From 32b6f0a8af90b3339168420cc92800bfc50023b1 Mon Sep 17 00:00:00 2001 From: BayoNet Date: Mon, 23 Apr 2018 09:20:21 +0300 Subject: [PATCH 001/231] English translation is updated. --- docs/en/agg_functions/combinators.md | 0 docs/en/agg_functions/index.md | 0 docs/en/agg_functions/parametric_functions.md | 0 docs/en/agg_functions/reference.md | 13 +- docs/en/data_types/array.md | 0 docs/en/data_types/boolean.md | 0 docs/en/data_types/date.md | 0 docs/en/data_types/datetime.md | 0 docs/en/data_types/enum.md | 0 docs/en/data_types/fixedstring.md | 0 docs/en/data_types/float.md | 3 +- docs/en/data_types/index.md | 0 docs/en/data_types/int_uint.md | 0 .../aggregatefunction.md | 0 .../nested_data_structures/index.md | 1 + .../nested_data_structures/nested.md | 0 .../special_data_types/expression.md | 0 .../en/data_types/special_data_types/index.md | 0 docs/en/data_types/special_data_types/set.md | 0 docs/en/data_types/string.md | 0 docs/en/data_types/tuple.md | 0 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new mode 100644 index 0eb896e4664..2b046d997cc --- a/docs/en/agg_functions/reference.md +++ b/docs/en/agg_functions/reference.md @@ -19,7 +19,7 @@ In some cases, you can rely on the order of execution. This applies to cases whe When a `SELECT` query has the `GROUP BY` clause or at least one aggregate function, ClickHouse (in contrast to MySQL) requires that all expressions in the `SELECT`, `HAVING`, and `ORDER BY` clauses be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. To get behavior like in MySQL, you can put the other columns in the `any` aggregate function. -## anyHeavy +## anyHeavy(x) Selects a frequently occurring value using the [heavy hitters](http://www.cs.umd.edu/~samir/498/karp.pdf) algorithm. If there is a value that occurs more than in half the cases in each of the query's execution threads, this value is returned. Normally, the result is nondeterministic. @@ -28,7 +28,6 @@ anyHeavy(column) ``` **Arguments** - - `column` – The column name. **Example** @@ -39,6 +38,7 @@ Take the [OnTime](../getting_started/example_datasets/ontime.md#example_datasets SELECT anyHeavy(AirlineID) AS res FROM ontime ``` + ``` ┌───res─┐ │ 19690 │ @@ -169,7 +169,7 @@ In some cases, you can still rely on the order of execution. This applies to cas -## groupArrayInsertAt +## groupArrayInsertAt(x) Inserts a value into the array in the specified position. @@ -256,7 +256,7 @@ The performance of the function is lower than for ` quantile`, ` quantileTiming` The result depends on the order of running the query, and is nondeterministic. -## median +## median(x) All the quantile functions have corresponding median functions: `median`, `medianDeterministic`, `medianTiming`, `medianTimingWeighted`, `medianExact`, `medianExactWeighted`, `medianTDigest`. They are synonyms and their behavior is identical. @@ -286,11 +286,11 @@ The result is equal to the square root of `varSamp(x)`. The result is equal to the square root of `varPop(x)`. -## topK +## topK(N)(column) Returns an array of the most frequent values in the specified column. The resulting array is sorted in descending order of frequency of values (not by the values themselves). -Implements the [ Filtered Space-Saving](http://www.l2f.inesc-id.pt/~fmmb/wiki/uploads/Work/misnis.ref0a.pdf) algorithm for analyzing TopK, based on the reduce-and-combine algorithm from [Parallel Space Saving](https://arxiv.org/pdf/1401.0702.pdf). +Implements the [Filtered Space-Saving](http://www.l2f.inesc-id.pt/~fmmb/wiki/uploads/Work/misnis.ref0a.pdf) algorithm for analyzing TopK, based on the reduce-and-combine algorithm from [Parallel Space Saving](https://arxiv.org/pdf/1401.0702.pdf). ``` topK(N)(column) @@ -301,7 +301,6 @@ This function doesn't provide a guaranteed result. In certain situations, errors We recommend using the `N < 10 ` value; performance is reduced with large `N` values. Maximum value of ` N = 65536`. **Arguments** - - 'N' is the number of values. - ' x ' – The column. diff --git a/docs/en/data_types/array.md b/docs/en/data_types/array.md old mode 100755 new mode 100644 diff --git a/docs/en/data_types/boolean.md b/docs/en/data_types/boolean.md old mode 100755 new mode 100644 diff --git a/docs/en/data_types/date.md b/docs/en/data_types/date.md old mode 100755 new mode 100644 diff --git a/docs/en/data_types/datetime.md b/docs/en/data_types/datetime.md old mode 100755 new mode 100644 diff --git a/docs/en/data_types/enum.md b/docs/en/data_types/enum.md old mode 100755 new mode 100644 diff --git a/docs/en/data_types/fixedstring.md b/docs/en/data_types/fixedstring.md old mode 100755 new mode 100644 diff --git a/docs/en/data_types/float.md b/docs/en/data_types/float.md old mode 100755 new mode 100644 index 9d5cc2c01bb..031a7b63436 --- a/docs/en/data_types/float.md +++ b/docs/en/data_types/float.md @@ -5,7 +5,7 @@ Types are equivalent to types of C: - `Float32` - `float` -- `Float64` - ` double` +- `Float64` - `double` We recommend that you store data in integer form whenever possible. For example, convert fixed precision numbers to integer values, such as monetary amounts or page load times in milliseconds. @@ -16,7 +16,6 @@ We recommend that you store data in integer form whenever possible. For example, ```sql SELECT 1 - 0.9 ``` - ``` ┌───────minus(1, 0.9)─┐ │ 0.09999999999999998 │ diff --git a/docs/en/data_types/index.md b/docs/en/data_types/index.md old mode 100755 new mode 100644 diff --git a/docs/en/data_types/int_uint.md b/docs/en/data_types/int_uint.md old mode 100755 new mode 100644 diff --git a/docs/en/data_types/nested_data_structures/aggregatefunction.md b/docs/en/data_types/nested_data_structures/aggregatefunction.md old mode 100755 new mode 100644 diff --git a/docs/en/data_types/nested_data_structures/index.md b/docs/en/data_types/nested_data_structures/index.md old mode 100755 new mode 100644 index 06f95b4a1c1..6f842947d00 --- a/docs/en/data_types/nested_data_structures/index.md +++ b/docs/en/data_types/nested_data_structures/index.md @@ -1 +1,2 @@ # Nested data structures + diff --git a/docs/en/data_types/nested_data_structures/nested.md b/docs/en/data_types/nested_data_structures/nested.md old mode 100755 new mode 100644 diff --git a/docs/en/data_types/special_data_types/expression.md b/docs/en/data_types/special_data_types/expression.md old mode 100755 new mode 100644 diff --git a/docs/en/data_types/special_data_types/index.md b/docs/en/data_types/special_data_types/index.md old mode 100755 new mode 100644 diff --git a/docs/en/data_types/special_data_types/set.md b/docs/en/data_types/special_data_types/set.md old mode 100755 new mode 100644 diff --git a/docs/en/data_types/string.md b/docs/en/data_types/string.md old mode 100755 new mode 100644 diff --git a/docs/en/data_types/tuple.md b/docs/en/data_types/tuple.md old mode 100755 new mode 100644 diff --git a/docs/en/development/style.md b/docs/en/development/style.md old mode 100755 new mode 100644 index 700fede5373..d583e81319c --- a/docs/en/development/style.md +++ b/docs/en/development/style.md @@ -93,14 +93,14 @@ 14. In classes and structures, public, private, and protected are written on the same level as the class/struct, but all other internal elements should be deeper. ```cpp - template -class MultiVersion -{ -public: - /// Version of object for usage. shared_ptr manage lifetime of version. - using Version = std::shared_ptr; - ... -} + template + class MultiVersion + { + public: + /// Version of object for usage. shared_ptr manage lifetime of version. + using Version = std::shared_ptr; + ... + } ``` 15. If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace. @@ -108,9 +108,9 @@ public: 16. If the block for if, for, while... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets. ```cpp - /// Finish write. -for (auto & stream : streams) - stream.second->finalize(); + /// Finish write. + for (auto & stream : streams) + stream.second->finalize(); ``` 17. There should be any spaces at the ends of lines. @@ -178,7 +178,6 @@ for (auto & stream : streams) //correct std::cerr << static_cast(c) << std::endl; ``` - 28. In classes and structs, group members and functions separately inside each visibility scope. 29. For small classes and structs, it is not necessary to separate the method declaration from the implementation. @@ -202,11 +201,11 @@ for (auto & stream : streams) This is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong. ```cpp - /** How much of the piece of memory can be used. - * For example, if internal_buffer is 1 MB, and only 10 bytes were loaded to the buffer from the file for reading, - * then working_buffer will have a size of only 10 bytes - * (working_buffer.end() will point to the position right after those 10 bytes available for read). - */ + /** Part of piece of memory, that can be used. + * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading, + * then working_buffer will have size of only 10 bytes + * (working_buffer.end() will point to the position right after those 10 bytes available for read). + */ ``` 2. Comments can be as detailed as necessary. @@ -214,15 +213,15 @@ for (auto & stream : streams) 3. Place comments before the code they describe. In rare cases, comments can come after the code, on the same line. ```cpp - /** Parses and executes the query. - */ - void executeQuery( - ReadBuffer & istr, /// Where to read the query from (and data for INSERT, if applicable) - WriteBuffer & ostr, /// Where to write the result - Context & context, /// DB, tables, data types, engines, functions, aggregate functions... - BlockInputStreamPtr & query_plan, /// A description of query processing can be included here - QueryProcessingStage::Enum stage = QueryProcessingStage::Complete /// The last stage to process the SELECT query to - ) + /** Parses and executes the query. + */ + void executeQuery( + ReadBuffer & istr, /// Where to read the query from (and data for INSERT, if applicable) + WriteBuffer & ostr, /// Where to write the result + Context & context, /// DB, tables, data types, engines, functions, aggregate functions... + BlockInputStreamPtr & query_plan, /// A description of query processing can be included here + QueryProcessingStage::Enum stage = QueryProcessingStage::Complete /// The last stage to process the SELECT query to + ) ``` 4. Comments should be written in English only. @@ -438,22 +437,19 @@ for (auto & stream : streams) In servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler. - In thread functions, you should catch and keep all exceptions to rethrow them in the main thread after join. - - ```cpp - /// If there were no other calculations yet, do it synchronously - if (!started) - { - calculate(); - started = true; - } - else /// If the calculations are already in progress, wait for results - pool.wait(); - - if (exception) - exception->rethrow(); - ``` + ```cpp + /// If there were no other calculations yet, do it synchronously + if (!started) + { + calculate(); + started = true; + } + else /// If the calculations are already in progress, wait for results + pool.wait(); + if (exception) + exception->rethrow(); + ``` Never hide exceptions without handling. Never just blindly put all exceptions to log. Not `catch (...) {}`. @@ -497,17 +493,16 @@ This is not recommended, but it is allowed. You can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block. ```cpp - Block block = data.in->read(); + Block block = data.in->read(); - { - std::lock_guard lock(mutex); - data.ready = true; - data.block = block; - } - - ready_any.set(); - ``` + { + std::lock_guard lock(mutex); + data.ready = true; + data.block = block; + } + ready_any.set(); + ``` 7. Multithreading. For offline data processing applications: @@ -569,14 +564,14 @@ This is not recommended, but it is allowed. ```cpp using AggregateFunctionPtr = std::shared_ptr; - /** Creates an aggregate function by name. */ + /** Creates an aggregate function by name. + */ class AggregateFunctionFactory { public: - AggregateFunctionFactory(); + AggregateFunctionFactory(); AggregateFunctionPtr get(const String & name, const DataTypes & argument_types) const; ``` - 15. namespace. There is no need to use a separate namespace for application code or small libraries. @@ -598,10 +593,10 @@ This is not recommended, but it is allowed. If later you’ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use shared_ptr/unique_ptr. ```cpp - Loader(DB::Connection * connection_, const std::string & query, size_t max_block_size_); - - /// For delayed initialization - Loader() {} + Loader(DB::Connection * connection_, const std::string & query, size_t max_block_size_); + + /// For delayed initialization + Loader() {} ``` 17. Virtual functions. @@ -668,7 +663,6 @@ This is not recommended, but it is allowed. std::string s = "Hello"; std::string s{"Hello"}; ``` - 26. For virtual functions, write 'virtual' in the base class, but write 'override' in descendent classes. ## Unused features of C++ diff --git a/docs/en/dicts/external_dicts.md b/docs/en/dicts/external_dicts.md old mode 100755 new mode 100644 index a6af84a313f..673966dc711 --- a/docs/en/dicts/external_dicts.md +++ b/docs/en/dicts/external_dicts.md @@ -21,11 +21,12 @@ The dictionary config file has the following format: /etc/metrika.xml - + + - + - + ... @@ -43,11 +44,3 @@ See also "[Functions for working with external dictionaries](../functions/ext_di You can convert values ​​for a small dictionary by describing it in a `SELECT` query (see the [transform](../functions/other_functions.md#other_functions-transform) function). This functionality is not related to external dictionaries. - -```eval_rst -.. toctree:: - :glob: - - external_dicts_dict* -``` - diff --git a/docs/en/dicts/external_dicts_dict.md b/docs/en/dicts/external_dicts_dict.md old mode 100755 new mode 100644 index 0e9b6f578b4..df0927988b2 --- a/docs/en/dicts/external_dicts_dict.md +++ b/docs/en/dicts/external_dicts_dict.md @@ -27,7 +27,7 @@ The dictionary configuration has the following structure: ``` - name – The identifier that can be used to access the dictionary. Use the characters `[a-zA-Z0-9_\-]`. -- [source](external_dicts_dict_sources.md/#dicts-external_dicts_dict_sources) — Source of the dictionary . +- [source](external_dicts_dict_sources.md#dicts-external_dicts_dict_sources) — Source of the dictionary. - [layout](external_dicts_dict_layout.md#dicts-external_dicts_dict_layout) — Dictionary layout in memory. - [structure](external_dicts_dict_structure.md#dicts-external_dicts_dict_structure) — Structure of the dictionary . A key and attributes that can be retrieved by this key. - [lifetime](external_dicts_dict_lifetime.md#dicts-external_dicts_dict_lifetime) — Frequency of dictionary updates. diff --git a/docs/en/dicts/external_dicts_dict_layout.md b/docs/en/dicts/external_dicts_dict_layout.md old mode 100755 new mode 100644 index ad635db94f5..4f2a623d627 --- a/docs/en/dicts/external_dicts_dict_layout.md +++ b/docs/en/dicts/external_dicts_dict_layout.md @@ -2,11 +2,11 @@ # Storing dictionaries in memory -There are [many different ways](external_dicts_dict_layout#dicts-external_dicts_dict_layout-manner) to store dictionaries in memory. +There are a [variety of ways](#dicts-external_dicts_dict_layout-manner) to store dictionaries in memory. -We recommend [flat](external_dicts_dict_layout#dicts-external_dicts_dict_layout-flat), [hashed](external_dicts_dict_layout#dicts-external_dicts_dict_layout-hashed), and [complex_key_hashed](external_dicts_dict_layout#dicts-external_dicts_dict_layout-complex_key_hashed). which provide optimal processing speed. +We recommend [flat](#dicts-external_dicts_dict_layout-flat), [hashed](#dicts-external_dicts_dict_layout-hashed)and[complex_key_hashed](#dicts-external_dicts_dict_layout-complex_key_hashed). which provide optimal processing speed. -Caching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more about this in the "[cache](external_dicts_dict_layout#dicts-external_dicts_dict_layout-cache)" section. +Caching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section " [cache](#dicts-external_dicts_dict_layout-cache)". There are several ways to improve dictionary performance: @@ -88,7 +88,7 @@ Configuration example: ### complex_key_hashed -This type of storage is designed for use with compound [keys](external_dicts_dict_structure#dicts-external_dicts_dict_structure). It is similar to hashed. +This type of storage is for use with composite [keys](external_dicts_dict_structure.md#dicts-external_dicts_dict_structure). Similar to `hashed`. Configuration example: @@ -109,18 +109,18 @@ This storage method works the same way as hashed and allows using date/time rang Example: The table contains discounts for each advertiser in the format: ``` - +---------------+---------------------+-------------------+--------+ - | advertiser id | discount start date | discount end date | amount | - +===============+=====================+===================+========+ - | 123 | 2015-01-01 | 2015-01-15 | 0.15 | - +---------------+---------------------+-------------------+--------+ - | 123 | 2015-01-16 | 2015-01-31 | 0.25 | - +---------------+---------------------+-------------------+--------+ - | 456 | 2015-01-01 | 2015-01-15 | 0.05 | - +---------------+---------------------+-------------------+--------+ ++---------------+---------------------+-------------------+--------+ +| advertiser id | discount start date | discount end date | amount | ++===============+=====================+===================+========+ +| 123 | 2015-01-01 | 2015-01-15 | 0.15 | ++---------------+---------------------+-------------------+--------+ +| 123 | 2015-01-16 | 2015-01-31 | 0.25 | ++---------------+---------------------+-------------------+--------+ +| 456 | 2015-01-01 | 2015-01-15 | 0.05 | ++---------------+---------------------+-------------------+--------+ ``` -To use a sample for date ranges, define `range_min` and `range_max` in [structure](external_dicts_dict_structure#dicts-external_dicts_dict_structure). +To use a sample for date ranges, define the `range_min` and `range_max` elements in the [structure](external_dicts_dict_structure.md#dicts-external_dicts_dict_structure). Example: @@ -140,7 +140,9 @@ Example: To work with these dictionaries, you need to pass an additional date argument to the `dictGetT` function: - dictGetT('dict_name', 'attr_name', id, date) +``` +dictGetT('dict_name', 'attr_name', id, date) +``` This function returns the value for the specified `id`s and the date range that includes the passed date. @@ -191,13 +193,13 @@ The dictionary is stored in a cache that has a fixed number of cells. These cell When searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using ` SELECT attrs... FROM db.table WHERE id IN (k1, k2, ...)`. The received data is then written to the cache. -For cache dictionaries, the expiration (lifetime <dicts-external_dicts_dict_lifetime>) of data in the cache can be set. If more time than `lifetime` has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used. +For cache dictionaries, the expiration [lifetime](dicts-external_dicts_dict_lifetime.md#dicts-external_dicts_dict_lifetime) of data in the cache can be set. If more time than `lifetime` has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used. This is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the `system.dictionaries` table. To improve cache performance, use a subquery with ` LIMIT`, and call the function with the dictionary externally. -Supported [sources](external_dicts_dict_sources#dicts-external_dicts_dict_sources): MySQL, ClickHouse, executable, HTTP. +Supported [sources](external_dicts_dict_sources.md#dicts-external_dicts_dict_sources): MySQL, ClickHouse, executable, HTTP. Example of settings: @@ -205,7 +207,7 @@ Example of settings: - 1000000000 + 1000000000 ``` @@ -227,16 +229,15 @@ Do not use ClickHouse as a source, because it is slow to process queries with ra ### complex_key_cache -This type of storage is designed for use with compound [keys](external_dicts_dict_structure#dicts-external_dicts_dict_structure). Similar to `cache`. +This type of storage is for use with composite [keys](external_dicts_dict_structure.md#dicts-external_dicts_dict_structure). Similar to `cache`. ### ip_trie +This type of storage is for mapping network prefixes (IP addresses) to metadata such as ASN. -The table stores IP prefixes for each key (IP address), which makes it possible to map IP addresses to metadata such as ASN or threat score. - -Example: in the table there are prefixes matches to AS number and country: +Example: The table contains network prefixes and their corresponding AS number and country code: ``` +-----------------+-------+--------+ @@ -252,7 +253,7 @@ Example: in the table there are prefixes matches to AS number and country: +-----------------+-------+--------+ ``` -When using such a layout, the structure should have the "key" element. +When using this type of layout, the structure must have a composite key. Example: @@ -277,16 +278,20 @@ Example: ... ``` -These key must have only one attribute of type String, containing a valid IP prefix. Other types are not yet supported. +The key must have only one String type attribute that contains an allowed IP prefix. Other types are not supported yet. -For querying, same functions (dictGetT with tuple) as for complex key dictionaries have to be used: +For queries, you must use the same functions (`dictGetT` with a tuple) as for dictionaries with composite keys: - dictGetT('dict_name', 'attr_name', tuple(ip)) +``` +dictGetT('dict_name', 'attr_name', tuple(ip)) +``` -The function accepts either UInt32 for IPv4 address or FixedString(16) for IPv6 address in wire format: +The function takes either `UInt32` for IPv4, or `FixedString(16)` for IPv6: - dictGetString('prefix', 'asn', tuple(IPv6StringToNum('2001:db8::1'))) +``` +dictGetString('prefix', 'asn', tuple(IPv6StringToNum('2001:db8::1'))) +``` -No other type is supported. The function returns attribute for a prefix matching the given IP address. If there are overlapping prefixes, the most specific one is returned. +Other types are not supported yet. The function returns the attribute for the prefix that corresponds to this IP address. If there are overlapping prefixes, the most specific one is returned. -The data is stored currently in a bitwise trie, it has to fit in memory. +Data is stored in a `trie`. It must completely fit into RAM. diff --git a/docs/en/dicts/external_dicts_dict_lifetime.md b/docs/en/dicts/external_dicts_dict_lifetime.md old mode 100755 new mode 100644 diff --git a/docs/en/dicts/external_dicts_dict_sources.md b/docs/en/dicts/external_dicts_dict_sources.md old mode 100755 new mode 100644 index 6cb4e0ea44d..8d0e4952a3b --- a/docs/en/dicts/external_dicts_dict_sources.md +++ b/docs/en/dicts/external_dicts_dict_sources.md @@ -135,7 +135,7 @@ Installing unixODBC and the ODBC driver for PostgreSQL: Configuring `/etc/odbc.ini` (or `~/.odbc.ini`): ``` -[DEFAULT] + [DEFAULT] Driver = myconnection [myconnection] @@ -159,9 +159,9 @@ The dictionary configuration in ClickHouse: table_name - - - + + + DSN=myconnection postgresql_table
 @@ -195,7 +195,7 @@ Ubuntu OS. Installing the driver: : ``` -sudo apt-get install tdsodbc freetds-bin sqsh + sudo apt-get install tdsodbc freetds-bin sqsh ``` Configuring the driver: : diff --git a/docs/en/dicts/external_dicts_dict_structure.md b/docs/en/dicts/external_dicts_dict_structure.md old mode 100755 new mode 100644 index b6038010623..869d6f16ca5 --- a/docs/en/dicts/external_dicts_dict_structure.md +++ b/docs/en/dicts/external_dicts_dict_structure.md @@ -119,4 +119,3 @@ Configuration fields: - `hierarchical` – Hierarchical support. Mirrored to the parent identifier. By default, ` false`. - `injective` – Whether the `id -> attribute` image is injective. If ` true`, then you can optimize the ` GROUP BY` clause. By default, `false`. - `is_object_id` – Whether the query is executed for a MongoDB document by `ObjectID`. - diff --git a/docs/en/dicts/index.md b/docs/en/dicts/index.md old mode 100755 new mode 100644 diff --git a/docs/en/dicts/internal_dicts.md b/docs/en/dicts/internal_dicts.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/capnproto.md b/docs/en/formats/capnproto.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/csv.md b/docs/en/formats/csv.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/csvwithnames.md b/docs/en/formats/csvwithnames.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/index.md b/docs/en/formats/index.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/json.md b/docs/en/formats/json.md old mode 100755 new mode 100644 index 3b8354f0b88..635f37533cd --- a/docs/en/formats/json.md +++ b/docs/en/formats/json.md @@ -27,19 +27,19 @@ SELECT SearchPhrase, count() AS c FROM test.hits GROUP BY SearchPhrase WITH TOTA "c": "8267016" }, { - "SearchPhrase": "bathroom interior design", + "SearchPhrase": "интерьер ванной комнаты", "c": "2166" }, { - "SearchPhrase": "yandex", + "SearchPhrase": "яндекс", "c": "1655" }, { - "SearchPhrase": "spring 2014 fashion", + "SearchPhrase": "весна 2014 мода", "c": "1549" }, { - "SearchPhrase": "freeform photo", + "SearchPhrase": "фриформ фото", "c": "1480" } ], diff --git a/docs/en/formats/jsoncompact.md b/docs/en/formats/jsoncompact.md old mode 100755 new mode 100644 index d870b6dff08..e4ce0867bc2 --- a/docs/en/formats/jsoncompact.md +++ b/docs/en/formats/jsoncompact.md @@ -24,7 +24,7 @@ Example: ["bathroom interior design", "2166"], ["yandex", "1655"], ["spring 2014 fashion", "1549"], - ["freeform photo", "1480"] + ["freeform photos", "1480"] ], "totals": ["","8873898"], diff --git a/docs/en/formats/jsoneachrow.md b/docs/en/formats/jsoneachrow.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/native.md b/docs/en/formats/native.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/null.md b/docs/en/formats/null.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/pretty.md b/docs/en/formats/pretty.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/prettycompact.md b/docs/en/formats/prettycompact.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/prettycompactmonoblock.md b/docs/en/formats/prettycompactmonoblock.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/prettynoescapes.md b/docs/en/formats/prettynoescapes.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/prettyspace.md b/docs/en/formats/prettyspace.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/rowbinary.md b/docs/en/formats/rowbinary.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/tabseparated.md b/docs/en/formats/tabseparated.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/tabseparatedraw.md b/docs/en/formats/tabseparatedraw.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/tabseparatedwithnames.md b/docs/en/formats/tabseparatedwithnames.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/tabseparatedwithnamesandtypes.md b/docs/en/formats/tabseparatedwithnamesandtypes.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/tskv.md b/docs/en/formats/tskv.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/values.md b/docs/en/formats/values.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/vertical.md b/docs/en/formats/vertical.md old mode 100755 new mode 100644 diff --git a/docs/en/formats/verticalraw.md b/docs/en/formats/verticalraw.md index 9bb53ee1260..edff754a7cd 100644 --- a/docs/en/formats/verticalraw.md +++ b/docs/en/formats/verticalraw.md @@ -1,9 +1,10 @@ # VerticalRaw -Differs from `Vertical` format in that the rows are written without escaping. +Differs from `Vertical` format in that the rows are not escaped. This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). -Samples: +Examples: + ``` :) SHOW CREATE TABLE geonames FORMAT VerticalRaw; Row 1: @@ -15,8 +16,11 @@ Row 1: ────── test: string with 'quotes' and with some special characters +``` --- the same in Vertical format: +Compare with the Vertical format: + +``` :) SELECT 'string with \'quotes\' and \t with some special \n characters' AS test FORMAT Vertical; Row 1: ────── diff --git a/docs/en/formats/xml.md b/docs/en/formats/xml.md old mode 100755 new mode 100644 index 0da55875cc3..5188b9514a8 --- a/docs/en/formats/xml.md +++ b/docs/en/formats/xml.md @@ -35,7 +35,7 @@ XML format is suitable only for output, not for parsing. Example: 1549 - freeform photo + freeform photos 1480 @@ -69,5 +69,6 @@ Just as for JSON, invalid UTF-8 sequences are changed to the replacement charact In string values, the characters `<` and `&` are escaped as `<` and `&`. -Arrays are output as `HelloWorld...`,and tuples as `HelloWorld...`. +Arrays are output as `HelloWorld...`, +and tuples as `HelloWorld...`. diff --git a/docs/en/functions/arithmetic_functions.md b/docs/en/functions/arithmetic_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/array_functions.md b/docs/en/functions/array_functions.md old mode 100755 new mode 100644 index 6993132f423..20a1eac2919 --- a/docs/en/functions/array_functions.md +++ b/docs/en/functions/array_functions.md @@ -225,7 +225,6 @@ arrayPopFront(array) ```sql SELECT arrayPopFront([1, 2, 3]) AS res ``` - ``` ┌─res───┐ │ [2,3] │ @@ -250,6 +249,7 @@ arrayPushBack(array, single_value) ```sql SELECT arrayPushBack(['a'], 'b') AS res ``` + ``` ┌─res───────┐ │ ['a','b'] │ @@ -274,7 +274,6 @@ arrayPushFront(array, single_value) ```sql SELECT arrayPushBack(['b'], 'a') AS res ``` - ``` ┌─res───────┐ │ ['a','b'] │ diff --git a/docs/en/functions/array_join.md b/docs/en/functions/array_join.md old mode 100755 new mode 100644 index f94b2707f52..6e18f8203c0 --- a/docs/en/functions/array_join.md +++ b/docs/en/functions/array_join.md @@ -28,3 +28,4 @@ SELECT arrayJoin([1, 2, 3] AS src) AS dst, 'Hello', src │ 3 │ Hello │ [1,2,3] │ └─────┴───────────┴─────────┘ ``` + diff --git a/docs/en/functions/bit_functions.md b/docs/en/functions/bit_functions.md old mode 100755 new mode 100644 index c5a032aa5d6..523413f200a --- a/docs/en/functions/bit_functions.md +++ b/docs/en/functions/bit_functions.md @@ -15,3 +15,4 @@ The result type is an integer with bits equal to the maximum bits of its argumen ## bitShiftLeft(a, b) ## bitShiftRight(a, b) + diff --git a/docs/en/functions/comparison_functions.md b/docs/en/functions/comparison_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/conditional_functions.md b/docs/en/functions/conditional_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/date_time_functions.md b/docs/en/functions/date_time_functions.md old mode 100755 new mode 100644 index a7529e5f0e1..1299baa6c5a --- a/docs/en/functions/date_time_functions.md +++ b/docs/en/functions/date_time_functions.md @@ -143,7 +143,7 @@ The same as 'today() - 1'. ## timeSlot Rounds the time to the half hour. -This function is specific to Yandex.Metrica, since half an hour is the minimum amount of time for breaking a session into two sessions if a counter shows a single user's consecutive pageviews that differ in time by strictly more than this amount. This means that tuples (the counter number, user ID, and time slot) can be used to search for pageviews that are included in the corresponding session. +This function is specific to Yandex.Metrica, since half an hour is the minimum amount of time for breaking a session into two sessions if a tracking tag shows a single user's consecutive pageviews that differ in time by strictly more than this amount. This means that tuples (the tag ID, user ID, and time slot) can be used to search for pageviews that are included in the corresponding session. ## timeSlots(StartTime, Duration) diff --git a/docs/en/functions/encoding_functions.md b/docs/en/functions/encoding_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/ext_dict_functions.md b/docs/en/functions/ext_dict_functions.md old mode 100755 new mode 100644 index 002e2f55845..5d5e4461396 --- a/docs/en/functions/ext_dict_functions.md +++ b/docs/en/functions/ext_dict_functions.md @@ -15,12 +15,9 @@ For information on connecting and configuring external dictionaries, see "[Exter ## dictGetUUID ## dictGetString - `dictGetT('dict_name', 'attr_name', id)` -- Get the value of the attr_name attribute from the dict_name dictionary using the 'id' key. -`dict_name` and `attr_name` are constant strings. -`id`must be UInt64. +- Get the value of the attr_name attribute from the dict_name dictionary using the 'id' key.`dict_name` and `attr_name` are constant strings.`id`must be UInt64. If there is no `id` key in the dictionary, it returns the default value specified in the dictionary description. ## dictGetTOrDefault diff --git a/docs/en/functions/hash_functions.md b/docs/en/functions/hash_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/higher_order_functions.md b/docs/en/functions/higher_order_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/in_functions.md b/docs/en/functions/in_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/index.md b/docs/en/functions/index.md old mode 100755 new mode 100644 index 9f92d009113..15e1061d093 --- a/docs/en/functions/index.md +++ b/docs/en/functions/index.md @@ -10,7 +10,7 @@ In this section we discuss regular functions. For aggregate functions, see the s In contrast to standard SQL, ClickHouse has strong typing. In other words, it doesn't make implicit conversions between types. Each function works for a specific set of types. This means that sometimes you need to use type conversion functions. -## Сommon subexpression elimination +## Common subexpression elimination All expressions in a query that have the same AST (the same record or same result of syntactic parsing) are considered to have identical values. Such expressions are concatenated and executed once. Identical subqueries are also eliminated this way. diff --git a/docs/en/functions/ip_address_functions.md b/docs/en/functions/ip_address_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/json_functions.md b/docs/en/functions/json_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/logical_functions.md b/docs/en/functions/logical_functions.md old mode 100755 new mode 100644 index d396640a49d..4ef0fe5fd32 --- a/docs/en/functions/logical_functions.md +++ b/docs/en/functions/logical_functions.md @@ -11,3 +11,4 @@ Zero as an argument is considered "false," while any non-zero value is considere ## not, NOT operator ## xor + diff --git a/docs/en/functions/math_functions.md b/docs/en/functions/math_functions.md old mode 100755 new mode 100644 index 42e3f3e8018..d606c87a509 --- a/docs/en/functions/math_functions.md +++ b/docs/en/functions/math_functions.md @@ -97,3 +97,4 @@ The arc tangent. ## pow(x, y) xy. + diff --git a/docs/en/functions/other_functions.md b/docs/en/functions/other_functions.md old mode 100755 new mode 100644 index 8a0063750fe..781ac527e2b --- a/docs/en/functions/other_functions.md +++ b/docs/en/functions/other_functions.md @@ -59,8 +59,7 @@ For elements in a nested data structure, the function checks for the existence o Allows building a unicode-art diagram. -`bar (x, min, max, width)` – Draws a band with a width proportional to (x - min) and equal to 'width' characters when x == max. -`min, max` – Integer constants. The value must fit in Int64.`width` – Constant, positive number, may be a fraction. +`bar (x, min, max, width)` – Draws a band with a width proportional to (x - min) and equal to 'width' characters when x == max.`min, max` – Integer constants. The value must fit in Int64.`width` – Constant, positive number, may be a fraction. The band is drawn with accuracy to one eighth of a symbol. @@ -278,4 +277,3 @@ The inverse function of MACNumToString. If the MAC address has an invalid format ## MACStringToOUI(s) Accepts a MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form). Returns the first three octets as a UInt64 number. If the MAC address has an invalid format, it returns 0. - diff --git a/docs/en/functions/random_functions.md b/docs/en/functions/random_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/rounding_functions.md b/docs/en/functions/rounding_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/splitting_merging_functions.md b/docs/en/functions/splitting_merging_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/string_functions.md b/docs/en/functions/string_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/string_replace_functions.md b/docs/en/functions/string_replace_functions.md old mode 100755 new mode 100644 index d70d8f404de..d3773504278 --- a/docs/en/functions/string_replace_functions.md +++ b/docs/en/functions/string_replace_functions.md @@ -76,3 +76,4 @@ SELECT replaceRegexpAll('Hello, World!', '^', 'here: ') AS res │ here: Hello, World! │ └─────────────────────┘ ``` + diff --git a/docs/en/functions/string_search_functions.md b/docs/en/functions/string_search_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/type_conversion_functions.md b/docs/en/functions/type_conversion_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/url_functions.md b/docs/en/functions/url_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/functions/ym_dict_functions.md b/docs/en/functions/ym_dict_functions.md old mode 100755 new mode 100644 diff --git a/docs/en/getting_started/example_datasets/amplab_benchmark.md b/docs/en/getting_started/example_datasets/amplab_benchmark.md old mode 100755 new mode 100644 index 49265d5da85..60926f53e06 --- a/docs/en/getting_started/example_datasets/amplab_benchmark.md +++ b/docs/en/getting_started/example_datasets/amplab_benchmark.md @@ -118,4 +118,3 @@ GROUP BY sourceIP ORDER BY totalRevenue DESC LIMIT 1 ``` - diff --git a/docs/en/getting_started/example_datasets/criteo.md b/docs/en/getting_started/example_datasets/criteo.md old mode 100755 new mode 100644 diff --git a/docs/en/getting_started/example_datasets/nyc_taxi.md b/docs/en/getting_started/example_datasets/nyc_taxi.md old mode 100755 new mode 100644 index a9f04f595d1..04bb31cc7a6 --- a/docs/en/getting_started/example_datasets/nyc_taxi.md +++ b/docs/en/getting_started/example_datasets/nyc_taxi.md @@ -301,14 +301,19 @@ SELECT passenger_count, toYear(pickup_date) AS year, count(*) FROM trips_mergetr Q4: ```sql -SELECT passenger_count, toYear(pickup_date) AS year, round(trip_distance) AS distance, count(*)FROM trips_mergetreeGROUP BY passenger_count, year, distanceORDER BY year, count(*) DESC +SELECT passenger_count, toYear(pickup_date) AS year, round(trip_distance) AS distance, count(*) +FROM trips_mergetree +GROUP BY passenger_count, year, distance +ORDER BY year, count(*) DESC ``` 3.593 seconds. The following server was used: -Two Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical kernels total,128 GiB RAM,8x6 TB HD on hardware RAID-5 +Two Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical kernels total, +128 GiB RAM, +8x6 TB HD on hardware RAID-5 Execution time is the best of three runsBut starting from the second run, queries read data from the file system cache. No further caching occurs: the data is read out and processed in each run. @@ -361,4 +366,3 @@ nodes Q1 Q2 Q3 Q4 3 0.212 0.438 0.733 1.241 140 0.028 0.043 0.051 0.072 ``` - diff --git a/docs/en/getting_started/example_datasets/ontime.md b/docs/en/getting_started/example_datasets/ontime.md old mode 100755 new mode 100644 index 574e195e6b5..150fc8bb5bd --- a/docs/en/getting_started/example_datasets/ontime.md +++ b/docs/en/getting_started/example_datasets/ontime.md @@ -316,4 +316,3 @@ SELECT OriginCityName, DestCityName, count() AS c FROM ontime GROUP BY OriginCit SELECT OriginCityName, count() AS c FROM ontime GROUP BY OriginCityName ORDER BY c DESC LIMIT 10; ``` - diff --git a/docs/en/getting_started/example_datasets/star_schema.md b/docs/en/getting_started/example_datasets/star_schema.md old mode 100755 new mode 100644 index 664ba59f48c..98bad00de5e --- a/docs/en/getting_started/example_datasets/star_schema.md +++ b/docs/en/getting_started/example_datasets/star_schema.md @@ -82,4 +82,3 @@ Downloading data (change 'customer' to 'customerd' in the distributed version): cat customer.tbl | sed 's/$/2000-01-01/' | clickhouse-client --query "INSERT INTO customer FORMAT CSV" cat lineorder.tbl | clickhouse-client --query "INSERT INTO lineorder FORMAT CSV" ``` - diff --git a/docs/en/getting_started/example_datasets/wikistat.md b/docs/en/getting_started/example_datasets/wikistat.md old mode 100755 new mode 100644 index fee0a56b52c..81ab8c4545d --- a/docs/en/getting_started/example_datasets/wikistat.md +++ b/docs/en/getting_started/example_datasets/wikistat.md @@ -20,8 +20,5 @@ CREATE TABLE wikistat Loading data: ```bash -for i in {2007..2016}; do for j in {01..12}; do echo $i-$j >&2; curl -sSL "http://dumps.wikimedia.org/other/pagecounts-raw/$i/$i-$j/" | grep -oE 'pagecounts-[0-9]+-[0-9]+\.gz'; done; done | sort | uniq | tee links.txt -cat links.txt | while read link; do wget http://dumps.wikimedia.org/other/pagecounts-raw/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1/')/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1-\2/')/$link; done -ls -1 /opt/wikistat/ | grep gz | while read i; do echo $i; gzip -cd /opt/wikistat/$i | ./wikistat-loader --time="$(echo -n $i | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\.gz/\1-\2-\3 \4-00-00/')" | clickhouse-client --query="INSERT INTO wikistat FORMAT TabSeparated"; done +for i in {2007..2016}; do for j in {01..12}; do echo $i-$j >&2; curl -sSL "http://dumps.wikimedia.org/other/pagecounts-raw/$i/$i-$j/" | grep -oE 'pagecounts-[0-9]+-[0-9]+\.gz'; done; done | sort | uniq | tee links.txtcat links.txt | while read link; do wget http://dumps.wikimedia.org/other/pagecounts-raw/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1/')/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1-\2/')/$link; donels -1 /opt/wikistat/ | grep gz | while read i; do echo $i; gzip -cd /opt/wikistat/$i | ./wikistat-loader --time="$(echo -n $i | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\.gz/\1-\2-\3 \4-00-00/')" | clickhouse-client --query="INSERT INTO wikistat FORMAT TabSeparated"; done ``` - diff --git a/docs/en/getting_started/index.md b/docs/en/getting_started/index.md old mode 100755 new mode 100644 index d3e9ea03915..731ef56e146 --- a/docs/en/getting_started/index.md +++ b/docs/en/getting_started/index.md @@ -16,7 +16,7 @@ The terminal must use UTF-8 encoding (the default in Ubuntu). For testing and development, the system can be installed on a single server or on a desktop computer. -### Installing from packages Debian/Ubuntu +### Installing from packages for Debian/Ubuntu In `/etc/apt/sources.list` (or in a separate `/etc/apt/sources.list.d/clickhouse.list` file), add the repository: @@ -34,8 +34,7 @@ sudo apt-get update sudo apt-get install clickhouse-client clickhouse-server-common ``` -You can also download and install packages manually from here: - +You can also download and install packages manually from here: . ClickHouse contains access restriction settings. They are located in the 'users.xml' file (next to 'config.xml'). By default, access is allowed from anywhere for the 'default' user, without a password. See 'user/default/networks'. @@ -101,8 +100,7 @@ clickhouse-client ``` The default parameters indicate connecting with localhost:9000 on behalf of the user 'default' without a password. -The client can be used for connecting to a remote server. -Example: +The client can be used for connecting to a remote server. Example: ```bash clickhouse-client --host=example.com @@ -134,3 +132,4 @@ SELECT 1 **Congratulations, the system works!** To continue experimenting, you can try to download from the test data sets. + diff --git a/docs/en/index.md b/docs/en/index.md old mode 100755 new mode 100644 index cc9c806fe50..72efa70802b --- a/docs/en/index.md +++ b/docs/en/index.md @@ -39,7 +39,7 @@ We'll say that the following is true for the OLAP (online analytical processing) - Data is updated in fairly large batches (> 1000 rows), not by single rows; or it is not updated at all. - Data is added to the DB but is not modified. - For reads, quite a large number of rows are extracted from the DB, but only a small subset of columns. -- Tables are "wide", meaning they contain a large number of columns. +- Tables are "wide," meaning they contain a large number of columns. - Queries are relatively rare (usually hundreds of queries per server or less per second). - For simple queries, latencies around 50 ms are allowed. - Column values are fairly small: numbers and short strings (for example, 60 bytes per URL). @@ -120,3 +120,4 @@ There are two ways to do this: This is not done in "normal" databases, because it doesn't make sense when running simple queries. However, there are exceptions. For example, MemSQL uses code generation to reduce latency when processing SQL queries. (For comparison, analytical DBMSs require optimization of throughput, not latency.) Note that for CPU efficiency, the query language must be declarative (SQL or MDX), or at least a vector (J, K). The query should only contain implicit loops, allowing for optimization. + diff --git a/docs/en/interfaces/cli.md b/docs/en/interfaces/cli.md old mode 100755 new mode 100644 index 76549b46b36..ff27973624d --- a/docs/en/interfaces/cli.md +++ b/docs/en/interfaces/cli.md @@ -31,6 +31,7 @@ _EOF cat file.csv | clickhouse-client --database=test --query="INSERT INTO test FORMAT CSV"; ``` +In batch mode, the default data format is TabSeparated. You can set the format in the FORMAT clause of the query. By default, you can only process a single query in batch mode. To make multiple queries from a "script," use the --multiquery parameter. This works for all queries except INSERT. Query results are output consecutively without additional separators. Similarly, to process a large number of queries, you can run 'clickhouse-client' for each query. Note that it may take tens of milliseconds to launch the 'clickhouse-client' program. diff --git a/docs/en/interfaces/http_interface.md b/docs/en/interfaces/http_interface.md old mode 100755 new mode 100644 index 8c223cf69cf..602e18ca58a --- a/docs/en/interfaces/http_interface.md +++ b/docs/en/interfaces/http_interface.md @@ -130,14 +130,13 @@ POST 'http://localhost:8123/?query=DROP TABLE t' For successful requests that don't return a data table, an empty response body is returned. -You can use compression when transmitting data. +You can use the internal ClickHouse compression format when transmitting data. The compressed data has a non-standard format, and you will need to use the special clickhouse-compressor program to work with it (it is installed with the clickhouse-client package). -For using ClickHouse internal compression format, and you will need to use the special compressor program to work with it (sudo apt-get install compressor-metrika-yandex). If you specified 'compress=1' in the URL, the server will compress the data it sends you. If you specified 'decompress=1' in the URL, the server will decompress the same data that you pass in the POST method. -Also standard gzip-based HTTP compression can be used. To send gzip compressed POST data just add `Content-Encoding: gzip` to request headers, and gzip POST body. -To get response compressed, you need to add `Accept-Encoding: gzip` to request headers, and turn on ClickHouse setting called `enable_http_compression`. +It is also possible to use the standard gzip-based HTTP compression. To send a POST request compressed using gzip, append the request header `Content-Encoding: gzip`. +In order for ClickHouse to compress the response using gzip, you must append `Accept-Encoding: gzip` to the request headers, and enable the ClickHouse setting `enable_http_compression`. You can use this to reduce network traffic when transmitting a large amount of data, or for creating dumps that are immediately compressed. @@ -174,7 +173,8 @@ echo 'SELECT 1' | curl 'http://localhost:8123/?user=user&password=password' -d @ ``` If the user name is not indicated, the username 'default' is used. If the password is not indicated, an empty password is used. -You can also use the URL parameters to specify any settings for processing a single query, or entire profiles of settings. Example:http://localhost:8123/?profile=web&max_rows_to_read=1000000000&query=SELECT+1 +You can also use the URL parameters to specify any settings for processing a single query, or entire profiles of settings. Example: +http://localhost:8123/?profile=web&max_rows_to_read=1000000000&query=SELECT+1 For more information, see the section "Settings". @@ -194,11 +194,11 @@ $ echo 'SELECT number FROM system.numbers LIMIT 10' | curl 'http://localhost:812 For information about other parameters, see the section "SET". -You can use ClickHouse sessions in the HTTP protocol. To do this, you need to specify the `session_id` GET parameter in HTTP request. You can use any alphanumeric string as a session_id. By default session will be timed out after 60 seconds of inactivity. You can change that by setting `default_session_timeout` in server config file, or by adding GET parameter `session_timeout`. You can also check the status of the session by using GET parameter `session_check=1`. When using sessions you can't run 2 queries with the same session_id simultaneously. +Similarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to add the `session_id` GET parameter to the request. You can use any string as the session ID. By default, the session is terminated after 60 seconds of inactivity. To change this timeout, modify the `default_session_timeout` setting in the server configuration, or add the `session_timeout` GET parameter to the request. To check the session status, use the `session_check=1` parameter. Only one query at a time can be executed within a single session. -You can get the progress of query execution in X-ClickHouse-Progress headers, by enabling setting send_progress_in_http_headers. +You have the option to receive information about the progress of query execution in X-ClickHouse-Progress headers. To do this, enable the setting send_progress_in_http_headers. -Running query are not aborted automatically after closing HTTP connection. Parsing and data formatting are performed on the server side, and using the network might be ineffective. +Running requests don't stop automatically if the HTTP connection is lost. Parsing and data formatting are performed on the server side, and using the network might be ineffective. The optional 'query_id' parameter can be passed as the query ID (any string). For more information, see the section "Settings, replace_running_query". The optional 'quota_key' parameter can be passed as the quota key (any string). For more information, see the section "Quotas". @@ -220,3 +220,4 @@ curl -sS 'http://localhost:8123/?max_result_bytes=4000000&buffer_size=3000000&wa ``` Use buffering to avoid situations where a query processing error occurred after the response code and HTTP headers were sent to the client. In this situation, an error message is written at the end of the response body, and on the client side, the error can only be detected at the parsing stage. + diff --git a/docs/en/interfaces/index.md b/docs/en/interfaces/index.md old mode 100755 new mode 100644 diff --git a/docs/en/interfaces/jdbc.md b/docs/en/interfaces/jdbc.md old mode 100755 new mode 100644 diff --git a/docs/en/interfaces/tcp.md b/docs/en/interfaces/tcp.md old mode 100755 new mode 100644 diff --git a/docs/en/interfaces/third-party_client_libraries.md b/docs/en/interfaces/third-party_client_libraries.md old mode 100755 new mode 100644 index 10ef1e62b49..c3831e55ade --- a/docs/en/interfaces/third-party_client_libraries.md +++ b/docs/en/interfaces/third-party_client_libraries.md @@ -34,8 +34,8 @@ There are libraries for working with ClickHouse for: - C++ - [clickhouse-cpp](https://github.com/artpaul/clickhouse-cpp/) - Elixir - - [clickhousex](https://github.com/appodeal/clickhousex/) - - [clickhouse_ecto](https://github.com/appodeal/clickhouse_ecto) + - [clickhousex](https://github.com/appodeal/clickhousex/) + - [clickhouse_ecto](https://github.com/appodeal/clickhouse_ecto) We have not tested these libraries. They are listed in random order. diff --git a/docs/en/interfaces/third-party_gui.md b/docs/en/interfaces/third-party_gui.md old mode 100755 new mode 100644 diff --git a/docs/en/introduction/distinctive_features.md b/docs/en/introduction/distinctive_features.md old mode 100755 new mode 100644 diff --git a/docs/en/introduction/features_considered_disadvantages.md b/docs/en/introduction/features_considered_disadvantages.md old mode 100755 new mode 100644 diff --git a/docs/en/introduction/index.md b/docs/en/introduction/index.md old mode 100755 new mode 100644 diff --git a/docs/en/introduction/performance.md b/docs/en/introduction/performance.md old mode 100755 new mode 100644 diff --git a/docs/en/introduction/possible_silly_questions.md b/docs/en/introduction/possible_silly_questions.md old mode 100755 new mode 100644 diff --git a/docs/en/introduction/ya_metrika_task.md b/docs/en/introduction/ya_metrika_task.md old mode 100755 new mode 100644 index 10f45f061d6..9c16b4e708b --- a/docs/en/introduction/ya_metrika_task.md +++ b/docs/en/introduction/ya_metrika_task.md @@ -1,6 +1,6 @@ # Yandex.Metrica use case -ClickHouse currently powers [Yandex.Metrica](https://metrika.yandex.ru/), [the second largest web analytics platform in the world](http://w3techs.com/technologies/overview/traffic_analysis/all). With more than 13 trillion records in the database and more than 20 billion events daily, ClickHouse allows you generating custom reports on the fly directly from non-aggregated data. +ClickHouse currently powers [Yandex.Metrica](https://metrica.yandex.com/), [the second largest web analytics platform in the world](http://w3techs.com/technologies/overview/traffic_analysis/all). With more than 13 trillion records in the database and more than 20 billion events daily, ClickHouse allows you generating custom reports on the fly directly from non-aggregated data. We need to get custom reports based on hits and sessions, with custom segments set by the user. Data for the reports is updated in real-time. Queries must be run immediately (in online mode). We must be able to build reports for any time period. Complex aggregates must be calculated, such as the number of unique visitors. At this time (April 2014), Yandex.Metrica receives approximately 12 billion events (pageviews and mouse clicks) daily. All these events must be stored in order to build custom reports. A single query may require scanning hundreds of millions of rows over a few seconds, or millions of rows in no more than a few hundred milliseconds. diff --git a/docs/en/operations/access_rights.md b/docs/en/operations/access_rights.md old mode 100755 new mode 100644 index 1c72bf13b3e..63caa5c8d90 --- a/docs/en/operations/access_rights.md +++ b/docs/en/operations/access_rights.md @@ -9,50 +9,51 @@ Users are recorded in the 'users' section. Here is a fragment of the `users.xml` - + Example: 65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5 + + How to generate decent password: + Execute: PASSWORD=$(base64 < /dev/urandom | head -c8); echo "$PASSWORD"; echo -n "$PASSWORD" | sha256sum | tr -d '-' + In first line will be password and in second - corresponding SHA256. + --> - - - - - default - - - default - - - - - - - web - default - - test + --> + + + + default + + + default + + + + + + + web + default + + test ``` diff --git a/docs/en/operations/configuration_files.md b/docs/en/operations/configuration_files.md old mode 100755 new mode 100644 diff --git a/docs/en/operations/index.md b/docs/en/operations/index.md old mode 100755 new mode 100644 diff --git a/docs/en/operations/quotas.md b/docs/en/operations/quotas.md old mode 100755 new mode 100644 index fb1238b257d..41a4d398044 --- a/docs/en/operations/quotas.md +++ b/docs/en/operations/quotas.md @@ -13,20 +13,20 @@ In contrast to query complexity restrictions, quotas: Let's look at the section of the 'users.xml' file that defines quotas. ```xml - + - + - - - + + + 3600 - - 0 - 0 - 0 - 0 + + 0 + 0 + 0 + 0 0 @@ -37,21 +37,23 @@ The resource consumption calculated for each interval is output to the server lo ```xml - - - - 3600 - 1000 - 100 - 1000000000 - 100000000000 - 900 - + + + + 3600 - - 86400 - 10000 - 1000 + 1000 + 100 + 1000000000 + 100000000000 + 900 + + + + 86400 + + 10000 + 1000 5000000000 500000000000 7200 @@ -82,11 +84,14 @@ Quotas can use the "quota key" feature in order to report on resources for multi ```xml - ``` @@ -96,3 +101,4 @@ The quota is assigned to users in the 'users' section of the config. See the sec For distributed query processing, the accumulated amounts are stored on the requestor server. So if the user goes to another server, the quota there will "start over". When the server is restarted, quotas are reset. + diff --git a/docs/en/operations/server_settings/index.md b/docs/en/operations/server_settings/index.md old mode 100755 new mode 100644 diff --git a/docs/en/operations/server_settings/settings.md b/docs/en/operations/server_settings/settings.md old mode 100755 new mode 100644 index e9916b9a836..2a87f00d43f --- a/docs/en/operations/server_settings/settings.md +++ b/docs/en/operations/server_settings/settings.md @@ -348,7 +348,7 @@ For more information, see the section "[Creating replicated tables](../../table_ ## mark_cache_size -Approximate size (in bytes) of the cache of "marks" used by [MergeTree](../../table_engines/mergetree.md#table_engines-mergetree) family. +Approximate size (in bytes) of the cache of "marks" used by [MergeTree](../../table_engines/mergetree.md#table_engines-mergetree) engines. The cache is shared for the server and memory is allocated as needed. The cache size must be at least 5368709120. @@ -450,7 +450,7 @@ Keys for server/client settings: - verificationMode – The method for checking the node's certificates. Details are in the description of the [Context](https://github.com/ClickHouse-Extras/poco/blob/master/NetSSL_OpenSSL/include/Poco/Net/Context.h) class. Possible values: ``none``, ``relaxed``, ``strict``, ``once``. - verificationDepth – The maximum length of the verification chain. Verification will fail if the certificate chain length exceeds the set value. - loadDefaultCAFile – Indicates that built-in CA certificates for OpenSSL will be used. Acceptable values: `` true``, `` false``. | -- cipherList - Поддерживаемые OpenSSL-шифры. For example: `` ALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH``. +- cipherList – Supported OpenSSL encryptions. For example: `` ALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH``. - cacheSessions – Enables or disables caching sessions. Must be used in combination with ``sessionIdContext``. Acceptable values: `` true``, `` false``. - sessionIdContext – A unique set of random characters that the server appends to each generated identifier. The length of the string must not exceed ``SSL_MAX_SSL_SESSION_ID_LENGTH``. This parameter is always recommended, since it helps avoid problems both if the server caches the session and if the client requested caching. Default value: ``${application.name}``. - sessionCacheSize – The maximum number of sessions that the server caches. Default value: 1024\*20. 0 – Unlimited sessions. @@ -691,3 +691,4 @@ For more information, see the section "[Replication](../../table_engines/replica ```xml ``` + diff --git a/docs/en/operations/settings/index.md b/docs/en/operations/settings/index.md old mode 100755 new mode 100644 diff --git a/docs/en/operations/settings/query_complexity.md b/docs/en/operations/settings/query_complexity.md old mode 100755 new mode 100644 index 627c2b00ea1..bd46617eed0 --- a/docs/en/operations/settings/query_complexity.md +++ b/docs/en/operations/settings/query_complexity.md @@ -26,21 +26,40 @@ After enabling readonly mode, you can't disable it in the current session. When using the GET method in the HTTP interface, 'readonly = 1' is set automatically. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter. + + ## max_memory_usage -The maximum amount of memory consumption when running a query on a single server. By default, 10 GB. +The maximum amount of RAM to use for running a query on a single server. + +In the default configuration file, the maximum is 10 GB. The setting doesn't consider the volume of available memory or the total volume of memory on the machine. The restriction applies to a single query within a single server. -You can use SHOW PROCESSLIST to see the current memory consumption for each query. +You can use `SHOW PROCESSLIST` to see the current memory consumption for each query. In addition, the peak memory consumption is tracked for each query and written to the log. -Certain cases of memory consumption are not tracked: +Memory usage is not monitored for the states of certain aggregate functions. -- Large constants (for example, a very long string constant). -- The states of certain aggregate functions. +Memory usage is not fully tracked for states of the aggregate functions `min`, `max`, `any`, `anyLast`, `argMin`, `argMax` from `String` and `Array` arguments. -Memory consumption is not fully considered for aggregate function states 'min', 'max', 'any', 'anyLast', 'argMin', and 'argMax' from String and Array arguments. +Memory consumption is also restricted by the parameters `max_memory_usage_for_user` and `max_memory_usage_for_all_queries`. + +## max_memory_usage_for_user + +The maximum amount of RAM to use for running a user's queries on a single server. + +Default values are defined in [Settings.h](https://github.com/yandex/ClickHouse/blob/master/dbms/src/Interpreters/Settings.h#L244). By default, the amount is not restricted (`max_memory_usage_for_user = 0`). + +See also the descriptions of [max_memory_usage]( and #settings_max_memory_usage). + +## max_memory_usage_for_all_queries + +The maximum amount of RAM to use for running all queries on a single server. + +Default values are defined in [Settings.h](https://github.com/yandex/ClickHouse/blob/master/dbms/src/Interpreters/Settings.h#L245). By default, the amount is not restricted (`max_memory_usage_for_all_queries = 0`). + +See also the descriptions of [max_memory_usage]( and #settings_max_memory_usage). ## max_rows_to_read diff --git a/docs/en/operations/settings/settings.md b/docs/en/operations/settings/settings.md old mode 100755 new mode 100644 index 25c804b0035..bb75180b95a --- a/docs/en/operations/settings/settings.md +++ b/docs/en/operations/settings/settings.md @@ -338,3 +338,4 @@ It works for JSONEachRow and TSKV formats. ## output_format_json_quote_64bit_integers If the value is true, integers appear in quotes when using JSON\* Int64 and UInt64 formats (for compatibility with most JavaScript implementations); otherwise, integers are output without the quotes. + diff --git a/docs/en/operations/settings/settings_profiles.md b/docs/en/operations/settings/settings_profiles.md old mode 100755 new mode 100644 index f1fce41ba75..5f454c0724a --- a/docs/en/operations/settings/settings_profiles.md +++ b/docs/en/operations/settings/settings_profiles.md @@ -15,13 +15,9 @@ Example: ```xml - + - - 8 - - - + 8 1000000000 100000000000 diff --git a/docs/en/operations/tips.md b/docs/en/operations/tips.md old mode 100755 new mode 100644 index 652698fe24c..9378c25fab1 --- a/docs/en/operations/tips.md +++ b/docs/en/operations/tips.md @@ -174,7 +174,8 @@ dynamicConfigFile=/etc/zookeeper-{{ cluster['name'] }}/conf/zoo.cfg.dynamic Java version: ```text -Java(TM) SE Runtime Environment (build 1.8.0_25-b17)Java HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode) +Java(TM) SE Runtime Environment (build 1.8.0_25-b17) +Java HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode) ``` JVM parameters: diff --git a/docs/en/operators/index.md b/docs/en/operators/index.md old mode 100755 new mode 100644 diff --git a/docs/en/query_language/index.md b/docs/en/query_language/index.md old mode 100755 new mode 100644 diff --git a/docs/en/query_language/queries.md b/docs/en/query_language/queries.md old mode 100755 new mode 100644 index a8503a91bc2..b1d6d5a3b06 --- a/docs/en/query_language/queries.md +++ b/docs/en/query_language/queries.md @@ -183,7 +183,7 @@ Deletes all tables inside the 'db' database, then deletes the 'db' database itse If `IF EXISTS` is specified, it doesn't return an error if the database doesn't exist. ```sql -DROP TABLE [IF EXISTS] [db.]name [ON CLUSTER cluster] +DROP [TEMPORARY] TABLE [IF EXISTS] [db.]name [ON CLUSTER cluster] ``` Deletes the table. @@ -312,10 +312,10 @@ Data directory: `/var/lib/clickhouse/data/database/table/`,where `/var/lib/click ```bash $ ls -l /var/lib/clickhouse/data/test/visits/ total 48 -drwxrwxrwx 2 clickhouse clickhouse 20480 may 13 02:58 20140317_20140323_2_2_0 -drwxrwxrwx 2 clickhouse clickhouse 20480 may 13 02:58 20140317_20140323_4_4_0 -drwxrwxrwx 2 clickhouse clickhouse 4096 may 13 02:55 detached --rw-rw-rw- 1 clickhouse clickhouse 2 may 13 02:58 increment.txt +drwxrwxrwx 2 clickhouse clickhouse 20480 мая 13 02:58 20140317_20140323_2_2_0 +drwxrwxrwx 2 clickhouse clickhouse 20480 мая 13 02:58 20140317_20140323_4_4_0 +drwxrwxrwx 2 clickhouse clickhouse 4096 мая 13 02:55 detached +-rw-rw-rw- 1 clickhouse clickhouse 2 мая 13 02:58 increment.txt ``` Here, `20140317_20140323_2_2_0` and ` 20140317_20140323_4_4_0` are the directories of data parts. @@ -450,7 +450,7 @@ See also the section "Formats". ## SHOW TABLES ```sql -SHOW TABLES [FROM db] [LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format] +SHOW [TEMPORARY] TABLES [FROM db] [LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format] ``` Displays a list of tables @@ -497,7 +497,7 @@ watch -n1 "clickhouse-client --query='SHOW PROCESSLIST'" ## SHOW CREATE TABLE ```sql -SHOW CREATE TABLE [db.]table [INTO OUTFILE filename] [FORMAT format] +SHOW CREATE [TEMPORARY] TABLE [db.]table [INTO OUTFILE filename] [FORMAT format] ``` Returns a single `String`-type 'statement' column, which contains a single value – the `CREATE` query used for creating the specified table. @@ -515,7 +515,7 @@ Nested data structures are output in "expanded" format. Each column is shown sep ## EXISTS ```sql -EXISTS TABLE [db.]name [INTO OUTFILE filename] [FORMAT format] +EXISTS [TEMPORARY] TABLE [db.]name [INTO OUTFILE filename] [FORMAT format] ``` Returns a single `UInt8`-type column, which contains the single value `0` if the table or database doesn't exist, or `1` if the table exists in the specified database. @@ -1103,7 +1103,7 @@ Example: SELECT domainWithoutWWW(URL) AS domain, count(), - any(Title) AS title -- getting the first occurring page header for each domain. + any(Title) AS title -- getting the first occurred page header for each domain. FROM hits GROUP BY domain ``` @@ -1434,7 +1434,7 @@ and the result will be put in a temporary table in RAM. Then the request will be SELECT uniq(UserID) FROM local_table WHERE CounterID = 101500 AND UserID GLOBAL IN _data1 ``` -and the temporary table `_data1` will be sent to every remote server together with the query (the name of the temporary table is implementation-defined). +and the temporary table `_data1` will be sent to every remote server with the query (the name of the temporary table is implementation-defined). This is more optimal than using the normal IN. However, keep the following points in mind: @@ -1482,28 +1482,29 @@ KILL QUERY [FORMAT format] ``` -Attempts to terminate queries currently running. -The queries to terminate are selected from the system.processes table for which `WHERE` expression is true. +Attempts to forcibly terminate the currently running queries. +The queries to terminate are selected from the system.processes table using the criteria defined in the `WHERE` clause of the `KILL` query. Examples: ```sql --- Terminates all queries with the specified query_id. +-- Forcibly terminates all queries with the specified query_id: KILL QUERY WHERE query_id='2-857d-4a57-9ee0-327da5d60a90' --- Synchronously terminates all queries run by `username`. +-- Synchronously terminates all queries run by 'username': KILL QUERY WHERE user='username' SYNC ``` -Readonly-users can only terminate their own requests. +Read-only users can only stop their own queries. -By default, the asynchronous version of queries is used (`ASYNC`), which terminates without waiting for queries to complete. +By default, the asynchronous version of queries is used (`ASYNC`), which doesn't wait for confirmation that queries have stopped. -The synchronous version (`SYNC`) waits for all queries to be completed and displays information about each process as it terminates. +The synchronous version (`SYNC`) waits for all queries to stop and displays information about each process as it stops. The response contains the `kill_status` column, which can take the following values: -1. 'finished' – The query completed successfully. -2. 'waiting' – Waiting for the query to finish after sending it a signal to terminate. -3. The other values ​​explain why the query can't be terminated. +1. 'finished' – The query was terminated successfully. +2. 'waiting' – Waiting for the query to end after sending it a signal to terminate. +3. The other values ​​explain why the query can't be stopped. + +A test query (`TEST`) only checks the user's rights and displays a list of queries to stop. -A test query (`TEST`) only checks the user's rights and displays a list of queries to terminate. diff --git a/docs/en/query_language/syntax.md b/docs/en/query_language/syntax.md old mode 100755 new mode 100644 diff --git a/docs/en/roadmap.md b/docs/en/roadmap.md old mode 100755 new mode 100644 index 8241b0a65ae..3bf32517c46 --- a/docs/en/roadmap.md +++ b/docs/en/roadmap.md @@ -2,7 +2,7 @@ ## Q1 2018 -### New functionality +### New fuctionality - Support for `UPDATE` and `DELETE`. @@ -13,9 +13,9 @@ ```sql CREATE TABLE t ( - x Array(Array(String)), + x Array(Array(String)), z Nested( - x Array(String), + x Array(String), y Nested(...)) ) ENGINE = MergeTree ORDER BY x @@ -26,7 +26,7 @@ ENGINE = MergeTree ORDER BY x External tables can be integrated into ClickHouse using external dictionaries. This new functionality is a convenient alternative to connecting external tables. ```sql -SELECT ... +SELECT ... FROM mysql('host:port', 'db', 'table', 'user', 'password')` ``` @@ -34,8 +34,7 @@ FROM mysql('host:port', 'db', 'table', 'user', 'password')` - Effective data copying between ClickHouse clusters. - Now you can copy data with the remote() function. For example: ` -INSERT INTO t SELECT * FROM remote(...) `. + Now you can copy data with the remote() function. For example: `INSERT INTO t SELECT * FROM remote(...) `. This operation will have improved performance. @@ -48,7 +47,9 @@ INSERT INTO t SELECT * FROM remote(...) `. ### New functionality - UPDATE/DELETE conform to the EU GDPR. + - Protobuf and Parquet input and output formats. + - Creating dictionaries using DDL queries. Currently, dictionaries that are part of the database schema are defined in external XML files. This is inconvenient and counter-intuitive. The new approach should fix it. diff --git a/docs/en/system_tables/index.md b/docs/en/system_tables/index.md old mode 100755 new mode 100644 diff --git a/docs/en/system_tables/system.asynchronous_metrics.md b/docs/en/system_tables/system.asynchronous_metrics.md old mode 100755 new mode 100644 diff --git a/docs/en/system_tables/system.clusters.md b/docs/en/system_tables/system.clusters.md old mode 100755 new mode 100644 index bc8dab86b3c..1241b22f183 --- a/docs/en/system_tables/system.clusters.md +++ b/docs/en/system_tables/system.clusters.md @@ -4,12 +4,12 @@ Contains information about clusters available in the config file and the servers Columns: ```text -cluster String - Cluster name. -shard_num UInt32 - Number of a shard in the cluster, starting from 1. -shard_weight UInt32 - Relative weight of a shard when writing data. -replica_num UInt32 - Number of a replica in the shard, starting from 1. -host_name String - Host name as specified in the config. -host_address String - Host's IP address obtained from DNS. -port UInt16 - The port used to access the server. -user String - The username to use for connecting to the server. +cluster String – Cluster name. +shard_num UInt32 – Number of a shard in the cluster, starting from 1. +shard_weight UInt32 – Relative weight of a shard when writing data. +replica_num UInt32 – Number of a replica in the shard, starting from 1. +host_name String – Host name as specified in the config. +host_address String – Host's IP address obtained from DNS. +port UInt16 – The port used to access the server. +user String – The username to use for connecting to the server. ``` diff --git a/docs/en/system_tables/system.columns.md b/docs/en/system_tables/system.columns.md old mode 100755 new mode 100644 index bf05616fbef..975b84fe9d4 --- a/docs/en/system_tables/system.columns.md +++ b/docs/en/system_tables/system.columns.md @@ -11,3 +11,4 @@ type String - Column type. default_type String - Expression type (DEFAULT, MATERIALIZED, ALIAS) for the default value, or an empty string if it is not defined. default_expression String - Expression for the default value, or an empty string if it is not defined. ``` + diff --git a/docs/en/system_tables/system.databases.md b/docs/en/system_tables/system.databases.md old mode 100755 new mode 100644 diff --git a/docs/en/system_tables/system.dictionaries.md b/docs/en/system_tables/system.dictionaries.md old mode 100755 new mode 100644 index 4ef0d7707b8..d637ae5b1fb --- a/docs/en/system_tables/system.dictionaries.md +++ b/docs/en/system_tables/system.dictionaries.md @@ -5,19 +5,19 @@ Contains information about external dictionaries. Columns: ```text -name String - Dictionary name. -type String - Dictionary type: Flat, Hashed, Cache. -origin String - Path to the config file where the dictionary is described. -attribute.names Array(String) - Array of attribute names provided by the dictionary. -attribute.types Array(String) - Corresponding array of attribute types provided by the dictionary. -has_hierarchy UInt8 - Whether the dictionary is hierarchical. -bytes_allocated UInt64 - The amount of RAM used by the dictionary. -hit_rate Float64 - For cache dictionaries, the percent of usage for which the value was in the cache. -element_count UInt64 - The number of items stored in the dictionary. -load_factor Float64 - The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table). -creation_time DateTime - Time spent for the creation or last successful reload of the dictionary. -last_exception String - Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created. -source String - Text describing the data source for the dictionary. +name String – Dictionary name. +type String – Dictionary type: Flat, Hashed, Cache. +origin String – Path to the config file where the dictionary is described.attribute. +names Array(String) – Array of attribute names provided by the dictionary. +attribute.types Array(String) – Corresponding array of attribute types provided by the dictionary. +has_hierarchy UInt8 – Whether the dictionary is hierarchical. +bytes_allocated UInt64 – The amount of RAM used by the dictionary. +hit_rate Float64 – For cache dictionaries, the percent of usage for which the value was in the cache. +element_count UInt64 – The number of items stored in the dictionary. +load_factor Float64 – The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table). +creation_time DateTime – Time spent for the creation or last successful reload of the dictionary. +last_exception String – Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created. +source String – Text describing the data source for the dictionary. ``` Note that the amount of memory used by the dictionary is not proportional to the number of items stored in it. So for flat and cached dictionaries, all the memory cells are pre-assigned, regardless of how full the dictionary actually is. diff --git a/docs/en/system_tables/system.events.md b/docs/en/system_tables/system.events.md old mode 100755 new mode 100644 diff --git a/docs/en/system_tables/system.functions.md b/docs/en/system_tables/system.functions.md old mode 100755 new mode 100644 index a1022a5e557..ac550acc14b --- a/docs/en/system_tables/system.functions.md +++ b/docs/en/system_tables/system.functions.md @@ -6,6 +6,6 @@ Columns: ```text name String – Function name. -is_aggregate UInt8 – Whether it is an aggregate function. +is_aggregate UInt8 – Whether it is an aggregate function. ``` diff --git a/docs/en/system_tables/system.merges.md b/docs/en/system_tables/system.merges.md old mode 100755 new mode 100644 index 59870922ea5..2844f6ab837 --- a/docs/en/system_tables/system.merges.md +++ b/docs/en/system_tables/system.merges.md @@ -4,17 +4,15 @@ Contains information about merges currently in process for tables in the MergeTr Columns: -```text -database String - Name of the database the table is located in. -table String - Name of the table. -elapsed Float64 - Time in seconds since the merge started. -progress Float64 - Percent of progress made, from 0 to 1. -num_parts UInt64 - Number of parts to merge. -result_part_name String - Name of the part that will be formed as the result of the merge. -total_size_bytes_compressed UInt64 - Total size of compressed data in the parts being merged. -total_size_marks UInt64 - Total number of marks in the parts being merged. -bytes_read_uncompressed UInt64 - Amount of bytes read, decompressed. -rows_read UInt64 - Number of rows read. -bytes_written_uncompressed UInt64 - Amount of bytes written, uncompressed. -rows_written UInt64 - Number of rows written. -``` +- `database String` — Name of the database the table is located in. +- `table String` — Name of the table. +- `elapsed Float64` — Time in seconds since the merge started. +- `progress Float64` — Percent of progress made, from 0 to 1. +- `num_parts UInt64` — Number of parts to merge. +- `result_part_name String` — Name of the part that will be formed as the result of the merge. +- `total_size_bytes_compressed UInt64` — Total size of compressed data in the parts being merged. +- `total_size_marks UInt64` — Total number of marks in the parts being merged. +- `bytes_read_uncompressed UInt64` — Amount of bytes read, decompressed. +- `rows_read UInt64` — Number of rows read. +- `bytes_written_uncompressed UInt64` — Amount of bytes written, uncompressed. +- `rows_written UInt64` — Number of rows written. diff --git a/docs/en/system_tables/system.numbers.md b/docs/en/system_tables/system.numbers.md old mode 100755 new mode 100644 diff --git a/docs/en/system_tables/system.numbers_mt.md b/docs/en/system_tables/system.numbers_mt.md old mode 100755 new mode 100644 diff --git a/docs/en/system_tables/system.one.md b/docs/en/system_tables/system.one.md old mode 100755 new mode 100644 diff --git a/docs/en/system_tables/system.parts.md b/docs/en/system_tables/system.parts.md old mode 100755 new mode 100644 diff --git a/docs/en/system_tables/system.processes.md b/docs/en/system_tables/system.processes.md old mode 100755 new mode 100644 index 0802e555648..ba449c280e9 --- a/docs/en/system_tables/system.processes.md +++ b/docs/en/system_tables/system.processes.md @@ -6,19 +6,19 @@ Columns: ```text user String – Name of the user who made the request. For distributed query processing, this is the user who helped the requestor server send the query to this server, not the user who made the distributed request on the requestor server. -address String – The IP address that the query was made from. The same is true for distributed query processing. +address String – The IP address that the query was made from. The same is true for distributed query processing. elapsed Float64 – The time in seconds since request execution started. -rows_read UInt64 – The number of rows read from the table. For distributed processing, on the requestor server, this is the total for all remote servers. +rows_read UInt64 – The number of rows read from the table. For distributed processing, on the requestor server, this is the total for all remote servers. -bytes_read UInt64 – The number of uncompressed bytes read from the table. For distributed processing, on the requestor server, this is the total for all remote servers. +bytes_read UInt64 – The number of uncompressed bytes read from the table. For distributed processing, on the requestor server, this is the total for all remote servers. UInt64 total_rows_approx – The approximate total number of rows that must be read. For distributed processing, on the requestor server, this is the total for all remote servers. It can be updated during request processing, when new sources to process become known. memory_usage UInt64 – Memory consumption by the query. It might not include some types of dedicated memory. -Query String – The query text. For INSERT, it doesn't include the data to insert. +query String – The query text. For INSERT, it doesn't include the data to insert. query_id – Query ID, if defined. ``` diff --git a/docs/en/system_tables/system.replicas.md b/docs/en/system_tables/system.replicas.md old mode 100755 new mode 100644 index 75cd8e34340..c777e35bad0 --- a/docs/en/system_tables/system.replicas.md +++ b/docs/en/system_tables/system.replicas.md @@ -54,28 +54,32 @@ This mode is turned on if the config doesn't have sections with ZK, if an unknow is_session_expired: Whether the ZK session expired. Basically, the same thing as is_readonly. -future_parts: The number of data parts that will appear as the result of INSERTs or merges that haven't been done yet. +future_parts: The number of data parts that will appear as the result of INSERTs or merges that haven't been done yet. -parts_to_check: The number of data parts in the queue for verification. +parts_to_check: The number of data parts in the queue for verification. A part is put in the verification queue if there is suspicion that it might be damaged. -zookeeper_path: The path to the table data in ZK. +zookeeper_path: The path to the table data in ZK. replica_name: Name of the replica in ZK. Different replicas of the same table have different names. -replica_path: The path to the replica data in ZK. The same as concatenating zookeeper_path/replicas/replica_path. +replica_path: The path to the replica data in ZK. The same as concatenating zookeeper_path/replicas/replica_path. -columns_version: Version number of the table structure. Indicates how many times ALTER was performed. If replicas have different versions, it means some replicas haven't made all of the ALTERs yet. +columns_version: Version number of the table structure. +Indicates how many times ALTER was performed. If replicas have different versions, it means some replicas haven't made all of the ALTERs yet. -queue_size: Size of the queue for operations waiting to be performed. +queue_size: Size of the queue for operations waiting to be performed. Operations include inserting blocks of data, merges, and certain other actions. Normally coincides with future_parts. -inserts_in_queue: Number of inserts of blocks of data that need to be made. Insertions are usually replicated fairly quickly. If the number is high, something is wrong. +inserts_in_queue: Number of inserts of blocks of data that need to be made. +Insertions are usually replicated fairly quickly. If the number is high, something is wrong. -merges_in_queue: The number of merges waiting to be made. Sometimes merges are lengthy, so this value may be greater than zero for a long time. +merges_in_queue: The number of merges waiting to be made. +Sometimes merges are lengthy, so this value may be greater than zero for a long time. The next 4 columns have a non-null value only if the ZK session is active. -log_max_index: Maximum entry number in the log of general activity. log_pointer: Maximum entry number in the log of general activity that the replica copied to its execution queue, plus one. +log_max_index: Maximum entry number in the log of general activity. +log_pointer: Maximum entry number in the log of general activity that the replica copied to its execution queue, plus one. If log_pointer is much smaller than log_max_index, something is wrong. total_replicas: Total number of known replicas of this table. diff --git a/docs/en/system_tables/system.settings.md b/docs/en/system_tables/system.settings.md old mode 100755 new mode 100644 diff --git a/docs/en/system_tables/system.tables.md b/docs/en/system_tables/system.tables.md old mode 100755 new mode 100644 diff --git a/docs/en/system_tables/system.zookeeper.md b/docs/en/system_tables/system.zookeeper.md old mode 100755 new mode 100644 index 46b40e7a08f..d20f7620b38 --- a/docs/en/system_tables/system.zookeeper.md +++ b/docs/en/system_tables/system.zookeeper.md @@ -9,22 +9,21 @@ If the path specified in 'path' doesn't exist, an exception will be thrown. Columns: -```text -name String - Name of the node. -path String - Path to the node. -value String - Value of the node. -dataLength Int32 - Size of the value. -numChildren Int32 - Number of children. -czxid Int64 - ID of the transaction that created the node. -mzxid Int64 - ID of the transaction that last changed the node. -pzxid Int64 - ID of the transaction that last added or removed children. -ctime DateTime - Time of node creation. -mtime DateTime - Time of the last node modification. -version Int32 - Node version - the number of times the node was changed. -cversion Int32 - Number of added or removed children. -aversion Int32 - Number of changes to ACL. -ephemeralOwner Int64 - For ephemeral nodes, the ID of the session that owns this node. -``` +- `name String` — Name of the node. +- `path String` — Path to the node. +- `value String` — Value of the node. +- `dataLength Int32` — Size of the value. +- `numChildren Int32` — Number of children. +- `czxid Int64` — ID of the transaction that created the node. +- `mzxid Int64` — ID of the transaction that last changed the node. +- `pzxid Int64` — ID of the transaction that last added or removed children. +- `ctime DateTime` — Time of node creation. +- `mtime DateTime` — Time of the last node modification. +- `version Int32` — Node version - the number of times the node was changed. +- `cversion Int32` — Number of added or removed children. +- `aversion Int32` — Number of changes to ACL. +- `ephemeralOwner Int64` — For ephemeral nodes, the ID of the session that owns this node. + Example: diff --git a/docs/en/table_engines/aggregatingmergetree.md b/docs/en/table_engines/aggregatingmergetree.md old mode 100755 new mode 100644 diff --git a/docs/en/table_engines/buffer.md b/docs/en/table_engines/buffer.md old mode 100755 new mode 100644 diff --git a/docs/en/table_engines/collapsingmergetree.md b/docs/en/table_engines/collapsingmergetree.md old mode 100755 new mode 100644 diff --git a/docs/en/table_engines/custom_partitioning_key.md b/docs/en/table_engines/custom_partitioning_key.md old mode 100755 new mode 100644 diff --git a/docs/en/table_engines/dictionary.md b/docs/en/table_engines/dictionary.md old mode 100755 new mode 100644 index ae8cca90d7c..ab7ea29c3aa --- a/docs/en/table_engines/dictionary.md +++ b/docs/en/table_engines/dictionary.md @@ -54,6 +54,7 @@ SELECT FROM system.dictionaries WHERE name = 'products' ``` + ``` ┌─name─────┬─type─┬─key────┬─attribute.names─┬─attribute.types─┬─bytes_allocated─┬─element_count─┬─source──────────┐ │ products │ Flat │ UInt64 │ ['title'] │ ['String'] │ 23065376 │ 175032 │ ODBC: .products │ @@ -102,5 +103,5 @@ LIMIT 1 │ 152689 │ Некоторый товар │ └───────────────┴─────────────────┘ -1 rows in set. Elapsed: 0.006 sec. +1 rows in set. Elapsed: 0.006 sec. ``` diff --git a/docs/en/table_engines/distributed.md b/docs/en/table_engines/distributed.md old mode 100755 new mode 100644 index dd2ffe27fe5..ea0f9fb7b49 --- a/docs/en/table_engines/distributed.md +++ b/docs/en/table_engines/distributed.md @@ -26,28 +26,28 @@ Clusters are set like this: - 1 - - false - - example01-01-1 - 9000 - - - example01-01-2 - 9000 - - - - 2 - false - - example01-02-1 - 9000 - - - example01-02-2 - 9000 + 1 + + false + + example01-01-1 + 9000 + + + example01-01-2 + 9000 + + + + 2 + false + + example01-02-1 + 9000 + + + example01-02-2 + 9000 diff --git a/docs/en/table_engines/external_data.md b/docs/en/table_engines/external_data.md old mode 100755 new mode 100644 diff --git a/docs/en/table_engines/file.md b/docs/en/table_engines/file.md old mode 100755 new mode 100644 diff --git a/docs/en/table_engines/graphitemergetree.md b/docs/en/table_engines/graphitemergetree.md old mode 100755 new mode 100644 index a4b62424954..d53d871ba6e --- a/docs/en/table_engines/graphitemergetree.md +++ b/docs/en/table_engines/graphitemergetree.md @@ -83,3 +83,4 @@ Example of settings: ``` + diff --git a/docs/en/table_engines/index.md b/docs/en/table_engines/index.md old mode 100755 new mode 100644 index bb5e01e7903..212df9c0f67 --- a/docs/en/table_engines/index.md +++ b/docs/en/table_engines/index.md @@ -8,7 +8,8 @@ The table engine (type of table) determines: - Use of indexes, if present. - Whether multithreaded request execution is possible. - Data replication. -- When reading data, the engine is only required to extract the necessary set of columns.However, in some cases, the query may be partially processed inside the table engine. +- When reading data, the engine is only required to extract the necessary set of columns. + However, in some cases, the query may be partially processed inside the table engine. Note that for most serious tasks, you should use engines from the MergeTree family. diff --git a/docs/en/table_engines/join.md b/docs/en/table_engines/join.md old mode 100755 new mode 100644 diff --git a/docs/en/table_engines/kafka.md b/docs/en/table_engines/kafka.md old mode 100755 new mode 100644 index 4f10e55d029..9c766e40fb6 --- a/docs/en/table_engines/kafka.md +++ b/docs/en/table_engines/kafka.md @@ -1,100 +1,100 @@ -# Kafka - -The engine works with [Apache Kafka](http://kafka.apache.org/). - -Kafka lets you: - -- Publish or subscribe to data flows. -- Organize fault-tolerant storage. -- Process streams as they become available. - -``` -Kafka(broker_list, topic_list, group_name, format[, schema, num_consumers]) -``` - -Parameters: - -- `broker_list` – A comma-separated list of brokers (`localhost:9092`). -- `topic_list` – A list of Kafka topics (`my_topic`). -- `group_name` – A group of Kafka consumers (`group1`). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere. -- `format` – Message format. Uses the same notation as the SQL ` FORMAT` function, such as ` JSONEachRow`. For more information, see the section "Formats". -- `schema` – An optional parameter that must be used if the format requires a schema definition. For example, [Cap'n Proto](https://capnproto.org/) requires the path to the schema file and the name of the root ` schema.capnp:Message` object. -- `num_consumers` - Number of created consumers per engine. By default `1`. Create more consumers if the throughput of a single consumer is insufficient. The total number of consumers shouldn't exceed the number of partitions in given topic, as there can be at most 1 consumers assigned to any single partition. - -Example: - -```sql - CREATE TABLE queue ( - timestamp UInt64, - level String, - message String - ) ENGINE = Kafka('localhost:9092', 'topic', 'group1', 'JSONEachRow'); - - SELECT * FROM queue LIMIT 5; -``` - -The delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name. - -Groups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. For more information, see [http://kafka.apache.org/intro](http://kafka.apache.org/intro). - -`SELECT` is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. For this purpose, the following was done: - -1. Use the engine to create a Kafka consumer and consider it a data stream. -2. Create a table with the desired structure. -3. Create a materialized view that converts data from the engine and puts it into a previously created table. - -When the `MATERIALIZED VIEW` joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using `SELECT` - -Example: - -```sql - CREATE TABLE queue ( - timestamp UInt64, - level String, - message String - ) ENGINE = Kafka('localhost:9092', 'topic', 'group1', 'JSONEachRow'); - - CREATE TABLE daily ( - day Date, - level String, - total UInt64 - ) ENGINE = SummingMergeTree(day, (day, level), 8192); - - CREATE MATERIALIZED VIEW consumer TO daily - AS SELECT toDate(toDateTime(timestamp)) AS day, level, count() as total - FROM queue GROUP BY day, level; - - SELECT level, sum(total) FROM daily GROUP BY level; -``` - -To improve performance, received messages are grouped into blocks the size of [max_block_size](../operations/settings/settings.md#settings-settings-max_insert_block_size). If the block wasn't formed within [ stream_flush_interval_ms](../operations/settings/settings.md#settings-settings_stream_flush_interval_ms) milliseconds, the data will be flushed to the table regardless of the completeness of the block. - -To stop receiving topic data or to change the conversion logic, detach the materialized view: - -``` - DETACH TABLE consumer; - ATTACH MATERIALIZED VIEW consumer; -``` - -If you want to change the target table by using `ALTER` materialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view. - - -## Configuration - -Similarly to GraphiteMergeTree, Kafka engine supports extended configuration through the ClickHouse config file. There are two configuration keys you can use - global (`kafka`), and per-topic (`kafka_topic_*`). The global configuration is applied first, then per-topic configuration (if exists). - -```xml - - - cgrp - smallest - - - - - 250 - 100000 - -``` - -See [librdkafka configuration reference](https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md) for the list of possible configuration options. Use underscores instead of dots in the ClickHouse configuration, for example `check.crcs=true` would correspond to `true`. +# Kafka + +This engine works with [Apache Kafka](http://kafka.apache.org/). + +Kafka lets you: + +- Publish or subscribe to data flows. +- Organize fault-tolerant storage. +- Process streams as they become available. + +``` +Kafka(broker_list, topic_list, group_name, format[, schema, num_consumers]) +``` + +Parameters: + +- `broker_list` – A comma-separated list of brokers (`localhost:9092`). +- `topic_list` – A list of Kafka topics (`my_topic`). +- `group_name` – A group of Kafka consumers (`group1`). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere. +- `--format` – Message format. Uses the same notation as the SQL ` FORMAT` function, such as ` JSONEachRow`. For more information, see the "Formats" section. +- `schema` – An optional parameter that must be used if the format requires a schema definition. For example, [Cap'n Proto](https://capnproto.org/) requires the path to the schema file and the name of the root `schema.capnp:Message` object. +- `num_consumers` – The number of consumers per table. Default: `1`. Specify more consumers if the throughput of one consumer is insufficient. The total number of consumers should not exceed the number of partitions in the topic, since only one consumer can be assigned per partition. + +Example: + +```sql + CREATE TABLE queue ( + timestamp UInt64, + level String, + message String + ) ENGINE = Kafka('localhost:9092', 'topic', 'group1', 'JSONEachRow'); + + SELECT * FROM queue LIMIT 5; +``` + +The delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name. + +Groups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. Read more about this at [http://kafka.apache.org/intro](http://kafka.apache.org/intro). + +`SELECT` is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. To do this: + +1. Use the engine to create a Kafka consumer and consider it a data stream. +2. Create a table with the desired structure. +3. Create a materialized view that converts data from the engine and puts it into a previously created table. + +When the `MATERIALIZED VIEW` joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using `SELECT` + +Example: + +```sql + CREATE TABLE queue ( + timestamp UInt64, + level String, + message String + ) ENGINE = Kafka('localhost:9092', 'topic', 'group1', 'JSONEachRow'); + + CREATE TABLE daily ( + day Date, + level String, + total UInt64 + ) ENGINE = SummingMergeTree(day, (day, level), 8192); + + CREATE MATERIALIZED VIEW consumer TO daily + AS SELECT toDate(toDateTime(timestamp)) AS day, level, count() as total + FROM queue GROUP BY day, level; + + SELECT level, sum(total) FROM daily GROUP BY level; +``` + +To improve performance, received messages are grouped into blocks the size of [max_insert_block_size](../operations/settings/settings.md#settings-settings-max_insert_block_size). If the block wasn't formed within [stream_flush_interval_ms](../operations/settings/settings.md#settings-settings_stream_flush_interval_ms) milliseconds, the data will be flushed to the table regardless of the completeness of the block. + +To stop receiving topic data or to change the conversion logic, detach the materialized view: + +``` + DETACH TABLE consumer; + ATTACH MATERIALIZED VIEW consumer; +``` + +If you want to change the target table by using ` ALTER`materialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view. + +## Configuration + +Similar to GraphiteMergeTree, the Kafka engine supports extended configuration using the ClickHouse config file. There are two configuration keys that you can use: global (`kafka`) and topic-level (`kafka_topic_*`). The global configuration is applied first, and the topic-level configuration is second (if it exists). + +```xml + + + cgrp + smallest + + + + + 250 + 100000 + +``` + +For a list of possible configuration options, see the [librdkafka configuration reference](https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md). Use the underscore (`_`) instead of a dot in the ClickHouse configuration. For example, `check.crcs=true` will be `true`. + diff --git a/docs/en/table_engines/log.md b/docs/en/table_engines/log.md old mode 100755 new mode 100644 diff --git a/docs/en/table_engines/materializedview.md b/docs/en/table_engines/materializedview.md old mode 100755 new mode 100644 diff --git a/docs/en/table_engines/memory.md b/docs/en/table_engines/memory.md old mode 100755 new mode 100644 diff --git a/docs/en/table_engines/merge.md b/docs/en/table_engines/merge.md old mode 100755 new mode 100644 index 10424aa3f10..b0f07dd71d6 --- a/docs/en/table_engines/merge.md +++ b/docs/en/table_engines/merge.md @@ -2,21 +2,21 @@ The Merge engine (not to be confused with `MergeTree`) does not store data itself, but allows reading from any number of other tables simultaneously. Reading is automatically parallelized. Writing to a table is not supported. When reading, the indexes of tables that are actually being read are used, if they exist. -The Merge engine accepts parameters: the database name and a regular expression for tables. Example: +The Merge engine accepts parameters: the database name and a regular expression for tables. Example. ```text Merge(hits, '^WatchLog') ``` -- Data will be read from the tables in the 'hits' database that have names that match the regular expression '`^WatchLog`'. +Data will be read from the tables in the 'hits' database that have names that match the regular expression '`^WatchLog`'. Instead of the database name, you can use a constant expression that returns a string. For example, `currentDatabase()`. -Regular expressions are re2 (similar to PCRE), case-sensitive. +Regular expressions — [re2](https://github.com/google/re2) (supports a subset of PCRE), case-sensitive. See the notes about escaping symbols in regular expressions in the "match" section. When selecting tables to read, the Merge table itself will not be selected, even if it matches the regex. This is to avoid loops. -It is possible to create two Merge tables that will endlessly try to read each others' data. But don't do this. +It is possible to create two Merge tables that will endlessly try to read each others' data, but this is not a good idea. The typical way to use the Merge engine is for working with a large number of TinyLog tables as if with a single table. diff --git a/docs/en/table_engines/mergetree.md b/docs/en/table_engines/mergetree.md old mode 100755 new mode 100644 index fea02e01d72..7ee58165c80 --- a/docs/en/table_engines/mergetree.md +++ b/docs/en/table_engines/mergetree.md @@ -5,27 +5,27 @@ The MergeTree engine supports an index by primary key and by date, and provides the possibility to update data in real time. This is the most advanced table engine in ClickHouse. Don't confuse it with the Merge engine. -The engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity. Example: +The engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity. -Example without sampling support: +Example without sampling support. ```text MergeTree(EventDate, (CounterID, EventDate), 8192) ``` -Example with sampling support: +Example with sampling support. ```text MergeTree(EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID)), 8192) ``` -A MergeTree type table must have a separate column containing the date. In this example, it is the 'EventDate' column. The type of the date column must be 'Date' (not 'DateTime'). +A MergeTree table must have a separate column containing the date. Here, it is the EventDate column. The date column must have the 'Date' type (not 'DateTime'). The primary key may be a tuple from any expressions (usually this is just a tuple of columns), or a single expression. The sampling expression (optional) can be any expression. It must also be present in the primary key. The example uses a hash of user IDs to pseudo-randomly disperse data in the table for each CounterID and EventDate. In other words, when using the SAMPLE clause in a query, you get an evenly pseudo-random sample of data for a subset of users. -The table is implemented as a set of parts. Each part is sorted by the primary key. In addition, each part has the minimum and maximum date assigned. When inserting in the table, a new sorted part is created. The merge process is periodically initiated in the background. When merging, several parts are selected, usually the smallest ones, and then merged into one large sorted part. +The table is implemented as a set of parts. Each part is sorted by the primary key. In addition, each part has the minimum and maximum date assigned. When inserting in the table, a new sorted part is created. The merge process is periodically initiated in the background. When merging, several parts are selected (usually the smallest ones) and then merged into one large sorted part. In other words, incremental sorting occurs when inserting to the table. Merging is implemented so that the table always consists of a small number of sorted parts, and the merge itself doesn't do too much work. @@ -38,9 +38,9 @@ For each part, an index file is also written. The index file contains the primar For columns, "marks" are also written to each 'index_granularity' row so that data can be read in a specific range. When reading from a table, the SELECT query is analyzed for whether indexes can be used. -An index can be used if the WHERE or PREWHERE clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has IN above columns that are in the primary key or date, or Boolean operators over them. +An index can be used if the WHERE or PREWHERE clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has IN or LIKE with a fixed prefix on columns or expressions that are in the primary key or partitioning key, or on certain partially repetitive functions of these columns, or logical relationships of these expressions. -Thus, it is possible to quickly run queries on one or many ranges of the primary key. In the example given, queries will work quickly for a specific counter, for a specific counter and range of dates, for a specific counter and date, for multiple counters and a range of dates, and so on. +Thus, it is possible to quickly run queries on one or many ranges of the primary key. In this example, queries will be fast when run for a specific tracking tag; for a specific tag and date range; for a specific tag and date; for multiple tags with a date range, and so on. ```sql SELECT count() FROM table WHERE EventDate = toDate(now()) AND CounterID = 34 @@ -50,7 +50,7 @@ SELECT count() FROM table WHERE ((EventDate >= toDate('2014-01-01') AND EventDat All of these cases will use the index by date and by primary key. The index is used even for complex expressions. Reading from the table is organized so that using the index can't be slower than a full scan. -In this example, the index can't be used: +In this example, the index can't be used. ```sql SELECT count() FROM table WHERE CounterID = 34 OR URL LIKE '%upyachka%' diff --git a/docs/en/table_engines/mysql.md b/docs/en/table_engines/mysql.md new file mode 100644 index 00000000000..42a0e2d0c1b --- /dev/null +++ b/docs/en/table_engines/mysql.md @@ -0,0 +1,16 @@ + + +# MySQL + +The MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server. + +The engine takes 4 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example: + +```text +MySQL('host:port', 'database', 'table', 'user', 'password'); +``` + +At this time, simple WHERE clauses such as ```=, !=, >, >=, <, <=``` are executed on the MySQL server. + +The rest of the conditions and the LIMIT sampling constraint are executed in ClickHouse only after the query to MySQL finishes. + diff --git a/docs/en/table_engines/null.md b/docs/en/table_engines/null.md old mode 100755 new mode 100644 diff --git a/docs/en/table_engines/replacingmergetree.md b/docs/en/table_engines/replacingmergetree.md old mode 100755 new mode 100644 index 66332d44356..92f2ffb34bf --- a/docs/en/table_engines/replacingmergetree.md +++ b/docs/en/table_engines/replacingmergetree.md @@ -2,7 +2,7 @@ This engine table differs from `MergeTree` in that it removes duplicate entries with the same primary key value. -The last optional parameter for the table engine is the "version" column. When merging, it reduces all rows with the same primary key value to just one row. If the version column is specified, it leaves the row with the highest version; otherwise, it leaves the last row. +The last optional parameter for the table engine is the version column. When merging, it reduces all rows with the same primary key value to just one row. If the version column is specified, it leaves the row with the highest version; otherwise, it leaves the last row. The version column must have a type from the `UInt` family, `Date`, or `DateTime`. diff --git a/docs/en/table_engines/replication.md b/docs/en/table_engines/replication.md old mode 100755 new mode 100644 index 20dd17e444f..cdc9ce0d1e0 --- a/docs/en/table_engines/replication.md +++ b/docs/en/table_engines/replication.md @@ -2,26 +2,28 @@ # Data replication -## ReplicatedAggregatingMergeTree +Replication is only supported for tables in the MergeTree family: -## ReplicatedCollapsingMergeTree +- ReplicatedMergeTree +- ReplicatedSummingMergeTree +- ReplicatedReplacingMergeTree +- ReplicatedAggregatingMergeTree +- ReplicatedCollapsingMergeTree +- ReplicatedGraphiteMergeTree -## ReplicatedGraphiteMergeTree +Replication works at the level of an individual table, not the entire server. A server can store both replicated and non-replicated tables at the same time. -## ReplicatedMergeTree +Replication does not depend on sharding. Each shard has its own independent replication. -## ReplicatedReplacingMergeTree +Compressed data is replicated for `INSERT` and `ALTER` queries (see the description of the [ALTER](../query_language/queries.md#query_language_queries_alter) query). -## ReplicatedSummingMergeTree +`CREATE`, `DROP`, `ATTACH`, `DETACH` and `RENAME` queries are executed on a single server and are not replicated: -Replication is only supported for tables in the MergeTree family. Replication works at the level of an individual table, not the entire server. A server can store both replicated and non-replicated tables at the same time. +- `The CREATE TABLE` query creates a new replicatable table on the server where the query is run. If this table already exists on other servers, it adds a new replica. +- `The DROP TABLE` query deletes the replica located on the server where the query is run. +- `The RENAME` query renames the table on one of the replicas. In other words, replicated tables can have different names on different replicas. -INSERT and ALTER are replicated (for more information, see ALTER). Compressed data is replicated, not query texts. -The CREATE, DROP, ATTACH, DETACH, and RENAME queries are not replicated. In other words, they belong to a single server. The CREATE TABLE query creates a new replicatable table on the server where the query is run. If this table already exists on other servers, it adds a new replica. The DROP TABLE query deletes the replica located on the server where the query is run. The RENAME query renames the table on one of the replicas. In other words, replicated tables can have different names on different replicas. - -Replication is not related to sharding in any way. Replication works independently on each shard. - -Replication is an optional feature. To use replication, set the addresses of the ZooKeeper cluster in the config file. Example: +To use replication, set the addresses of the ZooKeeper cluster in the config file. Example: ```xml @@ -40,25 +42,25 @@ Replication is an optional feature. To use replication, set the addresses of the ``` -**Use ZooKeeper version 3.4.5 or later.** For example, the version in the Ubuntu Precise package is too old. +Use ZooKeeper version 3.4.5 or later. You can specify any existing ZooKeeper cluster and the system will use a directory on it for its own data (the directory is specified when creating a replicatable table). If ZooKeeper isn't set in the config file, you can't create replicated tables, and any existing replicated tables will be read-only. -ZooKeeper isn't used for SELECT queries. In other words, replication doesn't affect the productivity of SELECT queries – they work just as fast as for non-replicated tables. When querying distributed replicated tables, ClickHouse behavior is controlled by the settings [max_replica_delay_for_distributed_queries](../operations/settings/settings.md#settings_settings_max_replica_delay_for_distributed_queries) and [fallback_to_stale_replicas_for_distributed_queries](../operations/settings/settings.md#settings-settings-fallback_to_stale_replicas_for_distributed_queries). +ZooKeeper is not used in `SELECT` queries because replication does not affect the performance of `SELECT` and queries run just as fast as they do for non-replicated tables. When querying distributed replicated tables, ClickHouse behavior is controlled by the settings [max_replica_delay_for_distributed_queries](../operations/settings/settings.md#settings_settings_max_replica_delay_for_distributed_queries) and [fallback_to_stale_replicas_for_distributed_queries](../operations/settings/settings.md#settings-settings-fallback_to_stale_replicas_for_distributed_queries). -For each INSERT query (more precisely, for each inserted block of data; the INSERT query contains a single block, or per block for every max_insert_block_size = 1048576 rows), approximately ten entries are made in ZooKeeper in several transactions. This leads to slightly longer latencies for INSERT compared to non-replicated tables. But if you follow the recommendations to insert data in batches of no more than one INSERT per second, it doesn't create any problems. The entire ClickHouse cluster used for coordinating one ZooKeeper cluster has a total of several hundred INSERTs per second. The throughput on data inserts (the number of rows per second) is just as high as for non-replicated data. +For each `INSERT` query, approximately ten entries are added to ZooKeeper through several transactions. (To be more precise, this is for each inserted block of data; an INSERT query contains one block or one block per `max_insert_block_size = 1048576` rows.) This leads to slightly longer latencies for `INSERT` compared to non-replicated tables. But if you follow the recommendations to insert data in batches of no more than one `INSERT` per second, it doesn't create any problems. The entire ClickHouse cluster used for coordinating one ZooKeeper cluster has a total of several hundred `INSERTs` per second. The throughput on data inserts (the number of rows per second) is just as high as for non-replicated data. For very large clusters, you can use different ZooKeeper clusters for different shards. However, this hasn't proven necessary on the Yandex.Metrica cluster (approximately 300 servers). -Replication is asynchronous and multi-master. INSERT queries (as well as ALTER) can be sent to any available server. Data is inserted on this server, then sent to the other servers. Because it is asynchronous, recently inserted data appears on the other replicas with some latency. If part of the replicas are not available, the data on them is written when they become available. If a replica is available, the latency is the amount of time it takes to transfer the block of compressed data over the network. +Replication is asynchronous and multi-master. `INSERT` queries (as well as `ALTER`) can be sent to any available server. Data is inserted on the server where the query is run, and then it is copied to the other servers. Because it is asynchronous, recently inserted data appears on the other replicas with some latency. If part of the replicas are not available, the data is written when they become available. If a replica is available, the latency is the amount of time it takes to transfer the block of compressed data over the network. -There are no quorum writes. You can't write data with confirmation that it was received by more than one replica. If you write a batch of data to one replica and the server with this data ceases to exist before the data has time to get to the other replicas, this data will be lost. +By default, an INSERT query waits for confirmation of writing the data from only one replica. If the data was successfully written to only one replica and the server with this replica ceases to exist, the stored data will be lost. Tp enable getting confirmation of data writes from multiple replicas, use the `insert_quorum` option. -Each block of data is written atomically. The INSERT query is divided into blocks up to max_insert_block_size = 1048576 rows. In other words, if the INSERT query has less than 1048576 rows, it is made atomically. +Each block of data is written atomically. The INSERT query is divided into blocks up to `max_insert_block_size = 1048576` rows. In other words, if the `INSERT` query has less than 1048576 rows, it is made atomically. -Data blocks are deduplicated. For multiple writes of the same data block (data blocks of the same size containing the same rows in the same order), the block is only written once. The reason for this is in case of network failures when the client application doesn't know if the data was written to the DB, so the INSERT query can simply be repeated. It doesn't matter which replica INSERTs were sent to with identical data. INSERTs are idempotent. This only works for the last 100 blocks inserted in a table. +Data blocks are deduplicated. For multiple writes of the same data block (data blocks of the same size containing the same rows in the same order), the block is only written once. The reason for this is in case of network failures when the client application doesn't know if the data was written to the DB, so the `INSERT` query can simply be repeated. It doesn't matter which replica INSERTs were sent to with identical data. `INSERTs` are idempotent. Deduplication parameters are controlled by [merge_tree](../operations/server_settings/settings.md#server_settings-merge_tree) server settings. During replication, only the source data to insert is transferred over the network. Further data transformation (merging) is coordinated and performed on all the replicas in the same way. This minimizes network usage, which means that replication works well when replicas reside in different datacenters. (Note that duplicating data in different datacenters is the main goal of replication.) @@ -101,19 +103,21 @@ In this case, the path consists of the following parts: The replica name identifies different replicas of the same table. You can use the server name for this, as in the example. The name only needs to be unique within each shard. -You can define everything explicitly instead of using substitutions. This might be convenient for testing and for configuring small clusters, but it is inconvenient when working with large clusters. +You can define the parameters explicitly instead of using substitutions. This might be convenient for testing and for configuring small clusters. However, you can't use distributed DDL queries (`ON CLUSTER`) in this case. -Run CREATE TABLE on each replica. This query creates a new replicated table, or adds a new replica to an existing one. +When working with large clusters, we recommend using substitutions because they reduce the probability of error. + +Run the `CREATE TABLE` query on each replica. This query creates a new replicated table, or adds a new replica to an existing one. If you add a new replica after the table already contains some data on other replicas, the data will be copied from the other replicas to the new one after running the query. In other words, the new replica syncs itself with the others. -To delete a replica, run DROP TABLE. However, only one replica is deleted – the one that resides on the server where you run the query. +To delete a replica, run `DROP TABLE`. However, only one replica is deleted – the one that resides on the server where you run the query. ## Recovery after failures If ZooKeeper is unavailable when a server starts, replicated tables switch to read-only mode. The system periodically attempts to connect to ZooKeeper. -If ZooKeeper is unavailable during an INSERT, or an error occurs when interacting with ZooKeeper, an exception is thrown. +If ZooKeeper is unavailable during an `INSERT`, or an error occurs when interacting with ZooKeeper, an exception is thrown. After connecting to ZooKeeper, the system checks whether the set of data in the local file system matches the expected set of data (ZooKeeper stores this information). If there are minor inconsistencies, the system resolves them by syncing data with the replicas. @@ -121,7 +125,7 @@ If the system detects broken data parts (with the wrong size of files) or unreco Note that ClickHouse does not perform any destructive actions such as automatically deleting a large amount of data. -When the server starts (or establishes a new session with ZooKeeper), it only checks the quantity and sizes of all files. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a SELECT query. The query throws an exception about a non-matching checksum or size of a compressed block. In this case, data parts are added to the verification queue and copied from the replicas if necessary. +When the server starts (or establishes a new session with ZooKeeper), it only checks the quantity and sizes of all files. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a `SELECT` query. The query throws an exception about a non-matching checksum or size of a compressed block. In this case, data parts are added to the verification queue and copied from the replicas if necessary. If the local set of data differs too much from the expected one, a safety mechanism is triggered. The server enters this in the log and refuses to launch. The reason for this is that this case may indicate a configuration error, such as if a replica on a shard was accidentally configured like a replica on a different shard. However, the thresholds for this mechanism are set fairly low, and this situation might occur during normal failure recovery. In this case, data is restored semi-automatically - by "pushing a button". @@ -138,13 +142,13 @@ Then restart the server. On start, the server deletes these flags and starts rec If all data and metadata disappeared from one of the servers, follow these steps for recovery: 1. Install ClickHouse on the server. Define substitutions correctly in the config file that contains the shard identifier and replicas, if you use them. -2. If you had unreplicated tables that must be manually duplicated on the servers, copy their data from a replica (in the directory /var/lib/clickhouse/data/db_name/table_name/). -3. Copy table definitions located in /var/lib/clickhouse/metadata/ from a replica. If a shard or replica identifier is defined explicitly in the table definitions, correct it so that it corresponds to this replica. (Alternatively, launch the server and make all the ATTACH TABLE queries that should have been in the .sql files in /var/lib/clickhouse/metadata/.) -4. To start recovery, create the ZooKeeper node /path_to_table/replica_name/flags/force_restore_data with any content, or run the command to restore all replicated tables: `sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data` +2. If you had unreplicated tables that must be manually duplicated on the servers, copy their data from a replica (in the directory `/var/lib/clickhouse/data/db_name/table_name/`). +3. Copy table definitions located in `/var/lib/clickhouse/metadata/` from a replica. If a shard or replica identifier is defined explicitly in the table definitions, correct it so that it corresponds to this replica. (Alternatively, start the server and make all the `ATTACH TABLE` queries that should have been in the .sql files in `/var/lib/clickhouse/metadata/`.) +4. To start recovery, create the ZooKeeper node `/path_to_table/replica_name/flags/force_restore_data` with any content, or run the command to restore all replicated tables: `sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data` Then start the server (restart, if it is already running). Data will be downloaded from replicas. -An alternative recovery option is to delete information about the lost replica from ZooKeeper ( `/path_to_table/replica_name`), then create the replica again as described in "Creating replicated tables". +An alternative recovery option is to delete information about the lost replica from ZooKeeper (`/path_to_table/replica_name`), then create the replica again as described in "[Creating replicatable tables](#table_engines-replication-creation_of_rep_tables)". There is no restriction on network bandwidth during recovery. Keep this in mind if you are restoring many replicas at once. @@ -152,24 +156,24 @@ There is no restriction on network bandwidth during recovery. Keep this in mind We use the term `MergeTree` to refer to all table engines in the ` MergeTree family`, the same as for ` ReplicatedMergeTree`. -If you had a MergeTree table that was manually replicated, you can convert it to a replicatable table. You might need to do this if you have already collected a large amount of data in a MergeTree table and now you want to enable replication. +If you had a `MergeTree` table that was manually replicated, you can convert it to a replicatable table. You might need to do this if you have already collected a large amount of data in a `MergeTree` table and now you want to enable replication. If the data differs on various replicas, first sync it, or delete this data on all the replicas except one. -Rename the existing MergeTree table, then create a ReplicatedMergeTree table with the old name. +Rename the existing MergeTree table, then create a `ReplicatedMergeTree` table with the old name. Move the data from the old table to the 'detached' subdirectory inside the directory with the new table data (`/var/lib/clickhouse/data/db_name/table_name/`). -Then run ALTER TABLE ATTACH PARTITION on one of the replicas to add these data parts to the working set. +Then run `ALTER TABLE ATTACH PARTITION` on one of the replicas to add these data parts to the working set. ## Converting from ReplicatedMergeTree to MergeTree -Create a MergeTree table with a different name. Move all the data from the directory with the ReplicatedMergeTree table data to the new table's data directory. Then delete the ReplicatedMergeTree table and restart the server. +Create a MergeTree table with a different name. Move all the data from the directory with the `ReplicatedMergeTree` table data to the new table's data directory. Then delete the `ReplicatedMergeTree` table and restart the server. -If you want to get rid of a ReplicatedMergeTree table without launching the server: +If you want to get rid of a `ReplicatedMergeTree` table without launching the server: -- Delete the corresponding .sql file in the metadata directory (`/var/lib/clickhouse/metadata/`). +- Delete the corresponding `.sql` file in the metadata directory (`/var/lib/clickhouse/metadata/`). - Delete the corresponding path in ZooKeeper (`/path_to_table/replica_name`). -After this, you can launch the server, create a MergeTree table, move the data to its directory, and then restart the server. +After this, you can launch the server, create a `MergeTree` table, move the data to its directory, and then restart the server. ## Recovery when metadata in the ZooKeeper cluster is lost or damaged diff --git a/docs/en/table_engines/set.md b/docs/en/table_engines/set.md old mode 100755 new mode 100644 diff --git a/docs/en/table_engines/summingmergetree.md b/docs/en/table_engines/summingmergetree.md old mode 100755 new mode 100644 diff --git a/docs/en/table_engines/tinylog.md b/docs/en/table_engines/tinylog.md old mode 100755 new mode 100644 diff --git a/docs/en/table_engines/view.md b/docs/en/table_engines/view.md old mode 100755 new mode 100644 diff --git a/docs/en/table_functions/index.md b/docs/en/table_functions/index.md old mode 100755 new mode 100644 diff --git a/docs/en/table_functions/merge.md b/docs/en/table_functions/merge.md old mode 100755 new mode 100644 diff --git a/docs/en/table_functions/numbers.md b/docs/en/table_functions/numbers.md index b055f1cd56e..9b98d8747b6 100644 --- a/docs/en/table_functions/numbers.md +++ b/docs/en/table_functions/numbers.md @@ -1,17 +1,20 @@ # numbers -`numbers(N)` - returns the table with one column named `number` (UInt64 type), containing integer numbers from 0 to N-1. +`numbers(N)` – Returns a table with the single 'number' column (UInt64) that contains integers from 0 to N-1. -`numbers(N)` (like a table `system.numbers`) can be used in tests or for sequences generation. +Similar to the `system.numbers` table, it can be used for testing and generating successive values. + +The following two queries are equivalent: -Two following queries are equal: ```sql SELECT * FROM numbers(10); SELECT * FROM system.numbers LIMIT 10; ``` -Samples: +Examples: + ```sql --- generation of sequence of dates from 2010-01-01 to 2010-12-31 +-- Generate a sequence of dates from 2010-01-01 to 2010-12-31 select toDate('2010-01-01') + number as d FROM numbers(365); ``` + diff --git a/docs/en/table_functions/remote.md b/docs/en/table_functions/remote.md old mode 100755 new mode 100644 diff --git a/docs/en/utils/clickhouse-copier.md b/docs/en/utils/clickhouse-copier.md old mode 100755 new mode 100644 index 9d15053fe06..eeb5e077d6a --- a/docs/en/utils/clickhouse-copier.md +++ b/docs/en/utils/clickhouse-copier.md @@ -87,34 +87,32 @@ Parameters: They are overlaid by and respectively. --> 3 - + 1 - - + source_cluster test hits - + destination_cluster test hits2 - @@ -123,21 +121,22 @@ Parameters: ORDER BY (CounterID, EventDate) - + jumpConsistentHash(intHash64(UserID), 2) - + CounterID != 0 - diff --git a/docs/en/utils/clickhouse-local.md b/docs/en/utils/clickhouse-local.md old mode 100755 new mode 100644 diff --git a/docs/en/utils/index.md b/docs/en/utils/index.md old mode 100755 new mode 100644 diff --git a/docs/mkdocs_en.yml b/docs/mkdocs_en.yml index eeedc71a79b..012d498f3e2 100644 --- a/docs/mkdocs_en.yml +++ b/docs/mkdocs_en.yml @@ -96,6 +96,7 @@ pages: - 'View': 'table_engines/view.md' - 'MaterializedView': 'table_engines/materializedview.md' - 'Kafka': 'table_engines/kafka.md' + - 'MySQL': 'table_engines/mysql.md' - 'External data for query processing': 'table_engines/external_data.md' - 'System tables': diff --git a/docs/ru/dicts/external_dicts.md b/docs/ru/dicts/external_dicts.md index e77d5e6b841..c0b9f520b30 100644 --- a/docs/ru/dicts/external_dicts.md +++ b/docs/ru/dicts/external_dicts.md @@ -22,7 +22,7 @@ ClickHouse: /etc/metrika.xml - + @@ -44,10 +44,3 @@ ClickHouse: Вы можете преобразовывать значения по небольшому словарю, описав его в запросе `SELECT` (см. функцию [transform](../functions/other_functions.md#other_functions-transform)). Эта функциональность не связана с внешними словарями. - -```eval_rst -.. toctree:: - :glob: - - external_dicts_dict* -``` diff --git a/docs/ru/dicts/external_dicts_dict_layout.md b/docs/ru/dicts/external_dicts_dict_layout.md index defb0605c0f..e9e50abf164 100644 --- a/docs/ru/dicts/external_dicts_dict_layout.md +++ b/docs/ru/dicts/external_dicts_dict_layout.md @@ -2,11 +2,11 @@ # Хранение словарей в памяти -Словари можно размещать в памяти [множеством способов](external_dicts_dict_layout#dicts-external_dicts_dict_layout-manner). +Словари можно размещать в памяти [множеством способов](#dicts-external_dicts_dict_layout-manner). -Рекомендуем [flat](external_dicts_dict_layout#dicts-external_dicts_dict_layout-flat), [hashed](external_dicts_dict_layout#dicts-external_dicts_dict_layout-hashed) и [complex_key_hashed](external_dicts_dict_layout#dicts-external_dicts_dict_layout-complex_key_hashed). Скорость обработки словарей при этом максимальна. +Рекомендуем [flat](#dicts-external_dicts_dict_layout-flat), [hashed](#dicts-external_dicts_dict_layout-hashed) и [complex_key_hashed](#dicts-external_dicts_dict_layout-complex_key_hashed). Скорость обработки словарей при этом максимальна. -Размещение с кэшированием не рекомендуется использовать из-за потенциально низкой производительности и сложностей в подборе оптимальных параметров. Читайте об этом подробнее в разделе " [cache](external_dicts_dict_layout#dicts-external_dicts_dict_layout-cache)". +Размещение с кэшированием не рекомендуется использовать из-за потенциально низкой производительности и сложностей в подборе оптимальных параметров. Читайте об этом подробнее в разделе " [cache](#dicts-external_dicts_dict_layout-cache)". Повысить производительнось словарей можно следующими способами: @@ -88,7 +88,7 @@ ### complex_key_hashed -Тип размещения предназначен для использования с составными [ключами](external_dicts_dict_structure#dicts-external_dicts_dict_structure). Аналогичен `hashed`. +Тип размещения предназначен для использования с составными [ключами](external_dicts_dict_structure.md#dicts-external_dicts_dict_structure). Аналогичен `hashed`. Пример конфигурации: @@ -120,7 +120,7 @@ +---------------+---------------------+-------------------+--------+ ``` -Чтобы использовать выборку по диапазонам дат, необходимо в [structure](external_dicts_dict_structure#dicts-external_dicts_dict_structure) определить элементы `range_min`, `range_max`. +Чтобы использовать выборку по диапазонам дат, необходимо в [structure](external_dicts_dict_structure.md#dicts-external_dicts_dict_structure) определить элементы `range_min`, `range_max`. Пример: @@ -191,13 +191,13 @@ При поиске в словаре сначала просматривается кэш. На каждый блок данных, все не найденные в кэше или устаревшие ключи запрашиваются у источника с помощью `SELECT attrs... FROM db.table WHERE id IN (k1, k2, ...)`. Затем, полученные данные записываются в кэш. -Для cache-словарей может быть задано время устаревания (lifetime <dicts-external_dicts_dict_lifetime>) данных в кэше. Если от загрузки данных в ячейке прошло больше времени, чем `lifetime`, то значение не используется, и будет запрошено заново при следующей необходимости его использовать. +Для cache-словарей может быть задано время устаревания [lifetime](dicts-external_dicts_dict_lifetime.md#dicts-external_dicts_dict_lifetime) данных в кэше. Если от загрузки данных в ячейке прошло больше времени, чем `lifetime`, то значение не используется, и будет запрошено заново при следующей необходимости его использовать. Это наименее эффективный из всех способов размещения словарей. Скорость работы кэша очень сильно зависит от правильности настройки и сценария использования. Словарь типа cache показывает высокую производительность лишь при достаточно больших hit rate-ах (рекомендуется 99% и выше). Посмотреть средний hit rate можно в таблице `system.dictionaries`. Чтобы увеличить производительность кэша, используйте подзапрос с `LIMIT`, а снаружи вызывайте функцию со словарём. -Поддерживаются [источники](external_dicts_dict_sources#dicts-external_dicts_dict_sources): MySQL, ClickHouse, executable, HTTP. +Поддерживаются [источники](external_dicts_dict_sources.md#dicts-external_dicts_dict_sources): MySQL, ClickHouse, executable, HTTP. Пример настройки: @@ -227,7 +227,7 @@ ### complex_key_cache -Тип размещения предназначен для использования с составными [ключами](external_dicts_dict_structure#dicts-external_dicts_dict_structure). Аналогичен `cache`. +Тип размещения предназначен для использования с составными [ключами](external_dicts_dict_structure.md#dicts-external_dicts_dict_structure). Аналогичен `cache`. @@ -276,16 +276,20 @@ ... ``` -Этот ключ должен иметь только один атрибут типа String, содержащий допустимый префикс IP. Другие типы еще не поддерживаются. +Этот ключ должен иметь только один атрибут типа `String`, содержащий допустимый префикс IP. Другие типы еще не поддерживаются. Для запросов необходимо использовать те же функции (`dictGetT` с кортежем), что и для словарей с составными ключами: - dictGetT('dict_name', 'attr_name', tuple(ip)) +``` +dictGetT('dict_name', 'attr_name', tuple(ip)) +``` -Функция принимает либо UInt32 для адреса IPv4, либо FixedString(16) для адреса IPv6: +Функция принимает либо `UInt32` для IPv4, либо `FixedString(16)` для IPv6: - dictGetString('prefix', 'asn', tuple(IPv6StringToNum('2001:db8::1'))) +``` +dictGetString('prefix', 'asn', tuple(IPv6StringToNum('2001:db8::1'))) +``` Никакие другие типы не поддерживаются. Функция возвращает атрибут для префикса, соответствующего данному IP-адресу. Если есть перекрывающиеся префиксы, возвращается наиболее специфический. -Данные хранятся в побитовом дереве (trie), он должены полностью помещаться в оперативной памяти. +Данные хранятся в побитовом дереве (`trie`), он должены полностью помещаться в оперативной памяти. diff --git a/docs/ru/functions/other_functions.md b/docs/ru/functions/other_functions.md index d9648fb4efa..754dd56dce9 100644 --- a/docs/ru/functions/other_functions.md +++ b/docs/ru/functions/other_functions.md @@ -127,7 +127,7 @@ ORDER BY h ASC ```sql SELECT - transform(SearchEngineID, [2, 3], ['Яндекс', 'Google'], 'Остальные') AS title, + transform(SearchEngineID, [2, 3], ['Yandex', 'Google'], 'Other') AS title, count() AS c FROM test.hits WHERE SearchEngineID != 0 @@ -137,9 +137,9 @@ ORDER BY c DESC ```text ┌─title─────┬──────c─┐ -│ Яндекс │ 498635 │ +│ Yandex │ 498635 │ │ Google │ 229872 │ -│ Остальные │ 104472 │ +│ Other │ 104472 │ └───────────┴────────┘ ``` diff --git a/docs/ru/system_tables/system.merges.md b/docs/ru/system_tables/system.merges.md index 07439e04d75..c0b52a4675c 100644 --- a/docs/ru/system_tables/system.merges.md +++ b/docs/ru/system_tables/system.merges.md @@ -4,17 +4,15 @@ Столбцы: -```text -database String - имя базы данных, в которой находится таблица -table String - имя таблицы -elapsed Float64 - время в секундах, прошедшее от начала выполнения слияния -progress Float64 - доля выполненной работы от 0 до 1 -num_parts UInt64 - количество сливаемых кусков -result_part_name String - имя куска, который будет образован в результате слияния -total_size_bytes_compressed UInt64 - суммарный размер сжатых данных сливаемых кусков -total_size_marks UInt64 - суммарное количество засечек в сливаемых кусках -bytes_read_uncompressed UInt64 - количество прочитанных байт, разжатых -rows_read UInt64 - количество прочитанных строк -bytes_written_uncompressed UInt64 - количество записанных байт, несжатых -rows_written UInt64 - количество записанных строк -``` +- `database String` — Имя базы данных, в которой находится таблица. +- `table String` — Имя таблицы. +- `elapsed Float64` — Время в секундах, прошедшее от начала выполнения слияния. +- `progress Float64` — Доля выполненной работы от 0 до 1. +- `num_parts UInt64` — Количество сливаемых кусков. +- `result_part_name String` — Имя куска, который будет образован в результате слияния. +- `total_size_bytes_compressed UInt64` — Суммарный размер сжатых данных сливаемых кусков. +- `total_size_marks UInt64` — Суммарное количество засечек в сливаемых кусках. +- `bytes_read_uncompressed UInt64` — Количество прочитанных байт, разжатых. +- `rows_read UInt64` — Количество прочитанных строк. +- `bytes_written_uncompressed UInt64` — Количество записанных байт, несжатых. +- `rows_written UInt64` — Количество записанных строк. diff --git a/docs/ru/system_tables/system.zookeeper.md b/docs/ru/system_tables/system.zookeeper.md index 5753c6166db..be4222d1a76 100644 --- a/docs/ru/system_tables/system.zookeeper.md +++ b/docs/ru/system_tables/system.zookeeper.md @@ -9,22 +9,21 @@ Столбцы: -```text -name String - имя узла -path String - путь к узлу -value String - значение узла -dataLength Int32 - размер значения -numChildren Int32 - количество детей -czxid Int64 - идентификатор транзакции, в которой узел был создан -mzxid Int64 - идентификатор транзакции, в которой узел был последний раз изменён -pzxid Int64 - идентификатор транзакции, последний раз удаливший или добавивший детей -ctime DateTime - время создания узла -mtime DateTime - время последней модификации узла -version Int32 - версия узла - количество раз, когда узел был изменён -cversion Int32 - количество добавлений или удалений детей -aversion Int32 - количество изменений ACL -ephemeralOwner Int64 - для эфемерных узлов - идентификатор сессии, которая владеет этим узлом -``` +- `name String` — Имя узла. +- `path String` — Путь к узлу. +- `value String` — Значение узла. +- `dataLength Int32` — Размер значения. +- `numChildren Int32` — Количество детей. +- `czxid Int64` — Идентификатор транзакции, в которой узел был создан. +- `mzxid Int64` — Идентификатор транзакции, в которой узел был последний раз изменён. +- `pzxid Int64` — Идентификатор транзакции, последний раз удаливший или добавивший детей. +- `ctime DateTime` — Время создания узла. +- `mtime DateTime` — Время последней модификации узла. +- `version Int32` — Версия узла - количество раз, когда узел был изменён. +- `cversion Int32` — Количество добавлений или удалений детей. +- `aversion Int32` — Количество изменений ACL. +- `ephemeralOwner Int64` — Для эфемерных узлов - идентификатор сессии, которая владеет этим узлом. + Пример: diff --git a/docs/ru/table_engines/mysql.md b/docs/ru/table_engines/mysql.md index abdd511be35..5db09c25b71 100644 --- a/docs/ru/table_engines/mysql.md +++ b/docs/ru/table_engines/mysql.md @@ -2,14 +2,14 @@ # MySQL -Движок MySQL позволяет выполнять SELECT запросы над данными, хранящимися на удалённом MySQL сервере. +Движок MySQL позволяет выполнять `SELECT` запросы над данными, хранящимися на удалённом MySQL сервере. -Движок принимает 4 параметра: адрес сервера (хост и порт); имя базы данных; имя таблицы; имя пользоваля; пароль пользователя. Пример: +Формат вызова: -```text +``` MySQL('host:port', 'database', 'table', 'user', 'password'); ``` -На данный момент простые условия WHERE, такие как ```=, !=, >, >=, <, <=``` будут выполняться на стороне сервера MySQL. +На данный момент простые условия `WHERE`, такие как `=, !=, >, >=, <, <=` будут выполняться на стороне сервера MySQL. -Остальные условия и ограничение выборки LIMIT будут выполнены в ClickHouse только после выполнения запроса к MySQL. +Остальные условия и ограничение выборки `LIMIT` будут выполнены в ClickHouse только после выполнения запроса к MySQL. From 48ee13e2d9dba489691048bf61236483d8db641e Mon Sep 17 00:00:00 2001 From: BayoNet Date: Mon, 23 Apr 2018 10:34:55 +0300 Subject: [PATCH 002/231] Fixes of codeblock language and formatting. --- docs/en/formats/json.md | 9 +++--- docs/en/query_language/queries.md | 9 +++--- docs/en/system_tables/system.dictionaries.md | 29 +++++++++----------- docs/ru/formats/json.md | 8 +++--- docs/ru/query_language/queries.md | 8 +++--- docs/ru/system_tables/system.dictionaries.md | 29 ++++++++++---------- 6 files changed, 43 insertions(+), 49 deletions(-) diff --git a/docs/en/formats/json.md b/docs/en/formats/json.md index 635f37533cd..554510c2d7a 100644 --- a/docs/en/formats/json.md +++ b/docs/en/formats/json.md @@ -27,19 +27,19 @@ SELECT SearchPhrase, count() AS c FROM test.hits GROUP BY SearchPhrase WITH TOTA "c": "8267016" }, { - "SearchPhrase": "интерьер ванной комнаты", + "SearchPhrase": "bathroom interior design", "c": "2166" }, { - "SearchPhrase": "яндекс", + "SearchPhrase": "yandex", "c": "1655" }, { - "SearchPhrase": "весна 2014 мода", + "SearchPhrase": "spring 2014 fashion", "c": "1549" }, { - "SearchPhrase": "фриформ фото", + "SearchPhrase": "freeform photos", "c": "1480" } ], @@ -83,4 +83,3 @@ If the query contains GROUP BY, rows_before_limit_at_least is the exact number o This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). See also the JSONEachRow format. - diff --git a/docs/en/query_language/queries.md b/docs/en/query_language/queries.md index b1d6d5a3b06..4c13b0b01cf 100644 --- a/docs/en/query_language/queries.md +++ b/docs/en/query_language/queries.md @@ -312,10 +312,10 @@ Data directory: `/var/lib/clickhouse/data/database/table/`,where `/var/lib/click ```bash $ ls -l /var/lib/clickhouse/data/test/visits/ total 48 -drwxrwxrwx 2 clickhouse clickhouse 20480 мая 13 02:58 20140317_20140323_2_2_0 -drwxrwxrwx 2 clickhouse clickhouse 20480 мая 13 02:58 20140317_20140323_4_4_0 -drwxrwxrwx 2 clickhouse clickhouse 4096 мая 13 02:55 detached --rw-rw-rw- 1 clickhouse clickhouse 2 мая 13 02:58 increment.txt +drwxrwxrwx 2 clickhouse clickhouse 20480 may 13 02:58 20140317_20140323_2_2_0 +drwxrwxrwx 2 clickhouse clickhouse 20480 may 13 02:58 20140317_20140323_4_4_0 +drwxrwxrwx 2 clickhouse clickhouse 4096 may 13 02:55 detached +-rw-rw-rw- 1 clickhouse clickhouse 2 may 13 02:58 increment.txt ``` Here, `20140317_20140323_2_2_0` and ` 20140317_20140323_4_4_0` are the directories of data parts. @@ -1507,4 +1507,3 @@ The response contains the `kill_status` column, which can take the following val 3. The other values ​​explain why the query can't be stopped. A test query (`TEST`) only checks the user's rights and displays a list of queries to stop. - diff --git a/docs/en/system_tables/system.dictionaries.md b/docs/en/system_tables/system.dictionaries.md index d637ae5b1fb..0694902c656 100644 --- a/docs/en/system_tables/system.dictionaries.md +++ b/docs/en/system_tables/system.dictionaries.md @@ -4,21 +4,18 @@ Contains information about external dictionaries. Columns: -```text -name String – Dictionary name. -type String – Dictionary type: Flat, Hashed, Cache. -origin String – Path to the config file where the dictionary is described.attribute. -names Array(String) – Array of attribute names provided by the dictionary. -attribute.types Array(String) – Corresponding array of attribute types provided by the dictionary. -has_hierarchy UInt8 – Whether the dictionary is hierarchical. -bytes_allocated UInt64 – The amount of RAM used by the dictionary. -hit_rate Float64 – For cache dictionaries, the percent of usage for which the value was in the cache. -element_count UInt64 – The number of items stored in the dictionary. -load_factor Float64 – The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table). -creation_time DateTime – Time spent for the creation or last successful reload of the dictionary. -last_exception String – Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created. -source String – Text describing the data source for the dictionary. -``` +- `name String` – Dictionary name. +- `type String` – Dictionary type: Flat, Hashed, Cache. +- `origin String` – Path to the config file where the dictionary is described. +- `attribute.names Array(String)` – Array of attribute names provided by the dictionary. +- `attribute.types Array(String)` – Corresponding array of attribute types provided by the dictionary. +- `has_hierarchy UInt8` – Whether the dictionary is hierarchical. +- `bytes_allocated UInt64` – The amount of RAM used by the dictionary. +- `hit_rate Float64` – For cache dictionaries, the percent of usage for which the value was in the cache. +- `element_count UInt64` – The number of items stored in the dictionary. +- `load_factor Float64` – The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table). +- `creation_time DateTime` – Time spent for the creation or last successful reload of the dictionary. +- `last_exception String` – Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created. +- `source String` – Text describing the data source for the dictionary. Note that the amount of memory used by the dictionary is not proportional to the number of items stored in it. So for flat and cached dictionaries, all the memory cells are pre-assigned, regardless of how full the dictionary actually is. - diff --git a/docs/ru/formats/json.md b/docs/ru/formats/json.md index 00d26d9e597..e3eae2bd63b 100644 --- a/docs/ru/formats/json.md +++ b/docs/ru/formats/json.md @@ -27,19 +27,19 @@ SELECT SearchPhrase, count() AS c FROM test.hits GROUP BY SearchPhrase WITH TOTA "c": "8267016" }, { - "SearchPhrase": "интерьер ванной комнаты", + "SearchPhrase": "bathroom interior design", "c": "2166" }, { - "SearchPhrase": "яндекс", + "SearchPhrase": "yandex", "c": "1655" }, { - "SearchPhrase": "весна 2014 мода", + "SearchPhrase": "spring 2014 fashion", "c": "1549" }, { - "SearchPhrase": "фриформ фото", + "SearchPhrase": "freeform photos", "c": "1480" } ], diff --git a/docs/ru/query_language/queries.md b/docs/ru/query_language/queries.md index 5e37137d4a0..9a6aa20c737 100644 --- a/docs/ru/query_language/queries.md +++ b/docs/ru/query_language/queries.md @@ -308,10 +308,10 @@ SELECT * FROM system.parts WHERE active ```bash $ ls -l /var/lib/clickhouse/data/test/visits/ total 48 -drwxrwxrwx 2 clickhouse clickhouse 20480 мая 13 02:58 20140317_20140323_2_2_0 -drwxrwxrwx 2 clickhouse clickhouse 20480 мая 13 02:58 20140317_20140323_4_4_0 -drwxrwxrwx 2 clickhouse clickhouse 4096 мая 13 02:55 detached --rw-rw-rw- 1 clickhouse clickhouse 2 мая 13 02:58 increment.txt +drwxrwxrwx 2 clickhouse clickhouse 20480 may 13 02:58 20140317_20140323_2_2_0 +drwxrwxrwx 2 clickhouse clickhouse 20480 may 13 02:58 20140317_20140323_4_4_0 +drwxrwxrwx 2 clickhouse clickhouse 4096 may 13 02:55 detached +-rw-rw-rw- 1 clickhouse clickhouse 2 may 13 02:58 increment.txt ``` Здесь `20140317_20140323_2_2_0`, `20140317_20140323_4_4_0` - директории кусков. diff --git a/docs/ru/system_tables/system.dictionaries.md b/docs/ru/system_tables/system.dictionaries.md index 67b1af8c6b4..df588920bc1 100644 --- a/docs/ru/system_tables/system.dictionaries.md +++ b/docs/ru/system_tables/system.dictionaries.md @@ -4,20 +4,19 @@ Столбцы: -```text -name String - имя словаря -type String - тип словаря: Flat, Hashed, Cache -origin String - путь к конфигурационному файлу, в котором описан словарь -attribute.names Array(String) - массив имён атрибутов, предоставляемых словарём -attribute.types Array(String) - соответствующий массив типов атрибутов, предоставляемых словарём -has_hierarchy UInt8 - является ли словарь иерархическим -bytes_allocated UInt64 - количество оперативной памяти, которое использует словарь -hit_rate Float64 - для cache-словарей - доля использований, для которых значение было в кэше -element_count UInt64 - количество хранящихся в словаре элементов -load_factor Float64 - доля заполненности словаря (для hashed словаря - доля заполнения хэш-таблицы) -creation_time DateTime - время создания или последней успешной перезагрузки словаря -last_exception String - текст ошибки, возникшей при создании или перезагрузке словаря, если словарь не удалось создать -source String - текст, описывающий источник данных для словаря -``` +- `name String` — Имя словаря. +- `type String` — Тип словаря: Flat, Hashed, Cache. +- `origin String` — Путь к конфигурационному файлу, в котором описан словарь. +- `attribute.names Array(String)` — Массив имён атрибутов, предоставляемых словарём. +- `attribute.types Array(String)` — Соответствующий массив типов атрибутов, предоставляемых словарём. +- `has_hierarchy UInt8` — Является ли словарь иерархическим. +- `bytes_allocated UInt64` — Количество оперативной памяти, которое использует словарь. +- `hit_rate Float64` — Для cache-словарей - доля использований, для которых значение было в кэше. +- `element_count UInt64` — Количество хранящихся в словаре элементов. +- `load_factor Float64` — Доля заполненности словаря (для hashed словаря - доля заполнения хэш-таблицы). +- `creation_time DateTime` — Время создания или последней успешной перезагрузки словаря. +- `last_exception String` — Текст ошибки, возникшей при создании или перезагрузке словаря, если словарь не удалось создать. +- `source String` - Текст, описывающий источник данных для словаря. + Заметим, что количество оперативной памяти, которое использует словарь, не является пропорциональным количеству элементов, хранящихся в словаре. Так, для flat и cached словарей, все ячейки памяти выделяются заранее, независимо от реальной заполненности словаря. From 99b1cbb3c4e0c932312e84ca654176217d22f68e Mon Sep 17 00:00:00 2001 From: BayoNet Date: Tue, 24 Apr 2018 01:16:40 +0300 Subject: [PATCH 003/231] Fixed formatting of development/style.md --- docs/en/development/style.md | 1057 +++++++++++++++---------------- docs/ru/development/style.md | 1133 +++++++++++++++++++--------------- 2 files changed, 1158 insertions(+), 1032 deletions(-) diff --git a/docs/en/development/style.md b/docs/en/development/style.md index d583e81319c..0028feddc0e 100644 --- a/docs/en/development/style.md +++ b/docs/en/development/style.md @@ -2,810 +2,831 @@ ## General recommendations -1. The following are recommendations, not requirements. -2. If you are editing code, it makes sense to follow the formatting of the existing code. -3. Code style is needed for consistency. Consistency makes it easier to read the code, and it also makes it easier to search the code. -4. Many of the rules do not have logical reasons; they are dictated by established practices. +**1.** The following are recommendations, not requirements. + +**2.** If you are editing code, it makes sense to follow the formatting of the existing code. + +**3.** Code style is needed for consistency. Consistency makes it easier to read the code, and it also makes it easier to search the code. + +**4.** Many of the rules do not have logical reasons; they are dictated by established practices. ## Formatting -1. Most of the formatting will be done automatically by `clang-format`. +**1.** Most of the formatting will be done automatically by `clang-format`. -1. Offsets are 4 spaces. Configure your development environment so that a tab adds four spaces. +**2.** Offsets are 4 spaces. Configure your development environment so that a tab adds four spaces. -1. A left curly bracket must be separated on a new line. (And the right one, as well.) +**3.** A left curly bracket must be separated on a new line. (And the right one, as well.) - ```cpp - inline void readBoolText(bool & x, ReadBuffer & buf) - { - char tmp = '0'; - readChar(tmp, buf); - x = tmp != '0'; - } - ``` +```cpp +inline void readBoolText(bool & x, ReadBuffer & buf) +{ + char tmp = '0'; + readChar(tmp, buf); + x = tmp != '0'; +} +``` -1. But if the entire function body is quite short (a single statement), you can place it entirely on one line if you wish. Place spaces around curly braces (besides the space at the end of the line). +**4.** +But if the entire function body is quite short (a single statement), you can place it entirely on one line if you wish. Place spaces around curly braces (besides the space at the end of the line). - ```cpp - inline size_t mask() const { return buf_size() - 1; } - inline size_t place(HashValue x) const { return x & mask(); } - ``` +```cpp +inline size_t mask() const { return buf_size() - 1; } +inline size_t place(HashValue x) const { return x & mask(); } +``` -1. For functions, don't put spaces around brackets. +**5.** For functions, don't put spaces around brackets. - ```cpp - void reinsert(const Value & x) - ``` +```cpp +void reinsert(const Value & x) +memcpy(&buf[place_value], &x, sizeof(x)); +``` - ```cpp - memcpy(&buf[place_value], &x, sizeof(x)); - ``` +**6.** When using statements such as `if`, `for`, and `while` (unlike function calls), put a space before the opening bracket. -1. When using statements such as if, for, and while (unlike function calls), put a space before the opening bracket. + ```cpp + for (size_t i = 0; i < rows; i += storage.index_granularity) + ``` - ```cpp - for (size_t i = 0; i < rows; i += storage.index_granularity) - ``` +**7.** Put spaces around binary operators (`+`, `-`, `*`, `/`, `%`, ...), as well as the ternary operator `?:`. -1. Put spaces around binary operators (+,-, *,/,%, ...), as well as the ternary operator?:. +```cpp +UInt16 year = (s[0] - '0') * 1000 + (s[1] - '0') * 100 + (s[2] - '0') * 10 + (s[3] - '0'); +UInt8 month = (s[5] - '0') * 10 + (s[6] - '0'); +UInt8 day = (s[8] - '0') * 10 + (s[9] - '0'); +``` - ```cpp - UInt16 year = (s[0] - '0') * 1000 + (s[1] - '0') * 100 + (s[2] - '0') * 10 + (s[3] - '0'); - UInt8 month = (s[5] - '0') * 10 + (s[6] - '0'); - UInt8 day = (s[8] - '0') * 10 + (s[9] - '0'); - ``` +**8.** If a line feed is entered, put the operator on a new line and increase the indent before it. -1. If a line feed is entered, put the operator on a new line and increase the indent before it. +```cpp +if (elapsed_ns) + message << " (" + << rows_read_on_server * 1000000000 / elapsed_ns << " rows/s., " + << bytes_read_on_server * 1000.0 / elapsed_ns << " MB/s.) "; +``` - ```cpp - if (elapsed_ns) - message << " (" - << rows_read_on_server * 1000000000 / elapsed_ns << " rows/s., " - << bytes_read_on_server * 1000.0 / elapsed_ns << " MB/s.) "; - ``` +**9.** You can use spaces for alignment within a line, if desired. -1. You can use spaces for alignment within a line, if desired. +```cpp +dst.ClickLogID = click.LogID; +dst.ClickEventID = click.EventID; +dst.ClickGoodEvent = click.GoodEvent; +``` - ```cpp - dst.ClickLogID = click.LogID; - dst.ClickEventID = click.EventID; - dst.ClickGoodEvent = click.GoodEvent; - ``` +**10.** Don't use spaces around the operators `.`, `->` . -9. Don't use spaces around the operators `.`, `->` . +If necessary, the operator can be wrapped to the next line. In this case, the offset in front of it is increased. - If necessary, the operator can be wrapped to the next line. In this case, the offset in front of it is increased. +**11.** Do not use a space to separate unary operators (`-`, `+`, `*`, `&`, ...) from the argument. -10. Do not use a space to separate unary operators (`-, +, +, *, &`, ...) from the argument. +**12.** Put a space after a comma, but not before it. The same rule goes for a semicolon inside a for expression. -11. Put a space after a comma, but not before it. The same rule goes for a semicolon inside a for expression. +**13.** Do not use spaces to separate the `[]` operator. -12. Do not use spaces to separate the `[]` operator. +**14.** In a `template <...>` expression, use a space between `template` and `<`. No spaces after `<` or before `>`. -13. In a `template <...>` expression, use a space between `template`and`<`; no spaces after `<` or before `>`. +```cpp +template +struct AggregatedStatElement +{} +``` - ```cpp - template - struct AggregatedStatElement - {} - ``` +**15.** In classes and structures, public, private, and protected are written on the same level as the `class/struct`, but all other internal elements should be deeper. -14. In classes and structures, public, private, and protected are written on the same level as the class/struct, but all other internal elements should be deeper. +```cpp +template +class MultiVersion +{ +public: + /// Version of object for usage. shared_ptr manage lifetime of version. + using Version = std::shared_ptr; + ... +} +``` - ```cpp - template - class MultiVersion - { - public: - /// Version of object for usage. shared_ptr manage lifetime of version. - using Version = std::shared_ptr; - ... - } - ``` +**16.** If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace. -15. If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace. +**17.** If the block for `if`, `for`, `while`... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets. -16. If the block for if, for, while... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets. +```cpp +/// Finish write. +for (auto & stream : streams) + stream.second->finalize(); +``` - ```cpp - /// Finish write. - for (auto & stream : streams) - stream.second->finalize(); - ``` +**18.** There should be any spaces at the ends of lines. -17. There should be any spaces at the ends of lines. +**19.** Sources are UTF-8 encoded. -18. Sources are UTF-8 encoded. +**20.** Non-ASCII characters can be used in string literals. -19. Non-ASCII characters can be used in string literals. +```cpp +<< ", " << (timer.elapsed() / chunks_stats.hits) << " μsec/hit."; +``` - ```cpp - << ", " << (timer.elapsed() / chunks_stats.hits) << " μsec/hit."; - ``` +**21.** Do not write multiple expressions in a single line. -20. Do not write multiple expressions in a single line. +**22.** Group sections of code inside functions and separate them with no more than one empty line. -21. Group sections of code inside functions and separate them with no more than one empty line. +**23.** Separate functions, classes, and so on with one or two empty lines. -22. Separate functions, classes, and so on with at least one empty line (maximum – two empty lines). +**24.** A `const` (related to a value) must be written before the type name. -23. A const (related to a value) must be written before the type name. +```cpp +//correct +const char * pos +const std::string & s +//incorrect +char const * pos +``` - ``` - //correct - const char * pos - const std::string & s - //incorrect - char const * pos - ``` +**25.** When declaring a pointer or reference, the `*` and `&` symbols should be separated by spaces on both sides. -24. When declaring a pointer or reference, the \* and & symbols should be separated by spaces on both sides. +```cpp +//correct +const char * pos +//incorrect +const char* pos +const char *pos +``` - ``` - //correct - const char * pos - //incorrect - const char* pos - const char *pos - ``` +**26.** When using template types, alias them with the `using` keyword (except in the simplest cases). -25. When using template types, alias them with the `using` keyword (except in the simplest cases). +In other words, the template parameters are specified only in `using` and aren't repeated in the code. - In other words, the template parameters are specified only in `using` and aren't repeated in the code. +`using` can be declared locally, such as inside a function. - `using` can be declared locally, such as inside a function. +```cpp +//correct +using FileStreams = std::map>; +FileStreams streams; +//incorrect +std::map> streams; +``` - ``` - //correct - using FileStreams = std::map>; - FileStreams streams; - //incorrect - std::map> streams; - ``` +**27.** Do not declare several variables of different types in one statement. -26. Do not declare several variables of different types in one statement. +```cpp +//incorrect +int x, *y; +``` - ``` - //incorrect - int x, *y; - ``` +**28.** Do not use C-style casts. -27. Do not use C-style casts. +```cpp +//incorrect +std::cerr << (int)c <<; std::endl; +//correct +std::cerr << static_cast(c) << std::endl; +``` - ```cpp - //incorrect - std::cerr << (int)c <<; std::endl; - //correct - std::cerr << static_cast(c) << std::endl; - ``` -28. In classes and structs, group members and functions separately inside each visibility scope. +**29.** In classes and structs, group members and functions separately inside each visibility scope. -29. For small classes and structs, it is not necessary to separate the method declaration from the implementation. +**30.** For small classes and structs, it is not necessary to separate the method declaration from the implementation. - The same is true for small methods in any classes or structs. +The same is true for small methods in any classes or structs. - For templated classes and structs, don't separate the method declarations from the implementation (because otherwise they must be defined in the same translation unit). +For templated classes and structs, don't separate the method declarations from the implementation (because otherwise they must be defined in the same translation unit). -30. You can wrap lines at 140 characters, instead of 80. +**31.** You can wrap lines at 140 characters, instead of 80. -31. Always use the prefix increment/decrement operators if postfix is not required. +**32.** Always use the prefix increment/decrement operators if postfix is not required. - ```cpp - for (Names::const_iterator it = column_names.begin(); it != column_names.end(); ++it) - ``` +```cpp +for (Names::const_iterator it = column_names.begin(); it != column_names.end(); ++it) +``` ## Comments -1. Be sure to add comments for all non-trivial parts of code. +**1.** Be sure to add comments for all non-trivial parts of code. - This is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong. +This is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong. - ```cpp - /** Part of piece of memory, that can be used. - * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading, - * then working_buffer will have size of only 10 bytes - * (working_buffer.end() will point to the position right after those 10 bytes available for read). - */ - ``` +```cpp +/** Part of piece of memory, that can be used. + * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading, + * then working_buffer will have size of only 10 bytes + * (working_buffer.end() will point to the position right after those 10 bytes available for read). +*/ +``` -2. Comments can be as detailed as necessary. +**2.** Comments can be as detailed as necessary. -3. Place comments before the code they describe. In rare cases, comments can come after the code, on the same line. +**3.** Place comments before the code they describe. In rare cases, comments can come after the code, on the same line. - ```cpp - /** Parses and executes the query. - */ - void executeQuery( - ReadBuffer & istr, /// Where to read the query from (and data for INSERT, if applicable) - WriteBuffer & ostr, /// Where to write the result - Context & context, /// DB, tables, data types, engines, functions, aggregate functions... - BlockInputStreamPtr & query_plan, /// A description of query processing can be included here - QueryProcessingStage::Enum stage = QueryProcessingStage::Complete /// The last stage to process the SELECT query to - ) - ``` +```cpp +/** Parses and executes the query. +*/ +void executeQuery( + ReadBuffer & istr, /// Where to read the query from (and data for INSERT, if applicable) + WriteBuffer & ostr, /// Where to write the result + Context & context, /// DB, tables, data types, engines, functions, aggregate functions... + BlockInputStreamPtr & query_plan, /// A description of query processing can be included here + QueryProcessingStage::Enum stage = QueryProcessingStage::Complete /// The last stage to process the SELECT query to + ) +``` -4. Comments should be written in English only. +**4.** Comments should be written in English only. -5. If you are writing a library, include detailed comments explaining it in the main header file. +**5.** If you are writing a library, include detailed comments explaining it in the main header file. -6. Do not add comments that do not provide additional information. In particular, do not leave empty comments like this: +**6.** Do not add comments that do not provide additional information. In particular, do not leave empty comments like this: - ```cpp - /* - * Procedure Name: - * Original procedure name: - * Author: - * Date of creation: - * Dates of modification: - * Modification authors: - * Original file name: - * Purpose: - * Intent: - * Designation: - * Classes used: - * Constants: - * Local variables: - * Parameters: - * Date of creation: - * Purpose: - */ - ``` +```cpp +/* +* Procedure Name: +* Original procedure name: +* Author: +* Date of creation: +* Dates of modification: +* Modification authors: +* Original file name: +* Purpose: +* Intent: +* Designation: +* Classes used: +* Constants: +* Local variables: +* Parameters: +* Date of creation: +* Purpose: +*/ +``` - (the example is borrowed from the resource [http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/](http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/) +The example is borrowed from [http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/](http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/). -7. Do not write garbage comments (author, creation date ..) at the beginning of each file. +**7.** Do not write garbage comments (author, creation date ..) at the beginning of each file. -8. Single-line comments begin with three slashes: `///` and multi-line comments begin with `/**`. These comments are considered "documentation". +**8.** Single-line comments begin with three slashes: `///` and multi-line comments begin with `/**`. These comments are considered "documentation". - Note: You can use Doxygen to generate documentation from these comments. But Doxygen is not generally used because it is more convenient to navigate the code in the IDE. +Note: You can use Doxygen to generate documentation from these comments. But Doxygen is not generally used because it is more convenient to navigate the code in the IDE. -9. Multi-line comments must not have empty lines at the beginning and end (except the line that closes a multi-line comment). +**9.** Multi-line comments must not have empty lines at the beginning and end (except the line that closes a multi-line comment). -10. For commenting out code, use basic comments, not “documenting” comments. +**10.** For commenting out code, use basic comments, not "documenting" comments. -1. Delete the commented out parts of the code before commiting. +**11.** Delete the commented out parts of the code before commiting. -11. Do not use profanity in comments or code. +**12.** Do not use profanity in comments or code. -12. Do not use uppercase letters. Do not use excessive punctuation. +**13.** Do not use uppercase letters. Do not use excessive punctuation. - ```cpp - /// WHAT THE FAIL??? - ``` +```cpp +/// WHAT THE FAIL??? +``` -13. Do not make delimeters from comments. +**14.** Do not make delimeters from comments. - ``` - ///****************************************************** - ``` +``` +///****************************************************** +``` -14. Do not start discussions in comments. +**15.** Do not start discussions in comments. - ``` - /// Why did you do this stuff? - ``` +``` +/// Why did you do this stuff? +``` -15. There's no need to write a comment at the end of a block describing what it was about. +**16.** There's no need to write a comment at the end of a block describing what it was about. - ``` - /// for - ``` +``` +/// for +``` ## Names -1. The names of variables and class members use lowercase letters with underscores. +**1.** The names of variables and class members use lowercase letters with underscores. - ```cpp - size_t max_block_size; - ``` +```cpp +size_t max_block_size; +``` -2. The names of functions (methods) use camelCase beginning with a lowercase letter. +**2.** The names of functions (methods) use camelCase beginning with a lowercase letter. - ```cpp - std::string getName() const override { return "Memory"; } - ``` +```cpp +std::string getName() const override { return "Memory"; } +``` -3. The names of classes (structures) use CamelCase beginning with an uppercase letter. Prefixes other than I are not used for interfaces. +**3.** The names of classes (structures) use CamelCase beginning with an uppercase letter. Prefixes other than I are not used for interfaces. - ```cpp - class StorageMemory : public IStorage - ``` +```cpp +class StorageMemory : public IStorage +``` -4. The names of usings follow the same rules as classes, or you can add _t at the end. +**4.** The names of usings follow the same rules as classes, or you can add _t at the end. -5. Names of template type arguments for simple cases: T; T, U; T1, T2. +**5.** Names of template type arguments for simple cases: T; T, U; T1, T2. - For more complex cases, either follow the rules for class names, or add the prefix T. +For more complex cases, either follow the rules for class names, or add the prefix T. - ```cpp - template - struct AggregatedStatElement - ``` +```cpp +template +struct AggregatedStatElement +``` -6. Names of template constant arguments: either follow the rules for variable names, or use N in simple cases. +**6.** Names of template constant arguments: either follow the rules for variable names, or use N in simple cases. - ```cpp - template - struct ExtractDomain - ``` +```cpp +template +struct ExtractDomain +``` -7. For abstract classes (interfaces) you can add the I prefix. +**7.** For abstract classes (interfaces) you can add the I prefix. - ```cpp - class IBlockInputStream - ``` +```cpp +class IBlockInputStream +``` -8. If you use a variable locally, you can use the short name. +**8.** If you use a variable locally, you can use the short name. - In other cases, use a descriptive name that conveys the meaning. +In other cases, use a descriptive name that conveys the meaning. - ```cpp - bool info_successfully_loaded = false; - ``` +```cpp +bool info_successfully_loaded = false; +``` -9. define‘s should be in ALL_CAPS with underscores. The same is true for global constants. +**9.** `define`‘s should be in ALL_CAPS with underscores. The same is true for global constants. - ```cpp - #define MAX_SRC_TABLE_NAMES_TO_STORE 1000 - ``` +```cpp +#define MAX_SRC_TABLE_NAMES_TO_STORE 1000 +``` -10. File names should use the same style as their contents. +**10.** File names should use the same style as their contents. - If a file contains a single class, name the file the same way as the class, in CamelCase. +If a file contains a single class, name the file the same way as the class, in CamelCase. - If the file contains a single function, name the file the same way as the function, in camelCase. +If the file contains a single function, name the file the same way as the function, in camelCase. -11. If the name contains an abbreviation, then: - - For variable names, the abbreviation should use lowercase letters `mysql_connection` (not `mySQL_connection`). - - For names of classes and functions, keep the uppercase letters in the abbreviation`MySQLConnection` (not `MySqlConnection`). +**11.** If the name contains an abbreviation, then: -12. Constructor arguments that are used just to initialize the class members should be named the same way as the class members, but with an underscore at the end. +- For variable names, the abbreviation should use lowercase letters `mysql_connection` (not `mySQL_connection`). +- For names of classes and functions, keep the uppercase letters in the abbreviation `MySQLConnection` (not `MySqlConnection`). - ```cpp - FileQueueProcessor( - const std::string & path_, - const std::string & prefix_, - std::shared_ptr handler_) - : path(path_), - prefix(prefix_), - handler(handler_), - log(&Logger::get("FileQueueProcessor")) - { - } - ``` +**12.** Constructor arguments that are used just to initialize the class members should be named the same way as the class members, but with an underscore at the end. - The underscore suffix can be omitted if the argument is not used in the constructor body. +```cpp +FileQueueProcessor( + const std::string & path_, + const std::string & prefix_, + std::shared_ptr handler_) + : path(path_), + prefix(prefix_), + handler(handler_), + log(&Logger::get("FileQueueProcessor")) +{ +} +``` -13. There is no difference in the names of local variables and class members (no prefixes required). +The underscore suffix can be omitted if the argument is not used in the constructor body. - ``` - timer (not m_timer) - ``` +**13.** There is no difference in the names of local variables and class members (no prefixes required). -14. Constants in enums use CamelCase beginning with an uppercase letter. ALL_CAPS is also allowed. If the enum is not local, use enum class. +```cpp +timer (not m_timer) +``` - ```cpp - enum class CompressionMethod - { - QuickLZ = 0, - LZ4 = 1, - }; - ``` +**14.** Constants in enums use CamelCase beginning with an uppercase letter. ALL_CAPS is also allowed. If the enum is not local, use enum class. -15. All names must be in English. Transliteration of Russian words is not allowed. +```cpp +enum class CompressionMethod +{ + QuickLZ = 0, + LZ4 = 1, +}; +``` - ``` - not Stroka - ``` +**15.** All names must be in English. Transliteration of Russian words is not allowed. -16. Abbreviations are acceptable if they are well known (when you can easily find the meaning of the abbreviation in Wikipedia or in a search engine). +```cpp +not Stroka +``` - `AST`, `SQL`. +**16.** Abbreviations are acceptable if they are well known (when you can easily find the meaning of the abbreviation in Wikipedia or in a search engine). - Not `NVDH` (some random letters) +``` +`AST`, `SQL`. - Incomplete words are acceptable if the shortened version is common use. +Not `NVDH` (some random letters) +``` - You can also use an abbreviation if the full name is included next to it in the comments. +Incomplete words are acceptable if the shortened version is common use. -17. File names with C++ source code must have the .cpp extension. Header files must have the .h extension. +You can also use an abbreviation if the full name is included next to it in the comments. + +**17.** File names with C++ source code must have the `.cpp` extension. Header files must have the `.h` extension. ## How to write code -1. Memory management. +**1.** Memory management. - Manual memory deallocation (delete) can only be used in library code. +Manual memory deallocation (delete) can only be used in library code. - In library code, the delete operator can only be used in destructors. +In library code, the delete operator can only be used in destructors. - In application code, memory must be freed by the object that owns it. +In application code, memory must be freed by the object that owns it. - Examples: - - The easiest way is to place an object on the stack, or make it a member of another class. - - For a large number of small objects, use containers. - - For automatic deallocation of a small number of objects that reside in the heap, use shared_ptr/unique_ptr. +Examples: -2. Resource management. +- The easiest way is to place an object on the stack, or make it a member of another class. +- For a large number of small objects, use containers. +- For automatic deallocation of a small number of objects that reside in the heap, use shared_ptr/unique_ptr. - Use RAII and see the previous point. +**2.** Resource management. -3. Error handling. +Use RAII and see the previous point. - Use exceptions. In most cases, you only need to throw an exception, and don't need to catch it (because of RAII). +**3.** Error handling. - In offline data processing applications, it's often acceptable to not catch exceptions. +Use exceptions. In most cases, you only need to throw an exception, and don't need to catch it (because of RAII). - In servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler. +In offline data processing applications, it's often acceptable to not catch exceptions. - ```cpp - /// If there were no other calculations yet, do it synchronously - if (!started) - { - calculate(); - started = true; - } - else /// If the calculations are already in progress, wait for results - pool.wait(); +In servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler. - if (exception) - exception->rethrow(); - ``` - Never hide exceptions without handling. Never just blindly put all exceptions to log. +```cpp +/// If there were no other calculations yet, do it synchronously +if (!started) +{ + calculate(); + started = true; +} +else /// If the calculations are already in progress, wait for results + pool.wait(); - Not `catch (...) {}`. +if (exception) + exception->rethrow(); +``` +Never hide exceptions without handling. Never just blindly put all exceptions to log. - If you need to ignore some exceptions, do so only for specific ones and rethrow the rest. +Not `catch (...) {}`. - ```cpp - catch (const DB::Exception & e) - { - if (e.code() == ErrorCodes::UNKNOWN_AGGREGATE_FUNCTION) - return nullptr; - else - throw; - } - ``` +If you need to ignore some exceptions, do so only for specific ones and rethrow the rest. - When using functions with response codes or errno, always check the result and throw an exception in case of error. +```cpp +catch (const DB::Exception & e) +{ + if (e.code() == ErrorCodes::UNKNOWN_AGGREGATE_FUNCTION) + return nullptr; + else + throw; +} +``` - ```cpp - if (0 != close(fd)) - throwFromErrno("Cannot close file " + file_name, ErrorCodes::CANNOT_CLOSE_FILE); - ``` +When using functions with response codes or errno, always check the result and throw an exception in case of error. - Asserts are not used. +```cpp +if (0 != close(fd)) + throwFromErrno("Cannot close file " + file_name, ErrorCodes::CANNOT_CLOSE_FILE); +``` -4. Exception types. +Asserts are not used. - There is no need to use complex exception hierarchy in application code. The exception text should be understandable to a system administrator. +**4.** Exception types. + +There is no need to use complex exception hierarchy in application code. The exception text should be understandable to a system administrator. + +**5.** Throwing exceptions from destructors. -5. Throwing exceptions from destructors. This is not recommended, but it is allowed. - Use the following options: - - Create a (done() or finalize()) function that will do all the work in advance that might lead to an exception. If that function was called, there should be no exceptions in the destructor later. - - Tasks that are too complex (such as sending messages over the network) can be put in separate method that the class user will have to call before destruction. - - If there is an exception in the destructor, it’s better to log it than to hide it (if the logger is available). - - In simple applications, it is acceptable to rely on std::terminate (for cases of noexcept by default in C++11) to handle exceptions. +Use the following options: -6. Anonymous code blocks. +- Create a (done() or finalize()) function that will do all the work in advance that might lead to an exception. If that function was called, there should be no exceptions in the destructor later. +- Tasks that are too complex (such as sending messages over the network) can be put in separate method that the class user will have to call before destruction. +- If there is an exception in the destructor, it’s better to log it than to hide it (if the logger is available). +- In simple applications, it is acceptable to rely on std::terminate (for cases of noexcept by default in C++11) to handle exceptions. - You can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block. +**6.** Anonymous code blocks. - ```cpp - Block block = data.in->read(); +You can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block. - { - std::lock_guard lock(mutex); - data.ready = true; - data.block = block; - } +```cpp +Block block = data.in->read(); - ready_any.set(); - ``` -7. Multithreading. +{ + std::lock_guard lock(mutex); + data.ready = true; + data.block = block; +} - For offline data processing applications: - - Try to get the best possible performance on a single CPU core. You can then parallelize your code if necessary. +ready_any.set(); +``` - In server applications: - - Use the thread pool to process requests. At this point, we haven't had any tasks that required userspace context switching. +**7.** Multithreading. - Fork is not used for parallelization. +For offline data processing applications: -8. Synchronizing threads. +- Try to get the best possible performance on a single CPU core. You can then parallelize your code if necessary. - Often it is possible to make different threads use different memory cells (even better: different cache lines,) and to not use any thread synchronization (except joinAll). +In server applications: - If synchronization is required, in most cases, it is sufficient to use mutex under lock_guard. +- Use the thread pool to process requests. At this point, we haven't had any tasks that required userspace context switching. - In other cases use system synchronization primitives. Do not use busy wait. +Fork is not used for parallelization. - Atomic operations should be used only in the simplest cases. +**8.** Synchronizing threads. - Do not try to implement lock-free data structures unless it is your primary area of expertise. + Often it is possible to make different threads use different memory cells (even better: different cache lines,) and to not use any thread synchronization (except joinAll). -9. Pointers vs references. + If synchronization is required, in most cases, it is sufficient to use mutex under lock_guard. - In most cases, prefer references. + In other cases use system synchronization primitives. Do not use busy wait. -10. const. + Atomic operations should be used only in the simplest cases. - Use constant references, pointers to constants, const_iterator, const methods. + Do not try to implement lock-free data structures unless it is your primary area of expertise. - Consider const to be default and use non-const only when necessary. +**9.** Pointers vs references. - When passing variable by value, using const usually does not make sense. +In most cases, prefer references. -11. unsigned. +**10.** const. - Use unsigned, if needed. + Use constant references, pointers to constants, `const_iterator`, `const` methods. -12. Numeric types + Consider `const` to be default and use non-const only when necessary. - Use UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, and size_t, ssize_t, ptrdiff_t. + When passing variable by value, using `const` usually does not make sense. - Don't use signed/unsigned long, long long, short; signed char, unsigned char, or char types for numbers. +**11.** unsigned. -13. Passing arguments. +Use `unsigned`, if needed. - Pass complex values by reference (including std::string). +**12.** Numeric types - If a function captures ownership of an objected created in the heap, make the argument type shared_ptr or unique_ptr. +Use `UInt8`, `UInt16`, `UInt32`, `UInt64`, `Int8`, `Int16`, `Int32`, `Int64`, and `size_t`, `ssize_t`, `ptrdiff_t`. -14. Returning values. +Don't use `signed/unsigned long`, `long long`, `short`, `signed char`, `unsigned char`, or `char` types for numbers. - In most cases, just use return. Do not write [return std::move(res)]{.strike}. +**13.** Passing arguments. - If the function allocates an object on heap and returns it, use shared_ptr or unique_ptr. +Pass complex values by reference (including `std::string`). - In rare cases you might need to return the value via an argument. In this case, the argument should be a reference. +If a function captures ownership of an objected created in the heap, make the argument type `shared_ptr` or `unique_ptr`. - ```cpp - using AggregateFunctionPtr = std::shared_ptr; - - /** Creates an aggregate function by name. - */ - class AggregateFunctionFactory - { - public: - AggregateFunctionFactory(); - AggregateFunctionPtr get(const String & name, const DataTypes & argument_types) const; - ``` -15. namespace. +**14.** Returning values. - There is no need to use a separate namespace for application code or small libraries. +In most cases, just use return. Do not write `[return std::move(res)]{.strike}`. - or small libraries. +If the function allocates an object on heap and returns it, use `shared_ptr` or `unique_ptr`. - For medium to large libraries, put everything in the namespace. +In rare cases you might need to return the value via an argument. In this case, the argument should be a reference. - You can use the additional detail namespace in a library's .h file to hide implementation details. +``` +using AggregateFunctionPtr = std::shared_ptr; - In a .cpp file, you can use the static or anonymous namespace to hide symbols. +/** Creates an aggregate function by name. + */ +class AggregateFunctionFactory +{ +public: + AggregateFunctionFactory(); + AggregateFunctionPtr get(const String & name, const DataTypes & argument_types) const; +``` - You can also use namespace for enums to prevent its names from polluting the outer namespace, but it’s better to use the enum class. +**15.** namespace. -16. Delayed initialization. +There is no need to use a separate namespace for application code or small libraries. - If arguments are required for initialization then do not write a default constructor. +or small libraries. - If later you’ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use shared_ptr/unique_ptr. +For medium to large libraries, put everything in the namespace. - ```cpp - Loader(DB::Connection * connection_, const std::string & query, size_t max_block_size_); +You can use the additional detail namespace in a library's `.h` file to hide implementation details. - /// For delayed initialization - Loader() {} - ``` +In a `.cpp` file, you can use the static or anonymous namespace to hide symbols. -17. Virtual functions. +You can also use namespace for enums to prevent its names from polluting the outer namespace, but it’s better to use the enum class. - If the class is not intended for polymorphic use, you do not need to make functions virtual. This also applies to the destructor. +**16.** Delayed initialization. -18. Encodings. +If arguments are required for initialization then do not write a default constructor. - Use UTF-8 everywhere. Use `std::string`and`char *`. Do not use `std::wstring`and`wchar_t`. +If later you’ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use `shared_ptr/unique_ptr`. -19. Logging. +```cpp +Loader(DB::Connection * connection_, const std::string & query, size_t max_block_size_); - See the examples everywhere in the code. +/// For delayed initialization +Loader() {} +``` - Before committing, delete all meaningless and debug logging, and any other types of debug output. +**17.** Virtual functions. - Logging in cycles should be avoided, even on the Trace level. +If the class is not intended for polymorphic use, you do not need to make functions virtual. This also applies to the destructor. - Logs must be readable at any logging level. +**18.** Encodings. - Logging should only be used in application code, for the most part. +Use UTF-8 everywhere. Use `std::string`and`char *`. Do not use `std::wstring`and`wchar_t`. - Log messages must be written in English. +**19.** Logging. - The log should preferably be understandable for the system administrator. +See the examples everywhere in the code. - Do not use profanity in the log. +Before committing, delete all meaningless and debug logging, and any other types of debug output. - Use UTF-8 encoding in the log. In rare cases you can use non-ASCII characters in the log. +Logging in cycles should be avoided, even on the Trace level. -20. I/O. +Logs must be readable at any logging level. - Don't use iostreams in internal cycles that are critical for application performance (and never use stringstream). +Logging should only be used in application code, for the most part. - Use the DB/IO library instead. +Log messages must be written in English. -21. Date and time. +The log should preferably be understandable for the system administrator. - See the DateLUT library. +Do not use profanity in the log. -22. include. +Use UTF-8 encoding in the log. In rare cases you can use non-ASCII characters in the log. - Always use `#pragma once` instead of include guards. +**20.** I/O. -23. using. +Don't use iostreams in internal cycles that are critical for application performance (and never use stringstream). - The using namespace is not used. +Use the DB/IO library instead. - It's fine if you are 'using' something specific, but make it local inside a class or function. +**21.** Date and time. -24. Do not use trailing return type for functions unless necessary. +See the `DateLUT` library. - [auto f() -> void;]{.strike} +**22.** include. -25. Do not declare and init variables like this: +Always use `#pragma once` instead of include guards. - ```cpp - auto s = std::string{"Hello"}; - ``` +**23.** using. - Do it like this: +The `using namespace` is not used. - ```cpp - std::string s = "Hello"; - std::string s{"Hello"}; - ``` -26. For virtual functions, write 'virtual' in the base class, but write 'override' in descendent classes. +It's fine if you are 'using' something specific, but make it local inside a class or function. + +**24.** Do not use trailing return type for functions unless necessary. + +``` +[auto f() -> void;]{.strike} +``` + +**25.** Do not declare and init variables like this: + +```cpp +auto s = std::string{"Hello"}; +``` + +Do it like this: + +```cpp +std::string s = "Hello"; +std::string s{"Hello"}; +``` + +**26.** For virtual functions, write `virtual` in the base class, but write `override` in descendent classes. ## Unused features of C++ -1. Virtual inheritance is not used. -2. Exception specifiers from C++03 are not used. -3. Function try block is not used, except for the main function in tests. +**1.** Virtual inheritance is not used. + +**2.** Exception specifiers from C++03 are not used. + +**3.** Function try block is not used, except for the main function in tests. ## Platform -1. We write code for a specific platform. +**1.** We write code for a specific platform. - But other things being equal, cross-platform or portable code is preferred. +But other things being equal, cross-platform or portable code is preferred. -2. The language is C++17. +**2.** The language is C++17. -3. The compiler is gcc. At this time (December 2017), the code is compiled using version 7.2. (It can also be compiled using clang 5.) +**3.** The compiler is `gcc`. At this time (December 2017), the code is compiled using version 7.2. (It can also be compiled using clang 5.) - The standard library is used (implementation of libstdc++ or libc++). +The standard library is used (implementation of `libstdc++` or `libc++`). -4. OS: Linux Ubuntu, not older than Precise. +**4.** OS: Linux Ubuntu, not older than Precise. -5. Code is written for x86_64 CPU architecture. +**5.** Code is written for x86_64 CPU architecture. - The CPU instruction set is the minimum supported set among our servers. Currently, it is SSE 4.2. +The CPU instruction set is the minimum supported set among our servers. Currently, it is SSE 4.2. -6. Use `-Wall -Wextra -Werror` compilation flags. +**6.** Use `-Wall -Wextra -Werror` compilation flags. -7. Use static linking with all libraries except those that are difficult to connect to statically (see the output of the 'ldd' command). +**7.** Use static linking with all libraries except those that are difficult to connect to statically (see the output of the `ldd` command). -8. Code is developed and debugged with release settings. +**8.** Code is developed and debugged with release settings. ## Tools -1. KDevelop is a good IDE. +**1.** `KDevelop` is a good IDE. -2. For debugging, use gdb, valgrind (memcheck), strace,-fsanitize=..., tcmalloc_minimal_debug. +**2.** For debugging, use `gdb`, `valgrind` (`memcheck`), `strace`, `-fsanitize=`, ..., `tcmalloc_minimal_debug`. -3. For profiling, use Linux Perf valgrind (callgrind), strace-cf. +**3.** For profiling, use Linux Perf `valgrind` (`callgrind`), `strace-cf`. -4. Sources are in Git. +**4.** Sources are in Git. -5. Compilation is managed by CMake. +**5.** Compilation is managed by `CMake`. -6. Releases are in deb packages. +**6.** Releases are in `deb` packages. -7. Commits to master must not break the build. +**7.** Commits to master must not break the build. - Though only selected revisions are considered workable. +Though only selected revisions are considered workable. -8. Make commits as often as possible, even if the code is only partially ready. +**8.** Make commits as often as possible, even if the code is only partially ready. - Use branches for this purpose. +Use branches for this purpose. - If your code is not buildable yet, exclude it from the build before pushing to master. You'll need to finish it or remove it from master within a few days. +If your code is not buildable yet, exclude it from the build before pushing to master. You'll need to finish it or remove it from master within a few days. -9. For non-trivial changes, used branches and publish them on the server. +**9.** For non-trivial changes, used branches and publish them on the server. -10. Unused code is removed from the repository. +**10.** Unused code is removed from the repository. ## Libraries -1. The C++14 standard library is used (experimental extensions are fine), as well as boost and Poco frameworks. +**1.** The C++14 standard library is used (experimental extensions are fine), as well as boost and Poco frameworks. -2. If necessary, you can use any well-known libraries available in the OS package. +**2.** If necessary, you can use any well-known libraries available in the OS package. - If there is a good solution already available, then use it, even if it means you have to install another library. +If there is a good solution already available, then use it, even if it means you have to install another library. - (But be prepared to remove bad libraries from code.) +(But be prepared to remove bad libraries from code.) -3. You can install a library that isn't in the packages, if the packages don't have what you need or have an outdated version or the wrong type of compilation. +**3.** You can install a library that isn't in the packages, if the packages don't have what you need or have an outdated version or the wrong type of compilation. -4. If the library is small and doesn't have its own complex build system, put the source files in the contrib folder. +**4.** If the library is small and doesn't have its own complex build system, put the source files in the contrib folder. -5. Preference is always given to libraries that are already used. +**5.** Preference is always given to libraries that are already used. ## General recommendations -1. Write as little code as possible. -2. Try the simplest solution. -3. Don't write code until you know how it's going to work and how the inner loop will function. -4. In the simplest cases, use 'using' instead of classes or structs. -5. If possible, do not write copy constructors, assignment operators, destructors (other than a virtual one, if the class contains at least one virtual function), mpve-constructors and move assignment operators. In other words, the compiler-generated functions must work correctly. You can use 'default'. -6. Code simplification is encouraged. Reduce the size of your code where possible. +**1.** Write as little code as possible. + +**2.** Try the simplest solution. + +**3.** Don't write code until you know how it's going to work and how the inner loop will function. + +**4.** In the simplest cases, use 'using' instead of classes or structs. + +**5.** If possible, do not write copy constructors, assignment operators, destructors (other than a virtual one, if the class contains at least one virtual function), mpve-constructors and move assignment operators. In other words, the compiler-generated functions must work correctly. You can use 'default'. + +**6.** Code simplification is encouraged. Reduce the size of your code where possible. ## Additional recommendations -1. Explicit std:: for types from stddef.h +**1.** Explicit `std::` for types from `stddef.h` is not recommended. - is not recommended. We recommend writing size_t instead std::size_t because it's shorter. +We recommend writing `size_t` instead `std::size_t` because it's shorter. - But if you prefer, std:: is acceptable. +But if you prefer, `std::` is acceptable. -2. Explicit std:: for functions from the standard C library. +**2.** Explicit `std::` for functions from the standard C library is not recommended. - is not recommended. Write memcpy instead of std::memcpy. +Write `memcpy` instead of `std::memcpy`. - The reason is that there are similar non-standard functions, such as memmem. We do use these functions on occasion. These functions do not exist in namespace std. +The reason is that there are similar non-standard functions, such as `memmem`. We do use these functions on occasion. These functions do not exist in namespace `std`. - If you write std::memcpy instead of memcpy everywhere, then memmem without std:: will look awkward. +If you write `std::memcpy` instead of `memcpy` everywhere, then `memmem` without `std::` will look awkward. - Nevertheless, std:: is allowed if you prefer it. +Nevertheless, `std::` is allowed if you prefer it. -3. Using functions from C when the ones are available in the standard C++ library. +**3.** Using functions from C when the ones are available in the standard C++ library. - This is acceptable if it is more efficient. + This is acceptable if it is more efficient. - For example, use memcpy instead of std::copy for copying large chunks of memory. + For example, use `memcpy` instead of `std::copy` for copying large chunks of memory. -4. Multiline function arguments. +**4.** Multiline function arguments. - Any of the following wrapping styles are allowed: +Any of the following wrapping styles are allowed: - ```cpp - function( - T1 x1, - T2 x2) - ``` +```cpp +function( + T1 x1, + T2 x2) +``` - ```cpp - function( - size_t left, size_t right, +```cpp +function( + size_t left, size_t right, + const & RangesInDataParts ranges, + size_t limit) +``` + +```cpp +function(size_t left, size_t right, + const & RangesInDataParts ranges, + size_t limit) +``` + +```cpp +function(size_t left, size_t right, const & RangesInDataParts ranges, size_t limit) - ``` +``` - ```cpp - function(size_t left, size_t right, +```cpp +function( + size_t left, + size_t right, const & RangesInDataParts ranges, size_t limit) - ``` - - ```cpp - function(size_t left, size_t right, - const & RangesInDataParts ranges, - size_t limit) - ``` - - ```cpp - function( - size_t left, - size_t right, - const & RangesInDataParts ranges, - size_t limit) - ``` - +``` diff --git a/docs/ru/development/style.md b/docs/ru/development/style.md index b035ed098c8..4bfe3300c22 100644 --- a/docs/ru/development/style.md +++ b/docs/ru/development/style.md @@ -2,734 +2,839 @@ ## Общее -1. Этот текст носит рекомендательный характер. -2. Если вы редактируете код, то имеет смысл писать так, как уже написано. -3. Стиль нужен для единообразия. Единообразие нужно, чтобы было проще (удобнее) читать код. А также, чтобы было легче осуществлять поиск по коду. -4. Многие правила продиктованы не какими либо разумными соображениями, а сложившейся практикой. +**1.** Этот текст носит рекомендательный характер. + +**2.** Если вы редактируете код, то имеет смысл писать так, как уже написано. + +**3.** Стиль нужен для единообразия. Единообразие нужно, чтобы было проще (удобнее) читать код. А также, чтобы было легче осуществлять поиск по коду. + +**4.** Многие правила продиктованы не какими либо разумными соображениями, а сложившейся практикой. ## Форматирование -1. Большую часть форматирования сделает автоматически `clang-format`. -1. Отступы - 4 пробела. Настройте среду разработки так, чтобы таб добавлял четыре пробела. -1. Открывающая фигурная скобка на новой, отдельной строке. (Закрывающая - тоже.) +**1.** Большую часть форматирования сделает автоматически `clang-format`. - ```cpp - inline void readBoolText(bool & x, ReadBuffer & buf) - { - char tmp = '0'; - readChar(tmp, buf); - x = tmp != '0'; - } - ``` -1. Но если всё тело функции достаточно короткое (один statement) - при желании, его можно целиком разместить на одной строке. При этом, вокруг фигурных скобок ставятся пробелы (кроме пробела на конце строки). +**2.** Отступы — 4 пробела. Настройте среду разработки так, чтобы таб добавлял четыре пробела. - ```cpp - inline size_t mask() const { return buf_size() - 1; } - inline size_t place(HashValue x) const { return x & mask(); } - ``` -1. Для функций, пробелы вокруг скобок не ставятся. +**3.** Открывающая и закрывающие фигурные скобки на отдельной строке. - ```cpp - void reinsert(const Value & x) - ``` +```cpp +inline void readBoolText(bool & x, ReadBuffer & buf) +{ + char tmp = '0'; + readChar(tmp, buf); + x = tmp != '0'; +} +``` - ```cpp - memcpy(&buf[place_value], &x, sizeof(x)); - ``` -1. При использовании выражений if, for, while, ... (в отличие от вызовов функций) перед открывающей скобкой ставится пробел. +**4.** Если всё тело функции — один `statement`, то его можно разместить на одной строке. При этом, вокруг фигурных скобок ставятся пробелы (кроме пробела на конце строки). - ```cpp - for (size_t i = 0; i < rows; i += storage.index_granularity) - ``` -1. Вокруг бинарных операторов (+, -, \*, /, %, ...), а также тернарного оператора ?: ставятся пробелы. +```cpp +inline size_t mask() const { return buf_size() - 1; } +inline size_t place(HashValue x) const { return x & mask(); } +``` - ```cpp - UInt16 year = (s[0] - '0') * 1000 + (s[1] - '0') * 100 + (s[2] - '0') * 10 + (s[3] - '0'); - UInt8 month = (s[5] - '0') * 10 + (s[6] - '0'); - UInt8 day = (s[8] - '0') * 10 + (s[9] - '0'); - ``` -1. Если ставится перенос строки, то оператор пишется на новой строке, и перед ним увеличивается отступ. +**5.** Для функций. Пробелы вокруг скобок не ставятся. - ```cpp - if (elapsed_ns) - message << " (" - << rows_read_on_server * 1000000000 / elapsed_ns << " rows/s., " - << bytes_read_on_server * 1000.0 / elapsed_ns << " MB/s.) "; - ``` -1. Внутри строки можно, при желании, выполнять выравнивание с помощью пробелов. +```cpp +void reinsert(const Value & x) +``` - ```cpp - dst.ClickLogID = click.LogID; - dst.ClickEventID = click.EventID; - dst.ClickGoodEvent = click.GoodEvent; - ``` -9. Вокруг операторов `.`, `->` не ставятся пробелы. +```cpp +memcpy(&buf[place_value], &x, sizeof(x)); +``` - При необходимости, оператор может быть перенесён на новую строку. В этом случае, перед ним увеличивается отступ. -10. Унарные операторы (`--, ++, *, &`, ...) не отделяются от аргумента пробелом. -11. После запятой ставится пробел, а перед - нет. Аналогично для точки с запятой внутри выражения for. -12. Оператор `[]` не отделяется пробелами. -13. В выражении `template <...>`, между `template` и `<` ставится пробел; после `<` и до `>` - не ставится. +**6.** В выражениях `if`, `for`, `while` и т.д. перед открывающей скобкой ставится пробел (в отличие от вызовов функций). - ```cpp - template - struct AggregatedStatElement - {} - ``` -14. В классах и структурах, public, private, protected пишется на том же уровне, что и class/struct, а все остальные внутренности - глубже. +```cpp +for (size_t i = 0; i < rows; i += storage.index_granularity) +``` - ```cpp - template - class MultiVersion - { - public: - /// Version of object for usage. shared_ptr manage lifetime of version. - using Version = std::shared_ptr; - ... - } - ``` -15. Если на весь файл один namespace и кроме него ничего существенного нет - то отступ внутри namespace не нужен. -16. Если блок для выражения if, for, while... состоит из одного statement-а, то фигурные скобки писать не обязательно. Вместо этого поместите statement на отдельную строку. Этим statement-ом также может быть вложенный if, for, while... Но если внутренний statement содержит фигурные скобки или else, то внешний блок следует писать в фигурных скобках. +**7.** Вокруг бинарных операторов (`+`, `-`, `*`, `/`, `%`, ...), а также тернарного оператора `?:` ставятся пробелы. - ```cpp - /// Finish write. - for (auto & stream : streams) - stream.second->finalize(); - ``` +```cpp +UInt16 year = (s[0] - '0') * 1000 + (s[1] - '0') * 100 + (s[2] - '0') * 10 + (s[3] - '0'); +UInt8 month = (s[5] - '0') * 10 + (s[6] - '0'); +UInt8 day = (s[8] - '0') * 10 + (s[9] - '0'); +``` -17. Не должно быть пробелов на концах строк. -18. Исходники в кодировке UTF-8. -19. В строковых литералах можно использовать не-ASCII. +**8.** Если ставится перенос строки, то оператор пишется на новой строке, и перед ним увеличивается отступ. - ```cpp - << ", " << (timer.elapsed() / chunks_stats.hits) << " μsec/hit."; - ``` -20. Не пишите несколько выражений в одной строке. -21. Внутри функций, группируйте куски кода, отделяя их не более, чем одной пустой строкой. -22. Функции, классы, и т. п. отделяются друг от друга минимум одной, максимум двумя пустыми строками. -23. const (относящийся к значению) пишется до имени типа. +```cpp +if (elapsed_ns) + message << " (" + << rows_read_on_server * 1000000000 / elapsed_ns << " rows/s., " + << bytes_read_on_server * 1000.0 / elapsed_ns << " MB/s.) "; +``` - ``` - //correct - const char * pos - const std::string & s - //incorrect - char const * pos - ``` -24. При объявлении указателя или ссылки, символы \* и & отделяются пробелами с обеих сторон. +**9.** Внутри строки можно, выполнять выравнивание с помощью пробелов. - ``` - //correct - const char * pos - //incorrect - const char* pos - const char *pos - ``` -25. При использовании шаблонных типов, пишите `using` (кроме, возможно, простейших случаев). +```cpp +dst.ClickLogID = click.LogID; +dst.ClickEventID = click.EventID; +dst.ClickGoodEvent = click.GoodEvent; +``` - То есть, параметры шаблона указываются только в `using` и затем не повторяются в коде. +**10.** Вокруг операторов `.`, `->` не ставятся пробелы. - `using` может быть объявлен локально, например, внутри функции. +При необходимости, оператор может быть перенесён на новую строку. В этом случае, перед ним увеличивается отступ. - ``` - //correct - using FileStreams = std::map>; - FileStreams streams; - //incorrect - std::map> streams; - ``` -26. Нельзя объявлять несколько переменных разных типов в одном объявлении. +**11.** Унарные операторы `--`, `++`, `*`, `&`, ... не отделяются от аргумента пробелом. - ``` - //incorrect - int x, *y; - ``` -27. C-style cast не используется. +**12.** После запятой ставится пробел, а перед — нет. Аналогично для точки с запятой внутри выражения `for`. - ```cpp - //incorrect - std::cerr << (int)c <<; std::endl; - //correct - std::cerr << static_cast(c) << std::endl; - ``` -28. В классах и структурах, группируйте отдельно методы и отдельно члены, внутри каждой области видимости. -29. Для не очень большого класса/структуры, можно не отделять объявления методов от реализации. +**13.** Оператор `[]` не отделяется пробелами. - Аналогично для маленьких методов в любых классах/структурах. +**14.** В выражении `template <...>`, между `template` и `<` ставится пробел, а после `<` и до `>` не ставится. - Для шаблонных классов/структур, лучше не отделять объявления методов от реализации (так как иначе они всё равно должны быть определены в той же единице трансляции). -30. Не обязательно умещать код по ширине в 80 символов. Можно в 140. -31. Всегда используйте префиксный инкремент/декремент, если постфиксный не нужен. +```cpp +template +struct AggregatedStatElement +{} +``` - ```cpp - for (Names::const_iterator it = column_names.begin(); it != column_names.end(); ++it) - ``` +**15.** В классах и структурах, `public`, `private`, `protected` пишется на том же уровне, что и `class/struct`, а остальной код с отступом. +```cpp +template +class MultiVersion +{ +public: + /// Version of object for usage. shared_ptr manage lifetime of version. + using Version = std::shared_ptr; + ... +} +``` + +**16.** Если на весь файл один `namespace` и кроме него ничего существенного нет, то отступ внутри `namespace` не нужен. + +**17.** Если блок для выражения `if`, `for`, `while`, ... состоит из одного `statement`, то фигурные скобки не обязательны. Вместо этого поместите `statement` на отдельную строку. Это правило справедливо и для вложенных `if`, `for`, `while`, ... + +Если внутренний `statement` содержит фигурные скобки или `else`, то внешний блок следует писать в фигурных скобках. + +```cpp +/// Finish write. +for (auto & stream : streams) + stream.second->finalize(); +``` + +**18.** Не должно быть пробелов на концах строк. + +**19.** Исходники в кодировке UTF-8. + +**20.** В строковых литералах можно использовать не-ASCII. + +```cpp +<< ", " << (timer.elapsed() / chunks_stats.hits) << " μsec/hit."; +``` + +**21.** Не пишите несколько выражений в одной строке. + +**22.** Внутри функций группируйте блоки кода, отделяя их не более, чем одной пустой строкой. + +**23.** Функции, классы, и т. п. отделяются друг от друга одной или двумя пустыми строками. + +**24.** `const` (относящийся к значению) пишется до имени типа. + +```cpp +//correct +const char * pos +const std::string & s +//incorrect +char const * pos +``` + +**25.** При объявлении указателя или ссылки, символы `*` и `&` отделяются пробелами с обеих сторон. + +```cpp +//correct +const char * pos +//incorrect +const char* pos +const char *pos +``` + +**26.** При использовании шаблонных типов, пишите `using` (кроме, возможно, простейших случаев). + +То есть, параметры шаблона указываются только в `using` и затем не повторяются в коде. + +`using` может быть объявлен локально, например, внутри функции. + +```cpp +//correct +using FileStreams = std::map>; +FileStreams streams; +//incorrect +std::map> streams; +``` + +**27.** Нельзя объявлять несколько переменных разных типов в одном выражении. + +```cpp +//incorrect +int x, *y; +``` + +**28.** C-style cast не используется. + +```cpp +//incorrect +std::cerr << (int)c <<; std::endl; +//correct +std::cerr << static_cast(c) << std::endl; +``` + +**29.** В классах и структурах, группируйте отдельно методы и отдельно члены, внутри каждой области видимости. + +**30.** Для не очень большого класса/структуры, можно не отделять объявления методов от реализации. + +Аналогично для маленьких методов в любых классах/структурах. + +Для шаблонных классов/структур, лучше не отделять объявления методов от реализации (так как иначе они всё равно должны быть определены в той же единице трансляции). + +**31.** Не обязательно умещать код по ширине в 80 символов. Можно в 140. + +**32.** Всегда используйте префиксный инкремент/декремент, если постфиксный не нужен. + +```cpp +for (Names::const_iterator it = column_names.begin(); it != column_names.end(); ++it) +``` ## Комментарии -1. Необходимо обязательно писать комментарии во всех нетривиальных местах. +**1.** Необходимо обязательно писать комментарии во всех нетривиальных местах. - Это очень важно. При написании комментария, можно успеть понять, что код не нужен вообще, или что всё сделано неверно. +Это очень важно. При написании комментария, можно успеть понять, что код не нужен вообще, или что всё сделано неверно. - ```cpp - /** Part of piece of memory, that can be used. - * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading, - * then working_buffer will have size of only 10 bytes - * (working_buffer.end() will point to position right after those 10 bytes available for read). - */ - ``` -2. Комментарии могут быть сколь угодно подробными. -3. Комментарии пишутся до соответствующего кода. В редких случаях - после, на той же строке. +```cpp +/** Part of piece of memory, that can be used. + * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading, + * then working_buffer will have size of only 10 bytes + * (working_buffer.end() will point to position right after those 10 bytes available for read). + */ +``` - ```cpp - /** Parses and executes the query. - */ - void executeQuery( - ReadBuffer & istr, /// Where to read the query from (and data for INSERT, if applicable) - WriteBuffer & ostr, /// Where to write the result - Context & context, /// DB, tables, data types, engines, functions, aggregate functions... - BlockInputStreamPtr & query_plan, /// Here could be written the description on how query was executed - QueryProcessingStage::Enum stage = QueryProcessingStage::Complete /// Up to which stage process the SELECT query - ) - ``` -4. Комментарии следует писать только на английском языке. -5. При написании библиотеки, разместите подробный комментарий о том, что это такое, в самом главном заголовочном файле. -6. Нельзя писать комментарии, которые не дают дополнительной информации. В частности, нельзя писать пустые комментарии вроде этого: +**2.** Комментарии могут быть сколь угодно подробными. - ```cpp - /* - * Procedure Name: - * Original procedure name: - * Author: - * Date of creation: - * Dates of modification: - * Modification authors: - * Original file name: - * Purpose: - * Intent: - * Designation: - * Classes used: - * Constants: - * Local variables: - * Parameters: - * Date of creation: - * Purpose: - */ - ``` +**3.** Комментарии пишутся до соответствующего кода. В редких случаях после, на той же строке. - (пример взят с ресурса [http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/](http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/) -7. Нельзя писать мусорные комментарии (автор, дата создания...) в начале каждого файла. -8. Однострочные комментарии начинаются с трёх слешей: `///` , многострочные с `/**`. Такие комментарии считаются «документирующими». +```cpp +/** Parses and executes the query. +*/ +void executeQuery( + ReadBuffer & istr, /// Where to read the query from (and data for INSERT, if applicable) + WriteBuffer & ostr, /// Where to write the result + Context & context, /// DB, tables, data types, engines, functions, aggregate functions... + BlockInputStreamPtr & query_plan, /// Here could be written the description on how query was executed + QueryProcessingStage::Enum stage = QueryProcessingStage::Complete /// Up to which stage process the SELECT query + ) +``` - Замечание: такие комментарии могут использоваться для генерации документации с помощью Doxygen. Но, фактически, Doxygen не используется, так как для навигации по коду гораздо удобне использовать возможности IDE. -9. В начале и конце многострочного комментария, не должно быть пустых строк (кроме строки, на которой закрывается многострочный комментарий). -10. Для закомментированных кусков кода, используются обычные, не "документирующие" комментарии. -1. Удаляйте закомментированные куски кода перед коммитом. -11. Не нужно писать нецензурную брань в комментариях или коде. -12. Не пишите прописными буквами. Не используйте излишнее количество знаков препинания. +**4.** Комментарии следует писать только на английском языке. - ```cpp - /// WHAT THE FAIL??? - ``` -13. Не составляйте из комментариев строки-разделители. +**5.** При написании библиотеки, разместите подробный комментарий о том, что это такое, в самом главном заголовочном файле. - ``` - ///****************************************************** - ``` -14. Не нужно писать в комментарии диалог (лучше сказать устно). +**6.** Нельзя писать комментарии, которые не дают дополнительной информации. В частности, нельзя писать пустые комментарии вроде этого: - ``` - /// Why did you do this stuff? - ``` -15. Не нужно писать комментарий в конце блока о том, что представлял собой этот блок. +```cpp +/* +* Procedure Name: +* Original procedure name: +* Author: +* Date of creation: +* Dates of modification: +* Modification authors: +* Original file name: +* Purpose: +* Intent: +* Designation: +* Classes used: +* Constants: +* Local variables: +* Parameters: +* Date of creation: +* Purpose: +*/ +``` - ``` - /// for - ``` +Пример взят с ресурса [http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/](http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/). + +**7.** Нельзя писать мусорные комментарии (автор, дата создания...) в начале каждого файла. + +**8.** Однострочные комментарии начинаются с трёх слешей: `///` , многострочные с `/**`. Такие комментарии считаются «документирующими». + +Замечание: такие комментарии могут использоваться для генерации документации с помощью Doxygen. Но, фактически, Doxygen не используется, так как для навигации по коду гораздо удобне использовать возможности IDE. + +**9.** В начале и конце многострочного комментария, не должно быть пустых строк (кроме строки, на которой закрывается многострочный комментарий). + +**10.** Для закомментированных кусков кода, используются обычные, не "документирующие" комментарии. + +**11.** Удаляйте закомментированные куски кода перед коммитом. + +**12.** Не нужно писать нецензурную брань в комментариях или коде. + +**13.** Не пишите прописными буквами. Не используйте излишнее количество знаков препинания. + +```cpp +/// WHAT THE FAIL??? +``` + +**14.** Не составляйте из комментариев строки-разделители. + +```cpp +///****************************************************** +``` + +**15.** Не нужно писать в комментарии диалог (лучше сказать устно). + +```cpp +/// Why did you do this stuff? +``` + +**16.** Не нужно писать комментарий в конце блока о том, что представлял собой этот блок. + +```cpp +/// for +``` ## Имена -1. Имена переменных и членов класса - маленькими буквами с подчёркиванием. +**1.** В именах переменных и членов класса используйте маленькие буквами с подчёркиванием. - ```cpp - size_t max_block_size; - ``` -2. Имена функций (методов) - camelCase с маленькой буквы. +```cpp +size_t max_block_size; +``` + +**2.** Имена функций (методов) camelCase с маленькой буквы. ```cpp std::string getName() const override { return "Memory"; } ``` -3. Имена классов (структур) - CamelCase с большой буквы. Префиксы кроме I для интерфейсов - не используются. +**3.** Имена классов (структур) - CamelCase с большой буквы. Префиксы кроме I для интерфейсов - не используются. ```cpp class StorageMemory : public IStorage ``` -4. Имена using-ов - также, как классов, либо можно добавить _t на конце. -5. Имена типов - параметров шаблонов: в простых случаях - T; T, U; T1, T2. - В более сложных случаях - либо также, как имена классов, либо можно добавить в начало букву T. +**4.** `using` называются также, как классы, либо с `_t` на конце. - ```cpp - template - struct AggregatedStatElement - ``` -6. Имена констант - параметров шаблонов: либо также, как имена переменных, либо N - в простом случае. +**5.** Имена типов — параметров шаблонов: в простых случаях - `T`; `T`, `U`; `T1`, `T2`. - ```cpp - template - struct ExtractDomain - ``` +В более сложных случаях - либо также, как имена классов, либо можно добавить в начало букву `T`. -7. Для абстрактных классов (интерфейсов) можно добавить в начало имени букву I. +```cpp +template +struct AggregatedStatElement +``` - ```cpp - class IBlockInputStream - ``` +**6.** Имена констант — параметров шаблонов: либо также, как имена переменных, либо `N` в простом случае. -8. Если переменная используется достаточно локально, то можно использовать короткое имя. +```cpp +template +struct ExtractDomain +``` - В остальных случаях - используйте достаточно подробное имя, описывающее смысл. +**7.** Для абстрактных классов (интерфейсов) можно добавить в начало имени букву `I`. - ```cpp - bool info_successfully_loaded = false; - ``` -9. define-ы - ALL_CAPS с подчёркиванием. Глобальные константы - тоже. +```cpp +class IBlockInputStream +``` - ```cpp - #define MAX_SRC_TABLE_NAMES_TO_STORE 1000 - ``` -10. Имена файлов с кодом называйте по стилю соответственно тому, что в них находится. +**8.** Если переменная используется достаточно локально, то можно использовать короткое имя. - Если в файле находится один класс - назовите файл, как класс - в CamelCase. +В остальных случаях используйте имя, описывающее смысл. - Если в файле находится одна функция - назовите файл, как функцию - в camelCase. -11. Если имя содержит сокращение, то: - - для имён переменных, всё сокращение пишется маленькими буквами `mysql_connection` (не `mySQL_connection`). - - для имён классов и функций, сохраняются большие буквы в сокращении `MySQLConnection` (не `MySqlConnection`). -12. Параметры конструктора, использующиеся сразу же для инициализации соответствующих членов класса, следует назвать также, как и члены класса, добавив подчёркивание в конец. +```cpp +bool info_successfully_loaded = false; +``` - ```cpp - FileQueueProcessor( - const std::string & path_, - const std::string & prefix_, - std::shared_ptr handler_) - : path(path_), - prefix(prefix_), - handler(handler_), - log(&Logger::get("FileQueueProcessor")) - { - } - ``` +**9.** В именах `define` и глобальных констант используется ALL_CAPS с подчёркиванием. - Также можно называть параметры конструктора так же, как и члены класса (не добавлять подчёркивание), но только если этот параметр не используется в теле конструктора. +```cpp +#define MAX_SRC_TABLE_NAMES_TO_STORE 1000 +``` -13. Именование локальных переменных и членов класса никак не отличается (никакие префиксы не нужны). +**10.** Имена файлов с кодом называйте по стилю соответственно тому, что в них находится. - ``` - timer (not m_timer) - ``` -14. Константы в enum-е - CamelCase с большой буквы. Также допустимо ALL_CAPS. Если enum не локален, то используйте enum class. +Если в файле находится один класс, назовите файл, как класс (CamelCase). - ```cpp - enum class CompressionMethod - { - QuickLZ = 0, - LZ4 = 1, - }; - ``` -15. Все имена - по английски. Транслит с русского использовать нельзя. +Если в файле находится одна функция, назовите файл, как функцию (camelCase). - ``` - не Stroka - ``` -16. Сокращения (из нескольких букв разных слов) в именах можно использовать только если они являются общепринятыми (если для сокращения можно найти расшифровку в английской википедии или сделав поисковый запрос). +**11.** Если имя содержит сокращение, то: - `AST`, `SQL`. +- для имён переменных, всё сокращение пишется маленькими буквами `mysql_connection` (не `mySQL_connection`). +- для имён классов и функций, сохраняются большие буквы в сокращении `MySQLConnection` (не `MySqlConnection`). - Не `NVDH` (что-то неведомое) +**12.** Параметры конструктора, использующиеся сразу же для инициализации соответствующих членов класса, следует назвать также, как и члены класса, добавив подчёркивание в конец. - Сокращения в виде обрезанного слова можно использовать, только если такое сокращение является широко используемым. +```cpp +FileQueueProcessor( + const std::string & path_, + const std::string & prefix_, + std::shared_ptr handler_) + : path(path_), + prefix(prefix_), + handler(handler_), + log(&Logger::get("FileQueueProcessor")) +{ +} +``` + +Также можно называть параметры конструктора так же, как и члены класса (не добавлять подчёркивание), но только если этот параметр не используется в теле конструктора. + +**13.** Именование локальных переменных и членов класса никак не отличается (никакие префиксы не нужны). + +```cpp +timer (not m_timer) +``` + +**14.** Константы в `enum` — CamelCase с большой буквы. Также допустим ALL_CAPS. Если `enum` не локален, то используйте `enum class`. + +```cpp +enum class CompressionMethod +{ + QuickLZ = 0, + LZ4 = 1, +}; +``` + +**15.** Все имена - по английски. Транслит с русского использовать нельзя. + +``` +не Stroka +``` + +**16.** Сокращения (из нескольких букв разных слов) в именах можно использовать только если они являются общепринятыми (если для сокращения можно найти расшифровку в английской википедии или сделав поисковый запрос). + +``` +`AST`, `SQL`. + +Не `NVDH` (что-то неведомое) +``` + +Сокращения в виде обрезанного слова можно использовать, только если такое сокращение является широко используемым. + +Впрочем, сокращения также можно использовать, если расшифровка находится рядом в комментарии. + +**17.** Имена файлов с исходниками на C++ должны иметь расширение только `.cpp`. Заголовочные файлы - только `.h`. - Впрочем, сокращения также можно использовать, если расшифровка находится рядом в комментарии. -17. Имена файлов с исходниками на C++ должны иметь расширение только .cpp. Заголовочные файлы - только .h. ## Как писать код -1. Управление памятью. +**1.** Управление памятью. - Ручное освобождение памяти (delete) можно использовать только в библиотечном коде. +Ручное освобождение памяти (`delete`) можно использовать только в библиотечном коде. - В свою очередь, в библиотечном коде, оператор delete можно использовать только в деструкторах. +В свою очередь, в библиотечном коде, оператор `delete` можно использовать только в деструкторах. - В прикладном коде следует делать так, что память освобождается каким-либо объектом, который владеет ей. +В прикладном коде следует делать так, что память освобождается каким-либо объектом, который владеет ей. - Примеры: +Примеры: - - проще всего разместить объект на стеке, или сделать его членом другого класса. - - для большого количества маленьких объектов используйте контейнеры. - - для автоматического освобождения маленького количества объектов, выделенных на куче, используйте shared_ptr/unique_ptr. -2. Управление ресурсами. +- проще всего разместить объект на стеке, или сделать его членом другого класса. +- для большого количества маленьких объектов используйте контейнеры. +- для автоматического освобождения маленького количества объектов, выделенных на куче, используйте `shared_ptr/unique_ptr`. - Используйте RAII и см. пункт выше. +**2.** Управление ресурсами. -3. Обработка ошибок. +Используйте `RAII` и см. пункт выше. - Используйте исключения. В большинстве случаев, нужно только кидать исключения, а ловить - не нужно (потому что RAII). +**3.** Обработка ошибок. - В программах offline обработки данных, зачастую, можно не ловить исключения. +Используйте исключения. В большинстве случаев, нужно только кидать исключения, а ловить - не нужно (потому что `RAII`). - В серверах, обрабатывающих пользовательские запросы, как правило, достаточно ловить исключения на самом верху обработчика соединения. +В программах офлайн обработки данных, зачастую, можно не ловить исключения. - В функциях потока, следует ловить и запоминать все исключения, чтобы выкинуть их в основном потоке после join. +В серверах, обрабатывающих пользовательские запросы, как правило, достаточно ловить исключения на самом верху обработчика соединения. - ```cpp - /// Если вычислений ещё не было - вычислим первый блок синхронно - if (!started) - { - calculate(); - started = true; - } - else /// Если вычисления уже идут - подождём результата - pool.wait(); +В функциях потока, следует ловить и запоминать все исключения, чтобы выкинуть их в основном потоке после `join`. - if (exception) - exception->rethrow(); - ``` +```cpp +/// Если вычислений ещё не было - вычислим первый блок синхронно +if (!started) +{ + calculate(); + started = true; +} +else /// Если вычисления уже идут - подождём результата + pool.wait(); - Ни в коем случае не «проглатывайте» исключения без разбора. Ни в коем случае, не превращайте все исключения без разбора в сообщения в логе. +if (exception) + exception->rethrow(); +``` - Не `catch (...) {}`. +Ни в коем случае не «проглатывайте» исключения без разбора. Ни в коем случае, не превращайте все исключения без разбора в сообщения в логе. - Если вам нужно проигнорировать какие-то исключения, то игнорируйте только конкретные, а остальные - кидайте обратно. +```cpp +//Not correct +catch (...) {} +``` - ```cpp - catch (const DB::Exception & e) - { - if (e.code() == ErrorCodes::UNKNOWN_AGGREGATE_FUNCTION) - return nullptr; - else - throw; - } - ``` +Если вам нужно проигнорировать какие-то исключения, то игнорируйте только конкретные, а остальные кидайте обратно. - При использовании функций, использующих коды возврата или errno - проверяйте результат и кидайте исключение. +```cpp +catch (const DB::Exception & e) +{ + if (e.code() == ErrorCodes::UNKNOWN_AGGREGATE_FUNCTION) + return nullptr; + else + throw; +} +``` - ```cpp - if (0 != close(fd)) - throwFromErrno("Cannot close file " + file_name, ErrorCodes::CANNOT_CLOSE_FILE); - ``` +При использовании функций, использующих коды возврата или `errno`, проверяйте результат и кидайте исключение. - assert-ы не используются. +```cpp +if (0 != close(fd)) + throwFromErrno("Cannot close file " + file_name, ErrorCodes::CANNOT_CLOSE_FILE); +``` -4. Типы исключений. +`assert` не используются. - В прикладном коде не требуется использовать сложную иерархию исключений. Желательно, чтобы текст исключения был понятен системному администратору. +**4.** Типы исключений. -5. Исключения, вылетающие из деструкторов. - Использовать не рекомендуется, но допустимо. +В прикладном коде не требуется использовать сложную иерархию исключений. Желательно, чтобы текст исключения был понятен системному администратору. - Используйте следующие варианты: +**5.** Исключения, вылетающие из деструкторов. - - Сделайте функцию (done() или finalize()), которая позволяет заранее выполнить всю работу, в процессе которой может возникнуть исключение. Если эта функция была вызвана, то затем в деструкторе не должно возникать исключений. - - Слишком сложную работу (например, отправку данных по сети) можно вообще не делать в деструкторе, рассчитывая, что пользователь заранее позовёт метод для завершения работы. - - Если в деструкторе возникло исключение, желательно не "проглатывать" его, а вывести информацию в лог (если в этом месте доступен логгер). - - В простых программах, если соответствующие исключения не ловятся, и приводят к завершению работы с записью информации в лог, можно не беспокоиться об исключениях, вылетающих из деструкторов, так как вызов std::terminate (в случае noexcept по умолчанию в C++11), является приемлимым способом обработки исключения. +Использовать не рекомендуется, но допустимо. -6. Отдельные блоки кода. +Используйте следующие варианты: - Внутри одной функции, можно создать отдельный блок кода, для того, чтобы сделать некоторые переменные локальными в нём, и для того, чтобы соответствующие деструкторы были вызваны при выходе из блока. +- Сделайте функцию (`done()` или `finalize()`), которая позволяет заранее выполнить всю работу, в процессе которой может возникнуть исключение. Если эта функция была вызвана, то затем в деструкторе не должно возникать исключений. +- Слишком сложную работу (например, отправку данных по сети) можно вообще не делать в деструкторе, рассчитывая, что пользователь заранее позовёт метод для завершения работы. +- Если в деструкторе возникло исключение, желательно не "проглатывать" его, а вывести информацию в лог (если в этом месте доступен логгер). +- В простых программах, если соответствующие исключения не ловятся, и приводят к завершению работы с записью информации в лог, можно не беспокоиться об исключениях, вылетающих из деструкторов, так как вызов `std::terminate` (в случае `noexcept` по умолчанию в C++11), является приемлимым способом обработки исключения. - ```cpp - Block block = data.in->read(); +**6.** Отдельные блоки кода. - { - std::lock_guard lock(mutex); - data.ready = true; - data.block = block; - } +Внутри одной функции, можно создать отдельный блок кода, для того, чтобы сделать некоторые переменные локальными в нём, и для того, чтобы соответствующие деструкторы были вызваны при выходе из блока. - ready_any.set(); - ``` -7. Многопоточность. +```cpp +Block block = data.in->read(); - В программах offline обработки данных: - - cначала добейтесь более-менее максимальной производительности на одном процессорном ядре, потом можно распараллеливать код, но только если есть необходимость. +{ + std::lock_guard lock(mutex); + data.ready = true; + data.block = block; +} - В программах - серверах: - - используйте пул потоков для обработки запросов. На данный момент, у нас не было задач, в которых была бы необходимость использовать userspace context switching. +ready_any.set(); +``` - Fork для распараллеливания не используется. -8. Синхронизация потоков. +**7.** Многопоточность. - Часто можно сделать так, чтобы отдельные потоки писали данные в разные ячейки памяти (лучше - в разные кэш-линии), и не использовать синхронизацию потоков (кроме joinAll). +В программах офлайн обработки данных: - Если синхронизация нужна, то в большинстве случаев, достаточно использовать mutex под lock_guard-ом. +- cначала добейтесь более-менее максимальной производительности на одном процессорном ядре, потом можно распараллеливать код, но только если есть необходимость. - В остальных случаях, используйте системные примитивы синхронизации. Не используйте busy wait. +В программах - серверах: - Атомарные операции можно использовать только в простейших случаях. +- используйте пул потоков для обработки запросов. На данный момент, у нас не было задач, в которых была бы необходимость использовать userspace context switching. - Не нужно писать самостоятельно lock-free структуры данных, если вы не являетесь экспертом. -9. Ссылки и указатели. +Fork для распараллеливания не используется. - В большинстве случаев, предпочитайте ссылки. -10. const. +**8.** Синхронизация потоков. - Используйте константные ссылки, указатели на константу, const_iterator, константные методы. +Часто можно сделать так, чтобы отдельные потоки писали данные в разные ячейки памяти (лучше в разные кэш-линии), и не использовать синхронизацию потоков (кроме `joinAll`). - Считайте, что const - вариант написания «по умолчанию», а отсутствие const - только при необходимости. +Если синхронизация нужна, то в большинстве случаев, достаточно использовать mutex под `lock_guard`. - Для переменных, передающихся по значению, использовать const обычно не имеет смысла. -11. unsigned. +В остальных случаях, используйте системные примитивы синхронизации. Не используйте busy wait. - Используйте unsigned, если нужно. -12. Числовые типы. +Атомарные операции можно использовать только в простейших случаях. - Используйте типы UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, а также size_t, ssize_t, ptrdiff_t. +Не нужно писать самостоятельно lock-free структуры данных, если вы не являетесь экспертом. - Не используйте для чисел типы signed/unsigned long, long long, short; signed char, unsigned char, а также char. -13. Передача аргументов. +**9.** Ссылки и указатели. - Сложные значения передавайте по ссылке (включая std::string). +В большинстве случаев, предпочитайте ссылки. - Если функция захватывает владение объектом, созданным на куче, то сделайте типом аргумента shared_ptr или unique_ptr. -14. Возврат значений. +**10.** const. - В большинстве случаев, просто возвращайте значение с помощью return. Не пишите [return std::move(res)]{.strike}. +Используйте константные ссылки, указатели на константу, `const_iterator`, константные методы. - Если внутри функции создаётся объект на куче и отдаётся наружу, то возвращайте shared_ptr или unique_ptr. +Считайте, что `const` — вариант написания «по умолчанию», а отсутствие `const` только при необходимости. - В некоторых редких случаях, может потребоваться возвращать значение через аргумент функции. В этом случае, аргументом будет ссылка. +Для переменных, передающихся по значению, использовать `const` обычно не имеет смысла. - ```cpp - using AggregateFunctionPtr = std::shared_ptr; +**11.** unsigned. - /** Позволяет создать агрегатную функцию по её имени. - */ - class AggregateFunctionFactory - { - public: - AggregateFunctionFactory(); - AggregateFunctionPtr get(const String & name, const DataTypes & argument_types) const; - ``` -15. namespace. +Используйте `unsigned`, если нужно. - Для прикладного кода отдельный namespace использовать не нужно. +**12.** Числовые типы. - Для маленьких библиотек - не требуется. +Используйте типы `UInt8`, `UInt16`, `UInt32`, `UInt64`, `Int8`, `Int16`, `Int32`, `Int64`, а также `size_t`, `ssize_t`, `ptrdiff_t`. - Для не совсем маленьких библиотек - поместите всё в namespace. +Не используйте для чисел типы `signed/unsigned long`, `long long`, `short`, `signed/unsigned char`, `char`. - Внутри библиотеки в .h файле можно использовать namespace detail для деталей реализации, не нужных прикладному коду. +**13.** Передача аргументов. - В .cpp файле можно использовать static или анонимный namespace для скрытия символов. +Сложные значения передавайте по ссылке (включая `std::string`). - Также, namespace можно использовать для enum, чтобы соответствующие имена не попали во внешний namespace (но лучше использовать enum class). +Если функция захватывает владение объектом, созданным на куче, то сделайте типом аргумента `shared_ptr` или `unique_ptr`. -16. Отложенная инициализация. +**14.** Возврат значений. - Обычно, если для инициализации требуются аргументы, то не пишите конструктор по умопчанию. +В большинстве случаев, просто возвращайте значение с помощью `return`. Не пишите `[return std::move(res)]{.strike}`. - Если потом вам потребовалась отложенная инициализация, то вы можете дописать конструктор по умолчанию (который создаст объект с некорректным состоянием). Или, для небольшого количества объектов, можно использовать shared_ptr/unique_ptr. +Если внутри функции создаётся объект на куче и отдаётся наружу, то возвращайте `shared_ptr` или `unique_ptr`. - ```cpp - Loader(DB::Connection * connection_, const std::string & query, size_t max_block_size_); +В некоторых редких случаях, может потребоваться возвращать значение через аргумент функции. В этом случае, аргументом будет ссылка. - /// Для отложенной инициализации - Loader() {} - ``` -17. Виртуальные функции. +```cpp +using AggregateFunctionPtr = std::shared_ptr; - Если класс не предназначен для полиморфного использования, то не нужно делать функции виртуальными зря. Это относится и к деструктору. -18. Кодировки. +/** Позволяет создать агрегатную функцию по её имени. + */ +class AggregateFunctionFactory +{ +public: + AggregateFunctionFactory(); + AggregateFunctionPtr get(const String & name, const DataTypes & argument_types) const; +``` - Везде используется UTF-8. Используется `std::string`, `char *`. Не используется `std::wstring`, `wchar_t`. -19. Логгирование. +**15.** namespace. - См. примеры везде в коде. +Для прикладного кода отдельный `namespace` использовать не нужно. - Перед коммитом, удалите всё бессмысленное и отладочное логгирование, и другие виды отладочного вывода. +Для маленьких библиотек - не требуется. - Не должно быть логгирования на каждую итерацию внутреннего цикла, даже уровня Trace. +Для не совсем маленьких библиотек - поместите всё в `namespace`. - При любом уровне логгирования, логи должно быть возможно читать. +Внутри библиотеки в `.h` файле можно использовать `namespace detail` для деталей реализации, не нужных прикладному коду. - Логгирование следует использовать, в основном, только в прикладном коде. +В `.cpp` файле можно использовать `static` или анонимный namespace для скрытия символов. - Сообщения в логе должны быть написаны на английском языке. +Также, `namespace` можно использовать для `enum`, чтобы соответствующие имена не попали во внешний `namespace` (но лучше использовать `enum class`). - Желательно, чтобы лог был понятен системному администратору. +**16.** Отложенная инициализация. - Не нужно писать ругательства в лог. +Обычно, если для инициализации требуются аргументы, то не пишите конструктор по умолчанию. - В логе используется кодировка UTF-8. Изредка можно использовать в логе не-ASCII символы. -20. Ввод-вывод. +Если потом вам потребовалась отложенная инициализация, то вы можете дописать конструктор по умолчанию (который создаст объект с некорректным состоянием). Или, для небольшого количества объектов, можно использовать `shared_ptr/unique_ptr`. - Во внутренних циклах (в критичных по производительности участках программы) нельзя использовать iostreams (в том числе, ни в коем случае не используйте stringstream). +```cpp +Loader(DB::Connection * connection_, const std::string & query, size_t max_block_size_); - Вместо этого используйте библиотеку DB/IO. +/// Для отложенной инициализации +Loader() {} +``` -21. Дата и время. +**17.** Виртуальные функции. - См. библиотеку DateLUT. +Если класс не предназначен для полиморфного использования, то не нужно делать функции виртуальными зря. Это относится и к деструктору. -22. include. +**18.** Кодировки. - В заголовочном файле используется только `#pragma once`, а include guard-ы писать не нужно. +Везде используется UTF-8. Используется `std::string`, `char *`. Не используется `std::wstring`, `wchar_t`. -23. using. +**19.** Логгирование. - using namespace не используется. +См. примеры везде в коде. - using что-то конкретное - можно. Лучше локально - внутри класса или функции. +Перед коммитом, удалите всё бессмысленное и отладочное логгирование, и другие виды отладочного вывода. -24. Не нужно использовать trailing return type для функций, если в этом нет необходимости. +Не должно быть логгирования на каждую итерацию внутреннего цикла, даже уровня Trace. - [auto f() -> void;]{.strike} +При любом уровне логгирования, логи должно быть возможно читать. -25. Не нужно объявлять и инициализировать переменные так: +Логгирование следует использовать, в основном, только в прикладном коде. - ```cpp - auto s = std::string{"Hello"}; - ``` +Сообщения в логе должны быть написаны на английском языке. - Надо так: +Желательно, чтобы лог был понятен системному администратору. + +Не нужно писать ругательства в лог. + +В логе используется кодировка UTF-8. Изредка можно использовать в логе не-ASCII символы. + +**20.** Ввод-вывод. + +Во внутренних циклах (в критичных по производительности участках программы) нельзя использовать `iostreams` (в том числе, ни в коем случае не используйте `stringstream`). + +Вместо этого используйте библиотеку `DB/IO`. + +**21.** Дата и время. + +См. библиотеку `DateLUT`. + +**22.** include. + +В заголовочном файле используется только `#pragma once`, а include guards писать не нужно. + +**23.** using. + +`using namespace` не используется. Можно использовать `using` что-то конкретное. Лучше локально, внутри класса или функции. + +**24.** Не нужно использовать `trailing return type` для функций, если в этом нет необходимости. + +```cpp +[auto f() -> void;]{.strike} +``` + +**25.** Объявление и инициализация переменных. + +```cpp +//right way +std::string s = "Hello"; +std::string s{"Hello"}; + +//wrong way +auto s = std::string{"Hello"}; +``` + +**26.** Для виртуальных функций, пишите `virtual` в базовом классе, а в классах-наследниках, пишите `override` и не пишите `virtual`. - ```cpp - std::string s = "Hello"; - std::string s{"Hello"}; - ``` -26. Для виртуальных функций, пишите virtual в базовом классе, а в классах-наследниках, пишите override и не пишите virtual. ## Неиспользуемые возможности языка C++ -1. Виртуальное наследование не используется. -2. Спецификаторы исключений из C++03 не используются. -3. Function try block не используется, за исключением функции main в тестах. +**1.** Виртуальное наследование не используется. + +**2.** Спецификаторы исключений из C++03 не используются. + +**3.** Function try block не используется, за исключением функции main в тестах. ## Платформа -1. Мы пишем некроссплатформенный код (под конкретную платформу). +**1.** Мы пишем код под конкретную платформу. - Хотя, при прочих равных условиях, предпочитается более-менее кроссплатформенный или легко портируемый код. +Хотя, при прочих равных условиях, предпочитается более-менее кроссплатформенный или легко портируемый код. -2. Язык - C++17. +**2.** Язык - C++17. -3. Компилятор - gcc. На данный момент (декабрь 2017), код собирается версией 7.2. (Также код может быть собран clang 5) +**3.** Компилятор - `gcc`. На данный момент (декабрь 2017), код собирается версией 7.2. (Также код может быть собран `clang 5`) - Используется стандартная библиотека (реализация libstdc++ или libc++). +Используется стандартная библиотека (реализация `libstdc++` или `libc++`). -4. ОС - Linux Ubuntu, не более старая, чем Precise. +**4.** ОС - Linux Ubuntu, не более старая, чем Precise. -5. Код пишется под процессор с архитектурой x86_64. +**5.** Код пишется под процессор с архитектурой x86_64. - Набор инструкций - минимальный поддерживаемый среди наших серверов. Сейчас это - SSE4.2. +Набор инструкций минимальный из поддержаных нашими серверами. Сейчас это - SSE4.2. -6. Используются флаги компиляции `-Wall -Wextra -Werror`. +**6.** Используются флаги компиляции `-Wall -Wextra -Werror`. -7. Используется статическая линковка со всеми библиотеками кроме тех, которые трудно подключить статически (см. вывод команды ldd). +**7.** Используется статическая линковка со всеми библиотеками кроме тех, которые трудно подключить статически (см. вывод команды `ldd`). -8. Код разрабатывается и отлаживается с релизными параметрами сборки. +**8.** Код разрабатывается и отлаживается с релизными параметрами сборки. ## Инструментарий -1. Хорошая среда разработки - KDevelop. -2. Для отладки используется gdb, valgrind (memcheck), strace, -fsanitize=..., tcmalloc_minimal_debug. -3. Для профилирования используется Linux Perf, valgrind (callgrind), strace -cf. -4. Исходники в Git. -5. Сборка с помощью CMake. -6. Программы выкладываются с помощью deb пакетов. -7. Коммиты в master не должны ломать сборку проекта. +**1.** Хорошая среда разработки - KDevelop. - А работоспособность собранных программ гарантируется только для отдельных ревизий. -8. Коммитьте как можно чаще, в том числе и не рабочий код. +**2.** Для отладки используется `gdb`, `valgrind` (`memcheck`), `strace`, `-fsanitize=...`, `tcmalloc_minimal_debug`. - Для этого следует использовать бранчи. +**3.** Для профилирования используется `Linux Perf`, `valgrind` (`callgrind`), `strace -cf`. + +**4.** Исходники в Git. + +**5.** Сборка с помощью `CMake`. + +**6.** Программы выкладываются с помощью `deb` пакетов. + +**7.** Коммиты в master не должны ломать сборку проекта. + +А работоспособность собранных программ гарантируется только для отдельных ревизий. + +**8.** Коммитьте как можно чаще, в том числе и нерабочий код. + +Для этого следует использовать бранчи. + +Если ваш код в ветке `master` ещё не собирается, исключите его из сборки перед `push`, также вы будете должны его доработать или удалить в течение нескольких дней. + +**9.** Для нетривиальных изменений, используются бранчи. Следует загружать бранчи на сервер. + +**10.** Ненужный код удаляется из исходников. - Если ваш код в master-е ещё не собирается, перед push-ем - исключите его из сборки, также вы будете должны его доработать или удалить в течение нескольких дней. -9. Для нетривиальных изменений, используются бранчи. Следует загружать бранчи на сервер. -10. Ненужный код удаляется из исходников. ## Библиотеки -1. Используются стандартная библиотека C++14 (допустимо использовать experimental расширения) а также фреймворки boost, Poco. -2. При необходимости, можно использовать любые известные библиотеки, доступные в ОС из пакетов. +**1.** Используются стандартная библиотека C++14 (допустимо использовать экспериментальные расширения) а также фреймворки `boost`, `Poco`. - Если есть хорошее готовое решение, то оно используется, даже если для этого придётся установить ещё одну библиотеку. +**2.** При необходимости, можно использовать любые известные библиотеки, доступные в ОС из пакетов. - (Но будьте готовы к тому, что иногда вам придётся выкидывать плохие библиотеки из кода.) +Если есть хорошее готовое решение, то оно используется, даже если для этого придётся установить ещё одну библиотеку. -3. Если в пакетах нет нужной библиотеки, или её версия достаточно старая, или если она собрана не так, как нужно, то можно использовать библиотеку, устанавливаемую не из пакетов. -4. Если библиотека достаточно маленькая и у неё нет своей системы сборки, то следует включить её файлы в проект, в директорию contrib. -5. Предпочтение всегда отдаётся уже использующимся библиотекам. +(Но будьте готовы к тому, что иногда вам придётся выкидывать плохие библиотеки из кода.) + +**3.** Если в пакетах нет нужной библиотеки, или её версия достаточно старая, или если она собрана не так, как нужно, то можно использовать библиотеку, устанавливаемую не из пакетов. + +**4.** Если библиотека достаточно маленькая и у неё нет своей системы сборки, то следует включить её файлы в проект, в директорию `contrib`. + +**5.** Предпочтение всегда отдаётся уже использующимся библиотекам. ## Общее -1. Пишите как можно меньше кода. -2. Пробуйте самое простое решение. -3. Не нужно писать код, если вы ещё не знаете, что будет делать ваша программа, и как будет работать её внутренний цикл. -4. В простейших случаях, используйте using вместо классов/структур. -5. Если есть возможность - не пишите конструкторы копирования, операторы присваивания, деструктор (кроме виртуального, если класс содержит хотя бы одну виртуальную функцию), move-конструкторы и move-присваивания. То есть, чтобы соответствущие функции, генерируемые компилятором, работали правильно. Можно использовать default. -6. Приветствуется упрощение и уменьшение объёма кода. +**1.** Пишите как можно меньше кода. + +**2.** Пробуйте самое простое решение. + +**3.** Не нужно писать код, если вы ещё не знаете, что будет делать ваша программа, и как будет работать её внутренний цикл. + +**4.** В простейших случаях, используйте `using` вместо классов/структур. + +**5.** Если есть возможность - не пишите конструкторы копирования, операторы присваивания, деструктор (кроме виртуального, если класс содержит хотя бы одну виртуальную функцию), move-конструкторы и move-присваивания. То есть, чтобы соответствущие функции, генерируемые компилятором, работали правильно. Можно использовать `default`. + +**6.** Приветствуется упрощение и уменьшение объёма кода. ## Дополнительно -1. Явное указание std:: для типов из stddef.h. +**1.** Явное указание `std::` для типов из `stddef.h`. - Рекомендуется не указывать. То есть, рекомендуется писать size_t вместо std::size_t - потому что это короче. +Рекомендуется не указывать. То есть, рекомендуется писать `size_t` вместо `std::size_t`, это короче. - Но при желании, вы можете всё-таки приписать std:: - такой вариант тоже допустим. +При желании, можно дописать `std::`, этот вариант допустим. -2. Явное указание std:: для функций из стандартной библиотеки C. +**2.** Явное указание `std::` для функций из стандартной библиотеки C. - Не рекомендуется. То есть, пишите memcpy вместо std::memcpy. +Не рекомендуется. То есть, пишите `memcpy` вместо `std::memcpy`. - Причина - существуют похожие нестандартные функции, например, memmem. Мы можем использовать и изредка используем эти функции. Эти функции отсутствуют в namespace std. +Причина - существуют похожие нестандартные функции, например, `memmem`. Мы можем использовать и изредка используем эти функции. Эти функции отсутствуют в `namespace std`. - Если вы везде напишете std::memcpy вместо memcpy, то будет неудобно смотреться memmem без std::. +Если вы везде напишете `std::memcpy` вместо `memcpy`, то будет неудобно смотреться `memmem` без `std::`. - Тем не менее, указывать std:: тоже допустимо, если так больше нравится. -3. Использование функций из C при наличии аналогов в стандартной библиотеке C++. +Тем не менее, указывать `std::` тоже допустимо, если так больше нравится. - Допустимо, если это использование эффективнее. +**3.** Использование функций из C при наличии аналогов в стандартной библиотеке C++. - Для примера, для копирования длинных кусков памяти, используйте memcpy вместо std::copy. +Допустимо, если это использование эффективнее. -4. Перенос длинных аргументов функций. +Для примера, для копирования длинных кусков памяти, используйте `memcpy` вместо `std::copy`. - Допустимо использовать любой стиль переноса, похожий на приведённые ниже: +**4.** Перенос длинных аргументов функций. - ```cpp - function( - T1 x1, - T2 x2) - ``` +Допустимо использовать любой стиль переноса, похожий на приведённые ниже: - ```cpp - function( - size_t left, size_t right, - const & RangesInDataParts ranges, - size_t limit) - ``` +```cpp +function( + T1 x1, + T2 x2) +``` - ```cpp - function(size_t left, size_t right, - const & RangesInDataParts ranges, - size_t limit) - ``` +```cpp +function( + size_t left, size_t right, + const & RangesInDataParts ranges, + size_t limit) +``` - ```cpp - function(size_t left, size_t right, - const & RangesInDataParts ranges, - size_t limit) - ``` +```cpp +function(size_t left, size_t right, + const & RangesInDataParts ranges, + size_t limit) +``` - ```cpp - function( - size_t left, - size_t right, - const & RangesInDataParts ranges, - size_t limit) - ``` +```cpp +function(size_t left, size_t right, + const & RangesInDataParts ranges, + size_t limit) +``` +```cpp +function( + size_t left, + size_t right, + const & RangesInDataParts ranges, + size_t limit) +``` From 27d90fb941be6525139d4f5b868e048b02426f25 Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 23 Apr 2018 17:51:56 +0300 Subject: [PATCH 004/231] Add an example function that uses LLVM to compile its own body --- dbms/src/Functions/CMakeLists.txt | 8 +- dbms/src/Functions/FunctionsLLVMTest.cpp | 144 ++++++++++++++++++ dbms/src/Functions/registerFunctions.cpp | 2 + dbms/src/Server/Compiler-5.0.0/CMakeLists.txt | 5 + dbms/src/Server/Compiler-6.0.0/CMakeLists.txt | 5 + 5 files changed, 163 insertions(+), 1 deletion(-) create mode 100644 dbms/src/Functions/FunctionsLLVMTest.cpp diff --git a/dbms/src/Functions/CMakeLists.txt b/dbms/src/Functions/CMakeLists.txt index cbc5288eac5..bb08820a322 100644 --- a/dbms/src/Functions/CMakeLists.txt +++ b/dbms/src/Functions/CMakeLists.txt @@ -79,11 +79,17 @@ list(REMOVE_ITEM clickhouse_functions_headers IFunction.h FunctionFactory.h Func add_library(clickhouse_functions ${clickhouse_functions_sources}) -target_link_libraries(clickhouse_functions PUBLIC dbms PRIVATE libconsistent-hashing ${FARMHASH_LIBRARIES} ${METROHASH_LIBRARIES}) +llvm_map_components_to_libraries(REQUIRED_LLVM_LIBRARIES all) + +target_link_libraries(clickhouse_functions PUBLIC dbms PRIVATE libconsistent-hashing ${FARMHASH_LIBRARIES} ${METROHASH_LIBRARIES} ${REQUIRED_LLVM_LIBRARIES}) target_include_directories (clickhouse_functions BEFORE PUBLIC ${ClickHouse_SOURCE_DIR}/contrib/libfarmhash) target_include_directories (clickhouse_functions BEFORE PUBLIC ${ClickHouse_SOURCE_DIR}/contrib/libmetrohash/src) target_include_directories (clickhouse_functions BEFORE PUBLIC ${DIVIDE_INCLUDE_DIR}) +target_include_directories (clickhouse_functions BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) + +# LLVM 5.0 has a bunch of unused parameters in its header files. +set_source_files_properties(FunctionsLLVMTest.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter -g") if (CMAKE_BUILD_TYPE_UC STREQUAL "RELEASE" OR CMAKE_BUILD_TYPE_UC STREQUAL "RELWITHDEBINFO" OR CMAKE_BUILD_TYPE_UC STREQUAL "MINSIZEREL") # Won't generate debug info for files with heavy template instantiation to achieve faster linking and lower size. diff --git a/dbms/src/Functions/FunctionsLLVMTest.cpp b/dbms/src/Functions/FunctionsLLVMTest.cpp new file mode 100644 index 00000000000..c2e4bd12eca --- /dev/null +++ b/dbms/src/Functions/FunctionsLLVMTest.cpp @@ -0,0 +1,144 @@ +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + + +namespace +{ + +struct LLVMTargetInitializer { + LLVMTargetInitializer() { + llvm::InitializeNativeTarget(); + llvm::InitializeNativeTargetAsmPrinter(); + } +}; + +LLVMTargetInitializer llvmInit; + +} + + +namespace DB +{ + +namespace ErrorCodes { + extern const int ILLEGAL_TYPE_OF_ARGUMENT; +} + +class FunctionSomething : public IFunction +{ + llvm::LLVMContext context; + std::unique_ptr machine{llvm::EngineBuilder().selectTarget()}; + llvm::orc::RTDyldObjectLinkingLayer objectLayer{[]() { return std::make_shared(); }}; + llvm::orc::IRCompileLayer compileLayer{objectLayer, llvm::orc::SimpleCompiler(*machine)}; + double (*jitted)(double, double); + +public: + static constexpr auto name = "something"; + + FunctionSomething() { + llvm::DataLayout layout = machine->createDataLayout(); + auto module = std::make_shared("something", context); + module->setDataLayout(layout); + module->setTargetTriple(machine->getTargetTriple().getTriple()); + + { + auto doubleType = llvm::Type::getDoubleTy(context); + auto funcType = llvm::FunctionType::get(doubleType, {doubleType, doubleType}, /*isVarArg=*/false); + auto func = llvm::Function::Create(funcType, llvm::Function::ExternalLinkage, name, module.get()); + llvm::Argument * args[] = {nullptr, nullptr}; + size_t i = 0; + for (auto& arg : func->args()) + { + args[i++] = &arg; + } + llvm::IRBuilder<> builder(context); + builder.SetInsertPoint(llvm::BasicBlock::Create(context, name, func)); + builder.CreateRet(builder.CreateFAdd(args[0], args[1], "add")); + } + + std::string mangledName; + llvm::raw_string_ostream mangledNameStream(mangledName); + llvm::Mangler::getNameWithPrefix(mangledNameStream, name, layout); + llvm::cantFail(compileLayer.addModule(module, std::make_shared())); + jitted = reinterpret_cast(compileLayer.findSymbol(mangledNameStream.str(), false).getAddress().get()); + } + + static FunctionPtr create(const Context &) + { + return std::make_shared(); + } + + String getName() const override + { + return name; + } + + size_t getNumberOfArguments() const override + { + return 2; + } + + bool useDefaultImplementationForConstants() const override { return true; } + + DataTypePtr getReturnTypeImpl(const DataTypes &) const override + { + return std::make_shared(); + } + + void executeImpl(Block & block, const ColumnNumbers & arguments, size_t result) override + { + auto a = checkAndGetColumn>(block.getByPosition(arguments[0]).column.get()); + if (!a) + throw Exception("Argument #1 (" + block.getByPosition(arguments[0]).column->getName() + ") of function " + getName() + " has invalid type", + ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); + auto b = checkAndGetColumn>(block.getByPosition(arguments[1]).column.get()); + if (!b) + throw Exception("Argument #2 (" + block.getByPosition(arguments[1]).column->getName() + ") of function " + getName() + " has invalid type", + ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); + + auto col_res = ColumnVector::create(); + auto & vec_a = a->getData(); + auto & vec_b = b->getData(); + auto & vec_res = col_res->getData(); + vec_res.resize(a->size()); + for (size_t i = 0; i < vec_res.size(); ++i) + vec_res[i] = jitted(vec_a[i], vec_b[i]); + block.getByPosition(result).column = std::move(col_res); + } +}; + + +void registerFunctionsLLVMTest(FunctionFactory & factory) +{ + factory.registerFunction(); +} + +} diff --git a/dbms/src/Functions/registerFunctions.cpp b/dbms/src/Functions/registerFunctions.cpp index 0dcc66bfd77..b9d4f39087f 100644 --- a/dbms/src/Functions/registerFunctions.cpp +++ b/dbms/src/Functions/registerFunctions.cpp @@ -42,6 +42,7 @@ void registerFunctionsGeo(FunctionFactory &); void registerFunctionsCharset(FunctionFactory &); void registerFunctionsNull(FunctionFactory &); void registerFunctionsFindCluster(FunctionFactory &); +void registerFunctionsLLVMTest(FunctionFactory &); void registerFunctions() @@ -79,6 +80,7 @@ void registerFunctions() registerFunctionsCharset(factory); registerFunctionsNull(factory); registerFunctionsFindCluster(factory); + registerFunctionsLLVMTest(factory); } } diff --git a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt index 739db1cf448..bfc988af773 100644 --- a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt @@ -51,3 +51,8 @@ libtinfo.a PUBLIC ${ZLIB_LIBRARIES} ${EXECINFO_LIBRARY} Threads::Threads ) + +if (MAKE_STATIC_LIBRARIES) + # fix strange static error: undefined reference to 'std::error_category::~error_category()' + target_link_libraries(clickhouse-compiler-lib PUBLIC stdc++) +endif () diff --git a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt index 95db6a7e1d1..a4cb086c4cd 100644 --- a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt @@ -51,3 +51,8 @@ libtinfo.a PUBLIC ${ZLIB_LIBRARIES} ${EXECINFO_LIBRARY} Threads::Threads ) + +if (MAKE_STATIC_LIBRARIES) + # fix strange static error: undefined reference to 'std::error_category::~error_category()' + target_link_libraries(clickhouse-compiler-lib PUBLIC stdc++) +endif () From 851684de51c473e1b4b44ca34aa2fc1d64bcc824 Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 24 Apr 2018 01:29:39 +0300 Subject: [PATCH 005/231] Add a JIT interface for row-wise default-nullable functions. Not actually implemented, though. It does print out some jit-compiled stuff, but that's about it. For example, this query: select number from system.numbers where something(cast(number as Float64)) == 4 results in this on server's stderr: define double @"something(CAST(number, 'Float64'))"(void**, i8*, void*) { "something(CAST(number, 'Float64'))": ret double 1.234500e+04 } (and an exception, because that's what the non-jitted method does.) As one may notice, this function neither reads the input (first argument; tuple of arrays) nor writes the output (third argument; array), instead returning some general nonsense. In addition, `#if USE_EMBEDDED_COMPILER` doesn't work for some reason, including LLVM headers requires -Wno-unused-parameter, this probably only works on LLVM 5.0 due to rampant API instability, and I'm definitely no expert on CMake. In short, there's still a long way to go. --- dbms/CMakeLists.txt | 15 ++ dbms/src/Functions/CMakeLists.txt | 17 +- dbms/src/Functions/FunctionsLLVMTest.cpp | 127 +++----------- dbms/src/Functions/IFunction.h | 48 +++++- dbms/src/Interpreters/ExpressionActions.cpp | 21 +++ dbms/src/Interpreters/ExpressionActions.h | 2 + dbms/src/Interpreters/ExpressionJIT.cpp | 179 ++++++++++++++++++++ dbms/src/Interpreters/ExpressionJIT.h | 111 ++++++++++++ 8 files changed, 405 insertions(+), 115 deletions(-) create mode 100644 dbms/src/Interpreters/ExpressionJIT.cpp create mode 100644 dbms/src/Interpreters/ExpressionJIT.h diff --git a/dbms/CMakeLists.txt b/dbms/CMakeLists.txt index 906897fd0f4..0517c41951f 100644 --- a/dbms/CMakeLists.txt +++ b/dbms/CMakeLists.txt @@ -82,6 +82,15 @@ list (APPEND dbms_headers list (APPEND dbms_sources src/TableFunctions/ITableFunction.cpp src/TableFunctions/TableFunctionFactory.cpp) list (APPEND dbms_headers src/TableFunctions/ITableFunction.h src/TableFunctions/TableFunctionFactory.h) +if (USE_EMBEDDED_COMPILER) + # LLVM 5.0 has a bunch of unused parameters in its header files. + # TODO: global-disable this warning + set_source_files_properties(src/Interpreters/ExpressionJIT.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter") +else () + list (REMOVE dbms_sources src/Interpreters/ExpressionJIT.cpp) + list (REMOVE dbms_headers src/Interpreters/ExpressionJIT.h) +endif () + add_library(clickhouse_common_io ${SPLIT_SHARED} ${clickhouse_common_io_headers} ${clickhouse_common_io_sources}) if (ARCH_FREEBSD) @@ -99,6 +108,12 @@ else () install (TARGETS dbms LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR} COMPONENT clickhouse) endif () +if (USE_EMBEDDED_COMPILER) + llvm_map_components_to_libraries(REQUIRED_LLVM_LIBRARIES all) + target_link_libraries (dbms ${REQUIRED_LLVM_LIBRARIES}) + target_include_directories (dbms BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) +endif () + if (CMAKE_BUILD_TYPE_UC STREQUAL "RELEASE" OR CMAKE_BUILD_TYPE_UC STREQUAL "RELWITHDEBINFO" OR CMAKE_BUILD_TYPE_UC STREQUAL "MINSIZEREL") # Won't generate debug info for files with heavy template instantiation to achieve faster linking and lower size. diff --git a/dbms/src/Functions/CMakeLists.txt b/dbms/src/Functions/CMakeLists.txt index bb08820a322..2c6a77726f9 100644 --- a/dbms/src/Functions/CMakeLists.txt +++ b/dbms/src/Functions/CMakeLists.txt @@ -79,17 +79,11 @@ list(REMOVE_ITEM clickhouse_functions_headers IFunction.h FunctionFactory.h Func add_library(clickhouse_functions ${clickhouse_functions_sources}) -llvm_map_components_to_libraries(REQUIRED_LLVM_LIBRARIES all) - -target_link_libraries(clickhouse_functions PUBLIC dbms PRIVATE libconsistent-hashing ${FARMHASH_LIBRARIES} ${METROHASH_LIBRARIES} ${REQUIRED_LLVM_LIBRARIES}) +target_link_libraries(clickhouse_functions PUBLIC dbms PRIVATE libconsistent-hashing ${FARMHASH_LIBRARIES} ${METROHASH_LIBRARIES}) target_include_directories (clickhouse_functions BEFORE PUBLIC ${ClickHouse_SOURCE_DIR}/contrib/libfarmhash) target_include_directories (clickhouse_functions BEFORE PUBLIC ${ClickHouse_SOURCE_DIR}/contrib/libmetrohash/src) target_include_directories (clickhouse_functions BEFORE PUBLIC ${DIVIDE_INCLUDE_DIR}) -target_include_directories (clickhouse_functions BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) - -# LLVM 5.0 has a bunch of unused parameters in its header files. -set_source_files_properties(FunctionsLLVMTest.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter -g") if (CMAKE_BUILD_TYPE_UC STREQUAL "RELEASE" OR CMAKE_BUILD_TYPE_UC STREQUAL "RELWITHDEBINFO" OR CMAKE_BUILD_TYPE_UC STREQUAL "MINSIZEREL") # Won't generate debug info for files with heavy template instantiation to achieve faster linking and lower size. @@ -108,3 +102,12 @@ endif () if (ENABLE_TESTS) add_subdirectory (tests) endif () + +if (USE_EMBEDDED_COMPILER) + #llvm_map_components_to_libraries(REQUIRED_LLVM_LIBRARIES all) + #target_link_libraries(clickhouse_functions PRIVATE ${REQUIRED_LLVM_LIBRARIES}) + target_include_directories (clickhouse_functions BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) + # LLVM 5.0 has a bunch of unused parameters in its header files. + # TODO: global-disable this warning + set_source_files_properties(FunctionsLLVMTest.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter -g") +endif () diff --git a/dbms/src/Functions/FunctionsLLVMTest.cpp b/dbms/src/Functions/FunctionsLLVMTest.cpp index c2e4bd12eca..b65dbaa236e 100644 --- a/dbms/src/Functions/FunctionsLLVMTest.cpp +++ b/dbms/src/Functions/FunctionsLLVMTest.cpp @@ -1,137 +1,50 @@ -#include -#include -#include -#include #include -#include +#include +#include -#include -#include -#include -#include +//#if USE_EMBEDDED_COMPILER +#include #include -#include -#include -#include #include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include -#include - - -namespace -{ - -struct LLVMTargetInitializer { - LLVMTargetInitializer() { - llvm::InitializeNativeTarget(); - llvm::InitializeNativeTargetAsmPrinter(); - } -}; - -LLVMTargetInitializer llvmInit; - -} +//#endif namespace DB { -namespace ErrorCodes { +namespace ErrorCodes +{ extern const int ILLEGAL_TYPE_OF_ARGUMENT; } class FunctionSomething : public IFunction { - llvm::LLVMContext context; - std::unique_ptr machine{llvm::EngineBuilder().selectTarget()}; - llvm::orc::RTDyldObjectLinkingLayer objectLayer{[]() { return std::make_shared(); }}; - llvm::orc::IRCompileLayer compileLayer{objectLayer, llvm::orc::SimpleCompiler(*machine)}; - double (*jitted)(double, double); - public: static constexpr auto name = "something"; - FunctionSomething() { - llvm::DataLayout layout = machine->createDataLayout(); - auto module = std::make_shared("something", context); - module->setDataLayout(layout); - module->setTargetTriple(machine->getTargetTriple().getTriple()); - - { - auto doubleType = llvm::Type::getDoubleTy(context); - auto funcType = llvm::FunctionType::get(doubleType, {doubleType, doubleType}, /*isVarArg=*/false); - auto func = llvm::Function::Create(funcType, llvm::Function::ExternalLinkage, name, module.get()); - llvm::Argument * args[] = {nullptr, nullptr}; - size_t i = 0; - for (auto& arg : func->args()) - { - args[i++] = &arg; - } - llvm::IRBuilder<> builder(context); - builder.SetInsertPoint(llvm::BasicBlock::Create(context, name, func)); - builder.CreateRet(builder.CreateFAdd(args[0], args[1], "add")); - } - - std::string mangledName; - llvm::raw_string_ostream mangledNameStream(mangledName); - llvm::Mangler::getNameWithPrefix(mangledNameStream, name, layout); - llvm::cantFail(compileLayer.addModule(module, std::make_shared())); - jitted = reinterpret_cast(compileLayer.findSymbol(mangledNameStream.str(), false).getAddress().get()); - } - - static FunctionPtr create(const Context &) +//#if USE_EMBEDDED_COMPILER + llvm::Value * compile(llvm::IRBuilderBase & builder, const DataTypes &, const ValuePlaceholders &) const override { - return std::make_shared(); + // if (types.size() != 2 || types[0] != DataTypeFloat64 || types[1] != DataTypeFloat64) + // throw Exception("invalid arguments for " + name, ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); + // return static_cast>(builder).CreateFAdd(values[0], values[1], "add"); + return llvm::ConstantFP::get(builder.getDoubleTy(), 12345.0); } +//#endif - String getName() const override - { - return name; - } + static FunctionPtr create(const Context &) { return std::make_shared(); } - size_t getNumberOfArguments() const override - { - return 2; - } + String getName() const override { return name; } + + size_t getNumberOfArguments() const override { return 1; } bool useDefaultImplementationForConstants() const override { return true; } - DataTypePtr getReturnTypeImpl(const DataTypes &) const override - { - return std::make_shared(); - } + DataTypePtr getReturnTypeImpl(const DataTypes &) const override { return std::make_shared(); } void executeImpl(Block & block, const ColumnNumbers & arguments, size_t result) override { - auto a = checkAndGetColumn>(block.getByPosition(arguments[0]).column.get()); - if (!a) - throw Exception("Argument #1 (" + block.getByPosition(arguments[0]).column->getName() + ") of function " + getName() + " has invalid type", - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); - auto b = checkAndGetColumn>(block.getByPosition(arguments[1]).column.get()); - if (!b) - throw Exception("Argument #2 (" + block.getByPosition(arguments[1]).column->getName() + ") of function " + getName() + " has invalid type", - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); - - auto col_res = ColumnVector::create(); - auto & vec_a = a->getData(); - auto & vec_b = b->getData(); - auto & vec_res = col_res->getData(); - vec_res.resize(a->size()); - for (size_t i = 0; i < vec_res.size(); ++i) - vec_res[i] = jitted(vec_a[i], vec_b[i]); - block.getByPosition(result).column = std::move(col_res); + throw Exception("should've used the jitted version", ErrorCodes::NOT_IMPLEMENTED); } }; diff --git a/dbms/src/Functions/IFunction.h b/dbms/src/Functions/IFunction.h index b7791268c79..67149ebdba3 100644 --- a/dbms/src/Functions/IFunction.h +++ b/dbms/src/Functions/IFunction.h @@ -2,6 +2,7 @@ #include +#include #include #include #include @@ -9,6 +10,14 @@ #include +namespace llvm +{ + class LLVMContext; + class Value; + class IRBuilderBase; +} + + namespace DB { @@ -68,6 +77,8 @@ private: bool defaultImplementationForConstantArguments(Block & block, const ColumnNumbers & args, size_t result); }; +using ValuePlaceholders = std::vector; + /// Function with known arguments and return type. class IFunctionBase { @@ -80,6 +91,12 @@ public: virtual const DataTypes & getArgumentTypes() const = 0; virtual const DataTypePtr & getReturnType() const = 0; + /// Create an empty result column of a given size. Only called on JIT-compilable functions. + virtual IColumn::Ptr createResultColumn(size_t /*size*/) const + { + throw Exception("createResultColumn is not implemented in a non-jitted function", ErrorCodes::NOT_IMPLEMENTED); + } + /// Do preparations and return executable. /// sample_block should contain data types of arguments and values of constants, if relevant. virtual PreparedFunctionPtr prepare(const Block & sample_block) const = 0; @@ -90,6 +107,21 @@ public: return prepare(block)->execute(block, arguments, result); } + /** Produce LLVM IR code that operates on *scalar* values. Should return null if the function can't be compiled. + * JIT-compilation is only supported for native data types, i.e. numbers. This method will never be called + * if there is a non-number argument or a non-number result type. Also, for any compilable function default + * behavior on NULL values is assumed, i.e. the result is NULL if and only if any argument is NULL. + * + * NOTE: the builder is actually guaranteed to be exactly `llvm::IRBuilder<>`, so you may safely + * downcast it to that type. This method is specified with `IRBuilderBase` because forward-declaring + * templates with default arguments is impossible and including LLVM in such a generic header + * as this one is a major pain. + */ + virtual llvm::Value * compile(llvm::IRBuilderBase & /*builder*/, const ValuePlaceholders & /*values*/) const + { + return nullptr; + } + /** Should we evaluate this function while constant folding, if arguments are constants? * Usually this is true. Notable counterexample is function 'sleep'. * If we will call it during query analysis, we will sleep extra amount of time. @@ -267,16 +299,26 @@ public: throw Exception("prepare is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); } + virtual llvm::Value * compile(llvm::IRBuilderBase & /*builder*/, const DataTypes & /*types*/, const ValuePlaceholders & /*values*/) const + { + return nullptr; + } + const DataTypes & getArgumentTypes() const final { throw Exception("getArgumentTypes is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); } - const DataTypePtr & getReturnType() const override + const DataTypePtr & getReturnType() const final { throw Exception("getReturnType is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); } + IColumn::Ptr createResultColumn(const DataTypes & /*arguments*/, size_t /*size*/) const + { + throw Exception("createResultColumn is not implemented in a non-jitted function", ErrorCodes::NOT_IMPLEMENTED); + } + protected: FunctionBasePtr buildImpl(const ColumnsWithTypeAndName & /*arguments*/, const DataTypePtr & /*return_type*/) const final { @@ -317,6 +359,10 @@ public: const DataTypes & getArgumentTypes() const override { return arguments; } const DataTypePtr & getReturnType() const override { return return_type; } + IColumn::Ptr createResultColumn(size_t size) const override { return function->createResultColumn(arguments, size); } + + llvm::Value * compile(llvm::IRBuilderBase & builder, const ValuePlaceholders & values) const override { return function->compile(builder, arguments, values); } + PreparedFunctionPtr prepare(const Block & /*sample_block*/) const override { return std::make_shared(function); } bool isSuitableForConstantFolding() const override { return function->isSuitableForConstantFolding(); } diff --git a/dbms/src/Interpreters/ExpressionActions.cpp b/dbms/src/Interpreters/ExpressionActions.cpp index 14fdb99090c..4d1a15f3348 100644 --- a/dbms/src/Interpreters/ExpressionActions.cpp +++ b/dbms/src/Interpreters/ExpressionActions.cpp @@ -1,5 +1,6 @@ #include #include +#include #include #include #include @@ -907,6 +908,7 @@ std::string ExpressionActions::dumpActions() const void ExpressionActions::optimize() { optimizeArrayJoin(); + compileFunctions(); } void ExpressionActions::optimizeArrayJoin() @@ -990,6 +992,25 @@ void ExpressionActions::optimizeArrayJoin() } } +void ExpressionActions::compileFunctions() +{ +//#if USE_EMBEDDED_COMPILER + LLVMSharedDataPtr context; + for (auto & action : actions) + { + if (action.type != ExpressionAction::APPLY_FUNCTION) + continue; + // TODO: if a result of one action is only used once and even that is as an input to another, fuse them + if (auto fn = LLVMFunction::create({action}, context)) + { + action.function = fn; + action.argument_names = fn->getArgumentNames(); + } + } + context.finalize(); +//#endif +} + BlockInputStreamPtr ExpressionActions::createStreamWithNonJoinedDataIfFullOrRightJoin(const Block & source_header, size_t max_block_size) const { diff --git a/dbms/src/Interpreters/ExpressionActions.h b/dbms/src/Interpreters/ExpressionActions.h index f29e53a1d7e..58e1db6246d 100644 --- a/dbms/src/Interpreters/ExpressionActions.h +++ b/dbms/src/Interpreters/ExpressionActions.h @@ -211,6 +211,8 @@ private: void optimize(); /// Move all arrayJoin as close as possible to the end. void optimizeArrayJoin(); + /// Try to JIT-compile all functions and remove unnecessary materialization of intermediate results. + void compileFunctions(); }; using ExpressionActionsPtr = std::shared_ptr; diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp new file mode 100644 index 00000000000..00d067fb764 --- /dev/null +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -0,0 +1,179 @@ +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +namespace DB +{ + +struct LLVMSharedData +{ + mutable llvm::LLVMContext context; + std::shared_ptr module; + std::unique_ptr machine; + llvm::orc::RTDyldObjectLinkingLayer objectLayer; + llvm::orc::IRCompileLayer compileLayer; + llvm::DataLayout layout; + llvm::IRBuilder<> builder; + + LLVMSharedData() + : module(std::make_shared("jit", context)) + , machine(llvm::EngineBuilder().selectTarget()) + , objectLayer([]() { return std::make_shared(); }) + , compileLayer(objectLayer, llvm::orc::SimpleCompiler(*machine)) + , layout(machine->createDataLayout()) + , builder(context) + { + module->setDataLayout(layout); + module->setTargetTriple(machine->getTargetTriple().getTriple()); + // TODO: throw in some optimization & verification layers + } + + llvm::Type * toNativeType(const DataTypePtr & type) const + { + if (type->equals(DataTypeFloat64{})) + return llvm::Type::getDoubleTy(context); + // TODO: numbers + return nullptr; + } + + void finalize() + { + if (module->size()) + llvm::cantFail(compileLayer.addModule(module, std::make_shared())); + } + + LLVMCompiledFunction * lookup(const std::string& name) /* const */ + { + std::string mangledName; + llvm::raw_string_ostream mangledNameStream(mangledName); + llvm::Mangler::getNameWithPrefix(mangledNameStream, name, layout); + // why is `findSymbol` not const? we may never know. + return reinterpret_cast(compileLayer.findSymbol(mangledNameStream.str(), false).getAddress().get()); + } +}; + +LLVMSharedDataPtr::LLVMSharedDataPtr() + : std::shared_ptr(std::make_shared()) +{} + +void LLVMSharedDataPtr::finalize() +{ + (*this)->finalize(); +} + +LLVMPreparedFunction::LLVMPreparedFunction(LLVMSharedDataPtr context, std::shared_ptr parent) + : parent(parent), context(context), function(context->lookup(parent->getName())) +{} +#if 0 +template +static void unpack(It it, It end) +{ + if (it != end) + throw std::invalid_argument("unpacked range contains excess elements"); +} + +template +static void unpack(It it, It end, H& h, T&... t) +{ + if (it == end) + throw std::invalid_argument("unpacked range does not contain enough elements"); + h = *it; + unpack(++it, t...); +} +#endif +std::shared_ptr LLVMFunction::create(ExpressionActions::Actions actions, LLVMSharedDataPtr context) +{ + Names arg_names; + DataTypes arg_types; + std::unordered_map arg_index; + std::unordered_set seen; + for (const auto & action : actions) + seen.insert(action.result_name); + for (const auto & action : actions) + { + const auto & names = action.argument_names; + const auto & types = action.function->getArgumentTypes(); + for (size_t i = 0; i < names.size(); i++) + { + if (seen.emplace(names[i]).second) + { + arg_index[names[i]] = arg_names.size(); + arg_names.push_back(names[i]); + arg_types.push_back(types[i]); + } + } + } + + std::vector native_types(arg_types.size()); + for (size_t i = 0; i < arg_types.size(); i++) + if (!(native_types[i] = context->toNativeType(arg_types[i]))) + return nullptr; + llvm::Type * return_type = context->toNativeType(actions.back().function->getReturnType()); + if (!return_type) + return nullptr; + + auto & name = actions.back().result_name; + auto char_ptr = llvm::PointerType::getUnqual(context->builder.getInt8Ty()); + auto void_ptr = llvm::PointerType::getUnqual(context->builder.getVoidTy()); + auto void_ptr_ptr = llvm::PointerType::getUnqual(void_ptr); + auto func_type = llvm::FunctionType::get(context->builder.getDoubleTy(), {void_ptr_ptr, char_ptr, void_ptr}, /*isVarArg=*/false); + auto func = llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, name); +// llvm::Argument * in_arg, is_const_arg, out_arg; +// unpack(func->args().begin(), func->args().end(), in_arg, is_const_arg, out_arg); + context->builder.SetInsertPoint(llvm::BasicBlock::Create(context->context, name, func)); + // TODO: cast each element of void** to corresponding native type + for (const auto & action : actions) + { + // TODO: generate code to fill the next entry + if (auto * val = action.function->compile(context->builder, {})) + context->builder.CreateRet(val); + else + return nullptr; + } + // TODO: increment each pointer if column is not constant then loop + func->print(llvm::errs()); + // context->module->add(func); or something like this, don't know the api + // return std::make_shared(std::move(actions), std::move(arg_names), std::move(arg_types), context); + return nullptr; +} + +} + + +namespace +{ + +struct LLVMTargetInitializer +{ + LLVMTargetInitializer() + { + llvm::InitializeNativeTarget(); + llvm::InitializeNativeTargetAsmPrinter(); + } +}; + +} + +static LLVMTargetInitializer llvmInitializer; diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h new file mode 100644 index 00000000000..16557aa67d6 --- /dev/null +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -0,0 +1,111 @@ +#pragma once + +#include + +#include + +namespace DB +{ + +struct LLVMSharedData; + +struct LLVMSharedDataPtr : std::shared_ptr +{ + // just like `IFunctionBase::compile` accepting `llvm::IRBuilderBase`, this weird wrapper exists to allow + // other code not to depend on LLVM headers. + LLVMSharedDataPtr(); + + // also, this is not a destructor because it's probably not `noexcept`. + void finalize(); +}; + +// second array is of `char` because `LLVMPreparedFunction::executeImpl` can't use a `std::vector` for this +using LLVMCompiledFunction = void(const void ** inputs, const char * is_constant, void * output, size_t block_size); + +class LLVMPreparedFunction : public PreparedFunctionImpl +{ + std::shared_ptr parent; + LLVMSharedDataPtr context; + LLVMCompiledFunction * function; + +public: + LLVMPreparedFunction(LLVMSharedDataPtr context, std::shared_ptr parent); + + String getName() const override { return parent->getName(); } + + // TODO: more efficient implementation for constants + bool useDefaultImplementationForConstants() const override { return true; } + + void executeImpl(Block & block, const ColumnNumbers & arguments, size_t result) override + { + size_t block_size = 0; + std::vector columns(arguments.size()); + std::vector is_const(arguments.size()); + for (size_t i = 0; i < arguments.size(); i++) + { + auto * column = block.getByPosition(arguments[i]).column.get(); + if (column->size()) + // assume the column is a `ColumnVector`. there's probably no good way to actually + // check that at runtime, so let's just hope it's always true for columns containing types + // for which `LLVMSharedData::toNativeType` returns non-null. + columns[i] = column->getDataAt(0).data; + is_const[i] = column->isColumnConst(); + block_size = column->size(); + } + auto col_res = parent->createResultColumn(block_size); + if (!col_res->isColumnConst() && !col_res->isDummy() && block_size) + function(columns.data(), is_const.data(), const_cast(col_res->getDataAt(0).data), block_size); + block.getByPosition(result).column = std::move(col_res); + }; +}; + +class LLVMFunction : public IFunctionBase, std::enable_shared_from_this +{ + ExpressionActions::Actions actions; // all of them must have type APPLY_FUNCTION + Names arg_names; + DataTypes arg_types; + LLVMSharedDataPtr context; + + LLVMFunction(ExpressionActions::Actions actions, Names arg_names, DataTypes arg_types, LLVMSharedDataPtr context) + : actions(std::move(actions)), arg_names(std::move(arg_names)), arg_types(std::move(arg_types)), context(context) + {} + +public: + static std::shared_ptr create(ExpressionActions::Actions actions, LLVMSharedDataPtr context); + + String getName() const override { return actions.back().result_name; } + + const Names & getArgumentNames() const { return arg_names; } + + const DataTypes & getArgumentTypes() const override { return arg_types; } + + const DataTypePtr & getReturnType() const override { return actions.back().function->getReturnType(); } + + PreparedFunctionPtr prepare(const Block &) const override { return std::make_shared(context, shared_from_this()); } + + IColumn::Ptr createResultColumn(size_t size) const override { return actions.back().function->createResultColumn(size); } + + bool isDeterministic() override + { + for (const auto & action : actions) + if (!action.function->isDeterministic()) + return false; + return true; + } + + bool isDeterministicInScopeOfQuery() override + { + for (const auto & action : actions) + if (!action.function->isDeterministicInScopeOfQuery()) + return false; + return true; + } + + // TODO: these methods require reconstructing the call tree: + // bool isSuitableForConstantFolding() const; + // bool isInjective(const Block & sample_block); + // bool hasInformationAboutMonotonicity() const; + // Monotonicity getMonotonicityForRange(const IDataType & type, const Field & left, const Field & right) const; +}; + +} From b398ffbaba31f16a40217b6209d14e4310c7a1c5 Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 24 Apr 2018 01:50:29 +0300 Subject: [PATCH 006/231] Map all number types to LLVM types. The example from the previous commit doesn't need a cast to Float64 anymore. --- dbms/src/Interpreters/ExpressionJIT.cpp | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 00d067fb764..bb1ec72539e 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -29,7 +29,7 @@ namespace DB struct LLVMSharedData { - mutable llvm::LLVMContext context; + llvm::LLVMContext context; std::shared_ptr module; std::unique_ptr machine; llvm::orc::RTDyldObjectLinkingLayer objectLayer; @@ -50,11 +50,21 @@ struct LLVMSharedData // TODO: throw in some optimization & verification layers } - llvm::Type * toNativeType(const DataTypePtr & type) const + llvm::Type * toNativeType(const DataTypePtr & type) { + // LLVM doesn't have unsigned types, it has unsigned instructions. + if (type->equals(DataTypeInt8{}) || type->equals(DataTypeUInt8{})) + return builder.getInt8Ty(); + if (type->equals(DataTypeInt16{}) || type->equals(DataTypeUInt16{})) + return builder.getInt16Ty(); + if (type->equals(DataTypeInt32{}) || type->equals(DataTypeUInt32{})) + return builder.getInt32Ty(); + if (type->equals(DataTypeInt64{}) || type->equals(DataTypeUInt64{})) + return builder.getInt64Ty(); + if (type->equals(DataTypeFloat32{})) + return builder.getFloatTy(); if (type->equals(DataTypeFloat64{})) - return llvm::Type::getDoubleTy(context); - // TODO: numbers + return builder.getDoubleTy(); return nullptr; } @@ -64,7 +74,7 @@ struct LLVMSharedData llvm::cantFail(compileLayer.addModule(module, std::make_shared())); } - LLVMCompiledFunction * lookup(const std::string& name) /* const */ + LLVMCompiledFunction * lookup(const std::string& name) { std::string mangledName; llvm::raw_string_ostream mangledNameStream(mangledName); From e96a5e8344cfc1f610a0cd2794770e024ebe4ae4 Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 24 Apr 2018 02:52:54 +0300 Subject: [PATCH 007/231] Implement JIT compilation, without a loop for now. It actually seems to work, so long as you only have one row that is. E.g. > select something(cast(number + 6 as Float64), cast(number + 2 as Float64)) from system.numbers limit 1'; 8 with this IR: define void @"something(CAST(plus(number, 6), 'Float64'), CAST(plus(number, 2), 'Float64'))"(void**, i8*, double*) { entry: %3 = load void*, void** %0 %4 = bitcast void* %3 to double* %5 = load double, double* %4 %6 = getelementptr void*, void** %0, i32 1 %7 = load void*, void** %6 %8 = bitcast void* %7 to double* %9 = load double, double* %8 %10 = fadd double %5, %9 store double %10, double* %2 ret void } --- dbms/src/Functions/FunctionsLLVMTest.cpp | 21 +++++-- dbms/src/Functions/IFunction.h | 2 +- dbms/src/Interpreters/ExpressionJIT.cpp | 80 +++++++++++++----------- dbms/src/Interpreters/ExpressionJIT.h | 4 +- 4 files changed, 63 insertions(+), 44 deletions(-) diff --git a/dbms/src/Functions/FunctionsLLVMTest.cpp b/dbms/src/Functions/FunctionsLLVMTest.cpp index b65dbaa236e..86ded24c55c 100644 --- a/dbms/src/Functions/FunctionsLLVMTest.cpp +++ b/dbms/src/Functions/FunctionsLLVMTest.cpp @@ -1,3 +1,4 @@ +#include #include #include #include @@ -23,12 +24,20 @@ public: static constexpr auto name = "something"; //#if USE_EMBEDDED_COMPILER - llvm::Value * compile(llvm::IRBuilderBase & builder, const DataTypes &, const ValuePlaceholders &) const override + llvm::Value * compile(llvm::IRBuilderBase & builder, const DataTypes & types, const ValuePlaceholders & values) const override { - // if (types.size() != 2 || types[0] != DataTypeFloat64 || types[1] != DataTypeFloat64) - // throw Exception("invalid arguments for " + name, ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); - // return static_cast>(builder).CreateFAdd(values[0], values[1], "add"); - return llvm::ConstantFP::get(builder.getDoubleTy(), 12345.0); + if (types.size() != 2 || !types[0]->equals(DataTypeFloat64{}) || !types[1]->equals(DataTypeFloat64{})) + throw Exception("invalid arguments for " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); + return static_cast&>(builder).CreateFAdd(values[0], values[1]); + } + + IColumn::Ptr createResultColumn(const DataTypes &, size_t size) const + { + // actually probably better to put type checks here? then this function could be reused in `executeImpl`. + // should pass `NamesAndTypesList` instead of `DataTypes` for better error messages, though. + auto column = ColumnVector::create(); + column->getData().resize(size); + return column; } //#endif @@ -36,7 +45,7 @@ public: String getName() const override { return name; } - size_t getNumberOfArguments() const override { return 1; } + size_t getNumberOfArguments() const override { return 2; } bool useDefaultImplementationForConstants() const override { return true; } diff --git a/dbms/src/Functions/IFunction.h b/dbms/src/Functions/IFunction.h index 67149ebdba3..dfc745be595 100644 --- a/dbms/src/Functions/IFunction.h +++ b/dbms/src/Functions/IFunction.h @@ -314,7 +314,7 @@ public: throw Exception("getReturnType is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); } - IColumn::Ptr createResultColumn(const DataTypes & /*arguments*/, size_t /*size*/) const + virtual IColumn::Ptr createResultColumn(const DataTypes & /*arguments*/, size_t /*size*/) const { throw Exception("createResultColumn is not implemented in a non-jitted function", ErrorCodes::NOT_IMPLEMENTED); } diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index bb1ec72539e..7fcd753fe31 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -27,6 +27,11 @@ namespace DB { +namespace ErrorCodes +{ + extern const int LOGICAL_ERROR; +} + struct LLVMSharedData { llvm::LLVMContext context; @@ -96,28 +101,11 @@ void LLVMSharedDataPtr::finalize() LLVMPreparedFunction::LLVMPreparedFunction(LLVMSharedDataPtr context, std::shared_ptr parent) : parent(parent), context(context), function(context->lookup(parent->getName())) {} -#if 0 -template -static void unpack(It it, It end) -{ - if (it != end) - throw std::invalid_argument("unpacked range contains excess elements"); -} -template -static void unpack(It it, It end, H& h, T&... t) -{ - if (it == end) - throw std::invalid_argument("unpacked range does not contain enough elements"); - h = *it; - unpack(++it, t...); -} -#endif std::shared_ptr LLVMFunction::create(ExpressionActions::Actions actions, LLVMSharedDataPtr context) { Names arg_names; DataTypes arg_types; - std::unordered_map arg_index; std::unordered_set seen; for (const auto & action : actions) seen.insert(action.result_name); @@ -129,7 +117,6 @@ std::shared_ptr LLVMFunction::create(ExpressionActions::Actions ac { if (seen.emplace(names[i]).second) { - arg_index[names[i]] = arg_names.size(); arg_names.push_back(names[i]); arg_types.push_back(types[i]); } @@ -144,29 +131,52 @@ std::shared_ptr LLVMFunction::create(ExpressionActions::Actions ac if (!return_type) return nullptr; - auto & name = actions.back().result_name; - auto char_ptr = llvm::PointerType::getUnqual(context->builder.getInt8Ty()); - auto void_ptr = llvm::PointerType::getUnqual(context->builder.getVoidTy()); - auto void_ptr_ptr = llvm::PointerType::getUnqual(void_ptr); - auto func_type = llvm::FunctionType::get(context->builder.getDoubleTy(), {void_ptr_ptr, char_ptr, void_ptr}, /*isVarArg=*/false); - auto func = llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, name); -// llvm::Argument * in_arg, is_const_arg, out_arg; -// unpack(func->args().begin(), func->args().end(), in_arg, is_const_arg, out_arg); - context->builder.SetInsertPoint(llvm::BasicBlock::Create(context->context, name, func)); - // TODO: cast each element of void** to corresponding native type + llvm::FunctionType * func_type = llvm::FunctionType::get(context->builder.getVoidTy(), { + llvm::PointerType::getUnqual(llvm::PointerType::getUnqual(context->builder.getVoidTy())), + llvm::PointerType::getUnqual(context->builder.getInt8Ty()), + llvm::PointerType::getUnqual(return_type), + }, /*isVarArg=*/false); + std::unique_ptr func{llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, actions.back().result_name)}; + context->builder.SetInsertPoint(llvm::BasicBlock::Create(context->context, "entry", func.get())); + + // prologue: cast each input column to appropriate type + auto args = func->args().begin(); + llvm::Value * in_arg = &*args++; + llvm::Value * is_const_arg = &*args++; + llvm::Value * out_arg = &*args++; + std::unordered_map by_name; + for (size_t i = 0; i < native_types.size(); i++) + { + // not sure if this is the correct ir instruction + llvm::Value * ptr = i ? context->builder.CreateConstGEP1_32(in_arg, i) : in_arg; + ptr = context->builder.CreateLoad(ptr); + ptr = context->builder.CreatePointerCast(ptr, llvm::PointerType::getUnqual(native_types[i])); + if (!by_name.emplace(arg_names[i], context->builder.CreateLoad(ptr)).second) + throw Exception("duplicate input column name", ErrorCodes::LOGICAL_ERROR); + } + + // main loop over the columns + (void)is_const_arg; for (const auto & action : actions) { - // TODO: generate code to fill the next entry - if (auto * val = action.function->compile(context->builder, {})) - context->builder.CreateRet(val); - else + ValuePlaceholders inputs; + inputs.reserve(action.argument_names.size()); + for (const auto & name : action.argument_names) + inputs.push_back(by_name.at(name)); + llvm::Value * val = action.function->compile(context->builder, inputs); + if (!val) + // TODO: separate checks from compilation return nullptr; + if (!by_name.emplace(action.result_name, val).second) + throw Exception("duplicate action result name", ErrorCodes::LOGICAL_ERROR); } + context->builder.CreateStore(by_name.at(actions.back().result_name), out_arg); + context->builder.CreateRetVoid(); // TODO: increment each pointer if column is not constant then loop + func->print(llvm::errs()); - // context->module->add(func); or something like this, don't know the api - // return std::make_shared(std::move(actions), std::move(arg_names), std::move(arg_types), context); - return nullptr; + context->module->getFunctionList().push_back(func.release()); + return std::make_shared(std::move(actions), std::move(arg_names), std::move(arg_types), context); } } diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h index 16557aa67d6..17522fa1631 100644 --- a/dbms/src/Interpreters/ExpressionJIT.h +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -59,18 +59,18 @@ public: }; }; -class LLVMFunction : public IFunctionBase, std::enable_shared_from_this +class LLVMFunction : public std::enable_shared_from_this, public IFunctionBase { ExpressionActions::Actions actions; // all of them must have type APPLY_FUNCTION Names arg_names; DataTypes arg_types; LLVMSharedDataPtr context; +public: LLVMFunction(ExpressionActions::Actions actions, Names arg_names, DataTypes arg_types, LLVMSharedDataPtr context) : actions(std::move(actions)), arg_names(std::move(arg_names)), arg_types(std::move(arg_types)), context(context) {} -public: static std::shared_ptr create(ExpressionActions::Actions actions, LLVMSharedDataPtr context); String getName() const override { return actions.back().result_name; } From 407008a4d9cf3bb0d825f4c65f5d057e75147be3 Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 24 Apr 2018 13:25:18 +0300 Subject: [PATCH 008/231] Separate jit-compilability checks from actual compilation --- dbms/src/Functions/FunctionsLLVMTest.cpp | 9 ++-- dbms/src/Functions/IFunction.h | 22 +++++--- dbms/src/Interpreters/ExpressionActions.cpp | 12 ++--- dbms/src/Interpreters/ExpressionJIT.cpp | 57 +++++++++------------ dbms/src/Interpreters/ExpressionJIT.h | 31 +++++------ 5 files changed, 64 insertions(+), 67 deletions(-) diff --git a/dbms/src/Functions/FunctionsLLVMTest.cpp b/dbms/src/Functions/FunctionsLLVMTest.cpp index 86ded24c55c..94adb21b4c8 100644 --- a/dbms/src/Functions/FunctionsLLVMTest.cpp +++ b/dbms/src/Functions/FunctionsLLVMTest.cpp @@ -24,17 +24,18 @@ public: static constexpr auto name = "something"; //#if USE_EMBEDDED_COMPILER + bool isCompilable(const DataTypes & types) const override + { + return types.size() == 2 && types[0]->equals(DataTypeFloat64{}) && types[1]->equals(DataTypeFloat64{}); + } + llvm::Value * compile(llvm::IRBuilderBase & builder, const DataTypes & types, const ValuePlaceholders & values) const override { - if (types.size() != 2 || !types[0]->equals(DataTypeFloat64{}) || !types[1]->equals(DataTypeFloat64{})) - throw Exception("invalid arguments for " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); return static_cast&>(builder).CreateFAdd(values[0], values[1]); } IColumn::Ptr createResultColumn(const DataTypes &, size_t size) const { - // actually probably better to put type checks here? then this function could be reused in `executeImpl`. - // should pass `NamesAndTypesList` instead of `DataTypes` for better error messages, though. auto column = ColumnVector::create(); column->getData().resize(size); return column; diff --git a/dbms/src/Functions/IFunction.h b/dbms/src/Functions/IFunction.h index dfc745be595..c27d4640b78 100644 --- a/dbms/src/Functions/IFunction.h +++ b/dbms/src/Functions/IFunction.h @@ -94,7 +94,7 @@ public: /// Create an empty result column of a given size. Only called on JIT-compilable functions. virtual IColumn::Ptr createResultColumn(size_t /*size*/) const { - throw Exception("createResultColumn is not implemented in a non-jitted function", ErrorCodes::NOT_IMPLEMENTED); + throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED); } /// Do preparations and return executable. @@ -107,10 +107,12 @@ public: return prepare(block)->execute(block, arguments, result); } - /** Produce LLVM IR code that operates on *scalar* values. Should return null if the function can't be compiled. - * JIT-compilation is only supported for native data types, i.e. numbers. This method will never be called - * if there is a non-number argument or a non-number result type. Also, for any compilable function default - * behavior on NULL values is assumed, i.e. the result is NULL if and only if any argument is NULL. + virtual bool isCompilable() const { return false; } + + /** Produce LLVM IR code that operates on *scalar* values. JIT-compilation is only supported for native + * data types, i.e. numbers. This method will never be called if there is a non-number argument or + * a non-number result type. Also, for any compilable function default behavior on NULL values is assumed, + * i.e. the result is NULL if and only if any argument is NULL. * * NOTE: the builder is actually guaranteed to be exactly `llvm::IRBuilder<>`, so you may safely * downcast it to that type. This method is specified with `IRBuilderBase` because forward-declaring @@ -119,7 +121,7 @@ public: */ virtual llvm::Value * compile(llvm::IRBuilderBase & /*builder*/, const ValuePlaceholders & /*values*/) const { - return nullptr; + throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED); } /** Should we evaluate this function while constant folding, if arguments are constants? @@ -294,6 +296,8 @@ public: using FunctionBuilderImpl::getReturnType; + virtual bool isCompilable(const DataTypes & /*types*/) const { return false; } + PreparedFunctionPtr prepare(const Block & /*sample_block*/) const final { throw Exception("prepare is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); @@ -301,7 +305,7 @@ public: virtual llvm::Value * compile(llvm::IRBuilderBase & /*builder*/, const DataTypes & /*types*/, const ValuePlaceholders & /*values*/) const { - return nullptr; + throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED); } const DataTypes & getArgumentTypes() const final @@ -316,7 +320,7 @@ public: virtual IColumn::Ptr createResultColumn(const DataTypes & /*arguments*/, size_t /*size*/) const { - throw Exception("createResultColumn is not implemented in a non-jitted function", ErrorCodes::NOT_IMPLEMENTED); + throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED); } protected: @@ -361,6 +365,8 @@ public: IColumn::Ptr createResultColumn(size_t size) const override { return function->createResultColumn(arguments, size); } + bool isCompilable() const override { return function->isCompilable(arguments); } + llvm::Value * compile(llvm::IRBuilderBase & builder, const ValuePlaceholders & values) const override { return function->compile(builder, arguments, values); } PreparedFunctionPtr prepare(const Block & /*sample_block*/) const override { return std::make_shared(function); } diff --git a/dbms/src/Interpreters/ExpressionActions.cpp b/dbms/src/Interpreters/ExpressionActions.cpp index 4d1a15f3348..4f42b08afd5 100644 --- a/dbms/src/Interpreters/ExpressionActions.cpp +++ b/dbms/src/Interpreters/ExpressionActions.cpp @@ -995,17 +995,15 @@ void ExpressionActions::optimizeArrayJoin() void ExpressionActions::compileFunctions() { //#if USE_EMBEDDED_COMPILER - LLVMSharedDataPtr context; + LLVMContext context; for (auto & action : actions) { - if (action.type != ExpressionAction::APPLY_FUNCTION) + if (action.type != ExpressionAction::APPLY_FUNCTION || !context.isCompilable(*action.function)) continue; // TODO: if a result of one action is only used once and even that is as an input to another, fuse them - if (auto fn = LLVMFunction::create({action}, context)) - { - action.function = fn; - action.argument_names = fn->getArgumentNames(); - } + auto fn = std::make_shared(Actions{action}, context); + action.function = fn; + action.argument_names = fn->getArgumentNames(); } context.finalize(); //#endif diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 7fcd753fe31..3b785158c58 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -32,7 +32,7 @@ namespace ErrorCodes extern const int LOGICAL_ERROR; } -struct LLVMSharedData +struct LLVMContext::Data { llvm::LLVMContext context; std::shared_ptr module; @@ -42,7 +42,7 @@ struct LLVMSharedData llvm::DataLayout layout; llvm::IRBuilder<> builder; - LLVMSharedData() + Data() : module(std::make_shared("jit", context)) , machine(llvm::EngineBuilder().selectTarget()) , objectLayer([]() { return std::make_shared(); }) @@ -73,12 +73,6 @@ struct LLVMSharedData return nullptr; } - void finalize() - { - if (module->size()) - llvm::cantFail(compileLayer.addModule(module, std::make_shared())); - } - LLVMCompiledFunction * lookup(const std::string& name) { std::string mangledName; @@ -89,23 +83,33 @@ struct LLVMSharedData } }; -LLVMSharedDataPtr::LLVMSharedDataPtr() - : std::shared_ptr(std::make_shared()) +LLVMContext::LLVMContext() + : shared(std::make_shared()) {} -void LLVMSharedDataPtr::finalize() +void LLVMContext::finalize() { - (*this)->finalize(); + if (shared->module->size()) + llvm::cantFail(shared->compileLayer.addModule(shared->module, std::make_shared())); } -LLVMPreparedFunction::LLVMPreparedFunction(LLVMSharedDataPtr context, std::shared_ptr parent) +bool LLVMContext::isCompilable(const IFunctionBase& function) const +{ + if (!function.isCompilable() || !shared->toNativeType(function.getReturnType())) + return false; + for (const auto & type : function.getArgumentTypes()) + if (!shared->toNativeType(type)) + return false; + return true; +} + +LLVMPreparedFunction::LLVMPreparedFunction(LLVMContext context, std::shared_ptr parent) : parent(parent), context(context), function(context->lookup(parent->getName())) {} -std::shared_ptr LLVMFunction::create(ExpressionActions::Actions actions, LLVMSharedDataPtr context) +LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext context) + : actions(std::move(actions_)), context(context) { - Names arg_names; - DataTypes arg_types; std::unordered_set seen; for (const auto & action : actions) seen.insert(action.result_name); @@ -123,18 +127,10 @@ std::shared_ptr LLVMFunction::create(ExpressionActions::Actions ac } } - std::vector native_types(arg_types.size()); - for (size_t i = 0; i < arg_types.size(); i++) - if (!(native_types[i] = context->toNativeType(arg_types[i]))) - return nullptr; - llvm::Type * return_type = context->toNativeType(actions.back().function->getReturnType()); - if (!return_type) - return nullptr; - llvm::FunctionType * func_type = llvm::FunctionType::get(context->builder.getVoidTy(), { llvm::PointerType::getUnqual(llvm::PointerType::getUnqual(context->builder.getVoidTy())), llvm::PointerType::getUnqual(context->builder.getInt8Ty()), - llvm::PointerType::getUnqual(return_type), + llvm::PointerType::getUnqual(context->toNativeType(actions.back().function->getReturnType())), }, /*isVarArg=*/false); std::unique_ptr func{llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, actions.back().result_name)}; context->builder.SetInsertPoint(llvm::BasicBlock::Create(context->context, "entry", func.get())); @@ -145,12 +141,12 @@ std::shared_ptr LLVMFunction::create(ExpressionActions::Actions ac llvm::Value * is_const_arg = &*args++; llvm::Value * out_arg = &*args++; std::unordered_map by_name; - for (size_t i = 0; i < native_types.size(); i++) + for (size_t i = 0; i < arg_types.size(); i++) { // not sure if this is the correct ir instruction llvm::Value * ptr = i ? context->builder.CreateConstGEP1_32(in_arg, i) : in_arg; ptr = context->builder.CreateLoad(ptr); - ptr = context->builder.CreatePointerCast(ptr, llvm::PointerType::getUnqual(native_types[i])); + ptr = context->builder.CreatePointerCast(ptr, llvm::PointerType::getUnqual(context->toNativeType(arg_types[i]))); if (!by_name.emplace(arg_names[i], context->builder.CreateLoad(ptr)).second) throw Exception("duplicate input column name", ErrorCodes::LOGICAL_ERROR); } @@ -163,11 +159,7 @@ std::shared_ptr LLVMFunction::create(ExpressionActions::Actions ac inputs.reserve(action.argument_names.size()); for (const auto & name : action.argument_names) inputs.push_back(by_name.at(name)); - llvm::Value * val = action.function->compile(context->builder, inputs); - if (!val) - // TODO: separate checks from compilation - return nullptr; - if (!by_name.emplace(action.result_name, val).second) + if (!by_name.emplace(action.result_name, action.function->compile(context->builder, inputs)).second) throw Exception("duplicate action result name", ErrorCodes::LOGICAL_ERROR); } context->builder.CreateStore(by_name.at(actions.back().result_name), out_arg); @@ -176,7 +168,6 @@ std::shared_ptr LLVMFunction::create(ExpressionActions::Actions ac func->print(llvm::errs()); context->module->getFunctionList().push_back(func.release()); - return std::make_shared(std::move(actions), std::move(arg_names), std::move(arg_types), context); } } diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h index 17522fa1631..bfc2931f424 100644 --- a/dbms/src/Interpreters/ExpressionJIT.h +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -7,16 +7,21 @@ namespace DB { -struct LLVMSharedData; - -struct LLVMSharedDataPtr : std::shared_ptr +class LLVMContext { - // just like `IFunctionBase::compile` accepting `llvm::IRBuilderBase`, this weird wrapper exists to allow - // other code not to depend on LLVM headers. - LLVMSharedDataPtr(); + struct Data; + std::shared_ptr shared; + +public: + LLVMContext(); - // also, this is not a destructor because it's probably not `noexcept`. void finalize(); + + bool isCompilable(const IFunctionBase& function) const; + + Data * operator->() const { + return shared.get(); + } }; // second array is of `char` because `LLVMPreparedFunction::executeImpl` can't use a `std::vector` for this @@ -25,11 +30,11 @@ using LLVMCompiledFunction = void(const void ** inputs, const char * is_constant class LLVMPreparedFunction : public PreparedFunctionImpl { std::shared_ptr parent; - LLVMSharedDataPtr context; + LLVMContext context; LLVMCompiledFunction * function; public: - LLVMPreparedFunction(LLVMSharedDataPtr context, std::shared_ptr parent); + LLVMPreparedFunction(LLVMContext context, std::shared_ptr parent); String getName() const override { return parent->getName(); } @@ -64,14 +69,10 @@ class LLVMFunction : public std::enable_shared_from_this, public I ExpressionActions::Actions actions; // all of them must have type APPLY_FUNCTION Names arg_names; DataTypes arg_types; - LLVMSharedDataPtr context; + LLVMContext context; public: - LLVMFunction(ExpressionActions::Actions actions, Names arg_names, DataTypes arg_types, LLVMSharedDataPtr context) - : actions(std::move(actions)), arg_names(std::move(arg_names)), arg_types(std::move(arg_types)), context(context) - {} - - static std::shared_ptr create(ExpressionActions::Actions actions, LLVMSharedDataPtr context); + LLVMFunction(ExpressionActions::Actions actions, LLVMContext context); String getName() const override { return actions.back().result_name; } From 5f1bf11ede7e3500080d53afa976e2f84347a910 Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 24 Apr 2018 16:21:42 +0300 Subject: [PATCH 009/231] Implement a loop over the columns in jit-compiled code --- dbms/src/Interpreters/ExpressionJIT.cpp | 81 +++++++++++++++++-------- dbms/src/Interpreters/ExpressionJIT.h | 3 - 2 files changed, 56 insertions(+), 28 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 3b785158c58..2aba3d114ec 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -89,8 +89,10 @@ LLVMContext::LLVMContext() void LLVMContext::finalize() { + shared->module->print(llvm::errs(), nullptr, false, true); if (shared->module->size()) llvm::cantFail(shared->compileLayer.addModule(shared->module, std::make_shared())); + shared->module->print(llvm::errs(), nullptr, false, true); } bool LLVMContext::isCompilable(const IFunctionBase& function) const @@ -131,43 +133,72 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont llvm::PointerType::getUnqual(llvm::PointerType::getUnqual(context->builder.getVoidTy())), llvm::PointerType::getUnqual(context->builder.getInt8Ty()), llvm::PointerType::getUnqual(context->toNativeType(actions.back().function->getReturnType())), + context->builder.getIntNTy(sizeof(size_t) * 8), }, /*isVarArg=*/false); - std::unique_ptr func{llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, actions.back().result_name)}; - context->builder.SetInsertPoint(llvm::BasicBlock::Create(context->context, "entry", func.get())); - - // prologue: cast each input column to appropriate type + auto * func = llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, actions.back().result_name, context->module.get()); auto args = func->args().begin(); - llvm::Value * in_arg = &*args++; - llvm::Value * is_const_arg = &*args++; - llvm::Value * out_arg = &*args++; - std::unordered_map by_name; + llvm::Value * inputs = &*args++; // void** - tuple of columns, each a contiguous data block + llvm::Value * consts = &*args++; // char* - for each column, 0 if it is full, 1 if it points to a single constant value + llvm::Value * output = &*args++; // void* - space for the result + llvm::Value * counter = &*args++; // size_t - number of entries to read from non-const values and write to output + + auto * entry = llvm::BasicBlock::Create(context->context, "entry", func); + context->builder.SetInsertPoint(entry); + + std::vector inputs_v(arg_types.size()); + std::vector deltas_v(arg_types.size()); for (size_t i = 0; i < arg_types.size(); i++) { - // not sure if this is the correct ir instruction - llvm::Value * ptr = i ? context->builder.CreateConstGEP1_32(in_arg, i) : in_arg; - ptr = context->builder.CreateLoad(ptr); - ptr = context->builder.CreatePointerCast(ptr, llvm::PointerType::getUnqual(context->toNativeType(arg_types[i]))); - if (!by_name.emplace(arg_names[i], context->builder.CreateLoad(ptr)).second) - throw Exception("duplicate input column name", ErrorCodes::LOGICAL_ERROR); + if (i != 0) + { + inputs = context->builder.CreateConstGEP1_32(inputs, 1); + consts = context->builder.CreateConstGEP1_32(consts, 1); + } + auto * type = llvm::PointerType::getUnqual(context->toNativeType(arg_types[i])); + auto * step = context->builder.CreateICmpEQ(context->builder.CreateLoad(consts), llvm::ConstantInt::get(context->builder.getInt8Ty(), 0)); + inputs_v[i] = context->builder.CreatePointerCast(context->builder.CreateLoad(inputs), type); + deltas_v[i] = context->builder.CreateZExt(step, context->builder.getInt32Ty()); } - // main loop over the columns - (void)is_const_arg; + auto * loop = llvm::BasicBlock::Create(context->context, "loop", func); + context->builder.CreateBr(loop); // assume nonzero initial value in `counter` + context->builder.SetInsertPoint(loop); + + std::unordered_map by_name; + std::vector phi(inputs_v.size()); + for (size_t i = 0; i < inputs_v.size(); i++) + { + phi[i] = context->builder.CreatePHI(inputs_v[i]->getType(), 2); + phi[i]->addIncoming(inputs_v[i], entry); + } + auto * output_phi = context->builder.CreatePHI(output->getType(), 2); + auto * counter_phi = context->builder.CreatePHI(counter->getType(), 2); + output_phi->addIncoming(output, entry); + counter_phi->addIncoming(counter, entry); + + for (size_t i = 0; i < phi.size(); i++) + if (!by_name.emplace(arg_names[i], context->builder.CreateLoad(phi[i])).second) + throw Exception("duplicate input column name", ErrorCodes::LOGICAL_ERROR); for (const auto & action : actions) { - ValuePlaceholders inputs; - inputs.reserve(action.argument_names.size()); + ValuePlaceholders action_input; + action_input.reserve(action.argument_names.size()); for (const auto & name : action.argument_names) - inputs.push_back(by_name.at(name)); - if (!by_name.emplace(action.result_name, action.function->compile(context->builder, inputs)).second) + action_input.push_back(by_name.at(name)); + if (!by_name.emplace(action.result_name, action.function->compile(context->builder, action_input)).second) throw Exception("duplicate action result name", ErrorCodes::LOGICAL_ERROR); } - context->builder.CreateStore(by_name.at(actions.back().result_name), out_arg); - context->builder.CreateRetVoid(); - // TODO: increment each pointer if column is not constant then loop + context->builder.CreateStore(by_name.at(actions.back().result_name), output_phi); - func->print(llvm::errs()); - context->module->getFunctionList().push_back(func.release()); + for (size_t i = 0; i < phi.size(); i++) + phi[i]->addIncoming(context->builder.CreateGEP(phi[i], deltas_v[i]), loop); + output_phi->addIncoming(context->builder.CreateConstGEP1_32(output_phi, 1), loop); + counter_phi->addIncoming(context->builder.CreateSub(counter_phi, llvm::ConstantInt::get(counter_phi->getType(), 1)), loop); + + auto * end = llvm::BasicBlock::Create(context->context, "end", func); + context->builder.CreateCondBr(context->builder.CreateICmpNE(counter_phi, llvm::ConstantInt::get(counter_phi->getType(), 1)), loop, end); + context->builder.SetInsertPoint(end); + context->builder.CreateRetVoid(); } } diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h index bfc2931f424..1e5a8ebbd90 100644 --- a/dbms/src/Interpreters/ExpressionJIT.h +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -38,9 +38,6 @@ public: String getName() const override { return parent->getName(); } - // TODO: more efficient implementation for constants - bool useDefaultImplementationForConstants() const override { return true; } - void executeImpl(Block & block, const ColumnNumbers & arguments, size_t result) override { size_t block_size = 0; From 8c8a8f9c0fc9e9682751cc59b26b486c9cb0df8b Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 24 Apr 2018 17:11:53 +0300 Subject: [PATCH 010/231] Extend the test jit-compilable function to arbitrary numbers --- dbms/src/Functions/FunctionsLLVMTest.cpp | 35 +++++++++++++++++++----- 1 file changed, 28 insertions(+), 7 deletions(-) diff --git a/dbms/src/Functions/FunctionsLLVMTest.cpp b/dbms/src/Functions/FunctionsLLVMTest.cpp index 94adb21b4c8..6e63041f750 100644 --- a/dbms/src/Functions/FunctionsLLVMTest.cpp +++ b/dbms/src/Functions/FunctionsLLVMTest.cpp @@ -15,6 +15,7 @@ namespace DB namespace ErrorCodes { + extern const int LOGICAL_ERROR; extern const int ILLEGAL_TYPE_OF_ARGUMENT; } @@ -26,19 +27,39 @@ public: //#if USE_EMBEDDED_COMPILER bool isCompilable(const DataTypes & types) const override { - return types.size() == 2 && types[0]->equals(DataTypeFloat64{}) && types[1]->equals(DataTypeFloat64{}); + return types.size() == 2 && types[0]->equals(*types[1]); } llvm::Value * compile(llvm::IRBuilderBase & builder, const DataTypes & types, const ValuePlaceholders & values) const override { - return static_cast&>(builder).CreateFAdd(values[0], values[1]); + if (types[0]->equals(DataTypeFloat32{}) || types[0]->equals(DataTypeFloat64{})) + return static_cast&>(builder).CreateFAdd(values[0], values[1]); + return static_cast&>(builder).CreateAdd(values[0], values[1]); } - IColumn::Ptr createResultColumn(const DataTypes &, size_t size) const + IColumn::Ptr createResultColumn(const DataTypes & types, size_t size) const { - auto column = ColumnVector::create(); - column->getData().resize(size); - return column; + if (types[0]->equals(DataTypeInt8{})) + return ColumnVector::create(size); + if (types[0]->equals(DataTypeInt16{})) + return ColumnVector::create(size); + if (types[0]->equals(DataTypeInt32{})) + return ColumnVector::create(size); + if (types[0]->equals(DataTypeInt64{})) + return ColumnVector::create(size); + if (types[0]->equals(DataTypeUInt8{})) + return ColumnVector::create(size); + if (types[0]->equals(DataTypeUInt16{})) + return ColumnVector::create(size); + if (types[0]->equals(DataTypeUInt32{})) + return ColumnVector::create(size); + if (types[0]->equals(DataTypeUInt64{})) + return ColumnVector::create(size); + if (types[0]->equals(DataTypeFloat32{})) + return ColumnVector::create(size); + if (types[0]->equals(DataTypeFloat64{})) + return ColumnVector::create(size); + throw Exception("invalid input type", ErrorCodes::LOGICAL_ERROR); } //#endif @@ -50,7 +71,7 @@ public: bool useDefaultImplementationForConstants() const override { return true; } - DataTypePtr getReturnTypeImpl(const DataTypes &) const override { return std::make_shared(); } + DataTypePtr getReturnTypeImpl(const DataTypes & types) const override { return types[0]; } void executeImpl(Block & block, const ColumnNumbers & arguments, size_t result) override { From 6b526f784cfec59aaddf089c0dce867bf4565c94 Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 24 Apr 2018 17:12:45 +0300 Subject: [PATCH 011/231] Enable the default set of LLVM optimization passes I honestly can't tell if they work. LLVM has surprisingly bad API documentation. --- dbms/src/Interpreters/ExpressionJIT.cpp | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 2aba3d114ec..af385754552 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -21,6 +21,7 @@ #include #include #include +#include #include @@ -52,7 +53,6 @@ struct LLVMContext::Data { module->setDataLayout(layout); module->setTargetTriple(machine->getTargetTriple().getTriple()); - // TODO: throw in some optimization & verification layers } llvm::Type * toNativeType(const DataTypePtr & type) @@ -89,9 +89,16 @@ LLVMContext::LLVMContext() void LLVMContext::finalize() { + if (!shared->module->size()) + return; shared->module->print(llvm::errs(), nullptr, false, true); - if (shared->module->size()) - llvm::cantFail(shared->compileLayer.addModule(shared->module, std::make_shared())); + llvm::PassManagerBuilder builder; + llvm::legacy::FunctionPassManager fpm(shared->module.get()); + builder.OptLevel = 2; + builder.populateFunctionPassManager(fpm); + for (auto & function : *shared->module) + fpm.run(function); + llvm::cantFail(shared->compileLayer.addModule(shared->module, std::make_shared())); shared->module->print(llvm::errs(), nullptr, false, true); } From 3810173103e87b173ec993ce9e3d62445952eb69 Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 24 Apr 2018 17:28:01 +0300 Subject: [PATCH 012/231] Remove IFunction::createResultColumn. Given that the list of supported types is hardcoded in LLVMContext::Data::toNativeType, this method is redundant because LLVMPreparedFunction can create a ColumnVector itself. --- dbms/src/Functions/FunctionsLLVMTest.cpp | 26 --------------- dbms/src/Functions/IFunction.h | 14 --------- dbms/src/Interpreters/ExpressionJIT.cpp | 1 - dbms/src/Interpreters/ExpressionJIT.h | 40 +++++++++++++++++++++--- 4 files changed, 36 insertions(+), 45 deletions(-) diff --git a/dbms/src/Functions/FunctionsLLVMTest.cpp b/dbms/src/Functions/FunctionsLLVMTest.cpp index 6e63041f750..df7d5687584 100644 --- a/dbms/src/Functions/FunctionsLLVMTest.cpp +++ b/dbms/src/Functions/FunctionsLLVMTest.cpp @@ -1,4 +1,3 @@ -#include #include #include #include @@ -36,31 +35,6 @@ public: return static_cast&>(builder).CreateFAdd(values[0], values[1]); return static_cast&>(builder).CreateAdd(values[0], values[1]); } - - IColumn::Ptr createResultColumn(const DataTypes & types, size_t size) const - { - if (types[0]->equals(DataTypeInt8{})) - return ColumnVector::create(size); - if (types[0]->equals(DataTypeInt16{})) - return ColumnVector::create(size); - if (types[0]->equals(DataTypeInt32{})) - return ColumnVector::create(size); - if (types[0]->equals(DataTypeInt64{})) - return ColumnVector::create(size); - if (types[0]->equals(DataTypeUInt8{})) - return ColumnVector::create(size); - if (types[0]->equals(DataTypeUInt16{})) - return ColumnVector::create(size); - if (types[0]->equals(DataTypeUInt32{})) - return ColumnVector::create(size); - if (types[0]->equals(DataTypeUInt64{})) - return ColumnVector::create(size); - if (types[0]->equals(DataTypeFloat32{})) - return ColumnVector::create(size); - if (types[0]->equals(DataTypeFloat64{})) - return ColumnVector::create(size); - throw Exception("invalid input type", ErrorCodes::LOGICAL_ERROR); - } //#endif static FunctionPtr create(const Context &) { return std::make_shared(); } diff --git a/dbms/src/Functions/IFunction.h b/dbms/src/Functions/IFunction.h index c27d4640b78..3d6a02e61e7 100644 --- a/dbms/src/Functions/IFunction.h +++ b/dbms/src/Functions/IFunction.h @@ -2,7 +2,6 @@ #include -#include #include #include #include @@ -91,12 +90,6 @@ public: virtual const DataTypes & getArgumentTypes() const = 0; virtual const DataTypePtr & getReturnType() const = 0; - /// Create an empty result column of a given size. Only called on JIT-compilable functions. - virtual IColumn::Ptr createResultColumn(size_t /*size*/) const - { - throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED); - } - /// Do preparations and return executable. /// sample_block should contain data types of arguments and values of constants, if relevant. virtual PreparedFunctionPtr prepare(const Block & sample_block) const = 0; @@ -318,11 +311,6 @@ public: throw Exception("getReturnType is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); } - virtual IColumn::Ptr createResultColumn(const DataTypes & /*arguments*/, size_t /*size*/) const - { - throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED); - } - protected: FunctionBasePtr buildImpl(const ColumnsWithTypeAndName & /*arguments*/, const DataTypePtr & /*return_type*/) const final { @@ -363,8 +351,6 @@ public: const DataTypes & getArgumentTypes() const override { return arguments; } const DataTypePtr & getReturnType() const override { return return_type; } - IColumn::Ptr createResultColumn(size_t size) const override { return function->createResultColumn(arguments, size); } - bool isCompilable() const override { return function->isCompilable(arguments); } llvm::Value * compile(llvm::IRBuilderBase & builder, const ValuePlaceholders & values) const override { return function->compile(builder, arguments, values); } diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index af385754552..1e99cdea20c 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -1,4 +1,3 @@ -#include #include #include diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h index 1e5a8ebbd90..86cf8b74ac0 100644 --- a/dbms/src/Interpreters/ExpressionJIT.h +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -1,5 +1,7 @@ #pragma once +#include +#include #include #include @@ -7,6 +9,11 @@ namespace DB { +namespace ErrorCodes +{ + extern const int LOGICAL_ERROR; +} + class LLVMContext { struct Data; @@ -54,11 +61,38 @@ public: is_const[i] = column->isColumnConst(); block_size = column->size(); } - auto col_res = parent->createResultColumn(block_size); - if (!col_res->isColumnConst() && !col_res->isDummy() && block_size) + auto col_res = createColumn(parent->getReturnType(), block_size); + if (block_size) function(columns.data(), is_const.data(), const_cast(col_res->getDataAt(0).data), block_size); block.getByPosition(result).column = std::move(col_res); }; + +private: + static IColumn::Ptr createColumn(const DataTypePtr & type, size_t size) + { + if (type->equals(DataTypeInt8{})) + return ColumnVector::create(size); + if (type->equals(DataTypeInt16{})) + return ColumnVector::create(size); + if (type->equals(DataTypeInt32{})) + return ColumnVector::create(size); + if (type->equals(DataTypeInt64{})) + return ColumnVector::create(size); + if (type->equals(DataTypeUInt8{})) + return ColumnVector::create(size); + if (type->equals(DataTypeUInt16{})) + return ColumnVector::create(size); + if (type->equals(DataTypeUInt32{})) + return ColumnVector::create(size); + if (type->equals(DataTypeUInt64{})) + return ColumnVector::create(size); + if (type->equals(DataTypeFloat32{})) + return ColumnVector::create(size); + if (type->equals(DataTypeFloat64{})) + return ColumnVector::create(size); + throw Exception("LLVMPreparedFunction::createColumn received an unsupported data type; check " + "that the list is consistent with LLVMContext::Data::toNativeType", ErrorCodes::LOGICAL_ERROR); + } }; class LLVMFunction : public std::enable_shared_from_this, public IFunctionBase @@ -81,8 +115,6 @@ public: PreparedFunctionPtr prepare(const Block &) const override { return std::make_shared(context, shared_from_this()); } - IColumn::Ptr createResultColumn(size_t size) const override { return actions.back().function->createResultColumn(size); } - bool isDeterministic() override { for (const auto & action : actions) From b2077a466aea78196d9ce0bacc3d9a78ce074f6d Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 24 Apr 2018 19:52:57 +0300 Subject: [PATCH 013/231] Inline jit-compilable functions into other jit-compilable functions --- dbms/src/Interpreters/ExpressionActions.cpp | 96 ++++++++++++++++++--- dbms/src/Interpreters/ExpressionActions.h | 4 +- 2 files changed, 87 insertions(+), 13 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionActions.cpp b/dbms/src/Interpreters/ExpressionActions.cpp index 4f42b08afd5..67d4dbd0103 100644 --- a/dbms/src/Interpreters/ExpressionActions.cpp +++ b/dbms/src/Interpreters/ExpressionActions.cpp @@ -11,6 +11,7 @@ #include #include +#include namespace ProfileEvents @@ -880,7 +881,7 @@ void ExpressionActions::finalize(const Names & output_columns) std::cerr << action.toString() << "\n"; std::cerr << "\n";*/ - optimize(); + optimize(output_columns); checkLimits(sample_block); } @@ -905,10 +906,10 @@ std::string ExpressionActions::dumpActions() const return ss.str(); } -void ExpressionActions::optimize() +void ExpressionActions::optimize(const Names & output_columns) { optimizeArrayJoin(); - compileFunctions(); + compileFunctions(output_columns); } void ExpressionActions::optimizeArrayJoin() @@ -992,19 +993,92 @@ void ExpressionActions::optimizeArrayJoin() } } -void ExpressionActions::compileFunctions() +void ExpressionActions::compileFunctions(const Names & output_columns) { //#if USE_EMBEDDED_COMPILER LLVMContext context; - for (auto & action : actions) + std::vector redundant(actions.size()); + // an empty optional is a poisoned value prohibiting the column's producer from being removed + // (which it could be, if it was inlined into every dependent function). + std::unordered_map>> current_dependents; + for (const auto & name : output_columns) + current_dependents[name].emplace(); + // a snapshot of each compilable function's dependents at the time of its execution. + std::vector>> dependents(actions.size()); + for (size_t i = actions.size(); i--;) { - if (action.type != ExpressionAction::APPLY_FUNCTION || !context.isCompilable(*action.function)) - continue; - // TODO: if a result of one action is only used once and even that is as an input to another, fuse them - auto fn = std::make_shared(Actions{action}, context); - action.function = fn; - action.argument_names = fn->getArgumentNames(); + switch (actions[i].type) + { + case ExpressionAction::ADD_COLUMN: + break; + + case ExpressionAction::REMOVE_COLUMN: + current_dependents.erase(actions[i].source_name); + // temporarily discard all `REMOVE_COLUMN`s because inlining will change dependency sets. + // for example, if there's a column `x` and we want to compile `f(g(x))`, said `x` might get removed + // between `g(x)` and `f(g(x))`. it's easier to reintroduce removals later than move them around. + redundant[i] = true; + break; + + case ExpressionAction::COPY_COLUMN: + current_dependents[actions[i].source_name].emplace(); + break; + + case ExpressionAction::PROJECT: + current_dependents.clear(); + // unlike `REMOVE_COLUMN`, we know the exact set of columns that will survive a `PROJECT`, + // so we can simply poison them to prevent any inlining chain from crossing this barrier. + // note that this would generate suboptimal action sequences if, for example, in the example above + // `REMOVE_COLUMN x ` was replaced with `PROJECT {f(x)}` -- it is more optimal to remove the `PROJECT` + // and inline `g`. however, that sequence would at least still execute correctly. + for (const auto & proj : actions[i].projection) + current_dependents[proj.first].emplace(); + break; + + case ExpressionAction::ARRAY_JOIN: + case ExpressionAction::JOIN: + // assume these actions can read everything; all columns not removed before this point are poisoned. + for (size_t j = i; j--;) + current_dependents[actions[j].result_name].emplace(); + break; + + case ExpressionAction::APPLY_FUNCTION: + { + dependents[i] = current_dependents[actions[i].result_name]; + const bool compilable = context.isCompilable(*actions[i].function); + for (const auto & name : actions[i].argument_names) + { + if (compilable) + current_dependents[name].emplace(i); + else + current_dependents[name].emplace(); + } + break; + } + } } + + std::vector fused(actions.size()); + for (size_t i = 0; i < actions.size(); i++) + { + if (actions[i].type != ExpressionAction::APPLY_FUNCTION || !context.isCompilable(*actions[i].function)) + continue; + if (dependents[i].find({}) != dependents[i].end()) + { + fused[i].push_back(actions[i]); + auto fn = std::make_shared(std::move(fused[i]), context); + actions[i].function = fn; + actions[i].argument_names = fn->getArgumentNames(); + continue; + } + // TODO: determine whether it's profitable to inline the function if there's more than one dependent. + for (const auto & dep : dependents[i]) + fused[*dep].push_back(actions[i]); + redundant[i] = true; + } + size_t i = 0; + actions.erase(std::remove_if(actions.begin(), actions.end(), [&](const auto&) { return redundant[i++]; }), actions.end()); + // TODO: insert `REMOVE_COLUMN`s according to new dependency sets context.finalize(); //#endif } diff --git a/dbms/src/Interpreters/ExpressionActions.h b/dbms/src/Interpreters/ExpressionActions.h index 58e1db6246d..ee14048e0e5 100644 --- a/dbms/src/Interpreters/ExpressionActions.h +++ b/dbms/src/Interpreters/ExpressionActions.h @@ -208,11 +208,11 @@ private: void addImpl(ExpressionAction action, Names & new_names); /// Try to improve something without changing the lists of input and output columns. - void optimize(); + void optimize(const Names & output_columns); /// Move all arrayJoin as close as possible to the end. void optimizeArrayJoin(); /// Try to JIT-compile all functions and remove unnecessary materialization of intermediate results. - void compileFunctions(); + void compileFunctions(const Names & output_columns); }; using ExpressionActionsPtr = std::shared_ptr; From df2d2e0b2502714a7d2550351bb4177140bc20d4 Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 24 Apr 2018 21:10:22 +0300 Subject: [PATCH 014/231] Tweak the jit compilation API to be more amenable to lazy computation --- dbms/src/Functions/FunctionsLLVMTest.cpp | 4 ++-- dbms/src/Functions/IFunction.h | 2 +- dbms/src/Interpreters/ExpressionJIT.cpp | 19 ++++++++++++------- 3 files changed, 15 insertions(+), 10 deletions(-) diff --git a/dbms/src/Functions/FunctionsLLVMTest.cpp b/dbms/src/Functions/FunctionsLLVMTest.cpp index df7d5687584..41aa92ce00d 100644 --- a/dbms/src/Functions/FunctionsLLVMTest.cpp +++ b/dbms/src/Functions/FunctionsLLVMTest.cpp @@ -32,8 +32,8 @@ public: llvm::Value * compile(llvm::IRBuilderBase & builder, const DataTypes & types, const ValuePlaceholders & values) const override { if (types[0]->equals(DataTypeFloat32{}) || types[0]->equals(DataTypeFloat64{})) - return static_cast&>(builder).CreateFAdd(values[0], values[1]); - return static_cast&>(builder).CreateAdd(values[0], values[1]); + return static_cast&>(builder).CreateFAdd(values[0](), values[1]()); + return static_cast&>(builder).CreateAdd(values[0](), values[1]()); } //#endif diff --git a/dbms/src/Functions/IFunction.h b/dbms/src/Functions/IFunction.h index 3d6a02e61e7..28a87dddcda 100644 --- a/dbms/src/Functions/IFunction.h +++ b/dbms/src/Functions/IFunction.h @@ -76,7 +76,7 @@ private: bool defaultImplementationForConstantArguments(Block & block, const ColumnNumbers & args, size_t result); }; -using ValuePlaceholders = std::vector; +using ValuePlaceholders = std::vector>; /// Function with known arguments and return type. class IFunctionBase diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 1e99cdea20c..6a763020afc 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -170,7 +170,7 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont context->builder.CreateBr(loop); // assume nonzero initial value in `counter` context->builder.SetInsertPoint(loop); - std::unordered_map by_name; + std::unordered_map> by_name; std::vector phi(inputs_v.size()); for (size_t i = 0; i < inputs_v.size(); i++) { @@ -183,7 +183,7 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont counter_phi->addIncoming(counter, entry); for (size_t i = 0; i < phi.size(); i++) - if (!by_name.emplace(arg_names[i], context->builder.CreateLoad(phi[i])).second) + if (!by_name.emplace(arg_names[i], [&, i]() { return context->builder.CreateLoad(phi[i]); }).second) throw Exception("duplicate input column name", ErrorCodes::LOGICAL_ERROR); for (const auto & action : actions) { @@ -191,15 +191,20 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont action_input.reserve(action.argument_names.size()); for (const auto & name : action.argument_names) action_input.push_back(by_name.at(name)); - if (!by_name.emplace(action.result_name, action.function->compile(context->builder, action_input)).second) + auto generator = [&action, &context, action_input{std::move(action_input)}]() + { + return action.function->compile(context->builder, action_input); + }; + if (!by_name.emplace(action.result_name, std::move(generator)).second) throw Exception("duplicate action result name", ErrorCodes::LOGICAL_ERROR); } - context->builder.CreateStore(by_name.at(actions.back().result_name), output_phi); + context->builder.CreateStore(by_name.at(actions.back().result_name)(), output_phi); + auto * cur_block = context->builder.GetInsertBlock(); for (size_t i = 0; i < phi.size(); i++) - phi[i]->addIncoming(context->builder.CreateGEP(phi[i], deltas_v[i]), loop); - output_phi->addIncoming(context->builder.CreateConstGEP1_32(output_phi, 1), loop); - counter_phi->addIncoming(context->builder.CreateSub(counter_phi, llvm::ConstantInt::get(counter_phi->getType(), 1)), loop); + phi[i]->addIncoming(context->builder.CreateGEP(phi[i], deltas_v[i]), cur_block); + output_phi->addIncoming(context->builder.CreateConstGEP1_32(output_phi, 1), cur_block); + counter_phi->addIncoming(context->builder.CreateSub(counter_phi, llvm::ConstantInt::get(counter_phi->getType(), 1)), cur_block); auto * end = llvm::BasicBlock::Create(context->context, "end", func); context->builder.CreateCondBr(context->builder.CreateICmpNE(counter_phi, llvm::ConstantInt::get(counter_phi->getType(), 1)), loop, end); From 2b1be27b1bad004bd26f931cc8fe3616aabf1207 Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 24 Apr 2018 21:12:37 +0300 Subject: [PATCH 015/231] Add missing option to CMakeFiles.txt --- dbms/CMakeLists.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/dbms/CMakeLists.txt b/dbms/CMakeLists.txt index 0517c41951f..d3dc6bb9d57 100644 --- a/dbms/CMakeLists.txt +++ b/dbms/CMakeLists.txt @@ -84,8 +84,8 @@ list (APPEND dbms_headers src/TableFunctions/ITableFunction.h src/TableFunctions if (USE_EMBEDDED_COMPILER) # LLVM 5.0 has a bunch of unused parameters in its header files. - # TODO: global-disable this warning - set_source_files_properties(src/Interpreters/ExpressionJIT.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter") + # TODO: global-disable no-unused-parameter + set_source_files_properties(src/Interpreters/ExpressionJIT.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter -Wno-non-virtual-dtor") else () list (REMOVE dbms_sources src/Interpreters/ExpressionJIT.cpp) list (REMOVE dbms_headers src/Interpreters/ExpressionJIT.h) From 3789eba5c4bf433b18acec1d0e7121afb67406be Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 24 Apr 2018 21:27:53 +0300 Subject: [PATCH 016/231] Fix CMakeFiles syntax --- dbms/CMakeLists.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/dbms/CMakeLists.txt b/dbms/CMakeLists.txt index d3dc6bb9d57..db1e8453924 100644 --- a/dbms/CMakeLists.txt +++ b/dbms/CMakeLists.txt @@ -87,8 +87,8 @@ if (USE_EMBEDDED_COMPILER) # TODO: global-disable no-unused-parameter set_source_files_properties(src/Interpreters/ExpressionJIT.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter -Wno-non-virtual-dtor") else () - list (REMOVE dbms_sources src/Interpreters/ExpressionJIT.cpp) - list (REMOVE dbms_headers src/Interpreters/ExpressionJIT.h) + list (REMOVE_ITEM dbms_sources src/Interpreters/ExpressionJIT.cpp) + list (REMOVE_ITEM dbms_headers src/Interpreters/ExpressionJIT.h) endif () add_library(clickhouse_common_io ${SPLIT_SHARED} ${clickhouse_common_io_headers} ${clickhouse_common_io_sources}) From 4bd0906613843a31c0961c475dc77be9469ca027 Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 24 Apr 2018 21:46:30 +0300 Subject: [PATCH 017/231] Fix some comments --- dbms/src/Interpreters/ExpressionActions.cpp | 2 +- dbms/src/Interpreters/ExpressionJIT.h | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionActions.cpp b/dbms/src/Interpreters/ExpressionActions.cpp index 67d4dbd0103..33926f50116 100644 --- a/dbms/src/Interpreters/ExpressionActions.cpp +++ b/dbms/src/Interpreters/ExpressionActions.cpp @@ -1029,7 +1029,7 @@ void ExpressionActions::compileFunctions(const Names & output_columns) // unlike `REMOVE_COLUMN`, we know the exact set of columns that will survive a `PROJECT`, // so we can simply poison them to prevent any inlining chain from crossing this barrier. // note that this would generate suboptimal action sequences if, for example, in the example above - // `REMOVE_COLUMN x ` was replaced with `PROJECT {f(x)}` -- it is more optimal to remove the `PROJECT` + // `REMOVE_COLUMN x` was replaced with `PROJECT {g(x)}` -- it is more optimal to remove the `PROJECT` // and inline `g`. however, that sequence would at least still execute correctly. for (const auto & proj : actions[i].projection) current_dependents[proj.first].emplace(); diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h index 86cf8b74ac0..3b00e96daeb 100644 --- a/dbms/src/Interpreters/ExpressionJIT.h +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -56,7 +56,7 @@ public: if (column->size()) // assume the column is a `ColumnVector`. there's probably no good way to actually // check that at runtime, so let's just hope it's always true for columns containing types - // for which `LLVMSharedData::toNativeType` returns non-null. + // for which `LLVMContext::Data::toNativeType` returns non-null. columns[i] = column->getDataAt(0).data; is_const[i] = column->isColumnConst(); block_size = column->size(); From 1bece1de46859c9591b67baa30abcfdc8611ffc0 Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 24 Apr 2018 22:42:06 +0300 Subject: [PATCH 018/231] Support nullable columns (with default behavior) in jitted functions --- dbms/src/Interpreters/ExpressionJIT.cpp | 54 +++++++++++++++++++++--- dbms/src/Interpreters/ExpressionJIT.h | 56 +------------------------ 2 files changed, 49 insertions(+), 61 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 6a763020afc..95dc5708936 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -1,3 +1,7 @@ +#include +#include +#include +#include #include #include @@ -32,6 +36,14 @@ namespace ErrorCodes extern const int LOGICAL_ERROR; } +template +static bool typeIsA(const DataTypePtr & type) +{ + if (auto * nullable = typeid_cast(type.get())) + return typeIsA(nullable->getNestedType()); + return typeid_cast(type.get());; +} + struct LLVMContext::Data { llvm::LLVMContext context; @@ -57,17 +69,17 @@ struct LLVMContext::Data llvm::Type * toNativeType(const DataTypePtr & type) { // LLVM doesn't have unsigned types, it has unsigned instructions. - if (type->equals(DataTypeInt8{}) || type->equals(DataTypeUInt8{})) + if (typeIsA(type) || typeIsA(type)) return builder.getInt8Ty(); - if (type->equals(DataTypeInt16{}) || type->equals(DataTypeUInt16{})) + if (typeIsA(type) || typeIsA(type)) return builder.getInt16Ty(); - if (type->equals(DataTypeInt32{}) || type->equals(DataTypeUInt32{})) + if (typeIsA(type) || typeIsA(type)) return builder.getInt32Ty(); - if (type->equals(DataTypeInt64{}) || type->equals(DataTypeUInt64{})) + if (typeIsA(type) || typeIsA(type)) return builder.getInt64Ty(); - if (type->equals(DataTypeFloat32{})) + if (typeIsA(type)) return builder.getFloatTy(); - if (type->equals(DataTypeFloat64{})) + if (typeIsA(type)) return builder.getDoubleTy(); return nullptr; } @@ -115,6 +127,36 @@ LLVMPreparedFunction::LLVMPreparedFunction(LLVMContext context, std::shared_ptr< : parent(parent), context(context), function(context->lookup(parent->getName())) {} +static MutableColumnPtr createNonNullableColumn(const DataTypePtr & type) +{ + if (auto * nullable = typeid_cast(type.get())) + return createNonNullableColumn(nullable->getNestedType()); + return type->createColumn(); +} + +void LLVMPreparedFunction::executeImpl(Block & block, const ColumnNumbers & arguments, size_t result) +{ + size_t block_size = 0; + std::vector columns(arguments.size()); + std::vector is_const(arguments.size()); + for (size_t i = 0; i < arguments.size(); i++) + { + auto * column = block.getByPosition(arguments[i]).column.get(); + if (column->size()) + // assume the column is a `ColumnVector`. there's probably no good way to actually + // check that at runtime, so let's just hope it's always true for columns containing types + // for which `LLVMContext::Data::toNativeType` returns non-null. + columns[i] = column->getDataAt(0).data; + is_const[i] = column->isColumnConst(); + block_size = column->size(); + } + // assuming that the function has default behavior on NULL, the column will be wrapped by `PreparedFunctionImpl::execute`. + auto col_res = createNonNullableColumn(parent->getReturnType())->cloneResized(block_size); + if (block_size) + function(columns.data(), is_const.data(), const_cast(col_res->getDataAt(0).data), block_size); + block.getByPosition(result).column = std::move(col_res); +}; + LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext context) : actions(std::move(actions_)), context(context) { diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h index 3b00e96daeb..8adb6eeade3 100644 --- a/dbms/src/Interpreters/ExpressionJIT.h +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -1,7 +1,5 @@ #pragma once -#include -#include #include #include @@ -9,11 +7,6 @@ namespace DB { -namespace ErrorCodes -{ - extern const int LOGICAL_ERROR; -} - class LLVMContext { struct Data; @@ -45,54 +38,7 @@ public: String getName() const override { return parent->getName(); } - void executeImpl(Block & block, const ColumnNumbers & arguments, size_t result) override - { - size_t block_size = 0; - std::vector columns(arguments.size()); - std::vector is_const(arguments.size()); - for (size_t i = 0; i < arguments.size(); i++) - { - auto * column = block.getByPosition(arguments[i]).column.get(); - if (column->size()) - // assume the column is a `ColumnVector`. there's probably no good way to actually - // check that at runtime, so let's just hope it's always true for columns containing types - // for which `LLVMContext::Data::toNativeType` returns non-null. - columns[i] = column->getDataAt(0).data; - is_const[i] = column->isColumnConst(); - block_size = column->size(); - } - auto col_res = createColumn(parent->getReturnType(), block_size); - if (block_size) - function(columns.data(), is_const.data(), const_cast(col_res->getDataAt(0).data), block_size); - block.getByPosition(result).column = std::move(col_res); - }; - -private: - static IColumn::Ptr createColumn(const DataTypePtr & type, size_t size) - { - if (type->equals(DataTypeInt8{})) - return ColumnVector::create(size); - if (type->equals(DataTypeInt16{})) - return ColumnVector::create(size); - if (type->equals(DataTypeInt32{})) - return ColumnVector::create(size); - if (type->equals(DataTypeInt64{})) - return ColumnVector::create(size); - if (type->equals(DataTypeUInt8{})) - return ColumnVector::create(size); - if (type->equals(DataTypeUInt16{})) - return ColumnVector::create(size); - if (type->equals(DataTypeUInt32{})) - return ColumnVector::create(size); - if (type->equals(DataTypeUInt64{})) - return ColumnVector::create(size); - if (type->equals(DataTypeFloat32{})) - return ColumnVector::create(size); - if (type->equals(DataTypeFloat64{})) - return ColumnVector::create(size); - throw Exception("LLVMPreparedFunction::createColumn received an unsupported data type; check " - "that the list is consistent with LLVMContext::Data::toNativeType", ErrorCodes::LOGICAL_ERROR); - } + void executeImpl(Block & block, const ColumnNumbers & arguments, size_t result) override; }; class LLVMFunction : public std::enable_shared_from_this, public IFunctionBase From 0da110234c1700179b8d9e6e84b381b552b07e80 Mon Sep 17 00:00:00 2001 From: pyos Date: Wed, 25 Apr 2018 13:43:57 +0300 Subject: [PATCH 019/231] Do not compile the jit if USE_EMBEDDED_COMPILER is disabled --- dbms/src/Functions/FunctionsLLVMTest.cpp | 9 +++++---- dbms/src/Interpreters/ExpressionActions.cpp | 7 ++++--- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/dbms/src/Functions/FunctionsLLVMTest.cpp b/dbms/src/Functions/FunctionsLLVMTest.cpp index 41aa92ce00d..6342daa76c8 100644 --- a/dbms/src/Functions/FunctionsLLVMTest.cpp +++ b/dbms/src/Functions/FunctionsLLVMTest.cpp @@ -1,12 +1,13 @@ +#include #include #include #include -//#if USE_EMBEDDED_COMPILER +#if USE_EMBEDDED_COMPILER #include #include #include -//#endif +#endif namespace DB @@ -23,7 +24,7 @@ class FunctionSomething : public IFunction public: static constexpr auto name = "something"; -//#if USE_EMBEDDED_COMPILER +#if USE_EMBEDDED_COMPILER bool isCompilable(const DataTypes & types) const override { return types.size() == 2 && types[0]->equals(*types[1]); @@ -35,7 +36,7 @@ public: return static_cast&>(builder).CreateFAdd(values[0](), values[1]()); return static_cast&>(builder).CreateAdd(values[0](), values[1]()); } -//#endif +#endif static FunctionPtr create(const Context &) { return std::make_shared(); } diff --git a/dbms/src/Interpreters/ExpressionActions.cpp b/dbms/src/Interpreters/ExpressionActions.cpp index 33926f50116..f991abba206 100644 --- a/dbms/src/Interpreters/ExpressionActions.cpp +++ b/dbms/src/Interpreters/ExpressionActions.cpp @@ -1,3 +1,4 @@ +#include #include #include #include @@ -993,9 +994,9 @@ void ExpressionActions::optimizeArrayJoin() } } -void ExpressionActions::compileFunctions(const Names & output_columns) +void ExpressionActions::compileFunctions([[maybe_unused]] const Names & output_columns) { -//#if USE_EMBEDDED_COMPILER +#if USE_EMBEDDED_COMPILER LLVMContext context; std::vector redundant(actions.size()); // an empty optional is a poisoned value prohibiting the column's producer from being removed @@ -1080,7 +1081,7 @@ void ExpressionActions::compileFunctions(const Names & output_columns) actions.erase(std::remove_if(actions.begin(), actions.end(), [&](const auto&) { return redundant[i++]; }), actions.end()); // TODO: insert `REMOVE_COLUMN`s according to new dependency sets context.finalize(); -//#endif +#endif } From 162a0c8b33b0e4129ef04d20271b9b0f31bea867 Mon Sep 17 00:00:00 2001 From: pyos Date: Wed, 25 Apr 2018 14:05:10 +0300 Subject: [PATCH 020/231] Fix some comments' style --- dbms/src/Interpreters/ExpressionJIT.cpp | 23 ++++++++++++----------- dbms/src/Interpreters/ExpressionJIT.h | 17 ++++++++++------- 2 files changed, 22 insertions(+), 18 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 95dc5708936..eb301774987 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -68,7 +68,7 @@ struct LLVMContext::Data llvm::Type * toNativeType(const DataTypePtr & type) { - // LLVM doesn't have unsigned types, it has unsigned instructions. + /// LLVM doesn't have unsigned types, it has unsigned instructions. if (typeIsA(type) || typeIsA(type)) return builder.getInt8Ty(); if (typeIsA(type) || typeIsA(type)) @@ -89,7 +89,7 @@ struct LLVMContext::Data std::string mangledName; llvm::raw_string_ostream mangledNameStream(mangledName); llvm::Mangler::getNameWithPrefix(mangledNameStream, name, layout); - // why is `findSymbol` not const? we may never know. + /// why is `findSymbol` not const? we may never know. return reinterpret_cast(compileLayer.findSymbol(mangledNameStream.str(), false).getAddress().get()); } }; @@ -143,14 +143,14 @@ void LLVMPreparedFunction::executeImpl(Block & block, const ColumnNumbers & argu { auto * column = block.getByPosition(arguments[i]).column.get(); if (column->size()) - // assume the column is a `ColumnVector`. there's probably no good way to actually - // check that at runtime, so let's just hope it's always true for columns containing types - // for which `LLVMContext::Data::toNativeType` returns non-null. + /// assume the column is a `ColumnVector`. there's probably no good way to actually + /// check that at runtime, so let's just hope it's always true for columns containing types + /// for which `LLVMContext::Data::toNativeType` returns non-null. columns[i] = column->getDataAt(0).data; is_const[i] = column->isColumnConst(); block_size = column->size(); } - // assuming that the function has default behavior on NULL, the column will be wrapped by `PreparedFunctionImpl::execute`. + /// assuming that the function has default behavior on NULL, the column will be wrapped by `PreparedFunctionImpl::execute`. auto col_res = createNonNullableColumn(parent->getReturnType())->cloneResized(block_size); if (block_size) function(columns.data(), is_const.data(), const_cast(col_res->getDataAt(0).data), block_size); @@ -185,10 +185,10 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont }, /*isVarArg=*/false); auto * func = llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, actions.back().result_name, context->module.get()); auto args = func->args().begin(); - llvm::Value * inputs = &*args++; // void** - tuple of columns, each a contiguous data block - llvm::Value * consts = &*args++; // char* - for each column, 0 if it is full, 1 if it points to a single constant value - llvm::Value * output = &*args++; // void* - space for the result - llvm::Value * counter = &*args++; // size_t - number of entries to read from non-const values and write to output + llvm::Value * inputs = &*args++; /// void** - tuple of columns, each a contiguous data block + llvm::Value * consts = &*args++; /// char* - for each column, 0 if it is full, 1 if it points to a single constant value + llvm::Value * output = &*args++; /// void* - space for the result + llvm::Value * counter = &*args++; /// size_t - number of entries to read from non-const values and write to output auto * entry = llvm::BasicBlock::Create(context->context, "entry", func); context->builder.SetInsertPoint(entry); @@ -208,8 +208,9 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont deltas_v[i] = context->builder.CreateZExt(step, context->builder.getInt32Ty()); } + /// assume nonzero initial value in `counter` auto * loop = llvm::BasicBlock::Create(context->context, "loop", func); - context->builder.CreateBr(loop); // assume nonzero initial value in `counter` + context->builder.CreateBr(loop); context->builder.SetInsertPoint(loop); std::unordered_map> by_name; diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h index 8adb6eeade3..fd71804685e 100644 --- a/dbms/src/Interpreters/ExpressionJIT.h +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -24,7 +24,7 @@ public: } }; -// second array is of `char` because `LLVMPreparedFunction::executeImpl` can't use a `std::vector` for this +/// second array is of `char` because `LLVMPreparedFunction::executeImpl` can't use a `std::vector` for this using LLVMCompiledFunction = void(const void ** inputs, const char * is_constant, void * output, size_t block_size); class LLVMPreparedFunction : public PreparedFunctionImpl @@ -43,7 +43,8 @@ public: class LLVMFunction : public std::enable_shared_from_this, public IFunctionBase { - ExpressionActions::Actions actions; // all of them must have type APPLY_FUNCTION + /// all actions must have type APPLY_FUNCTION + ExpressionActions::Actions actions; Names arg_names; DataTypes arg_types; LLVMContext context; @@ -77,11 +78,13 @@ public: return true; } - // TODO: these methods require reconstructing the call tree: - // bool isSuitableForConstantFolding() const; - // bool isInjective(const Block & sample_block); - // bool hasInformationAboutMonotonicity() const; - // Monotonicity getMonotonicityForRange(const IDataType & type, const Field & left, const Field & right) const; + /// TODO: these methods require reconstructing the call tree: + /* + bool isSuitableForConstantFolding() const; + bool isInjective(const Block & sample_block); + bool hasInformationAboutMonotonicity() const; + Monotonicity getMonotonicityForRange(const IDataType & type, const Field & left, const Field & right) const; + */ }; } From af7ecd4c4aba163a0a381d3495c6a45e1d5e240a Mon Sep 17 00:00:00 2001 From: pyos Date: Wed, 25 Apr 2018 14:16:51 +0300 Subject: [PATCH 021/231] Move function compilation before insertion of REMOVE_COLUMNs --- dbms/src/Interpreters/ExpressionActions.cpp | 41 ++++++++------------- dbms/src/Interpreters/ExpressionActions.h | 4 +- 2 files changed, 17 insertions(+), 28 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionActions.cpp b/dbms/src/Interpreters/ExpressionActions.cpp index f991abba206..f99dd0d2c9d 100644 --- a/dbms/src/Interpreters/ExpressionActions.cpp +++ b/dbms/src/Interpreters/ExpressionActions.cpp @@ -823,6 +823,9 @@ void ExpressionActions::finalize(const Names & output_columns) } } + /// This has to be done before inserting REMOVE_COLUMNs because inlining may change dependency sets. + compileFunctions(final_columns); + /* std::cerr << "\n"; for (const auto & action : actions) std::cerr << action.toString() << "\n"; @@ -882,7 +885,7 @@ void ExpressionActions::finalize(const Names & output_columns) std::cerr << action.toString() << "\n"; std::cerr << "\n";*/ - optimize(output_columns); + optimizeArrayJoin(); checkLimits(sample_block); } @@ -907,12 +910,6 @@ std::string ExpressionActions::dumpActions() const return ss.str(); } -void ExpressionActions::optimize(const Names & output_columns) -{ - optimizeArrayJoin(); - compileFunctions(output_columns); -} - void ExpressionActions::optimizeArrayJoin() { const size_t NONE = actions.size(); @@ -994,17 +991,16 @@ void ExpressionActions::optimizeArrayJoin() } } -void ExpressionActions::compileFunctions([[maybe_unused]] const Names & output_columns) +void ExpressionActions::compileFunctions([[maybe_unused]] const NameSet & final_columns) { #if USE_EMBEDDED_COMPILER LLVMContext context; - std::vector redundant(actions.size()); - // an empty optional is a poisoned value prohibiting the column's producer from being removed - // (which it could be, if it was inlined into every dependent function). + /// an empty optional is a poisoned value prohibiting the column's producer from being removed + /// (which it could be, if it was inlined into every dependent function). std::unordered_map>> current_dependents; - for (const auto & name : output_columns) + for (const auto & name : final_columns) current_dependents[name].emplace(); - // a snapshot of each compilable function's dependents at the time of its execution. + /// a snapshot of each compilable function's dependents at the time of its execution. std::vector>> dependents(actions.size()); for (size_t i = actions.size(); i--;) { @@ -1015,10 +1011,9 @@ void ExpressionActions::compileFunctions([[maybe_unused]] const Names & output_c case ExpressionAction::REMOVE_COLUMN: current_dependents.erase(actions[i].source_name); - // temporarily discard all `REMOVE_COLUMN`s because inlining will change dependency sets. - // for example, if there's a column `x` and we want to compile `f(g(x))`, said `x` might get removed - // between `g(x)` and `f(g(x))`. it's easier to reintroduce removals later than move them around. - redundant[i] = true; + /// poison every other column used after this point so that inlining chains do not cross it. + for (auto & dep : current_dependents) + dep.second.emplace(); break; case ExpressionAction::COPY_COLUMN: @@ -1027,18 +1022,13 @@ void ExpressionActions::compileFunctions([[maybe_unused]] const Names & output_c case ExpressionAction::PROJECT: current_dependents.clear(); - // unlike `REMOVE_COLUMN`, we know the exact set of columns that will survive a `PROJECT`, - // so we can simply poison them to prevent any inlining chain from crossing this barrier. - // note that this would generate suboptimal action sequences if, for example, in the example above - // `REMOVE_COLUMN x` was replaced with `PROJECT {g(x)}` -- it is more optimal to remove the `PROJECT` - // and inline `g`. however, that sequence would at least still execute correctly. for (const auto & proj : actions[i].projection) current_dependents[proj.first].emplace(); break; case ExpressionAction::ARRAY_JOIN: case ExpressionAction::JOIN: - // assume these actions can read everything; all columns not removed before this point are poisoned. + /// assume these actions can read everything; all columns not removed before this point are poisoned. for (size_t j = i; j--;) current_dependents[actions[j].result_name].emplace(); break; @@ -1060,6 +1050,7 @@ void ExpressionActions::compileFunctions([[maybe_unused]] const Names & output_c } std::vector fused(actions.size()); + std::vector redundant(actions.size()); for (size_t i = 0; i < actions.size(); i++) { if (actions[i].type != ExpressionAction::APPLY_FUNCTION || !context.isCompilable(*actions[i].function)) @@ -1072,14 +1063,14 @@ void ExpressionActions::compileFunctions([[maybe_unused]] const Names & output_c actions[i].argument_names = fn->getArgumentNames(); continue; } - // TODO: determine whether it's profitable to inline the function if there's more than one dependent. + /// TODO: determine whether it's profitable to inline the function if there's more than one dependent. for (const auto & dep : dependents[i]) fused[*dep].push_back(actions[i]); redundant[i] = true; + sample_block.erase(actions[i].result_name); } size_t i = 0; actions.erase(std::remove_if(actions.begin(), actions.end(), [&](const auto&) { return redundant[i++]; }), actions.end()); - // TODO: insert `REMOVE_COLUMN`s according to new dependency sets context.finalize(); #endif } diff --git a/dbms/src/Interpreters/ExpressionActions.h b/dbms/src/Interpreters/ExpressionActions.h index ee14048e0e5..5df5e60913c 100644 --- a/dbms/src/Interpreters/ExpressionActions.h +++ b/dbms/src/Interpreters/ExpressionActions.h @@ -207,12 +207,10 @@ private: void addImpl(ExpressionAction action, Names & new_names); - /// Try to improve something without changing the lists of input and output columns. - void optimize(const Names & output_columns); /// Move all arrayJoin as close as possible to the end. void optimizeArrayJoin(); /// Try to JIT-compile all functions and remove unnecessary materialization of intermediate results. - void compileFunctions(const Names & output_columns); + void compileFunctions(const NameSet & final_columns); }; using ExpressionActionsPtr = std::shared_ptr; From 5482282943308447042c4dd2bd7807f379f8d51e Mon Sep 17 00:00:00 2001 From: pyos Date: Wed, 25 Apr 2018 14:55:54 +0300 Subject: [PATCH 022/231] Implement informational methods for LLVMFunction --- dbms/src/Interpreters/ExpressionJIT.cpp | 43 +++++++++++++++++++++++++ dbms/src/Interpreters/ExpressionJIT.h | 32 ++++++++++++++---- 2 files changed, 68 insertions(+), 7 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index eb301774987..eb5a289f4c1 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -1,3 +1,4 @@ +#include #include #include #include @@ -255,6 +256,48 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont context->builder.CreateRetVoid(); } +static Field evaluateFunction(IFunctionBase & function, const IDataType & type, const Field & arg) +{ + const auto & arg_types = function.getArgumentTypes(); + if (arg_types.size() != 1 || !arg_types[0]->equals(type)) + return {}; + auto column = arg_types[0]->createColumn(); + column->insert(arg); + Block block = {{ ColumnConst::create(std::move(column), 1), arg_types[0], "_arg" }, { nullptr, function.getReturnType(), "_result" }}; + function.execute(block, {0}, 1); + auto result = block.getByPosition(1).column; + return result && result->size() == 1 ? (*result)[0] : Field(); +} + +IFunctionBase::Monotonicity LLVMFunction::getMonotonicityForRange(const IDataType & type, const Field & left, const Field & right) const +{ + const IDataType * type_ = &type; + Field left_ = left; + Field right_ = right; + Monotonicity result(true, true, true); + /// monotonicity is only defined for unary functions, to the chain must describe a sequence of nested calls + for (size_t i = 0; i < actions.size(); i++) + { + Monotonicity m = actions[i].function->getMonotonicityForRange(type, left_, right_); + if (!m.is_monotonic) + return m; + result.is_positive ^= !m.is_positive; + result.is_always_monotonic &= m.is_always_monotonic; + if (i + 1 < actions.size()) + { + if (left_ != Field()) + left_ = evaluateFunction(*actions[i].function, *type_, left_); + if (right_ != Field()) + right_ = evaluateFunction(*actions[i].function, *type_, right_); + if (!m.is_positive) + std::swap(left_, right_); + type_ = actions[i].function->getReturnType().get(); + return Monotonicity{}; + } + } + return result; +} + } diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h index fd71804685e..bf5e593f9cf 100644 --- a/dbms/src/Interpreters/ExpressionJIT.h +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -78,13 +78,31 @@ public: return true; } - /// TODO: these methods require reconstructing the call tree: - /* - bool isSuitableForConstantFolding() const; - bool isInjective(const Block & sample_block); - bool hasInformationAboutMonotonicity() const; - Monotonicity getMonotonicityForRange(const IDataType & type, const Field & left, const Field & right) const; - */ + bool isSuitableForConstantFolding() const override + { + for (const auto & action : actions) + if (!action.function->isSuitableForConstantFolding()) + return false; + return true; + } + + bool isInjective(const Block & sample_block) override + { + for (const auto & action : actions) + if (!action.function->isInjective(sample_block)) + return false; + return true; + } + + bool hasInformationAboutMonotonicity() const override + { + for (const auto & action : actions) + if (!action.function->hasInformationAboutMonotonicity()) + return false; + return true; + } + + Monotonicity getMonotonicityForRange(const IDataType & type, const Field & left, const Field & right) const override; }; } From c419d5a1a59e54f755d6fd49468ecfecc3d38b72 Mon Sep 17 00:00:00 2001 From: pyos Date: Wed, 25 Apr 2018 15:51:38 +0300 Subject: [PATCH 023/231] Poison only columns actually used by ARRAY_JOIN and JOIN --- dbms/src/Interpreters/ExpressionActions.cpp | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionActions.cpp b/dbms/src/Interpreters/ExpressionActions.cpp index f99dd0d2c9d..e89df35cfdc 100644 --- a/dbms/src/Interpreters/ExpressionActions.cpp +++ b/dbms/src/Interpreters/ExpressionActions.cpp @@ -1006,9 +1006,6 @@ void ExpressionActions::compileFunctions([[maybe_unused]] const NameSet & final_ { switch (actions[i].type) { - case ExpressionAction::ADD_COLUMN: - break; - case ExpressionAction::REMOVE_COLUMN: current_dependents.erase(actions[i].source_name); /// poison every other column used after this point so that inlining chains do not cross it. @@ -1016,22 +1013,22 @@ void ExpressionActions::compileFunctions([[maybe_unused]] const NameSet & final_ dep.second.emplace(); break; - case ExpressionAction::COPY_COLUMN: - current_dependents[actions[i].source_name].emplace(); - break; - case ExpressionAction::PROJECT: current_dependents.clear(); for (const auto & proj : actions[i].projection) current_dependents[proj.first].emplace(); break; + case ExpressionAction::ADD_COLUMN: + case ExpressionAction::COPY_COLUMN: case ExpressionAction::ARRAY_JOIN: case ExpressionAction::JOIN: - /// assume these actions can read everything; all columns not removed before this point are poisoned. - for (size_t j = i; j--;) - current_dependents[actions[j].result_name].emplace(); + { + Names columns = actions[i].getNeededColumns(); + for (const auto & column : columns) + current_dependents[column].emplace(); break; + } case ExpressionAction::APPLY_FUNCTION: { From 6c275c27d0bcaec024d5353c02f1803864f31bb2 Mon Sep 17 00:00:00 2001 From: pyos Date: Wed, 25 Apr 2018 16:44:24 +0300 Subject: [PATCH 024/231] Remove an unnoticed debug `return` --- dbms/src/Interpreters/ExpressionJIT.cpp | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index eb5a289f4c1..f6f523c3c37 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -162,8 +162,6 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont : actions(std::move(actions_)), context(context) { std::unordered_set seen; - for (const auto & action : actions) - seen.insert(action.result_name); for (const auto & action : actions) { const auto & names = action.argument_names; @@ -176,6 +174,7 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont arg_types.push_back(types[i]); } } + seen.insert(action.result_name); } llvm::FunctionType * func_type = llvm::FunctionType::get(context->builder.getVoidTy(), { @@ -292,7 +291,6 @@ IFunctionBase::Monotonicity LLVMFunction::getMonotonicityForRange(const IDataTyp if (!m.is_positive) std::swap(left_, right_); type_ = actions[i].function->getReturnType().get(); - return Monotonicity{}; } } return result; From d59b0d7ec0633822a3e20f6c2d5f99c743117f63 Mon Sep 17 00:00:00 2001 From: pyos Date: Wed, 25 Apr 2018 18:16:48 +0300 Subject: [PATCH 025/231] Add IColumn::getRawData to fixed-contiguous columns --- dbms/src/Columns/ColumnConst.h | 1 + dbms/src/Columns/ColumnFixedString.h | 2 +- dbms/src/Columns/ColumnVector.h | 2 +- dbms/src/Columns/IColumn.h | 3 ++ dbms/src/Interpreters/ExpressionJIT.cpp | 45 +++++++++++-------------- 5 files changed, 25 insertions(+), 28 deletions(-) diff --git a/dbms/src/Columns/ColumnConst.h b/dbms/src/Columns/ColumnConst.h index 2e4a692451f..7631917da76 100644 --- a/dbms/src/Columns/ColumnConst.h +++ b/dbms/src/Columns/ColumnConst.h @@ -188,6 +188,7 @@ public: bool isFixedAndContiguous() const override { return data->isFixedAndContiguous(); } bool valuesHaveFixedSize() const override { return data->valuesHaveFixedSize(); } size_t sizeOfValueIfFixed() const override { return data->sizeOfValueIfFixed(); } + StringRef getRawData() const override { return data->getRawData(); } /// Not part of the common interface. diff --git a/dbms/src/Columns/ColumnFixedString.h b/dbms/src/Columns/ColumnFixedString.h index cd465a1814d..80b6ccd5456 100644 --- a/dbms/src/Columns/ColumnFixedString.h +++ b/dbms/src/Columns/ColumnFixedString.h @@ -129,7 +129,7 @@ public: bool isFixedAndContiguous() const override { return true; } size_t sizeOfValueIfFixed() const override { return n; } - + StringRef getRawData() const override { return StringRef(chars.data(), chars.size()); } /// Specialized part of interface, not from IColumn. diff --git a/dbms/src/Columns/ColumnVector.h b/dbms/src/Columns/ColumnVector.h index 5ce33e82028..ec940300c81 100644 --- a/dbms/src/Columns/ColumnVector.h +++ b/dbms/src/Columns/ColumnVector.h @@ -263,7 +263,7 @@ public: bool isFixedAndContiguous() const override { return true; } size_t sizeOfValueIfFixed() const override { return sizeof(T); } - + StringRef getRawData() const override { return StringRef(reinterpret_cast(data.data()), data.size()); } /** More efficient methods of manipulation - to manipulate with data directly. */ Container & getData() diff --git a/dbms/src/Columns/IColumn.h b/dbms/src/Columns/IColumn.h index 40577a11d3f..c69e47f9c7f 100644 --- a/dbms/src/Columns/IColumn.h +++ b/dbms/src/Columns/IColumn.h @@ -298,6 +298,9 @@ public: /// Values in column are represented as continuous memory segment of fixed size. Implies valuesHaveFixedSize. virtual bool isFixedAndContiguous() const { return false; } + /// If isFixedAndContiguous, returns the underlying data array, otherwise throws an exception. + virtual StringRef getRawData() const { throw Exception("Column " + getName() + " is not a contiguous block of memory", ErrorCodes::NOT_IMPLEMENTED); } + /// If valuesHaveFixedSize, returns size of value, otherwise throw an exception. virtual size_t sizeOfValueIfFixed() const { throw Exception("Values of column " + getName() + " are not fixed size.", ErrorCodes::CANNOT_GET_SIZE_OF_FIELD); } diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index f6f523c3c37..7340b511fe2 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -40,9 +40,7 @@ namespace ErrorCodes template static bool typeIsA(const DataTypePtr & type) { - if (auto * nullable = typeid_cast(type.get())) - return typeIsA(nullable->getNestedType()); - return typeid_cast(type.get());; + return typeid_cast(removeNullable(type).get());; } struct LLVMContext::Data @@ -128,33 +126,28 @@ LLVMPreparedFunction::LLVMPreparedFunction(LLVMContext context, std::shared_ptr< : parent(parent), context(context), function(context->lookup(parent->getName())) {} -static MutableColumnPtr createNonNullableColumn(const DataTypePtr & type) -{ - if (auto * nullable = typeid_cast(type.get())) - return createNonNullableColumn(nullable->getNestedType()); - return type->createColumn(); -} - void LLVMPreparedFunction::executeImpl(Block & block, const ColumnNumbers & arguments, size_t result) { - size_t block_size = 0; - std::vector columns(arguments.size()); - std::vector is_const(arguments.size()); - for (size_t i = 0; i < arguments.size(); i++) - { - auto * column = block.getByPosition(arguments[i]).column.get(); - if (column->size()) - /// assume the column is a `ColumnVector`. there's probably no good way to actually - /// check that at runtime, so let's just hope it's always true for columns containing types - /// for which `LLVMContext::Data::toNativeType` returns non-null. - columns[i] = column->getDataAt(0).data; - is_const[i] = column->isColumnConst(); - block_size = column->size(); - } /// assuming that the function has default behavior on NULL, the column will be wrapped by `PreparedFunctionImpl::execute`. - auto col_res = createNonNullableColumn(parent->getReturnType())->cloneResized(block_size); + size_t block_size = block.rows(); + auto col_res = removeNullable(parent->getReturnType())->createColumn()->cloneResized(block_size); if (block_size) - function(columns.data(), is_const.data(), const_cast(col_res->getDataAt(0).data), block_size); + { + std::vector columns(arguments.size()); + std::vector is_const(arguments.size()); + for (size_t i = 0; i < arguments.size(); i++) + { + auto * column = block.getByPosition(arguments[i]).column.get(); + if (!column) + throw Exception("column " + block.getByPosition(arguments[i]).name + " is missing", ErrorCodes::LOGICAL_ERROR); + if (!column->isFixedAndContiguous()) + throw Exception("column type " + column->getName() + " is not a contiguous array; its data type " + "should've had no native equivalent in LLVMContext::Data::toNativeType", ErrorCodes::LOGICAL_ERROR); + columns[i] = column->getRawData().data; + is_const[i] = column->isColumnConst(); + } + function(columns.data(), is_const.data(), const_cast(col_res->getRawData().data), block_size); + } block.getByPosition(result).column = std::move(col_res); }; From 854f85dd9baad0499b950364acdfde53c2e8019b Mon Sep 17 00:00:00 2001 From: pyos Date: Wed, 25 Apr 2018 18:19:22 +0300 Subject: [PATCH 026/231] Put #if USE_EMBEDDED_COMPILER in ExpressionJIT.{cpp,h} --- dbms/CMakeLists.txt | 12 +++--------- dbms/src/Interpreters/ExpressionJIT.cpp | 7 ++++++- dbms/src/Interpreters/ExpressionJIT.h | 6 ++++++ 3 files changed, 15 insertions(+), 10 deletions(-) diff --git a/dbms/CMakeLists.txt b/dbms/CMakeLists.txt index db1e8453924..c59bd21f516 100644 --- a/dbms/CMakeLists.txt +++ b/dbms/CMakeLists.txt @@ -82,15 +82,6 @@ list (APPEND dbms_headers list (APPEND dbms_sources src/TableFunctions/ITableFunction.cpp src/TableFunctions/TableFunctionFactory.cpp) list (APPEND dbms_headers src/TableFunctions/ITableFunction.h src/TableFunctions/TableFunctionFactory.h) -if (USE_EMBEDDED_COMPILER) - # LLVM 5.0 has a bunch of unused parameters in its header files. - # TODO: global-disable no-unused-parameter - set_source_files_properties(src/Interpreters/ExpressionJIT.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter -Wno-non-virtual-dtor") -else () - list (REMOVE_ITEM dbms_sources src/Interpreters/ExpressionJIT.cpp) - list (REMOVE_ITEM dbms_headers src/Interpreters/ExpressionJIT.h) -endif () - add_library(clickhouse_common_io ${SPLIT_SHARED} ${clickhouse_common_io_headers} ${clickhouse_common_io_sources}) if (ARCH_FREEBSD) @@ -112,6 +103,9 @@ if (USE_EMBEDDED_COMPILER) llvm_map_components_to_libraries(REQUIRED_LLVM_LIBRARIES all) target_link_libraries (dbms ${REQUIRED_LLVM_LIBRARIES}) target_include_directories (dbms BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) + # LLVM 5.0 has a bunch of unused parameters in its header files. + # TODO: global-disable no-unused-parameter + set_source_files_properties(src/Interpreters/ExpressionJIT.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter -Wno-non-virtual-dtor") endif () diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 7340b511fe2..eee73a09802 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -1,9 +1,12 @@ +#include + +#if USE_EMBEDDED_COMPILER + #include #include #include #include #include -#include #include #include @@ -307,3 +310,5 @@ struct LLVMTargetInitializer } static LLVMTargetInitializer llvmInitializer; + +#endif diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h index bf5e593f9cf..edf26cae96c 100644 --- a/dbms/src/Interpreters/ExpressionJIT.h +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -1,5 +1,9 @@ #pragma once +#include + +#if USE_EMBEDDED_COMPILER + #include #include @@ -106,3 +110,5 @@ public: }; } + +#endif From 9ae5fe1b6db454de29685755c5ae6ded78fccd8f Mon Sep 17 00:00:00 2001 From: pyos Date: Wed, 25 Apr 2018 18:30:57 +0300 Subject: [PATCH 027/231] Minor style fixes --- dbms/src/Interpreters/ExpressionJIT.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index eee73a09802..a197dded237 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -131,8 +131,8 @@ LLVMPreparedFunction::LLVMPreparedFunction(LLVMContext context, std::shared_ptr< void LLVMPreparedFunction::executeImpl(Block & block, const ColumnNumbers & arguments, size_t result) { - /// assuming that the function has default behavior on NULL, the column will be wrapped by `PreparedFunctionImpl::execute`. size_t block_size = block.rows(); + /// assuming that the function has default behavior on NULL, the column will be wrapped by `PreparedFunctionImpl::execute`. auto col_res = removeNullable(parent->getReturnType())->createColumn()->cloneResized(block_size); if (block_size) { @@ -209,7 +209,6 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont context->builder.CreateBr(loop); context->builder.SetInsertPoint(loop); - std::unordered_map> by_name; std::vector phi(inputs_v.size()); for (size_t i = 0; i < inputs_v.size(); i++) { @@ -221,6 +220,7 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont output_phi->addIncoming(output, entry); counter_phi->addIncoming(counter, entry); + std::unordered_map> by_name; for (size_t i = 0; i < phi.size(); i++) if (!by_name.emplace(arg_names[i], [&, i]() { return context->builder.CreateLoad(phi[i]); }).second) throw Exception("duplicate input column name", ErrorCodes::LOGICAL_ERROR); @@ -270,7 +270,7 @@ IFunctionBase::Monotonicity LLVMFunction::getMonotonicityForRange(const IDataTyp Field left_ = left; Field right_ = right; Monotonicity result(true, true, true); - /// monotonicity is only defined for unary functions, to the chain must describe a sequence of nested calls + /// monotonicity is only defined for unary functions, so the chain must describe a sequence of nested calls for (size_t i = 0; i < actions.size(); i++) { Monotonicity m = actions[i].function->getMonotonicityForRange(type, left_, right_); From c95f8a669fe5ad7f636ff821fdd2455731eb337e Mon Sep 17 00:00:00 2001 From: pyos Date: Wed, 25 Apr 2018 20:07:19 +0300 Subject: [PATCH 028/231] Throw in untyped versions of IFunction::{isCompilable,compile} IFunction inherits IFunctionBase for some reason despite not actually knowing the types, so these two methods make no sense. The versions with DataTypes& as an argument should be used instead. --- dbms/src/Functions/IFunction.h | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/dbms/src/Functions/IFunction.h b/dbms/src/Functions/IFunction.h index 28a87dddcda..a07f0a5c99e 100644 --- a/dbms/src/Functions/IFunction.h +++ b/dbms/src/Functions/IFunction.h @@ -291,6 +291,11 @@ public: virtual bool isCompilable(const DataTypes & /*types*/) const { return false; } + bool isCompilable() const final + { + throw Exception("isCompilable without explicit types is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); + } + PreparedFunctionPtr prepare(const Block & /*sample_block*/) const final { throw Exception("prepare is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); @@ -301,6 +306,11 @@ public: throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED); } + llvm::Value * compile(llvm::IRBuilderBase & /*builder*/, const ValuePlaceholders & /*values*/) const final + { + throw Exception("compile without explicit types is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); + } + const DataTypes & getArgumentTypes() const final { throw Exception("getArgumentTypes is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); From b4d527ee853b2cd2ae81a440014dcfd1dc5d3f08 Mon Sep 17 00:00:00 2001 From: pyos Date: Thu, 26 Apr 2018 14:09:10 +0300 Subject: [PATCH 029/231] Inline compile-time constants into jitted functions. --- dbms/src/Interpreters/ExpressionActions.cpp | 18 ++++++-------- dbms/src/Interpreters/ExpressionActions.h | 2 +- dbms/src/Interpreters/ExpressionJIT.cpp | 27 +++++++++++++++++---- dbms/src/Interpreters/ExpressionJIT.h | 2 +- 4 files changed, 31 insertions(+), 18 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionActions.cpp b/dbms/src/Interpreters/ExpressionActions.cpp index e89df35cfdc..d6806c263e4 100644 --- a/dbms/src/Interpreters/ExpressionActions.cpp +++ b/dbms/src/Interpreters/ExpressionActions.cpp @@ -706,6 +706,10 @@ void ExpressionActions::finalize(const Names & output_columns) final_columns.insert(name); } + /// This has to be done before removing redundant actions and inserting REMOVE_COLUMNs + /// because inlining may change dependency sets. + compileFunctions(output_columns); + /// Which columns are needed to perform actions from the current to the last. NameSet needed_columns = final_columns; /// Which columns nobody will touch from the current action to the last. @@ -823,9 +827,6 @@ void ExpressionActions::finalize(const Names & output_columns) } } - /// This has to be done before inserting REMOVE_COLUMNs because inlining may change dependency sets. - compileFunctions(final_columns); - /* std::cerr << "\n"; for (const auto & action : actions) std::cerr << action.toString() << "\n"; @@ -991,14 +992,14 @@ void ExpressionActions::optimizeArrayJoin() } } -void ExpressionActions::compileFunctions([[maybe_unused]] const NameSet & final_columns) +void ExpressionActions::compileFunctions([[maybe_unused]] const Names & output_columns) { #if USE_EMBEDDED_COMPILER LLVMContext context; /// an empty optional is a poisoned value prohibiting the column's producer from being removed /// (which it could be, if it was inlined into every dependent function). std::unordered_map>> current_dependents; - for (const auto & name : final_columns) + for (const auto & name : output_columns) current_dependents[name].emplace(); /// a snapshot of each compilable function's dependents at the time of its execution. std::vector>> dependents(actions.size()); @@ -1047,7 +1048,6 @@ void ExpressionActions::compileFunctions([[maybe_unused]] const NameSet & final_ } std::vector fused(actions.size()); - std::vector redundant(actions.size()); for (size_t i = 0; i < actions.size(); i++) { if (actions[i].type != ExpressionAction::APPLY_FUNCTION || !context.isCompilable(*actions[i].function)) @@ -1055,7 +1055,7 @@ void ExpressionActions::compileFunctions([[maybe_unused]] const NameSet & final_ if (dependents[i].find({}) != dependents[i].end()) { fused[i].push_back(actions[i]); - auto fn = std::make_shared(std::move(fused[i]), context); + auto fn = std::make_shared(std::move(fused[i]), context, sample_block); actions[i].function = fn; actions[i].argument_names = fn->getArgumentNames(); continue; @@ -1063,11 +1063,7 @@ void ExpressionActions::compileFunctions([[maybe_unused]] const NameSet & final_ /// TODO: determine whether it's profitable to inline the function if there's more than one dependent. for (const auto & dep : dependents[i]) fused[*dep].push_back(actions[i]); - redundant[i] = true; - sample_block.erase(actions[i].result_name); } - size_t i = 0; - actions.erase(std::remove_if(actions.begin(), actions.end(), [&](const auto&) { return redundant[i++]; }), actions.end()); context.finalize(); #endif } diff --git a/dbms/src/Interpreters/ExpressionActions.h b/dbms/src/Interpreters/ExpressionActions.h index 5df5e60913c..014f9d9e108 100644 --- a/dbms/src/Interpreters/ExpressionActions.h +++ b/dbms/src/Interpreters/ExpressionActions.h @@ -210,7 +210,7 @@ private: /// Move all arrayJoin as close as possible to the end. void optimizeArrayJoin(); /// Try to JIT-compile all functions and remove unnecessary materialization of intermediate results. - void compileFunctions(const NameSet & final_columns); + void compileFunctions(const Names & output_columns); }; using ExpressionActionsPtr = std::shared_ptr; diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index a197dded237..92d6b50ec2f 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -154,9 +154,27 @@ void LLVMPreparedFunction::executeImpl(Block & block, const ColumnNumbers & argu block.getByPosition(result).column = std::move(col_res); }; -LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext context) +LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext context, const Block & sample_block) : actions(std::move(actions_)), context(context) { + std::unordered_map> by_name; + for (const auto & c : sample_block) + { + auto generator = [&]() -> llvm::Value * + { + auto * type = context->toNativeType(c.type); + if (typeIsA(c.type)) + return llvm::ConstantFP::get(type, typeid_cast *>(c.column.get())->getElement(0)); + if (typeIsA(c.type)) + return llvm::ConstantFP::get(type, typeid_cast *>(c.column.get())->getElement(0)); + if (type && type->isIntegerTy()) + return llvm::ConstantInt::get(type, c.column->getUInt(0)); + return nullptr; + }; + if (c.column && generator() && !by_name.emplace(c.name, std::move(generator)).second) + throw Exception("duplicate constant column " + c.name, ErrorCodes::LOGICAL_ERROR); + } + std::unordered_set seen; for (const auto & action : actions) { @@ -164,7 +182,7 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont const auto & types = action.function->getArgumentTypes(); for (size_t i = 0; i < names.size(); i++) { - if (seen.emplace(names[i]).second) + if (seen.emplace(names[i]).second && by_name.find(names[i]) == by_name.end()) { arg_names.push_back(names[i]); arg_types.push_back(types[i]); @@ -220,10 +238,9 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont output_phi->addIncoming(output, entry); counter_phi->addIncoming(counter, entry); - std::unordered_map> by_name; for (size_t i = 0; i < phi.size(); i++) if (!by_name.emplace(arg_names[i], [&, i]() { return context->builder.CreateLoad(phi[i]); }).second) - throw Exception("duplicate input column name", ErrorCodes::LOGICAL_ERROR); + throw Exception("duplicate input column name " + arg_names[i], ErrorCodes::LOGICAL_ERROR); for (const auto & action : actions) { ValuePlaceholders action_input; @@ -235,7 +252,7 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont return action.function->compile(context->builder, action_input); }; if (!by_name.emplace(action.result_name, std::move(generator)).second) - throw Exception("duplicate action result name", ErrorCodes::LOGICAL_ERROR); + throw Exception("duplicate action result name " + action.result_name, ErrorCodes::LOGICAL_ERROR); } context->builder.CreateStore(by_name.at(actions.back().result_name)(), output_phi); diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h index edf26cae96c..04ebb67ac25 100644 --- a/dbms/src/Interpreters/ExpressionJIT.h +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -54,7 +54,7 @@ class LLVMFunction : public std::enable_shared_from_this, public I LLVMContext context; public: - LLVMFunction(ExpressionActions::Actions actions, LLVMContext context); + LLVMFunction(ExpressionActions::Actions actions, LLVMContext context, const Block & sample_block); String getName() const override { return actions.back().result_name; } From a9e0b6de9fcc62b4d18320f94ccd662579286e1c Mon Sep 17 00:00:00 2001 From: pyos Date: Thu, 26 Apr 2018 18:36:27 +0300 Subject: [PATCH 030/231] Use system LLVMConfig.cmake with minor tweaks. Should fix Travis build, finally. --- cmake/find_llvm.cmake | 104 ++---------------- dbms/CMakeLists.txt | 2 +- dbms/src/Server/Compiler-5.0.0/CMakeLists.txt | 12 +- dbms/src/Server/Compiler-6.0.0/CMakeLists.txt | 14 +-- 4 files changed, 13 insertions(+), 119 deletions(-) diff --git a/cmake/find_llvm.cmake b/cmake/find_llvm.cmake index 618eaadf41a..bc5bcd39ef7 100644 --- a/cmake/find_llvm.cmake +++ b/cmake/find_llvm.cmake @@ -1,107 +1,21 @@ option (ENABLE_EMBEDDED_COMPILER "Set to TRUE to enable support for 'compile' option for query execution" 1) if (ENABLE_EMBEDDED_COMPILER) - # Based on source code of YT. - # Authors: Ivan Puzyrevskiy, Alexey Lukyanchikov, Ruslan Savchenko. - - # Find LLVM includes and libraries. - # - # LLVM_VERSION - LLVM version. - # LLVM_INCLUDE_DIRS - Directory containing LLVM headers. - # LLVM_LIBRARY_DIRS - Directory containing LLVM libraries. - # LLVM_CXXFLAGS - C++ compiler flags for files that include LLVM headers. - # LLVM_FOUND - True if LLVM was found. - - # llvm_map_components_to_libraries - Maps LLVM used components to required libraries. - # Usage: llvm_map_components_to_libraries(REQUIRED_LLVM_LIBRARIES core jit interpreter native ...) - if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang") - set(LLVM_VERSION_POSTFIX "${COMPILER_POSTFIX}" CACHE STRING "") - else() - if (ARCH_FREEBSD) - set(LLVM_VERSION_POSTFIX "50" CACHE STRING "") - else() - set(LLVM_VERSION_POSTFIX "-5.0" CACHE STRING "") - endif() - endif() + find_package(LLVM CONFIG) + else () + find_package(LLVM 5 CONFIG) + endif () - find_program(LLVM_CONFIG_EXECUTABLE - NAMES llvm-config${LLVM_VERSION_POSTFIX} llvm-config llvm-config-devel - PATHS $ENV{LLVM_ROOT}/bin) - - mark_as_advanced(LLVM_CONFIG_EXECUTABLE) - - if(NOT LLVM_CONFIG_EXECUTABLE) - message(WARNING "Cannot find LLVM (looking for `llvm-config${LLVM_VERSION_POSTFIX}`, `llvm-config`, `llvm-config-devel`). Please, provide LLVM_ROOT environment variable.") - else() - set(LLVM_FOUND TRUE) - - execute_process( - COMMAND ${LLVM_CONFIG_EXECUTABLE} --version - OUTPUT_VARIABLE LLVM_VERSION - OUTPUT_STRIP_TRAILING_WHITESPACE) - - if(LLVM_VERSION VERSION_LESS "5") - message(FATAL_ERROR "LLVM 5+ is required. You have ${LLVM_VERSION} (${LLVM_CONFIG_EXECUTABLE})") - endif() - - message(STATUS "LLVM config: ${LLVM_CONFIG_EXECUTABLE}; version: ${LLVM_VERSION}") - - execute_process( - COMMAND ${LLVM_CONFIG_EXECUTABLE} --includedir - OUTPUT_VARIABLE LLVM_INCLUDE_DIRS - OUTPUT_STRIP_TRAILING_WHITESPACE) - - execute_process( - COMMAND ${LLVM_CONFIG_EXECUTABLE} --libdir - OUTPUT_VARIABLE LLVM_LIBRARY_DIRS - OUTPUT_STRIP_TRAILING_WHITESPACE) - - execute_process( - COMMAND ${LLVM_CONFIG_EXECUTABLE} --cxxflags - OUTPUT_VARIABLE LLVM_CXXFLAGS - OUTPUT_STRIP_TRAILING_WHITESPACE) - - execute_process( - COMMAND ${LLVM_CONFIG_EXECUTABLE} --targets-built - OUTPUT_VARIABLE LLVM_TARGETS_BUILT - OUTPUT_STRIP_TRAILING_WHITESPACE) - - string(REPLACE " " ";" LLVM_TARGETS_BUILT "${LLVM_TARGETS_BUILT}") - - if (USE_STATIC_LIBRARIES) - set (LLVM_CONFIG_ADD "--link-static") - endif() - - # Get the link libs we need. - function(llvm_map_components_to_libraries RESULT) - execute_process( - COMMAND ${LLVM_CONFIG_EXECUTABLE} ${LLVM_CONFIG_ADD} --libs ${ARGN} - OUTPUT_VARIABLE _tmp - OUTPUT_STRIP_TRAILING_WHITESPACE) - - string(REPLACE " " ";" _libs_module "${_tmp}") - - #message(STATUS "LLVM Libraries for '${ARGN}': ${_libs_module}") - - execute_process( - COMMAND ${LLVM_CONFIG_EXECUTABLE} --system-libs ${ARGN} - OUTPUT_VARIABLE _libs_system - OUTPUT_STRIP_TRAILING_WHITESPACE) - - string(REPLACE "\n" " " _libs_system "${_libs_system}") - string(REPLACE " " " " _libs_system "${_libs_system}") - string(REPLACE " " ";" _libs_system "${_libs_system}") - - set(${RESULT} ${_libs_module} ${_libs_system} PARENT_SCOPE) - endfunction(llvm_map_components_to_libraries) + if (LLVM_FOUND) + # Remove dynamically-linked zlib and libedit from LLVM's dependencies: + set_target_properties(LLVMSupport PROPERTIES INTERFACE_LINK_LIBRARIES "-lpthread;LLVMDemangle;stdc++") + set_target_properties(LLVMLineEditor PROPERTIES INTERFACE_LINK_LIBRARIES "LLVMSupport") + message(STATUS "LLVM version: ${LLVM_PACKAGE_VERSION}") message(STATUS "LLVM Include Directory: ${LLVM_INCLUDE_DIRS}") message(STATUS "LLVM Library Directory: ${LLVM_LIBRARY_DIRS}") message(STATUS "LLVM C++ Compiler: ${LLVM_CXXFLAGS}") - endif() - - if (LLVM_FOUND AND LLVM_INCLUDE_DIRS AND LLVM_LIBRARY_DIRS) set (USE_EMBEDDED_COMPILER 1) endif() endif() diff --git a/dbms/CMakeLists.txt b/dbms/CMakeLists.txt index c59bd21f516..2cd85d63700 100644 --- a/dbms/CMakeLists.txt +++ b/dbms/CMakeLists.txt @@ -100,7 +100,7 @@ else () endif () if (USE_EMBEDDED_COMPILER) - llvm_map_components_to_libraries(REQUIRED_LLVM_LIBRARIES all) + llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) target_link_libraries (dbms ${REQUIRED_LLVM_LIBRARIES}) target_include_directories (dbms BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) # LLVM 5.0 has a bunch of unused parameters in its header files. diff --git a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt index bfc988af773..4a133afbbae 100644 --- a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt @@ -8,12 +8,7 @@ add_library(clickhouse-compiler-lib target_compile_options(clickhouse-compiler-lib PRIVATE -fno-rtti -fno-exceptions -g0) -llvm_map_components_to_libraries(REQUIRED_LLVM_LIBRARIES all) - -# We link statically with zlib, and LLVM (sometimes) tries to bring its own dependency. -list(REMOVE_ITEM REQUIRED_LLVM_LIBRARIES "-lz") -# Wrong library in freebsd: -list(REMOVE_ITEM REQUIRED_LLVM_LIBRARIES "-l/usr/lib/libexecinfo.so") +llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") @@ -51,8 +46,3 @@ libtinfo.a PUBLIC ${ZLIB_LIBRARIES} ${EXECINFO_LIBRARY} Threads::Threads ) - -if (MAKE_STATIC_LIBRARIES) - # fix strange static error: undefined reference to 'std::error_category::~error_category()' - target_link_libraries(clickhouse-compiler-lib PUBLIC stdc++) -endif () diff --git a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt index a4cb086c4cd..eb43310ba51 100644 --- a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt @@ -8,12 +8,7 @@ add_library(clickhouse-compiler-lib target_compile_options(clickhouse-compiler-lib PRIVATE -fno-rtti -fno-exceptions -g0) -llvm_map_components_to_libraries(REQUIRED_LLVM_LIBRARIES all) - -# We link statically with zlib, and LLVM (sometimes) tries to bring its own dependency. -list(REMOVE_ITEM REQUIRED_LLVM_LIBRARIES "-lz") -# Wrong library in freebsd: -list(REMOVE_ITEM REQUIRED_LLVM_LIBRARIES "-l/usr/lib/libexecinfo.so") +llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") @@ -24,7 +19,7 @@ target_include_directories(clickhouse-compiler-lib PRIVATE ${LLVM_INCLUDE_DIRS}) target_link_libraries(clickhouse-compiler-lib PRIVATE clangBasic clangCodeGen clangDriver -clangFrontend +clangFrontend clangFrontendTool clangRewriteFrontend clangARCMigrate clangStaticAnalyzerFrontend clangParse clangSerialization clangSema clangEdit clangStaticAnalyzerCheckers @@ -51,8 +46,3 @@ libtinfo.a PUBLIC ${ZLIB_LIBRARIES} ${EXECINFO_LIBRARY} Threads::Threads ) - -if (MAKE_STATIC_LIBRARIES) - # fix strange static error: undefined reference to 'std::error_category::~error_category()' - target_link_libraries(clickhouse-compiler-lib PUBLIC stdc++) -endif () From 49b61cd27d0fc6329c677373572019fdf4f6449b Mon Sep 17 00:00:00 2001 From: pyos Date: Fri, 27 Apr 2018 18:44:38 +0300 Subject: [PATCH 031/231] Refactor LLVMFunction to make extending to DataTypeNullable easier --- dbms/src/Interpreters/ExpressionJIT.cpp | 121 ++++++++++++------------ dbms/src/Interpreters/ExpressionJIT.h | 5 +- 2 files changed, 64 insertions(+), 62 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 92d6b50ec2f..29d03db4f0d 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -86,13 +86,13 @@ struct LLVMContext::Data return nullptr; } - LLVMCompiledFunction * lookup(const std::string& name) + const void * lookup(const std::string& name) { std::string mangledName; llvm::raw_string_ostream mangledNameStream(mangledName); llvm::Mangler::getNameWithPrefix(mangledNameStream, name, layout); /// why is `findSymbol` not const? we may never know. - return reinterpret_cast(compileLayer.findSymbol(mangledNameStream.str(), false).getAddress().get()); + return reinterpret_cast(compileLayer.findSymbol(mangledNameStream.str(), false).getAddress().get()); } }; @@ -129,6 +129,24 @@ LLVMPreparedFunction::LLVMPreparedFunction(LLVMContext context, std::shared_ptr< : parent(parent), context(context), function(context->lookup(parent->getName())) {} +namespace +{ + struct ColumnData + { + const char * data; + size_t stride; + }; +} + +static ColumnData getColumnData(const IColumn * column) +{ + if (!column->isFixedAndContiguous()) + throw Exception("column type " + column->getName() + " is not a contiguous array; its data type " + "should've had no native equivalent in LLVMContext::Data::toNativeType", ErrorCodes::LOGICAL_ERROR); + /// TODO: handle ColumnNullable + return {column->getRawData().data, !column->isColumnConst() ? column->sizeOfValueIfFixed() : 0}; +} + void LLVMPreparedFunction::executeImpl(Block & block, const ColumnNumbers & arguments, size_t result) { size_t block_size = block.rows(); @@ -136,20 +154,16 @@ void LLVMPreparedFunction::executeImpl(Block & block, const ColumnNumbers & argu auto col_res = removeNullable(parent->getReturnType())->createColumn()->cloneResized(block_size); if (block_size) { - std::vector columns(arguments.size()); - std::vector is_const(arguments.size()); + std::vector columns(arguments.size() + 1); for (size_t i = 0; i < arguments.size(); i++) { auto * column = block.getByPosition(arguments[i]).column.get(); if (!column) throw Exception("column " + block.getByPosition(arguments[i]).name + " is missing", ErrorCodes::LOGICAL_ERROR); - if (!column->isFixedAndContiguous()) - throw Exception("column type " + column->getName() + " is not a contiguous array; its data type " - "should've had no native equivalent in LLVMContext::Data::toNativeType", ErrorCodes::LOGICAL_ERROR); - columns[i] = column->getRawData().data; - is_const[i] = column->isColumnConst(); + columns[i] = getColumnData(column); } - function(columns.data(), is_const.data(), const_cast(col_res->getRawData().data), block_size); + columns[arguments.size()] = getColumnData(col_res.get()); + reinterpret_cast(function)(block_size, columns.data()); } block.getByPosition(result).column = std::move(col_res); }; @@ -191,55 +205,47 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont seen.insert(action.result_name); } - llvm::FunctionType * func_type = llvm::FunctionType::get(context->builder.getVoidTy(), { - llvm::PointerType::getUnqual(llvm::PointerType::getUnqual(context->builder.getVoidTy())), - llvm::PointerType::getUnqual(context->builder.getInt8Ty()), - llvm::PointerType::getUnqual(context->toNativeType(actions.back().function->getReturnType())), - context->builder.getIntNTy(sizeof(size_t) * 8), - }, /*isVarArg=*/false); + auto * char_type = context->builder.getInt8Ty(); + auto * size_type = context->builder.getIntNTy(sizeof(size_t) * 8); + auto * data_type = llvm::StructType::get(llvm::PointerType::get(char_type, 0), size_type); + auto * func_type = llvm::FunctionType::get(context->builder.getVoidTy(), { size_type, llvm::PointerType::get(data_type, 0) }, /*isVarArg=*/false); auto * func = llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, actions.back().result_name, context->module.get()); auto args = func->args().begin(); - llvm::Value * inputs = &*args++; /// void** - tuple of columns, each a contiguous data block - llvm::Value * consts = &*args++; /// char* - for each column, 0 if it is full, 1 if it points to a single constant value - llvm::Value * output = &*args++; /// void* - space for the result - llvm::Value * counter = &*args++; /// size_t - number of entries to read from non-const values and write to output + llvm::Value * counter = &*args++; + llvm::Value * columns = &*args++; auto * entry = llvm::BasicBlock::Create(context->context, "entry", func); context->builder.SetInsertPoint(entry); - std::vector inputs_v(arg_types.size()); - std::vector deltas_v(arg_types.size()); - for (size_t i = 0; i < arg_types.size(); i++) + struct CastedColumnData { - if (i != 0) - { - inputs = context->builder.CreateConstGEP1_32(inputs, 1); - consts = context->builder.CreateConstGEP1_32(consts, 1); - } - auto * type = llvm::PointerType::getUnqual(context->toNativeType(arg_types[i])); - auto * step = context->builder.CreateICmpEQ(context->builder.CreateLoad(consts), llvm::ConstantInt::get(context->builder.getInt8Ty(), 0)); - inputs_v[i] = context->builder.CreatePointerCast(context->builder.CreateLoad(inputs), type); - deltas_v[i] = context->builder.CreateZExt(step, context->builder.getInt32Ty()); + llvm::PHINode * data; + llvm::Value * data_init; + llvm::Value * stride; + }; + std::vector columns_v(arg_types.size() + 1); + for (size_t i = 0; i <= arg_types.size(); i++) + { + auto * type = llvm::PointerType::getUnqual(context->toNativeType(i == arg_types.size() ? getReturnType() : arg_types[i])); + auto * data = context->builder.CreateConstInBoundsGEP2_32(data_type, columns, i, 0); + auto * stride = context->builder.CreateConstInBoundsGEP2_32(data_type, columns, i, 1); + columns_v[i] = { nullptr, context->builder.CreatePointerCast(context->builder.CreateLoad(data), type), context->builder.CreateLoad(stride) }; } /// assume nonzero initial value in `counter` auto * loop = llvm::BasicBlock::Create(context->context, "loop", func); context->builder.CreateBr(loop); context->builder.SetInsertPoint(loop); - - std::vector phi(inputs_v.size()); - for (size_t i = 0; i < inputs_v.size(); i++) - { - phi[i] = context->builder.CreatePHI(inputs_v[i]->getType(), 2); - phi[i]->addIncoming(inputs_v[i], entry); - } - auto * output_phi = context->builder.CreatePHI(output->getType(), 2); auto * counter_phi = context->builder.CreatePHI(counter->getType(), 2); - output_phi->addIncoming(output, entry); counter_phi->addIncoming(counter, entry); + for (auto & col : columns_v) + { + col.data = context->builder.CreatePHI(col.data_init->getType(), 2); + col.data->addIncoming(col.data_init, entry); + } - for (size_t i = 0; i < phi.size(); i++) - if (!by_name.emplace(arg_names[i], [&, i]() { return context->builder.CreateLoad(phi[i]); }).second) + for (size_t i = 0; i < arg_types.size(); i++) + if (!by_name.emplace(arg_names[i], [&, i]() { return context->builder.CreateLoad(columns_v[i].data); }).second) throw Exception("duplicate input column name " + arg_names[i], ErrorCodes::LOGICAL_ERROR); for (const auto & action : actions) { @@ -254,12 +260,15 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont if (!by_name.emplace(action.result_name, std::move(generator)).second) throw Exception("duplicate action result name " + action.result_name, ErrorCodes::LOGICAL_ERROR); } - context->builder.CreateStore(by_name.at(actions.back().result_name)(), output_phi); + context->builder.CreateStore(by_name.at(actions.back().result_name)(), columns_v[arg_types.size()].data); auto * cur_block = context->builder.GetInsertBlock(); - for (size_t i = 0; i < phi.size(); i++) - phi[i]->addIncoming(context->builder.CreateGEP(phi[i], deltas_v[i]), cur_block); - output_phi->addIncoming(context->builder.CreateConstGEP1_32(output_phi, 1), cur_block); + for (auto & col : columns_v) + { + auto * as_char = context->builder.CreatePointerCast(col.data, llvm::PointerType::get(char_type, 0)); + auto * as_type = context->builder.CreatePointerCast(context->builder.CreateGEP(as_char, col.stride), col.data->getType()); + col.data->addIncoming(as_type, cur_block); + } counter_phi->addIncoming(context->builder.CreateSub(counter_phi, llvm::ConstantInt::get(counter_phi->getType(), 1)), cur_block); auto * end = llvm::BasicBlock::Create(context->context, "end", func); @@ -314,18 +323,14 @@ IFunctionBase::Monotonicity LLVMFunction::getMonotonicityForRange(const IDataTyp namespace { - -struct LLVMTargetInitializer -{ - LLVMTargetInitializer() + struct LLVMTargetInitializer { - llvm::InitializeNativeTarget(); - llvm::InitializeNativeTargetAsmPrinter(); - } -}; - + LLVMTargetInitializer() + { + llvm::InitializeNativeTarget(); + llvm::InitializeNativeTargetAsmPrinter(); + } + } llvmInitializer; } -static LLVMTargetInitializer llvmInitializer; - #endif diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h index 04ebb67ac25..7aa7ee4098a 100644 --- a/dbms/src/Interpreters/ExpressionJIT.h +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -28,14 +28,11 @@ public: } }; -/// second array is of `char` because `LLVMPreparedFunction::executeImpl` can't use a `std::vector` for this -using LLVMCompiledFunction = void(const void ** inputs, const char * is_constant, void * output, size_t block_size); - class LLVMPreparedFunction : public PreparedFunctionImpl { std::shared_ptr parent; LLVMContext context; - LLVMCompiledFunction * function; + const void * function; public: LLVMPreparedFunction(LLVMContext context, std::shared_ptr parent); From 979c4d959f8310132f660441ac0e787342f118e4 Mon Sep 17 00:00:00 2001 From: pyos Date: Sat, 28 Apr 2018 00:30:38 +0300 Subject: [PATCH 032/231] Let jit-compilable functions deal with NULLs themselves. And provide a default implementation of compile() for nullable columns that actually works and is consistent with execute(). --- dbms/CMakeLists.txt | 1 + dbms/src/DataTypes/Native.h | 52 +++++ dbms/src/Functions/FunctionsLLVMTest.cpp | 4 +- dbms/src/Functions/IFunction.cpp | 91 +++++++- dbms/src/Functions/IFunction.h | 49 +++-- dbms/src/Interpreters/ExpressionJIT.cpp | 255 ++++++++++++----------- dbms/src/Interpreters/ExpressionJIT.h | 4 +- 7 files changed, 312 insertions(+), 144 deletions(-) create mode 100644 dbms/src/DataTypes/Native.h diff --git a/dbms/CMakeLists.txt b/dbms/CMakeLists.txt index 2cd85d63700..e3bf825226b 100644 --- a/dbms/CMakeLists.txt +++ b/dbms/CMakeLists.txt @@ -105,6 +105,7 @@ if (USE_EMBEDDED_COMPILER) target_include_directories (dbms BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) # LLVM 5.0 has a bunch of unused parameters in its header files. # TODO: global-disable no-unused-parameter + set_source_files_properties(src/Functions/IFunction.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter") set_source_files_properties(src/Interpreters/ExpressionJIT.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter -Wno-non-virtual-dtor") endif () diff --git a/dbms/src/DataTypes/Native.h b/dbms/src/DataTypes/Native.h new file mode 100644 index 00000000000..411ba6bb1da --- /dev/null +++ b/dbms/src/DataTypes/Native.h @@ -0,0 +1,52 @@ +#pragma once + +#include +#include +#include + +namespace llvm +{ + class IRBuilderBase; + class Type; +} + +#if USE_EMBEDDED_COMPILER +#include +#endif + +namespace DB +{ + +namespace ErrorCodes +{ + extern const int NOT_IMPLEMENTED; +} + +static llvm::Type * toNativeType([[maybe_unused]] llvm::IRBuilderBase & builder, [[maybe_unused]] const DataTypePtr & type) +{ +#if USE_EMBEDDED_COMPILER + if (auto * nullable = typeid_cast(type.get())) + { + auto * wrapped = toNativeType(builder, nullable->getNestedType()); + return wrapped ? llvm::PointerType::get(wrapped, 0) : nullptr; + } + /// LLVM doesn't have unsigned types, it has unsigned instructions. + if (typeid_cast(type.get()) || typeid_cast(type.get())) + return builder.getInt8Ty(); + if (typeid_cast(type.get()) || typeid_cast(type.get())) + return builder.getInt16Ty(); + if (typeid_cast(type.get()) || typeid_cast(type.get())) + return builder.getInt32Ty(); + if (typeid_cast(type.get()) || typeid_cast(type.get())) + return builder.getInt64Ty(); + if (typeid_cast(type.get())) + return builder.getFloatTy(); + if (typeid_cast(type.get())) + return builder.getDoubleTy(); + return nullptr; +#else + throw Exception("JIT-compilation is disabled", ErrorCodes::NOT_IMPLEMENTED); +#endif +} + +} diff --git a/dbms/src/Functions/FunctionsLLVMTest.cpp b/dbms/src/Functions/FunctionsLLVMTest.cpp index 6342daa76c8..8619c5b0201 100644 --- a/dbms/src/Functions/FunctionsLLVMTest.cpp +++ b/dbms/src/Functions/FunctionsLLVMTest.cpp @@ -25,12 +25,12 @@ public: static constexpr auto name = "something"; #if USE_EMBEDDED_COMPILER - bool isCompilable(const DataTypes & types) const override + bool isCompilableImpl(const DataTypes & types) const override { return types.size() == 2 && types[0]->equals(*types[1]); } - llvm::Value * compile(llvm::IRBuilderBase & builder, const DataTypes & types, const ValuePlaceholders & values) const override + llvm::Value * compileImpl(llvm::IRBuilderBase & builder, const DataTypes & types, ValuePlaceholders values) const override { if (types[0]->equals(DataTypeFloat32{}) || types[0]->equals(DataTypeFloat64{})) return static_cast&>(builder).CreateFAdd(values[0](), values[1]()); diff --git a/dbms/src/Functions/IFunction.cpp b/dbms/src/Functions/IFunction.cpp index 12e8dfabbd8..ca8df11719c 100644 --- a/dbms/src/Functions/IFunction.cpp +++ b/dbms/src/Functions/IFunction.cpp @@ -1,14 +1,20 @@ -#include -#include -#include -#include -#include -#include -#include +#include #include +#include +#include +#include +#include +#include +#include +#include +#include #include #include +#if USE_EMBEDDED_COMPILER +#include +#endif + namespace DB { @@ -254,4 +260,75 @@ DataTypePtr FunctionBuilderImpl::getReturnType(const ColumnsWithTypeAndName & ar return getReturnTypeImpl(arguments); } + +static bool anyNullable(const DataTypes & types) +{ + for (const auto & type : types) + if (typeid_cast(type.get())) + return true; + return false; +} + +bool IFunction::isCompilable(const DataTypes & arguments) const +{ + if (useDefaultImplementationForNulls() && anyNullable(arguments)) + { + DataTypes filtered; + for (const auto & type : arguments) + filtered.emplace_back(removeNullable(type)); + return isCompilableImpl(filtered); + } + return isCompilableImpl(arguments); +} + +std::vector IFunction::compilePrologue(llvm::IRBuilderBase & builder, const DataTypes & arguments) const +{ + auto result = compilePrologueImpl(builder, arguments); +#if USE_EMBEDDED_COMPILER + if (useDefaultImplementationForNulls() && anyNullable(arguments)) + result.push_back(static_cast &>(builder).CreateAlloca(toNativeType(builder, getReturnTypeImpl(arguments)))); +#endif + return result; +} + +llvm::Value * IFunction::compile(llvm::IRBuilderBase & builder, const DataTypes & arguments, ValuePlaceholders values) const +{ +#if USE_EMBEDDED_COMPILER + if (useDefaultImplementationForNulls() && anyNullable(arguments)) + { + /// FIXME: when only one column is nullable, this is actually slower than the non-jitted version + /// because this involves copying the null map while `wrapInNullable` reuses it. + auto & b = static_cast &>(builder); + auto * fail = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); + auto * join = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); + auto * space = values.back()(); + values.pop_back(); + for (size_t i = 0; i < arguments.size(); i++) + { + if (!arguments[i]->isNullable()) + continue; + values[i] = [&, previous = std::move(values[i])]() + { + auto * value = previous(); + auto * ok = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); + b.CreateCondBr(b.CreateIsNull(value), fail, ok); + b.SetInsertPoint(ok); + return b.CreateLoad(value); + }; + } + b.CreateStore(compileImpl(builder, arguments, std::move(values)), space); + b.CreateBr(join); + auto * result_block = b.GetInsertBlock(); + b.SetInsertPoint(fail); /// an empty joining block to avoid keeping track of where we could jump from + b.CreateBr(join); + b.SetInsertPoint(join); + auto * phi = b.CreatePHI(space->getType(), 2); + phi->addIncoming(space, result_block); + phi->addIncoming(llvm::ConstantPointerNull::get(static_cast(space->getType())), fail); + return phi; + } +#endif + return compileImpl(builder, arguments, std::move(values)); +} + } diff --git a/dbms/src/Functions/IFunction.h b/dbms/src/Functions/IFunction.h index a07f0a5c99e..43d3ea060e4 100644 --- a/dbms/src/Functions/IFunction.h +++ b/dbms/src/Functions/IFunction.h @@ -102,17 +102,25 @@ public: virtual bool isCompilable() const { return false; } - /** Produce LLVM IR code that operates on *scalar* values. JIT-compilation is only supported for native - * data types, i.e. numbers. This method will never be called if there is a non-number argument or - * a non-number result type. Also, for any compilable function default behavior on NULL values is assumed, - * i.e. the result is NULL if and only if any argument is NULL. + /// Produce LLVM IR code that runs before the loop over the input rows. Mostly useful for allocating stack variables. + virtual std::vector compilePrologue(llvm::IRBuilderBase &) const + { + return {}; + } + + /** Produce LLVM IR code that operates on scalar values. + * + * The first `getArgumentTypes().size()` values describe the current row of each column. Supported value types: + * - numbers, represented as native numbers; + * - nullable numbers, as pointers to native numbers or a null pointer. + * The rest are values returned by `compilePrologue`. * * NOTE: the builder is actually guaranteed to be exactly `llvm::IRBuilder<>`, so you may safely * downcast it to that type. This method is specified with `IRBuilderBase` because forward-declaring * templates with default arguments is impossible and including LLVM in such a generic header * as this one is a major pain. */ - virtual llvm::Value * compile(llvm::IRBuilderBase & /*builder*/, const ValuePlaceholders & /*values*/) const + virtual llvm::Value * compile(llvm::IRBuilderBase & /*builder*/, ValuePlaceholders /*values*/) const { throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED); } @@ -286,11 +294,8 @@ public: using PreparedFunctionImpl::execute; using FunctionBuilderImpl::getReturnTypeImpl; using FunctionBuilderImpl::getLambdaArgumentTypesImpl; - using FunctionBuilderImpl::getReturnType; - virtual bool isCompilable(const DataTypes & /*types*/) const { return false; } - bool isCompilable() const final { throw Exception("isCompilable without explicit types is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); @@ -301,12 +306,12 @@ public: throw Exception("prepare is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); } - virtual llvm::Value * compile(llvm::IRBuilderBase & /*builder*/, const DataTypes & /*types*/, const ValuePlaceholders & /*values*/) const + std::vector compilePrologue(llvm::IRBuilderBase &) const final { - throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED); + throw Exception("compilePrologue without explicit types is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); } - llvm::Value * compile(llvm::IRBuilderBase & /*builder*/, const ValuePlaceholders & /*values*/) const final + llvm::Value * compile(llvm::IRBuilderBase & /*builder*/, ValuePlaceholders /*values*/) const final { throw Exception("compile without explicit types is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); } @@ -321,7 +326,25 @@ public: throw Exception("getReturnType is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); } + bool isCompilable(const DataTypes & arguments) const; + + std::vector compilePrologue(llvm::IRBuilderBase &, const DataTypes & arguments) const; + + llvm::Value * compile(llvm::IRBuilderBase &, const DataTypes & arguments, ValuePlaceholders values) const; + protected: + virtual bool isCompilableImpl(const DataTypes &) const { return false; } + + virtual std::vector compilePrologueImpl(llvm::IRBuilderBase &, const DataTypes &) const + { + return {}; + } + + virtual llvm::Value * compileImpl(llvm::IRBuilderBase &, const DataTypes &, ValuePlaceholders) const + { + throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED); + } + FunctionBasePtr buildImpl(const ColumnsWithTypeAndName & /*arguments*/, const DataTypePtr & /*return_type*/) const final { throw Exception("buildImpl is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); @@ -363,7 +386,9 @@ public: bool isCompilable() const override { return function->isCompilable(arguments); } - llvm::Value * compile(llvm::IRBuilderBase & builder, const ValuePlaceholders & values) const override { return function->compile(builder, arguments, values); } + std::vector compilePrologue(llvm::IRBuilderBase & builder) const override { return function->compilePrologue(builder, arguments); } + + llvm::Value * compile(llvm::IRBuilderBase & builder, ValuePlaceholders values) const override { return function->compile(builder, arguments, std::move(values)); } PreparedFunctionPtr prepare(const Block & /*sample_block*/) const override { return std::make_shared(function); } diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 29d03db4f0d..ef46fb67f94 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -3,10 +3,12 @@ #if USE_EMBEDDED_COMPILER #include +#include #include #include #include #include +#include #include #include @@ -17,7 +19,6 @@ #include #include #include -#include #include #include #include @@ -25,12 +26,10 @@ #include #include #include -#include -#include #include +#include #include -#include namespace DB { @@ -40,12 +39,6 @@ namespace ErrorCodes extern const int LOGICAL_ERROR; } -template -static bool typeIsA(const DataTypePtr & type) -{ - return typeid_cast(removeNullable(type).get());; -} - struct LLVMContext::Data { llvm::LLVMContext context; @@ -67,33 +60,6 @@ struct LLVMContext::Data module->setDataLayout(layout); module->setTargetTriple(machine->getTargetTriple().getTriple()); } - - llvm::Type * toNativeType(const DataTypePtr & type) - { - /// LLVM doesn't have unsigned types, it has unsigned instructions. - if (typeIsA(type) || typeIsA(type)) - return builder.getInt8Ty(); - if (typeIsA(type) || typeIsA(type)) - return builder.getInt16Ty(); - if (typeIsA(type) || typeIsA(type)) - return builder.getInt32Ty(); - if (typeIsA(type) || typeIsA(type)) - return builder.getInt64Ty(); - if (typeIsA(type)) - return builder.getFloatTy(); - if (typeIsA(type)) - return builder.getDoubleTy(); - return nullptr; - } - - const void * lookup(const std::string& name) - { - std::string mangledName; - llvm::raw_string_ostream mangledNameStream(mangledName); - llvm::Mangler::getNameWithPrefix(mangledNameStream, name, layout); - /// why is `findSymbol` not const? we may never know. - return reinterpret_cast(compileLayer.findSymbol(mangledNameStream.str(), false).getAddress().get()); - } }; LLVMContext::LLVMContext() @@ -104,7 +70,6 @@ void LLVMContext::finalize() { if (!shared->module->size()) return; - shared->module->print(llvm::errs(), nullptr, false, true); llvm::PassManagerBuilder builder; llvm::legacy::FunctionPassManager fpm(shared->module.get()); builder.OptLevel = 2; @@ -112,46 +77,67 @@ void LLVMContext::finalize() for (auto & function : *shared->module) fpm.run(function); llvm::cantFail(shared->compileLayer.addModule(shared->module, std::make_shared())); - shared->module->print(llvm::errs(), nullptr, false, true); } bool LLVMContext::isCompilable(const IFunctionBase& function) const { - if (!function.isCompilable() || !shared->toNativeType(function.getReturnType())) + if (!function.isCompilable() || !toNativeType(shared->builder, function.getReturnType())) return false; for (const auto & type : function.getArgumentTypes()) - if (!shared->toNativeType(type)) + if (!toNativeType(shared->builder, type)) return false; return true; } LLVMPreparedFunction::LLVMPreparedFunction(LLVMContext context, std::shared_ptr parent) - : parent(parent), context(context), function(context->lookup(parent->getName())) -{} + : parent(parent), context(context) +{ + std::string mangledName; + llvm::raw_string_ostream mangledNameStream(mangledName); + llvm::Mangler::getNameWithPrefix(mangledNameStream, parent->getName(), context->layout); + function = reinterpret_cast(context->compileLayer.findSymbol(mangledNameStream.str(), false).getAddress().get()); +} namespace { struct ColumnData { - const char * data; + const char * data = nullptr; + const char * null = nullptr; size_t stride; }; + + struct ColumnDataPlaceholders + { + llvm::PHINode * data; + llvm::PHINode * null; + llvm::Value * data_init; + llvm::Value * null_init; + llvm::Value * stride; + llvm::Value * is_const; + }; } static ColumnData getColumnData(const IColumn * column) { - if (!column->isFixedAndContiguous()) - throw Exception("column type " + column->getName() + " is not a contiguous array; its data type " - "should've had no native equivalent in LLVMContext::Data::toNativeType", ErrorCodes::LOGICAL_ERROR); - /// TODO: handle ColumnNullable - return {column->getRawData().data, !column->isColumnConst() ? column->sizeOfValueIfFixed() : 0}; + ColumnData result; + const bool is_const = column->isColumnConst(); + if (is_const) + column = &reinterpret_cast(column)->getDataColumn(); + if (auto * nullable = typeid_cast(column)) + { + result.null = nullable->getNullMapColumn().getRawData().data; + column = &nullable->getNestedColumn(); + } + result.data = column->getRawData().data; + result.stride = is_const ? 0 : column->sizeOfValueIfFixed(); + return result; } -void LLVMPreparedFunction::executeImpl(Block & block, const ColumnNumbers & arguments, size_t result) +void LLVMPreparedFunction::execute(Block & block, const ColumnNumbers & arguments, size_t result) { size_t block_size = block.rows(); - /// assuming that the function has default behavior on NULL, the column will be wrapped by `PreparedFunctionImpl::execute`. - auto col_res = removeNullable(parent->getReturnType())->createColumn()->cloneResized(block_size); + auto col_res = parent->getReturnType()->createColumn()->cloneResized(block_size); if (block_size) { std::vector columns(arguments.size() + 1); @@ -171,22 +157,34 @@ void LLVMPreparedFunction::executeImpl(Block & block, const ColumnNumbers & argu LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext context, const Block & sample_block) : actions(std::move(actions_)), context(context) { + auto & b = context->builder; + auto * size_type = b.getIntNTy(sizeof(size_t) * 8); + auto * data_type = llvm::StructType::get(b.getInt8PtrTy(), b.getInt8PtrTy(), size_type); + auto * func_type = llvm::FunctionType::get(b.getVoidTy(), { size_type, llvm::PointerType::get(data_type, 0) }, /*isVarArg=*/false); + auto * func = llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, actions.back().result_name, context->module.get()); + auto args = func->args().begin(); + llvm::Value * counter = &*args++; + llvm::Value * columns = &*args++; + + auto * entry = llvm::BasicBlock::Create(context->context, "entry", func); + b.SetInsertPoint(entry); + std::unordered_map> by_name; for (const auto & c : sample_block) { - auto generator = [&]() -> llvm::Value * - { - auto * type = context->toNativeType(c.type); - if (typeIsA(c.type)) - return llvm::ConstantFP::get(type, typeid_cast *>(c.column.get())->getElement(0)); - if (typeIsA(c.type)) - return llvm::ConstantFP::get(type, typeid_cast *>(c.column.get())->getElement(0)); - if (type && type->isIntegerTy()) - return llvm::ConstantInt::get(type, c.column->getUInt(0)); - return nullptr; - }; - if (c.column && generator() && !by_name.emplace(c.name, std::move(generator)).second) - throw Exception("duplicate constant column " + c.name, ErrorCodes::LOGICAL_ERROR); + auto * type = toNativeType(b, c.type); + if (!type || !c.column) + continue; + llvm::Value * value = nullptr; + if (type->isFloatTy()) + value = llvm::ConstantFP::get(type, typeid_cast *>(c.column.get())->getElement(0)); + else if (type->isDoubleTy()) + value = llvm::ConstantFP::get(type, typeid_cast *>(c.column.get())->getElement(0)); + else if (type->isIntegerTy()) + value = llvm::ConstantInt::get(type, c.column->getUInt(0)); + /// TODO: handle nullable (create a pointer) + if (value) + by_name[c.name] = [=]() { return value; }; } std::unordered_set seen; @@ -196,85 +194,100 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont const auto & types = action.function->getArgumentTypes(); for (size_t i = 0; i < names.size(); i++) { - if (seen.emplace(names[i]).second && by_name.find(names[i]) == by_name.end()) - { - arg_names.push_back(names[i]); - arg_types.push_back(types[i]); - } + if (!seen.emplace(names[i]).second || by_name.find(names[i]) != by_name.end()) + continue; + arg_names.push_back(names[i]); + arg_types.push_back(types[i]); } seen.insert(action.result_name); } - auto * char_type = context->builder.getInt8Ty(); - auto * size_type = context->builder.getIntNTy(sizeof(size_t) * 8); - auto * data_type = llvm::StructType::get(llvm::PointerType::get(char_type, 0), size_type); - auto * func_type = llvm::FunctionType::get(context->builder.getVoidTy(), { size_type, llvm::PointerType::get(data_type, 0) }, /*isVarArg=*/false); - auto * func = llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, actions.back().result_name, context->module.get()); - auto args = func->args().begin(); - llvm::Value * counter = &*args++; - llvm::Value * columns = &*args++; - - auto * entry = llvm::BasicBlock::Create(context->context, "entry", func); - context->builder.SetInsertPoint(entry); - - struct CastedColumnData - { - llvm::PHINode * data; - llvm::Value * data_init; - llvm::Value * stride; - }; - std::vector columns_v(arg_types.size() + 1); + std::vector columns_v(arg_types.size() + 1); for (size_t i = 0; i <= arg_types.size(); i++) { - auto * type = llvm::PointerType::getUnqual(context->toNativeType(i == arg_types.size() ? getReturnType() : arg_types[i])); - auto * data = context->builder.CreateConstInBoundsGEP2_32(data_type, columns, i, 0); - auto * stride = context->builder.CreateConstInBoundsGEP2_32(data_type, columns, i, 1); - columns_v[i] = { nullptr, context->builder.CreatePointerCast(context->builder.CreateLoad(data), type), context->builder.CreateLoad(stride) }; + auto & column_type = (i == arg_types.size()) ? getReturnType() : arg_types[i]; + auto * type = llvm::PointerType::get(toNativeType(b, removeNullable(column_type)), 0); + columns_v[i].data_init = b.CreatePointerCast(b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 0)), type); + columns_v[i].stride = b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 2)); + if (column_type->isNullable()) + { + columns_v[i].null_init = b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 1)); + columns_v[i].is_const = b.CreateICmpEQ(columns_v[i].stride, b.getIntN(sizeof(size_t) * 8, 0)); + } + } + + for (size_t i = 0; i < arg_types.size(); i++) + { + by_name[arg_names[i]] = [&, &col = columns_v[i]]() -> llvm::Value * + { + if (!col.null) + return b.CreateLoad(col.data); + auto * is_valid = b.CreateICmpNE(b.CreateLoad(col.null), b.getInt8(1)); + auto * null_ptr = llvm::ConstantPointerNull::get(reinterpret_cast(col.data->getType())); + return b.CreateSelect(is_valid, col.data, null_ptr); + }; + } + for (const auto & action : actions) + { + ValuePlaceholders input; + for (const auto & name : action.argument_names) + input.push_back(by_name.at(name)); + /// TODO: pass compile-time constant arguments to `compilePrologue`? + auto extra = action.function->compilePrologue(b); + for (auto * value : extra) + input.emplace_back([=]() { return value; }); + by_name[action.result_name] = [&, input = std::move(input)]() { return action.function->compile(b, input); }; } /// assume nonzero initial value in `counter` auto * loop = llvm::BasicBlock::Create(context->context, "loop", func); - context->builder.CreateBr(loop); - context->builder.SetInsertPoint(loop); - auto * counter_phi = context->builder.CreatePHI(counter->getType(), 2); + b.CreateBr(loop); + b.SetInsertPoint(loop); + auto * counter_phi = b.CreatePHI(counter->getType(), 2); counter_phi->addIncoming(counter, entry); for (auto & col : columns_v) { - col.data = context->builder.CreatePHI(col.data_init->getType(), 2); + col.data = b.CreatePHI(col.data_init->getType(), 2); col.data->addIncoming(col.data_init, entry); - } - - for (size_t i = 0; i < arg_types.size(); i++) - if (!by_name.emplace(arg_names[i], [&, i]() { return context->builder.CreateLoad(columns_v[i].data); }).second) - throw Exception("duplicate input column name " + arg_names[i], ErrorCodes::LOGICAL_ERROR); - for (const auto & action : actions) - { - ValuePlaceholders action_input; - action_input.reserve(action.argument_names.size()); - for (const auto & name : action.argument_names) - action_input.push_back(by_name.at(name)); - auto generator = [&action, &context, action_input{std::move(action_input)}]() + if (col.null_init) { - return action.function->compile(context->builder, action_input); - }; - if (!by_name.emplace(action.result_name, std::move(generator)).second) - throw Exception("duplicate action result name " + action.result_name, ErrorCodes::LOGICAL_ERROR); + col.null = b.CreatePHI(col.null_init->getType(), 2); + col.null->addIncoming(col.null_init, entry); + } } - context->builder.CreateStore(by_name.at(actions.back().result_name)(), columns_v[arg_types.size()].data); - auto * cur_block = context->builder.GetInsertBlock(); + auto * result = by_name.at(actions.back().result_name)(); + if (columns_v[arg_types.size()].null) + { + auto * read = llvm::BasicBlock::Create(context->context, "not_null", func); + auto * join = llvm::BasicBlock::Create(context->context, "join", func); + b.CreateCondBr(b.CreateIsNull(result), join, read); + b.SetInsertPoint(read); + b.CreateStore(b.getInt8(0), columns_v[arg_types.size()].null); /// column initialized to all-NULL + b.CreateStore(b.CreateLoad(result), columns_v[arg_types.size()].data); + b.CreateBr(join); + b.SetInsertPoint(join); + } + else + { + b.CreateStore(result, columns_v[arg_types.size()].data); + } + + auto * cur_block = b.GetInsertBlock(); for (auto & col : columns_v) { - auto * as_char = context->builder.CreatePointerCast(col.data, llvm::PointerType::get(char_type, 0)); - auto * as_type = context->builder.CreatePointerCast(context->builder.CreateGEP(as_char, col.stride), col.data->getType()); + auto * as_char = b.CreatePointerCast(col.data, b.getInt8PtrTy()); + auto * as_type = b.CreatePointerCast(b.CreateGEP(as_char, col.stride), col.data->getType()); col.data->addIncoming(as_type, cur_block); + if (col.null) + col.null->addIncoming(b.CreateSelect(col.is_const, col.null, b.CreateConstGEP1_32(col.null, 1)), cur_block); } - counter_phi->addIncoming(context->builder.CreateSub(counter_phi, llvm::ConstantInt::get(counter_phi->getType(), 1)), cur_block); + counter_phi->addIncoming(b.CreateSub(counter_phi, llvm::ConstantInt::get(size_type, 1)), cur_block); auto * end = llvm::BasicBlock::Create(context->context, "end", func); - context->builder.CreateCondBr(context->builder.CreateICmpNE(counter_phi, llvm::ConstantInt::get(counter_phi->getType(), 1)), loop, end); - context->builder.SetInsertPoint(end); - context->builder.CreateRetVoid(); + b.CreateCondBr(b.CreateICmpNE(counter_phi, llvm::ConstantInt::get(size_type, 1)), loop, end); + b.SetInsertPoint(end); + b.CreateRetVoid(); } static Field evaluateFunction(IFunctionBase & function, const IDataType & type, const Field & arg) diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h index 7aa7ee4098a..75d16d9facf 100644 --- a/dbms/src/Interpreters/ExpressionJIT.h +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -28,7 +28,7 @@ public: } }; -class LLVMPreparedFunction : public PreparedFunctionImpl +class LLVMPreparedFunction : public IPreparedFunction { std::shared_ptr parent; LLVMContext context; @@ -39,7 +39,7 @@ public: String getName() const override { return parent->getName(); } - void executeImpl(Block & block, const ColumnNumbers & arguments, size_t result) override; + void execute(Block & block, const ColumnNumbers & arguments, size_t result) override; }; class LLVMFunction : public std::enable_shared_from_this, public IFunctionBase From 5c75342d54a5ffd2b89e9140180c8eb1cdf5d27a Mon Sep 17 00:00:00 2001 From: pyos Date: Sat, 28 Apr 2018 01:03:52 +0300 Subject: [PATCH 033/231] Check nativity of all types *before* calling isCompilable --- dbms/src/Interpreters/ExpressionJIT.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index ef46fb67f94..0d856393a50 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -81,12 +81,12 @@ void LLVMContext::finalize() bool LLVMContext::isCompilable(const IFunctionBase& function) const { - if (!function.isCompilable() || !toNativeType(shared->builder, function.getReturnType())) + if (!toNativeType(shared->builder, function.getReturnType())) return false; for (const auto & type : function.getArgumentTypes()) if (!toNativeType(shared->builder, type)) return false; - return true; + return function.isCompilable(); } LLVMPreparedFunction::LLVMPreparedFunction(LLVMContext context, std::shared_ptr parent) From 7558684e33767328e9f846cdd3d8d8da8525cd2b Mon Sep 17 00:00:00 2001 From: BayoNet Date: Sat, 28 Apr 2018 10:58:16 +0300 Subject: [PATCH 034/231] Multiple formatting and links fixes. --- docs/en/development/style.md | 2 +- docs/en/dicts/external_dicts_dict_layout.md | 2 +- docs/en/system_tables/system.functions.md | 7 ++----- docs/mkdocs_en.yml | 2 +- docs/mkdocs_ru.yml | 2 +- docs/ru/dicts/external_dicts.md | 16 ++++++++------ docs/ru/dicts/external_dicts_dict_layout.md | 2 +- docs/ru/dicts/index.md | 11 +++++++--- docs/ru/dicts/internal_dicts.md | 23 +++++++++++---------- docs/ru/functions/ym_dict_functions.md | 2 ++ docs/ru/system_tables/system.functions.md | 6 ++---- 11 files changed, 41 insertions(+), 34 deletions(-) diff --git a/docs/en/development/style.md b/docs/en/development/style.md index 0028feddc0e..546857a2351 100644 --- a/docs/en/development/style.md +++ b/docs/en/development/style.md @@ -14,7 +14,7 @@ **1.** Most of the formatting will be done automatically by `clang-format`. -**2.** Offsets are 4 spaces. Configure your development environment so that a tab adds four spaces. +**2.** Indents are 4 spaces. Configure your development environment so that a tab adds four spaces. **3.** A left curly bracket must be separated on a new line. (And the right one, as well.) diff --git a/docs/en/dicts/external_dicts_dict_layout.md b/docs/en/dicts/external_dicts_dict_layout.md index 4f2a623d627..227eaab6b19 100644 --- a/docs/en/dicts/external_dicts_dict_layout.md +++ b/docs/en/dicts/external_dicts_dict_layout.md @@ -193,7 +193,7 @@ The dictionary is stored in a cache that has a fixed number of cells. These cell When searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using ` SELECT attrs... FROM db.table WHERE id IN (k1, k2, ...)`. The received data is then written to the cache. -For cache dictionaries, the expiration [lifetime](dicts-external_dicts_dict_lifetime.md#dicts-external_dicts_dict_lifetime) of data in the cache can be set. If more time than `lifetime` has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used. +For cache dictionaries, the expiration [lifetime](external_dicts_dict_lifetime.md#dicts-external_dicts_dict_lifetime) of data in the cache can be set. If more time than `lifetime` has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used. This is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the `system.dictionaries` table. diff --git a/docs/en/system_tables/system.functions.md b/docs/en/system_tables/system.functions.md index ac550acc14b..a501dc54741 100644 --- a/docs/en/system_tables/system.functions.md +++ b/docs/en/system_tables/system.functions.md @@ -4,8 +4,5 @@ Contains information about normal and aggregate functions. Columns: -```text -name String – Function name. -is_aggregate UInt8 – Whether it is an aggregate function. -``` - +- `name` (`String`) – Function name. +- `is_aggregate` (`UInt8`) – Whether it is an aggregate function. diff --git a/docs/mkdocs_en.yml b/docs/mkdocs_en.yml index 012d498f3e2..08209f90550 100644 --- a/docs/mkdocs_en.yml +++ b/docs/mkdocs_en.yml @@ -213,7 +213,7 @@ pages: - 'Dictionaries': - 'Introduction': 'dicts/index.md' - 'External dictionaries': - - 'External dictionaries': 'dicts/external_dicts.md' + - 'General desription': 'dicts/external_dicts.md' - 'Configuring an external dictionary': 'dicts/external_dicts_dict.md' - 'Storing dictionaries in memory': 'dicts/external_dicts_dict_layout.md' - 'Dictionary updates': 'dicts/external_dicts_dict_lifetime.md' diff --git a/docs/mkdocs_ru.yml b/docs/mkdocs_ru.yml index 931925a2fc1..2e8eae30640 100644 --- a/docs/mkdocs_ru.yml +++ b/docs/mkdocs_ru.yml @@ -213,7 +213,7 @@ pages: - 'Словари': - 'Введение': 'dicts/index.md' - 'Внешние словари': - - 'Внешние словари': 'dicts/external_dicts.md' + - 'Общее описание': 'dicts/external_dicts.md' - 'Настройка внешнего словаря': 'dicts/external_dicts_dict.md' - 'Хранение словарей в памяти': 'dicts/external_dicts_dict_layout.md' - 'Обновление словарей': 'dicts/external_dicts_dict_lifetime.md' diff --git a/docs/ru/dicts/external_dicts.md b/docs/ru/dicts/external_dicts.md index c0b9f520b30..0b7b9566ff9 100644 --- a/docs/ru/dicts/external_dicts.md +++ b/docs/ru/dicts/external_dicts.md @@ -6,8 +6,8 @@ ClickHouse: -> - Полностью или частично хранит словари в оперативной памяти. -> - Периодически обновляет их и динамически подгружает отсутствующие значения. Т.е. словари можно подгружать динамически. +- Полностью или частично хранит словари в оперативной памяти. +- Периодически обновляет их и динамически подгружает отсутствующие значения. Т.е. словари можно подгружать динамически. Конфигурация внешних словарей находится в одном или нескольких файлах. Путь к конфигурации указывается в параметре [dictionaries_config](../operations/server_settings/settings.md#server_settings-dictionaries_config). @@ -37,10 +37,14 @@ ClickHouse: В одном файле можно [сконфигурировать](external_dicts_dict.md#dicts-external_dicts_dict) произвольное количество словарей. Формат файла сохраняется даже если словарь один (т.е. ` `). -Смотрите также "[Функции для работы с внешними словарями](../functions/ext_dict_functions.md#ext_dict_functions)" . +>Вы можете преобразовывать значения по небольшому словарю, описав его в запросе `SELECT` (см. функцию [transform](../functions/other_functions.md#other_functions-transform)). Эта функциональность не связана с внешними словарями. -
-Вы можете преобразовывать значения по небольшому словарю, описав его в запросе `SELECT` (см. функцию [transform](../functions/other_functions.md#other_functions-transform)). Эта функциональность не связана с внешними словарями. +Смотрите также: -
+- [Настройка внешнего словаря](external_dicts_dict.md#dicts-external_dicts_dict) +- [Хранение словарей в памяти](external_dicts_dict_layout.md#dicts-external_dicts_dict_layout) +- [Обновление словарей](external_dicts_dict_lifetime#dicts-external_dicts_dict_lifetime) +- [Источники внешних словарей](external_dicts_dict_sources.md#dicts-external_dicts_dict_sources) +- [Ключ и поля словаря](external_dicts_dict_structure.md#dicts-external_dicts_dict_structure) +- [Функции для работы с внешними словарями](../functions/ext_dict_functions.md#ext_dict_functions) diff --git a/docs/ru/dicts/external_dicts_dict_layout.md b/docs/ru/dicts/external_dicts_dict_layout.md index e9e50abf164..94108a1e818 100644 --- a/docs/ru/dicts/external_dicts_dict_layout.md +++ b/docs/ru/dicts/external_dicts_dict_layout.md @@ -191,7 +191,7 @@ При поиске в словаре сначала просматривается кэш. На каждый блок данных, все не найденные в кэше или устаревшие ключи запрашиваются у источника с помощью `SELECT attrs... FROM db.table WHERE id IN (k1, k2, ...)`. Затем, полученные данные записываются в кэш. -Для cache-словарей может быть задано время устаревания [lifetime](dicts-external_dicts_dict_lifetime.md#dicts-external_dicts_dict_lifetime) данных в кэше. Если от загрузки данных в ячейке прошло больше времени, чем `lifetime`, то значение не используется, и будет запрошено заново при следующей необходимости его использовать. +Для cache-словарей может быть задано время устаревания [lifetime](external_dicts_dict_lifetime.md#dicts-external_dicts_dict_lifetime) данных в кэше. Если от загрузки данных в ячейке прошло больше времени, чем `lifetime`, то значение не используется, и будет запрошено заново при следующей необходимости его использовать. Это наименее эффективный из всех способов размещения словарей. Скорость работы кэша очень сильно зависит от правильности настройки и сценария использования. Словарь типа cache показывает высокую производительность лишь при достаточно больших hit rate-ах (рекомендуется 99% и выше). Посмотреть средний hit rate можно в таблице `system.dictionaries`. diff --git a/docs/ru/dicts/index.md b/docs/ru/dicts/index.md index 6d673ccef96..f474a241db6 100644 --- a/docs/ru/dicts/index.md +++ b/docs/ru/dicts/index.md @@ -1,6 +1,11 @@ # Словари -`Словарь` - это отображение (ключ `->` атрибуты), которое можно использовать в запросе в виде функций. -Это можно рассматривать как более удобный и максимально эффективный вариант JOIN-а с таблицами-справочниками (dimension tables). +Словарь — это отображение (`ключ -> атрибуты`), которое удобно использовать для различного вида справочников. -Существуют встроенные и подключаемые (внешние) словари. +ClickHouse поддерживает специальные функции для работы со словарями, которые можно использовать в запросах. Проще и эффективнее использовать словари с помощью функций, чем `JOIN` с таблицами-справочниками. + + +ClickHouse поддерживает: + +- [Встроенные словари](internal_dicts.md#internal_dicts) со специфическим [набором функций](../functions/ym_dict_functions.md#ym_dict_functions). +- [Подключаемые (внешние) словари](external_dicts.md#dicts-external_dicts) с [набором функций](../functions/ext_dict_functions.md#ext_dict_functions). diff --git a/docs/ru/dicts/internal_dicts.md b/docs/ru/dicts/internal_dicts.md index 6b765c6f55f..a4b736567d8 100644 --- a/docs/ru/dicts/internal_dicts.md +++ b/docs/ru/dicts/internal_dicts.md @@ -1,3 +1,5 @@ + + # Встроенные словари ClickHouse содержит встроенную возможность работы с геобазой. @@ -15,32 +17,31 @@ ClickHouse содержит встроенную возможность рабо Для включения, раскомментируйте параметры `path_to_regions_hierarchy_file` и `path_to_regions_names_files` в конфигурационном файле сервера. Геобаза загружается из текстовых файлов. -Если вы работаете в Яндексе, то для их создания вы можете воспользоваться инструкцией: - +Если вы работаете в Яндексе, то для их создания вы можете воспользоваться [соответствующей инструкцией](https://github.yandex-team.ru/raw/Metrika/ClickHouse_private/master/doc/create_embedded_geobase_dictionaries.txt). -Положите файлы regions_hierarchy\*.txt в директорию path_to_regions_hierarchy_file. Этот конфигурационный параметр должен содержать путь к файлу regions_hierarchy.txt (иерархия регионов по умолчанию), а другие файлы (regions_hierarchy_ua.txt) должны находиться рядом в той же директории. +Положите файлы `regions_hierarchy*.txt` в директорию `path_to_regions_hierarchy_file`. Этот конфигурационный параметр должен содержать путь к файлу `regions_hierarchy.txt` (иерархия регионов по умолчанию), а другие файлы (`regions_hierarchy_ua.txt`) должны находиться рядом в той же директории. -Положите файлы `regions_names_*.txt` в директорию path_to_regions_names_files. +Положите файлы `regions_names_*.txt` в директорию `path_to_regions_names_files`. Также вы можете создать эти файлы самостоятельно. Формат файлов такой: `regions_hierarchy*.txt`: TabSeparated (без заголовка), столбцы: -- идентификатор региона (UInt32); -- идентификатор родительского региона (UInt32); -- тип региона (UInt8): 1 - континент, 3 - страна, 4 - федеральный округ, 5 - область, 6 - город; остальные типы не имеют значения; -- население (UInt32) - не обязательный столбец. +- идентификатор региона (`UInt32`); +- идентификатор родительского региона (`UInt32`); +- тип региона (`UInt8`): 1 - континент, 3 - страна, 4 - федеральный округ, 5 - область, 6 - город; остальные типы не имеют значения; +- население (`UInt32`) - не обязательный столбец. `regions_names_*.txt`: TabSeparated (без заголовка), столбцы: -- идентификатор региона (UInt32); -- имя региона (String) - не может содержать табы или переводы строк, даже экранированные. +- идентификатор региона (`UInt32`); +- имя региона (`String`) - не может содержать табы или переводы строк, даже экранированные. Для хранения в оперативке используется плоский массив. Поэтому, идентификаторы не должны быть больше миллиона. Словари могут обновляться без перезапуска сервера. Но набор доступных словарей не обновляется. Для обновления проверяется время модификации файлов; если файл изменился, то словарь будет обновлён. -Периодичность проверки настраивается конфигурационным параметром builtin_dictionaries_reload_interval. +Периодичность проверки настраивается конфигурационным параметром `builtin_dictionaries_reload_interval`. Обновление словарей (кроме загрузки при первом использовании) не блокирует запросы - во время обновления запросы используют старую версию словарей. Если при обновлении возникнет ошибка, то ошибка пишется в лог сервера, а запросы продолжат использовать старую версию словарей. Рекомендуется периодически обновлять словари с геобазой. При обновлении, генерируйте новые файлы, записывая их в отдельное место, а только когда всё готово - переименовывайте в файлы, которые использует сервер. diff --git a/docs/ru/functions/ym_dict_functions.md b/docs/ru/functions/ym_dict_functions.md index 4e0b6bd451d..1761a21a7dd 100644 --- a/docs/ru/functions/ym_dict_functions.md +++ b/docs/ru/functions/ym_dict_functions.md @@ -1,3 +1,5 @@ + + # Функции для работы со словарями Яндекс.Метрики Чтобы указанные ниже функции работали, в конфиге сервера должны быть указаны пути и адреса для получения всех словарей Яндекс.Метрики. Словари загружаются при первом вызове любой из этих функций. Если справочники не удаётся загрузить - будет выкинуто исключение. diff --git a/docs/ru/system_tables/system.functions.md b/docs/ru/system_tables/system.functions.md index 0f96a6fa167..f4ec19d1dbf 100644 --- a/docs/ru/system_tables/system.functions.md +++ b/docs/ru/system_tables/system.functions.md @@ -4,7 +4,5 @@ Столбцы: -```text -name String - имя функции -is_aggregate UInt8 - является ли функция агрегатной -``` +- `name` (`String`) – Имя функции. +- `is_aggregate` (`UInt8`) – Признак, является ли функция агрегатной. From ccc895d16200bdbe4566b449567e77b890047386 Mon Sep 17 00:00:00 2001 From: pyos Date: Sat, 28 Apr 2018 14:12:21 +0300 Subject: [PATCH 035/231] Represent nullable types as pairs instead of pointers. Turns out LLVM has insertvalue & extractvalue for struct in registers. This is faster than pointers because null checks are now subject to more optimizations. --- dbms/src/DataTypes/Native.h | 31 +++++---- dbms/src/Functions/IFunction.cpp | 93 +++++++++++++------------ dbms/src/Functions/IFunction.h | 5 +- dbms/src/Interpreters/ExpressionJIT.cpp | 33 ++++----- 4 files changed, 84 insertions(+), 78 deletions(-) diff --git a/dbms/src/DataTypes/Native.h b/dbms/src/DataTypes/Native.h index 411ba6bb1da..c8a342bd393 100644 --- a/dbms/src/DataTypes/Native.h +++ b/dbms/src/DataTypes/Native.h @@ -1,18 +1,13 @@ #pragma once #include + +#if USE_EMBEDDED_COMPILER + #include #include -namespace llvm -{ - class IRBuilderBase; - class Type; -} - -#if USE_EMBEDDED_COMPILER #include -#endif namespace DB { @@ -22,13 +17,12 @@ namespace ErrorCodes extern const int NOT_IMPLEMENTED; } -static llvm::Type * toNativeType([[maybe_unused]] llvm::IRBuilderBase & builder, [[maybe_unused]] const DataTypePtr & type) +static llvm::Type * toNativeType(llvm::IRBuilderBase & builder, const DataTypePtr & type) { -#if USE_EMBEDDED_COMPILER if (auto * nullable = typeid_cast(type.get())) { auto * wrapped = toNativeType(builder, nullable->getNestedType()); - return wrapped ? llvm::PointerType::get(wrapped, 0) : nullptr; + return wrapped ? llvm::StructType::get(wrapped, /* is null = */ builder.getInt1Ty()) : nullptr; } /// LLVM doesn't have unsigned types, it has unsigned instructions. if (typeid_cast(type.get()) || typeid_cast(type.get())) @@ -44,9 +38,18 @@ static llvm::Type * toNativeType([[maybe_unused]] llvm::IRBuilderBase & builder, if (typeid_cast(type.get())) return builder.getDoubleTy(); return nullptr; -#else - throw Exception("JIT-compilation is disabled", ErrorCodes::NOT_IMPLEMENTED); -#endif +} + +static llvm::Constant * getDefaultNativeValue(llvm::IRBuilder<> & builder, llvm::Type * type) +{ + if (type->isIntegerTy()) + return llvm::ConstantInt::get(type, 0); + if (type->isFloatTy() || type->isDoubleTy()) + return llvm::ConstantFP::get(type, 0.0); + auto * as_struct = static_cast(type); /// nullable + return llvm::ConstantStruct::get(as_struct, getDefaultNativeValue(builder, as_struct->getElementType(0)), builder.getTrue()); } } + +#endif diff --git a/dbms/src/Functions/IFunction.cpp b/dbms/src/Functions/IFunction.cpp index ca8df11719c..a28da9eb2e6 100644 --- a/dbms/src/Functions/IFunction.cpp +++ b/dbms/src/Functions/IFunction.cpp @@ -261,71 +261,74 @@ DataTypePtr FunctionBuilderImpl::getReturnType(const ColumnsWithTypeAndName & ar return getReturnTypeImpl(arguments); } -static bool anyNullable(const DataTypes & types) +static std::optional removeNullables(const DataTypes & types) { for (const auto & type : types) - if (typeid_cast(type.get())) - return true; - return false; + { + if (!typeid_cast(type.get())) + continue; + DataTypes filtered; + for (const auto & type : types) + filtered.emplace_back(removeNullable(type)); + return filtered; + } + return {}; } bool IFunction::isCompilable(const DataTypes & arguments) const { - if (useDefaultImplementationForNulls() && anyNullable(arguments)) - { - DataTypes filtered; - for (const auto & type : arguments) - filtered.emplace_back(removeNullable(type)); - return isCompilableImpl(filtered); - } + if (useDefaultImplementationForNulls()) + if (auto denulled = removeNullables(arguments)) + return isCompilableImpl(*denulled); return isCompilableImpl(arguments); } std::vector IFunction::compilePrologue(llvm::IRBuilderBase & builder, const DataTypes & arguments) const { - auto result = compilePrologueImpl(builder, arguments); -#if USE_EMBEDDED_COMPILER - if (useDefaultImplementationForNulls() && anyNullable(arguments)) - result.push_back(static_cast &>(builder).CreateAlloca(toNativeType(builder, getReturnTypeImpl(arguments)))); -#endif - return result; + if (useDefaultImplementationForNulls()) + if (auto denulled = removeNullables(arguments)) + return compilePrologueImpl(builder, *denulled); + return compilePrologueImpl(builder, arguments); } llvm::Value * IFunction::compile(llvm::IRBuilderBase & builder, const DataTypes & arguments, ValuePlaceholders values) const { #if USE_EMBEDDED_COMPILER - if (useDefaultImplementationForNulls() && anyNullable(arguments)) + if (useDefaultImplementationForNulls()) { - /// FIXME: when only one column is nullable, this is actually slower than the non-jitted version - /// because this involves copying the null map while `wrapInNullable` reuses it. - auto & b = static_cast &>(builder); - auto * fail = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); - auto * join = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); - auto * space = values.back()(); - values.pop_back(); - for (size_t i = 0; i < arguments.size(); i++) + if (auto denulled = removeNullables(arguments)) { - if (!arguments[i]->isNullable()) - continue; - values[i] = [&, previous = std::move(values[i])]() + /// FIXME: when only one column is nullable, this is actually slower than the non-jitted version + /// because this involves copying the null map while `wrapInNullable` reuses it. + auto & b = static_cast &>(builder); + auto * fail = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); + auto * join = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); + auto * init = getDefaultNativeValue(b, toNativeType(b, makeNullable(getReturnTypeImpl(*denulled)))); + for (size_t i = 0; i < arguments.size(); i++) { - auto * value = previous(); - auto * ok = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); - b.CreateCondBr(b.CreateIsNull(value), fail, ok); - b.SetInsertPoint(ok); - return b.CreateLoad(value); - }; + if (!arguments[i]->isNullable()) + continue; + values[i] = [&, previous = std::move(values[i])]() + { + auto * value = previous(); + auto * ok = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); + b.CreateCondBr(b.CreateExtractValue(value, {1}), fail, ok); + b.SetInsertPoint(ok); + return b.CreateExtractValue(value, {0}); + }; + } + auto * result = compileImpl(builder, *denulled, std::move(values)); + auto * result_nullable = b.CreateInsertValue(b.CreateInsertValue(init, result, {0}), b.getFalse(), {1}); + auto * result_block = b.GetInsertBlock(); + b.CreateBr(join); + b.SetInsertPoint(fail); /// an empty joining block to avoid keeping track of where we could jump from + b.CreateBr(join); + b.SetInsertPoint(join); + auto * phi = b.CreatePHI(result_nullable->getType(), 2); + phi->addIncoming(result_nullable, result_block); + phi->addIncoming(init, fail); + return phi; } - b.CreateStore(compileImpl(builder, arguments, std::move(values)), space); - b.CreateBr(join); - auto * result_block = b.GetInsertBlock(); - b.SetInsertPoint(fail); /// an empty joining block to avoid keeping track of where we could jump from - b.CreateBr(join); - b.SetInsertPoint(join); - auto * phi = b.CreatePHI(space->getType(), 2); - phi->addIncoming(space, result_block); - phi->addIncoming(llvm::ConstantPointerNull::get(static_cast(space->getType())), fail); - return phi; } #endif return compileImpl(builder, arguments, std::move(values)); diff --git a/dbms/src/Functions/IFunction.h b/dbms/src/Functions/IFunction.h index 43d3ea060e4..d5f7896c601 100644 --- a/dbms/src/Functions/IFunction.h +++ b/dbms/src/Functions/IFunction.h @@ -110,9 +110,8 @@ public: /** Produce LLVM IR code that operates on scalar values. * - * The first `getArgumentTypes().size()` values describe the current row of each column. Supported value types: - * - numbers, represented as native numbers; - * - nullable numbers, as pointers to native numbers or a null pointer. + * The first `getArgumentTypes().size()` values describe the current row of each column. (See + * `toNativeType` in DataTypes/Native.h for supported value types and how they map to LLVM types.) * The rest are values returned by `compilePrologue`. * * NOTE: the builder is actually guaranteed to be exactly `llvm::IRBuilder<>`, so you may safely diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 0d856393a50..f5712ff5976 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -72,7 +72,12 @@ void LLVMContext::finalize() return; llvm::PassManagerBuilder builder; llvm::legacy::FunctionPassManager fpm(shared->module.get()); - builder.OptLevel = 2; + builder.OptLevel = 3; + builder.SLPVectorize = true; + builder.LoopVectorize = true; + builder.RerollLoops = true; + builder.VerifyInput = true; + builder.VerifyOutput = true; builder.populateFunctionPassManager(fpm); for (auto & function : *shared->module) fpm.run(function); @@ -218,13 +223,14 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont for (size_t i = 0; i < arg_types.size(); i++) { - by_name[arg_names[i]] = [&, &col = columns_v[i]]() -> llvm::Value * + by_name[arg_names[i]] = [&, &col = columns_v[i], i]() -> llvm::Value * { + auto * value = b.CreateLoad(col.data); if (!col.null) - return b.CreateLoad(col.data); - auto * is_valid = b.CreateICmpNE(b.CreateLoad(col.null), b.getInt8(1)); - auto * null_ptr = llvm::ConstantPointerNull::get(reinterpret_cast(col.data->getType())); - return b.CreateSelect(is_valid, col.data, null_ptr); + return value; + auto * is_null = b.CreateICmpEQ(b.CreateLoad(col.null), b.getInt8(1)); + auto * nullable = getDefaultNativeValue(b, toNativeType(b, arg_types[i])); + return b.CreateInsertValue(b.CreateInsertValue(nullable, value, {0}), is_null, {1}); }; } for (const auto & action : actions) @@ -259,14 +265,9 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont auto * result = by_name.at(actions.back().result_name)(); if (columns_v[arg_types.size()].null) { - auto * read = llvm::BasicBlock::Create(context->context, "not_null", func); - auto * join = llvm::BasicBlock::Create(context->context, "join", func); - b.CreateCondBr(b.CreateIsNull(result), join, read); - b.SetInsertPoint(read); - b.CreateStore(b.getInt8(0), columns_v[arg_types.size()].null); /// column initialized to all-NULL - b.CreateStore(b.CreateLoad(result), columns_v[arg_types.size()].data); - b.CreateBr(join); - b.SetInsertPoint(join); + b.CreateStore(b.CreateExtractValue(result, {0}), columns_v[arg_types.size()].data); + /// XXX: should zero-extend it to 1 instead of sign-extending to -1? + b.CreateStore(b.CreateExtractValue(result, {1}), columns_v[arg_types.size()].null); } else { @@ -277,10 +278,10 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont for (auto & col : columns_v) { auto * as_char = b.CreatePointerCast(col.data, b.getInt8PtrTy()); - auto * as_type = b.CreatePointerCast(b.CreateGEP(as_char, col.stride), col.data->getType()); + auto * as_type = b.CreatePointerCast(b.CreateInBoundsGEP(as_char, col.stride), col.data->getType()); col.data->addIncoming(as_type, cur_block); if (col.null) - col.null->addIncoming(b.CreateSelect(col.is_const, col.null, b.CreateConstGEP1_32(col.null, 1)), cur_block); + col.null->addIncoming(b.CreateSelect(col.is_const, col.null, b.CreateConstInBoundsGEP1_32(b.getInt8Ty(), col.null, 1)), cur_block); } counter_phi->addIncoming(b.CreateSub(counter_phi, llvm::ConstantInt::get(size_type, 1)), cur_block); From 38c0442ee6bbf4054fa1a5d90cf75cd0d5c7097b Mon Sep 17 00:00:00 2001 From: BayoNet Date: Sat, 28 Apr 2018 14:45:37 +0300 Subject: [PATCH 036/231] Changes in accordance with comments from the developers. --- docs/en/functions/other_functions.md | 8 +++++- docs/en/functions/string_search_functions.md | 11 ++++---- .../example_datasets/wikistat.md | 4 ++- .../operations/settings/query_complexity.md | 5 ++-- .../operations/settings/settings_profiles.md | 16 +++++++----- docs/en/query_language/queries.md | 8 +++--- docs/en/table_engines/index.md | 5 ++-- docs/en/table_engines/merge.md | 7 ++--- docs/ru/functions/other_functions.md | 10 ++++--- docs/ru/functions/string_search_functions.md | 10 ++++--- .../operations/settings/settings_profiles.md | 14 +++++----- docs/ru/query_language/queries.md | 8 +++--- docs/ru/table_engines/index.md | 18 ++++++------- docs/ru/table_engines/merge.md | 26 ++++++++++--------- 14 files changed, 85 insertions(+), 65 deletions(-) diff --git a/docs/en/functions/other_functions.md b/docs/en/functions/other_functions.md index 781ac527e2b..a8d2a54fa6a 100644 --- a/docs/en/functions/other_functions.md +++ b/docs/en/functions/other_functions.md @@ -59,7 +59,13 @@ For elements in a nested data structure, the function checks for the existence o Allows building a unicode-art diagram. -`bar (x, min, max, width)` – Draws a band with a width proportional to (x - min) and equal to 'width' characters when x == max.`min, max` – Integer constants. The value must fit in Int64.`width` – Constant, positive number, may be a fraction. +`bar (x, min, max, width)` draws a band with a width proportional to `(x - min)` and equal to `width` characters when `x = max`. + +Parameters: + +- `x` – Value to display. +- `min, max` – Integer constants. The value must fit in Int64. +- `width` – Constant, positive number, may be a fraction. The band is drawn with accuracy to one eighth of a symbol. diff --git a/docs/en/functions/string_search_functions.md b/docs/en/functions/string_search_functions.md index ba3e53d4ee8..56644f00ba3 100644 --- a/docs/en/functions/string_search_functions.md +++ b/docs/en/functions/string_search_functions.md @@ -5,14 +5,16 @@ The search substring or regular expression must be a constant in all these funct ## position(haystack, needle) -Search for the 'needle' substring in the 'haystack' string. +Search for the `needle` substring in the `haystack` string. Returns the position (in bytes) of the found substring, starting from 1, or returns 0 if the substring was not found. -It has also chimpanzees. + +For case-insensitive search use `positionCaseInsensitive` function. ## positionUTF8(haystack, needle) -The same as 'position', but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception). -There is also a positionCaseInsensitiveUTF8 function. +The same as `position`, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception). + +For case-insensitive search use `positionCaseInsensitiveUTF8` function. ## match(haystack, pattern) @@ -49,4 +51,3 @@ For other regular expressions, the code is the same as for the 'match' function. ## notLike(haystack, pattern), haystack NOT LIKE pattern operator The same thing as 'like', but negative. - diff --git a/docs/en/getting_started/example_datasets/wikistat.md b/docs/en/getting_started/example_datasets/wikistat.md index 81ab8c4545d..9928328692a 100644 --- a/docs/en/getting_started/example_datasets/wikistat.md +++ b/docs/en/getting_started/example_datasets/wikistat.md @@ -20,5 +20,7 @@ CREATE TABLE wikistat Loading data: ```bash -for i in {2007..2016}; do for j in {01..12}; do echo $i-$j >&2; curl -sSL "http://dumps.wikimedia.org/other/pagecounts-raw/$i/$i-$j/" | grep -oE 'pagecounts-[0-9]+-[0-9]+\.gz'; done; done | sort | uniq | tee links.txtcat links.txt | while read link; do wget http://dumps.wikimedia.org/other/pagecounts-raw/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1/')/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1-\2/')/$link; donels -1 /opt/wikistat/ | grep gz | while read i; do echo $i; gzip -cd /opt/wikistat/$i | ./wikistat-loader --time="$(echo -n $i | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\.gz/\1-\2-\3 \4-00-00/')" | clickhouse-client --query="INSERT INTO wikistat FORMAT TabSeparated"; done +for i in {2007..2016}; do for j in {01..12}; do echo $i-$j >&2; curl -sSL "http://dumps.wikimedia.org/other/pagecounts-raw/$i/$i-$j/" | grep -oE 'pagecounts-[0-9]+-[0-9]+\.gz'; done; done | sort | uniq | tee links.txt +cat links.txt | while read link; do wget http://dumps.wikimedia.org/other/pagecounts-raw/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1/')/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1-\2/')/$link; done +ls -1 /opt/wikistat/ | grep gz | while read i; do echo $i; gzip -cd /opt/wikistat/$i | ./wikistat-loader --time="$(echo -n $i | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\.gz/\1-\2-\3 \4-00-00/')" | clickhouse-client --query="INSERT INTO wikistat FORMAT TabSeparated"; done ``` diff --git a/docs/en/operations/settings/query_complexity.md b/docs/en/operations/settings/query_complexity.md index bd46617eed0..2132557d699 100644 --- a/docs/en/operations/settings/query_complexity.md +++ b/docs/en/operations/settings/query_complexity.md @@ -51,7 +51,7 @@ The maximum amount of RAM to use for running a user's queries on a single server Default values are defined in [Settings.h](https://github.com/yandex/ClickHouse/blob/master/dbms/src/Interpreters/Settings.h#L244). By default, the amount is not restricted (`max_memory_usage_for_user = 0`). -See also the descriptions of [max_memory_usage]( and #settings_max_memory_usage). +See also the description of [max_memory_usage](#settings_max_memory_usage). ## max_memory_usage_for_all_queries @@ -59,7 +59,7 @@ The maximum amount of RAM to use for running all queries on a single server. Default values are defined in [Settings.h](https://github.com/yandex/ClickHouse/blob/master/dbms/src/Interpreters/Settings.h#L245). By default, the amount is not restricted (`max_memory_usage_for_all_queries = 0`). -See also the descriptions of [max_memory_usage]( and #settings_max_memory_usage). +See also the description of [max_memory_usage](#settings_max_memory_usage). ## max_rows_to_read @@ -193,4 +193,3 @@ Maximum number of bytes (uncompressed data) that can be passed to a remote serve ## transfer_overflow_mode What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw. - diff --git a/docs/en/operations/settings/settings_profiles.md b/docs/en/operations/settings/settings_profiles.md index 5f454c0724a..9ee7cd22b8c 100644 --- a/docs/en/operations/settings/settings_profiles.md +++ b/docs/en/operations/settings/settings_profiles.md @@ -1,21 +1,24 @@ # Settings profiles A settings profile is a collection of settings grouped under the same name. Each ClickHouse user has a profile. -To apply all the settings in a profile, set 'profile'. Example: +To apply all the settings in a profile, set `profile`. + +Example: + +Setting `web` profile. ```sql SET profile = 'web' ``` -- Load the 'web' profile. In other words, set all the options that belong to the 'web' profile. - Settings profiles are declared in the user config file. This is usually `users.xml`. + Example: ```xml - + 8 @@ -50,7 +53,6 @@ Example: ``` -The example specifies two profiles: `default` and `web`. The `default` profile has a special purpose: it must always be present and is applied when starting the server. In other words, the 'default' profile contains default settings. The 'web' profile is a regular profile that can be set using the SET query or using a URL parameter in an HTTP query. - -Settings profiles can inherit from each other. To use inheritance, indicate the 'profile' setting before the other settings that are listed in the profile. +The example specifies two profiles: `default` and `web`. The `default` profile has a special purpose: it must always be present and is applied when starting the server. In other words, the `default` profile contains default settings. The `web` profile is a regular profile that can be set using the `SET` query or using a URL parameter in an HTTP query. +Settings profiles can inherit from each other. To use inheritance, indicate the `profile` setting before the other settings that are listed in the profile. diff --git a/docs/en/query_language/queries.md b/docs/en/query_language/queries.md index 4c13b0b01cf..f732a91b696 100644 --- a/docs/en/query_language/queries.md +++ b/docs/en/query_language/queries.md @@ -312,10 +312,10 @@ Data directory: `/var/lib/clickhouse/data/database/table/`,where `/var/lib/click ```bash $ ls -l /var/lib/clickhouse/data/test/visits/ total 48 -drwxrwxrwx 2 clickhouse clickhouse 20480 may 13 02:58 20140317_20140323_2_2_0 -drwxrwxrwx 2 clickhouse clickhouse 20480 may 13 02:58 20140317_20140323_4_4_0 -drwxrwxrwx 2 clickhouse clickhouse 4096 may 13 02:55 detached --rw-rw-rw- 1 clickhouse clickhouse 2 may 13 02:58 increment.txt +drwxrwxrwx 2 clickhouse clickhouse 20480 May 5 02:58 20140317_20140323_2_2_0 +drwxrwxrwx 2 clickhouse clickhouse 20480 May 5 02:58 20140317_20140323_4_4_0 +drwxrwxrwx 2 clickhouse clickhouse 4096 May 5 02:55 detached +-rw-rw-rw- 1 clickhouse clickhouse 2 May 5 02:58 increment.txt ``` Here, `20140317_20140323_2_2_0` and ` 20140317_20140323_4_4_0` are the directories of data parts. diff --git a/docs/en/table_engines/index.md b/docs/en/table_engines/index.md index 212df9c0f67..b7ed13fcb42 100644 --- a/docs/en/table_engines/index.md +++ b/docs/en/table_engines/index.md @@ -8,8 +8,7 @@ The table engine (type of table) determines: - Use of indexes, if present. - Whether multithreaded request execution is possible. - Data replication. -- When reading data, the engine is only required to extract the necessary set of columns. - However, in some cases, the query may be partially processed inside the table engine. -Note that for most serious tasks, you should use engines from the MergeTree family. +When reading data, the engine is only required to extract the necessary set of columns. However, in some cases, the query may be partially processed inside the table engine. +Note that for most serious tasks, you should use engines from the `MergeTree` family. diff --git a/docs/en/table_engines/merge.md b/docs/en/table_engines/merge.md index b0f07dd71d6..08dfa2ba306 100644 --- a/docs/en/table_engines/merge.md +++ b/docs/en/table_engines/merge.md @@ -2,9 +2,11 @@ The Merge engine (not to be confused with `MergeTree`) does not store data itself, but allows reading from any number of other tables simultaneously. Reading is automatically parallelized. Writing to a table is not supported. When reading, the indexes of tables that are actually being read are used, if they exist. -The Merge engine accepts parameters: the database name and a regular expression for tables. Example. +The Merge engine accepts parameters: the database name and a regular expression for tables. -```text +Example: + +``` Merge(hits, '^WatchLog') ``` @@ -35,4 +37,3 @@ Virtual columns differ from normal columns in the following ways: A Merge type table contains a virtual _table column with the String type. (If the table already has a _table column, the virtual column is named _table1, and if it already has _table1, it is named _table2, and so on.) It contains the name of the table that data was read from. If the WHERE or PREWHERE clause contains conditions for the '_table' column that do not depend on other table columns (as one of the conjunction elements, or as an entire expression), these conditions are used as an index. The conditions are performed on a data set of table names to read data from, and the read operation will be performed from only those tables that the condition was triggered on. - diff --git a/docs/ru/functions/other_functions.md b/docs/ru/functions/other_functions.md index 754dd56dce9..b9aecec9f7d 100644 --- a/docs/ru/functions/other_functions.md +++ b/docs/ru/functions/other_functions.md @@ -48,9 +48,13 @@ Позволяет построить unicode-art диаграмму. -`bar(x, min, max, width)` - рисует полосу ширины пропорциональной (x - min) и равной width символов при x == max. -`min, max` - целочисленные константы, значение должно помещаться в Int64. -`width` - константа, положительное число, может быть дробным. +`bar(x, min, max, width)` рисует полосу ширины пропорциональной `(x - min)` и равной `width` символов при `x = max`. + +Параметры: + +- `x` — Величина для отображения. +- `min, max` — Целочисленные константы, значение должно помещаться в `Int64`. +- `width` — Константа, положительное число, может быть дробным. Полоса рисуется с точностью до одной восьмой символа. diff --git a/docs/ru/functions/string_search_functions.md b/docs/ru/functions/string_search_functions.md index 72f1c9d4d4b..99bdef12f29 100644 --- a/docs/ru/functions/string_search_functions.md +++ b/docs/ru/functions/string_search_functions.md @@ -4,13 +4,15 @@ Во всех функциях, подстрока для поиска или регулярное выражение, должно быть константой. ## position(haystack, needle) -Поиск подстроки needle в строке haystack. +Поиск подстроки `needle` в строке `haystack`. Возвращает позицию (в байтах) найденной подстроки, начиная с 1, или 0, если подстрока не найдена. -Есть также функция positionCaseInsensitive. + +Для поиска без учета регистра используйте функцию `positionCaseInsensitive`. ## positionUTF8(haystack, needle) -Так же, как position, но позиция возвращается в кодовых точках Unicode. Работает при допущении, что строка содержит набор байт, представляющий текст в кодировке UTF-8. Если допущение не выполнено - то возвращает какой-нибудь результат (не кидает исключение). -Есть также функция positionCaseInsensitiveUTF8. +Так же, как `position`, но позиция возвращается в кодовых точках Unicode. Работает при допущении, что строка содержит набор байт, представляющий текст в кодировке UTF-8. Если допущение не выполнено - то возвращает какой-нибудь результат (не кидает исключение). + +Для поиска без учета регистра используйте функцию `positionCaseInsensitiveUTF8`. ## match(haystack, pattern) Проверка строки на соответствие регулярному выражению pattern. Регулярное выражение re2. diff --git a/docs/ru/operations/settings/settings_profiles.md b/docs/ru/operations/settings/settings_profiles.md index 6ee633de21e..5d7874b2e52 100644 --- a/docs/ru/operations/settings/settings_profiles.md +++ b/docs/ru/operations/settings/settings_profiles.md @@ -1,15 +1,17 @@ # Профили настроек Профили настроек - это множество настроек, сгруппированных под одним именем. Для каждого пользователя ClickHouse указывается некоторый профиль. -Все настройки профиля можно применить, установив настройку с именем profile. Пример: +Все настройки профиля можно применить, установив настройку `profile`. + +Пример: + +Установить профиль `web`. ```sql SET profile = 'web' ``` -- установить профиль web - то есть, установить все настройки, относящиеся к профилю web. - -Профили настроек объявляются в конфигурационном файле пользователей. Обычно это - `users.xml`. +Профили настроек объявляются в конфигурационном файле пользователей. Обычно это `users.xml`. Пример: ```xml @@ -54,6 +56,6 @@ SET profile = 'web' ``` -В примере задано два профиля: `default` и `web`. Профиль `default` имеет специальное значение - он всегда обязан присутствовать и применяется при запуске сервера. То есть, профиль default содержит настройки по умолчанию. Профиль web - обычный профиль, который может быть установлен с помощью запроса SET или с помощью параметра URL при запросе по HTTP. +В примере задано два профиля: `default` и `web`. Профиль `default` имеет специальное значение - он всегда обязан присутствовать и применяется при запуске сервера. То есть, профиль `default` содержит настройки по умолчанию. Профиль `web` - обычный профиль, который может быть установлен с помощью запроса `SET` или с помощью параметра URL при запросе по HTTP. -Профили настроек могут наследоваться от друг-друга - это реализуется указанием настройки profile перед остальными настройками, перечисленными в профиле. +Профили настроек могут наследоваться от друг-друга - это реализуется указанием настройки `profile` перед остальными настройками, перечисленными в профиле. diff --git a/docs/ru/query_language/queries.md b/docs/ru/query_language/queries.md index 9a6aa20c737..8abe5d61b35 100644 --- a/docs/ru/query_language/queries.md +++ b/docs/ru/query_language/queries.md @@ -308,10 +308,10 @@ SELECT * FROM system.parts WHERE active ```bash $ ls -l /var/lib/clickhouse/data/test/visits/ total 48 -drwxrwxrwx 2 clickhouse clickhouse 20480 may 13 02:58 20140317_20140323_2_2_0 -drwxrwxrwx 2 clickhouse clickhouse 20480 may 13 02:58 20140317_20140323_4_4_0 -drwxrwxrwx 2 clickhouse clickhouse 4096 may 13 02:55 detached --rw-rw-rw- 1 clickhouse clickhouse 2 may 13 02:58 increment.txt +drwxrwxrwx 2 clickhouse clickhouse 20480 May 5 02:58 20140317_20140323_2_2_0 +drwxrwxrwx 2 clickhouse clickhouse 20480 May 5 02:58 20140317_20140323_4_4_0 +drwxrwxrwx 2 clickhouse clickhouse 4096 May 5 02:55 detached +-rw-rw-rw- 1 clickhouse clickhouse 2 May 5 02:58 increment.txt ``` Здесь `20140317_20140323_2_2_0`, `20140317_20140323_4_4_0` - директории кусков. diff --git a/docs/ru/table_engines/index.md b/docs/ru/table_engines/index.md index 48bebf422fb..811045a2581 100644 --- a/docs/ru/table_engines/index.md +++ b/docs/ru/table_engines/index.md @@ -2,13 +2,13 @@ Движок таблицы (тип таблицы) определяет: -- как и где хранятся данные - куда их писать и откуда читать; -- какие запросы поддерживаются, и каким образом; -- конкуррентный доступ к данным; -- использование индексов, если есть; -- возможно ли многопоточное выполнение запроса; -- репликацию данных; -- при чтении, движок обязан лишь достать нужный набор столбцов; - но в некоторых случаях, запрос может быть частично обработан в рамках движка таблицы. +- Как и где хранятся данные, куда их писать и откуда читать. +- Какие запросы поддерживаются и каким образом. +- Конкурентный доступ к данным. +- Использование индексов, если есть. +- Возможно ли многопоточное выполнение запроса. +- Параметры репликации данных. -Забегая вперёд, заметим, что для большинства серьёзных задач, следует использовать движки семейства MergeTree. +При чтении, движок обязан лишь выдать запрошенные столбцы, но в некоторых случаях движок может частично обрабатывать данные при ответе на запрос. + +Для большинства серьёзных задач, следует использовать движки семейства `MergeTree`. diff --git a/docs/ru/table_engines/merge.md b/docs/ru/table_engines/merge.md index 1124e54b5bb..aa5d44e71f5 100644 --- a/docs/ru/table_engines/merge.md +++ b/docs/ru/table_engines/merge.md @@ -1,37 +1,39 @@ # Merge -Движок Merge (не путайте с движком `MergeTree`) не хранит данные самостоятельно, а позволяет читать одновременно из произвольного количества других таблиц. +Движок `Merge` (не путайте с движком `MergeTree`) не хранит данные самостоятельно, а позволяет читать одновременно из произвольного количества других таблиц. Чтение автоматически распараллеливается. Запись в таблицу не поддерживается. При чтении будут использованы индексы тех таблиц, из которых реально идёт чтение, если они существуют. -Движок Merge принимает параметры: имя базы данных и регулярное выражение для таблиц. Пример. +Движок `Merge` принимает параметры: имя базы данных и регулярное выражение для таблиц. -```text +Пример: + +``` Merge(hits, '^WatchLog') ``` -Данные будут читаться из таблиц в базе hits, имена которых соответствуют регулярному выражению '`^WatchLog`'. +Данные будут читаться из таблиц в базе `hits`, имена которых соответствуют регулярному выражению '`^WatchLog`'. Вместо имени базы данных может использоваться константное выражение, возвращающее строку. Например, `currentDatabase()`. Регулярные выражения — [re2](https://github.com/google/re2) (поддерживает подмножество PCRE), регистрозависимые. Смотрите замечание об экранировании в регулярных выражениях в разделе "match". -При выборе таблиц для чтения, сама Merge-таблица не будет выбрана, даже если попадает под регулярное выражение, чтобы не возникло циклов. -Впрочем, вы можете создать две Merge-таблицы, которые будут пытаться бесконечно читать данные друг друга, но делать этого не нужно. +При выборе таблиц для чтения, сама `Merge`-таблица не будет выбрана, даже если попадает под регулярное выражение, чтобы не возникло циклов. +Впрочем, вы можете создать две `Merge`-таблицы, которые будут пытаться бесконечно читать данные друг друга, но делать этого не нужно. -Типичный способ использования движка Merge — работа с большим количеством таблиц типа TinyLog, как с одной. +Типичный способ использования движка `Merge` — работа с большим количеством таблиц типа `TinyLog`, как с одной. ## Виртуальные столбцы -Виртуальные столбцы — столбцы, предоставляемые движком таблиц независимо от определения таблицы. То есть, такие столбцы не указываются в CREATE TABLE, но доступны для SELECT-а. +Виртуальные столбцы — столбцы, предоставляемые движком таблиц независимо от определения таблицы. То есть, такие столбцы не указываются в `CREATE TABLE`, но доступны для `SELECT`. Виртуальные столбцы отличаются от обычных следующими особенностями: - они не указываются в определении таблицы; -- в них нельзя вставить данные при INSERT-е; -- при INSERT-е без указания списка столбцов виртуальные столбцы не учитываются; +- в них нельзя вставить данные при `INSERT`; +- при `INSERT` без указания списка столбцов виртуальные столбцы не учитываются; - они не выбираются при использовании звёздочки (`SELECT *`); - виртуальные столбцы не показываются в запросах `SHOW CREATE TABLE` и `DESC TABLE`; -Таблица типа Merge содержит виртуальный столбец _table типа String. (Если в таблице уже есть столбец _table, то виртуальный столбец называется _table1; если уже есть _table1, то _table2 и т. п.) Он содержит имя таблицы, из которой были прочитаны данные. +Таблица типа `Merge` содержит виртуальный столбец `_table` типа `String`. (Если в таблице уже есть столбец `_table`, то виртуальный столбец называется `_table1`; если уже есть `_table1`, то `_table2` и т. п.) Он содержит имя таблицы, из которой были прочитаны данные. -Если секция WHERE/PREWHERE содержит (в качестве одного из элементов конъюнкции или в качестве всего выражения) условия на столбец _table, не зависящие от других столбцов таблицы, то эти условия используются как индекс: условия выполняются над множеством имён таблиц, из которых нужно читать данные, и чтение будет производиться только из тех таблиц, для которых условия сработали. +Если секция `WHERE/PREWHERE` содержит (в качестве одного из элементов конъюнкции или в качестве всего выражения) условия на столбец `_table`, не зависящие от других столбцов таблицы, то эти условия используются как индекс: условия выполняются над множеством имён таблиц, из которых нужно читать данные, и чтение будет производиться только из тех таблиц, для которых условия сработали. From c3a47815abf427a6800c88dfaa233525b5fc1107 Mon Sep 17 00:00:00 2001 From: BayoNet Date: Sat, 28 Apr 2018 14:53:59 +0300 Subject: [PATCH 037/231] Codeblock formatting is fixed --- docs/en/operations/settings/settings_profiles.md | 6 +++++- docs/ru/operations/settings/settings_profiles.md | 1 + 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/docs/en/operations/settings/settings_profiles.md b/docs/en/operations/settings/settings_profiles.md index 9ee7cd22b8c..b0f2e0c3e35 100644 --- a/docs/en/operations/settings/settings_profiles.md +++ b/docs/en/operations/settings/settings_profiles.md @@ -20,7 +20,11 @@ Example: - 8 + + 8 + + + 1000000000 100000000000 diff --git a/docs/ru/operations/settings/settings_profiles.md b/docs/ru/operations/settings/settings_profiles.md index 5d7874b2e52..de41eb6666d 100644 --- a/docs/ru/operations/settings/settings_profiles.md +++ b/docs/ru/operations/settings/settings_profiles.md @@ -12,6 +12,7 @@ SET profile = 'web' ``` Профили настроек объявляются в конфигурационном файле пользователей. Обычно это `users.xml`. + Пример: ```xml From a1eb938ed26c0bc9bd0c356e8bc6f4391e635d67 Mon Sep 17 00:00:00 2001 From: pyos Date: Sat, 28 Apr 2018 17:12:00 +0300 Subject: [PATCH 038/231] Inline nullable number constants into compiled code. Also, protect against some segfaults during compilation by checking correctness of the type returned by compile(). --- dbms/src/DataTypes/Native.h | 8 ++-- dbms/src/Functions/IFunction.cpp | 4 +- dbms/src/Interpreters/ExpressionJIT.cpp | 53 +++++++++++++++---------- 3 files changed, 40 insertions(+), 25 deletions(-) diff --git a/dbms/src/DataTypes/Native.h b/dbms/src/DataTypes/Native.h index c8a342bd393..b0641d69183 100644 --- a/dbms/src/DataTypes/Native.h +++ b/dbms/src/DataTypes/Native.h @@ -40,14 +40,16 @@ static llvm::Type * toNativeType(llvm::IRBuilderBase & builder, const DataTypePt return nullptr; } -static llvm::Constant * getDefaultNativeValue(llvm::IRBuilder<> & builder, llvm::Type * type) +static llvm::Constant * getDefaultNativeValue(llvm::Type * type) { if (type->isIntegerTy()) return llvm::ConstantInt::get(type, 0); if (type->isFloatTy() || type->isDoubleTy()) return llvm::ConstantFP::get(type, 0.0); - auto * as_struct = static_cast(type); /// nullable - return llvm::ConstantStruct::get(as_struct, getDefaultNativeValue(builder, as_struct->getElementType(0)), builder.getTrue()); + /// else nullable + auto * value = getDefaultNativeValue(type->getContainedType(0)); + auto * is_null = llvm::ConstantInt::get(type->getContainedType(1), 1); + return llvm::ConstantStruct::get(static_cast(type), value, is_null); } } diff --git a/dbms/src/Functions/IFunction.cpp b/dbms/src/Functions/IFunction.cpp index a28da9eb2e6..fdb4fa673e0 100644 --- a/dbms/src/Functions/IFunction.cpp +++ b/dbms/src/Functions/IFunction.cpp @@ -298,12 +298,12 @@ llvm::Value * IFunction::compile(llvm::IRBuilderBase & builder, const DataTypes { if (auto denulled = removeNullables(arguments)) { - /// FIXME: when only one column is nullable, this is actually slower than the non-jitted version + /// FIXME: when only one column is nullable, this can actually be slower than the non-jitted version /// because this involves copying the null map while `wrapInNullable` reuses it. auto & b = static_cast &>(builder); auto * fail = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); auto * join = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); - auto * init = getDefaultNativeValue(b, toNativeType(b, makeNullable(getReturnTypeImpl(*denulled)))); + auto * init = getDefaultNativeValue(toNativeType(b, makeNullable(getReturnTypeImpl(*denulled)))); for (size_t i = 0; i < arguments.size(); i++) { if (!arguments[i]->isNullable()) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index f5712ff5976..6d107688dc2 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -159,6 +159,27 @@ void LLVMPreparedFunction::execute(Block & block, const ColumnNumbers & argument block.getByPosition(result).column = std::move(col_res); }; +static llvm::Constant * getConstantValue(const IColumn * column, llvm::Type * type) +{ + if (!column || !type) + return nullptr; + if (auto * constant = typeid_cast(column)) + return getConstantValue(&constant->getDataColumn(), type); + if (auto * nullable = typeid_cast(column)) + { + auto * value = getConstantValue(&nullable->getNestedColumn(), type->getContainedType(0)); + auto * is_null = llvm::ConstantInt::get(type->getContainedType(1), nullable->isNullAt(0)); + return value ? llvm::ConstantStruct::get(static_cast(type), value, is_null) : nullptr; + } + if (type->isFloatTy()) + return llvm::ConstantFP::get(type, static_cast *>(column)->getElement(0)); + if (type->isDoubleTy()) + return llvm::ConstantFP::get(type, static_cast *>(column)->getElement(0)); + if (type->isIntegerTy()) + return llvm::ConstantInt::get(type, column->getUInt(0)); + return nullptr; +} + LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext context, const Block & sample_block) : actions(std::move(actions_)), context(context) { @@ -176,21 +197,8 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont std::unordered_map> by_name; for (const auto & c : sample_block) - { - auto * type = toNativeType(b, c.type); - if (!type || !c.column) - continue; - llvm::Value * value = nullptr; - if (type->isFloatTy()) - value = llvm::ConstantFP::get(type, typeid_cast *>(c.column.get())->getElement(0)); - else if (type->isDoubleTy()) - value = llvm::ConstantFP::get(type, typeid_cast *>(c.column.get())->getElement(0)); - else if (type->isIntegerTy()) - value = llvm::ConstantInt::get(type, c.column->getUInt(0)); - /// TODO: handle nullable (create a pointer) - if (value) + if (auto * value = getConstantValue(c.column.get(), toNativeType(b, c.type))) by_name[c.name] = [=]() { return value; }; - } std::unordered_set seen; for (const auto & action : actions) @@ -228,8 +236,8 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont auto * value = b.CreateLoad(col.data); if (!col.null) return value; - auto * is_null = b.CreateICmpEQ(b.CreateLoad(col.null), b.getInt8(1)); - auto * nullable = getDefaultNativeValue(b, toNativeType(b, arg_types[i])); + auto * is_null = b.CreateICmpNE(b.CreateLoad(col.null), b.getInt8(0)); + auto * nullable = getDefaultNativeValue(toNativeType(b, arg_types[i])); return b.CreateInsertValue(b.CreateInsertValue(nullable, value, {0}), is_null, {1}); }; } @@ -242,7 +250,13 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont auto extra = action.function->compilePrologue(b); for (auto * value : extra) input.emplace_back([=]() { return value; }); - by_name[action.result_name] = [&, input = std::move(input)]() { return action.function->compile(b, input); }; + by_name[action.result_name] = [&, input = std::move(input)]() { + auto * result = action.function->compile(b, input); + if (result->getType() != toNativeType(b, action.function->getReturnType())) + throw Exception("function " + action.function->getName() + " generated an llvm::Value of invalid type", + ErrorCodes::LOGICAL_ERROR); + return result; + }; } /// assume nonzero initial value in `counter` @@ -262,12 +276,11 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont } } - auto * result = by_name.at(actions.back().result_name)(); + auto * result = by_name.at(getName())(); if (columns_v[arg_types.size()].null) { b.CreateStore(b.CreateExtractValue(result, {0}), columns_v[arg_types.size()].data); - /// XXX: should zero-extend it to 1 instead of sign-extending to -1? - b.CreateStore(b.CreateExtractValue(result, {1}), columns_v[arg_types.size()].null); + b.CreateStore(b.CreateSelect(b.CreateExtractValue(result, {1}), b.getInt8(1), b.getInt8(0)), columns_v[arg_types.size()].null); } else { From 1ffc2a07754a9897599fca978d658eeb21500dcc Mon Sep 17 00:00:00 2001 From: pyos Date: Sat, 28 Apr 2018 17:41:13 +0300 Subject: [PATCH 039/231] Make LLVMFunction monotonicity computation shorter (and fix a typo-bug) --- dbms/src/DataTypes/Native.h | 30 +++++++++++++----- dbms/src/Interpreters/ExpressionJIT.cpp | 42 +++++-------------------- 2 files changed, 31 insertions(+), 41 deletions(-) diff --git a/dbms/src/DataTypes/Native.h b/dbms/src/DataTypes/Native.h index b0641d69183..c9cd35b4e08 100644 --- a/dbms/src/DataTypes/Native.h +++ b/dbms/src/DataTypes/Native.h @@ -12,12 +12,7 @@ namespace DB { -namespace ErrorCodes -{ - extern const int NOT_IMPLEMENTED; -} - -static llvm::Type * toNativeType(llvm::IRBuilderBase & builder, const DataTypePtr & type) +static inline llvm::Type * toNativeType(llvm::IRBuilderBase & builder, const DataTypePtr & type) { if (auto * nullable = typeid_cast(type.get())) { @@ -40,7 +35,7 @@ static llvm::Type * toNativeType(llvm::IRBuilderBase & builder, const DataTypePt return nullptr; } -static llvm::Constant * getDefaultNativeValue(llvm::Type * type) +static inline llvm::Constant * getDefaultNativeValue(llvm::Type * type) { if (type->isIntegerTy()) return llvm::ConstantInt::get(type, 0); @@ -52,6 +47,27 @@ static llvm::Constant * getDefaultNativeValue(llvm::Type * type) return llvm::ConstantStruct::get(static_cast(type), value, is_null); } +static inline llvm::Constant * getNativeValue(llvm::Type * type, const IColumn * column, size_t i) +{ + if (!column || !type) + return nullptr; + if (auto * constant = typeid_cast(column)) + return getNativeValue(type, &constant->getDataColumn(), 0); + if (auto * nullable = typeid_cast(column)) + { + auto * value = getNativeValue(type->getContainedType(0), &nullable->getNestedColumn(), i); + auto * is_null = llvm::ConstantInt::get(type->getContainedType(1), nullable->isNullAt(i)); + return value ? llvm::ConstantStruct::get(static_cast(type), value, is_null) : nullptr; + } + if (type->isFloatTy()) + return llvm::ConstantFP::get(type, static_cast *>(column)->getElement(i)); + if (type->isDoubleTy()) + return llvm::ConstantFP::get(type, static_cast *>(column)->getElement(i)); + if (type->isIntegerTy()) + return llvm::ConstantInt::get(type, column->getUInt(i)); + return nullptr; +} + } #endif diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 6d107688dc2..91e0a49338a 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -159,27 +159,6 @@ void LLVMPreparedFunction::execute(Block & block, const ColumnNumbers & argument block.getByPosition(result).column = std::move(col_res); }; -static llvm::Constant * getConstantValue(const IColumn * column, llvm::Type * type) -{ - if (!column || !type) - return nullptr; - if (auto * constant = typeid_cast(column)) - return getConstantValue(&constant->getDataColumn(), type); - if (auto * nullable = typeid_cast(column)) - { - auto * value = getConstantValue(&nullable->getNestedColumn(), type->getContainedType(0)); - auto * is_null = llvm::ConstantInt::get(type->getContainedType(1), nullable->isNullAt(0)); - return value ? llvm::ConstantStruct::get(static_cast(type), value, is_null) : nullptr; - } - if (type->isFloatTy()) - return llvm::ConstantFP::get(type, static_cast *>(column)->getElement(0)); - if (type->isDoubleTy()) - return llvm::ConstantFP::get(type, static_cast *>(column)->getElement(0)); - if (type->isIntegerTy()) - return llvm::ConstantInt::get(type, column->getUInt(0)); - return nullptr; -} - LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext context, const Block & sample_block) : actions(std::move(actions_)), context(context) { @@ -197,7 +176,7 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont std::unordered_map> by_name; for (const auto & c : sample_block) - if (auto * value = getConstantValue(c.column.get(), toNativeType(b, c.type))) + if (auto * value = getNativeValue(toNativeType(b, c.type), c.column.get(), 0)) by_name[c.name] = [=]() { return value; }; std::unordered_set seen; @@ -304,17 +283,12 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont b.CreateRetVoid(); } -static Field evaluateFunction(IFunctionBase & function, const IDataType & type, const Field & arg) +static void applyFunction(IFunctionBase & function, Field & value) { - const auto & arg_types = function.getArgumentTypes(); - if (arg_types.size() != 1 || !arg_types[0]->equals(type)) - return {}; - auto column = arg_types[0]->createColumn(); - column->insert(arg); - Block block = {{ ColumnConst::create(std::move(column), 1), arg_types[0], "_arg" }, { nullptr, function.getReturnType(), "_result" }}; + const auto & type = function.getArgumentTypes().at(0); + Block block = {{ type->createColumnConst(1, value), type, "x" }, { nullptr, function.getReturnType(), "y" }}; function.execute(block, {0}, 1); - auto result = block.getByPosition(1).column; - return result && result->size() == 1 ? (*result)[0] : Field(); + block.safeGetByPosition(1).column->get(0, value); } IFunctionBase::Monotonicity LLVMFunction::getMonotonicityForRange(const IDataType & type, const Field & left, const Field & right) const @@ -326,7 +300,7 @@ IFunctionBase::Monotonicity LLVMFunction::getMonotonicityForRange(const IDataTyp /// monotonicity is only defined for unary functions, so the chain must describe a sequence of nested calls for (size_t i = 0; i < actions.size(); i++) { - Monotonicity m = actions[i].function->getMonotonicityForRange(type, left_, right_); + Monotonicity m = actions[i].function->getMonotonicityForRange(*type_, left_, right_); if (!m.is_monotonic) return m; result.is_positive ^= !m.is_positive; @@ -334,9 +308,9 @@ IFunctionBase::Monotonicity LLVMFunction::getMonotonicityForRange(const IDataTyp if (i + 1 < actions.size()) { if (left_ != Field()) - left_ = evaluateFunction(*actions[i].function, *type_, left_); + applyFunction(*actions[i].function, left_); if (right_ != Field()) - right_ = evaluateFunction(*actions[i].function, *type_, right_); + applyFunction(*actions[i].function, right_); if (!m.is_positive) std::swap(left_, right_); type_ = actions[i].function->getReturnType().get(); From 6e05c5ace401db078f15c1e79ae4a07491851ca4 Mon Sep 17 00:00:00 2001 From: pyos Date: Sat, 28 Apr 2018 18:11:23 +0300 Subject: [PATCH 040/231] compilePrologue() isn't particularly useful after all. Basically the only thing it can do that compile() can't is create 'alloca' instructions, which are only needed to get pointers to stack variables. Given that dynamically-sized allocations aren't possible with this API, such pointers are probably completely pointless (heh). --- dbms/src/Functions/IFunction.cpp | 8 -------- dbms/src/Functions/IFunction.h | 27 ++----------------------- dbms/src/Interpreters/ExpressionJIT.cpp | 13 ++++-------- 3 files changed, 6 insertions(+), 42 deletions(-) diff --git a/dbms/src/Functions/IFunction.cpp b/dbms/src/Functions/IFunction.cpp index fdb4fa673e0..c55d293ec29 100644 --- a/dbms/src/Functions/IFunction.cpp +++ b/dbms/src/Functions/IFunction.cpp @@ -283,14 +283,6 @@ bool IFunction::isCompilable(const DataTypes & arguments) const return isCompilableImpl(arguments); } -std::vector IFunction::compilePrologue(llvm::IRBuilderBase & builder, const DataTypes & arguments) const -{ - if (useDefaultImplementationForNulls()) - if (auto denulled = removeNullables(arguments)) - return compilePrologueImpl(builder, *denulled); - return compilePrologueImpl(builder, arguments); -} - llvm::Value * IFunction::compile(llvm::IRBuilderBase & builder, const DataTypes & arguments, ValuePlaceholders values) const { #if USE_EMBEDDED_COMPILER diff --git a/dbms/src/Functions/IFunction.h b/dbms/src/Functions/IFunction.h index d5f7896c601..107c38b7e84 100644 --- a/dbms/src/Functions/IFunction.h +++ b/dbms/src/Functions/IFunction.h @@ -102,17 +102,8 @@ public: virtual bool isCompilable() const { return false; } - /// Produce LLVM IR code that runs before the loop over the input rows. Mostly useful for allocating stack variables. - virtual std::vector compilePrologue(llvm::IRBuilderBase &) const - { - return {}; - } - - /** Produce LLVM IR code that operates on scalar values. - * - * The first `getArgumentTypes().size()` values describe the current row of each column. (See - * `toNativeType` in DataTypes/Native.h for supported value types and how they map to LLVM types.) - * The rest are values returned by `compilePrologue`. + /** Produce LLVM IR code that operates on scalar values. See `toNativeType` in DataTypes/Native.h + * for supported value types and how they map to LLVM types. * * NOTE: the builder is actually guaranteed to be exactly `llvm::IRBuilder<>`, so you may safely * downcast it to that type. This method is specified with `IRBuilderBase` because forward-declaring @@ -305,11 +296,6 @@ public: throw Exception("prepare is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); } - std::vector compilePrologue(llvm::IRBuilderBase &) const final - { - throw Exception("compilePrologue without explicit types is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); - } - llvm::Value * compile(llvm::IRBuilderBase & /*builder*/, ValuePlaceholders /*values*/) const final { throw Exception("compile without explicit types is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); @@ -327,18 +313,11 @@ public: bool isCompilable(const DataTypes & arguments) const; - std::vector compilePrologue(llvm::IRBuilderBase &, const DataTypes & arguments) const; - llvm::Value * compile(llvm::IRBuilderBase &, const DataTypes & arguments, ValuePlaceholders values) const; protected: virtual bool isCompilableImpl(const DataTypes &) const { return false; } - virtual std::vector compilePrologueImpl(llvm::IRBuilderBase &, const DataTypes &) const - { - return {}; - } - virtual llvm::Value * compileImpl(llvm::IRBuilderBase &, const DataTypes &, ValuePlaceholders) const { throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED); @@ -385,8 +364,6 @@ public: bool isCompilable() const override { return function->isCompilable(arguments); } - std::vector compilePrologue(llvm::IRBuilderBase & builder) const override { return function->compilePrologue(builder, arguments); } - llvm::Value * compile(llvm::IRBuilderBase & builder, ValuePlaceholders values) const override { return function->compile(builder, arguments, std::move(values)); } PreparedFunctionPtr prepare(const Block & /*sample_block*/) const override { return std::make_shared(function); } diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 91e0a49338a..9f997c578f0 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -119,7 +119,6 @@ namespace llvm::Value * data_init; llvm::Value * null_init; llvm::Value * stride; - llvm::Value * is_const; }; } @@ -202,10 +201,7 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont columns_v[i].data_init = b.CreatePointerCast(b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 0)), type); columns_v[i].stride = b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 2)); if (column_type->isNullable()) - { columns_v[i].null_init = b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 1)); - columns_v[i].is_const = b.CreateICmpEQ(columns_v[i].stride, b.getIntN(sizeof(size_t) * 8, 0)); - } } for (size_t i = 0; i < arg_types.size(); i++) @@ -225,10 +221,6 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont ValuePlaceholders input; for (const auto & name : action.argument_names) input.push_back(by_name.at(name)); - /// TODO: pass compile-time constant arguments to `compilePrologue`? - auto extra = action.function->compilePrologue(b); - for (auto * value : extra) - input.emplace_back([=]() { return value; }); by_name[action.result_name] = [&, input = std::move(input)]() { auto * result = action.function->compile(b, input); if (result->getType() != toNativeType(b, action.function->getReturnType())) @@ -273,7 +265,10 @@ LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext cont auto * as_type = b.CreatePointerCast(b.CreateInBoundsGEP(as_char, col.stride), col.data->getType()); col.data->addIncoming(as_type, cur_block); if (col.null) - col.null->addIncoming(b.CreateSelect(col.is_const, col.null, b.CreateConstInBoundsGEP1_32(b.getInt8Ty(), col.null, 1)), cur_block); + { + auto * is_const = b.CreateICmpEQ(col.stride, llvm::ConstantInt::get(size_type, 0)); + col.null->addIncoming(b.CreateSelect(is_const, col.null, b.CreateConstInBoundsGEP1_32(b.getInt8Ty(), col.null, 1)), cur_block); + } } counter_phi->addIncoming(b.CreateSub(counter_phi, llvm::ConstantInt::get(size_type, 1)), cur_block); From 08345628a2ac3b76948a63f3f17e8d812f65b570 Mon Sep 17 00:00:00 2001 From: pyos Date: Sat, 28 Apr 2018 18:53:50 +0300 Subject: [PATCH 041/231] Support {Date,DateTime,Interval,UUID,FixedString} in compiled functions --- dbms/src/DataTypes/Native.h | 41 +++++++++++++++++++++++++++++-------- 1 file changed, 32 insertions(+), 9 deletions(-) diff --git a/dbms/src/DataTypes/Native.h b/dbms/src/DataTypes/Native.h index c9cd35b4e08..bbf0d4c5ae3 100644 --- a/dbms/src/DataTypes/Native.h +++ b/dbms/src/DataTypes/Native.h @@ -4,43 +4,65 @@ #if USE_EMBEDDED_COMPILER +#include +#include +#include +#include #include #include +#include #include namespace DB { -static inline llvm::Type * toNativeType(llvm::IRBuilderBase & builder, const DataTypePtr & type) +template +static inline bool typeIsEither(const IDataType & type) { - if (auto * nullable = typeid_cast(type.get())) + return (typeid_cast(&type) || ...); +} + +static inline llvm::Type * toNativeType(llvm::IRBuilderBase & builder, const IDataType & type) +{ + if (auto * nullable = typeid_cast(&type)) { - auto * wrapped = toNativeType(builder, nullable->getNestedType()); + auto * wrapped = toNativeType(builder, *nullable->getNestedType()); return wrapped ? llvm::StructType::get(wrapped, /* is null = */ builder.getInt1Ty()) : nullptr; } /// LLVM doesn't have unsigned types, it has unsigned instructions. - if (typeid_cast(type.get()) || typeid_cast(type.get())) + if (typeIsEither(type)) return builder.getInt8Ty(); - if (typeid_cast(type.get()) || typeid_cast(type.get())) + if (typeIsEither(type)) return builder.getInt16Ty(); - if (typeid_cast(type.get()) || typeid_cast(type.get())) + if (typeIsEither(type)) return builder.getInt32Ty(); - if (typeid_cast(type.get()) || typeid_cast(type.get())) + if (typeIsEither(type)) return builder.getInt64Ty(); - if (typeid_cast(type.get())) + if (typeIsEither(type)) + return builder.getInt128Ty(); + if (typeIsEither(type)) return builder.getFloatTy(); - if (typeid_cast(type.get())) + if (typeIsEither(type)) return builder.getDoubleTy(); + if (auto * fixed_string = typeid_cast(&type)) + return llvm::VectorType::get(builder.getInt8Ty(), fixed_string->getN()); return nullptr; } +static inline llvm::Type * toNativeType(llvm::IRBuilderBase & builder, const DataTypePtr & type) +{ + return toNativeType(builder, *type); +} + static inline llvm::Constant * getDefaultNativeValue(llvm::Type * type) { if (type->isIntegerTy()) return llvm::ConstantInt::get(type, 0); if (type->isFloatTy() || type->isDoubleTy()) return llvm::ConstantFP::get(type, 0.0); + if (type->isVectorTy()) + return llvm::ConstantVector::getSplat(type->getVectorNumElements(), getDefaultNativeValue(type->getVectorElementType())); /// else nullable auto * value = getDefaultNativeValue(type->getContainedType(0)); auto * is_null = llvm::ConstantInt::get(type->getContainedType(1), 1); @@ -65,6 +87,7 @@ static inline llvm::Constant * getNativeValue(llvm::Type * type, const IColumn * return llvm::ConstantFP::get(type, static_cast *>(column)->getElement(i)); if (type->isIntegerTy()) return llvm::ConstantInt::get(type, column->getUInt(i)); + /// TODO: if (type->isVectorTy()) return nullptr; } From 4641e2960f5c92a06fd9c43e22b6e18d76dec793 Mon Sep 17 00:00:00 2001 From: pyos Date: Sun, 29 Apr 2018 04:00:26 +0300 Subject: [PATCH 042/231] Move ExpressionActions::compileFunctions to ExpressionJIT.cpp. This means ExpressionJIT.h only has to expose one function. --- dbms/src/Interpreters/ExpressionActions.cpp | 80 +-- dbms/src/Interpreters/ExpressionActions.h | 2 - dbms/src/Interpreters/ExpressionJIT.cpp | 597 ++++++++++++-------- dbms/src/Interpreters/ExpressionJIT.h | 102 +--- 4 files changed, 376 insertions(+), 405 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionActions.cpp b/dbms/src/Interpreters/ExpressionActions.cpp index d6806c263e4..fe5ee96cdb3 100644 --- a/dbms/src/Interpreters/ExpressionActions.cpp +++ b/dbms/src/Interpreters/ExpressionActions.cpp @@ -706,9 +706,11 @@ void ExpressionActions::finalize(const Names & output_columns) final_columns.insert(name); } +#if USE_EMBEDDED_COMPILER /// This has to be done before removing redundant actions and inserting REMOVE_COLUMNs /// because inlining may change dependency sets. - compileFunctions(output_columns); + compileFunctions(actions, output_columns, sample_block); +#endif /// Which columns are needed to perform actions from the current to the last. NameSet needed_columns = final_columns; @@ -992,82 +994,6 @@ void ExpressionActions::optimizeArrayJoin() } } -void ExpressionActions::compileFunctions([[maybe_unused]] const Names & output_columns) -{ -#if USE_EMBEDDED_COMPILER - LLVMContext context; - /// an empty optional is a poisoned value prohibiting the column's producer from being removed - /// (which it could be, if it was inlined into every dependent function). - std::unordered_map>> current_dependents; - for (const auto & name : output_columns) - current_dependents[name].emplace(); - /// a snapshot of each compilable function's dependents at the time of its execution. - std::vector>> dependents(actions.size()); - for (size_t i = actions.size(); i--;) - { - switch (actions[i].type) - { - case ExpressionAction::REMOVE_COLUMN: - current_dependents.erase(actions[i].source_name); - /// poison every other column used after this point so that inlining chains do not cross it. - for (auto & dep : current_dependents) - dep.second.emplace(); - break; - - case ExpressionAction::PROJECT: - current_dependents.clear(); - for (const auto & proj : actions[i].projection) - current_dependents[proj.first].emplace(); - break; - - case ExpressionAction::ADD_COLUMN: - case ExpressionAction::COPY_COLUMN: - case ExpressionAction::ARRAY_JOIN: - case ExpressionAction::JOIN: - { - Names columns = actions[i].getNeededColumns(); - for (const auto & column : columns) - current_dependents[column].emplace(); - break; - } - - case ExpressionAction::APPLY_FUNCTION: - { - dependents[i] = current_dependents[actions[i].result_name]; - const bool compilable = context.isCompilable(*actions[i].function); - for (const auto & name : actions[i].argument_names) - { - if (compilable) - current_dependents[name].emplace(i); - else - current_dependents[name].emplace(); - } - break; - } - } - } - - std::vector fused(actions.size()); - for (size_t i = 0; i < actions.size(); i++) - { - if (actions[i].type != ExpressionAction::APPLY_FUNCTION || !context.isCompilable(*actions[i].function)) - continue; - if (dependents[i].find({}) != dependents[i].end()) - { - fused[i].push_back(actions[i]); - auto fn = std::make_shared(std::move(fused[i]), context, sample_block); - actions[i].function = fn; - actions[i].argument_names = fn->getArgumentNames(); - continue; - } - /// TODO: determine whether it's profitable to inline the function if there's more than one dependent. - for (const auto & dep : dependents[i]) - fused[*dep].push_back(actions[i]); - } - context.finalize(); -#endif -} - BlockInputStreamPtr ExpressionActions::createStreamWithNonJoinedDataIfFullOrRightJoin(const Block & source_header, size_t max_block_size) const { diff --git a/dbms/src/Interpreters/ExpressionActions.h b/dbms/src/Interpreters/ExpressionActions.h index 014f9d9e108..c859efa98a6 100644 --- a/dbms/src/Interpreters/ExpressionActions.h +++ b/dbms/src/Interpreters/ExpressionActions.h @@ -209,8 +209,6 @@ private: /// Move all arrayJoin as close as possible to the end. void optimizeArrayJoin(); - /// Try to JIT-compile all functions and remove unnecessary materialization of intermediate results. - void compileFunctions(const Names & output_columns); }; using ExpressionActionsPtr = std::shared_ptr; diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 9f997c578f0..24a35ee9c4f 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -9,6 +9,7 @@ #include #include #include +#include #include #include @@ -39,70 +40,6 @@ namespace ErrorCodes extern const int LOGICAL_ERROR; } -struct LLVMContext::Data -{ - llvm::LLVMContext context; - std::shared_ptr module; - std::unique_ptr machine; - llvm::orc::RTDyldObjectLinkingLayer objectLayer; - llvm::orc::IRCompileLayer compileLayer; - llvm::DataLayout layout; - llvm::IRBuilder<> builder; - - Data() - : module(std::make_shared("jit", context)) - , machine(llvm::EngineBuilder().selectTarget()) - , objectLayer([]() { return std::make_shared(); }) - , compileLayer(objectLayer, llvm::orc::SimpleCompiler(*machine)) - , layout(machine->createDataLayout()) - , builder(context) - { - module->setDataLayout(layout); - module->setTargetTriple(machine->getTargetTriple().getTriple()); - } -}; - -LLVMContext::LLVMContext() - : shared(std::make_shared()) -{} - -void LLVMContext::finalize() -{ - if (!shared->module->size()) - return; - llvm::PassManagerBuilder builder; - llvm::legacy::FunctionPassManager fpm(shared->module.get()); - builder.OptLevel = 3; - builder.SLPVectorize = true; - builder.LoopVectorize = true; - builder.RerollLoops = true; - builder.VerifyInput = true; - builder.VerifyOutput = true; - builder.populateFunctionPassManager(fpm); - for (auto & function : *shared->module) - fpm.run(function); - llvm::cantFail(shared->compileLayer.addModule(shared->module, std::make_shared())); -} - -bool LLVMContext::isCompilable(const IFunctionBase& function) const -{ - if (!toNativeType(shared->builder, function.getReturnType())) - return false; - for (const auto & type : function.getArgumentTypes()) - if (!toNativeType(shared->builder, type)) - return false; - return function.isCompilable(); -} - -LLVMPreparedFunction::LLVMPreparedFunction(LLVMContext context, std::shared_ptr parent) - : parent(parent), context(context) -{ - std::string mangledName; - llvm::raw_string_ostream mangledNameStream(mangledName); - llvm::Mangler::getNameWithPrefix(mangledNameStream, parent->getName(), context->layout); - function = reinterpret_cast(context->compileLayer.findSymbol(mangledNameStream.str(), false).getAddress().get()); -} - namespace { struct ColumnData @@ -114,11 +51,11 @@ namespace struct ColumnDataPlaceholders { - llvm::PHINode * data; - llvm::PHINode * null; - llvm::Value * data_init; + llvm::Value * data_init; /// first row llvm::Value * null_init; llvm::Value * stride; + llvm::PHINode * data; /// current row + llvm::PHINode * null; }; } @@ -138,146 +75,6 @@ static ColumnData getColumnData(const IColumn * column) return result; } -void LLVMPreparedFunction::execute(Block & block, const ColumnNumbers & arguments, size_t result) -{ - size_t block_size = block.rows(); - auto col_res = parent->getReturnType()->createColumn()->cloneResized(block_size); - if (block_size) - { - std::vector columns(arguments.size() + 1); - for (size_t i = 0; i < arguments.size(); i++) - { - auto * column = block.getByPosition(arguments[i]).column.get(); - if (!column) - throw Exception("column " + block.getByPosition(arguments[i]).name + " is missing", ErrorCodes::LOGICAL_ERROR); - columns[i] = getColumnData(column); - } - columns[arguments.size()] = getColumnData(col_res.get()); - reinterpret_cast(function)(block_size, columns.data()); - } - block.getByPosition(result).column = std::move(col_res); -}; - -LLVMFunction::LLVMFunction(ExpressionActions::Actions actions_, LLVMContext context, const Block & sample_block) - : actions(std::move(actions_)), context(context) -{ - auto & b = context->builder; - auto * size_type = b.getIntNTy(sizeof(size_t) * 8); - auto * data_type = llvm::StructType::get(b.getInt8PtrTy(), b.getInt8PtrTy(), size_type); - auto * func_type = llvm::FunctionType::get(b.getVoidTy(), { size_type, llvm::PointerType::get(data_type, 0) }, /*isVarArg=*/false); - auto * func = llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, actions.back().result_name, context->module.get()); - auto args = func->args().begin(); - llvm::Value * counter = &*args++; - llvm::Value * columns = &*args++; - - auto * entry = llvm::BasicBlock::Create(context->context, "entry", func); - b.SetInsertPoint(entry); - - std::unordered_map> by_name; - for (const auto & c : sample_block) - if (auto * value = getNativeValue(toNativeType(b, c.type), c.column.get(), 0)) - by_name[c.name] = [=]() { return value; }; - - std::unordered_set seen; - for (const auto & action : actions) - { - const auto & names = action.argument_names; - const auto & types = action.function->getArgumentTypes(); - for (size_t i = 0; i < names.size(); i++) - { - if (!seen.emplace(names[i]).second || by_name.find(names[i]) != by_name.end()) - continue; - arg_names.push_back(names[i]); - arg_types.push_back(types[i]); - } - seen.insert(action.result_name); - } - - std::vector columns_v(arg_types.size() + 1); - for (size_t i = 0; i <= arg_types.size(); i++) - { - auto & column_type = (i == arg_types.size()) ? getReturnType() : arg_types[i]; - auto * type = llvm::PointerType::get(toNativeType(b, removeNullable(column_type)), 0); - columns_v[i].data_init = b.CreatePointerCast(b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 0)), type); - columns_v[i].stride = b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 2)); - if (column_type->isNullable()) - columns_v[i].null_init = b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 1)); - } - - for (size_t i = 0; i < arg_types.size(); i++) - { - by_name[arg_names[i]] = [&, &col = columns_v[i], i]() -> llvm::Value * - { - auto * value = b.CreateLoad(col.data); - if (!col.null) - return value; - auto * is_null = b.CreateICmpNE(b.CreateLoad(col.null), b.getInt8(0)); - auto * nullable = getDefaultNativeValue(toNativeType(b, arg_types[i])); - return b.CreateInsertValue(b.CreateInsertValue(nullable, value, {0}), is_null, {1}); - }; - } - for (const auto & action : actions) - { - ValuePlaceholders input; - for (const auto & name : action.argument_names) - input.push_back(by_name.at(name)); - by_name[action.result_name] = [&, input = std::move(input)]() { - auto * result = action.function->compile(b, input); - if (result->getType() != toNativeType(b, action.function->getReturnType())) - throw Exception("function " + action.function->getName() + " generated an llvm::Value of invalid type", - ErrorCodes::LOGICAL_ERROR); - return result; - }; - } - - /// assume nonzero initial value in `counter` - auto * loop = llvm::BasicBlock::Create(context->context, "loop", func); - b.CreateBr(loop); - b.SetInsertPoint(loop); - auto * counter_phi = b.CreatePHI(counter->getType(), 2); - counter_phi->addIncoming(counter, entry); - for (auto & col : columns_v) - { - col.data = b.CreatePHI(col.data_init->getType(), 2); - col.data->addIncoming(col.data_init, entry); - if (col.null_init) - { - col.null = b.CreatePHI(col.null_init->getType(), 2); - col.null->addIncoming(col.null_init, entry); - } - } - - auto * result = by_name.at(getName())(); - if (columns_v[arg_types.size()].null) - { - b.CreateStore(b.CreateExtractValue(result, {0}), columns_v[arg_types.size()].data); - b.CreateStore(b.CreateSelect(b.CreateExtractValue(result, {1}), b.getInt8(1), b.getInt8(0)), columns_v[arg_types.size()].null); - } - else - { - b.CreateStore(result, columns_v[arg_types.size()].data); - } - - auto * cur_block = b.GetInsertBlock(); - for (auto & col : columns_v) - { - auto * as_char = b.CreatePointerCast(col.data, b.getInt8PtrTy()); - auto * as_type = b.CreatePointerCast(b.CreateInBoundsGEP(as_char, col.stride), col.data->getType()); - col.data->addIncoming(as_type, cur_block); - if (col.null) - { - auto * is_const = b.CreateICmpEQ(col.stride, llvm::ConstantInt::get(size_type, 0)); - col.null->addIncoming(b.CreateSelect(is_const, col.null, b.CreateConstInBoundsGEP1_32(b.getInt8Ty(), col.null, 1)), cur_block); - } - } - counter_phi->addIncoming(b.CreateSub(counter_phi, llvm::ConstantInt::get(size_type, 1)), cur_block); - - auto * end = llvm::BasicBlock::Create(context->context, "end", func); - b.CreateCondBr(b.CreateICmpNE(counter_phi, llvm::ConstantInt::get(size_type, 1)), loop, end); - b.SetInsertPoint(end); - b.CreateRetVoid(); -} - static void applyFunction(IFunctionBase & function, Field & value) { const auto & type = function.getArgumentTypes().at(0); @@ -286,32 +83,376 @@ static void applyFunction(IFunctionBase & function, Field & value) block.safeGetByPosition(1).column->get(0, value); } -IFunctionBase::Monotonicity LLVMFunction::getMonotonicityForRange(const IDataType & type, const Field & left, const Field & right) const +struct LLVMContext { - const IDataType * type_ = &type; - Field left_ = left; - Field right_ = right; - Monotonicity result(true, true, true); - /// monotonicity is only defined for unary functions, so the chain must describe a sequence of nested calls - for (size_t i = 0; i < actions.size(); i++) + llvm::LLVMContext context; + std::shared_ptr module; + std::unique_ptr machine; + llvm::orc::RTDyldObjectLinkingLayer objectLayer; + llvm::orc::IRCompileLayer compileLayer; + llvm::DataLayout layout; + llvm::IRBuilder<> builder; + + LLVMContext() + : module(std::make_shared("jit", context)) + , machine(llvm::EngineBuilder().selectTarget()) + , objectLayer([]() { return std::make_shared(); }) + , compileLayer(objectLayer, llvm::orc::SimpleCompiler(*machine)) + , layout(machine->createDataLayout()) + , builder(context) { - Monotonicity m = actions[i].function->getMonotonicityForRange(*type_, left_, right_); - if (!m.is_monotonic) - return m; - result.is_positive ^= !m.is_positive; - result.is_always_monotonic &= m.is_always_monotonic; - if (i + 1 < actions.size()) + module->setDataLayout(layout); + module->setTargetTriple(machine->getTargetTriple().getTriple()); + } + + void finalize() + { + if (!module->size()) + return; + llvm::PassManagerBuilder builder; + llvm::legacy::FunctionPassManager fpm(module.get()); + builder.OptLevel = 3; + builder.SLPVectorize = true; + builder.LoopVectorize = true; + builder.RerollLoops = true; + builder.VerifyInput = true; + builder.VerifyOutput = true; + builder.populateFunctionPassManager(fpm); + for (auto & function : *module) + fpm.run(function); + llvm::cantFail(compileLayer.addModule(module, std::make_shared())); + } +}; + +class LLVMPreparedFunction : public IPreparedFunction +{ + std::string name; + std::shared_ptr context; + const void * function; + +public: + LLVMPreparedFunction(std::string name_, std::shared_ptr context) + : name(std::move(name_)), context(context) + { + std::string mangledName; + llvm::raw_string_ostream mangledNameStream(mangledName); + llvm::Mangler::getNameWithPrefix(mangledNameStream, name, context->layout); + function = reinterpret_cast(context->compileLayer.findSymbol(mangledNameStream.str(), false).getAddress().get()); + } + + String getName() const override { return name; } + + void execute(Block & block, const ColumnNumbers & arguments, size_t result) override + { + size_t block_size = block.rows(); + auto col_res = block.getByPosition(result).type->createColumn()->cloneResized(block_size); + if (block_size) { - if (left_ != Field()) - applyFunction(*actions[i].function, left_); - if (right_ != Field()) - applyFunction(*actions[i].function, right_); - if (!m.is_positive) - std::swap(left_, right_); - type_ = actions[i].function->getReturnType().get(); + std::vector columns(arguments.size() + 1); + for (size_t i = 0; i < arguments.size(); i++) + { + auto * column = block.getByPosition(arguments[i]).column.get(); + if (!column) + throw Exception("column " + block.getByPosition(arguments[i]).name + " is missing", ErrorCodes::LOGICAL_ERROR); + columns[i] = getColumnData(column); + } + columns[arguments.size()] = getColumnData(col_res.get()); + reinterpret_cast(function)(block_size, columns.data()); + } + block.getByPosition(result).column = std::move(col_res); + }; +}; + +class LLVMFunction : public IFunctionBase +{ + /// all actions must have type APPLY_FUNCTION + ExpressionActions::Actions actions; + Names arg_names; + DataTypes arg_types; + std::shared_ptr context; + +public: + LLVMFunction(ExpressionActions::Actions actions_, std::shared_ptr context, const Block & sample_block) + : actions(std::move(actions_)), context(context) + { + auto & b = context->builder; + auto * size_type = b.getIntNTy(sizeof(size_t) * 8); + auto * data_type = llvm::StructType::get(b.getInt8PtrTy(), b.getInt8PtrTy(), size_type); + auto * func_type = llvm::FunctionType::get(b.getVoidTy(), { size_type, llvm::PointerType::get(data_type, 0) }, /*isVarArg=*/false); + auto * func = llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, actions.back().result_name, context->module.get()); + auto args = func->args().begin(); + llvm::Value * counter = &*args++; + llvm::Value * columns = &*args++; + + auto * entry = llvm::BasicBlock::Create(context->context, "entry", func); + b.SetInsertPoint(entry); + + std::unordered_map> by_name; + for (const auto & c : sample_block) + if (auto * value = getNativeValue(toNativeType(b, c.type), c.column.get(), 0)) + by_name[c.name] = [=]() { return value; }; + + std::unordered_set seen; + for (const auto & action : actions) + { + const auto & names = action.argument_names; + const auto & types = action.function->getArgumentTypes(); + for (size_t i = 0; i < names.size(); i++) + { + if (!seen.emplace(names[i]).second || by_name.find(names[i]) != by_name.end()) + continue; + arg_names.push_back(names[i]); + arg_types.push_back(types[i]); + } + seen.insert(action.result_name); + } + + std::vector columns_v(arg_types.size() + 1); + for (size_t i = 0; i <= arg_types.size(); i++) + { + auto & column_type = (i == arg_types.size()) ? getReturnType() : arg_types[i]; + auto * type = llvm::PointerType::get(toNativeType(b, removeNullable(column_type)), 0); + columns_v[i].data_init = b.CreatePointerCast(b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 0)), type); + columns_v[i].stride = b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 2)); + if (column_type->isNullable()) + columns_v[i].null_init = b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 1)); + } + + for (size_t i = 0; i < arg_types.size(); i++) + { + by_name[arg_names[i]] = [&, &col = columns_v[i], i]() -> llvm::Value * + { + auto * value = b.CreateLoad(col.data); + if (!col.null) + return value; + auto * is_null = b.CreateICmpNE(b.CreateLoad(col.null), b.getInt8(0)); + auto * nullable = getDefaultNativeValue(toNativeType(b, arg_types[i])); + return b.CreateInsertValue(b.CreateInsertValue(nullable, value, {0}), is_null, {1}); + }; + } + for (const auto & action : actions) + { + ValuePlaceholders input; + for (const auto & name : action.argument_names) + input.push_back(by_name.at(name)); + by_name[action.result_name] = [&, input = std::move(input)]() { + auto * result = action.function->compile(b, input); + if (result->getType() != toNativeType(b, action.function->getReturnType())) + throw Exception("function " + action.function->getName() + " generated an llvm::Value of invalid type", + ErrorCodes::LOGICAL_ERROR); + return result; + }; + } + + /// assume nonzero initial value in `counter` + auto * loop = llvm::BasicBlock::Create(context->context, "loop", func); + b.CreateBr(loop); + b.SetInsertPoint(loop); + auto * counter_phi = b.CreatePHI(counter->getType(), 2); + counter_phi->addIncoming(counter, entry); + for (auto & col : columns_v) + { + col.data = b.CreatePHI(col.data_init->getType(), 2); + col.data->addIncoming(col.data_init, entry); + if (col.null_init) + { + col.null = b.CreatePHI(col.null_init->getType(), 2); + col.null->addIncoming(col.null_init, entry); + } + } + + auto * result = by_name.at(getName())(); + if (columns_v[arg_types.size()].null) + { + b.CreateStore(b.CreateExtractValue(result, {0}), columns_v[arg_types.size()].data); + b.CreateStore(b.CreateSelect(b.CreateExtractValue(result, {1}), b.getInt8(1), b.getInt8(0)), columns_v[arg_types.size()].null); + } + else + { + b.CreateStore(result, columns_v[arg_types.size()].data); + } + + auto * cur_block = b.GetInsertBlock(); + for (auto & col : columns_v) + { + auto * as_char = b.CreatePointerCast(col.data, b.getInt8PtrTy()); + auto * as_type = b.CreatePointerCast(b.CreateInBoundsGEP(as_char, col.stride), col.data->getType()); + col.data->addIncoming(as_type, cur_block); + if (col.null) + { + auto * is_const = b.CreateICmpEQ(col.stride, llvm::ConstantInt::get(size_type, 0)); + col.null->addIncoming(b.CreateSelect(is_const, col.null, b.CreateConstInBoundsGEP1_32(b.getInt8Ty(), col.null, 1)), cur_block); + } + } + counter_phi->addIncoming(b.CreateSub(counter_phi, llvm::ConstantInt::get(size_type, 1)), cur_block); + + auto * end = llvm::BasicBlock::Create(context->context, "end", func); + b.CreateCondBr(b.CreateICmpNE(counter_phi, llvm::ConstantInt::get(size_type, 1)), loop, end); + b.SetInsertPoint(end); + b.CreateRetVoid(); + } + + String getName() const override { return actions.back().result_name; } + + const Names & getArgumentNames() const { return arg_names; } + + const DataTypes & getArgumentTypes() const override { return arg_types; } + + const DataTypePtr & getReturnType() const override { return actions.back().function->getReturnType(); } + + PreparedFunctionPtr prepare(const Block &) const override { return std::make_shared(getName(), context); } + + bool isDeterministic() override + { + for (const auto & action : actions) + if (!action.function->isDeterministic()) + return false; + return true; + } + + bool isDeterministicInScopeOfQuery() override + { + for (const auto & action : actions) + if (!action.function->isDeterministicInScopeOfQuery()) + return false; + return true; + } + + bool isSuitableForConstantFolding() const override + { + for (const auto & action : actions) + if (!action.function->isSuitableForConstantFolding()) + return false; + return true; + } + + bool isInjective(const Block & sample_block) override + { + for (const auto & action : actions) + if (!action.function->isInjective(sample_block)) + return false; + return true; + } + + bool hasInformationAboutMonotonicity() const override + { + for (const auto & action : actions) + if (!action.function->hasInformationAboutMonotonicity()) + return false; + return true; + } + + Monotonicity getMonotonicityForRange(const IDataType & type, const Field & left, const Field & right) const override + { + const IDataType * type_ = &type; + Field left_ = left; + Field right_ = right; + Monotonicity result(true, true, true); + /// monotonicity is only defined for unary functions, so the chain must describe a sequence of nested calls + for (size_t i = 0; i < actions.size(); i++) + { + Monotonicity m = actions[i].function->getMonotonicityForRange(*type_, left_, right_); + if (!m.is_monotonic) + return m; + result.is_positive ^= !m.is_positive; + result.is_always_monotonic &= m.is_always_monotonic; + if (i + 1 < actions.size()) + { + if (left_ != Field()) + applyFunction(*actions[i].function, left_); + if (right_ != Field()) + applyFunction(*actions[i].function, right_); + if (!m.is_positive) + std::swap(left_, right_); + type_ = actions[i].function->getReturnType().get(); + } + } + return result; + } +}; + +static bool isCompilable(llvm::IRBuilderBase & builder, const IFunctionBase& function) +{ + if (!toNativeType(builder, function.getReturnType())) + return false; + for (const auto & type : function.getArgumentTypes()) + if (!toNativeType(builder, type)) + return false; + return function.isCompilable(); +} + +void compileFunctions(ExpressionActions::Actions & actions, const Names & output_columns, const Block & sample_block) +{ + auto context = std::make_shared(); + /// an empty optional is a poisoned value prohibiting the column's producer from being removed + /// (which it could be, if it was inlined into every dependent function). + std::unordered_map>> current_dependents; + for (const auto & name : output_columns) + current_dependents[name].emplace(); + /// a snapshot of each compilable function's dependents at the time of its execution. + std::vector>> dependents(actions.size()); + for (size_t i = actions.size(); i--;) + { + switch (actions[i].type) + { + case ExpressionAction::REMOVE_COLUMN: + current_dependents.erase(actions[i].source_name); + /// poison every other column used after this point so that inlining chains do not cross it. + for (auto & dep : current_dependents) + dep.second.emplace(); + break; + + case ExpressionAction::PROJECT: + current_dependents.clear(); + for (const auto & proj : actions[i].projection) + current_dependents[proj.first].emplace(); + break; + + case ExpressionAction::ADD_COLUMN: + case ExpressionAction::COPY_COLUMN: + case ExpressionAction::ARRAY_JOIN: + case ExpressionAction::JOIN: + { + Names columns = actions[i].getNeededColumns(); + for (const auto & column : columns) + current_dependents[column].emplace(); + break; + } + + case ExpressionAction::APPLY_FUNCTION: + { + dependents[i] = current_dependents[actions[i].result_name]; + const bool compilable = isCompilable(context->builder, *actions[i].function); + for (const auto & name : actions[i].argument_names) + { + if (compilable) + current_dependents[name].emplace(i); + else + current_dependents[name].emplace(); + } + break; + } } } - return result; + + std::vector fused(actions.size()); + for (size_t i = 0; i < actions.size(); i++) + { + if (actions[i].type != ExpressionAction::APPLY_FUNCTION || !isCompilable(context->builder, *actions[i].function)) + continue; + if (dependents[i].find({}) != dependents[i].end()) + { + fused[i].push_back(actions[i]); + auto fn = std::make_shared(std::move(fused[i]), context, sample_block); + actions[i].function = fn; + actions[i].argument_names = fn->getArgumentNames(); + continue; + } + /// TODO: determine whether it's profitable to inline the function if there's more than one dependent. + for (const auto & dep : dependents[i]) + fused[*dep].push_back(actions[i]); + } + context->finalize(); } } diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h index 75d16d9facf..5a7a39c9e21 100644 --- a/dbms/src/Interpreters/ExpressionJIT.h +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -1,110 +1,16 @@ #pragma once #include +#include #if USE_EMBEDDED_COMPILER -#include - -#include - namespace DB { -class LLVMContext -{ - struct Data; - std::shared_ptr shared; - -public: - LLVMContext(); - - void finalize(); - - bool isCompilable(const IFunctionBase& function) const; - - Data * operator->() const { - return shared.get(); - } -}; - -class LLVMPreparedFunction : public IPreparedFunction -{ - std::shared_ptr parent; - LLVMContext context; - const void * function; - -public: - LLVMPreparedFunction(LLVMContext context, std::shared_ptr parent); - - String getName() const override { return parent->getName(); } - - void execute(Block & block, const ColumnNumbers & arguments, size_t result) override; -}; - -class LLVMFunction : public std::enable_shared_from_this, public IFunctionBase -{ - /// all actions must have type APPLY_FUNCTION - ExpressionActions::Actions actions; - Names arg_names; - DataTypes arg_types; - LLVMContext context; - -public: - LLVMFunction(ExpressionActions::Actions actions, LLVMContext context, const Block & sample_block); - - String getName() const override { return actions.back().result_name; } - - const Names & getArgumentNames() const { return arg_names; } - - const DataTypes & getArgumentTypes() const override { return arg_types; } - - const DataTypePtr & getReturnType() const override { return actions.back().function->getReturnType(); } - - PreparedFunctionPtr prepare(const Block &) const override { return std::make_shared(context, shared_from_this()); } - - bool isDeterministic() override - { - for (const auto & action : actions) - if (!action.function->isDeterministic()) - return false; - return true; - } - - bool isDeterministicInScopeOfQuery() override - { - for (const auto & action : actions) - if (!action.function->isDeterministicInScopeOfQuery()) - return false; - return true; - } - - bool isSuitableForConstantFolding() const override - { - for (const auto & action : actions) - if (!action.function->isSuitableForConstantFolding()) - return false; - return true; - } - - bool isInjective(const Block & sample_block) override - { - for (const auto & action : actions) - if (!action.function->isInjective(sample_block)) - return false; - return true; - } - - bool hasInformationAboutMonotonicity() const override - { - for (const auto & action : actions) - if (!action.function->hasInformationAboutMonotonicity()) - return false; - return true; - } - - Monotonicity getMonotonicityForRange(const IDataType & type, const Field & left, const Field & right) const override; -}; +/// For each APPLY_FUNCTION action, try to compile the function to native code; if the only uses of a compilable +/// function's result are as arguments to other compilable functions, inline it and leave the now-redundant action as-is. +void compileFunctions(ExpressionActions::Actions & actions, const Names & output_columns, const Block & sample_block); } From fb577b1049f29bf7d957ccc5ba10f1a1fadfa24a Mon Sep 17 00:00:00 2001 From: pyos Date: Sun, 29 Apr 2018 13:47:03 +0300 Subject: [PATCH 043/231] Hide the whole JIT API behind #if USE_EMBEDDED_COMPILER Kind ugly, but at least the conditionals are used consistently now. --- dbms/src/Functions/FunctionsLLVMTest.cpp | 3 --- dbms/src/Functions/IFunction.cpp | 6 +++-- dbms/src/Functions/IFunction.h | 32 ++++++++++++++++++++---- 3 files changed, 31 insertions(+), 10 deletions(-) diff --git a/dbms/src/Functions/FunctionsLLVMTest.cpp b/dbms/src/Functions/FunctionsLLVMTest.cpp index 8619c5b0201..6664c204466 100644 --- a/dbms/src/Functions/FunctionsLLVMTest.cpp +++ b/dbms/src/Functions/FunctionsLLVMTest.cpp @@ -1,12 +1,9 @@ -#include #include #include #include #if USE_EMBEDDED_COMPILER -#include #include -#include #endif diff --git a/dbms/src/Functions/IFunction.cpp b/dbms/src/Functions/IFunction.cpp index c55d293ec29..158210406b7 100644 --- a/dbms/src/Functions/IFunction.cpp +++ b/dbms/src/Functions/IFunction.cpp @@ -261,6 +261,8 @@ DataTypePtr FunctionBuilderImpl::getReturnType(const ColumnsWithTypeAndName & ar return getReturnTypeImpl(arguments); } +#if USE_EMBEDDED_COMPILER + static std::optional removeNullables(const DataTypes & types) { for (const auto & type : types) @@ -285,7 +287,6 @@ bool IFunction::isCompilable(const DataTypes & arguments) const llvm::Value * IFunction::compile(llvm::IRBuilderBase & builder, const DataTypes & arguments, ValuePlaceholders values) const { -#if USE_EMBEDDED_COMPILER if (useDefaultImplementationForNulls()) { if (auto denulled = removeNullables(arguments)) @@ -322,8 +323,9 @@ llvm::Value * IFunction::compile(llvm::IRBuilderBase & builder, const DataTypes return phi; } } -#endif return compileImpl(builder, arguments, std::move(values)); } +#endif + } diff --git a/dbms/src/Functions/IFunction.h b/dbms/src/Functions/IFunction.h index 107c38b7e84..cd282d38180 100644 --- a/dbms/src/Functions/IFunction.h +++ b/dbms/src/Functions/IFunction.h @@ -2,6 +2,7 @@ #include +#include #include #include #include @@ -100,6 +101,8 @@ public: return prepare(block)->execute(block, arguments, result); } +#if USE_EMBEDDED_COMPILER + virtual bool isCompilable() const { return false; } /** Produce LLVM IR code that operates on scalar values. See `toNativeType` in DataTypes/Native.h @@ -115,6 +118,8 @@ public: throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED); } +#endif + /** Should we evaluate this function while constant folding, if arguments are constants? * Usually this is true. Notable counterexample is function 'sleep'. * If we will call it during query analysis, we will sleep extra amount of time. @@ -286,21 +291,25 @@ public: using FunctionBuilderImpl::getLambdaArgumentTypesImpl; using FunctionBuilderImpl::getReturnType; - bool isCompilable() const final - { - throw Exception("isCompilable without explicit types is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); - } - PreparedFunctionPtr prepare(const Block & /*sample_block*/) const final { throw Exception("prepare is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); } +#if USE_EMBEDDED_COMPILER + + bool isCompilable() const final + { + throw Exception("isCompilable without explicit types is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); + } + llvm::Value * compile(llvm::IRBuilderBase & /*builder*/, ValuePlaceholders /*values*/) const final { throw Exception("compile without explicit types is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); } +#endif + const DataTypes & getArgumentTypes() const final { throw Exception("getArgumentTypes is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); @@ -311,11 +320,18 @@ public: throw Exception("getReturnType is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); } +#if USE_EMBEDDED_COMPILER + bool isCompilable(const DataTypes & arguments) const; llvm::Value * compile(llvm::IRBuilderBase &, const DataTypes & arguments, ValuePlaceholders values) const; +#endif + protected: + +#if USE_EMBEDDED_COMPILER + virtual bool isCompilableImpl(const DataTypes &) const { return false; } virtual llvm::Value * compileImpl(llvm::IRBuilderBase &, const DataTypes &, ValuePlaceholders) const @@ -323,6 +339,8 @@ protected: throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED); } +#endif + FunctionBasePtr buildImpl(const ColumnsWithTypeAndName & /*arguments*/, const DataTypePtr & /*return_type*/) const final { throw Exception("buildImpl is not implemented for IFunction", ErrorCodes::NOT_IMPLEMENTED); @@ -362,10 +380,14 @@ public: const DataTypes & getArgumentTypes() const override { return arguments; } const DataTypePtr & getReturnType() const override { return return_type; } +#if USE_EMBEDDED_COMPILER + bool isCompilable() const override { return function->isCompilable(arguments); } llvm::Value * compile(llvm::IRBuilderBase & builder, ValuePlaceholders values) const override { return function->compile(builder, arguments, std::move(values)); } +#endif + PreparedFunctionPtr prepare(const Block & /*sample_block*/) const override { return std::make_shared(function); } bool isSuitableForConstantFolding() const override { return function->isSuitableForConstantFolding(); } From 1be009d4850f5038bf839003763e867b1b8d42de Mon Sep 17 00:00:00 2001 From: pyos Date: Sun, 29 Apr 2018 20:32:30 +0300 Subject: [PATCH 044/231] Remove getDefaultNativeValue in favor of llvm::Constant::getNullValue --- dbms/src/DataTypes/Native.h | 14 -------------- dbms/src/Functions/IFunction.cpp | 12 ++++++------ dbms/src/Interpreters/ExpressionJIT.cpp | 2 +- 3 files changed, 7 insertions(+), 21 deletions(-) diff --git a/dbms/src/DataTypes/Native.h b/dbms/src/DataTypes/Native.h index bbf0d4c5ae3..7da94a0e11c 100644 --- a/dbms/src/DataTypes/Native.h +++ b/dbms/src/DataTypes/Native.h @@ -55,20 +55,6 @@ static inline llvm::Type * toNativeType(llvm::IRBuilderBase & builder, const Dat return toNativeType(builder, *type); } -static inline llvm::Constant * getDefaultNativeValue(llvm::Type * type) -{ - if (type->isIntegerTy()) - return llvm::ConstantInt::get(type, 0); - if (type->isFloatTy() || type->isDoubleTy()) - return llvm::ConstantFP::get(type, 0.0); - if (type->isVectorTy()) - return llvm::ConstantVector::getSplat(type->getVectorNumElements(), getDefaultNativeValue(type->getVectorElementType())); - /// else nullable - auto * value = getDefaultNativeValue(type->getContainedType(0)); - auto * is_null = llvm::ConstantInt::get(type->getContainedType(1), 1); - return llvm::ConstantStruct::get(static_cast(type), value, is_null); -} - static inline llvm::Constant * getNativeValue(llvm::Type * type, const IColumn * column, size_t i) { if (!column || !type) diff --git a/dbms/src/Functions/IFunction.cpp b/dbms/src/Functions/IFunction.cpp index 158210406b7..2432cd43814 100644 --- a/dbms/src/Functions/IFunction.cpp +++ b/dbms/src/Functions/IFunction.cpp @@ -296,7 +296,7 @@ llvm::Value * IFunction::compile(llvm::IRBuilderBase & builder, const DataTypes auto & b = static_cast &>(builder); auto * fail = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); auto * join = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); - auto * init = getDefaultNativeValue(toNativeType(b, makeNullable(getReturnTypeImpl(*denulled)))); + auto * zero = llvm::Constant::getNullValue(toNativeType(b, makeNullable(getReturnTypeImpl(*denulled)))); for (size_t i = 0; i < arguments.size(); i++) { if (!arguments[i]->isNullable()) @@ -310,16 +310,16 @@ llvm::Value * IFunction::compile(llvm::IRBuilderBase & builder, const DataTypes return b.CreateExtractValue(value, {0}); }; } - auto * result = compileImpl(builder, *denulled, std::move(values)); - auto * result_nullable = b.CreateInsertValue(b.CreateInsertValue(init, result, {0}), b.getFalse(), {1}); + auto * result = b.CreateInsertValue(zero, compileImpl(builder, *denulled, std::move(values)), {0}); auto * result_block = b.GetInsertBlock(); b.CreateBr(join); b.SetInsertPoint(fail); /// an empty joining block to avoid keeping track of where we could jump from + auto * null = b.CreateInsertValue(zero, b.getTrue(), {1}); b.CreateBr(join); b.SetInsertPoint(join); - auto * phi = b.CreatePHI(result_nullable->getType(), 2); - phi->addIncoming(result_nullable, result_block); - phi->addIncoming(init, fail); + auto * phi = b.CreatePHI(result->getType(), 2); + phi->addIncoming(result, result_block); + phi->addIncoming(null, fail); return phi; } } diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 24a35ee9c4f..ed3a967be68 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -226,7 +226,7 @@ public: if (!col.null) return value; auto * is_null = b.CreateICmpNE(b.CreateLoad(col.null), b.getInt8(0)); - auto * nullable = getDefaultNativeValue(toNativeType(b, arg_types[i])); + auto * nullable = llvm::Constant::getNullValue(toNativeType(b, arg_types[i])); return b.CreateInsertValue(b.CreateInsertValue(nullable, value, {0}), is_null, {1}); }; } From 72f2fea837b84c77456484bba99a2930a4c0ca31 Mon Sep 17 00:00:00 2001 From: pyos Date: Sun, 29 Apr 2018 21:03:58 +0300 Subject: [PATCH 045/231] Extract the code that compiles a single IFunctionBase from LLVMFunction --- dbms/src/DataTypes/Native.h | 22 -- dbms/src/Interpreters/ExpressionJIT.cpp | 305 ++++++++++++++---------- 2 files changed, 175 insertions(+), 152 deletions(-) diff --git a/dbms/src/DataTypes/Native.h b/dbms/src/DataTypes/Native.h index 7da94a0e11c..6c5f224b698 100644 --- a/dbms/src/DataTypes/Native.h +++ b/dbms/src/DataTypes/Native.h @@ -55,28 +55,6 @@ static inline llvm::Type * toNativeType(llvm::IRBuilderBase & builder, const Dat return toNativeType(builder, *type); } -static inline llvm::Constant * getNativeValue(llvm::Type * type, const IColumn * column, size_t i) -{ - if (!column || !type) - return nullptr; - if (auto * constant = typeid_cast(column)) - return getNativeValue(type, &constant->getDataColumn(), 0); - if (auto * nullable = typeid_cast(column)) - { - auto * value = getNativeValue(type->getContainedType(0), &nullable->getNestedColumn(), i); - auto * is_null = llvm::ConstantInt::get(type->getContainedType(1), nullable->isNullAt(i)); - return value ? llvm::ConstantStruct::get(static_cast(type), value, is_null) : nullptr; - } - if (type->isFloatTy()) - return llvm::ConstantFP::get(type, static_cast *>(column)->getElement(i)); - if (type->isDoubleTy()) - return llvm::ConstantFP::get(type, static_cast *>(column)->getElement(i)); - if (type->isIntegerTy()) - return llvm::ConstantInt::get(type, column->getUInt(i)); - /// TODO: if (type->isVectorTy()) - return nullptr; -} - } #endif diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index ed3a967be68..ae0c36aa8b6 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -49,7 +49,7 @@ namespace size_t stride; }; - struct ColumnDataPlaceholders + struct ColumnDataPlaceholder { llvm::Value * data_init; /// first row llvm::Value * null_init; @@ -163,181 +163,226 @@ public: }; }; +static void compileFunction(std::shared_ptr & context, const IFunctionBase & f) +{ + auto & arg_types = f.getArgumentTypes(); + auto & b = context->builder; + auto * size_type = b.getIntNTy(sizeof(size_t) * 8); + auto * data_type = llvm::StructType::get(b.getInt8PtrTy(), b.getInt8PtrTy(), size_type); + auto * func_type = llvm::FunctionType::get(b.getVoidTy(), { size_type, llvm::PointerType::get(data_type, 0) }, /*isVarArg=*/false); + auto * func = llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, f.getName(), context->module.get()); + auto args = func->args().begin(); + llvm::Value * counter_arg = &*args++; + llvm::Value * columns_arg = &*args++; + + auto * entry = llvm::BasicBlock::Create(b.getContext(), "entry", func); + b.SetInsertPoint(entry); + std::vector columns(arg_types.size() + 1); + for (size_t i = 0; i <= arg_types.size(); i++) + { + auto & type = i == arg_types.size() ? f.getReturnType() : arg_types[i]; + auto * native = llvm::PointerType::get(toNativeType(b, removeNullable(type)), 0); + columns[i].data_init = b.CreatePointerCast(b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns_arg, i, 0)), native); + columns[i].null_init = type->isNullable() ? b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns_arg, i, 1)) : nullptr; + columns[i].stride = b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns_arg, i, 2)); + } + + /// assume nonzero initial value in `counter_arg` + auto * loop = llvm::BasicBlock::Create(b.getContext(), "loop", func); + b.CreateBr(loop); + b.SetInsertPoint(loop); + auto * counter_phi = b.CreatePHI(counter_arg->getType(), 2); + counter_phi->addIncoming(counter_arg, entry); + for (auto & col : columns) + { + col.data = b.CreatePHI(col.data_init->getType(), 2); + col.data->addIncoming(col.data_init, entry); + if (col.null_init) + { + col.null = b.CreatePHI(col.null_init->getType(), 2); + col.null->addIncoming(col.null_init, entry); + } + } + ValuePlaceholders arguments(arg_types.size()); + for (size_t i = 0; i < arguments.size(); i++) + { + arguments[i] = [&b, &col = columns[i], &type = arg_types[i]]() -> llvm::Value * + { + auto * value = b.CreateLoad(col.data); + if (!col.null) + return value; + auto * is_null = b.CreateICmpNE(b.CreateLoad(col.null), b.getInt8(0)); + auto * nullable = llvm::Constant::getNullValue(toNativeType(b, type)); + return b.CreateInsertValue(b.CreateInsertValue(nullable, value, {0}), is_null, {1}); + }; + } + auto * result = f.compile(b, std::move(arguments)); + if (columns.back().null) + { + b.CreateStore(b.CreateExtractValue(result, {0}), columns.back().data); + b.CreateStore(b.CreateSelect(b.CreateExtractValue(result, {1}), b.getInt8(1), b.getInt8(0)), columns.back().null); + } + else + { + b.CreateStore(result, columns.back().data); + } + auto * cur_block = b.GetInsertBlock(); + for (auto & col : columns) + { + auto * as_char = b.CreatePointerCast(col.data, b.getInt8PtrTy()); + auto * as_type = b.CreatePointerCast(b.CreateInBoundsGEP(as_char, col.stride), col.data->getType()); + col.data->addIncoming(as_type, cur_block); + if (col.null) + { + auto * is_const = b.CreateICmpEQ(col.stride, llvm::ConstantInt::get(size_type, 0)); + col.null->addIncoming(b.CreateSelect(is_const, col.null, b.CreateConstInBoundsGEP1_32(b.getInt8Ty(), col.null, 1)), cur_block); + } + } + counter_phi->addIncoming(b.CreateSub(counter_phi, llvm::ConstantInt::get(size_type, 1)), cur_block); + + auto * end = llvm::BasicBlock::Create(b.getContext(), "end", func); + b.CreateCondBr(b.CreateICmpNE(counter_phi, llvm::ConstantInt::get(size_type, 1)), loop, end); + b.SetInsertPoint(end); + b.CreateRetVoid(); +} + +static llvm::Constant * getNativeValue(llvm::Type * type, const IColumn & column, size_t i) +{ + if (!type) + return nullptr; + if (auto * constant = typeid_cast(&column)) + return getNativeValue(type, constant->getDataColumn(), 0); + if (auto * nullable = typeid_cast(&column)) + { + auto * value = getNativeValue(type->getContainedType(0), nullable->getNestedColumn(), i); + auto * is_null = llvm::ConstantInt::get(type->getContainedType(1), nullable->isNullAt(i)); + return value ? llvm::ConstantStruct::get(static_cast(type), value, is_null) : nullptr; + } + if (type->isFloatTy()) + return llvm::ConstantFP::get(type, static_cast &>(column).getElement(i)); + if (type->isDoubleTy()) + return llvm::ConstantFP::get(type, static_cast &>(column).getElement(i)); + if (type->isIntegerTy()) + return llvm::ConstantInt::get(type, column.getUInt(i)); + /// TODO: if (type->isVectorTy()) + return nullptr; +} + +/// Same as IFunctionBase::compile, but also for constants and input columns. +using CompilableExpression = std::function; + +static CompilableExpression subexpression(ColumnPtr c, DataTypePtr type) +{ + return [=](llvm::IRBuilderBase & b, const ValuePlaceholders &) { return getNativeValue(toNativeType(b, type), *c, 0); }; +} + +static CompilableExpression subexpression(size_t i) +{ + return [=](llvm::IRBuilderBase &, const ValuePlaceholders & inputs) { return inputs[i](); }; +} + +static CompilableExpression subexpression(const IFunctionBase & f, std::vector args) +{ + return [&, args = std::move(args)](llvm::IRBuilderBase & builder, const ValuePlaceholders & inputs) + { + ValuePlaceholders input; + for (const auto & arg : args) + input.push_back([&]() { return arg(builder, inputs); }); + auto * result = f.compile(builder, input); + if (result->getType() != toNativeType(builder, f.getReturnType())) + throw Exception("function " + f.getName() + " generated an llvm::Value of invalid type", ErrorCodes::LOGICAL_ERROR); + return result; + }; +} + class LLVMFunction : public IFunctionBase { - /// all actions must have type APPLY_FUNCTION - ExpressionActions::Actions actions; + std::string name; Names arg_names; DataTypes arg_types; std::shared_ptr context; + std::vector originals; + std::unordered_map subexpressions; public: - LLVMFunction(ExpressionActions::Actions actions_, std::shared_ptr context, const Block & sample_block) - : actions(std::move(actions_)), context(context) + LLVMFunction(const ExpressionActions::Actions & actions, std::shared_ptr context, const Block & sample_block) + : name(actions.back().result_name), context(context) { - auto & b = context->builder; - auto * size_type = b.getIntNTy(sizeof(size_t) * 8); - auto * data_type = llvm::StructType::get(b.getInt8PtrTy(), b.getInt8PtrTy(), size_type); - auto * func_type = llvm::FunctionType::get(b.getVoidTy(), { size_type, llvm::PointerType::get(data_type, 0) }, /*isVarArg=*/false); - auto * func = llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, actions.back().result_name, context->module.get()); - auto args = func->args().begin(); - llvm::Value * counter = &*args++; - llvm::Value * columns = &*args++; - - auto * entry = llvm::BasicBlock::Create(context->context, "entry", func); - b.SetInsertPoint(entry); - - std::unordered_map> by_name; for (const auto & c : sample_block) - if (auto * value = getNativeValue(toNativeType(b, c.type), c.column.get(), 0)) - by_name[c.name] = [=]() { return value; }; - - std::unordered_set seen; + /// TODO: implement `getNativeValue` for all types & replace the check with `c.column && toNativeType(...)` + if (c.column && getNativeValue(toNativeType(context->builder, c.type), *c.column, 0)) + subexpressions[c.name] = subexpression(c.column, c.type); for (const auto & action : actions) { const auto & names = action.argument_names; const auto & types = action.function->getArgumentTypes(); + std::vector args; for (size_t i = 0; i < names.size(); i++) { - if (!seen.emplace(names[i]).second || by_name.find(names[i]) != by_name.end()) - continue; - arg_names.push_back(names[i]); - arg_types.push_back(types[i]); + auto inserted = subexpressions.emplace(names[i], subexpression(arg_names.size())); + if (inserted.second) + { + arg_names.push_back(names[i]); + arg_types.push_back(types[i]); + } + args.push_back(inserted.first->second); } - seen.insert(action.result_name); + subexpressions[action.result_name] = subexpression(*action.function, std::move(args)); + originals.push_back(action.function); } - - std::vector columns_v(arg_types.size() + 1); - for (size_t i = 0; i <= arg_types.size(); i++) - { - auto & column_type = (i == arg_types.size()) ? getReturnType() : arg_types[i]; - auto * type = llvm::PointerType::get(toNativeType(b, removeNullable(column_type)), 0); - columns_v[i].data_init = b.CreatePointerCast(b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 0)), type); - columns_v[i].stride = b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 2)); - if (column_type->isNullable()) - columns_v[i].null_init = b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns, i, 1)); - } - - for (size_t i = 0; i < arg_types.size(); i++) - { - by_name[arg_names[i]] = [&, &col = columns_v[i], i]() -> llvm::Value * - { - auto * value = b.CreateLoad(col.data); - if (!col.null) - return value; - auto * is_null = b.CreateICmpNE(b.CreateLoad(col.null), b.getInt8(0)); - auto * nullable = llvm::Constant::getNullValue(toNativeType(b, arg_types[i])); - return b.CreateInsertValue(b.CreateInsertValue(nullable, value, {0}), is_null, {1}); - }; - } - for (const auto & action : actions) - { - ValuePlaceholders input; - for (const auto & name : action.argument_names) - input.push_back(by_name.at(name)); - by_name[action.result_name] = [&, input = std::move(input)]() { - auto * result = action.function->compile(b, input); - if (result->getType() != toNativeType(b, action.function->getReturnType())) - throw Exception("function " + action.function->getName() + " generated an llvm::Value of invalid type", - ErrorCodes::LOGICAL_ERROR); - return result; - }; - } - - /// assume nonzero initial value in `counter` - auto * loop = llvm::BasicBlock::Create(context->context, "loop", func); - b.CreateBr(loop); - b.SetInsertPoint(loop); - auto * counter_phi = b.CreatePHI(counter->getType(), 2); - counter_phi->addIncoming(counter, entry); - for (auto & col : columns_v) - { - col.data = b.CreatePHI(col.data_init->getType(), 2); - col.data->addIncoming(col.data_init, entry); - if (col.null_init) - { - col.null = b.CreatePHI(col.null_init->getType(), 2); - col.null->addIncoming(col.null_init, entry); - } - } - - auto * result = by_name.at(getName())(); - if (columns_v[arg_types.size()].null) - { - b.CreateStore(b.CreateExtractValue(result, {0}), columns_v[arg_types.size()].data); - b.CreateStore(b.CreateSelect(b.CreateExtractValue(result, {1}), b.getInt8(1), b.getInt8(0)), columns_v[arg_types.size()].null); - } - else - { - b.CreateStore(result, columns_v[arg_types.size()].data); - } - - auto * cur_block = b.GetInsertBlock(); - for (auto & col : columns_v) - { - auto * as_char = b.CreatePointerCast(col.data, b.getInt8PtrTy()); - auto * as_type = b.CreatePointerCast(b.CreateInBoundsGEP(as_char, col.stride), col.data->getType()); - col.data->addIncoming(as_type, cur_block); - if (col.null) - { - auto * is_const = b.CreateICmpEQ(col.stride, llvm::ConstantInt::get(size_type, 0)); - col.null->addIncoming(b.CreateSelect(is_const, col.null, b.CreateConstInBoundsGEP1_32(b.getInt8Ty(), col.null, 1)), cur_block); - } - } - counter_phi->addIncoming(b.CreateSub(counter_phi, llvm::ConstantInt::get(size_type, 1)), cur_block); - - auto * end = llvm::BasicBlock::Create(context->context, "end", func); - b.CreateCondBr(b.CreateICmpNE(counter_phi, llvm::ConstantInt::get(size_type, 1)), loop, end); - b.SetInsertPoint(end); - b.CreateRetVoid(); + compileFunction(context, *this); } - String getName() const override { return actions.back().result_name; } + bool isCompilable() const override { return true; } + + llvm::Value * compile(llvm::IRBuilderBase & builder, ValuePlaceholders values) const override { return subexpressions.at(name)(builder, values); } + + String getName() const override { return name; } const Names & getArgumentNames() const { return arg_names; } const DataTypes & getArgumentTypes() const override { return arg_types; } - const DataTypePtr & getReturnType() const override { return actions.back().function->getReturnType(); } + const DataTypePtr & getReturnType() const override { return originals.back()->getReturnType(); } - PreparedFunctionPtr prepare(const Block &) const override { return std::make_shared(getName(), context); } + PreparedFunctionPtr prepare(const Block &) const override { return std::make_shared(name, context); } bool isDeterministic() override { - for (const auto & action : actions) - if (!action.function->isDeterministic()) + for (const auto & f : originals) + if (!f->isDeterministic()) return false; return true; } bool isDeterministicInScopeOfQuery() override { - for (const auto & action : actions) - if (!action.function->isDeterministicInScopeOfQuery()) + for (const auto & f : originals) + if (!f->isDeterministicInScopeOfQuery()) return false; return true; } bool isSuitableForConstantFolding() const override { - for (const auto & action : actions) - if (!action.function->isSuitableForConstantFolding()) + for (const auto & f : originals) + if (!f->isSuitableForConstantFolding()) return false; return true; } bool isInjective(const Block & sample_block) override { - for (const auto & action : actions) - if (!action.function->isInjective(sample_block)) + for (const auto & f : originals) + if (!f->isInjective(sample_block)) return false; return true; } bool hasInformationAboutMonotonicity() const override { - for (const auto & action : actions) - if (!action.function->hasInformationAboutMonotonicity()) + for (const auto & f : originals) + if (!f->hasInformationAboutMonotonicity()) return false; return true; } @@ -349,22 +394,22 @@ public: Field right_ = right; Monotonicity result(true, true, true); /// monotonicity is only defined for unary functions, so the chain must describe a sequence of nested calls - for (size_t i = 0; i < actions.size(); i++) + for (size_t i = 0; i < originals.size(); i++) { - Monotonicity m = actions[i].function->getMonotonicityForRange(*type_, left_, right_); + Monotonicity m = originals[i]->getMonotonicityForRange(*type_, left_, right_); if (!m.is_monotonic) return m; result.is_positive ^= !m.is_positive; result.is_always_monotonic &= m.is_always_monotonic; - if (i + 1 < actions.size()) + if (i + 1 < originals.size()) { if (left_ != Field()) - applyFunction(*actions[i].function, left_); + applyFunction(*originals[i], left_); if (right_ != Field()) - applyFunction(*actions[i].function, right_); + applyFunction(*originals[i], right_); if (!m.is_positive) std::swap(left_, right_); - type_ = actions[i].function->getReturnType().get(); + type_ = originals[i]->getReturnType().get(); } } return result; From 7529aa55a47cb13a3df848fc3484f156783e0caf Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 30 Apr 2018 01:23:27 +0300 Subject: [PATCH 046/231] Fix a bug that limited inlining depth at 2 --- dbms/src/Interpreters/ExpressionActions.cpp | 1 - dbms/src/Interpreters/ExpressionJIT.cpp | 4 ++-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionActions.cpp b/dbms/src/Interpreters/ExpressionActions.cpp index fe5ee96cdb3..17934285129 100644 --- a/dbms/src/Interpreters/ExpressionActions.cpp +++ b/dbms/src/Interpreters/ExpressionActions.cpp @@ -12,7 +12,6 @@ #include #include -#include namespace ProfileEvents diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index ae0c36aa8b6..ea38c6a777d 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -485,9 +485,9 @@ void compileFunctions(ExpressionActions::Actions & actions, const Names & output { if (actions[i].type != ExpressionAction::APPLY_FUNCTION || !isCompilable(context->builder, *actions[i].function)) continue; + fused[i].push_back(actions[i]); if (dependents[i].find({}) != dependents[i].end()) { - fused[i].push_back(actions[i]); auto fn = std::make_shared(std::move(fused[i]), context, sample_block); actions[i].function = fn; actions[i].argument_names = fn->getArgumentNames(); @@ -495,7 +495,7 @@ void compileFunctions(ExpressionActions::Actions & actions, const Names & output } /// TODO: determine whether it's profitable to inline the function if there's more than one dependent. for (const auto & dep : dependents[i]) - fused[*dep].push_back(actions[i]); + fused[*dep].insert(fused[*dep].end(), fused[i].begin(), fused[i].end()); } context->finalize(); } From 059bbcacca0e7bb6891a1f1bc6453b67edf79c36 Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 30 Apr 2018 01:43:02 +0300 Subject: [PATCH 047/231] Implement jit for most arithmetic functions, remove the test function --- dbms/src/DataTypes/Native.h | 16 ++ dbms/src/Functions/CMakeLists.txt | 7 +- dbms/src/Functions/FunctionHelpers.cpp | 6 +- dbms/src/Functions/FunctionsArithmetic.h | 308 +++++++++++++++++++++-- dbms/src/Functions/FunctionsLLVMTest.cpp | 60 ----- dbms/src/Functions/FunctionsRound.h | 12 + dbms/src/Functions/registerFunctions.cpp | 2 - 7 files changed, 322 insertions(+), 89 deletions(-) delete mode 100644 dbms/src/Functions/FunctionsLLVMTest.cpp diff --git a/dbms/src/DataTypes/Native.h b/dbms/src/DataTypes/Native.h index 6c5f224b698..d3b7646188e 100644 --- a/dbms/src/DataTypes/Native.h +++ b/dbms/src/DataTypes/Native.h @@ -4,6 +4,7 @@ #if USE_EMBEDDED_COMPILER +#include #include #include #include @@ -55,6 +56,21 @@ static inline llvm::Type * toNativeType(llvm::IRBuilderBase & builder, const Dat return toNativeType(builder, *type); } +static inline llvm::Value * castNativeNumber(llvm::IRBuilder<> & builder, llvm::Value * value, llvm::Type * type, bool is_signed) +{ + if (value->getType() == type) + return value; + if (value->getType()->isIntegerTy()) + { + if (type->isIntegerTy()) + return builder.CreateIntCast(value, type, is_signed); + return is_signed ? builder.CreateSIToFP(value, type) : builder.CreateUIToFP(value, type); + } + if (type->isFloatingPointTy()) + return builder.CreateFPCast(value, type); + return is_signed ? builder.CreateFPToSI(value, type) : builder.CreateFPToUI(value, type); +} + } #endif diff --git a/dbms/src/Functions/CMakeLists.txt b/dbms/src/Functions/CMakeLists.txt index 2c6a77726f9..671e24c6fb3 100644 --- a/dbms/src/Functions/CMakeLists.txt +++ b/dbms/src/Functions/CMakeLists.txt @@ -104,10 +104,7 @@ if (ENABLE_TESTS) endif () if (USE_EMBEDDED_COMPILER) - #llvm_map_components_to_libraries(REQUIRED_LLVM_LIBRARIES all) - #target_link_libraries(clickhouse_functions PRIVATE ${REQUIRED_LLVM_LIBRARIES}) target_include_directories (clickhouse_functions BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) - # LLVM 5.0 has a bunch of unused parameters in its header files. - # TODO: global-disable this warning - set_source_files_properties(FunctionsLLVMTest.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter -g") + # LLVM has a bunch of unused parameters in its header files. + target_compile_options (clickhouse_functions PRIVATE "-Wno-unused-parameter") endif () diff --git a/dbms/src/Functions/FunctionHelpers.cpp b/dbms/src/Functions/FunctionHelpers.cpp index 33aa6928b5c..144a6c6fa4f 100644 --- a/dbms/src/Functions/FunctionHelpers.cpp +++ b/dbms/src/Functions/FunctionHelpers.cpp @@ -5,12 +5,16 @@ #include #include #include -#include "FunctionsArithmetic.h" namespace DB { +namespace ErrorCodes +{ + extern const int ILLEGAL_COLUMN; +} + const ColumnConst * checkAndGetColumnConstStringOrFixedString(const IColumn * column) { if (!column->isColumnConst()) diff --git a/dbms/src/Functions/FunctionsArithmetic.h b/dbms/src/Functions/FunctionsArithmetic.h index 69651f8c9de..31da1362e9b 100644 --- a/dbms/src/Functions/FunctionsArithmetic.h +++ b/dbms/src/Functions/FunctionsArithmetic.h @@ -4,6 +4,7 @@ #include #include #include +#include #include #include #include @@ -19,6 +20,10 @@ #include #include +#if USE_EMBEDDED_COMPILER +#include +#endif + namespace DB { @@ -106,6 +111,15 @@ struct PlusImpl /// Next everywhere, static_cast - so that there is no wrong result in expressions of the form Int64 c = UInt32(a) * Int32(-1). return static_cast(a) + b; } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * left, llvm::Value * right, bool) + { + return left->getType()->isIntegerTy() ? b.CreateAdd(left, right) : b.CreateFAdd(left, right); + } +#endif }; @@ -119,6 +133,15 @@ struct MultiplyImpl { return static_cast(a) * b; } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * left, llvm::Value * right, bool) + { + return left->getType()->isIntegerTy() ? b.CreateMul(left, right) : b.CreateFMul(left, right); + } +#endif }; template @@ -131,6 +154,15 @@ struct MinusImpl { return static_cast(a) - b; } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * left, llvm::Value * right, bool) + { + return left->getType()->isIntegerTy() ? b.CreateSub(left, right) : b.CreateFSub(left, right); + } +#endif }; template @@ -143,6 +175,17 @@ struct DivideFloatingImpl { return static_cast(a) / b; } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * left, llvm::Value * right, bool) + { + if (left->getType()->isIntegerTy()) + throw Exception("DivideFloatingImpl expected a floating-point type", ErrorCodes::LOGICAL_ERROR); + return b.CreateFDiv(left, right); + } +#endif }; @@ -189,6 +232,10 @@ struct DivideIntegralImpl throwIfDivisionLeadsToFPE(a, b); return a / b; } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = false; /// don't know how to throw from LLVM IR +#endif }; template @@ -201,6 +248,10 @@ struct DivideIntegralOrZeroImpl { return unlikely(divisionLeadsToFPE(a, b)) ? 0 : a / b; } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = false; /// TODO implement the checks +#endif }; template @@ -212,9 +263,12 @@ struct ModuloImpl static inline Result apply(A a, B b) { throwIfDivisionLeadsToFPE(typename NumberTraits::ToInteger::Type(a), typename NumberTraits::ToInteger::Type(b)); - return typename NumberTraits::ToInteger::Type(a) - % typename NumberTraits::ToInteger::Type(b); + return typename NumberTraits::ToInteger::Type(a) % typename NumberTraits::ToInteger::Type(b); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = false; /// don't know how to throw from LLVM IR +#endif }; template @@ -225,9 +279,19 @@ struct BitAndImpl template static inline Result apply(A a, B b) { - return static_cast(a) - & static_cast(b); + return static_cast(a) & static_cast(b); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * left, llvm::Value * right, bool) + { + if (!left->getType()->isIntegerTy()) + throw Exception("BitAndImpl expected an integral type", ErrorCodes::LOGICAL_ERROR); + return b.CreateAnd(left, right); + } +#endif }; template @@ -238,9 +302,19 @@ struct BitOrImpl template static inline Result apply(A a, B b) { - return static_cast(a) - | static_cast(b); + return static_cast(a) | static_cast(b); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * left, llvm::Value * right, bool) + { + if (!left->getType()->isIntegerTy()) + throw Exception("BitOrImpl expected an integral type", ErrorCodes::LOGICAL_ERROR); + return b.CreateOr(left, right); + } +#endif }; template @@ -251,9 +325,19 @@ struct BitXorImpl template static inline Result apply(A a, B b) { - return static_cast(a) - ^ static_cast(b); + return static_cast(a) ^ static_cast(b); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * left, llvm::Value * right, bool) + { + if (!left->getType()->isIntegerTy()) + throw Exception("BitXorImpl expected an integral type", ErrorCodes::LOGICAL_ERROR); + return b.CreateXor(left, right); + } +#endif }; template @@ -264,9 +348,19 @@ struct BitShiftLeftImpl template static inline Result apply(A a, B b) { - return static_cast(a) - << static_cast(b); + return static_cast(a) << static_cast(b); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * left, llvm::Value * right, bool) + { + if (!left->getType()->isIntegerTy()) + throw Exception("BitShiftLeftImpl expected an integral type", ErrorCodes::LOGICAL_ERROR); + return b.CreateShl(left, right); + } +#endif }; template @@ -277,9 +371,19 @@ struct BitShiftRightImpl template static inline Result apply(A a, B b) { - return static_cast(a) - >> static_cast(b); + return static_cast(a) >> static_cast(b); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * left, llvm::Value * right, bool is_signed) + { + if (!left->getType()->isIntegerTy()) + throw Exception("BitShiftRightImpl expected an integral type", ErrorCodes::LOGICAL_ERROR); + return is_signed ? b.CreateAShr(left, right) : b.CreateLShr(left, right); + } +#endif }; template @@ -293,6 +397,19 @@ struct BitRotateLeftImpl return (static_cast(a) << static_cast(b)) | (static_cast(a) >> ((sizeof(Result) * 8) - static_cast(b))); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * left, llvm::Value * right, bool) + { + if (!left->getType()->isIntegerTy()) + throw Exception("BitRotateLeftImpl expected an integral type", ErrorCodes::LOGICAL_ERROR); + auto * size = llvm::ConstantInt::get(left->getType(), left->getType()->getPrimitiveSizeInBits()); + /// XXX how is this supposed to behave in signed mode? + return b.CreateOr(b.CreateShl(left, right), b.CreateLShr(left, b.CreateSub(size, right))); + } +#endif }; template @@ -306,24 +423,35 @@ struct BitRotateRightImpl return (static_cast(a) >> static_cast(b)) | (static_cast(a) << ((sizeof(Result) * 8) - static_cast(b))); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * left, llvm::Value * right, bool) + { + if (!left->getType()->isIntegerTy()) + throw Exception("BitRotateRightImpl expected an integral type", ErrorCodes::LOGICAL_ERROR); + auto * size = llvm::ConstantInt::get(left->getType(), left->getType()->getPrimitiveSizeInBits()); + return b.CreateOr(b.CreateLShr(left, right), b.CreateShl(left, b.CreateSub(size, right))); + } +#endif }; - -template -std::enable_if_t, T> toInteger(T x) { return x; } - -template -std::enable_if_t, Int64> toInteger(T x) { return Int64(x); } - template struct BitTestImpl { using ResultType = UInt8; template - static inline Result apply(A a, B b) { return (toInteger(a) >> toInteger(b)) & 1; }; -}; + static inline Result apply(A a, B b) + { + return (typename NumberTraits::ToInteger::Type(a) >> typename NumberTraits::ToInteger::Type(b)) & 1; + }; +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = false; /// TODO +#endif +}; template struct LeastBaseImpl @@ -336,6 +464,18 @@ struct LeastBaseImpl /** gcc 4.9.2 successfully vectorizes a loop from this function. */ return static_cast(a) < static_cast(b) ? static_cast(a) : static_cast(b); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * left, llvm::Value * right, bool is_signed) + { + if (!left->getType()->isIntegerTy()) + /// XXX minnum is basically fmin(), it may or may not match whatever apply() does + return b.CreateMinNum(left, right); + return b.CreateSelect(is_signed ? b.CreateICmpSLT(left, right) : b.CreateICmpULT(left, right), left, right); + } +#endif }; template @@ -349,6 +489,10 @@ struct LeastSpecialImpl static_assert(std::is_same_v, "ResultType != Result"); return accurate::lessOp(a, b) ? static_cast(a) : static_cast(b); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = false; /// ??? +#endif }; template @@ -365,6 +509,18 @@ struct GreatestBaseImpl { return static_cast(a) > static_cast(b) ? static_cast(a) : static_cast(b); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * left, llvm::Value * right, bool is_signed) + { + if (!left->getType()->isIntegerTy()) + /// XXX maxnum is basically fmax(), it may or may not match whatever apply() does + return b.CreateMaxNum(left, right); + return b.CreateSelect(is_signed ? b.CreateICmpSGT(left, right) : b.CreateICmpUGT(left, right), left, right); + } +#endif }; template @@ -378,6 +534,10 @@ struct GreatestSpecialImpl static_assert(std::is_same_v, "ResultType != Result"); return accurate::greaterOp(a, b) ? static_cast(a) : static_cast(b); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = false; /// ??? +#endif }; template @@ -393,6 +553,15 @@ struct NegateImpl { return -static_cast(a); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * arg, bool) + { + return arg->getType()->isIntegerTy() ? b.CreateNeg(arg) : b.CreateFNeg(arg); + } +#endif }; template @@ -404,6 +573,17 @@ struct BitNotImpl { return ~static_cast(a); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * arg, bool) + { + if (!arg->getType()->isIntegerTy()) + throw Exception("BitNotImpl expected an integral type", ErrorCodes::LOGICAL_ERROR); + return b.CreateNot(arg); + } +#endif }; template @@ -420,6 +600,10 @@ struct AbsImpl else if constexpr (std::is_floating_point_v) return static_cast(std::abs(a)); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = false; /// special type handling, some other time +#endif }; template @@ -436,6 +620,10 @@ struct GCDImpl typename NumberTraits::ToInteger::Type(a), typename NumberTraits::ToInteger::Type(b)); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = false; /// exceptions (and a non-trivial algorithm) +#endif }; template @@ -452,6 +640,10 @@ struct LCMImpl typename NumberTraits::ToInteger::Type(a), typename NumberTraits::ToInteger::Type(b)); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = false; /// exceptions (and a non-trivial algorithm) +#endif }; template @@ -463,6 +655,10 @@ struct IntExp2Impl { return intExp2(a); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = false; /// library function +#endif }; template @@ -474,6 +670,10 @@ struct IntExp10Impl { return intExp10(a); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = false; /// library function +#endif }; /// Used to indicate undefined operation @@ -745,6 +945,43 @@ public: if (!valid) throw Exception(getName() + "'s arguments do not match the expected data types", ErrorCodes::LOGICAL_ERROR); } + +#if USE_EMBEDDED_COMPILER + bool isCompilableImpl(const DataTypes & arguments) const override + { + return castBothTypes(arguments[0].get(), arguments[1].get(), [&](const auto & left, const auto & right) + { + using LeftDataType = std::decay_t; + using RightDataType = std::decay_t; + using ResultDataType = typename BinaryOperationTraits::ResultDataType; + using OpSpec = Op; + return !std::is_same_v && OpSpec::compilable; + }); + } + + llvm::Value * compileImpl(llvm::IRBuilderBase & builder, const DataTypes & types, ValuePlaceholders values) const override + { + llvm::Value * result = nullptr; + castBothTypes(types[0].get(), types[1].get(), [&](const auto & left, const auto & right) + { + using LeftDataType = std::decay_t; + using RightDataType = std::decay_t; + using ResultDataType = typename BinaryOperationTraits::ResultDataType; + using OpSpec = Op; + if constexpr (!std::is_same_v && OpSpec::compilable) + { + auto & b = static_cast &>(builder); + auto * type = toNativeType(b, ResultDataType{}); + auto * lval = castNativeNumber(b, values[0](), type, std::is_signed_v); + auto * rval = castNativeNumber(b, values[1](), type, std::is_signed_v); + result = OpSpec::compile(b, lval, rval, std::is_signed_v); + return true; + } + return false; + }); + return result; + } +#endif }; @@ -821,6 +1058,35 @@ public: throw Exception(getName() + "'s argument does not match the expected data type", ErrorCodes::LOGICAL_ERROR); } +#if USE_EMBEDDED_COMPILER + bool isCompilableImpl(const DataTypes & arguments) const override + { + return castType(arguments[0].get(), [&](const auto & type) + { + return Op::FieldType>::compilable; + }); + } + + llvm::Value * compileImpl(llvm::IRBuilderBase & builder, const DataTypes & types, ValuePlaceholders values) const override + { + llvm::Value * result = nullptr; + castType(types[0].get(), [&](const auto & type) + { + using T0 = typename std::decay_t::FieldType; + using T1 = typename Op::ResultType; + if constexpr (Op::compilable) + { + auto & b = static_cast &>(builder); + auto * v = castNativeNumber(b, values[0](), toNativeType(b, DataTypeNumber{}), std::is_signed_v); + result = Op::compile(b, v, std::is_signed_v); + return true; + } + return false; + }); + return result; + } +#endif + bool hasInformationAboutMonotonicity() const override { return FunctionUnaryArithmeticMonotonicity::has(); diff --git a/dbms/src/Functions/FunctionsLLVMTest.cpp b/dbms/src/Functions/FunctionsLLVMTest.cpp deleted file mode 100644 index 6664c204466..00000000000 --- a/dbms/src/Functions/FunctionsLLVMTest.cpp +++ /dev/null @@ -1,60 +0,0 @@ -#include -#include -#include - -#if USE_EMBEDDED_COMPILER -#include -#endif - - -namespace DB -{ - -namespace ErrorCodes -{ - extern const int LOGICAL_ERROR; - extern const int ILLEGAL_TYPE_OF_ARGUMENT; -} - -class FunctionSomething : public IFunction -{ -public: - static constexpr auto name = "something"; - -#if USE_EMBEDDED_COMPILER - bool isCompilableImpl(const DataTypes & types) const override - { - return types.size() == 2 && types[0]->equals(*types[1]); - } - - llvm::Value * compileImpl(llvm::IRBuilderBase & builder, const DataTypes & types, ValuePlaceholders values) const override - { - if (types[0]->equals(DataTypeFloat32{}) || types[0]->equals(DataTypeFloat64{})) - return static_cast&>(builder).CreateFAdd(values[0](), values[1]()); - return static_cast&>(builder).CreateAdd(values[0](), values[1]()); - } -#endif - - static FunctionPtr create(const Context &) { return std::make_shared(); } - - String getName() const override { return name; } - - size_t getNumberOfArguments() const override { return 2; } - - bool useDefaultImplementationForConstants() const override { return true; } - - DataTypePtr getReturnTypeImpl(const DataTypes & types) const override { return types[0]; } - - void executeImpl(Block & block, const ColumnNumbers & arguments, size_t result) override - { - throw Exception("should've used the jitted version", ErrorCodes::NOT_IMPLEMENTED); - } -}; - - -void registerFunctionsLLVMTest(FunctionFactory & factory) -{ - factory.registerFunction(); -} - -} diff --git a/dbms/src/Functions/FunctionsRound.h b/dbms/src/Functions/FunctionsRound.h index d5429b76318..6d29d450de5 100644 --- a/dbms/src/Functions/FunctionsRound.h +++ b/dbms/src/Functions/FunctionsRound.h @@ -93,6 +93,10 @@ struct RoundToExp2Impl { return roundDownToPowerOfTwo(x); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = false; +#endif }; @@ -120,6 +124,10 @@ struct RoundDurationImpl : (x < 36000 ? 18000 : 36000)))))))))))))); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = false; +#endif }; template @@ -137,6 +145,10 @@ struct RoundAgeImpl : (x < 55 ? 45 : 55))))); } + +#if USE_EMBEDDED_COMPILER + static constexpr bool compilable = false; +#endif }; diff --git a/dbms/src/Functions/registerFunctions.cpp b/dbms/src/Functions/registerFunctions.cpp index b9d4f39087f..0dcc66bfd77 100644 --- a/dbms/src/Functions/registerFunctions.cpp +++ b/dbms/src/Functions/registerFunctions.cpp @@ -42,7 +42,6 @@ void registerFunctionsGeo(FunctionFactory &); void registerFunctionsCharset(FunctionFactory &); void registerFunctionsNull(FunctionFactory &); void registerFunctionsFindCluster(FunctionFactory &); -void registerFunctionsLLVMTest(FunctionFactory &); void registerFunctions() @@ -80,7 +79,6 @@ void registerFunctions() registerFunctionsCharset(factory); registerFunctionsNull(factory); registerFunctionsFindCluster(factory); - registerFunctionsLLVMTest(factory); } } From 039c377a7a40a6e063648e245d68adb6dfc3d022 Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 30 Apr 2018 02:07:39 +0300 Subject: [PATCH 048/231] Work around a bug in llvm::IRBuilder::CreateMaxNum --- dbms/src/Functions/FunctionsArithmetic.h | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/dbms/src/Functions/FunctionsArithmetic.h b/dbms/src/Functions/FunctionsArithmetic.h index 31da1362e9b..668e8be6c07 100644 --- a/dbms/src/Functions/FunctionsArithmetic.h +++ b/dbms/src/Functions/FunctionsArithmetic.h @@ -517,7 +517,8 @@ struct GreatestBaseImpl { if (!left->getType()->isIntegerTy()) /// XXX maxnum is basically fmax(), it may or may not match whatever apply() does - return b.CreateMaxNum(left, right); + /// XXX CreateMaxNum is broken on LLVM 5.0 and 6.0 (generates minnum instead; fixed in 7) + return b.CreateBinaryIntrinsic(llvm::Intrinsic::maxnum, left, right); return b.CreateSelect(is_signed ? b.CreateICmpSGT(left, right) : b.CreateICmpUGT(left, right), left, right); } #endif From 4970b06b57f2f10d19ebdc4059b10003b0f5ed73 Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 30 Apr 2018 02:21:45 +0300 Subject: [PATCH 049/231] Remove outdated comments --- dbms/CMakeLists.txt | 3 +-- dbms/src/Functions/IFunction.cpp | 2 +- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/dbms/CMakeLists.txt b/dbms/CMakeLists.txt index e3bf825226b..3c99505c622 100644 --- a/dbms/CMakeLists.txt +++ b/dbms/CMakeLists.txt @@ -103,8 +103,7 @@ if (USE_EMBEDDED_COMPILER) llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) target_link_libraries (dbms ${REQUIRED_LLVM_LIBRARIES}) target_include_directories (dbms BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) - # LLVM 5.0 has a bunch of unused parameters in its header files. - # TODO: global-disable no-unused-parameter + # LLVM has a bunch of unused parameters in its header files. set_source_files_properties(src/Functions/IFunction.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter") set_source_files_properties(src/Interpreters/ExpressionJIT.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter -Wno-non-virtual-dtor") endif () diff --git a/dbms/src/Functions/IFunction.cpp b/dbms/src/Functions/IFunction.cpp index 2432cd43814..e0e0eb27d07 100644 --- a/dbms/src/Functions/IFunction.cpp +++ b/dbms/src/Functions/IFunction.cpp @@ -313,7 +313,7 @@ llvm::Value * IFunction::compile(llvm::IRBuilderBase & builder, const DataTypes auto * result = b.CreateInsertValue(zero, compileImpl(builder, *denulled, std::move(values)), {0}); auto * result_block = b.GetInsertBlock(); b.CreateBr(join); - b.SetInsertPoint(fail); /// an empty joining block to avoid keeping track of where we could jump from + b.SetInsertPoint(fail); auto * null = b.CreateInsertValue(zero, b.getTrue(), {1}); b.CreateBr(join); b.SetInsertPoint(join); From e4ace21f245d724c6e9cadd48b71977eb2305333 Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 30 Apr 2018 15:32:56 +0300 Subject: [PATCH 050/231] Remove laziness on nullable arguments from default implementation It breaks semantics, sadly. --- dbms/src/Functions/IFunction.cpp | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/dbms/src/Functions/IFunction.cpp b/dbms/src/Functions/IFunction.cpp index e0e0eb27d07..f3c1e670e4c 100644 --- a/dbms/src/Functions/IFunction.cpp +++ b/dbms/src/Functions/IFunction.cpp @@ -301,14 +301,13 @@ llvm::Value * IFunction::compile(llvm::IRBuilderBase & builder, const DataTypes { if (!arguments[i]->isNullable()) continue; - values[i] = [&, previous = std::move(values[i])]() - { - auto * value = previous(); - auto * ok = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); - b.CreateCondBr(b.CreateExtractValue(value, {1}), fail, ok); - b.SetInsertPoint(ok); - return b.CreateExtractValue(value, {0}); - }; + /// Would be nice to evaluate all this lazily, but that'd change semantics: if only unevaluated + /// arguments happen to contain NULLs, the return value would not be NULL, though it should be. + auto * value = values[i](); + auto * ok = llvm::BasicBlock::Create(b.GetInsertBlock()->getContext(), "", b.GetInsertBlock()->getParent()); + b.CreateCondBr(b.CreateExtractValue(value, {1}), fail, ok); + b.SetInsertPoint(ok); + values[i] = [value = b.CreateExtractValue(value, {0})]() { return value; }; } auto * result = b.CreateInsertValue(zero, compileImpl(builder, *denulled, std::move(values)), {0}); auto * result_block = b.GetInsertBlock(); From 7483ed24f048f014f3062ed065750d26b1d68f77 Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 30 Apr 2018 15:33:40 +0300 Subject: [PATCH 051/231] Implement jit for logic functions --- dbms/src/Functions/FunctionsLogical.h | 105 ++++++++++++++++++++++---- 1 file changed, 92 insertions(+), 13 deletions(-) diff --git a/dbms/src/Functions/FunctionsLogical.h b/dbms/src/Functions/FunctionsLogical.h index 067ae067a4a..6a7b2f325bf 100644 --- a/dbms/src/Functions/FunctionsLogical.h +++ b/dbms/src/Functions/FunctionsLogical.h @@ -12,6 +12,10 @@ #include #include +#if USE_EMBEDDED_COMPILER +#include +#endif + namespace DB { @@ -31,65 +35,71 @@ namespace ErrorCodes * For example, 1 OR NULL returns 1, not NULL. */ - struct AndImpl { - static inline bool isSaturable() + static inline constexpr bool isSaturable() { return true; } - static inline bool isSaturatedValue(bool a) + static inline constexpr bool isSaturatedValue(bool a) { return !a; } - static inline bool apply(bool a, bool b) + static inline constexpr bool apply(bool a, bool b) { return a && b; } - static inline bool specialImplementationForNulls() { return false; } + static inline constexpr bool specialImplementationForNulls() { return false; } }; struct OrImpl { - static inline bool isSaturable() + static inline constexpr bool isSaturable() { return true; } - static inline bool isSaturatedValue(bool a) + static inline constexpr bool isSaturatedValue(bool a) { return a; } - static inline bool apply(bool a, bool b) + static inline constexpr bool apply(bool a, bool b) { return a || b; } - static inline bool specialImplementationForNulls() { return true; } + static inline constexpr bool specialImplementationForNulls() { return true; } }; struct XorImpl { - static inline bool isSaturable() + static inline constexpr bool isSaturable() { return false; } - static inline bool isSaturatedValue(bool) + static inline constexpr bool isSaturatedValue(bool) { return false; } - static inline bool apply(bool a, bool b) + static inline constexpr bool apply(bool a, bool b) { return a != b; } - static inline bool specialImplementationForNulls() { return false; } + static inline constexpr bool specialImplementationForNulls() { return false; } + +#if USE_EMBEDDED_COMPILER + static inline llvm::Value * apply(llvm::IRBuilder<> & builder, llvm::Value * a, llvm::Value * b) + { + return builder.CreateXor(a, b); + } +#endif }; template @@ -101,6 +111,13 @@ struct NotImpl { return !a; } + +#if USE_EMBEDDED_COMPILER + static inline llvm::Value * apply(llvm::IRBuilder<> & builder, llvm::Value * a) + { + return builder.CreateNot(a); + } +#endif }; @@ -172,6 +189,20 @@ struct AssociativeOperationImpl }; +#if USE_EMBEDDED_COMPILER +static llvm::Value * isNativeTrueValue(llvm::IRBuilder<> & b, const DataTypePtr & type, llvm::Value * x) +{ + if (type->isNullable()) + { + auto * subexpr = isNativeTrueValue(b, removeNullable(type), b.CreateExtractValue(x, {0})); + return b.CreateAnd(b.CreateNot(b.CreateExtractValue(x, {1})), subexpr); + } + auto * zero = llvm::Constant::getNullValue(x->getType()); + return x->getType()->isIntegerTy() ? b.CreateICmpNE(x, zero) : b.CreateFCmpONE(x, zero); /// QNaN -> false +} +#endif + + template class FunctionAnyArityLogical : public IFunction { @@ -364,6 +395,44 @@ public: block.getByPosition(result).column = std::move(col_res); } + +#if USE_EMBEDDED_COMPILER + bool isCompilableImpl(const DataTypes &) const override { return true; } + + llvm::Value * compileImpl(llvm::IRBuilderBase & builder, const DataTypes & types, ValuePlaceholders values) const override + { + auto & b = static_cast &>(builder); + if constexpr (!Impl::isSaturable()) + { + auto * result = isNativeTrueValue(b, types[0], values[0]()); + for (size_t i = 1; i < types.size(); i++) + result = Impl::apply(b, result, isNativeTrueValue(b, types[i], values[i]())); + return b.CreateSelect(result, b.getInt8(1), b.getInt8(0)); + } + constexpr bool breakOnTrue = Impl::isSaturatedValue(true); + auto * next = b.GetInsertBlock(); + auto * stop = llvm::BasicBlock::Create(next->getContext(), "", next->getParent()); + b.SetInsertPoint(stop); + auto * phi = b.CreatePHI(b.getInt8Ty(), values.size()); + for (size_t i = 0; i < types.size(); i++) + { + b.SetInsertPoint(next); + auto * value = values[i](); + auto * truth = isNativeTrueValue(b, types[i], value); + if (!types[i]->equals(DataTypeUInt8{})) + value = b.CreateSelect(truth, b.getInt8(1), b.getInt8(0)); + phi->addIncoming(value, b.GetInsertBlock()); + if (i + 1 < types.size()) + { + next = llvm::BasicBlock::Create(next->getContext(), "", next->getParent()); + b.CreateCondBr(truth, breakOnTrue ? stop : next, breakOnTrue ? next : stop); + } + } + b.CreateBr(stop); + b.SetInsertPoint(stop); + return phi; + } +#endif }; @@ -430,6 +499,16 @@ public: + " of argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN); } + +#if USE_EMBEDDED_COMPILER + bool isCompilableImpl(const DataTypes &) const override { return true; } + + llvm::Value * compileImpl(llvm::IRBuilderBase & builder, const DataTypes & types, ValuePlaceholders values) const override + { + auto & b = static_cast &>(builder); + return b.CreateSelect(Impl::apply(b, isNativeTrueValue(b, types[0], values[0]())), b.getInt8(1), b.getInt8(0)); + } +#endif }; From 900b92f744ba03be949d6e3a5807208af35a90c5 Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 1 May 2018 22:52:33 +0300 Subject: [PATCH 052/231] Merge API changes from upstream --- dbms/src/Interpreters/ExpressionJIT.cpp | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index ea38c6a777d..73ceb33d50c 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -79,7 +79,7 @@ static void applyFunction(IFunctionBase & function, Field & value) { const auto & type = function.getArgumentTypes().at(0); Block block = {{ type->createColumnConst(1, value), type, "x" }, { nullptr, function.getReturnType(), "y" }}; - function.execute(block, {0}, 1); + function.execute(block, {0}, 1, 1); block.safeGetByPosition(1).column->get(0, value); } @@ -142,9 +142,8 @@ public: String getName() const override { return name; } - void execute(Block & block, const ColumnNumbers & arguments, size_t result) override + void execute(Block & block, const ColumnNumbers & arguments, size_t result, size_t block_size) override { - size_t block_size = block.rows(); auto col_res = block.getByPosition(result).type->createColumn()->cloneResized(block_size); if (block_size) { From accbbdb9e3029630eea7beceae7855ff3bc7108d Mon Sep 17 00:00:00 2001 From: pyos Date: Thu, 3 May 2018 00:47:28 +0300 Subject: [PATCH 053/231] Add a setting that disables jit-compilation --- dbms/src/Interpreters/ExpressionActions.cpp | 3 ++- dbms/src/Interpreters/Settings.h | 1 + 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/dbms/src/Interpreters/ExpressionActions.cpp b/dbms/src/Interpreters/ExpressionActions.cpp index 353430ccbaa..8efd319d647 100644 --- a/dbms/src/Interpreters/ExpressionActions.cpp +++ b/dbms/src/Interpreters/ExpressionActions.cpp @@ -759,7 +759,8 @@ void ExpressionActions::finalize(const Names & output_columns) #if USE_EMBEDDED_COMPILER /// This has to be done before removing redundant actions and inserting REMOVE_COLUMNs /// because inlining may change dependency sets. - compileFunctions(actions, output_columns, sample_block); + if (settings.compile_expressions) + compileFunctions(actions, output_columns, sample_block); #endif /// Which columns are needed to perform actions from the current to the last. diff --git a/dbms/src/Interpreters/Settings.h b/dbms/src/Interpreters/Settings.h index b1442d65c0f..9696e7866bf 100644 --- a/dbms/src/Interpreters/Settings.h +++ b/dbms/src/Interpreters/Settings.h @@ -65,6 +65,7 @@ struct Settings M(SettingFloat, totals_auto_threshold, 0.5, "The threshold for totals_mode = 'auto'.") \ \ M(SettingBool, compile, false, "Whether query compilation is enabled.") \ + M(SettingBool, compile_expressions, true, "Compile some scalar functions and operators to native code.") \ M(SettingUInt64, min_count_to_compile, 3, "The number of structurally identical queries before they are compiled.") \ M(SettingUInt64, group_by_two_level_threshold, 100000, "From what number of keys, a two-level aggregation starts. 0 - the threshold is not set.") \ M(SettingUInt64, group_by_two_level_threshold_bytes, 100000000, "From what size of the aggregation state in bytes, a two-level aggregation begins to be used. 0 - the threshold is not set. Two-level aggregation is used when at least one of the thresholds is triggered.") \ From 1f89849650c1b3a63e65bee61d1c6d3b1edb9160 Mon Sep 17 00:00:00 2001 From: pyos Date: Thu, 3 May 2018 01:45:54 +0300 Subject: [PATCH 054/231] Remove dynamically-linked stdc++ from dependencies --- cmake/find_llvm.cmake | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cmake/find_llvm.cmake b/cmake/find_llvm.cmake index bc5bcd39ef7..21ee7f28e4a 100644 --- a/cmake/find_llvm.cmake +++ b/cmake/find_llvm.cmake @@ -9,7 +9,7 @@ if (ENABLE_EMBEDDED_COMPILER) if (LLVM_FOUND) # Remove dynamically-linked zlib and libedit from LLVM's dependencies: - set_target_properties(LLVMSupport PROPERTIES INTERFACE_LINK_LIBRARIES "-lpthread;LLVMDemangle;stdc++") + set_target_properties(LLVMSupport PROPERTIES INTERFACE_LINK_LIBRARIES "-lpthread;LLVMDemangle") set_target_properties(LLVMLineEditor PROPERTIES INTERFACE_LINK_LIBRARIES "LLVMSupport") message(STATUS "LLVM version: ${LLVM_PACKAGE_VERSION}") From 23bbf632e56eca668efde98789612c8d24664b17 Mon Sep 17 00:00:00 2001 From: pyos Date: Thu, 3 May 2018 13:22:41 +0300 Subject: [PATCH 055/231] If all inputs to a jitted function are constant, return a constant --- dbms/src/Interpreters/ExpressionJIT.cpp | 29 +++++++++++++------------ 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 73ceb33d50c..4d2c386e751 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -124,7 +124,7 @@ struct LLVMContext } }; -class LLVMPreparedFunction : public IPreparedFunction +class LLVMPreparedFunction : public PreparedFunctionImpl { std::string name; std::shared_ptr context; @@ -142,7 +142,11 @@ public: String getName() const override { return name; } - void execute(Block & block, const ColumnNumbers & arguments, size_t result, size_t block_size) override + bool useDefaultImplementationForNulls() const override { return false; } + + bool useDefaultImplementationForConstants() const override { return true; } + + void executeImpl(Block & block, const ColumnNumbers & arguments, size_t result, size_t block_size) override { auto col_res = block.getByPosition(result).type->createColumn()->cloneResized(block_size); if (block_size) @@ -168,7 +172,7 @@ static void compileFunction(std::shared_ptr & context, const IFunct auto & b = context->builder; auto * size_type = b.getIntNTy(sizeof(size_t) * 8); auto * data_type = llvm::StructType::get(b.getInt8PtrTy(), b.getInt8PtrTy(), size_type); - auto * func_type = llvm::FunctionType::get(b.getVoidTy(), { size_type, llvm::PointerType::get(data_type, 0) }, /*isVarArg=*/false); + auto * func_type = llvm::FunctionType::get(b.getVoidTy(), { size_type, data_type->getPointerTo() }, /*isVarArg=*/false); auto * func = llvm::Function::Create(func_type, llvm::Function::ExternalLinkage, f.getName(), context->module.get()); auto args = func->args().begin(); llvm::Value * counter_arg = &*args++; @@ -180,10 +184,10 @@ static void compileFunction(std::shared_ptr & context, const IFunct for (size_t i = 0; i <= arg_types.size(); i++) { auto & type = i == arg_types.size() ? f.getReturnType() : arg_types[i]; - auto * native = llvm::PointerType::get(toNativeType(b, removeNullable(type)), 0); - columns[i].data_init = b.CreatePointerCast(b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns_arg, i, 0)), native); - columns[i].null_init = type->isNullable() ? b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns_arg, i, 1)) : nullptr; - columns[i].stride = b.CreateLoad(b.CreateConstInBoundsGEP2_32(data_type, columns_arg, i, 2)); + auto * data = b.CreateLoad(b.CreateConstInBoundsGEP1_32(data_type, columns_arg, i)); + columns[i].data_init = b.CreatePointerCast(b.CreateExtractValue(data, {0}), toNativeType(b, removeNullable(type))->getPointerTo()); + columns[i].null_init = type->isNullable() ? b.CreateExtractValue(data, {1}) : nullptr; + columns[i].stride = b.CreateExtractValue(data, {2}); } /// assume nonzero initial value in `counter_arg` @@ -228,14 +232,11 @@ static void compileFunction(std::shared_ptr & context, const IFunct auto * cur_block = b.GetInsertBlock(); for (auto & col : columns) { - auto * as_char = b.CreatePointerCast(col.data, b.getInt8PtrTy()); - auto * as_type = b.CreatePointerCast(b.CreateInBoundsGEP(as_char, col.stride), col.data->getType()); - col.data->addIncoming(as_type, cur_block); + /// currently, stride is either 0 or size of native type + auto * is_const = b.CreateICmpEQ(col.stride, llvm::ConstantInt::get(size_type, 0)); + col.data->addIncoming(b.CreateSelect(is_const, col.data, b.CreateConstInBoundsGEP1_32(nullptr, col.data, 1)), cur_block); if (col.null) - { - auto * is_const = b.CreateICmpEQ(col.stride, llvm::ConstantInt::get(size_type, 0)); - col.null->addIncoming(b.CreateSelect(is_const, col.null, b.CreateConstInBoundsGEP1_32(b.getInt8Ty(), col.null, 1)), cur_block); - } + col.null->addIncoming(b.CreateSelect(is_const, col.null, b.CreateConstInBoundsGEP1_32(nullptr, col.null, 1)), cur_block); } counter_phi->addIncoming(b.CreateSub(counter_phi, llvm::ConstantInt::get(size_type, 1)), cur_block); From a286dea2e1b7c3932b1a0eef29f061b3bbbf80dc Mon Sep 17 00:00:00 2001 From: pyos Date: Thu, 3 May 2018 16:34:42 +0300 Subject: [PATCH 056/231] Don't waste time jit-compiling isolated functions. This is already done ahead of time when building the executable. --- dbms/src/Interpreters/ExpressionJIT.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 4d2c386e751..a8e310e8e4e 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -488,6 +488,9 @@ void compileFunctions(ExpressionActions::Actions & actions, const Names & output fused[i].push_back(actions[i]); if (dependents[i].find({}) != dependents[i].end()) { + /// the result of compiling one function in isolation is pretty much the same as its `execute` method. + if (fused[i].size() == 1) + continue; auto fn = std::make_shared(std::move(fused[i]), context, sample_block); actions[i].function = fn; actions[i].argument_names = fn->getArgumentNames(); From 88bb2f7c2540a4fbcab31ce139deb35c9a496cf6 Mon Sep 17 00:00:00 2001 From: pyos Date: Sat, 5 May 2018 00:38:17 +0300 Subject: [PATCH 057/231] Resolve symbols right after compiling. llvm::orc::RTDyldObjectLinkingLayer::findSymbol appears to be non-threadsafe. --- dbms/src/Interpreters/ExpressionJIT.cpp | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index a8e310e8e4e..161345f569c 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -92,6 +92,7 @@ struct LLVMContext llvm::orc::IRCompileLayer compileLayer; llvm::DataLayout layout; llvm::IRBuilder<> builder; + std::unordered_map symbols; LLVMContext() : module(std::make_shared("jit", context)) @@ -121,6 +122,14 @@ struct LLVMContext for (auto & function : *module) fpm.run(function); llvm::cantFail(compileLayer.addModule(module, std::make_shared())); + for (const auto & function : *module) + { + std::string mangledName; + llvm::raw_string_ostream mangledNameStream(mangledName); + llvm::Mangler::getNameWithPrefix(mangledNameStream, function.getName(), layout); + if (auto symbol = compileLayer.findSymbol(mangledNameStream.str(), false).getAddress()) + symbols[function.getName()] = reinterpret_cast(*symbol); + } } }; @@ -132,13 +141,8 @@ class LLVMPreparedFunction : public PreparedFunctionImpl public: LLVMPreparedFunction(std::string name_, std::shared_ptr context) - : name(std::move(name_)), context(context) - { - std::string mangledName; - llvm::raw_string_ostream mangledNameStream(mangledName); - llvm::Mangler::getNameWithPrefix(mangledNameStream, name, context->layout); - function = reinterpret_cast(context->compileLayer.findSymbol(mangledNameStream.str(), false).getAddress().get()); - } + : name(std::move(name_)), context(context), function(context->symbols.at(name)) + {} String getName() const override { return name; } From b1b95454cc8c25d335973dac1b1efbadef584534 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Sun, 6 May 2018 12:29:57 +0300 Subject: [PATCH 058/231] Make warning suppressions more local #2277 --- dbms/CMakeLists.txt | 3 --- dbms/src/DataTypes/Native.h | 6 ++++++ dbms/src/Functions/FunctionsArithmetic.h | 3 +++ dbms/src/Functions/FunctionsLogical.h | 3 +++ dbms/src/Functions/IFunction.cpp | 3 +++ dbms/src/Interpreters/ExpressionJIT.cpp | 5 +++++ 6 files changed, 20 insertions(+), 3 deletions(-) diff --git a/dbms/CMakeLists.txt b/dbms/CMakeLists.txt index 3c99505c622..e290fad9315 100644 --- a/dbms/CMakeLists.txt +++ b/dbms/CMakeLists.txt @@ -103,9 +103,6 @@ if (USE_EMBEDDED_COMPILER) llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) target_link_libraries (dbms ${REQUIRED_LLVM_LIBRARIES}) target_include_directories (dbms BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) - # LLVM has a bunch of unused parameters in its header files. - set_source_files_properties(src/Functions/IFunction.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter") - set_source_files_properties(src/Interpreters/ExpressionJIT.cpp PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter -Wno-non-virtual-dtor") endif () diff --git a/dbms/src/DataTypes/Native.h b/dbms/src/DataTypes/Native.h index d3b7646188e..61daececd3e 100644 --- a/dbms/src/DataTypes/Native.h +++ b/dbms/src/DataTypes/Native.h @@ -13,8 +13,14 @@ #include #include +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wunused-parameter" + #include +#pragma GCC diagnostic pop + + namespace DB { diff --git a/dbms/src/Functions/FunctionsArithmetic.h b/dbms/src/Functions/FunctionsArithmetic.h index 4725c8caa74..fc8a8252d70 100644 --- a/dbms/src/Functions/FunctionsArithmetic.h +++ b/dbms/src/Functions/FunctionsArithmetic.h @@ -21,7 +21,10 @@ #include #if USE_EMBEDDED_COMPILER +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wunused-parameter" #include +#pragma GCC diagnostic pop #endif diff --git a/dbms/src/Functions/FunctionsLogical.h b/dbms/src/Functions/FunctionsLogical.h index 5d3982e95de..c62816be734 100644 --- a/dbms/src/Functions/FunctionsLogical.h +++ b/dbms/src/Functions/FunctionsLogical.h @@ -13,7 +13,10 @@ #include #if USE_EMBEDDED_COMPILER +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wunused-parameter" #include +#pragma GCC diagnostic pop #endif diff --git a/dbms/src/Functions/IFunction.cpp b/dbms/src/Functions/IFunction.cpp index 45deb9aceeb..0fe1bb23952 100644 --- a/dbms/src/Functions/IFunction.cpp +++ b/dbms/src/Functions/IFunction.cpp @@ -14,7 +14,10 @@ #include #if USE_EMBEDDED_COMPILER +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wunused-parameter" #include +#pragma GCC diagnostic pop #endif diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 161345f569c..1bdaeb0ba48 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -11,6 +11,9 @@ #include #include +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wunused-parameter -Wnon-virtual-dtor" + #include #include #include @@ -31,6 +34,8 @@ #include #include +#pragma GCC diagnostic pop + namespace DB { From f495d8cfa5f65930a40e59b952c2dd2fe70f43ad Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Sun, 6 May 2018 12:30:35 +0300 Subject: [PATCH 059/231] Removed deprecated CMake function #2277 --- dbms/src/Server/Compiler-7.0.0/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt index 3edd7d6770d..39b27b92c0f 100644 --- a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt @@ -8,7 +8,7 @@ add_library(clickhouse-compiler-lib target_compile_options(clickhouse-compiler-lib PRIVATE -fno-rtti -fno-exceptions -g0) -llvm_map_components_to_libraries(REQUIRED_LLVM_LIBRARIES all) +llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) # We link statically with zlib, and LLVM (sometimes) tries to bring its own dependency. list(REMOVE_ITEM REQUIRED_LLVM_LIBRARIES "-lz") From c4a26764cee5fe12ddd9b031033fc96347ac8d49 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Sun, 6 May 2018 12:32:36 +0300 Subject: [PATCH 060/231] Miscellaneous #2277 --- dbms/src/Interpreters/ExpressionJIT.h | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.h b/dbms/src/Interpreters/ExpressionJIT.h index 5a7a39c9e21..799a80171b5 100644 --- a/dbms/src/Interpreters/ExpressionJIT.h +++ b/dbms/src/Interpreters/ExpressionJIT.h @@ -1,10 +1,11 @@ #pragma once #include -#include #if USE_EMBEDDED_COMPILER +#include + namespace DB { From 69c67b4cd469ab4d209c5c3b1350f7ec25d552b3 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Sun, 6 May 2018 12:37:16 +0300 Subject: [PATCH 061/231] Addition to prev. revision #2277 --- dbms/src/Interpreters/ExpressionJIT.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 1bdaeb0ba48..e2cb3475cb8 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -12,7 +12,8 @@ #include #pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wunused-parameter -Wnon-virtual-dtor" +#pragma GCC diagnostic ignored "-Wunused-parameter" +#pragma GCC diagnostic ignored "-Wnon-virtual-dtor" #include #include From 6c7f896f9f4dd39d82fc76b24697dbbe592e9fd9 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Sun, 6 May 2018 13:42:35 +0300 Subject: [PATCH 062/231] Miscellaneous #2277 --- dbms/src/Interpreters/ExpressionJIT.cpp | 24 +++++++++++++----------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index e2cb3475cb8..e2c68faca35 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -2,6 +2,8 @@ #if USE_EMBEDDED_COMPILER +#include + #include #include #include @@ -98,7 +100,7 @@ struct LLVMContext llvm::orc::IRCompileLayer compileLayer; llvm::DataLayout layout; llvm::IRBuilder<> builder; - std::unordered_map symbols; + std::unordered_map symbols; LLVMContext() : module(std::make_shared("jit", context)) @@ -134,7 +136,7 @@ struct LLVMContext llvm::raw_string_ostream mangledNameStream(mangledName); llvm::Mangler::getNameWithPrefix(mangledNameStream, function.getName(), layout); if (auto symbol = compileLayer.findSymbol(mangledNameStream.str(), false).getAddress()) - symbols[function.getName()] = reinterpret_cast(*symbol); + symbols[function.getName()] = reinterpret_cast(*symbol); } } }; @@ -143,7 +145,7 @@ class LLVMPreparedFunction : public PreparedFunctionImpl { std::string name; std::shared_ptr context; - const void * function; + void * function; public: LLVMPreparedFunction(std::string name_, std::shared_ptr context) @@ -162,11 +164,11 @@ public: if (block_size) { std::vector columns(arguments.size() + 1); - for (size_t i = 0; i < arguments.size(); i++) + for (size_t i = 0; i < arguments.size(); ++i) { auto * column = block.getByPosition(arguments[i]).column.get(); if (!column) - throw Exception("column " + block.getByPosition(arguments[i]).name + " is missing", ErrorCodes::LOGICAL_ERROR); + throw Exception("Column " + block.getByPosition(arguments[i]).name + " is missing", ErrorCodes::LOGICAL_ERROR); columns[i] = getColumnData(column); } columns[arguments.size()] = getColumnData(col_res.get()); @@ -191,7 +193,7 @@ static void compileFunction(std::shared_ptr & context, const IFunct auto * entry = llvm::BasicBlock::Create(b.getContext(), "entry", func); b.SetInsertPoint(entry); std::vector columns(arg_types.size() + 1); - for (size_t i = 0; i <= arg_types.size(); i++) + for (size_t i = 0; i <= arg_types.size(); ++i) { auto & type = i == arg_types.size() ? f.getReturnType() : arg_types[i]; auto * data = b.CreateLoad(b.CreateConstInBoundsGEP1_32(data_type, columns_arg, i)); @@ -217,7 +219,7 @@ static void compileFunction(std::shared_ptr & context, const IFunct } } ValuePlaceholders arguments(arg_types.size()); - for (size_t i = 0; i < arguments.size(); i++) + for (size_t i = 0; i < arguments.size(); ++i) { arguments[i] = [&b, &col = columns[i], &type = arg_types[i]]() -> llvm::Value * { @@ -300,7 +302,7 @@ static CompilableExpression subexpression(const IFunctionBase & f, std::vectorgetType() != toNativeType(builder, f.getReturnType())) - throw Exception("function " + f.getName() + " generated an llvm::Value of invalid type", ErrorCodes::LOGICAL_ERROR); + throw Exception("Function " + f.getName() + " generated an llvm::Value of invalid type", ErrorCodes::LOGICAL_ERROR); return result; }; } @@ -327,7 +329,7 @@ public: const auto & names = action.argument_names; const auto & types = action.function->getArgumentTypes(); std::vector args; - for (size_t i = 0; i < names.size(); i++) + for (size_t i = 0; i < names.size(); ++i) { auto inserted = subexpressions.emplace(names[i], subexpression(arg_names.size())); if (inserted.second) @@ -404,7 +406,7 @@ public: Field right_ = right; Monotonicity result(true, true, true); /// monotonicity is only defined for unary functions, so the chain must describe a sequence of nested calls - for (size_t i = 0; i < originals.size(); i++) + for (size_t i = 0; i < originals.size(); ++i) { Monotonicity m = originals[i]->getMonotonicityForRange(*type_, left_, right_); if (!m.is_monotonic) @@ -491,7 +493,7 @@ void compileFunctions(ExpressionActions::Actions & actions, const Names & output } std::vector fused(actions.size()); - for (size_t i = 0; i < actions.size(); i++) + for (size_t i = 0; i < actions.size(); ++i) { if (actions[i].type != ExpressionAction::APPLY_FUNCTION || !isCompilable(context->builder, *actions[i].function)) continue; From b580d1c4879a9397da05790f32681ead191e5f9d Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Sun, 6 May 2018 14:16:38 +0300 Subject: [PATCH 063/231] Allow to build with clang 7 --- dbms/src/Functions/IFunction.cpp | 1 + dbms/src/Interpreters/ExpressionJIT.cpp | 46 +++++++++++++++++++++---- 2 files changed, 41 insertions(+), 6 deletions(-) diff --git a/dbms/src/Functions/IFunction.cpp b/dbms/src/Functions/IFunction.cpp index 0fe1bb23952..d271e3e9744 100644 --- a/dbms/src/Functions/IFunction.cpp +++ b/dbms/src/Functions/IFunction.cpp @@ -12,6 +12,7 @@ #include #include #include +#include #if USE_EMBEDDED_COMPILER #pragma GCC diagnostic push diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index e2c68faca35..c1e530a9c71 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -17,6 +17,7 @@ #pragma GCC diagnostic ignored "-Wunused-parameter" #pragma GCC diagnostic ignored "-Wnon-virtual-dtor" +#include #include #include #include @@ -94,7 +95,12 @@ static void applyFunction(IFunctionBase & function, Field & value) struct LLVMContext { llvm::LLVMContext context; +#if LLVM_VERSION_MAJOR >= 7 + llvm::orc::ExecutionSession execution_session; + std::unique_ptr module; +#else std::shared_ptr module; +#endif std::unique_ptr machine; llvm::orc::RTDyldObjectLinkingLayer objectLayer; llvm::orc::IRCompileLayer compileLayer; @@ -103,9 +109,24 @@ struct LLVMContext std::unordered_map symbols; LLVMContext() +#if LLVM_VERSION_MAJOR >= 7 + : module(std::make_unique("jit", context)) +#else : module(std::make_shared("jit", context)) +#endif , machine(llvm::EngineBuilder().selectTarget()) +#if LLVM_VERSION_MAJOR >= 7 + , objectLayer(execution_session, [](llvm::orc::VModuleKey) + { + return llvm::orc::RTDyldObjectLinkingLayer::Resources + { + std::make_shared(), + std::make_shared() + }; + }) +#else , objectLayer([]() { return std::make_shared(); }) +#endif , compileLayer(objectLayer, llvm::orc::SimpleCompiler(*machine)) , layout(machine->createDataLayout()) , builder(context) @@ -129,15 +150,28 @@ struct LLVMContext builder.populateFunctionPassManager(fpm); for (auto & function : *module) fpm.run(function); - llvm::cantFail(compileLayer.addModule(module, std::make_shared())); + + /// name, mangled name + std::vector> function_names; + function_names.reserve(module->size()); for (const auto & function : *module) { - std::string mangledName; - llvm::raw_string_ostream mangledNameStream(mangledName); - llvm::Mangler::getNameWithPrefix(mangledNameStream, function.getName(), layout); - if (auto symbol = compileLayer.findSymbol(mangledNameStream.str(), false).getAddress()) - symbols[function.getName()] = reinterpret_cast(*symbol); + std::string mangled_name; + llvm::raw_string_ostream mangled_name_stream(mangled_name); + llvm::Mangler::getNameWithPrefix(mangled_name_stream, function.getName(), layout); + function_names.emplace_back(function.getName(), mangled_name); } + +#if LLVM_VERSION_MAJOR >= 7 + llvm::orc::VModuleKey module_key = execution_session.allocateVModule(); + llvm::cantFail(compileLayer.addModule(module_key, std::move(module))); +#else + llvm::cantFail(compileLayer.addModule(module, std::make_shared())); +#endif + + for (const auto & names : function_names) + if (auto symbol = compileLayer.findSymbol(names.second, false).getAddress()) + symbols[names.first] = reinterpret_cast(*symbol); } }; From 04d1c8c44905898a8590cc53c5bfed0f7f857444 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Sun, 6 May 2018 14:29:17 +0300 Subject: [PATCH 064/231] Fixed code #2272 --- dbms/src/Columns/ColumnConst.h | 4 ++-- dbms/src/Columns/ColumnNullable.h | 2 +- dbms/src/Columns/ColumnVector.h | 4 ++-- dbms/src/Columns/IColumn.h | 15 ++++++++----- dbms/src/DataTypes/IDataType.cpp | 2 +- dbms/src/Functions/FunctionsProjection.cpp | 11 +++++----- dbms/src/Interpreters/ExpressionActions.cpp | 24 +++++++++------------ 7 files changed, 32 insertions(+), 30 deletions(-) diff --git a/dbms/src/Columns/ColumnConst.h b/dbms/src/Columns/ColumnConst.h index eeda4dfd03a..d0cdaf97018 100644 --- a/dbms/src/Columns/ColumnConst.h +++ b/dbms/src/Columns/ColumnConst.h @@ -91,9 +91,9 @@ public: return data->getInt(0); } - UInt8 getBoolRepresentation(size_t) const override + bool getBool(size_t) const override { - return data->getBoolRepresentation(0); + return data->getBool(0); } bool isNullAt(size_t) const override diff --git a/dbms/src/Columns/ColumnNullable.h b/dbms/src/Columns/ColumnNullable.h index a96e9651909..73f8ddfb556 100644 --- a/dbms/src/Columns/ColumnNullable.h +++ b/dbms/src/Columns/ColumnNullable.h @@ -46,7 +46,7 @@ public: bool isNullAt(size_t n) const override { return static_cast(*null_map).getData()[n] != 0;} Field operator[](size_t n) const override; void get(size_t n, Field & res) const override; - UInt8 getBoolRepresentation(size_t n) const override { return isNullAt(n) ? 0 : nested_column->getBoolRepresentation(n); } + bool getBool(size_t n) const override { return isNullAt(n) ? 0 : nested_column->getBool(n); } UInt64 get64(size_t n) const override { return nested_column->get64(n); } StringRef getDataAt(size_t n) const override; void insertData(const char * pos, size_t length) override; diff --git a/dbms/src/Columns/ColumnVector.h b/dbms/src/Columns/ColumnVector.h index b2ab0e9d403..b267065622a 100644 --- a/dbms/src/Columns/ColumnVector.h +++ b/dbms/src/Columns/ColumnVector.h @@ -231,9 +231,9 @@ public: return UInt64(data[n]); } - UInt8 getBoolRepresentation(size_t n) const override + bool getBool(size_t n) const override { - return UInt8(!!data[n]); + return data[n]; } Int64 getInt(size_t n) const override diff --git a/dbms/src/Columns/IColumn.h b/dbms/src/Columns/IColumn.h index 003e1667350..3221b3f1044 100644 --- a/dbms/src/Columns/IColumn.h +++ b/dbms/src/Columns/IColumn.h @@ -88,6 +88,7 @@ public: } /** If column is numeric, return value of n-th element, casted to UInt64. + * For NULL values of Nullable column it is allowed to return arbitary value. * Otherwise throw an exception. */ virtual UInt64 getUInt(size_t /*n*/) const @@ -95,11 +96,6 @@ public: throw Exception("Method getUInt is not supported for " + getName(), ErrorCodes::NOT_IMPLEMENTED); } - virtual UInt8 getBoolRepresentation(size_t /*n*/) const - { - throw Exception("Method getBoolRepresentation is not supported for " + getName(), ErrorCodes::NOT_IMPLEMENTED); - } - virtual Int64 getInt(size_t /*n*/) const { throw Exception("Method getInt is not supported for " + getName(), ErrorCodes::NOT_IMPLEMENTED); @@ -107,6 +103,15 @@ public: virtual bool isNullAt(size_t /*n*/) const { return false; } + /** If column is numeric, return value of n-th element, casted to bool. + * For NULL values of Nullable column returns false. + * Otherwise throw an exception. + */ + virtual bool getBool(size_t /*n*/) const + { + throw Exception("Method getBool is not supported for " + getName(), ErrorCodes::NOT_IMPLEMENTED); + } + /// Removes all elements outside of specified range. /// Is used in LIMIT operation, for example. virtual Ptr cut(size_t start, size_t length) const diff --git a/dbms/src/DataTypes/IDataType.cpp b/dbms/src/DataTypes/IDataType.cpp index 87fbe31d1af..6a19e7b3e67 100644 --- a/dbms/src/DataTypes/IDataType.cpp +++ b/dbms/src/DataTypes/IDataType.cpp @@ -74,7 +74,7 @@ String IDataType::getFileNameForStream(const String & column_name, const IDataTy String nested_table_name = Nested::extractTableName(column_name); bool is_sizes_of_nested_type = - path.size() == 1 /// Nested structure may have arrays as nested elements (so effectively we have multidimentional arrays). + path.size() == 1 /// Nested structure may have arrays as nested elements (so effectively we have multidimensional arrays). /// Sizes of arrays are shared only at first level. && path[0].type == IDataType::Substream::ArraySizes && nested_table_name != column_name; diff --git a/dbms/src/Functions/FunctionsProjection.cpp b/dbms/src/Functions/FunctionsProjection.cpp index 9b569d223c5..c2ea1df35d6 100644 --- a/dbms/src/Functions/FunctionsProjection.cpp +++ b/dbms/src/Functions/FunctionsProjection.cpp @@ -33,7 +33,7 @@ void FunctionOneOrZero::executeImpl(Block & block, const ColumnNumbers & argumen vec_res.resize(data_column->size()); for (size_t i = 0; i < data_column->size(); ++i) { - if (data_column->getBoolRepresentation(i)) + if (data_column->getBool(i)) { vec_res[i] = 1; } @@ -80,7 +80,7 @@ void FunctionProject::executeImpl(Block & block, const ColumnNumbers & arguments } else if (const auto projection_column_uint8_const = checkAndGetColumnConst(projection_column.get())) { - if (projection_column_uint8_const->getBoolRepresentation(0)) + if (projection_column_uint8_const->getBool(0)) { block.getByPosition(result).column = data_column->cloneResized(data_column->size()); } @@ -133,13 +133,14 @@ void FunctionBuildProjectionComposition::executeImpl(Block & block, const Column size_t current_reserve_index = 0; for (size_t i = 0; i < first_projection_column->size(); ++i) { - if (first_projection_column->getBoolRepresentation(i) == 0) + if (!first_projection_column->getBool(i)) { vec_res[i] = 0; } else { - vec_res[i] = second_projection_column->getBoolRepresentation(current_reserve_index++); + vec_res[i] = second_projection_column->getBool(current_reserve_index); + ++current_reserve_index; } } if (current_reserve_index != second_projection_column->size()) @@ -190,7 +191,7 @@ void FunctionRestoreProjection::executeImpl(Block & block, const ColumnNumbers & std::vector override_indices(arguments.size() - 1, 0); for (size_t i = 0; i < projection_column->size(); ++i) { - size_t argument_index = projection_column->getBoolRepresentation(i); + size_t argument_index = projection_column->getBool(i); col_res->insertFrom(*block.getByPosition(arguments[argument_index + 1]).column, override_indices[argument_index]++); } block.getByPosition(result).column = std::move(col_res); diff --git a/dbms/src/Interpreters/ExpressionActions.cpp b/dbms/src/Interpreters/ExpressionActions.cpp index 8efd319d647..5b942843d92 100644 --- a/dbms/src/Interpreters/ExpressionActions.cpp +++ b/dbms/src/Interpreters/ExpressionActions.cpp @@ -290,35 +290,31 @@ void ExpressionAction::prepare(Block & sample_block) size_t ExpressionAction::getInputRowsCount(Block & block, std::unordered_map & input_rows_counts) const { auto it = input_rows_counts.find(row_projection_column); - size_t projection_space_dimention; + size_t projection_space_dimension; if (it == input_rows_counts.end()) { const auto & projection_column = block.getByName(row_projection_column).column; - projection_space_dimention = 0; + projection_space_dimension = 0; for (size_t i = 0; i < projection_column->size(); ++i) - { - if (projection_column->getBoolRepresentation(i) > 0) - { - ++projection_space_dimention; - } - } + if (projection_column->getBool(i)) + ++projection_space_dimension; - input_rows_counts[row_projection_column] = projection_space_dimention; + input_rows_counts[row_projection_column] = projection_space_dimension; } else { - projection_space_dimention = it->second; + projection_space_dimension = it->second; } - size_t parent_space_dimention; + size_t parent_space_dimension; if (row_projection_column.empty()) { - parent_space_dimention = input_rows_counts[""]; + parent_space_dimension = input_rows_counts[""]; } else { - parent_space_dimention = block.getByName(row_projection_column).column->size(); + parent_space_dimension = block.getByName(row_projection_column).column->size(); } - return is_row_projection_complementary ? parent_space_dimention - projection_space_dimention : projection_space_dimention; + return is_row_projection_complementary ? parent_space_dimension - projection_space_dimension : projection_space_dimension; } void ExpressionAction::execute(Block & block, std::unordered_map & input_rows_counts) const From 63625c0a30c1e367969b5fbfc562b0745f24024a Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Sun, 6 May 2018 14:33:03 +0300 Subject: [PATCH 065/231] Fixed code #2272 --- dbms/src/Columns/ColumnVector.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dbms/src/Columns/ColumnVector.h b/dbms/src/Columns/ColumnVector.h index b267065622a..da4bf0dabab 100644 --- a/dbms/src/Columns/ColumnVector.h +++ b/dbms/src/Columns/ColumnVector.h @@ -233,7 +233,7 @@ public: bool getBool(size_t n) const override { - return data[n]; + return bool(data[n]); } Int64 getInt(size_t n) const override From 9da677719cb8e2e590c396bc86c433647e0c922d Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Sun, 6 May 2018 14:34:16 +0300 Subject: [PATCH 066/231] Fixed code #2272 --- dbms/src/Interpreters/ExpressionActions.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/dbms/src/Interpreters/ExpressionActions.cpp b/dbms/src/Interpreters/ExpressionActions.cpp index 5b942843d92..f472416f689 100644 --- a/dbms/src/Interpreters/ExpressionActions.cpp +++ b/dbms/src/Interpreters/ExpressionActions.cpp @@ -288,7 +288,8 @@ void ExpressionAction::prepare(Block & sample_block) } } -size_t ExpressionAction::getInputRowsCount(Block & block, std::unordered_map & input_rows_counts) const { +size_t ExpressionAction::getInputRowsCount(Block & block, std::unordered_map & input_rows_counts) const +{ auto it = input_rows_counts.find(row_projection_column); size_t projection_space_dimension; if (it == input_rows_counts.end()) @@ -314,6 +315,7 @@ size_t ExpressionAction::getInputRowsCount(Block & block, std::unordered_mapsize(); } + return is_row_projection_complementary ? parent_space_dimension - projection_space_dimension : projection_space_dimension; } From a687c901008c0caf01836f044c12767f46dbc7b9 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 03:00:12 +0300 Subject: [PATCH 067/231] Allow to link with LLVM if it was compiled without RTTI #2277 --- dbms/src/Interpreters/ExpressionJIT.cpp | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index c1e530a9c71..2a0984728c1 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -41,6 +41,23 @@ #pragma GCC diagnostic pop +/** HACK + * Allow to link with LLVM that was compiled without RTTI. + * This is the default option when you build LLVM from sources. + * We define fake symbols for RTTI to help linker. + * This assumes that enabling/disabling RTTI doesn't change memory layout of objects + * in any significant way and it doesn't affect the code that isn't actually using RTTI. + * Proper solution: recompile LLVM with enabled RTTI. + */ +extern "C" +{ + +__attribute__((__weak__)) int _ZTIN4llvm13ErrorInfoBaseE = 0; +__attribute__((__weak__)) int _ZTIN4llvm12MemoryBufferE = 0; + +} + + namespace DB { From 956128a67a97c6a8839e2054cf3d8768149089d8 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 05:01:11 +0300 Subject: [PATCH 068/231] Fixed style a bit [#CLICKHOUSE-2] --- cmake/dbms_generate_function.cmake | 13 +- .../AggregateFunctionSequenceMatch.h | 19 +-- .../ReservoirSamplerDeterministic.h | 8 +- dbms/src/Common/Config/ConfigProcessor.cpp | 3 +- dbms/src/Common/ExternalTable.h | 2 +- dbms/src/Common/Macros.cpp | 2 +- dbms/src/Common/Macros.h | 2 +- dbms/src/Common/StringSearcher.h | 20 ++- dbms/src/Core/BlockInfo.cpp | 2 +- dbms/src/Dictionaries/CacheDictionary.cpp | 41 ++---- .../ComplexKeyCacheDictionary.cpp | 24 +--- .../ComplexKeyHashedDictionary.cpp | 60 ++++---- .../Dictionaries/DictionarySourceFactory.cpp | 24 +--- dbms/src/Dictionaries/DictionaryStructure.cpp | 38 ++--- .../Embedded/GeoDictionariesLoader.cpp | 2 +- .../Embedded/RegionsHierarchy.cpp | 2 +- .../ExternalResultDescription.cpp | 4 +- dbms/src/Dictionaries/FlatDictionary.cpp | 43 ++---- dbms/src/Dictionaries/HashedDictionary.cpp | 39 ++---- .../Dictionaries/MongoDBBlockInputStream.cpp | 15 +- .../Dictionaries/MySQLBlockInputStream.cpp | 4 +- .../src/Dictionaries/ODBCBlockInputStream.cpp | 6 +- .../Dictionaries/RangeHashedDictionary.cpp | 19 +-- dbms/src/Dictionaries/TrieDictionary.cpp | 44 ++---- dbms/src/Functions/CMakeLists.txt | 130 +++++++++--------- dbms/src/Functions/FunctionsArithmetic.h | 22 +-- dbms/src/Functions/FunctionsArray.cpp | 15 +- dbms/src/Functions/FunctionsArray.h | 6 +- dbms/src/Functions/FunctionsComparison.h | 6 +- dbms/src/Functions/FunctionsConversion.h | 4 +- dbms/src/Functions/FunctionsDateTime.h | 19 ++- dbms/src/Functions/FunctionsHashing.h | 30 ++-- dbms/src/Functions/FunctionsMath.h | 22 +-- dbms/src/Functions/FunctionsString.cpp | 12 +- dbms/src/Functions/FunctionsTransform.h | 25 +--- .../Functions/registerFunctions_area.cpp.in | 2 - dbms/src/IO/ReadBufferAIO.cpp | 4 +- .../IExternalLoaderConfigRepository.h | 2 +- .../Interpreters/InterpreterCreateQuery.cpp | 2 +- dbms/src/Interpreters/PartLog.cpp | 2 +- dbms/src/Interpreters/SettingsCommon.h | 3 +- dbms/src/Parsers/iostream_debug_helpers.cpp | 7 +- dbms/src/Server/HTTPHandler.h | 2 +- dbms/src/Server/Server.cpp | 3 +- dbms/src/Server/TCPHandler.h | 4 +- dbms/src/Storages/AlterCommands.cpp | 14 +- .../Storages/Distributed/DirectoryMonitor.cpp | 7 +- .../DistributedBlockOutputStream.h | 2 +- dbms/src/Storages/MergeTree/MergeTreeData.cpp | 5 +- .../Storages/MergeTree/MergeTreeDataWriter.h | 2 +- .../MergeTree/MergedBlockOutputStream.cpp | 2 +- .../MergeTree/registerStorageMergeTree.cpp | 2 +- dbms/src/Storages/StorageJoin.cpp | 4 +- dbms/src/Storages/tests/merge_selector2.cpp | 6 +- 54 files changed, 282 insertions(+), 520 deletions(-) diff --git a/cmake/dbms_generate_function.cmake b/cmake/dbms_generate_function.cmake index 0ed4d6002a5..ef35a623e43 100644 --- a/cmake/dbms_generate_function.cmake +++ b/cmake/dbms_generate_function.cmake @@ -1,13 +1,10 @@ - function(generate_function_register FUNCTION_AREA) - - foreach(FUNCTION IN LISTS ARGN) - configure_file (registerFunction.h.in register${FUNCTION}.h) - configure_file (registerFunction.cpp.in register${FUNCTION}.cpp) - set(REGISTER_HEADERS "${REGISTER_HEADERS} #include \"register${FUNCTION}.h\"\n") - set(REGISTER_FUNCTIONS "${REGISTER_FUNCTIONS} register${FUNCTION}(factory);\n") + foreach(FUNCTION IN LISTS ARGN) + configure_file (registerFunction.h.in register${FUNCTION}.h) + configure_file (registerFunction.cpp.in register${FUNCTION}.cpp) + set(REGISTER_HEADERS "${REGISTER_HEADERS}#include \"register${FUNCTION}.h\"\n") + set(REGISTER_FUNCTIONS "${REGISTER_FUNCTIONS} register${FUNCTION}(factory);\n") endforeach() configure_file (registerFunctions_area.cpp.in registerFunctions${FUNCTION_AREA}.cpp) - endfunction() diff --git a/dbms/src/AggregateFunctions/AggregateFunctionSequenceMatch.h b/dbms/src/AggregateFunctions/AggregateFunctionSequenceMatch.h index b0e11e84d9e..48d059b0f14 100644 --- a/dbms/src/AggregateFunctions/AggregateFunctionSequenceMatch.h +++ b/dbms/src/AggregateFunctions/AggregateFunctionSequenceMatch.h @@ -242,9 +242,7 @@ private: auto throw_exception = [&](const std::string & msg) { - throw Exception{ - msg + " '" + std::string(pos, end) + "' at position " + toString(pos - begin), - ErrorCodes::SYNTAX_ERROR}; + throw Exception{msg + " '" + std::string(pos, end) + "' at position " + toString(pos - begin), ErrorCodes::SYNTAX_ERROR}; }; auto match = [&pos, end](const char * str) mutable @@ -286,9 +284,7 @@ private: if (actions.back().type != PatternActionType::SpecificEvent && actions.back().type != PatternActionType::AnyEvent && actions.back().type != PatternActionType::KleeneStar) - throw Exception{ - "Temporal condition should be preceeded by an event condition", - ErrorCodes::BAD_ARGUMENTS}; + throw Exception{"Temporal condition should be preceeded by an event condition", ErrorCodes::BAD_ARGUMENTS}; actions.emplace_back(type, duration); } @@ -301,9 +297,7 @@ private: throw_exception("Could not parse number"); if (event_number > arg_count - 1) - throw Exception{ - "Event number " + toString(event_number) + " is out of range", - ErrorCodes::BAD_ARGUMENTS}; + throw Exception{"Event number " + toString(event_number) + " is out of range", ErrorCodes::BAD_ARGUMENTS}; actions.emplace_back(PatternActionType::SpecificEvent, event_number - 1); } @@ -428,13 +422,10 @@ protected: break; } else - throw Exception{ - "Unknown PatternActionType", - ErrorCodes::LOGICAL_ERROR}; + throw Exception{"Unknown PatternActionType", ErrorCodes::LOGICAL_ERROR}; if (++i > sequence_match_max_iterations) - throw Exception{ - "Pattern application proves too difficult, exceeding max iterations (" + toString(sequence_match_max_iterations) + ")", + throw Exception{"Pattern application proves too difficult, exceeding max iterations (" + toString(sequence_match_max_iterations) + ")", ErrorCodes::TOO_SLOW}; } diff --git a/dbms/src/AggregateFunctions/ReservoirSamplerDeterministic.h b/dbms/src/AggregateFunctions/ReservoirSamplerDeterministic.h index 44760d8e5a5..c543e662b2a 100644 --- a/dbms/src/AggregateFunctions/ReservoirSamplerDeterministic.h +++ b/dbms/src/AggregateFunctions/ReservoirSamplerDeterministic.h @@ -15,10 +15,10 @@ #include - /// Implementation of Reservoir Sampling algorithm. Incrementally selects from the added objects a random subset of the `sample_count` size. - /// Can approximately get quantiles. - /// The `quantile` call takes O(sample_count log sample_count), if after the previous call `quantile` there was at least one call to insert. Otherwise, O(1). - /// That is, it makes sense to first add, then get quantiles without adding. +/// Implementation of Reservoir Sampling algorithm. Incrementally selects from the added objects a random subset of the `sample_count` size. +/// Can approximately get quantiles. +/// The `quantile` call takes O(sample_count log sample_count), if after the previous call `quantile` there was at least one call to insert. Otherwise, O(1). +/// That is, it makes sense to first add, then get quantiles without adding. namespace DB diff --git a/dbms/src/Common/Config/ConfigProcessor.cpp b/dbms/src/Common/Config/ConfigProcessor.cpp index e303b580ba7..54f9ca58823 100644 --- a/dbms/src/Common/Config/ConfigProcessor.cpp +++ b/dbms/src/Common/Config/ConfigProcessor.cpp @@ -373,7 +373,8 @@ ConfigProcessor::Files ConfigProcessor::getConfigMergeFiles(const std::string & std::vector merge_dirs; merge_dirs.push_back(merge_dir_path.toString()); - if (merge_dir_path.getBaseName() != "conf") { + if (merge_dir_path.getBaseName() != "conf") + { merge_dir_path.setBaseName("conf"); merge_dirs.push_back(merge_dir_path.toString()); } diff --git a/dbms/src/Common/ExternalTable.h b/dbms/src/Common/ExternalTable.h index 7cc8069108b..c894b09c99b 100644 --- a/dbms/src/Common/ExternalTable.h +++ b/dbms/src/Common/ExternalTable.h @@ -165,7 +165,7 @@ public: /// Parsing of external table used when sending tables via http /// The `handlePart` function will be called for each table passed, - /// so it's also necessary to call `clean` at the end of the `handlePart`. +/// so it's also necessary to call `clean` at the end of the `handlePart`. class ExternalTablesHandler : public Poco::Net::PartHandler, BaseExternalTable { public: diff --git a/dbms/src/Common/Macros.cpp b/dbms/src/Common/Macros.cpp index 5d111abb0c6..d0bb1235741 100644 --- a/dbms/src/Common/Macros.cpp +++ b/dbms/src/Common/Macros.cpp @@ -76,7 +76,7 @@ Names Macros::expand(const Names & source_names, size_t level) const for (const String & name : source_names) result_names.push_back(expand(name, level)); - + return result_names; } } diff --git a/dbms/src/Common/Macros.h b/dbms/src/Common/Macros.h index 583aff7f18d..ce905723433 100644 --- a/dbms/src/Common/Macros.h +++ b/dbms/src/Common/Macros.h @@ -30,7 +30,7 @@ public: * level - the level of recursion. */ String expand(const String & s, size_t level = 0) const; - + /** Apply expand for the list. */ Names expand(const Names & source_names, size_t level = 0) const; diff --git a/dbms/src/Common/StringSearcher.h b/dbms/src/Common/StringSearcher.h index 4812dd3d329..f43fe6c717c 100644 --- a/dbms/src/Common/StringSearcher.h +++ b/dbms/src/Common/StringSearcher.h @@ -121,7 +121,7 @@ public: continue; } - const auto src_len = DB::UTF8::seqLength(*needle_pos); + const auto src_len = UTF8::seqLength(*needle_pos); const auto c_u32 = utf8.convert(needle_pos); const auto c_l_u32 = Poco::Unicode::toLower(c_u32); @@ -132,9 +132,7 @@ public: /// @note Unicode standard states it is a rare but possible occasion if (!(dst_l_len == dst_u_len && dst_u_len == src_len)) - throw DB::Exception{ - "UTF8 sequences with different lowercase and uppercase lengths are not supported", - DB::ErrorCodes::UNSUPPORTED_PARAMETER}; + throw Exception{"UTF8 sequences with different lowercase and uppercase lengths are not supported", ErrorCodes::UNSUPPORTED_PARAMETER}; cache_actual_len += src_len; if (cache_actual_len < n) @@ -183,7 +181,7 @@ public: Poco::Unicode::toLower(utf8.convert(needle_pos))) { /// @note assuming sequences for lowercase and uppercase have exact same length - const auto len = DB::UTF8::seqLength(*pos); + const auto len = UTF8::seqLength(*pos); pos += len, needle_pos += len; } @@ -207,7 +205,7 @@ public: Poco::Unicode::toLower(utf8.convert(pos)) == Poco::Unicode::toLower(utf8.convert(needle_pos))) { - const auto len = DB::UTF8::seqLength(*pos); + const auto len = UTF8::seqLength(*pos); pos += len, needle_pos += len; } @@ -240,7 +238,7 @@ public: if (mask == 0) { haystack += n; - DB::UTF8::syncForward(haystack, haystack_end); + UTF8::syncForward(haystack, haystack_end); continue; } @@ -267,7 +265,7 @@ public: Poco::Unicode::toLower(utf8.convert(needle_pos))) { /// @note assuming sequences for lowercase and uppercase have exact same length - const auto len = DB::UTF8::seqLength(*haystack_pos); + const auto len = UTF8::seqLength(*haystack_pos); haystack_pos += len, needle_pos += len; } @@ -279,7 +277,7 @@ public: return haystack; /// first octet was ok, but not the first 16, move to start of next sequence and reapply - haystack += DB::UTF8::seqLength(*haystack); + haystack += UTF8::seqLength(*haystack); continue; } } @@ -297,7 +295,7 @@ public: Poco::Unicode::toLower(utf8.convert(haystack_pos)) == Poco::Unicode::toLower(utf8.convert(needle_pos))) { - const auto len = DB::UTF8::seqLength(*haystack_pos); + const auto len = UTF8::seqLength(*haystack_pos); haystack_pos += len, needle_pos += len; } @@ -306,7 +304,7 @@ public: } /// advance to the start of the next sequence - haystack += DB::UTF8::seqLength(*haystack); + haystack += UTF8::seqLength(*haystack); } return haystack_end; diff --git a/dbms/src/Core/BlockInfo.cpp b/dbms/src/Core/BlockInfo.cpp index 9169288c7c1..77ef2e01007 100644 --- a/dbms/src/Core/BlockInfo.cpp +++ b/dbms/src/Core/BlockInfo.cpp @@ -20,7 +20,7 @@ namespace ErrorCodes /// Write values in binary form. NOTE: You could use protobuf, but it would be overkill for this case. void BlockInfo::write(WriteBuffer & out) const { - /// Set of pairs `FIELD_NUM`, value in binary form. Then 0. +/// Set of pairs `FIELD_NUM`, value in binary form. Then 0. #define WRITE_FIELD(TYPE, NAME, DEFAULT, FIELD_NUM) \ writeVarUInt(FIELD_NUM, out); \ writeBinary(NAME, out); diff --git a/dbms/src/Dictionaries/CacheDictionary.cpp b/dbms/src/Dictionaries/CacheDictionary.cpp index 37ed8173449..cae70322587 100644 --- a/dbms/src/Dictionaries/CacheDictionary.cpp +++ b/dbms/src/Dictionaries/CacheDictionary.cpp @@ -70,9 +70,7 @@ CacheDictionary::CacheDictionary(const std::string & name, const DictionaryStruc rnd_engine(randomSeed()) { if (!this->source_ptr->supportsSelectiveLoad()) - throw Exception{ - name + ": source cannot be used with CacheDictionary", - ErrorCodes::UNSUPPORTED_METHOD}; + throw Exception{name + ": source cannot be used with CacheDictionary", ErrorCodes::UNSUPPORTED_METHOD}; createAttributes(); } @@ -215,9 +213,7 @@ void CacheDictionary::get##TYPE(const std::string & attribute_name, const Padded {\ auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ const auto null_value = std::get(attribute.null_values);\ \ @@ -240,9 +236,7 @@ void CacheDictionary::getString(const std::string & attribute_name, const Padded { auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; const auto null_value = StringRef{std::get(attribute.null_values)}; @@ -256,9 +250,7 @@ void CacheDictionary::get##TYPE(\ {\ auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ getItemsNumber(attribute, ids, out, [&] (const size_t row) { return def[row]; });\ } @@ -281,9 +273,7 @@ void CacheDictionary::getString( { auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; getItemsString(attribute, ids, out, [&] (const size_t row) { return def->getDataAt(row); }); } @@ -294,9 +284,7 @@ void CacheDictionary::get##TYPE(\ {\ auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ getItemsNumber(attribute, ids, out, [&] (const size_t) { return def; });\ } @@ -319,9 +307,7 @@ void CacheDictionary::getString( { auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; getItemsString(attribute, ids, out, [&] (const size_t) { return StringRef{def}; }); } @@ -449,9 +435,7 @@ void CacheDictionary::createAttributes() hierarchical_attribute = &attributes.back(); if (hierarchical_attribute->type != AttributeUnderlyingType::UInt64) - throw Exception{ - name + ": hierarchical attribute must be UInt64.", - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": hierarchical attribute must be UInt64.", ErrorCodes::TYPE_MISMATCH}; } } } @@ -798,9 +782,7 @@ void CacheDictionary::update( { const auto id_column = typeid_cast(block.safeGetByPosition(0).column.get()); if (!id_column) - throw Exception{ - name + ": id column has type different from UInt64.", - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": id column has type different from UInt64.", ErrorCodes::TYPE_MISMATCH}; const auto & ids = id_column->getData(); @@ -973,10 +955,7 @@ CacheDictionary::Attribute & CacheDictionary::getAttribute(const std::string & a { const auto it = attribute_index_by_name.find(attribute_name); if (it == std::end(attribute_index_by_name)) - throw Exception{ - name + ": no such attribute '" + attribute_name + "'", - ErrorCodes::BAD_ARGUMENTS - }; + throw Exception{name + ": no such attribute '" + attribute_name + "'", ErrorCodes::BAD_ARGUMENTS}; return attributes[it->second]; } diff --git a/dbms/src/Dictionaries/ComplexKeyCacheDictionary.cpp b/dbms/src/Dictionaries/ComplexKeyCacheDictionary.cpp index fcd73cdb889..86fbfbb474e 100644 --- a/dbms/src/Dictionaries/ComplexKeyCacheDictionary.cpp +++ b/dbms/src/Dictionaries/ComplexKeyCacheDictionary.cpp @@ -59,9 +59,7 @@ ComplexKeyCacheDictionary::ComplexKeyCacheDictionary(const std::string & name, c rnd_engine(randomSeed()) { if (!this->source_ptr->supportsSelectiveLoad()) - throw Exception{ - name + ": source cannot be used with ComplexKeyCacheDictionary", - ErrorCodes::UNSUPPORTED_METHOD}; + throw Exception{name + ": source cannot be used with ComplexKeyCacheDictionary", ErrorCodes::UNSUPPORTED_METHOD}; createAttributes(); } @@ -78,9 +76,7 @@ void ComplexKeyCacheDictionary::getString( auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; const auto null_value = StringRef{std::get(attribute.null_values)}; @@ -95,9 +91,7 @@ void ComplexKeyCacheDictionary::getString( auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; getItemsString(attribute, key_columns, out, [&] (const size_t row) { return def->getDataAt(row); }); } @@ -110,9 +104,7 @@ void ComplexKeyCacheDictionary::getString( auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; getItemsString(attribute, key_columns, out, [&] (const size_t) { return StringRef{def}; }); } @@ -248,9 +240,7 @@ void ComplexKeyCacheDictionary::createAttributes() attributes.push_back(createAttributeWithType(attribute.underlying_type, attribute.null_value)); if (attribute.hierarchical) - throw Exception{ - name + ": hierarchical attributes not supported for dictionary of type " + getTypeName(), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": hierarchical attributes not supported for dictionary of type " + getTypeName(), ErrorCodes::TYPE_MISMATCH}; } } @@ -258,9 +248,7 @@ ComplexKeyCacheDictionary::Attribute & ComplexKeyCacheDictionary::getAttribute(c { const auto it = attribute_index_by_name.find(attribute_name); if (it == std::end(attribute_index_by_name)) - throw Exception{ - name + ": no such attribute '" + attribute_name + "'", - ErrorCodes::BAD_ARGUMENTS}; + throw Exception{name + ": no such attribute '" + attribute_name + "'", ErrorCodes::BAD_ARGUMENTS}; return attributes[it->second]; } diff --git a/dbms/src/Dictionaries/ComplexKeyHashedDictionary.cpp b/dbms/src/Dictionaries/ComplexKeyHashedDictionary.cpp index eaaeeab4e0a..1b84b1c66e0 100644 --- a/dbms/src/Dictionaries/ComplexKeyHashedDictionary.cpp +++ b/dbms/src/Dictionaries/ComplexKeyHashedDictionary.cpp @@ -51,9 +51,7 @@ void ComplexKeyHashedDictionary::get##TYPE(\ \ const auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ const auto null_value = std::get(attribute.null_values);\ \ @@ -82,9 +80,7 @@ void ComplexKeyHashedDictionary::getString( const auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; const auto & null_value = StringRef{std::get(attribute.null_values)}; @@ -102,9 +98,7 @@ void ComplexKeyHashedDictionary::get##TYPE(\ \ const auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ getItemsNumber(attribute, key_columns,\ [&] (const size_t row, const auto value) { out[row] = value; },\ @@ -131,9 +125,7 @@ void ComplexKeyHashedDictionary::getString( const auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; getItemsImpl(attribute, key_columns, [&] (const size_t, const StringRef value) { out->insertData(value.data, value.size); }, @@ -149,9 +141,7 @@ void ComplexKeyHashedDictionary::get##TYPE(\ \ const auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ getItemsNumber(attribute, key_columns,\ [&] (const size_t row, const auto value) { out[row] = value; },\ @@ -178,9 +168,7 @@ void ComplexKeyHashedDictionary::getString( const auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; getItemsImpl(attribute, key_columns, [&] (const size_t, const StringRef value) { out->insertData(value.data, value.size); }, @@ -221,9 +209,7 @@ void ComplexKeyHashedDictionary::createAttributes() attributes.push_back(createAttributeWithType(attribute.underlying_type, attribute.null_value)); if (attribute.hierarchical) - throw Exception{ - name + ": hierarchical attributes not supported for dictionary of type " + getTypeName(), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": hierarchical attributes not supported for dictionary of type " + getTypeName(), ErrorCodes::TYPE_MISMATCH}; } } @@ -238,23 +224,26 @@ void ComplexKeyHashedDictionary::blockToAttributes(const Block & block) element_count += rows; const auto key_column_ptrs = ext::map(ext::range(0, keys_size), - [&](const size_t attribute_idx) { - return block.safeGetByPosition(attribute_idx).column; - }); + [&](const size_t attribute_idx) + { + return block.safeGetByPosition(attribute_idx).column; + }); const auto attribute_column_ptrs = ext::map(ext::range(0, attributes_size), - [&](const size_t attribute_idx) { - return block.safeGetByPosition( - keys_size + attribute_idx).column; - }); + [&](const size_t attribute_idx) + { + return block.safeGetByPosition(keys_size + attribute_idx).column; + }); - for (const auto row_idx : ext::range(0, rows)) { + for (const auto row_idx : ext::range(0, rows)) + { /// calculate key once per row const auto key = placeKeysInPool(row_idx, key_column_ptrs, keys, keys_pool); auto should_rollback = false; - for (const auto attribute_idx : ext::range(0, attributes_size)) { + for (const auto attribute_idx : ext::range(0, attributes_size)) + { const auto &attribute_column = *attribute_column_ptrs[attribute_idx]; auto &attribute = attributes[attribute_idx]; const auto inserted = setAttributeValue(attribute, key, attribute_column[row_idx]); @@ -354,7 +343,8 @@ void ComplexKeyHashedDictionary::updateData() void ComplexKeyHashedDictionary::loadData() { - if (!source_ptr->hasUpdateField()) { + if (!source_ptr->hasUpdateField()) + { auto stream = source_ptr->loadAll(); stream->readPrefix(); @@ -367,9 +357,7 @@ void ComplexKeyHashedDictionary::loadData() updateData(); if (require_nonempty && 0 == element_count) - throw Exception{ - name + ": dictionary source is empty and 'require_nonempty' property is set.", - ErrorCodes::DICTIONARY_IS_EMPTY}; + throw Exception{name + ": dictionary source is empty and 'require_nonempty' property is set.", ErrorCodes::DICTIONARY_IS_EMPTY}; } template @@ -546,9 +534,7 @@ const ComplexKeyHashedDictionary::Attribute & ComplexKeyHashedDictionary::getAtt { const auto it = attribute_index_by_name.find(attribute_name); if (it == std::end(attribute_index_by_name)) - throw Exception{ - name + ": no such attribute '" + attribute_name + "'", - ErrorCodes::BAD_ARGUMENTS}; + throw Exception{name + ": no such attribute '" + attribute_name + "'", ErrorCodes::BAD_ARGUMENTS}; return attributes[it->second]; } diff --git a/dbms/src/Dictionaries/DictionarySourceFactory.cpp b/dbms/src/Dictionaries/DictionarySourceFactory.cpp index 6c9c355893d..e77a1189233 100644 --- a/dbms/src/Dictionaries/DictionarySourceFactory.cpp +++ b/dbms/src/Dictionaries/DictionarySourceFactory.cpp @@ -53,8 +53,7 @@ Block createSampleBlock(const DictionaryStructure & dict_struct) Block block; if (dict_struct.id) - block.insert(ColumnWithTypeAndName{ - ColumnUInt64::create(1, 0), std::make_shared(), dict_struct.id->name}); + block.insert(ColumnWithTypeAndName{ColumnUInt64::create(1, 0), std::make_shared(), dict_struct.id->name}); if (dict_struct.key) { @@ -109,10 +108,7 @@ DictionarySourcePtr DictionarySourceFactory::create( Poco::Util::AbstractConfiguration::Keys keys; config.keys(config_prefix, keys); if (keys.size() != 1) - throw Exception{ - name +": element dictionary.source should have exactly one child element", - ErrorCodes::EXCESSIVE_ELEMENT_IN_CONFIG - }; + throw Exception{name +": element dictionary.source should have exactly one child element", ErrorCodes::EXCESSIVE_ELEMENT_IN_CONFIG}; auto sample_block = createSampleBlock(dict_struct); @@ -121,9 +117,7 @@ DictionarySourcePtr DictionarySourceFactory::create( if ("file" == source_type) { if (dict_struct.has_expressions) - throw Exception{ - "Dictionary source of type `file` does not support attribute expressions", - ErrorCodes::LOGICAL_ERROR}; + throw Exception{"Dictionary source of type `file` does not support attribute expressions", ErrorCodes::LOGICAL_ERROR}; const auto filename = config.getString(config_prefix + ".file.path"); const auto format = config.getString(config_prefix + ".file.format"); @@ -164,9 +158,7 @@ DictionarySourcePtr DictionarySourceFactory::create( else if ("executable" == source_type) { if (dict_struct.has_expressions) - throw Exception{ - "Dictionary source of type `executable` does not support attribute expressions", - ErrorCodes::LOGICAL_ERROR}; + throw Exception{"Dictionary source of type `executable` does not support attribute expressions", ErrorCodes::LOGICAL_ERROR}; return std::make_unique(dict_struct, config, config_prefix + ".executable", sample_block, context); } @@ -174,9 +166,7 @@ DictionarySourcePtr DictionarySourceFactory::create( { if (dict_struct.has_expressions) - throw Exception{ - "Dictionary source of type `http` does not support attribute expressions", - ErrorCodes::LOGICAL_ERROR}; + throw Exception{"Dictionary source of type `http` does not support attribute expressions", ErrorCodes::LOGICAL_ERROR}; #if Poco_NetSSL_FOUND // Used for https queries @@ -199,9 +189,7 @@ DictionarySourcePtr DictionarySourceFactory::create( } } - throw Exception{ - name + ": unknown dictionary source type: " + source_type, - ErrorCodes::UNKNOWN_ELEMENT_IN_CONFIG}; + throw Exception{name + ": unknown dictionary source type: " + source_type, ErrorCodes::UNKNOWN_ELEMENT_IN_CONFIG}; } } diff --git a/dbms/src/Dictionaries/DictionaryStructure.cpp b/dbms/src/Dictionaries/DictionaryStructure.cpp index 9989bb64173..f5e84cd0110 100644 --- a/dbms/src/Dictionaries/DictionaryStructure.cpp +++ b/dbms/src/Dictionaries/DictionaryStructure.cpp @@ -83,9 +83,7 @@ AttributeUnderlyingType getAttributeUnderlyingType(const std::string & type) if (it != std::end(dictionary)) return it->second; - throw Exception{ - "Unknown type " + type, - ErrorCodes::UNKNOWN_TYPE}; + throw Exception{"Unknown type " + type, ErrorCodes::UNKNOWN_TYPE}; } @@ -107,9 +105,7 @@ std::string toString(const AttributeUnderlyingType type) case AttributeUnderlyingType::String: return "String"; } - throw Exception{ - "Unknown attribute_type " + toString(static_cast(type)), - ErrorCodes::ARGUMENT_OUT_OF_BOUND}; + throw Exception{"Unknown attribute_type " + toString(static_cast(type)), ErrorCodes::ARGUMENT_OUT_OF_BOUND}; } @@ -118,9 +114,7 @@ DictionarySpecialAttribute::DictionarySpecialAttribute(const Poco::Util::Abstrac expression{config.getString(config_prefix + ".expression", "")} { if (name.empty() && !expression.empty()) - throw Exception{ - "Element " + config_prefix + ".name is empty", - ErrorCodes::BAD_ARGUMENTS}; + throw Exception{"Element " + config_prefix + ".name is empty", ErrorCodes::BAD_ARGUMENTS}; } @@ -169,9 +163,7 @@ DictionaryStructure::DictionaryStructure(const Poco::Util::AbstractConfiguration void DictionaryStructure::validateKeyTypes(const DataTypes & key_types) const { if (key_types.size() != key->size()) - throw Exception{ - "Key structure does not match, expected " + getKeyDescription(), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{"Key structure does not match, expected " + getKeyDescription(), ErrorCodes::TYPE_MISMATCH}; for (const auto i : ext::range(0, key_types.size())) { @@ -179,10 +171,8 @@ void DictionaryStructure::validateKeyTypes(const DataTypes & key_types) const const auto & actual_type = key_types[i]->getName(); if (expected_type != actual_type) - throw Exception{ - "Key type at position " + std::to_string(i) + " does not match, expected " + expected_type + - ", found " + actual_type, - ErrorCodes::TYPE_MISMATCH}; + throw Exception{"Key type at position " + std::to_string(i) + " does not match, expected " + expected_type + + ", found " + actual_type, ErrorCodes::TYPE_MISMATCH}; } } @@ -240,9 +230,7 @@ static void CheckAttributeKeys(const Poco::Util::AbstractConfiguration::Keys & k for (const auto & key : keys) { if (valid_keys.find(key) == valid_keys.end()) - throw Exception{ - "Unknown key '" + key + "' inside attribute section", - ErrorCodes::BAD_ARGUMENTS}; + throw Exception{"Unknown key '" + key + "' inside attribute section", ErrorCodes::BAD_ARGUMENTS}; } } @@ -298,19 +286,13 @@ std::vector DictionaryStructure::getAttributes( const auto injective = config.getBool(prefix + "injective", false); const auto is_object_id = config.getBool(prefix + "is_object_id", false); if (name.empty()) - throw Exception{ - "Properties 'name' and 'type' of an attribute cannot be empty", - ErrorCodes::BAD_ARGUMENTS}; + throw Exception{"Properties 'name' and 'type' of an attribute cannot be empty", ErrorCodes::BAD_ARGUMENTS}; if (has_hierarchy && !hierarchy_allowed) - throw Exception{ - "Hierarchy not allowed in '" + prefix, - ErrorCodes::BAD_ARGUMENTS}; + throw Exception{"Hierarchy not allowed in '" + prefix, ErrorCodes::BAD_ARGUMENTS}; if (has_hierarchy && hierarchical) - throw Exception{ - "Only one hierarchical attribute supported", - ErrorCodes::BAD_ARGUMENTS}; + throw Exception{"Only one hierarchical attribute supported", ErrorCodes::BAD_ARGUMENTS}; has_hierarchy = has_hierarchy || hierarchical; diff --git a/dbms/src/Dictionaries/Embedded/GeoDictionariesLoader.cpp b/dbms/src/Dictionaries/Embedded/GeoDictionariesLoader.cpp index 7a32fb31be1..2d2967e72a1 100644 --- a/dbms/src/Dictionaries/Embedded/GeoDictionariesLoader.cpp +++ b/dbms/src/Dictionaries/Embedded/GeoDictionariesLoader.cpp @@ -13,7 +13,7 @@ std::unique_ptr GeoDictionariesLoader::reloadRegionsHierarch return {}; const auto default_hierarchy_file = config.getString(config_key); - auto data_provider = std::make_unique(default_hierarchy_file); + auto data_provider = std::make_unique(default_hierarchy_file); return std::make_unique(std::move(data_provider)); } diff --git a/dbms/src/Dictionaries/Embedded/RegionsHierarchy.cpp b/dbms/src/Dictionaries/Embedded/RegionsHierarchy.cpp index de39090568f..2dbab26acc1 100644 --- a/dbms/src/Dictionaries/Embedded/RegionsHierarchy.cpp +++ b/dbms/src/Dictionaries/Embedded/RegionsHierarchy.cpp @@ -42,7 +42,7 @@ void RegionsHierarchy::reload() RegionID max_region_id = 0; - auto regions_reader = data_source->createReader(); + auto regions_reader = data_source->createReader(); RegionEntry region_entry; while (regions_reader->readNext(region_entry)) diff --git a/dbms/src/Dictionaries/ExternalResultDescription.cpp b/dbms/src/Dictionaries/ExternalResultDescription.cpp index 5eca329e5f2..18788606be7 100644 --- a/dbms/src/Dictionaries/ExternalResultDescription.cpp +++ b/dbms/src/Dictionaries/ExternalResultDescription.cpp @@ -56,9 +56,7 @@ void ExternalResultDescription::init(const Block & sample_block_) else if (typeid_cast(type)) types.push_back(ValueType::DateTime); else - throw Exception{ - "Unsupported type " + type->getName(), - ErrorCodes::UNKNOWN_TYPE}; + throw Exception{"Unsupported type " + type->getName(), ErrorCodes::UNKNOWN_TYPE}; names.emplace_back(column.name); sample_columns.emplace_back(column.column); diff --git a/dbms/src/Dictionaries/FlatDictionary.cpp b/dbms/src/Dictionaries/FlatDictionary.cpp index e1341907649..c758b0d0a9a 100644 --- a/dbms/src/Dictionaries/FlatDictionary.cpp +++ b/dbms/src/Dictionaries/FlatDictionary.cpp @@ -117,9 +117,7 @@ void FlatDictionary::get##TYPE(const std::string & attribute_name, const PaddedP {\ const auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ const auto null_value = std::get(attribute.null_values);\ \ @@ -144,9 +142,7 @@ void FlatDictionary::getString(const std::string & attribute_name, const PaddedP { const auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; const auto & null_value = std::get(attribute.null_values); @@ -162,9 +158,7 @@ void FlatDictionary::get##TYPE(\ {\ const auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ getItemsNumber(attribute, ids,\ [&] (const size_t row, const auto value) { out[row] = value; },\ @@ -189,9 +183,7 @@ void FlatDictionary::getString( { const auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; getItemsImpl(attribute, ids, [&] (const size_t, const StringRef value) { out->insertData(value.data, value.size); }, @@ -205,9 +197,7 @@ void FlatDictionary::get##TYPE(\ {\ const auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ getItemsNumber(attribute, ids,\ [&] (const size_t row, const auto value) { out[row] = value; },\ @@ -232,9 +222,7 @@ void FlatDictionary::getString( { const auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; FlatDictionary::getItemsImpl(attribute, ids, [&] (const size_t, const StringRef value) { out->insertData(value.data, value.size); }, @@ -279,9 +267,7 @@ void FlatDictionary::createAttributes() hierarchical_attribute = &attributes.back(); if (hierarchical_attribute->type != AttributeUnderlyingType::UInt64) - throw Exception{ - name + ": hierarchical attribute must be UInt64.", - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": hierarchical attribute must be UInt64.", ErrorCodes::TYPE_MISMATCH}; } } } @@ -374,7 +360,8 @@ void FlatDictionary::updateData() void FlatDictionary::loadData() { - if (!source_ptr->hasUpdateField()) { + if (!source_ptr->hasUpdateField()) + { auto stream = source_ptr->loadAll(); stream->readPrefix(); @@ -387,9 +374,7 @@ void FlatDictionary::loadData() updateData(); if (require_nonempty && 0 == element_count) - throw Exception{ - name + ": dictionary source is empty and 'require_nonempty' property is set.", - ErrorCodes::DICTIONARY_IS_EMPTY}; + throw Exception{name + ": dictionary source is empty and 'require_nonempty' property is set.", ErrorCodes::DICTIONARY_IS_EMPTY}; } @@ -530,9 +515,7 @@ template void FlatDictionary::resize(Attribute & attribute, const Key id) { if (id >= max_array_size) - throw Exception{ - name + ": identifier should be less than " + toString(max_array_size), - ErrorCodes::ARGUMENT_OUT_OF_BOUND}; + throw Exception{name + ": identifier should be less than " + toString(max_array_size), ErrorCodes::ARGUMENT_OUT_OF_BOUND}; auto & array = *std::get>(attribute.arrays); if (id >= array.size()) @@ -586,9 +569,7 @@ const FlatDictionary::Attribute & FlatDictionary::getAttribute(const std::string { const auto it = attribute_index_by_name.find(attribute_name); if (it == std::end(attribute_index_by_name)) - throw Exception{ - name + ": no such attribute '" + attribute_name + "'", - ErrorCodes::BAD_ARGUMENTS}; + throw Exception{name + ": no such attribute '" + attribute_name + "'", ErrorCodes::BAD_ARGUMENTS}; return attributes[it->second]; } diff --git a/dbms/src/Dictionaries/HashedDictionary.cpp b/dbms/src/Dictionaries/HashedDictionary.cpp index 09880cdbeb5..708ef118016 100644 --- a/dbms/src/Dictionaries/HashedDictionary.cpp +++ b/dbms/src/Dictionaries/HashedDictionary.cpp @@ -114,9 +114,7 @@ void HashedDictionary::get##TYPE(const std::string & attribute_name, const Padde {\ const auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ const auto null_value = std::get(attribute.null_values);\ \ @@ -141,9 +139,7 @@ void HashedDictionary::getString(const std::string & attribute_name, const Padde { const auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; const auto & null_value = StringRef{std::get(attribute.null_values)}; @@ -159,9 +155,7 @@ void HashedDictionary::get##TYPE(\ {\ const auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ getItemsNumber(attribute, ids,\ [&] (const size_t row, const auto value) { out[row] = value; },\ @@ -186,9 +180,7 @@ void HashedDictionary::getString( { const auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; getItemsImpl(attribute, ids, [&] (const size_t, const StringRef value) { out->insertData(value.data, value.size); }, @@ -201,9 +193,7 @@ void HashedDictionary::get##TYPE(\ {\ const auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ getItemsNumber(attribute, ids,\ [&] (const size_t row, const auto value) { out[row] = value; },\ @@ -228,9 +218,7 @@ void HashedDictionary::getString( { const auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; getItemsImpl(attribute, ids, [&] (const size_t, const StringRef value) { out->insertData(value.data, value.size); }, @@ -273,9 +261,7 @@ void HashedDictionary::createAttributes() hierarchical_attribute = &attributes.back(); if (hierarchical_attribute->type != AttributeUnderlyingType::UInt64) - throw Exception{ - name + ": hierarchical attribute must be UInt64.", - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": hierarchical attribute must be UInt64.", ErrorCodes::TYPE_MISMATCH}; } } } @@ -368,7 +354,8 @@ void HashedDictionary::updateData() void HashedDictionary::loadData() { - if (!source_ptr->hasUpdateField()) { + if (!source_ptr->hasUpdateField()) + { auto stream = source_ptr->loadAll(); stream->readPrefix(); @@ -381,9 +368,7 @@ void HashedDictionary::loadData() updateData(); if (require_nonempty && 0 == element_count) - throw Exception{ - name + ": dictionary source is empty and 'require_nonempty' property is set.", - ErrorCodes::DICTIONARY_IS_EMPTY}; + throw Exception{name + ": dictionary source is empty and 'require_nonempty' property is set.", ErrorCodes::DICTIONARY_IS_EMPTY}; } template @@ -545,9 +530,7 @@ const HashedDictionary::Attribute & HashedDictionary::getAttribute(const std::st { const auto it = attribute_index_by_name.find(attribute_name); if (it == std::end(attribute_index_by_name)) - throw Exception{ - name + ": no such attribute '" + attribute_name + "'", - ErrorCodes::BAD_ARGUMENTS}; + throw Exception{name + ": no such attribute '" + attribute_name + "'", ErrorCodes::BAD_ARGUMENTS}; return attributes[it->second]; } diff --git a/dbms/src/Dictionaries/MongoDBBlockInputStream.cpp b/dbms/src/Dictionaries/MongoDBBlockInputStream.cpp index b5f1d1f6f69..20c2d655d85 100644 --- a/dbms/src/Dictionaries/MongoDBBlockInputStream.cpp +++ b/dbms/src/Dictionaries/MongoDBBlockInputStream.cpp @@ -104,17 +104,15 @@ namespace break; } - throw Exception{ - "Type mismatch, expected String, got type id = " + toString(value.type()) + - " for column " + name, ErrorCodes::TYPE_MISMATCH}; + throw Exception{"Type mismatch, expected String, got type id = " + toString(value.type()) + + " for column " + name, ErrorCodes::TYPE_MISMATCH}; } case ValueType::Date: { if (value.type() != Poco::MongoDB::ElementTraits::TypeId) - throw Exception{ - "Type mismatch, expected Timestamp, got type id = " + toString(value.type()) + - " for column " + name, ErrorCodes::TYPE_MISMATCH}; + throw Exception{"Type mismatch, expected Timestamp, got type id = " + toString(value.type()) + + " for column " + name, ErrorCodes::TYPE_MISMATCH}; static_cast(column).getData().push_back( UInt16{DateLUT::instance().toDayNum( @@ -125,9 +123,8 @@ namespace case ValueType::DateTime: { if (value.type() != Poco::MongoDB::ElementTraits::TypeId) - throw Exception{ - "Type mismatch, expected Timestamp, got type id = " + toString(value.type()) + - " for column " + name, ErrorCodes::TYPE_MISMATCH}; + throw Exception{"Type mismatch, expected Timestamp, got type id = " + toString(value.type()) + + " for column " + name, ErrorCodes::TYPE_MISMATCH}; static_cast(column).getData().push_back( static_cast &>(value).value().epochTime()); diff --git a/dbms/src/Dictionaries/MySQLBlockInputStream.cpp b/dbms/src/Dictionaries/MySQLBlockInputStream.cpp index 6e2275cd105..3f2d192cce9 100644 --- a/dbms/src/Dictionaries/MySQLBlockInputStream.cpp +++ b/dbms/src/Dictionaries/MySQLBlockInputStream.cpp @@ -24,9 +24,7 @@ MySQLBlockInputStream::MySQLBlockInputStream( max_block_size{max_block_size} { if (sample_block.columns() != result.getNumFields()) - throw Exception{ - "mysqlxx::UseQueryResult contains " + toString(result.getNumFields()) + " columns while " + - toString(sample_block.columns()) + " expected", + throw Exception{"mysqlxx::UseQueryResult contains " + toString(result.getNumFields()) + " columns while " + toString(sample_block.columns()) + " expected", ErrorCodes::NUMBER_OF_COLUMNS_DOESNT_MATCH}; description.init(sample_block); diff --git a/dbms/src/Dictionaries/ODBCBlockInputStream.cpp b/dbms/src/Dictionaries/ODBCBlockInputStream.cpp index 8b186f6791e..a90c770b8b2 100644 --- a/dbms/src/Dictionaries/ODBCBlockInputStream.cpp +++ b/dbms/src/Dictionaries/ODBCBlockInputStream.cpp @@ -29,10 +29,8 @@ ODBCBlockInputStream::ODBCBlockInputStream( log(&Logger::get("ODBCBlockInputStream")) { if (sample_block.columns() != result.columnCount()) - throw Exception{ - "RecordSet contains " + toString(result.columnCount()) + " columns while " + - toString(sample_block.columns()) + " expected", - ErrorCodes::NUMBER_OF_COLUMNS_DOESNT_MATCH}; + throw Exception{"RecordSet contains " + toString(result.columnCount()) + " columns while " + + toString(sample_block.columns()) + " expected", ErrorCodes::NUMBER_OF_COLUMNS_DOESNT_MATCH}; description.init(sample_block); } diff --git a/dbms/src/Dictionaries/RangeHashedDictionary.cpp b/dbms/src/Dictionaries/RangeHashedDictionary.cpp index 2a73a43b84e..fc938143176 100644 --- a/dbms/src/Dictionaries/RangeHashedDictionary.cpp +++ b/dbms/src/Dictionaries/RangeHashedDictionary.cpp @@ -102,9 +102,7 @@ void RangeHashedDictionary::createAttributes() attributes.push_back(createAttributeWithType(attribute.underlying_type, attribute.null_value)); if (attribute.hierarchical) - throw Exception{ - name + ": hierarchical attributes not supported by " + getName() + " dictionary.", - ErrorCodes::BAD_ARGUMENTS}; + throw Exception{name + ": hierarchical attributes not supported by " + getName() + " dictionary.", ErrorCodes::BAD_ARGUMENTS}; } } @@ -136,9 +134,7 @@ void RangeHashedDictionary::loadData() stream->readSuffix(); if (require_nonempty && 0 == element_count) - throw Exception{ - name + ": dictionary source is empty and 'require_nonempty' property is set.", - ErrorCodes::DICTIONARY_IS_EMPTY}; + throw Exception{name + ": dictionary source is empty and 'require_nonempty' property is set.", ErrorCodes::DICTIONARY_IS_EMPTY}; } template @@ -284,7 +280,8 @@ void RangeHashedDictionary::setAttributeValueImpl(Attribute & attribute, const K auto & values = it->second; const auto insert_it = std::lower_bound(std::begin(values), std::end(values), range, - [] (const Value & lhs, const Range & range) { + [] (const Value & lhs, const Range & range) + { return lhs.range < range; }); @@ -342,9 +339,7 @@ const RangeHashedDictionary::Attribute & RangeHashedDictionary::getAttribute(con { const auto it = attribute_index_by_name.find(attribute_name); if (it == std::end(attribute_index_by_name)) - throw Exception{ - name + ": no such attribute '" + attribute_name + "'", - ErrorCodes::BAD_ARGUMENTS}; + throw Exception{name + ": no such attribute '" + attribute_name + "'", ErrorCodes::BAD_ARGUMENTS}; return attributes[it->second]; } @@ -353,9 +348,7 @@ const RangeHashedDictionary::Attribute & RangeHashedDictionary::getAttributeWith { const auto & attribute = getAttribute(name); if (attribute.type != type) - throw Exception{ - name + ": type mismatch: attribute " + name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; return attribute; } diff --git a/dbms/src/Dictionaries/TrieDictionary.cpp b/dbms/src/Dictionaries/TrieDictionary.cpp index 99cf29c8ca1..52227a6c1d4 100644 --- a/dbms/src/Dictionaries/TrieDictionary.cpp +++ b/dbms/src/Dictionaries/TrieDictionary.cpp @@ -68,9 +68,7 @@ void TrieDictionary::get##TYPE(\ \ const auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ const auto null_value = std::get(attribute.null_values);\ \ @@ -99,9 +97,7 @@ void TrieDictionary::getString( const auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; const auto & null_value = StringRef{std::get(attribute.null_values)}; @@ -119,9 +115,7 @@ void TrieDictionary::get##TYPE(\ \ const auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ getItemsNumber(attribute, key_columns,\ [&] (const size_t row, const auto value) { out[row] = value; },\ @@ -148,9 +142,7 @@ void TrieDictionary::getString( const auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; getItemsImpl(attribute, key_columns, [&] (const size_t, const StringRef value) { out->insertData(value.data, value.size); }, @@ -166,9 +158,7 @@ void TrieDictionary::get##TYPE(\ \ const auto & attribute = getAttribute(attribute_name);\ if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::TYPE))\ - throw Exception{\ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type),\ - ErrorCodes::TYPE_MISMATCH};\ + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH};\ \ getItemsNumber(attribute, key_columns,\ [&] (const size_t row, const auto value) { out[row] = value; },\ @@ -195,9 +185,7 @@ void TrieDictionary::getString( const auto & attribute = getAttribute(attribute_name); if (!isAttributeTypeConvertibleTo(attribute.type, AttributeUnderlyingType::String)) - throw Exception{ - name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": type mismatch: attribute " + attribute_name + " has type " + toString(attribute.type), ErrorCodes::TYPE_MISMATCH}; getItemsImpl(attribute, key_columns, [&] (const size_t, const StringRef value) { out->insertData(value.data, value.size); }, @@ -238,9 +226,7 @@ void TrieDictionary::createAttributes() attributes.push_back(createAttributeWithType(attribute.underlying_type, attribute.null_value)); if (attribute.hierarchical) - throw Exception{ - name + ": hierarchical attributes not supported for dictionary of type " + getTypeName(), - ErrorCodes::TYPE_MISMATCH}; + throw Exception{name + ": hierarchical attributes not supported for dictionary of type " + getTypeName(), ErrorCodes::TYPE_MISMATCH}; } } @@ -290,9 +276,7 @@ void TrieDictionary::loadData() stream->readSuffix(); if (require_nonempty && 0 == element_count) - throw Exception{ - name + ": dictionary source is empty and 'require_nonempty' property is set.", - ErrorCodes::DICTIONARY_IS_EMPTY}; + throw Exception{name + ": dictionary source is empty and 'require_nonempty' property is set.", ErrorCodes::DICTIONARY_IS_EMPTY}; } template @@ -338,16 +322,12 @@ void TrieDictionary::calculateBytesAllocated() void TrieDictionary::validateKeyTypes(const DataTypes & key_types) const { if (key_types.size() != 1) - throw Exception{ - "Expected a single IP address", - ErrorCodes::TYPE_MISMATCH}; + throw Exception{"Expected a single IP address", ErrorCodes::TYPE_MISMATCH}; const auto & actual_type = key_types[0]->getName(); if (actual_type != "UInt32" && actual_type != "FixedString(16)") - throw Exception{ - "Key does not match, expected either UInt32 or FixedString(16)", - ErrorCodes::TYPE_MISMATCH}; + throw Exception{"Key does not match, expected either UInt32 or FixedString(16)", ErrorCodes::TYPE_MISMATCH}; } @@ -526,9 +506,7 @@ const TrieDictionary::Attribute & TrieDictionary::getAttribute(const std::string { const auto it = attribute_index_by_name.find(attribute_name); if (it == std::end(attribute_index_by_name)) - throw Exception{ - name + ": no such attribute '" + attribute_name + "'", - ErrorCodes::BAD_ARGUMENTS}; + throw Exception{name + ": no such attribute '" + attribute_name + "'", ErrorCodes::BAD_ARGUMENTS}; return attributes[it->second]; } diff --git a/dbms/src/Functions/CMakeLists.txt b/dbms/src/Functions/CMakeLists.txt index 4882710578e..48735b7a4aa 100644 --- a/dbms/src/Functions/CMakeLists.txt +++ b/dbms/src/Functions/CMakeLists.txt @@ -2,77 +2,77 @@ include(${ClickHouse_SOURCE_DIR}/cmake/dbms_glob_sources.cmake) include(${ClickHouse_SOURCE_DIR}/cmake/dbms_generate_function.cmake) generate_function_register(Arithmetic - FunctionPlus - FunctionMinus - FunctionMultiply - FunctionDivideFloating - FunctionDivideIntegral - FunctionDivideIntegralOrZero - FunctionModulo - FunctionNegate - FunctionAbs - FunctionBitAnd - FunctionBitOr - FunctionBitXor - FunctionBitNot - FunctionBitShiftLeft - FunctionBitShiftRight - FunctionBitRotateLeft - FunctionBitRotateRight - FunctionLeast - FunctionGreatest - FunctionBitTest - FunctionBitTestAny - FunctionBitTestAll - FunctionGCD - FunctionLCM - FunctionIntExp2 - FunctionIntExp10 + FunctionPlus + FunctionMinus + FunctionMultiply + FunctionDivideFloating + FunctionDivideIntegral + FunctionDivideIntegralOrZero + FunctionModulo + FunctionNegate + FunctionAbs + FunctionBitAnd + FunctionBitOr + FunctionBitXor + FunctionBitNot + FunctionBitShiftLeft + FunctionBitShiftRight + FunctionBitRotateLeft + FunctionBitRotateRight + FunctionLeast + FunctionGreatest + FunctionBitTest + FunctionBitTestAny + FunctionBitTestAll + FunctionGCD + FunctionLCM + FunctionIntExp2 + FunctionIntExp10 ) generate_function_register(Array - FunctionArray - FunctionArrayElement - FunctionHas - FunctionIndexOf - FunctionCountEqual - FunctionArrayEnumerate - FunctionArrayEnumerateUniq - FunctionArrayUniq - FunctionEmptyArrayUInt8 - FunctionEmptyArrayUInt16 - FunctionEmptyArrayUInt32 - FunctionEmptyArrayUInt64 - FunctionEmptyArrayInt8 - FunctionEmptyArrayInt16 - FunctionEmptyArrayInt32 - FunctionEmptyArrayInt64 - FunctionEmptyArrayFloat32 - FunctionEmptyArrayFloat64 - FunctionEmptyArrayDate - FunctionEmptyArrayDateTime - FunctionEmptyArrayString - FunctionEmptyArrayToSingle - FunctionRange - FunctionArrayReduce - FunctionArrayReverse - FunctionArrayConcat - FunctionArraySlice - FunctionArrayPushBack - FunctionArrayPushFront - FunctionArrayPopBack - FunctionArrayPopFront - FunctionArrayHasAll - FunctionArrayHasAny - FunctionArrayIntersect - FunctionArrayResize + FunctionArray + FunctionArrayElement + FunctionHas + FunctionIndexOf + FunctionCountEqual + FunctionArrayEnumerate + FunctionArrayEnumerateUniq + FunctionArrayUniq + FunctionEmptyArrayUInt8 + FunctionEmptyArrayUInt16 + FunctionEmptyArrayUInt32 + FunctionEmptyArrayUInt64 + FunctionEmptyArrayInt8 + FunctionEmptyArrayInt16 + FunctionEmptyArrayInt32 + FunctionEmptyArrayInt64 + FunctionEmptyArrayFloat32 + FunctionEmptyArrayFloat64 + FunctionEmptyArrayDate + FunctionEmptyArrayDateTime + FunctionEmptyArrayString + FunctionEmptyArrayToSingle + FunctionRange + FunctionArrayReduce + FunctionArrayReverse + FunctionArrayConcat + FunctionArraySlice + FunctionArrayPushBack + FunctionArrayPushFront + FunctionArrayPopBack + FunctionArrayPopFront + FunctionArrayHasAll + FunctionArrayHasAny + FunctionArrayIntersect + FunctionArrayResize ) generate_function_register(Projection - FunctionOneOrZero - FunctionProject - FunctionBuildProjectionComposition - FunctionRestoreProjection + FunctionOneOrZero + FunctionProject + FunctionBuildProjectionComposition + FunctionRestoreProjection ) diff --git a/dbms/src/Functions/FunctionsArithmetic.h b/dbms/src/Functions/FunctionsArithmetic.h index fc8a8252d70..40cc27115b7 100644 --- a/dbms/src/Functions/FunctionsArithmetic.h +++ b/dbms/src/Functions/FunctionsArithmetic.h @@ -1384,17 +1384,13 @@ public: DataTypePtr getReturnTypeImpl(const DataTypes & arguments) const override { if (arguments.size() < 2) - throw Exception{ - "Number of arguments for function " + getName() + " doesn't match: passed " - + toString(arguments.size()) + ", should be at least 2.", - ErrorCodes::TOO_LESS_ARGUMENTS_FOR_FUNCTION}; + throw Exception{"Number of arguments for function " + getName() + " doesn't match: passed " + + toString(arguments.size()) + ", should be at least 2.", ErrorCodes::TOO_LESS_ARGUMENTS_FOR_FUNCTION}; const auto first_arg = arguments.front().get(); if (!first_arg->isInteger()) - throw Exception{ - "Illegal type " + first_arg->getName() + " of first argument of function " + getName(), - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; + throw Exception{"Illegal type " + first_arg->getName() + " of first argument of function " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; for (const auto i : ext::range(1, arguments.size())) @@ -1402,9 +1398,7 @@ public: const auto pos_arg = arguments[i].get(); if (!pos_arg->isUnsignedInteger()) - throw Exception{ - "Illegal type " + pos_arg->getName() + " of " + toString(i) + " argument of function " + getName(), - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; + throw Exception{"Illegal type " + pos_arg->getName() + " of " + toString(i) + " argument of function " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; } return std::make_shared(); @@ -1422,9 +1416,7 @@ public: && !execute(block, arguments, result, value_col) && !execute(block, arguments, result, value_col) && !execute(block, arguments, result, value_col)) - throw Exception{ - "Illegal column " + value_col->getName() + " of argument of function " + getName(), - ErrorCodes::ILLEGAL_COLUMN}; + throw Exception{"Illegal column " + value_col->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } private: @@ -1525,9 +1517,7 @@ private: && !addToMaskImpl(mask, pos_col) && !addToMaskImpl(mask, pos_col) && !addToMaskImpl(mask, pos_col)) - throw Exception{ - "Illegal column " + pos_col->getName() + " of argument of function " + getName(), - ErrorCodes::ILLEGAL_COLUMN}; + throw Exception{"Illegal column " + pos_col->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } return mask; diff --git a/dbms/src/Functions/FunctionsArray.cpp b/dbms/src/Functions/FunctionsArray.cpp index e61b22b5acc..bdab0ddc3b6 100644 --- a/dbms/src/Functions/FunctionsArray.cpp +++ b/dbms/src/Functions/FunctionsArray.cpp @@ -1962,8 +1962,7 @@ DataTypePtr FunctionRange::getReturnTypeImpl(const DataTypes & arguments) const const DataTypePtr & arg = arguments.front(); if (!arg->isUnsignedInteger()) - throw Exception{ - "Illegal type " + arg->getName() + " of argument of function " + getName(), + throw Exception{"Illegal type " + arg->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; return std::make_shared(arg); @@ -1982,17 +1981,15 @@ bool FunctionRange::executeInternal(Block & block, const IColumn * arg, const si { const auto sum = lhs + rhs; if (sum < lhs) - throw Exception{ - "A call to function " + getName() + " overflows, investigate the values of arguments you are passing", + throw Exception{"A call to function " + getName() + " overflows, investigate the values of arguments you are passing", ErrorCodes::ARGUMENT_OUT_OF_BOUND}; return sum; }); if (total_values > max_elements) - throw Exception{ - "A call to function " + getName() + " would produce " + std::to_string(total_values) + - " array elements, which is greater than the allowed maximum of " + std::to_string(max_elements), + throw Exception{"A call to function " + getName() + " would produce " + std::to_string(total_values) + + " array elements, which is greater than the allowed maximum of " + std::to_string(max_elements), ErrorCodes::ARGUMENT_OUT_OF_BOUND}; auto data_col = ColumnVector::create(total_values); @@ -2027,9 +2024,7 @@ void FunctionRange::executeImpl(Block & block, const ColumnNumbers & arguments, !executeInternal(block, col, result) && !executeInternal(block, col, result)) { - throw Exception{ - "Illegal column " + col->getName() + " of argument of function " + getName(), - ErrorCodes::ILLEGAL_COLUMN}; + throw Exception{"Illegal column " + col->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } } diff --git a/dbms/src/Functions/FunctionsArray.h b/dbms/src/Functions/FunctionsArray.h index ff6c1d79dad..865864fb890 100644 --- a/dbms/src/Functions/FunctionsArray.h +++ b/dbms/src/Functions/FunctionsArray.h @@ -1166,10 +1166,8 @@ private: || executeConst(block, arguments, result) || executeString(block, arguments, result) || executeGeneric(block, arguments, result))) - throw Exception{ - "Illegal column " + block.getByPosition(arguments[0]).column->getName() - + " of first argument of function " + getName(), - ErrorCodes::ILLEGAL_COLUMN}; + throw Exception{"Illegal column " + block.getByPosition(arguments[0]).column->getName() + + " of first argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } }; diff --git a/dbms/src/Functions/FunctionsComparison.h b/dbms/src/Functions/FunctionsComparison.h index 0aac1c13f82..0cf19833821 100644 --- a/dbms/src/Functions/FunctionsComparison.h +++ b/dbms/src/Functions/FunctionsComparison.h @@ -779,10 +779,8 @@ private: const auto column_string = checkAndGetColumnConst(column_string_untyped); if (!column_string || !legal_types) - throw Exception{ - "Illegal columns " + col_left_untyped->getName() + " and " + col_right_untyped->getName() - + " of arguments of function " + getName(), - ErrorCodes::ILLEGAL_COLUMN}; + throw Exception{"Illegal columns " + col_left_untyped->getName() + " and " + col_right_untyped->getName() + + " of arguments of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; StringRef string_value = column_string->getDataAt(0); diff --git a/dbms/src/Functions/FunctionsConversion.h b/dbms/src/Functions/FunctionsConversion.h index 48296002088..5a284f4c435 100644 --- a/dbms/src/Functions/FunctionsConversion.h +++ b/dbms/src/Functions/FunctionsConversion.h @@ -942,9 +942,7 @@ public: { const auto src_n = column_fixed_string->getN(); if (src_n > n) - throw Exception{ - "String too long for type FixedString(" + toString(n) + ")", - ErrorCodes::TOO_LARGE_STRING_SIZE}; + throw Exception{"String too long for type FixedString(" + toString(n) + ")", ErrorCodes::TOO_LARGE_STRING_SIZE}; auto column_fixed = ColumnFixedString::create(n); diff --git a/dbms/src/Functions/FunctionsDateTime.h b/dbms/src/Functions/FunctionsDateTime.h index a44c5dda122..9bf68024584 100644 --- a/dbms/src/Functions/FunctionsDateTime.h +++ b/dbms/src/Functions/FunctionsDateTime.h @@ -638,20 +638,19 @@ public: if (arguments.size() == 1) { if (!arguments[0].type->isDateOrDateTime()) - throw Exception{ - "Illegal type " + arguments[0].type->getName() + " of argument of function " + getName() + - ". Should be a date or a date with time", ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; + throw Exception("Illegal type " + arguments[0].type->getName() + " of argument of function " + getName() + + ". Should be a date or a date with time", ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); } else if (arguments.size() == 2) { if (!checkDataType(arguments[0].type.get()) || !checkDataType(arguments[1].type.get())) - throw Exception{ + throw Exception( "Function " + getName() + " supports 1 or 2 arguments. The 1st argument " "must be of type Date or DateTime. The 2nd argument (optional) must be " "a constant string with timezone name. The timezone argument is allowed " "only when the 1st argument has the type DateTime", - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; + ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); } else throw Exception("Number of arguments for function " + getName() + " doesn't match: passed " @@ -953,21 +952,20 @@ public: if (arguments.size() == 2) { if (!arguments[0].type->isDateOrDateTime()) - throw Exception{ - "Illegal type " + arguments[0].type->getName() + " of argument of function " + getName() + + throw Exception{"Illegal type " + arguments[0].type->getName() + " of argument of function " + getName() + ". Should be a date or a date with time", ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; } else { if (!checkDataType(arguments[0].type.get()) || !checkDataType(arguments[2].type.get())) - throw Exception{ + throw Exception( "Function " + getName() + " supports 2 or 3 arguments. The 1st argument " "must be of type Date or DateTime. The 2nd argument must be number. " "The 3rd argument (optional) must be " "a constant string with timezone name. The timezone argument is allowed " "only when the 1st argument has the type DateTime", - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; + ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); } if (checkDataType(arguments[0].type.get())) @@ -1299,8 +1297,7 @@ public: ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); if (!checkDataType(arguments[0].type.get())) - throw Exception{ - "Illegal type " + arguments[0].type->getName() + " of argument of function " + getName() + + throw Exception{"Illegal type " + arguments[0].type->getName() + " of argument of function " + getName() + ". Should be DateTime", ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; String time_zone_name = extractTimeZoneNameFromFunctionArguments(arguments, 1, 0); diff --git a/dbms/src/Functions/FunctionsHashing.h b/dbms/src/Functions/FunctionsHashing.h index 39184e4dbfc..89b2d7f8861 100644 --- a/dbms/src/Functions/FunctionsHashing.h +++ b/dbms/src/Functions/FunctionsHashing.h @@ -702,24 +702,18 @@ public: { const auto arg_count = arguments.size(); if (arg_count != 1 && arg_count != 2) - throw Exception{ - "Number of arguments for function " + getName() + " doesn't match: passed " + - toString(arg_count) + ", should be 1 or 2.", - ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH}; + throw Exception{"Number of arguments for function " + getName() + " doesn't match: passed " + + toString(arg_count) + ", should be 1 or 2.", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH}; const auto first_arg = arguments.front().get(); if (!checkDataType(first_arg)) - throw Exception{ - "Illegal type " + first_arg->getName() + " of argument of function " + getName(), - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; + throw Exception{"Illegal type " + first_arg->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; if (arg_count == 2) { const auto second_arg = arguments.back().get(); if (!second_arg->isInteger()) - throw Exception{ - "Illegal type " + second_arg->getName() + " of argument of function " + getName(), - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; + throw Exception{"Illegal type " + second_arg->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; } return std::make_shared(); @@ -762,19 +756,15 @@ private: block.getByPosition(result).column = std::move(col_to); } else - throw Exception{ - "Illegal column " + block.getByPosition(arguments[0]).column->getName() + - " of argument of function " + getName(), - ErrorCodes::ILLEGAL_COLUMN}; + throw Exception{"Illegal column " + block.getByPosition(arguments[0]).column->getName() + + " of argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } void executeTwoArgs(Block & block, const ColumnNumbers & arguments, const size_t result) const { const auto level_col = block.getByPosition(arguments.back()).column.get(); if (!level_col->isColumnConst()) - throw Exception{ - "Second argument of function " + getName() + " must be an integral constant", - ErrorCodes::ILLEGAL_COLUMN}; + throw Exception{"Second argument of function " + getName() + " must be an integral constant", ErrorCodes::ILLEGAL_COLUMN}; const auto level = level_col->get64(0); @@ -796,10 +786,8 @@ private: block.getByPosition(result).column = std::move(col_to); } else - throw Exception{ - "Illegal column " + block.getByPosition(arguments[0]).column->getName() + - " of argument of function " + getName(), - ErrorCodes::ILLEGAL_COLUMN}; + throw Exception{"Illegal column " + block.getByPosition(arguments[0]).column->getName() + + " of argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } }; diff --git a/dbms/src/Functions/FunctionsMath.h b/dbms/src/Functions/FunctionsMath.h index 5b178c6cd42..c12ed5ef436 100644 --- a/dbms/src/Functions/FunctionsMath.h +++ b/dbms/src/Functions/FunctionsMath.h @@ -79,9 +79,7 @@ private: DataTypePtr getReturnTypeImpl(const DataTypes & arguments) const override { if (!arguments.front()->isNumber()) - throw Exception{ - "Illegal type " + arguments.front()->getName() + " of argument of function " + getName(), - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; + throw Exception{"Illegal type " + arguments.front()->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; return std::make_shared(); } @@ -140,9 +138,7 @@ private: !execute(block, arg, result) && !execute(block, arg, result)) { - throw Exception{ - "Illegal column " + arg->getName() + " of argument of function " + getName(), - ErrorCodes::ILLEGAL_COLUMN}; + throw Exception{"Illegal column " + arg->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } } }; @@ -204,8 +200,7 @@ private: const auto check_argument_type = [this] (const IDataType * arg) { if (!arg->isNumber()) - throw Exception{ - "Illegal type " + arg->getName() + " of argument of function " + getName(), + throw Exception{"Illegal type " + arg->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; }; @@ -352,8 +347,7 @@ private: } else { - throw Exception{ - "Illegal column " + block.getByPosition(arguments[1]).column->getName() + + throw Exception{"Illegal column " + block.getByPosition(arguments[1]).column->getName() + " of second argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } @@ -377,8 +371,7 @@ private: } else { - throw Exception{ - "Illegal column " + block.getByPosition(arguments[1]).column->getName() + + throw Exception{"Illegal column " + block.getByPosition(arguments[1]).column->getName() + " of second argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } @@ -402,8 +395,7 @@ private: !executeLeft(block, arguments, result, left_arg) && !executeLeft(block, arguments, result, left_arg)) { - throw Exception{ - "Illegal column " + left_arg->getName() + " of argument of function " + getName(), + throw Exception{"Illegal column " + left_arg->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } } @@ -484,7 +476,7 @@ using FunctionExp = FunctionMathUnaryFloat64>; using FunctionExp2 = FunctionMathUnaryFloat64>; using FunctionLog2 = FunctionMathUnaryFloat64>; -using FunctionExp10 = FunctionMathUnaryFloat64isStringOrFixedString()) - throw Exception{ - "Illegal type " + arg->getName() + " of argument " + std::to_string(arg_idx + 1) + " of function " + getName(), + throw Exception{"Illegal type " + arg->getName() + " of argument " + std::to_string(arg_idx + 1) + " of function " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; } @@ -1017,12 +1016,10 @@ private: DataTypePtr getReturnTypeImpl(const DataTypes & arguments) const override { if (!arguments[0]->isString()) - throw Exception{ - "Illegal type " + arguments[0]->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; + throw Exception{"Illegal type " + arguments[0]->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; if (!arguments[1]->isString()) - throw Exception{ - "Illegal type " + arguments[1]->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; + throw Exception{"Illegal type " + arguments[1]->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; return std::make_shared(); } @@ -1081,8 +1078,7 @@ private: block.getByPosition(result).column = std::move(col_res); } else - throw Exception{ - "Illegal column " + block.getByPosition(arguments[0]).column->getName() + " of argument of function " + getName(), + throw Exception{"Illegal column " + block.getByPosition(arguments[0]).column->getName() + " of argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } }; diff --git a/dbms/src/Functions/FunctionsTransform.h b/dbms/src/Functions/FunctionsTransform.h index 68b7c023064..bf604ea488b 100644 --- a/dbms/src/Functions/FunctionsTransform.h +++ b/dbms/src/Functions/FunctionsTransform.h @@ -72,9 +72,7 @@ public: { const auto args_size = arguments.size(); if (args_size != 3 && args_size != 4) - throw Exception{ - "Number of arguments for function " + getName() + " doesn't match: passed " + - toString(args_size) + ", should be 3 or 4", + throw Exception{"Number of arguments for function " + getName() + " doesn't match: passed " + toString(args_size) + ", should be 3 or 4", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH}; const DataTypePtr & type_x = arguments[0]; @@ -180,9 +178,7 @@ public: && !executeNum(in, out, default_column) && !executeString(in, out, default_column)) { - throw Exception{ - "Illegal column " + in->getName() + " of first argument of function " + getName(), - ErrorCodes::ILLEGAL_COLUMN}; + throw Exception{"Illegal column " + in->getName() + " of first argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } block.getByPosition(result).column = std::move(column_result); @@ -246,8 +242,7 @@ private: && !executeNumToNumWithConstDefault(in, out_untyped) && !executeNumToStringWithConstDefault(in, out_untyped)) { - throw Exception{ - "Illegal column " + in->getName() + " of elements of array of second argument of function " + getName(), + throw Exception{"Illegal column " + in->getName() + " of elements of array of second argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } } @@ -265,8 +260,7 @@ private: && !executeNumToNumWithNonConstDefault(in, out_untyped, default_untyped) && !executeNumToStringWithNonConstDefault(in, out_untyped, default_untyped)) { - throw Exception{ - "Illegal column " + in->getName() + " of elements of array of second argument of function " + getName(), + throw Exception{"Illegal column " + in->getName() + " of elements of array of second argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } } @@ -284,11 +278,8 @@ private: if (!default_untyped) { if (!executeStringToString(in, out_untyped)) - { - throw Exception{ - "Illegal column " + in->getName() + " of elements of array of second argument of function " + getName(), + throw Exception{"Illegal column " + in->getName() + " of elements of array of second argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; - } } else if (default_untyped->isColumnConst()) { @@ -304,8 +295,7 @@ private: && !executeStringToNumWithConstDefault(in, out_untyped) && !executeStringToStringWithConstDefault(in, out_untyped)) { - throw Exception{ - "Illegal column " + in->getName() + " of elements of array of second argument of function " + getName(), + throw Exception{"Illegal column " + in->getName() + " of elements of array of second argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } } @@ -323,8 +313,7 @@ private: && !executeStringToNumWithNonConstDefault(in, out_untyped, default_untyped) && !executeStringToStringWithNonConstDefault(in, out_untyped, default_untyped)) { - throw Exception{ - "Illegal column " + in->getName() + " of elements of array of second argument of function " + getName(), + throw Exception{"Illegal column " + in->getName() + " of elements of array of second argument of function " + getName(), ErrorCodes::ILLEGAL_COLUMN}; } } diff --git a/dbms/src/Functions/registerFunctions_area.cpp.in b/dbms/src/Functions/registerFunctions_area.cpp.in index bf7c0407675..cd727885d57 100644 --- a/dbms/src/Functions/registerFunctions_area.cpp.in +++ b/dbms/src/Functions/registerFunctions_area.cpp.in @@ -8,9 +8,7 @@ namespace DB void registerFunctions@FUNCTION_AREA@(FunctionFactory & factory) { - @REGISTER_FUNCTIONS@ - } } diff --git a/dbms/src/IO/ReadBufferAIO.cpp b/dbms/src/IO/ReadBufferAIO.cpp index b9f760b51da..1c4b7ab49e3 100644 --- a/dbms/src/IO/ReadBufferAIO.cpp +++ b/dbms/src/IO/ReadBufferAIO.cpp @@ -85,8 +85,8 @@ void ReadBufferAIO::setMaxBytes(size_t max_bytes_read_) bool ReadBufferAIO::nextImpl() { - /// If the end of the file has already been reached by calling this function, - /// then the current call is wrong. + /// If the end of the file has already been reached by calling this function, + /// then the current call is wrong. if (is_eof) return false; diff --git a/dbms/src/Interpreters/IExternalLoaderConfigRepository.h b/dbms/src/Interpreters/IExternalLoaderConfigRepository.h index 93159eb4ba6..79615780242 100644 --- a/dbms/src/Interpreters/IExternalLoaderConfigRepository.h +++ b/dbms/src/Interpreters/IExternalLoaderConfigRepository.h @@ -12,7 +12,7 @@ namespace DB /** Repository with configurations of user-defined objects (dictionaries, models). * Used by ExternalLoader. - */ + */ class IExternalLoaderConfigRepository { public: diff --git a/dbms/src/Interpreters/InterpreterCreateQuery.cpp b/dbms/src/Interpreters/InterpreterCreateQuery.cpp index 9b4deee75e1..0b4caa95157 100644 --- a/dbms/src/Interpreters/InterpreterCreateQuery.cpp +++ b/dbms/src/Interpreters/InterpreterCreateQuery.cpp @@ -522,7 +522,7 @@ BlockIO InterpreterCreateQuery::createTable(ASTCreateQuery & create) } else if (context.tryGetExternalTable(table_name) && create.if_not_exists) return {}; - + res = StorageFactory::instance().get(create, data_path, table_name, diff --git a/dbms/src/Interpreters/PartLog.cpp b/dbms/src/Interpreters/PartLog.cpp index 646aae527b1..dc5d5e07a41 100644 --- a/dbms/src/Interpreters/PartLog.cpp +++ b/dbms/src/Interpreters/PartLog.cpp @@ -49,7 +49,7 @@ Block PartLogElement::createBlock() /// Is there an error during the execution or commit {ColumnUInt16::create(), std::make_shared(), "error"}, {ColumnString::create(), std::make_shared(), "exception"}, - }; + }; } void PartLogElement::appendToBlock(Block & block) const diff --git a/dbms/src/Interpreters/SettingsCommon.h b/dbms/src/Interpreters/SettingsCommon.h index 8b53484ce51..9b1e9cb1ee2 100644 --- a/dbms/src/Interpreters/SettingsCommon.h +++ b/dbms/src/Interpreters/SettingsCommon.h @@ -730,7 +730,8 @@ public: return String(1, value); } - void set(char x) { + void set(char x) + { value = x; changed = true; } diff --git a/dbms/src/Parsers/iostream_debug_helpers.cpp b/dbms/src/Parsers/iostream_debug_helpers.cpp index ee5d7d5d781..61dd08ecddc 100644 --- a/dbms/src/Parsers/iostream_debug_helpers.cpp +++ b/dbms/src/Parsers/iostream_debug_helpers.cpp @@ -6,13 +6,14 @@ namespace DB { - -std::ostream & operator<<(std::ostream & stream, const Token & what) { +std::ostream & operator<<(std::ostream & stream, const Token & what) +{ stream << "Token (type="<< static_cast(what.type) <<"){"<< std::string{what.begin, what.end} << "}"; return stream; } -std::ostream & operator<<(std::ostream & stream, const Expected & what) { +std::ostream & operator<<(std::ostream & stream, const Expected & what) +{ stream << "Expected {variants="; dumpValue(stream, what.variants) << "; max_parsed_pos=" << what.max_parsed_pos << "}"; diff --git a/dbms/src/Server/HTTPHandler.h b/dbms/src/Server/HTTPHandler.h index 72606afa827..58cb6bb67f1 100644 --- a/dbms/src/Server/HTTPHandler.h +++ b/dbms/src/Server/HTTPHandler.h @@ -55,7 +55,7 @@ private: IServer & server; Poco::Logger * log; - /// It is the name of the server that will be sent in an http-header X-ClickHouse-Server-Display-Name. + /// It is the name of the server that will be sent in an http-header X-ClickHouse-Server-Display-Name. String server_display_name; CurrentMetrics::Increment metric_increment{CurrentMetrics::HTTPConnection}; diff --git a/dbms/src/Server/Server.cpp b/dbms/src/Server/Server.cpp index 19a9809db7c..d8e5870f8e6 100644 --- a/dbms/src/Server/Server.cpp +++ b/dbms/src/Server/Server.cpp @@ -259,7 +259,8 @@ int Server::main(const std::vector & /*args*/) /* already_loaded = */ false); /// Reload config in SYSTEM RELOAD CONFIG query. - global_context->setConfigReloadCallback([&]() { + global_context->setConfigReloadCallback([&]() + { main_config_reloader->reload(); users_config_reloader->reload(); }); diff --git a/dbms/src/Server/TCPHandler.h b/dbms/src/Server/TCPHandler.h index 7202067dab8..e01987d3bbd 100644 --- a/dbms/src/Server/TCPHandler.h +++ b/dbms/src/Server/TCPHandler.h @@ -117,8 +117,8 @@ private: QueryState state; CurrentMetrics::Increment metric_increment{CurrentMetrics::TCPConnection}; - - /// It is the name of the server that will be sent to the client. + + /// It is the name of the server that will be sent to the client. String server_display_name; void runImpl(); diff --git a/dbms/src/Storages/AlterCommands.cpp b/dbms/src/Storages/AlterCommands.cpp index 274f9315f5f..f30dc9df53a 100644 --- a/dbms/src/Storages/AlterCommands.cpp +++ b/dbms/src/Storages/AlterCommands.cpp @@ -26,11 +26,7 @@ void AlterCommand::apply(ColumnsDescription & columns_description) const if (type == ADD_COLUMN) { if (columns_description.getAll().contains(column_name)) - { - throw Exception{ - "Cannot add column " + column_name + ": column with this name already exists", - ErrorCodes::ILLEGAL_COLUMN}; - } + throw Exception{"Cannot add column " + column_name + ": column with this name already exists", ErrorCodes::ILLEGAL_COLUMN}; const auto add_column = [this] (NamesAndTypesList & columns) { @@ -196,17 +192,13 @@ void AlterCommands::validate(IStorage * table, const Context & context) if (command.type == AlterCommand::ADD_COLUMN) { if (std::end(all_columns) != column_it) - throw Exception{ - "Cannot add column " + column_name + ": column with this name already exists", - ErrorCodes::ILLEGAL_COLUMN}; + throw Exception{"Cannot add column " + column_name + ": column with this name already exists", ErrorCodes::ILLEGAL_COLUMN}; } else if (command.type == AlterCommand::MODIFY_COLUMN) { if (std::end(all_columns) == column_it) - throw Exception{ - "Wrong column name. Cannot find column " + column_name + " to modify", - ErrorCodes::ILLEGAL_COLUMN}; + throw Exception{"Wrong column name. Cannot find column " + column_name + " to modify", ErrorCodes::ILLEGAL_COLUMN}; all_columns.erase(column_it); defaults.erase(column_name); diff --git a/dbms/src/Storages/Distributed/DirectoryMonitor.cpp b/dbms/src/Storages/Distributed/DirectoryMonitor.cpp index 19981be3f06..1c1e1f83033 100644 --- a/dbms/src/Storages/Distributed/DirectoryMonitor.cpp +++ b/dbms/src/Storages/Distributed/DirectoryMonitor.cpp @@ -63,16 +63,13 @@ namespace const char * user_pw_end = strchr(address.data(), '@'); const char * colon = strchr(address.data(), ':'); if (!user_pw_end || !colon) - throw Exception{ - "Shard address '" + address + "' does not match to 'user[:password]@host:port#default_database' pattern", + throw Exception{"Shard address '" + address + "' does not match to 'user[:password]@host:port#default_database' pattern", ErrorCodes::INCORRECT_FILE_NAME}; const bool has_pw = colon < user_pw_end; const char * host_end = has_pw ? strchr(user_pw_end + 1, ':') : colon; if (!host_end) - throw Exception{ - "Shard address '" + address + "' does not contain port", - ErrorCodes::INCORRECT_FILE_NAME}; + throw Exception{"Shard address '" + address + "' does not contain port", ErrorCodes::INCORRECT_FILE_NAME}; const char * has_db = strchr(address.data(), '#'); const char * port_end = has_db ? has_db : address_end; diff --git a/dbms/src/Storages/Distributed/DistributedBlockOutputStream.h b/dbms/src/Storages/Distributed/DistributedBlockOutputStream.h index b08a21bbc07..6b3349eb16d 100644 --- a/dbms/src/Storages/Distributed/DistributedBlockOutputStream.h +++ b/dbms/src/Storages/Distributed/DistributedBlockOutputStream.h @@ -128,7 +128,7 @@ private: size_t remote_jobs_count = 0; size_t local_jobs_count = 0; - + std::atomic finished_jobs_count{0}; Poco::Logger * log; diff --git a/dbms/src/Storages/MergeTree/MergeTreeData.cpp b/dbms/src/Storages/MergeTree/MergeTreeData.cpp index 8acdd4584b8..20d6aa545c7 100644 --- a/dbms/src/Storages/MergeTree/MergeTreeData.cpp +++ b/dbms/src/Storages/MergeTree/MergeTreeData.cpp @@ -774,10 +774,7 @@ void MergeTreeData::clearOldPartsFromFilesystem() void MergeTreeData::setPath(const String & new_full_path) { if (Poco::File{new_full_path}.exists()) - throw Exception{ - "Target path already exists: " + new_full_path, - /// @todo existing target can also be a file, not directory - ErrorCodes::DIRECTORY_ALREADY_EXISTS}; + throw Exception{"Target path already exists: " + new_full_path, ErrorCodes::DIRECTORY_ALREADY_EXISTS}; Poco::File(full_path).renameTo(new_full_path); diff --git a/dbms/src/Storages/MergeTree/MergeTreeDataWriter.h b/dbms/src/Storages/MergeTree/MergeTreeDataWriter.h index 021e5a7eea8..93d8ecb98ae 100644 --- a/dbms/src/Storages/MergeTree/MergeTreeDataWriter.h +++ b/dbms/src/Storages/MergeTree/MergeTreeDataWriter.h @@ -30,7 +30,7 @@ struct BlockWithPartition using BlocksWithPartition = std::vector; - /** Writes new parts of data to the merge tree. +/** Writes new parts of data to the merge tree. */ class MergeTreeDataWriter { diff --git a/dbms/src/Storages/MergeTree/MergedBlockOutputStream.cpp b/dbms/src/Storages/MergeTree/MergedBlockOutputStream.cpp index 6c9409be833..e99609b0c09 100644 --- a/dbms/src/Storages/MergeTree/MergedBlockOutputStream.cpp +++ b/dbms/src/Storages/MergeTree/MergedBlockOutputStream.cpp @@ -260,7 +260,7 @@ std::string MergedBlockOutputStream::getPartPath() const return part_path; } - /// If data is pre-sorted. +/// If data is pre-sorted. void MergedBlockOutputStream::write(const Block & block) { writeImpl(block, nullptr); diff --git a/dbms/src/Storages/MergeTree/registerStorageMergeTree.cpp b/dbms/src/Storages/MergeTree/registerStorageMergeTree.cpp index 4cdff05c2e3..a875b743760 100644 --- a/dbms/src/Storages/MergeTree/registerStorageMergeTree.cpp +++ b/dbms/src/Storages/MergeTree/registerStorageMergeTree.cpp @@ -233,7 +233,7 @@ Date column must exist in the table and have type Date (not DateTime). It is used for internal data partitioning and works like some kind of index. If your source data doesn't have a column of type Date, but has a DateTime column, you may add values for Date column while loading, - or you may INSERT your source data to a table of type Log and then transform it with INSERT INTO t SELECT toDate(time) AS date, * FROM ... + or you may INSERT your source data to a table of type Log and then transform it with INSERT INTO t SELECT toDate(time) AS date, * FROM ... If your source data doesn't have any date or time, you may just pass any constant for a date column while loading. Next parameter is optional sampling expression. Sampling expression is used to implement SAMPLE clause in query for approximate query execution. diff --git a/dbms/src/Storages/StorageJoin.cpp b/dbms/src/Storages/StorageJoin.cpp index aab48c3ed2a..e17129610d3 100644 --- a/dbms/src/Storages/StorageJoin.cpp +++ b/dbms/src/Storages/StorageJoin.cpp @@ -30,9 +30,7 @@ StorageJoin::StorageJoin( { for (const auto & key : key_names) if (!getColumns().hasPhysical(key)) - throw Exception{ - "Key column (" + key + ") does not exist in table declaration.", - ErrorCodes::NO_SUCH_COLUMN_IN_TABLE}; + throw Exception{"Key column (" + key + ") does not exist in table declaration.", ErrorCodes::NO_SUCH_COLUMN_IN_TABLE}; /// NOTE StorageJoin doesn't use join_use_nulls setting. diff --git a/dbms/src/Storages/tests/merge_selector2.cpp b/dbms/src/Storages/tests/merge_selector2.cpp index 7eb7e1f4f0b..a2c30733326 100644 --- a/dbms/src/Storages/tests/merge_selector2.cpp +++ b/dbms/src/Storages/tests/merge_selector2.cpp @@ -10,9 +10,9 @@ /** This program tests merge-selecting algorithm. * Usage: clickhouse-client --query=" - SELECT bytes, now() - modification_time, level, name - FROM system.parts - WHERE table = 'visits' AND active AND partition = '201610'" | ./merge_selector2 + SELECT bytes, now() - modification_time, level, name + FROM system.parts + WHERE table = 'visits' AND active AND partition = '201610'" | ./merge_selector2 */ int main(int, char **) From 598c7fddb7bbfa50b4002602bcd2162dfc808a34 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 05:06:00 +0300 Subject: [PATCH 069/231] Applied clang-format to some imported code [#CLICKHOUSE-2] --- dbms/src/Common/tests/AvalancheTest.cpp | 64 ++--- dbms/src/Common/tests/AvalancheTest.h | 326 ++++++++++++------------ dbms/src/Common/tests/Random.h | 186 +++++++------- 3 files changed, 300 insertions(+), 276 deletions(-) diff --git a/dbms/src/Common/tests/AvalancheTest.cpp b/dbms/src/Common/tests/AvalancheTest.cpp index 2f19a431b59..098ec232dea 100644 --- a/dbms/src/Common/tests/AvalancheTest.cpp +++ b/dbms/src/Common/tests/AvalancheTest.cpp @@ -4,55 +4,57 @@ //----------------------------------------------------------------------------- -void PrintAvalancheDiagram ( int x, int y, int reps, double scale, int * bins ) +void PrintAvalancheDiagram(int x, int y, int reps, double scale, int * bins) { - const char * symbols = ".123456789X"; + const char * symbols = ".123456789X"; - for(int i = 0; i < y; i++) - { - printf("["); - for(int j = 0; j < x; j++) + for (int i = 0; i < y; i++) { - int k = (y - i) -1; + printf("["); + for (int j = 0; j < x; j++) + { + int k = (y - i) - 1; - int bin = bins[k + (j*y)]; + int bin = bins[k + (j * y)]; - double b = double(bin) / double(reps); - b = fabs(b*2 - 1); + double b = double(bin) / double(reps); + b = fabs(b * 2 - 1); - b *= scale; + b *= scale; - int s = static_cast(floor(b*10)); + int s = static_cast(floor(b * 10)); - if(s > 10) s = 10; - if(s < 0) s = 0; + if (s > 10) + s = 10; + if (s < 0) + s = 0; - printf("%c",symbols[s]); + printf("%c", symbols[s]); + } + + printf("]\n"); } - - printf("]\n"); - } } //---------------------------------------------------------------------------- -double maxBias ( std::vector & counts, int reps ) +double maxBias(std::vector & counts, int reps) { - double worst = 0; + double worst = 0; - for(int i = 0; i < static_cast(counts.size()); i++) - { - double c = static_cast(counts[i]) / static_cast(reps); - - double d = fabs(c * 2 - 1); - - if(d > worst) + for (int i = 0; i < static_cast(counts.size()); i++) { - worst = d; - } - } + double c = static_cast(counts[i]) / static_cast(reps); - return worst; + double d = fabs(c * 2 - 1); + + if (d > worst) + { + worst = d; + } + } + + return worst; } //----------------------------------------------------------------------------- diff --git a/dbms/src/Common/tests/AvalancheTest.h b/dbms/src/Common/tests/AvalancheTest.h index 0c0c09fc30e..a53b2ee97b9 100644 --- a/dbms/src/Common/tests/AvalancheTest.h +++ b/dbms/src/Common/tests/AvalancheTest.h @@ -14,131 +14,134 @@ #include "Random.h" #include -#include #include +#include // Avalanche fails if a bit is biased by more than 1% #define AVALANCHE_FAIL 0.01 -double maxBias ( std::vector & counts, int reps ); +double maxBias(std::vector & counts, int reps); -typedef void (*pfHash) ( const void * blob, const int len, const uint32_t seed, void * out ); +typedef void (*pfHash)(const void * blob, const int len, const uint32_t seed, void * out); -inline uint32_t getbit ( const void * block, int len, uint32_t bit ) +inline uint32_t getbit(const void * block, int len, uint32_t bit) { - uint8_t * b = reinterpret_cast(const_cast(block)); + uint8_t * b = reinterpret_cast(const_cast(block)); - int byte = bit >> 3; - bit = bit & 0x7; + int byte = bit >> 3; + bit = bit & 0x7; - if(byte < len) return (b[byte] >> bit) & 1; + if (byte < len) + return (b[byte] >> bit) & 1; - return 0; + return 0; } -template < typename T > -inline uint32_t getbit ( T & blob, uint32_t bit ) +template +inline uint32_t getbit(T & blob, uint32_t bit) { - return getbit(&blob,sizeof(blob),bit); + return getbit(&blob, sizeof(blob), bit); } -inline void flipbit ( void * block, int len, uint32_t bit ) +inline void flipbit(void * block, int len, uint32_t bit) { - uint8_t * b = reinterpret_cast(block); + uint8_t * b = reinterpret_cast(block); - int byte = bit >> 3; - bit = bit & 0x7; + int byte = bit >> 3; + bit = bit & 0x7; - if(byte < len) b[byte] ^= (1 << bit); + if (byte < len) + b[byte] ^= (1 << bit); } -template < typename T > -inline void flipbit ( T & blob, uint32_t bit ) +template +inline void flipbit(T & blob, uint32_t bit) { - flipbit(&blob,sizeof(blob),bit); + flipbit(&blob, sizeof(blob), bit); } //----------------------------------------------------------------------------- -template < typename keytype, typename hashtype > -void calcBias ( pfHash hash, std::vector & counts, int reps, Rand & r ) +template +void calcBias(pfHash hash, std::vector & counts, int reps, Rand & r) { - const int keybytes = sizeof(keytype); - const int hashbytes = sizeof(hashtype); + const int keybytes = sizeof(keytype); + const int hashbytes = sizeof(hashtype); - const int keybits = keybytes * 8; - const int hashbits = hashbytes * 8; + const int keybits = keybytes * 8; + const int hashbits = hashbytes * 8; - keytype K; - hashtype A,B; + keytype K; + hashtype A, B; - for(int irep = 0; irep < reps; irep++) - { - if(irep % (reps/10) == 0) printf("."); - - r.rand_p(&K,keybytes); - - hash(&K,keybytes,0,&A); - - int * cursor = &counts[0]; - - for(int iBit = 0; iBit < keybits; iBit++) + for (int irep = 0; irep < reps; irep++) { - flipbit(&K,keybytes,iBit); - hash(&K,keybytes,0,&B); - flipbit(&K,keybytes,iBit); + if (irep % (reps / 10) == 0) + printf("."); - for(int iOut = 0; iOut < hashbits; iOut++) - { - int bitA = getbit(&A,hashbytes,iOut); - int bitB = getbit(&B,hashbytes,iOut); + r.rand_p(&K, keybytes); - (*cursor++) += (bitA ^ bitB); - } + hash(&K, keybytes, 0, &A); + + int * cursor = &counts[0]; + + for (int iBit = 0; iBit < keybits; iBit++) + { + flipbit(&K, keybytes, iBit); + hash(&K, keybytes, 0, &B); + flipbit(&K, keybytes, iBit); + + for (int iOut = 0; iOut < hashbits; iOut++) + { + int bitA = getbit(&A, hashbytes, iOut); + int bitB = getbit(&B, hashbytes, iOut); + + (*cursor++) += (bitA ^ bitB); + } + } } - } } //----------------------------------------------------------------------------- -template < typename keytype, typename hashtype > -bool AvalancheTest ( pfHash hash, const int reps ) +template +bool AvalancheTest(pfHash hash, const int reps) { - Rand r(48273); + Rand r(48273); - const int keybytes = sizeof(keytype); - const int hashbytes = sizeof(hashtype); + const int keybytes = sizeof(keytype); + const int hashbytes = sizeof(hashtype); - const int keybits = keybytes * 8; - const int hashbits = hashbytes * 8; + const int keybits = keybytes * 8; + const int hashbits = hashbytes * 8; - printf("Testing %3d-bit keys -> %3d-bit hashes, %8d reps",keybits,hashbits,reps); + printf("Testing %3d-bit keys -> %3d-bit hashes, %8d reps", keybits, hashbits, reps); - //---------- + //---------- - std::vector bins(keybits*hashbits,0); + std::vector bins(keybits * hashbits, 0); - calcBias(hash,bins,reps,r); + calcBias(hash, bins, reps, r); - //---------- + //---------- - bool result = true; + bool result = true; - double b = maxBias(bins,reps); + double b = maxBias(bins, reps); - printf(" worst bias is %f%%",b * 100.0); + printf(" worst bias is %f%%", b * 100.0); - if(b > AVALANCHE_FAIL) - { - printf(" !!!!! "); - result = false; - } + if (b > AVALANCHE_FAIL) + { + printf(" !!!!! "); + result = false; + } - printf("\n"); + printf("\n"); - return result; + return result; } @@ -146,107 +149,116 @@ bool AvalancheTest ( pfHash hash, const int reps ) // BIC test variant - store all intermediate data in a table, draw diagram // afterwards (much faster) -template < typename keytype, typename hashtype > -void BicTest3 ( pfHash hash, const int reps, bool verbose = true ) +template +void BicTest3(pfHash hash, const int reps, bool verbose = true) { - const int keybytes = sizeof(keytype); - const int keybits = keybytes * 8; - const int hashbytes = sizeof(hashtype); - const int hashbits = hashbytes * 8; - const int pagesize = hashbits*hashbits*4; + const int keybytes = sizeof(keytype); + const int keybits = keybytes * 8; + const int hashbytes = sizeof(hashtype); + const int hashbits = hashbytes * 8; + const int pagesize = hashbits * hashbits * 4; - Rand r(11938); + Rand r(11938); - double maxBias = 0; - int maxK = 0; - int maxA = 0; - int maxB = 0; + double maxBias = 0; + int maxK = 0; + int maxA = 0; + int maxB = 0; - keytype key; - hashtype h1,h2; + keytype key; + hashtype h1, h2; - std::vector bins(keybits*pagesize,0); + std::vector bins(keybits * pagesize, 0); - for(int keybit = 0; keybit < keybits; keybit++) - { - if(keybit % (keybits/10) == 0) printf("."); - - int * page = &bins[keybit*pagesize]; - - for(int irep = 0; irep < reps; irep++) + for (int keybit = 0; keybit < keybits; keybit++) { - r.rand_p(&key,keybytes); - hash(&key,keybytes,0,&h1); - flipbit(key,keybit); - hash(&key,keybytes,0,&h2); + if (keybit % (keybits / 10) == 0) + printf("."); - hashtype d = h1 ^ h2; + int * page = &bins[keybit * pagesize]; - for(int out1 = 0; out1 < hashbits-1; out1++) - for(int out2 = out1+1; out2 < hashbits; out2++) - { - int * b = &page[(out1*hashbits+out2)*4]; - - uint32_t x = getbit(d,out1) | (getbit(d,out2) << 1); - - b[x]++; - } - } - } - - printf("\n"); - - for(int out1 = 0; out1 < hashbits-1; out1++) - { - for(int out2 = out1+1; out2 < hashbits; out2++) - { - if(verbose) printf("(%3d,%3d) - ",out1,out2); - - for(int keybit = 0; keybit < keybits; keybit++) - { - int * page = &bins[keybit*pagesize]; - int * bins = &page[(out1*hashbits+out2)*4]; - - double bias = 0; - - for(int b = 0; b < 4; b++) + for (int irep = 0; irep < reps; irep++) { - double b2 = static_cast(bins[b]) / static_cast(reps / 2); - b2 = fabs(b2 * 2 - 1); + r.rand_p(&key, keybytes); + hash(&key, keybytes, 0, &h1); + flipbit(key, keybit); + hash(&key, keybytes, 0, &h2); - if(b2 > bias) bias = b2; + hashtype d = h1 ^ h2; + + for (int out1 = 0; out1 < hashbits - 1; out1++) + for (int out2 = out1 + 1; out2 < hashbits; out2++) + { + int * b = &page[(out1 * hashbits + out2) * 4]; + + uint32_t x = getbit(d, out1) | (getbit(d, out2) << 1); + + b[x]++; + } } - - if(bias > maxBias) - { - maxBias = bias; - maxK = keybit; - maxA = out1; - maxB = out2; - } - - if(verbose) - { - if (bias < 0.01) printf("."); - else if(bias < 0.05) printf("o"); - else if(bias < 0.33) printf("O"); - else printf("X"); - } - } - - // Finished keybit - - if(verbose) printf("\n"); } - if(verbose) - { - for(int i = 0; i < keybits+12; i++) printf("-"); - printf("\n"); - } - } + printf("\n"); - printf("Max bias %f - (%3d : %3d,%3d)\n",maxBias,maxK,maxA,maxB); + for (int out1 = 0; out1 < hashbits - 1; out1++) + { + for (int out2 = out1 + 1; out2 < hashbits; out2++) + { + if (verbose) + printf("(%3d,%3d) - ", out1, out2); + + for (int keybit = 0; keybit < keybits; keybit++) + { + int * page = &bins[keybit * pagesize]; + int * bins = &page[(out1 * hashbits + out2) * 4]; + + double bias = 0; + + for (int b = 0; b < 4; b++) + { + double b2 = static_cast(bins[b]) / static_cast(reps / 2); + b2 = fabs(b2 * 2 - 1); + + if (b2 > bias) + bias = b2; + } + + if (bias > maxBias) + { + maxBias = bias; + maxK = keybit; + maxA = out1; + maxB = out2; + } + + if (verbose) + { + if (bias < 0.01) + printf("."); + else if (bias < 0.05) + printf("o"); + else if (bias < 0.33) + printf("O"); + else + printf("X"); + } + } + + // Finished keybit + + if (verbose) + printf("\n"); + } + + if (verbose) + { + for (int i = 0; i < keybits + 12; i++) + printf("-"); + printf("\n"); + } + } + + printf("Max bias %f - (%3d : %3d,%3d)\n", maxBias, maxK, maxA, maxB); } //----------------------------------------------------------------------------- diff --git a/dbms/src/Common/tests/Random.h b/dbms/src/Common/tests/Random.h index 65d55c1d32a..ea341b55e3a 100644 --- a/dbms/src/Common/tests/Random.h +++ b/dbms/src/Common/tests/Random.h @@ -10,110 +10,120 @@ struct Rand { - uint32_t x; - uint32_t y; - uint32_t z; - uint32_t w; + uint32_t x; + uint32_t y; + uint32_t z; + uint32_t w; - Rand() - { - reseed(static_cast(0)); - } - - explicit Rand( uint32_t seed ) - { - reseed(seed); - } - - void reseed ( uint32_t seed ) - { - x = 0x498b3bc5 ^ seed; - y = 0; - z = 0; - w = 0; - - for(int i = 0; i < 10; i++) mix(); - } - - void reseed ( uint64_t seed ) - { - x = 0x498b3bc5 ^ static_cast(seed >> 0); - y = 0x5a05089a ^ static_cast(seed >> 32); - z = 0; - w = 0; - - for(int i = 0; i < 10; i++) mix(); - } - - //----------------------------------------------------------------------------- - - void mix ( void ) - { - uint32_t t = x ^ (x << 11); - x = y; y = z; z = w; - w = w ^ (w >> 19) ^ t ^ (t >> 8); - } - - uint32_t rand_u32 ( void ) - { - mix(); - - return x; - } - - uint64_t rand_u64 ( void ) - { - mix(); - - uint64_t a = x; - uint64_t b = y; - - return (a << 32) | b; - } - - void rand_p ( void * blob, int bytes ) - { - uint32_t * blocks = reinterpret_cast(blob); - - while(bytes >= 4) + Rand() { - blocks[0] = rand_u32(); - blocks++; - bytes -= 4; + reseed(static_cast(0)); } - uint8_t * tail = reinterpret_cast(blocks); - - for(int i = 0; i < bytes; i++) + explicit Rand(uint32_t seed) { - tail[i] = static_cast(rand_u32()); + reseed(seed); + } + + void reseed(uint32_t seed) + { + x = 0x498b3bc5 ^ seed; + y = 0; + z = 0; + w = 0; + + for (int i = 0; i < 10; i++) + mix(); + } + + void reseed(uint64_t seed) + { + x = 0x498b3bc5 ^ static_cast(seed >> 0); + y = 0x5a05089a ^ static_cast(seed >> 32); + z = 0; + w = 0; + + for (int i = 0; i < 10; i++) + mix(); + } + + //----------------------------------------------------------------------------- + + void mix(void) + { + uint32_t t = x ^ (x << 11); + x = y; + y = z; + z = w; + w = w ^ (w >> 19) ^ t ^ (t >> 8); + } + + uint32_t rand_u32(void) + { + mix(); + + return x; + } + + uint64_t rand_u64(void) + { + mix(); + + uint64_t a = x; + uint64_t b = y; + + return (a << 32) | b; + } + + void rand_p(void * blob, int bytes) + { + uint32_t * blocks = reinterpret_cast(blob); + + while (bytes >= 4) + { + blocks[0] = rand_u32(); + blocks++; + bytes -= 4; + } + + uint8_t * tail = reinterpret_cast(blocks); + + for (int i = 0; i < bytes; i++) + { + tail[i] = static_cast(rand_u32()); + } } - } }; //----------------------------------------------------------------------------- extern Rand g_rand1; -inline uint32_t rand_u32 ( void ) { return g_rand1.rand_u32(); } -inline uint64_t rand_u64 ( void ) { return g_rand1.rand_u64(); } - -inline void rand_p ( void * blob, int bytes ) +inline uint32_t rand_u32(void) { - uint32_t * blocks = static_cast(blob); + return g_rand1.rand_u32(); +} +inline uint64_t rand_u64(void) +{ + return g_rand1.rand_u64(); +} - while(bytes >= 4) - { - *blocks++ = rand_u32(); - bytes -= 4; - } +inline void rand_p(void * blob, int bytes) +{ + uint32_t * blocks = static_cast(blob); - uint8_t * tail = reinterpret_cast(blocks); + while (bytes >= 4) + { + *blocks++ = rand_u32(); + bytes -= 4; + } - for(int i = 0; i < bytes; i++) - { - tail[i] = static_cast(rand_u32()); - } + uint8_t * tail = reinterpret_cast(blocks); + + for (int i = 0; i < bytes; i++) + { + tail[i] = static_cast(rand_u32()); + } } //----------------------------------------------------------------------------- From 90427db8547021429b11560fb11ec85fb7fec9c0 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 05:06:55 +0300 Subject: [PATCH 070/231] Applied clang-format #2272 --- dbms/src/Functions/FunctionsProjection.cpp | 29 +++++---- dbms/src/Functions/FunctionsProjection.h | 16 +++-- .../src/Interpreters/ProjectionManipulation.h | 63 ++++++++++--------- 3 files changed, 59 insertions(+), 49 deletions(-) diff --git a/dbms/src/Functions/FunctionsProjection.cpp b/dbms/src/Functions/FunctionsProjection.cpp index c2ea1df35d6..3b67fbc0fc2 100644 --- a/dbms/src/Functions/FunctionsProjection.cpp +++ b/dbms/src/Functions/FunctionsProjection.cpp @@ -1,10 +1,10 @@ -#include -#include #include #include +#include +#include -namespace DB { - +namespace DB +{ FunctionPtr FunctionOneOrZero::create(const Context &) { return std::make_shared(); @@ -64,8 +64,8 @@ DataTypePtr FunctionProject::getReturnTypeImpl(const DataTypes & arguments) cons { if (!checkAndGetDataType(arguments[1].get())) { - throw Exception("Illegal type " + arguments[1]->getName() + " of 2nd argument of function " + getName(), - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); + throw Exception( + "Illegal type " + arguments[1]->getName() + " of 2nd argument of function " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); } return arguments[0]; } @@ -116,14 +116,16 @@ DataTypePtr FunctionBuildProjectionComposition::getReturnTypeImpl(const DataType { if (!checkAndGetDataType(arguments[i].get())) { - throw Exception("Illegal type " + arguments[i]->getName() + " of " + std::to_string(i + 1) + " argument of function " + getName(), - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); + throw Exception( + "Illegal type " + arguments[i]->getName() + " of " + std::to_string(i + 1) + " argument of function " + getName(), + ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); } } return std::make_shared(); } -void FunctionBuildProjectionComposition::executeImpl(Block & block, const ColumnNumbers & arguments, size_t result, size_t /*input_rows_count*/) +void FunctionBuildProjectionComposition::executeImpl( + Block & block, const ColumnNumbers & arguments, size_t result, size_t /*input_rows_count*/) { const auto & first_projection_column = block.getByPosition(arguments[0]).column; const auto & second_projection_column = block.getByPosition(arguments[1]).column; @@ -145,9 +147,9 @@ void FunctionBuildProjectionComposition::executeImpl(Block & block, const Column } if (current_reserve_index != second_projection_column->size()) { - throw Exception("Second argument size is not appropriate: " + std::to_string(second_projection_column->size()) - + " instead of " + std::to_string(current_reserve_index), - ErrorCodes::BAD_ARGUMENTS); + throw Exception("Second argument size is not appropriate: " + std::to_string(second_projection_column->size()) + " instead of " + + std::to_string(current_reserve_index), + ErrorCodes::BAD_ARGUMENTS); } block.getByPosition(result).column = std::move(col_res); } @@ -162,7 +164,8 @@ String FunctionRestoreProjection::getName() const return name; } -bool FunctionRestoreProjection::isVariadic() const { +bool FunctionRestoreProjection::isVariadic() const +{ return true; } diff --git a/dbms/src/Functions/FunctionsProjection.h b/dbms/src/Functions/FunctionsProjection.h index 6972c341833..bbb1951fc5b 100644 --- a/dbms/src/Functions/FunctionsProjection.h +++ b/dbms/src/Functions/FunctionsProjection.h @@ -3,13 +3,14 @@ #include #include "FunctionsConversion.h" -namespace DB { - +namespace DB +{ /* * This function accepts one column and converts it to UInt8, replacing values, which evaluate to true, with 1, and values, * which evaluate to false with 0 */ -class FunctionOneOrZero final : public IFunction { +class FunctionOneOrZero final : public IFunction +{ public: static constexpr auto name = "one_or_zero"; static FunctionPtr create(const Context &); @@ -25,7 +26,8 @@ public: * This function builds a column of a smaller, which contains values of the data column at the positions where * the projection column contained 1. The size of result column equals the count of ones in the projection column. */ -class FunctionProject final : public IFunction { +class FunctionProject final : public IFunction +{ public: static constexpr auto name = "__inner_project__"; static FunctionPtr create(const Context &); @@ -39,7 +41,8 @@ public: * FunctionBuildProjectionComposition constructs the composition of two projection columns. The size of * second projection column should equal the count of ones in the first input projection column. */ -class FunctionBuildProjectionComposition final : public IFunction { +class FunctionBuildProjectionComposition final : public IFunction +{ public: static constexpr auto name = "__inner_build_projection_composition__"; static FunctionPtr create(const Context &); @@ -53,7 +56,8 @@ public: * Accepts mapping column with values from range [0, N) and N more columns as arguments. * Forms a column by taking value from column, which number is in the mapping column. */ -class FunctionRestoreProjection final : public IFunction { +class FunctionRestoreProjection final : public IFunction +{ public: static constexpr auto name = "__inner_restore_projection__"; static FunctionPtr create(const Context &); diff --git a/dbms/src/Interpreters/ProjectionManipulation.h b/dbms/src/Interpreters/ProjectionManipulation.h index 6bc4815b547..3d7f49b4f39 100644 --- a/dbms/src/Interpreters/ProjectionManipulation.h +++ b/dbms/src/Interpreters/ProjectionManipulation.h @@ -3,21 +3,23 @@ #include #include -namespace DB { - +namespace DB +{ class ExpressionAnalyzer; struct ScopeStack; -namespace ErrorCodes { -extern const int CONDITIONAL_TREE_PARENT_NOT_FOUND; -extern const int ILLEGAL_PROJECTION_MANIPULATOR; +namespace ErrorCodes +{ + extern const int CONDITIONAL_TREE_PARENT_NOT_FOUND; + extern const int ILLEGAL_PROJECTION_MANIPULATOR; } /* * This is a base class for the ConditionalTree. Look at the description of ConditionalTree. */ -struct ProjectionManipulatorBase { +struct ProjectionManipulatorBase +{ public: virtual bool tryToGetFromUpperProjection(const std::string & column_name) = 0; @@ -37,9 +39,11 @@ using ProjectionManipulatorPtr = std::shared_ptr; * For the better understanding of what ProjectionManipulator does, * look at the description of ConditionalTree. */ -struct DefaultProjectionManipulator : public ProjectionManipulatorBase { +struct DefaultProjectionManipulator : public ProjectionManipulatorBase +{ private: ScopeStack & scopes; + public: explicit DefaultProjectionManipulator(ScopeStack & scopes); @@ -86,9 +90,11 @@ public: * understand whether we need to scan the expression deeply, or can it be easily computed just with the projection * from one of the higher projection layers. */ -struct ConditionalTree : public ProjectionManipulatorBase { +struct ConditionalTree : public ProjectionManipulatorBase +{ private: - struct Node { + struct Node + { Node(); size_t getParentNode() const; @@ -103,21 +109,20 @@ private: ScopeStack & scopes; const Context & context; std::unordered_map projection_expression_index; + private: std::string getColumnNameByIndex(const std::string & col_name, size_t node) const; - std::string getProjectionColumnName(const std::string & first_projection_expr, - const std::string & second_projection_expr) const; + std::string getProjectionColumnName(const std::string & first_projection_expr, const std::string & second_projection_expr) const; std::string getProjectionColumnName(size_t first_index, size_t second_index) const; - void buildProjectionCompositionRecursive(const std::vector & path, - size_t child_index, - size_t parent_index); + void buildProjectionCompositionRecursive(const std::vector & path, size_t child_index, size_t parent_index); void buildProjectionComposition(size_t child_node, size_t parent_node); std::string getProjectionSourceColumn(size_t node) const; + public: ConditionalTree(ScopeStack & scopes, const Context & context); @@ -128,11 +133,7 @@ public: std::string buildRestoreProjectionAndGetName(size_t levels_up); void restoreColumn( - const std::string & default_values_name, - const std::string & new_values_name, - size_t levels_up, - const std::string & result_name - ); + const std::string & default_values_name, const std::string & new_values_name, size_t levels_up, const std::string & result_name); void goUp(size_t levels_up); @@ -150,7 +151,8 @@ using ConditionalTreePtr = std::shared_ptr; * This class has two inherited classes: DefaultProjectionAction, which does nothing, and AndOperatorProjectionAction, * which represents how function "and" uses projection manipulator. */ -class ProjectionActionBase { +class ProjectionActionBase +{ public: /* * What to do before scanning the function argument (each of it) @@ -177,7 +179,8 @@ public: using ProjectionActionPtr = std::shared_ptr; -class DefaultProjectionAction : public ProjectionActionBase { +class DefaultProjectionAction : public ProjectionActionBase +{ public: void preArgumentAction() final; @@ -191,7 +194,8 @@ public: /* * This is a specification of ProjectionAction specifically for the 'and' operation */ -class AndOperatorProjectionAction : public ProjectionActionBase { +class AndOperatorProjectionAction : public ProjectionActionBase +{ private: ScopeStack & scopes; ProjectionManipulatorPtr projection_manipulator; @@ -205,11 +209,10 @@ private: std::string getFinalColumnName(); void createZerosColumn(const std::string & restore_projection_name); + public: - AndOperatorProjectionAction(ScopeStack & scopes, - ProjectionManipulatorPtr projection_manipulator, - const std::string & expression_name, - const Context& context); + AndOperatorProjectionAction( + ScopeStack & scopes, ProjectionManipulatorPtr projection_manipulator, const std::string & expression_name, const Context & context); /* * Before scanning each argument, we should go to the next projection layer. For example, if the expression is @@ -241,9 +244,9 @@ public: * it returns the pointer to AndOperatorProjectionAction. */ ProjectionActionPtr getProjectionAction(const std::string & node_name, - ScopeStack & scopes, - ProjectionManipulatorPtr projection_manipulator, - const std::string & expression_name, - const Context & context); + ScopeStack & scopes, + ProjectionManipulatorPtr projection_manipulator, + const std::string & expression_name, + const Context & context); } From 619a0dbd8c7c6dfebcf3bcf006b050e960dfd84e Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 05:07:17 +0300 Subject: [PATCH 071/231] Fixed style [#CLICKHOUSE-2] --- dbms/src/Functions/FunctionsTransform.h | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/dbms/src/Functions/FunctionsTransform.h b/dbms/src/Functions/FunctionsTransform.h index bf604ea488b..ede02e64f7b 100644 --- a/dbms/src/Functions/FunctionsTransform.h +++ b/dbms/src/Functions/FunctionsTransform.h @@ -220,10 +220,8 @@ private: auto out = typeid_cast *>(out_untyped); if (!out) { - throw Exception{ - "Illegal column " + out_untyped->getName() + " of elements of array of third argument of function " + getName() - + ", must be " + in->getName(), - ErrorCodes::ILLEGAL_COLUMN}; + throw Exception{"Illegal column " + out_untyped->getName() + " of elements of array of third argument of function " + getName() + + ", must be " + in->getName(), ErrorCodes::ILLEGAL_COLUMN}; } executeImplNumToNum(in->getData(), out->getData()); From 59bc8e1b480c925f217ae1952888a8cde2031db4 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 05:07:47 +0300 Subject: [PATCH 072/231] Applied clang-format to some imported code [#CLICKHOUSE-2] --- .../Functions/FunctionsConsistentHashing.h | 89 ++++++++++++------- 1 file changed, 57 insertions(+), 32 deletions(-) diff --git a/dbms/src/Functions/FunctionsConsistentHashing.h b/dbms/src/Functions/FunctionsConsistentHashing.h index 65f9bea9c57..75971d57693 100644 --- a/dbms/src/Functions/FunctionsConsistentHashing.h +++ b/dbms/src/Functions/FunctionsConsistentHashing.h @@ -1,20 +1,19 @@ #pragma once -#include -#include -#include #include -#include +#include +#include #include +#include +#include #include -#include #include +#include namespace DB { - namespace ErrorCodes { extern const int LOGICAL_ERROR; @@ -42,9 +41,11 @@ struct YandexConsistentHashImpl /// Code from https://arxiv.org/pdf/1406.2294.pdf -static inline int32_t JumpConsistentHash(uint64_t key, int32_t num_buckets) { +static inline int32_t JumpConsistentHash(uint64_t key, int32_t num_buckets) +{ int64_t b = -1, j = 0; - while (j < num_buckets) { + while (j < num_buckets) + { b = j; key = key * 2862933555777941757ULL + 1; j = static_cast((b + 1) * (double(1LL << 31) / double((key >> 33) + 1))); @@ -86,14 +87,22 @@ template class FunctionConsistentHashImpl : public IFunction { public: - static constexpr auto name = Impl::name; - static FunctionPtr create(const Context &) { return std::make_shared>(); }; + static FunctionPtr create(const Context &) + { + return std::make_shared>(); + }; - String getName() const override { return name; } + String getName() const override + { + return name; + } - size_t getNumberOfArguments() const override { return 2; } + size_t getNumberOfArguments() const override + { + return 2; + } DataTypePtr getReturnTypeImpl(const DataTypes & arguments) const override { @@ -103,7 +112,8 @@ public: if (arguments[0]->getSizeOfValueInMemory() > sizeof(HashType)) throw Exception("Function " + getName() + " accepts " + std::to_string(sizeof(HashType) * 8) + "-bit integers at most" - + ", got " + arguments[0]->getName(), ErrorCodes::BAD_ARGUMENTS); + + ", got " + arguments[0]->getName(), + ErrorCodes::BAD_ARGUMENTS); if (!arguments[1]->isInteger()) throw Exception("Illegal type " + arguments[1]->getName() + " of the second argument of function " + getName(), @@ -112,19 +122,25 @@ public: return std::make_shared>(); } - bool useDefaultImplementationForConstants() const override { return true; } - ColumnNumbers getArgumentsThatAreAlwaysConstant() const override { return {1}; } + bool useDefaultImplementationForConstants() const override + { + return true; + } + ColumnNumbers getArgumentsThatAreAlwaysConstant() const override + { + return {1}; + } void executeImpl(Block & block, const ColumnNumbers & arguments, size_t result, size_t /*input_rows_count*/) override { if (block.getByPosition(arguments[1]).column->isColumnConst()) executeConstBuckets(block, arguments, result); else - throw Exception("The second argument of function " + getName() + " (number of buckets) must be constant", ErrorCodes::BAD_ARGUMENTS); + throw Exception( + "The second argument of function " + getName() + " (number of buckets) must be constant", ErrorCodes::BAD_ARGUMENTS); } private: - using HashType = typename Impl::HashType; using ResultType = typename Impl::ResultType; using BucketsType = typename Impl::BucketsCountType; @@ -134,12 +150,13 @@ private: inline BucketsType checkBucketsRange(T buckets) { if (unlikely(buckets <= 0)) - throw Exception("The second argument of function " + getName() + " (number of buckets) must be positive number", - ErrorCodes::BAD_ARGUMENTS); + throw Exception( + "The second argument of function " + getName() + " (number of buckets) must be positive number", ErrorCodes::BAD_ARGUMENTS); if (unlikely(static_cast(buckets) > max_buckets)) - throw Exception("The value of the second argument of function " + getName() + " (number of buckets) is not fit to " + - DataTypeNumber().getName(), ErrorCodes::BAD_ARGUMENTS); + throw Exception("The value of the second argument of function " + getName() + " (number of buckets) is not fit to " + + DataTypeNumber().getName(), + ErrorCodes::BAD_ARGUMENTS); return static_cast(buckets); } @@ -155,23 +172,31 @@ private: num_buckets = checkBucketsRange(buckets_field.get()); else throw Exception("Illegal type " + String(buckets_field.getTypeName()) + " of the second argument of function " + getName(), - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); + ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); const auto & hash_col = block.getByPosition(arguments[0]).column; const IDataType * hash_type = block.getByPosition(arguments[0]).type.get(); auto res_col = ColumnVector::create(); - if (checkDataType(hash_type)) executeType(hash_col, num_buckets, res_col.get()); - else if (checkDataType(hash_type)) executeType(hash_col, num_buckets, res_col.get()); - else if (checkDataType(hash_type)) executeType(hash_col, num_buckets, res_col.get()); - else if (checkDataType(hash_type)) executeType(hash_col, num_buckets, res_col.get()); - else if (checkDataType(hash_type)) executeType(hash_col, num_buckets, res_col.get()); - else if (checkDataType(hash_type)) executeType(hash_col, num_buckets, res_col.get()); - else if (checkDataType(hash_type)) executeType(hash_col, num_buckets, res_col.get()); - else if (checkDataType(hash_type)) executeType(hash_col, num_buckets, res_col.get()); + if (checkDataType(hash_type)) + executeType(hash_col, num_buckets, res_col.get()); + else if (checkDataType(hash_type)) + executeType(hash_col, num_buckets, res_col.get()); + else if (checkDataType(hash_type)) + executeType(hash_col, num_buckets, res_col.get()); + else if (checkDataType(hash_type)) + executeType(hash_col, num_buckets, res_col.get()); + else if (checkDataType(hash_type)) + executeType(hash_col, num_buckets, res_col.get()); + else if (checkDataType(hash_type)) + executeType(hash_col, num_buckets, res_col.get()); + else if (checkDataType(hash_type)) + executeType(hash_col, num_buckets, res_col.get()); + else if (checkDataType(hash_type)) + executeType(hash_col, num_buckets, res_col.get()); else throw Exception("Illegal type " + hash_type->getName() + " of the first argument of function " + getName(), - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); + ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); block.getByPosition(result).column = std::move(res_col); } @@ -182,7 +207,7 @@ private: auto col_hash = checkAndGetColumn>(col_hash_ptr.get()); if (!col_hash) throw Exception("Illegal type of the first argument of function " + getName(), ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); - + auto & vec_result = col_result->getData(); const auto & vec_hash = col_hash->getData(); From b2edcfaa50309ddb60512dfd43d6b599c789d090 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 05:09:29 +0300 Subject: [PATCH 073/231] Applied clang-format #2272 --- .../Interpreters/ProjectionManipulation.cpp | 191 ++++++++---------- 1 file changed, 80 insertions(+), 111 deletions(-) diff --git a/dbms/src/Interpreters/ProjectionManipulation.cpp b/dbms/src/Interpreters/ProjectionManipulation.cpp index 69a81618174..1fb3bb071fb 100644 --- a/dbms/src/Interpreters/ProjectionManipulation.cpp +++ b/dbms/src/Interpreters/ProjectionManipulation.cpp @@ -1,23 +1,20 @@ -#include -#include -#include -#include -#include -#include -#include -#include -#include #include +#include +#include +#include +#include +#include +#include +#include +#include +#include #include -namespace DB { +namespace DB +{ +ProjectionManipulatorBase::~ProjectionManipulatorBase() {} -ProjectionManipulatorBase::~ProjectionManipulatorBase() -{} - -DefaultProjectionManipulator::DefaultProjectionManipulator(ScopeStack & scopes) - : scopes(scopes) -{} +DefaultProjectionManipulator::DefaultProjectionManipulator(ScopeStack & scopes) : scopes(scopes) {} bool DefaultProjectionManipulator::tryToGetFromUpperProjection(const std::string & column_name) { @@ -34,22 +31,19 @@ std::string DefaultProjectionManipulator::getProjectionExpression() return ""; } -std::string DefaultProjectionManipulator::getProjectionSourceColumn() const { +std::string DefaultProjectionManipulator::getProjectionSourceColumn() const +{ return ""; } -ConditionalTree::Node::Node() - : projection_expression_string(), - parent_node(0), - is_root(false) -{} +ConditionalTree::Node::Node() : projection_expression_string(), parent_node(0), is_root(false) {} size_t ConditionalTree::Node::getParentNode() const { if (is_root) { - throw Exception("Failed to get parent projection node of node " + projection_expression_string, - ErrorCodes::CONDITIONAL_TREE_PARENT_NOT_FOUND); + throw Exception( + "Failed to get parent projection node of node " + projection_expression_string, ErrorCodes::CONDITIONAL_TREE_PARENT_NOT_FOUND); } else { @@ -75,22 +69,19 @@ std::string ConditionalTree::getColumnName(const std::string & col_name) const return getColumnNameByIndex(col_name, current_node); } -std::string ConditionalTree::getProjectionColumnName(const std::string & first_projection_expr, - const std::string & second_projection_expr) const +std::string ConditionalTree::getProjectionColumnName( + const std::string & first_projection_expr, const std::string & second_projection_expr) const { return std::string("P<") + first_projection_expr + "><" + second_projection_expr + ">"; } std::string ConditionalTree::getProjectionColumnName(const size_t first_index, const size_t second_index) const { - return getProjectionColumnName( - nodes[first_index].projection_expression_string, - nodes[second_index].projection_expression_string); + return getProjectionColumnName(nodes[first_index].projection_expression_string, nodes[second_index].projection_expression_string); } -void ConditionalTree::buildProjectionCompositionRecursive(const std::vector & path, - const size_t child_index, - const size_t parent_index) +void ConditionalTree::buildProjectionCompositionRecursive( + const std::vector & path, const size_t child_index, const size_t parent_index) { std::string projection_name = getProjectionColumnName(path[parent_index], path[child_index]); if (parent_index - child_index >= 2 && !scopes.getSampleBlock().has(projection_name)) @@ -98,15 +89,12 @@ void ConditionalTree::buildProjectionCompositionRecursive(const std::vectorgetColumnName(getZerosColumnName()); if (!scopes.getSampleBlock().has(zeros_column_name)) { - scopes.addAction(ExpressionAction::addColumn(ColumnWithTypeAndName( - ColumnUInt8::create(0, 1), std::make_shared(), zeros_column_name), - restore_projection_name, true)); + scopes.addAction(ExpressionAction::addColumn( + ColumnWithTypeAndName(ColumnUInt8::create(0, 1), std::make_shared(), zeros_column_name), + restore_projection_name, + true)); } } @@ -309,7 +282,8 @@ void AndOperatorProjectionAction::preArgumentAction() } else { - throw Exception("Illegal projection manipulator used in AndOperatorProjectionAction", ErrorCodes::ILLEGAL_PROJECTION_MANIPULATOR); + throw Exception( + "Illegal projection manipulator used in AndOperatorProjectionAction", ErrorCodes::ILLEGAL_PROJECTION_MANIPULATOR); } ++projection_levels_count; } @@ -326,17 +300,13 @@ void AndOperatorProjectionAction::preCalculation() { auto final_column = getFinalColumnName(); const FunctionBuilderPtr & function_builder = FunctionFactory::instance().get("one_or_zero", context); - scopes.addAction(ExpressionAction::applyFunction( - function_builder, - { - projection_manipulator->getColumnName(previous_argument_name) - }, - projection_manipulator->getColumnName(final_column), - projection_manipulator->getProjectionSourceColumn())); + scopes.addAction(ExpressionAction::applyFunction(function_builder, + {projection_manipulator->getColumnName(previous_argument_name)}, + projection_manipulator->getColumnName(final_column), + projection_manipulator->getProjectionSourceColumn())); std::string restore_projection_name = conditional_tree->buildRestoreProjectionAndGetName(projection_levels_count); createZerosColumn(restore_projection_name); - conditional_tree->restoreColumn(getZerosColumnName(), final_column, - projection_levels_count, expression_name); + conditional_tree->restoreColumn(getZerosColumnName(), final_column, projection_levels_count, expression_name); conditional_tree->goUp(projection_levels_count); } else @@ -350,14 +320,13 @@ bool AndOperatorProjectionAction::isCalculationRequired() return false; } -ProjectionActionBase::~ProjectionActionBase() -{} +ProjectionActionBase::~ProjectionActionBase() {} ProjectionActionPtr getProjectionAction(const std::string & node_name, - ScopeStack & scopes, - ProjectionManipulatorPtr projection_manipulator, - const std::string & expression_name, - const Context & context) + ScopeStack & scopes, + ProjectionManipulatorPtr projection_manipulator, + const std::string & expression_name, + const Context & context) { if (typeid_cast(projection_manipulator.get()) && node_name == "and") { From fd0ee5c6b1143cae627343f0260e9487c9036cf9 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 05:14:24 +0300 Subject: [PATCH 074/231] Style [#CLICKHOUSE-2] --- dbms/src/Common/tests/sip_hash.cpp | 83 ++++++++++--------- .../Interpreters/tests/hash_map_string_2.cpp | 42 +++++----- .../Interpreters/tests/hash_map_string_3.cpp | 42 +++++----- 3 files changed, 84 insertions(+), 83 deletions(-) diff --git a/dbms/src/Common/tests/sip_hash.cpp b/dbms/src/Common/tests/sip_hash.cpp index 1708a3dd044..1c3629c46e3 100644 --- a/dbms/src/Common/tests/sip_hash.cpp +++ b/dbms/src/Common/tests/sip_hash.cpp @@ -1,8 +1,8 @@ #include #include -#include #include +#include #include @@ -91,49 +91,50 @@ uint8_t vectors[64][8] = int test_vectors() { #define MAXLEN 64 - char in[MAXLEN]; + char in[MAXLEN]; - union - { - char out[16]; - uint64_t out64[2]; - }; - - union - { - char k[16]; - uint64_t k64[2]; - }; - - int i; - int ok = 1; - - for( i = 0; i < 16; ++i ) k[i] = i; - - for( i = 0; i < MAXLEN; ++i ) - { - in[i] = i; - - size_t part = i == 0 ? 0 : (rand() % i); - - SipHash hash(k64[0], k64[1]); - - hash.update(in, part); - hash.update(in + part, i - part); - - hash.get128(out); - - uint64_t test_vector; - memcpy(&test_vector, vectors[i], 8); - - if ((out64[0] ^ out64[1]) != test_vector) + union { - std::cerr << "test vector failed for " << i << " bytes" << std::endl; - ok = 0; - } - } + char out[16]; + uint64_t out64[2]; + }; - return ok; + union + { + char k[16]; + uint64_t k64[2]; + }; + + int i; + int ok = 1; + + for (i = 0; i < 16; ++i) + k[i] = i; + + for (i = 0; i < MAXLEN; ++i) + { + in[i] = i; + + size_t part = i == 0 ? 0 : (rand() % i); + + SipHash hash(k64[0], k64[1]); + + hash.update(in, part); + hash.update(in + part, i - part); + + hash.get128(out); + + uint64_t test_vector; + memcpy(&test_vector, vectors[i], 8); + + if ((out64[0] ^ out64[1]) != test_vector) + { + std::cerr << "test vector failed for " << i << " bytes" << std::endl; + ok = 0; + } + } + + return ok; } diff --git a/dbms/src/Interpreters/tests/hash_map_string_2.cpp b/dbms/src/Interpreters/tests/hash_map_string_2.cpp index 3bb17e7ad3f..52de8cee2e3 100644 --- a/dbms/src/Interpreters/tests/hash_map_string_2.cpp +++ b/dbms/src/Interpreters/tests/hash_map_string_2.cpp @@ -22,27 +22,27 @@ /** Do this: for file in MobilePhoneModel PageCharset Params URLDomain UTMSource Referer URL Title; do - for size in 30000 100000 300000 1000000 5000000; do - echo - BEST_METHOD=0 - BEST_RESULT=0 - for method in {1..12}; do - echo -ne $file $size $method ''; - TOTAL_ELEMS=0 - for i in {0..1000}; do - TOTAL_ELEMS=$(( $TOTAL_ELEMS + $size )) - if [[ $TOTAL_ELEMS -gt 25000000 ]]; then break; fi - ./hash_map_string_2 $size $method < ${file}.bin 2>&1 | - grep HashMap | grep -oE '[0-9\.]+ elem'; - done | awk -W interactive '{ if ($1 > x) { x = $1 }; printf(".") } END { print x }' | tee /tmp/hash_map_string_2_res; - CUR_RESULT=$(cat /tmp/hash_map_string_2_res | tr -d '.') - if [[ $CUR_RESULT -gt $BEST_RESULT ]]; then - BEST_METHOD=$method - BEST_RESULT=$CUR_RESULT - fi; - done; - echo Best: $BEST_METHOD - $BEST_RESULT - done; + for size in 30000 100000 300000 1000000 5000000; do + echo + BEST_METHOD=0 + BEST_RESULT=0 + for method in {1..12}; do + echo -ne $file $size $method ''; + TOTAL_ELEMS=0 + for i in {0..1000}; do + TOTAL_ELEMS=$(( $TOTAL_ELEMS + $size )) + if [[ $TOTAL_ELEMS -gt 25000000 ]]; then break; fi + ./hash_map_string_2 $size $method < ${file}.bin 2>&1 | + grep HashMap | grep -oE '[0-9\.]+ elem'; + done | awk -W interactive '{ if ($1 > x) { x = $1 }; printf(".") } END { print x }' | tee /tmp/hash_map_string_2_res; + CUR_RESULT=$(cat /tmp/hash_map_string_2_res | tr -d '.') + if [[ $CUR_RESULT -gt $BEST_RESULT ]]; then + BEST_METHOD=$method + BEST_RESULT=$CUR_RESULT + fi; + done; + echo Best: $BEST_METHOD - $BEST_RESULT + done; done */ diff --git a/dbms/src/Interpreters/tests/hash_map_string_3.cpp b/dbms/src/Interpreters/tests/hash_map_string_3.cpp index 9b3bf47ae10..3fd1adc5ab1 100644 --- a/dbms/src/Interpreters/tests/hash_map_string_3.cpp +++ b/dbms/src/Interpreters/tests/hash_map_string_3.cpp @@ -25,27 +25,27 @@ /** Do this: for file in MobilePhoneModel PageCharset Params URLDomain UTMSource Referer URL Title; do - for size in 30000 100000 300000 1000000 5000000; do - echo - BEST_METHOD=0 - BEST_RESULT=0 - for method in {1..11}; do - echo -ne $file $size $method ''; - TOTAL_ELEMS=0 - for i in {0..1000}; do - TOTAL_ELEMS=$(( $TOTAL_ELEMS + $size )) - if [[ $TOTAL_ELEMS -gt 25000000 ]]; then break; fi - ./hash_map_string_3 $size $method < ${file}.bin 2>&1 | - grep HashMap | grep -oE '[0-9\.]+ elem'; - done | awk -W interactive '{ if ($1 > x) { x = $1 }; printf(".") } END { print x }' | tee /tmp/hash_map_string_3_res; - CUR_RESULT=$(cat /tmp/hash_map_string_3_res | tr -d '.') - if [[ $CUR_RESULT -gt $BEST_RESULT ]]; then - BEST_METHOD=$method - BEST_RESULT=$CUR_RESULT - fi; - done; - echo Best: $BEST_METHOD - $BEST_RESULT - done; + for size in 30000 100000 300000 1000000 5000000; do + echo + BEST_METHOD=0 + BEST_RESULT=0 + for method in {1..11}; do + echo -ne $file $size $method ''; + TOTAL_ELEMS=0 + for i in {0..1000}; do + TOTAL_ELEMS=$(( $TOTAL_ELEMS + $size )) + if [[ $TOTAL_ELEMS -gt 25000000 ]]; then break; fi + ./hash_map_string_3 $size $method < ${file}.bin 2>&1 | + grep HashMap | grep -oE '[0-9\.]+ elem'; + done | awk -W interactive '{ if ($1 > x) { x = $1 }; printf(".") } END { print x }' | tee /tmp/hash_map_string_3_res; + CUR_RESULT=$(cat /tmp/hash_map_string_3_res | tr -d '.') + if [[ $CUR_RESULT -gt $BEST_RESULT ]]; then + BEST_METHOD=$method + BEST_RESULT=$CUR_RESULT + fi; + done; + echo Best: $BEST_METHOD - $BEST_RESULT + done; done */ From a916d2760e9cc5a9acb00f7f95d3f7b71bf23390 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 05:15:24 +0300 Subject: [PATCH 075/231] Style [#CLICKHOUSE-2] --- dbms/src/Common/tests/sip_hash.cpp | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/dbms/src/Common/tests/sip_hash.cpp b/dbms/src/Common/tests/sip_hash.cpp index 1c3629c46e3..ac08a2de584 100644 --- a/dbms/src/Common/tests/sip_hash.cpp +++ b/dbms/src/Common/tests/sip_hash.cpp @@ -9,15 +9,15 @@ /// Adapted version https://www.131002.net/siphash/siphash24.c /* - SipHash-2-4 output with - k = 00 01 02 ... - and - in = (empty string) - in = 00 (1 byte) - in = 00 01 (2 bytes) - in = 00 01 02 (3 bytes) - ... - in = 00 01 02 ... 3e (63 bytes) + SipHash-2-4 output with + k = 00 01 02 ... + and + in = (empty string) + in = 00 (1 byte) + in = 00 01 (2 bytes) + in = 00 01 02 (3 bytes) + ... + in = 00 01 02 ... 3e (63 bytes) */ uint8_t vectors[64][8] = { From 711bc0ed7fa542a8b7b22941f9a643f63ea56ae3 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 05:27:11 +0300 Subject: [PATCH 076/231] Added a tool to fix code style [#CLICKHOUSE-2] --- utils/check-style/check-style | 20 ++++++++++++++++++++ utils/check-style/fix-style | 3 +++ 2 files changed, 23 insertions(+) create mode 100755 utils/check-style/check-style create mode 100755 utils/check-style/fix-style diff --git a/utils/check-style/check-style b/utils/check-style/check-style new file mode 100755 index 00000000000..41f329f3dd1 --- /dev/null +++ b/utils/check-style/check-style @@ -0,0 +1,20 @@ +#!/usr/bin/env bash + +# For code formatting we have clang-format. +# +# But it's not sane to apply clang-format for whole code base, +# because it sometimes makes worse for properly formatted files. +# +# It's only reasonable to blindly apply clang-format only in cases +# when the code is likely to be out of style. +# +# For this purpose we have a script that will use very primitive heuristics +# (simple regexps) to check if the code is likely to have basic style violations. +# and then to run formatter only for the specified files. + +ROOT_PATH=$(git rev-parse --show-toplevel) + +find $ROOT_PATH/dbms -name '*.h' -or -name '*.cpp' | + grep -vP 'Compiler|build' | + xargs grep $@ -P '((class|struct|namespace|enum|if|for|while|else|throw|switch).*|\)(\s*const)?(\s*override)?\s*)\{$|\s$|\t|^ {1,3}[^\* ]\S' +# a curly brace not in a new line, but not for the case of C++11 init or agg. initialization | trailing whitespace | number of ws not a multiple of 4, but not in the case of comment continuation diff --git a/utils/check-style/fix-style b/utils/check-style/fix-style new file mode 100755 index 00000000000..51d2a28f542 --- /dev/null +++ b/utils/check-style/fix-style @@ -0,0 +1,3 @@ +#!/usr/bin/env bash + +$(dirname ${BASH_SOURCE[0]})/check-style -l | xargs --no-run-if-empty clang-format -i From a0cdfaaba1fcd7fb1b932d9c905345d47c155716 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 05:29:50 +0300 Subject: [PATCH 077/231] Addition to prev. revision [#CLICKHOUSE-2] --- utils/check-style/check-style | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/utils/check-style/check-style b/utils/check-style/check-style index 41f329f3dd1..53acaee55ae 100755 --- a/utils/check-style/check-style +++ b/utils/check-style/check-style @@ -16,5 +16,5 @@ ROOT_PATH=$(git rev-parse --show-toplevel) find $ROOT_PATH/dbms -name '*.h' -or -name '*.cpp' | grep -vP 'Compiler|build' | - xargs grep $@ -P '((class|struct|namespace|enum|if|for|while|else|throw|switch).*|\)(\s*const)?(\s*override)?\s*)\{$|\s$|\t|^ {1,3}[^\* ]\S' -# a curly brace not in a new line, but not for the case of C++11 init or agg. initialization | trailing whitespace | number of ws not a multiple of 4, but not in the case of comment continuation + xargs grep $@ -P '((class|struct|namespace|enum|if|for|while|else|throw|switch).*|\)(\s*const)?(\s*override)?\s*)\{$|\s$|\t|^ {1,3}[^\* ]\S|\t' +# a curly brace not in a new line, but not for the case of C++11 init or agg. initialization | trailing whitespace | number of ws not a multiple of 4, but not in the case of comment continuation | a tab character From d1b4b5c8364bd8d66e89a490532e4268e81919f2 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 09:23:18 +0300 Subject: [PATCH 078/231] Fixed error; added ProfileEvent #2277 --- dbms/src/Common/ProfileEvents.cpp | 2 ++ dbms/src/Functions/IFunction.cpp | 1 + dbms/src/Interpreters/ExpressionJIT.cpp | 21 +++++++++++++++++++-- 3 files changed, 22 insertions(+), 2 deletions(-) diff --git a/dbms/src/Common/ProfileEvents.cpp b/dbms/src/Common/ProfileEvents.cpp index 027f52b2f37..7151b6eab38 100644 --- a/dbms/src/Common/ProfileEvents.cpp +++ b/dbms/src/Common/ProfileEvents.cpp @@ -79,6 +79,8 @@ M(CompileAttempt) \ M(CompileSuccess) \ \ + M(CompileFunction) \ + \ M(ExternalSortWritePart) \ M(ExternalSortMerge) \ M(ExternalAggregationWritePart) \ diff --git a/dbms/src/Functions/IFunction.cpp b/dbms/src/Functions/IFunction.cpp index d271e3e9744..f9727c3b3ce 100644 --- a/dbms/src/Functions/IFunction.cpp +++ b/dbms/src/Functions/IFunction.cpp @@ -31,6 +31,7 @@ namespace ErrorCodes extern const int ILLEGAL_COLUMN; } + namespace { diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 2a0984728c1..17fc97b77eb 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -8,6 +8,7 @@ #include #include #include +#include #include #include #include @@ -58,6 +59,11 @@ __attribute__((__weak__)) int _ZTIN4llvm12MemoryBufferE = 0; } +namespace ProfileEvents +{ + extern const Event CompileFunction; +} + namespace DB { @@ -176,6 +182,7 @@ struct LLVMContext std::string mangled_name; llvm::raw_string_ostream mangled_name_stream(mangled_name); llvm::Mangler::getNameWithPrefix(mangled_name_stream, function.getName(), layout); + mangled_name_stream.flush(); function_names.emplace_back(function.getName(), mangled_name); } @@ -187,8 +194,13 @@ struct LLVMContext #endif for (const auto & names : function_names) - if (auto symbol = compileLayer.findSymbol(names.second, false).getAddress()) - symbols[names.first] = reinterpret_cast(*symbol); + { + if (auto symbol = compileLayer.findSymbol(names.second, false)) + { + if (auto address_or_error = symbol.getAddress()) + symbols[names.first] = reinterpret_cast(*address_or_error); + } + } } }; @@ -231,6 +243,8 @@ public: static void compileFunction(std::shared_ptr & context, const IFunctionBase & f) { + ProfileEvents::increment(ProfileEvents::CompileFunction); + auto & arg_types = f.getArgumentTypes(); auto & b = context->builder; auto * size_type = b.getIntNTy(sizeof(size_t) * 8); @@ -548,6 +562,7 @@ void compileFunctions(ExpressionActions::Actions & actions, const Names & output { if (actions[i].type != ExpressionAction::APPLY_FUNCTION || !isCompilable(context->builder, *actions[i].function)) continue; + fused[i].push_back(actions[i]); if (dependents[i].find({}) != dependents[i].end()) { @@ -559,10 +574,12 @@ void compileFunctions(ExpressionActions::Actions & actions, const Names & output actions[i].argument_names = fn->getArgumentNames(); continue; } + /// TODO: determine whether it's profitable to inline the function if there's more than one dependent. for (const auto & dep : dependents[i]) fused[*dep].insert(fused[*dep].end(), fused[i].begin(), fused[i].end()); } + context->finalize(); } From 147919928d7922073c614a7591d14e4e12e3dfaa Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 09:49:56 +0300 Subject: [PATCH 079/231] Better error checks #2277 --- dbms/src/Interpreters/ExpressionJIT.cpp | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 17fc97b77eb..86b1d16d4c8 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -70,6 +70,7 @@ namespace DB namespace ErrorCodes { extern const int LOGICAL_ERROR; + extern const int CANNOT_COMPILE_CODE; } namespace @@ -188,9 +189,11 @@ struct LLVMContext #if LLVM_VERSION_MAJOR >= 7 llvm::orc::VModuleKey module_key = execution_session.allocateVModule(); - llvm::cantFail(compileLayer.addModule(module_key, std::move(module))); + if (compileLayer.addModule(module_key, std::move(module))) + throw Exception("Cannot add module to compile layer", ErrorCodes::CANNOT_COMPILE_CODE); #else - llvm::cantFail(compileLayer.addModule(module, std::make_shared())); + if (compileLayer.addModule(module, std::make_shared())) + throw Exception("Cannot add module to compile layer", ErrorCodes::CANNOT_COMPILE_CODE); #endif for (const auto & names : function_names) @@ -199,7 +202,11 @@ struct LLVMContext { if (auto address_or_error = symbol.getAddress()) symbols[names.first] = reinterpret_cast(*address_or_error); + else + throw Exception("Cannot get an address of compiled symbol from a module", ErrorCodes::CANNOT_COMPILE_CODE); } + else + throw Exception("Cannot find compiled symbol in a module", ErrorCodes::CANNOT_COMPILE_CODE); } } }; From 01bbf650f88582befd4aef7908fb7624516be101 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 11:59:24 +0300 Subject: [PATCH 080/231] Build fixes #2277 --- cmake/find_llvm.cmake | 9 +++++++-- dbms/src/Common/config.h.in | 1 + dbms/src/Interpreters/ExpressionJIT.cpp | 4 ++++ 3 files changed, 12 insertions(+), 2 deletions(-) diff --git a/cmake/find_llvm.cmake b/cmake/find_llvm.cmake index 21ee7f28e4a..b4d64ce5aab 100644 --- a/cmake/find_llvm.cmake +++ b/cmake/find_llvm.cmake @@ -1,10 +1,12 @@ option (ENABLE_EMBEDDED_COMPILER "Set to TRUE to enable support for 'compile' option for query execution" 1) if (ENABLE_EMBEDDED_COMPILER) + set (LLVM_PATHS "/usr/local/lib/llvm") + if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang") - find_package(LLVM CONFIG) + find_package(LLVM CONFIG PATHS ${LLVM_PATHS}) else () - find_package(LLVM 5 CONFIG) + find_package(LLVM 5 CONFIG PATHS ${LLVM_PATHS}) endif () if (LLVM_FOUND) @@ -16,6 +18,9 @@ if (ENABLE_EMBEDDED_COMPILER) message(STATUS "LLVM Include Directory: ${LLVM_INCLUDE_DIRS}") message(STATUS "LLVM Library Directory: ${LLVM_LIBRARY_DIRS}") message(STATUS "LLVM C++ Compiler: ${LLVM_CXXFLAGS}") + + option(LLVM_HAS_RTTI "Enable if LLVM was build with RTTI enabled" ON) + set (USE_EMBEDDED_COMPILER 1) endif() endif() diff --git a/dbms/src/Common/config.h.in b/dbms/src/Common/config.h.in index 2531d9ced70..f4d155de2c8 100644 --- a/dbms/src/Common/config.h.in +++ b/dbms/src/Common/config.h.in @@ -9,6 +9,7 @@ #cmakedefine01 USE_RDKAFKA #cmakedefine01 USE_CAPNP #cmakedefine01 USE_EMBEDDED_COMPILER +#cmakedefine01 LLVM_HAS_RTTI #cmakedefine01 Poco_SQLODBC_FOUND #cmakedefine01 Poco_DataODBC_FOUND #cmakedefine01 Poco_MongoDB_FOUND diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 86b1d16d4c8..3abc9b34167 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -42,6 +42,8 @@ #pragma GCC diagnostic pop +#if !LLVM_HAS_RTTI + /** HACK * Allow to link with LLVM that was compiled without RTTI. * This is the default option when you build LLVM from sources. @@ -58,6 +60,8 @@ __attribute__((__weak__)) int _ZTIN4llvm12MemoryBufferE = 0; } +#endif + namespace ProfileEvents { From 00a5162503ed4424c22215e4139b97ea98f0e297 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 13:29:19 +0300 Subject: [PATCH 081/231] Removed useless library [#CLICKHOUSE-2] --- .travis.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index 7af12080396..1e73e39fcc1 100644 --- a/.travis.yml +++ b/.travis.yml @@ -13,7 +13,7 @@ matrix: # apt: # sources: # - ubuntu-toolchain-r-test -# packages: [ g++-7, libicu-dev, libreadline-dev, libmysqlclient-dev, unixodbc-dev, libltdl-dev, libssl-dev, libboost-dev, zlib1g-dev, libdouble-conversion-dev, libzookeeper-mt-dev, libsparsehash-dev, librdkafka-dev, libcapnp-dev, libsparsehash-dev, libgoogle-perftools-dev, bash, expect, python, python-lxml, python-termcolor, curl, perl, sudo, openssl ] +# packages: [ g++-7, libicu-dev, libreadline-dev, libmysqlclient-dev, unixodbc-dev, libltdl-dev, libssl-dev, libboost-dev, zlib1g-dev, libdouble-conversion-dev, libsparsehash-dev, librdkafka-dev, libcapnp-dev, libsparsehash-dev, libgoogle-perftools-dev, bash, expect, python, python-lxml, python-termcolor, curl, perl, sudo, openssl ] # # env: # - MATRIX_EVAL="export CC=gcc-7 && export CXX=g++-7" @@ -36,7 +36,7 @@ matrix: sources: - ubuntu-toolchain-r-test - llvm-toolchain-trusty-5.0 - packages: [ g++-7, clang-5.0, lld-5.0, libicu-dev, libreadline-dev, libmysqlclient-dev, unixodbc-dev, libltdl-dev, libssl-dev, libboost-dev, zlib1g-dev, libdouble-conversion-dev, libzookeeper-mt-dev, libsparsehash-dev, librdkafka-dev, libcapnp-dev, libsparsehash-dev, libgoogle-perftools-dev, bash, expect, python, python-lxml, python-termcolor, curl, perl, sudo, openssl] + packages: [ g++-7, clang-5.0, lld-5.0, libicu-dev, libreadline-dev, libmysqlclient-dev, unixodbc-dev, libltdl-dev, libssl-dev, libboost-dev, zlib1g-dev, libdouble-conversion-dev, libsparsehash-dev, librdkafka-dev, libcapnp-dev, libsparsehash-dev, libgoogle-perftools-dev, bash, expect, python, python-lxml, python-termcolor, curl, perl, sudo, openssl] env: - MATRIX_EVAL="export CC=clang-5.0 && export CXX=clang++-5.0" From 863531ccf0b880d84da5ed1acc4979442717141b Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 13:42:57 +0300 Subject: [PATCH 082/231] Removed useless library [#CLICKHOUSE-2] --- debian/.pbuilderrc | 3 --- 1 file changed, 3 deletions(-) diff --git a/debian/.pbuilderrc b/debian/.pbuilderrc index bedbe74881b..8d29bab3dd5 100644 --- a/debian/.pbuilderrc +++ b/debian/.pbuilderrc @@ -174,9 +174,6 @@ else fi -# bundled zookeepeer have broken asm -[[ "$ARCH" == "arm64" ]] && EXTRAPACKAGES+=" libzookeeper-mt-dev " - # will test symbols #EXTRAPACKAGES+=" gdb " From 0e091384054b803f33141547d8ed4efc17dc058e Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 7 May 2018 16:03:26 +0300 Subject: [PATCH 083/231] Assume output and one input of LLVMFunction is non-const --- dbms/src/Interpreters/ExpressionJIT.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 3abc9b34167..f6e03c25d37 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -320,8 +320,8 @@ static void compileFunction(std::shared_ptr & context, const IFunct auto * cur_block = b.GetInsertBlock(); for (auto & col : columns) { - /// currently, stride is either 0 or size of native type - auto * is_const = b.CreateICmpEQ(col.stride, llvm::ConstantInt::get(size_type, 0)); + /// stride is either 0 or size of native type; output column is never constant; neither is at least one input + auto * is_const = &col == &columns.back() || columns.size() <= 2 ? b.getFalse() : b.CreateICmpEQ(col.stride, llvm::ConstantInt::get(size_type, 0)); col.data->addIncoming(b.CreateSelect(is_const, col.data, b.CreateConstInBoundsGEP1_32(nullptr, col.data, 1)), cur_block); if (col.null) col.null->addIncoming(b.CreateSelect(is_const, col.null, b.CreateConstInBoundsGEP1_32(nullptr, col.null, 1)), cur_block); From d4b5c01a1e9beb0d13dc38b87fc394167cd8d3ce Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 7 May 2018 16:04:04 +0300 Subject: [PATCH 084/231] Fix addModule check for LLVM < 7 It returns `Expected`, so false-y is a failure. (In >= 7 it returns an `Error`, so truth-y is a failure. Wow, that's confusing.) --- dbms/src/Interpreters/ExpressionJIT.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index f6e03c25d37..312452631c9 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -196,7 +196,7 @@ struct LLVMContext if (compileLayer.addModule(module_key, std::move(module))) throw Exception("Cannot add module to compile layer", ErrorCodes::CANNOT_COMPILE_CODE); #else - if (compileLayer.addModule(module, std::make_shared())) + if (!compileLayer.addModule(module, std::make_shared())) throw Exception("Cannot add module to compile layer", ErrorCodes::CANNOT_COMPILE_CODE); #endif From b08dbf2b228b0c6788e9f6f0a72883487b3c76e0 Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 7 May 2018 16:33:07 +0300 Subject: [PATCH 085/231] Remove a redundant target_compile_options --- dbms/src/Functions/CMakeLists.txt | 2 -- 1 file changed, 2 deletions(-) diff --git a/dbms/src/Functions/CMakeLists.txt b/dbms/src/Functions/CMakeLists.txt index 48735b7a4aa..2306f0c109d 100644 --- a/dbms/src/Functions/CMakeLists.txt +++ b/dbms/src/Functions/CMakeLists.txt @@ -112,6 +112,4 @@ endif () if (USE_EMBEDDED_COMPILER) target_include_directories (clickhouse_functions BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) - # LLVM has a bunch of unused parameters in its header files. - target_compile_options (clickhouse_functions PRIVATE "-Wno-unused-parameter") endif () From 686c1f73fdd8dfe123b2946bb6299510d3fcb949 Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 7 May 2018 16:35:33 +0300 Subject: [PATCH 086/231] Implement IntExp2Impl::compile --- dbms/src/Functions/FunctionsArithmetic.h | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/dbms/src/Functions/FunctionsArithmetic.h b/dbms/src/Functions/FunctionsArithmetic.h index 40cc27115b7..cef7ec4b924 100644 --- a/dbms/src/Functions/FunctionsArithmetic.h +++ b/dbms/src/Functions/FunctionsArithmetic.h @@ -661,7 +661,14 @@ struct IntExp2Impl } #if USE_EMBEDDED_COMPILER - static constexpr bool compilable = false; /// library function + static constexpr bool compilable = true; + + static inline llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * arg, bool) + { + if (!arg->getType()->isIntegerTy()) + throw Exception("IntExp2Impl expected an integral type", ErrorCodes::LOGICAL_ERROR); + return b.CreateShl(llvm::ConstantInt::get(arg->getType(), 1), arg); + } #endif }; From 32fd1230103b21a21348b9b4cd5f4b259fb8a057 Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 7 May 2018 19:06:13 +0300 Subject: [PATCH 087/231] Select an *exact* target machine, not an approximation. Required for enabling advanced features such as AVX and AVX2. Code mostly copied from LLVM's tools/opt/opt.cpp. --- dbms/src/Interpreters/ExpressionJIT.cpp | 38 ++++++++++++++++++++++++- 1 file changed, 37 insertions(+), 1 deletion(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 312452631c9..51e6ca5da86 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -18,6 +18,7 @@ #pragma GCC diagnostic ignored "-Wunused-parameter" #pragma GCC diagnostic ignored "-Wnon-virtual-dtor" +#include #include #include #include @@ -36,6 +37,9 @@ #include #include #include +#include +#include +#include #include #include @@ -120,6 +124,30 @@ static void applyFunction(IFunctionBase & function, Field & value) block.safeGetByPosition(1).column->get(0, value); } +static llvm::TargetMachine * getNativeMachine() +{ + std::string error; + auto cpu = llvm::sys::getHostCPUName(); + auto triple = llvm::sys::getProcessTriple(); + auto target = llvm::TargetRegistry::lookupTarget(triple, error); + if (!target) + throw Exception("Could not initialize native target: " + error, ErrorCodes::CANNOT_COMPILE_CODE); + llvm::SubtargetFeatures features; + llvm::StringMap feature_map; + if (llvm::sys::getHostCPUFeatures(feature_map)) + for (auto& f : feature_map) + features.AddFeature(f.first(), f.second); + llvm::TargetOptions options; + return target->createTargetMachine( + triple, cpu, features.getString(), options, llvm::None, +#if LLVM_VERSION_MAJOR >= 6 + llvm::None, llvm::CodeGenOpt::Default, /*jit=*/true +#else + llvm::CodeModel::Default, llvm::CodeGenOpt::Default +#endif + ); +} + struct LLVMContext { llvm::LLVMContext context; @@ -142,7 +170,7 @@ struct LLVMContext #else : module(std::make_shared("jit", context)) #endif - , machine(llvm::EngineBuilder().selectTarget()) + , machine(getNativeMachine()) #if LLVM_VERSION_MAJOR >= 7 , objectLayer(execution_session, [](llvm::orc::VModuleKey) { @@ -168,6 +196,7 @@ struct LLVMContext if (!module->size()) return; llvm::PassManagerBuilder builder; + llvm::legacy::PassManager mpm; llvm::legacy::FunctionPassManager fpm(module.get()); builder.OptLevel = 3; builder.SLPVectorize = true; @@ -175,9 +204,16 @@ struct LLVMContext builder.RerollLoops = true; builder.VerifyInput = true; builder.VerifyOutput = true; + machine->adjustPassManager(builder); + fpm.add(llvm::createTargetTransformInfoWrapperPass(machine->getTargetIRAnalysis())); + mpm.add(llvm::createTargetTransformInfoWrapperPass(machine->getTargetIRAnalysis())); builder.populateFunctionPassManager(fpm); + builder.populateModulePassManager(mpm); + fpm.doInitialization(); for (auto & function : *module) fpm.run(function); + fpm.doFinalization(); + mpm.run(*module); /// name, mangled name std::vector> function_names; From cfc41e1a640c779e0d1f198ccb0ee2a5bd52ebdd Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 7 May 2018 19:14:00 +0300 Subject: [PATCH 088/231] Copy changes from LLVM {5,6} CMakeLists to 7 --- dbms/src/Server/Compiler-7.0.0/CMakeLists.txt | 5 ----- 1 file changed, 5 deletions(-) diff --git a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt index 7114fb785cb..c6e725d3bad 100644 --- a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt @@ -10,11 +10,6 @@ target_compile_options(clickhouse-compiler-lib PRIVATE -fno-rtti -fno-exceptions llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) -# We link statically with zlib, and LLVM (sometimes) tries to bring its own dependency. -list(REMOVE_ITEM REQUIRED_LLVM_LIBRARIES "-lz") -# Wrong library in freebsd: -list(REMOVE_ITEM REQUIRED_LLVM_LIBRARIES "-l/usr/lib/libexecinfo.so") - message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") target_include_directories(clickhouse-compiler-lib PRIVATE ${LLVM_INCLUDE_DIRS}) From 089ef3277fa79ffde3d2a7eb31c405a61f40696b Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 7 May 2018 19:30:47 +0300 Subject: [PATCH 089/231] Removed useless code [#CLICKHOUSE-2] --- dbms/src/Common/ZooKeeper/ZooKeeper.cpp | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/dbms/src/Common/ZooKeeper/ZooKeeper.cpp b/dbms/src/Common/ZooKeeper/ZooKeeper.cpp index c348a5fab01..dbcc51ccc92 100644 --- a/dbms/src/Common/ZooKeeper/ZooKeeper.cpp +++ b/dbms/src/Common/ZooKeeper/ZooKeeper.cpp @@ -745,14 +745,10 @@ int32_t ZooKeeper::tryMultiNoThrow(const Requests & requests, Responses & respon { return multiImpl(requests, responses); } - catch (ZooKeeperImpl::Exception & e) + catch (const ZooKeeperImpl::Exception & e) { return e.code; } - catch (...) - { - throw; - } } From 1433e6e849af13e19b949b1bbc00db5ce66c1cd4 Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 7 May 2018 22:21:23 +0300 Subject: [PATCH 090/231] Extract native bool cast; generalize number cast to nullables --- dbms/src/DataTypes/Native.h | 55 +++++++++++++++++++----- dbms/src/Functions/FunctionsArithmetic.h | 8 ++-- dbms/src/Functions/FunctionsLogical.h | 22 ++-------- 3 files changed, 53 insertions(+), 32 deletions(-) diff --git a/dbms/src/DataTypes/Native.h b/dbms/src/DataTypes/Native.h index 61daececd3e..6a793d13ca4 100644 --- a/dbms/src/DataTypes/Native.h +++ b/dbms/src/DataTypes/Native.h @@ -62,19 +62,54 @@ static inline llvm::Type * toNativeType(llvm::IRBuilderBase & builder, const Dat return toNativeType(builder, *type); } -static inline llvm::Value * castNativeNumber(llvm::IRBuilder<> & builder, llvm::Value * value, llvm::Type * type, bool is_signed) +static inline llvm::Value * nativeBoolCast(llvm::IRBuilder<> & b, const DataTypePtr & from, llvm::Value * value) { - if (value->getType() == type) - return value; - if (value->getType()->isIntegerTy()) + if (from->isNullable()) { - if (type->isIntegerTy()) - return builder.CreateIntCast(value, type, is_signed); - return is_signed ? builder.CreateSIToFP(value, type) : builder.CreateUIToFP(value, type); + auto * inner = nativeBoolCast(b, removeNullable(from), b.CreateExtractValue(value, {0})); + return b.CreateAnd(b.CreateNot(b.CreateExtractValue(value, {1})), inner); } - if (type->isFloatingPointTy()) - return builder.CreateFPCast(value, type); - return is_signed ? builder.CreateFPToSI(value, type) : builder.CreateFPToUI(value, type); + auto * zero = llvm::Constant::getNullValue(value->getType()); + if (value->getType()->isIntegerTy()) + return b.CreateICmpNE(value, zero); + if (value->getType()->isFloatingPointTy()) + return b.CreateFCmpONE(value, zero); /// QNaN is false + throw Exception("Cannot cast non-number " + from->getName() + " to bool", ErrorCodes::NOT_IMPLEMENTED); +} + +static inline llvm::Value * nativeCast(llvm::IRBuilder<> & b, const DataTypePtr & from, llvm::Value * value, const DataTypePtr & to) +{ + auto * n_from = value->getType(); + auto * n_to = toNativeType(b, to); + if (n_from == n_to) + return value; + if (from->isNullable() && to->isNullable()) + { + auto * inner = nativeCast(b, removeNullable(from), b.CreateExtractValue(value, {0}), to); + return b.CreateInsertValue(inner, b.CreateExtractValue(value, {1}), {1}); + } + if (from->isNullable()) + return nativeCast(b, removeNullable(from), b.CreateExtractValue(value, {0}), to); + if (to->isNullable()) + { + auto * inner = nativeCast(b, from, value, removeNullable(to)); + return b.CreateInsertValue(llvm::Constant::getNullValue(n_to), inner, {0}); + } + + bool is_signed = typeIsEither< + DataTypeInt8, DataTypeInt16, DataTypeInt32, DataTypeInt64, + DataTypeFloat32, DataTypeFloat64, + DataTypeDate, DataTypeDateTime, DataTypeInterval + >(*from); + if (n_from->isIntegerTy() && n_to->isFloatingPointTy()) + return is_signed ? b.CreateSIToFP(value, n_to) : b.CreateUIToFP(value, n_to); + if (n_from->isFloatingPointTy() && n_to->isIntegerTy()) + return is_signed ? b.CreateFPToSI(value, n_to) : b.CreateFPToUI(value, n_to); + if (n_from->isIntegerTy() && n_to->isIntegerTy()) + return b.CreateIntCast(value, n_to, is_signed); + if (n_from->isFloatingPointTy() && n_to->isFloatingPointTy()) + return b.CreateFPCast(value, n_to); + throw Exception("Cannot cast " + from->getName() + " to " + to->getName(), ErrorCodes::NOT_IMPLEMENTED); } } diff --git a/dbms/src/Functions/FunctionsArithmetic.h b/dbms/src/Functions/FunctionsArithmetic.h index cef7ec4b924..a8ec0cf4942 100644 --- a/dbms/src/Functions/FunctionsArithmetic.h +++ b/dbms/src/Functions/FunctionsArithmetic.h @@ -982,9 +982,9 @@ public: if constexpr (!std::is_same_v && OpSpec::compilable) { auto & b = static_cast &>(builder); - auto * type = toNativeType(b, ResultDataType{}); - auto * lval = castNativeNumber(b, values[0](), type, std::is_signed_v); - auto * rval = castNativeNumber(b, values[1](), type, std::is_signed_v); + auto type = std::make_shared(); + auto * lval = nativeCast(b, types[0], values[0](), type); + auto * rval = nativeCast(b, types[1], values[1](), type); result = OpSpec::compile(b, lval, rval, std::is_signed_v); return true; } @@ -1088,7 +1088,7 @@ public: if constexpr (Op::compilable) { auto & b = static_cast &>(builder); - auto * v = castNativeNumber(b, values[0](), toNativeType(b, DataTypeNumber{}), std::is_signed_v); + auto * v = nativeCast(b, types[0], values[0](), std::make_shared>()); result = Op::compile(b, v, std::is_signed_v); return true; } diff --git a/dbms/src/Functions/FunctionsLogical.h b/dbms/src/Functions/FunctionsLogical.h index c62816be734..d5e77c4a450 100644 --- a/dbms/src/Functions/FunctionsLogical.h +++ b/dbms/src/Functions/FunctionsLogical.h @@ -192,20 +192,6 @@ struct AssociativeOperationImpl }; -#if USE_EMBEDDED_COMPILER -static llvm::Value * isNativeTrueValue(llvm::IRBuilder<> & b, const DataTypePtr & type, llvm::Value * x) -{ - if (type->isNullable()) - { - auto * subexpr = isNativeTrueValue(b, removeNullable(type), b.CreateExtractValue(x, {0})); - return b.CreateAnd(b.CreateNot(b.CreateExtractValue(x, {1})), subexpr); - } - auto * zero = llvm::Constant::getNullValue(x->getType()); - return x->getType()->isIntegerTy() ? b.CreateICmpNE(x, zero) : b.CreateFCmpONE(x, zero); /// QNaN -> false -} -#endif - - template class FunctionAnyArityLogical : public IFunction { @@ -407,9 +393,9 @@ public: auto & b = static_cast &>(builder); if constexpr (!Impl::isSaturable()) { - auto * result = isNativeTrueValue(b, types[0], values[0]()); + auto * result = nativeBoolCast(b, types[0], values[0]()); for (size_t i = 1; i < types.size(); i++) - result = Impl::apply(b, result, isNativeTrueValue(b, types[i], values[i]())); + result = Impl::apply(b, result, nativeBoolCast(b, types[i], values[i]())); return b.CreateSelect(result, b.getInt8(1), b.getInt8(0)); } constexpr bool breakOnTrue = Impl::isSaturatedValue(true); @@ -421,7 +407,7 @@ public: { b.SetInsertPoint(next); auto * value = values[i](); - auto * truth = isNativeTrueValue(b, types[i], value); + auto * truth = nativeBoolCast(b, types[i], value); if (!types[i]->equals(DataTypeUInt8{})) value = b.CreateSelect(truth, b.getInt8(1), b.getInt8(0)); phi->addIncoming(value, b.GetInsertBlock()); @@ -509,7 +495,7 @@ public: llvm::Value * compileImpl(llvm::IRBuilderBase & builder, const DataTypes & types, ValuePlaceholders values) const override { auto & b = static_cast &>(builder); - return b.CreateSelect(Impl::apply(b, isNativeTrueValue(b, types[0], values[0]())), b.getInt8(1), b.getInt8(0)); + return b.CreateSelect(Impl::apply(b, nativeBoolCast(b, types[0], values[0]())), b.getInt8(1), b.getInt8(0)); } #endif }; From 2d70d9d6013de93b71e1f1f0c26a69de46b8edf7 Mon Sep 17 00:00:00 2001 From: pyos Date: Mon, 7 May 2018 22:46:11 +0300 Subject: [PATCH 091/231] Implement jit for numeric if and multiIf --- dbms/src/Functions/FunctionsConditional.h | 64 +++++++++++++++++++++-- 1 file changed, 61 insertions(+), 3 deletions(-) diff --git a/dbms/src/Functions/FunctionsConditional.h b/dbms/src/Functions/FunctionsConditional.h index 4dae3fcc424..ad3e6625396 100644 --- a/dbms/src/Functions/FunctionsConditional.h +++ b/dbms/src/Functions/FunctionsConditional.h @@ -6,6 +6,7 @@ #include #include #include +#include #include #include #include @@ -113,7 +114,64 @@ public: }; -class FunctionIf : public IFunction +template +class FunctionIfBase : public IFunction +{ +#if USE_EMBEDDED_COMPILER +public: + bool isCompilableImpl(const DataTypes & types) const override + { + for (const auto & type : types) + if (!removeNullable(type)->isValueRepresentedByNumber()) + return false; + return true; + } + + llvm::Value * compileImpl(llvm::IRBuilderBase & builder, const DataTypes & types, ValuePlaceholders values) const override + { + auto & b = static_cast &>(builder); + auto type = getReturnTypeImpl(types); + llvm::Value * null = nullptr; + if (!null_is_false && type->isNullable()) + null = b.CreateInsertValue(llvm::Constant::getNullValue(toNativeType(b, type)), b.getTrue(), {1}); + auto * head = b.GetInsertBlock(); + auto * join = llvm::BasicBlock::Create(head->getContext(), "", head->getParent()); + std::vector> returns; + for (size_t i = 0; i + 1 < types.size(); i += 2) + { + auto * then = llvm::BasicBlock::Create(head->getContext(), "", head->getParent()); + auto * next = llvm::BasicBlock::Create(head->getContext(), "", head->getParent()); + auto * cond = values[i](); + if (!null_is_false && types[i]->isNullable()) + { + returns.emplace_back(head, null); + auto * nonnull = llvm::BasicBlock::Create(head->getContext(), "", head->getParent()); + b.CreateCondBr(b.CreateExtractValue(cond, {1}), join, nonnull); + b.SetInsertPoint(nonnull); + b.CreateCondBr(nativeBoolCast(b, removeNullable(types[i]), b.CreateExtractValue(cond, {0})), then, next); + } + else + { + b.CreateCondBr(nativeBoolCast(b, types[i], cond), then, next); + } + b.SetInsertPoint(then); + returns.emplace_back(then, nativeCast(b, types[i + 1], values[i + 1](), type)); + b.CreateBr(join); + b.SetInsertPoint(next); + head = next; + } + returns.emplace_back(head, nativeCast(b, types.back(), values.back()(), type)); + b.CreateBr(join); + b.SetInsertPoint(join); + auto * phi = b.CreatePHI(toNativeType(b, type), returns.size()); + for (const auto & r : returns) + phi->addIncoming(r.second, r.first); + return phi; + } +#endif +}; + +class FunctionIf : public FunctionIfBase { public: static constexpr auto name = "if"; @@ -916,8 +974,8 @@ public: /// - arrays of such types. /// /// Additionally the arguments, conditions or branches, support nullable types -/// and the NULL value. -class FunctionMultiIf final : public IFunction +/// and the NULL value, with a NULL condition treated as false. +class FunctionMultiIf final : public FunctionIfBase { public: static constexpr auto name = "multiIf"; From cbaf78df4f93457a1f569ca88e0728fb05cb2362 Mon Sep 17 00:00:00 2001 From: Atri Sharma Date: Mon, 7 May 2018 20:05:45 +0530 Subject: [PATCH 092/231] Implement CGroups Limit for CPU --- dbms/src/Common/getNumberOfPhysicalCPUCores.cpp | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp b/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp index 463b9065dde..cdedfa44c04 100644 --- a/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp +++ b/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp @@ -1,5 +1,6 @@ #include #include +#include #if defined(__x86_64__) @@ -15,6 +16,21 @@ unsigned getNumberOfPhysicalCPUCores() { #if defined(__x86_64__) + std::ifstream cgroup_read_in("/sys/fs/cgroup/cpu/cpu.cfs_quota_us"); + if (cgroup_read_in.is_open()) + { + std::string allocated_cpus_str{ std::istreambuf_iterator(cgroup_read_in), std::istreambuf_iterator() }; + int allocated_cpus_int = std::stoi(allocated_cpus_str); + + cgroup_read_in.close(); + + // If a valid value is present + if(allocated_cpus_int > -1) + { + return (unsigned) allocated_cpus_int; + } + } + cpu_raw_data_t raw_data; if (0 != cpuid_get_raw_data(&raw_data)) throw DB::Exception("Cannot cpuid_get_raw_data: " + std::string(cpuid_error()), DB::ErrorCodes::CPUID_ERROR); From 9b5ecc83ac947d95f44b12f7160d395846d27deb Mon Sep 17 00:00:00 2001 From: Atri Sharma Date: Mon, 7 May 2018 22:05:26 +0530 Subject: [PATCH 093/231] Fix quota issue --- dbms/src/Common/getNumberOfPhysicalCPUCores.cpp | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp b/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp index cdedfa44c04..59585a3467e 100644 --- a/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp +++ b/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp @@ -19,15 +19,16 @@ unsigned getNumberOfPhysicalCPUCores() std::ifstream cgroup_read_in("/sys/fs/cgroup/cpu/cpu.cfs_quota_us"); if (cgroup_read_in.is_open()) { - std::string allocated_cpus_str{ std::istreambuf_iterator(cgroup_read_in), std::istreambuf_iterator() }; - int allocated_cpus_int = std::stoi(allocated_cpus_str); + std::string allocated_cpus_share_str{ std::istreambuf_iterator(cgroup_read_in), std::istreambuf_iterator() }; + int allocated_cpus_share_int = std::stoi(allocated_cpus_share_str); cgroup_read_in.close(); // If a valid value is present - if(allocated_cpus_int > -1) + if(allocated_cpus_share_int > -1) { - return (unsigned) allocated_cpus_int; + unsigned int allocated_cpus = allocated_cpus_share_int / 1000; + return allocated_cpus; } } From 22530c38e4aed1d233bd8582cd6b8b47b5be4426 Mon Sep 17 00:00:00 2001 From: Atri Sharma Date: Mon, 7 May 2018 23:05:03 +0530 Subject: [PATCH 094/231] Add Rounding to nearest number --- dbms/src/Common/getNumberOfPhysicalCPUCores.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp b/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp index 59585a3467e..5d436f9a608 100644 --- a/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp +++ b/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp @@ -1,6 +1,7 @@ #include #include #include +#include #if defined(__x86_64__) @@ -27,7 +28,7 @@ unsigned getNumberOfPhysicalCPUCores() // If a valid value is present if(allocated_cpus_share_int > -1) { - unsigned int allocated_cpus = allocated_cpus_share_int / 1000; + unsigned int allocated_cpus = round (allocated_cpus_share_int / 1000); return allocated_cpus; } } From 14cc5308331708148231eb7bafa40de96e50c0aa Mon Sep 17 00:00:00 2001 From: Atri Sharma Date: Tue, 8 May 2018 13:34:23 +0530 Subject: [PATCH 095/231] Update based on comments --- dbms/src/Common/getNumberOfPhysicalCPUCores.cpp | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp b/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp index 5d436f9a608..261a495455d 100644 --- a/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp +++ b/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp @@ -1,7 +1,6 @@ #include #include #include -#include #if defined(__x86_64__) @@ -17,6 +16,7 @@ unsigned getNumberOfPhysicalCPUCores() { #if defined(__x86_64__) +#if defined(__linux__) std::ifstream cgroup_read_in("/sys/fs/cgroup/cpu/cpu.cfs_quota_us"); if (cgroup_read_in.is_open()) { @@ -26,12 +26,13 @@ unsigned getNumberOfPhysicalCPUCores() cgroup_read_in.close(); // If a valid value is present - if(allocated_cpus_share_int > -1) + if(allocated_cpus_share_int > 0) { - unsigned int allocated_cpus = round (allocated_cpus_share_int / 1000); + unsigned int allocated_cpus = (allocated_cpus_share_int + 999) / 1000; return allocated_cpus; } } +#endif cpu_raw_data_t raw_data; if (0 != cpuid_get_raw_data(&raw_data)) From fd9938cc463456513eba5dce06a0e75b43997d69 Mon Sep 17 00:00:00 2001 From: alexey-milovidov Date: Tue, 8 May 2018 12:43:31 +0300 Subject: [PATCH 096/231] Update getNumberOfPhysicalCPUCores.cpp --- dbms/src/Common/getNumberOfPhysicalCPUCores.cpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp b/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp index 261a495455d..832f53e0d86 100644 --- a/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp +++ b/dbms/src/Common/getNumberOfPhysicalCPUCores.cpp @@ -14,9 +14,8 @@ unsigned getNumberOfPhysicalCPUCores() { -#if defined(__x86_64__) - #if defined(__linux__) + /// On Linux we try to look at Cgroups limit if it is available. std::ifstream cgroup_read_in("/sys/fs/cgroup/cpu/cpu.cfs_quota_us"); if (cgroup_read_in.is_open()) { @@ -26,14 +25,15 @@ unsigned getNumberOfPhysicalCPUCores() cgroup_read_in.close(); // If a valid value is present - if(allocated_cpus_share_int > 0) + if (allocated_cpus_share_int > 0) { - unsigned int allocated_cpus = (allocated_cpus_share_int + 999) / 1000; + unsigned allocated_cpus = (allocated_cpus_share_int + 999) / 1000; return allocated_cpus; } } #endif +#if defined(__x86_64__) cpu_raw_data_t raw_data; if (0 != cpuid_get_raw_data(&raw_data)) throw DB::Exception("Cannot cpuid_get_raw_data: " + std::string(cpuid_error()), DB::ErrorCodes::CPUID_ERROR); From 10f68290ee13fe0605e1d9520fb810147ef5a8d9 Mon Sep 17 00:00:00 2001 From: robot-metrika-test Date: Tue, 8 May 2018 14:40:41 +0300 Subject: [PATCH 097/231] Auto version update to [54382] --- dbms/cmake/version.cmake | 6 +++--- debian/changelog | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/dbms/cmake/version.cmake b/dbms/cmake/version.cmake index edaafe61522..c87c695ec01 100644 --- a/dbms/cmake/version.cmake +++ b/dbms/cmake/version.cmake @@ -1,7 +1,7 @@ # This strings autochanged from release_lib.sh: -set(VERSION_DESCRIBE v1.1.54380-testing) -set(VERSION_REVISION 54380) -set(VERSION_GITHASH fb4a44a9132ee6c84aa3b0adff9c8d09a8473a15) +set(VERSION_DESCRIBE v1.1.54382-testing) +set(VERSION_REVISION 54382) +set(VERSION_GITHASH fd9938cc463456513eba5dce06a0e75b43997d69) # end of autochange set (VERSION_MAJOR 1) diff --git a/debian/changelog b/debian/changelog index 2c5d782f65a..fa2d6d409d9 100644 --- a/debian/changelog +++ b/debian/changelog @@ -1,5 +1,5 @@ -clickhouse (1.1.54380) unstable; urgency=low +clickhouse (1.1.54382) unstable; urgency=low * Modified source code - -- Fri, 20 Apr 2018 22:47:20 +0300 + -- Tue, 08 May 2018 14:40:41 +0300 From 195f142b60870bfc824177a722a86686b0120f7e Mon Sep 17 00:00:00 2001 From: BayoNet Date: Tue, 8 May 2018 14:50:38 +0300 Subject: [PATCH 098/231] Typo fixes. --- docs/en/dicts/external_dicts_dict_layout.md | 2 +- docs/en/table_engines/dictionary.md | 29 +++++++++---------- docs/ru/dicts/external_dicts_dict_layout.md | 2 +- docs/ru/table_engines/dictionary.md | 31 +++++++++++---------- 4 files changed, 33 insertions(+), 31 deletions(-) diff --git a/docs/en/dicts/external_dicts_dict_layout.md b/docs/en/dicts/external_dicts_dict_layout.md index 227eaab6b19..ef59cefae7d 100644 --- a/docs/en/dicts/external_dicts_dict_layout.md +++ b/docs/en/dicts/external_dicts_dict_layout.md @@ -6,7 +6,7 @@ There are a [variety of ways](#dicts-external_dicts_dict_layout-manner) to store We recommend [flat](#dicts-external_dicts_dict_layout-flat), [hashed](#dicts-external_dicts_dict_layout-hashed)and[complex_key_hashed](#dicts-external_dicts_dict_layout-complex_key_hashed). which provide optimal processing speed. -Caching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section " [cache](#dicts-external_dicts_dict_layout-cache)". +Caching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section "[cache](#dicts-external_dicts_dict_layout-cache)". There are several ways to improve dictionary performance: diff --git a/docs/en/table_engines/dictionary.md b/docs/en/table_engines/dictionary.md index ab7ea29c3aa..db24e39e07a 100644 --- a/docs/en/table_engines/dictionary.md +++ b/docs/en/table_engines/dictionary.md @@ -42,16 +42,16 @@ Query the dictionary data: ```sql select name, type, key, attribute.names, attribute.types, bytes_allocated, element_count,source from system.dictionaries where name = 'products'; -SELECT - name, - type, - key, - attribute.names, - attribute.types, - bytes_allocated, - element_count, +SELECT + name, + type, + key, + attribute.names, + attribute.types, + bytes_allocated, + element_count, source -FROM system.dictionaries +FROM system.dictionaries WHERE name = 'products' ``` @@ -78,8 +78,8 @@ create table products (product_id UInt64, title String) Engine = Dictionary(prod CREATE TABLE products ( - product_id UInt64, - title String, + product_id UInt64, + title String, ) ENGINE = Dictionary(products) ``` @@ -95,13 +95,14 @@ Take a look at what's in the table. select * from products limit 1; SELECT * -FROM products +FROM products LIMIT 1 ``` + ``` ┌────product_id─┬─title───────────┐ -│ 152689 │ Некоторый товар │ +│ 152689 │ Some item │ └───────────────┴─────────────────┘ -1 rows in set. Elapsed: 0.006 sec. +1 rows in set. Elapsed: 0.006 sec. ``` diff --git a/docs/ru/dicts/external_dicts_dict_layout.md b/docs/ru/dicts/external_dicts_dict_layout.md index 94108a1e818..efe872f616c 100644 --- a/docs/ru/dicts/external_dicts_dict_layout.md +++ b/docs/ru/dicts/external_dicts_dict_layout.md @@ -6,7 +6,7 @@ Рекомендуем [flat](#dicts-external_dicts_dict_layout-flat), [hashed](#dicts-external_dicts_dict_layout-hashed) и [complex_key_hashed](#dicts-external_dicts_dict_layout-complex_key_hashed). Скорость обработки словарей при этом максимальна. -Размещение с кэшированием не рекомендуется использовать из-за потенциально низкой производительности и сложностей в подборе оптимальных параметров. Читайте об этом подробнее в разделе " [cache](#dicts-external_dicts_dict_layout-cache)". +Размещение с кэшированием не рекомендуется использовать из-за потенциально низкой производительности и сложностей в подборе оптимальных параметров. Читайте об этом подробнее в разделе "[cache](#dicts-external_dicts_dict_layout-cache)". Повысить производительнось словарей можно следующими способами: diff --git a/docs/ru/table_engines/dictionary.md b/docs/ru/table_engines/dictionary.md index 255bb9aaa24..6ea26ff6e79 100644 --- a/docs/ru/table_engines/dictionary.md +++ b/docs/ru/table_engines/dictionary.md @@ -42,16 +42,16 @@ ```sql select name, type, key, attribute.names, attribute.types, bytes_allocated, element_count,source from system.dictionaries where name = 'products'; -SELECT - name, - type, - key, - attribute.names, - attribute.types, - bytes_allocated, - element_count, +SELECT + name, + type, + key, + attribute.names, + attribute.types, + bytes_allocated, + element_count, source -FROM system.dictionaries +FROM system.dictionaries WHERE name = 'products' ``` ``` @@ -78,15 +78,15 @@ create table products (product_id UInt64, title String) Engine = Dictionary(prod CREATE TABLE products ( - product_id UInt64, - title String, + product_id UInt64, + title String, ) ENGINE = Dictionary(products) ``` ``` Ok. -0 rows in set. Elapsed: 0.004 sec. +0 rows in set. Elapsed: 0.004 sec. ``` Проверим что у нас в таблице? @@ -95,13 +95,14 @@ Ok. select * from products limit 1; SELECT * -FROM products +FROM products LIMIT 1 ``` + ``` ┌────product_id─┬─title───────────┐ -│ 152689 │ Некоторый товар │ +│ 152689 │ Some item │ └───────────────┴─────────────────┘ -1 rows in set. Elapsed: 0.006 sec. +1 rows in set. Elapsed: 0.006 sec. ``` From 7244dfe1f538dcd9a3ef48dc78a56a59e10fb829 Mon Sep 17 00:00:00 2001 From: Alex Zatelepin Date: Tue, 8 May 2018 15:47:06 +0300 Subject: [PATCH 099/231] better detached part name --- .../Storages/MergeTree/ReplicatedMergeTreeRestartingThread.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dbms/src/Storages/MergeTree/ReplicatedMergeTreeRestartingThread.cpp b/dbms/src/Storages/MergeTree/ReplicatedMergeTreeRestartingThread.cpp index 925e02b599f..3fe59ea940f 100644 --- a/dbms/src/Storages/MergeTree/ReplicatedMergeTreeRestartingThread.cpp +++ b/dbms/src/Storages/MergeTree/ReplicatedMergeTreeRestartingThread.cpp @@ -263,7 +263,7 @@ void ReplicatedMergeTreeRestartingThread::removeFailedQuorumParts() if (code == ZooKeeperImpl::ZooKeeper::ZNONODE) LOG_WARNING(log, "Part " << part_name << " with failed quorum is not in ZooKeeper. This shouldn't happen often."); - storage.data.renameAndDetachPart(part, "noquorum"); + storage.data.renameAndDetachPart(part, "noquorum_"); } } } From f060883c4b7a3403d5ced448cb765aaca82d22d0 Mon Sep 17 00:00:00 2001 From: pyos Date: Tue, 8 May 2018 16:02:32 +0300 Subject: [PATCH 100/231] Fix a typo --- dbms/src/Functions/FunctionsArithmetic.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dbms/src/Functions/FunctionsArithmetic.h b/dbms/src/Functions/FunctionsArithmetic.h index a8ec0cf4942..28a28d9c705 100644 --- a/dbms/src/Functions/FunctionsArithmetic.h +++ b/dbms/src/Functions/FunctionsArithmetic.h @@ -1085,7 +1085,7 @@ public: { using T0 = typename std::decay_t::FieldType; using T1 = typename Op::ResultType; - if constexpr (Op::compilable) + if constexpr (Op::compilable) { auto & b = static_cast &>(builder); auto * v = nativeCast(b, types[0], values[0](), std::make_shared>()); From b6ffdd93e11cd93392c6f775431b86ab8acd61c7 Mon Sep 17 00:00:00 2001 From: proller Date: Tue, 8 May 2018 17:42:22 +0300 Subject: [PATCH 101/231] Ignore werror in Core/Field.h --- dbms/src/Core/Field.h | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/dbms/src/Core/Field.h b/dbms/src/Core/Field.h index 795bc4b7070..738479b4682 100644 --- a/dbms/src/Core/Field.h +++ b/dbms/src/Core/Field.h @@ -361,10 +361,19 @@ private: switch (field.which) { case Types::Null: f(field.template get()); return; + +// gcc 7.3.0 +#if !__clang__ +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wmaybe-uninitialized" +#endif case Types::UInt64: f(field.template get()); return; case Types::UInt128: f(field.template get()); return; case Types::Int64: f(field.template get()); return; case Types::Float64: f(field.template get()); return; +#if !__clang__ +#pragma GCC diagnostic pop +#endif case Types::String: f(field.template get()); return; case Types::Array: f(field.template get()); return; case Types::Tuple: f(field.template get()); return; From eb772b800151f6bab6854fff929b31b55620937e Mon Sep 17 00:00:00 2001 From: proller Date: Tue, 8 May 2018 22:44:54 +0300 Subject: [PATCH 102/231] gcc8 fixes (memset, uncaught_exception) --- cmake/arch.cmake | 4 ++++ cmake/find_llvm.cmake | 2 +- contrib/libsparsehash/sparsehash/sparsetable | 4 ++-- dbms/src/Common/HashTable/HashTable.h | 6 +++--- dbms/src/Common/StringSearcher.h | 4 ++-- .../DataStreams/CollapsingFinalBlockInputStream.h | 2 +- .../MergeTree/MergeTreeBaseBlockInputStream.cpp | 4 ++++ dbms/src/Storages/MergeTree/checkDataPart.cpp | 4 ++-- dbms/tests/server-test.xml | 1 + debian/.pbuilderrc | 5 ++++- docker/builder/build.sh | 2 +- libs/libcommon/include/ext/bit_cast.h | 2 +- release | 12 ++++++------ utils/travis/pbuilder.sh | 2 +- 14 files changed, 33 insertions(+), 21 deletions(-) diff --git a/cmake/arch.cmake b/cmake/arch.cmake index f61bac96ab0..ba446d95676 100644 --- a/cmake/arch.cmake +++ b/cmake/arch.cmake @@ -22,6 +22,10 @@ if (NOT MSVC) set (NOT_MSVC 1) endif () +if (NOT APPLE) + set (NOT_APPLE 1) +endif () + if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU") set (COMPILER_GCC 1) elseif (CMAKE_CXX_COMPILER_ID STREQUAL "Clang") diff --git a/cmake/find_llvm.cmake b/cmake/find_llvm.cmake index 618eaadf41a..8a8ad33a38c 100644 --- a/cmake/find_llvm.cmake +++ b/cmake/find_llvm.cmake @@ -1,4 +1,4 @@ -option (ENABLE_EMBEDDED_COMPILER "Set to TRUE to enable support for 'compile' option for query execution" 1) +option (ENABLE_EMBEDDED_COMPILER "Set to TRUE to enable support for 'compile' option for query execution" ${NOT_APPLE}) if (ENABLE_EMBEDDED_COMPILER) # Based on source code of YT. diff --git a/contrib/libsparsehash/sparsehash/sparsetable b/contrib/libsparsehash/sparsehash/sparsetable index efbeaac0a69..d162623a5f5 100644 --- a/contrib/libsparsehash/sparsehash/sparsetable +++ b/contrib/libsparsehash/sparsehash/sparsetable @@ -1088,7 +1088,7 @@ class sparsegroup { // This is equivalent to memmove(), but faster on my Intel P4, // at least with gcc4.1 -O2 / glibc 2.3.6. for (size_type i = settings.num_buckets; i > offset; --i) - memcpy(group + i, group + i-1, sizeof(*group)); + memcpy(static_cast(group + i), group + i-1, sizeof(*group)); } // Create space at group[offset], without special assumptions about value_type @@ -1154,7 +1154,7 @@ class sparsegroup { // at lesat with gcc4.1 -O2 / glibc 2.3.6. assert(settings.num_buckets > 0); for (size_type i = offset; i < settings.num_buckets-1; ++i) - memcpy(group + i, group + i+1, sizeof(*group)); // hopefully inlined! + memcpy(static_cast(group + i), group + i+1, sizeof(*group)); // hopefully inlined! group = settings.realloc_or_die(group, settings.num_buckets-1); } diff --git a/dbms/src/Common/HashTable/HashTable.h b/dbms/src/Common/HashTable/HashTable.h index b04d3444f93..9fe446ccb8f 100644 --- a/dbms/src/Common/HashTable/HashTable.h +++ b/dbms/src/Common/HashTable/HashTable.h @@ -408,7 +408,7 @@ protected: /// Copy to a new location and zero the old one. x.setHash(hash_value); - memcpy(&buf[place_value], &x, sizeof(x)); + memcpy(static_cast(&buf[place_value]), &x, sizeof(x)); x.setZero(); /// Then the elements that previously were in collision with this can move to the old place. @@ -726,7 +726,7 @@ public: { size_t place_value = findEmptyCell(grower.place(hash_value)); - memcpy(&buf[place_value], cell, sizeof(*cell)); + memcpy(static_cast(&buf[place_value]), cell, sizeof(*cell)); ++m_size; if (unlikely(grower.overflow(m_size))) @@ -897,7 +897,7 @@ public: this->clearHasZero(); m_size = 0; - memset(buf, 0, grower.bufSize() * sizeof(*buf)); + memset(static_cast(buf), 0, grower.bufSize() * sizeof(*buf)); } /// After executing this function, the table can only be destroyed, diff --git a/dbms/src/Common/StringSearcher.h b/dbms/src/Common/StringSearcher.h index f43fe6c717c..7392e08ea25 100644 --- a/dbms/src/Common/StringSearcher.h +++ b/dbms/src/Common/StringSearcher.h @@ -86,8 +86,8 @@ public: if (*needle < 0x80u) { first_needle_symbol_is_ascii = true; - l = static_cast(std::tolower(*needle)); - u = static_cast(std::toupper(*needle)); + l = std::tolower(*needle); + u = std::toupper(*needle); } else { diff --git a/dbms/src/DataStreams/CollapsingFinalBlockInputStream.h b/dbms/src/DataStreams/CollapsingFinalBlockInputStream.h index db06a2aa9d3..607149bbcd2 100644 --- a/dbms/src/DataStreams/CollapsingFinalBlockInputStream.h +++ b/dbms/src/DataStreams/CollapsingFinalBlockInputStream.h @@ -150,7 +150,7 @@ private: --ptr->refcount; if (!ptr->refcount) { - if (std::uncaught_exception()) + if (std::uncaught_exceptions()) delete ptr; else ptr->output_blocks->push_back(ptr); diff --git a/dbms/src/Storages/MergeTree/MergeTreeBaseBlockInputStream.cpp b/dbms/src/Storages/MergeTree/MergeTreeBaseBlockInputStream.cpp index 9f8525d2092..a405620e7d8 100644 --- a/dbms/src/Storages/MergeTree/MergeTreeBaseBlockInputStream.cpp +++ b/dbms/src/Storages/MergeTree/MergeTreeBaseBlockInputStream.cpp @@ -147,8 +147,12 @@ Block MergeTreeBaseBlockInputStream::readFromPart() } size_t recommended_rows = estimateNumRows(*task, task->range_reader); + +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wignored-qualifiers" size_t rows_to_read = std::max(static_cast(1), std::min(max_block_size_rows, recommended_rows)); +#pragma GCC diagnostic pop auto read_result = task->range_reader.read(rows_to_read, task->mark_ranges); diff --git a/dbms/src/Storages/MergeTree/checkDataPart.cpp b/dbms/src/Storages/MergeTree/checkDataPart.cpp index 1c25112ca39..ff36619756b 100644 --- a/dbms/src/Storages/MergeTree/checkDataPart.cpp +++ b/dbms/src/Storages/MergeTree/checkDataPart.cpp @@ -69,8 +69,8 @@ public: readIntBinary(mrk_mark.offset_in_decompressed_block, mrk_hashing_buf); bool has_alternative_mark = false; - MarkInCompressedFile alternative_data_mark; - MarkInCompressedFile data_mark; + MarkInCompressedFile alternative_data_mark = {}; + MarkInCompressedFile data_mark = {}; /// If the mark should be exactly at the border of blocks, we can also use a mark pointing to the end of previous block, /// and the beginning of next. diff --git a/dbms/tests/server-test.xml b/dbms/tests/server-test.xml index 9e4480cb0d3..9c392d0e518 100644 --- a/dbms/tests/server-test.xml +++ b/dbms/tests/server-test.xml @@ -71,6 +71,7 @@ + 3600 diff --git a/debian/.pbuilderrc b/debian/.pbuilderrc index 82c88b6bece..9edf5f91239 100644 --- a/debian/.pbuilderrc +++ b/debian/.pbuilderrc @@ -173,9 +173,12 @@ else export CMAKE_FLAGS="-DENABLE_EMBEDDED_COMPILER=0 $CMAKE_FLAGS" fi -# will test symbols +# Will test symbols #EXTRAPACKAGES+=" gdb " +# For killall in pbuilder-hooks: +EXTRAPACKAGES+=" psmisc " + [[ $CCACHE_PREFIX == 'distcc' ]] && EXTRAPACKAGES+=" $CCACHE_PREFIX " export DEB_BUILD_OPTIONS=parallel=`nproc` diff --git a/docker/builder/build.sh b/docker/builder/build.sh index a392638a319..f63dbb65558 100644 --- a/docker/builder/build.sh +++ b/docker/builder/build.sh @@ -2,6 +2,6 @@ mkdir -p /server/build_docker cd /server/build_docker -cmake /server -DENABLE_EMBEDDED_COMPILER=1 -DENABLE_TESTS=0 +cmake /server -DENABLE_TESTS=0 make -j $(nproc || grep -c ^processor /proc/cpuinfo) #ctest -V -j $(nproc || grep -c ^processor /proc/cpuinfo) diff --git a/libs/libcommon/include/ext/bit_cast.h b/libs/libcommon/include/ext/bit_cast.h index 49c48493d68..7bc70edcfe0 100644 --- a/libs/libcommon/include/ext/bit_cast.h +++ b/libs/libcommon/include/ext/bit_cast.h @@ -14,7 +14,7 @@ namespace ext std::decay_t bit_cast(const From & from) { To res {}; - memcpy(&res, &from, std::min(sizeof(res), sizeof(from))); + memcpy(static_cast(&res), &from, std::min(sizeof(res), sizeof(from))); return res; }; diff --git a/release b/release index 831c38daff7..cfc81791657 100755 --- a/release +++ b/release @@ -6,7 +6,7 @@ # Clang6 build: # env DIST=bionic EXTRAPACKAGES="clang-6.0 libstdc++-8-dev lld-6.0 liblld-6.0-dev libclang-6.0-dev liblld-6.0" DEB_CC=clang-6.0 DEB_CXX=clang++-6.0 CMAKE_FLAGS=" -DLLVM_VERSION_POSTFIX=-6.0 -DNO_WERROR=1 " ./release # Clang7 build: -# env DIST=unstable EXTRAPACKAGES="clang-7 libstdc++-8-dev lld-7 liblld-7-dev libclang-7-dev llvm-7-dev liblld-7" DEB_CC=clang-7 DEB_CXX=clang++-7 CMAKE_FLAGS=" -DLLVM_VERSION_POSTFIX=-7 -DNO_WERROR=1 " ./release +# env DIST=unstable EXTRAPACKAGES="clang-7 libstdc++-8-dev lld-7 liblld-7-dev libclang-7-dev liblld-7" DEB_CC=clang-7 DEB_CXX=clang++-7 CMAKE_FLAGS=" -DLLVM_VERSION_POSTFIX=-7 -DNO_WERROR=1 " ./release # Clang6 without internal compiler (for low memory arm64): # env DIST=bionic DISABLE_PARALLEL=1 EXTRAPACKAGES="clang-6.0 libstdc++-8-dev" DEB_CC=clang-6.0 DEB_CXX=clang++-6.0 CMAKE_FLAGS=" -DNO_WERROR=1 " ./release @@ -33,7 +33,7 @@ while [[ $1 == --* ]] do if [[ $1 == '--test' ]]; then TEST='yes' - VERSION_POSTFIX+=-test + VERSION_POSTFIX+=+test shift elif [[ $1 == '--check-build-dependencies' ]]; then DEBUILD_NODEPS_OPTIONS="" @@ -66,7 +66,7 @@ done if [ -n "$SANITIZER" ] then CMAKE_BUILD_TYPE=$SANITIZER - VERSION_POSTFIX+=-${SANITIZER,,} + VERSION_POSTFIX+=+${SANITIZER,,} # todo: нужно ли отключить libtcmalloc? LIBTCMALLOC_OPTS="-DENABLE_TCMALLOC=0" # GLIBC_COMPATIBILITY отключен по умолчанию @@ -75,14 +75,14 @@ then EXTRAPACKAGES="$EXTRAPACKAGES clang-5.0 lld-5.0" elif [[ $BUILD_TYPE == 'valgrind' ]]; then LIBTCMALLOC_OPTS="-DENABLE_TCMALLOC=0" - VERSION_POSTFIX+=-$BUILD_TYPE + VERSION_POSTFIX+=+$BUILD_TYPE elif [[ $BUILD_TYPE == 'debug' ]]; then CMAKE_BUILD_TYPE=Debug LIBTCMALLOC_OPTS="-DDEBUG_TCMALLOC=1" - VERSION_POSTFIX+=-$BUILD_TYPE + VERSION_POSTFIX+=+$BUILD_TYPE fi -CMAKE_FLAGS=" $LIBTCMALLOC_OPTS -DCMAKE_BUILD_TYPE=$CMAKE_BUILD_TYPE -DENABLE_EMBEDDED_COMPILER=1 $CMAKE_FLAGS" +CMAKE_FLAGS=" $LIBTCMALLOC_OPTS -DCMAKE_BUILD_TYPE=$CMAKE_BUILD_TYPE $CMAKE_FLAGS" export CMAKE_FLAGS export EXTRAPACKAGES diff --git a/utils/travis/pbuilder.sh b/utils/travis/pbuilder.sh index 613ecd61e0a..f5a3ee6c14a 100755 --- a/utils/travis/pbuilder.sh +++ b/utils/travis/pbuilder.sh @@ -24,7 +24,7 @@ env TEST_RUN=${TEST_RUN=1} \ DEB_CC=${DEB_CC=$CC} DEB_CXX=${DEB_CXX=$CXX} \ CCACHE_SIZE=${CCACHE_SIZE:=4G} \ `# Disable all features` \ - CMAKE_FLAGS="-DCMAKE_BUILD_TYPE=Debug -DUNBUNDLED=1 -DENABLE_UNWIND=0 -DENABLE_MYSQL=0 -DENABLE_CAPNP=0 -DENABLE_RDKAFKA=0 -DENABLE_EMBEDDED_COMPILER=1 -DCMAKE_C_FLAGS_ADD='-O0 -g0' -DCMAKE_CXX_FLAGS_ADD='-O0 -g0' $CMAKE_FLAGS" \ + CMAKE_FLAGS="-DCMAKE_BUILD_TYPE=Debug -DUNBUNDLED=1 -DENABLE_UNWIND=0 -DENABLE_MYSQL=0 -DENABLE_CAPNP=0 -DENABLE_RDKAFKA=0 -DCMAKE_C_FLAGS_ADD='-O0 -g0' -DCMAKE_CXX_FLAGS_ADD='-O0 -g0' $CMAKE_FLAGS" \ `# Use all possible contrib libs from system` \ `# psmisc - killall` \ EXTRAPACKAGES="psmisc clang-5.0 lld-5.0 liblld-5.0-dev libclang-5.0-dev liblld-5.0 libc++abi-dev libc++-dev libboost-program-options-dev libboost-system-dev libboost-filesystem-dev libboost-thread-dev zlib1g-dev liblz4-dev libdouble-conversion-dev libsparsehash-dev librdkafka-dev libpoco-dev libsparsehash-dev libgoogle-perftools-dev libzstd-dev libre2-dev $EXTRAPACKAGES" \ From 14223a88a3ea9185643037920098ab7d657dcf27 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Wed, 9 May 2018 07:21:40 +0300 Subject: [PATCH 103/231] Fixed build [#CLICKHOUSE-2] --- dbms/src/Functions/FunctionsArithmetic.h | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/dbms/src/Functions/FunctionsArithmetic.h b/dbms/src/Functions/FunctionsArithmetic.h index 94c56afee67..326b39ceb09 100644 --- a/dbms/src/Functions/FunctionsArithmetic.h +++ b/dbms/src/Functions/FunctionsArithmetic.h @@ -17,7 +17,7 @@ #include #include #include -#include +#include namespace DB @@ -432,7 +432,7 @@ struct GCDImpl { throwIfDivisionLeadsToFPE(typename NumberTraits::ToInteger::Type(a), typename NumberTraits::ToInteger::Type(b)); throwIfDivisionLeadsToFPE(typename NumberTraits::ToInteger::Type(b), typename NumberTraits::ToInteger::Type(a)); - return boost::math::gcd( + return boost::integer::gcd( typename NumberTraits::ToInteger::Type(a), typename NumberTraits::ToInteger::Type(b)); } @@ -448,7 +448,7 @@ struct LCMImpl { throwIfDivisionLeadsToFPE(typename NumberTraits::ToInteger::Type(a), typename NumberTraits::ToInteger::Type(b)); throwIfDivisionLeadsToFPE(typename NumberTraits::ToInteger::Type(b), typename NumberTraits::ToInteger::Type(a)); - return boost::math::lcm( + return boost::integer::lcm( typename NumberTraits::ToInteger::Type(a), typename NumberTraits::ToInteger::Type(b)); } From b442cd9c6490d0e1095f28c4b932f01cc41c848b Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Wed, 9 May 2018 07:22:30 +0300 Subject: [PATCH 104/231] Miscellaneous (no effect) [#CLICKHOUSE-2] --- dbms/src/Common/AIO.h | 1 + dbms/src/Common/Exception.cpp | 1 - dbms/src/Common/localBackup.cpp | 1 + dbms/src/IO/ReadBufferAIO.cpp | 1 + dbms/src/IO/ReadBufferFromFile.cpp | 1 + dbms/src/IO/WriteBufferAIO.cpp | 1 + dbms/src/IO/WriteBufferFromFile.cpp | 1 + dbms/src/Server/InterruptListener.h | 1 + dbms/src/Server/Server.cpp | 1 + dbms/src/Server/StatusFile.cpp | 1 + dbms/src/Storages/StorageStripeLog.cpp | 1 + dbms/src/Storages/StorageTinyLog.cpp | 1 + 12 files changed, 11 insertions(+), 1 deletion(-) diff --git a/dbms/src/Common/AIO.h b/dbms/src/Common/AIO.h index 254a7f02ecb..133da5f04eb 100644 --- a/dbms/src/Common/AIO.h +++ b/dbms/src/Common/AIO.h @@ -15,6 +15,7 @@ #include #include #include +#include /** Small wrappers for asynchronous I/O. diff --git a/dbms/src/Common/Exception.cpp b/dbms/src/Common/Exception.cpp index be44b98dc20..95bc2cd0660 100644 --- a/dbms/src/Common/Exception.cpp +++ b/dbms/src/Common/Exception.cpp @@ -1,4 +1,3 @@ -#include #include #include diff --git a/dbms/src/Common/localBackup.cpp b/dbms/src/Common/localBackup.cpp index eb1f76d8c60..50f0757a008 100644 --- a/dbms/src/Common/localBackup.cpp +++ b/dbms/src/Common/localBackup.cpp @@ -5,6 +5,7 @@ #include #include #include +#include namespace DB diff --git a/dbms/src/IO/ReadBufferAIO.cpp b/dbms/src/IO/ReadBufferAIO.cpp index 1c4b7ab49e3..8225f27ecd4 100644 --- a/dbms/src/IO/ReadBufferAIO.cpp +++ b/dbms/src/IO/ReadBufferAIO.cpp @@ -7,6 +7,7 @@ #include #include +#include #include diff --git a/dbms/src/IO/ReadBufferFromFile.cpp b/dbms/src/IO/ReadBufferFromFile.cpp index 2061410aeec..bd69ac8a90c 100644 --- a/dbms/src/IO/ReadBufferFromFile.cpp +++ b/dbms/src/IO/ReadBufferFromFile.cpp @@ -3,6 +3,7 @@ #include #include #include +#include namespace ProfileEvents diff --git a/dbms/src/IO/WriteBufferAIO.cpp b/dbms/src/IO/WriteBufferAIO.cpp index 087717b452d..ffd2cdfa0cf 100644 --- a/dbms/src/IO/WriteBufferAIO.cpp +++ b/dbms/src/IO/WriteBufferAIO.cpp @@ -6,6 +6,7 @@ #include #include #include +#include namespace ProfileEvents diff --git a/dbms/src/IO/WriteBufferFromFile.cpp b/dbms/src/IO/WriteBufferFromFile.cpp index 4ec4956dbf5..e31120d0844 100644 --- a/dbms/src/IO/WriteBufferFromFile.cpp +++ b/dbms/src/IO/WriteBufferFromFile.cpp @@ -1,5 +1,6 @@ #include #include +#include #include diff --git a/dbms/src/Server/InterruptListener.h b/dbms/src/Server/InterruptListener.h index 8e04f59bb36..eb818671037 100644 --- a/dbms/src/Server/InterruptListener.h +++ b/dbms/src/Server/InterruptListener.h @@ -1,6 +1,7 @@ #pragma once #include +#include #include diff --git a/dbms/src/Server/Server.cpp b/dbms/src/Server/Server.cpp index d8e5870f8e6..dba8c04ce28 100644 --- a/dbms/src/Server/Server.cpp +++ b/dbms/src/Server/Server.cpp @@ -2,6 +2,7 @@ #include #include +#include #include #include #include diff --git a/dbms/src/Server/StatusFile.cpp b/dbms/src/Server/StatusFile.cpp index 3c32e39db9f..463d04b62e7 100644 --- a/dbms/src/Server/StatusFile.cpp +++ b/dbms/src/Server/StatusFile.cpp @@ -4,6 +4,7 @@ #include #include #include +#include #include #include diff --git a/dbms/src/Storages/StorageStripeLog.cpp b/dbms/src/Storages/StorageStripeLog.cpp index 1bebca62322..2b239eb250d 100644 --- a/dbms/src/Storages/StorageStripeLog.cpp +++ b/dbms/src/Storages/StorageStripeLog.cpp @@ -1,5 +1,6 @@ #include #include +#include #include #include diff --git a/dbms/src/Storages/StorageTinyLog.cpp b/dbms/src/Storages/StorageTinyLog.cpp index cb62c911e63..1a0cb0c2179 100644 --- a/dbms/src/Storages/StorageTinyLog.cpp +++ b/dbms/src/Storages/StorageTinyLog.cpp @@ -1,5 +1,6 @@ #include #include +#include #include From 180fbead430506467cf8559e6e1645555d1860bd Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Wed, 9 May 2018 07:24:36 +0300 Subject: [PATCH 105/231] Removed questionable code from CMakeLists (and obsolete test) [#CLICKHOUSE-2] --- cmake/find_rt.cmake | 7 ----- libs/libmysqlxx/src/tests/CMakeLists.txt | 5 ---- libs/libmysqlxx/src/tests/failover.cpp | 36 ------------------------ 3 files changed, 48 deletions(-) delete mode 100644 libs/libmysqlxx/src/tests/failover.cpp diff --git a/cmake/find_rt.cmake b/cmake/find_rt.cmake index 56c7f3cd777..43c653df3e1 100644 --- a/cmake/find_rt.cmake +++ b/cmake/find_rt.cmake @@ -6,10 +6,3 @@ else () endif () message(STATUS "Using rt: ${RT_LIBRARY}") - -function (target_link_rt_by_force TARGET) - if (NOT APPLE) - set (FLAGS "-Wl,-no-as-needed -lrt -Wl,-as-needed") - set_property (TARGET ${TARGET} APPEND PROPERTY LINK_FLAGS "${FLAGS}") - endif () -endfunction () diff --git a/libs/libmysqlxx/src/tests/CMakeLists.txt b/libs/libmysqlxx/src/tests/CMakeLists.txt index 3646a35c58f..d2901513808 100644 --- a/libs/libmysqlxx/src/tests/CMakeLists.txt +++ b/libs/libmysqlxx/src/tests/CMakeLists.txt @@ -1,7 +1,2 @@ - add_executable (mysqlxx_test mysqlxx_test.cpp) -add_executable (failover failover.cpp) - target_link_libraries (mysqlxx_test mysqlxx) -target_link_libraries (failover mysqlxx ${Poco_Util_LIBRARY} ${Poco_Foundation_LIBRARY}) -target_link_rt_by_force (failover) diff --git a/libs/libmysqlxx/src/tests/failover.cpp b/libs/libmysqlxx/src/tests/failover.cpp deleted file mode 100644 index 46b2621bb2d..00000000000 --- a/libs/libmysqlxx/src/tests/failover.cpp +++ /dev/null @@ -1,36 +0,0 @@ -#include -#include -#include -#include -#include -#include - - -class App : public Poco::Util::Application -{ -public: - App() {} -}; - -int main() -{ - App app; - app.loadConfiguration("failover.xml"); - - Poco::AutoPtr channel = new Poco::ConsoleChannel(std::cerr); - Poco::Logger::root().setChannel(channel); - Poco::Logger::root().setLevel("trace"); - - mysqlxx::PoolWithFailover pool("mysql_goals"); - - for (size_t i = 0; i < 10; ++i) - { - mysqlxx::PoolWithFailover::Entry conn = pool.Get(); - mysqlxx::Query Q = conn->query(); - Q << "SELECT count(*) FROM counters"; - mysqlxx::UseQueryResult R = Q.use(); - std::cout << R.fetch_row()[0] << std::endl; - } - - return 0; -} From bd7924268843d797a1438e1c5c3d410f59bc8ac4 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Wed, 9 May 2018 07:49:34 +0300 Subject: [PATCH 106/231] Better #2328 --- .../Storages/MergeTree/MergeTreeBaseBlockInputStream.cpp | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/dbms/src/Storages/MergeTree/MergeTreeBaseBlockInputStream.cpp b/dbms/src/Storages/MergeTree/MergeTreeBaseBlockInputStream.cpp index a405620e7d8..9c0b96d6615 100644 --- a/dbms/src/Storages/MergeTree/MergeTreeBaseBlockInputStream.cpp +++ b/dbms/src/Storages/MergeTree/MergeTreeBaseBlockInputStream.cpp @@ -147,12 +147,7 @@ Block MergeTreeBaseBlockInputStream::readFromPart() } size_t recommended_rows = estimateNumRows(*task, task->range_reader); - -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wignored-qualifiers" - size_t rows_to_read = std::max(static_cast(1), - std::min(max_block_size_rows, recommended_rows)); -#pragma GCC diagnostic pop + size_t rows_to_read = std::max(1, std::min(max_block_size_rows, recommended_rows)); auto read_result = task->range_reader.read(rows_to_read, task->mark_ranges); From 76468d8d899570d8f667fadb830698da7f6d83dc Mon Sep 17 00:00:00 2001 From: proller Date: Wed, 9 May 2018 07:50:54 +0300 Subject: [PATCH 107/231] Change build system DIST from artful to bionic (#2330) * Pbuilder: use ubuntu-ports mirror (with arm64 packages) * Fix arm64 * Fixed tests isolation. [#CLICKHOUSE-2] * Fix nodes leak in case of session expiration. [#CLICKHOUSE-2] * fix * Add new clang versions * ubuntu bionic && gcc-8 fixes * Fixes * wip * Change build system DIST from artful to bionic --- .gitignore | 2 +- debian/control | 3 ++- release | 2 +- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/.gitignore b/.gitignore index f4e9bab7a7a..df18591e21a 100644 --- a/.gitignore +++ b/.gitignore @@ -9,7 +9,7 @@ # auto generated files *.logrt -build +/build /docs/en_single_page/ /docs/ru_single_page/ /docs/venv/ diff --git a/debian/control b/debian/control index 91a275cad93..591c930b206 100644 --- a/debian/control +++ b/debian/control @@ -5,7 +5,8 @@ Maintainer: Alexey Milovidov Build-Depends: debhelper (>= 9), cmake3 | cmake, ninja-build, - gcc-7, g++-7, + gcc-7 [amd64 i386], g++-7 [amd64 i386], + clang-6.0 [arm64 armhf] | clang-5.0 [arm64 armhf], libc6-dev, libmariadbclient-dev | default-libmysqlclient-dev | libmysqlclient-dev, libicu-dev, diff --git a/release b/release index cfc81791657..d5bae8c9fe8 100755 --- a/release +++ b/release @@ -97,7 +97,7 @@ if [ -z "$USE_PBUILDER" ] ; then -e DEB_CC=$DEB_CC -e DEB_CXX=$DEB_CXX -e CMAKE_FLAGS="$CMAKE_FLAGS" \ -b ${DEBUILD_NOSIGN_OPTIONS} ${DEBUILD_NODEPS_OPTIONS} else - export DIST=${DIST:=artful} + export DIST=${DIST:=bionic} export SET_BUILDRESULT=${SET_BUILDRESULT:=$CURDIR/..} . $CURDIR/debian/.pbuilderrc From 8fd72a6777baf2af70b224a1397c208374b615a0 Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Mon, 26 Mar 2018 17:12:07 +0300 Subject: [PATCH 108/231] Add automatic DROP DNS CACHE, update of SYSTEM queries. [#CLICKHOUSE-3645] --- dbms/src/Common/ProfileEvents.cpp | 4 +- dbms/src/Interpreters/Context.cpp | 25 +++- dbms/src/Interpreters/DDLWorker.cpp | 3 +- dbms/src/Interpreters/DNSCacheUpdater.cpp | 116 ++++++++++++++++++ dbms/src/Interpreters/DNSCacheUpdater.h | 42 +++++++ .../Interpreters/InterpreterSystemQuery.cpp | 21 ++-- dbms/src/Interpreters/executeQuery.cpp | 3 + dbms/src/Parsers/ASTSystemQuery.cpp | 2 + dbms/src/Parsers/ASTSystemQuery.h | 1 + dbms/src/Server/Server.cpp | 6 + .../MergeTree/BackgroundProcessingPool.cpp | 6 +- .../MergeTree/BackgroundProcessingPool.h | 54 ++++---- .../integration/test_system_queries/test.py | 17 +++ 13 files changed, 262 insertions(+), 38 deletions(-) create mode 100644 dbms/src/Interpreters/DNSCacheUpdater.cpp create mode 100644 dbms/src/Interpreters/DNSCacheUpdater.h diff --git a/dbms/src/Common/ProfileEvents.cpp b/dbms/src/Common/ProfileEvents.cpp index 027f52b2f37..418d5ef715d 100644 --- a/dbms/src/Common/ProfileEvents.cpp +++ b/dbms/src/Common/ProfileEvents.cpp @@ -138,7 +138,9 @@ M(RWLockAcquiredReadLocks) \ M(RWLockAcquiredWriteLocks) \ M(RWLockReadersWaitMilliseconds) \ - M(RWLockWritersWaitMilliseconds) + M(RWLockWritersWaitMilliseconds) \ + \ + M(NetworkErrors) namespace ProfileEvents { diff --git a/dbms/src/Interpreters/Context.cpp b/dbms/src/Interpreters/Context.cpp index 6453dc38b35..2df56e876fc 100644 --- a/dbms/src/Interpreters/Context.cpp +++ b/dbms/src/Interpreters/Context.cpp @@ -1406,9 +1406,28 @@ std::shared_ptr Context::tryGetCluster(const std::string & cluster_name void Context::reloadClusterConfig() { - std::lock_guard lock(shared->clusters_mutex); - auto & config = shared->clusters_config ? *shared->clusters_config : getConfigRef(); - shared->clusters = std::make_unique(config, settings); + while (true) + { + ConfigurationPtr cluster_config; + { + std::lock_guard lock(shared->clusters_mutex); + cluster_config = shared->clusters_config; + } + + auto & config = cluster_config ? *cluster_config : getConfigRef(); + auto new_clusters = std::make_unique(config, settings); + + { + std::lock_guard lock(shared->clusters_mutex); + if (shared->clusters_config.get() == cluster_config.get()) + { + shared->clusters = std::move(new_clusters); + return; + } + + /// Clusters config has been suddenly changed, recompute clusters + } + } } diff --git a/dbms/src/Interpreters/DDLWorker.cpp b/dbms/src/Interpreters/DDLWorker.cpp index 3025f841ade..f9d1d0969e4 100644 --- a/dbms/src/Interpreters/DDLWorker.cpp +++ b/dbms/src/Interpreters/DDLWorker.cpp @@ -39,6 +39,7 @@ #include #include +#include namespace DB @@ -95,7 +96,7 @@ struct HostID { return DB::isLocalAddress(Poco::Net::SocketAddress(host_name, port), clickhouse_port); } - catch (const Poco::Exception & e) + catch (const Poco::Net::NetException & e) { /// Avoid "Host not found" exceptions return false; diff --git a/dbms/src/Interpreters/DNSCacheUpdater.cpp b/dbms/src/Interpreters/DNSCacheUpdater.cpp new file mode 100644 index 00000000000..2dc75d96dc7 --- /dev/null +++ b/dbms/src/Interpreters/DNSCacheUpdater.cpp @@ -0,0 +1,116 @@ +#include "DNSCacheUpdater.h" +#include +#include +#include +#include +#include +#include + + +namespace ProfileEvents +{ + extern Event NetworkErrors; +} + + +namespace DB +{ + +using BackgroundProcessingPoolTaskInfo = BackgroundProcessingPool::TaskInfo; + +namespace ErrorCodes +{ + extern const int TIMEOUT_EXCEEDED; + extern const int ALL_CONNECTION_TRIES_FAILED; +} + + +DNSCacheUpdater::DNSCacheUpdater(Context & context_) + : context(context_), pool(context_.getBackgroundPool()) +{ + task_handle = pool.addTask([this] () { return run(); }); +} + +bool DNSCacheUpdater::run() +{ + /// TODO: Ensusre that we get global counter (not thread local) + auto num_current_network_exceptions = ProfileEvents::counters[ProfileEvents::NetworkErrors].load(std::memory_order_relaxed); + + if (num_current_network_exceptions >= last_num_network_erros + min_errors_to_update_cache + && time(nullptr) > last_update_time + min_update_period_seconds) + { + try + { + LOG_INFO(&Poco::Logger::get("DNSCacheUpdater"), "Updating DNS cache"); + + DNSCache::instance().drop(); + context.reloadClusterConfig(); + + last_num_network_erros = num_current_network_exceptions; + last_update_time = time(nullptr); + + return true; + } + catch (...) + { + /// Do not increment ProfileEvents::NetworkErrors twice + if (isNetworkError()) + return false; + + throw; + } + } + + /// According to BackgroundProcessingPool logic, if task has done work, it could be executed again immediately. + return false; +} + +DNSCacheUpdater::~DNSCacheUpdater() +{ + if (task_handle) + pool.removeTask(task_handle); + task_handle.reset(); +} + + +bool DNSCacheUpdater::incrementNetworkErrors() +{ + if (isNetworkError()) + { + ProfileEvents::increment(ProfileEvents::NetworkErrors); + return true; + } + + return false; +} + +bool DNSCacheUpdater::isNetworkError() +{ + try + { + throw; + } + catch (const Exception & e) + { + if (e.code() == ErrorCodes::TIMEOUT_EXCEEDED || e.code() == ErrorCodes::ALL_CONNECTION_TRIES_FAILED) + return true; + } + catch (Poco::Net::DNSException & e) + { + return true; + } + catch (Poco::TimeoutException & e) + { + return true; + } + catch (...) + { + /// Do nothing + } + + return false; +} + + +} + diff --git a/dbms/src/Interpreters/DNSCacheUpdater.h b/dbms/src/Interpreters/DNSCacheUpdater.h new file mode 100644 index 00000000000..9c45c84af90 --- /dev/null +++ b/dbms/src/Interpreters/DNSCacheUpdater.h @@ -0,0 +1,42 @@ +#pragma once +#include + + +namespace DB +{ + +class Context; +class BackgroundProcessingPool; +class BackgroundProcessingPoolTaskInfo; + + +/// Add a task to BackgroundProcessingPool that watch for ProfileEvents::NetworkErrors and updates DNS cache if it has increased +class DNSCacheUpdater +{ +public: + + DNSCacheUpdater(Context & context); + ~DNSCacheUpdater(); + + /// Call it inside catch section + /// Returns true if it is a network error + static bool isNetworkError(); + + /// Checks if it is a network error and increments ProfileEvents::NetworkErrors + static bool incrementNetworkErrors(); + +private: + bool run(); + + Context & context; + BackgroundProcessingPool & pool; + std::shared_ptr task_handle; + size_t last_num_network_erros = 0; + time_t last_update_time = 0; + + static constexpr size_t min_errors_to_update_cache = 3; + static constexpr time_t min_update_period_seconds = 10; +}; + + +} diff --git a/dbms/src/Interpreters/InterpreterSystemQuery.cpp b/dbms/src/Interpreters/InterpreterSystemQuery.cpp index ef8197f5ffb..d06a540376d 100644 --- a/dbms/src/Interpreters/InterpreterSystemQuery.cpp +++ b/dbms/src/Interpreters/InterpreterSystemQuery.cpp @@ -63,6 +63,10 @@ BlockIO InterpreterSystemQuery::execute() using Type = ASTSystemQuery::Type; + /// Use global context with fresh system profile settings + Context system_context = context.getGlobalContext(); + system_context.setSetting("profile", context.getSystemProfileName()); + switch (query.type) { case Type::SHUTDOWN: @@ -76,29 +80,32 @@ BlockIO InterpreterSystemQuery::execute() case Type::DROP_DNS_CACHE: DNSCache::instance().drop(); /// Reinitialize clusters to update their resolved_addresses - context.reloadClusterConfig(); + system_context.reloadClusterConfig(); break; case Type::DROP_MARK_CACHE: - context.dropMarkCache(); + system_context.dropMarkCache(); break; case Type::DROP_UNCOMPRESSED_CACHE: - context.dropUncompressedCache(); + system_context.dropUncompressedCache(); break; case Type::RELOAD_DICTIONARY: - context.getExternalDictionaries().reloadDictionary(query.target_dictionary); + system_context.getExternalDictionaries().reloadDictionary(query.target_dictionary); break; case Type::RELOAD_DICTIONARIES: { auto status = getOverallExecutionStatusOfCommands( - [&] { context.getExternalDictionaries().reload(); }, - [&] { context.getEmbeddedDictionaries().reload(); } + [&] { system_context.getExternalDictionaries().reload(); }, + [&] { system_context.getEmbeddedDictionaries().reload(); } ); if (status.code != 0) throw Exception(status.message, status.code); break; } + case Type::RELOAD_EMBEDDED_DICTIONARIES: + system_context.getEmbeddedDictionaries().reload(); + break; case Type::RELOAD_CONFIG: - context.reloadConfig(); + system_context.reloadConfig(); break; case Type::STOP_LISTEN_QUERIES: case Type::START_LISTEN_QUERIES: diff --git a/dbms/src/Interpreters/executeQuery.cpp b/dbms/src/Interpreters/executeQuery.cpp index bac8cef33c5..5137f103457 100644 --- a/dbms/src/Interpreters/executeQuery.cpp +++ b/dbms/src/Interpreters/executeQuery.cpp @@ -23,6 +23,7 @@ #include #include #include +#include "DNSCacheUpdater.h" namespace ProfileEvents @@ -377,6 +378,8 @@ static std::tuple executeQueryImpl( if (!internal) onExceptionBeforeStart(query, context, current_time); + DNSCacheUpdater::incrementNetworkErrors(); + throw; } diff --git a/dbms/src/Parsers/ASTSystemQuery.cpp b/dbms/src/Parsers/ASTSystemQuery.cpp index a50a98f03d6..03a7123b66b 100644 --- a/dbms/src/Parsers/ASTSystemQuery.cpp +++ b/dbms/src/Parsers/ASTSystemQuery.cpp @@ -39,6 +39,8 @@ const char * ASTSystemQuery::typeToString(Type type) return "RELOAD DICTIONARY"; case Type::RELOAD_DICTIONARIES: return "RELOAD DICTIONARIES"; + case Type::RELOAD_EMBEDDED_DICTIONARIES: + return "RELOAD EMBEDDED DICTIONARIES"; case Type::RELOAD_CONFIG: return "RELOAD CONFIG"; case Type::STOP_MERGES: diff --git a/dbms/src/Parsers/ASTSystemQuery.h b/dbms/src/Parsers/ASTSystemQuery.h index 87eaded44ef..520114e24d7 100644 --- a/dbms/src/Parsers/ASTSystemQuery.h +++ b/dbms/src/Parsers/ASTSystemQuery.h @@ -24,6 +24,7 @@ public: SYNC_REPLICA, RELOAD_DICTIONARY, RELOAD_DICTIONARIES, + RELOAD_EMBEDDED_DICTIONARIES, RELOAD_CONFIG, STOP_MERGES, START_MERGES, diff --git a/dbms/src/Server/Server.cpp b/dbms/src/Server/Server.cpp index dba8c04ce28..86afae947ae 100644 --- a/dbms/src/Server/Server.cpp +++ b/dbms/src/Server/Server.cpp @@ -40,6 +40,9 @@ #if Poco_NetSSL_FOUND #include #include +#include + + #endif namespace CurrentMetrics @@ -322,6 +325,9 @@ int Server::main(const std::vector & /*args*/) global_context->setDDLWorker(std::make_shared(ddl_zookeeper_path, *global_context, &config(), "distributed_ddl")); } + /// Initialize a watcher updating DNS cache in case of network errors + DNSCacheUpdater dns_cache_updater(*global_context); + { Poco::Timespan keep_alive_timeout(config().getUInt("keep_alive_timeout", 10), 0); diff --git a/dbms/src/Storages/MergeTree/BackgroundProcessingPool.cpp b/dbms/src/Storages/MergeTree/BackgroundProcessingPool.cpp index 12b7edf32dc..c9081324121 100644 --- a/dbms/src/Storages/MergeTree/BackgroundProcessingPool.cpp +++ b/dbms/src/Storages/MergeTree/BackgroundProcessingPool.cpp @@ -6,6 +6,7 @@ #include #include #include +#include #include #include @@ -25,7 +26,7 @@ constexpr double BackgroundProcessingPool::sleep_seconds; constexpr double BackgroundProcessingPool::sleep_seconds_random_part; -void BackgroundProcessingPool::TaskInfo::wake() +void BackgroundProcessingPoolTaskInfo::wake() { if (removed) return; @@ -36,7 +37,7 @@ void BackgroundProcessingPool::TaskInfo::wake() std::unique_lock lock(pool.tasks_mutex); auto next_time_to_execute = iterator->first; - TaskHandle this_task_handle = iterator->second; + auto this_task_handle = iterator->second; /// If this task was done nothing at previous time and it has to sleep, then cancel sleep time. if (next_time_to_execute > current_time) @@ -180,6 +181,7 @@ void BackgroundProcessingPool::threadFunction() catch (...) { tryLogCurrentException(__PRETTY_FUNCTION__); + DNSCacheUpdater::incrementNetworkErrors(); } if (shutdown) diff --git a/dbms/src/Storages/MergeTree/BackgroundProcessingPool.h b/dbms/src/Storages/MergeTree/BackgroundProcessingPool.h index a659bd0280c..0495b5e8c9d 100644 --- a/dbms/src/Storages/MergeTree/BackgroundProcessingPool.h +++ b/dbms/src/Storages/MergeTree/BackgroundProcessingPool.h @@ -16,6 +16,9 @@ namespace DB { +class BackgroundProcessingPool; +class BackgroundProcessingPoolTaskInfo; + /** Using a fixed number of threads, perform an arbitrary number of tasks in an infinite loop. * In this case, one task can run simultaneously from different threads. * Designed for tasks that perform continuous background work (for example, merge). @@ -27,29 +30,7 @@ class BackgroundProcessingPool public: /// Returns true, if some useful work was done. In that case, thread will not sleep before next run of this task. using Task = std::function; - - - class TaskInfo - { - public: - /// Wake up any thread. - void wake(); - - TaskInfo(BackgroundProcessingPool & pool_, const Task & function_) : pool(pool_), function(function_) {} - - private: - friend class BackgroundProcessingPool; - - BackgroundProcessingPool & pool; - Task function; - - /// Read lock is hold when task is executed. - std::shared_mutex rwlock; - std::atomic removed {false}; - - std::multimap>::iterator iterator; - }; - + using TaskInfo = BackgroundProcessingPoolTaskInfo; using TaskHandle = std::shared_ptr; @@ -65,7 +46,9 @@ public: ~BackgroundProcessingPool(); -private: +protected: + friend class BackgroundProcessingPoolTaskInfo; + using Tasks = std::multimap; /// key is desired next time to execute (priority). using Threads = std::vector; @@ -87,4 +70,27 @@ private: using BackgroundProcessingPoolPtr = std::shared_ptr; + +class BackgroundProcessingPoolTaskInfo +{ +public: + /// Wake up any thread. + void wake(); + + BackgroundProcessingPoolTaskInfo(BackgroundProcessingPool & pool_, const BackgroundProcessingPool::Task & function_) + : pool(pool_), function(function_) {} + +protected: + friend class BackgroundProcessingPool; + + BackgroundProcessingPool & pool; + BackgroundProcessingPool::Task function; + + /// Read lock is hold when task is executed. + std::shared_mutex rwlock; + std::atomic removed {false}; + + std::multimap>::iterator iterator; +}; + } diff --git a/dbms/tests/integration/test_system_queries/test.py b/dbms/tests/integration/test_system_queries/test.py index 46ab2561a83..762b70324a7 100644 --- a/dbms/tests/integration/test_system_queries/test.py +++ b/dbms/tests/integration/test_system_queries/test.py @@ -58,6 +58,7 @@ def test_DROP_DNS_CACHE(started_cluster): instance = cluster.instances['ch1'] instance.exec_in_container(['bash', '-c', 'echo 127.255.255.255 lost_host > /etc/hosts'], privileged=True, user='root') + instance.query("SYSTEM DROP DNS CACHE") with pytest.raises(QueryRuntimeException): instance.query("SELECT * FROM remote('lost_host', 'system', 'one')") @@ -74,6 +75,22 @@ def test_DROP_DNS_CACHE(started_cluster): assert TSV(instance.query("SELECT DISTINCT host_name, host_address FROM system.clusters WHERE cluster='lost_host_cluster'")) == TSV("lost_host\t127.0.0.1\n") +def test_automatic_DROP_DNS_CACHE(started_cluster): + instance = cluster.instances['ch1'] + + instance.exec_in_container(['bash', '-c', 'echo 127.255.255.255 lost_host > /etc/hosts'], privileged=True, user='root') + instance.query("SYSTEM DROP DNS CACHE") + + for i in xrange(5): + with pytest.raises(QueryRuntimeException): + instance.query("SELECT * FROM remote('lost_host', 'system', 'one')") + + time.sleep(20) + + # DNS cache should be automatically updated after this delay + instance.query("SELECT * FROM remote('lost_host', 'system', 'one')") + + def test_RELOAD_CONFIG_AND_MACROS(started_cluster): macros = "ro" From 704583968fac77e1b0f8652509feaf712cb116d3 Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Thu, 29 Mar 2018 23:21:01 +0300 Subject: [PATCH 109/231] Do not save resolved addresses in Connections. [#CLICKHOSUE-2] Add disable_internal_dns_cache main config option. --- dbms/src/Client/Connection.cpp | 17 +++++++- dbms/src/Client/Connection.h | 43 ++++--------------- dbms/src/Client/ConnectionPool.h | 26 +---------- dbms/src/Common/DNSCache.cpp | 20 +++++++-- dbms/src/Common/DNSCache.h | 8 +++- dbms/src/Common/isLocalAddress.cpp | 8 ++-- dbms/src/Common/isLocalAddress.h | 6 ++- dbms/src/Interpreters/Cluster.cpp | 26 +++++------ dbms/src/Interpreters/Cluster.h | 15 ++++++- .../ClusterProxy/DescribeStreamFactory.cpp | 2 +- dbms/src/Interpreters/DDLWorker.cpp | 6 +-- dbms/src/Interpreters/DNSCacheUpdater.h | 2 +- dbms/src/Server/ClusterCopier.cpp | 4 +- dbms/src/Server/Server.cpp | 14 +++++- dbms/src/Server/config.xml | 3 ++ .../Storages/System/StorageSystemClusters.cpp | 3 +- .../integration/test_system_queries/test.py | 16 ------- 17 files changed, 110 insertions(+), 109 deletions(-) diff --git a/dbms/src/Client/Connection.cpp b/dbms/src/Client/Connection.cpp index 8c6d1d0e583..76209b4e43f 100644 --- a/dbms/src/Client/Connection.cpp +++ b/dbms/src/Client/Connection.cpp @@ -17,6 +17,7 @@ #include #include #include +#include #include #include @@ -66,7 +67,10 @@ void Connection::connect() { socket = std::make_unique(); } - socket->connect(resolved_address, timeouts.connection_timeout); + + current_resolved_address = DNSCache::instance().resolveAddress(host, port); + + socket->connect(current_resolved_address, timeouts.connection_timeout); socket->setReceiveTimeout(timeouts.receive_timeout); socket->setSendTimeout(timeouts.send_timeout); socket->setNoDelay(true); @@ -462,6 +466,14 @@ void Connection::sendExternalTablesData(ExternalTablesData & data) LOG_DEBUG(log_wrapper.get(), msg.rdbuf()); } +Poco::Net::SocketAddress Connection::getResolvedAddress() const +{ + if (connected) + return current_resolved_address; + + return DNSCache::instance().resolveAddress(host, port); +} + bool Connection::poll(size_t timeout_microseconds) { @@ -571,6 +583,7 @@ void Connection::initBlockInput() void Connection::setDescription() { + auto resolved_address = getResolvedAddress(); description = host + ":" + toString(resolved_address.port()); auto ip_address = resolved_address.host().toString(); @@ -610,7 +623,7 @@ void Connection::fillBlockExtraInfo(BlockExtraInfo & info) const { info.is_valid = true; info.host = host; - info.resolved_address = resolved_address.toString(); + info.resolved_address = getResolvedAddress().toString(); info.port = port; info.user = user; } diff --git a/dbms/src/Client/Connection.h b/dbms/src/Client/Connection.h index 539c8245aeb..ef941224c9f 100644 --- a/dbms/src/Client/Connection.h +++ b/dbms/src/Client/Connection.h @@ -53,32 +53,7 @@ class Connection : private boost::noncopyable friend class MultiplexedConnections; public: - Connection(const String & host_, UInt16 port_, const String & default_database_, - const String & user_, const String & password_, - const ConnectionTimeouts & timeouts_, - const String & client_name_ = "client", - Protocol::Compression compression_ = Protocol::Compression::Enable, - Protocol::Secure secure_ = Protocol::Secure::Disable, - Poco::Timespan sync_request_timeout_ = Poco::Timespan(DBMS_DEFAULT_SYNC_REQUEST_TIMEOUT_SEC, 0)) - : - host(host_), port(port_), default_database(default_database_), - user(user_), password(password_), resolved_address(host, port), - client_name(client_name_), - compression(compression_), - secure(secure_), - timeouts(timeouts_), - sync_request_timeout(sync_request_timeout_), - log_wrapper(*this) - { - /// Don't connect immediately, only on first need. - - if (user.empty()) - user = "default"; - - setDescription(); - } - - Connection(const String & host_, UInt16 port_, const Poco::Net::SocketAddress & resolved_address_, + Connection(const String & host_, UInt16 port_, const String & default_database_, const String & user_, const String & password_, const ConnectionTimeouts & timeouts_, @@ -87,10 +62,8 @@ public: Protocol::Secure secure_ = Protocol::Secure::Disable, Poco::Timespan sync_request_timeout_ = Poco::Timespan(DBMS_DEFAULT_SYNC_REQUEST_TIMEOUT_SEC, 0)) : - host(host_), port(port_), - default_database(default_database_), - user(user_), password(password_), - resolved_address(resolved_address_), + host(host_), port(port_), default_database(default_database_), + user(user_), password(password_), current_resolved_address(host, port), client_name(client_name_), compression(compression_), secure(secure_), @@ -189,6 +162,9 @@ public: size_t outBytesCount() const { return out ? out->count() : 0; } size_t inBytesCount() const { return in ? in->count() : 0; } + /// Returns initially resolved address + Poco::Net::SocketAddress getResolvedAddress() const; + private: String host; UInt16 port; @@ -196,10 +172,9 @@ private: String user; String password; - /** Address could be resolved beforehand and passed to constructor. Then 'host' and 'port' fields are used just for logging. - * Otherwise address is resolved in constructor. Thus, DNS based load balancing is not supported. - */ - Poco::Net::SocketAddress resolved_address; + /// Address is resolved during the first connection (or the following reconnects) + /// Use it only for logging purposes + Poco::Net::SocketAddress current_resolved_address; /// For messages in log and in exceptions. String description; diff --git a/dbms/src/Client/ConnectionPool.h b/dbms/src/Client/ConnectionPool.h index c07396fe842..12d9ca1d8ee 100644 --- a/dbms/src/Client/ConnectionPool.h +++ b/dbms/src/Client/ConnectionPool.h @@ -54,24 +54,7 @@ public: Protocol::Secure secure_ = Protocol::Secure::Disable) : Base(max_connections_, &Logger::get("ConnectionPool (" + host_ + ":" + toString(port_) + ")")), host(host_), port(port_), default_database(default_database_), - user(user_), password(password_), resolved_address(host_, port_), - client_name(client_name_), compression(compression_), - secure{secure_}, - timeouts(timeouts) - { - } - - ConnectionPool(unsigned max_connections_, - const String & host_, UInt16 port_, const Poco::Net::SocketAddress & resolved_address_, - const String & default_database_, - const String & user_, const String & password_, - const ConnectionTimeouts & timeouts, - const String & client_name_ = "client", - Protocol::Compression compression_ = Protocol::Compression::Enable, - Protocol::Secure secure_ = Protocol::Secure::Disable) - : Base(max_connections_, &Logger::get("ConnectionPool (" + host_ + ":" + toString(port_) + ")")), - host(host_), port(port_), default_database(default_database_), - user(user_), password(password_), resolved_address(resolved_address_), + user(user_), password(password_), client_name(client_name_), compression(compression_), secure{secure_}, timeouts(timeouts) @@ -102,7 +85,7 @@ protected: ConnectionPtr allocObject() override { return std::make_shared( - host, port, resolved_address, + host, port, default_database, user, password, timeouts, client_name, compression, secure); } @@ -114,11 +97,6 @@ private: String user; String password; - /** The address can be resolved in advance and passed to the constructor. Then `host` and `port` fields are meaningful only for logging. - * Otherwise, address is resolved in constructor. That is, DNS balancing is not supported. - */ - Poco::Net::SocketAddress resolved_address; - String client_name; Protocol::Compression compression; /// Whether to compress data when interacting with the server. Protocol::Secure secure; /// Whether to encrypt data when interacting with the server. diff --git a/dbms/src/Common/DNSCache.cpp b/dbms/src/Common/DNSCache.cpp index e4ea9e119da..37b03e191b5 100644 --- a/dbms/src/Common/DNSCache.cpp +++ b/dbms/src/Common/DNSCache.cpp @@ -6,6 +6,7 @@ #include #include #include +#include namespace DB @@ -77,6 +78,9 @@ static Poco::Net::IPAddress resolveIPAddressImpl(const std::string & host) struct DNSCache::Impl { SimpleCache cache_host; + + /// If disabled, will not make cache lookups, will resolve addresses manually on each call + std::atomic is_disabled{false}; }; @@ -84,16 +88,21 @@ DNSCache::DNSCache() : impl(std::make_unique()) {} Poco::Net::IPAddress DNSCache::resolveHost(const std::string & host) { - return impl->cache_host(host); + return !impl->is_disabled ? impl->cache_host(host) : resolveIPAddressImpl(host); } -Poco::Net::SocketAddress DNSCache::resolveHostAndPort(const std::string & host_and_port) +Poco::Net::SocketAddress DNSCache::resolveAddress(const std::string & host_and_port) { String host; UInt16 port; splitHostAndPort(host_and_port, host, port); - return Poco::Net::SocketAddress(impl->cache_host(host), port); + return !impl->is_disabled ? Poco::Net::SocketAddress(impl->cache_host(host), port) : Poco::Net::SocketAddress(host_and_port); +} + +Poco::Net::SocketAddress DNSCache::resolveAddress(const std::string & host, UInt16 port) +{ + return !impl->is_disabled ? Poco::Net::SocketAddress(impl->cache_host(host), port) : Poco::Net::SocketAddress(host, port); } void DNSCache::drop() @@ -101,6 +110,11 @@ void DNSCache::drop() impl->cache_host.drop(); } +void DNSCache::setDisableFlag(bool is_disabled) +{ + impl->is_disabled = is_disabled; +} + DNSCache::~DNSCache() = default; diff --git a/dbms/src/Common/DNSCache.h b/dbms/src/Common/DNSCache.h index 46722f43a64..6df001f2a11 100644 --- a/dbms/src/Common/DNSCache.h +++ b/dbms/src/Common/DNSCache.h @@ -3,6 +3,7 @@ #include #include #include +#include namespace DB @@ -20,7 +21,12 @@ public: Poco::Net::IPAddress resolveHost(const std::string & host); /// Accepts host names like 'example.com:port' or '127.0.0.1:port' or '[::1]:port' and resolve its IP and port - Poco::Net::SocketAddress resolveHostAndPort(const std::string & host_and_port); + Poco::Net::SocketAddress resolveAddress(const std::string & host_and_port); + + Poco::Net::SocketAddress resolveAddress(const std::string & host, UInt16 port); + + /// Disables caching + void setDisableFlag(bool is_disabled = true); /// Drops all caches void drop(); diff --git a/dbms/src/Common/isLocalAddress.cpp b/dbms/src/Common/isLocalAddress.cpp index 742eac967ba..3e81ecd935c 100644 --- a/dbms/src/Common/isLocalAddress.cpp +++ b/dbms/src/Common/isLocalAddress.cpp @@ -10,7 +10,7 @@ namespace DB { -bool isLocalAddress(const Poco::Net::SocketAddress & address) +bool isLocalAddress(const Poco::Net::IPAddress & address) { static auto interfaces = Poco::Net::NetworkInterface::list(); @@ -21,14 +21,14 @@ bool isLocalAddress(const Poco::Net::SocketAddress & address) * Theoretically, this may not be correct - depends on `route` setting * - through which interface we will actually access the specified address. */ - return interface.address().length() == address.host().length() - && 0 == memcmp(interface.address().addr(), address.host().addr(), address.host().length()); + return interface.address().length() == address.length() + && 0 == memcmp(interface.address().addr(), address.addr(), address.length()); }); } bool isLocalAddress(const Poco::Net::SocketAddress & address, UInt16 clickhouse_port) { - return clickhouse_port == address.port() && isLocalAddress(address); + return clickhouse_port == address.port() && isLocalAddress(address.host()); } diff --git a/dbms/src/Common/isLocalAddress.h b/dbms/src/Common/isLocalAddress.h index 981d343a97f..ffa03977a3f 100644 --- a/dbms/src/Common/isLocalAddress.h +++ b/dbms/src/Common/isLocalAddress.h @@ -1,6 +1,8 @@ #pragma once #include +#include + namespace Poco { @@ -20,10 +22,12 @@ namespace DB * - only the first address is taken for each network interface; * - the routing rules that affect which network interface we go to the specified address are not checked. */ - bool isLocalAddress(const Poco::Net::SocketAddress & address, UInt16 clickhouse_port); + bool isLocalAddress(const Poco::Net::SocketAddress & address, UInt16 clickhouse_port); bool isLocalAddress(const Poco::Net::SocketAddress & address); + bool isLocalAddress(const Poco::Net::IPAddress & address); + /// Returns number of different bytes in hostnames, used for load balancing size_t getHostNameDifference(const std::string & local_hostname, const std::string & host); } diff --git a/dbms/src/Interpreters/Cluster.cpp b/dbms/src/Interpreters/Cluster.cpp index 4f76505fdd9..38d54ecbcc1 100644 --- a/dbms/src/Interpreters/Cluster.cpp +++ b/dbms/src/Interpreters/Cluster.cpp @@ -29,7 +29,7 @@ namespace /// Default shard weight. static constexpr UInt32 default_weight = 1; -inline bool isLocal(const Cluster::Address & address, UInt16 clickhouse_port) +inline bool isLocal(const Cluster::Address & address, const Poco::Net::SocketAddress & resolved_address, UInt16 clickhouse_port) { /// If there is replica, for which: /// - its port is the same that the server is listening; @@ -41,7 +41,7 @@ inline bool isLocal(const Cluster::Address & address, UInt16 clickhouse_port) /// Also, replica is considered non-local, if it has default database set /// (only reason is to avoid query rewrite). - return address.default_database.empty() && isLocalAddress(address.resolved_address, clickhouse_port); + return address.default_database.empty() && isLocalAddress(resolved_address, clickhouse_port); } @@ -56,15 +56,15 @@ Poco::Net::SocketAddress resolveSocketAddress(const String & host, UInt16 port) Cluster::Address::Address(Poco::Util::AbstractConfiguration & config, const String & config_prefix) { - UInt16 clickhouse_port = config.getInt("tcp_port", 0); + UInt16 clickhouse_port = static_cast(config.getInt("tcp_port", 0)); host_name = config.getString(config_prefix + ".host"); port = static_cast(config.getInt(config_prefix + ".port")); - resolved_address = resolveSocketAddress(host_name, port); user = config.getString(config_prefix + ".user", "default"); password = config.getString(config_prefix + ".password", ""); default_database = config.getString(config_prefix + ".default_database", ""); - is_local = isLocal(*this, clickhouse_port); + initially_resolved_address = resolveSocketAddress(host_name, port); + is_local = isLocal(*this, initially_resolved_address, clickhouse_port); secure = config.getBool(config_prefix + ".secure", false) ? Protocol::Secure::Enable : Protocol::Secure::Disable; compression = config.getBool(config_prefix + ".compression", true) ? Protocol::Compression::Enable : Protocol::Compression::Disable; } @@ -74,11 +74,11 @@ Cluster::Address::Address(const String & host_port_, const String & user_, const : user(user_), password(password_) { auto parsed_host_port = parseAddress(host_port_, clickhouse_port); - - resolved_address = resolveSocketAddress(parsed_host_port.first, parsed_host_port.second); host_name = parsed_host_port.first; port = parsed_host_port.second; - is_local = isLocal(*this, clickhouse_port); + + initially_resolved_address = resolveSocketAddress(parsed_host_port.first, parsed_host_port.second); + is_local = isLocal(*this, initially_resolved_address, clickhouse_port); } @@ -113,8 +113,8 @@ String Cluster::Address::toStringFull() const return escapeForFileName(user) + (password.empty() ? "" : (':' + escapeForFileName(password))) + '@' + - escapeForFileName(resolved_address.host().toString()) + ':' + - std::to_string(resolved_address.port()) + + escapeForFileName(initially_resolved_address.host().toString()) + ':' + + std::to_string(initially_resolved_address.port()) + (default_database.empty() ? "" : ('#' + escapeForFileName(default_database))) + ((secure == Protocol::Secure::Enable) ? "+secure" : ""); } @@ -220,7 +220,7 @@ Cluster::Cluster(Poco::Util::AbstractConfiguration & config, const Settings & se { ConnectionPoolPtr pool = std::make_shared( settings.distributed_connections_pool_size, - address.host_name, address.port, address.resolved_address, + address.host_name, address.port, address.default_database, address.user, address.password, ConnectionTimeouts::getTCPTimeoutsWithoutFailover(settings).getSaturated(settings.max_execution_time), "server", address.compression, address.secure); @@ -303,7 +303,7 @@ Cluster::Cluster(Poco::Util::AbstractConfiguration & config, const Settings & se { auto replica_pool = std::make_shared( settings.distributed_connections_pool_size, - replica.host_name, replica.port, replica.resolved_address, + replica.host_name, replica.port, replica.default_database, replica.user, replica.password, ConnectionTimeouts::getTCPTimeoutsWithFailover(settings).getSaturated(settings.max_execution_time), "server", replica.compression, replica.secure); @@ -367,7 +367,7 @@ Cluster::Cluster(const Settings & settings, const std::vector( settings.distributed_connections_pool_size, - replica.host_name, replica.port, replica.resolved_address, + replica.host_name, replica.port, replica.default_database, replica.user, replica.password, ConnectionTimeouts::getTCPTimeoutsWithFailover(settings).getSaturated(settings.max_execution_time), "server", replica.compression, replica.secure); diff --git a/dbms/src/Interpreters/Cluster.h b/dbms/src/Interpreters/Cluster.h index d15b7eb3223..2bf13baa326 100644 --- a/dbms/src/Interpreters/Cluster.h +++ b/dbms/src/Interpreters/Cluster.h @@ -52,13 +52,15 @@ public: * * */ - Poco::Net::SocketAddress resolved_address; + String host_name; UInt16 port; String user; String password; - String default_database; /// this database is selected when no database is specified for Distributed table + /// This database is selected when no database is specified for Distributed table + String default_database; UInt32 replica_num; + /// The locality is determined at the initialization, and is not changed even if DNS is changed bool is_local; Protocol::Compression compression = Protocol::Compression::Enable; Protocol::Secure secure = Protocol::Secure::Disable; @@ -79,6 +81,15 @@ public: /// Retrurns escaped user:password@resolved_host_address:resolved_host_port#default_database String toStringFull() const; + + /// Returns intially resolved address + Poco::Net::SocketAddress getResolvedAddress() const + { + return initially_resolved_address; + } + + private: + Poco::Net::SocketAddress initially_resolved_address; }; using Addresses = std::vector
; diff --git a/dbms/src/Interpreters/ClusterProxy/DescribeStreamFactory.cpp b/dbms/src/Interpreters/ClusterProxy/DescribeStreamFactory.cpp index 8f98e660aca..2638399f8ff 100644 --- a/dbms/src/Interpreters/ClusterProxy/DescribeStreamFactory.cpp +++ b/dbms/src/Interpreters/ClusterProxy/DescribeStreamFactory.cpp @@ -14,7 +14,7 @@ BlockExtraInfo toBlockExtraInfo(const Cluster::Address & address) { BlockExtraInfo block_extra_info; block_extra_info.host = address.host_name; - block_extra_info.resolved_address = address.resolved_address.toString(); + block_extra_info.resolved_address = address.getResolvedAddress().toString(); block_extra_info.port = address.port; block_extra_info.user = address.user; block_extra_info.is_valid = true; diff --git a/dbms/src/Interpreters/DDLWorker.cpp b/dbms/src/Interpreters/DDLWorker.cpp index f9d1d0969e4..8d9b19e433b 100644 --- a/dbms/src/Interpreters/DDLWorker.cpp +++ b/dbms/src/Interpreters/DDLWorker.cpp @@ -94,7 +94,7 @@ struct HostID { try { - return DB::isLocalAddress(Poco::Net::SocketAddress(host_name, port), clickhouse_port); + return DB::isLocalAddress(DNSCache::instance().resolveAddress(host_name, port), clickhouse_port); } catch (const Poco::Net::NetException & e) { @@ -481,7 +481,7 @@ void DDLWorker::parseQueryAndResolveHost(DDLTask & task) { const Cluster::Address & address = shards[shard_num][replica_num]; - if (isLocalAddress(address.resolved_address, context.getTCPPort())) + if (isLocalAddress(address.getResolvedAddress(), context.getTCPPort())) { if (found_via_resolving) { @@ -651,7 +651,7 @@ void DDLWorker::processTaskAlter( /// FIXME: this replica_name could be changed after replica restart Strings replica_names; for (const Cluster::Address & address : task.cluster->getShardsAddresses().at(task.host_shard_num)) - replica_names.emplace_back(address.resolved_address.host().toString()); + replica_names.emplace_back(address.getResolvedAddress().host().toString()); std::sort(replica_names.begin(), replica_names.end()); String shard_node_name; diff --git a/dbms/src/Interpreters/DNSCacheUpdater.h b/dbms/src/Interpreters/DNSCacheUpdater.h index 9c45c84af90..01193185e07 100644 --- a/dbms/src/Interpreters/DNSCacheUpdater.h +++ b/dbms/src/Interpreters/DNSCacheUpdater.h @@ -35,7 +35,7 @@ private: time_t last_update_time = 0; static constexpr size_t min_errors_to_update_cache = 3; - static constexpr time_t min_update_period_seconds = 10; + static constexpr time_t min_update_period_seconds = 45; }; diff --git a/dbms/src/Server/ClusterCopier.cpp b/dbms/src/Server/ClusterCopier.cpp index 4fb8c543eca..67910945173 100644 --- a/dbms/src/Server/ClusterCopier.cpp +++ b/dbms/src/Server/ClusterCopier.cpp @@ -29,6 +29,7 @@ #include #include #include +#include #include #include #include @@ -64,6 +65,7 @@ #include #include #include +#include namespace DB @@ -610,7 +612,7 @@ static ShardPriority getReplicasPriority(const Cluster::Addresses & replicas, co res.is_remote = 1; for (auto & replica : replicas) { - if (isLocalAddress(replica.resolved_address)) + if (isLocalAddress(DNSCache::instance().resolveHost(replica.host_name))) { res.is_remote = 0; break; diff --git a/dbms/src/Server/Server.cpp b/dbms/src/Server/Server.cpp index 86afae947ae..0e8ec48abee 100644 --- a/dbms/src/Server/Server.cpp +++ b/dbms/src/Server/Server.cpp @@ -11,6 +11,7 @@ #include #include #include +#include #include #include #include @@ -325,8 +326,17 @@ int Server::main(const std::vector & /*args*/) global_context->setDDLWorker(std::make_shared(ddl_zookeeper_path, *global_context, &config(), "distributed_ddl")); } - /// Initialize a watcher updating DNS cache in case of network errors - DNSCacheUpdater dns_cache_updater(*global_context); + std::unique_ptr dns_cache_updater; + if (config().has("disable_internal_dns_cache") && config().getInt("disable_internal_dns_cache")) + { + /// Disable DNS caching at all + DNSCache::instance().setDisableFlag(); + } + else + { + /// Initialize a watcher updating DNS cache in case of network errors + dns_cache_updater = std::make_unique(*global_context); + } { Poco::Timespan keep_alive_timeout(config().getUInt("keep_alive_timeout", 10), 0); diff --git a/dbms/src/Server/config.xml b/dbms/src/Server/config.xml index 5506fc055b6..7dd7a00517e 100644 --- a/dbms/src/Server/config.xml +++ b/dbms/src/Server/config.xml @@ -362,4 +362,7 @@ The directory will be created if it doesn't exist. --> /var/lib/clickhouse/format_schemas/ + + + diff --git a/dbms/src/Storages/System/StorageSystemClusters.cpp b/dbms/src/Storages/System/StorageSystemClusters.cpp index 904d22d180e..2093d284416 100644 --- a/dbms/src/Storages/System/StorageSystemClusters.cpp +++ b/dbms/src/Storages/System/StorageSystemClusters.cpp @@ -5,6 +5,7 @@ #include #include #include +#include #include namespace DB @@ -51,7 +52,7 @@ BlockInputStreams StorageSystemClusters::read( res_columns[i++]->insert(static_cast(shard_info.weight)); res_columns[i++]->insert(static_cast(address.replica_num)); res_columns[i++]->insert(address.host_name); - res_columns[i++]->insert(address.resolved_address.host().toString()); + res_columns[i++]->insert(DNSCache::instance().resolveHost(address.host_name).toString()); res_columns[i++]->insert(static_cast(address.port)); res_columns[i++]->insert(static_cast(shard_info.isLocal())); res_columns[i++]->insert(address.user); diff --git a/dbms/tests/integration/test_system_queries/test.py b/dbms/tests/integration/test_system_queries/test.py index 762b70324a7..79c81570dd8 100644 --- a/dbms/tests/integration/test_system_queries/test.py +++ b/dbms/tests/integration/test_system_queries/test.py @@ -75,22 +75,6 @@ def test_DROP_DNS_CACHE(started_cluster): assert TSV(instance.query("SELECT DISTINCT host_name, host_address FROM system.clusters WHERE cluster='lost_host_cluster'")) == TSV("lost_host\t127.0.0.1\n") -def test_automatic_DROP_DNS_CACHE(started_cluster): - instance = cluster.instances['ch1'] - - instance.exec_in_container(['bash', '-c', 'echo 127.255.255.255 lost_host > /etc/hosts'], privileged=True, user='root') - instance.query("SYSTEM DROP DNS CACHE") - - for i in xrange(5): - with pytest.raises(QueryRuntimeException): - instance.query("SELECT * FROM remote('lost_host', 'system', 'one')") - - time.sleep(20) - - # DNS cache should be automatically updated after this delay - instance.query("SELECT * FROM remote('lost_host', 'system', 'one')") - - def test_RELOAD_CONFIG_AND_MACROS(started_cluster): macros = "ro" From 1f05000c2c8b267358cb25ad46ca13b35d10beaa Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Thu, 19 Apr 2018 16:56:14 +0300 Subject: [PATCH 110/231] Better naming. [#CLICKHOUSE-3645] --- dbms/src/Client/Connection.cpp | 6 ++-- .../Common/{DNSCache.cpp => DNSResolver.cpp} | 28 +++++++++---------- dbms/src/Common/{DNSCache.h => DNSResolver.h} | 18 ++++++------ dbms/src/IO/ReadWriteBufferFromHTTP.cpp | 4 +-- dbms/src/Interpreters/Cluster.cpp | 4 +-- dbms/src/Interpreters/Context.cpp | 2 +- dbms/src/Interpreters/DDLWorker.cpp | 4 +-- dbms/src/Interpreters/DNSCacheUpdater.cpp | 4 +-- .../Interpreters/InterpreterSystemQuery.cpp | 4 +-- dbms/src/Server/ClusterCopier.cpp | 4 +-- dbms/src/Server/Server.cpp | 4 +-- .../Storages/System/StorageSystemClusters.cpp | 4 +-- 12 files changed, 43 insertions(+), 43 deletions(-) rename dbms/src/Common/{DNSCache.cpp => DNSResolver.cpp} (71%) rename dbms/src/Common/{DNSCache.h => DNSResolver.h} (64%) diff --git a/dbms/src/Client/Connection.cpp b/dbms/src/Client/Connection.cpp index 76209b4e43f..16f5bde4f71 100644 --- a/dbms/src/Client/Connection.cpp +++ b/dbms/src/Client/Connection.cpp @@ -17,7 +17,7 @@ #include #include #include -#include +#include #include #include @@ -68,7 +68,7 @@ void Connection::connect() socket = std::make_unique(); } - current_resolved_address = DNSCache::instance().resolveAddress(host, port); + current_resolved_address = DNSResolver::instance().resolveAddress(host, port); socket->connect(current_resolved_address, timeouts.connection_timeout); socket->setReceiveTimeout(timeouts.receive_timeout); @@ -471,7 +471,7 @@ Poco::Net::SocketAddress Connection::getResolvedAddress() const if (connected) return current_resolved_address; - return DNSCache::instance().resolveAddress(host, port); + return DNSResolver::instance().resolveAddress(host, port); } diff --git a/dbms/src/Common/DNSCache.cpp b/dbms/src/Common/DNSResolver.cpp similarity index 71% rename from dbms/src/Common/DNSCache.cpp rename to dbms/src/Common/DNSResolver.cpp index 37b03e191b5..e3d9442deea 100644 --- a/dbms/src/Common/DNSCache.cpp +++ b/dbms/src/Common/DNSResolver.cpp @@ -1,4 +1,4 @@ -#include "DNSCache.h" +#include "DNSResolver.h" #include #include #include @@ -75,47 +75,47 @@ static Poco::Net::IPAddress resolveIPAddressImpl(const std::string & host) } -struct DNSCache::Impl +struct DNSResolver::Impl { SimpleCache cache_host; /// If disabled, will not make cache lookups, will resolve addresses manually on each call - std::atomic is_disabled{false}; + std::atomic disable_cache{false}; }; -DNSCache::DNSCache() : impl(std::make_unique()) {} +DNSResolver::DNSResolver() : impl(std::make_unique()) {} -Poco::Net::IPAddress DNSCache::resolveHost(const std::string & host) +Poco::Net::IPAddress DNSResolver::resolveHost(const std::string & host) { - return !impl->is_disabled ? impl->cache_host(host) : resolveIPAddressImpl(host); + return !impl->disable_cache ? impl->cache_host(host) : resolveIPAddressImpl(host); } -Poco::Net::SocketAddress DNSCache::resolveAddress(const std::string & host_and_port) +Poco::Net::SocketAddress DNSResolver::resolveAddress(const std::string & host_and_port) { String host; UInt16 port; splitHostAndPort(host_and_port, host, port); - return !impl->is_disabled ? Poco::Net::SocketAddress(impl->cache_host(host), port) : Poco::Net::SocketAddress(host_and_port); + return !impl->disable_cache ? Poco::Net::SocketAddress(impl->cache_host(host), port) : Poco::Net::SocketAddress(host_and_port); } -Poco::Net::SocketAddress DNSCache::resolveAddress(const std::string & host, UInt16 port) +Poco::Net::SocketAddress DNSResolver::resolveAddress(const std::string & host, UInt16 port) { - return !impl->is_disabled ? Poco::Net::SocketAddress(impl->cache_host(host), port) : Poco::Net::SocketAddress(host, port); + return !impl->disable_cache ? Poco::Net::SocketAddress(impl->cache_host(host), port) : Poco::Net::SocketAddress(host, port); } -void DNSCache::drop() +void DNSResolver::dropCache() { impl->cache_host.drop(); } -void DNSCache::setDisableFlag(bool is_disabled) +void DNSResolver::setDisableCacheFlag(bool is_disabled) { - impl->is_disabled = is_disabled; + impl->disable_cache = is_disabled; } -DNSCache::~DNSCache() = default; +DNSResolver::~DNSResolver() = default; } diff --git a/dbms/src/Common/DNSCache.h b/dbms/src/Common/DNSResolver.h similarity index 64% rename from dbms/src/Common/DNSCache.h rename to dbms/src/Common/DNSResolver.h index 6df001f2a11..fb3892e101f 100644 --- a/dbms/src/Common/DNSCache.h +++ b/dbms/src/Common/DNSResolver.h @@ -9,13 +9,13 @@ namespace DB { -/// A singleton implementing global and permanent DNS cache -/// It could be updated only manually via drop() method -class DNSCache : public ext::singleton +/// A singleton implementing DNS names resolving with optional permanent DNS cache +/// The cache could be updated only manually via drop() method +class DNSResolver : public ext::singleton { public: - DNSCache(const DNSCache &) = delete; + DNSResolver(const DNSResolver &) = delete; /// Accepts host names like 'example.com' or '127.0.0.1' or '::1' and resolve its IP Poco::Net::IPAddress resolveHost(const std::string & host); @@ -26,18 +26,18 @@ public: Poco::Net::SocketAddress resolveAddress(const std::string & host, UInt16 port); /// Disables caching - void setDisableFlag(bool is_disabled = true); + void setDisableCacheFlag(bool is_disabled = true); /// Drops all caches - void drop(); + void dropCache(); - ~DNSCache(); + ~DNSResolver(); protected: - DNSCache(); + DNSResolver(); - friend class ext::singleton; + friend class ext::singleton; struct Impl; std::unique_ptr impl; diff --git a/dbms/src/IO/ReadWriteBufferFromHTTP.cpp b/dbms/src/IO/ReadWriteBufferFromHTTP.cpp index 12954b616ad..ad2240609b4 100644 --- a/dbms/src/IO/ReadWriteBufferFromHTTP.cpp +++ b/dbms/src/IO/ReadWriteBufferFromHTTP.cpp @@ -4,7 +4,7 @@ #include #include #include -#include +#include #include #include #include @@ -43,7 +43,7 @@ ReadWriteBufferFromHTTP::ReadWriteBufferFromHTTP(const Poco::URI & uri, new Poco::Net::HTTPClientSession) } { - session->setHost(DNSCache::instance().resolveHost(uri.getHost()).toString()); + session->setHost(DNSResolver::instance().resolveHost(uri.getHost()).toString()); session->setPort(uri.getPort()); #if POCO_CLICKHOUSE_PATCH || POCO_VERSION >= 0x02000000 diff --git a/dbms/src/Interpreters/Cluster.cpp b/dbms/src/Interpreters/Cluster.cpp index 38d54ecbcc1..3ce6b456748 100644 --- a/dbms/src/Interpreters/Cluster.cpp +++ b/dbms/src/Interpreters/Cluster.cpp @@ -1,5 +1,5 @@ #include -#include +#include #include #include #include @@ -47,7 +47,7 @@ inline bool isLocal(const Cluster::Address & address, const Poco::Net::SocketAdd Poco::Net::SocketAddress resolveSocketAddress(const String & host, UInt16 port) { - return Poco::Net::SocketAddress(DNSCache::instance().resolveHost(host), port); + return Poco::Net::SocketAddress(DNSResolver::instance().resolveHost(host), port); } } diff --git a/dbms/src/Interpreters/Context.cpp b/dbms/src/Interpreters/Context.cpp index 2df56e876fc..fffa43b5dcc 100644 --- a/dbms/src/Interpreters/Context.cpp +++ b/dbms/src/Interpreters/Context.cpp @@ -39,7 +39,7 @@ #include #include #include -#include +#include #include #include #include diff --git a/dbms/src/Interpreters/DDLWorker.cpp b/dbms/src/Interpreters/DDLWorker.cpp index 8d9b19e433b..2c4da5f7f21 100644 --- a/dbms/src/Interpreters/DDLWorker.cpp +++ b/dbms/src/Interpreters/DDLWorker.cpp @@ -16,7 +16,7 @@ #include #include -#include +#include #include #include @@ -94,7 +94,7 @@ struct HostID { try { - return DB::isLocalAddress(DNSCache::instance().resolveAddress(host_name, port), clickhouse_port); + return DB::isLocalAddress(DNSResolver::instance().resolveAddress(host_name, port), clickhouse_port); } catch (const Poco::Net::NetException & e) { diff --git a/dbms/src/Interpreters/DNSCacheUpdater.cpp b/dbms/src/Interpreters/DNSCacheUpdater.cpp index 2dc75d96dc7..1bb34eee63a 100644 --- a/dbms/src/Interpreters/DNSCacheUpdater.cpp +++ b/dbms/src/Interpreters/DNSCacheUpdater.cpp @@ -1,5 +1,5 @@ #include "DNSCacheUpdater.h" -#include +#include #include #include #include @@ -43,7 +43,7 @@ bool DNSCacheUpdater::run() { LOG_INFO(&Poco::Logger::get("DNSCacheUpdater"), "Updating DNS cache"); - DNSCache::instance().drop(); + DNSResolver::instance().dropCache(); context.reloadClusterConfig(); last_num_network_erros = num_current_network_exceptions; diff --git a/dbms/src/Interpreters/InterpreterSystemQuery.cpp b/dbms/src/Interpreters/InterpreterSystemQuery.cpp index d06a540376d..b697bcf6968 100644 --- a/dbms/src/Interpreters/InterpreterSystemQuery.cpp +++ b/dbms/src/Interpreters/InterpreterSystemQuery.cpp @@ -1,5 +1,5 @@ #include -#include +#include #include #include #include @@ -78,7 +78,7 @@ BlockIO InterpreterSystemQuery::execute() throwFromErrno("System call kill(0, SIGKILL) failed", ErrorCodes::CANNOT_KILL); break; case Type::DROP_DNS_CACHE: - DNSCache::instance().drop(); + DNSResolver::instance().dropCache(); /// Reinitialize clusters to update their resolved_addresses system_context.reloadClusterConfig(); break; diff --git a/dbms/src/Server/ClusterCopier.cpp b/dbms/src/Server/ClusterCopier.cpp index 67910945173..c0d9c95b427 100644 --- a/dbms/src/Server/ClusterCopier.cpp +++ b/dbms/src/Server/ClusterCopier.cpp @@ -29,7 +29,7 @@ #include #include #include -#include +#include #include #include #include @@ -612,7 +612,7 @@ static ShardPriority getReplicasPriority(const Cluster::Addresses & replicas, co res.is_remote = 1; for (auto & replica : replicas) { - if (isLocalAddress(DNSCache::instance().resolveHost(replica.host_name))) + if (isLocalAddress(DNSResolver::instance().resolveHost(replica.host_name))) { res.is_remote = 0; break; diff --git a/dbms/src/Server/Server.cpp b/dbms/src/Server/Server.cpp index 0e8ec48abee..918ea19c92e 100644 --- a/dbms/src/Server/Server.cpp +++ b/dbms/src/Server/Server.cpp @@ -11,7 +11,7 @@ #include #include #include -#include +#include #include #include #include @@ -330,7 +330,7 @@ int Server::main(const std::vector & /*args*/) if (config().has("disable_internal_dns_cache") && config().getInt("disable_internal_dns_cache")) { /// Disable DNS caching at all - DNSCache::instance().setDisableFlag(); + DNSResolver::instance().setDisableCacheFlag(); } else { diff --git a/dbms/src/Storages/System/StorageSystemClusters.cpp b/dbms/src/Storages/System/StorageSystemClusters.cpp index 2093d284416..fb5c4e41b82 100644 --- a/dbms/src/Storages/System/StorageSystemClusters.cpp +++ b/dbms/src/Storages/System/StorageSystemClusters.cpp @@ -5,7 +5,7 @@ #include #include #include -#include +#include #include namespace DB @@ -52,7 +52,7 @@ BlockInputStreams StorageSystemClusters::read( res_columns[i++]->insert(static_cast(shard_info.weight)); res_columns[i++]->insert(static_cast(address.replica_num)); res_columns[i++]->insert(address.host_name); - res_columns[i++]->insert(DNSCache::instance().resolveHost(address.host_name).toString()); + res_columns[i++]->insert(DNSResolver::instance().resolveHost(address.host_name).toString()); res_columns[i++]->insert(static_cast(address.port)); res_columns[i++]->insert(static_cast(shard_info.isLocal())); res_columns[i++]->insert(address.user); From 874614996555ae64c7262643d85da7353f3bc916 Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Thu, 19 Apr 2018 22:25:54 +0300 Subject: [PATCH 111/231] More persistent directory names for replicas. [#CLICKHOUSE-2] --- dbms/src/Interpreters/Cluster.cpp | 27 ++++++----- dbms/src/Interpreters/Cluster.h | 2 +- dbms/src/Interpreters/DDLWorker.cpp | 47 ++++++++++++------- ...ptimize_on_nonleader_replica_zookeeper.sql | 1 + 4 files changed, 46 insertions(+), 31 deletions(-) diff --git a/dbms/src/Interpreters/Cluster.cpp b/dbms/src/Interpreters/Cluster.cpp index 3ce6b456748..efa2ad60732 100644 --- a/dbms/src/Interpreters/Cluster.cpp +++ b/dbms/src/Interpreters/Cluster.cpp @@ -44,12 +44,6 @@ inline bool isLocal(const Cluster::Address & address, const Poco::Net::SocketAdd return address.default_database.empty() && isLocalAddress(resolved_address, clickhouse_port); } - -Poco::Net::SocketAddress resolveSocketAddress(const String & host, UInt16 port) -{ - return Poco::Net::SocketAddress(DNSResolver::instance().resolveHost(host), port); -} - } /// Implementation of Cluster::Address class @@ -63,7 +57,7 @@ Cluster::Address::Address(Poco::Util::AbstractConfiguration & config, const Stri user = config.getString(config_prefix + ".user", "default"); password = config.getString(config_prefix + ".password", ""); default_database = config.getString(config_prefix + ".default_database", ""); - initially_resolved_address = resolveSocketAddress(host_name, port); + initially_resolved_address = DNSResolver::instance().resolveAddress(host_name, port); is_local = isLocal(*this, initially_resolved_address, clickhouse_port); secure = config.getBool(config_prefix + ".secure", false) ? Protocol::Secure::Enable : Protocol::Secure::Disable; compression = config.getBool(config_prefix + ".compression", true) ? Protocol::Compression::Enable : Protocol::Compression::Disable; @@ -77,7 +71,7 @@ Cluster::Address::Address(const String & host_port_, const String & user_, const host_name = parsed_host_port.first; port = parsed_host_port.second; - initially_resolved_address = resolveSocketAddress(parsed_host_port.first, parsed_host_port.second); + initially_resolved_address = DNSResolver::instance().resolveAddress(parsed_host_port.first, parsed_host_port.second); is_local = isLocal(*this, initially_resolved_address, clickhouse_port); } @@ -94,7 +88,16 @@ String Cluster::Address::toString(const String & host_name, UInt16 port) String Cluster::Address::readableString() const { - return host_name + ':' + DB::toString(port); + String res; + + /// If it looks like IPv6 address add braces to avoid ambiguity in ipv6_host:port notation + if (host_name.find_first_of(':') != std::string::npos && !host_name.empty() && host_name.back() != ']') + res += '[' + host_name + ']'; + else + res += host_name; + + res += ':' + DB::toString(port); + return res; } void Cluster::Address::fromString(const String & host_port_string, String & host_name, UInt16 & port) @@ -113,8 +116,8 @@ String Cluster::Address::toStringFull() const return escapeForFileName(user) + (password.empty() ? "" : (':' + escapeForFileName(password))) + '@' + - escapeForFileName(initially_resolved_address.host().toString()) + ':' + - std::to_string(initially_resolved_address.port()) + + escapeForFileName(host_name) + ':' + + std::to_string(port) + (default_database.empty() ? "" : ('#' + escapeForFileName(default_database))) + ((secure == Protocol::Secure::Enable) ? "+secure" : ""); } @@ -252,7 +255,7 @@ Cluster::Cluster(Poco::Util::AbstractConfiguration & config, const Settings & se bool internal_replication = config.getBool(partial_prefix + ".internal_replication", false); - /// in case of internal_replication we will be appending names to dir_name_for_internal_replication + /// In case of internal_replication we will be appending names to dir_name_for_internal_replication std::string dir_name_for_internal_replication; auto first = true; diff --git a/dbms/src/Interpreters/Cluster.h b/dbms/src/Interpreters/Cluster.h index 2bf13baa326..cc1f43a05ca 100644 --- a/dbms/src/Interpreters/Cluster.h +++ b/dbms/src/Interpreters/Cluster.h @@ -82,7 +82,7 @@ public: /// Retrurns escaped user:password@resolved_host_address:resolved_host_port#default_database String toStringFull() const; - /// Returns intially resolved address + /// Returns initially resolved address Poco::Net::SocketAddress getResolvedAddress() const { return initially_resolved_address; diff --git a/dbms/src/Interpreters/DDLWorker.cpp b/dbms/src/Interpreters/DDLWorker.cpp index 2c4da5f7f21..dd8d1e7fe66 100644 --- a/dbms/src/Interpreters/DDLWorker.cpp +++ b/dbms/src/Interpreters/DDLWorker.cpp @@ -635,43 +635,51 @@ void DDLWorker::processTaskAlter( if (execute_once_on_replica && !config_is_replicated_shard) { throw Exception("Table " + ast_alter->table + " is replicated, but shard #" + toString(task.host_shard_num + 1) + - " isn't replicated according to its cluster definition", ErrorCodes::INCONSISTENT_CLUSTER_DEFINITION); + " isn't replicated according to its cluster definition." + " Possibly true is forgotten in the cluster config.", + ErrorCodes::INCONSISTENT_CLUSTER_DEFINITION); } - else if (!execute_once_on_replica && config_is_replicated_shard) + if (!execute_once_on_replica && config_is_replicated_shard) { throw Exception("Table " + ast_alter->table + " isn't replicated, but shard #" + toString(task.host_shard_num + 1) + " is replicated according to its cluster definition", ErrorCodes::INCONSISTENT_CLUSTER_DEFINITION); } - if (execute_once_on_replica) + /// Generate unique name for shard node, it will be used to execute the query by only single host + /// Shard node name has format 'replica_name1,replica_name2,...,replica_nameN' + /// Where replica_name is 'replica_config_host_name:replica_port' + auto get_shard_name = [] (const Cluster::Addresses & shard_addresses) { - /// Generate unique name for shard node, it will be used to execute the query by only single host - /// Shard node name has format 'replica_name1,replica_name2,...,replica_nameN' - /// Where replica_name is 'escape(replica_ip_address):replica_port' - /// FIXME: this replica_name could be changed after replica restart Strings replica_names; - for (const Cluster::Address & address : task.cluster->getShardsAddresses().at(task.host_shard_num)) - replica_names.emplace_back(address.getResolvedAddress().host().toString()); + for (const Cluster::Address & address : shard_addresses) + replica_names.emplace_back(address.readableString()); std::sort(replica_names.begin(), replica_names.end()); - String shard_node_name; + String res; for (auto it = replica_names.begin(); it != replica_names.end(); ++it) - shard_node_name += *it + (std::next(it) != replica_names.end() ? "," : ""); + res += *it + (std::next(it) != replica_names.end() ? "," : ""); + return res; + }; + + if (execute_once_on_replica) + { + String shard_node_name = get_shard_name(task.cluster->getShardsAddresses().at(task.host_shard_num)); String shard_path = node_path + "/shards/" + shard_node_name; String is_executed_path = shard_path + "/executed"; zookeeper->createAncestors(shard_path + "/"); - bool alter_executed_by_any_replica = false; + bool is_executed_by_any_replica = false; { auto lock = createSimpleZooKeeperLock(zookeeper, shard_path, "lock", task.host_id_str); pcg64 rng(randomSeed()); - for (int num_tries = 0; num_tries < 10; ++num_tries) + static const size_t max_tries = 20; + for (size_t num_tries = 0; num_tries < max_tries; ++num_tries) { if (zookeeper->exists(is_executed_path)) { - alter_executed_by_any_replica = true; + is_executed_by_any_replica = true; break; } @@ -686,16 +694,19 @@ void DDLWorker::processTaskAlter( zookeeper->create(is_executed_path, task.host_id_str, zkutil::CreateMode::Persistent); lock->unlock(); - alter_executed_by_any_replica = true; + is_executed_by_any_replica = true; break; } - std::this_thread::sleep_for(std::chrono::duration(std::uniform_real_distribution(0, 1)(rng))); + std::this_thread::sleep_for(std::chrono::milliseconds(std::uniform_int_distribution(0, 1000)(rng))); } } - if (!alter_executed_by_any_replica) - task.execution_status = ExecutionStatus(ErrorCodes::NOT_IMPLEMENTED, "Cannot enqueue replicated DDL query"); + if (!is_executed_by_any_replica) + { + task.execution_status = ExecutionStatus(ErrorCodes::NOT_IMPLEMENTED, + "Cannot enqueue replicated DDL query for a replicated shard"); + } } else { diff --git a/dbms/tests/queries/0_stateless/00620_optimize_on_nonleader_replica_zookeeper.sql b/dbms/tests/queries/0_stateless/00620_optimize_on_nonleader_replica_zookeeper.sql index 5e463ede704..e042486bef2 100644 --- a/dbms/tests/queries/0_stateless/00620_optimize_on_nonleader_replica_zookeeper.sql +++ b/dbms/tests/queries/0_stateless/00620_optimize_on_nonleader_replica_zookeeper.sql @@ -1,5 +1,6 @@ DROP TABLE IF EXISTS test.rename1; DROP TABLE IF EXISTS test.rename2; +DROP TABLE IF EXISTS test.rename3; CREATE TABLE test.rename1 (p Int64, i Int64, v UInt64) ENGINE = ReplicatedReplacingMergeTree('/clickhouse/test/tables/rename', '1', v) PARTITION BY p ORDER BY i; CREATE TABLE test.rename2 (p Int64, i Int64, v UInt64) ENGINE = ReplicatedReplacingMergeTree('/clickhouse/test/tables/rename', '2', v) PARTITION BY p ORDER BY i; From 864dc0546b8278f4a1de34d9341b77d54dc6b2ff Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Mon, 23 Apr 2018 17:40:23 +0300 Subject: [PATCH 112/231] Add requested changes. [#CLICKHOUSE-3645] --- dbms/src/Interpreters/DNSCacheUpdater.cpp | 60 ++++++++++--------- dbms/src/Interpreters/DNSCacheUpdater.h | 8 +-- dbms/src/Interpreters/executeQuery.cpp | 2 +- .../MergeTree/BackgroundProcessingPool.cpp | 2 +- 4 files changed, 35 insertions(+), 37 deletions(-) diff --git a/dbms/src/Interpreters/DNSCacheUpdater.cpp b/dbms/src/Interpreters/DNSCacheUpdater.cpp index 1bb34eee63a..16653dff3c8 100644 --- a/dbms/src/Interpreters/DNSCacheUpdater.cpp +++ b/dbms/src/Interpreters/DNSCacheUpdater.cpp @@ -25,6 +25,36 @@ namespace ErrorCodes } +/// Call it inside catch section +/// Returns true if it is a network error +static bool isNetworkError() +{ + try + { + throw; + } + catch (const Exception & e) + { + if (e.code() == ErrorCodes::TIMEOUT_EXCEEDED || e.code() == ErrorCodes::ALL_CONNECTION_TRIES_FAILED) + return true; + } + catch (Poco::Net::DNSException & e) + { + return true; + } + catch (Poco::TimeoutException & e) + { + return true; + } + catch (...) + { + /// Do nothing + } + + return false; +} + + DNSCacheUpdater::DNSCacheUpdater(Context & context_) : context(context_), pool(context_.getBackgroundPool()) { @@ -73,7 +103,7 @@ DNSCacheUpdater::~DNSCacheUpdater() } -bool DNSCacheUpdater::incrementNetworkErrors() +bool DNSCacheUpdater::incrementNetworkErrorEventsIfNeeded() { if (isNetworkError()) { @@ -84,33 +114,5 @@ bool DNSCacheUpdater::incrementNetworkErrors() return false; } -bool DNSCacheUpdater::isNetworkError() -{ - try - { - throw; - } - catch (const Exception & e) - { - if (e.code() == ErrorCodes::TIMEOUT_EXCEEDED || e.code() == ErrorCodes::ALL_CONNECTION_TRIES_FAILED) - return true; - } - catch (Poco::Net::DNSException & e) - { - return true; - } - catch (Poco::TimeoutException & e) - { - return true; - } - catch (...) - { - /// Do nothing - } - - return false; -} - - } diff --git a/dbms/src/Interpreters/DNSCacheUpdater.h b/dbms/src/Interpreters/DNSCacheUpdater.h index 01193185e07..4c1939d2f8e 100644 --- a/dbms/src/Interpreters/DNSCacheUpdater.h +++ b/dbms/src/Interpreters/DNSCacheUpdater.h @@ -15,15 +15,11 @@ class DNSCacheUpdater { public: - DNSCacheUpdater(Context & context); + explicit DNSCacheUpdater(Context & context); ~DNSCacheUpdater(); - /// Call it inside catch section - /// Returns true if it is a network error - static bool isNetworkError(); - /// Checks if it is a network error and increments ProfileEvents::NetworkErrors - static bool incrementNetworkErrors(); + static bool incrementNetworkErrorEventsIfNeeded(); private: bool run(); diff --git a/dbms/src/Interpreters/executeQuery.cpp b/dbms/src/Interpreters/executeQuery.cpp index 5137f103457..4fdd12d9089 100644 --- a/dbms/src/Interpreters/executeQuery.cpp +++ b/dbms/src/Interpreters/executeQuery.cpp @@ -378,7 +378,7 @@ static std::tuple executeQueryImpl( if (!internal) onExceptionBeforeStart(query, context, current_time); - DNSCacheUpdater::incrementNetworkErrors(); + DNSCacheUpdater::incrementNetworkErrorEventsIfNeeded(); throw; } diff --git a/dbms/src/Storages/MergeTree/BackgroundProcessingPool.cpp b/dbms/src/Storages/MergeTree/BackgroundProcessingPool.cpp index c9081324121..bd4a64371c3 100644 --- a/dbms/src/Storages/MergeTree/BackgroundProcessingPool.cpp +++ b/dbms/src/Storages/MergeTree/BackgroundProcessingPool.cpp @@ -181,7 +181,7 @@ void BackgroundProcessingPool::threadFunction() catch (...) { tryLogCurrentException(__PRETTY_FUNCTION__); - DNSCacheUpdater::incrementNetworkErrors(); + DNSCacheUpdater::incrementNetworkErrorEventsIfNeeded(); } if (shutdown) From 845d6372e7d74008366fa999a860c31c85d00297 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Wed, 9 May 2018 21:33:29 +0300 Subject: [PATCH 113/231] Fixed linking of tests #2277 --- dbms/src/Functions/CMakeLists.txt | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/dbms/src/Functions/CMakeLists.txt b/dbms/src/Functions/CMakeLists.txt index 2306f0c109d..1a6ab2caca2 100644 --- a/dbms/src/Functions/CMakeLists.txt +++ b/dbms/src/Functions/CMakeLists.txt @@ -86,7 +86,12 @@ list(REMOVE_ITEM clickhouse_functions_headers IFunction.h FunctionFactory.h Func add_library(clickhouse_functions ${clickhouse_functions_sources}) -target_link_libraries(clickhouse_functions PUBLIC dbms PRIVATE libconsistent-hashing ${FARMHASH_LIBRARIES} ${METROHASH_LIBRARIES}) +if (USE_EMBEDDED_COMPILER) + # It is needed for llvm::sys::Process::FileDescriptorHasColors. + set (CLICKHOUSE_FUNCTIONS_ADDITIONAL_LIBRARIES libtinfo.a) +endif () + +target_link_libraries(clickhouse_functions PUBLIC dbms PRIVATE libconsistent-hashing ${FARMHASH_LIBRARIES} ${METROHASH_LIBRARIES} ${CLICKHOUSE_FUNCTIONS_ADDITIONAL_LIBRARIES}) target_include_directories (clickhouse_functions BEFORE PUBLIC ${ClickHouse_SOURCE_DIR}/contrib/libfarmhash) target_include_directories (clickhouse_functions BEFORE PUBLIC ${ClickHouse_SOURCE_DIR}/contrib/libmetrohash/src) From 4d2989e42f5f2e16835385246b6be509a31e5847 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Wed, 9 May 2018 23:31:03 +0300 Subject: [PATCH 114/231] Added support for bundled LLVM libraries #2277 --- .gitmodules | 3 + cmake/find_llvm.cmake | 131 +++++++------------------- contrib/CMakeLists.txt | 4 + contrib/llvm | 1 + dbms/src/Server/Compiler-7.0.0bundled | 1 + 5 files changed, 42 insertions(+), 98 deletions(-) create mode 160000 contrib/llvm create mode 120000 dbms/src/Server/Compiler-7.0.0bundled diff --git a/.gitmodules b/.gitmodules index a1ba915b91e..1f392b73c83 100644 --- a/.gitmodules +++ b/.gitmodules @@ -34,3 +34,6 @@ [submodule "contrib/boost"] path = contrib/boost url = https://github.com/ClickHouse-Extras/boost.git +[submodule "contrib/llvm"] + path = contrib/llvm + url = https://github.com/ClickHouse-Extras/llvm diff --git a/cmake/find_llvm.cmake b/cmake/find_llvm.cmake index 8a8ad33a38c..f12798449d7 100644 --- a/cmake/find_llvm.cmake +++ b/cmake/find_llvm.cmake @@ -1,107 +1,42 @@ -option (ENABLE_EMBEDDED_COMPILER "Set to TRUE to enable support for 'compile' option for query execution" ${NOT_APPLE}) +option (ENABLE_EMBEDDED_COMPILER "Set to TRUE to enable support for 'compile' option for query execution" 1) +option (USE_INTERNAL_LLVM_LIBRARY "Use bundled or system LLVM library. Default: system library for quicker developer builds." 0) if (ENABLE_EMBEDDED_COMPILER) - # Based on source code of YT. - # Authors: Ivan Puzyrevskiy, Alexey Lukyanchikov, Ruslan Savchenko. - # Find LLVM includes and libraries. - # - # LLVM_VERSION - LLVM version. - # LLVM_INCLUDE_DIRS - Directory containing LLVM headers. - # LLVM_LIBRARY_DIRS - Directory containing LLVM libraries. - # LLVM_CXXFLAGS - C++ compiler flags for files that include LLVM headers. - # LLVM_FOUND - True if LLVM was found. + if (USE_INTERNAL_LLVM_LIBRARY AND NOT EXISTS "${ClickHouse_SOURCE_DIR}/contrib/llvm/llvm/CMakeLists.txt") + message (WARNING "submodule contrib/llvm is missing. to fix try run: \n git submodule update --init --recursive") + set (USE_INTERNAL_LLVM_LIBRARY 0) + endif () - # llvm_map_components_to_libraries - Maps LLVM used components to required libraries. - # Usage: llvm_map_components_to_libraries(REQUIRED_LLVM_LIBRARIES core jit interpreter native ...) + if (NOT USE_INTERNAL_LZ4_LIBRARY) + set (LLVM_PATHS "/usr/local/lib/llvm") - if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang") - set(LLVM_VERSION_POSTFIX "${COMPILER_POSTFIX}" CACHE STRING "") + if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang") + find_package(LLVM CONFIG PATHS ${LLVM_PATHS}) + else () + find_package(LLVM 5 CONFIG PATHS ${LLVM_PATHS}) + endif () + + if (LLVM_FOUND) + # Remove dynamically-linked zlib and libedit from LLVM's dependencies: + set_target_properties(LLVMSupport PROPERTIES INTERFACE_LINK_LIBRARIES "-lpthread;LLVMDemangle") + set_target_properties(LLVMLineEditor PROPERTIES INTERFACE_LINK_LIBRARIES "LLVMSupport") + + option(LLVM_HAS_RTTI "Enable if LLVM was build with RTTI enabled" ON) + set (USE_EMBEDDED_COMPILER 1) + endif() else() - if (ARCH_FREEBSD) - set(LLVM_VERSION_POSTFIX "50" CACHE STRING "") - else() - set(LLVM_VERSION_POSTFIX "-5.0" CACHE STRING "") - endif() - endif() - - find_program(LLVM_CONFIG_EXECUTABLE - NAMES llvm-config${LLVM_VERSION_POSTFIX} llvm-config llvm-config-devel - PATHS $ENV{LLVM_ROOT}/bin) - - mark_as_advanced(LLVM_CONFIG_EXECUTABLE) - - if(NOT LLVM_CONFIG_EXECUTABLE) - message(WARNING "Cannot find LLVM (looking for `llvm-config${LLVM_VERSION_POSTFIX}`, `llvm-config`, `llvm-config-devel`). Please, provide LLVM_ROOT environment variable.") - else() - set(LLVM_FOUND TRUE) - - execute_process( - COMMAND ${LLVM_CONFIG_EXECUTABLE} --version - OUTPUT_VARIABLE LLVM_VERSION - OUTPUT_STRIP_TRAILING_WHITESPACE) - - if(LLVM_VERSION VERSION_LESS "5") - message(FATAL_ERROR "LLVM 5+ is required. You have ${LLVM_VERSION} (${LLVM_CONFIG_EXECUTABLE})") - endif() - - message(STATUS "LLVM config: ${LLVM_CONFIG_EXECUTABLE}; version: ${LLVM_VERSION}") - - execute_process( - COMMAND ${LLVM_CONFIG_EXECUTABLE} --includedir - OUTPUT_VARIABLE LLVM_INCLUDE_DIRS - OUTPUT_STRIP_TRAILING_WHITESPACE) - - execute_process( - COMMAND ${LLVM_CONFIG_EXECUTABLE} --libdir - OUTPUT_VARIABLE LLVM_LIBRARY_DIRS - OUTPUT_STRIP_TRAILING_WHITESPACE) - - execute_process( - COMMAND ${LLVM_CONFIG_EXECUTABLE} --cxxflags - OUTPUT_VARIABLE LLVM_CXXFLAGS - OUTPUT_STRIP_TRAILING_WHITESPACE) - - execute_process( - COMMAND ${LLVM_CONFIG_EXECUTABLE} --targets-built - OUTPUT_VARIABLE LLVM_TARGETS_BUILT - OUTPUT_STRIP_TRAILING_WHITESPACE) - - string(REPLACE " " ";" LLVM_TARGETS_BUILT "${LLVM_TARGETS_BUILT}") - - if (USE_STATIC_LIBRARIES) - set (LLVM_CONFIG_ADD "--link-static") - endif() - - # Get the link libs we need. - function(llvm_map_components_to_libraries RESULT) - execute_process( - COMMAND ${LLVM_CONFIG_EXECUTABLE} ${LLVM_CONFIG_ADD} --libs ${ARGN} - OUTPUT_VARIABLE _tmp - OUTPUT_STRIP_TRAILING_WHITESPACE) - - string(REPLACE " " ";" _libs_module "${_tmp}") - - #message(STATUS "LLVM Libraries for '${ARGN}': ${_libs_module}") - - execute_process( - COMMAND ${LLVM_CONFIG_EXECUTABLE} --system-libs ${ARGN} - OUTPUT_VARIABLE _libs_system - OUTPUT_STRIP_TRAILING_WHITESPACE) - - string(REPLACE "\n" " " _libs_system "${_libs_system}") - string(REPLACE " " " " _libs_system "${_libs_system}") - string(REPLACE " " ";" _libs_system "${_libs_system}") - - set(${RESULT} ${_libs_module} ${_libs_system} PARENT_SCOPE) - endfunction(llvm_map_components_to_libraries) - - message(STATUS "LLVM Include Directory: ${LLVM_INCLUDE_DIRS}") - message(STATUS "LLVM Library Directory: ${LLVM_LIBRARY_DIRS}") - message(STATUS "LLVM C++ Compiler: ${LLVM_CXXFLAGS}") - endif() - - if (LLVM_FOUND AND LLVM_INCLUDE_DIRS AND LLVM_LIBRARY_DIRS) + set (LLVM_FOUND 1) set (USE_EMBEDDED_COMPILER 1) + set (LLVM_VERSION "7.0.0bundled") + set (LLVM_INCLUDE_DIRS ${ClickHouse_SOURCE_DIR}/contrib/llvm/llvm/include ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm/include) + set (LLVM_LIBRARY_DIRS ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm) + endif() + + if (LLVM_FOUND) + message(STATUS "LLVM version: ${LLVM_PACKAGE_VERSION}") + message(STATUS "LLVM include Directory: ${LLVM_INCLUDE_DIRS}") + message(STATUS "LLVM library Directory: ${LLVM_LIBRARY_DIRS}") + message(STATUS "LLVM C++ compiler flags: ${LLVM_CXXFLAGS}") endif() endif() diff --git a/contrib/CMakeLists.txt b/contrib/CMakeLists.txt index 118c7009044..18cdc15b3fd 100644 --- a/contrib/CMakeLists.txt +++ b/contrib/CMakeLists.txt @@ -150,3 +150,7 @@ if (USE_INTERNAL_POCO_LIBRARY) target_include_directories(Crypto PUBLIC ${OPENSSL_INCLUDE_DIR}) endif () endif () + +if (USE_INTERNAL_LLVM_LIBRARY) + add_subdirectory (llvm/llvm) +endif () diff --git a/contrib/llvm b/contrib/llvm new file mode 160000 index 00000000000..6b3975cf38d --- /dev/null +++ b/contrib/llvm @@ -0,0 +1 @@ +Subproject commit 6b3975cf38d5c9436e1311b7e54ad93ef1a9aa9c diff --git a/dbms/src/Server/Compiler-7.0.0bundled b/dbms/src/Server/Compiler-7.0.0bundled new file mode 120000 index 00000000000..eeeb5bbc2c0 --- /dev/null +++ b/dbms/src/Server/Compiler-7.0.0bundled @@ -0,0 +1 @@ +Compiler-7.0.0 \ No newline at end of file From f82ef70b4f469d58ba1ee752d0bac2d454281f01 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Wed, 9 May 2018 23:36:33 +0300 Subject: [PATCH 115/231] Updated submodules #2277 --- dbms/src/Analyzers/tests/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dbms/src/Analyzers/tests/CMakeLists.txt b/dbms/src/Analyzers/tests/CMakeLists.txt index a4f331dbd3a..b1abc236793 100644 --- a/dbms/src/Analyzers/tests/CMakeLists.txt +++ b/dbms/src/Analyzers/tests/CMakeLists.txt @@ -12,7 +12,7 @@ target_link_libraries(type_and_constant_inference clickhouse_storages_system clickhouse_functions clickhouse_aggregate_functions clickhouse_table_functions) add_executable(analyze_result_of_query analyze_result_of_query.cpp) -target_link_libraries(analyze_result_of_query dbms clickhouse_storages_system) +target_link_libraries(analyze_result_of_query dbms clickhouse_storages_system libtinfo.a) add_executable(translate_positional_arguments translate_positional_arguments.cpp) target_link_libraries(translate_positional_arguments dbms) From 1f2011ec14f8015be17a2204bfad22790de2df0a Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Wed, 9 May 2018 23:38:03 +0300 Subject: [PATCH 116/231] Fixed error #2277 --- cmake/find_llvm.cmake | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cmake/find_llvm.cmake b/cmake/find_llvm.cmake index 0148362f32e..50e9d1180b9 100644 --- a/cmake/find_llvm.cmake +++ b/cmake/find_llvm.cmake @@ -7,7 +7,7 @@ if (ENABLE_EMBEDDED_COMPILER) set (USE_INTERNAL_LLVM_LIBRARY 0) endif () - if (NOT USE_INTERNAL_LZ4_LIBRARY) + if (NOT USE_INTERNAL_LLVM_LIBRARY) set (LLVM_PATHS "/usr/local/lib/llvm") if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang") From c926a880a41e2cf2a5d0329a991a766024c4dd4c Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Wed, 9 May 2018 23:38:03 +0300 Subject: [PATCH 117/231] Fixed error #2277 --- cmake/find_llvm.cmake | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cmake/find_llvm.cmake b/cmake/find_llvm.cmake index f12798449d7..6b2408789ec 100644 --- a/cmake/find_llvm.cmake +++ b/cmake/find_llvm.cmake @@ -8,7 +8,7 @@ if (ENABLE_EMBEDDED_COMPILER) set (USE_INTERNAL_LLVM_LIBRARY 0) endif () - if (NOT USE_INTERNAL_LZ4_LIBRARY) + if (NOT USE_INTERNAL_LLVM_LIBRARY) set (LLVM_PATHS "/usr/local/lib/llvm") if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang") From 72ab282bcaa97fd4d0440fff11dd76fafbd828f9 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 00:03:03 +0300 Subject: [PATCH 118/231] Removed hack #2277 --- dbms/src/Interpreters/ExpressionJIT.cpp | 21 --------------------- 1 file changed, 21 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 51e6ca5da86..14e131d731a 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -46,27 +46,6 @@ #pragma GCC diagnostic pop -#if !LLVM_HAS_RTTI - -/** HACK - * Allow to link with LLVM that was compiled without RTTI. - * This is the default option when you build LLVM from sources. - * We define fake symbols for RTTI to help linker. - * This assumes that enabling/disabling RTTI doesn't change memory layout of objects - * in any significant way and it doesn't affect the code that isn't actually using RTTI. - * Proper solution: recompile LLVM with enabled RTTI. - */ -extern "C" -{ - -__attribute__((__weak__)) int _ZTIN4llvm13ErrorInfoBaseE = 0; -__attribute__((__weak__)) int _ZTIN4llvm12MemoryBufferE = 0; - -} - -#endif - - namespace ProfileEvents { extern const Event CompileFunction; From 9ec6bb77acf326cacbdc728fdc24e4a1831b0fce Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 00:07:52 +0300 Subject: [PATCH 119/231] Fixed include paths #2277 --- cmake/find_llvm.cmake | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/cmake/find_llvm.cmake b/cmake/find_llvm.cmake index 50e9d1180b9..9142b77019d 100644 --- a/cmake/find_llvm.cmake +++ b/cmake/find_llvm.cmake @@ -28,7 +28,13 @@ if (ENABLE_EMBEDDED_COMPILER) set (LLVM_FOUND 1) set (USE_EMBEDDED_COMPILER 1) set (LLVM_VERSION "7.0.0bundled") - set (LLVM_INCLUDE_DIRS ${ClickHouse_SOURCE_DIR}/contrib/llvm/llvm/include ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm/include) + set (LLVM_INCLUDE_DIRS + ${ClickHouse_SOURCE_DIR}/contrib/llvm/llvm/include + ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm/include + ${ClickHouse_SOURCE_DIR}/contrib/llvm/clang/include + ${ClickHouse_BINARY_DIR}/contrib/llvm/clang/include + ${ClickHouse_SOURCE_DIR}/contrib/llvm/lld/include + ${ClickHouse_BINARY_DIR}/contrib/llvm/lld/include) set (LLVM_LIBRARY_DIRS ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm) endif() From c8b46d4465bc398d898d62e7c781564437b7e282 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 00:07:52 +0300 Subject: [PATCH 120/231] Fixed include paths #2277 --- cmake/find_llvm.cmake | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/cmake/find_llvm.cmake b/cmake/find_llvm.cmake index 6b2408789ec..3ede3756031 100644 --- a/cmake/find_llvm.cmake +++ b/cmake/find_llvm.cmake @@ -29,7 +29,13 @@ if (ENABLE_EMBEDDED_COMPILER) set (LLVM_FOUND 1) set (USE_EMBEDDED_COMPILER 1) set (LLVM_VERSION "7.0.0bundled") - set (LLVM_INCLUDE_DIRS ${ClickHouse_SOURCE_DIR}/contrib/llvm/llvm/include ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm/include) + set (LLVM_INCLUDE_DIRS + ${ClickHouse_SOURCE_DIR}/contrib/llvm/llvm/include + ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm/include + ${ClickHouse_SOURCE_DIR}/contrib/llvm/clang/include + ${ClickHouse_BINARY_DIR}/contrib/llvm/clang/include + ${ClickHouse_SOURCE_DIR}/contrib/llvm/lld/include + ${ClickHouse_BINARY_DIR}/contrib/llvm/lld/include) set (LLVM_LIBRARY_DIRS ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm) endif() From 10eae3ff6a1bd64f48f6e4a90b15e970b6ebc37d Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 00:12:15 +0300 Subject: [PATCH 121/231] Fixed include paths #2277 --- cmake/find_llvm.cmake | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/cmake/find_llvm.cmake b/cmake/find_llvm.cmake index 9142b77019d..57700df0c0e 100644 --- a/cmake/find_llvm.cmake +++ b/cmake/find_llvm.cmake @@ -33,8 +33,10 @@ if (ENABLE_EMBEDDED_COMPILER) ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm/include ${ClickHouse_SOURCE_DIR}/contrib/llvm/clang/include ${ClickHouse_BINARY_DIR}/contrib/llvm/clang/include + ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm/tools/clang/include ${ClickHouse_SOURCE_DIR}/contrib/llvm/lld/include - ${ClickHouse_BINARY_DIR}/contrib/llvm/lld/include) + ${ClickHouse_BINARY_DIR}/contrib/llvm/lld/include + ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm/tools/lld/include) set (LLVM_LIBRARY_DIRS ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm) endif() From 5197a77782bf2934c8d450d19d9c06c7e21fd3ce Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 00:12:15 +0300 Subject: [PATCH 122/231] Fixed include paths #2277 --- cmake/find_llvm.cmake | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/cmake/find_llvm.cmake b/cmake/find_llvm.cmake index 3ede3756031..029355ea1a1 100644 --- a/cmake/find_llvm.cmake +++ b/cmake/find_llvm.cmake @@ -34,8 +34,10 @@ if (ENABLE_EMBEDDED_COMPILER) ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm/include ${ClickHouse_SOURCE_DIR}/contrib/llvm/clang/include ${ClickHouse_BINARY_DIR}/contrib/llvm/clang/include + ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm/tools/clang/include ${ClickHouse_SOURCE_DIR}/contrib/llvm/lld/include - ${ClickHouse_BINARY_DIR}/contrib/llvm/lld/include) + ${ClickHouse_BINARY_DIR}/contrib/llvm/lld/include + ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm/tools/lld/include) set (LLVM_LIBRARY_DIRS ${ClickHouse_BINARY_DIR}/contrib/llvm/llvm) endif() From a950ef6038de41c28ad025f55018ef1eb700cb5d Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 00:33:11 +0300 Subject: [PATCH 123/231] Updated submodule #2277 --- contrib/llvm | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/llvm b/contrib/llvm index 6b3975cf38d..b3fc4f68d27 160000 --- a/contrib/llvm +++ b/contrib/llvm @@ -1 +1 @@ -Subproject commit 6b3975cf38d5c9436e1311b7e54ad93ef1a9aa9c +Subproject commit b3fc4f68d2721e0ec584c8a1c0b229cc9b7cac55 From e6369d41681d93e3646cc3ee37a7759a0edbe2a4 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 00:46:44 +0300 Subject: [PATCH 124/231] Updated submodule #2277 --- contrib/llvm | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/llvm b/contrib/llvm index b3fc4f68d27..35efe59586d 160000 --- a/contrib/llvm +++ b/contrib/llvm @@ -1 +1 @@ -Subproject commit b3fc4f68d2721e0ec584c8a1c0b229cc9b7cac55 +Subproject commit 35efe59586d017b200e60c7913517a036027d906 From 3679925233bb7dae902c2aa1e03437cdd4ac73c6 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 01:07:40 +0300 Subject: [PATCH 125/231] Updated submodule #2277 --- contrib/llvm | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/llvm b/contrib/llvm index 35efe59586d..010759d2849 160000 --- a/contrib/llvm +++ b/contrib/llvm @@ -1 +1 @@ -Subproject commit 35efe59586d017b200e60c7913517a036027d906 +Subproject commit 010759d2849cda0c8e3d199508fcf45b97ec761d From 3f34eced723970a117b4886de2df036b62d86322 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 01:09:02 +0300 Subject: [PATCH 126/231] Updated submodule #2277 --- contrib/llvm | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/llvm b/contrib/llvm index 010759d2849..84b29e5ad03 160000 --- a/contrib/llvm +++ b/contrib/llvm @@ -1 +1 @@ -Subproject commit 010759d2849cda0c8e3d199508fcf45b97ec761d +Subproject commit 84b29e5ad03195dd11827f4bcec8cfd4afcd4454 From 6f69c41bddb1f7b34cc3a3ed455c2d5faf25d7f1 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 01:29:23 +0300 Subject: [PATCH 127/231] Fixed error #2277 --- dbms/src/Server/Compiler-7.0.0/cc1as_main.cpp | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/dbms/src/Server/Compiler-7.0.0/cc1as_main.cpp b/dbms/src/Server/Compiler-7.0.0/cc1as_main.cpp index 7a467229cd0..ce23422077f 100644 --- a/dbms/src/Server/Compiler-7.0.0/cc1as_main.cpp +++ b/dbms/src/Server/Compiler-7.0.0/cc1as_main.cpp @@ -60,16 +60,6 @@ using namespace llvm; using namespace llvm::opt; -/// Clang 7 with debug variant of libc++ cannot compile itself without this patch. -namespace llvm -{ - inline bool operator<(const StringRef & s, const SubtargetFeatureKV & feature) - { - return s < StringRef(feature.Key); - } -} - - namespace { /// \brief Helper class for representing a single invocation of the assembler. From 2d0e0059f81c3d44333c68506eea4a25770984b4 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 02:07:10 +0300 Subject: [PATCH 128/231] Updated submodule #2277 --- contrib/llvm | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/llvm b/contrib/llvm index 84b29e5ad03..10fce862f82 160000 --- a/contrib/llvm +++ b/contrib/llvm @@ -1 +1 @@ -Subproject commit 84b29e5ad03195dd11827f4bcec8cfd4afcd4454 +Subproject commit 10fce862f8217aea47738fa608a1c277631930dc From 5704de87747810b0e68b653a18d01cf2c76dd656 Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Fri, 20 Apr 2018 18:32:40 +0300 Subject: [PATCH 129/231] Fixed settings passing in clickhouse-local and tmp dirs. [#CLICKHOUSE-3713] --- .../MergeSortingBlockInputStream.cpp | 1 + dbms/src/Interpreters/Aggregator.cpp | 1 + dbms/src/Server/LocalServer.cpp | 61 +++++++++++-------- dbms/src/Server/LocalServer.h | 4 +- ...ge_file_and_clickhouse-local_app.reference | 4 ++ ...5_storage_file_and_clickhouse-local_app.sh | 5 ++ 6 files changed, 50 insertions(+), 26 deletions(-) diff --git a/dbms/src/DataStreams/MergeSortingBlockInputStream.cpp b/dbms/src/DataStreams/MergeSortingBlockInputStream.cpp index 2506810be80..432bb0216c7 100644 --- a/dbms/src/DataStreams/MergeSortingBlockInputStream.cpp +++ b/dbms/src/DataStreams/MergeSortingBlockInputStream.cpp @@ -108,6 +108,7 @@ Block MergeSortingBlockInputStream::readImpl() */ if (max_bytes_before_external_sort && sum_bytes_in_blocks > max_bytes_before_external_sort) { + Poco::File(tmp_path).createDirectories(); temporary_files.emplace_back(new Poco::TemporaryFile(tmp_path)); const std::string & path = temporary_files.back()->path(); WriteBufferFromFile file_buf(path); diff --git a/dbms/src/Interpreters/Aggregator.cpp b/dbms/src/Interpreters/Aggregator.cpp index 0b458de253c..ef6075e5761 100644 --- a/dbms/src/Interpreters/Aggregator.cpp +++ b/dbms/src/Interpreters/Aggregator.cpp @@ -837,6 +837,7 @@ void Aggregator::writeToTemporaryFile(AggregatedDataVariants & data_variants) Stopwatch watch; size_t rows = data_variants.size(); + Poco::File(params.tmp_path).createDirectories(); auto file = std::make_unique(params.tmp_path); const std::string & path = file->path(); WriteBufferFromFile file_buf(path); diff --git a/dbms/src/Server/LocalServer.cpp b/dbms/src/Server/LocalServer.cpp index 035e08c4d3f..90199dc57fd 100644 --- a/dbms/src/Server/LocalServer.cpp +++ b/dbms/src/Server/LocalServer.cpp @@ -154,27 +154,25 @@ void LocalServer::defineOptions(Poco::Util::OptionSet& _options) .binding("help") .callback(Poco::Util::OptionCallback(this, &LocalServer::handleHelp))); - /// These arrays prevent "variable tracking size limit exceeded" compiler notice. - static const char * settings_names[] = { -#define DECLARE_SETTING(TYPE, NAME, DEFAULT, DESCRIPTION) #NAME, - APPLY_FOR_SETTINGS(DECLARE_SETTING) +#define DECLARE_SETTING(TYPE, NAME, DEFAULT, DESCRIPTION) \ + _options.addOption(Poco::Util::Option(#NAME, "", DESCRIPTION).required(false).argument("arg").repeatable(false).binding(#NAME)); + APPLY_FOR_SETTINGS(DECLARE_SETTING); #undef DECLARE_SETTING - nullptr}; - - for (const char ** name = settings_names; *name; ++name) - _options.addOption(Poco::Util::Option(*name, "", "Settings.h").required(false).argument("") - .repeatable(false).binding(*name)); } -void LocalServer::applyOptions() +void LocalServer::applyCmdOptions() { context->setDefaultFormat(config().getString("output-format", config().getString("format", "TSV"))); + applyCmdSettings(*context); +} - /// settings and limits could be specified in config file, but passed settings has higher priority +void LocalServer::applyCmdSettings(Context & context) +{ + /// settings could be specified in config file, but passed settings has higher priority #define EXTRACT_SETTING(TYPE, NAME, DEFAULT, DESCRIPTION) \ - if (config().has(#NAME) && !context->getSettingsRef().NAME.changed) \ - context->setSetting(#NAME, config().getString(#NAME)); + if (config().has(#NAME)) \ + context.setSetting(#NAME, config().getString(#NAME)); APPLY_FOR_SETTINGS(EXTRACT_SETTING) #undef EXTRACT_SETTING } @@ -187,8 +185,8 @@ void LocalServer::displayHelp() helpFormatter.setUsage("[initial table definition] [--query ]"); helpFormatter.setHeader("\n" "clickhouse-local allows to execute SQL queries on your data files via single command line call.\n" - "To do so, intially you need to define your data source and its format.\n" - "After you can execute your SQL queries in the usual manner.\n" + "To do so, initially you need to define your data source and its format.\n" + "After you can execute your SQL queries in an usual manner.\n" "There are two ways to define initial table keeping your data:\n" "either just in first query like this:\n" " CREATE TABLE () ENGINE = File(, );\n" @@ -213,18 +211,23 @@ void LocalServer::handleHelp(const std::string & /*name*/, const std::string & / /// If path is specified and not empty, will try to setup server environment and load existing metadata void LocalServer::tryInitPath() { - if (!config().has("path") || (path = config().getString("path")).empty()) - return; - + path = config().getString("path", ""); Poco::trimInPlace(path); - if (path.empty()) + + if (!path.empty()) + { + if (path.back() != '/') + path += '/'; + + context->setPath(path); return; - if (path.back() != '/') - path += '/'; + } - context->setPath(path); - - StatusFile status{path + "status"}; + /// In case of empty path set paths to helpful directories + std::string cd = Poco::Path::current(); + context->setTemporaryPath(cd + "tmp"); + context->setFlagsPath(cd + "flags"); + context->setUserFilesPath(""); // user's files are everywhere } @@ -258,7 +261,7 @@ try context->setApplicationType(Context::ApplicationType::LOCAL); tryInitPath(); - applyOptions(); + std::optional status; /// Skip temp path installation @@ -307,9 +310,13 @@ try const std::string default_database = "_local"; context->addDatabase(default_database, std::make_shared(default_database)); context->setCurrentDatabase(default_database); + applyCmdOptions(); if (!path.empty()) { + /// Lock path directory before read + status.emplace(context->getPath() + "status"); + LOG_DEBUG(log, "Loading metadata from " << path); loadMetadataSystem(*context); attachSystemTables(); @@ -410,8 +417,12 @@ void LocalServer::processQueries() if (!parse_res.second) throw Exception("Cannot parse and execute the following part of query: " + String(parse_res.first), ErrorCodes::SYNTAX_ERROR); + context->setSessionContext(*context); + context->setQueryContext(*context); + context->setUser("default", "", Poco::Net::SocketAddress{}, ""); context->setCurrentQueryId(""); + applyCmdSettings(*context); for (const auto & query : queries) { diff --git a/dbms/src/Server/LocalServer.h b/dbms/src/Server/LocalServer.h index 5a4bb28233b..828171f755c 100644 --- a/dbms/src/Server/LocalServer.h +++ b/dbms/src/Server/LocalServer.h @@ -3,6 +3,7 @@ #include #include + namespace DB { @@ -34,7 +35,8 @@ private: std::string getInitialCreateTableQuery(); void tryInitPath(); - void applyOptions(); + void applyCmdOptions(); + void applyCmdSettings(Context & context); void attachSystemTables(); void processQueries(); void setupUsers(); diff --git a/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.reference b/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.reference index 6546f7e455d..9e4689a3e61 100644 --- a/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.reference +++ b/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.reference @@ -6,4 +6,8 @@ 0 0 0 0 0 0 +max_rows_in_distinct 33 +max_rows_in_distinct 33 +0 + 1 diff --git a/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh b/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh index 858016efd91..284781ae0d5 100755 --- a/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh +++ b/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh @@ -38,6 +38,11 @@ pack_unpack_compare "SELECT name, is_aggregate FROM system.functions" "name Stri pack_unpack_compare "SELECT name, is_aggregate FROM system.functions" "name String, is_aggregate UInt8" "Native" pack_unpack_compare "SELECT name, is_aggregate FROM system.functions" "name String, is_aggregate UInt8" "TSKV" echo +# Check settings are passed correctly +${CLICKHOUSE_LOCAL} -s --max_rows_in_distinct=33 -q "SELECT name, value FROM system.settings WHERE name = 'max_rows_in_distinct'" +${CLICKHOUSE_LOCAL} -s -q "SET max_rows_in_distinct=33; SELECT name, value FROM system.settings WHERE name = 'max_rows_in_distinct'" +${CLICKHOUSE_LOCAL} -s --max_bytes_before_external_group_by=1 -q "SELECT sum(ignore(*)) FROM (SELECT number, count() FROM numbers(1000) GROUP BY number)" +echo ${CLICKHOUSE_LOCAL} -s -q "CREATE TABLE sophisticated_default ( a UInt8 DEFAULT From b4025e312e4f5fcc96df5c4bc2d10295aee5ef69 Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Fri, 20 Apr 2018 22:31:19 +0300 Subject: [PATCH 130/231] Use neat boost::program_options, more parameters. [#CLICKHOUSE-3713] --- dbms/src/Server/Client.cpp | 6 +- dbms/src/Server/LocalServer.cpp | 336 +++++++++--------- dbms/src/Server/LocalServer.h | 15 +- ...ge_file_and_clickhouse-local_app.reference | 4 + ...5_storage_file_and_clickhouse-local_app.sh | 17 +- 5 files changed, 191 insertions(+), 187 deletions(-) diff --git a/dbms/src/Server/Client.cpp b/dbms/src/Server/Client.cpp index 362fdc2f657..d4c4bfc9043 100644 --- a/dbms/src/Server/Client.cpp +++ b/dbms/src/Server/Client.cpp @@ -1362,10 +1362,12 @@ public: } } + ioctl(0, TIOCGWINSZ, &terminal_size); + #define DECLARE_SETTING(TYPE, NAME, DEFAULT, DESCRIPTION) (#NAME, boost::program_options::value (), DESCRIPTION) /// Main commandline options related to client functionality and all parameters from Settings. - boost::program_options::options_description main_description("Main options"); + boost::program_options::options_description main_description("Main options", terminal_size.ws_col); main_description.add_options() ("help", "produce help message") ("config-file,c", boost::program_options::value(), "config-file path") @@ -1451,7 +1453,7 @@ public: } } - /// Extract settings and limits from the options. + /// Extract settings from the options. #define EXTRACT_SETTING(TYPE, NAME, DEFAULT, DESCRIPTION) \ if (options.count(#NAME)) \ context.setSetting(#NAME, options[#NAME].as()); diff --git a/dbms/src/Server/LocalServer.cpp b/dbms/src/Server/LocalServer.cpp index 90199dc57fd..c1ac861b9bd 100644 --- a/dbms/src/Server/LocalServer.cpp +++ b/dbms/src/Server/LocalServer.cpp @@ -16,6 +16,7 @@ #include #include #include +#include #include #include #include @@ -27,6 +28,8 @@ #include #include #include +#include +#include namespace DB @@ -53,165 +56,26 @@ void LocalServer::initialize(Poco::Util::Application & self) Poco::Util::Application::initialize(self); // Turn off server logging to stderr - if (config().has("silent")) + if (!config().has("verbose")) { Poco::Logger::root().setLevel("none"); Poco::Logger::root().setChannel(Poco::AutoPtr(new Poco::NullChannel())); } } - -void LocalServer::defineOptions(Poco::Util::OptionSet& _options) -{ - Poco::Util::Application::defineOptions (_options); - - _options.addOption( - Poco::Util::Option("config-file", "", "Load configuration from a given file") - .required(false) - .repeatable(false) - .argument("[config.xml]") - .binding("config-file")); - - /// Arguments that define first query creating initial table: - /// (If structure argument is omitted then initial query is not generated) - _options.addOption( - Poco::Util::Option("structure", "S", "Structure of initial table(list columns names with their types)") - .required(false) - .repeatable(false) - .argument("[name Type]") - .binding("table-structure")); - - /// Turn off logging - _options.addOption( - Poco::Util::Option("silent", "s", "Quiet mode, print only errors") - .required(false) - .repeatable(false) - .binding("silent")); - - _options.addOption( - Poco::Util::Option("table", "N", "Name of initial table") - .required(false) - .repeatable(false) - .argument("[table]") - .binding("table-name")); - - _options.addOption( - Poco::Util::Option("file", "f", "Path to file with data of initial table (stdin if not specified)") - .required(false) - .repeatable(false) - .argument(" stdin") - .binding("table-file")); - - _options.addOption( - Poco::Util::Option("input-format", "if", "Input format of initial table data") - .required(false) - .repeatable(false) - .argument("") - .binding("table-data-format")); - - /// List of queries to execute - _options.addOption( - Poco::Util::Option("query", "q", "Queries to execute") - .required(false) - .repeatable(false) - .argument("") - .binding("query")); - - /// Default Output format - _options.addOption( - Poco::Util::Option("output-format", "of", "Default output format") - .required(false) - .repeatable(false) - .argument("[TSV]", true) - .binding("output-format")); - - /// Alias for previous one, required for clickhouse-client compatibility - _options.addOption( - Poco::Util::Option("format", "", "Default output format") - .required(false) - .repeatable(false) - .argument("[TSV]", true) - .binding("format")); - - _options.addOption( - Poco::Util::Option("stacktrace", "", "Print stack traces of exceptions") - .required(false) - .repeatable(false) - .binding("stacktrace")); - - _options.addOption( - Poco::Util::Option("verbose", "", "Print info about execution of queries") - .required(false) - .repeatable(false) - .noArgument() - .binding("verbose")); - - _options.addOption( - Poco::Util::Option("help", "", "Display help information") - .required(false) - .repeatable(false) - .noArgument() - .binding("help") - .callback(Poco::Util::OptionCallback(this, &LocalServer::handleHelp))); - -#define DECLARE_SETTING(TYPE, NAME, DEFAULT, DESCRIPTION) \ - _options.addOption(Poco::Util::Option(#NAME, "", DESCRIPTION).required(false).argument("arg").repeatable(false).binding(#NAME)); - APPLY_FOR_SETTINGS(DECLARE_SETTING); -#undef DECLARE_SETTING -} - - -void LocalServer::applyCmdOptions() -{ - context->setDefaultFormat(config().getString("output-format", config().getString("format", "TSV"))); - applyCmdSettings(*context); -} - void LocalServer::applyCmdSettings(Context & context) { - /// settings could be specified in config file, but passed settings has higher priority #define EXTRACT_SETTING(TYPE, NAME, DEFAULT, DESCRIPTION) \ - if (config().has(#NAME)) \ - context.setSetting(#NAME, config().getString(#NAME)); + if (cmd_settings.NAME.changed) \ + context.getSettingsRef().NAME = cmd_settings.NAME; APPLY_FOR_SETTINGS(EXTRACT_SETTING) #undef EXTRACT_SETTING } - -void LocalServer::displayHelp() -{ - Poco::Util::HelpFormatter helpFormatter(options()); - helpFormatter.setCommand(commandName()); - helpFormatter.setUsage("[initial table definition] [--query ]"); - helpFormatter.setHeader("\n" - "clickhouse-local allows to execute SQL queries on your data files via single command line call.\n" - "To do so, initially you need to define your data source and its format.\n" - "After you can execute your SQL queries in an usual manner.\n" - "There are two ways to define initial table keeping your data:\n" - "either just in first query like this:\n" - " CREATE TABLE
() ENGINE = File(, );\n" - "either through corresponding command line parameters." - ); - helpFormatter.setWidth(132); /// 80 is ugly due to wide settings params - - helpFormatter.format(std::cerr); - std::cerr << "Example printing memory used by each Unix user:\n" - "ps aux | tail -n +2 | awk '{ printf(\"%s\\t%s\\n\", $1, $4) }' | " - "clickhouse-local -S \"user String, mem Float64\" -q \"SELECT user, round(sum(mem), 2) as memTotal FROM table GROUP BY user ORDER BY memTotal DESC FORMAT Pretty\"\n"; -} - - -void LocalServer::handleHelp(const std::string & /*name*/, const std::string & /*value*/) -{ - displayHelp(); - stopOptionsProcessing(); -} - - /// If path is specified and not empty, will try to setup server environment and load existing metadata void LocalServer::tryInitPath() { - path = config().getString("path", ""); + std::string path = config().getString("path", ""); Poco::trimInPlace(path); if (!path.empty()) @@ -238,11 +102,8 @@ try if (!config().has("query") && !config().has("table-structure")) /// Nothing to process { - if (!config().hasOption("help")) - { + if (!config().hasOption("silent")) std::cerr << "There are no queries to process." << std::endl; - displayHelp(); - } return Application::EXIT_OK; } @@ -307,17 +168,17 @@ try * Otherwise, metadata of temporary File(format, EXPLICIT_PATH) tables will pollute metadata/ directory; * if such tables will not be dropped, clickhouse-server will not be able to load them due to security reasons. */ - const std::string default_database = "_local"; + std::string default_database = config().getString("default_database", "_local"); context->addDatabase(default_database, std::make_shared(default_database)); context->setCurrentDatabase(default_database); - applyCmdOptions(); + applyCmdSettings(*context); - if (!path.empty()) + if (!context->getPath().empty()) { /// Lock path directory before read status.emplace(context->getPath() + "status"); - LOG_DEBUG(log, "Loading metadata from " << path); + LOG_DEBUG(log, "Loading metadata from " << context->getPath()); loadMetadataSystem(*context); attachSystemTables(); loadMetadata(*context); @@ -337,20 +198,8 @@ try } catch (const Exception & e) { - bool print_stack_trace = config().has("stacktrace"); - - std::string text = e.displayText(); - - auto embedded_stack_trace_pos = text.find("Stack trace"); - if (std::string::npos != embedded_stack_trace_pos && !print_stack_trace) - text.resize(embedded_stack_trace_pos); - - std::cerr << "Code: " << e.code() << ". " << text << std::endl << std::endl; - - if (print_stack_trace && std::string::npos == embedded_stack_trace_pos) - { - std::cerr << "Stack trace:" << std::endl << e.getStackTrace().toString(); - } + if (!config().hasOption("silent")) + std::cerr << getCurrentExceptionMessage(config().hasOption("stacktrace")); /// If exception code isn't zero, we should return non-zero return code anyway. return e.code() ? e.code() : -1; @@ -404,13 +253,9 @@ void LocalServer::attachSystemTables() void LocalServer::processQueries() { - Logger * log = &logger(); - String initial_create_query = getInitialCreateTableQuery(); String queries_str = initial_create_query + config().getString("query"); - bool verbose = config().hasOption("verbose"); - std::vector queries; auto parse_res = splitMultipartQuery(queries_str, queries); @@ -424,16 +269,36 @@ void LocalServer::processQueries() context->setCurrentQueryId(""); applyCmdSettings(*context); + bool echo_query = config().hasOption("echo") || config().hasOption("verbose"); + std::exception_ptr exception; + for (const auto & query : queries) { ReadBufferFromString read_buf(query); WriteBufferFromFileDescriptor write_buf(STDOUT_FILENO); - if (verbose) - LOG_INFO(log, "Executing query: " << query); + if (echo_query) + std::cerr << query << "\n"; - executeQuery(read_buf, write_buf, /* allow_into_outfile = */ true, *context, {}); + try + { + executeQuery(read_buf, write_buf, /* allow_into_outfile = */ true, *context, {}); + } + catch (...) + { + if (!config().hasOption("ignore-error")) + throw; + + if (!exception) + exception = std::current_exception(); + + if (!config().has("silent")) + std::cerr << getCurrentExceptionMessage(config().hasOption("stacktrace")); + } } + + if (exception) + std::rethrow_exception(exception); } static const char * minimal_default_user_xml = @@ -488,6 +353,133 @@ void LocalServer::setupUsers() throw Exception("Can't load config for users", ErrorCodes::CANNOT_LOAD_CONFIG); } +static void showClientVersion() +{ + std::cout << "ClickHouse client version " << DBMS_VERSION_MAJOR + << "." << DBMS_VERSION_MINOR + << "." << ClickHouseRevision::get() + << "." << std::endl; +} + +std::string LocalServer::getHelpHeader() const +{ + return + "usage: clickhouse-local [initial table definition] [--query ]\n" + + "clickhouse-local allows to execute SQL queries on your data files via single command line call." + " To do so, initially you need to define your data source and its format." + " After you can execute your SQL queries in usual manner.\n" + + "There are two ways to define initial table keeping your data." + " Either just in first query like this:\n" + " CREATE TABLE
() ENGINE = File(, );\n" + "Either through corresponding command line parameters --table --structure --input-format and --file."; +} + +std::string LocalServer::getHelpFooter() const +{ + return + "Example printing memory used by each Unix user:\n" + "ps aux | tail -n +2 | awk '{ printf(\"%s\\t%s\\n\", $1, $4) }' | " + "clickhouse-local -S \"user String, mem Float64\" -q" + " \"SELECT user, round(sum(mem), 2) as mem_total FROM table GROUP BY user ORDER" + " BY mem_total DESC FORMAT PrettyCompact\""; +} + +void LocalServer::init(int argc, char ** argv) +{ + /// Don't parse options with Poco library, we prefer neat boost::program_options + stopOptionsProcessing(); + + winsize terminal_size{}; + ioctl(0, TIOCGWINSZ, &terminal_size); + + namespace po = boost::program_options; + +#define DECLARE_SETTING(TYPE, NAME, DEFAULT, DESCRIPTION) (#NAME, po::value (), DESCRIPTION) + po::options_description description("Main options", terminal_size.ws_col); + description.add_options() + ("help", "produce help message") + ("config-file,c", po::value(), "config-file path") + ("query,q", po::value(), "query") + ("database,d", po::value(), "database") + + ("table,N", po::value(), "name of the initial table") + /// If structure argument is omitted then initial query is not generated + ("structure,S", po::value(), "structure of the initial table (list of column and type names)") + ("file,f", po::value(), "path to file with data of the initial table (stdin if not specified)") + ("input-format", po::value(), "input format of the initial table data") + ("format,f", po::value(), "default output format (clickhouse-client compatibility)") + ("output-format", po::value(), "default output format") + + ("silent,s", "quiet mode, do not print errors") + ("stacktrace", "print stack traces of exceptions") + ("echo", "print query before execution") + ("verbose", "print query and other debugging info") + ("ignore-error", "do not stop processing if a query failed") + ("version,V", "print version information and exit") + APPLY_FOR_SETTINGS(DECLARE_SETTING); +#undef DECLARE_SETTING + + /// Parse main commandline options. + po::parsed_options parsed = po::command_line_parser(argc, argv).options(description).run(); + po::variables_map options; + po::store(parsed, options); + + if (options.count("version") || options.count("V")) + { + showClientVersion(); + exit(0); + } + + if (options.count("help")) + { + std::cout << getHelpHeader() << "\n"; + std::cout << description << "\n"; + std::cout << getHelpFooter() << "\n"; + exit(0); + } + + /// Extract settings and limits from the options. +#define EXTRACT_SETTING(TYPE, NAME, DEFAULT, DESCRIPTION) \ + if (options.count(#NAME)) \ + cmd_settings.set(#NAME, options[#NAME].as()); + APPLY_FOR_SETTINGS(EXTRACT_SETTING) +#undef EXTRACT_SETTING + + /// Save received data into the internal config. + if (options.count("config-file")) + config().setString("config-file", options["config-file"].as()); + if (options.count("query")) + config().setString("query", options["query"].as()); + if (options.count("database")) + config().setString("default_database", options["database"].as()); + + if (options.count("table")) + config().setString("table-name", options["table"].as()); + if (options.count("file")) + config().setString("table-file", options["file"].as()); + if (options.count("structure")) + config().setString("table-structure", options["structure"].as()); + if (options.count("input-format")) + config().setString("table-data-format", options["input-format"].as()); + if (options.count("format")) + config().setString("format", options["format"].as()); + if (options.count("output-format")) + config().setString("output-format", options["output-format"].as()); + + if (options.count("silent")) + config().setBool("silent", true); + if (options.count("stacktrace")) + config().setBool("stacktrace", true); + if (options.count("echo")) + config().setBool("echo", true); + if (options.count("verbose")) + config().setBool("verbose", true); + if (options.count("ignore-error")) + config().setBool("ignore-error", true); +} + } int mainEntryClickHouseLocal(int argc, char ** argv) diff --git a/dbms/src/Server/LocalServer.h b/dbms/src/Server/LocalServer.h index 828171f755c..5aee2739823 100644 --- a/dbms/src/Server/LocalServer.h +++ b/dbms/src/Server/LocalServer.h @@ -1,5 +1,6 @@ #pragma once +#include #include #include @@ -20,10 +21,10 @@ public: void initialize(Poco::Util::Application & self) override; - void defineOptions(Poco::Util::OptionSet& _options) override; - int main(const std::vector & args) override; + void init(int argc, char ** argv); + ~LocalServer(); private: @@ -35,18 +36,20 @@ private: std::string getInitialCreateTableQuery(); void tryInitPath(); - void applyCmdOptions(); void applyCmdSettings(Context & context); void attachSystemTables(); void processQueries(); void setupUsers(); - void displayHelp(); - void handleHelp(const std::string & name, const std::string & value); + + std::string getHelpHeader() const; + std::string getHelpFooter() const; protected: std::unique_ptr context; - std::string path; + + /// Settings specified via command line args + Settings cmd_settings; }; } diff --git a/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.reference b/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.reference index 9e4689a3e61..cbc42e98660 100644 --- a/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.reference +++ b/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.reference @@ -10,4 +10,8 @@ max_rows_in_distinct 33 max_rows_in_distinct 33 0 +SELECT nothing_to_do(); +SELECT 42; +42 + 1 diff --git a/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh b/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh index 284781ae0d5..4eff70f62d6 100755 --- a/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh +++ b/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh @@ -20,8 +20,8 @@ function pack_unpack_compare() local res_db_file=$(${CLICKHOUSE_CLIENT} --max_threads=1 --query "SELECT $TABLE_HASH FROM test.buf_file") ${CLICKHOUSE_CLIENT} --max_threads=1 --query "SELECT * FROM test.buf FORMAT $3" > "$buf_file" - local res_ch_local1=$(${CLICKHOUSE_LOCAL} -s --structure "$2" --file "$buf_file" --table "my super table" --input-format "$3" --output-format TabSeparated --query "SELECT $TABLE_HASH FROM \`my super table\`") - local res_ch_local2=$(${CLICKHOUSE_LOCAL} -s --structure "$2" --table "my super table" --input-format "$3" --output-format TabSeparated --query "SELECT $TABLE_HASH FROM \`my super table\`" < "$buf_file") + local res_ch_local1=$(${CLICKHOUSE_LOCAL} --structure "$2" --file "$buf_file" --table "my super table" --input-format "$3" --output-format TabSeparated --query "SELECT $TABLE_HASH FROM \`my super table\`") + local res_ch_local2=$(${CLICKHOUSE_LOCAL} --structure "$2" --table "my super table" --input-format "$3" --output-format TabSeparated --query "SELECT $TABLE_HASH FROM \`my super table\`" < "$buf_file") ${CLICKHOUSE_CLIENT} --query "DROP TABLE IF EXISTS test.buf" ${CLICKHOUSE_CLIENT} --query "DROP TABLE IF EXISTS test.buf_file" @@ -39,11 +39,14 @@ pack_unpack_compare "SELECT name, is_aggregate FROM system.functions" "name Stri pack_unpack_compare "SELECT name, is_aggregate FROM system.functions" "name String, is_aggregate UInt8" "TSKV" echo # Check settings are passed correctly -${CLICKHOUSE_LOCAL} -s --max_rows_in_distinct=33 -q "SELECT name, value FROM system.settings WHERE name = 'max_rows_in_distinct'" -${CLICKHOUSE_LOCAL} -s -q "SET max_rows_in_distinct=33; SELECT name, value FROM system.settings WHERE name = 'max_rows_in_distinct'" -${CLICKHOUSE_LOCAL} -s --max_bytes_before_external_group_by=1 -q "SELECT sum(ignore(*)) FROM (SELECT number, count() FROM numbers(1000) GROUP BY number)" +${CLICKHOUSE_LOCAL} --max_rows_in_distinct=33 -q "SELECT name, value FROM system.settings WHERE name = 'max_rows_in_distinct'" +${CLICKHOUSE_LOCAL} -q "SET max_rows_in_distinct=33; SELECT name, value FROM system.settings WHERE name = 'max_rows_in_distinct'" +${CLICKHOUSE_LOCAL} --max_bytes_before_external_group_by=1 --max_block_size=10 -q "SELECT sum(ignore(*)) FROM (SELECT number, count() FROM numbers(1000) GROUP BY number)" echo -${CLICKHOUSE_LOCAL} -s -q "CREATE TABLE sophisticated_default +# Check exta options +(${CLICKHOUSE_LOCAL} --ignore-error --echo --silent -q "SELECT nothing_to_do();SELECT 42;" 2>&1 && echo "Wrong RC") || true +echo +${CLICKHOUSE_LOCAL} -q "CREATE TABLE sophisticated_default ( a UInt8 DEFAULT ( @@ -57,4 +60,4 @@ ${CLICKHOUSE_LOCAL} -s -q "CREATE TABLE sophisticated_default ) ENGINE = Memory; SELECT count() FROM system.tables WHERE name='sophisticated_default';" # Help is not skipped -[[ `${CLICKHOUSE_LOCAL} -s --help 2>&1 | wc -l` > 100 ]] \ No newline at end of file +[[ `${CLICKHOUSE_LOCAL} --help 2>&1 | wc -l` > 100 ]] \ No newline at end of file From 990bcbb00781bacca78f984095617f82d7395554 Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Tue, 24 Apr 2018 13:40:03 +0300 Subject: [PATCH 131/231] Add test for --help. [#CLICKHOUSE-3713] --- ...385_storage_file_and_clickhouse-local_app.sh | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh b/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh index 4eff70f62d6..7dad87341e4 100755 --- a/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh +++ b/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh @@ -60,4 +60,19 @@ ${CLICKHOUSE_LOCAL} -q "CREATE TABLE sophisticated_default ) ENGINE = Memory; SELECT count() FROM system.tables WHERE name='sophisticated_default';" # Help is not skipped -[[ `${CLICKHOUSE_LOCAL} --help 2>&1 | wc -l` > 100 ]] \ No newline at end of file +[[ `${CLICKHOUSE_LOCAL} --help | wc -l` > 100 ]] + +# Check that help width is adaptive +stty cols 99999 +rows1=`${CLICKHOUSE_LOCAL} --help | wc -l` +stty cols 80 +rows2=`${CLICKHOUSE_LOCAL} --help | wc -l` +[[ $rows1 < $rows2 ]] + +stty cols 99999 +rows1=`${CLICKHOUSE_CLIENT} --help | wc -l` +stty cols 80 +rows2=`${CLICKHOUSE_CLIENT} --help | wc -l` +[[ $rows1 < $rows2 ]] + +shopt -s checkwinsize || true From 01beee93ee947f05a360afc69a09dc32ec51de8a Mon Sep 17 00:00:00 2001 From: BayoNet Date: Wed, 9 May 2018 14:12:15 +0300 Subject: [PATCH 132/231] Editing of external contributions in the documentation. --- docs/en/operations/settings/settings.md | 2 ++ docs/ru/interfaces/third-party_gui.md | 18 ++++++++++++++++-- docs/ru/operations/access_rights.md | 14 +++++++------- docs/ru/operations/quotas.md | 2 ++ docs/ru/operations/settings/settings.md | 2 ++ .../operations/settings/settings_profiles.md | 2 ++ 6 files changed, 31 insertions(+), 9 deletions(-) diff --git a/docs/en/operations/settings/settings.md b/docs/en/operations/settings/settings.md index 8768bf89b2f..79d84f2f9e8 100644 --- a/docs/en/operations/settings/settings.md +++ b/docs/en/operations/settings/settings.md @@ -339,6 +339,8 @@ It works for JSONEachRow and TSKV formats. If the value is true, integers appear in quotes when using JSON\* Int64 and UInt64 formats (for compatibility with most JavaScript implementations); otherwise, integers are output without the quotes. + + ## format_csv_delimiter The character to be considered as a delimiter in CSV data. By default, `,`. diff --git a/docs/ru/interfaces/third-party_gui.md b/docs/ru/interfaces/third-party_gui.md index 85b38412228..676b58a7ba0 100644 --- a/docs/ru/interfaces/third-party_gui.md +++ b/docs/ru/interfaces/third-party_gui.md @@ -4,7 +4,8 @@ Веб-интерфейс для ClickHouse в проекте [Tabix](https://github.com/tabixio/tabix). -Основные особенности: +Основные возможности: + - Работает с ClickHouse напрямую из браузера, без необходимости установки дополнительного ПО. - Редактор запросов с подсветкой синтаксиса. - Автодополнение команд. @@ -15,4 +16,17 @@ ## HouseOps -[HouseOps](https://github.com/HouseOps/HouseOps) is a unique Desktop ClickHouse Ops UI / IDE for OSX, Linux and Windows. +[HouseOps](https://github.com/HouseOps/HouseOps) — UI/IDE для OSX, Linux и Windows. + +Основные возможности: + +- Создание запросов. + +Планируется разработка следующих возможностей: + +- Управление базами. +- Управление пользователями. +- Управление кластером. +- Анализ данных в режиме реального времени. +- Мониторинг кластера. +- Мониторинг реплицированных и Kafka таблиц. diff --git a/docs/ru/operations/access_rights.md b/docs/ru/operations/access_rights.md index 54809b27ffe..2e5fac14200 100644 --- a/docs/ru/operations/access_rights.md +++ b/docs/ru/operations/access_rights.md @@ -2,7 +2,7 @@ Пользователи и права доступа настраиваются в конфиге пользователей. Обычно это `users.xml`. -Пользователи прописаны в секции users. Рассмотрим фрагмент файла `users.xml`: +Пользователи прописаны в секции `users`. Рассмотрим фрагмент файла `users.xml`: ```xml @@ -67,7 +67,7 @@ Пароль указывается либо в открытом виде (не рекомендуется), либо в виде SHA-256. Хэш не содержит соль. В связи с этим, не следует рассматривать такие пароли, как защиту от потенциального злоумышленника. Скорее, они нужны для защиты от сотрудников. -Указывается список сетей, из которых разрешён доступ. В этом примере, список сетей для обеих пользователей, загружается из отдельного файла (/etc/metrika.xml), содержащего подстановку networks. Вот его фрагмент: +Указывается список сетей, из которых разрешён доступ. В этом примере, список сетей для обеих пользователей, загружается из отдельного файла (`/etc/metrika.xml`), содержащего подстановку `networks`. Вот его фрагмент: ```xml @@ -81,17 +81,17 @@ ``` -Можно было бы указать этот список сетей непосредственно в users.xml, или в файле в директории users.d (подробнее смотрите раздел "Конфигурационные файлы"). +Можно было бы указать этот список сетей непосредственно в `users.xml`, или в файле в директории `users.d` (подробнее смотрите раздел "[Конфигурационные файлы](configuration_files.md#configuration_files)"). В конфиге приведён комментарий, указывающий, как можно открыть доступ отовсюду. -Для продакшен использования, указывайте только элементы вида ip (IP-адреса и их маски), так как использование host и host_regexp может вызывать лишние задержки. +Для продакшен использования, указывайте только элементы вида `ip` (IP-адреса и их маски), так как использование `host` и `host_regexp` может вызывать лишние задержки. -Далее указывается используемый профиль настроек пользователя (смотрите раздел "Профили настроек"). Вы можете указать профиль по умолчанию - `default`. Профиль может называться как угодно; один и тот же профиль может быть указан для разных пользователей. Наиболее важная вещь, которую вы можете прописать в профиле настроек - настройку readonly, равную 1, что обеспечивает доступ только на чтение. +Далее указывается используемый профиль настроек пользователя (смотрите раздел "[Профили настроек](settings/settings_profiles.md#settings_profiles)"). Вы можете указать профиль по умолчанию - `default`. Профиль может называться как угодно; один и тот же профиль может быть указан для разных пользователей. Наиболее важная вещь, которую вы можете прописать в профиле настроек `readonly=1`, что обеспечивает доступ только на чтение. -Затем указывается используемая квота (смотрите раздел "Квоты"). Вы можете указать квоту по умолчанию - `default`. Она настроена в конфиге по умолчанию так, что только считает использование ресурсов, но никак их не ограничивает. Квота может называться как угодно; одна и та же квота может быть указана для разных пользователей - в этом случае, подсчёт использования ресурсов делается для каждого пользователя по отдельности. +Затем указывается используемая квота (смотрите раздел "[Квоты](quotas.md#quotas)"). Вы можете указать квоту по умолчанию — `default`. Она настроена в конфиге по умолчанию так, что только считает использование ресурсов, но никак их не ограничивает. Квота может называться как угодно. Одна и та же квота может быть указана для разных пользователей, в этом случае подсчёт использования ресурсов делается для каждого пользователя по отдельности. -Также в необязательном разделе `` можно указать перечень баз, к которым у пользователя будет доступ. По умолчанию пользователю доступны все базы. Можно указать базу данных `default`, в этом случае пользователь получит доступ к базе данных по умолчанию. +Также, в необязательном разделе `` можно указать перечень баз, к которым у пользователя будет доступ. По умолчанию пользователю доступны все базы. Можно указать базу данных `default`, в этом случае пользователь получит доступ к базе данных по умолчанию. Доступ к БД `system` всегда считается разрешённым (так как эта БД используется для выполнения запросов). diff --git a/docs/ru/operations/quotas.md b/docs/ru/operations/quotas.md index a084017b2ca..1a56ff9fe62 100644 --- a/docs/ru/operations/quotas.md +++ b/docs/ru/operations/quotas.md @@ -1,3 +1,5 @@ + + # Квоты Квоты позволяют ограничить использование ресурсов за некоторый интервал времени, или просто подсчитывать использование ресурсов. diff --git a/docs/ru/operations/settings/settings.md b/docs/ru/operations/settings/settings.md index 0e1752f49a7..69fd8ab3978 100644 --- a/docs/ru/operations/settings/settings.md +++ b/docs/ru/operations/settings/settings.md @@ -336,6 +336,8 @@ ClickHouse применяет настройку в том случае, ког Если значение истинно, то при использовании JSON\* форматов UInt64 и Int64 числа выводятся в кавычках (из соображений совместимости с большинством реализаций JavaScript), иначе - без кавычек. + + ## format_csv_delimiter Символ, интерпретируемый как разделитель в данных формата CSV. По умолчанию — `,`. diff --git a/docs/ru/operations/settings/settings_profiles.md b/docs/ru/operations/settings/settings_profiles.md index de41eb6666d..8e30d76107e 100644 --- a/docs/ru/operations/settings/settings_profiles.md +++ b/docs/ru/operations/settings/settings_profiles.md @@ -1,3 +1,5 @@ + + # Профили настроек Профили настроек - это множество настроек, сгруппированных под одним именем. Для каждого пользователя ClickHouse указывается некоторый профиль. From 0db2e5565f68aa9f066f79f475667ad2fcdd8c63 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 02:37:31 +0300 Subject: [PATCH 133/231] Updated submodule #2277 --- contrib/llvm | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/llvm b/contrib/llvm index 10fce862f82..5618c710d9d 160000 --- a/contrib/llvm +++ b/contrib/llvm @@ -1 +1 @@ -Subproject commit 10fce862f8217aea47738fa608a1c277631930dc +Subproject commit 5618c710d9d5cc17d01bac3200340563ecd816ae From 12905f5c6f621e7e1051e818f04bb4d56cbf06bb Mon Sep 17 00:00:00 2001 From: proller Date: Thu, 10 May 2018 15:31:30 +0300 Subject: [PATCH 134/231] Try fix travis (#2340) --- utils/travis/pbuilder.sh | 2 ++ 1 file changed, 2 insertions(+) diff --git a/utils/travis/pbuilder.sh b/utils/travis/pbuilder.sh index f5a3ee6c14a..dee1176d840 100755 --- a/utils/travis/pbuilder.sh +++ b/utils/travis/pbuilder.sh @@ -28,6 +28,8 @@ env TEST_RUN=${TEST_RUN=1} \ `# Use all possible contrib libs from system` \ `# psmisc - killall` \ EXTRAPACKAGES="psmisc clang-5.0 lld-5.0 liblld-5.0-dev libclang-5.0-dev liblld-5.0 libc++abi-dev libc++-dev libboost-program-options-dev libboost-system-dev libboost-filesystem-dev libboost-thread-dev zlib1g-dev liblz4-dev libdouble-conversion-dev libsparsehash-dev librdkafka-dev libpoco-dev libsparsehash-dev libgoogle-perftools-dev libzstd-dev libre2-dev $EXTRAPACKAGES" \ + `# Travis trusty cant unpack bionic: E: debootstrap failed, TODO: check again, can be fixed` \ + DIST=${DIST=artful} \ $CUR_DIR/../../release $RELEASE_OPT date From bd332b917156ef5694f797b00b2eeeeae3846c84 Mon Sep 17 00:00:00 2001 From: pyos Date: Thu, 10 May 2018 17:00:29 +0300 Subject: [PATCH 135/231] Allow calling into libc from jitted code. Mostly for intrinsics like memcpy/memset/memmove, which are inserted during optimization by LLVM itself. (With a null resolver, a compiled version of something like `Uint64 < 0` would segfault.) --- dbms/src/Interpreters/ExpressionJIT.cpp | 63 ++++++++++--------------- 1 file changed, 26 insertions(+), 37 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 14e131d731a..9e1a7a2d069 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -34,10 +34,10 @@ #include #include #include -#include #include #include #include +#include #include #include #include @@ -137,8 +137,9 @@ struct LLVMContext std::shared_ptr module; #endif std::unique_ptr machine; - llvm::orc::RTDyldObjectLinkingLayer objectLayer; - llvm::orc::IRCompileLayer compileLayer; + std::shared_ptr memory_manager; + llvm::orc::RTDyldObjectLinkingLayer object_layer; + llvm::orc::IRCompileLayer compile_layer; llvm::DataLayout layout; llvm::IRBuilder<> builder; std::unordered_map symbols; @@ -150,19 +151,16 @@ struct LLVMContext : module(std::make_shared("jit", context)) #endif , machine(getNativeMachine()) + , memory_manager(std::make_shared()) #if LLVM_VERSION_MAJOR >= 7 - , objectLayer(execution_session, [](llvm::orc::VModuleKey) + , object_layer(execution_session, [this](llvm::orc::VModuleKey) { - return llvm::orc::RTDyldObjectLinkingLayer::Resources - { - std::make_shared(), - std::make_shared() - }; + return llvm::orc::RTDyldObjectLinkingLayer::Resources{memory_manager, memory_manager}; }) #else - , objectLayer([]() { return std::make_shared(); }) + , object_layer([this]() { return memory_manager; }) #endif - , compileLayer(objectLayer, llvm::orc::SimpleCompiler(*machine)) + , compile_layer(object_layer, llvm::orc::SimpleCompiler(*machine)) , layout(machine->createDataLayout()) , builder(context) { @@ -194,38 +192,28 @@ struct LLVMContext fpm.doFinalization(); mpm.run(*module); - /// name, mangled name - std::vector> function_names; - function_names.reserve(module->size()); +#if LLVM_VERSION_MAJOR >= 7 + llvm::orc::VModuleKey module_key = execution_session.allocateVModule(); + if (compile_layer.addModule(module_key, std::move(module))) + throw Exception("Cannot add module to compile layer", ErrorCodes::CANNOT_COMPILE_CODE); +#else + if (!compile_layer.addModule(module, memory_manager)) + throw Exception("Cannot add module to compile layer", ErrorCodes::CANNOT_COMPILE_CODE); +#endif + for (const auto & function : *module) { std::string mangled_name; llvm::raw_string_ostream mangled_name_stream(mangled_name); llvm::Mangler::getNameWithPrefix(mangled_name_stream, function.getName(), layout); mangled_name_stream.flush(); - function_names.emplace_back(function.getName(), mangled_name); - } - -#if LLVM_VERSION_MAJOR >= 7 - llvm::orc::VModuleKey module_key = execution_session.allocateVModule(); - if (compileLayer.addModule(module_key, std::move(module))) - throw Exception("Cannot add module to compile layer", ErrorCodes::CANNOT_COMPILE_CODE); -#else - if (!compileLayer.addModule(module, std::make_shared())) - throw Exception("Cannot add module to compile layer", ErrorCodes::CANNOT_COMPILE_CODE); -#endif - - for (const auto & names : function_names) - { - if (auto symbol = compileLayer.findSymbol(names.second, false)) - { - if (auto address_or_error = symbol.getAddress()) - symbols[names.first] = reinterpret_cast(*address_or_error); - else - throw Exception("Cannot get an address of compiled symbol from a module", ErrorCodes::CANNOT_COMPILE_CODE); - } - else - throw Exception("Cannot find compiled symbol in a module", ErrorCodes::CANNOT_COMPILE_CODE); + auto symbol = compile_layer.findSymbol(mangled_name, false); + if (!symbol) + continue; /// external function (e.g. an intrinsic that calls into libc) + auto address = symbol.getAddress(); + if (!address) + throw Exception(("Function " + function.getName() + " failed to link").str(), ErrorCodes::CANNOT_COMPILE_CODE); + symbols[function.getName()] = reinterpret_cast(*address); } } }; @@ -620,6 +608,7 @@ namespace { llvm::InitializeNativeTarget(); llvm::InitializeNativeTargetAsmPrinter(); + llvm::sys::DynamicLibrary::LoadLibraryPermanently(nullptr); } } llvmInitializer; } From 6d2259f2cff73e251934e2189fe11bddd9a044df Mon Sep 17 00:00:00 2001 From: pyos Date: Thu, 10 May 2018 17:02:25 +0300 Subject: [PATCH 136/231] Implement jit for comparisons, except for (double, int). That one has some edge cases which I can't be bothered to code. --- dbms/src/DataTypes/Native.h | 44 ++++++++------ dbms/src/Functions/FunctionsComparison.h | 77 ++++++++++++++++++++++++ 2 files changed, 104 insertions(+), 17 deletions(-) diff --git a/dbms/src/DataTypes/Native.h b/dbms/src/DataTypes/Native.h index 6a793d13ca4..e6167b03a73 100644 --- a/dbms/src/DataTypes/Native.h +++ b/dbms/src/DataTypes/Native.h @@ -30,6 +30,15 @@ static inline bool typeIsEither(const IDataType & type) return (typeid_cast(&type) || ...); } +static inline bool typeIsSigned(const IDataType & type) +{ + return typeIsEither< + DataTypeInt8, DataTypeInt16, DataTypeInt32, DataTypeInt64, + DataTypeFloat32, DataTypeFloat64, + DataTypeDate, DataTypeDateTime, DataTypeInterval + >(type); +} + static inline llvm::Type * toNativeType(llvm::IRBuilderBase & builder, const IDataType & type) { if (auto * nullable = typeid_cast(&type)) @@ -77,11 +86,26 @@ static inline llvm::Value * nativeBoolCast(llvm::IRBuilder<> & b, const DataType throw Exception("Cannot cast non-number " + from->getName() + " to bool", ErrorCodes::NOT_IMPLEMENTED); } -static inline llvm::Value * nativeCast(llvm::IRBuilder<> & b, const DataTypePtr & from, llvm::Value * value, const DataTypePtr & to) +static inline llvm::Value * nativeCast(llvm::IRBuilder<> & b, const DataTypePtr & from, llvm::Value * value, llvm::Type * to) { auto * n_from = value->getType(); + if (n_from == to) + return value; + if (n_from->isIntegerTy() && to->isFloatingPointTy()) + return typeIsSigned(*from) ? b.CreateSIToFP(value, to) : b.CreateUIToFP(value, to); + if (n_from->isFloatingPointTy() && to->isIntegerTy()) + return typeIsSigned(*from) ? b.CreateFPToSI(value, to) : b.CreateFPToUI(value, to); + if (n_from->isIntegerTy() && to->isIntegerTy()) + return b.CreateIntCast(value, to, typeIsSigned(*from)); + if (n_from->isFloatingPointTy() && to->isFloatingPointTy()) + return b.CreateFPCast(value, to); + throw Exception("Cannot cast " + from->getName() + " to requested type", ErrorCodes::NOT_IMPLEMENTED); +} + +static inline llvm::Value * nativeCast(llvm::IRBuilder<> & b, const DataTypePtr & from, llvm::Value * value, const DataTypePtr & to) +{ auto * n_to = toNativeType(b, to); - if (n_from == n_to) + if (value->getType() == n_to) return value; if (from->isNullable() && to->isNullable()) { @@ -95,21 +119,7 @@ static inline llvm::Value * nativeCast(llvm::IRBuilder<> & b, const DataTypePtr auto * inner = nativeCast(b, from, value, removeNullable(to)); return b.CreateInsertValue(llvm::Constant::getNullValue(n_to), inner, {0}); } - - bool is_signed = typeIsEither< - DataTypeInt8, DataTypeInt16, DataTypeInt32, DataTypeInt64, - DataTypeFloat32, DataTypeFloat64, - DataTypeDate, DataTypeDateTime, DataTypeInterval - >(*from); - if (n_from->isIntegerTy() && n_to->isFloatingPointTy()) - return is_signed ? b.CreateSIToFP(value, n_to) : b.CreateUIToFP(value, n_to); - if (n_from->isFloatingPointTy() && n_to->isIntegerTy()) - return is_signed ? b.CreateFPToSI(value, n_to) : b.CreateFPToUI(value, n_to); - if (n_from->isIntegerTy() && n_to->isIntegerTy()) - return b.CreateIntCast(value, n_to, is_signed); - if (n_from->isFloatingPointTy() && n_to->isFloatingPointTy()) - return b.CreateFPCast(value, n_to); - throw Exception("Cannot cast " + from->getName() + " to " + to->getName(), ErrorCodes::NOT_IMPLEMENTED); + return nativeCast(b, from, value, n_to); } } diff --git a/dbms/src/Functions/FunctionsComparison.h b/dbms/src/Functions/FunctionsComparison.h index 0cf19833821..229cbfe20e2 100644 --- a/dbms/src/Functions/FunctionsComparison.h +++ b/dbms/src/Functions/FunctionsComparison.h @@ -53,12 +53,26 @@ template struct EqualsOp using SymmetricOp = EqualsOp; static UInt8 apply(A a, B b) { return accurate::equalsOp(a, b); } + +#if USE_EMBEDDED_COMPILER + static llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * x, llvm::Value * y, bool /*is_signed*/) + { + return x->getType()->isIntegerTy() ? b.CreateICmpEQ(x, y) : b.CreateFCmpOEQ(x, y); /// qNaNs always compare false + } +#endif }; template struct NotEqualsOp { using SymmetricOp = NotEqualsOp; static UInt8 apply(A a, B b) { return accurate::notEqualsOp(a, b); } + +#if USE_EMBEDDED_COMPILER + static llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * x, llvm::Value * y, bool /*is_signed*/) + { + return x->getType()->isIntegerTy() ? b.CreateICmpNE(x, y) : b.CreateFCmpONE(x, y); + } +#endif }; template struct GreaterOp; @@ -67,12 +81,26 @@ template struct LessOp { using SymmetricOp = GreaterOp; static UInt8 apply(A a, B b) { return accurate::lessOp(a, b); } + +#if USE_EMBEDDED_COMPILER + static llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * x, llvm::Value * y, bool is_signed) + { + return x->getType()->isIntegerTy() ? (is_signed ? b.CreateICmpSLT(x, y) : b.CreateICmpULT(x, y)) : b.CreateFCmpOLT(x, y); + } +#endif }; template struct GreaterOp { using SymmetricOp = LessOp; static UInt8 apply(A a, B b) { return accurate::greaterOp(a, b); } + +#if USE_EMBEDDED_COMPILER + static llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * x, llvm::Value * y, bool is_signed) + { + return x->getType()->isIntegerTy() ? (is_signed ? b.CreateICmpSGT(x, y) : b.CreateICmpUGT(x, y)) : b.CreateFCmpOGT(x, y); + } +#endif }; template struct GreaterOrEqualsOp; @@ -81,12 +109,26 @@ template struct LessOrEqualsOp { using SymmetricOp = GreaterOrEqualsOp; static UInt8 apply(A a, B b) { return accurate::lessOrEqualsOp(a, b); } + +#if USE_EMBEDDED_COMPILER + static llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * x, llvm::Value * y, bool is_signed) + { + return x->getType()->isIntegerTy() ? (is_signed ? b.CreateICmpSLE(x, y) : b.CreateICmpULE(x, y)) : b.CreateFCmpOLE(x, y); + } +#endif }; template struct GreaterOrEqualsOp { using SymmetricOp = LessOrEqualsOp; static UInt8 apply(A a, B b) { return accurate::greaterOrEqualsOp(a, b); } + +#if USE_EMBEDDED_COMPILER + static llvm::Value * compile(llvm::IRBuilder<> & b, llvm::Value * x, llvm::Value * y, bool is_signed) + { + return x->getType()->isIntegerTy() ? (is_signed ? b.CreateICmpSGE(x, y) : b.CreateICmpUGE(x, y)) : b.CreateFCmpOGE(x, y); + } +#endif }; @@ -1136,6 +1178,41 @@ public: col_with_type_and_name_left.type, col_with_type_and_name_right.type, left_is_num, input_rows_count); } + +#if USE_EMBEDDED_COMPILER + bool isCompilableImpl(const DataTypes & types) const override + { + auto isBigInteger = &typeIsEither; + auto isFloatingPoint = &typeIsEither; + if ((isBigInteger(*types[0]) && isFloatingPoint(*types[1])) || (isBigInteger(*types[1]) && isFloatingPoint(*types[0]))) + return false; /// TODO: implement (double, int_N where N > double's mantissa width) + return types[0]->isValueRepresentedByNumber() && types[1]->isValueRepresentedByNumber(); + } + + llvm::Value * compileImpl(llvm::IRBuilderBase & builder, const DataTypes & types, ValuePlaceholders values) const override + { + auto & b = static_cast &>(builder); + auto * x = values[0](); + auto * y = values[1](); + if (!types[0]->equals(*types[1])) + { + llvm::Type * common; + if (x->getType()->isIntegerTy() && y->getType()->isIntegerTy()) + common = b.getIntNTy(std::max( + /// if one integer has a sign bit, make sure the other does as well. llvm generates optimal code + /// (e.g. uses overflow flag on x86) for (word size + 1)-bit integer operations. + x->getType()->getIntegerBitWidth() + (!typeIsSigned(*types[0]) && typeIsSigned(*types[1])), + y->getType()->getIntegerBitWidth() + (!typeIsSigned(*types[1]) && typeIsSigned(*types[0])))); + else + /// (double, float) or (double, int_N where N <= double's mantissa width) -> double + common = b.getDoubleTy(); + x = nativeCast(b, types[0], x, common); + y = nativeCast(b, types[1], y, common); + } + auto * result = Op::compile(b, x, y, typeIsSigned(*types[0]) || typeIsSigned(*types[1])); + return b.CreateSelect(result, b.getInt8(1), b.getInt8(0)); + } +#endif }; From 6ecc6d8ea4ffb668b719f5538893702565f791ab Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 17:44:45 +0300 Subject: [PATCH 137/231] Changed some doc files to symlinks #2277 --- docs/ru/development/architecture.md | 193 +--------------------------- docs/ru/development/build.md | 128 +----------------- docs/ru/development/build_osx.md | 45 +------ docs/ru/development/index.md | 2 +- docs/ru/development/tests.md | 33 +---- 5 files changed, 5 insertions(+), 396 deletions(-) mode change 100644 => 120000 docs/ru/development/architecture.md mode change 100644 => 120000 docs/ru/development/build.md mode change 100644 => 120000 docs/ru/development/build_osx.md mode change 100644 => 120000 docs/ru/development/index.md mode change 100644 => 120000 docs/ru/development/tests.md diff --git a/docs/ru/development/architecture.md b/docs/ru/development/architecture.md deleted file mode 100644 index 5258907f9d1..00000000000 --- a/docs/ru/development/architecture.md +++ /dev/null @@ -1,192 +0,0 @@ -# Overview of ClickHouse architecture - -ClickHouse is a true column-oriented DBMS. Data is stored by columns, and during query execution data is processed by arrays (vectors or chunks of columns). Whenever possible, operations are dispatched on arrays, rather than on individual values. This is called "vectorized query execution," and it helps lower dispatch cost relative to the cost of actual data processing. - -> This idea is nothing new. It dates back to the `APL` programming language and its descendants: `A+`, `J`, `K`, and `Q`. Array programming is widely used in scientific data processing. Neither is this idea something new in relational databases: for example, it is used in the `Vectorwise` system. -> -> There are two different approaches for speeding up query processing: vectorized query execution and runtime code generation. In the latter, the code is generated for every kind of query on the fly, removing all indirection and dynamic dispatch. Neither of these approaches is strictly better than the other. Runtime code generation can be better when it fuses many operations together, thus fully utilizing CPU execution units and the pipeline. Vectorized query execution can be less practical, because it involves temporary vectors that must be written to cache and read back. If the temporary data does not fit in the L2 cache, this becomes an issue. But vectorized query execution more easily utilizes the SIMD capabilities of the CPU. A [research paper](http://15721.courses.cs.cmu.edu/spring2016/papers/p5-sompolski.pdf) written by our friends shows that it is better to combine both approaches. ClickHouse mainly uses vectorized query execution and has limited initial support for runtime code generation (only the inner loop of the first stage of GROUP BY can be compiled). - -## Columns - -To represent columns in memory (actually, chunks of columns), the `IColumn` interface is used. This interface provides helper methods for implementation of various relational operators. Almost all operations are immutable: they do not modify the original column, but create a new modified one. For example, the `IColumn::filter` method accepts a filter byte mask and creates a new filtered column. It is used for the `WHERE` and `HAVING` relational operators. Additional examples: the `IColumn::permute` method to support `ORDER BY`, the `IColumn::cut` method to support `LIMIT`, and so on. - -Various `IColumn` implementations (`ColumnUInt8`, `ColumnString` and so on) are responsible for the memory layout of columns. Memory layout is usually a contiguous array. For the integer type of columns it is just one contiguous array, like `std::vector`. For `String` and `Array` columns, it is two vectors: one for all array elements, placed contiguously, and a second one for offsets to the beginning of each array. There is also `ColumnConst` that stores just one value in memory, but looks like a column. - -## Field - -Nevertheless, it is possible to work with individual values as well. To represent an individual value, `Field` is used. `Field` is just a discriminated union of `UInt64`, `Int64`, `Float64`, `String` and `Array`. `IColumn` has the `operator[]` method to get the n-th value as a `Field`, and the `insert` method to append a `Field` to the end of a column. These methods are not very efficient, because they require dealing with temporary `Field` objects representing an individual value. There are more efficient methods, such as `insertFrom`, `insertRangeFrom`, and so on. - -`Field` doesn't have enough information about a specific data type for a table. For example, `UInt8`, `UInt16`, `UInt32`, and `UInt64` are all represented as `UInt64` in a `Field`. - -## Leaky abstractions - -`IColumn` has methods for common relational transformations of data, but they don't meet all needs. For example, `ColumnUInt64` doesn't have a method to calculate the sum of two columns, and `ColumnString` doesn't have a method to run a substring search. These countless routines are implemented outside of `IColumn`. - -Various functions on columns can be implemented in a generic, non-efficient way using `IColumn` methods to extract `Field` values, or in a specialized way using knowledge of inner memory layout of data in a specific `IColumn` implementation. To do this, functions are cast to a specific `IColumn` type and deal with internal representation directly. For example, `ColumnUInt64` has the `getData` method that returns a reference to an internal array, then a separate routine reads or fills that array directly. In fact, we have "leaky abstractions" to allow efficient specializations of various routines. - -## Data types - -`IDataType` is responsible for serialization and deserialization: for reading and writing chunks of columns or individual values in binary or text form. -`IDataType` directly corresponds to data types in tables. For example, there are `DataTypeUInt32`, `DataTypeDateTime`, `DataTypeString` and so on. - -`IDataType` and `IColumn` are only loosely related to each other. Different data types can be represented in memory by the same `IColumn` implementations. For example, `DataTypeUInt32` and `DataTypeDateTime` are both represented by `ColumnUInt32` or `ColumnConstUInt32`. In addition, the same data type can be represented by different `IColumn` implementations. For example, `DataTypeUInt8` can be represented by `ColumnUInt8` or `ColumnConstUInt8`. - -`IDataType` only stores metadata. For instance, `DataTypeUInt8` doesn't store anything at all (except vptr) and `DataTypeFixedString` stores just `N` (the size of fixed-size strings). - -`IDataType` has helper methods for various data formats. Examples are methods to serialize a value with possible quoting, to serialize a value for JSON, and to serialize a value as part of XML format. There is no direct correspondence to data formats. For example, the different data formats `Pretty` and `TabSeparated` can use the same `serializeTextEscaped` helper method from the `IDataType` interface. - -## Block - -A `Block` is a container that represents a subset (chunk) of a table in memory. It is just a set of triples: `(IColumn, IDataType, column name)`. During query execution, data is processed by `Block`s. If we have a `Block`, we have data (in the `IColumn` object), we have information about its type (in `IDataType`) that tells us how to deal with that column, and we have the column name (either the original column name from the table, or some artificial name assigned for getting temporary results of calculations). - -When we calculate some function over columns in a block, we add another column with its result to the block, and we don't touch columns for arguments of the function because operations are immutable. Later, unneeded columns can be removed from the block, but not modified. This is convenient for elimination of common subexpressions. - -Blocks are created for every processed chunk of data. Note that for the same type of calculation, the column names and types remain the same for different blocks, and only column data changes. It is better to split block data from the block header, because small block sizes will have a high overhead of temporary strings for copying shared_ptrs and column names. - -## Block Streams - -Block streams are for processing data. We use streams of blocks to read data from somewhere, perform data transformations, or write data to somewhere. `IBlockInputStream` has the `read` method to fetch the next block while available. `IBlockOutputStream` has the `write` method to push the block somewhere. - -Streams are responsible for: - -1. Reading or writing to a table. The table just returns a stream for reading or writing blocks. -2. Implementing data formats. For example, if you want to output data to a terminal in `Pretty` format, you create a block output stream where you push blocks, and it formats them. -3. Performing data transformations. Let's say you have `IBlockInputStream` and want to create a filtered stream. You create `FilterBlockInputStream` and initialize it with your stream. Then when you pull a block from `FilterBlockInputStream`, it pulls a block from your stream, filters it, and returns the filtered block to you. Query execution pipelines are represented this way. - -There are more sophisticated transformations. For example, when you pull from `AggregatingBlockInputStream`, it reads all data from its source, aggregates it, and then returns a stream of aggregated data for you. Another example: `UnionBlockInputStream` accepts many input sources in the constructor and also a number of threads. It launches multiple threads and reads from multiple sources in parallel. - -> Block streams use the "pull" approach to control flow: when you pull a block from the first stream, it consequently pulls the required blocks from nested streams, and the entire execution pipeline will work. Neither "pull" nor "push" is the best solution, because control flow is implicit, and that limits implementation of various features like simultaneous execution of multiple queries (merging many pipelines together). This limitation could be overcome with coroutines or just running extra threads that wait for each other. We may have more possibilities if we make control flow explicit: if we locate the logic for passing data from one calculation unit to another outside of those calculation units. Read this [nice article](http://journal.stuffwithstuff.com/2013/01/13/iteration-inside-and-out/) for more thoughts. -> -> We should note that the query execution pipeline creates temporary data at each step. We try to keep block size small enough so that temporary data fits in the CPU cache. With that assumption, writing and reading temporary data is almost free in comparison with other calculations. We could consider an alternative, which is to fuse many operations in the pipeline together, to make the pipeline as short as possible and remove much of the temporary data. This could be an advantage, but it also has drawbacks. For example, a split pipeline makes it easy to implement caching intermediate data, stealing intermediate data from similar queries running at the same time, and merging pipelines for similar queries. - -## Formats - -Data formats are implemented with block streams. There are "presentational" formats only suitable for output of data to the client, such as `Pretty` format, which provides only `IBlockOutputStream`. And there are input/output formats, such as `TabSeparated` or `JSONEachRow`. - -There are also row streams: `IRowInputStream` and `IRowOutputStream`. They allow you to pull/push data by individual rows, not by blocks. And they are only needed to simplify implementation of row-oriented formats. The wrappers `BlockInputStreamFromRowInputStream` and `BlockOutputStreamFromRowOutputStream` allow you to convert row-oriented streams to regular block-oriented streams. - -## I/O - -For byte-oriented input/output, there are `ReadBuffer` and `WriteBuffer` abstract classes. They are used instead of C++ `iostream`'s. Don't worry: every mature C++ project is using something other than `iostream`'s for good reasons. - -`ReadBuffer` and `WriteBuffer` are just a contiguous buffer and a cursor pointing to the position in that buffer. Implementations may own or not own the memory for the buffer. There is a virtual method to fill the buffer with the following data (for `ReadBuffer`) or to flush the buffer somewhere (for `WriteBuffer`). The virtual methods are rarely called. - -Implementations of `ReadBuffer`/`WriteBuffer` are used for working with files and file descriptors and network sockets, for implementing compression (`CompressedWriteBuffer` is initialized with another WriteBuffer and performs compression before writing data to it), and for other purposes – the names `ConcatReadBuffer`, `LimitReadBuffer`, and `HashingWriteBuffer` speak for themselves. - -Read/WriteBuffers only deal with bytes. To help with formatted input/output (for instance, to write a number in decimal format), there are functions from `ReadHelpers` and `WriteHelpers` header files. - -Let's look at what happens when you want to write a result set in `JSON` format to stdout. You have a result set ready to be fetched from `IBlockInputStream`. You create `WriteBufferFromFileDescriptor(STDOUT_FILENO)` to write bytes to stdout. You create `JSONRowOutputStream`, initialized with that `WriteBuffer`, to write rows in `JSON` to stdout. You create `BlockOutputStreamFromRowOutputStream` on top of it, to represent it as `IBlockOutputStream`. Then you call `copyData` to transfer data from `IBlockInputStream` to `IBlockOutputStream`, and everything works. Internally, `JSONRowOutputStream` will write various JSON delimiters and call the `IDataType::serializeTextJSON` method with a reference to `IColumn` and the row number as arguments. Consequently, `IDataType::serializeTextJSON` will call a method from `WriteHelpers.h`: for example, `writeText` for numeric types and `writeJSONString` for `DataTypeString`. - -## Tables - -Tables are represented by the `IStorage` interface. Different implementations of that interface are different table engines. Examples are `StorageMergeTree`, `StorageMemory`, and so on. Instances of these classes are just tables. - -The most important `IStorage` methods are `read` and `write`. There are also `alter`, `rename`, `drop`, and so on. The `read` method accepts the following arguments: the set of columns to read from a table, the `AST` query to consider, and the desired number of streams to return. It returns one or multiple `IBlockInputStream` objects and information about the stage of data processing that was completed inside a table engine during query execution. - -In most cases, the read method is only responsible for reading the specified columns from a table, not for any further data processing. All further data processing is done by the query interpreter and is outside the responsibility of `IStorage`. - -But there are notable exceptions: -- The AST query is passed to the `read` method and the table engine can use it to derive index usage and to read less data from a table. -- Sometimes the table engine can process data itself to a specific stage. For example, `StorageDistributed` can send a query to remote servers, ask them to process data to a stage where data from different remote servers can be merged, and return that preprocessed data. -The query interpreter then finishes processing the data. - -The table's `read` method can return multiple `IBlockInputStream` objects to allow parallel data processing. These multiple block input streams can read from a table in parallel. Then you can wrap these streams with various transformations (such as expression evaluation or filtering) that can be calculated independently and create a `UnionBlockInputStream` on top of them, to read from multiple streams in parallel. - -There are also `TableFunction`s. These are functions that return a temporary `IStorage` object to use in the `FROM` clause of a query. - -To get a quick idea of how to implement your own table engine, look at something simple, like `StorageMemory` or `StorageTinyLog`. - -> As the result of the `read` method, `IStorage` returns `QueryProcessingStage` – information about what parts of the query were already calculated inside storage. Currently we have only very coarse granularity for that information. There is no way for the storage to say "I have already processed this part of the expression in WHERE, for this range of data". We need to work on that. - -## Parsers - -A query is parsed by a hand-written recursive descent parser. For example, `ParserSelectQuery` just recursively calls the underlying parsers for various parts of the query. Parsers create an `AST`. The `AST` is represented by nodes, which are instances of `IAST`. - -> Parser generators are not used for historical reasons. - -## Interpreters - -Interpreters are responsible for creating the query execution pipeline from an `AST`. There are simple interpreters, such as `InterpreterExistsQuery`and `InterpreterDropQuery`, or the more sophisticated `InterpreterSelectQuery`. The query execution pipeline is a combination of block input or output streams. For example, the result of interpreting the `SELECT` query is the `IBlockInputStream` to read the result set from; the result of the INSERT query is the `IBlockOutputStream` to write data for insertion to; and the result of interpreting the `INSERT SELECT` query is the `IBlockInputStream` that returns an empty result set on the first read, but that copies data from `SELECT` to `INSERT` at the same time. - -`InterpreterSelectQuery` uses `ExpressionAnalyzer` and `ExpressionActions` machinery for query analysis and transformations. This is where most rule-based query optimizations are done. `ExpressionAnalyzer` is quite messy and should be rewritten: various query transformations and optimizations should be extracted to separate classes to allow modular transformations or query. - -## Functions - -There are ordinary functions and aggregate functions. For aggregate functions, see the next section. - -Ordinary functions don't change the number of rows – they work as if they are processing each row independently. In fact, functions are not called for individual rows, but for `Block`'s of data to implement vectorized query execution. - -There are some miscellaneous functions, like `blockSize`, `rowNumberInBlock`, and `runningAccumulate`, that exploit block processing and violate the independence of rows. - -ClickHouse has strong typing, so implicit type conversion doesn't occur. If a function doesn't support a specific combination of types, an exception will be thrown. But functions can work (be overloaded) for many different combinations of types. For example, the `plus` function (to implement the `+` operator) works for any combination of numeric types: `UInt8` + `Float32`, `UInt16` + `Int8`, and so on. Also, some variadic functions can accept any number of arguments, such as the `concat` function. - -Implementing a function may be slightly inconvenient because a function explicitly dispatches supported data types and supported `IColumns`. For example, the `plus` function has code generated by instantiation of a C++ template for each combination of numeric types, and for constant or non-constant left and right arguments. - -> This is a nice place to implement runtime code generation to avoid template code bloat. Also, it will make it possible to add fused functions like fused multiply-add, or to make multiple comparisons in one loop iteration. -> -> Due to vectorized query execution, functions are not short-circuit. For example, if you write `WHERE f(x) AND g(y)`, both sides will be calculated, even for rows, when `f(x)` is zero (except when `f(x)` is a zero constant expression). But if selectivity of the `f(x)` condition is high, and calculation of `f(x)` is much cheaper than `g(y)`, it's better to implement multi-pass calculation: first calculate `f(x)`, then filter columns by the result, and then calculate `g(y)` only for smaller, filtered chunks of data. - -## Aggregate Functions - -Aggregate functions are stateful functions. They accumulate passed values into some state, and allow you to get results from that state. They are managed with the `IAggregateFunction` interface. States can be rather simple (the state for `AggregateFunctionCount` is just a single `UInt64` value) or quite complex (the state of `AggregateFunctionUniqCombined` is a combination of a linear array, a hash table and a `HyperLogLog` probabilistic data structure). - -To deal with multiple states while executing a high-cardinality `GROUP BY` query, states are allocated in `Arena` (a memory pool), or they could be allocated in any suitable piece of memory. States can have a non-trivial constructor and destructor: for example, complex aggregation states can allocate additional memory themselves. This requires some attention to creating and destroying states and properly passing their ownership, to keep track of who and when will destroy states. - -Aggregation states can be serialized and deserialized to pass over the network during distributed query execution or to write them on disk where there is not enough RAM. They can even be stored in a table with the `DataTypeAggregateFunction` to allow incremental aggregation of data. - -> The serialized data format for aggregate function states is not versioned right now. This is ok if aggregate states are only stored temporarily. But we have the `AggregatingMergeTree` table engine for incremental aggregation, and people are already using it in production. This is why we should add support for backward compatibility when changing the serialized format for any aggregate function in the future. - -## Server - -The server implements several different interfaces: -- An HTTP interface for any foreign clients. -- A TCP interface for the native ClickHouse client and for cross-server communication during distributed query execution. -- An interface for transferring data for replication. - -Internally, it is just a basic multithreaded server without coroutines, fibers, etc. Since the server is not designed to process a high rate of simple queries but is intended to process a relatively low rate of complex queries, each of them can process a vast amount of data for analytics. - -The server initializes the `Context` class with the necessary environment for query execution: the list of available databases, users and access rights, settings, clusters, the process list, the query log, and so on. This environment is used by interpreters. - -We maintain full backward and forward compatibility for the server TCP protocol: old clients can talk to new servers and new clients can talk to old servers. But we don't want to maintain it eternally, and we are removing support for old versions after about one year. - -> For all external applications, we recommend using the HTTP interface because it is simple and easy to use. The TCP protocol is more tightly linked to internal data structures: it uses an internal format for passing blocks of data and it uses custom framing for compressed data. We haven't released a C library for that protocol because it requires linking most of the ClickHouse codebase, which is not practical. - -## Distributed query execution - -Servers in a cluster setup are mostly independent. You can create a `Distributed` table on one or all servers in a cluster. The `Distributed` table does not store data itself – it only provides a "view" to all local tables on multiple nodes of a cluster. When you SELECT from a `Distributed` table, it rewrites that query, chooses remote nodes according to load balancing settings, and sends the query to them. The `Distributed` table requests remote servers to process a query just up to a stage where intermediate results from different servers can be merged. Then it receives the intermediate results and merges them. The distributed table tries to distribute as much work as possible to remote servers, and does not send much intermediate data over the network. - -> Things become more complicated when you have subqueries in IN or JOIN clauses and each of them uses a `Distributed` table. We have different strategies for execution of these queries. -> -> There is no global query plan for distributed query execution. Each node has its own local query plan for its part of the job. We only have simple one-pass distributed query execution: we send queries for remote nodes and then merge the results. But this is not feasible for difficult queries with high cardinality GROUP BYs or with a large amount of temporary data for JOIN: in such cases, we need to "reshuffle" data between servers, which requires additional coordination. ClickHouse does not support that kind of query execution, and we need to work on it. - -## Merge Tree - -`MergeTree` is a family of storage engines that supports indexing by primary key. The primary key can be an arbitary tuple of columns or expressions. Data in a `MergeTree` table is stored in "parts". Each part stores data in the primary key order (data is ordered lexicographically by the primary key tuple). All the table columns are stored in separate `column.bin` files in these parts. The files consist of compressed blocks. Each block is usually from 64 KB to 1 MB of uncompressed data, depending on the average value size. The blocks consist of column values placed contiguously one after the other. Column values are in the same order for each column (the order is defined by the primary key), so when you iterate by many columns, you get values for the corresponding rows. - -The primary key itself is "sparse". It doesn't address each single row, but only some ranges of data. A separate `primary.idx` file has the value of the primary key for each N-th row, where N is called `index_granularity` (usually, N = 8192). Also, for each column, we have `column.mrk` files with "marks," which are offsets to each N-th row in the data file. Each mark is a pair: the offset in the file to the beginning of the compressed block, and the offset in the decompressed block to the beginning of data. Usually compressed blocks are aligned by marks, and the offset in the decompressed block is zero. Data for `primary.idx` always resides in memory and data for `column.mrk` files is cached. - -When we are going to read something from a part in `MergeTree`, we look at `primary.idx` data and locate ranges that could possibly contain requested data, then look at `column.mrk` data and calculate offsets for where to start reading those ranges. Because of sparseness, excess data may be read. ClickHouse is not suitable for a high load of simple point queries, because the entire range with `index_granularity` rows must be read for each key, and the entire compressed block must be decompressed for each column. We made the index sparse because we must be able to maintain trillions of rows per single server without noticeable memory consumption for the index. Also, because the primary key is sparse, it is not unique: it cannot check the existence of the key in the table at INSERT time. You could have many rows with the same key in a table. - -When you `INSERT` a bunch of data into `MergeTree`, that bunch is sorted by primary key order and forms a new part. To keep the number of parts relatively low, there are background threads that periodically select some parts and merge them to a single sorted part. That's why it is called `MergeTree`. Of course, merging leads to "write amplification". All parts are immutable: they are only created and deleted, but not modified. When SELECT is run, it holds a snapshot of the table (a set of parts). After merging, we also keep old parts for some time to make recovery after failure easier, so if we see that some merged part is probably broken, we can replace it with its source parts. - -`MergeTree` is not an LSM tree because it doesn't contain "memtable" and "log": inserted data is written directly to the filesystem. This makes it suitable only to INSERT data in batches, not by individual row and not very frequently – about once per second is ok, but a thousand times a second is not. We did it this way for simplicity's sake, and because we are already inserting data in batches in our applications. - -> MergeTree tables can only have one (primary) index: there aren't any secondary indices. It would be nice to allow multiple physical representations under one logical table, for example, to store data in more than one physical order or even to allow representations with pre-aggregated data along with original data. -> -> There are MergeTree engines that are doing additional work during background merges. Examples are `CollapsingMergeTree` and `AggregatingMergeTree`. This could be treated as special support for updates. Keep in mind that these are not real updates because users usually have no control over the time when background merges will be executed, and data in a `MergeTree` table is almost always stored in more than one part, not in completely merged form. - -## Replication - -Replication in ClickHouse is implemented on a per-table basis. You could have some replicated and some non-replicated tables on the same server. You could also have tables replicated in different ways, such as one table with two-factor replication and another with three-factor. - -Replication is implemented in the `ReplicatedMergeTree` storage engine. The path in `ZooKeeper` is specified as a parameter for the storage engine. All tables with the same path in `ZooKeeper` become replicas of each other: they synchronise their data and maintain consistency. Replicas can be added and removed dynamically simply by creating or dropping a table. - -Replication uses an asynchronous multi-master scheme. You can insert data into any replica that has a session with `ZooKeeper`, and data is replicated to all other replicas asynchronously. Because ClickHouse doesn't support UPDATEs, replication is conflict-free. As there is no quorum acknowledgment of inserts, just-inserted data might be lost if one node fails. - -Metadata for replication is stored in ZooKeeper. There is a replication log that lists what actions to do. Actions are: get part; merge parts; drop partition, etc. Each replica copies the replication log to its queue and then executes the actions from the queue. For example, on insertion, the "get part" action is created in the log, and every replica downloads that part. Merges are coordinated between replicas to get byte-identical results. All parts are merged in the same way on all replicas. To achieve this, one replica is elected as the leader, and that replica initiates merges and writes "merge parts" actions to the log. - -Replication is physical: only compressed parts are transferred between nodes, not queries. To lower the network cost (to avoid network amplification), merges are processed on each replica independently in most cases. Large merged parts are sent over the network only in cases of significant replication lag. - -In addition, each replica stores its state in ZooKeeper as the set of parts and its checksums. When the state on the local filesystem diverges from the reference state in ZooKeeper, the replica restores its consistency by downloading missing and broken parts from other replicas. When there is some unexpected or broken data in the local filesystem, ClickHouse does not remove it, but moves it to a separate directory and forgets it. - -> The ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is not elastic, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load will be uneven. This implementation gives you more control, and it is fine for relatively small clusters such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that will span its data across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically. diff --git a/docs/ru/development/architecture.md b/docs/ru/development/architecture.md new file mode 120000 index 00000000000..abda4dd48a8 --- /dev/null +++ b/docs/ru/development/architecture.md @@ -0,0 +1 @@ +../../en/development/architecture.md \ No newline at end of file diff --git a/docs/ru/development/build.md b/docs/ru/development/build.md deleted file mode 100644 index 73554285fb3..00000000000 --- a/docs/ru/development/build.md +++ /dev/null @@ -1,127 +0,0 @@ -# How to build ClickHouse on Linux - -Build should work on Linux Ubuntu 12.04, 14.04 or newer. -With appropriate changes, build should work on any other Linux distribution. -Build is not intended to work on Mac OS X. -Only x86_64 with SSE 4.2 is supported. Support for AArch64 is experimental. - -To test for SSE 4.2, do - -```bash -grep -q sse4_2 /proc/cpuinfo && echo "SSE 4.2 supported" || echo "SSE 4.2 not supported" -``` - -## Install Git and CMake - -```bash -sudo apt-get install git cmake3 -``` - -Or just cmake on newer systems. - -## Detect number of threads - -```bash -export THREADS=$(grep -c ^processor /proc/cpuinfo) -``` - -## Install GCC 7 - -There are several ways to do it. - -### Install from PPA package - -```bash -sudo apt-get install software-properties-common -sudo apt-add-repository ppa:ubuntu-toolchain-r/test -sudo apt-get update -sudo apt-get install gcc-7 g++-7 -``` - -### Install from sources - -Look at [https://github.com/yandex/ClickHouse/blob/master/utils/prepare-environment/install-gcc.sh] - -## Use GCC 7 for builds - -```bash -export CC=gcc-7 -export CXX=g++-7 -``` - -## Install required libraries from packages - -```bash -sudo apt-get install libicu-dev libreadline-dev libmysqlclient-dev libssl-dev unixodbc-dev ninja-build -``` - -## Checkout ClickHouse sources - -To get latest stable version: - -```bash -git clone -b stable --recursive git@github.com:yandex/ClickHouse.git -# or: git clone -b stable --recursive https://github.com/yandex/ClickHouse.git - -cd ClickHouse -``` - -For development, switch to the `master` branch. -For latest release candidate, switch to the `testing` branch. - -## Build ClickHouse - -There are two variants of build. - -### Build release package - -Install prerequisites to build debian packages. - -```bash -sudo apt-get install devscripts dupload fakeroot debhelper -``` - -Install recent version of clang. - -Clang is embedded into ClickHouse package and used at runtime. Minimum version is 5.0. It is optional. - -To install clang, look at `utils/prepare-environment/install-clang.sh` - -You may also build ClickHouse with clang for development purposes. -For production releases, GCC is used. - -Run release script: - -```bash -rm -f ../clickhouse*.deb -./release -``` - -You will find built packages in parent directory: - -```bash -ls -l ../clickhouse*.deb -``` - -Note that usage of debian packages is not required. -ClickHouse has no runtime dependencies except libc, so it could work on almost any Linux. - -Installing just built packages on development server: - -```bash -sudo dpkg -i ../clickhouse*.deb -sudo service clickhouse-server start -``` - -### Build to work with code - -```bash -mkdir build -cd build -cmake .. -make -j $THREADS -cd .. -``` - -To create an executable, run `make clickhouse`. -This will create the `dbms/src/Server/clickhouse` executable, which can be used with `client` or `server` arguments. diff --git a/docs/ru/development/build.md b/docs/ru/development/build.md new file mode 120000 index 00000000000..480dbc2e9f5 --- /dev/null +++ b/docs/ru/development/build.md @@ -0,0 +1 @@ +../../en/development/build.md \ No newline at end of file diff --git a/docs/ru/development/build_osx.md b/docs/ru/development/build_osx.md deleted file mode 100644 index 199e90c2d6e..00000000000 --- a/docs/ru/development/build_osx.md +++ /dev/null @@ -1,44 +0,0 @@ -# How to build ClickHouse on Mac OS X - -Build should work on Mac OS X 10.12. If you're using earlier version, you can try to build ClickHouse using Gentoo Prefix and clang sl in this instruction. -With appropriate changes, build should work on any other OS X distribution. - -## Install Homebrew - -```bash -/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" -``` - -## Install required compilers, tools, libraries - -```bash -brew install cmake gcc icu4c mysql openssl unixodbc libtool gettext zlib readline boost --cc=gcc-7 -``` - -## Checkout ClickHouse sources - -To get the latest stable version: - -```bash -git clone -b stable --recursive --depth=10 git@github.com:yandex/ClickHouse.git -# or: git clone -b stable --recursive --depth=10 https://github.com/yandex/ClickHouse.git - -cd ClickHouse -``` - -For development, switch to the `master` branch. -For the latest release candidate, switch to the `testing` branch. - -## Build ClickHouse - -```bash -mkdir build -cd build -cmake .. -DCMAKE_CXX_COMPILER=`which g++-7` -DCMAKE_C_COMPILER=`which gcc-7` -make -j `sysctl -n hw.ncpu` -cd .. -``` - -## Caveats - -If you intend to run clickhouse-server, make sure to increase system's maxfiles variable. See [MacOS.md](https://github.com/yandex/ClickHouse/blob/master/MacOS.md) for more details. diff --git a/docs/ru/development/build_osx.md b/docs/ru/development/build_osx.md new file mode 120000 index 00000000000..f9adaf24584 --- /dev/null +++ b/docs/ru/development/build_osx.md @@ -0,0 +1 @@ +../../en/development/build_osx.md \ No newline at end of file diff --git a/docs/ru/development/index.md b/docs/ru/development/index.md deleted file mode 100644 index 34b1dab5c5b..00000000000 --- a/docs/ru/development/index.md +++ /dev/null @@ -1 +0,0 @@ -# ClickHouse Development diff --git a/docs/ru/development/index.md b/docs/ru/development/index.md new file mode 120000 index 00000000000..1e2ad97dcc5 --- /dev/null +++ b/docs/ru/development/index.md @@ -0,0 +1 @@ +../../en/development/index.md \ No newline at end of file diff --git a/docs/ru/development/tests.md b/docs/ru/development/tests.md deleted file mode 100644 index c7f90cdcd45..00000000000 --- a/docs/ru/development/tests.md +++ /dev/null @@ -1,32 +0,0 @@ -# How to run ClickHouse tests - -The `clickhouse-test` utility that is used for functional testing is written using Python 2.x. -It also requires you to have some third-party packages: - -```bash -$ pip install lxml termcolor -``` - -In a nutshell: - -- Put the `clickhouse` program to `/usr/bin` (or `PATH`) -- Create a `clickhouse-client` symlink in `/usr/bin` pointing to `clickhouse` -- Start the `clickhouse` server -- `cd dbms/tests/` -- Run `./clickhouse-test` - -## Example usage - -Run `./clickhouse-test --help` to see available options. - -To run tests without having to create a symlink or mess with `PATH`: - -```bash -./clickhouse-test -c "../../build/dbms/src/Server/clickhouse --client" -``` - -To run a single test, i.e. `00395_nullable`: - -```bash -./clickhouse-test 00395 -``` diff --git a/docs/ru/development/tests.md b/docs/ru/development/tests.md new file mode 120000 index 00000000000..c03d36c3916 --- /dev/null +++ b/docs/ru/development/tests.md @@ -0,0 +1 @@ +../../en/development/tests.md \ No newline at end of file From 7df26c93ee5c1b92e903b42a8d3a4233587ebe62 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 17:45:52 +0300 Subject: [PATCH 138/231] Minor modifications in build instruction #2277 --- docs/en/development/build.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/en/development/build.md b/docs/en/development/build.md index 2caf33fd593..fbc2333f583 100644 --- a/docs/en/development/build.md +++ b/docs/en/development/build.md @@ -14,10 +14,10 @@ grep -q sse4_2 /proc/cpuinfo && echo "SSE 4.2 supported" || echo "SSE 4.2 not su ## Install Git and CMake ```bash -sudo apt-get install git cmake3 +sudo apt-get install git cmake ``` -Or just cmake on newer systems. +Or cmake3 instead of cmake on older systems. ## Detect the number of threads From c123be1fe449983bc0d9cd7644597ed0024900fe Mon Sep 17 00:00:00 2001 From: pyos Date: Thu, 10 May 2018 17:49:25 +0300 Subject: [PATCH 139/231] Fix incorrect phi node edges in `if` --- dbms/src/Functions/FunctionsConditional.h | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/dbms/src/Functions/FunctionsConditional.h b/dbms/src/Functions/FunctionsConditional.h index ad3e6625396..355e271127e 100644 --- a/dbms/src/Functions/FunctionsConditional.h +++ b/dbms/src/Functions/FunctionsConditional.h @@ -144,8 +144,8 @@ public: auto * cond = values[i](); if (!null_is_false && types[i]->isNullable()) { - returns.emplace_back(head, null); auto * nonnull = llvm::BasicBlock::Create(head->getContext(), "", head->getParent()); + returns.emplace_back(b.GetInsertBlock(), null); b.CreateCondBr(b.CreateExtractValue(cond, {1}), join, nonnull); b.SetInsertPoint(nonnull); b.CreateCondBr(nativeBoolCast(b, removeNullable(types[i]), b.CreateExtractValue(cond, {0})), then, next); @@ -155,12 +155,13 @@ public: b.CreateCondBr(nativeBoolCast(b, types[i], cond), then, next); } b.SetInsertPoint(then); - returns.emplace_back(then, nativeCast(b, types[i + 1], values[i + 1](), type)); + auto * value = nativeCast(b, types[i + 1], values[i + 1](), type); + returns.emplace_back(b.GetInsertBlock(), value); b.CreateBr(join); b.SetInsertPoint(next); - head = next; } - returns.emplace_back(head, nativeCast(b, types.back(), values.back()(), type)); + auto * value = nativeCast(b, types.back(), values.back()(), type); + returns.emplace_back(b.GetInsertBlock(), value); b.CreateBr(join); b.SetInsertPoint(join); auto * phi = b.CreatePHI(toNativeType(b, type), returns.size()); From 93352237d931283b67678ca3e2884a3f3881ca1c Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 18:20:19 +0300 Subject: [PATCH 140/231] Better exception message [#CLICKHOUSE-2] --- dbms/src/Functions/FunctionsConversion.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dbms/src/Functions/FunctionsConversion.cpp b/dbms/src/Functions/FunctionsConversion.cpp index 380a5b20687..b0f5bbb2ed6 100644 --- a/dbms/src/Functions/FunctionsConversion.cpp +++ b/dbms/src/Functions/FunctionsConversion.cpp @@ -21,7 +21,7 @@ void throwExceptionForIncompletelyParsedValue( message_buf << " at begin of string"; if (to_type.isNumber()) - message_buf << ". Note: there are to" << to_type.getName() << "OrZero function, which returns zero instead of throwing exception."; + message_buf << ". Note: there are to" << to_type.getName() << "OrZero and to" << to_type.getName() << "OrNull functions, which returns zero/NULL instead of throwing exception."; throw Exception(message_buf.str(), ErrorCodes::CANNOT_PARSE_TEXT); } From c00b5fd55dd0ff655e7bc92b2129b4256e70c507 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 17:44:45 +0300 Subject: [PATCH 141/231] Changed some doc files to symlinks #2277 --- docs/ru/development/architecture.md | 193 +--------------------------- docs/ru/development/build.md | 128 +----------------- docs/ru/development/build_osx.md | 45 +------ docs/ru/development/index.md | 2 +- docs/ru/development/tests.md | 33 +---- 5 files changed, 5 insertions(+), 396 deletions(-) mode change 100644 => 120000 docs/ru/development/architecture.md mode change 100644 => 120000 docs/ru/development/build.md mode change 100644 => 120000 docs/ru/development/build_osx.md mode change 100644 => 120000 docs/ru/development/index.md mode change 100644 => 120000 docs/ru/development/tests.md diff --git a/docs/ru/development/architecture.md b/docs/ru/development/architecture.md deleted file mode 100644 index 5258907f9d1..00000000000 --- a/docs/ru/development/architecture.md +++ /dev/null @@ -1,192 +0,0 @@ -# Overview of ClickHouse architecture - -ClickHouse is a true column-oriented DBMS. Data is stored by columns, and during query execution data is processed by arrays (vectors or chunks of columns). Whenever possible, operations are dispatched on arrays, rather than on individual values. This is called "vectorized query execution," and it helps lower dispatch cost relative to the cost of actual data processing. - -> This idea is nothing new. It dates back to the `APL` programming language and its descendants: `A+`, `J`, `K`, and `Q`. Array programming is widely used in scientific data processing. Neither is this idea something new in relational databases: for example, it is used in the `Vectorwise` system. -> -> There are two different approaches for speeding up query processing: vectorized query execution and runtime code generation. In the latter, the code is generated for every kind of query on the fly, removing all indirection and dynamic dispatch. Neither of these approaches is strictly better than the other. Runtime code generation can be better when it fuses many operations together, thus fully utilizing CPU execution units and the pipeline. Vectorized query execution can be less practical, because it involves temporary vectors that must be written to cache and read back. If the temporary data does not fit in the L2 cache, this becomes an issue. But vectorized query execution more easily utilizes the SIMD capabilities of the CPU. A [research paper](http://15721.courses.cs.cmu.edu/spring2016/papers/p5-sompolski.pdf) written by our friends shows that it is better to combine both approaches. ClickHouse mainly uses vectorized query execution and has limited initial support for runtime code generation (only the inner loop of the first stage of GROUP BY can be compiled). - -## Columns - -To represent columns in memory (actually, chunks of columns), the `IColumn` interface is used. This interface provides helper methods for implementation of various relational operators. Almost all operations are immutable: they do not modify the original column, but create a new modified one. For example, the `IColumn::filter` method accepts a filter byte mask and creates a new filtered column. It is used for the `WHERE` and `HAVING` relational operators. Additional examples: the `IColumn::permute` method to support `ORDER BY`, the `IColumn::cut` method to support `LIMIT`, and so on. - -Various `IColumn` implementations (`ColumnUInt8`, `ColumnString` and so on) are responsible for the memory layout of columns. Memory layout is usually a contiguous array. For the integer type of columns it is just one contiguous array, like `std::vector`. For `String` and `Array` columns, it is two vectors: one for all array elements, placed contiguously, and a second one for offsets to the beginning of each array. There is also `ColumnConst` that stores just one value in memory, but looks like a column. - -## Field - -Nevertheless, it is possible to work with individual values as well. To represent an individual value, `Field` is used. `Field` is just a discriminated union of `UInt64`, `Int64`, `Float64`, `String` and `Array`. `IColumn` has the `operator[]` method to get the n-th value as a `Field`, and the `insert` method to append a `Field` to the end of a column. These methods are not very efficient, because they require dealing with temporary `Field` objects representing an individual value. There are more efficient methods, such as `insertFrom`, `insertRangeFrom`, and so on. - -`Field` doesn't have enough information about a specific data type for a table. For example, `UInt8`, `UInt16`, `UInt32`, and `UInt64` are all represented as `UInt64` in a `Field`. - -## Leaky abstractions - -`IColumn` has methods for common relational transformations of data, but they don't meet all needs. For example, `ColumnUInt64` doesn't have a method to calculate the sum of two columns, and `ColumnString` doesn't have a method to run a substring search. These countless routines are implemented outside of `IColumn`. - -Various functions on columns can be implemented in a generic, non-efficient way using `IColumn` methods to extract `Field` values, or in a specialized way using knowledge of inner memory layout of data in a specific `IColumn` implementation. To do this, functions are cast to a specific `IColumn` type and deal with internal representation directly. For example, `ColumnUInt64` has the `getData` method that returns a reference to an internal array, then a separate routine reads or fills that array directly. In fact, we have "leaky abstractions" to allow efficient specializations of various routines. - -## Data types - -`IDataType` is responsible for serialization and deserialization: for reading and writing chunks of columns or individual values in binary or text form. -`IDataType` directly corresponds to data types in tables. For example, there are `DataTypeUInt32`, `DataTypeDateTime`, `DataTypeString` and so on. - -`IDataType` and `IColumn` are only loosely related to each other. Different data types can be represented in memory by the same `IColumn` implementations. For example, `DataTypeUInt32` and `DataTypeDateTime` are both represented by `ColumnUInt32` or `ColumnConstUInt32`. In addition, the same data type can be represented by different `IColumn` implementations. For example, `DataTypeUInt8` can be represented by `ColumnUInt8` or `ColumnConstUInt8`. - -`IDataType` only stores metadata. For instance, `DataTypeUInt8` doesn't store anything at all (except vptr) and `DataTypeFixedString` stores just `N` (the size of fixed-size strings). - -`IDataType` has helper methods for various data formats. Examples are methods to serialize a value with possible quoting, to serialize a value for JSON, and to serialize a value as part of XML format. There is no direct correspondence to data formats. For example, the different data formats `Pretty` and `TabSeparated` can use the same `serializeTextEscaped` helper method from the `IDataType` interface. - -## Block - -A `Block` is a container that represents a subset (chunk) of a table in memory. It is just a set of triples: `(IColumn, IDataType, column name)`. During query execution, data is processed by `Block`s. If we have a `Block`, we have data (in the `IColumn` object), we have information about its type (in `IDataType`) that tells us how to deal with that column, and we have the column name (either the original column name from the table, or some artificial name assigned for getting temporary results of calculations). - -When we calculate some function over columns in a block, we add another column with its result to the block, and we don't touch columns for arguments of the function because operations are immutable. Later, unneeded columns can be removed from the block, but not modified. This is convenient for elimination of common subexpressions. - -Blocks are created for every processed chunk of data. Note that for the same type of calculation, the column names and types remain the same for different blocks, and only column data changes. It is better to split block data from the block header, because small block sizes will have a high overhead of temporary strings for copying shared_ptrs and column names. - -## Block Streams - -Block streams are for processing data. We use streams of blocks to read data from somewhere, perform data transformations, or write data to somewhere. `IBlockInputStream` has the `read` method to fetch the next block while available. `IBlockOutputStream` has the `write` method to push the block somewhere. - -Streams are responsible for: - -1. Reading or writing to a table. The table just returns a stream for reading or writing blocks. -2. Implementing data formats. For example, if you want to output data to a terminal in `Pretty` format, you create a block output stream where you push blocks, and it formats them. -3. Performing data transformations. Let's say you have `IBlockInputStream` and want to create a filtered stream. You create `FilterBlockInputStream` and initialize it with your stream. Then when you pull a block from `FilterBlockInputStream`, it pulls a block from your stream, filters it, and returns the filtered block to you. Query execution pipelines are represented this way. - -There are more sophisticated transformations. For example, when you pull from `AggregatingBlockInputStream`, it reads all data from its source, aggregates it, and then returns a stream of aggregated data for you. Another example: `UnionBlockInputStream` accepts many input sources in the constructor and also a number of threads. It launches multiple threads and reads from multiple sources in parallel. - -> Block streams use the "pull" approach to control flow: when you pull a block from the first stream, it consequently pulls the required blocks from nested streams, and the entire execution pipeline will work. Neither "pull" nor "push" is the best solution, because control flow is implicit, and that limits implementation of various features like simultaneous execution of multiple queries (merging many pipelines together). This limitation could be overcome with coroutines or just running extra threads that wait for each other. We may have more possibilities if we make control flow explicit: if we locate the logic for passing data from one calculation unit to another outside of those calculation units. Read this [nice article](http://journal.stuffwithstuff.com/2013/01/13/iteration-inside-and-out/) for more thoughts. -> -> We should note that the query execution pipeline creates temporary data at each step. We try to keep block size small enough so that temporary data fits in the CPU cache. With that assumption, writing and reading temporary data is almost free in comparison with other calculations. We could consider an alternative, which is to fuse many operations in the pipeline together, to make the pipeline as short as possible and remove much of the temporary data. This could be an advantage, but it also has drawbacks. For example, a split pipeline makes it easy to implement caching intermediate data, stealing intermediate data from similar queries running at the same time, and merging pipelines for similar queries. - -## Formats - -Data formats are implemented with block streams. There are "presentational" formats only suitable for output of data to the client, such as `Pretty` format, which provides only `IBlockOutputStream`. And there are input/output formats, such as `TabSeparated` or `JSONEachRow`. - -There are also row streams: `IRowInputStream` and `IRowOutputStream`. They allow you to pull/push data by individual rows, not by blocks. And they are only needed to simplify implementation of row-oriented formats. The wrappers `BlockInputStreamFromRowInputStream` and `BlockOutputStreamFromRowOutputStream` allow you to convert row-oriented streams to regular block-oriented streams. - -## I/O - -For byte-oriented input/output, there are `ReadBuffer` and `WriteBuffer` abstract classes. They are used instead of C++ `iostream`'s. Don't worry: every mature C++ project is using something other than `iostream`'s for good reasons. - -`ReadBuffer` and `WriteBuffer` are just a contiguous buffer and a cursor pointing to the position in that buffer. Implementations may own or not own the memory for the buffer. There is a virtual method to fill the buffer with the following data (for `ReadBuffer`) or to flush the buffer somewhere (for `WriteBuffer`). The virtual methods are rarely called. - -Implementations of `ReadBuffer`/`WriteBuffer` are used for working with files and file descriptors and network sockets, for implementing compression (`CompressedWriteBuffer` is initialized with another WriteBuffer and performs compression before writing data to it), and for other purposes – the names `ConcatReadBuffer`, `LimitReadBuffer`, and `HashingWriteBuffer` speak for themselves. - -Read/WriteBuffers only deal with bytes. To help with formatted input/output (for instance, to write a number in decimal format), there are functions from `ReadHelpers` and `WriteHelpers` header files. - -Let's look at what happens when you want to write a result set in `JSON` format to stdout. You have a result set ready to be fetched from `IBlockInputStream`. You create `WriteBufferFromFileDescriptor(STDOUT_FILENO)` to write bytes to stdout. You create `JSONRowOutputStream`, initialized with that `WriteBuffer`, to write rows in `JSON` to stdout. You create `BlockOutputStreamFromRowOutputStream` on top of it, to represent it as `IBlockOutputStream`. Then you call `copyData` to transfer data from `IBlockInputStream` to `IBlockOutputStream`, and everything works. Internally, `JSONRowOutputStream` will write various JSON delimiters and call the `IDataType::serializeTextJSON` method with a reference to `IColumn` and the row number as arguments. Consequently, `IDataType::serializeTextJSON` will call a method from `WriteHelpers.h`: for example, `writeText` for numeric types and `writeJSONString` for `DataTypeString`. - -## Tables - -Tables are represented by the `IStorage` interface. Different implementations of that interface are different table engines. Examples are `StorageMergeTree`, `StorageMemory`, and so on. Instances of these classes are just tables. - -The most important `IStorage` methods are `read` and `write`. There are also `alter`, `rename`, `drop`, and so on. The `read` method accepts the following arguments: the set of columns to read from a table, the `AST` query to consider, and the desired number of streams to return. It returns one or multiple `IBlockInputStream` objects and information about the stage of data processing that was completed inside a table engine during query execution. - -In most cases, the read method is only responsible for reading the specified columns from a table, not for any further data processing. All further data processing is done by the query interpreter and is outside the responsibility of `IStorage`. - -But there are notable exceptions: -- The AST query is passed to the `read` method and the table engine can use it to derive index usage and to read less data from a table. -- Sometimes the table engine can process data itself to a specific stage. For example, `StorageDistributed` can send a query to remote servers, ask them to process data to a stage where data from different remote servers can be merged, and return that preprocessed data. -The query interpreter then finishes processing the data. - -The table's `read` method can return multiple `IBlockInputStream` objects to allow parallel data processing. These multiple block input streams can read from a table in parallel. Then you can wrap these streams with various transformations (such as expression evaluation or filtering) that can be calculated independently and create a `UnionBlockInputStream` on top of them, to read from multiple streams in parallel. - -There are also `TableFunction`s. These are functions that return a temporary `IStorage` object to use in the `FROM` clause of a query. - -To get a quick idea of how to implement your own table engine, look at something simple, like `StorageMemory` or `StorageTinyLog`. - -> As the result of the `read` method, `IStorage` returns `QueryProcessingStage` – information about what parts of the query were already calculated inside storage. Currently we have only very coarse granularity for that information. There is no way for the storage to say "I have already processed this part of the expression in WHERE, for this range of data". We need to work on that. - -## Parsers - -A query is parsed by a hand-written recursive descent parser. For example, `ParserSelectQuery` just recursively calls the underlying parsers for various parts of the query. Parsers create an `AST`. The `AST` is represented by nodes, which are instances of `IAST`. - -> Parser generators are not used for historical reasons. - -## Interpreters - -Interpreters are responsible for creating the query execution pipeline from an `AST`. There are simple interpreters, such as `InterpreterExistsQuery`and `InterpreterDropQuery`, or the more sophisticated `InterpreterSelectQuery`. The query execution pipeline is a combination of block input or output streams. For example, the result of interpreting the `SELECT` query is the `IBlockInputStream` to read the result set from; the result of the INSERT query is the `IBlockOutputStream` to write data for insertion to; and the result of interpreting the `INSERT SELECT` query is the `IBlockInputStream` that returns an empty result set on the first read, but that copies data from `SELECT` to `INSERT` at the same time. - -`InterpreterSelectQuery` uses `ExpressionAnalyzer` and `ExpressionActions` machinery for query analysis and transformations. This is where most rule-based query optimizations are done. `ExpressionAnalyzer` is quite messy and should be rewritten: various query transformations and optimizations should be extracted to separate classes to allow modular transformations or query. - -## Functions - -There are ordinary functions and aggregate functions. For aggregate functions, see the next section. - -Ordinary functions don't change the number of rows – they work as if they are processing each row independently. In fact, functions are not called for individual rows, but for `Block`'s of data to implement vectorized query execution. - -There are some miscellaneous functions, like `blockSize`, `rowNumberInBlock`, and `runningAccumulate`, that exploit block processing and violate the independence of rows. - -ClickHouse has strong typing, so implicit type conversion doesn't occur. If a function doesn't support a specific combination of types, an exception will be thrown. But functions can work (be overloaded) for many different combinations of types. For example, the `plus` function (to implement the `+` operator) works for any combination of numeric types: `UInt8` + `Float32`, `UInt16` + `Int8`, and so on. Also, some variadic functions can accept any number of arguments, such as the `concat` function. - -Implementing a function may be slightly inconvenient because a function explicitly dispatches supported data types and supported `IColumns`. For example, the `plus` function has code generated by instantiation of a C++ template for each combination of numeric types, and for constant or non-constant left and right arguments. - -> This is a nice place to implement runtime code generation to avoid template code bloat. Also, it will make it possible to add fused functions like fused multiply-add, or to make multiple comparisons in one loop iteration. -> -> Due to vectorized query execution, functions are not short-circuit. For example, if you write `WHERE f(x) AND g(y)`, both sides will be calculated, even for rows, when `f(x)` is zero (except when `f(x)` is a zero constant expression). But if selectivity of the `f(x)` condition is high, and calculation of `f(x)` is much cheaper than `g(y)`, it's better to implement multi-pass calculation: first calculate `f(x)`, then filter columns by the result, and then calculate `g(y)` only for smaller, filtered chunks of data. - -## Aggregate Functions - -Aggregate functions are stateful functions. They accumulate passed values into some state, and allow you to get results from that state. They are managed with the `IAggregateFunction` interface. States can be rather simple (the state for `AggregateFunctionCount` is just a single `UInt64` value) or quite complex (the state of `AggregateFunctionUniqCombined` is a combination of a linear array, a hash table and a `HyperLogLog` probabilistic data structure). - -To deal with multiple states while executing a high-cardinality `GROUP BY` query, states are allocated in `Arena` (a memory pool), or they could be allocated in any suitable piece of memory. States can have a non-trivial constructor and destructor: for example, complex aggregation states can allocate additional memory themselves. This requires some attention to creating and destroying states and properly passing their ownership, to keep track of who and when will destroy states. - -Aggregation states can be serialized and deserialized to pass over the network during distributed query execution or to write them on disk where there is not enough RAM. They can even be stored in a table with the `DataTypeAggregateFunction` to allow incremental aggregation of data. - -> The serialized data format for aggregate function states is not versioned right now. This is ok if aggregate states are only stored temporarily. But we have the `AggregatingMergeTree` table engine for incremental aggregation, and people are already using it in production. This is why we should add support for backward compatibility when changing the serialized format for any aggregate function in the future. - -## Server - -The server implements several different interfaces: -- An HTTP interface for any foreign clients. -- A TCP interface for the native ClickHouse client and for cross-server communication during distributed query execution. -- An interface for transferring data for replication. - -Internally, it is just a basic multithreaded server without coroutines, fibers, etc. Since the server is not designed to process a high rate of simple queries but is intended to process a relatively low rate of complex queries, each of them can process a vast amount of data for analytics. - -The server initializes the `Context` class with the necessary environment for query execution: the list of available databases, users and access rights, settings, clusters, the process list, the query log, and so on. This environment is used by interpreters. - -We maintain full backward and forward compatibility for the server TCP protocol: old clients can talk to new servers and new clients can talk to old servers. But we don't want to maintain it eternally, and we are removing support for old versions after about one year. - -> For all external applications, we recommend using the HTTP interface because it is simple and easy to use. The TCP protocol is more tightly linked to internal data structures: it uses an internal format for passing blocks of data and it uses custom framing for compressed data. We haven't released a C library for that protocol because it requires linking most of the ClickHouse codebase, which is not practical. - -## Distributed query execution - -Servers in a cluster setup are mostly independent. You can create a `Distributed` table on one or all servers in a cluster. The `Distributed` table does not store data itself – it only provides a "view" to all local tables on multiple nodes of a cluster. When you SELECT from a `Distributed` table, it rewrites that query, chooses remote nodes according to load balancing settings, and sends the query to them. The `Distributed` table requests remote servers to process a query just up to a stage where intermediate results from different servers can be merged. Then it receives the intermediate results and merges them. The distributed table tries to distribute as much work as possible to remote servers, and does not send much intermediate data over the network. - -> Things become more complicated when you have subqueries in IN or JOIN clauses and each of them uses a `Distributed` table. We have different strategies for execution of these queries. -> -> There is no global query plan for distributed query execution. Each node has its own local query plan for its part of the job. We only have simple one-pass distributed query execution: we send queries for remote nodes and then merge the results. But this is not feasible for difficult queries with high cardinality GROUP BYs or with a large amount of temporary data for JOIN: in such cases, we need to "reshuffle" data between servers, which requires additional coordination. ClickHouse does not support that kind of query execution, and we need to work on it. - -## Merge Tree - -`MergeTree` is a family of storage engines that supports indexing by primary key. The primary key can be an arbitary tuple of columns or expressions. Data in a `MergeTree` table is stored in "parts". Each part stores data in the primary key order (data is ordered lexicographically by the primary key tuple). All the table columns are stored in separate `column.bin` files in these parts. The files consist of compressed blocks. Each block is usually from 64 KB to 1 MB of uncompressed data, depending on the average value size. The blocks consist of column values placed contiguously one after the other. Column values are in the same order for each column (the order is defined by the primary key), so when you iterate by many columns, you get values for the corresponding rows. - -The primary key itself is "sparse". It doesn't address each single row, but only some ranges of data. A separate `primary.idx` file has the value of the primary key for each N-th row, where N is called `index_granularity` (usually, N = 8192). Also, for each column, we have `column.mrk` files with "marks," which are offsets to each N-th row in the data file. Each mark is a pair: the offset in the file to the beginning of the compressed block, and the offset in the decompressed block to the beginning of data. Usually compressed blocks are aligned by marks, and the offset in the decompressed block is zero. Data for `primary.idx` always resides in memory and data for `column.mrk` files is cached. - -When we are going to read something from a part in `MergeTree`, we look at `primary.idx` data and locate ranges that could possibly contain requested data, then look at `column.mrk` data and calculate offsets for where to start reading those ranges. Because of sparseness, excess data may be read. ClickHouse is not suitable for a high load of simple point queries, because the entire range with `index_granularity` rows must be read for each key, and the entire compressed block must be decompressed for each column. We made the index sparse because we must be able to maintain trillions of rows per single server without noticeable memory consumption for the index. Also, because the primary key is sparse, it is not unique: it cannot check the existence of the key in the table at INSERT time. You could have many rows with the same key in a table. - -When you `INSERT` a bunch of data into `MergeTree`, that bunch is sorted by primary key order and forms a new part. To keep the number of parts relatively low, there are background threads that periodically select some parts and merge them to a single sorted part. That's why it is called `MergeTree`. Of course, merging leads to "write amplification". All parts are immutable: they are only created and deleted, but not modified. When SELECT is run, it holds a snapshot of the table (a set of parts). After merging, we also keep old parts for some time to make recovery after failure easier, so if we see that some merged part is probably broken, we can replace it with its source parts. - -`MergeTree` is not an LSM tree because it doesn't contain "memtable" and "log": inserted data is written directly to the filesystem. This makes it suitable only to INSERT data in batches, not by individual row and not very frequently – about once per second is ok, but a thousand times a second is not. We did it this way for simplicity's sake, and because we are already inserting data in batches in our applications. - -> MergeTree tables can only have one (primary) index: there aren't any secondary indices. It would be nice to allow multiple physical representations under one logical table, for example, to store data in more than one physical order or even to allow representations with pre-aggregated data along with original data. -> -> There are MergeTree engines that are doing additional work during background merges. Examples are `CollapsingMergeTree` and `AggregatingMergeTree`. This could be treated as special support for updates. Keep in mind that these are not real updates because users usually have no control over the time when background merges will be executed, and data in a `MergeTree` table is almost always stored in more than one part, not in completely merged form. - -## Replication - -Replication in ClickHouse is implemented on a per-table basis. You could have some replicated and some non-replicated tables on the same server. You could also have tables replicated in different ways, such as one table with two-factor replication and another with three-factor. - -Replication is implemented in the `ReplicatedMergeTree` storage engine. The path in `ZooKeeper` is specified as a parameter for the storage engine. All tables with the same path in `ZooKeeper` become replicas of each other: they synchronise their data and maintain consistency. Replicas can be added and removed dynamically simply by creating or dropping a table. - -Replication uses an asynchronous multi-master scheme. You can insert data into any replica that has a session with `ZooKeeper`, and data is replicated to all other replicas asynchronously. Because ClickHouse doesn't support UPDATEs, replication is conflict-free. As there is no quorum acknowledgment of inserts, just-inserted data might be lost if one node fails. - -Metadata for replication is stored in ZooKeeper. There is a replication log that lists what actions to do. Actions are: get part; merge parts; drop partition, etc. Each replica copies the replication log to its queue and then executes the actions from the queue. For example, on insertion, the "get part" action is created in the log, and every replica downloads that part. Merges are coordinated between replicas to get byte-identical results. All parts are merged in the same way on all replicas. To achieve this, one replica is elected as the leader, and that replica initiates merges and writes "merge parts" actions to the log. - -Replication is physical: only compressed parts are transferred between nodes, not queries. To lower the network cost (to avoid network amplification), merges are processed on each replica independently in most cases. Large merged parts are sent over the network only in cases of significant replication lag. - -In addition, each replica stores its state in ZooKeeper as the set of parts and its checksums. When the state on the local filesystem diverges from the reference state in ZooKeeper, the replica restores its consistency by downloading missing and broken parts from other replicas. When there is some unexpected or broken data in the local filesystem, ClickHouse does not remove it, but moves it to a separate directory and forgets it. - -> The ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is not elastic, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load will be uneven. This implementation gives you more control, and it is fine for relatively small clusters such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that will span its data across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically. diff --git a/docs/ru/development/architecture.md b/docs/ru/development/architecture.md new file mode 120000 index 00000000000..abda4dd48a8 --- /dev/null +++ b/docs/ru/development/architecture.md @@ -0,0 +1 @@ +../../en/development/architecture.md \ No newline at end of file diff --git a/docs/ru/development/build.md b/docs/ru/development/build.md deleted file mode 100644 index 73554285fb3..00000000000 --- a/docs/ru/development/build.md +++ /dev/null @@ -1,127 +0,0 @@ -# How to build ClickHouse on Linux - -Build should work on Linux Ubuntu 12.04, 14.04 or newer. -With appropriate changes, build should work on any other Linux distribution. -Build is not intended to work on Mac OS X. -Only x86_64 with SSE 4.2 is supported. Support for AArch64 is experimental. - -To test for SSE 4.2, do - -```bash -grep -q sse4_2 /proc/cpuinfo && echo "SSE 4.2 supported" || echo "SSE 4.2 not supported" -``` - -## Install Git and CMake - -```bash -sudo apt-get install git cmake3 -``` - -Or just cmake on newer systems. - -## Detect number of threads - -```bash -export THREADS=$(grep -c ^processor /proc/cpuinfo) -``` - -## Install GCC 7 - -There are several ways to do it. - -### Install from PPA package - -```bash -sudo apt-get install software-properties-common -sudo apt-add-repository ppa:ubuntu-toolchain-r/test -sudo apt-get update -sudo apt-get install gcc-7 g++-7 -``` - -### Install from sources - -Look at [https://github.com/yandex/ClickHouse/blob/master/utils/prepare-environment/install-gcc.sh] - -## Use GCC 7 for builds - -```bash -export CC=gcc-7 -export CXX=g++-7 -``` - -## Install required libraries from packages - -```bash -sudo apt-get install libicu-dev libreadline-dev libmysqlclient-dev libssl-dev unixodbc-dev ninja-build -``` - -## Checkout ClickHouse sources - -To get latest stable version: - -```bash -git clone -b stable --recursive git@github.com:yandex/ClickHouse.git -# or: git clone -b stable --recursive https://github.com/yandex/ClickHouse.git - -cd ClickHouse -``` - -For development, switch to the `master` branch. -For latest release candidate, switch to the `testing` branch. - -## Build ClickHouse - -There are two variants of build. - -### Build release package - -Install prerequisites to build debian packages. - -```bash -sudo apt-get install devscripts dupload fakeroot debhelper -``` - -Install recent version of clang. - -Clang is embedded into ClickHouse package and used at runtime. Minimum version is 5.0. It is optional. - -To install clang, look at `utils/prepare-environment/install-clang.sh` - -You may also build ClickHouse with clang for development purposes. -For production releases, GCC is used. - -Run release script: - -```bash -rm -f ../clickhouse*.deb -./release -``` - -You will find built packages in parent directory: - -```bash -ls -l ../clickhouse*.deb -``` - -Note that usage of debian packages is not required. -ClickHouse has no runtime dependencies except libc, so it could work on almost any Linux. - -Installing just built packages on development server: - -```bash -sudo dpkg -i ../clickhouse*.deb -sudo service clickhouse-server start -``` - -### Build to work with code - -```bash -mkdir build -cd build -cmake .. -make -j $THREADS -cd .. -``` - -To create an executable, run `make clickhouse`. -This will create the `dbms/src/Server/clickhouse` executable, which can be used with `client` or `server` arguments. diff --git a/docs/ru/development/build.md b/docs/ru/development/build.md new file mode 120000 index 00000000000..480dbc2e9f5 --- /dev/null +++ b/docs/ru/development/build.md @@ -0,0 +1 @@ +../../en/development/build.md \ No newline at end of file diff --git a/docs/ru/development/build_osx.md b/docs/ru/development/build_osx.md deleted file mode 100644 index 199e90c2d6e..00000000000 --- a/docs/ru/development/build_osx.md +++ /dev/null @@ -1,44 +0,0 @@ -# How to build ClickHouse on Mac OS X - -Build should work on Mac OS X 10.12. If you're using earlier version, you can try to build ClickHouse using Gentoo Prefix and clang sl in this instruction. -With appropriate changes, build should work on any other OS X distribution. - -## Install Homebrew - -```bash -/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" -``` - -## Install required compilers, tools, libraries - -```bash -brew install cmake gcc icu4c mysql openssl unixodbc libtool gettext zlib readline boost --cc=gcc-7 -``` - -## Checkout ClickHouse sources - -To get the latest stable version: - -```bash -git clone -b stable --recursive --depth=10 git@github.com:yandex/ClickHouse.git -# or: git clone -b stable --recursive --depth=10 https://github.com/yandex/ClickHouse.git - -cd ClickHouse -``` - -For development, switch to the `master` branch. -For the latest release candidate, switch to the `testing` branch. - -## Build ClickHouse - -```bash -mkdir build -cd build -cmake .. -DCMAKE_CXX_COMPILER=`which g++-7` -DCMAKE_C_COMPILER=`which gcc-7` -make -j `sysctl -n hw.ncpu` -cd .. -``` - -## Caveats - -If you intend to run clickhouse-server, make sure to increase system's maxfiles variable. See [MacOS.md](https://github.com/yandex/ClickHouse/blob/master/MacOS.md) for more details. diff --git a/docs/ru/development/build_osx.md b/docs/ru/development/build_osx.md new file mode 120000 index 00000000000..f9adaf24584 --- /dev/null +++ b/docs/ru/development/build_osx.md @@ -0,0 +1 @@ +../../en/development/build_osx.md \ No newline at end of file diff --git a/docs/ru/development/index.md b/docs/ru/development/index.md deleted file mode 100644 index 34b1dab5c5b..00000000000 --- a/docs/ru/development/index.md +++ /dev/null @@ -1 +0,0 @@ -# ClickHouse Development diff --git a/docs/ru/development/index.md b/docs/ru/development/index.md new file mode 120000 index 00000000000..1e2ad97dcc5 --- /dev/null +++ b/docs/ru/development/index.md @@ -0,0 +1 @@ +../../en/development/index.md \ No newline at end of file diff --git a/docs/ru/development/tests.md b/docs/ru/development/tests.md deleted file mode 100644 index c7f90cdcd45..00000000000 --- a/docs/ru/development/tests.md +++ /dev/null @@ -1,32 +0,0 @@ -# How to run ClickHouse tests - -The `clickhouse-test` utility that is used for functional testing is written using Python 2.x. -It also requires you to have some third-party packages: - -```bash -$ pip install lxml termcolor -``` - -In a nutshell: - -- Put the `clickhouse` program to `/usr/bin` (or `PATH`) -- Create a `clickhouse-client` symlink in `/usr/bin` pointing to `clickhouse` -- Start the `clickhouse` server -- `cd dbms/tests/` -- Run `./clickhouse-test` - -## Example usage - -Run `./clickhouse-test --help` to see available options. - -To run tests without having to create a symlink or mess with `PATH`: - -```bash -./clickhouse-test -c "../../build/dbms/src/Server/clickhouse --client" -``` - -To run a single test, i.e. `00395_nullable`: - -```bash -./clickhouse-test 00395 -``` diff --git a/docs/ru/development/tests.md b/docs/ru/development/tests.md new file mode 120000 index 00000000000..c03d36c3916 --- /dev/null +++ b/docs/ru/development/tests.md @@ -0,0 +1 @@ +../../en/development/tests.md \ No newline at end of file From 01656e8fe113101b5b020b8f07cf2e5ca72a1441 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 17:45:52 +0300 Subject: [PATCH 142/231] Minor modifications in build instruction #2277 --- docs/en/development/build.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/en/development/build.md b/docs/en/development/build.md index 2caf33fd593..fbc2333f583 100644 --- a/docs/en/development/build.md +++ b/docs/en/development/build.md @@ -14,10 +14,10 @@ grep -q sse4_2 /proc/cpuinfo && echo "SSE 4.2 supported" || echo "SSE 4.2 not su ## Install Git and CMake ```bash -sudo apt-get install git cmake3 +sudo apt-get install git cmake ``` -Or just cmake on newer systems. +Or cmake3 instead of cmake on older systems. ## Detect the number of threads From da1cab72dbb5db7cf24bbef1dd5d10db1262a949 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 18:20:19 +0300 Subject: [PATCH 143/231] Better exception message [#CLICKHOUSE-2] --- dbms/src/Functions/FunctionsConversion.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dbms/src/Functions/FunctionsConversion.cpp b/dbms/src/Functions/FunctionsConversion.cpp index 380a5b20687..b0f5bbb2ed6 100644 --- a/dbms/src/Functions/FunctionsConversion.cpp +++ b/dbms/src/Functions/FunctionsConversion.cpp @@ -21,7 +21,7 @@ void throwExceptionForIncompletelyParsedValue( message_buf << " at begin of string"; if (to_type.isNumber()) - message_buf << ". Note: there are to" << to_type.getName() << "OrZero function, which returns zero instead of throwing exception."; + message_buf << ". Note: there are to" << to_type.getName() << "OrZero and to" << to_type.getName() << "OrNull functions, which returns zero/NULL instead of throwing exception."; throw Exception(message_buf.str(), ErrorCodes::CANNOT_PARSE_TEXT); } From d3b7bafedf3dc8c0efd506e7a3acccab88f306cc Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 18:22:54 +0300 Subject: [PATCH 144/231] Miscellaneous #2277 --- dbms/src/Interpreters/ExpressionJIT.cpp | 24 +++++++++++------------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 14e131d731a..b9e0da9aa65 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -531,6 +531,17 @@ static bool isCompilable(llvm::IRBuilderBase & builder, const IFunctionBase& fun void compileFunctions(ExpressionActions::Actions & actions, const Names & output_columns, const Block & sample_block) { + struct LLVMTargetInitializer + { + LLVMTargetInitializer() + { + llvm::InitializeNativeTarget(); + llvm::InitializeNativeTargetAsmPrinter(); + } + }; + + static LLVMTargetInitializer initializer; + auto context = std::make_shared(); /// an empty optional is a poisoned value prohibiting the column's producer from being removed /// (which it could be, if it was inlined into every dependent function). @@ -611,17 +622,4 @@ void compileFunctions(ExpressionActions::Actions & actions, const Names & output } - -namespace -{ - struct LLVMTargetInitializer - { - LLVMTargetInitializer() - { - llvm::InitializeNativeTarget(); - llvm::InitializeNativeTargetAsmPrinter(); - } - } llvmInitializer; -} - #endif From 1718e575a7987040144485e98ab30d1c3042c289 Mon Sep 17 00:00:00 2001 From: pyos Date: Thu, 10 May 2018 20:31:24 +0300 Subject: [PATCH 145/231] Bridge between incompatible LLVM APIs --- dbms/src/Interpreters/ExpressionJIT.cpp | 35 ++++++++++++++++++++++++- 1 file changed, 34 insertions(+), 1 deletion(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index 9e1a7a2d069..da069c73ec4 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -127,6 +127,39 @@ static llvm::TargetMachine * getNativeMachine() ); } +#if LLVM_VERSION_MAJOR >= 7 +auto wrapJITSymbolResolver(llvm::JITSymbolResolver & jsr) +{ + auto flags = [&](llvm::orc::SymbolFlagsMap & flags, const llvm::orc::SymbolNameSet & symbols) + { + llvm::orc::SymbolNameSet missing; + for (const auto & symbol : symbols) + { + auto resolved = jsr.lookupFlags({*symbol}); + if (resolved && resolved->size()) + flags.emplace(symbol, resolved->begin()->second); + else + missing.emplace(symbol); + } + return missing; + }; + auto symbols = [&](std::shared_ptr query, llvm::orc::SymbolNameSet symbols) + { + llvm::orc::SymbolNameSet missing; + for (const auto & symbol : symbols) + { + auto resolved = jsr.lookup({*symbol}); + if (resolved && resolved->size()) + query->resolve(symbol, resolved->begin()->second); + else + missing.emplace(symbol); + } + return missing; + }; + return llvm::orc::createSymbolResolver(flags, symbols); +} +#endif + struct LLVMContext { llvm::LLVMContext context; @@ -155,7 +188,7 @@ struct LLVMContext #if LLVM_VERSION_MAJOR >= 7 , object_layer(execution_session, [this](llvm::orc::VModuleKey) { - return llvm::orc::RTDyldObjectLinkingLayer::Resources{memory_manager, memory_manager}; + return llvm::orc::RTDyldObjectLinkingLayer::Resources{memory_manager, wrapJITSymbolResolver(*memory_manager)}; }) #else , object_layer([this]() { return memory_manager; }) From e5ebc24657421260318f4d2a0d85a1ac373e10d1 Mon Sep 17 00:00:00 2001 From: pyos Date: Thu, 10 May 2018 21:19:41 +0300 Subject: [PATCH 146/231] Revert the part of bd332b that moved a read after std::move. --- dbms/src/Interpreters/ExpressionJIT.cpp | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionJIT.cpp b/dbms/src/Interpreters/ExpressionJIT.cpp index da069c73ec4..57f716a746f 100644 --- a/dbms/src/Interpreters/ExpressionJIT.cpp +++ b/dbms/src/Interpreters/ExpressionJIT.cpp @@ -225,6 +225,11 @@ struct LLVMContext fpm.doFinalization(); mpm.run(*module); + std::vector functions; + functions.reserve(module->size()); + for (const auto & function : *module) + functions.emplace_back(function.getName()); + #if LLVM_VERSION_MAJOR >= 7 llvm::orc::VModuleKey module_key = execution_session.allocateVModule(); if (compile_layer.addModule(module_key, std::move(module))) @@ -234,19 +239,19 @@ struct LLVMContext throw Exception("Cannot add module to compile layer", ErrorCodes::CANNOT_COMPILE_CODE); #endif - for (const auto & function : *module) + for (const auto & name : functions) { std::string mangled_name; llvm::raw_string_ostream mangled_name_stream(mangled_name); - llvm::Mangler::getNameWithPrefix(mangled_name_stream, function.getName(), layout); + llvm::Mangler::getNameWithPrefix(mangled_name_stream, name, layout); mangled_name_stream.flush(); auto symbol = compile_layer.findSymbol(mangled_name, false); if (!symbol) continue; /// external function (e.g. an intrinsic that calls into libc) auto address = symbol.getAddress(); if (!address) - throw Exception(("Function " + function.getName() + " failed to link").str(), ErrorCodes::CANNOT_COMPILE_CODE); - symbols[function.getName()] = reinterpret_cast(*address); + throw Exception("Function " + name + " failed to link", ErrorCodes::CANNOT_COMPILE_CODE); + symbols[name] = reinterpret_cast(*address); } } }; From 497181108ef41e676b6ceac30700532dcae78f30 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 10 May 2018 23:44:01 +0300 Subject: [PATCH 147/231] Updated submodule #2277 --- contrib/llvm | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/llvm b/contrib/llvm index 5618c710d9d..2cb22978148 160000 --- a/contrib/llvm +++ b/contrib/llvm @@ -1 +1 @@ -Subproject commit 5618c710d9d5cc17d01bac3200340563ecd816ae +Subproject commit 2cb22978148deed81eff850c8a90ad5471d5dc87 From 582085bf15229fd7d2aab3458132a4b2ca56870f Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Fri, 11 May 2018 00:29:44 +0300 Subject: [PATCH 148/231] Be more conservative about new feature #2277 --- dbms/src/Interpreters/Settings.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dbms/src/Interpreters/Settings.h b/dbms/src/Interpreters/Settings.h index 9696e7866bf..0d0e47c598c 100644 --- a/dbms/src/Interpreters/Settings.h +++ b/dbms/src/Interpreters/Settings.h @@ -65,7 +65,7 @@ struct Settings M(SettingFloat, totals_auto_threshold, 0.5, "The threshold for totals_mode = 'auto'.") \ \ M(SettingBool, compile, false, "Whether query compilation is enabled.") \ - M(SettingBool, compile_expressions, true, "Compile some scalar functions and operators to native code.") \ + M(SettingBool, compile_expressions, false, "Compile some scalar functions and operators to native code.") \ M(SettingUInt64, min_count_to_compile, 3, "The number of structurally identical queries before they are compiled.") \ M(SettingUInt64, group_by_two_level_threshold, 100000, "From what number of keys, a two-level aggregation starts. 0 - the threshold is not set.") \ M(SettingUInt64, group_by_two_level_threshold_bytes, 100000000, "From what size of the aggregation state in bytes, a two-level aggregation begins to be used. 0 - the threshold is not set. Two-level aggregation is used when at least one of the thresholds is triggered.") \ From 646bea46d883950df4a9ad4b23b32800f438812e Mon Sep 17 00:00:00 2001 From: alexey-milovidov Date: Fri, 11 May 2018 00:47:05 +0300 Subject: [PATCH 149/231] Update replication.md --- docs/ru/table_engines/replication.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/docs/ru/table_engines/replication.md b/docs/ru/table_engines/replication.md index 30d8882d4eb..22eab99739f 100644 --- a/docs/ru/table_engines/replication.md +++ b/docs/ru/table_engines/replication.md @@ -178,5 +178,3 @@ sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data ## Восстановление в случае потери или повреждения метаданных на ZooKeeper кластере Если данные в ZooKeeper оказались утеряны или повреждены, то вы можете сохранить данные, переместив их в нереплицируемую таблицу, как описано в пункте выше. - -Если на остальных репликах есть точно такие же куски, они будут добавлены в рабочий набор на них. Если нет - куски будут скачаны с той реплики, где они есть. From 9fa5952b0f6e7496d2608689556ee72432e13b9d Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Fri, 11 May 2018 02:20:48 +0300 Subject: [PATCH 150/231] Updated submodule #2277 --- contrib/llvm | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/llvm b/contrib/llvm index 2cb22978148..163def21781 160000 --- a/contrib/llvm +++ b/contrib/llvm @@ -1 +1 @@ -Subproject commit 2cb22978148deed81eff850c8a90ad5471d5dc87 +Subproject commit 163def217817c90fb982a6daf384744d8472b92b From 294b468a3230025cbacbd14e4455d932b11245f4 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Fri, 11 May 2018 02:25:46 +0300 Subject: [PATCH 151/231] Avoid warnings in LLVM code #2277 --- dbms/src/Server/Compiler-5.0.0/CMakeLists.txt | 2 +- dbms/src/Server/Compiler-6.0.0/CMakeLists.txt | 2 +- dbms/src/Server/Compiler-7.0.0/CMakeLists.txt | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt index e72e3f6753b..fcae3c108ca 100644 --- a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt @@ -1,4 +1,4 @@ -set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error") +add_definitions(-Wno-error -Wno-unused-parameter -Wno-non-virtual-dtor -U_LIBCPP_DEBUG) add_library(clickhouse-compiler-lib driver.cpp diff --git a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt index a66af8bbc7a..e136b8fc363 100644 --- a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt @@ -1,4 +1,4 @@ -set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error") +add_definitions(-Wno-error -Wno-unused-parameter -Wno-non-virtual-dtor -U_LIBCPP_DEBUG) add_library(clickhouse-compiler-lib driver.cpp diff --git a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt index c6e725d3bad..96f50b2f899 100644 --- a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt @@ -1,4 +1,4 @@ -set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error") +add_definitions(-Wno-error -Wno-unused-parameter -Wno-non-virtual-dtor -U_LIBCPP_DEBUG) add_library(clickhouse-compiler-lib driver.cpp From 8b05841142db6accb1b62bb6e1ce8c3f26443736 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Fri, 11 May 2018 03:08:49 +0300 Subject: [PATCH 152/231] Removed useless files [#CLICKHOUSE-2] --- dbms/src/IO/RemoteReadBuffer.h | 88 ------ dbms/src/IO/RemoteWriteBuffer.h | 264 ------------------ dbms/src/IO/tests/CMakeLists.txt | 50 ++-- .../src/IO/tests/remote_read_write_buffer.cpp | 19 -- 4 files changed, 23 insertions(+), 398 deletions(-) delete mode 100644 dbms/src/IO/RemoteReadBuffer.h delete mode 100644 dbms/src/IO/RemoteWriteBuffer.h delete mode 100644 dbms/src/IO/tests/remote_read_write_buffer.cpp diff --git a/dbms/src/IO/RemoteReadBuffer.h b/dbms/src/IO/RemoteReadBuffer.h deleted file mode 100644 index d962a5c2af5..00000000000 --- a/dbms/src/IO/RemoteReadBuffer.h +++ /dev/null @@ -1,88 +0,0 @@ -#pragma once - -#include -#include "ReadHelpers.h" - -#define DEFAULT_REMOTE_READ_BUFFER_CONNECTION_TIMEOUT 1 -#define DEFAULT_REMOTE_READ_BUFFER_RECEIVE_TIMEOUT 1800 -#define DEFAULT_REMOTE_READ_BUFFER_SEND_TIMEOUT 1800 - -namespace DB -{ - -/** Allows you to read a file from a remote server via riod. - */ -class RemoteReadBuffer : public ReadBuffer -{ -private: - std::unique_ptr impl; - -public: - RemoteReadBuffer( - const std::string & host, - int port, - const std::string & path, - bool compress = true, - size_t buffer_size = DBMS_DEFAULT_BUFFER_SIZE, - const Poco::Timespan & connection_timeout = Poco::Timespan(DEFAULT_REMOTE_READ_BUFFER_CONNECTION_TIMEOUT, 0), - const Poco::Timespan & send_timeout = Poco::Timespan(DEFAULT_REMOTE_READ_BUFFER_SEND_TIMEOUT, 0), - const Poco::Timespan & receive_timeout = Poco::Timespan(DEFAULT_REMOTE_READ_BUFFER_RECEIVE_TIMEOUT, 0)) - : ReadBuffer(nullptr, 0) - { - Poco::URI uri; - uri.setScheme("http"); - uri.setHost(host); - uri.setPort(port); - uri.setQueryParameters( - { - std::make_pair("action", "read"), - std::make_pair("path", path), - std::make_pair("compress", (compress ? "true" : "false")) - }); - - ConnectionTimeouts timeouts(connection_timeout, send_timeout, receive_timeout); - ReadWriteBufferFromHTTP::OutStreamCallback callback; - impl = std::make_unique(uri, std::string(), callback, timeouts, buffer_size); - } - - bool nextImpl() override - { - if (!impl->next()) - return false; - internal_buffer = impl->buffer(); - working_buffer = internal_buffer; - return true; - } - - /// Return the list of file names in the directory. - static std::vector listFiles( - const std::string & host, - int port, - const std::string & path, - const ConnectionTimeouts & timeouts) - { - Poco::URI uri; - uri.setScheme("http"); - uri.setHost(host); - uri.setPort(port); - uri.setQueryParameters( - { - std::make_pair("action", "list"), - std::make_pair("path", path)}); - - ReadWriteBufferFromHTTP in(uri, {}, {}, timeouts); - - std::vector files; - while (!in.eof()) - { - std::string s; - readString(s, in); - skipWhitespaceIfAny(in); - files.push_back(s); - } - - return files; - } -}; - -} diff --git a/dbms/src/IO/RemoteWriteBuffer.h b/dbms/src/IO/RemoteWriteBuffer.h deleted file mode 100644 index af25e72c8a8..00000000000 --- a/dbms/src/IO/RemoteWriteBuffer.h +++ /dev/null @@ -1,264 +0,0 @@ -#pragma once - -#include -#include -#include -#include -#include -#include - -#include -#include -#include - -#include - -#define DEFAULT_REMOTE_WRITE_BUFFER_CONNECTION_TIMEOUT 1 -#define DEFAULT_REMOTE_WRITE_BUFFER_RECEIVE_TIMEOUT 1800 -#define DEFAULT_REMOTE_WRITE_BUFFER_SEND_TIMEOUT 1800 - - -namespace DB -{ - -namespace ErrorCodes -{ - extern const int CANNOT_WRITE_TO_OSTREAM; - extern const int RECEIVED_ERROR_FROM_REMOTE_IO_SERVER; -} - -/** Allows you to write a file to a remote server. - */ -class RemoteWriteBuffer : public WriteBuffer -{ -private: - std::string host; - int port; - std::string path; - std::string encoded_path; - std::string encoded_tmp_path; - std::string tmp_path; - std::string if_exists; - bool decompress; - unsigned connection_retries; - - std::string uri_str; - - Poco::Net::HTTPClientSession session; - std::ostream * ostr; /// this is owned by session - std::unique_ptr impl; - - /// Have sent all the data and renamed the file - bool finalized; -public: - /** If tmp_path is not empty, it writes first the temporary file, and then renames it, - * deleting existing files, if any. - * Otherwise, if_exists parameter is used. - */ - RemoteWriteBuffer(const std::string & host_, int port_, const std::string & path_, - const std::string & tmp_path_ = "", const std::string & if_exists_ = "remove", - bool decompress_ = false, - unsigned connection_retries_ = 3, - size_t buffer_size_ = DBMS_DEFAULT_BUFFER_SIZE, - const Poco::Timespan & connection_timeout = Poco::Timespan(DEFAULT_REMOTE_WRITE_BUFFER_CONNECTION_TIMEOUT, 0), - const Poco::Timespan & send_timeout = Poco::Timespan(DEFAULT_REMOTE_WRITE_BUFFER_SEND_TIMEOUT, 0), - const Poco::Timespan & receive_timeout = Poco::Timespan(DEFAULT_REMOTE_WRITE_BUFFER_RECEIVE_TIMEOUT, 0)) - : WriteBuffer(nullptr, 0), host(host_), port(port_), path(path_), - tmp_path(tmp_path_), if_exists(if_exists_), - decompress(decompress_), connection_retries(connection_retries_), finalized(false) - { - Poco::URI::encode(path, "&#", encoded_path); - Poco::URI::encode(tmp_path, "&#", encoded_tmp_path); - - std::stringstream uri; - uri << "http://" << host << ":" << port - << "/?action=write" - << "&path=" << (tmp_path.empty() ? encoded_path : encoded_tmp_path) - << "&if_exists=" << if_exists - << "&decompress=" << (decompress ? "true" : "false"); - - uri_str = Poco::URI(uri.str()).getPathAndQuery(); - - session.setHost(host); - session.setPort(port); - session.setKeepAlive(true); - - /// set the timeout -#if POCO_CLICKHOUSE_PATCH || POCO_VERSION >= 0x02000000 - session.setTimeout(connection_timeout, send_timeout, receive_timeout); -#else - session.setTimeout(connection_timeout); -#endif - - Poco::Net::HTTPRequest request(Poco::Net::HTTPRequest::HTTP_POST, uri_str, Poco::Net::HTTPRequest::HTTP_1_1); - - request.setChunkedTransferEncoding(true); - - for (unsigned i = 0; i < connection_retries; ++i) - { - LOG_TRACE((&Logger::get("RemoteWriteBuffer")), "Sending write request to " << host << ":" << port << uri_str); - - try - { - ostr = &session.sendRequest(request); - } - catch (const Poco::Net::NetException & e) - { - if (i + 1 == connection_retries) - throw; - - LOG_WARNING((&Logger::get("RemoteWriteBuffer")), e.displayText() << ", URL: " << host << ":" << port << uri_str << ", try No " << i + 1 << "."); - session.reset(); - continue; - } - catch (const Poco::TimeoutException & e) - { - if (i + 1 == connection_retries) - throw; - - LOG_WARNING((&Logger::get("RemoteWriteBuffer")), "Connection timeout from " << host << ":" << port << uri_str << ", try No " << i + 1 << "."); - session.reset(); - continue; - } - - break; - } - - impl = std::make_unique(*ostr, buffer_size_); - - set(impl->buffer().begin(), impl->buffer().size()); - } - - void nextImpl() override - { - if (!offset() || finalized) - return; - - /// For correct work with AsynchronousWriteBuffer, which replaces buffers. - impl->set(buffer().begin(), buffer().size()); - - impl->position() = pos; - - try - { - impl->next(); - } - catch (const Exception & e) - { - if (e.code() == ErrorCodes::CANNOT_WRITE_TO_OSTREAM) - checkStatus(); /// Change the error message to a clearer one. - throw; - } - } - - void finalize() - { - if (finalized) - return; - - next(); - checkStatus(); - - /// Rename the file if necessary. - if (!tmp_path.empty()) - rename(); - - finalized = true; - } - - void cancel() - { - finalized = true; - } - - ~RemoteWriteBuffer() - { - try - { - finalize(); - } - catch (...) - { - tryLogCurrentException(__PRETTY_FUNCTION__); - } - } - - -private: - - void checkStatus() - { - Poco::Net::HTTPResponse response; - std::istream & istr = session.receiveResponse(response); - Poco::Net::HTTPResponse::HTTPStatus status = response.getStatus(); - - std::stringstream message; - message << istr.rdbuf(); - - if (status != Poco::Net::HTTPResponse::HTTP_OK || message.str() != "Ok.\n") - { - std::stringstream error_message; - error_message << "Received error from remote server " << uri_str << ", body: " << message.str(); - - throw Exception(error_message.str(), ErrorCodes::RECEIVED_ERROR_FROM_REMOTE_IO_SERVER); - } - } - - void rename() - { - std::stringstream uri; - uri << "http://" << host << ":" << port - << "/?action=rename" - << "&from=" << encoded_tmp_path - << "&to=" << encoded_path; - - uri_str = Poco::URI(uri.str()).getPathAndQuery(); - - Poco::Net::HTTPRequest request(Poco::Net::HTTPRequest::HTTP_GET, uri_str, Poco::Net::HTTPRequest::HTTP_1_1); - - for (unsigned i = 0; i < connection_retries; ++i) - { - LOG_TRACE((&Logger::get("RemoteWriteBuffer")), "Sending rename request to " << host << ":" << port << uri_str); - - try - { - session.sendRequest(request); - checkStatus(); - } - catch (const Poco::Net::NetException & e) - { - if (i + 1 == connection_retries) - throw; - - LOG_WARNING((&Logger::get("RemoteWriteBuffer")), e.what() << ", message: " << e.displayText() - << ", URL: " << host << ":" << port << uri_str << ", try No " << i + 1 << "."); - session.reset(); - continue; - } - catch (const Poco::TimeoutException & e) - { - if (i + 1 == connection_retries) - throw; - - LOG_WARNING((&Logger::get("RemoteWriteBuffer")), "Connection timeout from " << host << ":" << port << uri_str << ", try No " << i + 1 << "."); - session.reset(); - continue; - } - catch (const Exception & e) - { - /// If in the last attempt we did not receive a response from the server, but the file was renamed already. - if (i != 0 && e.code() == ErrorCodes::RECEIVED_ERROR_FROM_REMOTE_IO_SERVER - && nullptr != strstr(e.displayText().data(), "File not found")) - { - LOG_TRACE((&Logger::get("RemoteWriteBuffer")), "File already renamed"); - } - else - throw; - } - - break; - } - } -}; - -} diff --git a/dbms/src/IO/tests/CMakeLists.txt b/dbms/src/IO/tests/CMakeLists.txt index fcaf88d3d64..1bda663e847 100644 --- a/dbms/src/IO/tests/CMakeLists.txt +++ b/dbms/src/IO/tests/CMakeLists.txt @@ -1,58 +1,57 @@ include_directories (${CMAKE_CURRENT_BINARY_DIR}) -set(SRCS ) -add_executable (read_buffer read_buffer.cpp ${SRCS}) +add_executable (read_buffer read_buffer.cpp) target_link_libraries (read_buffer clickhouse_common_io) -add_executable (read_buffer_perf read_buffer_perf.cpp ${SRCS}) +add_executable (read_buffer_perf read_buffer_perf.cpp) target_link_libraries (read_buffer_perf clickhouse_common_io) -add_executable (read_float_perf read_float_perf.cpp ${SRCS}) +add_executable (read_float_perf read_float_perf.cpp) target_link_libraries (read_float_perf clickhouse_common_io) -add_executable (write_buffer write_buffer.cpp ${SRCS}) +add_executable (write_buffer write_buffer.cpp) target_link_libraries (write_buffer clickhouse_common_io) -add_executable (write_buffer_perf write_buffer_perf.cpp ${SRCS}) +add_executable (write_buffer_perf write_buffer_perf.cpp) target_link_libraries (write_buffer_perf clickhouse_common_io) -add_executable (valid_utf8_perf valid_utf8_perf.cpp ${SRCS}) +add_executable (valid_utf8_perf valid_utf8_perf.cpp) target_link_libraries (valid_utf8_perf clickhouse_common_io) -add_executable (valid_utf8 valid_utf8.cpp ${SRCS}) +add_executable (valid_utf8 valid_utf8.cpp) target_link_libraries (valid_utf8 clickhouse_common_io) -add_executable (compressed_buffer compressed_buffer.cpp ${SRCS}) +add_executable (compressed_buffer compressed_buffer.cpp) target_link_libraries (compressed_buffer clickhouse_common_io) -add_executable (var_uint var_uint.cpp ${SRCS}) +add_executable (var_uint var_uint.cpp) target_link_libraries (var_uint clickhouse_common_io) -add_executable (read_escaped_string read_escaped_string.cpp ${SRCS}) +add_executable (read_escaped_string read_escaped_string.cpp) target_link_libraries (read_escaped_string clickhouse_common_io) -add_executable (async_write async_write.cpp ${SRCS}) +add_executable (async_write async_write.cpp) target_link_libraries (async_write clickhouse_common_io) -add_executable (parse_int_perf parse_int_perf.cpp ${SRCS}) +add_executable (parse_int_perf parse_int_perf.cpp) target_link_libraries (parse_int_perf clickhouse_common_io) -add_executable (parse_int_perf2 parse_int_perf2.cpp ${SRCS}) +add_executable (parse_int_perf2 parse_int_perf2.cpp) target_link_libraries (parse_int_perf2 clickhouse_common_io) -add_executable (read_write_int read_write_int.cpp ${SRCS}) +add_executable (read_write_int read_write_int.cpp) target_link_libraries (read_write_int clickhouse_common_io) -add_executable (mempbrk mempbrk.cpp ${SRCS}) +add_executable (mempbrk mempbrk.cpp) target_link_libraries (mempbrk clickhouse_common_io) -add_executable (cached_compressed_read_buffer cached_compressed_read_buffer.cpp ${SRCS}) +add_executable (cached_compressed_read_buffer cached_compressed_read_buffer.cpp) target_link_libraries (cached_compressed_read_buffer clickhouse_common_io) -add_executable (o_direct_and_dirty_pages o_direct_and_dirty_pages.cpp ${SRCS}) +add_executable (o_direct_and_dirty_pages o_direct_and_dirty_pages.cpp) target_link_libraries (o_direct_and_dirty_pages clickhouse_common_io) -add_executable (hashing_write_buffer hashing_write_buffer.cpp ${SRCS}) +add_executable (hashing_write_buffer hashing_write_buffer.cpp) target_link_libraries (hashing_write_buffer clickhouse_common_io) add_check(hashing_write_buffer) @@ -60,7 +59,7 @@ add_executable (hashing_read_buffer hashing_read_buffer.cpp) target_link_libraries (hashing_read_buffer clickhouse_common_io) add_check (hashing_read_buffer) -add_executable (io_operators operators.cpp ${SRCS}) +add_executable (io_operators operators.cpp) target_link_libraries (io_operators clickhouse_common_io) if (NOT APPLE AND NOT ARCH_FREEBSD) @@ -71,17 +70,14 @@ if (NOT APPLE AND NOT ARCH_FREEBSD) target_link_libraries (read_buffer_aio clickhouse_common_io ${Boost_FILESYSTEM_LIBRARY}) endif () -add_executable (zlib_buffers zlib_buffers.cpp ${SRCS}) +add_executable (zlib_buffers zlib_buffers.cpp) target_link_libraries (zlib_buffers clickhouse_common_io) -add_executable (remote_read_write_buffer remote_read_write_buffer.cpp ${SRCS}) -target_link_libraries (remote_read_write_buffer clickhouse_common_io) - -add_executable (limit_read_buffer limit_read_buffer.cpp ${SRCS}) +add_executable (limit_read_buffer limit_read_buffer.cpp) target_link_libraries (limit_read_buffer clickhouse_common_io) -add_executable (limit_read_buffer2 limit_read_buffer2.cpp ${SRCS}) +add_executable (limit_read_buffer2 limit_read_buffer2.cpp) target_link_libraries (limit_read_buffer2 clickhouse_common_io) -add_executable (parse_date_time_best_effort parse_date_time_best_effort.cpp ${SRCS}) +add_executable (parse_date_time_best_effort parse_date_time_best_effort.cpp) target_link_libraries (parse_date_time_best_effort clickhouse_common_io) diff --git a/dbms/src/IO/tests/remote_read_write_buffer.cpp b/dbms/src/IO/tests/remote_read_write_buffer.cpp deleted file mode 100644 index 6359b92bd3f..00000000000 --- a/dbms/src/IO/tests/remote_read_write_buffer.cpp +++ /dev/null @@ -1,19 +0,0 @@ -#include - -#include -#include - - -int main(int, char **) -try -{ - DB::RemoteReadBuffer({}, {}, {}); - DB::RemoteWriteBuffer({}, {}, {}); - - return 0; -} -catch (const DB::Exception & e) -{ - std::cerr << e.what() << ", " << e.displayText() << std::endl; - return 1; -} From 471eb5c8ca51233af8e254ed737ff333d0e9f96a Mon Sep 17 00:00:00 2001 From: Alexey 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-0.8999999 -1 -2 -1 -1 -1 -1 -1 -1.5 -1 -1 -2 -0.8999999 -1.25 -1 -1 -1.5 -1 -1 -1 -1 -1 -1 -2 -1 -1 -1 -1.25 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1.5 -1 -1 -1 -1 -1 -1.4 -1.25 -1 -1 -1 -1 -1.6 -1 -1.1 -1 -4 -2 -1 -0 -1 -2 -1 -1 -1 -1 -1 -1 -1 -0 -2 -1 -1 -1.5 -2 -1 -0 -1 -1.25 -0 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1.25 -0 -1 -3 -1 -1 -1.25 -1 -1 -1 -0 -2 -0.8999999 -1.6 -1 -1 -1 -4 -1 -1 -1 -1.25 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 -0 -1.1 -4 -0.8999999 -1 -2 -1 -1.25 -1 -1 -0.8955225 -1 -1.3312501 -1 -1 -2 -1.5 -0 -1 -1 -1 -1 -1.3202168 -1.2 -1 -1 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -2 -1.5 -2 -1 -1 -1 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1.25 -1 -1 -0.8999999 -1 -1 -1 -1.3299999 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1.0499998 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1.75 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 From ed70e468f9305038c9a40affb9a758f939781c60 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Fri, 11 May 2018 03:56:31 +0300 Subject: [PATCH 154/231] Removed useless library [#CLICKHOUSE-2] --- cmake/find_rt.cmake | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/cmake/find_rt.cmake b/cmake/find_rt.cmake index 43c653df3e1..82ec314d195 100644 --- a/cmake/find_rt.cmake +++ b/cmake/find_rt.cmake @@ -1,8 +1,10 @@ if (APPLE) # lib from libs/libcommon set (RT_LIBRARY "apple_rt") -else () +elseif (ARCH_FREEBSD) find_library (RT_LIBRARY rt) +else () + set (RT_LIBRARY "") endif () message(STATUS "Using rt: ${RT_LIBRARY}") From 715a9a723311f7b77a18ec4a5ee22fbe38f25a33 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Fri, 11 May 2018 14:04:12 +0300 Subject: [PATCH 155/231] Miscellaneous [#CLICKHOUSE-2] --- contrib/libtcmalloc/CMakeLists.txt | 1 - dbms/src/IO/ReadWriteBufferFromHTTP.cpp | 1 - 2 files changed, 2 deletions(-) diff --git a/contrib/libtcmalloc/CMakeLists.txt b/contrib/libtcmalloc/CMakeLists.txt index 5bf61f98bea..d7f52e1d384 100644 --- a/contrib/libtcmalloc/CMakeLists.txt +++ b/contrib/libtcmalloc/CMakeLists.txt @@ -1,4 +1,3 @@ - message (STATUS "Building: tcmalloc_minimal_internal") add_library (tcmalloc_minimal_internal diff --git a/dbms/src/IO/ReadWriteBufferFromHTTP.cpp b/dbms/src/IO/ReadWriteBufferFromHTTP.cpp index ad2240609b4..0ec26f684f1 100644 --- a/dbms/src/IO/ReadWriteBufferFromHTTP.cpp +++ b/dbms/src/IO/ReadWriteBufferFromHTTP.cpp @@ -1,6 +1,5 @@ #include -#include #include #include #include From 38a559061f014545ce428d6d1532da55dfeebdb9 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Fri, 11 May 2018 14:04:39 +0300 Subject: [PATCH 156/231] Removed garbage (fixed build) [#CLICKHOUSE-2] --- contrib/CMakeLists.txt | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/contrib/CMakeLists.txt b/contrib/CMakeLists.txt index 18cdc15b3fd..104db478ef0 100644 --- a/contrib/CMakeLists.txt +++ b/contrib/CMakeLists.txt @@ -21,7 +21,7 @@ if (USE_INTERNAL_RE2_LIBRARY) endif () if (USE_INTERNAL_DOUBLE_CONVERSION_LIBRARY) - set (BUILD_TESTING ${ENABLE_TESTS} CACHE INTERNAL "") + set (BUILD_TESTING 0 CACHE INTERNAL "") add_subdirectory (double-conversion) endif () @@ -113,11 +113,7 @@ if (USE_INTERNAL_RDKAFKA_LIBRARY) endif () if (USE_INTERNAL_CAPNP_LIBRARY) - if (APPLE) # tests never end - set (BUILD_TESTING 0 CACHE INTERNAL "") - else () - set (BUILD_TESTING ${ENABLE_TESTS} CACHE INTERNAL "") - endif () + set (BUILD_TESTING 0 CACHE INTERNAL "") set (_save ${CMAKE_CXX_EXTENSIONS}) set (CMAKE_CXX_EXTENSIONS) add_subdirectory (capnproto/c++) From e14dedb76b618e8c5d7596b091dd43f2e7ce539e Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Fri, 11 May 2018 14:23:52 +0300 Subject: [PATCH 157/231] Removed ANL because we don't really need it #2240 --- contrib/poco | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/poco b/contrib/poco index 2d5a158303a..3a2d0a833a2 160000 --- a/contrib/poco +++ b/contrib/poco @@ -1 +1 @@ -Subproject commit 2d5a158303adf9d47b980cdcfdb26cee1460704e +Subproject commit 3a2d0a833a22ef5e1164a9ada54e3253cb038904 From 274704d0dfeddebba31d70776cff16878f1dda16 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Fri, 11 May 2018 14:34:02 +0300 Subject: [PATCH 158/231] Added a script to validate dependencies on shared libraries #2240 --- utils/link-validate/link-validate.sh | 42 ++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100755 utils/link-validate/link-validate.sh diff --git a/utils/link-validate/link-validate.sh b/utils/link-validate/link-validate.sh new file mode 100755 index 00000000000..612956d292a --- /dev/null +++ b/utils/link-validate/link-validate.sh @@ -0,0 +1,42 @@ +#/bin/sh +# +# This script is used to validate the shared libraries +# +# Authors: FoundationDB team, https://github.com/apple/foundationdb/blame/master/build/link-validate.sh +# License: Apache License 2.0 + +verlte() { + [ "$1" = "`echo -e "$1\n$2" | sort -V | head -n1`" ] +} + +ALLOWED_SHARED_LIBS=("libdl.so.2" "libpthread.so.0" "librt.so.1" "libm.so.6" "libc.so.6" "ld-linux-x86-64.so.2") + +if [ "$#" -ne 2 ]; then + echo "USAGE: link-validate.sh BINNAME GLIBC_VERSION" + exit 1 +fi + +# Step 1: glibc version + +for i in $(objdump -T "$1" | awk '{print $5}' | grep GLIBC | sed 's/ *$//g' | sed 's/GLIBC_//' | sort | uniq); do + if ! verlte "$i" "$2"; then + echo "Dependency on newer libc detected: $i" + exit 1 + fi +done + +# Step 2: Other dynamic dependencies + +for j in $(objdump -p "$1" | grep NEEDED | awk '{print $2}'); do + PRESENT=0 + for k in ${ALLOWED_SHARED_LIBS[@]}; do + if [[ "$k" == "$j" ]]; then + PRESENT=1 + break + fi + done + if ! [[ $PRESENT == 1 ]]; then + echo "Unexpected shared object dependency detected: $j" + exit 1 + fi +done From 3ff088f27076dd1d9264620b2d5e4f4f6b4d4e39 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Fri, 11 May 2018 14:54:25 +0300 Subject: [PATCH 159/231] Get rid of GLIBC_2.27 symbols #2240 --- libs/libglibc-compatibility/CMakeLists.txt | 4 +- libs/libglibc-compatibility/musl/exp2f.c | 127 +++++++++++ libs/libglibc-compatibility/musl/glob.c | 240 +++++++++++++++++++++ 3 files changed, 370 insertions(+), 1 deletion(-) create mode 100644 libs/libglibc-compatibility/musl/exp2f.c create mode 100644 libs/libglibc-compatibility/musl/glob.c diff --git a/libs/libglibc-compatibility/CMakeLists.txt b/libs/libglibc-compatibility/CMakeLists.txt index f9139d2ccde..3aeea0ffd05 100644 --- a/libs/libglibc-compatibility/CMakeLists.txt +++ b/libs/libglibc-compatibility/CMakeLists.txt @@ -10,6 +10,8 @@ musl/posix_spawn.c musl/futimens.c musl/syscall.s musl/syscall_ret.c -musl/sched_cpucount.c) +musl/sched_cpucount.c +musl/glob.c +musl/exp2f.c) add_subdirectory (tests) diff --git a/libs/libglibc-compatibility/musl/exp2f.c b/libs/libglibc-compatibility/musl/exp2f.c new file mode 100644 index 00000000000..e8289b7dbaf --- /dev/null +++ b/libs/libglibc-compatibility/musl/exp2f.c @@ -0,0 +1,127 @@ +/* origin: FreeBSD /usr/src/lib/msun/src/s_exp2f.c */ +/*- + * Copyright (c) 2005 David Schultz + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND + * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE + * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS + * OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY + * OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF + * SUCH DAMAGE. + */ + +#include +#include + +#define TBLSIZE 16 + +static const float +redux = 0x1.8p23f / TBLSIZE, +P1 = 0x1.62e430p-1f, +P2 = 0x1.ebfbe0p-3f, +P3 = 0x1.c6b348p-5f, +P4 = 0x1.3b2c9cp-7f; + +static const double exp2ft[TBLSIZE] = { + 0x1.6a09e667f3bcdp-1, + 0x1.7a11473eb0187p-1, + 0x1.8ace5422aa0dbp-1, + 0x1.9c49182a3f090p-1, + 0x1.ae89f995ad3adp-1, + 0x1.c199bdd85529cp-1, + 0x1.d5818dcfba487p-1, + 0x1.ea4afa2a490dap-1, + 0x1.0000000000000p+0, + 0x1.0b5586cf9890fp+0, + 0x1.172b83c7d517bp+0, + 0x1.2387a6e756238p+0, + 0x1.306fe0a31b715p+0, + 0x1.3dea64c123422p+0, + 0x1.4bfdad5362a27p+0, + 0x1.5ab07dd485429p+0, +}; + +/* + * exp2f(x): compute the base 2 exponential of x + * + * Accuracy: Peak error < 0.501 ulp; location of peak: -0.030110927. + * + * Method: (equally-spaced tables) + * + * Reduce x: + * x = k + y, for integer k and |y| <= 1/2. + * Thus we have exp2f(x) = 2**k * exp2(y). + * + * Reduce y: + * y = i/TBLSIZE + z for integer i near y * TBLSIZE. + * Thus we have exp2(y) = exp2(i/TBLSIZE) * exp2(z), + * with |z| <= 2**-(TBLSIZE+1). + * + * We compute exp2(i/TBLSIZE) via table lookup and exp2(z) via a + * degree-4 minimax polynomial with maximum error under 1.4 * 2**-33. + * Using double precision for everything except the reduction makes + * roundoff error insignificant and simplifies the scaling step. + * + * This method is due to Tang, but I do not use his suggested parameters: + * + * Tang, P. Table-driven Implementation of the Exponential Function + * in IEEE Floating-Point Arithmetic. TOMS 15(2), 144-157 (1989). + */ +float exp2f(float x) +{ + double_t t, r, z; + union {float f; uint32_t i;} u = {x}; + union {double f; uint64_t i;} uk; + uint32_t ix, i0, k; + + /* Filter out exceptional cases. */ + ix = u.i & 0x7fffffff; + if (ix > 0x42fc0000) { /* |x| > 126 */ + if (ix > 0x7f800000) /* NaN */ + return x; + if (u.i >= 0x43000000 && u.i < 0x80000000) { /* x >= 128 */ + x *= 0x1p127f; + return x; + } + if (u.i >= 0x80000000) { /* x < -126 */ + if (u.i >= 0xc3160000 || (u.i & 0x0000ffff)) + { volatile float tmp; tmp = (-0x1p-149f/x); } + if (u.i >= 0xc3160000) /* x <= -150 */ + return 0; + } + } else if (ix <= 0x33000000) { /* |x| <= 0x1p-25 */ + return 1.0f + x; + } + + /* Reduce x, computing z, i0, and k. */ + u.f = x + redux; + i0 = u.i; + i0 += TBLSIZE / 2; + k = i0 / TBLSIZE; + uk.i = (uint64_t)(0x3ff + k)<<52; + i0 &= TBLSIZE - 1; + u.f -= redux; + z = x - u.f; + /* Compute r = exp2(y) = exp2ft[i0] * p(z). */ + r = exp2ft[i0]; + t = r * z; + r = r + t * (P1 + z * P2) + t * (z * z) * (P3 + z * P4); + + /* Scale by 2**k */ + return r * uk.f; +} diff --git a/libs/libglibc-compatibility/musl/glob.c b/libs/libglibc-compatibility/musl/glob.c new file mode 100644 index 00000000000..256f4161cfc --- /dev/null +++ b/libs/libglibc-compatibility/musl/glob.c @@ -0,0 +1,240 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include + +struct match +{ + struct match *next; + char name[1]; +}; + +static int is_literal(const char *p, int useesc) +{ + int bracket = 0; + for (; *p; p++) { + switch (*p) { + case '\\': + if (!useesc) break; + case '?': + case '*': + return 0; + case '[': + bracket = 1; + break; + case ']': + if (bracket) return 0; + break; + } + } + return 1; +} + +static int append(struct match **tail, const char *name, size_t len, int mark) +{ + struct match *new = malloc(sizeof(struct match) + len + 1); + if (!new) return -1; + (*tail)->next = new; + new->next = NULL; + strcpy(new->name, name); + if (mark) strcat(new->name, "/"); + *tail = new; + return 0; +} + +static int match_in_dir(const char *d, const char *p, int flags, int (*errfunc)(const char *path, int err), struct match **tail) +{ + DIR *dir; + struct dirent *de; + char pat[strlen(p)+1]; + char *p2; + size_t l = strlen(d); + int literal; + int fnm_flags= ((flags & GLOB_NOESCAPE) ? FNM_NOESCAPE : 0) + | ((!(flags & GLOB_PERIOD)) ? FNM_PERIOD : 0); + int error; + + if ((p2 = strchr(p, '/'))) { + strcpy(pat, p); + pat[p2-p] = 0; + for (; *p2 == '/'; p2++); + p = pat; + } + literal = is_literal(p, !(flags & GLOB_NOESCAPE)); + if (*d == '/' && !*(d+1)) l = 0; + + /* rely on opendir failing for nondirectory objects */ + dir = opendir(*d ? d : "."); + error = errno; + if (!dir) { + /* this is not an error -- we let opendir call stat for us */ + if (error == ENOTDIR) return 0; + if (error == EACCES && !*p) { + struct stat st; + if (!stat(d, &st) && S_ISDIR(st.st_mode)) { + if (append(tail, d, l, l)) + return GLOB_NOSPACE; + return 0; + } + } + if (errfunc(d, error) || (flags & GLOB_ERR)) + return GLOB_ABORTED; + return 0; + } + if (!*p) { + error = append(tail, d, l, l) ? GLOB_NOSPACE : 0; + closedir(dir); + return error; + } + while ((de = readdir(dir))) { + char namebuf[l+de->d_reclen+2], *name = namebuf; + if (!literal && fnmatch(p, de->d_name, fnm_flags)) + continue; + if (literal && strcmp(p, de->d_name)) + continue; + if (p2 && de->d_type && !S_ISDIR(de->d_type<<12) && !S_ISLNK(de->d_type<<12)) + continue; + /* With GLOB_PERIOD, don't allow matching . or .. unless + * fnmatch would match them with FNM_PERIOD rules in effect. */ + if (p2 && (flags & GLOB_PERIOD) && de->d_name[0]=='.' + && (!de->d_name[1] || (de->d_name[1]=='.' && !de->d_name[2])) + && fnmatch(p, de->d_name, fnm_flags | FNM_PERIOD)) + continue; + if (*d) { + memcpy(name, d, l); + name[l] = '/'; + strcpy(name+l+1, de->d_name); + } else { + name = de->d_name; + } + if (p2) { + if ((error = match_in_dir(name, p2, flags, errfunc, tail))) { + closedir(dir); + return error; + } + } else { + int mark = 0; + if (flags & GLOB_MARK) { + if (de->d_type && !S_ISLNK(de->d_type<<12)) + mark = S_ISDIR(de->d_type<<12); + else { + struct stat st; + stat(name, &st); + mark = S_ISDIR(st.st_mode); + } + } + if (append(tail, name, l+de->d_reclen+1, mark)) { + closedir(dir); + return GLOB_NOSPACE; + } + } + } + closedir(dir); + if (error && (errfunc(d, error) || (flags & GLOB_ERR))) + return GLOB_ABORTED; + return 0; +} + +static int ignore_err(const char *path, int err) +{ + return 0; +} + +static void freelist(struct match *head) +{ + struct match *match, *next; + for (match=head->next; match; match=next) { + next = match->next; + free(match); + } +} + +static int sort(const void *a, const void *b) +{ + return strcmp(*(const char **)a, *(const char **)b); +} + +int glob(const char *restrict pat, int flags, int (*errfunc)(const char *path, int err), glob_t *restrict g) +{ + const char *p=pat, *d; + struct match head = { .next = NULL }, *tail = &head; + size_t cnt, i; + size_t offs = (flags & GLOB_DOOFFS) ? g->gl_offs : 0; + int error = 0; + + if (*p == '/') { + for (; *p == '/'; p++); + d = "/"; + } else { + d = ""; + } + + if (!errfunc) errfunc = ignore_err; + + if (!(flags & GLOB_APPEND)) { + g->gl_offs = offs; + g->gl_pathc = 0; + g->gl_pathv = NULL; + } + + if (strnlen(p, PATH_MAX+1) > PATH_MAX) return GLOB_NOSPACE; + + if (*pat) error = match_in_dir(d, p, flags, errfunc, &tail); + if (error == GLOB_NOSPACE) { + freelist(&head); + return error; + } + + for (cnt=0, tail=head.next; tail; tail=tail->next, cnt++); + if (!cnt) { + if (flags & GLOB_NOCHECK) { + tail = &head; + if (append(&tail, pat, strlen(pat), 0)) + return GLOB_NOSPACE; + cnt++; + } else + return GLOB_NOMATCH; + } + + if (flags & GLOB_APPEND) { + char **pathv = realloc(g->gl_pathv, (offs + g->gl_pathc + cnt + 1) * sizeof(char *)); + if (!pathv) { + freelist(&head); + return GLOB_NOSPACE; + } + g->gl_pathv = pathv; + offs += g->gl_pathc; + } else { + g->gl_pathv = malloc((offs + cnt + 1) * sizeof(char *)); + if (!g->gl_pathv) { + freelist(&head); + return GLOB_NOSPACE; + } + for (i=0; igl_pathv[i] = NULL; + } + for (i=0, tail=head.next; inext, i++) + g->gl_pathv[offs + i] = tail->name; + g->gl_pathv[offs + i] = NULL; + g->gl_pathc += cnt; + + if (!(flags & GLOB_NOSORT)) + qsort(g->gl_pathv+offs, cnt, sizeof(char *), sort); + + return error; +} + +void globfree(glob_t *g) +{ + size_t i; + for (i=0; igl_pathc; i++) + free(g->gl_pathv[g->gl_offs + i] - offsetof(struct match, name)); + free(g->gl_pathv); + g->gl_pathc = 0; + g->gl_pathv = NULL; +} From 11313e94c7e9fe30495e78ea863958b915c7bebc Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Fri, 11 May 2018 14:58:11 +0300 Subject: [PATCH 160/231] Get rid of GLIBC_2.27 symbols #2240 --- libs/libglibc-compatibility/musl/exp2f.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/libglibc-compatibility/musl/exp2f.c b/libs/libglibc-compatibility/musl/exp2f.c index e8289b7dbaf..8aaedfb9821 100644 --- a/libs/libglibc-compatibility/musl/exp2f.c +++ b/libs/libglibc-compatibility/musl/exp2f.c @@ -100,7 +100,7 @@ float exp2f(float x) } if (u.i >= 0x80000000) { /* x < -126 */ if (u.i >= 0xc3160000 || (u.i & 0x0000ffff)) - { volatile float tmp; tmp = (-0x1p-149f/x); } + { volatile float tmp; tmp = (-0x1p-149f/x); (void)tmp; } if (u.i >= 0xc3160000) /* x <= -150 */ return 0; } From 5241a810153184f89dd87eaad68d11de515df1a3 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Fri, 11 May 2018 15:00:02 +0300 Subject: [PATCH 161/231] Better check #2240 --- utils/link-validate/link-validate.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/utils/link-validate/link-validate.sh b/utils/link-validate/link-validate.sh index 612956d292a..2d8d57b95fc 100755 --- a/utils/link-validate/link-validate.sh +++ b/utils/link-validate/link-validate.sh @@ -11,7 +11,7 @@ verlte() { ALLOWED_SHARED_LIBS=("libdl.so.2" "libpthread.so.0" "librt.so.1" "libm.so.6" "libc.so.6" "ld-linux-x86-64.so.2") -if [ "$#" -ne 2 ]; then +if [ "$#" -lt 1 ]; then echo "USAGE: link-validate.sh BINNAME GLIBC_VERSION" exit 1 fi @@ -19,7 +19,7 @@ fi # Step 1: glibc version for i in $(objdump -T "$1" | awk '{print $5}' | grep GLIBC | sed 's/ *$//g' | sed 's/GLIBC_//' | sort | uniq); do - if ! verlte "$i" "$2"; then + if ! verlte "$i" "${2:-2.10}"; then echo "Dependency on newer libc detected: $i" exit 1 fi From d292190d4bc5bd3467002809c7769d5d8fcd710b Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Fri, 11 May 2018 16:52:40 +0300 Subject: [PATCH 162/231] Try to fix broken test. [#CLICKHOUSE-2] --- ...5_storage_file_and_clickhouse-local_app.sh | 29 +++++++++++-------- 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh b/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh index 7dad87341e4..cf8424e7556 100755 --- a/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh +++ b/dbms/tests/queries/0_stateless/00385_storage_file_and_clickhouse-local_app.sh @@ -62,17 +62,22 @@ ${CLICKHOUSE_LOCAL} -q "CREATE TABLE sophisticated_default # Help is not skipped [[ `${CLICKHOUSE_LOCAL} --help | wc -l` > 100 ]] -# Check that help width is adaptive -stty cols 99999 -rows1=`${CLICKHOUSE_LOCAL} --help | wc -l` -stty cols 80 -rows2=`${CLICKHOUSE_LOCAL} --help | wc -l` -[[ $rows1 < $rows2 ]] -stty cols 99999 -rows1=`${CLICKHOUSE_CLIENT} --help | wc -l` -stty cols 80 -rows2=`${CLICKHOUSE_CLIENT} --help | wc -l` -[[ $rows1 < $rows2 ]] +if [ -t 0 ] ; then + # this shell has a std-input, so we're not in batch mode -shopt -s checkwinsize || true + # Check that help width is adaptive + stty cols 99999 + rows1=`${CLICKHOUSE_LOCAL} --help | wc -l` + stty cols 80 + rows2=`${CLICKHOUSE_LOCAL} --help | wc -l` + [[ $rows1 < $rows2 ]] + + stty cols 99999 + rows1=`${CLICKHOUSE_CLIENT} --help | wc -l` + stty cols 80 + rows2=`${CLICKHOUSE_CLIENT} --help | wc -l` + [[ $rows1 < $rows2 ]] + + shopt -s checkwinsize || true +fi \ No newline at end of file From f4a3daaed06545ce48129a074f56455b27c1fe53 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Fri, 11 May 2018 17:13:44 +0300 Subject: [PATCH 163/231] Fixed linking with newer libmysqlclient when using lld. TODO: add mysqlclient to contrib [#CLICKHOUSE-2] --- libs/libmysqlxx/patch.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/libs/libmysqlxx/patch.sh b/libs/libmysqlxx/patch.sh index 4ed1abf39ba..90c1646ffb1 100755 --- a/libs/libmysqlxx/patch.sh +++ b/libs/libmysqlxx/patch.sh @@ -5,12 +5,12 @@ LIB=$1 OUT=$2 -ZLIB_OBJS_REGEX="(adler32.c.o|compress.c.o|crc32.c.o|deflate.c.o|gzio.c.o|infback.c.o|inffast.c.o|inflate.c.o|inftrees.c.o|trees.c.o|uncompr.c.o|zutil.c.o)" +ZLIB_OBJS_REGEX="(my_new.cc.o|adler32.c.o|compress.c.o|crc32.c.o|deflate.c.o|gzio.c.o|infback.c.o|inffast.c.o|inflate.c.o|inftrees.c.o|trees.c.o|uncompr.c.o|zutil.c.o|my_sha2.cc.o)" mkdir -p tmp cd tmp ar x $LIB -ar t $LIB | grep -v 'my_new.cc.o' | egrep --word-regex -v $ZLIB_OBJS_REGEX | xargs ar rcs $OUT +ar t $LIB | egrep --word-regex -v $ZLIB_OBJS_REGEX | xargs ar rcs $OUT cd .. From efe661b6201cfc87efddfc903c2ebf968bd7eb7d Mon Sep 17 00:00:00 2001 From: proller Date: Fri, 11 May 2018 17:16:16 +0300 Subject: [PATCH 164/231] Build fixes (#2347) * Try fix travis * fix * Fix clickhouse-local shared-split link * fix * fix * fix * Build fixes * Fix tinfo * fix * tinfo -> termcap --- .travis.yml | 7 ++++- CMakeLists.txt | 1 + cmake/find_termcap.cmake | 5 ++++ dbms/src/Analyzers/tests/CMakeLists.txt | 2 +- dbms/src/Functions/CMakeLists.txt | 7 +---- dbms/src/Server/CMakeLists.txt | 2 +- dbms/src/Server/Compiler-5.0.0/CMakeLists.txt | 4 +-- dbms/src/Server/Compiler-6.0.0/CMakeLists.txt | 4 +-- dbms/src/Server/Compiler-7.0.0/CMakeLists.txt | 4 +-- dbms/tests/clickhouse-test-server | 28 +++++++++++-------- dbms/tests/queries/shell_config.sh | 2 +- debian/control | 6 ++-- utils/travis/normal.sh | 2 +- 13 files changed, 42 insertions(+), 32 deletions(-) create mode 100644 cmake/find_termcap.cmake diff --git a/.travis.yml b/.travis.yml index 1e73e39fcc1..f06efc80f15 100644 --- a/.travis.yml +++ b/.travis.yml @@ -11,6 +11,7 @@ matrix: # # addons: # apt: +# update: true # sources: # - ubuntu-toolchain-r-test # packages: [ g++-7, libicu-dev, libreadline-dev, libmysqlclient-dev, unixodbc-dev, libltdl-dev, libssl-dev, libboost-dev, zlib1g-dev, libdouble-conversion-dev, libsparsehash-dev, librdkafka-dev, libcapnp-dev, libsparsehash-dev, libgoogle-perftools-dev, bash, expect, python, python-lxml, python-termcolor, curl, perl, sudo, openssl ] @@ -33,6 +34,7 @@ matrix: addons: apt: + update: true sources: - ubuntu-toolchain-r-test - llvm-toolchain-trusty-5.0 @@ -77,6 +79,7 @@ matrix: addons: apt: + update: true packages: [ pbuilder, fakeroot, debhelper ] script: @@ -94,6 +97,7 @@ matrix: # # addons: # apt: +# update: true # packages: [ pbuilder, fakeroot, debhelper ] # # env: @@ -115,6 +119,7 @@ matrix: # # addons: # apt: +# update: true # packages: [ pbuilder, fakeroot, debhelper ] # # env: @@ -137,7 +142,7 @@ matrix: # - brew link --overwrite gcc || true # # env: -# - MATRIX_EVAL="export CC=gcc-7 && export CXX=g++-7" +# - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8" # # script: # - env CMAKE_FLAGS="-DUSE_INTERNAL_BOOST_LIBRARY=1" utils/travis/normal.sh diff --git a/CMakeLists.txt b/CMakeLists.txt index c10a35f935d..ce26a33ec4e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -247,6 +247,7 @@ include (cmake/find_boost.cmake) include (cmake/find_zlib.cmake) include (cmake/find_zstd.cmake) include (cmake/find_ltdl.cmake) # for odbc +include (cmake/find_termcap.cmake) if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/contrib/poco/cmake/FindODBC.cmake) include (${CMAKE_CURRENT_SOURCE_DIR}/contrib/poco/cmake/FindODBC.cmake) # for poco else () diff --git a/cmake/find_termcap.cmake b/cmake/find_termcap.cmake new file mode 100644 index 00000000000..47b772331bb --- /dev/null +++ b/cmake/find_termcap.cmake @@ -0,0 +1,5 @@ +find_library (TERMCAP_LIBRARY termcap) +if (NOT TERMCAP_LIBRARY) + find_library (TERMCAP_LIBRARY tinfo) +endif() +message (STATUS "Using termcap: ${TERMCAP_LIBRARY}") diff --git a/dbms/src/Analyzers/tests/CMakeLists.txt b/dbms/src/Analyzers/tests/CMakeLists.txt index b1abc236793..a4f331dbd3a 100644 --- a/dbms/src/Analyzers/tests/CMakeLists.txt +++ b/dbms/src/Analyzers/tests/CMakeLists.txt @@ -12,7 +12,7 @@ target_link_libraries(type_and_constant_inference clickhouse_storages_system clickhouse_functions clickhouse_aggregate_functions clickhouse_table_functions) add_executable(analyze_result_of_query analyze_result_of_query.cpp) -target_link_libraries(analyze_result_of_query dbms clickhouse_storages_system libtinfo.a) +target_link_libraries(analyze_result_of_query dbms clickhouse_storages_system) add_executable(translate_positional_arguments translate_positional_arguments.cpp) target_link_libraries(translate_positional_arguments dbms) diff --git a/dbms/src/Functions/CMakeLists.txt b/dbms/src/Functions/CMakeLists.txt index 1a6ab2caca2..2306f0c109d 100644 --- a/dbms/src/Functions/CMakeLists.txt +++ b/dbms/src/Functions/CMakeLists.txt @@ -86,12 +86,7 @@ list(REMOVE_ITEM clickhouse_functions_headers IFunction.h FunctionFactory.h Func add_library(clickhouse_functions ${clickhouse_functions_sources}) -if (USE_EMBEDDED_COMPILER) - # It is needed for llvm::sys::Process::FileDescriptorHasColors. - set (CLICKHOUSE_FUNCTIONS_ADDITIONAL_LIBRARIES libtinfo.a) -endif () - -target_link_libraries(clickhouse_functions PUBLIC dbms PRIVATE libconsistent-hashing ${FARMHASH_LIBRARIES} ${METROHASH_LIBRARIES} ${CLICKHOUSE_FUNCTIONS_ADDITIONAL_LIBRARIES}) +target_link_libraries(clickhouse_functions PUBLIC dbms PRIVATE libconsistent-hashing ${FARMHASH_LIBRARIES} ${METROHASH_LIBRARIES}) target_include_directories (clickhouse_functions BEFORE PUBLIC ${ClickHouse_SOURCE_DIR}/contrib/libfarmhash) target_include_directories (clickhouse_functions BEFORE PUBLIC ${ClickHouse_SOURCE_DIR}/contrib/libmetrohash/src) diff --git a/dbms/src/Server/CMakeLists.txt b/dbms/src/Server/CMakeLists.txt index b2d72fb3c8b..747cab4c94a 100644 --- a/dbms/src/Server/CMakeLists.txt +++ b/dbms/src/Server/CMakeLists.txt @@ -31,7 +31,7 @@ target_link_libraries (clickhouse-server-lib clickhouse_common_io daemon clickho target_include_directories (clickhouse-server-lib PUBLIC ${ClickHouse_SOURCE_DIR}/libs/libdaemon/include) add_library (clickhouse-local-lib LocalServer.cpp) -target_link_libraries (clickhouse-local-lib clickhouse-server-lib clickhouse_functions clickhouse_aggregate_functions clickhouse_table_functions) +target_link_libraries (clickhouse-local-lib clickhouse-server-lib clickhouse_functions clickhouse_aggregate_functions clickhouse_table_functions ${Boost_PROGRAM_OPTIONS_LIBRARY}) add_library (clickhouse-extract-from-config-lib ${SPLIT_SHARED} ExtractFromConfig.cpp) target_link_libraries (clickhouse-extract-from-config-lib clickhouse_common_config clickhouse_common_io ${Boost_PROGRAM_OPTIONS_LIBRARY}) diff --git a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt index fcae3c108ca..ee21c283ff3 100644 --- a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt @@ -45,6 +45,6 @@ LLVMSupport PUBLIC ${ZLIB_LIBRARIES} ${EXECINFO_LIBRARY} Threads::Threads ) -if (NOT APPLE) - target_link_libraries(clickhouse-compiler-lib PRIVATE libtinfo.a) +if (TERMCAP_LIBRARY) + target_link_libraries(clickhouse-compiler-lib PUBLIC ${TERMCAP_LIBRARY}) endif() diff --git a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt index e136b8fc363..17e4ab928a5 100644 --- a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt @@ -45,6 +45,6 @@ ${REQUIRED_LLVM_LIBRARIES} PUBLIC ${ZLIB_LIBRARIES} ${EXECINFO_LIBRARY} Threads::Threads ) -if (NOT APPLE) - target_link_libraries(clickhouse-compiler-lib PRIVATE libtinfo.a) +if (TERMCAP_LIBRARY) + target_link_libraries(clickhouse-compiler-lib PUBLIC ${TERMCAP_LIBRARY}) endif() diff --git a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt index 96f50b2f899..e3c9beee997 100644 --- a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt @@ -41,6 +41,6 @@ ${REQUIRED_LLVM_LIBRARIES} PUBLIC ${ZLIB_LIBRARIES} ${EXECINFO_LIBRARY} Threads::Threads ) -if (NOT APPLE) - target_link_libraries(clickhouse-compiler-lib PRIVATE libtinfo.a) +if (TERMCAP_LIBRARY) + target_link_libraries(clickhouse-compiler-lib PUBLIC ${TERMCAP_LIBRARY}) endif() diff --git a/dbms/tests/clickhouse-test-server b/dbms/tests/clickhouse-test-server index 38b6515a746..82f5e88f612 100755 --- a/dbms/tests/clickhouse-test-server +++ b/dbms/tests/clickhouse-test-server @@ -9,24 +9,26 @@ ROOT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && cd ../.. && pwd) DATA_DIR=${DATA_DIR:=/tmp/clickhouse} LOG_DIR=${LOG_DIR:=$DATA_DIR/log} BUILD_DIR=${BUILD_DIR:=$ROOT_DIR/build${BUILD_TYPE}} -[ -x "${CUR_DIR}/clickhouse-server" ] && [ -x "${CUR_DIR}/clickhouse-client" ] && BIN_DIR= # Allow run in /usr/bin -[ -x "${BUILD_DIR}/dbms/src/Server/clickhouse-server" ] && BIN_DIR=${BIN_DIR=$BUILD_DIR/dbms/src/Server/} -[ -f "${CUR_DIR}/server-test.xml" ] && CONFIG_DIR=${CONFIG_DIR=$CUR_DIR}/ -CONFIG_CLIENT_DIR=${CONFIG_CLIENT_DIR=${CONFIG_DIR}} -CONFIG_SERVER_DIR=${CONFIG_SERVER_DIR=${CONFIG_DIR}} +export CLICKHOUSE_BINARY=${CLICKHOUSE_BINARY:="clickhouse"} +[ -x "$CUR_DIR/clickhouse-server" ] && [ -x "${CUR_DIR}/${CLICKHOUSE_BINARY}-client" ] && BIN_DIR= # Allow run in /usr/bin +[ -x "$BUILD_DIR/dbms/src/Server/${CLICKHOUSE_BINARY}-server" ] && BIN_DIR=${BIN_DIR=$BUILD_DIR/dbms/src/Server/} +[ -f "$CUR_DIR/server-test.xml" ] && CONFIG_DIR=${CONFIG_DIR=$CUR_DIR}/ +CONFIG_CLIENT_DIR=${CONFIG_CLIENT_DIR=$CONFIG_DIR} +CONFIG_SERVER_DIR=${CONFIG_SERVER_DIR=$CONFIG_DIR} [ ! -f "${CONFIG_CLIENT_DIR}client-test.xml" ] && CONFIG_CLIENT_DIR=${CONFIG_CLIENT_DIR:=/etc/clickhouse-client/} [ ! -f "${CONFIG_SERVER_DIR}server-test.xml" ] && CONFIG_SERVER_DIR=${CONFIG_SERVER_DIR:=/etc/clickhouse-server/} CONFIG_CLIENT=${CONFIG_CLIENT:=${CONFIG_CLIENT_DIR}client-test.xml} export CLICKHOUSE_CONFIG=${CLICKHOUSE_CONFIG:=${CONFIG_SERVER_DIR}server-test.xml} -[ -x "${CUR_DIR}/clickhouse-test" ] && TEST_DIR=${TEST_DIR=${CUR_DIR}/} -[ -d "${CUR_DIR}/queries" ] && QUERIES_DIR=${QUERIES_DIR=${CUR_DIR}/queries} +[ -x "$CUR_DIR/clickhouse-test" ] && TEST_DIR=${TEST_DIR=$CUR_DIR/} +[ -d "$CUR_DIR/queries" ] && QUERIES_DIR=${QUERIES_DIR=$CUR_DIR/queries} [ ! -d "$QUERIES_DIR" ] && QUERIES_DIR=${QUERIES_DIR=/usr/share/clickhouse-test/queries} +CLICKHOUSE_EXTRACT_CONFIG=${CLICKHOUSE_EXTRACT_CONFIG:="${BIN_DIR}${CLICKHOUSE_BINARY}-extract-from-config --config=$CLICKHOUSE_CONFIG"} rm -rf $DATA_DIR mkdir -p $LOG_DIR -openssl dhparam -out `${BIN_DIR}clickhouse-extract-from-config --config=$CLICKHOUSE_CONFIG --key=openSSL.server.dhParamsFile` 256 -openssl req -subj "/CN=localhost" -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout `${BIN_DIR}clickhouse-extract-from-config --config=$CLICKHOUSE_CONFIG --key=openSSL.server.privateKeyFile` -out `${BIN_DIR}clickhouse-extract-from-config --config=$CLICKHOUSE_CONFIG --key=openSSL.server.certificateFile` +openssl dhparam -out `$CLICKHOUSE_EXTRACT_CONFIG --key=openSSL.server.dhParamsFile` 256 +openssl req -subj "/CN=localhost" -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout `${BIN_DIR}clickhouse-extract-from-config --config=$CLICKHOUSE_CONFIG --key=openSSL.server.privateKeyFile` -out `${BIN_DIR}clickhouse-extract-from-config --config=$CLICKHOUSE_CONFIG --key=openSSL.server.certificateFile` if [ "$TEST_GDB" ]; then echo -e "run \nset pagination off \nset logging file $DATA_DIR/gdb.log \nset logging on \nthread apply all backtrace \ndetach \nquit " > $DATA_DIR/gdb.cmd @@ -35,7 +37,7 @@ fi # Start a local clickhouse server which will be used to run tests #PATH=$PATH:$BIN_DIR \ -$GDB ${BIN_DIR}clickhouse-server --config-file=${CLICKHOUSE_CONFIG} > $LOG_DIR/stdout 2>&1 & +$GDB ${BIN_DIR}clickhouse-server --config-file=$CLICKHOUSE_CONFIG > $LOG_DIR/stdout 2>&1 & CH_PID=$! sleep 3 @@ -62,7 +64,9 @@ trap finish EXIT SIGINT SIGQUIT SIGTERM if [ -n "$*" ]; then $* else + TEST_RUN=${TEST_RUN=1} + TEST_PERF=${TEST_PERF=1} ${BIN_DIR}clickhouse-client --config ${CONFIG_CLIENT} -q 'SELECT * from system.build_options;' - PATH=$PATH:$BIN_DIR \ - ${TEST_DIR}clickhouse-test --binary ${BIN_DIR}clickhouse --configclient ${CONFIG_CLIENT} --configserver ${CLICKHOUSE_CONFIG} --tmp $DATA_DIR/tmp --queries ${QUERIES_DIR} $TEST_OPT0 $TEST_OPT + [ "$TEST_RUN" ] && env PATH=$PATH:$BIN_DIR ${TEST_DIR}clickhouse-test --binary ${BIN_DIR}clickhouse --configclient $CONFIG_CLIENT --configserver $CLICKHOUSE_CONFIG --tmp $DATA_DIR/tmp --queries $QUERIES_DIR $TEST_OPT0 $TEST_OPT + [ "$TEST_PERF" ] && ${BIN_DIR}clickhouse-performance-test --port `$CLICKHOUSE_EXTRACT_CONFIG --key=tcp_port` --r $CUR_DIR/performance --skip-tags=long $* fi diff --git a/dbms/tests/queries/shell_config.sh b/dbms/tests/queries/shell_config.sh index 1eae3159870..0c12418b602 100644 --- a/dbms/tests/queries/shell_config.sh +++ b/dbms/tests/queries/shell_config.sh @@ -4,7 +4,7 @@ export CLICKHOUSE_CLIENT=${CLICKHOUSE_CLIENT:="${CLICKHOUSE_BINARY}-client"} export CLICKHOUSE_LOCAL=${CLICKHOUSE_LOCAL:="${CLICKHOUSE_BINARY}-local"} export CLICKHOUSE_CONFIG=${CLICKHOUSE_CONFIG:="/etc/clickhouse-server/config.xml"} -export CLICKHOUSE_EXTRACT_CONFIG=${CLICKHOUSE_EXTRACT_CONFIG:="$CLICKHOUSE_BINARY-extract-from-config -c $CLICKHOUSE_CONFIG"} +export CLICKHOUSE_EXTRACT_CONFIG=${CLICKHOUSE_EXTRACT_CONFIG:="$CLICKHOUSE_BINARY-extract-from-config --config=$CLICKHOUSE_CONFIG"} export CLICKHOUSE_CONFIG_GREP=${CLICKHOUSE_CONFIG_GREP:="/etc/clickhouse-server/config-preprocessed.xml"} export CLICKHOUSE_HOST=${CLICKHOUSE_HOST:="localhost"} diff --git a/debian/control b/debian/control index 591c930b206..f3aa1be41ca 100644 --- a/debian/control +++ b/debian/control @@ -5,14 +5,14 @@ Maintainer: Alexey Milovidov Build-Depends: debhelper (>= 9), cmake3 | cmake, ninja-build, - gcc-7 [amd64 i386], g++-7 [amd64 i386], - clang-6.0 [arm64 armhf] | clang-5.0 [arm64 armhf], + gcc-7 [amd64 i386] | gcc-8 [amd64 i386], g++-7 [amd64 i386] | g++-8 [amd64 i386], + clang-6.0 [arm64 armhf] | clang-5.0 [arm64 armhf] | clang-7 [arm64 armhf], libc6-dev, libmariadbclient-dev | default-libmysqlclient-dev | libmysqlclient-dev, libicu-dev, libltdl-dev, libreadline-dev, - libssl-dev, + libssl1.0-dev | libssl-dev, unixodbc-dev Standards-Version: 3.9.8 diff --git a/utils/travis/normal.sh b/utils/travis/normal.sh index dfdc74fe3b4..573b486762f 100755 --- a/utils/travis/normal.sh +++ b/utils/travis/normal.sh @@ -35,6 +35,6 @@ cmake $CUR_DIR/../.. -DCMAKE_CXX_COMPILER=`which $DEB_CXX $CXX` -DCMAKE_C_COMPIL `# Skip tests:` \ `# 00281 requires internal compiler` \ `# 00428 requires sudo (not all vms allow this)` \ - && ( [ ! ${TEST_RUN=1} ] || ( ( cd $CUR_DIR/../.. && env TEST_OPT="--skip long compile 00428 $TEST_OPT" bash -x dbms/tests/clickhouse-test-server ) || ${TEST_TRUE=false} ) ) + && ( [ ! ${TEST_RUN=1} ] || ( ( cd $CUR_DIR/../.. && env TEST_OPT="--skip long compile 00428 $TEST_OPT" TEST_PERF= bash -x dbms/tests/clickhouse-test-server ) || ${TEST_TRUE=false} ) ) date From adbbbb3c4897f48433250b800aeee01e802f2f15 Mon Sep 17 00:00:00 2001 From: Winter Zhang Date: Fri, 11 May 2018 22:35:32 +0800 Subject: [PATCH 165/231] ISSUES-2343 fix failed test (#2344) --- dbms/src/Server/LocalServer.cpp | 8 +++++++- dbms/src/Server/LocalServer.h | 1 + 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/dbms/src/Server/LocalServer.cpp b/dbms/src/Server/LocalServer.cpp index c1ac861b9bd..77e46cde7eb 100644 --- a/dbms/src/Server/LocalServer.cpp +++ b/dbms/src/Server/LocalServer.cpp @@ -171,7 +171,7 @@ try std::string default_database = config().getString("default_database", "_local"); context->addDatabase(default_database, std::make_shared(default_database)); context->setCurrentDatabase(default_database); - applyCmdSettings(*context); + applyCmdOptions(*context); if (!context->getPath().empty()) { @@ -480,6 +480,12 @@ void LocalServer::init(int argc, char ** argv) config().setBool("ignore-error", true); } +void LocalServer::applyCmdOptions(Context & context) +{ + context.setDefaultFormat(config().getString("output-format", config().getString("format", "TSV"))); + applyCmdSettings(context); +} + } int mainEntryClickHouseLocal(int argc, char ** argv) diff --git a/dbms/src/Server/LocalServer.h b/dbms/src/Server/LocalServer.h index 5aee2739823..5a981216953 100644 --- a/dbms/src/Server/LocalServer.h +++ b/dbms/src/Server/LocalServer.h @@ -36,6 +36,7 @@ private: std::string getInitialCreateTableQuery(); void tryInitPath(); + void applyCmdOptions(Context & context); void applyCmdSettings(Context & context); void attachSystemTables(); void processQueries(); From 0b386381aca79c5a6a4a00ebb5e24a2e13177126 Mon Sep 17 00:00:00 2001 From: proller Date: Fri, 11 May 2018 21:00:24 +0300 Subject: [PATCH 166/231] Build fixes --- CMakeLists.txt | 1 - dbms/CMakeLists.txt | 3 +++ dbms/src/Server/Compiler-5.0.0/CMakeLists.txt | 7 +++---- dbms/src/Server/Compiler-6.0.0/CMakeLists.txt | 7 +++---- dbms/src/Server/Compiler-7.0.0/CMakeLists.txt | 7 +++---- dbms/tests/clickhouse-test-server | 2 +- release | 4 ++-- 7 files changed, 15 insertions(+), 16 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index ce26a33ec4e..4056bd84cf0 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -17,7 +17,6 @@ else () message (WARNING "You are using an unsupported compiler! Compilation has only been tested with Clang 5+ and GCC 7+.") endif () - # Write compile_commands.json set(CMAKE_EXPORT_COMPILE_COMMANDS 1) diff --git a/dbms/CMakeLists.txt b/dbms/CMakeLists.txt index e290fad9315..2dee7937e8d 100644 --- a/dbms/CMakeLists.txt +++ b/dbms/CMakeLists.txt @@ -101,6 +101,9 @@ endif () if (USE_EMBEDDED_COMPILER) llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) + if (TERMCAP_LIBRARY) + list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) + endif() target_link_libraries (dbms ${REQUIRED_LLVM_LIBRARIES}) target_include_directories (dbms BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) endif () diff --git a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt index ee21c283ff3..2fa435f451f 100644 --- a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt @@ -9,6 +9,9 @@ add_library(clickhouse-compiler-lib target_compile_options(clickhouse-compiler-lib PRIVATE -fno-rtti -fno-exceptions -g0) llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) +if (TERMCAP_LIBRARY) + list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) +endif () message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") @@ -44,7 +47,3 @@ LLVMSupport PUBLIC ${ZLIB_LIBRARIES} ${EXECINFO_LIBRARY} Threads::Threads ) - -if (TERMCAP_LIBRARY) - target_link_libraries(clickhouse-compiler-lib PUBLIC ${TERMCAP_LIBRARY}) -endif() diff --git a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt index 17e4ab928a5..481b0cc39e7 100644 --- a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt @@ -9,6 +9,9 @@ add_library(clickhouse-compiler-lib target_compile_options(clickhouse-compiler-lib PRIVATE -fno-rtti -fno-exceptions -g0) llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) +if (TERMCAP_LIBRARY) + list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) +endif () message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") @@ -44,7 +47,3 @@ ${REQUIRED_LLVM_LIBRARIES} PUBLIC ${ZLIB_LIBRARIES} ${EXECINFO_LIBRARY} Threads::Threads ) - -if (TERMCAP_LIBRARY) - target_link_libraries(clickhouse-compiler-lib PUBLIC ${TERMCAP_LIBRARY}) -endif() diff --git a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt index e3c9beee997..a72f09b3a58 100644 --- a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt @@ -9,6 +9,9 @@ add_library(clickhouse-compiler-lib target_compile_options(clickhouse-compiler-lib PRIVATE -fno-rtti -fno-exceptions -g0) llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) +if (TERMCAP_LIBRARY) + list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) +endif () message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") @@ -40,7 +43,3 @@ ${REQUIRED_LLVM_LIBRARIES} PUBLIC ${ZLIB_LIBRARIES} ${EXECINFO_LIBRARY} Threads::Threads ) - -if (TERMCAP_LIBRARY) - target_link_libraries(clickhouse-compiler-lib PUBLIC ${TERMCAP_LIBRARY}) -endif() diff --git a/dbms/tests/clickhouse-test-server b/dbms/tests/clickhouse-test-server index 82f5e88f612..75d597eb3ae 100755 --- a/dbms/tests/clickhouse-test-server +++ b/dbms/tests/clickhouse-test-server @@ -68,5 +68,5 @@ else TEST_PERF=${TEST_PERF=1} ${BIN_DIR}clickhouse-client --config ${CONFIG_CLIENT} -q 'SELECT * from system.build_options;' [ "$TEST_RUN" ] && env PATH=$PATH:$BIN_DIR ${TEST_DIR}clickhouse-test --binary ${BIN_DIR}clickhouse --configclient $CONFIG_CLIENT --configserver $CLICKHOUSE_CONFIG --tmp $DATA_DIR/tmp --queries $QUERIES_DIR $TEST_OPT0 $TEST_OPT - [ "$TEST_PERF" ] && ${BIN_DIR}clickhouse-performance-test --port `$CLICKHOUSE_EXTRACT_CONFIG --key=tcp_port` --r $CUR_DIR/performance --skip-tags=long $* + ( [ "$TEST_PERF" ] && ${BIN_DIR}clickhouse-performance-test --port `$CLICKHOUSE_EXTRACT_CONFIG --key=tcp_port` --r $CUR_DIR/performance --skip-tags=long $* ) || true fi diff --git a/release b/release index d5bae8c9fe8..0e785aa0886 100755 --- a/release +++ b/release @@ -4,9 +4,9 @@ # Test gcc-8: # env DIST=bionic EXTRAPACKAGES="gcc-8 g++-8" DEB_CC=gcc-8 DEB_CXX=g++-8 CMAKE_FLAGS=" -DNO_WERROR=1 " ./release # Clang6 build: -# env DIST=bionic EXTRAPACKAGES="clang-6.0 libstdc++-8-dev lld-6.0 liblld-6.0-dev libclang-6.0-dev liblld-6.0" DEB_CC=clang-6.0 DEB_CXX=clang++-6.0 CMAKE_FLAGS=" -DLLVM_VERSION_POSTFIX=-6.0 -DNO_WERROR=1 " ./release +# env DIST=bionic EXTRAPACKAGES="clang-6.0 libstdc++-8-dev lld-6.0 liblld-6.0-dev libclang-6.0-dev liblld-6.0" DEB_CC=clang-6.0 DEB_CXX=clang++-6.0 CMAKE_FLAGS=" -DNO_WERROR=1 " ./release # Clang7 build: -# env DIST=unstable EXTRAPACKAGES="clang-7 libstdc++-8-dev lld-7 liblld-7-dev libclang-7-dev liblld-7" DEB_CC=clang-7 DEB_CXX=clang++-7 CMAKE_FLAGS=" -DLLVM_VERSION_POSTFIX=-7 -DNO_WERROR=1 " ./release +# env DIST=unstable EXTRAPACKAGES="clang-7 libstdc++-8-dev lld-7 liblld-7-dev libclang-7-dev liblld-7" DEB_CC=clang-7 DEB_CXX=clang++-7 CMAKE_FLAGS=" -DNO_WERROR=1 " ./release # Clang6 without internal compiler (for low memory arm64): # env DIST=bionic DISABLE_PARALLEL=1 EXTRAPACKAGES="clang-6.0 libstdc++-8-dev" DEB_CC=clang-6.0 DEB_CXX=clang++-6.0 CMAKE_FLAGS=" -DNO_WERROR=1 " ./release From 64b1b6d764b41f4c1ab2cd3babf5e672fc4cebb0 Mon Sep 17 00:00:00 2001 From: sundy-li <543950155@qq.com> Date: Sat, 12 May 2018 23:49:17 +0800 Subject: [PATCH 167/231] Add windowFunnel AggregateFunction --- .../AggregateFunctionWindowFunnel.cpp | 28 ++ .../AggregateFunctionWindowFunnel.h | 262 ++++++++++++++++++ .../registerAggregateFunctions.cpp | 2 + 3 files changed, 292 insertions(+) create mode 100644 dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.cpp create mode 100644 dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h diff --git a/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.cpp b/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.cpp new file mode 100644 index 00000000000..164693e1873 --- /dev/null +++ b/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.cpp @@ -0,0 +1,28 @@ +#include +#include +#include +#include + + +namespace DB +{ + +namespace +{ + +AggregateFunctionPtr createAggregateFunctionWindowFunnel(const std::string & name, const DataTypes & arguments, const Array & params) +{ + + if (params.size() <= 0 || params.size() > 32) + throw Exception("Aggregate function " + name + " requires (1, 32] event ids.", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); + return std::make_shared(arguments, params); +} + +} + +void registerAggregateFunctionWindowFunnel(AggregateFunctionFactory & factory) +{ + factory.registerFunction("windowFunnel", createAggregateFunctionWindowFunnel, AggregateFunctionFactory::CaseInsensitive); +} + +} diff --git a/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h b/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h new file mode 100644 index 00000000000..4dfa9d31aea --- /dev/null +++ b/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h @@ -0,0 +1,262 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include + + +namespace DB +{ + +namespace ErrorCodes +{ + extern const int NUMBER_OF_ARGUMENTS_DOESNT_MATCH; + extern const int TOO_MANY_ARGUMENTS_FOR_FUNCTION; +} + +struct ComparePairFirst final +{ + template + bool operator()(const std::pair & lhs, const std::pair & rhs) const + { + return lhs.first < rhs.first; + } +}; + +struct AggregateFunctionWindowFunnelData +{ + static constexpr auto max_events = 32; + using TimestampEvent = std::pair; + + static constexpr size_t bytes_on_stack = 64; + using TimestampEvents = PODArray, bytes_on_stack>>; + + using Comparator = ComparePairFirst; + + bool sorted = true; + TimestampEvents events_list; + + size_t size() const + { + return events_list.size(); + } + + void add(UInt64 timestamp, UInt8 event) + { + // Since most events should have already been sorted by timestamp. + if (sorted && events_list.size() > 0 && events_list.back().first > timestamp) + sorted = false; + events_list.emplace_back(timestamp, event); + } + + void merge(const AggregateFunctionWindowFunnelData & other) + { + const auto size = events_list.size(); + + events_list.insert(std::begin(other.events_list), std::end(other.events_list)); + + /// either sort whole container or do so partially merging ranges afterwards + if (!sorted && !other.sorted) + std::sort(std::begin(events_list), std::end(events_list), Comparator{}); + else + { + const auto begin = std::begin(events_list); + const auto middle = std::next(begin, size); + const auto end = std::end(events_list); + + if (!sorted) + std::sort(begin, middle, Comparator{}); + + if (!other.sorted) + std::sort(middle, end, Comparator{}); + + std::inplace_merge(begin, middle, end, Comparator{}); + } + + sorted = true; + } + + void sort() + { + if (!sorted) + { + std::sort(std::begin(events_list), std::end(events_list), Comparator{}); + sorted = true; + } + } + + void serialize(WriteBuffer & buf) const + { + writeBinary(sorted, buf); + writeBinary(events_list.size(), buf); + + for (const auto & events : events_list) + { + writeBinary(events.first, buf); + writeBinary(events.second, buf); + } + } + + void deserialize(ReadBuffer & buf) + { + readBinary(sorted, buf); + + size_t size; + readBinary(size, buf); + + events_list.clear(); + events_list.resize(size); + + UInt64 timestamp; + UInt8 event; + + for (size_t i = 0; i < size; ++i) + { + readBinary(timestamp, buf); + readBinary(event, buf); + events_list.emplace_back(timestamp, event); + } + } + +}; + +/** Calculates the max event level in a sliding window. + * The max size of events is 32, that's enough for funnel analytics + * + * Usage: + * - windowFunnel(window_size)(window_column, event_condition1, event_condition2, event_condition3, ....) + */ + +class AggregateFunctionWindowFunnel final : public IAggregateFunctionDataHelper +{ +private: + UInt64 window; + Logger * log = &Logger::get("AggregateFunctionWindowFunnel"); + UInt8 check_events_size; + + + // Loop through the entire events_list + // If the timestamp window size between current event and pre event( that's event-1) is less than the window value, then update current event's timestamp. + // Returns the max event level. + // The Algorithm complexity is O(n). + + UInt8 match(const AggregateFunctionWindowFunnelData & data) const + { + if(data.events_list.empty()) return 0; + if (check_events_size == 1) + return 1; + + const_cast(data).sort(); + + std::vector events_timestamp(check_events_size,0); + for(const auto i : ext::range(0, data.size())) + { + const auto & event = (data.events_list)[i - 1].second - 1; + const auto & timestamp = (data.events_list)[i - 1].first; + if(event == 0) + events_timestamp[0] = timestamp; + else if(timestamp <= events_timestamp[event - 1] + window) + { + events_timestamp[event] = timestamp; + if(event == check_events_size) return check_events_size; + } + } + + for(const auto i : ext::range(data.size() - 1, 0)) + { + if(events_timestamp[i]) return i + 1; + } + return 0; + } + +public: + + String getName() const override { return "windowFunnel"; } + + AggregateFunctionWindowFunnel(const DataTypes & arguments, const Array & params) + { + DataTypePtr timestampType = arguments[0]; + + if (!(timestampType->isUnsignedInteger())) + throw Exception("Illegal type " + timestampType->getName() + " of argument for aggregate function " + getName() + " (1 arg, timestamp: UIntXX)", + ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); + + check_events_size = arguments.size() - 1; + if(check_events_size > AggregateFunctionWindowFunnelData::max_events) + throw Exception{"Aggregate function " + getName() + " supports up to " + + toString(AggregateFunctionWindowFunnelData::max_events) + " event arguments.", + ErrorCodes::TOO_MANY_ARGUMENTS_FOR_FUNCTION}; + + for(const auto i : ext::range(1, arguments.size())) + { + auto cond_arg = arguments[i].get(); + if (!typeid_cast(cond_arg)) + throw Exception{"Illegal type " + cond_arg->getName() + " of argument " + toString(i + 1) + + " of aggregate function " + getName() + ", must be UInt8", + ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; + } + + if (params.size() != 1) + throw Exception("Aggregate function " + getName() + " requires exactly 1 args(window_num).", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); + + window = params[0].safeGet(); + LOG_TRACE(log, std::fixed << std::setprecision(3) << "setParameters, window: " << window << " check events:" << check_events_size); + } + + + DataTypePtr getReturnType() const override + { + return std::make_shared(); + } + + void add(AggregateDataPtr place, const IColumn ** columns, const size_t row_num, Arena *) const override + { + UInt8 event_level = 0; + for(const auto i : ext::range(1,check_events_size)) + { + auto event = static_cast *>(columns[i])->getData()[row_num]; + if(event){ + event_level = i; + break; + } + } + this->data(place).add( // + static_cast *>(columns[0])->getData()[row_num], + event_level + ); + } + + void merge(AggregateDataPtr place, ConstAggregateDataPtr rhs, Arena *) const override + { + this->data(place).merge(this->data(rhs)); + } + + void serialize(ConstAggregateDataPtr place, WriteBuffer & buf) const override + { + this->data(place).serialize(buf); + } + + void deserialize(AggregateDataPtr place, ReadBuffer & buf, Arena *) const override + { + this->data(place).deserialize(buf); + } + + void insertResultInto(ConstAggregateDataPtr place, IColumn & to) const override + { + static_cast(to).getData().push_back(match(this->data(place))); + } + + const char * getHeaderFilePath() const override { return __FILE__; } +}; + +} diff --git a/dbms/src/AggregateFunctions/registerAggregateFunctions.cpp b/dbms/src/AggregateFunctions/registerAggregateFunctions.cpp index 644543e4f2e..be566e442b2 100644 --- a/dbms/src/AggregateFunctions/registerAggregateFunctions.cpp +++ b/dbms/src/AggregateFunctions/registerAggregateFunctions.cpp @@ -14,6 +14,7 @@ void registerAggregateFunctionGroupUniqArray(AggregateFunctionFactory &); void registerAggregateFunctionGroupArrayInsertAt(AggregateFunctionFactory &); void registerAggregateFunctionsQuantile(AggregateFunctionFactory &); void registerAggregateFunctionsSequenceMatch(AggregateFunctionFactory &); +void registerAggregateFunctionWindowFunnel(AggregateFunctionFactory &); void registerAggregateFunctionsMinMaxAny(AggregateFunctionFactory &); void registerAggregateFunctionsStatisticsStable(AggregateFunctionFactory &); void registerAggregateFunctionsStatisticsSimple(AggregateFunctionFactory &); @@ -45,6 +46,7 @@ void registerAggregateFunctions() registerAggregateFunctionGroupArrayInsertAt(factory); registerAggregateFunctionsQuantile(factory); registerAggregateFunctionsSequenceMatch(factory); + registerAggregateFunctionWindowFunnel(factory); registerAggregateFunctionsMinMaxAny(factory); registerAggregateFunctionsStatisticsStable(factory); registerAggregateFunctionsStatisticsSimple(factory); From ddf2744095f7782d747c497932cb80f927014989 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Sun, 13 May 2018 01:08:53 +0300 Subject: [PATCH 168/231] Actualized Docker builder; added README #268 --- docker/builder/Dockerfile | 13 ++++++------- docker/builder/Makefile | 4 ++-- docker/builder/README.md | 33 +++++++++++++++++++++++++++++++++ docker/builder/build.sh | 2 +- 4 files changed, 42 insertions(+), 10 deletions(-) create mode 100644 docker/builder/README.md diff --git a/docker/builder/Dockerfile b/docker/builder/Dockerfile index 2b3646367d5..195455d422f 100644 --- a/docker/builder/Dockerfile +++ b/docker/builder/Dockerfile @@ -1,12 +1,11 @@ FROM ubuntu:17.10 -RUN apt update -y && \ - apt install -y cmake libssl-dev libcrypto++-dev \ - libglib2.0-dev libltdl-dev libicu-dev libmysql++-dev \ - libreadline-dev libmysqlclient-dev unixodbc-dev \ - gcc-7 g++-7 unixodbc-dev devscripts dupload fakeroot debhelper \ - liblld-5.0-dev libclang-5.0-dev liblld-5.0 - # For tests: # bash expect python python-lxml python-termcolor curl perl sudo tzdata +RUN apt-get update -y && \ + apt-get install -y \ + cmake pkg-config gcc-7 g++-7 \ + liblld-5.0-dev libclang-5.0-dev liblld-5.0 \ + libssl-dev libicu-dev libmysql++-dev libreadline-dev libmysqlclient-dev unixodbc-dev + # For tests: bash expect python python-lxml python-termcolor curl perl sudo tzdata ADD build.sh / RUN chmod +x /build.sh diff --git a/docker/builder/Makefile b/docker/builder/Makefile index 4a6df269e9e..770aa6f8bfe 100644 --- a/docker/builder/Makefile +++ b/docker/builder/Makefile @@ -1,11 +1,11 @@ build: - docker run --rm --workdir /server -v $(realpath ../..):/server -it yandex/clickhouse-builder + docker run --network=host --rm --workdir /server --volume $(realpath ../..):/server -it yandex/clickhouse-builder pull: docker pull yandex/clickhouse-builder image: - docker build -t yandex/clickhouse-builder . + docker build --network=host -t yandex/clickhouse-builder . image_push: docker push yandex/clickhouse-builder diff --git a/docker/builder/README.md b/docker/builder/README.md new file mode 100644 index 00000000000..55bd23acb1d --- /dev/null +++ b/docker/builder/README.md @@ -0,0 +1,33 @@ +Allows to build ClickHouse in Docker. +This is useful if you have an old OS distribution and you don't want to build fresh gcc or clang from sources. + +Usage: + +Prepare image: +``` +make image +``` + +Run build: +``` +make build +``` + +Before run, ensure that your user has access to docker: +To check, that you have access to Docker, run `docker ps`. +If not, you must add this user to `docker` group: `sudo usermod -aG docker $USER` and relogin. +(You must close all your sessions. For example, restart your computer.) + +Build results are available in `build_docker` directory at top level of your working copy. +It builds only binaries, not packages. + +For example, run server: +``` +cd $(git rev-parse --show-toplevel)/dbms/src/Server +$(git rev-parse --show-toplevel)/build_docker/dbms/src/Server/clickhouse server +``` + +Run client: +``` +$(git rev-parse --show-toplevel)/build_docker/dbms/src/Server/clickhouse client +``` diff --git a/docker/builder/build.sh b/docker/builder/build.sh index f63dbb65558..b62ad97579b 100644 --- a/docker/builder/build.sh +++ b/docker/builder/build.sh @@ -2,6 +2,6 @@ mkdir -p /server/build_docker cd /server/build_docker -cmake /server -DENABLE_TESTS=0 +cmake /server -D ENABLE_TESTS=0 make -j $(nproc || grep -c ^processor /proc/cpuinfo) #ctest -V -j $(nproc || grep -c ^processor /proc/cpuinfo) From 46070ab698ac4b5675c0946ce877181e8143d167 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Sun, 13 May 2018 04:15:32 +0300 Subject: [PATCH 169/231] Removed trash library that we never used [#CLICKHOUSE-2] --- docker/builder/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docker/builder/Dockerfile b/docker/builder/Dockerfile index 195455d422f..65180f4daef 100644 --- a/docker/builder/Dockerfile +++ b/docker/builder/Dockerfile @@ -4,7 +4,7 @@ RUN apt-get update -y && \ apt-get install -y \ cmake pkg-config gcc-7 g++-7 \ liblld-5.0-dev libclang-5.0-dev liblld-5.0 \ - libssl-dev libicu-dev libmysql++-dev libreadline-dev libmysqlclient-dev unixodbc-dev + libssl-dev libicu-dev libreadline-dev libmysqlclient-dev unixodbc-dev # For tests: bash expect python python-lxml python-termcolor curl perl sudo tzdata ADD build.sh / From c56e9967f27ed0b63ca14ae529c5166754589cd7 Mon Sep 17 00:00:00 2001 From: sundy-li <543950155@qq.com> Date: Sun, 13 May 2018 16:18:35 +0800 Subject: [PATCH 170/231] Add tests && docs --- .../AggregateFunctionWindowFunnel.h | 83 +- 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100644 docs/build/docs/en/table_functions/numbers/index.html create mode 100644 docs/build/docs/en/table_functions/remote/index.html create mode 100644 docs/build/docs/en/utils/clickhouse-copier/index.html create mode 100644 docs/build/docs/en/utils/clickhouse-local/index.html create mode 100644 docs/build/docs/en/utils/index.html diff --git a/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h b/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h index 4dfa9d31aea..ed1628c3bf8 100644 --- a/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h +++ b/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h @@ -2,11 +2,11 @@ #include #include -#include #include #include #include #include +#include #include #include #include @@ -36,7 +36,7 @@ struct ComparePairFirst final struct AggregateFunctionWindowFunnelData { static constexpr auto max_events = 32; - using TimestampEvent = std::pair; + using TimestampEvent = std::pair; static constexpr size_t bytes_on_stack = 64; using TimestampEvents = PODArray, bytes_on_stack>>; @@ -51,7 +51,7 @@ struct AggregateFunctionWindowFunnelData return events_list.size(); } - void add(UInt64 timestamp, UInt8 event) + void add(UInt32 timestamp, UInt8 event) { // Since most events should have already been sorted by timestamp. if (sorted && events_list.size() > 0 && events_list.back().first > timestamp) @@ -117,7 +117,7 @@ struct AggregateFunctionWindowFunnelData events_list.clear(); events_list.resize(size); - UInt64 timestamp; + UInt32 timestamp; UInt8 event; for (size_t i = 0; i < size; ++i) @@ -134,47 +134,45 @@ struct AggregateFunctionWindowFunnelData * The max size of events is 32, that's enough for funnel analytics * * Usage: - * - windowFunnel(window_size)(window_column, event_condition1, event_condition2, event_condition3, ....) + * - windowFunnel(window)(timestamp, cond1, cond2, cond3, ....) */ - class AggregateFunctionWindowFunnel final : public IAggregateFunctionDataHelper { private: - UInt64 window; - Logger * log = &Logger::get("AggregateFunctionWindowFunnel"); - UInt8 check_events_size; + UInt32 window; + UInt8 events_size; - // Loop through the entire events_list - // If the timestamp window size between current event and pre event( that's event-1) is less than the window value, then update current event's timestamp. - // Returns the max event level. + // Loop through the entire events_list, update the event timestamp value + // The level path must be 1---2---3---...---check_events_size, find the max event level that statisfied the path in the sliding window. + // If found, returns the max event level, else return 0. // The Algorithm complexity is O(n). - - UInt8 match(const AggregateFunctionWindowFunnelData & data) const + UInt8 getEventLevel(const AggregateFunctionWindowFunnelData & data) const { - if(data.events_list.empty()) return 0; - if (check_events_size == 1) + if(data.size() == 0) return 0; + if (events_size == 1) return 1; const_cast(data).sort(); - std::vector events_timestamp(check_events_size,0); + // events_timestamp stores the timestamp that lastest level 1 happen. + // timestamp defaults to -1, which unsigned timestamp value never meet + std::vector events_timestamp(events_size, -1); for(const auto i : ext::range(0, data.size())) { - const auto & event = (data.events_list)[i - 1].second - 1; - const auto & timestamp = (data.events_list)[i - 1].first; - if(event == 0) + const auto & timestamp = (data.events_list)[i].first; + const auto & event_idx = (data.events_list)[i].second - 1; + if(event_idx == 0) events_timestamp[0] = timestamp; - else if(timestamp <= events_timestamp[event - 1] + window) + else if(events_timestamp[event_idx - 1] >= 0 && timestamp <= events_timestamp[event_idx - 1] + window) { - events_timestamp[event] = timestamp; - if(event == check_events_size) return check_events_size; + events_timestamp[event_idx] = events_timestamp[event_idx - 1]; + if(event_idx + 1 == events_size) return events_size; } } - - for(const auto i : ext::range(data.size() - 1, 0)) + for(size_t event = events_timestamp.size(); event > 0; --event) { - if(events_timestamp[i]) return i + 1; + if(events_timestamp[event - 1] >= 0) return event; } return 0; } @@ -185,14 +183,14 @@ public: AggregateFunctionWindowFunnel(const DataTypes & arguments, const Array & params) { - DataTypePtr timestampType = arguments[0]; + DataTypePtr windowType = arguments[0]; - if (!(timestampType->isUnsignedInteger())) - throw Exception("Illegal type " + timestampType->getName() + " of argument for aggregate function " + getName() + " (1 arg, timestamp: UIntXX)", - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); + const auto time_arg = arguments.front().get(); + if (!typeid_cast(time_arg) && !typeid_cast(time_arg) ) + throw Exception{"Illegal type " + time_arg->getName() + " of first argument of aggregate function " + + getName() + ", must be DateTime or UInt32"}; - check_events_size = arguments.size() - 1; - if(check_events_size > AggregateFunctionWindowFunnelData::max_events) + if(arguments.size() - 1 > AggregateFunctionWindowFunnelData::max_events) throw Exception{"Aggregate function " + getName() + " supports up to " + toString(AggregateFunctionWindowFunnelData::max_events) + " event arguments.", ErrorCodes::TOO_MANY_ARGUMENTS_FOR_FUNCTION}; @@ -206,11 +204,11 @@ public: ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; } - if (params.size() != 1) - throw Exception("Aggregate function " + getName() + " requires exactly 1 args(window_num).", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); + if (params.size() != 1) + throw Exception("Aggregate function " + getName() + " requires exactly 1 args(timestamp_window).", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); + events_size = arguments.size() - 1; window = params[0].safeGet(); - LOG_TRACE(log, std::fixed << std::setprecision(3) << "setParameters, window: " << window << " check events:" << check_events_size); } @@ -222,7 +220,7 @@ public: void add(AggregateDataPtr place, const IColumn ** columns, const size_t row_num, Arena *) const override { UInt8 event_level = 0; - for(const auto i : ext::range(1,check_events_size)) + for(const auto i : ext::range(1, events_size + 1)) { auto event = static_cast *>(columns[i])->getData()[row_num]; if(event){ @@ -230,10 +228,13 @@ public: break; } } - this->data(place).add( // - static_cast *>(columns[0])->getData()[row_num], - event_level - ); + if(event_level) + { + this->data(place).add( + static_cast *>(columns[0])->getData()[row_num], + event_level + ); + } } void merge(AggregateDataPtr place, ConstAggregateDataPtr rhs, Arena *) const override @@ -253,7 +254,7 @@ public: void insertResultInto(ConstAggregateDataPtr place, IColumn & to) const override { - static_cast(to).getData().push_back(match(this->data(place))); + static_cast(to).getData().push_back(getEventLevel(this->data(place))); } const char * getHeaderFilePath() const override { return __FILE__; } diff --git a/dbms/tests/queries/0_stateless/00632_aggregation_window_funnel.reference b/dbms/tests/queries/0_stateless/00632_aggregation_window_funnel.reference new file mode 100644 index 00000000000..4dff9ef38ef --- /dev/null +++ b/dbms/tests/queries/0_stateless/00632_aggregation_window_funnel.reference @@ -0,0 +1,12 @@ +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 diff --git a/dbms/tests/queries/0_stateless/00632_aggregation_window_funnel.sql b/dbms/tests/queries/0_stateless/00632_aggregation_window_funnel.sql new file mode 100644 index 00000000000..338c201e59d --- /dev/null +++ b/dbms/tests/queries/0_stateless/00632_aggregation_window_funnel.sql @@ -0,0 +1,30 @@ +drop table if exists funnel_test; + +create table funnel_test (timestamp UInt32, event UInt32) engine=Memory; +insert into funnel_test values (0,1000),(1,1001),(2,1002),(3,1003),(4,1004),(5,1005),(6,1006),(7,1007),(8,1008); + +select 1 = windowFunnel(10000)(timestamp, event = 1000) from funnel_test; +select 2 = windowFunnel(10000)(timestamp, event = 1000, event = 1001) from funnel_test; +select 3 = windowFunnel(10000)(timestamp, event = 1000, event = 1001, event = 1002) from funnel_test; +select 4 = windowFunnel(10000)(timestamp, event = 1000, event = 1001, event = 1002, event = 1008) from funnel_test; + + + +select 1 = windowFunnel(1)(timestamp, event = 1000) from funnel_test; +select 3 = windowFunnel(2)(timestamp, event = 1003, event = 1004, event = 1005, event = 1006, event = 1007) from funnel_test; +select 4 = windowFunnel(3)(timestamp, event = 1003, event = 1004, event = 1005, event = 1006, event = 1007) from funnel_test; +select 5 = windowFunnel(4)(timestamp, event = 1003, event = 1004, event = 1005, event = 1006, event = 1007) from funnel_test; + + +drop table if exists funnel_test2; +create table funnel_test2 (uid UInt32 default 1,timestamp DateTime, event UInt32) engine=Memory; +insert into funnel_test2(timestamp, event) values (now() + 1,1001),(now() + 2,1002),(now() + 3,1003),(now() + 4,1004),(now() + 5,1005),(now() + 6,1006),(now() + 7,1007),(now() + 8,1008); + + +select 5 = windowFunnel(4)(timestamp, event = 1003, event = 1004, event = 1005, event = 1006, event = 1007) from funnel_test2; +select 2 = windowFunnel(10000)(timestamp, event = 1001, event = 1008) from funnel_test2; +select 1 = windowFunnel(10000)(timestamp, event = 1008, event = 1001) from funnel_test2; +select 5 = windowFunnel(4)(timestamp, event = 1003, event = 1004, event = 1005, event = 1006, event = 1007) from funnel_test2; + +drop table funnel_test; +drop table funnel_test2; \ No newline at end of file diff --git a/docs/build/docs/en/404.html b/docs/build/docs/en/404.html new file mode 100644 index 00000000000..770661d52e8 --- /dev/null +++ b/docs/build/docs/en/404.html @@ -0,0 +1,2817 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/agg_functions/combinators/index.html b/docs/build/docs/en/agg_functions/combinators/index.html new file mode 100644 index 00000000000..190bb098bb0 --- /dev/null +++ b/docs/build/docs/en/agg_functions/combinators/index.html @@ -0,0 +1,3017 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Aggregate function combinators - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Aggregate function combinators

+

The name of an aggregate function can have a suffix appended to it. This changes the way the aggregate function works.

+

-If

+

The suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument – a condition (Uint8 type). The aggregate function processes only the rows that trigger the condition. If the condition was not triggered even once, it returns a default value (usually zeros or empty strings).

+

Examples: sumIf(column, cond), countIf(cond), avgIf(x, cond), quantilesTimingIf(level1, level2)(x, cond), argMinIf(arg, val, cond) and so on.

+

With conditional aggregate functions, you can calculate aggregates for several conditions at once, without using subqueries and JOINs. For example, in Yandex.Metrica, conditional aggregate functions are used to implement the segment comparison functionality.

+

-Array

+

The -Array suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the 'Array(T)' type (arrays) instead of 'T' type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. When processing arrays, the aggregate function works like the original aggregate function across all array elements.

+

Example 1: sumArray(arr) - Totals all the elements of all 'arr' arrays. In this example, it could have been written more simply: sum(arraySum(arr)).

+

Example 2: uniqArray(arr) – Count the number of unique elements in all 'arr' arrays. This could be done an easier way: uniq(arrayJoin(arr)), but it's not always possible to add 'arrayJoin' to a query.

+

-If and -Array can be combined. However, 'Array' must come first, then 'If'. Examples: uniqArrayIf(arr, cond), quantilesTimingArrayIf(level1, level2)(arr, cond). Due to this order, the 'cond' argument can't be an array.

+

-State

+

If you apply this combinator, the aggregate function doesn't return the resulting value (such as the number of unique values for the 'uniq' function), but an intermediate state of the aggregation (for uniq, this is the hash table for calculating the number of unique values). This is an AggregateFunction(...) that can be used for further processing or stored in a table to finish aggregating later. See the sections "AggregatingMergeTree" and "Functions for working with intermediate aggregation states".

+

-Merge

+

If you apply this combinator, the aggregate function takes the intermediate aggregation state as an argument, combines the states to finish aggregation, and returns the resulting value.

+

-MergeState.

+

Merges the intermediate aggregation states in the same way as the -Merge combinator. However, it doesn't return the resulting value, but an intermediate aggregation state, similar to the -State combinator.

+

-ForEach

+

Converts an aggregate function for tables into an aggregate function for arrays that aggregates the corresponding array items and returns an array of results. For example, sumForEach for the arrays [1, 2], [3, 4, 5]and[6, 7]returns the result [10, 13, 5] after adding together the corresponding array items.

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/agg_functions/index.html b/docs/build/docs/en/agg_functions/index.html new file mode 100644 index 00000000000..837705e3017 --- /dev/null +++ b/docs/build/docs/en/agg_functions/index.html @@ -0,0 +1,2894 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Introduction - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Aggregate functions

+

Aggregate functions work in the normal way as expected by database experts.

+

ClickHouse also supports:

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/agg_functions/parametric_functions/index.html b/docs/build/docs/en/agg_functions/parametric_functions/index.html new file mode 100644 index 00000000000..171b2057e82 --- /dev/null +++ b/docs/build/docs/en/agg_functions/parametric_functions/index.html @@ -0,0 +1,3037 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Parametric aggregate functions - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Parametric aggregate functions

+

Some aggregate functions can accept not only argument columns (used for compression), but a set of parameters – constants for initialization. The syntax is two pairs of brackets instead of one. The first is for parameters, and the second is for arguments.

+

sequenceMatch(pattern)(time, cond1, cond2, ...)

+

Pattern matching for event chains.

+

pattern is a string containing a pattern to match. The pattern is similar to a regular expression.

+

time is the time of the event with the DateTime type.

+

cond1, cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event.

+

The function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern. +It returns UInt8: 0 if the pattern isn't matched, or 1 if it matches.

+

Example: sequenceMatch ('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%')

+
    +
  • whether there was a chain of events in which a pageview with 'company' in the address occurred earlier than a pageview with 'cart' in the address.
    • +
+

This is a singular example. You could write it using other aggregate functions:

+
minIf(EventTime, URL LIKE '%company%') < maxIf(EventTime, URL LIKE '%cart%').
+
+ + +

However, there is no such solution for more complex situations.

+

Pattern syntax:

+

(?1) refers to the condition (any number can be used in place of 1).

+

.* is any number of any events.

+

(?t>=1800) is a time condition.

+

Any quantity of any type of events is allowed over the specified time.

+

Instead of >=, the following operators can be used:<, >, <=.

+

Any number may be specified in place of 1800.

+

Events that occur during the same second can be put in the chain in any order. This may affect the result of the function.

+

sequenceCount(pattern)(time, cond1, cond2, ...)

+

Works the same way as the sequenceMatch function, but instead of returning whether there is an event chain, it returns UInt64 with the number of event chains found. +Chains are searched for without overlapping. In other words, the next chain can start only after the end of the previous one.

+

windowFunnel(window)(timestamp, cond1, cond2, cond3, ....)

+

Window funnel matching for event chains, calculates the max event level in a sliding window.

+

window is the timestamp window value, such as 3600.

+

timestamp is the time of the event with the DateTime type or UInt32 type.

+

cond1, cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event

+

Example:

+

Consider you are doing a website analytics, intend to find out the user counts clicked login button( event = 1001 ), then the user counts followed by searched the phones( event = 1003 and product = 'phone' ) , then the user counts followed by made an order ( event = 1009 ). And all event chains must be in a 3600 seconds sliding window.

+

This could be easily calculate by windowFunnel

+
SELECT
+    level,
+    count() AS c
+FROM
+(
+    SELECT
+        user_id,
+        windowFunnel(3600)(timestamp, event_id = 1001, event_id = 1003 AND product = 'phone', event_id = 1009) AS level
+    FROM trend_event
+    WHERE (event_date >= '2017-01-01') AND (event_date <= '2017-01-31')
+    GROUP BY user_id
+)
+GROUP BY level
+ORDER BY level
+
+ + +

Simply, the level could only be 0,1,2,3, it means the maxium event action stage that one user could reach.

+

uniqUpTo(N)(x)

+

Calculates the number of different argument values ​​if it is less than or equal to N. If the number of different argument values is greater than N, it returns N + 1.

+

Recommended for use with small Ns, up to 10. The maximum value of N is 100.

+

For the state of an aggregate function, it uses the amount of memory equal to 1 + N * the size of one value of bytes. +For strings, it stores a non-cryptographic hash of 8 bytes. That is, the calculation is approximated for strings.

+

The function also works for several arguments.

+

It works as fast as possible, except for cases when a large N value is used and the number of unique values is slightly less than N.

+

Usage example:

+
Problem: Generate a report that shows only keywords that produced at least 5 unique users.
+Solution: Write in the GROUP BY query SearchPhrase HAVING uniqUpTo(4)(UserID) >= 5
+
+ + + + + + + +
+
+
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/agg_functions/reference/index.html b/docs/build/docs/en/agg_functions/reference/index.html new file mode 100644 index 00000000000..574a70d50f6 --- /dev/null +++ b/docs/build/docs/en/agg_functions/reference/index.html @@ -0,0 +1,3631 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Function reference - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Function reference

+

count()

+

Counts the number of rows. Accepts zero arguments and returns UInt64. +The syntax COUNT(DISTINCT x) is not supported. The separate uniq aggregate function exists for this purpose.

+

A SELECT count() FROM table query is not optimized, because the number of entries in the table is not stored separately. It will select some small column from the table and count the number of values in it.

+

any(x)

+

Selects the first encountered value. +The query can be executed in any order and even in a different order each time, so the result of this function is indeterminate. +To get a determinate result, you can use the 'min' or 'max' function instead of 'any'.

+

In some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.

+

When a SELECT query has the GROUP BY clause or at least one aggregate function, ClickHouse (in contrast to MySQL) requires that all expressions in the SELECT, HAVING, and ORDER BY clauses be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. To get behavior like in MySQL, you can put the other columns in the any aggregate function.

+

anyHeavy(x)

+

Selects a frequently occurring value using the heavy hitters algorithm. If there is a value that occurs more than in half the cases in each of the query's execution threads, this value is returned. Normally, the result is nondeterministic.

+
anyHeavy(column)
+
+ + +

Arguments +- column – The column name.

+

Example

+

Take the OnTime data set and select any frequently occurring value in the AirlineID column.

+
SELECT anyHeavy(AirlineID) AS res
+FROM ontime
+
+ + +
┌───res─┐
+│ 19690 │
+└───────┘
+
+ + +

anyLast(x)

+

Selects the last value encountered. +The result is just as indeterminate as for the any function.

+

min(x)

+

Calculates the minimum.

+

max(x)

+

Calculates the maximum.

+

argMin(arg, val)

+

Calculates the 'arg' value for a minimal 'val' value. If there are several different values of 'arg' for minimal values of 'val', the first of these values encountered is output.

+

argMax(arg, val)

+

Calculates the 'arg' value for a maximum 'val' value. If there are several different values of 'arg' for maximum values of 'val', the first of these values encountered is output.

+

sum(x)

+

Calculates the sum. +Only works for numbers.

+

sumWithOverflow(x)

+

Computes the sum of the numbers, using the same data type for the result as for the input parameters. If the sum exceeds the maximum value for this data type, the function returns an error.

+

Only works for numbers.

+

sumMap(key, value)

+

Totals the 'value' array according to the keys specified in the 'key' array. +The number of elements in 'key' and 'value' must be the same for each row that is totaled. +Returns a tuple of two arrays: keys in sorted order, and values ​​summed for the corresponding keys.

+

Example:

+
CREATE TABLE sum_map(
+    date Date,
+    timeslot DateTime,
+    statusMap Nested(
+        status UInt16,
+        requests UInt64
+    )
+) ENGINE = Log;
+INSERT INTO sum_map VALUES
+    ('2000-01-01', '2000-01-01 00:00:00', [1, 2, 3], [10, 10, 10]),
+    ('2000-01-01', '2000-01-01 00:00:00', [3, 4, 5], [10, 10, 10]),
+    ('2000-01-01', '2000-01-01 00:01:00', [4, 5, 6], [10, 10, 10]),
+    ('2000-01-01', '2000-01-01 00:01:00', [6, 7, 8], [10, 10, 10]);
+SELECT
+    timeslot,
+    sumMap(statusMap.status, statusMap.requests)
+FROM sum_map
+GROUP BY timeslot
+
+ + +
┌────────────timeslot─┬─sumMap(statusMap.status, statusMap.requests)─┐
+│ 2000-01-01 00:00:00 │ ([1,2,3,4,5],[10,10,20,10,10])               │
+│ 2000-01-01 00:01:00 │ ([4,5,6,7,8],[10,10,20,10,10])               │
+└─────────────────────┴──────────────────────────────────────────────┘
+
+ + +

avg(x)

+

Calculates the average. +Only works for numbers. +The result is always Float64.

+

uniq(x)

+

Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.

+

Uses an adaptive sampling algorithm: for the calculation state, it uses a sample of element hash values with a size up to 65536. +This algorithm is also very accurate for data sets with low cardinality (up to 65536) and very efficient on CPU (when computing not too many of these functions, using uniq is almost as fast as using other aggregate functions).

+

The result is determinate (it doesn't depend on the order of query processing).

+

This function provides excellent accuracy even for data sets with extremely high cardinality (over 10 billion elements). It is recommended for default use.

+

uniqCombined(x)

+

Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.

+

A combination of three algorithms is used: array, hash table and HyperLogLog with an error correction table. The memory consumption is several times smaller than for the uniq function, and the accuracy is several times higher. Performance is slightly lower than for the uniq function, but sometimes it can be even higher than it, such as with distributed queries that transmit a large number of aggregation states over the network. The maximum state size is 96 KiB (HyperLogLog of 217 6-bit cells).

+

The result is determinate (it doesn't depend on the order of query processing).

+

The uniqCombined function is a good default choice for calculating the number of different values, but keep in mind that the estimation error will increase for high-cardinality data sets (200M+ elements), and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).

+

uniqHLL12(x)

+

Uses the HyperLogLog algorithm to approximate the number of different values of the argument. +212 5-bit cells are used. The size of the state is slightly more than 2.5 KB. The result is not very accurate (up to ~10% error) for small data sets (<10K elements). However, the result is fairly accurate for high-cardinality data sets (10K-100M), with a maximum error of ~1.6%. Starting from 100M, the estimation error increases, and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).

+

The result is determinate (it doesn't depend on the order of query processing).

+

We don't recommend using this function. In most cases, use the uniq or uniqCombined function.

+

uniqExact(x)

+

Calculates the number of different values of the argument, exactly. +There is no reason to fear approximations. It's better to use the uniq function. +Use the uniqExact function if you definitely need an exact result.

+

The uniqExact function uses more memory than the uniq function, because the size of the state has unbounded growth as the number of different values increases.

+

groupArray(x), groupArray(max_size)(x)

+

Creates an array of argument values. +Values can be added to the array in any (indeterminate) order.

+

The second version (with the max_size parameter) limits the size of the resulting array to max_size elements. +For example, groupArray (1) (x) is equivalent to [any (x)].

+

In some cases, you can still rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.

+

+

groupArrayInsertAt(x)

+

Inserts a value into the array in the specified position.

+

Accepts the value and position as input. If several values ​​are inserted into the same position, any of them might end up in the resulting array (the first one will be used in the case of single-threaded execution). If no value is inserted into a position, the position is assigned the default value.

+

Optional parameters:

+
    +
  • The default value for substituting in empty positions.
    • +
  • The length of the resulting array. This allows you to receive arrays of the same size for all the aggregate keys. When using this parameter, the default value must be specified.
    • +
+

groupUniqArray(x)

+

Creates an array from different argument values. Memory consumption is the same as for the uniqExact function.

+

quantile(level)(x)

+

Approximates the 'level' quantile. 'level' is a constant, a floating-point number from 0 to 1. +We recommend using a 'level' value in the range of 0.01..0.99 +Don't use a 'level' value equal to 0 or 1 – use the 'min' and 'max' functions for these cases.

+

In this function, as well as in all functions for calculating quantiles, the 'level' parameter can be omitted. In this case, it is assumed to be equal to 0.5 (in other words, the function will calculate the median).

+

Works for numbers, dates, and dates with times. +Returns: for numbers – Float64; for dates – a date; for dates with times – a date with time.

+

Uses reservoir sampling with a reservoir size up to 8192. +If necessary, the result is output with linear approximation from the two neighboring values. +This algorithm provides very low accuracy. See also: quantileTiming, quantileTDigest, quantileExact.

+

The result depends on the order of running the query, and is nondeterministic.

+

When using multiple quantile (and similar) functions with different levels in a query, the internal states are not combined (that is, the query works less efficiently than it could). In this case, use the quantiles (and similar) functions.

+

quantileDeterministic(level)(x, determinator)

+

Works the same way as the quantile function, but the result is deterministic and does not depend on the order of query execution.

+

To achieve this, the function takes a second argument – the "determinator". This is a number whose hash is used instead of a random number generator in the reservoir sampling algorithm. For the function to work correctly, the same determinator value should not occur too often. For the determinator, you can use an event ID, user ID, and so on.

+

Don't use this function for calculating timings. There is a more suitable function for this purpose: quantileTiming.

+

quantileTiming(level)(x)

+

Computes the quantile of 'level' with a fixed precision. +Works for numbers. Intended for calculating quantiles of page loading time in milliseconds.

+

If the value is greater than 30,000 (a page loading time of more than 30 seconds), the result is equated to 30,000.

+

If the total value is not more than about 5670, then the calculation is accurate.

+

Otherwise:

+
    +
  • if the time is less than 1024 ms, then the calculation is accurate.
    • +
  • otherwise the calculation is rounded to a multiple of 16 ms.
    • +
+

When passing negative values to the function, the behavior is undefined.

+

The returned value has the Float32 type. If no values were passed to the function (when using quantileTimingIf), 'nan' is returned. The purpose of this is to differentiate these instances from zeros. See the note on sorting NaNs in "ORDER BY clause".

+

The result is determinate (it doesn't depend on the order of query processing).

+

For its purpose (calculating quantiles of page loading times), using this function is more effective and the result is more accurate than for the quantile function.

+

quantileTimingWeighted(level)(x, weight)

+

Differs from the quantileTiming function in that it has a second argument, "weights". Weight is a non-negative integer. +The result is calculated as if the x value were passed weight number of times to the quantileTiming function.

+

quantileExact(level)(x)

+

Computes the quantile of 'level' exactly. To do this, all the passed values ​​are combined into an array, which is then partially sorted. Therefore, the function consumes O(n) memory, where 'n' is the number of values that were passed. However, for a small number of values, the function is very effective.

+

quantileExactWeighted(level)(x, weight)

+

Computes the quantile of 'level' exactly. In addition, each value is counted with its weight, as if it is present 'weight' times. The arguments of the function can be considered as histograms, where the value 'x' corresponds to a histogram "column" of the height 'weight', and the function itself can be considered as a summation of histograms.

+

A hash table is used as the algorithm. Because of this, if the passed values ​​are frequently repeated, the function consumes less RAM than quantileExact. You can use this function instead of quantileExact and specify the weight as 1.

+

quantileTDigest(level)(x)

+

Approximates the quantile level using the t-digest algorithm. The maximum error is 1%. Memory consumption by State is proportional to the logarithm of the number of passed values.

+

The performance of the function is lower than for quantile, quantileTiming. In terms of the ratio of State size to precision, this function is much better than quantile.

+

The result depends on the order of running the query, and is nondeterministic.

+

median(x)

+

All the quantile functions have corresponding median functions: median, medianDeterministic, medianTiming, medianTimingWeighted, medianExact, medianExactWeighted, medianTDigest. They are synonyms and their behavior is identical.

+

quantiles(level1, level2, ...)(x)

+

All the quantile functions also have corresponding quantiles functions: quantiles, quantilesDeterministic, quantilesTiming, quantilesTimingWeighted, quantilesExact, quantilesExactWeighted, quantilesTDigest. These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.

+

varSamp(x)

+

Calculates the amount Σ((x - x̅)^2) / (n - 1), where n is the sample size and x̅is the average value of x.

+

It represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount.

+

Returns Float64. When n <= 1, returns +∞.

+

varPop(x)

+

Calculates the amount Σ((x - x̅)^2) / (n - 1), where n is the sample size and x̅is the average value of x.

+

In other words, dispersion for a set of values. Returns Float64.

+

stddevSamp(x)

+

The result is equal to the square root of varSamp(x).

+

stddevPop(x)

+

The result is equal to the square root of varPop(x).

+

topK(N)(column)

+

Returns an array of the most frequent values in the specified column. The resulting array is sorted in descending order of frequency of values (not by the values themselves).

+

Implements the Filtered Space-Saving algorithm for analyzing TopK, based on the reduce-and-combine algorithm from Parallel Space Saving.

+
topK(N)(column)
+
+ + +

This function doesn't provide a guaranteed result. In certain situations, errors might occur and it might return frequent values that aren't the most frequent values.

+

We recommend using the N < 10 value; performance is reduced with large N values. Maximum value of N = 65536.

+

Arguments +- 'N' is the number of values. +- ' x ' – The column.

+

Example

+

Take the OnTime data set and select the three most frequently occurring values in the AirlineID column.

+
SELECT topK(3)(AirlineID) AS res
+FROM ontime
+
+ + +
┌─res─────────────────┐
+│ [19393,19790,19805] │
+└─────────────────────┘
+
+ + +

covarSamp(x, y)

+

Calculates the value of Σ((x - x̅)(y - y̅)) / (n - 1).

+

Returns Float64. When n <= 1, returns +∞.

+

covarPop(x, y)

+

Calculates the value of Σ((x - x̅)(y - y̅)) / n.

+

corr(x, y)

+

Calculates the Pearson correlation coefficient: Σ((x - x̅)(y - y̅)) / sqrt(Σ((x - x̅)^2) * Σ((y - y̅)^2)).

+ + + + + + + +
+
+
+
+ + + + +
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Please include / require Lunr stemmer support before this script.");e.da=function(){this.pipeline.reset(),this.pipeline.add(e.da.trimmer,e.da.stopWordFilter,e.da.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.da.stemmer))},e.da.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.da.trimmer=e.trimmerSupport.generateTrimmer(e.da.wordCharacters),e.Pipeline.registerFunction(e.da.trimmer,"trimmer-da"),e.da.stemmer=function(){var r=e.stemmerSupport.Among,i=e.stemmerSupport.SnowballProgram,n=new function(){function e(){var e,r=l.limit-l.cursor;l.cursor>=t&&(e=l.limit_backward,l.limit_backward=t,l.ket=l.cursor,l.find_among_b(a,4)?(l.bra=l.cursor,l.limit_backward=e,l.cursor=l.limit-r,l.cursor>l.limit_backward&&(l.cursor--,l.bra=l.cursor,l.slice_del())):l.limit_backward=e)}var n,t,s,o=[new r("hed",-1,1),new r("ethed",0,1),new r("ered",-1,1),new r("e",-1,1),new r("erede",3,1),new r("ende",3,1),new r("erende",5,1),new r("ene",3,1),new r("erne",3,1),new r("ere",3,1),new r("en",-1,1),new r("heden",10,1),new r("eren",10,1),new r("er",-1,1),new r("heder",13,1),new r("erer",13,1),new r("s",-1,2),new r("heds",16,1),new r("es",16,1),new r("endes",18,1),new r("erendes",19,1),new r("enes",18,1),new r("ernes",18,1),new r("eres",18,1),new r("ens",16,1),new r("hedens",24,1),new r("erens",24,1),new r("ers",16,1),new r("ets",16,1),new r("erets",28,1),new r("et",-1,1),new r("eret",30,1)],a=[new r("gd",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1)],d=[new r("ig",-1,1),new r("lig",0,1),new r("elig",1,1),new r("els",-1,1),new r("løst",-1,2)],u=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],c=[239,254,42,3,0,0,0,0,0,0,0,0,0,0,0,0,16],l=new i;this.setCurrent=function(e){l.setCurrent(e)},this.getCurrent=function(){return l.getCurrent()},this.stem=function(){var r=l.cursor;return function(){var e,r=l.cursor+3;if(t=l.limit,0<=r&&r<=l.limit){for(n=r;;){if(e=l.cursor,l.in_grouping(u,97,248)){l.cursor=e;break}if(l.cursor=e,e>=l.limit)return;l.cursor++}for(;!l.out_grouping(u,97,248);){if(l.cursor>=l.limit)return;l.cursor++}(t=l.cursor)=t&&(r=l.limit_backward,l.limit_backward=t,l.ket=l.cursor,e=l.find_among_b(o,32),l.limit_backward=r,e))switch(l.bra=l.cursor,e){case 1:l.slice_del();break;case 2:l.in_grouping_b(c,97,229)&&l.slice_del()}}(),l.cursor=l.limit,e(),l.cursor=l.limit,function(){var r,i,n,s=l.limit-l.cursor;if(l.ket=l.cursor,l.eq_s_b(2,"st")&&(l.bra=l.cursor,l.eq_s_b(2,"ig")&&l.slice_del()),l.cursor=l.limit-s,l.cursor>=t&&(i=l.limit_backward,l.limit_backward=t,l.ket=l.cursor,r=l.find_among_b(d,5),l.limit_backward=i,r))switch(l.bra=l.cursor,r){case 1:l.slice_del(),n=l.limit-l.cursor,e(),l.cursor=l.limit-n;break;case 2:l.slice_from("løs")}}(),l.cursor=l.limit,function(){var e;l.cursor>=t&&(e=l.limit_backward,l.limit_backward=t,l.ket=l.cursor,l.out_grouping_b(u,97,248)?(l.bra=l.cursor,s=l.slice_to(s),l.limit_backward=e,l.eq_v_b(s)&&l.slice_del()):l.limit_backward=e)}(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}}(),e.Pipeline.registerFunction(e.da.stemmer,"stemmer-da"),e.da.stopWordFilter=e.generateStopWordFilter("ad af alle alt anden at blev blive bliver da de dem den denne der deres det dette dig din disse dog du efter eller en end er et for fra ham han hans har havde have hende hendes her hos hun hvad hvis hvor i ikke ind jeg jer jo kunne man mange med meget men mig min mine mit mod ned noget nogle nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}}); \ No newline at end of file diff --git a/docs/build/docs/en/assets/javascripts/lunr/lunr.de.js b/docs/build/docs/en/assets/javascripts/lunr/lunr.de.js new file mode 100644 index 00000000000..576a2192311 --- /dev/null +++ b/docs/build/docs/en/assets/javascripts/lunr/lunr.de.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.de=function(){this.pipeline.reset(),this.pipeline.add(e.de.trimmer,e.de.stopWordFilter,e.de.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.de.stemmer))},e.de.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.de.trimmer=e.trimmerSupport.generateTrimmer(e.de.wordCharacters),e.Pipeline.registerFunction(e.de.trimmer,"trimmer-de"),e.de.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){function e(e,r,n){return!(!_.eq_s(1,e)||(_.ket=_.cursor,!_.in_grouping(w,97,252)))&&(_.slice_from(r),_.cursor=n,!0)}function i(){for(;!_.in_grouping(w,97,252);){if(_.cursor>=_.limit)return!0;_.cursor++}for(;!_.out_grouping(w,97,252);){if(_.cursor>=_.limit)return!0;_.cursor++}return!1}function s(){return u<=_.cursor}function t(){return c<=_.cursor}var o,c,u,a=[new r("",-1,6),new r("U",0,2),new r("Y",0,1),new r("ä",0,3),new r("ö",0,4),new r("ü",0,5)],d=[new r("e",-1,2),new r("em",-1,1),new r("en",-1,2),new r("ern",-1,1),new r("er",-1,1),new r("s",-1,3),new r("es",5,2)],l=[new r("en",-1,1),new r("er",-1,1),new r("st",-1,2),new r("est",2,1)],m=[new r("ig",-1,1),new r("lich",-1,1)],h=[new r("end",-1,1),new r("ig",-1,2),new r("ung",-1,1),new r("lich",-1,3),new r("isch",-1,2),new r("ik",-1,2),new r("heit",-1,3),new r("keit",-1,4)],w=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,32,8],f=[117,30,5],b=[117,30,4],_=new n;this.setCurrent=function(e){_.setCurrent(e)},this.getCurrent=function(){return _.getCurrent()},this.stem=function(){var r=_.cursor;return function(){for(var r,n,i,s,t=_.cursor;;)if(r=_.cursor,_.bra=r,_.eq_s(1,"ß"))_.ket=_.cursor,_.slice_from("ss");else{if(r>=_.limit)break;_.cursor=r+1}for(_.cursor=t;;)for(n=_.cursor;;){if(i=_.cursor,_.in_grouping(w,97,252)){if(s=_.cursor,_.bra=s,e("u","U",i))break;if(_.cursor=s,e("y","Y",i))break}if(i>=_.limit)return void(_.cursor=n);_.cursor=i+1}}(),_.cursor=r,function(){u=_.limit,c=u;var e=_.cursor+3;0<=e&&e<=_.limit&&(o=e,i()||((u=_.cursor)=_.limit)return;_.cursor++}}}(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.de.stemmer,"stemmer-de"),e.de.stopWordFilter=e.generateStopWordFilter("aber alle allem allen aller alles als also am an ander andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines können könnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher solches soll sollte sondern sonst um und uns unse unsem unsen unser unses unter viel vom von vor war waren warst was weg weil weiter welche welchem welchen welcher welches wenn werde werden wie wieder will wir wird wirst wo wollen wollte während würde würden zu zum zur zwar zwischen über".split(" ")),e.Pipeline.registerFunction(e.de.stopWordFilter,"stopWordFilter-de")}}); \ No newline at end of file diff --git a/docs/build/docs/en/assets/javascripts/lunr/lunr.du.js b/docs/build/docs/en/assets/javascripts/lunr/lunr.du.js new file mode 100644 index 00000000000..c317652d6bd --- /dev/null +++ b/docs/build/docs/en/assets/javascripts/lunr/lunr.du.js @@ -0,0 +1 @@ +!function(r,e){"function"==typeof define&&define.amd?define(e):"object"==typeof exports?module.exports=e():e()(r.lunr)}(this,function(){return function(r){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");r.du=function(){this.pipeline.reset(),this.pipeline.add(r.du.trimmer,r.du.stopWordFilter,r.du.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(r.du.stemmer))},r.du.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",r.du.trimmer=r.trimmerSupport.generateTrimmer(r.du.wordCharacters),r.Pipeline.registerFunction(r.du.trimmer,"trimmer-du"),r.du.stemmer=function(){var e=r.stemmerSupport.Among,i=r.stemmerSupport.SnowballProgram,n=new function(){function r(r){return v.cursor=r,r>=v.limit||(v.cursor++,!1)}function n(){for(;!v.in_grouping(g,97,232);){if(v.cursor>=v.limit)return!0;v.cursor++}for(;!v.out_grouping(g,97,232);){if(v.cursor>=v.limit)return!0;v.cursor++}return!1}function o(){return l<=v.cursor}function t(){return a<=v.cursor}function s(){var r=v.limit-v.cursor;v.find_among_b(_,3)&&(v.cursor=v.limit-r,v.ket=v.cursor,v.cursor>v.limit_backward&&(v.cursor--,v.bra=v.cursor,v.slice_del()))}function u(){var r;m=!1,v.ket=v.cursor,v.eq_s_b(1,"e")&&(v.bra=v.cursor,o()&&(r=v.limit-v.cursor,v.out_grouping_b(g,97,232)&&(v.cursor=v.limit-r,v.slice_del(),m=!0,s())))}function c(){var r;o()&&(r=v.limit-v.cursor,v.out_grouping_b(g,97,232)&&(v.cursor=v.limit-r,v.eq_s_b(3,"gem")||(v.cursor=v.limit-r,v.slice_del(),s())))}var a,l,m,d=[new e("",-1,6),new e("á",0,1),new e("ä",0,1),new e("é",0,2),new e("ë",0,2),new e("í",0,3),new e("ï",0,3),new e("ó",0,4),new e("ö",0,4),new e("ú",0,5),new e("ü",0,5)],f=[new e("",-1,3),new e("I",0,2),new e("Y",0,1)],_=[new e("dd",-1,-1),new e("kk",-1,-1),new e("tt",-1,-1)],w=[new e("ene",-1,2),new e("se",-1,3),new e("en",-1,2),new e("heden",2,1),new e("s",-1,3)],b=[new e("end",-1,1),new e("ig",-1,2),new e("ing",-1,1),new e("lijk",-1,3),new e("baar",-1,4),new e("bar",-1,5)],p=[new e("aa",-1,-1),new e("ee",-1,-1),new e("oo",-1,-1),new e("uu",-1,-1)],g=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],h=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],k=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],v=new i;this.setCurrent=function(r){v.setCurrent(r)},this.getCurrent=function(){return v.getCurrent()},this.stem=function(){var e=v.cursor;return function(){for(var e,i,n,o=v.cursor;;){if(v.bra=v.cursor,e=v.find_among(d,11))switch(v.ket=v.cursor,e){case 1:v.slice_from("a");continue;case 2:v.slice_from("e");continue;case 3:v.slice_from("i");continue;case 4:v.slice_from("o");continue;case 5:v.slice_from("u");continue;case 6:if(v.cursor>=v.limit)break;v.cursor++;continue}break}for(v.cursor=o,v.bra=o,v.eq_s(1,"y")?(v.ket=v.cursor,v.slice_from("Y")):v.cursor=o;;)if(i=v.cursor,v.in_grouping(g,97,232)){if(n=v.cursor,v.bra=n,v.eq_s(1,"i"))v.ket=v.cursor,v.in_grouping(g,97,232)&&(v.slice_from("I"),v.cursor=i);else if(v.cursor=n,v.eq_s(1,"y"))v.ket=v.cursor,v.slice_from("Y"),v.cursor=i;else if(r(i))break}else if(r(i))break}(),v.cursor=e,l=v.limit,a=l,n()||((l=v.cursor)<3&&(l=3),n()||(a=v.cursor)),v.limit_backward=e,v.cursor=v.limit,function(){var r,e,i,n,a,l,d=v.limit-v.cursor;if(v.ket=v.cursor,r=v.find_among_b(w,5))switch(v.bra=v.cursor,r){case 1:o()&&v.slice_from("heid");break;case 2:c();break;case 3:o()&&v.out_grouping_b(k,97,232)&&v.slice_del()}if(v.cursor=v.limit-d,u(),v.cursor=v.limit-d,v.ket=v.cursor,v.eq_s_b(4,"heid")&&(v.bra=v.cursor,t()&&(e=v.limit-v.cursor,v.eq_s_b(1,"c")||(v.cursor=v.limit-e,v.slice_del(),v.ket=v.cursor,v.eq_s_b(2,"en")&&(v.bra=v.cursor,c())))),v.cursor=v.limit-d,v.ket=v.cursor,r=v.find_among_b(b,6))switch(v.bra=v.cursor,r){case 1:if(t()){if(v.slice_del(),i=v.limit-v.cursor,v.ket=v.cursor,v.eq_s_b(2,"ig")&&(v.bra=v.cursor,t()&&(n=v.limit-v.cursor,!v.eq_s_b(1,"e")))){v.cursor=v.limit-n,v.slice_del();break}v.cursor=v.limit-i,s()}break;case 2:t()&&(a=v.limit-v.cursor,v.eq_s_b(1,"e")||(v.cursor=v.limit-a,v.slice_del()));break;case 3:t()&&(v.slice_del(),u());break;case 4:t()&&v.slice_del();break;case 5:t()&&m&&v.slice_del()}v.cursor=v.limit-d,v.out_grouping_b(h,73,232)&&(l=v.limit-v.cursor,v.find_among_b(p,4)&&v.out_grouping_b(g,97,232)&&(v.cursor=v.limit-l,v.ket=v.cursor,v.cursor>v.limit_backward&&(v.cursor--,v.bra=v.cursor,v.slice_del())))}(),v.cursor=v.limit_backward,function(){for(var r;;)if(v.bra=v.cursor,r=v.find_among(f,3))switch(v.ket=v.cursor,r){case 1:v.slice_from("y");break;case 2:v.slice_from("i");break;case 3:if(v.cursor>=v.limit)return;v.cursor++}}(),!0}};return function(r){return"function"==typeof r.update?r.update(function(r){return n.setCurrent(r),n.stem(),n.getCurrent()}):(n.setCurrent(r),n.stem(),n.getCurrent())}}(),r.Pipeline.registerFunction(r.du.stemmer,"stemmer-du"),r.du.stopWordFilter=r.generateStopWordFilter(" aan al alles als altijd andere ben bij daar dan dat de der deze die dit doch doen door dus een eens en er ge geen geweest haar had heb hebben heeft hem het hier hij hoe hun iemand iets ik in is ja je kan kon kunnen maar me meer men met mij mijn moet na naar niet niets nog nu of om omdat onder ons ook op over reeds te tegen toch toen tot u uit uw van veel voor want waren was wat werd wezen wie wil worden wordt zal ze zelf zich zij zijn zo zonder zou".split(" ")),r.Pipeline.registerFunction(r.du.stopWordFilter,"stopWordFilter-du")}}); \ No newline at end of file diff --git a/docs/build/docs/en/assets/javascripts/lunr/lunr.es.js b/docs/build/docs/en/assets/javascripts/lunr/lunr.es.js new file mode 100644 index 00000000000..5098feba48b --- /dev/null +++ b/docs/build/docs/en/assets/javascripts/lunr/lunr.es.js @@ -0,0 +1 @@ +!function(e,s){"function"==typeof define&&define.amd?define(s):"object"==typeof exports?module.exports=s():s()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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fie fiecare fii fim fiu fiţi frumos fără graţie halbă iar ieri la le li lor lui lângă lîngă mai mea mei mele mereu meu mi mie mine mult multă mulţi mulţumesc mâine mîine mă ne nevoie nici nicăieri nimeni nimeri nimic nişte noastre noastră noi noroc nostru nouă noştri nu opt ori oricare orice oricine oricum oricând oricât oricînd oricît oriunde patra patru patrulea pe pentru peste pic poate pot prea prima primul prin puţin puţina puţină până pînă rog sa sale sau se spate spre sub sunt suntem sunteţi sută sînt sîntem sînteţi să săi său ta tale te timp tine toate toată tot totuşi toţi trei treia treilea tu tăi tău un una unde undeva unei uneia unele uneori unii unor unora unu unui unuia unul vi voastre voastră voi vostru vouă voştri vreme vreo vreun vă zece zero zi zice îi îl îmi împotriva în înainte înaintea încotro încât încît între întrucât întrucît îţi ăla ălea ăsta ăstea ăştia şapte şase şi ştiu ţi ţie".split(" 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n("ии",6,1),new n("ами",6,1),new n("ями",6,1),new n("иями",10,1),new n("й",-1,1),new n("ей",12,1),new n("ией",13,1),new n("ий",12,1),new n("ой",12,1),new n("ам",-1,1),new n("ем",-1,1),new n("ием",18,1),new n("ом",-1,1),new n("ям",-1,1),new n("иям",21,1),new n("о",-1,1),new n("у",-1,1),new n("ах",-1,1),new n("ях",-1,1),new n("иях",26,1),new n("ы",-1,1),new n("ь",-1,1),new n("ю",-1,1),new n("ию",30,1),new n("ью",30,1),new n("я",-1,1),new n("ия",33,1),new n("ья",33,1)],_=[new n("ост",-1,1),new n("ость",-1,1)],b=[new n("ейше",-1,1),new n("н",-1,2),new n("ейш",-1,1),new n("ь",-1,3)],h=[33,65,8,232],g=new r;this.setCurrent=function(e){g.setCurrent(e)},this.getCurrent=function(){return g.getCurrent()},this.stem=function(){return c=g.limit,o=c,e()&&(c=g.cursor,t()&&e()&&t()&&(o=g.cursor)),g.cursor=g.limit,!(g.cursor=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},in_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},out_grouping:function(t,i,s){if(this.cursors||e>3]&1<<(7&e)))return this.cursor++,!0}return!1},out_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e>s||e>3]&1<<(7&e)))return this.cursor--,!0}return!1},eq_s:function(t,i){if(this.limit-this.cursor>1),f=0,l=o0||e==s||c)break;c=!0}}for(;;){if(o>=(_=t[s]).s_size){if(this.cursor=n+_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n+_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},find_among_b:function(t,i){for(var s=0,e=i,n=this.cursor,u=this.limit_backward,o=0,h=0,c=!1;;){for(var a=s+(e-s>>1),f=0,l=o=0;_--){if(n-l==u){f=-1;break}if(f=r.charCodeAt(n-1-l)-m.s[_])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0||e==s||c)break;c=!0}}for(;;){var m=t[s];if(o>=m.s_size){if(this.cursor=n-m.s_size,!m.method)return m.result;var b=m.method();if(this.cursor=n-m.s_size,b)return m.result}if((s=m.substring_i)<0)return 0}},replace_s:function(t,i,s){var e=s.length-(i-t),n=r.substring(0,t),u=r.substring(i);return r=n+s+u,this.limit+=e,this.cursor>=i?this.cursor+=e:this.cursor>t&&(this.cursor=t),e},slice_check:function(){if(this.bra<0||this.bra>this.ket||this.ket>this.limit||this.limit>r.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),r.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}}); \ No newline at end of file diff --git a/docs/build/docs/en/assets/javascripts/lunr/lunr.sv.js b/docs/build/docs/en/assets/javascripts/lunr/lunr.sv.js new file mode 100644 index 00000000000..70211fd77d9 --- /dev/null +++ b/docs/build/docs/en/assets/javascripts/lunr/lunr.sv.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sv=function(){this.pipeline.reset(),this.pipeline.add(e.sv.trimmer,e.sv.stopWordFilter,e.sv.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sv.stemmer))},e.sv.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.sv.trimmer=e.trimmerSupport.generateTrimmer(e.sv.wordCharacters),e.Pipeline.registerFunction(e.sv.trimmer,"trimmer-sv"),e.sv.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,t=new function(){var e,t,i=[new r("a",-1,1),new r("arna",0,1),new r("erna",0,1),new r("heterna",2,1),new r("orna",0,1),new r("ad",-1,1),new r("e",-1,1),new r("ade",6,1),new r("ande",6,1),new r("arne",6,1),new r("are",6,1),new r("aste",6,1),new r("en",-1,1),new r("anden",12,1),new r("aren",12,1),new r("heten",12,1),new r("ern",-1,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",18,1),new r("or",-1,1),new r("s",-1,2),new r("as",21,1),new r("arnas",22,1),new r("ernas",22,1),new r("ornas",22,1),new r("es",21,1),new r("ades",26,1),new r("andes",26,1),new r("ens",21,1),new r("arens",29,1),new r("hetens",29,1),new r("erns",21,1),new r("at",-1,1),new r("andet",-1,1),new r("het",-1,1),new r("ast",-1,1)],s=[new r("dd",-1,-1),new r("gd",-1,-1),new r("nn",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1),new r("tt",-1,-1)],a=[new r("ig",-1,1),new r("lig",0,1),new r("els",-1,1),new r("fullt",-1,3),new r("löst",-1,2)],o=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,24,0,32],u=[119,127,149],c=new n;this.setCurrent=function(e){c.setCurrent(e)},this.getCurrent=function(){return c.getCurrent()},this.stem=function(){var r=c.cursor;return function(){var r,n=c.cursor+3;if(t=c.limit,0<=n||n<=c.limit){for(e=n;;){if(r=c.cursor,c.in_grouping(o,97,246)){c.cursor=r;break}if(c.cursor=r,c.cursor>=c.limit)return;c.cursor++}for(;!c.out_grouping(o,97,246);){if(c.cursor>=c.limit)return;c.cursor++}(t=c.cursor)=t&&(c.limit_backward=t,c.cursor=c.limit,c.ket=c.cursor,e=c.find_among_b(i,37),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del();break;case 2:c.in_grouping_b(u,98,121)&&c.slice_del()}}(),c.cursor=c.limit,function(){var e=c.limit_backward;c.cursor>=t&&(c.limit_backward=t,c.cursor=c.limit,c.find_among_b(s,7)&&(c.cursor=c.limit,c.ket=c.cursor,c.cursor>c.limit_backward&&(c.bra=--c.cursor,c.slice_del())),c.limit_backward=e)}(),c.cursor=c.limit,function(){var e,r;if(c.cursor>=t){if(r=c.limit_backward,c.limit_backward=t,c.cursor=c.limit,c.ket=c.cursor,e=c.find_among_b(a,5))switch(c.bra=c.cursor,e){case 1:c.slice_del();break;case 2:c.slice_from("lös");break;case 3:c.slice_from("full")}c.limit_backward=r}}(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return t.setCurrent(e),t.stem(),t.getCurrent()}):(t.setCurrent(e),t.stem(),t.getCurrent())}}(),e.Pipeline.registerFunction(e.sv.stemmer,"stemmer-sv"),e.sv.stopWordFilter=e.generateStopWordFilter("alla allt att av blev bli blir blivit de dem den denna deras dess dessa det detta dig din dina ditt du där då efter ej eller en er era ert ett från för ha hade han hans har henne hennes hon honom hur här i icke ingen inom inte jag ju kan kunde man med mellan men mig min mina mitt mot mycket ni nu när någon något några och om oss på samma sedan sig sin sina sitta själv skulle som så sådan sådana sådant till under upp ut utan vad var vara varför varit varje vars vart vem vi vid vilka vilkas vilken vilket vår våra vårt än är åt över".split(" ")),e.Pipeline.registerFunction(e.sv.stopWordFilter,"stopWordFilter-sv")}}); \ No newline at end of file diff --git a/docs/build/docs/en/assets/javascripts/lunr/lunr.tr.js b/docs/build/docs/en/assets/javascripts/lunr/lunr.tr.js new file mode 100644 index 00000000000..db7c908a524 --- /dev/null +++ b/docs/build/docs/en/assets/javascripts/lunr/lunr.tr.js @@ -0,0 +1 @@ +!function(r,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(r.lunr)}(this,function(){return function(r){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");r.tr=function(){this.pipeline.reset(),this.pipeline.add(r.tr.trimmer,r.tr.stopWordFilter,r.tr.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(r.tr.stemmer))},r.tr.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",r.tr.trimmer=r.trimmerSupport.generateTrimmer(r.tr.wordCharacters),r.Pipeline.registerFunction(r.tr.trimmer,"trimmer-tr"),r.tr.stemmer=function(){var i=r.stemmerSupport.Among,e=r.stemmerSupport.SnowballProgram,n=new function(){function r(r,i,e){for(;;){var n=fr.limit-fr.cursor;if(fr.in_grouping_b(r,i,e)){fr.cursor=fr.limit-n;break}if(fr.cursor=fr.limit-n,fr.cursor<=fr.limit_backward)return!1;fr.cursor--}return!0}function n(){var i,e;i=fr.limit-fr.cursor,r(cr,97,305);for(var 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.md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=light-blue]{background-color:#03a9f4}[data-md-color-primary=light-blue] .md-typeset a{color:#03a9f4}[data-md-color-primary=light-blue] .md-header,[data-md-color-primary=light-blue] .md-hero{background-color:#03a9f4}[data-md-color-primary=light-blue] .md-nav__link--active,[data-md-color-primary=light-blue] .md-nav__link:active{color:#03a9f4}[data-md-color-primary=light-blue] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=cyan]{background-color:#00bcd4}[data-md-color-primary=cyan] .md-typeset a{color:#00bcd4}[data-md-color-primary=cyan] .md-header,[data-md-color-primary=cyan] .md-hero{background-color:#00bcd4}[data-md-color-primary=cyan] .md-nav__link--active,[data-md-color-primary=cyan] .md-nav__link:active{color:#00bcd4}[data-md-color-primary=cyan] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=teal]{background-color:#009688}[data-md-color-primary=teal] .md-typeset a{color:#009688}[data-md-color-primary=teal] .md-header,[data-md-color-primary=teal] .md-hero{background-color:#009688}[data-md-color-primary=teal] .md-nav__link--active,[data-md-color-primary=teal] .md-nav__link:active{color:#009688}[data-md-color-primary=teal] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=green]{background-color:#4caf50}[data-md-color-primary=green] .md-typeset a{color:#4caf50}[data-md-color-primary=green] .md-header,[data-md-color-primary=green] .md-hero{background-color:#4caf50}[data-md-color-primary=green] .md-nav__link--active,[data-md-color-primary=green] .md-nav__link:active{color:#4caf50}[data-md-color-primary=green] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=light-green]{background-color:#7cb342}[data-md-color-primary=light-green] .md-typeset a{color:#7cb342}[data-md-color-primary=light-green] .md-header,[data-md-color-primary=light-green] .md-hero{background-color:#7cb342}[data-md-color-primary=light-green] .md-nav__link--active,[data-md-color-primary=light-green] .md-nav__link:active{color:#7cb342}[data-md-color-primary=light-green] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=lime]{background-color:#c0ca33}[data-md-color-primary=lime] .md-typeset a{color:#c0ca33}[data-md-color-primary=lime] .md-header,[data-md-color-primary=lime] .md-hero{background-color:#c0ca33}[data-md-color-primary=lime] .md-nav__link--active,[data-md-color-primary=lime] .md-nav__link:active{color:#c0ca33}[data-md-color-primary=lime] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=yellow]{background-color:#f9a825}[data-md-color-primary=yellow] .md-typeset a{color:#f9a825}[data-md-color-primary=yellow] .md-header,[data-md-color-primary=yellow] .md-hero{background-color:#f9a825}[data-md-color-primary=yellow] .md-nav__link--active,[data-md-color-primary=yellow] .md-nav__link:active{color:#f9a825}[data-md-color-primary=yellow] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=amber]{background-color:#ffa000}[data-md-color-primary=amber] .md-typeset a{color:#ffa000}[data-md-color-primary=amber] .md-header,[data-md-color-primary=amber] .md-hero{background-color:#ffa000}[data-md-color-primary=amber] .md-nav__link--active,[data-md-color-primary=amber] .md-nav__link:active{color:#ffa000}[data-md-color-primary=amber] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=orange]{background-color:#fb8c00}[data-md-color-primary=orange] .md-typeset a{color:#fb8c00}[data-md-color-primary=orange] .md-header,[data-md-color-primary=orange] .md-hero{background-color:#fb8c00}[data-md-color-primary=orange] .md-nav__link--active,[data-md-color-primary=orange] .md-nav__link:active{color:#fb8c00}[data-md-color-primary=orange] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=deep-orange]{background-color:#ff7043}[data-md-color-primary=deep-orange] .md-typeset a{color:#ff7043}[data-md-color-primary=deep-orange] .md-header,[data-md-color-primary=deep-orange] .md-hero{background-color:#ff7043}[data-md-color-primary=deep-orange] .md-nav__link--active,[data-md-color-primary=deep-orange] .md-nav__link:active{color:#ff7043}[data-md-color-primary=deep-orange] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=brown]{background-color:#795548}[data-md-color-primary=brown] .md-typeset a{color:#795548}[data-md-color-primary=brown] .md-header,[data-md-color-primary=brown] .md-hero{background-color:#795548}[data-md-color-primary=brown] .md-nav__link--active,[data-md-color-primary=brown] .md-nav__link:active{color:#795548}[data-md-color-primary=brown] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=grey]{background-color:#757575}[data-md-color-primary=grey] .md-typeset a{color:#757575}[data-md-color-primary=grey] .md-header,[data-md-color-primary=grey] .md-hero{background-color:#757575}[data-md-color-primary=grey] .md-nav__link--active,[data-md-color-primary=grey] .md-nav__link:active{color:#757575}[data-md-color-primary=grey] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=blue-grey]{background-color:#546e7a}[data-md-color-primary=blue-grey] .md-typeset a{color:#546e7a}[data-md-color-primary=blue-grey] .md-header,[data-md-color-primary=blue-grey] .md-hero{background-color:#546e7a}[data-md-color-primary=blue-grey] .md-nav__link--active,[data-md-color-primary=blue-grey] .md-nav__link:active{color:#546e7a}[data-md-color-primary=blue-grey] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=white]{-webkit-box-shadow:0 0 .1rem rgba(0,0,0,.54) inset;box-shadow:inset 0 0 .1rem rgba(0,0,0,.54)}[data-md-color-primary=white] .md-header,[data-md-color-primary=white] .md-hero,button[data-md-color-primary=white]{background-color:#fff;color:rgba(0,0,0,.87)}[data-md-color-primary=white] .md-hero--expand{border-bottom:.1rem solid rgba(0,0,0,.07)}button[data-md-color-accent=red]{background-color:#ff1744}[data-md-color-accent=red] .md-typeset a:active,[data-md-color-accent=red] .md-typeset a:hover{color:#ff1744}[data-md-color-accent=red] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=red] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-nav__link:focus,[data-md-color-accent=red] .md-nav__link:hover,[data-md-color-accent=red] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=red] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=red] .md-typeset .md-clipboard:active:before,[data-md-color-accent=red] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=red] .md-typeset [id] .headerlink:focus,[data-md-color-accent=red] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=red] .md-typeset [id]:target .headerlink{color:#ff1744}[data-md-color-accent=red] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-search-result__link:hover,[data-md-color-accent=red] .md-search-result__link[data-md-state=active]{background-color:rgba(255,23,68,.1)}[data-md-color-accent=red] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-source-file:hover:before{background-color:#ff1744}button[data-md-color-accent=pink]{background-color:#f50057}[data-md-color-accent=pink] .md-typeset a:active,[data-md-color-accent=pink] .md-typeset a:hover{color:#f50057}[data-md-color-accent=pink] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=pink] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-nav__link:focus,[data-md-color-accent=pink] .md-nav__link:hover,[data-md-color-accent=pink] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=pink] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=pink] .md-typeset .md-clipboard:active:before,[data-md-color-accent=pink] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=pink] .md-typeset [id] .headerlink:focus,[data-md-color-accent=pink] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=pink] .md-typeset [id]:target .headerlink{color:#f50057}[data-md-color-accent=pink] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-search-result__link:hover,[data-md-color-accent=pink] .md-search-result__link[data-md-state=active]{background-color:rgba(245,0,87,.1)}[data-md-color-accent=pink] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-source-file:hover:before{background-color:#f50057}button[data-md-color-accent=purple]{background-color:#e040fb}[data-md-color-accent=purple] .md-typeset a:active,[data-md-color-accent=purple] .md-typeset a:hover{color:#e040fb}[data-md-color-accent=purple] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=purple] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-nav__link:focus,[data-md-color-accent=purple] .md-nav__link:hover,[data-md-color-accent=purple] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=purple] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=purple] .md-typeset .md-clipboard:active:before,[data-md-color-accent=purple] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=purple] .md-typeset [id] .headerlink:focus,[data-md-color-accent=purple] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=purple] .md-typeset [id]:target .headerlink{color:#e040fb}[data-md-color-accent=purple] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-search-result__link:hover,[data-md-color-accent=purple] .md-search-result__link[data-md-state=active]{background-color:rgba(224,64,251,.1)}[data-md-color-accent=purple] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-source-file:hover:before{background-color:#e040fb}button[data-md-color-accent=deep-purple]{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-typeset a:active,[data-md-color-accent=deep-purple] .md-typeset a:hover{color:#7c4dff}[data-md-color-accent=deep-purple] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=deep-purple] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-nav__link:focus,[data-md-color-accent=deep-purple] .md-nav__link:hover,[data-md-color-accent=deep-purple] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=deep-purple] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=deep-purple] .md-typeset .md-clipboard:active:before,[data-md-color-accent=deep-purple] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=deep-purple] .md-typeset [id] .headerlink:focus,[data-md-color-accent=deep-purple] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=deep-purple] .md-typeset [id]:target .headerlink{color:#7c4dff}[data-md-color-accent=deep-purple] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-search-result__link:hover,[data-md-color-accent=deep-purple] .md-search-result__link[data-md-state=active]{background-color:rgba(124,77,255,.1)}[data-md-color-accent=deep-purple] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-source-file:hover:before{background-color:#7c4dff}button[data-md-color-accent=indigo]{background-color:#536dfe}[data-md-color-accent=indigo] .md-typeset a:active,[data-md-color-accent=indigo] .md-typeset a:hover{color:#536dfe}[data-md-color-accent=indigo] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=indigo] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-nav__link:focus,[data-md-color-accent=indigo] .md-nav__link:hover,[data-md-color-accent=indigo] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=indigo] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=indigo] .md-typeset .md-clipboard:active:before,[data-md-color-accent=indigo] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=indigo] .md-typeset [id] .headerlink:focus,[data-md-color-accent=indigo] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=indigo] .md-typeset [id]:target .headerlink{color:#536dfe}[data-md-color-accent=indigo] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-search-result__link:hover,[data-md-color-accent=indigo] .md-search-result__link[data-md-state=active]{background-color:rgba(83,109,254,.1)}[data-md-color-accent=indigo] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-source-file:hover:before{background-color:#536dfe}button[data-md-color-accent=blue]{background-color:#448aff}[data-md-color-accent=blue] .md-typeset a:active,[data-md-color-accent=blue] .md-typeset a:hover{color:#448aff}[data-md-color-accent=blue] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=blue] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-nav__link:focus,[data-md-color-accent=blue] .md-nav__link:hover,[data-md-color-accent=blue] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=blue] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=blue] .md-typeset .md-clipboard:active:before,[data-md-color-accent=blue] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=blue] .md-typeset [id] .headerlink:focus,[data-md-color-accent=blue] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=blue] .md-typeset [id]:target .headerlink{color:#448aff}[data-md-color-accent=blue] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-search-result__link:hover,[data-md-color-accent=blue] .md-search-result__link[data-md-state=active]{background-color:rgba(68,138,255,.1)}[data-md-color-accent=blue] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-source-file:hover:before{background-color:#448aff}button[data-md-color-accent=light-blue]{background-color:#0091ea}[data-md-color-accent=light-blue] .md-typeset a:active,[data-md-color-accent=light-blue] .md-typeset a:hover{color:#0091ea}[data-md-color-accent=light-blue] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=light-blue] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-nav__link:focus,[data-md-color-accent=light-blue] .md-nav__link:hover,[data-md-color-accent=light-blue] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=light-blue] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=light-blue] .md-typeset .md-clipboard:active:before,[data-md-color-accent=light-blue] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=light-blue] .md-typeset [id] .headerlink:focus,[data-md-color-accent=light-blue] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=light-blue] .md-typeset [id]:target .headerlink{color:#0091ea}[data-md-color-accent=light-blue] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-search-result__link:hover,[data-md-color-accent=light-blue] .md-search-result__link[data-md-state=active]{background-color:rgba(0,145,234,.1)}[data-md-color-accent=light-blue] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-source-file:hover:before{background-color:#0091ea}button[data-md-color-accent=cyan]{background-color:#00b8d4}[data-md-color-accent=cyan] .md-typeset a:active,[data-md-color-accent=cyan] .md-typeset a:hover{color:#00b8d4}[data-md-color-accent=cyan] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=cyan] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-nav__link:focus,[data-md-color-accent=cyan] .md-nav__link:hover,[data-md-color-accent=cyan] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=cyan] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=cyan] .md-typeset .md-clipboard:active:before,[data-md-color-accent=cyan] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=cyan] .md-typeset [id] .headerlink:focus,[data-md-color-accent=cyan] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=cyan] .md-typeset [id]:target .headerlink{color:#00b8d4}[data-md-color-accent=cyan] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-search-result__link:hover,[data-md-color-accent=cyan] .md-search-result__link[data-md-state=active]{background-color:rgba(0,184,212,.1)}[data-md-color-accent=cyan] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-source-file:hover:before{background-color:#00b8d4}button[data-md-color-accent=teal]{background-color:#00bfa5}[data-md-color-accent=teal] .md-typeset a:active,[data-md-color-accent=teal] .md-typeset a:hover{color:#00bfa5}[data-md-color-accent=teal] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=teal] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#00bfa5}[data-md-color-accent=teal] .md-nav__link:focus,[data-md-color-accent=teal] .md-nav__link:hover,[data-md-color-accent=teal] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=teal] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=teal] .md-typeset .md-clipboard:active:before,[data-md-color-accent=teal] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=teal] .md-typeset [id] .headerlink:focus,[data-md-color-accent=teal] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=teal] .md-typeset [id]:target .headerlink{color:#00bfa5}[data-md-color-accent=teal] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00bfa5}[data-md-color-accent=teal] .md-search-result__link:hover,[data-md-color-accent=teal] .md-search-result__link[data-md-state=active]{background-color:rgba(0,191,165,.1)}[data-md-color-accent=teal] 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!important; + text-decoration: none; +} + +a:hover, a:active { + color: #f00; + text-decoration: underline; +} + +.md-typeset pre { + font: 13px/18px monospace, "Courier New"; + display: block; + padding: 1rem 3rem 1rem 1rem; + overflow: scroll; +} + +h1, h2, h3, .md-logo { + font-family: 'Yandex Sans Display Web', Arial, sans-serif; + color: #000 !important; +} + +.md-logo { + padding: 0; +} + +.md-header { + border-bottom: 1px solid #efefef; +} + +.md-header-nav__title { + font-size: 3rem; + font-family: 'Yandex Sans Display Web', Arial, sans-serif; +} + +.md-content__icon:hover { + text-decoration: none !important; + color: #08f !important; +} + +.md-search-result__link { + text-decoration: none !important; +} diff --git a/docs/build/docs/en/data_types/array/index.html b/docs/build/docs/en/data_types/array/index.html new file mode 100644 index 00000000000..268cf7bf644 --- /dev/null +++ b/docs/build/docs/en/data_types/array/index.html @@ -0,0 +1,2889 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Array(T) - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Array(T)

+

An array of elements of type T. The T type can be any type, including an array. +We don't recommend using multidimensional arrays, because they are not well supported (for example, you can't store multidimensional arrays in tables with a MergeTree engine).

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/data_types/boolean/index.html b/docs/build/docs/en/data_types/boolean/index.html new file mode 100644 index 00000000000..b449b6ee1e7 --- /dev/null +++ b/docs/build/docs/en/data_types/boolean/index.html @@ -0,0 +1,2888 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Boolean values - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Boolean values

+

There isn't a separate type for boolean values. They use the UInt8 type, restricted to the values 0 or 1.

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/data_types/date/index.html b/docs/build/docs/en/data_types/date/index.html new file mode 100644 index 00000000000..0312126811b --- /dev/null +++ b/docs/build/docs/en/data_types/date/index.html @@ -0,0 +1,2890 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Date - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Date

+

A date. Stored in two bytes as the number of days since 1970-01-01 (unsigned). Allows storing values from just after the beginning of the Unix Epoch to the upper threshold defined by a constant at the compilation stage (currently, this is until the year 2106, but the final fully-supported year is 2105). +The minimum value is output as 0000-00-00.

+

The date is stored without the time zone.

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/data_types/datetime/index.html b/docs/build/docs/en/data_types/datetime/index.html new file mode 100644 index 00000000000..ca682be71aa --- /dev/null +++ b/docs/build/docs/en/data_types/datetime/index.html @@ -0,0 +1,2935 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + DateTime - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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DateTime

+

Date with time. Stored in four bytes as a Unix timestamp (unsigned). Allows storing values in the same range as for the Date type. The minimal value is output as 0000-00-00 00:00:00. +The time is stored with accuracy up to one second (without leap seconds).

+

Time zones

+

The date with time is converted from text (divided into component parts) to binary and back, using the system's time zone at the time the client or server starts. In text format, information about daylight savings is lost.

+

By default, the client switches to the timezone of the server when it connects. You can change this behavior by enabling the client command-line option --use_client_time_zone.

+

Supports only those time zones that never had the time differ from UTC for a partial number of hours (without leap seconds) over the entire time range you will be working with.

+

So when working with a textual date (for example, when saving text dumps), keep in mind that there may be ambiguity during changes for daylight savings time, and there may be problems matching data if the time zone changed.

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/data_types/enum/index.html b/docs/build/docs/en/data_types/enum/index.html new file mode 100644 index 00000000000..aa09b042c11 --- /dev/null +++ b/docs/build/docs/en/data_types/enum/index.html @@ -0,0 +1,2908 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Enum - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Enum

+

Enum8 or Enum16. A finite set of string values that can be stored more efficiently than the String data type.

+

Example:

+
Enum8('hello' = 1, 'world' = 2)
+
+ + +
    +
  • A data type with two possible values: 'hello' and 'world'.
    • +
+

Each of the values is assigned a number in the range -128 ... 127 for Enum8 or in the range -32768 ... 32767 for Enum16. All the strings and numbers must be different. An empty string is allowed. If this type is specified (in a table definition), numbers can be in an arbitrary order. However, the order does not matter.

+

In RAM, this type of column is stored in the same way as Int8 or Int16 of the corresponding numerical values. +When reading in text form, ClickHouse parses the value as a string and searches for the corresponding string from the set of Enum values. If it is not found, an exception is thrown. When reading in text format, the string is read and the corresponding numeric value is looked up. An exception will be thrown if it is not found. +When writing in text form, it writes the value as the corresponding string. If column data contains garbage (numbers that are not from the valid set), an exception is thrown. When reading and writing in binary form, it works the same way as for Int8 and Int16 data types. +The implicit default value is the value with the lowest number.

+

During ORDER BY, GROUP BY, IN, DISTINCT and so on, Enums behave the same way as the corresponding numbers. For example, ORDER BY sorts them numerically. Equality and comparison operators work the same way on Enums as they do on the underlying numeric values.

+

Enum values cannot be compared with numbers. Enums can be compared to a constant string. If the string compared to is not a valid value for the Enum, an exception will be thrown. The IN operator is supported with the Enum on the left hand side and a set of strings on the right hand side. The strings are the values of the corresponding Enum.

+

Most numeric and string operations are not defined for Enum values, e.g. adding a number to an Enum or concatenating a string to an Enum. +However, the Enum has a natural toString function that returns its string value.

+

Enum values are also convertible to numeric types using the toT function, where T is a numeric type. When T corresponds to the enum’s underlying numeric type, this conversion is zero-cost. +The Enum type can be changed without cost using ALTER, if only the set of values is changed. It is possible to both add and remove members of the Enum using ALTER (removing is safe only if the removed value has never been used in the table). As a safeguard, changing the numeric value of a previously defined Enum member will throw an exception.

+

Using ALTER, it is possible to change an Enum8 to an Enum16 or vice versa, just like changing an Int8 to Int16.

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/data_types/fixedstring/index.html b/docs/build/docs/en/data_types/fixedstring/index.html new file mode 100644 index 00000000000..5384662f216 --- /dev/null +++ b/docs/build/docs/en/data_types/fixedstring/index.html @@ -0,0 +1,2893 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + FixedString(N) - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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FixedString(N)

+

A fixed-length string of N bytes (not characters or code points). N must be a strictly positive natural number. +When the server reads a string that contains fewer bytes (such as when parsing INSERT data), the string is padded to N bytes by appending null bytes at the right. +When the server reads a string that contains more bytes, an error message is returned. +When the server writes a string (such as when outputting the result of a SELECT query), null bytes are not trimmed off of the end of the string, but are output. +Note that this behavior differs from MySQL behavior for the CHAR type (where strings are padded with spaces, and the spaces are removed for output).

+

Fewer functions can work with the FixedString(N) type than with String, so it is less convenient to use.

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/data_types/float/index.html b/docs/build/docs/en/data_types/float/index.html new file mode 100644 index 00000000000..2c2a2ab4316 --- /dev/null +++ b/docs/build/docs/en/data_types/float/index.html @@ -0,0 +1,3010 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Float32, Float64 - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Float32, Float64

+

Floating point numbers.

+

Types are equivalent to types of C:

+
    +
  • Float32 - float
    • +
  • Float64 - double
    • +
+

We recommend that you store data in integer form whenever possible. For example, convert fixed precision numbers to integer values, such as monetary amounts or page load times in milliseconds.

+

Using floating-point numbers

+
    +
  • Computations with floating-point numbers might produce a rounding error.
    • +
+
SELECT 1 - 0.9
+
+ + +
┌───────minus(1, 0.9)─┐
+│ 0.09999999999999998 │
+└─────────────────────┘
+
+ + +
    +
  • The result of the calculation depends on the calculation method (the processor type and architecture of the computer system).
    • +
  • Floating-point calculations might result in numbers such as infinity (Inf) and "not-a-number" (NaN). This should be taken into account when processing the results of calculations.
    • +
  • When reading floating point numbers from rows, the result might not be the nearest machine-representable number.
    • +
+

NaN and Inf

+

In contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers:

+
    +
  • Inf – Infinity.
    • +
+
SELECT 0.5 / 0
+
+ + +
┌─divide(0.5, 0)─┐
+│            inf │
+└────────────────┘
+
+ + +
    +
  • -Inf – Negative infinity.
    • +
+
SELECT -0.5 / 0
+
+ + +
┌─divide(-0.5, 0)─┐
+│            -inf │
+└─────────────────┘
+
+ + +
    +
  • NaN – Not a number.
    • +
+
SELECT 0 / 0
+
+ + +
┌─divide(0, 0)─┐
+│          nan │
+└──────────────┘
+
+ + +

See the rules for NaN sorting in the section ORDER BY clause.

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/data_types/index.html b/docs/build/docs/en/data_types/index.html new file mode 100644 index 00000000000..a6747a23078 --- /dev/null +++ b/docs/build/docs/en/data_types/index.html @@ -0,0 +1,2890 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Introduction - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Data types

+

ClickHouse can store various types of data in table cells.

+

This section describes the supported data types and special considerations when using and/or implementing them, if any.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/data_types/int_uint/index.html b/docs/build/docs/en/data_types/int_uint/index.html new file mode 100644 index 00000000000..fd020287afe --- /dev/null +++ b/docs/build/docs/en/data_types/int_uint/index.html @@ -0,0 +1,2957 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64 - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64

+

Fixed-length integers, with or without a sign.

+

Int ranges

+
    +
  • Int8 - [-128 : 127]
    • +
  • Int16 - [-32768 : 32767]
    • +
  • Int32 - [-2147483648 : 2147483647]
    • +
  • Int64 - [-9223372036854775808 : 9223372036854775807]
    • +
+

Uint ranges

+
    +
  • UInt8 - [0 : 255]
    • +
  • UInt16 - [0 : 65535]
    • +
  • UInt32 - [0 : 4294967295]
    • +
  • UInt64 - [0 : 18446744073709551615]
    • +
+ + + + + + + +
+
+
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+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/data_types/nested_data_structures/aggregatefunction/index.html b/docs/build/docs/en/data_types/nested_data_structures/aggregatefunction/index.html new file mode 100644 index 00000000000..d8ead59bfd7 --- /dev/null +++ b/docs/build/docs/en/data_types/nested_data_structures/aggregatefunction/index.html @@ -0,0 +1,2888 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + AggregateFunction(name, types_of_arguments...) - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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AggregateFunction(name, types_of_arguments...)

+

The intermediate state of an aggregate function. To get it, use aggregate functions with the '-State' suffix. For more information, see "AggregatingMergeTree".

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/data_types/nested_data_structures/nested/index.html b/docs/build/docs/en/data_types/nested_data_structures/nested/index.html new file mode 100644 index 00000000000..c81b4782aa5 --- /dev/null +++ b/docs/build/docs/en/data_types/nested_data_structures/nested/index.html @@ -0,0 +1,2973 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Nested(Name1 Type1, Name2 Type2, ...) - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Nested(Name1 Type1, Name2 Type2, ...)

+

A nested data structure is like a nested table. The parameters of a nested data structure – the column names and types – are specified the same way as in a CREATE query. Each table row can correspond to any number of rows in a nested data structure.

+

Example:

+
CREATE TABLE test.visits
+(
+    CounterID UInt32,
+    StartDate Date,
+    Sign Int8,
+    IsNew UInt8,
+    VisitID UInt64,
+    UserID UInt64,
+    ...
+    Goals Nested
+    (
+        ID UInt32,
+        Serial UInt32,
+        EventTime DateTime,
+        Price Int64,
+        OrderID String,
+        CurrencyID UInt32
+    ),
+    ...
+) ENGINE = CollapsingMergeTree(StartDate, intHash32(UserID), (CounterID, StartDate, intHash32(UserID), VisitID), 8192, Sign)
+
+ + +

This example declares the Goals nested data structure, which contains data about conversions (goals reached). Each row in the 'visits' table can correspond to zero or any number of conversions.

+

Only a single nesting level is supported. Columns of nested structures containing arrays are equivalent to multidimensional arrays, so they have limited support (there is no support for storing these columns in tables with the MergeTree engine).

+

In most cases, when working with a nested data structure, its individual columns are specified. To do this, the column names are separated by a dot. These columns make up an array of matching types. All the column arrays of a single nested data structure have the same length.

+

Example:

+
SELECT
+    Goals.ID,
+    Goals.EventTime
+FROM test.visits
+WHERE CounterID = 101500 AND length(Goals.ID) < 5
+LIMIT 10
+
+ + +
┌─Goals.ID───────────────────────┬─Goals.EventTime───────────────────────────────────────────────────────────────────────────┐
+│ [1073752,591325,591325]        │ ['2014-03-17 16:38:10','2014-03-17 16:38:48','2014-03-17 16:42:27']                       │
+│ [1073752]                      │ ['2014-03-17 00:28:25']                                                                   │
+│ [1073752]                      │ ['2014-03-17 10:46:20']                                                                   │
+│ [1073752,591325,591325,591325] │ ['2014-03-17 13:59:20','2014-03-17 22:17:55','2014-03-17 22:18:07','2014-03-17 22:18:51'] │
+│ []                             │ []                                                                                        │
+│ [1073752,591325,591325]        │ ['2014-03-17 11:37:06','2014-03-17 14:07:47','2014-03-17 14:36:21']                       │
+│ []                             │ []                                                                                        │
+│ []                             │ []                                                                                        │
+│ [591325,1073752]               │ ['2014-03-17 00:46:05','2014-03-17 00:46:05']                                             │
+│ [1073752,591325,591325,591325] │ ['2014-03-17 13:28:33','2014-03-17 13:30:26','2014-03-17 18:51:21','2014-03-17 18:51:45'] │
+└────────────────────────────────┴───────────────────────────────────────────────────────────────────────────────────────────┘
+
+ + +

It is easiest to think of a nested data structure as a set of multiple column arrays of the same length.

+

The only place where a SELECT query can specify the name of an entire nested data structure instead of individual columns is the ARRAY JOIN clause. For more information, see "ARRAY JOIN clause". Example:

+
SELECT
+    Goal.ID,
+    Goal.EventTime
+FROM test.visits
+ARRAY JOIN Goals AS Goal
+WHERE CounterID = 101500 AND length(Goals.ID) < 5
+LIMIT 10
+
+ + +
┌─Goal.ID─┬──────Goal.EventTime─┐
+│ 1073752 │ 2014-03-17 16:38:10 │
+│  591325 │ 2014-03-17 16:38:48 │
+│  591325 │ 2014-03-17 16:42:27 │
+│ 1073752 │ 2014-03-17 00:28:25 │
+│ 1073752 │ 2014-03-17 10:46:20 │
+│ 1073752 │ 2014-03-17 13:59:20 │
+│  591325 │ 2014-03-17 22:17:55 │
+│  591325 │ 2014-03-17 22:18:07 │
+│  591325 │ 2014-03-17 22:18:51 │
+│ 1073752 │ 2014-03-17 11:37:06 │
+└─────────┴─────────────────────┘
+
+ + +

You can't perform SELECT for an entire nested data structure. You can only explicitly list individual columns that are part of it.

+

For an INSERT query, you should pass all the component column arrays of a nested data structure separately (as if they were individual column arrays). During insertion, the system checks that they have the same length.

+

For a DESCRIBE query, the columns in a nested data structure are listed separately in the same way.

+

The ALTER query is very limited for elements in a nested data structure.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/data_types/special_data_types/expression/index.html b/docs/build/docs/en/data_types/special_data_types/expression/index.html new file mode 100644 index 00000000000..495c18daa01 --- /dev/null +++ b/docs/build/docs/en/data_types/special_data_types/expression/index.html @@ -0,0 +1,2890 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Expression - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Expression

+

Used for representing lambda expressions in high-order functions.

+ + + + + + + +
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+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/data_types/special_data_types/set/index.html b/docs/build/docs/en/data_types/special_data_types/set/index.html new file mode 100644 index 00000000000..cf924e4ace9 --- /dev/null +++ b/docs/build/docs/en/data_types/special_data_types/set/index.html @@ -0,0 +1,2890 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Set - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Set

+

Used for the right half of an IN expression.

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/data_types/string/index.html b/docs/build/docs/en/data_types/string/index.html new file mode 100644 index 00000000000..217fc7f6187 --- /dev/null +++ b/docs/build/docs/en/data_types/string/index.html @@ -0,0 +1,2935 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + String - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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String

+

Strings of an arbitrary length. The length is not limited. The value can contain an arbitrary set of bytes, including null bytes. +The String type replaces the types VARCHAR, BLOB, CLOB, and others from other DBMSs.

+

Encodings

+

ClickHouse doesn't have the concept of encodings. Strings can contain an arbitrary set of bytes, which are stored and output as-is. +If you need to store texts, we recommend using UTF-8 encoding. At the very least, if your terminal uses UTF-8 (as recommended), you can read and write your values without making conversions. +Similarly, certain functions for working with strings have separate variations that work under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. +For example, the 'length' function calculates the string length in bytes, while the 'lengthUTF8' function calculates the string length in Unicode code points, assuming that the value is UTF-8 encoded.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/data_types/tuple/index.html b/docs/build/docs/en/data_types/tuple/index.html new file mode 100644 index 00000000000..1d129d87f7a --- /dev/null +++ b/docs/build/docs/en/data_types/tuple/index.html @@ -0,0 +1,2889 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Tuple(T1, T2, ...) - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Tuple(T1, T2, ...)

+

Tuples can't be written to tables (other than Memory tables). They are used for temporary column grouping. Columns can be grouped when an IN expression is used in a query, and for specifying certain formal parameters of lambda functions. For more information, see "IN operators" and "Higher order functions".

+

Tuples can be output as the result of running a query. In this case, for text formats other than JSON*, values are comma-separated in brackets. In JSON* formats, tuples are output as arrays (in square brackets).

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/development/architecture/index.html b/docs/build/docs/en/development/architecture/index.html new file mode 100644 index 00000000000..935d7de87c9 --- /dev/null +++ b/docs/build/docs/en/development/architecture/index.html @@ -0,0 +1,3278 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Overview of ClickHouse architecture - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
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+ + +
+
+ + + + + + + +

Overview of ClickHouse architecture

+

ClickHouse is a true column-oriented DBMS. Data is stored by columns, and during the execution of arrays (vectors or chunks of columns). Whenever possible, operations are dispatched on arrays, rather than on individual values. This is called "vectorized query execution," and it helps lower the cost of actual data processing.

+
+

This idea is nothing new. It dates back to the APL programming language and its descendants: A +, J, K, and Q. Array programming is used in scientific data processing. Neither is this idea something new in relational databases: for example, it is used in the Vectorwise system.

+
+

There are two different approaches for speeding up the query processing: vectorized query execution and runtime code generation. In the latter, the code is generated for every kind of query on the fly, removing all indirection and dynamic dispatch. Neither of these approaches is strictly better than the other. Runtime code generation can be better when it's fuses many operations together, thus fully utilizing CPU execution units and the pipeline. Vectorized query execution can be less practical, because it involves the temporary vectors that must be written to the cache and read back. If the temporary data does not fit in the L2 cache, this becomes an issue. But vectorized query execution more easily utilizes the SIMD capabilities of the CPU. A research paper written by our friends shows that it is better to combine both approaches. ClickHouse uses vectorized query execution and has limited initial support for runtime code.

+

Columns

+

To represent columns in memory (actually, chunks of columns), the IColumn interface is used. This interface provides helper methods for implementation of various relational operators. Almost all operations are immutable: they do not modify the original column, but create a new modified one. For example, the IColumn :: filter method accepts a filter byte mask. It is used for the WHERE and HAVING relational operators. Additional examples: the IColumn :: permute method to support ORDER BY, the IColumn :: cut method to support LIMIT, and so on.

+

Various IColumn implementations (ColumnUInt8, ColumnString and so on) are responsible for the memory layout of columns. Memory layout is usually a contiguous array. For the integer type of columns it is just one contiguous array, like std :: vector. For String and Array columns, it is two vectors: one for all array elements, placed contiguously, and a second one for offsets to the beginning of each array. There is also ColumnConst that stores just one value in memory, but looks like a column.

+

Field

+

Nevertheless, it is possible to work with individual values as well. To represent an individual value, the Field is used. Field is just a discriminated union of UInt64, Int64, Float64, String and Array. IColumn has the operator[] method to get the n-th value as a Field, and the insert method to append a Field to the end of a column. These methods are not very efficient, because they require dealing with temporary Field objects representing an individual value. There are more efficient methods, such as insertFrom, insertRangeFrom, and so on.

+

Field doesn't have enough information about a specific data type for a table. For example, UInt8, UInt16, UInt32, and UInt64 are all represented as UInt64 in a Field.

+

Leaky abstractions

+

IColumn has methods for common relational transformations of data, but they don't meet all needs. For example, ColumnUInt64 doesn't have a method to calculate the sum of two columns, and ColumnString doesn't have a method to run a substring search. These countless routines are implemented outside of IColumn.

+

Various functions on columns can be implemented in a generic, non-efficient way using IColumn methods to extract Field values, or in a specialized way using knowledge of inner memory layout of data in a specific IColumn implementation. To do this, functions are cast to a specific IColumn type and deal with internal representation directly. For example, ColumnUInt64 has the getData method that returns a reference to an internal array, then a separate routine reads or fills that array directly. In fact, we have "leaky abstractions" to allow efficient specializations of various routines.

+

Data types

+

IDataType is responsible for serialization and deserialization: for reading and writing chunks of columns or individual values in binary or text form. +IDataType directly corresponds to data types in tables. For example, there are DataTypeUInt32, DataTypeDateTime, DataTypeString and so on.

+

IDataType and IColumn are only loosely related to each other. Different data types can be represented in memory by the same IColumn implementations. For example, DataTypeUInt32 and DataTypeDateTime are both represented by ColumnUInt32 or ColumnConstUInt32. In addition, the same data type can be represented by different IColumn implementations. For example, DataTypeUInt8 can be represented by ColumnUInt8 or ColumnConstUInt8.

+

IDataType only stores metadata. For instance, DataTypeUInt8 doesn't store anything at all (except vptr) and DataTypeFixedString stores just N (the size of fixed-size strings).

+

IDataType has helper methods for various data formats. Examples are methods to serialize a value with possible quoting, to serialize a value for JSON, and to serialize a value as part of XML format. There is no direct correspondence to data formats. For example, the different data formats Pretty and TabSeparated can use the same serializeTextEscaped helper method from the IDataType interface.

+

Block

+

A Block is a container that represents a subset (chunk) of a table in memory. It is just a set of triples: (IColumn, IDataType, column name). During query execution, data is processed by Blocks. If we have a Block, we have data (in the IColumn object), we have information about its type (in IDataType) that tells us how to deal with that column, and we have the column name (either the original column name from the table, or some artificial name assigned for getting temporary results of calculations).

+

When we calculate some function over columns in a block, we add another column with its result to the block, and we don't touch columns for arguments of the function because operations are immutable. Later, unneeded columns can be removed from the block, but not modified. This is convenient for elimination of common subexpressions.

+

Blocks are created for every processed chunk of data. Note that for the same type of calculation, the column names and types remain the same for different blocks, and only column data changes. It is better to split block data from the block header, because small block sizes will have a high overhead of temporary strings for copying shared_ptrs and column names.

+

Block Streams

+

Block streams are for processing data. We use streams of blocks to read data from somewhere, perform data transformations, or write data to somewhere. IBlockInputStream has the read method to fetch the next block while available. IBlockOutputStream has the write method to push the block somewhere.

+

Streams are responsible for:

+
    +
  1. Reading or writing to a table. The table just returns a stream for reading or writing blocks.
    2. +
  2. Implementing data formats. For example, if you want to output data to a terminal in Pretty format, you create a block output stream where you push blocks, and it formats them.
    3. +
  3. Performing data transformations. Let's say you have IBlockInputStream and want to create a filtered stream. You create FilterBlockInputStream and initialize it with your stream. Then when you pull a block from FilterBlockInputStream, it pulls a block from your stream, filters it, and returns the filtered block to you. Query execution pipelines are represented this way.
    4. +
+

There are more sophisticated transformations. For example, when you pull from AggregatingBlockInputStream, it reads all data from its source, aggregates it, and then returns a stream of aggregated data for you. Another example: UnionBlockInputStream accepts many input sources in the constructor and also a number of threads. It launches multiple threads and reads from multiple sources in parallel.

+
+

Block streams use the "pull" approach to control flow: when you pull a block from the first stream, it consequently pulls the required blocks from nested streams, and the entire execution pipeline will work. Neither "pull" nor "push" is the best solution, because control flow is implicit, and that limits implementation of various features like simultaneous execution of multiple queries (merging many pipelines together). This limitation could be overcome with coroutines or just running extra threads that wait for each other. We may have more possibilities if we make control flow explicit: if we locate the logic for passing data from one calculation unit to another outside of those calculation units. Read this article for more thoughts.

+
+

We should note that the query execution pipeline creates temporary data at each step. We try to keep block size small enough so that temporary data fits in the CPU cache. With that assumption, writing and reading temporary data is almost free in comparison with other calculations. We could consider an alternative, which is to fuse many operations in the pipeline together, to make the pipeline as short as possible and remove much of the temporary data. This could be an advantage, but it also has drawbacks. For example, a split pipeline makes it easy to implement caching intermediate data, stealing intermediate data from similar queries running at the same time, and merging pipelines for similar queries.

+

Formats

+

Data formats are implemented with block streams. There are "presentational" formats only suitable for output of data to the client, such as Pretty format, which provides only IBlockOutputStream. And there are input/output formats, such as TabSeparated or JSONEachRow.

+

There are also row streams: IRowInputStream and IRowOutputStream. They allow you to pull/push data by individual rows, not by blocks. And they are only needed to simplify implementation of row-oriented formats. The wrappers BlockInputStreamFromRowInputStream and BlockOutputStreamFromRowOutputStream allow you to convert row-oriented streams to regular block-oriented streams.

+

I/O

+

For byte-oriented input/output, there are ReadBuffer and WriteBuffer abstract classes. They are used instead of C++ iostream's. Don't worry: every mature C++ project is using something other than iostream's for good reasons.

+

ReadBuffer and WriteBuffer are just a contiguous buffer and a cursor pointing to the position in that buffer. Implementations may own or not own the memory for the buffer. There is a virtual method to fill the buffer with the following data (for ReadBuffer) or to flush the buffer somewhere (for WriteBuffer). The virtual methods are rarely called.

+

Implementations of ReadBuffer/WriteBuffer are used for working with files and file descriptors and network sockets, for implementing compression (CompressedWriteBuffer is initialized with another WriteBuffer and performs compression before writing data to it), and for other purposes – the names ConcatReadBuffer, LimitReadBuffer, and HashingWriteBuffer speak for themselves.

+

Read/WriteBuffers only deal with bytes. To help with formatted input/output (for instance, to write a number in decimal format), there are functions from ReadHelpers and WriteHelpers header files.

+

Let's look at what happens when you want to write a result set in JSON format to stdout. You have a result set ready to be fetched from IBlockInputStream. You create WriteBufferFromFileDescriptor(STDOUT_FILENO) to write bytes to stdout. You create JSONRowOutputStream, initialized with that WriteBuffer, to write rows in JSON to stdout. You create BlockOutputStreamFromRowOutputStream on top of it, to represent it as IBlockOutputStream. Then you call copyData to transfer data from IBlockInputStream to IBlockOutputStream, and everything works. Internally, JSONRowOutputStream will write various JSON delimiters and call the IDataType::serializeTextJSON method with a reference to IColumn and the row number as arguments. Consequently, IDataType::serializeTextJSON will call a method from WriteHelpers.h: for example, writeText for numeric types and writeJSONString for DataTypeString.

+

Tables

+

Tables are represented by the IStorage interface. Different implementations of that interface are different table engines. Examples are StorageMergeTree, StorageMemory, and so on. Instances of these classes are just tables.

+

The most important IStorage methods are read and write. There are also alter, rename, drop, and so on. The read method accepts the following arguments: the set of columns to read from a table, the AST query to consider, and the desired number of streams to return. It returns one or multiple IBlockInputStream objects and information about the stage of data processing that was completed inside a table engine during query execution.

+

In most cases, the read method is only responsible for reading the specified columns from a table, not for any further data processing. All further data processing is done by the query interpreter and is outside the responsibility of IStorage.

+

But there are notable exceptions:

+
    +
  • The AST query is passed to the read method and the table engine can use it to derive index usage and to read less data from a table.
    • +
  • Sometimes the table engine can process data itself to a specific stage. For example, StorageDistributed can send a query to remote servers, ask them to process data to a stage where data from different remote servers can be merged, and return that preprocessed data. +The query interpreter then finishes processing the data.
    • +
+

The table's read method can return multiple IBlockInputStream objects to allow parallel data processing. These multiple block input streams can read from a table in parallel. Then you can wrap these streams with various transformations (such as expression evaluation or filtering) that can be calculated independently and create a UnionBlockInputStream on top of them, to read from multiple streams in parallel.

+

There are also TableFunctions. These are functions that return a temporary IStorage object to use in the FROM clause of a query.

+

To get a quick idea of how to implement your own table engine, look at something simple, like StorageMemory or StorageTinyLog.

+
+

As the result of the read method, IStorage returns QueryProcessingStage – information about what parts of the query were already calculated inside storage. Currently we have only very coarse granularity for that information. There is no way for the storage to say "I have already processed this part of the expression in WHERE, for this range of data". We need to work on that.

+
+

Parsers

+

A query is parsed by a hand-written recursive descent parser. For example, ParserSelectQuery just recursively calls the underlying parsers for various parts of the query. Parsers create an AST. The AST is represented by nodes, which are instances of IAST.

+
+

Parser generators are not used for historical reasons.

+
+

Interpreters

+

Interpreters are responsible for creating the query execution pipeline from an AST. There are simple interpreters, such as InterpreterExistsQueryand InterpreterDropQuery, or the more sophisticated InterpreterSelectQuery. The query execution pipeline is a combination of block input or output streams. For example, the result of interpreting the SELECT query is the IBlockInputStream to read the result set from; the result of the INSERT query is the IBlockOutputStream to write data for insertion to; and the result of interpreting the INSERT SELECT query is the IBlockInputStream that returns an empty result set on the first read, but that copies data from SELECT to INSERT at the same time.

+

InterpreterSelectQuery uses ExpressionAnalyzer and ExpressionActions machinery for query analysis and transformations. This is where most rule-based query optimizations are done. ExpressionAnalyzer is quite messy and should be rewritten: various query transformations and optimizations should be extracted to separate classes to allow modular transformations or query.

+

Functions

+

There are ordinary functions and aggregate functions. For aggregate functions, see the next section.

+

Ordinary functions don't change the number of rows – they work as if they are processing each row independently. In fact, functions are not called for individual rows, but for Block's of data to implement vectorized query execution.

+

There are some miscellaneous functions, like blockSize, rowNumberInBlock, and runningAccumulate, that exploit block processing and violate the independence of rows.

+

ClickHouse has strong typing, so implicit type conversion doesn't occur. If a function doesn't support a specific combination of types, an exception will be thrown. But functions can work (be overloaded) for many different combinations of types. For example, the plus function (to implement the + operator) works for any combination of numeric types: UInt8 + Float32, UInt16 + Int8, and so on. Also, some variadic functions can accept any number of arguments, such as the concat function.

+

Implementing a function may be slightly inconvenient because a function explicitly dispatches supported data types and supported IColumns. For example, the plus function has code generated by instantiation of a C++ template for each combination of numeric types, and for constant or non-constant left and right arguments.

+
+

This is a nice place to implement runtime code generation to avoid template code bloat. Also, it will make it possible to add fused functions like fused multiply-add, or to make multiple comparisons in one loop iteration.

+
+

Due to vectorized query execution, functions are not short-circuit. For example, if you write WHERE f(x) AND g(y), both sides will be calculated, even for rows, when f(x) is zero (except when f(x) is a zero constant expression). But if selectivity of the f(x) condition is high, and calculation of f(x) is much cheaper than g(y), it's better to implement multi-pass calculation: first calculate f(x), then filter columns by the result, and then calculate g(y) only for smaller, filtered chunks of data.

+

Aggregate Functions

+

Aggregate functions are stateful functions. They accumulate passed values into some state, and allow you to get results from that state. They are managed with the IAggregateFunction interface. States can be rather simple (the state for AggregateFunctionCount is just a single UInt64 value) or quite complex (the state of AggregateFunctionUniqCombined is a combination of a linear array, a hash table and a HyperLogLog probabilistic data structure).

+

To deal with multiple states while executing a high-cardinality GROUP BY query, states are allocated in Arena (a memory pool), or they could be allocated in any suitable piece of memory. States can have a non-trivial constructor and destructor: for example, complex aggregation states can allocate additional memory themselves. This requires some attention to creating and destroying states and properly passing their ownership, to keep track of who and when will destroy states.

+

Aggregation states can be serialized and deserialized to pass over the network during distributed query execution or to write them on disk where there is not enough RAM. They can even be stored in a table with the DataTypeAggregateFunction to allow incremental aggregation of data.

+
+

The serialized data format for aggregate function states is not versioned right now. This is ok if aggregate states are only stored temporarily. But we have the AggregatingMergeTree table engine for incremental aggregation, and people are already using it in production. This is why we should add support for backward compatibility when changing the serialized format for any aggregate function in the future.

+
+

Server

+

The server implements several different interfaces:

+
    +
  • An HTTP interface for any foreign clients.
    • +
  • A TCP interface for the native ClickHouse client and for cross-server communication during distributed query execution.
    • +
  • An interface for transferring data for replication.
    • +
+

Internally, it is just a basic multithreaded server without coroutines, fibers, etc. Since the server is not designed to process a high rate of simple queries but is intended to process a relatively low rate of complex queries, each of them can process a vast amount of data for analytics.

+

The server initializes the Context class with the necessary environment for query execution: the list of available databases, users and access rights, settings, clusters, the process list, the query log, and so on. This environment is used by interpreters.

+

We maintain full backward and forward compatibility for the server TCP protocol: old clients can talk to new servers and new clients can talk to old servers. But we don't want to maintain it eternally, and we are removing support for old versions after about one year.

+
+

For all external applications, we recommend using the HTTP interface because it is simple and easy to use. The TCP protocol is more tightly linked to internal data structures: it uses an internal format for passing blocks of data and it uses custom framing for compressed data. We haven't released a C library for that protocol because it requires linking most of the ClickHouse codebase, which is not practical.

+
+

Distributed query execution

+

Servers in a cluster setup are mostly independent. You can create a Distributed table on one or all servers in a cluster. The Distributed table does not store data itself – it only provides a "view" to all local tables on multiple nodes of a cluster. When you SELECT from a Distributed table, it rewrites that query, chooses remote nodes according to load balancing settings, and sends the query to them. The Distributed table requests remote servers to process a query just up to a stage where intermediate results from different servers can be merged. Then it receives the intermediate results and merges them. The distributed table tries to distribute as much work as possible to remote servers, and does not send much intermediate data over the network.

+
+

Things become more complicated when you have subqueries in IN or JOIN clauses and each of them uses a Distributed table. We have different strategies for execution of these queries.

+
+

There is no global query plan for distributed query execution. Each node has its own local query plan for its part of the job. We only have simple one-pass distributed query execution: we send queries for remote nodes and then merge the results. But this is not feasible for difficult queries with high cardinality GROUP BYs or with a large amount of temporary data for JOIN: in such cases, we need to "reshuffle" data between servers, which requires additional coordination. ClickHouse does not support that kind of query execution, and we need to work on it.

+

Merge Tree

+

MergeTree is a family of storage engines that supports indexing by primary key. The primary key can be an arbitary tuple of columns or expressions. Data in a MergeTree table is stored in "parts". Each part stores data in the primary key order (data is ordered lexicographically by the primary key tuple). All the table columns are stored in separate column.bin files in these parts. The files consist of compressed blocks. Each block is usually from 64 KB to 1 MB of uncompressed data, depending on the average value size. The blocks consist of column values placed contiguously one after the other. Column values are in the same order for each column (the order is defined by the primary key), so when you iterate by many columns, you get values for the corresponding rows.

+

The primary key itself is "sparse". It doesn't address each single row, but only some ranges of data. A separate primary.idx file has the value of the primary key for each N-th row, where N is called index_granularity (usually, N = 8192). Also, for each column, we have column.mrk files with "marks," which are offsets to each N-th row in the data file. Each mark is a pair: the offset in the file to the beginning of the compressed block, and the offset in the decompressed block to the beginning of data. Usually compressed blocks are aligned by marks, and the offset in the decompressed block is zero. Data for primary.idx always resides in memory and data for column.mrk files is cached.

+

When we are going to read something from a part in MergeTree, we look at primary.idx data and locate ranges that could possibly contain requested data, then look at column.mrk data and calculate offsets for where to start reading those ranges. Because of sparseness, excess data may be read. ClickHouse is not suitable for a high load of simple point queries, because the entire range with index_granularity rows must be read for each key, and the entire compressed block must be decompressed for each column. We made the index sparse because we must be able to maintain trillions of rows per single server without noticeable memory consumption for the index. Also, because the primary key is sparse, it is not unique: it cannot check the existence of the key in the table at INSERT time. You could have many rows with the same key in a table.

+

When you INSERT a bunch of data into MergeTree, that bunch is sorted by primary key order and forms a new part. To keep the number of parts relatively low, there are background threads that periodically select some parts and merge them to a single sorted part. That's why it is called MergeTree. Of course, merging leads to "write amplification". All parts are immutable: they are only created and deleted, but not modified. When SELECT is run, it holds a snapshot of the table (a set of parts). After merging, we also keep old parts for some time to make recovery after failure easier, so if we see that some merged part is probably broken, we can replace it with its source parts.

+

MergeTree is not an LSM tree because it doesn't contain "memtable" and "log": inserted data is written directly to the filesystem. This makes it suitable only to INSERT data in batches, not by individual row and not very frequently – about once per second is ok, but a thousand times a second is not. We did it this way for simplicity's sake, and because we are already inserting data in batches in our applications.

+
+

MergeTree tables can only have one (primary) index: there aren't any secondary indices. It would be nice to allow multiple physical representations under one logical table, for example, to store data in more than one physical order or even to allow representations with pre-aggregated data along with original data.

+
+

There are MergeTree engines that are doing additional work during background merges. Examples are CollapsingMergeTree and AggregatingMergeTree. This could be treated as special support for updates. Keep in mind that these are not real updates because users usually have no control over the time when background merges will be executed, and data in a MergeTree table is almost always stored in more than one part, not in completely merged form.

+

Replication

+

Replication in ClickHouse is implemented on a per-table basis. You could have some replicated and some non-replicated tables on the same server. You could also have tables replicated in different ways, such as one table with two-factor replication and another with three-factor.

+

Replication is implemented in the ReplicatedMergeTree storage engine. The path in ZooKeeper is specified as a parameter for the storage engine. All tables with the same path in ZooKeeper become replicas of each other: they synchronize their data and maintain consistency. Replicas can be added and removed dynamically simply by creating or dropping a table.

+

Replication uses an asynchronous multi-master scheme. You can insert data into any replica that has a session with ZooKeeper, and data is replicated to all other replicas asynchronously. Because ClickHouse doesn't support UPDATEs, replication is conflict-free. As there is no quorum acknowledgment of inserts, just-inserted data might be lost if one node fails.

+

Metadata for replication is stored in ZooKeeper. There is a replication log that lists what actions to do. Actions are: get part; merge parts; drop partition, etc. Each replica copies the replication log to its queue and then executes the actions from the queue. For example, on insertion, the "get part" action is created in the log, and every replica downloads that part. Merges are coordinated between replicas to get byte-identical results. All parts are merged in the same way on all replicas. To achieve this, one replica is elected as the leader, and that replica initiates merges and writes "merge parts" actions to the log.

+

Replication is physical: only compressed parts are transferred between nodes, not queries. To lower the network cost (to avoid network amplification), merges are processed on each replica independently in most cases. Large merged parts are sent over the network only in cases of significant replication lag.

+

In addition, each replica stores its state in ZooKeeper as the set of parts and its checksums. When the state on the local filesystem diverges from the reference state in ZooKeeper, the replica restores its consistency by downloading missing and broken parts from other replicas. When there is some unexpected or broken data in the local filesystem, ClickHouse does not remove it, but moves it to a separate directory and forgets it.

+
+

The ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is not elastic, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load will be uneven. This implementation gives you more control, and it is fine for relatively small clusters such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that will span its data across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically.

+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/development/build/index.html b/docs/build/docs/en/development/build/index.html new file mode 100644 index 00000000000..42bc7f7f23b --- /dev/null +++ b/docs/build/docs/en/development/build/index.html @@ -0,0 +1,3189 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + How to build ClickHouse on Linux - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

How to build ClickHouse on Linux

+

Build should work on Linux Ubuntu 12.04, 14.04 or newer. +With appropriate changes, it should also work on any other Linux distribution. +The build process is not intended to work on Mac OS X. +Only x86_64 with SSE 4.2 is supported. Support for AArch64 is experimental.

+

To test for SSE 4.2, do

+
grep -q sse4_2 /proc/cpuinfo && echo "SSE 4.2 supported" || echo "SSE 4.2 not supported"
+
+ + +

Install Git and CMake

+
sudo apt-get install git cmake
+
+ + +

Or cmake3 instead of cmake on older systems.

+

Detect the number of threads

+
export THREADS=$(grep -c ^processor /proc/cpuinfo)
+
+ + +

Install GCC 7

+

There are several ways to do this.

+

Install from a PPA package

+
sudo apt-get install software-properties-common
+sudo apt-add-repository ppa:ubuntu-toolchain-r/test
+sudo apt-get update
+sudo apt-get install gcc-7 g++-7
+
+ + +

Install from sources

+

Look at [https://github.com/yandex/ClickHouse/blob/master/utils/prepare-environment/install-gcc.sh]

+

Use GCC 7 for builds

+
export CC=gcc-7
+export CXX=g++-7
+
+ + +

Install required libraries from packages

+
sudo apt-get install libicu-dev libreadline-dev libmysqlclient-dev libssl-dev unixodbc-dev ninja-build
+
+ + +

Checkout ClickHouse sources

+

To get the latest stable version:

+
git clone -b stable --recursive git@github.com:yandex/ClickHouse.git
+# or: git clone -b stable --recursive https://github.com/yandex/ClickHouse.git
+
+cd ClickHouse
+
+ + +

For development, switch to the master branch. +For the latest release candidate, switch to the testing branch.

+

Build ClickHouse

+

There are two build variants.

+

Build release package

+

Install prerequisites to build Debian packages.

+
sudo apt-get install devscripts dupload fakeroot debhelper
+
+ + +

Install the most recent version of Clang.

+

Clang is embedded into the ClickHouse package and used at runtime. The minimum version is 5.0. It is optional.

+

To install clang, see utils/prepare-environment/install-clang.sh

+

You may also build ClickHouse with Clang for development purposes. +For production releases, GCC is used.

+

Run the release script:

+
rm -f ../clickhouse*.deb
+./release
+
+ + +

You will find built packages in the parent directory:

+
ls -l ../clickhouse*.deb
+
+ + +

Note that usage of debian packages is not required. +ClickHouse has no runtime dependencies except libc, so it could work on almost any Linux.

+

Installing freshly built packages on a development server:

+
sudo dpkg -i ../clickhouse*.deb
+sudo service clickhouse-server start
+
+ + +

Build to work with code

+
mkdir build
+cd build
+cmake ..
+make -j $THREADS
+cd ..
+
+ + +

To create an executable, run make clickhouse. +This will create the dbms/src/Server/clickhouse executable, which can be used with client or server arguments.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/development/build_osx/index.html b/docs/build/docs/en/development/build_osx/index.html new file mode 100644 index 00000000000..f206e1644ce --- /dev/null +++ b/docs/build/docs/en/development/build_osx/index.html @@ -0,0 +1,3018 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + How to build ClickHouse on Mac OS X - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
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+ + +
+
+ + + + + + + +

How to build ClickHouse on Mac OS X

+

Build should work on Mac OS X 10.12. If you're using earlier version, you can try to build ClickHouse using Gentoo Prefix and clang sl in this instruction. +With appropriate changes, it should also work on any other Linux distribution.

+

Install Homebrew

+
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
+
+ + +

Install required compilers, tools, and libraries

+
brew install cmake gcc icu4c mysql openssl unixodbc libtool gettext zlib readline boost --cc=gcc-7
+
+ + +

Checkout ClickHouse sources

+

To get the latest stable version:

+
git clone -b stable --recursive --depth=10 git@github.com:yandex/ClickHouse.git
+# or: git clone -b stable --recursive --depth=10 https://github.com/yandex/ClickHouse.git
+
+cd ClickHouse
+
+ + +

For development, switch to the master branch. +For the latest release candidate, switch to the testing branch.

+

Build ClickHouse

+
mkdir build
+cd build
+cmake .. -DCMAKE_CXX_COMPILER=`which g++-7` -DCMAKE_C_COMPILER=`which gcc-7`
+make -j `sysctl -n hw.ncpu`
+cd ..
+
+ + +

Caveats

+

If you intend to run clickhouse-server, make sure to increase the system's maxfiles variable. See MacOS.md for more details.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/development/style/index.html b/docs/build/docs/en/development/style/index.html new file mode 100644 index 00000000000..82996b3c415 --- /dev/null +++ b/docs/build/docs/en/development/style/index.html @@ -0,0 +1,3664 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + How to write C++ code - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

How to write C++ code

+

General recommendations

+

1. The following are recommendations, not requirements.

+

2. If you are editing code, it makes sense to follow the formatting of the existing code.

+

3. Code style is needed for consistency. Consistency makes it easier to read the code, and it also makes it easier to search the code.

+

4. Many of the rules do not have logical reasons; they are dictated by established practices.

+

Formatting

+

1. Most of the formatting will be done automatically by clang-format.

+

2. Indents are 4 spaces. Configure your development environment so that a tab adds four spaces.

+

3. A left curly bracket must be separated on a new line. (And the right one, as well.)

+
inline void readBoolText(bool & x, ReadBuffer & buf)
+{
+    char tmp = '0';
+    readChar(tmp, buf);
+    x = tmp != '0';
+}
+
+ + +

4. +But if the entire function body is quite short (a single statement), you can place it entirely on one line if you wish. Place spaces around curly braces (besides the space at the end of the line).

+
inline size_t mask() const                { return buf_size() - 1; }
+inline size_t place(HashValue x) const    { return x & mask(); }
+
+ + +

5. For functions, don't put spaces around brackets.

+
void reinsert(const Value & x)
+memcpy(&buf[place_value], &x, sizeof(x));
+
+ + +

6. When using statements such as if, for, and while (unlike function calls), put a space before the opening bracket.

+

cpp + for (size_t i = 0; i < rows; i += storage.index_granularity)

+

7. Put spaces around binary operators (+, -, *, /, %, ...), as well as the ternary operator ?:.

+
UInt16 year = (s[0] - '0') * 1000 + (s[1] - '0') * 100 + (s[2] - '0') * 10 + (s[3] - '0');
+UInt8 month = (s[5] - '0') * 10 + (s[6] - '0');
+UInt8 day = (s[8] - '0') * 10 + (s[9] - '0');
+
+ + +

8. If a line feed is entered, put the operator on a new line and increase the indent before it.

+
if (elapsed_ns)
+    message << " ("
+         << rows_read_on_server * 1000000000 / elapsed_ns << " rows/s., "
+        << bytes_read_on_server * 1000.0 / elapsed_ns << " MB/s.) ";
+
+ + +

9. You can use spaces for alignment within a line, if desired.

+
dst.ClickLogID         = click.LogID;
+dst.ClickEventID       = click.EventID;
+dst.ClickGoodEvent     = click.GoodEvent;
+
+ + +

10. Don't use spaces around the operators ., -> .

+

If necessary, the operator can be wrapped to the next line. In this case, the offset in front of it is increased.

+

11. Do not use a space to separate unary operators (-, +, *, &, ...) from the argument.

+

12. Put a space after a comma, but not before it. The same rule goes for a semicolon inside a for expression.

+

13. Do not use spaces to separate the [] operator.

+

14. In a template <...> expression, use a space between template and <. No spaces after < or before >.

+
template <typename TKey, typename TValue>
+struct AggregatedStatElement
+{}
+
+ + +

15. In classes and structures, public, private, and protected are written on the same level as the class/struct, but all other internal elements should be deeper.

+
template <typename T>
+class MultiVersion
+{
+public:
+    /// Version of object for usage. shared_ptr manage lifetime of version.
+    using Version = std::shared_ptr<const T>;
+    ...
+}
+
+ + +

16. If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace.

+

17. If the block for if, for, while... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets.

+
/// Finish write.
+for (auto & stream : streams)
+    stream.second->finalize();
+
+ + +

18. There should be any spaces at the ends of lines.

+

19. Sources are UTF-8 encoded.

+

20. Non-ASCII characters can be used in string literals.

+
<< ", " << (timer.elapsed() / chunks_stats.hits) << " μsec/hit.";
+
+ + +

21. Do not write multiple expressions in a single line.

+

22. Group sections of code inside functions and separate them with no more than one empty line.

+

23. Separate functions, classes, and so on with one or two empty lines.

+

24. A const (related to a value) must be written before the type name.

+
//correct
+const char * pos
+const std::string & s
+//incorrect
+char const * pos
+
+ + +

25. When declaring a pointer or reference, the * and & symbols should be separated by spaces on both sides.

+
//correct
+const char * pos
+//incorrect
+const char* pos
+const char *pos
+
+ + +

26. When using template types, alias them with the using keyword (except in the simplest cases).

+

In other words, the template parameters are specified only in using and aren't repeated in the code.

+

using can be declared locally, such as inside a function.

+
//correct
+using FileStreams = std::map<std::string, std::shared_ptr<Stream>>;
+FileStreams streams;
+//incorrect
+std::map<std::string, std::shared_ptr<Stream>> streams;
+
+ + +

27. Do not declare several variables of different types in one statement.

+
//incorrect
+int x, *y;
+
+ + +

28. Do not use C-style casts.

+
//incorrect
+std::cerr << (int)c <<; std::endl;
+//correct
+std::cerr << static_cast<int>(c) << std::endl;
+
+ + +

29. In classes and structs, group members and functions separately inside each visibility scope.

+

30. For small classes and structs, it is not necessary to separate the method declaration from the implementation.

+

The same is true for small methods in any classes or structs.

+

For templated classes and structs, don't separate the method declarations from the implementation (because otherwise they must be defined in the same translation unit).

+

31. You can wrap lines at 140 characters, instead of 80.

+

32. Always use the prefix increment/decrement operators if postfix is not required.

+
for (Names::const_iterator it = column_names.begin(); it != column_names.end(); ++it)
+
+ + +

Comments

+

1. Be sure to add comments for all non-trivial parts of code.

+

This is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong.

+
/** Part of piece of memory, that can be used.
+  * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading,
+  * then working_buffer will have size of only 10 bytes
+  * (working_buffer.end() will point to the position right after those 10 bytes available for read).
+*/
+
+ + +

2. Comments can be as detailed as necessary.

+

3. Place comments before the code they describe. In rare cases, comments can come after the code, on the same line.

+
/** Parses and executes the query.
+*/
+void executeQuery(
+    ReadBuffer & istr, /// Where to read the query from (and data for INSERT, if applicable)
+    WriteBuffer & ostr, /// Where to write the result
+    Context & context, /// DB, tables, data types, engines, functions, aggregate functions...
+    BlockInputStreamPtr & query_plan, /// A description of query processing can be included here
+    QueryProcessingStage::Enum stage = QueryProcessingStage::Complete /// The last stage to process the SELECT query to
+    )
+
+ + +

4. Comments should be written in English only.

+

5. If you are writing a library, include detailed comments explaining it in the main header file.

+

6. Do not add comments that do not provide additional information. In particular, do not leave empty comments like this:

+
/*
+* Procedure Name:
+* Original procedure name:
+* Author:
+* Date of creation:
+* Dates of modification:
+* Modification authors:
+* Original file name:
+* Purpose:
+* Intent:
+* Designation:
+* Classes used:
+* Constants:
+* Local variables:
+* Parameters:
+* Date of creation:
+* Purpose:
+*/
+
+ + +

The example is borrowed from http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/.

+

7. Do not write garbage comments (author, creation date ..) at the beginning of each file.

+

8. Single-line comments begin with three slashes: /// and multi-line comments begin with /**. These comments are considered "documentation".

+

Note: You can use Doxygen to generate documentation from these comments. But Doxygen is not generally used because it is more convenient to navigate the code in the IDE.

+

9. Multi-line comments must not have empty lines at the beginning and end (except the line that closes a multi-line comment).

+

10. For commenting out code, use basic comments, not "documenting" comments.

+

11. Delete the commented out parts of the code before commiting.

+

12. Do not use profanity in comments or code.

+

13. Do not use uppercase letters. Do not use excessive punctuation.

+
/// WHAT THE FAIL???
+
+ + +

14. Do not make delimeters from comments.

+
///******************************************************
+
+ + +

15. Do not start discussions in comments.

+
/// Why did you do this stuff?
+
+ + +

16. There's no need to write a comment at the end of a block describing what it was about.

+
/// for
+
+ + +

Names

+

1. The names of variables and class members use lowercase letters with underscores.

+
size_t max_block_size;
+
+ + +

2. The names of functions (methods) use camelCase beginning with a lowercase letter.

+
std::string getName() const override { return "Memory"; }
+
+ + +

3. The names of classes (structures) use CamelCase beginning with an uppercase letter. Prefixes other than I are not used for interfaces.

+
class StorageMemory : public IStorage
+
+ + +

4. The names of usings follow the same rules as classes, or you can add _t at the end.

+

5. Names of template type arguments for simple cases: T; T, U; T1, T2.

+

For more complex cases, either follow the rules for class names, or add the prefix T.

+
template <typename TKey, typename TValue>
+struct AggregatedStatElement
+
+ + +

6. Names of template constant arguments: either follow the rules for variable names, or use N in simple cases.

+
template <bool without_www>
+struct ExtractDomain
+
+ + +

7. For abstract classes (interfaces) you can add the I prefix.

+
class IBlockInputStream
+
+ + +

8. If you use a variable locally, you can use the short name.

+

In other cases, use a descriptive name that conveys the meaning.

+
bool info_successfully_loaded = false;
+
+ + +

9. define‘s should be in ALL_CAPS with underscores. The same is true for global constants.

+
#define MAX_SRC_TABLE_NAMES_TO_STORE 1000
+
+ + +

10. File names should use the same style as their contents.

+

If a file contains a single class, name the file the same way as the class, in CamelCase.

+

If the file contains a single function, name the file the same way as the function, in camelCase.

+

11. If the name contains an abbreviation, then:

+
    +
  • For variable names, the abbreviation should use lowercase letters mysql_connection (not mySQL_connection).
    • +
  • For names of classes and functions, keep the uppercase letters in the abbreviation MySQLConnection (not MySqlConnection).
    • +
+

12. Constructor arguments that are used just to initialize the class members should be named the same way as the class members, but with an underscore at the end.

+
FileQueueProcessor(
+    const std::string & path_,
+    const std::string & prefix_,
+    std::shared_ptr<FileHandler> handler_)
+    : path(path_),
+    prefix(prefix_),
+    handler(handler_),
+    log(&Logger::get("FileQueueProcessor"))
+{
+}
+
+ + +

The underscore suffix can be omitted if the argument is not used in the constructor body.

+

13. There is no difference in the names of local variables and class members (no prefixes required).

+
timer (not m_timer)
+
+ + +

14. Constants in enums use CamelCase beginning with an uppercase letter. ALL_CAPS is also allowed. If the enum is not local, use enum class.

+
enum class CompressionMethod
+{
+    QuickLZ = 0,
+    LZ4     = 1,
+};
+
+ + +

15. All names must be in English. Transliteration of Russian words is not allowed.

+
not Stroka
+
+ + +

16. Abbreviations are acceptable if they are well known (when you can easily find the meaning of the abbreviation in Wikipedia or in a search engine).

+
`AST`, `SQL`.
+
+Not `NVDH` (some random letters)
+
+ + +

Incomplete words are acceptable if the shortened version is common use.

+

You can also use an abbreviation if the full name is included next to it in the comments.

+

17. File names with C++ source code must have the .cpp extension. Header files must have the .h extension.

+

How to write code

+

1. Memory management.

+

Manual memory deallocation (delete) can only be used in library code.

+

In library code, the delete operator can only be used in destructors.

+

In application code, memory must be freed by the object that owns it.

+

Examples:

+
    +
  • The easiest way is to place an object on the stack, or make it a member of another class.
    • +
  • For a large number of small objects, use containers.
    • +
  • For automatic deallocation of a small number of objects that reside in the heap, use shared_ptr/unique_ptr.
    • +
+

2. Resource management.

+

Use RAII and see the previous point.

+

3. Error handling.

+

Use exceptions. In most cases, you only need to throw an exception, and don't need to catch it (because of RAII).

+

In offline data processing applications, it's often acceptable to not catch exceptions.

+

In servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler.

+
/// If there were no other calculations yet, do it synchronously
+if (!started)
+{
+    calculate();
+    started = true;
+}
+else    /// If the calculations are already in progress, wait for results
+    pool.wait();
+
+if (exception)
+    exception->rethrow();
+
+ + +

Never hide exceptions without handling. Never just blindly put all exceptions to log.

+

Not catch (...) {}.

+

If you need to ignore some exceptions, do so only for specific ones and rethrow the rest.

+
catch (const DB::Exception & e)
+{
+    if (e.code() == ErrorCodes::UNKNOWN_AGGREGATE_FUNCTION)
+        return nullptr;
+    else
+        throw;
+}
+
+ + +

When using functions with response codes or errno, always check the result and throw an exception in case of error.

+
if (0 != close(fd))
+    throwFromErrno("Cannot close file " + file_name, ErrorCodes::CANNOT_CLOSE_FILE);
+
+ + +

Asserts are not used.

+

4. Exception types.

+

There is no need to use complex exception hierarchy in application code. The exception text should be understandable to a system administrator.

+

5. Throwing exceptions from destructors.

+

This is not recommended, but it is allowed.

+

Use the following options:

+
    +
  • Create a (done() or finalize()) function that will do all the work in advance that might lead to an exception. If that function was called, there should be no exceptions in the destructor later.
    • +
  • Tasks that are too complex (such as sending messages over the network) can be put in separate method that the class user will have to call before destruction.
    • +
  • If there is an exception in the destructor, it’s better to log it than to hide it (if the logger is available).
    • +
  • In simple applications, it is acceptable to rely on std::terminate (for cases of noexcept by default in C++11) to handle exceptions.
    • +
+

6. Anonymous code blocks.

+

You can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block.

+
Block block = data.in->read();
+
+{
+    std::lock_guard<std::mutex> lock(mutex);
+    data.ready = true;
+    data.block = block;
+}
+
+ready_any.set();
+
+ + +

7. Multithreading.

+

For offline data processing applications:

+
    +
  • Try to get the best possible performance on a single CPU core. You can then parallelize your code if necessary.
    • +
+

In server applications:

+
    +
  • Use the thread pool to process requests. At this point, we haven't had any tasks that required userspace context switching.
    • +
+

Fork is not used for parallelization.

+

8. Synchronizing threads.

+

Often it is possible to make different threads use different memory cells (even better: different cache lines,) and to not use any thread synchronization (except joinAll).

+

If synchronization is required, in most cases, it is sufficient to use mutex under lock_guard.

+

In other cases use system synchronization primitives. Do not use busy wait.

+

Atomic operations should be used only in the simplest cases.

+

Do not try to implement lock-free data structures unless it is your primary area of expertise.

+

9. Pointers vs references.

+

In most cases, prefer references.

+

10. const.

+

Use constant references, pointers to constants, const_iterator, const methods.

+

Consider const to be default and use non-const only when necessary.

+

When passing variable by value, using const usually does not make sense.

+

11. unsigned.

+

Use unsigned, if needed.

+

12. Numeric types

+

Use UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, and size_t, ssize_t, ptrdiff_t.

+

Don't use signed/unsigned long, long long, short, signed char, unsigned char, or char types for numbers.

+

13. Passing arguments.

+

Pass complex values by reference (including std::string).

+

If a function captures ownership of an objected created in the heap, make the argument type shared_ptr or unique_ptr.

+

14. Returning values.

+

In most cases, just use return. Do not write [return std::move(res)]{.strike}.

+

If the function allocates an object on heap and returns it, use shared_ptr or unique_ptr.

+

In rare cases you might need to return the value via an argument. In this case, the argument should be a reference.

+
using AggregateFunctionPtr = std::shared_ptr<IAggregateFunction>;
+
+/** Creates an aggregate function by name.
+ */
+class AggregateFunctionFactory
+{
+public:
+   AggregateFunctionFactory();
+   AggregateFunctionPtr get(const String & name, const DataTypes & argument_types) const;
+
+ + +

15. namespace.

+

There is no need to use a separate namespace for application code or small libraries.

+

or small libraries.

+

For medium to large libraries, put everything in the namespace.

+

You can use the additional detail namespace in a library's .h file to hide implementation details.

+

In a .cpp file, you can use the static or anonymous namespace to hide symbols.

+

You can also use namespace for enums to prevent its names from polluting the outer namespace, but it’s better to use the enum class.

+

16. Delayed initialization.

+

If arguments are required for initialization then do not write a default constructor.

+

If later you’ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use shared_ptr/unique_ptr.

+
Loader(DB::Connection * connection_, const std::string & query, size_t max_block_size_);
+
+/// For delayed initialization
+Loader() {}
+
+ + +

17. Virtual functions.

+

If the class is not intended for polymorphic use, you do not need to make functions virtual. This also applies to the destructor.

+

18. Encodings.

+

Use UTF-8 everywhere. Use std::stringandchar *. Do not use std::wstringandwchar_t.

+

19. Logging.

+

See the examples everywhere in the code.

+

Before committing, delete all meaningless and debug logging, and any other types of debug output.

+

Logging in cycles should be avoided, even on the Trace level.

+

Logs must be readable at any logging level.

+

Logging should only be used in application code, for the most part.

+

Log messages must be written in English.

+

The log should preferably be understandable for the system administrator.

+

Do not use profanity in the log.

+

Use UTF-8 encoding in the log. In rare cases you can use non-ASCII characters in the log.

+

20. I/O.

+

Don't use iostreams in internal cycles that are critical for application performance (and never use stringstream).

+

Use the DB/IO library instead.

+

21. Date and time.

+

See the DateLUT library.

+

22. include.

+

Always use #pragma once instead of include guards.

+

23. using.

+

The using namespace is not used.

+

It's fine if you are 'using' something specific, but make it local inside a class or function.

+

24. Do not use trailing return type for functions unless necessary.

+
[auto f() -&gt; void;]{.strike}
+
+ + +

25. Do not declare and init variables like this:

+
auto s = std::string{"Hello"};
+
+ + +

Do it like this:

+
std::string s = "Hello";
+std::string s{"Hello"};
+
+ + +

26. For virtual functions, write virtual in the base class, but write override in descendent classes.

+

Unused features of C++

+

1. Virtual inheritance is not used.

+

2. Exception specifiers from C++03 are not used.

+

3. Function try block is not used, except for the main function in tests.

+

Platform

+

1. We write code for a specific platform.

+

But other things being equal, cross-platform or portable code is preferred.

+

2. The language is C++17.

+

3. The compiler is gcc. At this time (December 2017), the code is compiled using version 7.2. (It can also be compiled using clang 5.)

+

The standard library is used (implementation of libstdc++ or libc++).

+

4. OS: Linux Ubuntu, not older than Precise.

+

5. Code is written for x86_64 CPU architecture.

+

The CPU instruction set is the minimum supported set among our servers. Currently, it is SSE 4.2.

+

6. Use -Wall -Wextra -Werror compilation flags.

+

7. Use static linking with all libraries except those that are difficult to connect to statically (see the output of the ldd command).

+

8. Code is developed and debugged with release settings.

+

Tools

+

1. KDevelop is a good IDE.

+

2. For debugging, use gdb, valgrind (memcheck), strace, -fsanitize=, ..., tcmalloc_minimal_debug.

+

3. For profiling, use Linux Perf valgrind (callgrind), strace-cf.

+

4. Sources are in Git.

+

5. Compilation is managed by CMake.

+

6. Releases are in deb packages.

+

7. Commits to master must not break the build.

+

Though only selected revisions are considered workable.

+

8. Make commits as often as possible, even if the code is only partially ready.

+

Use branches for this purpose.

+

If your code is not buildable yet, exclude it from the build before pushing to master. You'll need to finish it or remove it from master within a few days.

+

9. For non-trivial changes, used branches and publish them on the server.

+

10. Unused code is removed from the repository.

+

Libraries

+

1. The C++14 standard library is used (experimental extensions are fine), as well as boost and Poco frameworks.

+

2. If necessary, you can use any well-known libraries available in the OS package.

+

If there is a good solution already available, then use it, even if it means you have to install another library.

+

(But be prepared to remove bad libraries from code.)

+

3. You can install a library that isn't in the packages, if the packages don't have what you need or have an outdated version or the wrong type of compilation.

+

4. If the library is small and doesn't have its own complex build system, put the source files in the contrib folder.

+

5. Preference is always given to libraries that are already used.

+

General recommendations

+

1. Write as little code as possible.

+

2. Try the simplest solution.

+

3. Don't write code until you know how it's going to work and how the inner loop will function.

+

4. In the simplest cases, use 'using' instead of classes or structs.

+

5. If possible, do not write copy constructors, assignment operators, destructors (other than a virtual one, if the class contains at least one virtual function), mpve-constructors and move assignment operators. In other words, the compiler-generated functions must work correctly. You can use 'default'.

+

6. Code simplification is encouraged. Reduce the size of your code where possible.

+

Additional recommendations

+

1. Explicit std:: for types from stddef.h is not recommended.

+

We recommend writing size_t instead std::size_t because it's shorter.

+

But if you prefer, std:: is acceptable.

+

2. Explicit std:: for functions from the standard C library is not recommended.

+

Write memcpy instead of std::memcpy.

+

The reason is that there are similar non-standard functions, such as memmem. We do use these functions on occasion. These functions do not exist in namespace std.

+

If you write std::memcpy instead of memcpy everywhere, then memmem without std:: will look awkward.

+

Nevertheless, std:: is allowed if you prefer it.

+

3. Using functions from C when the ones are available in the standard C++ library.

+

This is acceptable if it is more efficient.

+

For example, use memcpy instead of std::copy for copying large chunks of memory.

+

4. Multiline function arguments.

+

Any of the following wrapping styles are allowed:

+
function(
+    T1 x1,
+    T2 x2)
+
+ + +
function(
+    size_t left, size_t right,
+    const & RangesInDataParts ranges,
+    size_t limit)
+
+ + +
function(size_t left, size_t right,
+    const & RangesInDataParts ranges,
+    size_t limit)
+
+ + +
function(size_t left, size_t right,
+        const & RangesInDataParts ranges,
+        size_t limit)
+
+ + +
function(
+        size_t left,
+        size_t right,
+        const & RangesInDataParts ranges,
+        size_t limit)
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/development/tests/index.html b/docs/build/docs/en/development/tests/index.html new file mode 100644 index 00000000000..b161a82c24f --- /dev/null +++ b/docs/build/docs/en/development/tests/index.html @@ -0,0 +1,2951 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + How to run ClickHouse tests - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

How to run ClickHouse tests

+

The clickhouse-test utility that is used for functional testing is written using Python 2.x.It also requires you to have some third-party packages:

+
$ pip install lxml termcolor
+
+ + +

In a nutshell:

+
    +
  • Put the clickhouse program to /usr/bin (or PATH)
    • +
  • Create a clickhouse-client symlink in /usr/bin pointing to clickhouse
    • +
  • Start the clickhouse server
    • +
  • cd dbms/tests/
    • +
  • Run ./clickhouse-test
    • +
+

Example usage

+

Run ./clickhouse-test --help to see available options.

+

To run tests without having to create a symlink or mess with PATH:

+
./clickhouse-test -c "../../build/dbms/src/Server/clickhouse --client"
+
+ + +

To run a single test, i.e. 00395_nullable:

+
./clickhouse-test 00395
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/dicts/external_dicts/index.html b/docs/build/docs/en/dicts/external_dicts/index.html new file mode 100644 index 00000000000..95a082ea7cc --- /dev/null +++ b/docs/build/docs/en/dicts/external_dicts/index.html @@ -0,0 +1,2928 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + General desription - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

+

External dictionaries

+

You can add your own dictionaries from various data sources. The data source for a dictionary can be a local text or executable file, an HTTP(s) resource, or another DBMS. For more information, see "Sources for external dictionaries".

+

ClickHouse:

+
+
    +
  • Fully or partially stores dictionaries in RAM.
    • +
  • Periodically updates dictionaries and dynamically loads missing values. In other words, dictionaries can be loaded dynamically.
    • +
+
+

The configuration of external dictionaries is located in one or more files. The path to the configuration is specified in the dictionaries_config parameter.

+

Dictionaries can be loaded at server startup or at first use, depending on the dictionaries_lazy_load setting.

+

The dictionary config file has the following format:

+
<yandex>
+    <comment>An optional element with any content. Ignored by the ClickHouse server.</comment>
+
+    <!--Optional element. File name with substitutions-->
+    <include_from>/etc/metrika.xml</include_from>
+
+
+    <dictionary>
+        <!-- Dictionary configuration -->
+    </dictionary>
+
+    ...
+
+    <dictionary>
+        <!-- Dictionary configuration -->
+    </dictionary>
+</yandex>
+
+ + +

You can configure any number of dictionaries in the same file. The file format is preserved even if there is only one dictionary (i.e. <yandex><dictionary> <!--configuration -> </dictionary></yandex> ).

+

See also "Functions for working with external dictionaries".

+
+ +You can convert values ​​for a small dictionary by describing it in a `SELECT` query (see the [transform](../functions/other_functions.md#other_functions-transform) function). This functionality is not related to external dictionaries. + +
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/dicts/external_dicts_dict/index.html b/docs/build/docs/en/dicts/external_dicts_dict/index.html new file mode 100644 index 00000000000..3fd94aeaece --- /dev/null +++ b/docs/build/docs/en/dicts/external_dicts_dict/index.html @@ -0,0 +1,2920 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Configuring an external dictionary - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

+

Configuring an external dictionary

+

The dictionary configuration has the following structure:

+
<dictionary>
+    <name>dict_name</name>
+
+    <source>
+      <!-- Source configuration -->
+    </source>
+
+    <layout>
+      <!-- Memory layout configuration -->
+    </layout>
+
+    <structure>
+      <!-- Complex key configuration -->
+    </structure>
+
+    <lifetime>
+      <!-- Lifetime of dictionary in memory -->
+    </lifetime>
+</dictionary>
+
+ + +
    +
  • name – The identifier that can be used to access the dictionary. Use the characters [a-zA-Z0-9_\-].
    • +
  • source — Source of the dictionary.
    • +
  • layout — Dictionary layout in memory.
    • +
  • structure — Structure of the dictionary . A key and attributes that can be retrieved by this key.
    • +
  • lifetime — Frequency of dictionary updates.
    • +
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/dicts/external_dicts_dict_layout/index.html b/docs/build/docs/en/dicts/external_dicts_dict_layout/index.html new file mode 100644 index 00000000000..0c9312a97d9 --- /dev/null +++ b/docs/build/docs/en/dicts/external_dicts_dict_layout/index.html @@ -0,0 +1,3280 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Storing dictionaries in memory - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

+

Storing dictionaries in memory

+

There are a variety of ways to store dictionaries in memory.

+

We recommend flat, hashedandcomplex_key_hashed. which provide optimal processing speed.

+

Caching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section "cache".

+

There are several ways to improve dictionary performance:

+
    +
  • Call the function for working with the dictionary after GROUP BY.
    • +
  • Mark attributes to extract as injective. An attribute is called injective if different attribute values correspond to different keys. So when GROUP BY uses a function that fetches an attribute value by the key, this function is automatically taken out of GROUP BY.
    • +
+

ClickHouse generates an exception for errors with dictionaries. Examples of errors:

+
    +
  • The dictionary being accessed could not be loaded.
    • +
  • Error querying a cached dictionary.
    • +
+

You can view the list of external dictionaries and their statuses in the system.dictionaries table.

+

The configuration looks like this:

+
<yandex>
+    <dictionary>
+        ...
+        <layout>
+            <layout_type>
+                <!-- layout settings -->
+            </layout_type>
+        </layout>
+        ...
+    </dictionary>
+</yandex>
+
+ + +

+

Ways to store dictionaries in memory

+ +

+

flat

+

The dictionary is completely stored in memory in the form of flat arrays. How much memory does the dictionary use? The amount is proportional to the size of the largest key (in space used).

+

The dictionary key has the UInt64 type and the value is limited to 500,000. If a larger key is discovered when creating the dictionary, ClickHouse throws an exception and does not create the dictionary.

+

All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.

+

This method provides the best performance among all available methods of storing the dictionary.

+

Configuration example:

+
<layout>
+  <flat />
+</layout>
+
+ + +

+

hashed

+

The dictionary is completely stored in memory in the form of a hash table. The dictionary can contain any number of elements with any identifiers In practice, the number of keys can reach tens of millions of items.

+

All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.

+

Configuration example:

+
<layout>
+  <hashed />
+</layout>
+
+ + +

+

complex_key_hashed

+

This type of storage is for use with composite keys. Similar to hashed.

+

Configuration example:

+
<layout>
+  <complex_key_hashed />
+</layout>
+
+ + +

+

range_hashed

+

The dictionary is stored in memory in the form of a hash table with an ordered array of ranges and their corresponding values.

+

This storage method works the same way as hashed and allows using date/time ranges in addition to the key, if they appear in the dictionary.

+

Example: The table contains discounts for each advertiser in the format:

+
+---------------+---------------------+-------------------+--------+
+| advertiser id | discount start date | discount end date | amount |
++===============+=====================+===================+========+
+| 123           | 2015-01-01          | 2015-01-15        | 0.15   |
++---------------+---------------------+-------------------+--------+
+| 123           | 2015-01-16          | 2015-01-31        | 0.25   |
++---------------+---------------------+-------------------+--------+
+| 456           | 2015-01-01          | 2015-01-15        | 0.05   |
++---------------+---------------------+-------------------+--------+
+
+ + +

To use a sample for date ranges, define the range_min and range_max elements in the structure.

+

Example:

+
<structure>
+    <id>
+        <name>Id</name>
+    </id>
+    <range_min>
+        <name>first</name>
+    </range_min>
+    <range_max>
+        <name>last</name>
+    </range_max>
+    ...
+
+ + +

To work with these dictionaries, you need to pass an additional date argument to the dictGetT function:

+
dictGetT('dict_name', 'attr_name', id, date)
+
+ + +

This function returns the value for the specified ids and the date range that includes the passed date.

+

Details of the algorithm:

+
    +
  • If the id is not found or a range is not found for the id, it returns the default value for the dictionary.
    • +
  • If there are overlapping ranges, you can use any.
    • +
  • If the range delimiter is NULL or an invalid date (such as 1900-01-01 or 2039-01-01), the range is left open. The range can be open on both sides.
    • +
+

Configuration example:

+
<yandex>
+        <dictionary>
+
+                ...
+
+                <layout>
+                        <range_hashed />
+                </layout>
+
+                <structure>
+                        <id>
+                                <name>Abcdef</name>
+                        </id>
+                        <range_min>
+                                <name>StartDate</name>
+                        </range_min>
+                        <range_max>
+                                <name>EndDate</name>
+                        </range_max>
+                        <attribute>
+                                <name>XXXType</name>
+                                <type>String</type>
+                                <null_value />
+                        </attribute>
+                </structure>
+
+        </dictionary>
+</yandex>
+
+ + +

+

cache

+

The dictionary is stored in a cache that has a fixed number of cells. These cells contain frequently used elements.

+

When searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using SELECT attrs... FROM db.table WHERE id IN (k1, k2, ...). The received data is then written to the cache.

+

For cache dictionaries, the expiration lifetime of data in the cache can be set. If more time than lifetime has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used.

+

This is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the system.dictionaries table.

+

To improve cache performance, use a subquery with LIMIT, and call the function with the dictionary externally.

+

Supported sources: MySQL, ClickHouse, executable, HTTP.

+

Example of settings:

+
<layout>
+    <cache>
+        <!-- The size of the cache, in number of cells. Rounded up to a power of two. -->
+        <size_in_cells>1000000000</size_in_cells>
+    </cache>
+</layout>
+
+ + +

Set a large enough cache size. You need to experiment to select the number of cells:

+
    +
  1. Set some value.
    2. +
  2. Run queries until the cache is completely full.
    3. +
  3. Assess memory consumption using the system.dictionaries table.
    4. +
  4. Increase or decrease the number of cells until the required memory consumption is reached.
    5. +
+
+ +Do not use ClickHouse as a source, because it is slow to process queries with random reads. + +
+ +

+

complex_key_cache

+

This type of storage is for use with composite keys. Similar to cache.

+

+

ip_trie

+

This type of storage is for mapping network prefixes (IP addresses) to metadata such as ASN.

+

Example: The table contains network prefixes and their corresponding AS number and country code:

+
  +-----------------+-------+--------+
+  | prefix          | asn   | cca2   |
+  +=================+=======+========+
+  | 202.79.32.0/20  | 17501 | NP     |
+  +-----------------+-------+--------+
+  | 2620:0:870::/48 | 3856  | US     |
+  +-----------------+-------+--------+
+  | 2a02:6b8:1::/48 | 13238 | RU     |
+  +-----------------+-------+--------+
+  | 2001:db8::/32   | 65536 | ZZ     |
+  +-----------------+-------+--------+
+
+ + +

When using this type of layout, the structure must have a composite key.

+

Example:

+
<structure>
+    <key>
+        <attribute>
+            <name>prefix</name>
+            <type>String</type>
+        </attribute>
+    </key>
+    <attribute>
+            <name>asn</name>
+            <type>UInt32</type>
+            <null_value />
+    </attribute>
+    <attribute>
+            <name>cca2</name>
+            <type>String</type>
+            <null_value>??</null_value>
+    </attribute>
+    ...
+
+ + +

The key must have only one String type attribute that contains an allowed IP prefix. Other types are not supported yet.

+

For queries, you must use the same functions (dictGetT with a tuple) as for dictionaries with composite keys:

+
dictGetT('dict_name', 'attr_name', tuple(ip))
+
+ + +

The function takes either UInt32 for IPv4, or FixedString(16) for IPv6:

+
dictGetString('prefix', 'asn', tuple(IPv6StringToNum('2001:db8::1')))
+
+ + +

Other types are not supported yet. The function returns the attribute for the prefix that corresponds to this IP address. If there are overlapping prefixes, the most specific one is returned.

+

Data is stored in a trie. It must completely fit into RAM.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/dicts/external_dicts_dict_lifetime/index.html b/docs/build/docs/en/dicts/external_dicts_dict_lifetime/index.html new file mode 100644 index 00000000000..d6a482738aa --- /dev/null +++ b/docs/build/docs/en/dicts/external_dicts_dict_lifetime/index.html @@ -0,0 +1,2940 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Dictionary updates - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

+

Dictionary updates

+

ClickHouse periodically updates the dictionaries. The update interval for fully downloaded dictionaries and the invalidation interval for cached dictionaries are defined in the <lifetime> tag in seconds.

+

Dictionary updates (other than loading for first use) do not block queries. During updates, the old version of a dictionary is used. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.

+

Example of settings:

+
<dictionary>
+    ...
+    <lifetime>300</lifetime>
+    ...
+</dictionary>
+
+ + +

Setting <lifetime> 0</lifetime> prevents updating dictionaries.

+

You can set a time interval for upgrades, and ClickHouse will choose a uniformly random time within this range. This is necessary in order to distribute the load on the dictionary source when upgrading on a large number of servers.

+

Example of settings:

+
<dictionary>
+    ...
+    <lifetime>
+        <min>300</min>
+        <max>360</max>
+    </lifetime>
+    ...
+</dictionary>
+
+ + +

When upgrading the dictionaries, the ClickHouse server applies different logic depending on the type of source:

+
+
    +
  • For a text file, it checks the time of modification. If the time differs from the previously recorded time, the dictionary is updated.
    • +
  • For MyISAM tables, the time of modification is checked using a SHOW TABLE STATUS query.
    • +
  • Dictionaries from other sources are updated every time by default.
    • +
+
+

For MySQL (InnoDB) and ODBC sources, you can set up a query that will update the dictionaries only if they really changed, rather than each time. To do this, follow these steps:

+
+
    +
  • The dictionary table must have a field that always changes when the source data is updated.
    • +
  • The settings of the source must specify a query that retrieves the changing field. The ClickHouse server interprets the query result as a row, and if this row has changed relative to its previous state, the dictionary is updated. Specify the query in the <invalidate_query> field in the settings for the source.
    • +
+
+

Example of settings:

+
<dictionary>
+    ...
+    <odbc>
+      ...
+      <invalidate_query>SELECT update_time FROM dictionary_source where id = 1</invalidate_query>
+    </odbc>
+    ...
+</dictionary>
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/dicts/external_dicts_dict_sources/index.html b/docs/build/docs/en/dicts/external_dicts_dict_sources/index.html new file mode 100644 index 00000000000..e8b95de0985 --- /dev/null +++ b/docs/build/docs/en/dicts/external_dicts_dict_sources/index.html @@ -0,0 +1,3446 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Sources of external dictionaries - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

+

Sources of external dictionaries

+

An external dictionary can be connected from many different sources.

+

The configuration looks like this:

+
<yandex>
+  <dictionary>
+    ...
+    <source>
+      <source_type>
+        <!-- Source configuration -->
+      </source_type>
+    </source>
+    ...
+  </dictionary>
+  ...
+</yandex>
+
+ + +

The source is configured in the source section.

+

Types of sources (source_type):

+ +

+

Local file

+

Example of settings:

+
<source>
+  <file>
+    <path>/opt/dictionaries/os.tsv</path>
+    <format>TabSeparated</format>
+  </file>
+</source>
+
+ + +

Setting fields:

+
    +
  • path – The absolute path to the file.
    • +
  • format – The file format. All the formats described in "Formats" are supported.
    • +
+

+

Executable file

+

Working with executable files depends on how the dictionary is stored in memory. If the dictionary is stored using cache and complex_key_cache, ClickHouse requests the necessary keys by sending a request to the executable file's STDIN.

+

Example of settings:

+
<source>
+    <executable>
+        <command>cat /opt/dictionaries/os.tsv</command>
+        <format>TabSeparated</format>
+    </executable>
+</source>
+
+ + +

Setting fields:

+
    +
  • command – The absolute path to the executable file, or the file name (if the program directory is written to PATH).
    • +
  • format – The file format. All the formats described in "Formats" are supported.
    • +
+

+

HTTP(s)

+

Working with an HTTP(s) server depends on how the dictionary is stored in memory. If the dictionary is stored using cache and complex_key_cache, ClickHouse requests the necessary keys by sending a request via the POST method.

+

Example of settings:

+
<source>
+    <http>
+        <url>http://[::1]/os.tsv</url>
+        <format>TabSeparated</format>
+    </http>
+</source>
+
+ + +

In order for ClickHouse to access an HTTPS resource, you must configure openSSL in the server configuration.

+

Setting fields:

+
    +
  • url – The source URL.
    • +
  • format – The file format. All the formats described in "Formats" are supported.
    • +
+

+

ODBC

+

You can use this method to connect any database that has an ODBC driver.

+

Example of settings:

+
<odbc>
+    <db>DatabaseName</db>
+    <table>TableName</table>
+    <connection_string>DSN=some_parameters</connection_string>
+    <invalidate_query>SQL_QUERY</invalidate_query>
+</odbc>
+
+ + +

Setting fields:

+
    +
  • db – Name of the database. Omit it if the database name is set in the <connection_string> parameters.
    • +
  • table – Name of the table.
    • +
  • connection_string – Connection string.
    • +
  • invalidate_query – Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries.
    • +
+

Example of connecting PostgreSQL

+

Ubuntu OS.

+

Installing unixODBC and the ODBC driver for PostgreSQL:

+
sudo apt-get install -y unixodbc odbcinst odbc-postgresql
+
+ + +

Configuring /etc/odbc.ini (or ~/.odbc.ini):

+
    [DEFAULT]
+    Driver = myconnection
+
+    [myconnection]
+    Description         = PostgreSQL connection to my_db
+    Driver              = PostgreSQL Unicode
+    Database            = my_db
+    Servername          = 127.0.0.1
+    UserName            = username
+    Password            = password
+    Port                = 5432
+    Protocol            = 9.3
+    ReadOnly            = No
+    RowVersioning       = No
+    ShowSystemTables    = No
+    ConnSettings        =
+
+ + +

The dictionary configuration in ClickHouse:

+
<dictionary>
+    <name>table_name</name>
+    <source>
+    <odbc>
+        <!-- You can specifiy the following parameters in connection_string: -->
+        <!-- DSN=myconnection;UID=username;PWD=password;HOST=127.0.0.1;PORT=5432;DATABASE=my_db -->
+            <connection_string>DSN=myconnection</connection_string>
+            <table>postgresql_table</table>
+        </odbc>
+    </source>
+    <lifetime>
+        <min>300</min>
+        <max>360</max>
+    </lifetime>
+    <layout>
+        <hashed/>
+    </layout>
+    <structure>
+        <id>
+            <name>id</name>
+        </id>
+        <attribute>
+            <name>some_column</name>
+            <type>UInt64</type>
+            <null_value>0</null_value>
+        </attribute>
+    </structure>
+</dictionary>
+
+ + +

You may need to edit odbc.ini to specify the full path to the library with the driver DRIVER=/usr/local/lib/psqlodbcw.so.

+

Example of connecting MS SQL Server

+

Ubuntu OS.

+

Installing the driver: :

+
    sudo apt-get install tdsodbc freetds-bin sqsh
+
+ + +

Configuring the driver: :

+
    $ cat /etc/freetds/freetds.conf 
+    ...
+
+    [MSSQL]
+    host = 192.168.56.101
+    port = 1433
+    tds version = 7.0
+    client charset = UTF-8
+
+    $ cat /etc/odbcinst.ini 
+    ...
+
+    [FreeTDS]
+    Description     = FreeTDS
+    Driver          = /usr/lib/x86_64-linux-gnu/odbc/libtdsodbc.so
+    Setup           = /usr/lib/x86_64-linux-gnu/odbc/libtdsS.so
+    FileUsage       = 1
+    UsageCount      = 5
+
+    $ cat ~/.odbc.ini 
+    ...
+
+    [MSSQL]
+    Description     = FreeTDS
+    Driver          = FreeTDS
+    Servername      = MSSQL
+    Database        = test
+    UID             = test
+    PWD             = test
+    Port            = 1433
+
+ + +

Configuring the dictionary in ClickHouse:

+
<yandex>
+    <dictionary>
+        <name>test</name>
+        <source>
+            <odbc>
+                <table>dict</table>
+                <connection_string>DSN=MSSQL;UID=test;PWD=test</connection_string>
+            </odbc>
+        </source>
+
+        <lifetime>
+            <min>300</min>
+            <max>360</max>
+        </lifetime>
+
+        <layout>
+            <flat />
+        </layout>
+
+        <structure>
+            <id>
+                <name>k</name>
+            </id>
+            <attribute>
+                <name>s</name>
+                <type>String</type>
+                <null_value></null_value>
+            </attribute>
+        </structure>
+    </dictionary>
+</yandex>
+
+ + +

DBMS

+

+

MySQL

+

Example of settings:

+
<source>
+  <mysql>
+      <port>3306</port>
+      <user>clickhouse</user>
+      <password>qwerty</password>
+      <replica>
+          <host>example01-1</host>
+          <priority>1</priority>
+      </replica>
+      <replica>
+          <host>example01-2</host>
+          <priority>1</priority>
+      </replica>
+      <db>db_name</db>
+      <table>table_name</table>
+      <where>id=10</where>
+      <invalidate_query>SQL_QUERY</invalidate_query>
+  </mysql>
+</source>
+
+ + +

Setting fields:

+
    +
  • +

    port – The port on the MySQL server. You can specify it for all replicas, or for each one individually (inside <replica>).

    +
    • +
  • +

    user – Name of the MySQL user. You can specify it for all replicas, or for each one individually (inside <replica>).

    +
    • +
  • +

    password – Password of the MySQL user. You can specify it for all replicas, or for each one individually (inside <replica>).

    +
    • +
  • +

    replica – Section of replica configurations. There can be multiple sections.

    +
    • +
  • replica/host – The MySQL host.
    • +
+

* replica/priority – The replica priority. When attempting to connect, ClickHouse traverses the replicas in order of priority. The lower the number, the higher the priority.

+
    +
  • +

    db – Name of the database.

    +
    • +
  • +

    table – Name of the table.

    +
    • +
  • +

    where – The selection criteria. Optional parameter.

    +
    • +
  • +

    invalidate_query – Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries.

    +
    • +
+

MySQL can be connected on a local host via sockets. To do this, set host and socket.

+

Example of settings:

+
<source>
+  <mysql>
+      <host>localhost</host>
+      <socket>/path/to/socket/file.sock</socket>
+      <user>clickhouse</user>
+      <password>qwerty</password>
+      <db>db_name</db>
+      <table>table_name</table>
+      <where>id=10</where>
+      <invalidate_query>SQL_QUERY</invalidate_query>
+  </mysql>
+</source>
+
+ + +

+

ClickHouse

+

Example of settings:

+
<source>
+    <clickhouse>
+        <host>example01-01-1</host>
+        <port>9000</port>
+        <user>default</user>
+        <password></password>
+        <db>default</db>
+        <table>ids</table>
+        <where>id=10</where>
+    </clickhouse>
+</source>
+
+ + +

Setting fields:

+
    +
  • host – The ClickHouse host. If it is a local host, the query is processed without any network activity. To improve fault tolerance, you can create a Distributed table and enter it in subsequent configurations.
    • +
  • port – The port on the ClickHouse server.
    • +
  • user – Name of the ClickHouse user.
    • +
  • password – Password of the ClickHouse user.
    • +
  • db – Name of the database.
    • +
  • table – Name of the table.
    • +
  • where – The selection criteria. May be omitted.
    • +
+

+

MongoDB

+

Example of settings:

+
<source>
+    <mongodb>
+        <host>localhost</host>
+        <port>27017</port>
+        <user></user>
+        <password></password>
+        <db>test</db>
+        <collection>dictionary_source</collection>
+    </mongodb>
+</source>
+
+ + +

Setting fields:

+
    +
  • host – The MongoDB host.
    • +
  • port – The port on the MongoDB server.
    • +
  • user – Name of the MongoDB user.
    • +
  • password – Password of the MongoDB user.
    • +
  • db – Name of the database.
    • +
  • collection – Name of the collection.
    • +
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/dicts/external_dicts_dict_structure/index.html b/docs/build/docs/en/dicts/external_dicts_dict_structure/index.html new file mode 100644 index 00000000000..8b62d2974fa --- /dev/null +++ b/docs/build/docs/en/dicts/external_dicts_dict_structure/index.html @@ -0,0 +1,3088 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Dictionary key and fields - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

+

Dictionary key and fields

+

The <structure> clause describes the dictionary key and fields available for queries.

+

Overall structure:

+
<dictionary>
+    <structure>
+        <id>
+            <name>Id</name>
+        </id>
+
+        <attribute>
+            <!-- Attribute parameters -->
+        </attribute>
+
+        ...
+
+    </structure>
+</dictionary>
+
+ + +

Columns are described in the structure:

+ +

+

Key

+

ClickHouse supports the following types of keys:

+
    +
  • Numeric key. UInt64. Defined in the tag <id> .
    • +
  • Composite key. Set of values of different types. Defined in the tag <key> .
    • +
+

A structure can contain either <id> or <key> .

+
+ +The key doesn't need to be defined separately in attributes. + +
+ +

Numeric key

+

Format: UInt64.

+

Configuration example:

+
<id>
+    <name>Id</name>
+</id>
+
+ + +

Configuration fields:

+
    +
  • name – The name of the column with keys.
    • +
+

Composite key

+

The key can be a tuple from any types of fields. The layout in this case must be complex_key_hashed or complex_key_cache.

+
+A composite key can consist of a single element. This makes it possible to use a string as the key, for instance. +
+ +

The key structure is set in the element <key>. Key fields are specified in the same format as the dictionary attributes. Example:

+
<structure>
+    <key>
+        <attribute>
+            <name>field1</name>
+            <type>String</type>
+        </attribute>
+        <attribute>
+            <name>field2</name>
+            <type>UInt32</type>
+        </attribute>
+        ...
+    </key>
+...
+
+ + +

For a query to the dictGet* function, a tuple is passed as the key. Example: dictGetString('dict_name', 'attr_name', tuple('string for field1', num_for_field2)).

+

+

Attributes

+

Configuration example:

+
<structure>
+    ...
+    <attribute>
+        <name>Name</name>
+        <type>Type</type>
+        <null_value></null_value>
+        <expression>rand64()</expression>
+        <hierarchical>true</hierarchical>
+        <injective>true</injective>
+        <is_object_id>true</is_object_id>
+    </attribute>
+</structure>
+
+ + +

Configuration fields:

+
    +
  • name – The column name.
    • +
  • type – The column type. Sets the method for interpreting data in the source. For example, for MySQL, the field might be TEXT, VARCHAR, or BLOB in the source table, but it can be uploaded as String.
    • +
  • null_value – The default value for a non-existing element. In the example, it is an empty string.
    • +
  • expression – The attribute can be an expression. The tag is not required.
    • +
  • hierarchical – Hierarchical support. Mirrored to the parent identifier. By default, false.
    • +
  • injective – Whether the id -> attribute image is injective. If true, then you can optimize the GROUP BY clause. By default, false.
    • +
  • is_object_id – Whether the query is executed for a MongoDB document by ObjectID.
    • +
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/dicts/index.html b/docs/build/docs/en/dicts/index.html new file mode 100644 index 00000000000..6f141a86a09 --- /dev/null +++ b/docs/build/docs/en/dicts/index.html @@ -0,0 +1,2890 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Introduction - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Dictionaries

+

A dictionary is a mapping (key -> attributes) that can be used in a query as functions. +You can think of this as a more convenient and efficient type of JOIN with dimension tables.

+

There are built-in (internal) and add-on (external) dictionaries.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/dicts/internal_dicts/index.html b/docs/build/docs/en/dicts/internal_dicts/index.html new file mode 100644 index 00000000000..5af82b29b0f --- /dev/null +++ b/docs/build/docs/en/dicts/internal_dicts/index.html @@ -0,0 +1,2923 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Internal dictionaries - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
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+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Internal dictionaries

+

ClickHouse contains a built-in feature for working with a geobase.

+

This allows you to:

+
    +
  • Use a region's ID to get its name in the desired language.
    • +
  • Use a region's ID to get the ID of a city, area, federal district, country, or continent.
    • +
  • Check whether a region is part of another region.
    • +
  • Get a chain of parent regions.
    • +
+

All the functions support "translocality," the ability to simultaneously use different perspectives on region ownership. For more information, see the section "Functions for working with Yandex.Metrica dictionaries".

+

The internal dictionaries are disabled in the default package. +To enable them, uncomment the parameters path_to_regions_hierarchy_file and path_to_regions_names_files in the server configuration file.

+

The geobase is loaded from text files. +If you work at Yandex, you can follow these instructions to create them: +https://github.yandex-team.ru/raw/Metrika/ClickHouse_private/master/doc/create_embedded_geobase_dictionaries.txt

+

Put the regions_hierarchy*.txt files in the path_to_regions_hierarchy_file directory. This configuration parameter must contain the path to the regions_hierarchy.txt file (the default regional hierarchy), and the other files (regions_hierarchy_ua.txt) must be located in the same directory.

+

Put the regions_names_*.txt files in the path_to_regions_names_files directory.

+

You can also create these files yourself. The file format is as follows:

+

regions_hierarchy*.txt: TabSeparated (no header), columns:

+
    +
  • Region ID (UInt32)
    • +
  • Parent region ID (UInt32)
    • +
  • Region type (UInt8): 1 - continent, 3 - country, 4 - federal district, 5 - region, 6 - city; other types don't have values.
    • +
  • Population (UInt32) - Optional column.
    • +
+

regions_names_*.txt: TabSeparated (no header), columns:

+
    +
  • Region ID (UInt32)
    • +
  • Region name (String) - Can't contain tabs or line feeds, even escaped ones.
    • +
+

A flat array is used for storing in RAM. For this reason, IDs shouldn't be more than a million.

+

Dictionaries can be updated without restarting the server. However, the set of available dictionaries is not updated. +For updates, the file modification times are checked. If a file has changed, the dictionary is updated. +The interval to check for changes is configured in the 'builtin_dictionaries_reload_interval' parameter. +Dictionary updates (other than loading at first use) do not block queries. During updates, queries use the old versions of dictionaries. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.

+

We recommend periodically updating the dictionaries with the geobase. During an update, generate new files and write them to a separate location. When everything is ready, rename them to the files used by the server.

+

There are also functions for working with OS identifiers and Yandex.Metrica search engines, but they shouldn't be used.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/capnproto/index.html b/docs/build/docs/en/formats/capnproto/index.html new file mode 100644 index 00000000000..688943935f0 --- /dev/null +++ b/docs/build/docs/en/formats/capnproto/index.html @@ -0,0 +1,2905 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + CapnProto - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

+

CapnProto

+

Cap'n Proto is a binary message format similar to Protocol Buffers and Thrift, but not like JSON or MessagePack.

+

Cap'n Proto messages are strictly typed and not self-describing, meaning they need an external schema description. The schema is applied on the fly and cached for each query.

+
SELECT SearchPhrase, count() AS c FROM test.hits
+       GROUP BY SearchPhrase FORMAT CapnProto SETTINGS schema = 'schema:Message'
+
+ + +

Where schema.capnp looks like this:

+
struct Message {
+  SearchPhrase @0 :Text;
+  c @1 :Uint64;
+}
+
+ + +

Schema files are in the file that is located in the directory specified in format_schema_path in the server configuration.

+

Deserialization is effective and usually doesn't increase the system load.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/csv/index.html b/docs/build/docs/en/formats/csv/index.html new file mode 100644 index 00000000000..3f24e9c7582 --- /dev/null +++ b/docs/build/docs/en/formats/csv/index.html @@ -0,0 +1,2892 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + CSV - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

CSV

+

Comma Separated Values format (RFC).

+

When formatting, rows are enclosed in double quotes. A double quote inside a string is output as two double quotes in a row. There are no other rules for escaping characters. Date and date-time are enclosed in double quotes. Numbers are output without quotes. Values ​​are separated by a delimiter*. Rows are separated using the Unix line feed (LF). Arrays are serialized in CSV as follows: first the array is serialized to a string as in TabSeparated format, and then the resulting string is output to CSV in double quotes. Tuples in CSV format are serialized as separate columns (that is, their nesting in the tuple is lost).

+

*By default — ,. See a format_csv_delimiter setting for additional info.

+

When parsing, all values can be parsed either with or without quotes. Both double and single quotes are supported. Rows can also be arranged without quotes. In this case, they are parsed up to a delimiter or line feed (CR or LF). In violation of the RFC, when parsing rows without quotes, the leading and trailing spaces and tabs are ignored. For the line feed, Unix (LF), Windows (CR LF) and Mac OS Classic (CR LF) are all supported.

+

The CSV format supports the output of totals and extremes the same way as TabSeparated.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/csvwithnames/index.html b/docs/build/docs/en/formats/csvwithnames/index.html new file mode 100644 index 00000000000..d665c015f6f --- /dev/null +++ b/docs/build/docs/en/formats/csvwithnames/index.html @@ -0,0 +1,2888 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + CSVWithNames - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

CSVWithNames

+

Also prints the header row, similar to TabSeparatedWithNames.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/index.html b/docs/build/docs/en/formats/index.html new file mode 100644 index 00000000000..2f7f68b7056 --- /dev/null +++ b/docs/build/docs/en/formats/index.html @@ -0,0 +1,2889 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Introduction - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

+

Formats

+

The format determines how data is returned to you after SELECTs (how it is written and formatted by the server), and how it is accepted for INSERTs (how it is read and parsed by the server).

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/json/index.html b/docs/build/docs/en/formats/json/index.html new file mode 100644 index 00000000000..4a96abbaac7 --- /dev/null +++ b/docs/build/docs/en/formats/json/index.html @@ -0,0 +1,2964 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + JSON - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ + +
+
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+ +
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+ + +
+
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+ + +
+
+
+ + +
+
+ + + + + + + +

JSON

+

Outputs data in JSON format. Besides data tables, it also outputs column names and types, along with some additional information: the total number of output rows, and the number of rows that could have been output if there weren't a LIMIT. Example:

+
SELECT SearchPhrase, count() AS c FROM test.hits GROUP BY SearchPhrase WITH TOTALS ORDER BY c DESC LIMIT 5 FORMAT JSON
+
+ + +
{
+        "meta":
+        [
+                {
+                        "name": "SearchPhrase",
+                        "type": "String"
+                },
+                {
+                        "name": "c",
+                        "type": "UInt64"
+                }
+        ],
+
+        "data":
+        [
+                {
+                        "SearchPhrase": "",
+                        "c": "8267016"
+                },
+                {
+                        "SearchPhrase": "bathroom interior design",
+                        "c": "2166"
+                },
+                {
+                        "SearchPhrase": "yandex",
+                        "c": "1655"
+                },
+                {
+                        "SearchPhrase": "spring 2014 fashion",
+                        "c": "1549"
+                },
+                {
+                        "SearchPhrase": "freeform photos",
+                        "c": "1480"
+                }
+        ],
+
+        "totals":
+        {
+                "SearchPhrase": "",
+                "c": "8873898"
+        },
+
+        "extremes":
+        {
+                "min":
+                {
+                        "SearchPhrase": "",
+                        "c": "1480"
+                },
+                "max":
+                {
+                        "SearchPhrase": "",
+                        "c": "8267016"
+                }
+        },
+
+        "rows": 5,
+
+        "rows_before_limit_at_least": 141137
+}
+
+ + +

The JSON is compatible with JavaScript. To ensure this, some characters are additionally escaped: the slash / is escaped as \/; alternative line breaks U+2028 and U+2029, which break some browsers, are escaped as \uXXXX. ASCII control characters are escaped: backspace, form feed, line feed, carriage return, and horizontal tab are replaced with \b, \f, \n, \r, \t , as well as the remaining bytes in the 00-1F range using \uXXXX sequences. Invalid UTF-8 sequences are changed to the replacement character � so the output text will consist of valid UTF-8 sequences. For compatibility with JavaScript, Int64 and UInt64 integers are enclosed in double quotes by default. To remove the quotes, you can set the configuration parameter output_format_json_quote_64bit_integers to 0.

+

rows – The total number of output rows.

+

rows_before_limit_at_least The minimal number of rows there would have been without LIMIT. Output only if the query contains LIMIT. +If the query contains GROUP BY, rows_before_limit_at_least is the exact number of rows there would have been without a LIMIT.

+

totals – Total values (when using WITH TOTALS).

+

extremes – Extreme values (when extremes is set to 1).

+

This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). +See also the JSONEachRow format.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/jsoncompact/index.html b/docs/build/docs/en/formats/jsoncompact/index.html new file mode 100644 index 00000000000..af4007c3380 --- /dev/null +++ b/docs/build/docs/en/formats/jsoncompact/index.html @@ -0,0 +1,2928 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + JSONCompact - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ + +
+
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+ +
+
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+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

JSONCompact

+

Differs from JSON only in that data rows are output in arrays, not in objects.

+

Example:

+
{
+        "meta":
+        [
+                {
+                        "name": "SearchPhrase",
+                        "type": "String"
+                },
+                {
+                        "name": "c",
+                        "type": "UInt64"
+                }
+        ],
+
+        "data":
+        [
+                ["", "8267016"],
+                ["bathroom interior design", "2166"],
+                ["yandex", "1655"],
+                ["spring 2014 fashion", "1549"],
+                ["freeform photos", "1480"]
+        ],
+
+        "totals": ["","8873898"],
+
+        "extremes":
+        {
+                "min": ["","1480"],
+                "max": ["","8267016"]
+        },
+
+        "rows": 5,
+
+        "rows_before_limit_at_least": 141137
+}
+
+ + +

This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). +See also the JSONEachRow format.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/jsoneachrow/index.html b/docs/build/docs/en/formats/jsoneachrow/index.html new file mode 100644 index 00000000000..774767fe6af --- /dev/null +++ b/docs/build/docs/en/formats/jsoneachrow/index.html @@ -0,0 +1,2903 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + JSONEachRow - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

JSONEachRow

+

Outputs data as separate JSON objects for each row (newline delimited JSON).

+
{"SearchPhrase":"","count()":"8267016"}
+{"SearchPhrase":"bathroom interior design","count()":"2166"}
+{"SearchPhrase":"yandex","count()":"1655"}
+{"SearchPhrase":"spring 2014 fashion","count()":"1549"}
+{"SearchPhrase":"freeform photo","count()":"1480"}
+{"SearchPhrase":"angelina jolie","count()":"1245"}
+{"SearchPhrase":"omsk","count()":"1112"}
+{"SearchPhrase":"photos of dog breeds","count()":"1091"}
+{"SearchPhrase":"curtain design","count()":"1064"}
+{"SearchPhrase":"baku","count()":"1000"}
+
+ + +

Unlike the JSON format, there is no substitution of invalid UTF-8 sequences. Any set of bytes can be output in the rows. This is necessary so that data can be formatted without losing any information. Values are escaped in the same way as for JSON.

+

For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted – they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Whitespace between elements is ignored. If a comma is placed after the objects, it is ignored. Objects don't necessarily have to be separated by new lines.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/native/index.html b/docs/build/docs/en/formats/native/index.html new file mode 100644 index 00000000000..5d155e52763 --- /dev/null +++ b/docs/build/docs/en/formats/native/index.html @@ -0,0 +1,2889 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Native - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Native

+

The most efficient format. Data is written and read by blocks in binary format. For each block, the number of rows, number of columns, column names and types, and parts of columns in this block are recorded one after another. In other words, this format is "columnar" – it doesn't convert columns to rows. This is the format used in the native interface for interaction between servers, for using the command-line client, and for C++ clients.

+

You can use this format to quickly generate dumps that can only be read by the ClickHouse DBMS. It doesn't make sense to work with this format yourself.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/null/index.html b/docs/build/docs/en/formats/null/index.html new file mode 100644 index 00000000000..dbd84278e46 --- /dev/null +++ b/docs/build/docs/en/formats/null/index.html @@ -0,0 +1,2889 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Null - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Null

+

Nothing is output. However, the query is processed, and when using the command-line client, data is transmitted to the client. This is used for tests, including productivity testing. +Obviously, this format is only appropriate for output, not for parsing.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/pretty/index.html b/docs/build/docs/en/formats/pretty/index.html new file mode 100644 index 00000000000..f297b9af1b9 --- /dev/null +++ b/docs/build/docs/en/formats/pretty/index.html @@ -0,0 +1,2918 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Pretty - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Pretty

+

Outputs data as Unicode-art tables, also using ANSI-escape sequences for setting colors in the terminal. +A full grid of the table is drawn, and each row occupies two lines in the terminal. +Each result block is output as a separate table. This is necessary so that blocks can be output without buffering results (buffering would be necessary in order to pre-calculate the visible width of all the values). +To avoid dumping too much data to the terminal, only the first 10,000 rows are printed. If the number of rows is greater than or equal to 10,000, the message "Showed first 10 000" is printed. +This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

+

The Pretty format supports outputting total values (when using WITH TOTALS) and extremes (when 'extremes' is set to 1). In these cases, total values and extreme values are output after the main data, in separate tables. Example (shown for the PrettyCompact format):

+
SELECT EventDate, count() AS c FROM test.hits GROUP BY EventDate WITH TOTALS ORDER BY EventDate FORMAT PrettyCompact
+
+ + +
┌──EventDate─┬───────c─┐
+│ 2014-03-17 │ 1406958 │
+│ 2014-03-18 │ 1383658 │
+│ 2014-03-19 │ 1405797 │
+│ 2014-03-20 │ 1353623 │
+│ 2014-03-21 │ 1245779 │
+│ 2014-03-22 │ 1031592 │
+│ 2014-03-23 │ 1046491 │
+└────────────┴─────────┘
+
+Totals:
+┌──EventDate─┬───────c─┐
+│ 0000-00-00 │ 8873898 │
+└────────────┴─────────┘
+
+Extremes:
+┌──EventDate─┬───────c─┐
+│ 2014-03-17 │ 1031592 │
+│ 2014-03-23 │ 1406958 │
+└────────────┴─────────┘
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/prettycompact/index.html b/docs/build/docs/en/formats/prettycompact/index.html new file mode 100644 index 00000000000..8c1910df21c --- /dev/null +++ b/docs/build/docs/en/formats/prettycompact/index.html @@ -0,0 +1,2889 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + PrettyCompact - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

PrettyCompact

+

Differs from Pretty in that the grid is drawn between rows and the result is more compact. +This format is used by default in the command-line client in interactive mode.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/prettycompactmonoblock/index.html b/docs/build/docs/en/formats/prettycompactmonoblock/index.html new file mode 100644 index 00000000000..be2c7281fbc --- /dev/null +++ b/docs/build/docs/en/formats/prettycompactmonoblock/index.html @@ -0,0 +1,2888 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + PrettyCompactMonoBlock - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

PrettyCompactMonoBlock

+

Differs from PrettyCompact in that up to 10,000 rows are buffered, then output as a single table, not by blocks.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/prettynoescapes/index.html b/docs/build/docs/en/formats/prettynoescapes/index.html new file mode 100644 index 00000000000..5c086736685 --- /dev/null +++ b/docs/build/docs/en/formats/prettynoescapes/index.html @@ -0,0 +1,2953 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + PrettyNoEscapes - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

PrettyNoEscapes

+

Differs from Pretty in that ANSI-escape sequences aren't used. This is necessary for displaying this format in a browser, as well as for using the 'watch' command-line utility.

+

Example:

+
watch -n1 "clickhouse-client --query='SELECT * FROM system.events FORMAT PrettyCompactNoEscapes'"
+
+ + +

You can use the HTTP interface for displaying in the browser.

+

PrettyCompactNoEscapes

+

The same as the previous setting.

+

PrettySpaceNoEscapes

+

The same as the previous setting.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/prettyspace/index.html b/docs/build/docs/en/formats/prettyspace/index.html new file mode 100644 index 00000000000..54bd86de855 --- /dev/null +++ b/docs/build/docs/en/formats/prettyspace/index.html @@ -0,0 +1,2888 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + PrettySpace - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

PrettySpace

+

Differs from PrettyCompact in that whitespace (space characters) is used instead of the grid.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/rowbinary/index.html b/docs/build/docs/en/formats/rowbinary/index.html new file mode 100644 index 00000000000..62b75ac87f9 --- /dev/null +++ b/docs/build/docs/en/formats/rowbinary/index.html @@ -0,0 +1,2895 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + RowBinary - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

RowBinary

+

Formats and parses data by row in binary format. Rows and values are listed consecutively, without separators. +This format is less efficient than the Native format, since it is row-based.

+

Integers use fixed-length little endian representation. For example, UInt64 uses 8 bytes. +DateTime is represented as UInt32 containing the Unix timestamp as the value. +Date is represented as a UInt16 object that contains the number of days since 1970-01-01 as the value. +String is represented as a varint length (unsigned LEB128), followed by the bytes of the string. +FixedString is represented simply as a sequence of bytes.

+

Array is represented as a varint length (unsigned LEB128), followed by successive elements of the array.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/tabseparated/index.html b/docs/build/docs/en/formats/tabseparated/index.html new file mode 100644 index 00000000000..83f6b742e80 --- /dev/null +++ b/docs/build/docs/en/formats/tabseparated/index.html @@ -0,0 +1,2931 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + TabSeparated - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

TabSeparated

+

In TabSeparated format, data is written by row. Each row contains values separated by tabs. Each value is follow by a tab, except the last value in the row, which is followed by a line feed. Strictly Unix line feeds are assumed everywhere. The last row also must contain a line feed at the end. Values are written in text format, without enclosing quotation marks, and with special characters escaped.

+

Integer numbers are written in decimal form. Numbers can contain an extra "+" character at the beginning (ignored when parsing, and not recorded when formatting). Non-negative numbers can't contain the negative sign. When reading, it is allowed to parse an empty string as a zero, or (for signed types) a string consisting of just a minus sign as a zero. Numbers that do not fit into the corresponding data type may be parsed as a different number, without an error message.

+

Floating-point numbers are written in decimal form. The dot is used as the decimal separator. Exponential entries are supported, as are 'inf', '+inf', '-inf', and 'nan'. An entry of floating-point numbers may begin or end with a decimal point. +During formatting, accuracy may be lost on floating-point numbers. +During parsing, it is not strictly required to read the nearest machine-representable number.

+

Dates are written in YYYY-MM-DD format and parsed in the same format, but with any characters as separators. +Dates with times are written in the format YYYY-MM-DD hh:mm:ss and parsed in the same format, but with any characters as separators. +This all occurs in the system time zone at the time the client or server starts (depending on which one formats data). For dates with times, daylight saving time is not specified. So if a dump has times during daylight saving time, the dump does not unequivocally match the data, and parsing will select one of the two times. +During a read operation, incorrect dates and dates with times can be parsed with natural overflow or as null dates and times, without an error message.

+

As an exception, parsing dates with times is also supported in Unix timestamp format, if it consists of exactly 10 decimal digits. The result is not time zone-dependent. The formats YYYY-MM-DD hh:mm:ss and NNNNNNNNNN are differentiated automatically.

+

Strings are output with backslash-escaped special characters. The following escape sequences are used for output: \b, \f, \r, \n, \t, \0, \', \\. Parsing also supports the sequences \a, \v, and \xHH (hex escape sequences) and any \c sequences, where c is any character (these sequences are converted to c). Thus, reading data supports formats where a line feed can be written as \n or \, or as a line feed. For example, the string Hello world with a line feed between the words instead of a space can be parsed in any of the following variations:

+
Hello\nworld
+
+Hello\
+world
+
+ + +

The second variant is supported because MySQL uses it when writing tab-separated dumps.

+

The minimum set of characters that you need to escape when passing data in TabSeparated format: tab, line feed (LF) and backslash.

+

Only a small set of symbols are escaped. You can easily stumble onto a string value that your terminal will ruin in output.

+

Arrays are written as a list of comma-separated values in square brackets. Number items in the array are fomratted as normally, but dates, dates with times, and strings are written in single quotes with the same escaping rules as above.

+

The TabSeparated format is convenient for processing data using custom programs and scripts. It is used by default in the HTTP interface, and in the command-line client's batch mode. This format also allows transferring data between different DBMSs. For example, you can get a dump from MySQL and upload it to ClickHouse, or vice versa.

+

The TabSeparated format supports outputting total values (when using WITH TOTALS) and extreme values (when 'extremes' is set to 1). In these cases, the total values and extremes are output after the main data. The main result, total values, and extremes are separated from each other by an empty line. Example:

+
SELECT EventDate, count() AS c FROM test.hits GROUP BY EventDate WITH TOTALS ORDER BY EventDate FORMAT TabSeparated``
+
+ + +
2014-03-17      1406958
+2014-03-18      1383658
+2014-03-19      1405797
+2014-03-20      1353623
+2014-03-21      1245779
+2014-03-22      1031592
+2014-03-23      1046491
+
+0000-00-00      8873898
+
+2014-03-17      1031592
+2014-03-23      1406958
+
+ + +

This format is also available under the name TSV.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/tabseparatedraw/index.html b/docs/build/docs/en/formats/tabseparatedraw/index.html new file mode 100644 index 00000000000..2a54fdd7e52 --- /dev/null +++ b/docs/build/docs/en/formats/tabseparatedraw/index.html @@ -0,0 +1,2890 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + TabSeparatedRaw - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

TabSeparatedRaw

+

Differs from TabSeparated format in that the rows are written without escaping. +This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

+

This format is also available under the name TSVRaw.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/tabseparatedwithnames/index.html b/docs/build/docs/en/formats/tabseparatedwithnames/index.html new file mode 100644 index 00000000000..320821d4727 --- /dev/null +++ b/docs/build/docs/en/formats/tabseparatedwithnames/index.html @@ -0,0 +1,2891 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + TabSeparatedWithNames - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

TabSeparatedWithNames

+

Differs from the TabSeparated format in that the column names are written in the first row. +During parsing, the first row is completely ignored. You can't use column names to determine their position or to check their correctness. +(Support for parsing the header row may be added in the future.)

+

This format is also available under the name TSVWithNames.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/tabseparatedwithnamesandtypes/index.html b/docs/build/docs/en/formats/tabseparatedwithnamesandtypes/index.html new file mode 100644 index 00000000000..267ba41bfc3 --- /dev/null +++ b/docs/build/docs/en/formats/tabseparatedwithnamesandtypes/index.html @@ -0,0 +1,2890 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + TabSeparatedWithNamesAndTypes - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
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+
+ +
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+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

TabSeparatedWithNamesAndTypes

+

Differs from the TabSeparated format in that the column names are written to the first row, while the column types are in the second row. +During parsing, the first and second rows are completely ignored.

+

This format is also available under the name TSVWithNamesAndTypes.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/tskv/index.html b/docs/build/docs/en/formats/tskv/index.html new file mode 100644 index 00000000000..fd274f7d5c0 --- /dev/null +++ b/docs/build/docs/en/formats/tskv/index.html @@ -0,0 +1,2904 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + TSKV - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

TSKV

+

Similar to TabSeparated, but outputs a value in name=value format. Names are escaped the same way as in TabSeparated format, and the = symbol is also escaped.

+
SearchPhrase=   count()=8267016
+SearchPhrase=bathroom interior design    count()=2166
+SearchPhrase=yandex     count()=1655
+SearchPhrase=spring 2014 fashion    count()=1549
+SearchPhrase=freeform photos       count()=1480
+SearchPhrase=angelina jolia    count()=1245
+SearchPhrase=omsk       count()=1112
+SearchPhrase=photos of dog breeds    count()=1091
+SearchPhrase=curtain design        count()=1064
+SearchPhrase=baku       count()=1000
+
+ + +

When there is a large number of small columns, this format is ineffective, and there is generally no reason to use it. It is used in some departments of Yandex.

+

Both data output and parsing are supported in this format. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted – they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults.

+

Parsing allows the presence of the additional field tskv without the equal sign or a value. This field is ignored.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/values/index.html b/docs/build/docs/en/formats/values/index.html new file mode 100644 index 00000000000..92f8442d5ec --- /dev/null +++ b/docs/build/docs/en/formats/values/index.html @@ -0,0 +1,2890 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Values - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ + +
+
+
+ +
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+
+ + +
+
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+ + +
+
+
+ + +
+
+ + + + + + + +

Values

+

Prints every row in brackets. Rows are separated by commas. There is no comma after the last row. The values inside the brackets are also comma-separated. Numbers are output in decimal format without quotes. Arrays are output in square brackets. Strings, dates, and dates with times are output in quotes. Escaping rules and parsing are similar to the TabSeparated format. During formatting, extra spaces aren't inserted, but during parsing, they are allowed and skipped (except for spaces inside array values, which are not allowed).

+

The minimum set of characters that you need to escape when passing data in Values ​​format: single quotes and backslashes.

+

This is the format that is used in INSERT INTO t VALUES ..., but you can also use it for formatting query results.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/vertical/index.html b/docs/build/docs/en/formats/vertical/index.html new file mode 100644 index 00000000000..461b379eea4 --- /dev/null +++ b/docs/build/docs/en/formats/vertical/index.html @@ -0,0 +1,2889 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Vertical - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+
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+ +
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+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Vertical

+

Prints each value on a separate line with the column name specified. This format is convenient for printing just one or a few rows, if each row consists of a large number of columns. +This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/verticalraw/index.html b/docs/build/docs/en/formats/verticalraw/index.html new file mode 100644 index 00000000000..c90750ea6af --- /dev/null +++ b/docs/build/docs/en/formats/verticalraw/index.html @@ -0,0 +1,2909 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + VerticalRaw - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

VerticalRaw

+

Differs from Vertical format in that the rows are not escaped. +This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

+

Examples:

+
:) SHOW CREATE TABLE geonames FORMAT VerticalRaw;
+Row 1:
+──────
+statement: CREATE TABLE default.geonames ( geonameid UInt32, date Date DEFAULT CAST('2017-12-08' AS Date)) ENGINE = MergeTree(date, geonameid, 8192)
+
+:) SELECT 'string with \'quotes\' and \t with some special \n characters' AS test FORMAT VerticalRaw;
+Row 1:
+──────
+test: string with 'quotes' and   with some special
+ characters
+
+ + +

Compare with the Vertical format:

+
:) SELECT 'string with \'quotes\' and \t with some special \n characters' AS test FORMAT Vertical;
+Row 1:
+──────
+test: string with \'quotes\' and \t with some special \n characters
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/formats/xml/index.html b/docs/build/docs/en/formats/xml/index.html new file mode 100644 index 00000000000..93714b3e878 --- /dev/null +++ b/docs/build/docs/en/formats/xml/index.html @@ -0,0 +1,2955 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + XML - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

XML

+

XML format is suitable only for output, not for parsing. Example:

+
<?xml version='1.0' encoding='UTF-8' ?>
+<result>
+        <meta>
+                <columns>
+                        <column>
+                                <name>SearchPhrase</name>
+                                <type>String</type>
+                        </column>
+                        <column>
+                                <name>count()</name>
+                                <type>UInt64</type>
+                        </column>
+                </columns>
+        </meta>
+        <data>
+                <row>
+                        <SearchPhrase></SearchPhrase>
+                        <field>8267016</field>
+                </row>
+                <row>
+                        <SearchPhrase>bathroom interior design</SearchPhrase>
+                        <field>2166</field>
+                </row>
+                <row>
+                        <SearchPhrase>yandex</SearchPhrase>
+                        <field>1655</field>
+                </row>
+                <row>
+                        <SearchPhrase>spring 2014 fashion</SearchPhrase>
+                        <field>1549</field>
+                </row>
+                <row>
+                        <SearchPhrase>freeform photos</SearchPhrase>
+                        <field>1480</field>
+                </row>
+                <row>
+                        <SearchPhrase>angelina jolie</SearchPhrase>
+                        <field>1245</field>
+                </row>
+                <row>
+                        <SearchPhrase>omsk</SearchPhrase>
+                        <field>1112</field>
+                </row>
+                <row>
+                        <SearchPhrase>photos of dog breeds</SearchPhrase>
+                        <field>1091</field>
+                </row>
+                <row>
+                        <SearchPhrase>curtain design</SearchPhrase>
+                        <field>1064</field>
+                </row>
+                <row>
+                        <SearchPhrase>baku</SearchPhrase>
+                        <field>1000</field>
+                </row>
+        </data>
+        <rows>10</rows>
+        <rows_before_limit_at_least>141137</rows_before_limit_at_least>
+</result>
+
+ + +

If the column name does not have an acceptable format, just 'field' is used as the element name. In general, the XML structure follows the JSON structure. +Just as for JSON, invalid UTF-8 sequences are changed to the replacement character � so the output text will consist of valid UTF-8 sequences.

+

In string values, the characters < and & are escaped as < and &.

+

Arrays are output as <array><elem>Hello</elem><elem>World</elem>...</array>, +and tuples as <tuple><elem>Hello</elem><elem>World</elem>...</tuple>.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/arithmetic_functions/index.html b/docs/build/docs/en/functions/arithmetic_functions/index.html new file mode 100644 index 00000000000..1adc5631f97 --- /dev/null +++ b/docs/build/docs/en/functions/arithmetic_functions/index.html @@ -0,0 +1,3114 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Arithmetic functions - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Arithmetic functions

+

For all arithmetic functions, the result type is calculated as the smallest number type that the result fits in, if there is such a type. The minimum is taken simultaneously based on the number of bits, whether it is signed, and whether it floats. If there are not enough bits, the highest bit type is taken.

+

Example:

+
SELECT toTypeName(0), toTypeName(0 + 0), toTypeName(0 + 0 + 0), toTypeName(0 + 0 + 0 + 0)
+
+ + +
┌─toTypeName(0)─┬─toTypeName(plus(0, 0))─┬─toTypeName(plus(plus(0, 0), 0))─┬─toTypeName(plus(plus(plus(0, 0), 0), 0))─┐
+│ UInt8         │ UInt16                 │ UInt32                          │ UInt64                                   │
+└───────────────┴────────────────────────┴─────────────────────────────────┴──────────────────────────────────────────┘
+
+ + +

Arithmetic functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.

+

Overflow is produced the same way as in C++.

+

plus(a, b), a + b operator

+

Calculates the sum of the numbers. +You can also add integer numbers with a date or date and time. In the case of a date, adding an integer means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds.

+

minus(a, b), a - b operator

+

Calculates the difference. The result is always signed.

+

You can also calculate integer numbers from a date or date with time. The idea is the same – see above for 'plus'.

+

multiply(a, b), a * b operator

+

Calculates the product of the numbers.

+

divide(a, b), a / b operator

+

Calculates the quotient of the numbers. The result type is always a floating-point type. +It is not integer division. For integer division, use the 'intDiv' function. +When dividing by zero you get 'inf', '-inf', or 'nan'.

+

intDiv(a, b)

+

Calculates the quotient of the numbers. Divides into integers, rounding down (by the absolute value). +An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

+

intDivOrZero(a, b)

+

Differs from 'intDiv' in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one.

+

modulo(a, b), a % b operator

+

Calculates the remainder after division. +If arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion. +The remainder is taken in the same sense as in C++. Truncated division is used for negative numbers. +An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

+

negate(a), -a operator

+

Calculates a number with the reverse sign. The result is always signed.

+

abs(a)

+

Calculates the absolute value of the number (a). That is, if a < 0, it returns -a. For unsigned types it doesn't do anything. For signed integer types, it returns an unsigned number.

+

gcd(a, b)

+

Returns the greatest common divisor of the numbers. +An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

+

lcm(a, b)

+

Returns the least common multiple of the numbers. +An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/array_functions/index.html b/docs/build/docs/en/functions/array_functions/index.html new file mode 100644 index 00000000000..d990f22411a --- /dev/null +++ b/docs/build/docs/en/functions/array_functions/index.html @@ -0,0 +1,3524 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Functions for working with arrays - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Functions for working with arrays

+

empty

+

Returns 1 for an empty array, or 0 for a non-empty array. +The result type is UInt8. +The function also works for strings.

+

notEmpty

+

Returns 0 for an empty array, or 1 for a non-empty array. +The result type is UInt8. +The function also works for strings.

+

length

+

Returns the number of items in the array. +The result type is UInt64. +The function also works for strings.

+

emptyArrayUInt8, emptyArrayUInt16, emptyArrayUInt32, emptyArrayUInt64

+

emptyArrayInt8, emptyArrayInt16, emptyArrayInt32, emptyArrayInt64

+

emptyArrayFloat32, emptyArrayFloat64

+

emptyArrayDate, emptyArrayDateTime

+

emptyArrayString

+

Accepts zero arguments and returns an empty array of the appropriate type.

+

emptyArrayToSingle

+

Accepts an empty array and returns a one-element array that is equal to the default value.

+

range(N)

+

Returns an array of numbers from 0 to N-1. +Just in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block.

+

array(x1, ...), operator [x1, ...]

+

Creates an array from the function arguments. +The arguments must be constants and have types that have the smallest common type. At least one argument must be passed, because otherwise it isn't clear which type of array to create. That is, you can't use this function to create an empty array (to do that, use the 'emptyArray*' function described above). +Returns an 'Array(T)' type result, where 'T' is the smallest common type out of the passed arguments.

+

arrayConcat

+

Combines arrays passed as arguments.

+
arrayConcat(arrays)
+
+ + +

Arguments

+
    +
  • arrays – Arrays of comma-separated [values].
    • +
+

Example

+
SELECT arrayConcat([1, 2], [3, 4], [5, 6]) AS res
+
+ + +
┌─res───────────┐
+│ [1,2,3,4,5,6] │
+└───────────────┘
+
+ + +

arrayElement(arr, n), operator arr[n]

+

Get the element with the index 'n' from the array 'arr'.'n' must be any integer type. +Indexes in an array begin from one. +Negative indexes are supported. In this case, it selects the corresponding element numbered from the end. For example, 'arr[-1]' is the last item in the array.

+

If the index falls outside of the bounds of an array, it returns some default value (0 for numbers, an empty string for strings, etc.).

+

has(arr, elem)

+

Checks whether the 'arr' array has the 'elem' element. +Returns 0 if the the element is not in the array, or 1 if it is.

+

indexOf(arr, x)

+

Returns the index of the 'x' element (starting from 1) if it is in the array, or 0 if it is not.

+

countEqual(arr, x)

+

Returns the number of elements in the array equal to x. Equivalent to arrayCount (elem-> elem = x, arr).

+

arrayEnumerate(arr)

+

Returns the array [1, 2, 3, ..., length (arr) ]

+

This function is normally used with ARRAY JOIN. It allows counting something just once for each array after applying ARRAY JOIN. Example:

+
SELECT
+    count() AS Reaches,
+    countIf(num = 1) AS Hits
+FROM test.hits
+ARRAY JOIN
+    GoalsReached,
+    arrayEnumerate(GoalsReached) AS num
+WHERE CounterID = 160656
+LIMIT 10
+
+ + +
┌─Reaches─┬──Hits─┐
+│   95606 │ 31406 │
+└─────────┴───────┘
+
+ + +

In this example, Reaches is the number of conversions (the strings received after applying ARRAY JOIN), and Hits is the number of pageviews (strings before ARRAY JOIN). In this particular case, you can get the same result in an easier way:

+
SELECT
+    sum(length(GoalsReached)) AS Reaches,
+    count() AS Hits
+FROM test.hits
+WHERE (CounterID = 160656) AND notEmpty(GoalsReached)
+
+ + +
┌─Reaches─┬──Hits─┐
+│   95606 │ 31406 │
+└─────────┴───────┘
+
+ + +

This function can also be used in higher-order functions. For example, you can use it to get array indexes for elements that match a condition.

+

arrayEnumerateUniq(arr, ...)

+

Returns an array the same size as the source array, indicating for each element what its position is among elements with the same value. +For example: arrayEnumerateUniq([10, 20, 10, 30]) = [1, 1, 2, 1].

+

This function is useful when using ARRAY JOIN and aggregation of array elements. +Example:

+
SELECT
+    Goals.ID AS GoalID,
+    sum(Sign) AS Reaches,
+    sumIf(Sign, num = 1) AS Visits
+FROM test.visits
+ARRAY JOIN
+    Goals,
+    arrayEnumerateUniq(Goals.ID) AS num
+WHERE CounterID = 160656
+GROUP BY GoalID
+ORDER BY Reaches DESC
+LIMIT 10
+
+ + +
┌──GoalID─┬─Reaches─┬─Visits─┐
+│   53225 │    3214 │   1097 │
+│ 2825062 │    3188 │   1097 │
+│   56600 │    2803 │    488 │
+│ 1989037 │    2401 │    365 │
+│ 2830064 │    2396 │    910 │
+│ 1113562 │    2372 │    373 │
+│ 3270895 │    2262 │    812 │
+│ 1084657 │    2262 │    345 │
+│   56599 │    2260 │    799 │
+│ 3271094 │    2256 │    812 │
+└─────────┴─────────┴────────┘
+
+ + +

In this example, each goal ID has a calculation of the number of conversions (each element in the Goals nested data structure is a goal that was reached, which we refer to as a conversion) and the number of sessions. Without ARRAY JOIN, we would have counted the number of sessions as sum(Sign). But in this particular case, the rows were multiplied by the nested Goals structure, so in order to count each session one time after this, we apply a condition to the value of the arrayEnumerateUniq(Goals.ID) function.

+

The arrayEnumerateUniq function can take multiple arrays of the same size as arguments. In this case, uniqueness is considered for tuples of elements in the same positions in all the arrays.

+
SELECT arrayEnumerateUniq([1, 1, 1, 2, 2, 2], [1, 1, 2, 1, 1, 2]) AS res
+
+ + +
┌─res───────────┐
+│ [1,2,1,1,2,1] │
+└───────────────┘
+
+ + +

This is necessary when using ARRAY JOIN with a nested data structure and further aggregation across multiple elements in this structure.

+

arrayPopBack

+

Removes the last item from the array.

+
arrayPopBack(array)
+
+ + +

Arguments

+
    +
  • array – Array.
    • +
+

Example

+
SELECT arrayPopBack([1, 2, 3]) AS res
+
+ + +
┌─res───┐
+│ [1,2] │
+└───────┘
+
+ + +

arrayPopFront

+

Removes the first item from the array.

+
arrayPopFront(array)
+
+ + +

Arguments

+
    +
  • array – Array.
    • +
+

Example

+
SELECT arrayPopFront([1, 2, 3]) AS res
+
+ + +
┌─res───┐
+│ [2,3] │
+└───────┘
+
+ + +

arrayPushBack

+

Adds one item to the end of the array.

+
arrayPushBack(array, single_value)
+
+ + +

Arguments

+
    +
  • array – Array.
    • +
  • single_value – A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section "Data types".
    • +
+

Example

+
SELECT arrayPushBack(['a'], 'b') AS res
+
+ + +
┌─res───────┐
+│ ['a','b'] │
+└───────────┘
+
+ + +

arrayPushFront

+

Adds one element to the beginning of the array.

+
arrayPushFront(array, single_value)
+
+ + +

Arguments

+
    +
  • array – Array.
    • +
  • single_value – A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section "Data types".
    • +
+

Example

+
SELECT arrayPushBack(['b'], 'a') AS res
+
+ + +
┌─res───────┐
+│ ['a','b'] │
+└───────────┘
+
+ + +

arraySlice

+

Returns a slice of the array.

+
arraySlice(array, offset[, length])
+
+ + +

Arguments

+
    +
  • array – Array of data.
    • +
  • offset – Indent from the edge of the array. A positive value indicates an offset on the left, and a negative value is an indent on the right. Numbering of the array items begins with 1.
    • +
  • length - The length of the required slice. If you specify a negative value, the function returns an open slice [offset, array_length - length). If you omit the value, the function returns the slice [offset, the_end_of_array].
    • +
+

Example

+
SELECT arraySlice([1, 2, 3, 4, 5], 2, 3) AS res
+
+ + +
┌─res─────┐
+│ [2,3,4] │
+└─────────┘
+
+ + +

arrayUniq(arr, ...)

+

If one argument is passed, it counts the number of different elements in the array. +If multiple arguments are passed, it counts the number of different tuples of elements at corresponding positions in multiple arrays.

+

If you want to get a list of unique items in an array, you can use arrayReduce('groupUniqArray', arr).

+

arrayJoin(arr)

+

A special function. See the section "ArrayJoin function".

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/array_join/index.html b/docs/build/docs/en/functions/array_join/index.html new file mode 100644 index 00000000000..5bfa32bd023 --- /dev/null +++ b/docs/build/docs/en/functions/array_join/index.html @@ -0,0 +1,2907 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + arrayJoin function - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

+

arrayJoin function

+

This is a very unusual function.

+

Normal functions don't change a set of rows, but just change the values in each row (map). +Aggregate functions compress a set of rows (fold or reduce). +The 'arrayJoin' function takes each row and generates a set of rows (unfold).

+

This function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array. +All the values in columns are simply copied, except the values in the column where this function is applied; it is replaced with the corresponding array value.

+

A query can use multiple arrayJoin functions. In this case, the transformation is performed multiple times.

+

Note the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities.

+

Example:

+
SELECT arrayJoin([1, 2, 3] AS src) AS dst, 'Hello', src
+
+ + +
┌─dst─┬─\'Hello\'─┬─src─────┐
+│   1 │ Hello     │ [1,2,3] │
+│   2 │ Hello     │ [1,2,3] │
+│   3 │ Hello     │ [1,2,3] │
+└─────┴───────────┴─────────┘
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/bit_functions/index.html b/docs/build/docs/en/functions/bit_functions/index.html new file mode 100644 index 00000000000..f84f68bf867 --- /dev/null +++ b/docs/build/docs/en/functions/bit_functions/index.html @@ -0,0 +1,3006 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Bit functions - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Bit functions

+

Bit functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.

+

The result type is an integer with bits equal to the maximum bits of its arguments. If at least one of the arguments is signed, the result is a signed number. If an argument is a floating-point number, it is cast to Int64.

+

bitAnd(a, b)

+

bitOr(a, b)

+

bitXor(a, b)

+

bitNot(a)

+

bitShiftLeft(a, b)

+

bitShiftRight(a, b)

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/comparison_functions/index.html b/docs/build/docs/en/functions/comparison_functions/index.html new file mode 100644 index 00000000000..616cb8ea1a9 --- /dev/null +++ b/docs/build/docs/en/functions/comparison_functions/index.html @@ -0,0 +1,3016 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Comparison functions - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Comparison functions

+

Comparison functions always return 0 or 1 (Uint8).

+

The following types can be compared:

+
    +
  • numbers
    • +
  • strings and fixed strings
    • +
  • dates
    • +
  • dates with times
    • +
+

within each group, but not between different groups.

+

For example, you can't compare a date with a string. You have to use a function to convert the string to a date, or vice versa.

+

Strings are compared by bytes. A shorter string is smaller than all strings that start with it and that contain at least one more character.

+

Note. Up until version 1.1.54134, signed and unsigned numbers were compared the same way as in C++. In other words, you could get an incorrect result in cases like SELECT 9223372036854775807 > -1. This behavior changed in version 1.1.54134 and is now mathematically correct.

+

equals, a = b and a == b operator

+

notEquals, a ! operator= b and a <> b

+

less, < operator

+

greater, > operator

+

lessOrEquals, <= operator

+

greaterOrEquals, >= operator

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/conditional_functions/index.html b/docs/build/docs/en/functions/conditional_functions/index.html new file mode 100644 index 00000000000..aaee2a9ca48 --- /dev/null +++ b/docs/build/docs/en/functions/conditional_functions/index.html @@ -0,0 +1,2930 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Conditional functions - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ + +
+
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+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Conditional functions

+

if(cond, then, else), cond ? operator then : else

+

Returns 'then' if cond !or 'else' if cond = 0.'cond' must be UInt 8, and 'then' and 'else' must be a type that has the smallest common type.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/date_time_functions/index.html b/docs/build/docs/en/functions/date_time_functions/index.html new file mode 100644 index 00000000000..5fed4d61956 --- /dev/null +++ b/docs/build/docs/en/functions/date_time_functions/index.html @@ -0,0 +1,3409 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Functions for working with dates and times - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
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+ + +
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+ + + + + + + +

Functions for working with dates and times

+

Support for time zones

+

All functions for working with the date and time that have a logical use for the time zone can accept a second optional time zone argument. Example: Asia/Yekaterinburg. In this case, they use the specified time zone instead of the local (default) one.

+
SELECT
+    toDateTime('2016-06-15 23:00:00') AS time,
+    toDate(time) AS date_local,
+    toDate(time, 'Asia/Yekaterinburg') AS date_yekat,
+    toString(time, 'US/Samoa') AS time_samoa
+
+ + +
┌────────────────time─┬─date_local─┬─date_yekat─┬─time_samoa──────────┐
+│ 2016-06-15 23:00:00 │ 2016-06-15 │ 2016-06-16 │ 2016-06-15 09:00:00 │
+└─────────────────────┴────────────┴────────────┴─────────────────────┘
+
+ + +

Only time zones that differ from UTC by a whole number of hours are supported.

+

toYear

+

Converts a date or date with time to a UInt16 number containing the year number (AD).

+

toMonth

+

Converts a date or date with time to a UInt8 number containing the month number (1-12).

+

toDayOfMonth

+

-Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31).

+

toDayOfWeek

+

Converts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7).

+

toHour

+

Converts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23). +This function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true – even in Moscow the clocks were twice changed at a different time).

+

toMinute

+

Converts a date with time to a UInt8 number containing the number of the minute of the hour (0-59).

+

toSecond

+

Converts a date with time to a UInt8 number containing the number of the second in the minute (0-59). +Leap seconds are not accounted for.

+

toMonday

+

Rounds down a date or date with time to the nearest Monday. +Returns the date.

+

toStartOfMonth

+

Rounds down a date or date with time to the first day of the month. +Returns the date.

+

toStartOfQuarter

+

Rounds down a date or date with time to the first day of the quarter. +The first day of the quarter is either 1 January, 1 April, 1 July, or 1 October. +Returns the date.

+

toStartOfYear

+

Rounds down a date or date with time to the first day of the year. +Returns the date.

+

toStartOfMinute

+

Rounds down a date with time to the start of the minute.

+

toStartOfFiveMinute

+

Rounds down a date with time to the start of the hour.

+

toStartOfFifteenMinutes

+

Rounds down the date with time to the start of the fifteen-minute interval.

+

Note: If you need to round a date with time to any other number of seconds, minutes, or hours, you can convert it into a number by using the toUInt32 function, then round the number using intDiv and multiplication, and convert it back using the toDateTime function.

+

toStartOfHour

+

Rounds down a date with time to the start of the hour.

+

toStartOfDay

+

Rounds down a date with time to the start of the day.

+

toTime

+

Converts a date with time to a certain fixed date, while preserving the time.

+

toRelativeYearNum

+

Converts a date with time or date to the number of the year, starting from a certain fixed point in the past.

+

toRelativeMonthNum

+

Converts a date with time or date to the number of the month, starting from a certain fixed point in the past.

+

toRelativeWeekNum

+

Converts a date with time or date to the number of the week, starting from a certain fixed point in the past.

+

toRelativeDayNum

+

Converts a date with time or date to the number of the day, starting from a certain fixed point in the past.

+

toRelativeHourNum

+

Converts a date with time or date to the number of the hour, starting from a certain fixed point in the past.

+

toRelativeMinuteNum

+

Converts a date with time or date to the number of the minute, starting from a certain fixed point in the past.

+

toRelativeSecondNum

+

Converts a date with time or date to the number of the second, starting from a certain fixed point in the past.

+

now

+

Accepts zero arguments and returns the current time at one of the moments of request execution. +This function returns a constant, even if the request took a long time to complete.

+

today

+

Accepts zero arguments and returns the current date at one of the moments of request execution. +The same as 'toDate(now())'.

+

yesterday

+

Accepts zero arguments and returns yesterday's date at one of the moments of request execution. +The same as 'today() - 1'.

+

timeSlot

+

Rounds the time to the half hour. +This function is specific to Yandex.Metrica, since half an hour is the minimum amount of time for breaking a session into two sessions if a tracking tag shows a single user's consecutive pageviews that differ in time by strictly more than this amount. This means that tuples (the tag ID, user ID, and time slot) can be used to search for pageviews that are included in the corresponding session.

+

timeSlots(StartTime, Duration)

+

For a time interval starting at 'StartTime' and continuing for 'Duration' seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour. +For example, timeSlots(toDateTime('2012-01-01 12:20:00'), 600) = [toDateTime('2012-01-01 12:00:00'), toDateTime('2012-01-01 12:30:00')]. +This is necessary for searching for pageviews in the corresponding session.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/encoding_functions/index.html b/docs/build/docs/en/functions/encoding_functions/index.html new file mode 100644 index 00000000000..5adbc3f10e6 --- /dev/null +++ b/docs/build/docs/en/functions/encoding_functions/index.html @@ -0,0 +1,3011 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Encoding functions - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ + +
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+ +
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+
+ + +
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+ + +
+
+
+ + +
+
+ + + + + + + +

Encoding functions

+

hex

+

Accepts arguments of types: String, unsigned integer, Date, or DateTime. Returns a string containing the argument's hexadecimal representation. Uses uppercase letters A-F. Does not use 0x prefixes or h suffixes. For strings, all bytes are simply encoded as two hexadecimal numbers. Numbers are converted to big endian ("human readable") format. For numbers, older zeros are trimmed, but only by entire bytes. For example, hex (1) = '01'. Date is encoded as the number of days since the beginning of the Unix epoch. DateTime is encoded as the number of seconds since the beginning of the Unix epoch.

+

unhex(str)

+

Accepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits does not have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn't thrown). +If you want to convert the result to a number, you can use the 'reverse' and 'reinterpretAsType' functions.

+

UUIDStringToNum(str)

+

Accepts a string containing 36 characters in the format 123e4567-e89b-12d3-a456-426655440000, and returns it as a set of bytes in a FixedString(16).

+

UUIDNumToString(str)

+

Accepts a FixedString(16) value. Returns a string containing 36 characters in text format.

+

bitmaskToList(num)

+

Accepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order.

+

bitmaskToArray(num)

+

Accepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/ext_dict_functions/index.html b/docs/build/docs/en/functions/ext_dict_functions/index.html new file mode 100644 index 00000000000..7d053388c9d --- /dev/null +++ b/docs/build/docs/en/functions/ext_dict_functions/index.html @@ -0,0 +1,3085 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Functions for working with external dictionaries - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
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+ +
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+ + +
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+ + + + + + + +

+

Functions for working with external dictionaries

+

For information on connecting and configuring external dictionaries, see "External dictionaries".

+

dictGetUInt8, dictGetUInt16, dictGetUInt32, dictGetUInt64

+

dictGetInt8, dictGetInt16, dictGetInt32, dictGetInt64

+

dictGetFloat32, dictGetFloat64

+

dictGetDate, dictGetDateTime

+

dictGetUUID

+

dictGetString

+

dictGetT('dict_name', 'attr_name', id)

+
    +
  • Get the value of the attr_name attribute from the dict_name dictionary using the 'id' key.dict_name and attr_name are constant strings.idmust be UInt64. +If there is no id key in the dictionary, it returns the default value specified in the dictionary description.
    • +
+

dictGetTOrDefault

+

dictGetT('dict_name', 'attr_name', id, default)

+

The same as the dictGetT functions, but the default value is taken from the function's last argument.

+

dictIsIn

+

dictIsIn('dict_name', child_id, ancestor_id)

+
    +
  • For the 'dict_name' hierarchical dictionary, finds out whether the 'child_id' key is located inside 'ancestor_id' (or matches 'ancestor_id'). Returns UInt8.
    • +
+

dictGetHierarchy

+

dictGetHierarchy('dict_name', id)

+
    +
  • For the 'dict_name' hierarchical dictionary, returns an array of dictionary keys starting from 'id' and continuing along the chain of parent elements. Returns Array(UInt64).
    • +
+

dictHas

+

dictHas('dict_name', id)

+
    +
  • Check whether the dictionary has the key. Returns a UInt8 value equal to 0 if there is no key and 1 if there is a key.
    • +
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/hash_functions/index.html b/docs/build/docs/en/functions/hash_functions/index.html new file mode 100644 index 00000000000..1616b188758 --- /dev/null +++ b/docs/build/docs/en/functions/hash_functions/index.html @@ -0,0 +1,3111 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Hash functions - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ +
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+ + +
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+
+ + +
+
+ + + + + + + +

Hash functions

+

Hash functions can be used for deterministic pseudo-random shuffling of elements.

+

halfMD5

+

Calculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian. +Accepts a String-type argument. Returns UInt64. +This function works fairly slowly (5 million short strings per second per processor core). +If you don't need MD5 in particular, use the 'sipHash64' function instead.

+

MD5

+

Calculates the MD5 from a string and returns the resulting set of bytes as FixedString(16). +If you don't need MD5 in particular, but you need a decent cryptographic 128-bit hash, use the 'sipHash128' function instead. +If you want to get the same result as output by the md5sum utility, use lower(hex(MD5(s))).

+

sipHash64

+

Calculates SipHash from a string. +Accepts a String-type argument. Returns UInt64. +SipHash is a cryptographic hash function. It works at least three times faster than MD5. +For more information, see the link: https://131002.net/siphash/

+

sipHash128

+

Calculates SipHash from a string. +Accepts a String-type argument. Returns FixedString(16). +Differs from sipHash64 in that the final xor-folding state is only done up to 128 bytes.

+

cityHash64

+

Calculates CityHash64 from a string or a similar hash function for any number of any type of arguments. +For String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality. +For other types of arguments, a decent implementation-specific fast non-cryptographic hash function is used. +If multiple arguments are passed, the function is calculated using the same rules and chain combinations using the CityHash combinator. +For example, you can compute the checksum of an entire table with accuracy up to the row order: SELECT sum(cityHash64(*)) FROM table.

+

intHash32

+

Calculates a 32-bit hash code from any type of integer. +This is a relatively fast non-cryptographic hash function of average quality for numbers.

+

intHash64

+

Calculates a 64-bit hash code from any type of integer. +It works faster than intHash32. Average quality.

+

SHA1

+

SHA224

+

SHA256

+

Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32). +The function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million). +We recommend using this function only in cases when you need a specific hash function and you can't select it. +Even in these cases, we recommend applying the function offline and pre-calculating values when inserting them into the table, instead of applying it in SELECTS.

+

URLHash(url[, N])

+

A fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization. +URLHash(s) – Calculates a hash from a string without one of the trailing symbols /,? or # at the end, if present. +URLHash(s, N) – Calculates a hash from a string up to the N level in the URL hierarchy, without one of the trailing symbols /,? or # at the end, if present. +Levels are the same as in URLHierarchy. This function is specific to Yandex.Metrica.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/higher_order_functions/index.html b/docs/build/docs/en/functions/higher_order_functions/index.html new file mode 100644 index 00000000000..258b403f5ff --- /dev/null +++ b/docs/build/docs/en/functions/higher_order_functions/index.html @@ -0,0 +1,3171 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Higher-order functions - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
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+ +
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+ + +
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+ + + + + + + +

Higher-order functions

+

-> operator, lambda(params, expr) function

+

Allows describing a lambda function for passing to a higher-order function. The left side of the arrow has a formal parameter, which is any ID, or multiple formal parameters – any IDs in a tuple. The right side of the arrow has an expression that can use these formal parameters, as well as any table columns.

+

Examples: x -> 2 * x, str -> str != Referer.

+

Higher-order functions can only accept lambda functions as their functional argument.

+

A lambda function that accepts multiple arguments can be passed to a higher-order function. In this case, the higher-order function is passed several arrays of identical length that these arguments will correspond to.

+

For all functions other than 'arrayMap' and 'arrayFilter', the first argument (the lambda function) can be omitted. In this case, identical mapping is assumed.

+

arrayMap(func, arr1, ...)

+

Returns an array obtained from the original application of the 'func' function to each element in the 'arr' array.

+

arrayFilter(func, arr1, ...)

+

Returns an array containing only the elements in 'arr1' for which 'func' returns something other than 0.

+

Examples:

+
SELECT arrayFilter(x -> x LIKE '%World%', ['Hello', 'abc World']) AS res
+
+ + +
┌─res───────────┐
+│ ['abc World'] │
+└───────────────┘
+
+ + +
SELECT
+    arrayFilter(
+        (i, x) -> x LIKE '%World%',
+        arrayEnumerate(arr),
+        ['Hello', 'abc World'] AS arr)
+    AS res
+
+ + +
┌─res─┐
+│ [2] │
+└─────┘
+
+ + +

arrayCount([func,] arr1, ...)

+

Returns the number of elements in the arr array for which func returns something other than 0. If 'func' is not specified, it returns the number of non-zero elements in the array.

+

arrayExists([func,] arr1, ...)

+

Returns 1 if there is at least one element in 'arr' for which 'func' returns something other than 0. Otherwise, it returns 0.

+

arrayAll([func,] arr1, ...)

+

Returns 1 if 'func' returns something other than 0 for all the elements in 'arr'. Otherwise, it returns 0.

+

arraySum([func,] arr1, ...)

+

Returns the sum of the 'func' values. If the function is omitted, it just returns the sum of the array elements.

+

arrayFirst(func, arr1, ...)

+

Returns the first element in the 'arr1' array for which 'func' returns something other than 0.

+

arrayFirstIndex(func, arr1, ...)

+

Returns the index of the first element in the 'arr1' array for which 'func' returns something other than 0.

+

arrayCumSum([func,] arr1, ...)

+

Returns an array of partial sums of elements in the source array (a running sum). If the func function is specified, then the values of the array elements are converted by this function before summing.

+

Example:

+
SELECT arrayCumSum([1, 1, 1, 1]) AS res
+
+ + +
┌─res──────────┐
+│ [1, 2, 3, 4] │
+└──────────────┘
+
+ + +

arraySort([func,] arr1, ...)

+

Returns an array as result of sorting the elements of arr1 in ascending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays)

+

The Schwartzian transform is used to impove sorting efficiency.

+

Example:

+
SELECT arraySort((x, y) -> y, ['hello', 'world'], [2, 1]);
+
+ + +
┌─res────────────────┐
+│ ['world', 'hello'] │
+└────────────────────┘
+
+ + +

arrayReverseSort([func,] arr1, ...)

+

Returns an array as result of sorting the elements of arr1 in descending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays)

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/in_functions/index.html b/docs/build/docs/en/functions/in_functions/index.html new file mode 100644 index 00000000000..915717faf9c --- /dev/null +++ b/docs/build/docs/en/functions/in_functions/index.html @@ -0,0 +1,2966 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Functions for implementing the IN operator - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
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+ + +
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+ +
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+ + +
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+ + +
+
+
+ + +
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+ + + + + + + +

Functions for implementing the IN operator

+

in, notIn, globalIn, globalNotIn

+

See the section "IN operators".

+

tuple(x, y, ...), operator (x, y, ...)

+

A function that allows grouping multiple columns. +For columns with the types T1, T2, ..., it returns a Tuple(T1, T2, ...) type tuple containing these columns. There is no cost to execute the function. +Tuples are normally used as intermediate values for an argument of IN operators, or for creating a list of formal parameters of lambda functions. Tuples can't be written to a table.

+

tupleElement(tuple, n), operator x.N

+

A function that allows getting a column from a tuple. +'N' is the column index, starting from 1. N must be a constant. 'N' must be a constant. 'N' must be a strict postive integer no greater than the size of the tuple. +There is no cost to execute the function.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/index.html b/docs/build/docs/en/functions/index.html new file mode 100644 index 00000000000..ac07712399a --- /dev/null +++ b/docs/build/docs/en/functions/index.html @@ -0,0 +1,3060 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Introduction - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ + +
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+ +
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+ + +
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+ + +
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+ + + + + + + +

Functions

+

There are at least* two types of functions - regular functions (they are just called "functions") and aggregate functions. These are completely different concepts. Regular functions work as if they are applied to each row separately (for each row, the result of the function doesn't depend on the other rows). Aggregate functions accumulate a set of values from various rows (i.e. they depend on the entire set of rows).

+

In this section we discuss regular functions. For aggregate functions, see the section "Aggregate functions".

+

* - There is a third type of function that the 'arrayJoin' function belongs to; table functions can also be mentioned separately.*

+

Strong typing

+

In contrast to standard SQL, ClickHouse has strong typing. In other words, it doesn't make implicit conversions between types. Each function works for a specific set of types. This means that sometimes you need to use type conversion functions.

+

Common subexpression elimination

+

All expressions in a query that have the same AST (the same record or same result of syntactic parsing) are considered to have identical values. Such expressions are concatenated and executed once. Identical subqueries are also eliminated this way.

+

Types of results

+

All functions return a single return as the result (not several values, and not zero values). The type of result is usually defined only by the types of arguments, not by the values. Exceptions are the tupleElement function (the a.N operator), and the toFixedString function.

+

Constants

+

For simplicity, certain functions can only work with constants for some arguments. For example, the right argument of the LIKE operator must be a constant. +Almost all functions return a constant for constant arguments. The exception is functions that generate random numbers. +The 'now' function returns different values for queries that were run at different times, but the result is considered a constant, since constancy is only important within a single query. +A constant expression is also considered a constant (for example, the right half of the LIKE operator can be constructed from multiple constants).

+

Functions can be implemented in different ways for constant and non-constant arguments (different code is executed). But the results for a constant and for a true column containing only the same value should match each other.

+

Constancy

+

Functions can't change the values of their arguments – any changes are returned as the result. Thus, the result of calculating separate functions does not depend on the order in which the functions are written in the query.

+

Error handling

+

Some functions might throw an exception if the data is invalid. In this case, the query is canceled and an error text is returned to the client. For distributed processing, when an exception occurs on one of the servers, the other servers also attempt to abort the query.

+

Evaluation of argument expressions

+

In almost all programming languages, one of the arguments might not be evaluated for certain operators. This is usually the operators &&, ||, and ?:. +But in ClickHouse, arguments of functions (operators) are always evaluated. This is because entire parts of columns are evaluated at once, instead of calculating each row separately.

+

Performing functions for distributed query processing

+

For distributed query processing, as many stages of query processing as possible are performed on remote servers, and the rest of the stages (merging intermediate results and everything after that) are performed on the requestor server.

+

This means that functions can be performed on different servers. +For example, in the query SELECT f(sum(g(x))) FROM distributed_table GROUP BY h(y),

+
    +
  • if a distributed_table has at least two shards, the functions 'g' and 'h' are performed on remote servers, and the function 'f' is performed on the requestor server.
    • +
  • if a distributed_table has only one shard, all the 'f', 'g', and 'h' functions are performed on this shard's server.
    • +
+

The result of a function usually doesn't depend on which server it is performed on. However, sometimes this is important. +For example, functions that work with dictionaries use the dictionary that exists on the server they are running on. +Another example is the hostName function, which returns the name of the server it is running on in order to make GROUP BY by servers in a SELECT query.

+

If a function in a query is performed on the requestor server, but you need to perform it on remote servers, you can wrap it in an 'any' aggregate function or add it to a key in GROUP BY.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/ip_address_functions/index.html b/docs/build/docs/en/functions/ip_address_functions/index.html new file mode 100644 index 00000000000..9a1d93f9ebe --- /dev/null +++ b/docs/build/docs/en/functions/ip_address_functions/index.html @@ -0,0 +1,3097 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Functions for working with IP addresses - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Functions for working with IP addresses

+

IPv4NumToString(num)

+

Takes a UInt32 number. Interprets it as an IPv4 address in big endian. Returns a string containing the corresponding IPv4 address in the format A.B.C.d (dot-separated numbers in decimal form).

+

IPv4StringToNum(s)

+

The reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0.

+

IPv4NumToStringClassC(num)

+

Similar to IPv4NumToString, but using xxx instead of the last octet.

+

Example:

+
SELECT
+    IPv4NumToStringClassC(ClientIP) AS k,
+    count() AS c
+FROM test.hits
+GROUP BY k
+ORDER BY c DESC
+LIMIT 10
+
+ + +
┌─k──────────────┬─────c─┐
+│ 83.149.9.xxx   │ 26238 │
+│ 217.118.81.xxx │ 26074 │
+│ 213.87.129.xxx │ 25481 │
+│ 83.149.8.xxx   │ 24984 │
+│ 217.118.83.xxx │ 22797 │
+│ 78.25.120.xxx  │ 22354 │
+│ 213.87.131.xxx │ 21285 │
+│ 78.25.121.xxx  │ 20887 │
+│ 188.162.65.xxx │ 19694 │
+│ 83.149.48.xxx  │ 17406 │
+└────────────────┴───────┘
+
+ + +

Since using 'xxx' is highly unusual, this may be changed in the future. We recommend that you don't rely on the exact format of this fragment.

+

IPv6NumToString(x)

+

Accepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format. +IPv6-mapped IPv4 addresses are output in the format ::ffff:111.222.33.44. Examples:

+
SELECT IPv6NumToString(toFixedString(unhex('2A0206B8000000000000000000000011'), 16)) AS addr
+
+ + +
┌─addr─────────┐
+│ 2a02:6b8::11 │
+└──────────────┘
+
+ + +
SELECT
+    IPv6NumToString(ClientIP6 AS k),
+    count() AS c
+FROM hits_all
+WHERE EventDate = today() AND substring(ClientIP6, 1, 12) != unhex('00000000000000000000FFFF')
+GROUP BY k
+ORDER BY c DESC
+LIMIT 10
+
+ + +
┌─IPv6NumToString(ClientIP6)──────────────┬─────c─┐
+│ 2a02:2168:aaa:bbbb::2                   │ 24695 │
+│ 2a02:2698:abcd:abcd:abcd:abcd:8888:5555 │ 22408 │
+│ 2a02:6b8:0:fff::ff                      │ 16389 │
+│ 2a01:4f8:111:6666::2                    │ 16016 │
+│ 2a02:2168:888:222::1                    │ 15896 │
+│ 2a01:7e00::ffff:ffff:ffff:222           │ 14774 │
+│ 2a02:8109:eee:ee:eeee:eeee:eeee:eeee    │ 14443 │
+│ 2a02:810b:8888:888:8888:8888:8888:8888  │ 14345 │
+│ 2a02:6b8:0:444:4444:4444:4444:4444      │ 14279 │
+│ 2a01:7e00::ffff:ffff:ffff:ffff          │ 13880 │
+└─────────────────────────────────────────┴───────┘
+
+ + +
SELECT
+    IPv6NumToString(ClientIP6 AS k),
+    count() AS c
+FROM hits_all
+WHERE EventDate = today()
+GROUP BY k
+ORDER BY c DESC
+LIMIT 10
+
+ + +
┌─IPv6NumToString(ClientIP6)─┬──────c─┐
+│ ::ffff:94.26.111.111       │ 747440 │
+│ ::ffff:37.143.222.4        │ 529483 │
+│ ::ffff:5.166.111.99        │ 317707 │
+│ ::ffff:46.38.11.77         │ 263086 │
+│ ::ffff:79.105.111.111      │ 186611 │
+│ ::ffff:93.92.111.88        │ 176773 │
+│ ::ffff:84.53.111.33        │ 158709 │
+│ ::ffff:217.118.11.22       │ 154004 │
+│ ::ffff:217.118.11.33       │ 148449 │
+│ ::ffff:217.118.11.44       │ 148243 │
+└────────────────────────────┴────────┘
+
+ + +

IPv6StringToNum(s)

+

The reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes. +HEX can be uppercase or lowercase.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/json_functions/index.html b/docs/build/docs/en/functions/json_functions/index.html new file mode 100644 index 00000000000..497770ca32b --- /dev/null +++ b/docs/build/docs/en/functions/json_functions/index.html @@ -0,0 +1,3049 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Functions for working with JSON. - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Functions for working with JSON

+

In Yandex.Metrica, JSON is transmitted by users as session parameters. There are some special functions for working with this JSON. (Although in most of the cases, the JSONs are additionally pre-processed, and the resulting values are put in separate columns in their processed format.) All these functions are based on strong assumptions about what the JSON can be, but they try to do as little as possible to get the job done.

+

The following assumptions are made:

+
    +
  1. The field name (function argument) must be a constant.
    2. +
  2. The field name is somehow canonically encoded in JSON. For example: visitParamHas('{"abc":"def"}', 'abc') = 1, but visitParamHas('{"\\u0061\\u0062\\u0063":"def"}', 'abc') = 0
    3. +
  3. Fields are searched for on any nesting level, indiscriminately. If there are multiple matching fields, the first occurrence is used.
    4. +
  4. The JSON doesn't have space characters outside of string literals.
    5. +
+

visitParamHas(params, name)

+

Checks whether there is a field with the 'name' name.

+

visitParamExtractUInt(params, name)

+

Parses UInt64 from the value of the field named 'name'. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn't exist, or it exists but doesn't contain a number, it returns 0.

+

visitParamExtractInt(params, name)

+

The same as for Int64.

+

visitParamExtractFloat(params, name)

+

The same as for Float64.

+

visitParamExtractBool(params, name)

+

Parses a true/false value. The result is UInt8.

+

visitParamExtractRaw(params, name)

+

Returns the value of a field, including separators.

+

Examples:

+
visitParamExtractRaw('{"abc":"\\n\\u0000"}', 'abc') = '"\\n\\u0000"'
+visitParamExtractRaw('{"abc":{"def":[1,2,3]}}', 'abc') = '{"def":[1,2,3]}'
+
+ + +

visitParamExtractString(params, name)

+

Parses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string.

+

Examples:

+
visitParamExtractString('{"abc":"\\n\\u0000"}', 'abc') = '\n\0'
+visitParamExtractString('{"abc":"\\u263a"}', 'abc') = '☺'
+visitParamExtractString('{"abc":"\\u263"}', 'abc') = ''
+visitParamExtractString('{"abc":"hello}', 'abc') = ''
+
+ + +

There is currently no support for code points in the format \uXXXX\uYYYY that are not from the basic multilingual plane (they are converted to CESU-8 instead of UTF-8).

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/logical_functions/index.html b/docs/build/docs/en/functions/logical_functions/index.html new file mode 100644 index 00000000000..85a057e6f59 --- /dev/null +++ b/docs/build/docs/en/functions/logical_functions/index.html @@ -0,0 +1,2976 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Logical functions - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Logical functions

+

Logical functions accept any numeric types, but return a UInt8 number equal to 0 or 1.

+

Zero as an argument is considered "false," while any non-zero value is considered "true".

+

and, AND operator

+

or, OR operator

+

not, NOT operator

+

xor

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/math_functions/index.html b/docs/build/docs/en/functions/math_functions/index.html new file mode 100644 index 00000000000..fbabb8d1217 --- /dev/null +++ b/docs/build/docs/en/functions/math_functions/index.html @@ -0,0 +1,3262 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Mathematical functions - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Mathematical functions

+

All the functions return a Float64 number. The accuracy of the result is close to the maximum precision possible, but the result might not coincide with the machine representable number nearest to the corresponding real number.

+

e()

+

Returns a Float64 number close to the e number.

+

pi()

+

Returns a Float64 number close to π.

+

exp(x)

+

Accepts a numeric argument and returns a Float64 number close to the exponent of the argument.

+

log(x)

+

Accepts a numeric argument and returns a Float64 number close to the natural logarithm of the argument.

+

exp2(x)

+

Accepts a numeric argument and returns a Float64 number close to 2^x.

+

log2(x)

+

Accepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument.

+

exp10(x)

+

Accepts a numeric argument and returns a Float64 number close to 10^x.

+

log10(x)

+

Accepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument.

+

sqrt(x)

+

Accepts a numeric argument and returns a Float64 number close to the square root of the argument.

+

cbrt(x)

+

Accepts a numeric argument and returns a Float64 number close to the cubic root of the argument.

+

erf(x)

+

If 'x' is non-negative, then erf(x / σ√2) is the probability that a random variable having a normal distribution with standard deviation 'σ' takes the value that is separated from the expected value by more than 'x'.

+

Example (three sigma rule):

+
SELECT erf(3 / sqrt(2))
+
+ + +
┌─erf(divide(3, sqrt(2)))─┐
+│      0.9973002039367398 │
+└─────────────────────────┘
+
+ + +

erfc(x)

+

Accepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large 'x' values.

+

lgamma(x)

+

The logarithm of the gamma function.

+

tgamma(x)

+

Gamma function.

+

sin(x)

+

The sine.

+

cos(x)

+

The cosine.

+

tan(x)

+

The tangent.

+

asin(x)

+

The arc sine.

+

acos(x)

+

The arc cosine.

+

atan(x)

+

The arc tangent.

+

pow(x, y)

+

Accepts two numeric arguments and returns a Float64 number close to x^y.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/other_functions/index.html b/docs/build/docs/en/functions/other_functions/index.html new file mode 100644 index 00000000000..e034988f1ad --- /dev/null +++ b/docs/build/docs/en/functions/other_functions/index.html @@ -0,0 +1,3467 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Other functions - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Other functions

+

hostName()

+

Returns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server.

+

visibleWidth(x)

+

Calculates the approximate width when outputting values to the console in text format (tab-separated). +This function is used by the system for implementing Pretty formats.

+

toTypeName(x)

+

Returns a string containing the type name of the passed argument.

+

blockSize()

+

Gets the size of the block. +In ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for.

+

materialize(x)

+

Turns a constant into a full column containing just one value. +In ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior.

+

ignore(...)

+

Accepts any arguments and always returns 0. +However, the argument is still evaluated. This can be used for benchmarks.

+

sleep(seconds)

+

Sleeps 'seconds' seconds on each data block. You can specify an integer or a floating-point number.

+

currentDatabase()

+

Returns the name of the current database. +You can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database.

+

isFinite(x)

+

Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0.

+

isInfinite(x)

+

Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. Note that 0 is returned for a NaN.

+

isNaN(x)

+

Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0.

+

hasColumnInTable(['hostname'[, 'username'[, 'password']],] 'database', 'table', 'column')

+

Accepts constant strings: database name, table name, and column name. Returns a UInt8 constant expression equal to 1 if there is a column, otherwise 0. If the hostname parameter is set, the test will run on a remote server. +The function throws an exception if the table does not exist. +For elements in a nested data structure, the function checks for the existence of a column. For the nested data structure itself, the function returns 0.

+

bar

+

Allows building a unicode-art diagram.

+

bar (x, min, max, width) draws a band with a width proportional to (x - min) and equal to width characters when x = max.

+

Parameters:

+
    +
  • x – Value to display.
    • +
  • min, max – Integer constants. The value must fit in Int64.
    • +
  • width – Constant, positive number, may be a fraction.
    • +
+

The band is drawn with accuracy to one eighth of a symbol.

+

Example:

+
SELECT
+    toHour(EventTime) AS h,
+    count() AS c,
+    bar(c, 0, 600000, 20) AS bar
+FROM test.hits
+GROUP BY h
+ORDER BY h ASC
+
+ + +
┌──h─┬──────c─┬─bar────────────────┐
+│  0 │ 292907 │ █████████▋         │
+│  1 │ 180563 │ ██████             │
+│  2 │ 114861 │ ███▋               │
+│  3 │  85069 │ ██▋                │
+│  4 │  68543 │ ██▎                │
+│  5 │  78116 │ ██▌                │
+│  6 │ 113474 │ ███▋               │
+│  7 │ 170678 │ █████▋             │
+│  8 │ 278380 │ █████████▎         │
+│  9 │ 391053 │ █████████████      │
+│ 10 │ 457681 │ ███████████████▎   │
+│ 11 │ 493667 │ ████████████████▍  │
+│ 12 │ 509641 │ ████████████████▊  │
+│ 13 │ 522947 │ █████████████████▍ │
+│ 14 │ 539954 │ █████████████████▊ │
+│ 15 │ 528460 │ █████████████████▌ │
+│ 16 │ 539201 │ █████████████████▊ │
+│ 17 │ 523539 │ █████████████████▍ │
+│ 18 │ 506467 │ ████████████████▊  │
+│ 19 │ 520915 │ █████████████████▎ │
+│ 20 │ 521665 │ █████████████████▍ │
+│ 21 │ 542078 │ ██████████████████ │
+│ 22 │ 493642 │ ████████████████▍  │
+│ 23 │ 400397 │ █████████████▎     │
+└────┴────────┴────────────────────┘
+
+ + +

+

transform

+

Transforms a value according to the explicitly defined mapping of some elements to other ones. +There are two variations of this function:

+
    +
  1. transform(x, array_from, array_to, default)
    2. +
+

x – What to transform.

+

array_from – Constant array of values for converting.

+

array_to – Constant array of values to convert the values in 'from' to.

+

default – Which value to use if 'x' is not equal to any of the values in 'from'.

+

array_from and array_to – Arrays of the same size.

+

Types:

+

transform(T, Array(T), Array(U), U) -> U

+

T and U can be numeric, string, or Date or DateTime types. +Where the same letter is indicated (T or U), for numeric types these might not be matching types, but types that have a common type. +For example, the first argument can have the Int64 type, while the second has the Array(Uint16) type.

+

If the 'x' value is equal to one of the elements in the 'array_from' array, it returns the existing element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'default'. If there are multiple matching elements in 'array_from', it returns one of the matches.

+

Example:

+
SELECT
+    transform(SearchEngineID, [2, 3], ['Yandex', 'Google'], 'Other') AS title,
+    count() AS c
+FROM test.hits
+WHERE SearchEngineID != 0
+GROUP BY title
+ORDER BY c DESC
+
+ + +
┌─title─────┬──────c─┐
+│ Yandex    │ 498635 │
+│ Google    │ 229872 │
+│ Other     │ 104472 │
+└───────────┴────────┘
+
+ + +
    +
  1. transform(x, array_from, array_to)
    2. +
+

Differs from the first variation in that the 'default' argument is omitted. +If the 'x' value is equal to one of the elements in the 'array_from' array, it returns the matching element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'x'.

+

Types:

+

transform(T, Array(T), Array(T)) -> T

+

Example:

+
SELECT
+    transform(domain(Referer), ['yandex.ru', 'google.ru', 'vk.com'], ['www.yandex', 'example.com']) AS s,
+    count() AS c
+FROM test.hits
+GROUP BY domain(Referer)
+ORDER BY count() DESC
+LIMIT 10
+
+ + +
┌─s──────────────┬───────c─┐
+│                │ 2906259 │
+│ www.yandex     │  867767 │
+│ ███████.ru     │  313599 │
+│ mail.yandex.ru │  107147 │
+│ ██████.ru      │  100355 │
+│ █████████.ru   │   65040 │
+│ news.yandex.ru │   64515 │
+│ ██████.net     │   59141 │
+│ example.com    │   57316 │
+└────────────────┴─────────┘
+
+ + +

formatReadableSize(x)

+

Accepts the size (number of bytes). Returns a rounded size with a suffix (KiB, MiB, etc.) as a string.

+

Example:

+
SELECT
+    arrayJoin([1, 1024, 1024*1024, 192851925]) AS filesize_bytes,
+    formatReadableSize(filesize_bytes) AS filesize
+
+ + +
┌─filesize_bytes─┬─filesize───┐
+│              1 │ 1.00 B     │
+│           1024 │ 1.00 KiB   │
+│        1048576 │ 1.00 MiB   │
+│      192851925 │ 183.92 MiB │
+└────────────────┴────────────┘
+
+ + +

least(a, b)

+

Returns the smallest value from a and b.

+

greatest(a, b)

+

Returns the largest value of a and b.

+

uptime()

+

Returns the server's uptime in seconds.

+

version()

+

Returns the version of the server as a string.

+

rowNumberInAllBlocks()

+

Returns the ordinal number of the row in the data block. This function only considers the affected data blocks.

+

runningDifference(x)

+

Calculates the difference between successive row values ​​in the data block. +Returns 0 for the first row and the difference from the previous row for each subsequent row.

+

The result of the function depends on the affected data blocks and the order of data in the block. +If you make a subquery with ORDER BY and call the function from outside the subquery, you can get the expected result.

+

Example:

+
SELECT
+    EventID,
+    EventTime,
+    runningDifference(EventTime) AS delta
+FROM
+(
+    SELECT
+        EventID,
+        EventTime
+    FROM events
+    WHERE EventDate = '2016-11-24'
+    ORDER BY EventTime ASC
+    LIMIT 5
+)
+
+ + +
┌─EventID─┬───────────EventTime─┬─delta─┐
+│    1106 │ 2016-11-24 00:00:04 │     0 │
+│    1107 │ 2016-11-24 00:00:05 │     1 │
+│    1108 │ 2016-11-24 00:00:05 │     0 │
+│    1109 │ 2016-11-24 00:00:09 │     4 │
+│    1110 │ 2016-11-24 00:00:10 │     1 │
+└─────────┴─────────────────────┴───────┘
+
+ + +

MACNumToString(num)

+

Accepts a UInt64 number. Interprets it as a MAC address in big endian. Returns a string containing the corresponding MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form).

+

MACStringToNum(s)

+

The inverse function of MACNumToString. If the MAC address has an invalid format, it returns 0.

+

MACStringToOUI(s)

+

Accepts a MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form). Returns the first three octets as a UInt64 number. If the MAC address has an invalid format, it returns 0.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/random_functions/index.html b/docs/build/docs/en/functions/random_functions/index.html new file mode 100644 index 00000000000..93db7008d30 --- /dev/null +++ b/docs/build/docs/en/functions/random_functions/index.html @@ -0,0 +1,2952 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Functions for generating pseudo-random numbers - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Functions for generating pseudo-random numbers

+

Non-cryptographic generators of pseudo-random numbers are used.

+

All the functions accept zero arguments or one argument. +If an argument is passed, it can be any type, and its value is not used for anything. +The only purpose of this argument is to prevent common subexpression elimination, so that two different instances of the same function return different columns with different random numbers.

+

rand

+

Returns a pseudo-random UInt32 number, evenly distributed among all UInt32-type numbers. +Uses a linear congruential generator.

+

rand64

+

Returns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers. +Uses a linear congruential generator.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/rounding_functions/index.html b/docs/build/docs/en/functions/rounding_functions/index.html new file mode 100644 index 00000000000..8a74ae11d7d --- /dev/null +++ b/docs/build/docs/en/functions/rounding_functions/index.html @@ -0,0 +1,3018 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Rounding functions - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Rounding functions

+

floor(x[, N])

+

Returns the largest round number that is less than or equal to x. A round number is a multiple of 1/10N, or the nearest number of the appropriate data type if 1 / 10N isn't exact. +'N' is an integer constant, optional parameter. By default it is zero, which means to round to an integer. +'N' may be negative.

+

Examples: floor(123.45, 1) = 123.4, floor(123.45, -1) = 120.

+

x is any numeric type. The result is a number of the same type. +For integer arguments, it makes sense to round with a negative 'N' value (for non-negative 'N', the function doesn't do anything). +If rounding causes overflow (for example, floor(-128, -1)), an implementation-specific result is returned.

+

ceil(x[, N])

+

Returns the smallest round number that is greater than or equal to 'x'. In every other way, it is the same as the 'floor' function (see above).

+

round(x[, N])

+

Returns the round number nearest to 'num', which may be less than, greater than, or equal to 'x'.If 'x' is exactly in the middle between the nearest round numbers, one of them is returned (implementation-specific). +The number '-0.' may or may not be considered round (implementation-specific). +In every other way, this function is the same as 'floor' and 'ceil' described above.

+

roundToExp2(num)

+

Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two.

+

roundDuration(num)

+

Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length

+

roundAge(num)

+

Accepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to a number from the set: 18, 25, 35, 45, 55. This function is specific to Yandex.Metrica and used for implementing the report on user age.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/splitting_merging_functions/index.html b/docs/build/docs/en/functions/splitting_merging_functions/index.html new file mode 100644 index 00000000000..3eeb8eb0f6d --- /dev/null +++ b/docs/build/docs/en/functions/splitting_merging_functions/index.html @@ -0,0 +1,2980 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Functions for splitting and merging strings and arrays - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Functions for splitting and merging strings and arrays

+

splitByChar(separator, s)

+

Splits a string into substrings separated by 'separator'.'separator' must be a string constant consisting of exactly one character. +Returns an array of selected substrings. Empty substrings may be selected if the separator occurs at the beginning or end of the string, or if there are multiple consecutive separators.

+

splitByString(separator, s)

+

The same as above, but it uses a string of multiple characters as the separator. The string must be non-empty.

+

arrayStringConcat(arr[, separator])

+

Concatenates the strings listed in the array with the separator.'separator' is an optional parameter: a constant string, set to an empty string by default. +Returns the string.

+

alphaTokens(s)

+

Selects substrings of consecutive bytes from the ranges a-z and A-Z.Returns an array of substrings.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/string_functions/index.html b/docs/build/docs/en/functions/string_functions/index.html new file mode 100644 index 00000000000..97a253ca1f0 --- /dev/null +++ b/docs/build/docs/en/functions/string_functions/index.html @@ -0,0 +1,3168 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Functions for working with strings - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Functions for working with strings

+

empty

+

Returns 1 for an empty string or 0 for a non-empty string. +The result type is UInt8. +A string is considered non-empty if it contains at least one byte, even if this is a space or a null byte. +The function also works for arrays.

+

notEmpty

+

Returns 0 for an empty string or 1 for a non-empty string. +The result type is UInt8. +The function also works for arrays.

+

length

+

Returns the length of a string in bytes (not in characters, and not in code points). +The result type is UInt64. +The function also works for arrays.

+

lengthUTF8

+

Returns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception). +The result type is UInt64.

+

lower

+

Converts ASCII Latin symbols in a string to lowercase.

+

upper

+

Converts ASCII Latin symbols in a string to uppercase.

+

lowerUTF8

+

Converts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. +It doesn't detect the language. So for Turkish the result might not be exactly correct. +If the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point. +If the string contains a set of bytes that is not UTF-8, then the behavior is undefined.

+

upperUTF8

+

Converts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. +It doesn't detect the language. So for Turkish the result might not be exactly correct. +If the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point. +If the string contains a set of bytes that is not UTF-8, then the behavior is undefined.

+

reverse

+

Reverses the string (as a sequence of bytes).

+

reverseUTF8

+

Reverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn't throw an exception).

+

concat(s1, s2, ...)

+

Concatenates the strings listed in the arguments, without a separator.

+

substring(s, offset, length)

+

Returns a substring starting with the byte from the 'offset' index that is 'length' bytes long. Character indexing starts from one (as in standard SQL). The 'offset' and 'length' arguments must be constants.

+

substringUTF8(s, offset, length)

+

The same as 'substring', but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).

+

appendTrailingCharIfAbsent(s, c)

+

If the 's' string is non-empty and does not contain the 'c' character at the end, it appends the 'c' character to the end.

+

convertCharset(s, from, to)

+

Returns the string 's' that was converted from the encoding in 'from' to the encoding in 'to'.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/string_replace_functions/index.html b/docs/build/docs/en/functions/string_replace_functions/index.html new file mode 100644 index 00000000000..7923f539f70 --- /dev/null +++ b/docs/build/docs/en/functions/string_replace_functions/index.html @@ -0,0 +1,3034 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Functions for searching and replacing in strings - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Functions for searching and replacing in strings

+

replaceOne(haystack, pattern, replacement)

+

Replaces the first occurrence, if it exists, of the 'pattern' substring in 'haystack' with the 'replacement' substring. +Hereafter, 'pattern' and 'replacement' must be constants.

+

replaceAll(haystack, pattern, replacement)

+

Replaces all occurrences of the 'pattern' substring in 'haystack' with the 'replacement' substring.

+

replaceRegexpOne(haystack, pattern, replacement)

+

Replacement using the 'pattern' regular expression. A re2 regular expression. +Replaces only the first occurrence, if it exists. +A pattern can be specified as 'replacement'. This pattern can include substitutions \0-\9. +The substitution \0 includes the entire regular expression. Substitutions \1-\9 correspond to the subpattern numbers.To use the \ character in a template, escape it using \. +Also keep in mind that a string literal requires an extra escape.

+

Example 1. Converting the date to American format:

+
SELECT DISTINCT
+    EventDate,
+    replaceRegexpOne(toString(EventDate), '(\\d{4})-(\\d{2})-(\\d{2})', '\\2/\\3/\\1') AS res
+FROM test.hits
+LIMIT 7
+FORMAT TabSeparated
+
+ + +
2014-03-17      03/17/2014
+2014-03-18      03/18/2014
+2014-03-19      03/19/2014
+2014-03-20      03/20/2014
+2014-03-21      03/21/2014
+2014-03-22      03/22/2014
+2014-03-23      03/23/2014
+
+ + +

Example 2. Copying a string ten times:

+
SELECT replaceRegexpOne('Hello, World!', '.*', '\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0') AS res
+
+ + +
┌─res────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
+│ Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World! │
+└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
+
+ + +

replaceRegexpAll(haystack, pattern, replacement)

+

This does the same thing, but replaces all the occurrences. Example:

+
SELECT replaceRegexpAll('Hello, World!', '.', '\\0\\0') AS res
+
+ + +
┌─res────────────────────────┐
+│ HHeelllloo,,  WWoorrlldd!! │
+└────────────────────────────┘
+
+ + +

As an exception, if a regular expression worked on an empty substring, the replacement is not made more than once. +Example:

+
SELECT replaceRegexpAll('Hello, World!', '^', 'here: ') AS res
+
+ + +
┌─res─────────────────┐
+│ here: Hello, World! │
+└─────────────────────┘
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/string_search_functions/index.html b/docs/build/docs/en/functions/string_search_functions/index.html new file mode 100644 index 00000000000..7db18e5a569 --- /dev/null +++ b/docs/build/docs/en/functions/string_search_functions/index.html @@ -0,0 +1,3041 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Functions for searching strings - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Functions for searching strings

+

The search is case-sensitive in all these functions. +The search substring or regular expression must be a constant in all these functions.

+

position(haystack, needle)

+

Search for the needle substring in the haystack string. +Returns the position (in bytes) of the found substring, starting from 1, or returns 0 if the substring was not found.

+

For case-insensitive search use positionCaseInsensitive function.

+

positionUTF8(haystack, needle)

+

The same as position, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).

+

For case-insensitive search use positionCaseInsensitiveUTF8 function.

+

match(haystack, pattern)

+

Checks whether the string matches the 'pattern' regular expression. A re2 regular expression. +Returns 0 if it doesn't match, or 1 if it matches.

+

Note that the backslash symbol (\) is used for escaping in the regular expression. The same symbol is used for escaping in string literals. So in order to escape the symbol in a regular expression, you must write two backslashes (\) in a string literal.

+

The regular expression works with the string as if it is a set of bytes. The regular expression can't contain null bytes. +For patterns to search for substrings in a string, it is better to use LIKE or 'position', since they work much faster.

+

extract(haystack, pattern)

+

Extracts a fragment of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. If the regex doesn't contain subpatterns, it takes the fragment that matches the entire regex. Otherwise, it takes the fragment that matches the first subpattern.

+

extractAll(haystack, pattern)

+

Extracts all the fragments of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex. In general, the behavior is the same as the 'extract' function (it takes the first subpattern, or the entire expression if there isn't a subpattern).

+

like(haystack, pattern), haystack LIKE pattern operator

+

Checks whether a string matches a simple regular expression. +The regular expression can contain the metasymbols % and _.

+

``% indicates any quantity of any bytes (including zero characters).

+

_ indicates any one byte.

+

Use the backslash (\) for escaping metasymbols. See the note on escaping in the description of the 'match' function.

+

For regular expressions like %needle%, the code is more optimal and works as fast as the position function. +For other regular expressions, the code is the same as for the 'match' function.

+

notLike(haystack, pattern), haystack NOT LIKE pattern operator

+

The same thing as 'like', but negative.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/type_conversion_functions/index.html b/docs/build/docs/en/functions/type_conversion_functions/index.html new file mode 100644 index 00000000000..c5a855ca5fd --- /dev/null +++ b/docs/build/docs/en/functions/type_conversion_functions/index.html @@ -0,0 +1,3197 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Type conversion functions - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

+

Type conversion functions

+

toUInt8, toUInt16, toUInt32, toUInt64

+

toInt8, toInt16, toInt32, toInt64

+

toFloat32, toFloat64

+

toUInt8OrZero, toUInt16OrZero, toUInt32OrZero, toUInt64OrZero, toInt8OrZero, toInt16OrZero, toInt32OrZero, toInt64OrZero, toFloat32OrZero, toFloat64OrZero

+

toDate, toDateTime

+

toString

+

Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. +All these functions accept one argument.

+

When converting to or from a string, the value is formatted or parsed using the same rules as for the TabSeparated format (and almost all other text formats). If the string can't be parsed, an exception is thrown and the request is canceled.

+

When converting dates to numbers or vice versa, the date corresponds to the number of days since the beginning of the Unix epoch. +When converting dates with times to numbers or vice versa, the date with time corresponds to the number of seconds since the beginning of the Unix epoch.

+

The date and date-with-time formats for the toDate/toDateTime functions are defined as follows:

+
YYYY-MM-DD
+YYYY-MM-DD hh:mm:ss
+
+ + +

As an exception, if converting from UInt32, Int32, UInt64, or Int64 numeric types to Date, and if the number is greater than or equal to 65536, the number is interpreted as a Unix timestamp (and not as the number of days) and is rounded to the date. This allows support for the common occurrence of writing 'toDate(unix_timestamp)', which otherwise would be an error and would require writing the more cumbersome 'toDate(toDateTime(unix_timestamp))'.

+

Conversion between a date and date with time is performed the natural way: by adding a null time or dropping the time.

+

Conversion between numeric types uses the same rules as assignments between different numeric types in C++.

+

Additionally, the toString function of the DateTime argument can take a second String argument containing the name of the time zone. Example: Asia/Yekaterinburg In this case, the time is formatted according to the specified time zone.

+
SELECT
+    now() AS now_local,
+    toString(now(), 'Asia/Yekaterinburg') AS now_yekat
+
+ + +
┌───────────now_local─┬─now_yekat───────────┐
+│ 2016-06-15 00:11:21 │ 2016-06-15 02:11:21 │
+└─────────────────────┴─────────────────────┘
+
+ + +

Also see the toUnixTimestamp function.

+

toFixedString(s, N)

+

Converts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant. +If the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown.

+

toStringCutToZero(s)

+

Accepts a String or FixedString argument. Returns the String with the content truncated at the first zero byte found.

+

Example:

+
SELECT toFixedString('foo', 8) AS s, toStringCutToZero(s) AS s_cut
+
+ + +
┌─s─────────────┬─s_cut─┐
+│ foo\0\0\0\0\0 │ foo   │
+└───────────────┴───────┘
+
+ + +
SELECT toFixedString('foo\0bar', 8) AS s, toStringCutToZero(s) AS s_cut
+
+ + +
┌─s──────────┬─s_cut─┐
+│ foo\0bar\0 │ foo   │
+└────────────┴───────┘
+
+ + +

reinterpretAsUInt8, reinterpretAsUInt16, reinterpretAsUInt32, reinterpretAsUInt64

+

reinterpretAsInt8, reinterpretAsInt16, reinterpretAsInt32, reinterpretAsInt64

+

reinterpretAsFloat32, reinterpretAsFloat64

+

reinterpretAsDate, reinterpretAsDateTime

+

These functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.

+

reinterpretAsString

+

This function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long.

+

CAST(x, t)

+

Converts 'x' to the 't' data type. The syntax CAST(x AS t) is also supported.

+

Example:

+
SELECT
+    '2016-06-15 23:00:00' AS timestamp,
+    CAST(timestamp AS DateTime) AS datetime,
+    CAST(timestamp AS Date) AS date,
+    CAST(timestamp, 'String') AS string,
+    CAST(timestamp, 'FixedString(22)') AS fixed_string
+
+ + +
┌─timestamp───────────┬────────────datetime─┬───────date─┬─string──────────────┬─fixed_string──────────────┐
+│ 2016-06-15 23:00:00 │ 2016-06-15 23:00:00 │ 2016-06-15 │ 2016-06-15 23:00:00 │ 2016-06-15 23:00:00\0\0\0 │
+└─────────────────────┴─────────────────────┴────────────┴─────────────────────┴───────────────────────────┘
+
+ + +

Conversion to FixedString (N) only works for arguments of type String or FixedString (N).

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/url_functions/index.html b/docs/build/docs/en/functions/url_functions/index.html new file mode 100644 index 00000000000..5c9ba333209 --- /dev/null +++ b/docs/build/docs/en/functions/url_functions/index.html @@ -0,0 +1,3343 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Functions for working with URLs - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Functions for working with URLs

+

All these functions don't follow the RFC. They are maximally simplified for improved performance.

+

Functions that extract part of a URL

+

If there isn't anything similar in a URL, an empty string is returned.

+

protocol

+

Returns the protocol. Examples: http, ftp, mailto, magnet...

+

domain

+

Gets the domain.

+

domainWithoutWWW

+

Returns the domain and removes no more than one 'www.' from the beginning of it, if present.

+

topLevelDomain

+

Returns the top-level domain. Example: .ru.

+

firstSignificantSubdomain

+

Returns the "first significant subdomain". This is a non-standard concept specific to Yandex.Metrica. The first significant subdomain is a second-level domain if it is 'com', 'net', 'org', or 'co'. Otherwise, it is a third-level domain. For example, firstSignificantSubdomain ('https://news.yandex.ru/') = 'yandex ', firstSignificantSubdomain ('https://news.yandex.com.tr/') = 'yandex '. The list of "insignificant" second-level domains and other implementation details may change in the future.

+

cutToFirstSignificantSubdomain

+

Returns the part of the domain that includes top-level subdomains up to the "first significant subdomain" (see the explanation above).

+

For example, cutToFirstSignificantSubdomain('https://news.yandex.com.tr/') = 'yandex.com.tr'.

+

path

+

Returns the path. Example: /top/news.html The path does not include the query string.

+

pathFull

+

The same as above, but including query string and fragment. Example: /top/news.html?page=2#comments

+

queryString

+

Returns the query string. Example: page=1&lr=213. query-string does not include the initial question mark, as well as # and everything after #.

+

fragment

+

Returns the fragment identifier. fragment does not include the initial hash symbol.

+

queryStringAndFragment

+

Returns the query string and fragment identifier. Example: page=1#29390.

+

extractURLParameter(URL, name)

+

Returns the value of the 'name' parameter in the URL, if present. Otherwise, an empty string. If there are many parameters with this name, it returns the first occurrence. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.

+

extractURLParameters(URL)

+

Returns an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way.

+

extractURLParameterNames(URL)

+

Returns an array of name strings corresponding to the names of URL parameters. The values are not decoded in any way.

+

URLHierarchy(URL)

+

Returns an array containing the URL, truncated at the end by the symbols /,? in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:

+

URLPathHierarchy(URL)

+

The same as above, but without the protocol and host in the result. The / element (root) is not included. Example: the function is used to implement tree reports the URL in Yandex. Metric.

+
URLPathHierarchy('https://example.com/browse/CONV-6788') =
+[
+    '/browse/',
+    '/browse/CONV-6788'
+]
+
+ + +

decodeURLComponent(URL)

+

Returns the decoded URL. +Example:

+
SELECT decodeURLComponent('http://127.0.0.1:8123/?query=SELECT%201%3B') AS DecodedURL;
+
+ + +
┌─DecodedURL─────────────────────────────┐
+│ http://127.0.0.1:8123/?query=SELECT 1; │
+└────────────────────────────────────────┘
+
+ + +

Functions that remove part of a URL.

+

If the URL doesn't have anything similar, the URL remains unchanged.

+

cutWWW

+

Removes no more than one 'www.' from the beginning of the URL's domain, if present.

+

cutQueryString

+

Removes query string. The question mark is also removed.

+

cutFragment

+

Removes the fragment identifier. The number sign is also removed.

+

cutQueryStringAndFragment

+

Removes the query string and fragment identifier. The question mark and number sign are also removed.

+

cutURLParameter(URL, name)

+

Removes the 'name' URL parameter, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/functions/ym_dict_functions/index.html b/docs/build/docs/en/functions/ym_dict_functions/index.html new file mode 100644 index 00000000000..f813423a342 --- /dev/null +++ b/docs/build/docs/en/functions/ym_dict_functions/index.html @@ -0,0 +1,3161 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Functions for working with Yandex.Metrica dictionaries - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Functions for working with Yandex.Metrica dictionaries

+

In order for the functions below to work, the server config must specify the paths and addresses for getting all the Yandex.Metrica dictionaries. The dictionaries are loaded at the first call of any of these functions. If the reference lists can't be loaded, an exception is thrown.

+

For information about creating reference lists, see the section "Dictionaries".

+

Multiple geobases

+

ClickHouse supports working with multiple alternative geobases (regional hierarchies) simultaneously, in order to support various perspectives on which countries certain regions belong to.

+

The 'clickhouse-server' config specifies the file with the regional hierarchy::<path_to_regions_hierarchy_file>/opt/geo/regions_hierarchy.txt</path_to_regions_hierarchy_file>

+

Besides this file, it also searches for files nearby that have the _ symbol and any suffix appended to the name (before the file extension). +For example, it will also find the file /opt/geo/regions_hierarchy_ua.txt, if present.

+

ua is called the dictionary key. For a dictionary without a suffix, the key is an empty string.

+

All the dictionaries are re-loaded in runtime (once every certain number of seconds, as defined in the builtin_dictionaries_reload_interval config parameter, or once an hour by default). However, the list of available dictionaries is defined one time, when the server starts.

+

All functions for working with regions have an optional argument at the end – the dictionary key. It is referred to as the geobase. +Example:

+
regionToCountry(RegionID) – Uses the default dictionary: /opt/geo/regions_hierarchy.txt
+regionToCountry(RegionID, '') – Uses the default dictionary: /opt/geo/regions_hierarchy.txt
+regionToCountry(RegionID, 'ua') – Uses the dictionary for the 'ua' key: /opt/geo/regions_hierarchy_ua.txt
+
+ + +

regionToCity(id[, geobase])

+

Accepts a UInt32 number – the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0.

+

regionToArea(id[, geobase])

+

Converts a region to an area (type 5 in the geobase). In every other way, this function is the same as 'regionToCity'.

+
SELECT DISTINCT regionToName(regionToArea(toUInt32(number), 'ua'))
+FROM system.numbers
+LIMIT 15
+
+ + +
┌─regionToName(regionToArea(toUInt32(number), \'ua\'))─┐
+│                                                      │
+│ Moscow and Moscow region                             │
+│ St. Petersburg and Leningrad region                  │
+│ Belgorod region                                      │
+│ Ivanovsk region                                      │
+│ Kaluga region                                        │
+│ Kostroma region                                      │
+│ Kursk region                                         │
+│ Lipetsk region                                       │
+│ Orlov region                                         │
+│ Ryazan region                                        │
+│ Smolensk region                                      │
+│ Tambov region                                        │
+│ Tver region                                          │
+│ Tula region                                          │
+└──────────────────────────────────────────────────────┘
+
+ + +

regionToDistrict(id[, geobase])

+

Converts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as 'regionToCity'.

+
SELECT DISTINCT regionToName(regionToDistrict(toUInt32(number), 'ua'))
+FROM system.numbers
+LIMIT 15
+
+ + +
┌─regionToName(regionToDistrict(toUInt32(number), \'ua\'))─┐
+│                                                          │
+│ Central federal district                                 │
+│ Northwest federal district                               │
+│ South federal district                                   │
+│ North Caucases federal district                          │
+│ Privolga federal district                                │
+│ Ural federal district                                    │
+│ Siberian federal district                                │
+│ Far East federal district                                │
+│ Scotland                                                 │
+│ Faroe Islands                                            │
+│ Flemish region                                           │
+│ Brussels capital region                                  │
+│ Wallonia                                                 │
+│ Federation of Bosnia and Herzegovina                     │
+└──────────────────────────────────────────────────────────┘
+
+ + +

regionToCountry(id[, geobase])

+

Converts a region to a country. In every other way, this function is the same as 'regionToCity'. +Example: regionToCountry(toUInt32(213)) = 225 converts Moscow (213) to Russia (225).

+

regionToContinent(id[, geobase])

+

Converts a region to a continent. In every other way, this function is the same as 'regionToCity'. +Example: regionToContinent(toUInt32(213)) = 10001 converts Moscow (213) to Eurasia (10001).

+

regionToPopulation(id[, geobase])

+

Gets the population for a region. +The population can be recorded in files with the geobase. See the section "External dictionaries". +If the population is not recorded for the region, it returns 0. +In the Yandex geobase, the population might be recorded for child regions, but not for parent regions.

+

regionIn(lhs, rhs[, geobase])

+

Checks whether a 'lhs' region belongs to a 'rhs' region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn't belong. +The relationship is reflexive – any region also belongs to itself.

+

regionHierarchy(id[, geobase])

+

Accepts a UInt32 number – the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain. +Example: regionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000].

+

regionToName(id[, lang])

+

Accepts a UInt32 number – the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language 'ru' is used. If the language is not supported, an exception is thrown. Returns a string – the name of the region in the corresponding language. If the region with the specified ID doesn't exist, an empty string is returned.

+

ua and uk both mean Ukrainian.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/getting_started/example_datasets/amplab_benchmark/index.html b/docs/build/docs/en/getting_started/example_datasets/amplab_benchmark/index.html new file mode 100644 index 00000000000..081fe88290a --- /dev/null +++ b/docs/build/docs/en/getting_started/example_datasets/amplab_benchmark/index.html @@ -0,0 +1,3000 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + AMPLab Big Data Benchmark - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

AMPLab Big Data Benchmark

+

See https://amplab.cs.berkeley.edu/benchmark/

+

Sign up for a free account at https://aws.amazon.com. You will need a credit card, email and phone number.Get a new access key at https://console.aws.amazon.com/iam/home?nc2=h_m_sc#security_credential

+

Run the following in the console:

+
sudo apt-get install s3cmd
+mkdir tiny; cd tiny;
+s3cmd sync s3://big-data-benchmark/pavlo/text-deflate/tiny/ .
+cd ..
+mkdir 1node; cd 1node;
+s3cmd sync s3://big-data-benchmark/pavlo/text-deflate/1node/ .
+cd ..
+mkdir 5nodes; cd 5nodes;
+s3cmd sync s3://big-data-benchmark/pavlo/text-deflate/5nodes/ .
+cd ..
+
+ + +

Run the following ClickHouse queries:

+
CREATE TABLE rankings_tiny
+(
+    pageURL String,
+    pageRank UInt32,
+    avgDuration UInt32
+) ENGINE = Log;
+
+CREATE TABLE uservisits_tiny
+(
+    sourceIP String,
+    destinationURL String,
+    visitDate Date,
+    adRevenue Float32,
+    UserAgent String,
+    cCode FixedString(3),
+    lCode FixedString(6),
+    searchWord String,
+    duration UInt32
+) ENGINE = MergeTree(visitDate, visitDate, 8192);
+
+CREATE TABLE rankings_1node
+(
+    pageURL String,
+    pageRank UInt32,
+    avgDuration UInt32
+) ENGINE = Log;
+
+CREATE TABLE uservisits_1node
+(
+    sourceIP String,
+    destinationURL String,
+    visitDate Date,
+    adRevenue Float32,
+    UserAgent String,
+    cCode FixedString(3),
+    lCode FixedString(6),
+    searchWord String,
+    duration UInt32
+) ENGINE = MergeTree(visitDate, visitDate, 8192);
+
+CREATE TABLE rankings_5nodes_on_single
+(
+    pageURL String,
+    pageRank UInt32,
+    avgDuration UInt32
+) ENGINE = Log;
+
+CREATE TABLE uservisits_5nodes_on_single
+(
+    sourceIP String,
+    destinationURL String,
+    visitDate Date,
+    adRevenue Float32,
+    UserAgent String,
+    cCode FixedString(3),
+    lCode FixedString(6),
+    searchWord String,
+    duration UInt32
+) ENGINE = MergeTree(visitDate, visitDate, 8192);
+
+ + +

Go back to the console:

+
for i in tiny/rankings/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO rankings_tiny FORMAT CSV"; done
+for i in tiny/uservisits/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO uservisits_tiny FORMAT CSV"; done
+for i in 1node/rankings/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO rankings_1node FORMAT CSV"; done
+for i in 1node/uservisits/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO uservisits_1node FORMAT CSV"; done
+for i in 5nodes/rankings/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO rankings_5nodes_on_single FORMAT CSV"; done
+for i in 5nodes/uservisits/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO uservisits_5nodes_on_single FORMAT CSV"; done
+
+ + +

Queries for obtaining data samples:

+
SELECT pageURL, pageRank FROM rankings_1node WHERE pageRank > 1000
+
+SELECT substring(sourceIP, 1, 8), sum(adRevenue) FROM uservisits_1node GROUP BY substring(sourceIP, 1, 8)
+
+SELECT
+    sourceIP,
+    sum(adRevenue) AS totalRevenue,
+    avg(pageRank) AS pageRank
+FROM rankings_1node ALL INNER JOIN
+(
+    SELECT
+        sourceIP,
+        destinationURL AS pageURL,
+        adRevenue
+    FROM uservisits_1node
+    WHERE (visitDate > '1980-01-01') AND (visitDate < '1980-04-01')
+) USING pageURL
+GROUP BY sourceIP
+ORDER BY totalRevenue DESC
+LIMIT 1
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/getting_started/example_datasets/criteo/index.html b/docs/build/docs/en/getting_started/example_datasets/criteo/index.html new file mode 100644 index 00000000000..e05aaa979b6 --- /dev/null +++ b/docs/build/docs/en/getting_started/example_datasets/criteo/index.html @@ -0,0 +1,2953 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Terabyte click logs from Criteo - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Terabyte of click logs from Criteo

+

Download the data from http://labs.criteo.com/downloads/download-terabyte-click-logs/

+

Create a table to import the log to:

+
CREATE TABLE criteo_log (date Date, clicked UInt8, int1 Int32, int2 Int32, int3 Int32, int4 Int32, int5 Int32, int6 Int32, int7 Int32, int8 Int32, int9 Int32, int10 Int32, int11 Int32, int12 Int32, int13 Int32, cat1 String, cat2 String, cat3 String, cat4 String, cat5 String, cat6 String, cat7 String, cat8 String, cat9 String, cat10 String, cat11 String, cat12 String, cat13 String, cat14 String, cat15 String, cat16 String, cat17 String, cat18 String, cat19 String, cat20 String, cat21 String, cat22 String, cat23 String, cat24 String, cat25 String, cat26 String) ENGINE = Log
+
+ + +

Download the data:

+
for i in {00..23}; do echo $i; zcat datasets/criteo/day_${i#0}.gz | sed -r 's/^/2000-01-'${i/00/24}'\t/' | clickhouse-client --host=example-perftest01j --query="INSERT INTO criteo_log FORMAT TabSeparated"; done
+
+ + +

Create a table for the converted data:

+
CREATE TABLE criteo
+(
+    date Date,
+    clicked UInt8,
+    int1 Int32,
+    int2 Int32,
+    int3 Int32,
+    int4 Int32,
+    int5 Int32,
+    int6 Int32,
+    int7 Int32,
+    int8 Int32,
+    int9 Int32,
+    int10 Int32,
+    int11 Int32,
+    int12 Int32,
+    int13 Int32,
+    icat1 UInt32,
+    icat2 UInt32,
+    icat3 UInt32,
+    icat4 UInt32,
+    icat5 UInt32,
+    icat6 UInt32,
+    icat7 UInt32,
+    icat8 UInt32,
+    icat9 UInt32,
+    icat10 UInt32,
+    icat11 UInt32,
+    icat12 UInt32,
+    icat13 UInt32,
+    icat14 UInt32,
+    icat15 UInt32,
+    icat16 UInt32,
+    icat17 UInt32,
+    icat18 UInt32,
+    icat19 UInt32,
+    icat20 UInt32,
+    icat21 UInt32,
+    icat22 UInt32,
+    icat23 UInt32,
+    icat24 UInt32,
+    icat25 UInt32,
+    icat26 UInt32
+) ENGINE = MergeTree(date, intHash32(icat1), (date, intHash32(icat1)), 8192)
+
+ + +

Transform data from the raw log and put it in the second table:

+
INSERT INTO criteo SELECT date, clicked, int1, int2, int3, int4, int5, int6, int7, int8, int9, int10, int11, int12, int13, reinterpretAsUInt32(unhex(cat1)) AS icat1, reinterpretAsUInt32(unhex(cat2)) AS icat2, reinterpretAsUInt32(unhex(cat3)) AS icat3, reinterpretAsUInt32(unhex(cat4)) AS icat4, reinterpretAsUInt32(unhex(cat5)) AS icat5, reinterpretAsUInt32(unhex(cat6)) AS icat6, reinterpretAsUInt32(unhex(cat7)) AS icat7, reinterpretAsUInt32(unhex(cat8)) AS icat8, reinterpretAsUInt32(unhex(cat9)) AS icat9, reinterpretAsUInt32(unhex(cat10)) AS icat10, reinterpretAsUInt32(unhex(cat11)) AS icat11, reinterpretAsUInt32(unhex(cat12)) AS icat12, reinterpretAsUInt32(unhex(cat13)) AS icat13, reinterpretAsUInt32(unhex(cat14)) AS icat14, reinterpretAsUInt32(unhex(cat15)) AS icat15, reinterpretAsUInt32(unhex(cat16)) AS icat16, reinterpretAsUInt32(unhex(cat17)) AS icat17, reinterpretAsUInt32(unhex(cat18)) AS icat18, reinterpretAsUInt32(unhex(cat19)) AS icat19, reinterpretAsUInt32(unhex(cat20)) AS icat20, reinterpretAsUInt32(unhex(cat21)) AS icat21, reinterpretAsUInt32(unhex(cat22)) AS icat22, reinterpretAsUInt32(unhex(cat23)) AS icat23, reinterpretAsUInt32(unhex(cat24)) AS icat24, reinterpretAsUInt32(unhex(cat25)) AS icat25, reinterpretAsUInt32(unhex(cat26)) AS icat26 FROM criteo_log;
+
+DROP TABLE criteo_log;
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/getting_started/example_datasets/nyc_taxi/index.html b/docs/build/docs/en/getting_started/example_datasets/nyc_taxi/index.html new file mode 100644 index 00000000000..7ee92ad3acf --- /dev/null +++ b/docs/build/docs/en/getting_started/example_datasets/nyc_taxi/index.html @@ -0,0 +1,3277 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + New York Taxi data - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

New York Taxi data

+

How to import the raw data

+

See https://github.com/toddwschneider/nyc-taxi-data and http://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html for the description of the dataset and instructions for downloading.

+

Downloading will result in about 227 GB of uncompressed data in CSV files. The download takes about an hour over a 1 Gbit connection (parallel downloading from s3.amazonaws.com recovers at least half of a 1 Gbit channel). +Some of the files might not download fully. Check the file sizes and re-download any that seem doubtful.

+

Some of the files might contain invalid rows. You can fix them as follows:

+
sed -E '/(.*,){18,}/d' data/yellow_tripdata_2010-02.csv > data/yellow_tripdata_2010-02.csv_
+sed -E '/(.*,){18,}/d' data/yellow_tripdata_2010-03.csv > data/yellow_tripdata_2010-03.csv_
+mv data/yellow_tripdata_2010-02.csv_ data/yellow_tripdata_2010-02.csv
+mv data/yellow_tripdata_2010-03.csv_ data/yellow_tripdata_2010-03.csv
+
+ + +

Then the data must be pre-processed in PostgreSQL. This will create selections of points in the polygons (to match points on the map with the boroughs of New York City) and combine all the data into a single denormalized flat table by using a JOIN. To do this, you will need to install PostgreSQL with PostGIS support.

+

Be careful when running initialize_database.sh and manually re-check that all the tables were created correctly.

+

It takes about 20-30 minutes to process each month's worth of data in PostgreSQL, for a total of about 48 hours.

+

You can check the number of downloaded rows as follows:

+
time psql nyc-taxi-data -c "SELECT count(*) FROM trips;"
+##    count
+ 1298979494
+(1 row)
+
+real    7m9.164s
+
+ + +

(This is slightly more than 1.1 billion rows reported by Mark Litwintschik in a series of blog posts.)

+

The data in PostgreSQL uses 370 GB of space.

+

Exporting the data from PostgreSQL:

+
COPY
+(
+    SELECT trips.id,
+           trips.vendor_id,
+           trips.pickup_datetime,
+           trips.dropoff_datetime,
+           trips.store_and_fwd_flag,
+           trips.rate_code_id,
+           trips.pickup_longitude,
+           trips.pickup_latitude,
+           trips.dropoff_longitude,
+           trips.dropoff_latitude,
+           trips.passenger_count,
+           trips.trip_distance,
+           trips.fare_amount,
+           trips.extra,
+           trips.mta_tax,
+           trips.tip_amount,
+           trips.tolls_amount,
+           trips.ehail_fee,
+           trips.improvement_surcharge,
+           trips.total_amount,
+           trips.payment_type,
+           trips.trip_type,
+           trips.pickup,
+           trips.dropoff,
+
+           cab_types.type cab_type,
+
+           weather.precipitation_tenths_of_mm rain,
+           weather.snow_depth_mm,
+           weather.snowfall_mm,
+           weather.max_temperature_tenths_degrees_celsius max_temp,
+           weather.min_temperature_tenths_degrees_celsius min_temp,
+           weather.average_wind_speed_tenths_of_meters_per_second wind,
+
+           pick_up.gid pickup_nyct2010_gid,
+           pick_up.ctlabel pickup_ctlabel,
+           pick_up.borocode pickup_borocode,
+           pick_up.boroname pickup_boroname,
+           pick_up.ct2010 pickup_ct2010,
+           pick_up.boroct2010 pickup_boroct2010,
+           pick_up.cdeligibil pickup_cdeligibil,
+           pick_up.ntacode pickup_ntacode,
+           pick_up.ntaname pickup_ntaname,
+           pick_up.puma pickup_puma,
+
+           drop_off.gid dropoff_nyct2010_gid,
+           drop_off.ctlabel dropoff_ctlabel,
+           drop_off.borocode dropoff_borocode,
+           drop_off.boroname dropoff_boroname,
+           drop_off.ct2010 dropoff_ct2010,
+           drop_off.boroct2010 dropoff_boroct2010,
+           drop_off.cdeligibil dropoff_cdeligibil,
+           drop_off.ntacode dropoff_ntacode,
+           drop_off.ntaname dropoff_ntaname,
+           drop_off.puma dropoff_puma
+    FROM trips
+    LEFT JOIN cab_types
+        ON trips.cab_type_id = cab_types.id
+    LEFT JOIN central_park_weather_observations_raw weather
+        ON weather.date = trips.pickup_datetime::date
+    LEFT JOIN nyct2010 pick_up
+        ON pick_up.gid = trips.pickup_nyct2010_gid
+    LEFT JOIN nyct2010 drop_off
+        ON drop_off.gid = trips.dropoff_nyct2010_gid
+) TO '/opt/milovidov/nyc-taxi-data/trips.tsv';
+
+ + +

The data snapshot is created at a speed of about 50 MB per second. While creating the snapshot, PostgreSQL reads from the disk at a speed of about 28 MB per second. +This takes about 5 hours. The resulting TSV file is 590612904969 bytes.

+

Create a temporary table in ClickHouse:

+
CREATE TABLE trips
+(
+trip_id                 UInt32,
+vendor_id               String,
+pickup_datetime         DateTime,
+dropoff_datetime        Nullable(DateTime),
+store_and_fwd_flag      Nullable(FixedString(1)),
+rate_code_id            Nullable(UInt8),
+pickup_longitude        Nullable(Float64),
+pickup_latitude         Nullable(Float64),
+dropoff_longitude       Nullable(Float64),
+dropoff_latitude        Nullable(Float64),
+passenger_count         Nullable(UInt8),
+trip_distance           Nullable(Float64),
+fare_amount             Nullable(Float32),
+extra                   Nullable(Float32),
+mta_tax                 Nullable(Float32),
+tip_amount              Nullable(Float32),
+tolls_amount            Nullable(Float32),
+ehail_fee               Nullable(Float32),
+improvement_surcharge   Nullable(Float32),
+total_amount            Nullable(Float32),
+payment_type            Nullable(String),
+trip_type               Nullable(UInt8),
+pickup                  Nullable(String),
+dropoff                 Nullable(String),
+cab_type                Nullable(String),
+precipitation           Nullable(UInt8),
+snow_depth              Nullable(UInt8),
+snowfall                Nullable(UInt8),
+max_temperature         Nullable(UInt8),
+min_temperature         Nullable(UInt8),
+average_wind_speed      Nullable(UInt8),
+pickup_nyct2010_gid     Nullable(UInt8),
+pickup_ctlabel          Nullable(String),
+pickup_borocode         Nullable(UInt8),
+pickup_boroname         Nullable(String),
+pickup_ct2010           Nullable(String),
+pickup_boroct2010       Nullable(String),
+pickup_cdeligibil       Nullable(FixedString(1)),
+pickup_ntacode          Nullable(String),
+pickup_ntaname          Nullable(String),
+pickup_puma             Nullable(String),
+dropoff_nyct2010_gid    Nullable(UInt8),
+dropoff_ctlabel         Nullable(String),
+dropoff_borocode        Nullable(UInt8),
+dropoff_boroname        Nullable(String),
+dropoff_ct2010          Nullable(String),
+dropoff_boroct2010      Nullable(String),
+dropoff_cdeligibil      Nullable(String),
+dropoff_ntacode         Nullable(String),
+dropoff_ntaname         Nullable(String),
+dropoff_puma            Nullable(String)
+) ENGINE = Log;
+
+ + +

It is needed for converting fields to more correct data types and, if possible, to eliminate NULLs.

+
time clickhouse-client --query="INSERT INTO trips FORMAT TabSeparated" < trips.tsv
+
+real    75m56.214s
+
+ + +

Data is read at a speed of 112-140 Mb/second. +Loading data into a Log type table in one stream took 76 minutes. +The data in this table uses 142 GB.

+

(Importing data directly from Postgres is also possible using COPY ... TO PROGRAM.)

+

Unfortunately, all the fields associated with the weather (precipitation...average_wind_speed) were filled with NULL. Because of this, we will remove them from the final data set.

+

To start, we'll create a table on a single server. Later we will make the table distributed.

+

Create and populate a summary table:

+
CREATE TABLE trips_mergetree
+ENGINE = MergeTree(pickup_date, pickup_datetime, 8192)
+AS SELECT
+
+trip_id,
+CAST(vendor_id AS Enum8('1' = 1, '2' = 2, 'CMT' = 3, 'VTS' = 4, 'DDS' = 5, 'B02512' = 10, 'B02598' = 11, 'B02617' = 12, 'B02682' = 13, 'B02764' = 14)) AS vendor_id,
+toDate(pickup_datetime) AS pickup_date,
+ifNull(pickup_datetime, toDateTime(0)) AS pickup_datetime,
+toDate(dropoff_datetime) AS dropoff_date,
+ifNull(dropoff_datetime, toDateTime(0)) AS dropoff_datetime,
+assumeNotNull(store_and_fwd_flag) IN ('Y', '1', '2') AS store_and_fwd_flag,
+assumeNotNull(rate_code_id) AS rate_code_id,
+assumeNotNull(pickup_longitude) AS pickup_longitude,
+assumeNotNull(pickup_latitude) AS pickup_latitude,
+assumeNotNull(dropoff_longitude) AS dropoff_longitude,
+assumeNotNull(dropoff_latitude) AS dropoff_latitude,
+assumeNotNull(passenger_count) AS passenger_count,
+assumeNotNull(trip_distance) AS trip_distance,
+assumeNotNull(fare_amount) AS fare_amount,
+assumeNotNull(extra) AS extra,
+assumeNotNull(mta_tax) AS mta_tax,
+assumeNotNull(tip_amount) AS tip_amount,
+assumeNotNull(tolls_amount) AS tolls_amount,
+assumeNotNull(ehail_fee) AS ehail_fee,
+assumeNotNull(improvement_surcharge) AS improvement_surcharge,
+assumeNotNull(total_amount) AS total_amount,
+CAST((assumeNotNull(payment_type) AS pt) IN ('CSH', 'CASH', 'Cash', 'CAS', 'Cas', '1') ? 'CSH' : (pt IN ('CRD', 'Credit', 'Cre', 'CRE', 'CREDIT', '2') ? 'CRE' : (pt IN ('NOC', 'No Charge', 'No', '3') ? 'NOC' : (pt IN ('DIS', 'Dispute', 'Dis', '4') ? 'DIS' : 'UNK'))) AS Enum8('CSH' = 1, 'CRE' = 2, 'UNK' = 0, 'NOC' = 3, 'DIS' = 4)) AS payment_type_,
+assumeNotNull(trip_type) AS trip_type,
+ifNull(toFixedString(unhex(pickup), 25), toFixedString('', 25)) AS pickup,
+ifNull(toFixedString(unhex(dropoff), 25), toFixedString('', 25)) AS dropoff,
+CAST(assumeNotNull(cab_type) AS Enum8('yellow' = 1, 'green' = 2, 'uber' = 3)) AS cab_type,
+
+assumeNotNull(pickup_nyct2010_gid) AS pickup_nyct2010_gid,
+toFloat32(ifNull(pickup_ctlabel, '0')) AS pickup_ctlabel,
+assumeNotNull(pickup_borocode) AS pickup_borocode,
+CAST(assumeNotNull(pickup_boroname) AS Enum8('Manhattan' = 1, 'Queens' = 4, 'Brooklyn' = 3, '' = 0, 'Bronx' = 2, 'Staten Island' = 5)) AS pickup_boroname,
+toFixedString(ifNull(pickup_ct2010, '000000'), 6) AS pickup_ct2010,
+toFixedString(ifNull(pickup_boroct2010, '0000000'), 7) AS pickup_boroct2010,
+CAST(assumeNotNull(ifNull(pickup_cdeligibil, ' ')) AS Enum8(' ' = 0, 'E' = 1, 'I' = 2)) AS pickup_cdeligibil,
+toFixedString(ifNull(pickup_ntacode, '0000'), 4) AS pickup_ntacode,
+
+CAST(assumeNotNull(pickup_ntaname) AS Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195)) AS pickup_ntaname,
+
+toUInt16(ifNull(pickup_puma, '0')) AS pickup_puma,
+
+assumeNotNull(dropoff_nyct2010_gid) AS dropoff_nyct2010_gid,
+toFloat32(ifNull(dropoff_ctlabel, '0')) AS dropoff_ctlabel,
+assumeNotNull(dropoff_borocode) AS dropoff_borocode,
+CAST(assumeNotNull(dropoff_boroname) AS Enum8('Manhattan' = 1, 'Queens' = 4, 'Brooklyn' = 3, '' = 0, 'Bronx' = 2, 'Staten Island' = 5)) AS dropoff_boroname,
+toFixedString(ifNull(dropoff_ct2010, '000000'), 6) AS dropoff_ct2010,
+toFixedString(ifNull(dropoff_boroct2010, '0000000'), 7) AS dropoff_boroct2010,
+CAST(assumeNotNull(ifNull(dropoff_cdeligibil, ' ')) AS Enum8(' ' = 0, 'E' = 1, 'I' = 2)) AS dropoff_cdeligibil,
+toFixedString(ifNull(dropoff_ntacode, '0000'), 4) AS dropoff_ntacode,
+
+CAST(assumeNotNull(dropoff_ntaname) AS Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195)) AS dropoff_ntaname,
+
+toUInt16(ifNull(dropoff_puma, '0')) AS dropoff_puma
+
+FROM trips
+
+ + +

This takes 3030 seconds at a speed of about 428,000 rows per second. +To load it faster, you can create the table with the Log engine instead of MergeTree. In this case, the download works faster than 200 seconds.

+

The table uses 126 GB of disk space.

+
:) SELECT formatReadableSize(sum(bytes)) FROM system.parts WHERE table = 'trips_mergetree' AND active
+
+SELECT formatReadableSize(sum(bytes))
+FROM system.parts
+WHERE (table = 'trips_mergetree') AND active
+
+┌─formatReadableSize(sum(bytes))─┐
+│ 126.18 GiB                     │
+└────────────────────────────────┘
+
+ + +

Among other things, you can run the OPTIMIZE query on MergeTree. But it's not required, since everything will be fine without it.

+

Results on single server

+

Q1:

+
SELECT cab_type, count(*) FROM trips_mergetree GROUP BY cab_type
+
+ + +

0.490 seconds.

+

Q2:

+
SELECT passenger_count, avg(total_amount) FROM trips_mergetree GROUP BY passenger_count
+
+ + +

1.224 seconds.

+

Q3:

+
SELECT passenger_count, toYear(pickup_date) AS year, count(*) FROM trips_mergetree GROUP BY passenger_count, year
+
+ + +

2.104 seconds.

+

Q4:

+
SELECT passenger_count, toYear(pickup_date) AS year, round(trip_distance) AS distance, count(*)
+FROM trips_mergetree
+GROUP BY passenger_count, year, distance
+ORDER BY year, count(*) DESC
+
+ + +

3.593 seconds.

+

The following server was used:

+

Two Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical kernels total, +128 GiB RAM, +8x6 TB HD on hardware RAID-5

+

Execution time is the best of three runsBut starting from the second run, queries read data from the file system cache. No further caching occurs: the data is read out and processed in each run.

+

Creating a table on three servers:

+

On each server:

+
CREATE TABLE default.trips_mergetree_third ( trip_id UInt32,  vendor_id Enum8('1' = 1, '2' = 2, 'CMT' = 3, 'VTS' = 4, 'DDS' = 5, 'B02512' = 10, 'B02598' = 11, 'B02617' = 12, 'B02682' = 13, 'B02764' = 14),  pickup_date Date,  pickup_datetime DateTime,  dropoff_date Date,  dropoff_datetime DateTime,  store_and_fwd_flag UInt8,  rate_code_id UInt8,  pickup_longitude Float64,  pickup_latitude Float64,  dropoff_longitude Float64,  dropoff_latitude Float64,  passenger_count UInt8,  trip_distance Float64,  fare_amount Float32,  extra Float32,  mta_tax Float32,  tip_amount Float32,  tolls_amount Float32,  ehail_fee Float32,  improvement_surcharge Float32,  total_amount Float32,  payment_type_ Enum8('UNK' = 0, 'CSH' = 1, 'CRE' = 2, 'NOC' = 3, 'DIS' = 4),  trip_type UInt8,  pickup FixedString(25),  dropoff FixedString(25),  cab_type Enum8('yellow' = 1, 'green' = 2, 'uber' = 3),  pickup_nyct2010_gid UInt8,  pickup_ctlabel Float32,  pickup_borocode UInt8,  pickup_boroname Enum8('' = 0, 'Manhattan' = 1, 'Bronx' = 2, 'Brooklyn' = 3, 'Queens' = 4, 'Staten Island' = 5),  pickup_ct2010 FixedString(6),  pickup_boroct2010 FixedString(7),  pickup_cdeligibil Enum8(' ' = 0, 'E' = 1, 'I' = 2),  pickup_ntacode FixedString(4),  pickup_ntaname Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195),  pickup_puma UInt16,  dropoff_nyct2010_gid UInt8,  dropoff_ctlabel Float32,  dropoff_borocode UInt8,  dropoff_boroname Enum8('' = 0, 'Manhattan' = 1, 'Bronx' = 2, 'Brooklyn' = 3, 'Queens' = 4, 'Staten Island' = 5),  dropoff_ct2010 FixedString(6),  dropoff_boroct2010 FixedString(7),  dropoff_cdeligibil Enum8(' ' = 0, 'E' = 1, 'I' = 2),  dropoff_ntacode FixedString(4),  dropoff_ntaname Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195),  dropoff_puma UInt16) ENGINE = MergeTree(pickup_date, pickup_datetime, 8192)
+
+ + +

On the source server:

+
CREATE TABLE trips_mergetree_x3 AS trips_mergetree_third ENGINE = Distributed(perftest, default, trips_mergetree_third, rand())
+
+ + +

The following query redistributes data:

+
INSERT INTO trips_mergetree_x3 SELECT * FROM trips_mergetree
+
+ + +

This takes 2454 seconds.

+

On three servers:

+

Q1: 0.212 seconds. +Q2: 0.438 seconds. +Q3: 0.733 seconds. +Q4: 1.241 seconds.

+

No surprises here, since the queries are scaled linearly.

+

We also have results from a cluster of 140 servers:

+

Q1: 0.028 sec. +Q2: 0.043 sec. +Q3: 0.051 sec. +Q4: 0.072 sec.

+

In this case, the query processing time is determined above all by network latency. +We ran queries using a client located in a Yandex datacenter in Finland on a cluster in Russia, which added about 20 ms of latency.

+

Summary

+
nodes   Q1     Q2     Q3     Q4
+  1  0.490  1.224  2.104  3.593
+  3  0.212  0.438  0.733  1.241
+140  0.028  0.043  0.051  0.072
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/getting_started/example_datasets/ontime/index.html b/docs/build/docs/en/getting_started/example_datasets/ontime/index.html new file mode 100644 index 00000000000..00d7d760897 --- /dev/null +++ b/docs/build/docs/en/getting_started/example_datasets/ontime/index.html @@ -0,0 +1,3183 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + OnTime - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

+

OnTime

+

This performance test was created by Vadim Tkachenko. See:

+ +

Downloading data:

+
for s in `seq 1987 2017`
+do
+for m in `seq 1 12`
+do
+wget http://transtats.bts.gov/PREZIP/On_Time_On_Time_Performance_${s}_${m}.zip
+done
+done
+
+ + +

(from https://github.com/Percona-Lab/ontime-airline-performance/blob/master/download.sh )

+

Creating a table:

+
CREATE TABLE `ontime` (
+  `Year` UInt16,
+  `Quarter` UInt8,
+  `Month` UInt8,
+  `DayofMonth` UInt8,
+  `DayOfWeek` UInt8,
+  `FlightDate` Date,
+  `UniqueCarrier` FixedString(7),
+  `AirlineID` Int32,
+  `Carrier` FixedString(2),
+  `TailNum` String,
+  `FlightNum` String,
+  `OriginAirportID` Int32,
+  `OriginAirportSeqID` Int32,
+  `OriginCityMarketID` Int32,
+  `Origin` FixedString(5),
+  `OriginCityName` String,
+  `OriginState` FixedString(2),
+  `OriginStateFips` String,
+  `OriginStateName` String,
+  `OriginWac` Int32,
+  `DestAirportID` Int32,
+  `DestAirportSeqID` Int32,
+  `DestCityMarketID` Int32,
+  `Dest` FixedString(5),
+  `DestCityName` String,
+  `DestState` FixedString(2),
+  `DestStateFips` String,
+  `DestStateName` String,
+  `DestWac` Int32,
+  `CRSDepTime` Int32,
+  `DepTime` Int32,
+  `DepDelay` Int32,
+  `DepDelayMinutes` Int32,
+  `DepDel15` Int32,
+  `DepartureDelayGroups` String,
+  `DepTimeBlk` String,
+  `TaxiOut` Int32,
+  `WheelsOff` Int32,
+  `WheelsOn` Int32,
+  `TaxiIn` Int32,
+  `CRSArrTime` Int32,
+  `ArrTime` Int32,
+  `ArrDelay` Int32,
+  `ArrDelayMinutes` Int32,
+  `ArrDel15` Int32,
+  `ArrivalDelayGroups` Int32,
+  `ArrTimeBlk` String,
+  `Cancelled` UInt8,
+  `CancellationCode` FixedString(1),
+  `Diverted` UInt8,
+  `CRSElapsedTime` Int32,
+  `ActualElapsedTime` Int32,
+  `AirTime` Int32,
+  `Flights` Int32,
+  `Distance` Int32,
+  `DistanceGroup` UInt8,
+  `CarrierDelay` Int32,
+  `WeatherDelay` Int32,
+  `NASDelay` Int32,
+  `SecurityDelay` Int32,
+  `LateAircraftDelay` Int32,
+  `FirstDepTime` String,
+  `TotalAddGTime` String,
+  `LongestAddGTime` String,
+  `DivAirportLandings` String,
+  `DivReachedDest` String,
+  `DivActualElapsedTime` String,
+  `DivArrDelay` String,
+  `DivDistance` String,
+  `Div1Airport` String,
+  `Div1AirportID` Int32,
+  `Div1AirportSeqID` Int32,
+  `Div1WheelsOn` String,
+  `Div1TotalGTime` String,
+  `Div1LongestGTime` String,
+  `Div1WheelsOff` String,
+  `Div1TailNum` String,
+  `Div2Airport` String,
+  `Div2AirportID` Int32,
+  `Div2AirportSeqID` Int32,
+  `Div2WheelsOn` String,
+  `Div2TotalGTime` String,
+  `Div2LongestGTime` String,
+  `Div2WheelsOff` String,
+  `Div2TailNum` String,
+  `Div3Airport` String,
+  `Div3AirportID` Int32,
+  `Div3AirportSeqID` Int32,
+  `Div3WheelsOn` String,
+  `Div3TotalGTime` String,
+  `Div3LongestGTime` String,
+  `Div3WheelsOff` String,
+  `Div3TailNum` String,
+  `Div4Airport` String,
+  `Div4AirportID` Int32,
+  `Div4AirportSeqID` Int32,
+  `Div4WheelsOn` String,
+  `Div4TotalGTime` String,
+  `Div4LongestGTime` String,
+  `Div4WheelsOff` String,
+  `Div4TailNum` String,
+  `Div5Airport` String,
+  `Div5AirportID` Int32,
+  `Div5AirportSeqID` Int32,
+  `Div5WheelsOn` String,
+  `Div5TotalGTime` String,
+  `Div5LongestGTime` String,
+  `Div5WheelsOff` String,
+  `Div5TailNum` String
+) ENGINE = MergeTree(FlightDate, (Year, FlightDate), 8192)
+
+ + +

Loading data:

+
for i in *.zip; do echo $i; unzip -cq $i '*.csv' | sed 's/\.00//g' | clickhouse-client --host=example-perftest01j --query="INSERT INTO ontime FORMAT CSVWithNames"; done
+
+ + +

Queries:

+

Q0.

+
select avg(c1) from (select Year, Month, count(*) as c1 from ontime group by Year, Month);
+
+ + +

Q1. The number of flights per day from the year 2000 to 2008

+
SELECT DayOfWeek, count(*) AS c FROM ontime WHERE Year >= 2000 AND Year <= 2008 GROUP BY DayOfWeek ORDER BY c DESC;
+
+ + +

Q2. The number of flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008

+
SELECT DayOfWeek, count(*) AS c FROM ontime WHERE DepDelay>10 AND Year >= 2000 AND Year <= 2008 GROUP BY DayOfWeek ORDER BY c DESC
+
+ + +

Q3. The number of delays by airport for 2000-2008

+
SELECT Origin, count(*) AS c FROM ontime WHERE DepDelay>10 AND Year >= 2000 AND Year <= 2008 GROUP BY Origin ORDER BY c DESC LIMIT 10
+
+ + +

Q4. The number of delays by carrier for 2007

+
SELECT Carrier, count(*) FROM ontime WHERE DepDelay>10  AND Year = 2007 GROUP BY Carrier ORDER BY count(*) DESC
+
+ + +

Q5. The percentage of delays by carrier for 2007

+
SELECT Carrier, c, c2, c*1000/c2 as c3
+FROM
+(
+    SELECT
+        Carrier,
+        count(*) AS c
+    FROM ontime
+    WHERE DepDelay>10
+        AND Year=2007
+    GROUP BY Carrier
+)
+ANY INNER JOIN
+(
+    SELECT
+        Carrier,
+        count(*) AS c2
+    FROM ontime
+    WHERE Year=2007
+    GROUP BY Carrier
+) USING Carrier
+ORDER BY c3 DESC;
+
+ + +

Better version of the same query:

+
SELECT Carrier, avg(DepDelay > 10) * 1000 AS c3 FROM ontime WHERE Year = 2007 GROUP BY Carrier ORDER BY Carrier
+
+ + +

Q6. The previous request for a broader range of years, 2000-2008

+
SELECT Carrier, c, c2, c*1000/c2 as c3
+FROM
+(
+    SELECT
+        Carrier,
+        count(*) AS c
+    FROM ontime
+    WHERE DepDelay>10
+        AND Year >= 2000 AND Year <= 2008
+    GROUP BY Carrier
+)
+ANY INNER JOIN
+(
+    SELECT
+        Carrier,
+        count(*) AS c2
+    FROM ontime
+    WHERE Year >= 2000 AND Year <= 2008
+    GROUP BY Carrier
+) USING Carrier
+ORDER BY c3 DESC;
+
+ + +

Better version of the same query:

+
SELECT Carrier, avg(DepDelay > 10) * 1000 AS c3 FROM ontime WHERE Year >= 2000 AND Year <= 2008 GROUP BY Carrier ORDER BY Carrier
+
+ + +

Q7. Percentage of flights delayed for more than 10 minutes, by year

+
SELECT Year, c1/c2
+FROM
+(
+    select
+        Year,
+        count(*)*1000 as c1
+    from ontime
+    WHERE DepDelay>10
+    GROUP BY Year
+)
+ANY INNER JOIN
+(
+    select
+        Year,
+        count(*) as c2
+    from ontime
+    GROUP BY Year
+) USING (Year)
+ORDER BY Year
+
+ + +

Better version of the same query:

+
SELECT Year, avg(DepDelay > 10) FROM ontime GROUP BY Year ORDER BY Year
+
+ + +

Q8. The most popular destinations by the number of directly connected cities for various year ranges

+
SELECT DestCityName, uniqExact(OriginCityName) AS u FROM ontime WHERE Year >= 2000 and Year <= 2010 GROUP BY DestCityName ORDER BY u DESC LIMIT 10;
+
+ + +

Q9.

+
select Year, count(*) as c1 from ontime group by Year;
+
+ + +

Q10.

+
select
+   min(Year), max(Year), Carrier, count(*) as cnt,
+   sum(ArrDelayMinutes>30) as flights_delayed,
+   round(sum(ArrDelayMinutes>30)/count(*),2) as rate
+FROM ontime
+WHERE
+   DayOfWeek not in (6,7) and OriginState not in ('AK', 'HI', 'PR', 'VI')
+   and DestState not in ('AK', 'HI', 'PR', 'VI')
+   and FlightDate < '2010-01-01'
+GROUP by Carrier
+HAVING cnt > 100000 and max(Year) > 1990
+ORDER by rate DESC
+LIMIT 1000;
+
+ + +

Bonus:

+
SELECT avg(cnt) FROM (SELECT Year,Month,count(*) AS cnt FROM ontime WHERE DepDel15=1 GROUP BY Year,Month)
+
+select avg(c1) from (select Year,Month,count(*) as c1 from ontime group by Year,Month)
+
+SELECT DestCityName, uniqExact(OriginCityName) AS u FROM ontime GROUP BY DestCityName ORDER BY u DESC LIMIT 10;
+
+SELECT OriginCityName, DestCityName, count() AS c FROM ontime GROUP BY OriginCityName, DestCityName ORDER BY c DESC LIMIT 10;
+
+SELECT OriginCityName, count() AS c FROM ontime GROUP BY OriginCityName ORDER BY c DESC LIMIT 10;
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/getting_started/example_datasets/star_schema/index.html b/docs/build/docs/en/getting_started/example_datasets/star_schema/index.html new file mode 100644 index 00000000000..8d13719226f --- /dev/null +++ b/docs/build/docs/en/getting_started/example_datasets/star_schema/index.html @@ -0,0 +1,2963 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Star Schema Benchmark - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ + +
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+ +
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+ + +
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+ + +
+
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+ + +
+
+ + + + + + + +

Star Schema Benchmark

+

Compiling dbgen: https://github.com/vadimtk/ssb-dbgen

+
git clone git@github.com:vadimtk/ssb-dbgen.git
+cd ssb-dbgen
+make
+
+ + +

There will be some warnings during the process, but this is normal.

+

Place dbgen and dists.dss in any location with 800 GB of free disk space.

+

Generating data:

+
./dbgen -s 1000 -T c
+./dbgen -s 1000 -T l
+
+ + +

Creating tables in ClickHouse:

+
CREATE TABLE lineorder (
+        LO_ORDERKEY             UInt32,
+        LO_LINENUMBER           UInt8,
+        LO_CUSTKEY              UInt32,
+        LO_PARTKEY              UInt32,
+        LO_SUPPKEY              UInt32,
+        LO_ORDERDATE            Date,
+        LO_ORDERPRIORITY        String,
+        LO_SHIPPRIORITY         UInt8,
+        LO_QUANTITY             UInt8,
+        LO_EXTENDEDPRICE        UInt32,
+        LO_ORDTOTALPRICE        UInt32,
+        LO_DISCOUNT             UInt8,
+        LO_REVENUE              UInt32,
+        LO_SUPPLYCOST           UInt32,
+        LO_TAX                  UInt8,
+        LO_COMMITDATE           Date,
+        LO_SHIPMODE             String
+)Engine=MergeTree(LO_ORDERDATE,(LO_ORDERKEY,LO_LINENUMBER,LO_ORDERDATE),8192);
+
+CREATE TABLE customer (
+        C_CUSTKEY       UInt32,
+        C_NAME          String,
+        C_ADDRESS       String,
+        C_CITY          String,
+        C_NATION        String,
+        C_REGION        String,
+        C_PHONE         String,
+        C_MKTSEGMENT    String,
+        C_FAKEDATE      Date
+)Engine=MergeTree(C_FAKEDATE,(C_CUSTKEY,C_FAKEDATE),8192);
+
+CREATE TABLE part (
+        P_PARTKEY       UInt32,
+        P_NAME          String,
+        P_MFGR          String,
+        P_CATEGORY      String,
+        P_BRAND         String,
+        P_COLOR         String,
+        P_TYPE          String,
+        P_SIZE          UInt8,
+        P_CONTAINER     String,
+        P_FAKEDATE      Date
+)Engine=MergeTree(P_FAKEDATE,(P_PARTKEY,P_FAKEDATE),8192);
+
+CREATE TABLE lineorderd AS lineorder ENGINE = Distributed(perftest_3shards_1replicas, default, lineorder, rand());
+CREATE TABLE customerd AS customer ENGINE = Distributed(perftest_3shards_1replicas, default, customer, rand());
+CREATE TABLE partd AS part ENGINE = Distributed(perftest_3shards_1replicas, default, part, rand());
+
+ + +

For testing on a single server, just use MergeTree tables. +For distributed testing, you need to configure the perftest_3shards_1replicas cluster in the config file. +Next, create MergeTree tables on each server and a Distributed above them.

+

Downloading data (change 'customer' to 'customerd' in the distributed version):

+
cat customer.tbl | sed 's/$/2000-01-01/' | clickhouse-client --query "INSERT INTO customer FORMAT CSV"
+cat lineorder.tbl | clickhouse-client --query "INSERT INTO lineorder FORMAT CSV"
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/getting_started/example_datasets/wikistat/index.html b/docs/build/docs/en/getting_started/example_datasets/wikistat/index.html new file mode 100644 index 00000000000..ffdb2145385 --- /dev/null +++ b/docs/build/docs/en/getting_started/example_datasets/wikistat/index.html @@ -0,0 +1,2909 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + WikiStat - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ + +
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+ + +
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+ + + + + + + +

WikiStat

+

See: http://dumps.wikimedia.org/other/pagecounts-raw/

+

Creating a table:

+
CREATE TABLE wikistat
+(
+    date Date,
+    time DateTime,
+    project String,
+    subproject String,
+    path String,
+    hits UInt64,
+    size UInt64
+) ENGINE = MergeTree(date, (path, time), 8192);
+
+ + +

Loading data:

+
for i in {2007..2016}; do for j in {01..12}; do echo $i-$j >&2; curl -sSL "http://dumps.wikimedia.org/other/pagecounts-raw/$i/$i-$j/" | grep -oE 'pagecounts-[0-9]+-[0-9]+\.gz'; done; done | sort | uniq | tee links.txt
+cat links.txt | while read link; do wget http://dumps.wikimedia.org/other/pagecounts-raw/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1/')/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1-\2/')/$link; done
+ls -1 /opt/wikistat/ | grep gz | while read i; do echo $i; gzip -cd /opt/wikistat/$i | ./wikistat-loader --time="$(echo -n $i | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\.gz/\1-\2-\3 \4-00-00/')" | clickhouse-client --query="INSERT INTO wikistat FORMAT TabSeparated"; done
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/getting_started/index.html b/docs/build/docs/en/getting_started/index.html new file mode 100644 index 00000000000..83406a5ee01 --- /dev/null +++ b/docs/build/docs/en/getting_started/index.html @@ -0,0 +1,3110 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Deploying and running - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Getting started

+

System requirements

+

This is not a cross-platform system. It requires Linux Ubuntu Precise (12.04) or newer, with x86_64 architecture and support for the SSE 4.2 instruction set. +To check for SSE 4.2:

+
grep -q sse4_2 /proc/cpuinfo && echo "SSE 4.2 supported" || echo "SSE 4.2 not supported"
+
+ + +

We recommend using Ubuntu Trusty, Ubuntu Xenial, or Ubuntu Precise. +The terminal must use UTF-8 encoding (the default in Ubuntu).

+

Installation

+

For testing and development, the system can be installed on a single server or on a desktop computer.

+

Installing from packages for Debian/Ubuntu

+

In /etc/apt/sources.list (or in a separate /etc/apt/sources.list.d/clickhouse.list file), add the repository:

+
deb http://repo.yandex.ru/clickhouse/deb/stable/ main/
+
+ + +

If you want to use the most recent test version, replace 'stable' with 'testing'.

+

Then run:

+
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4    # optional
+sudo apt-get update
+sudo apt-get install clickhouse-client clickhouse-server
+
+ + +

You can also download and install packages manually from here: https://repo.yandex.ru/clickhouse/deb/stable/main/.

+

ClickHouse contains access restriction settings. They are located in the 'users.xml' file (next to 'config.xml'). +By default, access is allowed from anywhere for the 'default' user, without a password. See 'user/default/networks'. +For more information, see the section "Configuration files".

+

Installing from sources

+

To compile, follow the instructions: build.md

+

You can compile packages and install them. +You can also use programs without installing packages.

+
Client: dbms/src/Client/
+Server: dbms/src/Server/
+
+ + +

For the server, create a catalog with data, such as:

+
/opt/clickhouse/data/default/
+/opt/clickhouse/metadata/default/
+
+ + +

(Configurable in the server config.) +Run 'chown' for the desired user.

+

Note the path to logs in the server config (src/dbms/src/Server/config.xml).

+

Other installation methods

+

Docker image: https://hub.docker.com/r/yandex/clickhouse-server/

+

RPM packages for CentOS or RHEL: https://github.com/Altinity/clickhouse-rpm-install

+

Gentoo overlay: https://github.com/kmeaw/clickhouse-overlay

+

Launch

+

To start the server (as a daemon), run:

+
sudo service clickhouse-server start
+
+ + +

See the logs in the /var/log/clickhouse-server/ directory.

+

If the server doesn't start, check the configurations in the file /etc/clickhouse-server/config.xml.

+

You can also launch the server from the console:

+
clickhouse-server --config-file=/etc/clickhouse-server/config.xml
+
+ + +

In this case, the log will be printed to the console, which is convenient during development. +If the configuration file is in the current directory, you don't need to specify the '--config-file' parameter. By default, it uses './config.xml'.

+

You can use the command-line client to connect to the server:

+
clickhouse-client
+
+ + +

The default parameters indicate connecting with localhost:9000 on behalf of the user 'default' without a password. +The client can be used for connecting to a remote server. Example:

+
clickhouse-client --host=example.com
+
+ + +

For more information, see the section "Command-line client".

+

Checking the system:

+
milovidov@hostname:~/work/metrica/src/dbms/src/Client$ ./clickhouse-client
+ClickHouse client version 0.0.18749.
+Connecting to localhost:9000.
+Connected to ClickHouse server version 0.0.18749.
+
+:) SELECT 1
+
+SELECT 1
+
+┌─1─┐
+│ 1 │
+└───┘
+
+1 rows in set. Elapsed: 0.003 sec.
+
+:)
+
+ + +

Congratulations, the system works!

+

To continue experimenting, you can try to download from the test data sets.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/images/logo.svg b/docs/build/docs/en/images/logo.svg new file mode 100644 index 00000000000..70662da887e --- /dev/null +++ b/docs/build/docs/en/images/logo.svg @@ -0,0 +1,12 @@ + + + + + + + + + diff --git a/docs/build/docs/en/index.html b/docs/build/docs/en/index.html new file mode 100644 index 00000000000..786770ee69e --- /dev/null +++ b/docs/build/docs/en/index.html @@ -0,0 +1,2982 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
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+ + +
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+ + +
+
+ + + + + + + +

What is ClickHouse?

+

ClickHouse is a columnar DBMS for OLAP.

+

In a "normal" row-oriented DBMS, data is stored in this order:

+
5123456789123456789     1       Eurobasket - Greece - Bosnia and Herzegovina - example.com      1       2011-09-01 01:03:02     6274717   1294101174      11409   612345678912345678      0       33      6       http://www.example.com/basketball/team/123/match/456789.html http://www.example.com/basketball/team/123/match/987654.html       0       1366    768     32      10      3183      0       0       13      0\0     1       1       0       0                       2011142 -1      0               0       01321     613     660     2011-09-01 08:01:17     0       0       0       0       utf-8   1466    0       0       0       5678901234567890123               277789954       0       0       0       0       0
+5234985259563631958     0       Consulting, Tax assessment, Accounting, Law       1       2011-09-01 01:03:02     6320881   2111222333      213     6458937489576391093     0       3       2       http://www.example.ru/         0       800     600       16      10      2       153.1   0       0       10      63      1       1       0       0                       2111678 000       0       588     368     240     2011-09-01 01:03:17     4       0       60310   0       windows-1251    1466    0       000               778899001       0       0       0       0       0
+...
+
+ + +

In order words, all the values related to a row are stored next to each other. +Examples of a row-oriented DBMS are MySQL, Postgres, MS SQL Server, and others.

+

In a column-oriented DBMS, data is stored like this:

+
WatchID:    5385521489354350662     5385521490329509958     5385521489953706054     5385521490476781638     5385521490583269446     5385521490218868806     5385521491437850694   5385521491090174022      5385521490792669254     5385521490420695110     5385521491532181574     5385521491559694406     5385521491459625030     5385521492275175494   5385521492781318214      5385521492710027334     5385521492955615302     5385521493708759110     5385521494506434630     5385521493104611398
+JavaEnable: 1       0       1       0       0       0       1       0       1       1       1       1       1       1       0       1       0       0       1       1
+Title:      Yandex  Announcements - Investor Relations - Yandex     Yandex — Contact us — Moscow    Yandex — Mission        Ru      Yandex — History — History of Yandex    Yandex Financial Releases - Investor Relations - Yandex Yandex — Locations      Yandex Board of Directors - Corporate Governance - Yandex       Yandex — Technologies
+GoodEvent:  1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1
+EventTime:  2016-05-18 05:19:20     2016-05-18 08:10:20     2016-05-18 07:38:00     2016-05-18 01:13:08     2016-05-18 00:04:06     2016-05-18 04:21:30     2016-05-18 00:34:16     2016-05-18 07:35:49     2016-05-18 11:41:59     2016-05-18 01:13:32
+
+ + +

These examples only show the order that data is arranged in. +The values from different columns are stored separately, and data from the same column is stored together.

+

Examples of column-oriented DBMSs: Vertica, Paraccel (Actian Matrix) (Amazon Redshift), Sybase IQ, Exasol, Infobright, InfiniDB, MonetDB (VectorWise) (Actian Vector), LucidDB, SAP HANA, Google Dremel, Google PowerDrill, Druid, kdb+, and so on.

+

Different orders for storing data are better suited to different scenarios. +The data access scenario refers to what queries are made, how often, and in what proportion; how much data is read for each type of query – rows, columns, and bytes; the relationship between reading and updating data; the working size of the data and how locally it is used; whether transactions are used, and how isolated they are; requirements for data replication and logical integrity; requirements for latency and throughput for each type of query, and so on.

+

The higher the load on the system, the more important it is to customize the system to the scenario, and the more specific this customization becomes. There is no system that is equally well-suited to significantly different scenarios. If a system is adaptable to a wide set of scenarios, under a high load, the system will handle all the scenarios equally poorly, or will work well for just one of the scenarios.

+

We'll say that the following is true for the OLAP (online analytical processing) scenario:

+
    +
  • The vast majority of requests are for read access.
    • +
  • Data is updated in fairly large batches (> 1000 rows), not by single rows; or it is not updated at all.
    • +
  • Data is added to the DB but is not modified.
    • +
  • For reads, quite a large number of rows are extracted from the DB, but only a small subset of columns.
    • +
  • Tables are "wide," meaning they contain a large number of columns.
    • +
  • Queries are relatively rare (usually hundreds of queries per server or less per second).
    • +
  • For simple queries, latencies around 50 ms are allowed.
    • +
  • Column values are fairly small: numbers and short strings (for example, 60 bytes per URL).
    • +
  • Requires high throughput when processing a single query (up to billions of rows per second per server).
    • +
  • There are no transactions.
    • +
  • Low requirements for data consistency.
    • +
  • There is one large table per query. All tables are small, except for one.
    • +
  • A query result is significantly smaller than the source data. In other words, data is filtered or aggregated. The result fits in a single server's RAM.
    • +
+

It is easy to see that the OLAP scenario is very different from other popular scenarios (such as OLTP or Key-Value access). So it doesn't make sense to try to use OLTP or a Key-Value DB for processing analytical queries if you want to get decent performance. For example, if you try to use MongoDB or Elliptics for analytics, you will get very poor performance compared to OLAP databases.

+

Columnar-oriented databases are better suited to OLAP scenarios (at least 100 times better in processing speed for most queries), for the following reasons:

+
    +
  1. For I/O.
    2. +
  2. For an analytical query, only a small number of table columns need to be read. In a column-oriented database, you can read just the data you need. For example, if you need 5 columns out of 100, you can expect a 20-fold reduction in I/O.
    3. +
  3. Since data is read in packets, it is easier to compress. Data in columns is also easier to compress. This further reduces the I/O volume.
    4. +
  4. Due to the reduced I/O, more data fits in the system cache.
    5. +
+

For example, the query "count the number of records for each advertising platform" requires reading one "advertising platform ID" column, which takes up 1 byte uncompressed. If most of the traffic was not from advertising platforms, you can expect at least 10-fold compression of this column. When using a quick compression algorithm, data decompression is possible at a speed of at least several gigabytes of uncompressed data per second. In other words, this query can be processed at a speed of approximately several billion rows per second on a single server. This speed is actually achieved in practice.

+

Example:

+
milovidov@hostname:~$ clickhouse-client
+ClickHouse client version 0.0.52053.
+Connecting to localhost:9000.
+Connected to ClickHouse server version 0.0.52053.
+
+:) SELECT CounterID, count() FROM hits GROUP BY CounterID ORDER BY count() DESC LIMIT 20
+
+SELECT
+    CounterID,
+    count()
+FROM hits
+GROUP BY CounterID
+ORDER BY count() DESC
+LIMIT 20
+
+┌─CounterID─┬──count()─┐
+│    114208 │ 56057344 │
+│    115080 │ 51619590 │
+│      3228 │ 44658301 │
+│     38230 │ 42045932 │
+│    145263 │ 42042158 │
+│     91244 │ 38297270 │
+│    154139 │ 26647572 │
+│    150748 │ 24112755 │
+│    242232 │ 21302571 │
+│    338158 │ 13507087 │
+│     62180 │ 12229491 │
+│     82264 │ 12187441 │
+│    232261 │ 12148031 │
+│    146272 │ 11438516 │
+│    168777 │ 11403636 │
+│   4120072 │ 11227824 │
+│  10938808 │ 10519739 │
+│     74088 │  9047015 │
+│    115079 │  8837972 │
+│    337234 │  8205961 │
+└───────────┴──────────┘
+
+20 rows in set. Elapsed: 0.153 sec. Processed 1.00 billion rows, 4.00 GB (6.53 billion rows/s., 26.10 GB/s.)
+
+:)
+
+ + +
    +
  1. For CPU.
    2. +
+

Since executing a query requires processing a large number of rows, it helps to dispatch all operations for entire vectors instead of for separate rows, or to implement the query engine so that there is almost no dispatching cost. If you don't do this, with any half-decent disk subsystem, the query interpreter inevitably stalls the CPU. +It makes sense to both store data in columns and process it, when possible, by columns.

+

There are two ways to do this:

+
    +
  1. +

    A vector engine. All operations are written for vectors, instead of for separate values. This means you don't need to call operations very often, and dispatching costs are negligible. Operation code contains an optimized internal cycle.

    +
    2. +
  2. +

    Code generation. The code generated for the query has all the indirect calls in it.

    +
    3. +
+

This is not done in "normal" databases, because it doesn't make sense when running simple queries. However, there are exceptions. For example, MemSQL uses code generation to reduce latency when processing SQL queries. (For comparison, analytical DBMSs require optimization of throughput, not latency.)

+

Note that for CPU efficiency, the query language must be declarative (SQL or MDX), or at least a vector (J, K). The query should only contain implicit loops, allowing for optimization.

+ + + + + + + +
+
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+
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Command-line client

+

To work from the command line, you can use clickhouse-client:

+
$ clickhouse-client
+ClickHouse client version 0.0.26176.
+Connecting to localhost:9000.
+Connected to ClickHouse server version 0.0.26176.
+
+:)
+
+ + +

The client supports command-line options and configuration files. For more information, see "Configuring".

+

Usage

+

The client can be used in interactive and non-interactive (batch) mode. +To use batch mode, specify the 'query' parameter, or send data to 'stdin' (it verifies that 'stdin' is not a terminal), or both. +Similar to the HTTP interface, when using the 'query' parameter and sending data to 'stdin', the request is a concatenation of the 'query' parameter, a line feed, and the data in 'stdin'. This is convenient for large INSERT queries.

+

Example of using the client to insert data:

+
echo -ne "1, 'some text', '2016-08-14 00:00:00'\n2, 'some more text', '2016-08-14 00:00:01'" | clickhouse-client --database=test --query="INSERT INTO test FORMAT CSV";
+
+cat <<_EOF | clickhouse-client --database=test --query="INSERT INTO test FORMAT CSV";
+3, 'some text', '2016-08-14 00:00:00'
+4, 'some more text', '2016-08-14 00:00:01'
+_EOF
+
+cat file.csv | clickhouse-client --database=test --query="INSERT INTO test FORMAT CSV";
+
+ + +

In batch mode, the default data format is TabSeparated. You can set the format in the FORMAT clause of the query.

+

By default, you can only process a single query in batch mode. To make multiple queries from a "script," use the --multiquery parameter. This works for all queries except INSERT. Query results are output consecutively without additional separators. +Similarly, to process a large number of queries, you can run 'clickhouse-client' for each query. Note that it may take tens of milliseconds to launch the 'clickhouse-client' program.

+

In interactive mode, you get a command line where you can enter queries.

+

If 'multiline' is not specified (the default):To run the query, press Enter. The semicolon is not necessary at the end of the query. To enter a multiline query, enter a backslash \ before the line feed. After you press Enter, you will be asked to enter the next line of the query.

+

If multiline is specified:To run a query, end it with a semicolon and press Enter. If the semicolon was omitted at the end of the entered line, you will be asked to enter the next line of the query.

+

Only a single query is run, so everything after the semicolon is ignored.

+

You can specify \G instead of or after the semicolon. This indicates Vertical format. In this format, each value is printed on a separate line, which is convenient for wide tables. This unusual feature was added for compatibility with the MySQL CLI.

+

The command line is based on 'readline' (and 'history' or 'libedit', or without a library, depending on the build). In other words, it uses the familiar keyboard shortcuts and keeps a history. +The history is written to ~/.clickhouse-client-history.

+

By default, the format used is PrettyCompact. You can change the format in the FORMAT clause of the query, or by specifying \G at the end of the query, using the --format or --vertical argument in the command line, or using the client configuration file.

+

To exit the client, press Ctrl+D (or Ctrl+C), or enter one of the following instead of a query:"exit", "quit", "logout", "учше", "йгше", "дщпщге", "exit;", "quit;", "logout;", "учшеж", "йгшеж", "дщпщгеж", "q", "й", "q", "Q", ":q", "й", "Й", "Жй"

+

When processing a query, the client shows:

+
    +
  1. Progress, which is updated no more than 10 times per second (by default). For quick queries, the progress might not have time to be displayed.
    2. +
  2. The formatted query after parsing, for debugging.
    3. +
  3. The result in the specified format.
    4. +
  4. The number of lines in the result, the time passed, and the average speed of query processing.
    5. +
+

You can cancel a long query by pressing Ctrl+C. However, you will still need to wait a little for the server to abort the request. It is not possible to cancel a query at certain stages. If you don't wait and press Ctrl+C a second time, the client will exit.

+

The command-line client allows passing external data (external temporary tables) for querying. For more information, see the section "External data for query processing".

+

+

Configuring

+

You can pass parameters to clickhouse-client (all parameters have a default value) using:

+
    +
  • From the Command Line
    • +
+

Command-line options override the default values and settings in configuration files.

+
    +
  • Configuration files.
    • +
+

Settings in the configuration files override the default values.

+

Command line options

+
    +
  • --host, -h -– The server name, 'localhost' by default. You can use either the name or the IPv4 or IPv6 address.
    • +
  • --port – The port to connect to. Default value: 9000. Note that the HTTP interface and the native interface use different ports.
    • +
  • --user, -u – The username. Default value: default.
    • +
  • --password – The password. Default value: empty string.
    • +
  • --query, -q – The query to process when using non-interactive mode.
    • +
  • --database, -d – Select the current default database. Default value: the current database from the server settings ('default' by default).
    • +
  • --multiline, -m – If specified, allow multiline queries (do not send the query on Enter).
    • +
  • --multiquery, -n – If specified, allow processing multiple queries separated by commas. Only works in non-interactive mode.
    • +
  • --format, -f – Use the specified default format to output the result.
    • +
  • --vertical, -E – If specified, use the Vertical format by default to output the result. This is the same as '--format=Vertical'. In this format, each value is printed on a separate line, which is helpful when displaying wide tables.
    • +
  • --time, -t – If specified, print the query execution time to 'stderr' in non-interactive mode.
    • +
  • --stacktrace – If specified, also print the stack trace if an exception occurs.
    • +
  • -config-file – The name of the configuration file.
    • +
+

Configuration files

+

clickhouse-client uses the first existing file of the following:

+
    +
  • Defined in the -config-file parameter.
    • +
  • ./clickhouse-client.xml
    • +
  • \~/.clickhouse-client/config.xml
    • +
  • /etc/clickhouse-client/config.xml
    • +
+

Example of a config file:

+
<config>
+    <user>username</user>
+    <password>password</password>
+</config>
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/interfaces/http_interface/index.html b/docs/build/docs/en/interfaces/http_interface/index.html new file mode 100644 index 00000000000..9abf810db4c --- /dev/null +++ b/docs/build/docs/en/interfaces/http_interface/index.html @@ -0,0 +1,3112 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + HTTP interface - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

HTTP interface

+

The HTTP interface lets you use ClickHouse on any platform from any programming language. We use it for working from Java and Perl, as well as shell scripts. In other departments, the HTTP interface is used from Perl, Python, and Go. The HTTP interface is more limited than the native interface, but it has better compatibility.

+

By default, clickhouse-server listens for HTTP on port 8123 (this can be changed in the config). +If you make a GET / request without parameters, it returns the string "Ok" (with a line feed at the end). You can use this in health-check scripts.

+
$ curl 'http://localhost:8123/'
+Ok.
+
+ + +

Send the request as a URL 'query' parameter, or as a POST. Or send the beginning of the query in the 'query' parameter, and the rest in the POST (we'll explain later why this is necessary). The size of the URL is limited to 16 KB, so keep this in mind when sending large queries.

+

If successful, you receive the 200 response code and the result in the response body. +If an error occurs, you receive the 500 response code and an error description text in the response body.

+

When using the GET method, 'readonly' is set. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.

+

Examples:

+
$ curl 'http://localhost:8123/?query=SELECT%201'
+1
+
+$ wget -O- -q 'http://localhost:8123/?query=SELECT 1'
+1
+
+$ GET 'http://localhost:8123/?query=SELECT 1'
+1
+
+$ echo -ne 'GET /?query=SELECT%201 HTTP/1.0\r\n\r\n' | nc localhost 8123
+HTTP/1.0 200 OK
+Connection: Close
+Date: Fri, 16 Nov 2012 19:21:50 GMT
+
+1
+
+ + +

As you can see, curl is somewhat inconvenient in that spaces must be URL escaped.Although wget escapes everything itself, we don't recommend using it because it doesn't work well over HTTP 1.1 when using keep-alive and Transfer-Encoding: chunked.

+
$ echo 'SELECT 1' | curl 'http://localhost:8123/' --data-binary @-
+1
+
+$ echo 'SELECT 1' | curl 'http://localhost:8123/?query=' --data-binary @-
+1
+
+$ echo '1' | curl 'http://localhost:8123/?query=SELECT' --data-binary @-
+1
+
+ + +

If part of the query is sent in the parameter, and part in the POST, a line feed is inserted between these two data parts. +Example (this won't work):

+
$ echo 'ECT 1' | curl 'http://localhost:8123/?query=SEL' --data-binary @-
+Code: 59, e.displayText() = DB::Exception: Syntax error: failed at position 0: SEL
+ECT 1
+, expected One of: SHOW TABLES, SHOW DATABASES, SELECT, INSERT, CREATE, ATTACH, RENAME, DROP, DETACH, USE, SET, OPTIMIZE., e.what() = DB::Exception
+
+ + +

By default, data is returned in TabSeparated format (for more information, see the "Formats" section). +You use the FORMAT clause of the query to request any other format.

+
$ echo 'SELECT 1 FORMAT Pretty' | curl 'http://localhost:8123/?' --data-binary @-
+┏━━━┓
+┃ 1 ┃
+┡━━━┩
+│ 1 │
+└───┘
+
+ + +

The POST method of transmitting data is necessary for INSERT queries. In this case, you can write the beginning of the query in the URL parameter, and use POST to pass the data to insert. The data to insert could be, for example, a tab-separated dump from MySQL. In this way, the INSERT query replaces LOAD DATA LOCAL INFILE from MySQL.

+

Examples: Creating a table:

+
echo 'CREATE TABLE t (a UInt8) ENGINE = Memory' | POST 'http://localhost:8123/'
+
+ + +

Using the familiar INSERT query for data insertion:

+
echo 'INSERT INTO t VALUES (1),(2),(3)' | POST 'http://localhost:8123/'
+
+ + +

Data can be sent separately from the query:

+
echo '(4),(5),(6)' | POST 'http://localhost:8123/?query=INSERT INTO t VALUES'
+
+ + +

You can specify any data format. The 'Values' format is the same as what is used when writing INSERT INTO t VALUES:

+
echo '(7),(8),(9)' | POST 'http://localhost:8123/?query=INSERT INTO t FORMAT Values'
+
+ + +

To insert data from a tab-separated dump, specify the corresponding format:

+
echo -ne '10\n11\n12\n' | POST 'http://localhost:8123/?query=INSERT INTO t FORMAT TabSeparated'
+
+ + +

Reading the table contents. Data is output in random order due to parallel query processing:

+
$ GET 'http://localhost:8123/?query=SELECT a FROM t'
+7
+8
+9
+10
+11
+12
+1
+2
+3
+4
+5
+6
+
+ + +

Deleting the table.

+
POST 'http://localhost:8123/?query=DROP TABLE t'
+
+ + +

For successful requests that don't return a data table, an empty response body is returned.

+

You can use the internal ClickHouse compression format when transmitting data. The compressed data has a non-standard format, and you will need to use the special clickhouse-compressor program to work with it (it is installed with the clickhouse-client package).

+

If you specified 'compress=1' in the URL, the server will compress the data it sends you. +If you specified 'decompress=1' in the URL, the server will decompress the same data that you pass in the POST method.

+

It is also possible to use the standard gzip-based HTTP compression. To send a POST request compressed using gzip, append the request header Content-Encoding: gzip. +In order for ClickHouse to compress the response using gzip, you must append Accept-Encoding: gzip to the request headers, and enable the ClickHouse setting enable_http_compression.

+

You can use this to reduce network traffic when transmitting a large amount of data, or for creating dumps that are immediately compressed.

+

You can use the 'database' URL parameter to specify the default database.

+
$ echo 'SELECT number FROM numbers LIMIT 10' | curl 'http://localhost:8123/?database=system' --data-binary @-
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+
+ + +

By default, the database that is registered in the server settings is used as the default database. By default, this is the database called 'default'. Alternatively, you can always specify the database using a dot before the table name.

+

The username and password can be indicated in one of two ways:

+
    +
  1. Using HTTP Basic Authentication. Example:
    2. +
+
echo 'SELECT 1' | curl 'http://user:password@localhost:8123/' -d @-
+
+ + +
    +
  1. In the 'user' and 'password' URL parameters. Example:
    2. +
+
echo 'SELECT 1' | curl 'http://localhost:8123/?user=user&password=password' -d @-
+
+ + +

If the user name is not indicated, the username 'default' is used. If the password is not indicated, an empty password is used. +You can also use the URL parameters to specify any settings for processing a single query, or entire profiles of settings. Example: +http://localhost:8123/?profile=web&max_rows_to_read=1000000000&query=SELECT+1

+

For more information, see the section "Settings".

+
$ echo 'SELECT number FROM system.numbers LIMIT 10' | curl 'http://localhost:8123/?' --data-binary @-
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+
+ + +

For information about other parameters, see the section "SET".

+

Similarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to add the session_id GET parameter to the request. You can use any string as the session ID. By default, the session is terminated after 60 seconds of inactivity. To change this timeout, modify the default_session_timeout setting in the server configuration, or add the session_timeout GET parameter to the request. To check the session status, use the session_check=1 parameter. Only one query at a time can be executed within a single session.

+

You have the option to receive information about the progress of query execution in X-ClickHouse-Progress headers. To do this, enable the setting send_progress_in_http_headers.

+

Running requests don't stop automatically if the HTTP connection is lost. Parsing and data formatting are performed on the server side, and using the network might be ineffective. +The optional 'query_id' parameter can be passed as the query ID (any string). For more information, see the section "Settings, replace_running_query".

+

The optional 'quota_key' parameter can be passed as the quota key (any string). For more information, see the section "Quotas".

+

The HTTP interface allows passing external data (external temporary tables) for querying. For more information, see the section "External data for query processing".

+

Response buffering

+

You can enable response buffering on the server side. The buffer_size and wait_end_of_query URL parameters are provided for this purpose.

+

buffer_size determines the number of bytes in the result to buffer in the server memory. If the result body is larger than this threshold, the buffer is written to the HTTP channel, and the remaining data is sent directly to the HTTP channel.

+

To ensure that the entire response is buffered, set wait_end_of_query=1. In this case, the data that is not stored in memory will be buffered in a temporary server file.

+

Example:

+
curl -sS 'http://localhost:8123/?max_result_bytes=4000000&buffer_size=3000000&wait_end_of_query=1' -d 'SELECT toUInt8(number) FROM system.numbers LIMIT 9000000 FORMAT RowBinary'
+
+ + +

Use buffering to avoid situations where a query processing error occurred after the response code and HTTP headers were sent to the client. In this situation, an error message is written at the end of the response body, and on the client side, the error can only be detected at the parsing stage.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/interfaces/index.html b/docs/build/docs/en/interfaces/index.html new file mode 100644 index 00000000000..6ed57577bc1 --- /dev/null +++ b/docs/build/docs/en/interfaces/index.html @@ -0,0 +1,2889 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Introduction - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
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+ +
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+ + + + + + + +

+

Interfaces

+

To explore the system's capabilities, download data to tables, or make manual queries, use the clickhouse-client program.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/interfaces/jdbc/index.html b/docs/build/docs/en/interfaces/jdbc/index.html new file mode 100644 index 00000000000..cb26fd088ef --- /dev/null +++ b/docs/build/docs/en/interfaces/jdbc/index.html @@ -0,0 +1,2888 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + JDBC driver - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ +
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+ + +
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+ + +
+
+ + + + + + + +

JDBC driver

+

There is an official JDBC driver for ClickHouse. See here .

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/interfaces/tcp/index.html b/docs/build/docs/en/interfaces/tcp/index.html new file mode 100644 index 00000000000..ba8fe0980a9 --- /dev/null +++ b/docs/build/docs/en/interfaces/tcp/index.html @@ -0,0 +1,2888 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Native interface (TCP) - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + +
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+ + +
+
+ + + + + + + +

Native interface (TCP)

+

The native interface is used in the "clickhouse-client" command-line client for interaction between servers with distributed query processing, and also in C++ programs. We will only cover the command-line client.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/interfaces/third-party_client_libraries/index.html b/docs/build/docs/en/interfaces/third-party_client_libraries/index.html new file mode 100644 index 00000000000..31451895fbc --- /dev/null +++ b/docs/build/docs/en/interfaces/third-party_client_libraries/index.html @@ -0,0 +1,2949 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Libraries from third-party developers - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
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+ + +
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+ + + + + + + +

Libraries from third-party developers

+

There are libraries for working with ClickHouse for:

+ +

We have not tested these libraries. They are listed in random order.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/interfaces/third-party_gui/index.html b/docs/build/docs/en/interfaces/third-party_gui/index.html new file mode 100644 index 00000000000..8dfbbdba5c6 --- /dev/null +++ b/docs/build/docs/en/interfaces/third-party_gui/index.html @@ -0,0 +1,3018 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Visual interfaces from third-party developers - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
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Visual interfaces from third-party developers

+

Tabix

+

Web interface for ClickHouse in the Tabix project.

+

Features:

+
    +
  • Works with ClickHouse directly from the browser, without the need to install additional software.
    • +
  • Query editor with syntax highlighting.
    • +
  • Auto-completion of commands.
    • +
  • Tools for graphical analysis of query execution.
    • +
  • Color scheme options.
    • +
+

Tabix documentation.

+

HouseOps

+

HouseOps is a unique Desktop ClickHouse Ops UI / IDE for OSX, Linux and Windows.

+

Features:

+
    +
  • Query builder;
    • +
  • Database manangement (soon);
    • +
  • Users manangement (soon);
    • +
  • Real-Time Data Analytics (soon);
    • +
  • Cluster/Infra monitoring (soon);
    • +
  • Cluster manangement (soon);
    • +
  • Kafka and Replicated tables monitoring (soon);
    • +
  • And a lot of others features (soon) for you take a beautiful implementation of ClickHouse.
    • +
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/introduction/distinctive_features/index.html b/docs/build/docs/en/introduction/distinctive_features/index.html new file mode 100644 index 00000000000..f74c24a7930 --- /dev/null +++ b/docs/build/docs/en/introduction/distinctive_features/index.html @@ -0,0 +1,3118 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Distinctive features of ClickHouse - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Distinctive features of ClickHouse

+

True column-oriented DBMS

+

In a true column-oriented DBMS, there isn't any "garbage" stored with the values. Among other things, this means that constant-length values must be supported, to avoid storing their length "number" next to the values. As an example, a billion UInt8-type values should actually consume around 1 GB uncompressed, or this will strongly affect the CPU use. It is very important to store data compactly (without any "garbage") even when uncompressed, since the speed of decompression (CPU usage) depends mainly on the volume of uncompressed data.

+

This is worth noting because there are systems that can store values of separate columns separately, but that can't effectively process analytical queries due to their optimization for other scenarios. Examples are HBase, BigTable, Cassandra, and HyperTable. In these systems, you will get throughput around a hundred thousand rows per second, but not hundreds of millions of rows per second.

+

Also note that ClickHouse is a DBMS, not a single database. ClickHouse allows creating tables and databases in runtime, loading data, and running queries without reconfiguring and restarting the server.

+

Data compression

+

Some column-oriented DBMSs (InfiniDB CE and MonetDB) do not use data compression. However, data compression really improves performance.

+

Disk storage of data

+

Many column-oriented DBMSs (such as SAP HANA and Google PowerDrill) can only work in RAM. But even on thousands of servers, the RAM is too small for storing all the pageviews and sessions in Yandex.Metrica.

+

Parallel processing on multiple cores

+

Large queries are parallelized in a natural way.

+

Distributed processing on multiple servers

+

Almost none of the columnar DBMSs listed above have support for distributed processing. +In ClickHouse, data can reside on different shards. Each shard can be a group of replicas that are used for fault tolerance. The query is processed on all the shards in parallel. This is transparent for the user.

+

SQL support

+

If you are familiar with standard SQL, we can't really talk about SQL support. +All the functions have different names. +However, this is a declarative query language based on SQL that can't be differentiated from SQL in many instances. +JOINs are supported. Subqueries are supported in FROM, IN, and JOIN clauses, as well as scalar subqueries. +Dependent subqueries are not supported.

+

Vector engine

+

Data is not only stored by columns, but is processed by vectors (parts of columns). This allows us to achieve high CPU performance.

+

Real-time data updates

+

ClickHouse supports primary key tables. In order to quickly perform queries on the range of the primary key, the data is sorted incrementally using the merge tree. Due to this, data can continually be added to the table. There is no locking when adding data.

+

Indexes

+

Having a primary key makes it possible to extract data for specific clients (for instance, Yandex.Metrica tracking tags) for a specific time range, with low latency less than several dozen milliseconds.

+

Suitable for online queries

+

This lets us use the system as the back-end for a web interface. Low latency means queries can be processed without delay, while the Yandex.Metrica interface page is loading. In other words, in online mode.

+

Support for approximated calculations

+
    +
  1. The system contains aggregate functions for approximated calculation of the number of various values, medians, and quantiles.
    2. +
  2. Supports running a query based on a part (sample) of data and getting an approximated result. In this case, proportionally less data is retrieved from the disk.
    3. +
  3. Supports running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reasonably accurate result while using fewer resources.
    4. +
+

Data replication and support for data integrity on replicas

+

Uses asynchronous multimaster replication. After being written to any available replica, data is distributed to all the remaining replicas. The system maintains identical data on different replicas. Data is restored automatically after a failure, or using a "button" for complex cases. +For more information, see the section Data replication.

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/introduction/features_considered_disadvantages/index.html b/docs/build/docs/en/introduction/features_considered_disadvantages/index.html new file mode 100644 index 00000000000..64af89227dd --- /dev/null +++ b/docs/build/docs/en/introduction/features_considered_disadvantages/index.html @@ -0,0 +1,2892 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ClickHouse features that can be considered disadvantages - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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ClickHouse features that can be considered disadvantages

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  1. No transactions.
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  2. For aggregation, query results must fit in the RAM on a single server. However, the volume of source data for a query may be indefinitely large.
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  3. Lack of full-fledged UPDATE/DELETE implementation.
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Performance

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According to internal testing results, ClickHouse shows the best performance for comparable operating scenarios among systems of its class that were available for testing. This includes the highest throughput for long queries, and the lowest latency on short queries. Testing results are shown on a separate page.

+

Throughput for a single large query

+

Throughput can be measured in rows per second or in megabytes per second. If the data is placed in the page cache, a query that is not too complex is processed on modern hardware at a speed of approximately 2-10 GB/s of uncompressed data on a single server (for the simplest cases, the speed may reach 30 GB/s). If data is not placed in the page cache, the speed depends on the disk subsystem and the data compression rate. For example, if the disk subsystem allows reading data at 400 MB/s, and the data compression rate is 3, the speed will be around 1.2 GB/s. To get the speed in rows per second, divide the speed in bytes per second by the total size of the columns used in the query. For example, if 10 bytes of columns are extracted, the speed will be around 100-200 million rows per second.

+

The processing speed increases almost linearly for distributed processing, but only if the number of rows resulting from aggregation or sorting is not too large.

+

Latency when processing short queries

+

If a query uses a primary key and does not select too many rows to process (hundreds of thousands), and does not use too many columns, we can expect less than 50 milliseconds of latency (single digits of milliseconds in the best case) if data is placed in the page cache. Otherwise, latency is calculated from the number of seeks. If you use rotating drives, for a system that is not overloaded, the latency is calculated by this formula: seek time (10 ms) * number of columns queried * number of data parts.

+

Throughput when processing a large quantity of short queries

+

Under the same conditions, ClickHouse can handle several hundred queries per second on a single server (up to several thousand in the best case). Since this scenario is not typical for analytical DBMSs, we recommend expecting a maximum of 100 queries per second.

+

Performance when inserting data

+

We recommend inserting data in packets of at least 1000 rows, or no more than a single request per second. When inserting to a MergeTree table from a tab-separated dump, the insertion speed will be from 50 to 200 MB/s. If the inserted rows are around 1 Kb in size, the speed will be from 50,000 to 200,000 rows per second. If the rows are small, the performance will be higher in rows per second (on Banner System data -> 500,000 rows per second; on Graphite data -> 1,000,000 rows per second). To improve performance, you can make multiple INSERT queries in parallel, and performance will increase linearly.

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Questions you were afraid to ask

+

Why not use something like MapReduce?

+

We can refer to systems like map-reduce as distributed computing systems in which the reduce operation is based on distributed sorting. In this sense, they include Hadoop, and YT (YT is developed at Yandex for internal use).

+

These systems aren't appropriate for online queries due to their high latency. In other words, they can't be used as the back-end for a web interface. +These types of systems aren't useful for real-time data updates. +Distributed sorting isn't the best way to perform reduce operations if the result of the operation and all the intermediate results (if there are any) are located in the RAM of a single server, which is usually the case for online queries. In such a case, a hash table is the optimal way to perform reduce operations. A common approach to optimizing map-reduce tasks is pre-aggregation (partial reduce) using a hash table in RAM. The user performs this optimization manually. +Distributed sorting is one of the main causes of reduced performance when running simple map-reduce tasks.

+

Systems like map-reduce allow executing any code on the cluster. But a declarative query language is better suited to OLAP in order to run experiments quickly. For example, Hadoop has Hive and Pig. Also consider Cloudera Impala, Shark (outdated) for Spark, and Spark SQL, Presto, and Apache Drill. Performance when running such tasks is highly sub-optimal compared to specialized systems, but relatively high latency makes it unrealistic to use these systems as the backend for a web interface.

+

YT allows storing groups of columns separately. But YT can't be considered a true column-based system because it doesn't have fixed-length data types (for efficiently storing numbers without extra "garbage"), and also due to its lack of a vector engine. Tasks are performed in YT using custom code in streaming mode, so they cannot be optimized enough (up to hundreds of millions of rows per second per server). "Dynamic table sorting" is under development in YT using MergeTree, strict value typing, and a query language similar to SQL. Dynamically sorted tables are not appropriate for OLAP tasks because the data is stored by row. The YT query language is still under development, so we can't yet rely on this functionality. YT developers are considering using dynamically sorted tables in OLTP and Key-Value scenarios.

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Yandex.Metrica use case

+

ClickHouse currently powers Yandex.Metrica, the second largest web analytics platform in the world. With more than 13 trillion records in the database and more than 20 billion events daily, ClickHouse allows you generating custom reports on the fly directly from non-aggregated data.

+

We need to get custom reports based on hits and sessions, with custom segments set by the user. Data for the reports is updated in real-time. Queries must be run immediately (in online mode). We must be able to build reports for any time period. Complex aggregates must be calculated, such as the number of unique visitors. +At this time (April 2014), Yandex.Metrica receives approximately 12 billion events (pageviews and mouse clicks) daily. All these events must be stored in order to build custom reports. A single query may require scanning hundreds of millions of rows over a few seconds, or millions of rows in no more than a few hundred milliseconds.

+

Usage in Yandex.Metrica and other Yandex services

+

ClickHouse is used for multiple purposes in Yandex.Metrica. +Its main task is to build reports in online mode using non-aggregated data. It uses a cluster of 374 servers, which store over 20.3 trillion rows in the database. The volume of compressed data, without counting duplication and replication, is about 2 PB. The volume of uncompressed data (in TSV format) would be approximately 17 PB.

+

ClickHouse is also used for:

+
    +
  • Storing data for Session Replay from Yandex.Metrica.
    • +
  • Processing intermediate data.
    • +
  • Building global reports with Analytics.
    • +
  • Running queries for debugging the Yandex.Metrica engine.
    • +
  • Analyzing logs from the API and the user interface.
    • +
+

ClickHouse has at least a dozen installations in other Yandex services: in search verticals, Market, Direct, business analytics, mobile development, AdFox, personal services, and others.

+

Aggregated and non-aggregated data

+

There is a popular opinion that in order to effectively calculate statistics, you must aggregate data, since this reduces the volume of data.

+

But data aggregation is a very limited solution, for the following reasons:

+
    +
  • You must have a pre-defined list of reports the user will need.
    • +
  • The user can't make custom reports.
    • +
  • When aggregating a large quantity of keys, the volume of data is not reduced, and aggregation is useless.
    • +
  • For a large number of reports, there are too many aggregation variations (combinatorial explosion).
    • +
  • When aggregating keys with high cardinality (such as URLs), the volume of data is not reduced by much (less than twofold).
    • +
  • For this reason, the volume of data with aggregation might grow instead of shrink.
    • +
  • Users do not view all the reports we generate for them. A large portion of calculations are useless.
    • +
  • The logical integrity of data may be violated for various aggregations.
    • +
+

If we do not aggregate anything and work with non-aggregated data, this might actually reduce the volume of calculations.

+

However, with aggregation, a significant part of the work is taken offline and completed relatively calmly. In contrast, online calculations require calculating as fast as possible, since the user is waiting for the result.

+

Yandex.Metrica has a specialized system for aggregating data called Metrage, which is used for the majority of reports. +Starting in 2009, Yandex.Metrica also used a specialized OLAP database for non-aggregated data called OLAPServer, which was previously used for the report builder. +OLAPServer worked well for non-aggregated data, but it had many restrictions that did not allow it to be used for all reports as desired. These included the lack of support for data types (only numbers), and the inability to incrementally update data in real-time (it could only be done by rewriting data daily). OLAPServer is not a DBMS, but a specialized DB.

+

To remove the limitations of OLAPServer and solve the problem of working with non-aggregated data for all reports, we developed the ClickHouse DBMS.

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/operations/access_rights/index.html b/docs/build/docs/en/operations/access_rights/index.html new file mode 100644 index 00000000000..7af6a2da7e1 --- /dev/null +++ b/docs/build/docs/en/operations/access_rights/index.html @@ -0,0 +1,2970 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Access rights - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Access rights

+

Users and access rights are set up in the user config. This is usually users.xml.

+

Users are recorded in the users section. Here is a fragment of the users.xml file:

+
<!-- Users and ACL. -->
+<users>
+    <!-- If the user name is not specified, the 'default' user is used. -->
+    <default>
+        <!-- Password could be specified in plaintext or in SHA256 (in hex format).
+
+             If you want to specify the password in plain text (not recommended), place it in the 'password' element.
+             Example: <password>qwerty</password>.
+             Password can be empty.
+
+             If you want to specify SHA256, place it in the 'password_sha256_hex' element.
+                          Example: <password_sha256_hex>65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5</password_sha256_hex>
+
+             How to generate decent password:
+             Execute: PASSWORD=$(base64 < /dev/urandom | head -c8); echo "$PASSWORD"; echo -n "$PASSWORD" | sha256sum | tr -d '-'
+             In first line will be password and in second - corresponding SHA256.
+        -->
+        <password></password>
+        <!-- A list of networks that access is allowed from.
+            Each list item has one of the following forms:
+            <ip>IP address or subnet mask. For example: 198.51.100.0/24 or 2001:DB8::/32.
+            <host> Host name. For example: example01. A DNS query is made for verification, and all addresses obtained are compared with the address of the customer.
+            <host_regexp> Regular expression for host names. For example: ^example\d\d-\d\d-\d\.yandex\.ru$
+                For verification, a DNS PTR query is made for the customer's address and a regular expression is applied to the result.
+                Then another DNS query is made for the result of the PTR query, and all received address are compared to the client address.
+                We strongly recommend that the regex ends with \.yandex\.ru$.
+
+            If you are installing ClickHouse yourself, enter:
+                <networks>
+                        <ip>::/0</ip>
+                </networks>
+        -->
+        <networks incl="networks" />
+
+        <!-- Settings profile for the user. -->
+        <profile>default</profile>
+
+        <!-- Quota for the user. -->
+        <quota>default</quota>
+    </default>
+
+    <!-- For requests from the Yandex.Metrica user interface via the API for data on specific counters. -->
+    <web>
+        <password></password>
+        <networks incl="networks" />
+        <profile>web</profile>
+        <quota>default</quota>
+        <allow_databases>
+        <database>test</database>
+        </allow_databases>
+    </web>
+</users>
+
+ + +

You can see a declaration from two users: default and web. We added the web user separately.

+

The default user is chosen in cases when the username is not passed. The default user is also used for distributed query processing, if the configuration of the server or cluster doesn't specify the user and password (see the section on the Distributed engine).

+

The user that is used for exchanging information between servers combined in a cluster must not have substantial restrictions or quotas – otherwise, distributed queries will fail.

+

The password is specified in open format (not recommended) or in SHA-256. The hash isn't salted. In this regard, you should not consider these passwords as providing security against potential malicious attacks. Rather, they are necessary for protection from employees.

+

A list of networks is specified that access is allowed from. In this example, the list of networks for both users is loaded from a separate file (/etc/metrika.xml) containing the 'networks' substitution. Here is a fragment of it:

+
<yandex>
+    ...
+    <networks>
+        <ip>::/64</ip>
+        <ip>203.0.113.0/24</ip>
+        <ip>2001:DB8::/32</ip>
+        ...
+    </networks>
+</yandex>
+
+ + +

We could have defined this list of networks directly in 'users.xml', or in a file in the 'users.d' directory (for more information, see the section "Configuration files").

+

The config includes comments explaining how to open access from everywhere.

+

For use in production, only specify IP elements (IP addresses and their masks), since using 'host' and 'hoost_regexp' might cause extra latency.

+

Next the user settings profile is specified (see the section "Settings profiles"). You can specify the default profile, default. The profile can have any name. You can specify the same profile for different users. The most important thing you can write in the settings profile is 'readonly' set to 1, which provides read-only access.

+

After this, the quota is defined (see the section "Quotas"). You can specify the default quota, default. It is set in the config by default so that it only counts resource usage, but does not restrict it. The quota can have any name. You can specify the same quota for different users – in this case, resource usage is calculated for each user individually.

+

In the optional <allow_databases> section, you can also specify a list of databases that the user can access. By default, all databases are available to the user. You can specify the default database. In this case, the user will receive access to the database by default.

+

Access to the system database is always allowed (since this database is used for processing queries).

+

The user can get a list of all databases and tables in them by using SHOW queries or system tables, even if access to individual databases isn't allowed.

+

Database access is not related to the readonly setting. You can't grant full access to one database and readonly access to another one.

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/operations/configuration_files/index.html b/docs/build/docs/en/operations/configuration_files/index.html new file mode 100644 index 00000000000..a6119116c29 --- /dev/null +++ b/docs/build/docs/en/operations/configuration_files/index.html @@ -0,0 +1,2900 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Configuration files - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Configuration files

+

The main server config file is config.xml. It resides in the /etc/clickhouse-server/ directory.

+

Individual settings can be overridden in the *.xmland*.conf files in the conf.d and config.d directories next to the config file.

+

The replace or remove attributes can be specified for the elements of these config files.

+

If neither is specified, it combines the contents of elements recursively, replacing values of duplicate children.

+

If replace is specified, it replaces the entire element with the specified one.

+

If remove is specified, it deletes the element.

+

The config can also define "substitutions". If an element has the incl attribute, the corresponding substitution from the file will be used as the value. By default, the path to the file with substitutions is /etc/metrika.xml. This can be changed in the include_from element in the server config. The substitution values are specified in /yandex/substitution_name elements in this file. If a substitution specified in incl does not exist, it is recorded in the log. To prevent ClickHouse from logging missing substitutions, specify the optional="true" attribute (for example, settings for macrosserver_settings/settings.md#server_settings-macros)).

+

Substitutions can also be performed from ZooKeeper. To do this, specify the attribute from_zk = "/path/to/node". The element value is replaced with the contents of the node at /path/to/node in ZooKeeper. You can also put an entire XML subtree on the ZooKeeper node and it will be fully inserted into the source element.

+

The config.xml file can specify a separate config with user settings, profiles, and quotas. The relative path to this config is set in the 'users_config' element. By default, it is users.xml. If users_config is omitted, the user settings, profiles, and quotas are specified directly in config.xml.

+

In addition, users_config may have overrides in files from the users_config.d directory (for example, users.d) and substitutions.

+

For each config file, the server also generates file-preprocessed.xml files when starting. These files contain all the completed substitutions and overrides, and they are intended for informational use. If ZooKeeper substitutions were used in the config files but ZooKeeper is not available on the server start, the server loads the configuration from the preprocessed file.

+

The server tracks changes in config files, as well as files and ZooKeeper nodes that were used when performing substitutions and overrides, and reloads the settings for users and clusters on the fly. This means that you can modify the cluster, users, and their settings without restarting the server.

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/operations/quotas/index.html b/docs/build/docs/en/operations/quotas/index.html new file mode 100644 index 00000000000..9bb75021d91 --- /dev/null +++ b/docs/build/docs/en/operations/quotas/index.html @@ -0,0 +1,2972 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Quotas - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Quotas

+

Quotas allow you to limit resource usage over a period of time, or simply track the use of resources. +Quotas are set up in the user config. This is usually 'users.xml'.

+

The system also has a feature for limiting the complexity of a single query. See the section "Restrictions on query complexity").

+

In contrast to query complexity restrictions, quotas:

+
    +
  • Place restrictions on a set of queries that can be run over a period of time, instead of limiting a single query.
    • +
  • Account for resources spent on all remote servers for distributed query processing.
    • +
+

Let's look at the section of the 'users.xml' file that defines quotas.

+
<!-- Quotas. -->
+<quotas>
+    <!-- Quota name. -->
+    <default>
+        <!-- Restrictions for a time period. You can set many intervals with different restrictions. -->
+        <interval>
+            <!-- Length of the interval. -->
+            <duration>3600</duration>
+
+            <!-- Unlimited. Just collect data for the specified time interval. -->
+            <queries>0</queries>
+            <errors>0</errors>
+            <result_rows>0</result_rows>
+            <read_rows>0</read_rows>
+            <execution_time>0</execution_time>
+        </interval>
+    </default>
+
+ + +

By default, the quota just tracks resource consumption for each hour, without limiting usage. +The resource consumption calculated for each interval is output to the server log after each request.

+
<statbox>
+    <!-- Restrictions for a time period. You can set many intervals with different restrictions. -->
+    <interval>
+        <!-- Length of the interval. -->
+        <duration>3600</duration>
+
+        <queries>1000</queries>
+        <errors>100</errors>
+        <result_rows>1000000000</result_rows>
+        <read_rows>100000000000</read_rows>
+        <execution_time>900</execution_time>
+    </interval>
+
+    <interval>
+        <duration>86400</duration>
+
+        <queries>10000</queries>
+        <errors>1000</errors>
+        <result_rows>5000000000</result_rows>
+        <read_rows>500000000000</read_rows>
+        <execution_time>7200</execution_time>
+    </interval>
+</statbox>
+
+ + +

For the 'statbox' quota, restrictions are set for every hour and for every 24 hours (86,400 seconds). The time interval is counted starting from an implementation-defined fixed moment in time. In other words, the 24-hour interval doesn't necessarily begin at midnight.

+

When the interval ends, all collected values are cleared. For the next hour, the quota calculation starts over.

+

Here are the amounts that can be restricted:

+

queries – The total number of requests.

+

errors – The number of queries that threw an exception.

+

result_rows – The total number of rows given as the result.

+

read_rows – The total number of source rows read from tables for running the query, on all remote servers.

+

execution_time – The total query execution time, in seconds (wall time).

+

If the limit is exceeded for at least one time interval, an exception is thrown with a text about which restriction was exceeded, for which interval, and when the new interval begins (when queries can be sent again).

+

Quotas can use the "quota key" feature in order to report on resources for multiple keys independently. Here is an example of this:

+
<!-- For the global reports designer. -->
+<web_global>
+    <!-- keyed - The quota_key "key" is passed in the query parameter,
+            and the quota is tracked separately for each key value.
+        For example, you can pass a Yandex.Metrica username as the key,
+            so the quota will be counted separately for each username.
+        Using keys makes sense only if quota_key is transmitted by the program, not by a user.
+
+        You can also write <keyed_by_ip /> so the IP address is used as the quota key.
+        (But keep in mind that users can change the IPv6 address fairly easily.)
+    -->
+    <keyed />
+
+ + +

The quota is assigned to users in the 'users' section of the config. See the section "Access rights".

+

For distributed query processing, the accumulated amounts are stored on the requestor server. So if the user goes to another server, the quota there will "start over".

+

When the server is restarted, quotas are reset.

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/operations/server_settings/index.html b/docs/build/docs/en/operations/server_settings/index.html new file mode 100644 index 00000000000..32e971104bd --- /dev/null +++ b/docs/build/docs/en/operations/server_settings/index.html @@ -0,0 +1,2894 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Introduction - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + + + +

+

Server configuration parameters

+

This section contains descriptions of server settings that cannot be changed at the session or query level.

+

These settings are stored in the config.xml file on the ClickHouse server.

+

Other settings are described in the "Settings" section.

+

Before studying the settings, read the Configuration files section and note the use of substitutions (the incl and optional attributes).

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/operations/server_settings/settings/index.html b/docs/build/docs/en/operations/server_settings/settings/index.html new file mode 100644 index 00000000000..94fbefe347e --- /dev/null +++ b/docs/build/docs/en/operations/server_settings/settings/index.html @@ -0,0 +1,3915 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Server settings - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + + + +

Server settings

+

+

builtin_dictionaries_reload_interval

+

The interval in seconds before reloading built-in dictionaries.

+

ClickHouse reloads built-in dictionaries every x seconds. This makes it possible to edit dictionaries "on the fly" without restarting the server.

+

Default value: 3600.

+

Example

+
<builtin_dictionaries_reload_interval>3600</builtin_dictionaries_reload_interval>
+
+ + +

+

compression

+

Data compression settings.

+
+ +Don't use it if you have just started using ClickHouse. + +
+ +

The configuration looks like this:

+
<compression>
+    <case>
+      <parameters/>
+    </case>
+    ...
+</compression>
+
+ + +

You can configure multiple sections <case>.

+

Block field <case>:

+
    +
  • min_part_size – The minimum size of a table part.
    • +
  • min_part_size_ratio – The ratio of the minimum size of a table part to the full size of the table.
    • +
  • method – Compression method. Acceptable values ​: lz4 or zstd(experimental).
    • +
+

ClickHouse checks min_part_size and min_part_size_ratio and processes the case blocks that match these conditions. If none of the <case> matches, ClickHouse applies the lz4 compression algorithm.

+

Example

+
<compression incl="clickhouse_compression">
+    <case>
+        <min_part_size>10000000000</min_part_size>
+        <min_part_size_ratio>0.01</min_part_size_ratio>
+        <method>zstd</method>
+    </case>
+</compression>
+
+ + +

+

default_database

+

The default database.

+

To get a list of databases, use the SHOW DATABASES.

+

Example

+
<default_database>default</default_database>
+
+ + +

+

default_profile

+

Default settings profile.

+

Settings profiles are located in the file specified in the parameter user_config.

+

Example

+
<default_profile>default</default_profile>
+
+ + +

+

dictionaries_config

+

The path to the config file for external dictionaries.

+

Path:

+
    +
  • Specify the absolute path or the path relative to the server config file.
    • +
  • The path can contain wildcards * and ?.
    • +
+

See also "External dictionaries".

+

Example

+
<dictionaries_config>*_dictionary.xml</dictionaries_config>
+
+ + +

+

dictionaries_lazy_load

+

Lazy loading of dictionaries.

+

If true, then each dictionary is created on first use. If dictionary creation failed, the function that was using the dictionary throws an exception.

+

If false, all dictionaries are created when the server starts, and if there is an error, the server shuts down.

+

The default is true.

+

Example

+
<dictionaries_lazy_load>true</dictionaries_lazy_load>
+
+ + +

+

format_schema_path

+

The path to the directory with the schemes for the input data, such as schemas for the CapnProto format.

+

Example

+
  <!-- Directory containing schema files for various input formats. -->
+  <format_schema_path>format_schemas/</format_schema_path>
+
+ + +

+

graphite

+

Sending data to Graphite.

+

Settings:

+
    +
  • host – The Graphite server.
    • +
  • port – The port on the Graphite server.
    • +
  • interval – The interval for sending, in seconds.
    • +
  • timeout – The timeout for sending data, in seconds.
    • +
  • root_path – Prefix for keys.
    • +
  • metrics – Sending data from a :ref:system_tables-system.metrics table.
    • +
  • events – Sending data from a :ref:system_tables-system.events table.
    • +
  • asynchronous_metrics – Sending data from a :ref:system_tables-system.asynchronous_metrics table.
    • +
+

You can configure multiple <graphite> clauses. For instance, you can use this for sending different data at different intervals.

+

Example

+
<graphite>
+    <host>localhost</host>
+    <port>42000</port>
+    <timeout>0.1</timeout>
+    <interval>60</interval>
+    <root_path>one_min</root_path>
+    <metrics>true</metrics>
+    <events>true</events>
+    <asynchronous_metrics>true</asynchronous_metrics>
+</graphite>
+
+ + +

+

graphite_rollup

+

Settings for thinning data for Graphite.

+

For more information, see GraphiteMergeTree.

+

Example

+
<graphite_rollup_example>
+    <default>
+        <function>max</function>
+        <retention>
+            <age>0</age>
+            <precision>60</precision>
+        </retention>
+        <retention>
+            <age>3600</age>
+            <precision>300</precision>
+        </retention>
+        <retention>
+            <age>86400</age>
+            <precision>3600</precision>
+        </retention>
+    </default>
+</graphite_rollup_example>
+
+ + +

+

http_port/https_port

+

The port for connecting to the server over HTTP(s).

+

If https_port is specified, openSSL must be configured.

+

If http_port is specified, the openSSL configuration is ignored even if it is set.

+

Example

+
<https>0000</https>
+
+ + +

+

http_server_default_response

+

The page that is shown by default when you access the ClickHouse HTTP(s) server.

+

Example

+

Opens https://tabix.io/ when accessing http://localhost: http_port.

+
<http_server_default_response>
+  <![CDATA[<html ng-app="SMI2"><head><base href="http://ui.tabix.io/"></head><body><div ui-view="" class="content-ui"></div><script src="http://loader.tabix.io/master.js"></script></body></html>]]>
+</http_server_default_response>
+
+ + +

+

include_from

+

The path to the file with substitutions.

+

For more information, see the section "Configuration files".

+

Example

+
<include_from>/etc/metrica.xml</include_from>
+
+ + +

+

interserver_http_port

+

Port for exchanging data between ClickHouse servers.

+

Example

+
<interserver_http_port>9009</interserver_http_port>
+
+ + +

+

interserver_http_host

+

The host name that can be used by other servers to access this server.

+

If omitted, it is defined in the same way as the hostname-f command.

+

Useful for breaking away from a specific network interface.

+

Example

+
<interserver_http_host>example.yandex.ru</interserver_http_host>
+
+ + +

+

keep_alive_timeout

+

The number of milliseconds that ClickHouse waits for incoming requests before closing the connection.

+

Example

+
<keep_alive_timeout>3</keep_alive_timeout>
+
+ + +

+

listen_host

+

Restriction on hosts that requests can come from. If you want the server to answer all of them, specify ::.

+

Examples:

+
<listen_host>::1</listen_host>
+<listen_host>127.0.0.1</listen_host>
+
+ + +

+

logger

+

Logging settings.

+

Keys:

+
    +
  • level – Logging level. Acceptable values: trace, debug, information, warning, error.
    • +
  • log – The log file. Contains all the entries according to level.
    • +
  • errorlog – Error log file.
    • +
  • size – Size of the file. Applies to loganderrorlog. Once the file reaches size, ClickHouse archives and renames it, and creates a new log file in its place.
    • +
  • count – The number of archived log files that ClickHouse stores.
    • +
+

Example

+
<logger>
+    <level>trace</level>
+    <log>/var/log/clickhouse-server/clickhouse-server.log</log>
+    <errorlog>/var/log/clickhouse-server/clickhouse-server.err.log</errorlog>
+    <size>1000M</size>
+    <count>10</count>
+</logger>
+
+ + +

+

macros

+

Parameter substitutions for replicated tables.

+

Can be omitted if replicated tables are not used.

+

For more information, see the section "Creating replicated tables".

+

Example

+
<macros incl="macros" optional="true" />
+
+ + +

+

mark_cache_size

+

Approximate size (in bytes) of the cache of "marks" used by MergeTree engines.

+

The cache is shared for the server and memory is allocated as needed. The cache size must be at least 5368709120.

+

Example

+
<mark_cache_size>5368709120</mark_cache_size>
+
+ + +

+

max_concurrent_queries

+

The maximum number of simultaneously processed requests.

+

Example

+
<max_concurrent_queries>100</max_concurrent_queries>
+
+ + +

+

max_connections

+

The maximum number of inbound connections.

+

Example

+
<max_connections>4096</max_connections>
+
+ + +

+

max_open_files

+

The maximum number of open files.

+

By default: maximum.

+

We recommend using this option in Mac OS X, since the getrlimit() function returns an incorrect value.

+

Example

+
<max_open_files>262144</max_open_files>
+
+ + +

+

max_table_size_to_drop

+

Restriction on deleting tables.

+

If the size of a MergeTree type table exceeds max_table_size_to_drop (in bytes), you can't delete it using a DROP query.

+

If you still need to delete the table without restarting the ClickHouse server, create the <clickhouse-path>/flags/force_drop_table file and run the DROP query.

+

Default value: 50 GB.

+

The value 0 means that you can delete all tables without any restrictions.

+

Example

+
<max_table_size_to_drop>0</max_table_size_to_drop>
+
+ + +

+

merge_tree

+

Fine tuning for tables in the MergeTree family.

+

For more information, see the MergeTreeSettings.h header file.

+

Example

+
<merge_tree>
+    <max_suspicious_broken_parts>5</max_suspicious_broken_parts>
+</merge_tree>
+
+ + +

+

openSSL

+

SSL client/server configuration.

+

Support for SSL is provided by the libpoco library. The interface is described in the file SSLManager.h

+

Keys for server/client settings:

+
    +
  • privateKeyFile – The path to the file with the secret key of the PEM certificate. The file may contain a key and certificate at the same time.
    • +
  • certificateFile – The path to the client/server certificate file in PEM format. You can omit it if privateKeyFile contains the certificate.
    • +
  • caConfig – The path to the file or directory that contains trusted root certificates.
    • +
  • verificationMode – The method for checking the node's certificates. Details are in the description of the Context class. Possible values: none, relaxed, strict, once.
    • +
  • verificationDepth – The maximum length of the verification chain. Verification will fail if the certificate chain length exceeds the set value.
    • +
  • loadDefaultCAFile – Indicates that built-in CA certificates for OpenSSL will be used. Acceptable values: true, false. |
    • +
  • cipherList – Supported OpenSSL encryptions. For example: ALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH.
    • +
  • cacheSessions – Enables or disables caching sessions. Must be used in combination with sessionIdContext. Acceptable values: true, false.
    • +
  • sessionIdContext – A unique set of random characters that the server appends to each generated identifier. The length of the string must not exceed SSL_MAX_SSL_SESSION_ID_LENGTH. This parameter is always recommended, since it helps avoid problems both if the server caches the session and if the client requested caching. Default value: ${application.name}.
    • +
  • sessionCacheSize – The maximum number of sessions that the server caches. Default value: 1024*20. 0 – Unlimited sessions.
    • +
  • sessionTimeout – Time for caching the session on the server.
    • +
  • extendedVerification – Automatically extended verification of certificates after the session ends. Acceptable values: true, false.
    • +
  • requireTLSv1 – Require a TLSv1 connection. Acceptable values: true, false.
    • +
  • requireTLSv1_1 – Require a TLSv1.1 connection. Acceptable values: true, false.
    • +
  • requireTLSv1 – Require a TLSv1.2 connection. Acceptable values: true, false.
    • +
  • fips – Activates OpenSSL FIPS mode. Supported if the library's OpenSSL version supports FIPS.
    • +
  • privateKeyPassphraseHandler – Class (PrivateKeyPassphraseHandler subclass) that requests the passphrase for accessing the private key. For example: <privateKeyPassphraseHandler>, <name>KeyFileHandler</name>, <options><password>test</password></options>, </privateKeyPassphraseHandler>.
    • +
  • invalidCertificateHandler – Class (subclass of CertificateHandler) for verifying invalid certificates. For example: <invalidCertificateHandler> <name>ConsoleCertificateHandler</name> </invalidCertificateHandler> .
    • +
  • disableProtocols – Protocols that are not allowed to use.
    • +
  • preferServerCiphers – Preferred server ciphers on the client.
    • +
+

Example of settings:

+
<openSSL>
+    <server>
+        <!-- openssl req -subj "/CN=localhost" -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout /etc/clickhouse-server/server.key -out /etc/clickhouse-server/server.crt -->
+        <certificateFile>/etc/clickhouse-server/server.crt</certificateFile>
+        <privateKeyFile>/etc/clickhouse-server/server.key</privateKeyFile>
+        <!-- openssl dhparam -out /etc/clickhouse-server/dhparam.pem 4096 -->
+        <dhParamsFile>/etc/clickhouse-server/dhparam.pem</dhParamsFile>
+        <verificationMode>none</verificationMode>
+        <loadDefaultCAFile>true</loadDefaultCAFile>
+        <cacheSessions>true</cacheSessions>
+        <disableProtocols>sslv2,sslv3</disableProtocols>
+        <preferServerCiphers>true</preferServerCiphers>
+    </server>
+    <client>
+        <loadDefaultCAFile>true</loadDefaultCAFile>
+        <cacheSessions>true</cacheSessions>
+        <disableProtocols>sslv2,sslv3</disableProtocols>
+        <preferServerCiphers>true</preferServerCiphers>
+        <!-- Use for self-signed: <verificationMode>none</verificationMode> -->
+        <invalidCertificateHandler>
+            <!-- Use for self-signed: <name>AcceptCertificateHandler</name> -->
+            <name>RejectCertificateHandler</name>
+        </invalidCertificateHandler>
+    </client>
+</openSSL>
+
+ + +

+

part_log

+

Logging events that are associated with MergeTree data. For instance, adding or merging data. You can use the log to simulate merge algorithms and compare their characteristics. You can visualize the merge process.

+

Queries are logged in the ClickHouse table, not in a separate file.

+

Columns in the log:

+
    +
  • event_time – Date of the event.
    • +
  • duration_ms – Duration of the event.
    • +
  • event_type – Type of event. 1 – new data part; 2 – merge result; 3 – data part downloaded from replica; 4 – data part deleted.
    • +
  • database_name – The name of the database.
    • +
  • table_name – Name of the table.
    • +
  • part_name – Name of the data part.
    • +
  • size_in_bytes – Size of the data part in bytes.
    • +
  • merged_from – An array of names of data parts that make up the merge (also used when downloading a merged part).
    • +
  • merge_time_ms – Time spent on the merge.
    • +
+

Use the following parameters to configure logging:

+
    +
  • database – Name of the database.
    • +
  • table – Name of the table.
    • +
  • partition_by – Sets a custom partitioning key.
    • +
  • flush_interval_milliseconds – Interval for flushing data from memory to the disk.
    • +
+

Example

+
<part_log>
+    <database>system</database>
+    <table>part_log</table>
+    <partition_by>toMonday(event_date)</partition_by>
+    <flush_interval_milliseconds>7500</flush_interval_milliseconds>
+</part_log>
+
+ + +

+

path

+

The path to the directory containing data.

+
+ +The end slash is mandatory. + +
+ +

Example

+
<path>/var/lib/clickhouse/</path>
+
+ + +

+

query_log

+

Setting for logging queries received with the log_queries=1 setting.

+

Queries are logged in the ClickHouse table, not in a separate file.

+

Use the following parameters to configure logging:

+
    +
  • database – Name of the database.
    • +
  • table – Name of the table.
    • +
  • partition_by – Sets a custom partitioning key.
    • +
  • flush_interval_milliseconds – Interval for flushing data from memory to the disk.
    • +
+

If the table doesn't exist, ClickHouse will create it. If the structure of the query log changed when the ClickHouse server was updated, the table with the old structure is renamed, and a new table is created automatically.

+

Example

+
<query_log>
+    <database>system</database>
+    <table>query_log</table>
+    <partition_by>toMonday(event_date)</partition_by>
+    <flush_interval_milliseconds>7500</flush_interval_milliseconds>
+</query_log>
+
+ + +

+

remote_servers

+

Configuration of clusters used by the Distributed table engine.

+

For more information, see the section "Table engines/Distributed".

+

Example

+
<remote_servers incl="clickhouse_remote_servers" />
+
+ + +

For the value of the incl attribute, see the section "Configuration files".

+

+

timezone

+

The server's time zone.

+

Specified as an IANA identifier for the UTC time zone or geographic location (for example, Africa/Abidjan).

+

The time zone is necessary for conversions between String and DateTime formats when DateTime fields are output to text format (printed on the screen or in a file), and when getting DateTime from a string. In addition, the time zone is used in functions that work with the time and date if they didn't receive the time zone in the input parameters.

+

Example

+
<timezone>Europe/Moscow</timezone>
+
+ + +

+

tcp_port

+

Port for communicating with clients over the TCP protocol.

+

Example

+
<tcp_port>9000</tcp_port>
+
+ + +

+

tmp_path

+

Path to temporary data for processing large queries.

+
+ +The end slash is mandatory. + +
+ +

Example

+
<tmp_path>/var/lib/clickhouse/tmp/</tmp_path>
+
+ + +

+

uncompressed_cache_size

+

Cache size (in bytes) for uncompressed data used by table engines from the MergeTree family.

+

There is one shared cache for the server. Memory is allocated on demand. The cache is used if the option use_uncompressed_cache is enabled.

+

The uncompressed cache is advantageous for very short queries in individual cases.

+

Example

+
<uncompressed_cache_size>8589934592</uncompressed_cache_size>
+
+ + +

+

users_config

+

Path to the file that contains:

+
    +
  • User configurations.
    • +
  • Access rights.
    • +
  • Settings profiles.
    • +
  • Quota settings.
    • +
+

Example

+
<users_config>users.xml</users_config>
+
+ + +

+

zookeeper

+

Configuration of ZooKeeper servers.

+

ClickHouse uses ZooKeeper for storing replica metadata when using replicated tables.

+

This parameter can be omitted if replicated tables are not used.

+

For more information, see the section "Replication".

+

Example

+
<zookeeper incl="zookeeper-servers" optional="true" />
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/operations/settings/index.html b/docs/build/docs/en/operations/settings/index.html new file mode 100644 index 00000000000..b59d1a20354 --- /dev/null +++ b/docs/build/docs/en/operations/settings/index.html @@ -0,0 +1,2908 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Introduction - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

+

Settings

+

There are multiple ways to make all the settings described below. +Settings are configured in layers, so each subsequent layer redefines the previous settings.

+

Ways to configure settings, in order of priority:

+
    +
  • Settings in the server config file.
    • +
+

Settings from user profiles.

+
    +
  • Session settings.
    • +
+

Send SET setting=value from the ClickHouse console client in interactive mode. +Similarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to specify the session_id HTTP parameter.

+
    +
  • For a query.
    • +
  • When starting the ClickHouse console client in non-interactive mode, set the startup parameter --setting=value.
    • +
  • When using the HTTP API, pass CGI parameters (URL?setting_1=value&setting_2=value...).
    • +
+

Settings that can only be made in the server config file are not covered in this section.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/operations/settings/query_complexity/index.html b/docs/build/docs/en/operations/settings/query_complexity/index.html new file mode 100644 index 00000000000..46f480f68bd --- /dev/null +++ b/docs/build/docs/en/operations/settings/query_complexity/index.html @@ -0,0 +1,3511 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Restrictions on query complexity - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Restrictions on query complexity

+

Restrictions on query complexity are part of the settings. +They are used in order to provide safer execution from the user interface. +Almost all the restrictions only apply to SELECTs.For distributed query processing, restrictions are applied on each server separately.

+

Restrictions on the "maximum amount of something" can take the value 0, which means "unrestricted". +Most restrictions also have an 'overflow_mode' setting, meaning what to do when the limit is exceeded. +It can take one of two values: throw or break. Restrictions on aggregation (group_by_overflow_mode) also have the value any.

+

throw – Throw an exception (default).

+

break – Stop executing the query and return the partial result, as if the source data ran out.

+

any (only for group_by_overflow_mode) – Continuing aggregation for the keys that got into the set, but don't add new keys to the set.

+

+

readonly

+

With a value of 0, you can execute any queries. +With a value of 1, you can only execute read requests (such as SELECT and SHOW). Requests for writing and changing settings (INSERT, SET) are prohibited. +With a value of 2, you can process read queries (SELECT, SHOW) and change settings (SET).

+

After enabling readonly mode, you can't disable it in the current session.

+

When using the GET method in the HTTP interface, 'readonly = 1' is set automatically. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.

+

+

max_memory_usage

+

The maximum amount of RAM to use for running a query on a single server.

+

In the default configuration file, the maximum is 10 GB.

+

The setting doesn't consider the volume of available memory or the total volume of memory on the machine. +The restriction applies to a single query within a single server. +You can use SHOW PROCESSLIST to see the current memory consumption for each query. +In addition, the peak memory consumption is tracked for each query and written to the log.

+

Memory usage is not monitored for the states of certain aggregate functions.

+

Memory usage is not fully tracked for states of the aggregate functions min, max, any, anyLast, argMin, argMax from String and Array arguments.

+

Memory consumption is also restricted by the parameters max_memory_usage_for_user and max_memory_usage_for_all_queries.

+

max_memory_usage_for_user

+

The maximum amount of RAM to use for running a user's queries on a single server.

+

Default values are defined in Settings.h. By default, the amount is not restricted (max_memory_usage_for_user = 0).

+

See also the description of max_memory_usage.

+

max_memory_usage_for_all_queries

+

The maximum amount of RAM to use for running all queries on a single server.

+

Default values are defined in Settings.h. By default, the amount is not restricted (max_memory_usage_for_all_queries = 0).

+

See also the description of max_memory_usage.

+

max_rows_to_read

+

The following restrictions can be checked on each block (instead of on each row). That is, the restrictions can be broken a little. +When running a query in multiple threads, the following restrictions apply to each thread separately.

+

Maximum number of rows that can be read from a table when running a query.

+

max_bytes_to_read

+

Maximum number of bytes (uncompressed data) that can be read from a table when running a query.

+

read_overflow_mode

+

What to do when the volume of data read exceeds one of the limits: 'throw' or 'break'. By default, throw.

+

max_rows_to_group_by

+

Maximum number of unique keys received from aggregation. This setting lets you limit memory consumption when aggregating.

+

group_by_overflow_mode

+

What to do when the number of unique keys for aggregation exceeds the limit: 'throw', 'break', or 'any'. By default, throw. +Using the 'any' value lets you run an approximation of GROUP BY. The quality of this approximation depends on the statistical nature of the data.

+

max_rows_to_sort

+

Maximum number of rows before sorting. This allows you to limit memory consumption when sorting.

+

max_bytes_to_sort

+

Maximum number of bytes before sorting.

+

sort_overflow_mode

+

What to do if the number of rows received before sorting exceeds one of the limits: 'throw' or 'break'. By default, throw.

+

max_result_rows

+

Limit on the number of rows in the result. Also checked for subqueries, and on remote servers when running parts of a distributed query.

+

max_result_bytes

+

Limit on the number of bytes in the result. The same as the previous setting.

+

result_overflow_mode

+

What to do if the volume of the result exceeds one of the limits: 'throw' or 'break'. By default, throw. +Using 'break' is similar to using LIMIT.

+

max_execution_time

+

Maximum query execution time in seconds. +At this time, it is not checked for one of the sorting stages, or when merging and finalizing aggregate functions.

+

timeout_overflow_mode

+

What to do if the query is run longer than 'max_execution_time': 'throw' or 'break'. By default, throw.

+

min_execution_speed

+

Minimal execution speed in rows per second. Checked on every data block when 'timeout_before_checking_execution_speed' expires. If the execution speed is lower, an exception is thrown.

+

timeout_before_checking_execution_speed

+

Checks that execution speed is not too slow (no less than 'min_execution_speed'), after the specified time in seconds has expired.

+

max_columns_to_read

+

Maximum number of columns that can be read from a table in a single query. If a query requires reading a greater number of columns, it throws an exception.

+

max_temporary_columns

+

Maximum number of temporary columns that must be kept in RAM at the same time when running a query, including constant columns. If there are more temporary columns than this, it throws an exception.

+

max_temporary_non_const_columns

+

The same thing as 'max_temporary_columns', but without counting constant columns. +Note that constant columns are formed fairly often when running a query, but they require approximately zero computing resources.

+

max_subquery_depth

+

Maximum nesting depth of subqueries. If subqueries are deeper, an exception is thrown. By default, 100.

+

max_pipeline_depth

+

Maximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000.

+

max_ast_depth

+

Maximum nesting depth of a query syntactic tree. If exceeded, an exception is thrown. +At this time, it isn't checked during parsing, but only after parsing the query. That is, a syntactic tree that is too deep can be created during parsing, but the query will fail. By default, 1000.

+

max_ast_elements

+

Maximum number of elements in a query syntactic tree. If exceeded, an exception is thrown. +In the same way as the previous setting, it is checked only after parsing the query. By default, 10,000.

+

max_rows_in_set

+

Maximum number of rows for a data set in the IN clause created from a subquery.

+

max_bytes_in_set

+

Maximum number of bytes (uncompressed data) used by a set in the IN clause created from a subquery.

+

set_overflow_mode

+

What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.

+

max_rows_in_distinct

+

Maximum number of different rows when using DISTINCT.

+

max_bytes_in_distinct

+

Maximum number of bytes used by a hash table when using DISTINCT.

+

distinct_overflow_mode

+

What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.

+

max_rows_to_transfer

+

Maximum number of rows that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.

+

max_bytes_to_transfer

+

Maximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.

+

transfer_overflow_mode

+

What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/operations/settings/settings/index.html b/docs/build/docs/en/operations/settings/settings/index.html new file mode 100644 index 00000000000..63fa565651e --- /dev/null +++ b/docs/build/docs/en/operations/settings/settings/index.html @@ -0,0 +1,3725 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Settings - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Settings

+

+

distributed_product_mode

+

Changes the behavior of distributed subqueries, i.e. in cases when the query contains the product of distributed tables.

+

ClickHouse applies the configuration if the subqueries on any level have a distributed table that exists on the local server and has more than one shard.

+

Restrictions:

+
    +
  • Only applied for IN and JOIN subqueries.
    • +
  • Used only if a distributed table is used in the FROM clause.
    • +
  • Not used for a table-valued remote function.
    • +
+

The possible values ​​are:

+

+

fallback_to_stale_replicas_for_distributed_queries

+

Forces a query to an out-of-date replica if updated data is not available. See "Replication".

+

ClickHouse selects the most relevant from the outdated replicas of the table.

+

Used when performing SELECT from a distributed table that points to replicated tables.

+

By default, 1 (enabled).

+

+

force_index_by_date

+

Disables query execution if the index can't be used by date.

+

Works with tables in the MergeTree family.

+

If force_index_by_date=1, ClickHouse checks whether the query has a date key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For example, the condition Date != ' 2000-01-01 ' is acceptable even when it matches all the data in the table (i.e., running the query requires a full scan). For more information about ranges of data in MergeTree tables, see "MergeTree".

+

+

force_primary_key

+

Disables query execution if indexing by the primary key is not possible.

+

Works with tables in the MergeTree family.

+

If force_primary_key=1, ClickHouse checks to see if the query has a primary key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For more information about data ranges in MergeTree tables, see "MergeTree".

+

+

fsync_metadata

+

Enable or disable fsync when writing .sql files. By default, it is enabled.

+

It makes sense to disable it if the server has millions of tiny table chunks that are constantly being created and destroyed.

+

input_format_allow_errors_num

+

Sets the maximum number of acceptable errors when reading from text formats (CSV, TSV, etc.).

+

The default value is 0.

+

Always pair it with input_format_allow_errors_ratio. To skip errors, both settings must be greater than 0.

+

If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_num, ClickHouse ignores the row and moves on to the next one.

+

If input_format_allow_errors_numis exceeded, ClickHouse throws an exception.

+

input_format_allow_errors_ratio

+

Sets the maximum percentage of errors allowed when reading from text formats (CSV, TSV, etc.). +The percentage of errors is set as a floating-point number between 0 and 1.

+

The default value is 0.

+

Always pair it with input_format_allow_errors_num. To skip errors, both settings must be greater than 0.

+

If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_ratio, ClickHouse ignores the row and moves on to the next one.

+

If input_format_allow_errors_ratio is exceeded, ClickHouse throws an exception.

+

max_block_size

+

In ClickHouse, data is processed by blocks (sets of column parts). The internal processing cycles for a single block are efficient enough, but there are noticeable expenditures on each block. max_block_size is a recommendation for what size of block (in number of rows) to load from tables. The block size shouldn't be too small, so that the expenditures on each block are still noticeable, but not too large, so that the query with LIMIT that is completed after the first block is processed quickly, so that too much memory isn't consumed when extracting a large number of columns in multiple threads, and so that at least some cache locality is preserved.

+

By default, 65,536.

+

Blocks the size of max_block_size are not always loaded from the table. If it is obvious that less data needs to be retrieved, a smaller block is processed.

+

preferred_block_size_bytes

+

Used for the same purpose as max_block_size, but it sets the recommended block size in bytes by adapting it to the number of rows in the block. +However, the block size cannot be more than max_block_size rows. +Disabled by default (set to 0). It only works when reading from MergeTree engines.

+

+

log_queries

+

Setting up query the logging.

+

Queries sent to ClickHouse with this setup are logged according to the rules in the query_log server configuration parameter.

+

Example:

+
log_queries=1
+
+ + +

+

max_insert_block_size

+

The size of blocks to form for insertion into a table. +This setting only applies in cases when the server forms the blocks. +For example, for an INSERT via the HTTP interface, the server parses the data format and forms blocks of the specified size. +But when using clickhouse-client, the client parses the data itself, and the 'max_insert_block_size' setting on the server doesn't affect the size of the inserted blocks. +The setting also doesn't have a purpose when using INSERT SELECT, since data is inserted using the same blocks that are formed after SELECT.

+

By default, it is 1,048,576.

+

This is slightly more than max_block_size. The reason for this is because certain table engines (*MergeTree) form a data part on the disk for each inserted block, which is a fairly large entity. Similarly, *MergeTree tables sort data during insertion, and a large enough block size allows sorting more data in RAM.

+

+

max_replica_delay_for_distributed_queries

+

Disables lagging replicas for distributed queries. See "Replication".

+

Sets the time in seconds. If a replica lags more than the set value, this replica is not used.

+

Default value: 0 (off).

+

Used when performing SELECT from a distributed table that points to replicated tables.

+

max_threads

+

The maximum number of query processing threads

+
    +
  • excluding threads for retrieving data from remote servers (see the 'max_distributed_connections' parameter).
    • +
+

This parameter applies to threads that perform the same stages of the query processing pipeline in parallel. +For example, if reading from a table, evaluating expressions with functions, filtering with WHERE and pre-aggregating for GROUP BY can all be done in parallel using at least 'max_threads' number of threads, then 'max_threads' are used.

+

By default, 8.

+

If less than one SELECT query is normally run on a server at a time, set this parameter to a value slightly less than the actual number of processor cores.

+

For queries that are completed quickly because of a LIMIT, you can set a lower 'max_threads'. For example, if the necessary number of entries are located in every block and max_threads = 8, 8 blocks are retrieved, although it would have been enough to read just one.

+

The smaller the max_threads value, the less memory is consumed.

+

max_compress_block_size

+

The maximum size of blocks of uncompressed data before compressing for writing to a table. By default, 1,048,576 (1 MiB). If the size is reduced, the compression rate is significantly reduced, the compression and decompression speed increases slightly due to cache locality, and memory consumption is reduced. There usually isn't any reason to change this setting.

+

Don't confuse blocks for compression (a chunk of memory consisting of bytes) and blocks for query processing (a set of rows from a table).

+

min_compress_block_size

+

For MergeTree" tables. In order to reduce latency when processing queries, a block is compressed when writing the next mark if its size is at least 'min_compress_block_size'. By default, 65,536.

+

The actual size of the block, if the uncompressed data is less than 'max_compress_block_size', is no less than this value and no less than the volume of data for one mark.

+

Let's look at an example. Assume that 'index_granularity' was set to 8192 during table creation.

+

We are writing a UInt32-type column (4 bytes per value). When writing 8192 rows, the total will be 32 KB of data. Since min_compress_block_size = 65,536, a compressed block will be formed for every two marks.

+

We are writing a URL column with the String type (average size of 60 bytes per value). When writing 8192 rows, the average will be slightly less than 500 KB of data. Since this is more than 65,536, a compressed block will be formed for each mark. In this case, when reading data from the disk in the range of a single mark, extra data won't be decompressed.

+

There usually isn't any reason to change this setting.

+

max_query_size

+

The maximum part of a query that can be taken to RAM for parsing with the SQL parser. +The INSERT query also contains data for INSERT that is processed by a separate stream parser (that consumes O(1) RAM), which is not included in this restriction.

+

The default is 256 KiB.

+

interactive_delay

+

The interval in microseconds for checking whether request execution has been canceled and sending the progress.

+

By default, 100,000 (check for canceling and send progress ten times per second).

+

connect_timeout

+

receive_timeout

+

send_timeout

+

Timeouts in seconds on the socket used for communicating with the client.

+

By default, 10, 300, 300.

+

poll_interval

+

Lock in a wait loop for the specified number of seconds.

+

By default, 10.

+

max_distributed_connections

+

The maximum number of simultaneous connections with remote servers for distributed processing of a single query to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.

+

By default, 100.

+

The following parameters are only used when creating Distributed tables (and when launching a server), so there is no reason to change them at runtime.

+

distributed_connections_pool_size

+

The maximum number of simultaneous connections with remote servers for distributed processing of all queries to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.

+

By default, 128.

+

connect_timeout_with_failover_ms

+

The timeout in milliseconds for connecting to a remote server for a Distributed table engine, if the 'shard' and 'replica' sections are used in the cluster definition. +If unsuccessful, several attempts are made to connect to various replicas.

+

By default, 50.

+

connections_with_failover_max_tries

+

The maximum number of connection attempts with each replica, for the Distributed table engine.

+

By default, 3.

+

extremes

+

Whether to count extreme values (the minimums and maximums in columns of a query result). Accepts 0 or 1. By default, 0 (disabled). +For more information, see the section "Extreme values".

+

+

use_uncompressed_cache

+

Whether to use a cache of uncompressed blocks. Accepts 0 or 1. By default, 0 (disabled). +The uncompressed cache (only for tables in the MergeTree family) allows significantly reducing latency and increasing throughput when working with a large number of short queries. Enable this setting for users who send frequent short requests. Also pay attention to the 'uncompressed_cache_size' configuration parameter (only set in the config file) – the size of uncompressed cache blocks. By default, it is 8 GiB. The uncompressed cache is filled in as needed; the least-used data is automatically deleted.

+

For queries that read at least a somewhat large volume of data (one million rows or more), the uncompressed cache is disabled automatically in order to save space for truly small queries. So you can keep the 'use_uncompressed_cache' setting always set to 1.

+

replace_running_query

+

When using the HTTP interface, the 'query_id' parameter can be passed. This is any string that serves as the query identifier. +If a query from the same user with the same 'query_id' already exists at this time, the behavior depends on the 'replace_running_query' parameter.

+

0 (default) – Throw an exception (don't allow the query to run if a query with the same 'query_id' is already running).

+

1 – Cancel the old query and start running the new one.

+

Yandex.Metrica uses this parameter set to 1 for implementing suggestions for segmentation conditions. After entering the next character, if the old query hasn't finished yet, it should be canceled.

+

schema

+

This parameter is useful when you are using formats that require a schema definition, such as Cap'n Proto. The value depends on the format.

+

+

stream_flush_interval_ms

+

Works for tables with streaming in the case of a timeout, or when a thread generatesmax_insert_block_size rows.

+

The default value is 7500.

+

The smaller the value, the more often data is flushed into the table. Setting the value too low leads to poor performance.

+

+

load_balancing

+

Which replicas (among healthy replicas) to preferably send a query to (on the first attempt) for distributed processing.

+

random (default)

+

The number of errors is counted for each replica. The query is sent to the replica with the fewest errors, and if there are several of these, to any one of them. +Disadvantages: Server proximity is not accounted for; if the replicas have different data, you will also get different data.

+

nearest_hostname

+

The number of errors is counted for each replica. Every 5 minutes, the number of errors is integrally divided by 2. Thus, the number of errors is calculated for a recent time with exponential smoothing. If there is one replica with a minimal number of errors (i.e. errors occurred recently on the other replicas), the query is sent to it. If there are multiple replicas with the same minimal number of errors, the query is sent to the replica with a host name that is most similar to the server's host name in the config file (for the number of different characters in identical positions, up to the minimum length of both host names).

+

For instance, example01-01-1 and example01-01-2.yandex.ru are different in one position, while example01-01-1 and example01-02-2 differ in two places. +This method might seem a little stupid, but it doesn't use external data about network topology, and it doesn't compare IP addresses, which would be complicated for our IPv6 addresses.

+

Thus, if there are equivalent replicas, the closest one by name is preferred. +We can also assume that when sending a query to the same server, in the absence of failures, a distributed query will also go to the same servers. So even if different data is placed on the replicas, the query will return mostly the same results.

+

in_order

+

Replicas are accessed in the same order as they are specified. The number of errors does not matter. +This method is appropriate when you know exactly which replica is preferable.

+

totals_mode

+

How to calculate TOTALS when HAVING is present, as well as when max_rows_to_group_by and group_by_overflow_mode = 'any' are present. +See the section "WITH TOTALS modifier".

+

totals_auto_threshold

+

The threshold for totals_mode = 'auto'. +See the section "WITH TOTALS modifier".

+

default_sample

+

Floating-point number from 0 to 1. By default, 1. +Allows you to set the default sampling ratio for all SELECT queries. +(For tables that do not support sampling, it throws an exception.) +If set to 1, sampling is not performed by default.

+

max_parallel_replicas

+

The maximum number of replicas for each shard when executing a query. +For consistency (to get different parts of the same data split), this option only works when the sampling key is set. +Replica lag is not controlled.

+

compile

+

Enable compilation of queries. By default, 0 (disabled).

+

Compilation is only used for part of the query-processing pipeline: for the first stage of aggregation (GROUP BY). +If this portion of the pipeline was compiled, the query may run faster due to deployment of short cycles and inlining aggregate function calls. The maximum performance improvement (up to four times faster in rare cases) is seen for queries with multiple simple aggregate functions. Typically, the performance gain is insignificant. In very rare cases, it may slow down query execution.

+

min_count_to_compile

+

How many times to potentially use a compiled chunk of code before running compilation. By default, 3. +If the value is zero, then compilation runs synchronously and the query waits for the end of the compilation process before continuing execution. This can be used for testing; otherwise, use values ​​starting with 1. Compilation normally takes about 5-10 seconds. +If the value is 1 or more, compilation occurs asynchronously in a separate thread. The result will be used as soon as it is ready, including by queries that are currently running.

+

Compiled code is required for each different combination of aggregate functions used in the query and the type of keys in the GROUP BY clause. +The results of compilation are saved in the build directory in the form of .so files. There is no restriction on the number of compilation results, since they don't use very much space. Old results will be used after server restarts, except in the case of a server upgrade – in this case, the old results are deleted.

+

input_format_skip_unknown_fields

+

If the value is true, running INSERT skips input data from columns with unknown names. Otherwise, this situation will generate an exception. +It works for JSONEachRow and TSKV formats.

+

output_format_json_quote_64bit_integers

+

If the value is true, integers appear in quotes when using JSON* Int64 and UInt64 formats (for compatibility with most JavaScript implementations); otherwise, integers are output without the quotes.

+

+

format_csv_delimiter

+

The character to be considered as a delimiter in CSV data. By default, ,.

+ + + + + + + +
+
+
+
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/operations/settings/settings_profiles/index.html b/docs/build/docs/en/operations/settings/settings_profiles/index.html new file mode 100644 index 00000000000..54dd3770f7a --- /dev/null +++ b/docs/build/docs/en/operations/settings/settings_profiles/index.html @@ -0,0 +1,2943 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Settings profiles - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Settings profiles

+

A settings profile is a collection of settings grouped under the same name. Each ClickHouse user has a profile. +To apply all the settings in a profile, set profile.

+

Example:

+

Setting web profile.

+
SET profile = 'web'
+
+ + +

Settings profiles are declared in the user config file. This is usually users.xml.

+

Example:

+
<!-- Settings profiles -->
+<profiles>
+    <!-- Default settings -->
+    <default>
+        <!-- The maximum number of threads when running a single query. -->
+        <max_threads>8</max_threads>
+    </default>
+
+    <!-- Settings for quries from the user interface -->
+    <web>
+        <max_rows_to_read>1000000000</max_rows_to_read>
+        <max_bytes_to_read>100000000000</max_bytes_to_read>
+
+        <max_rows_to_group_by>1000000</max_rows_to_group_by>
+        <group_by_overflow_mode>any</group_by_overflow_mode>
+
+        <max_rows_to_sort>1000000</max_rows_to_sort>
+        <max_bytes_to_sort>1000000000</max_bytes_to_sort>
+
+        <max_result_rows>100000</max_result_rows>
+        <max_result_bytes>100000000</max_result_bytes>
+        <result_overflow_mode>break</result_overflow_mode>
+
+        <max_execution_time>600</max_execution_time>
+        <min_execution_speed>1000000</min_execution_speed>
+        <timeout_before_checking_execution_speed>15</timeout_before_checking_execution_speed>
+
+        <max_columns_to_read>25</max_columns_to_read>
+        <max_temporary_columns>100</max_temporary_columns>
+        <max_temporary_non_const_columns>50</max_temporary_non_const_columns>
+
+        <max_subquery_depth>2</max_subquery_depth>
+        <max_pipeline_depth>25</max_pipeline_depth>
+        <max_ast_depth>50</max_ast_depth>
+        <max_ast_elements>100</max_ast_elements>
+
+        <readonly>1</readonly>
+    </web>
+</profiles>
+
+ + +

The example specifies two profiles: default and web. The default profile has a special purpose: it must always be present and is applied when starting the server. In other words, the default profile contains default settings. The web profile is a regular profile that can be set using the SET query or using a URL parameter in an HTTP query.

+

Settings profiles can inherit from each other. To use inheritance, indicate the profile setting before the other settings that are listed in the profile.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/operations/tips/index.html b/docs/build/docs/en/operations/tips/index.html new file mode 100644 index 00000000000..afe3ba8538b --- /dev/null +++ b/docs/build/docs/en/operations/tips/index.html @@ -0,0 +1,3314 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Usage recommendations - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+
+ + +
+
+ + + + + + + +

Usage recommendations

+

CPU

+

The SSE 4.2 instruction set must be supported. Modern processors (since 2008) support it.

+

When choosing a processor, prefer a large number of cores and slightly slower clock rate over fewer cores and a higher clock rate. +For example, 16 cores with 2600 MHz is better than 8 cores with 3600 MHz.

+

Hyper-threading

+

Don't disable hyper-threading. It helps for some queries, but not for others.

+

Turbo Boost

+

Turbo Boost is highly recommended. It significantly improves performance with a typical load. +You can use turbostat to view the CPU's actual clock rate under a load.

+

CPU scaling governor

+

Always use the performance scaling governor. The on-demand scaling governor works much worse with constantly high demand.

+
sudo echo 'performance' | tee /sys/devices/system/cpu/cpu\*/cpufreq/scaling_governor
+
+ + +

CPU limitations

+

Processors can overheat. Use dmesg to see if the CPU's clock rate was limited due to overheating. +The restriction can also be set externally at the datacenter level. You can use turbostat to monitor it under a load.

+

RAM

+

For small amounts of data (up to \~200 GB compressed), it is best to use as much memory as the volume of data. +For large amounts of data and when processing interactive (online) queries, you should use a reasonable amount of RAM (128 GB or more) so the hot data subset will fit in the cache of pages. +Even for data volumes of \~50 TB per server, using 128 GB of RAM significantly improves query performance compared to 64 GB.

+

Swap file

+

Always disable the swap file. The only reason for not doing this is if you are using ClickHouse on your personal laptop.

+

Huge pages

+

Always disable transparent huge pages. It interferes with memory allocators, which leads to significant performance degradation.

+
echo 'never' | sudo tee /sys/kernel/mm/transparent_hugepage/enabled
+
+ + +

Use perf top to watch the time spent in the kernel for memory management. +Permanent huge pages also do not need to be allocated.

+

Storage subsystem

+

If your budget allows you to use SSD, use SSD. +If not, use HDD. SATA HDDs 7200 RPM will do.

+

Give preference to a lot of servers with local hard drives over a smaller number of servers with attached disk shelves. +But for storing archives with rare queries, shelves will work.

+

RAID

+

When using HDD, you can combine their RAID-10, RAID-5, RAID-6 or RAID-50. +For Linux, software RAID is better (with mdadm). We don't recommend using LVM. +When creating RAID-10, select the far layout. +If your budget allows, choose RAID-10.

+

If you have more than 4 disks, use RAID-6 (preferred) or RAID-50, instead of RAID-5. +When using RAID-5, RAID-6 or RAID-50, always increase stripe_cache_size, since the default value is usually not the best choice.

+
echo 4096 | sudo tee /sys/block/md2/md/stripe_cache_size
+
+ + +

Calculate the exact number from the number of devices and the block size, using the formula: 2 * num_devices * chunk_size_in_bytes / 4096.

+

A block size of 1025 KB is sufficient for all RAID configurations. +Never set the block size too small or too large.

+

You can use RAID-0 on SSD. +Regardless of RAID use, always use replication for data security.

+

Enable NCQ with a long queue. For HDD, choose the CFQ scheduler, and for SSD, choose noop. Don't reduce the 'readahead' setting. +For HDD, enable the write cache.

+

File system

+

Ext4 is the most reliable option. Set the mount options noatime, nobarrier. +XFS is also suitable, but it hasn't been as thoroughly tested with ClickHouse. +Most other file systems should also work fine. File systems with delayed allocation work better.

+

Linux kernel

+

Don't use an outdated Linux kernel. In 2015, 3.18.19 was new enough. +Consider using the kernel build from Yandex:https://github.com/yandex/smart – it provides at least a 5% performance increase.

+

Network

+

If you are using IPv6, increase the size of the route cache. +The Linux kernel prior to 3.2 had a multitude of problems with IPv6 implementation.

+

Use at least a 10 GB network, if possible. 1 Gb will also work, but it will be much worse for patching replicas with tens of terabytes of data, or for processing distributed queries with a large amount of intermediate data.

+

ZooKeeper

+

You are probably already using ZooKeeper for other purposes. You can use the same installation of ZooKeeper, if it isn't already overloaded.

+

It's best to use a fresh version of ZooKeeper – 3.4.9 or later. The version in stable Linux distributions may be outdated.

+

With the default settings, ZooKeeper is a time bomb:

+
+

The ZooKeeper server won't delete files from old snapshots and logs when using the default configuration (see autopurge), and this is the responsibility of the operator.

+
+

This bomb must be defused.

+

The ZooKeeper (3.5.1) configuration below is used in the Yandex.Metrica production environment as of May 20, 2017:

+

zoo.cfg:

+
# http://hadoop.apache.org/zookeeper/docs/current/zookeeperAdmin.html
+
+# The number of milliseconds of each tick
+tickTime=2000
+# The number of ticks that the initial
+# synchronization phase can take
+initLimit=30000
+# The number of ticks that can pass between
+# sending a request and getting an acknowledgement
+syncLimit=10
+
+maxClientCnxns=2000
+
+maxSessionTimeout=60000000
+# the directory where the snapshot is stored.
+dataDir=/opt/zookeeper/{{ cluster['name'] }}/data
+# Place the dataLogDir to a separate physical disc for better performance
+dataLogDir=/opt/zookeeper/{{ cluster['name'] }}/logs
+
+autopurge.snapRetainCount=10
+autopurge.purgeInterval=1
+
+
+# To avoid seeks ZooKeeper allocates space in the transaction log file in
+# blocks of preAllocSize kilobytes. The default block size is 64M. One reason
+# for changing the size of the blocks is to reduce the block size if snapshots
+# are taken more often. (Also, see snapCount).
+preAllocSize=131072
+
+# Clients can submit requests faster than ZooKeeper can process them,
+# especially if there are a lot of clients. To prevent ZooKeeper from running
+# out of memory due to queued requests, ZooKeeper will throttle clients so that
+# there is no more than globalOutstandingLimit outstanding requests in the
+# system. The default limit is 1,000.ZooKeeper logs transactions to a
+# transaction log. After snapCount transactions are written to a log file a
+# snapshot is started and a new transaction log file is started. The default
+# snapCount is 10,000.
+snapCount=3000000
+
+# If this option is defined, requests will be will logged to a trace file named
+# traceFile.year.month.day.
+#traceFile=
+
+# Leader accepts client connections. Default value is "yes". The leader machine
+# coordinates updates. For higher update throughput at thes slight expense of
+# read throughput the leader can be configured to not accept clients and focus
+# on coordination.
+leaderServes=yes
+
+standaloneEnabled=false
+dynamicConfigFile=/etc/zookeeper-{{ cluster['name'] }}/conf/zoo.cfg.dynamic
+
+ + +

Java version:

+
Java(TM) SE Runtime Environment (build 1.8.0_25-b17)
+Java HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode)
+
+ + +

JVM parameters:

+
NAME=zookeeper-{{ cluster['name'] }}
+ZOOCFGDIR=/etc/$NAME/conf
+
+# TODO this is really ugly
+# How to find out, which jars are needed?
+# seems, that log4j requires the log4j.properties file to be in the classpath
+CLASSPATH="$ZOOCFGDIR:/usr/build/classes:/usr/build/lib/*.jar:/usr/share/zookeeper/zookeeper-3.5.1-metrika.jar:/usr/share/zookeeper/slf4j-log4j12-1.7.5.jar:/usr/share/zookeeper/slf4j-api-1.7.5.jar:/usr/share/zookeeper/servlet-api-2.5-20081211.jar:/usr/share/zookeeper/netty-3.7.0.Final.jar:/usr/share/zookeeper/log4j-1.2.16.jar:/usr/share/zookeeper/jline-2.11.jar:/usr/share/zookeeper/jetty-util-6.1.26.jar:/usr/share/zookeeper/jetty-6.1.26.jar:/usr/share/zookeeper/javacc.jar:/usr/share/zookeeper/jackson-mapper-asl-1.9.11.jar:/usr/share/zookeeper/jackson-core-asl-1.9.11.jar:/usr/share/zookeeper/commons-cli-1.2.jar:/usr/src/java/lib/*.jar:/usr/etc/zookeeper"
+
+ZOOCFG="$ZOOCFGDIR/zoo.cfg"
+ZOO_LOG_DIR=/var/log/$NAME
+USER=zookeeper
+GROUP=zookeeper
+PIDDIR=/var/run/$NAME
+PIDFILE=$PIDDIR/$NAME.pid
+SCRIPTNAME=/etc/init.d/$NAME
+JAVA=/usr/bin/java
+ZOOMAIN="org.apache.zookeeper.server.quorum.QuorumPeerMain"
+ZOO_LOG4J_PROP="INFO,ROLLINGFILE"
+JMXLOCALONLY=false
+JAVA_OPTS="-Xms{{ cluster.get('xms','128M') }} \
+    -Xmx{{ cluster.get('xmx','1G') }} \
+    -Xloggc:/var/log/$NAME/zookeeper-gc.log \
+    -XX:+UseGCLogFileRotation \
+    -XX:NumberOfGCLogFiles=16 \
+    -XX:GCLogFileSize=16M \
+    -verbose:gc \
+    -XX:+PrintGCTimeStamps \
+    -XX:+PrintGCDateStamps \
+    -XX:+PrintGCDetails
+    -XX:+PrintTenuringDistribution \
+    -XX:+PrintGCApplicationStoppedTime \
+    -XX:+PrintGCApplicationConcurrentTime \
+    -XX:+PrintSafepointStatistics \
+    -XX:+UseParNewGC \
+    -XX:+UseConcMarkSweepGC \
+-XX:+CMSParallelRemarkEnabled"
+
+ + +

Salt init:

+
description "zookeeper-{{ cluster['name'] }} centralized coordination service"
+
+start on runlevel [2345]
+stop on runlevel [!2345]
+
+respawn
+
+limit nofile 8192 8192
+
+pre-start script
+    [ -r "/etc/zookeeper-{{ cluster['name'] }}/conf/environment" ] || exit 0
+    . /etc/zookeeper-{{ cluster['name'] }}/conf/environment
+    [ -d $ZOO_LOG_DIR ] || mkdir -p $ZOO_LOG_DIR
+    chown $USER:$GROUP $ZOO_LOG_DIR
+end script
+
+script
+    . /etc/zookeeper-{{ cluster['name'] }}/conf/environment
+    [ -r /etc/default/zookeeper ] && . /etc/default/zookeeper
+    if [ -z "$JMXDISABLE" ]; then
+        JAVA_OPTS="$JAVA_OPTS -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.local.only=$JMXLOCALONLY"
+    fi
+    exec start-stop-daemon --start -c $USER --exec $JAVA --name zookeeper-{{ cluster['name'] }} \
+        -- -cp $CLASSPATH $JAVA_OPTS -Dzookeeper.log.dir=${ZOO_LOG_DIR} \
+        -Dzookeeper.root.logger=${ZOO_LOG4J_PROP} $ZOOMAIN $ZOOCFG
+end script
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/operators/index.html b/docs/build/docs/en/operators/index.html new file mode 100644 index 00000000000..a152ed4dc55 --- /dev/null +++ b/docs/build/docs/en/operators/index.html @@ -0,0 +1,3201 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Operators - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Operators

+

All operators are transformed to the corresponding functions at the query parsing stage, in accordance with their precedence and associativity. +Groups of operators are listed in order of priority (the higher it is in the list, the earlier the operator is connected to its arguments).

+

Access operators

+

a[N] Access to an element of an array; arrayElement(a, N) function.

+

a.N – Access to a tuble element; tupleElement(a, N) function.

+

Numeric negation operator

+

-a – The negate (a) function.

+

Multiplication and division operators

+

a * b – The multiply (a, b) function.

+

a / b – The divide(a, b) function.

+

a % b – The modulo(a, b) function.

+

Addition and subtraction operators

+

a + b – The plus(a, b) function.

+

a - b – The minus(a, b) function.

+

Comparison operators

+

a = b – The equals(a, b) function.

+

a == b – The equals(a, b) function.

+

a != b – The notEquals(a, b) function.

+

a <> b – The notEquals(a, b) function.

+

a <= b – The lessOrEquals(a, b) function.

+

a >= b – The greaterOrEquals(a, b) function.

+

a < b – The less(a, b) function.

+

a > b – The greater(a, b) function.

+

a LIKE s – The like(a, b) function.

+

a NOT LIKE s – The notLike(a, b) function.

+

a BETWEEN b AND c – The same as a >= b AND a <= c.

+

Operators for working with data sets

+

See the section "IN operators".

+

a IN ... – The in(a, b) function

+

a NOT IN ... – The notIn(a, b) function.

+

a GLOBAL IN ... – The globalIn(a, b) function.

+

a GLOBAL NOT IN ... – The globalNotIn(a, b) function.

+

Logical negation operator

+

NOT a The not(a) function.

+

Logical AND operator

+

a AND b – Theand(a, b) function.

+

Logical OR operator

+

a OR b – The or(a, b) function.

+

Conditional operator

+

a ? b : c – The if(a, b, c) function.

+

Note:

+

The conditional operator calculates the values of b and c, then checks whether condition a is met, and then returns the corresponding value. If "b" or "c" is an arrayJoin() function, each row will be replicated regardless of the "a" condition.

+

Conditional expression

+
CASE [x]
+    WHEN a THEN b
+    [WHEN ... THEN ...]
+    ELSE c
+END
+
+ + +

If "x" is specified, then transform(x, [a, ...], [b, ...], c). Otherwise – multiIf(a, b, ..., c).

+

Concatenation operator

+

s1 || s2 – The concat(s1, s2) function.

+

Lambda creation operator

+

x -> expr – The lambda(x, expr) function.

+

The following operators do not have a priority, since they are brackets:

+

Array creation operator

+

[x1, ...] – The array(x1, ...) function.

+

Tuple creation operator

+

(x1, x2, ...) – The tuple(x2, x2, ...) function.

+

Associativity

+

All binary operators have left associativity. For example, 1 + 2 + 3 is transformed to plus(plus(1, 2), 3). +Sometimes this doesn't work the way you expect. For example, SELECT 4 > 2 > 3 will result in 0.

+

For efficiency, the and and or functions accept any number of arguments. The corresponding chains of AND and OR operators are transformed to a single call of these functions.

+ + + + + + + +
+
+
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/query_language/queries/index.html b/docs/build/docs/en/query_language/queries/index.html new file mode 100644 index 00000000000..522909c1541 --- /dev/null +++ b/docs/build/docs/en/query_language/queries/index.html @@ -0,0 +1,4850 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Queries - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Queries

+

CREATE DATABASE

+

Creating db_name databases

+
CREATE DATABASE [IF NOT EXISTS] db_name
+
+ + +

A database is just a directory for tables. +If IF NOT EXISTS is included, the query won't return an error if the database already exists.

+

+

CREATE TABLE

+

The CREATE TABLE query can have several forms.

+
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db.]name [ON CLUSTER cluster]
+(
+    name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1],
+    name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2],
+    ...
+) ENGINE = engine
+
+ + +

Creates a table named 'name' in the 'db' database or the current database if 'db' is not set, with the structure specified in brackets and the 'engine' engine. +The structure of the table is a list of column descriptions. If indexes are supported by the engine, they are indicated as parameters for the table engine.

+

A column description is name type in the simplest case. Example: RegionID UInt32. +Expressions can also be defined for default values (see below).

+
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db.]name AS [db2.]name2 [ENGINE = engine]
+
+ + +

Creates a table with the same structure as another table. You can specify a different engine for the table. If the engine is not specified, the same engine will be used as for the db2.name2 table.

+
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db.]name ENGINE = engine AS SELECT ...
+
+ + +

Creates a table with a structure like the result of the SELECT query, with the 'engine' engine, and fills it with data from SELECT.

+

In all cases, if IF NOT EXISTS is specified, the query won't return an error if the table already exists. In this case, the query won't do anything.

+

Default values

+

The column description can specify an expression for a default value, in one of the following ways:DEFAULT expr, MATERIALIZED expr, ALIAS expr. +Example: URLDomain String DEFAULT domain(URL).

+

If an expression for the default value is not defined, the default values will be set to zeros for numbers, empty strings for strings, empty arrays for arrays, and 0000-00-00 for dates or 0000-00-00 00:00:00 for dates with time. NULLs are not supported.

+

If the default expression is defined, the column type is optional. If there isn't an explicitly defined type, the default expression type is used. Example: EventDate DEFAULT toDate(EventTime) – the 'Date' type will be used for the 'EventDate' column.

+

If the data type and default expression are defined explicitly, this expression will be cast to the specified type using type casting functions. Example: Hits UInt32 DEFAULT 0 means the same thing as Hits UInt32 DEFAULT toUInt32(0).

+

Default expressions may be defined as an arbitrary expression from table constants and columns. When creating and changing the table structure, it checks that expressions don't contain loops. For INSERT, it checks that expressions are resolvable – that all columns they can be calculated from have been passed.

+

DEFAULT expr

+

Normal default value. If the INSERT query doesn't specify the corresponding column, it will be filled in by computing the corresponding expression.

+

MATERIALIZED expr

+

Materialized expression. Such a column can't be specified for INSERT, because it is always calculated. +For an INSERT without a list of columns, these columns are not considered. +In addition, this column is not substituted when using an asterisk in a SELECT query. This is to preserve the invariant that the dump obtained using SELECT * can be inserted back into the table using INSERT without specifying the list of columns.

+

ALIAS expr

+

Synonym. Such a column isn't stored in the table at all. +Its values can't be inserted in a table, and it is not substituted when using an asterisk in a SELECT query. +It can be used in SELECTs if the alias is expanded during query parsing.

+

When using the ALTER query to add new columns, old data for these columns is not written. Instead, when reading old data that does not have values for the new columns, expressions are computed on the fly by default. However, if running the expressions requires different columns that are not indicated in the query, these columns will additionally be read, but only for the blocks of data that need it.

+

If you add a new column to a table but later change its default expression, the values used for old data will change (for data where values were not stored on the disk). Note that when running background merges, data for columns that are missing in one of the merging parts is written to the merged part.

+

It is not possible to set default values for elements in nested data structures.

+

Temporary tables

+

In all cases, if TEMPORARY is specified, a temporary table will be created. Temporary tables have the following characteristics:

+
    +
  • Temporary tables disappear when the session ends, including if the connection is lost.
    • +
  • A temporary table is created with the Memory engine. The other table engines are not supported.
    • +
  • The DB can't be specified for a temporary table. It is created outside of databases.
    • +
  • If a temporary table has the same name as another one and a query specifies the table name without specifying the DB, the temporary table will be used.
    • +
  • For distributed query processing, temporary tables used in a query are passed to remote servers.
    • +
+

In most cases, temporary tables are not created manually, but when using external data for a query, or for distributed (GLOBAL) IN. For more information, see the appropriate sections

+

Distributed DDL queries (ON CLUSTER clause)

+

The CREATE, DROP, ALTER, and RENAME queries support distributed execution on a cluster. +For example, the following query creates the all_hits Distributed table on each host in cluster:

+
CREATE TABLE IF NOT EXISTS all_hits ON CLUSTER cluster (p Date, i Int32) ENGINE = Distributed(cluster, default, hits)
+
+ + +

In order to run these queries correctly, each host must have the same cluster definition (to simplify syncing configs, you can use substitutions from ZooKeeper). They must also connect to the ZooKeeper servers. +The local version of the query will eventually be implemented on each host in the cluster, even if some hosts are currently not available. The order for executing queries within a single host is guaranteed. +ALTER queries are not yet supported for replicated tables.

+

CREATE VIEW

+
CREATE [MATERIALIZED] VIEW [IF NOT EXISTS] [db.]name [TO[db.]name] [ENGINE = engine] [POPULATE] AS SELECT ...
+
+ + +

Creates a view. There are two types of views: normal and MATERIALIZED.

+

When creating a materialized view, you must specify ENGINE – the table engine for storing data.

+

A materialized view works as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.

+

Normal views don't store any data, but just perform a read from another table. In other words, a normal view is nothing more than a saved query. When reading from a view, this saved query is used as a subquery in the FROM clause.

+

As an example, assume you've created a view:

+
CREATE VIEW view AS SELECT ...
+
+ + +

and written a query:

+
SELECT a, b, c FROM view
+
+ + +

This query is fully equivalent to using the subquery:

+
SELECT a, b, c FROM (SELECT ...)
+
+ + +

Materialized views store data transformed by the corresponding SELECT query.

+

When creating a materialized view, you must specify ENGINE – the table engine for storing data.

+

A materialized view is arranged as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.

+

If you specify POPULATE, the existing table data is inserted in the view when creating it, as if making a CREATE TABLE ... AS SELECT ... . Otherwise, the query contains only the data inserted in the table after creating the view. We don't recommend using POPULATE, since data inserted in the table during the view creation will not be inserted in it.

+

A SELECT query can contain DISTINCT, GROUP BY, ORDER BY, LIMIT... Note that the corresponding conversions are performed independently on each block of inserted data. For example, if GROUP BY is set, data is aggregated during insertion, but only within a single packet of inserted data. The data won't be further aggregated. The exception is when using an ENGINE that independently performs data aggregation, such as SummingMergeTree.

+

The execution of ALTER queries on materialized views has not been fully developed, so they might be inconvenient. If the materialized view uses the construction TO [db.]name, you can DETACH the view, run ALTER for the target table, and then ATTACH the previously detached (DETACH) view.

+

Views look the same as normal tables. For example, they are listed in the result of the SHOW TABLES query.

+

There isn't a separate query for deleting views. To delete a view, use DROP TABLE.

+

ATTACH

+

This query is exactly the same as CREATE, but

+
    +
  • instead of the word CREATE it uses the word ATTACH.
    • +
  • The query doesn't create data on the disk, but assumes that data is already in the appropriate places, and just adds information about the table to the server. +After executing an ATTACH query, the server will know about the existence of the table.
    • +
+

If the table was previously detached (DETACH), meaning that its structure is known, you can use shorthand without defining the structure.

+
ATTACH TABLE [IF NOT EXISTS] [db.]name
+
+ + +

This query is used when starting the server. The server stores table metadata as files with ATTACH queries, which it simply runs at launch (with the exception of system tables, which are explicitly created on the server).

+

DROP

+

This query has two types: DROP DATABASE and DROP TABLE.

+
DROP DATABASE [IF EXISTS] db [ON CLUSTER cluster]
+
+ + +

Deletes all tables inside the 'db' database, then deletes the 'db' database itself. +If IF EXISTS is specified, it doesn't return an error if the database doesn't exist.

+
DROP [TEMPORARY] TABLE [IF EXISTS] [db.]name [ON CLUSTER cluster]
+
+ + +

Deletes the table. +If IF EXISTS is specified, it doesn't return an error if the table doesn't exist or the database doesn't exist.

+

DETACH

+

Deletes information about the 'name' table from the server. The server stops knowing about the table's existence.

+
DETACH TABLE [IF EXISTS] [db.]name
+
+ + +

This does not delete the table's data or metadata. On the next server launch, the server will read the metadata and find out about the table again. +Similarly, a "detached" table can be re-attached using the ATTACH query (with the exception of system tables, which do not have metadata stored for them).

+

There is no DETACH DATABASE query.

+

RENAME

+

Renames one or more tables.

+
RENAME TABLE [db11.]name11 TO [db12.]name12, [db21.]name21 TO [db22.]name22, ... [ON CLUSTER cluster]
+
+ + +

All tables are renamed under global locking. Renaming tables is a light operation. If you indicated another database after TO, the table will be moved to this database. However, the directories with databases must reside in the same file system (otherwise, an error is returned).

+

+

ALTER

+

The ALTER query is only supported for *MergeTree tables, as well as MergeandDistributed. The query has several variations.

+

Column manipulations

+

Changing the table structure.

+
ALTER TABLE [db].name [ON CLUSTER cluster] ADD|DROP|MODIFY COLUMN ...
+
+ + +

In the query, specify a list of one or more comma-separated actions. +Each action is an operation on a column.

+

The following actions are supported:

+
ADD COLUMN name [type] [default_expr] [AFTER name_after]
+
+ + +

Adds a new column to the table with the specified name, type, and default_expr (see the section "Default expressions"). If you specify AFTER name_after (the name of another column), the column is added after the specified one in the list of table columns. Otherwise, the column is added to the end of the table. Note that there is no way to add a column to the beginning of a table. For a chain of actions, 'name_after' can be the name of a column that is added in one of the previous actions.

+

Adding a column just changes the table structure, without performing any actions with data. The data doesn't appear on the disk after ALTER. If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). The column appears on the disk after merging data parts (see MergeTree).

+

This approach allows us to complete the ALTER query instantly, without increasing the volume of old data.

+
DROP COLUMN name
+
+ + +

Deletes the column with the name 'name'. +Deletes data from the file system. Since this deletes entire files, the query is completed almost instantly.

+
MODIFY COLUMN name [type] [default_expr]
+
+ + +

Changes the 'name' column's type to 'type' and/or the default expression to 'default_expr'. When changing the type, values are converted as if the 'toType' function were applied to them.

+

If only the default expression is changed, the query doesn't do anything complex, and is completed almost instantly.

+

Changing the column type is the only complex action – it changes the contents of files with data. For large tables, this may take a long time.

+

There are several processing stages:

+
    +
  • Preparing temporary (new) files with modified data.
    • +
  • Renaming old files.
    • +
  • Renaming the temporary (new) files to the old names.
    • +
  • Deleting the old files.
    • +
+

Only the first stage takes time. If there is a failure at this stage, the data is not changed. +If there is a failure during one of the successive stages, data can be restored manually. The exception is if the old files were deleted from the file system but the data for the new files did not get written to the disk and was lost.

+

There is no support for changing the column type in arrays and nested data structures.

+

The ALTER query lets you create and delete separate elements (columns) in nested data structures, but not whole nested data structures. To add a nested data structure, you can add columns with a name like name.nested_name and the type Array(T). A nested data structure is equivalent to multiple array columns with a name that has the same prefix before the dot.

+

There is no support for deleting columns in the primary key or the sampling key (columns that are in the ENGINE expression). Changing the type for columns that are included in the primary key is only possible if this change does not cause the data to be modified (for example, it is allowed to add values to an Enum or change a type with DateTime to UInt32).

+

If the ALTER query is not sufficient for making the table changes you need, you can create a new table, copy the data to it using the INSERT SELECT query, then switch the tables using the RENAME query and delete the old table.

+

The ALTER query blocks all reads and writes for the table. In other words, if a long SELECT is running at the time of the ALTER query, the ALTER query will wait for it to complete. At the same time, all new queries to the same table will wait while this ALTER is running.

+

For tables that don't store data themselves (such as Merge and Distributed), ALTER just changes the table structure, and does not change the structure of subordinate tables. For example, when running ALTER for a Distributed table, you will also need to run ALTER for the tables on all remote servers.

+

The ALTER query for changing columns is replicated. The instructions are saved in ZooKeeper, then each replica applies them. All ALTER queries are run in the same order. The query waits for the appropriate actions to be completed on the other replicas. However, a query to change columns in a replicated table can be interrupted, and all actions will be performed asynchronously.

+

Manipulations with partitions and parts

+

It only works for tables in the MergeTree family. The following operations are available:

+
    +
  • DETACH PARTITION – Move a partition to the 'detached' directory and forget it.
    • +
  • DROP PARTITION – Delete a partition.
    • +
  • ATTACH PART|PARTITION – Add a new part or partition from the detached directory to the table.
    • +
  • FREEZE PARTITION – Create a backup of a partition.
    • +
  • FETCH PARTITION – Download a partition from another server.
    • +
+

Each type of query is covered separately below.

+

A partition in a table is data for a single calendar month. This is determined by the values of the date key specified in the table engine parameters. Each month's data is stored separately in order to simplify manipulations with this data.

+

A "part" in the table is part of the data from a single partition, sorted by the primary key.

+

You can use the system.parts table to view the set of table parts and partitions:

+
SELECT * FROM system.parts WHERE active
+
+ + +

active – Only count active parts. Inactive parts are, for example, source parts remaining after merging to a larger part – these parts are deleted approximately 10 minutes after merging.

+

Another way to view a set of parts and partitions is to go into the directory with table data. +Data directory: /var/lib/clickhouse/data/database/table/,where /var/lib/clickhouse/ is the path to the ClickHouse data, 'database' is the database name, and 'table' is the table name. Example:

+
$ ls -l /var/lib/clickhouse/data/test/visits/
+total 48
+drwxrwxrwx 2 clickhouse clickhouse 20480 May  5 02:58 20140317_20140323_2_2_0
+drwxrwxrwx 2 clickhouse clickhouse 20480 May  5 02:58 20140317_20140323_4_4_0
+drwxrwxrwx 2 clickhouse clickhouse  4096 May  5 02:55 detached
+-rw-rw-rw- 1 clickhouse clickhouse     2 May  5 02:58 increment.txt
+
+ + +

Here, 20140317_20140323_2_2_0 and 20140317_20140323_4_4_0 are the directories of data parts.

+

Let's break down the name of the first part: 20140317_20140323_2_2_0.

+
    +
  • 20140317 is the minimum date of the data in the chunk.
    • +
  • 20140323 is the maximum date of the data in the chunk.
    • +
  • 2 is the minimum number of the data block.
    • +
  • 2 is the maximum number of the data block.
    • +
  • 0 is the chunk level (the depth of the merge tree it is formed from).
    • +
+

Each piece relates to a single partition and contains data for just one month. +201403 is the name of the partition. A partition is a set of parts for a single month.

+

On an operating server, you can't manually change the set of parts or their data on the file system, since the server won't know about it. +For non-replicated tables, you can do this when the server is stopped, but we don't recommended it. +For replicated tables, the set of parts can't be changed in any case.

+

The detached directory contains parts that are not used by the server - detached from the table using the ALTER ... DETACH query. Parts that are damaged are also moved to this directory, instead of deleting them. You can add, delete, or modify the data in the 'detached' directory at any time – the server won't know about this until you make the ALTER TABLE ... ATTACH query.

+
ALTER TABLE [db.]table DETACH PARTITION 'name'
+
+ + +

Move all data for partitions named 'name' to the 'detached' directory and forget about them. +The partition name is specified in YYYYMM format. It can be indicated in single quotes or without them.

+

After the query is executed, you can do whatever you want with the data in the 'detached' directory — delete it from the file system, or just leave it.

+

The query is replicated – data will be moved to the 'detached' directory and forgotten on all replicas. The query can only be sent to a leader replica. To find out if a replica is a leader, perform SELECT to the 'system.replicas' system table. Alternatively, it is easier to make a query on all replicas, and all except one will throw an exception.

+
ALTER TABLE [db.]table DROP PARTITION 'name'
+
+ + +

The same as the DETACH operation. Deletes data from the table. Data parts will be tagged as inactive and will be completely deleted in approximately 10 minutes. The query is replicated – data will be deleted on all replicas.

+
ALTER TABLE [db.]table ATTACH PARTITION|PART 'name'
+
+ + +

Adds data to the table from the 'detached' directory.

+

It is possible to add data for an entire partition or a separate part. For a part, specify the full name of the part in single quotes.

+

The query is replicated. Each replica checks whether there is data in the 'detached' directory. If there is data, it checks the integrity, verifies that it matches the data on the server that initiated the query, and then adds it if everything is correct. If not, it downloads data from the query requestor replica, or from another replica where the data has already been added.

+

So you can put data in the 'detached' directory on one replica, and use the ALTER ... ATTACH query to add it to the table on all replicas.

+
ALTER TABLE [db.]table FREEZE PARTITION 'name'
+
+ + +

Creates a local backup of one or multiple partitions. The name can be the full name of the partition (for example, 201403), or its prefix (for example, 2014): then the backup will be created for all the corresponding partitions.

+

The query does the following: for a data snapshot at the time of execution, it creates hardlinks to table data in the directory /var/lib/clickhouse/shadow/N/...

+

/var/lib/clickhouse/ is the working ClickHouse directory from the config. +N is the incremental number of the backup.

+

The same structure of directories is created inside the backup as inside /var/lib/clickhouse/. +It also performs 'chmod' for all files, forbidding writes to them.

+

The backup is created almost instantly (but first it waits for current queries to the corresponding table to finish running). At first, the backup doesn't take any space on the disk. As the system works, the backup can take disk space, as data is modified. If the backup is made for old enough data, it won't take space on the disk.

+

After creating the backup, data from /var/lib/clickhouse/shadow/ can be copied to the remote server and then deleted on the local server. +The entire backup process is performed without stopping the server.

+

The ALTER ... FREEZE PARTITION query is not replicated. A local backup is only created on the local server.

+

As an alternative, you can manually copy data from the /var/lib/clickhouse/data/database/table directory. +But if you do this while the server is running, race conditions are possible when copying directories with files being added or changed, and the backup may be inconsistent. You can do this if the server isn't running – then the resulting data will be the same as after the ALTER TABLE t FREEZE PARTITION query.

+

ALTER TABLE ... FREEZE PARTITION only copies data, not table metadata. To make a backup of table metadata, copy the file /var/lib/clickhouse/metadata/database/table.sql

+

To restore from a backup:

+
+
    +
  • Use the CREATE query to create the table if it doesn't exist. The query can be taken from an .sql file (replace ATTACH in it with CREATE).
    • +
  • Copy the data from the data/database/table/ directory inside the backup to the /var/lib/clickhouse/data/database/table/detached/ directory.
    • +
  • Run ALTER TABLE ... ATTACH PARTITION YYYYMM queries, where YYYYMM is the month, for every month.
    • +
+
+

In this way, data from the backup will be added to the table. +Restoring from a backup doesn't require stopping the server.

+

Backups and replication

+

Replication provides protection from device failures. If all data disappeared on one of your replicas, follow the instructions in the "Restoration after failure" section to restore it.

+

For protection from device failures, you must use replication. For more information about replication, see the section "Data replication".

+

Backups protect against human error (accidentally deleting data, deleting the wrong data or in the wrong cluster, or corrupting data). +For high-volume databases, it can be difficult to copy backups to remote servers. In such cases, to protect from human error, you can keep a backup on the same server (it will reside in /var/lib/clickhouse/shadow/).

+
ALTER TABLE [db.]table FETCH PARTITION 'name' FROM 'path-in-zookeeper'
+
+ + +

This query only works for replicatable tables.

+

It downloads the specified partition from the shard that has its ZooKeeper path specified in the FROM clause, then puts it in the detached directory for the specified table.

+

Although the query is called ALTER TABLE, it does not change the table structure, and does not immediately change the data available in the table.

+

Data is placed in the detached directory. You can use the ALTER TABLE ... ATTACH query to attach the data.

+

The FROM clause specifies the path in ZooKeeper. For example, /clickhouse/tables/01-01/visits. +Before downloading, the system checks that the partition exists and the table structure matches. The most appropriate replica is selected automatically from the healthy replicas.

+

The ALTER ... FETCH PARTITION query is not replicated. The partition will be downloaded to the 'detached' directory only on the local server. Note that if after this you use the ALTER TABLE ... ATTACH query to add data to the table, the data will be added on all replicas (on one of the replicas it will be added from the 'detached' directory, and on the rest it will be loaded from neighboring replicas).

+

Synchronicity of ALTER queries

+

For non-replicatable tables, all ALTER queries are performed synchronously. For replicatable tables, the query just adds instructions for the appropriate actions to ZooKeeper, and the actions themselves are performed as soon as possible. However, the query can wait for these actions to be completed on all the replicas.

+

For ALTER ... ATTACH|DETACH|DROP queries, you can use the replication_alter_partitions_sync setting to set up waiting. +Possible values: 0 – do not wait; 1 – only wait for own execution (default); 2 – wait for all.

+

+

SHOW DATABASES

+
SHOW DATABASES [INTO OUTFILE filename] [FORMAT format]
+
+ + +

Prints a list of all databases. +This query is identical to SELECT name FROM system.databases [INTO OUTFILE filename] [FORMAT format].

+

See also the section "Formats".

+

SHOW TABLES

+
SHOW [TEMPORARY] TABLES [FROM db] [LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format]
+
+ + +

Displays a list of tables

+
    +
  • tables from the current database, or from the 'db' database if "FROM db" is specified.
    • +
  • all tables, or tables whose name matches the pattern, if "LIKE 'pattern'" is specified.
    • +
+

This query is identical to: SELECT name FROM system.tables WHERE database = 'db' [AND name LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format].

+

See also the section "LIKE operator".

+

SHOW PROCESSLIST

+
SHOW PROCESSLIST [INTO OUTFILE filename] [FORMAT format]
+
+ + +

Outputs a list of queries currently being processed, other than SHOW PROCESSLIST queries.

+

Prints a table containing the columns:

+

user – The user who made the query. Keep in mind that for distributed processing, queries are sent to remote servers under the 'default' user. SHOW PROCESSLIST shows the username for a specific query, not for a query that this query initiated.

+

address – The name of the host that the query was sent from. For distributed processing, on remote servers, this is the name of the query requestor host. To track where a distributed query was originally made from, look at SHOW PROCESSLIST on the query requestor server.

+

elapsed – The execution time, in seconds. Queries are output in order of decreasing execution time.

+

rows_read, bytes_read – How many rows and bytes of uncompressed data were read when processing the query. For distributed processing, data is totaled from all the remote servers. This is the data used for restrictions and quotas.

+

memory_usage – Current RAM usage in bytes. See the setting 'max_memory_usage'.

+

query – The query itself. In INSERT queries, the data for insertion is not output.

+

query_id – The query identifier. Non-empty only if it was explicitly defined by the user. For distributed processing, the query ID is not passed to remote servers.

+

This query is identical to: SELECT * FROM system.processes [INTO OUTFILE filename] [FORMAT format].

+

Tip (execute in the console):

+
watch -n1 "clickhouse-client --query='SHOW PROCESSLIST'"
+
+ + +

SHOW CREATE TABLE

+
SHOW CREATE [TEMPORARY] TABLE [db.]table [INTO OUTFILE filename] [FORMAT format]
+
+ + +

Returns a single String-type 'statement' column, which contains a single value – the CREATE query used for creating the specified table.

+

DESCRIBE TABLE

+
DESC|DESCRIBE TABLE [db.]table [INTO OUTFILE filename] [FORMAT format]
+
+ + +

Returns two String-type columns: name and type, which indicate the names and types of columns in the specified table.

+

Nested data structures are output in "expanded" format. Each column is shown separately, with the name after a dot.

+

EXISTS

+
EXISTS [TEMPORARY] TABLE [db.]name [INTO OUTFILE filename] [FORMAT format]
+
+ + +

Returns a single UInt8-type column, which contains the single value 0 if the table or database doesn't exist, or 1 if the table exists in the specified database.

+

USE

+
USE db
+
+ + +

Lets you set the current database for the session. +The current database is used for searching for tables if the database is not explicitly defined in the query with a dot before the table name. +This query can't be made when using the HTTP protocol, since there is no concept of a session.

+

SET

+
SET param = value
+
+ + +

Allows you to set param to value. You can also make all the settings from the specified settings profile in a single query. To do this, specify 'profile' as the setting name. For more information, see the section "Settings". +The setting is made for the session, or for the server (globally) if GLOBAL is specified. +When making a global setting, the setting is not applied to sessions already running, including the current session. It will only be used for new sessions.

+

When the server is restarted, global settings made using SET are lost. +To make settings that persist after a server restart, you can only use the server's config file.

+

OPTIMIZE

+
OPTIMIZE TABLE [db.]name [PARTITION partition] [FINAL]
+
+ + +

Asks the table engine to do something for optimization. +Supported only by *MergeTree engines, in which this query initializes a non-scheduled merge of data parts. +If you specify a PARTITION, only the specified partition will be optimized. +If you specify FINAL, optimization will be performed even when all the data is already in one part.

+

+

INSERT

+

Adding data.

+

Basic query format:

+
INSERT INTO [db.]table [(c1, c2, c3)] VALUES (v11, v12, v13), (v21, v22, v23), ...
+
+ + +

The query can specify a list of columns to insert [(c1, c2, c3)]. In this case, the rest of the columns are filled with:

+
    +
  • The values calculated from the DEFAULT expressions specified in the table definition.
    • +
  • Zeros and empty strings, if DEFAULT expressions are not defined.
    • +
+

If strict_insert_defaults=1, columns that do not have DEFAULT defined must be listed in the query.

+

Data can be passed to the INSERT in any format supported by ClickHouse. The format must be specified explicitly in the query:

+
INSERT INTO [db.]table [(c1, c2, c3)] FORMAT format_name data_set
+
+ + +

For example, the following query format is identical to the basic version of INSERT ... VALUES:

+
INSERT INTO [db.]table [(c1, c2, c3)] FORMAT Values (v11, v12, v13), (v21, v22, v23), ...
+
+ + +

ClickHouse removes all spaces and one line feed (if there is one) before the data. When forming a query, we recommend putting the data on a new line after the query operators (this is important if the data begins with spaces).

+

Example:

+
INSERT INTO t FORMAT TabSeparated
+11  Hello, world!
+22  Qwerty
+
+ + +

You can insert data separately from the query by using the command-line client or the HTTP interface. For more information, see the section "Interfaces".

+

Inserting the results of SELECT

+
INSERT INTO [db.]table [(c1, c2, c3)] SELECT ...
+
+ + +

Columns are mapped according to their position in the SELECT clause. However, their names in the SELECT expression and the table for INSERT may differ. If necessary, type casting is performed.

+

None of the data formats except Values allow setting values to expressions such as now(), 1 + 2, and so on. The Values format allows limited use of expressions, but this is not recommended, because in this case inefficient code is used for their execution.

+

Other queries for modifying data parts are not supported: UPDATE, DELETE, REPLACE, MERGE, UPSERT, INSERT UPDATE. +However, you can delete old data using ALTER TABLE ... DROP PARTITION.

+

Performance considerations

+

INSERT sorts the input data by primary key and splits them into partitions by month. If you insert data for mixed months, it can significantly reduce the performance of the INSERT query. To avoid this:

+
    +
  • Add data in fairly large batches, such as 100,000 rows at a time.
    • +
  • Group data by month before uploading it to ClickHouse.
    • +
+

Performance will not decrease if:

+
    +
  • Data is added in real time.
    • +
  • You upload data that is usually sorted by time.
    • +
+

SELECT

+

Data sampling.

+
SELECT [DISTINCT] expr_list
+    [FROM [db.]table | (subquery) | table_function] [FINAL]
+    [SAMPLE sample_coeff]
+    [ARRAY JOIN ...]
+    [GLOBAL] ANY|ALL INNER|LEFT JOIN (subquery)|table USING columns_list
+    [PREWHERE expr]
+    [WHERE expr]
+    [GROUP BY expr_list] [WITH TOTALS]
+    [HAVING expr]
+    [ORDER BY expr_list]
+    [LIMIT [n, ]m]
+    [UNION ALL ...]
+    [INTO OUTFILE filename]
+    [FORMAT format]
+    [LIMIT n BY columns]
+
+ + +

All the clauses are optional, except for the required list of expressions immediately after SELECT. +The clauses below are described in almost the same order as in the query execution conveyor.

+

If the query omits the DISTINCT, GROUP BY and ORDER BY clauses and the IN and JOIN subqueries, the query will be completely stream processed, using O(1) amount of RAM. +Otherwise, the query might consume a lot of RAM if the appropriate restrictions are not specified: max_memory_usage, max_rows_to_group_by, max_rows_to_sort, max_rows_in_distinct, max_bytes_in_distinct, max_rows_in_set, max_bytes_in_set, max_rows_in_join, max_bytes_in_join, max_bytes_before_external_sort, max_bytes_before_external_group_by. For more information, see the section "Settings". It is possible to use external sorting (saving temporary tables to a disk) and external aggregation. The system does not have "merge join".

+

FROM clause

+

If the FROM clause is omitted, data will be read from the system.one table. +The 'system.one' table contains exactly one row (this table fulfills the same purpose as the DUAL table found in other DBMSs).

+

The FROM clause specifies the table to read data from, or a subquery, or a table function; ARRAY JOIN and the regular JOIN may also be included (see below).

+

Instead of a table, the SELECT subquery may be specified in brackets. +In this case, the subquery processing pipeline will be built into the processing pipeline of an external query. +In contrast to standard SQL, a synonym does not need to be specified after a subquery. For compatibility, it is possible to write 'AS name' after a subquery, but the specified name isn't used anywhere.

+

A table function may be specified instead of a table. For more information, see the section "Table functions".

+

To execute a query, all the columns listed in the query are extracted from the appropriate table. Any columns not needed for the external query are thrown out of the subqueries. +If a query does not list any columns (for example, SELECT count() FROM t), some column is extracted from the table anyway (the smallest one is preferred), in order to calculate the number of rows.

+

The FINAL modifier can be used only for a SELECT from a CollapsingMergeTree table. When you specify FINAL, data is selected fully "collapsed". Keep in mind that using FINAL leads to a selection that includes columns related to the primary key, in addition to the columns specified in the SELECT. Additionally, the query will be executed in a single stream, and data will be merged during query execution. This means that when using FINAL, the query is processed more slowly. In most cases, you should avoid using FINAL. For more information, see the section "CollapsingMergeTree engine".

+

SAMPLE clause

+

The SAMPLE clause allows for approximated query processing. Approximated query processing is only supported by MergeTree* type tables, and only if the sampling expression was specified during table creation (see the section "MergeTree engine").

+

SAMPLE has the format SAMPLE k, where k is a decimal number from 0 to 1, or SAMPLE n, where 'n' is a sufficiently large integer.

+

In the first case, the query will be executed on 'k' percent of data. For example, SAMPLE 0.1 runs the query on 10% of data. +In the second case, the query will be executed on a sample of no more than 'n' rows. For example, SAMPLE 10000000 runs the query on a maximum of 10,000,000 rows.

+

Example:

+
SELECT
+    Title,
+    count() * 10 AS PageViews
+FROM hits_distributed
+SAMPLE 0.1
+WHERE
+    CounterID = 34
+    AND toDate(EventDate) >= toDate('2013-01-29')
+    AND toDate(EventDate) <= toDate('2013-02-04')
+    AND NOT DontCountHits
+    AND NOT Refresh
+    AND Title != ''
+GROUP BY Title
+ORDER BY PageViews DESC LIMIT 1000
+
+ + +

In this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value 'count()' is manually multiplied by 10.

+

When using something like SAMPLE 10000000, there isn't any information about which relative percent of data was processed or what the aggregate functions should be multiplied by, so this method of writing is not always appropriate to the situation.

+

A sample with a relative coefficient is "consistent": if we look at all possible data that could be in the table, a sample (when using a single sampling expression specified during table creation) with the same coefficient always selects the same subset of possible data. In other words, a sample from different tables on different servers at different times is made the same way.

+

For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This allows using the sample in subqueries in the IN clause, as well as for manually correlating results of different queries with samples.

+

ARRAY JOIN clause

+

Allows executing JOIN with an array or nested data structure. The intent is similar to the 'arrayJoin' function, but its functionality is broader.

+

ARRAY JOIN is essentially INNER JOIN with an array. Example:

+
:) CREATE TABLE arrays_test (s String, arr Array(UInt8)) ENGINE = Memory
+
+CREATE TABLE arrays_test
+(
+    s String,
+    arr Array(UInt8)
+) ENGINE = Memory
+
+Ok.
+
+0 rows in set. Elapsed: 0.001 sec.
+
+:) INSERT INTO arrays_test VALUES ('Hello', [1,2]), ('World', [3,4,5]), ('Goodbye', [])
+
+INSERT INTO arrays_test VALUES
+
+Ok.
+
+3 rows in set. Elapsed: 0.001 sec.
+
+:) SELECT * FROM arrays_test
+
+SELECT *
+FROM arrays_test
+
+┌─s───────┬─arr─────┐
+│ Hello   │ [1,2]   │
+│ World   │ [3,4,5] │
+│ Goodbye │ []      │
+└─────────┴─────────┘
+
+3 rows in set. Elapsed: 0.001 sec.
+
+:) SELECT s, arr FROM arrays_test ARRAY JOIN arr
+
+SELECT s, arr
+FROM arrays_test
+ARRAY JOIN arr
+
+┌─s─────┬─arr─┐
+│ Hello │   1 │
+│ Hello │   2 │
+│ World │   3 │
+│ World │   4 │
+│ World │   5 │
+└───────┴─────┘
+
+5 rows in set. Elapsed: 0.001 sec.
+
+ + +

An alias can be specified for an array in the ARRAY JOIN clause. In this case, an array item can be accessed by this alias, but the array itself by the original name. Example:

+
:) SELECT s, arr, a FROM arrays_test ARRAY JOIN arr AS a
+
+SELECT s, arr, a
+FROM arrays_test
+ARRAY JOIN arr AS a
+
+┌─s─────┬─arr─────┬─a─┐
+│ Hello │ [1,2]   │ 1 │
+│ Hello │ [1,2]   │ 2 │
+│ World │ [3,4,5] │ 3 │
+│ World │ [3,4,5] │ 4 │
+│ World │ [3,4,5] │ 5 │
+└───────┴─────────┴───┘
+
+5 rows in set. Elapsed: 0.001 sec.
+
+ + +

Multiple arrays of the same size can be comma-separated in the ARRAY JOIN clause. In this case, JOIN is performed with them simultaneously (the direct sum, not the direct product). Example:

+
:) SELECT s, arr, a, num, mapped FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(x -> x + 1, arr) AS mapped
+
+SELECT s, arr, a, num, mapped
+FROM arrays_test
+ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(lambda(tuple(x), plus(x, 1)), arr) AS mapped
+
+┌─s─────┬─arr─────┬─a─┬─num─┬─mapped─┐
+│ Hello │ [1,2]   │ 1 │   1 │      2 │
+│ Hello │ [1,2]   │ 2 │   2 │      3 │
+│ World │ [3,4,5] │ 3 │   1 │      4 │
+│ World │ [3,4,5] │ 4 │   2 │      5 │
+│ World │ [3,4,5] │ 5 │   3 │      6 │
+└───────┴─────────┴───┴─────┴────────┘
+
+5 rows in set. Elapsed: 0.002 sec.
+
+:) SELECT s, arr, a, num, arrayEnumerate(arr) FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num
+
+SELECT s, arr, a, num, arrayEnumerate(arr)
+FROM arrays_test
+ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num
+
+┌─s─────┬─arr─────┬─a─┬─num─┬─arrayEnumerate(arr)─┐
+│ Hello │ [1,2]   │ 1 │   1 │ [1,2]               │
+│ Hello │ [1,2]   │ 2 │   2 │ [1,2]               │
+│ World │ [3,4,5] │ 3 │   1 │ [1,2,3]             │
+│ World │ [3,4,5] │ 4 │   2 │ [1,2,3]             │
+│ World │ [3,4,5] │ 5 │   3 │ [1,2,3]             │
+└───────┴─────────┴───┴─────┴─────────────────────┘
+
+5 rows in set. Elapsed: 0.002 sec.
+
+ + +

ARRAY JOIN also works with nested data structures. Example:

+
:) CREATE TABLE nested_test (s String, nest Nested(x UInt8, y UInt32)) ENGINE = Memory
+
+CREATE TABLE nested_test
+(
+    s String,
+    nest Nested(
+    x UInt8,
+    y UInt32)
+) ENGINE = Memory
+
+Ok.
+
+0 rows in set. Elapsed: 0.006 sec.
+
+:) INSERT INTO nested_test VALUES ('Hello', [1,2], [10,20]), ('World', [3,4,5], [30,40,50]), ('Goodbye', [], [])
+
+INSERT INTO nested_test VALUES
+
+Ok.
+
+3 rows in set. Elapsed: 0.001 sec.
+
+:) SELECT * FROM nested_test
+
+SELECT *
+FROM nested_test
+
+┌─s───────┬─nest.x──┬─nest.y─────┐
+│ Hello   │ [1,2]   │ [10,20]    │
+│ World   │ [3,4,5] │ [30,40,50] │
+│ Goodbye │ []      │ []         │
+└─────────┴─────────┴────────────┘
+
+3 rows in set. Elapsed: 0.001 sec.
+
+:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest
+
+SELECT s, `nest.x`, `nest.y`
+FROM nested_test
+ARRAY JOIN nest
+
+┌─s─────┬─nest.x─┬─nest.y─┐
+│ Hello │      1 │     10 │
+│ Hello │      2 │     20 │
+│ World │      3 │     30 │
+│ World │      4 │     40 │
+│ World │      5 │     50 │
+└───────┴────────┴────────┘
+
+5 rows in set. Elapsed: 0.001 sec.
+
+ + +

When specifying names of nested data structures in ARRAY JOIN, the meaning is the same as ARRAY JOIN with all the array elements that it consists of. Example:

+
:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x, nest.y
+
+SELECT s, `nest.x`, `nest.y`
+FROM nested_test
+ARRAY JOIN `nest.x`, `nest.y`
+
+┌─s─────┬─nest.x─┬─nest.y─┐
+│ Hello │      1 │     10 │
+│ Hello │      2 │     20 │
+│ World │      3 │     30 │
+│ World │      4 │     40 │
+│ World │      5 │     50 │
+└───────┴────────┴────────┘
+
+5 rows in set. Elapsed: 0.001 sec.
+
+ + +

This variation also makes sense:

+
:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x
+
+SELECT s, `nest.x`, `nest.y`
+FROM nested_test
+ARRAY JOIN `nest.x`
+
+┌─s─────┬─nest.x─┬─nest.y─────┐
+│ Hello │      1 │ [10,20]    │
+│ Hello │      2 │ [10,20]    │
+│ World │      3 │ [30,40,50] │
+│ World │      4 │ [30,40,50] │
+│ World │      5 │ [30,40,50] │
+└───────┴────────┴────────────┘
+
+5 rows in set. Elapsed: 0.001 sec.
+
+ + +

An alias may be used for a nested data structure, in order to select either the JOIN result or the source array. Example:

+
:) SELECT s, n.x, n.y, nest.x, nest.y FROM nested_test ARRAY JOIN nest AS n
+
+SELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`
+FROM nested_test
+ARRAY JOIN nest AS n
+
+┌─s─────┬─n.x─┬─n.y─┬─nest.x──┬─nest.y─────┐
+│ Hello │   1 │  10 │ [1,2]   │ [10,20]    │
+│ Hello │   2 │  20 │ [1,2]   │ [10,20]    │
+│ World │   3 │  30 │ [3,4,5] │ [30,40,50] │
+│ World │   4 │  40 │ [3,4,5] │ [30,40,50] │
+│ World │   5 │  50 │ [3,4,5] │ [30,40,50] │
+└───────┴─────┴─────┴─────────┴────────────┘
+
+5 rows in set. Elapsed: 0.001 sec.
+
+ + +

Example of using the arrayEnumerate function:

+
:) SELECT s, n.x, n.y, nest.x, nest.y, num FROM nested_test ARRAY JOIN nest AS n, arrayEnumerate(nest.x) AS num
+
+SELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`, num
+FROM nested_test
+ARRAY JOIN nest AS n, arrayEnumerate(`nest.x`) AS num
+
+┌─s─────┬─n.x─┬─n.y─┬─nest.x──┬─nest.y─────┬─num─┐
+│ Hello │   1 │  10 │ [1,2]   │ [10,20]    │   1 │
+│ Hello │   2 │  20 │ [1,2]   │ [10,20]    │   2 │
+│ World │   3 │  30 │ [3,4,5] │ [30,40,50] │   1 │
+│ World │   4 │  40 │ [3,4,5] │ [30,40,50] │   2 │
+│ World │   5 │  50 │ [3,4,5] │ [30,40,50] │   3 │
+└───────┴─────┴─────┴─────────┴────────────┴─────┘
+
+5 rows in set. Elapsed: 0.002 sec.
+
+ + +

The query can only specify a single ARRAY JOIN clause.

+

The corresponding conversion can be performed before the WHERE/PREWHERE clause (if its result is needed in this clause), or after completing WHERE/PREWHERE (to reduce the volume of calculations).

+

JOIN clause

+

The normal JOIN, which is not related to ARRAY JOIN described above.

+
[GLOBAL] ANY|ALL INNER|LEFT [OUTER] JOIN (subquery)|table USING columns_list
+
+ + +

Performs joins with data from the subquery. At the beginning of query processing, the subquery specified after JOIN is run, and its result is saved in memory. Then it is read from the "left" table specified in the FROM clause, and while it is being read, for each of the read rows from the "left" table, rows are selected from the subquery results table (the "right" table) that meet the condition for matching the values of the columns specified in USING.

+

The table name can be specified instead of a subquery. This is equivalent to the SELECT * FROM table subquery, except in a special case when the table has the Join engine – an array prepared for joining.

+

All columns that are not needed for the JOIN are deleted from the subquery.

+

There are several types of JOINs:

+

INNER or LEFT type:If INNER is specified, the result will contain only those rows that have a matching row in the right table. +If LEFT is specified, any rows in the left table that don't have matching rows in the right table will be assigned the default value - zeros or empty rows. LEFT OUTER may be written instead of LEFT; the word OUTER does not affect anything.

+

ANY or ALL stringency:If ANY is specified and the right table has several matching rows, only the first one found is joined. +If ALL is specified and the right table has several matching rows, the data will be multiplied by the number of these rows.

+

Using ALL corresponds to the normal JOIN semantic from standard SQL. +Using ANY is optimal. If the right table has only one matching row, the results of ANY and ALL are the same. You must specify either ANY or ALL (neither of them is selected by default).

+

GLOBAL distribution:

+

When using a normal JOIN, the query is sent to remote servers. Subqueries are run on each of them in order to make the right table, and the join is performed with this table. In other words, the right table is formed on each server separately.

+

When using GLOBAL ... JOIN, first the requestor server runs a subquery to calculate the right table. This temporary table is passed to each remote server, and queries are run on them using the temporary data that was transmitted.

+

Be careful when using GLOBAL JOINs. For more information, see the section "Distributed subqueries".

+

Any combination of JOINs is possible. For example, GLOBAL ANY LEFT OUTER JOIN.

+

When running a JOIN, there is no optimization of the order of execution in relation to other stages of the query. The join (a search in the right table) is run before filtering in WHERE and before aggregation. In order to explicitly set the processing order, we recommend running a JOIN subquery with a subquery.

+

Example:

+
SELECT
+    CounterID,
+    hits,
+    visits
+FROM
+(
+    SELECT
+        CounterID,
+        count() AS hits
+    FROM test.hits
+    GROUP BY CounterID
+) ANY LEFT JOIN
+(
+    SELECT
+        CounterID,
+        sum(Sign) AS visits
+    FROM test.visits
+    GROUP BY CounterID
+) USING CounterID
+ORDER BY hits DESC
+LIMIT 10
+
+ + +
┌─CounterID─┬───hits─┬─visits─┐
+│   1143050 │ 523264 │  13665 │
+│    731962 │ 475698 │ 102716 │
+│    722545 │ 337212 │ 108187 │
+│    722889 │ 252197 │  10547 │
+│   2237260 │ 196036 │   9522 │
+│  23057320 │ 147211 │   7689 │
+│    722818 │  90109 │  17847 │
+│     48221 │  85379 │   4652 │
+│  19762435 │  77807 │   7026 │
+│    722884 │  77492 │  11056 │
+└───────────┴────────┴────────┘
+
+ + +

Subqueries don't allow you to set names or use them for referencing a column from a specific subquery. +The columns specified in USING must have the same names in both subqueries, and the other columns must be named differently. You can use aliases to change the names of columns in subqueries (the example uses the aliases 'hits' and 'visits').

+

The USING clause specifies one or more columns to join, which establishes the equality of these columns. The list of columns is set without brackets. More complex join conditions are not supported.

+

The right table (the subquery result) resides in RAM. If there isn't enough memory, you can't run a JOIN.

+

Only one JOIN can be specified in a query (on a single level). To run multiple JOINs, you can put them in subqueries.

+

Each time a query is run with the same JOIN, the subquery is run again – the result is not cached. To avoid this, use the special 'Join' table engine, which is a prepared array for joining that is always in RAM. For more information, see the section "Table engines, Join".

+

In some cases, it is more efficient to use IN instead of JOIN. +Among the various types of JOINs, the most efficient is ANY LEFT JOIN, then ANY INNER JOIN. The least efficient are ALL LEFT JOIN and ALL INNER JOIN.

+

If you need a JOIN for joining with dimension tables (these are relatively small tables that contain dimension properties, such as names for advertising campaigns), a JOIN might not be very convenient due to the bulky syntax and the fact that the right table is re-accessed for every query. For such cases, there is an "external dictionaries" feature that you should use instead of JOIN. For more information, see the section "External dictionaries".

+

WHERE clause

+

If there is a WHERE clause, it must contain an expression with the UInt8 type. This is usually an expression with comparison and logical operators. +This expression will be used for filtering data before all other transformations.

+

If indexes are supported by the database table engine, the expression is evaluated on the ability to use indexes.

+

PREWHERE clause

+

This clause has the same meaning as the WHERE clause. The difference is in which data is read from the table. +When using PREWHERE, first only the columns necessary for executing PREWHERE are read. Then the other columns are read that are needed for running the query, but only those blocks where the PREWHERE expression is true.

+

It makes sense to use PREWHERE if there are filtration conditions that are not suitable for indexes that are used by a minority of the columns in the query, but that provide strong data filtration. This reduces the volume of data to read.

+

For example, it is useful to write PREWHERE for queries that extract a large number of columns, but that only have filtration for a few columns.

+

PREWHERE is only supported by tables from the *MergeTree family.

+

A query may simultaneously specify PREWHERE and WHERE. In this case, PREWHERE precedes WHERE.

+

Keep in mind that it does not make much sense for PREWHERE to only specify those columns that have an index, because when using an index, only the data blocks that match the index are read.

+

If the 'optimize_move_to_prewhere' setting is set to 1 and PREWHERE is omitted, the system uses heuristics to automatically move parts of expressions from WHERE to PREWHERE.

+

GROUP BY clause

+

This is one of the most important parts of a column-oriented DBMS.

+

If there is a GROUP BY clause, it must contain a list of expressions. Each expression will be referred to here as a "key". +All the expressions in the SELECT, HAVING, and ORDER BY clauses must be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions.

+

If a query contains only table columns inside aggregate functions, the GROUP BY clause can be omitted, and aggregation by an empty set of keys is assumed.

+

Example:

+
SELECT
+    count(),
+    median(FetchTiming > 60 ? 60 : FetchTiming),
+    count() - sum(Refresh)
+FROM hits
+
+ + +

However, in contrast to standard SQL, if the table doesn't have any rows (either there aren't any at all, or there aren't any after using WHERE to filter), an empty result is returned, and not the result from one of the rows containing the initial values of aggregate functions.

+

As opposed to MySQL (and conforming to standard SQL), you can't get some value of some column that is not in a key or aggregate function (except constant expressions). To work around this, you can use the 'any' aggregate function (get the first encountered value) or 'min/max'.

+

Example:

+
SELECT
+    domainWithoutWWW(URL) AS domain,
+    count(),
+    any(Title) AS title -- getting the first occurred page header for each domain.
+FROM hits
+GROUP BY domain
+
+ + +

For every different key value encountered, GROUP BY calculates a set of aggregate function values.

+

GROUP BY is not supported for array columns.

+

A constant can't be specified as arguments for aggregate functions. Example: sum(1). Instead of this, you can get rid of the constant. Example: count().

+

WITH TOTALS modifier

+

If the WITH TOTALS modifier is specified, another row will be calculated. This row will have key columns containing default values (zeros or empty lines), and columns of aggregate functions with the values calculated across all the rows (the "total" values).

+

This extra row is output in JSON*, TabSeparated*, and Pretty* formats, separately from the other rows. In the other formats, this row is not output.

+

In JSON* formats, this row is output as a separate 'totals' field. In TabSeparated* formats, the row comes after the main result, preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result.

+

WITH TOTALS can be run in different ways when HAVING is present. The behavior depends on the 'totals_mode' setting. +By default, totals_mode = 'before_having'. In this case, 'totals' is calculated across all rows, including the ones that don't pass through HAVING and 'max_rows_to_group_by'.

+

The other alternatives include only the rows that pass through HAVING in 'totals', and behave differently with the setting max_rows_to_group_by and group_by_overflow_mode = 'any'.

+

after_having_exclusive – Don't include rows that didn't pass through max_rows_to_group_by. In other words, 'totals' will have less than or the same number of rows as it would if max_rows_to_group_by were omitted.

+

after_having_inclusive – Include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. In other words, 'totals' will have more than or the same number of rows as it would if max_rows_to_group_by were omitted.

+

after_having_auto – Count the number of rows that passed through HAVING. If it is more than a certain amount (by default, 50%), include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. Otherwise, do not include them.

+

totals_auto_threshold – By default, 0.5. The coefficient for after_having_auto.

+

If max_rows_to_group_by and group_by_overflow_mode = 'any' are not used, all variations of after_having are the same, and you can use any of them (for example, after_having_auto).

+

You can use WITH TOTALS in subqueries, including subqueries in the JOIN clause (in this case, the respective total values are combined).

+

GROUP BY in external memory

+

You can enable dumping temporary data to the disk to restrict memory usage during GROUP BY. +The max_bytes_before_external_group_by setting determines the threshold RAM consumption for dumping GROUP BY temporary data to the file system. If set to 0 (the default), it is disabled.

+

When using max_bytes_before_external_group_by, we recommend that you set max_memory_usage about twice as high. This is necessary because there are two stages to aggregation: reading the date and forming intermediate data (1) and merging the intermediate data (2). Dumping data to the file system can only occur during stage 1. If the temporary data wasn't dumped, then stage 2 might require up to the same amount of memory as in stage 1.

+

For example, if max_memory_usage was set to 10000000000 and you want to use external aggregation, it makes sense to set max_bytes_before_external_group_by to 10000000000, and max_memory_usage to 20000000000. When external aggregation is triggered (if there was at least one dump of temporary data), maximum consumption of RAM is only slightly more than max_bytes_before_external_group_by.

+

With distributed query processing, external aggregation is performed on remote servers. In order for the requestor server to use only a small amount of RAM, set distributed_aggregation_memory_efficient to 1.

+

When merging data flushed to the disk, as well as when merging results from remote servers when the distributed_aggregation_memory_efficient setting is enabled, consumes up to 1/256 * the number of threads from the total amount of RAM.

+

When external aggregation is enabled, if there was less than max_bytes_before_external_group_by of data (i.e. data was not flushed), the query runs just as fast as without external aggregation. If any temporary data was flushed, the run time will be several times longer (approximately three times).

+

If you have an ORDER BY with a small LIMIT after GROUP BY, then the ORDER BY CLAUSE will not use significant amounts of RAM. +But if the ORDER BY doesn't have LIMIT, don't forget to enable external sorting (max_bytes_before_external_sort).

+

LIMIT N BY clause

+

LIMIT N BY COLUMNS selects the top N rows for each group of COLUMNS. LIMIT N BY is not related to LIMIT; they can both be used in the same query. The key for LIMIT N BY can contain any number of columns or expressions.

+

Example:

+
SELECT
+    domainWithoutWWW(URL) AS domain,
+    domainWithoutWWW(REFERRER_URL) AS referrer,
+    device_type,
+    count() cnt
+FROM hits
+GROUP BY domain, referrer, device_type
+ORDER BY cnt DESC
+LIMIT 5 BY domain, device_type
+LIMIT 100
+
+ + +

The query will select the top 5 referrers for each domain, device_type pair, but not more than 100 rows (LIMIT n BY + LIMIT).

+

HAVING clause

+

Allows filtering the result received after GROUP BY, similar to the WHERE clause. +WHERE and HAVING differ in that WHERE is performed before aggregation (GROUP BY), while HAVING is performed after it. +If aggregation is not performed, HAVING can't be used.

+

+

ORDER BY clause

+

The ORDER BY clause contains a list of expressions, which can each be assigned DESC or ASC (the sorting direction). If the direction is not specified, ASC is assumed. ASC is sorted in ascending order, and DESC in descending order. The sorting direction applies to a single expression, not to the entire list. Example: ORDER BY Visits DESC, SearchPhrase

+

For sorting by String values, you can specify collation (comparison). Example: ORDER BY SearchPhrase COLLATE 'tr' - for sorting by keyword in ascending order, using the Turkish alphabet, case insensitive, assuming that strings are UTF-8 encoded. COLLATE can be specified or not for each expression in ORDER BY independently. If ASC or DESC is specified, COLLATE is specified after it. When using COLLATE, sorting is always case-insensitive.

+

We only recommend using COLLATE for final sorting of a small number of rows, since sorting with COLLATE is less efficient than normal sorting by bytes.

+

Rows that have identical values for the list of sorting expressions are output in an arbitrary order, which can also be nondeterministic (different each time). +If the ORDER BY clause is omitted, the order of the rows is also undefined, and may be nondeterministic as well.

+

When floating point numbers are sorted, NaNs are separate from the other values. Regardless of the sorting order, NaNs come at the end. In other words, for ascending sorting they are placed as if they are larger than all the other numbers, while for descending sorting they are placed as if they are smaller than the rest.

+

Less RAM is used if a small enough LIMIT is specified in addition to ORDER BY. Otherwise, the amount of memory spent is proportional to the volume of data for sorting. For distributed query processing, if GROUP BY is omitted, sorting is partially done on remote servers, and the results are merged on the requestor server. This means that for distributed sorting, the volume of data to sort can be greater than the amount of memory on a single server.

+

If there is not enough RAM, it is possible to perform sorting in external memory (creating temporary files on a disk). Use the setting max_bytes_before_external_sort for this purpose. If it is set to 0 (the default), external sorting is disabled. If it is enabled, when the volume of data to sort reaches the specified number of bytes, the collected data is sorted and dumped into a temporary file. After all data is read, all the sorted files are merged and the results are output. Files are written to the /var/lib/clickhouse/tmp/ directory in the config (by default, but you can use the 'tmp_path' parameter to change this setting).

+

Running a query may use more memory than 'max_bytes_before_external_sort'. For this reason, this setting must have a value significantly smaller than 'max_memory_usage'. As an example, if your server has 128 GB of RAM and you need to run a single query, set 'max_memory_usage' to 100 GB, and 'max_bytes_before_external_sort' to 80 GB.

+

External sorting works much less effectively than sorting in RAM.

+

SELECT clause

+

The expressions specified in the SELECT clause are analyzed after the calculations for all the clauses listed above are completed. +More specifically, expressions are analyzed that are above the aggregate functions, if there are any aggregate functions. +The aggregate functions and everything below them are calculated during aggregation (GROUP BY). +These expressions work as if they are applied to separate rows in the result.

+

DISTINCT clause

+

If DISTINCT is specified, only a single row will remain out of all the sets of fully matching rows in the result. +The result will be the same as if GROUP BY were specified across all the fields specified in SELECT without aggregate functions. But there are several differences from GROUP BY:

+
    +
  • DISTINCT can be applied together with GROUP BY.
    • +
  • When ORDER BY is omitted and LIMIT is defined, the query stops running immediately after the required number of different rows has been read.
    • +
  • Data blocks are output as they are processed, without waiting for the entire query to finish running.
    • +
+

DISTINCT is not supported if SELECT has at least one array column.

+

LIMIT clause

+

LIMIT m allows you to select the first 'm' rows from the result. +LIMIT n, m allows you to select the first 'm' rows from the result after skipping the first 'n' rows.

+

'n' and 'm' must be non-negative integers.

+

If there isn't an ORDER BY clause that explicitly sorts results, the result may be arbitrary and nondeterministic.

+

UNION ALL clause

+

You can use UNION ALL to combine any number of queries. Example:

+
SELECT CounterID, 1 AS table, toInt64(count()) AS c
+    FROM test.hits
+    GROUP BY CounterID
+
+UNION ALL
+
+SELECT CounterID, 2 AS table, sum(Sign) AS c
+    FROM test.visits
+    GROUP BY CounterID
+    HAVING c > 0
+
+ + +

Only UNION ALL is supported. The regular UNION (UNION DISTINCT) is not supported. If you need UNION DISTINCT, you can write SELECT DISTINCT from a subquery containing UNION ALL.

+

Queries that are parts of UNION ALL can be run simultaneously, and their results can be mixed together.

+

The structure of results (the number and type of columns) must match for the queries. But the column names can differ. In this case, the column names for the final result will be taken from the first query.

+

Queries that are parts of UNION ALL can't be enclosed in brackets. ORDER BY and LIMIT are applied to separate queries, not to the final result. If you need to apply a conversion to the final result, you can put all the queries with UNION ALL in a subquery in the FROM clause.

+

INTO OUTFILE clause

+

Add the INTO OUTFILE filename clause (where filename is a string literal) to redirect query output to the specified file. +In contrast to MySQL, the file is created on the client side. The query will fail if a file with the same filename already exists. +This functionality is available in the command-line client and clickhouse-local (a query sent via HTTP interface will fail).

+

The default output format is TabSeparated (the same as in the command-line client batch mode).

+

FORMAT clause

+

Specify 'FORMAT format' to get data in any specified format. +You can use this for convenience, or for creating dumps. +For more information, see the section "Formats". +If the FORMAT clause is omitted, the default format is used, which depends on both the settings and the interface used for accessing the DB. For the HTTP interface and the command-line client in batch mode, the default format is TabSeparated. For the command-line client in interactive mode, the default format is PrettyCompact (it has attractive and compact tables).

+

When using the command-line client, data is passed to the client in an internal efficient format. The client independently interprets the FORMAT clause of the query and formats the data itself (thus relieving the network and the server from the load).

+

IN operators

+

The IN, NOT IN, GLOBAL IN, and GLOBAL NOT IN operators are covered separately, since their functionality is quite rich.

+

The left side of the operator is either a single column or a tuple.

+

Examples:

+
SELECT UserID IN (123, 456) FROM ...
+SELECT (CounterID, UserID) IN ((34, 123), (101500, 456)) FROM ...
+
+ + +

If the left side is a single column that is in the index, and the right side is a set of constants, the system uses the index for processing the query.

+

Don't list too many values explicitly (i.e. millions). If a data set is large, put it in a temporary table (for example, see the section "External data for query processing"), then use a subquery.

+

The right side of the operator can be a set of constant expressions, a set of tuples with constant expressions (shown in the examples above), or the name of a database table or SELECT subquery in brackets.

+

If the right side of the operator is the name of a table (for example, UserID IN users), this is equivalent to the subquery UserID IN (SELECT * FROM users). Use this when working with external data that is sent along with the query. For example, the query can be sent together with a set of user IDs loaded to the 'users' temporary table, which should be filtered.

+

If the right side of the operator is a table name that has the Set engine (a prepared data set that is always in RAM), the data set will not be created over again for each query.

+

The subquery may specify more than one column for filtering tuples. +Example:

+
SELECT (CounterID, UserID) IN (SELECT CounterID, UserID FROM ...) FROM ...
+
+ + +

The columns to the left and right of the IN operator should have the same type.

+

The IN operator and subquery may occur in any part of the query, including in aggregate functions and lambda functions. +Example:

+
SELECT
+    EventDate,
+    avg(UserID IN
+    (
+        SELECT UserID
+        FROM test.hits
+        WHERE EventDate = toDate('2014-03-17')
+    )) AS ratio
+FROM test.hits
+GROUP BY EventDate
+ORDER BY EventDate ASC
+
+ + +
┌──EventDate─┬────ratio─┐
+│ 2014-03-17 │        1 │
+│ 2014-03-18 │ 0.807696 │
+│ 2014-03-19 │ 0.755406 │
+│ 2014-03-20 │ 0.723218 │
+│ 2014-03-21 │ 0.697021 │
+│ 2014-03-22 │ 0.647851 │
+│ 2014-03-23 │ 0.648416 │
+└────────────┴──────────┘
+
+ + +

For each day after March 17th, count the percentage of pageviews made by users who visited the site on March 17th. +A subquery in the IN clause is always run just one time on a single server. There are no dependent subqueries.

+

+

Distributed subqueries

+

There are two options for IN-s with subqueries (similar to JOINs): normal IN / OIN and IN GLOBAL / GLOBAL JOIN. They differ in how they are run for distributed query processing.

+
+ +Remember that the algorithms described below may work differently depending on the [settings](../operations/settings/settings.md#settings-distributed_product_mode) `distributed_product_mode` setting. + +
+ +

When using the regular IN, the query is sent to remote servers, and each of them runs the subqueries in the IN or JOIN clause.

+

When using GLOBAL IN / GLOBAL JOINs, first all the subqueries are run for GLOBAL IN / GLOBAL JOINs, and the results are collected in temporary tables. Then the temporary tables are sent to each remote server, where the queries are run using this temporary data.

+

For a non-distributed query, use the regular IN / JOIN.

+

Be careful when using subqueries in the IN / JOIN clauses for distributed query processing.

+

Let's look at some examples. Assume that each server in the cluster has a normal local_table. Each server also has a distributed_table table with the Distributed type, which looks at all the servers in the cluster.

+

For a query to the distributed_table, the query will be sent to all the remote servers and run on them using the local_table.

+

For example, the query

+
SELECT uniq(UserID) FROM distributed_table
+
+ + +

will be sent to all remote servers as

+
SELECT uniq(UserID) FROM local_table
+
+ + +

and run on each of them in parallel, until it reaches the stage where intermediate results can be combined. Then the intermediate results will be returned to the requestor server and merged on it, and the final result will be sent to the client.

+

Now let's examine a query with IN:

+
SELECT uniq(UserID) FROM distributed_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM local_table WHERE CounterID = 34)
+
+ + +
    +
  • Calculation of the intersection of audiences of two sites.
    • +
+

This query will be sent to all remote servers as

+
SELECT uniq(UserID) FROM local_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM local_table WHERE CounterID = 34)
+
+ + +

In other words, the data set in the IN clause will be collected on each server independently, only across the data that is stored locally on each of the servers.

+

This will work correctly and optimally if you are prepared for this case and have spread data across the cluster servers such that the data for a single UserID resides entirely on a single server. In this case, all the necessary data will be available locally on each server. Otherwise, the result will be inaccurate. We refer to this variation of the query as "local IN".

+

To correct how the query works when data is spread randomly across the cluster servers, you could specify distributed_table inside a subquery. The query would look like this:

+
SELECT uniq(UserID) FROM distributed_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM distributed_table WHERE CounterID = 34)
+
+ + +

This query will be sent to all remote servers as

+
SELECT uniq(UserID) FROM local_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM distributed_table WHERE CounterID = 34)
+
+ + +

The subquery will begin running on each remote server. Since the subquery uses a distributed table, the subquery that is on each remote server will be resent to every remote server as

+
SELECT UserID FROM local_table WHERE CounterID = 34
+
+ + +

For example, if you have a cluster of 100 servers, executing the entire query will require 10,000 elementary requests, which is generally considered unacceptable.

+

In such cases, you should always use GLOBAL IN instead of IN. Let's look at how it works for the query

+
SELECT uniq(UserID) FROM distributed_table WHERE CounterID = 101500 AND UserID GLOBAL IN (SELECT UserID FROM distributed_table WHERE CounterID = 34)
+
+ + +

The requestor server will run the subquery

+
SELECT UserID FROM distributed_table WHERE CounterID = 34
+
+ + +

and the result will be put in a temporary table in RAM. Then the request will be sent to each remote server as

+
SELECT uniq(UserID) FROM local_table WHERE CounterID = 101500 AND UserID GLOBAL IN _data1
+
+ + +

and the temporary table _data1 will be sent to every remote server with the query (the name of the temporary table is implementation-defined).

+

This is more optimal than using the normal IN. However, keep the following points in mind:

+
    +
  1. When creating a temporary table, data is not made unique. To reduce the volume of data transmitted over the network, specify DISTINCT in the subquery. (You don't need to do this for a normal IN.)
    2. +
  2. The temporary table will be sent to all the remote servers. Transmission does not account for network topology. For example, if 10 remote servers reside in a datacenter that is very remote in relation to the requestor server, the data will be sent 10 times over the channel to the remote datacenter. Try to avoid large data sets when using GLOBAL IN.
    3. +
  3. When transmitting data to remote servers, restrictions on network bandwidth are not configurable. You might overload the network.
    4. +
  4. Try to distribute data across servers so that you don't need to use GLOBAL IN on a regular basis.
    5. +
  5. If you need to use GLOBAL IN often, plan the location of the ClickHouse cluster so that a single group of replicas resides in no more than one data center with a fast network between them, so that a query can be processed entirely within a single data center.
    6. +
+

It also makes sense to specify a local table in the GLOBAL IN clause, in case this local table is only available on the requestor server and you want to use data from it on remote servers.

+

Extreme values

+

In addition to results, you can also get minimum and maximum values for the results columns. To do this, set the extremes setting to 1. Minimums and maximums are calculated for numeric types, dates, and dates with times. For other columns, the default values are output.

+

An extra two rows are calculated – the minimums and maximums, respectively. These extra two rows are output in JSON*, TabSeparated*, and Pretty* formats, separate from the other rows. They are not output for other formats.

+

In JSON* formats, the extreme values are output in a separate 'extremes' field. In TabSeparated* formats, the row comes after the main result, and after 'totals' if present. It is preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result, and after 'totals' if present.

+

Extreme values are calculated for rows that have passed through LIMIT. However, when using 'LIMIT offset, size', the rows before 'offset' are included in 'extremes'. In stream requests, the result may also include a small number of rows that passed through LIMIT.

+

Notes

+

The GROUP BY and ORDER BY clauses do not support positional arguments. This contradicts MySQL, but conforms to standard SQL. +For example, GROUP BY 1, 2 will be interpreted as grouping by constants (i.e. aggregation of all rows into one).

+

You can use synonyms (AS aliases) in any part of a query.

+

You can put an asterisk in any part of a query instead of an expression. When the query is analyzed, the asterisk is expanded to a list of all table columns (excluding the MATERIALIZED and ALIAS columns). There are only a few cases when using an asterisk is justified:

+
    +
  • When creating a table dump.
    • +
  • For tables containing just a few columns, such as system tables.
    • +
  • For getting information about what columns are in a table. In this case, set LIMIT 1. But it is better to use the DESC TABLE query.
    • +
  • When there is strong filtration on a small number of columns using PREWHERE.
    • +
  • In subqueries (since columns that aren't needed for the external query are excluded from subqueries).
    • +
+

In all other cases, we don't recommend using the asterisk, since it only gives you the drawbacks of a columnar DBMS instead of the advantages. In other words using the asterisk is not recommended.

+

KILL QUERY

+
KILL QUERY
+  WHERE <where expression to SELECT FROM system.processes query>
+  [SYNC|ASYNC|TEST]
+  [FORMAT format]
+
+ + +

Attempts to forcibly terminate the currently running queries. +The queries to terminate are selected from the system.processes table using the criteria defined in the WHERE clause of the KILL query.

+

Examples:

+
-- Forcibly terminates all queries with the specified query_id:
+KILL QUERY WHERE query_id='2-857d-4a57-9ee0-327da5d60a90'
+
+-- Synchronously terminates all queries run by 'username':
+KILL QUERY WHERE user='username' SYNC
+
+ + +

Read-only users can only stop their own queries.

+

By default, the asynchronous version of queries is used (ASYNC), which doesn't wait for confirmation that queries have stopped.

+

The synchronous version (SYNC) waits for all queries to stop and displays information about each process as it stops. +The response contains the kill_status column, which can take the following values:

+
    +
  1. 'finished' – The query was terminated successfully.
    2. +
  2. 'waiting' – Waiting for the query to end after sending it a signal to terminate.
    3. +
  3. The other values ​​explain why the query can't be stopped.
    4. +
+

A test query (TEST) only checks the user's rights and displays a list of queries to stop.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/query_language/syntax/index.html b/docs/build/docs/en/query_language/syntax/index.html new file mode 100644 index 00000000000..5ff8edd39e7 --- /dev/null +++ b/docs/build/docs/en/query_language/syntax/index.html @@ -0,0 +1,3191 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Syntax - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Syntax

+

There are two types of parsers in the system: the full SQL parser (a recursive descent parser), and the data format parser (a fast stream parser). +In all cases except the INSERT query, only the full SQL parser is used. +The INSERT query uses both parsers:

+
INSERT INTO t VALUES (1, 'Hello, world'), (2, 'abc'), (3, 'def')
+
+ + +

The INSERT INTO t VALUES fragment is parsed by the full parser, and the data (1, 'Hello, world'), (2, 'abc'), (3, 'def') is parsed by the fast stream parser. +Data can have any format. When a query is received, the server calculates no more than max_query_size bytes of the request in RAM (by default, 1 MB), and the rest is stream parsed. +This means the system doesn't have problems with large INSERT queries, like MySQL does.

+

When using the Values format in an INSERT query, it may seem that data is parsed the same as expressions in a SELECT query, but this is not true. The Values format is much more limited.

+

Next we will cover the full parser. For more information about format parsers, see the section "Formats".

+

Spaces

+

There may be any number of space symbols between syntactical constructions (including the beginning and end of a query). Space symbols include the space, tab, line feed, CR, and form feed.

+

Comments

+

SQL-style and C-style comments are supported. +SQL-style comments: from -- to the end of the line. The space after -- can be omitted. +Comments in C-style: from /* to */. These comments can be multiline. Spaces are not required here, either.

+

Keywords

+

Keywords (such as SELECT) are not case-sensitive. Everything else (column names, functions, and so on), in contrast to standard SQL, is case-sensitive. Keywords are not reserved (they are just parsed as keywords in the corresponding context).

+

Identifiers

+

Identifiers (column names, functions, and data types) can be quoted or non-quoted. +Non-quoted identifiers start with a Latin letter or underscore, and continue with a Latin letter, underscore, or number. In other words, they must match the regex ^[a-zA-Z_][0-9a-zA-Z_]*$. Examples: x, _1, X_y__Z123_.

+

Quoted identifiers are placed in reversed quotation marks `id` (the same as in MySQL), and can indicate any set of bytes (non-empty). In addition, symbols (for example, the reverse quotation mark) inside this type of identifier can be backslash-escaped. Escaping rules are the same as for string literals (see below). +We recommend using identifiers that do not need to be quoted.

+

Literals

+

There are numeric literals, string literals, and compound literals.

+

Numeric literals

+

A numeric literal tries to be parsed:

+
    +
  • First as a 64-bit signed number, using the 'strtoull' function.
    • +
  • If unsuccessful, as a 64-bit unsigned number, using the 'strtoll' function.
    • +
  • If unsuccessful, as a floating-point number using the 'strtod' function.
    • +
  • Otherwise, an error is returned.
    • +
+

The corresponding value will have the smallest type that the value fits in. +For example, 1 is parsed as UInt8, but 256 is parsed as UInt16. For more information, see "Data types".

+

Examples: 1, 18446744073709551615, 0xDEADBEEF, 01, 0.1, 1e100, -1e-100, inf, nan.

+

String literals

+

Only string literals in single quotes are supported. The enclosed characters can be backslash-escaped. The following escape sequences have a corresponding special value: \b, \f, \r, \n, \t, \0, \a, \v, \xHH. In all other cases, escape sequences in the format \c, where "c" is any character, are converted to "c". This means that you can use the sequences \'and\\. The value will have the String type.

+

The minimum set of characters that you need to escape in string literals: ' and \.

+

Compound literals

+

Constructions are supported for arrays: [1, 2, 3] and tuples: (1, 'Hello, world!', 2).. +Actually, these are not literals, but expressions with the array creation operator and the tuple creation operator, respectively. +For more information, see the section "Operators2". +An array must consist of at least one item, and a tuple must have at least two items. +Tuples have a special purpose for use in the IN clause of a SELECT query. Tuples can be obtained as the result of a query, but they can't be saved to a database (with the exception of Memory-type tables).

+

Functions

+

Functions are written like an identifier with a list of arguments (possibly empty) in brackets. In contrast to standard SQL, the brackets are required, even for an empty arguments list. Example: now(). +There are regular and aggregate functions (see the section "Aggregate functions"). Some aggregate functions can contain two lists of arguments in brackets. Example: quantile (0.9) (x). These aggregate functions are called "parametric" functions, and the arguments in the first list are called "parameters". The syntax of aggregate functions without parameters is the same as for regular functions.

+

Operators

+

Operators are converted to their corresponding functions during query parsing, taking their priority and associativity into account. +For example, the expression 1 + 2 * 3 + 4 is transformed to plus(plus(1, multiply(2, 3)), 4). +For more information, see the section "Operators" below.

+

Data types and database table engines

+

Data types and table engines in the CREATE query are written the same way as identifiers or functions. In other words, they may or may not contain an arguments list in brackets. For more information, see the sections "Data types," "Table engines," and "CREATE".

+

Synonyms

+

In the SELECT query, expressions can specify synonyms using the AS keyword. Any expression is placed to the left of AS. The identifier name for the synonym is placed to the right of AS. As opposed to standard SQL, synonyms are not only declared on the top level of expressions:

+
SELECT (1 AS n) + 2, n
+
+ + +

In contrast to standard SQL, synonyms can be used in all parts of a query, not just SELECT.

+

Asterisk

+

In a SELECT query, an asterisk can replace the expression. For more information, see the section "SELECT".

+

Expressions

+

An expression is a function, identifier, literal, application of an operator, expression in brackets, subquery, or asterisk. It can also contain a synonym. +A list of expressions is one or more expressions separated by commas. +Functions and operators, in turn, can have expressions as arguments.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/roadmap/index.html b/docs/build/docs/en/roadmap/index.html new file mode 100644 index 00000000000..fb7984d36eb --- /dev/null +++ b/docs/build/docs/en/roadmap/index.html @@ -0,0 +1,3119 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Roadmap - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

Roadmap

+

Q1 2018

+

New fuctionality

+
    +
  • +

    Support for UPDATE and DELETE.

    +
    • +
  • +

    Multidimensional and nested arrays.

    +
    • +
+

It can look something like this:

+
CREATE TABLE t
+(
+    x Array(Array(String)),
+    z Nested(
+        x Array(String),
+        y Nested(...))
+)
+ENGINE = MergeTree ORDER BY x
+
+ + +
    +
  • External MySQL and ODBC tables.
    • +
+

External tables can be integrated into ClickHouse using external dictionaries. This new functionality is a convenient alternative to connecting external tables.

+
SELECT ...
+FROM mysql('host:port', 'db', 'table', 'user', 'password')`
+
+ + +

Improvements

+
    +
  • Effective data copying between ClickHouse clusters.
    • +
+

Now you can copy data with the remote() function. For example: INSERT INTO t SELECT * FROM remote(...).

+

This operation will have improved performance.

+
    +
  • O_DIRECT for merges.
    • +
+

This will improve the performance of the OS cache and "hot" queries.

+

Q2 2018

+

New functionality

+
    +
  • +

    UPDATE/DELETE conform to the EU GDPR.

    +
    • +
  • +

    Protobuf and Parquet input and output formats.

    +
    • +
  • +

    Creating dictionaries using DDL queries.

    +
    • +
+

Currently, dictionaries that are part of the database schema are defined in external XML files. This is inconvenient and counter-intuitive. The new approach should fix it.

+
    +
  • +

    Integration with LDAP.

    +
    • +
  • +

    WITH ROLLUP and WITH CUBE for GROUP BY.

    +
    • +
  • +

    Custom encoding and compression for each column individually.

    +
    • +
+

As of now, ClickHouse supports LZ4 and ZSTD compression of columns, and compression settings are global (see the article Compression in ClickHouse). Per-column compression and encoding will provide more efficient data storage, which in turn will speed up queries.

+
    +
  • Storing data on multiple disks on the same server.
    • +
+

This functionality will make it easier to extend the disk space, since different disk systems can be used for different databases or tables. Currently, users are forced to use symbolic links if the databases and tables must be stored on a different disk.

+

Improvements

+

Many improvements and fixes are planned for the query execution system. For example:

+
    +
  • Using an index for in (subquery).
    • +
+

The index is not used right now, which reduces performance.

+
    +
  • Passing predicates from where to subqueries, and passing predicates to views.
    • +
+

The predicates must be passed, since the view is changed by the subquery. Performance is still low for view filters, and views can't use the primary key of the original table, which makes views useless for large tables.

+
    +
  • Optimizing branching operations (ternary operator, if, multiIf).
    • +
+

ClickHouse currently performs all branches, even if they aren't necessary.

+
    +
  • Using a primary key for GROUP BY and ORDER BY.
    • +
+

This will speed up certain types of queries with partially sorted data.

+

Q3-Q4 2018

+

We don't have any set plans yet, but the main projects will be:

+
    +
  • Resource pools for executing queries.
    • +
+

This will make load management more efficient.

+
    +
  • ANSI SQL JOIN syntax.
    • +
+

Improve ClickHouse compatibility with many SQL tools.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/search/search_index.json b/docs/build/docs/en/search/search_index.json new file mode 100644 index 00000000000..6ae9746f5ad --- /dev/null +++ b/docs/build/docs/en/search/search_index.json @@ -0,0 +1,4984 @@ +{ + "docs": [ + { + "location": "/", + "text": "What is ClickHouse?\n\n\nClickHouse is a columnar DBMS for OLAP.\n\n\nIn a \"normal\" row-oriented DBMS, data is stored in this order:\n\n\n5123456789123456789 1 Eurobasket - Greece - Bosnia and Herzegovina - example.com 1 2011-09-01 01:03:02 6274717 1294101174 11409 612345678912345678 0 33 6 http://www.example.com/basketball/team/123/match/456789.html http://www.example.com/basketball/team/123/match/987654.html 0 1366 768 32 10 3183 0 0 13 0\\0 1 1 0 0 2011142 -1 0 0 01321 613 660 2011-09-01 08:01:17 0 0 0 0 utf-8 1466 0 0 0 5678901234567890123 277789954 0 0 0 0 0\n5234985259563631958 0 Consulting, Tax assessment, Accounting, Law 1 2011-09-01 01:03:02 6320881 2111222333 213 6458937489576391093 0 3 2 http://www.example.ru/ 0 800 600 16 10 2 153.1 0 0 10 63 1 1 0 0 2111678 000 0 588 368 240 2011-09-01 01:03:17 4 0 60310 0 windows-1251 1466 0 000 778899001 0 0 0 0 0\n...\n\n\n\n\n\nIn order words, all the values related to a row are stored next to each other.\nExamples of a row-oriented DBMS are MySQL, Postgres, MS SQL Server, and others.\n\n\nIn a column-oriented DBMS, data is stored like this:\n\n\nWatchID: 5385521489354350662 5385521490329509958 5385521489953706054 5385521490476781638 5385521490583269446 5385521490218868806 5385521491437850694 5385521491090174022 5385521490792669254 5385521490420695110 5385521491532181574 5385521491559694406 5385521491459625030 5385521492275175494 5385521492781318214 5385521492710027334 5385521492955615302 5385521493708759110 5385521494506434630 5385521493104611398\nJavaEnable: 1 0 1 0 0 0 1 0 1 1 1 1 1 1 0 1 0 0 1 1\nTitle: Yandex Announcements - Investor Relations - Yandex Yandex \u2014 Contact us \u2014 Moscow Yandex \u2014 Mission Ru Yandex \u2014 History \u2014 History of Yandex Yandex Financial Releases - Investor Relations - Yandex Yandex \u2014 Locations Yandex Board of Directors - Corporate Governance - Yandex Yandex \u2014 Technologies\nGoodEvent: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\nEventTime: 2016-05-18 05:19:20 2016-05-18 08:10:20 2016-05-18 07:38:00 2016-05-18 01:13:08 2016-05-18 00:04:06 2016-05-18 04:21:30 2016-05-18 00:34:16 2016-05-18 07:35:49 2016-05-18 11:41:59 2016-05-18 01:13:32\n\n\n\n\n\nThese examples only show the order that data is arranged in.\nThe values from different columns are stored separately, and data from the same column is stored together.\n\n\nExamples of column-oriented DBMSs: \nVertica\n, \nParaccel (Actian Matrix) (Amazon Redshift)\n, \nSybase IQ\n, \nExasol\n, \nInfobright\n, \nInfiniDB\n, \nMonetDB (VectorWise) (Actian Vector)\n, \nLucidDB\n, \nSAP HANA\n, \nGoogle Dremel\n, \nGoogle PowerDrill\n, \nDruid\n, \nkdb+\n, and so on.\n\n\nDifferent orders for storing data are better suited to different scenarios.\nThe data access scenario refers to what queries are made, how often, and in what proportion; how much data is read for each type of query \u2013 rows, columns, and bytes; the relationship between reading and updating data; the working size of the data and how locally it is used; whether transactions are used, and how isolated they are; requirements for data replication and logical integrity; requirements for latency and throughput for each type of query, and so on.\n\n\nThe higher the load on the system, the more important it is to customize the system to the scenario, and the more specific this customization becomes. There is no system that is equally well-suited to significantly different scenarios. If a system is adaptable to a wide set of scenarios, under a high load, the system will handle all the scenarios equally poorly, or will work well for just one of the scenarios.\n\n\nWe'll say that the following is true for the OLAP (online analytical processing) scenario:\n\n\n\n\nThe vast majority of requests are for read access.\n\n\nData is updated in fairly large batches (\n 1000 rows), not by single rows; or it is not updated at all.\n\n\nData is added to the DB but is not modified.\n\n\nFor reads, quite a large number of rows are extracted from the DB, but only a small subset of columns.\n\n\nTables are \"wide,\" meaning they contain a large number of columns.\n\n\nQueries are relatively rare (usually hundreds of queries per server or less per second).\n\n\nFor simple queries, latencies around 50 ms are allowed.\n\n\nColumn values are fairly small: numbers and short strings (for example, 60 bytes per URL).\n\n\nRequires high throughput when processing a single query (up to billions of rows per second per server).\n\n\nThere are no transactions.\n\n\nLow requirements for data consistency.\n\n\nThere is one large table per query. All tables are small, except for one.\n\n\nA query result is significantly smaller than the source data. In other words, data is filtered or aggregated. The result fits in a single server's RAM.\n\n\n\n\nIt is easy to see that the OLAP scenario is very different from other popular scenarios (such as OLTP or Key-Value access). So it doesn't make sense to try to use OLTP or a Key-Value DB for processing analytical queries if you want to get decent performance. For example, if you try to use MongoDB or Elliptics for analytics, you will get very poor performance compared to OLAP databases.\n\n\nColumnar-oriented databases are better suited to OLAP scenarios (at least 100 times better in processing speed for most queries), for the following reasons:\n\n\n\n\nFor I/O.\n\n\nFor an analytical query, only a small number of table columns need to be read. In a column-oriented database, you can read just the data you need. For example, if you need 5 columns out of 100, you can expect a 20-fold reduction in I/O.\n\n\nSince data is read in packets, it is easier to compress. Data in columns is also easier to compress. This further reduces the I/O volume.\n\n\nDue to the reduced I/O, more data fits in the system cache.\n\n\n\n\nFor example, the query \"count the number of records for each advertising platform\" requires reading one \"advertising platform ID\" column, which takes up 1 byte uncompressed. If most of the traffic was not from advertising platforms, you can expect at least 10-fold compression of this column. When using a quick compression algorithm, data decompression is possible at a speed of at least several gigabytes of uncompressed data per second. In other words, this query can be processed at a speed of approximately several billion rows per second on a single server. This speed is actually achieved in practice.\n\n\nExample:\n\n\nmilovidov@hostname:~$ clickhouse-client\nClickHouse client version \n0\n.0.52053.\nConnecting to localhost:9000.\nConnected to ClickHouse server version \n0\n.0.52053.\n\n:\n)\n SELECT CounterID, count\n()\n FROM hits GROUP BY CounterID ORDER BY count\n()\n DESC LIMIT \n20\n\n\nSELECT\n CounterID,\n count\n()\n\nFROM hits\nGROUP BY CounterID\nORDER BY count\n()\n DESC\nLIMIT \n20\n\n\n\u250c\u2500CounterID\u2500\u252c\u2500\u2500count\n()\n\u2500\u2510\n\u2502 \n114208\n \u2502 \n56057344\n \u2502\n\u2502 \n115080\n \u2502 \n51619590\n \u2502\n\u2502 \n3228\n \u2502 \n44658301\n \u2502\n\u2502 \n38230\n \u2502 \n42045932\n \u2502\n\u2502 \n145263\n \u2502 \n42042158\n \u2502\n\u2502 \n91244\n \u2502 \n38297270\n \u2502\n\u2502 \n154139\n \u2502 \n26647572\n \u2502\n\u2502 \n150748\n \u2502 \n24112755\n \u2502\n\u2502 \n242232\n \u2502 \n21302571\n \u2502\n\u2502 \n338158\n \u2502 \n13507087\n \u2502\n\u2502 \n62180\n \u2502 \n12229491\n \u2502\n\u2502 \n82264\n \u2502 \n12187441\n \u2502\n\u2502 \n232261\n \u2502 \n12148031\n \u2502\n\u2502 \n146272\n \u2502 \n11438516\n \u2502\n\u2502 \n168777\n \u2502 \n11403636\n \u2502\n\u2502 \n4120072\n \u2502 \n11227824\n \u2502\n\u2502 \n10938808\n \u2502 \n10519739\n \u2502\n\u2502 \n74088\n \u2502 \n9047015\n \u2502\n\u2502 \n115079\n \u2502 \n8837972\n \u2502\n\u2502 \n337234\n \u2502 \n8205961\n \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n20\n rows in set. Elapsed: \n0\n.153 sec. Processed \n1\n.00 billion rows, \n4\n.00 GB \n(\n6\n.53 billion rows/s., \n26\n.10 GB/s.\n)\n\n\n:\n)\n\n\n\n\n\n\n\n\nFor CPU.\n\n\n\n\nSince executing a query requires processing a large number of rows, it helps to dispatch all operations for entire vectors instead of for separate rows, or to implement the query engine so that there is almost no dispatching cost. If you don't do this, with any half-decent disk subsystem, the query interpreter inevitably stalls the CPU.\nIt makes sense to both store data in columns and process it, when possible, by columns.\n\n\nThere are two ways to do this:\n\n\n\n\n\n\nA vector engine. All operations are written for vectors, instead of for separate values. This means you don't need to call operations very often, and dispatching costs are negligible. Operation code contains an optimized internal cycle.\n\n\n\n\n\n\nCode generation. The code generated for the query has all the indirect calls in it.\n\n\n\n\n\n\nThis is not done in \"normal\" databases, because it doesn't make sense when running simple queries. However, there are exceptions. For example, MemSQL uses code generation to reduce latency when processing SQL queries. (For comparison, analytical DBMSs require optimization of throughput, not latency.)\n\n\nNote that for CPU efficiency, the query language must be declarative (SQL or MDX), or at least a vector (J, K). The query should only contain implicit loops, allowing for optimization.", + "title": "ClickHouse" + }, + { + "location": "/#what-is-clickhouse", + "text": "ClickHouse is a columnar DBMS for OLAP. In a \"normal\" row-oriented DBMS, data is stored in this order: 5123456789123456789 1 Eurobasket - Greece - Bosnia and Herzegovina - example.com 1 2011-09-01 01:03:02 6274717 1294101174 11409 612345678912345678 0 33 6 http://www.example.com/basketball/team/123/match/456789.html http://www.example.com/basketball/team/123/match/987654.html 0 1366 768 32 10 3183 0 0 13 0\\0 1 1 0 0 2011142 -1 0 0 01321 613 660 2011-09-01 08:01:17 0 0 0 0 utf-8 1466 0 0 0 5678901234567890123 277789954 0 0 0 0 0\n5234985259563631958 0 Consulting, Tax assessment, Accounting, Law 1 2011-09-01 01:03:02 6320881 2111222333 213 6458937489576391093 0 3 2 http://www.example.ru/ 0 800 600 16 10 2 153.1 0 0 10 63 1 1 0 0 2111678 000 0 588 368 240 2011-09-01 01:03:17 4 0 60310 0 windows-1251 1466 0 000 778899001 0 0 0 0 0\n... In order words, all the values related to a row are stored next to each other.\nExamples of a row-oriented DBMS are MySQL, Postgres, MS SQL Server, and others. In a column-oriented DBMS, data is stored like this: WatchID: 5385521489354350662 5385521490329509958 5385521489953706054 5385521490476781638 5385521490583269446 5385521490218868806 5385521491437850694 5385521491090174022 5385521490792669254 5385521490420695110 5385521491532181574 5385521491559694406 5385521491459625030 5385521492275175494 5385521492781318214 5385521492710027334 5385521492955615302 5385521493708759110 5385521494506434630 5385521493104611398\nJavaEnable: 1 0 1 0 0 0 1 0 1 1 1 1 1 1 0 1 0 0 1 1\nTitle: Yandex Announcements - Investor Relations - Yandex Yandex \u2014 Contact us \u2014 Moscow Yandex \u2014 Mission Ru Yandex \u2014 History \u2014 History of Yandex Yandex Financial Releases - Investor Relations - Yandex Yandex \u2014 Locations Yandex Board of Directors - Corporate Governance - Yandex Yandex \u2014 Technologies\nGoodEvent: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\nEventTime: 2016-05-18 05:19:20 2016-05-18 08:10:20 2016-05-18 07:38:00 2016-05-18 01:13:08 2016-05-18 00:04:06 2016-05-18 04:21:30 2016-05-18 00:34:16 2016-05-18 07:35:49 2016-05-18 11:41:59 2016-05-18 01:13:32 These examples only show the order that data is arranged in.\nThe values from different columns are stored separately, and data from the same column is stored together. Examples of column-oriented DBMSs: Vertica , Paraccel (Actian Matrix) (Amazon Redshift) , Sybase IQ , Exasol , Infobright , InfiniDB , MonetDB (VectorWise) (Actian Vector) , LucidDB , SAP HANA , Google Dremel , Google PowerDrill , Druid , kdb+ , and so on. Different orders for storing data are better suited to different scenarios.\nThe data access scenario refers to what queries are made, how often, and in what proportion; how much data is read for each type of query \u2013 rows, columns, and bytes; the relationship between reading and updating data; the working size of the data and how locally it is used; whether transactions are used, and how isolated they are; requirements for data replication and logical integrity; requirements for latency and throughput for each type of query, and so on. The higher the load on the system, the more important it is to customize the system to the scenario, and the more specific this customization becomes. There is no system that is equally well-suited to significantly different scenarios. If a system is adaptable to a wide set of scenarios, under a high load, the system will handle all the scenarios equally poorly, or will work well for just one of the scenarios. We'll say that the following is true for the OLAP (online analytical processing) scenario: The vast majority of requests are for read access. Data is updated in fairly large batches ( 1000 rows), not by single rows; or it is not updated at all. Data is added to the DB but is not modified. For reads, quite a large number of rows are extracted from the DB, but only a small subset of columns. Tables are \"wide,\" meaning they contain a large number of columns. Queries are relatively rare (usually hundreds of queries per server or less per second). For simple queries, latencies around 50 ms are allowed. Column values are fairly small: numbers and short strings (for example, 60 bytes per URL). Requires high throughput when processing a single query (up to billions of rows per second per server). There are no transactions. Low requirements for data consistency. There is one large table per query. All tables are small, except for one. A query result is significantly smaller than the source data. In other words, data is filtered or aggregated. The result fits in a single server's RAM. It is easy to see that the OLAP scenario is very different from other popular scenarios (such as OLTP or Key-Value access). So it doesn't make sense to try to use OLTP or a Key-Value DB for processing analytical queries if you want to get decent performance. For example, if you try to use MongoDB or Elliptics for analytics, you will get very poor performance compared to OLAP databases. Columnar-oriented databases are better suited to OLAP scenarios (at least 100 times better in processing speed for most queries), for the following reasons: For I/O. For an analytical query, only a small number of table columns need to be read. In a column-oriented database, you can read just the data you need. For example, if you need 5 columns out of 100, you can expect a 20-fold reduction in I/O. Since data is read in packets, it is easier to compress. Data in columns is also easier to compress. This further reduces the I/O volume. Due to the reduced I/O, more data fits in the system cache. For example, the query \"count the number of records for each advertising platform\" requires reading one \"advertising platform ID\" column, which takes up 1 byte uncompressed. If most of the traffic was not from advertising platforms, you can expect at least 10-fold compression of this column. When using a quick compression algorithm, data decompression is possible at a speed of at least several gigabytes of uncompressed data per second. In other words, this query can be processed at a speed of approximately several billion rows per second on a single server. This speed is actually achieved in practice. Example: milovidov@hostname:~$ clickhouse-client\nClickHouse client version 0 .0.52053.\nConnecting to localhost:9000.\nConnected to ClickHouse server version 0 .0.52053.\n\n: ) SELECT CounterID, count () FROM hits GROUP BY CounterID ORDER BY count () DESC LIMIT 20 \n\nSELECT\n CounterID,\n count () \nFROM hits\nGROUP BY CounterID\nORDER BY count () DESC\nLIMIT 20 \n\n\u250c\u2500CounterID\u2500\u252c\u2500\u2500count () \u2500\u2510\n\u2502 114208 \u2502 56057344 \u2502\n\u2502 115080 \u2502 51619590 \u2502\n\u2502 3228 \u2502 44658301 \u2502\n\u2502 38230 \u2502 42045932 \u2502\n\u2502 145263 \u2502 42042158 \u2502\n\u2502 91244 \u2502 38297270 \u2502\n\u2502 154139 \u2502 26647572 \u2502\n\u2502 150748 \u2502 24112755 \u2502\n\u2502 242232 \u2502 21302571 \u2502\n\u2502 338158 \u2502 13507087 \u2502\n\u2502 62180 \u2502 12229491 \u2502\n\u2502 82264 \u2502 12187441 \u2502\n\u2502 232261 \u2502 12148031 \u2502\n\u2502 146272 \u2502 11438516 \u2502\n\u2502 168777 \u2502 11403636 \u2502\n\u2502 4120072 \u2502 11227824 \u2502\n\u2502 10938808 \u2502 10519739 \u2502\n\u2502 74088 \u2502 9047015 \u2502\n\u2502 115079 \u2502 8837972 \u2502\n\u2502 337234 \u2502 8205961 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 20 rows in set. Elapsed: 0 .153 sec. Processed 1 .00 billion rows, 4 .00 GB ( 6 .53 billion rows/s., 26 .10 GB/s. ) \n\n: ) For CPU. Since executing a query requires processing a large number of rows, it helps to dispatch all operations for entire vectors instead of for separate rows, or to implement the query engine so that there is almost no dispatching cost. If you don't do this, with any half-decent disk subsystem, the query interpreter inevitably stalls the CPU.\nIt makes sense to both store data in columns and process it, when possible, by columns. There are two ways to do this: A vector engine. All operations are written for vectors, instead of for separate values. This means you don't need to call operations very often, and dispatching costs are negligible. Operation code contains an optimized internal cycle. Code generation. The code generated for the query has all the indirect calls in it. This is not done in \"normal\" databases, because it doesn't make sense when running simple queries. However, there are exceptions. For example, MemSQL uses code generation to reduce latency when processing SQL queries. (For comparison, analytical DBMSs require optimization of throughput, not latency.) Note that for CPU efficiency, the query language must be declarative (SQL or MDX), or at least a vector (J, K). The query should only contain implicit loops, allowing for optimization.", + "title": "What is ClickHouse?" + }, + { + "location": "/introduction/distinctive_features/", + "text": "Distinctive features of ClickHouse\n\n\nTrue column-oriented DBMS\n\n\nIn a true column-oriented DBMS, there isn't any \"garbage\" stored with the values. Among other things, this means that constant-length values must be supported, to avoid storing their length \"number\" next to the values. As an example, a billion UInt8-type values should actually consume around 1 GB uncompressed, or this will strongly affect the CPU use. It is very important to store data compactly (without any \"garbage\") even when uncompressed, since the speed of decompression (CPU usage) depends mainly on the volume of uncompressed data.\n\n\nThis is worth noting because there are systems that can store values of separate columns separately, but that can't effectively process analytical queries due to their optimization for other scenarios. Examples are HBase, BigTable, Cassandra, and HyperTable. In these systems, you will get throughput around a hundred thousand rows per second, but not hundreds of millions of rows per second.\n\n\nAlso note that ClickHouse is a DBMS, not a single database. ClickHouse allows creating tables and databases in runtime, loading data, and running queries without reconfiguring and restarting the server.\n\n\nData compression\n\n\nSome column-oriented DBMSs (InfiniDB CE and MonetDB) do not use data compression. However, data compression really improves performance.\n\n\nDisk storage of data\n\n\nMany column-oriented DBMSs (such as SAP HANA and Google PowerDrill) can only work in RAM. But even on thousands of servers, the RAM is too small for storing all the pageviews and sessions in Yandex.Metrica.\n\n\nParallel processing on multiple cores\n\n\nLarge queries are parallelized in a natural way.\n\n\nDistributed processing on multiple servers\n\n\nAlmost none of the columnar DBMSs listed above have support for distributed processing.\nIn ClickHouse, data can reside on different shards. Each shard can be a group of replicas that are used for fault tolerance. The query is processed on all the shards in parallel. This is transparent for the user.\n\n\nSQL support\n\n\nIf you are familiar with standard SQL, we can't really talk about SQL support.\nAll the functions have different names.\nHowever, this is a declarative query language based on SQL that can't be differentiated from SQL in many instances.\nJOINs are supported. Subqueries are supported in FROM, IN, and JOIN clauses, as well as scalar subqueries.\nDependent subqueries are not supported.\n\n\nVector engine\n\n\nData is not only stored by columns, but is processed by vectors (parts of columns). This allows us to achieve high CPU performance.\n\n\nReal-time data updates\n\n\nClickHouse supports primary key tables. In order to quickly perform queries on the range of the primary key, the data is sorted incrementally using the merge tree. Due to this, data can continually be added to the table. There is no locking when adding data.\n\n\nIndexes\n\n\nHaving a primary key makes it possible to extract data for specific clients (for instance, Yandex.Metrica tracking tags) for a specific time range, with low latency less than several dozen milliseconds.\n\n\nSuitable for online queries\n\n\nThis lets us use the system as the back-end for a web interface. Low latency means queries can be processed without delay, while the Yandex.Metrica interface page is loading. In other words, in online mode.\n\n\nSupport for approximated calculations\n\n\n\n\nThe system contains aggregate functions for approximated calculation of the number of various values, medians, and quantiles.\n\n\nSupports running a query based on a part (sample) of data and getting an approximated result. In this case, proportionally less data is retrieved from the disk.\n\n\nSupports running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reasonably accurate result while using fewer resources.\n\n\n\n\nData replication and support for data integrity on replicas\n\n\nUses asynchronous multimaster replication. After being written to any available replica, data is distributed to all the remaining replicas. The system maintains identical data on different replicas. Data is restored automatically after a failure, or using a \"button\" for complex cases.\nFor more information, see the section \nData replication\n.", + "title": "Distinctive features of ClickHouse" + }, + { + "location": "/introduction/distinctive_features/#distinctive-features-of-clickhouse", + "text": "", + "title": "Distinctive features of ClickHouse" + }, + { + "location": "/introduction/distinctive_features/#true-column-oriented-dbms", + "text": "In a true column-oriented DBMS, there isn't any \"garbage\" stored with the values. Among other things, this means that constant-length values must be supported, to avoid storing their length \"number\" next to the values. As an example, a billion UInt8-type values should actually consume around 1 GB uncompressed, or this will strongly affect the CPU use. It is very important to store data compactly (without any \"garbage\") even when uncompressed, since the speed of decompression (CPU usage) depends mainly on the volume of uncompressed data. This is worth noting because there are systems that can store values of separate columns separately, but that can't effectively process analytical queries due to their optimization for other scenarios. Examples are HBase, BigTable, Cassandra, and HyperTable. In these systems, you will get throughput around a hundred thousand rows per second, but not hundreds of millions of rows per second. Also note that ClickHouse is a DBMS, not a single database. ClickHouse allows creating tables and databases in runtime, loading data, and running queries without reconfiguring and restarting the server.", + "title": "True column-oriented DBMS" + }, + { + "location": "/introduction/distinctive_features/#data-compression", + "text": "Some column-oriented DBMSs (InfiniDB CE and MonetDB) do not use data compression. However, data compression really improves performance.", + "title": "Data compression" + }, + { + "location": "/introduction/distinctive_features/#disk-storage-of-data", + "text": "Many column-oriented DBMSs (such as SAP HANA and Google PowerDrill) can only work in RAM. But even on thousands of servers, the RAM is too small for storing all the pageviews and sessions in Yandex.Metrica.", + "title": "Disk storage of data" + }, + { + "location": "/introduction/distinctive_features/#parallel-processing-on-multiple-cores", + "text": "Large queries are parallelized in a natural way.", + "title": "Parallel processing on multiple cores" + }, + { + "location": "/introduction/distinctive_features/#distributed-processing-on-multiple-servers", + "text": "Almost none of the columnar DBMSs listed above have support for distributed processing.\nIn ClickHouse, data can reside on different shards. Each shard can be a group of replicas that are used for fault tolerance. The query is processed on all the shards in parallel. This is transparent for the user.", + "title": "Distributed processing on multiple servers" + }, + { + "location": "/introduction/distinctive_features/#sql-support", + "text": "If you are familiar with standard SQL, we can't really talk about SQL support.\nAll the functions have different names.\nHowever, this is a declarative query language based on SQL that can't be differentiated from SQL in many instances.\nJOINs are supported. Subqueries are supported in FROM, IN, and JOIN clauses, as well as scalar subqueries.\nDependent subqueries are not supported.", + "title": "SQL support" + }, + { + "location": "/introduction/distinctive_features/#vector-engine", + "text": "Data is not only stored by columns, but is processed by vectors (parts of columns). This allows us to achieve high CPU performance.", + "title": "Vector engine" + }, + { + "location": "/introduction/distinctive_features/#real-time-data-updates", + "text": "ClickHouse supports primary key tables. In order to quickly perform queries on the range of the primary key, the data is sorted incrementally using the merge tree. Due to this, data can continually be added to the table. There is no locking when adding data.", + "title": "Real-time data updates" + }, + { + "location": "/introduction/distinctive_features/#indexes", + "text": "Having a primary key makes it possible to extract data for specific clients (for instance, Yandex.Metrica tracking tags) for a specific time range, with low latency less than several dozen milliseconds.", + "title": "Indexes" + }, + { + "location": "/introduction/distinctive_features/#suitable-for-online-queries", + "text": "This lets us use the system as the back-end for a web interface. Low latency means queries can be processed without delay, while the Yandex.Metrica interface page is loading. In other words, in online mode.", + "title": "Suitable for online queries" + }, + { + "location": "/introduction/distinctive_features/#support-for-approximated-calculations", + "text": "The system contains aggregate functions for approximated calculation of the number of various values, medians, and quantiles. Supports running a query based on a part (sample) of data and getting an approximated result. In this case, proportionally less data is retrieved from the disk. Supports running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reasonably accurate result while using fewer resources.", + "title": "Support for approximated calculations" + }, + { + "location": "/introduction/distinctive_features/#data-replication-and-support-for-data-integrity-on-replicas", + "text": "Uses asynchronous multimaster replication. After being written to any available replica, data is distributed to all the remaining replicas. The system maintains identical data on different replicas. Data is restored automatically after a failure, or using a \"button\" for complex cases.\nFor more information, see the section Data replication .", + "title": "Data replication and support for data integrity on replicas" + }, + { + "location": "/introduction/features_considered_disadvantages/", + "text": "ClickHouse features that can be considered disadvantages\n\n\n\n\nNo transactions.\n\n\nFor aggregation, query results must fit in the RAM on a single server. However, the volume of source data for a query may be indefinitely large.\n\n\nLack of full-fledged UPDATE/DELETE implementation.", + "title": "ClickHouse features that can be considered disadvantages" + }, + { + "location": "/introduction/features_considered_disadvantages/#clickhouse-features-that-can-be-considered-disadvantages", + "text": "No transactions. For aggregation, query results must fit in the RAM on a single server. However, the volume of source data for a query may be indefinitely large. Lack of full-fledged UPDATE/DELETE implementation.", + "title": "ClickHouse features that can be considered disadvantages" + }, + { + "location": "/introduction/ya_metrika_task/", + "text": "Yandex.Metrica use case\n\n\nClickHouse currently powers \nYandex.Metrica\n, \nthe second largest web analytics platform in the world\n. With more than 13 trillion records in the database and more than 20 billion events daily, ClickHouse allows you generating custom reports on the fly directly from non-aggregated data.\n\n\nWe need to get custom reports based on hits and sessions, with custom segments set by the user. Data for the reports is updated in real-time. Queries must be run immediately (in online mode). We must be able to build reports for any time period. Complex aggregates must be calculated, such as the number of unique visitors.\nAt this time (April 2014), Yandex.Metrica receives approximately 12 billion events (pageviews and mouse clicks) daily. All these events must be stored in order to build custom reports. A single query may require scanning hundreds of millions of rows over a few seconds, or millions of rows in no more than a few hundred milliseconds.\n\n\nUsage in Yandex.Metrica and other Yandex services\n\n\nClickHouse is used for multiple purposes in Yandex.Metrica.\nIts main task is to build reports in online mode using non-aggregated data. It uses a cluster of 374 servers, which store over 20.3 trillion rows in the database. The volume of compressed data, without counting duplication and replication, is about 2 PB. The volume of uncompressed data (in TSV format) would be approximately 17 PB.\n\n\nClickHouse is also used for:\n\n\n\n\nStoring data for Session Replay from Yandex.Metrica.\n\n\nProcessing intermediate data.\n\n\nBuilding global reports with Analytics.\n\n\nRunning queries for debugging the Yandex.Metrica engine.\n\n\nAnalyzing logs from the API and the user interface.\n\n\n\n\nClickHouse has at least a dozen installations in other Yandex services: in search verticals, Market, Direct, business analytics, mobile development, AdFox, personal services, and others.\n\n\nAggregated and non-aggregated data\n\n\nThere is a popular opinion that in order to effectively calculate statistics, you must aggregate data, since this reduces the volume of data.\n\n\nBut data aggregation is a very limited solution, for the following reasons:\n\n\n\n\nYou must have a pre-defined list of reports the user will need.\n\n\nThe user can't make custom reports.\n\n\nWhen aggregating a large quantity of keys, the volume of data is not reduced, and aggregation is useless.\n\n\nFor a large number of reports, there are too many aggregation variations (combinatorial explosion).\n\n\nWhen aggregating keys with high cardinality (such as URLs), the volume of data is not reduced by much (less than twofold).\n\n\nFor this reason, the volume of data with aggregation might grow instead of shrink.\n\n\nUsers do not view all the reports we generate for them. A large portion of calculations are useless.\n\n\nThe logical integrity of data may be violated for various aggregations.\n\n\n\n\nIf we do not aggregate anything and work with non-aggregated data, this might actually reduce the volume of calculations.\n\n\nHowever, with aggregation, a significant part of the work is taken offline and completed relatively calmly. In contrast, online calculations require calculating as fast as possible, since the user is waiting for the result.\n\n\nYandex.Metrica has a specialized system for aggregating data called Metrage, which is used for the majority of reports.\nStarting in 2009, Yandex.Metrica also used a specialized OLAP database for non-aggregated data called OLAPServer, which was previously used for the report builder.\nOLAPServer worked well for non-aggregated data, but it had many restrictions that did not allow it to be used for all reports as desired. These included the lack of support for data types (only numbers), and the inability to incrementally update data in real-time (it could only be done by rewriting data daily). OLAPServer is not a DBMS, but a specialized DB.\n\n\nTo remove the limitations of OLAPServer and solve the problem of working with non-aggregated data for all reports, we developed the ClickHouse DBMS.", + "title": "The Yandex.Metrica task" + }, + { + "location": "/introduction/ya_metrika_task/#yandexmetrica-use-case", + "text": "ClickHouse currently powers Yandex.Metrica , the second largest web analytics platform in the world . With more than 13 trillion records in the database and more than 20 billion events daily, ClickHouse allows you generating custom reports on the fly directly from non-aggregated data. We need to get custom reports based on hits and sessions, with custom segments set by the user. Data for the reports is updated in real-time. Queries must be run immediately (in online mode). We must be able to build reports for any time period. Complex aggregates must be calculated, such as the number of unique visitors.\nAt this time (April 2014), Yandex.Metrica receives approximately 12 billion events (pageviews and mouse clicks) daily. All these events must be stored in order to build custom reports. A single query may require scanning hundreds of millions of rows over a few seconds, or millions of rows in no more than a few hundred milliseconds.", + "title": "Yandex.Metrica use case" + }, + { + "location": "/introduction/ya_metrika_task/#usage-in-yandexmetrica-and-other-yandex-services", + "text": "ClickHouse is used for multiple purposes in Yandex.Metrica.\nIts main task is to build reports in online mode using non-aggregated data. It uses a cluster of 374 servers, which store over 20.3 trillion rows in the database. The volume of compressed data, without counting duplication and replication, is about 2 PB. The volume of uncompressed data (in TSV format) would be approximately 17 PB. ClickHouse is also used for: Storing data for Session Replay from Yandex.Metrica. Processing intermediate data. Building global reports with Analytics. Running queries for debugging the Yandex.Metrica engine. Analyzing logs from the API and the user interface. ClickHouse has at least a dozen installations in other Yandex services: in search verticals, Market, Direct, business analytics, mobile development, AdFox, personal services, and others.", + "title": "Usage in Yandex.Metrica and other Yandex services" + }, + { + "location": "/introduction/ya_metrika_task/#aggregated-and-non-aggregated-data", + "text": "There is a popular opinion that in order to effectively calculate statistics, you must aggregate data, since this reduces the volume of data. But data aggregation is a very limited solution, for the following reasons: You must have a pre-defined list of reports the user will need. The user can't make custom reports. When aggregating a large quantity of keys, the volume of data is not reduced, and aggregation is useless. For a large number of reports, there are too many aggregation variations (combinatorial explosion). When aggregating keys with high cardinality (such as URLs), the volume of data is not reduced by much (less than twofold). For this reason, the volume of data with aggregation might grow instead of shrink. Users do not view all the reports we generate for them. A large portion of calculations are useless. The logical integrity of data may be violated for various aggregations. If we do not aggregate anything and work with non-aggregated data, this might actually reduce the volume of calculations. However, with aggregation, a significant part of the work is taken offline and completed relatively calmly. In contrast, online calculations require calculating as fast as possible, since the user is waiting for the result. Yandex.Metrica has a specialized system for aggregating data called Metrage, which is used for the majority of reports.\nStarting in 2009, Yandex.Metrica also used a specialized OLAP database for non-aggregated data called OLAPServer, which was previously used for the report builder.\nOLAPServer worked well for non-aggregated data, but it had many restrictions that did not allow it to be used for all reports as desired. These included the lack of support for data types (only numbers), and the inability to incrementally update data in real-time (it could only be done by rewriting data daily). OLAPServer is not a DBMS, but a specialized DB. To remove the limitations of OLAPServer and solve the problem of working with non-aggregated data for all reports, we developed the ClickHouse DBMS.", + "title": "Aggregated and non-aggregated data" + }, + { + "location": "/introduction/possible_silly_questions/", + "text": "Questions you were afraid to ask\n\n\nWhy not use something like MapReduce?\n\n\nWe can refer to systems like map-reduce as distributed computing systems in which the reduce operation is based on distributed sorting. In this sense, they include Hadoop, and YT (YT is developed at Yandex for internal use).\n\n\nThese systems aren't appropriate for online queries due to their high latency. In other words, they can't be used as the back-end for a web interface.\nThese types of systems aren't useful for real-time data updates.\nDistributed sorting isn't the best way to perform reduce operations if the result of the operation and all the intermediate results (if there are any) are located in the RAM of a single server, which is usually the case for online queries. In such a case, a hash table is the optimal way to perform reduce operations. A common approach to optimizing map-reduce tasks is pre-aggregation (partial reduce) using a hash table in RAM. The user performs this optimization manually.\nDistributed sorting is one of the main causes of reduced performance when running simple map-reduce tasks.\n\n\nSystems like map-reduce allow executing any code on the cluster. But a declarative query language is better suited to OLAP in order to run experiments quickly. For example, Hadoop has Hive and Pig. Also consider Cloudera Impala, Shark (outdated) for Spark, and Spark SQL, Presto, and Apache Drill. Performance when running such tasks is highly sub-optimal compared to specialized systems, but relatively high latency makes it unrealistic to use these systems as the backend for a web interface.\n\n\nYT allows storing groups of columns separately. But YT can't be considered a true column-based system because it doesn't have fixed-length data types (for efficiently storing numbers without extra \"garbage\"), and also due to its lack of a vector engine. Tasks are performed in YT using custom code in streaming mode, so they cannot be optimized enough (up to hundreds of millions of rows per second per server). \"Dynamic table sorting\" is under development in YT using MergeTree, strict value typing, and a query language similar to SQL. Dynamically sorted tables are not appropriate for OLAP tasks because the data is stored by row. The YT query language is still under development, so we can't yet rely on this functionality. YT developers are considering using dynamically sorted tables in OLTP and Key-Value scenarios.", + "title": "Everything you were afraid to ask" + }, + { + "location": "/introduction/possible_silly_questions/#questions-you-were-afraid-to-ask", + "text": "", + "title": "Questions you were afraid to ask" + }, + { + "location": "/introduction/possible_silly_questions/#why-not-use-something-like-mapreduce", + "text": "We can refer to systems like map-reduce as distributed computing systems in which the reduce operation is based on distributed sorting. In this sense, they include Hadoop, and YT (YT is developed at Yandex for internal use). These systems aren't appropriate for online queries due to their high latency. In other words, they can't be used as the back-end for a web interface.\nThese types of systems aren't useful for real-time data updates.\nDistributed sorting isn't the best way to perform reduce operations if the result of the operation and all the intermediate results (if there are any) are located in the RAM of a single server, which is usually the case for online queries. In such a case, a hash table is the optimal way to perform reduce operations. A common approach to optimizing map-reduce tasks is pre-aggregation (partial reduce) using a hash table in RAM. The user performs this optimization manually.\nDistributed sorting is one of the main causes of reduced performance when running simple map-reduce tasks. Systems like map-reduce allow executing any code on the cluster. But a declarative query language is better suited to OLAP in order to run experiments quickly. For example, Hadoop has Hive and Pig. Also consider Cloudera Impala, Shark (outdated) for Spark, and Spark SQL, Presto, and Apache Drill. Performance when running such tasks is highly sub-optimal compared to specialized systems, but relatively high latency makes it unrealistic to use these systems as the backend for a web interface. YT allows storing groups of columns separately. But YT can't be considered a true column-based system because it doesn't have fixed-length data types (for efficiently storing numbers without extra \"garbage\"), and also due to its lack of a vector engine. Tasks are performed in YT using custom code in streaming mode, so they cannot be optimized enough (up to hundreds of millions of rows per second per server). \"Dynamic table sorting\" is under development in YT using MergeTree, strict value typing, and a query language similar to SQL. Dynamically sorted tables are not appropriate for OLAP tasks because the data is stored by row. The YT query language is still under development, so we can't yet rely on this functionality. YT developers are considering using dynamically sorted tables in OLTP and Key-Value scenarios.", + "title": "Why not use something like MapReduce?" + }, + { + "location": "/introduction/performance/", + "text": "Performance\n\n\nAccording to internal testing results, ClickHouse shows the best performance for comparable operating scenarios among systems of its class that were available for testing. This includes the highest throughput for long queries, and the lowest latency on short queries. Testing results are shown on a separate page.\n\n\nThroughput for a single large query\n\n\nThroughput can be measured in rows per second or in megabytes per second. If the data is placed in the page cache, a query that is not too complex is processed on modern hardware at a speed of approximately 2-10 GB/s of uncompressed data on a single server (for the simplest cases, the speed may reach 30 GB/s). If data is not placed in the page cache, the speed depends on the disk subsystem and the data compression rate. For example, if the disk subsystem allows reading data at 400 MB/s, and the data compression rate is 3, the speed will be around 1.2 GB/s. To get the speed in rows per second, divide the speed in bytes per second by the total size of the columns used in the query. For example, if 10 bytes of columns are extracted, the speed will be around 100-200 million rows per second.\n\n\nThe processing speed increases almost linearly for distributed processing, but only if the number of rows resulting from aggregation or sorting is not too large.\n\n\nLatency when processing short queries\n\n\nIf a query uses a primary key and does not select too many rows to process (hundreds of thousands), and does not use too many columns, we can expect less than 50 milliseconds of latency (single digits of milliseconds in the best case) if data is placed in the page cache. Otherwise, latency is calculated from the number of seeks. If you use rotating drives, for a system that is not overloaded, the latency is calculated by this formula: seek time (10 ms) * number of columns queried * number of data parts.\n\n\nThroughput when processing a large quantity of short queries\n\n\nUnder the same conditions, ClickHouse can handle several hundred queries per second on a single server (up to several thousand in the best case). Since this scenario is not typical for analytical DBMSs, we recommend expecting a maximum of 100 queries per second.\n\n\nPerformance when inserting data\n\n\nWe recommend inserting data in packets of at least 1000 rows, or no more than a single request per second. When inserting to a MergeTree table from a tab-separated dump, the insertion speed will be from 50 to 200 MB/s. If the inserted rows are around 1 Kb in size, the speed will be from 50,000 to 200,000 rows per second. If the rows are small, the performance will be higher in rows per second (on Banner System data -\n 500,000 rows per second; on Graphite data -\n 1,000,000 rows per second). To improve performance, you can make multiple INSERT queries in parallel, and performance will increase linearly.", + "title": "Performance" + }, + { + "location": "/introduction/performance/#performance", + "text": "According to internal testing results, ClickHouse shows the best performance for comparable operating scenarios among systems of its class that were available for testing. This includes the highest throughput for long queries, and the lowest latency on short queries. Testing results are shown on a separate page.", + "title": "Performance" + }, + { + "location": "/introduction/performance/#throughput-for-a-single-large-query", + "text": "Throughput can be measured in rows per second or in megabytes per second. If the data is placed in the page cache, a query that is not too complex is processed on modern hardware at a speed of approximately 2-10 GB/s of uncompressed data on a single server (for the simplest cases, the speed may reach 30 GB/s). If data is not placed in the page cache, the speed depends on the disk subsystem and the data compression rate. For example, if the disk subsystem allows reading data at 400 MB/s, and the data compression rate is 3, the speed will be around 1.2 GB/s. To get the speed in rows per second, divide the speed in bytes per second by the total size of the columns used in the query. For example, if 10 bytes of columns are extracted, the speed will be around 100-200 million rows per second. The processing speed increases almost linearly for distributed processing, but only if the number of rows resulting from aggregation or sorting is not too large.", + "title": "Throughput for a single large query" + }, + { + "location": "/introduction/performance/#latency-when-processing-short-queries", + "text": "If a query uses a primary key and does not select too many rows to process (hundreds of thousands), and does not use too many columns, we can expect less than 50 milliseconds of latency (single digits of milliseconds in the best case) if data is placed in the page cache. Otherwise, latency is calculated from the number of seeks. If you use rotating drives, for a system that is not overloaded, the latency is calculated by this formula: seek time (10 ms) * number of columns queried * number of data parts.", + "title": "Latency when processing short queries" + }, + { + "location": "/introduction/performance/#throughput-when-processing-a-large-quantity-of-short-queries", + "text": "Under the same conditions, ClickHouse can handle several hundred queries per second on a single server (up to several thousand in the best case). Since this scenario is not typical for analytical DBMSs, we recommend expecting a maximum of 100 queries per second.", + "title": "Throughput when processing a large quantity of short queries" + }, + { + "location": "/introduction/performance/#performance-when-inserting-data", + "text": "We recommend inserting data in packets of at least 1000 rows, or no more than a single request per second. When inserting to a MergeTree table from a tab-separated dump, the insertion speed will be from 50 to 200 MB/s. If the inserted rows are around 1 Kb in size, the speed will be from 50,000 to 200,000 rows per second. If the rows are small, the performance will be higher in rows per second (on Banner System data - 500,000 rows per second; on Graphite data - 1,000,000 rows per second). To improve performance, you can make multiple INSERT queries in parallel, and performance will increase linearly.", + "title": "Performance when inserting data" + }, + { + "location": "/getting_started/", + "text": "Getting started\n\n\nSystem requirements\n\n\nThis is not a cross-platform system. It requires Linux Ubuntu Precise (12.04) or newer, with x86_64 architecture and support for the SSE 4.2 instruction set.\nTo check for SSE 4.2:\n\n\ngrep -q sse4_2 /proc/cpuinfo \n \necho\n \nSSE 4.2 supported\n \n||\n \necho\n \nSSE 4.2 not supported\n\n\n\n\n\n\nWe recommend using Ubuntu Trusty, Ubuntu Xenial, or Ubuntu Precise.\nThe terminal must use UTF-8 encoding (the default in Ubuntu).\n\n\nInstallation\n\n\nFor testing and development, the system can be installed on a single server or on a desktop computer.\n\n\nInstalling from packages for Debian/Ubuntu\n\n\nIn \n/etc/apt/sources.list\n (or in a separate \n/etc/apt/sources.list.d/clickhouse.list\n file), add the repository:\n\n\ndeb http://repo.yandex.ru/clickhouse/deb/stable/ main/\n\n\n\n\n\nIf you want to use the most recent test version, replace 'stable' with 'testing'.\n\n\nThen run:\n\n\nsudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4 \n# optional\n\nsudo apt-get update\nsudo apt-get install clickhouse-client clickhouse-server\n\n\n\n\n\nYou can also download and install packages manually from here: \nhttps://repo.yandex.ru/clickhouse/deb/stable/main/\n.\n\n\nClickHouse contains access restriction settings. They are located in the 'users.xml' file (next to 'config.xml').\nBy default, access is allowed from anywhere for the 'default' user, without a password. See 'user/default/networks'.\nFor more information, see the section \"Configuration files\".\n\n\nInstalling from sources\n\n\nTo compile, follow the instructions: build.md\n\n\nYou can compile packages and install them.\nYou can also use programs without installing packages.\n\n\nClient: dbms/src/Client/\nServer: dbms/src/Server/\n\n\n\n\n\nFor the server, create a catalog with data, such as:\n\n\n/opt/clickhouse/data/default/\n/opt/clickhouse/metadata/default/\n\n\n\n\n\n(Configurable in the server config.)\nRun 'chown' for the desired user.\n\n\nNote the path to logs in the server config (src/dbms/src/Server/config.xml).\n\n\nOther installation methods\n\n\nDocker image: \nhttps://hub.docker.com/r/yandex/clickhouse-server/\n\n\nRPM packages for CentOS or RHEL: \nhttps://github.com/Altinity/clickhouse-rpm-install\n\n\nGentoo overlay: \nhttps://github.com/kmeaw/clickhouse-overlay\n\n\nLaunch\n\n\nTo start the server (as a daemon), run:\n\n\nsudo service clickhouse-server start\n\n\n\n\n\nSee the logs in the \n/var/log/clickhouse-server/ directory.\n\n\nIf the server doesn't start, check the configurations in the file \n/etc/clickhouse-server/config.xml.\n\n\nYou can also launch the server from the console:\n\n\nclickhouse-server --config-file\n=\n/etc/clickhouse-server/config.xml\n\n\n\n\n\nIn this case, the log will be printed to the console, which is convenient during development.\nIf the configuration file is in the current directory, you don't need to specify the '--config-file' parameter. By default, it uses './config.xml'.\n\n\nYou can use the command-line client to connect to the server:\n\n\nclickhouse-client\n\n\n\n\n\nThe default parameters indicate connecting with localhost:9000 on behalf of the user 'default' without a password.\nThe client can be used for connecting to a remote server. Example:\n\n\nclickhouse-client --host\n=\nexample.com\n\n\n\n\n\nFor more information, see the section \"Command-line client\".\n\n\nChecking the system:\n\n\nmilovidov@hostname:~/work/metrica/src/dbms/src/Client$ ./clickhouse-client\nClickHouse client version \n0\n.0.18749.\nConnecting to localhost:9000.\nConnected to ClickHouse server version \n0\n.0.18749.\n\n:\n)\n SELECT \n1\n\n\nSELECT \n1\n\n\n\u250c\u25001\u2500\u2510\n\u2502 \n1\n \u2502\n\u2514\u2500\u2500\u2500\u2518\n\n\n1\n rows in set. Elapsed: \n0\n.003 sec.\n\n:\n)\n\n\n\n\n\n\nCongratulations, the system works!\n\n\nTo continue experimenting, you can try to download from the test data sets.", + "title": "Deploying and running" + }, + { + "location": "/getting_started/#getting-started", + "text": "", + "title": "Getting started" + }, + { + "location": "/getting_started/#system-requirements", + "text": "This is not a cross-platform system. It requires Linux Ubuntu Precise (12.04) or newer, with x86_64 architecture and support for the SSE 4.2 instruction set.\nTo check for SSE 4.2: grep -q sse4_2 /proc/cpuinfo echo SSE 4.2 supported || echo SSE 4.2 not supported We recommend using Ubuntu Trusty, Ubuntu Xenial, or Ubuntu Precise.\nThe terminal must use UTF-8 encoding (the default in Ubuntu).", + "title": "System requirements" + }, + { + "location": "/getting_started/#installation", + "text": "For testing and development, the system can be installed on a single server or on a desktop computer.", + "title": "Installation" + }, + { + "location": "/getting_started/#installing-from-packages-for-debianubuntu", + "text": "In /etc/apt/sources.list (or in a separate /etc/apt/sources.list.d/clickhouse.list file), add the repository: deb http://repo.yandex.ru/clickhouse/deb/stable/ main/ If you want to use the most recent test version, replace 'stable' with 'testing'. Then run: sudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4 # optional \nsudo apt-get update\nsudo apt-get install clickhouse-client clickhouse-server You can also download and install packages manually from here: https://repo.yandex.ru/clickhouse/deb/stable/main/ . ClickHouse contains access restriction settings. They are located in the 'users.xml' file (next to 'config.xml').\nBy default, access is allowed from anywhere for the 'default' user, without a password. See 'user/default/networks'.\nFor more information, see the section \"Configuration files\".", + "title": "Installing from packages for Debian/Ubuntu" + }, + { + "location": "/getting_started/#installing-from-sources", + "text": "To compile, follow the instructions: build.md You can compile packages and install them.\nYou can also use programs without installing packages. Client: dbms/src/Client/\nServer: dbms/src/Server/ For the server, create a catalog with data, such as: /opt/clickhouse/data/default/\n/opt/clickhouse/metadata/default/ (Configurable in the server config.)\nRun 'chown' for the desired user. Note the path to logs in the server config (src/dbms/src/Server/config.xml).", + "title": "Installing from sources" + }, + { + "location": "/getting_started/#other-installation-methods", + "text": "Docker image: https://hub.docker.com/r/yandex/clickhouse-server/ RPM packages for CentOS or RHEL: https://github.com/Altinity/clickhouse-rpm-install Gentoo overlay: https://github.com/kmeaw/clickhouse-overlay", + "title": "Other installation methods" + }, + { + "location": "/getting_started/#launch", + "text": "To start the server (as a daemon), run: sudo service clickhouse-server start See the logs in the /var/log/clickhouse-server/ directory. If the server doesn't start, check the configurations in the file /etc/clickhouse-server/config.xml. You can also launch the server from the console: clickhouse-server --config-file = /etc/clickhouse-server/config.xml In this case, the log will be printed to the console, which is convenient during development.\nIf the configuration file is in the current directory, you don't need to specify the '--config-file' parameter. By default, it uses './config.xml'. You can use the command-line client to connect to the server: clickhouse-client The default parameters indicate connecting with localhost:9000 on behalf of the user 'default' without a password.\nThe client can be used for connecting to a remote server. Example: clickhouse-client --host = example.com For more information, see the section \"Command-line client\". Checking the system: milovidov@hostname:~/work/metrica/src/dbms/src/Client$ ./clickhouse-client\nClickHouse client version 0 .0.18749.\nConnecting to localhost:9000.\nConnected to ClickHouse server version 0 .0.18749.\n\n: ) SELECT 1 \n\nSELECT 1 \n\n\u250c\u25001\u2500\u2510\n\u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2518 1 rows in set. Elapsed: 0 .003 sec.\n\n: ) Congratulations, the system works! To continue experimenting, you can try to download from the test data sets.", + "title": "Launch" + }, + { + "location": "/getting_started/example_datasets/ontime/", + "text": "OnTime\n\n\nThis performance test was created by Vadim Tkachenko. See:\n\n\n\n\nhttps://www.percona.com/blog/2009/10/02/analyzing-air-traffic-performance-with-infobright-and-monetdb/\n\n\nhttps://www.percona.com/blog/2009/10/26/air-traffic-queries-in-luciddb/\n\n\nhttps://www.percona.com/blog/2009/11/02/air-traffic-queries-in-infinidb-early-alpha/\n\n\nhttps://www.percona.com/blog/2014/04/21/using-apache-hadoop-and-impala-together-with-mysql-for-data-analysis/\n\n\nhttps://www.percona.com/blog/2016/01/07/apache-spark-with-air-ontime-performance-data/\n\n\nhttp://nickmakos.blogspot.ru/2012/08/analyzing-air-traffic-performance-with.html\n\n\n\n\nDownloading data:\n\n\nfor\n s in \n`\nseq \n1987\n \n2017\n`\n\n\ndo\n\n\nfor\n m in \n`\nseq \n1\n \n12\n`\n\n\ndo\n\nwget http://transtats.bts.gov/PREZIP/On_Time_On_Time_Performance_\n${\ns\n}\n_\n${\nm\n}\n.zip\n\ndone\n\n\ndone\n\n\n\n\n\n\n(from \nhttps://github.com/Percona-Lab/ontime-airline-performance/blob/master/download.sh\n )\n\n\nCreating a table:\n\n\nCREATE\n \nTABLE\n \n`\nontime\n`\n \n(\n\n \n`\nYear\n`\n \nUInt16\n,\n\n \n`\nQuarter\n`\n \nUInt8\n,\n\n \n`\nMonth\n`\n \nUInt8\n,\n\n \n`\nDayofMonth\n`\n \nUInt8\n,\n\n \n`\nDayOfWeek\n`\n \nUInt8\n,\n\n \n`\nFlightDate\n`\n \nDate\n,\n\n \n`\nUniqueCarrier\n`\n \nFixedString\n(\n7\n),\n\n \n`\nAirlineID\n`\n \nInt32\n,\n\n \n`\nCarrier\n`\n \nFixedString\n(\n2\n),\n\n \n`\nTailNum\n`\n \nString\n,\n\n \n`\nFlightNum\n`\n \nString\n,\n\n \n`\nOriginAirportID\n`\n \nInt32\n,\n\n \n`\nOriginAirportSeqID\n`\n \nInt32\n,\n\n \n`\nOriginCityMarketID\n`\n \nInt32\n,\n\n \n`\nOrigin\n`\n \nFixedString\n(\n5\n),\n\n \n`\nOriginCityName\n`\n \nString\n,\n\n \n`\nOriginState\n`\n \nFixedString\n(\n2\n),\n\n \n`\nOriginStateFips\n`\n \nString\n,\n\n \n`\nOriginStateName\n`\n \nString\n,\n\n \n`\nOriginWac\n`\n \nInt32\n,\n\n \n`\nDestAirportID\n`\n \nInt32\n,\n\n \n`\nDestAirportSeqID\n`\n \nInt32\n,\n\n \n`\nDestCityMarketID\n`\n \nInt32\n,\n\n \n`\nDest\n`\n \nFixedString\n(\n5\n),\n\n \n`\nDestCityName\n`\n \nString\n,\n\n \n`\nDestState\n`\n \nFixedString\n(\n2\n),\n\n \n`\nDestStateFips\n`\n \nString\n,\n\n \n`\nDestStateName\n`\n \nString\n,\n\n \n`\nDestWac\n`\n \nInt32\n,\n\n \n`\nCRSDepTime\n`\n \nInt32\n,\n\n \n`\nDepTime\n`\n \nInt32\n,\n\n \n`\nDepDelay\n`\n \nInt32\n,\n\n \n`\nDepDelayMinutes\n`\n \nInt32\n,\n\n \n`\nDepDel15\n`\n \nInt32\n,\n\n \n`\nDepartureDelayGroups\n`\n \nString\n,\n\n \n`\nDepTimeBlk\n`\n \nString\n,\n\n \n`\nTaxiOut\n`\n \nInt32\n,\n\n \n`\nWheelsOff\n`\n \nInt32\n,\n\n \n`\nWheelsOn\n`\n \nInt32\n,\n\n \n`\nTaxiIn\n`\n \nInt32\n,\n\n \n`\nCRSArrTime\n`\n \nInt32\n,\n\n \n`\nArrTime\n`\n \nInt32\n,\n\n \n`\nArrDelay\n`\n \nInt32\n,\n\n \n`\nArrDelayMinutes\n`\n \nInt32\n,\n\n \n`\nArrDel15\n`\n \nInt32\n,\n\n \n`\nArrivalDelayGroups\n`\n \nInt32\n,\n\n \n`\nArrTimeBlk\n`\n \nString\n,\n\n \n`\nCancelled\n`\n \nUInt8\n,\n\n \n`\nCancellationCode\n`\n \nFixedString\n(\n1\n),\n\n \n`\nDiverted\n`\n \nUInt8\n,\n\n \n`\nCRSElapsedTime\n`\n \nInt32\n,\n\n \n`\nActualElapsedTime\n`\n \nInt32\n,\n\n \n`\nAirTime\n`\n \nInt32\n,\n\n \n`\nFlights\n`\n \nInt32\n,\n\n \n`\nDistance\n`\n \nInt32\n,\n\n \n`\nDistanceGroup\n`\n \nUInt8\n,\n\n \n`\nCarrierDelay\n`\n \nInt32\n,\n\n \n`\nWeatherDelay\n`\n \nInt32\n,\n\n \n`\nNASDelay\n`\n \nInt32\n,\n\n \n`\nSecurityDelay\n`\n \nInt32\n,\n\n \n`\nLateAircraftDelay\n`\n \nInt32\n,\n\n \n`\nFirstDepTime\n`\n \nString\n,\n\n \n`\nTotalAddGTime\n`\n \nString\n,\n\n \n`\nLongestAddGTime\n`\n \nString\n,\n\n \n`\nDivAirportLandings\n`\n \nString\n,\n\n \n`\nDivReachedDest\n`\n \nString\n,\n\n \n`\nDivActualElapsedTime\n`\n \nString\n,\n\n \n`\nDivArrDelay\n`\n \nString\n,\n\n \n`\nDivDistance\n`\n \nString\n,\n\n \n`\nDiv1Airport\n`\n \nString\n,\n\n \n`\nDiv1AirportID\n`\n \nInt32\n,\n\n \n`\nDiv1AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv1WheelsOn\n`\n \nString\n,\n\n \n`\nDiv1TotalGTime\n`\n \nString\n,\n\n \n`\nDiv1LongestGTime\n`\n \nString\n,\n\n \n`\nDiv1WheelsOff\n`\n \nString\n,\n\n \n`\nDiv1TailNum\n`\n \nString\n,\n\n \n`\nDiv2Airport\n`\n \nString\n,\n\n \n`\nDiv2AirportID\n`\n \nInt32\n,\n\n \n`\nDiv2AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv2WheelsOn\n`\n \nString\n,\n\n \n`\nDiv2TotalGTime\n`\n \nString\n,\n\n \n`\nDiv2LongestGTime\n`\n \nString\n,\n\n \n`\nDiv2WheelsOff\n`\n \nString\n,\n\n \n`\nDiv2TailNum\n`\n \nString\n,\n\n \n`\nDiv3Airport\n`\n \nString\n,\n\n \n`\nDiv3AirportID\n`\n \nInt32\n,\n\n \n`\nDiv3AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv3WheelsOn\n`\n \nString\n,\n\n \n`\nDiv3TotalGTime\n`\n \nString\n,\n\n \n`\nDiv3LongestGTime\n`\n \nString\n,\n\n \n`\nDiv3WheelsOff\n`\n \nString\n,\n\n \n`\nDiv3TailNum\n`\n \nString\n,\n\n \n`\nDiv4Airport\n`\n \nString\n,\n\n \n`\nDiv4AirportID\n`\n \nInt32\n,\n\n \n`\nDiv4AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv4WheelsOn\n`\n \nString\n,\n\n \n`\nDiv4TotalGTime\n`\n \nString\n,\n\n \n`\nDiv4LongestGTime\n`\n \nString\n,\n\n \n`\nDiv4WheelsOff\n`\n \nString\n,\n\n \n`\nDiv4TailNum\n`\n \nString\n,\n\n \n`\nDiv5Airport\n`\n \nString\n,\n\n \n`\nDiv5AirportID\n`\n \nInt32\n,\n\n \n`\nDiv5AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv5WheelsOn\n`\n \nString\n,\n\n \n`\nDiv5TotalGTime\n`\n \nString\n,\n\n \n`\nDiv5LongestGTime\n`\n \nString\n,\n\n \n`\nDiv5WheelsOff\n`\n \nString\n,\n\n \n`\nDiv5TailNum\n`\n \nString\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nFlightDate\n,\n \n(\nYear\n,\n \nFlightDate\n),\n \n8192\n)\n\n\n\n\n\n\nLoading data:\n\n\nfor\n i in *.zip\n;\n \ndo\n \necho\n \n$i\n;\n unzip -cq \n$i\n \n*.csv\n \n|\n sed \ns/\\.00//g\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO ontime FORMAT CSVWithNames\n;\n \ndone\n\n\n\n\n\n\nQueries:\n\n\nQ0.\n\n\nselect\n \navg\n(\nc1\n)\n \nfrom\n \n(\nselect\n \nYear\n,\n \nMonth\n,\n \ncount\n(\n*\n)\n \nas\n \nc1\n \nfrom\n \nontime\n \ngroup\n \nby\n \nYear\n,\n \nMonth\n);\n\n\n\n\n\n\nQ1. The number of flights per day from the year 2000 to 2008\n\n\nSELECT\n \nDayOfWeek\n,\n \ncount\n(\n*\n)\n \nAS\n \nc\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nDayOfWeek\n \nORDER\n \nBY\n \nc\n \nDESC\n;\n\n\n\n\n\n\nQ2. The number of flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008\n\n\nSELECT\n \nDayOfWeek\n,\n \ncount\n(\n*\n)\n \nAS\n \nc\n \nFROM\n \nontime\n \nWHERE\n \nDepDelay\n10\n \nAND\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nDayOfWeek\n \nORDER\n \nBY\n \nc\n \nDESC\n\n\n\n\n\n\nQ3. The number of delays by airport for 2000-2008\n\n\nSELECT\n \nOrigin\n,\n \ncount\n(\n*\n)\n \nAS\n \nc\n \nFROM\n \nontime\n \nWHERE\n \nDepDelay\n10\n \nAND\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nOrigin\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n10\n\n\n\n\n\n\nQ4. The number of delays by carrier for 2007\n\n\nSELECT\n \nCarrier\n,\n \ncount\n(\n*\n)\n \nFROM\n \nontime\n \nWHERE\n \nDepDelay\n10\n \nAND\n \nYear\n \n=\n \n2007\n \nGROUP\n \nBY\n \nCarrier\n \nORDER\n \nBY\n \ncount\n(\n*\n)\n \nDESC\n\n\n\n\n\n\nQ5. The percentage of delays by carrier for 2007\n\n\nSELECT\n \nCarrier\n,\n \nc\n,\n \nc2\n,\n \nc\n*\n1000\n/\nc2\n \nas\n \nc3\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc\n\n \nFROM\n \nontime\n\n \nWHERE\n \nDepDelay\n10\n\n \nAND\n \nYear\n=\n2007\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n\n\nANY\n \nINNER\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc2\n\n \nFROM\n \nontime\n\n \nWHERE\n \nYear\n=\n2007\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n \nUSING\n \nCarrier\n\n\nORDER\n \nBY\n \nc3\n \nDESC\n;\n\n\n\n\n\n\nBetter version of the same query:\n\n\nSELECT\n \nCarrier\n,\n \navg\n(\nDepDelay\n \n \n10\n)\n \n*\n \n1000\n \nAS\n \nc3\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2007\n \nGROUP\n \nBY\n \nCarrier\n \nORDER\n \nBY\n \nCarrier\n\n\n\n\n\n\nQ6. The previous request for a broader range of years, 2000-2008\n\n\nSELECT\n \nCarrier\n,\n \nc\n,\n \nc2\n,\n \nc\n*\n1000\n/\nc2\n \nas\n \nc3\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc\n\n \nFROM\n \nontime\n\n \nWHERE\n \nDepDelay\n10\n\n \nAND\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n\n\nANY\n \nINNER\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc2\n\n \nFROM\n \nontime\n\n \nWHERE\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n \nUSING\n \nCarrier\n\n\nORDER\n \nBY\n \nc3\n \nDESC\n;\n\n\n\n\n\n\nBetter version of the same query:\n\n\nSELECT\n \nCarrier\n,\n \navg\n(\nDepDelay\n \n \n10\n)\n \n*\n \n1000\n \nAS\n \nc3\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nCarrier\n \nORDER\n \nBY\n \nCarrier\n\n\n\n\n\n\nQ7. Percentage of flights delayed for more than 10 minutes, by year\n\n\nSELECT\n \nYear\n,\n \nc1\n/\nc2\n\n\nFROM\n\n\n(\n\n \nselect\n\n \nYear\n,\n\n \ncount\n(\n*\n)\n*\n1000\n \nas\n \nc1\n\n \nfrom\n \nontime\n\n \nWHERE\n \nDepDelay\n10\n\n \nGROUP\n \nBY\n \nYear\n\n\n)\n\n\nANY\n \nINNER\n \nJOIN\n\n\n(\n\n \nselect\n\n \nYear\n,\n\n \ncount\n(\n*\n)\n \nas\n \nc2\n\n \nfrom\n \nontime\n\n \nGROUP\n \nBY\n \nYear\n\n\n)\n \nUSING\n \n(\nYear\n)\n\n\nORDER\n \nBY\n \nYear\n\n\n\n\n\n\nBetter version of the same query:\n\n\nSELECT\n \nYear\n,\n \navg\n(\nDepDelay\n \n \n10\n)\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nYear\n \nORDER\n \nBY\n \nYear\n\n\n\n\n\n\nQ8. The most popular destinations by the number of directly connected cities for various year ranges\n\n\nSELECT\n \nDestCityName\n,\n \nuniqExact\n(\nOriginCityName\n)\n \nAS\n \nu\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2000\n \nand\n \nYear\n \n=\n \n2010\n \nGROUP\n \nBY\n \nDestCityName\n \nORDER\n \nBY\n \nu\n \nDESC\n \nLIMIT\n \n10\n;\n\n\n\n\n\n\nQ9.\n\n\nselect\n \nYear\n,\n \ncount\n(\n*\n)\n \nas\n \nc1\n \nfrom\n \nontime\n \ngroup\n \nby\n \nYear\n;\n\n\n\n\n\n\nQ10.\n\n\nselect\n\n \nmin\n(\nYear\n),\n \nmax\n(\nYear\n),\n \nCarrier\n,\n \ncount\n(\n*\n)\n \nas\n \ncnt\n,\n\n \nsum\n(\nArrDelayMinutes\n30\n)\n \nas\n \nflights_delayed\n,\n\n \nround\n(\nsum\n(\nArrDelayMinutes\n30\n)\n/\ncount\n(\n*\n),\n2\n)\n \nas\n \nrate\n\n\nFROM\n \nontime\n\n\nWHERE\n\n \nDayOfWeek\n \nnot\n \nin\n \n(\n6\n,\n7\n)\n \nand\n \nOriginState\n \nnot\n \nin\n \n(\nAK\n,\n \nHI\n,\n \nPR\n,\n \nVI\n)\n\n \nand\n \nDestState\n \nnot\n \nin\n \n(\nAK\n,\n \nHI\n,\n \nPR\n,\n \nVI\n)\n\n \nand\n \nFlightDate\n \n \n2010-01-01\n\n\nGROUP\n \nby\n \nCarrier\n\n\nHAVING\n \ncnt\n \n \n100000\n \nand\n \nmax\n(\nYear\n)\n \n \n1990\n\n\nORDER\n \nby\n \nrate\n \nDESC\n\n\nLIMIT\n \n1000\n;\n\n\n\n\n\n\nBonus:\n\n\nSELECT\n \navg\n(\ncnt\n)\n \nFROM\n \n(\nSELECT\n \nYear\n,\nMonth\n,\ncount\n(\n*\n)\n \nAS\n \ncnt\n \nFROM\n \nontime\n \nWHERE\n \nDepDel15\n=\n1\n \nGROUP\n \nBY\n \nYear\n,\nMonth\n)\n\n\n\nselect\n \navg\n(\nc1\n)\n \nfrom\n \n(\nselect\n \nYear\n,\nMonth\n,\ncount\n(\n*\n)\n \nas\n \nc1\n \nfrom\n \nontime\n \ngroup\n \nby\n \nYear\n,\nMonth\n)\n\n\n\nSELECT\n \nDestCityName\n,\n \nuniqExact\n(\nOriginCityName\n)\n \nAS\n \nu\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nDestCityName\n \nORDER\n \nBY\n \nu\n \nDESC\n \nLIMIT\n \n10\n;\n\n\n\nSELECT\n \nOriginCityName\n,\n \nDestCityName\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nOriginCityName\n,\n \nDestCityName\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n10\n;\n\n\n\nSELECT\n \nOriginCityName\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nOriginCityName\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n10\n;", + "title": "OnTime" + }, + { + "location": "/getting_started/example_datasets/ontime/#ontime", + "text": "This performance test was created by Vadim Tkachenko. See: https://www.percona.com/blog/2009/10/02/analyzing-air-traffic-performance-with-infobright-and-monetdb/ https://www.percona.com/blog/2009/10/26/air-traffic-queries-in-luciddb/ https://www.percona.com/blog/2009/11/02/air-traffic-queries-in-infinidb-early-alpha/ https://www.percona.com/blog/2014/04/21/using-apache-hadoop-and-impala-together-with-mysql-for-data-analysis/ https://www.percona.com/blog/2016/01/07/apache-spark-with-air-ontime-performance-data/ http://nickmakos.blogspot.ru/2012/08/analyzing-air-traffic-performance-with.html Downloading data: for s in ` seq 1987 2017 ` do for m in ` seq 1 12 ` do \nwget http://transtats.bts.gov/PREZIP/On_Time_On_Time_Performance_ ${ s } _ ${ m } .zip done done (from https://github.com/Percona-Lab/ontime-airline-performance/blob/master/download.sh ) Creating a table: CREATE TABLE ` ontime ` ( \n ` Year ` UInt16 , \n ` Quarter ` UInt8 , \n ` Month ` UInt8 , \n ` DayofMonth ` UInt8 , \n ` DayOfWeek ` UInt8 , \n ` FlightDate ` Date , \n ` UniqueCarrier ` FixedString ( 7 ), \n ` AirlineID ` Int32 , \n ` Carrier ` FixedString ( 2 ), \n ` TailNum ` String , \n ` FlightNum ` String , \n ` OriginAirportID ` Int32 , \n ` OriginAirportSeqID ` Int32 , \n ` OriginCityMarketID ` Int32 , \n ` Origin ` FixedString ( 5 ), \n ` OriginCityName ` String , \n ` OriginState ` FixedString ( 2 ), \n ` OriginStateFips ` String , \n ` OriginStateName ` String , \n ` OriginWac ` Int32 , \n ` DestAirportID ` Int32 , \n ` DestAirportSeqID ` Int32 , \n ` DestCityMarketID ` Int32 , \n ` Dest ` FixedString ( 5 ), \n ` DestCityName ` String , \n ` DestState ` FixedString ( 2 ), \n ` DestStateFips ` String , \n ` DestStateName ` String , \n ` DestWac ` Int32 , \n ` CRSDepTime ` Int32 , \n ` DepTime ` Int32 , \n ` DepDelay ` Int32 , \n ` DepDelayMinutes ` Int32 , \n ` DepDel15 ` Int32 , \n ` DepartureDelayGroups ` String , \n ` DepTimeBlk ` String , \n ` TaxiOut ` Int32 , \n ` WheelsOff ` Int32 , \n ` WheelsOn ` Int32 , \n ` TaxiIn ` Int32 , \n ` CRSArrTime ` Int32 , \n ` ArrTime ` Int32 , \n ` ArrDelay ` Int32 , \n ` ArrDelayMinutes ` Int32 , \n ` ArrDel15 ` Int32 , \n ` ArrivalDelayGroups ` Int32 , \n ` ArrTimeBlk ` String , \n ` Cancelled ` UInt8 , \n ` CancellationCode ` FixedString ( 1 ), \n ` Diverted ` UInt8 , \n ` CRSElapsedTime ` Int32 , \n ` ActualElapsedTime ` Int32 , \n ` AirTime ` Int32 , \n ` Flights ` Int32 , \n ` Distance ` Int32 , \n ` DistanceGroup ` UInt8 , \n ` CarrierDelay ` Int32 , \n ` WeatherDelay ` Int32 , \n ` NASDelay ` Int32 , \n ` SecurityDelay ` Int32 , \n ` LateAircraftDelay ` Int32 , \n ` FirstDepTime ` String , \n ` TotalAddGTime ` String , \n ` LongestAddGTime ` String , \n ` DivAirportLandings ` String , \n ` DivReachedDest ` String , \n ` DivActualElapsedTime ` String , \n ` DivArrDelay ` String , \n ` DivDistance ` String , \n ` Div1Airport ` String , \n ` Div1AirportID ` Int32 , \n ` Div1AirportSeqID ` Int32 , \n ` Div1WheelsOn ` String , \n ` Div1TotalGTime ` String , \n ` Div1LongestGTime ` String , \n ` Div1WheelsOff ` String , \n ` Div1TailNum ` String , \n ` Div2Airport ` String , \n ` Div2AirportID ` Int32 , \n ` Div2AirportSeqID ` Int32 , \n ` Div2WheelsOn ` String , \n ` Div2TotalGTime ` String , \n ` Div2LongestGTime ` String , \n ` Div2WheelsOff ` String , \n ` Div2TailNum ` String , \n ` Div3Airport ` String , \n ` Div3AirportID ` Int32 , \n ` Div3AirportSeqID ` Int32 , \n ` Div3WheelsOn ` String , \n ` Div3TotalGTime ` String , \n ` Div3LongestGTime ` String , \n ` Div3WheelsOff ` String , \n ` Div3TailNum ` String , \n ` Div4Airport ` String , \n ` Div4AirportID ` Int32 , \n ` Div4AirportSeqID ` Int32 , \n ` Div4WheelsOn ` String , \n ` Div4TotalGTime ` String , \n ` Div4LongestGTime ` String , \n ` Div4WheelsOff ` String , \n ` Div4TailNum ` String , \n ` Div5Airport ` String , \n ` Div5AirportID ` Int32 , \n ` Div5AirportSeqID ` Int32 , \n ` Div5WheelsOn ` String , \n ` Div5TotalGTime ` String , \n ` Div5LongestGTime ` String , \n ` Div5WheelsOff ` String , \n ` Div5TailNum ` String ) ENGINE = MergeTree ( FlightDate , ( Year , FlightDate ), 8192 ) Loading data: for i in *.zip ; do echo $i ; unzip -cq $i *.csv | sed s/\\.00//g | clickhouse-client --host = example-perftest01j --query = INSERT INTO ontime FORMAT CSVWithNames ; done Queries: Q0. select avg ( c1 ) from ( select Year , Month , count ( * ) as c1 from ontime group by Year , Month ); Q1. The number of flights per day from the year 2000 to 2008 SELECT DayOfWeek , count ( * ) AS c FROM ontime WHERE Year = 2000 AND Year = 2008 GROUP BY DayOfWeek ORDER BY c DESC ; Q2. The number of flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008 SELECT DayOfWeek , count ( * ) AS c FROM ontime WHERE DepDelay 10 AND Year = 2000 AND Year = 2008 GROUP BY DayOfWeek ORDER BY c DESC Q3. The number of delays by airport for 2000-2008 SELECT Origin , count ( * ) AS c FROM ontime WHERE DepDelay 10 AND Year = 2000 AND Year = 2008 GROUP BY Origin ORDER BY c DESC LIMIT 10 Q4. The number of delays by carrier for 2007 SELECT Carrier , count ( * ) FROM ontime WHERE DepDelay 10 AND Year = 2007 GROUP BY Carrier ORDER BY count ( * ) DESC Q5. The percentage of delays by carrier for 2007 SELECT Carrier , c , c2 , c * 1000 / c2 as c3 FROM ( \n SELECT \n Carrier , \n count ( * ) AS c \n FROM ontime \n WHERE DepDelay 10 \n AND Year = 2007 \n GROUP BY Carrier ) ANY INNER JOIN ( \n SELECT \n Carrier , \n count ( * ) AS c2 \n FROM ontime \n WHERE Year = 2007 \n GROUP BY Carrier ) USING Carrier ORDER BY c3 DESC ; Better version of the same query: SELECT Carrier , avg ( DepDelay 10 ) * 1000 AS c3 FROM ontime WHERE Year = 2007 GROUP BY Carrier ORDER BY Carrier Q6. The previous request for a broader range of years, 2000-2008 SELECT Carrier , c , c2 , c * 1000 / c2 as c3 FROM ( \n SELECT \n Carrier , \n count ( * ) AS c \n FROM ontime \n WHERE DepDelay 10 \n AND Year = 2000 AND Year = 2008 \n GROUP BY Carrier ) ANY INNER JOIN ( \n SELECT \n Carrier , \n count ( * ) AS c2 \n FROM ontime \n WHERE Year = 2000 AND Year = 2008 \n GROUP BY Carrier ) USING Carrier ORDER BY c3 DESC ; Better version of the same query: SELECT Carrier , avg ( DepDelay 10 ) * 1000 AS c3 FROM ontime WHERE Year = 2000 AND Year = 2008 GROUP BY Carrier ORDER BY Carrier Q7. Percentage of flights delayed for more than 10 minutes, by year SELECT Year , c1 / c2 FROM ( \n select \n Year , \n count ( * ) * 1000 as c1 \n from ontime \n WHERE DepDelay 10 \n GROUP BY Year ) ANY INNER JOIN ( \n select \n Year , \n count ( * ) as c2 \n from ontime \n GROUP BY Year ) USING ( Year ) ORDER BY Year Better version of the same query: SELECT Year , avg ( DepDelay 10 ) FROM ontime GROUP BY Year ORDER BY Year Q8. The most popular destinations by the number of directly connected cities for various year ranges SELECT DestCityName , uniqExact ( OriginCityName ) AS u FROM ontime WHERE Year = 2000 and Year = 2010 GROUP BY DestCityName ORDER BY u DESC LIMIT 10 ; Q9. select Year , count ( * ) as c1 from ontime group by Year ; Q10. select \n min ( Year ), max ( Year ), Carrier , count ( * ) as cnt , \n sum ( ArrDelayMinutes 30 ) as flights_delayed , \n round ( sum ( ArrDelayMinutes 30 ) / count ( * ), 2 ) as rate FROM ontime WHERE \n DayOfWeek not in ( 6 , 7 ) and OriginState not in ( AK , HI , PR , VI ) \n and DestState not in ( AK , HI , PR , VI ) \n and FlightDate 2010-01-01 GROUP by Carrier HAVING cnt 100000 and max ( Year ) 1990 ORDER by rate DESC LIMIT 1000 ; Bonus: SELECT avg ( cnt ) FROM ( SELECT Year , Month , count ( * ) AS cnt FROM ontime WHERE DepDel15 = 1 GROUP BY Year , Month ) select avg ( c1 ) from ( select Year , Month , count ( * ) as c1 from ontime group by Year , Month ) SELECT DestCityName , uniqExact ( OriginCityName ) AS u FROM ontime GROUP BY DestCityName ORDER BY u DESC LIMIT 10 ; SELECT OriginCityName , DestCityName , count () AS c FROM ontime GROUP BY OriginCityName , DestCityName ORDER BY c DESC LIMIT 10 ; SELECT OriginCityName , count () AS c FROM ontime GROUP BY OriginCityName ORDER BY c DESC LIMIT 10 ;", + "title": "OnTime" + }, + { + "location": "/getting_started/example_datasets/nyc_taxi/", + "text": "New York Taxi data\n\n\nHow to import the raw data\n\n\nSee \nhttps://github.com/toddwschneider/nyc-taxi-data\n and \nhttp://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html\n for the description of the dataset and instructions for downloading.\n\n\nDownloading will result in about 227 GB of uncompressed data in CSV files. The download takes about an hour over a 1 Gbit connection (parallel downloading from s3.amazonaws.com recovers at least half of a 1 Gbit channel).\nSome of the files might not download fully. Check the file sizes and re-download any that seem doubtful.\n\n\nSome of the files might contain invalid rows. You can fix them as follows:\n\n\nsed -E \n/(.*,){18,}/d\n data/yellow_tripdata_2010-02.csv \n data/yellow_tripdata_2010-02.csv_\nsed -E \n/(.*,){18,}/d\n data/yellow_tripdata_2010-03.csv \n data/yellow_tripdata_2010-03.csv_\nmv data/yellow_tripdata_2010-02.csv_ data/yellow_tripdata_2010-02.csv\nmv data/yellow_tripdata_2010-03.csv_ data/yellow_tripdata_2010-03.csv\n\n\n\n\n\nThen the data must be pre-processed in PostgreSQL. This will create selections of points in the polygons (to match points on the map with the boroughs of New York City) and combine all the data into a single denormalized flat table by using a JOIN. To do this, you will need to install PostgreSQL with PostGIS support.\n\n\nBe careful when running \ninitialize_database.sh\n and manually re-check that all the tables were created correctly.\n\n\nIt takes about 20-30 minutes to process each month's worth of data in PostgreSQL, for a total of about 48 hours.\n\n\nYou can check the number of downloaded rows as follows:\n\n\ntime psql nyc-taxi-data -c \nSELECT count(*) FROM trips;\n\n## count\n 1298979494\n(1 row)\n\nreal 7m9.164s\n\n\n\n\n\n(This is slightly more than 1.1 billion rows reported by Mark Litwintschik in a series of blog posts.)\n\n\nThe data in PostgreSQL uses 370 GB of space.\n\n\nExporting the data from PostgreSQL:\n\n\nCOPY\n\n\n(\n\n \nSELECT\n \ntrips\n.\nid\n,\n\n \ntrips\n.\nvendor_id\n,\n\n \ntrips\n.\npickup_datetime\n,\n\n \ntrips\n.\ndropoff_datetime\n,\n\n \ntrips\n.\nstore_and_fwd_flag\n,\n\n \ntrips\n.\nrate_code_id\n,\n\n \ntrips\n.\npickup_longitude\n,\n\n \ntrips\n.\npickup_latitude\n,\n\n \ntrips\n.\ndropoff_longitude\n,\n\n \ntrips\n.\ndropoff_latitude\n,\n\n \ntrips\n.\npassenger_count\n,\n\n \ntrips\n.\ntrip_distance\n,\n\n \ntrips\n.\nfare_amount\n,\n\n \ntrips\n.\nextra\n,\n\n \ntrips\n.\nmta_tax\n,\n\n \ntrips\n.\ntip_amount\n,\n\n \ntrips\n.\ntolls_amount\n,\n\n \ntrips\n.\nehail_fee\n,\n\n \ntrips\n.\nimprovement_surcharge\n,\n\n \ntrips\n.\ntotal_amount\n,\n\n \ntrips\n.\npayment_type\n,\n\n \ntrips\n.\ntrip_type\n,\n\n \ntrips\n.\npickup\n,\n\n \ntrips\n.\ndropoff\n,\n\n\n \ncab_types\n.\ntype\n \ncab_type\n,\n\n\n \nweather\n.\nprecipitation_tenths_of_mm\n \nrain\n,\n\n \nweather\n.\nsnow_depth_mm\n,\n\n \nweather\n.\nsnowfall_mm\n,\n\n \nweather\n.\nmax_temperature_tenths_degrees_celsius\n \nmax_temp\n,\n\n \nweather\n.\nmin_temperature_tenths_degrees_celsius\n \nmin_temp\n,\n\n \nweather\n.\naverage_wind_speed_tenths_of_meters_per_second\n \nwind\n,\n\n\n \npick_up\n.\ngid\n \npickup_nyct2010_gid\n,\n\n \npick_up\n.\nctlabel\n \npickup_ctlabel\n,\n\n \npick_up\n.\nborocode\n \npickup_borocode\n,\n\n \npick_up\n.\nboroname\n \npickup_boroname\n,\n\n \npick_up\n.\nct2010\n \npickup_ct2010\n,\n\n \npick_up\n.\nboroct2010\n \npickup_boroct2010\n,\n\n \npick_up\n.\ncdeligibil\n \npickup_cdeligibil\n,\n\n \npick_up\n.\nntacode\n \npickup_ntacode\n,\n\n \npick_up\n.\nntaname\n \npickup_ntaname\n,\n\n \npick_up\n.\npuma\n \npickup_puma\n,\n\n\n \ndrop_off\n.\ngid\n \ndropoff_nyct2010_gid\n,\n\n \ndrop_off\n.\nctlabel\n \ndropoff_ctlabel\n,\n\n \ndrop_off\n.\nborocode\n \ndropoff_borocode\n,\n\n \ndrop_off\n.\nboroname\n \ndropoff_boroname\n,\n\n \ndrop_off\n.\nct2010\n \ndropoff_ct2010\n,\n\n \ndrop_off\n.\nboroct2010\n \ndropoff_boroct2010\n,\n\n \ndrop_off\n.\ncdeligibil\n \ndropoff_cdeligibil\n,\n\n \ndrop_off\n.\nntacode\n \ndropoff_ntacode\n,\n\n \ndrop_off\n.\nntaname\n \ndropoff_ntaname\n,\n\n \ndrop_off\n.\npuma\n \ndropoff_puma\n\n \nFROM\n \ntrips\n\n \nLEFT\n \nJOIN\n \ncab_types\n\n \nON\n \ntrips\n.\ncab_type_id\n \n=\n \ncab_types\n.\nid\n\n \nLEFT\n \nJOIN\n \ncentral_park_weather_observations_raw\n \nweather\n\n \nON\n \nweather\n.\ndate\n \n=\n \ntrips\n.\npickup_datetime\n::\ndate\n\n \nLEFT\n \nJOIN\n \nnyct2010\n \npick_up\n\n \nON\n \npick_up\n.\ngid\n \n=\n \ntrips\n.\npickup_nyct2010_gid\n\n \nLEFT\n \nJOIN\n \nnyct2010\n \ndrop_off\n\n \nON\n \ndrop_off\n.\ngid\n \n=\n \ntrips\n.\ndropoff_nyct2010_gid\n\n\n)\n \nTO\n \n/opt/milovidov/nyc-taxi-data/trips.tsv\n;\n\n\n\n\n\n\nThe data snapshot is created at a speed of about 50 MB per second. While creating the snapshot, PostgreSQL reads from the disk at a speed of about 28 MB per second.\nThis takes about 5 hours. The resulting TSV file is 590612904969 bytes.\n\n\nCreate a temporary table in ClickHouse:\n\n\nCREATE\n \nTABLE\n \ntrips\n\n\n(\n\n\ntrip_id\n \nUInt32\n,\n\n\nvendor_id\n \nString\n,\n\n\npickup_datetime\n \nDateTime\n,\n\n\ndropoff_datetime\n \nNullable\n(\nDateTime\n),\n\n\nstore_and_fwd_flag\n \nNullable\n(\nFixedString\n(\n1\n)),\n\n\nrate_code_id\n \nNullable\n(\nUInt8\n),\n\n\npickup_longitude\n \nNullable\n(\nFloat64\n),\n\n\npickup_latitude\n \nNullable\n(\nFloat64\n),\n\n\ndropoff_longitude\n \nNullable\n(\nFloat64\n),\n\n\ndropoff_latitude\n \nNullable\n(\nFloat64\n),\n\n\npassenger_count\n \nNullable\n(\nUInt8\n),\n\n\ntrip_distance\n \nNullable\n(\nFloat64\n),\n\n\nfare_amount\n \nNullable\n(\nFloat32\n),\n\n\nextra\n \nNullable\n(\nFloat32\n),\n\n\nmta_tax\n \nNullable\n(\nFloat32\n),\n\n\ntip_amount\n \nNullable\n(\nFloat32\n),\n\n\ntolls_amount\n \nNullable\n(\nFloat32\n),\n\n\nehail_fee\n \nNullable\n(\nFloat32\n),\n\n\nimprovement_surcharge\n \nNullable\n(\nFloat32\n),\n\n\ntotal_amount\n \nNullable\n(\nFloat32\n),\n\n\npayment_type\n \nNullable\n(\nString\n),\n\n\ntrip_type\n \nNullable\n(\nUInt8\n),\n\n\npickup\n \nNullable\n(\nString\n),\n\n\ndropoff\n \nNullable\n(\nString\n),\n\n\ncab_type\n \nNullable\n(\nString\n),\n\n\nprecipitation\n \nNullable\n(\nUInt8\n),\n\n\nsnow_depth\n \nNullable\n(\nUInt8\n),\n\n\nsnowfall\n \nNullable\n(\nUInt8\n),\n\n\nmax_temperature\n \nNullable\n(\nUInt8\n),\n\n\nmin_temperature\n \nNullable\n(\nUInt8\n),\n\n\naverage_wind_speed\n \nNullable\n(\nUInt8\n),\n\n\npickup_nyct2010_gid\n \nNullable\n(\nUInt8\n),\n\n\npickup_ctlabel\n \nNullable\n(\nString\n),\n\n\npickup_borocode\n \nNullable\n(\nUInt8\n),\n\n\npickup_boroname\n \nNullable\n(\nString\n),\n\n\npickup_ct2010\n \nNullable\n(\nString\n),\n\n\npickup_boroct2010\n \nNullable\n(\nString\n),\n\n\npickup_cdeligibil\n \nNullable\n(\nFixedString\n(\n1\n)),\n\n\npickup_ntacode\n \nNullable\n(\nString\n),\n\n\npickup_ntaname\n \nNullable\n(\nString\n),\n\n\npickup_puma\n \nNullable\n(\nString\n),\n\n\ndropoff_nyct2010_gid\n \nNullable\n(\nUInt8\n),\n\n\ndropoff_ctlabel\n \nNullable\n(\nString\n),\n\n\ndropoff_borocode\n \nNullable\n(\nUInt8\n),\n\n\ndropoff_boroname\n \nNullable\n(\nString\n),\n\n\ndropoff_ct2010\n \nNullable\n(\nString\n),\n\n\ndropoff_boroct2010\n \nNullable\n(\nString\n),\n\n\ndropoff_cdeligibil\n \nNullable\n(\nString\n),\n\n\ndropoff_ntacode\n \nNullable\n(\nString\n),\n\n\ndropoff_ntaname\n \nNullable\n(\nString\n),\n\n\ndropoff_puma\n \nNullable\n(\nString\n)\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\n\n\n\nIt is needed for converting fields to more correct data types and, if possible, to eliminate NULLs.\n\n\ntime clickhouse-client --query=\nINSERT INTO trips FORMAT TabSeparated\n \n trips.tsv\n\nreal 75m56.214s\n\n\n\n\n\nData is read at a speed of 112-140 Mb/second.\nLoading data into a Log type table in one stream took 76 minutes.\nThe data in this table uses 142 GB.\n\n\n(Importing data directly from Postgres is also possible using \nCOPY ... TO PROGRAM\n.)\n\n\nUnfortunately, all the fields associated with the weather (precipitation...average_wind_speed) were filled with NULL. Because of this, we will remove them from the final data set.\n\n\nTo start, we'll create a table on a single server. Later we will make the table distributed.\n\n\nCreate and populate a summary table:\n\n\nCREATE TABLE trips_mergetree\nENGINE = MergeTree(pickup_date, pickup_datetime, 8192)\nAS SELECT\n\ntrip_id,\nCAST(vendor_id AS Enum8(\n1\n = 1, \n2\n = 2, \nCMT\n = 3, \nVTS\n = 4, \nDDS\n = 5, \nB02512\n = 10, \nB02598\n = 11, \nB02617\n = 12, \nB02682\n = 13, \nB02764\n = 14)) AS vendor_id,\ntoDate(pickup_datetime) AS pickup_date,\nifNull(pickup_datetime, toDateTime(0)) AS pickup_datetime,\ntoDate(dropoff_datetime) AS dropoff_date,\nifNull(dropoff_datetime, toDateTime(0)) AS dropoff_datetime,\nassumeNotNull(store_and_fwd_flag) IN (\nY\n, \n1\n, \n2\n) AS store_and_fwd_flag,\nassumeNotNull(rate_code_id) AS rate_code_id,\nassumeNotNull(pickup_longitude) AS pickup_longitude,\nassumeNotNull(pickup_latitude) AS pickup_latitude,\nassumeNotNull(dropoff_longitude) AS dropoff_longitude,\nassumeNotNull(dropoff_latitude) AS dropoff_latitude,\nassumeNotNull(passenger_count) AS passenger_count,\nassumeNotNull(trip_distance) AS trip_distance,\nassumeNotNull(fare_amount) AS fare_amount,\nassumeNotNull(extra) AS extra,\nassumeNotNull(mta_tax) AS mta_tax,\nassumeNotNull(tip_amount) AS tip_amount,\nassumeNotNull(tolls_amount) AS tolls_amount,\nassumeNotNull(ehail_fee) AS ehail_fee,\nassumeNotNull(improvement_surcharge) AS improvement_surcharge,\nassumeNotNull(total_amount) AS total_amount,\nCAST((assumeNotNull(payment_type) AS pt) IN (\nCSH\n, \nCASH\n, \nCash\n, \nCAS\n, \nCas\n, \n1\n) ? \nCSH\n : (pt IN (\nCRD\n, \nCredit\n, \nCre\n, \nCRE\n, \nCREDIT\n, \n2\n) ? \nCRE\n : (pt IN (\nNOC\n, \nNo Charge\n, \nNo\n, \n3\n) ? \nNOC\n : (pt IN (\nDIS\n, \nDispute\n, \nDis\n, \n4\n) ? \nDIS\n : \nUNK\n))) AS Enum8(\nCSH\n = 1, \nCRE\n = 2, \nUNK\n = 0, \nNOC\n = 3, \nDIS\n = 4)) AS payment_type_,\nassumeNotNull(trip_type) AS trip_type,\nifNull(toFixedString(unhex(pickup), 25), toFixedString(\n, 25)) AS pickup,\nifNull(toFixedString(unhex(dropoff), 25), toFixedString(\n, 25)) AS dropoff,\nCAST(assumeNotNull(cab_type) AS Enum8(\nyellow\n = 1, \ngreen\n = 2, \nuber\n = 3)) AS cab_type,\n\nassumeNotNull(pickup_nyct2010_gid) AS pickup_nyct2010_gid,\ntoFloat32(ifNull(pickup_ctlabel, \n0\n)) AS pickup_ctlabel,\nassumeNotNull(pickup_borocode) AS pickup_borocode,\nCAST(assumeNotNull(pickup_boroname) AS Enum8(\nManhattan\n = 1, \nQueens\n = 4, \nBrooklyn\n = 3, \n = 0, \nBronx\n = 2, \nStaten Island\n = 5)) AS pickup_boroname,\ntoFixedString(ifNull(pickup_ct2010, \n000000\n), 6) AS pickup_ct2010,\ntoFixedString(ifNull(pickup_boroct2010, \n0000000\n), 7) AS pickup_boroct2010,\nCAST(assumeNotNull(ifNull(pickup_cdeligibil, \n \n)) AS Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2)) AS pickup_cdeligibil,\ntoFixedString(ifNull(pickup_ntacode, \n0000\n), 4) AS pickup_ntacode,\n\nCAST(assumeNotNull(pickup_ntaname) AS Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195)) AS pickup_ntaname,\n\ntoUInt16(ifNull(pickup_puma, \n0\n)) AS pickup_puma,\n\nassumeNotNull(dropoff_nyct2010_gid) AS dropoff_nyct2010_gid,\ntoFloat32(ifNull(dropoff_ctlabel, \n0\n)) AS dropoff_ctlabel,\nassumeNotNull(dropoff_borocode) AS dropoff_borocode,\nCAST(assumeNotNull(dropoff_boroname) AS Enum8(\nManhattan\n = 1, \nQueens\n = 4, \nBrooklyn\n = 3, \n = 0, \nBronx\n = 2, \nStaten Island\n = 5)) AS dropoff_boroname,\ntoFixedString(ifNull(dropoff_ct2010, \n000000\n), 6) AS dropoff_ct2010,\ntoFixedString(ifNull(dropoff_boroct2010, \n0000000\n), 7) AS dropoff_boroct2010,\nCAST(assumeNotNull(ifNull(dropoff_cdeligibil, \n \n)) AS Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2)) AS dropoff_cdeligibil,\ntoFixedString(ifNull(dropoff_ntacode, \n0000\n), 4) AS dropoff_ntacode,\n\nCAST(assumeNotNull(dropoff_ntaname) AS Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195)) AS dropoff_ntaname,\n\ntoUInt16(ifNull(dropoff_puma, \n0\n)) AS dropoff_puma\n\nFROM trips\n\n\n\n\n\nThis takes 3030 seconds at a speed of about 428,000 rows per second.\nTo load it faster, you can create the table with the \nLog\n engine instead of \nMergeTree\n. In this case, the download works faster than 200 seconds.\n\n\nThe table uses 126 GB of disk space.\n\n\n:) SELECT formatReadableSize(sum(bytes)) FROM system.parts WHERE table = \ntrips_mergetree\n AND active\n\nSELECT formatReadableSize(sum(bytes))\nFROM system.parts\nWHERE (table = \ntrips_mergetree\n) AND active\n\n\u250c\u2500formatReadableSize(sum(bytes))\u2500\u2510\n\u2502 126.18 GiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nAmong other things, you can run the OPTIMIZE query on MergeTree. But it's not required, since everything will be fine without it.\n\n\nResults on single server\n\n\nQ1:\n\n\nSELECT\n \ncab_type\n,\n \ncount\n(\n*\n)\n \nFROM\n \ntrips_mergetree\n \nGROUP\n \nBY\n \ncab_type\n\n\n\n\n\n\n0.490 seconds.\n\n\nQ2:\n\n\nSELECT\n \npassenger_count\n,\n \navg\n(\ntotal_amount\n)\n \nFROM\n \ntrips_mergetree\n \nGROUP\n \nBY\n \npassenger_count\n\n\n\n\n\n\n1.224 seconds.\n\n\nQ3:\n\n\nSELECT\n \npassenger_count\n,\n \ntoYear\n(\npickup_date\n)\n \nAS\n \nyear\n,\n \ncount\n(\n*\n)\n \nFROM\n \ntrips_mergetree\n \nGROUP\n \nBY\n \npassenger_count\n,\n \nyear\n\n\n\n\n\n\n2.104 seconds.\n\n\nQ4:\n\n\nSELECT\n \npassenger_count\n,\n \ntoYear\n(\npickup_date\n)\n \nAS\n \nyear\n,\n \nround\n(\ntrip_distance\n)\n \nAS\n \ndistance\n,\n \ncount\n(\n*\n)\n\n\nFROM\n \ntrips_mergetree\n\n\nGROUP\n \nBY\n \npassenger_count\n,\n \nyear\n,\n \ndistance\n\n\nORDER\n \nBY\n \nyear\n,\n \ncount\n(\n*\n)\n \nDESC\n\n\n\n\n\n\n3.593 seconds.\n\n\nThe following server was used:\n\n\nTwo Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical kernels total,\n128 GiB RAM,\n8x6 TB HD on hardware RAID-5\n\n\nExecution time is the best of three runsBut starting from the second run, queries read data from the file system cache. No further caching occurs: the data is read out and processed in each run.\n\n\nCreating a table on three servers:\n\n\nOn each server:\n\n\nCREATE TABLE default.trips_mergetree_third ( trip_id UInt32, vendor_id Enum8(\n1\n = 1, \n2\n = 2, \nCMT\n = 3, \nVTS\n = 4, \nDDS\n = 5, \nB02512\n = 10, \nB02598\n = 11, \nB02617\n = 12, \nB02682\n = 13, \nB02764\n = 14), pickup_date Date, pickup_datetime DateTime, dropoff_date Date, dropoff_datetime DateTime, store_and_fwd_flag UInt8, rate_code_id UInt8, pickup_longitude Float64, pickup_latitude Float64, dropoff_longitude Float64, dropoff_latitude Float64, passenger_count UInt8, trip_distance Float64, fare_amount Float32, extra Float32, mta_tax Float32, tip_amount Float32, tolls_amount Float32, ehail_fee Float32, improvement_surcharge Float32, total_amount Float32, payment_type_ Enum8(\nUNK\n = 0, \nCSH\n = 1, \nCRE\n = 2, \nNOC\n = 3, \nDIS\n = 4), trip_type UInt8, pickup FixedString(25), dropoff FixedString(25), cab_type Enum8(\nyellow\n = 1, \ngreen\n = 2, \nuber\n = 3), pickup_nyct2010_gid UInt8, pickup_ctlabel Float32, pickup_borocode UInt8, pickup_boroname Enum8(\n = 0, \nManhattan\n = 1, \nBronx\n = 2, \nBrooklyn\n = 3, \nQueens\n = 4, \nStaten Island\n = 5), pickup_ct2010 FixedString(6), pickup_boroct2010 FixedString(7), pickup_cdeligibil Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2), pickup_ntacode FixedString(4), pickup_ntaname Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195), pickup_puma UInt16, dropoff_nyct2010_gid UInt8, dropoff_ctlabel Float32, dropoff_borocode UInt8, dropoff_boroname Enum8(\n = 0, \nManhattan\n = 1, \nBronx\n = 2, \nBrooklyn\n = 3, \nQueens\n = 4, \nStaten Island\n = 5), dropoff_ct2010 FixedString(6), dropoff_boroct2010 FixedString(7), dropoff_cdeligibil Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2), dropoff_ntacode FixedString(4), dropoff_ntaname Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195), dropoff_puma UInt16) ENGINE = MergeTree(pickup_date, pickup_datetime, 8192)\n\n\n\n\n\nOn the source server:\n\n\nCREATE\n \nTABLE\n \ntrips_mergetree_x3\n \nAS\n \ntrips_mergetree_third\n \nENGINE\n \n=\n \nDistributed\n(\nperftest\n,\n \ndefault\n,\n \ntrips_mergetree_third\n,\n \nrand\n())\n\n\n\n\n\n\nThe following query redistributes data:\n\n\nINSERT\n \nINTO\n \ntrips_mergetree_x3\n \nSELECT\n \n*\n \nFROM\n \ntrips_mergetree\n\n\n\n\n\n\nThis takes 2454 seconds.\n\n\nOn three servers:\n\n\nQ1: 0.212 seconds.\nQ2: 0.438 seconds.\nQ3: 0.733 seconds.\nQ4: 1.241 seconds.\n\n\nNo surprises here, since the queries are scaled linearly.\n\n\nWe also have results from a cluster of 140 servers:\n\n\nQ1: 0.028 sec.\nQ2: 0.043 sec.\nQ3: 0.051 sec.\nQ4: 0.072 sec.\n\n\nIn this case, the query processing time is determined above all by network latency.\nWe ran queries using a client located in a Yandex datacenter in Finland on a cluster in Russia, which added about 20 ms of latency.\n\n\nSummary\n\n\nnodes Q1 Q2 Q3 Q4\n 1 0.490 1.224 2.104 3.593\n 3 0.212 0.438 0.733 1.241\n140 0.028 0.043 0.051 0.072", + "title": "New York Taxi data" + }, + { + "location": "/getting_started/example_datasets/nyc_taxi/#new-york-taxi-data", + "text": "", + "title": "New York Taxi data" + }, + { + "location": "/getting_started/example_datasets/nyc_taxi/#how-to-import-the-raw-data", + "text": "See https://github.com/toddwschneider/nyc-taxi-data and http://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html for the description of the dataset and instructions for downloading. Downloading will result in about 227 GB of uncompressed data in CSV files. The download takes about an hour over a 1 Gbit connection (parallel downloading from s3.amazonaws.com recovers at least half of a 1 Gbit channel).\nSome of the files might not download fully. Check the file sizes and re-download any that seem doubtful. Some of the files might contain invalid rows. You can fix them as follows: sed -E /(.*,){18,}/d data/yellow_tripdata_2010-02.csv data/yellow_tripdata_2010-02.csv_\nsed -E /(.*,){18,}/d data/yellow_tripdata_2010-03.csv data/yellow_tripdata_2010-03.csv_\nmv data/yellow_tripdata_2010-02.csv_ data/yellow_tripdata_2010-02.csv\nmv data/yellow_tripdata_2010-03.csv_ data/yellow_tripdata_2010-03.csv Then the data must be pre-processed in PostgreSQL. This will create selections of points in the polygons (to match points on the map with the boroughs of New York City) and combine all the data into a single denormalized flat table by using a JOIN. To do this, you will need to install PostgreSQL with PostGIS support. Be careful when running initialize_database.sh and manually re-check that all the tables were created correctly. It takes about 20-30 minutes to process each month's worth of data in PostgreSQL, for a total of about 48 hours. You can check the number of downloaded rows as follows: time psql nyc-taxi-data -c SELECT count(*) FROM trips; \n## count\n 1298979494\n(1 row)\n\nreal 7m9.164s (This is slightly more than 1.1 billion rows reported by Mark Litwintschik in a series of blog posts.) The data in PostgreSQL uses 370 GB of space. Exporting the data from PostgreSQL: COPY ( \n SELECT trips . id , \n trips . vendor_id , \n trips . pickup_datetime , \n trips . dropoff_datetime , \n trips . store_and_fwd_flag , \n trips . rate_code_id , \n trips . pickup_longitude , \n trips . pickup_latitude , \n trips . dropoff_longitude , \n trips . dropoff_latitude , \n trips . passenger_count , \n trips . trip_distance , \n trips . fare_amount , \n trips . extra , \n trips . mta_tax , \n trips . tip_amount , \n trips . tolls_amount , \n trips . ehail_fee , \n trips . improvement_surcharge , \n trips . total_amount , \n trips . payment_type , \n trips . trip_type , \n trips . pickup , \n trips . dropoff , \n\n cab_types . type cab_type , \n\n weather . precipitation_tenths_of_mm rain , \n weather . snow_depth_mm , \n weather . snowfall_mm , \n weather . max_temperature_tenths_degrees_celsius max_temp , \n weather . min_temperature_tenths_degrees_celsius min_temp , \n weather . average_wind_speed_tenths_of_meters_per_second wind , \n\n pick_up . gid pickup_nyct2010_gid , \n pick_up . ctlabel pickup_ctlabel , \n pick_up . borocode pickup_borocode , \n pick_up . boroname pickup_boroname , \n pick_up . ct2010 pickup_ct2010 , \n pick_up . boroct2010 pickup_boroct2010 , \n pick_up . cdeligibil pickup_cdeligibil , \n pick_up . ntacode pickup_ntacode , \n pick_up . ntaname pickup_ntaname , \n pick_up . puma pickup_puma , \n\n drop_off . gid dropoff_nyct2010_gid , \n drop_off . ctlabel dropoff_ctlabel , \n drop_off . borocode dropoff_borocode , \n drop_off . boroname dropoff_boroname , \n drop_off . ct2010 dropoff_ct2010 , \n drop_off . boroct2010 dropoff_boroct2010 , \n drop_off . cdeligibil dropoff_cdeligibil , \n drop_off . ntacode dropoff_ntacode , \n drop_off . ntaname dropoff_ntaname , \n drop_off . puma dropoff_puma \n FROM trips \n LEFT JOIN cab_types \n ON trips . cab_type_id = cab_types . id \n LEFT JOIN central_park_weather_observations_raw weather \n ON weather . date = trips . pickup_datetime :: date \n LEFT JOIN nyct2010 pick_up \n ON pick_up . gid = trips . pickup_nyct2010_gid \n LEFT JOIN nyct2010 drop_off \n ON drop_off . gid = trips . dropoff_nyct2010_gid ) TO /opt/milovidov/nyc-taxi-data/trips.tsv ; The data snapshot is created at a speed of about 50 MB per second. While creating the snapshot, PostgreSQL reads from the disk at a speed of about 28 MB per second.\nThis takes about 5 hours. The resulting TSV file is 590612904969 bytes. Create a temporary table in ClickHouse: CREATE TABLE trips ( trip_id UInt32 , vendor_id String , pickup_datetime DateTime , dropoff_datetime Nullable ( DateTime ), store_and_fwd_flag Nullable ( FixedString ( 1 )), rate_code_id Nullable ( UInt8 ), pickup_longitude Nullable ( Float64 ), pickup_latitude Nullable ( Float64 ), dropoff_longitude Nullable ( Float64 ), dropoff_latitude Nullable ( Float64 ), passenger_count Nullable ( UInt8 ), trip_distance Nullable ( Float64 ), fare_amount Nullable ( Float32 ), extra Nullable ( Float32 ), mta_tax Nullable ( Float32 ), tip_amount Nullable ( Float32 ), tolls_amount Nullable ( Float32 ), ehail_fee Nullable ( Float32 ), improvement_surcharge Nullable ( Float32 ), total_amount Nullable ( Float32 ), payment_type Nullable ( String ), trip_type Nullable ( UInt8 ), pickup Nullable ( String ), dropoff Nullable ( String ), cab_type Nullable ( String ), precipitation Nullable ( UInt8 ), snow_depth Nullable ( UInt8 ), snowfall Nullable ( UInt8 ), max_temperature Nullable ( UInt8 ), min_temperature Nullable ( UInt8 ), average_wind_speed Nullable ( UInt8 ), pickup_nyct2010_gid Nullable ( UInt8 ), pickup_ctlabel Nullable ( String ), pickup_borocode Nullable ( UInt8 ), pickup_boroname Nullable ( String ), pickup_ct2010 Nullable ( String ), pickup_boroct2010 Nullable ( String ), pickup_cdeligibil Nullable ( FixedString ( 1 )), pickup_ntacode Nullable ( String ), pickup_ntaname Nullable ( String ), pickup_puma Nullable ( String ), dropoff_nyct2010_gid Nullable ( UInt8 ), dropoff_ctlabel Nullable ( String ), dropoff_borocode Nullable ( UInt8 ), dropoff_boroname Nullable ( String ), dropoff_ct2010 Nullable ( String ), dropoff_boroct2010 Nullable ( String ), dropoff_cdeligibil Nullable ( String ), dropoff_ntacode Nullable ( String ), dropoff_ntaname Nullable ( String ), dropoff_puma Nullable ( String ) ) ENGINE = Log ; It is needed for converting fields to more correct data types and, if possible, to eliminate NULLs. time clickhouse-client --query= INSERT INTO trips FORMAT TabSeparated trips.tsv\n\nreal 75m56.214s Data is read at a speed of 112-140 Mb/second.\nLoading data into a Log type table in one stream took 76 minutes.\nThe data in this table uses 142 GB. (Importing data directly from Postgres is also possible using COPY ... TO PROGRAM .) Unfortunately, all the fields associated with the weather (precipitation...average_wind_speed) were filled with NULL. Because of this, we will remove them from the final data set. To start, we'll create a table on a single server. Later we will make the table distributed. Create and populate a summary table: CREATE TABLE trips_mergetree\nENGINE = MergeTree(pickup_date, pickup_datetime, 8192)\nAS SELECT\n\ntrip_id,\nCAST(vendor_id AS Enum8( 1 = 1, 2 = 2, CMT = 3, VTS = 4, DDS = 5, B02512 = 10, B02598 = 11, B02617 = 12, B02682 = 13, B02764 = 14)) AS vendor_id,\ntoDate(pickup_datetime) AS pickup_date,\nifNull(pickup_datetime, toDateTime(0)) AS pickup_datetime,\ntoDate(dropoff_datetime) AS dropoff_date,\nifNull(dropoff_datetime, toDateTime(0)) AS dropoff_datetime,\nassumeNotNull(store_and_fwd_flag) IN ( Y , 1 , 2 ) AS store_and_fwd_flag,\nassumeNotNull(rate_code_id) AS rate_code_id,\nassumeNotNull(pickup_longitude) AS pickup_longitude,\nassumeNotNull(pickup_latitude) AS pickup_latitude,\nassumeNotNull(dropoff_longitude) AS dropoff_longitude,\nassumeNotNull(dropoff_latitude) AS dropoff_latitude,\nassumeNotNull(passenger_count) AS passenger_count,\nassumeNotNull(trip_distance) AS trip_distance,\nassumeNotNull(fare_amount) AS fare_amount,\nassumeNotNull(extra) AS extra,\nassumeNotNull(mta_tax) AS mta_tax,\nassumeNotNull(tip_amount) AS tip_amount,\nassumeNotNull(tolls_amount) AS tolls_amount,\nassumeNotNull(ehail_fee) AS ehail_fee,\nassumeNotNull(improvement_surcharge) AS improvement_surcharge,\nassumeNotNull(total_amount) AS total_amount,\nCAST((assumeNotNull(payment_type) AS pt) IN ( CSH , CASH , Cash , CAS , Cas , 1 ) ? CSH : (pt IN ( CRD , Credit , Cre , CRE , CREDIT , 2 ) ? CRE : (pt IN ( NOC , No Charge , No , 3 ) ? NOC : (pt IN ( DIS , Dispute , Dis , 4 ) ? DIS : UNK ))) AS Enum8( CSH = 1, CRE = 2, UNK = 0, NOC = 3, DIS = 4)) AS payment_type_,\nassumeNotNull(trip_type) AS trip_type,\nifNull(toFixedString(unhex(pickup), 25), toFixedString( , 25)) AS pickup,\nifNull(toFixedString(unhex(dropoff), 25), toFixedString( , 25)) AS dropoff,\nCAST(assumeNotNull(cab_type) AS Enum8( yellow = 1, green = 2, uber = 3)) AS cab_type,\n\nassumeNotNull(pickup_nyct2010_gid) AS pickup_nyct2010_gid,\ntoFloat32(ifNull(pickup_ctlabel, 0 )) AS pickup_ctlabel,\nassumeNotNull(pickup_borocode) AS pickup_borocode,\nCAST(assumeNotNull(pickup_boroname) AS Enum8( Manhattan = 1, Queens = 4, Brooklyn = 3, = 0, Bronx = 2, Staten Island = 5)) AS pickup_boroname,\ntoFixedString(ifNull(pickup_ct2010, 000000 ), 6) AS pickup_ct2010,\ntoFixedString(ifNull(pickup_boroct2010, 0000000 ), 7) AS pickup_boroct2010,\nCAST(assumeNotNull(ifNull(pickup_cdeligibil, )) AS Enum8( = 0, E = 1, I = 2)) AS pickup_cdeligibil,\ntoFixedString(ifNull(pickup_ntacode, 0000 ), 4) AS pickup_ntacode,\n\nCAST(assumeNotNull(pickup_ntaname) AS Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195)) AS pickup_ntaname,\n\ntoUInt16(ifNull(pickup_puma, 0 )) AS pickup_puma,\n\nassumeNotNull(dropoff_nyct2010_gid) AS dropoff_nyct2010_gid,\ntoFloat32(ifNull(dropoff_ctlabel, 0 )) AS dropoff_ctlabel,\nassumeNotNull(dropoff_borocode) AS dropoff_borocode,\nCAST(assumeNotNull(dropoff_boroname) AS Enum8( Manhattan = 1, Queens = 4, Brooklyn = 3, = 0, Bronx = 2, Staten Island = 5)) AS dropoff_boroname,\ntoFixedString(ifNull(dropoff_ct2010, 000000 ), 6) AS dropoff_ct2010,\ntoFixedString(ifNull(dropoff_boroct2010, 0000000 ), 7) AS dropoff_boroct2010,\nCAST(assumeNotNull(ifNull(dropoff_cdeligibil, )) AS Enum8( = 0, E = 1, I = 2)) AS dropoff_cdeligibil,\ntoFixedString(ifNull(dropoff_ntacode, 0000 ), 4) AS dropoff_ntacode,\n\nCAST(assumeNotNull(dropoff_ntaname) AS Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195)) AS dropoff_ntaname,\n\ntoUInt16(ifNull(dropoff_puma, 0 )) AS dropoff_puma\n\nFROM trips This takes 3030 seconds at a speed of about 428,000 rows per second.\nTo load it faster, you can create the table with the Log engine instead of MergeTree . In this case, the download works faster than 200 seconds. The table uses 126 GB of disk space. :) SELECT formatReadableSize(sum(bytes)) FROM system.parts WHERE table = trips_mergetree AND active\n\nSELECT formatReadableSize(sum(bytes))\nFROM system.parts\nWHERE (table = trips_mergetree ) AND active\n\n\u250c\u2500formatReadableSize(sum(bytes))\u2500\u2510\n\u2502 126.18 GiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Among other things, you can run the OPTIMIZE query on MergeTree. But it's not required, since everything will be fine without it.", + "title": "How to import the raw data" + }, + { + "location": "/getting_started/example_datasets/nyc_taxi/#results-on-single-server", + "text": "Q1: SELECT cab_type , count ( * ) FROM trips_mergetree GROUP BY cab_type 0.490 seconds. Q2: SELECT passenger_count , avg ( total_amount ) FROM trips_mergetree GROUP BY passenger_count 1.224 seconds. Q3: SELECT passenger_count , toYear ( pickup_date ) AS year , count ( * ) FROM trips_mergetree GROUP BY passenger_count , year 2.104 seconds. Q4: SELECT passenger_count , toYear ( pickup_date ) AS year , round ( trip_distance ) AS distance , count ( * ) FROM trips_mergetree GROUP BY passenger_count , year , distance ORDER BY year , count ( * ) DESC 3.593 seconds. The following server was used: Two Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical kernels total,\n128 GiB RAM,\n8x6 TB HD on hardware RAID-5 Execution time is the best of three runsBut starting from the second run, queries read data from the file system cache. No further caching occurs: the data is read out and processed in each run. Creating a table on three servers: On each server: CREATE TABLE default.trips_mergetree_third ( trip_id UInt32, vendor_id Enum8( 1 = 1, 2 = 2, CMT = 3, VTS = 4, DDS = 5, B02512 = 10, B02598 = 11, B02617 = 12, B02682 = 13, B02764 = 14), pickup_date Date, pickup_datetime DateTime, dropoff_date Date, dropoff_datetime DateTime, store_and_fwd_flag UInt8, rate_code_id UInt8, pickup_longitude Float64, pickup_latitude Float64, dropoff_longitude Float64, dropoff_latitude Float64, passenger_count UInt8, trip_distance Float64, fare_amount Float32, extra Float32, mta_tax Float32, tip_amount Float32, tolls_amount Float32, ehail_fee Float32, improvement_surcharge Float32, total_amount Float32, payment_type_ Enum8( UNK = 0, CSH = 1, CRE = 2, NOC = 3, DIS = 4), trip_type UInt8, pickup FixedString(25), dropoff FixedString(25), cab_type Enum8( yellow = 1, green = 2, uber = 3), pickup_nyct2010_gid UInt8, pickup_ctlabel Float32, pickup_borocode UInt8, pickup_boroname Enum8( = 0, Manhattan = 1, Bronx = 2, Brooklyn = 3, Queens = 4, Staten Island = 5), pickup_ct2010 FixedString(6), pickup_boroct2010 FixedString(7), pickup_cdeligibil Enum8( = 0, E = 1, I = 2), pickup_ntacode FixedString(4), pickup_ntaname Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195), pickup_puma UInt16, dropoff_nyct2010_gid UInt8, dropoff_ctlabel Float32, dropoff_borocode UInt8, dropoff_boroname Enum8( = 0, Manhattan = 1, Bronx = 2, Brooklyn = 3, Queens = 4, Staten Island = 5), dropoff_ct2010 FixedString(6), dropoff_boroct2010 FixedString(7), dropoff_cdeligibil Enum8( = 0, E = 1, I = 2), dropoff_ntacode FixedString(4), dropoff_ntaname Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195), dropoff_puma UInt16) ENGINE = MergeTree(pickup_date, pickup_datetime, 8192) On the source server: CREATE TABLE trips_mergetree_x3 AS trips_mergetree_third ENGINE = Distributed ( perftest , default , trips_mergetree_third , rand ()) The following query redistributes data: INSERT INTO trips_mergetree_x3 SELECT * FROM trips_mergetree This takes 2454 seconds. On three servers: Q1: 0.212 seconds.\nQ2: 0.438 seconds.\nQ3: 0.733 seconds.\nQ4: 1.241 seconds. No surprises here, since the queries are scaled linearly. We also have results from a cluster of 140 servers: Q1: 0.028 sec.\nQ2: 0.043 sec.\nQ3: 0.051 sec.\nQ4: 0.072 sec. In this case, the query processing time is determined above all by network latency.\nWe ran queries using a client located in a Yandex datacenter in Finland on a cluster in Russia, which added about 20 ms of latency.", + "title": "Results on single server" + }, + { + "location": "/getting_started/example_datasets/nyc_taxi/#summary", + "text": "nodes Q1 Q2 Q3 Q4\n 1 0.490 1.224 2.104 3.593\n 3 0.212 0.438 0.733 1.241\n140 0.028 0.043 0.051 0.072", + "title": "Summary" + }, + { + "location": "/getting_started/example_datasets/amplab_benchmark/", + "text": "AMPLab Big Data Benchmark\n\n\nSee \nhttps://amplab.cs.berkeley.edu/benchmark/\n\n\nSign up for a free account at \nhttps://aws.amazon.com\n. You will need a credit card, email and phone number.Get a new access key at \nhttps://console.aws.amazon.com/iam/home?nc2=h_m_sc#security_credential\n\n\nRun the following in the console:\n\n\nsudo apt-get install s3cmd\nmkdir tiny\n;\n \ncd\n tiny\n;\n\ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/tiny/ .\n\ncd\n ..\nmkdir 1node\n;\n \ncd\n 1node\n;\n\ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/1node/ .\n\ncd\n ..\nmkdir 5nodes\n;\n \ncd\n 5nodes\n;\n\ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/5nodes/ .\n\ncd\n ..\n\n\n\n\n\nRun the following ClickHouse queries:\n\n\nCREATE\n \nTABLE\n \nrankings_tiny\n\n\n(\n\n \npageURL\n \nString\n,\n\n \npageRank\n \nUInt32\n,\n\n \navgDuration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\nCREATE\n \nTABLE\n \nuservisits_tiny\n\n\n(\n\n \nsourceIP\n \nString\n,\n\n \ndestinationURL\n \nString\n,\n\n \nvisitDate\n \nDate\n,\n\n \nadRevenue\n \nFloat32\n,\n\n \nUserAgent\n \nString\n,\n\n \ncCode\n \nFixedString\n(\n3\n),\n\n \nlCode\n \nFixedString\n(\n6\n),\n\n \nsearchWord\n \nString\n,\n\n \nduration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nvisitDate\n,\n \nvisitDate\n,\n \n8192\n);\n\n\n\nCREATE\n \nTABLE\n \nrankings_1node\n\n\n(\n\n \npageURL\n \nString\n,\n\n \npageRank\n \nUInt32\n,\n\n \navgDuration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\nCREATE\n \nTABLE\n \nuservisits_1node\n\n\n(\n\n \nsourceIP\n \nString\n,\n\n \ndestinationURL\n \nString\n,\n\n \nvisitDate\n \nDate\n,\n\n \nadRevenue\n \nFloat32\n,\n\n \nUserAgent\n \nString\n,\n\n \ncCode\n \nFixedString\n(\n3\n),\n\n \nlCode\n \nFixedString\n(\n6\n),\n\n \nsearchWord\n \nString\n,\n\n \nduration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nvisitDate\n,\n \nvisitDate\n,\n \n8192\n);\n\n\n\nCREATE\n \nTABLE\n \nrankings_5nodes_on_single\n\n\n(\n\n \npageURL\n \nString\n,\n\n \npageRank\n \nUInt32\n,\n\n \navgDuration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\nCREATE\n \nTABLE\n \nuservisits_5nodes_on_single\n\n\n(\n\n \nsourceIP\n \nString\n,\n\n \ndestinationURL\n \nString\n,\n\n \nvisitDate\n \nDate\n,\n\n \nadRevenue\n \nFloat32\n,\n\n \nUserAgent\n \nString\n,\n\n \ncCode\n \nFixedString\n(\n3\n),\n\n \nlCode\n \nFixedString\n(\n6\n),\n\n \nsearchWord\n \nString\n,\n\n \nduration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nvisitDate\n,\n \nvisitDate\n,\n \n8192\n);\n\n\n\n\n\n\nGo back to the console:\n\n\nfor\n i in tiny/rankings/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO rankings_tiny FORMAT CSV\n;\n \ndone\n\n\nfor\n i in tiny/uservisits/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO uservisits_tiny FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 1node/rankings/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO rankings_1node FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 1node/uservisits/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO uservisits_1node FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 5nodes/rankings/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO rankings_5nodes_on_single FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 5nodes/uservisits/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO uservisits_5nodes_on_single FORMAT CSV\n;\n \ndone\n\n\n\n\n\n\nQueries for obtaining data samples:\n\n\nSELECT\n \npageURL\n,\n \npageRank\n \nFROM\n \nrankings_1node\n \nWHERE\n \npageRank\n \n \n1000\n\n\n\nSELECT\n \nsubstring\n(\nsourceIP\n,\n \n1\n,\n \n8\n),\n \nsum\n(\nadRevenue\n)\n \nFROM\n \nuservisits_1node\n \nGROUP\n \nBY\n \nsubstring\n(\nsourceIP\n,\n \n1\n,\n \n8\n)\n\n\n\nSELECT\n\n \nsourceIP\n,\n\n \nsum\n(\nadRevenue\n)\n \nAS\n \ntotalRevenue\n,\n\n \navg\n(\npageRank\n)\n \nAS\n \npageRank\n\n\nFROM\n \nrankings_1node\n \nALL\n \nINNER\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nsourceIP\n,\n\n \ndestinationURL\n \nAS\n \npageURL\n,\n\n \nadRevenue\n\n \nFROM\n \nuservisits_1node\n\n \nWHERE\n \n(\nvisitDate\n \n \n1980-01-01\n)\n \nAND\n \n(\nvisitDate\n \n \n1980-04-01\n)\n\n\n)\n \nUSING\n \npageURL\n\n\nGROUP\n \nBY\n \nsourceIP\n\n\nORDER\n \nBY\n \ntotalRevenue\n \nDESC\n\n\nLIMIT\n \n1", + "title": "AMPLab Big Data Benchmark" + }, + { + "location": "/getting_started/example_datasets/amplab_benchmark/#amplab-big-data-benchmark", + "text": "See https://amplab.cs.berkeley.edu/benchmark/ Sign up for a free account at https://aws.amazon.com . You will need a credit card, email and phone number.Get a new access key at https://console.aws.amazon.com/iam/home?nc2=h_m_sc#security_credential Run the following in the console: sudo apt-get install s3cmd\nmkdir tiny ; cd tiny ; \ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/tiny/ . cd ..\nmkdir 1node ; cd 1node ; \ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/1node/ . cd ..\nmkdir 5nodes ; cd 5nodes ; \ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/5nodes/ . cd .. Run the following ClickHouse queries: CREATE TABLE rankings_tiny ( \n pageURL String , \n pageRank UInt32 , \n avgDuration UInt32 ) ENGINE = Log ; CREATE TABLE uservisits_tiny ( \n sourceIP String , \n destinationURL String , \n visitDate Date , \n adRevenue Float32 , \n UserAgent String , \n cCode FixedString ( 3 ), \n lCode FixedString ( 6 ), \n searchWord String , \n duration UInt32 ) ENGINE = MergeTree ( visitDate , visitDate , 8192 ); CREATE TABLE rankings_1node ( \n pageURL String , \n pageRank UInt32 , \n avgDuration UInt32 ) ENGINE = Log ; CREATE TABLE uservisits_1node ( \n sourceIP String , \n destinationURL String , \n visitDate Date , \n adRevenue Float32 , \n UserAgent String , \n cCode FixedString ( 3 ), \n lCode FixedString ( 6 ), \n searchWord String , \n duration UInt32 ) ENGINE = MergeTree ( visitDate , visitDate , 8192 ); CREATE TABLE rankings_5nodes_on_single ( \n pageURL String , \n pageRank UInt32 , \n avgDuration UInt32 ) ENGINE = Log ; CREATE TABLE uservisits_5nodes_on_single ( \n sourceIP String , \n destinationURL String , \n visitDate Date , \n adRevenue Float32 , \n UserAgent String , \n cCode FixedString ( 3 ), \n lCode FixedString ( 6 ), \n searchWord String , \n duration UInt32 ) ENGINE = MergeTree ( visitDate , visitDate , 8192 ); Go back to the console: for i in tiny/rankings/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO rankings_tiny FORMAT CSV ; done for i in tiny/uservisits/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO uservisits_tiny FORMAT CSV ; done for i in 1node/rankings/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO rankings_1node FORMAT CSV ; done for i in 1node/uservisits/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO uservisits_1node FORMAT CSV ; done for i in 5nodes/rankings/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO rankings_5nodes_on_single FORMAT CSV ; done for i in 5nodes/uservisits/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO uservisits_5nodes_on_single FORMAT CSV ; done Queries for obtaining data samples: SELECT pageURL , pageRank FROM rankings_1node WHERE pageRank 1000 SELECT substring ( sourceIP , 1 , 8 ), sum ( adRevenue ) FROM uservisits_1node GROUP BY substring ( sourceIP , 1 , 8 ) SELECT \n sourceIP , \n sum ( adRevenue ) AS totalRevenue , \n avg ( pageRank ) AS pageRank FROM rankings_1node ALL INNER JOIN ( \n SELECT \n sourceIP , \n destinationURL AS pageURL , \n adRevenue \n FROM uservisits_1node \n WHERE ( visitDate 1980-01-01 ) AND ( visitDate 1980-04-01 ) ) USING pageURL GROUP BY sourceIP ORDER BY totalRevenue DESC LIMIT 1", + "title": "AMPLab Big Data Benchmark" + }, + { + "location": "/getting_started/example_datasets/wikistat/", + "text": "WikiStat\n\n\nSee: \nhttp://dumps.wikimedia.org/other/pagecounts-raw/\n\n\nCreating a table:\n\n\nCREATE\n \nTABLE\n \nwikistat\n\n\n(\n\n \ndate\n \nDate\n,\n\n \ntime\n \nDateTime\n,\n\n \nproject\n \nString\n,\n\n \nsubproject\n \nString\n,\n\n \npath\n \nString\n,\n\n \nhits\n \nUInt64\n,\n\n \nsize\n \nUInt64\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\ndate\n,\n \n(\npath\n,\n \ntime\n),\n \n8192\n);\n\n\n\n\n\n\nLoading data:\n\n\nfor\n i in \n{\n2007\n..2016\n}\n;\n \ndo\n \nfor\n j in \n{\n01\n..12\n}\n;\n \ndo\n \necho\n \n$i\n-\n$j\n \n2\n;\n curl -sSL \nhttp://dumps.wikimedia.org/other/pagecounts-raw/\n$i\n/\n$i\n-\n$j\n/\n \n|\n grep -oE \npagecounts-[0-9]+-[0-9]+\\.gz\n;\n \ndone\n;\n \ndone\n \n|\n sort \n|\n uniq \n|\n tee links.txt\ncat links.txt \n|\n \nwhile\n \nread\n link\n;\n \ndo\n wget http://dumps.wikimedia.org/other/pagecounts-raw/\n$(\necho\n \n$link\n \n|\n sed -r \ns/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1/\n)\n/\n$(\necho\n \n$link\n \n|\n sed -r \ns/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1-\\2/\n)\n/\n$link\n;\n \ndone\n\nls -1 /opt/wikistat/ \n|\n grep gz \n|\n \nwhile\n \nread\n i\n;\n \ndo\n \necho\n \n$i\n;\n gzip -cd /opt/wikistat/\n$i\n \n|\n ./wikistat-loader --time\n=\n$(\necho\n -n \n$i\n \n|\n sed -r \ns/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\\.gz/\\1-\\2-\\3 \\4-00-00/\n)\n \n|\n clickhouse-client --query\n=\nINSERT INTO wikistat FORMAT TabSeparated\n;\n \ndone", + "title": "WikiStat" + }, + { + "location": "/getting_started/example_datasets/wikistat/#wikistat", + "text": "See: http://dumps.wikimedia.org/other/pagecounts-raw/ Creating a table: CREATE TABLE wikistat ( \n date Date , \n time DateTime , \n project String , \n subproject String , \n path String , \n hits UInt64 , \n size UInt64 ) ENGINE = MergeTree ( date , ( path , time ), 8192 ); Loading data: for i in { 2007 ..2016 } ; do for j in { 01 ..12 } ; do echo $i - $j 2 ; curl -sSL http://dumps.wikimedia.org/other/pagecounts-raw/ $i / $i - $j / | grep -oE pagecounts-[0-9]+-[0-9]+\\.gz ; done ; done | sort | uniq | tee links.txt\ncat links.txt | while read link ; do wget http://dumps.wikimedia.org/other/pagecounts-raw/ $( echo $link | sed -r s/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1/ ) / $( echo $link | sed -r s/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1-\\2/ ) / $link ; done \nls -1 /opt/wikistat/ | grep gz | while read i ; do echo $i ; gzip -cd /opt/wikistat/ $i | ./wikistat-loader --time = $( echo -n $i | sed -r s/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\\.gz/\\1-\\2-\\3 \\4-00-00/ ) | clickhouse-client --query = INSERT INTO wikistat FORMAT TabSeparated ; done", + "title": "WikiStat" + }, + { + "location": "/getting_started/example_datasets/criteo/", + "text": "Terabyte of click logs from Criteo\n\n\nDownload the data from \nhttp://labs.criteo.com/downloads/download-terabyte-click-logs/\n\n\nCreate a table to import the log to:\n\n\nCREATE\n \nTABLE\n \ncriteo_log\n \n(\ndate\n \nDate\n,\n \nclicked\n \nUInt8\n,\n \nint1\n \nInt32\n,\n \nint2\n \nInt32\n,\n \nint3\n \nInt32\n,\n \nint4\n \nInt32\n,\n \nint5\n \nInt32\n,\n \nint6\n \nInt32\n,\n \nint7\n \nInt32\n,\n \nint8\n \nInt32\n,\n \nint9\n \nInt32\n,\n \nint10\n \nInt32\n,\n \nint11\n \nInt32\n,\n \nint12\n \nInt32\n,\n \nint13\n \nInt32\n,\n \ncat1\n \nString\n,\n \ncat2\n \nString\n,\n \ncat3\n \nString\n,\n \ncat4\n \nString\n,\n \ncat5\n \nString\n,\n \ncat6\n \nString\n,\n \ncat7\n \nString\n,\n \ncat8\n \nString\n,\n \ncat9\n \nString\n,\n \ncat10\n \nString\n,\n \ncat11\n \nString\n,\n \ncat12\n \nString\n,\n \ncat13\n \nString\n,\n \ncat14\n \nString\n,\n \ncat15\n \nString\n,\n \ncat16\n \nString\n,\n \ncat17\n \nString\n,\n \ncat18\n \nString\n,\n \ncat19\n \nString\n,\n \ncat20\n \nString\n,\n \ncat21\n \nString\n,\n \ncat22\n \nString\n,\n \ncat23\n \nString\n,\n \ncat24\n \nString\n,\n \ncat25\n \nString\n,\n \ncat26\n \nString\n)\n \nENGINE\n \n=\n \nLog\n\n\n\n\n\n\nDownload the data:\n\n\nfor\n i in \n{\n00\n..23\n}\n;\n \ndo\n \necho\n \n$i\n;\n zcat datasets/criteo/day_\n${\ni\n#0\n}\n.gz \n|\n sed -r \ns/^/2000-01-\n${\ni\n/00/24\n}\n\\t/\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO criteo_log FORMAT TabSeparated\n;\n \ndone\n\n\n\n\n\n\nCreate a table for the converted data:\n\n\nCREATE\n \nTABLE\n \ncriteo\n\n\n(\n\n \ndate\n \nDate\n,\n\n \nclicked\n \nUInt8\n,\n\n \nint1\n \nInt32\n,\n\n \nint2\n \nInt32\n,\n\n \nint3\n \nInt32\n,\n\n \nint4\n \nInt32\n,\n\n \nint5\n \nInt32\n,\n\n \nint6\n \nInt32\n,\n\n \nint7\n \nInt32\n,\n\n \nint8\n \nInt32\n,\n\n \nint9\n \nInt32\n,\n\n \nint10\n \nInt32\n,\n\n \nint11\n \nInt32\n,\n\n \nint12\n \nInt32\n,\n\n \nint13\n \nInt32\n,\n\n \nicat1\n \nUInt32\n,\n\n \nicat2\n \nUInt32\n,\n\n \nicat3\n \nUInt32\n,\n\n \nicat4\n \nUInt32\n,\n\n \nicat5\n \nUInt32\n,\n\n \nicat6\n \nUInt32\n,\n\n \nicat7\n \nUInt32\n,\n\n \nicat8\n \nUInt32\n,\n\n \nicat9\n \nUInt32\n,\n\n \nicat10\n \nUInt32\n,\n\n \nicat11\n \nUInt32\n,\n\n \nicat12\n \nUInt32\n,\n\n \nicat13\n \nUInt32\n,\n\n \nicat14\n \nUInt32\n,\n\n \nicat15\n \nUInt32\n,\n\n \nicat16\n \nUInt32\n,\n\n \nicat17\n \nUInt32\n,\n\n \nicat18\n \nUInt32\n,\n\n \nicat19\n \nUInt32\n,\n\n \nicat20\n \nUInt32\n,\n\n \nicat21\n \nUInt32\n,\n\n \nicat22\n \nUInt32\n,\n\n \nicat23\n \nUInt32\n,\n\n \nicat24\n \nUInt32\n,\n\n \nicat25\n \nUInt32\n,\n\n \nicat26\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\ndate\n,\n \nintHash32\n(\nicat1\n),\n \n(\ndate\n,\n \nintHash32\n(\nicat1\n)),\n \n8192\n)\n\n\n\n\n\n\nTransform data from the raw log and put it in the second table:\n\n\nINSERT\n \nINTO\n \ncriteo\n \nSELECT\n \ndate\n,\n \nclicked\n,\n \nint1\n,\n \nint2\n,\n \nint3\n,\n \nint4\n,\n \nint5\n,\n \nint6\n,\n \nint7\n,\n \nint8\n,\n \nint9\n,\n \nint10\n,\n \nint11\n,\n \nint12\n,\n \nint13\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat1\n))\n \nAS\n \nicat1\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat2\n))\n \nAS\n \nicat2\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat3\n))\n \nAS\n \nicat3\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat4\n))\n \nAS\n \nicat4\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat5\n))\n \nAS\n \nicat5\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat6\n))\n \nAS\n \nicat6\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat7\n))\n \nAS\n \nicat7\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat8\n))\n \nAS\n \nicat8\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat9\n))\n \nAS\n \nicat9\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat10\n))\n \nAS\n \nicat10\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat11\n))\n \nAS\n \nicat11\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat12\n))\n \nAS\n \nicat12\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat13\n))\n \nAS\n \nicat13\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat14\n))\n \nAS\n \nicat14\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat15\n))\n \nAS\n \nicat15\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat16\n))\n \nAS\n \nicat16\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat17\n))\n \nAS\n \nicat17\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat18\n))\n \nAS\n \nicat18\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat19\n))\n \nAS\n \nicat19\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat20\n))\n \nAS\n \nicat20\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat21\n))\n \nAS\n \nicat21\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat22\n))\n \nAS\n \nicat22\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat23\n))\n \nAS\n \nicat23\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat24\n))\n \nAS\n \nicat24\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat25\n))\n \nAS\n \nicat25\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat26\n))\n \nAS\n \nicat26\n \nFROM\n \ncriteo_log\n;\n\n\n\nDROP\n \nTABLE\n \ncriteo_log\n;", + "title": "Terabyte click logs from Criteo" + }, + { + "location": "/getting_started/example_datasets/criteo/#terabyte-of-click-logs-from-criteo", + "text": "Download the data from http://labs.criteo.com/downloads/download-terabyte-click-logs/ Create a table to import the log to: CREATE TABLE criteo_log ( date Date , clicked UInt8 , int1 Int32 , int2 Int32 , int3 Int32 , int4 Int32 , int5 Int32 , int6 Int32 , int7 Int32 , int8 Int32 , int9 Int32 , int10 Int32 , int11 Int32 , int12 Int32 , int13 Int32 , cat1 String , cat2 String , cat3 String , cat4 String , cat5 String , cat6 String , cat7 String , cat8 String , cat9 String , cat10 String , cat11 String , cat12 String , cat13 String , cat14 String , cat15 String , cat16 String , cat17 String , cat18 String , cat19 String , cat20 String , cat21 String , cat22 String , cat23 String , cat24 String , cat25 String , cat26 String ) ENGINE = Log Download the data: for i in { 00 ..23 } ; do echo $i ; zcat datasets/criteo/day_ ${ i #0 } .gz | sed -r s/^/2000-01- ${ i /00/24 } \\t/ | clickhouse-client --host = example-perftest01j --query = INSERT INTO criteo_log FORMAT TabSeparated ; done Create a table for the converted data: CREATE TABLE criteo ( \n date Date , \n clicked UInt8 , \n int1 Int32 , \n int2 Int32 , \n int3 Int32 , \n int4 Int32 , \n int5 Int32 , \n int6 Int32 , \n int7 Int32 , \n int8 Int32 , \n int9 Int32 , \n int10 Int32 , \n int11 Int32 , \n int12 Int32 , \n int13 Int32 , \n icat1 UInt32 , \n icat2 UInt32 , \n icat3 UInt32 , \n icat4 UInt32 , \n icat5 UInt32 , \n icat6 UInt32 , \n icat7 UInt32 , \n icat8 UInt32 , \n icat9 UInt32 , \n icat10 UInt32 , \n icat11 UInt32 , \n icat12 UInt32 , \n icat13 UInt32 , \n icat14 UInt32 , \n icat15 UInt32 , \n icat16 UInt32 , \n icat17 UInt32 , \n icat18 UInt32 , \n icat19 UInt32 , \n icat20 UInt32 , \n icat21 UInt32 , \n icat22 UInt32 , \n icat23 UInt32 , \n icat24 UInt32 , \n icat25 UInt32 , \n icat26 UInt32 ) ENGINE = MergeTree ( date , intHash32 ( icat1 ), ( date , intHash32 ( icat1 )), 8192 ) Transform data from the raw log and put it in the second table: INSERT INTO criteo SELECT date , clicked , int1 , int2 , int3 , int4 , int5 , int6 , int7 , int8 , int9 , int10 , int11 , int12 , int13 , reinterpretAsUInt32 ( unhex ( cat1 )) AS icat1 , reinterpretAsUInt32 ( unhex ( cat2 )) AS icat2 , reinterpretAsUInt32 ( unhex ( cat3 )) AS icat3 , reinterpretAsUInt32 ( unhex ( cat4 )) AS icat4 , reinterpretAsUInt32 ( unhex ( cat5 )) AS icat5 , reinterpretAsUInt32 ( unhex ( cat6 )) AS icat6 , reinterpretAsUInt32 ( unhex ( cat7 )) AS icat7 , reinterpretAsUInt32 ( unhex ( cat8 )) AS icat8 , reinterpretAsUInt32 ( unhex ( cat9 )) AS icat9 , reinterpretAsUInt32 ( unhex ( cat10 )) AS icat10 , reinterpretAsUInt32 ( unhex ( cat11 )) AS icat11 , reinterpretAsUInt32 ( unhex ( cat12 )) AS icat12 , reinterpretAsUInt32 ( unhex ( cat13 )) AS icat13 , reinterpretAsUInt32 ( unhex ( cat14 )) AS icat14 , reinterpretAsUInt32 ( unhex ( cat15 )) AS icat15 , reinterpretAsUInt32 ( unhex ( cat16 )) AS icat16 , reinterpretAsUInt32 ( unhex ( cat17 )) AS icat17 , reinterpretAsUInt32 ( unhex ( cat18 )) AS icat18 , reinterpretAsUInt32 ( unhex ( cat19 )) AS icat19 , reinterpretAsUInt32 ( unhex ( cat20 )) AS icat20 , reinterpretAsUInt32 ( unhex ( cat21 )) AS icat21 , reinterpretAsUInt32 ( unhex ( cat22 )) AS icat22 , reinterpretAsUInt32 ( unhex ( cat23 )) AS icat23 , reinterpretAsUInt32 ( unhex ( cat24 )) AS icat24 , reinterpretAsUInt32 ( unhex ( cat25 )) AS icat25 , reinterpretAsUInt32 ( unhex ( cat26 )) AS icat26 FROM criteo_log ; DROP TABLE criteo_log ;", + "title": "Terabyte of click logs from Criteo" + }, + { + "location": "/getting_started/example_datasets/star_schema/", + "text": "Star Schema Benchmark\n\n\nCompiling dbgen: \nhttps://github.com/vadimtk/ssb-dbgen\n\n\ngit clone git@github.com:vadimtk/ssb-dbgen.git\n\ncd\n ssb-dbgen\nmake\n\n\n\n\n\nThere will be some warnings during the process, but this is normal.\n\n\nPlace \ndbgen\n and \ndists.dss\n in any location with 800 GB of free disk space.\n\n\nGenerating data:\n\n\n./dbgen -s \n1000\n -T c\n./dbgen -s \n1000\n -T l\n\n\n\n\n\nCreating tables in ClickHouse:\n\n\nCREATE\n \nTABLE\n \nlineorder\n \n(\n\n \nLO_ORDERKEY\n \nUInt32\n,\n\n \nLO_LINENUMBER\n \nUInt8\n,\n\n \nLO_CUSTKEY\n \nUInt32\n,\n\n \nLO_PARTKEY\n \nUInt32\n,\n\n \nLO_SUPPKEY\n \nUInt32\n,\n\n \nLO_ORDERDATE\n \nDate\n,\n\n \nLO_ORDERPRIORITY\n \nString\n,\n\n \nLO_SHIPPRIORITY\n \nUInt8\n,\n\n \nLO_QUANTITY\n \nUInt8\n,\n\n \nLO_EXTENDEDPRICE\n \nUInt32\n,\n\n \nLO_ORDTOTALPRICE\n \nUInt32\n,\n\n \nLO_DISCOUNT\n \nUInt8\n,\n\n \nLO_REVENUE\n \nUInt32\n,\n\n \nLO_SUPPLYCOST\n \nUInt32\n,\n\n \nLO_TAX\n \nUInt8\n,\n\n \nLO_COMMITDATE\n \nDate\n,\n\n \nLO_SHIPMODE\n \nString\n\n\n)\nEngine\n=\nMergeTree\n(\nLO_ORDERDATE\n,(\nLO_ORDERKEY\n,\nLO_LINENUMBER\n,\nLO_ORDERDATE\n),\n8192\n);\n\n\n\nCREATE\n \nTABLE\n \ncustomer\n \n(\n\n \nC_CUSTKEY\n \nUInt32\n,\n\n \nC_NAME\n \nString\n,\n\n \nC_ADDRESS\n \nString\n,\n\n \nC_CITY\n \nString\n,\n\n \nC_NATION\n \nString\n,\n\n \nC_REGION\n \nString\n,\n\n \nC_PHONE\n \nString\n,\n\n \nC_MKTSEGMENT\n \nString\n,\n\n \nC_FAKEDATE\n \nDate\n\n\n)\nEngine\n=\nMergeTree\n(\nC_FAKEDATE\n,(\nC_CUSTKEY\n,\nC_FAKEDATE\n),\n8192\n);\n\n\n\nCREATE\n \nTABLE\n \npart\n \n(\n\n \nP_PARTKEY\n \nUInt32\n,\n\n \nP_NAME\n \nString\n,\n\n \nP_MFGR\n \nString\n,\n\n \nP_CATEGORY\n \nString\n,\n\n \nP_BRAND\n \nString\n,\n\n \nP_COLOR\n \nString\n,\n\n \nP_TYPE\n \nString\n,\n\n \nP_SIZE\n \nUInt8\n,\n\n \nP_CONTAINER\n \nString\n,\n\n \nP_FAKEDATE\n \nDate\n\n\n)\nEngine\n=\nMergeTree\n(\nP_FAKEDATE\n,(\nP_PARTKEY\n,\nP_FAKEDATE\n),\n8192\n);\n\n\n\nCREATE\n \nTABLE\n \nlineorderd\n \nAS\n \nlineorder\n \nENGINE\n \n=\n \nDistributed\n(\nperftest_3shards_1replicas\n,\n \ndefault\n,\n \nlineorder\n,\n \nrand\n());\n\n\nCREATE\n \nTABLE\n \ncustomerd\n \nAS\n \ncustomer\n \nENGINE\n \n=\n \nDistributed\n(\nperftest_3shards_1replicas\n,\n \ndefault\n,\n \ncustomer\n,\n \nrand\n());\n\n\nCREATE\n \nTABLE\n \npartd\n \nAS\n \npart\n \nENGINE\n \n=\n \nDistributed\n(\nperftest_3shards_1replicas\n,\n \ndefault\n,\n \npart\n,\n \nrand\n());\n\n\n\n\n\n\nFor testing on a single server, just use MergeTree tables.\nFor distributed testing, you need to configure the \nperftest_3shards_1replicas\n cluster in the config file.\nNext, create MergeTree tables on each server and a Distributed above them.\n\n\nDownloading data (change 'customer' to 'customerd' in the distributed version):\n\n\ncat customer.tbl \n|\n sed \ns/$/2000-01-01/\n \n|\n clickhouse-client --query \nINSERT INTO customer FORMAT CSV\n\ncat lineorder.tbl \n|\n clickhouse-client --query \nINSERT INTO lineorder FORMAT CSV", + "title": "Star Schema Benchmark" + }, + { + "location": "/getting_started/example_datasets/star_schema/#star-schema-benchmark", + "text": "Compiling dbgen: https://github.com/vadimtk/ssb-dbgen git clone git@github.com:vadimtk/ssb-dbgen.git cd ssb-dbgen\nmake There will be some warnings during the process, but this is normal. Place dbgen and dists.dss in any location with 800 GB of free disk space. Generating data: ./dbgen -s 1000 -T c\n./dbgen -s 1000 -T l Creating tables in ClickHouse: CREATE TABLE lineorder ( \n LO_ORDERKEY UInt32 , \n LO_LINENUMBER UInt8 , \n LO_CUSTKEY UInt32 , \n LO_PARTKEY UInt32 , \n LO_SUPPKEY UInt32 , \n LO_ORDERDATE Date , \n LO_ORDERPRIORITY String , \n LO_SHIPPRIORITY UInt8 , \n LO_QUANTITY UInt8 , \n LO_EXTENDEDPRICE UInt32 , \n LO_ORDTOTALPRICE UInt32 , \n LO_DISCOUNT UInt8 , \n LO_REVENUE UInt32 , \n LO_SUPPLYCOST UInt32 , \n LO_TAX UInt8 , \n LO_COMMITDATE Date , \n LO_SHIPMODE String ) Engine = MergeTree ( LO_ORDERDATE ,( LO_ORDERKEY , LO_LINENUMBER , LO_ORDERDATE ), 8192 ); CREATE TABLE customer ( \n C_CUSTKEY UInt32 , \n C_NAME String , \n C_ADDRESS String , \n C_CITY String , \n C_NATION String , \n C_REGION String , \n C_PHONE String , \n C_MKTSEGMENT String , \n C_FAKEDATE Date ) Engine = MergeTree ( C_FAKEDATE ,( C_CUSTKEY , C_FAKEDATE ), 8192 ); CREATE TABLE part ( \n P_PARTKEY UInt32 , \n P_NAME String , \n P_MFGR String , \n P_CATEGORY String , \n P_BRAND String , \n P_COLOR String , \n P_TYPE String , \n P_SIZE UInt8 , \n P_CONTAINER String , \n P_FAKEDATE Date ) Engine = MergeTree ( P_FAKEDATE ,( P_PARTKEY , P_FAKEDATE ), 8192 ); CREATE TABLE lineorderd AS lineorder ENGINE = Distributed ( perftest_3shards_1replicas , default , lineorder , rand ()); CREATE TABLE customerd AS customer ENGINE = Distributed ( perftest_3shards_1replicas , default , customer , rand ()); CREATE TABLE partd AS part ENGINE = Distributed ( perftest_3shards_1replicas , default , part , rand ()); For testing on a single server, just use MergeTree tables.\nFor distributed testing, you need to configure the perftest_3shards_1replicas cluster in the config file.\nNext, create MergeTree tables on each server and a Distributed above them. Downloading data (change 'customer' to 'customerd' in the distributed version): cat customer.tbl | sed s/$/2000-01-01/ | clickhouse-client --query INSERT INTO customer FORMAT CSV \ncat lineorder.tbl | clickhouse-client --query INSERT INTO lineorder FORMAT CSV", + "title": "Star Schema Benchmark" + }, + { + "location": "/interfaces/", + "text": "Interfaces\n\n\nTo explore the system's capabilities, download data to tables, or make manual queries, use the clickhouse-client program.", + "title": "Introduction" + }, + { + "location": "/interfaces/#interfaces", + "text": "To explore the system's capabilities, download data to tables, or make manual queries, use the clickhouse-client program.", + "title": "Interfaces" + }, + { + "location": "/interfaces/cli/", + "text": "Command-line client\n\n\nTo work from the command line, you can use \nclickhouse-client\n:\n\n\n$ clickhouse-client\nClickHouse client version \n0\n.0.26176.\nConnecting to localhost:9000.\nConnected to ClickHouse server version \n0\n.0.26176.\n\n:\n)\n\n\n\n\n\n\nThe client supports command-line options and configuration files. For more information, see \"\nConfiguring\n\".\n\n\nUsage\n\n\nThe client can be used in interactive and non-interactive (batch) mode.\nTo use batch mode, specify the 'query' parameter, or send data to 'stdin' (it verifies that 'stdin' is not a terminal), or both.\nSimilar to the HTTP interface, when using the 'query' parameter and sending data to 'stdin', the request is a concatenation of the 'query' parameter, a line feed, and the data in 'stdin'. This is convenient for large INSERT queries.\n\n\nExample of using the client to insert data:\n\n\necho\n -ne \n1, \nsome text\n, \n2016-08-14 00:00:00\n\\n2, \nsome more text\n, \n2016-08-14 00:00:01\n \n|\n clickhouse-client --database\n=\ntest\n --query\n=\nINSERT INTO test FORMAT CSV\n;\n\n\ncat \n_EOF | clickhouse-client --database=test --query=\nINSERT INTO test FORMAT CSV\n;\n\n\n3, \nsome text\n, \n2016-08-14 00:00:00\n\n\n4, \nsome more text\n, \n2016-08-14 00:00:01\n\n\n_EOF\n\n\ncat file.csv \n|\n clickhouse-client --database\n=\ntest\n --query\n=\nINSERT INTO test FORMAT CSV\n;\n\n\n\n\n\n\nIn batch mode, the default data format is TabSeparated. You can set the format in the FORMAT clause of the query.\n\n\nBy default, you can only process a single query in batch mode. To make multiple queries from a \"script,\" use the --multiquery parameter. This works for all queries except INSERT. Query results are output consecutively without additional separators.\nSimilarly, to process a large number of queries, you can run 'clickhouse-client' for each query. Note that it may take tens of milliseconds to launch the 'clickhouse-client' program.\n\n\nIn interactive mode, you get a command line where you can enter queries.\n\n\nIf 'multiline' is not specified (the default):To run the query, press Enter. The semicolon is not necessary at the end of the query. To enter a multiline query, enter a backslash \n\\\n before the line feed. After you press Enter, you will be asked to enter the next line of the query.\n\n\nIf multiline is specified:To run a query, end it with a semicolon and press Enter. If the semicolon was omitted at the end of the entered line, you will be asked to enter the next line of the query.\n\n\nOnly a single query is run, so everything after the semicolon is ignored.\n\n\nYou can specify \n\\G\n instead of or after the semicolon. This indicates Vertical format. In this format, each value is printed on a separate line, which is convenient for wide tables. This unusual feature was added for compatibility with the MySQL CLI.\n\n\nThe command line is based on 'readline' (and 'history' or 'libedit', or without a library, depending on the build). In other words, it uses the familiar keyboard shortcuts and keeps a history.\nThe history is written to \n~/.clickhouse-client-history\n.\n\n\nBy default, the format used is PrettyCompact. You can change the format in the FORMAT clause of the query, or by specifying \n\\G\n at the end of the query, using the \n--format\n or \n--vertical\n argument in the command line, or using the client configuration file.\n\n\nTo exit the client, press Ctrl+D (or Ctrl+C), or enter one of the following instead of a query:\"exit\", \"quit\", \"logout\", \"\u0443\u0447\u0448\u0435\", \"\u0439\u0433\u0448\u0435\", \"\u0434\u0449\u043f\u0449\u0433\u0435\", \"exit;\", \"quit;\", \"logout;\", \"\u0443\u0447\u0448\u0435\u0436\", \"\u0439\u0433\u0448\u0435\u0436\", \"\u0434\u0449\u043f\u0449\u0433\u0435\u0436\", \"q\", \"\u0439\", \"q\", \"Q\", \":q\", \"\u0439\", \"\u0419\", \"\u0416\u0439\"\n\n\nWhen processing a query, the client shows:\n\n\n\n\nProgress, which is updated no more than 10 times per second (by default). For quick queries, the progress might not have time to be displayed.\n\n\nThe formatted query after parsing, for debugging.\n\n\nThe result in the specified format.\n\n\nThe number of lines in the result, the time passed, and the average speed of query processing.\n\n\n\n\nYou can cancel a long query by pressing Ctrl+C. However, you will still need to wait a little for the server to abort the request. It is not possible to cancel a query at certain stages. If you don't wait and press Ctrl+C a second time, the client will exit.\n\n\nThe command-line client allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".\n\n\n\n\nConfiguring\n\n\nYou can pass parameters to \nclickhouse-client\n (all parameters have a default value) using:\n\n\n\n\nFrom the Command Line\n\n\n\n\nCommand-line options override the default values and settings in configuration files.\n\n\n\n\nConfiguration files.\n\n\n\n\nSettings in the configuration files override the default values.\n\n\nCommand line options\n\n\n\n\n--host, -h\n -\u2013 The server name, 'localhost' by default. You can use either the name or the IPv4 or IPv6 address.\n\n\n--port\n \u2013 The port to connect to. Default value: 9000. Note that the HTTP interface and the native interface use different ports.\n\n\n--user, -u\n \u2013 The username. Default value: default.\n\n\n--password\n \u2013 The password. Default value: empty string.\n\n\n--query, -q\n \u2013 The query to process when using non-interactive mode.\n\n\n--database, -d\n \u2013 Select the current default database. Default value: the current database from the server settings ('default' by default).\n\n\n--multiline, -m\n \u2013 If specified, allow multiline queries (do not send the query on Enter).\n\n\n--multiquery, -n\n \u2013 If specified, allow processing multiple queries separated by commas. Only works in non-interactive mode.\n\n\n--format, -f\n \u2013 Use the specified default format to output the result.\n\n\n--vertical, -E\n \u2013 If specified, use the Vertical format by default to output the result. This is the same as '--format=Vertical'. In this format, each value is printed on a separate line, which is helpful when displaying wide tables.\n\n\n--time, -t\n \u2013 If specified, print the query execution time to 'stderr' in non-interactive mode.\n\n\n--stacktrace\n \u2013 If specified, also print the stack trace if an exception occurs.\n\n\n-config-file\n \u2013 The name of the configuration file.\n\n\n\n\nConfiguration files\n\n\nclickhouse-client\n uses the first existing file of the following:\n\n\n\n\nDefined in the \n-config-file\n parameter.\n\n\n./clickhouse-client.xml\n\n\n\\~/.clickhouse-client/config.xml\n\n\n/etc/clickhouse-client/config.xml\n\n\n\n\nExample of a config file:\n\n\nconfig\n\n \nuser\nusername\n/user\n\n \npassword\npassword\n/password\n\n\n/config", + "title": "Command-line client" + }, + { + "location": "/interfaces/cli/#command-line-client", + "text": "To work from the command line, you can use clickhouse-client : $ clickhouse-client\nClickHouse client version 0 .0.26176.\nConnecting to localhost:9000.\nConnected to ClickHouse server version 0 .0.26176.\n\n: ) The client supports command-line options and configuration files. For more information, see \" Configuring \".", + "title": "Command-line client" + }, + { + "location": "/interfaces/cli/#usage", + "text": "The client can be used in interactive and non-interactive (batch) mode.\nTo use batch mode, specify the 'query' parameter, or send data to 'stdin' (it verifies that 'stdin' is not a terminal), or both.\nSimilar to the HTTP interface, when using the 'query' parameter and sending data to 'stdin', the request is a concatenation of the 'query' parameter, a line feed, and the data in 'stdin'. This is convenient for large INSERT queries. Example of using the client to insert data: echo -ne 1, some text , 2016-08-14 00:00:00 \\n2, some more text , 2016-08-14 00:00:01 | clickhouse-client --database = test --query = INSERT INTO test FORMAT CSV ; \n\ncat _EOF | clickhouse-client --database=test --query= INSERT INTO test FORMAT CSV ; 3, some text , 2016-08-14 00:00:00 4, some more text , 2016-08-14 00:00:01 _EOF \n\ncat file.csv | clickhouse-client --database = test --query = INSERT INTO test FORMAT CSV ; In batch mode, the default data format is TabSeparated. You can set the format in the FORMAT clause of the query. By default, you can only process a single query in batch mode. To make multiple queries from a \"script,\" use the --multiquery parameter. This works for all queries except INSERT. Query results are output consecutively without additional separators.\nSimilarly, to process a large number of queries, you can run 'clickhouse-client' for each query. Note that it may take tens of milliseconds to launch the 'clickhouse-client' program. In interactive mode, you get a command line where you can enter queries. If 'multiline' is not specified (the default):To run the query, press Enter. The semicolon is not necessary at the end of the query. To enter a multiline query, enter a backslash \\ before the line feed. After you press Enter, you will be asked to enter the next line of the query. If multiline is specified:To run a query, end it with a semicolon and press Enter. If the semicolon was omitted at the end of the entered line, you will be asked to enter the next line of the query. Only a single query is run, so everything after the semicolon is ignored. You can specify \\G instead of or after the semicolon. This indicates Vertical format. In this format, each value is printed on a separate line, which is convenient for wide tables. This unusual feature was added for compatibility with the MySQL CLI. The command line is based on 'readline' (and 'history' or 'libedit', or without a library, depending on the build). In other words, it uses the familiar keyboard shortcuts and keeps a history.\nThe history is written to ~/.clickhouse-client-history . By default, the format used is PrettyCompact. You can change the format in the FORMAT clause of the query, or by specifying \\G at the end of the query, using the --format or --vertical argument in the command line, or using the client configuration file. To exit the client, press Ctrl+D (or Ctrl+C), or enter one of the following instead of a query:\"exit\", \"quit\", \"logout\", \"\u0443\u0447\u0448\u0435\", \"\u0439\u0433\u0448\u0435\", \"\u0434\u0449\u043f\u0449\u0433\u0435\", \"exit;\", \"quit;\", \"logout;\", \"\u0443\u0447\u0448\u0435\u0436\", \"\u0439\u0433\u0448\u0435\u0436\", \"\u0434\u0449\u043f\u0449\u0433\u0435\u0436\", \"q\", \"\u0439\", \"q\", \"Q\", \":q\", \"\u0439\", \"\u0419\", \"\u0416\u0439\" When processing a query, the client shows: Progress, which is updated no more than 10 times per second (by default). For quick queries, the progress might not have time to be displayed. The formatted query after parsing, for debugging. The result in the specified format. The number of lines in the result, the time passed, and the average speed of query processing. You can cancel a long query by pressing Ctrl+C. However, you will still need to wait a little for the server to abort the request. It is not possible to cancel a query at certain stages. If you don't wait and press Ctrl+C a second time, the client will exit. The command-line client allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".", + "title": "Usage" + }, + { + "location": "/interfaces/cli/#configuring", + "text": "You can pass parameters to clickhouse-client (all parameters have a default value) using: From the Command Line Command-line options override the default values and settings in configuration files. Configuration files. Settings in the configuration files override the default values.", + "title": "Configuring" + }, + { + "location": "/interfaces/cli/#command-line-options", + "text": "--host, -h -\u2013 The server name, 'localhost' by default. You can use either the name or the IPv4 or IPv6 address. --port \u2013 The port to connect to. Default value: 9000. Note that the HTTP interface and the native interface use different ports. --user, -u \u2013 The username. Default value: default. --password \u2013 The password. Default value: empty string. --query, -q \u2013 The query to process when using non-interactive mode. --database, -d \u2013 Select the current default database. Default value: the current database from the server settings ('default' by default). --multiline, -m \u2013 If specified, allow multiline queries (do not send the query on Enter). --multiquery, -n \u2013 If specified, allow processing multiple queries separated by commas. Only works in non-interactive mode. --format, -f \u2013 Use the specified default format to output the result. --vertical, -E \u2013 If specified, use the Vertical format by default to output the result. This is the same as '--format=Vertical'. In this format, each value is printed on a separate line, which is helpful when displaying wide tables. --time, -t \u2013 If specified, print the query execution time to 'stderr' in non-interactive mode. --stacktrace \u2013 If specified, also print the stack trace if an exception occurs. -config-file \u2013 The name of the configuration file.", + "title": "Command line options" + }, + { + "location": "/interfaces/cli/#configuration-files", + "text": "clickhouse-client uses the first existing file of the following: Defined in the -config-file parameter. ./clickhouse-client.xml \\~/.clickhouse-client/config.xml /etc/clickhouse-client/config.xml Example of a config file: config \n user username /user \n password password /password /config", + "title": "Configuration files" + }, + { + "location": "/interfaces/http_interface/", + "text": "HTTP interface\n\n\nThe HTTP interface lets you use ClickHouse on any platform from any programming language. We use it for working from Java and Perl, as well as shell scripts. In other departments, the HTTP interface is used from Perl, Python, and Go. The HTTP interface is more limited than the native interface, but it has better compatibility.\n\n\nBy default, clickhouse-server listens for HTTP on port 8123 (this can be changed in the config).\nIf you make a GET / request without parameters, it returns the string \"Ok\" (with a line feed at the end). You can use this in health-check scripts.\n\n\n$ curl \nhttp://localhost:8123/\n\nOk.\n\n\n\n\n\nSend the request as a URL 'query' parameter, or as a POST. Or send the beginning of the query in the 'query' parameter, and the rest in the POST (we'll explain later why this is necessary). The size of the URL is limited to 16 KB, so keep this in mind when sending large queries.\n\n\nIf successful, you receive the 200 response code and the result in the response body.\nIf an error occurs, you receive the 500 response code and an error description text in the response body.\n\n\nWhen using the GET method, 'readonly' is set. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.\n\n\nExamples:\n\n\n$ curl \nhttp://localhost:8123/?query=SELECT%201\n\n\n1\n\n\n$ wget -O- -q \nhttp://localhost:8123/?query=SELECT 1\n\n\n1\n\n\n$ GET \nhttp://localhost:8123/?query=SELECT 1\n\n\n1\n\n\n$ \necho\n -ne \nGET /?query=SELECT%201 HTTP/1.0\\r\\n\\r\\n\n \n|\n nc localhost \n8123\n\nHTTP/1.0 \n200\n OK\nConnection: Close\nDate: Fri, \n16\n Nov \n2012\n \n19\n:21:50 GMT\n\n\n1\n\n\n\n\n\n\nAs you can see, curl is somewhat inconvenient in that spaces must be URL escaped.Although wget escapes everything itself, we don't recommend using it because it doesn't work well over HTTP 1.1 when using keep-alive and Transfer-Encoding: chunked.\n\n\n$ \necho\n \nSELECT 1\n \n|\n curl \nhttp://localhost:8123/\n --data-binary @-\n\n1\n\n\n$ \necho\n \nSELECT 1\n \n|\n curl \nhttp://localhost:8123/?query=\n --data-binary @-\n\n1\n\n\n$ \necho\n \n1\n \n|\n curl \nhttp://localhost:8123/?query=SELECT\n --data-binary @-\n\n1\n\n\n\n\n\n\nIf part of the query is sent in the parameter, and part in the POST, a line feed is inserted between these two data parts.\nExample (this won't work):\n\n\n$ \necho\n \nECT 1\n \n|\n curl \nhttp://localhost:8123/?query=SEL\n --data-binary @-\nCode: \n59\n, e.displayText\n()\n \n=\n DB::Exception: Syntax error: failed at position \n0\n: SEL\nECT \n1\n\n, expected One of: SHOW TABLES, SHOW DATABASES, SELECT, INSERT, CREATE, ATTACH, RENAME, DROP, DETACH, USE, SET, OPTIMIZE., e.what\n()\n \n=\n DB::Exception\n\n\n\n\n\nBy default, data is returned in TabSeparated format (for more information, see the \"Formats\" section).\nYou use the FORMAT clause of the query to request any other format.\n\n\n$ \necho\n \nSELECT 1 FORMAT Pretty\n \n|\n curl \nhttp://localhost:8123/?\n --data-binary @-\n\u250f\u2501\u2501\u2501\u2513\n\u2503 \n1\n \u2503\n\u2521\u2501\u2501\u2501\u2529\n\u2502 \n1\n \u2502\n\u2514\u2500\u2500\u2500\u2518\n\n\n\n\n\nThe POST method of transmitting data is necessary for INSERT queries. In this case, you can write the beginning of the query in the URL parameter, and use POST to pass the data to insert. The data to insert could be, for example, a tab-separated dump from MySQL. In this way, the INSERT query replaces LOAD DATA LOCAL INFILE from MySQL.\n\n\nExamples: Creating a table:\n\n\necho\n \nCREATE TABLE t (a UInt8) ENGINE = Memory\n \n|\n POST \nhttp://localhost:8123/\n\n\n\n\n\n\nUsing the familiar INSERT query for data insertion:\n\n\necho\n \nINSERT INTO t VALUES (1),(2),(3)\n \n|\n POST \nhttp://localhost:8123/\n\n\n\n\n\n\nData can be sent separately from the query:\n\n\necho\n \n(4),(5),(6)\n \n|\n POST \nhttp://localhost:8123/?query=INSERT INTO t VALUES\n\n\n\n\n\n\nYou can specify any data format. The 'Values' format is the same as what is used when writing INSERT INTO t VALUES:\n\n\necho\n \n(7),(8),(9)\n \n|\n POST \nhttp://localhost:8123/?query=INSERT INTO t FORMAT Values\n\n\n\n\n\n\nTo insert data from a tab-separated dump, specify the corresponding format:\n\n\necho\n -ne \n10\\n11\\n12\\n\n \n|\n POST \nhttp://localhost:8123/?query=INSERT INTO t FORMAT TabSeparated\n\n\n\n\n\n\nReading the table contents. Data is output in random order due to parallel query processing:\n\n\n$ GET \nhttp://localhost:8123/?query=SELECT a FROM t\n\n\n7\n\n\n8\n\n\n9\n\n\n10\n\n\n11\n\n\n12\n\n\n1\n\n\n2\n\n\n3\n\n\n4\n\n\n5\n\n\n6\n\n\n\n\n\n\nDeleting the table.\n\n\nPOST \nhttp://localhost:8123/?query=DROP TABLE t\n\n\n\n\n\n\nFor successful requests that don't return a data table, an empty response body is returned.\n\n\nYou can use the internal ClickHouse compression format when transmitting data. The compressed data has a non-standard format, and you will need to use the special clickhouse-compressor program to work with it (it is installed with the clickhouse-client package).\n\n\nIf you specified 'compress=1' in the URL, the server will compress the data it sends you.\nIf you specified 'decompress=1' in the URL, the server will decompress the same data that you pass in the POST method.\n\n\nIt is also possible to use the standard gzip-based HTTP compression. To send a POST request compressed using gzip, append the request header \nContent-Encoding: gzip\n.\nIn order for ClickHouse to compress the response using gzip, you must append \nAccept-Encoding: gzip\n to the request headers, and enable the ClickHouse setting \nenable_http_compression\n.\n\n\nYou can use this to reduce network traffic when transmitting a large amount of data, or for creating dumps that are immediately compressed.\n\n\nYou can use the 'database' URL parameter to specify the default database.\n\n\n$ \necho\n \nSELECT number FROM numbers LIMIT 10\n \n|\n curl \nhttp://localhost:8123/?database=system\n --data-binary @-\n\n0\n\n\n1\n\n\n2\n\n\n3\n\n\n4\n\n\n5\n\n\n6\n\n\n7\n\n\n8\n\n\n9\n\n\n\n\n\n\nBy default, the database that is registered in the server settings is used as the default database. By default, this is the database called 'default'. Alternatively, you can always specify the database using a dot before the table name.\n\n\nThe username and password can be indicated in one of two ways:\n\n\n\n\nUsing HTTP Basic Authentication. Example:\n\n\n\n\necho\n \nSELECT 1\n \n|\n curl \nhttp://user:password@localhost:8123/\n -d @-\n\n\n\n\n\n\n\nIn the 'user' and 'password' URL parameters. Example:\n\n\n\n\necho\n \nSELECT 1\n \n|\n curl \nhttp://localhost:8123/?user=user\npassword=password\n -d @-\n\n\n\n\n\nIf the user name is not indicated, the username 'default' is used. If the password is not indicated, an empty password is used.\nYou can also use the URL parameters to specify any settings for processing a single query, or entire profiles of settings. Example:\nhttp://localhost:8123/?profile=web\nmax_rows_to_read=1000000000\nquery=SELECT+1\n\n\nFor more information, see the section \"Settings\".\n\n\n$ \necho\n \nSELECT number FROM system.numbers LIMIT 10\n \n|\n curl \nhttp://localhost:8123/?\n --data-binary @-\n\n0\n\n\n1\n\n\n2\n\n\n3\n\n\n4\n\n\n5\n\n\n6\n\n\n7\n\n\n8\n\n\n9\n\n\n\n\n\n\nFor information about other parameters, see the section \"SET\".\n\n\nSimilarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to add the \nsession_id\n GET parameter to the request. You can use any string as the session ID. By default, the session is terminated after 60 seconds of inactivity. To change this timeout, modify the \ndefault_session_timeout\n setting in the server configuration, or add the \nsession_timeout\n GET parameter to the request. To check the session status, use the \nsession_check=1\n parameter. Only one query at a time can be executed within a single session.\n\n\nYou have the option to receive information about the progress of query execution in X-ClickHouse-Progress headers. To do this, enable the setting send_progress_in_http_headers.\n\n\nRunning requests don't stop automatically if the HTTP connection is lost. Parsing and data formatting are performed on the server side, and using the network might be ineffective.\nThe optional 'query_id' parameter can be passed as the query ID (any string). For more information, see the section \"Settings, replace_running_query\".\n\n\nThe optional 'quota_key' parameter can be passed as the quota key (any string). For more information, see the section \"Quotas\".\n\n\nThe HTTP interface allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".\n\n\nResponse buffering\n\n\nYou can enable response buffering on the server side. The \nbuffer_size\n and \nwait_end_of_query\n URL parameters are provided for this purpose.\n\n\nbuffer_size\n determines the number of bytes in the result to buffer in the server memory. If the result body is larger than this threshold, the buffer is written to the HTTP channel, and the remaining data is sent directly to the HTTP channel.\n\n\nTo ensure that the entire response is buffered, set \nwait_end_of_query=1\n. In this case, the data that is not stored in memory will be buffered in a temporary server file.\n\n\nExample:\n\n\ncurl -sS \nhttp://localhost:8123/?max_result_bytes=4000000\nbuffer_size=3000000\nwait_end_of_query=1\n -d \nSELECT toUInt8(number) FROM system.numbers LIMIT 9000000 FORMAT RowBinary\n\n\n\n\n\n\nUse buffering to avoid situations where a query processing error occurred after the response code and HTTP headers were sent to the client. In this situation, an error message is written at the end of the response body, and on the client side, the error can only be detected at the parsing stage.", + "title": "HTTP interface" + }, + { + "location": "/interfaces/http_interface/#http-interface", + "text": "The HTTP interface lets you use ClickHouse on any platform from any programming language. We use it for working from Java and Perl, as well as shell scripts. In other departments, the HTTP interface is used from Perl, Python, and Go. The HTTP interface is more limited than the native interface, but it has better compatibility. By default, clickhouse-server listens for HTTP on port 8123 (this can be changed in the config).\nIf you make a GET / request without parameters, it returns the string \"Ok\" (with a line feed at the end). You can use this in health-check scripts. $ curl http://localhost:8123/ \nOk. Send the request as a URL 'query' parameter, or as a POST. Or send the beginning of the query in the 'query' parameter, and the rest in the POST (we'll explain later why this is necessary). The size of the URL is limited to 16 KB, so keep this in mind when sending large queries. If successful, you receive the 200 response code and the result in the response body.\nIf an error occurs, you receive the 500 response code and an error description text in the response body. When using the GET method, 'readonly' is set. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter. Examples: $ curl http://localhost:8123/?query=SELECT%201 1 \n\n$ wget -O- -q http://localhost:8123/?query=SELECT 1 1 \n\n$ GET http://localhost:8123/?query=SELECT 1 1 \n\n$ echo -ne GET /?query=SELECT%201 HTTP/1.0\\r\\n\\r\\n | nc localhost 8123 \nHTTP/1.0 200 OK\nConnection: Close\nDate: Fri, 16 Nov 2012 19 :21:50 GMT 1 As you can see, curl is somewhat inconvenient in that spaces must be URL escaped.Although wget escapes everything itself, we don't recommend using it because it doesn't work well over HTTP 1.1 when using keep-alive and Transfer-Encoding: chunked. $ echo SELECT 1 | curl http://localhost:8123/ --data-binary @- 1 \n\n$ echo SELECT 1 | curl http://localhost:8123/?query= --data-binary @- 1 \n\n$ echo 1 | curl http://localhost:8123/?query=SELECT --data-binary @- 1 If part of the query is sent in the parameter, and part in the POST, a line feed is inserted between these two data parts.\nExample (this won't work): $ echo ECT 1 | curl http://localhost:8123/?query=SEL --data-binary @-\nCode: 59 , e.displayText () = DB::Exception: Syntax error: failed at position 0 : SEL\nECT 1 \n, expected One of: SHOW TABLES, SHOW DATABASES, SELECT, INSERT, CREATE, ATTACH, RENAME, DROP, DETACH, USE, SET, OPTIMIZE., e.what () = DB::Exception By default, data is returned in TabSeparated format (for more information, see the \"Formats\" section).\nYou use the FORMAT clause of the query to request any other format. $ echo SELECT 1 FORMAT Pretty | curl http://localhost:8123/? --data-binary @-\n\u250f\u2501\u2501\u2501\u2513\n\u2503 1 \u2503\n\u2521\u2501\u2501\u2501\u2529\n\u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2518 The POST method of transmitting data is necessary for INSERT queries. In this case, you can write the beginning of the query in the URL parameter, and use POST to pass the data to insert. The data to insert could be, for example, a tab-separated dump from MySQL. In this way, the INSERT query replaces LOAD DATA LOCAL INFILE from MySQL. Examples: Creating a table: echo CREATE TABLE t (a UInt8) ENGINE = Memory | POST http://localhost:8123/ Using the familiar INSERT query for data insertion: echo INSERT INTO t VALUES (1),(2),(3) | POST http://localhost:8123/ Data can be sent separately from the query: echo (4),(5),(6) | POST http://localhost:8123/?query=INSERT INTO t VALUES You can specify any data format. The 'Values' format is the same as what is used when writing INSERT INTO t VALUES: echo (7),(8),(9) | POST http://localhost:8123/?query=INSERT INTO t FORMAT Values To insert data from a tab-separated dump, specify the corresponding format: echo -ne 10\\n11\\n12\\n | POST http://localhost:8123/?query=INSERT INTO t FORMAT TabSeparated Reading the table contents. Data is output in random order due to parallel query processing: $ GET http://localhost:8123/?query=SELECT a FROM t 7 8 9 10 11 12 1 2 3 4 5 6 Deleting the table. POST http://localhost:8123/?query=DROP TABLE t For successful requests that don't return a data table, an empty response body is returned. You can use the internal ClickHouse compression format when transmitting data. The compressed data has a non-standard format, and you will need to use the special clickhouse-compressor program to work with it (it is installed with the clickhouse-client package). If you specified 'compress=1' in the URL, the server will compress the data it sends you.\nIf you specified 'decompress=1' in the URL, the server will decompress the same data that you pass in the POST method. It is also possible to use the standard gzip-based HTTP compression. To send a POST request compressed using gzip, append the request header Content-Encoding: gzip .\nIn order for ClickHouse to compress the response using gzip, you must append Accept-Encoding: gzip to the request headers, and enable the ClickHouse setting enable_http_compression . You can use this to reduce network traffic when transmitting a large amount of data, or for creating dumps that are immediately compressed. You can use the 'database' URL parameter to specify the default database. $ echo SELECT number FROM numbers LIMIT 10 | curl http://localhost:8123/?database=system --data-binary @- 0 1 2 3 4 5 6 7 8 9 By default, the database that is registered in the server settings is used as the default database. By default, this is the database called 'default'. Alternatively, you can always specify the database using a dot before the table name. The username and password can be indicated in one of two ways: Using HTTP Basic Authentication. Example: echo SELECT 1 | curl http://user:password@localhost:8123/ -d @- In the 'user' and 'password' URL parameters. Example: echo SELECT 1 | curl http://localhost:8123/?user=user password=password -d @- If the user name is not indicated, the username 'default' is used. If the password is not indicated, an empty password is used.\nYou can also use the URL parameters to specify any settings for processing a single query, or entire profiles of settings. Example:\nhttp://localhost:8123/?profile=web max_rows_to_read=1000000000 query=SELECT+1 For more information, see the section \"Settings\". $ echo SELECT number FROM system.numbers LIMIT 10 | curl http://localhost:8123/? --data-binary @- 0 1 2 3 4 5 6 7 8 9 For information about other parameters, see the section \"SET\". Similarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to add the session_id GET parameter to the request. You can use any string as the session ID. By default, the session is terminated after 60 seconds of inactivity. To change this timeout, modify the default_session_timeout setting in the server configuration, or add the session_timeout GET parameter to the request. To check the session status, use the session_check=1 parameter. Only one query at a time can be executed within a single session. You have the option to receive information about the progress of query execution in X-ClickHouse-Progress headers. To do this, enable the setting send_progress_in_http_headers. Running requests don't stop automatically if the HTTP connection is lost. Parsing and data formatting are performed on the server side, and using the network might be ineffective.\nThe optional 'query_id' parameter can be passed as the query ID (any string). For more information, see the section \"Settings, replace_running_query\". The optional 'quota_key' parameter can be passed as the quota key (any string). For more information, see the section \"Quotas\". The HTTP interface allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".", + "title": "HTTP interface" + }, + { + "location": "/interfaces/http_interface/#response-buffering", + "text": "You can enable response buffering on the server side. The buffer_size and wait_end_of_query URL parameters are provided for this purpose. buffer_size determines the number of bytes in the result to buffer in the server memory. If the result body is larger than this threshold, the buffer is written to the HTTP channel, and the remaining data is sent directly to the HTTP channel. To ensure that the entire response is buffered, set wait_end_of_query=1 . In this case, the data that is not stored in memory will be buffered in a temporary server file. Example: curl -sS http://localhost:8123/?max_result_bytes=4000000 buffer_size=3000000 wait_end_of_query=1 -d SELECT toUInt8(number) FROM system.numbers LIMIT 9000000 FORMAT RowBinary Use buffering to avoid situations where a query processing error occurred after the response code and HTTP headers were sent to the client. In this situation, an error message is written at the end of the response body, and on the client side, the error can only be detected at the parsing stage.", + "title": "Response buffering" + }, + { + "location": "/interfaces/jdbc/", + "text": "JDBC driver\n\n\nThere is an official JDBC driver for ClickHouse. See \nhere\n .", + "title": "JDBC driver" + }, + { + "location": "/interfaces/jdbc/#jdbc-driver", + "text": "There is an official JDBC driver for ClickHouse. See here .", + "title": "JDBC driver" + }, + { + "location": "/interfaces/tcp/", + "text": "Native interface (TCP)\n\n\nThe native interface is used in the \"clickhouse-client\" command-line client for interaction between servers with distributed query processing, and also in C++ programs. We will only cover the command-line client.", + "title": "Native interface (TCP)" + }, + { + "location": "/interfaces/tcp/#native-interface-tcp", + "text": "The native interface is used in the \"clickhouse-client\" command-line client for interaction between servers with distributed query processing, and also in C++ programs. We will only cover the command-line client.", + "title": "Native interface (TCP)" + }, + { + "location": "/interfaces/third-party_client_libraries/", + "text": "Libraries from third-party developers\n\n\nThere are libraries for working with ClickHouse for:\n\n\n\n\nPython\n\n\ninfi.clickhouse_orm\n\n\nsqlalchemy-clickhouse\n\n\nclickhouse-driver\n\n\nclickhouse-client\n\n\n\n\n\n\nPHP\n\n\nclickhouse-php-client\n\n\nPhpClickHouseClient\n\n\nphpClickHouse\n\n\nclickhouse-client\n\n\n\n\n\n\nGo\n\n\nclickhouse\n\n\ngo-clickhouse\n\n\nmailrugo-clickhouse\n\n\ngolang-clickhouse\n\n\n\n\n\n\nNodeJs\n\n\nclickhouse (NodeJs)\n\n\nnode-clickhouse\n\n\n\n\n\n\nPerl\n\n\nperl-DBD-ClickHouse\n\n\nHTTP-ClickHouse\n\n\nAnyEvent-ClickHouse\n\n\n\n\n\n\nRuby\n\n\nclickhouse (Ruby)\n\n\n\n\n\n\nR\n\n\nclickhouse-r\n\n\nRClickhouse\n\n\n\n\n\n\n.NET\n\n\nClickHouse-Net\n\n\n\n\n\n\nC++\n\n\nclickhouse-cpp\n\n\n\n\n\n\nElixir\n\n\nclickhousex\n\n\nclickhouse_ecto\n\n\n\n\n\n\nJava\n\n\nclickhouse-client-java\n\n\n\n\n\n\n\n\nWe have not tested these libraries. They are listed in random order.", + "title": "Libraries from third-party developers" + }, + { + "location": "/interfaces/third-party_client_libraries/#libraries-from-third-party-developers", + "text": "There are libraries for working with ClickHouse for: Python infi.clickhouse_orm sqlalchemy-clickhouse clickhouse-driver clickhouse-client PHP clickhouse-php-client PhpClickHouseClient phpClickHouse clickhouse-client Go clickhouse go-clickhouse mailrugo-clickhouse golang-clickhouse NodeJs clickhouse (NodeJs) node-clickhouse Perl perl-DBD-ClickHouse HTTP-ClickHouse AnyEvent-ClickHouse Ruby clickhouse (Ruby) R clickhouse-r RClickhouse .NET ClickHouse-Net C++ clickhouse-cpp Elixir clickhousex clickhouse_ecto Java clickhouse-client-java We have not tested these libraries. They are listed in random order.", + "title": "Libraries from third-party developers" + }, + { + "location": "/interfaces/third-party_gui/", + "text": "Visual interfaces from third-party developers\n\n\nTabix\n\n\nWeb interface for ClickHouse in the \nTabix\n project.\n\n\nFeatures:\n\n\n\n\nWorks with ClickHouse directly from the browser, without the need to install additional software.\n\n\nQuery editor with syntax highlighting.\n\n\nAuto-completion of commands.\n\n\nTools for graphical analysis of query execution.\n\n\nColor scheme options.\n\n\n\n\nTabix documentation\n.\n\n\nHouseOps\n\n\nHouseOps\n is a unique Desktop ClickHouse Ops UI / IDE for OSX, Linux and Windows.\n\n\nFeatures:\n\n\n\n\nQuery builder;\n\n\nDatabase manangement (soon);\n\n\nUsers manangement (soon);\n\n\nReal-Time Data Analytics (soon);\n\n\nCluster/Infra monitoring (soon);\n\n\nCluster manangement (soon);\n\n\nKafka and Replicated tables monitoring (soon);\n\n\nAnd a lot of others features (soon) for you take a beautiful implementation of ClickHouse.", + "title": "Visual interfaces from third-party developers" + }, + { + "location": "/interfaces/third-party_gui/#visual-interfaces-from-third-party-developers", + "text": "", + "title": "Visual interfaces from third-party developers" + }, + { + "location": "/interfaces/third-party_gui/#tabix", + "text": "Web interface for ClickHouse in the Tabix project.", + "title": "Tabix" + }, + { + "location": "/interfaces/third-party_gui/#features", + "text": "Works with ClickHouse directly from the browser, without the need to install additional software. Query editor with syntax highlighting. Auto-completion of commands. Tools for graphical analysis of query execution. Color scheme options. Tabix documentation .", + "title": "Features:" + }, + { + "location": "/interfaces/third-party_gui/#houseops", + "text": "HouseOps is a unique Desktop ClickHouse Ops UI / IDE for OSX, Linux and Windows.", + "title": "HouseOps" + }, + { + "location": "/interfaces/third-party_gui/#features_1", + "text": "Query builder; Database manangement (soon); Users manangement (soon); Real-Time Data Analytics (soon); Cluster/Infra monitoring (soon); Cluster manangement (soon); Kafka and Replicated tables monitoring (soon); And a lot of others features (soon) for you take a beautiful implementation of ClickHouse.", + "title": "Features:" + }, + { + "location": "/query_language/queries/", + "text": "Queries\n\n\nCREATE DATABASE\n\n\nCreating db_name databases\n\n\nCREATE\n \nDATABASE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \ndb_name\n\n\n\n\n\n\nA database\n is just a directory for tables.\nIf \nIF NOT EXISTS\n is included, the query won't return an error if the database already exists.\n\n\n\n\nCREATE TABLE\n\n\nThe \nCREATE TABLE\n query can have several forms.\n\n\nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n(\n\n \nname1\n \n[\ntype1\n]\n \n[\nDEFAULT\n|\nMATERIALIZED\n|\nALIAS\n \nexpr1\n],\n\n \nname2\n \n[\ntype2\n]\n \n[\nDEFAULT\n|\nMATERIALIZED\n|\nALIAS\n \nexpr2\n],\n\n \n...\n\n\n)\n \nENGINE\n \n=\n \nengine\n\n\n\n\n\n\nCreates a table named 'name' in the 'db' database or the current database if 'db' is not set, with the structure specified in brackets and the 'engine' engine.\nThe structure of the table is a list of column descriptions. If indexes are supported by the engine, they are indicated as parameters for the table engine.\n\n\nA column description is \nname type\n in the simplest case. Example: \nRegionID UInt32\n.\nExpressions can also be defined for default values (see below).\n\n\nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \nAS\n \n[\ndb2\n.]\nname2\n \n[\nENGINE\n \n=\n \nengine\n]\n\n\n\n\n\n\nCreates a table with the same structure as another table. You can specify a different engine for the table. If the engine is not specified, the same engine will be used as for the \ndb2.name2\n table.\n\n\nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \nENGINE\n \n=\n \nengine\n \nAS\n \nSELECT\n \n...\n\n\n\n\n\n\nCreates a table with a structure like the result of the \nSELECT\n query, with the 'engine' engine, and fills it with data from SELECT.\n\n\nIn all cases, if \nIF NOT EXISTS\n is specified, the query won't return an error if the table already exists. In this case, the query won't do anything.\n\n\nDefault values\n\n\nThe column description can specify an expression for a default value, in one of the following ways:\nDEFAULT expr\n, \nMATERIALIZED expr\n, \nALIAS expr\n.\nExample: \nURLDomain String DEFAULT domain(URL)\n.\n\n\nIf an expression for the default value is not defined, the default values will be set to zeros for numbers, empty strings for strings, empty arrays for arrays, and \n0000-00-00\n for dates or \n0000-00-00 00:00:00\n for dates with time. NULLs are not supported.\n\n\nIf the default expression is defined, the column type is optional. If there isn't an explicitly defined type, the default expression type is used. Example: \nEventDate DEFAULT toDate(EventTime)\n \u2013 the 'Date' type will be used for the 'EventDate' column.\n\n\nIf the data type and default expression are defined explicitly, this expression will be cast to the specified type using type casting functions. Example: \nHits UInt32 DEFAULT 0\n means the same thing as \nHits UInt32 DEFAULT toUInt32(0)\n.\n\n\nDefault expressions may be defined as an arbitrary expression from table constants and columns. When creating and changing the table structure, it checks that expressions don't contain loops. For INSERT, it checks that expressions are resolvable \u2013 that all columns they can be calculated from have been passed.\n\n\nDEFAULT expr\n\n\nNormal default value. If the INSERT query doesn't specify the corresponding column, it will be filled in by computing the corresponding expression.\n\n\nMATERIALIZED expr\n\n\nMaterialized expression. Such a column can't be specified for INSERT, because it is always calculated.\nFor an INSERT without a list of columns, these columns are not considered.\nIn addition, this column is not substituted when using an asterisk in a SELECT query. This is to preserve the invariant that the dump obtained using \nSELECT *\n can be inserted back into the table using INSERT without specifying the list of columns.\n\n\nALIAS expr\n\n\nSynonym. Such a column isn't stored in the table at all.\nIts values can't be inserted in a table, and it is not substituted when using an asterisk in a SELECT query.\nIt can be used in SELECTs if the alias is expanded during query parsing.\n\n\nWhen using the ALTER query to add new columns, old data for these columns is not written. Instead, when reading old data that does not have values for the new columns, expressions are computed on the fly by default. However, if running the expressions requires different columns that are not indicated in the query, these columns will additionally be read, but only for the blocks of data that need it.\n\n\nIf you add a new column to a table but later change its default expression, the values used for old data will change (for data where values were not stored on the disk). Note that when running background merges, data for columns that are missing in one of the merging parts is written to the merged part.\n\n\nIt is not possible to set default values for elements in nested data structures.\n\n\nTemporary tables\n\n\nIn all cases, if \nTEMPORARY\n is specified, a temporary table will be created. Temporary tables have the following characteristics:\n\n\n\n\nTemporary tables disappear when the session ends, including if the connection is lost.\n\n\nA temporary table is created with the Memory engine. The other table engines are not supported.\n\n\nThe DB can't be specified for a temporary table. It is created outside of databases.\n\n\nIf a temporary table has the same name as another one and a query specifies the table name without specifying the DB, the temporary table will be used.\n\n\nFor distributed query processing, temporary tables used in a query are passed to remote servers.\n\n\n\n\nIn most cases, temporary tables are not created manually, but when using external data for a query, or for distributed \n(GLOBAL) IN\n. For more information, see the appropriate sections\n\n\nDistributed DDL queries (ON CLUSTER clause)\n\n\nThe \nCREATE\n, \nDROP\n, \nALTER\n, and \nRENAME\n queries support distributed execution on a cluster.\nFor example, the following query creates the \nall_hits\n \nDistributed\n table on each host in \ncluster\n:\n\n\nCREATE\n \nTABLE\n \nIF\n \nNOT\n \nEXISTS\n \nall_hits\n \nON\n \nCLUSTER\n \ncluster\n \n(\np\n \nDate\n,\n \ni\n \nInt32\n)\n \nENGINE\n \n=\n \nDistributed\n(\ncluster\n,\n \ndefault\n,\n \nhits\n)\n\n\n\n\n\n\nIn order to run these queries correctly, each host must have the same cluster definition (to simplify syncing configs, you can use substitutions from ZooKeeper). They must also connect to the ZooKeeper servers.\nThe local version of the query will eventually be implemented on each host in the cluster, even if some hosts are currently not available. The order for executing queries within a single host is guaranteed.\n\nALTER\n queries are not yet supported for replicated tables.\n\n\nCREATE VIEW\n\n\nCREATE\n \n[\nMATERIALIZED\n]\n \nVIEW\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \n[\nTO\n[\ndb\n.]\nname\n]\n \n[\nENGINE\n \n=\n \nengine\n]\n \n[\nPOPULATE\n]\n \nAS\n \nSELECT\n \n...\n\n\n\n\n\n\nCreates a view. There are two types of views: normal and MATERIALIZED.\n\n\nWhen creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data.\n\n\nA materialized view works as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.\n\n\nNormal views don't store any data, but just perform a read from another table. In other words, a normal view is nothing more than a saved query. When reading from a view, this saved query is used as a subquery in the FROM clause.\n\n\nAs an example, assume you've created a view:\n\n\nCREATE\n \nVIEW\n \nview\n \nAS\n \nSELECT\n \n...\n\n\n\n\n\n\nand written a query:\n\n\nSELECT\n \na\n,\n \nb\n,\n \nc\n \nFROM\n \nview\n\n\n\n\n\n\nThis query is fully equivalent to using the subquery:\n\n\nSELECT\n \na\n,\n \nb\n,\n \nc\n \nFROM\n \n(\nSELECT\n \n...)\n\n\n\n\n\n\nMaterialized views store data transformed by the corresponding SELECT query.\n\n\nWhen creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data.\n\n\nA materialized view is arranged as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.\n\n\nIf you specify POPULATE, the existing table data is inserted in the view when creating it, as if making a \nCREATE TABLE ... AS SELECT ...\n . Otherwise, the query contains only the data inserted in the table after creating the view. We don't recommend using POPULATE, since data inserted in the table during the view creation will not be inserted in it.\n\n\nA \nSELECT\n query can contain \nDISTINCT\n, \nGROUP BY\n, \nORDER BY\n, \nLIMIT\n... Note that the corresponding conversions are performed independently on each block of inserted data. For example, if \nGROUP BY\n is set, data is aggregated during insertion, but only within a single packet of inserted data. The data won't be further aggregated. The exception is when using an ENGINE that independently performs data aggregation, such as \nSummingMergeTree\n.\n\n\nThe execution of \nALTER\n queries on materialized views has not been fully developed, so they might be inconvenient. If the materialized view uses the construction \nTO [db.]name\n, you can \nDETACH\n the view, run \nALTER\n for the target table, and then \nATTACH\n the previously detached (\nDETACH\n) view.\n\n\nViews look the same as normal tables. For example, they are listed in the result of the \nSHOW TABLES\n query.\n\n\nThere isn't a separate query for deleting views. To delete a view, use \nDROP TABLE\n.\n\n\nATTACH\n\n\nThis query is exactly the same as \nCREATE\n, but\n\n\n\n\ninstead of the word \nCREATE\n it uses the word \nATTACH\n.\n\n\nThe query doesn't create data on the disk, but assumes that data is already in the appropriate places, and just adds information about the table to the server.\nAfter executing an ATTACH query, the server will know about the existence of the table.\n\n\n\n\nIf the table was previously detached (\nDETACH\n), meaning that its structure is known, you can use shorthand without defining the structure.\n\n\nATTACH\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n\n\n\n\n\n\nThis query is used when starting the server. The server stores table metadata as files with \nATTACH\n queries, which it simply runs at launch (with the exception of system tables, which are explicitly created on the server).\n\n\nDROP\n\n\nThis query has two types: \nDROP DATABASE\n and \nDROP TABLE\n.\n\n\nDROP\n \nDATABASE\n \n[\nIF\n \nEXISTS\n]\n \ndb\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n\n\n\n\nDeletes all tables inside the 'db' database, then deletes the 'db' database itself.\nIf \nIF EXISTS\n is specified, it doesn't return an error if the database doesn't exist.\n\n\nDROP\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n\n\n\n\nDeletes the table.\nIf \nIF EXISTS\n is specified, it doesn't return an error if the table doesn't exist or the database doesn't exist.\n\n\nDETACH\n\n\nDeletes information about the 'name' table from the server. The server stops knowing about the table's existence.\n\n\nDETACH\n \nTABLE\n \n[\nIF\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n\n\n\n\n\n\nThis does not delete the table's data or metadata. On the next server launch, the server will read the metadata and find out about the table again.\nSimilarly, a \"detached\" table can be re-attached using the \nATTACH\n query (with the exception of system tables, which do not have metadata stored for them).\n\n\nThere is no \nDETACH DATABASE\n query.\n\n\nRENAME\n\n\nRenames one or more tables.\n\n\nRENAME\n \nTABLE\n \n[\ndb11\n.]\nname11\n \nTO\n \n[\ndb12\n.]\nname12\n,\n \n[\ndb21\n.]\nname21\n \nTO\n \n[\ndb22\n.]\nname22\n,\n \n...\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n\n\n\n\nAll tables are renamed under global locking. Renaming tables is a light operation. If you indicated another database after TO, the table will be moved to this database. However, the directories with databases must reside in the same file system (otherwise, an error is returned).\n\n\n\n\nALTER\n\n\nThe \nALTER\n query is only supported for \n*MergeTree\n tables, as well as \nMerge\nand\nDistributed\n. The query has several variations.\n\n\nColumn manipulations\n\n\nChanging the table structure.\n\n\nALTER\n \nTABLE\n \n[\ndb\n].\nname\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n \nADD\n|\nDROP\n|\nMODIFY\n \nCOLUMN\n \n...\n\n\n\n\n\n\nIn the query, specify a list of one or more comma-separated actions.\nEach action is an operation on a column.\n\n\nThe following actions are supported:\n\n\nADD\n \nCOLUMN\n \nname\n \n[\ntype\n]\n \n[\ndefault_expr\n]\n \n[\nAFTER\n \nname_after\n]\n\n\n\n\n\n\nAdds a new column to the table with the specified name, type, and \ndefault_expr\n (see the section \"Default expressions\"). If you specify \nAFTER name_after\n (the name of another column), the column is added after the specified one in the list of table columns. Otherwise, the column is added to the end of the table. Note that there is no way to add a column to the beginning of a table. For a chain of actions, 'name_after' can be the name of a column that is added in one of the previous actions.\n\n\nAdding a column just changes the table structure, without performing any actions with data. The data doesn't appear on the disk after ALTER. If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). The column appears on the disk after merging data parts (see MergeTree).\n\n\nThis approach allows us to complete the ALTER query instantly, without increasing the volume of old data.\n\n\nDROP\n \nCOLUMN\n \nname\n\n\n\n\n\n\nDeletes the column with the name 'name'.\nDeletes data from the file system. Since this deletes entire files, the query is completed almost instantly.\n\n\nMODIFY\n \nCOLUMN\n \nname\n \n[\ntype\n]\n \n[\ndefault_expr\n]\n\n\n\n\n\n\nChanges the 'name' column's type to 'type' and/or the default expression to 'default_expr'. When changing the type, values are converted as if the 'toType' function were applied to them.\n\n\nIf only the default expression is changed, the query doesn't do anything complex, and is completed almost instantly.\n\n\nChanging the column type is the only complex action \u2013 it changes the contents of files with data. For large tables, this may take a long time.\n\n\nThere are several processing stages:\n\n\n\n\nPreparing temporary (new) files with modified data.\n\n\nRenaming old files.\n\n\nRenaming the temporary (new) files to the old names.\n\n\nDeleting the old files.\n\n\n\n\nOnly the first stage takes time. If there is a failure at this stage, the data is not changed.\nIf there is a failure during one of the successive stages, data can be restored manually. The exception is if the old files were deleted from the file system but the data for the new files did not get written to the disk and was lost.\n\n\nThere is no support for changing the column type in arrays and nested data structures.\n\n\nThe \nALTER\n query lets you create and delete separate elements (columns) in nested data structures, but not whole nested data structures. To add a nested data structure, you can add columns with a name like \nname.nested_name\n and the type \nArray(T)\n. A nested data structure is equivalent to multiple array columns with a name that has the same prefix before the dot.\n\n\nThere is no support for deleting columns in the primary key or the sampling key (columns that are in the \nENGINE\n expression). Changing the type for columns that are included in the primary key is only possible if this change does not cause the data to be modified (for example, it is allowed to add values to an Enum or change a type with \nDateTime\n to \nUInt32\n).\n\n\nIf the \nALTER\n query is not sufficient for making the table changes you need, you can create a new table, copy the data to it using the \nINSERT SELECT\n query, then switch the tables using the \nRENAME\n query and delete the old table.\n\n\nThe \nALTER\n query blocks all reads and writes for the table. In other words, if a long \nSELECT\n is running at the time of the \nALTER\n query, the \nALTER\n query will wait for it to complete. At the same time, all new queries to the same table will wait while this \nALTER\n is running.\n\n\nFor tables that don't store data themselves (such as \nMerge\n and \nDistributed\n), \nALTER\n just changes the table structure, and does not change the structure of subordinate tables. For example, when running ALTER for a \nDistributed\n table, you will also need to run \nALTER\n for the tables on all remote servers.\n\n\nThe \nALTER\n query for changing columns is replicated. The instructions are saved in ZooKeeper, then each replica applies them. All \nALTER\n queries are run in the same order. The query waits for the appropriate actions to be completed on the other replicas. However, a query to change columns in a replicated table can be interrupted, and all actions will be performed asynchronously.\n\n\nManipulations with partitions and parts\n\n\nIt only works for tables in the \nMergeTree\n family. The following operations are available:\n\n\n\n\nDETACH PARTITION\n \u2013 Move a partition to the 'detached' directory and forget it.\n\n\nDROP PARTITION\n \u2013 Delete a partition.\n\n\nATTACH PART|PARTITION\n \u2013 Add a new part or partition from the \ndetached\n directory to the table.\n\n\nFREEZE PARTITION\n \u2013 Create a backup of a partition.\n\n\nFETCH PARTITION\n \u2013 Download a partition from another server.\n\n\n\n\nEach type of query is covered separately below.\n\n\nA partition in a table is data for a single calendar month. This is determined by the values of the date key specified in the table engine parameters. Each month's data is stored separately in order to simplify manipulations with this data.\n\n\nA \"part\" in the table is part of the data from a single partition, sorted by the primary key.\n\n\nYou can use the \nsystem.parts\n table to view the set of table parts and partitions:\n\n\nSELECT\n \n*\n \nFROM\n \nsystem\n.\nparts\n \nWHERE\n \nactive\n\n\n\n\n\n\nactive\n \u2013 Only count active parts. Inactive parts are, for example, source parts remaining after merging to a larger part \u2013 these parts are deleted approximately 10 minutes after merging.\n\n\nAnother way to view a set of parts and partitions is to go into the directory with table data.\nData directory: \n/var/lib/clickhouse/data/database/table/\n,where \n/var/lib/clickhouse/\n is the path to the ClickHouse data, 'database' is the database name, and 'table' is the table name. Example:\n\n\n$ ls -l /var/lib/clickhouse/data/test/visits/\ntotal \n48\n\ndrwxrwxrwx \n2\n clickhouse clickhouse \n20480\n May \n5\n \n02\n:58 20140317_20140323_2_2_0\ndrwxrwxrwx \n2\n clickhouse clickhouse \n20480\n May \n5\n \n02\n:58 20140317_20140323_4_4_0\ndrwxrwxrwx \n2\n clickhouse clickhouse \n4096\n May \n5\n \n02\n:55 detached\n-rw-rw-rw- \n1\n clickhouse clickhouse \n2\n May \n5\n \n02\n:58 increment.txt\n\n\n\n\n\nHere, \n20140317_20140323_2_2_0\n and \n20140317_20140323_4_4_0\n are the directories of data parts.\n\n\nLet's break down the name of the first part: \n20140317_20140323_2_2_0\n.\n\n\n\n\n20140317\n is the minimum date of the data in the chunk.\n\n\n20140323\n is the maximum date of the data in the chunk.\n\n\n2\n is the minimum number of the data block.\n\n\n2\n is the maximum number of the data block.\n\n\n0\n is the chunk level (the depth of the merge tree it is formed from).\n\n\n\n\nEach piece relates to a single partition and contains data for just one month.\n\n201403\n is the name of the partition. A partition is a set of parts for a single month.\n\n\nOn an operating server, you can't manually change the set of parts or their data on the file system, since the server won't know about it.\nFor non-replicated tables, you can do this when the server is stopped, but we don't recommended it.\nFor replicated tables, the set of parts can't be changed in any case.\n\n\nThe \ndetached\n directory contains parts that are not used by the server - detached from the table using the \nALTER ... DETACH\n query. Parts that are damaged are also moved to this directory, instead of deleting them. You can add, delete, or modify the data in the 'detached' directory at any time \u2013 the server won't know about this until you make the \nALTER TABLE ... ATTACH\n query.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nDETACH\n \nPARTITION\n \nname\n\n\n\n\n\n\nMove all data for partitions named 'name' to the 'detached' directory and forget about them.\nThe partition name is specified in YYYYMM format. It can be indicated in single quotes or without them.\n\n\nAfter the query is executed, you can do whatever you want with the data in the 'detached' directory \u2014 delete it from the file system, or just leave it.\n\n\nThe query is replicated \u2013 data will be moved to the 'detached' directory and forgotten on all replicas. The query can only be sent to a leader replica. To find out if a replica is a leader, perform SELECT to the 'system.replicas' system table. Alternatively, it is easier to make a query on all replicas, and all except one will throw an exception.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nDROP\n \nPARTITION\n \nname\n\n\n\n\n\n\nThe same as the \nDETACH\n operation. Deletes data from the table. Data parts will be tagged as inactive and will be completely deleted in approximately 10 minutes. The query is replicated \u2013 data will be deleted on all replicas.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nATTACH\n \nPARTITION\n|\nPART\n \nname\n\n\n\n\n\n\nAdds data to the table from the 'detached' directory.\n\n\nIt is possible to add data for an entire partition or a separate part. For a part, specify the full name of the part in single quotes.\n\n\nThe query is replicated. Each replica checks whether there is data in the 'detached' directory. If there is data, it checks the integrity, verifies that it matches the data on the server that initiated the query, and then adds it if everything is correct. If not, it downloads data from the query requestor replica, or from another replica where the data has already been added.\n\n\nSo you can put data in the 'detached' directory on one replica, and use the ALTER ... ATTACH query to add it to the table on all replicas.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nFREEZE\n \nPARTITION\n \nname\n\n\n\n\n\n\nCreates a local backup of one or multiple partitions. The name can be the full name of the partition (for example, 201403), or its prefix (for example, 2014): then the backup will be created for all the corresponding partitions.\n\n\nThe query does the following: for a data snapshot at the time of execution, it creates hardlinks to table data in the directory \n/var/lib/clickhouse/shadow/N/...\n\n\n/var/lib/clickhouse/\n is the working ClickHouse directory from the config.\n\nN\n is the incremental number of the backup.\n\n\nThe same structure of directories is created inside the backup as inside \n/var/lib/clickhouse/\n.\nIt also performs 'chmod' for all files, forbidding writes to them.\n\n\nThe backup is created almost instantly (but first it waits for current queries to the corresponding table to finish running). At first, the backup doesn't take any space on the disk. As the system works, the backup can take disk space, as data is modified. If the backup is made for old enough data, it won't take space on the disk.\n\n\nAfter creating the backup, data from \n/var/lib/clickhouse/shadow/\n can be copied to the remote server and then deleted on the local server.\nThe entire backup process is performed without stopping the server.\n\n\nThe \nALTER ... FREEZE PARTITION\n query is not replicated. A local backup is only created on the local server.\n\n\nAs an alternative, you can manually copy data from the \n/var/lib/clickhouse/data/database/table\n directory.\nBut if you do this while the server is running, race conditions are possible when copying directories with files being added or changed, and the backup may be inconsistent. You can do this if the server isn't running \u2013 then the resulting data will be the same as after the \nALTER TABLE t FREEZE PARTITION\n query.\n\n\nALTER TABLE ... FREEZE PARTITION\n only copies data, not table metadata. To make a backup of table metadata, copy the file \n/var/lib/clickhouse/metadata/database/table.sql\n\n\nTo restore from a backup:\n\n\n\n\n\n\nUse the CREATE query to create the table if it doesn't exist. The query can be taken from an .sql file (replace \nATTACH\n in it with \nCREATE\n).\n\n\nCopy the data from the data/database/table/ directory inside the backup to the \n/var/lib/clickhouse/data/database/table/detached/ directory.\n\n\nRun \nALTER TABLE ... ATTACH PARTITION YYYYMM\n queries, where \nYYYYMM\n is the month, for every month.\n\n\n\n\n\n\nIn this way, data from the backup will be added to the table.\nRestoring from a backup doesn't require stopping the server.\n\n\nBackups and replication\n\n\nReplication provides protection from device failures. If all data disappeared on one of your replicas, follow the instructions in the \"Restoration after failure\" section to restore it.\n\n\nFor protection from device failures, you must use replication. For more information about replication, see the section \"Data replication\".\n\n\nBackups protect against human error (accidentally deleting data, deleting the wrong data or in the wrong cluster, or corrupting data).\nFor high-volume databases, it can be difficult to copy backups to remote servers. In such cases, to protect from human error, you can keep a backup on the same server (it will reside in \n/var/lib/clickhouse/shadow/\n).\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nFETCH\n \nPARTITION\n \nname\n \nFROM\n \npath-in-zookeeper\n\n\n\n\n\n\nThis query only works for replicatable tables.\n\n\nIt downloads the specified partition from the shard that has its \nZooKeeper path\n specified in the \nFROM\n clause, then puts it in the \ndetached\n directory for the specified table.\n\n\nAlthough the query is called \nALTER TABLE\n, it does not change the table structure, and does not immediately change the data available in the table.\n\n\nData is placed in the \ndetached\n directory. You can use the \nALTER TABLE ... ATTACH\n query to attach the data.\n\n\nThe \nFROM\n clause specifies the path in \nZooKeeper\n. For example, \n/clickhouse/tables/01-01/visits\n.\nBefore downloading, the system checks that the partition exists and the table structure matches. The most appropriate replica is selected automatically from the healthy replicas.\n\n\nThe \nALTER ... FETCH PARTITION\n query is not replicated. The partition will be downloaded to the 'detached' directory only on the local server. Note that if after this you use the \nALTER TABLE ... ATTACH\n query to add data to the table, the data will be added on all replicas (on one of the replicas it will be added from the 'detached' directory, and on the rest it will be loaded from neighboring replicas).\n\n\nSynchronicity of ALTER queries\n\n\nFor non-replicatable tables, all \nALTER\n queries are performed synchronously. For replicatable tables, the query just adds instructions for the appropriate actions to \nZooKeeper\n, and the actions themselves are performed as soon as possible. However, the query can wait for these actions to be completed on all the replicas.\n\n\nFor \nALTER ... ATTACH|DETACH|DROP\n queries, you can use the \nreplication_alter_partitions_sync\n setting to set up waiting.\nPossible values: \n0\n \u2013 do not wait; \n1\n \u2013 only wait for own execution (default); \n2\n \u2013 wait for all.\n\n\n\n\nSHOW DATABASES\n\n\nSHOW\n \nDATABASES\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nPrints a list of all databases.\nThis query is identical to \nSELECT name FROM system.databases [INTO OUTFILE filename] [FORMAT format]\n.\n\n\nSee also the section \"Formats\".\n\n\nSHOW TABLES\n\n\nSHOW\n \n[\nTEMPORARY\n]\n \nTABLES\n \n[\nFROM\n \ndb\n]\n \n[\nLIKE\n \npattern\n]\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nDisplays a list of tables\n\n\n\n\ntables from the current database, or from the 'db' database if \"FROM db\" is specified.\n\n\nall tables, or tables whose name matches the pattern, if \"LIKE 'pattern'\" is specified.\n\n\n\n\nThis query is identical to: \nSELECT name FROM system.tables WHERE database = 'db' [AND name LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format]\n.\n\n\nSee also the section \"LIKE operator\".\n\n\nSHOW PROCESSLIST\n\n\nSHOW\n \nPROCESSLIST\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nOutputs a list of queries currently being processed, other than \nSHOW PROCESSLIST\n queries.\n\n\nPrints a table containing the columns:\n\n\nuser\n \u2013 The user who made the query. Keep in mind that for distributed processing, queries are sent to remote servers under the 'default' user. SHOW PROCESSLIST shows the username for a specific query, not for a query that this query initiated.\n\n\naddress\n \u2013 The name of the host that the query was sent from. For distributed processing, on remote servers, this is the name of the query requestor host. To track where a distributed query was originally made from, look at SHOW PROCESSLIST on the query requestor server.\n\n\nelapsed\n \u2013 The execution time, in seconds. Queries are output in order of decreasing execution time.\n\n\nrows_read\n, \nbytes_read\n \u2013 How many rows and bytes of uncompressed data were read when processing the query. For distributed processing, data is totaled from all the remote servers. This is the data used for restrictions and quotas.\n\n\nmemory_usage\n \u2013 Current RAM usage in bytes. See the setting 'max_memory_usage'.\n\n\nquery\n \u2013 The query itself. In INSERT queries, the data for insertion is not output.\n\n\nquery_id\n \u2013 The query identifier. Non-empty only if it was explicitly defined by the user. For distributed processing, the query ID is not passed to remote servers.\n\n\nThis query is identical to: \nSELECT * FROM system.processes [INTO OUTFILE filename] [FORMAT format]\n.\n\n\nTip (execute in the console):\n\n\nwatch -n1 \nclickhouse-client --query=\nSHOW PROCESSLIST\n\n\n\n\n\n\nSHOW CREATE TABLE\n\n\nSHOW\n \nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\ndb\n.]\ntable\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nReturns a single \nString\n-type 'statement' column, which contains a single value \u2013 the \nCREATE\n query used for creating the specified table.\n\n\nDESCRIBE TABLE\n\n\nDESC\n|\nDESCRIBE\n \nTABLE\n \n[\ndb\n.]\ntable\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nReturns two \nString\n-type columns: \nname\n and \ntype\n, which indicate the names and types of columns in the specified table.\n\n\nNested data structures are output in \"expanded\" format. Each column is shown separately, with the name after a dot.\n\n\nEXISTS\n\n\nEXISTS\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\ndb\n.]\nname\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nReturns a single \nUInt8\n-type column, which contains the single value \n0\n if the table or database doesn't exist, or \n1\n if the table exists in the specified database.\n\n\nUSE\n\n\nUSE\n \ndb\n\n\n\n\n\n\nLets you set the current database for the session.\nThe current database is used for searching for tables if the database is not explicitly defined in the query with a dot before the table name.\nThis query can't be made when using the HTTP protocol, since there is no concept of a session.\n\n\nSET\n\n\nSET\n \nparam\n \n=\n \nvalue\n\n\n\n\n\n\nAllows you to set \nparam\n to \nvalue\n. You can also make all the settings from the specified settings profile in a single query. To do this, specify 'profile' as the setting name. For more information, see the section \"Settings\".\nThe setting is made for the session, or for the server (globally) if \nGLOBAL\n is specified.\nWhen making a global setting, the setting is not applied to sessions already running, including the current session. It will only be used for new sessions.\n\n\nWhen the server is restarted, global settings made using \nSET\n are lost.\nTo make settings that persist after a server restart, you can only use the server's config file.\n\n\nOPTIMIZE\n\n\nOPTIMIZE\n \nTABLE\n \n[\ndb\n.]\nname\n \n[\nPARTITION\n \npartition\n]\n \n[\nFINAL\n]\n\n\n\n\n\n\nAsks the table engine to do something for optimization.\nSupported only by \n*MergeTree\n engines, in which this query initializes a non-scheduled merge of data parts.\nIf you specify a \nPARTITION\n, only the specified partition will be optimized.\nIf you specify \nFINAL\n, optimization will be performed even when all the data is already in one part.\n\n\n\n\nINSERT\n\n\nAdding data.\n\n\nBasic query format:\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nVALUES\n \n(\nv11\n,\n \nv12\n,\n \nv13\n),\n \n(\nv21\n,\n \nv22\n,\n \nv23\n),\n \n...\n\n\n\n\n\n\nThe query can specify a list of columns to insert \n[(c1, c2, c3)]\n. In this case, the rest of the columns are filled with:\n\n\n\n\nThe values calculated from the \nDEFAULT\n expressions specified in the table definition.\n\n\nZeros and empty strings, if \nDEFAULT\n expressions are not defined.\n\n\n\n\nIf \nstrict_insert_defaults=1\n, columns that do not have \nDEFAULT\n defined must be listed in the query.\n\n\nData can be passed to the INSERT in any \nformat\n supported by ClickHouse. The format must be specified explicitly in the query:\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nFORMAT\n \nformat_name\n \ndata_set\n\n\n\n\n\n\nFor example, the following query format is identical to the basic version of INSERT ... VALUES:\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nFORMAT\n \nValues\n \n(\nv11\n,\n \nv12\n,\n \nv13\n),\n \n(\nv21\n,\n \nv22\n,\n \nv23\n),\n \n...\n\n\n\n\n\n\nClickHouse removes all spaces and one line feed (if there is one) before the data. When forming a query, we recommend putting the data on a new line after the query operators (this is important if the data begins with spaces).\n\n\nExample:\n\n\nINSERT\n \nINTO\n \nt\n \nFORMAT\n \nTabSeparated\n\n\n11\n \nHello\n,\n \nworld\n!\n\n\n22\n \nQwerty\n\n\n\n\n\n\nYou can insert data separately from the query by using the command-line client or the HTTP interface. For more information, see the section \"\nInterfaces\n\".\n\n\nInserting the results of \nSELECT\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nSELECT\n \n...\n\n\n\n\n\n\nColumns are mapped according to their position in the SELECT clause. However, their names in the SELECT expression and the table for INSERT may differ. If necessary, type casting is performed.\n\n\nNone of the data formats except Values allow setting values to expressions such as \nnow()\n, \n1 + 2\n, and so on. The Values format allows limited use of expressions, but this is not recommended, because in this case inefficient code is used for their execution.\n\n\nOther queries for modifying data parts are not supported: \nUPDATE\n, \nDELETE\n, \nREPLACE\n, \nMERGE\n, \nUPSERT\n, \nINSERT UPDATE\n.\nHowever, you can delete old data using \nALTER TABLE ... DROP PARTITION\n.\n\n\nPerformance considerations\n\n\nINSERT\n sorts the input data by primary key and splits them into partitions by month. If you insert data for mixed months, it can significantly reduce the performance of the \nINSERT\n query. To avoid this:\n\n\n\n\nAdd data in fairly large batches, such as 100,000 rows at a time.\n\n\nGroup data by month before uploading it to ClickHouse.\n\n\n\n\nPerformance will not decrease if:\n\n\n\n\nData is added in real time.\n\n\nYou upload data that is usually sorted by time.\n\n\n\n\nSELECT\n\n\nData sampling.\n\n\nSELECT\n \n[\nDISTINCT\n]\n \nexpr_list\n\n \n[\nFROM\n \n[\ndb\n.]\ntable\n \n|\n \n(\nsubquery\n)\n \n|\n \ntable_function\n]\n \n[\nFINAL\n]\n\n \n[\nSAMPLE\n \nsample_coeff\n]\n\n \n[\nARRAY\n \nJOIN\n \n...]\n\n \n[\nGLOBAL\n]\n \nANY\n|\nALL\n \nINNER\n|\nLEFT\n \nJOIN\n \n(\nsubquery\n)\n|\ntable\n \nUSING\n \ncolumns_list\n\n \n[\nPREWHERE\n \nexpr\n]\n\n \n[\nWHERE\n \nexpr\n]\n\n \n[\nGROUP\n \nBY\n \nexpr_list\n]\n \n[\nWITH\n \nTOTALS\n]\n\n \n[\nHAVING\n \nexpr\n]\n\n \n[\nORDER\n \nBY\n \nexpr_list\n]\n\n \n[\nLIMIT\n \n[\nn\n,\n \n]\nm\n]\n\n \n[\nUNION\n \nALL\n \n...]\n\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n\n \n[\nFORMAT\n \nformat\n]\n\n \n[\nLIMIT\n \nn\n \nBY\n \ncolumns\n]\n\n\n\n\n\n\nAll the clauses are optional, except for the required list of expressions immediately after SELECT.\nThe clauses below are described in almost the same order as in the query execution conveyor.\n\n\nIf the query omits the \nDISTINCT\n, \nGROUP BY\n and \nORDER BY\n clauses and the \nIN\n and \nJOIN\n subqueries, the query will be completely stream processed, using O(1) amount of RAM.\nOtherwise, the query might consume a lot of RAM if the appropriate restrictions are not specified: \nmax_memory_usage\n, \nmax_rows_to_group_by\n, \nmax_rows_to_sort\n, \nmax_rows_in_distinct\n, \nmax_bytes_in_distinct\n, \nmax_rows_in_set\n, \nmax_bytes_in_set\n, \nmax_rows_in_join\n, \nmax_bytes_in_join\n, \nmax_bytes_before_external_sort\n, \nmax_bytes_before_external_group_by\n. For more information, see the section \"Settings\". It is possible to use external sorting (saving temporary tables to a disk) and external aggregation. \nThe system does not have \"merge join\"\n.\n\n\nFROM clause\n\n\nIf the FROM clause is omitted, data will be read from the \nsystem.one\n table.\nThe 'system.one' table contains exactly one row (this table fulfills the same purpose as the DUAL table found in other DBMSs).\n\n\nThe FROM clause specifies the table to read data from, or a subquery, or a table function; ARRAY JOIN and the regular JOIN may also be included (see below).\n\n\nInstead of a table, the SELECT subquery may be specified in brackets.\nIn this case, the subquery processing pipeline will be built into the processing pipeline of an external query.\nIn contrast to standard SQL, a synonym does not need to be specified after a subquery. For compatibility, it is possible to write 'AS name' after a subquery, but the specified name isn't used anywhere.\n\n\nA table function may be specified instead of a table. For more information, see the section \"Table functions\".\n\n\nTo execute a query, all the columns listed in the query are extracted from the appropriate table. Any columns not needed for the external query are thrown out of the subqueries.\nIf a query does not list any columns (for example, SELECT count() FROM t), some column is extracted from the table anyway (the smallest one is preferred), in order to calculate the number of rows.\n\n\nThe FINAL modifier can be used only for a SELECT from a CollapsingMergeTree table. When you specify FINAL, data is selected fully \"collapsed\". Keep in mind that using FINAL leads to a selection that includes columns related to the primary key, in addition to the columns specified in the SELECT. Additionally, the query will be executed in a single stream, and data will be merged during query execution. This means that when using FINAL, the query is processed more slowly. In most cases, you should avoid using FINAL. For more information, see the section \"CollapsingMergeTree engine\".\n\n\nSAMPLE clause\n\n\nThe SAMPLE clause allows for approximated query processing. Approximated query processing is only supported by MergeTree* type tables, and only if the sampling expression was specified during table creation (see the section \"MergeTree engine\").\n\n\nSAMPLE\n has the \nformat SAMPLE k\n, where \nk\n is a decimal number from 0 to 1, or \nSAMPLE n\n, where 'n' is a sufficiently large integer.\n\n\nIn the first case, the query will be executed on 'k' percent of data. For example, \nSAMPLE 0.1\n runs the query on 10% of data.\nIn the second case, the query will be executed on a sample of no more than 'n' rows. For example, \nSAMPLE 10000000\n runs the query on a maximum of 10,000,000 rows.\n\n\nExample:\n\n\nSELECT\n\n \nTitle\n,\n\n \ncount\n()\n \n*\n \n10\n \nAS\n \nPageViews\n\n\nFROM\n \nhits_distributed\n\n\nSAMPLE\n \n0\n.\n1\n\n\nWHERE\n\n \nCounterID\n \n=\n \n34\n\n \nAND\n \ntoDate\n(\nEventDate\n)\n \n=\n \ntoDate\n(\n2013-01-29\n)\n\n \nAND\n \ntoDate\n(\nEventDate\n)\n \n=\n \ntoDate\n(\n2013-02-04\n)\n\n \nAND\n \nNOT\n \nDontCountHits\n\n \nAND\n \nNOT\n \nRefresh\n\n \nAND\n \nTitle\n \n!=\n \n\n\nGROUP\n \nBY\n \nTitle\n\n\nORDER\n \nBY\n \nPageViews\n \nDESC\n \nLIMIT\n \n1000\n\n\n\n\n\n\nIn this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value 'count()' is manually multiplied by 10.\n\n\nWhen using something like \nSAMPLE 10000000\n, there isn't any information about which relative percent of data was processed or what the aggregate functions should be multiplied by, so this method of writing is not always appropriate to the situation.\n\n\nA sample with a relative coefficient is \"consistent\": if we look at all possible data that could be in the table, a sample (when using a single sampling expression specified during table creation) with the same coefficient always selects the same subset of possible data. In other words, a sample from different tables on different servers at different times is made the same way.\n\n\nFor example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This allows using the sample in subqueries in the IN clause, as well as for manually correlating results of different queries with samples.\n\n\nARRAY JOIN clause\n\n\nAllows executing JOIN with an array or nested data structure. The intent is similar to the 'arrayJoin' function, but its functionality is broader.\n\n\nARRAY JOIN\n is essentially \nINNER JOIN\n with an array. Example:\n\n\n:) CREATE TABLE arrays_test (s String, arr Array(UInt8)) ENGINE = Memory\n\nCREATE TABLE arrays_test\n(\n s String,\n arr Array(UInt8)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.001 sec.\n\n:) INSERT INTO arrays_test VALUES (\nHello\n, [1,2]), (\nWorld\n, [3,4,5]), (\nGoodbye\n, [])\n\nINSERT INTO arrays_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM arrays_test\n\nSELECT *\nFROM arrays_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502\n\u2502 Goodbye \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, arr FROM arrays_test ARRAY JOIN arr\n\nSELECT s, arr\nFROM arrays_test\nARRAY JOIN arr\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2510\n\u2502 Hello \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502\n\u2502 World \u2502 4 \u2502\n\u2502 World \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nAn alias can be specified for an array in the ARRAY JOIN clause. In this case, an array item can be accessed by this alias, but the array itself by the original name. Example:\n\n\n:) SELECT s, arr, a FROM arrays_test ARRAY JOIN arr AS a\n\nSELECT s, arr, a\nFROM arrays_test\nARRAY JOIN arr AS a\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nMultiple arrays of the same size can be comma-separated in the ARRAY JOIN clause. In this case, JOIN is performed with them simultaneously (the direct sum, not the direct product). Example:\n\n\n:) SELECT s, arr, a, num, mapped FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(x -\n x + 1, arr) AS mapped\n\nSELECT s, arr, a, num, mapped\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(lambda(tuple(x), plus(x, 1)), arr) AS mapped\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500mapped\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 2 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 5 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 6 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n:) SELECT s, arr, a, num, arrayEnumerate(arr) FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\nSELECT s, arr, a, num, arrayEnumerate(arr)\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500arrayEnumerate(arr)\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 [1,2] \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n\n\n\n\nARRAY JOIN also works with nested data structures. Example:\n\n\n:) CREATE TABLE nested_test (s String, nest Nested(x UInt8, y UInt32)) ENGINE = Memory\n\nCREATE TABLE nested_test\n(\n s String,\n nest Nested(\n x UInt8,\n y UInt32)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.006 sec.\n\n:) INSERT INTO nested_test VALUES (\nHello\n, [1,2], [10,20]), (\nWorld\n, [3,4,5], [30,40,50]), (\nGoodbye\n, [], [])\n\nINSERT INTO nested_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM nested_test\n\nSELECT *\nFROM nested_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 Goodbye \u2502 [] \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nWhen specifying names of nested data structures in ARRAY JOIN, the meaning is the same as ARRAY JOIN with all the array elements that it consists of. Example:\n\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x, nest.y\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`, `nest.y`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nThis variation also makes sense:\n\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nAn alias may be used for a nested data structure, in order to select either the JOIN result or the source array. Example:\n\n\n:) SELECT s, n.x, n.y, nest.x, nest.y FROM nested_test ARRAY JOIN nest AS n\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest AS n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nExample of using the arrayEnumerate function:\n\n\n:) SELECT s, n.x, n.y, nest.x, nest.y, num FROM nested_test ARRAY JOIN nest AS n, arrayEnumerate(nest.x) AS num\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`, num\nFROM nested_test\nARRAY JOIN nest AS n, arrayEnumerate(`nest.x`) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u252c\u2500num\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 1 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 2 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 3 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n\n\n\n\nThe query can only specify a single ARRAY JOIN clause.\n\n\nThe corresponding conversion can be performed before the WHERE/PREWHERE clause (if its result is needed in this clause), or after completing WHERE/PREWHERE (to reduce the volume of calculations).\n\n\nJOIN clause\n\n\nThe normal JOIN, which is not related to ARRAY JOIN described above.\n\n\n[\nGLOBAL\n]\n \nANY\n|\nALL\n \nINNER\n|\nLEFT\n \n[\nOUTER\n]\n \nJOIN\n \n(\nsubquery\n)\n|\ntable\n \nUSING\n \ncolumns_list\n\n\n\n\n\n\nPerforms joins with data from the subquery. At the beginning of query processing, the subquery specified after JOIN is run, and its result is saved in memory. Then it is read from the \"left\" table specified in the FROM clause, and while it is being read, for each of the read rows from the \"left\" table, rows are selected from the subquery results table (the \"right\" table) that meet the condition for matching the values of the columns specified in USING.\n\n\nThe table name can be specified instead of a subquery. This is equivalent to the \nSELECT * FROM table\n subquery, except in a special case when the table has the Join engine \u2013 an array prepared for joining.\n\n\nAll columns that are not needed for the JOIN are deleted from the subquery.\n\n\nThere are several types of JOINs:\n\n\nINNER\n or \nLEFT\n type:If INNER is specified, the result will contain only those rows that have a matching row in the right table.\nIf LEFT is specified, any rows in the left table that don't have matching rows in the right table will be assigned the default value - zeros or empty rows. LEFT OUTER may be written instead of LEFT; the word OUTER does not affect anything.\n\n\nANY\n or \nALL\n stringency:If \nANY\n is specified and the right table has several matching rows, only the first one found is joined.\nIf \nALL\n is specified and the right table has several matching rows, the data will be multiplied by the number of these rows.\n\n\nUsing ALL corresponds to the normal JOIN semantic from standard SQL.\nUsing ANY is optimal. If the right table has only one matching row, the results of ANY and ALL are the same. You must specify either ANY or ALL (neither of them is selected by default).\n\n\nGLOBAL\n distribution:\n\n\nWhen using a normal JOIN, the query is sent to remote servers. Subqueries are run on each of them in order to make the right table, and the join is performed with this table. In other words, the right table is formed on each server separately.\n\n\nWhen using \nGLOBAL ... JOIN\n, first the requestor server runs a subquery to calculate the right table. This temporary table is passed to each remote server, and queries are run on them using the temporary data that was transmitted.\n\n\nBe careful when using GLOBAL JOINs. For more information, see the section \"Distributed subqueries\".\n\n\nAny combination of JOINs is possible. For example, \nGLOBAL ANY LEFT OUTER JOIN\n.\n\n\nWhen running a JOIN, there is no optimization of the order of execution in relation to other stages of the query. The join (a search in the right table) is run before filtering in WHERE and before aggregation. In order to explicitly set the processing order, we recommend running a JOIN subquery with a subquery.\n\n\nExample:\n\n\nSELECT\n\n \nCounterID\n,\n\n \nhits\n,\n\n \nvisits\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nCounterID\n,\n\n \ncount\n()\n \nAS\n \nhits\n\n \nFROM\n \ntest\n.\nhits\n\n \nGROUP\n \nBY\n \nCounterID\n\n\n)\n \nANY\n \nLEFT\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nCounterID\n,\n\n \nsum\n(\nSign\n)\n \nAS\n \nvisits\n\n \nFROM\n \ntest\n.\nvisits\n\n \nGROUP\n \nBY\n \nCounterID\n\n\n)\n \nUSING\n \nCounterID\n\n\nORDER\n \nBY\n \nhits\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500CounterID\u2500\u252c\u2500\u2500\u2500hits\u2500\u252c\u2500visits\u2500\u2510\n\u2502 1143050 \u2502 523264 \u2502 13665 \u2502\n\u2502 731962 \u2502 475698 \u2502 102716 \u2502\n\u2502 722545 \u2502 337212 \u2502 108187 \u2502\n\u2502 722889 \u2502 252197 \u2502 10547 \u2502\n\u2502 2237260 \u2502 196036 \u2502 9522 \u2502\n\u2502 23057320 \u2502 147211 \u2502 7689 \u2502\n\u2502 722818 \u2502 90109 \u2502 17847 \u2502\n\u2502 48221 \u2502 85379 \u2502 4652 \u2502\n\u2502 19762435 \u2502 77807 \u2502 7026 \u2502\n\u2502 722884 \u2502 77492 \u2502 11056 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSubqueries don't allow you to set names or use them for referencing a column from a specific subquery.\nThe columns specified in USING must have the same names in both subqueries, and the other columns must be named differently. You can use aliases to change the names of columns in subqueries (the example uses the aliases 'hits' and 'visits').\n\n\nThe USING clause specifies one or more columns to join, which establishes the equality of these columns. The list of columns is set without brackets. More complex join conditions are not supported.\n\n\nThe right table (the subquery result) resides in RAM. If there isn't enough memory, you can't run a JOIN.\n\n\nOnly one JOIN can be specified in a query (on a single level). To run multiple JOINs, you can put them in subqueries.\n\n\nEach time a query is run with the same JOIN, the subquery is run again \u2013 the result is not cached. To avoid this, use the special 'Join' table engine, which is a prepared array for joining that is always in RAM. For more information, see the section \"Table engines, Join\".\n\n\nIn some cases, it is more efficient to use IN instead of JOIN.\nAmong the various types of JOINs, the most efficient is ANY LEFT JOIN, then ANY INNER JOIN. The least efficient are ALL LEFT JOIN and ALL INNER JOIN.\n\n\nIf you need a JOIN for joining with dimension tables (these are relatively small tables that contain dimension properties, such as names for advertising campaigns), a JOIN might not be very convenient due to the bulky syntax and the fact that the right table is re-accessed for every query. For such cases, there is an \"external dictionaries\" feature that you should use instead of JOIN. For more information, see the section \"External dictionaries\".\n\n\nWHERE clause\n\n\nIf there is a WHERE clause, it must contain an expression with the UInt8 type. This is usually an expression with comparison and logical operators.\nThis expression will be used for filtering data before all other transformations.\n\n\nIf indexes are supported by the database table engine, the expression is evaluated on the ability to use indexes.\n\n\nPREWHERE clause\n\n\nThis clause has the same meaning as the WHERE clause. The difference is in which data is read from the table.\nWhen using PREWHERE, first only the columns necessary for executing PREWHERE are read. Then the other columns are read that are needed for running the query, but only those blocks where the PREWHERE expression is true.\n\n\nIt makes sense to use PREWHERE if there are filtration conditions that are not suitable for indexes that are used by a minority of the columns in the query, but that provide strong data filtration. This reduces the volume of data to read.\n\n\nFor example, it is useful to write PREWHERE for queries that extract a large number of columns, but that only have filtration for a few columns.\n\n\nPREWHERE is only supported by tables from the \n*MergeTree\n family.\n\n\nA query may simultaneously specify PREWHERE and WHERE. In this case, PREWHERE precedes WHERE.\n\n\nKeep in mind that it does not make much sense for PREWHERE to only specify those columns that have an index, because when using an index, only the data blocks that match the index are read.\n\n\nIf the 'optimize_move_to_prewhere' setting is set to 1 and PREWHERE is omitted, the system uses heuristics to automatically move parts of expressions from WHERE to PREWHERE.\n\n\nGROUP BY clause\n\n\nThis is one of the most important parts of a column-oriented DBMS.\n\n\nIf there is a GROUP BY clause, it must contain a list of expressions. Each expression will be referred to here as a \"key\".\nAll the expressions in the SELECT, HAVING, and ORDER BY clauses must be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions.\n\n\nIf a query contains only table columns inside aggregate functions, the GROUP BY clause can be omitted, and aggregation by an empty set of keys is assumed.\n\n\nExample:\n\n\nSELECT\n\n \ncount\n(),\n\n \nmedian\n(\nFetchTiming\n \n \n60\n \n?\n \n60\n \n:\n \nFetchTiming\n),\n\n \ncount\n()\n \n-\n \nsum\n(\nRefresh\n)\n\n\nFROM\n \nhits\n\n\n\n\n\n\nHowever, in contrast to standard SQL, if the table doesn't have any rows (either there aren't any at all, or there aren't any after using WHERE to filter), an empty result is returned, and not the result from one of the rows containing the initial values of aggregate functions.\n\n\nAs opposed to MySQL (and conforming to standard SQL), you can't get some value of some column that is not in a key or aggregate function (except constant expressions). To work around this, you can use the 'any' aggregate function (get the first encountered value) or 'min/max'.\n\n\nExample:\n\n\nSELECT\n\n \ndomainWithoutWWW\n(\nURL\n)\n \nAS\n \ndomain\n,\n\n \ncount\n(),\n\n \nany\n(\nTitle\n)\n \nAS\n \ntitle\n \n-- getting the first occurred page header for each domain.\n\n\nFROM\n \nhits\n\n\nGROUP\n \nBY\n \ndomain\n\n\n\n\n\n\nFor every different key value encountered, GROUP BY calculates a set of aggregate function values.\n\n\nGROUP BY is not supported for array columns.\n\n\nA constant can't be specified as arguments for aggregate functions. Example: sum(1). Instead of this, you can get rid of the constant. Example: \ncount()\n.\n\n\nWITH TOTALS modifier\n\n\nIf the WITH TOTALS modifier is specified, another row will be calculated. This row will have key columns containing default values (zeros or empty lines), and columns of aggregate functions with the values calculated across all the rows (the \"total\" values).\n\n\nThis extra row is output in JSON*, TabSeparated*, and Pretty* formats, separately from the other rows. In the other formats, this row is not output.\n\n\nIn JSON* formats, this row is output as a separate 'totals' field. In TabSeparated* formats, the row comes after the main result, preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result.\n\n\nWITH TOTALS\n can be run in different ways when HAVING is present. The behavior depends on the 'totals_mode' setting.\nBy default, \ntotals_mode = 'before_having'\n. In this case, 'totals' is calculated across all rows, including the ones that don't pass through HAVING and 'max_rows_to_group_by'.\n\n\nThe other alternatives include only the rows that pass through HAVING in 'totals', and behave differently with the setting \nmax_rows_to_group_by\n and \ngroup_by_overflow_mode = 'any'\n.\n\n\nafter_having_exclusive\n \u2013 Don't include rows that didn't pass through \nmax_rows_to_group_by\n. In other words, 'totals' will have less than or the same number of rows as it would if \nmax_rows_to_group_by\n were omitted.\n\n\nafter_having_inclusive\n \u2013 Include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. In other words, 'totals' will have more than or the same number of rows as it would if \nmax_rows_to_group_by\n were omitted.\n\n\nafter_having_auto\n \u2013 Count the number of rows that passed through HAVING. If it is more than a certain amount (by default, 50%), include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. Otherwise, do not include them.\n\n\ntotals_auto_threshold\n \u2013 By default, 0.5. The coefficient for \nafter_having_auto\n.\n\n\nIf \nmax_rows_to_group_by\n and \ngroup_by_overflow_mode = 'any'\n are not used, all variations of \nafter_having\n are the same, and you can use any of them (for example, \nafter_having_auto\n).\n\n\nYou can use WITH TOTALS in subqueries, including subqueries in the JOIN clause (in this case, the respective total values are combined).\n\n\nGROUP BY in external memory\n\n\nYou can enable dumping temporary data to the disk to restrict memory usage during GROUP BY.\nThe \nmax_bytes_before_external_group_by\n setting determines the threshold RAM consumption for dumping GROUP BY temporary data to the file system. If set to 0 (the default), it is disabled.\n\n\nWhen using \nmax_bytes_before_external_group_by\n, we recommend that you set max_memory_usage about twice as high. This is necessary because there are two stages to aggregation: reading the date and forming intermediate data (1) and merging the intermediate data (2). Dumping data to the file system can only occur during stage 1. If the temporary data wasn't dumped, then stage 2 might require up to the same amount of memory as in stage 1.\n\n\nFor example, if \nmax_memory_usage\n was set to 10000000000 and you want to use external aggregation, it makes sense to set \nmax_bytes_before_external_group_by\n to 10000000000, and max_memory_usage to 20000000000. When external aggregation is triggered (if there was at least one dump of temporary data), maximum consumption of RAM is only slightly more than \nmax_bytes_before_external_group_by\n.\n\n\nWith distributed query processing, external aggregation is performed on remote servers. In order for the requestor server to use only a small amount of RAM, set \ndistributed_aggregation_memory_efficient\n to 1.\n\n\nWhen merging data flushed to the disk, as well as when merging results from remote servers when the \ndistributed_aggregation_memory_efficient\n setting is enabled, consumes up to 1/256 * the number of threads from the total amount of RAM.\n\n\nWhen external aggregation is enabled, if there was less than \nmax_bytes_before_external_group_by\n of data (i.e. data was not flushed), the query runs just as fast as without external aggregation. If any temporary data was flushed, the run time will be several times longer (approximately three times).\n\n\nIf you have an ORDER BY with a small LIMIT after GROUP BY, then the ORDER BY CLAUSE will not use significant amounts of RAM.\nBut if the ORDER BY doesn't have LIMIT, don't forget to enable external sorting (\nmax_bytes_before_external_sort\n).\n\n\nLIMIT N BY clause\n\n\nLIMIT N BY COLUMNS selects the top N rows for each group of COLUMNS. LIMIT N BY is not related to LIMIT; they can both be used in the same query. The key for LIMIT N BY can contain any number of columns or expressions.\n\n\nExample:\n\n\nSELECT\n\n \ndomainWithoutWWW\n(\nURL\n)\n \nAS\n \ndomain\n,\n\n \ndomainWithoutWWW\n(\nREFERRER_URL\n)\n \nAS\n \nreferrer\n,\n\n \ndevice_type\n,\n\n \ncount\n()\n \ncnt\n\n\nFROM\n \nhits\n\n\nGROUP\n \nBY\n \ndomain\n,\n \nreferrer\n,\n \ndevice_type\n\n\nORDER\n \nBY\n \ncnt\n \nDESC\n\n\nLIMIT\n \n5\n \nBY\n \ndomain\n,\n \ndevice_type\n\n\nLIMIT\n \n100\n\n\n\n\n\n\nThe query will select the top 5 referrers for each \ndomain, device_type\n pair, but not more than 100 rows (\nLIMIT n BY + LIMIT\n).\n\n\nHAVING clause\n\n\nAllows filtering the result received after GROUP BY, similar to the WHERE clause.\nWHERE and HAVING differ in that WHERE is performed before aggregation (GROUP BY), while HAVING is performed after it.\nIf aggregation is not performed, HAVING can't be used.\n\n\n\n\nORDER BY clause\n\n\nThe ORDER BY clause contains a list of expressions, which can each be assigned DESC or ASC (the sorting direction). If the direction is not specified, ASC is assumed. ASC is sorted in ascending order, and DESC in descending order. The sorting direction applies to a single expression, not to the entire list. Example: \nORDER BY Visits DESC, SearchPhrase\n\n\nFor sorting by String values, you can specify collation (comparison). Example: \nORDER BY SearchPhrase COLLATE 'tr'\n - for sorting by keyword in ascending order, using the Turkish alphabet, case insensitive, assuming that strings are UTF-8 encoded. COLLATE can be specified or not for each expression in ORDER BY independently. If ASC or DESC is specified, COLLATE is specified after it. When using COLLATE, sorting is always case-insensitive.\n\n\nWe only recommend using COLLATE for final sorting of a small number of rows, since sorting with COLLATE is less efficient than normal sorting by bytes.\n\n\nRows that have identical values for the list of sorting expressions are output in an arbitrary order, which can also be nondeterministic (different each time).\nIf the ORDER BY clause is omitted, the order of the rows is also undefined, and may be nondeterministic as well.\n\n\nWhen floating point numbers are sorted, NaNs are separate from the other values. Regardless of the sorting order, NaNs come at the end. In other words, for ascending sorting they are placed as if they are larger than all the other numbers, while for descending sorting they are placed as if they are smaller than the rest.\n\n\nLess RAM is used if a small enough LIMIT is specified in addition to ORDER BY. Otherwise, the amount of memory spent is proportional to the volume of data for sorting. For distributed query processing, if GROUP BY is omitted, sorting is partially done on remote servers, and the results are merged on the requestor server. This means that for distributed sorting, the volume of data to sort can be greater than the amount of memory on a single server.\n\n\nIf there is not enough RAM, it is possible to perform sorting in external memory (creating temporary files on a disk). Use the setting \nmax_bytes_before_external_sort\n for this purpose. If it is set to 0 (the default), external sorting is disabled. If it is enabled, when the volume of data to sort reaches the specified number of bytes, the collected data is sorted and dumped into a temporary file. After all data is read, all the sorted files are merged and the results are output. Files are written to the /var/lib/clickhouse/tmp/ directory in the config (by default, but you can use the 'tmp_path' parameter to change this setting).\n\n\nRunning a query may use more memory than 'max_bytes_before_external_sort'. For this reason, this setting must have a value significantly smaller than 'max_memory_usage'. As an example, if your server has 128 GB of RAM and you need to run a single query, set 'max_memory_usage' to 100 GB, and 'max_bytes_before_external_sort' to 80 GB.\n\n\nExternal sorting works much less effectively than sorting in RAM.\n\n\nSELECT clause\n\n\nThe expressions specified in the SELECT clause are analyzed after the calculations for all the clauses listed above are completed.\nMore specifically, expressions are analyzed that are above the aggregate functions, if there are any aggregate functions.\nThe aggregate functions and everything below them are calculated during aggregation (GROUP BY).\nThese expressions work as if they are applied to separate rows in the result.\n\n\nDISTINCT clause\n\n\nIf DISTINCT is specified, only a single row will remain out of all the sets of fully matching rows in the result.\nThe result will be the same as if GROUP BY were specified across all the fields specified in SELECT without aggregate functions. But there are several differences from GROUP BY:\n\n\n\n\nDISTINCT can be applied together with GROUP BY.\n\n\nWhen ORDER BY is omitted and LIMIT is defined, the query stops running immediately after the required number of different rows has been read.\n\n\nData blocks are output as they are processed, without waiting for the entire query to finish running.\n\n\n\n\nDISTINCT is not supported if SELECT has at least one array column.\n\n\nLIMIT clause\n\n\nLIMIT m allows you to select the first 'm' rows from the result.\nLIMIT n, m allows you to select the first 'm' rows from the result after skipping the first 'n' rows.\n\n\n'n' and 'm' must be non-negative integers.\n\n\nIf there isn't an ORDER BY clause that explicitly sorts results, the result may be arbitrary and nondeterministic.\n\n\nUNION ALL clause\n\n\nYou can use UNION ALL to combine any number of queries. Example:\n\n\nSELECT\n \nCounterID\n,\n \n1\n \nAS\n \ntable\n,\n \ntoInt64\n(\ncount\n())\n \nAS\n \nc\n\n \nFROM\n \ntest\n.\nhits\n\n \nGROUP\n \nBY\n \nCounterID\n\n\n\nUNION\n \nALL\n\n\n\nSELECT\n \nCounterID\n,\n \n2\n \nAS\n \ntable\n,\n \nsum\n(\nSign\n)\n \nAS\n \nc\n\n \nFROM\n \ntest\n.\nvisits\n\n \nGROUP\n \nBY\n \nCounterID\n\n \nHAVING\n \nc\n \n \n0\n\n\n\n\n\n\nOnly UNION ALL is supported. The regular UNION (UNION DISTINCT) is not supported. If you need UNION DISTINCT, you can write SELECT DISTINCT from a subquery containing UNION ALL.\n\n\nQueries that are parts of UNION ALL can be run simultaneously, and their results can be mixed together.\n\n\nThe structure of results (the number and type of columns) must match for the queries. But the column names can differ. In this case, the column names for the final result will be taken from the first query.\n\n\nQueries that are parts of UNION ALL can't be enclosed in brackets. ORDER BY and LIMIT are applied to separate queries, not to the final result. If you need to apply a conversion to the final result, you can put all the queries with UNION ALL in a subquery in the FROM clause.\n\n\nINTO OUTFILE clause\n\n\nAdd the \nINTO OUTFILE filename\n clause (where filename is a string literal) to redirect query output to the specified file.\nIn contrast to MySQL, the file is created on the client side. The query will fail if a file with the same filename already exists.\nThis functionality is available in the command-line client and clickhouse-local (a query sent via HTTP interface will fail).\n\n\nThe default output format is TabSeparated (the same as in the command-line client batch mode).\n\n\nFORMAT clause\n\n\nSpecify 'FORMAT format' to get data in any specified format.\nYou can use this for convenience, or for creating dumps.\nFor more information, see the section \"Formats\".\nIf the FORMAT clause is omitted, the default format is used, which depends on both the settings and the interface used for accessing the DB. For the HTTP interface and the command-line client in batch mode, the default format is TabSeparated. For the command-line client in interactive mode, the default format is PrettyCompact (it has attractive and compact tables).\n\n\nWhen using the command-line client, data is passed to the client in an internal efficient format. The client independently interprets the FORMAT clause of the query and formats the data itself (thus relieving the network and the server from the load).\n\n\nIN operators\n\n\nThe \nIN\n, \nNOT IN\n, \nGLOBAL IN\n, and \nGLOBAL NOT IN\n operators are covered separately, since their functionality is quite rich.\n\n\nThe left side of the operator is either a single column or a tuple.\n\n\nExamples:\n\n\nSELECT\n \nUserID\n \nIN\n \n(\n123\n,\n \n456\n)\n \nFROM\n \n...\n\n\nSELECT\n \n(\nCounterID\n,\n \nUserID\n)\n \nIN\n \n((\n34\n,\n \n123\n),\n \n(\n101500\n,\n \n456\n))\n \nFROM\n \n...\n\n\n\n\n\n\nIf the left side is a single column that is in the index, and the right side is a set of constants, the system uses the index for processing the query.\n\n\nDon't list too many values explicitly (i.e. millions). If a data set is large, put it in a temporary table (for example, see the section \"External data for query processing\"), then use a subquery.\n\n\nThe right side of the operator can be a set of constant expressions, a set of tuples with constant expressions (shown in the examples above), or the name of a database table or SELECT subquery in brackets.\n\n\nIf the right side of the operator is the name of a table (for example, \nUserID IN users\n), this is equivalent to the subquery \nUserID IN (SELECT * FROM users)\n. Use this when working with external data that is sent along with the query. For example, the query can be sent together with a set of user IDs loaded to the 'users' temporary table, which should be filtered.\n\n\nIf the right side of the operator is a table name that has the Set engine (a prepared data set that is always in RAM), the data set will not be created over again for each query.\n\n\nThe subquery may specify more than one column for filtering tuples.\nExample:\n\n\nSELECT\n \n(\nCounterID\n,\n \nUserID\n)\n \nIN\n \n(\nSELECT\n \nCounterID\n,\n \nUserID\n \nFROM\n \n...)\n \nFROM\n \n...\n\n\n\n\n\n\nThe columns to the left and right of the IN operator should have the same type.\n\n\nThe IN operator and subquery may occur in any part of the query, including in aggregate functions and lambda functions.\nExample:\n\n\nSELECT\n\n \nEventDate\n,\n\n \navg\n(\nUserID\n \nIN\n\n \n(\n\n \nSELECT\n \nUserID\n\n \nFROM\n \ntest\n.\nhits\n\n \nWHERE\n \nEventDate\n \n=\n \ntoDate\n(\n2014-03-17\n)\n\n \n))\n \nAS\n \nratio\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \nEventDate\n\n\nORDER\n \nBY\n \nEventDate\n \nASC\n\n\n\n\n\n\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500ratio\u2500\u2510\n\u2502 2014-03-17 \u2502 1 \u2502\n\u2502 2014-03-18 \u2502 0.807696 \u2502\n\u2502 2014-03-19 \u2502 0.755406 \u2502\n\u2502 2014-03-20 \u2502 0.723218 \u2502\n\u2502 2014-03-21 \u2502 0.697021 \u2502\n\u2502 2014-03-22 \u2502 0.647851 \u2502\n\u2502 2014-03-23 \u2502 0.648416 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nFor each day after March 17th, count the percentage of pageviews made by users who visited the site on March 17th.\nA subquery in the IN clause is always run just one time on a single server. There are no dependent subqueries.\n\n\n\n\nDistributed subqueries\n\n\nThere are two options for IN-s with subqueries (similar to JOINs): normal \nIN\n / \nOIN\n and \nIN GLOBAL\n / \nGLOBAL JOIN\n. They differ in how they are run for distributed query processing.\n\n\n\n\nRemember that the algorithms described below may work differently depending on the [settings](../operations/settings/settings.md#settings-distributed_product_mode) `distributed_product_mode` setting.\n\n\n\n\n\nWhen using the regular IN, the query is sent to remote servers, and each of them runs the subqueries in the \nIN\n or \nJOIN\n clause.\n\n\nWhen using \nGLOBAL IN\n / \nGLOBAL JOINs\n, first all the subqueries are run for \nGLOBAL IN\n / \nGLOBAL JOINs\n, and the results are collected in temporary tables. Then the temporary tables are sent to each remote server, where the queries are run using this temporary data.\n\n\nFor a non-distributed query, use the regular \nIN\n / \nJOIN\n.\n\n\nBe careful when using subqueries in the \nIN\n / \nJOIN\n clauses for distributed query processing.\n\n\nLet's look at some examples. Assume that each server in the cluster has a normal \nlocal_table\n. Each server also has a \ndistributed_table\n table with the \nDistributed\n type, which looks at all the servers in the cluster.\n\n\nFor a query to the \ndistributed_table\n, the query will be sent to all the remote servers and run on them using the \nlocal_table\n.\n\n\nFor example, the query\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n\n\n\n\n\n\nwill be sent to all remote servers as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n\n\n\n\n\n\nand run on each of them in parallel, until it reaches the stage where intermediate results can be combined. Then the intermediate results will be returned to the requestor server and merged on it, and the final result will be sent to the client.\n\n\nNow let's examine a query with IN:\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\n\n\nCalculation of the intersection of audiences of two sites.\n\n\n\n\nThis query will be sent to all remote servers as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nIn other words, the data set in the IN clause will be collected on each server independently, only across the data that is stored locally on each of the servers.\n\n\nThis will work correctly and optimally if you are prepared for this case and have spread data across the cluster servers such that the data for a single UserID resides entirely on a single server. In this case, all the necessary data will be available locally on each server. Otherwise, the result will be inaccurate. We refer to this variation of the query as \"local IN\".\n\n\nTo correct how the query works when data is spread randomly across the cluster servers, you could specify \ndistributed_table\n inside a subquery. The query would look like this:\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nThis query will be sent to all remote servers as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nThe subquery will begin running on each remote server. Since the subquery uses a distributed table, the subquery that is on each remote server will be resent to every remote server as\n\n\nSELECT\n \nUserID\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n\n\n\n\n\n\nFor example, if you have a cluster of 100 servers, executing the entire query will require 10,000 elementary requests, which is generally considered unacceptable.\n\n\nIn such cases, you should always use GLOBAL IN instead of IN. Let's look at how it works for the query\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nGLOBAL\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nThe requestor server will run the subquery\n\n\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n\n\n\n\n\n\nand the result will be put in a temporary table in RAM. Then the request will be sent to each remote server as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nGLOBAL\n \nIN\n \n_data1\n\n\n\n\n\n\nand the temporary table \n_data1\n will be sent to every remote server with the query (the name of the temporary table is implementation-defined).\n\n\nThis is more optimal than using the normal IN. However, keep the following points in mind:\n\n\n\n\nWhen creating a temporary table, data is not made unique. To reduce the volume of data transmitted over the network, specify DISTINCT in the subquery. (You don't need to do this for a normal IN.)\n\n\nThe temporary table will be sent to all the remote servers. Transmission does not account for network topology. For example, if 10 remote servers reside in a datacenter that is very remote in relation to the requestor server, the data will be sent 10 times over the channel to the remote datacenter. Try to avoid large data sets when using GLOBAL IN.\n\n\nWhen transmitting data to remote servers, restrictions on network bandwidth are not configurable. You might overload the network.\n\n\nTry to distribute data across servers so that you don't need to use GLOBAL IN on a regular basis.\n\n\nIf you need to use GLOBAL IN often, plan the location of the ClickHouse cluster so that a single group of replicas resides in no more than one data center with a fast network between them, so that a query can be processed entirely within a single data center.\n\n\n\n\nIt also makes sense to specify a local table in the \nGLOBAL IN\n clause, in case this local table is only available on the requestor server and you want to use data from it on remote servers.\n\n\nExtreme values\n\n\nIn addition to results, you can also get minimum and maximum values for the results columns. To do this, set the \nextremes\n setting to 1. Minimums and maximums are calculated for numeric types, dates, and dates with times. For other columns, the default values are output.\n\n\nAn extra two rows are calculated \u2013 the minimums and maximums, respectively. These extra two rows are output in JSON*, TabSeparated*, and Pretty* formats, separate from the other rows. They are not output for other formats.\n\n\nIn JSON* formats, the extreme values are output in a separate 'extremes' field. In TabSeparated* formats, the row comes after the main result, and after 'totals' if present. It is preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result, and after 'totals' if present.\n\n\nExtreme values are calculated for rows that have passed through LIMIT. However, when using 'LIMIT offset, size', the rows before 'offset' are included in 'extremes'. In stream requests, the result may also include a small number of rows that passed through LIMIT.\n\n\nNotes\n\n\nThe \nGROUP BY\n and \nORDER BY\n clauses do not support positional arguments. This contradicts MySQL, but conforms to standard SQL.\nFor example, \nGROUP BY 1, 2\n will be interpreted as grouping by constants (i.e. aggregation of all rows into one).\n\n\nYou can use synonyms (\nAS\n aliases) in any part of a query.\n\n\nYou can put an asterisk in any part of a query instead of an expression. When the query is analyzed, the asterisk is expanded to a list of all table columns (excluding the \nMATERIALIZED\n and \nALIAS\n columns). There are only a few cases when using an asterisk is justified:\n\n\n\n\nWhen creating a table dump.\n\n\nFor tables containing just a few columns, such as system tables.\n\n\nFor getting information about what columns are in a table. In this case, set \nLIMIT 1\n. But it is better to use the \nDESC TABLE\n query.\n\n\nWhen there is strong filtration on a small number of columns using \nPREWHERE\n.\n\n\nIn subqueries (since columns that aren't needed for the external query are excluded from subqueries).\n\n\n\n\nIn all other cases, we don't recommend using the asterisk, since it only gives you the drawbacks of a columnar DBMS instead of the advantages. In other words using the asterisk is not recommended.\n\n\nKILL QUERY\n\n\nKILL\n \nQUERY\n\n \nWHERE\n \nwhere\n \nexpression\n \nto\n \nSELECT\n \nFROM\n \nsystem\n.\nprocesses\n \nquery\n\n \n[\nSYNC\n|\nASYNC\n|\nTEST\n]\n\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nAttempts to forcibly terminate the currently running queries.\nThe queries to terminate are selected from the system.processes table using the criteria defined in the \nWHERE\n clause of the \nKILL\n query.\n\n\nExamples:\n\n\n-- Forcibly terminates all queries with the specified query_id:\n\n\nKILL\n \nQUERY\n \nWHERE\n \nquery_id\n=\n2-857d-4a57-9ee0-327da5d60a90\n\n\n\n-- Synchronously terminates all queries run by \nusername\n:\n\n\nKILL\n \nQUERY\n \nWHERE\n \nuser\n=\nusername\n \nSYNC\n\n\n\n\n\n\nRead-only users can only stop their own queries.\n\n\nBy default, the asynchronous version of queries is used (\nASYNC\n), which doesn't wait for confirmation that queries have stopped.\n\n\nThe synchronous version (\nSYNC\n) waits for all queries to stop and displays information about each process as it stops.\nThe response contains the \nkill_status\n column, which can take the following values:\n\n\n\n\n'finished' \u2013 The query was terminated successfully.\n\n\n'waiting' \u2013 Waiting for the query to end after sending it a signal to terminate.\n\n\nThe other values \u200b\u200bexplain why the query can't be stopped.\n\n\n\n\nA test query (\nTEST\n) only checks the user's rights and displays a list of queries to stop.", + "title": "Queries" + }, + { + "location": "/query_language/queries/#queries", + "text": "", + "title": "Queries" + }, + { + "location": "/query_language/queries/#create-database", + "text": "Creating db_name databases CREATE DATABASE [ IF NOT EXISTS ] db_name A database is just a directory for tables.\nIf IF NOT EXISTS is included, the query won't return an error if the database already exists.", + "title": "CREATE DATABASE" + }, + { + "location": "/query_language/queries/#create-table", + "text": "The CREATE TABLE query can have several forms. CREATE [ TEMPORARY ] TABLE [ IF NOT EXISTS ] [ db .] name [ ON CLUSTER cluster ] ( \n name1 [ type1 ] [ DEFAULT | MATERIALIZED | ALIAS expr1 ], \n name2 [ type2 ] [ DEFAULT | MATERIALIZED | ALIAS expr2 ], \n ... ) ENGINE = engine Creates a table named 'name' in the 'db' database or the current database if 'db' is not set, with the structure specified in brackets and the 'engine' engine.\nThe structure of the table is a list of column descriptions. If indexes are supported by the engine, they are indicated as parameters for the table engine. A column description is name type in the simplest case. Example: RegionID UInt32 .\nExpressions can also be defined for default values (see below). CREATE [ TEMPORARY ] TABLE [ IF NOT EXISTS ] [ db .] name AS [ db2 .] name2 [ ENGINE = engine ] Creates a table with the same structure as another table. You can specify a different engine for the table. If the engine is not specified, the same engine will be used as for the db2.name2 table. CREATE [ TEMPORARY ] TABLE [ IF NOT EXISTS ] [ db .] name ENGINE = engine AS SELECT ... Creates a table with a structure like the result of the SELECT query, with the 'engine' engine, and fills it with data from SELECT. In all cases, if IF NOT EXISTS is specified, the query won't return an error if the table already exists. In this case, the query won't do anything.", + "title": "CREATE TABLE" + }, + { + "location": "/query_language/queries/#default-values", + "text": "The column description can specify an expression for a default value, in one of the following ways: DEFAULT expr , MATERIALIZED expr , ALIAS expr .\nExample: URLDomain String DEFAULT domain(URL) . If an expression for the default value is not defined, the default values will be set to zeros for numbers, empty strings for strings, empty arrays for arrays, and 0000-00-00 for dates or 0000-00-00 00:00:00 for dates with time. NULLs are not supported. If the default expression is defined, the column type is optional. If there isn't an explicitly defined type, the default expression type is used. Example: EventDate DEFAULT toDate(EventTime) \u2013 the 'Date' type will be used for the 'EventDate' column. If the data type and default expression are defined explicitly, this expression will be cast to the specified type using type casting functions. Example: Hits UInt32 DEFAULT 0 means the same thing as Hits UInt32 DEFAULT toUInt32(0) . Default expressions may be defined as an arbitrary expression from table constants and columns. When creating and changing the table structure, it checks that expressions don't contain loops. For INSERT, it checks that expressions are resolvable \u2013 that all columns they can be calculated from have been passed. DEFAULT expr Normal default value. If the INSERT query doesn't specify the corresponding column, it will be filled in by computing the corresponding expression. MATERIALIZED expr Materialized expression. Such a column can't be specified for INSERT, because it is always calculated.\nFor an INSERT without a list of columns, these columns are not considered.\nIn addition, this column is not substituted when using an asterisk in a SELECT query. This is to preserve the invariant that the dump obtained using SELECT * can be inserted back into the table using INSERT without specifying the list of columns. ALIAS expr Synonym. Such a column isn't stored in the table at all.\nIts values can't be inserted in a table, and it is not substituted when using an asterisk in a SELECT query.\nIt can be used in SELECTs if the alias is expanded during query parsing. When using the ALTER query to add new columns, old data for these columns is not written. Instead, when reading old data that does not have values for the new columns, expressions are computed on the fly by default. However, if running the expressions requires different columns that are not indicated in the query, these columns will additionally be read, but only for the blocks of data that need it. If you add a new column to a table but later change its default expression, the values used for old data will change (for data where values were not stored on the disk). Note that when running background merges, data for columns that are missing in one of the merging parts is written to the merged part. It is not possible to set default values for elements in nested data structures.", + "title": "Default values" + }, + { + "location": "/query_language/queries/#temporary-tables", + "text": "In all cases, if TEMPORARY is specified, a temporary table will be created. Temporary tables have the following characteristics: Temporary tables disappear when the session ends, including if the connection is lost. A temporary table is created with the Memory engine. The other table engines are not supported. The DB can't be specified for a temporary table. It is created outside of databases. If a temporary table has the same name as another one and a query specifies the table name without specifying the DB, the temporary table will be used. For distributed query processing, temporary tables used in a query are passed to remote servers. In most cases, temporary tables are not created manually, but when using external data for a query, or for distributed (GLOBAL) IN . For more information, see the appropriate sections", + "title": "Temporary tables" + }, + { + "location": "/query_language/queries/#distributed-ddl-queries-on-cluster-clause", + "text": "The CREATE , DROP , ALTER , and RENAME queries support distributed execution on a cluster.\nFor example, the following query creates the all_hits Distributed table on each host in cluster : CREATE TABLE IF NOT EXISTS all_hits ON CLUSTER cluster ( p Date , i Int32 ) ENGINE = Distributed ( cluster , default , hits ) In order to run these queries correctly, each host must have the same cluster definition (to simplify syncing configs, you can use substitutions from ZooKeeper). They must also connect to the ZooKeeper servers.\nThe local version of the query will eventually be implemented on each host in the cluster, even if some hosts are currently not available. The order for executing queries within a single host is guaranteed. ALTER queries are not yet supported for replicated tables.", + "title": "Distributed DDL queries (ON CLUSTER clause)" + }, + { + "location": "/query_language/queries/#create-view", + "text": "CREATE [ MATERIALIZED ] VIEW [ IF NOT EXISTS ] [ db .] name [ TO [ db .] name ] [ ENGINE = engine ] [ POPULATE ] AS SELECT ... Creates a view. There are two types of views: normal and MATERIALIZED. When creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data. A materialized view works as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view. Normal views don't store any data, but just perform a read from another table. In other words, a normal view is nothing more than a saved query. When reading from a view, this saved query is used as a subquery in the FROM clause. As an example, assume you've created a view: CREATE VIEW view AS SELECT ... and written a query: SELECT a , b , c FROM view This query is fully equivalent to using the subquery: SELECT a , b , c FROM ( SELECT ...) Materialized views store data transformed by the corresponding SELECT query. When creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data. A materialized view is arranged as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view. If you specify POPULATE, the existing table data is inserted in the view when creating it, as if making a CREATE TABLE ... AS SELECT ... . Otherwise, the query contains only the data inserted in the table after creating the view. We don't recommend using POPULATE, since data inserted in the table during the view creation will not be inserted in it. A SELECT query can contain DISTINCT , GROUP BY , ORDER BY , LIMIT ... Note that the corresponding conversions are performed independently on each block of inserted data. For example, if GROUP BY is set, data is aggregated during insertion, but only within a single packet of inserted data. The data won't be further aggregated. The exception is when using an ENGINE that independently performs data aggregation, such as SummingMergeTree . The execution of ALTER queries on materialized views has not been fully developed, so they might be inconvenient. If the materialized view uses the construction TO [db.]name , you can DETACH the view, run ALTER for the target table, and then ATTACH the previously detached ( DETACH ) view. Views look the same as normal tables. For example, they are listed in the result of the SHOW TABLES query. There isn't a separate query for deleting views. To delete a view, use DROP TABLE .", + "title": "CREATE VIEW" + }, + { + "location": "/query_language/queries/#attach", + "text": "This query is exactly the same as CREATE , but instead of the word CREATE it uses the word ATTACH . The query doesn't create data on the disk, but assumes that data is already in the appropriate places, and just adds information about the table to the server.\nAfter executing an ATTACH query, the server will know about the existence of the table. If the table was previously detached ( DETACH ), meaning that its structure is known, you can use shorthand without defining the structure. ATTACH TABLE [ IF NOT EXISTS ] [ db .] name This query is used when starting the server. The server stores table metadata as files with ATTACH queries, which it simply runs at launch (with the exception of system tables, which are explicitly created on the server).", + "title": "ATTACH" + }, + { + "location": "/query_language/queries/#drop", + "text": "This query has two types: DROP DATABASE and DROP TABLE . DROP DATABASE [ IF EXISTS ] db [ ON CLUSTER cluster ] Deletes all tables inside the 'db' database, then deletes the 'db' database itself.\nIf IF EXISTS is specified, it doesn't return an error if the database doesn't exist. DROP [ TEMPORARY ] TABLE [ IF EXISTS ] [ db .] name [ ON CLUSTER cluster ] Deletes the table.\nIf IF EXISTS is specified, it doesn't return an error if the table doesn't exist or the database doesn't exist.", + "title": "DROP" + }, + { + "location": "/query_language/queries/#detach", + "text": "Deletes information about the 'name' table from the server. The server stops knowing about the table's existence. DETACH TABLE [ IF EXISTS ] [ db .] name This does not delete the table's data or metadata. On the next server launch, the server will read the metadata and find out about the table again.\nSimilarly, a \"detached\" table can be re-attached using the ATTACH query (with the exception of system tables, which do not have metadata stored for them). There is no DETACH DATABASE query.", + "title": "DETACH" + }, + { + "location": "/query_language/queries/#rename", + "text": "Renames one or more tables. RENAME TABLE [ db11 .] name11 TO [ db12 .] name12 , [ db21 .] name21 TO [ db22 .] name22 , ... [ ON CLUSTER cluster ] All tables are renamed under global locking. Renaming tables is a light operation. If you indicated another database after TO, the table will be moved to this database. However, the directories with databases must reside in the same file system (otherwise, an error is returned).", + "title": "RENAME" + }, + { + "location": "/query_language/queries/#alter", + "text": "The ALTER query is only supported for *MergeTree tables, as well as Merge and Distributed . The query has several variations.", + "title": "ALTER" + }, + { + "location": "/query_language/queries/#column-manipulations", + "text": "Changing the table structure. ALTER TABLE [ db ]. name [ ON CLUSTER cluster ] ADD | DROP | MODIFY COLUMN ... In the query, specify a list of one or more comma-separated actions.\nEach action is an operation on a column. The following actions are supported: ADD COLUMN name [ type ] [ default_expr ] [ AFTER name_after ] Adds a new column to the table with the specified name, type, and default_expr (see the section \"Default expressions\"). If you specify AFTER name_after (the name of another column), the column is added after the specified one in the list of table columns. Otherwise, the column is added to the end of the table. Note that there is no way to add a column to the beginning of a table. For a chain of actions, 'name_after' can be the name of a column that is added in one of the previous actions. Adding a column just changes the table structure, without performing any actions with data. The data doesn't appear on the disk after ALTER. If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). The column appears on the disk after merging data parts (see MergeTree). This approach allows us to complete the ALTER query instantly, without increasing the volume of old data. DROP COLUMN name Deletes the column with the name 'name'.\nDeletes data from the file system. Since this deletes entire files, the query is completed almost instantly. MODIFY COLUMN name [ type ] [ default_expr ] Changes the 'name' column's type to 'type' and/or the default expression to 'default_expr'. When changing the type, values are converted as if the 'toType' function were applied to them. If only the default expression is changed, the query doesn't do anything complex, and is completed almost instantly. Changing the column type is the only complex action \u2013 it changes the contents of files with data. For large tables, this may take a long time. There are several processing stages: Preparing temporary (new) files with modified data. Renaming old files. Renaming the temporary (new) files to the old names. Deleting the old files. Only the first stage takes time. If there is a failure at this stage, the data is not changed.\nIf there is a failure during one of the successive stages, data can be restored manually. The exception is if the old files were deleted from the file system but the data for the new files did not get written to the disk and was lost. There is no support for changing the column type in arrays and nested data structures. The ALTER query lets you create and delete separate elements (columns) in nested data structures, but not whole nested data structures. To add a nested data structure, you can add columns with a name like name.nested_name and the type Array(T) . A nested data structure is equivalent to multiple array columns with a name that has the same prefix before the dot. There is no support for deleting columns in the primary key or the sampling key (columns that are in the ENGINE expression). Changing the type for columns that are included in the primary key is only possible if this change does not cause the data to be modified (for example, it is allowed to add values to an Enum or change a type with DateTime to UInt32 ). If the ALTER query is not sufficient for making the table changes you need, you can create a new table, copy the data to it using the INSERT SELECT query, then switch the tables using the RENAME query and delete the old table. The ALTER query blocks all reads and writes for the table. In other words, if a long SELECT is running at the time of the ALTER query, the ALTER query will wait for it to complete. At the same time, all new queries to the same table will wait while this ALTER is running. For tables that don't store data themselves (such as Merge and Distributed ), ALTER just changes the table structure, and does not change the structure of subordinate tables. For example, when running ALTER for a Distributed table, you will also need to run ALTER for the tables on all remote servers. The ALTER query for changing columns is replicated. The instructions are saved in ZooKeeper, then each replica applies them. All ALTER queries are run in the same order. The query waits for the appropriate actions to be completed on the other replicas. However, a query to change columns in a replicated table can be interrupted, and all actions will be performed asynchronously.", + "title": "Column manipulations" + }, + { + "location": "/query_language/queries/#manipulations-with-partitions-and-parts", + "text": "It only works for tables in the MergeTree family. The following operations are available: DETACH PARTITION \u2013 Move a partition to the 'detached' directory and forget it. DROP PARTITION \u2013 Delete a partition. ATTACH PART|PARTITION \u2013 Add a new part or partition from the detached directory to the table. FREEZE PARTITION \u2013 Create a backup of a partition. FETCH PARTITION \u2013 Download a partition from another server. Each type of query is covered separately below. A partition in a table is data for a single calendar month. This is determined by the values of the date key specified in the table engine parameters. Each month's data is stored separately in order to simplify manipulations with this data. A \"part\" in the table is part of the data from a single partition, sorted by the primary key. You can use the system.parts table to view the set of table parts and partitions: SELECT * FROM system . parts WHERE active active \u2013 Only count active parts. Inactive parts are, for example, source parts remaining after merging to a larger part \u2013 these parts are deleted approximately 10 minutes after merging. Another way to view a set of parts and partitions is to go into the directory with table data.\nData directory: /var/lib/clickhouse/data/database/table/ ,where /var/lib/clickhouse/ is the path to the ClickHouse data, 'database' is the database name, and 'table' is the table name. Example: $ ls -l /var/lib/clickhouse/data/test/visits/\ntotal 48 \ndrwxrwxrwx 2 clickhouse clickhouse 20480 May 5 02 :58 20140317_20140323_2_2_0\ndrwxrwxrwx 2 clickhouse clickhouse 20480 May 5 02 :58 20140317_20140323_4_4_0\ndrwxrwxrwx 2 clickhouse clickhouse 4096 May 5 02 :55 detached\n-rw-rw-rw- 1 clickhouse clickhouse 2 May 5 02 :58 increment.txt Here, 20140317_20140323_2_2_0 and 20140317_20140323_4_4_0 are the directories of data parts. Let's break down the name of the first part: 20140317_20140323_2_2_0 . 20140317 is the minimum date of the data in the chunk. 20140323 is the maximum date of the data in the chunk. 2 is the minimum number of the data block. 2 is the maximum number of the data block. 0 is the chunk level (the depth of the merge tree it is formed from). Each piece relates to a single partition and contains data for just one month. 201403 is the name of the partition. A partition is a set of parts for a single month. On an operating server, you can't manually change the set of parts or their data on the file system, since the server won't know about it.\nFor non-replicated tables, you can do this when the server is stopped, but we don't recommended it.\nFor replicated tables, the set of parts can't be changed in any case. The detached directory contains parts that are not used by the server - detached from the table using the ALTER ... DETACH query. Parts that are damaged are also moved to this directory, instead of deleting them. You can add, delete, or modify the data in the 'detached' directory at any time \u2013 the server won't know about this until you make the ALTER TABLE ... ATTACH query. ALTER TABLE [ db .] table DETACH PARTITION name Move all data for partitions named 'name' to the 'detached' directory and forget about them.\nThe partition name is specified in YYYYMM format. It can be indicated in single quotes or without them. After the query is executed, you can do whatever you want with the data in the 'detached' directory \u2014 delete it from the file system, or just leave it. The query is replicated \u2013 data will be moved to the 'detached' directory and forgotten on all replicas. The query can only be sent to a leader replica. To find out if a replica is a leader, perform SELECT to the 'system.replicas' system table. Alternatively, it is easier to make a query on all replicas, and all except one will throw an exception. ALTER TABLE [ db .] table DROP PARTITION name The same as the DETACH operation. Deletes data from the table. Data parts will be tagged as inactive and will be completely deleted in approximately 10 minutes. The query is replicated \u2013 data will be deleted on all replicas. ALTER TABLE [ db .] table ATTACH PARTITION | PART name Adds data to the table from the 'detached' directory. It is possible to add data for an entire partition or a separate part. For a part, specify the full name of the part in single quotes. The query is replicated. Each replica checks whether there is data in the 'detached' directory. If there is data, it checks the integrity, verifies that it matches the data on the server that initiated the query, and then adds it if everything is correct. If not, it downloads data from the query requestor replica, or from another replica where the data has already been added. So you can put data in the 'detached' directory on one replica, and use the ALTER ... ATTACH query to add it to the table on all replicas. ALTER TABLE [ db .] table FREEZE PARTITION name Creates a local backup of one or multiple partitions. The name can be the full name of the partition (for example, 201403), or its prefix (for example, 2014): then the backup will be created for all the corresponding partitions. The query does the following: for a data snapshot at the time of execution, it creates hardlinks to table data in the directory /var/lib/clickhouse/shadow/N/... /var/lib/clickhouse/ is the working ClickHouse directory from the config. N is the incremental number of the backup. The same structure of directories is created inside the backup as inside /var/lib/clickhouse/ .\nIt also performs 'chmod' for all files, forbidding writes to them. The backup is created almost instantly (but first it waits for current queries to the corresponding table to finish running). At first, the backup doesn't take any space on the disk. As the system works, the backup can take disk space, as data is modified. If the backup is made for old enough data, it won't take space on the disk. After creating the backup, data from /var/lib/clickhouse/shadow/ can be copied to the remote server and then deleted on the local server.\nThe entire backup process is performed without stopping the server. The ALTER ... FREEZE PARTITION query is not replicated. A local backup is only created on the local server. As an alternative, you can manually copy data from the /var/lib/clickhouse/data/database/table directory.\nBut if you do this while the server is running, race conditions are possible when copying directories with files being added or changed, and the backup may be inconsistent. You can do this if the server isn't running \u2013 then the resulting data will be the same as after the ALTER TABLE t FREEZE PARTITION query. ALTER TABLE ... FREEZE PARTITION only copies data, not table metadata. To make a backup of table metadata, copy the file /var/lib/clickhouse/metadata/database/table.sql To restore from a backup: Use the CREATE query to create the table if it doesn't exist. The query can be taken from an .sql file (replace ATTACH in it with CREATE ). Copy the data from the data/database/table/ directory inside the backup to the /var/lib/clickhouse/data/database/table/detached/ directory. Run ALTER TABLE ... ATTACH PARTITION YYYYMM queries, where YYYYMM is the month, for every month. In this way, data from the backup will be added to the table.\nRestoring from a backup doesn't require stopping the server.", + "title": "Manipulations with partitions and parts" + }, + { + "location": "/query_language/queries/#backups-and-replication", + "text": "Replication provides protection from device failures. If all data disappeared on one of your replicas, follow the instructions in the \"Restoration after failure\" section to restore it. For protection from device failures, you must use replication. For more information about replication, see the section \"Data replication\". Backups protect against human error (accidentally deleting data, deleting the wrong data or in the wrong cluster, or corrupting data).\nFor high-volume databases, it can be difficult to copy backups to remote servers. In such cases, to protect from human error, you can keep a backup on the same server (it will reside in /var/lib/clickhouse/shadow/ ). ALTER TABLE [ db .] table FETCH PARTITION name FROM path-in-zookeeper This query only works for replicatable tables. It downloads the specified partition from the shard that has its ZooKeeper path specified in the FROM clause, then puts it in the detached directory for the specified table. Although the query is called ALTER TABLE , it does not change the table structure, and does not immediately change the data available in the table. Data is placed in the detached directory. You can use the ALTER TABLE ... ATTACH query to attach the data. The FROM clause specifies the path in ZooKeeper . For example, /clickhouse/tables/01-01/visits .\nBefore downloading, the system checks that the partition exists and the table structure matches. The most appropriate replica is selected automatically from the healthy replicas. The ALTER ... FETCH PARTITION query is not replicated. The partition will be downloaded to the 'detached' directory only on the local server. Note that if after this you use the ALTER TABLE ... ATTACH query to add data to the table, the data will be added on all replicas (on one of the replicas it will be added from the 'detached' directory, and on the rest it will be loaded from neighboring replicas).", + "title": "Backups and replication" + }, + { + "location": "/query_language/queries/#synchronicity-of-alter-queries", + "text": "For non-replicatable tables, all ALTER queries are performed synchronously. For replicatable tables, the query just adds instructions for the appropriate actions to ZooKeeper , and the actions themselves are performed as soon as possible. However, the query can wait for these actions to be completed on all the replicas. For ALTER ... ATTACH|DETACH|DROP queries, you can use the replication_alter_partitions_sync setting to set up waiting.\nPossible values: 0 \u2013 do not wait; 1 \u2013 only wait for own execution (default); 2 \u2013 wait for all.", + "title": "Synchronicity of ALTER queries" + }, + { + "location": "/query_language/queries/#show-databases", + "text": "SHOW DATABASES [ INTO OUTFILE filename ] [ FORMAT format ] Prints a list of all databases.\nThis query is identical to SELECT name FROM system.databases [INTO OUTFILE filename] [FORMAT format] . See also the section \"Formats\".", + "title": "SHOW DATABASES" + }, + { + "location": "/query_language/queries/#show-tables", + "text": "SHOW [ TEMPORARY ] TABLES [ FROM db ] [ LIKE pattern ] [ INTO OUTFILE filename ] [ FORMAT format ] Displays a list of tables tables from the current database, or from the 'db' database if \"FROM db\" is specified. all tables, or tables whose name matches the pattern, if \"LIKE 'pattern'\" is specified. This query is identical to: SELECT name FROM system.tables WHERE database = 'db' [AND name LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format] . See also the section \"LIKE operator\".", + "title": "SHOW TABLES" + }, + { + "location": "/query_language/queries/#show-processlist", + "text": "SHOW PROCESSLIST [ INTO OUTFILE filename ] [ FORMAT format ] Outputs a list of queries currently being processed, other than SHOW PROCESSLIST queries. Prints a table containing the columns: user \u2013 The user who made the query. Keep in mind that for distributed processing, queries are sent to remote servers under the 'default' user. SHOW PROCESSLIST shows the username for a specific query, not for a query that this query initiated. address \u2013 The name of the host that the query was sent from. For distributed processing, on remote servers, this is the name of the query requestor host. To track where a distributed query was originally made from, look at SHOW PROCESSLIST on the query requestor server. elapsed \u2013 The execution time, in seconds. Queries are output in order of decreasing execution time. rows_read , bytes_read \u2013 How many rows and bytes of uncompressed data were read when processing the query. For distributed processing, data is totaled from all the remote servers. This is the data used for restrictions and quotas. memory_usage \u2013 Current RAM usage in bytes. See the setting 'max_memory_usage'. query \u2013 The query itself. In INSERT queries, the data for insertion is not output. query_id \u2013 The query identifier. Non-empty only if it was explicitly defined by the user. For distributed processing, the query ID is not passed to remote servers. This query is identical to: SELECT * FROM system.processes [INTO OUTFILE filename] [FORMAT format] . Tip (execute in the console): watch -n1 clickhouse-client --query= SHOW PROCESSLIST", + "title": "SHOW PROCESSLIST" + }, + { + "location": "/query_language/queries/#show-create-table", + "text": "SHOW CREATE [ TEMPORARY ] TABLE [ db .] table [ INTO OUTFILE filename ] [ FORMAT format ] Returns a single String -type 'statement' column, which contains a single value \u2013 the CREATE query used for creating the specified table.", + "title": "SHOW CREATE TABLE" + }, + { + "location": "/query_language/queries/#describe-table", + "text": "DESC | DESCRIBE TABLE [ db .] table [ INTO OUTFILE filename ] [ FORMAT format ] Returns two String -type columns: name and type , which indicate the names and types of columns in the specified table. Nested data structures are output in \"expanded\" format. Each column is shown separately, with the name after a dot.", + "title": "DESCRIBE TABLE" + }, + { + "location": "/query_language/queries/#exists", + "text": "EXISTS [ TEMPORARY ] TABLE [ db .] name [ INTO OUTFILE filename ] [ FORMAT format ] Returns a single UInt8 -type column, which contains the single value 0 if the table or database doesn't exist, or 1 if the table exists in the specified database.", + "title": "EXISTS" + }, + { + "location": "/query_language/queries/#use", + "text": "USE db Lets you set the current database for the session.\nThe current database is used for searching for tables if the database is not explicitly defined in the query with a dot before the table name.\nThis query can't be made when using the HTTP protocol, since there is no concept of a session.", + "title": "USE" + }, + { + "location": "/query_language/queries/#set", + "text": "SET param = value Allows you to set param to value . You can also make all the settings from the specified settings profile in a single query. To do this, specify 'profile' as the setting name. For more information, see the section \"Settings\".\nThe setting is made for the session, or for the server (globally) if GLOBAL is specified.\nWhen making a global setting, the setting is not applied to sessions already running, including the current session. It will only be used for new sessions. When the server is restarted, global settings made using SET are lost.\nTo make settings that persist after a server restart, you can only use the server's config file.", + "title": "SET" + }, + { + "location": "/query_language/queries/#optimize", + "text": "OPTIMIZE TABLE [ db .] name [ PARTITION partition ] [ FINAL ] Asks the table engine to do something for optimization.\nSupported only by *MergeTree engines, in which this query initializes a non-scheduled merge of data parts.\nIf you specify a PARTITION , only the specified partition will be optimized.\nIf you specify FINAL , optimization will be performed even when all the data is already in one part.", + "title": "OPTIMIZE" + }, + { + "location": "/query_language/queries/#insert", + "text": "Adding data. Basic query format: INSERT INTO [ db .] table [( c1 , c2 , c3 )] VALUES ( v11 , v12 , v13 ), ( v21 , v22 , v23 ), ... The query can specify a list of columns to insert [(c1, c2, c3)] . In this case, the rest of the columns are filled with: The values calculated from the DEFAULT expressions specified in the table definition. Zeros and empty strings, if DEFAULT expressions are not defined. If strict_insert_defaults=1 , columns that do not have DEFAULT defined must be listed in the query. Data can be passed to the INSERT in any format supported by ClickHouse. The format must be specified explicitly in the query: INSERT INTO [ db .] table [( c1 , c2 , c3 )] FORMAT format_name data_set For example, the following query format is identical to the basic version of INSERT ... VALUES: INSERT INTO [ db .] table [( c1 , c2 , c3 )] FORMAT Values ( v11 , v12 , v13 ), ( v21 , v22 , v23 ), ... ClickHouse removes all spaces and one line feed (if there is one) before the data. When forming a query, we recommend putting the data on a new line after the query operators (this is important if the data begins with spaces). Example: INSERT INTO t FORMAT TabSeparated 11 Hello , world ! 22 Qwerty You can insert data separately from the query by using the command-line client or the HTTP interface. For more information, see the section \" Interfaces \".", + "title": "INSERT" + }, + { + "location": "/query_language/queries/#inserting-the-results-of-select", + "text": "INSERT INTO [ db .] table [( c1 , c2 , c3 )] SELECT ... Columns are mapped according to their position in the SELECT clause. However, their names in the SELECT expression and the table for INSERT may differ. If necessary, type casting is performed. None of the data formats except Values allow setting values to expressions such as now() , 1 + 2 , and so on. The Values format allows limited use of expressions, but this is not recommended, because in this case inefficient code is used for their execution. Other queries for modifying data parts are not supported: UPDATE , DELETE , REPLACE , MERGE , UPSERT , INSERT UPDATE .\nHowever, you can delete old data using ALTER TABLE ... DROP PARTITION .", + "title": "Inserting the results of SELECT" + }, + { + "location": "/query_language/queries/#performance-considerations", + "text": "INSERT sorts the input data by primary key and splits them into partitions by month. If you insert data for mixed months, it can significantly reduce the performance of the INSERT query. To avoid this: Add data in fairly large batches, such as 100,000 rows at a time. Group data by month before uploading it to ClickHouse. Performance will not decrease if: Data is added in real time. You upload data that is usually sorted by time.", + "title": "Performance considerations" + }, + { + "location": "/query_language/queries/#select", + "text": "Data sampling. SELECT [ DISTINCT ] expr_list \n [ FROM [ db .] table | ( subquery ) | table_function ] [ FINAL ] \n [ SAMPLE sample_coeff ] \n [ ARRAY JOIN ...] \n [ GLOBAL ] ANY | ALL INNER | LEFT JOIN ( subquery ) | table USING columns_list \n [ PREWHERE expr ] \n [ WHERE expr ] \n [ GROUP BY expr_list ] [ WITH TOTALS ] \n [ HAVING expr ] \n [ ORDER BY expr_list ] \n [ LIMIT [ n , ] m ] \n [ UNION ALL ...] \n [ INTO OUTFILE filename ] \n [ FORMAT format ] \n [ LIMIT n BY columns ] All the clauses are optional, except for the required list of expressions immediately after SELECT.\nThe clauses below are described in almost the same order as in the query execution conveyor. If the query omits the DISTINCT , GROUP BY and ORDER BY clauses and the IN and JOIN subqueries, the query will be completely stream processed, using O(1) amount of RAM.\nOtherwise, the query might consume a lot of RAM if the appropriate restrictions are not specified: max_memory_usage , max_rows_to_group_by , max_rows_to_sort , max_rows_in_distinct , max_bytes_in_distinct , max_rows_in_set , max_bytes_in_set , max_rows_in_join , max_bytes_in_join , max_bytes_before_external_sort , max_bytes_before_external_group_by . For more information, see the section \"Settings\". It is possible to use external sorting (saving temporary tables to a disk) and external aggregation. The system does not have \"merge join\" .", + "title": "SELECT" + }, + { + "location": "/query_language/queries/#from-clause", + "text": "If the FROM clause is omitted, data will be read from the system.one table.\nThe 'system.one' table contains exactly one row (this table fulfills the same purpose as the DUAL table found in other DBMSs). The FROM clause specifies the table to read data from, or a subquery, or a table function; ARRAY JOIN and the regular JOIN may also be included (see below). Instead of a table, the SELECT subquery may be specified in brackets.\nIn this case, the subquery processing pipeline will be built into the processing pipeline of an external query.\nIn contrast to standard SQL, a synonym does not need to be specified after a subquery. For compatibility, it is possible to write 'AS name' after a subquery, but the specified name isn't used anywhere. A table function may be specified instead of a table. For more information, see the section \"Table functions\". To execute a query, all the columns listed in the query are extracted from the appropriate table. Any columns not needed for the external query are thrown out of the subqueries.\nIf a query does not list any columns (for example, SELECT count() FROM t), some column is extracted from the table anyway (the smallest one is preferred), in order to calculate the number of rows. The FINAL modifier can be used only for a SELECT from a CollapsingMergeTree table. When you specify FINAL, data is selected fully \"collapsed\". Keep in mind that using FINAL leads to a selection that includes columns related to the primary key, in addition to the columns specified in the SELECT. Additionally, the query will be executed in a single stream, and data will be merged during query execution. This means that when using FINAL, the query is processed more slowly. In most cases, you should avoid using FINAL. For more information, see the section \"CollapsingMergeTree engine\".", + "title": "FROM clause" + }, + { + "location": "/query_language/queries/#sample-clause", + "text": "The SAMPLE clause allows for approximated query processing. Approximated query processing is only supported by MergeTree* type tables, and only if the sampling expression was specified during table creation (see the section \"MergeTree engine\"). SAMPLE has the format SAMPLE k , where k is a decimal number from 0 to 1, or SAMPLE n , where 'n' is a sufficiently large integer. In the first case, the query will be executed on 'k' percent of data. For example, SAMPLE 0.1 runs the query on 10% of data.\nIn the second case, the query will be executed on a sample of no more than 'n' rows. For example, SAMPLE 10000000 runs the query on a maximum of 10,000,000 rows. Example: SELECT \n Title , \n count () * 10 AS PageViews FROM hits_distributed SAMPLE 0 . 1 WHERE \n CounterID = 34 \n AND toDate ( EventDate ) = toDate ( 2013-01-29 ) \n AND toDate ( EventDate ) = toDate ( 2013-02-04 ) \n AND NOT DontCountHits \n AND NOT Refresh \n AND Title != GROUP BY Title ORDER BY PageViews DESC LIMIT 1000 In this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value 'count()' is manually multiplied by 10. When using something like SAMPLE 10000000 , there isn't any information about which relative percent of data was processed or what the aggregate functions should be multiplied by, so this method of writing is not always appropriate to the situation. A sample with a relative coefficient is \"consistent\": if we look at all possible data that could be in the table, a sample (when using a single sampling expression specified during table creation) with the same coefficient always selects the same subset of possible data. In other words, a sample from different tables on different servers at different times is made the same way. For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This allows using the sample in subqueries in the IN clause, as well as for manually correlating results of different queries with samples.", + "title": "SAMPLE clause" + }, + { + "location": "/query_language/queries/#array-join-clause", + "text": "Allows executing JOIN with an array or nested data structure. The intent is similar to the 'arrayJoin' function, but its functionality is broader. ARRAY JOIN is essentially INNER JOIN with an array. Example: :) CREATE TABLE arrays_test (s String, arr Array(UInt8)) ENGINE = Memory\n\nCREATE TABLE arrays_test\n(\n s String,\n arr Array(UInt8)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.001 sec.\n\n:) INSERT INTO arrays_test VALUES ( Hello , [1,2]), ( World , [3,4,5]), ( Goodbye , [])\n\nINSERT INTO arrays_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM arrays_test\n\nSELECT *\nFROM arrays_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502\n\u2502 Goodbye \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, arr FROM arrays_test ARRAY JOIN arr\n\nSELECT s, arr\nFROM arrays_test\nARRAY JOIN arr\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2510\n\u2502 Hello \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502\n\u2502 World \u2502 4 \u2502\n\u2502 World \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. An alias can be specified for an array in the ARRAY JOIN clause. In this case, an array item can be accessed by this alias, but the array itself by the original name. Example: :) SELECT s, arr, a FROM arrays_test ARRAY JOIN arr AS a\n\nSELECT s, arr, a\nFROM arrays_test\nARRAY JOIN arr AS a\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. Multiple arrays of the same size can be comma-separated in the ARRAY JOIN clause. In this case, JOIN is performed with them simultaneously (the direct sum, not the direct product). Example: :) SELECT s, arr, a, num, mapped FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(x - x + 1, arr) AS mapped\n\nSELECT s, arr, a, num, mapped\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(lambda(tuple(x), plus(x, 1)), arr) AS mapped\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500mapped\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 2 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 5 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 6 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n:) SELECT s, arr, a, num, arrayEnumerate(arr) FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\nSELECT s, arr, a, num, arrayEnumerate(arr)\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500arrayEnumerate(arr)\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 [1,2] \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec. ARRAY JOIN also works with nested data structures. Example: :) CREATE TABLE nested_test (s String, nest Nested(x UInt8, y UInt32)) ENGINE = Memory\n\nCREATE TABLE nested_test\n(\n s String,\n nest Nested(\n x UInt8,\n y UInt32)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.006 sec.\n\n:) INSERT INTO nested_test VALUES ( Hello , [1,2], [10,20]), ( World , [3,4,5], [30,40,50]), ( Goodbye , [], [])\n\nINSERT INTO nested_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM nested_test\n\nSELECT *\nFROM nested_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 Goodbye \u2502 [] \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. When specifying names of nested data structures in ARRAY JOIN, the meaning is the same as ARRAY JOIN with all the array elements that it consists of. Example: :) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x, nest.y\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`, `nest.y`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. This variation also makes sense: :) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. An alias may be used for a nested data structure, in order to select either the JOIN result or the source array. Example: :) SELECT s, n.x, n.y, nest.x, nest.y FROM nested_test ARRAY JOIN nest AS n\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest AS n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. Example of using the arrayEnumerate function: :) SELECT s, n.x, n.y, nest.x, nest.y, num FROM nested_test ARRAY JOIN nest AS n, arrayEnumerate(nest.x) AS num\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`, num\nFROM nested_test\nARRAY JOIN nest AS n, arrayEnumerate(`nest.x`) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u252c\u2500num\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 1 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 2 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 3 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec. The query can only specify a single ARRAY JOIN clause. The corresponding conversion can be performed before the WHERE/PREWHERE clause (if its result is needed in this clause), or after completing WHERE/PREWHERE (to reduce the volume of calculations).", + "title": "ARRAY JOIN clause" + }, + { + "location": "/query_language/queries/#join-clause", + "text": "The normal JOIN, which is not related to ARRAY JOIN described above. [ GLOBAL ] ANY | ALL INNER | LEFT [ OUTER ] JOIN ( subquery ) | table USING columns_list Performs joins with data from the subquery. At the beginning of query processing, the subquery specified after JOIN is run, and its result is saved in memory. Then it is read from the \"left\" table specified in the FROM clause, and while it is being read, for each of the read rows from the \"left\" table, rows are selected from the subquery results table (the \"right\" table) that meet the condition for matching the values of the columns specified in USING. The table name can be specified instead of a subquery. This is equivalent to the SELECT * FROM table subquery, except in a special case when the table has the Join engine \u2013 an array prepared for joining. All columns that are not needed for the JOIN are deleted from the subquery. There are several types of JOINs: INNER or LEFT type:If INNER is specified, the result will contain only those rows that have a matching row in the right table.\nIf LEFT is specified, any rows in the left table that don't have matching rows in the right table will be assigned the default value - zeros or empty rows. LEFT OUTER may be written instead of LEFT; the word OUTER does not affect anything. ANY or ALL stringency:If ANY is specified and the right table has several matching rows, only the first one found is joined.\nIf ALL is specified and the right table has several matching rows, the data will be multiplied by the number of these rows. Using ALL corresponds to the normal JOIN semantic from standard SQL.\nUsing ANY is optimal. If the right table has only one matching row, the results of ANY and ALL are the same. You must specify either ANY or ALL (neither of them is selected by default). GLOBAL distribution: When using a normal JOIN, the query is sent to remote servers. Subqueries are run on each of them in order to make the right table, and the join is performed with this table. In other words, the right table is formed on each server separately. When using GLOBAL ... JOIN , first the requestor server runs a subquery to calculate the right table. This temporary table is passed to each remote server, and queries are run on them using the temporary data that was transmitted. Be careful when using GLOBAL JOINs. For more information, see the section \"Distributed subqueries\". Any combination of JOINs is possible. For example, GLOBAL ANY LEFT OUTER JOIN . When running a JOIN, there is no optimization of the order of execution in relation to other stages of the query. The join (a search in the right table) is run before filtering in WHERE and before aggregation. In order to explicitly set the processing order, we recommend running a JOIN subquery with a subquery. Example: SELECT \n CounterID , \n hits , \n visits FROM ( \n SELECT \n CounterID , \n count () AS hits \n FROM test . hits \n GROUP BY CounterID ) ANY LEFT JOIN ( \n SELECT \n CounterID , \n sum ( Sign ) AS visits \n FROM test . visits \n GROUP BY CounterID ) USING CounterID ORDER BY hits DESC LIMIT 10 \u250c\u2500CounterID\u2500\u252c\u2500\u2500\u2500hits\u2500\u252c\u2500visits\u2500\u2510\n\u2502 1143050 \u2502 523264 \u2502 13665 \u2502\n\u2502 731962 \u2502 475698 \u2502 102716 \u2502\n\u2502 722545 \u2502 337212 \u2502 108187 \u2502\n\u2502 722889 \u2502 252197 \u2502 10547 \u2502\n\u2502 2237260 \u2502 196036 \u2502 9522 \u2502\n\u2502 23057320 \u2502 147211 \u2502 7689 \u2502\n\u2502 722818 \u2502 90109 \u2502 17847 \u2502\n\u2502 48221 \u2502 85379 \u2502 4652 \u2502\n\u2502 19762435 \u2502 77807 \u2502 7026 \u2502\n\u2502 722884 \u2502 77492 \u2502 11056 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Subqueries don't allow you to set names or use them for referencing a column from a specific subquery.\nThe columns specified in USING must have the same names in both subqueries, and the other columns must be named differently. You can use aliases to change the names of columns in subqueries (the example uses the aliases 'hits' and 'visits'). The USING clause specifies one or more columns to join, which establishes the equality of these columns. The list of columns is set without brackets. More complex join conditions are not supported. The right table (the subquery result) resides in RAM. If there isn't enough memory, you can't run a JOIN. Only one JOIN can be specified in a query (on a single level). To run multiple JOINs, you can put them in subqueries. Each time a query is run with the same JOIN, the subquery is run again \u2013 the result is not cached. To avoid this, use the special 'Join' table engine, which is a prepared array for joining that is always in RAM. For more information, see the section \"Table engines, Join\". In some cases, it is more efficient to use IN instead of JOIN.\nAmong the various types of JOINs, the most efficient is ANY LEFT JOIN, then ANY INNER JOIN. The least efficient are ALL LEFT JOIN and ALL INNER JOIN. If you need a JOIN for joining with dimension tables (these are relatively small tables that contain dimension properties, such as names for advertising campaigns), a JOIN might not be very convenient due to the bulky syntax and the fact that the right table is re-accessed for every query. For such cases, there is an \"external dictionaries\" feature that you should use instead of JOIN. For more information, see the section \"External dictionaries\".", + "title": "JOIN clause" + }, + { + "location": "/query_language/queries/#where-clause", + "text": "If there is a WHERE clause, it must contain an expression with the UInt8 type. This is usually an expression with comparison and logical operators.\nThis expression will be used for filtering data before all other transformations. If indexes are supported by the database table engine, the expression is evaluated on the ability to use indexes.", + "title": "WHERE clause" + }, + { + "location": "/query_language/queries/#prewhere-clause", + "text": "This clause has the same meaning as the WHERE clause. The difference is in which data is read from the table.\nWhen using PREWHERE, first only the columns necessary for executing PREWHERE are read. Then the other columns are read that are needed for running the query, but only those blocks where the PREWHERE expression is true. It makes sense to use PREWHERE if there are filtration conditions that are not suitable for indexes that are used by a minority of the columns in the query, but that provide strong data filtration. This reduces the volume of data to read. For example, it is useful to write PREWHERE for queries that extract a large number of columns, but that only have filtration for a few columns. PREWHERE is only supported by tables from the *MergeTree family. A query may simultaneously specify PREWHERE and WHERE. In this case, PREWHERE precedes WHERE. Keep in mind that it does not make much sense for PREWHERE to only specify those columns that have an index, because when using an index, only the data blocks that match the index are read. If the 'optimize_move_to_prewhere' setting is set to 1 and PREWHERE is omitted, the system uses heuristics to automatically move parts of expressions from WHERE to PREWHERE.", + "title": "PREWHERE clause" + }, + { + "location": "/query_language/queries/#group-by-clause", + "text": "This is one of the most important parts of a column-oriented DBMS. If there is a GROUP BY clause, it must contain a list of expressions. Each expression will be referred to here as a \"key\".\nAll the expressions in the SELECT, HAVING, and ORDER BY clauses must be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. If a query contains only table columns inside aggregate functions, the GROUP BY clause can be omitted, and aggregation by an empty set of keys is assumed. Example: SELECT \n count (), \n median ( FetchTiming 60 ? 60 : FetchTiming ), \n count () - sum ( Refresh ) FROM hits However, in contrast to standard SQL, if the table doesn't have any rows (either there aren't any at all, or there aren't any after using WHERE to filter), an empty result is returned, and not the result from one of the rows containing the initial values of aggregate functions. As opposed to MySQL (and conforming to standard SQL), you can't get some value of some column that is not in a key or aggregate function (except constant expressions). To work around this, you can use the 'any' aggregate function (get the first encountered value) or 'min/max'. Example: SELECT \n domainWithoutWWW ( URL ) AS domain , \n count (), \n any ( Title ) AS title -- getting the first occurred page header for each domain. FROM hits GROUP BY domain For every different key value encountered, GROUP BY calculates a set of aggregate function values. GROUP BY is not supported for array columns. A constant can't be specified as arguments for aggregate functions. Example: sum(1). Instead of this, you can get rid of the constant. Example: count() .", + "title": "GROUP BY clause" + }, + { + "location": "/query_language/queries/#with-totals-modifier", + "text": "If the WITH TOTALS modifier is specified, another row will be calculated. This row will have key columns containing default values (zeros or empty lines), and columns of aggregate functions with the values calculated across all the rows (the \"total\" values). This extra row is output in JSON*, TabSeparated*, and Pretty* formats, separately from the other rows. In the other formats, this row is not output. In JSON* formats, this row is output as a separate 'totals' field. In TabSeparated* formats, the row comes after the main result, preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result. WITH TOTALS can be run in different ways when HAVING is present. The behavior depends on the 'totals_mode' setting.\nBy default, totals_mode = 'before_having' . In this case, 'totals' is calculated across all rows, including the ones that don't pass through HAVING and 'max_rows_to_group_by'. The other alternatives include only the rows that pass through HAVING in 'totals', and behave differently with the setting max_rows_to_group_by and group_by_overflow_mode = 'any' . after_having_exclusive \u2013 Don't include rows that didn't pass through max_rows_to_group_by . In other words, 'totals' will have less than or the same number of rows as it would if max_rows_to_group_by were omitted. after_having_inclusive \u2013 Include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. In other words, 'totals' will have more than or the same number of rows as it would if max_rows_to_group_by were omitted. after_having_auto \u2013 Count the number of rows that passed through HAVING. If it is more than a certain amount (by default, 50%), include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. Otherwise, do not include them. totals_auto_threshold \u2013 By default, 0.5. The coefficient for after_having_auto . If max_rows_to_group_by and group_by_overflow_mode = 'any' are not used, all variations of after_having are the same, and you can use any of them (for example, after_having_auto ). You can use WITH TOTALS in subqueries, including subqueries in the JOIN clause (in this case, the respective total values are combined).", + "title": "WITH TOTALS modifier" + }, + { + "location": "/query_language/queries/#group-by-in-external-memory", + "text": "You can enable dumping temporary data to the disk to restrict memory usage during GROUP BY.\nThe max_bytes_before_external_group_by setting determines the threshold RAM consumption for dumping GROUP BY temporary data to the file system. If set to 0 (the default), it is disabled. When using max_bytes_before_external_group_by , we recommend that you set max_memory_usage about twice as high. This is necessary because there are two stages to aggregation: reading the date and forming intermediate data (1) and merging the intermediate data (2). Dumping data to the file system can only occur during stage 1. If the temporary data wasn't dumped, then stage 2 might require up to the same amount of memory as in stage 1. For example, if max_memory_usage was set to 10000000000 and you want to use external aggregation, it makes sense to set max_bytes_before_external_group_by to 10000000000, and max_memory_usage to 20000000000. When external aggregation is triggered (if there was at least one dump of temporary data), maximum consumption of RAM is only slightly more than max_bytes_before_external_group_by . With distributed query processing, external aggregation is performed on remote servers. In order for the requestor server to use only a small amount of RAM, set distributed_aggregation_memory_efficient to 1. When merging data flushed to the disk, as well as when merging results from remote servers when the distributed_aggregation_memory_efficient setting is enabled, consumes up to 1/256 * the number of threads from the total amount of RAM. When external aggregation is enabled, if there was less than max_bytes_before_external_group_by of data (i.e. data was not flushed), the query runs just as fast as without external aggregation. If any temporary data was flushed, the run time will be several times longer (approximately three times). If you have an ORDER BY with a small LIMIT after GROUP BY, then the ORDER BY CLAUSE will not use significant amounts of RAM.\nBut if the ORDER BY doesn't have LIMIT, don't forget to enable external sorting ( max_bytes_before_external_sort ).", + "title": "GROUP BY in external memory" + }, + { + "location": "/query_language/queries/#limit-n-by-clause", + "text": "LIMIT N BY COLUMNS selects the top N rows for each group of COLUMNS. LIMIT N BY is not related to LIMIT; they can both be used in the same query. The key for LIMIT N BY can contain any number of columns or expressions. Example: SELECT \n domainWithoutWWW ( URL ) AS domain , \n domainWithoutWWW ( REFERRER_URL ) AS referrer , \n device_type , \n count () cnt FROM hits GROUP BY domain , referrer , device_type ORDER BY cnt DESC LIMIT 5 BY domain , device_type LIMIT 100 The query will select the top 5 referrers for each domain, device_type pair, but not more than 100 rows ( LIMIT n BY + LIMIT ).", + "title": "LIMIT N BY clause" + }, + { + "location": "/query_language/queries/#having-clause", + "text": "Allows filtering the result received after GROUP BY, similar to the WHERE clause.\nWHERE and HAVING differ in that WHERE is performed before aggregation (GROUP BY), while HAVING is performed after it.\nIf aggregation is not performed, HAVING can't be used.", + "title": "HAVING clause" + }, + { + "location": "/query_language/queries/#order-by-clause", + "text": "The ORDER BY clause contains a list of expressions, which can each be assigned DESC or ASC (the sorting direction). If the direction is not specified, ASC is assumed. ASC is sorted in ascending order, and DESC in descending order. The sorting direction applies to a single expression, not to the entire list. Example: ORDER BY Visits DESC, SearchPhrase For sorting by String values, you can specify collation (comparison). Example: ORDER BY SearchPhrase COLLATE 'tr' - for sorting by keyword in ascending order, using the Turkish alphabet, case insensitive, assuming that strings are UTF-8 encoded. COLLATE can be specified or not for each expression in ORDER BY independently. If ASC or DESC is specified, COLLATE is specified after it. When using COLLATE, sorting is always case-insensitive. We only recommend using COLLATE for final sorting of a small number of rows, since sorting with COLLATE is less efficient than normal sorting by bytes. Rows that have identical values for the list of sorting expressions are output in an arbitrary order, which can also be nondeterministic (different each time).\nIf the ORDER BY clause is omitted, the order of the rows is also undefined, and may be nondeterministic as well. When floating point numbers are sorted, NaNs are separate from the other values. Regardless of the sorting order, NaNs come at the end. In other words, for ascending sorting they are placed as if they are larger than all the other numbers, while for descending sorting they are placed as if they are smaller than the rest. Less RAM is used if a small enough LIMIT is specified in addition to ORDER BY. Otherwise, the amount of memory spent is proportional to the volume of data for sorting. For distributed query processing, if GROUP BY is omitted, sorting is partially done on remote servers, and the results are merged on the requestor server. This means that for distributed sorting, the volume of data to sort can be greater than the amount of memory on a single server. If there is not enough RAM, it is possible to perform sorting in external memory (creating temporary files on a disk). Use the setting max_bytes_before_external_sort for this purpose. If it is set to 0 (the default), external sorting is disabled. If it is enabled, when the volume of data to sort reaches the specified number of bytes, the collected data is sorted and dumped into a temporary file. After all data is read, all the sorted files are merged and the results are output. Files are written to the /var/lib/clickhouse/tmp/ directory in the config (by default, but you can use the 'tmp_path' parameter to change this setting). Running a query may use more memory than 'max_bytes_before_external_sort'. For this reason, this setting must have a value significantly smaller than 'max_memory_usage'. As an example, if your server has 128 GB of RAM and you need to run a single query, set 'max_memory_usage' to 100 GB, and 'max_bytes_before_external_sort' to 80 GB. External sorting works much less effectively than sorting in RAM.", + "title": "ORDER BY clause" + }, + { + "location": "/query_language/queries/#select-clause", + "text": "The expressions specified in the SELECT clause are analyzed after the calculations for all the clauses listed above are completed.\nMore specifically, expressions are analyzed that are above the aggregate functions, if there are any aggregate functions.\nThe aggregate functions and everything below them are calculated during aggregation (GROUP BY).\nThese expressions work as if they are applied to separate rows in the result.", + "title": "SELECT clause" + }, + { + "location": "/query_language/queries/#distinct-clause", + "text": "If DISTINCT is specified, only a single row will remain out of all the sets of fully matching rows in the result.\nThe result will be the same as if GROUP BY were specified across all the fields specified in SELECT without aggregate functions. But there are several differences from GROUP BY: DISTINCT can be applied together with GROUP BY. When ORDER BY is omitted and LIMIT is defined, the query stops running immediately after the required number of different rows has been read. Data blocks are output as they are processed, without waiting for the entire query to finish running. DISTINCT is not supported if SELECT has at least one array column.", + "title": "DISTINCT clause" + }, + { + "location": "/query_language/queries/#limit-clause", + "text": "LIMIT m allows you to select the first 'm' rows from the result.\nLIMIT n, m allows you to select the first 'm' rows from the result after skipping the first 'n' rows. 'n' and 'm' must be non-negative integers. If there isn't an ORDER BY clause that explicitly sorts results, the result may be arbitrary and nondeterministic.", + "title": "LIMIT clause" + }, + { + "location": "/query_language/queries/#union-all-clause", + "text": "You can use UNION ALL to combine any number of queries. Example: SELECT CounterID , 1 AS table , toInt64 ( count ()) AS c \n FROM test . hits \n GROUP BY CounterID UNION ALL SELECT CounterID , 2 AS table , sum ( Sign ) AS c \n FROM test . visits \n GROUP BY CounterID \n HAVING c 0 Only UNION ALL is supported. The regular UNION (UNION DISTINCT) is not supported. If you need UNION DISTINCT, you can write SELECT DISTINCT from a subquery containing UNION ALL. Queries that are parts of UNION ALL can be run simultaneously, and their results can be mixed together. The structure of results (the number and type of columns) must match for the queries. But the column names can differ. In this case, the column names for the final result will be taken from the first query. Queries that are parts of UNION ALL can't be enclosed in brackets. ORDER BY and LIMIT are applied to separate queries, not to the final result. If you need to apply a conversion to the final result, you can put all the queries with UNION ALL in a subquery in the FROM clause.", + "title": "UNION ALL clause" + }, + { + "location": "/query_language/queries/#into-outfile-clause", + "text": "Add the INTO OUTFILE filename clause (where filename is a string literal) to redirect query output to the specified file.\nIn contrast to MySQL, the file is created on the client side. The query will fail if a file with the same filename already exists.\nThis functionality is available in the command-line client and clickhouse-local (a query sent via HTTP interface will fail). The default output format is TabSeparated (the same as in the command-line client batch mode).", + "title": "INTO OUTFILE clause" + }, + { + "location": "/query_language/queries/#format-clause", + "text": "Specify 'FORMAT format' to get data in any specified format.\nYou can use this for convenience, or for creating dumps.\nFor more information, see the section \"Formats\".\nIf the FORMAT clause is omitted, the default format is used, which depends on both the settings and the interface used for accessing the DB. For the HTTP interface and the command-line client in batch mode, the default format is TabSeparated. For the command-line client in interactive mode, the default format is PrettyCompact (it has attractive and compact tables). When using the command-line client, data is passed to the client in an internal efficient format. The client independently interprets the FORMAT clause of the query and formats the data itself (thus relieving the network and the server from the load).", + "title": "FORMAT clause" + }, + { + "location": "/query_language/queries/#in-operators", + "text": "The IN , NOT IN , GLOBAL IN , and GLOBAL NOT IN operators are covered separately, since their functionality is quite rich. The left side of the operator is either a single column or a tuple. Examples: SELECT UserID IN ( 123 , 456 ) FROM ... SELECT ( CounterID , UserID ) IN (( 34 , 123 ), ( 101500 , 456 )) FROM ... If the left side is a single column that is in the index, and the right side is a set of constants, the system uses the index for processing the query. Don't list too many values explicitly (i.e. millions). If a data set is large, put it in a temporary table (for example, see the section \"External data for query processing\"), then use a subquery. The right side of the operator can be a set of constant expressions, a set of tuples with constant expressions (shown in the examples above), or the name of a database table or SELECT subquery in brackets. If the right side of the operator is the name of a table (for example, UserID IN users ), this is equivalent to the subquery UserID IN (SELECT * FROM users) . Use this when working with external data that is sent along with the query. For example, the query can be sent together with a set of user IDs loaded to the 'users' temporary table, which should be filtered. If the right side of the operator is a table name that has the Set engine (a prepared data set that is always in RAM), the data set will not be created over again for each query. The subquery may specify more than one column for filtering tuples.\nExample: SELECT ( CounterID , UserID ) IN ( SELECT CounterID , UserID FROM ...) FROM ... The columns to the left and right of the IN operator should have the same type. The IN operator and subquery may occur in any part of the query, including in aggregate functions and lambda functions.\nExample: SELECT \n EventDate , \n avg ( UserID IN \n ( \n SELECT UserID \n FROM test . hits \n WHERE EventDate = toDate ( 2014-03-17 ) \n )) AS ratio FROM test . hits GROUP BY EventDate ORDER BY EventDate ASC \u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500ratio\u2500\u2510\n\u2502 2014-03-17 \u2502 1 \u2502\n\u2502 2014-03-18 \u2502 0.807696 \u2502\n\u2502 2014-03-19 \u2502 0.755406 \u2502\n\u2502 2014-03-20 \u2502 0.723218 \u2502\n\u2502 2014-03-21 \u2502 0.697021 \u2502\n\u2502 2014-03-22 \u2502 0.647851 \u2502\n\u2502 2014-03-23 \u2502 0.648416 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 For each day after March 17th, count the percentage of pageviews made by users who visited the site on March 17th.\nA subquery in the IN clause is always run just one time on a single server. There are no dependent subqueries.", + "title": "IN operators" + }, + { + "location": "/query_language/queries/#distributed-subqueries", + "text": "There are two options for IN-s with subqueries (similar to JOINs): normal IN / OIN and IN GLOBAL / GLOBAL JOIN . They differ in how they are run for distributed query processing. \n\nRemember that the algorithms described below may work differently depending on the [settings](../operations/settings/settings.md#settings-distributed_product_mode) `distributed_product_mode` setting. When using the regular IN, the query is sent to remote servers, and each of them runs the subqueries in the IN or JOIN clause. When using GLOBAL IN / GLOBAL JOINs , first all the subqueries are run for GLOBAL IN / GLOBAL JOINs , and the results are collected in temporary tables. Then the temporary tables are sent to each remote server, where the queries are run using this temporary data. For a non-distributed query, use the regular IN / JOIN . Be careful when using subqueries in the IN / JOIN clauses for distributed query processing. Let's look at some examples. Assume that each server in the cluster has a normal local_table . Each server also has a distributed_table table with the Distributed type, which looks at all the servers in the cluster. For a query to the distributed_table , the query will be sent to all the remote servers and run on them using the local_table . For example, the query SELECT uniq ( UserID ) FROM distributed_table will be sent to all remote servers as SELECT uniq ( UserID ) FROM local_table and run on each of them in parallel, until it reaches the stage where intermediate results can be combined. Then the intermediate results will be returned to the requestor server and merged on it, and the final result will be sent to the client. Now let's examine a query with IN: SELECT uniq ( UserID ) FROM distributed_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM local_table WHERE CounterID = 34 ) Calculation of the intersection of audiences of two sites. This query will be sent to all remote servers as SELECT uniq ( UserID ) FROM local_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM local_table WHERE CounterID = 34 ) In other words, the data set in the IN clause will be collected on each server independently, only across the data that is stored locally on each of the servers. This will work correctly and optimally if you are prepared for this case and have spread data across the cluster servers such that the data for a single UserID resides entirely on a single server. In this case, all the necessary data will be available locally on each server. Otherwise, the result will be inaccurate. We refer to this variation of the query as \"local IN\". To correct how the query works when data is spread randomly across the cluster servers, you could specify distributed_table inside a subquery. The query would look like this: SELECT uniq ( UserID ) FROM distributed_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM distributed_table WHERE CounterID = 34 ) This query will be sent to all remote servers as SELECT uniq ( UserID ) FROM local_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM distributed_table WHERE CounterID = 34 ) The subquery will begin running on each remote server. Since the subquery uses a distributed table, the subquery that is on each remote server will be resent to every remote server as SELECT UserID FROM local_table WHERE CounterID = 34 For example, if you have a cluster of 100 servers, executing the entire query will require 10,000 elementary requests, which is generally considered unacceptable. In such cases, you should always use GLOBAL IN instead of IN. Let's look at how it works for the query SELECT uniq ( UserID ) FROM distributed_table WHERE CounterID = 101500 AND UserID GLOBAL IN ( SELECT UserID FROM distributed_table WHERE CounterID = 34 ) The requestor server will run the subquery SELECT UserID FROM distributed_table WHERE CounterID = 34 and the result will be put in a temporary table in RAM. Then the request will be sent to each remote server as SELECT uniq ( UserID ) FROM local_table WHERE CounterID = 101500 AND UserID GLOBAL IN _data1 and the temporary table _data1 will be sent to every remote server with the query (the name of the temporary table is implementation-defined). This is more optimal than using the normal IN. However, keep the following points in mind: When creating a temporary table, data is not made unique. To reduce the volume of data transmitted over the network, specify DISTINCT in the subquery. (You don't need to do this for a normal IN.) The temporary table will be sent to all the remote servers. Transmission does not account for network topology. For example, if 10 remote servers reside in a datacenter that is very remote in relation to the requestor server, the data will be sent 10 times over the channel to the remote datacenter. Try to avoid large data sets when using GLOBAL IN. When transmitting data to remote servers, restrictions on network bandwidth are not configurable. You might overload the network. Try to distribute data across servers so that you don't need to use GLOBAL IN on a regular basis. If you need to use GLOBAL IN often, plan the location of the ClickHouse cluster so that a single group of replicas resides in no more than one data center with a fast network between them, so that a query can be processed entirely within a single data center. It also makes sense to specify a local table in the GLOBAL IN clause, in case this local table is only available on the requestor server and you want to use data from it on remote servers.", + "title": "Distributed subqueries" + }, + { + "location": "/query_language/queries/#extreme-values", + "text": "In addition to results, you can also get minimum and maximum values for the results columns. To do this, set the extremes setting to 1. Minimums and maximums are calculated for numeric types, dates, and dates with times. For other columns, the default values are output. An extra two rows are calculated \u2013 the minimums and maximums, respectively. These extra two rows are output in JSON*, TabSeparated*, and Pretty* formats, separate from the other rows. They are not output for other formats. In JSON* formats, the extreme values are output in a separate 'extremes' field. In TabSeparated* formats, the row comes after the main result, and after 'totals' if present. It is preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result, and after 'totals' if present. Extreme values are calculated for rows that have passed through LIMIT. However, when using 'LIMIT offset, size', the rows before 'offset' are included in 'extremes'. In stream requests, the result may also include a small number of rows that passed through LIMIT.", + "title": "Extreme values" + }, + { + "location": "/query_language/queries/#notes", + "text": "The GROUP BY and ORDER BY clauses do not support positional arguments. This contradicts MySQL, but conforms to standard SQL.\nFor example, GROUP BY 1, 2 will be interpreted as grouping by constants (i.e. aggregation of all rows into one). You can use synonyms ( AS aliases) in any part of a query. You can put an asterisk in any part of a query instead of an expression. When the query is analyzed, the asterisk is expanded to a list of all table columns (excluding the MATERIALIZED and ALIAS columns). There are only a few cases when using an asterisk is justified: When creating a table dump. For tables containing just a few columns, such as system tables. For getting information about what columns are in a table. In this case, set LIMIT 1 . But it is better to use the DESC TABLE query. When there is strong filtration on a small number of columns using PREWHERE . In subqueries (since columns that aren't needed for the external query are excluded from subqueries). In all other cases, we don't recommend using the asterisk, since it only gives you the drawbacks of a columnar DBMS instead of the advantages. In other words using the asterisk is not recommended.", + "title": "Notes" + }, + { + "location": "/query_language/queries/#kill-query", + "text": "KILL QUERY \n WHERE where expression to SELECT FROM system . processes query \n [ SYNC | ASYNC | TEST ] \n [ FORMAT format ] Attempts to forcibly terminate the currently running queries.\nThe queries to terminate are selected from the system.processes table using the criteria defined in the WHERE clause of the KILL query. Examples: -- Forcibly terminates all queries with the specified query_id: KILL QUERY WHERE query_id = 2-857d-4a57-9ee0-327da5d60a90 -- Synchronously terminates all queries run by username : KILL QUERY WHERE user = username SYNC Read-only users can only stop their own queries. By default, the asynchronous version of queries is used ( ASYNC ), which doesn't wait for confirmation that queries have stopped. The synchronous version ( SYNC ) waits for all queries to stop and displays information about each process as it stops.\nThe response contains the kill_status column, which can take the following values: 'finished' \u2013 The query was terminated successfully. 'waiting' \u2013 Waiting for the query to end after sending it a signal to terminate. The other values \u200b\u200bexplain why the query can't be stopped. A test query ( TEST ) only checks the user's rights and displays a list of queries to stop.", + "title": "KILL QUERY" + }, + { + "location": "/query_language/syntax/", + "text": "Syntax\n\n\nThere are two types of parsers in the system: the full SQL parser (a recursive descent parser), and the data format parser (a fast stream parser).\nIn all cases except the INSERT query, only the full SQL parser is used.\nThe INSERT query uses both parsers:\n\n\nINSERT\n \nINTO\n \nt\n \nVALUES\n \n(\n1\n,\n \nHello, world\n),\n \n(\n2\n,\n \nabc\n),\n \n(\n3\n,\n \ndef\n)\n\n\n\n\n\n\nThe \nINSERT INTO t VALUES\n fragment is parsed by the full parser, and the data \n(1, 'Hello, world'), (2, 'abc'), (3, 'def')\n is parsed by the fast stream parser.\nData can have any format. When a query is received, the server calculates no more than \nmax_query_size\n bytes of the request in RAM (by default, 1 MB), and the rest is stream parsed.\nThis means the system doesn't have problems with large INSERT queries, like MySQL does.\n\n\nWhen using the Values format in an INSERT query, it may seem that data is parsed the same as expressions in a SELECT query, but this is not true. The Values format is much more limited.\n\n\nNext we will cover the full parser. For more information about format parsers, see the section \"Formats\".\n\n\nSpaces\n\n\nThere may be any number of space symbols between syntactical constructions (including the beginning and end of a query). Space symbols include the space, tab, line feed, CR, and form feed.\n\n\nComments\n\n\nSQL-style and C-style comments are supported.\nSQL-style comments: from \n--\n to the end of the line. The space after \n--\n can be omitted.\nComments in C-style: from \n/*\n to \n*/\n. These comments can be multiline. Spaces are not required here, either.\n\n\nKeywords\n\n\nKeywords (such as \nSELECT\n) are not case-sensitive. Everything else (column names, functions, and so on), in contrast to standard SQL, is case-sensitive. Keywords are not reserved (they are just parsed as keywords in the corresponding context).\n\n\nIdentifiers\n\n\nIdentifiers (column names, functions, and data types) can be quoted or non-quoted.\nNon-quoted identifiers start with a Latin letter or underscore, and continue with a Latin letter, underscore, or number. In other words, they must match the regex \n^[a-zA-Z_][0-9a-zA-Z_]*$\n. Examples: \nx, _1, X_y__Z123_.\n\n\nQuoted identifiers are placed in reversed quotation marks \n`id`\n (the same as in MySQL), and can indicate any set of bytes (non-empty). In addition, symbols (for example, the reverse quotation mark) inside this type of identifier can be backslash-escaped. Escaping rules are the same as for string literals (see below).\nWe recommend using identifiers that do not need to be quoted.\n\n\nLiterals\n\n\nThere are numeric literals, string literals, and compound literals.\n\n\nNumeric literals\n\n\nA numeric literal tries to be parsed:\n\n\n\n\nFirst as a 64-bit signed number, using the 'strtoull' function.\n\n\nIf unsuccessful, as a 64-bit unsigned number, using the 'strtoll' function.\n\n\nIf unsuccessful, as a floating-point number using the 'strtod' function.\n\n\nOtherwise, an error is returned.\n\n\n\n\nThe corresponding value will have the smallest type that the value fits in.\nFor example, 1 is parsed as UInt8, but 256 is parsed as UInt16. For more information, see \"Data types\".\n\n\nExamples: \n1\n, \n18446744073709551615\n, \n0xDEADBEEF\n, \n01\n, \n0.1\n, \n1e100\n, \n-1e-100\n, \ninf\n, \nnan\n.\n\n\nString literals\n\n\nOnly string literals in single quotes are supported. The enclosed characters can be backslash-escaped. The following escape sequences have a corresponding special value: \n\\b\n, \n\\f\n, \n\\r\n, \n\\n\n, \n\\t\n, \n\\0\n, \n\\a\n, \n\\v\n, \n\\xHH\n. In all other cases, escape sequences in the format \n\\c\n, where \"c\" is any character, are converted to \"c\". This means that you can use the sequences \n\\'\nand\n\\\\\n. The value will have the String type.\n\n\nThe minimum set of characters that you need to escape in string literals: \n'\n and \n\\\n.\n\n\nCompound literals\n\n\nConstructions are supported for arrays: \n[1, 2, 3]\n and tuples: \n(1, 'Hello, world!', 2)\n..\nActually, these are not literals, but expressions with the array creation operator and the tuple creation operator, respectively.\nFor more information, see the section \"Operators2\".\nAn array must consist of at least one item, and a tuple must have at least two items.\nTuples have a special purpose for use in the IN clause of a SELECT query. Tuples can be obtained as the result of a query, but they can't be saved to a database (with the exception of Memory-type tables).\n\n\nFunctions\n\n\nFunctions are written like an identifier with a list of arguments (possibly empty) in brackets. In contrast to standard SQL, the brackets are required, even for an empty arguments list. Example: \nnow()\n.\nThere are regular and aggregate functions (see the section \"Aggregate functions\"). Some aggregate functions can contain two lists of arguments in brackets. Example: \nquantile (0.9) (x)\n. These aggregate functions are called \"parametric\" functions, and the arguments in the first list are called \"parameters\". The syntax of aggregate functions without parameters is the same as for regular functions.\n\n\nOperators\n\n\nOperators are converted to their corresponding functions during query parsing, taking their priority and associativity into account.\nFor example, the expression \n1 + 2 * 3 + 4\n is transformed to \nplus(plus(1, multiply(2, 3)), 4)\n.\nFor more information, see the section \"Operators\" below.\n\n\nData types and database table engines\n\n\nData types and table engines in the \nCREATE\n query are written the same way as identifiers or functions. In other words, they may or may not contain an arguments list in brackets. For more information, see the sections \"Data types,\" \"Table engines,\" and \"CREATE\".\n\n\nSynonyms\n\n\nIn the SELECT query, expressions can specify synonyms using the AS keyword. Any expression is placed to the left of AS. The identifier name for the synonym is placed to the right of AS. As opposed to standard SQL, synonyms are not only declared on the top level of expressions:\n\n\nSELECT\n \n(\n1\n \nAS\n \nn\n)\n \n+\n \n2\n,\n \nn\n\n\n\n\n\n\nIn contrast to standard SQL, synonyms can be used in all parts of a query, not just \nSELECT\n.\n\n\nAsterisk\n\n\nIn a \nSELECT\n query, an asterisk can replace the expression. For more information, see the section \"SELECT\".\n\n\nExpressions\n\n\nAn expression is a function, identifier, literal, application of an operator, expression in brackets, subquery, or asterisk. It can also contain a synonym.\nA list of expressions is one or more expressions separated by commas.\nFunctions and operators, in turn, can have expressions as arguments.", + "title": "Syntax" + }, + { + "location": "/query_language/syntax/#syntax", + "text": "There are two types of parsers in the system: the full SQL parser (a recursive descent parser), and the data format parser (a fast stream parser).\nIn all cases except the INSERT query, only the full SQL parser is used.\nThe INSERT query uses both parsers: INSERT INTO t VALUES ( 1 , Hello, world ), ( 2 , abc ), ( 3 , def ) The INSERT INTO t VALUES fragment is parsed by the full parser, and the data (1, 'Hello, world'), (2, 'abc'), (3, 'def') is parsed by the fast stream parser.\nData can have any format. When a query is received, the server calculates no more than max_query_size bytes of the request in RAM (by default, 1 MB), and the rest is stream parsed.\nThis means the system doesn't have problems with large INSERT queries, like MySQL does. When using the Values format in an INSERT query, it may seem that data is parsed the same as expressions in a SELECT query, but this is not true. The Values format is much more limited. Next we will cover the full parser. For more information about format parsers, see the section \"Formats\".", + "title": "Syntax" + }, + { + "location": "/query_language/syntax/#spaces", + "text": "There may be any number of space symbols between syntactical constructions (including the beginning and end of a query). Space symbols include the space, tab, line feed, CR, and form feed.", + "title": "Spaces" + }, + { + "location": "/query_language/syntax/#comments", + "text": "SQL-style and C-style comments are supported.\nSQL-style comments: from -- to the end of the line. The space after -- can be omitted.\nComments in C-style: from /* to */ . These comments can be multiline. Spaces are not required here, either.", + "title": "Comments" + }, + { + "location": "/query_language/syntax/#keywords", + "text": "Keywords (such as SELECT ) are not case-sensitive. Everything else (column names, functions, and so on), in contrast to standard SQL, is case-sensitive. Keywords are not reserved (they are just parsed as keywords in the corresponding context).", + "title": "Keywords" + }, + { + "location": "/query_language/syntax/#identifiers", + "text": "Identifiers (column names, functions, and data types) can be quoted or non-quoted.\nNon-quoted identifiers start with a Latin letter or underscore, and continue with a Latin letter, underscore, or number. In other words, they must match the regex ^[a-zA-Z_][0-9a-zA-Z_]*$ . Examples: x, _1, X_y__Z123_. Quoted identifiers are placed in reversed quotation marks `id` (the same as in MySQL), and can indicate any set of bytes (non-empty). In addition, symbols (for example, the reverse quotation mark) inside this type of identifier can be backslash-escaped. Escaping rules are the same as for string literals (see below).\nWe recommend using identifiers that do not need to be quoted.", + "title": "Identifiers" + }, + { + "location": "/query_language/syntax/#literals", + "text": "There are numeric literals, string literals, and compound literals.", + "title": "Literals" + }, + { + "location": "/query_language/syntax/#numeric-literals", + "text": "A numeric literal tries to be parsed: First as a 64-bit signed number, using the 'strtoull' function. If unsuccessful, as a 64-bit unsigned number, using the 'strtoll' function. If unsuccessful, as a floating-point number using the 'strtod' function. Otherwise, an error is returned. The corresponding value will have the smallest type that the value fits in.\nFor example, 1 is parsed as UInt8, but 256 is parsed as UInt16. For more information, see \"Data types\". Examples: 1 , 18446744073709551615 , 0xDEADBEEF , 01 , 0.1 , 1e100 , -1e-100 , inf , nan .", + "title": "Numeric literals" + }, + { + "location": "/query_language/syntax/#string-literals", + "text": "Only string literals in single quotes are supported. The enclosed characters can be backslash-escaped. The following escape sequences have a corresponding special value: \\b , \\f , \\r , \\n , \\t , \\0 , \\a , \\v , \\xHH . In all other cases, escape sequences in the format \\c , where \"c\" is any character, are converted to \"c\". This means that you can use the sequences \\' and \\\\ . The value will have the String type. The minimum set of characters that you need to escape in string literals: ' and \\ .", + "title": "String literals" + }, + { + "location": "/query_language/syntax/#compound-literals", + "text": "Constructions are supported for arrays: [1, 2, 3] and tuples: (1, 'Hello, world!', 2) ..\nActually, these are not literals, but expressions with the array creation operator and the tuple creation operator, respectively.\nFor more information, see the section \"Operators2\".\nAn array must consist of at least one item, and a tuple must have at least two items.\nTuples have a special purpose for use in the IN clause of a SELECT query. Tuples can be obtained as the result of a query, but they can't be saved to a database (with the exception of Memory-type tables).", + "title": "Compound literals" + }, + { + "location": "/query_language/syntax/#functions", + "text": "Functions are written like an identifier with a list of arguments (possibly empty) in brackets. In contrast to standard SQL, the brackets are required, even for an empty arguments list. Example: now() .\nThere are regular and aggregate functions (see the section \"Aggregate functions\"). Some aggregate functions can contain two lists of arguments in brackets. Example: quantile (0.9) (x) . These aggregate functions are called \"parametric\" functions, and the arguments in the first list are called \"parameters\". The syntax of aggregate functions without parameters is the same as for regular functions.", + "title": "Functions" + }, + { + "location": "/query_language/syntax/#operators", + "text": "Operators are converted to their corresponding functions during query parsing, taking their priority and associativity into account.\nFor example, the expression 1 + 2 * 3 + 4 is transformed to plus(plus(1, multiply(2, 3)), 4) .\nFor more information, see the section \"Operators\" below.", + "title": "Operators" + }, + { + "location": "/query_language/syntax/#data-types-and-database-table-engines", + "text": "Data types and table engines in the CREATE query are written the same way as identifiers or functions. In other words, they may or may not contain an arguments list in brackets. For more information, see the sections \"Data types,\" \"Table engines,\" and \"CREATE\".", + "title": "Data types and database table engines" + }, + { + "location": "/query_language/syntax/#synonyms", + "text": "In the SELECT query, expressions can specify synonyms using the AS keyword. Any expression is placed to the left of AS. The identifier name for the synonym is placed to the right of AS. As opposed to standard SQL, synonyms are not only declared on the top level of expressions: SELECT ( 1 AS n ) + 2 , n In contrast to standard SQL, synonyms can be used in all parts of a query, not just SELECT .", + "title": "Synonyms" + }, + { + "location": "/query_language/syntax/#asterisk", + "text": "In a SELECT query, an asterisk can replace the expression. For more information, see the section \"SELECT\".", + "title": "Asterisk" + }, + { + "location": "/query_language/syntax/#expressions", + "text": "An expression is a function, identifier, literal, application of an operator, expression in brackets, subquery, or asterisk. It can also contain a synonym.\nA list of expressions is one or more expressions separated by commas.\nFunctions and operators, in turn, can have expressions as arguments.", + "title": "Expressions" + }, + { + "location": "/table_engines/", + "text": "Table engines\n\n\nThe table engine (type of table) determines:\n\n\n\n\nHow and where data is stored: where to write it to, and where to read it from.\n\n\nWhich queries are supported, and how.\n\n\nConcurrent data access.\n\n\nUse of indexes, if present.\n\n\nWhether multithreaded request execution is possible.\n\n\nData replication.\n\n\n\n\nWhen reading data, the engine is only required to extract the necessary set of columns. However, in some cases, the query may be partially processed inside the table engine.\n\n\nNote that for most serious tasks, you should use engines from the \nMergeTree\n family.", + "title": "Introduction" + }, + { + "location": "/table_engines/#table-engines", + "text": "The table engine (type of table) determines: How and where data is stored: where to write it to, and where to read it from. Which queries are supported, and how. Concurrent data access. Use of indexes, if present. Whether multithreaded request execution is possible. Data replication. When reading data, the engine is only required to extract the necessary set of columns. However, in some cases, the query may be partially processed inside the table engine. Note that for most serious tasks, you should use engines from the MergeTree family.", + "title": "Table engines" + }, + { + "location": "/table_engines/tinylog/", + "text": "TinyLog\n\n\nThe simplest table engine, which stores data on a disk.\nEach column is stored in a separate compressed file.\nWhen writing, data is appended to the end of files.\n\n\nConcurrent data access is not restricted in any way:\n\n\n\n\nIf you are simultaneously reading from a table and writing to it in a different query, the read operation will complete with an error.\n\n\nIf you are writing to a table in multiple queries simultaneously, the data will be broken.\n\n\n\n\nThe typical way to use this table is write-once: first just write the data one time, then read it as many times as needed.\nQueries are executed in a single stream. In other words, this engine is intended for relatively small tables (recommended up to 1,000,000 rows).\nIt makes sense to use this table engine if you have many small tables, since it is simpler than the Log engine (fewer files need to be opened).\nThe situation when you have a large number of small tables guarantees poor productivity, but may already be used when working with another DBMS, and you may find it easier to switch to using TinyLog types of tables.\n\nIndexes are not supported.\n\n\nIn Yandex.Metrica, TinyLog tables are used for intermediary data that is processed in small batches.", + "title": "TinyLog" + }, + { + "location": "/table_engines/tinylog/#tinylog", + "text": "The simplest table engine, which stores data on a disk.\nEach column is stored in a separate compressed file.\nWhen writing, data is appended to the end of files. Concurrent data access is not restricted in any way: If you are simultaneously reading from a table and writing to it in a different query, the read operation will complete with an error. If you are writing to a table in multiple queries simultaneously, the data will be broken. The typical way to use this table is write-once: first just write the data one time, then read it as many times as needed.\nQueries are executed in a single stream. In other words, this engine is intended for relatively small tables (recommended up to 1,000,000 rows).\nIt makes sense to use this table engine if you have many small tables, since it is simpler than the Log engine (fewer files need to be opened).\nThe situation when you have a large number of small tables guarantees poor productivity, but may already be used when working with another DBMS, and you may find it easier to switch to using TinyLog types of tables. Indexes are not supported. In Yandex.Metrica, TinyLog tables are used for intermediary data that is processed in small batches.", + "title": "TinyLog" + }, + { + "location": "/table_engines/log/", + "text": "Log\n\n\nLog differs from TinyLog in that a small file of \"marks\" resides with the column files. These marks are written on every data block and contain offsets that indicate where to start reading the file in order to skip the specified number of rows. This makes it possible to read table data in multiple threads.\nFor concurrent data access, the read operations can be performed simultaneously, while write operations block reads and each other.\nThe Log engine does not support indexes. Similarly, if writing to a table failed, the table is broken, and reading from it returns an error. The Log engine is appropriate for temporary data, write-once tables, and for testing or demonstration purposes.", + "title": "Log" + }, + { + "location": "/table_engines/log/#log", + "text": "Log differs from TinyLog in that a small file of \"marks\" resides with the column files. These marks are written on every data block and contain offsets that indicate where to start reading the file in order to skip the specified number of rows. This makes it possible to read table data in multiple threads.\nFor concurrent data access, the read operations can be performed simultaneously, while write operations block reads and each other.\nThe Log engine does not support indexes. Similarly, if writing to a table failed, the table is broken, and reading from it returns an error. The Log engine is appropriate for temporary data, write-once tables, and for testing or demonstration purposes.", + "title": "Log" + }, + { + "location": "/table_engines/memory/", + "text": "Memory\n\n\nThe Memory engine stores data in RAM, in uncompressed form. Data is stored in exactly the same form as it is received when read. In other words, reading from this table is completely free.\nConcurrent data access is synchronized. Locks are short: read and write operations don't block each other.\nIndexes are not supported. Reading is parallelized.\nMaximal productivity (over 10 GB/sec) is reached on simple queries, because there is no reading from the disk, decompressing, or deserializing data. (We should note that in many cases, the productivity of the MergeTree engine is almost as high.)\nWhen restarting a server, data disappears from the table and the table becomes empty.\nNormally, using this table engine is not justified. However, it can be used for tests, and for tasks where maximum speed is required on a relatively small number of rows (up to approximately 100,000,000).\n\n\nThe Memory engine is used by the system for temporary tables with external query data (see the section \"External data for processing a query\"), and for implementing GLOBAL IN (see the section \"IN operators\").", + "title": "Memory" + }, + { + "location": "/table_engines/memory/#memory", + "text": "The Memory engine stores data in RAM, in uncompressed form. Data is stored in exactly the same form as it is received when read. In other words, reading from this table is completely free.\nConcurrent data access is synchronized. Locks are short: read and write operations don't block each other.\nIndexes are not supported. Reading is parallelized.\nMaximal productivity (over 10 GB/sec) is reached on simple queries, because there is no reading from the disk, decompressing, or deserializing data. (We should note that in many cases, the productivity of the MergeTree engine is almost as high.)\nWhen restarting a server, data disappears from the table and the table becomes empty.\nNormally, using this table engine is not justified. However, it can be used for tests, and for tasks where maximum speed is required on a relatively small number of rows (up to approximately 100,000,000). The Memory engine is used by the system for temporary tables with external query data (see the section \"External data for processing a query\"), and for implementing GLOBAL IN (see the section \"IN operators\").", + "title": "Memory" + }, + { + "location": "/table_engines/mergetree/", + "text": "MergeTree\n\n\nThe MergeTree engine supports an index by primary key and by date, and provides the possibility to update data in real time.\nThis is the most advanced table engine in ClickHouse. Don't confuse it with the Merge engine.\n\n\nThe engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity.\n\n\nExample without sampling support.\n\n\nMergeTree(EventDate, (CounterID, EventDate), 8192)\n\n\n\n\n\nExample with sampling support.\n\n\nMergeTree(EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID)), 8192)\n\n\n\n\n\nA MergeTree table must have a separate column containing the date. Here, it is the EventDate column. The date column must have the 'Date' type (not 'DateTime').\n\n\nThe primary key may be a tuple from any expressions (usually this is just a tuple of columns), or a single expression.\n\n\nThe sampling expression (optional) can be any expression. It must also be present in the primary key. The example uses a hash of user IDs to pseudo-randomly disperse data in the table for each CounterID and EventDate. In other words, when using the SAMPLE clause in a query, you get an evenly pseudo-random sample of data for a subset of users.\n\n\nThe table is implemented as a set of parts. Each part is sorted by the primary key. In addition, each part has the minimum and maximum date assigned. When inserting in the table, a new sorted part is created. The merge process is periodically initiated in the background. When merging, several parts are selected (usually the smallest ones) and then merged into one large sorted part.\n\n\nIn other words, incremental sorting occurs when inserting to the table. Merging is implemented so that the table always consists of a small number of sorted parts, and the merge itself doesn't do too much work.\n\n\nDuring insertion, data belonging to different months is separated into different parts. The parts that correspond to different months are never combined. The purpose of this is to provide local data modification (for ease in backups).\n\n\nParts are combined up to a certain size threshold, so there aren't any merges that are too long.\n\n\nFor each part, an index file is also written. The index file contains the primary key value for every 'index_granularity' row in the table. In other words, this is an abbreviated index of sorted data.\n\n\nFor columns, \"marks\" are also written to each 'index_granularity' row so that data can be read in a specific range.\n\n\nWhen reading from a table, the SELECT query is analyzed for whether indexes can be used.\nAn index can be used if the WHERE or PREWHERE clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has IN or LIKE with a fixed prefix on columns or expressions that are in the primary key or partitioning key, or on certain partially repetitive functions of these columns, or logical relationships of these expressions.\n\n\nThus, it is possible to quickly run queries on one or many ranges of the primary key. In this example, queries will be fast when run for a specific tracking tag; for a specific tag and date range; for a specific tag and date; for multiple tags with a date range, and so on.\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \nEventDate\n \n=\n \ntoDate\n(\nnow\n())\n \nAND\n \nCounterID\n \n=\n \n34\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \nEventDate\n \n=\n \ntoDate\n(\nnow\n())\n \nAND\n \n(\nCounterID\n \n=\n \n34\n \nOR\n \nCounterID\n \n=\n \n42\n)\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \n((\nEventDate\n \n=\n \ntoDate\n(\n2014-01-01\n)\n \nAND\n \nEventDate\n \n=\n \ntoDate\n(\n2014-01-31\n))\n \nOR\n \nEventDate\n \n=\n \ntoDate\n(\n2014-05-01\n))\n \nAND\n \nCounterID\n \nIN\n \n(\n101500\n,\n \n731962\n,\n \n160656\n)\n \nAND\n \n(\nCounterID\n \n=\n \n101500\n \nOR\n \nEventDate\n \n!=\n \ntoDate\n(\n2014-05-01\n))\n\n\n\n\n\n\nAll of these cases will use the index by date and by primary key. The index is used even for complex expressions. Reading from the table is organized so that using the index can't be slower than a full scan.\n\n\nIn this example, the index can't be used.\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \nCounterID\n \n=\n \n34\n \nOR\n \nURL\n \nLIKE\n \n%upyachka%\n\n\n\n\n\n\nTo check whether ClickHouse can use the index when executing the query, use the settings \nforce_index_by_date\nand\nforce_primary_key\n.\n\n\nThe index by date only allows reading those parts that contain dates from the desired range. However, a data part may contain data for many dates (up to an entire month), while within a single part the data is ordered by the primary key, which might not contain the date as the first column. Because of this, using a query with only a date condition that does not specify the primary key prefix will cause more data to be read than for a single date.\n\n\nFor concurrent table access, we use multi-versioning. In other words, when a table is simultaneously read and updated, data is read from a set of parts that is current at the time of the query. There are no lengthy locks. Inserts do not get in the way of read operations.\n\n\nReading from a table is automatically parallelized.\n\n\nThe \nOPTIMIZE\n query is supported, which calls an extra merge step.\n\n\nYou can use a single large table and continually add data to it in small chunks \u2013 this is what MergeTree is intended for.\n\n\nData replication is possible for all types of tables in the MergeTree family (see the section \"Data replication\").", + "title": "MergeTree" + }, + { + "location": "/table_engines/mergetree/#mergetree", + "text": "The MergeTree engine supports an index by primary key and by date, and provides the possibility to update data in real time.\nThis is the most advanced table engine in ClickHouse. Don't confuse it with the Merge engine. The engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity. Example without sampling support. MergeTree(EventDate, (CounterID, EventDate), 8192) Example with sampling support. MergeTree(EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID)), 8192) A MergeTree table must have a separate column containing the date. Here, it is the EventDate column. The date column must have the 'Date' type (not 'DateTime'). The primary key may be a tuple from any expressions (usually this is just a tuple of columns), or a single expression. The sampling expression (optional) can be any expression. It must also be present in the primary key. The example uses a hash of user IDs to pseudo-randomly disperse data in the table for each CounterID and EventDate. In other words, when using the SAMPLE clause in a query, you get an evenly pseudo-random sample of data for a subset of users. The table is implemented as a set of parts. Each part is sorted by the primary key. In addition, each part has the minimum and maximum date assigned. When inserting in the table, a new sorted part is created. The merge process is periodically initiated in the background. When merging, several parts are selected (usually the smallest ones) and then merged into one large sorted part. In other words, incremental sorting occurs when inserting to the table. Merging is implemented so that the table always consists of a small number of sorted parts, and the merge itself doesn't do too much work. During insertion, data belonging to different months is separated into different parts. The parts that correspond to different months are never combined. The purpose of this is to provide local data modification (for ease in backups). Parts are combined up to a certain size threshold, so there aren't any merges that are too long. For each part, an index file is also written. The index file contains the primary key value for every 'index_granularity' row in the table. In other words, this is an abbreviated index of sorted data. For columns, \"marks\" are also written to each 'index_granularity' row so that data can be read in a specific range. When reading from a table, the SELECT query is analyzed for whether indexes can be used.\nAn index can be used if the WHERE or PREWHERE clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has IN or LIKE with a fixed prefix on columns or expressions that are in the primary key or partitioning key, or on certain partially repetitive functions of these columns, or logical relationships of these expressions. Thus, it is possible to quickly run queries on one or many ranges of the primary key. In this example, queries will be fast when run for a specific tracking tag; for a specific tag and date range; for a specific tag and date; for multiple tags with a date range, and so on. SELECT count () FROM table WHERE EventDate = toDate ( now ()) AND CounterID = 34 SELECT count () FROM table WHERE EventDate = toDate ( now ()) AND ( CounterID = 34 OR CounterID = 42 ) SELECT count () FROM table WHERE (( EventDate = toDate ( 2014-01-01 ) AND EventDate = toDate ( 2014-01-31 )) OR EventDate = toDate ( 2014-05-01 )) AND CounterID IN ( 101500 , 731962 , 160656 ) AND ( CounterID = 101500 OR EventDate != toDate ( 2014-05-01 )) All of these cases will use the index by date and by primary key. The index is used even for complex expressions. Reading from the table is organized so that using the index can't be slower than a full scan. In this example, the index can't be used. SELECT count () FROM table WHERE CounterID = 34 OR URL LIKE %upyachka% To check whether ClickHouse can use the index when executing the query, use the settings force_index_by_date and force_primary_key . The index by date only allows reading those parts that contain dates from the desired range. However, a data part may contain data for many dates (up to an entire month), while within a single part the data is ordered by the primary key, which might not contain the date as the first column. Because of this, using a query with only a date condition that does not specify the primary key prefix will cause more data to be read than for a single date. For concurrent table access, we use multi-versioning. In other words, when a table is simultaneously read and updated, data is read from a set of parts that is current at the time of the query. There are no lengthy locks. Inserts do not get in the way of read operations. Reading from a table is automatically parallelized. The OPTIMIZE query is supported, which calls an extra merge step. You can use a single large table and continually add data to it in small chunks \u2013 this is what MergeTree is intended for. Data replication is possible for all types of tables in the MergeTree family (see the section \"Data replication\").", + "title": "MergeTree" + }, + { + "location": "/table_engines/custom_partitioning_key/", + "text": "Custom partitioning key\n\n\nStarting with version 1.1.54310, you can create tables in the MergeTree family with any partitioning expression (not only partitioning by month).\n\n\nThe partition key can be an expression from the table columns, or a tuple of such expressions (similar to the primary key). The partition key can be omitted. When creating a table, specify the partition key in the ENGINE description with the new syntax:\n\n\nENGINE [=] Name(...) [PARTITION BY expr] [ORDER BY expr] [SAMPLE BY expr] [SETTINGS name=value, ...]\n\n\n\n\n\nFor MergeTree tables, the partition expression is specified after \nPARTITION BY\n, the primary key after \nORDER BY\n, the sampling key after \nSAMPLE BY\n, and \nSETTINGS\n can specify \nindex_granularity\n (optional; the default value is 8192), as well as other settings from \nMergeTreeSettings.h\n. The other engine parameters are specified in parentheses after the engine name, as previously. Example:\n\n\nENGINE\n \n=\n \nReplicatedCollapsingMergeTree\n(\n/clickhouse/tables/name\n,\n \nreplica1\n,\n \nSign\n)\n\n \nPARTITION\n \nBY\n \n(\ntoMonday\n(\nStartDate\n),\n \nEventType\n)\n\n \nORDER\n \nBY\n \n(\nCounterID\n,\n \nStartDate\n,\n \nintHash32\n(\nUserID\n))\n\n \nSAMPLE\n \nBY\n \nintHash32\n(\nUserID\n)\n\n\n\n\n\n\nThe traditional partitioning by month is expressed as \ntoYYYYMM(date_column)\n.\n\n\nYou can't convert an old-style table to a table with custom partitions (only via INSERT SELECT).\n\n\nAfter this table is created, merge will only work for data parts that have the same value for the partitioning expression. Note: This means that you shouldn't make overly granular partitions (more than about a thousand partitions), or SELECT will perform poorly.\n\n\nTo specify a partition in ALTER PARTITION commands, specify the value of the partition expression (or a tuple). Constants and constant expressions are supported. Example:\n\n\nALTER\n \nTABLE\n \ntable\n \nDROP\n \nPARTITION\n \n(\ntoMonday\n(\ntoday\n()),\n \n1\n)\n\n\n\n\n\n\nDeletes the partition for the current week with event type 1. The same is true for the OPTIMIZE query. To specify the only partition in a non-partitioned table, specify \nPARTITION tuple()\n.\n\n\nNote: For old-style tables, the partition can be specified either as a number \n201710\n or a string \n'201710'\n. The syntax for the new style of tables is stricter with types (similar to the parser for the VALUES input format). In addition, ALTER TABLE FREEZE PARTITION uses exact match for new-style tables (not prefix match).\n\n\nIn the \nsystem.parts\n table, the \npartition\n column specifies the value of the partition expression to use in ALTER queries (if quotas are removed). The \nname\n column should specify the name of the data part that has a new format.\n\n\nWas: \n20140317_20140323_2_2_0\n (minimum date - maximum date - minimum block number - maximum block number - level).\n\n\nNow: \n201403_2_2_0\n (partition ID - minimum block number - maximum block number - level).\n\n\nThe partition ID is its string identifier (human-readable, if possible) that is used for the names of data parts in the file system and in ZooKeeper. You can specify it in ALTER queries in place of the partition key. Example: Partition key \ntoYYYYMM(EventDate)\n; ALTER can specify either \nPARTITION 201710\n or \nPARTITION ID '201710'\n.\n\n\nFor more examples, see the tests \n00502_custom_partitioning_local\n and \n00502_custom_partitioning_replicated_zookeeper\n.", + "title": "Custom partitioning key" + }, + { + "location": "/table_engines/custom_partitioning_key/#custom-partitioning-key", + "text": "Starting with version 1.1.54310, you can create tables in the MergeTree family with any partitioning expression (not only partitioning by month). The partition key can be an expression from the table columns, or a tuple of such expressions (similar to the primary key). The partition key can be omitted. When creating a table, specify the partition key in the ENGINE description with the new syntax: ENGINE [=] Name(...) [PARTITION BY expr] [ORDER BY expr] [SAMPLE BY expr] [SETTINGS name=value, ...] For MergeTree tables, the partition expression is specified after PARTITION BY , the primary key after ORDER BY , the sampling key after SAMPLE BY , and SETTINGS can specify index_granularity (optional; the default value is 8192), as well as other settings from MergeTreeSettings.h . The other engine parameters are specified in parentheses after the engine name, as previously. Example: ENGINE = ReplicatedCollapsingMergeTree ( /clickhouse/tables/name , replica1 , Sign ) \n PARTITION BY ( toMonday ( StartDate ), EventType ) \n ORDER BY ( CounterID , StartDate , intHash32 ( UserID )) \n SAMPLE BY intHash32 ( UserID ) The traditional partitioning by month is expressed as toYYYYMM(date_column) . You can't convert an old-style table to a table with custom partitions (only via INSERT SELECT). After this table is created, merge will only work for data parts that have the same value for the partitioning expression. Note: This means that you shouldn't make overly granular partitions (more than about a thousand partitions), or SELECT will perform poorly. To specify a partition in ALTER PARTITION commands, specify the value of the partition expression (or a tuple). Constants and constant expressions are supported. Example: ALTER TABLE table DROP PARTITION ( toMonday ( today ()), 1 ) Deletes the partition for the current week with event type 1. The same is true for the OPTIMIZE query. To specify the only partition in a non-partitioned table, specify PARTITION tuple() . Note: For old-style tables, the partition can be specified either as a number 201710 or a string '201710' . The syntax for the new style of tables is stricter with types (similar to the parser for the VALUES input format). In addition, ALTER TABLE FREEZE PARTITION uses exact match for new-style tables (not prefix match). In the system.parts table, the partition column specifies the value of the partition expression to use in ALTER queries (if quotas are removed). The name column should specify the name of the data part that has a new format. Was: 20140317_20140323_2_2_0 (minimum date - maximum date - minimum block number - maximum block number - level). Now: 201403_2_2_0 (partition ID - minimum block number - maximum block number - level). The partition ID is its string identifier (human-readable, if possible) that is used for the names of data parts in the file system and in ZooKeeper. You can specify it in ALTER queries in place of the partition key. Example: Partition key toYYYYMM(EventDate) ; ALTER can specify either PARTITION 201710 or PARTITION ID '201710' . For more examples, see the tests 00502_custom_partitioning_local and 00502_custom_partitioning_replicated_zookeeper .", + "title": "Custom partitioning key" + }, + { + "location": "/table_engines/replacingmergetree/", + "text": "ReplacingMergeTree\n\n\nThis engine table differs from \nMergeTree\n in that it removes duplicate entries with the same primary key value.\n\n\nThe last optional parameter for the table engine is the version column. When merging, it reduces all rows with the same primary key value to just one row. If the version column is specified, it leaves the row with the highest version; otherwise, it leaves the last row.\n\n\nThe version column must have a type from the \nUInt\n family, \nDate\n, or \nDateTime\n.\n\n\nReplacingMergeTree\n(\nEventDate\n,\n \n(\nOrderID\n,\n \nEventDate\n,\n \nBannerID\n,\n \n...),\n \n8192\n,\n \nver\n)\n\n\n\n\n\n\nNote that data is only deduplicated during merges. Merging occurs in the background at an unknown time, so you can't plan for it. Some of the data may remain unprocessed. Although you can run an unscheduled merge using the OPTIMIZE query, don't count on using it, because the OPTIMIZE query will read and write a large amount of data.\n\n\nThus, \nReplacingMergeTree\n is suitable for clearing out duplicate data in the background in order to save space, but it doesn't guarantee the absence of duplicates.\n\n\nThis engine is not used in Yandex.Metrica, but it has been applied in other Yandex projects.", + "title": "ReplacingMergeTree" + }, + { + "location": "/table_engines/replacingmergetree/#replacingmergetree", + "text": "This engine table differs from MergeTree in that it removes duplicate entries with the same primary key value. The last optional parameter for the table engine is the version column. When merging, it reduces all rows with the same primary key value to just one row. If the version column is specified, it leaves the row with the highest version; otherwise, it leaves the last row. The version column must have a type from the UInt family, Date , or DateTime . ReplacingMergeTree ( EventDate , ( OrderID , EventDate , BannerID , ...), 8192 , ver ) Note that data is only deduplicated during merges. Merging occurs in the background at an unknown time, so you can't plan for it. Some of the data may remain unprocessed. Although you can run an unscheduled merge using the OPTIMIZE query, don't count on using it, because the OPTIMIZE query will read and write a large amount of data. Thus, ReplacingMergeTree is suitable for clearing out duplicate data in the background in order to save space, but it doesn't guarantee the absence of duplicates. This engine is not used in Yandex.Metrica, but it has been applied in other Yandex projects.", + "title": "ReplacingMergeTree" + }, + { + "location": "/table_engines/summingmergetree/", + "text": "SummingMergeTree\n\n\nThis engine differs from \nMergeTree\n in that it totals data while merging.\n\n\nSummingMergeTree\n(\nEventDate\n,\n \n(\nOrderID\n,\n \nEventDate\n,\n \nBannerID\n,\n \n...),\n \n8192\n)\n\n\n\n\n\n\nThe columns to total are implicit. When merging, all rows with the same primary key value (in the example, OrderId, EventDate, BannerID, ...) have their values totaled in numeric columns that are not part of the primary key.\n\n\nSummingMergeTree\n(\nEventDate\n,\n \n(\nOrderID\n,\n \nEventDate\n,\n \nBannerID\n,\n \n...),\n \n8192\n,\n \n(\nShows\n,\n \nClicks\n,\n \nCost\n,\n \n...))\n\n\n\n\n\n\nThe columns to total are set explicitly (the last parameter \u2013 Shows, Clicks, Cost, ...). When merging, all rows with the same primary key value have their values totaled in the specified columns. The specified columns also must be numeric and must not be part of the primary key.\n\n\nIf the values were null in all of these columns, the row is deleted. (The exception is cases when the data part would not have any rows left in it.)\n\n\nFor the other rows that are not part of the primary key, the first value that occurs is selected when merging.\n\n\nSummation is not performed for a read operation. If it is necessary, write the appropriate GROUP BY.\n\n\nIn addition, a table can have nested data structures that are processed in a special way.\nIf the name of a nested table ends in 'Map' and it contains at least two columns that meet the following criteria:\n\n\n\n\nThe first table is numeric ((U)IntN, Date, DateTime), which we'll refer to as the 'key'.\n\n\nThe other columns are arithmetic ((U)IntN, Float32/64), which we'll refer to as '(values...)'. Then this nested table is interpreted as a mapping of key =\n (values...), and when merging its rows, the elements of two data sets are merged by 'key' with a summation of the corresponding (values...).\n\n\n\n\nExamples:\n\n\n[(1, 100)] + [(2, 150)] -\n [(1, 100), (2, 150)]\n[(1, 100)] + [(1, 150)] -\n [(1, 250)]\n[(1, 100)] + [(1, 150), (2, 150)] -\n [(1, 250), (2, 150)]\n[(1, 100), (2, 150)] + [(1, -100)] -\n [(2, 150)]\n\n\n\n\n\nFor aggregation of Map, use the function sumMap(key, value).\n\n\nFor nested data structures, you don't need to specify the columns as a list of columns for totaling.\n\n\nThis table engine is not particularly useful. Remember that when saving just pre-aggregated data, you lose some of the system's advantages.", + "title": "SummingMergeTree" + }, + { + "location": "/table_engines/summingmergetree/#summingmergetree", + "text": "This engine differs from MergeTree in that it totals data while merging. SummingMergeTree ( EventDate , ( OrderID , EventDate , BannerID , ...), 8192 ) The columns to total are implicit. When merging, all rows with the same primary key value (in the example, OrderId, EventDate, BannerID, ...) have their values totaled in numeric columns that are not part of the primary key. SummingMergeTree ( EventDate , ( OrderID , EventDate , BannerID , ...), 8192 , ( Shows , Clicks , Cost , ...)) The columns to total are set explicitly (the last parameter \u2013 Shows, Clicks, Cost, ...). When merging, all rows with the same primary key value have their values totaled in the specified columns. The specified columns also must be numeric and must not be part of the primary key. If the values were null in all of these columns, the row is deleted. (The exception is cases when the data part would not have any rows left in it.) For the other rows that are not part of the primary key, the first value that occurs is selected when merging. Summation is not performed for a read operation. If it is necessary, write the appropriate GROUP BY. In addition, a table can have nested data structures that are processed in a special way.\nIf the name of a nested table ends in 'Map' and it contains at least two columns that meet the following criteria: The first table is numeric ((U)IntN, Date, DateTime), which we'll refer to as the 'key'. The other columns are arithmetic ((U)IntN, Float32/64), which we'll refer to as '(values...)'. Then this nested table is interpreted as a mapping of key = (values...), and when merging its rows, the elements of two data sets are merged by 'key' with a summation of the corresponding (values...). Examples: [(1, 100)] + [(2, 150)] - [(1, 100), (2, 150)]\n[(1, 100)] + [(1, 150)] - [(1, 250)]\n[(1, 100)] + [(1, 150), (2, 150)] - [(1, 250), (2, 150)]\n[(1, 100), (2, 150)] + [(1, -100)] - [(2, 150)] For aggregation of Map, use the function sumMap(key, value). For nested data structures, you don't need to specify the columns as a list of columns for totaling. This table engine is not particularly useful. Remember that when saving just pre-aggregated data, you lose some of the system's advantages.", + "title": "SummingMergeTree" + }, + { + "location": "/table_engines/aggregatingmergetree/", + "text": "AggregatingMergeTree\n\n\nThis engine differs from \nMergeTree\n in that the merge combines the states of aggregate functions stored in the table for rows with the same primary key value.\n\n\nFor this to work, it uses the \nAggregateFunction\n data type, as well as \n-State\n and \n-Merge\n modifiers for aggregate functions. Let's examine it more closely.\n\n\nThere is an \nAggregateFunction\n data type. It is a parametric data type. As parameters, the name of the aggregate function is passed, then the types of its arguments.\n\n\nExamples:\n\n\nCREATE\n \nTABLE\n \nt\n\n\n(\n\n \ncolumn1\n \nAggregateFunction\n(\nuniq\n,\n \nUInt64\n),\n\n \ncolumn2\n \nAggregateFunction\n(\nanyIf\n,\n \nString\n,\n \nUInt8\n),\n\n \ncolumn3\n \nAggregateFunction\n(\nquantiles\n(\n0\n.\n5\n,\n \n0\n.\n9\n),\n \nUInt64\n)\n\n\n)\n \nENGINE\n \n=\n \n...\n\n\n\n\n\n\nThis type of column stores the state of an aggregate function.\n\n\nTo get this type of value, use aggregate functions with the \nState\n suffix.\n\n\nExample:\n\nuniqState(UserID), quantilesState(0.5, 0.9)(SendTiming)\n\n\nIn contrast to the corresponding \nuniq\n and \nquantiles\n functions, these functions return the state, rather than the prepared value. In other words, they return an \nAggregateFunction\n type value.\n\n\nAn \nAggregateFunction\n type value can't be output in Pretty formats. In other formats, these types of values are output as implementation-specific binary data. The \nAggregateFunction\n type values are not intended for output or saving in a dump.\n\n\nThe only useful thing you can do with \nAggregateFunction\n type values is combine the states and get a result, which essentially means to finish aggregation. Aggregate functions with the 'Merge' suffix are used for this purpose.\nExample: \nuniqMerge(UserIDState), where UserIDState has the AggregateFunction\n type.\n\n\nIn other words, an aggregate function with the 'Merge' suffix takes a set of states, combines them, and returns the result.\nAs an example, these two queries return the same result:\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ntable\n\n\n\nSELECT\n \nuniqMerge\n(\nstate\n)\n \nFROM\n \n(\nSELECT\n \nuniqState\n(\nUserID\n)\n \nAS\n \nstate\n \nFROM\n \ntable\n \nGROUP\n \nBY\n \nRegionID\n)\n\n\n\n\n\n\nThere is an \nAggregatingMergeTree\n engine. Its job during a merge is to combine the states of aggregate functions from different table rows with the same primary key value.\n\n\nYou can't use a normal INSERT to insert a row in a table containing \nAggregateFunction\n columns, because you can't explicitly define the \nAggregateFunction\n value. Instead, use \nINSERT SELECT\n with \n-State\n aggregate functions for inserting data.\n\n\nWith SELECT from an \nAggregatingMergeTree\n table, use GROUP BY and aggregate functions with the '-Merge' modifier in order to complete data aggregation.\n\n\nYou can use \nAggregatingMergeTree\n tables for incremental data aggregation, including for aggregated materialized views.\n\n\nExample:\n\n\nCreate an \nAggregatingMergeTree\n materialized view that watches the \ntest.visits\n table:\n\n\nCREATE\n \nMATERIALIZED\n \nVIEW\n \ntest\n.\nbasic\n\n\nENGINE\n \n=\n \nAggregatingMergeTree\n(\nStartDate\n,\n \n(\nCounterID\n,\n \nStartDate\n),\n \n8192\n)\n\n\nAS\n \nSELECT\n\n \nCounterID\n,\n\n \nStartDate\n,\n\n \nsumState\n(\nSign\n)\n \nAS\n \nVisits\n,\n\n \nuniqState\n(\nUserID\n)\n \nAS\n \nUsers\n\n\nFROM\n \ntest\n.\nvisits\n\n\nGROUP\n \nBY\n \nCounterID\n,\n \nStartDate\n;\n\n\n\n\n\n\nInsert data in the \ntest.visits\n table. Data will also be inserted in the view, where it will be aggregated:\n\n\nINSERT\n \nINTO\n \ntest\n.\nvisits\n \n...\n\n\n\n\n\n\nPerform \nSELECT\n from the view using \nGROUP BY\n in order to complete data aggregation:\n\n\nSELECT\n\n \nStartDate\n,\n\n \nsumMerge\n(\nVisits\n)\n \nAS\n \nVisits\n,\n\n \nuniqMerge\n(\nUsers\n)\n \nAS\n \nUsers\n\n\nFROM\n \ntest\n.\nbasic\n\n\nGROUP\n \nBY\n \nStartDate\n\n\nORDER\n \nBY\n \nStartDate\n;\n\n\n\n\n\n\nYou can create a materialized view like this and assign a normal view to it that finishes data aggregation.\n\n\nNote that in most cases, using \nAggregatingMergeTree\n is not justified, since queries can be run efficiently enough on non-aggregated data.", + "title": "AggregatingMergeTree" + }, + { + "location": "/table_engines/aggregatingmergetree/#aggregatingmergetree", + "text": "This engine differs from MergeTree in that the merge combines the states of aggregate functions stored in the table for rows with the same primary key value. For this to work, it uses the AggregateFunction data type, as well as -State and -Merge modifiers for aggregate functions. Let's examine it more closely. There is an AggregateFunction data type. It is a parametric data type. As parameters, the name of the aggregate function is passed, then the types of its arguments. Examples: CREATE TABLE t ( \n column1 AggregateFunction ( uniq , UInt64 ), \n column2 AggregateFunction ( anyIf , String , UInt8 ), \n column3 AggregateFunction ( quantiles ( 0 . 5 , 0 . 9 ), UInt64 ) ) ENGINE = ... This type of column stores the state of an aggregate function. To get this type of value, use aggregate functions with the State suffix. Example: uniqState(UserID), quantilesState(0.5, 0.9)(SendTiming) In contrast to the corresponding uniq and quantiles functions, these functions return the state, rather than the prepared value. In other words, they return an AggregateFunction type value. An AggregateFunction type value can't be output in Pretty formats. In other formats, these types of values are output as implementation-specific binary data. The AggregateFunction type values are not intended for output or saving in a dump. The only useful thing you can do with AggregateFunction type values is combine the states and get a result, which essentially means to finish aggregation. Aggregate functions with the 'Merge' suffix are used for this purpose.\nExample: uniqMerge(UserIDState), where UserIDState has the AggregateFunction type. In other words, an aggregate function with the 'Merge' suffix takes a set of states, combines them, and returns the result.\nAs an example, these two queries return the same result: SELECT uniq ( UserID ) FROM table SELECT uniqMerge ( state ) FROM ( SELECT uniqState ( UserID ) AS state FROM table GROUP BY RegionID ) There is an AggregatingMergeTree engine. Its job during a merge is to combine the states of aggregate functions from different table rows with the same primary key value. You can't use a normal INSERT to insert a row in a table containing AggregateFunction columns, because you can't explicitly define the AggregateFunction value. Instead, use INSERT SELECT with -State aggregate functions for inserting data. With SELECT from an AggregatingMergeTree table, use GROUP BY and aggregate functions with the '-Merge' modifier in order to complete data aggregation. You can use AggregatingMergeTree tables for incremental data aggregation, including for aggregated materialized views. Example: Create an AggregatingMergeTree materialized view that watches the test.visits table: CREATE MATERIALIZED VIEW test . basic ENGINE = AggregatingMergeTree ( StartDate , ( CounterID , StartDate ), 8192 ) AS SELECT \n CounterID , \n StartDate , \n sumState ( Sign ) AS Visits , \n uniqState ( UserID ) AS Users FROM test . visits GROUP BY CounterID , StartDate ; Insert data in the test.visits table. Data will also be inserted in the view, where it will be aggregated: INSERT INTO test . visits ... Perform SELECT from the view using GROUP BY in order to complete data aggregation: SELECT \n StartDate , \n sumMerge ( Visits ) AS Visits , \n uniqMerge ( Users ) AS Users FROM test . basic GROUP BY StartDate ORDER BY StartDate ; You can create a materialized view like this and assign a normal view to it that finishes data aggregation. Note that in most cases, using AggregatingMergeTree is not justified, since queries can be run efficiently enough on non-aggregated data.", + "title": "AggregatingMergeTree" + }, + { + "location": "/table_engines/collapsingmergetree/", + "text": "CollapsingMergeTree\n\n\nThis engine is used specifically for Yandex.Metrica.\n\n\nIt differs from \nMergeTree\n in that it allows automatic deletion, or \"collapsing\" certain pairs of rows when merging.\n\n\nYandex.Metrica has normal logs (such as hit logs) and change logs. Change logs are used for incrementally calculating statistics on data that is constantly changing. Examples are the log of session changes, or logs of changes to user histories. Sessions are constantly changing in Yandex.Metrica. For example, the number of hits per session increases. We refer to changes in any object as a pair (?old values, ?new values). Old values may be missing if the object was created. New values may be missing if the object was deleted. If the object was changed, but existed previously and was not deleted, both values are present. In the change log, one or two entries are made for each change. Each entry contains all the attributes that the object has, plus a special attribute for differentiating between the old and new values. When objects change, only the new entries are added to the change log, and the existing ones are not touched.\n\n\nThe change log makes it possible to incrementally calculate almost any statistics. To do this, we need to consider \"new\" rows with a plus sign, and \"old\" rows with a minus sign. In other words, incremental calculation is possible for all statistics whose algebraic structure contains an operation for taking the inverse of an element. This is true of most statistics. We can also calculate \"idempotent\" statistics, such as the number of unique visitors, since the unique visitors are not deleted when making changes to sessions.\n\n\nThis is the main concept that allows Yandex.Metrica to work in real time.\n\n\nCollapsingMergeTree accepts an additional parameter - the name of an Int8-type column that contains the row's \"sign\". Example:\n\n\nCollapsingMergeTree\n(\nEventDate\n,\n \n(\nCounterID\n,\n \nEventDate\n,\n \nintHash32\n(\nUniqID\n),\n \nVisitID\n),\n \n8192\n,\n \nSign\n)\n\n\n\n\n\n\nHere, \nSign\n is a column containing -1 for \"old\" values and 1 for \"new\" values.\n\n\nWhen merging, each group of consecutive identical primary key values (columns for sorting data) is reduced to no more than one row with the column value 'sign_column = -1' (the \"negative row\") and no more than one row with the column value 'sign_column = 1' (the \"positive row\"). In other words, entries from the change log are collapsed.\n\n\nIf the number of positive and negative rows matches, the first negative row and the last positive row are written.\nIf there is one more positive row than negative rows, only the last positive row is written.\nIf there is one more negative row than positive rows, only the first negative row is written.\nOtherwise, there will be a logical error and none of the rows will be written. (A logical error can occur if the same section of the log was accidentally inserted more than once. The error is just recorded in the server log, and the merge continues.)\n\n\nThus, collapsing should not change the results of calculating statistics.\nChanges are gradually collapsed so that in the end only the last value of almost every object is left.\nCompared to MergeTree, the CollapsingMergeTree engine allows a multifold reduction of data volume.\n\n\nThere are several ways to get completely \"collapsed\" data from a \nCollapsingMergeTree\n table:\n\n\n\n\nWrite a query with GROUP BY and aggregate functions that accounts for the sign. For example, to calculate quantity, write 'sum(Sign)' instead of 'count()'. To calculate the sum of something, write 'sum(Sign * x)' instead of 'sum(x)', and so on, and also add 'HAVING sum(Sign) \n 0'. Not all amounts can be calculated this way. For example, the aggregate functions 'min' and 'max' can't be rewritten.\n\n\nIf you must extract data without aggregation (for example, to check whether rows are present whose newest values match certain conditions), you can use the FINAL modifier for the FROM clause. This approach is significantly less efficient.", + "title": "CollapsingMergeTree" + }, + { + "location": "/table_engines/collapsingmergetree/#collapsingmergetree", + "text": "This engine is used specifically for Yandex.Metrica. It differs from MergeTree in that it allows automatic deletion, or \"collapsing\" certain pairs of rows when merging. Yandex.Metrica has normal logs (such as hit logs) and change logs. Change logs are used for incrementally calculating statistics on data that is constantly changing. Examples are the log of session changes, or logs of changes to user histories. Sessions are constantly changing in Yandex.Metrica. For example, the number of hits per session increases. We refer to changes in any object as a pair (?old values, ?new values). Old values may be missing if the object was created. New values may be missing if the object was deleted. If the object was changed, but existed previously and was not deleted, both values are present. In the change log, one or two entries are made for each change. Each entry contains all the attributes that the object has, plus a special attribute for differentiating between the old and new values. When objects change, only the new entries are added to the change log, and the existing ones are not touched. The change log makes it possible to incrementally calculate almost any statistics. To do this, we need to consider \"new\" rows with a plus sign, and \"old\" rows with a minus sign. In other words, incremental calculation is possible for all statistics whose algebraic structure contains an operation for taking the inverse of an element. This is true of most statistics. We can also calculate \"idempotent\" statistics, such as the number of unique visitors, since the unique visitors are not deleted when making changes to sessions. This is the main concept that allows Yandex.Metrica to work in real time. CollapsingMergeTree accepts an additional parameter - the name of an Int8-type column that contains the row's \"sign\". Example: CollapsingMergeTree ( EventDate , ( CounterID , EventDate , intHash32 ( UniqID ), VisitID ), 8192 , Sign ) Here, Sign is a column containing -1 for \"old\" values and 1 for \"new\" values. When merging, each group of consecutive identical primary key values (columns for sorting data) is reduced to no more than one row with the column value 'sign_column = -1' (the \"negative row\") and no more than one row with the column value 'sign_column = 1' (the \"positive row\"). In other words, entries from the change log are collapsed. If the number of positive and negative rows matches, the first negative row and the last positive row are written.\nIf there is one more positive row than negative rows, only the last positive row is written.\nIf there is one more negative row than positive rows, only the first negative row is written.\nOtherwise, there will be a logical error and none of the rows will be written. (A logical error can occur if the same section of the log was accidentally inserted more than once. The error is just recorded in the server log, and the merge continues.) Thus, collapsing should not change the results of calculating statistics.\nChanges are gradually collapsed so that in the end only the last value of almost every object is left.\nCompared to MergeTree, the CollapsingMergeTree engine allows a multifold reduction of data volume. There are several ways to get completely \"collapsed\" data from a CollapsingMergeTree table: Write a query with GROUP BY and aggregate functions that accounts for the sign. For example, to calculate quantity, write 'sum(Sign)' instead of 'count()'. To calculate the sum of something, write 'sum(Sign * x)' instead of 'sum(x)', and so on, and also add 'HAVING sum(Sign) 0'. Not all amounts can be calculated this way. For example, the aggregate functions 'min' and 'max' can't be rewritten. If you must extract data without aggregation (for example, to check whether rows are present whose newest values match certain conditions), you can use the FINAL modifier for the FROM clause. This approach is significantly less efficient.", + "title": "CollapsingMergeTree" + }, + { + "location": "/table_engines/graphitemergetree/", + "text": "GraphiteMergeTree\n\n\nThis engine is designed for rollup (thinning and aggregating/averaging) \nGraphite\n data. It may be helpful to developers who want to use ClickHouse as a data store for Graphite.\n\n\nGraphite stores full data in ClickHouse, and data can be retrieved in the following ways:\n\n\n\n\nWithout thinning.\n\n\n\n\nUses the \nMergeTree\n engine.\n\n\n\n\nWith thinning.\n\n\n\n\nUsing the \nGraphiteMergeTree\n engine.\n\n\nThe engine inherits properties from MergeTree. The settings for thinning data are defined by the \ngraphite_rollup\n parameter in the server configuration.\n\n\nUsing the engine\n\n\nThe Graphite data table must contain the following fields at minimum:\n\n\n\n\nPath\n \u2013 The metric name (Graphite sensor).\n\n\nTime\n \u2013 The time for measuring the metric.\n\n\nValue\n \u2013 The value of the metric at the time set in Time.\n\n\nVersion\n \u2013 Determines which value of the metric with the same Path and Time will remain in the database.\n\n\n\n\nRollup pattern:\n\n\npattern\n regexp\n function\n age -\n precision\n ...\npattern\n ...\ndefault\n function\n age -\n precision\n ...\n\n\n\n\n\nWhen processing a record, ClickHouse will check the rules in the \npattern\nclause. If the metric name matches the \nregexp\n, the rules from \npattern\n are applied; otherwise, the rules from \ndefault\n are used.\n\n\nFields in the pattern.\n\n\n\n\nage\n \u2013 The minimum age of the data in seconds.\n\n\nfunction\n \u2013 The name of the aggregating function to apply to data whose age falls within the range \n[age, age + precision]\n.\n\n\nprecision\n\u2013 How precisely to define the age of the data in seconds.\n\n\nregexp\n\u2013 A pattern for the metric name.\n\n\n\n\nExample of settings:\n\n\ngraphite_rollup\n\n \npattern\n\n \nregexp\nclick_cost\n/regexp\n\n \nfunction\nany\n/function\n\n \nretention\n\n \nage\n0\n/age\n\n \nprecision\n5\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n86400\n/age\n\n \nprecision\n60\n/precision\n\n \n/retention\n\n \n/pattern\n\n \ndefault\n\n \nfunction\nmax\n/function\n\n \nretention\n\n \nage\n0\n/age\n\n \nprecision\n60\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n3600\n/age\n\n \nprecision\n300\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n86400\n/age\n\n \nprecision\n3600\n/precision\n\n \n/retention\n\n \n/default\n\n\n/graphite_rollup", + "title": "GraphiteMergeTree" + }, + { + "location": "/table_engines/graphitemergetree/#graphitemergetree", + "text": "This engine is designed for rollup (thinning and aggregating/averaging) Graphite data. It may be helpful to developers who want to use ClickHouse as a data store for Graphite. Graphite stores full data in ClickHouse, and data can be retrieved in the following ways: Without thinning. Uses the MergeTree engine. With thinning. Using the GraphiteMergeTree engine. The engine inherits properties from MergeTree. The settings for thinning data are defined by the graphite_rollup parameter in the server configuration.", + "title": "GraphiteMergeTree" + }, + { + "location": "/table_engines/graphitemergetree/#using-the-engine", + "text": "The Graphite data table must contain the following fields at minimum: Path \u2013 The metric name (Graphite sensor). Time \u2013 The time for measuring the metric. Value \u2013 The value of the metric at the time set in Time. Version \u2013 Determines which value of the metric with the same Path and Time will remain in the database. Rollup pattern: pattern\n regexp\n function\n age - precision\n ...\npattern\n ...\ndefault\n function\n age - precision\n ... When processing a record, ClickHouse will check the rules in the pattern clause. If the metric name matches the regexp , the rules from pattern are applied; otherwise, the rules from default are used. Fields in the pattern. age \u2013 The minimum age of the data in seconds. function \u2013 The name of the aggregating function to apply to data whose age falls within the range [age, age + precision] . precision \u2013 How precisely to define the age of the data in seconds. regexp \u2013 A pattern for the metric name. Example of settings: graphite_rollup \n pattern \n regexp click_cost /regexp \n function any /function \n retention \n age 0 /age \n precision 5 /precision \n /retention \n retention \n age 86400 /age \n precision 60 /precision \n /retention \n /pattern \n default \n function max /function \n retention \n age 0 /age \n precision 60 /precision \n /retention \n retention \n age 3600 /age \n precision 300 /precision \n /retention \n retention \n age 86400 /age \n precision 3600 /precision \n /retention \n /default /graphite_rollup", + "title": "Using the engine" + }, + { + "location": "/table_engines/replication/", + "text": "Data replication\n\n\nReplication is only supported for tables in the MergeTree family:\n\n\n\n\nReplicatedMergeTree\n\n\nReplicatedSummingMergeTree\n\n\nReplicatedReplacingMergeTree\n\n\nReplicatedAggregatingMergeTree\n\n\nReplicatedCollapsingMergeTree\n\n\nReplicatedGraphiteMergeTree\n\n\n\n\nReplication works at the level of an individual table, not the entire server. A server can store both replicated and non-replicated tables at the same time.\n\n\nReplication does not depend on sharding. Each shard has its own independent replication.\n\n\nCompressed data is replicated for \nINSERT\n and \nALTER\n queries (see the description of the \nALTER\n query).\n\n\nCREATE\n, \nDROP\n, \nATTACH\n, \nDETACH\n and \nRENAME\n queries are executed on a single server and are not replicated:\n\n\n\n\nThe CREATE TABLE\n query creates a new replicatable table on the server where the query is run. If this table already exists on other servers, it adds a new replica.\n\n\nThe DROP TABLE\n query deletes the replica located on the server where the query is run.\n\n\nThe RENAME\n query renames the table on one of the replicas. In other words, replicated tables can have different names on different replicas.\n\n\n\n\nTo use replication, set the addresses of the ZooKeeper cluster in the config file. Example:\n\n\nzookeeper\n\n \nnode\n \nindex=\n1\n\n \nhost\nexample1\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n \nnode\n \nindex=\n2\n\n \nhost\nexample2\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n \nnode\n \nindex=\n3\n\n \nhost\nexample3\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n\n/zookeeper\n\n\n\n\n\n\nUse ZooKeeper version 3.4.5 or later.\n\n\nYou can specify any existing ZooKeeper cluster and the system will use a directory on it for its own data (the directory is specified when creating a replicatable table).\n\n\nIf ZooKeeper isn't set in the config file, you can't create replicated tables, and any existing replicated tables will be read-only.\n\n\nZooKeeper is not used in \nSELECT\n queries because replication does not affect the performance of \nSELECT\n and queries run just as fast as they do for non-replicated tables. When querying distributed replicated tables, ClickHouse behavior is controlled by the settings \nmax_replica_delay_for_distributed_queries\n and \nfallback_to_stale_replicas_for_distributed_queries\n.\n\n\nFor each \nINSERT\n query, approximately ten entries are added to ZooKeeper through several transactions. (To be more precise, this is for each inserted block of data; an INSERT query contains one block or one block per \nmax_insert_block_size = 1048576\n rows.) This leads to slightly longer latencies for \nINSERT\n compared to non-replicated tables. But if you follow the recommendations to insert data in batches of no more than one \nINSERT\n per second, it doesn't create any problems. The entire ClickHouse cluster used for coordinating one ZooKeeper cluster has a total of several hundred \nINSERTs\n per second. The throughput on data inserts (the number of rows per second) is just as high as for non-replicated data.\n\n\nFor very large clusters, you can use different ZooKeeper clusters for different shards. However, this hasn't proven necessary on the Yandex.Metrica cluster (approximately 300 servers).\n\n\nReplication is asynchronous and multi-master. \nINSERT\n queries (as well as \nALTER\n) can be sent to any available server. Data is inserted on the server where the query is run, and then it is copied to the other servers. Because it is asynchronous, recently inserted data appears on the other replicas with some latency. If part of the replicas are not available, the data is written when they become available. If a replica is available, the latency is the amount of time it takes to transfer the block of compressed data over the network.\n\n\nBy default, an INSERT query waits for confirmation of writing the data from only one replica. If the data was successfully written to only one replica and the server with this replica ceases to exist, the stored data will be lost. Tp enable getting confirmation of data writes from multiple replicas, use the \ninsert_quorum\n option.\n\n\nEach block of data is written atomically. The INSERT query is divided into blocks up to \nmax_insert_block_size = 1048576\n rows. In other words, if the \nINSERT\n query has less than 1048576 rows, it is made atomically.\n\n\nData blocks are deduplicated. For multiple writes of the same data block (data blocks of the same size containing the same rows in the same order), the block is only written once. The reason for this is in case of network failures when the client application doesn't know if the data was written to the DB, so the \nINSERT\n query can simply be repeated. It doesn't matter which replica INSERTs were sent to with identical data. \nINSERTs\n are idempotent. Deduplication parameters are controlled by \nmerge_tree\n server settings.\n\n\nDuring replication, only the source data to insert is transferred over the network. Further data transformation (merging) is coordinated and performed on all the replicas in the same way. This minimizes network usage, which means that replication works well when replicas reside in different datacenters. (Note that duplicating data in different datacenters is the main goal of replication.)\n\n\nYou can have any number of replicas of the same data. Yandex.Metrica uses double replication in production. Each server uses RAID-5 or RAID-6, and RAID-10 in some cases. This is a relatively reliable and convenient solution.\n\n\nThe system monitors data synchronicity on replicas and is able to recover after a failure. Failover is automatic (for small differences in data) or semi-automatic (when data differs too much, which may indicate a configuration error).\n\n\n\n\nCreating replicated tables\n\n\nThe \nReplicated\n prefix is added to the table engine name. For example:\nReplicatedMergeTree\n.\n\n\nTwo parameters are also added in the beginning of the parameters list \u2013 the path to the table in ZooKeeper, and the replica name in ZooKeeper.\n\n\nExample:\n\n\nReplicatedMergeTree(\n/clickhouse/tables/{layer}-{shard}/hits\n, \n{replica}\n, EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID), EventTime), 8192)\n\n\n\n\n\nAs the example shows, these parameters can contain substitutions in curly brackets. The substituted values are taken from the 'macros' section of the config file. Example:\n\n\nmacros\n\n \nlayer\n05\n/layer\n\n \nshard\n02\n/shard\n\n \nreplica\nexample05-02-1.yandex.ru\n/replica\n\n\n/macros\n\n\n\n\n\n\nThe path to the table in ZooKeeper should be unique for each replicated table. Tables on different shards should have different paths.\nIn this case, the path consists of the following parts:\n\n\n/clickhouse/tables/\n is the common prefix. We recommend using exactly this one.\n\n\n{layer}-{shard}\n is the shard identifier. In this example it consists of two parts, since the Yandex.Metrica cluster uses bi-level sharding. For most tasks, you can leave just the {shard} substitution, which will be expanded to the shard identifier.\n\n\nhits\n is the name of the node for the table in ZooKeeper. It is a good idea to make it the same as the table name. It is defined explicitly, because in contrast to the table name, it doesn't change after a RENAME query.\n\n\nThe replica name identifies different replicas of the same table. You can use the server name for this, as in the example. The name only needs to be unique within each shard.\n\n\nYou can define the parameters explicitly instead of using substitutions. This might be convenient for testing and for configuring small clusters. However, you can't use distributed DDL queries (\nON CLUSTER\n) in this case.\n\n\nWhen working with large clusters, we recommend using substitutions because they reduce the probability of error.\n\n\nRun the \nCREATE TABLE\n query on each replica. This query creates a new replicated table, or adds a new replica to an existing one.\n\n\nIf you add a new replica after the table already contains some data on other replicas, the data will be copied from the other replicas to the new one after running the query. In other words, the new replica syncs itself with the others.\n\n\nTo delete a replica, run \nDROP TABLE\n. However, only one replica is deleted \u2013 the one that resides on the server where you run the query.\n\n\nRecovery after failures\n\n\nIf ZooKeeper is unavailable when a server starts, replicated tables switch to read-only mode. The system periodically attempts to connect to ZooKeeper.\n\n\nIf ZooKeeper is unavailable during an \nINSERT\n, or an error occurs when interacting with ZooKeeper, an exception is thrown.\n\n\nAfter connecting to ZooKeeper, the system checks whether the set of data in the local file system matches the expected set of data (ZooKeeper stores this information). If there are minor inconsistencies, the system resolves them by syncing data with the replicas.\n\n\nIf the system detects broken data parts (with the wrong size of files) or unrecognized parts (parts written to the file system but not recorded in ZooKeeper), it moves them to the 'detached' subdirectory (they are not deleted). Any missing parts are copied from the replicas.\n\n\nNote that ClickHouse does not perform any destructive actions such as automatically deleting a large amount of data.\n\n\nWhen the server starts (or establishes a new session with ZooKeeper), it only checks the quantity and sizes of all files. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a \nSELECT\n query. The query throws an exception about a non-matching checksum or size of a compressed block. In this case, data parts are added to the verification queue and copied from the replicas if necessary.\n\n\nIf the local set of data differs too much from the expected one, a safety mechanism is triggered. The server enters this in the log and refuses to launch. The reason for this is that this case may indicate a configuration error, such as if a replica on a shard was accidentally configured like a replica on a different shard. However, the thresholds for this mechanism are set fairly low, and this situation might occur during normal failure recovery. In this case, data is restored semi-automatically - by \"pushing a button\".\n\n\nTo start recovery, create the node \n/path_to_table/replica_name/flags/force_restore_data\n in ZooKeeper with any content, or run the command to restore all replicated tables:\n\n\nsudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data\n\n\n\n\n\nThen restart the server. On start, the server deletes these flags and starts recovery.\n\n\nRecovery after complete data loss\n\n\nIf all data and metadata disappeared from one of the servers, follow these steps for recovery:\n\n\n\n\nInstall ClickHouse on the server. Define substitutions correctly in the config file that contains the shard identifier and replicas, if you use them.\n\n\nIf you had unreplicated tables that must be manually duplicated on the servers, copy their data from a replica (in the directory \n/var/lib/clickhouse/data/db_name/table_name/\n).\n\n\nCopy table definitions located in \n/var/lib/clickhouse/metadata/\n from a replica. If a shard or replica identifier is defined explicitly in the table definitions, correct it so that it corresponds to this replica. (Alternatively, start the server and make all the \nATTACH TABLE\n queries that should have been in the .sql files in \n/var/lib/clickhouse/metadata/\n.)\n\n\nTo start recovery, create the ZooKeeper node \n/path_to_table/replica_name/flags/force_restore_data\n with any content, or run the command to restore all replicated tables: \nsudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data\n\n\n\n\nThen start the server (restart, if it is already running). Data will be downloaded from replicas.\n\n\nAn alternative recovery option is to delete information about the lost replica from ZooKeeper (\n/path_to_table/replica_name\n), then create the replica again as described in \"\nCreating replicatable tables\n\".\n\n\nThere is no restriction on network bandwidth during recovery. Keep this in mind if you are restoring many replicas at once.\n\n\nConverting from MergeTree to ReplicatedMergeTree\n\n\nWe use the term \nMergeTree\n to refer to all table engines in the \nMergeTree family\n, the same as for \nReplicatedMergeTree\n.\n\n\nIf you had a \nMergeTree\n table that was manually replicated, you can convert it to a replicatable table. You might need to do this if you have already collected a large amount of data in a \nMergeTree\n table and now you want to enable replication.\n\n\nIf the data differs on various replicas, first sync it, or delete this data on all the replicas except one.\n\n\nRename the existing MergeTree table, then create a \nReplicatedMergeTree\n table with the old name.\nMove the data from the old table to the 'detached' subdirectory inside the directory with the new table data (\n/var/lib/clickhouse/data/db_name/table_name/\n).\nThen run \nALTER TABLE ATTACH PARTITION\n on one of the replicas to add these data parts to the working set.\n\n\nConverting from ReplicatedMergeTree to MergeTree\n\n\nCreate a MergeTree table with a different name. Move all the data from the directory with the \nReplicatedMergeTree\n table data to the new table's data directory. Then delete the \nReplicatedMergeTree\n table and restart the server.\n\n\nIf you want to get rid of a \nReplicatedMergeTree\n table without launching the server:\n\n\n\n\nDelete the corresponding \n.sql\n file in the metadata directory (\n/var/lib/clickhouse/metadata/\n).\n\n\nDelete the corresponding path in ZooKeeper (\n/path_to_table/replica_name\n).\n\n\n\n\nAfter this, you can launch the server, create a \nMergeTree\n table, move the data to its directory, and then restart the server.\n\n\nRecovery when metadata in the ZooKeeper cluster is lost or damaged\n\n\nIf the data in ZooKeeper was lost or damaged, you can save data by moving it to an unreplicated table as described above.\n\n\nIf exactly the same parts exist on the other replicas, they are added to the working set on them. If not, the parts are downloaded from the replica that has them.", + "title": "Data replication" + }, + { + "location": "/table_engines/replication/#data-replication", + "text": "Replication is only supported for tables in the MergeTree family: ReplicatedMergeTree ReplicatedSummingMergeTree ReplicatedReplacingMergeTree ReplicatedAggregatingMergeTree ReplicatedCollapsingMergeTree ReplicatedGraphiteMergeTree Replication works at the level of an individual table, not the entire server. A server can store both replicated and non-replicated tables at the same time. Replication does not depend on sharding. Each shard has its own independent replication. Compressed data is replicated for INSERT and ALTER queries (see the description of the ALTER query). CREATE , DROP , ATTACH , DETACH and RENAME queries are executed on a single server and are not replicated: The CREATE TABLE query creates a new replicatable table on the server where the query is run. If this table already exists on other servers, it adds a new replica. The DROP TABLE query deletes the replica located on the server where the query is run. The RENAME query renames the table on one of the replicas. In other words, replicated tables can have different names on different replicas. To use replication, set the addresses of the ZooKeeper cluster in the config file. Example: zookeeper \n node index= 1 \n host example1 /host \n port 2181 /port \n /node \n node index= 2 \n host example2 /host \n port 2181 /port \n /node \n node index= 3 \n host example3 /host \n port 2181 /port \n /node /zookeeper Use ZooKeeper version 3.4.5 or later. You can specify any existing ZooKeeper cluster and the system will use a directory on it for its own data (the directory is specified when creating a replicatable table). If ZooKeeper isn't set in the config file, you can't create replicated tables, and any existing replicated tables will be read-only. ZooKeeper is not used in SELECT queries because replication does not affect the performance of SELECT and queries run just as fast as they do for non-replicated tables. When querying distributed replicated tables, ClickHouse behavior is controlled by the settings max_replica_delay_for_distributed_queries and fallback_to_stale_replicas_for_distributed_queries . For each INSERT query, approximately ten entries are added to ZooKeeper through several transactions. (To be more precise, this is for each inserted block of data; an INSERT query contains one block or one block per max_insert_block_size = 1048576 rows.) This leads to slightly longer latencies for INSERT compared to non-replicated tables. But if you follow the recommendations to insert data in batches of no more than one INSERT per second, it doesn't create any problems. The entire ClickHouse cluster used for coordinating one ZooKeeper cluster has a total of several hundred INSERTs per second. The throughput on data inserts (the number of rows per second) is just as high as for non-replicated data. For very large clusters, you can use different ZooKeeper clusters for different shards. However, this hasn't proven necessary on the Yandex.Metrica cluster (approximately 300 servers). Replication is asynchronous and multi-master. INSERT queries (as well as ALTER ) can be sent to any available server. Data is inserted on the server where the query is run, and then it is copied to the other servers. Because it is asynchronous, recently inserted data appears on the other replicas with some latency. If part of the replicas are not available, the data is written when they become available. If a replica is available, the latency is the amount of time it takes to transfer the block of compressed data over the network. By default, an INSERT query waits for confirmation of writing the data from only one replica. If the data was successfully written to only one replica and the server with this replica ceases to exist, the stored data will be lost. Tp enable getting confirmation of data writes from multiple replicas, use the insert_quorum option. Each block of data is written atomically. The INSERT query is divided into blocks up to max_insert_block_size = 1048576 rows. In other words, if the INSERT query has less than 1048576 rows, it is made atomically. Data blocks are deduplicated. For multiple writes of the same data block (data blocks of the same size containing the same rows in the same order), the block is only written once. The reason for this is in case of network failures when the client application doesn't know if the data was written to the DB, so the INSERT query can simply be repeated. It doesn't matter which replica INSERTs were sent to with identical data. INSERTs are idempotent. Deduplication parameters are controlled by merge_tree server settings. During replication, only the source data to insert is transferred over the network. Further data transformation (merging) is coordinated and performed on all the replicas in the same way. This minimizes network usage, which means that replication works well when replicas reside in different datacenters. (Note that duplicating data in different datacenters is the main goal of replication.) You can have any number of replicas of the same data. Yandex.Metrica uses double replication in production. Each server uses RAID-5 or RAID-6, and RAID-10 in some cases. This is a relatively reliable and convenient solution. The system monitors data synchronicity on replicas and is able to recover after a failure. Failover is automatic (for small differences in data) or semi-automatic (when data differs too much, which may indicate a configuration error).", + "title": "Data replication" + }, + { + "location": "/table_engines/replication/#creating-replicated-tables", + "text": "The Replicated prefix is added to the table engine name. For example: ReplicatedMergeTree . Two parameters are also added in the beginning of the parameters list \u2013 the path to the table in ZooKeeper, and the replica name in ZooKeeper. Example: ReplicatedMergeTree( /clickhouse/tables/{layer}-{shard}/hits , {replica} , EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID), EventTime), 8192) As the example shows, these parameters can contain substitutions in curly brackets. The substituted values are taken from the 'macros' section of the config file. Example: macros \n layer 05 /layer \n shard 02 /shard \n replica example05-02-1.yandex.ru /replica /macros The path to the table in ZooKeeper should be unique for each replicated table. Tables on different shards should have different paths.\nIn this case, the path consists of the following parts: /clickhouse/tables/ is the common prefix. We recommend using exactly this one. {layer}-{shard} is the shard identifier. In this example it consists of two parts, since the Yandex.Metrica cluster uses bi-level sharding. For most tasks, you can leave just the {shard} substitution, which will be expanded to the shard identifier. hits is the name of the node for the table in ZooKeeper. It is a good idea to make it the same as the table name. It is defined explicitly, because in contrast to the table name, it doesn't change after a RENAME query. The replica name identifies different replicas of the same table. You can use the server name for this, as in the example. The name only needs to be unique within each shard. You can define the parameters explicitly instead of using substitutions. This might be convenient for testing and for configuring small clusters. However, you can't use distributed DDL queries ( ON CLUSTER ) in this case. When working with large clusters, we recommend using substitutions because they reduce the probability of error. Run the CREATE TABLE query on each replica. This query creates a new replicated table, or adds a new replica to an existing one. If you add a new replica after the table already contains some data on other replicas, the data will be copied from the other replicas to the new one after running the query. In other words, the new replica syncs itself with the others. To delete a replica, run DROP TABLE . However, only one replica is deleted \u2013 the one that resides on the server where you run the query.", + "title": "Creating replicated tables" + }, + { + "location": "/table_engines/replication/#recovery-after-failures", + "text": "If ZooKeeper is unavailable when a server starts, replicated tables switch to read-only mode. The system periodically attempts to connect to ZooKeeper. If ZooKeeper is unavailable during an INSERT , or an error occurs when interacting with ZooKeeper, an exception is thrown. After connecting to ZooKeeper, the system checks whether the set of data in the local file system matches the expected set of data (ZooKeeper stores this information). If there are minor inconsistencies, the system resolves them by syncing data with the replicas. If the system detects broken data parts (with the wrong size of files) or unrecognized parts (parts written to the file system but not recorded in ZooKeeper), it moves them to the 'detached' subdirectory (they are not deleted). Any missing parts are copied from the replicas. Note that ClickHouse does not perform any destructive actions such as automatically deleting a large amount of data. When the server starts (or establishes a new session with ZooKeeper), it only checks the quantity and sizes of all files. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a SELECT query. The query throws an exception about a non-matching checksum or size of a compressed block. In this case, data parts are added to the verification queue and copied from the replicas if necessary. If the local set of data differs too much from the expected one, a safety mechanism is triggered. The server enters this in the log and refuses to launch. The reason for this is that this case may indicate a configuration error, such as if a replica on a shard was accidentally configured like a replica on a different shard. However, the thresholds for this mechanism are set fairly low, and this situation might occur during normal failure recovery. In this case, data is restored semi-automatically - by \"pushing a button\". To start recovery, create the node /path_to_table/replica_name/flags/force_restore_data in ZooKeeper with any content, or run the command to restore all replicated tables: sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data Then restart the server. On start, the server deletes these flags and starts recovery.", + "title": "Recovery after failures" + }, + { + "location": "/table_engines/replication/#recovery-after-complete-data-loss", + "text": "If all data and metadata disappeared from one of the servers, follow these steps for recovery: Install ClickHouse on the server. Define substitutions correctly in the config file that contains the shard identifier and replicas, if you use them. If you had unreplicated tables that must be manually duplicated on the servers, copy their data from a replica (in the directory /var/lib/clickhouse/data/db_name/table_name/ ). Copy table definitions located in /var/lib/clickhouse/metadata/ from a replica. If a shard or replica identifier is defined explicitly in the table definitions, correct it so that it corresponds to this replica. (Alternatively, start the server and make all the ATTACH TABLE queries that should have been in the .sql files in /var/lib/clickhouse/metadata/ .) To start recovery, create the ZooKeeper node /path_to_table/replica_name/flags/force_restore_data with any content, or run the command to restore all replicated tables: sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data Then start the server (restart, if it is already running). Data will be downloaded from replicas. An alternative recovery option is to delete information about the lost replica from ZooKeeper ( /path_to_table/replica_name ), then create the replica again as described in \" Creating replicatable tables \". There is no restriction on network bandwidth during recovery. Keep this in mind if you are restoring many replicas at once.", + "title": "Recovery after complete data loss" + }, + { + "location": "/table_engines/replication/#converting-from-mergetree-to-replicatedmergetree", + "text": "We use the term MergeTree to refer to all table engines in the MergeTree family , the same as for ReplicatedMergeTree . If you had a MergeTree table that was manually replicated, you can convert it to a replicatable table. You might need to do this if you have already collected a large amount of data in a MergeTree table and now you want to enable replication. If the data differs on various replicas, first sync it, or delete this data on all the replicas except one. Rename the existing MergeTree table, then create a ReplicatedMergeTree table with the old name.\nMove the data from the old table to the 'detached' subdirectory inside the directory with the new table data ( /var/lib/clickhouse/data/db_name/table_name/ ).\nThen run ALTER TABLE ATTACH PARTITION on one of the replicas to add these data parts to the working set.", + "title": "Converting from MergeTree to ReplicatedMergeTree" + }, + { + "location": "/table_engines/replication/#converting-from-replicatedmergetree-to-mergetree", + "text": "Create a MergeTree table with a different name. Move all the data from the directory with the ReplicatedMergeTree table data to the new table's data directory. Then delete the ReplicatedMergeTree table and restart the server. If you want to get rid of a ReplicatedMergeTree table without launching the server: Delete the corresponding .sql file in the metadata directory ( /var/lib/clickhouse/metadata/ ). Delete the corresponding path in ZooKeeper ( /path_to_table/replica_name ). After this, you can launch the server, create a MergeTree table, move the data to its directory, and then restart the server.", + "title": "Converting from ReplicatedMergeTree to MergeTree" + }, + { + "location": "/table_engines/replication/#recovery-when-metadata-in-the-zookeeper-cluster-is-lost-or-damaged", + "text": "If the data in ZooKeeper was lost or damaged, you can save data by moving it to an unreplicated table as described above. If exactly the same parts exist on the other replicas, they are added to the working set on them. If not, the parts are downloaded from the replica that has them.", + "title": "Recovery when metadata in the ZooKeeper cluster is lost or damaged" + }, + { + "location": "/table_engines/distributed/", + "text": "Distributed\n\n\nThe Distributed engine does not store data itself\n, but allows distributed query processing on multiple servers.\nReading is automatically parallelized. During a read, the table indexes on remote servers are used, if there are any.\nThe Distributed engine accepts parameters: the cluster name in the server's config file, the name of a remote database, the name of a remote table, and (optionally) a sharding key.\nExample:\n\n\nDistributed(logs, default, hits[, sharding_key])\n\n\n\n\n\nData will be read from all servers in the 'logs' cluster, from the default.hits table located on every server in the cluster.\nData is not only read, but is partially processed on the remote servers (to the extent that this is possible).\nFor example, for a query with GROUP BY, data will be aggregated on remote servers, and the intermediate states of aggregate functions will be sent to the requestor server. Then data will be further aggregated.\n\n\nInstead of the database name, you can use a constant expression that returns a string. For example: currentDatabase().\n\n\nlogs \u2013 The cluster name in the server's config file.\n\n\nClusters are set like this:\n\n\nremote_servers\n\n \nlogs\n\n \nshard\n\n \n!-- Optional. Shard weight when writing data. Default: 1. --\n\n \nweight\n1\n/weight\n\n \n!-- Optional. Whether to write data to just one of the replicas. Default: false (write data to all replicas). --\n\n \ninternal_replication\nfalse\n/internal_replication\n\n \nreplica\n\n \nhost\nexample01-01-1\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \nreplica\n\n \nhost\nexample01-01-2\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \n/shard\n\n \nshard\n\n \nweight\n2\n/weight\n\n \ninternal_replication\nfalse\n/internal_replication\n\n \nreplica\n\n \nhost\nexample01-02-1\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \nreplica\n\n \nhost\nexample01-02-2\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \n/shard\n\n \n/logs\n\n\n/remote_servers\n\n\n\n\n\n\nHere a cluster is defined with the name 'logs' that consists of two shards, each of which contains two replicas.\nShards refer to the servers that contain different parts of the data (in order to read all the data, you must access all the shards).\nReplicas are duplicating servers (in order to read all the data, you can access the data on any one of the replicas).\n\n\nThe parameters \nhost\n, \nport\n, and optionally \nuser\n and \npassword\n are specified for each server:\n\n\n: - \nhost\n \u2013 The address of the remote server. You can use either the domain or the IPv4 or IPv6 address. If you specify the domain, the server makes a DNS request when it starts, and the result is stored as long as the server is running. If the DNS request fails, the server doesn't start. If you change the DNS record, restart the server.\n- \nport\n\u2013 The TCP port for messenger activity ('tcp_port' in the config, usually set to 9000). Do not confuse it with http_port.\n- \nuser\n\u2013 Name of the user for connecting to a remote server. Default value: default. This user must have access to connect to the specified server. Access is configured in the users.xml file. For more information, see the section \"Access rights\".\n- \npassword\n \u2013 The password for connecting to a remote server (not masked). Default value: empty string.\n\n\nWhen specifying replicas, one of the available replicas will be selected for each of the shards when reading. You can configure the algorithm for load balancing (the preference for which replica to access) \u2013 see the 'load_balancing' setting.\nIf the connection with the server is not established, there will be an attempt to connect with a short timeout. If the connection failed, the next replica will be selected, and so on for all the replicas. If the connection attempt failed for all the replicas, the attempt will be repeated the same way, several times.\nThis works in favor of resiliency, but does not provide complete fault tolerance: a remote server might accept the connection, but might not work, or work poorly.\n\n\nYou can specify just one of the shards (in this case, query processing should be called remote, rather than distributed) or up to any number of shards. In each shard, you can specify from one to any number of replicas. You can specify a different number of replicas for each shard.\n\n\nYou can specify as many clusters as you wish in the configuration.\n\n\nTo view your clusters, use the 'system.clusters' table.\n\n\nThe Distributed engine allows working with a cluster like a local server. However, the cluster is inextensible: you must write its configuration in the server config file (even better, for all the cluster's servers).\n\n\nThere is no support for Distributed tables that look at other Distributed tables (except in cases when a Distributed table only has one shard). As an alternative, make the Distributed table look at the \"final\" tables.\n\n\nThe Distributed engine requires writing clusters to the config file. Clusters from the config file are updated on the fly, without restarting the server. If you need to send a query to an unknown set of shards and replicas each time, you don't need to create a Distributed table \u2013 use the 'remote' table function instead. See the section \"Table functions\".\n\n\nThere are two methods for writing data to a cluster:\n\n\nFirst, you can define which servers to write which data to, and perform the write directly on each shard. In other words, perform INSERT in the tables that the distributed table \"looks at\".\nThis is the most flexible solution \u2013 you can use any sharding scheme, which could be non-trivial due to the requirements of the subject area.\nThis is also the most optimal solution, since data can be written to different shards completely independently.\n\n\nSecond, you can perform INSERT in a Distributed table. In this case, the table will distribute the inserted data across servers itself.\nIn order to write to a Distributed table, it must have a sharding key set (the last parameter). In addition, if there is only one shard, the write operation works without specifying the sharding key, since it doesn't have any meaning in this case.\n\n\nEach shard can have a weight defined in the config file. By default, the weight is equal to one. Data is distributed across shards in the amount proportional to the shard weight. For example, if there are two shards and the first has a weight of 9 while the second has a weight of 10, the first will be sent 9 / 19 parts of the rows, and the second will be sent 10 / 19.\n\n\nEach shard can have the 'internal_replication' parameter defined in the config file.\n\n\nIf this parameter is set to 'true', the write operation selects the first healthy replica and writes data to it. Use this alternative if the Distributed table \"looks at\" replicated tables. In other words, if the table where data will be written is going to replicate them itself.\n\n\nIf it is set to 'false' (the default), data is written to all replicas. In essence, this means that the Distributed table replicates data itself. This is worse than using replicated tables, because the consistency of replicas is not checked, and over time they will contain slightly different data.\n\n\nTo select the shard that a row of data is sent to, the sharding expression is analyzed, and its remainder is taken from dividing it by the total weight of the shards. The row is sent to the shard that corresponds to the half-interval of the remainders from 'prev_weight' to 'prev_weights + weight', where 'prev_weights' is the total weight of the shards with the smallest number, and 'weight' is the weight of this shard. For example, if there are two shards, and the first has a weight of 9 while the second has a weight of 10, the row will be sent to the first shard for the remainders from the range [0, 9), and to the second for the remainders from the range [9, 19).\n\n\nThe sharding expression can be any expression from constants and table columns that returns an integer. For example, you can use the expression 'rand()' for random distribution of data, or 'UserID' for distribution by the remainder from dividing the user's ID (then the data of a single user will reside on a single shard, which simplifies running IN and JOIN by users). If one of the columns is not distributed evenly enough, you can wrap it in a hash function: intHash64(UserID).\n\n\nA simple remainder from division is a limited solution for sharding and isn't always appropriate. It works for medium and large volumes of data (dozens of servers), but not for very large volumes of data (hundreds of servers or more). In the latter case, use the sharding scheme required by the subject area, rather than using entries in Distributed tables.\n\n\nSELECT queries are sent to all the shards, and work regardless of how data is distributed across the shards (they can be distributed completely randomly). When you add a new shard, you don't have to transfer the old data to it. You can write new data with a heavier weight \u2013 the data will be distributed slightly unevenly, but queries will work correctly and efficiently.\n\n\nYou should be concerned about the sharding scheme in the following cases:\n\n\n\n\nQueries are used that require joining data (IN or JOIN) by a specific key. If data is sharded by this key, you can use local IN or JOIN instead of GLOBAL IN or GLOBAL JOIN, which is much more efficient.\n\n\nA large number of servers is used (hundreds or more) with a large number of small queries (queries of individual clients - websites, advertisers, or partners). In order for the small queries to not affect the entire cluster, it makes sense to locate data for a single client on a single shard. Alternatively, as we've done in Yandex.Metrica, you can set up bi-level sharding: divide the entire cluster into \"layers\", where a layer may consist of multiple shards. Data for a single client is located on a single layer, but shards can be added to a layer as necessary, and data is randomly distributed within them. Distributed tables are created for each layer, and a single shared distributed table is created for global queries.\n\n\n\n\nData is written asynchronously. For an INSERT to a Distributed table, the data block is just written to the local file system. The data is sent to the remote servers in the background as soon as possible. You should check whether data is sent successfully by checking the list of files (data waiting to be sent) in the table directory: /var/lib/clickhouse/data/database/table/.\n\n\nIf the server ceased to exist or had a rough restart (for example, after a device failure) after an INSERT to a Distributed table, the inserted data might be lost. If a damaged data part is detected in the table directory, it is transferred to the 'broken' subdirectory and no longer used.\n\n\nWhen the max_parallel_replicas option is enabled, query processing is parallelized across all replicas within a single shard. For more information, see the section \"Settings, max_parallel_replicas\".", + "title": "Distributed" + }, + { + "location": "/table_engines/distributed/#distributed", + "text": "The Distributed engine does not store data itself , but allows distributed query processing on multiple servers.\nReading is automatically parallelized. During a read, the table indexes on remote servers are used, if there are any.\nThe Distributed engine accepts parameters: the cluster name in the server's config file, the name of a remote database, the name of a remote table, and (optionally) a sharding key.\nExample: Distributed(logs, default, hits[, sharding_key]) Data will be read from all servers in the 'logs' cluster, from the default.hits table located on every server in the cluster.\nData is not only read, but is partially processed on the remote servers (to the extent that this is possible).\nFor example, for a query with GROUP BY, data will be aggregated on remote servers, and the intermediate states of aggregate functions will be sent to the requestor server. Then data will be further aggregated. Instead of the database name, you can use a constant expression that returns a string. For example: currentDatabase(). logs \u2013 The cluster name in the server's config file. Clusters are set like this: remote_servers \n logs \n shard \n !-- Optional. Shard weight when writing data. Default: 1. -- \n weight 1 /weight \n !-- Optional. Whether to write data to just one of the replicas. Default: false (write data to all replicas). -- \n internal_replication false /internal_replication \n replica \n host example01-01-1 /host \n port 9000 /port \n /replica \n replica \n host example01-01-2 /host \n port 9000 /port \n /replica \n /shard \n shard \n weight 2 /weight \n internal_replication false /internal_replication \n replica \n host example01-02-1 /host \n port 9000 /port \n /replica \n replica \n host example01-02-2 /host \n port 9000 /port \n /replica \n /shard \n /logs /remote_servers Here a cluster is defined with the name 'logs' that consists of two shards, each of which contains two replicas.\nShards refer to the servers that contain different parts of the data (in order to read all the data, you must access all the shards).\nReplicas are duplicating servers (in order to read all the data, you can access the data on any one of the replicas). The parameters host , port , and optionally user and password are specified for each server: : - host \u2013 The address of the remote server. You can use either the domain or the IPv4 or IPv6 address. If you specify the domain, the server makes a DNS request when it starts, and the result is stored as long as the server is running. If the DNS request fails, the server doesn't start. If you change the DNS record, restart the server.\n- port \u2013 The TCP port for messenger activity ('tcp_port' in the config, usually set to 9000). Do not confuse it with http_port.\n- user \u2013 Name of the user for connecting to a remote server. Default value: default. This user must have access to connect to the specified server. Access is configured in the users.xml file. For more information, see the section \"Access rights\".\n- password \u2013 The password for connecting to a remote server (not masked). Default value: empty string. When specifying replicas, one of the available replicas will be selected for each of the shards when reading. You can configure the algorithm for load balancing (the preference for which replica to access) \u2013 see the 'load_balancing' setting.\nIf the connection with the server is not established, there will be an attempt to connect with a short timeout. If the connection failed, the next replica will be selected, and so on for all the replicas. If the connection attempt failed for all the replicas, the attempt will be repeated the same way, several times.\nThis works in favor of resiliency, but does not provide complete fault tolerance: a remote server might accept the connection, but might not work, or work poorly. You can specify just one of the shards (in this case, query processing should be called remote, rather than distributed) or up to any number of shards. In each shard, you can specify from one to any number of replicas. You can specify a different number of replicas for each shard. You can specify as many clusters as you wish in the configuration. To view your clusters, use the 'system.clusters' table. The Distributed engine allows working with a cluster like a local server. However, the cluster is inextensible: you must write its configuration in the server config file (even better, for all the cluster's servers). There is no support for Distributed tables that look at other Distributed tables (except in cases when a Distributed table only has one shard). As an alternative, make the Distributed table look at the \"final\" tables. The Distributed engine requires writing clusters to the config file. Clusters from the config file are updated on the fly, without restarting the server. If you need to send a query to an unknown set of shards and replicas each time, you don't need to create a Distributed table \u2013 use the 'remote' table function instead. See the section \"Table functions\". There are two methods for writing data to a cluster: First, you can define which servers to write which data to, and perform the write directly on each shard. In other words, perform INSERT in the tables that the distributed table \"looks at\".\nThis is the most flexible solution \u2013 you can use any sharding scheme, which could be non-trivial due to the requirements of the subject area.\nThis is also the most optimal solution, since data can be written to different shards completely independently. Second, you can perform INSERT in a Distributed table. In this case, the table will distribute the inserted data across servers itself.\nIn order to write to a Distributed table, it must have a sharding key set (the last parameter). In addition, if there is only one shard, the write operation works without specifying the sharding key, since it doesn't have any meaning in this case. Each shard can have a weight defined in the config file. By default, the weight is equal to one. Data is distributed across shards in the amount proportional to the shard weight. For example, if there are two shards and the first has a weight of 9 while the second has a weight of 10, the first will be sent 9 / 19 parts of the rows, and the second will be sent 10 / 19. Each shard can have the 'internal_replication' parameter defined in the config file. If this parameter is set to 'true', the write operation selects the first healthy replica and writes data to it. Use this alternative if the Distributed table \"looks at\" replicated tables. In other words, if the table where data will be written is going to replicate them itself. If it is set to 'false' (the default), data is written to all replicas. In essence, this means that the Distributed table replicates data itself. This is worse than using replicated tables, because the consistency of replicas is not checked, and over time they will contain slightly different data. To select the shard that a row of data is sent to, the sharding expression is analyzed, and its remainder is taken from dividing it by the total weight of the shards. The row is sent to the shard that corresponds to the half-interval of the remainders from 'prev_weight' to 'prev_weights + weight', where 'prev_weights' is the total weight of the shards with the smallest number, and 'weight' is the weight of this shard. For example, if there are two shards, and the first has a weight of 9 while the second has a weight of 10, the row will be sent to the first shard for the remainders from the range [0, 9), and to the second for the remainders from the range [9, 19). The sharding expression can be any expression from constants and table columns that returns an integer. For example, you can use the expression 'rand()' for random distribution of data, or 'UserID' for distribution by the remainder from dividing the user's ID (then the data of a single user will reside on a single shard, which simplifies running IN and JOIN by users). If one of the columns is not distributed evenly enough, you can wrap it in a hash function: intHash64(UserID). A simple remainder from division is a limited solution for sharding and isn't always appropriate. It works for medium and large volumes of data (dozens of servers), but not for very large volumes of data (hundreds of servers or more). In the latter case, use the sharding scheme required by the subject area, rather than using entries in Distributed tables. SELECT queries are sent to all the shards, and work regardless of how data is distributed across the shards (they can be distributed completely randomly). When you add a new shard, you don't have to transfer the old data to it. You can write new data with a heavier weight \u2013 the data will be distributed slightly unevenly, but queries will work correctly and efficiently. You should be concerned about the sharding scheme in the following cases: Queries are used that require joining data (IN or JOIN) by a specific key. If data is sharded by this key, you can use local IN or JOIN instead of GLOBAL IN or GLOBAL JOIN, which is much more efficient. A large number of servers is used (hundreds or more) with a large number of small queries (queries of individual clients - websites, advertisers, or partners). In order for the small queries to not affect the entire cluster, it makes sense to locate data for a single client on a single shard. Alternatively, as we've done in Yandex.Metrica, you can set up bi-level sharding: divide the entire cluster into \"layers\", where a layer may consist of multiple shards. Data for a single client is located on a single layer, but shards can be added to a layer as necessary, and data is randomly distributed within them. Distributed tables are created for each layer, and a single shared distributed table is created for global queries. Data is written asynchronously. For an INSERT to a Distributed table, the data block is just written to the local file system. The data is sent to the remote servers in the background as soon as possible. You should check whether data is sent successfully by checking the list of files (data waiting to be sent) in the table directory: /var/lib/clickhouse/data/database/table/. If the server ceased to exist or had a rough restart (for example, after a device failure) after an INSERT to a Distributed table, the inserted data might be lost. If a damaged data part is detected in the table directory, it is transferred to the 'broken' subdirectory and no longer used. When the max_parallel_replicas option is enabled, query processing is parallelized across all replicas within a single shard. For more information, see the section \"Settings, max_parallel_replicas\".", + "title": "Distributed" + }, + { + "location": "/table_engines/dictionary/", + "text": "Dictionary\n\n\nThe \nDictionary\n engine displays the dictionary data as a ClickHouse table.\n\n\nAs an example, consider a dictionary of \nproducts\n with the following configuration:\n\n\ndictionaries\n\n\ndictionary\n\n \nname\nproducts\n/name\n\n \nsource\n\n \nodbc\n\n \ntable\nproducts\n/table\n\n \nconnection_string\nDSN=some-db-server\n/connection_string\n\n \n/odbc\n\n \n/source\n\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n \nlayout\n\n \nflat/\n\n \n/layout\n\n \nstructure\n\n \nid\n\n \nname\nproduct_id\n/name\n\n \n/id\n\n \nattribute\n\n \nname\ntitle\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n/null_value\n\n \n/attribute\n\n \n/structure\n\n\n/dictionary\n\n\n/dictionaries\n\n\n\n\n\n\nQuery the dictionary data:\n\n\nselect\n \nname\n,\n \ntype\n,\n \nkey\n,\n \nattribute\n.\nnames\n,\n \nattribute\n.\ntypes\n,\n \nbytes_allocated\n,\n \nelement_count\n,\nsource\n \nfrom\n \nsystem\n.\ndictionaries\n \nwhere\n \nname\n \n=\n \nproducts\n;\n \n\n\nSELECT\n\n \nname\n,\n\n \ntype\n,\n\n \nkey\n,\n\n \nattribute\n.\nnames\n,\n\n \nattribute\n.\ntypes\n,\n\n \nbytes_allocated\n,\n\n \nelement_count\n,\n\n \nsource\n\n\nFROM\n \nsystem\n.\ndictionaries\n\n\nWHERE\n \nname\n \n=\n \nproducts\n\n\n\n\n\n\n\u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u252c\u2500type\u2500\u252c\u2500key\u2500\u2500\u2500\u2500\u252c\u2500attribute.names\u2500\u252c\u2500attribute.types\u2500\u252c\u2500bytes_allocated\u2500\u252c\u2500element_count\u2500\u252c\u2500source\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 products \u2502 Flat \u2502 UInt64 \u2502 [\ntitle\n] \u2502 [\nString\n] \u2502 23065376 \u2502 175032 \u2502 ODBC: .products \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nYou can use the \ndictGet*\n function to get the dictionary data in this format.\n\n\nThis view isn't helpful when you need to get raw data, or when performing a \nJOIN\n operation. For these cases, you can use the \nDictionary\n engine, which displays the dictionary data in a table.\n\n\nSyntax:\n\n\nCREATE TABLE %table_name% (%fields%) engine = Dictionary(%dictionary_name%)`\n\n\n\n\n\nUsage example:\n\n\ncreate\n \ntable\n \nproducts\n \n(\nproduct_id\n \nUInt64\n,\n \ntitle\n \nString\n)\n \nEngine\n \n=\n \nDictionary\n(\nproducts\n);\n\n\n\nCREATE\n \nTABLE\n \nproducts\n\n\n(\n\n \nproduct_id\n \nUInt64\n,\n\n \ntitle\n \nString\n,\n\n\n)\n\n\nENGINE\n \n=\n \nDictionary\n(\nproducts\n)\n\n\n\n\n\n\nOk.\n\n0 rows in set. Elapsed: 0.004 sec.\n\n\n\n\n\nTake a look at what's in the table.\n\n\nselect\n \n*\n \nfrom\n \nproducts\n \nlimit\n \n1\n;\n\n\n\nSELECT\n \n*\n\n\nFROM\n \nproducts\n\n\nLIMIT\n \n1\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500product_id\u2500\u252c\u2500title\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 152689 \u2502 Some item \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n1 rows in set. Elapsed: 0.006 sec.", + "title": "Dictionary" + }, + { + "location": "/table_engines/dictionary/#dictionary", + "text": "The Dictionary engine displays the dictionary data as a ClickHouse table. As an example, consider a dictionary of products with the following configuration: dictionaries dictionary \n name products /name \n source \n odbc \n table products /table \n connection_string DSN=some-db-server /connection_string \n /odbc \n /source \n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n layout \n flat/ \n /layout \n structure \n id \n name product_id /name \n /id \n attribute \n name title /name \n type String /type \n null_value /null_value \n /attribute \n /structure /dictionary /dictionaries Query the dictionary data: select name , type , key , attribute . names , attribute . types , bytes_allocated , element_count , source from system . dictionaries where name = products ; SELECT \n name , \n type , \n key , \n attribute . names , \n attribute . types , \n bytes_allocated , \n element_count , \n source FROM system . dictionaries WHERE name = products \u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u252c\u2500type\u2500\u252c\u2500key\u2500\u2500\u2500\u2500\u252c\u2500attribute.names\u2500\u252c\u2500attribute.types\u2500\u252c\u2500bytes_allocated\u2500\u252c\u2500element_count\u2500\u252c\u2500source\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 products \u2502 Flat \u2502 UInt64 \u2502 [ title ] \u2502 [ String ] \u2502 23065376 \u2502 175032 \u2502 ODBC: .products \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 You can use the dictGet* function to get the dictionary data in this format. This view isn't helpful when you need to get raw data, or when performing a JOIN operation. For these cases, you can use the Dictionary engine, which displays the dictionary data in a table. Syntax: CREATE TABLE %table_name% (%fields%) engine = Dictionary(%dictionary_name%)` Usage example: create table products ( product_id UInt64 , title String ) Engine = Dictionary ( products ); CREATE TABLE products ( \n product_id UInt64 , \n title String , ) ENGINE = Dictionary ( products ) Ok.\n\n0 rows in set. Elapsed: 0.004 sec. Take a look at what's in the table. select * from products limit 1 ; SELECT * FROM products LIMIT 1 \u250c\u2500\u2500\u2500\u2500product_id\u2500\u252c\u2500title\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 152689 \u2502 Some item \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n1 rows in set. Elapsed: 0.006 sec.", + "title": "Dictionary" + }, + { + "location": "/table_engines/merge/", + "text": "Merge\n\n\nThe Merge engine (not to be confused with \nMergeTree\n) does not store data itself, but allows reading from any number of other tables simultaneously.\nReading is automatically parallelized. Writing to a table is not supported. When reading, the indexes of tables that are actually being read are used, if they exist.\nThe Merge engine accepts parameters: the database name and a regular expression for tables.\n\n\nExample:\n\n\nMerge(hits, \n^WatchLog\n)\n\n\n\n\n\nData will be read from the tables in the 'hits' database that have names that match the regular expression '\n^WatchLog\n'.\n\n\nInstead of the database name, you can use a constant expression that returns a string. For example, \ncurrentDatabase()\n.\n\n\nRegular expressions \u2014 \nre2\n (supports a subset of PCRE), case-sensitive.\nSee the notes about escaping symbols in regular expressions in the \"match\" section.\n\n\nWhen selecting tables to read, the Merge table itself will not be selected, even if it matches the regex. This is to avoid loops.\nIt is possible to create two Merge tables that will endlessly try to read each others' data, but this is not a good idea.\n\n\nThe typical way to use the Merge engine is for working with a large number of TinyLog tables as if with a single table.\n\n\nVirtual columns\n\n\nVirtual columns are columns that are provided by the table engine, regardless of the table definition. In other words, these columns are not specified in CREATE TABLE, but they are accessible for SELECT.\n\n\nVirtual columns differ from normal columns in the following ways:\n\n\n\n\nThey are not specified in table definitions.\n\n\nData can't be added to them with INSERT.\n\n\nWhen using INSERT without specifying the list of columns, virtual columns are ignored.\n\n\nThey are not selected when using the asterisk (\nSELECT *\n).\n\n\nVirtual columns are not shown in \nSHOW CREATE TABLE\n and \nDESC TABLE\n queries.\n\n\n\n\nA Merge type table contains a virtual _table column with the String type. (If the table already has a _table column, the virtual column is named _table1, and if it already has _table1, it is named _table2, and so on.) It contains the name of the table that data was read from.\n\n\nIf the WHERE or PREWHERE clause contains conditions for the '_table' column that do not depend on other table columns (as one of the conjunction elements, or as an entire expression), these conditions are used as an index. The conditions are performed on a data set of table names to read data from, and the read operation will be performed from only those tables that the condition was triggered on.", + "title": "Merge" + }, + { + "location": "/table_engines/merge/#merge", + "text": "The Merge engine (not to be confused with MergeTree ) does not store data itself, but allows reading from any number of other tables simultaneously.\nReading is automatically parallelized. Writing to a table is not supported. When reading, the indexes of tables that are actually being read are used, if they exist.\nThe Merge engine accepts parameters: the database name and a regular expression for tables. Example: Merge(hits, ^WatchLog ) Data will be read from the tables in the 'hits' database that have names that match the regular expression ' ^WatchLog '. Instead of the database name, you can use a constant expression that returns a string. For example, currentDatabase() . Regular expressions \u2014 re2 (supports a subset of PCRE), case-sensitive.\nSee the notes about escaping symbols in regular expressions in the \"match\" section. When selecting tables to read, the Merge table itself will not be selected, even if it matches the regex. This is to avoid loops.\nIt is possible to create two Merge tables that will endlessly try to read each others' data, but this is not a good idea. The typical way to use the Merge engine is for working with a large number of TinyLog tables as if with a single table.", + "title": "Merge" + }, + { + "location": "/table_engines/merge/#virtual-columns", + "text": "Virtual columns are columns that are provided by the table engine, regardless of the table definition. In other words, these columns are not specified in CREATE TABLE, but they are accessible for SELECT. Virtual columns differ from normal columns in the following ways: They are not specified in table definitions. Data can't be added to them with INSERT. When using INSERT without specifying the list of columns, virtual columns are ignored. They are not selected when using the asterisk ( SELECT * ). Virtual columns are not shown in SHOW CREATE TABLE and DESC TABLE queries. A Merge type table contains a virtual _table column with the String type. (If the table already has a _table column, the virtual column is named _table1, and if it already has _table1, it is named _table2, and so on.) It contains the name of the table that data was read from. If the WHERE or PREWHERE clause contains conditions for the '_table' column that do not depend on other table columns (as one of the conjunction elements, or as an entire expression), these conditions are used as an index. The conditions are performed on a data set of table names to read data from, and the read operation will be performed from only those tables that the condition was triggered on.", + "title": "Virtual columns" + }, + { + "location": "/table_engines/buffer/", + "text": "Buffer\n\n\nBuffers the data to write in RAM, periodically flushing it to another table. During the read operation, data is read from the buffer and the other table simultaneously.\n\n\nBuffer(database, table, num_layers, min_time, max_time, min_rows, max_rows, min_bytes, max_bytes)\n\n\n\n\n\nEngine parameters:database, table \u2013 The table to flush data to. Instead of the database name, you can use a constant expression that returns a string.num_layers \u2013 Parallelism layer. Physically, the table will be represented as 'num_layers' of independent buffers. Recommended value: 16.min_time, max_time, min_rows, max_rows, min_bytes, and max_bytes are conditions for flushing data from the buffer.\n\n\nData is flushed from the buffer and written to the destination table if all the 'min' conditions or at least one 'max' condition are met.min_time, max_time \u2013 Condition for the time in seconds from the moment of the first write to the buffer.min_rows, max_rows \u2013 Condition for the number of rows in the buffer.min_bytes, max_bytes \u2013 Condition for the number of bytes in the buffer.\n\n\nDuring the write operation, data is inserted to a 'num_layers' number of random buffers. Or, if the data part to insert is large enough (greater than 'max_rows' or 'max_bytes'), it is written directly to the destination table, omitting the buffer.\n\n\nThe conditions for flushing the data are calculated separately for each of the 'num_layers' buffers. For example, if num_layers = 16 and max_bytes = 100000000, the maximum RAM consumption is 1.6 GB.\n\n\nExample:\n\n\nCREATE\n \nTABLE\n \nmerge\n.\nhits_buffer\n \nAS\n \nmerge\n.\nhits\n \nENGINE\n \n=\n \nBuffer\n(\nmerge\n,\n \nhits\n,\n \n16\n,\n \n10\n,\n \n100\n,\n \n10000\n,\n \n1000000\n,\n \n10000000\n,\n \n100000000\n)\n\n\n\n\n\n\nCreating a 'merge.hits_buffer' table with the same structure as 'merge.hits' and using the Buffer engine. When writing to this table, data is buffered in RAM and later written to the 'merge.hits' table. 16 buffers are created. The data in each of them is flushed if either 100 seconds have passed, or one million rows have been written, or 100 MB of data have been written; or if simultaneously 10 seconds have passed and 10,000 rows and 10 MB of data have been written. For example, if just one row has been written, after 100 seconds it will be flushed, no matter what. But if many rows have been written, the data will be flushed sooner.\n\n\nWhen the server is stopped, with DROP TABLE or DETACH TABLE, buffer data is also flushed to the destination table.\n\n\nYou can set empty strings in single quotation marks for the database and table name. This indicates the absence of a destination table. In this case, when the data flush conditions are reached, the buffer is simply cleared. This may be useful for keeping a window of data in memory.\n\n\nWhen reading from a Buffer table, data is processed both from the buffer and from the destination table (if there is one).\nNote that the Buffer tables does not support an index. In other words, data in the buffer is fully scanned, which might be slow for large buffers. (For data in a subordinate table, the index that it supports will be used.)\n\n\nIf the set of columns in the Buffer table doesn't match the set of columns in a subordinate table, a subset of columns that exist in both tables is inserted.\n\n\nIf the types don't match for one of the columns in the Buffer table and a subordinate table, an error message is entered in the server log and the buffer is cleared.\nThe same thing happens if the subordinate table doesn't exist when the buffer is flushed.\n\n\nIf you need to run ALTER for a subordinate table and the Buffer table, we recommend first deleting the Buffer table, running ALTER for the subordinate table, then creating the Buffer table again.\n\n\nIf the server is restarted abnormally, the data in the buffer is lost.\n\n\nPREWHERE, FINAL and SAMPLE do not work correctly for Buffer tables. These conditions are passed to the destination table, but are not used for processing data in the buffer. Because of this, we recommend only using the Buffer table for writing, while reading from the destination table.\n\n\nWhen adding data to a Buffer, one of the buffers is locked. This causes delays if a read operation is simultaneously being performed from the table.\n\n\nData that is inserted to a Buffer table may end up in the subordinate table in a different order and in different blocks. Because of this, a Buffer table is difficult to use for writing to a CollapsingMergeTree correctly. To avoid problems, you can set 'num_layers' to 1.\n\n\nIf the destination table is replicated, some expected characteristics of replicated tables are lost when writing to a Buffer table. The random changes to the order of rows and sizes of data parts cause data deduplication to quit working, which means it is not possible to have a reliable 'exactly once' write to replicated tables.\n\n\nDue to these disadvantages, we can only recommend using a Buffer table in rare cases.\n\n\nA Buffer table is used when too many INSERTs are received from a large number of servers over a unit of time and data can't be buffered before insertion, which means the INSERTs can't run fast enough.\n\n\nNote that it doesn't make sense to insert data one row at a time, even for Buffer tables. This will only produce a speed of a few thousand rows per second, while inserting larger blocks of data can produce over a million rows per second (see the section \"Performance\").", + "title": "Buffer" + }, + { + "location": "/table_engines/buffer/#buffer", + "text": "Buffers the data to write in RAM, periodically flushing it to another table. During the read operation, data is read from the buffer and the other table simultaneously. Buffer(database, table, num_layers, min_time, max_time, min_rows, max_rows, min_bytes, max_bytes) Engine parameters:database, table \u2013 The table to flush data to. Instead of the database name, you can use a constant expression that returns a string.num_layers \u2013 Parallelism layer. Physically, the table will be represented as 'num_layers' of independent buffers. Recommended value: 16.min_time, max_time, min_rows, max_rows, min_bytes, and max_bytes are conditions for flushing data from the buffer. Data is flushed from the buffer and written to the destination table if all the 'min' conditions or at least one 'max' condition are met.min_time, max_time \u2013 Condition for the time in seconds from the moment of the first write to the buffer.min_rows, max_rows \u2013 Condition for the number of rows in the buffer.min_bytes, max_bytes \u2013 Condition for the number of bytes in the buffer. During the write operation, data is inserted to a 'num_layers' number of random buffers. Or, if the data part to insert is large enough (greater than 'max_rows' or 'max_bytes'), it is written directly to the destination table, omitting the buffer. The conditions for flushing the data are calculated separately for each of the 'num_layers' buffers. For example, if num_layers = 16 and max_bytes = 100000000, the maximum RAM consumption is 1.6 GB. Example: CREATE TABLE merge . hits_buffer AS merge . hits ENGINE = Buffer ( merge , hits , 16 , 10 , 100 , 10000 , 1000000 , 10000000 , 100000000 ) Creating a 'merge.hits_buffer' table with the same structure as 'merge.hits' and using the Buffer engine. When writing to this table, data is buffered in RAM and later written to the 'merge.hits' table. 16 buffers are created. The data in each of them is flushed if either 100 seconds have passed, or one million rows have been written, or 100 MB of data have been written; or if simultaneously 10 seconds have passed and 10,000 rows and 10 MB of data have been written. For example, if just one row has been written, after 100 seconds it will be flushed, no matter what. But if many rows have been written, the data will be flushed sooner. When the server is stopped, with DROP TABLE or DETACH TABLE, buffer data is also flushed to the destination table. You can set empty strings in single quotation marks for the database and table name. This indicates the absence of a destination table. In this case, when the data flush conditions are reached, the buffer is simply cleared. This may be useful for keeping a window of data in memory. When reading from a Buffer table, data is processed both from the buffer and from the destination table (if there is one).\nNote that the Buffer tables does not support an index. In other words, data in the buffer is fully scanned, which might be slow for large buffers. (For data in a subordinate table, the index that it supports will be used.) If the set of columns in the Buffer table doesn't match the set of columns in a subordinate table, a subset of columns that exist in both tables is inserted. If the types don't match for one of the columns in the Buffer table and a subordinate table, an error message is entered in the server log and the buffer is cleared.\nThe same thing happens if the subordinate table doesn't exist when the buffer is flushed. If you need to run ALTER for a subordinate table and the Buffer table, we recommend first deleting the Buffer table, running ALTER for the subordinate table, then creating the Buffer table again. If the server is restarted abnormally, the data in the buffer is lost. PREWHERE, FINAL and SAMPLE do not work correctly for Buffer tables. These conditions are passed to the destination table, but are not used for processing data in the buffer. Because of this, we recommend only using the Buffer table for writing, while reading from the destination table. When adding data to a Buffer, one of the buffers is locked. This causes delays if a read operation is simultaneously being performed from the table. Data that is inserted to a Buffer table may end up in the subordinate table in a different order and in different blocks. Because of this, a Buffer table is difficult to use for writing to a CollapsingMergeTree correctly. To avoid problems, you can set 'num_layers' to 1. If the destination table is replicated, some expected characteristics of replicated tables are lost when writing to a Buffer table. The random changes to the order of rows and sizes of data parts cause data deduplication to quit working, which means it is not possible to have a reliable 'exactly once' write to replicated tables. Due to these disadvantages, we can only recommend using a Buffer table in rare cases. A Buffer table is used when too many INSERTs are received from a large number of servers over a unit of time and data can't be buffered before insertion, which means the INSERTs can't run fast enough. Note that it doesn't make sense to insert data one row at a time, even for Buffer tables. This will only produce a speed of a few thousand rows per second, while inserting larger blocks of data can produce over a million rows per second (see the section \"Performance\").", + "title": "Buffer" + }, + { + "location": "/table_engines/file/", + "text": "File(InputFormat)\n\n\nThe data source is a file that stores data in one of the supported input formats (TabSeparated, Native, etc.).", + "title": "File" + }, + { + "location": "/table_engines/file/#fileinputformat", + "text": "The data source is a file that stores data in one of the supported input formats (TabSeparated, Native, etc.).", + "title": "File(InputFormat)" + }, + { + "location": "/table_engines/null/", + "text": "Null\n\n\nWhen writing to a Null table, data is ignored. When reading from a Null table, the response is empty.\n\n\nHowever, you can create a materialized view on a Null table. So the data written to the table will end up in the view.", + "title": "Null" + }, + { + "location": "/table_engines/null/#null", + "text": "When writing to a Null table, data is ignored. When reading from a Null table, the response is empty. However, you can create a materialized view on a Null table. So the data written to the table will end up in the view.", + "title": "Null" + }, + { + "location": "/table_engines/set/", + "text": "Set\n\n\nA data set that is always in RAM. It is intended for use on the right side of the IN operator (see the section \"IN operators\").\n\n\nYou can use INSERT to insert data in the table. New elements will be added to the data set, while duplicates will be ignored.\nBut you can't perform SELECT from the table. The only way to retrieve data is by using it in the right half of the IN operator.\n\n\nData is always located in RAM. For INSERT, the blocks of inserted data are also written to the directory of tables on the disk. When starting the server, this data is loaded to RAM. In other words, after restarting, the data remains in place.\n\n\nFor a rough server restart, the block of data on the disk might be lost or damaged. In the latter case, you may need to manually delete the file with damaged data.", + "title": "Set" + }, + { + "location": "/table_engines/set/#set", + "text": "A data set that is always in RAM. It is intended for use on the right side of the IN operator (see the section \"IN operators\"). You can use INSERT to insert data in the table. New elements will be added to the data set, while duplicates will be ignored.\nBut you can't perform SELECT from the table. The only way to retrieve data is by using it in the right half of the IN operator. Data is always located in RAM. For INSERT, the blocks of inserted data are also written to the directory of tables on the disk. When starting the server, this data is loaded to RAM. In other words, after restarting, the data remains in place. For a rough server restart, the block of data on the disk might be lost or damaged. In the latter case, you may need to manually delete the file with damaged data.", + "title": "Set" + }, + { + "location": "/table_engines/join/", + "text": "Join\n\n\nA prepared data structure for JOIN that is always located in RAM.\n\n\nJoin(ANY|ALL, LEFT|INNER, k1[, k2, ...])\n\n\n\n\n\nEngine parameters: \nANY|ALL\n \u2013 strictness; \nLEFT|INNER\n \u2013 type.\nThese parameters are set without quotes and must match the JOIN that the table will be used for. k1, k2, ... are the key columns from the USING clause that the join will be made on.\n\n\nThe table can't be used for GLOBAL JOINs.\n\n\nYou can use INSERT to add data to the table, similar to the Set engine. For ANY, data for duplicated keys will be ignored. For ALL, it will be counted. You can't perform SELECT directly from the table. The only way to retrieve data is to use it as the \"right-hand\" table for JOIN.\n\n\nStoring data on the disk is the same as for the Set engine.", + "title": "Join" + }, + { + "location": "/table_engines/join/#join", + "text": "A prepared data structure for JOIN that is always located in RAM. Join(ANY|ALL, LEFT|INNER, k1[, k2, ...]) Engine parameters: ANY|ALL \u2013 strictness; LEFT|INNER \u2013 type.\nThese parameters are set without quotes and must match the JOIN that the table will be used for. k1, k2, ... are the key columns from the USING clause that the join will be made on. The table can't be used for GLOBAL JOINs. You can use INSERT to add data to the table, similar to the Set engine. For ANY, data for duplicated keys will be ignored. For ALL, it will be counted. You can't perform SELECT directly from the table. The only way to retrieve data is to use it as the \"right-hand\" table for JOIN. Storing data on the disk is the same as for the Set engine.", + "title": "Join" + }, + { + "location": "/table_engines/view/", + "text": "View\n\n\nUsed for implementing views (for more information, see the \nCREATE VIEW query\n). It does not store data, but only stores the specified \nSELECT\n query. When reading from a table, it runs this query (and deletes all unnecessary columns from the query).", + "title": "View" + }, + { + "location": "/table_engines/view/#view", + "text": "Used for implementing views (for more information, see the CREATE VIEW query ). It does not store data, but only stores the specified SELECT query. When reading from a table, it runs this query (and deletes all unnecessary columns from the query).", + "title": "View" + }, + { + "location": "/table_engines/materializedview/", + "text": "MaterializedView\n\n\nUsed for implementing materialized views (for more information, see the \nCREATE TABLE\n) query. For storing data, it uses a different engine that was specified when creating the view. When reading from a table, it just uses this engine.", + "title": "MaterializedView" + }, + { + "location": "/table_engines/materializedview/#materializedview", + "text": "Used for implementing materialized views (for more information, see the CREATE TABLE ) query. For storing data, it uses a different engine that was specified when creating the view. When reading from a table, it just uses this engine.", + "title": "MaterializedView" + }, + { + "location": "/table_engines/kafka/", + "text": "Kafka\n\n\nThis engine works with \nApache Kafka\n.\n\n\nKafka lets you:\n\n\n\n\nPublish or subscribe to data flows.\n\n\nOrganize fault-tolerant storage.\n\n\nProcess streams as they become available.\n\n\n\n\nKafka(broker_list, topic_list, group_name, format[, schema, num_consumers])\n\n\n\n\n\nParameters:\n\n\n\n\nbroker_list\n \u2013 A comma-separated list of brokers (\nlocalhost:9092\n).\n\n\ntopic_list\n \u2013 A list of Kafka topics (\nmy_topic\n).\n\n\ngroup_name\n \u2013 A group of Kafka consumers (\ngroup1\n). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere.\n\n\n--format\n \u2013 Message format. Uses the same notation as the SQL \nFORMAT\n function, such as \nJSONEachRow\n. For more information, see the \"Formats\" section.\n\n\nschema\n \u2013 An optional parameter that must be used if the format requires a schema definition. For example, \nCap'n Proto\n requires the path to the schema file and the name of the root \nschema.capnp:Message\n object.\n\n\nnum_consumers\n \u2013 The number of consumers per table. Default: \n1\n. Specify more consumers if the throughput of one consumer is insufficient. The total number of consumers should not exceed the number of partitions in the topic, since only one consumer can be assigned per partition.\n\n\n\n\nExample:\n\n\n \nCREATE\n \nTABLE\n \nqueue\n \n(\n\n \ntimestamp\n \nUInt64\n,\n\n \nlevel\n \nString\n,\n\n \nmessage\n \nString\n\n \n)\n \nENGINE\n \n=\n \nKafka\n(\nlocalhost:9092\n,\n \ntopic\n,\n \ngroup1\n,\n \nJSONEachRow\n);\n\n\n \nSELECT\n \n*\n \nFROM\n \nqueue\n \nLIMIT\n \n5\n;\n\n\n\n\n\n\nThe delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name.\n\n\nGroups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. Read more about this at \nhttp://kafka.apache.org/intro\n.\n\n\nSELECT\n is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. To do this:\n\n\n\n\nUse the engine to create a Kafka consumer and consider it a data stream.\n\n\nCreate a table with the desired structure.\n\n\nCreate a materialized view that converts data from the engine and puts it into a previously created table.\n\n\n\n\nWhen the \nMATERIALIZED VIEW\n joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using \nSELECT\n\n\nExample:\n\n\n \nCREATE\n \nTABLE\n \nqueue\n \n(\n\n \ntimestamp\n \nUInt64\n,\n\n \nlevel\n \nString\n,\n\n \nmessage\n \nString\n\n \n)\n \nENGINE\n \n=\n \nKafka\n(\nlocalhost:9092\n,\n \ntopic\n,\n \ngroup1\n,\n \nJSONEachRow\n);\n\n\n \nCREATE\n \nTABLE\n \ndaily\n \n(\n\n \nday\n \nDate\n,\n\n \nlevel\n \nString\n,\n\n \ntotal\n \nUInt64\n\n \n)\n \nENGINE\n \n=\n \nSummingMergeTree\n(\nday\n,\n \n(\nday\n,\n \nlevel\n),\n \n8192\n);\n\n\n \nCREATE\n \nMATERIALIZED\n \nVIEW\n \nconsumer\n \nTO\n \ndaily\n\n \nAS\n \nSELECT\n \ntoDate\n(\ntoDateTime\n(\ntimestamp\n))\n \nAS\n \nday\n,\n \nlevel\n,\n \ncount\n()\n \nas\n \ntotal\n\n \nFROM\n \nqueue\n \nGROUP\n \nBY\n \nday\n,\n \nlevel\n;\n\n\n \nSELECT\n \nlevel\n,\n \nsum\n(\ntotal\n)\n \nFROM\n \ndaily\n \nGROUP\n \nBY\n \nlevel\n;\n\n\n\n\n\n\nTo improve performance, received messages are grouped into blocks the size of \nmax_insert_block_size\n. If the block wasn't formed within \nstream_flush_interval_ms\n milliseconds, the data will be flushed to the table regardless of the completeness of the block.\n\n\nTo stop receiving topic data or to change the conversion logic, detach the materialized view:\n\n\n DETACH TABLE consumer;\n ATTACH MATERIALIZED VIEW consumer;\n\n\n\n\n\nIf you want to change the target table by using \nALTER\nmaterialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view.\n\n\nConfiguration\n\n\nSimilar to GraphiteMergeTree, the Kafka engine supports extended configuration using the ClickHouse config file. There are two configuration keys that you can use: global (\nkafka\n) and topic-level (\nkafka_topic_*\n). The global configuration is applied first, and the topic-level configuration is second (if it exists).\n\n\n \n!-- Global configuration options for all tables of Kafka engine type --\n\n \nkafka\n\n \ndebug\ncgrp\n/debug\n\n \nauto_offset_reset\nsmallest\n/auto_offset_reset\n\n \n/kafka\n\n\n \n!-- Configuration specific for topic \nlogs\n --\n\n \nkafka_topic_logs\n\n \nretry_backoff_ms\n250\n/retry_backoff_ms\n\n \nfetch_min_bytes\n100000\n/fetch_min_bytes\n\n \n/kafka_topic_logs\n\n\n\n\n\n\nFor a list of possible configuration options, see the \nlibrdkafka configuration reference\n. Use the underscore (\n_\n) instead of a dot in the ClickHouse configuration. For example, \ncheck.crcs=true\n will be \ncheck_crcs\ntrue\n/check_crcs\n.", + "title": "Kafka" + }, + { + "location": "/table_engines/kafka/#kafka", + "text": "This engine works with Apache Kafka . Kafka lets you: Publish or subscribe to data flows. Organize fault-tolerant storage. Process streams as they become available. Kafka(broker_list, topic_list, group_name, format[, schema, num_consumers]) Parameters: broker_list \u2013 A comma-separated list of brokers ( localhost:9092 ). topic_list \u2013 A list of Kafka topics ( my_topic ). group_name \u2013 A group of Kafka consumers ( group1 ). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere. --format \u2013 Message format. Uses the same notation as the SQL FORMAT function, such as JSONEachRow . For more information, see the \"Formats\" section. schema \u2013 An optional parameter that must be used if the format requires a schema definition. For example, Cap'n Proto requires the path to the schema file and the name of the root schema.capnp:Message object. num_consumers \u2013 The number of consumers per table. Default: 1 . Specify more consumers if the throughput of one consumer is insufficient. The total number of consumers should not exceed the number of partitions in the topic, since only one consumer can be assigned per partition. Example: CREATE TABLE queue ( \n timestamp UInt64 , \n level String , \n message String \n ) ENGINE = Kafka ( localhost:9092 , topic , group1 , JSONEachRow ); \n\n SELECT * FROM queue LIMIT 5 ; The delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name. Groups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. Read more about this at http://kafka.apache.org/intro . SELECT is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. To do this: Use the engine to create a Kafka consumer and consider it a data stream. Create a table with the desired structure. Create a materialized view that converts data from the engine and puts it into a previously created table. When the MATERIALIZED VIEW joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using SELECT Example: CREATE TABLE queue ( \n timestamp UInt64 , \n level String , \n message String \n ) ENGINE = Kafka ( localhost:9092 , topic , group1 , JSONEachRow ); \n\n CREATE TABLE daily ( \n day Date , \n level String , \n total UInt64 \n ) ENGINE = SummingMergeTree ( day , ( day , level ), 8192 ); \n\n CREATE MATERIALIZED VIEW consumer TO daily \n AS SELECT toDate ( toDateTime ( timestamp )) AS day , level , count () as total \n FROM queue GROUP BY day , level ; \n\n SELECT level , sum ( total ) FROM daily GROUP BY level ; To improve performance, received messages are grouped into blocks the size of max_insert_block_size . If the block wasn't formed within stream_flush_interval_ms milliseconds, the data will be flushed to the table regardless of the completeness of the block. To stop receiving topic data or to change the conversion logic, detach the materialized view: DETACH TABLE consumer;\n ATTACH MATERIALIZED VIEW consumer; If you want to change the target table by using ALTER materialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view.", + "title": "Kafka" + }, + { + "location": "/table_engines/kafka/#configuration", + "text": "Similar to GraphiteMergeTree, the Kafka engine supports extended configuration using the ClickHouse config file. There are two configuration keys that you can use: global ( kafka ) and topic-level ( kafka_topic_* ). The global configuration is applied first, and the topic-level configuration is second (if it exists). !-- Global configuration options for all tables of Kafka engine type -- \n kafka \n debug cgrp /debug \n auto_offset_reset smallest /auto_offset_reset \n /kafka \n\n !-- Configuration specific for topic logs -- \n kafka_topic_logs \n retry_backoff_ms 250 /retry_backoff_ms \n fetch_min_bytes 100000 /fetch_min_bytes \n /kafka_topic_logs For a list of possible configuration options, see the librdkafka configuration reference . Use the underscore ( _ ) instead of a dot in the ClickHouse configuration. For example, check.crcs=true will be check_crcs true /check_crcs .", + "title": "Configuration" + }, + { + "location": "/table_engines/mysql/", + "text": "MySQL\n\n\nThe MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server.\n\n\nThe engine takes 4 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example:\n\n\nMySQL(\nhost:port\n, \ndatabase\n, \ntable\n, \nuser\n, \npassword\n);\n\n\n\n\n\nAt this time, simple WHERE clauses such as \n=, !=, \n, \n=, \n, \n=\n are executed on the MySQL server.\n\n\nThe rest of the conditions and the LIMIT sampling constraint are executed in ClickHouse only after the query to MySQL finishes.", + "title": "MySQL" + }, + { + "location": "/table_engines/mysql/#mysql", + "text": "The MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server. The engine takes 4 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example: MySQL( host:port , database , table , user , password ); At this time, simple WHERE clauses such as =, !=, , =, , = are executed on the MySQL server. The rest of the conditions and the LIMIT sampling constraint are executed in ClickHouse only after the query to MySQL finishes.", + "title": "MySQL" + }, + { + "location": "/table_engines/external_data/", + "text": "External data for query processing\n\n\nClickHouse allows sending a server the data that is needed for processing a query, together with a SELECT query. This data is put in a temporary table (see the section \"Temporary tables\") and can be used in the query (for example, in IN operators).\n\n\nFor example, if you have a text file with important user identifiers, you can upload it to the server along with a query that uses filtration by this list.\n\n\nIf you need to run more than one query with a large volume of external data, don't use this feature. It is better to upload the data to the DB ahead of time.\n\n\nExternal data can be uploaded using the command-line client (in non-interactive mode), or using the HTTP interface.\n\n\nIn the command-line client, you can specify a parameters section in the format\n\n\n--external --file\n=\n... \n[\n--name\n=\n...\n]\n \n[\n--format\n=\n...\n]\n \n[\n--types\n=\n...\n|\n--structure\n=\n...\n]\n\n\n\n\n\n\nYou may have multiple sections like this, for the number of tables being transmitted.\n\n\n--external\n \u2013 Marks the beginning of a clause.\n\n--file\n \u2013 Path to the file with the table dump, or -, which refers to stdin.\nOnly a single table can be retrieved from stdin.\n\n\nThe following parameters are optional: \n--name\n\u2013 Name of the table. If omitted, _data is used.\n\n--format\n \u2013 Data format in the file. If omitted, TabSeparated is used.\n\n\nOne of the following parameters is required:\n--types\n \u2013 A list of comma-separated column types. For example: \nUInt64,String\n. The columns will be named _1, _2, ...\n\n--structure\n\u2013 The table structure in the format\nUserID UInt64\n, \nURL String\n. Defines the column names and types.\n\n\nThe files specified in 'file' will be parsed by the format specified in 'format', using the data types specified in 'types' or 'structure'. The table will be uploaded to the server and accessible there as a temporary table with the name in 'name'.\n\n\nExamples:\n\n\necho\n -ne \n1\\n2\\n3\\n\n \n|\n clickhouse-client --query\n=\nSELECT count() FROM test.visits WHERE TraficSourceID IN _data\n --external --file\n=\n- --types\n=\nInt8\n\n849897\n\ncat /etc/passwd \n|\n sed \ns/:/\\t/g\n \n|\n clickhouse-client --query\n=\nSELECT shell, count() AS c FROM passwd GROUP BY shell ORDER BY c DESC\n --external --file\n=\n- --name\n=\npasswd --structure\n=\nlogin String, unused String, uid UInt16, gid UInt16, comment String, home String, shell String\n\n/bin/sh \n20\n\n/bin/false \n5\n\n/bin/bash \n4\n\n/usr/sbin/nologin \n1\n\n/bin/sync \n1\n\n\n\n\n\n\nWhen using the HTTP interface, external data is passed in the multipart/form-data format. Each table is transmitted as a separate file. The table name is taken from the file name. The 'query_string' is passed the parameters 'name_format', 'name_types', and 'name_structure', where 'name' is the name of the table that these parameters correspond to. The meaning of the parameters is the same as when using the command-line client.\n\n\nExample:\n\n\ncat /etc/passwd \n|\n sed \ns/:/\\t/g\n \n passwd.tsv\n\ncurl -F \npasswd=@passwd.tsv;\n \nhttp://localhost:8123/?query=SELECT+shell,+count()+AS+c+FROM+passwd+GROUP+BY+shell+ORDER+BY+c+DESC\npasswd_structure=login+String,+unused+String,+uid+UInt16,+gid+UInt16,+comment+String,+home+String,+shell+String\n\n/bin/sh \n20\n\n/bin/false \n5\n\n/bin/bash \n4\n\n/usr/sbin/nologin \n1\n\n/bin/sync \n1\n\n\n\n\n\n\nFor distributed query processing, the temporary tables are sent to all the remote servers.", + "title": "External data for query processing" + }, + { + "location": "/table_engines/external_data/#external-data-for-query-processing", + "text": "ClickHouse allows sending a server the data that is needed for processing a query, together with a SELECT query. This data is put in a temporary table (see the section \"Temporary tables\") and can be used in the query (for example, in IN operators). For example, if you have a text file with important user identifiers, you can upload it to the server along with a query that uses filtration by this list. If you need to run more than one query with a large volume of external data, don't use this feature. It is better to upload the data to the DB ahead of time. External data can be uploaded using the command-line client (in non-interactive mode), or using the HTTP interface. In the command-line client, you can specify a parameters section in the format --external --file = ... [ --name = ... ] [ --format = ... ] [ --types = ... | --structure = ... ] You may have multiple sections like this, for the number of tables being transmitted. --external \u2013 Marks the beginning of a clause. --file \u2013 Path to the file with the table dump, or -, which refers to stdin.\nOnly a single table can be retrieved from stdin. The following parameters are optional: --name \u2013 Name of the table. If omitted, _data is used. --format \u2013 Data format in the file. If omitted, TabSeparated is used. One of the following parameters is required: --types \u2013 A list of comma-separated column types. For example: UInt64,String . The columns will be named _1, _2, ... --structure \u2013 The table structure in the format UserID UInt64 , URL String . Defines the column names and types. The files specified in 'file' will be parsed by the format specified in 'format', using the data types specified in 'types' or 'structure'. The table will be uploaded to the server and accessible there as a temporary table with the name in 'name'. Examples: echo -ne 1\\n2\\n3\\n | clickhouse-client --query = SELECT count() FROM test.visits WHERE TraficSourceID IN _data --external --file = - --types = Int8 849897 \ncat /etc/passwd | sed s/:/\\t/g | clickhouse-client --query = SELECT shell, count() AS c FROM passwd GROUP BY shell ORDER BY c DESC --external --file = - --name = passwd --structure = login String, unused String, uid UInt16, gid UInt16, comment String, home String, shell String \n/bin/sh 20 \n/bin/false 5 \n/bin/bash 4 \n/usr/sbin/nologin 1 \n/bin/sync 1 When using the HTTP interface, external data is passed in the multipart/form-data format. Each table is transmitted as a separate file. The table name is taken from the file name. The 'query_string' is passed the parameters 'name_format', 'name_types', and 'name_structure', where 'name' is the name of the table that these parameters correspond to. The meaning of the parameters is the same as when using the command-line client. Example: cat /etc/passwd | sed s/:/\\t/g passwd.tsv\n\ncurl -F passwd=@passwd.tsv; http://localhost:8123/?query=SELECT+shell,+count()+AS+c+FROM+passwd+GROUP+BY+shell+ORDER+BY+c+DESC passwd_structure=login+String,+unused+String,+uid+UInt16,+gid+UInt16,+comment+String,+home+String,+shell+String \n/bin/sh 20 \n/bin/false 5 \n/bin/bash 4 \n/usr/sbin/nologin 1 \n/bin/sync 1 For distributed query processing, the temporary tables are sent to all the remote servers.", + "title": "External data for query processing" + }, + { + "location": "/system_tables/", + "text": "System tables\n\n\nSystem tables are used for implementing part of the system's functionality, and for providing access to information about how the system is working.\nYou can't delete a system table (but you can perform DETACH).\nSystem tables don't have files with data on the disk or files with metadata. The server creates all the system tables when it starts.\nSystem tables are read-only.\nThey are located in the 'system' database.", + "title": "Introduction" + }, + { + "location": "/system_tables/#system-tables", + "text": "System tables are used for implementing part of the system's functionality, and for providing access to information about how the system is working.\nYou can't delete a system table (but you can perform DETACH).\nSystem tables don't have files with data on the disk or files with metadata. The server creates all the system tables when it starts.\nSystem tables are read-only.\nThey are located in the 'system' database.", + "title": "System tables" + }, + { + "location": "/system_tables/system.one/", + "text": "system.one\n\n\nThis table contains a single row with a single 'dummy' UInt8 column containing the value 0.\nThis table is used if a SELECT query doesn't specify the FROM clause.\nThis is similar to the DUAL table found in other DBMSs.", + "title": "system.one" + }, + { + "location": "/system_tables/system.one/#systemone", + "text": "This table contains a single row with a single 'dummy' UInt8 column containing the value 0.\nThis table is used if a SELECT query doesn't specify the FROM clause.\nThis is similar to the DUAL table found in other DBMSs.", + "title": "system.one" + }, + { + "location": "/system_tables/system.numbers/", + "text": "system.numbers\n\n\nThis table contains a single UInt64 column named 'number' that contains almost all the natural numbers starting from zero.\nYou can use this table for tests, or if you need to do a brute force search.\nReads from this table are not parallelized.", + "title": "system.numbers" + }, + { + "location": "/system_tables/system.numbers/#systemnumbers", + "text": "This table contains a single UInt64 column named 'number' that contains almost all the natural numbers starting from zero.\nYou can use this table for tests, or if you need to do a brute force search.\nReads from this table are not parallelized.", + "title": "system.numbers" + }, + { + "location": "/system_tables/system.numbers_mt/", + "text": "system.numbers_mt\n\n\nThe same as 'system.numbers' but reads are parallelized. The numbers can be returned in any order.\nUsed for tests.", + "title": "system.numbers_mt" + }, + { + "location": "/system_tables/system.numbers_mt/#systemnumbers_mt", + "text": "The same as 'system.numbers' but reads are parallelized. The numbers can be returned in any order.\nUsed for tests.", + "title": "system.numbers_mt" + }, + { + "location": "/system_tables/system.databases/", + "text": "system.databases\n\n\nThis table contains a single String column called 'name' \u2013 the name of a database.\nEach database that the server knows about has a corresponding entry in the table.\nThis system table is used for implementing the \nSHOW DATABASES\n query.", + "title": "system.databases" + }, + { + "location": "/system_tables/system.databases/#systemdatabases", + "text": "This table contains a single String column called 'name' \u2013 the name of a database.\nEach database that the server knows about has a corresponding entry in the table.\nThis system table is used for implementing the SHOW DATABASES query.", + "title": "system.databases" + }, + { + "location": "/system_tables/system.tables/", + "text": "system.tables\n\n\nThis table contains the String columns 'database', 'name', and 'engine'.\nThe table also contains three virtual columns: metadata_modification_time (DateTime type), create_table_query, and engine_full (String type).\nEach table that the server knows about is entered in the 'system.tables' table.\nThis system table is used for implementing SHOW TABLES queries.", + "title": "system.tables" + }, + { + "location": "/system_tables/system.tables/#systemtables", + "text": "This table contains the String columns 'database', 'name', and 'engine'.\nThe table also contains three virtual columns: metadata_modification_time (DateTime type), create_table_query, and engine_full (String type).\nEach table that the server knows about is entered in the 'system.tables' table.\nThis system table is used for implementing SHOW TABLES queries.", + "title": "system.tables" + }, + { + "location": "/system_tables/system.columns/", + "text": "system.columns\n\n\nContains information about the columns in all tables.\nYou can use this table to get information similar to \nDESCRIBE TABLE\n, but for multiple tables at once.\n\n\ndatabase String - Name of the database the table is located in.\ntable String - Table name.\nname String - Column name.\ntype String - Column type.\ndefault_type String - Expression type (DEFAULT, MATERIALIZED, ALIAS) for the default value, or an empty string if it is not defined.\ndefault_expression String - Expression for the default value, or an empty string if it is not defined.", + "title": "system.columns" + }, + { + "location": "/system_tables/system.columns/#systemcolumns", + "text": "Contains information about the columns in all tables.\nYou can use this table to get information similar to DESCRIBE TABLE , but for multiple tables at once. database String - Name of the database the table is located in.\ntable String - Table name.\nname String - Column name.\ntype String - Column type.\ndefault_type String - Expression type (DEFAULT, MATERIALIZED, ALIAS) for the default value, or an empty string if it is not defined.\ndefault_expression String - Expression for the default value, or an empty string if it is not defined.", + "title": "system.columns" + }, + { + "location": "/system_tables/system.parts/", + "text": "system.parts\n\n\nContains information about parts of a table in the \nMergeTree\n family.\n\n\nEach row describes one part of the data.\n\n\nColumns:\n\n\n\n\npartition (String) \u2013 The partition name. YYYYMM format. To learn what a partition is, see the description of the \nALTER\n query.\n\n\nname (String) \u2013 Name of the data part.\n\n\nactive (UInt8) \u2013 Indicates whether the part is active. If a part is active, it is used in a table; otherwise, it will be deleted. Inactive data parts remain after merging.\n\n\nmarks (UInt64) \u2013 The number of marks. To get the approximate number of rows in a data part, multiply \nmarks\n by the index granularity (usually 8192).\n\n\nmarks_size (UInt64) \u2013 The size of the file with marks.\n\n\nrows (UInt64) \u2013 The number of rows.\n\n\nbytes (UInt64) \u2013 The number of bytes when compressed.\n\n\nmodification_time (DateTime) \u2013 The modification time of the directory with the data part. This usually corresponds to the time of data part creation.|\n\n\nremove_time (DateTime) \u2013 The time when the data part became inactive.\n\n\nrefcount (UInt32) \u2013 The number of places where the data part is used. A value greater than 2 indicates that the data part is used in queries or merges.\n\n\nmin_date (Date) \u2013 The minimum value of the date key in the data part.\n\n\nmax_date (Date) \u2013 The maximum value of the date key in the data part.\n\n\nmin_block_number (UInt64) \u2013 The minimum number of data parts that make up the current part after merging.\n\n\nmax_block_number (UInt64) \u2013 The maximum number of data parts that make up the current part after merging.\n\n\nlevel (UInt32) \u2013 Depth of the merge tree. If a merge was not performed, \nlevel=0\n.\n\n\nprimary_key_bytes_in_memory (UInt64) \u2013 The amount of memory (in bytes) used by primary key values.\n\n\nprimary_key_bytes_in_memory_allocated (UInt64) \u2013 The amount of memory (in bytes) reserved for primary key values.\n\n\ndatabase (String) \u2013 Name of the database.\n\n\ntable (String) \u2013 Name of the table.\n\n\nengine (String) \u2013 Name of the table engine without parameters.", + "title": "system.parts" + }, + { + "location": "/system_tables/system.parts/#systemparts", + "text": "Contains information about parts of a table in the MergeTree family. Each row describes one part of the data. Columns: partition (String) \u2013 The partition name. YYYYMM format. To learn what a partition is, see the description of the ALTER query. name (String) \u2013 Name of the data part. active (UInt8) \u2013 Indicates whether the part is active. If a part is active, it is used in a table; otherwise, it will be deleted. Inactive data parts remain after merging. marks (UInt64) \u2013 The number of marks. To get the approximate number of rows in a data part, multiply marks by the index granularity (usually 8192). marks_size (UInt64) \u2013 The size of the file with marks. rows (UInt64) \u2013 The number of rows. bytes (UInt64) \u2013 The number of bytes when compressed. modification_time (DateTime) \u2013 The modification time of the directory with the data part. This usually corresponds to the time of data part creation.| remove_time (DateTime) \u2013 The time when the data part became inactive. refcount (UInt32) \u2013 The number of places where the data part is used. A value greater than 2 indicates that the data part is used in queries or merges. min_date (Date) \u2013 The minimum value of the date key in the data part. max_date (Date) \u2013 The maximum value of the date key in the data part. min_block_number (UInt64) \u2013 The minimum number of data parts that make up the current part after merging. max_block_number (UInt64) \u2013 The maximum number of data parts that make up the current part after merging. level (UInt32) \u2013 Depth of the merge tree. If a merge was not performed, level=0 . primary_key_bytes_in_memory (UInt64) \u2013 The amount of memory (in bytes) used by primary key values. primary_key_bytes_in_memory_allocated (UInt64) \u2013 The amount of memory (in bytes) reserved for primary key values. database (String) \u2013 Name of the database. table (String) \u2013 Name of the table. engine (String) \u2013 Name of the table engine without parameters.", + "title": "system.parts" + }, + { + "location": "/system_tables/system.processes/", + "text": "system.processes\n\n\nThis system table is used for implementing the \nSHOW PROCESSLIST\n query.\nColumns:\n\n\nuser String \u2013 Name of the user who made the request. For distributed query processing, this is the user who helped the requestor server send the query to this server, not the user who made the distributed request on the requestor server.\n\naddress String \u2013 The IP address that the query was made from. The same is true for distributed query processing.\n\nelapsed Float64 \u2013 The time in seconds since request execution started.\n\nrows_read UInt64 \u2013 The number of rows read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nbytes_read UInt64 \u2013 The number of uncompressed bytes read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nUInt64 total_rows_approx \u2013 The approximate total number of rows that must be read. For distributed processing, on the requestor server, this is the total for all remote servers. It can be updated during request processing, when new sources to process become known.\n\nmemory_usage UInt64 \u2013 Memory consumption by the query. It might not include some types of dedicated memory.\n\nquery String \u2013 The query text. For INSERT, it doesn\nt include the data to insert.\n\nquery_id \u2013 Query ID, if defined.", + "title": "system.processes" + }, + { + "location": "/system_tables/system.processes/#systemprocesses", + "text": "This system table is used for implementing the SHOW PROCESSLIST query.\nColumns: user String \u2013 Name of the user who made the request. For distributed query processing, this is the user who helped the requestor server send the query to this server, not the user who made the distributed request on the requestor server.\n\naddress String \u2013 The IP address that the query was made from. The same is true for distributed query processing.\n\nelapsed Float64 \u2013 The time in seconds since request execution started.\n\nrows_read UInt64 \u2013 The number of rows read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nbytes_read UInt64 \u2013 The number of uncompressed bytes read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nUInt64 total_rows_approx \u2013 The approximate total number of rows that must be read. For distributed processing, on the requestor server, this is the total for all remote servers. It can be updated during request processing, when new sources to process become known.\n\nmemory_usage UInt64 \u2013 Memory consumption by the query. It might not include some types of dedicated memory.\n\nquery String \u2013 The query text. For INSERT, it doesn t include the data to insert.\n\nquery_id \u2013 Query ID, if defined.", + "title": "system.processes" + }, + { + "location": "/system_tables/system.merges/", + "text": "system.merges\n\n\nContains information about merges currently in process for tables in the MergeTree family.\n\n\nColumns:\n\n\n\n\ndatabase String\n \u2014 Name of the database the table is located in.\n\n\ntable String\n \u2014 Name of the table.\n\n\nelapsed Float64\n \u2014 Time in seconds since the merge started.\n\n\nprogress Float64\n \u2014 Percent of progress made, from 0 to 1.\n\n\nnum_parts UInt64\n \u2014 Number of parts to merge.\n\n\nresult_part_name String\n \u2014 Name of the part that will be formed as the result of the merge.\n\n\ntotal_size_bytes_compressed UInt64\n \u2014 Total size of compressed data in the parts being merged.\n\n\ntotal_size_marks UInt64\n \u2014 Total number of marks in the parts being merged.\n\n\nbytes_read_uncompressed UInt64\n \u2014 Amount of bytes read, decompressed.\n\n\nrows_read UInt64\n \u2014 Number of rows read.\n\n\nbytes_written_uncompressed UInt64\n \u2014 Amount of bytes written, uncompressed.\n\n\nrows_written UInt64\n \u2014 Number of rows written.", + "title": "system.merges" + }, + { + "location": "/system_tables/system.merges/#systemmerges", + "text": "Contains information about merges currently in process for tables in the MergeTree family. Columns: database String \u2014 Name of the database the table is located in. table String \u2014 Name of the table. elapsed Float64 \u2014 Time in seconds since the merge started. progress Float64 \u2014 Percent of progress made, from 0 to 1. num_parts UInt64 \u2014 Number of parts to merge. result_part_name String \u2014 Name of the part that will be formed as the result of the merge. total_size_bytes_compressed UInt64 \u2014 Total size of compressed data in the parts being merged. total_size_marks UInt64 \u2014 Total number of marks in the parts being merged. bytes_read_uncompressed UInt64 \u2014 Amount of bytes read, decompressed. rows_read UInt64 \u2014 Number of rows read. bytes_written_uncompressed UInt64 \u2014 Amount of bytes written, uncompressed. rows_written UInt64 \u2014 Number of rows written.", + "title": "system.merges" + }, + { + "location": "/system_tables/system.events/", + "text": "system.events\n\n\nContains information about the number of events that have occurred in the system. This is used for profiling and monitoring purposes.\nExample: The number of processed SELECT queries.\nColumns: 'event String' \u2013 the event name, and 'value UInt64' \u2013 the quantity.", + "title": "system.events" + }, + { + "location": "/system_tables/system.events/#systemevents", + "text": "Contains information about the number of events that have occurred in the system. This is used for profiling and monitoring purposes.\nExample: The number of processed SELECT queries.\nColumns: 'event String' \u2013 the event name, and 'value UInt64' \u2013 the quantity.", + "title": "system.events" + }, + { + "location": "/system_tables/system.metrics/", + "text": "system.metrics", + "title": "system.metrics" + }, + { + "location": "/system_tables/system.metrics/#systemmetrics", + "text": "", + "title": "system.metrics" + }, + { + "location": "/system_tables/system.asynchronous_metrics/", + "text": "system.asynchronous_metrics\n\n\nContain metrics used for profiling and monitoring.\nThey usually reflect the number of events currently in the system, or the total resources consumed by the system.\nExample: The number of SELECT queries currently running; the amount of memory in use.\nsystem.asynchronous_metrics\nand\nsystem.metrics\n differ in their sets of metrics and how they are calculated.", + "title": "system.asynchronous_metrics" + }, + { + "location": "/system_tables/system.asynchronous_metrics/#systemasynchronous_metrics", + "text": "Contain metrics used for profiling and monitoring.\nThey usually reflect the number of events currently in the system, or the total resources consumed by the system.\nExample: The number of SELECT queries currently running; the amount of memory in use. system.asynchronous_metrics and system.metrics differ in their sets of metrics and how they are calculated.", + "title": "system.asynchronous_metrics" + }, + { + "location": "/system_tables/system.replicas/", + "text": "system.replicas\n\n\nContains information and status for replicated tables residing on the local server.\nThis table can be used for monitoring. The table contains a row for every Replicated* table.\n\n\nExample:\n\n\nSELECT\n \n*\n\n\nFROM\n \nsystem\n.\nreplicas\n\n\nWHERE\n \ntable\n \n=\n \nvisits\n\n\nFORMAT\n \nVertical\n\n\n\n\n\n\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ndatabase: merge\ntable: visits\nengine: ReplicatedCollapsingMergeTree\nis_leader: 1\nis_readonly: 0\nis_session_expired: 0\nfuture_parts: 1\nparts_to_check: 0\nzookeeper_path: /clickhouse/tables/01-06/visits\nreplica_name: example01-06-1.yandex.ru\nreplica_path: /clickhouse/tables/01-06/visits/replicas/example01-06-1.yandex.ru\ncolumns_version: 9\nqueue_size: 1\ninserts_in_queue: 0\nmerges_in_queue: 1\nlog_max_index: 596273\nlog_pointer: 596274\ntotal_replicas: 2\nactive_replicas: 2\n\n\n\n\n\nColumns:\n\n\ndatabase: database name\ntable: table name\nengine: table engine name\n\nis_leader: whether the replica is the leader\n\nOnly one replica at a time can be the leader. The leader is responsible for selecting background merges to perform.\nNote that writes can be performed to any replica that is available and has a session in ZK, regardless of whether it is a leader.\n\nis_readonly: Whether the replica is in read-only mode.\nThis mode is turned on if the config doesn\nt have sections with ZK, if an unknown error occurred when reinitializing sessions in ZK, and during session reinitialization in ZK.\n\nis_session_expired: Whether the ZK session expired.\nBasically, the same thing as is_readonly.\n\nfuture_parts: The number of data parts that will appear as the result of INSERTs or merges that haven\nt been done yet. \n\nparts_to_check: The number of data parts in the queue for verification.\nA part is put in the verification queue if there is suspicion that it might be damaged.\n\nzookeeper_path: The path to the table data in ZK. \nreplica_name: Name of the replica in ZK. Different replicas of the same table have different names. \nreplica_path: The path to the replica data in ZK. The same as concatenating zookeeper_path/replicas/replica_path.\n\ncolumns_version: Version number of the table structure.\nIndicates how many times ALTER was performed. If replicas have different versions, it means some replicas haven\nt made all of the ALTERs yet.\n\nqueue_size: Size of the queue for operations waiting to be performed.\nOperations include inserting blocks of data, merges, and certain other actions.\nNormally coincides with future_parts.\n\ninserts_in_queue: Number of inserts of blocks of data that need to be made.\nInsertions are usually replicated fairly quickly. If the number is high, something is wrong.\n\nmerges_in_queue: The number of merges waiting to be made. \nSometimes merges are lengthy, so this value may be greater than zero for a long time.\n\nThe next 4 columns have a non-null value only if the ZK session is active.\n\nlog_max_index: Maximum entry number in the log of general activity.\nlog_pointer: Maximum entry number in the log of general activity that the replica copied to its execution queue, plus one.\nIf log_pointer is much smaller than log_max_index, something is wrong.\n\ntotal_replicas: Total number of known replicas of this table.\nactive_replicas: Number of replicas of this table that have a ZK session (the number of active replicas).\n\n\n\n\n\nIf you request all the columns, the table may work a bit slowly, since several reads from ZK are made for each row.\nIf you don't request the last 4 columns (log_max_index, log_pointer, total_replicas, active_replicas), the table works quickly.\n\n\nFor example, you can check that everything is working correctly like this:\n\n\nSELECT\n\n \ndatabase\n,\n\n \ntable\n,\n\n \nis_leader\n,\n\n \nis_readonly\n,\n\n \nis_session_expired\n,\n\n \nfuture_parts\n,\n\n \nparts_to_check\n,\n\n \ncolumns_version\n,\n\n \nqueue_size\n,\n\n \ninserts_in_queue\n,\n\n \nmerges_in_queue\n,\n\n \nlog_max_index\n,\n\n \nlog_pointer\n,\n\n \ntotal_replicas\n,\n\n \nactive_replicas\n\n\nFROM\n \nsystem\n.\nreplicas\n\n\nWHERE\n\n \nis_readonly\n\n \nOR\n \nis_session_expired\n\n \nOR\n \nfuture_parts\n \n \n20\n\n \nOR\n \nparts_to_check\n \n \n10\n\n \nOR\n \nqueue_size\n \n \n20\n\n \nOR\n \ninserts_in_queue\n \n \n10\n\n \nOR\n \nlog_max_index\n \n-\n \nlog_pointer\n \n \n10\n\n \nOR\n \ntotal_replicas\n \n \n2\n\n \nOR\n \nactive_replicas\n \n \ntotal_replicas\n\n\n\n\n\n\nIf this query doesn't return anything, it means that everything is fine.", + "title": "system.replicas" + }, + { + "location": "/system_tables/system.replicas/#systemreplicas", + "text": "Contains information and status for replicated tables residing on the local server.\nThis table can be used for monitoring. The table contains a row for every Replicated* table. Example: SELECT * FROM system . replicas WHERE table = visits FORMAT Vertical Row 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ndatabase: merge\ntable: visits\nengine: ReplicatedCollapsingMergeTree\nis_leader: 1\nis_readonly: 0\nis_session_expired: 0\nfuture_parts: 1\nparts_to_check: 0\nzookeeper_path: /clickhouse/tables/01-06/visits\nreplica_name: example01-06-1.yandex.ru\nreplica_path: /clickhouse/tables/01-06/visits/replicas/example01-06-1.yandex.ru\ncolumns_version: 9\nqueue_size: 1\ninserts_in_queue: 0\nmerges_in_queue: 1\nlog_max_index: 596273\nlog_pointer: 596274\ntotal_replicas: 2\nactive_replicas: 2 Columns: database: database name\ntable: table name\nengine: table engine name\n\nis_leader: whether the replica is the leader\n\nOnly one replica at a time can be the leader. The leader is responsible for selecting background merges to perform.\nNote that writes can be performed to any replica that is available and has a session in ZK, regardless of whether it is a leader.\n\nis_readonly: Whether the replica is in read-only mode.\nThis mode is turned on if the config doesn t have sections with ZK, if an unknown error occurred when reinitializing sessions in ZK, and during session reinitialization in ZK.\n\nis_session_expired: Whether the ZK session expired.\nBasically, the same thing as is_readonly.\n\nfuture_parts: The number of data parts that will appear as the result of INSERTs or merges that haven t been done yet. \n\nparts_to_check: The number of data parts in the queue for verification.\nA part is put in the verification queue if there is suspicion that it might be damaged.\n\nzookeeper_path: The path to the table data in ZK. \nreplica_name: Name of the replica in ZK. Different replicas of the same table have different names. \nreplica_path: The path to the replica data in ZK. The same as concatenating zookeeper_path/replicas/replica_path.\n\ncolumns_version: Version number of the table structure.\nIndicates how many times ALTER was performed. If replicas have different versions, it means some replicas haven t made all of the ALTERs yet.\n\nqueue_size: Size of the queue for operations waiting to be performed.\nOperations include inserting blocks of data, merges, and certain other actions.\nNormally coincides with future_parts.\n\ninserts_in_queue: Number of inserts of blocks of data that need to be made.\nInsertions are usually replicated fairly quickly. If the number is high, something is wrong.\n\nmerges_in_queue: The number of merges waiting to be made. \nSometimes merges are lengthy, so this value may be greater than zero for a long time.\n\nThe next 4 columns have a non-null value only if the ZK session is active.\n\nlog_max_index: Maximum entry number in the log of general activity.\nlog_pointer: Maximum entry number in the log of general activity that the replica copied to its execution queue, plus one.\nIf log_pointer is much smaller than log_max_index, something is wrong.\n\ntotal_replicas: Total number of known replicas of this table.\nactive_replicas: Number of replicas of this table that have a ZK session (the number of active replicas). If you request all the columns, the table may work a bit slowly, since several reads from ZK are made for each row.\nIf you don't request the last 4 columns (log_max_index, log_pointer, total_replicas, active_replicas), the table works quickly. For example, you can check that everything is working correctly like this: SELECT \n database , \n table , \n is_leader , \n is_readonly , \n is_session_expired , \n future_parts , \n parts_to_check , \n columns_version , \n queue_size , \n inserts_in_queue , \n merges_in_queue , \n log_max_index , \n log_pointer , \n total_replicas , \n active_replicas FROM system . replicas WHERE \n is_readonly \n OR is_session_expired \n OR future_parts 20 \n OR parts_to_check 10 \n OR queue_size 20 \n OR inserts_in_queue 10 \n OR log_max_index - log_pointer 10 \n OR total_replicas 2 \n OR active_replicas total_replicas If this query doesn't return anything, it means that everything is fine.", + "title": "system.replicas" + }, + { + "location": "/system_tables/system.dictionaries/", + "text": "system.dictionaries\n\n\nContains information about external dictionaries.\n\n\nColumns:\n\n\n\n\nname String\n \u2013 Dictionary name.\n\n\ntype String\n \u2013 Dictionary type: Flat, Hashed, Cache.\n\n\norigin String\n \u2013 Path to the config file where the dictionary is described.\n\n\nattribute.names Array(String)\n \u2013 Array of attribute names provided by the dictionary.\n\n\nattribute.types Array(String)\n \u2013 Corresponding array of attribute types provided by the dictionary.\n\n\nhas_hierarchy UInt8\n \u2013 Whether the dictionary is hierarchical.\n\n\nbytes_allocated UInt64\n \u2013 The amount of RAM used by the dictionary.\n\n\nhit_rate Float64\n \u2013 For cache dictionaries, the percent of usage for which the value was in the cache.\n\n\nelement_count UInt64\n \u2013 The number of items stored in the dictionary.\n\n\nload_factor Float64\n \u2013 The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table).\n\n\ncreation_time DateTime\n \u2013 Time spent for the creation or last successful reload of the dictionary.\n\n\nlast_exception String\n \u2013 Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created.\n\n\nsource String\n \u2013 Text describing the data source for the dictionary.\n\n\n\n\nNote that the amount of memory used by the dictionary is not proportional to the number of items stored in it. So for flat and cached dictionaries, all the memory cells are pre-assigned, regardless of how full the dictionary actually is.", + "title": "system.dictionaries" + }, + { + "location": "/system_tables/system.dictionaries/#systemdictionaries", + "text": "Contains information about external dictionaries. Columns: name String \u2013 Dictionary name. type String \u2013 Dictionary type: Flat, Hashed, Cache. origin String \u2013 Path to the config file where the dictionary is described. attribute.names Array(String) \u2013 Array of attribute names provided by the dictionary. attribute.types Array(String) \u2013 Corresponding array of attribute types provided by the dictionary. has_hierarchy UInt8 \u2013 Whether the dictionary is hierarchical. bytes_allocated UInt64 \u2013 The amount of RAM used by the dictionary. hit_rate Float64 \u2013 For cache dictionaries, the percent of usage for which the value was in the cache. element_count UInt64 \u2013 The number of items stored in the dictionary. load_factor Float64 \u2013 The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table). creation_time DateTime \u2013 Time spent for the creation or last successful reload of the dictionary. last_exception String \u2013 Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created. source String \u2013 Text describing the data source for the dictionary. Note that the amount of memory used by the dictionary is not proportional to the number of items stored in it. So for flat and cached dictionaries, all the memory cells are pre-assigned, regardless of how full the dictionary actually is.", + "title": "system.dictionaries" + }, + { + "location": "/system_tables/system.clusters/", + "text": "system.clusters\n\n\nContains information about clusters available in the config file and the servers in them.\nColumns:\n\n\ncluster String \u2013 Cluster name.\nshard_num UInt32 \u2013 Number of a shard in the cluster, starting from 1.\nshard_weight UInt32 \u2013 Relative weight of a shard when writing data.\nreplica_num UInt32 \u2013 Number of a replica in the shard, starting from 1.\nhost_name String \u2013 Host name as specified in the config.\nhost_address String \u2013 Host\ns IP address obtained from DNS.\nport UInt16 \u2013 The port used to access the server.\nuser String \u2013 The username to use for connecting to the server.", + "title": "system.clusters" + }, + { + "location": "/system_tables/system.clusters/#systemclusters", + "text": "Contains information about clusters available in the config file and the servers in them.\nColumns: cluster String \u2013 Cluster name.\nshard_num UInt32 \u2013 Number of a shard in the cluster, starting from 1.\nshard_weight UInt32 \u2013 Relative weight of a shard when writing data.\nreplica_num UInt32 \u2013 Number of a replica in the shard, starting from 1.\nhost_name String \u2013 Host name as specified in the config.\nhost_address String \u2013 Host s IP address obtained from DNS.\nport UInt16 \u2013 The port used to access the server.\nuser String \u2013 The username to use for connecting to the server.", + "title": "system.clusters" + }, + { + "location": "/system_tables/system.functions/", + "text": "system.functions\n\n\nContains information about normal and aggregate functions.\n\n\nColumns:\n\n\n\n\nname\n (\nString\n) \u2013 Function name.\n\n\nis_aggregate\n (\nUInt8\n) \u2013 Whether it is an aggregate function.", + "title": "system.functions" + }, + { + "location": "/system_tables/system.functions/#systemfunctions", + "text": "Contains information about normal and aggregate functions. Columns: name ( String ) \u2013 Function name. is_aggregate ( UInt8 ) \u2013 Whether it is an aggregate function.", + "title": "system.functions" + }, + { + "location": "/system_tables/system.settings/", + "text": "system.settings\n\n\nContains information about settings that are currently in use.\nI.e. used for executing the query you are using to read from the system.settings table).\n\n\nColumns:\n\n\nname String \u2013 Setting name.\nvalue String \u2013 Setting value.\nchanged UInt8 - Whether the setting was explicitly defined in the config or explicitly changed.\n\n\n\n\n\nExample:\n\n\nSELECT\n \n*\n\n\nFROM\n \nsystem\n.\nsettings\n\n\nWHERE\n \nchanged\n\n\n\n\n\n\n\u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500value\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500changed\u2500\u2510\n\u2502 max_threads \u2502 8 \u2502 1 \u2502\n\u2502 use_uncompressed_cache \u2502 0 \u2502 1 \u2502\n\u2502 load_balancing \u2502 random \u2502 1 \u2502\n\u2502 max_memory_usage \u2502 10000000000 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "system.settings" + }, + { + "location": "/system_tables/system.settings/#systemsettings", + "text": "Contains information about settings that are currently in use.\nI.e. used for executing the query you are using to read from the system.settings table). Columns: name String \u2013 Setting name.\nvalue String \u2013 Setting value.\nchanged UInt8 - Whether the setting was explicitly defined in the config or explicitly changed. Example: SELECT * FROM system . settings WHERE changed \u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500value\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500changed\u2500\u2510\n\u2502 max_threads \u2502 8 \u2502 1 \u2502\n\u2502 use_uncompressed_cache \u2502 0 \u2502 1 \u2502\n\u2502 load_balancing \u2502 random \u2502 1 \u2502\n\u2502 max_memory_usage \u2502 10000000000 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "system.settings" + }, + { + "location": "/system_tables/system.zookeeper/", + "text": "system.zookeeper\n\n\nAllows reading data from the ZooKeeper cluster defined in the config.\nThe query must have a 'path' equality condition in the WHERE clause. This is the path in ZooKeeper for the children that you want to get data for.\n\n\nThe query \nSELECT * FROM system.zookeeper WHERE path = '/clickhouse'\n outputs data for all children on the \n/clickhouse\n node.\nTo output data for all root nodes, write path = '/'.\nIf the path specified in 'path' doesn't exist, an exception will be thrown.\n\n\nColumns:\n\n\n\n\nname String\n \u2014 Name of the node.\n\n\npath String\n \u2014 Path to the node.\n\n\nvalue String\n \u2014 Value of the node.\n\n\ndataLength Int32\n \u2014 Size of the value.\n\n\nnumChildren Int32\n \u2014 Number of children.\n\n\nczxid Int64\n \u2014 ID of the transaction that created the node.\n\n\nmzxid Int64\n \u2014 ID of the transaction that last changed the node.\n\n\npzxid Int64\n \u2014 ID of the transaction that last added or removed children.\n\n\nctime DateTime\n \u2014 Time of node creation.\n\n\nmtime DateTime\n \u2014 Time of the last node modification.\n\n\nversion Int32\n \u2014 Node version - the number of times the node was changed.\n\n\ncversion Int32\n \u2014 Number of added or removed children.\n\n\naversion Int32\n \u2014 Number of changes to ACL.\n\n\nephemeralOwner Int64\n \u2014 For ephemeral nodes, the ID of the session that owns this node.\n\n\n\n\nExample:\n\n\nSELECT\n \n*\n\n\nFROM\n \nsystem\n.\nzookeeper\n\n\nWHERE\n \npath\n \n=\n \n/clickhouse/tables/01-08/visits/replicas\n\n\nFORMAT\n \nVertical\n\n\n\n\n\n\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-1.yandex.ru\nvalue:\nczxid: 932998691229\nmzxid: 932998691229\nctime: 2015-03-27 16:49:51\nmtime: 2015-03-27 16:49:51\nversion: 0\ncversion: 47\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021031383\npath: /clickhouse/tables/01-08/visits/replicas\n\nRow 2:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-2.yandex.ru\nvalue:\nczxid: 933002738135\nmzxid: 933002738135\nctime: 2015-03-27 16:57:01\nmtime: 2015-03-27 16:57:01\nversion: 0\ncversion: 37\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021252247\npath: /clickhouse/tables/01-08/visits/replicas", + "title": "system.zookeeper" + }, + { + "location": "/system_tables/system.zookeeper/#systemzookeeper", + "text": "Allows reading data from the ZooKeeper cluster defined in the config.\nThe query must have a 'path' equality condition in the WHERE clause. This is the path in ZooKeeper for the children that you want to get data for. The query SELECT * FROM system.zookeeper WHERE path = '/clickhouse' outputs data for all children on the /clickhouse node.\nTo output data for all root nodes, write path = '/'.\nIf the path specified in 'path' doesn't exist, an exception will be thrown. Columns: name String \u2014 Name of the node. path String \u2014 Path to the node. value String \u2014 Value of the node. dataLength Int32 \u2014 Size of the value. numChildren Int32 \u2014 Number of children. czxid Int64 \u2014 ID of the transaction that created the node. mzxid Int64 \u2014 ID of the transaction that last changed the node. pzxid Int64 \u2014 ID of the transaction that last added or removed children. ctime DateTime \u2014 Time of node creation. mtime DateTime \u2014 Time of the last node modification. version Int32 \u2014 Node version - the number of times the node was changed. cversion Int32 \u2014 Number of added or removed children. aversion Int32 \u2014 Number of changes to ACL. ephemeralOwner Int64 \u2014 For ephemeral nodes, the ID of the session that owns this node. Example: SELECT * FROM system . zookeeper WHERE path = /clickhouse/tables/01-08/visits/replicas FORMAT Vertical Row 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-1.yandex.ru\nvalue:\nczxid: 932998691229\nmzxid: 932998691229\nctime: 2015-03-27 16:49:51\nmtime: 2015-03-27 16:49:51\nversion: 0\ncversion: 47\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021031383\npath: /clickhouse/tables/01-08/visits/replicas\n\nRow 2:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-2.yandex.ru\nvalue:\nczxid: 933002738135\nmzxid: 933002738135\nctime: 2015-03-27 16:57:01\nmtime: 2015-03-27 16:57:01\nversion: 0\ncversion: 37\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021252247\npath: /clickhouse/tables/01-08/visits/replicas", + "title": "system.zookeeper" + }, + { + "location": "/table_functions/", + "text": "Table functions\n\n\nTable functions can be specified in the FROM clause instead of the database and table names.\nTable functions can only be used if 'readonly' is not set.\nTable functions aren't related to other functions.", + "title": "Introduction" + }, + { + "location": "/table_functions/#table-functions", + "text": "Table functions can be specified in the FROM clause instead of the database and table names.\nTable functions can only be used if 'readonly' is not set.\nTable functions aren't related to other functions.", + "title": "Table functions" + }, + { + "location": "/table_functions/remote/", + "text": "remote\n\n\nAllows you to access remote servers without creating a \nDistributed\n table.\n\n\nSignatures:\n\n\nremote\n(\naddresses_expr\n,\n \ndb\n,\n \ntable\n[,\n \nuser\n[,\n \npassword\n]])\n\n\nremote\n(\naddresses_expr\n,\n \ndb\n.\ntable\n[,\n \nuser\n[,\n \npassword\n]])\n\n\n\n\n\n\naddresses_expr\n \u2013 An expression that generates addresses of remote servers. This may be just one server address. The server address is \nhost:port\n, or just \nhost\n. The host can be specified as the server name, or as the IPv4 or IPv6 address. An IPv6 address is specified in square brackets. The port is the TCP port on the remote server. If the port is omitted, it uses \ntcp_port\n from the server's config file (by default, 9000).\n\n\n\n\nThe port is required for an IPv6 address.\n\n\n\n\n\nExamples:\n\n\nexample01-01-1\nexample01-01-1:9000\nlocalhost\n127.0.0.1\n[::]:9000\n[2a02:6b8:0:1111::11]:9000\n\n\n\n\n\nMultiple addresses can be comma-separated. In this case, ClickHouse will use distributed processing, so it will send the query to all specified addresses (like to shards with different data).\n\n\nExample:\n\n\nexample01-01-1,example01-02-1\n\n\n\n\n\nPart of the expression can be specified in curly brackets. The previous example can be written as follows:\n\n\nexample01-0{1,2}-1\n\n\n\n\n\nCurly brackets can contain a range of numbers separated by two dots (non-negative integers). In this case, the range is expanded to a set of values that generate shard addresses. If the first number starts with zero, the values are formed with the same zero alignment. The previous example can be written as follows:\n\n\nexample01-{01..02}-1\n\n\n\n\n\nIf you have multiple pairs of curly brackets, it generates the direct product of the corresponding sets.\n\n\nAddresses and parts of addresses in curly brackets can be separated by the pipe symbol (|). In this case, the corresponding sets of addresses are interpreted as replicas, and the query will be sent to the first healthy replica. However, the replicas are iterated in the order currently set in the \nload_balancing\n setting.\n\n\nExample:\n\n\nexample01-{01..02}-{1|2}\n\n\n\n\n\nThis example specifies two shards that each have two replicas.\n\n\nThe number of addresses generated is limited by a constant. Right now this is 1000 addresses.\n\n\nUsing the \nremote\n table function is less optimal than creating a \nDistributed\n table, because in this case, the server connection is re-established for every request. In addition, if host names are set, the names are resolved, and errors are not counted when working with various replicas. When processing a large number of queries, always create the \nDistributed\n table ahead of time, and don't use the \nremote\n table function.\n\n\nThe \nremote\n table function can be useful in the following cases:\n\n\n\n\nAccessing a specific server for data comparison, debugging, and testing.\n\n\nQueries between various ClickHouse clusters for research purposes.\n\n\nInfrequent distributed requests that are made manually.\n\n\nDistributed requests where the set of servers is re-defined each time.\n\n\n\n\nIf the user is not specified, \ndefault\n is used.\nIf the password is not specified, an empty password is used.", + "title": "remote" + }, + { + "location": "/table_functions/remote/#remote", + "text": "Allows you to access remote servers without creating a Distributed table. Signatures: remote ( addresses_expr , db , table [, user [, password ]]) remote ( addresses_expr , db . table [, user [, password ]]) addresses_expr \u2013 An expression that generates addresses of remote servers. This may be just one server address. The server address is host:port , or just host . The host can be specified as the server name, or as the IPv4 or IPv6 address. An IPv6 address is specified in square brackets. The port is the TCP port on the remote server. If the port is omitted, it uses tcp_port from the server's config file (by default, 9000). \n\nThe port is required for an IPv6 address. Examples: example01-01-1\nexample01-01-1:9000\nlocalhost\n127.0.0.1\n[::]:9000\n[2a02:6b8:0:1111::11]:9000 Multiple addresses can be comma-separated. In this case, ClickHouse will use distributed processing, so it will send the query to all specified addresses (like to shards with different data). Example: example01-01-1,example01-02-1 Part of the expression can be specified in curly brackets. The previous example can be written as follows: example01-0{1,2}-1 Curly brackets can contain a range of numbers separated by two dots (non-negative integers). In this case, the range is expanded to a set of values that generate shard addresses. If the first number starts with zero, the values are formed with the same zero alignment. The previous example can be written as follows: example01-{01..02}-1 If you have multiple pairs of curly brackets, it generates the direct product of the corresponding sets. Addresses and parts of addresses in curly brackets can be separated by the pipe symbol (|). In this case, the corresponding sets of addresses are interpreted as replicas, and the query will be sent to the first healthy replica. However, the replicas are iterated in the order currently set in the load_balancing setting. Example: example01-{01..02}-{1|2} This example specifies two shards that each have two replicas. The number of addresses generated is limited by a constant. Right now this is 1000 addresses. Using the remote table function is less optimal than creating a Distributed table, because in this case, the server connection is re-established for every request. In addition, if host names are set, the names are resolved, and errors are not counted when working with various replicas. When processing a large number of queries, always create the Distributed table ahead of time, and don't use the remote table function. The remote table function can be useful in the following cases: Accessing a specific server for data comparison, debugging, and testing. Queries between various ClickHouse clusters for research purposes. Infrequent distributed requests that are made manually. Distributed requests where the set of servers is re-defined each time. If the user is not specified, default is used.\nIf the password is not specified, an empty password is used.", + "title": "remote" + }, + { + "location": "/table_functions/merge/", + "text": "merge\n\n\nmerge(db_name, 'tables_regexp')\n \u2013 Creates a temporary Merge table. For more information, see the section \"Table engines, Merge\".\n\n\nThe table structure is taken from the first table encountered that matches the regular expression.", + "title": "merge" + }, + { + "location": "/table_functions/merge/#merge", + "text": "merge(db_name, 'tables_regexp') \u2013 Creates a temporary Merge table. For more information, see the section \"Table engines, Merge\". The table structure is taken from the first table encountered that matches the regular expression.", + "title": "merge" + }, + { + "location": "/table_functions/numbers/", + "text": "numbers\n\n\nnumbers(N)\n \u2013 Returns a table with the single 'number' column (UInt64) that contains integers from 0 to N-1.\n\n\nSimilar to the \nsystem.numbers\n table, it can be used for testing and generating successive values.\n\n\nThe following two queries are equivalent:\n\n\nSELECT\n \n*\n \nFROM\n \nnumbers\n(\n10\n);\n\n\nSELECT\n \n*\n \nFROM\n \nsystem\n.\nnumbers\n \nLIMIT\n \n10\n;\n\n\n\n\n\n\nExamples:\n\n\n-- Generate a sequence of dates from 2010-01-01 to 2010-12-31\n\n\nselect\n \ntoDate\n(\n2010-01-01\n)\n \n+\n \nnumber\n \nas\n \nd\n \nFROM\n \nnumbers\n(\n365\n);", + "title": "numbers" + }, + { + "location": "/table_functions/numbers/#numbers", + "text": "numbers(N) \u2013 Returns a table with the single 'number' column (UInt64) that contains integers from 0 to N-1. Similar to the system.numbers table, it can be used for testing and generating successive values. The following two queries are equivalent: SELECT * FROM numbers ( 10 ); SELECT * FROM system . numbers LIMIT 10 ; Examples: -- Generate a sequence of dates from 2010-01-01 to 2010-12-31 select toDate ( 2010-01-01 ) + number as d FROM numbers ( 365 );", + "title": "numbers" + }, + { + "location": "/formats/", + "text": "Formats\n\n\nThe format determines how data is returned to you after SELECTs (how it is written and formatted by the server), and how it is accepted for INSERTs (how it is read and parsed by the server).", + "title": "Introduction" + }, + { + "location": "/formats/#formats", + "text": "The format determines how data is returned to you after SELECTs (how it is written and formatted by the server), and how it is accepted for INSERTs (how it is read and parsed by the server).", + "title": "Formats" + }, + { + "location": "/formats/tabseparated/", + "text": "TabSeparated\n\n\nIn TabSeparated format, data is written by row. Each row contains values separated by tabs. Each value is follow by a tab, except the last value in the row, which is followed by a line feed. Strictly Unix line feeds are assumed everywhere. The last row also must contain a line feed at the end. Values are written in text format, without enclosing quotation marks, and with special characters escaped.\n\n\nInteger numbers are written in decimal form. Numbers can contain an extra \"+\" character at the beginning (ignored when parsing, and not recorded when formatting). Non-negative numbers can't contain the negative sign. When reading, it is allowed to parse an empty string as a zero, or (for signed types) a string consisting of just a minus sign as a zero. Numbers that do not fit into the corresponding data type may be parsed as a different number, without an error message.\n\n\nFloating-point numbers are written in decimal form. The dot is used as the decimal separator. Exponential entries are supported, as are 'inf', '+inf', '-inf', and 'nan'. An entry of floating-point numbers may begin or end with a decimal point.\nDuring formatting, accuracy may be lost on floating-point numbers.\nDuring parsing, it is not strictly required to read the nearest machine-representable number.\n\n\nDates are written in YYYY-MM-DD format and parsed in the same format, but with any characters as separators.\nDates with times are written in the format YYYY-MM-DD hh:mm:ss and parsed in the same format, but with any characters as separators.\nThis all occurs in the system time zone at the time the client or server starts (depending on which one formats data). For dates with times, daylight saving time is not specified. So if a dump has times during daylight saving time, the dump does not unequivocally match the data, and parsing will select one of the two times.\nDuring a read operation, incorrect dates and dates with times can be parsed with natural overflow or as null dates and times, without an error message.\n\n\nAs an exception, parsing dates with times is also supported in Unix timestamp format, if it consists of exactly 10 decimal digits. The result is not time zone-dependent. The formats YYYY-MM-DD hh:mm:ss and NNNNNNNNNN are differentiated automatically.\n\n\nStrings are output with backslash-escaped special characters. The following escape sequences are used for output: \n\\b\n, \n\\f\n, \n\\r\n, \n\\n\n, \n\\t\n, \n\\0\n, \n\\'\n, \n\\\\\n. Parsing also supports the sequences \n\\a\n, \n\\v\n, and \n\\xHH\n (hex escape sequences) and any \n\\c\n sequences, where \nc\n is any character (these sequences are converted to \nc\n). Thus, reading data supports formats where a line feed can be written as \n\\n\n or \n\\\n, or as a line feed. For example, the string \nHello world\n with a line feed between the words instead of a space can be parsed in any of the following variations:\n\n\nHello\\nworld\n\nHello\\\nworld\n\n\n\n\n\nThe second variant is supported because MySQL uses it when writing tab-separated dumps.\n\n\nThe minimum set of characters that you need to escape when passing data in TabSeparated format: tab, line feed (LF) and backslash.\n\n\nOnly a small set of symbols are escaped. You can easily stumble onto a string value that your terminal will ruin in output.\n\n\nArrays are written as a list of comma-separated values in square brackets. Number items in the array are fomratted as normally, but dates, dates with times, and strings are written in single quotes with the same escaping rules as above.\n\n\nThe TabSeparated format is convenient for processing data using custom programs and scripts. It is used by default in the HTTP interface, and in the command-line client's batch mode. This format also allows transferring data between different DBMSs. For example, you can get a dump from MySQL and upload it to ClickHouse, or vice versa.\n\n\nThe TabSeparated format supports outputting total values (when using WITH TOTALS) and extreme values (when 'extremes' is set to 1). In these cases, the total values and extremes are output after the main data. The main result, total values, and extremes are separated from each other by an empty line. Example:\n\n\nSELECT\n \nEventDate\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n \nGROUP\n \nBY\n \nEventDate\n \nWITH\n \nTOTALS\n \nORDER\n \nBY\n \nEventDate\n \nFORMAT\n \nTabSeparated\n``\n\n\n\n\n\n\n2014-03-17 1406958\n2014-03-18 1383658\n2014-03-19 1405797\n2014-03-20 1353623\n2014-03-21 1245779\n2014-03-22 1031592\n2014-03-23 1046491\n\n0000-00-00 8873898\n\n2014-03-17 1031592\n2014-03-23 1406958\n\n\n\n\n\nThis format is also available under the name \nTSV\n.", + "title": "TabSeparated" + }, + { + "location": "/formats/tabseparated/#tabseparated", + "text": "In TabSeparated format, data is written by row. Each row contains values separated by tabs. Each value is follow by a tab, except the last value in the row, which is followed by a line feed. Strictly Unix line feeds are assumed everywhere. The last row also must contain a line feed at the end. Values are written in text format, without enclosing quotation marks, and with special characters escaped. Integer numbers are written in decimal form. Numbers can contain an extra \"+\" character at the beginning (ignored when parsing, and not recorded when formatting). Non-negative numbers can't contain the negative sign. When reading, it is allowed to parse an empty string as a zero, or (for signed types) a string consisting of just a minus sign as a zero. Numbers that do not fit into the corresponding data type may be parsed as a different number, without an error message. Floating-point numbers are written in decimal form. The dot is used as the decimal separator. Exponential entries are supported, as are 'inf', '+inf', '-inf', and 'nan'. An entry of floating-point numbers may begin or end with a decimal point.\nDuring formatting, accuracy may be lost on floating-point numbers.\nDuring parsing, it is not strictly required to read the nearest machine-representable number. Dates are written in YYYY-MM-DD format and parsed in the same format, but with any characters as separators.\nDates with times are written in the format YYYY-MM-DD hh:mm:ss and parsed in the same format, but with any characters as separators.\nThis all occurs in the system time zone at the time the client or server starts (depending on which one formats data). For dates with times, daylight saving time is not specified. So if a dump has times during daylight saving time, the dump does not unequivocally match the data, and parsing will select one of the two times.\nDuring a read operation, incorrect dates and dates with times can be parsed with natural overflow or as null dates and times, without an error message. As an exception, parsing dates with times is also supported in Unix timestamp format, if it consists of exactly 10 decimal digits. The result is not time zone-dependent. The formats YYYY-MM-DD hh:mm:ss and NNNNNNNNNN are differentiated automatically. Strings are output with backslash-escaped special characters. The following escape sequences are used for output: \\b , \\f , \\r , \\n , \\t , \\0 , \\' , \\\\ . Parsing also supports the sequences \\a , \\v , and \\xHH (hex escape sequences) and any \\c sequences, where c is any character (these sequences are converted to c ). Thus, reading data supports formats where a line feed can be written as \\n or \\ , or as a line feed. For example, the string Hello world with a line feed between the words instead of a space can be parsed in any of the following variations: Hello\\nworld\n\nHello\\\nworld The second variant is supported because MySQL uses it when writing tab-separated dumps. The minimum set of characters that you need to escape when passing data in TabSeparated format: tab, line feed (LF) and backslash. Only a small set of symbols are escaped. You can easily stumble onto a string value that your terminal will ruin in output. Arrays are written as a list of comma-separated values in square brackets. Number items in the array are fomratted as normally, but dates, dates with times, and strings are written in single quotes with the same escaping rules as above. The TabSeparated format is convenient for processing data using custom programs and scripts. It is used by default in the HTTP interface, and in the command-line client's batch mode. This format also allows transferring data between different DBMSs. For example, you can get a dump from MySQL and upload it to ClickHouse, or vice versa. The TabSeparated format supports outputting total values (when using WITH TOTALS) and extreme values (when 'extremes' is set to 1). In these cases, the total values and extremes are output after the main data. The main result, total values, and extremes are separated from each other by an empty line. Example: SELECT EventDate , count () AS c FROM test . hits GROUP BY EventDate WITH TOTALS ORDER BY EventDate FORMAT TabSeparated `` 2014-03-17 1406958\n2014-03-18 1383658\n2014-03-19 1405797\n2014-03-20 1353623\n2014-03-21 1245779\n2014-03-22 1031592\n2014-03-23 1046491\n\n0000-00-00 8873898\n\n2014-03-17 1031592\n2014-03-23 1406958 This format is also available under the name TSV .", + "title": "TabSeparated" + }, + { + "location": "/formats/tabseparatedraw/", + "text": "TabSeparatedRaw\n\n\nDiffers from \nTabSeparated\n format in that the rows are written without escaping.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\n\n\nThis format is also available under the name \nTSVRaw\n.", + "title": "TabSeparatedRaw" + }, + { + "location": "/formats/tabseparatedraw/#tabseparatedraw", + "text": "Differs from TabSeparated format in that the rows are written without escaping.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). This format is also available under the name TSVRaw .", + "title": "TabSeparatedRaw" + }, + { + "location": "/formats/tabseparatedwithnames/", + "text": "TabSeparatedWithNames\n\n\nDiffers from the \nTabSeparated\n format in that the column names are written in the first row.\nDuring parsing, the first row is completely ignored. You can't use column names to determine their position or to check their correctness.\n(Support for parsing the header row may be added in the future.)\n\n\nThis format is also available under the name \nTSVWithNames\n.", + "title": "TabSeparatedWithNames" + }, + { + "location": "/formats/tabseparatedwithnames/#tabseparatedwithnames", + "text": "Differs from the TabSeparated format in that the column names are written in the first row.\nDuring parsing, the first row is completely ignored. You can't use column names to determine their position or to check their correctness.\n(Support for parsing the header row may be added in the future.) This format is also available under the name TSVWithNames .", + "title": "TabSeparatedWithNames" + }, + { + "location": "/formats/tabseparatedwithnamesandtypes/", + "text": "TabSeparatedWithNamesAndTypes\n\n\nDiffers from the \nTabSeparated\n format in that the column names are written to the first row, while the column types are in the second row.\nDuring parsing, the first and second rows are completely ignored.\n\n\nThis format is also available under the name \nTSVWithNamesAndTypes\n.", + "title": "TabSeparatedWithNamesAndTypes" + }, + { + "location": "/formats/tabseparatedwithnamesandtypes/#tabseparatedwithnamesandtypes", + "text": "Differs from the TabSeparated format in that the column names are written to the first row, while the column types are in the second row.\nDuring parsing, the first and second rows are completely ignored. This format is also available under the name TSVWithNamesAndTypes .", + "title": "TabSeparatedWithNamesAndTypes" + }, + { + "location": "/formats/csv/", + "text": "CSV\n\n\nComma Separated Values format (\nRFC\n).\n\n\nWhen formatting, rows are enclosed in double quotes. A double quote inside a string is output as two double quotes in a row. There are no other rules for escaping characters. Date and date-time are enclosed in double quotes. Numbers are output without quotes. Values \u200b\u200bare separated by a delimiter\n. Rows are separated using the Unix line feed (LF). Arrays are serialized in CSV as follows: first the array is serialized to a string as in TabSeparated format, and then the resulting string is output to CSV in double quotes. Tuples in CSV format are serialized as separate columns (that is, their nesting in the tuple is lost).\n\n\nBy default \u2014 \n,\n. See a \nformat_csv_delimiter\n setting for additional info.\n\n\nWhen parsing, all values can be parsed either with or without quotes. Both double and single quotes are supported. Rows can also be arranged without quotes. In this case, they are parsed up to a delimiter or line feed (CR or LF). In violation of the RFC, when parsing rows without quotes, the leading and trailing spaces and tabs are ignored. For the line feed, Unix (LF), Windows (CR LF) and Mac OS Classic (CR LF) are all supported.\n\n\nThe CSV format supports the output of totals and extremes the same way as \nTabSeparated\n.", + "title": "CSV" + }, + { + "location": "/formats/csv/#csv", + "text": "Comma Separated Values format ( RFC ). When formatting, rows are enclosed in double quotes. A double quote inside a string is output as two double quotes in a row. There are no other rules for escaping characters. Date and date-time are enclosed in double quotes. Numbers are output without quotes. Values \u200b\u200bare separated by a delimiter . Rows are separated using the Unix line feed (LF). Arrays are serialized in CSV as follows: first the array is serialized to a string as in TabSeparated format, and then the resulting string is output to CSV in double quotes. Tuples in CSV format are serialized as separate columns (that is, their nesting in the tuple is lost). By default \u2014 , . See a format_csv_delimiter setting for additional info. When parsing, all values can be parsed either with or without quotes. Both double and single quotes are supported. Rows can also be arranged without quotes. In this case, they are parsed up to a delimiter or line feed (CR or LF). In violation of the RFC, when parsing rows without quotes, the leading and trailing spaces and tabs are ignored. For the line feed, Unix (LF), Windows (CR LF) and Mac OS Classic (CR LF) are all supported. The CSV format supports the output of totals and extremes the same way as TabSeparated .", + "title": "CSV" + }, + { + "location": "/formats/csvwithnames/", + "text": "CSVWithNames\n\n\nAlso prints the header row, similar to \nTabSeparatedWithNames\n.", + "title": "CSVWithNames" + }, + { + "location": "/formats/csvwithnames/#csvwithnames", + "text": "Also prints the header row, similar to TabSeparatedWithNames .", + "title": "CSVWithNames" + }, + { + "location": "/formats/values/", + "text": "Values\n\n\nPrints every row in brackets. Rows are separated by commas. There is no comma after the last row. The values inside the brackets are also comma-separated. Numbers are output in decimal format without quotes. Arrays are output in square brackets. Strings, dates, and dates with times are output in quotes. Escaping rules and parsing are similar to the TabSeparated format. During formatting, extra spaces aren't inserted, but during parsing, they are allowed and skipped (except for spaces inside array values, which are not allowed).\n\n\nThe minimum set of characters that you need to escape when passing data in Values \u200b\u200bformat: single quotes and backslashes.\n\n\nThis is the format that is used in \nINSERT INTO t VALUES ...\n, but you can also use it for formatting query results.", + "title": "Values" + }, + { + "location": "/formats/values/#values", + "text": "Prints every row in brackets. Rows are separated by commas. There is no comma after the last row. The values inside the brackets are also comma-separated. Numbers are output in decimal format without quotes. Arrays are output in square brackets. Strings, dates, and dates with times are output in quotes. Escaping rules and parsing are similar to the TabSeparated format. During formatting, extra spaces aren't inserted, but during parsing, they are allowed and skipped (except for spaces inside array values, which are not allowed). The minimum set of characters that you need to escape when passing data in Values \u200b\u200bformat: single quotes and backslashes. This is the format that is used in INSERT INTO t VALUES ... , but you can also use it for formatting query results.", + "title": "Values" + }, + { + "location": "/formats/vertical/", + "text": "Vertical\n\n\nPrints each value on a separate line with the column name specified. This format is convenient for printing just one or a few rows, if each row consists of a large number of columns.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).", + "title": "Vertical" + }, + { + "location": "/formats/vertical/#vertical", + "text": "Prints each value on a separate line with the column name specified. This format is convenient for printing just one or a few rows, if each row consists of a large number of columns.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).", + "title": "Vertical" + }, + { + "location": "/formats/verticalraw/", + "text": "VerticalRaw\n\n\nDiffers from \nVertical\n format in that the rows are not escaped.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\n\n\nExamples:\n\n\n:) SHOW CREATE TABLE geonames FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nstatement: CREATE TABLE default.geonames ( geonameid UInt32, date Date DEFAULT CAST(\n2017-12-08\n AS Date)) ENGINE = MergeTree(date, geonameid, 8192)\n\n:) SELECT \nstring with \\\nquotes\\\n and \\t with some special \\n characters\n AS test FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with \nquotes\n and with some special\n characters\n\n\n\n\n\nCompare with the Vertical format:\n\n\n:) SELECT \nstring with \\\nquotes\\\n and \\t with some special \\n characters\n AS test FORMAT Vertical;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with \\\nquotes\\\n and \\t with some special \\n characters", + "title": "VerticalRaw" + }, + { + "location": "/formats/verticalraw/#verticalraw", + "text": "Differs from Vertical format in that the rows are not escaped.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). Examples: :) SHOW CREATE TABLE geonames FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nstatement: CREATE TABLE default.geonames ( geonameid UInt32, date Date DEFAULT CAST( 2017-12-08 AS Date)) ENGINE = MergeTree(date, geonameid, 8192)\n\n:) SELECT string with \\ quotes\\ and \\t with some special \\n characters AS test FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with quotes and with some special\n characters Compare with the Vertical format: :) SELECT string with \\ quotes\\ and \\t with some special \\n characters AS test FORMAT Vertical;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with \\ quotes\\ and \\t with some special \\n characters", + "title": "VerticalRaw" + }, + { + "location": "/formats/json/", + "text": "JSON\n\n\nOutputs data in JSON format. Besides data tables, it also outputs column names and types, along with some additional information: the total number of output rows, and the number of rows that could have been output if there weren't a LIMIT. Example:\n\n\nSELECT\n \nSearchPhrase\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n \nGROUP\n \nBY\n \nSearchPhrase\n \nWITH\n \nTOTALS\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n5\n \nFORMAT\n \nJSON\n\n\n\n\n\n\n{\n\n \nmeta\n:\n\n \n[\n\n \n{\n\n \nname\n:\n \nSearchPhrase\n,\n\n \ntype\n:\n \nString\n\n \n},\n\n \n{\n\n \nname\n:\n \nc\n,\n\n \ntype\n:\n \nUInt64\n\n \n}\n\n \n],\n\n\n \ndata\n:\n\n \n[\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n8267016\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nbathroom interior design\n,\n\n \nc\n:\n \n2166\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nyandex\n,\n\n \nc\n:\n \n1655\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nspring 2014 fashion\n,\n\n \nc\n:\n \n1549\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nfreeform photos\n,\n\n \nc\n:\n \n1480\n\n \n}\n\n \n],\n\n\n \ntotals\n:\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n8873898\n\n \n},\n\n\n \nextremes\n:\n\n \n{\n\n \nmin\n:\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n1480\n\n \n},\n\n \nmax\n:\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n8267016\n\n \n}\n\n \n},\n\n\n \nrows\n:\n \n5\n,\n\n\n \nrows_before_limit_at_least\n:\n \n141137\n\n\n}\n\n\n\n\n\n\nThe JSON is compatible with JavaScript. To ensure this, some characters are additionally escaped: the slash \n/\n is escaped as \n\\/\n; alternative line breaks \nU+2028\n and \nU+2029\n, which break some browsers, are escaped as \n\\uXXXX\n. ASCII control characters are escaped: backspace, form feed, line feed, carriage return, and horizontal tab are replaced with \n\\b\n, \n\\f\n, \n\\n\n, \n\\r\n, \n\\t\n , as well as the remaining bytes in the 00-1F range using \n\\uXXXX\n sequences. Invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences. For compatibility with JavaScript, Int64 and UInt64 integers are enclosed in double quotes by default. To remove the quotes, you can set the configuration parameter output_format_json_quote_64bit_integers to 0.\n\n\nrows\n \u2013 The total number of output rows.\n\n\nrows_before_limit_at_least\n The minimal number of rows there would have been without LIMIT. Output only if the query contains LIMIT.\nIf the query contains GROUP BY, rows_before_limit_at_least is the exact number of rows there would have been without a LIMIT.\n\n\ntotals\n \u2013 Total values (when using WITH TOTALS).\n\n\nextremes\n \u2013 Extreme values (when extremes is set to 1).\n\n\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the JSONEachRow format.", + "title": "JSON" + }, + { + "location": "/formats/json/#json", + "text": "Outputs data in JSON format. Besides data tables, it also outputs column names and types, along with some additional information: the total number of output rows, and the number of rows that could have been output if there weren't a LIMIT. Example: SELECT SearchPhrase , count () AS c FROM test . hits GROUP BY SearchPhrase WITH TOTALS ORDER BY c DESC LIMIT 5 FORMAT JSON { \n meta : \n [ \n { \n name : SearchPhrase , \n type : String \n }, \n { \n name : c , \n type : UInt64 \n } \n ], \n\n data : \n [ \n { \n SearchPhrase : , \n c : 8267016 \n }, \n { \n SearchPhrase : bathroom interior design , \n c : 2166 \n }, \n { \n SearchPhrase : yandex , \n c : 1655 \n }, \n { \n SearchPhrase : spring 2014 fashion , \n c : 1549 \n }, \n { \n SearchPhrase : freeform photos , \n c : 1480 \n } \n ], \n\n totals : \n { \n SearchPhrase : , \n c : 8873898 \n }, \n\n extremes : \n { \n min : \n { \n SearchPhrase : , \n c : 1480 \n }, \n max : \n { \n SearchPhrase : , \n c : 8267016 \n } \n }, \n\n rows : 5 , \n\n rows_before_limit_at_least : 141137 } The JSON is compatible with JavaScript. To ensure this, some characters are additionally escaped: the slash / is escaped as \\/ ; alternative line breaks U+2028 and U+2029 , which break some browsers, are escaped as \\uXXXX . ASCII control characters are escaped: backspace, form feed, line feed, carriage return, and horizontal tab are replaced with \\b , \\f , \\n , \\r , \\t , as well as the remaining bytes in the 00-1F range using \\uXXXX sequences. Invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences. For compatibility with JavaScript, Int64 and UInt64 integers are enclosed in double quotes by default. To remove the quotes, you can set the configuration parameter output_format_json_quote_64bit_integers to 0. rows \u2013 The total number of output rows. rows_before_limit_at_least The minimal number of rows there would have been without LIMIT. Output only if the query contains LIMIT.\nIf the query contains GROUP BY, rows_before_limit_at_least is the exact number of rows there would have been without a LIMIT. totals \u2013 Total values (when using WITH TOTALS). extremes \u2013 Extreme values (when extremes is set to 1). This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the JSONEachRow format.", + "title": "JSON" + }, + { + "location": "/formats/jsoncompact/", + "text": "JSONCompact\n\n\nDiffers from JSON only in that data rows are output in arrays, not in objects.\n\n\nExample:\n\n\n{\n\n \nmeta\n:\n\n \n[\n\n \n{\n\n \nname\n:\n \nSearchPhrase\n,\n\n \ntype\n:\n \nString\n\n \n},\n\n \n{\n\n \nname\n:\n \nc\n,\n\n \ntype\n:\n \nUInt64\n\n \n}\n\n \n],\n\n\n \ndata\n:\n\n \n[\n\n \n[\n,\n \n8267016\n],\n\n \n[\nbathroom interior design\n,\n \n2166\n],\n\n \n[\nyandex\n,\n \n1655\n],\n\n \n[\nspring 2014 fashion\n,\n \n1549\n],\n\n \n[\nfreeform photos\n,\n \n1480\n]\n\n \n],\n\n\n \ntotals\n:\n \n[\n,\n8873898\n],\n\n\n \nextremes\n:\n\n \n{\n\n \nmin\n:\n \n[\n,\n1480\n],\n\n \nmax\n:\n \n[\n,\n8267016\n]\n\n \n},\n\n\n \nrows\n:\n \n5\n,\n\n\n \nrows_before_limit_at_least\n:\n \n141137\n\n\n}\n\n\n\n\n\n\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the \nJSONEachRow\n format.", + "title": "JSONCompact" + }, + { + "location": "/formats/jsoncompact/#jsoncompact", + "text": "Differs from JSON only in that data rows are output in arrays, not in objects. Example: { \n meta : \n [ \n { \n name : SearchPhrase , \n type : String \n }, \n { \n name : c , \n type : UInt64 \n } \n ], \n\n data : \n [ \n [ , 8267016 ], \n [ bathroom interior design , 2166 ], \n [ yandex , 1655 ], \n [ spring 2014 fashion , 1549 ], \n [ freeform photos , 1480 ] \n ], \n\n totals : [ , 8873898 ], \n\n extremes : \n { \n min : [ , 1480 ], \n max : [ , 8267016 ] \n }, \n\n rows : 5 , \n\n rows_before_limit_at_least : 141137 } This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the JSONEachRow format.", + "title": "JSONCompact" + }, + { + "location": "/formats/jsoneachrow/", + "text": "JSONEachRow\n\n\nOutputs data as separate JSON objects for each row (newline delimited JSON).\n\n\n{\nSearchPhrase\n:\n,\ncount()\n:\n8267016\n}\n\n\n{\nSearchPhrase\n:\nbathroom interior design\n,\ncount()\n:\n2166\n}\n\n\n{\nSearchPhrase\n:\nyandex\n,\ncount()\n:\n1655\n}\n\n\n{\nSearchPhrase\n:\nspring 2014 fashion\n,\ncount()\n:\n1549\n}\n\n\n{\nSearchPhrase\n:\nfreeform photo\n,\ncount()\n:\n1480\n}\n\n\n{\nSearchPhrase\n:\nangelina jolie\n,\ncount()\n:\n1245\n}\n\n\n{\nSearchPhrase\n:\nomsk\n,\ncount()\n:\n1112\n}\n\n\n{\nSearchPhrase\n:\nphotos of dog breeds\n,\ncount()\n:\n1091\n}\n\n\n{\nSearchPhrase\n:\ncurtain design\n,\ncount()\n:\n1064\n}\n\n\n{\nSearchPhrase\n:\nbaku\n,\ncount()\n:\n1000\n}\n\n\n\n\n\n\nUnlike the JSON format, there is no substitution of invalid UTF-8 sequences. Any set of bytes can be output in the rows. This is necessary so that data can be formatted without losing any information. Values are escaped in the same way as for JSON.\n\n\nFor parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Whitespace between elements is ignored. If a comma is placed after the objects, it is ignored. Objects don't necessarily have to be separated by new lines.", + "title": "JSONEachRow" + }, + { + "location": "/formats/jsoneachrow/#jsoneachrow", + "text": "Outputs data as separate JSON objects for each row (newline delimited JSON). { SearchPhrase : , count() : 8267016 } { SearchPhrase : bathroom interior design , count() : 2166 } { SearchPhrase : yandex , count() : 1655 } { SearchPhrase : spring 2014 fashion , count() : 1549 } { SearchPhrase : freeform photo , count() : 1480 } { SearchPhrase : angelina jolie , count() : 1245 } { SearchPhrase : omsk , count() : 1112 } { SearchPhrase : photos of dog breeds , count() : 1091 } { SearchPhrase : curtain design , count() : 1064 } { SearchPhrase : baku , count() : 1000 } Unlike the JSON format, there is no substitution of invalid UTF-8 sequences. Any set of bytes can be output in the rows. This is necessary so that data can be formatted without losing any information. Values are escaped in the same way as for JSON. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Whitespace between elements is ignored. If a comma is placed after the objects, it is ignored. Objects don't necessarily have to be separated by new lines.", + "title": "JSONEachRow" + }, + { + "location": "/formats/tskv/", + "text": "TSKV\n\n\nSimilar to TabSeparated, but outputs a value in name=value format. Names are escaped the same way as in TabSeparated format, and the = symbol is also escaped.\n\n\nSearchPhrase= count()=8267016\nSearchPhrase=bathroom interior design count()=2166\nSearchPhrase=yandex count()=1655\nSearchPhrase=spring 2014 fashion count()=1549\nSearchPhrase=freeform photos count()=1480\nSearchPhrase=angelina jolia count()=1245\nSearchPhrase=omsk count()=1112\nSearchPhrase=photos of dog breeds count()=1091\nSearchPhrase=curtain design count()=1064\nSearchPhrase=baku count()=1000\n\n\n\n\n\nWhen there is a large number of small columns, this format is ineffective, and there is generally no reason to use it. It is used in some departments of Yandex.\n\n\nBoth data output and parsing are supported in this format. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults.\n\n\nParsing allows the presence of the additional field \ntskv\n without the equal sign or a value. This field is ignored.", + "title": "TSKV" + }, + { + "location": "/formats/tskv/#tskv", + "text": "Similar to TabSeparated, but outputs a value in name=value format. Names are escaped the same way as in TabSeparated format, and the = symbol is also escaped. SearchPhrase= count()=8267016\nSearchPhrase=bathroom interior design count()=2166\nSearchPhrase=yandex count()=1655\nSearchPhrase=spring 2014 fashion count()=1549\nSearchPhrase=freeform photos count()=1480\nSearchPhrase=angelina jolia count()=1245\nSearchPhrase=omsk count()=1112\nSearchPhrase=photos of dog breeds count()=1091\nSearchPhrase=curtain design count()=1064\nSearchPhrase=baku count()=1000 When there is a large number of small columns, this format is ineffective, and there is generally no reason to use it. It is used in some departments of Yandex. Both data output and parsing are supported in this format. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Parsing allows the presence of the additional field tskv without the equal sign or a value. This field is ignored.", + "title": "TSKV" + }, + { + "location": "/formats/pretty/", + "text": "Pretty\n\n\nOutputs data as Unicode-art tables, also using ANSI-escape sequences for setting colors in the terminal.\nA full grid of the table is drawn, and each row occupies two lines in the terminal.\nEach result block is output as a separate table. This is necessary so that blocks can be output without buffering results (buffering would be necessary in order to pre-calculate the visible width of all the values).\nTo avoid dumping too much data to the terminal, only the first 10,000 rows are printed. If the number of rows is greater than or equal to 10,000, the message \"Showed first 10 000\" is printed.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\n\n\nThe Pretty format supports outputting total values (when using WITH TOTALS) and extremes (when 'extremes' is set to 1). In these cases, total values and extreme values are output after the main data, in separate tables. Example (shown for the PrettyCompact format):\n\n\nSELECT\n \nEventDate\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n \nGROUP\n \nBY\n \nEventDate\n \nWITH\n \nTOTALS\n \nORDER\n \nBY\n \nEventDate\n \nFORMAT\n \nPrettyCompact\n\n\n\n\n\n\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1406958 \u2502\n\u2502 2014-03-18 \u2502 1383658 \u2502\n\u2502 2014-03-19 \u2502 1405797 \u2502\n\u2502 2014-03-20 \u2502 1353623 \u2502\n\u2502 2014-03-21 \u2502 1245779 \u2502\n\u2502 2014-03-22 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1046491 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nTotals:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 0000-00-00 \u2502 8873898 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nExtremes:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1406958 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "Pretty" + }, + { + "location": "/formats/pretty/#pretty", + "text": "Outputs data as Unicode-art tables, also using ANSI-escape sequences for setting colors in the terminal.\nA full grid of the table is drawn, and each row occupies two lines in the terminal.\nEach result block is output as a separate table. This is necessary so that blocks can be output without buffering results (buffering would be necessary in order to pre-calculate the visible width of all the values).\nTo avoid dumping too much data to the terminal, only the first 10,000 rows are printed. If the number of rows is greater than or equal to 10,000, the message \"Showed first 10 000\" is printed.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). The Pretty format supports outputting total values (when using WITH TOTALS) and extremes (when 'extremes' is set to 1). In these cases, total values and extreme values are output after the main data, in separate tables. Example (shown for the PrettyCompact format): SELECT EventDate , count () AS c FROM test . hits GROUP BY EventDate WITH TOTALS ORDER BY EventDate FORMAT PrettyCompact \u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1406958 \u2502\n\u2502 2014-03-18 \u2502 1383658 \u2502\n\u2502 2014-03-19 \u2502 1405797 \u2502\n\u2502 2014-03-20 \u2502 1353623 \u2502\n\u2502 2014-03-21 \u2502 1245779 \u2502\n\u2502 2014-03-22 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1046491 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nTotals:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 0000-00-00 \u2502 8873898 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nExtremes:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1406958 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "Pretty" + }, + { + "location": "/formats/prettycompact/", + "text": "PrettyCompact\n\n\nDiffers from \nPretty\n in that the grid is drawn between rows and the result is more compact.\nThis format is used by default in the command-line client in interactive mode.", + "title": "PrettyCompact" + }, + { + "location": "/formats/prettycompact/#prettycompact", + "text": "Differs from Pretty in that the grid is drawn between rows and the result is more compact.\nThis format is used by default in the command-line client in interactive mode.", + "title": "PrettyCompact" + }, + { + "location": "/formats/prettycompactmonoblock/", + "text": "PrettyCompactMonoBlock\n\n\nDiffers from \nPrettyCompact\n in that up to 10,000 rows are buffered, then output as a single table, not by blocks.", + "title": "PrettyCompactMonoBlock" + }, + { + "location": "/formats/prettycompactmonoblock/#prettycompactmonoblock", + "text": "Differs from PrettyCompact in that up to 10,000 rows are buffered, then output as a single table, not by blocks.", + "title": "PrettyCompactMonoBlock" + }, + { + "location": "/formats/prettynoescapes/", + "text": "PrettyNoEscapes\n\n\nDiffers from Pretty in that ANSI-escape sequences aren't used. This is necessary for displaying this format in a browser, as well as for using the 'watch' command-line utility.\n\n\nExample:\n\n\nwatch -n1 \nclickhouse-client --query=\nSELECT * FROM system.events FORMAT PrettyCompactNoEscapes\n\n\n\n\n\n\nYou can use the HTTP interface for displaying in the browser.\n\n\nPrettyCompactNoEscapes\n\n\nThe same as the previous setting.\n\n\nPrettySpaceNoEscapes\n\n\nThe same as the previous setting.", + "title": "PrettyNoEscapes" + }, + { + "location": "/formats/prettynoescapes/#prettynoescapes", + "text": "Differs from Pretty in that ANSI-escape sequences aren't used. This is necessary for displaying this format in a browser, as well as for using the 'watch' command-line utility. Example: watch -n1 clickhouse-client --query= SELECT * FROM system.events FORMAT PrettyCompactNoEscapes You can use the HTTP interface for displaying in the browser.", + "title": "PrettyNoEscapes" + }, + { + "location": "/formats/prettynoescapes/#prettycompactnoescapes", + "text": "The same as the previous setting.", + "title": "PrettyCompactNoEscapes" + }, + { + "location": "/formats/prettynoescapes/#prettyspacenoescapes", + "text": "The same as the previous setting.", + "title": "PrettySpaceNoEscapes" + }, + { + "location": "/formats/prettyspace/", + "text": "PrettySpace\n\n\nDiffers from \nPrettyCompact\n in that whitespace (space characters) is used instead of the grid.", + "title": "PrettySpace" + }, + { + "location": "/formats/prettyspace/#prettyspace", + "text": "Differs from PrettyCompact in that whitespace (space characters) is used instead of the grid.", + "title": "PrettySpace" + }, + { + "location": "/formats/rowbinary/", + "text": "RowBinary\n\n\nFormats and parses data by row in binary format. Rows and values are listed consecutively, without separators.\nThis format is less efficient than the Native format, since it is row-based.\n\n\nIntegers use fixed-length little endian representation. For example, UInt64 uses 8 bytes.\nDateTime is represented as UInt32 containing the Unix timestamp as the value.\nDate is represented as a UInt16 object that contains the number of days since 1970-01-01 as the value.\nString is represented as a varint length (unsigned \nLEB128\n), followed by the bytes of the string.\nFixedString is represented simply as a sequence of bytes.\n\n\nArray is represented as a varint length (unsigned \nLEB128\n), followed by successive elements of the array.", + "title": "RowBinary" + }, + { + "location": "/formats/rowbinary/#rowbinary", + "text": "Formats and parses data by row in binary format. Rows and values are listed consecutively, without separators.\nThis format is less efficient than the Native format, since it is row-based. Integers use fixed-length little endian representation. For example, UInt64 uses 8 bytes.\nDateTime is represented as UInt32 containing the Unix timestamp as the value.\nDate is represented as a UInt16 object that contains the number of days since 1970-01-01 as the value.\nString is represented as a varint length (unsigned LEB128 ), followed by the bytes of the string.\nFixedString is represented simply as a sequence of bytes. Array is represented as a varint length (unsigned LEB128 ), followed by successive elements of the array.", + "title": "RowBinary" + }, + { + "location": "/formats/native/", + "text": "Native\n\n\nThe most efficient format. Data is written and read by blocks in binary format. For each block, the number of rows, number of columns, column names and types, and parts of columns in this block are recorded one after another. In other words, this format is \"columnar\" \u2013 it doesn't convert columns to rows. This is the format used in the native interface for interaction between servers, for using the command-line client, and for C++ clients.\n\n\nYou can use this format to quickly generate dumps that can only be read by the ClickHouse DBMS. It doesn't make sense to work with this format yourself.", + "title": "Native" + }, + { + "location": "/formats/native/#native", + "text": "The most efficient format. Data is written and read by blocks in binary format. For each block, the number of rows, number of columns, column names and types, and parts of columns in this block are recorded one after another. In other words, this format is \"columnar\" \u2013 it doesn't convert columns to rows. This is the format used in the native interface for interaction between servers, for using the command-line client, and for C++ clients. You can use this format to quickly generate dumps that can only be read by the ClickHouse DBMS. It doesn't make sense to work with this format yourself.", + "title": "Native" + }, + { + "location": "/formats/null/", + "text": "Null\n\n\nNothing is output. However, the query is processed, and when using the command-line client, data is transmitted to the client. This is used for tests, including productivity testing.\nObviously, this format is only appropriate for output, not for parsing.", + "title": "Null" + }, + { + "location": "/formats/null/#null", + "text": "Nothing is output. However, the query is processed, and when using the command-line client, data is transmitted to the client. This is used for tests, including productivity testing.\nObviously, this format is only appropriate for output, not for parsing.", + "title": "Null" + }, + { + "location": "/formats/xml/", + "text": "XML\n\n\nXML format is suitable only for output, not for parsing. Example:\n\n\n?xml version=\n1.0\n encoding=\nUTF-8\n ?\n\n\nresult\n\n \nmeta\n\n \ncolumns\n\n \ncolumn\n\n \nname\nSearchPhrase\n/name\n\n \ntype\nString\n/type\n\n \n/column\n\n \ncolumn\n\n \nname\ncount()\n/name\n\n \ntype\nUInt64\n/type\n\n \n/column\n\n \n/columns\n\n \n/meta\n\n \ndata\n\n \nrow\n\n \nSearchPhrase\n/SearchPhrase\n\n \nfield\n8267016\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nbathroom interior design\n/SearchPhrase\n\n \nfield\n2166\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nyandex\n/SearchPhrase\n\n \nfield\n1655\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nspring 2014 fashion\n/SearchPhrase\n\n \nfield\n1549\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nfreeform photos\n/SearchPhrase\n\n \nfield\n1480\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nangelina jolie\n/SearchPhrase\n\n \nfield\n1245\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nomsk\n/SearchPhrase\n\n \nfield\n1112\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nphotos of dog breeds\n/SearchPhrase\n\n \nfield\n1091\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\ncurtain design\n/SearchPhrase\n\n \nfield\n1064\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nbaku\n/SearchPhrase\n\n \nfield\n1000\n/field\n\n \n/row\n\n \n/data\n\n \nrows\n10\n/rows\n\n \nrows_before_limit_at_least\n141137\n/rows_before_limit_at_least\n\n\n/result\n\n\n\n\n\n\nIf the column name does not have an acceptable format, just 'field' is used as the element name. In general, the XML structure follows the JSON structure.\nJust as for JSON, invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences.\n\n\nIn string values, the characters \n and \n are escaped as \n and \n.\n\n\nArrays are output as \narray\nelem\nHello\n/elem\nelem\nWorld\n/elem\n...\n/array\n,\nand tuples as \ntuple\nelem\nHello\n/elem\nelem\nWorld\n/elem\n...\n/tuple\n.", + "title": "XML" + }, + { + "location": "/formats/xml/#xml", + "text": "XML format is suitable only for output, not for parsing. Example: ?xml version= 1.0 encoding= UTF-8 ? result \n meta \n columns \n column \n name SearchPhrase /name \n type String /type \n /column \n column \n name count() /name \n type UInt64 /type \n /column \n /columns \n /meta \n data \n row \n SearchPhrase /SearchPhrase \n field 8267016 /field \n /row \n row \n SearchPhrase bathroom interior design /SearchPhrase \n field 2166 /field \n /row \n row \n SearchPhrase yandex /SearchPhrase \n field 1655 /field \n /row \n row \n SearchPhrase spring 2014 fashion /SearchPhrase \n field 1549 /field \n /row \n row \n SearchPhrase freeform photos /SearchPhrase \n field 1480 /field \n /row \n row \n SearchPhrase angelina jolie /SearchPhrase \n field 1245 /field \n /row \n row \n SearchPhrase omsk /SearchPhrase \n field 1112 /field \n /row \n row \n SearchPhrase photos of dog breeds /SearchPhrase \n field 1091 /field \n /row \n row \n SearchPhrase curtain design /SearchPhrase \n field 1064 /field \n /row \n row \n SearchPhrase baku /SearchPhrase \n field 1000 /field \n /row \n /data \n rows 10 /rows \n rows_before_limit_at_least 141137 /rows_before_limit_at_least /result If the column name does not have an acceptable format, just 'field' is used as the element name. In general, the XML structure follows the JSON structure.\nJust as for JSON, invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences. In string values, the characters and are escaped as and . Arrays are output as array elem Hello /elem elem World /elem ... /array ,\nand tuples as tuple elem Hello /elem elem World /elem ... /tuple .", + "title": "XML" + }, + { + "location": "/formats/capnproto/", + "text": "CapnProto\n\n\nCap'n Proto is a binary message format similar to Protocol Buffers and Thrift, but not like JSON or MessagePack.\n\n\nCap'n Proto messages are strictly typed and not self-describing, meaning they need an external schema description. The schema is applied on the fly and cached for each query.\n\n\nSELECT\n \nSearchPhrase\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n\n \nGROUP\n \nBY\n \nSearchPhrase\n \nFORMAT\n \nCapnProto\n \nSETTINGS\n \nschema\n \n=\n \nschema:Message\n\n\n\n\n\n\nWhere \nschema.capnp\n looks like this:\n\n\nstruct\n \nMessage\n \n{\n\n \nSearchPhrase\n \n@0\n \n:\nText\n;\n\n \nc\n \n@1\n \n:\nUint64\n;\n\n\n}\n\n\n\n\n\n\nSchema files are in the file that is located in the directory specified in \n format_schema_path\n in the server configuration.\n\n\nDeserialization is effective and usually doesn't increase the system load.", + "title": "CapnProto" + }, + { + "location": "/formats/capnproto/#capnproto", + "text": "Cap'n Proto is a binary message format similar to Protocol Buffers and Thrift, but not like JSON or MessagePack. Cap'n Proto messages are strictly typed and not self-describing, meaning they need an external schema description. The schema is applied on the fly and cached for each query. SELECT SearchPhrase , count () AS c FROM test . hits \n GROUP BY SearchPhrase FORMAT CapnProto SETTINGS schema = schema:Message Where schema.capnp looks like this: struct Message { \n SearchPhrase @0 : Text ; \n c @1 : Uint64 ; } Schema files are in the file that is located in the directory specified in format_schema_path in the server configuration. Deserialization is effective and usually doesn't increase the system load.", + "title": "CapnProto" + }, + { + "location": "/data_types/", + "text": "Data types\n\n\nClickHouse can store various types of data in table cells.\n\n\nThis section describes the supported data types and special considerations when using and/or implementing them, if any.", + "title": "Introduction" + }, + { + "location": "/data_types/#data-types", + "text": "ClickHouse can store various types of data in table cells. This section describes the supported data types and special considerations when using and/or implementing them, if any.", + "title": "Data types" + }, + { + "location": "/data_types/int_uint/", + "text": "UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64\n\n\nFixed-length integers, with or without a sign.\n\n\nInt ranges\n\n\n\n\nInt8 - [-128 : 127]\n\n\nInt16 - [-32768 : 32767]\n\n\nInt32 - [-2147483648 : 2147483647]\n\n\nInt64 - [-9223372036854775808 : 9223372036854775807]\n\n\n\n\nUint ranges\n\n\n\n\nUInt8 - [0 : 255]\n\n\nUInt16 - [0 : 65535]\n\n\nUInt32 - [0 : 4294967295]\n\n\nUInt64 - [0 : 18446744073709551615]", + "title": "UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64" + }, + { + "location": "/data_types/int_uint/#uint8-uint16-uint32-uint64-int8-int16-int32-int64", + "text": "Fixed-length integers, with or without a sign.", + "title": "UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64" + }, + { + "location": "/data_types/int_uint/#int-ranges", + "text": "Int8 - [-128 : 127] Int16 - [-32768 : 32767] Int32 - [-2147483648 : 2147483647] Int64 - [-9223372036854775808 : 9223372036854775807]", + "title": "Int ranges" + }, + { + "location": "/data_types/int_uint/#uint-ranges", + "text": "UInt8 - [0 : 255] UInt16 - [0 : 65535] UInt32 - [0 : 4294967295] UInt64 - [0 : 18446744073709551615]", + "title": "Uint ranges" + }, + { + "location": "/data_types/float/", + "text": "Float32, Float64\n\n\nFloating point numbers\n.\n\n\nTypes are equivalent to types of C:\n\n\n\n\nFloat32\n - \nfloat\n\n\nFloat64\n - \ndouble\n\n\n\n\nWe recommend that you store data in integer form whenever possible. For example, convert fixed precision numbers to integer values, such as monetary amounts or page load times in milliseconds.\n\n\nUsing floating-point numbers\n\n\n\n\nComputations with floating-point numbers might produce a rounding error.\n\n\n\n\nSELECT\n \n1\n \n-\n \n0\n.\n9\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500minus(1, 0.9)\u2500\u2510\n\u2502 0.09999999999999998 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\nThe result of the calculation depends on the calculation method (the processor type and architecture of the computer system).\n\n\nFloating-point calculations might result in numbers such as infinity (\nInf\n) and \"not-a-number\" (\nNaN\n). This should be taken into account when processing the results of calculations.\n\n\nWhen reading floating point numbers from rows, the result might not be the nearest machine-representable number.\n\n\n\n\nNaN and Inf\n\n\nIn contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers:\n\n\n\n\nInf\n \u2013 Infinity.\n\n\n\n\nSELECT\n \n0\n.\n5\n \n/\n \n0\n\n\n\n\n\n\n\u250c\u2500divide(0.5, 0)\u2500\u2510\n\u2502 inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\n-Inf\n \u2013 Negative infinity.\n\n\n\n\nSELECT\n \n-\n0\n.\n5\n \n/\n \n0\n\n\n\n\n\n\n\u250c\u2500divide(-0.5, 0)\u2500\u2510\n\u2502 -inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\nNaN\n \u2013 Not a number.\n\n\n\n\nSELECT 0 / 0\n\n\n\n\n\n\u250c\u2500divide(0, 0)\u2500\u2510\n\u2502 nan \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSee the rules for \nNaN\n sorting in the section \nORDER BY clause\n.", + "title": "Float32, Float64" + }, + { + "location": "/data_types/float/#float32-float64", + "text": "Floating point numbers . Types are equivalent to types of C: Float32 - float Float64 - double We recommend that you store data in integer form whenever possible. For example, convert fixed precision numbers to integer values, such as monetary amounts or page load times in milliseconds.", + "title": "Float32, Float64" + }, + { + "location": "/data_types/float/#using-floating-point-numbers", + "text": "Computations with floating-point numbers might produce a rounding error. SELECT 1 - 0 . 9 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500minus(1, 0.9)\u2500\u2510\n\u2502 0.09999999999999998 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 The result of the calculation depends on the calculation method (the processor type and architecture of the computer system). Floating-point calculations might result in numbers such as infinity ( Inf ) and \"not-a-number\" ( NaN ). This should be taken into account when processing the results of calculations. When reading floating point numbers from rows, the result might not be the nearest machine-representable number.", + "title": "Using floating-point numbers" + }, + { + "location": "/data_types/float/#nan-and-inf", + "text": "In contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers: Inf \u2013 Infinity. SELECT 0 . 5 / 0 \u250c\u2500divide(0.5, 0)\u2500\u2510\n\u2502 inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 -Inf \u2013 Negative infinity. SELECT - 0 . 5 / 0 \u250c\u2500divide(-0.5, 0)\u2500\u2510\n\u2502 -inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 NaN \u2013 Not a number. SELECT 0 / 0 \u250c\u2500divide(0, 0)\u2500\u2510\n\u2502 nan \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 See the rules for NaN sorting in the section ORDER BY clause .", + "title": "NaN and Inf" + }, + { + "location": "/data_types/boolean/", + "text": "Boolean values\n\n\nThere isn't a separate type for boolean values. They use the UInt8 type, restricted to the values 0 or 1.", + "title": "Boolean values" + }, + { + "location": "/data_types/boolean/#boolean-values", + "text": "There isn't a separate type for boolean values. They use the UInt8 type, restricted to the values 0 or 1.", + "title": "Boolean values" + }, + { + "location": "/data_types/string/", + "text": "String\n\n\nStrings of an arbitrary length. The length is not limited. The value can contain an arbitrary set of bytes, including null bytes.\nThe String type replaces the types VARCHAR, BLOB, CLOB, and others from other DBMSs.\n\n\nEncodings\n\n\nClickHouse doesn't have the concept of encodings. Strings can contain an arbitrary set of bytes, which are stored and output as-is.\nIf you need to store texts, we recommend using UTF-8 encoding. At the very least, if your terminal uses UTF-8 (as recommended), you can read and write your values without making conversions.\nSimilarly, certain functions for working with strings have separate variations that work under the assumption that the string contains a set of bytes representing a UTF-8 encoded text.\nFor example, the 'length' function calculates the string length in bytes, while the 'lengthUTF8' function calculates the string length in Unicode code points, assuming that the value is UTF-8 encoded.", + "title": "String" + }, + { + "location": "/data_types/string/#string", + "text": "Strings of an arbitrary length. The length is not limited. The value can contain an arbitrary set of bytes, including null bytes.\nThe String type replaces the types VARCHAR, BLOB, CLOB, and others from other DBMSs.", + "title": "String" + }, + { + "location": "/data_types/string/#encodings", + "text": "ClickHouse doesn't have the concept of encodings. Strings can contain an arbitrary set of bytes, which are stored and output as-is.\nIf you need to store texts, we recommend using UTF-8 encoding. At the very least, if your terminal uses UTF-8 (as recommended), you can read and write your values without making conversions.\nSimilarly, certain functions for working with strings have separate variations that work under the assumption that the string contains a set of bytes representing a UTF-8 encoded text.\nFor example, the 'length' function calculates the string length in bytes, while the 'lengthUTF8' function calculates the string length in Unicode code points, assuming that the value is UTF-8 encoded.", + "title": "Encodings" + }, + { + "location": "/data_types/fixedstring/", + "text": "FixedString(N)\n\n\nA fixed-length string of N bytes (not characters or code points). N must be a strictly positive natural number.\nWhen the server reads a string that contains fewer bytes (such as when parsing INSERT data), the string is padded to N bytes by appending null bytes at the right.\nWhen the server reads a string that contains more bytes, an error message is returned.\nWhen the server writes a string (such as when outputting the result of a SELECT query), null bytes are not trimmed off of the end of the string, but are output.\nNote that this behavior differs from MySQL behavior for the CHAR type (where strings are padded with spaces, and the spaces are removed for output).\n\n\nFewer functions can work with the FixedString(N) type than with String, so it is less convenient to use.", + "title": "FixedString(N)" + }, + { + "location": "/data_types/fixedstring/#fixedstringn", + "text": "A fixed-length string of N bytes (not characters or code points). N must be a strictly positive natural number.\nWhen the server reads a string that contains fewer bytes (such as when parsing INSERT data), the string is padded to N bytes by appending null bytes at the right.\nWhen the server reads a string that contains more bytes, an error message is returned.\nWhen the server writes a string (such as when outputting the result of a SELECT query), null bytes are not trimmed off of the end of the string, but are output.\nNote that this behavior differs from MySQL behavior for the CHAR type (where strings are padded with spaces, and the spaces are removed for output). Fewer functions can work with the FixedString(N) type than with String, so it is less convenient to use.", + "title": "FixedString(N)" + }, + { + "location": "/data_types/date/", + "text": "Date\n\n\nA date. Stored in two bytes as the number of days since 1970-01-01 (unsigned). Allows storing values from just after the beginning of the Unix Epoch to the upper threshold defined by a constant at the compilation stage (currently, this is until the year 2106, but the final fully-supported year is 2105).\nThe minimum value is output as 0000-00-00.\n\n\nThe date is stored without the time zone.", + "title": "Date" + }, + { + "location": "/data_types/date/#date", + "text": "A date. Stored in two bytes as the number of days since 1970-01-01 (unsigned). Allows storing values from just after the beginning of the Unix Epoch to the upper threshold defined by a constant at the compilation stage (currently, this is until the year 2106, but the final fully-supported year is 2105).\nThe minimum value is output as 0000-00-00. The date is stored without the time zone.", + "title": "Date" + }, + { + "location": "/data_types/datetime/", + "text": "DateTime\n\n\nDate with time. Stored in four bytes as a Unix timestamp (unsigned). Allows storing values in the same range as for the Date type. The minimal value is output as 0000-00-00 00:00:00.\nThe time is stored with accuracy up to one second (without leap seconds).\n\n\nTime zones\n\n\nThe date with time is converted from text (divided into component parts) to binary and back, using the system's time zone at the time the client or server starts. In text format, information about daylight savings is lost.\n\n\nBy default, the client switches to the timezone of the server when it connects. You can change this behavior by enabling the client command-line option \n--use_client_time_zone\n.\n\n\nSupports only those time zones that never had the time differ from UTC for a partial number of hours (without leap seconds) over the entire time range you will be working with.\n\n\nSo when working with a textual date (for example, when saving text dumps), keep in mind that there may be ambiguity during changes for daylight savings time, and there may be problems matching data if the time zone changed.", + "title": "DateTime" + }, + { + "location": "/data_types/datetime/#datetime", + "text": "Date with time. Stored in four bytes as a Unix timestamp (unsigned). Allows storing values in the same range as for the Date type. The minimal value is output as 0000-00-00 00:00:00.\nThe time is stored with accuracy up to one second (without leap seconds).", + "title": "DateTime" + }, + { + "location": "/data_types/datetime/#time-zones", + "text": "The date with time is converted from text (divided into component parts) to binary and back, using the system's time zone at the time the client or server starts. In text format, information about daylight savings is lost. By default, the client switches to the timezone of the server when it connects. You can change this behavior by enabling the client command-line option --use_client_time_zone . Supports only those time zones that never had the time differ from UTC for a partial number of hours (without leap seconds) over the entire time range you will be working with. So when working with a textual date (for example, when saving text dumps), keep in mind that there may be ambiguity during changes for daylight savings time, and there may be problems matching data if the time zone changed.", + "title": "Time zones" + }, + { + "location": "/data_types/enum/", + "text": "Enum\n\n\nEnum8 or Enum16. A finite set of string values that can be stored more efficiently than the \nString\n data type.\n\n\nExample:\n\n\nEnum8(\nhello\n = 1, \nworld\n = 2)\n\n\n\n\n\n\n\nA data type with two possible values: 'hello' and 'world'.\n\n\n\n\nEach of the values is assigned a number in the range \n-128 ... 127\n for \nEnum8\n or in the range \n-32768 ... 32767\n for \nEnum16\n. All the strings and numbers must be different. An empty string is allowed. If this type is specified (in a table definition), numbers can be in an arbitrary order. However, the order does not matter.\n\n\nIn RAM, this type of column is stored in the same way as \nInt8\n or \nInt16\n of the corresponding numerical values.\nWhen reading in text form, ClickHouse parses the value as a string and searches for the corresponding string from the set of Enum values. If it is not found, an exception is thrown. When reading in text format, the string is read and the corresponding numeric value is looked up. An exception will be thrown if it is not found.\nWhen writing in text form, it writes the value as the corresponding string. If column data contains garbage (numbers that are not from the valid set), an exception is thrown. When reading and writing in binary form, it works the same way as for Int8 and Int16 data types.\nThe implicit default value is the value with the lowest number.\n\n\nDuring \nORDER BY\n, \nGROUP BY\n, \nIN\n, \nDISTINCT\n and so on, Enums behave the same way as the corresponding numbers. For example, ORDER BY sorts them numerically. Equality and comparison operators work the same way on Enums as they do on the underlying numeric values.\n\n\nEnum values cannot be compared with numbers. Enums can be compared to a constant string. If the string compared to is not a valid value for the Enum, an exception will be thrown. The IN operator is supported with the Enum on the left hand side and a set of strings on the right hand side. The strings are the values of the corresponding Enum.\n\n\nMost numeric and string operations are not defined for Enum values, e.g. adding a number to an Enum or concatenating a string to an Enum.\nHowever, the Enum has a natural \ntoString\n function that returns its string value.\n\n\nEnum values are also convertible to numeric types using the \ntoT\n function, where T is a numeric type. When T corresponds to the enum\u2019s underlying numeric type, this conversion is zero-cost.\nThe Enum type can be changed without cost using ALTER, if only the set of values is changed. It is possible to both add and remove members of the Enum using ALTER (removing is safe only if the removed value has never been used in the table). As a safeguard, changing the numeric value of a previously defined Enum member will throw an exception.\n\n\nUsing ALTER, it is possible to change an Enum8 to an Enum16 or vice versa, just like changing an Int8 to Int16.", + "title": "Enum" + }, + { + "location": "/data_types/enum/#enum", + "text": "Enum8 or Enum16. A finite set of string values that can be stored more efficiently than the String data type. Example: Enum8( hello = 1, world = 2) A data type with two possible values: 'hello' and 'world'. Each of the values is assigned a number in the range -128 ... 127 for Enum8 or in the range -32768 ... 32767 for Enum16 . All the strings and numbers must be different. An empty string is allowed. If this type is specified (in a table definition), numbers can be in an arbitrary order. However, the order does not matter. In RAM, this type of column is stored in the same way as Int8 or Int16 of the corresponding numerical values.\nWhen reading in text form, ClickHouse parses the value as a string and searches for the corresponding string from the set of Enum values. If it is not found, an exception is thrown. When reading in text format, the string is read and the corresponding numeric value is looked up. An exception will be thrown if it is not found.\nWhen writing in text form, it writes the value as the corresponding string. If column data contains garbage (numbers that are not from the valid set), an exception is thrown. When reading and writing in binary form, it works the same way as for Int8 and Int16 data types.\nThe implicit default value is the value with the lowest number. During ORDER BY , GROUP BY , IN , DISTINCT and so on, Enums behave the same way as the corresponding numbers. For example, ORDER BY sorts them numerically. Equality and comparison operators work the same way on Enums as they do on the underlying numeric values. Enum values cannot be compared with numbers. Enums can be compared to a constant string. If the string compared to is not a valid value for the Enum, an exception will be thrown. The IN operator is supported with the Enum on the left hand side and a set of strings on the right hand side. The strings are the values of the corresponding Enum. Most numeric and string operations are not defined for Enum values, e.g. adding a number to an Enum or concatenating a string to an Enum.\nHowever, the Enum has a natural toString function that returns its string value. Enum values are also convertible to numeric types using the toT function, where T is a numeric type. When T corresponds to the enum\u2019s underlying numeric type, this conversion is zero-cost.\nThe Enum type can be changed without cost using ALTER, if only the set of values is changed. It is possible to both add and remove members of the Enum using ALTER (removing is safe only if the removed value has never been used in the table). As a safeguard, changing the numeric value of a previously defined Enum member will throw an exception. Using ALTER, it is possible to change an Enum8 to an Enum16 or vice versa, just like changing an Int8 to Int16.", + "title": "Enum" + }, + { + "location": "/data_types/array/", + "text": "Array(T)\n\n\nAn array of elements of type T. The T type can be any type, including an array.\nWe don't recommend using multidimensional arrays, because they are not well supported (for example, you can't store multidimensional arrays in tables with a MergeTree engine).", + "title": "Array(T)" + }, + { + "location": "/data_types/array/#arrayt", + "text": "An array of elements of type T. The T type can be any type, including an array.\nWe don't recommend using multidimensional arrays, because they are not well supported (for example, you can't store multidimensional arrays in tables with a MergeTree engine).", + "title": "Array(T)" + }, + { + "location": "/data_types/nested_data_structures/aggregatefunction/", + "text": "AggregateFunction(name, types_of_arguments...)\n\n\nThe intermediate state of an aggregate function. To get it, use aggregate functions with the '-State' suffix. For more information, see \"AggregatingMergeTree\".", + "title": "AggregateFunction(name, types_of_arguments...)" + }, + { + "location": "/data_types/nested_data_structures/aggregatefunction/#aggregatefunctionname-types_of_arguments", + "text": "The intermediate state of an aggregate function. To get it, use aggregate functions with the '-State' suffix. For more information, see \"AggregatingMergeTree\".", + "title": "AggregateFunction(name, types_of_arguments...)" + }, + { + "location": "/data_types/tuple/", + "text": "Tuple(T1, T2, ...)\n\n\nTuples can't be written to tables (other than Memory tables). They are used for temporary column grouping. Columns can be grouped when an IN expression is used in a query, and for specifying certain formal parameters of lambda functions. For more information, see \"IN operators\" and \"Higher order functions\".\n\n\nTuples can be output as the result of running a query. In this case, for text formats other than JSON*, values are comma-separated in brackets. In JSON* formats, tuples are output as arrays (in square brackets).", + "title": "Tuple(T1, T2, ...)" + }, + { + "location": "/data_types/tuple/#tuplet1-t2", + "text": "Tuples can't be written to tables (other than Memory tables). They are used for temporary column grouping. Columns can be grouped when an IN expression is used in a query, and for specifying certain formal parameters of lambda functions. For more information, see \"IN operators\" and \"Higher order functions\". Tuples can be output as the result of running a query. In this case, for text formats other than JSON*, values are comma-separated in brackets. In JSON* formats, tuples are output as arrays (in square brackets).", + "title": "Tuple(T1, T2, ...)" + }, + { + "location": "/data_types/nested_data_structures/nested/", + "text": "Nested(Name1 Type1, Name2 Type2, ...)\n\n\nA nested data structure is like a nested table. The parameters of a nested data structure \u2013 the column names and types \u2013 are specified the same way as in a CREATE query. Each table row can correspond to any number of rows in a nested data structure.\n\n\nExample:\n\n\nCREATE\n \nTABLE\n \ntest\n.\nvisits\n\n\n(\n\n \nCounterID\n \nUInt32\n,\n\n \nStartDate\n \nDate\n,\n\n \nSign\n \nInt8\n,\n\n \nIsNew\n \nUInt8\n,\n\n \nVisitID\n \nUInt64\n,\n\n \nUserID\n \nUInt64\n,\n\n \n...\n\n \nGoals\n \nNested\n\n \n(\n\n \nID\n \nUInt32\n,\n\n \nSerial\n \nUInt32\n,\n\n \nEventTime\n \nDateTime\n,\n\n \nPrice\n \nInt64\n,\n\n \nOrderID\n \nString\n,\n\n \nCurrencyID\n \nUInt32\n\n \n),\n\n \n...\n\n\n)\n \nENGINE\n \n=\n \nCollapsingMergeTree\n(\nStartDate\n,\n \nintHash32\n(\nUserID\n),\n \n(\nCounterID\n,\n \nStartDate\n,\n \nintHash32\n(\nUserID\n),\n \nVisitID\n),\n \n8192\n,\n \nSign\n)\n\n\n\n\n\n\nThis example declares the \nGoals\n nested data structure, which contains data about conversions (goals reached). Each row in the 'visits' table can correspond to zero or any number of conversions.\n\n\nOnly a single nesting level is supported. Columns of nested structures containing arrays are equivalent to multidimensional arrays, so they have limited support (there is no support for storing these columns in tables with the MergeTree engine).\n\n\nIn most cases, when working with a nested data structure, its individual columns are specified. To do this, the column names are separated by a dot. These columns make up an array of matching types. All the column arrays of a single nested data structure have the same length.\n\n\nExample:\n\n\nSELECT\n\n \nGoals\n.\nID\n,\n\n \nGoals\n.\nEventTime\n\n\nFROM\n \ntest\n.\nvisits\n\n\nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nlength\n(\nGoals\n.\nID\n)\n \n \n5\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500Goals.ID\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500Goals.EventTime\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1073752,591325,591325] \u2502 [\n2014-03-17 16:38:10\n,\n2014-03-17 16:38:48\n,\n2014-03-17 16:42:27\n] \u2502\n\u2502 [1073752] \u2502 [\n2014-03-17 00:28:25\n] \u2502\n\u2502 [1073752] \u2502 [\n2014-03-17 10:46:20\n] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [\n2014-03-17 13:59:20\n,\n2014-03-17 22:17:55\n,\n2014-03-17 22:18:07\n,\n2014-03-17 22:18:51\n] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [1073752,591325,591325] \u2502 [\n2014-03-17 11:37:06\n,\n2014-03-17 14:07:47\n,\n2014-03-17 14:36:21\n] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [591325,1073752] \u2502 [\n2014-03-17 00:46:05\n,\n2014-03-17 00:46:05\n] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [\n2014-03-17 13:28:33\n,\n2014-03-17 13:30:26\n,\n2014-03-17 18:51:21\n,\n2014-03-17 18:51:45\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIt is easiest to think of a nested data structure as a set of multiple column arrays of the same length.\n\n\nThe only place where a SELECT query can specify the name of an entire nested data structure instead of individual columns is the ARRAY JOIN clause. For more information, see \"ARRAY JOIN clause\". Example:\n\n\nSELECT\n\n \nGoal\n.\nID\n,\n\n \nGoal\n.\nEventTime\n\n\nFROM\n \ntest\n.\nvisits\n\n\nARRAY\n \nJOIN\n \nGoals\n \nAS\n \nGoal\n\n\nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nlength\n(\nGoals\n.\nID\n)\n \n \n5\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500Goal.ID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500Goal.EventTime\u2500\u2510\n\u2502 1073752 \u2502 2014-03-17 16:38:10 \u2502\n\u2502 591325 \u2502 2014-03-17 16:38:48 \u2502\n\u2502 591325 \u2502 2014-03-17 16:42:27 \u2502\n\u2502 1073752 \u2502 2014-03-17 00:28:25 \u2502\n\u2502 1073752 \u2502 2014-03-17 10:46:20 \u2502\n\u2502 1073752 \u2502 2014-03-17 13:59:20 \u2502\n\u2502 591325 \u2502 2014-03-17 22:17:55 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:07 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:51 \u2502\n\u2502 1073752 \u2502 2014-03-17 11:37:06 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nYou can't perform SELECT for an entire nested data structure. You can only explicitly list individual columns that are part of it.\n\n\nFor an INSERT query, you should pass all the component column arrays of a nested data structure separately (as if they were individual column arrays). During insertion, the system checks that they have the same length.\n\n\nFor a DESCRIBE query, the columns in a nested data structure are listed separately in the same way.\n\n\nThe ALTER query is very limited for elements in a nested data structure.", + "title": "Nested(Name1 Type1, Name2 Type2, ...)" + }, + { + "location": "/data_types/nested_data_structures/nested/#nestedname1-type1-name2-type2", + "text": "A nested data structure is like a nested table. The parameters of a nested data structure \u2013 the column names and types \u2013 are specified the same way as in a CREATE query. Each table row can correspond to any number of rows in a nested data structure. Example: CREATE TABLE test . visits ( \n CounterID UInt32 , \n StartDate Date , \n Sign Int8 , \n IsNew UInt8 , \n VisitID UInt64 , \n UserID UInt64 , \n ... \n Goals Nested \n ( \n ID UInt32 , \n Serial UInt32 , \n EventTime DateTime , \n Price Int64 , \n OrderID String , \n CurrencyID UInt32 \n ), \n ... ) ENGINE = CollapsingMergeTree ( StartDate , intHash32 ( UserID ), ( CounterID , StartDate , intHash32 ( UserID ), VisitID ), 8192 , Sign ) This example declares the Goals nested data structure, which contains data about conversions (goals reached). Each row in the 'visits' table can correspond to zero or any number of conversions. Only a single nesting level is supported. Columns of nested structures containing arrays are equivalent to multidimensional arrays, so they have limited support (there is no support for storing these columns in tables with the MergeTree engine). In most cases, when working with a nested data structure, its individual columns are specified. To do this, the column names are separated by a dot. These columns make up an array of matching types. All the column arrays of a single nested data structure have the same length. Example: SELECT \n Goals . ID , \n Goals . EventTime FROM test . visits WHERE CounterID = 101500 AND length ( Goals . ID ) 5 LIMIT 10 \u250c\u2500Goals.ID\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500Goals.EventTime\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1073752,591325,591325] \u2502 [ 2014-03-17 16:38:10 , 2014-03-17 16:38:48 , 2014-03-17 16:42:27 ] \u2502\n\u2502 [1073752] \u2502 [ 2014-03-17 00:28:25 ] \u2502\n\u2502 [1073752] \u2502 [ 2014-03-17 10:46:20 ] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [ 2014-03-17 13:59:20 , 2014-03-17 22:17:55 , 2014-03-17 22:18:07 , 2014-03-17 22:18:51 ] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [1073752,591325,591325] \u2502 [ 2014-03-17 11:37:06 , 2014-03-17 14:07:47 , 2014-03-17 14:36:21 ] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [591325,1073752] \u2502 [ 2014-03-17 00:46:05 , 2014-03-17 00:46:05 ] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [ 2014-03-17 13:28:33 , 2014-03-17 13:30:26 , 2014-03-17 18:51:21 , 2014-03-17 18:51:45 ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 It is easiest to think of a nested data structure as a set of multiple column arrays of the same length. The only place where a SELECT query can specify the name of an entire nested data structure instead of individual columns is the ARRAY JOIN clause. For more information, see \"ARRAY JOIN clause\". Example: SELECT \n Goal . ID , \n Goal . EventTime FROM test . visits ARRAY JOIN Goals AS Goal WHERE CounterID = 101500 AND length ( Goals . ID ) 5 LIMIT 10 \u250c\u2500Goal.ID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500Goal.EventTime\u2500\u2510\n\u2502 1073752 \u2502 2014-03-17 16:38:10 \u2502\n\u2502 591325 \u2502 2014-03-17 16:38:48 \u2502\n\u2502 591325 \u2502 2014-03-17 16:42:27 \u2502\n\u2502 1073752 \u2502 2014-03-17 00:28:25 \u2502\n\u2502 1073752 \u2502 2014-03-17 10:46:20 \u2502\n\u2502 1073752 \u2502 2014-03-17 13:59:20 \u2502\n\u2502 591325 \u2502 2014-03-17 22:17:55 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:07 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:51 \u2502\n\u2502 1073752 \u2502 2014-03-17 11:37:06 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 You can't perform SELECT for an entire nested data structure. You can only explicitly list individual columns that are part of it. For an INSERT query, you should pass all the component column arrays of a nested data structure separately (as if they were individual column arrays). During insertion, the system checks that they have the same length. For a DESCRIBE query, the columns in a nested data structure are listed separately in the same way. The ALTER query is very limited for elements in a nested data structure.", + "title": "Nested(Name1 Type1, Name2 Type2, ...)" + }, + { + "location": "/data_types/special_data_types/expression/", + "text": "Expression\n\n\nUsed for representing lambda expressions in high-order functions.", + "title": "Expression" + }, + { + "location": "/data_types/special_data_types/expression/#expression", + "text": "Used for representing lambda expressions in high-order functions.", + "title": "Expression" + }, + { + "location": "/data_types/special_data_types/set/", + "text": "Set\n\n\nUsed for the right half of an IN expression.", + "title": "Set" + }, + { + "location": "/data_types/special_data_types/set/#set", + "text": "Used for the right half of an IN expression.", + "title": "Set" + }, + { + "location": "/operators/", + "text": "Operators\n\n\nAll operators are transformed to the corresponding functions at the query parsing stage, in accordance with their precedence and associativity.\nGroups of operators are listed in order of priority (the higher it is in the list, the earlier the operator is connected to its arguments).\n\n\nAccess operators\n\n\na[N]\n Access to an element of an array; \narrayElement(a, N) function\n.\n\n\na.N\n \u2013 Access to a tuble element; \ntupleElement(a, N)\n function.\n\n\nNumeric negation operator\n\n\n-a\n \u2013 The \nnegate (a)\n function.\n\n\nMultiplication and division operators\n\n\na * b\n \u2013 The \nmultiply (a, b) function.\n\n\na / b\n \u2013 The \ndivide(a, b) function.\n\n\na % b\n \u2013 The \nmodulo(a, b) function.\n\n\nAddition and subtraction operators\n\n\na + b\n \u2013 The \nplus(a, b) function.\n\n\na - b\n \u2013 The \nminus(a, b) function.\n\n\nComparison operators\n\n\na = b\n \u2013 The \nequals(a, b) function.\n\n\na == b\n \u2013 The \nequals(a, b) function.\n\n\na != b\n \u2013 The \nnotEquals(a, b) function.\n\n\na \n b\n \u2013 The \nnotEquals(a, b) function.\n\n\na \n= b\n \u2013 The \nlessOrEquals(a, b) function.\n\n\na \n= b\n \u2013 The \ngreaterOrEquals(a, b) function.\n\n\na \n b\n \u2013 The \nless(a, b) function.\n\n\na \n b\n \u2013 The \ngreater(a, b) function.\n\n\na LIKE s\n \u2013 The \nlike(a, b) function.\n\n\na NOT LIKE s\n \u2013 The \nnotLike(a, b) function.\n\n\na BETWEEN b AND c\n \u2013 The same as \na \n= b AND a \n= c.\n\n\nOperators for working with data sets\n\n\nSee the section \"IN operators\".\n\n\na IN ...\n \u2013 The \nin(a, b) function\n\n\na NOT IN ...\n \u2013 The \nnotIn(a, b) function.\n\n\na GLOBAL IN ...\n \u2013 The \nglobalIn(a, b) function.\n\n\na GLOBAL NOT IN ...\n \u2013 The \nglobalNotIn(a, b) function.\n\n\nLogical negation operator\n\n\nNOT a\n The \nnot(a) function.\n\n\nLogical AND operator\n\n\na AND b\n \u2013 The\nand(a, b) function.\n\n\nLogical OR operator\n\n\na OR b\n \u2013 The \nor(a, b) function.\n\n\nConditional operator\n\n\na ? b : c\n \u2013 The \nif(a, b, c) function.\n\n\nNote:\n\n\nThe conditional operator calculates the values of b and c, then checks whether condition a is met, and then returns the corresponding value. If \"b\" or \"c\" is an arrayJoin() function, each row will be replicated regardless of the \"a\" condition.\n\n\nConditional expression\n\n\nCASE\n \n[\nx\n]\n\n \nWHEN\n \na\n \nTHEN\n \nb\n\n \n[\nWHEN\n \n...\n \nTHEN\n \n...]\n\n \nELSE\n \nc\n\n\nEND\n\n\n\n\n\n\nIf \"x\" is specified, then transform(x, [a, ...], [b, ...], c). Otherwise \u2013 multiIf(a, b, ..., c).\n\n\nConcatenation operator\n\n\ns1 || s2\n \u2013 The \nconcat(s1, s2) function.\n\n\nLambda creation operator\n\n\nx -\n expr\n \u2013 The \nlambda(x, expr) function.\n\n\nThe following operators do not have a priority, since they are brackets:\n\n\nArray creation operator\n\n\n[x1, ...]\n \u2013 The \narray(x1, ...) function.\n\n\nTuple creation operator\n\n\n(x1, x2, ...)\n \u2013 The \ntuple(x2, x2, ...) function.\n\n\nAssociativity\n\n\nAll binary operators have left associativity. For example, \n1 + 2 + 3\n is transformed to \nplus(plus(1, 2), 3)\n.\nSometimes this doesn't work the way you expect. For example, \nSELECT 4 \n 2 \n 3\n will result in 0.\n\n\nFor efficiency, the \nand\n and \nor\n functions accept any number of arguments. The corresponding chains of \nAND\n and \nOR\n operators are transformed to a single call of these functions.", + "title": "Operators" + }, + { + "location": "/operators/#operators", + "text": "All operators are transformed to the corresponding functions at the query parsing stage, in accordance with their precedence and associativity.\nGroups of operators are listed in order of priority (the higher it is in the list, the earlier the operator is connected to its arguments).", + "title": "Operators" + }, + { + "location": "/operators/#access-operators", + "text": "a[N] Access to an element of an array; arrayElement(a, N) function . a.N \u2013 Access to a tuble element; tupleElement(a, N) function.", + "title": "Access operators" + }, + { + "location": "/operators/#numeric-negation-operator", + "text": "-a \u2013 The negate (a) function.", + "title": "Numeric negation operator" + }, + { + "location": "/operators/#multiplication-and-division-operators", + "text": "a * b \u2013 The multiply (a, b) function. a / b \u2013 The divide(a, b) function. a % b \u2013 The modulo(a, b) function.", + "title": "Multiplication and division operators" + }, + { + "location": "/operators/#addition-and-subtraction-operators", + "text": "a + b \u2013 The plus(a, b) function. a - b \u2013 The minus(a, b) function.", + "title": "Addition and subtraction operators" + }, + { + "location": "/operators/#comparison-operators", + "text": "a = b \u2013 The equals(a, b) function. a == b \u2013 The equals(a, b) function. a != b \u2013 The notEquals(a, b) function. a b \u2013 The notEquals(a, b) function. a = b \u2013 The lessOrEquals(a, b) function. a = b \u2013 The greaterOrEquals(a, b) function. a b \u2013 The less(a, b) function. a b \u2013 The greater(a, b) function. a LIKE s \u2013 The like(a, b) function. a NOT LIKE s \u2013 The notLike(a, b) function. a BETWEEN b AND c \u2013 The same as a = b AND a = c.", + "title": "Comparison operators" + }, + { + "location": "/operators/#operators-for-working-with-data-sets", + "text": "See the section \"IN operators\". a IN ... \u2013 The in(a, b) function a NOT IN ... \u2013 The notIn(a, b) function. a GLOBAL IN ... \u2013 The globalIn(a, b) function. a GLOBAL NOT IN ... \u2013 The globalNotIn(a, b) function.", + "title": "Operators for working with data sets" + }, + { + "location": "/operators/#logical-negation-operator", + "text": "NOT a The not(a) function.", + "title": "Logical negation operator" + }, + { + "location": "/operators/#logical-and-operator", + "text": "a AND b \u2013 The and(a, b) function.", + "title": "Logical AND operator" + }, + { + "location": "/operators/#logical-or-operator", + "text": "a OR b \u2013 The or(a, b) function.", + "title": "Logical OR operator" + }, + { + "location": "/operators/#conditional-operator", + "text": "a ? b : c \u2013 The if(a, b, c) function. Note: The conditional operator calculates the values of b and c, then checks whether condition a is met, and then returns the corresponding value. If \"b\" or \"c\" is an arrayJoin() function, each row will be replicated regardless of the \"a\" condition.", + "title": "Conditional operator" + }, + { + "location": "/operators/#conditional-expression", + "text": "CASE [ x ] \n WHEN a THEN b \n [ WHEN ... THEN ...] \n ELSE c END If \"x\" is specified, then transform(x, [a, ...], [b, ...], c). Otherwise \u2013 multiIf(a, b, ..., c).", + "title": "Conditional expression" + }, + { + "location": "/operators/#concatenation-operator", + "text": "s1 || s2 \u2013 The concat(s1, s2) function.", + "title": "Concatenation operator" + }, + { + "location": "/operators/#lambda-creation-operator", + "text": "x - expr \u2013 The lambda(x, expr) function. The following operators do not have a priority, since they are brackets:", + "title": "Lambda creation operator" + }, + { + "location": "/operators/#array-creation-operator", + "text": "[x1, ...] \u2013 The array(x1, ...) function.", + "title": "Array creation operator" + }, + { + "location": "/operators/#tuple-creation-operator", + "text": "(x1, x2, ...) \u2013 The tuple(x2, x2, ...) function.", + "title": "Tuple creation operator" + }, + { + "location": "/operators/#associativity", + "text": "All binary operators have left associativity. For example, 1 + 2 + 3 is transformed to plus(plus(1, 2), 3) .\nSometimes this doesn't work the way you expect. For example, SELECT 4 2 3 will result in 0. For efficiency, the and and or functions accept any number of arguments. The corresponding chains of AND and OR operators are transformed to a single call of these functions.", + "title": "Associativity" + }, + { + "location": "/functions/", + "text": "Functions\n\n\nThere are at least* two types of functions - regular functions (they are just called \"functions\") and aggregate functions. These are completely different concepts. Regular functions work as if they are applied to each row separately (for each row, the result of the function doesn't depend on the other rows). Aggregate functions accumulate a set of values from various rows (i.e. they depend on the entire set of rows).\n\n\nIn this section we discuss regular functions. For aggregate functions, see the section \"Aggregate functions\".\n\n\n* - There is a third type of function that the 'arrayJoin' function belongs to; table functions can also be mentioned separately.*\n\n\nStrong typing\n\n\nIn contrast to standard SQL, ClickHouse has strong typing. In other words, it doesn't make implicit conversions between types. Each function works for a specific set of types. This means that sometimes you need to use type conversion functions.\n\n\nCommon subexpression elimination\n\n\nAll expressions in a query that have the same AST (the same record or same result of syntactic parsing) are considered to have identical values. Such expressions are concatenated and executed once. Identical subqueries are also eliminated this way.\n\n\nTypes of results\n\n\nAll functions return a single return as the result (not several values, and not zero values). The type of result is usually defined only by the types of arguments, not by the values. Exceptions are the tupleElement function (the a.N operator), and the toFixedString function.\n\n\nConstants\n\n\nFor simplicity, certain functions can only work with constants for some arguments. For example, the right argument of the LIKE operator must be a constant.\nAlmost all functions return a constant for constant arguments. The exception is functions that generate random numbers.\nThe 'now' function returns different values for queries that were run at different times, but the result is considered a constant, since constancy is only important within a single query.\nA constant expression is also considered a constant (for example, the right half of the LIKE operator can be constructed from multiple constants).\n\n\nFunctions can be implemented in different ways for constant and non-constant arguments (different code is executed). But the results for a constant and for a true column containing only the same value should match each other.\n\n\nConstancy\n\n\nFunctions can't change the values of their arguments \u2013 any changes are returned as the result. Thus, the result of calculating separate functions does not depend on the order in which the functions are written in the query.\n\n\nError handling\n\n\nSome functions might throw an exception if the data is invalid. In this case, the query is canceled and an error text is returned to the client. For distributed processing, when an exception occurs on one of the servers, the other servers also attempt to abort the query.\n\n\nEvaluation of argument expressions\n\n\nIn almost all programming languages, one of the arguments might not be evaluated for certain operators. This is usually the operators \n, \n||\n, and \n?:\n.\nBut in ClickHouse, arguments of functions (operators) are always evaluated. This is because entire parts of columns are evaluated at once, instead of calculating each row separately.\n\n\nPerforming functions for distributed query processing\n\n\nFor distributed query processing, as many stages of query processing as possible are performed on remote servers, and the rest of the stages (merging intermediate results and everything after that) are performed on the requestor server.\n\n\nThis means that functions can be performed on different servers.\nFor example, in the query \nSELECT f(sum(g(x))) FROM distributed_table GROUP BY h(y),\n\n\n\n\nif a \ndistributed_table\n has at least two shards, the functions 'g' and 'h' are performed on remote servers, and the function 'f' is performed on the requestor server.\n\n\nif a \ndistributed_table\n has only one shard, all the 'f', 'g', and 'h' functions are performed on this shard's server.\n\n\n\n\nThe result of a function usually doesn't depend on which server it is performed on. However, sometimes this is important.\nFor example, functions that work with dictionaries use the dictionary that exists on the server they are running on.\nAnother example is the \nhostName\n function, which returns the name of the server it is running on in order to make \nGROUP BY\n by servers in a \nSELECT\n query.\n\n\nIf a function in a query is performed on the requestor server, but you need to perform it on remote servers, you can wrap it in an 'any' aggregate function or add it to a key in \nGROUP BY\n.", + "title": "Introduction" + }, + { + "location": "/functions/#functions", + "text": "There are at least* two types of functions - regular functions (they are just called \"functions\") and aggregate functions. These are completely different concepts. Regular functions work as if they are applied to each row separately (for each row, the result of the function doesn't depend on the other rows). Aggregate functions accumulate a set of values from various rows (i.e. they depend on the entire set of rows). In this section we discuss regular functions. For aggregate functions, see the section \"Aggregate functions\". * - There is a third type of function that the 'arrayJoin' function belongs to; table functions can also be mentioned separately.*", + "title": "Functions" + }, + { + "location": "/functions/#strong-typing", + "text": "In contrast to standard SQL, ClickHouse has strong typing. In other words, it doesn't make implicit conversions between types. Each function works for a specific set of types. This means that sometimes you need to use type conversion functions.", + "title": "Strong typing" + }, + { + "location": "/functions/#common-subexpression-elimination", + "text": "All expressions in a query that have the same AST (the same record or same result of syntactic parsing) are considered to have identical values. Such expressions are concatenated and executed once. Identical subqueries are also eliminated this way.", + "title": "Common subexpression elimination" + }, + { + "location": "/functions/#types-of-results", + "text": "All functions return a single return as the result (not several values, and not zero values). The type of result is usually defined only by the types of arguments, not by the values. Exceptions are the tupleElement function (the a.N operator), and the toFixedString function.", + "title": "Types of results" + }, + { + "location": "/functions/#constants", + "text": "For simplicity, certain functions can only work with constants for some arguments. For example, the right argument of the LIKE operator must be a constant.\nAlmost all functions return a constant for constant arguments. The exception is functions that generate random numbers.\nThe 'now' function returns different values for queries that were run at different times, but the result is considered a constant, since constancy is only important within a single query.\nA constant expression is also considered a constant (for example, the right half of the LIKE operator can be constructed from multiple constants). Functions can be implemented in different ways for constant and non-constant arguments (different code is executed). But the results for a constant and for a true column containing only the same value should match each other.", + "title": "Constants" + }, + { + "location": "/functions/#constancy", + "text": "Functions can't change the values of their arguments \u2013 any changes are returned as the result. Thus, the result of calculating separate functions does not depend on the order in which the functions are written in the query.", + "title": "Constancy" + }, + { + "location": "/functions/#error-handling", + "text": "Some functions might throw an exception if the data is invalid. In this case, the query is canceled and an error text is returned to the client. For distributed processing, when an exception occurs on one of the servers, the other servers also attempt to abort the query.", + "title": "Error handling" + }, + { + "location": "/functions/#evaluation-of-argument-expressions", + "text": "In almost all programming languages, one of the arguments might not be evaluated for certain operators. This is usually the operators , || , and ?: .\nBut in ClickHouse, arguments of functions (operators) are always evaluated. This is because entire parts of columns are evaluated at once, instead of calculating each row separately.", + "title": "Evaluation of argument expressions" + }, + { + "location": "/functions/#performing-functions-for-distributed-query-processing", + "text": "For distributed query processing, as many stages of query processing as possible are performed on remote servers, and the rest of the stages (merging intermediate results and everything after that) are performed on the requestor server. This means that functions can be performed on different servers.\nFor example, in the query SELECT f(sum(g(x))) FROM distributed_table GROUP BY h(y), if a distributed_table has at least two shards, the functions 'g' and 'h' are performed on remote servers, and the function 'f' is performed on the requestor server. if a distributed_table has only one shard, all the 'f', 'g', and 'h' functions are performed on this shard's server. The result of a function usually doesn't depend on which server it is performed on. However, sometimes this is important.\nFor example, functions that work with dictionaries use the dictionary that exists on the server they are running on.\nAnother example is the hostName function, which returns the name of the server it is running on in order to make GROUP BY by servers in a SELECT query. If a function in a query is performed on the requestor server, but you need to perform it on remote servers, you can wrap it in an 'any' aggregate function or add it to a key in GROUP BY .", + "title": "Performing functions for distributed query processing" + }, + { + "location": "/functions/arithmetic_functions/", + "text": "Arithmetic functions\n\n\nFor all arithmetic functions, the result type is calculated as the smallest number type that the result fits in, if there is such a type. The minimum is taken simultaneously based on the number of bits, whether it is signed, and whether it floats. If there are not enough bits, the highest bit type is taken.\n\n\nExample:\n\n\nSELECT\n \ntoTypeName\n(\n0\n),\n \ntoTypeName\n(\n0\n \n+\n \n0\n),\n \ntoTypeName\n(\n0\n \n+\n \n0\n \n+\n \n0\n),\n \ntoTypeName\n(\n0\n \n+\n \n0\n \n+\n \n0\n \n+\n \n0\n)\n\n\n\n\n\n\n\u250c\u2500toTypeName(0)\u2500\u252c\u2500toTypeName(plus(0, 0))\u2500\u252c\u2500toTypeName(plus(plus(0, 0), 0))\u2500\u252c\u2500toTypeName(plus(plus(plus(0, 0), 0), 0))\u2500\u2510\n\u2502 UInt8 \u2502 UInt16 \u2502 UInt32 \u2502 UInt64 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nArithmetic functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.\n\n\nOverflow is produced the same way as in C++.\n\n\nplus(a, b), a + b operator\n\n\nCalculates the sum of the numbers.\nYou can also add integer numbers with a date or date and time. In the case of a date, adding an integer means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds.\n\n\nminus(a, b), a - b operator\n\n\nCalculates the difference. The result is always signed.\n\n\nYou can also calculate integer numbers from a date or date with time. The idea is the same \u2013 see above for 'plus'.\n\n\nmultiply(a, b), a * b operator\n\n\nCalculates the product of the numbers.\n\n\ndivide(a, b), a / b operator\n\n\nCalculates the quotient of the numbers. The result type is always a floating-point type.\nIt is not integer division. For integer division, use the 'intDiv' function.\nWhen dividing by zero you get 'inf', '-inf', or 'nan'.\n\n\nintDiv(a, b)\n\n\nCalculates the quotient of the numbers. Divides into integers, rounding down (by the absolute value).\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nintDivOrZero(a, b)\n\n\nDiffers from 'intDiv' in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nmodulo(a, b), a % b operator\n\n\nCalculates the remainder after division.\nIf arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion.\nThe remainder is taken in the same sense as in C++. Truncated division is used for negative numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nnegate(a), -a operator\n\n\nCalculates a number with the reverse sign. The result is always signed.\n\n\nabs(a)\n\n\nCalculates the absolute value of the number (a). That is, if a \n 0, it returns -a. For unsigned types it doesn't do anything. For signed integer types, it returns an unsigned number.\n\n\ngcd(a, b)\n\n\nReturns the greatest common divisor of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nlcm(a, b)\n\n\nReturns the least common multiple of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", + "title": "Arithmetic functions" + }, + { + "location": "/functions/arithmetic_functions/#arithmetic-functions", + "text": "For all arithmetic functions, the result type is calculated as the smallest number type that the result fits in, if there is such a type. The minimum is taken simultaneously based on the number of bits, whether it is signed, and whether it floats. If there are not enough bits, the highest bit type is taken. Example: SELECT toTypeName ( 0 ), toTypeName ( 0 + 0 ), toTypeName ( 0 + 0 + 0 ), toTypeName ( 0 + 0 + 0 + 0 ) \u250c\u2500toTypeName(0)\u2500\u252c\u2500toTypeName(plus(0, 0))\u2500\u252c\u2500toTypeName(plus(plus(0, 0), 0))\u2500\u252c\u2500toTypeName(plus(plus(plus(0, 0), 0), 0))\u2500\u2510\n\u2502 UInt8 \u2502 UInt16 \u2502 UInt32 \u2502 UInt64 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Arithmetic functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64. Overflow is produced the same way as in C++.", + "title": "Arithmetic functions" + }, + { + "location": "/functions/arithmetic_functions/#plusa-b-a-b-operator", + "text": "Calculates the sum of the numbers.\nYou can also add integer numbers with a date or date and time. In the case of a date, adding an integer means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds.", + "title": "plus(a, b), a + b operator" + }, + { + "location": "/functions/arithmetic_functions/#minusa-b-a-b-operator", + "text": "Calculates the difference. The result is always signed. You can also calculate integer numbers from a date or date with time. The idea is the same \u2013 see above for 'plus'.", + "title": "minus(a, b), a - b operator" + }, + { + "location": "/functions/arithmetic_functions/#multiplya-b-a-42-b-operator", + "text": "Calculates the product of the numbers.", + "title": "multiply(a, b), a * b operator" + }, + { + "location": "/functions/arithmetic_functions/#dividea-b-a-b-operator", + "text": "Calculates the quotient of the numbers. The result type is always a floating-point type.\nIt is not integer division. For integer division, use the 'intDiv' function.\nWhen dividing by zero you get 'inf', '-inf', or 'nan'.", + "title": "divide(a, b), a / b operator" + }, + { + "location": "/functions/arithmetic_functions/#intdiva-b", + "text": "Calculates the quotient of the numbers. Divides into integers, rounding down (by the absolute value).\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", + "title": "intDiv(a, b)" + }, + { + "location": "/functions/arithmetic_functions/#intdivorzeroa-b", + "text": "Differs from 'intDiv' in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one.", + "title": "intDivOrZero(a, b)" + }, + { + "location": "/functions/arithmetic_functions/#moduloa-b-a-b-operator", + "text": "Calculates the remainder after division.\nIf arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion.\nThe remainder is taken in the same sense as in C++. Truncated division is used for negative numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", + "title": "modulo(a, b), a % b operator" + }, + { + "location": "/functions/arithmetic_functions/#negatea-a-operator", + "text": "Calculates a number with the reverse sign. The result is always signed.", + "title": "negate(a), -a operator" + }, + { + "location": "/functions/arithmetic_functions/#absa", + "text": "Calculates the absolute value of the number (a). That is, if a 0, it returns -a. For unsigned types it doesn't do anything. For signed integer types, it returns an unsigned number.", + "title": "abs(a)" + }, + { + "location": "/functions/arithmetic_functions/#gcda-b", + "text": "Returns the greatest common divisor of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", + "title": "gcd(a, b)" + }, + { + "location": "/functions/arithmetic_functions/#lcma-b", + "text": "Returns the least common multiple of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", + "title": "lcm(a, b)" + }, + { + "location": "/functions/comparison_functions/", + "text": "Comparison functions\n\n\nComparison functions always return 0 or 1 (Uint8).\n\n\nThe following types can be compared:\n\n\n\n\nnumbers\n\n\nstrings and fixed strings\n\n\ndates\n\n\ndates with times\n\n\n\n\nwithin each group, but not between different groups.\n\n\nFor example, you can't compare a date with a string. You have to use a function to convert the string to a date, or vice versa.\n\n\nStrings are compared by bytes. A shorter string is smaller than all strings that start with it and that contain at least one more character.\n\n\nNote. Up until version 1.1.54134, signed and unsigned numbers were compared the same way as in C++. In other words, you could get an incorrect result in cases like SELECT 9223372036854775807 \n -1. This behavior changed in version 1.1.54134 and is now mathematically correct.\n\n\nequals, a = b and a == b operator\n\n\nnotEquals, a ! operator= b and a \n b\n\n\nless, \n operator\n\n\ngreater, \n operator\n\n\nlessOrEquals, \n= operator\n\n\ngreaterOrEquals, \n= operator", + "title": "Comparison functions" + }, + { + "location": "/functions/comparison_functions/#comparison-functions", + "text": "Comparison functions always return 0 or 1 (Uint8). The following types can be compared: numbers strings and fixed strings dates dates with times within each group, but not between different groups. For example, you can't compare a date with a string. You have to use a function to convert the string to a date, or vice versa. Strings are compared by bytes. A shorter string is smaller than all strings that start with it and that contain at least one more character. Note. Up until version 1.1.54134, signed and unsigned numbers were compared the same way as in C++. In other words, you could get an incorrect result in cases like SELECT 9223372036854775807 -1. This behavior changed in version 1.1.54134 and is now mathematically correct.", + "title": "Comparison functions" + }, + { + "location": "/functions/comparison_functions/#equals-a-b-and-a-b-operator", + "text": "", + "title": "equals, a = b and a == b operator" + }, + { + "location": "/functions/comparison_functions/#notequals-a-operator-b-and-a-b", + "text": "", + "title": "notEquals, a ! operator= b and a <> b" + }, + { + "location": "/functions/comparison_functions/#less-operator", + "text": "", + "title": "less, < operator" + }, + { + "location": "/functions/comparison_functions/#greater-operator", + "text": "", + "title": "greater, > operator" + }, + { + "location": "/functions/comparison_functions/#lessorequals-operator", + "text": "", + "title": "lessOrEquals, <= operator" + }, + { + "location": "/functions/comparison_functions/#greaterorequals-operator", + "text": "", + "title": "greaterOrEquals, >= operator" + }, + { + "location": "/functions/logical_functions/", + "text": "Logical functions\n\n\nLogical functions accept any numeric types, but return a UInt8 number equal to 0 or 1.\n\n\nZero as an argument is considered \"false,\" while any non-zero value is considered \"true\".\n\n\nand, AND operator\n\n\nor, OR operator\n\n\nnot, NOT operator\n\n\nxor", + "title": "Logical functions" + }, + { + "location": "/functions/logical_functions/#logical-functions", + "text": "Logical functions accept any numeric types, but return a UInt8 number equal to 0 or 1. Zero as an argument is considered \"false,\" while any non-zero value is considered \"true\".", + "title": "Logical functions" + }, + { + "location": "/functions/logical_functions/#and-and-operator", + "text": "", + "title": "and, AND operator" + }, + { + "location": "/functions/logical_functions/#or-or-operator", + "text": "", + "title": "or, OR operator" + }, + { + "location": "/functions/logical_functions/#not-not-operator", + "text": "", + "title": "not, NOT operator" + }, + { + "location": "/functions/logical_functions/#xor", + "text": "", + "title": "xor" + }, + { + "location": "/functions/type_conversion_functions/", + "text": "Type conversion functions\n\n\ntoUInt8, toUInt16, toUInt32, toUInt64\n\n\ntoInt8, toInt16, toInt32, toInt64\n\n\ntoFloat32, toFloat64\n\n\ntoUInt8OrZero, toUInt16OrZero, toUInt32OrZero, toUInt64OrZero, toInt8OrZero, toInt16OrZero, toInt32OrZero, toInt64OrZero, toFloat32OrZero, toFloat64OrZero\n\n\ntoDate, toDateTime\n\n\ntoString\n\n\nFunctions for converting between numbers, strings (but not fixed strings), dates, and dates with times.\nAll these functions accept one argument.\n\n\nWhen converting to or from a string, the value is formatted or parsed using the same rules as for the TabSeparated format (and almost all other text formats). If the string can't be parsed, an exception is thrown and the request is canceled.\n\n\nWhen converting dates to numbers or vice versa, the date corresponds to the number of days since the beginning of the Unix epoch.\nWhen converting dates with times to numbers or vice versa, the date with time corresponds to the number of seconds since the beginning of the Unix epoch.\n\n\nThe date and date-with-time formats for the toDate/toDateTime functions are defined as follows:\n\n\nYYYY-MM-DD\nYYYY-MM-DD hh:mm:ss\n\n\n\n\n\nAs an exception, if converting from UInt32, Int32, UInt64, or Int64 numeric types to Date, and if the number is greater than or equal to 65536, the number is interpreted as a Unix timestamp (and not as the number of days) and is rounded to the date. This allows support for the common occurrence of writing 'toDate(unix_timestamp)', which otherwise would be an error and would require writing the more cumbersome 'toDate(toDateTime(unix_timestamp))'.\n\n\nConversion between a date and date with time is performed the natural way: by adding a null time or dropping the time.\n\n\nConversion between numeric types uses the same rules as assignments between different numeric types in C++.\n\n\nAdditionally, the toString function of the DateTime argument can take a second String argument containing the name of the time zone. Example: \nAsia/Yekaterinburg\n In this case, the time is formatted according to the specified time zone.\n\n\nSELECT\n\n \nnow\n()\n \nAS\n \nnow_local\n,\n\n \ntoString\n(\nnow\n(),\n \nAsia/Yekaterinburg\n)\n \nAS\n \nnow_yekat\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500now_local\u2500\u252c\u2500now_yekat\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 00:11:21 \u2502 2016-06-15 02:11:21 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nAlso see the \ntoUnixTimestamp\n function.\n\n\ntoFixedString(s, N)\n\n\nConverts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant.\nIf the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown.\n\n\ntoStringCutToZero(s)\n\n\nAccepts a String or FixedString argument. Returns the String with the content truncated at the first zero byte found.\n\n\nExample:\n\n\nSELECT\n \ntoFixedString\n(\nfoo\n,\n \n8\n)\n \nAS\n \ns\n,\n \ntoStringCutToZero\n(\ns\n)\n \nAS\n \ns_cut\n\n\n\n\n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0\\0\\0\\0\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n \ntoFixedString\n(\nfoo\\0bar\n,\n \n8\n)\n \nAS\n \ns\n,\n \ntoStringCutToZero\n(\ns\n)\n \nAS\n \ns_cut\n\n\n\n\n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0bar\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nreinterpretAsUInt8, reinterpretAsUInt16, reinterpretAsUInt32, reinterpretAsUInt64\n\n\nreinterpretAsInt8, reinterpretAsInt16, reinterpretAsInt32, reinterpretAsInt64\n\n\nreinterpretAsFloat32, reinterpretAsFloat64\n\n\nreinterpretAsDate, reinterpretAsDateTime\n\n\nThese functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\n\n\nreinterpretAsString\n\n\nThis function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long.\n\n\nCAST(x, t)\n\n\nConverts 'x' to the 't' data type. The syntax CAST(x AS t) is also supported.\n\n\nExample:\n\n\nSELECT\n\n \n2016-06-15 23:00:00\n \nAS\n \ntimestamp\n,\n\n \nCAST\n(\ntimestamp\n \nAS\n \nDateTime\n)\n \nAS\n \ndatetime\n,\n\n \nCAST\n(\ntimestamp\n \nAS\n \nDate\n)\n \nAS\n \ndate\n,\n\n \nCAST\n(\ntimestamp\n,\n \nString\n)\n \nAS\n \nstring\n,\n\n \nCAST\n(\ntimestamp\n,\n \nFixedString(22)\n)\n \nAS\n \nfixed_string\n\n\n\n\n\n\n\u250c\u2500timestamp\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500datetime\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500date\u2500\u252c\u2500string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500fixed_string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00\\0\\0\\0 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nConversion to FixedString (N) only works for arguments of type String or FixedString (N).", + "title": "Type conversion functions" + }, + { + "location": "/functions/type_conversion_functions/#type-conversion-functions", + "text": "", + "title": "Type conversion functions" + }, + { + "location": "/functions/type_conversion_functions/#touint8-touint16-touint32-touint64", + "text": "", + "title": "toUInt8, toUInt16, toUInt32, toUInt64" + }, + { + "location": "/functions/type_conversion_functions/#toint8-toint16-toint32-toint64", + "text": "", + "title": "toInt8, toInt16, toInt32, toInt64" + }, + { + "location": "/functions/type_conversion_functions/#tofloat32-tofloat64", + "text": "", + "title": "toFloat32, toFloat64" + }, + { + "location": "/functions/type_conversion_functions/#touint8orzero-touint16orzero-touint32orzero-touint64orzero-toint8orzero-toint16orzero-toint32orzero-toint64orzero-tofloat32orzero-tofloat64orzero", + "text": "", + "title": "toUInt8OrZero, toUInt16OrZero, toUInt32OrZero, toUInt64OrZero, toInt8OrZero, toInt16OrZero, toInt32OrZero, toInt64OrZero, toFloat32OrZero, toFloat64OrZero" + }, + { + "location": "/functions/type_conversion_functions/#todate-todatetime", + "text": "", + "title": "toDate, toDateTime" + }, + { + "location": "/functions/type_conversion_functions/#tostring", + "text": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times.\nAll these functions accept one argument. When converting to or from a string, the value is formatted or parsed using the same rules as for the TabSeparated format (and almost all other text formats). If the string can't be parsed, an exception is thrown and the request is canceled. When converting dates to numbers or vice versa, the date corresponds to the number of days since the beginning of the Unix epoch.\nWhen converting dates with times to numbers or vice versa, the date with time corresponds to the number of seconds since the beginning of the Unix epoch. The date and date-with-time formats for the toDate/toDateTime functions are defined as follows: YYYY-MM-DD\nYYYY-MM-DD hh:mm:ss As an exception, if converting from UInt32, Int32, UInt64, or Int64 numeric types to Date, and if the number is greater than or equal to 65536, the number is interpreted as a Unix timestamp (and not as the number of days) and is rounded to the date. This allows support for the common occurrence of writing 'toDate(unix_timestamp)', which otherwise would be an error and would require writing the more cumbersome 'toDate(toDateTime(unix_timestamp))'. Conversion between a date and date with time is performed the natural way: by adding a null time or dropping the time. Conversion between numeric types uses the same rules as assignments between different numeric types in C++. Additionally, the toString function of the DateTime argument can take a second String argument containing the name of the time zone. Example: Asia/Yekaterinburg In this case, the time is formatted according to the specified time zone. SELECT \n now () AS now_local , \n toString ( now (), Asia/Yekaterinburg ) AS now_yekat \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500now_local\u2500\u252c\u2500now_yekat\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 00:11:21 \u2502 2016-06-15 02:11:21 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Also see the toUnixTimestamp function.", + "title": "toString" + }, + { + "location": "/functions/type_conversion_functions/#tofixedstrings-n", + "text": "Converts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant.\nIf the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown.", + "title": "toFixedString(s, N)" + }, + { + "location": "/functions/type_conversion_functions/#tostringcuttozeros", + "text": "Accepts a String or FixedString argument. Returns the String with the content truncated at the first zero byte found. Example: SELECT toFixedString ( foo , 8 ) AS s , toStringCutToZero ( s ) AS s_cut \u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0\\0\\0\\0\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT toFixedString ( foo\\0bar , 8 ) AS s , toStringCutToZero ( s ) AS s_cut \u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0bar\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "toStringCutToZero(s)" + }, + { + "location": "/functions/type_conversion_functions/#reinterpretasuint8-reinterpretasuint16-reinterpretasuint32-reinterpretasuint64", + "text": "", + "title": "reinterpretAsUInt8, reinterpretAsUInt16, reinterpretAsUInt32, reinterpretAsUInt64" + }, + { + "location": "/functions/type_conversion_functions/#reinterpretasint8-reinterpretasint16-reinterpretasint32-reinterpretasint64", + "text": "", + "title": "reinterpretAsInt8, reinterpretAsInt16, reinterpretAsInt32, reinterpretAsInt64" + }, + { + "location": "/functions/type_conversion_functions/#reinterpretasfloat32-reinterpretasfloat64", + "text": "", + "title": "reinterpretAsFloat32, reinterpretAsFloat64" + }, + { + "location": "/functions/type_conversion_functions/#reinterpretasdate-reinterpretasdatetime", + "text": "These functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.", + "title": "reinterpretAsDate, reinterpretAsDateTime" + }, + { + "location": "/functions/type_conversion_functions/#reinterpretasstring", + "text": "This function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long.", + "title": "reinterpretAsString" + }, + { + "location": "/functions/type_conversion_functions/#castx-t", + "text": "Converts 'x' to the 't' data type. The syntax CAST(x AS t) is also supported. Example: SELECT \n 2016-06-15 23:00:00 AS timestamp , \n CAST ( timestamp AS DateTime ) AS datetime , \n CAST ( timestamp AS Date ) AS date , \n CAST ( timestamp , String ) AS string , \n CAST ( timestamp , FixedString(22) ) AS fixed_string \u250c\u2500timestamp\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500datetime\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500date\u2500\u252c\u2500string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500fixed_string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00\\0\\0\\0 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Conversion to FixedString (N) only works for arguments of type String or FixedString (N).", + "title": "CAST(x, t)" + }, + { + "location": "/functions/date_time_functions/", + "text": "Functions for working with dates and times\n\n\nSupport for time zones\n\n\nAll functions for working with the date and time that have a logical use for the time zone can accept a second optional time zone argument. Example: Asia/Yekaterinburg. In this case, they use the specified time zone instead of the local (default) one.\n\n\nSELECT\n\n \ntoDateTime\n(\n2016-06-15 23:00:00\n)\n \nAS\n \ntime\n,\n\n \ntoDate\n(\ntime\n)\n \nAS\n \ndate_local\n,\n\n \ntoDate\n(\ntime\n,\n \nAsia/Yekaterinburg\n)\n \nAS\n \ndate_yekat\n,\n\n \ntoString\n(\ntime\n,\n \nUS/Samoa\n)\n \nAS\n \ntime_samoa\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500time\u2500\u252c\u2500date_local\u2500\u252c\u2500date_yekat\u2500\u252c\u2500time_samoa\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-16 \u2502 2016-06-15 09:00:00 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nOnly time zones that differ from UTC by a whole number of hours are supported.\n\n\ntoYear\n\n\nConverts a date or date with time to a UInt16 number containing the year number (AD).\n\n\ntoMonth\n\n\nConverts a date or date with time to a UInt8 number containing the month number (1-12).\n\n\ntoDayOfMonth\n\n\n-Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31).\n\n\ntoDayOfWeek\n\n\nConverts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7).\n\n\ntoHour\n\n\nConverts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23).\nThis function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true \u2013 even in Moscow the clocks were twice changed at a different time).\n\n\ntoMinute\n\n\nConverts a date with time to a UInt8 number containing the number of the minute of the hour (0-59).\n\n\ntoSecond\n\n\nConverts a date with time to a UInt8 number containing the number of the second in the minute (0-59).\nLeap seconds are not accounted for.\n\n\ntoMonday\n\n\nRounds down a date or date with time to the nearest Monday.\nReturns the date.\n\n\ntoStartOfMonth\n\n\nRounds down a date or date with time to the first day of the month.\nReturns the date.\n\n\ntoStartOfQuarter\n\n\nRounds down a date or date with time to the first day of the quarter.\nThe first day of the quarter is either 1 January, 1 April, 1 July, or 1 October.\nReturns the date.\n\n\ntoStartOfYear\n\n\nRounds down a date or date with time to the first day of the year.\nReturns the date.\n\n\ntoStartOfMinute\n\n\nRounds down a date with time to the start of the minute.\n\n\ntoStartOfFiveMinute\n\n\nRounds down a date with time to the start of the hour.\n\n\ntoStartOfFifteenMinutes\n\n\nRounds down the date with time to the start of the fifteen-minute interval.\n\n\nNote: If you need to round a date with time to any other number of seconds, minutes, or hours, you can convert it into a number by using the toUInt32 function, then round the number using intDiv and multiplication, and convert it back using the toDateTime function.\n\n\ntoStartOfHour\n\n\nRounds down a date with time to the start of the hour.\n\n\ntoStartOfDay\n\n\nRounds down a date with time to the start of the day.\n\n\ntoTime\n\n\nConverts a date with time to a certain fixed date, while preserving the time.\n\n\ntoRelativeYearNum\n\n\nConverts a date with time or date to the number of the year, starting from a certain fixed point in the past.\n\n\ntoRelativeMonthNum\n\n\nConverts a date with time or date to the number of the month, starting from a certain fixed point in the past.\n\n\ntoRelativeWeekNum\n\n\nConverts a date with time or date to the number of the week, starting from a certain fixed point in the past.\n\n\ntoRelativeDayNum\n\n\nConverts a date with time or date to the number of the day, starting from a certain fixed point in the past.\n\n\ntoRelativeHourNum\n\n\nConverts a date with time or date to the number of the hour, starting from a certain fixed point in the past.\n\n\ntoRelativeMinuteNum\n\n\nConverts a date with time or date to the number of the minute, starting from a certain fixed point in the past.\n\n\ntoRelativeSecondNum\n\n\nConverts a date with time or date to the number of the second, starting from a certain fixed point in the past.\n\n\nnow\n\n\nAccepts zero arguments and returns the current time at one of the moments of request execution.\nThis function returns a constant, even if the request took a long time to complete.\n\n\ntoday\n\n\nAccepts zero arguments and returns the current date at one of the moments of request execution.\nThe same as 'toDate(now())'.\n\n\nyesterday\n\n\nAccepts zero arguments and returns yesterday's date at one of the moments of request execution.\nThe same as 'today() - 1'.\n\n\ntimeSlot\n\n\nRounds the time to the half hour.\nThis function is specific to Yandex.Metrica, since half an hour is the minimum amount of time for breaking a session into two sessions if a tracking tag shows a single user's consecutive pageviews that differ in time by strictly more than this amount. This means that tuples (the tag ID, user ID, and time slot) can be used to search for pageviews that are included in the corresponding session.\n\n\ntimeSlots(StartTime, Duration)\n\n\nFor a time interval starting at 'StartTime' and continuing for 'Duration' seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour.\nFor example, \ntimeSlots(toDateTime('2012-01-01 12:20:00'), 600) = [toDateTime('2012-01-01 12:00:00'), toDateTime('2012-01-01 12:30:00')]\n.\nThis is necessary for searching for pageviews in the corresponding session.", + "title": "Functions for working with dates and times" + }, + { + "location": "/functions/date_time_functions/#functions-for-working-with-dates-and-times", + "text": "Support for time zones All functions for working with the date and time that have a logical use for the time zone can accept a second optional time zone argument. Example: Asia/Yekaterinburg. In this case, they use the specified time zone instead of the local (default) one. SELECT \n toDateTime ( 2016-06-15 23:00:00 ) AS time , \n toDate ( time ) AS date_local , \n toDate ( time , Asia/Yekaterinburg ) AS date_yekat , \n toString ( time , US/Samoa ) AS time_samoa \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500time\u2500\u252c\u2500date_local\u2500\u252c\u2500date_yekat\u2500\u252c\u2500time_samoa\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-16 \u2502 2016-06-15 09:00:00 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Only time zones that differ from UTC by a whole number of hours are supported.", + "title": "Functions for working with dates and times" + }, + { + "location": "/functions/date_time_functions/#toyear", + "text": "Converts a date or date with time to a UInt16 number containing the year number (AD).", + "title": "toYear" + }, + { + "location": "/functions/date_time_functions/#tomonth", + "text": "Converts a date or date with time to a UInt8 number containing the month number (1-12).", + "title": "toMonth" + }, + { + "location": "/functions/date_time_functions/#todayofmonth", + "text": "-Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31).", + "title": "toDayOfMonth" + }, + { + "location": "/functions/date_time_functions/#todayofweek", + "text": "Converts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7).", + "title": "toDayOfWeek" + }, + { + "location": "/functions/date_time_functions/#tohour", + "text": "Converts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23).\nThis function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true \u2013 even in Moscow the clocks were twice changed at a different time).", + "title": "toHour" + }, + { + "location": "/functions/date_time_functions/#tominute", + "text": "Converts a date with time to a UInt8 number containing the number of the minute of the hour (0-59).", + "title": "toMinute" + }, + { + "location": "/functions/date_time_functions/#tosecond", + "text": "Converts a date with time to a UInt8 number containing the number of the second in the minute (0-59).\nLeap seconds are not accounted for.", + "title": "toSecond" + }, + { + "location": "/functions/date_time_functions/#tomonday", + "text": "Rounds down a date or date with time to the nearest Monday.\nReturns the date.", + "title": "toMonday" + }, + { + "location": "/functions/date_time_functions/#tostartofmonth", + "text": "Rounds down a date or date with time to the first day of the month.\nReturns the date.", + "title": "toStartOfMonth" + }, + { + "location": "/functions/date_time_functions/#tostartofquarter", + "text": "Rounds down a date or date with time to the first day of the quarter.\nThe first day of the quarter is either 1 January, 1 April, 1 July, or 1 October.\nReturns the date.", + "title": "toStartOfQuarter" + }, + { + "location": "/functions/date_time_functions/#tostartofyear", + "text": "Rounds down a date or date with time to the first day of the year.\nReturns the date.", + "title": "toStartOfYear" + }, + { + "location": "/functions/date_time_functions/#tostartofminute", + "text": "Rounds down a date with time to the start of the minute.", + "title": "toStartOfMinute" + }, + { + "location": "/functions/date_time_functions/#tostartoffiveminute", + "text": "Rounds down a date with time to the start of the hour.", + "title": "toStartOfFiveMinute" + }, + { + "location": "/functions/date_time_functions/#tostartoffifteenminutes", + "text": "Rounds down the date with time to the start of the fifteen-minute interval. Note: If you need to round a date with time to any other number of seconds, minutes, or hours, you can convert it into a number by using the toUInt32 function, then round the number using intDiv and multiplication, and convert it back using the toDateTime function.", + "title": "toStartOfFifteenMinutes" + }, + { + "location": "/functions/date_time_functions/#tostartofhour", + "text": "Rounds down a date with time to the start of the hour.", + "title": "toStartOfHour" + }, + { + "location": "/functions/date_time_functions/#tostartofday", + "text": "Rounds down a date with time to the start of the day.", + "title": "toStartOfDay" + }, + { + "location": "/functions/date_time_functions/#totime", + "text": "Converts a date with time to a certain fixed date, while preserving the time.", + "title": "toTime" + }, + { + "location": "/functions/date_time_functions/#torelativeyearnum", + "text": "Converts a date with time or date to the number of the year, starting from a certain fixed point in the past.", + "title": "toRelativeYearNum" + }, + { + "location": "/functions/date_time_functions/#torelativemonthnum", + "text": "Converts a date with time or date to the number of the month, starting from a certain fixed point in the past.", + "title": "toRelativeMonthNum" + }, + { + "location": "/functions/date_time_functions/#torelativeweeknum", + "text": "Converts a date with time or date to the number of the week, starting from a certain fixed point in the past.", + "title": "toRelativeWeekNum" + }, + { + "location": "/functions/date_time_functions/#torelativedaynum", + "text": "Converts a date with time or date to the number of the day, starting from a certain fixed point in the past.", + "title": "toRelativeDayNum" + }, + { + "location": "/functions/date_time_functions/#torelativehournum", + "text": "Converts a date with time or date to the number of the hour, starting from a certain fixed point in the past.", + "title": "toRelativeHourNum" + }, + { + "location": "/functions/date_time_functions/#torelativeminutenum", + "text": "Converts a date with time or date to the number of the minute, starting from a certain fixed point in the past.", + "title": "toRelativeMinuteNum" + }, + { + "location": "/functions/date_time_functions/#torelativesecondnum", + "text": "Converts a date with time or date to the number of the second, starting from a certain fixed point in the past.", + "title": "toRelativeSecondNum" + }, + { + "location": "/functions/date_time_functions/#now", + "text": "Accepts zero arguments and returns the current time at one of the moments of request execution.\nThis function returns a constant, even if the request took a long time to complete.", + "title": "now" + }, + { + "location": "/functions/date_time_functions/#today", + "text": "Accepts zero arguments and returns the current date at one of the moments of request execution.\nThe same as 'toDate(now())'.", + "title": "today" + }, + { + "location": "/functions/date_time_functions/#yesterday", + "text": "Accepts zero arguments and returns yesterday's date at one of the moments of request execution.\nThe same as 'today() - 1'.", + "title": "yesterday" + }, + { + "location": "/functions/date_time_functions/#timeslot", + "text": "Rounds the time to the half hour.\nThis function is specific to Yandex.Metrica, since half an hour is the minimum amount of time for breaking a session into two sessions if a tracking tag shows a single user's consecutive pageviews that differ in time by strictly more than this amount. This means that tuples (the tag ID, user ID, and time slot) can be used to search for pageviews that are included in the corresponding session.", + "title": "timeSlot" + }, + { + "location": "/functions/date_time_functions/#timeslotsstarttime-duration", + "text": "For a time interval starting at 'StartTime' and continuing for 'Duration' seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour.\nFor example, timeSlots(toDateTime('2012-01-01 12:20:00'), 600) = [toDateTime('2012-01-01 12:00:00'), toDateTime('2012-01-01 12:30:00')] .\nThis is necessary for searching for pageviews in the corresponding session.", + "title": "timeSlots(StartTime, Duration)" + }, + { + "location": "/functions/string_functions/", + "text": "Functions for working with strings\n\n\nempty\n\n\nReturns 1 for an empty string or 0 for a non-empty string.\nThe result type is UInt8.\nA string is considered non-empty if it contains at least one byte, even if this is a space or a null byte.\nThe function also works for arrays.\n\n\nnotEmpty\n\n\nReturns 0 for an empty string or 1 for a non-empty string.\nThe result type is UInt8.\nThe function also works for arrays.\n\n\nlength\n\n\nReturns the length of a string in bytes (not in characters, and not in code points).\nThe result type is UInt64.\nThe function also works for arrays.\n\n\nlengthUTF8\n\n\nReturns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\nThe result type is UInt64.\n\n\nlower\n\n\nConverts ASCII Latin symbols in a string to lowercase.\n\n\nupper\n\n\nConverts ASCII Latin symbols in a string to uppercase.\n\n\nlowerUTF8\n\n\nConverts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.\n\n\nupperUTF8\n\n\nConverts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.\n\n\nreverse\n\n\nReverses the string (as a sequence of bytes).\n\n\nreverseUTF8\n\n\nReverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn't throw an exception).\n\n\nconcat(s1, s2, ...)\n\n\nConcatenates the strings listed in the arguments, without a separator.\n\n\nsubstring(s, offset, length)\n\n\nReturns a substring starting with the byte from the 'offset' index that is 'length' bytes long. Character indexing starts from one (as in standard SQL). The 'offset' and 'length' arguments must be constants.\n\n\nsubstringUTF8(s, offset, length)\n\n\nThe same as 'substring', but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\n\n\nappendTrailingCharIfAbsent(s, c)\n\n\nIf the 's' string is non-empty and does not contain the 'c' character at the end, it appends the 'c' character to the end.\n\n\nconvertCharset(s, from, to)\n\n\nReturns the string 's' that was converted from the encoding in 'from' to the encoding in 'to'.", + "title": "Functions for working with strings" + }, + { + "location": "/functions/string_functions/#functions-for-working-with-strings", + "text": "", + "title": "Functions for working with strings" + }, + { + "location": "/functions/string_functions/#empty", + "text": "Returns 1 for an empty string or 0 for a non-empty string.\nThe result type is UInt8.\nA string is considered non-empty if it contains at least one byte, even if this is a space or a null byte.\nThe function also works for arrays.", + "title": "empty" + }, + { + "location": "/functions/string_functions/#notempty", + "text": "Returns 0 for an empty string or 1 for a non-empty string.\nThe result type is UInt8.\nThe function also works for arrays.", + "title": "notEmpty" + }, + { + "location": "/functions/string_functions/#length", + "text": "Returns the length of a string in bytes (not in characters, and not in code points).\nThe result type is UInt64.\nThe function also works for arrays.", + "title": "length" + }, + { + "location": "/functions/string_functions/#lengthutf8", + "text": "Returns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\nThe result type is UInt64.", + "title": "lengthUTF8" + }, + { + "location": "/functions/string_functions/#lower", + "text": "Converts ASCII Latin symbols in a string to lowercase.", + "title": "lower" + }, + { + "location": "/functions/string_functions/#upper", + "text": "Converts ASCII Latin symbols in a string to uppercase.", + "title": "upper" + }, + { + "location": "/functions/string_functions/#lowerutf8", + "text": "Converts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.", + "title": "lowerUTF8" + }, + { + "location": "/functions/string_functions/#upperutf8", + "text": "Converts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.", + "title": "upperUTF8" + }, + { + "location": "/functions/string_functions/#reverse", + "text": "Reverses the string (as a sequence of bytes).", + "title": "reverse" + }, + { + "location": "/functions/string_functions/#reverseutf8", + "text": "Reverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn't throw an exception).", + "title": "reverseUTF8" + }, + { + "location": "/functions/string_functions/#concats1-s2", + "text": "Concatenates the strings listed in the arguments, without a separator.", + "title": "concat(s1, s2, ...)" + }, + { + "location": "/functions/string_functions/#substrings-offset-length", + "text": "Returns a substring starting with the byte from the 'offset' index that is 'length' bytes long. Character indexing starts from one (as in standard SQL). The 'offset' and 'length' arguments must be constants.", + "title": "substring(s, offset, length)" + }, + { + "location": "/functions/string_functions/#substringutf8s-offset-length", + "text": "The same as 'substring', but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).", + "title": "substringUTF8(s, offset, length)" + }, + { + "location": "/functions/string_functions/#appendtrailingcharifabsents-c", + "text": "If the 's' string is non-empty and does not contain the 'c' character at the end, it appends the 'c' character to the end.", + "title": "appendTrailingCharIfAbsent(s, c)" + }, + { + "location": "/functions/string_functions/#convertcharsets-from-to", + "text": "Returns the string 's' that was converted from the encoding in 'from' to the encoding in 'to'.", + "title": "convertCharset(s, from, to)" + }, + { + "location": "/functions/string_search_functions/", + "text": "Functions for searching strings\n\n\nThe search is case-sensitive in all these functions.\nThe search substring or regular expression must be a constant in all these functions.\n\n\nposition(haystack, needle)\n\n\nSearch for the \nneedle\n substring in the \nhaystack\n string.\nReturns the position (in bytes) of the found substring, starting from 1, or returns 0 if the substring was not found.\n\n\nFor case-insensitive search use \npositionCaseInsensitive\n function.\n\n\npositionUTF8(haystack, needle)\n\n\nThe same as \nposition\n, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\n\n\nFor case-insensitive search use \npositionCaseInsensitiveUTF8\n function.\n\n\nmatch(haystack, pattern)\n\n\nChecks whether the string matches the 'pattern' regular expression. A re2 regular expression.\nReturns 0 if it doesn't match, or 1 if it matches.\n\n\nNote that the backslash symbol (\n\\\n) is used for escaping in the regular expression. The same symbol is used for escaping in string literals. So in order to escape the symbol in a regular expression, you must write two backslashes (\\) in a string literal.\n\n\nThe regular expression works with the string as if it is a set of bytes. The regular expression can't contain null bytes.\nFor patterns to search for substrings in a string, it is better to use LIKE or 'position', since they work much faster.\n\n\nextract(haystack, pattern)\n\n\nExtracts a fragment of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. If the regex doesn't contain subpatterns, it takes the fragment that matches the entire regex. Otherwise, it takes the fragment that matches the first subpattern.\n\n\nextractAll(haystack, pattern)\n\n\nExtracts all the fragments of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex. In general, the behavior is the same as the 'extract' function (it takes the first subpattern, or the entire expression if there isn't a subpattern).\n\n\nlike(haystack, pattern), haystack LIKE pattern operator\n\n\nChecks whether a string matches a simple regular expression.\nThe regular expression can contain the metasymbols \n%\n and \n_\n.\n\n\n``% indicates any quantity of any bytes (including zero characters).\n\n\n_\n indicates any one byte.\n\n\nUse the backslash (\n\\\n) for escaping metasymbols. See the note on escaping in the description of the 'match' function.\n\n\nFor regular expressions like \n%needle%\n, the code is more optimal and works as fast as the \nposition\n function.\nFor other regular expressions, the code is the same as for the 'match' function.\n\n\nnotLike(haystack, pattern), haystack NOT LIKE pattern operator\n\n\nThe same thing as 'like', but negative.", + "title": "Functions for searching strings" + }, + { + "location": "/functions/string_search_functions/#functions-for-searching-strings", + "text": "The search is case-sensitive in all these functions.\nThe search substring or regular expression must be a constant in all these functions.", + "title": "Functions for searching strings" + }, + { + "location": "/functions/string_search_functions/#positionhaystack-needle", + "text": "Search for the needle substring in the haystack string.\nReturns the position (in bytes) of the found substring, starting from 1, or returns 0 if the substring was not found. For case-insensitive search use positionCaseInsensitive function.", + "title": "position(haystack, needle)" + }, + { + "location": "/functions/string_search_functions/#positionutf8haystack-needle", + "text": "The same as position , but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception). For case-insensitive search use positionCaseInsensitiveUTF8 function.", + "title": "positionUTF8(haystack, needle)" + }, + { + "location": "/functions/string_search_functions/#matchhaystack-pattern", + "text": "Checks whether the string matches the 'pattern' regular expression. A re2 regular expression.\nReturns 0 if it doesn't match, or 1 if it matches. Note that the backslash symbol ( \\ ) is used for escaping in the regular expression. The same symbol is used for escaping in string literals. So in order to escape the symbol in a regular expression, you must write two backslashes (\\) in a string literal. The regular expression works with the string as if it is a set of bytes. The regular expression can't contain null bytes.\nFor patterns to search for substrings in a string, it is better to use LIKE or 'position', since they work much faster.", + "title": "match(haystack, pattern)" + }, + { + "location": "/functions/string_search_functions/#extracthaystack-pattern", + "text": "Extracts a fragment of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. If the regex doesn't contain subpatterns, it takes the fragment that matches the entire regex. Otherwise, it takes the fragment that matches the first subpattern.", + "title": "extract(haystack, pattern)" + }, + { + "location": "/functions/string_search_functions/#extractallhaystack-pattern", + "text": "Extracts all the fragments of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex. In general, the behavior is the same as the 'extract' function (it takes the first subpattern, or the entire expression if there isn't a subpattern).", + "title": "extractAll(haystack, pattern)" + }, + { + "location": "/functions/string_search_functions/#likehaystack-pattern-haystack-like-pattern-operator", + "text": "Checks whether a string matches a simple regular expression.\nThe regular expression can contain the metasymbols % and _ . ``% indicates any quantity of any bytes (including zero characters). _ indicates any one byte. Use the backslash ( \\ ) for escaping metasymbols. See the note on escaping in the description of the 'match' function. For regular expressions like %needle% , the code is more optimal and works as fast as the position function.\nFor other regular expressions, the code is the same as for the 'match' function.", + "title": "like(haystack, pattern), haystack LIKE pattern operator" + }, + { + "location": "/functions/string_search_functions/#notlikehaystack-pattern-haystack-not-like-pattern-operator", + "text": "The same thing as 'like', but negative.", + "title": "notLike(haystack, pattern), haystack NOT LIKE pattern operator" + }, + { + "location": "/functions/string_replace_functions/", + "text": "Functions for searching and replacing in strings\n\n\nreplaceOne(haystack, pattern, replacement)\n\n\nReplaces the first occurrence, if it exists, of the 'pattern' substring in 'haystack' with the 'replacement' substring.\nHereafter, 'pattern' and 'replacement' must be constants.\n\n\nreplaceAll(haystack, pattern, replacement)\n\n\nReplaces all occurrences of the 'pattern' substring in 'haystack' with the 'replacement' substring.\n\n\nreplaceRegexpOne(haystack, pattern, replacement)\n\n\nReplacement using the 'pattern' regular expression. A re2 regular expression.\nReplaces only the first occurrence, if it exists.\nA pattern can be specified as 'replacement'. This pattern can include substitutions \n\\0-\\9\n.\nThe substitution \n\\0\n includes the entire regular expression. Substitutions \n\\1-\\9\n correspond to the subpattern numbers.To use the \n\\\n character in a template, escape it using \n\\\n.\nAlso keep in mind that a string literal requires an extra escape.\n\n\nExample 1. Converting the date to American format:\n\n\nSELECT\n \nDISTINCT\n\n \nEventDate\n,\n\n \nreplaceRegexpOne\n(\ntoString\n(\nEventDate\n),\n \n(\\\\d{4})-(\\\\d{2})-(\\\\d{2})\n,\n \n\\\\2/\\\\3/\\\\1\n)\n \nAS\n \nres\n\n\nFROM\n \ntest\n.\nhits\n\n\nLIMIT\n \n7\n\n\nFORMAT\n \nTabSeparated\n\n\n\n\n\n\n2014-03-17 03/17/2014\n2014-03-18 03/18/2014\n2014-03-19 03/19/2014\n2014-03-20 03/20/2014\n2014-03-21 03/21/2014\n2014-03-22 03/22/2014\n2014-03-23 03/23/2014\n\n\n\n\n\nExample 2. Copying a string ten times:\n\n\nSELECT\n \nreplaceRegexpOne\n(\nHello, World!\n,\n \n.*\n,\n \n\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nreplaceRegexpAll(haystack, pattern, replacement)\n\n\nThis does the same thing, but replaces all the occurrences. Example:\n\n\nSELECT\n \nreplaceRegexpAll\n(\nHello, World!\n,\n \n.\n,\n \n\\\\0\\\\0\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 HHeelllloo,, WWoorrlldd!! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nAs an exception, if a regular expression worked on an empty substring, the replacement is not made more than once.\nExample:\n\n\nSELECT\n \nreplaceRegexpAll\n(\nHello, World!\n,\n \n^\n,\n \nhere: \n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 here: Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "Functions for searching and replacing in strings" + }, + { + "location": "/functions/string_replace_functions/#functions-for-searching-and-replacing-in-strings", + "text": "", + "title": "Functions for searching and replacing in strings" + }, + { + "location": "/functions/string_replace_functions/#replaceonehaystack-pattern-replacement", + "text": "Replaces the first occurrence, if it exists, of the 'pattern' substring in 'haystack' with the 'replacement' substring.\nHereafter, 'pattern' and 'replacement' must be constants.", + "title": "replaceOne(haystack, pattern, replacement)" + }, + { + "location": "/functions/string_replace_functions/#replaceallhaystack-pattern-replacement", + "text": "Replaces all occurrences of the 'pattern' substring in 'haystack' with the 'replacement' substring.", + "title": "replaceAll(haystack, pattern, replacement)" + }, + { + "location": "/functions/string_replace_functions/#replaceregexponehaystack-pattern-replacement", + "text": "Replacement using the 'pattern' regular expression. A re2 regular expression.\nReplaces only the first occurrence, if it exists.\nA pattern can be specified as 'replacement'. This pattern can include substitutions \\0-\\9 .\nThe substitution \\0 includes the entire regular expression. Substitutions \\1-\\9 correspond to the subpattern numbers.To use the \\ character in a template, escape it using \\ .\nAlso keep in mind that a string literal requires an extra escape. Example 1. Converting the date to American format: SELECT DISTINCT \n EventDate , \n replaceRegexpOne ( toString ( EventDate ), (\\\\d{4})-(\\\\d{2})-(\\\\d{2}) , \\\\2/\\\\3/\\\\1 ) AS res FROM test . hits LIMIT 7 FORMAT TabSeparated 2014-03-17 03/17/2014\n2014-03-18 03/18/2014\n2014-03-19 03/19/2014\n2014-03-20 03/20/2014\n2014-03-21 03/21/2014\n2014-03-22 03/22/2014\n2014-03-23 03/23/2014 Example 2. Copying a string ten times: SELECT replaceRegexpOne ( Hello, World! , .* , \\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0 ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "replaceRegexpOne(haystack, pattern, replacement)" + }, + { + "location": "/functions/string_replace_functions/#replaceregexpallhaystack-pattern-replacement", + "text": "This does the same thing, but replaces all the occurrences. Example: SELECT replaceRegexpAll ( Hello, World! , . , \\\\0\\\\0 ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 HHeelllloo,, WWoorrlldd!! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 As an exception, if a regular expression worked on an empty substring, the replacement is not made more than once.\nExample: SELECT replaceRegexpAll ( Hello, World! , ^ , here: ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 here: Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "replaceRegexpAll(haystack, pattern, replacement)" + }, + { + "location": "/functions/conditional_functions/", + "text": "Conditional functions\n\n\nif(cond, then, else), cond ? operator then : else\n\n\nReturns 'then' if cond !or 'else' if cond = 0.'cond' must be UInt 8, and 'then' and 'else' must be a type that has the smallest common type.", + "title": "Conditional functions" + }, + { + "location": "/functions/conditional_functions/#conditional-functions", + "text": "", + "title": "Conditional functions" + }, + { + "location": "/functions/conditional_functions/#ifcond-then-else-cond-operator-then-else", + "text": "Returns 'then' if cond !or 'else' if cond = 0.'cond' must be UInt 8, and 'then' and 'else' must be a type that has the smallest common type.", + "title": "if(cond, then, else), cond ? operator then : else" + }, + { + "location": "/functions/math_functions/", + "text": "Mathematical functions\n\n\nAll the functions return a Float64 number. The accuracy of the result is close to the maximum precision possible, but the result might not coincide with the machine representable number nearest to the corresponding real number.\n\n\ne()\n\n\nReturns a Float64 number close to the e number.\n\n\npi()\n\n\nReturns a Float64 number close to \u03c0.\n\n\nexp(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the exponent of the argument.\n\n\nlog(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the natural logarithm of the argument.\n\n\nexp2(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to 2^x.\n\n\nlog2(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument.\n\n\nexp10(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to 10^x.\n\n\nlog10(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument.\n\n\nsqrt(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the square root of the argument.\n\n\ncbrt(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the cubic root of the argument.\n\n\nerf(x)\n\n\nIf 'x' is non-negative, then erf(x / \u03c3\u221a2)\n is the probability that a random variable having a normal distribution with standard deviation '\u03c3' takes the value that is separated from the expected value by more than 'x'.\n\n\nExample (three sigma rule):\n\n\nSELECT\n \nerf\n(\n3\n \n/\n \nsqrt\n(\n2\n))\n\n\n\n\n\n\n\u250c\u2500erf(divide(3, sqrt(2)))\u2500\u2510\n\u2502 0.9973002039367398 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nerfc(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large 'x' values.\n\n\nlgamma(x)\n\n\nThe logarithm of the gamma function.\n\n\ntgamma(x)\n\n\nGamma function.\n\n\nsin(x)\n\n\nThe sine.\n\n\ncos(x)\n\n\nThe cosine.\n\n\ntan(x)\n\n\nThe tangent.\n\n\nasin(x)\n\n\nThe arc sine.\n\n\nacos(x)\n\n\nThe arc cosine.\n\n\natan(x)\n\n\nThe arc tangent.\n\n\npow(x, y)\n\n\nAccepts two numeric arguments and returns a Float64 number close to x^y.", + "title": "Mathematical functions" + }, + { + "location": "/functions/math_functions/#mathematical-functions", + "text": "All the functions return a Float64 number. The accuracy of the result is close to the maximum precision possible, but the result might not coincide with the machine representable number nearest to the corresponding real number.", + "title": "Mathematical functions" + }, + { + "location": "/functions/math_functions/#e", + "text": "Returns a Float64 number close to the e number.", + "title": "e()" + }, + { + "location": "/functions/math_functions/#pi", + "text": "Returns a Float64 number close to \u03c0.", + "title": "pi()" + }, + { + "location": "/functions/math_functions/#expx", + "text": "Accepts a numeric argument and returns a Float64 number close to the exponent of the argument.", + "title": "exp(x)" + }, + { + "location": "/functions/math_functions/#logx", + "text": "Accepts a numeric argument and returns a Float64 number close to the natural logarithm of the argument.", + "title": "log(x)" + }, + { + "location": "/functions/math_functions/#exp2x", + "text": "Accepts a numeric argument and returns a Float64 number close to 2^x.", + "title": "exp2(x)" + }, + { + "location": "/functions/math_functions/#log2x", + "text": "Accepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument.", + "title": "log2(x)" + }, + { + "location": "/functions/math_functions/#exp10x", + "text": "Accepts a numeric argument and returns a Float64 number close to 10^x.", + "title": "exp10(x)" + }, + { + "location": "/functions/math_functions/#log10x", + "text": "Accepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument.", + "title": "log10(x)" + }, + { + "location": "/functions/math_functions/#sqrtx", + "text": "Accepts a numeric argument and returns a Float64 number close to the square root of the argument.", + "title": "sqrt(x)" + }, + { + "location": "/functions/math_functions/#cbrtx", + "text": "Accepts a numeric argument and returns a Float64 number close to the cubic root of the argument.", + "title": "cbrt(x)" + }, + { + "location": "/functions/math_functions/#erfx", + "text": "If 'x' is non-negative, then erf(x / \u03c3\u221a2) is the probability that a random variable having a normal distribution with standard deviation '\u03c3' takes the value that is separated from the expected value by more than 'x'. Example (three sigma rule): SELECT erf ( 3 / sqrt ( 2 )) \u250c\u2500erf(divide(3, sqrt(2)))\u2500\u2510\n\u2502 0.9973002039367398 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "erf(x)" + }, + { + "location": "/functions/math_functions/#erfcx", + "text": "Accepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large 'x' values.", + "title": "erfc(x)" + }, + { + "location": "/functions/math_functions/#lgammax", + "text": "The logarithm of the gamma function.", + "title": "lgamma(x)" + }, + { + "location": "/functions/math_functions/#tgammax", + "text": "Gamma function.", + "title": "tgamma(x)" + }, + { + "location": "/functions/math_functions/#sinx", + "text": "The sine.", + "title": "sin(x)" + }, + { + "location": "/functions/math_functions/#cosx", + "text": "The cosine.", + "title": "cos(x)" + }, + { + "location": "/functions/math_functions/#tanx", + "text": "The tangent.", + "title": "tan(x)" + }, + { + "location": "/functions/math_functions/#asinx", + "text": "The arc sine.", + "title": "asin(x)" + }, + { + "location": "/functions/math_functions/#acosx", + "text": "The arc cosine.", + "title": "acos(x)" + }, + { + "location": "/functions/math_functions/#atanx", + "text": "The arc tangent.", + "title": "atan(x)" + }, + { + "location": "/functions/math_functions/#powx-y", + "text": "Accepts two numeric arguments and returns a Float64 number close to x^y.", + "title": "pow(x, y)" + }, + { + "location": "/functions/rounding_functions/", + "text": "Rounding functions\n\n\nfloor(x[, N])\n\n\nReturns the largest round number that is less than or equal to x. A round number is a multiple of 1/10N, or the nearest number of the appropriate data type if 1 / 10N isn't exact.\n'N' is an integer constant, optional parameter. By default it is zero, which means to round to an integer.\n'N' may be negative.\n\n\nExamples: \nfloor(123.45, 1) = 123.4, floor(123.45, -1) = 120.\n\n\nx\n is any numeric type. The result is a number of the same type.\nFor integer arguments, it makes sense to round with a negative 'N' value (for non-negative 'N', the function doesn't do anything).\nIf rounding causes overflow (for example, floor(-128, -1)), an implementation-specific result is returned.\n\n\nceil(x[, N])\n\n\nReturns the smallest round number that is greater than or equal to 'x'. In every other way, it is the same as the 'floor' function (see above).\n\n\nround(x[, N])\n\n\nReturns the round number nearest to 'num', which may be less than, greater than, or equal to 'x'.If 'x' is exactly in the middle between the nearest round numbers, one of them is returned (implementation-specific).\nThe number '-0.' may or may not be considered round (implementation-specific).\nIn every other way, this function is the same as 'floor' and 'ceil' described above.\n\n\nroundToExp2(num)\n\n\nAccepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two.\n\n\nroundDuration(num)\n\n\nAccepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length\n\n\nroundAge(num)\n\n\nAccepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to a number from the set: 18, 25, 35, 45, 55. This function is specific to Yandex.Metrica and used for implementing the report on user age.", + "title": "Rounding functions" + }, + { + "location": "/functions/rounding_functions/#rounding-functions", + "text": "", + "title": "Rounding functions" + }, + { + "location": "/functions/rounding_functions/#floorx91-n93", + "text": "Returns the largest round number that is less than or equal to x. A round number is a multiple of 1/10N, or the nearest number of the appropriate data type if 1 / 10N isn't exact.\n'N' is an integer constant, optional parameter. By default it is zero, which means to round to an integer.\n'N' may be negative. Examples: floor(123.45, 1) = 123.4, floor(123.45, -1) = 120. x is any numeric type. The result is a number of the same type.\nFor integer arguments, it makes sense to round with a negative 'N' value (for non-negative 'N', the function doesn't do anything).\nIf rounding causes overflow (for example, floor(-128, -1)), an implementation-specific result is returned.", + "title": "floor(x[, N])" + }, + { + "location": "/functions/rounding_functions/#ceilx91-n93", + "text": "Returns the smallest round number that is greater than or equal to 'x'. In every other way, it is the same as the 'floor' function (see above).", + "title": "ceil(x[, N])" + }, + { + "location": "/functions/rounding_functions/#roundx91-n93", + "text": "Returns the round number nearest to 'num', which may be less than, greater than, or equal to 'x'.If 'x' is exactly in the middle between the nearest round numbers, one of them is returned (implementation-specific).\nThe number '-0.' may or may not be considered round (implementation-specific).\nIn every other way, this function is the same as 'floor' and 'ceil' described above.", + "title": "round(x[, N])" + }, + { + "location": "/functions/rounding_functions/#roundtoexp2num", + "text": "Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two.", + "title": "roundToExp2(num)" + }, + { + "location": "/functions/rounding_functions/#rounddurationnum", + "text": "Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length", + "title": "roundDuration(num)" + }, + { + "location": "/functions/rounding_functions/#roundagenum", + "text": "Accepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to a number from the set: 18, 25, 35, 45, 55. This function is specific to Yandex.Metrica and used for implementing the report on user age.", + "title": "roundAge(num)" + }, + { + "location": "/functions/array_functions/", + "text": "Functions for working with arrays\n\n\nempty\n\n\nReturns 1 for an empty array, or 0 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.\n\n\nnotEmpty\n\n\nReturns 0 for an empty array, or 1 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.\n\n\nlength\n\n\nReturns the number of items in the array.\nThe result type is UInt64.\nThe function also works for strings.\n\n\nemptyArrayUInt8, emptyArrayUInt16, emptyArrayUInt32, emptyArrayUInt64\n\n\nemptyArrayInt8, emptyArrayInt16, emptyArrayInt32, emptyArrayInt64\n\n\nemptyArrayFloat32, emptyArrayFloat64\n\n\nemptyArrayDate, emptyArrayDateTime\n\n\nemptyArrayString\n\n\nAccepts zero arguments and returns an empty array of the appropriate type.\n\n\nemptyArrayToSingle\n\n\nAccepts an empty array and returns a one-element array that is equal to the default value.\n\n\nrange(N)\n\n\nReturns an array of numbers from 0 to N-1.\nJust in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block.\n\n\narray(x1, ...), operator [x1, ...]\n\n\nCreates an array from the function arguments.\nThe arguments must be constants and have types that have the smallest common type. At least one argument must be passed, because otherwise it isn't clear which type of array to create. That is, you can't use this function to create an empty array (to do that, use the 'emptyArray*' function described above).\nReturns an 'Array(T)' type result, where 'T' is the smallest common type out of the passed arguments.\n\n\narrayConcat\n\n\nCombines arrays passed as arguments.\n\n\narrayConcat(arrays)\n\n\n\n\n\nArguments\n\n\n\n\narrays\n \u2013 Arrays of comma-separated \n[values]\n.\n\n\n\n\nExample\n\n\nSELECT\n \narrayConcat\n([\n1\n,\n \n2\n],\n \n[\n3\n,\n \n4\n],\n \n[\n5\n,\n \n6\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,3,4,5,6] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayElement(arr, n), operator arr[n]\n\n\nGet the element with the index 'n' from the array 'arr'.'n' must be any integer type.\nIndexes in an array begin from one.\nNegative indexes are supported. In this case, it selects the corresponding element numbered from the end. For example, 'arr[-1]' is the last item in the array.\n\n\nIf the index falls outside of the bounds of an array, it returns some default value (0 for numbers, an empty string for strings, etc.).\n\n\nhas(arr, elem)\n\n\nChecks whether the 'arr' array has the 'elem' element.\nReturns 0 if the the element is not in the array, or 1 if it is.\n\n\nindexOf(arr, x)\n\n\nReturns the index of the 'x' element (starting from 1) if it is in the array, or 0 if it is not.\n\n\ncountEqual(arr, x)\n\n\nReturns the number of elements in the array equal to x. Equivalent to arrayCount (elem-\n elem = x, arr).\n\n\narrayEnumerate(arr)\n\n\nReturns the array [1, 2, 3, ..., length (arr) ]\n\n\nThis function is normally used with ARRAY JOIN. It allows counting something just once for each array after applying ARRAY JOIN. Example:\n\n\nSELECT\n\n \ncount\n()\n \nAS\n \nReaches\n,\n\n \ncountIf\n(\nnum\n \n=\n \n1\n)\n \nAS\n \nHits\n\n\nFROM\n \ntest\n.\nhits\n\n\nARRAY\n \nJOIN\n\n \nGoalsReached\n,\n\n \narrayEnumerate\n(\nGoalsReached\n)\n \nAS\n \nnum\n\n\nWHERE\n \nCounterID\n \n=\n \n160656\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIn this example, Reaches is the number of conversions (the strings received after applying ARRAY JOIN), and Hits is the number of pageviews (strings before ARRAY JOIN). In this particular case, you can get the same result in an easier way:\n\n\nSELECT\n\n \nsum\n(\nlength\n(\nGoalsReached\n))\n \nAS\n \nReaches\n,\n\n \ncount\n()\n \nAS\n \nHits\n\n\nFROM\n \ntest\n.\nhits\n\n\nWHERE\n \n(\nCounterID\n \n=\n \n160656\n)\n \nAND\n \nnotEmpty\n(\nGoalsReached\n)\n\n\n\n\n\n\n\u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nThis function can also be used in higher-order functions. For example, you can use it to get array indexes for elements that match a condition.\n\n\narrayEnumerateUniq(arr, ...)\n\n\nReturns an array the same size as the source array, indicating for each element what its position is among elements with the same value.\nFor example: arrayEnumerateUniq([10, 20, 10, 30]) = [1, 1, 2, 1].\n\n\nThis function is useful when using ARRAY JOIN and aggregation of array elements.\nExample:\n\n\nSELECT\n\n \nGoals\n.\nID\n \nAS\n \nGoalID\n,\n\n \nsum\n(\nSign\n)\n \nAS\n \nReaches\n,\n\n \nsumIf\n(\nSign\n,\n \nnum\n \n=\n \n1\n)\n \nAS\n \nVisits\n\n\nFROM\n \ntest\n.\nvisits\n\n\nARRAY\n \nJOIN\n\n \nGoals\n,\n\n \narrayEnumerateUniq\n(\nGoals\n.\nID\n)\n \nAS\n \nnum\n\n\nWHERE\n \nCounterID\n \n=\n \n160656\n\n\nGROUP\n \nBY\n \nGoalID\n\n\nORDER\n \nBY\n \nReaches\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500\u2500GoalID\u2500\u252c\u2500Reaches\u2500\u252c\u2500Visits\u2500\u2510\n\u2502 53225 \u2502 3214 \u2502 1097 \u2502\n\u2502 2825062 \u2502 3188 \u2502 1097 \u2502\n\u2502 56600 \u2502 2803 \u2502 488 \u2502\n\u2502 1989037 \u2502 2401 \u2502 365 \u2502\n\u2502 2830064 \u2502 2396 \u2502 910 \u2502\n\u2502 1113562 \u2502 2372 \u2502 373 \u2502\n\u2502 3270895 \u2502 2262 \u2502 812 \u2502\n\u2502 1084657 \u2502 2262 \u2502 345 \u2502\n\u2502 56599 \u2502 2260 \u2502 799 \u2502\n\u2502 3271094 \u2502 2256 \u2502 812 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIn this example, each goal ID has a calculation of the number of conversions (each element in the Goals nested data structure is a goal that was reached, which we refer to as a conversion) and the number of sessions. Without ARRAY JOIN, we would have counted the number of sessions as sum(Sign). But in this particular case, the rows were multiplied by the nested Goals structure, so in order to count each session one time after this, we apply a condition to the value of the arrayEnumerateUniq(Goals.ID) function.\n\n\nThe arrayEnumerateUniq function can take multiple arrays of the same size as arguments. In this case, uniqueness is considered for tuples of elements in the same positions in all the arrays.\n\n\nSELECT\n \narrayEnumerateUniq\n([\n1\n,\n \n1\n,\n \n1\n,\n \n2\n,\n \n2\n,\n \n2\n],\n \n[\n1\n,\n \n1\n,\n \n2\n,\n \n1\n,\n \n1\n,\n \n2\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,1,1,2,1] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nThis is necessary when using ARRAY JOIN with a nested data structure and further aggregation across multiple elements in this structure.\n\n\narrayPopBack\n\n\nRemoves the last item from the array.\n\n\narrayPopBack(array)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\n\n\nExample\n\n\nSELECT\n \narrayPopBack\n([\n1\n,\n \n2\n,\n \n3\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [1,2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayPopFront\n\n\nRemoves the first item from the array.\n\n\narrayPopFront(array)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\n\n\nExample\n\n\nSELECT\n \narrayPopFront\n([\n1\n,\n \n2\n,\n \n3\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayPushBack\n\n\nAdds one item to the end of the array.\n\n\narrayPushBack(array, single_value)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\nsingle_value\n \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the \nsingle_value\n type for the data type of the array. For more information about ClickHouse data types, read the section \"\nData types\n\".\n\n\n\n\nExample\n\n\nSELECT\n \narrayPushBack\n([\na\n],\n \nb\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\na\n,\nb\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayPushFront\n\n\nAdds one element to the beginning of the array.\n\n\narrayPushFront(array, single_value)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\nsingle_value\n \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the \nsingle_value\n type for the data type of the array. For more information about ClickHouse data types, read the section \"\nData types\n\".\n\n\n\n\nExample\n\n\nSELECT\n \narrayPushBack\n([\nb\n],\n \na\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\na\n,\nb\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narraySlice\n\n\nReturns a slice of the array.\n\n\narraySlice(array, offset[, length])\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array of data.\n\n\noffset\n \u2013 Indent from the edge of the array. A positive value indicates an offset on the left, and a negative value is an indent on the right. Numbering of the array items begins with 1.\n\n\nlength\n - The length of the required slice. If you specify a negative value, the function returns an open slice \n[offset, array_length - length)\n. If you omit the value, the function returns the slice \n[offset, the_end_of_array]\n.\n\n\n\n\nExample\n\n\nSELECT\n \narraySlice\n([\n1\n,\n \n2\n,\n \n3\n,\n \n4\n,\n \n5\n],\n \n2\n,\n \n3\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [2,3,4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayUniq(arr, ...)\n\n\nIf one argument is passed, it counts the number of different elements in the array.\nIf multiple arguments are passed, it counts the number of different tuples of elements at corresponding positions in multiple arrays.\n\n\nIf you want to get a list of unique items in an array, you can use arrayReduce('groupUniqArray', arr).\n\n\narrayJoin(arr)\n\n\nA special function. See the section \n\"ArrayJoin function\"\n.", + "title": "Functions for working with arrays" + }, + { + "location": "/functions/array_functions/#functions-for-working-with-arrays", + "text": "", + "title": "Functions for working with arrays" + }, + { + "location": "/functions/array_functions/#empty", + "text": "Returns 1 for an empty array, or 0 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.", + "title": "empty" + }, + { + "location": "/functions/array_functions/#notempty", + "text": "Returns 0 for an empty array, or 1 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.", + "title": "notEmpty" + }, + { + "location": "/functions/array_functions/#length", + "text": "Returns the number of items in the array.\nThe result type is UInt64.\nThe function also works for strings.", + "title": "length" + }, + { + "location": "/functions/array_functions/#emptyarrayuint8-emptyarrayuint16-emptyarrayuint32-emptyarrayuint64", + "text": "", + "title": "emptyArrayUInt8, emptyArrayUInt16, emptyArrayUInt32, emptyArrayUInt64" + }, + { + "location": "/functions/array_functions/#emptyarrayint8-emptyarrayint16-emptyarrayint32-emptyarrayint64", + "text": "", + "title": "emptyArrayInt8, emptyArrayInt16, emptyArrayInt32, emptyArrayInt64" + }, + { + "location": "/functions/array_functions/#emptyarrayfloat32-emptyarrayfloat64", + "text": "", + "title": "emptyArrayFloat32, emptyArrayFloat64" + }, + { + "location": "/functions/array_functions/#emptyarraydate-emptyarraydatetime", + "text": "", + "title": "emptyArrayDate, emptyArrayDateTime" + }, + { + "location": "/functions/array_functions/#emptyarraystring", + "text": "Accepts zero arguments and returns an empty array of the appropriate type.", + "title": "emptyArrayString" + }, + { + "location": "/functions/array_functions/#emptyarraytosingle", + "text": "Accepts an empty array and returns a one-element array that is equal to the default value.", + "title": "emptyArrayToSingle" + }, + { + "location": "/functions/array_functions/#rangen", + "text": "Returns an array of numbers from 0 to N-1.\nJust in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block.", + "title": "range(N)" + }, + { + "location": "/functions/array_functions/#arrayx1-operator-91x1-93", + "text": "Creates an array from the function arguments.\nThe arguments must be constants and have types that have the smallest common type. At least one argument must be passed, because otherwise it isn't clear which type of array to create. That is, you can't use this function to create an empty array (to do that, use the 'emptyArray*' function described above).\nReturns an 'Array(T)' type result, where 'T' is the smallest common type out of the passed arguments.", + "title": "array(x1, ...), operator [x1, ...]" + }, + { + "location": "/functions/array_functions/#arrayconcat", + "text": "Combines arrays passed as arguments. arrayConcat(arrays) Arguments arrays \u2013 Arrays of comma-separated [values] . Example SELECT arrayConcat ([ 1 , 2 ], [ 3 , 4 ], [ 5 , 6 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,3,4,5,6] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayConcat" + }, + { + "location": "/functions/array_functions/#arrayelementarr-n-operator-arrn", + "text": "Get the element with the index 'n' from the array 'arr'.'n' must be any integer type.\nIndexes in an array begin from one.\nNegative indexes are supported. In this case, it selects the corresponding element numbered from the end. For example, 'arr[-1]' is the last item in the array. If the index falls outside of the bounds of an array, it returns some default value (0 for numbers, an empty string for strings, etc.).", + "title": "arrayElement(arr, n), operator arr[n]" + }, + { + "location": "/functions/array_functions/#hasarr-elem", + "text": "Checks whether the 'arr' array has the 'elem' element.\nReturns 0 if the the element is not in the array, or 1 if it is.", + "title": "has(arr, elem)" + }, + { + "location": "/functions/array_functions/#indexofarr-x", + "text": "Returns the index of the 'x' element (starting from 1) if it is in the array, or 0 if it is not.", + "title": "indexOf(arr, x)" + }, + { + "location": "/functions/array_functions/#countequalarr-x", + "text": "Returns the number of elements in the array equal to x. Equivalent to arrayCount (elem- elem = x, arr).", + "title": "countEqual(arr, x)" + }, + { + "location": "/functions/array_functions/#arrayenumeratearr", + "text": "Returns the array [1, 2, 3, ..., length (arr) ] This function is normally used with ARRAY JOIN. It allows counting something just once for each array after applying ARRAY JOIN. Example: SELECT \n count () AS Reaches , \n countIf ( num = 1 ) AS Hits FROM test . hits ARRAY JOIN \n GoalsReached , \n arrayEnumerate ( GoalsReached ) AS num WHERE CounterID = 160656 LIMIT 10 \u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 In this example, Reaches is the number of conversions (the strings received after applying ARRAY JOIN), and Hits is the number of pageviews (strings before ARRAY JOIN). In this particular case, you can get the same result in an easier way: SELECT \n sum ( length ( GoalsReached )) AS Reaches , \n count () AS Hits FROM test . hits WHERE ( CounterID = 160656 ) AND notEmpty ( GoalsReached ) \u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 This function can also be used in higher-order functions. For example, you can use it to get array indexes for elements that match a condition.", + "title": "arrayEnumerate(arr)" + }, + { + "location": "/functions/array_functions/#arrayenumerateuniqarr", + "text": "Returns an array the same size as the source array, indicating for each element what its position is among elements with the same value.\nFor example: arrayEnumerateUniq([10, 20, 10, 30]) = [1, 1, 2, 1]. This function is useful when using ARRAY JOIN and aggregation of array elements.\nExample: SELECT \n Goals . ID AS GoalID , \n sum ( Sign ) AS Reaches , \n sumIf ( Sign , num = 1 ) AS Visits FROM test . visits ARRAY JOIN \n Goals , \n arrayEnumerateUniq ( Goals . ID ) AS num WHERE CounterID = 160656 GROUP BY GoalID ORDER BY Reaches DESC LIMIT 10 \u250c\u2500\u2500GoalID\u2500\u252c\u2500Reaches\u2500\u252c\u2500Visits\u2500\u2510\n\u2502 53225 \u2502 3214 \u2502 1097 \u2502\n\u2502 2825062 \u2502 3188 \u2502 1097 \u2502\n\u2502 56600 \u2502 2803 \u2502 488 \u2502\n\u2502 1989037 \u2502 2401 \u2502 365 \u2502\n\u2502 2830064 \u2502 2396 \u2502 910 \u2502\n\u2502 1113562 \u2502 2372 \u2502 373 \u2502\n\u2502 3270895 \u2502 2262 \u2502 812 \u2502\n\u2502 1084657 \u2502 2262 \u2502 345 \u2502\n\u2502 56599 \u2502 2260 \u2502 799 \u2502\n\u2502 3271094 \u2502 2256 \u2502 812 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 In this example, each goal ID has a calculation of the number of conversions (each element in the Goals nested data structure is a goal that was reached, which we refer to as a conversion) and the number of sessions. Without ARRAY JOIN, we would have counted the number of sessions as sum(Sign). But in this particular case, the rows were multiplied by the nested Goals structure, so in order to count each session one time after this, we apply a condition to the value of the arrayEnumerateUniq(Goals.ID) function. The arrayEnumerateUniq function can take multiple arrays of the same size as arguments. In this case, uniqueness is considered for tuples of elements in the same positions in all the arrays. SELECT arrayEnumerateUniq ([ 1 , 1 , 1 , 2 , 2 , 2 ], [ 1 , 1 , 2 , 1 , 1 , 2 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,1,1,2,1] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 This is necessary when using ARRAY JOIN with a nested data structure and further aggregation across multiple elements in this structure.", + "title": "arrayEnumerateUniq(arr, ...)" + }, + { + "location": "/functions/array_functions/#arraypopback", + "text": "Removes the last item from the array. arrayPopBack(array) Arguments array \u2013 Array. Example SELECT arrayPopBack ([ 1 , 2 , 3 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [1,2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayPopBack" + }, + { + "location": "/functions/array_functions/#arraypopfront", + "text": "Removes the first item from the array. arrayPopFront(array) Arguments array \u2013 Array. Example SELECT arrayPopFront ([ 1 , 2 , 3 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayPopFront" + }, + { + "location": "/functions/array_functions/#arraypushback", + "text": "Adds one item to the end of the array. arrayPushBack(array, single_value) Arguments array \u2013 Array. single_value \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section \" Data types \". Example SELECT arrayPushBack ([ a ], b ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ a , b ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayPushBack" + }, + { + "location": "/functions/array_functions/#arraypushfront", + "text": "Adds one element to the beginning of the array. arrayPushFront(array, single_value) Arguments array \u2013 Array. single_value \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section \" Data types \". Example SELECT arrayPushBack ([ b ], a ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ a , b ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayPushFront" + }, + { + "location": "/functions/array_functions/#arrayslice", + "text": "Returns a slice of the array. arraySlice(array, offset[, length]) Arguments array \u2013 Array of data. offset \u2013 Indent from the edge of the array. A positive value indicates an offset on the left, and a negative value is an indent on the right. Numbering of the array items begins with 1. length - The length of the required slice. If you specify a negative value, the function returns an open slice [offset, array_length - length) . If you omit the value, the function returns the slice [offset, the_end_of_array] . Example SELECT arraySlice ([ 1 , 2 , 3 , 4 , 5 ], 2 , 3 ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [2,3,4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arraySlice" + }, + { + "location": "/functions/array_functions/#arrayuniqarr", + "text": "If one argument is passed, it counts the number of different elements in the array.\nIf multiple arguments are passed, it counts the number of different tuples of elements at corresponding positions in multiple arrays. If you want to get a list of unique items in an array, you can use arrayReduce('groupUniqArray', arr).", + "title": "arrayUniq(arr, ...)" + }, + { + "location": "/functions/array_functions/#arrayjoinarr", + "text": "A special function. See the section \"ArrayJoin function\" .", + "title": "arrayJoin(arr)" + }, + { + "location": "/functions/splitting_merging_functions/", + "text": "Functions for splitting and merging strings and arrays\n\n\nsplitByChar(separator, s)\n\n\nSplits a string into substrings separated by 'separator'.'separator' must be a string constant consisting of exactly one character.\nReturns an array of selected substrings. Empty substrings may be selected if the separator occurs at the beginning or end of the string, or if there are multiple consecutive separators.\n\n\nsplitByString(separator, s)\n\n\nThe same as above, but it uses a string of multiple characters as the separator. The string must be non-empty.\n\n\narrayStringConcat(arr[, separator])\n\n\nConcatenates the strings listed in the array with the separator.'separator' is an optional parameter: a constant string, set to an empty string by default.\nReturns the string.\n\n\nalphaTokens(s)\n\n\nSelects substrings of consecutive bytes from the ranges a-z and A-Z.Returns an array of substrings.", + "title": "Functions for splitting and merging strings and arrays" + }, + { + "location": "/functions/splitting_merging_functions/#functions-for-splitting-and-merging-strings-and-arrays", + "text": "", + "title": "Functions for splitting and merging strings and arrays" + }, + { + "location": "/functions/splitting_merging_functions/#splitbycharseparator-s", + "text": "Splits a string into substrings separated by 'separator'.'separator' must be a string constant consisting of exactly one character.\nReturns an array of selected substrings. Empty substrings may be selected if the separator occurs at the beginning or end of the string, or if there are multiple consecutive separators.", + "title": "splitByChar(separator, s)" + }, + { + "location": "/functions/splitting_merging_functions/#splitbystringseparator-s", + "text": "The same as above, but it uses a string of multiple characters as the separator. The string must be non-empty.", + "title": "splitByString(separator, s)" + }, + { + "location": "/functions/splitting_merging_functions/#arraystringconcatarr91-separator93", + "text": "Concatenates the strings listed in the array with the separator.'separator' is an optional parameter: a constant string, set to an empty string by default.\nReturns the string.", + "title": "arrayStringConcat(arr[, separator])" + }, + { + "location": "/functions/splitting_merging_functions/#alphatokenss", + "text": "Selects substrings of consecutive bytes from the ranges a-z and A-Z.Returns an array of substrings.", + "title": "alphaTokens(s)" + }, + { + "location": "/functions/bit_functions/", + "text": "Bit functions\n\n\nBit functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.\n\n\nThe result type is an integer with bits equal to the maximum bits of its arguments. If at least one of the arguments is signed, the result is a signed number. If an argument is a floating-point number, it is cast to Int64.\n\n\nbitAnd(a, b)\n\n\nbitOr(a, b)\n\n\nbitXor(a, b)\n\n\nbitNot(a)\n\n\nbitShiftLeft(a, b)\n\n\nbitShiftRight(a, b)", + "title": "Bit functions" + }, + { + "location": "/functions/bit_functions/#bit-functions", + "text": "Bit functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64. The result type is an integer with bits equal to the maximum bits of its arguments. If at least one of the arguments is signed, the result is a signed number. If an argument is a floating-point number, it is cast to Int64.", + "title": "Bit functions" + }, + { + "location": "/functions/bit_functions/#bitanda-b", + "text": "", + "title": "bitAnd(a, b)" + }, + { + "location": "/functions/bit_functions/#bitora-b", + "text": "", + "title": "bitOr(a, b)" + }, + { + "location": "/functions/bit_functions/#bitxora-b", + "text": "", + "title": "bitXor(a, b)" + }, + { + "location": "/functions/bit_functions/#bitnota", + "text": "", + "title": "bitNot(a)" + }, + { + "location": "/functions/bit_functions/#bitshiftlefta-b", + "text": "", + "title": "bitShiftLeft(a, b)" + }, + { + "location": "/functions/bit_functions/#bitshiftrighta-b", + "text": "", + "title": "bitShiftRight(a, b)" + }, + { + "location": "/functions/hash_functions/", + "text": "Hash functions\n\n\nHash functions can be used for deterministic pseudo-random shuffling of elements.\n\n\nhalfMD5\n\n\nCalculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian.\nAccepts a String-type argument. Returns UInt64.\nThis function works fairly slowly (5 million short strings per second per processor core).\nIf you don't need MD5 in particular, use the 'sipHash64' function instead.\n\n\nMD5\n\n\nCalculates the MD5 from a string and returns the resulting set of bytes as FixedString(16).\nIf you don't need MD5 in particular, but you need a decent cryptographic 128-bit hash, use the 'sipHash128' function instead.\nIf you want to get the same result as output by the md5sum utility, use lower(hex(MD5(s))).\n\n\nsipHash64\n\n\nCalculates SipHash from a string.\nAccepts a String-type argument. Returns UInt64.\nSipHash is a cryptographic hash function. It works at least three times faster than MD5.\nFor more information, see the link: \nhttps://131002.net/siphash/\n\n\nsipHash128\n\n\nCalculates SipHash from a string.\nAccepts a String-type argument. Returns FixedString(16).\nDiffers from sipHash64 in that the final xor-folding state is only done up to 128 bytes.\n\n\ncityHash64\n\n\nCalculates CityHash64 from a string or a similar hash function for any number of any type of arguments.\nFor String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality.\nFor other types of arguments, a decent implementation-specific fast non-cryptographic hash function is used.\nIf multiple arguments are passed, the function is calculated using the same rules and chain combinations using the CityHash combinator.\nFor example, you can compute the checksum of an entire table with accuracy up to the row order: \nSELECT sum(cityHash64(*)) FROM table\n.\n\n\nintHash32\n\n\nCalculates a 32-bit hash code from any type of integer.\nThis is a relatively fast non-cryptographic hash function of average quality for numbers.\n\n\nintHash64\n\n\nCalculates a 64-bit hash code from any type of integer.\nIt works faster than intHash32. Average quality.\n\n\nSHA1\n\n\nSHA224\n\n\nSHA256\n\n\nCalculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32).\nThe function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million).\nWe recommend using this function only in cases when you need a specific hash function and you can't select it.\nEven in these cases, we recommend applying the function offline and pre-calculating values when inserting them into the table, instead of applying it in SELECTS.\n\n\nURLHash(url[, N])\n\n\nA fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization.\n\nURLHash(s)\n \u2013 Calculates a hash from a string without one of the trailing symbols \n/\n,\n?\n or \n#\n at the end, if present.\n\nURLHash(s, N)\n \u2013 Calculates a hash from a string up to the N level in the URL hierarchy, without one of the trailing symbols \n/\n,\n?\n or \n#\n at the end, if present.\nLevels are the same as in URLHierarchy. This function is specific to Yandex.Metrica.", + "title": "Hash functions" + }, + { + "location": "/functions/hash_functions/#hash-functions", + "text": "Hash functions can be used for deterministic pseudo-random shuffling of elements.", + "title": "Hash functions" + }, + { + "location": "/functions/hash_functions/#halfmd5", + "text": "Calculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian.\nAccepts a String-type argument. Returns UInt64.\nThis function works fairly slowly (5 million short strings per second per processor core).\nIf you don't need MD5 in particular, use the 'sipHash64' function instead.", + "title": "halfMD5" + }, + { + "location": "/functions/hash_functions/#md5", + "text": "Calculates the MD5 from a string and returns the resulting set of bytes as FixedString(16).\nIf you don't need MD5 in particular, but you need a decent cryptographic 128-bit hash, use the 'sipHash128' function instead.\nIf you want to get the same result as output by the md5sum utility, use lower(hex(MD5(s))).", + "title": "MD5" + }, + { + "location": "/functions/hash_functions/#siphash64", + "text": "Calculates SipHash from a string.\nAccepts a String-type argument. Returns UInt64.\nSipHash is a cryptographic hash function. It works at least three times faster than MD5.\nFor more information, see the link: https://131002.net/siphash/", + "title": "sipHash64" + }, + { + "location": "/functions/hash_functions/#siphash128", + "text": "Calculates SipHash from a string.\nAccepts a String-type argument. Returns FixedString(16).\nDiffers from sipHash64 in that the final xor-folding state is only done up to 128 bytes.", + "title": "sipHash128" + }, + { + "location": "/functions/hash_functions/#cityhash64", + "text": "Calculates CityHash64 from a string or a similar hash function for any number of any type of arguments.\nFor String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality.\nFor other types of arguments, a decent implementation-specific fast non-cryptographic hash function is used.\nIf multiple arguments are passed, the function is calculated using the same rules and chain combinations using the CityHash combinator.\nFor example, you can compute the checksum of an entire table with accuracy up to the row order: SELECT sum(cityHash64(*)) FROM table .", + "title": "cityHash64" + }, + { + "location": "/functions/hash_functions/#inthash32", + "text": "Calculates a 32-bit hash code from any type of integer.\nThis is a relatively fast non-cryptographic hash function of average quality for numbers.", + "title": "intHash32" + }, + { + "location": "/functions/hash_functions/#inthash64", + "text": "Calculates a 64-bit hash code from any type of integer.\nIt works faster than intHash32. Average quality.", + "title": "intHash64" + }, + { + "location": "/functions/hash_functions/#sha1", + "text": "", + "title": "SHA1" + }, + { + "location": "/functions/hash_functions/#sha224", + "text": "", + "title": "SHA224" + }, + { + "location": "/functions/hash_functions/#sha256", + "text": "Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32).\nThe function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million).\nWe recommend using this function only in cases when you need a specific hash function and you can't select it.\nEven in these cases, we recommend applying the function offline and pre-calculating values when inserting them into the table, instead of applying it in SELECTS.", + "title": "SHA256" + }, + { + "location": "/functions/hash_functions/#urlhashurl91-n93", + "text": "A fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization. URLHash(s) \u2013 Calculates a hash from a string without one of the trailing symbols / , ? or # at the end, if present. URLHash(s, N) \u2013 Calculates a hash from a string up to the N level in the URL hierarchy, without one of the trailing symbols / , ? or # at the end, if present.\nLevels are the same as in URLHierarchy. This function is specific to Yandex.Metrica.", + "title": "URLHash(url[, N])" + }, + { + "location": "/functions/random_functions/", + "text": "Functions for generating pseudo-random numbers\n\n\nNon-cryptographic generators of pseudo-random numbers are used.\n\n\nAll the functions accept zero arguments or one argument.\nIf an argument is passed, it can be any type, and its value is not used for anything.\nThe only purpose of this argument is to prevent common subexpression elimination, so that two different instances of the same function return different columns with different random numbers.\n\n\nrand\n\n\nReturns a pseudo-random UInt32 number, evenly distributed among all UInt32-type numbers.\nUses a linear congruential generator.\n\n\nrand64\n\n\nReturns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers.\nUses a linear congruential generator.", + "title": "Functions for generating pseudo-random numbers" + }, + { + "location": "/functions/random_functions/#functions-for-generating-pseudo-random-numbers", + "text": "Non-cryptographic generators of pseudo-random numbers are used. All the functions accept zero arguments or one argument.\nIf an argument is passed, it can be any type, and its value is not used for anything.\nThe only purpose of this argument is to prevent common subexpression elimination, so that two different instances of the same function return different columns with different random numbers.", + "title": "Functions for generating pseudo-random numbers" + }, + { + "location": "/functions/random_functions/#rand", + "text": "Returns a pseudo-random UInt32 number, evenly distributed among all UInt32-type numbers.\nUses a linear congruential generator.", + "title": "rand" + }, + { + "location": "/functions/random_functions/#rand64", + "text": "Returns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers.\nUses a linear congruential generator.", + "title": "rand64" + }, + { + "location": "/functions/encoding_functions/", + "text": "Encoding functions\n\n\nhex\n\n\nAccepts arguments of types: \nString\n, \nunsigned integer\n, \nDate\n, or \nDateTime\n. Returns a string containing the argument's hexadecimal representation. Uses uppercase letters \nA-F\n. Does not use \n0x\n prefixes or \nh\n suffixes. For strings, all bytes are simply encoded as two hexadecimal numbers. Numbers are converted to big endian (\"human readable\") format. For numbers, older zeros are trimmed, but only by entire bytes. For example, \nhex (1) = '01'\n. \nDate\n is encoded as the number of days since the beginning of the Unix epoch. \nDateTime\n is encoded as the number of seconds since the beginning of the Unix epoch.\n\n\nunhex(str)\n\n\nAccepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits does not have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn't thrown).\nIf you want to convert the result to a number, you can use the 'reverse' and 'reinterpretAsType' functions.\n\n\nUUIDStringToNum(str)\n\n\nAccepts a string containing 36 characters in the format \n123e4567-e89b-12d3-a456-426655440000\n, and returns it as a set of bytes in a FixedString(16).\n\n\nUUIDNumToString(str)\n\n\nAccepts a FixedString(16) value. Returns a string containing 36 characters in text format.\n\n\nbitmaskToList(num)\n\n\nAccepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order.\n\n\nbitmaskToArray(num)\n\n\nAccepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order.", + "title": "Encoding functions" + }, + { + "location": "/functions/encoding_functions/#encoding-functions", + "text": "", + "title": "Encoding functions" + }, + { + "location": "/functions/encoding_functions/#hex", + "text": "Accepts arguments of types: String , unsigned integer , Date , or DateTime . Returns a string containing the argument's hexadecimal representation. Uses uppercase letters A-F . Does not use 0x prefixes or h suffixes. For strings, all bytes are simply encoded as two hexadecimal numbers. Numbers are converted to big endian (\"human readable\") format. For numbers, older zeros are trimmed, but only by entire bytes. For example, hex (1) = '01' . Date is encoded as the number of days since the beginning of the Unix epoch. DateTime is encoded as the number of seconds since the beginning of the Unix epoch.", + "title": "hex" + }, + { + "location": "/functions/encoding_functions/#unhexstr", + "text": "Accepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits does not have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn't thrown).\nIf you want to convert the result to a number, you can use the 'reverse' and 'reinterpretAsType' functions.", + "title": "unhex(str)" + }, + { + "location": "/functions/encoding_functions/#uuidstringtonumstr", + "text": "Accepts a string containing 36 characters in the format 123e4567-e89b-12d3-a456-426655440000 , and returns it as a set of bytes in a FixedString(16).", + "title": "UUIDStringToNum(str)" + }, + { + "location": "/functions/encoding_functions/#uuidnumtostringstr", + "text": "Accepts a FixedString(16) value. Returns a string containing 36 characters in text format.", + "title": "UUIDNumToString(str)" + }, + { + "location": "/functions/encoding_functions/#bitmasktolistnum", + "text": "Accepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order.", + "title": "bitmaskToList(num)" + }, + { + "location": "/functions/encoding_functions/#bitmasktoarraynum", + "text": "Accepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order.", + "title": "bitmaskToArray(num)" + }, + { + "location": "/functions/url_functions/", + "text": "Functions for working with URLs\n\n\nAll these functions don't follow the RFC. They are maximally simplified for improved performance.\n\n\nFunctions that extract part of a URL\n\n\nIf there isn't anything similar in a URL, an empty string is returned.\n\n\nprotocol\n\n\nReturns the protocol. Examples: http, ftp, mailto, magnet...\n\n\ndomain\n\n\nGets the domain.\n\n\ndomainWithoutWWW\n\n\nReturns the domain and removes no more than one 'www.' from the beginning of it, if present.\n\n\ntopLevelDomain\n\n\nReturns the top-level domain. Example: .ru.\n\n\nfirstSignificantSubdomain\n\n\nReturns the \"first significant subdomain\". This is a non-standard concept specific to Yandex.Metrica. The first significant subdomain is a second-level domain if it is 'com', 'net', 'org', or 'co'. Otherwise, it is a third-level domain. For example, firstSignificantSubdomain ('\nhttps://news.yandex.ru/\n') = 'yandex ', firstSignificantSubdomain ('\nhttps://news.yandex.com.tr/\n') = 'yandex '. The list of \"insignificant\" second-level domains and other implementation details may change in the future.\n\n\ncutToFirstSignificantSubdomain\n\n\nReturns the part of the domain that includes top-level subdomains up to the \"first significant subdomain\" (see the explanation above).\n\n\nFor example, \ncutToFirstSignificantSubdomain('https://news.yandex.com.tr/') = 'yandex.com.tr'\n.\n\n\npath\n\n\nReturns the path. Example: \n/top/news.html\n The path does not include the query string.\n\n\npathFull\n\n\nThe same as above, but including query string and fragment. Example: /top/news.html?page=2#comments\n\n\nqueryString\n\n\nReturns the query string. Example: page=1\nlr=213. query-string does not include the initial question mark, as well as # and everything after #.\n\n\nfragment\n\n\nReturns the fragment identifier. fragment does not include the initial hash symbol.\n\n\nqueryStringAndFragment\n\n\nReturns the query string and fragment identifier. Example: page=1#29390.\n\n\nextractURLParameter(URL, name)\n\n\nReturns the value of the 'name' parameter in the URL, if present. Otherwise, an empty string. If there are many parameters with this name, it returns the first occurrence. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.\n\n\nextractURLParameters(URL)\n\n\nReturns an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way.\n\n\nextractURLParameterNames(URL)\n\n\nReturns an array of name strings corresponding to the names of URL parameters. The values are not decoded in any way.\n\n\nURLHierarchy(URL)\n\n\nReturns an array containing the URL, truncated at the end by the symbols /,? in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:\n\n\nURLPathHierarchy(URL)\n\n\nThe same as above, but without the protocol and host in the result. The / element (root) is not included. Example: the function is used to implement tree reports the URL in Yandex. Metric.\n\n\nURLPathHierarchy(\nhttps://example.com/browse/CONV-6788\n) =\n[\n \n/browse/\n,\n \n/browse/CONV-6788\n\n]\n\n\n\n\n\ndecodeURLComponent(URL)\n\n\nReturns the decoded URL.\nExample:\n\n\nSELECT\n \ndecodeURLComponent\n(\nhttp://127.0.0.1:8123/?query=SELECT%201%3B\n)\n \nAS\n \nDecodedURL\n;\n\n\n\n\n\n\n\u250c\u2500DecodedURL\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 http://127.0.0.1:8123/?query=SELECT 1; \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nFunctions that remove part of a URL.\n\n\nIf the URL doesn't have anything similar, the URL remains unchanged.\n\n\ncutWWW\n\n\nRemoves no more than one 'www.' from the beginning of the URL's domain, if present.\n\n\ncutQueryString\n\n\nRemoves query string. The question mark is also removed.\n\n\ncutFragment\n\n\nRemoves the fragment identifier. The number sign is also removed.\n\n\ncutQueryStringAndFragment\n\n\nRemoves the query string and fragment identifier. The question mark and number sign are also removed.\n\n\ncutURLParameter(URL, name)\n\n\nRemoves the 'name' URL parameter, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.", + "title": "Functions for working with URLs" + }, + { + "location": "/functions/url_functions/#functions-for-working-with-urls", + "text": "All these functions don't follow the RFC. They are maximally simplified for improved performance.", + "title": "Functions for working with URLs" + }, + { + "location": "/functions/url_functions/#functions-that-extract-part-of-a-url", + "text": "If there isn't anything similar in a URL, an empty string is returned.", + "title": "Functions that extract part of a URL" + }, + { + "location": "/functions/url_functions/#protocol", + "text": "Returns the protocol. Examples: http, ftp, mailto, magnet...", + "title": "protocol" + }, + { + "location": "/functions/url_functions/#domain", + "text": "Gets the domain.", + "title": "domain" + }, + { + "location": "/functions/url_functions/#domainwithoutwww", + "text": "Returns the domain and removes no more than one 'www.' from the beginning of it, if present.", + "title": "domainWithoutWWW" + }, + { + "location": "/functions/url_functions/#topleveldomain", + "text": "Returns the top-level domain. Example: .ru.", + "title": "topLevelDomain" + }, + { + "location": "/functions/url_functions/#firstsignificantsubdomain", + "text": "Returns the \"first significant subdomain\". This is a non-standard concept specific to Yandex.Metrica. The first significant subdomain is a second-level domain if it is 'com', 'net', 'org', or 'co'. Otherwise, it is a third-level domain. For example, firstSignificantSubdomain (' https://news.yandex.ru/ ') = 'yandex ', firstSignificantSubdomain (' https://news.yandex.com.tr/ ') = 'yandex '. The list of \"insignificant\" second-level domains and other implementation details may change in the future.", + "title": "firstSignificantSubdomain" + }, + { + "location": "/functions/url_functions/#cuttofirstsignificantsubdomain", + "text": "Returns the part of the domain that includes top-level subdomains up to the \"first significant subdomain\" (see the explanation above). For example, cutToFirstSignificantSubdomain('https://news.yandex.com.tr/') = 'yandex.com.tr' .", + "title": "cutToFirstSignificantSubdomain" + }, + { + "location": "/functions/url_functions/#path", + "text": "Returns the path. Example: /top/news.html The path does not include the query string.", + "title": "path" + }, + { + "location": "/functions/url_functions/#pathfull", + "text": "The same as above, but including query string and fragment. Example: /top/news.html?page=2#comments", + "title": "pathFull" + }, + { + "location": "/functions/url_functions/#querystring", + "text": "Returns the query string. Example: page=1 lr=213. query-string does not include the initial question mark, as well as # and everything after #.", + "title": "queryString" + }, + { + "location": "/functions/url_functions/#fragment", + "text": "Returns the fragment identifier. fragment does not include the initial hash symbol.", + "title": "fragment" + }, + { + "location": "/functions/url_functions/#querystringandfragment", + "text": "Returns the query string and fragment identifier. Example: page=1#29390.", + "title": "queryStringAndFragment" + }, + { + "location": "/functions/url_functions/#extracturlparameterurl-name", + "text": "Returns the value of the 'name' parameter in the URL, if present. Otherwise, an empty string. If there are many parameters with this name, it returns the first occurrence. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.", + "title": "extractURLParameter(URL, name)" + }, + { + "location": "/functions/url_functions/#extracturlparametersurl", + "text": "Returns an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way.", + "title": "extractURLParameters(URL)" + }, + { + "location": "/functions/url_functions/#extracturlparameternamesurl", + "text": "Returns an array of name strings corresponding to the names of URL parameters. The values are not decoded in any way.", + "title": "extractURLParameterNames(URL)" + }, + { + "location": "/functions/url_functions/#urlhierarchyurl", + "text": "Returns an array containing the URL, truncated at the end by the symbols /,? in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:", + "title": "URLHierarchy(URL)" + }, + { + "location": "/functions/url_functions/#urlpathhierarchyurl", + "text": "The same as above, but without the protocol and host in the result. The / element (root) is not included. Example: the function is used to implement tree reports the URL in Yandex. Metric. URLPathHierarchy( https://example.com/browse/CONV-6788 ) =\n[\n /browse/ ,\n /browse/CONV-6788 \n]", + "title": "URLPathHierarchy(URL)" + }, + { + "location": "/functions/url_functions/#decodeurlcomponenturl", + "text": "Returns the decoded URL.\nExample: SELECT decodeURLComponent ( http://127.0.0.1:8123/?query=SELECT%201%3B ) AS DecodedURL ; \u250c\u2500DecodedURL\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 http://127.0.0.1:8123/?query=SELECT 1; \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "decodeURLComponent(URL)" + }, + { + "location": "/functions/url_functions/#functions-that-remove-part-of-a-url", + "text": "If the URL doesn't have anything similar, the URL remains unchanged.", + "title": "Functions that remove part of a URL." + }, + { + "location": "/functions/url_functions/#cutwww", + "text": "Removes no more than one 'www.' from the beginning of the URL's domain, if present.", + "title": "cutWWW" + }, + { + "location": "/functions/url_functions/#cutquerystring", + "text": "Removes query string. The question mark is also removed.", + "title": "cutQueryString" + }, + { + "location": "/functions/url_functions/#cutfragment", + "text": "Removes the fragment identifier. The number sign is also removed.", + "title": "cutFragment" + }, + { + "location": "/functions/url_functions/#cutquerystringandfragment", + "text": "Removes the query string and fragment identifier. The question mark and number sign are also removed.", + "title": "cutQueryStringAndFragment" + }, + { + "location": "/functions/url_functions/#cuturlparameterurl-name", + "text": "Removes the 'name' URL parameter, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.", + "title": "cutURLParameter(URL, name)" + }, + { + "location": "/functions/ip_address_functions/", + "text": "Functions for working with IP addresses\n\n\nIPv4NumToString(num)\n\n\nTakes a UInt32 number. Interprets it as an IPv4 address in big endian. Returns a string containing the corresponding IPv4 address in the format A.B.C.d (dot-separated numbers in decimal form).\n\n\nIPv4StringToNum(s)\n\n\nThe reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0.\n\n\nIPv4NumToStringClassC(num)\n\n\nSimilar to IPv4NumToString, but using xxx instead of the last octet.\n\n\nExample:\n\n\nSELECT\n\n \nIPv4NumToStringClassC\n(\nClientIP\n)\n \nAS\n \nk\n,\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \nk\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500k\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 83.149.9.xxx \u2502 26238 \u2502\n\u2502 217.118.81.xxx \u2502 26074 \u2502\n\u2502 213.87.129.xxx \u2502 25481 \u2502\n\u2502 83.149.8.xxx \u2502 24984 \u2502\n\u2502 217.118.83.xxx \u2502 22797 \u2502\n\u2502 78.25.120.xxx \u2502 22354 \u2502\n\u2502 213.87.131.xxx \u2502 21285 \u2502\n\u2502 78.25.121.xxx \u2502 20887 \u2502\n\u2502 188.162.65.xxx \u2502 19694 \u2502\n\u2502 83.149.48.xxx \u2502 17406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSince using 'xxx' is highly unusual, this may be changed in the future. We recommend that you don't rely on the exact format of this fragment.\n\n\nIPv6NumToString(x)\n\n\nAccepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format.\nIPv6-mapped IPv4 addresses are output in the format ::ffff:111.222.33.44. Examples:\n\n\nSELECT\n \nIPv6NumToString\n(\ntoFixedString\n(\nunhex\n(\n2A0206B8000000000000000000000011\n),\n \n16\n))\n \nAS\n \naddr\n\n\n\n\n\n\n\u250c\u2500addr\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2a02:6b8::11 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n\n \nIPv6NumToString\n(\nClientIP6\n \nAS\n \nk\n),\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \nhits_all\n\n\nWHERE\n \nEventDate\n \n=\n \ntoday\n()\n \nAND\n \nsubstring\n(\nClientIP6\n,\n \n1\n,\n \n12\n)\n \n!=\n \nunhex\n(\n00000000000000000000FFFF\n)\n\n\nGROUP\n \nBY\n \nk\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500IPv6NumToString(ClientIP6)\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2a02:2168:aaa:bbbb::2 \u2502 24695 \u2502\n\u2502 2a02:2698:abcd:abcd:abcd:abcd:8888:5555 \u2502 22408 \u2502\n\u2502 2a02:6b8:0:fff::ff \u2502 16389 \u2502\n\u2502 2a01:4f8:111:6666::2 \u2502 16016 \u2502\n\u2502 2a02:2168:888:222::1 \u2502 15896 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:222 \u2502 14774 \u2502\n\u2502 2a02:8109:eee:ee:eeee:eeee:eeee:eeee \u2502 14443 \u2502\n\u2502 2a02:810b:8888:888:8888:8888:8888:8888 \u2502 14345 \u2502\n\u2502 2a02:6b8:0:444:4444:4444:4444:4444 \u2502 14279 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:ffff \u2502 13880 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n\n \nIPv6NumToString\n(\nClientIP6\n \nAS\n \nk\n),\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \nhits_all\n\n\nWHERE\n \nEventDate\n \n=\n \ntoday\n()\n\n\nGROUP\n \nBY\n \nk\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500IPv6NumToString(ClientIP6)\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 ::ffff:94.26.111.111 \u2502 747440 \u2502\n\u2502 ::ffff:37.143.222.4 \u2502 529483 \u2502\n\u2502 ::ffff:5.166.111.99 \u2502 317707 \u2502\n\u2502 ::ffff:46.38.11.77 \u2502 263086 \u2502\n\u2502 ::ffff:79.105.111.111 \u2502 186611 \u2502\n\u2502 ::ffff:93.92.111.88 \u2502 176773 \u2502\n\u2502 ::ffff:84.53.111.33 \u2502 158709 \u2502\n\u2502 ::ffff:217.118.11.22 \u2502 154004 \u2502\n\u2502 ::ffff:217.118.11.33 \u2502 148449 \u2502\n\u2502 ::ffff:217.118.11.44 \u2502 148243 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIPv6StringToNum(s)\n\n\nThe reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes.\nHEX can be uppercase or lowercase.", + "title": "Functions for working with IP addresses" + }, + { + "location": "/functions/ip_address_functions/#functions-for-working-with-ip-addresses", + "text": "", + "title": "Functions for working with IP addresses" + }, + { + "location": "/functions/ip_address_functions/#ipv4numtostringnum", + "text": "Takes a UInt32 number. Interprets it as an IPv4 address in big endian. Returns a string containing the corresponding IPv4 address in the format A.B.C.d (dot-separated numbers in decimal form).", + "title": "IPv4NumToString(num)" + }, + { + "location": "/functions/ip_address_functions/#ipv4stringtonums", + "text": "The reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0.", + "title": "IPv4StringToNum(s)" + }, + { + "location": "/functions/ip_address_functions/#ipv4numtostringclasscnum", + "text": "Similar to IPv4NumToString, but using xxx instead of the last octet. Example: SELECT \n IPv4NumToStringClassC ( ClientIP ) AS k , \n count () AS c FROM test . hits GROUP BY k ORDER BY c DESC LIMIT 10 \u250c\u2500k\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 83.149.9.xxx \u2502 26238 \u2502\n\u2502 217.118.81.xxx \u2502 26074 \u2502\n\u2502 213.87.129.xxx \u2502 25481 \u2502\n\u2502 83.149.8.xxx \u2502 24984 \u2502\n\u2502 217.118.83.xxx \u2502 22797 \u2502\n\u2502 78.25.120.xxx \u2502 22354 \u2502\n\u2502 213.87.131.xxx \u2502 21285 \u2502\n\u2502 78.25.121.xxx \u2502 20887 \u2502\n\u2502 188.162.65.xxx \u2502 19694 \u2502\n\u2502 83.149.48.xxx \u2502 17406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Since using 'xxx' is highly unusual, this may be changed in the future. We recommend that you don't rely on the exact format of this fragment.", + "title": "IPv4NumToStringClassC(num)" + }, + { + "location": "/functions/ip_address_functions/#ipv6numtostringx", + "text": "Accepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format.\nIPv6-mapped IPv4 addresses are output in the format ::ffff:111.222.33.44. Examples: SELECT IPv6NumToString ( toFixedString ( unhex ( 2A0206B8000000000000000000000011 ), 16 )) AS addr \u250c\u2500addr\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2a02:6b8::11 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT \n IPv6NumToString ( ClientIP6 AS k ), \n count () AS c FROM hits_all WHERE EventDate = today () AND substring ( ClientIP6 , 1 , 12 ) != unhex ( 00000000000000000000FFFF ) GROUP BY k ORDER BY c DESC LIMIT 10 \u250c\u2500IPv6NumToString(ClientIP6)\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2a02:2168:aaa:bbbb::2 \u2502 24695 \u2502\n\u2502 2a02:2698:abcd:abcd:abcd:abcd:8888:5555 \u2502 22408 \u2502\n\u2502 2a02:6b8:0:fff::ff \u2502 16389 \u2502\n\u2502 2a01:4f8:111:6666::2 \u2502 16016 \u2502\n\u2502 2a02:2168:888:222::1 \u2502 15896 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:222 \u2502 14774 \u2502\n\u2502 2a02:8109:eee:ee:eeee:eeee:eeee:eeee \u2502 14443 \u2502\n\u2502 2a02:810b:8888:888:8888:8888:8888:8888 \u2502 14345 \u2502\n\u2502 2a02:6b8:0:444:4444:4444:4444:4444 \u2502 14279 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:ffff \u2502 13880 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT \n IPv6NumToString ( ClientIP6 AS k ), \n count () AS c FROM hits_all WHERE EventDate = today () GROUP BY k ORDER BY c DESC LIMIT 10 \u250c\u2500IPv6NumToString(ClientIP6)\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 ::ffff:94.26.111.111 \u2502 747440 \u2502\n\u2502 ::ffff:37.143.222.4 \u2502 529483 \u2502\n\u2502 ::ffff:5.166.111.99 \u2502 317707 \u2502\n\u2502 ::ffff:46.38.11.77 \u2502 263086 \u2502\n\u2502 ::ffff:79.105.111.111 \u2502 186611 \u2502\n\u2502 ::ffff:93.92.111.88 \u2502 176773 \u2502\n\u2502 ::ffff:84.53.111.33 \u2502 158709 \u2502\n\u2502 ::ffff:217.118.11.22 \u2502 154004 \u2502\n\u2502 ::ffff:217.118.11.33 \u2502 148449 \u2502\n\u2502 ::ffff:217.118.11.44 \u2502 148243 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "IPv6NumToString(x)" + }, + { + "location": "/functions/ip_address_functions/#ipv6stringtonums", + "text": "The reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes.\nHEX can be uppercase or lowercase.", + "title": "IPv6StringToNum(s)" + }, + { + "location": "/functions/json_functions/", + "text": "Functions for working with JSON\n\n\nIn Yandex.Metrica, JSON is transmitted by users as session parameters. There are some special functions for working with this JSON. (Although in most of the cases, the JSONs are additionally pre-processed, and the resulting values are put in separate columns in their processed format.) All these functions are based on strong assumptions about what the JSON can be, but they try to do as little as possible to get the job done.\n\n\nThe following assumptions are made:\n\n\n\n\nThe field name (function argument) must be a constant.\n\n\nThe field name is somehow canonically encoded in JSON. For example: \nvisitParamHas('{\"abc\":\"def\"}', 'abc') = 1\n, but \nvisitParamHas('{\"\\\\u0061\\\\u0062\\\\u0063\":\"def\"}', 'abc') = 0\n\n\nFields are searched for on any nesting level, indiscriminately. If there are multiple matching fields, the first occurrence is used.\n\n\nThe JSON doesn't have space characters outside of string literals.\n\n\n\n\nvisitParamHas(params, name)\n\n\nChecks whether there is a field with the 'name' name.\n\n\nvisitParamExtractUInt(params, name)\n\n\nParses UInt64 from the value of the field named 'name'. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn't exist, or it exists but doesn't contain a number, it returns 0.\n\n\nvisitParamExtractInt(params, name)\n\n\nThe same as for Int64.\n\n\nvisitParamExtractFloat(params, name)\n\n\nThe same as for Float64.\n\n\nvisitParamExtractBool(params, name)\n\n\nParses a true/false value. The result is UInt8.\n\n\nvisitParamExtractRaw(params, name)\n\n\nReturns the value of a field, including separators.\n\n\nExamples:\n\n\nvisitParamExtractRaw(\n{\nabc\n:\n\\\\n\\\\u0000\n}\n, \nabc\n) = \n\\\\n\\\\u0000\n\nvisitParamExtractRaw(\n{\nabc\n:{\ndef\n:[1,2,3]}}\n, \nabc\n) = \n{\ndef\n:[1,2,3]}\n\n\n\n\n\n\nvisitParamExtractString(params, name)\n\n\nParses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string.\n\n\nExamples:\n\n\nvisitParamExtractString(\n{\nabc\n:\n\\\\n\\\\u0000\n}\n, \nabc\n) = \n\\n\\0\n\nvisitParamExtractString(\n{\nabc\n:\n\\\\u263a\n}\n, \nabc\n) = \n\u263a\n\nvisitParamExtractString(\n{\nabc\n:\n\\\\u263\n}\n, \nabc\n) = \n\nvisitParamExtractString(\n{\nabc\n:\nhello}\n, \nabc\n) = \n\n\n\n\n\n\nThere is currently no support for code points in the format \n\\uXXXX\\uYYYY\n that are not from the basic multilingual plane (they are converted to CESU-8 instead of UTF-8).", + "title": "Functions for working with JSON." + }, + { + "location": "/functions/json_functions/#functions-for-working-with-json", + "text": "In Yandex.Metrica, JSON is transmitted by users as session parameters. There are some special functions for working with this JSON. (Although in most of the cases, the JSONs are additionally pre-processed, and the resulting values are put in separate columns in their processed format.) All these functions are based on strong assumptions about what the JSON can be, but they try to do as little as possible to get the job done. The following assumptions are made: The field name (function argument) must be a constant. The field name is somehow canonically encoded in JSON. For example: visitParamHas('{\"abc\":\"def\"}', 'abc') = 1 , but visitParamHas('{\"\\\\u0061\\\\u0062\\\\u0063\":\"def\"}', 'abc') = 0 Fields are searched for on any nesting level, indiscriminately. If there are multiple matching fields, the first occurrence is used. The JSON doesn't have space characters outside of string literals.", + "title": "Functions for working with JSON" + }, + { + "location": "/functions/json_functions/#visitparamhasparams-name", + "text": "Checks whether there is a field with the 'name' name.", + "title": "visitParamHas(params, name)" + }, + { + "location": "/functions/json_functions/#visitparamextractuintparams-name", + "text": "Parses UInt64 from the value of the field named 'name'. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn't exist, or it exists but doesn't contain a number, it returns 0.", + "title": "visitParamExtractUInt(params, name)" + }, + { + "location": "/functions/json_functions/#visitparamextractintparams-name", + "text": "The same as for Int64.", + "title": "visitParamExtractInt(params, name)" + }, + { + "location": "/functions/json_functions/#visitparamextractfloatparams-name", + "text": "The same as for Float64.", + "title": "visitParamExtractFloat(params, name)" + }, + { + "location": "/functions/json_functions/#visitparamextractboolparams-name", + "text": "Parses a true/false value. The result is UInt8.", + "title": "visitParamExtractBool(params, name)" + }, + { + "location": "/functions/json_functions/#visitparamextractrawparams-name", + "text": "Returns the value of a field, including separators. Examples: visitParamExtractRaw( { abc : \\\\n\\\\u0000 } , abc ) = \\\\n\\\\u0000 \nvisitParamExtractRaw( { abc :{ def :[1,2,3]}} , abc ) = { def :[1,2,3]}", + "title": "visitParamExtractRaw(params, name)" + }, + { + "location": "/functions/json_functions/#visitparamextractstringparams-name", + "text": "Parses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string. Examples: visitParamExtractString( { abc : \\\\n\\\\u0000 } , abc ) = \\n\\0 \nvisitParamExtractString( { abc : \\\\u263a } , abc ) = \u263a \nvisitParamExtractString( { abc : \\\\u263 } , abc ) = \nvisitParamExtractString( { abc : hello} , abc ) = There is currently no support for code points in the format \\uXXXX\\uYYYY that are not from the basic multilingual plane (they are converted to CESU-8 instead of UTF-8).", + "title": "visitParamExtractString(params, name)" + }, + { + "location": "/functions/higher_order_functions/", + "text": "Higher-order functions\n\n\n-\n operator, lambda(params, expr) function\n\n\nAllows describing a lambda function for passing to a higher-order function. The left side of the arrow has a formal parameter, which is any ID, or multiple formal parameters \u2013 any IDs in a tuple. The right side of the arrow has an expression that can use these formal parameters, as well as any table columns.\n\n\nExamples: \nx -\n 2 * x, str -\n str != Referer.\n\n\nHigher-order functions can only accept lambda functions as their functional argument.\n\n\nA lambda function that accepts multiple arguments can be passed to a higher-order function. In this case, the higher-order function is passed several arrays of identical length that these arguments will correspond to.\n\n\nFor all functions other than 'arrayMap' and 'arrayFilter', the first argument (the lambda function) can be omitted. In this case, identical mapping is assumed.\n\n\narrayMap(func, arr1, ...)\n\n\nReturns an array obtained from the original application of the 'func' function to each element in the 'arr' array.\n\n\narrayFilter(func, arr1, ...)\n\n\nReturns an array containing only the elements in 'arr1' for which 'func' returns something other than 0.\n\n\nExamples:\n\n\nSELECT\n \narrayFilter\n(\nx\n \n-\n \nx\n \nLIKE\n \n%World%\n,\n \n[\nHello\n,\n \nabc World\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\nabc World\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n\n \narrayFilter\n(\n\n \n(\ni\n,\n \nx\n)\n \n-\n \nx\n \nLIKE\n \n%World%\n,\n\n \narrayEnumerate\n(\narr\n),\n\n \n[\nHello\n,\n \nabc World\n]\n \nAS\n \narr\n)\n\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2510\n\u2502 [2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayCount([func,] arr1, ...)\n\n\nReturns the number of elements in the arr array for which func returns something other than 0. If 'func' is not specified, it returns the number of non-zero elements in the array.\n\n\narrayExists([func,] arr1, ...)\n\n\nReturns 1 if there is at least one element in 'arr' for which 'func' returns something other than 0. Otherwise, it returns 0.\n\n\narrayAll([func,] arr1, ...)\n\n\nReturns 1 if 'func' returns something other than 0 for all the elements in 'arr'. Otherwise, it returns 0.\n\n\narraySum([func,] arr1, ...)\n\n\nReturns the sum of the 'func' values. If the function is omitted, it just returns the sum of the array elements.\n\n\narrayFirst(func, arr1, ...)\n\n\nReturns the first element in the 'arr1' array for which 'func' returns something other than 0.\n\n\narrayFirstIndex(func, arr1, ...)\n\n\nReturns the index of the first element in the 'arr1' array for which 'func' returns something other than 0.\n\n\narrayCumSum([func,] arr1, ...)\n\n\nReturns an array of partial sums of elements in the source array (a running sum). If the \nfunc\n function is specified, then the values of the array elements are converted by this function before summing.\n\n\nExample:\n\n\nSELECT\n \narrayCumSum\n([\n1\n,\n \n1\n,\n \n1\n,\n \n1\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1, 2, 3, 4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narraySort([func,] arr1, ...)\n\n\nReturns an array as result of sorting the elements of \narr1\n in ascending order. If the \nfunc\n function is specified, sorting order is determined by the result of the function \nfunc\n applied to the elements of array (arrays) \n\n\nThe \nSchwartzian transform\n is used to impove sorting efficiency.\n\n\nExample:\n\n\nSELECT\n \narraySort\n((\nx\n,\n \ny\n)\n \n-\n \ny\n,\n \n[\nhello\n,\n \nworld\n],\n \n[\n2\n,\n \n1\n]);\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\nworld\n, \nhello\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayReverseSort([func,] arr1, ...)\n\n\nReturns an array as result of sorting the elements of \narr1\n in descending order. If the \nfunc\n function is specified, sorting order is determined by the result of the function \nfunc\n applied to the elements of array (arrays)", + "title": "Higher-order functions" + }, + { + "location": "/functions/higher_order_functions/#higher-order-functions", + "text": "", + "title": "Higher-order functions" + }, + { + "location": "/functions/higher_order_functions/#-operator-lambdaparams-expr-function", + "text": "Allows describing a lambda function for passing to a higher-order function. The left side of the arrow has a formal parameter, which is any ID, or multiple formal parameters \u2013 any IDs in a tuple. The right side of the arrow has an expression that can use these formal parameters, as well as any table columns. Examples: x - 2 * x, str - str != Referer. Higher-order functions can only accept lambda functions as their functional argument. A lambda function that accepts multiple arguments can be passed to a higher-order function. In this case, the higher-order function is passed several arrays of identical length that these arguments will correspond to. For all functions other than 'arrayMap' and 'arrayFilter', the first argument (the lambda function) can be omitted. In this case, identical mapping is assumed.", + "title": "-> operator, lambda(params, expr) function" + }, + { + "location": "/functions/higher_order_functions/#arraymapfunc-arr1", + "text": "Returns an array obtained from the original application of the 'func' function to each element in the 'arr' array.", + "title": "arrayMap(func, arr1, ...)" + }, + { + "location": "/functions/higher_order_functions/#arrayfilterfunc-arr1", + "text": "Returns an array containing only the elements in 'arr1' for which 'func' returns something other than 0. Examples: SELECT arrayFilter ( x - x LIKE %World% , [ Hello , abc World ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ abc World ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT \n arrayFilter ( \n ( i , x ) - x LIKE %World% , \n arrayEnumerate ( arr ), \n [ Hello , abc World ] AS arr ) \n AS res \u250c\u2500res\u2500\u2510\n\u2502 [2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayFilter(func, arr1, ...)" + }, + { + "location": "/functions/higher_order_functions/#arraycount91func93-arr1", + "text": "Returns the number of elements in the arr array for which func returns something other than 0. If 'func' is not specified, it returns the number of non-zero elements in the array.", + "title": "arrayCount([func,] arr1, ...)" + }, + { + "location": "/functions/higher_order_functions/#arrayexists91func93-arr1", + "text": "Returns 1 if there is at least one element in 'arr' for which 'func' returns something other than 0. Otherwise, it returns 0.", + "title": "arrayExists([func,] arr1, ...)" + }, + { + "location": "/functions/higher_order_functions/#arrayall91func93-arr1", + "text": "Returns 1 if 'func' returns something other than 0 for all the elements in 'arr'. Otherwise, it returns 0.", + "title": "arrayAll([func,] arr1, ...)" + }, + { + "location": "/functions/higher_order_functions/#arraysum91func93-arr1", + "text": "Returns the sum of the 'func' values. If the function is omitted, it just returns the sum of the array elements.", + "title": "arraySum([func,] arr1, ...)" + }, + { + "location": "/functions/higher_order_functions/#arrayfirstfunc-arr1", + "text": "Returns the first element in the 'arr1' array for which 'func' returns something other than 0.", + "title": "arrayFirst(func, arr1, ...)" + }, + { + "location": "/functions/higher_order_functions/#arrayfirstindexfunc-arr1", + "text": "Returns the index of the first element in the 'arr1' array for which 'func' returns something other than 0.", + "title": "arrayFirstIndex(func, arr1, ...)" + }, + { + "location": "/functions/higher_order_functions/#arraycumsum91func93-arr1", + "text": "Returns an array of partial sums of elements in the source array (a running sum). If the func function is specified, then the values of the array elements are converted by this function before summing. Example: SELECT arrayCumSum ([ 1 , 1 , 1 , 1 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1, 2, 3, 4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayCumSum([func,] arr1, ...)" + }, + { + "location": "/functions/higher_order_functions/#arraysort91func93-arr1", + "text": "Returns an array as result of sorting the elements of arr1 in ascending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays) The Schwartzian transform is used to impove sorting efficiency. Example: SELECT arraySort (( x , y ) - y , [ hello , world ], [ 2 , 1 ]); \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ world , hello ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arraySort([func,] arr1, ...)" + }, + { + "location": "/functions/higher_order_functions/#arrayreversesort91func93-arr1", + "text": "Returns an array as result of sorting the elements of arr1 in descending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays)", + "title": "arrayReverseSort([func,] arr1, ...)" + }, + { + "location": "/functions/other_functions/", + "text": "Other functions\n\n\nhostName()\n\n\nReturns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server.\n\n\nvisibleWidth(x)\n\n\nCalculates the approximate width when outputting values to the console in text format (tab-separated).\nThis function is used by the system for implementing Pretty formats.\n\n\ntoTypeName(x)\n\n\nReturns a string containing the type name of the passed argument.\n\n\nblockSize()\n\n\nGets the size of the block.\nIn ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for.\n\n\nmaterialize(x)\n\n\nTurns a constant into a full column containing just one value.\nIn ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior.\n\n\nignore(...)\n\n\nAccepts any arguments and always returns 0.\nHowever, the argument is still evaluated. This can be used for benchmarks.\n\n\nsleep(seconds)\n\n\nSleeps 'seconds' seconds on each data block. You can specify an integer or a floating-point number.\n\n\ncurrentDatabase()\n\n\nReturns the name of the current database.\nYou can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database.\n\n\nisFinite(x)\n\n\nAccepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0.\n\n\nisInfinite(x)\n\n\nAccepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. Note that 0 is returned for a NaN.\n\n\nisNaN(x)\n\n\nAccepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0.\n\n\nhasColumnInTable(['hostname'[, 'username'[, 'password']],] 'database', 'table', 'column')\n\n\nAccepts constant strings: database name, table name, and column name. Returns a UInt8 constant expression equal to 1 if there is a column, otherwise 0. If the hostname parameter is set, the test will run on a remote server.\nThe function throws an exception if the table does not exist.\nFor elements in a nested data structure, the function checks for the existence of a column. For the nested data structure itself, the function returns 0.\n\n\nbar\n\n\nAllows building a unicode-art diagram.\n\n\nbar (x, min, max, width)\n draws a band with a width proportional to \n(x - min)\n and equal to \nwidth\n characters when \nx = max\n.\n\n\nParameters:\n\n\n\n\nx\n \u2013 Value to display.\n\n\nmin, max\n \u2013 Integer constants. The value must fit in Int64.\n\n\nwidth\n \u2013 Constant, positive number, may be a fraction.\n\n\n\n\nThe band is drawn with accuracy to one eighth of a symbol.\n\n\nExample:\n\n\nSELECT\n\n \ntoHour\n(\nEventTime\n)\n \nAS\n \nh\n,\n\n \ncount\n()\n \nAS\n \nc\n,\n\n \nbar\n(\nc\n,\n \n0\n,\n \n600000\n,\n \n20\n)\n \nAS\n \nbar\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \nh\n\n\nORDER\n \nBY\n \nh\n \nASC\n\n\n\n\n\n\n\u250c\u2500\u2500h\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u252c\u2500bar\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 0 \u2502 292907 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 1 \u2502 180563 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 2 \u2502 114861 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 3 \u2502 85069 \u2502 \u2588\u2588\u258b \u2502\n\u2502 4 \u2502 68543 \u2502 \u2588\u2588\u258e \u2502\n\u2502 5 \u2502 78116 \u2502 \u2588\u2588\u258c \u2502\n\u2502 6 \u2502 113474 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 7 \u2502 170678 \u2502 \u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 8 \u2502 278380 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 9 \u2502 391053 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 10 \u2502 457681 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 11 \u2502 493667 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 12 \u2502 509641 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 13 \u2502 522947 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 14 \u2502 539954 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 15 \u2502 528460 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c \u2502\n\u2502 16 \u2502 539201 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 17 \u2502 523539 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 18 \u2502 506467 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 19 \u2502 520915 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 20 \u2502 521665 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 21 \u2502 542078 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 22 \u2502 493642 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 23 \u2502 400397 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2514\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\ntransform\n\n\nTransforms a value according to the explicitly defined mapping of some elements to other ones.\nThere are two variations of this function:\n\n\n\n\ntransform(x, array_from, array_to, default)\n\n\n\n\nx\n \u2013 What to transform.\n\n\narray_from\n \u2013 Constant array of values for converting.\n\n\narray_to\n \u2013 Constant array of values to convert the values in 'from' to.\n\n\ndefault\n \u2013 Which value to use if 'x' is not equal to any of the values in 'from'.\n\n\narray_from\n and \narray_to\n \u2013 Arrays of the same size.\n\n\nTypes:\n\n\ntransform(T, Array(T), Array(U), U) -\n U\n\n\nT\n and \nU\n can be numeric, string, or Date or DateTime types.\nWhere the same letter is indicated (T or U), for numeric types these might not be matching types, but types that have a common type.\nFor example, the first argument can have the Int64 type, while the second has the Array(Uint16) type.\n\n\nIf the 'x' value is equal to one of the elements in the 'array_from' array, it returns the existing element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'default'. If there are multiple matching elements in 'array_from', it returns one of the matches.\n\n\nExample:\n\n\nSELECT\n\n \ntransform\n(\nSearchEngineID\n,\n \n[\n2\n,\n \n3\n],\n \n[\nYandex\n,\n \nGoogle\n],\n \nOther\n)\n \nAS\n \ntitle\n,\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \ntest\n.\nhits\n\n\nWHERE\n \nSearchEngineID\n \n!=\n \n0\n\n\nGROUP\n \nBY\n \ntitle\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\n\n\n\n\n\u250c\u2500title\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 Yandex \u2502 498635 \u2502\n\u2502 Google \u2502 229872 \u2502\n\u2502 Other \u2502 104472 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\ntransform(x, array_from, array_to)\n\n\n\n\nDiffers from the first variation in that the 'default' argument is omitted.\nIf the 'x' value is equal to one of the elements in the 'array_from' array, it returns the matching element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'x'.\n\n\nTypes:\n\n\ntransform(T, Array(T), Array(T)) -\n T\n\n\nExample:\n\n\nSELECT\n\n \ntransform\n(\ndomain\n(\nReferer\n),\n \n[\nyandex.ru\n,\n \ngoogle.ru\n,\n \nvk.com\n],\n \n[\nwww.yandex\n,\n \nexample.com\n])\n \nAS\n \ns\n,\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \ndomain\n(\nReferer\n)\n\n\nORDER\n \nBY\n \ncount\n()\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 \u2502 2906259 \u2502\n\u2502 www.yandex \u2502 867767 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 313599 \u2502\n\u2502 mail.yandex.ru \u2502 107147 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 100355 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 65040 \u2502\n\u2502 news.yandex.ru \u2502 64515 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.net \u2502 59141 \u2502\n\u2502 example.com \u2502 57316 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nformatReadableSize(x)\n\n\nAccepts the size (number of bytes). Returns a rounded size with a suffix (KiB, MiB, etc.) as a string.\n\n\nExample:\n\n\nSELECT\n\n \narrayJoin\n([\n1\n,\n \n1024\n,\n \n1024\n*\n1024\n,\n \n192851925\n])\n \nAS\n \nfilesize_bytes\n,\n\n \nformatReadableSize\n(\nfilesize_bytes\n)\n \nAS\n \nfilesize\n\n\n\n\n\n\n\u250c\u2500filesize_bytes\u2500\u252c\u2500filesize\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 1.00 B \u2502\n\u2502 1024 \u2502 1.00 KiB \u2502\n\u2502 1048576 \u2502 1.00 MiB \u2502\n\u2502 192851925 \u2502 183.92 MiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nleast(a, b)\n\n\nReturns the smallest value from a and b.\n\n\ngreatest(a, b)\n\n\nReturns the largest value of a and b.\n\n\nuptime()\n\n\nReturns the server's uptime in seconds.\n\n\nversion()\n\n\nReturns the version of the server as a string.\n\n\nrowNumberInAllBlocks()\n\n\nReturns the ordinal number of the row in the data block. This function only considers the affected data blocks.\n\n\nrunningDifference(x)\n\n\nCalculates the difference between successive row values \u200b\u200bin the data block.\nReturns 0 for the first row and the difference from the previous row for each subsequent row.\n\n\nThe result of the function depends on the affected data blocks and the order of data in the block.\nIf you make a subquery with ORDER BY and call the function from outside the subquery, you can get the expected result.\n\n\nExample:\n\n\nSELECT\n\n \nEventID\n,\n\n \nEventTime\n,\n\n \nrunningDifference\n(\nEventTime\n)\n \nAS\n \ndelta\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nEventID\n,\n\n \nEventTime\n\n \nFROM\n \nevents\n\n \nWHERE\n \nEventDate\n \n=\n \n2016-11-24\n\n \nORDER\n \nBY\n \nEventTime\n \nASC\n\n \nLIMIT\n \n5\n\n\n)\n\n\n\n\n\n\n\u250c\u2500EventID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500EventTime\u2500\u252c\u2500delta\u2500\u2510\n\u2502 1106 \u2502 2016-11-24 00:00:04 \u2502 0 \u2502\n\u2502 1107 \u2502 2016-11-24 00:00:05 \u2502 1 \u2502\n\u2502 1108 \u2502 2016-11-24 00:00:05 \u2502 0 \u2502\n\u2502 1109 \u2502 2016-11-24 00:00:09 \u2502 4 \u2502\n\u2502 1110 \u2502 2016-11-24 00:00:10 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nMACNumToString(num)\n\n\nAccepts a UInt64 number. Interprets it as a MAC address in big endian. Returns a string containing the corresponding MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form).\n\n\nMACStringToNum(s)\n\n\nThe inverse function of MACNumToString. If the MAC address has an invalid format, it returns 0.\n\n\nMACStringToOUI(s)\n\n\nAccepts a MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form). Returns the first three octets as a UInt64 number. If the MAC address has an invalid format, it returns 0.", + "title": "Other functions" + }, + { + "location": "/functions/other_functions/#other-functions", + "text": "", + "title": "Other functions" + }, + { + "location": "/functions/other_functions/#hostname", + "text": "Returns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server.", + "title": "hostName()" + }, + { + "location": "/functions/other_functions/#visiblewidthx", + "text": "Calculates the approximate width when outputting values to the console in text format (tab-separated).\nThis function is used by the system for implementing Pretty formats.", + "title": "visibleWidth(x)" + }, + { + "location": "/functions/other_functions/#totypenamex", + "text": "Returns a string containing the type name of the passed argument.", + "title": "toTypeName(x)" + }, + { + "location": "/functions/other_functions/#blocksize", + "text": "Gets the size of the block.\nIn ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for.", + "title": "blockSize()" + }, + { + "location": "/functions/other_functions/#materializex", + "text": "Turns a constant into a full column containing just one value.\nIn ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior.", + "title": "materialize(x)" + }, + { + "location": "/functions/other_functions/#ignore", + "text": "Accepts any arguments and always returns 0.\nHowever, the argument is still evaluated. This can be used for benchmarks.", + "title": "ignore(...)" + }, + { + "location": "/functions/other_functions/#sleepseconds", + "text": "Sleeps 'seconds' seconds on each data block. You can specify an integer or a floating-point number.", + "title": "sleep(seconds)" + }, + { + "location": "/functions/other_functions/#currentdatabase", + "text": "Returns the name of the current database.\nYou can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database.", + "title": "currentDatabase()" + }, + { + "location": "/functions/other_functions/#isfinitex", + "text": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0.", + "title": "isFinite(x)" + }, + { + "location": "/functions/other_functions/#isinfinitex", + "text": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. Note that 0 is returned for a NaN.", + "title": "isInfinite(x)" + }, + { + "location": "/functions/other_functions/#isnanx", + "text": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0.", + "title": "isNaN(x)" + }, + { + "location": "/functions/other_functions/#hascolumnintable91hostname91-username91-password939393-database-table-column", + "text": "Accepts constant strings: database name, table name, and column name. Returns a UInt8 constant expression equal to 1 if there is a column, otherwise 0. If the hostname parameter is set, the test will run on a remote server.\nThe function throws an exception if the table does not exist.\nFor elements in a nested data structure, the function checks for the existence of a column. For the nested data structure itself, the function returns 0.", + "title": "hasColumnInTable(['hostname'[, 'username'[, 'password']],] 'database', 'table', 'column')" + }, + { + "location": "/functions/other_functions/#bar", + "text": "Allows building a unicode-art diagram. bar (x, min, max, width) draws a band with a width proportional to (x - min) and equal to width characters when x = max . Parameters: x \u2013 Value to display. min, max \u2013 Integer constants. The value must fit in Int64. width \u2013 Constant, positive number, may be a fraction. The band is drawn with accuracy to one eighth of a symbol. Example: SELECT \n toHour ( EventTime ) AS h , \n count () AS c , \n bar ( c , 0 , 600000 , 20 ) AS bar FROM test . hits GROUP BY h ORDER BY h ASC \u250c\u2500\u2500h\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u252c\u2500bar\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 0 \u2502 292907 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 1 \u2502 180563 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 2 \u2502 114861 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 3 \u2502 85069 \u2502 \u2588\u2588\u258b \u2502\n\u2502 4 \u2502 68543 \u2502 \u2588\u2588\u258e \u2502\n\u2502 5 \u2502 78116 \u2502 \u2588\u2588\u258c \u2502\n\u2502 6 \u2502 113474 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 7 \u2502 170678 \u2502 \u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 8 \u2502 278380 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 9 \u2502 391053 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 10 \u2502 457681 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 11 \u2502 493667 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 12 \u2502 509641 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 13 \u2502 522947 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 14 \u2502 539954 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 15 \u2502 528460 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c \u2502\n\u2502 16 \u2502 539201 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 17 \u2502 523539 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 18 \u2502 506467 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 19 \u2502 520915 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 20 \u2502 521665 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 21 \u2502 542078 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 22 \u2502 493642 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 23 \u2502 400397 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2514\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "bar" + }, + { + "location": "/functions/other_functions/#transform", + "text": "Transforms a value according to the explicitly defined mapping of some elements to other ones.\nThere are two variations of this function: transform(x, array_from, array_to, default) x \u2013 What to transform. array_from \u2013 Constant array of values for converting. array_to \u2013 Constant array of values to convert the values in 'from' to. default \u2013 Which value to use if 'x' is not equal to any of the values in 'from'. array_from and array_to \u2013 Arrays of the same size. Types: transform(T, Array(T), Array(U), U) - U T and U can be numeric, string, or Date or DateTime types.\nWhere the same letter is indicated (T or U), for numeric types these might not be matching types, but types that have a common type.\nFor example, the first argument can have the Int64 type, while the second has the Array(Uint16) type. If the 'x' value is equal to one of the elements in the 'array_from' array, it returns the existing element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'default'. If there are multiple matching elements in 'array_from', it returns one of the matches. Example: SELECT \n transform ( SearchEngineID , [ 2 , 3 ], [ Yandex , Google ], Other ) AS title , \n count () AS c FROM test . hits WHERE SearchEngineID != 0 GROUP BY title ORDER BY c DESC \u250c\u2500title\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 Yandex \u2502 498635 \u2502\n\u2502 Google \u2502 229872 \u2502\n\u2502 Other \u2502 104472 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 transform(x, array_from, array_to) Differs from the first variation in that the 'default' argument is omitted.\nIf the 'x' value is equal to one of the elements in the 'array_from' array, it returns the matching element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'x'. Types: transform(T, Array(T), Array(T)) - T Example: SELECT \n transform ( domain ( Referer ), [ yandex.ru , google.ru , vk.com ], [ www.yandex , example.com ]) AS s , \n count () AS c FROM test . hits GROUP BY domain ( Referer ) ORDER BY count () DESC LIMIT 10 \u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 \u2502 2906259 \u2502\n\u2502 www.yandex \u2502 867767 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 313599 \u2502\n\u2502 mail.yandex.ru \u2502 107147 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 100355 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 65040 \u2502\n\u2502 news.yandex.ru \u2502 64515 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.net \u2502 59141 \u2502\n\u2502 example.com \u2502 57316 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "transform" + }, + { + "location": "/functions/other_functions/#formatreadablesizex", + "text": "Accepts the size (number of bytes). Returns a rounded size with a suffix (KiB, MiB, etc.) as a string. Example: SELECT \n arrayJoin ([ 1 , 1024 , 1024 * 1024 , 192851925 ]) AS filesize_bytes , \n formatReadableSize ( filesize_bytes ) AS filesize \u250c\u2500filesize_bytes\u2500\u252c\u2500filesize\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 1.00 B \u2502\n\u2502 1024 \u2502 1.00 KiB \u2502\n\u2502 1048576 \u2502 1.00 MiB \u2502\n\u2502 192851925 \u2502 183.92 MiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "formatReadableSize(x)" + }, + { + "location": "/functions/other_functions/#leasta-b", + "text": "Returns the smallest value from a and b.", + "title": "least(a, b)" + }, + { + "location": "/functions/other_functions/#greatesta-b", + "text": "Returns the largest value of a and b.", + "title": "greatest(a, b)" + }, + { + "location": "/functions/other_functions/#uptime", + "text": "Returns the server's uptime in seconds.", + "title": "uptime()" + }, + { + "location": "/functions/other_functions/#version", + "text": "Returns the version of the server as a string.", + "title": "version()" + }, + { + "location": "/functions/other_functions/#rownumberinallblocks", + "text": "Returns the ordinal number of the row in the data block. This function only considers the affected data blocks.", + "title": "rowNumberInAllBlocks()" + }, + { + "location": "/functions/other_functions/#runningdifferencex", + "text": "Calculates the difference between successive row values \u200b\u200bin the data block.\nReturns 0 for the first row and the difference from the previous row for each subsequent row. The result of the function depends on the affected data blocks and the order of data in the block.\nIf you make a subquery with ORDER BY and call the function from outside the subquery, you can get the expected result. Example: SELECT \n EventID , \n EventTime , \n runningDifference ( EventTime ) AS delta FROM ( \n SELECT \n EventID , \n EventTime \n FROM events \n WHERE EventDate = 2016-11-24 \n ORDER BY EventTime ASC \n LIMIT 5 ) \u250c\u2500EventID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500EventTime\u2500\u252c\u2500delta\u2500\u2510\n\u2502 1106 \u2502 2016-11-24 00:00:04 \u2502 0 \u2502\n\u2502 1107 \u2502 2016-11-24 00:00:05 \u2502 1 \u2502\n\u2502 1108 \u2502 2016-11-24 00:00:05 \u2502 0 \u2502\n\u2502 1109 \u2502 2016-11-24 00:00:09 \u2502 4 \u2502\n\u2502 1110 \u2502 2016-11-24 00:00:10 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "runningDifference(x)" + }, + { + "location": "/functions/other_functions/#macnumtostringnum", + "text": "Accepts a UInt64 number. Interprets it as a MAC address in big endian. Returns a string containing the corresponding MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form).", + "title": "MACNumToString(num)" + }, + { + "location": "/functions/other_functions/#macstringtonums", + "text": "The inverse function of MACNumToString. If the MAC address has an invalid format, it returns 0.", + "title": "MACStringToNum(s)" + }, + { + "location": "/functions/other_functions/#macstringtoouis", + "text": "Accepts a MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form). Returns the first three octets as a UInt64 number. If the MAC address has an invalid format, it returns 0.", + "title": "MACStringToOUI(s)" + }, + { + "location": "/functions/ext_dict_functions/", + "text": "Functions for working with external dictionaries\n\n\nFor information on connecting and configuring external dictionaries, see \"\nExternal dictionaries\n\".\n\n\ndictGetUInt8, dictGetUInt16, dictGetUInt32, dictGetUInt64\n\n\ndictGetInt8, dictGetInt16, dictGetInt32, dictGetInt64\n\n\ndictGetFloat32, dictGetFloat64\n\n\ndictGetDate, dictGetDateTime\n\n\ndictGetUUID\n\n\ndictGetString\n\n\ndictGetT('dict_name', 'attr_name', id)\n\n\n\n\nGet the value of the attr_name attribute from the dict_name dictionary using the 'id' key.\ndict_name\n and \nattr_name\n are constant strings.\nid\nmust be UInt64.\nIf there is no \nid\n key in the dictionary, it returns the default value specified in the dictionary description.\n\n\n\n\ndictGetTOrDefault\n\n\ndictGetT('dict_name', 'attr_name', id, default)\n\n\nThe same as the \ndictGetT\n functions, but the default value is taken from the function's last argument.\n\n\ndictIsIn\n\n\ndictIsIn('dict_name', child_id, ancestor_id)\n\n\n\n\nFor the 'dict_name' hierarchical dictionary, finds out whether the 'child_id' key is located inside 'ancestor_id' (or matches 'ancestor_id'). Returns UInt8.\n\n\n\n\ndictGetHierarchy\n\n\ndictGetHierarchy('dict_name', id)\n\n\n\n\nFor the 'dict_name' hierarchical dictionary, returns an array of dictionary keys starting from 'id' and continuing along the chain of parent elements. Returns Array(UInt64).\n\n\n\n\ndictHas\n\n\ndictHas('dict_name', id)\n\n\n\n\nCheck whether the dictionary has the key. Returns a UInt8 value equal to 0 if there is no key and 1 if there is a key.", + "title": "Functions for working with external dictionaries" + }, + { + "location": "/functions/ext_dict_functions/#functions-for-working-with-external-dictionaries", + "text": "For information on connecting and configuring external dictionaries, see \" External dictionaries \".", + "title": "Functions for working with external dictionaries" + }, + { + "location": "/functions/ext_dict_functions/#dictgetuint8-dictgetuint16-dictgetuint32-dictgetuint64", + "text": "", + "title": "dictGetUInt8, dictGetUInt16, dictGetUInt32, dictGetUInt64" + }, + { + "location": "/functions/ext_dict_functions/#dictgetint8-dictgetint16-dictgetint32-dictgetint64", + "text": "", + "title": "dictGetInt8, dictGetInt16, dictGetInt32, dictGetInt64" + }, + { + "location": "/functions/ext_dict_functions/#dictgetfloat32-dictgetfloat64", + "text": "", + "title": "dictGetFloat32, dictGetFloat64" + }, + { + "location": "/functions/ext_dict_functions/#dictgetdate-dictgetdatetime", + "text": "", + "title": "dictGetDate, dictGetDateTime" + }, + { + "location": "/functions/ext_dict_functions/#dictgetuuid", + "text": "", + "title": "dictGetUUID" + }, + { + "location": "/functions/ext_dict_functions/#dictgetstring", + "text": "dictGetT('dict_name', 'attr_name', id) Get the value of the attr_name attribute from the dict_name dictionary using the 'id' key. dict_name and attr_name are constant strings. id must be UInt64.\nIf there is no id key in the dictionary, it returns the default value specified in the dictionary description.", + "title": "dictGetString" + }, + { + "location": "/functions/ext_dict_functions/#dictgettordefault", + "text": "dictGetT('dict_name', 'attr_name', id, default) The same as the dictGetT functions, but the default value is taken from the function's last argument.", + "title": "dictGetTOrDefault" + }, + { + "location": "/functions/ext_dict_functions/#dictisin", + "text": "dictIsIn('dict_name', child_id, ancestor_id) For the 'dict_name' hierarchical dictionary, finds out whether the 'child_id' key is located inside 'ancestor_id' (or matches 'ancestor_id'). Returns UInt8.", + "title": "dictIsIn" + }, + { + "location": "/functions/ext_dict_functions/#dictgethierarchy", + "text": "dictGetHierarchy('dict_name', id) For the 'dict_name' hierarchical dictionary, returns an array of dictionary keys starting from 'id' and continuing along the chain of parent elements. Returns Array(UInt64).", + "title": "dictGetHierarchy" + }, + { + "location": "/functions/ext_dict_functions/#dicthas", + "text": "dictHas('dict_name', id) Check whether the dictionary has the key. Returns a UInt8 value equal to 0 if there is no key and 1 if there is a key.", + "title": "dictHas" + }, + { + "location": "/functions/ym_dict_functions/", + "text": "Functions for working with Yandex.Metrica dictionaries\n\n\nIn order for the functions below to work, the server config must specify the paths and addresses for getting all the Yandex.Metrica dictionaries. The dictionaries are loaded at the first call of any of these functions. If the reference lists can't be loaded, an exception is thrown.\n\n\nFor information about creating reference lists, see the section \"Dictionaries\".\n\n\nMultiple geobases\n\n\nClickHouse supports working with multiple alternative geobases (regional hierarchies) simultaneously, in order to support various perspectives on which countries certain regions belong to.\n\n\nThe 'clickhouse-server' config specifies the file with the regional hierarchy::\npath_to_regions_hierarchy_file\n/opt/geo/regions_hierarchy.txt\n/path_to_regions_hierarchy_file\n\n\nBesides this file, it also searches for files nearby that have the _ symbol and any suffix appended to the name (before the file extension).\nFor example, it will also find the file \n/opt/geo/regions_hierarchy_ua.txt\n, if present.\n\n\nua\n is called the dictionary key. For a dictionary without a suffix, the key is an empty string.\n\n\nAll the dictionaries are re-loaded in runtime (once every certain number of seconds, as defined in the builtin_dictionaries_reload_interval config parameter, or once an hour by default). However, the list of available dictionaries is defined one time, when the server starts.\n\n\nAll functions for working with regions have an optional argument at the end \u2013 the dictionary key. It is referred to as the geobase.\nExample:\n\n\nregionToCountry(RegionID) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, \n) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, \nua\n) \u2013 Uses the dictionary for the \nua\n key: /opt/geo/regions_hierarchy_ua.txt\n\n\n\n\n\nregionToCity(id[, geobase])\n\n\nAccepts a UInt32 number \u2013 the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0.\n\n\nregionToArea(id[, geobase])\n\n\nConverts a region to an area (type 5 in the geobase). In every other way, this function is the same as 'regionToCity'.\n\n\nSELECT\n \nDISTINCT\n \nregionToName\n(\nregionToArea\n(\ntoUInt32\n(\nnumber\n),\n \nua\n))\n\n\nFROM\n \nsystem\n.\nnumbers\n\n\nLIMIT\n \n15\n\n\n\n\n\n\n\u250c\u2500regionToName(regionToArea(toUInt32(number), \\\nua\\\n))\u2500\u2510\n\u2502 \u2502\n\u2502 Moscow and Moscow region \u2502\n\u2502 St. Petersburg and Leningrad region \u2502\n\u2502 Belgorod region \u2502\n\u2502 Ivanovsk region \u2502\n\u2502 Kaluga region \u2502\n\u2502 Kostroma region \u2502\n\u2502 Kursk region \u2502\n\u2502 Lipetsk region \u2502\n\u2502 Orlov region \u2502\n\u2502 Ryazan region \u2502\n\u2502 Smolensk region \u2502\n\u2502 Tambov region \u2502\n\u2502 Tver region \u2502\n\u2502 Tula region \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nregionToDistrict(id[, geobase])\n\n\nConverts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as 'regionToCity'.\n\n\nSELECT\n \nDISTINCT\n \nregionToName\n(\nregionToDistrict\n(\ntoUInt32\n(\nnumber\n),\n \nua\n))\n\n\nFROM\n \nsystem\n.\nnumbers\n\n\nLIMIT\n \n15\n\n\n\n\n\n\n\u250c\u2500regionToName(regionToDistrict(toUInt32(number), \\\nua\\\n))\u2500\u2510\n\u2502 \u2502\n\u2502 Central federal district \u2502\n\u2502 Northwest federal district \u2502\n\u2502 South federal district \u2502\n\u2502 North Caucases federal district \u2502\n\u2502 Privolga federal district \u2502\n\u2502 Ural federal district \u2502\n\u2502 Siberian federal district \u2502\n\u2502 Far East federal district \u2502\n\u2502 Scotland \u2502\n\u2502 Faroe Islands \u2502\n\u2502 Flemish region \u2502\n\u2502 Brussels capital region \u2502\n\u2502 Wallonia \u2502\n\u2502 Federation of Bosnia and Herzegovina \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nregionToCountry(id[, geobase])\n\n\nConverts a region to a country. In every other way, this function is the same as 'regionToCity'.\nExample: \nregionToCountry(toUInt32(213)) = 225\n converts Moscow (213) to Russia (225).\n\n\nregionToContinent(id[, geobase])\n\n\nConverts a region to a continent. In every other way, this function is the same as 'regionToCity'.\nExample: \nregionToContinent(toUInt32(213)) = 10001\n converts Moscow (213) to Eurasia (10001).\n\n\nregionToPopulation(id[, geobase])\n\n\nGets the population for a region.\nThe population can be recorded in files with the geobase. See the section \"External dictionaries\".\nIf the population is not recorded for the region, it returns 0.\nIn the Yandex geobase, the population might be recorded for child regions, but not for parent regions.\n\n\nregionIn(lhs, rhs[, geobase])\n\n\nChecks whether a 'lhs' region belongs to a 'rhs' region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn't belong.\nThe relationship is reflexive \u2013 any region also belongs to itself.\n\n\nregionHierarchy(id[, geobase])\n\n\nAccepts a UInt32 number \u2013 the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain.\nExample: \nregionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000]\n.\n\n\nregionToName(id[, lang])\n\n\nAccepts a UInt32 number \u2013 the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language 'ru' is used. If the language is not supported, an exception is thrown. Returns a string \u2013 the name of the region in the corresponding language. If the region with the specified ID doesn't exist, an empty string is returned.\n\n\nua\n and \nuk\n both mean Ukrainian.", + "title": "Functions for working with Yandex.Metrica dictionaries" + }, + { + "location": "/functions/ym_dict_functions/#functions-for-working-with-yandexmetrica-dictionaries", + "text": "In order for the functions below to work, the server config must specify the paths and addresses for getting all the Yandex.Metrica dictionaries. The dictionaries are loaded at the first call of any of these functions. If the reference lists can't be loaded, an exception is thrown. For information about creating reference lists, see the section \"Dictionaries\".", + "title": "Functions for working with Yandex.Metrica dictionaries" + }, + { + "location": "/functions/ym_dict_functions/#multiple-geobases", + "text": "ClickHouse supports working with multiple alternative geobases (regional hierarchies) simultaneously, in order to support various perspectives on which countries certain regions belong to. The 'clickhouse-server' config specifies the file with the regional hierarchy:: path_to_regions_hierarchy_file /opt/geo/regions_hierarchy.txt /path_to_regions_hierarchy_file Besides this file, it also searches for files nearby that have the _ symbol and any suffix appended to the name (before the file extension).\nFor example, it will also find the file /opt/geo/regions_hierarchy_ua.txt , if present. ua is called the dictionary key. For a dictionary without a suffix, the key is an empty string. All the dictionaries are re-loaded in runtime (once every certain number of seconds, as defined in the builtin_dictionaries_reload_interval config parameter, or once an hour by default). However, the list of available dictionaries is defined one time, when the server starts. All functions for working with regions have an optional argument at the end \u2013 the dictionary key. It is referred to as the geobase.\nExample: regionToCountry(RegionID) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, ) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, ua ) \u2013 Uses the dictionary for the ua key: /opt/geo/regions_hierarchy_ua.txt", + "title": "Multiple geobases" + }, + { + "location": "/functions/ym_dict_functions/#regiontocityid-geobase", + "text": "Accepts a UInt32 number \u2013 the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0.", + "title": "regionToCity(id[, geobase])" + }, + { + "location": "/functions/ym_dict_functions/#regiontoareaid91-geobase93", + "text": "Converts a region to an area (type 5 in the geobase). In every other way, this function is the same as 'regionToCity'. SELECT DISTINCT regionToName ( regionToArea ( toUInt32 ( number ), ua )) FROM system . numbers LIMIT 15 \u250c\u2500regionToName(regionToArea(toUInt32(number), \\ ua\\ ))\u2500\u2510\n\u2502 \u2502\n\u2502 Moscow and Moscow region \u2502\n\u2502 St. Petersburg and Leningrad region \u2502\n\u2502 Belgorod region \u2502\n\u2502 Ivanovsk region \u2502\n\u2502 Kaluga region \u2502\n\u2502 Kostroma region \u2502\n\u2502 Kursk region \u2502\n\u2502 Lipetsk region \u2502\n\u2502 Orlov region \u2502\n\u2502 Ryazan region \u2502\n\u2502 Smolensk region \u2502\n\u2502 Tambov region \u2502\n\u2502 Tver region \u2502\n\u2502 Tula region \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "regionToArea(id[, geobase])" + }, + { + "location": "/functions/ym_dict_functions/#regiontodistrictid-geobase", + "text": "Converts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as 'regionToCity'. SELECT DISTINCT regionToName ( regionToDistrict ( toUInt32 ( number ), ua )) FROM system . numbers LIMIT 15 \u250c\u2500regionToName(regionToDistrict(toUInt32(number), \\ ua\\ ))\u2500\u2510\n\u2502 \u2502\n\u2502 Central federal district \u2502\n\u2502 Northwest federal district \u2502\n\u2502 South federal district \u2502\n\u2502 North Caucases federal district \u2502\n\u2502 Privolga federal district \u2502\n\u2502 Ural federal district \u2502\n\u2502 Siberian federal district \u2502\n\u2502 Far East federal district \u2502\n\u2502 Scotland \u2502\n\u2502 Faroe Islands \u2502\n\u2502 Flemish region \u2502\n\u2502 Brussels capital region \u2502\n\u2502 Wallonia \u2502\n\u2502 Federation of Bosnia and Herzegovina \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "regionToDistrict(id[, geobase])" + }, + { + "location": "/functions/ym_dict_functions/#regiontocountryid-geobase", + "text": "Converts a region to a country. In every other way, this function is the same as 'regionToCity'.\nExample: regionToCountry(toUInt32(213)) = 225 converts Moscow (213) to Russia (225).", + "title": "regionToCountry(id[, geobase])" + }, + { + "location": "/functions/ym_dict_functions/#regiontocontinentid-geobase", + "text": "Converts a region to a continent. In every other way, this function is the same as 'regionToCity'.\nExample: regionToContinent(toUInt32(213)) = 10001 converts Moscow (213) to Eurasia (10001).", + "title": "regionToContinent(id[, geobase])" + }, + { + "location": "/functions/ym_dict_functions/#regiontopopulationid-geobase", + "text": "Gets the population for a region.\nThe population can be recorded in files with the geobase. See the section \"External dictionaries\".\nIf the population is not recorded for the region, it returns 0.\nIn the Yandex geobase, the population might be recorded for child regions, but not for parent regions.", + "title": "regionToPopulation(id[, geobase])" + }, + { + "location": "/functions/ym_dict_functions/#regioninlhs-rhs-geobase", + "text": "Checks whether a 'lhs' region belongs to a 'rhs' region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn't belong.\nThe relationship is reflexive \u2013 any region also belongs to itself.", + "title": "regionIn(lhs, rhs[, geobase])" + }, + { + "location": "/functions/ym_dict_functions/#regionhierarchyid91-geobase93", + "text": "Accepts a UInt32 number \u2013 the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain.\nExample: regionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000] .", + "title": "regionHierarchy(id[, geobase])" + }, + { + "location": "/functions/ym_dict_functions/#regiontonameid91-lang93", + "text": "Accepts a UInt32 number \u2013 the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language 'ru' is used. If the language is not supported, an exception is thrown. Returns a string \u2013 the name of the region in the corresponding language. If the region with the specified ID doesn't exist, an empty string is returned. ua and uk both mean Ukrainian.", + "title": "regionToName(id[, lang])" + }, + { + "location": "/functions/in_functions/", + "text": "Functions for implementing the IN operator\n\n\nin, notIn, globalIn, globalNotIn\n\n\nSee the section \"IN operators\".\n\n\ntuple(x, y, ...), operator (x, y, ...)\n\n\nA function that allows grouping multiple columns.\nFor columns with the types T1, T2, ..., it returns a Tuple(T1, T2, ...) type tuple containing these columns. There is no cost to execute the function.\nTuples are normally used as intermediate values for an argument of IN operators, or for creating a list of formal parameters of lambda functions. Tuples can't be written to a table.\n\n\ntupleElement(tuple, n), operator x.N\n\n\nA function that allows getting a column from a tuple.\n'N' is the column index, starting from 1. N must be a constant. 'N' must be a constant. 'N' must be a strict postive integer no greater than the size of the tuple.\nThere is no cost to execute the function.", + "title": "Functions for implementing the IN operator" + }, + { + "location": "/functions/in_functions/#functions-for-implementing-the-in-operator", + "text": "", + "title": "Functions for implementing the IN operator" + }, + { + "location": "/functions/in_functions/#in-notin-globalin-globalnotin", + "text": "See the section \"IN operators\".", + "title": "in, notIn, globalIn, globalNotIn" + }, + { + "location": "/functions/in_functions/#tuplex-y-operator-x-y", + "text": "A function that allows grouping multiple columns.\nFor columns with the types T1, T2, ..., it returns a Tuple(T1, T2, ...) type tuple containing these columns. There is no cost to execute the function.\nTuples are normally used as intermediate values for an argument of IN operators, or for creating a list of formal parameters of lambda functions. Tuples can't be written to a table.", + "title": "tuple(x, y, ...), operator (x, y, ...)" + }, + { + "location": "/functions/in_functions/#tupleelementtuple-n-operator-xn", + "text": "A function that allows getting a column from a tuple.\n'N' is the column index, starting from 1. N must be a constant. 'N' must be a constant. 'N' must be a strict postive integer no greater than the size of the tuple.\nThere is no cost to execute the function.", + "title": "tupleElement(tuple, n), operator x.N" + }, + { + "location": "/functions/array_join/", + "text": "arrayJoin function\n\n\nThis is a very unusual function.\n\n\nNormal functions don't change a set of rows, but just change the values in each row (map).\nAggregate functions compress a set of rows (fold or reduce).\nThe 'arrayJoin' function takes each row and generates a set of rows (unfold).\n\n\nThis function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array.\nAll the values in columns are simply copied, except the values in the column where this function is applied; it is replaced with the corresponding array value.\n\n\nA query can use multiple \narrayJoin\n functions. In this case, the transformation is performed multiple times.\n\n\nNote the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities.\n\n\nExample:\n\n\nSELECT\n \narrayJoin\n([\n1\n,\n \n2\n,\n \n3\n]\n \nAS\n \nsrc\n)\n \nAS\n \ndst\n,\n \nHello\n,\n \nsrc\n\n\n\n\n\n\n\u250c\u2500dst\u2500\u252c\u2500\\\nHello\\\n\u2500\u252c\u2500src\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 2 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 3 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayJoin function" + }, + { + "location": "/functions/array_join/#arrayjoin-function", + "text": "This is a very unusual function. Normal functions don't change a set of rows, but just change the values in each row (map).\nAggregate functions compress a set of rows (fold or reduce).\nThe 'arrayJoin' function takes each row and generates a set of rows (unfold). This function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array.\nAll the values in columns are simply copied, except the values in the column where this function is applied; it is replaced with the corresponding array value. A query can use multiple arrayJoin functions. In this case, the transformation is performed multiple times. Note the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities. Example: SELECT arrayJoin ([ 1 , 2 , 3 ] AS src ) AS dst , Hello , src \u250c\u2500dst\u2500\u252c\u2500\\ Hello\\ \u2500\u252c\u2500src\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 2 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 3 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayJoin function" + }, + { + "location": "/agg_functions/", + "text": "Aggregate functions\n\n\nAggregate functions work in the \nnormal\n way as expected by database experts.\n\n\nClickHouse also supports:\n\n\n\n\nParametric aggregate functions\n, which accept other parameters in addition to columns.\n\n\nCombinators\n, which change the behavior of aggregate functions.", + "title": "Introduction" + }, + { + "location": "/agg_functions/#aggregate-functions", + "text": "Aggregate functions work in the normal way as expected by database experts. ClickHouse also supports: Parametric aggregate functions , which accept other parameters in addition to columns. Combinators , which change the behavior of aggregate functions.", + "title": "Aggregate functions" + }, + { + "location": "/agg_functions/reference/", + "text": "Function reference\n\n\ncount()\n\n\nCounts the number of rows. Accepts zero arguments and returns UInt64.\nThe syntax \nCOUNT(DISTINCT x)\n is not supported. The separate \nuniq\n aggregate function exists for this purpose.\n\n\nA \nSELECT count() FROM table\n query is not optimized, because the number of entries in the table is not stored separately. It will select some small column from the table and count the number of values in it.\n\n\nany(x)\n\n\nSelects the first encountered value.\nThe query can be executed in any order and even in a different order each time, so the result of this function is indeterminate.\nTo get a determinate result, you can use the 'min' or 'max' function instead of 'any'.\n\n\nIn some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.\n\n\nWhen a \nSELECT\n query has the \nGROUP BY\n clause or at least one aggregate function, ClickHouse (in contrast to MySQL) requires that all expressions in the \nSELECT\n, \nHAVING\n, and \nORDER BY\n clauses be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. To get behavior like in MySQL, you can put the other columns in the \nany\n aggregate function.\n\n\nanyHeavy(x)\n\n\nSelects a frequently occurring value using the \nheavy hitters\n algorithm. If there is a value that occurs more than in half the cases in each of the query's execution threads, this value is returned. Normally, the result is nondeterministic.\n\n\nanyHeavy(column)\n\n\n\n\n\nArguments\n\n- \ncolumn\n \u2013 The column name.\n\n\nExample\n\n\nTake the \nOnTime\n data set and select any frequently occurring value in the \nAirlineID\n column.\n\n\nSELECT\n \nanyHeavy\n(\nAirlineID\n)\n \nAS\n \nres\n\n\nFROM\n \nontime\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500res\u2500\u2510\n\u2502 19690 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nanyLast(x)\n\n\nSelects the last value encountered.\nThe result is just as indeterminate as for the \nany\n function.\n\n\nmin(x)\n\n\nCalculates the minimum.\n\n\nmax(x)\n\n\nCalculates the maximum.\n\n\nargMin(arg, val)\n\n\nCalculates the 'arg' value for a minimal 'val' value. If there are several different values of 'arg' for minimal values of 'val', the first of these values encountered is output.\n\n\nargMax(arg, val)\n\n\nCalculates the 'arg' value for a maximum 'val' value. If there are several different values of 'arg' for maximum values of 'val', the first of these values encountered is output.\n\n\nsum(x)\n\n\nCalculates the sum.\nOnly works for numbers.\n\n\nsumWithOverflow(x)\n\n\nComputes the sum of the numbers, using the same data type for the result as for the input parameters. If the sum exceeds the maximum value for this data type, the function returns an error.\n\n\nOnly works for numbers.\n\n\nsumMap(key, value)\n\n\nTotals the 'value' array according to the keys specified in the 'key' array.\nThe number of elements in 'key' and 'value' must be the same for each row that is totaled.\nReturns a tuple of two arrays: keys in sorted order, and values \u200b\u200bsummed for the corresponding keys.\n\n\nExample:\n\n\nCREATE\n \nTABLE\n \nsum_map\n(\n\n \ndate\n \nDate\n,\n\n \ntimeslot\n \nDateTime\n,\n\n \nstatusMap\n \nNested\n(\n\n \nstatus\n \nUInt16\n,\n\n \nrequests\n \nUInt64\n\n \n)\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\nINSERT\n \nINTO\n \nsum_map\n \nVALUES\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:00:00\n,\n \n[\n1\n,\n \n2\n,\n \n3\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]),\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:00:00\n,\n \n[\n3\n,\n \n4\n,\n \n5\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]),\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:01:00\n,\n \n[\n4\n,\n \n5\n,\n \n6\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]),\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:01:00\n,\n \n[\n6\n,\n \n7\n,\n \n8\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]);\n\n\nSELECT\n\n \ntimeslot\n,\n\n \nsumMap\n(\nstatusMap\n.\nstatus\n,\n \nstatusMap\n.\nrequests\n)\n\n\nFROM\n \nsum_map\n\n\nGROUP\n \nBY\n \ntimeslot\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500timeslot\u2500\u252c\u2500sumMap(statusMap.status, statusMap.requests)\u2500\u2510\n\u2502 2000-01-01 00:00:00 \u2502 ([1,2,3,4,5],[10,10,20,10,10]) \u2502\n\u2502 2000-01-01 00:01:00 \u2502 ([4,5,6,7,8],[10,10,20,10,10]) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\navg(x)\n\n\nCalculates the average.\nOnly works for numbers.\nThe result is always Float64.\n\n\nuniq(x)\n\n\nCalculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.\n\n\nUses an adaptive sampling algorithm: for the calculation state, it uses a sample of element hash values with a size up to 65536.\nThis algorithm is also very accurate for data sets with low cardinality (up to 65536) and very efficient on CPU (when computing not too many of these functions, using \nuniq\n is almost as fast as using other aggregate functions).\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nThis function provides excellent accuracy even for data sets with extremely high cardinality (over 10 billion elements). It is recommended for default use.\n\n\nuniqCombined(x)\n\n\nCalculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.\n\n\nA combination of three algorithms is used: array, hash table and \nHyperLogLog\n with an error correction table. The memory consumption is several times smaller than for the \nuniq\n function, and the accuracy is several times higher. Performance is slightly lower than for the \nuniq\n function, but sometimes it can be even higher than it, such as with distributed queries that transmit a large number of aggregation states over the network. The maximum state size is 96 KiB (HyperLogLog of 217 6-bit cells).\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nThe \nuniqCombined\n function is a good default choice for calculating the number of different values, but keep in mind that the estimation error will increase for high-cardinality data sets (200M+ elements), and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).\n\n\nuniqHLL12(x)\n\n\nUses the \nHyperLogLog\n algorithm to approximate the number of different values of the argument.\n212 5-bit cells are used. The size of the state is slightly more than 2.5 KB. The result is not very accurate (up to ~10% error) for small data sets (\n10K elements). However, the result is fairly accurate for high-cardinality data sets (10K-100M), with a maximum error of ~1.6%. Starting from 100M, the estimation error increases, and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nWe don't recommend using this function. In most cases, use the \nuniq\n or \nuniqCombined\n function.\n\n\nuniqExact(x)\n\n\nCalculates the number of different values of the argument, exactly.\nThere is no reason to fear approximations. It's better to use the \nuniq\n function.\nUse the \nuniqExact\n function if you definitely need an exact result.\n\n\nThe \nuniqExact\n function uses more memory than the \nuniq\n function, because the size of the state has unbounded growth as the number of different values increases.\n\n\ngroupArray(x), groupArray(max_size)(x)\n\n\nCreates an array of argument values.\nValues can be added to the array in any (indeterminate) order.\n\n\nThe second version (with the \nmax_size\n parameter) limits the size of the resulting array to \nmax_size\n elements.\nFor example, \ngroupArray (1) (x)\n is equivalent to \n[any (x)]\n.\n\n\nIn some cases, you can still rely on the order of execution. This applies to cases when \nSELECT\n comes from a subquery that uses \nORDER BY\n.\n\n\n\n\ngroupArrayInsertAt(x)\n\n\nInserts a value into the array in the specified position.\n\n\nAccepts the value and position as input. If several values \u200b\u200bare inserted into the same position, any of them might end up in the resulting array (the first one will be used in the case of single-threaded execution). If no value is inserted into a position, the position is assigned the default value.\n\n\nOptional parameters:\n\n\n\n\nThe default value for substituting in empty positions.\n\n\nThe length of the resulting array. This allows you to receive arrays of the same size for all the aggregate keys. When using this parameter, the default value must be specified.\n\n\n\n\ngroupUniqArray(x)\n\n\nCreates an array from different argument values. Memory consumption is the same as for the \nuniqExact\n function.\n\n\nquantile(level)(x)\n\n\nApproximates the 'level' quantile. 'level' is a constant, a floating-point number from 0 to 1.\nWe recommend using a 'level' value in the range of 0.01..0.99\nDon't use a 'level' value equal to 0 or 1 \u2013 use the 'min' and 'max' functions for these cases.\n\n\nIn this function, as well as in all functions for calculating quantiles, the 'level' parameter can be omitted. In this case, it is assumed to be equal to 0.5 (in other words, the function will calculate the median).\n\n\nWorks for numbers, dates, and dates with times.\nReturns: for numbers \u2013 Float64; for dates \u2013 a date; for dates with times \u2013 a date with time.\n\n\nUses \nreservoir sampling\n with a reservoir size up to 8192.\nIf necessary, the result is output with linear approximation from the two neighboring values.\nThis algorithm provides very low accuracy. See also: \nquantileTiming\n, \nquantileTDigest\n, \nquantileExact\n.\n\n\nThe result depends on the order of running the query, and is nondeterministic.\n\n\nWhen using multiple \nquantile\n (and similar) functions with different levels in a query, the internal states are not combined (that is, the query works less efficiently than it could). In this case, use the \nquantiles\n (and similar) functions.\n\n\nquantileDeterministic(level)(x, determinator)\n\n\nWorks the same way as the \nquantile\n function, but the result is deterministic and does not depend on the order of query execution.\n\n\nTo achieve this, the function takes a second argument \u2013 the \"determinator\". This is a number whose hash is used instead of a random number generator in the reservoir sampling algorithm. For the function to work correctly, the same determinator value should not occur too often. For the determinator, you can use an event ID, user ID, and so on.\n\n\nDon't use this function for calculating timings. There is a more suitable function for this purpose: \nquantileTiming\n.\n\n\nquantileTiming(level)(x)\n\n\nComputes the quantile of 'level' with a fixed precision.\nWorks for numbers. Intended for calculating quantiles of page loading time in milliseconds.\n\n\nIf the value is greater than 30,000 (a page loading time of more than 30 seconds), the result is equated to 30,000.\n\n\nIf the total value is not more than about 5670, then the calculation is accurate.\n\n\nOtherwise:\n\n\n\n\nif the time is less than 1024 ms, then the calculation is accurate.\n\n\notherwise the calculation is rounded to a multiple of 16 ms.\n\n\n\n\nWhen passing negative values to the function, the behavior is undefined.\n\n\nThe returned value has the Float32 type. If no values were passed to the function (when using \nquantileTimingIf\n), 'nan' is returned. The purpose of this is to differentiate these instances from zeros. See the note on sorting NaNs in \"ORDER BY clause\".\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nFor its purpose (calculating quantiles of page loading times), using this function is more effective and the result is more accurate than for the \nquantile\n function.\n\n\nquantileTimingWeighted(level)(x, weight)\n\n\nDiffers from the \nquantileTiming\n function in that it has a second argument, \"weights\". Weight is a non-negative integer.\nThe result is calculated as if the \nx\n value were passed \nweight\n number of times to the \nquantileTiming\n function.\n\n\nquantileExact(level)(x)\n\n\nComputes the quantile of 'level' exactly. To do this, all the passed values \u200b\u200bare combined into an array, which is then partially sorted. Therefore, the function consumes O(n) memory, where 'n' is the number of values that were passed. However, for a small number of values, the function is very effective.\n\n\nquantileExactWeighted(level)(x, weight)\n\n\nComputes the quantile of 'level' exactly. In addition, each value is counted with its weight, as if it is present 'weight' times. The arguments of the function can be considered as histograms, where the value 'x' corresponds to a histogram \"column\" of the height 'weight', and the function itself can be considered as a summation of histograms.\n\n\nA hash table is used as the algorithm. Because of this, if the passed values \u200b\u200bare frequently repeated, the function consumes less RAM than \nquantileExact\n. You can use this function instead of \nquantileExact\n and specify the weight as 1.\n\n\nquantileTDigest(level)(x)\n\n\nApproximates the quantile level using the \nt-digest\n algorithm. The maximum error is 1%. Memory consumption by State is proportional to the logarithm of the number of passed values.\n\n\nThe performance of the function is lower than for \nquantile\n, \nquantileTiming\n. In terms of the ratio of State size to precision, this function is much better than \nquantile\n.\n\n\nThe result depends on the order of running the query, and is nondeterministic.\n\n\nmedian(x)\n\n\nAll the quantile functions have corresponding median functions: \nmedian\n, \nmedianDeterministic\n, \nmedianTiming\n, \nmedianTimingWeighted\n, \nmedianExact\n, \nmedianExactWeighted\n, \nmedianTDigest\n. They are synonyms and their behavior is identical.\n\n\nquantiles(level1, level2, ...)(x)\n\n\nAll the quantile functions also have corresponding quantiles functions: \nquantiles\n, \nquantilesDeterministic\n, \nquantilesTiming\n, \nquantilesTimingWeighted\n, \nquantilesExact\n, \nquantilesExactWeighted\n, \nquantilesTDigest\n. These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.\n\n\nvarSamp(x)\n\n\nCalculates the amount \n\u03a3((x - x\u0305)^2) / (n - 1)\n, where \nn\n is the sample size and \nx\u0305\nis the average value of \nx\n.\n\n\nIt represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount.\n\n\nReturns \nFloat64\n. When \nn \n= 1\n, returns \n+\u221e\n.\n\n\nvarPop(x)\n\n\nCalculates the amount \n\u03a3((x - x\u0305)^2) / (n - 1)\n, where \nn\n is the sample size and \nx\u0305\nis the average value of \nx\n.\n\n\nIn other words, dispersion for a set of values. Returns \nFloat64\n.\n\n\nstddevSamp(x)\n\n\nThe result is equal to the square root of \nvarSamp(x)\n.\n\n\nstddevPop(x)\n\n\nThe result is equal to the square root of \nvarPop(x)\n.\n\n\ntopK(N)(column)\n\n\nReturns an array of the most frequent values in the specified column. The resulting array is sorted in descending order of frequency of values (not by the values themselves).\n\n\nImplements the \nFiltered Space-Saving\n algorithm for analyzing TopK, based on the reduce-and-combine algorithm from \nParallel Space Saving\n.\n\n\ntopK(N)(column)\n\n\n\n\n\nThis function doesn't provide a guaranteed result. In certain situations, errors might occur and it might return frequent values that aren't the most frequent values.\n\n\nWe recommend using the \nN \n 10\n value; performance is reduced with large \nN\n values. Maximum value of \nN = 65536\n.\n\n\nArguments\n\n- 'N' is the number of values.\n- ' x ' \u2013 The column.\n\n\nExample\n\n\nTake the \nOnTime\n data set and select the three most frequently occurring values in the \nAirlineID\n column.\n\n\nSELECT\n \ntopK\n(\n3\n)(\nAirlineID\n)\n \nAS\n \nres\n\n\nFROM\n \nontime\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [19393,19790,19805] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\ncovarSamp(x, y)\n\n\nCalculates the value of \n\u03a3((x - x\u0305)(y - y\u0305)) / (n - 1)\n.\n\n\nReturns Float64. When \nn \n= 1\n, returns +\u221e.\n\n\ncovarPop(x, y)\n\n\nCalculates the value of \n\u03a3((x - x\u0305)(y - y\u0305)) / n\n.\n\n\ncorr(x, y)\n\n\nCalculates the Pearson correlation coefficient: \n\u03a3((x - x\u0305)(y - y\u0305)) / sqrt(\u03a3((x - x\u0305)^2) * \u03a3((y - y\u0305)^2))\n.", + "title": "Function reference" + }, + { + "location": "/agg_functions/reference/#function-reference", + "text": "", + "title": "Function reference" + }, + { + "location": "/agg_functions/reference/#count", + "text": "Counts the number of rows. Accepts zero arguments and returns UInt64.\nThe syntax COUNT(DISTINCT x) is not supported. The separate uniq aggregate function exists for this purpose. A SELECT count() FROM table query is not optimized, because the number of entries in the table is not stored separately. It will select some small column from the table and count the number of values in it.", + "title": "count()" + }, + { + "location": "/agg_functions/reference/#anyx", + "text": "Selects the first encountered value.\nThe query can be executed in any order and even in a different order each time, so the result of this function is indeterminate.\nTo get a determinate result, you can use the 'min' or 'max' function instead of 'any'. In some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY. When a SELECT query has the GROUP BY clause or at least one aggregate function, ClickHouse (in contrast to MySQL) requires that all expressions in the SELECT , HAVING , and ORDER BY clauses be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. To get behavior like in MySQL, you can put the other columns in the any aggregate function.", + "title": "any(x)" + }, + { + "location": "/agg_functions/reference/#anyheavyx", + "text": "Selects a frequently occurring value using the heavy hitters algorithm. If there is a value that occurs more than in half the cases in each of the query's execution threads, this value is returned. Normally, the result is nondeterministic. anyHeavy(column) Arguments \n- column \u2013 The column name. Example Take the OnTime data set and select any frequently occurring value in the AirlineID column. SELECT anyHeavy ( AirlineID ) AS res FROM ontime \u250c\u2500\u2500\u2500res\u2500\u2510\n\u2502 19690 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "anyHeavy(x)" + }, + { + "location": "/agg_functions/reference/#anylastx", + "text": "Selects the last value encountered.\nThe result is just as indeterminate as for the any function.", + "title": "anyLast(x)" + }, + { + "location": "/agg_functions/reference/#minx", + "text": "Calculates the minimum.", + "title": "min(x)" + }, + { + "location": "/agg_functions/reference/#maxx", + "text": "Calculates the maximum.", + "title": "max(x)" + }, + { + "location": "/agg_functions/reference/#argminarg-val", + "text": "Calculates the 'arg' value for a minimal 'val' value. If there are several different values of 'arg' for minimal values of 'val', the first of these values encountered is output.", + "title": "argMin(arg, val)" + }, + { + "location": "/agg_functions/reference/#argmaxarg-val", + "text": "Calculates the 'arg' value for a maximum 'val' value. If there are several different values of 'arg' for maximum values of 'val', the first of these values encountered is output.", + "title": "argMax(arg, val)" + }, + { + "location": "/agg_functions/reference/#sumx", + "text": "Calculates the sum.\nOnly works for numbers.", + "title": "sum(x)" + }, + { + "location": "/agg_functions/reference/#sumwithoverflowx", + "text": "Computes the sum of the numbers, using the same data type for the result as for the input parameters. If the sum exceeds the maximum value for this data type, the function returns an error. Only works for numbers.", + "title": "sumWithOverflow(x)" + }, + { + "location": "/agg_functions/reference/#summapkey-value", + "text": "Totals the 'value' array according to the keys specified in the 'key' array.\nThe number of elements in 'key' and 'value' must be the same for each row that is totaled.\nReturns a tuple of two arrays: keys in sorted order, and values \u200b\u200bsummed for the corresponding keys. Example: CREATE TABLE sum_map ( \n date Date , \n timeslot DateTime , \n statusMap Nested ( \n status UInt16 , \n requests UInt64 \n ) ) ENGINE = Log ; INSERT INTO sum_map VALUES \n ( 2000-01-01 , 2000-01-01 00:00:00 , [ 1 , 2 , 3 ], [ 10 , 10 , 10 ]), \n ( 2000-01-01 , 2000-01-01 00:00:00 , [ 3 , 4 , 5 ], [ 10 , 10 , 10 ]), \n ( 2000-01-01 , 2000-01-01 00:01:00 , [ 4 , 5 , 6 ], [ 10 , 10 , 10 ]), \n ( 2000-01-01 , 2000-01-01 00:01:00 , [ 6 , 7 , 8 ], [ 10 , 10 , 10 ]); SELECT \n timeslot , \n sumMap ( statusMap . status , statusMap . requests ) FROM sum_map GROUP BY timeslot \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500timeslot\u2500\u252c\u2500sumMap(statusMap.status, statusMap.requests)\u2500\u2510\n\u2502 2000-01-01 00:00:00 \u2502 ([1,2,3,4,5],[10,10,20,10,10]) \u2502\n\u2502 2000-01-01 00:01:00 \u2502 ([4,5,6,7,8],[10,10,20,10,10]) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "sumMap(key, value)" + }, + { + "location": "/agg_functions/reference/#avgx", + "text": "Calculates the average.\nOnly works for numbers.\nThe result is always Float64.", + "title": "avg(x)" + }, + { + "location": "/agg_functions/reference/#uniqx", + "text": "Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments. Uses an adaptive sampling algorithm: for the calculation state, it uses a sample of element hash values with a size up to 65536.\nThis algorithm is also very accurate for data sets with low cardinality (up to 65536) and very efficient on CPU (when computing not too many of these functions, using uniq is almost as fast as using other aggregate functions). The result is determinate (it doesn't depend on the order of query processing). This function provides excellent accuracy even for data sets with extremely high cardinality (over 10 billion elements). It is recommended for default use.", + "title": "uniq(x)" + }, + { + "location": "/agg_functions/reference/#uniqcombinedx", + "text": "Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments. A combination of three algorithms is used: array, hash table and HyperLogLog with an error correction table. The memory consumption is several times smaller than for the uniq function, and the accuracy is several times higher. Performance is slightly lower than for the uniq function, but sometimes it can be even higher than it, such as with distributed queries that transmit a large number of aggregation states over the network. The maximum state size is 96 KiB (HyperLogLog of 217 6-bit cells). The result is determinate (it doesn't depend on the order of query processing). The uniqCombined function is a good default choice for calculating the number of different values, but keep in mind that the estimation error will increase for high-cardinality data sets (200M+ elements), and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).", + "title": "uniqCombined(x)" + }, + { + "location": "/agg_functions/reference/#uniqhll12x", + "text": "Uses the HyperLogLog algorithm to approximate the number of different values of the argument.\n212 5-bit cells are used. The size of the state is slightly more than 2.5 KB. The result is not very accurate (up to ~10% error) for small data sets ( 10K elements). However, the result is fairly accurate for high-cardinality data sets (10K-100M), with a maximum error of ~1.6%. Starting from 100M, the estimation error increases, and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements). The result is determinate (it doesn't depend on the order of query processing). We don't recommend using this function. In most cases, use the uniq or uniqCombined function.", + "title": "uniqHLL12(x)" + }, + { + "location": "/agg_functions/reference/#uniqexactx", + "text": "Calculates the number of different values of the argument, exactly.\nThere is no reason to fear approximations. It's better to use the uniq function.\nUse the uniqExact function if you definitely need an exact result. The uniqExact function uses more memory than the uniq function, because the size of the state has unbounded growth as the number of different values increases.", + "title": "uniqExact(x)" + }, + { + "location": "/agg_functions/reference/#grouparrayx-grouparraymax_sizex", + "text": "Creates an array of argument values.\nValues can be added to the array in any (indeterminate) order. The second version (with the max_size parameter) limits the size of the resulting array to max_size elements.\nFor example, groupArray (1) (x) is equivalent to [any (x)] . In some cases, you can still rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY .", + "title": "groupArray(x), groupArray(max_size)(x)" + }, + { + "location": "/agg_functions/reference/#grouparrayinsertatx", + "text": "Inserts a value into the array in the specified position. Accepts the value and position as input. If several values \u200b\u200bare inserted into the same position, any of them might end up in the resulting array (the first one will be used in the case of single-threaded execution). If no value is inserted into a position, the position is assigned the default value. Optional parameters: The default value for substituting in empty positions. The length of the resulting array. This allows you to receive arrays of the same size for all the aggregate keys. When using this parameter, the default value must be specified.", + "title": "groupArrayInsertAt(x)" + }, + { + "location": "/agg_functions/reference/#groupuniqarrayx", + "text": "Creates an array from different argument values. Memory consumption is the same as for the uniqExact function.", + "title": "groupUniqArray(x)" + }, + { + "location": "/agg_functions/reference/#quantilelevelx", + "text": "Approximates the 'level' quantile. 'level' is a constant, a floating-point number from 0 to 1.\nWe recommend using a 'level' value in the range of 0.01..0.99\nDon't use a 'level' value equal to 0 or 1 \u2013 use the 'min' and 'max' functions for these cases. In this function, as well as in all functions for calculating quantiles, the 'level' parameter can be omitted. In this case, it is assumed to be equal to 0.5 (in other words, the function will calculate the median). Works for numbers, dates, and dates with times.\nReturns: for numbers \u2013 Float64; for dates \u2013 a date; for dates with times \u2013 a date with time. Uses reservoir sampling with a reservoir size up to 8192.\nIf necessary, the result is output with linear approximation from the two neighboring values.\nThis algorithm provides very low accuracy. See also: quantileTiming , quantileTDigest , quantileExact . The result depends on the order of running the query, and is nondeterministic. When using multiple quantile (and similar) functions with different levels in a query, the internal states are not combined (that is, the query works less efficiently than it could). In this case, use the quantiles (and similar) functions.", + "title": "quantile(level)(x)" + }, + { + "location": "/agg_functions/reference/#quantiledeterministiclevelx-determinator", + "text": "Works the same way as the quantile function, but the result is deterministic and does not depend on the order of query execution. To achieve this, the function takes a second argument \u2013 the \"determinator\". This is a number whose hash is used instead of a random number generator in the reservoir sampling algorithm. For the function to work correctly, the same determinator value should not occur too often. For the determinator, you can use an event ID, user ID, and so on. Don't use this function for calculating timings. There is a more suitable function for this purpose: quantileTiming .", + "title": "quantileDeterministic(level)(x, determinator)" + }, + { + "location": "/agg_functions/reference/#quantiletiminglevelx", + "text": "Computes the quantile of 'level' with a fixed precision.\nWorks for numbers. Intended for calculating quantiles of page loading time in milliseconds. If the value is greater than 30,000 (a page loading time of more than 30 seconds), the result is equated to 30,000. If the total value is not more than about 5670, then the calculation is accurate. Otherwise: if the time is less than 1024 ms, then the calculation is accurate. otherwise the calculation is rounded to a multiple of 16 ms. When passing negative values to the function, the behavior is undefined. The returned value has the Float32 type. If no values were passed to the function (when using quantileTimingIf ), 'nan' is returned. The purpose of this is to differentiate these instances from zeros. See the note on sorting NaNs in \"ORDER BY clause\". The result is determinate (it doesn't depend on the order of query processing). For its purpose (calculating quantiles of page loading times), using this function is more effective and the result is more accurate than for the quantile function.", + "title": "quantileTiming(level)(x)" + }, + { + "location": "/agg_functions/reference/#quantiletimingweightedlevelx-weight", + "text": "Differs from the quantileTiming function in that it has a second argument, \"weights\". Weight is a non-negative integer.\nThe result is calculated as if the x value were passed weight number of times to the quantileTiming function.", + "title": "quantileTimingWeighted(level)(x, weight)" + }, + { + "location": "/agg_functions/reference/#quantileexactlevelx", + "text": "Computes the quantile of 'level' exactly. To do this, all the passed values \u200b\u200bare combined into an array, which is then partially sorted. Therefore, the function consumes O(n) memory, where 'n' is the number of values that were passed. However, for a small number of values, the function is very effective.", + "title": "quantileExact(level)(x)" + }, + { + "location": "/agg_functions/reference/#quantileexactweightedlevelx-weight", + "text": "Computes the quantile of 'level' exactly. In addition, each value is counted with its weight, as if it is present 'weight' times. The arguments of the function can be considered as histograms, where the value 'x' corresponds to a histogram \"column\" of the height 'weight', and the function itself can be considered as a summation of histograms. A hash table is used as the algorithm. Because of this, if the passed values \u200b\u200bare frequently repeated, the function consumes less RAM than quantileExact . You can use this function instead of quantileExact and specify the weight as 1.", + "title": "quantileExactWeighted(level)(x, weight)" + }, + { + "location": "/agg_functions/reference/#quantiletdigestlevelx", + "text": "Approximates the quantile level using the t-digest algorithm. The maximum error is 1%. Memory consumption by State is proportional to the logarithm of the number of passed values. The performance of the function is lower than for quantile , quantileTiming . In terms of the ratio of State size to precision, this function is much better than quantile . The result depends on the order of running the query, and is nondeterministic.", + "title": "quantileTDigest(level)(x)" + }, + { + "location": "/agg_functions/reference/#medianx", + "text": "All the quantile functions have corresponding median functions: median , medianDeterministic , medianTiming , medianTimingWeighted , medianExact , medianExactWeighted , medianTDigest . They are synonyms and their behavior is identical.", + "title": "median(x)" + }, + { + "location": "/agg_functions/reference/#quantileslevel1-level2-x", + "text": "All the quantile functions also have corresponding quantiles functions: quantiles , quantilesDeterministic , quantilesTiming , quantilesTimingWeighted , quantilesExact , quantilesExactWeighted , quantilesTDigest . These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.", + "title": "quantiles(level1, level2, ...)(x)" + }, + { + "location": "/agg_functions/reference/#varsampx", + "text": "Calculates the amount \u03a3((x - x\u0305)^2) / (n - 1) , where n is the sample size and x\u0305 is the average value of x . It represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount. Returns Float64 . When n = 1 , returns +\u221e .", + "title": "varSamp(x)" + }, + { + "location": "/agg_functions/reference/#varpopx", + "text": "Calculates the amount \u03a3((x - x\u0305)^2) / (n - 1) , where n is the sample size and x\u0305 is the average value of x . In other words, dispersion for a set of values. Returns Float64 .", + "title": "varPop(x)" + }, + { + "location": "/agg_functions/reference/#stddevsampx", + "text": "The result is equal to the square root of varSamp(x) .", + "title": "stddevSamp(x)" + }, + { + "location": "/agg_functions/reference/#stddevpopx", + "text": "The result is equal to the square root of varPop(x) .", + "title": "stddevPop(x)" + }, + { + "location": "/agg_functions/reference/#topkncolumn", + "text": "Returns an array of the most frequent values in the specified column. The resulting array is sorted in descending order of frequency of values (not by the values themselves). Implements the Filtered Space-Saving algorithm for analyzing TopK, based on the reduce-and-combine algorithm from Parallel Space Saving . topK(N)(column) This function doesn't provide a guaranteed result. In certain situations, errors might occur and it might return frequent values that aren't the most frequent values. We recommend using the N 10 value; performance is reduced with large N values. Maximum value of N = 65536 . Arguments \n- 'N' is the number of values.\n- ' x ' \u2013 The column. Example Take the OnTime data set and select the three most frequently occurring values in the AirlineID column. SELECT topK ( 3 )( AirlineID ) AS res FROM ontime \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [19393,19790,19805] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "topK(N)(column)" + }, + { + "location": "/agg_functions/reference/#covarsampx-y", + "text": "Calculates the value of \u03a3((x - x\u0305)(y - y\u0305)) / (n - 1) . Returns Float64. When n = 1 , returns +\u221e.", + "title": "covarSamp(x, y)" + }, + { + "location": "/agg_functions/reference/#covarpopx-y", + "text": "Calculates the value of \u03a3((x - x\u0305)(y - y\u0305)) / n .", + "title": "covarPop(x, y)" + }, + { + "location": "/agg_functions/reference/#corrx-y", + "text": "Calculates the Pearson correlation coefficient: \u03a3((x - x\u0305)(y - y\u0305)) / sqrt(\u03a3((x - x\u0305)^2) * \u03a3((y - y\u0305)^2)) .", + "title": "corr(x, y)" + }, + { + "location": "/agg_functions/combinators/", + "text": "Aggregate function combinators\n\n\nThe name of an aggregate function can have a suffix appended to it. This changes the way the aggregate function works.\n\n\n-If\n\n\nThe suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument \u2013 a condition (Uint8 type). The aggregate function processes only the rows that trigger the condition. If the condition was not triggered even once, it returns a default value (usually zeros or empty strings).\n\n\nExamples: \nsumIf(column, cond)\n, \ncountIf(cond)\n, \navgIf(x, cond)\n, \nquantilesTimingIf(level1, level2)(x, cond)\n, \nargMinIf(arg, val, cond)\n and so on.\n\n\nWith conditional aggregate functions, you can calculate aggregates for several conditions at once, without using subqueries and \nJOIN\ns. For example, in Yandex.Metrica, conditional aggregate functions are used to implement the segment comparison functionality.\n\n\n-Array\n\n\nThe -Array suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the 'Array(T)' type (arrays) instead of 'T' type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. When processing arrays, the aggregate function works like the original aggregate function across all array elements.\n\n\nExample 1: \nsumArray(arr)\n - Totals all the elements of all 'arr' arrays. In this example, it could have been written more simply: \nsum(arraySum(arr))\n.\n\n\nExample 2: \nuniqArray(arr)\n \u2013 Count the number of unique elements in all 'arr' arrays. This could be done an easier way: \nuniq(arrayJoin(arr))\n, but it's not always possible to add 'arrayJoin' to a query.\n\n\n-If and -Array can be combined. However, 'Array' must come first, then 'If'. Examples: \nuniqArrayIf(arr, cond)\n, \nquantilesTimingArrayIf(level1, level2)(arr, cond)\n. Due to this order, the 'cond' argument can't be an array.\n\n\n-State\n\n\nIf you apply this combinator, the aggregate function doesn't return the resulting value (such as the number of unique values for the 'uniq' function), but an intermediate state of the aggregation (for \nuniq\n, this is the hash table for calculating the number of unique values). This is an AggregateFunction(...) that can be used for further processing or stored in a table to finish aggregating later. See the sections \"AggregatingMergeTree\" and \"Functions for working with intermediate aggregation states\".\n\n\n-Merge\n\n\nIf you apply this combinator, the aggregate function takes the intermediate aggregation state as an argument, combines the states to finish aggregation, and returns the resulting value.\n\n\n-MergeState.\n\n\nMerges the intermediate aggregation states in the same way as the -Merge combinator. However, it doesn't return the resulting value, but an intermediate aggregation state, similar to the -State combinator.\n\n\n-ForEach\n\n\nConverts an aggregate function for tables into an aggregate function for arrays that aggregates the corresponding array items and returns an array of results. For example, \nsumForEach\n for the arrays \n[1, 2]\n, \n[3, 4, 5]\nand\n[6, 7]\nreturns the result \n[10, 13, 5]\n after adding together the corresponding array items.", + "title": "Aggregate function combinators" + }, + { + "location": "/agg_functions/combinators/#aggregate-function-combinators", + "text": "The name of an aggregate function can have a suffix appended to it. This changes the way the aggregate function works.", + "title": "Aggregate function combinators" + }, + { + "location": "/agg_functions/combinators/#-if", + "text": "The suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument \u2013 a condition (Uint8 type). The aggregate function processes only the rows that trigger the condition. If the condition was not triggered even once, it returns a default value (usually zeros or empty strings). Examples: sumIf(column, cond) , countIf(cond) , avgIf(x, cond) , quantilesTimingIf(level1, level2)(x, cond) , argMinIf(arg, val, cond) and so on. With conditional aggregate functions, you can calculate aggregates for several conditions at once, without using subqueries and JOIN s. For example, in Yandex.Metrica, conditional aggregate functions are used to implement the segment comparison functionality.", + "title": "-If" + }, + { + "location": "/agg_functions/combinators/#-array", + "text": "The -Array suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the 'Array(T)' type (arrays) instead of 'T' type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. When processing arrays, the aggregate function works like the original aggregate function across all array elements. Example 1: sumArray(arr) - Totals all the elements of all 'arr' arrays. In this example, it could have been written more simply: sum(arraySum(arr)) . Example 2: uniqArray(arr) \u2013 Count the number of unique elements in all 'arr' arrays. This could be done an easier way: uniq(arrayJoin(arr)) , but it's not always possible to add 'arrayJoin' to a query. -If and -Array can be combined. However, 'Array' must come first, then 'If'. Examples: uniqArrayIf(arr, cond) , quantilesTimingArrayIf(level1, level2)(arr, cond) . Due to this order, the 'cond' argument can't be an array.", + "title": "-Array" + }, + { + "location": "/agg_functions/combinators/#-state", + "text": "If you apply this combinator, the aggregate function doesn't return the resulting value (such as the number of unique values for the 'uniq' function), but an intermediate state of the aggregation (for uniq , this is the hash table for calculating the number of unique values). This is an AggregateFunction(...) that can be used for further processing or stored in a table to finish aggregating later. See the sections \"AggregatingMergeTree\" and \"Functions for working with intermediate aggregation states\".", + "title": "-State" + }, + { + "location": "/agg_functions/combinators/#-merge", + "text": "If you apply this combinator, the aggregate function takes the intermediate aggregation state as an argument, combines the states to finish aggregation, and returns the resulting value.", + "title": "-Merge" + }, + { + "location": "/agg_functions/combinators/#-mergestate", + "text": "Merges the intermediate aggregation states in the same way as the -Merge combinator. However, it doesn't return the resulting value, but an intermediate aggregation state, similar to the -State combinator.", + "title": "-MergeState." + }, + { + "location": "/agg_functions/combinators/#-foreach", + "text": "Converts an aggregate function for tables into an aggregate function for arrays that aggregates the corresponding array items and returns an array of results. For example, sumForEach for the arrays [1, 2] , [3, 4, 5] and [6, 7] returns the result [10, 13, 5] after adding together the corresponding array items.", + "title": "-ForEach" + }, + { + "location": "/agg_functions/parametric_functions/", + "text": "Parametric aggregate functions\n\n\nSome aggregate functions can accept not only argument columns (used for compression), but a set of parameters \u2013 constants for initialization. The syntax is two pairs of brackets instead of one. The first is for parameters, and the second is for arguments.\n\n\nsequenceMatch(pattern)(time, cond1, cond2, ...)\n\n\nPattern matching for event chains.\n\n\npattern\n is a string containing a pattern to match. The pattern is similar to a regular expression.\n\n\ntime\n is the time of the event with the DateTime type.\n\n\ncond1\n, \ncond2\n ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event.\n\n\nThe function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern.\nIt returns UInt8: 0 if the pattern isn't matched, or 1 if it matches.\n\n\nExample: \nsequenceMatch ('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%')\n\n\n\n\nwhether there was a chain of events in which a pageview with 'company' in the address occurred earlier than a pageview with 'cart' in the address.\n\n\n\n\nThis is a singular example. You could write it using other aggregate functions:\n\n\nminIf(EventTime, URL LIKE \n%company%\n) \n maxIf(EventTime, URL LIKE \n%cart%\n).\n\n\n\n\n\nHowever, there is no such solution for more complex situations.\n\n\nPattern syntax:\n\n\n(?1)\n refers to the condition (any number can be used in place of 1).\n\n\n.*\n is any number of any events.\n\n\n(?t\n=1800)\n is a time condition.\n\n\nAny quantity of any type of events is allowed over the specified time.\n\n\nInstead of \n=\n, the following operators can be used:\n, \n, \n=\n.\n\n\nAny number may be specified in place of 1800.\n\n\nEvents that occur during the same second can be put in the chain in any order. This may affect the result of the function.\n\n\nsequenceCount(pattern)(time, cond1, cond2, ...)\n\n\nWorks the same way as the sequenceMatch function, but instead of returning whether there is an event chain, it returns UInt64 with the number of event chains found.\nChains are searched for without overlapping. In other words, the next chain can start only after the end of the previous one.\n\n\nwindowFunnel(window)(timestamp, cond1, cond2, cond3, ....)\n\n\nWindow funnel matching for event chains, calculates the max event level in a sliding window.\n\n\nwindow\n is the timestamp window value, such as 3600.\n\n\ntimestamp\n is the time of the event with the DateTime type or UInt32 type.\n\n\ncond1\n, \ncond2\n ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event\n\n\nExample: \n\n\nConsider you are doing a website analytics, intend to find out the user counts clicked login button( event = 1001 ), then the user counts followed by searched the phones( event = 1003 and product = 'phone' ) , then the user counts followed by made an order ( event = 1009 ). And all event chains must be in a 3600 seconds sliding window. \n\n\nThis could be easily calculate by \nwindowFunnel\n\n\nSELECT\n level,\n count() AS c\nFROM\n(\n SELECT\n user_id,\n windowFunnel(3600)(timestamp, event_id = 1001, event_id = 1003 AND product = \nphone\n, event_id = 1009) AS level\n FROM trend_event\n WHERE (event_date \n= \n2017-01-01\n) AND (event_date \n= \n2017-01-31\n)\n GROUP BY user_id\n)\nGROUP BY level\nORDER BY level\n\n\n\n\n\nSimply, the level could only be 0,1,2,3, it means the maxium event action stage that one user could reach.\n\n\nuniqUpTo(N)(x)\n\n\nCalculates the number of different argument values \u200b\u200bif it is less than or equal to N. If the number of different argument values is greater than N, it returns N + 1.\n\n\nRecommended for use with small Ns, up to 10. The maximum value of N is 100.\n\n\nFor the state of an aggregate function, it uses the amount of memory equal to 1 + N * the size of one value of bytes.\nFor strings, it stores a non-cryptographic hash of 8 bytes. That is, the calculation is approximated for strings.\n\n\nThe function also works for several arguments.\n\n\nIt works as fast as possible, except for cases when a large N value is used and the number of unique values is slightly less than N.\n\n\nUsage example:\n\n\nProblem: Generate a report that shows only keywords that produced at least 5 unique users.\nSolution: Write in the GROUP BY query SearchPhrase HAVING uniqUpTo(4)(UserID) \n= 5", + "title": "Parametric aggregate functions" + }, + { + "location": "/agg_functions/parametric_functions/#parametric-aggregate-functions", + "text": "Some aggregate functions can accept not only argument columns (used for compression), but a set of parameters \u2013 constants for initialization. The syntax is two pairs of brackets instead of one. The first is for parameters, and the second is for arguments.", + "title": "Parametric aggregate functions" + }, + { + "location": "/agg_functions/parametric_functions/#sequencematchpatterntime-cond1-cond2", + "text": "Pattern matching for event chains. pattern is a string containing a pattern to match. The pattern is similar to a regular expression. time is the time of the event with the DateTime type. cond1 , cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event. The function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern.\nIt returns UInt8: 0 if the pattern isn't matched, or 1 if it matches. Example: sequenceMatch ('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%') whether there was a chain of events in which a pageview with 'company' in the address occurred earlier than a pageview with 'cart' in the address. This is a singular example. You could write it using other aggregate functions: minIf(EventTime, URL LIKE %company% ) maxIf(EventTime, URL LIKE %cart% ). However, there is no such solution for more complex situations. Pattern syntax: (?1) refers to the condition (any number can be used in place of 1). .* is any number of any events. (?t =1800) is a time condition. Any quantity of any type of events is allowed over the specified time. Instead of = , the following operators can be used: , , = . Any number may be specified in place of 1800. Events that occur during the same second can be put in the chain in any order. This may affect the result of the function.", + "title": "sequenceMatch(pattern)(time, cond1, cond2, ...)" + }, + { + "location": "/agg_functions/parametric_functions/#sequencecountpatterntime-cond1-cond2", + "text": "Works the same way as the sequenceMatch function, but instead of returning whether there is an event chain, it returns UInt64 with the number of event chains found.\nChains are searched for without overlapping. In other words, the next chain can start only after the end of the previous one.", + "title": "sequenceCount(pattern)(time, cond1, cond2, ...)" + }, + { + "location": "/agg_functions/parametric_functions/#windowfunnelwindowtimestamp-cond1-cond2-cond3", + "text": "Window funnel matching for event chains, calculates the max event level in a sliding window. window is the timestamp window value, such as 3600. timestamp is the time of the event with the DateTime type or UInt32 type. cond1 , cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event Example: Consider you are doing a website analytics, intend to find out the user counts clicked login button( event = 1001 ), then the user counts followed by searched the phones( event = 1003 and product = 'phone' ) , then the user counts followed by made an order ( event = 1009 ). And all event chains must be in a 3600 seconds sliding window. This could be easily calculate by windowFunnel SELECT\n level,\n count() AS c\nFROM\n(\n SELECT\n user_id,\n windowFunnel(3600)(timestamp, event_id = 1001, event_id = 1003 AND product = phone , event_id = 1009) AS level\n FROM trend_event\n WHERE (event_date = 2017-01-01 ) AND (event_date = 2017-01-31 )\n GROUP BY user_id\n)\nGROUP BY level\nORDER BY level Simply, the level could only be 0,1,2,3, it means the maxium event action stage that one user could reach.", + "title": "windowFunnel(window)(timestamp, cond1, cond2, cond3, ....)" + }, + { + "location": "/agg_functions/parametric_functions/#uniquptonx", + "text": "Calculates the number of different argument values \u200b\u200bif it is less than or equal to N. If the number of different argument values is greater than N, it returns N + 1. Recommended for use with small Ns, up to 10. The maximum value of N is 100. For the state of an aggregate function, it uses the amount of memory equal to 1 + N * the size of one value of bytes.\nFor strings, it stores a non-cryptographic hash of 8 bytes. That is, the calculation is approximated for strings. The function also works for several arguments. It works as fast as possible, except for cases when a large N value is used and the number of unique values is slightly less than N. Usage example: Problem: Generate a report that shows only keywords that produced at least 5 unique users.\nSolution: Write in the GROUP BY query SearchPhrase HAVING uniqUpTo(4)(UserID) = 5", + "title": "uniqUpTo(N)(x)" + }, + { + "location": "/dicts/", + "text": "Dictionaries\n\n\nA dictionary\n is a mapping (key \n-\n attributes) that can be used in a query as functions.\nYou can think of this as a more convenient and efficient type of JOIN with dimension tables.\n\n\nThere are built-in (internal) and add-on (external) dictionaries.", + "title": "Introduction" + }, + { + "location": "/dicts/#dictionaries", + "text": "A dictionary is a mapping (key - attributes) that can be used in a query as functions.\nYou can think of this as a more convenient and efficient type of JOIN with dimension tables. There are built-in (internal) and add-on (external) dictionaries.", + "title": "Dictionaries" + }, + { + "location": "/dicts/external_dicts/", + "text": "External dictionaries\n\n\nYou can add your own dictionaries from various data sources. The data source for a dictionary can be a local text or executable file, an HTTP(s) resource, or another DBMS. For more information, see \"\nSources for external dictionaries\n\".\n\n\nClickHouse:\n\n\n\n\n\n\nFully or partially stores dictionaries in RAM.\n\n\nPeriodically updates dictionaries and dynamically loads missing values. In other words, dictionaries can be loaded dynamically.\n\n\n\n\n\n\nThe configuration of external dictionaries is located in one or more files. The path to the configuration is specified in the \ndictionaries_config\n parameter.\n\n\nDictionaries can be loaded at server startup or at first use, depending on the \ndictionaries_lazy_load\n setting.\n\n\nThe dictionary config file has the following format:\n\n\nyandex\n\n \ncomment\nAn optional element with any content. Ignored by the ClickHouse server.\n/comment\n\n\n \n!--Optional element. File name with substitutions--\n\n \ninclude_from\n/etc/metrika.xml\n/include_from\n\n\n\n \ndictionary\n\n \n!-- Dictionary configuration --\n\n \n/dictionary\n\n\n ...\n\n \ndictionary\n\n \n!-- Dictionary configuration --\n\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\nYou can \nconfigure\n any number of dictionaries in the same file. The file format is preserved even if there is only one dictionary (i.e. \nyandex\ndictionary\n \n!--configuration -\n \n/dictionary\n/yandex\n ).\n\n\nSee also \"\nFunctions for working with external dictionaries\n\".\n\n\n\n\nYou can convert values \u200b\u200bfor a small dictionary by describing it in a `SELECT` query (see the [transform](../functions/other_functions.md#other_functions-transform) function). This functionality is not related to external dictionaries.", + "title": "General desription" + }, + { + "location": "/dicts/external_dicts/#external-dictionaries", + "text": "You can add your own dictionaries from various data sources. The data source for a dictionary can be a local text or executable file, an HTTP(s) resource, or another DBMS. For more information, see \" Sources for external dictionaries \". ClickHouse: Fully or partially stores dictionaries in RAM. Periodically updates dictionaries and dynamically loads missing values. In other words, dictionaries can be loaded dynamically. The configuration of external dictionaries is located in one or more files. The path to the configuration is specified in the dictionaries_config parameter. Dictionaries can be loaded at server startup or at first use, depending on the dictionaries_lazy_load setting. The dictionary config file has the following format: yandex \n comment An optional element with any content. Ignored by the ClickHouse server. /comment \n\n !--Optional element. File name with substitutions-- \n include_from /etc/metrika.xml /include_from \n\n\n dictionary \n !-- Dictionary configuration -- \n /dictionary \n\n ...\n\n dictionary \n !-- Dictionary configuration -- \n /dictionary /yandex You can configure any number of dictionaries in the same file. The file format is preserved even if there is only one dictionary (i.e. yandex dictionary !--configuration - /dictionary /yandex ). See also \" Functions for working with external dictionaries \". \n\nYou can convert values \u200b\u200bfor a small dictionary by describing it in a `SELECT` query (see the [transform](../functions/other_functions.md#other_functions-transform) function). This functionality is not related to external dictionaries.", + "title": "External dictionaries" + }, + { + "location": "/dicts/external_dicts_dict/", + "text": "Configuring an external dictionary\n\n\nThe dictionary configuration has the following structure:\n\n\ndictionary\n\n \nname\ndict_name\n/name\n\n\n \nsource\n\n \n!-- Source configuration --\n\n \n/source\n\n\n \nlayout\n\n \n!-- Memory layout configuration --\n\n \n/layout\n\n\n \nstructure\n\n \n!-- Complex key configuration --\n\n \n/structure\n\n\n \nlifetime\n\n \n!-- Lifetime of dictionary in memory --\n\n \n/lifetime\n\n\n/dictionary\n\n\n\n\n\n\n\n\nname \u2013 The identifier that can be used to access the dictionary. Use the characters \n[a-zA-Z0-9_\\-]\n.\n\n\nsource\n \u2014 Source of the dictionary.\n\n\nlayout\n \u2014 Dictionary layout in memory.\n\n\nstructure\n \u2014 Structure of the dictionary . A key and attributes that can be retrieved by this key.\n\n\nlifetime\n \u2014 Frequency of dictionary updates.", + "title": "Configuring an external dictionary" + }, + { + "location": "/dicts/external_dicts_dict/#configuring-an-external-dictionary", + "text": "The dictionary configuration has the following structure: dictionary \n name dict_name /name \n\n source \n !-- Source configuration -- \n /source \n\n layout \n !-- Memory layout configuration -- \n /layout \n\n structure \n !-- Complex key configuration -- \n /structure \n\n lifetime \n !-- Lifetime of dictionary in memory -- \n /lifetime /dictionary name \u2013 The identifier that can be used to access the dictionary. Use the characters [a-zA-Z0-9_\\-] . source \u2014 Source of the dictionary. layout \u2014 Dictionary layout in memory. structure \u2014 Structure of the dictionary . A key and attributes that can be retrieved by this key. lifetime \u2014 Frequency of dictionary updates.", + "title": "Configuring an external dictionary" + }, + { + "location": "/dicts/external_dicts_dict_layout/", + "text": "Storing dictionaries in memory\n\n\nThere are a \nvariety of ways\n to store dictionaries in memory.\n\n\nWe recommend \nflat\n, \nhashed\nand\ncomplex_key_hashed\n. which provide optimal processing speed.\n\n\nCaching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section \"\ncache\n\".\n\n\nThere are several ways to improve dictionary performance:\n\n\n\n\nCall the function for working with the dictionary after \nGROUP BY\n.\n\n\nMark attributes to extract as injective. An attribute is called injective if different attribute values correspond to different keys. So when \nGROUP BY\n uses a function that fetches an attribute value by the key, this function is automatically taken out of \nGROUP BY\n.\n\n\n\n\nClickHouse generates an exception for errors with dictionaries. Examples of errors:\n\n\n\n\nThe dictionary being accessed could not be loaded.\n\n\nError querying a \ncached\n dictionary.\n\n\n\n\nYou can view the list of external dictionaries and their statuses in the \nsystem.dictionaries\n table.\n\n\nThe configuration looks like this:\n\n\nyandex\n\n \ndictionary\n\n ...\n \nlayout\n\n \nlayout_type\n\n \n!-- layout settings --\n\n \n/layout_type\n\n \n/layout\n\n ...\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\n\n\nWays to store dictionaries in memory\n\n\n\n\nflat\n\n\nhashed\n\n\ncache\n\n\nrange_hashed\n\n\ncomplex_key_hashed\n\n\ncomplex_key_cache\n\n\nip_trie\n\n\n\n\n\n\nflat\n\n\nThe dictionary is completely stored in memory in the form of flat arrays. How much memory does the dictionary use? The amount is proportional to the size of the largest key (in space used).\n\n\nThe dictionary key has the \nUInt64\n type and the value is limited to 500,000. If a larger key is discovered when creating the dictionary, ClickHouse throws an exception and does not create the dictionary.\n\n\nAll types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.\n\n\nThis method provides the best performance among all available methods of storing the dictionary.\n\n\nConfiguration example:\n\n\nlayout\n\n \nflat\n \n/\n\n\n/layout\n\n\n\n\n\n\n\n\nhashed\n\n\nThe dictionary is completely stored in memory in the form of a hash table. The dictionary can contain any number of elements with any identifiers In practice, the number of keys can reach tens of millions of items.\n\n\nAll types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.\n\n\nConfiguration example:\n\n\nlayout\n\n \nhashed\n \n/\n\n\n/layout\n\n\n\n\n\n\n\n\ncomplex_key_hashed\n\n\nThis type of storage is for use with composite \nkeys\n. Similar to \nhashed\n.\n\n\nConfiguration example:\n\n\nlayout\n\n \ncomplex_key_hashed\n \n/\n\n\n/layout\n\n\n\n\n\n\n\n\nrange_hashed\n\n\nThe dictionary is stored in memory in the form of a hash table with an ordered array of ranges and their corresponding values.\n\n\nThis storage method works the same way as hashed and allows using date/time ranges in addition to the key, if they appear in the dictionary.\n\n\nExample: The table contains discounts for each advertiser in the format:\n\n\n+---------------+---------------------+-------------------+--------+\n| advertiser id | discount start date | discount end date | amount |\n+===============+=====================+===================+========+\n| 123 | 2015-01-01 | 2015-01-15 | 0.15 |\n+---------------+---------------------+-------------------+--------+\n| 123 | 2015-01-16 | 2015-01-31 | 0.25 |\n+---------------+---------------------+-------------------+--------+\n| 456 | 2015-01-01 | 2015-01-15 | 0.05 |\n+---------------+---------------------+-------------------+--------+\n\n\n\n\n\nTo use a sample for date ranges, define the \nrange_min\n and \nrange_max\n elements in the \nstructure\n.\n\n\nExample:\n\n\nstructure\n\n \nid\n\n \nname\nId\n/name\n\n \n/id\n\n \nrange_min\n\n \nname\nfirst\n/name\n\n \n/range_min\n\n \nrange_max\n\n \nname\nlast\n/name\n\n \n/range_max\n\n ...\n\n\n\n\n\nTo work with these dictionaries, you need to pass an additional date argument to the \ndictGetT\n function:\n\n\ndictGetT(\ndict_name\n, \nattr_name\n, id, date)\n\n\n\n\n\nThis function returns the value for the specified \nid\ns and the date range that includes the passed date.\n\n\nDetails of the algorithm:\n\n\n\n\nIf the \nid\n is not found or a range is not found for the \nid\n, it returns the default value for the dictionary.\n\n\nIf there are overlapping ranges, you can use any.\n\n\nIf the range delimiter is \nNULL\n or an invalid date (such as 1900-01-01 or 2039-01-01), the range is left open. The range can be open on both sides.\n\n\n\n\nConfiguration example:\n\n\nyandex\n\n \ndictionary\n\n\n ...\n\n \nlayout\n\n \nrange_hashed\n \n/\n\n \n/layout\n\n\n \nstructure\n\n \nid\n\n \nname\nAbcdef\n/name\n\n \n/id\n\n \nrange_min\n\n \nname\nStartDate\n/name\n\n \n/range_min\n\n \nrange_max\n\n \nname\nEndDate\n/name\n\n \n/range_max\n\n \nattribute\n\n \nname\nXXXType\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n \n/\n\n \n/attribute\n\n \n/structure\n\n\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\n\n\ncache\n\n\nThe dictionary is stored in a cache that has a fixed number of cells. These cells contain frequently used elements.\n\n\nWhen searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using \nSELECT attrs... FROM db.table WHERE id IN (k1, k2, ...)\n. The received data is then written to the cache.\n\n\nFor cache dictionaries, the expiration \nlifetime\n of data in the cache can be set. If more time than \nlifetime\n has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used.\n\n\nThis is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the \nsystem.dictionaries\n table.\n\n\nTo improve cache performance, use a subquery with \nLIMIT\n, and call the function with the dictionary externally.\n\n\nSupported \nsources\n: MySQL, ClickHouse, executable, HTTP.\n\n\nExample of settings:\n\n\nlayout\n\n \ncache\n\n \n!-- The size of the cache, in number of cells. Rounded up to a power of two. --\n\n \nsize_in_cells\n1000000000\n/size_in_cells\n\n \n/cache\n\n\n/layout\n\n\n\n\n\n\nSet a large enough cache size. You need to experiment to select the number of cells:\n\n\n\n\nSet some value.\n\n\nRun queries until the cache is completely full.\n\n\nAssess memory consumption using the \nsystem.dictionaries\n table.\n\n\nIncrease or decrease the number of cells until the required memory consumption is reached.\n\n\n\n\n\n\nDo not use ClickHouse as a source, because it is slow to process queries with random reads.\n\n\n\n\n\n\n\ncomplex_key_cache\n\n\nThis type of storage is for use with composite \nkeys\n. Similar to \ncache\n.\n\n\n\n\nip_trie\n\n\nThis type of storage is for mapping network prefixes (IP addresses) to metadata such as ASN.\n\n\nExample: The table contains network prefixes and their corresponding AS number and country code:\n\n\n +-----------------+-------+--------+\n | prefix | asn | cca2 |\n +=================+=======+========+\n | 202.79.32.0/20 | 17501 | NP |\n +-----------------+-------+--------+\n | 2620:0:870::/48 | 3856 | US |\n +-----------------+-------+--------+\n | 2a02:6b8:1::/48 | 13238 | RU |\n +-----------------+-------+--------+\n | 2001:db8::/32 | 65536 | ZZ |\n +-----------------+-------+--------+\n\n\n\n\n\nWhen using this type of layout, the structure must have a composite key.\n\n\nExample:\n\n\nstructure\n\n \nkey\n\n \nattribute\n\n \nname\nprefix\n/name\n\n \ntype\nString\n/type\n\n \n/attribute\n\n \n/key\n\n \nattribute\n\n \nname\nasn\n/name\n\n \ntype\nUInt32\n/type\n\n \nnull_value\n \n/\n\n \n/attribute\n\n \nattribute\n\n \nname\ncca2\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n??\n/null_value\n\n \n/attribute\n\n ...\n\n\n\n\n\nThe key must have only one String type attribute that contains an allowed IP prefix. Other types are not supported yet.\n\n\nFor queries, you must use the same functions (\ndictGetT\n with a tuple) as for dictionaries with composite keys:\n\n\ndictGetT(\ndict_name\n, \nattr_name\n, tuple(ip))\n\n\n\n\n\nThe function takes either \nUInt32\n for IPv4, or \nFixedString(16)\n for IPv6:\n\n\ndictGetString(\nprefix\n, \nasn\n, tuple(IPv6StringToNum(\n2001:db8::1\n)))\n\n\n\n\n\nOther types are not supported yet. The function returns the attribute for the prefix that corresponds to this IP address. If there are overlapping prefixes, the most specific one is returned.\n\n\nData is stored in a \ntrie\n. It must completely fit into RAM.", + "title": "Storing dictionaries in memory" + }, + { + "location": "/dicts/external_dicts_dict_layout/#storing-dictionaries-in-memory", + "text": "There are a variety of ways to store dictionaries in memory. We recommend flat , hashed and complex_key_hashed . which provide optimal processing speed. Caching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section \" cache \". There are several ways to improve dictionary performance: Call the function for working with the dictionary after GROUP BY . Mark attributes to extract as injective. An attribute is called injective if different attribute values correspond to different keys. So when GROUP BY uses a function that fetches an attribute value by the key, this function is automatically taken out of GROUP BY . ClickHouse generates an exception for errors with dictionaries. Examples of errors: The dictionary being accessed could not be loaded. Error querying a cached dictionary. You can view the list of external dictionaries and their statuses in the system.dictionaries table. The configuration looks like this: yandex \n dictionary \n ...\n layout \n layout_type \n !-- layout settings -- \n /layout_type \n /layout \n ...\n /dictionary /yandex", + "title": "Storing dictionaries in memory" + }, + { + "location": "/dicts/external_dicts_dict_layout/#ways-to-store-dictionaries-in-memory", + "text": "flat hashed cache range_hashed complex_key_hashed complex_key_cache ip_trie", + "title": "Ways to store dictionaries in memory" + }, + { + "location": "/dicts/external_dicts_dict_layout/#flat", + "text": "The dictionary is completely stored in memory in the form of flat arrays. How much memory does the dictionary use? The amount is proportional to the size of the largest key (in space used). The dictionary key has the UInt64 type and the value is limited to 500,000. If a larger key is discovered when creating the dictionary, ClickHouse throws an exception and does not create the dictionary. All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety. This method provides the best performance among all available methods of storing the dictionary. Configuration example: layout \n flat / /layout", + "title": "flat" + }, + { + "location": "/dicts/external_dicts_dict_layout/#hashed", + "text": "The dictionary is completely stored in memory in the form of a hash table. The dictionary can contain any number of elements with any identifiers In practice, the number of keys can reach tens of millions of items. All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety. Configuration example: layout \n hashed / /layout", + "title": "hashed" + }, + { + "location": "/dicts/external_dicts_dict_layout/#complex_key_hashed", + "text": "This type of storage is for use with composite keys . Similar to hashed . Configuration example: layout \n complex_key_hashed / /layout", + "title": "complex_key_hashed" + }, + { + "location": "/dicts/external_dicts_dict_layout/#range_hashed", + "text": "The dictionary is stored in memory in the form of a hash table with an ordered array of ranges and their corresponding values. This storage method works the same way as hashed and allows using date/time ranges in addition to the key, if they appear in the dictionary. Example: The table contains discounts for each advertiser in the format: +---------------+---------------------+-------------------+--------+\n| advertiser id | discount start date | discount end date | amount |\n+===============+=====================+===================+========+\n| 123 | 2015-01-01 | 2015-01-15 | 0.15 |\n+---------------+---------------------+-------------------+--------+\n| 123 | 2015-01-16 | 2015-01-31 | 0.25 |\n+---------------+---------------------+-------------------+--------+\n| 456 | 2015-01-01 | 2015-01-15 | 0.05 |\n+---------------+---------------------+-------------------+--------+ To use a sample for date ranges, define the range_min and range_max elements in the structure . Example: structure \n id \n name Id /name \n /id \n range_min \n name first /name \n /range_min \n range_max \n name last /name \n /range_max \n ... To work with these dictionaries, you need to pass an additional date argument to the dictGetT function: dictGetT( dict_name , attr_name , id, date) This function returns the value for the specified id s and the date range that includes the passed date. Details of the algorithm: If the id is not found or a range is not found for the id , it returns the default value for the dictionary. If there are overlapping ranges, you can use any. If the range delimiter is NULL or an invalid date (such as 1900-01-01 or 2039-01-01), the range is left open. The range can be open on both sides. Configuration example: yandex \n dictionary \n\n ...\n\n layout \n range_hashed / \n /layout \n\n structure \n id \n name Abcdef /name \n /id \n range_min \n name StartDate /name \n /range_min \n range_max \n name EndDate /name \n /range_max \n attribute \n name XXXType /name \n type String /type \n null_value / \n /attribute \n /structure \n\n /dictionary /yandex", + "title": "range_hashed" + }, + { + "location": "/dicts/external_dicts_dict_layout/#cache", + "text": "The dictionary is stored in a cache that has a fixed number of cells. These cells contain frequently used elements. When searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using SELECT attrs... FROM db.table WHERE id IN (k1, k2, ...) . The received data is then written to the cache. For cache dictionaries, the expiration lifetime of data in the cache can be set. If more time than lifetime has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used. This is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the system.dictionaries table. To improve cache performance, use a subquery with LIMIT , and call the function with the dictionary externally. Supported sources : MySQL, ClickHouse, executable, HTTP. Example of settings: layout \n cache \n !-- The size of the cache, in number of cells. Rounded up to a power of two. -- \n size_in_cells 1000000000 /size_in_cells \n /cache /layout Set a large enough cache size. You need to experiment to select the number of cells: Set some value. Run queries until the cache is completely full. Assess memory consumption using the system.dictionaries table. Increase or decrease the number of cells until the required memory consumption is reached. \n\nDo not use ClickHouse as a source, because it is slow to process queries with random reads.", + "title": "cache" + }, + { + "location": "/dicts/external_dicts_dict_layout/#complex_key_cache", + "text": "This type of storage is for use with composite keys . Similar to cache .", + "title": "complex_key_cache" + }, + { + "location": "/dicts/external_dicts_dict_layout/#ip_trie", + "text": "This type of storage is for mapping network prefixes (IP addresses) to metadata such as ASN. Example: The table contains network prefixes and their corresponding AS number and country code: +-----------------+-------+--------+\n | prefix | asn | cca2 |\n +=================+=======+========+\n | 202.79.32.0/20 | 17501 | NP |\n +-----------------+-------+--------+\n | 2620:0:870::/48 | 3856 | US |\n +-----------------+-------+--------+\n | 2a02:6b8:1::/48 | 13238 | RU |\n +-----------------+-------+--------+\n | 2001:db8::/32 | 65536 | ZZ |\n +-----------------+-------+--------+ When using this type of layout, the structure must have a composite key. Example: structure \n key \n attribute \n name prefix /name \n type String /type \n /attribute \n /key \n attribute \n name asn /name \n type UInt32 /type \n null_value / \n /attribute \n attribute \n name cca2 /name \n type String /type \n null_value ?? /null_value \n /attribute \n ... The key must have only one String type attribute that contains an allowed IP prefix. Other types are not supported yet. For queries, you must use the same functions ( dictGetT with a tuple) as for dictionaries with composite keys: dictGetT( dict_name , attr_name , tuple(ip)) The function takes either UInt32 for IPv4, or FixedString(16) for IPv6: dictGetString( prefix , asn , tuple(IPv6StringToNum( 2001:db8::1 ))) Other types are not supported yet. The function returns the attribute for the prefix that corresponds to this IP address. If there are overlapping prefixes, the most specific one is returned. Data is stored in a trie . It must completely fit into RAM.", + "title": "ip_trie" + }, + { + "location": "/dicts/external_dicts_dict_lifetime/", + "text": "Dictionary updates\n\n\nClickHouse periodically updates the dictionaries. The update interval for fully downloaded dictionaries and the invalidation interval for cached dictionaries are defined in the \nlifetime\n tag in seconds.\n\n\nDictionary updates (other than loading for first use) do not block queries. During updates, the old version of a dictionary is used. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.\n\n\nExample of settings:\n\n\ndictionary\n\n ...\n \nlifetime\n300\n/lifetime\n\n ...\n\n/dictionary\n\n\n\n\n\n\nSetting \nlifetime\n 0\n/lifetime\n prevents updating dictionaries.\n\n\nYou can set a time interval for upgrades, and ClickHouse will choose a uniformly random time within this range. This is necessary in order to distribute the load on the dictionary source when upgrading on a large number of servers.\n\n\nExample of settings:\n\n\ndictionary\n\n ...\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n ...\n\n/dictionary\n\n\n\n\n\n\nWhen upgrading the dictionaries, the ClickHouse server applies different logic depending on the type of \n source\n:\n\n\n\n\n\n\nFor a text file, it checks the time of modification. If the time differs from the previously recorded time, the dictionary is updated.\n\n\nFor MyISAM tables, the time of modification is checked using a \nSHOW TABLE STATUS\n query.\n\n\nDictionaries from other sources are updated every time by default.\n\n\n\n\n\n\nFor MySQL (InnoDB) and ODBC sources, you can set up a query that will update the dictionaries only if they really changed, rather than each time. To do this, follow these steps:\n\n\n\n\n\n\nThe dictionary table must have a field that always changes when the source data is updated.\n\n\nThe settings of the source must specify a query that retrieves the changing field. The ClickHouse server interprets the query result as a row, and if this row has changed relative to its previous state, the dictionary is updated. Specify the query in the \ninvalidate_query\n field in the settings for the \nsource\n.\n\n\n\n\n\n\nExample of settings:\n\n\ndictionary\n\n ...\n \nodbc\n\n ...\n \ninvalidate_query\nSELECT update_time FROM dictionary_source where id = 1\n/invalidate_query\n\n \n/odbc\n\n ...\n\n/dictionary", + "title": "Dictionary updates" + }, + { + "location": "/dicts/external_dicts_dict_lifetime/#dictionary-updates", + "text": "ClickHouse periodically updates the dictionaries. The update interval for fully downloaded dictionaries and the invalidation interval for cached dictionaries are defined in the lifetime tag in seconds. Dictionary updates (other than loading for first use) do not block queries. During updates, the old version of a dictionary is used. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries. Example of settings: dictionary \n ...\n lifetime 300 /lifetime \n ... /dictionary Setting lifetime 0 /lifetime prevents updating dictionaries. You can set a time interval for upgrades, and ClickHouse will choose a uniformly random time within this range. This is necessary in order to distribute the load on the dictionary source when upgrading on a large number of servers. Example of settings: dictionary \n ...\n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n ... /dictionary When upgrading the dictionaries, the ClickHouse server applies different logic depending on the type of source : For a text file, it checks the time of modification. If the time differs from the previously recorded time, the dictionary is updated. For MyISAM tables, the time of modification is checked using a SHOW TABLE STATUS query. Dictionaries from other sources are updated every time by default. For MySQL (InnoDB) and ODBC sources, you can set up a query that will update the dictionaries only if they really changed, rather than each time. To do this, follow these steps: The dictionary table must have a field that always changes when the source data is updated. The settings of the source must specify a query that retrieves the changing field. The ClickHouse server interprets the query result as a row, and if this row has changed relative to its previous state, the dictionary is updated. Specify the query in the invalidate_query field in the settings for the source . Example of settings: dictionary \n ...\n odbc \n ...\n invalidate_query SELECT update_time FROM dictionary_source where id = 1 /invalidate_query \n /odbc \n ... /dictionary", + "title": "Dictionary updates" + }, + { + "location": "/dicts/external_dicts_dict_sources/", + "text": "Sources of external dictionaries\n\n\nAn external dictionary can be connected from many different sources.\n\n\nThe configuration looks like this:\n\n\nyandex\n\n \ndictionary\n\n ...\n \nsource\n\n \nsource_type\n\n \n!-- Source configuration --\n\n \n/source_type\n\n \n/source\n\n ...\n \n/dictionary\n\n ...\n\n/yandex\n\n\n\n\n\n\nThe source is configured in the \nsource\n section.\n\n\nTypes of sources (\nsource_type\n):\n\n\n\n\nLocal file\n\n\nExecutable file\n\n\nHTTP(s)\n\n\nODBC\n\n\nDBMS\n\n\nMySQL\n\n\nClickHouse\n\n\nMongoDB\n\n\n\n\n\n\nLocal file\n\n\nExample of settings:\n\n\nsource\n\n \nfile\n\n \npath\n/opt/dictionaries/os.tsv\n/path\n\n \nformat\nTabSeparated\n/format\n\n \n/file\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\npath\n \u2013 The absolute path to the file.\n\n\nformat\n \u2013 The file format. All the formats described in \"\nFormats\n\" are supported.\n\n\n\n\n\n\nExecutable file\n\n\nWorking with executable files depends on \nhow the dictionary is stored in memory\n. If the dictionary is stored using \ncache\n and \ncomplex_key_cache\n, ClickHouse requests the necessary keys by sending a request to the executable file's \nSTDIN\n.\n\n\nExample of settings:\n\n\nsource\n\n \nexecutable\n\n \ncommand\ncat /opt/dictionaries/os.tsv\n/command\n\n \nformat\nTabSeparated\n/format\n\n \n/executable\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\ncommand\n \u2013 The absolute path to the executable file, or the file name (if the program directory is written to \nPATH\n).\n\n\nformat\n \u2013 The file format. All the formats described in \"\nFormats\n\" are supported.\n\n\n\n\n\n\nHTTP(s)\n\n\nWorking with an HTTP(s) server depends on \nhow the dictionary is stored in memory\n. If the dictionary is stored using \ncache\n and \ncomplex_key_cache\n, ClickHouse requests the necessary keys by sending a request via the \nPOST\n method.\n\n\nExample of settings:\n\n\nsource\n\n \nhttp\n\n \nurl\nhttp://[::1]/os.tsv\n/url\n\n \nformat\nTabSeparated\n/format\n\n \n/http\n\n\n/source\n\n\n\n\n\n\nIn order for ClickHouse to access an HTTPS resource, you must \nconfigure openSSL\n in the server configuration.\n\n\nSetting fields:\n\n\n\n\nurl\n \u2013 The source URL.\n\n\nformat\n \u2013 The file format. All the formats described in \"\nFormats\n\" are supported.\n\n\n\n\n\n\nODBC\n\n\nYou can use this method to connect any database that has an ODBC driver.\n\n\nExample of settings:\n\n\nodbc\n\n \ndb\nDatabaseName\n/db\n\n \ntable\nTableName\n/table\n\n \nconnection_string\nDSN=some_parameters\n/connection_string\n\n \ninvalidate_query\nSQL_QUERY\n/invalidate_query\n\n\n/odbc\n\n\n\n\n\n\nSetting fields:\n\n\n\n\ndb\n \u2013 Name of the database. Omit it if the database name is set in the \nconnection_string\n parameters.\n\n\ntable\n \u2013 Name of the table.\n\n\nconnection_string\n \u2013 Connection string.\n\n\ninvalidate_query\n \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section \nUpdating dictionaries\n.\n\n\n\n\nExample of connecting PostgreSQL\n\n\nUbuntu OS.\n\n\nInstalling unixODBC and the ODBC driver for PostgreSQL:\n\n\nsudo apt-get install -y unixodbc odbcinst odbc-postgresql\n\n\n\n\n\nConfiguring \n/etc/odbc.ini\n (or \n~/.odbc.ini\n):\n\n\n [DEFAULT]\n Driver = myconnection\n\n [myconnection]\n Description = PostgreSQL connection to my_db\n Driver = PostgreSQL Unicode\n Database = my_db\n Servername = 127.0.0.1\n UserName = username\n Password = password\n Port = 5432\n Protocol = 9.3\n ReadOnly = No\n RowVersioning = No\n ShowSystemTables = No\n ConnSettings =\n\n\n\n\n\nThe dictionary configuration in ClickHouse:\n\n\ndictionary\n\n \nname\ntable_name\n/name\n\n \nsource\n\n \nodbc\n\n \n!-- You can specifiy the following parameters in connection_string: --\n\n \n!-- DSN=myconnection;UID=username;PWD=password;HOST=127.0.0.1;PORT=5432;DATABASE=my_db --\n\n \nconnection_string\nDSN=myconnection\n/connection_string\n\n \ntable\npostgresql_table\n/table\n\n \n/odbc\n\n \n/source\n\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n \nlayout\n\n \nhashed/\n\n \n/layout\n\n \nstructure\n\n \nid\n\n \nname\nid\n/name\n\n \n/id\n\n \nattribute\n\n \nname\nsome_column\n/name\n\n \ntype\nUInt64\n/type\n\n \nnull_value\n0\n/null_value\n\n \n/attribute\n\n \n/structure\n\n\n/dictionary\n\n\n\n\n\n\nYou may need to edit \nodbc.ini\n to specify the full path to the library with the driver \nDRIVER=/usr/local/lib/psqlodbcw.so\n.\n\n\nExample of connecting MS SQL Server\n\n\nUbuntu OS.\n\n\nInstalling the driver: :\n\n\n sudo apt-get install tdsodbc freetds-bin sqsh\n\n\n\n\n\nConfiguring the driver: :\n\n\n $ cat /etc/freetds/freetds.conf \n ...\n\n [MSSQL]\n host = 192.168.56.101\n port = 1433\n tds version = 7.0\n client charset = UTF-8\n\n $ cat /etc/odbcinst.ini \n ...\n\n [FreeTDS]\n Description = FreeTDS\n Driver = /usr/lib/x86_64-linux-gnu/odbc/libtdsodbc.so\n Setup = /usr/lib/x86_64-linux-gnu/odbc/libtdsS.so\n FileUsage = 1\n UsageCount = 5\n\n $ cat ~/.odbc.ini \n ...\n\n [MSSQL]\n Description = FreeTDS\n Driver = FreeTDS\n Servername = MSSQL\n Database = test\n UID = test\n PWD = test\n Port = 1433\n\n\n\n\n\nConfiguring the dictionary in ClickHouse:\n\n\nyandex\n\n \ndictionary\n\n \nname\ntest\n/name\n\n \nsource\n\n \nodbc\n\n \ntable\ndict\n/table\n\n \nconnection_string\nDSN=MSSQL;UID=test;PWD=test\n/connection_string\n\n \n/odbc\n\n \n/source\n\n\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n\n \nlayout\n\n \nflat\n \n/\n\n \n/layout\n\n\n \nstructure\n\n \nid\n\n \nname\nk\n/name\n\n \n/id\n\n \nattribute\n\n \nname\ns\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n/null_value\n\n \n/attribute\n\n \n/structure\n\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\nDBMS\n\n\n\n\nMySQL\n\n\nExample of settings:\n\n\nsource\n\n \nmysql\n\n \nport\n3306\n/port\n\n \nuser\nclickhouse\n/user\n\n \npassword\nqwerty\n/password\n\n \nreplica\n\n \nhost\nexample01-1\n/host\n\n \npriority\n1\n/priority\n\n \n/replica\n\n \nreplica\n\n \nhost\nexample01-2\n/host\n\n \npriority\n1\n/priority\n\n \n/replica\n\n \ndb\ndb_name\n/db\n\n \ntable\ntable_name\n/table\n\n \nwhere\nid=10\n/where\n\n \ninvalidate_query\nSQL_QUERY\n/invalidate_query\n\n \n/mysql\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\n\n\nport\n \u2013 The port on the MySQL server. You can specify it for all replicas, or for each one individually (inside \nreplica\n).\n\n\n\n\n\n\nuser\n \u2013 Name of the MySQL user. You can specify it for all replicas, or for each one individually (inside \nreplica\n).\n\n\n\n\n\n\npassword\n \u2013 Password of the MySQL user. You can specify it for all replicas, or for each one individually (inside \nreplica\n).\n\n\n\n\n\n\nreplica\n \u2013 Section of replica configurations. There can be multiple sections.\n\n\n\n\nreplica/host\n \u2013 The MySQL host.\n\n\n\n\n* \nreplica/priority\n \u2013 The replica priority. When attempting to connect, ClickHouse traverses the replicas in order of priority. The lower the number, the higher the priority.\n\n\n\n\n\n\ndb\n \u2013 Name of the database.\n\n\n\n\n\n\ntable\n \u2013 Name of the table.\n\n\n\n\n\n\nwhere\n \u2013 The selection criteria. Optional parameter.\n\n\n\n\n\n\ninvalidate_query\n \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section \nUpdating dictionaries\n.\n\n\n\n\n\n\nMySQL can be connected on a local host via sockets. To do this, set \nhost\n and \nsocket\n.\n\n\nExample of settings:\n\n\nsource\n\n \nmysql\n\n \nhost\nlocalhost\n/host\n\n \nsocket\n/path/to/socket/file.sock\n/socket\n\n \nuser\nclickhouse\n/user\n\n \npassword\nqwerty\n/password\n\n \ndb\ndb_name\n/db\n\n \ntable\ntable_name\n/table\n\n \nwhere\nid=10\n/where\n\n \ninvalidate_query\nSQL_QUERY\n/invalidate_query\n\n \n/mysql\n\n\n/source\n\n\n\n\n\n\n\n\nClickHouse\n\n\nExample of settings:\n\n\nsource\n\n \nclickhouse\n\n \nhost\nexample01-01-1\n/host\n\n \nport\n9000\n/port\n\n \nuser\ndefault\n/user\n\n \npassword\n/password\n\n \ndb\ndefault\n/db\n\n \ntable\nids\n/table\n\n \nwhere\nid=10\n/where\n\n \n/clickhouse\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\nhost\n \u2013 The ClickHouse host. If it is a local host, the query is processed without any network activity. To improve fault tolerance, you can create a \nDistributed\n table and enter it in subsequent configurations.\n\n\nport\n \u2013 The port on the ClickHouse server.\n\n\nuser\n \u2013 Name of the ClickHouse user.\n\n\npassword\n \u2013 Password of the ClickHouse user.\n\n\ndb\n \u2013 Name of the database.\n\n\ntable\n \u2013 Name of the table.\n\n\nwhere\n \u2013 The selection criteria. May be omitted.\n\n\n\n\n\n\nMongoDB\n\n\nExample of settings:\n\n\nsource\n\n \nmongodb\n\n \nhost\nlocalhost\n/host\n\n \nport\n27017\n/port\n\n \nuser\n/user\n\n \npassword\n/password\n\n \ndb\ntest\n/db\n\n \ncollection\ndictionary_source\n/collection\n\n \n/mongodb\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\nhost\n \u2013 The MongoDB host.\n\n\nport\n \u2013 The port on the MongoDB server.\n\n\nuser\n \u2013 Name of the MongoDB user.\n\n\npassword\n \u2013 Password of the MongoDB user.\n\n\ndb\n \u2013 Name of the database.\n\n\ncollection\n \u2013 Name of the collection.", + "title": "Sources of external dictionaries" + }, + { + "location": "/dicts/external_dicts_dict_sources/#sources-of-external-dictionaries", + "text": "An external dictionary can be connected from many different sources. The configuration looks like this: yandex \n dictionary \n ...\n source \n source_type \n !-- Source configuration -- \n /source_type \n /source \n ...\n /dictionary \n ... /yandex The source is configured in the source section. Types of sources ( source_type ): Local file Executable file HTTP(s) ODBC DBMS MySQL ClickHouse MongoDB", + "title": "Sources of external dictionaries" + }, + { + "location": "/dicts/external_dicts_dict_sources/#local-file", + "text": "Example of settings: source \n file \n path /opt/dictionaries/os.tsv /path \n format TabSeparated /format \n /file /source Setting fields: path \u2013 The absolute path to the file. format \u2013 The file format. All the formats described in \" Formats \" are supported.", + "title": "Local file" + }, + { + "location": "/dicts/external_dicts_dict_sources/#executable-file", + "text": "Working with executable files depends on how the dictionary is stored in memory . If the dictionary is stored using cache and complex_key_cache , ClickHouse requests the necessary keys by sending a request to the executable file's STDIN . Example of settings: source \n executable \n command cat /opt/dictionaries/os.tsv /command \n format TabSeparated /format \n /executable /source Setting fields: command \u2013 The absolute path to the executable file, or the file name (if the program directory is written to PATH ). format \u2013 The file format. All the formats described in \" Formats \" are supported.", + "title": "Executable file" + }, + { + "location": "/dicts/external_dicts_dict_sources/#https", + "text": "Working with an HTTP(s) server depends on how the dictionary is stored in memory . If the dictionary is stored using cache and complex_key_cache , ClickHouse requests the necessary keys by sending a request via the POST method. Example of settings: source \n http \n url http://[::1]/os.tsv /url \n format TabSeparated /format \n /http /source In order for ClickHouse to access an HTTPS resource, you must configure openSSL in the server configuration. Setting fields: url \u2013 The source URL. format \u2013 The file format. All the formats described in \" Formats \" are supported.", + "title": "HTTP(s)" + }, + { + "location": "/dicts/external_dicts_dict_sources/#odbc", + "text": "You can use this method to connect any database that has an ODBC driver. Example of settings: odbc \n db DatabaseName /db \n table TableName /table \n connection_string DSN=some_parameters /connection_string \n invalidate_query SQL_QUERY /invalidate_query /odbc Setting fields: db \u2013 Name of the database. Omit it if the database name is set in the connection_string parameters. table \u2013 Name of the table. connection_string \u2013 Connection string. invalidate_query \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries .", + "title": "ODBC" + }, + { + "location": "/dicts/external_dicts_dict_sources/#example-of-connecting-postgresql", + "text": "Ubuntu OS. Installing unixODBC and the ODBC driver for PostgreSQL: sudo apt-get install -y unixodbc odbcinst odbc-postgresql Configuring /etc/odbc.ini (or ~/.odbc.ini ): [DEFAULT]\n Driver = myconnection\n\n [myconnection]\n Description = PostgreSQL connection to my_db\n Driver = PostgreSQL Unicode\n Database = my_db\n Servername = 127.0.0.1\n UserName = username\n Password = password\n Port = 5432\n Protocol = 9.3\n ReadOnly = No\n RowVersioning = No\n ShowSystemTables = No\n ConnSettings = The dictionary configuration in ClickHouse: dictionary \n name table_name /name \n source \n odbc \n !-- You can specifiy the following parameters in connection_string: -- \n !-- DSN=myconnection;UID=username;PWD=password;HOST=127.0.0.1;PORT=5432;DATABASE=my_db -- \n connection_string DSN=myconnection /connection_string \n table postgresql_table /table \n /odbc \n /source \n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n layout \n hashed/ \n /layout \n structure \n id \n name id /name \n /id \n attribute \n name some_column /name \n type UInt64 /type \n null_value 0 /null_value \n /attribute \n /structure /dictionary You may need to edit odbc.ini to specify the full path to the library with the driver DRIVER=/usr/local/lib/psqlodbcw.so .", + "title": "Example of connecting PostgreSQL" + }, + { + "location": "/dicts/external_dicts_dict_sources/#example-of-connecting-ms-sql-server", + "text": "Ubuntu OS. Installing the driver: : sudo apt-get install tdsodbc freetds-bin sqsh Configuring the driver: : $ cat /etc/freetds/freetds.conf \n ...\n\n [MSSQL]\n host = 192.168.56.101\n port = 1433\n tds version = 7.0\n client charset = UTF-8\n\n $ cat /etc/odbcinst.ini \n ...\n\n [FreeTDS]\n Description = FreeTDS\n Driver = /usr/lib/x86_64-linux-gnu/odbc/libtdsodbc.so\n Setup = /usr/lib/x86_64-linux-gnu/odbc/libtdsS.so\n FileUsage = 1\n UsageCount = 5\n\n $ cat ~/.odbc.ini \n ...\n\n [MSSQL]\n Description = FreeTDS\n Driver = FreeTDS\n Servername = MSSQL\n Database = test\n UID = test\n PWD = test\n Port = 1433 Configuring the dictionary in ClickHouse: yandex \n dictionary \n name test /name \n source \n odbc \n table dict /table \n connection_string DSN=MSSQL;UID=test;PWD=test /connection_string \n /odbc \n /source \n\n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n\n layout \n flat / \n /layout \n\n structure \n id \n name k /name \n /id \n attribute \n name s /name \n type String /type \n null_value /null_value \n /attribute \n /structure \n /dictionary /yandex", + "title": "Example of connecting MS SQL Server" + }, + { + "location": "/dicts/external_dicts_dict_sources/#dbms", + "text": "", + "title": "DBMS" + }, + { + "location": "/dicts/external_dicts_dict_sources/#mysql", + "text": "Example of settings: source \n mysql \n port 3306 /port \n user clickhouse /user \n password qwerty /password \n replica \n host example01-1 /host \n priority 1 /priority \n /replica \n replica \n host example01-2 /host \n priority 1 /priority \n /replica \n db db_name /db \n table table_name /table \n where id=10 /where \n invalidate_query SQL_QUERY /invalidate_query \n /mysql /source Setting fields: port \u2013 The port on the MySQL server. You can specify it for all replicas, or for each one individually (inside replica ). user \u2013 Name of the MySQL user. You can specify it for all replicas, or for each one individually (inside replica ). password \u2013 Password of the MySQL user. You can specify it for all replicas, or for each one individually (inside replica ). replica \u2013 Section of replica configurations. There can be multiple sections. replica/host \u2013 The MySQL host. * replica/priority \u2013 The replica priority. When attempting to connect, ClickHouse traverses the replicas in order of priority. The lower the number, the higher the priority. db \u2013 Name of the database. table \u2013 Name of the table. where \u2013 The selection criteria. Optional parameter. invalidate_query \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries . MySQL can be connected on a local host via sockets. To do this, set host and socket . Example of settings: source \n mysql \n host localhost /host \n socket /path/to/socket/file.sock /socket \n user clickhouse /user \n password qwerty /password \n db db_name /db \n table table_name /table \n where id=10 /where \n invalidate_query SQL_QUERY /invalidate_query \n /mysql /source", + "title": "MySQL" + }, + { + "location": "/dicts/external_dicts_dict_sources/#clickhouse", + "text": "Example of settings: source \n clickhouse \n host example01-01-1 /host \n port 9000 /port \n user default /user \n password /password \n db default /db \n table ids /table \n where id=10 /where \n /clickhouse /source Setting fields: host \u2013 The ClickHouse host. If it is a local host, the query is processed without any network activity. To improve fault tolerance, you can create a Distributed table and enter it in subsequent configurations. port \u2013 The port on the ClickHouse server. user \u2013 Name of the ClickHouse user. password \u2013 Password of the ClickHouse user. db \u2013 Name of the database. table \u2013 Name of the table. where \u2013 The selection criteria. May be omitted.", + "title": "ClickHouse" + }, + { + "location": "/dicts/external_dicts_dict_sources/#mongodb", + "text": "Example of settings: source \n mongodb \n host localhost /host \n port 27017 /port \n user /user \n password /password \n db test /db \n collection dictionary_source /collection \n /mongodb /source Setting fields: host \u2013 The MongoDB host. port \u2013 The port on the MongoDB server. user \u2013 Name of the MongoDB user. password \u2013 Password of the MongoDB user. db \u2013 Name of the database. collection \u2013 Name of the collection.", + "title": "MongoDB" + }, + { + "location": "/dicts/external_dicts_dict_structure/", + "text": "Dictionary key and fields\n\n\nThe \nstructure\n clause describes the dictionary key and fields available for queries.\n\n\nOverall structure:\n\n\ndictionary\n\n \nstructure\n\n \nid\n\n \nname\nId\n/name\n\n \n/id\n\n\n \nattribute\n\n \n!-- Attribute parameters --\n\n \n/attribute\n\n\n ...\n\n \n/structure\n\n\n/dictionary\n\n\n\n\n\n\nColumns are described in the structure:\n\n\n\n\nid\n - \nkey column\n.\n\n\nattribute\n - \ndata column\n. There can be a large number of columns.\n\n\n\n\n\n\nKey\n\n\nClickHouse supports the following types of keys:\n\n\n\n\nNumeric key. UInt64. Defined in the tag \nid\n .\n\n\nComposite key. Set of values of different types. Defined in the tag \nkey\n .\n\n\n\n\nA structure can contain either \nid\n or \nkey\n .\n\n\n\n\nThe key doesn't need to be defined separately in attributes.\n\n\n\n\n\nNumeric key\n\n\nFormat: \nUInt64\n.\n\n\nConfiguration example:\n\n\nid\n\n \nname\nId\n/name\n\n\n/id\n\n\n\n\n\n\nConfiguration fields:\n\n\n\n\nname \u2013 The name of the column with keys.\n\n\n\n\nComposite key\n\n\nThe key can be a \ntuple\n from any types of fields. The \nlayout\n in this case must be \ncomplex_key_hashed\n or \ncomplex_key_cache\n.\n\n\n\nA composite key can consist of a single element. This makes it possible to use a string as the key, for instance.\n\n\n\n\nThe key structure is set in the element \nkey\n. Key fields are specified in the same format as the dictionary \nattributes\n. Example:\n\n\nstructure\n\n \nkey\n\n \nattribute\n\n \nname\nfield1\n/name\n\n \ntype\nString\n/type\n\n \n/attribute\n\n \nattribute\n\n \nname\nfield2\n/name\n\n \ntype\nUInt32\n/type\n\n \n/attribute\n\n ...\n \n/key\n\n...\n\n\n\n\n\nFor a query to the \ndictGet*\n function, a tuple is passed as the key. Example: \ndictGetString('dict_name', 'attr_name', tuple('string for field1', num_for_field2))\n.\n\n\n\n\nAttributes\n\n\nConfiguration example:\n\n\nstructure\n\n ...\n \nattribute\n\n \nname\nName\n/name\n\n \ntype\nType\n/type\n\n \nnull_value\n/null_value\n\n \nexpression\nrand64()\n/expression\n\n \nhierarchical\ntrue\n/hierarchical\n\n \ninjective\ntrue\n/injective\n\n \nis_object_id\ntrue\n/is_object_id\n\n \n/attribute\n\n\n/structure\n\n\n\n\n\n\nConfiguration fields:\n\n\n\n\nname\n \u2013 The column name.\n\n\ntype\n \u2013 The column type. Sets the method for interpreting data in the source. For example, for MySQL, the field might be \nTEXT\n, \nVARCHAR\n, or \nBLOB\n in the source table, but it can be uploaded as \nString\n.\n\n\nnull_value\n \u2013 The default value for a non-existing element. In the example, it is an empty string.\n\n\nexpression\n \u2013 The attribute can be an expression. The tag is not required.\n\n\nhierarchical\n \u2013 Hierarchical support. Mirrored to the parent identifier. By default, \nfalse\n.\n\n\ninjective\n \u2013 Whether the \nid -\n attribute\n image is injective. If \ntrue\n, then you can optimize the \nGROUP BY\n clause. By default, \nfalse\n.\n\n\nis_object_id\n \u2013 Whether the query is executed for a MongoDB document by \nObjectID\n.", + "title": "Dictionary key and fields" + }, + { + "location": "/dicts/external_dicts_dict_structure/#dictionary-key-and-fields", + "text": "The structure clause describes the dictionary key and fields available for queries. Overall structure: dictionary \n structure \n id \n name Id /name \n /id \n\n attribute \n !-- Attribute parameters -- \n /attribute \n\n ...\n\n /structure /dictionary Columns are described in the structure: id - key column . attribute - data column . There can be a large number of columns.", + "title": "Dictionary key and fields" + }, + { + "location": "/dicts/external_dicts_dict_structure/#key", + "text": "ClickHouse supports the following types of keys: Numeric key. UInt64. Defined in the tag id . Composite key. Set of values of different types. Defined in the tag key . A structure can contain either id or key . \n\nThe key doesn't need to be defined separately in attributes.", + "title": "Key" + }, + { + "location": "/dicts/external_dicts_dict_structure/#numeric-key", + "text": "Format: UInt64 . Configuration example: id \n name Id /name /id Configuration fields: name \u2013 The name of the column with keys.", + "title": "Numeric key" + }, + { + "location": "/dicts/external_dicts_dict_structure/#composite-key", + "text": "The key can be a tuple from any types of fields. The layout in this case must be complex_key_hashed or complex_key_cache . \nA composite key can consist of a single element. This makes it possible to use a string as the key, for instance. The key structure is set in the element key . Key fields are specified in the same format as the dictionary attributes . Example: structure \n key \n attribute \n name field1 /name \n type String /type \n /attribute \n attribute \n name field2 /name \n type UInt32 /type \n /attribute \n ...\n /key \n... For a query to the dictGet* function, a tuple is passed as the key. Example: dictGetString('dict_name', 'attr_name', tuple('string for field1', num_for_field2)) .", + "title": "Composite key" + }, + { + "location": "/dicts/external_dicts_dict_structure/#attributes", + "text": "Configuration example: structure \n ...\n attribute \n name Name /name \n type Type /type \n null_value /null_value \n expression rand64() /expression \n hierarchical true /hierarchical \n injective true /injective \n is_object_id true /is_object_id \n /attribute /structure Configuration fields: name \u2013 The column name. type \u2013 The column type. Sets the method for interpreting data in the source. For example, for MySQL, the field might be TEXT , VARCHAR , or BLOB in the source table, but it can be uploaded as String . null_value \u2013 The default value for a non-existing element. In the example, it is an empty string. expression \u2013 The attribute can be an expression. The tag is not required. hierarchical \u2013 Hierarchical support. Mirrored to the parent identifier. By default, false . injective \u2013 Whether the id - attribute image is injective. If true , then you can optimize the GROUP BY clause. By default, false . is_object_id \u2013 Whether the query is executed for a MongoDB document by ObjectID .", + "title": "Attributes" + }, + { + "location": "/dicts/internal_dicts/", + "text": "Internal dictionaries\n\n\nClickHouse contains a built-in feature for working with a geobase.\n\n\nThis allows you to:\n\n\n\n\nUse a region's ID to get its name in the desired language.\n\n\nUse a region's ID to get the ID of a city, area, federal district, country, or continent.\n\n\nCheck whether a region is part of another region.\n\n\nGet a chain of parent regions.\n\n\n\n\nAll the functions support \"translocality,\" the ability to simultaneously use different perspectives on region ownership. For more information, see the section \"Functions for working with Yandex.Metrica dictionaries\".\n\n\nThe internal dictionaries are disabled in the default package.\nTo enable them, uncomment the parameters \npath_to_regions_hierarchy_file\n and \npath_to_regions_names_files\n in the server configuration file.\n\n\nThe geobase is loaded from text files.\nIf you work at Yandex, you can follow these instructions to create them:\n\nhttps://github.yandex-team.ru/raw/Metrika/ClickHouse_private/master/doc/create_embedded_geobase_dictionaries.txt\n\n\nPut the regions_hierarchy*.txt files in the path_to_regions_hierarchy_file directory. This configuration parameter must contain the path to the regions_hierarchy.txt file (the default regional hierarchy), and the other files (regions_hierarchy_ua.txt) must be located in the same directory.\n\n\nPut the \nregions_names_*.txt\n files in the path_to_regions_names_files directory.\n\n\nYou can also create these files yourself. The file format is as follows:\n\n\nregions_hierarchy*.txt\n: TabSeparated (no header), columns:\n\n\n\n\nRegion ID (UInt32)\n\n\nParent region ID (UInt32)\n\n\nRegion type (UInt8): 1 - continent, 3 - country, 4 - federal district, 5 - region, 6 - city; other types don't have values.\n\n\nPopulation (UInt32) - Optional column.\n\n\n\n\nregions_names_*.txt\n: TabSeparated (no header), columns:\n\n\n\n\nRegion ID (UInt32)\n\n\nRegion name (String) - Can't contain tabs or line feeds, even escaped ones.\n\n\n\n\nA flat array is used for storing in RAM. For this reason, IDs shouldn't be more than a million.\n\n\nDictionaries can be updated without restarting the server. However, the set of available dictionaries is not updated.\nFor updates, the file modification times are checked. If a file has changed, the dictionary is updated.\nThe interval to check for changes is configured in the 'builtin_dictionaries_reload_interval' parameter.\nDictionary updates (other than loading at first use) do not block queries. During updates, queries use the old versions of dictionaries. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.\n\n\nWe recommend periodically updating the dictionaries with the geobase. During an update, generate new files and write them to a separate location. When everything is ready, rename them to the files used by the server.\n\n\nThere are also functions for working with OS identifiers and Yandex.Metrica search engines, but they shouldn't be used.", + "title": "Internal dictionaries" + }, + { + "location": "/dicts/internal_dicts/#internal-dictionaries", + "text": "ClickHouse contains a built-in feature for working with a geobase. This allows you to: Use a region's ID to get its name in the desired language. Use a region's ID to get the ID of a city, area, federal district, country, or continent. Check whether a region is part of another region. Get a chain of parent regions. All the functions support \"translocality,\" the ability to simultaneously use different perspectives on region ownership. For more information, see the section \"Functions for working with Yandex.Metrica dictionaries\". The internal dictionaries are disabled in the default package.\nTo enable them, uncomment the parameters path_to_regions_hierarchy_file and path_to_regions_names_files in the server configuration file. The geobase is loaded from text files.\nIf you work at Yandex, you can follow these instructions to create them: https://github.yandex-team.ru/raw/Metrika/ClickHouse_private/master/doc/create_embedded_geobase_dictionaries.txt Put the regions_hierarchy*.txt files in the path_to_regions_hierarchy_file directory. This configuration parameter must contain the path to the regions_hierarchy.txt file (the default regional hierarchy), and the other files (regions_hierarchy_ua.txt) must be located in the same directory. Put the regions_names_*.txt files in the path_to_regions_names_files directory. You can also create these files yourself. The file format is as follows: regions_hierarchy*.txt : TabSeparated (no header), columns: Region ID (UInt32) Parent region ID (UInt32) Region type (UInt8): 1 - continent, 3 - country, 4 - federal district, 5 - region, 6 - city; other types don't have values. Population (UInt32) - Optional column. regions_names_*.txt : TabSeparated (no header), columns: Region ID (UInt32) Region name (String) - Can't contain tabs or line feeds, even escaped ones. A flat array is used for storing in RAM. For this reason, IDs shouldn't be more than a million. Dictionaries can be updated without restarting the server. However, the set of available dictionaries is not updated.\nFor updates, the file modification times are checked. If a file has changed, the dictionary is updated.\nThe interval to check for changes is configured in the 'builtin_dictionaries_reload_interval' parameter.\nDictionary updates (other than loading at first use) do not block queries. During updates, queries use the old versions of dictionaries. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries. We recommend periodically updating the dictionaries with the geobase. During an update, generate new files and write them to a separate location. When everything is ready, rename them to the files used by the server. There are also functions for working with OS identifiers and Yandex.Metrica search engines, but they shouldn't be used.", + "title": "Internal dictionaries" + }, + { + "location": "/operations/access_rights/", + "text": "Access rights\n\n\nUsers and access rights are set up in the user config. This is usually \nusers.xml\n.\n\n\nUsers are recorded in the \nusers\n section. Here is a fragment of the \nusers.xml\n file:\n\n\n!-- Users and ACL. --\n\n\nusers\n\n \n!-- If the user name is not specified, the \ndefault\n user is used. --\n\n \ndefault\n\n \n!-- Password could be specified in plaintext or in SHA256 (in hex format).\n\n\n\n If you want to specify the password in plain text (not recommended), place it in the \npassword\n element.\n\n\n Example: \npassword\nqwerty\n/password\n.\n\n\n Password can be empty.\n\n\n\n If you want to specify SHA256, place it in the \npassword_sha256_hex\n element.\n\n\n Example: \npassword_sha256_hex\n65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5\n/password_sha256_hex\n\n\n\n How to generate decent password:\n\n\n Execute: PASSWORD=$(base64 \n /dev/urandom | head -c8); echo \n$PASSWORD\n; echo -n \n$PASSWORD\n | sha256sum | tr -d \n-\n\n\n In first line will be password and in second - corresponding SHA256.\n\n\n --\n\n \npassword\n/password\n\n \n!-- A list of networks that access is allowed from.\n\n\n Each list item has one of the following forms:\n\n\n \nip\nIP address or subnet mask. For example: 198.51.100.0/24 or 2001:DB8::/32.\n\n\n \nhost\n Host name. For example: example01. A DNS query is made for verification, and all addresses obtained are compared with the address of the customer.\n\n\n \nhost_regexp\n Regular expression for host names. For example: ^example\\d\\d-\\d\\d-\\d\\.yandex\\.ru$\n\n\n For verification, a DNS PTR query is made for the customer\ns address and a regular expression is applied to the result.\n\n\n Then another DNS query is made for the result of the PTR query, and all received address are compared to the client address.\n\n\n We strongly recommend that the regex ends with \\.yandex\\.ru$.\n\n\n\n If you are installing ClickHouse yourself, enter:\n\n\n \nnetworks\n\n\n \nip\n::/0\n/ip\n\n\n \n/networks\n\n\n --\n\n \nnetworks\n \nincl=\nnetworks\n \n/\n\n\n \n!-- Settings profile for the user. --\n\n \nprofile\ndefault\n/profile\n\n\n \n!-- Quota for the user. --\n\n \nquota\ndefault\n/quota\n\n \n/default\n\n\n \n!-- For requests from the Yandex.Metrica user interface via the API for data on specific counters. --\n\n \nweb\n\n \npassword\n/password\n\n \nnetworks\n \nincl=\nnetworks\n \n/\n\n \nprofile\nweb\n/profile\n\n \nquota\ndefault\n/quota\n\n \nallow_databases\n\n \ndatabase\ntest\n/database\n\n \n/allow_databases\n\n \n/web\n\n\n/users\n\n\n\n\n\n\nYou can see a declaration from two users: \ndefault\n and \nweb\n. We added the \nweb\n user separately.\n\n\nThe \ndefault\n user is chosen in cases when the username is not passed. The \ndefault\n user is also used for distributed query processing, if the configuration of the server or cluster doesn't specify the \nuser\n and \npassword\n (see the section on the \nDistributed\n engine).\n\n\nThe user that is used for exchanging information between servers combined in a cluster must not have substantial restrictions or quotas \u2013 otherwise, distributed queries will fail.\n\n\nThe password is specified in open format (not recommended) or in SHA-256. The hash isn't salted. In this regard, you should not consider these passwords as providing security against potential malicious attacks. Rather, they are necessary for protection from employees.\n\n\nA list of networks is specified that access is allowed from. In this example, the list of networks for both users is loaded from a separate file (/etc/metrika.xml) containing the 'networks' substitution. Here is a fragment of it:\n\n\nyandex\n\n ...\n \nnetworks\n\n \nip\n::/64\n/ip\n\n \nip\n203.0.113.0/24\n/ip\n\n \nip\n2001:DB8::/32\n/ip\n\n ...\n \n/networks\n\n\n/yandex\n\n\n\n\n\n\nWe could have defined this list of networks directly in 'users.xml', or in a file in the 'users.d' directory (for more information, see the section \"Configuration files\").\n\n\nThe config includes comments explaining how to open access from everywhere.\n\n\nFor use in production, only specify IP elements (IP addresses and their masks), since using 'host' and 'hoost_regexp' might cause extra latency.\n\n\nNext the user settings profile is specified (see the section \"Settings profiles\"). You can specify the default profile, \ndefault\n. The profile can have any name. You can specify the same profile for different users. The most important thing you can write in the settings profile is 'readonly' set to 1, which provides read-only access.\n\n\nAfter this, the quota is defined (see the section \"Quotas\"). You can specify the default quota, \ndefault\n. It is set in the config by default so that it only counts resource usage, but does not restrict it. The quota can have any name. You can specify the same quota for different users \u2013 in this case, resource usage is calculated for each user individually.\n\n\nIn the optional \nallow_databases\n section, you can also specify a list of databases that the user can access. By default, all databases are available to the user. You can specify the \ndefault\n database. In this case, the user will receive access to the database by default.\n\n\nAccess to the \nsystem\n database is always allowed (since this database is used for processing queries).\n\n\nThe user can get a list of all databases and tables in them by using \nSHOW\n queries or system tables, even if access to individual databases isn't allowed.\n\n\nDatabase access is not related to the \nreadonly\n setting. You can't grant full access to one database and \nreadonly\n access to another one.", + "title": "Access rights" + }, + { + "location": "/operations/access_rights/#access-rights", + "text": "Users and access rights are set up in the user config. This is usually users.xml . Users are recorded in the users section. Here is a fragment of the users.xml file: !-- Users and ACL. -- users \n !-- If the user name is not specified, the default user is used. -- \n default \n !-- Password could be specified in plaintext or in SHA256 (in hex format). If you want to specify the password in plain text (not recommended), place it in the password element. Example: password qwerty /password . Password can be empty. If you want to specify SHA256, place it in the password_sha256_hex element. Example: password_sha256_hex 65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5 /password_sha256_hex How to generate decent password: Execute: PASSWORD=$(base64 /dev/urandom | head -c8); echo $PASSWORD ; echo -n $PASSWORD | sha256sum | tr -d - In first line will be password and in second - corresponding SHA256. -- \n password /password \n !-- A list of networks that access is allowed from. Each list item has one of the following forms: ip IP address or subnet mask. For example: 198.51.100.0/24 or 2001:DB8::/32. host Host name. For example: example01. A DNS query is made for verification, and all addresses obtained are compared with the address of the customer. host_regexp Regular expression for host names. For example: ^example\\d\\d-\\d\\d-\\d\\.yandex\\.ru$ For verification, a DNS PTR query is made for the customer s address and a regular expression is applied to the result. Then another DNS query is made for the result of the PTR query, and all received address are compared to the client address. We strongly recommend that the regex ends with \\.yandex\\.ru$. If you are installing ClickHouse yourself, enter: networks ip ::/0 /ip /networks -- \n networks incl= networks / \n\n !-- Settings profile for the user. -- \n profile default /profile \n\n !-- Quota for the user. -- \n quota default /quota \n /default \n\n !-- For requests from the Yandex.Metrica user interface via the API for data on specific counters. -- \n web \n password /password \n networks incl= networks / \n profile web /profile \n quota default /quota \n allow_databases \n database test /database \n /allow_databases \n /web /users You can see a declaration from two users: default and web . We added the web user separately. The default user is chosen in cases when the username is not passed. The default user is also used for distributed query processing, if the configuration of the server or cluster doesn't specify the user and password (see the section on the Distributed engine). The user that is used for exchanging information between servers combined in a cluster must not have substantial restrictions or quotas \u2013 otherwise, distributed queries will fail. The password is specified in open format (not recommended) or in SHA-256. The hash isn't salted. In this regard, you should not consider these passwords as providing security against potential malicious attacks. Rather, they are necessary for protection from employees. A list of networks is specified that access is allowed from. In this example, the list of networks for both users is loaded from a separate file (/etc/metrika.xml) containing the 'networks' substitution. Here is a fragment of it: yandex \n ...\n networks \n ip ::/64 /ip \n ip 203.0.113.0/24 /ip \n ip 2001:DB8::/32 /ip \n ...\n /networks /yandex We could have defined this list of networks directly in 'users.xml', or in a file in the 'users.d' directory (for more information, see the section \"Configuration files\"). The config includes comments explaining how to open access from everywhere. For use in production, only specify IP elements (IP addresses and their masks), since using 'host' and 'hoost_regexp' might cause extra latency. Next the user settings profile is specified (see the section \"Settings profiles\"). You can specify the default profile, default . The profile can have any name. You can specify the same profile for different users. The most important thing you can write in the settings profile is 'readonly' set to 1, which provides read-only access. After this, the quota is defined (see the section \"Quotas\"). You can specify the default quota, default . It is set in the config by default so that it only counts resource usage, but does not restrict it. The quota can have any name. You can specify the same quota for different users \u2013 in this case, resource usage is calculated for each user individually. In the optional allow_databases section, you can also specify a list of databases that the user can access. By default, all databases are available to the user. You can specify the default database. In this case, the user will receive access to the database by default. Access to the system database is always allowed (since this database is used for processing queries). The user can get a list of all databases and tables in them by using SHOW queries or system tables, even if access to individual databases isn't allowed. Database access is not related to the readonly setting. You can't grant full access to one database and readonly access to another one.", + "title": "Access rights" + }, + { + "location": "/operations/configuration_files/", + "text": "Configuration files\n\n\nThe main server config file is \nconfig.xml\n. It resides in the \n/etc/clickhouse-server/\n directory.\n\n\nIndividual settings can be overridden in the \n*.xml\nand\n*.conf\n files in the \nconf.d\n and \nconfig.d\n directories next to the config file.\n\n\nThe \nreplace\n or \nremove\n attributes can be specified for the elements of these config files.\n\n\nIf neither is specified, it combines the contents of elements recursively, replacing values of duplicate children.\n\n\nIf \nreplace\n is specified, it replaces the entire element with the specified one.\n\n\nIf \nremove\n is specified, it deletes the element.\n\n\nThe config can also define \"substitutions\". If an element has the \nincl\n attribute, the corresponding substitution from the file will be used as the value. By default, the path to the file with substitutions is \n/etc/metrika.xml\n. This can be changed in the \ninclude_from\n element in the server config. The substitution values are specified in \n/yandex/substitution_name\n elements in this file. If a substitution specified in \nincl\n does not exist, it is recorded in the log. To prevent ClickHouse from logging missing substitutions, specify the \noptional=\"true\"\n attribute (for example, settings for \nmacros\nserver_settings/settings.md#server_settings-macros)).\n\n\nSubstitutions can also be performed from ZooKeeper. To do this, specify the attribute \nfrom_zk = \"/path/to/node\"\n. The element value is replaced with the contents of the node at \n/path/to/node\n in ZooKeeper. You can also put an entire XML subtree on the ZooKeeper node and it will be fully inserted into the source element.\n\n\nThe \nconfig.xml\n file can specify a separate config with user settings, profiles, and quotas. The relative path to this config is set in the 'users_config' element. By default, it is \nusers.xml\n. If \nusers_config\n is omitted, the user settings, profiles, and quotas are specified directly in \nconfig.xml\n.\n\n\nIn addition, \nusers_config\n may have overrides in files from the \nusers_config.d\n directory (for example, \nusers.d\n) and substitutions.\n\n\nFor each config file, the server also generates \nfile-preprocessed.xml\n files when starting. These files contain all the completed substitutions and overrides, and they are intended for informational use. If ZooKeeper substitutions were used in the config files but ZooKeeper is not available on the server start, the server loads the configuration from the preprocessed file.\n\n\nThe server tracks changes in config files, as well as files and ZooKeeper nodes that were used when performing substitutions and overrides, and reloads the settings for users and clusters on the fly. This means that you can modify the cluster, users, and their settings without restarting the server.", + "title": "Configuration files" + }, + { + "location": "/operations/configuration_files/#configuration-files", + "text": "The main server config file is config.xml . It resides in the /etc/clickhouse-server/ directory. Individual settings can be overridden in the *.xml and *.conf files in the conf.d and config.d directories next to the config file. The replace or remove attributes can be specified for the elements of these config files. If neither is specified, it combines the contents of elements recursively, replacing values of duplicate children. If replace is specified, it replaces the entire element with the specified one. If remove is specified, it deletes the element. The config can also define \"substitutions\". If an element has the incl attribute, the corresponding substitution from the file will be used as the value. By default, the path to the file with substitutions is /etc/metrika.xml . This can be changed in the include_from element in the server config. The substitution values are specified in /yandex/substitution_name elements in this file. If a substitution specified in incl does not exist, it is recorded in the log. To prevent ClickHouse from logging missing substitutions, specify the optional=\"true\" attribute (for example, settings for macros server_settings/settings.md#server_settings-macros)). Substitutions can also be performed from ZooKeeper. To do this, specify the attribute from_zk = \"/path/to/node\" . The element value is replaced with the contents of the node at /path/to/node in ZooKeeper. You can also put an entire XML subtree on the ZooKeeper node and it will be fully inserted into the source element. The config.xml file can specify a separate config with user settings, profiles, and quotas. The relative path to this config is set in the 'users_config' element. By default, it is users.xml . If users_config is omitted, the user settings, profiles, and quotas are specified directly in config.xml . In addition, users_config may have overrides in files from the users_config.d directory (for example, users.d ) and substitutions. For each config file, the server also generates file-preprocessed.xml files when starting. These files contain all the completed substitutions and overrides, and they are intended for informational use. If ZooKeeper substitutions were used in the config files but ZooKeeper is not available on the server start, the server loads the configuration from the preprocessed file. The server tracks changes in config files, as well as files and ZooKeeper nodes that were used when performing substitutions and overrides, and reloads the settings for users and clusters on the fly. This means that you can modify the cluster, users, and their settings without restarting the server.", + "title": "Configuration files" + }, + { + "location": "/operations/quotas/", + "text": "Quotas\n\n\nQuotas allow you to limit resource usage over a period of time, or simply track the use of resources.\nQuotas are set up in the user config. This is usually 'users.xml'.\n\n\nThe system also has a feature for limiting the complexity of a single query. See the section \"Restrictions on query complexity\").\n\n\nIn contrast to query complexity restrictions, quotas:\n\n\n\n\nPlace restrictions on a set of queries that can be run over a period of time, instead of limiting a single query.\n\n\nAccount for resources spent on all remote servers for distributed query processing.\n\n\n\n\nLet's look at the section of the 'users.xml' file that defines quotas.\n\n\n!-- Quotas. --\n\n\nquotas\n\n \n!-- Quota name. --\n\n \ndefault\n\n \n!-- Restrictions for a time period. You can set many intervals with different restrictions. --\n\n \ninterval\n\n \n!-- Length of the interval. --\n\n \nduration\n3600\n/duration\n\n\n \n!-- Unlimited. Just collect data for the specified time interval. --\n\n \nqueries\n0\n/queries\n\n \nerrors\n0\n/errors\n\n \nresult_rows\n0\n/result_rows\n\n \nread_rows\n0\n/read_rows\n\n \nexecution_time\n0\n/execution_time\n\n \n/interval\n\n \n/default\n\n\n\n\n\n\nBy default, the quota just tracks resource consumption for each hour, without limiting usage.\nThe resource consumption calculated for each interval is output to the server log after each request.\n\n\nstatbox\n\n \n!-- Restrictions for a time period. You can set many intervals with different restrictions. --\n\n \ninterval\n\n \n!-- Length of the interval. --\n\n \nduration\n3600\n/duration\n\n\n \nqueries\n1000\n/queries\n\n \nerrors\n100\n/errors\n\n \nresult_rows\n1000000000\n/result_rows\n\n \nread_rows\n100000000000\n/read_rows\n\n \nexecution_time\n900\n/execution_time\n\n \n/interval\n\n\n \ninterval\n\n \nduration\n86400\n/duration\n\n\n \nqueries\n10000\n/queries\n\n \nerrors\n1000\n/errors\n\n \nresult_rows\n5000000000\n/result_rows\n\n \nread_rows\n500000000000\n/read_rows\n\n \nexecution_time\n7200\n/execution_time\n\n \n/interval\n\n\n/statbox\n\n\n\n\n\n\nFor the 'statbox' quota, restrictions are set for every hour and for every 24 hours (86,400 seconds). The time interval is counted starting from an implementation-defined fixed moment in time. In other words, the 24-hour interval doesn't necessarily begin at midnight.\n\n\nWhen the interval ends, all collected values are cleared. For the next hour, the quota calculation starts over.\n\n\nHere are the amounts that can be restricted:\n\n\nqueries\n \u2013 The total number of requests.\n\n\nerrors\n \u2013 The number of queries that threw an exception.\n\n\nresult_rows\n \u2013 The total number of rows given as the result.\n\n\nread_rows\n \u2013 The total number of source rows read from tables for running the query, on all remote servers.\n\n\nexecution_time\n \u2013 The total query execution time, in seconds (wall time).\n\n\nIf the limit is exceeded for at least one time interval, an exception is thrown with a text about which restriction was exceeded, for which interval, and when the new interval begins (when queries can be sent again).\n\n\nQuotas can use the \"quota key\" feature in order to report on resources for multiple keys independently. Here is an example of this:\n\n\n!-- For the global reports designer. --\n\n\nweb_global\n\n \n!-- keyed - The quota_key \nkey\n is passed in the query parameter,\n\n\n and the quota is tracked separately for each key value.\n\n\n For example, you can pass a Yandex.Metrica username as the key,\n\n\n so the quota will be counted separately for each username.\n\n\n Using keys makes sense only if quota_key is transmitted by the program, not by a user.\n\n\n\n You can also write \nkeyed_by_ip /\n so the IP address is used as the quota key.\n\n\n (But keep in mind that users can change the IPv6 address fairly easily.)\n\n\n --\n\n \nkeyed\n \n/\n\n\n\n\n\n\nThe quota is assigned to users in the 'users' section of the config. See the section \"Access rights\".\n\n\nFor distributed query processing, the accumulated amounts are stored on the requestor server. So if the user goes to another server, the quota there will \"start over\".\n\n\nWhen the server is restarted, quotas are reset.", + "title": "Quotas" + }, + { + "location": "/operations/quotas/#quotas", + "text": "Quotas allow you to limit resource usage over a period of time, or simply track the use of resources.\nQuotas are set up in the user config. This is usually 'users.xml'. The system also has a feature for limiting the complexity of a single query. See the section \"Restrictions on query complexity\"). In contrast to query complexity restrictions, quotas: Place restrictions on a set of queries that can be run over a period of time, instead of limiting a single query. Account for resources spent on all remote servers for distributed query processing. Let's look at the section of the 'users.xml' file that defines quotas. !-- Quotas. -- quotas \n !-- Quota name. -- \n default \n !-- Restrictions for a time period. You can set many intervals with different restrictions. -- \n interval \n !-- Length of the interval. -- \n duration 3600 /duration \n\n !-- Unlimited. Just collect data for the specified time interval. -- \n queries 0 /queries \n errors 0 /errors \n result_rows 0 /result_rows \n read_rows 0 /read_rows \n execution_time 0 /execution_time \n /interval \n /default By default, the quota just tracks resource consumption for each hour, without limiting usage.\nThe resource consumption calculated for each interval is output to the server log after each request. statbox \n !-- Restrictions for a time period. You can set many intervals with different restrictions. -- \n interval \n !-- Length of the interval. -- \n duration 3600 /duration \n\n queries 1000 /queries \n errors 100 /errors \n result_rows 1000000000 /result_rows \n read_rows 100000000000 /read_rows \n execution_time 900 /execution_time \n /interval \n\n interval \n duration 86400 /duration \n\n queries 10000 /queries \n errors 1000 /errors \n result_rows 5000000000 /result_rows \n read_rows 500000000000 /read_rows \n execution_time 7200 /execution_time \n /interval /statbox For the 'statbox' quota, restrictions are set for every hour and for every 24 hours (86,400 seconds). The time interval is counted starting from an implementation-defined fixed moment in time. In other words, the 24-hour interval doesn't necessarily begin at midnight. When the interval ends, all collected values are cleared. For the next hour, the quota calculation starts over. Here are the amounts that can be restricted: queries \u2013 The total number of requests. errors \u2013 The number of queries that threw an exception. result_rows \u2013 The total number of rows given as the result. read_rows \u2013 The total number of source rows read from tables for running the query, on all remote servers. execution_time \u2013 The total query execution time, in seconds (wall time). If the limit is exceeded for at least one time interval, an exception is thrown with a text about which restriction was exceeded, for which interval, and when the new interval begins (when queries can be sent again). Quotas can use the \"quota key\" feature in order to report on resources for multiple keys independently. Here is an example of this: !-- For the global reports designer. -- web_global \n !-- keyed - The quota_key key is passed in the query parameter, and the quota is tracked separately for each key value. For example, you can pass a Yandex.Metrica username as the key, so the quota will be counted separately for each username. Using keys makes sense only if quota_key is transmitted by the program, not by a user. You can also write keyed_by_ip / so the IP address is used as the quota key. (But keep in mind that users can change the IPv6 address fairly easily.) -- \n keyed / The quota is assigned to users in the 'users' section of the config. See the section \"Access rights\". For distributed query processing, the accumulated amounts are stored on the requestor server. So if the user goes to another server, the quota there will \"start over\". When the server is restarted, quotas are reset.", + "title": "Quotas" + }, + { + "location": "/operations/tips/", + "text": "Usage recommendations\n\n\nCPU\n\n\nThe SSE 4.2 instruction set must be supported. Modern processors (since 2008) support it.\n\n\nWhen choosing a processor, prefer a large number of cores and slightly slower clock rate over fewer cores and a higher clock rate.\nFor example, 16 cores with 2600 MHz is better than 8 cores with 3600 MHz.\n\n\nHyper-threading\n\n\nDon't disable hyper-threading. It helps for some queries, but not for others.\n\n\nTurbo Boost\n\n\nTurbo Boost is highly recommended. It significantly improves performance with a typical load.\nYou can use \nturbostat\n to view the CPU's actual clock rate under a load.\n\n\nCPU scaling governor\n\n\nAlways use the \nperformance\n scaling governor. The \non-demand\n scaling governor works much worse with constantly high demand.\n\n\nsudo \necho\n \nperformance\n \n|\n tee /sys/devices/system/cpu/cpu\n\\*\n/cpufreq/scaling_governor\n\n\n\n\n\nCPU limitations\n\n\nProcessors can overheat. Use \ndmesg\n to see if the CPU's clock rate was limited due to overheating.\nThe restriction can also be set externally at the datacenter level. You can use \nturbostat\n to monitor it under a load.\n\n\nRAM\n\n\nFor small amounts of data (up to \\~200 GB compressed), it is best to use as much memory as the volume of data.\nFor large amounts of data and when processing interactive (online) queries, you should use a reasonable amount of RAM (128 GB or more) so the hot data subset will fit in the cache of pages.\nEven for data volumes of \\~50 TB per server, using 128 GB of RAM significantly improves query performance compared to 64 GB.\n\n\nSwap file\n\n\nAlways disable the swap file. The only reason for not doing this is if you are using ClickHouse on your personal laptop.\n\n\nHuge pages\n\n\nAlways disable transparent huge pages. It interferes with memory allocators, which leads to significant performance degradation.\n\n\necho\n \nnever\n \n|\n sudo tee /sys/kernel/mm/transparent_hugepage/enabled\n\n\n\n\n\nUse \nperf top\n to watch the time spent in the kernel for memory management.\nPermanent huge pages also do not need to be allocated.\n\n\nStorage subsystem\n\n\nIf your budget allows you to use SSD, use SSD.\nIf not, use HDD. SATA HDDs 7200 RPM will do.\n\n\nGive preference to a lot of servers with local hard drives over a smaller number of servers with attached disk shelves.\nBut for storing archives with rare queries, shelves will work.\n\n\nRAID\n\n\nWhen using HDD, you can combine their RAID-10, RAID-5, RAID-6 or RAID-50.\nFor Linux, software RAID is better (with \nmdadm\n). We don't recommend using LVM.\nWhen creating RAID-10, select the \nfar\n layout.\nIf your budget allows, choose RAID-10.\n\n\nIf you have more than 4 disks, use RAID-6 (preferred) or RAID-50, instead of RAID-5.\nWhen using RAID-5, RAID-6 or RAID-50, always increase stripe_cache_size, since the default value is usually not the best choice.\n\n\necho\n \n4096\n \n|\n sudo tee /sys/block/md2/md/stripe_cache_size\n\n\n\n\n\nCalculate the exact number from the number of devices and the block size, using the formula: \n2 * num_devices * chunk_size_in_bytes / 4096\n.\n\n\nA block size of 1025 KB is sufficient for all RAID configurations.\nNever set the block size too small or too large.\n\n\nYou can use RAID-0 on SSD.\nRegardless of RAID use, always use replication for data security.\n\n\nEnable NCQ with a long queue. For HDD, choose the CFQ scheduler, and for SSD, choose noop. Don't reduce the 'readahead' setting.\nFor HDD, enable the write cache.\n\n\nFile system\n\n\nExt4 is the most reliable option. Set the mount options \nnoatime, nobarrier\n.\nXFS is also suitable, but it hasn't been as thoroughly tested with ClickHouse.\nMost other file systems should also work fine. File systems with delayed allocation work better.\n\n\nLinux kernel\n\n\nDon't use an outdated Linux kernel. In 2015, 3.18.19 was new enough.\nConsider using the kernel build from Yandex:\nhttps://github.com/yandex/smart\n \u2013 it provides at least a 5% performance increase.\n\n\nNetwork\n\n\nIf you are using IPv6, increase the size of the route cache.\nThe Linux kernel prior to 3.2 had a multitude of problems with IPv6 implementation.\n\n\nUse at least a 10 GB network, if possible. 1 Gb will also work, but it will be much worse for patching replicas with tens of terabytes of data, or for processing distributed queries with a large amount of intermediate data.\n\n\nZooKeeper\n\n\nYou are probably already using ZooKeeper for other purposes. You can use the same installation of ZooKeeper, if it isn't already overloaded.\n\n\nIt's best to use a fresh version of ZooKeeper \u2013 3.4.9 or later. The version in stable Linux distributions may be outdated.\n\n\nWith the default settings, ZooKeeper is a time bomb:\n\n\n\n\nThe ZooKeeper server won't delete files from old snapshots and logs when using the default configuration (see autopurge), and this is the responsibility of the operator.\n\n\n\n\nThis bomb must be defused.\n\n\nThe ZooKeeper (3.5.1) configuration below is used in the Yandex.Metrica production environment as of May 20, 2017:\n\n\nzoo.cfg:\n\n\n# http://hadoop.apache.org/zookeeper/docs/current/zookeeperAdmin.html\n\n\n\n# The number of milliseconds of each tick\n\n\ntickTime\n=\n2000\n\n\n# The number of ticks that the initial\n\n\n# synchronization phase can take\n\n\ninitLimit\n=\n30000\n\n\n# The number of ticks that can pass between\n\n\n# sending a request and getting an acknowledgement\n\n\nsyncLimit\n=\n10\n\n\n\nmaxClientCnxns\n=\n2000\n\n\n\nmaxSessionTimeout\n=\n60000000\n\n\n# the directory where the snapshot is stored.\n\n\ndataDir\n=\n/opt/zookeeper/\n{{\n cluster\n[\nname\n]\n \n}}\n/data\n\n# Place the dataLogDir to a separate physical disc for better performance\n\n\ndataLogDir\n=\n/opt/zookeeper/\n{{\n cluster\n[\nname\n]\n \n}}\n/logs\n\nautopurge.snapRetainCount\n=\n10\n\nautopurge.purgeInterval\n=\n1\n\n\n\n\n# To avoid seeks ZooKeeper allocates space in the transaction log file in\n\n\n# blocks of preAllocSize kilobytes. The default block size is 64M. One reason\n\n\n# for changing the size of the blocks is to reduce the block size if snapshots\n\n\n# are taken more often. (Also, see snapCount).\n\n\npreAllocSize\n=\n131072\n\n\n\n# Clients can submit requests faster than ZooKeeper can process them,\n\n\n# especially if there are a lot of clients. To prevent ZooKeeper from running\n\n\n# out of memory due to queued requests, ZooKeeper will throttle clients so that\n\n\n# there is no more than globalOutstandingLimit outstanding requests in the\n\n\n# system. The default limit is 1,000.ZooKeeper logs transactions to a\n\n\n# transaction log. After snapCount transactions are written to a log file a\n\n\n# snapshot is started and a new transaction log file is started. The default\n\n\n# snapCount is 10,000.\n\n\nsnapCount\n=\n3000000\n\n\n\n# If this option is defined, requests will be will logged to a trace file named\n\n\n# traceFile.year.month.day.\n\n\n#traceFile=\n\n\n\n# Leader accepts client connections. Default value is \nyes\n. The leader machine\n\n\n# coordinates updates. For higher update throughput at thes slight expense of\n\n\n# read throughput the leader can be configured to not accept clients and focus\n\n\n# on coordination.\n\n\nleaderServes\n=\nyes\n\n\nstandaloneEnabled\n=\nfalse\n\n\ndynamicConfigFile\n=\n/etc/zookeeper-\n{{\n cluster\n[\nname\n]\n \n}}\n/conf/zoo.cfg.dynamic\n\n\n\n\n\nJava version:\n\n\nJava(TM) SE Runtime Environment (build 1.8.0_25-b17)\nJava HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode)\n\n\n\n\n\nJVM parameters:\n\n\nNAME\n=\nzookeeper-\n{{\n cluster\n[\nname\n]\n \n}}\n\n\nZOOCFGDIR\n=\n/etc/\n$NAME\n/conf\n\n\n# TODO this is really ugly\n\n\n# How to find out, which jars are needed?\n\n\n# seems, that log4j requires the log4j.properties file to be in the classpath\n\n\nCLASSPATH\n=\n$ZOOCFGDIR\n:/usr/build/classes:/usr/build/lib/*.jar:/usr/share/zookeeper/zookeeper-3.5.1-metrika.jar:/usr/share/zookeeper/slf4j-log4j12-1.7.5.jar:/usr/share/zookeeper/slf4j-api-1.7.5.jar:/usr/share/zookeeper/servlet-api-2.5-20081211.jar:/usr/share/zookeeper/netty-3.7.0.Final.jar:/usr/share/zookeeper/log4j-1.2.16.jar:/usr/share/zookeeper/jline-2.11.jar:/usr/share/zookeeper/jetty-util-6.1.26.jar:/usr/share/zookeeper/jetty-6.1.26.jar:/usr/share/zookeeper/javacc.jar:/usr/share/zookeeper/jackson-mapper-asl-1.9.11.jar:/usr/share/zookeeper/jackson-core-asl-1.9.11.jar:/usr/share/zookeeper/commons-cli-1.2.jar:/usr/src/java/lib/*.jar:/usr/etc/zookeeper\n\n\n\nZOOCFG\n=\n$ZOOCFGDIR\n/zoo.cfg\n\n\nZOO_LOG_DIR\n=\n/var/log/\n$NAME\n\n\nUSER\n=\nzookeeper\n\nGROUP\n=\nzookeeper\n\nPIDDIR\n=\n/var/run/\n$NAME\n\n\nPIDFILE\n=\n$PIDDIR\n/\n$NAME\n.pid\n\nSCRIPTNAME\n=\n/etc/init.d/\n$NAME\n\n\nJAVA\n=\n/usr/bin/java\n\nZOOMAIN\n=\norg.apache.zookeeper.server.quorum.QuorumPeerMain\n\n\nZOO_LOG4J_PROP\n=\nINFO,ROLLINGFILE\n\n\nJMXLOCALONLY\n=\nfalse\n\n\nJAVA_OPTS\n=\n-Xms{{ cluster.get(\nxms\n,\n128M\n) }} \\\n\n\n -Xmx{{ cluster.get(\nxmx\n,\n1G\n) }} \\\n\n\n -Xloggc:/var/log/\n$NAME\n/zookeeper-gc.log \\\n\n\n -XX:+UseGCLogFileRotation \\\n\n\n -XX:NumberOfGCLogFiles=16 \\\n\n\n -XX:GCLogFileSize=16M \\\n\n\n -verbose:gc \\\n\n\n -XX:+PrintGCTimeStamps \\\n\n\n -XX:+PrintGCDateStamps \\\n\n\n -XX:+PrintGCDetails\n\n\n -XX:+PrintTenuringDistribution \\\n\n\n -XX:+PrintGCApplicationStoppedTime \\\n\n\n -XX:+PrintGCApplicationConcurrentTime \\\n\n\n -XX:+PrintSafepointStatistics \\\n\n\n -XX:+UseParNewGC \\\n\n\n -XX:+UseConcMarkSweepGC \\\n\n\n-XX:+CMSParallelRemarkEnabled\n\n\n\n\n\n\nSalt init:\n\n\ndescription \nzookeeper-{{ cluster[\nname\n] }} centralized coordination service\n\n\nstart on runlevel [2345]\nstop on runlevel [!2345]\n\nrespawn\n\nlimit nofile 8192 8192\n\npre-start script\n [ -r \n/etc/zookeeper-{{ cluster[\nname\n] }}/conf/environment\n ] || exit 0\n . /etc/zookeeper-{{ cluster[\nname\n] }}/conf/environment\n [ -d $ZOO_LOG_DIR ] || mkdir -p $ZOO_LOG_DIR\n chown $USER:$GROUP $ZOO_LOG_DIR\nend script\n\nscript\n . /etc/zookeeper-{{ cluster[\nname\n] }}/conf/environment\n [ -r /etc/default/zookeeper ] \n . /etc/default/zookeeper\n if [ -z \n$JMXDISABLE\n ]; then\n JAVA_OPTS=\n$JAVA_OPTS -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.local.only=$JMXLOCALONLY\n\n fi\n exec start-stop-daemon --start -c $USER --exec $JAVA --name zookeeper-{{ cluster[\nname\n] }} \\\n -- -cp $CLASSPATH $JAVA_OPTS -Dzookeeper.log.dir=${ZOO_LOG_DIR} \\\n -Dzookeeper.root.logger=${ZOO_LOG4J_PROP} $ZOOMAIN $ZOOCFG\nend script", + "title": "Usage recommendations" + }, + { + "location": "/operations/tips/#usage-recommendations", + "text": "", + "title": "Usage recommendations" + }, + { + "location": "/operations/tips/#cpu", + "text": "The SSE 4.2 instruction set must be supported. Modern processors (since 2008) support it. When choosing a processor, prefer a large number of cores and slightly slower clock rate over fewer cores and a higher clock rate.\nFor example, 16 cores with 2600 MHz is better than 8 cores with 3600 MHz.", + "title": "CPU" + }, + { + "location": "/operations/tips/#hyper-threading", + "text": "Don't disable hyper-threading. It helps for some queries, but not for others.", + "title": "Hyper-threading" + }, + { + "location": "/operations/tips/#turbo-boost", + "text": "Turbo Boost is highly recommended. It significantly improves performance with a typical load.\nYou can use turbostat to view the CPU's actual clock rate under a load.", + "title": "Turbo Boost" + }, + { + "location": "/operations/tips/#cpu-scaling-governor", + "text": "Always use the performance scaling governor. The on-demand scaling governor works much worse with constantly high demand. sudo echo performance | tee /sys/devices/system/cpu/cpu \\* /cpufreq/scaling_governor", + "title": "CPU scaling governor" + }, + { + "location": "/operations/tips/#cpu-limitations", + "text": "Processors can overheat. Use dmesg to see if the CPU's clock rate was limited due to overheating.\nThe restriction can also be set externally at the datacenter level. You can use turbostat to monitor it under a load.", + "title": "CPU limitations" + }, + { + "location": "/operations/tips/#ram", + "text": "For small amounts of data (up to \\~200 GB compressed), it is best to use as much memory as the volume of data.\nFor large amounts of data and when processing interactive (online) queries, you should use a reasonable amount of RAM (128 GB or more) so the hot data subset will fit in the cache of pages.\nEven for data volumes of \\~50 TB per server, using 128 GB of RAM significantly improves query performance compared to 64 GB.", + "title": "RAM" + }, + { + "location": "/operations/tips/#swap-file", + "text": "Always disable the swap file. The only reason for not doing this is if you are using ClickHouse on your personal laptop.", + "title": "Swap file" + }, + { + "location": "/operations/tips/#huge-pages", + "text": "Always disable transparent huge pages. It interferes with memory allocators, which leads to significant performance degradation. echo never | sudo tee /sys/kernel/mm/transparent_hugepage/enabled Use perf top to watch the time spent in the kernel for memory management.\nPermanent huge pages also do not need to be allocated.", + "title": "Huge pages" + }, + { + "location": "/operations/tips/#storage-subsystem", + "text": "If your budget allows you to use SSD, use SSD.\nIf not, use HDD. SATA HDDs 7200 RPM will do. Give preference to a lot of servers with local hard drives over a smaller number of servers with attached disk shelves.\nBut for storing archives with rare queries, shelves will work.", + "title": "Storage subsystem" + }, + { + "location": "/operations/tips/#raid", + "text": "When using HDD, you can combine their RAID-10, RAID-5, RAID-6 or RAID-50.\nFor Linux, software RAID is better (with mdadm ). We don't recommend using LVM.\nWhen creating RAID-10, select the far layout.\nIf your budget allows, choose RAID-10. If you have more than 4 disks, use RAID-6 (preferred) or RAID-50, instead of RAID-5.\nWhen using RAID-5, RAID-6 or RAID-50, always increase stripe_cache_size, since the default value is usually not the best choice. echo 4096 | sudo tee /sys/block/md2/md/stripe_cache_size Calculate the exact number from the number of devices and the block size, using the formula: 2 * num_devices * chunk_size_in_bytes / 4096 . A block size of 1025 KB is sufficient for all RAID configurations.\nNever set the block size too small or too large. You can use RAID-0 on SSD.\nRegardless of RAID use, always use replication for data security. Enable NCQ with a long queue. For HDD, choose the CFQ scheduler, and for SSD, choose noop. Don't reduce the 'readahead' setting.\nFor HDD, enable the write cache.", + "title": "RAID" + }, + { + "location": "/operations/tips/#file-system", + "text": "Ext4 is the most reliable option. Set the mount options noatime, nobarrier .\nXFS is also suitable, but it hasn't been as thoroughly tested with ClickHouse.\nMost other file systems should also work fine. File systems with delayed allocation work better.", + "title": "File system" + }, + { + "location": "/operations/tips/#linux-kernel", + "text": "Don't use an outdated Linux kernel. In 2015, 3.18.19 was new enough.\nConsider using the kernel build from Yandex: https://github.com/yandex/smart \u2013 it provides at least a 5% performance increase.", + "title": "Linux kernel" + }, + { + "location": "/operations/tips/#network", + "text": "If you are using IPv6, increase the size of the route cache.\nThe Linux kernel prior to 3.2 had a multitude of problems with IPv6 implementation. Use at least a 10 GB network, if possible. 1 Gb will also work, but it will be much worse for patching replicas with tens of terabytes of data, or for processing distributed queries with a large amount of intermediate data.", + "title": "Network" + }, + { + "location": "/operations/tips/#zookeeper", + "text": "You are probably already using ZooKeeper for other purposes. You can use the same installation of ZooKeeper, if it isn't already overloaded. It's best to use a fresh version of ZooKeeper \u2013 3.4.9 or later. The version in stable Linux distributions may be outdated. With the default settings, ZooKeeper is a time bomb: The ZooKeeper server won't delete files from old snapshots and logs when using the default configuration (see autopurge), and this is the responsibility of the operator. This bomb must be defused. The ZooKeeper (3.5.1) configuration below is used in the Yandex.Metrica production environment as of May 20, 2017: zoo.cfg: # http://hadoop.apache.org/zookeeper/docs/current/zookeeperAdmin.html # The number of milliseconds of each tick tickTime = 2000 # The number of ticks that the initial # synchronization phase can take initLimit = 30000 # The number of ticks that can pass between # sending a request and getting an acknowledgement syncLimit = 10 maxClientCnxns = 2000 maxSessionTimeout = 60000000 # the directory where the snapshot is stored. dataDir = /opt/zookeeper/ {{ cluster [ name ] }} /data # Place the dataLogDir to a separate physical disc for better performance dataLogDir = /opt/zookeeper/ {{ cluster [ name ] }} /logs\n\nautopurge.snapRetainCount = 10 \nautopurge.purgeInterval = 1 # To avoid seeks ZooKeeper allocates space in the transaction log file in # blocks of preAllocSize kilobytes. The default block size is 64M. One reason # for changing the size of the blocks is to reduce the block size if snapshots # are taken more often. (Also, see snapCount). preAllocSize = 131072 # Clients can submit requests faster than ZooKeeper can process them, # especially if there are a lot of clients. To prevent ZooKeeper from running # out of memory due to queued requests, ZooKeeper will throttle clients so that # there is no more than globalOutstandingLimit outstanding requests in the # system. The default limit is 1,000.ZooKeeper logs transactions to a # transaction log. After snapCount transactions are written to a log file a # snapshot is started and a new transaction log file is started. The default # snapCount is 10,000. snapCount = 3000000 # If this option is defined, requests will be will logged to a trace file named # traceFile.year.month.day. #traceFile= # Leader accepts client connections. Default value is yes . The leader machine # coordinates updates. For higher update throughput at thes slight expense of # read throughput the leader can be configured to not accept clients and focus # on coordination. leaderServes = yes standaloneEnabled = false dynamicConfigFile = /etc/zookeeper- {{ cluster [ name ] }} /conf/zoo.cfg.dynamic Java version: Java(TM) SE Runtime Environment (build 1.8.0_25-b17)\nJava HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode) JVM parameters: NAME = zookeeper- {{ cluster [ name ] }} ZOOCFGDIR = /etc/ $NAME /conf # TODO this is really ugly # How to find out, which jars are needed? # seems, that log4j requires the log4j.properties file to be in the classpath CLASSPATH = $ZOOCFGDIR :/usr/build/classes:/usr/build/lib/*.jar:/usr/share/zookeeper/zookeeper-3.5.1-metrika.jar:/usr/share/zookeeper/slf4j-log4j12-1.7.5.jar:/usr/share/zookeeper/slf4j-api-1.7.5.jar:/usr/share/zookeeper/servlet-api-2.5-20081211.jar:/usr/share/zookeeper/netty-3.7.0.Final.jar:/usr/share/zookeeper/log4j-1.2.16.jar:/usr/share/zookeeper/jline-2.11.jar:/usr/share/zookeeper/jetty-util-6.1.26.jar:/usr/share/zookeeper/jetty-6.1.26.jar:/usr/share/zookeeper/javacc.jar:/usr/share/zookeeper/jackson-mapper-asl-1.9.11.jar:/usr/share/zookeeper/jackson-core-asl-1.9.11.jar:/usr/share/zookeeper/commons-cli-1.2.jar:/usr/src/java/lib/*.jar:/usr/etc/zookeeper ZOOCFG = $ZOOCFGDIR /zoo.cfg ZOO_LOG_DIR = /var/log/ $NAME USER = zookeeper GROUP = zookeeper PIDDIR = /var/run/ $NAME PIDFILE = $PIDDIR / $NAME .pid SCRIPTNAME = /etc/init.d/ $NAME JAVA = /usr/bin/java ZOOMAIN = org.apache.zookeeper.server.quorum.QuorumPeerMain ZOO_LOG4J_PROP = INFO,ROLLINGFILE JMXLOCALONLY = false JAVA_OPTS = -Xms{{ cluster.get( xms , 128M ) }} \\ -Xmx{{ cluster.get( xmx , 1G ) }} \\ -Xloggc:/var/log/ $NAME /zookeeper-gc.log \\ -XX:+UseGCLogFileRotation \\ -XX:NumberOfGCLogFiles=16 \\ -XX:GCLogFileSize=16M \\ -verbose:gc \\ -XX:+PrintGCTimeStamps \\ -XX:+PrintGCDateStamps \\ -XX:+PrintGCDetails -XX:+PrintTenuringDistribution \\ -XX:+PrintGCApplicationStoppedTime \\ -XX:+PrintGCApplicationConcurrentTime \\ -XX:+PrintSafepointStatistics \\ -XX:+UseParNewGC \\ -XX:+UseConcMarkSweepGC \\ -XX:+CMSParallelRemarkEnabled Salt init: description zookeeper-{{ cluster[ name ] }} centralized coordination service \n\nstart on runlevel [2345]\nstop on runlevel [!2345]\n\nrespawn\n\nlimit nofile 8192 8192\n\npre-start script\n [ -r /etc/zookeeper-{{ cluster[ name ] }}/conf/environment ] || exit 0\n . /etc/zookeeper-{{ cluster[ name ] }}/conf/environment\n [ -d $ZOO_LOG_DIR ] || mkdir -p $ZOO_LOG_DIR\n chown $USER:$GROUP $ZOO_LOG_DIR\nend script\n\nscript\n . /etc/zookeeper-{{ cluster[ name ] }}/conf/environment\n [ -r /etc/default/zookeeper ] . /etc/default/zookeeper\n if [ -z $JMXDISABLE ]; then\n JAVA_OPTS= $JAVA_OPTS -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.local.only=$JMXLOCALONLY \n fi\n exec start-stop-daemon --start -c $USER --exec $JAVA --name zookeeper-{{ cluster[ name ] }} \\\n -- -cp $CLASSPATH $JAVA_OPTS -Dzookeeper.log.dir=${ZOO_LOG_DIR} \\\n -Dzookeeper.root.logger=${ZOO_LOG4J_PROP} $ZOOMAIN $ZOOCFG\nend script", + "title": "ZooKeeper" + }, + { + "location": "/operations/server_settings/", + "text": "Server configuration parameters\n\n\nThis section contains descriptions of server settings that cannot be changed at the session or query level.\n\n\nThese settings are stored in the \nconfig.xml\n file on the ClickHouse server.\n\n\nOther settings are described in the \"\nSettings\n\" section.\n\n\nBefore studying the settings, read the \nConfiguration files\n section and note the use of substitutions (the \nincl\n and \noptional\n attributes).", + "title": "Introduction" + }, + { + "location": "/operations/server_settings/#server-configuration-parameters", + "text": "This section contains descriptions of server settings that cannot be changed at the session or query level. These settings are stored in the config.xml file on the ClickHouse server. Other settings are described in the \" Settings \" section. Before studying the settings, read the Configuration files section and note the use of substitutions (the incl and optional attributes).", + "title": "Server configuration parameters" + }, + { + "location": "/operations/server_settings/settings/", + "text": "Server settings\n\n\n\n\nbuiltin_dictionaries_reload_interval\n\n\nThe interval in seconds before reloading built-in dictionaries.\n\n\nClickHouse reloads built-in dictionaries every x seconds. This makes it possible to edit dictionaries \"on the fly\" without restarting the server.\n\n\nDefault value: 3600.\n\n\nExample\n\n\nbuiltin_dictionaries_reload_interval\n3600\n/builtin_dictionaries_reload_interval\n\n\n\n\n\n\n\n\ncompression\n\n\nData compression settings.\n\n\n\n\nDon't use it if you have just started using ClickHouse.\n\n\n\n\n\nThe configuration looks like this:\n\n\ncompression\n\n \ncase\n\n \nparameters/\n\n \n/case\n\n ...\n\n/compression\n\n\n\n\n\n\nYou can configure multiple sections \ncase\n.\n\n\nBlock field \ncase\n:\n\n\n\n\nmin_part_size\n \u2013 The minimum size of a table part.\n\n\nmin_part_size_ratio\n \u2013 The ratio of the minimum size of a table part to the full size of the table.\n\n\nmethod\n \u2013 Compression method. Acceptable values \u200b: \nlz4\n or \nzstd\n(experimental).\n\n\n\n\nClickHouse checks \nmin_part_size\n and \nmin_part_size_ratio\n and processes the \ncase\n blocks that match these conditions. If none of the \ncase\n matches, ClickHouse applies the \nlz4\n compression algorithm.\n\n\nExample\n\n\ncompression\n \nincl=\nclickhouse_compression\n\n \ncase\n\n \nmin_part_size\n10000000000\n/min_part_size\n\n \nmin_part_size_ratio\n0.01\n/min_part_size_ratio\n\n \nmethod\nzstd\n/method\n\n \n/case\n\n\n/compression\n\n\n\n\n\n\n\n\ndefault_database\n\n\nThe default database.\n\n\nTo get a list of databases, use the \nSHOW DATABASES\n.\n\n\nExample\n\n\ndefault_database\ndefault\n/default_database\n\n\n\n\n\n\n\n\ndefault_profile\n\n\nDefault settings profile.\n\n\nSettings profiles are located in the file specified in the parameter \nuser_config\n.\n\n\nExample\n\n\ndefault_profile\ndefault\n/default_profile\n\n\n\n\n\n\n\n\ndictionaries_config\n\n\nThe path to the config file for external dictionaries.\n\n\nPath:\n\n\n\n\nSpecify the absolute path or the path relative to the server config file.\n\n\nThe path can contain wildcards * and ?.\n\n\n\n\nSee also \"\nExternal dictionaries\n\".\n\n\nExample\n\n\ndictionaries_config\n*_dictionary.xml\n/dictionaries_config\n\n\n\n\n\n\n\n\ndictionaries_lazy_load\n\n\nLazy loading of dictionaries.\n\n\nIf \ntrue\n, then each dictionary is created on first use. If dictionary creation failed, the function that was using the dictionary throws an exception.\n\n\nIf \nfalse\n, all dictionaries are created when the server starts, and if there is an error, the server shuts down.\n\n\nThe default is \ntrue\n.\n\n\nExample\n\n\ndictionaries_lazy_load\ntrue\n/dictionaries_lazy_load\n\n\n\n\n\n\n\n\nformat_schema_path\n\n\nThe path to the directory with the schemes for the input data, such as schemas for the \nCapnProto\n format.\n\n\nExample\n\n\n \n!-- Directory containing schema files for various input formats. --\n\n \nformat_schema_path\nformat_schemas/\n/format_schema_path\n\n\n\n\n\n\n\n\ngraphite\n\n\nSending data to \nGraphite\n.\n\n\nSettings:\n\n\n\n\nhost \u2013 The Graphite server.\n\n\nport \u2013 The port on the Graphite server.\n\n\ninterval \u2013 The interval for sending, in seconds.\n\n\ntimeout \u2013 The timeout for sending data, in seconds.\n\n\nroot_path \u2013 Prefix for keys.\n\n\nmetrics \u2013 Sending data from a :ref:\nsystem_tables-system.metrics\n table.\n\n\nevents \u2013 Sending data from a :ref:\nsystem_tables-system.events\n table.\n\n\nasynchronous_metrics \u2013 Sending data from a :ref:\nsystem_tables-system.asynchronous_metrics\n table.\n\n\n\n\nYou can configure multiple \ngraphite\n clauses. For instance, you can use this for sending different data at different intervals.\n\n\nExample\n\n\ngraphite\n\n \nhost\nlocalhost\n/host\n\n \nport\n42000\n/port\n\n \ntimeout\n0.1\n/timeout\n\n \ninterval\n60\n/interval\n\n \nroot_path\none_min\n/root_path\n\n \nmetrics\ntrue\n/metrics\n\n \nevents\ntrue\n/events\n\n \nasynchronous_metrics\ntrue\n/asynchronous_metrics\n\n\n/graphite\n\n\n\n\n\n\n\n\ngraphite_rollup\n\n\nSettings for thinning data for Graphite.\n\n\nFor more information, see \nGraphiteMergeTree\n.\n\n\nExample\n\n\ngraphite_rollup_example\n\n \ndefault\n\n \nfunction\nmax\n/function\n\n \nretention\n\n \nage\n0\n/age\n\n \nprecision\n60\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n3600\n/age\n\n \nprecision\n300\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n86400\n/age\n\n \nprecision\n3600\n/precision\n\n \n/retention\n\n \n/default\n\n\n/graphite_rollup_example\n\n\n\n\n\n\n\n\nhttp_port/https_port\n\n\nThe port for connecting to the server over HTTP(s).\n\n\nIf \nhttps_port\n is specified, \nopenSSL\n must be configured.\n\n\nIf \nhttp_port\n is specified, the openSSL configuration is ignored even if it is set.\n\n\nExample\n\n\nhttps\n0000\n/https\n\n\n\n\n\n\n\n\nhttp_server_default_response\n\n\nThe page that is shown by default when you access the ClickHouse HTTP(s) server.\n\n\nExample\n\n\nOpens \nhttps://tabix.io/\n when accessing \nhttp://localhost: http_port\n.\n\n\nhttp_server_default_response\n\n \n![CDATA[\nhtml ng-app=\nSMI2\nhead\nbase href=\nhttp://ui.tabix.io/\n/head\nbody\ndiv ui-view=\n class=\ncontent-ui\n/div\nscript src=\nhttp://loader.tabix.io/master.js\n/script\n/body\n/html\n]]\n\n\n/http_server_default_response\n\n\n\n\n\n\n\n\ninclude_from\n\n\nThe path to the file with substitutions.\n\n\nFor more information, see the section \"\nConfiguration files\n\".\n\n\nExample\n\n\ninclude_from\n/etc/metrica.xml\n/include_from\n\n\n\n\n\n\n\n\ninterserver_http_port\n\n\nPort for exchanging data between ClickHouse servers.\n\n\nExample\n\n\ninterserver_http_port\n9009\n/interserver_http_port\n\n\n\n\n\n\n\n\ninterserver_http_host\n\n\nThe host name that can be used by other servers to access this server.\n\n\nIf omitted, it is defined in the same way as the \nhostname-f\n command.\n\n\nUseful for breaking away from a specific network interface.\n\n\nExample\n\n\ninterserver_http_host\nexample.yandex.ru\n/interserver_http_host\n\n\n\n\n\n\n\n\nkeep_alive_timeout\n\n\nThe number of milliseconds that ClickHouse waits for incoming requests before closing the connection.\n\n\nExample\n\n\nkeep_alive_timeout\n3\n/keep_alive_timeout\n\n\n\n\n\n\n\n\nlisten_host\n\n\nRestriction on hosts that requests can come from. If you want the server to answer all of them, specify \n::\n.\n\n\nExamples:\n\n\nlisten_host\n::1\n/listen_host\n\n\nlisten_host\n127.0.0.1\n/listen_host\n\n\n\n\n\n\n\n\nlogger\n\n\nLogging settings.\n\n\nKeys:\n\n\n\n\nlevel \u2013 Logging level. Acceptable values: \ntrace\n, \ndebug\n, \ninformation\n, \nwarning\n, \nerror\n.\n\n\nlog \u2013 The log file. Contains all the entries according to \nlevel\n.\n\n\nerrorlog \u2013 Error log file.\n\n\nsize \u2013 Size of the file. Applies to \nlog\nand\nerrorlog\n. Once the file reaches \nsize\n, ClickHouse archives and renames it, and creates a new log file in its place.\n\n\ncount \u2013 The number of archived log files that ClickHouse stores.\n\n\n\n\nExample\n\n\nlogger\n\n \nlevel\ntrace\n/level\n\n \nlog\n/var/log/clickhouse-server/clickhouse-server.log\n/log\n\n \nerrorlog\n/var/log/clickhouse-server/clickhouse-server.err.log\n/errorlog\n\n \nsize\n1000M\n/size\n\n \ncount\n10\n/count\n\n\n/logger\n\n\n\n\n\n\n\n\nmacros\n\n\nParameter substitutions for replicated tables.\n\n\nCan be omitted if replicated tables are not used.\n\n\nFor more information, see the section \"\nCreating replicated tables\n\".\n\n\nExample\n\n\nmacros\n \nincl=\nmacros\n \noptional=\ntrue\n \n/\n\n\n\n\n\n\n\n\nmark_cache_size\n\n\nApproximate size (in bytes) of the cache of \"marks\" used by \nMergeTree\n engines.\n\n\nThe cache is shared for the server and memory is allocated as needed. The cache size must be at least 5368709120.\n\n\nExample\n\n\nmark_cache_size\n5368709120\n/mark_cache_size\n\n\n\n\n\n\n\n\nmax_concurrent_queries\n\n\nThe maximum number of simultaneously processed requests.\n\n\nExample\n\n\nmax_concurrent_queries\n100\n/max_concurrent_queries\n\n\n\n\n\n\n\n\nmax_connections\n\n\nThe maximum number of inbound connections.\n\n\nExample\n\n\nmax_connections\n4096\n/max_connections\n\n\n\n\n\n\n\n\nmax_open_files\n\n\nThe maximum number of open files.\n\n\nBy default: \nmaximum\n.\n\n\nWe recommend using this option in Mac OS X, since the \ngetrlimit()\n function returns an incorrect value.\n\n\nExample\n\n\nmax_open_files\n262144\n/max_open_files\n\n\n\n\n\n\n\n\nmax_table_size_to_drop\n\n\nRestriction on deleting tables.\n\n\nIf the size of a \nMergeTree\n type table exceeds \nmax_table_size_to_drop\n (in bytes), you can't delete it using a DROP query.\n\n\nIf you still need to delete the table without restarting the ClickHouse server, create the \nclickhouse-path\n/flags/force_drop_table\n file and run the DROP query.\n\n\nDefault value: 50 GB.\n\n\nThe value 0 means that you can delete all tables without any restrictions.\n\n\nExample\n\n\nmax_table_size_to_drop\n0\n/max_table_size_to_drop\n\n\n\n\n\n\n\n\nmerge_tree\n\n\nFine tuning for tables in the \n MergeTree\n family.\n\n\nFor more information, see the MergeTreeSettings.h header file.\n\n\nExample\n\n\nmerge_tree\n\n \nmax_suspicious_broken_parts\n5\n/max_suspicious_broken_parts\n\n\n/merge_tree\n\n\n\n\n\n\n\n\nopenSSL\n\n\nSSL client/server configuration.\n\n\nSupport for SSL is provided by the \nlibpoco\n library. The interface is described in the file \nSSLManager.h\n\n\nKeys for server/client settings:\n\n\n\n\nprivateKeyFile \u2013 The path to the file with the secret key of the PEM certificate. The file may contain a key and certificate at the same time.\n\n\ncertificateFile \u2013 The path to the client/server certificate file in PEM format. You can omit it if \nprivateKeyFile\n contains the certificate.\n\n\ncaConfig \u2013 The path to the file or directory that contains trusted root certificates.\n\n\nverificationMode \u2013 The method for checking the node's certificates. Details are in the description of the \nContext\n class. Possible values: \nnone\n, \nrelaxed\n, \nstrict\n, \nonce\n.\n\n\nverificationDepth \u2013 The maximum length of the verification chain. Verification will fail if the certificate chain length exceeds the set value.\n\n\nloadDefaultCAFile \u2013 Indicates that built-in CA certificates for OpenSSL will be used. Acceptable values: \ntrue\n, \nfalse\n. |\n\n\ncipherList \u2013 Supported OpenSSL encryptions. For example: \nALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH\n.\n\n\ncacheSessions \u2013 Enables or disables caching sessions. Must be used in combination with \nsessionIdContext\n. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nsessionIdContext \u2013 A unique set of random characters that the server appends to each generated identifier. The length of the string must not exceed \nSSL_MAX_SSL_SESSION_ID_LENGTH\n. This parameter is always recommended, since it helps avoid problems both if the server caches the session and if the client requested caching. Default value: \n${application.name}\n.\n\n\nsessionCacheSize \u2013 The maximum number of sessions that the server caches. Default value: 1024*20. 0 \u2013 Unlimited sessions.\n\n\nsessionTimeout \u2013 Time for caching the session on the server.\n\n\nextendedVerification \u2013 Automatically extended verification of certificates after the session ends. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nrequireTLSv1 \u2013 Require a TLSv1 connection. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nrequireTLSv1_1 \u2013 Require a TLSv1.1 connection. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nrequireTLSv1 \u2013 Require a TLSv1.2 connection. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nfips \u2013 Activates OpenSSL FIPS mode. Supported if the library's OpenSSL version supports FIPS.\n\n\nprivateKeyPassphraseHandler \u2013 Class (PrivateKeyPassphraseHandler subclass) that requests the passphrase for accessing the private key. For example: \nprivateKeyPassphraseHandler\n, \nname\nKeyFileHandler\n/name\n, \noptions\npassword\ntest\n/password\n/options\n, \n/privateKeyPassphraseHandler\n.\n\n\ninvalidCertificateHandler \u2013 Class (subclass of CertificateHandler) for verifying invalid certificates. For example: \ninvalidCertificateHandler\n \nname\nConsoleCertificateHandler\n/name\n \n/invalidCertificateHandler\n .\n\n\ndisableProtocols \u2013 Protocols that are not allowed to use.\n\n\npreferServerCiphers \u2013 Preferred server ciphers on the client.\n\n\n\n\nExample of settings:\n\n\nopenSSL\n\n \nserver\n\n \n!-- openssl req -subj \n/CN=localhost\n -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout /etc/clickhouse-server/server.key -out /etc/clickhouse-server/server.crt --\n\n \ncertificateFile\n/etc/clickhouse-server/server.crt\n/certificateFile\n\n \nprivateKeyFile\n/etc/clickhouse-server/server.key\n/privateKeyFile\n\n \n!-- openssl dhparam -out /etc/clickhouse-server/dhparam.pem 4096 --\n\n \ndhParamsFile\n/etc/clickhouse-server/dhparam.pem\n/dhParamsFile\n\n \nverificationMode\nnone\n/verificationMode\n\n \nloadDefaultCAFile\ntrue\n/loadDefaultCAFile\n\n \ncacheSessions\ntrue\n/cacheSessions\n\n \ndisableProtocols\nsslv2,sslv3\n/disableProtocols\n\n \npreferServerCiphers\ntrue\n/preferServerCiphers\n\n \n/server\n\n \nclient\n\n \nloadDefaultCAFile\ntrue\n/loadDefaultCAFile\n\n \ncacheSessions\ntrue\n/cacheSessions\n\n \ndisableProtocols\nsslv2,sslv3\n/disableProtocols\n\n \npreferServerCiphers\ntrue\n/preferServerCiphers\n\n \n!-- Use for self-signed: \nverificationMode\nnone\n/verificationMode\n --\n\n \ninvalidCertificateHandler\n\n \n!-- Use for self-signed: \nname\nAcceptCertificateHandler\n/name\n --\n\n \nname\nRejectCertificateHandler\n/name\n\n \n/invalidCertificateHandler\n\n \n/client\n\n\n/openSSL\n\n\n\n\n\n\n\n\npart_log\n\n\nLogging events that are associated with \nMergeTree\n data. For instance, adding or merging data. You can use the log to simulate merge algorithms and compare their characteristics. You can visualize the merge process.\n\n\nQueries are logged in the ClickHouse table, not in a separate file.\n\n\nColumns in the log:\n\n\n\n\nevent_time \u2013 Date of the event.\n\n\nduration_ms \u2013 Duration of the event.\n\n\nevent_type \u2013 Type of event. 1 \u2013 new data part; 2 \u2013 merge result; 3 \u2013 data part downloaded from replica; 4 \u2013 data part deleted.\n\n\ndatabase_name \u2013 The name of the database.\n\n\ntable_name \u2013 Name of the table.\n\n\npart_name \u2013 Name of the data part.\n\n\nsize_in_bytes \u2013 Size of the data part in bytes.\n\n\nmerged_from \u2013 An array of names of data parts that make up the merge (also used when downloading a merged part).\n\n\nmerge_time_ms \u2013 Time spent on the merge.\n\n\n\n\nUse the following parameters to configure logging:\n\n\n\n\ndatabase \u2013 Name of the database.\n\n\ntable \u2013 Name of the table.\n\n\npartition_by \u2013 Sets a \ncustom partitioning key\n.\n\n\nflush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk.\n\n\n\n\nExample\n\n\npart_log\n\n \ndatabase\nsystem\n/database\n\n \ntable\npart_log\n/table\n\n \npartition_by\ntoMonday(event_date)\n/partition_by\n\n \nflush_interval_milliseconds\n7500\n/flush_interval_milliseconds\n\n\n/part_log\n\n\n\n\n\n\n\n\npath\n\n\nThe path to the directory containing data.\n\n\n\n\nThe end slash is mandatory.\n\n\n\n\n\nExample\n\n\npath\n/var/lib/clickhouse/\n/path\n\n\n\n\n\n\n\n\nquery_log\n\n\nSetting for logging queries received with the \nlog_queries=1\n setting.\n\n\nQueries are logged in the ClickHouse table, not in a separate file.\n\n\nUse the following parameters to configure logging:\n\n\n\n\ndatabase \u2013 Name of the database.\n\n\ntable \u2013 Name of the table.\n\n\npartition_by \u2013 Sets a \ncustom partitioning key\n.\n\n\nflush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk.\n\n\n\n\nIf the table doesn't exist, ClickHouse will create it. If the structure of the query log changed when the ClickHouse server was updated, the table with the old structure is renamed, and a new table is created automatically.\n\n\nExample\n\n\nquery_log\n\n \ndatabase\nsystem\n/database\n\n \ntable\nquery_log\n/table\n\n \npartition_by\ntoMonday(event_date)\n/partition_by\n\n \nflush_interval_milliseconds\n7500\n/flush_interval_milliseconds\n\n\n/query_log\n\n\n\n\n\n\n\n\nremote_servers\n\n\nConfiguration of clusters used by the Distributed table engine.\n\n\nFor more information, see the section \"\nTable engines/Distributed\n\".\n\n\nExample\n\n\nremote_servers\n \nincl=\nclickhouse_remote_servers\n \n/\n\n\n\n\n\n\nFor the value of the \nincl\n attribute, see the section \"\nConfiguration files\n\".\n\n\n\n\ntimezone\n\n\nThe server's time zone.\n\n\nSpecified as an IANA identifier for the UTC time zone or geographic location (for example, Africa/Abidjan).\n\n\nThe time zone is necessary for conversions between String and DateTime formats when DateTime fields are output to text format (printed on the screen or in a file), and when getting DateTime from a string. In addition, the time zone is used in functions that work with the time and date if they didn't receive the time zone in the input parameters.\n\n\nExample\n\n\ntimezone\nEurope/Moscow\n/timezone\n\n\n\n\n\n\n\n\ntcp_port\n\n\nPort for communicating with clients over the TCP protocol.\n\n\nExample\n\n\ntcp_port\n9000\n/tcp_port\n\n\n\n\n\n\n\n\ntmp_path\n\n\nPath to temporary data for processing large queries.\n\n\n\n\nThe end slash is mandatory.\n\n\n\n\n\nExample\n\n\ntmp_path\n/var/lib/clickhouse/tmp/\n/tmp_path\n\n\n\n\n\n\n\n\nuncompressed_cache_size\n\n\nCache size (in bytes) for uncompressed data used by table engines from the \nMergeTree\n family.\n\n\nThere is one shared cache for the server. Memory is allocated on demand. The cache is used if the option \nuse_uncompressed_cache\n is enabled.\n\n\nThe uncompressed cache is advantageous for very short queries in individual cases.\n\n\nExample\n\n\nuncompressed_cache_size\n8589934592\n/uncompressed_cache_size\n\n\n\n\n\n\n\n\nusers_config\n\n\nPath to the file that contains:\n\n\n\n\nUser configurations.\n\n\nAccess rights.\n\n\nSettings profiles.\n\n\nQuota settings.\n\n\n\n\nExample\n\n\nusers_config\nusers.xml\n/users_config\n\n\n\n\n\n\n\n\nzookeeper\n\n\nConfiguration of ZooKeeper servers.\n\n\nClickHouse uses ZooKeeper for storing replica metadata when using replicated tables.\n\n\nThis parameter can be omitted if replicated tables are not used.\n\n\nFor more information, see the section \"\nReplication\n\".\n\n\nExample\n\n\nzookeeper\n \nincl=\nzookeeper-servers\n \noptional=\ntrue\n \n/", + "title": "Server settings" + }, + { + "location": "/operations/server_settings/settings/#server-settings", + "text": "", + "title": "Server settings" + }, + { + "location": "/operations/server_settings/settings/#builtin_dictionaries_reload_interval", + "text": "The interval in seconds before reloading built-in dictionaries. ClickHouse reloads built-in dictionaries every x seconds. This makes it possible to edit dictionaries \"on the fly\" without restarting the server. Default value: 3600. Example builtin_dictionaries_reload_interval 3600 /builtin_dictionaries_reload_interval", + "title": "builtin_dictionaries_reload_interval" + }, + { + "location": "/operations/server_settings/settings/#compression", + "text": "Data compression settings. \n\nDon't use it if you have just started using ClickHouse. The configuration looks like this: compression \n case \n parameters/ \n /case \n ... /compression You can configure multiple sections case . Block field case : min_part_size \u2013 The minimum size of a table part. min_part_size_ratio \u2013 The ratio of the minimum size of a table part to the full size of the table. method \u2013 Compression method. Acceptable values \u200b: lz4 or zstd (experimental). ClickHouse checks min_part_size and min_part_size_ratio and processes the case blocks that match these conditions. If none of the case matches, ClickHouse applies the lz4 compression algorithm. Example compression incl= clickhouse_compression \n case \n min_part_size 10000000000 /min_part_size \n min_part_size_ratio 0.01 /min_part_size_ratio \n method zstd /method \n /case /compression", + "title": "compression" + }, + { + "location": "/operations/server_settings/settings/#default_database", + "text": "The default database. To get a list of databases, use the SHOW DATABASES . Example default_database default /default_database", + "title": "default_database" + }, + { + "location": "/operations/server_settings/settings/#default_profile", + "text": "Default settings profile. Settings profiles are located in the file specified in the parameter user_config . Example default_profile default /default_profile", + "title": "default_profile" + }, + { + "location": "/operations/server_settings/settings/#dictionaries_config", + "text": "The path to the config file for external dictionaries. Path: Specify the absolute path or the path relative to the server config file. The path can contain wildcards * and ?. See also \" External dictionaries \". Example dictionaries_config *_dictionary.xml /dictionaries_config", + "title": "dictionaries_config" + }, + { + "location": "/operations/server_settings/settings/#dictionaries_lazy_load", + "text": "Lazy loading of dictionaries. If true , then each dictionary is created on first use. If dictionary creation failed, the function that was using the dictionary throws an exception. If false , all dictionaries are created when the server starts, and if there is an error, the server shuts down. The default is true . Example dictionaries_lazy_load true /dictionaries_lazy_load", + "title": "dictionaries_lazy_load" + }, + { + "location": "/operations/server_settings/settings/#format_schema_path", + "text": "The path to the directory with the schemes for the input data, such as schemas for the CapnProto format. Example !-- Directory containing schema files for various input formats. -- \n format_schema_path format_schemas/ /format_schema_path", + "title": "format_schema_path" + }, + { + "location": "/operations/server_settings/settings/#graphite", + "text": "Sending data to Graphite . Settings: host \u2013 The Graphite server. port \u2013 The port on the Graphite server. interval \u2013 The interval for sending, in seconds. timeout \u2013 The timeout for sending data, in seconds. root_path \u2013 Prefix for keys. metrics \u2013 Sending data from a :ref: system_tables-system.metrics table. events \u2013 Sending data from a :ref: system_tables-system.events table. asynchronous_metrics \u2013 Sending data from a :ref: system_tables-system.asynchronous_metrics table. You can configure multiple graphite clauses. For instance, you can use this for sending different data at different intervals. Example graphite \n host localhost /host \n port 42000 /port \n timeout 0.1 /timeout \n interval 60 /interval \n root_path one_min /root_path \n metrics true /metrics \n events true /events \n asynchronous_metrics true /asynchronous_metrics /graphite", + "title": "graphite" + }, + { + "location": "/operations/server_settings/settings/#graphite_rollup", + "text": "Settings for thinning data for Graphite. For more information, see GraphiteMergeTree . Example graphite_rollup_example \n default \n function max /function \n retention \n age 0 /age \n precision 60 /precision \n /retention \n retention \n age 3600 /age \n precision 300 /precision \n /retention \n retention \n age 86400 /age \n precision 3600 /precision \n /retention \n /default /graphite_rollup_example", + "title": "graphite_rollup" + }, + { + "location": "/operations/server_settings/settings/#http_porthttps_port", + "text": "The port for connecting to the server over HTTP(s). If https_port is specified, openSSL must be configured. If http_port is specified, the openSSL configuration is ignored even if it is set. Example https 0000 /https", + "title": "http_port/https_port" + }, + { + "location": "/operations/server_settings/settings/#http_server_default_response", + "text": "The page that is shown by default when you access the ClickHouse HTTP(s) server. Example Opens https://tabix.io/ when accessing http://localhost: http_port . http_server_default_response \n ![CDATA[ html ng-app= SMI2 head base href= http://ui.tabix.io/ /head body div ui-view= class= content-ui /div script src= http://loader.tabix.io/master.js /script /body /html ]] /http_server_default_response", + "title": "http_server_default_response" + }, + { + "location": "/operations/server_settings/settings/#include_from", + "text": "The path to the file with substitutions. For more information, see the section \" Configuration files \". Example include_from /etc/metrica.xml /include_from", + "title": "include_from" + }, + { + "location": "/operations/server_settings/settings/#interserver_http_port", + "text": "Port for exchanging data between ClickHouse servers. Example interserver_http_port 9009 /interserver_http_port", + "title": "interserver_http_port" + }, + { + "location": "/operations/server_settings/settings/#interserver_http_host", + "text": "The host name that can be used by other servers to access this server. If omitted, it is defined in the same way as the hostname-f command. Useful for breaking away from a specific network interface. Example interserver_http_host example.yandex.ru /interserver_http_host", + "title": "interserver_http_host" + }, + { + "location": "/operations/server_settings/settings/#keep_alive_timeout", + "text": "The number of milliseconds that ClickHouse waits for incoming requests before closing the connection. Example keep_alive_timeout 3 /keep_alive_timeout", + "title": "keep_alive_timeout" + }, + { + "location": "/operations/server_settings/settings/#listen_host", + "text": "Restriction on hosts that requests can come from. If you want the server to answer all of them, specify :: . Examples: listen_host ::1 /listen_host listen_host 127.0.0.1 /listen_host", + "title": "listen_host" + }, + { + "location": "/operations/server_settings/settings/#logger", + "text": "Logging settings. Keys: level \u2013 Logging level. Acceptable values: trace , debug , information , warning , error . log \u2013 The log file. Contains all the entries according to level . errorlog \u2013 Error log file. size \u2013 Size of the file. Applies to log and errorlog . Once the file reaches size , ClickHouse archives and renames it, and creates a new log file in its place. count \u2013 The number of archived log files that ClickHouse stores. Example logger \n level trace /level \n log /var/log/clickhouse-server/clickhouse-server.log /log \n errorlog /var/log/clickhouse-server/clickhouse-server.err.log /errorlog \n size 1000M /size \n count 10 /count /logger", + "title": "logger" + }, + { + "location": "/operations/server_settings/settings/#macros", + "text": "Parameter substitutions for replicated tables. Can be omitted if replicated tables are not used. For more information, see the section \" Creating replicated tables \". Example macros incl= macros optional= true /", + "title": "macros" + }, + { + "location": "/operations/server_settings/settings/#mark_cache_size", + "text": "Approximate size (in bytes) of the cache of \"marks\" used by MergeTree engines. The cache is shared for the server and memory is allocated as needed. The cache size must be at least 5368709120. Example mark_cache_size 5368709120 /mark_cache_size", + "title": "mark_cache_size" + }, + { + "location": "/operations/server_settings/settings/#max_concurrent_queries", + "text": "The maximum number of simultaneously processed requests. Example max_concurrent_queries 100 /max_concurrent_queries", + "title": "max_concurrent_queries" + }, + { + "location": "/operations/server_settings/settings/#max_connections", + "text": "The maximum number of inbound connections. Example max_connections 4096 /max_connections", + "title": "max_connections" + }, + { + "location": "/operations/server_settings/settings/#max_open_files", + "text": "The maximum number of open files. By default: maximum . We recommend using this option in Mac OS X, since the getrlimit() function returns an incorrect value. Example max_open_files 262144 /max_open_files", + "title": "max_open_files" + }, + { + "location": "/operations/server_settings/settings/#max_table_size_to_drop", + "text": "Restriction on deleting tables. If the size of a MergeTree type table exceeds max_table_size_to_drop (in bytes), you can't delete it using a DROP query. If you still need to delete the table without restarting the ClickHouse server, create the clickhouse-path /flags/force_drop_table file and run the DROP query. Default value: 50 GB. The value 0 means that you can delete all tables without any restrictions. Example max_table_size_to_drop 0 /max_table_size_to_drop", + "title": "max_table_size_to_drop" + }, + { + "location": "/operations/server_settings/settings/#merge_tree", + "text": "Fine tuning for tables in the MergeTree family. For more information, see the MergeTreeSettings.h header file. Example merge_tree \n max_suspicious_broken_parts 5 /max_suspicious_broken_parts /merge_tree", + "title": "merge_tree" + }, + { + "location": "/operations/server_settings/settings/#openssl", + "text": "SSL client/server configuration. Support for SSL is provided by the libpoco library. The interface is described in the file SSLManager.h Keys for server/client settings: privateKeyFile \u2013 The path to the file with the secret key of the PEM certificate. The file may contain a key and certificate at the same time. certificateFile \u2013 The path to the client/server certificate file in PEM format. You can omit it if privateKeyFile contains the certificate. caConfig \u2013 The path to the file or directory that contains trusted root certificates. verificationMode \u2013 The method for checking the node's certificates. Details are in the description of the Context class. Possible values: none , relaxed , strict , once . verificationDepth \u2013 The maximum length of the verification chain. Verification will fail if the certificate chain length exceeds the set value. loadDefaultCAFile \u2013 Indicates that built-in CA certificates for OpenSSL will be used. Acceptable values: true , false . | cipherList \u2013 Supported OpenSSL encryptions. For example: ALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH . cacheSessions \u2013 Enables or disables caching sessions. Must be used in combination with sessionIdContext . Acceptable values: true , false . sessionIdContext \u2013 A unique set of random characters that the server appends to each generated identifier. The length of the string must not exceed SSL_MAX_SSL_SESSION_ID_LENGTH . This parameter is always recommended, since it helps avoid problems both if the server caches the session and if the client requested caching. Default value: ${application.name} . sessionCacheSize \u2013 The maximum number of sessions that the server caches. Default value: 1024*20. 0 \u2013 Unlimited sessions. sessionTimeout \u2013 Time for caching the session on the server. extendedVerification \u2013 Automatically extended verification of certificates after the session ends. Acceptable values: true , false . requireTLSv1 \u2013 Require a TLSv1 connection. Acceptable values: true , false . requireTLSv1_1 \u2013 Require a TLSv1.1 connection. Acceptable values: true , false . requireTLSv1 \u2013 Require a TLSv1.2 connection. Acceptable values: true , false . fips \u2013 Activates OpenSSL FIPS mode. Supported if the library's OpenSSL version supports FIPS. privateKeyPassphraseHandler \u2013 Class (PrivateKeyPassphraseHandler subclass) that requests the passphrase for accessing the private key. For example: privateKeyPassphraseHandler , name KeyFileHandler /name , options password test /password /options , /privateKeyPassphraseHandler . invalidCertificateHandler \u2013 Class (subclass of CertificateHandler) for verifying invalid certificates. For example: invalidCertificateHandler name ConsoleCertificateHandler /name /invalidCertificateHandler . disableProtocols \u2013 Protocols that are not allowed to use. preferServerCiphers \u2013 Preferred server ciphers on the client. Example of settings: openSSL \n server \n !-- openssl req -subj /CN=localhost -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout /etc/clickhouse-server/server.key -out /etc/clickhouse-server/server.crt -- \n certificateFile /etc/clickhouse-server/server.crt /certificateFile \n privateKeyFile /etc/clickhouse-server/server.key /privateKeyFile \n !-- openssl dhparam -out /etc/clickhouse-server/dhparam.pem 4096 -- \n dhParamsFile /etc/clickhouse-server/dhparam.pem /dhParamsFile \n verificationMode none /verificationMode \n loadDefaultCAFile true /loadDefaultCAFile \n cacheSessions true /cacheSessions \n disableProtocols sslv2,sslv3 /disableProtocols \n preferServerCiphers true /preferServerCiphers \n /server \n client \n loadDefaultCAFile true /loadDefaultCAFile \n cacheSessions true /cacheSessions \n disableProtocols sslv2,sslv3 /disableProtocols \n preferServerCiphers true /preferServerCiphers \n !-- Use for self-signed: verificationMode none /verificationMode -- \n invalidCertificateHandler \n !-- Use for self-signed: name AcceptCertificateHandler /name -- \n name RejectCertificateHandler /name \n /invalidCertificateHandler \n /client /openSSL", + "title": "openSSL" + }, + { + "location": "/operations/server_settings/settings/#part_log", + "text": "Logging events that are associated with MergeTree data. For instance, adding or merging data. You can use the log to simulate merge algorithms and compare their characteristics. You can visualize the merge process. Queries are logged in the ClickHouse table, not in a separate file. Columns in the log: event_time \u2013 Date of the event. duration_ms \u2013 Duration of the event. event_type \u2013 Type of event. 1 \u2013 new data part; 2 \u2013 merge result; 3 \u2013 data part downloaded from replica; 4 \u2013 data part deleted. database_name \u2013 The name of the database. table_name \u2013 Name of the table. part_name \u2013 Name of the data part. size_in_bytes \u2013 Size of the data part in bytes. merged_from \u2013 An array of names of data parts that make up the merge (also used when downloading a merged part). merge_time_ms \u2013 Time spent on the merge. Use the following parameters to configure logging: database \u2013 Name of the database. table \u2013 Name of the table. partition_by \u2013 Sets a custom partitioning key . flush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk. Example part_log \n database system /database \n table part_log /table \n partition_by toMonday(event_date) /partition_by \n flush_interval_milliseconds 7500 /flush_interval_milliseconds /part_log", + "title": "part_log" + }, + { + "location": "/operations/server_settings/settings/#path", + "text": "The path to the directory containing data. \n\nThe end slash is mandatory. Example path /var/lib/clickhouse/ /path", + "title": "path" + }, + { + "location": "/operations/server_settings/settings/#query_log", + "text": "Setting for logging queries received with the log_queries=1 setting. Queries are logged in the ClickHouse table, not in a separate file. Use the following parameters to configure logging: database \u2013 Name of the database. table \u2013 Name of the table. partition_by \u2013 Sets a custom partitioning key . flush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk. If the table doesn't exist, ClickHouse will create it. If the structure of the query log changed when the ClickHouse server was updated, the table with the old structure is renamed, and a new table is created automatically. Example query_log \n database system /database \n table query_log /table \n partition_by toMonday(event_date) /partition_by \n flush_interval_milliseconds 7500 /flush_interval_milliseconds /query_log", + "title": "query_log" + }, + { + "location": "/operations/server_settings/settings/#remote_servers", + "text": "Configuration of clusters used by the Distributed table engine. For more information, see the section \" Table engines/Distributed \". Example remote_servers incl= clickhouse_remote_servers / For the value of the incl attribute, see the section \" Configuration files \".", + "title": "remote_servers" + }, + { + "location": "/operations/server_settings/settings/#timezone", + "text": "The server's time zone. Specified as an IANA identifier for the UTC time zone or geographic location (for example, Africa/Abidjan). The time zone is necessary for conversions between String and DateTime formats when DateTime fields are output to text format (printed on the screen or in a file), and when getting DateTime from a string. In addition, the time zone is used in functions that work with the time and date if they didn't receive the time zone in the input parameters. Example timezone Europe/Moscow /timezone", + "title": "timezone" + }, + { + "location": "/operations/server_settings/settings/#tcp_port", + "text": "Port for communicating with clients over the TCP protocol. Example tcp_port 9000 /tcp_port", + "title": "tcp_port" + }, + { + "location": "/operations/server_settings/settings/#tmp_path", + "text": "Path to temporary data for processing large queries. \n\nThe end slash is mandatory. Example tmp_path /var/lib/clickhouse/tmp/ /tmp_path", + "title": "tmp_path" + }, + { + "location": "/operations/server_settings/settings/#uncompressed_cache_size", + "text": "Cache size (in bytes) for uncompressed data used by table engines from the MergeTree family. There is one shared cache for the server. Memory is allocated on demand. The cache is used if the option use_uncompressed_cache is enabled. The uncompressed cache is advantageous for very short queries in individual cases. Example uncompressed_cache_size 8589934592 /uncompressed_cache_size", + "title": "uncompressed_cache_size" + }, + { + "location": "/operations/server_settings/settings/#users_config", + "text": "Path to the file that contains: User configurations. Access rights. Settings profiles. Quota settings. Example users_config users.xml /users_config", + "title": "users_config" + }, + { + "location": "/operations/server_settings/settings/#zookeeper", + "text": "Configuration of ZooKeeper servers. ClickHouse uses ZooKeeper for storing replica metadata when using replicated tables. This parameter can be omitted if replicated tables are not used. For more information, see the section \" Replication \". Example zookeeper incl= zookeeper-servers optional= true /", + "title": "zookeeper" + }, + { + "location": "/operations/settings/", + "text": "Settings\n\n\nThere are multiple ways to make all the settings described below.\nSettings are configured in layers, so each subsequent layer redefines the previous settings.\n\n\nWays to configure settings, in order of priority:\n\n\n\n\nSettings in the server config file.\n\n\n\n\nSettings from user profiles.\n\n\n\n\nSession settings.\n\n\n\n\nSend \nSET setting=value\n from the ClickHouse console client in interactive mode.\nSimilarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to specify the \nsession_id\n HTTP parameter.\n\n\n\n\nFor a query.\n\n\nWhen starting the ClickHouse console client in non-interactive mode, set the startup parameter \n--setting=value\n.\n\n\nWhen using the HTTP API, pass CGI parameters (\nURL?setting_1=value\nsetting_2=value...\n).\n\n\n\n\nSettings that can only be made in the server config file are not covered in this section.", + "title": "Introduction" + }, + { + "location": "/operations/settings/#settings", + "text": "There are multiple ways to make all the settings described below.\nSettings are configured in layers, so each subsequent layer redefines the previous settings. Ways to configure settings, in order of priority: Settings in the server config file. Settings from user profiles. Session settings. Send SET setting=value from the ClickHouse console client in interactive mode.\nSimilarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to specify the session_id HTTP parameter. For a query. When starting the ClickHouse console client in non-interactive mode, set the startup parameter --setting=value . When using the HTTP API, pass CGI parameters ( URL?setting_1=value setting_2=value... ). Settings that can only be made in the server config file are not covered in this section.", + "title": "Settings" + }, + { + "location": "/operations/settings/query_complexity/", + "text": "Restrictions on query complexity\n\n\nRestrictions on query complexity are part of the settings.\nThey are used in order to provide safer execution from the user interface.\nAlmost all the restrictions only apply to SELECTs.For distributed query processing, restrictions are applied on each server separately.\n\n\nRestrictions on the \"maximum amount of something\" can take the value 0, which means \"unrestricted\".\nMost restrictions also have an 'overflow_mode' setting, meaning what to do when the limit is exceeded.\nIt can take one of two values: \nthrow\n or \nbreak\n. Restrictions on aggregation (group_by_overflow_mode) also have the value \nany\n.\n\n\nthrow\n \u2013 Throw an exception (default).\n\n\nbreak\n \u2013 Stop executing the query and return the partial result, as if the source data ran out.\n\n\nany (only for group_by_overflow_mode)\n \u2013 Continuing aggregation for the keys that got into the set, but don't add new keys to the set.\n\n\n\n\nreadonly\n\n\nWith a value of 0, you can execute any queries.\nWith a value of 1, you can only execute read requests (such as SELECT and SHOW). Requests for writing and changing settings (INSERT, SET) are prohibited.\nWith a value of 2, you can process read queries (SELECT, SHOW) and change settings (SET).\n\n\nAfter enabling readonly mode, you can't disable it in the current session.\n\n\nWhen using the GET method in the HTTP interface, 'readonly = 1' is set automatically. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.\n\n\n\n\nmax_memory_usage\n\n\nThe maximum amount of RAM to use for running a query on a single server.\n\n\nIn the default configuration file, the maximum is 10 GB.\n\n\nThe setting doesn't consider the volume of available memory or the total volume of memory on the machine.\nThe restriction applies to a single query within a single server.\nYou can use \nSHOW PROCESSLIST\n to see the current memory consumption for each query.\nIn addition, the peak memory consumption is tracked for each query and written to the log.\n\n\nMemory usage is not monitored for the states of certain aggregate functions.\n\n\nMemory usage is not fully tracked for states of the aggregate functions \nmin\n, \nmax\n, \nany\n, \nanyLast\n, \nargMin\n, \nargMax\n from \nString\n and \nArray\n arguments.\n\n\nMemory consumption is also restricted by the parameters \nmax_memory_usage_for_user\n and \nmax_memory_usage_for_all_queries\n.\n\n\nmax_memory_usage_for_user\n\n\nThe maximum amount of RAM to use for running a user's queries on a single server.\n\n\nDefault values are defined in \nSettings.h\n. By default, the amount is not restricted (\nmax_memory_usage_for_user = 0\n).\n\n\nSee also the description of \nmax_memory_usage\n.\n\n\nmax_memory_usage_for_all_queries\n\n\nThe maximum amount of RAM to use for running all queries on a single server.\n\n\nDefault values are defined in \nSettings.h\n. By default, the amount is not restricted (\nmax_memory_usage_for_all_queries = 0\n).\n\n\nSee also the description of \nmax_memory_usage\n.\n\n\nmax_rows_to_read\n\n\nThe following restrictions can be checked on each block (instead of on each row). That is, the restrictions can be broken a little.\nWhen running a query in multiple threads, the following restrictions apply to each thread separately.\n\n\nMaximum number of rows that can be read from a table when running a query.\n\n\nmax_bytes_to_read\n\n\nMaximum number of bytes (uncompressed data) that can be read from a table when running a query.\n\n\nread_overflow_mode\n\n\nWhat to do when the volume of data read exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_rows_to_group_by\n\n\nMaximum number of unique keys received from aggregation. This setting lets you limit memory consumption when aggregating.\n\n\ngroup_by_overflow_mode\n\n\nWhat to do when the number of unique keys for aggregation exceeds the limit: 'throw', 'break', or 'any'. By default, throw.\nUsing the 'any' value lets you run an approximation of GROUP BY. The quality of this approximation depends on the statistical nature of the data.\n\n\nmax_rows_to_sort\n\n\nMaximum number of rows before sorting. This allows you to limit memory consumption when sorting.\n\n\nmax_bytes_to_sort\n\n\nMaximum number of bytes before sorting.\n\n\nsort_overflow_mode\n\n\nWhat to do if the number of rows received before sorting exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_result_rows\n\n\nLimit on the number of rows in the result. Also checked for subqueries, and on remote servers when running parts of a distributed query.\n\n\nmax_result_bytes\n\n\nLimit on the number of bytes in the result. The same as the previous setting.\n\n\nresult_overflow_mode\n\n\nWhat to do if the volume of the result exceeds one of the limits: 'throw' or 'break'. By default, throw.\nUsing 'break' is similar to using LIMIT.\n\n\nmax_execution_time\n\n\nMaximum query execution time in seconds.\nAt this time, it is not checked for one of the sorting stages, or when merging and finalizing aggregate functions.\n\n\ntimeout_overflow_mode\n\n\nWhat to do if the query is run longer than 'max_execution_time': 'throw' or 'break'. By default, throw.\n\n\nmin_execution_speed\n\n\nMinimal execution speed in rows per second. Checked on every data block when 'timeout_before_checking_execution_speed' expires. If the execution speed is lower, an exception is thrown.\n\n\ntimeout_before_checking_execution_speed\n\n\nChecks that execution speed is not too slow (no less than 'min_execution_speed'), after the specified time in seconds has expired.\n\n\nmax_columns_to_read\n\n\nMaximum number of columns that can be read from a table in a single query. If a query requires reading a greater number of columns, it throws an exception.\n\n\nmax_temporary_columns\n\n\nMaximum number of temporary columns that must be kept in RAM at the same time when running a query, including constant columns. If there are more temporary columns than this, it throws an exception.\n\n\nmax_temporary_non_const_columns\n\n\nThe same thing as 'max_temporary_columns', but without counting constant columns.\nNote that constant columns are formed fairly often when running a query, but they require approximately zero computing resources.\n\n\nmax_subquery_depth\n\n\nMaximum nesting depth of subqueries. If subqueries are deeper, an exception is thrown. By default, 100.\n\n\nmax_pipeline_depth\n\n\nMaximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000.\n\n\nmax_ast_depth\n\n\nMaximum nesting depth of a query syntactic tree. If exceeded, an exception is thrown.\nAt this time, it isn't checked during parsing, but only after parsing the query. That is, a syntactic tree that is too deep can be created during parsing, but the query will fail. By default, 1000.\n\n\nmax_ast_elements\n\n\nMaximum number of elements in a query syntactic tree. If exceeded, an exception is thrown.\nIn the same way as the previous setting, it is checked only after parsing the query. By default, 10,000.\n\n\nmax_rows_in_set\n\n\nMaximum number of rows for a data set in the IN clause created from a subquery.\n\n\nmax_bytes_in_set\n\n\nMaximum number of bytes (uncompressed data) used by a set in the IN clause created from a subquery.\n\n\nset_overflow_mode\n\n\nWhat to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_rows_in_distinct\n\n\nMaximum number of different rows when using DISTINCT.\n\n\nmax_bytes_in_distinct\n\n\nMaximum number of bytes used by a hash table when using DISTINCT.\n\n\ndistinct_overflow_mode\n\n\nWhat to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_rows_to_transfer\n\n\nMaximum number of rows that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.\n\n\nmax_bytes_to_transfer\n\n\nMaximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.\n\n\ntransfer_overflow_mode\n\n\nWhat to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.", + "title": "Restrictions on query complexity" + }, + { + "location": "/operations/settings/query_complexity/#restrictions-on-query-complexity", + "text": "Restrictions on query complexity are part of the settings.\nThey are used in order to provide safer execution from the user interface.\nAlmost all the restrictions only apply to SELECTs.For distributed query processing, restrictions are applied on each server separately. Restrictions on the \"maximum amount of something\" can take the value 0, which means \"unrestricted\".\nMost restrictions also have an 'overflow_mode' setting, meaning what to do when the limit is exceeded.\nIt can take one of two values: throw or break . Restrictions on aggregation (group_by_overflow_mode) also have the value any . throw \u2013 Throw an exception (default). break \u2013 Stop executing the query and return the partial result, as if the source data ran out. any (only for group_by_overflow_mode) \u2013 Continuing aggregation for the keys that got into the set, but don't add new keys to the set.", + "title": "Restrictions on query complexity" + }, + { + "location": "/operations/settings/query_complexity/#readonly", + "text": "With a value of 0, you can execute any queries.\nWith a value of 1, you can only execute read requests (such as SELECT and SHOW). Requests for writing and changing settings (INSERT, SET) are prohibited.\nWith a value of 2, you can process read queries (SELECT, SHOW) and change settings (SET). After enabling readonly mode, you can't disable it in the current session. When using the GET method in the HTTP interface, 'readonly = 1' is set automatically. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.", + "title": "readonly" + }, + { + "location": "/operations/settings/query_complexity/#max_memory_usage", + "text": "The maximum amount of RAM to use for running a query on a single server. In the default configuration file, the maximum is 10 GB. The setting doesn't consider the volume of available memory or the total volume of memory on the machine.\nThe restriction applies to a single query within a single server.\nYou can use SHOW PROCESSLIST to see the current memory consumption for each query.\nIn addition, the peak memory consumption is tracked for each query and written to the log. Memory usage is not monitored for the states of certain aggregate functions. Memory usage is not fully tracked for states of the aggregate functions min , max , any , anyLast , argMin , argMax from String and Array arguments. Memory consumption is also restricted by the parameters max_memory_usage_for_user and max_memory_usage_for_all_queries .", + "title": "max_memory_usage" + }, + { + "location": "/operations/settings/query_complexity/#max_memory_usage_for_user", + "text": "The maximum amount of RAM to use for running a user's queries on a single server. Default values are defined in Settings.h . By default, the amount is not restricted ( max_memory_usage_for_user = 0 ). See also the description of max_memory_usage .", + "title": "max_memory_usage_for_user" + }, + { + "location": "/operations/settings/query_complexity/#max_memory_usage_for_all_queries", + "text": "The maximum amount of RAM to use for running all queries on a single server. Default values are defined in Settings.h . By default, the amount is not restricted ( max_memory_usage_for_all_queries = 0 ). See also the description of max_memory_usage .", + "title": "max_memory_usage_for_all_queries" + }, + { + "location": "/operations/settings/query_complexity/#max_rows_to_read", + "text": "The following restrictions can be checked on each block (instead of on each row). That is, the restrictions can be broken a little.\nWhen running a query in multiple threads, the following restrictions apply to each thread separately. Maximum number of rows that can be read from a table when running a query.", + "title": "max_rows_to_read" + }, + { + "location": "/operations/settings/query_complexity/#max_bytes_to_read", + "text": "Maximum number of bytes (uncompressed data) that can be read from a table when running a query.", + "title": "max_bytes_to_read" + }, + { + "location": "/operations/settings/query_complexity/#read_overflow_mode", + "text": "What to do when the volume of data read exceeds one of the limits: 'throw' or 'break'. By default, throw.", + "title": "read_overflow_mode" + }, + { + "location": "/operations/settings/query_complexity/#max_rows_to_group_by", + "text": "Maximum number of unique keys received from aggregation. This setting lets you limit memory consumption when aggregating.", + "title": "max_rows_to_group_by" + }, + { + "location": "/operations/settings/query_complexity/#group_by_overflow_mode", + "text": "What to do when the number of unique keys for aggregation exceeds the limit: 'throw', 'break', or 'any'. By default, throw.\nUsing the 'any' value lets you run an approximation of GROUP BY. The quality of this approximation depends on the statistical nature of the data.", + "title": "group_by_overflow_mode" + }, + { + "location": "/operations/settings/query_complexity/#max_rows_to_sort", + "text": "Maximum number of rows before sorting. This allows you to limit memory consumption when sorting.", + "title": "max_rows_to_sort" + }, + { + "location": "/operations/settings/query_complexity/#max_bytes_to_sort", + "text": "Maximum number of bytes before sorting.", + "title": "max_bytes_to_sort" + }, + { + "location": "/operations/settings/query_complexity/#sort_overflow_mode", + "text": "What to do if the number of rows received before sorting exceeds one of the limits: 'throw' or 'break'. By default, throw.", + "title": "sort_overflow_mode" + }, + { + "location": "/operations/settings/query_complexity/#max_result_rows", + "text": "Limit on the number of rows in the result. Also checked for subqueries, and on remote servers when running parts of a distributed query.", + "title": "max_result_rows" + }, + { + "location": "/operations/settings/query_complexity/#max_result_bytes", + "text": "Limit on the number of bytes in the result. The same as the previous setting.", + "title": "max_result_bytes" + }, + { + "location": "/operations/settings/query_complexity/#result_overflow_mode", + "text": "What to do if the volume of the result exceeds one of the limits: 'throw' or 'break'. By default, throw.\nUsing 'break' is similar to using LIMIT.", + "title": "result_overflow_mode" + }, + { + "location": "/operations/settings/query_complexity/#max_execution_time", + "text": "Maximum query execution time in seconds.\nAt this time, it is not checked for one of the sorting stages, or when merging and finalizing aggregate functions.", + "title": "max_execution_time" + }, + { + "location": "/operations/settings/query_complexity/#timeout_overflow_mode", + "text": "What to do if the query is run longer than 'max_execution_time': 'throw' or 'break'. By default, throw.", + "title": "timeout_overflow_mode" + }, + { + "location": "/operations/settings/query_complexity/#min_execution_speed", + "text": "Minimal execution speed in rows per second. Checked on every data block when 'timeout_before_checking_execution_speed' expires. If the execution speed is lower, an exception is thrown.", + "title": "min_execution_speed" + }, + { + "location": "/operations/settings/query_complexity/#timeout_before_checking_execution_speed", + "text": "Checks that execution speed is not too slow (no less than 'min_execution_speed'), after the specified time in seconds has expired.", + "title": "timeout_before_checking_execution_speed" + }, + { + "location": "/operations/settings/query_complexity/#max_columns_to_read", + "text": "Maximum number of columns that can be read from a table in a single query. If a query requires reading a greater number of columns, it throws an exception.", + "title": "max_columns_to_read" + }, + { + "location": "/operations/settings/query_complexity/#max_temporary_columns", + "text": "Maximum number of temporary columns that must be kept in RAM at the same time when running a query, including constant columns. If there are more temporary columns than this, it throws an exception.", + "title": "max_temporary_columns" + }, + { + "location": "/operations/settings/query_complexity/#max_temporary_non_const_columns", + "text": "The same thing as 'max_temporary_columns', but without counting constant columns.\nNote that constant columns are formed fairly often when running a query, but they require approximately zero computing resources.", + "title": "max_temporary_non_const_columns" + }, + { + "location": "/operations/settings/query_complexity/#max_subquery_depth", + "text": "Maximum nesting depth of subqueries. If subqueries are deeper, an exception is thrown. By default, 100.", + "title": "max_subquery_depth" + }, + { + "location": "/operations/settings/query_complexity/#max_pipeline_depth", + "text": "Maximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000.", + "title": "max_pipeline_depth" + }, + { + "location": "/operations/settings/query_complexity/#max_ast_depth", + "text": "Maximum nesting depth of a query syntactic tree. If exceeded, an exception is thrown.\nAt this time, it isn't checked during parsing, but only after parsing the query. That is, a syntactic tree that is too deep can be created during parsing, but the query will fail. By default, 1000.", + "title": "max_ast_depth" + }, + { + "location": "/operations/settings/query_complexity/#max_ast_elements", + "text": "Maximum number of elements in a query syntactic tree. If exceeded, an exception is thrown.\nIn the same way as the previous setting, it is checked only after parsing the query. By default, 10,000.", + "title": "max_ast_elements" + }, + { + "location": "/operations/settings/query_complexity/#max_rows_in_set", + "text": "Maximum number of rows for a data set in the IN clause created from a subquery.", + "title": "max_rows_in_set" + }, + { + "location": "/operations/settings/query_complexity/#max_bytes_in_set", + "text": "Maximum number of bytes (uncompressed data) used by a set in the IN clause created from a subquery.", + "title": "max_bytes_in_set" + }, + { + "location": "/operations/settings/query_complexity/#set_overflow_mode", + "text": "What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.", + "title": "set_overflow_mode" + }, + { + "location": "/operations/settings/query_complexity/#max_rows_in_distinct", + "text": "Maximum number of different rows when using DISTINCT.", + "title": "max_rows_in_distinct" + }, + { + "location": "/operations/settings/query_complexity/#max_bytes_in_distinct", + "text": "Maximum number of bytes used by a hash table when using DISTINCT.", + "title": "max_bytes_in_distinct" + }, + { + "location": "/operations/settings/query_complexity/#distinct_overflow_mode", + "text": "What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.", + "title": "distinct_overflow_mode" + }, + { + "location": "/operations/settings/query_complexity/#max_rows_to_transfer", + "text": "Maximum number of rows that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.", + "title": "max_rows_to_transfer" + }, + { + "location": "/operations/settings/query_complexity/#max_bytes_to_transfer", + "text": "Maximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.", + "title": "max_bytes_to_transfer" + }, + { + "location": "/operations/settings/query_complexity/#transfer_overflow_mode", + "text": "What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.", + "title": "transfer_overflow_mode" + }, + { + "location": "/operations/settings/settings/", + "text": "Settings\n\n\n\n\ndistributed_product_mode\n\n\nChanges the behavior of \ndistributed subqueries\n, i.e. in cases when the query contains the product of distributed tables.\n\n\nClickHouse applies the configuration if the subqueries on any level have a distributed table that exists on the local server and has more than one shard.\n\n\nRestrictions:\n\n\n\n\nOnly applied for IN and JOIN subqueries.\n\n\nUsed only if a distributed table is used in the FROM clause.\n\n\nNot used for a table-valued \n remote\n function.\n\n\n\n\nThe possible values \u200b\u200bare:\n\n\n\n\nfallback_to_stale_replicas_for_distributed_queries\n\n\nForces a query to an out-of-date replica if updated data is not available. See \"\nReplication\n\".\n\n\nClickHouse selects the most relevant from the outdated replicas of the table.\n\n\nUsed when performing \nSELECT\n from a distributed table that points to replicated tables.\n\n\nBy default, 1 (enabled).\n\n\n\n\nforce_index_by_date\n\n\nDisables query execution if the index can't be used by date.\n\n\nWorks with tables in the MergeTree family.\n\n\nIf \nforce_index_by_date=1\n, ClickHouse checks whether the query has a date key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For example, the condition \nDate != ' 2000-01-01 '\n is acceptable even when it matches all the data in the table (i.e., running the query requires a full scan). For more information about ranges of data in MergeTree tables, see \"\nMergeTree\n\".\n\n\n\n\nforce_primary_key\n\n\nDisables query execution if indexing by the primary key is not possible.\n\n\nWorks with tables in the MergeTree family.\n\n\nIf \nforce_primary_key=1\n, ClickHouse checks to see if the query has a primary key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For more information about data ranges in MergeTree tables, see \"\nMergeTree\n\".\n\n\n\n\nfsync_metadata\n\n\nEnable or disable fsync when writing .sql files. By default, it is enabled.\n\n\nIt makes sense to disable it if the server has millions of tiny table chunks that are constantly being created and destroyed.\n\n\ninput_format_allow_errors_num\n\n\nSets the maximum number of acceptable errors when reading from text formats (CSV, TSV, etc.).\n\n\nThe default value is 0.\n\n\nAlways pair it with \ninput_format_allow_errors_ratio\n. To skip errors, both settings must be greater than 0.\n\n\nIf an error occurred while reading rows but the error counter is still less than \ninput_format_allow_errors_num\n, ClickHouse ignores the row and moves on to the next one.\n\n\nIf \ninput_format_allow_errors_num\nis exceeded, ClickHouse throws an exception.\n\n\ninput_format_allow_errors_ratio\n\n\nSets the maximum percentage of errors allowed when reading from text formats (CSV, TSV, etc.).\nThe percentage of errors is set as a floating-point number between 0 and 1.\n\n\nThe default value is 0.\n\n\nAlways pair it with \ninput_format_allow_errors_num\n. To skip errors, both settings must be greater than 0.\n\n\nIf an error occurred while reading rows but the error counter is still less than \ninput_format_allow_errors_ratio\n, ClickHouse ignores the row and moves on to the next one.\n\n\nIf \ninput_format_allow_errors_ratio\n is exceeded, ClickHouse throws an exception.\n\n\nmax_block_size\n\n\nIn ClickHouse, data is processed by blocks (sets of column parts). The internal processing cycles for a single block are efficient enough, but there are noticeable expenditures on each block. \nmax_block_size\n is a recommendation for what size of block (in number of rows) to load from tables. The block size shouldn't be too small, so that the expenditures on each block are still noticeable, but not too large, so that the query with LIMIT that is completed after the first block is processed quickly, so that too much memory isn't consumed when extracting a large number of columns in multiple threads, and so that at least some cache locality is preserved.\n\n\nBy default, 65,536.\n\n\nBlocks the size of \nmax_block_size\n are not always loaded from the table. If it is obvious that less data needs to be retrieved, a smaller block is processed.\n\n\npreferred_block_size_bytes\n\n\nUsed for the same purpose as \nmax_block_size\n, but it sets the recommended block size in bytes by adapting it to the number of rows in the block.\nHowever, the block size cannot be more than \nmax_block_size\n rows.\nDisabled by default (set to 0). It only works when reading from MergeTree engines.\n\n\n\n\nlog_queries\n\n\nSetting up query the logging.\n\n\nQueries sent to ClickHouse with this setup are logged according to the rules in the \nquery_log\n server configuration parameter.\n\n\nExample\n:\n\n\nlog_queries=1\n\n\n\n\n\n\n\nmax_insert_block_size\n\n\nThe size of blocks to form for insertion into a table.\nThis setting only applies in cases when the server forms the blocks.\nFor example, for an INSERT via the HTTP interface, the server parses the data format and forms blocks of the specified size.\nBut when using clickhouse-client, the client parses the data itself, and the 'max_insert_block_size' setting on the server doesn't affect the size of the inserted blocks.\nThe setting also doesn't have a purpose when using INSERT SELECT, since data is inserted using the same blocks that are formed after SELECT.\n\n\nBy default, it is 1,048,576.\n\n\nThis is slightly more than \nmax_block_size\n. The reason for this is because certain table engines (\n*MergeTree\n) form a data part on the disk for each inserted block, which is a fairly large entity. Similarly, \n*MergeTree\n tables sort data during insertion, and a large enough block size allows sorting more data in RAM.\n\n\n\n\nmax_replica_delay_for_distributed_queries\n\n\nDisables lagging replicas for distributed queries. See \"\nReplication\n\".\n\n\nSets the time in seconds. If a replica lags more than the set value, this replica is not used.\n\n\nDefault value: 0 (off).\n\n\nUsed when performing \nSELECT\n from a distributed table that points to replicated tables.\n\n\nmax_threads\n\n\nThe maximum number of query processing threads\n\n\n\n\nexcluding threads for retrieving data from remote servers (see the 'max_distributed_connections' parameter).\n\n\n\n\nThis parameter applies to threads that perform the same stages of the query processing pipeline in parallel.\nFor example, if reading from a table, evaluating expressions with functions, filtering with WHERE and pre-aggregating for GROUP BY can all be done in parallel using at least 'max_threads' number of threads, then 'max_threads' are used.\n\n\nBy default, 8.\n\n\nIf less than one SELECT query is normally run on a server at a time, set this parameter to a value slightly less than the actual number of processor cores.\n\n\nFor queries that are completed quickly because of a LIMIT, you can set a lower 'max_threads'. For example, if the necessary number of entries are located in every block and max_threads = 8, 8 blocks are retrieved, although it would have been enough to read just one.\n\n\nThe smaller the \nmax_threads\n value, the less memory is consumed.\n\n\nmax_compress_block_size\n\n\nThe maximum size of blocks of uncompressed data before compressing for writing to a table. By default, 1,048,576 (1 MiB). If the size is reduced, the compression rate is significantly reduced, the compression and decompression speed increases slightly due to cache locality, and memory consumption is reduced. There usually isn't any reason to change this setting.\n\n\nDon't confuse blocks for compression (a chunk of memory consisting of bytes) and blocks for query processing (a set of rows from a table).\n\n\nmin_compress_block_size\n\n\nFor \nMergeTree\n\" tables. In order to reduce latency when processing queries, a block is compressed when writing the next mark if its size is at least 'min_compress_block_size'. By default, 65,536.\n\n\nThe actual size of the block, if the uncompressed data is less than 'max_compress_block_size', is no less than this value and no less than the volume of data for one mark.\n\n\nLet's look at an example. Assume that 'index_granularity' was set to 8192 during table creation.\n\n\nWe are writing a UInt32-type column (4 bytes per value). When writing 8192 rows, the total will be 32 KB of data. Since min_compress_block_size = 65,536, a compressed block will be formed for every two marks.\n\n\nWe are writing a URL column with the String type (average size of 60 bytes per value). When writing 8192 rows, the average will be slightly less than 500 KB of data. Since this is more than 65,536, a compressed block will be formed for each mark. In this case, when reading data from the disk in the range of a single mark, extra data won't be decompressed.\n\n\nThere usually isn't any reason to change this setting.\n\n\nmax_query_size\n\n\nThe maximum part of a query that can be taken to RAM for parsing with the SQL parser.\nThe INSERT query also contains data for INSERT that is processed by a separate stream parser (that consumes O(1) RAM), which is not included in this restriction.\n\n\nThe default is 256 KiB.\n\n\ninteractive_delay\n\n\nThe interval in microseconds for checking whether request execution has been canceled and sending the progress.\n\n\nBy default, 100,000 (check for canceling and send progress ten times per second).\n\n\nconnect_timeout\n\n\nreceive_timeout\n\n\nsend_timeout\n\n\nTimeouts in seconds on the socket used for communicating with the client.\n\n\nBy default, 10, 300, 300.\n\n\npoll_interval\n\n\nLock in a wait loop for the specified number of seconds.\n\n\nBy default, 10.\n\n\nmax_distributed_connections\n\n\nThe maximum number of simultaneous connections with remote servers for distributed processing of a single query to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.\n\n\nBy default, 100.\n\n\nThe following parameters are only used when creating Distributed tables (and when launching a server), so there is no reason to change them at runtime.\n\n\ndistributed_connections_pool_size\n\n\nThe maximum number of simultaneous connections with remote servers for distributed processing of all queries to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.\n\n\nBy default, 128.\n\n\nconnect_timeout_with_failover_ms\n\n\nThe timeout in milliseconds for connecting to a remote server for a Distributed table engine, if the 'shard' and 'replica' sections are used in the cluster definition.\nIf unsuccessful, several attempts are made to connect to various replicas.\n\n\nBy default, 50.\n\n\nconnections_with_failover_max_tries\n\n\nThe maximum number of connection attempts with each replica, for the Distributed table engine.\n\n\nBy default, 3.\n\n\nextremes\n\n\nWhether to count extreme values (the minimums and maximums in columns of a query result). Accepts 0 or 1. By default, 0 (disabled).\nFor more information, see the section \"Extreme values\".\n\n\n\n\nuse_uncompressed_cache\n\n\nWhether to use a cache of uncompressed blocks. Accepts 0 or 1. By default, 0 (disabled).\nThe uncompressed cache (only for tables in the MergeTree family) allows significantly reducing latency and increasing throughput when working with a large number of short queries. Enable this setting for users who send frequent short requests. Also pay attention to the 'uncompressed_cache_size' configuration parameter (only set in the config file) \u2013 the size of uncompressed cache blocks. By default, it is 8 GiB. The uncompressed cache is filled in as needed; the least-used data is automatically deleted.\n\n\nFor queries that read at least a somewhat large volume of data (one million rows or more), the uncompressed cache is disabled automatically in order to save space for truly small queries. So you can keep the 'use_uncompressed_cache' setting always set to 1.\n\n\nreplace_running_query\n\n\nWhen using the HTTP interface, the 'query_id' parameter can be passed. This is any string that serves as the query identifier.\nIf a query from the same user with the same 'query_id' already exists at this time, the behavior depends on the 'replace_running_query' parameter.\n\n\n0\n (default) \u2013 Throw an exception (don't allow the query to run if a query with the same 'query_id' is already running).\n\n\n1\n \u2013 Cancel the old query and start running the new one.\n\n\nYandex.Metrica uses this parameter set to 1 for implementing suggestions for segmentation conditions. After entering the next character, if the old query hasn't finished yet, it should be canceled.\n\n\nschema\n\n\nThis parameter is useful when you are using formats that require a schema definition, such as \nCap'n Proto\n. The value depends on the format.\n\n\n\n\nstream_flush_interval_ms\n\n\nWorks for tables with streaming in the case of a timeout, or when a thread generates\nmax_insert_block_size\n rows.\n\n\nThe default value is 7500.\n\n\nThe smaller the value, the more often data is flushed into the table. Setting the value too low leads to poor performance.\n\n\n\n\nload_balancing\n\n\nWhich replicas (among healthy replicas) to preferably send a query to (on the first attempt) for distributed processing.\n\n\nrandom (default)\n\n\nThe number of errors is counted for each replica. The query is sent to the replica with the fewest errors, and if there are several of these, to any one of them.\nDisadvantages: Server proximity is not accounted for; if the replicas have different data, you will also get different data.\n\n\nnearest_hostname\n\n\nThe number of errors is counted for each replica. Every 5 minutes, the number of errors is integrally divided by 2. Thus, the number of errors is calculated for a recent time with exponential smoothing. If there is one replica with a minimal number of errors (i.e. errors occurred recently on the other replicas), the query is sent to it. If there are multiple replicas with the same minimal number of errors, the query is sent to the replica with a host name that is most similar to the server's host name in the config file (for the number of different characters in identical positions, up to the minimum length of both host names).\n\n\nFor instance, example01-01-1 and example01-01-2.yandex.ru are different in one position, while example01-01-1 and example01-02-2 differ in two places.\nThis method might seem a little stupid, but it doesn't use external data about network topology, and it doesn't compare IP addresses, which would be complicated for our IPv6 addresses.\n\n\nThus, if there are equivalent replicas, the closest one by name is preferred.\nWe can also assume that when sending a query to the same server, in the absence of failures, a distributed query will also go to the same servers. So even if different data is placed on the replicas, the query will return mostly the same results.\n\n\nin_order\n\n\nReplicas are accessed in the same order as they are specified. The number of errors does not matter.\nThis method is appropriate when you know exactly which replica is preferable.\n\n\ntotals_mode\n\n\nHow to calculate TOTALS when HAVING is present, as well as when max_rows_to_group_by and group_by_overflow_mode = 'any' are present.\nSee the section \"WITH TOTALS modifier\".\n\n\ntotals_auto_threshold\n\n\nThe threshold for \ntotals_mode = 'auto'\n.\nSee the section \"WITH TOTALS modifier\".\n\n\ndefault_sample\n\n\nFloating-point number from 0 to 1. By default, 1.\nAllows you to set the default sampling ratio for all SELECT queries.\n(For tables that do not support sampling, it throws an exception.)\nIf set to 1, sampling is not performed by default.\n\n\nmax_parallel_replicas\n\n\nThe maximum number of replicas for each shard when executing a query.\nFor consistency (to get different parts of the same data split), this option only works when the sampling key is set.\nReplica lag is not controlled.\n\n\ncompile\n\n\nEnable compilation of queries. By default, 0 (disabled).\n\n\nCompilation is only used for part of the query-processing pipeline: for the first stage of aggregation (GROUP BY).\nIf this portion of the pipeline was compiled, the query may run faster due to deployment of short cycles and inlining aggregate function calls. The maximum performance improvement (up to four times faster in rare cases) is seen for queries with multiple simple aggregate functions. Typically, the performance gain is insignificant. In very rare cases, it may slow down query execution.\n\n\nmin_count_to_compile\n\n\nHow many times to potentially use a compiled chunk of code before running compilation. By default, 3.\nIf the value is zero, then compilation runs synchronously and the query waits for the end of the compilation process before continuing execution. This can be used for testing; otherwise, use values \u200b\u200bstarting with 1. Compilation normally takes about 5-10 seconds.\nIf the value is 1 or more, compilation occurs asynchronously in a separate thread. The result will be used as soon as it is ready, including by queries that are currently running.\n\n\nCompiled code is required for each different combination of aggregate functions used in the query and the type of keys in the GROUP BY clause.\nThe results of compilation are saved in the build directory in the form of .so files. There is no restriction on the number of compilation results, since they don't use very much space. Old results will be used after server restarts, except in the case of a server upgrade \u2013 in this case, the old results are deleted.\n\n\ninput_format_skip_unknown_fields\n\n\nIf the value is true, running INSERT skips input data from columns with unknown names. Otherwise, this situation will generate an exception.\nIt works for JSONEachRow and TSKV formats.\n\n\noutput_format_json_quote_64bit_integers\n\n\nIf the value is true, integers appear in quotes when using JSON* Int64 and UInt64 formats (for compatibility with most JavaScript implementations); otherwise, integers are output without the quotes.\n\n\n\n\nformat_csv_delimiter\n\n\nThe character to be considered as a delimiter in CSV data. By default, \n,\n.", + "title": "Settings" + }, + { + "location": "/operations/settings/settings/#settings", + "text": "", + "title": "Settings" + }, + { + "location": "/operations/settings/settings/#distributed_product_mode", + "text": "Changes the behavior of distributed subqueries , i.e. in cases when the query contains the product of distributed tables. ClickHouse applies the configuration if the subqueries on any level have a distributed table that exists on the local server and has more than one shard. Restrictions: Only applied for IN and JOIN subqueries. Used only if a distributed table is used in the FROM clause. Not used for a table-valued remote function. The possible values \u200b\u200bare:", + "title": "distributed_product_mode" + }, + { + "location": "/operations/settings/settings/#fallback_to_stale_replicas_for_distributed_queries", + "text": "Forces a query to an out-of-date replica if updated data is not available. See \" Replication \". ClickHouse selects the most relevant from the outdated replicas of the table. Used when performing SELECT from a distributed table that points to replicated tables. By default, 1 (enabled).", + "title": "fallback_to_stale_replicas_for_distributed_queries" + }, + { + "location": "/operations/settings/settings/#force_index_by_date", + "text": "Disables query execution if the index can't be used by date. Works with tables in the MergeTree family. If force_index_by_date=1 , ClickHouse checks whether the query has a date key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For example, the condition Date != ' 2000-01-01 ' is acceptable even when it matches all the data in the table (i.e., running the query requires a full scan). For more information about ranges of data in MergeTree tables, see \" MergeTree \".", + "title": "force_index_by_date" + }, + { + "location": "/operations/settings/settings/#force_primary_key", + "text": "Disables query execution if indexing by the primary key is not possible. Works with tables in the MergeTree family. If force_primary_key=1 , ClickHouse checks to see if the query has a primary key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For more information about data ranges in MergeTree tables, see \" MergeTree \".", + "title": "force_primary_key" + }, + { + "location": "/operations/settings/settings/#fsync_metadata", + "text": "Enable or disable fsync when writing .sql files. By default, it is enabled. It makes sense to disable it if the server has millions of tiny table chunks that are constantly being created and destroyed.", + "title": "fsync_metadata" + }, + { + "location": "/operations/settings/settings/#input_format_allow_errors_num", + "text": "Sets the maximum number of acceptable errors when reading from text formats (CSV, TSV, etc.). The default value is 0. Always pair it with input_format_allow_errors_ratio . To skip errors, both settings must be greater than 0. If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_num , ClickHouse ignores the row and moves on to the next one. If input_format_allow_errors_num is exceeded, ClickHouse throws an exception.", + "title": "input_format_allow_errors_num" + }, + { + "location": "/operations/settings/settings/#input_format_allow_errors_ratio", + "text": "Sets the maximum percentage of errors allowed when reading from text formats (CSV, TSV, etc.).\nThe percentage of errors is set as a floating-point number between 0 and 1. The default value is 0. Always pair it with input_format_allow_errors_num . To skip errors, both settings must be greater than 0. If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_ratio , ClickHouse ignores the row and moves on to the next one. If input_format_allow_errors_ratio is exceeded, ClickHouse throws an exception.", + "title": "input_format_allow_errors_ratio" + }, + { + "location": "/operations/settings/settings/#max_block_size", + "text": "In ClickHouse, data is processed by blocks (sets of column parts). The internal processing cycles for a single block are efficient enough, but there are noticeable expenditures on each block. max_block_size is a recommendation for what size of block (in number of rows) to load from tables. The block size shouldn't be too small, so that the expenditures on each block are still noticeable, but not too large, so that the query with LIMIT that is completed after the first block is processed quickly, so that too much memory isn't consumed when extracting a large number of columns in multiple threads, and so that at least some cache locality is preserved. By default, 65,536. Blocks the size of max_block_size are not always loaded from the table. If it is obvious that less data needs to be retrieved, a smaller block is processed.", + "title": "max_block_size" + }, + { + "location": "/operations/settings/settings/#preferred_block_size_bytes", + "text": "Used for the same purpose as max_block_size , but it sets the recommended block size in bytes by adapting it to the number of rows in the block.\nHowever, the block size cannot be more than max_block_size rows.\nDisabled by default (set to 0). It only works when reading from MergeTree engines.", + "title": "preferred_block_size_bytes" + }, + { + "location": "/operations/settings/settings/#log_queries", + "text": "Setting up query the logging. Queries sent to ClickHouse with this setup are logged according to the rules in the query_log server configuration parameter. Example : log_queries=1", + "title": "log_queries" + }, + { + "location": "/operations/settings/settings/#max_insert_block_size", + "text": "The size of blocks to form for insertion into a table.\nThis setting only applies in cases when the server forms the blocks.\nFor example, for an INSERT via the HTTP interface, the server parses the data format and forms blocks of the specified size.\nBut when using clickhouse-client, the client parses the data itself, and the 'max_insert_block_size' setting on the server doesn't affect the size of the inserted blocks.\nThe setting also doesn't have a purpose when using INSERT SELECT, since data is inserted using the same blocks that are formed after SELECT. By default, it is 1,048,576. This is slightly more than max_block_size . The reason for this is because certain table engines ( *MergeTree ) form a data part on the disk for each inserted block, which is a fairly large entity. Similarly, *MergeTree tables sort data during insertion, and a large enough block size allows sorting more data in RAM.", + "title": "max_insert_block_size" + }, + { + "location": "/operations/settings/settings/#max_replica_delay_for_distributed_queries", + "text": "Disables lagging replicas for distributed queries. See \" Replication \". Sets the time in seconds. If a replica lags more than the set value, this replica is not used. Default value: 0 (off). Used when performing SELECT from a distributed table that points to replicated tables.", + "title": "max_replica_delay_for_distributed_queries" + }, + { + "location": "/operations/settings/settings/#max_threads", + "text": "The maximum number of query processing threads excluding threads for retrieving data from remote servers (see the 'max_distributed_connections' parameter). This parameter applies to threads that perform the same stages of the query processing pipeline in parallel.\nFor example, if reading from a table, evaluating expressions with functions, filtering with WHERE and pre-aggregating for GROUP BY can all be done in parallel using at least 'max_threads' number of threads, then 'max_threads' are used. By default, 8. If less than one SELECT query is normally run on a server at a time, set this parameter to a value slightly less than the actual number of processor cores. For queries that are completed quickly because of a LIMIT, you can set a lower 'max_threads'. For example, if the necessary number of entries are located in every block and max_threads = 8, 8 blocks are retrieved, although it would have been enough to read just one. The smaller the max_threads value, the less memory is consumed.", + "title": "max_threads" + }, + { + "location": "/operations/settings/settings/#max_compress_block_size", + "text": "The maximum size of blocks of uncompressed data before compressing for writing to a table. By default, 1,048,576 (1 MiB). If the size is reduced, the compression rate is significantly reduced, the compression and decompression speed increases slightly due to cache locality, and memory consumption is reduced. There usually isn't any reason to change this setting. Don't confuse blocks for compression (a chunk of memory consisting of bytes) and blocks for query processing (a set of rows from a table).", + "title": "max_compress_block_size" + }, + { + "location": "/operations/settings/settings/#min_compress_block_size", + "text": "For MergeTree \" tables. In order to reduce latency when processing queries, a block is compressed when writing the next mark if its size is at least 'min_compress_block_size'. By default, 65,536. The actual size of the block, if the uncompressed data is less than 'max_compress_block_size', is no less than this value and no less than the volume of data for one mark. Let's look at an example. Assume that 'index_granularity' was set to 8192 during table creation. We are writing a UInt32-type column (4 bytes per value). When writing 8192 rows, the total will be 32 KB of data. Since min_compress_block_size = 65,536, a compressed block will be formed for every two marks. We are writing a URL column with the String type (average size of 60 bytes per value). When writing 8192 rows, the average will be slightly less than 500 KB of data. Since this is more than 65,536, a compressed block will be formed for each mark. In this case, when reading data from the disk in the range of a single mark, extra data won't be decompressed. There usually isn't any reason to change this setting.", + "title": "min_compress_block_size" + }, + { + "location": "/operations/settings/settings/#max_query_size", + "text": "The maximum part of a query that can be taken to RAM for parsing with the SQL parser.\nThe INSERT query also contains data for INSERT that is processed by a separate stream parser (that consumes O(1) RAM), which is not included in this restriction. The default is 256 KiB.", + "title": "max_query_size" + }, + { + "location": "/operations/settings/settings/#interactive_delay", + "text": "The interval in microseconds for checking whether request execution has been canceled and sending the progress. By default, 100,000 (check for canceling and send progress ten times per second).", + "title": "interactive_delay" + }, + { + "location": "/operations/settings/settings/#connect_timeout", + "text": "", + "title": "connect_timeout" + }, + { + "location": "/operations/settings/settings/#receive_timeout", + "text": "", + "title": "receive_timeout" + }, + { + "location": "/operations/settings/settings/#send_timeout", + "text": "Timeouts in seconds on the socket used for communicating with the client. By default, 10, 300, 300.", + "title": "send_timeout" + }, + { + "location": "/operations/settings/settings/#poll_interval", + "text": "Lock in a wait loop for the specified number of seconds. By default, 10.", + "title": "poll_interval" + }, + { + "location": "/operations/settings/settings/#max_distributed_connections", + "text": "The maximum number of simultaneous connections with remote servers for distributed processing of a single query to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster. By default, 100. The following parameters are only used when creating Distributed tables (and when launching a server), so there is no reason to change them at runtime.", + "title": "max_distributed_connections" + }, + { + "location": "/operations/settings/settings/#distributed_connections_pool_size", + "text": "The maximum number of simultaneous connections with remote servers for distributed processing of all queries to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster. By default, 128.", + "title": "distributed_connections_pool_size" + }, + { + "location": "/operations/settings/settings/#connect_timeout_with_failover_ms", + "text": "The timeout in milliseconds for connecting to a remote server for a Distributed table engine, if the 'shard' and 'replica' sections are used in the cluster definition.\nIf unsuccessful, several attempts are made to connect to various replicas. By default, 50.", + "title": "connect_timeout_with_failover_ms" + }, + { + "location": "/operations/settings/settings/#connections_with_failover_max_tries", + "text": "The maximum number of connection attempts with each replica, for the Distributed table engine. By default, 3.", + "title": "connections_with_failover_max_tries" + }, + { + "location": "/operations/settings/settings/#extremes", + "text": "Whether to count extreme values (the minimums and maximums in columns of a query result). Accepts 0 or 1. By default, 0 (disabled).\nFor more information, see the section \"Extreme values\".", + "title": "extremes" + }, + { + "location": "/operations/settings/settings/#use_uncompressed_cache", + "text": "Whether to use a cache of uncompressed blocks. Accepts 0 or 1. By default, 0 (disabled).\nThe uncompressed cache (only for tables in the MergeTree family) allows significantly reducing latency and increasing throughput when working with a large number of short queries. Enable this setting for users who send frequent short requests. Also pay attention to the 'uncompressed_cache_size' configuration parameter (only set in the config file) \u2013 the size of uncompressed cache blocks. By default, it is 8 GiB. The uncompressed cache is filled in as needed; the least-used data is automatically deleted. For queries that read at least a somewhat large volume of data (one million rows or more), the uncompressed cache is disabled automatically in order to save space for truly small queries. So you can keep the 'use_uncompressed_cache' setting always set to 1.", + "title": "use_uncompressed_cache" + }, + { + "location": "/operations/settings/settings/#replace_running_query", + "text": "When using the HTTP interface, the 'query_id' parameter can be passed. This is any string that serves as the query identifier.\nIf a query from the same user with the same 'query_id' already exists at this time, the behavior depends on the 'replace_running_query' parameter. 0 (default) \u2013 Throw an exception (don't allow the query to run if a query with the same 'query_id' is already running). 1 \u2013 Cancel the old query and start running the new one. Yandex.Metrica uses this parameter set to 1 for implementing suggestions for segmentation conditions. After entering the next character, if the old query hasn't finished yet, it should be canceled.", + "title": "replace_running_query" + }, + { + "location": "/operations/settings/settings/#schema", + "text": "This parameter is useful when you are using formats that require a schema definition, such as Cap'n Proto . The value depends on the format.", + "title": "schema" + }, + { + "location": "/operations/settings/settings/#stream_flush_interval_ms", + "text": "Works for tables with streaming in the case of a timeout, or when a thread generates max_insert_block_size rows. The default value is 7500. The smaller the value, the more often data is flushed into the table. Setting the value too low leads to poor performance.", + "title": "stream_flush_interval_ms" + }, + { + "location": "/operations/settings/settings/#load_balancing", + "text": "Which replicas (among healthy replicas) to preferably send a query to (on the first attempt) for distributed processing.", + "title": "load_balancing" + }, + { + "location": "/operations/settings/settings/#random-default", + "text": "The number of errors is counted for each replica. The query is sent to the replica with the fewest errors, and if there are several of these, to any one of them.\nDisadvantages: Server proximity is not accounted for; if the replicas have different data, you will also get different data.", + "title": "random (default)" + }, + { + "location": "/operations/settings/settings/#nearest_hostname", + "text": "The number of errors is counted for each replica. Every 5 minutes, the number of errors is integrally divided by 2. Thus, the number of errors is calculated for a recent time with exponential smoothing. If there is one replica with a minimal number of errors (i.e. errors occurred recently on the other replicas), the query is sent to it. If there are multiple replicas with the same minimal number of errors, the query is sent to the replica with a host name that is most similar to the server's host name in the config file (for the number of different characters in identical positions, up to the minimum length of both host names). For instance, example01-01-1 and example01-01-2.yandex.ru are different in one position, while example01-01-1 and example01-02-2 differ in two places.\nThis method might seem a little stupid, but it doesn't use external data about network topology, and it doesn't compare IP addresses, which would be complicated for our IPv6 addresses. Thus, if there are equivalent replicas, the closest one by name is preferred.\nWe can also assume that when sending a query to the same server, in the absence of failures, a distributed query will also go to the same servers. So even if different data is placed on the replicas, the query will return mostly the same results.", + "title": "nearest_hostname" + }, + { + "location": "/operations/settings/settings/#in_order", + "text": "Replicas are accessed in the same order as they are specified. The number of errors does not matter.\nThis method is appropriate when you know exactly which replica is preferable.", + "title": "in_order" + }, + { + "location": "/operations/settings/settings/#totals_mode", + "text": "How to calculate TOTALS when HAVING is present, as well as when max_rows_to_group_by and group_by_overflow_mode = 'any' are present.\nSee the section \"WITH TOTALS modifier\".", + "title": "totals_mode" + }, + { + "location": "/operations/settings/settings/#totals_auto_threshold", + "text": "The threshold for totals_mode = 'auto' .\nSee the section \"WITH TOTALS modifier\".", + "title": "totals_auto_threshold" + }, + { + "location": "/operations/settings/settings/#default_sample", + "text": "Floating-point number from 0 to 1. By default, 1.\nAllows you to set the default sampling ratio for all SELECT queries.\n(For tables that do not support sampling, it throws an exception.)\nIf set to 1, sampling is not performed by default.", + "title": "default_sample" + }, + { + "location": "/operations/settings/settings/#max_parallel_replicas", + "text": "The maximum number of replicas for each shard when executing a query.\nFor consistency (to get different parts of the same data split), this option only works when the sampling key is set.\nReplica lag is not controlled.", + "title": "max_parallel_replicas" + }, + { + "location": "/operations/settings/settings/#compile", + "text": "Enable compilation of queries. By default, 0 (disabled). Compilation is only used for part of the query-processing pipeline: for the first stage of aggregation (GROUP BY).\nIf this portion of the pipeline was compiled, the query may run faster due to deployment of short cycles and inlining aggregate function calls. The maximum performance improvement (up to four times faster in rare cases) is seen for queries with multiple simple aggregate functions. Typically, the performance gain is insignificant. In very rare cases, it may slow down query execution.", + "title": "compile" + }, + { + "location": "/operations/settings/settings/#min_count_to_compile", + "text": "How many times to potentially use a compiled chunk of code before running compilation. By default, 3.\nIf the value is zero, then compilation runs synchronously and the query waits for the end of the compilation process before continuing execution. This can be used for testing; otherwise, use values \u200b\u200bstarting with 1. Compilation normally takes about 5-10 seconds.\nIf the value is 1 or more, compilation occurs asynchronously in a separate thread. The result will be used as soon as it is ready, including by queries that are currently running. Compiled code is required for each different combination of aggregate functions used in the query and the type of keys in the GROUP BY clause.\nThe results of compilation are saved in the build directory in the form of .so files. There is no restriction on the number of compilation results, since they don't use very much space. Old results will be used after server restarts, except in the case of a server upgrade \u2013 in this case, the old results are deleted.", + "title": "min_count_to_compile" + }, + { + "location": "/operations/settings/settings/#input_format_skip_unknown_fields", + "text": "If the value is true, running INSERT skips input data from columns with unknown names. Otherwise, this situation will generate an exception.\nIt works for JSONEachRow and TSKV formats.", + "title": "input_format_skip_unknown_fields" + }, + { + "location": "/operations/settings/settings/#output_format_json_quote_64bit_integers", + "text": "If the value is true, integers appear in quotes when using JSON* Int64 and UInt64 formats (for compatibility with most JavaScript implementations); otherwise, integers are output without the quotes.", + "title": "output_format_json_quote_64bit_integers" + }, + { + "location": "/operations/settings/settings/#format_csv_delimiter", + "text": "The character to be considered as a delimiter in CSV data. By default, , .", + "title": "format_csv_delimiter" + }, + { + "location": "/operations/settings/settings_profiles/", + "text": "Settings profiles\n\n\nA settings profile is a collection of settings grouped under the same name. Each ClickHouse user has a profile.\nTo apply all the settings in a profile, set \nprofile\n.\n\n\nExample:\n\n\nSetting \nweb\n profile.\n\n\nSET\n \nprofile\n \n=\n \nweb\n\n\n\n\n\n\nSettings profiles are declared in the user config file. This is usually \nusers.xml\n.\n\n\nExample:\n\n\n!-- Settings profiles --\n\n\nprofiles\n\n \n!-- Default settings --\n\n \ndefault\n\n \n!-- The maximum number of threads when running a single query. --\n\n \nmax_threads\n8\n/max_threads\n\n \n/default\n\n\n \n!-- Settings for quries from the user interface --\n\n \nweb\n\n \nmax_rows_to_read\n1000000000\n/max_rows_to_read\n\n \nmax_bytes_to_read\n100000000000\n/max_bytes_to_read\n\n\n \nmax_rows_to_group_by\n1000000\n/max_rows_to_group_by\n\n \ngroup_by_overflow_mode\nany\n/group_by_overflow_mode\n\n\n \nmax_rows_to_sort\n1000000\n/max_rows_to_sort\n\n \nmax_bytes_to_sort\n1000000000\n/max_bytes_to_sort\n\n\n \nmax_result_rows\n100000\n/max_result_rows\n\n \nmax_result_bytes\n100000000\n/max_result_bytes\n\n \nresult_overflow_mode\nbreak\n/result_overflow_mode\n\n\n \nmax_execution_time\n600\n/max_execution_time\n\n \nmin_execution_speed\n1000000\n/min_execution_speed\n\n \ntimeout_before_checking_execution_speed\n15\n/timeout_before_checking_execution_speed\n\n\n \nmax_columns_to_read\n25\n/max_columns_to_read\n\n \nmax_temporary_columns\n100\n/max_temporary_columns\n\n \nmax_temporary_non_const_columns\n50\n/max_temporary_non_const_columns\n\n\n \nmax_subquery_depth\n2\n/max_subquery_depth\n\n \nmax_pipeline_depth\n25\n/max_pipeline_depth\n\n \nmax_ast_depth\n50\n/max_ast_depth\n\n \nmax_ast_elements\n100\n/max_ast_elements\n\n\n \nreadonly\n1\n/readonly\n\n \n/web\n\n\n/profiles\n\n\n\n\n\n\nThe example specifies two profiles: \ndefault\n and \nweb\n. The \ndefault\n profile has a special purpose: it must always be present and is applied when starting the server. In other words, the \ndefault\n profile contains default settings. The \nweb\n profile is a regular profile that can be set using the \nSET\n query or using a URL parameter in an HTTP query.\n\n\nSettings profiles can inherit from each other. To use inheritance, indicate the \nprofile\n setting before the other settings that are listed in the profile.", + "title": "Settings profiles" + }, + { + "location": "/operations/settings/settings_profiles/#settings-profiles", + "text": "A settings profile is a collection of settings grouped under the same name. Each ClickHouse user has a profile.\nTo apply all the settings in a profile, set profile . Example: Setting web profile. SET profile = web Settings profiles are declared in the user config file. This is usually users.xml . Example: !-- Settings profiles -- profiles \n !-- Default settings -- \n default \n !-- The maximum number of threads when running a single query. -- \n max_threads 8 /max_threads \n /default \n\n !-- Settings for quries from the user interface -- \n web \n max_rows_to_read 1000000000 /max_rows_to_read \n max_bytes_to_read 100000000000 /max_bytes_to_read \n\n max_rows_to_group_by 1000000 /max_rows_to_group_by \n group_by_overflow_mode any /group_by_overflow_mode \n\n max_rows_to_sort 1000000 /max_rows_to_sort \n max_bytes_to_sort 1000000000 /max_bytes_to_sort \n\n max_result_rows 100000 /max_result_rows \n max_result_bytes 100000000 /max_result_bytes \n result_overflow_mode break /result_overflow_mode \n\n max_execution_time 600 /max_execution_time \n min_execution_speed 1000000 /min_execution_speed \n timeout_before_checking_execution_speed 15 /timeout_before_checking_execution_speed \n\n max_columns_to_read 25 /max_columns_to_read \n max_temporary_columns 100 /max_temporary_columns \n max_temporary_non_const_columns 50 /max_temporary_non_const_columns \n\n max_subquery_depth 2 /max_subquery_depth \n max_pipeline_depth 25 /max_pipeline_depth \n max_ast_depth 50 /max_ast_depth \n max_ast_elements 100 /max_ast_elements \n\n readonly 1 /readonly \n /web /profiles The example specifies two profiles: default and web . The default profile has a special purpose: it must always be present and is applied when starting the server. In other words, the default profile contains default settings. The web profile is a regular profile that can be set using the SET query or using a URL parameter in an HTTP query. Settings profiles can inherit from each other. To use inheritance, indicate the profile setting before the other settings that are listed in the profile.", + "title": "Settings profiles" + }, + { + "location": "/utils/", + "text": "ClickHouse utility\n\n\n\n\nclickhouse-local\n \u2014 Allows running SQL queries on data without stopping the ClickHouse server, similar to how \nawk\n does this.\n\n\nclickhouse-copier\n \u2014 Copies (and reshards) data from one cluster to another cluster.", + "title": "Introduction" + }, + { + "location": "/utils/#clickhouse-utility", + "text": "clickhouse-local \u2014 Allows running SQL queries on data without stopping the ClickHouse server, similar to how awk does this. clickhouse-copier \u2014 Copies (and reshards) data from one cluster to another cluster.", + "title": "ClickHouse utility" + }, + { + "location": "/utils/clickhouse-copier/", + "text": "clickhouse-copier\n\n\nCopies data from the tables in one cluster to tables in another (or the same) cluster.\n\n\nYou can run multiple \nclickhouse-copier\n instances on different servers to perform the same job. ZooKeeper is used for syncing the processes.\n\n\nAfter starting, \nclickhouse-copier\n:\n\n\n\n\nConnects to ZooKeeper and receives:\n\n\nCopying jobs.\n\n\n\n\nThe state of the copying jobs.\n\n\n\n\n\n\nIt performs the jobs.\n\n\n\n\n\n\nEach running process chooses the \"closest\" shard of the source cluster and copies the data into the destination cluster, resharding the data if necessary.\n\n\nclickhouse-copier\n tracks the changes in ZooKeeper and applies them on the fly.\n\n\nTo reduce network traffic, we recommend running \nclickhouse-copier\n on the same server where the source data is located.\n\n\nRunning clickhouse-copier\n\n\nThe utility should be run manually:\n\n\nclickhouse-copier copier --daemon --config zookeeper.xml --task-path /task/path --base-dir /path/to/dir\n\n\n\n\n\nParameters:\n\n\n\n\ndaemon\n \u2014 Starts \nclickhouse-copier\n in daemon mode.\n\n\nconfig\n \u2014 The path to the \nzookeeper.xml\n file with the parameters for the connection to ZooKeeper.\n\n\ntask-path\n \u2014 The path to the ZooKeeper node. This node is used for syncing \nclickhouse-copier\n processes and storing tasks. Tasks are stored in \n$task-path/description\n.\n\n\nbase-dir\n \u2014 The path to logs and auxiliary files. When it starts, \nclickhouse-copier\n creates \nclickhouse-copier_YYYYMMHHSS_\nPID\n subdirectories in \n$base-dir\n. If this parameter is omitted, the directories are created in the directory where \nclickhouse-copier\n was launched.\n\n\n\n\nFormat of zookeeper.xml\n\n\nyandex\n\n \nzookeeper\n\n \nnode\n \nindex=\n1\n\n \nhost\n127.0.0.1\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n \n/zookeeper\n\n\n/yandex\n\n\n\n\n\n\nConfiguration of copying tasks\n\n\nyandex\n\n \n!-- Configuration of clusters as in an ordinary server config --\n\n \nremote_servers\n\n \nsource_cluster\n\n \nshard\n\n \ninternal_replication\nfalse\n/internal_replication\n\n \nreplica\n\n \nhost\n127.0.0.1\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \n/shard\n\n ...\n \n/source_cluster\n\n\n \ndestination_cluster\n\n ...\n \n/destination_cluster\n\n \n/remote_servers\n\n\n \n!-- How many simultaneously active workers are possible. If you run more workers superfluous workers will sleep. --\n\n \nmax_workers\n2\n/max_workers\n\n\n \n!-- Setting used to fetch (pull) data from source cluster tables --\n\n \nsettings_pull\n\n \nreadonly\n1\n/readonly\n\n \n/settings_pull\n\n\n \n!-- Setting used to insert (push) data to destination cluster tables --\n\n \nsettings_push\n\n \nreadonly\n0\n/readonly\n\n \n/settings_push\n\n\n \n!-- Common setting for fetch (pull) and insert (push) operations. The copier process context also uses it.\n\n\n They are overlaid by \nsettings_pull/\n and \nsettings_push/\n respectively. --\n\n \nsettings\n\n \nconnect_timeout\n3\n/connect_timeout\n\n \n!-- Sync insert is set forcibly, leave it here just in case. --\n\n \ninsert_distributed_sync\n1\n/insert_distributed_sync\n\n \n/settings\n\n\n \n!-- Copying description of tasks.\n\n\n You can specify several table tasks in the same task description (in the same ZooKeeper node), and they will be performed sequentially.\n\n\n --\n\n \ntables\n\n \n!-- A table task that copies one table. --\n\n \ntable_hits\n\n \n!-- Source cluster name (from the \nremote_servers/\n section) and tables in it that should be copied --\n\n \ncluster_pull\nsource_cluster\n/cluster_pull\n\n \ndatabase_pull\ntest\n/database_pull\n\n \ntable_pull\nhits\n/table_pull\n\n\n \n!-- Destination cluster name and tables in which the data should be inserted --\n\n \ncluster_push\ndestination_cluster\n/cluster_push\n\n \ndatabase_push\ntest\n/database_push\n\n \ntable_push\nhits2\n/table_push\n\n\n \n!-- Engine of destination tables.\n\n\n If the destination tables have not been created yet, workers create them using column definitions from source tables and the engine definition from here.\n\n\n\n NOTE: If the first worker starts to insert data and detects that the destination partition is not empty, then the partition will\n\n\n be dropped and refilled. Take this into account if you already have some data in destination tables. You can directly \n\n\n specify partitions that should be copied in \nenabled_partitions/\n. They should be in quoted format like the partition column in the \n\n\n system.parts table.\n\n\n --\n\n \nengine\n\n ENGINE=ReplicatedMergeTree(\n/clickhouse/tables/{cluster}/{shard}/hits2\n, \n{replica}\n)\n PARTITION BY toMonday(date)\n ORDER BY (CounterID, EventDate)\n \n/engine\n\n\n \n!-- Sharding key used to insert data to destination cluster --\n\n \nsharding_key\njumpConsistentHash(intHash64(UserID), 2)\n/sharding_key\n\n\n \n!-- Optional expression that filter data while pull them from source servers --\n\n \nwhere_condition\nCounterID != 0\n/where_condition\n\n\n \n!-- This section specifies partitions that should be copied, other partition will be ignored.\n\n\n Partition names should have the same format as\n\n\n partition column of system.parts table (i.e. a quoted text).\n\n\n Since partition key of source and destination cluster could be different,\n\n\n these partition names specify destination partitions.\n\n\n\n Note: Although this section is optional (if it omitted, all partitions will be copied), \n\n\n it is strongly recommended to specify the partitions explicitly.\n\n\n If you already have some partitions ready on the destination cluster, they \n\n\n will be removed at the start of the copying, because they will be interpreted \n\n\n as unfinished data from the previous copying.\n\n\n --\n\n \nenabled_partitions\n\n \npartition\n2018-02-26\n/partition\n\n \npartition\n2018-03-05\n/partition\n\n ...\n \n/enabled_partitions\n\n \n/table_hits\n\n\n \n!-- Next table to copy. It is not copied until the previous table is copying. --\n\n \n/table_visits\n\n ...\n \n/table_visits\n\n ...\n \n/tables\n\n\n/yandex\n\n\n\n\n\n\nclickhouse-copier\n tracks the changes in \n/task/path/description\n and applies them on the fly. For instance, if you change the value of \nmax_workers\n, the number of processes running tasks will also change.", + "title": "clickhouse-copier" + }, + { + "location": "/utils/clickhouse-copier/#clickhouse-copier", + "text": "Copies data from the tables in one cluster to tables in another (or the same) cluster. You can run multiple clickhouse-copier instances on different servers to perform the same job. ZooKeeper is used for syncing the processes. After starting, clickhouse-copier : Connects to ZooKeeper and receives: Copying jobs. The state of the copying jobs. It performs the jobs. Each running process chooses the \"closest\" shard of the source cluster and copies the data into the destination cluster, resharding the data if necessary. clickhouse-copier tracks the changes in ZooKeeper and applies them on the fly. To reduce network traffic, we recommend running clickhouse-copier on the same server where the source data is located.", + "title": "clickhouse-copier" + }, + { + "location": "/utils/clickhouse-copier/#running-clickhouse-copier", + "text": "The utility should be run manually: clickhouse-copier copier --daemon --config zookeeper.xml --task-path /task/path --base-dir /path/to/dir Parameters: daemon \u2014 Starts clickhouse-copier in daemon mode. config \u2014 The path to the zookeeper.xml file with the parameters for the connection to ZooKeeper. task-path \u2014 The path to the ZooKeeper node. This node is used for syncing clickhouse-copier processes and storing tasks. Tasks are stored in $task-path/description . base-dir \u2014 The path to logs and auxiliary files. When it starts, clickhouse-copier creates clickhouse-copier_YYYYMMHHSS_ PID subdirectories in $base-dir . If this parameter is omitted, the directories are created in the directory where clickhouse-copier was launched.", + "title": "Running clickhouse-copier" + }, + { + "location": "/utils/clickhouse-copier/#format-of-zookeeperxml", + "text": "yandex \n zookeeper \n node index= 1 \n host 127.0.0.1 /host \n port 2181 /port \n /node \n /zookeeper /yandex", + "title": "Format of zookeeper.xml" + }, + { + "location": "/utils/clickhouse-copier/#configuration-of-copying-tasks", + "text": "yandex \n !-- Configuration of clusters as in an ordinary server config -- \n remote_servers \n source_cluster \n shard \n internal_replication false /internal_replication \n replica \n host 127.0.0.1 /host \n port 9000 /port \n /replica \n /shard \n ...\n /source_cluster \n\n destination_cluster \n ...\n /destination_cluster \n /remote_servers \n\n !-- How many simultaneously active workers are possible. If you run more workers superfluous workers will sleep. -- \n max_workers 2 /max_workers \n\n !-- Setting used to fetch (pull) data from source cluster tables -- \n settings_pull \n readonly 1 /readonly \n /settings_pull \n\n !-- Setting used to insert (push) data to destination cluster tables -- \n settings_push \n readonly 0 /readonly \n /settings_push \n\n !-- Common setting for fetch (pull) and insert (push) operations. The copier process context also uses it. They are overlaid by settings_pull/ and settings_push/ respectively. -- \n settings \n connect_timeout 3 /connect_timeout \n !-- Sync insert is set forcibly, leave it here just in case. -- \n insert_distributed_sync 1 /insert_distributed_sync \n /settings \n\n !-- Copying description of tasks. You can specify several table tasks in the same task description (in the same ZooKeeper node), and they will be performed sequentially. -- \n tables \n !-- A table task that copies one table. -- \n table_hits \n !-- Source cluster name (from the remote_servers/ section) and tables in it that should be copied -- \n cluster_pull source_cluster /cluster_pull \n database_pull test /database_pull \n table_pull hits /table_pull \n\n !-- Destination cluster name and tables in which the data should be inserted -- \n cluster_push destination_cluster /cluster_push \n database_push test /database_push \n table_push hits2 /table_push \n\n !-- Engine of destination tables. If the destination tables have not been created yet, workers create them using column definitions from source tables and the engine definition from here. NOTE: If the first worker starts to insert data and detects that the destination partition is not empty, then the partition will be dropped and refilled. Take this into account if you already have some data in destination tables. You can directly specify partitions that should be copied in enabled_partitions/ . They should be in quoted format like the partition column in the system.parts table. -- \n engine \n ENGINE=ReplicatedMergeTree( /clickhouse/tables/{cluster}/{shard}/hits2 , {replica} )\n PARTITION BY toMonday(date)\n ORDER BY (CounterID, EventDate)\n /engine \n\n !-- Sharding key used to insert data to destination cluster -- \n sharding_key jumpConsistentHash(intHash64(UserID), 2) /sharding_key \n\n !-- Optional expression that filter data while pull them from source servers -- \n where_condition CounterID != 0 /where_condition \n\n !-- This section specifies partitions that should be copied, other partition will be ignored. Partition names should have the same format as partition column of system.parts table (i.e. a quoted text). Since partition key of source and destination cluster could be different, these partition names specify destination partitions. Note: Although this section is optional (if it omitted, all partitions will be copied), it is strongly recommended to specify the partitions explicitly. If you already have some partitions ready on the destination cluster, they will be removed at the start of the copying, because they will be interpreted as unfinished data from the previous copying. -- \n enabled_partitions \n partition 2018-02-26 /partition \n partition 2018-03-05 /partition \n ...\n /enabled_partitions \n /table_hits \n\n !-- Next table to copy. It is not copied until the previous table is copying. -- \n /table_visits \n ...\n /table_visits \n ...\n /tables /yandex clickhouse-copier tracks the changes in /task/path/description and applies them on the fly. For instance, if you change the value of max_workers , the number of processes running tasks will also change.", + "title": "Configuration of copying tasks" + }, + { + "location": "/utils/clickhouse-local/", + "text": "clickhouse-local\n\n\nThe \nclickhouse-local\n program enables you to perform fast processing on local files that store tables, without having to deploy and configure the ClickHouse server.", + "title": "clickhouse-local" + }, + { + "location": "/utils/clickhouse-local/#clickhouse-local", + "text": "The clickhouse-local program enables you to perform fast processing on local files that store tables, without having to deploy and configure the ClickHouse server.", + "title": "clickhouse-local" + }, + { + "location": "/development/architecture/", + "text": "Overview of ClickHouse architecture\n\n\nClickHouse is a true column-oriented DBMS. Data is stored by columns, and during the execution of arrays (vectors or chunks of columns). Whenever possible, operations are dispatched on arrays, rather than on individual values. This is called \"vectorized query execution,\" and it helps lower the cost of actual data processing.\n\n\n\n\nThis idea is nothing new. It dates back to the \nAPL\n programming language and its descendants: \nA +\n, \nJ\n, \nK\n, and \nQ\n. Array programming is used in scientific data processing. Neither is this idea something new in relational databases: for example, it is used in the \nVectorwise\n system.\n\n\n\n\nThere are two different approaches for speeding up the query processing: vectorized query execution and runtime code generation. In the latter, the code is generated for every kind of query on the fly, removing all indirection and dynamic dispatch. Neither of these approaches is strictly better than the other. Runtime code generation can be better when it's fuses many operations together, thus fully utilizing CPU execution units and the pipeline. Vectorized query execution can be less practical, because it involves the temporary vectors that must be written to the cache and read back. If the temporary data does not fit in the L2 cache, this becomes an issue. But vectorized query execution more easily utilizes the SIMD capabilities of the CPU. A \nresearch paper\n written by our friends shows that it is better to combine both approaches. ClickHouse uses vectorized query execution and has limited initial support for runtime code.\n\n\nColumns\n\n\nTo represent columns in memory (actually, chunks of columns), the \nIColumn\n interface is used. This interface provides helper methods for implementation of various relational operators. Almost all operations are immutable: they do not modify the original column, but create a new modified one. For example, the \nIColumn :: filter\n method accepts a filter byte mask. It is used for the \nWHERE\n and \nHAVING\n relational operators. Additional examples: the \nIColumn :: permute\n method to support \nORDER BY\n, the \nIColumn :: cut\n method to support \nLIMIT\n, and so on.\n\n\nVarious \nIColumn\n implementations (\nColumnUInt8\n, \nColumnString\n and so on) are responsible for the memory layout of columns. Memory layout is usually a contiguous array. For the integer type of columns it is just one contiguous array, like \nstd :: vector\n. For \nString\n and \nArray\n columns, it is two vectors: one for all array elements, placed contiguously, and a second one for offsets to the beginning of each array. There is also \nColumnConst\n that stores just one value in memory, but looks like a column.\n\n\nField\n\n\nNevertheless, it is possible to work with individual values as well. To represent an individual value, the \nField\n is used. \nField\n is just a discriminated union of \nUInt64\n, \nInt64\n, \nFloat64\n, \nString\n and \nArray\n. \nIColumn\n has the \noperator[]\n method to get the n-th value as a \nField\n, and the \ninsert\n method to append a \nField\n to the end of a column. These methods are not very efficient, because they require dealing with temporary \nField\n objects representing an individual value. There are more efficient methods, such as \ninsertFrom\n, \ninsertRangeFrom\n, and so on.\n\n\nField\n doesn't have enough information about a specific data type for a table. For example, \nUInt8\n, \nUInt16\n, \nUInt32\n, and \nUInt64\n are all represented as \nUInt64\n in a \nField\n.\n\n\nLeaky abstractions\n\n\nIColumn\n has methods for common relational transformations of data, but they don't meet all needs. For example, \nColumnUInt64\n doesn't have a method to calculate the sum of two columns, and \nColumnString\n doesn't have a method to run a substring search. These countless routines are implemented outside of \nIColumn\n.\n\n\nVarious functions on columns can be implemented in a generic, non-efficient way using \nIColumn\n methods to extract \nField\n values, or in a specialized way using knowledge of inner memory layout of data in a specific \nIColumn\n implementation. To do this, functions are cast to a specific \nIColumn\n type and deal with internal representation directly. For example, \nColumnUInt64\n has the \ngetData\n method that returns a reference to an internal array, then a separate routine reads or fills that array directly. In fact, we have \"leaky abstractions\" to allow efficient specializations of various routines.\n\n\nData types\n\n\nIDataType\n is responsible for serialization and deserialization: for reading and writing chunks of columns or individual values in binary or text form.\n\nIDataType\n directly corresponds to data types in tables. For example, there are \nDataTypeUInt32\n, \nDataTypeDateTime\n, \nDataTypeString\n and so on.\n\n\nIDataType\n and \nIColumn\n are only loosely related to each other. Different data types can be represented in memory by the same \nIColumn\n implementations. For example, \nDataTypeUInt32\n and \nDataTypeDateTime\n are both represented by \nColumnUInt32\n or \nColumnConstUInt32\n. In addition, the same data type can be represented by different \nIColumn\n implementations. For example, \nDataTypeUInt8\n can be represented by \nColumnUInt8\n or \nColumnConstUInt8\n.\n\n\nIDataType\n only stores metadata. For instance, \nDataTypeUInt8\n doesn't store anything at all (except vptr) and \nDataTypeFixedString\n stores just \nN\n (the size of fixed-size strings).\n\n\nIDataType\n has helper methods for various data formats. Examples are methods to serialize a value with possible quoting, to serialize a value for JSON, and to serialize a value as part of XML format. There is no direct correspondence to data formats. For example, the different data formats \nPretty\n and \nTabSeparated\n can use the same \nserializeTextEscaped\n helper method from the \nIDataType\n interface.\n\n\nBlock\n\n\nA \nBlock\n is a container that represents a subset (chunk) of a table in memory. It is just a set of triples: \n(IColumn, IDataType, column name)\n. During query execution, data is processed by \nBlock\ns. If we have a \nBlock\n, we have data (in the \nIColumn\n object), we have information about its type (in \nIDataType\n) that tells us how to deal with that column, and we have the column name (either the original column name from the table, or some artificial name assigned for getting temporary results of calculations).\n\n\nWhen we calculate some function over columns in a block, we add another column with its result to the block, and we don't touch columns for arguments of the function because operations are immutable. Later, unneeded columns can be removed from the block, but not modified. This is convenient for elimination of common subexpressions.\n\n\nBlocks are created for every processed chunk of data. Note that for the same type of calculation, the column names and types remain the same for different blocks, and only column data changes. It is better to split block data from the block header, because small block sizes will have a high overhead of temporary strings for copying shared_ptrs and column names.\n\n\nBlock Streams\n\n\nBlock streams are for processing data. We use streams of blocks to read data from somewhere, perform data transformations, or write data to somewhere. \nIBlockInputStream\n has the \nread\n method to fetch the next block while available. \nIBlockOutputStream\n has the \nwrite\n method to push the block somewhere.\n\n\nStreams are responsible for:\n\n\n\n\nReading or writing to a table. The table just returns a stream for reading or writing blocks.\n\n\nImplementing data formats. For example, if you want to output data to a terminal in \nPretty\n format, you create a block output stream where you push blocks, and it formats them.\n\n\nPerforming data transformations. Let's say you have \nIBlockInputStream\n and want to create a filtered stream. You create \nFilterBlockInputStream\n and initialize it with your stream. Then when you pull a block from \nFilterBlockInputStream\n, it pulls a block from your stream, filters it, and returns the filtered block to you. Query execution pipelines are represented this way.\n\n\n\n\nThere are more sophisticated transformations. For example, when you pull from \nAggregatingBlockInputStream\n, it reads all data from its source, aggregates it, and then returns a stream of aggregated data for you. Another example: \nUnionBlockInputStream\n accepts many input sources in the constructor and also a number of threads. It launches multiple threads and reads from multiple sources in parallel.\n\n\n\n\nBlock streams use the \"pull\" approach to control flow: when you pull a block from the first stream, it consequently pulls the required blocks from nested streams, and the entire execution pipeline will work. Neither \"pull\" nor \"push\" is the best solution, because control flow is implicit, and that limits implementation of various features like simultaneous execution of multiple queries (merging many pipelines together). This limitation could be overcome with coroutines or just running extra threads that wait for each other. We may have more possibilities if we make control flow explicit: if we locate the logic for passing data from one calculation unit to another outside of those calculation units. Read this \narticle\n for more thoughts.\n\n\n\n\nWe should note that the query execution pipeline creates temporary data at each step. We try to keep block size small enough so that temporary data fits in the CPU cache. With that assumption, writing and reading temporary data is almost free in comparison with other calculations. We could consider an alternative, which is to fuse many operations in the pipeline together, to make the pipeline as short as possible and remove much of the temporary data. This could be an advantage, but it also has drawbacks. For example, a split pipeline makes it easy to implement caching intermediate data, stealing intermediate data from similar queries running at the same time, and merging pipelines for similar queries.\n\n\nFormats\n\n\nData formats are implemented with block streams. There are \"presentational\" formats only suitable for output of data to the client, such as \nPretty\n format, which provides only \nIBlockOutputStream\n. And there are input/output formats, such as \nTabSeparated\n or \nJSONEachRow\n.\n\n\nThere are also row streams: \nIRowInputStream\n and \nIRowOutputStream\n. They allow you to pull/push data by individual rows, not by blocks. And they are only needed to simplify implementation of row-oriented formats. The wrappers \nBlockInputStreamFromRowInputStream\n and \nBlockOutputStreamFromRowOutputStream\n allow you to convert row-oriented streams to regular block-oriented streams.\n\n\nI/O\n\n\nFor byte-oriented input/output, there are \nReadBuffer\n and \nWriteBuffer\n abstract classes. They are used instead of C++ \niostream\n's. Don't worry: every mature C++ project is using something other than \niostream\n's for good reasons.\n\n\nReadBuffer\n and \nWriteBuffer\n are just a contiguous buffer and a cursor pointing to the position in that buffer. Implementations may own or not own the memory for the buffer. There is a virtual method to fill the buffer with the following data (for \nReadBuffer\n) or to flush the buffer somewhere (for \nWriteBuffer\n). The virtual methods are rarely called.\n\n\nImplementations of \nReadBuffer\n/\nWriteBuffer\n are used for working with files and file descriptors and network sockets, for implementing compression (\nCompressedWriteBuffer\n is initialized with another WriteBuffer and performs compression before writing data to it), and for other purposes \u2013 the names \nConcatReadBuffer\n, \nLimitReadBuffer\n, and \nHashingWriteBuffer\n speak for themselves.\n\n\nRead/WriteBuffers only deal with bytes. To help with formatted input/output (for instance, to write a number in decimal format), there are functions from \nReadHelpers\n and \nWriteHelpers\n header files.\n\n\nLet's look at what happens when you want to write a result set in \nJSON\n format to stdout. You have a result set ready to be fetched from \nIBlockInputStream\n. You create \nWriteBufferFromFileDescriptor(STDOUT_FILENO)\n to write bytes to stdout. You create \nJSONRowOutputStream\n, initialized with that \nWriteBuffer\n, to write rows in \nJSON\n to stdout. You create \nBlockOutputStreamFromRowOutputStream\n on top of it, to represent it as \nIBlockOutputStream\n. Then you call \ncopyData\n to transfer data from \nIBlockInputStream\n to \nIBlockOutputStream\n, and everything works. Internally, \nJSONRowOutputStream\n will write various JSON delimiters and call the \nIDataType::serializeTextJSON\n method with a reference to \nIColumn\n and the row number as arguments. Consequently, \nIDataType::serializeTextJSON\n will call a method from \nWriteHelpers.h\n: for example, \nwriteText\n for numeric types and \nwriteJSONString\n for \nDataTypeString\n.\n\n\nTables\n\n\nTables are represented by the \nIStorage\n interface. Different implementations of that interface are different table engines. Examples are \nStorageMergeTree\n, \nStorageMemory\n, and so on. Instances of these classes are just tables.\n\n\nThe most important \nIStorage\n methods are \nread\n and \nwrite\n. There are also \nalter\n, \nrename\n, \ndrop\n, and so on. The \nread\n method accepts the following arguments: the set of columns to read from a table, the \nAST\n query to consider, and the desired number of streams to return. It returns one or multiple \nIBlockInputStream\n objects and information about the stage of data processing that was completed inside a table engine during query execution.\n\n\nIn most cases, the read method is only responsible for reading the specified columns from a table, not for any further data processing. All further data processing is done by the query interpreter and is outside the responsibility of \nIStorage\n.\n\n\nBut there are notable exceptions:\n\n\n\n\nThe AST query is passed to the \nread\n method and the table engine can use it to derive index usage and to read less data from a table.\n\n\nSometimes the table engine can process data itself to a specific stage. For example, \nStorageDistributed\n can send a query to remote servers, ask them to process data to a stage where data from different remote servers can be merged, and return that preprocessed data.\nThe query interpreter then finishes processing the data.\n\n\n\n\nThe table's \nread\n method can return multiple \nIBlockInputStream\n objects to allow parallel data processing. These multiple block input streams can read from a table in parallel. Then you can wrap these streams with various transformations (such as expression evaluation or filtering) that can be calculated independently and create a \nUnionBlockInputStream\n on top of them, to read from multiple streams in parallel.\n\n\nThere are also \nTableFunction\ns. These are functions that return a temporary \nIStorage\n object to use in the \nFROM\n clause of a query.\n\n\nTo get a quick idea of how to implement your own table engine, look at something simple, like \nStorageMemory\n or \nStorageTinyLog\n.\n\n\n\n\nAs the result of the \nread\n method, \nIStorage\n returns \nQueryProcessingStage\n \u2013 information about what parts of the query were already calculated inside storage. Currently we have only very coarse granularity for that information. There is no way for the storage to say \"I have already processed this part of the expression in WHERE, for this range of data\". We need to work on that.\n\n\n\n\nParsers\n\n\nA query is parsed by a hand-written recursive descent parser. For example, \nParserSelectQuery\n just recursively calls the underlying parsers for various parts of the query. Parsers create an \nAST\n. The \nAST\n is represented by nodes, which are instances of \nIAST\n.\n\n\n\n\nParser generators are not used for historical reasons.\n\n\n\n\nInterpreters\n\n\nInterpreters are responsible for creating the query execution pipeline from an \nAST\n. There are simple interpreters, such as \nInterpreterExistsQuery\nand \nInterpreterDropQuery\n, or the more sophisticated \nInterpreterSelectQuery\n. The query execution pipeline is a combination of block input or output streams. For example, the result of interpreting the \nSELECT\n query is the \nIBlockInputStream\n to read the result set from; the result of the INSERT query is the \nIBlockOutputStream\n to write data for insertion to; and the result of interpreting the \nINSERT SELECT\n query is the \nIBlockInputStream\n that returns an empty result set on the first read, but that copies data from \nSELECT\n to \nINSERT\n at the same time.\n\n\nInterpreterSelectQuery\n uses \nExpressionAnalyzer\n and \nExpressionActions\n machinery for query analysis and transformations. This is where most rule-based query optimizations are done. \nExpressionAnalyzer\n is quite messy and should be rewritten: various query transformations and optimizations should be extracted to separate classes to allow modular transformations or query.\n\n\nFunctions\n\n\nThere are ordinary functions and aggregate functions. For aggregate functions, see the next section.\n\n\nOrdinary functions don't change the number of rows \u2013 they work as if they are processing each row independently. In fact, functions are not called for individual rows, but for \nBlock\n's of data to implement vectorized query execution.\n\n\nThere are some miscellaneous functions, like \nblockSize\n, \nrowNumberInBlock\n, and \nrunningAccumulate\n, that exploit block processing and violate the independence of rows.\n\n\nClickHouse has strong typing, so implicit type conversion doesn't occur. If a function doesn't support a specific combination of types, an exception will be thrown. But functions can work (be overloaded) for many different combinations of types. For example, the \nplus\n function (to implement the \n+\n operator) works for any combination of numeric types: \nUInt8\n + \nFloat32\n, \nUInt16\n + \nInt8\n, and so on. Also, some variadic functions can accept any number of arguments, such as the \nconcat\n function.\n\n\nImplementing a function may be slightly inconvenient because a function explicitly dispatches supported data types and supported \nIColumns\n. For example, the \nplus\n function has code generated by instantiation of a C++ template for each combination of numeric types, and for constant or non-constant left and right arguments.\n\n\n\n\nThis is a nice place to implement runtime code generation to avoid template code bloat. Also, it will make it possible to add fused functions like fused multiply-add, or to make multiple comparisons in one loop iteration.\n\n\n\n\nDue to vectorized query execution, functions are not short-circuit. For example, if you write \nWHERE f(x) AND g(y)\n, both sides will be calculated, even for rows, when \nf(x)\n is zero (except when \nf(x)\n is a zero constant expression). But if selectivity of the \nf(x)\n condition is high, and calculation of \nf(x)\n is much cheaper than \ng(y)\n, it's better to implement multi-pass calculation: first calculate \nf(x)\n, then filter columns by the result, and then calculate \ng(y)\n only for smaller, filtered chunks of data.\n\n\nAggregate Functions\n\n\nAggregate functions are stateful functions. They accumulate passed values into some state, and allow you to get results from that state. They are managed with the \nIAggregateFunction\n interface. States can be rather simple (the state for \nAggregateFunctionCount\n is just a single \nUInt64\n value) or quite complex (the state of \nAggregateFunctionUniqCombined\n is a combination of a linear array, a hash table and a \nHyperLogLog\n probabilistic data structure).\n\n\nTo deal with multiple states while executing a high-cardinality \nGROUP BY\n query, states are allocated in \nArena\n (a memory pool), or they could be allocated in any suitable piece of memory. States can have a non-trivial constructor and destructor: for example, complex aggregation states can allocate additional memory themselves. This requires some attention to creating and destroying states and properly passing their ownership, to keep track of who and when will destroy states.\n\n\nAggregation states can be serialized and deserialized to pass over the network during distributed query execution or to write them on disk where there is not enough RAM. They can even be stored in a table with the \nDataTypeAggregateFunction\n to allow incremental aggregation of data.\n\n\n\n\nThe serialized data format for aggregate function states is not versioned right now. This is ok if aggregate states are only stored temporarily. But we have the \nAggregatingMergeTree\n table engine for incremental aggregation, and people are already using it in production. This is why we should add support for backward compatibility when changing the serialized format for any aggregate function in the future.\n\n\n\n\nServer\n\n\nThe server implements several different interfaces:\n\n\n\n\nAn HTTP interface for any foreign clients.\n\n\nA TCP interface for the native ClickHouse client and for cross-server communication during distributed query execution.\n\n\nAn interface for transferring data for replication.\n\n\n\n\nInternally, it is just a basic multithreaded server without coroutines, fibers, etc. Since the server is not designed to process a high rate of simple queries but is intended to process a relatively low rate of complex queries, each of them can process a vast amount of data for analytics.\n\n\nThe server initializes the \nContext\n class with the necessary environment for query execution: the list of available databases, users and access rights, settings, clusters, the process list, the query log, and so on. This environment is used by interpreters.\n\n\nWe maintain full backward and forward compatibility for the server TCP protocol: old clients can talk to new servers and new clients can talk to old servers. But we don't want to maintain it eternally, and we are removing support for old versions after about one year.\n\n\n\n\nFor all external applications, we recommend using the HTTP interface because it is simple and easy to use. The TCP protocol is more tightly linked to internal data structures: it uses an internal format for passing blocks of data and it uses custom framing for compressed data. We haven't released a C library for that protocol because it requires linking most of the ClickHouse codebase, which is not practical.\n\n\n\n\nDistributed query execution\n\n\nServers in a cluster setup are mostly independent. You can create a \nDistributed\n table on one or all servers in a cluster. The \nDistributed\n table does not store data itself \u2013 it only provides a \"view\" to all local tables on multiple nodes of a cluster. When you SELECT from a \nDistributed\n table, it rewrites that query, chooses remote nodes according to load balancing settings, and sends the query to them. The \nDistributed\n table requests remote servers to process a query just up to a stage where intermediate results from different servers can be merged. Then it receives the intermediate results and merges them. The distributed table tries to distribute as much work as possible to remote servers, and does not send much intermediate data over the network.\n\n\n\n\nThings become more complicated when you have subqueries in IN or JOIN clauses and each of them uses a \nDistributed\n table. We have different strategies for execution of these queries.\n\n\n\n\nThere is no global query plan for distributed query execution. Each node has its own local query plan for its part of the job. We only have simple one-pass distributed query execution: we send queries for remote nodes and then merge the results. But this is not feasible for difficult queries with high cardinality GROUP BYs or with a large amount of temporary data for JOIN: in such cases, we need to \"reshuffle\" data between servers, which requires additional coordination. ClickHouse does not support that kind of query execution, and we need to work on it.\n\n\nMerge Tree\n\n\nMergeTree\n is a family of storage engines that supports indexing by primary key. The primary key can be an arbitary tuple of columns or expressions. Data in a \nMergeTree\n table is stored in \"parts\". Each part stores data in the primary key order (data is ordered lexicographically by the primary key tuple). All the table columns are stored in separate \ncolumn.bin\n files in these parts. The files consist of compressed blocks. Each block is usually from 64 KB to 1 MB of uncompressed data, depending on the average value size. The blocks consist of column values placed contiguously one after the other. Column values are in the same order for each column (the order is defined by the primary key), so when you iterate by many columns, you get values for the corresponding rows.\n\n\nThe primary key itself is \"sparse\". It doesn't address each single row, but only some ranges of data. A separate \nprimary.idx\n file has the value of the primary key for each N-th row, where N is called \nindex_granularity\n (usually, N = 8192). Also, for each column, we have \ncolumn.mrk\n files with \"marks,\" which are offsets to each N-th row in the data file. Each mark is a pair: the offset in the file to the beginning of the compressed block, and the offset in the decompressed block to the beginning of data. Usually compressed blocks are aligned by marks, and the offset in the decompressed block is zero. Data for \nprimary.idx\n always resides in memory and data for \ncolumn.mrk\n files is cached.\n\n\nWhen we are going to read something from a part in \nMergeTree\n, we look at \nprimary.idx\n data and locate ranges that could possibly contain requested data, then look at \ncolumn.mrk\n data and calculate offsets for where to start reading those ranges. Because of sparseness, excess data may be read. ClickHouse is not suitable for a high load of simple point queries, because the entire range with \nindex_granularity\n rows must be read for each key, and the entire compressed block must be decompressed for each column. We made the index sparse because we must be able to maintain trillions of rows per single server without noticeable memory consumption for the index. Also, because the primary key is sparse, it is not unique: it cannot check the existence of the key in the table at INSERT time. You could have many rows with the same key in a table.\n\n\nWhen you \nINSERT\n a bunch of data into \nMergeTree\n, that bunch is sorted by primary key order and forms a new part. To keep the number of parts relatively low, there are background threads that periodically select some parts and merge them to a single sorted part. That's why it is called \nMergeTree\n. Of course, merging leads to \"write amplification\". All parts are immutable: they are only created and deleted, but not modified. When SELECT is run, it holds a snapshot of the table (a set of parts). After merging, we also keep old parts for some time to make recovery after failure easier, so if we see that some merged part is probably broken, we can replace it with its source parts.\n\n\nMergeTree\n is not an LSM tree because it doesn't contain \"memtable\" and \"log\": inserted data is written directly to the filesystem. This makes it suitable only to INSERT data in batches, not by individual row and not very frequently \u2013 about once per second is ok, but a thousand times a second is not. We did it this way for simplicity's sake, and because we are already inserting data in batches in our applications.\n\n\n\n\nMergeTree tables can only have one (primary) index: there aren't any secondary indices. It would be nice to allow multiple physical representations under one logical table, for example, to store data in more than one physical order or even to allow representations with pre-aggregated data along with original data.\n\n\n\n\nThere are MergeTree engines that are doing additional work during background merges. Examples are \nCollapsingMergeTree\n and \nAggregatingMergeTree\n. This could be treated as special support for updates. Keep in mind that these are not real updates because users usually have no control over the time when background merges will be executed, and data in a \nMergeTree\n table is almost always stored in more than one part, not in completely merged form.\n\n\nReplication\n\n\nReplication in ClickHouse is implemented on a per-table basis. You could have some replicated and some non-replicated tables on the same server. You could also have tables replicated in different ways, such as one table with two-factor replication and another with three-factor.\n\n\nReplication is implemented in the \nReplicatedMergeTree\n storage engine. The path in \nZooKeeper\n is specified as a parameter for the storage engine. All tables with the same path in \nZooKeeper\n become replicas of each other: they synchronize their data and maintain consistency. Replicas can be added and removed dynamically simply by creating or dropping a table.\n\n\nReplication uses an asynchronous multi-master scheme. You can insert data into any replica that has a session with \nZooKeeper\n, and data is replicated to all other replicas asynchronously. Because ClickHouse doesn't support UPDATEs, replication is conflict-free. As there is no quorum acknowledgment of inserts, just-inserted data might be lost if one node fails.\n\n\nMetadata for replication is stored in ZooKeeper. There is a replication log that lists what actions to do. Actions are: get part; merge parts; drop partition, etc. Each replica copies the replication log to its queue and then executes the actions from the queue. For example, on insertion, the \"get part\" action is created in the log, and every replica downloads that part. Merges are coordinated between replicas to get byte-identical results. All parts are merged in the same way on all replicas. To achieve this, one replica is elected as the leader, and that replica initiates merges and writes \"merge parts\" actions to the log.\n\n\nReplication is physical: only compressed parts are transferred between nodes, not queries. To lower the network cost (to avoid network amplification), merges are processed on each replica independently in most cases. Large merged parts are sent over the network only in cases of significant replication lag.\n\n\nIn addition, each replica stores its state in ZooKeeper as the set of parts and its checksums. When the state on the local filesystem diverges from the reference state in ZooKeeper, the replica restores its consistency by downloading missing and broken parts from other replicas. When there is some unexpected or broken data in the local filesystem, ClickHouse does not remove it, but moves it to a separate directory and forgets it.\n\n\n\n\nThe ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is not elastic, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load will be uneven. This implementation gives you more control, and it is fine for relatively small clusters such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that will span its data across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically.", + "title": "Overview of ClickHouse architecture" + }, + { + "location": "/development/architecture/#overview-of-clickhouse-architecture", + "text": "ClickHouse is a true column-oriented DBMS. Data is stored by columns, and during the execution of arrays (vectors or chunks of columns). Whenever possible, operations are dispatched on arrays, rather than on individual values. This is called \"vectorized query execution,\" and it helps lower the cost of actual data processing. This idea is nothing new. It dates back to the APL programming language and its descendants: A + , J , K , and Q . Array programming is used in scientific data processing. Neither is this idea something new in relational databases: for example, it is used in the Vectorwise system. There are two different approaches for speeding up the query processing: vectorized query execution and runtime code generation. In the latter, the code is generated for every kind of query on the fly, removing all indirection and dynamic dispatch. Neither of these approaches is strictly better than the other. Runtime code generation can be better when it's fuses many operations together, thus fully utilizing CPU execution units and the pipeline. Vectorized query execution can be less practical, because it involves the temporary vectors that must be written to the cache and read back. If the temporary data does not fit in the L2 cache, this becomes an issue. But vectorized query execution more easily utilizes the SIMD capabilities of the CPU. A research paper written by our friends shows that it is better to combine both approaches. ClickHouse uses vectorized query execution and has limited initial support for runtime code.", + "title": "Overview of ClickHouse architecture" + }, + { + "location": "/development/architecture/#columns", + "text": "To represent columns in memory (actually, chunks of columns), the IColumn interface is used. This interface provides helper methods for implementation of various relational operators. Almost all operations are immutable: they do not modify the original column, but create a new modified one. For example, the IColumn :: filter method accepts a filter byte mask. It is used for the WHERE and HAVING relational operators. Additional examples: the IColumn :: permute method to support ORDER BY , the IColumn :: cut method to support LIMIT , and so on. Various IColumn implementations ( ColumnUInt8 , ColumnString and so on) are responsible for the memory layout of columns. Memory layout is usually a contiguous array. For the integer type of columns it is just one contiguous array, like std :: vector . For String and Array columns, it is two vectors: one for all array elements, placed contiguously, and a second one for offsets to the beginning of each array. There is also ColumnConst that stores just one value in memory, but looks like a column.", + "title": "Columns" + }, + { + "location": "/development/architecture/#field", + "text": "Nevertheless, it is possible to work with individual values as well. To represent an individual value, the Field is used. Field is just a discriminated union of UInt64 , Int64 , Float64 , String and Array . IColumn has the operator[] method to get the n-th value as a Field , and the insert method to append a Field to the end of a column. These methods are not very efficient, because they require dealing with temporary Field objects representing an individual value. There are more efficient methods, such as insertFrom , insertRangeFrom , and so on. Field doesn't have enough information about a specific data type for a table. For example, UInt8 , UInt16 , UInt32 , and UInt64 are all represented as UInt64 in a Field .", + "title": "Field" + }, + { + "location": "/development/architecture/#leaky-abstractions", + "text": "IColumn has methods for common relational transformations of data, but they don't meet all needs. For example, ColumnUInt64 doesn't have a method to calculate the sum of two columns, and ColumnString doesn't have a method to run a substring search. These countless routines are implemented outside of IColumn . Various functions on columns can be implemented in a generic, non-efficient way using IColumn methods to extract Field values, or in a specialized way using knowledge of inner memory layout of data in a specific IColumn implementation. To do this, functions are cast to a specific IColumn type and deal with internal representation directly. For example, ColumnUInt64 has the getData method that returns a reference to an internal array, then a separate routine reads or fills that array directly. In fact, we have \"leaky abstractions\" to allow efficient specializations of various routines.", + "title": "Leaky abstractions" + }, + { + "location": "/development/architecture/#data-types", + "text": "IDataType is responsible for serialization and deserialization: for reading and writing chunks of columns or individual values in binary or text form. IDataType directly corresponds to data types in tables. For example, there are DataTypeUInt32 , DataTypeDateTime , DataTypeString and so on. IDataType and IColumn are only loosely related to each other. Different data types can be represented in memory by the same IColumn implementations. For example, DataTypeUInt32 and DataTypeDateTime are both represented by ColumnUInt32 or ColumnConstUInt32 . In addition, the same data type can be represented by different IColumn implementations. For example, DataTypeUInt8 can be represented by ColumnUInt8 or ColumnConstUInt8 . IDataType only stores metadata. For instance, DataTypeUInt8 doesn't store anything at all (except vptr) and DataTypeFixedString stores just N (the size of fixed-size strings). IDataType has helper methods for various data formats. Examples are methods to serialize a value with possible quoting, to serialize a value for JSON, and to serialize a value as part of XML format. There is no direct correspondence to data formats. For example, the different data formats Pretty and TabSeparated can use the same serializeTextEscaped helper method from the IDataType interface.", + "title": "Data types" + }, + { + "location": "/development/architecture/#block", + "text": "A Block is a container that represents a subset (chunk) of a table in memory. It is just a set of triples: (IColumn, IDataType, column name) . During query execution, data is processed by Block s. If we have a Block , we have data (in the IColumn object), we have information about its type (in IDataType ) that tells us how to deal with that column, and we have the column name (either the original column name from the table, or some artificial name assigned for getting temporary results of calculations). When we calculate some function over columns in a block, we add another column with its result to the block, and we don't touch columns for arguments of the function because operations are immutable. Later, unneeded columns can be removed from the block, but not modified. This is convenient for elimination of common subexpressions. Blocks are created for every processed chunk of data. Note that for the same type of calculation, the column names and types remain the same for different blocks, and only column data changes. It is better to split block data from the block header, because small block sizes will have a high overhead of temporary strings for copying shared_ptrs and column names.", + "title": "Block" + }, + { + "location": "/development/architecture/#block-streams", + "text": "Block streams are for processing data. We use streams of blocks to read data from somewhere, perform data transformations, or write data to somewhere. IBlockInputStream has the read method to fetch the next block while available. IBlockOutputStream has the write method to push the block somewhere. Streams are responsible for: Reading or writing to a table. The table just returns a stream for reading or writing blocks. Implementing data formats. For example, if you want to output data to a terminal in Pretty format, you create a block output stream where you push blocks, and it formats them. Performing data transformations. Let's say you have IBlockInputStream and want to create a filtered stream. You create FilterBlockInputStream and initialize it with your stream. Then when you pull a block from FilterBlockInputStream , it pulls a block from your stream, filters it, and returns the filtered block to you. Query execution pipelines are represented this way. There are more sophisticated transformations. For example, when you pull from AggregatingBlockInputStream , it reads all data from its source, aggregates it, and then returns a stream of aggregated data for you. Another example: UnionBlockInputStream accepts many input sources in the constructor and also a number of threads. It launches multiple threads and reads from multiple sources in parallel. Block streams use the \"pull\" approach to control flow: when you pull a block from the first stream, it consequently pulls the required blocks from nested streams, and the entire execution pipeline will work. Neither \"pull\" nor \"push\" is the best solution, because control flow is implicit, and that limits implementation of various features like simultaneous execution of multiple queries (merging many pipelines together). This limitation could be overcome with coroutines or just running extra threads that wait for each other. We may have more possibilities if we make control flow explicit: if we locate the logic for passing data from one calculation unit to another outside of those calculation units. Read this article for more thoughts. We should note that the query execution pipeline creates temporary data at each step. We try to keep block size small enough so that temporary data fits in the CPU cache. With that assumption, writing and reading temporary data is almost free in comparison with other calculations. We could consider an alternative, which is to fuse many operations in the pipeline together, to make the pipeline as short as possible and remove much of the temporary data. This could be an advantage, but it also has drawbacks. For example, a split pipeline makes it easy to implement caching intermediate data, stealing intermediate data from similar queries running at the same time, and merging pipelines for similar queries.", + "title": "Block Streams" + }, + { + "location": "/development/architecture/#formats", + "text": "Data formats are implemented with block streams. There are \"presentational\" formats only suitable for output of data to the client, such as Pretty format, which provides only IBlockOutputStream . And there are input/output formats, such as TabSeparated or JSONEachRow . There are also row streams: IRowInputStream and IRowOutputStream . They allow you to pull/push data by individual rows, not by blocks. And they are only needed to simplify implementation of row-oriented formats. The wrappers BlockInputStreamFromRowInputStream and BlockOutputStreamFromRowOutputStream allow you to convert row-oriented streams to regular block-oriented streams.", + "title": "Formats" + }, + { + "location": "/development/architecture/#io", + "text": "For byte-oriented input/output, there are ReadBuffer and WriteBuffer abstract classes. They are used instead of C++ iostream 's. Don't worry: every mature C++ project is using something other than iostream 's for good reasons. ReadBuffer and WriteBuffer are just a contiguous buffer and a cursor pointing to the position in that buffer. Implementations may own or not own the memory for the buffer. There is a virtual method to fill the buffer with the following data (for ReadBuffer ) or to flush the buffer somewhere (for WriteBuffer ). The virtual methods are rarely called. Implementations of ReadBuffer / WriteBuffer are used for working with files and file descriptors and network sockets, for implementing compression ( CompressedWriteBuffer is initialized with another WriteBuffer and performs compression before writing data to it), and for other purposes \u2013 the names ConcatReadBuffer , LimitReadBuffer , and HashingWriteBuffer speak for themselves. Read/WriteBuffers only deal with bytes. To help with formatted input/output (for instance, to write a number in decimal format), there are functions from ReadHelpers and WriteHelpers header files. Let's look at what happens when you want to write a result set in JSON format to stdout. You have a result set ready to be fetched from IBlockInputStream . You create WriteBufferFromFileDescriptor(STDOUT_FILENO) to write bytes to stdout. You create JSONRowOutputStream , initialized with that WriteBuffer , to write rows in JSON to stdout. You create BlockOutputStreamFromRowOutputStream on top of it, to represent it as IBlockOutputStream . Then you call copyData to transfer data from IBlockInputStream to IBlockOutputStream , and everything works. Internally, JSONRowOutputStream will write various JSON delimiters and call the IDataType::serializeTextJSON method with a reference to IColumn and the row number as arguments. Consequently, IDataType::serializeTextJSON will call a method from WriteHelpers.h : for example, writeText for numeric types and writeJSONString for DataTypeString .", + "title": "I/O" + }, + { + "location": "/development/architecture/#tables", + "text": "Tables are represented by the IStorage interface. Different implementations of that interface are different table engines. Examples are StorageMergeTree , StorageMemory , and so on. Instances of these classes are just tables. The most important IStorage methods are read and write . There are also alter , rename , drop , and so on. The read method accepts the following arguments: the set of columns to read from a table, the AST query to consider, and the desired number of streams to return. It returns one or multiple IBlockInputStream objects and information about the stage of data processing that was completed inside a table engine during query execution. In most cases, the read method is only responsible for reading the specified columns from a table, not for any further data processing. All further data processing is done by the query interpreter and is outside the responsibility of IStorage . But there are notable exceptions: The AST query is passed to the read method and the table engine can use it to derive index usage and to read less data from a table. Sometimes the table engine can process data itself to a specific stage. For example, StorageDistributed can send a query to remote servers, ask them to process data to a stage where data from different remote servers can be merged, and return that preprocessed data.\nThe query interpreter then finishes processing the data. The table's read method can return multiple IBlockInputStream objects to allow parallel data processing. These multiple block input streams can read from a table in parallel. Then you can wrap these streams with various transformations (such as expression evaluation or filtering) that can be calculated independently and create a UnionBlockInputStream on top of them, to read from multiple streams in parallel. There are also TableFunction s. These are functions that return a temporary IStorage object to use in the FROM clause of a query. To get a quick idea of how to implement your own table engine, look at something simple, like StorageMemory or StorageTinyLog . As the result of the read method, IStorage returns QueryProcessingStage \u2013 information about what parts of the query were already calculated inside storage. Currently we have only very coarse granularity for that information. There is no way for the storage to say \"I have already processed this part of the expression in WHERE, for this range of data\". We need to work on that.", + "title": "Tables" + }, + { + "location": "/development/architecture/#parsers", + "text": "A query is parsed by a hand-written recursive descent parser. For example, ParserSelectQuery just recursively calls the underlying parsers for various parts of the query. Parsers create an AST . The AST is represented by nodes, which are instances of IAST . Parser generators are not used for historical reasons.", + "title": "Parsers" + }, + { + "location": "/development/architecture/#interpreters", + "text": "Interpreters are responsible for creating the query execution pipeline from an AST . There are simple interpreters, such as InterpreterExistsQuery and InterpreterDropQuery , or the more sophisticated InterpreterSelectQuery . The query execution pipeline is a combination of block input or output streams. For example, the result of interpreting the SELECT query is the IBlockInputStream to read the result set from; the result of the INSERT query is the IBlockOutputStream to write data for insertion to; and the result of interpreting the INSERT SELECT query is the IBlockInputStream that returns an empty result set on the first read, but that copies data from SELECT to INSERT at the same time. InterpreterSelectQuery uses ExpressionAnalyzer and ExpressionActions machinery for query analysis and transformations. This is where most rule-based query optimizations are done. ExpressionAnalyzer is quite messy and should be rewritten: various query transformations and optimizations should be extracted to separate classes to allow modular transformations or query.", + "title": "Interpreters" + }, + { + "location": "/development/architecture/#functions", + "text": "There are ordinary functions and aggregate functions. For aggregate functions, see the next section. Ordinary functions don't change the number of rows \u2013 they work as if they are processing each row independently. In fact, functions are not called for individual rows, but for Block 's of data to implement vectorized query execution. There are some miscellaneous functions, like blockSize , rowNumberInBlock , and runningAccumulate , that exploit block processing and violate the independence of rows. ClickHouse has strong typing, so implicit type conversion doesn't occur. If a function doesn't support a specific combination of types, an exception will be thrown. But functions can work (be overloaded) for many different combinations of types. For example, the plus function (to implement the + operator) works for any combination of numeric types: UInt8 + Float32 , UInt16 + Int8 , and so on. Also, some variadic functions can accept any number of arguments, such as the concat function. Implementing a function may be slightly inconvenient because a function explicitly dispatches supported data types and supported IColumns . For example, the plus function has code generated by instantiation of a C++ template for each combination of numeric types, and for constant or non-constant left and right arguments. This is a nice place to implement runtime code generation to avoid template code bloat. Also, it will make it possible to add fused functions like fused multiply-add, or to make multiple comparisons in one loop iteration. Due to vectorized query execution, functions are not short-circuit. For example, if you write WHERE f(x) AND g(y) , both sides will be calculated, even for rows, when f(x) is zero (except when f(x) is a zero constant expression). But if selectivity of the f(x) condition is high, and calculation of f(x) is much cheaper than g(y) , it's better to implement multi-pass calculation: first calculate f(x) , then filter columns by the result, and then calculate g(y) only for smaller, filtered chunks of data.", + "title": "Functions" + }, + { + "location": "/development/architecture/#aggregate-functions", + "text": "Aggregate functions are stateful functions. They accumulate passed values into some state, and allow you to get results from that state. They are managed with the IAggregateFunction interface. States can be rather simple (the state for AggregateFunctionCount is just a single UInt64 value) or quite complex (the state of AggregateFunctionUniqCombined is a combination of a linear array, a hash table and a HyperLogLog probabilistic data structure). To deal with multiple states while executing a high-cardinality GROUP BY query, states are allocated in Arena (a memory pool), or they could be allocated in any suitable piece of memory. States can have a non-trivial constructor and destructor: for example, complex aggregation states can allocate additional memory themselves. This requires some attention to creating and destroying states and properly passing their ownership, to keep track of who and when will destroy states. Aggregation states can be serialized and deserialized to pass over the network during distributed query execution or to write them on disk where there is not enough RAM. They can even be stored in a table with the DataTypeAggregateFunction to allow incremental aggregation of data. The serialized data format for aggregate function states is not versioned right now. This is ok if aggregate states are only stored temporarily. But we have the AggregatingMergeTree table engine for incremental aggregation, and people are already using it in production. This is why we should add support for backward compatibility when changing the serialized format for any aggregate function in the future.", + "title": "Aggregate Functions" + }, + { + "location": "/development/architecture/#server", + "text": "The server implements several different interfaces: An HTTP interface for any foreign clients. A TCP interface for the native ClickHouse client and for cross-server communication during distributed query execution. An interface for transferring data for replication. Internally, it is just a basic multithreaded server without coroutines, fibers, etc. Since the server is not designed to process a high rate of simple queries but is intended to process a relatively low rate of complex queries, each of them can process a vast amount of data for analytics. The server initializes the Context class with the necessary environment for query execution: the list of available databases, users and access rights, settings, clusters, the process list, the query log, and so on. This environment is used by interpreters. We maintain full backward and forward compatibility for the server TCP protocol: old clients can talk to new servers and new clients can talk to old servers. But we don't want to maintain it eternally, and we are removing support for old versions after about one year. For all external applications, we recommend using the HTTP interface because it is simple and easy to use. The TCP protocol is more tightly linked to internal data structures: it uses an internal format for passing blocks of data and it uses custom framing for compressed data. We haven't released a C library for that protocol because it requires linking most of the ClickHouse codebase, which is not practical.", + "title": "Server" + }, + { + "location": "/development/architecture/#distributed-query-execution", + "text": "Servers in a cluster setup are mostly independent. You can create a Distributed table on one or all servers in a cluster. The Distributed table does not store data itself \u2013 it only provides a \"view\" to all local tables on multiple nodes of a cluster. When you SELECT from a Distributed table, it rewrites that query, chooses remote nodes according to load balancing settings, and sends the query to them. The Distributed table requests remote servers to process a query just up to a stage where intermediate results from different servers can be merged. Then it receives the intermediate results and merges them. The distributed table tries to distribute as much work as possible to remote servers, and does not send much intermediate data over the network. Things become more complicated when you have subqueries in IN or JOIN clauses and each of them uses a Distributed table. We have different strategies for execution of these queries. There is no global query plan for distributed query execution. Each node has its own local query plan for its part of the job. We only have simple one-pass distributed query execution: we send queries for remote nodes and then merge the results. But this is not feasible for difficult queries with high cardinality GROUP BYs or with a large amount of temporary data for JOIN: in such cases, we need to \"reshuffle\" data between servers, which requires additional coordination. ClickHouse does not support that kind of query execution, and we need to work on it.", + "title": "Distributed query execution" + }, + { + "location": "/development/architecture/#merge-tree", + "text": "MergeTree is a family of storage engines that supports indexing by primary key. The primary key can be an arbitary tuple of columns or expressions. Data in a MergeTree table is stored in \"parts\". Each part stores data in the primary key order (data is ordered lexicographically by the primary key tuple). All the table columns are stored in separate column.bin files in these parts. The files consist of compressed blocks. Each block is usually from 64 KB to 1 MB of uncompressed data, depending on the average value size. The blocks consist of column values placed contiguously one after the other. Column values are in the same order for each column (the order is defined by the primary key), so when you iterate by many columns, you get values for the corresponding rows. The primary key itself is \"sparse\". It doesn't address each single row, but only some ranges of data. A separate primary.idx file has the value of the primary key for each N-th row, where N is called index_granularity (usually, N = 8192). Also, for each column, we have column.mrk files with \"marks,\" which are offsets to each N-th row in the data file. Each mark is a pair: the offset in the file to the beginning of the compressed block, and the offset in the decompressed block to the beginning of data. Usually compressed blocks are aligned by marks, and the offset in the decompressed block is zero. Data for primary.idx always resides in memory and data for column.mrk files is cached. When we are going to read something from a part in MergeTree , we look at primary.idx data and locate ranges that could possibly contain requested data, then look at column.mrk data and calculate offsets for where to start reading those ranges. Because of sparseness, excess data may be read. ClickHouse is not suitable for a high load of simple point queries, because the entire range with index_granularity rows must be read for each key, and the entire compressed block must be decompressed for each column. We made the index sparse because we must be able to maintain trillions of rows per single server without noticeable memory consumption for the index. Also, because the primary key is sparse, it is not unique: it cannot check the existence of the key in the table at INSERT time. You could have many rows with the same key in a table. When you INSERT a bunch of data into MergeTree , that bunch is sorted by primary key order and forms a new part. To keep the number of parts relatively low, there are background threads that periodically select some parts and merge them to a single sorted part. That's why it is called MergeTree . Of course, merging leads to \"write amplification\". All parts are immutable: they are only created and deleted, but not modified. When SELECT is run, it holds a snapshot of the table (a set of parts). After merging, we also keep old parts for some time to make recovery after failure easier, so if we see that some merged part is probably broken, we can replace it with its source parts. MergeTree is not an LSM tree because it doesn't contain \"memtable\" and \"log\": inserted data is written directly to the filesystem. This makes it suitable only to INSERT data in batches, not by individual row and not very frequently \u2013 about once per second is ok, but a thousand times a second is not. We did it this way for simplicity's sake, and because we are already inserting data in batches in our applications. MergeTree tables can only have one (primary) index: there aren't any secondary indices. It would be nice to allow multiple physical representations under one logical table, for example, to store data in more than one physical order or even to allow representations with pre-aggregated data along with original data. There are MergeTree engines that are doing additional work during background merges. Examples are CollapsingMergeTree and AggregatingMergeTree . This could be treated as special support for updates. Keep in mind that these are not real updates because users usually have no control over the time when background merges will be executed, and data in a MergeTree table is almost always stored in more than one part, not in completely merged form.", + "title": "Merge Tree" + }, + { + "location": "/development/architecture/#replication", + "text": "Replication in ClickHouse is implemented on a per-table basis. You could have some replicated and some non-replicated tables on the same server. You could also have tables replicated in different ways, such as one table with two-factor replication and another with three-factor. Replication is implemented in the ReplicatedMergeTree storage engine. The path in ZooKeeper is specified as a parameter for the storage engine. All tables with the same path in ZooKeeper become replicas of each other: they synchronize their data and maintain consistency. Replicas can be added and removed dynamically simply by creating or dropping a table. Replication uses an asynchronous multi-master scheme. You can insert data into any replica that has a session with ZooKeeper , and data is replicated to all other replicas asynchronously. Because ClickHouse doesn't support UPDATEs, replication is conflict-free. As there is no quorum acknowledgment of inserts, just-inserted data might be lost if one node fails. Metadata for replication is stored in ZooKeeper. There is a replication log that lists what actions to do. Actions are: get part; merge parts; drop partition, etc. Each replica copies the replication log to its queue and then executes the actions from the queue. For example, on insertion, the \"get part\" action is created in the log, and every replica downloads that part. Merges are coordinated between replicas to get byte-identical results. All parts are merged in the same way on all replicas. To achieve this, one replica is elected as the leader, and that replica initiates merges and writes \"merge parts\" actions to the log. Replication is physical: only compressed parts are transferred between nodes, not queries. To lower the network cost (to avoid network amplification), merges are processed on each replica independently in most cases. Large merged parts are sent over the network only in cases of significant replication lag. In addition, each replica stores its state in ZooKeeper as the set of parts and its checksums. When the state on the local filesystem diverges from the reference state in ZooKeeper, the replica restores its consistency by downloading missing and broken parts from other replicas. When there is some unexpected or broken data in the local filesystem, ClickHouse does not remove it, but moves it to a separate directory and forgets it. The ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is not elastic, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load will be uneven. This implementation gives you more control, and it is fine for relatively small clusters such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that will span its data across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically.", + "title": "Replication" + }, + { + "location": "/development/build/", + "text": "How to build ClickHouse on Linux\n\n\nBuild should work on Linux Ubuntu 12.04, 14.04 or newer.\nWith appropriate changes, it should also work on any other Linux distribution.\nThe build process is not intended to work on Mac OS X.\nOnly x86_64 with SSE 4.2 is supported. Support for AArch64 is experimental.\n\n\nTo test for SSE 4.2, do\n\n\ngrep -q sse4_2 /proc/cpuinfo \n \necho\n \nSSE 4.2 supported\n \n||\n \necho\n \nSSE 4.2 not supported\n\n\n\n\n\n\nInstall Git and CMake\n\n\nsudo apt-get install git cmake\n\n\n\n\n\nOr cmake3 instead of cmake on older systems.\n\n\nDetect the number of threads\n\n\nexport\n \nTHREADS\n=\n$(\ngrep -c ^processor /proc/cpuinfo\n)\n\n\n\n\n\n\nInstall GCC 7\n\n\nThere are several ways to do this.\n\n\nInstall from a PPA package\n\n\nsudo apt-get install software-properties-common\nsudo apt-add-repository ppa:ubuntu-toolchain-r/test\nsudo apt-get update\nsudo apt-get install gcc-7 g++-7\n\n\n\n\n\nInstall from sources\n\n\nLook at [https://github.com/yandex/ClickHouse/blob/master/utils/prepare-environment/install-gcc.sh]\n\n\nUse GCC 7 for builds\n\n\nexport\n \nCC\n=\ngcc-7\n\nexport\n \nCXX\n=\ng++-7\n\n\n\n\n\nInstall required libraries from packages\n\n\nsudo apt-get install libicu-dev libreadline-dev libmysqlclient-dev libssl-dev unixodbc-dev ninja-build\n\n\n\n\n\nCheckout ClickHouse sources\n\n\nTo get the latest stable version:\n\n\ngit clone -b stable --recursive git@github.com:yandex/ClickHouse.git\n\n# or: git clone -b stable --recursive https://github.com/yandex/ClickHouse.git\n\n\n\ncd\n ClickHouse\n\n\n\n\n\nFor development, switch to the \nmaster\n branch.\nFor the latest release candidate, switch to the \ntesting\n branch.\n\n\nBuild ClickHouse\n\n\nThere are two build variants.\n\n\nBuild release package\n\n\nInstall prerequisites to build Debian packages.\n\n\nsudo apt-get install devscripts dupload fakeroot debhelper\n\n\n\n\n\nInstall the most recent version of Clang.\n\n\nClang is embedded into the ClickHouse package and used at runtime. The minimum version is 5.0. It is optional.\n\n\nTo install clang, see \nutils/prepare-environment/install-clang.sh\n\n\nYou may also build ClickHouse with Clang for development purposes.\nFor production releases, GCC is used.\n\n\nRun the release script:\n\n\nrm -f ../clickhouse*.deb\n./release\n\n\n\n\n\nYou will find built packages in the parent directory:\n\n\nls -l ../clickhouse*.deb\n\n\n\n\n\nNote that usage of debian packages is not required.\nClickHouse has no runtime dependencies except libc, so it could work on almost any Linux.\n\n\nInstalling freshly built packages on a development server:\n\n\nsudo dpkg -i ../clickhouse*.deb\nsudo service clickhouse-server start\n\n\n\n\n\nBuild to work with code\n\n\nmkdir build\n\ncd\n build\ncmake ..\nmake -j \n$THREADS\n\n\ncd\n ..\n\n\n\n\n\nTo create an executable, run \nmake clickhouse\n.\nThis will create the \ndbms/src/Server/clickhouse\n executable, which can be used with \nclient\n or \nserver\n arguments.", + "title": "How to build ClickHouse on Linux" + }, + { + "location": "/development/build/#how-to-build-clickhouse-on-linux", + "text": "Build should work on Linux Ubuntu 12.04, 14.04 or newer.\nWith appropriate changes, it should also work on any other Linux distribution.\nThe build process is not intended to work on Mac OS X.\nOnly x86_64 with SSE 4.2 is supported. Support for AArch64 is experimental. To test for SSE 4.2, do grep -q sse4_2 /proc/cpuinfo echo SSE 4.2 supported || echo SSE 4.2 not supported", + "title": "How to build ClickHouse on Linux" + }, + { + "location": "/development/build/#install-git-and-cmake", + "text": "sudo apt-get install git cmake Or cmake3 instead of cmake on older systems.", + "title": "Install Git and CMake" + }, + { + "location": "/development/build/#detect-the-number-of-threads", + "text": "export THREADS = $( grep -c ^processor /proc/cpuinfo )", + "title": "Detect the number of threads" + }, + { + "location": "/development/build/#install-gcc-7", + "text": "There are several ways to do this.", + "title": "Install GCC 7" + }, + { + "location": "/development/build/#install-from-a-ppa-package", + "text": "sudo apt-get install software-properties-common\nsudo apt-add-repository ppa:ubuntu-toolchain-r/test\nsudo apt-get update\nsudo apt-get install gcc-7 g++-7", + "title": "Install from a PPA package" + }, + { + "location": "/development/build/#install-from-sources", + "text": "Look at [https://github.com/yandex/ClickHouse/blob/master/utils/prepare-environment/install-gcc.sh]", + "title": "Install from sources" + }, + { + "location": "/development/build/#use-gcc-7-for-builds", + "text": "export CC = gcc-7 export CXX = g++-7", + "title": "Use GCC 7 for builds" + }, + { + "location": "/development/build/#install-required-libraries-from-packages", + "text": "sudo apt-get install libicu-dev libreadline-dev libmysqlclient-dev libssl-dev unixodbc-dev ninja-build", + "title": "Install required libraries from packages" + }, + { + "location": "/development/build/#checkout-clickhouse-sources", + "text": "To get the latest stable version: git clone -b stable --recursive git@github.com:yandex/ClickHouse.git # or: git clone -b stable --recursive https://github.com/yandex/ClickHouse.git cd ClickHouse For development, switch to the master branch.\nFor the latest release candidate, switch to the testing branch.", + "title": "Checkout ClickHouse sources" + }, + { + "location": "/development/build/#build-clickhouse", + "text": "There are two build variants.", + "title": "Build ClickHouse" + }, + { + "location": "/development/build/#build-release-package", + "text": "Install prerequisites to build Debian packages. sudo apt-get install devscripts dupload fakeroot debhelper Install the most recent version of Clang. Clang is embedded into the ClickHouse package and used at runtime. The minimum version is 5.0. It is optional. To install clang, see utils/prepare-environment/install-clang.sh You may also build ClickHouse with Clang for development purposes.\nFor production releases, GCC is used. Run the release script: rm -f ../clickhouse*.deb\n./release You will find built packages in the parent directory: ls -l ../clickhouse*.deb Note that usage of debian packages is not required.\nClickHouse has no runtime dependencies except libc, so it could work on almost any Linux. Installing freshly built packages on a development server: sudo dpkg -i ../clickhouse*.deb\nsudo service clickhouse-server start", + "title": "Build release package" + }, + { + "location": "/development/build/#build-to-work-with-code", + "text": "mkdir build cd build\ncmake ..\nmake -j $THREADS cd .. To create an executable, run make clickhouse .\nThis will create the dbms/src/Server/clickhouse executable, which can be used with client or server arguments.", + "title": "Build to work with code" + }, + { + "location": "/development/build_osx/", + "text": "How to build ClickHouse on Mac OS X\n\n\nBuild should work on Mac OS X 10.12. If you're using earlier version, you can try to build ClickHouse using Gentoo Prefix and clang sl in this instruction.\nWith appropriate changes, it should also work on any other Linux distribution.\n\n\nInstall Homebrew\n\n\n/usr/bin/ruby -e \n$(\ncurl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install\n)\n\n\n\n\n\n\nInstall required compilers, tools, and libraries\n\n\nbrew install cmake gcc icu4c mysql openssl unixodbc libtool gettext zlib readline boost --cc\n=\ngcc-7\n\n\n\n\n\nCheckout ClickHouse sources\n\n\nTo get the latest stable version:\n\n\ngit clone -b stable --recursive --depth\n=\n10\n git@github.com:yandex/ClickHouse.git\n\n# or: git clone -b stable --recursive --depth=10 https://github.com/yandex/ClickHouse.git\n\n\n\ncd\n ClickHouse\n\n\n\n\n\nFor development, switch to the \nmaster\n branch.\nFor the latest release candidate, switch to the \ntesting\n branch.\n\n\nBuild ClickHouse\n\n\nmkdir build\n\ncd\n build\ncmake .. -DCMAKE_CXX_COMPILER\n=\n`\nwhich g++-7\n`\n -DCMAKE_C_COMPILER\n=\n`\nwhich gcc-7\n`\n\nmake -j \n`\nsysctl -n hw.ncpu\n`\n\n\ncd\n ..\n\n\n\n\n\nCaveats\n\n\nIf you intend to run clickhouse-server, make sure to increase the system's maxfiles variable. See \nMacOS.md\n for more details.", + "title": "How to build ClickHouse on Mac OS X" + }, + { + "location": "/development/build_osx/#how-to-build-clickhouse-on-mac-os-x", + "text": "Build should work on Mac OS X 10.12. If you're using earlier version, you can try to build ClickHouse using Gentoo Prefix and clang sl in this instruction.\nWith appropriate changes, it should also work on any other Linux distribution.", + "title": "How to build ClickHouse on Mac OS X" + }, + { + "location": "/development/build_osx/#install-homebrew", + "text": "/usr/bin/ruby -e $( curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install )", + "title": "Install Homebrew" + }, + { + "location": "/development/build_osx/#install-required-compilers-tools-and-libraries", + "text": "brew install cmake gcc icu4c mysql openssl unixodbc libtool gettext zlib readline boost --cc = gcc-7", + "title": "Install required compilers, tools, and libraries" + }, + { + "location": "/development/build_osx/#checkout-clickhouse-sources", + "text": "To get the latest stable version: git clone -b stable --recursive --depth = 10 git@github.com:yandex/ClickHouse.git # or: git clone -b stable --recursive --depth=10 https://github.com/yandex/ClickHouse.git cd ClickHouse For development, switch to the master branch.\nFor the latest release candidate, switch to the testing branch.", + "title": "Checkout ClickHouse sources" + }, + { + "location": "/development/build_osx/#build-clickhouse", + "text": "mkdir build cd build\ncmake .. -DCMAKE_CXX_COMPILER = ` which g++-7 ` -DCMAKE_C_COMPILER = ` which gcc-7 ` \nmake -j ` sysctl -n hw.ncpu ` cd ..", + "title": "Build ClickHouse" + }, + { + "location": "/development/build_osx/#caveats", + "text": "If you intend to run clickhouse-server, make sure to increase the system's maxfiles variable. See MacOS.md for more details.", + "title": "Caveats" + }, + { + "location": "/development/style/", + "text": "How to write C++ code\n\n\nGeneral recommendations\n\n\n1.\n The following are recommendations, not requirements.\n\n\n2.\n If you are editing code, it makes sense to follow the formatting of the existing code.\n\n\n3.\n Code style is needed for consistency. Consistency makes it easier to read the code, and it also makes it easier to search the code.\n\n\n4.\n Many of the rules do not have logical reasons; they are dictated by established practices.\n\n\nFormatting\n\n\n1.\n Most of the formatting will be done automatically by \nclang-format\n.\n\n\n2.\n Indents are 4 spaces. Configure your development environment so that a tab adds four spaces.\n\n\n3.\n A left curly bracket must be separated on a new line. (And the right one, as well.)\n\n\ninline\n \nvoid\n \nreadBoolText\n(\nbool\n \n \nx\n,\n \nReadBuffer\n \n \nbuf\n)\n\n\n{\n\n \nchar\n \ntmp\n \n=\n \n0\n;\n\n \nreadChar\n(\ntmp\n,\n \nbuf\n);\n\n \nx\n \n=\n \ntmp\n \n!=\n \n0\n;\n\n\n}\n\n\n\n\n\n\n4.\n\nBut if the entire function body is quite short (a single statement), you can place it entirely on one line if you wish. Place spaces around curly braces (besides the space at the end of the line).\n\n\ninline\n \nsize_t\n \nmask\n()\n \nconst\n \n{\n \nreturn\n \nbuf_size\n()\n \n-\n \n1\n;\n \n}\n\n\ninline\n \nsize_t\n \nplace\n(\nHashValue\n \nx\n)\n \nconst\n \n{\n \nreturn\n \nx\n \n \nmask\n();\n \n}\n\n\n\n\n\n\n5.\n For functions, don't put spaces around brackets.\n\n\nvoid\n \nreinsert\n(\nconst\n \nValue\n \n \nx\n)\n\n\nmemcpy\n(\nbuf\n[\nplace_value\n],\n \nx\n,\n \nsizeof\n(\nx\n));\n\n\n\n\n\n\n6.\n When using statements such as \nif\n, \nfor\n, and \nwhile\n (unlike function calls), put a space before the opening bracket.\n\n\ncpp\n for (size_t i = 0; i \n rows; i += storage.index_granularity)\n\n\n7.\n Put spaces around binary operators (\n+\n, \n-\n, \n*\n, \n/\n, \n%\n, ...), as well as the ternary operator \n?:\n.\n\n\nUInt16\n \nyear\n \n=\n \n(\ns\n[\n0\n]\n \n-\n \n0\n)\n \n*\n \n1000\n \n+\n \n(\ns\n[\n1\n]\n \n-\n \n0\n)\n \n*\n \n100\n \n+\n \n(\ns\n[\n2\n]\n \n-\n \n0\n)\n \n*\n \n10\n \n+\n \n(\ns\n[\n3\n]\n \n-\n \n0\n);\n\n\nUInt8\n \nmonth\n \n=\n \n(\ns\n[\n5\n]\n \n-\n \n0\n)\n \n*\n \n10\n \n+\n \n(\ns\n[\n6\n]\n \n-\n \n0\n);\n\n\nUInt8\n \nday\n \n=\n \n(\ns\n[\n8\n]\n \n-\n \n0\n)\n \n*\n \n10\n \n+\n \n(\ns\n[\n9\n]\n \n-\n \n0\n);\n\n\n\n\n\n\n8.\n If a line feed is entered, put the operator on a new line and increase the indent before it.\n\n\nif\n \n(\nelapsed_ns\n)\n\n \nmessage\n \n \n (\n\n \n \nrows_read_on_server\n \n*\n \n1000000000\n \n/\n \nelapsed_ns\n \n \n rows/s., \n\n \n \nbytes_read_on_server\n \n*\n \n1000.0\n \n/\n \nelapsed_ns\n \n \n MB/s.) \n;\n\n\n\n\n\n\n9.\n You can use spaces for alignment within a line, if desired.\n\n\ndst\n.\nClickLogID\n \n=\n \nclick\n.\nLogID\n;\n\n\ndst\n.\nClickEventID\n \n=\n \nclick\n.\nEventID\n;\n\n\ndst\n.\nClickGoodEvent\n \n=\n \nclick\n.\nGoodEvent\n;\n\n\n\n\n\n\n10.\n Don't use spaces around the operators \n.\n, \n-\n .\n\n\nIf necessary, the operator can be wrapped to the next line. In this case, the offset in front of it is increased.\n\n\n11.\n Do not use a space to separate unary operators (\n-\n, \n+\n, \n*\n, \n, ...) from the argument.\n\n\n12.\n Put a space after a comma, but not before it. The same rule goes for a semicolon inside a for expression.\n\n\n13.\n Do not use spaces to separate the \n[]\n operator.\n\n\n14.\n In a \ntemplate \n...\n expression, use a space between \ntemplate\n and \n. No spaces after \n or before \n.\n\n\ntemplate\n \ntypename\n \nTKey\n,\n \ntypename\n \nTValue\n\n\nstruct\n \nAggregatedStatElement\n\n\n{}\n\n\n\n\n\n\n15.\n In classes and structures, public, private, and protected are written on the same level as the \nclass/struct\n, but all other internal elements should be deeper.\n\n\ntemplate\n \ntypename\n \nT\n\n\nclass\n \nMultiVersion\n\n\n{\n\n\npublic\n:\n\n \n/// Version of object for usage. shared_ptr manage lifetime of version.\n\n \nusing\n \nVersion\n \n=\n \nstd\n::\nshared_ptr\nconst\n \nT\n;\n\n \n...\n\n\n}\n\n\n\n\n\n\n16.\n If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace.\n\n\n17.\n If the block for \nif\n, \nfor\n, \nwhile\n... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets.\n\n\n/// Finish write.\n\n\nfor\n \n(\nauto\n \n \nstream\n \n:\n \nstreams\n)\n\n \nstream\n.\nsecond\n-\nfinalize\n();\n\n\n\n\n\n\n18.\n There should be any spaces at the ends of lines.\n\n\n19.\n Sources are UTF-8 encoded.\n\n\n20.\n Non-ASCII characters can be used in string literals.\n\n\n \n, \n \n \n(\ntimer\n.\nelapsed\n()\n \n/\n \nchunks_stats\n.\nhits\n)\n \n \n \u03bcsec/hit.\n;\n\n\n\n\n\n\n21.\n Do not write multiple expressions in a single line.\n\n\n22.\n Group sections of code inside functions and separate them with no more than one empty line.\n\n\n23.\n Separate functions, classes, and so on with one or two empty lines.\n\n\n24.\n A \nconst\n (related to a value) must be written before the type name.\n\n\n//correct\n\n\nconst\n \nchar\n \n*\n \npos\n\n\nconst\n \nstd\n::\nstring\n \n \ns\n\n\n//incorrect\n\n\nchar\n \nconst\n \n*\n \npos\n\n\n\n\n\n\n25.\n When declaring a pointer or reference, the \n*\n and \n symbols should be separated by spaces on both sides.\n\n\n//correct\n\n\nconst\n \nchar\n \n*\n \npos\n\n\n//incorrect\n\n\nconst\n \nchar\n*\n \npos\n\n\nconst\n \nchar\n \n*\npos\n\n\n\n\n\n\n26.\n When using template types, alias them with the \nusing\n keyword (except in the simplest cases).\n\n\nIn other words, the template parameters are specified only in \nusing\n and aren't repeated in the code.\n\n\nusing\n can be declared locally, such as inside a function.\n\n\n//correct\n\n\nusing\n \nFileStreams\n \n=\n \nstd\n::\nmap\nstd\n::\nstring\n,\n \nstd\n::\nshared_ptr\nStream\n;\n\n\nFileStreams\n \nstreams\n;\n\n\n//incorrect\n\n\nstd\n::\nmap\nstd\n::\nstring\n,\n \nstd\n::\nshared_ptr\nStream\n \nstreams\n;\n\n\n\n\n\n\n27.\n Do not declare several variables of different types in one statement.\n\n\n//incorrect\n\n\nint\n \nx\n,\n \n*\ny\n;\n\n\n\n\n\n\n28.\n Do not use C-style casts.\n\n\n//incorrect\n\n\nstd\n::\ncerr\n \n \n(\nint\n)\nc\n \n;\n \nstd\n::\nendl\n;\n\n\n//correct\n\n\nstd\n::\ncerr\n \n \nstatic_cast\nint\n(\nc\n)\n \n \nstd\n::\nendl\n;\n\n\n\n\n\n\n29.\n In classes and structs, group members and functions separately inside each visibility scope.\n\n\n30.\n For small classes and structs, it is not necessary to separate the method declaration from the implementation.\n\n\nThe same is true for small methods in any classes or structs.\n\n\nFor templated classes and structs, don't separate the method declarations from the implementation (because otherwise they must be defined in the same translation unit).\n\n\n31.\n You can wrap lines at 140 characters, instead of 80.\n\n\n32.\n Always use the prefix increment/decrement operators if postfix is not required.\n\n\nfor\n \n(\nNames\n::\nconst_iterator\n \nit\n \n=\n \ncolumn_names\n.\nbegin\n();\n \nit\n \n!=\n \ncolumn_names\n.\nend\n();\n \n++\nit\n)\n\n\n\n\n\n\nComments\n\n\n1.\n Be sure to add comments for all non-trivial parts of code.\n\n\nThis is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong.\n\n\n/** Part of piece of memory, that can be used.\n\n\n * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading,\n\n\n * then working_buffer will have size of only 10 bytes\n\n\n * (working_buffer.end() will point to the position right after those 10 bytes available for read).\n\n\n*/\n\n\n\n\n\n\n2.\n Comments can be as detailed as necessary.\n\n\n3.\n Place comments before the code they describe. In rare cases, comments can come after the code, on the same line.\n\n\n/** Parses and executes the query.\n\n\n*/\n\n\nvoid\n \nexecuteQuery\n(\n\n \nReadBuffer\n \n \nistr\n,\n \n/// Where to read the query from (and data for INSERT, if applicable)\n\n \nWriteBuffer\n \n \nostr\n,\n \n/// Where to write the result\n\n \nContext\n \n \ncontext\n,\n \n/// DB, tables, data types, engines, functions, aggregate functions...\n\n \nBlockInputStreamPtr\n \n \nquery_plan\n,\n \n/// A description of query processing can be included here\n\n \nQueryProcessingStage\n::\nEnum\n \nstage\n \n=\n \nQueryProcessingStage\n::\nComplete\n \n/// The last stage to process the SELECT query to\n\n \n)\n\n\n\n\n\n\n4.\n Comments should be written in English only.\n\n\n5.\n If you are writing a library, include detailed comments explaining it in the main header file.\n\n\n6.\n Do not add comments that do not provide additional information. In particular, do not leave empty comments like this:\n\n\n/*\n\n\n* Procedure Name:\n\n\n* Original procedure name:\n\n\n* Author:\n\n\n* Date of creation:\n\n\n* Dates of modification:\n\n\n* Modification authors:\n\n\n* Original file name:\n\n\n* Purpose:\n\n\n* Intent:\n\n\n* Designation:\n\n\n* Classes used:\n\n\n* Constants:\n\n\n* Local variables:\n\n\n* Parameters:\n\n\n* Date of creation:\n\n\n* Purpose:\n\n\n*/\n\n\n\n\n\n\nThe example is borrowed from \nhttp://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/\n.\n\n\n7.\n Do not write garbage comments (author, creation date ..) at the beginning of each file.\n\n\n8.\n Single-line comments begin with three slashes: \n///\n and multi-line comments begin with \n/**\n. These comments are considered \"documentation\".\n\n\nNote: You can use Doxygen to generate documentation from these comments. But Doxygen is not generally used because it is more convenient to navigate the code in the IDE.\n\n\n9.\n Multi-line comments must not have empty lines at the beginning and end (except the line that closes a multi-line comment).\n\n\n10.\n For commenting out code, use basic comments, not \"documenting\" comments.\n\n\n11.\n Delete the commented out parts of the code before commiting.\n\n\n12.\n Do not use profanity in comments or code.\n\n\n13.\n Do not use uppercase letters. Do not use excessive punctuation.\n\n\n/// WHAT THE FAIL???\n\n\n\n\n\n\n14.\n Do not make delimeters from comments.\n\n\n///******************************************************\n\n\n\n\n\n15.\n Do not start discussions in comments.\n\n\n/// Why did you do this stuff?\n\n\n\n\n\n16.\n There's no need to write a comment at the end of a block describing what it was about.\n\n\n/// for\n\n\n\n\n\nNames\n\n\n1.\n The names of variables and class members use lowercase letters with underscores.\n\n\nsize_t\n \nmax_block_size\n;\n\n\n\n\n\n\n2.\n The names of functions (methods) use camelCase beginning with a lowercase letter.\n\n\nstd\n::\nstring\n \ngetName\n()\n \nconst\n \noverride\n \n{\n \nreturn\n \nMemory\n;\n \n}\n\n\n\n\n\n\n3.\n The names of classes (structures) use CamelCase beginning with an uppercase letter. Prefixes other than I are not used for interfaces.\n\n\nclass\n \nStorageMemory\n \n:\n \npublic\n \nIStorage\n\n\n\n\n\n\n4.\n The names of usings follow the same rules as classes, or you can add _t at the end.\n\n\n5.\n Names of template type arguments for simple cases: T; T, U; T1, T2.\n\n\nFor more complex cases, either follow the rules for class names, or add the prefix T.\n\n\ntemplate\n \ntypename\n \nTKey\n,\n \ntypename\n \nTValue\n\n\nstruct\n \nAggregatedStatElement\n\n\n\n\n\n\n6.\n Names of template constant arguments: either follow the rules for variable names, or use N in simple cases.\n\n\ntemplate\n \nbool\n \nwithout_www\n\n\nstruct\n \nExtractDomain\n\n\n\n\n\n\n7.\n For abstract classes (interfaces) you can add the I prefix.\n\n\nclass\n \nIBlockInputStream\n\n\n\n\n\n\n8.\n If you use a variable locally, you can use the short name.\n\n\nIn other cases, use a descriptive name that conveys the meaning.\n\n\nbool\n \ninfo_successfully_loaded\n \n=\n \nfalse\n;\n\n\n\n\n\n\n9.\n \ndefine\n\u2018s should be in ALL_CAPS with underscores. The same is true for global constants.\n\n\n#define MAX_SRC_TABLE_NAMES_TO_STORE 1000\n\n\n\n\n\n\n10.\n File names should use the same style as their contents.\n\n\nIf a file contains a single class, name the file the same way as the class, in CamelCase.\n\n\nIf the file contains a single function, name the file the same way as the function, in camelCase.\n\n\n11.\n If the name contains an abbreviation, then:\n\n\n\n\nFor variable names, the abbreviation should use lowercase letters \nmysql_connection\n (not \nmySQL_connection\n).\n\n\nFor names of classes and functions, keep the uppercase letters in the abbreviation \nMySQLConnection\n (not \nMySqlConnection\n).\n\n\n\n\n12.\n Constructor arguments that are used just to initialize the class members should be named the same way as the class members, but with an underscore at the end.\n\n\nFileQueueProcessor\n(\n\n \nconst\n \nstd\n::\nstring\n \n \npath_\n,\n\n \nconst\n \nstd\n::\nstring\n \n \nprefix_\n,\n\n \nstd\n::\nshared_ptr\nFileHandler\n \nhandler_\n)\n\n \n:\n \npath\n(\npath_\n),\n\n \nprefix\n(\nprefix_\n),\n\n \nhandler\n(\nhandler_\n),\n\n \nlog\n(\nLogger\n::\nget\n(\nFileQueueProcessor\n))\n\n\n{\n\n\n}\n\n\n\n\n\n\nThe underscore suffix can be omitted if the argument is not used in the constructor body.\n\n\n13.\n There is no difference in the names of local variables and class members (no prefixes required).\n\n\ntimer\n \n(\nnot\n \nm_timer\n)\n\n\n\n\n\n\n14.\n Constants in enums use CamelCase beginning with an uppercase letter. ALL_CAPS is also allowed. If the enum is not local, use enum class.\n\n\nenum\n \nclass\n \nCompressionMethod\n\n\n{\n\n \nQuickLZ\n \n=\n \n0\n,\n\n \nLZ4\n \n=\n \n1\n,\n\n\n};\n\n\n\n\n\n\n15.\n All names must be in English. Transliteration of Russian words is not allowed.\n\n\nnot\n \nStroka\n\n\n\n\n\n\n16.\n Abbreviations are acceptable if they are well known (when you can easily find the meaning of the abbreviation in Wikipedia or in a search engine).\n\n\n`AST`, `SQL`.\n\nNot `NVDH` (some random letters)\n\n\n\n\n\nIncomplete words are acceptable if the shortened version is common use.\n\n\nYou can also use an abbreviation if the full name is included next to it in the comments.\n\n\n17.\n File names with C++ source code must have the \n.cpp\n extension. Header files must have the \n.h\n extension.\n\n\nHow to write code\n\n\n1.\n Memory management.\n\n\nManual memory deallocation (delete) can only be used in library code.\n\n\nIn library code, the delete operator can only be used in destructors.\n\n\nIn application code, memory must be freed by the object that owns it.\n\n\nExamples:\n\n\n\n\nThe easiest way is to place an object on the stack, or make it a member of another class.\n\n\nFor a large number of small objects, use containers.\n\n\nFor automatic deallocation of a small number of objects that reside in the heap, use shared_ptr/unique_ptr.\n\n\n\n\n2.\n Resource management.\n\n\nUse RAII and see the previous point.\n\n\n3.\n Error handling.\n\n\nUse exceptions. In most cases, you only need to throw an exception, and don't need to catch it (because of RAII).\n\n\nIn offline data processing applications, it's often acceptable to not catch exceptions.\n\n\nIn servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler.\n\n\n/// If there were no other calculations yet, do it synchronously\n\n\nif\n \n(\n!\nstarted\n)\n\n\n{\n\n \ncalculate\n();\n\n \nstarted\n \n=\n \ntrue\n;\n\n\n}\n\n\nelse\n \n/// If the calculations are already in progress, wait for results\n\n \npool\n.\nwait\n();\n\n\n\nif\n \n(\nexception\n)\n\n \nexception\n-\nrethrow\n();\n\n\n\n\n\n\nNever hide exceptions without handling. Never just blindly put all exceptions to log.\n\n\nNot \ncatch (...) {}\n.\n\n\nIf you need to ignore some exceptions, do so only for specific ones and rethrow the rest.\n\n\ncatch\n \n(\nconst\n \nDB\n::\nException\n \n \ne\n)\n\n\n{\n\n \nif\n \n(\ne\n.\ncode\n()\n \n==\n \nErrorCodes\n::\nUNKNOWN_AGGREGATE_FUNCTION\n)\n\n \nreturn\n \nnullptr\n;\n\n \nelse\n\n \nthrow\n;\n\n\n}\n\n\n\n\n\n\nWhen using functions with response codes or errno, always check the result and throw an exception in case of error.\n\n\nif\n \n(\n0\n \n!=\n \nclose\n(\nfd\n))\n\n \nthrowFromErrno\n(\nCannot close file \n \n+\n \nfile_name\n,\n \nErrorCodes\n::\nCANNOT_CLOSE_FILE\n);\n\n\n\n\n\n\nAsserts are not used.\n\n\n4.\n Exception types.\n\n\nThere is no need to use complex exception hierarchy in application code. The exception text should be understandable to a system administrator.\n\n\n5.\n Throwing exceptions from destructors.\n\n\nThis is not recommended, but it is allowed.\n\n\nUse the following options:\n\n\n\n\nCreate a (done() or finalize()) function that will do all the work in advance that might lead to an exception. If that function was called, there should be no exceptions in the destructor later.\n\n\nTasks that are too complex (such as sending messages over the network) can be put in separate method that the class user will have to call before destruction.\n\n\nIf there is an exception in the destructor, it\u2019s better to log it than to hide it (if the logger is available).\n\n\nIn simple applications, it is acceptable to rely on std::terminate (for cases of noexcept by default in C++11) to handle exceptions.\n\n\n\n\n6.\n Anonymous code blocks.\n\n\nYou can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block.\n\n\nBlock\n \nblock\n \n=\n \ndata\n.\nin\n-\nread\n();\n\n\n\n{\n\n \nstd\n::\nlock_guard\nstd\n::\nmutex\n \nlock\n(\nmutex\n);\n\n \ndata\n.\nready\n \n=\n \ntrue\n;\n\n \ndata\n.\nblock\n \n=\n \nblock\n;\n\n\n}\n\n\n\nready_any\n.\nset\n();\n\n\n\n\n\n\n7.\n Multithreading.\n\n\nFor offline data processing applications:\n\n\n\n\nTry to get the best possible performance on a single CPU core. You can then parallelize your code if necessary.\n\n\n\n\nIn server applications:\n\n\n\n\nUse the thread pool to process requests. At this point, we haven't had any tasks that required userspace context switching.\n\n\n\n\nFork is not used for parallelization.\n\n\n8.\n Synchronizing threads.\n\n\nOften it is possible to make different threads use different memory cells (even better: different cache lines,) and to not use any thread synchronization (except joinAll).\n\n\nIf synchronization is required, in most cases, it is sufficient to use mutex under lock_guard.\n\n\nIn other cases use system synchronization primitives. Do not use busy wait.\n\n\nAtomic operations should be used only in the simplest cases.\n\n\nDo not try to implement lock-free data structures unless it is your primary area of expertise.\n\n\n9.\n Pointers vs references.\n\n\nIn most cases, prefer references.\n\n\n10.\n const.\n\n\nUse constant references, pointers to constants, \nconst_iterator\n, \nconst\n methods.\n\n\nConsider \nconst\n to be default and use non-const only when necessary.\n\n\nWhen passing variable by value, using \nconst\n usually does not make sense.\n\n\n11.\n unsigned.\n\n\nUse \nunsigned\n, if needed.\n\n\n12.\n Numeric types\n\n\nUse \nUInt8\n, \nUInt16\n, \nUInt32\n, \nUInt64\n, \nInt8\n, \nInt16\n, \nInt32\n, \nInt64\n, and \nsize_t\n, \nssize_t\n, \nptrdiff_t\n.\n\n\nDon't use \nsigned/unsigned long\n, \nlong long\n, \nshort\n, \nsigned char\n, \nunsigned char\n, or \nchar\n types for numbers.\n\n\n13.\n Passing arguments.\n\n\nPass complex values by reference (including \nstd::string\n).\n\n\nIf a function captures ownership of an objected created in the heap, make the argument type \nshared_ptr\n or \nunique_ptr\n.\n\n\n14.\n Returning values.\n\n\nIn most cases, just use return. Do not write \n[return std::move(res)]{.strike}\n.\n\n\nIf the function allocates an object on heap and returns it, use \nshared_ptr\n or \nunique_ptr\n.\n\n\nIn rare cases you might need to return the value via an argument. In this case, the argument should be a reference.\n\n\nusing\n \nAggregateFunctionPtr\n \n=\n \nstd\n::\nshared_ptr\nIAggregateFunction\n;\n\n\n\n/** Creates an aggregate function by name.\n\n\n */\n\n\nclass\n \nAggregateFunctionFactory\n\n\n{\n\n\npublic\n:\n\n \nAggregateFunctionFactory\n();\n\n \nAggregateFunctionPtr\n \nget\n(\nconst\n \nString\n \n \nname\n,\n \nconst\n \nDataTypes\n \n \nargument_types\n)\n \nconst\n;\n\n\n\n\n\n\n15.\n namespace.\n\n\nThere is no need to use a separate namespace for application code or small libraries.\n\n\nor small libraries.\n\n\nFor medium to large libraries, put everything in the namespace.\n\n\nYou can use the additional detail namespace in a library's \n.h\n file to hide implementation details.\n\n\nIn a \n.cpp\n file, you can use the static or anonymous namespace to hide symbols.\n\n\nYou can also use namespace for enums to prevent its names from polluting the outer namespace, but it\u2019s better to use the enum class.\n\n\n16.\n Delayed initialization.\n\n\nIf arguments are required for initialization then do not write a default constructor.\n\n\nIf later you\u2019ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use \nshared_ptr/unique_ptr\n.\n\n\nLoader\n(\nDB\n::\nConnection\n \n*\n \nconnection_\n,\n \nconst\n \nstd\n::\nstring\n \n \nquery\n,\n \nsize_t\n \nmax_block_size_\n);\n\n\n\n/// For delayed initialization\n\n\nLoader\n()\n \n{}\n\n\n\n\n\n\n17.\n Virtual functions.\n\n\nIf the class is not intended for polymorphic use, you do not need to make functions virtual. This also applies to the destructor.\n\n\n18.\n Encodings.\n\n\nUse UTF-8 everywhere. Use \nstd::string\nand\nchar *\n. Do not use \nstd::wstring\nand\nwchar_t\n.\n\n\n19.\n Logging.\n\n\nSee the examples everywhere in the code.\n\n\nBefore committing, delete all meaningless and debug logging, and any other types of debug output.\n\n\nLogging in cycles should be avoided, even on the Trace level.\n\n\nLogs must be readable at any logging level.\n\n\nLogging should only be used in application code, for the most part.\n\n\nLog messages must be written in English.\n\n\nThe log should preferably be understandable for the system administrator.\n\n\nDo not use profanity in the log.\n\n\nUse UTF-8 encoding in the log. In rare cases you can use non-ASCII characters in the log.\n\n\n20.\n I/O.\n\n\nDon't use iostreams in internal cycles that are critical for application performance (and never use stringstream).\n\n\nUse the DB/IO library instead.\n\n\n21.\n Date and time.\n\n\nSee the \nDateLUT\n library.\n\n\n22.\n include.\n\n\nAlways use \n#pragma once\n instead of include guards.\n\n\n23.\n using.\n\n\nThe \nusing namespace\n is not used.\n\n\nIt's fine if you are 'using' something specific, but make it local inside a class or function.\n\n\n24.\n Do not use trailing return type for functions unless necessary.\n\n\n[auto f() -\ngt; void;]{.strike}\n\n\n\n\n\n25.\n Do not declare and init variables like this:\n\n\nauto\n \ns\n \n=\n \nstd\n::\nstring\n{\nHello\n};\n\n\n\n\n\n\nDo it like this:\n\n\nstd\n::\nstring\n \ns\n \n=\n \nHello\n;\n\n\nstd\n::\nstring\n \ns\n{\nHello\n};\n\n\n\n\n\n\n26.\n For virtual functions, write \nvirtual\n in the base class, but write \noverride\n in descendent classes.\n\n\nUnused features of C++\n\n\n1.\n Virtual inheritance is not used.\n\n\n2.\n Exception specifiers from C++03 are not used.\n\n\n3.\n Function try block is not used, except for the main function in tests.\n\n\nPlatform\n\n\n1.\n We write code for a specific platform.\n\n\nBut other things being equal, cross-platform or portable code is preferred.\n\n\n2.\n The language is C++17.\n\n\n3.\n The compiler is \ngcc\n. At this time (December 2017), the code is compiled using version 7.2. (It can also be compiled using clang 5.)\n\n\nThe standard library is used (implementation of \nlibstdc++\n or \nlibc++\n).\n\n\n4.\n OS: Linux Ubuntu, not older than Precise.\n\n\n5.\n Code is written for x86_64 CPU architecture.\n\n\nThe CPU instruction set is the minimum supported set among our servers. Currently, it is SSE 4.2.\n\n\n6.\n Use \n-Wall -Wextra -Werror\n compilation flags.\n\n\n7.\n Use static linking with all libraries except those that are difficult to connect to statically (see the output of the \nldd\n command).\n\n\n8.\n Code is developed and debugged with release settings.\n\n\nTools\n\n\n1.\n \nKDevelop\n is a good IDE.\n\n\n2.\n For debugging, use \ngdb\n, \nvalgrind\n (\nmemcheck\n), \nstrace\n, \n-fsanitize=\n, ..., \ntcmalloc_minimal_debug\n.\n\n\n3.\n For profiling, use Linux Perf \nvalgrind\n (\ncallgrind\n), \nstrace-cf\n.\n\n\n4.\n Sources are in Git.\n\n\n5.\n Compilation is managed by \nCMake\n.\n\n\n6.\n Releases are in \ndeb\n packages.\n\n\n7.\n Commits to master must not break the build.\n\n\nThough only selected revisions are considered workable.\n\n\n8.\n Make commits as often as possible, even if the code is only partially ready.\n\n\nUse branches for this purpose.\n\n\nIf your code is not buildable yet, exclude it from the build before pushing to master. You'll need to finish it or remove it from master within a few days.\n\n\n9.\n For non-trivial changes, used branches and publish them on the server.\n\n\n10.\n Unused code is removed from the repository.\n\n\nLibraries\n\n\n1.\n The C++14 standard library is used (experimental extensions are fine), as well as boost and Poco frameworks.\n\n\n2.\n If necessary, you can use any well-known libraries available in the OS package.\n\n\nIf there is a good solution already available, then use it, even if it means you have to install another library.\n\n\n(But be prepared to remove bad libraries from code.)\n\n\n3.\n You can install a library that isn't in the packages, if the packages don't have what you need or have an outdated version or the wrong type of compilation.\n\n\n4.\n If the library is small and doesn't have its own complex build system, put the source files in the contrib folder.\n\n\n5.\n Preference is always given to libraries that are already used.\n\n\nGeneral recommendations\n\n\n1.\n Write as little code as possible.\n\n\n2.\n Try the simplest solution.\n\n\n3.\n Don't write code until you know how it's going to work and how the inner loop will function.\n\n\n4.\n In the simplest cases, use 'using' instead of classes or structs.\n\n\n5.\n If possible, do not write copy constructors, assignment operators, destructors (other than a virtual one, if the class contains at least one virtual function), mpve-constructors and move assignment operators. In other words, the compiler-generated functions must work correctly. You can use 'default'.\n\n\n6.\n Code simplification is encouraged. Reduce the size of your code where possible.\n\n\nAdditional recommendations\n\n\n1.\n Explicit \nstd::\n for types from \nstddef.h\n is not recommended.\n\n\nWe recommend writing \nsize_t\n instead \nstd::size_t\n because it's shorter.\n\n\nBut if you prefer, \nstd::\n is acceptable.\n\n\n2.\n Explicit \nstd::\n for functions from the standard C library is not recommended.\n\n\nWrite \nmemcpy\n instead of \nstd::memcpy\n.\n\n\nThe reason is that there are similar non-standard functions, such as \nmemmem\n. We do use these functions on occasion. These functions do not exist in namespace \nstd\n.\n\n\nIf you write \nstd::memcpy\n instead of \nmemcpy\n everywhere, then \nmemmem\n without \nstd::\n will look awkward.\n\n\nNevertheless, \nstd::\n is allowed if you prefer it.\n\n\n3.\n Using functions from C when the ones are available in the standard C++ library.\n\n\nThis is acceptable if it is more efficient.\n\n\nFor example, use \nmemcpy\n instead of \nstd::copy\n for copying large chunks of memory.\n\n\n4.\n Multiline function arguments.\n\n\nAny of the following wrapping styles are allowed:\n\n\nfunction\n(\n\n \nT1\n \nx1\n,\n\n \nT2\n \nx2\n)\n\n\n\n\n\n\nfunction\n(\n\n \nsize_t\n \nleft\n,\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)\n\n\n\n\n\n\nfunction\n(\nsize_t\n \nleft\n,\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)\n\n\n\n\n\n\nfunction\n(\nsize_t\n \nleft\n,\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)\n\n\n\n\n\n\nfunction\n(\n\n \nsize_t\n \nleft\n,\n\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)", + "title": "How to write C++ code" + }, + { + "location": "/development/style/#how-to-write-c-code", + "text": "", + "title": "How to write C++ code" + }, + { + "location": "/development/style/#general-recommendations", + "text": "1. The following are recommendations, not requirements. 2. If you are editing code, it makes sense to follow the formatting of the existing code. 3. Code style is needed for consistency. Consistency makes it easier to read the code, and it also makes it easier to search the code. 4. Many of the rules do not have logical reasons; they are dictated by established practices.", + "title": "General recommendations" + }, + { + "location": "/development/style/#formatting", + "text": "1. Most of the formatting will be done automatically by clang-format . 2. Indents are 4 spaces. Configure your development environment so that a tab adds four spaces. 3. A left curly bracket must be separated on a new line. (And the right one, as well.) inline void readBoolText ( bool x , ReadBuffer buf ) { \n char tmp = 0 ; \n readChar ( tmp , buf ); \n x = tmp != 0 ; } 4. \nBut if the entire function body is quite short (a single statement), you can place it entirely on one line if you wish. Place spaces around curly braces (besides the space at the end of the line). inline size_t mask () const { return buf_size () - 1 ; } inline size_t place ( HashValue x ) const { return x mask (); } 5. For functions, don't put spaces around brackets. void reinsert ( const Value x ) memcpy ( buf [ place_value ], x , sizeof ( x )); 6. When using statements such as if , for , and while (unlike function calls), put a space before the opening bracket. cpp\n for (size_t i = 0; i rows; i += storage.index_granularity) 7. Put spaces around binary operators ( + , - , * , / , % , ...), as well as the ternary operator ?: . UInt16 year = ( s [ 0 ] - 0 ) * 1000 + ( s [ 1 ] - 0 ) * 100 + ( s [ 2 ] - 0 ) * 10 + ( s [ 3 ] - 0 ); UInt8 month = ( s [ 5 ] - 0 ) * 10 + ( s [ 6 ] - 0 ); UInt8 day = ( s [ 8 ] - 0 ) * 10 + ( s [ 9 ] - 0 ); 8. If a line feed is entered, put the operator on a new line and increase the indent before it. if ( elapsed_ns ) \n message ( \n rows_read_on_server * 1000000000 / elapsed_ns rows/s., \n bytes_read_on_server * 1000.0 / elapsed_ns MB/s.) ; 9. You can use spaces for alignment within a line, if desired. dst . ClickLogID = click . LogID ; dst . ClickEventID = click . EventID ; dst . ClickGoodEvent = click . GoodEvent ; 10. Don't use spaces around the operators . , - . If necessary, the operator can be wrapped to the next line. In this case, the offset in front of it is increased. 11. Do not use a space to separate unary operators ( - , + , * , , ...) from the argument. 12. Put a space after a comma, but not before it. The same rule goes for a semicolon inside a for expression. 13. Do not use spaces to separate the [] operator. 14. In a template ... expression, use a space between template and . No spaces after or before . template typename TKey , typename TValue struct AggregatedStatElement {} 15. In classes and structures, public, private, and protected are written on the same level as the class/struct , but all other internal elements should be deeper. template typename T class MultiVersion { public : \n /// Version of object for usage. shared_ptr manage lifetime of version. \n using Version = std :: shared_ptr const T ; \n ... } 16. If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace. 17. If the block for if , for , while ... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets. /// Finish write. for ( auto stream : streams ) \n stream . second - finalize (); 18. There should be any spaces at the ends of lines. 19. Sources are UTF-8 encoded. 20. Non-ASCII characters can be used in string literals. , ( timer . elapsed () / chunks_stats . hits ) \u03bcsec/hit. ; 21. Do not write multiple expressions in a single line. 22. Group sections of code inside functions and separate them with no more than one empty line. 23. Separate functions, classes, and so on with one or two empty lines. 24. A const (related to a value) must be written before the type name. //correct const char * pos const std :: string s //incorrect char const * pos 25. When declaring a pointer or reference, the * and symbols should be separated by spaces on both sides. //correct const char * pos //incorrect const char * pos const char * pos 26. When using template types, alias them with the using keyword (except in the simplest cases). In other words, the template parameters are specified only in using and aren't repeated in the code. using can be declared locally, such as inside a function. //correct using FileStreams = std :: map std :: string , std :: shared_ptr Stream ; FileStreams streams ; //incorrect std :: map std :: string , std :: shared_ptr Stream streams ; 27. Do not declare several variables of different types in one statement. //incorrect int x , * y ; 28. Do not use C-style casts. //incorrect std :: cerr ( int ) c ; std :: endl ; //correct std :: cerr static_cast int ( c ) std :: endl ; 29. In classes and structs, group members and functions separately inside each visibility scope. 30. For small classes and structs, it is not necessary to separate the method declaration from the implementation. The same is true for small methods in any classes or structs. For templated classes and structs, don't separate the method declarations from the implementation (because otherwise they must be defined in the same translation unit). 31. You can wrap lines at 140 characters, instead of 80. 32. Always use the prefix increment/decrement operators if postfix is not required. for ( Names :: const_iterator it = column_names . begin (); it != column_names . end (); ++ it )", + "title": "Formatting" + }, + { + "location": "/development/style/#comments", + "text": "1. Be sure to add comments for all non-trivial parts of code. This is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong. /** Part of piece of memory, that can be used. * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading, * then working_buffer will have size of only 10 bytes * (working_buffer.end() will point to the position right after those 10 bytes available for read). */ 2. Comments can be as detailed as necessary. 3. Place comments before the code they describe. In rare cases, comments can come after the code, on the same line. /** Parses and executes the query. */ void executeQuery ( \n ReadBuffer istr , /// Where to read the query from (and data for INSERT, if applicable) \n WriteBuffer ostr , /// Where to write the result \n Context context , /// DB, tables, data types, engines, functions, aggregate functions... \n BlockInputStreamPtr query_plan , /// A description of query processing can be included here \n QueryProcessingStage :: Enum stage = QueryProcessingStage :: Complete /// The last stage to process the SELECT query to \n ) 4. Comments should be written in English only. 5. If you are writing a library, include detailed comments explaining it in the main header file. 6. Do not add comments that do not provide additional information. In particular, do not leave empty comments like this: /* * Procedure Name: * Original procedure name: * Author: * Date of creation: * Dates of modification: * Modification authors: * Original file name: * Purpose: * Intent: * Designation: * Classes used: * Constants: * Local variables: * Parameters: * Date of creation: * Purpose: */ The example is borrowed from http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/ . 7. Do not write garbage comments (author, creation date ..) at the beginning of each file. 8. Single-line comments begin with three slashes: /// and multi-line comments begin with /** . These comments are considered \"documentation\". Note: You can use Doxygen to generate documentation from these comments. But Doxygen is not generally used because it is more convenient to navigate the code in the IDE. 9. Multi-line comments must not have empty lines at the beginning and end (except the line that closes a multi-line comment). 10. For commenting out code, use basic comments, not \"documenting\" comments. 11. Delete the commented out parts of the code before commiting. 12. Do not use profanity in comments or code. 13. Do not use uppercase letters. Do not use excessive punctuation. /// WHAT THE FAIL??? 14. Do not make delimeters from comments. ///****************************************************** 15. Do not start discussions in comments. /// Why did you do this stuff? 16. There's no need to write a comment at the end of a block describing what it was about. /// for", + "title": "Comments" + }, + { + "location": "/development/style/#names", + "text": "1. The names of variables and class members use lowercase letters with underscores. size_t max_block_size ; 2. The names of functions (methods) use camelCase beginning with a lowercase letter. std :: string getName () const override { return Memory ; } 3. The names of classes (structures) use CamelCase beginning with an uppercase letter. Prefixes other than I are not used for interfaces. class StorageMemory : public IStorage 4. The names of usings follow the same rules as classes, or you can add _t at the end. 5. Names of template type arguments for simple cases: T; T, U; T1, T2. For more complex cases, either follow the rules for class names, or add the prefix T. template typename TKey , typename TValue struct AggregatedStatElement 6. Names of template constant arguments: either follow the rules for variable names, or use N in simple cases. template bool without_www struct ExtractDomain 7. For abstract classes (interfaces) you can add the I prefix. class IBlockInputStream 8. If you use a variable locally, you can use the short name. In other cases, use a descriptive name that conveys the meaning. bool info_successfully_loaded = false ; 9. define \u2018s should be in ALL_CAPS with underscores. The same is true for global constants. #define MAX_SRC_TABLE_NAMES_TO_STORE 1000 10. File names should use the same style as their contents. If a file contains a single class, name the file the same way as the class, in CamelCase. If the file contains a single function, name the file the same way as the function, in camelCase. 11. If the name contains an abbreviation, then: For variable names, the abbreviation should use lowercase letters mysql_connection (not mySQL_connection ). For names of classes and functions, keep the uppercase letters in the abbreviation MySQLConnection (not MySqlConnection ). 12. Constructor arguments that are used just to initialize the class members should be named the same way as the class members, but with an underscore at the end. FileQueueProcessor ( \n const std :: string path_ , \n const std :: string prefix_ , \n std :: shared_ptr FileHandler handler_ ) \n : path ( path_ ), \n prefix ( prefix_ ), \n handler ( handler_ ), \n log ( Logger :: get ( FileQueueProcessor )) { } The underscore suffix can be omitted if the argument is not used in the constructor body. 13. There is no difference in the names of local variables and class members (no prefixes required). timer ( not m_timer ) 14. Constants in enums use CamelCase beginning with an uppercase letter. ALL_CAPS is also allowed. If the enum is not local, use enum class. enum class CompressionMethod { \n QuickLZ = 0 , \n LZ4 = 1 , }; 15. All names must be in English. Transliteration of Russian words is not allowed. not Stroka 16. Abbreviations are acceptable if they are well known (when you can easily find the meaning of the abbreviation in Wikipedia or in a search engine). `AST`, `SQL`.\n\nNot `NVDH` (some random letters) Incomplete words are acceptable if the shortened version is common use. You can also use an abbreviation if the full name is included next to it in the comments. 17. File names with C++ source code must have the .cpp extension. Header files must have the .h extension.", + "title": "Names" + }, + { + "location": "/development/style/#how-to-write-code", + "text": "1. Memory management. Manual memory deallocation (delete) can only be used in library code. In library code, the delete operator can only be used in destructors. In application code, memory must be freed by the object that owns it. Examples: The easiest way is to place an object on the stack, or make it a member of another class. For a large number of small objects, use containers. For automatic deallocation of a small number of objects that reside in the heap, use shared_ptr/unique_ptr. 2. Resource management. Use RAII and see the previous point. 3. Error handling. Use exceptions. In most cases, you only need to throw an exception, and don't need to catch it (because of RAII). In offline data processing applications, it's often acceptable to not catch exceptions. In servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler. /// If there were no other calculations yet, do it synchronously if ( ! started ) { \n calculate (); \n started = true ; } else /// If the calculations are already in progress, wait for results \n pool . wait (); if ( exception ) \n exception - rethrow (); Never hide exceptions without handling. Never just blindly put all exceptions to log. Not catch (...) {} . If you need to ignore some exceptions, do so only for specific ones and rethrow the rest. catch ( const DB :: Exception e ) { \n if ( e . code () == ErrorCodes :: UNKNOWN_AGGREGATE_FUNCTION ) \n return nullptr ; \n else \n throw ; } When using functions with response codes or errno, always check the result and throw an exception in case of error. if ( 0 != close ( fd )) \n throwFromErrno ( Cannot close file + file_name , ErrorCodes :: CANNOT_CLOSE_FILE ); Asserts are not used. 4. Exception types. There is no need to use complex exception hierarchy in application code. The exception text should be understandable to a system administrator. 5. Throwing exceptions from destructors. This is not recommended, but it is allowed. Use the following options: Create a (done() or finalize()) function that will do all the work in advance that might lead to an exception. If that function was called, there should be no exceptions in the destructor later. Tasks that are too complex (such as sending messages over the network) can be put in separate method that the class user will have to call before destruction. If there is an exception in the destructor, it\u2019s better to log it than to hide it (if the logger is available). In simple applications, it is acceptable to rely on std::terminate (for cases of noexcept by default in C++11) to handle exceptions. 6. Anonymous code blocks. You can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block. Block block = data . in - read (); { \n std :: lock_guard std :: mutex lock ( mutex ); \n data . ready = true ; \n data . block = block ; } ready_any . set (); 7. Multithreading. For offline data processing applications: Try to get the best possible performance on a single CPU core. You can then parallelize your code if necessary. In server applications: Use the thread pool to process requests. At this point, we haven't had any tasks that required userspace context switching. Fork is not used for parallelization. 8. Synchronizing threads. Often it is possible to make different threads use different memory cells (even better: different cache lines,) and to not use any thread synchronization (except joinAll). If synchronization is required, in most cases, it is sufficient to use mutex under lock_guard. In other cases use system synchronization primitives. Do not use busy wait. Atomic operations should be used only in the simplest cases. Do not try to implement lock-free data structures unless it is your primary area of expertise. 9. Pointers vs references. In most cases, prefer references. 10. const. Use constant references, pointers to constants, const_iterator , const methods. Consider const to be default and use non-const only when necessary. When passing variable by value, using const usually does not make sense. 11. unsigned. Use unsigned , if needed. 12. Numeric types Use UInt8 , UInt16 , UInt32 , UInt64 , Int8 , Int16 , Int32 , Int64 , and size_t , ssize_t , ptrdiff_t . Don't use signed/unsigned long , long long , short , signed char , unsigned char , or char types for numbers. 13. Passing arguments. Pass complex values by reference (including std::string ). If a function captures ownership of an objected created in the heap, make the argument type shared_ptr or unique_ptr . 14. Returning values. In most cases, just use return. Do not write [return std::move(res)]{.strike} . If the function allocates an object on heap and returns it, use shared_ptr or unique_ptr . In rare cases you might need to return the value via an argument. In this case, the argument should be a reference. using AggregateFunctionPtr = std :: shared_ptr IAggregateFunction ; /** Creates an aggregate function by name. */ class AggregateFunctionFactory { public : \n AggregateFunctionFactory (); \n AggregateFunctionPtr get ( const String name , const DataTypes argument_types ) const ; 15. namespace. There is no need to use a separate namespace for application code or small libraries. or small libraries. For medium to large libraries, put everything in the namespace. You can use the additional detail namespace in a library's .h file to hide implementation details. In a .cpp file, you can use the static or anonymous namespace to hide symbols. You can also use namespace for enums to prevent its names from polluting the outer namespace, but it\u2019s better to use the enum class. 16. Delayed initialization. If arguments are required for initialization then do not write a default constructor. If later you\u2019ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use shared_ptr/unique_ptr . Loader ( DB :: Connection * connection_ , const std :: string query , size_t max_block_size_ ); /// For delayed initialization Loader () {} 17. Virtual functions. If the class is not intended for polymorphic use, you do not need to make functions virtual. This also applies to the destructor. 18. Encodings. Use UTF-8 everywhere. Use std::string and char * . Do not use std::wstring and wchar_t . 19. Logging. See the examples everywhere in the code. Before committing, delete all meaningless and debug logging, and any other types of debug output. Logging in cycles should be avoided, even on the Trace level. Logs must be readable at any logging level. Logging should only be used in application code, for the most part. Log messages must be written in English. The log should preferably be understandable for the system administrator. Do not use profanity in the log. Use UTF-8 encoding in the log. In rare cases you can use non-ASCII characters in the log. 20. I/O. Don't use iostreams in internal cycles that are critical for application performance (and never use stringstream). Use the DB/IO library instead. 21. Date and time. See the DateLUT library. 22. include. Always use #pragma once instead of include guards. 23. using. The using namespace is not used. It's fine if you are 'using' something specific, but make it local inside a class or function. 24. Do not use trailing return type for functions unless necessary. [auto f() - gt; void;]{.strike} 25. Do not declare and init variables like this: auto s = std :: string { Hello }; Do it like this: std :: string s = Hello ; std :: string s { Hello }; 26. For virtual functions, write virtual in the base class, but write override in descendent classes.", + "title": "How to write code" + }, + { + "location": "/development/style/#unused-features-of-c", + "text": "1. Virtual inheritance is not used. 2. Exception specifiers from C++03 are not used. 3. Function try block is not used, except for the main function in tests.", + "title": "Unused features of C++" + }, + { + "location": "/development/style/#platform", + "text": "1. We write code for a specific platform. But other things being equal, cross-platform or portable code is preferred. 2. The language is C++17. 3. The compiler is gcc . At this time (December 2017), the code is compiled using version 7.2. (It can also be compiled using clang 5.) The standard library is used (implementation of libstdc++ or libc++ ). 4. OS: Linux Ubuntu, not older than Precise. 5. Code is written for x86_64 CPU architecture. The CPU instruction set is the minimum supported set among our servers. Currently, it is SSE 4.2. 6. Use -Wall -Wextra -Werror compilation flags. 7. Use static linking with all libraries except those that are difficult to connect to statically (see the output of the ldd command). 8. Code is developed and debugged with release settings.", + "title": "Platform" + }, + { + "location": "/development/style/#tools", + "text": "1. KDevelop is a good IDE. 2. For debugging, use gdb , valgrind ( memcheck ), strace , -fsanitize= , ..., tcmalloc_minimal_debug . 3. For profiling, use Linux Perf valgrind ( callgrind ), strace-cf . 4. Sources are in Git. 5. Compilation is managed by CMake . 6. Releases are in deb packages. 7. Commits to master must not break the build. Though only selected revisions are considered workable. 8. Make commits as often as possible, even if the code is only partially ready. Use branches for this purpose. If your code is not buildable yet, exclude it from the build before pushing to master. You'll need to finish it or remove it from master within a few days. 9. For non-trivial changes, used branches and publish them on the server. 10. Unused code is removed from the repository.", + "title": "Tools" + }, + { + "location": "/development/style/#libraries", + "text": "1. The C++14 standard library is used (experimental extensions are fine), as well as boost and Poco frameworks. 2. If necessary, you can use any well-known libraries available in the OS package. If there is a good solution already available, then use it, even if it means you have to install another library. (But be prepared to remove bad libraries from code.) 3. You can install a library that isn't in the packages, if the packages don't have what you need or have an outdated version or the wrong type of compilation. 4. If the library is small and doesn't have its own complex build system, put the source files in the contrib folder. 5. Preference is always given to libraries that are already used.", + "title": "Libraries" + }, + { + "location": "/development/style/#general-recommendations_1", + "text": "1. Write as little code as possible. 2. Try the simplest solution. 3. Don't write code until you know how it's going to work and how the inner loop will function. 4. In the simplest cases, use 'using' instead of classes or structs. 5. If possible, do not write copy constructors, assignment operators, destructors (other than a virtual one, if the class contains at least one virtual function), mpve-constructors and move assignment operators. In other words, the compiler-generated functions must work correctly. You can use 'default'. 6. Code simplification is encouraged. Reduce the size of your code where possible.", + "title": "General recommendations" + }, + { + "location": "/development/style/#additional-recommendations", + "text": "1. Explicit std:: for types from stddef.h is not recommended. We recommend writing size_t instead std::size_t because it's shorter. But if you prefer, std:: is acceptable. 2. Explicit std:: for functions from the standard C library is not recommended. Write memcpy instead of std::memcpy . The reason is that there are similar non-standard functions, such as memmem . We do use these functions on occasion. These functions do not exist in namespace std . If you write std::memcpy instead of memcpy everywhere, then memmem without std:: will look awkward. Nevertheless, std:: is allowed if you prefer it. 3. Using functions from C when the ones are available in the standard C++ library. This is acceptable if it is more efficient. For example, use memcpy instead of std::copy for copying large chunks of memory. 4. Multiline function arguments. Any of the following wrapping styles are allowed: function ( \n T1 x1 , \n T2 x2 ) function ( \n size_t left , size_t right , \n const RangesInDataParts ranges , \n size_t limit ) function ( size_t left , size_t right , \n const RangesInDataParts ranges , \n size_t limit ) function ( size_t left , size_t right , \n const RangesInDataParts ranges , \n size_t limit ) function ( \n size_t left , \n size_t right , \n const RangesInDataParts ranges , \n size_t limit )", + "title": "Additional recommendations" + }, + { + "location": "/development/tests/", + "text": "How to run ClickHouse tests\n\n\nThe \nclickhouse-test\n utility that is used for functional testing is written using Python 2.x.It also requires you to have some third-party packages:\n\n\n$ pip install lxml termcolor\n\n\n\n\n\nIn a nutshell:\n\n\n\n\nPut the \nclickhouse\n program to \n/usr/bin\n (or \nPATH\n)\n\n\nCreate a \nclickhouse-client\n symlink in \n/usr/bin\n pointing to \nclickhouse\n\n\nStart the \nclickhouse\n server\n\n\ncd dbms/tests/\n\n\nRun \n./clickhouse-test\n\n\n\n\nExample usage\n\n\nRun \n./clickhouse-test --help\n to see available options.\n\n\nTo run tests without having to create a symlink or mess with \nPATH\n:\n\n\n./clickhouse-test -c \n../../build/dbms/src/Server/clickhouse --client\n\n\n\n\n\n\nTo run a single test, i.e. \n00395_nullable\n:\n\n\n./clickhouse-test \n00395", + "title": "How to run ClickHouse tests" + }, + { + "location": "/development/tests/#how-to-run-clickhouse-tests", + "text": "The clickhouse-test utility that is used for functional testing is written using Python 2.x.It also requires you to have some third-party packages: $ pip install lxml termcolor In a nutshell: Put the clickhouse program to /usr/bin (or PATH ) Create a clickhouse-client symlink in /usr/bin pointing to clickhouse Start the clickhouse server cd dbms/tests/ Run ./clickhouse-test", + "title": "How to run ClickHouse tests" + }, + { + "location": "/development/tests/#example-usage", + "text": "Run ./clickhouse-test --help to see available options. To run tests without having to create a symlink or mess with PATH : ./clickhouse-test -c ../../build/dbms/src/Server/clickhouse --client To run a single test, i.e. 00395_nullable : ./clickhouse-test 00395", + "title": "Example usage" + }, + { + "location": "/roadmap/", + "text": "Roadmap\n\n\nQ1 2018\n\n\nNew fuctionality\n\n\n\n\n\n\nSupport for \nUPDATE\n and \nDELETE\n.\n\n\n\n\n\n\nMultidimensional and nested arrays.\n\n\n\n\n\n\nIt can look something like this:\n\n\nCREATE\n \nTABLE\n \nt\n\n\n(\n\n \nx\n \nArray\n(\nArray\n(\nString\n)),\n\n \nz\n \nNested\n(\n\n \nx\n \nArray\n(\nString\n),\n\n \ny\n \nNested\n(...))\n\n\n)\n\n\nENGINE\n \n=\n \nMergeTree\n \nORDER\n \nBY\n \nx\n\n\n\n\n\n\n\n\nExternal MySQL and ODBC tables.\n\n\n\n\nExternal tables can be integrated into ClickHouse using external dictionaries. This new functionality is a convenient alternative to connecting external tables.\n\n\nSELECT\n \n...\n\n\nFROM\n \nmysql\n(\nhost:port\n,\n \ndb\n,\n \ntable\n,\n \nuser\n,\n \npassword\n)\n`\n\n\n\n\n\n\nImprovements\n\n\n\n\nEffective data copying between ClickHouse clusters.\n\n\n\n\nNow you can copy data with the remote() function. For example: \nINSERT INTO t SELECT * FROM remote(...)\n.\n\n\nThis operation will have improved performance.\n\n\n\n\nO_DIRECT for merges.\n\n\n\n\nThis will improve the performance of the OS cache and \"hot\" queries.\n\n\nQ2 2018\n\n\nNew functionality\n\n\n\n\n\n\nUPDATE/DELETE conform to the EU GDPR.\n\n\n\n\n\n\nProtobuf and Parquet input and output formats.\n\n\n\n\n\n\nCreating dictionaries using DDL queries.\n\n\n\n\n\n\nCurrently, dictionaries that are part of the database schema are defined in external XML files. This is inconvenient and counter-intuitive. The new approach should fix it.\n\n\n\n\n\n\nIntegration with LDAP.\n\n\n\n\n\n\nWITH ROLLUP and WITH CUBE for GROUP BY.\n\n\n\n\n\n\nCustom encoding and compression for each column individually.\n\n\n\n\n\n\nAs of now, ClickHouse supports LZ4 and ZSTD compression of columns, and compression settings are global (see the article \nCompression in ClickHouse\n). Per-column compression and encoding will provide more efficient data storage, which in turn will speed up queries.\n\n\n\n\nStoring data on multiple disks on the same server.\n\n\n\n\nThis functionality will make it easier to extend the disk space, since different disk systems can be used for different databases or tables. Currently, users are forced to use symbolic links if the databases and tables must be stored on a different disk.\n\n\nImprovements\n\n\nMany improvements and fixes are planned for the query execution system. For example:\n\n\n\n\nUsing an index for \nin (subquery)\n.\n\n\n\n\nThe index is not used right now, which reduces performance.\n\n\n\n\nPassing predicates from \nwhere\n to subqueries, and passing predicates to views.\n\n\n\n\nThe predicates must be passed, since the view is changed by the subquery. Performance is still low for view filters, and views can't use the primary key of the original table, which makes views useless for large tables.\n\n\n\n\nOptimizing branching operations (ternary operator, if, multiIf).\n\n\n\n\nClickHouse currently performs all branches, even if they aren't necessary.\n\n\n\n\nUsing a primary key for GROUP BY and ORDER BY.\n\n\n\n\nThis will speed up certain types of queries with partially sorted data.\n\n\nQ3-Q4 2018\n\n\nWe don't have any set plans yet, but the main projects will be:\n\n\n\n\nResource pools for executing queries.\n\n\n\n\nThis will make load management more efficient.\n\n\n\n\nANSI SQL JOIN syntax.\n\n\n\n\nImprove ClickHouse compatibility with many SQL tools.", + "title": "Roadmap" + }, + { + "location": "/roadmap/#roadmap", + "text": "", + "title": "Roadmap" + }, + { + "location": "/roadmap/#q1-2018", + "text": "", + "title": "Q1 2018" + }, + { + "location": "/roadmap/#new-fuctionality", + "text": "Support for UPDATE and DELETE . Multidimensional and nested arrays. It can look something like this: CREATE TABLE t ( \n x Array ( Array ( String )), \n z Nested ( \n x Array ( String ), \n y Nested (...)) ) ENGINE = MergeTree ORDER BY x External MySQL and ODBC tables. External tables can be integrated into ClickHouse using external dictionaries. This new functionality is a convenient alternative to connecting external tables. SELECT ... FROM mysql ( host:port , db , table , user , password ) `", + "title": "New fuctionality" + }, + { + "location": "/roadmap/#improvements", + "text": "Effective data copying between ClickHouse clusters. Now you can copy data with the remote() function. For example: INSERT INTO t SELECT * FROM remote(...) . This operation will have improved performance. O_DIRECT for merges. This will improve the performance of the OS cache and \"hot\" queries.", + "title": "Improvements" + }, + { + "location": "/roadmap/#q2-2018", + "text": "", + "title": "Q2 2018" + }, + { + "location": "/roadmap/#new-functionality", + "text": "UPDATE/DELETE conform to the EU GDPR. Protobuf and Parquet input and output formats. Creating dictionaries using DDL queries. Currently, dictionaries that are part of the database schema are defined in external XML files. This is inconvenient and counter-intuitive. The new approach should fix it. Integration with LDAP. WITH ROLLUP and WITH CUBE for GROUP BY. Custom encoding and compression for each column individually. As of now, ClickHouse supports LZ4 and ZSTD compression of columns, and compression settings are global (see the article Compression in ClickHouse ). Per-column compression and encoding will provide more efficient data storage, which in turn will speed up queries. Storing data on multiple disks on the same server. This functionality will make it easier to extend the disk space, since different disk systems can be used for different databases or tables. Currently, users are forced to use symbolic links if the databases and tables must be stored on a different disk.", + "title": "New functionality" + }, + { + "location": "/roadmap/#improvements_1", + "text": "Many improvements and fixes are planned for the query execution system. For example: Using an index for in (subquery) . The index is not used right now, which reduces performance. Passing predicates from where to subqueries, and passing predicates to views. The predicates must be passed, since the view is changed by the subquery. Performance is still low for view filters, and views can't use the primary key of the original table, which makes views useless for large tables. Optimizing branching operations (ternary operator, if, multiIf). ClickHouse currently performs all branches, even if they aren't necessary. Using a primary key for GROUP BY and ORDER BY. This will speed up certain types of queries with partially sorted data.", + "title": "Improvements" + }, + { + "location": "/roadmap/#q3-q4-2018", + "text": "We don't have any set plans yet, but the main projects will be: Resource pools for executing queries. This will make load management more efficient. ANSI SQL JOIN syntax. Improve ClickHouse compatibility with many SQL tools.", + "title": "Q3-Q4 2018" + } + ] +} \ No newline at end of file diff --git a/docs/build/docs/en/single/404.html b/docs/build/docs/en/single/404.html new file mode 100644 index 00000000000..b7c03b8124c --- /dev/null +++ b/docs/build/docs/en/single/404.html @@ -0,0 +1,293 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Please include / require Lunr stemmer support before this script.");e.de=function(){this.pipeline.reset(),this.pipeline.add(e.de.trimmer,e.de.stopWordFilter,e.de.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.de.stemmer))},e.de.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.de.trimmer=e.trimmerSupport.generateTrimmer(e.de.wordCharacters),e.Pipeline.registerFunction(e.de.trimmer,"trimmer-de"),e.de.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){function e(e,r,n){return!(!_.eq_s(1,e)||(_.ket=_.cursor,!_.in_grouping(w,97,252)))&&(_.slice_from(r),_.cursor=n,!0)}function i(){for(;!_.in_grouping(w,97,252);){if(_.cursor>=_.limit)return!0;_.cursor++}for(;!_.out_grouping(w,97,252);){if(_.cursor>=_.limit)return!0;_.cursor++}return!1}function s(){return u<=_.cursor}function t(){return c<=_.cursor}var o,c,u,a=[new r("",-1,6),new r("U",0,2),new r("Y",0,1),new r("ä",0,3),new r("ö",0,4),new r("ü",0,5)],d=[new r("e",-1,2),new r("em",-1,1),new r("en",-1,2),new r("ern",-1,1),new r("er",-1,1),new r("s",-1,3),new r("es",5,2)],l=[new r("en",-1,1),new r("er",-1,1),new r("st",-1,2),new r("est",2,1)],m=[new r("ig",-1,1),new r("lich",-1,1)],h=[new r("end",-1,1),new r("ig",-1,2),new r("ung",-1,1),new r("lich",-1,3),new r("isch",-1,2),new r("ik",-1,2),new r("heit",-1,3),new r("keit",-1,4)],w=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,32,8],f=[117,30,5],b=[117,30,4],_=new n;this.setCurrent=function(e){_.setCurrent(e)},this.getCurrent=function(){return _.getCurrent()},this.stem=function(){var r=_.cursor;return function(){for(var r,n,i,s,t=_.cursor;;)if(r=_.cursor,_.bra=r,_.eq_s(1,"ß"))_.ket=_.cursor,_.slice_from("ss");else{if(r>=_.limit)break;_.cursor=r+1}for(_.cursor=t;;)for(n=_.cursor;;){if(i=_.cursor,_.in_grouping(w,97,252)){if(s=_.cursor,_.bra=s,e("u","U",i))break;if(_.cursor=s,e("y","Y",i))break}if(i>=_.limit)return void(_.cursor=n);_.cursor=i+1}}(),_.cursor=r,function(){u=_.limit,c=u;var e=_.cursor+3;0<=e&&e<=_.limit&&(o=e,i()||((u=_.cursor)=_.limit)return;_.cursor++}}}(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.de.stemmer,"stemmer-de"),e.de.stopWordFilter=e.generateStopWordFilter("aber alle allem allen aller alles als also am an ander andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines können könnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher solches soll sollte sondern sonst um und uns unse unsem unsen unser unses unter viel vom von vor war waren warst was weg weil weiter welche welchem welchen welcher welches wenn werde werden wie wieder will wir wird wirst wo wollen wollte während würde würden zu zum zur zwar zwischen über".split(" ")),e.Pipeline.registerFunction(e.de.stopWordFilter,"stopWordFilter-de")}}); \ No newline at end of file diff --git a/docs/build/docs/en/single/assets/javascripts/lunr/lunr.du.js b/docs/build/docs/en/single/assets/javascripts/lunr/lunr.du.js new file mode 100644 index 00000000000..c317652d6bd --- /dev/null +++ b/docs/build/docs/en/single/assets/javascripts/lunr/lunr.du.js @@ -0,0 +1 @@ +!function(r,e){"function"==typeof define&&define.amd?define(e):"object"==typeof exports?module.exports=e():e()(r.lunr)}(this,function(){return function(r){if(void 0===r)throw new Error("Lunr is not present. 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Please include / require Lunr stemmer support before this script.");r.du=function(){this.pipeline.reset(),this.pipeline.add(r.du.trimmer,r.du.stopWordFilter,r.du.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(r.du.stemmer))},r.du.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",r.du.trimmer=r.trimmerSupport.generateTrimmer(r.du.wordCharacters),r.Pipeline.registerFunction(r.du.trimmer,"trimmer-du"),r.du.stemmer=function(){var e=r.stemmerSupport.Among,i=r.stemmerSupport.SnowballProgram,n=new function(){function r(r){return v.cursor=r,r>=v.limit||(v.cursor++,!1)}function n(){for(;!v.in_grouping(g,97,232);){if(v.cursor>=v.limit)return!0;v.cursor++}for(;!v.out_grouping(g,97,232);){if(v.cursor>=v.limit)return!0;v.cursor++}return!1}function o(){return l<=v.cursor}function t(){return a<=v.cursor}function s(){var r=v.limit-v.cursor;v.find_among_b(_,3)&&(v.cursor=v.limit-r,v.ket=v.cursor,v.cursor>v.limit_backward&&(v.cursor--,v.bra=v.cursor,v.slice_del()))}function u(){var r;m=!1,v.ket=v.cursor,v.eq_s_b(1,"e")&&(v.bra=v.cursor,o()&&(r=v.limit-v.cursor,v.out_grouping_b(g,97,232)&&(v.cursor=v.limit-r,v.slice_del(),m=!0,s())))}function c(){var r;o()&&(r=v.limit-v.cursor,v.out_grouping_b(g,97,232)&&(v.cursor=v.limit-r,v.eq_s_b(3,"gem")||(v.cursor=v.limit-r,v.slice_del(),s())))}var a,l,m,d=[new e("",-1,6),new e("á",0,1),new e("ä",0,1),new e("é",0,2),new e("ë",0,2),new e("í",0,3),new e("ï",0,3),new e("ó",0,4),new e("ö",0,4),new e("ú",0,5),new e("ü",0,5)],f=[new e("",-1,3),new e("I",0,2),new e("Y",0,1)],_=[new e("dd",-1,-1),new e("kk",-1,-1),new e("tt",-1,-1)],w=[new e("ene",-1,2),new e("se",-1,3),new e("en",-1,2),new e("heden",2,1),new e("s",-1,3)],b=[new e("end",-1,1),new e("ig",-1,2),new e("ing",-1,1),new e("lijk",-1,3),new e("baar",-1,4),new e("bar",-1,5)],p=[new e("aa",-1,-1),new e("ee",-1,-1),new 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if(r(i))break}(),v.cursor=e,l=v.limit,a=l,n()||((l=v.cursor)<3&&(l=3),n()||(a=v.cursor)),v.limit_backward=e,v.cursor=v.limit,function(){var r,e,i,n,a,l,d=v.limit-v.cursor;if(v.ket=v.cursor,r=v.find_among_b(w,5))switch(v.bra=v.cursor,r){case 1:o()&&v.slice_from("heid");break;case 2:c();break;case 3:o()&&v.out_grouping_b(k,97,232)&&v.slice_del()}if(v.cursor=v.limit-d,u(),v.cursor=v.limit-d,v.ket=v.cursor,v.eq_s_b(4,"heid")&&(v.bra=v.cursor,t()&&(e=v.limit-v.cursor,v.eq_s_b(1,"c")||(v.cursor=v.limit-e,v.slice_del(),v.ket=v.cursor,v.eq_s_b(2,"en")&&(v.bra=v.cursor,c())))),v.cursor=v.limit-d,v.ket=v.cursor,r=v.find_among_b(b,6))switch(v.bra=v.cursor,r){case 1:if(t()){if(v.slice_del(),i=v.limit-v.cursor,v.ket=v.cursor,v.eq_s_b(2,"ig")&&(v.bra=v.cursor,t()&&(n=v.limit-v.cursor,!v.eq_s_b(1,"e")))){v.cursor=v.limit-n,v.slice_del();break}v.cursor=v.limit-i,s()}break;case 2:t()&&(a=v.limit-v.cursor,v.eq_s_b(1,"e")||(v.cursor=v.limit-a,v.slice_del()));break;case 3:t()&&(v.slice_del(),u());break;case 4:t()&&v.slice_del();break;case 5:t()&&m&&v.slice_del()}v.cursor=v.limit-d,v.out_grouping_b(h,73,232)&&(l=v.limit-v.cursor,v.find_among_b(p,4)&&v.out_grouping_b(g,97,232)&&(v.cursor=v.limit-l,v.ket=v.cursor,v.cursor>v.limit_backward&&(v.cursor--,v.bra=v.cursor,v.slice_del())))}(),v.cursor=v.limit_backward,function(){for(var r;;)if(v.bra=v.cursor,r=v.find_among(f,3))switch(v.ket=v.cursor,r){case 1:v.slice_from("y");break;case 2:v.slice_from("i");break;case 3:if(v.cursor>=v.limit)return;v.cursor++}}(),!0}};return function(r){return"function"==typeof r.update?r.update(function(r){return n.setCurrent(r),n.stem(),n.getCurrent()}):(n.setCurrent(r),n.stem(),n.getCurrent())}}(),r.Pipeline.registerFunction(r.du.stemmer,"stemmer-du"),r.du.stopWordFilter=r.generateStopWordFilter(" aan al alles als altijd andere ben bij daar dan dat de der deze die dit doch doen door dus een eens en er ge geen geweest haar had heb hebben heeft hem het hier hij hoe hun iemand iets ik in is ja je kan kon kunnen maar me meer men met mij mijn moet na naar niet niets nog nu of om omdat onder ons ook op over reeds te tegen toch toen tot u uit uw van veel voor want waren was wat werd wezen wie wil worden wordt zal ze zelf zich zij zijn zo zonder zou".split(" ")),r.Pipeline.registerFunction(r.du.stopWordFilter,"stopWordFilter-du")}}); \ No newline at end of file diff --git a/docs/build/docs/en/single/assets/javascripts/lunr/lunr.es.js b/docs/build/docs/en/single/assets/javascripts/lunr/lunr.es.js new file mode 100644 index 00000000000..5098feba48b --- /dev/null +++ b/docs/build/docs/en/single/assets/javascripts/lunr/lunr.es.js @@ -0,0 +1 @@ +!function(e,s){"function"==typeof define&&define.amd?define(s):"object"==typeof exports?module.exports=s():s()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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s;this.setCurrent=function(e){j.setCurrent(e)},this.getCurrent=function(){return j.getCurrent()},this.stem=function(){var e=j.cursor;return function(){for(var e;;){if(j.bra=j.cursor,e=j.find_among(f,3))switch(j.ket=j.cursor,e){case 1:j.slice_from("a~");continue;case 2:j.slice_from("o~");continue;case 3:if(j.cursor>=j.limit)break;j.cursor++;continue}break}}(),j.cursor=e,function(){var e=j.cursor;l=j.limit,c=l,m=l,n(),j.cursor=e,i()&&(c=j.cursor,i()&&(m=j.cursor))}(),j.limit_backward=e,j.cursor=j.limit,w(),j.cursor=j.limit,function(){var e;if(j.ket=j.cursor,e=j.find_among_b(k,4))switch(j.bra=j.cursor,e){case 1:o()&&(j.slice_del(),j.ket=j.cursor,j.limit,j.cursor,u("u","g")&&u("i","c"));break;case 2:j.slice_from("c")}}(),j.cursor=j.limit_backward,function(){for(var e;;){if(j.bra=j.cursor,e=j.find_among(d,3))switch(j.ket=j.cursor,e){case 1:j.slice_from("ã");continue;case 2:j.slice_from("õ");continue;case 3:if(j.cursor>=j.limit)break;j.cursor++;continue}break}}(),!0}};return 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nas nem no nos nossa nossas nosso nossos num numa não nós o os ou para pela pelas pelo pelos por qual quando que quem se seja sejam sejamos sem serei seremos seria seriam será serão seríamos seu seus somos sou sua suas são só também te tem temos tenha tenham tenhamos tenho terei teremos teria teriam terá terão teríamos teu teus teve tinha tinham tive tivemos tiver tivera tiveram tiverem tivermos tivesse tivessem tivéramos tivéssemos tu tua tuas tém tínhamos um uma você vocês vos à às éramos".split(" ")),e.Pipeline.registerFunction(e.pt.stopWordFilter,"stopWordFilter-pt")}}); \ No newline at end of file diff --git a/docs/build/docs/en/single/assets/javascripts/lunr/lunr.ro.js b/docs/build/docs/en/single/assets/javascripts/lunr/lunr.ro.js new file mode 100644 index 00000000000..9b5612891c5 --- /dev/null +++ b/docs/build/docs/en/single/assets/javascripts/lunr/lunr.ro.js @@ -0,0 +1 @@ +!function(e,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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fie fiecare fii fim fiu fiţi frumos fără graţie halbă iar ieri la le li lor lui lângă lîngă mai mea mei mele mereu meu mi mie mine mult multă mulţi mulţumesc mâine mîine mă ne nevoie nici nicăieri nimeni nimeri nimic nişte noastre noastră noi noroc nostru nouă noştri nu opt ori oricare orice oricine oricum oricând oricât oricînd oricît oriunde patra patru patrulea pe pentru peste pic poate pot prea prima primul prin puţin puţina puţină până pînă rog sa sale sau se spate spre sub sunt suntem sunteţi sută sînt sîntem sînteţi să săi său ta tale te timp tine toate toată tot totuşi toţi trei treia treilea tu tăi tău un una unde undeva unei uneia unele uneori unii unor unora unu unui unuia unul vi voastre voastră voi vostru vouă voştri vreme vreo vreun vă zece zero zi zice îi îl îmi împotriva în înainte înaintea încotro încât încît între întrucât întrucît îţi ăla ălea ăsta ăstea ăştia şapte şase şi ştiu ţi ţie".split(" 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.md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=light-blue]{background-color:#03a9f4}[data-md-color-primary=light-blue] .md-typeset a{color:#03a9f4}[data-md-color-primary=light-blue] .md-header,[data-md-color-primary=light-blue] .md-hero{background-color:#03a9f4}[data-md-color-primary=light-blue] .md-nav__link--active,[data-md-color-primary=light-blue] .md-nav__link:active{color:#03a9f4}[data-md-color-primary=light-blue] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=cyan]{background-color:#00bcd4}[data-md-color-primary=cyan] .md-typeset a{color:#00bcd4}[data-md-color-primary=cyan] .md-header,[data-md-color-primary=cyan] .md-hero{background-color:#00bcd4}[data-md-color-primary=cyan] .md-nav__link--active,[data-md-color-primary=cyan] .md-nav__link:active{color:#00bcd4}[data-md-color-primary=cyan] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=teal]{background-color:#009688}[data-md-color-primary=teal] .md-typeset a{color:#009688}[data-md-color-primary=teal] .md-header,[data-md-color-primary=teal] .md-hero{background-color:#009688}[data-md-color-primary=teal] .md-nav__link--active,[data-md-color-primary=teal] .md-nav__link:active{color:#009688}[data-md-color-primary=teal] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=green]{background-color:#4caf50}[data-md-color-primary=green] .md-typeset a{color:#4caf50}[data-md-color-primary=green] .md-header,[data-md-color-primary=green] .md-hero{background-color:#4caf50}[data-md-color-primary=green] .md-nav__link--active,[data-md-color-primary=green] .md-nav__link:active{color:#4caf50}[data-md-color-primary=green] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=light-green]{background-color:#7cb342}[data-md-color-primary=light-green] .md-typeset a{color:#7cb342}[data-md-color-primary=light-green] .md-header,[data-md-color-primary=light-green] .md-hero{background-color:#7cb342}[data-md-color-primary=light-green] .md-nav__link--active,[data-md-color-primary=light-green] .md-nav__link:active{color:#7cb342}[data-md-color-primary=light-green] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=lime]{background-color:#c0ca33}[data-md-color-primary=lime] .md-typeset a{color:#c0ca33}[data-md-color-primary=lime] .md-header,[data-md-color-primary=lime] .md-hero{background-color:#c0ca33}[data-md-color-primary=lime] .md-nav__link--active,[data-md-color-primary=lime] .md-nav__link:active{color:#c0ca33}[data-md-color-primary=lime] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=yellow]{background-color:#f9a825}[data-md-color-primary=yellow] .md-typeset a{color:#f9a825}[data-md-color-primary=yellow] .md-header,[data-md-color-primary=yellow] .md-hero{background-color:#f9a825}[data-md-color-primary=yellow] .md-nav__link--active,[data-md-color-primary=yellow] .md-nav__link:active{color:#f9a825}[data-md-color-primary=yellow] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=amber]{background-color:#ffa000}[data-md-color-primary=amber] .md-typeset a{color:#ffa000}[data-md-color-primary=amber] .md-header,[data-md-color-primary=amber] .md-hero{background-color:#ffa000}[data-md-color-primary=amber] .md-nav__link--active,[data-md-color-primary=amber] .md-nav__link:active{color:#ffa000}[data-md-color-primary=amber] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=orange]{background-color:#fb8c00}[data-md-color-primary=orange] .md-typeset a{color:#fb8c00}[data-md-color-primary=orange] .md-header,[data-md-color-primary=orange] .md-hero{background-color:#fb8c00}[data-md-color-primary=orange] .md-nav__link--active,[data-md-color-primary=orange] .md-nav__link:active{color:#fb8c00}[data-md-color-primary=orange] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=deep-orange]{background-color:#ff7043}[data-md-color-primary=deep-orange] .md-typeset a{color:#ff7043}[data-md-color-primary=deep-orange] .md-header,[data-md-color-primary=deep-orange] .md-hero{background-color:#ff7043}[data-md-color-primary=deep-orange] .md-nav__link--active,[data-md-color-primary=deep-orange] .md-nav__link:active{color:#ff7043}[data-md-color-primary=deep-orange] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=brown]{background-color:#795548}[data-md-color-primary=brown] .md-typeset a{color:#795548}[data-md-color-primary=brown] .md-header,[data-md-color-primary=brown] .md-hero{background-color:#795548}[data-md-color-primary=brown] .md-nav__link--active,[data-md-color-primary=brown] .md-nav__link:active{color:#795548}[data-md-color-primary=brown] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=grey]{background-color:#757575}[data-md-color-primary=grey] .md-typeset a{color:#757575}[data-md-color-primary=grey] .md-header,[data-md-color-primary=grey] .md-hero{background-color:#757575}[data-md-color-primary=grey] .md-nav__link--active,[data-md-color-primary=grey] .md-nav__link:active{color:#757575}[data-md-color-primary=grey] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=blue-grey]{background-color:#546e7a}[data-md-color-primary=blue-grey] .md-typeset a{color:#546e7a}[data-md-color-primary=blue-grey] .md-header,[data-md-color-primary=blue-grey] .md-hero{background-color:#546e7a}[data-md-color-primary=blue-grey] .md-nav__link--active,[data-md-color-primary=blue-grey] .md-nav__link:active{color:#546e7a}[data-md-color-primary=blue-grey] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=white]{-webkit-box-shadow:0 0 .1rem rgba(0,0,0,.54) inset;box-shadow:inset 0 0 .1rem rgba(0,0,0,.54)}[data-md-color-primary=white] .md-header,[data-md-color-primary=white] .md-hero,button[data-md-color-primary=white]{background-color:#fff;color:rgba(0,0,0,.87)}[data-md-color-primary=white] .md-hero--expand{border-bottom:.1rem solid rgba(0,0,0,.07)}button[data-md-color-accent=red]{background-color:#ff1744}[data-md-color-accent=red] .md-typeset a:active,[data-md-color-accent=red] .md-typeset a:hover{color:#ff1744}[data-md-color-accent=red] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=red] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-nav__link:focus,[data-md-color-accent=red] .md-nav__link:hover,[data-md-color-accent=red] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=red] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=red] .md-typeset .md-clipboard:active:before,[data-md-color-accent=red] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=red] .md-typeset [id] .headerlink:focus,[data-md-color-accent=red] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=red] .md-typeset [id]:target .headerlink{color:#ff1744}[data-md-color-accent=red] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-search-result__link:hover,[data-md-color-accent=red] .md-search-result__link[data-md-state=active]{background-color:rgba(255,23,68,.1)}[data-md-color-accent=red] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-source-file:hover:before{background-color:#ff1744}button[data-md-color-accent=pink]{background-color:#f50057}[data-md-color-accent=pink] .md-typeset a:active,[data-md-color-accent=pink] .md-typeset a:hover{color:#f50057}[data-md-color-accent=pink] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=pink] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-nav__link:focus,[data-md-color-accent=pink] .md-nav__link:hover,[data-md-color-accent=pink] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=pink] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=pink] .md-typeset .md-clipboard:active:before,[data-md-color-accent=pink] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=pink] .md-typeset [id] .headerlink:focus,[data-md-color-accent=pink] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=pink] .md-typeset [id]:target .headerlink{color:#f50057}[data-md-color-accent=pink] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-search-result__link:hover,[data-md-color-accent=pink] .md-search-result__link[data-md-state=active]{background-color:rgba(245,0,87,.1)}[data-md-color-accent=pink] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-source-file:hover:before{background-color:#f50057}button[data-md-color-accent=purple]{background-color:#e040fb}[data-md-color-accent=purple] .md-typeset a:active,[data-md-color-accent=purple] .md-typeset a:hover{color:#e040fb}[data-md-color-accent=purple] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=purple] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-nav__link:focus,[data-md-color-accent=purple] .md-nav__link:hover,[data-md-color-accent=purple] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=purple] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=purple] .md-typeset .md-clipboard:active:before,[data-md-color-accent=purple] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=purple] .md-typeset [id] .headerlink:focus,[data-md-color-accent=purple] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=purple] .md-typeset [id]:target .headerlink{color:#e040fb}[data-md-color-accent=purple] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-search-result__link:hover,[data-md-color-accent=purple] .md-search-result__link[data-md-state=active]{background-color:rgba(224,64,251,.1)}[data-md-color-accent=purple] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-source-file:hover:before{background-color:#e040fb}button[data-md-color-accent=deep-purple]{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-typeset a:active,[data-md-color-accent=deep-purple] .md-typeset a:hover{color:#7c4dff}[data-md-color-accent=deep-purple] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=deep-purple] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-nav__link:focus,[data-md-color-accent=deep-purple] .md-nav__link:hover,[data-md-color-accent=deep-purple] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=deep-purple] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=deep-purple] .md-typeset .md-clipboard:active:before,[data-md-color-accent=deep-purple] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=deep-purple] .md-typeset [id] .headerlink:focus,[data-md-color-accent=deep-purple] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=deep-purple] .md-typeset [id]:target .headerlink{color:#7c4dff}[data-md-color-accent=deep-purple] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-search-result__link:hover,[data-md-color-accent=deep-purple] .md-search-result__link[data-md-state=active]{background-color:rgba(124,77,255,.1)}[data-md-color-accent=deep-purple] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-source-file:hover:before{background-color:#7c4dff}button[data-md-color-accent=indigo]{background-color:#536dfe}[data-md-color-accent=indigo] .md-typeset a:active,[data-md-color-accent=indigo] .md-typeset a:hover{color:#536dfe}[data-md-color-accent=indigo] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=indigo] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-nav__link:focus,[data-md-color-accent=indigo] .md-nav__link:hover,[data-md-color-accent=indigo] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=indigo] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=indigo] .md-typeset .md-clipboard:active:before,[data-md-color-accent=indigo] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=indigo] .md-typeset [id] .headerlink:focus,[data-md-color-accent=indigo] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=indigo] .md-typeset [id]:target .headerlink{color:#536dfe}[data-md-color-accent=indigo] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-search-result__link:hover,[data-md-color-accent=indigo] .md-search-result__link[data-md-state=active]{background-color:rgba(83,109,254,.1)}[data-md-color-accent=indigo] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-source-file:hover:before{background-color:#536dfe}button[data-md-color-accent=blue]{background-color:#448aff}[data-md-color-accent=blue] .md-typeset a:active,[data-md-color-accent=blue] .md-typeset a:hover{color:#448aff}[data-md-color-accent=blue] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=blue] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-nav__link:focus,[data-md-color-accent=blue] .md-nav__link:hover,[data-md-color-accent=blue] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=blue] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=blue] .md-typeset .md-clipboard:active:before,[data-md-color-accent=blue] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=blue] .md-typeset [id] .headerlink:focus,[data-md-color-accent=blue] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=blue] .md-typeset [id]:target .headerlink{color:#448aff}[data-md-color-accent=blue] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-search-result__link:hover,[data-md-color-accent=blue] .md-search-result__link[data-md-state=active]{background-color:rgba(68,138,255,.1)}[data-md-color-accent=blue] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-source-file:hover:before{background-color:#448aff}button[data-md-color-accent=light-blue]{background-color:#0091ea}[data-md-color-accent=light-blue] .md-typeset a:active,[data-md-color-accent=light-blue] .md-typeset a:hover{color:#0091ea}[data-md-color-accent=light-blue] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=light-blue] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-nav__link:focus,[data-md-color-accent=light-blue] .md-nav__link:hover,[data-md-color-accent=light-blue] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=light-blue] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=light-blue] .md-typeset .md-clipboard:active:before,[data-md-color-accent=light-blue] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=light-blue] .md-typeset [id] .headerlink:focus,[data-md-color-accent=light-blue] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=light-blue] .md-typeset [id]:target .headerlink{color:#0091ea}[data-md-color-accent=light-blue] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-search-result__link:hover,[data-md-color-accent=light-blue] .md-search-result__link[data-md-state=active]{background-color:rgba(0,145,234,.1)}[data-md-color-accent=light-blue] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-source-file:hover:before{background-color:#0091ea}button[data-md-color-accent=cyan]{background-color:#00b8d4}[data-md-color-accent=cyan] .md-typeset a:active,[data-md-color-accent=cyan] .md-typeset a:hover{color:#00b8d4}[data-md-color-accent=cyan] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=cyan] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-nav__link:focus,[data-md-color-accent=cyan] .md-nav__link:hover,[data-md-color-accent=cyan] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=cyan] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=cyan] .md-typeset .md-clipboard:active:before,[data-md-color-accent=cyan] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=cyan] .md-typeset [id] .headerlink:focus,[data-md-color-accent=cyan] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=cyan] .md-typeset [id]:target .headerlink{color:#00b8d4}[data-md-color-accent=cyan] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-search-result__link:hover,[data-md-color-accent=cyan] .md-search-result__link[data-md-state=active]{background-color:rgba(0,184,212,.1)}[data-md-color-accent=cyan] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-source-file:hover:before{background-color:#00b8d4}button[data-md-color-accent=teal]{background-color:#00bfa5}[data-md-color-accent=teal] .md-typeset a:active,[data-md-color-accent=teal] .md-typeset a:hover{color:#00bfa5}[data-md-color-accent=teal] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=teal] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#00bfa5}[data-md-color-accent=teal] .md-nav__link:focus,[data-md-color-accent=teal] .md-nav__link:hover,[data-md-color-accent=teal] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=teal] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=teal] .md-typeset .md-clipboard:active:before,[data-md-color-accent=teal] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=teal] .md-typeset [id] .headerlink:focus,[data-md-color-accent=teal] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=teal] .md-typeset [id]:target .headerlink{color:#00bfa5}[data-md-color-accent=teal] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00bfa5}[data-md-color-accent=teal] .md-search-result__link:hover,[data-md-color-accent=teal] .md-search-result__link[data-md-state=active]{background-color:rgba(0,191,165,.1)}[data-md-color-accent=teal] 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What is ClickHouse?

+

ClickHouse is a columnar DBMS for OLAP.

+

In a "normal" row-oriented DBMS, data is stored in this order:

+
5123456789123456789     1       Eurobasket - Greece - Bosnia and Herzegovina - example.com      1       2011-09-01 01:03:02     6274717   1294101174      11409   612345678912345678      0       33      6       http://www.example.com/basketball/team/123/match/456789.html http://www.example.com/basketball/team/123/match/987654.html       0       1366    768     32      10      3183      0       0       13      0\0     1       1       0       0                       2011142 -1      0               0       01321     613     660     2011-09-01 08:01:17     0       0       0       0       utf-8   1466    0       0       0       5678901234567890123               277789954       0       0       0       0       0
+5234985259563631958     0       Consulting, Tax assessment, Accounting, Law       1       2011-09-01 01:03:02     6320881   2111222333      213     6458937489576391093     0       3       2       http://www.example.ru/         0       800     600       16      10      2       153.1   0       0       10      63      1       1       0       0                       2111678 000       0       588     368     240     2011-09-01 01:03:17     4       0       60310   0       windows-1251    1466    0       000               778899001       0       0       0       0       0
+...
+
+ + +

In order words, all the values related to a row are stored next to each other. +Examples of a row-oriented DBMS are MySQL, Postgres, MS SQL Server, and others.

+

In a column-oriented DBMS, data is stored like this:

+
WatchID:    5385521489354350662     5385521490329509958     5385521489953706054     5385521490476781638     5385521490583269446     5385521490218868806     5385521491437850694   5385521491090174022      5385521490792669254     5385521490420695110     5385521491532181574     5385521491559694406     5385521491459625030     5385521492275175494   5385521492781318214      5385521492710027334     5385521492955615302     5385521493708759110     5385521494506434630     5385521493104611398
+JavaEnable: 1       0       1       0       0       0       1       0       1       1       1       1       1       1       0       1       0       0       1       1
+Title:      Yandex  Announcements - Investor Relations - Yandex     Yandex — Contact us — Moscow    Yandex — Mission        Ru      Yandex — History — History of Yandex    Yandex Financial Releases - Investor Relations - Yandex Yandex — Locations      Yandex Board of Directors - Corporate Governance - Yandex       Yandex — Technologies
+GoodEvent:  1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1
+EventTime:  2016-05-18 05:19:20     2016-05-18 08:10:20     2016-05-18 07:38:00     2016-05-18 01:13:08     2016-05-18 00:04:06     2016-05-18 04:21:30     2016-05-18 00:34:16     2016-05-18 07:35:49     2016-05-18 11:41:59     2016-05-18 01:13:32
+
+ + +

These examples only show the order that data is arranged in. +The values from different columns are stored separately, and data from the same column is stored together.

+

Examples of column-oriented DBMSs: Vertica, Paraccel (Actian Matrix) (Amazon Redshift), Sybase IQ, Exasol, Infobright, InfiniDB, MonetDB (VectorWise) (Actian Vector), LucidDB, SAP HANA, Google Dremel, Google PowerDrill, Druid, kdb+, and so on.

+

Different orders for storing data are better suited to different scenarios. +The data access scenario refers to what queries are made, how often, and in what proportion; how much data is read for each type of query – rows, columns, and bytes; the relationship between reading and updating data; the working size of the data and how locally it is used; whether transactions are used, and how isolated they are; requirements for data replication and logical integrity; requirements for latency and throughput for each type of query, and so on.

+

The higher the load on the system, the more important it is to customize the system to the scenario, and the more specific this customization becomes. There is no system that is equally well-suited to significantly different scenarios. If a system is adaptable to a wide set of scenarios, under a high load, the system will handle all the scenarios equally poorly, or will work well for just one of the scenarios.

+

We'll say that the following is true for the OLAP (online analytical processing) scenario:

+
    +
  • The vast majority of requests are for read access.
    • +
  • Data is updated in fairly large batches (> 1000 rows), not by single rows; or it is not updated at all.
    • +
  • Data is added to the DB but is not modified.
    • +
  • For reads, quite a large number of rows are extracted from the DB, but only a small subset of columns.
    • +
  • Tables are "wide," meaning they contain a large number of columns.
    • +
  • Queries are relatively rare (usually hundreds of queries per server or less per second).
    • +
  • For simple queries, latencies around 50 ms are allowed.
    • +
  • Column values are fairly small: numbers and short strings (for example, 60 bytes per URL).
    • +
  • Requires high throughput when processing a single query (up to billions of rows per second per server).
    • +
  • There are no transactions.
    • +
  • Low requirements for data consistency.
    • +
  • There is one large table per query. All tables are small, except for one.
    • +
  • A query result is significantly smaller than the source data. In other words, data is filtered or aggregated. The result fits in a single server's RAM.
    • +
+

It is easy to see that the OLAP scenario is very different from other popular scenarios (such as OLTP or Key-Value access). So it doesn't make sense to try to use OLTP or a Key-Value DB for processing analytical queries if you want to get decent performance. For example, if you try to use MongoDB or Elliptics for analytics, you will get very poor performance compared to OLAP databases.

+

Columnar-oriented databases are better suited to OLAP scenarios (at least 100 times better in processing speed for most queries), for the following reasons:

+
    +
  1. For I/O.
    2. +
  2. For an analytical query, only a small number of table columns need to be read. In a column-oriented database, you can read just the data you need. For example, if you need 5 columns out of 100, you can expect a 20-fold reduction in I/O.
    3. +
  3. Since data is read in packets, it is easier to compress. Data in columns is also easier to compress. This further reduces the I/O volume.
    4. +
  4. Due to the reduced I/O, more data fits in the system cache.
    5. +
+

For example, the query "count the number of records for each advertising platform" requires reading one "advertising platform ID" column, which takes up 1 byte uncompressed. If most of the traffic was not from advertising platforms, you can expect at least 10-fold compression of this column. When using a quick compression algorithm, data decompression is possible at a speed of at least several gigabytes of uncompressed data per second. In other words, this query can be processed at a speed of approximately several billion rows per second on a single server. This speed is actually achieved in practice.

+

Example:

+
milovidov@hostname:~$ clickhouse-client
+ClickHouse client version 0.0.52053.
+Connecting to localhost:9000.
+Connected to ClickHouse server version 0.0.52053.
+
+:) SELECT CounterID, count() FROM hits GROUP BY CounterID ORDER BY count() DESC LIMIT 20
+
+SELECT
+    CounterID,
+    count()
+FROM hits
+GROUP BY CounterID
+ORDER BY count() DESC
+LIMIT 20
+
+┌─CounterID─┬──count()─┐
+│    114208 │ 56057344 │
+│    115080 │ 51619590 │
+│      3228 │ 44658301 │
+│     38230 │ 42045932 │
+│    145263 │ 42042158 │
+│     91244 │ 38297270 │
+│    154139 │ 26647572 │
+│    150748 │ 24112755 │
+│    242232 │ 21302571 │
+│    338158 │ 13507087 │
+│     62180 │ 12229491 │
+│     82264 │ 12187441 │
+│    232261 │ 12148031 │
+│    146272 │ 11438516 │
+│    168777 │ 11403636 │
+│   4120072 │ 11227824 │
+│  10938808 │ 10519739 │
+│     74088 │  9047015 │
+│    115079 │  8837972 │
+│    337234 │  8205961 │
+└───────────┴──────────┘
+
+20 rows in set. Elapsed: 0.153 sec. Processed 1.00 billion rows, 4.00 GB (6.53 billion rows/s., 26.10 GB/s.)
+
+:)
+
+ + +
    +
  1. For CPU.
    2. +
+

Since executing a query requires processing a large number of rows, it helps to dispatch all operations for entire vectors instead of for separate rows, or to implement the query engine so that there is almost no dispatching cost. If you don't do this, with any half-decent disk subsystem, the query interpreter inevitably stalls the CPU. +It makes sense to both store data in columns and process it, when possible, by columns.

+

There are two ways to do this:

+
    +
  1. +

    A vector engine. All operations are written for vectors, instead of for separate values. This means you don't need to call operations very often, and dispatching costs are negligible. Operation code contains an optimized internal cycle.

    +
    2. +
  2. +

    Code generation. The code generated for the query has all the indirect calls in it.

    +
    3. +
+

This is not done in "normal" databases, because it doesn't make sense when running simple queries. However, there are exceptions. For example, MemSQL uses code generation to reduce latency when processing SQL queries. (For comparison, analytical DBMSs require optimization of throughput, not latency.)

+

Note that for CPU efficiency, the query language must be declarative (SQL or MDX), or at least a vector (J, K). The query should only contain implicit loops, allowing for optimization.

+

Introduction

+

Distinctive features of ClickHouse

+

True column-oriented DBMS

+

In a true column-oriented DBMS, there isn't any "garbage" stored with the values. Among other things, this means that constant-length values must be supported, to avoid storing their length "number" next to the values. As an example, a billion UInt8-type values should actually consume around 1 GB uncompressed, or this will strongly affect the CPU use. It is very important to store data compactly (without any "garbage") even when uncompressed, since the speed of decompression (CPU usage) depends mainly on the volume of uncompressed data.

+

This is worth noting because there are systems that can store values of separate columns separately, but that can't effectively process analytical queries due to their optimization for other scenarios. Examples are HBase, BigTable, Cassandra, and HyperTable. In these systems, you will get throughput around a hundred thousand rows per second, but not hundreds of millions of rows per second.

+

Also note that ClickHouse is a DBMS, not a single database. ClickHouse allows creating tables and databases in runtime, loading data, and running queries without reconfiguring and restarting the server.

+

Data compression

+

Some column-oriented DBMSs (InfiniDB CE and MonetDB) do not use data compression. However, data compression really improves performance.

+

Disk storage of data

+

Many column-oriented DBMSs (such as SAP HANA and Google PowerDrill) can only work in RAM. But even on thousands of servers, the RAM is too small for storing all the pageviews and sessions in Yandex.Metrica.

+

Parallel processing on multiple cores

+

Large queries are parallelized in a natural way.

+

Distributed processing on multiple servers

+

Almost none of the columnar DBMSs listed above have support for distributed processing. +In ClickHouse, data can reside on different shards. Each shard can be a group of replicas that are used for fault tolerance. The query is processed on all the shards in parallel. This is transparent for the user.

+

SQL support

+

If you are familiar with standard SQL, we can't really talk about SQL support. +All the functions have different names. +However, this is a declarative query language based on SQL that can't be differentiated from SQL in many instances. +JOINs are supported. Subqueries are supported in FROM, IN, and JOIN clauses, as well as scalar subqueries. +Dependent subqueries are not supported.

+

Vector engine

+

Data is not only stored by columns, but is processed by vectors (parts of columns). This allows us to achieve high CPU performance.

+

Real-time data updates

+

ClickHouse supports primary key tables. In order to quickly perform queries on the range of the primary key, the data is sorted incrementally using the merge tree. Due to this, data can continually be added to the table. There is no locking when adding data.

+

Indexes

+

Having a primary key makes it possible to extract data for specific clients (for instance, Yandex.Metrica tracking tags) for a specific time range, with low latency less than several dozen milliseconds.

+

Suitable for online queries

+

This lets us use the system as the back-end for a web interface. Low latency means queries can be processed without delay, while the Yandex.Metrica interface page is loading. In other words, in online mode.

+

Support for approximated calculations

+
    +
  1. The system contains aggregate functions for approximated calculation of the number of various values, medians, and quantiles.
    2. +
  2. Supports running a query based on a part (sample) of data and getting an approximated result. In this case, proportionally less data is retrieved from the disk.
    3. +
  3. Supports running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reasonably accurate result while using fewer resources.
    4. +
+

Data replication and support for data integrity on replicas

+

Uses asynchronous multimaster replication. After being written to any available replica, data is distributed to all the remaining replicas. The system maintains identical data on different replicas. Data is restored automatically after a failure, or using a "button" for complex cases. +For more information, see the section Data replication.

+

ClickHouse features that can be considered disadvantages

+
    +
  1. No transactions.
    2. +
  2. For aggregation, query results must fit in the RAM on a single server. However, the volume of source data for a query may be indefinitely large.
    3. +
  3. Lack of full-fledged UPDATE/DELETE implementation.
    4. +
+

Yandex.Metrica use case

+

ClickHouse currently powers Yandex.Metrica, the second largest web analytics platform in the world. With more than 13 trillion records in the database and more than 20 billion events daily, ClickHouse allows you generating custom reports on the fly directly from non-aggregated data.

+

We need to get custom reports based on hits and sessions, with custom segments set by the user. Data for the reports is updated in real-time. Queries must be run immediately (in online mode). We must be able to build reports for any time period. Complex aggregates must be calculated, such as the number of unique visitors. +At this time (April 2014), Yandex.Metrica receives approximately 12 billion events (pageviews and mouse clicks) daily. All these events must be stored in order to build custom reports. A single query may require scanning hundreds of millions of rows over a few seconds, or millions of rows in no more than a few hundred milliseconds.

+

Usage in Yandex.Metrica and other Yandex services

+

ClickHouse is used for multiple purposes in Yandex.Metrica. +Its main task is to build reports in online mode using non-aggregated data. It uses a cluster of 374 servers, which store over 20.3 trillion rows in the database. The volume of compressed data, without counting duplication and replication, is about 2 PB. The volume of uncompressed data (in TSV format) would be approximately 17 PB.

+

ClickHouse is also used for:

+
    +
  • Storing data for Session Replay from Yandex.Metrica.
    • +
  • Processing intermediate data.
    • +
  • Building global reports with Analytics.
    • +
  • Running queries for debugging the Yandex.Metrica engine.
    • +
  • Analyzing logs from the API and the user interface.
    • +
+

ClickHouse has at least a dozen installations in other Yandex services: in search verticals, Market, Direct, business analytics, mobile development, AdFox, personal services, and others.

+

Aggregated and non-aggregated data

+

There is a popular opinion that in order to effectively calculate statistics, you must aggregate data, since this reduces the volume of data.

+

But data aggregation is a very limited solution, for the following reasons:

+
    +
  • You must have a pre-defined list of reports the user will need.
    • +
  • The user can't make custom reports.
    • +
  • When aggregating a large quantity of keys, the volume of data is not reduced, and aggregation is useless.
    • +
  • For a large number of reports, there are too many aggregation variations (combinatorial explosion).
    • +
  • When aggregating keys with high cardinality (such as URLs), the volume of data is not reduced by much (less than twofold).
    • +
  • For this reason, the volume of data with aggregation might grow instead of shrink.
    • +
  • Users do not view all the reports we generate for them. A large portion of calculations are useless.
    • +
  • The logical integrity of data may be violated for various aggregations.
    • +
+

If we do not aggregate anything and work with non-aggregated data, this might actually reduce the volume of calculations.

+

However, with aggregation, a significant part of the work is taken offline and completed relatively calmly. In contrast, online calculations require calculating as fast as possible, since the user is waiting for the result.

+

Yandex.Metrica has a specialized system for aggregating data called Metrage, which is used for the majority of reports. +Starting in 2009, Yandex.Metrica also used a specialized OLAP database for non-aggregated data called OLAPServer, which was previously used for the report builder. +OLAPServer worked well for non-aggregated data, but it had many restrictions that did not allow it to be used for all reports as desired. These included the lack of support for data types (only numbers), and the inability to incrementally update data in real-time (it could only be done by rewriting data daily). OLAPServer is not a DBMS, but a specialized DB.

+

To remove the limitations of OLAPServer and solve the problem of working with non-aggregated data for all reports, we developed the ClickHouse DBMS.

+

Questions you were afraid to ask

+

Why not use something like MapReduce?

+

We can refer to systems like map-reduce as distributed computing systems in which the reduce operation is based on distributed sorting. In this sense, they include Hadoop, and YT (YT is developed at Yandex for internal use).

+

These systems aren't appropriate for online queries due to their high latency. In other words, they can't be used as the back-end for a web interface. +These types of systems aren't useful for real-time data updates. +Distributed sorting isn't the best way to perform reduce operations if the result of the operation and all the intermediate results (if there are any) are located in the RAM of a single server, which is usually the case for online queries. In such a case, a hash table is the optimal way to perform reduce operations. A common approach to optimizing map-reduce tasks is pre-aggregation (partial reduce) using a hash table in RAM. The user performs this optimization manually. +Distributed sorting is one of the main causes of reduced performance when running simple map-reduce tasks.

+

Systems like map-reduce allow executing any code on the cluster. But a declarative query language is better suited to OLAP in order to run experiments quickly. For example, Hadoop has Hive and Pig. Also consider Cloudera Impala, Shark (outdated) for Spark, and Spark SQL, Presto, and Apache Drill. Performance when running such tasks is highly sub-optimal compared to specialized systems, but relatively high latency makes it unrealistic to use these systems as the backend for a web interface.

+

YT allows storing groups of columns separately. But YT can't be considered a true column-based system because it doesn't have fixed-length data types (for efficiently storing numbers without extra "garbage"), and also due to its lack of a vector engine. Tasks are performed in YT using custom code in streaming mode, so they cannot be optimized enough (up to hundreds of millions of rows per second per server). "Dynamic table sorting" is under development in YT using MergeTree, strict value typing, and a query language similar to SQL. Dynamically sorted tables are not appropriate for OLAP tasks because the data is stored by row. The YT query language is still under development, so we can't yet rely on this functionality. YT developers are considering using dynamically sorted tables in OLTP and Key-Value scenarios.

+

Performance

+

According to internal testing results, ClickHouse shows the best performance for comparable operating scenarios among systems of its class that were available for testing. This includes the highest throughput for long queries, and the lowest latency on short queries. Testing results are shown on a separate page.

+

Throughput for a single large query

+

Throughput can be measured in rows per second or in megabytes per second. If the data is placed in the page cache, a query that is not too complex is processed on modern hardware at a speed of approximately 2-10 GB/s of uncompressed data on a single server (for the simplest cases, the speed may reach 30 GB/s). If data is not placed in the page cache, the speed depends on the disk subsystem and the data compression rate. For example, if the disk subsystem allows reading data at 400 MB/s, and the data compression rate is 3, the speed will be around 1.2 GB/s. To get the speed in rows per second, divide the speed in bytes per second by the total size of the columns used in the query. For example, if 10 bytes of columns are extracted, the speed will be around 100-200 million rows per second.

+

The processing speed increases almost linearly for distributed processing, but only if the number of rows resulting from aggregation or sorting is not too large.

+

Latency when processing short queries

+

If a query uses a primary key and does not select too many rows to process (hundreds of thousands), and does not use too many columns, we can expect less than 50 milliseconds of latency (single digits of milliseconds in the best case) if data is placed in the page cache. Otherwise, latency is calculated from the number of seeks. If you use rotating drives, for a system that is not overloaded, the latency is calculated by this formula: seek time (10 ms) * number of columns queried * number of data parts.

+

Throughput when processing a large quantity of short queries

+

Under the same conditions, ClickHouse can handle several hundred queries per second on a single server (up to several thousand in the best case). Since this scenario is not typical for analytical DBMSs, we recommend expecting a maximum of 100 queries per second.

+

Performance when inserting data

+

We recommend inserting data in packets of at least 1000 rows, or no more than a single request per second. When inserting to a MergeTree table from a tab-separated dump, the insertion speed will be from 50 to 200 MB/s. If the inserted rows are around 1 Kb in size, the speed will be from 50,000 to 200,000 rows per second. If the rows are small, the performance will be higher in rows per second (on Banner System data -> 500,000 rows per second; on Graphite data -> 1,000,000 rows per second). To improve performance, you can make multiple INSERT queries in parallel, and performance will increase linearly.

+

Getting started

+

System requirements

+

This is not a cross-platform system. It requires Linux Ubuntu Precise (12.04) or newer, with x86_64 architecture and support for the SSE 4.2 instruction set. +To check for SSE 4.2:

+
grep -q sse4_2 /proc/cpuinfo && echo "SSE 4.2 supported" || echo "SSE 4.2 not supported"
+
+ + +

We recommend using Ubuntu Trusty, Ubuntu Xenial, or Ubuntu Precise. +The terminal must use UTF-8 encoding (the default in Ubuntu).

+

Installation

+

For testing and development, the system can be installed on a single server or on a desktop computer.

+

Installing from packages for Debian/Ubuntu

+

In /etc/apt/sources.list (or in a separate /etc/apt/sources.list.d/clickhouse.list file), add the repository:

+
deb http://repo.yandex.ru/clickhouse/deb/stable/ main/
+
+ + +

If you want to use the most recent test version, replace 'stable' with 'testing'.

+

Then run:

+
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4    # optional
+sudo apt-get update
+sudo apt-get install clickhouse-client clickhouse-server
+
+ + +

You can also download and install packages manually from here: https://repo.yandex.ru/clickhouse/deb/stable/main/.

+

ClickHouse contains access restriction settings. They are located in the 'users.xml' file (next to 'config.xml'). +By default, access is allowed from anywhere for the 'default' user, without a password. See 'user/default/networks'. +For more information, see the section "Configuration files".

+

Installing from sources

+

To compile, follow the instructions: build.md

+

You can compile packages and install them. +You can also use programs without installing packages.

+
Client: dbms/src/Client/
+Server: dbms/src/Server/
+
+ + +

For the server, create a catalog with data, such as:

+
/opt/clickhouse/data/default/
+/opt/clickhouse/metadata/default/
+
+ + +

(Configurable in the server config.) +Run 'chown' for the desired user.

+

Note the path to logs in the server config (src/dbms/src/Server/config.xml).

+

Other installation methods

+

Docker image: https://hub.docker.com/r/yandex/clickhouse-server/

+

RPM packages for CentOS or RHEL: https://github.com/Altinity/clickhouse-rpm-install

+

Gentoo overlay: https://github.com/kmeaw/clickhouse-overlay

+

Launch

+

To start the server (as a daemon), run:

+
sudo service clickhouse-server start
+
+ + +

See the logs in the /var/log/clickhouse-server/ directory.

+

If the server doesn't start, check the configurations in the file /etc/clickhouse-server/config.xml.

+

You can also launch the server from the console:

+
clickhouse-server --config-file=/etc/clickhouse-server/config.xml
+
+ + +

In this case, the log will be printed to the console, which is convenient during development. +If the configuration file is in the current directory, you don't need to specify the '--config-file' parameter. By default, it uses './config.xml'.

+

You can use the command-line client to connect to the server:

+
clickhouse-client
+
+ + +

The default parameters indicate connecting with localhost:9000 on behalf of the user 'default' without a password. +The client can be used for connecting to a remote server. Example:

+
clickhouse-client --host=example.com
+
+ + +

For more information, see the section "Command-line client".

+

Checking the system:

+
milovidov@hostname:~/work/metrica/src/dbms/src/Client$ ./clickhouse-client
+ClickHouse client version 0.0.18749.
+Connecting to localhost:9000.
+Connected to ClickHouse server version 0.0.18749.
+
+:) SELECT 1
+
+SELECT 1
+
+┌─1─┐
+│ 1 │
+└───┘
+
+1 rows in set. Elapsed: 0.003 sec.
+
+:)
+
+ + +

Congratulations, the system works!

+

To continue experimenting, you can try to download from the test data sets.

+

+

OnTime

+

This performance test was created by Vadim Tkachenko. See:

+ +

Downloading data:

+
for s in `seq 1987 2017`
+do
+for m in `seq 1 12`
+do
+wget http://transtats.bts.gov/PREZIP/On_Time_On_Time_Performance_${s}_${m}.zip
+done
+done
+
+ + +

(from https://github.com/Percona-Lab/ontime-airline-performance/blob/master/download.sh )

+

Creating a table:

+
CREATE TABLE `ontime` (
+  `Year` UInt16,
+  `Quarter` UInt8,
+  `Month` UInt8,
+  `DayofMonth` UInt8,
+  `DayOfWeek` UInt8,
+  `FlightDate` Date,
+  `UniqueCarrier` FixedString(7),
+  `AirlineID` Int32,
+  `Carrier` FixedString(2),
+  `TailNum` String,
+  `FlightNum` String,
+  `OriginAirportID` Int32,
+  `OriginAirportSeqID` Int32,
+  `OriginCityMarketID` Int32,
+  `Origin` FixedString(5),
+  `OriginCityName` String,
+  `OriginState` FixedString(2),
+  `OriginStateFips` String,
+  `OriginStateName` String,
+  `OriginWac` Int32,
+  `DestAirportID` Int32,
+  `DestAirportSeqID` Int32,
+  `DestCityMarketID` Int32,
+  `Dest` FixedString(5),
+  `DestCityName` String,
+  `DestState` FixedString(2),
+  `DestStateFips` String,
+  `DestStateName` String,
+  `DestWac` Int32,
+  `CRSDepTime` Int32,
+  `DepTime` Int32,
+  `DepDelay` Int32,
+  `DepDelayMinutes` Int32,
+  `DepDel15` Int32,
+  `DepartureDelayGroups` String,
+  `DepTimeBlk` String,
+  `TaxiOut` Int32,
+  `WheelsOff` Int32,
+  `WheelsOn` Int32,
+  `TaxiIn` Int32,
+  `CRSArrTime` Int32,
+  `ArrTime` Int32,
+  `ArrDelay` Int32,
+  `ArrDelayMinutes` Int32,
+  `ArrDel15` Int32,
+  `ArrivalDelayGroups` Int32,
+  `ArrTimeBlk` String,
+  `Cancelled` UInt8,
+  `CancellationCode` FixedString(1),
+  `Diverted` UInt8,
+  `CRSElapsedTime` Int32,
+  `ActualElapsedTime` Int32,
+  `AirTime` Int32,
+  `Flights` Int32,
+  `Distance` Int32,
+  `DistanceGroup` UInt8,
+  `CarrierDelay` Int32,
+  `WeatherDelay` Int32,
+  `NASDelay` Int32,
+  `SecurityDelay` Int32,
+  `LateAircraftDelay` Int32,
+  `FirstDepTime` String,
+  `TotalAddGTime` String,
+  `LongestAddGTime` String,
+  `DivAirportLandings` String,
+  `DivReachedDest` String,
+  `DivActualElapsedTime` String,
+  `DivArrDelay` String,
+  `DivDistance` String,
+  `Div1Airport` String,
+  `Div1AirportID` Int32,
+  `Div1AirportSeqID` Int32,
+  `Div1WheelsOn` String,
+  `Div1TotalGTime` String,
+  `Div1LongestGTime` String,
+  `Div1WheelsOff` String,
+  `Div1TailNum` String,
+  `Div2Airport` String,
+  `Div2AirportID` Int32,
+  `Div2AirportSeqID` Int32,
+  `Div2WheelsOn` String,
+  `Div2TotalGTime` String,
+  `Div2LongestGTime` String,
+  `Div2WheelsOff` String,
+  `Div2TailNum` String,
+  `Div3Airport` String,
+  `Div3AirportID` Int32,
+  `Div3AirportSeqID` Int32,
+  `Div3WheelsOn` String,
+  `Div3TotalGTime` String,
+  `Div3LongestGTime` String,
+  `Div3WheelsOff` String,
+  `Div3TailNum` String,
+  `Div4Airport` String,
+  `Div4AirportID` Int32,
+  `Div4AirportSeqID` Int32,
+  `Div4WheelsOn` String,
+  `Div4TotalGTime` String,
+  `Div4LongestGTime` String,
+  `Div4WheelsOff` String,
+  `Div4TailNum` String,
+  `Div5Airport` String,
+  `Div5AirportID` Int32,
+  `Div5AirportSeqID` Int32,
+  `Div5WheelsOn` String,
+  `Div5TotalGTime` String,
+  `Div5LongestGTime` String,
+  `Div5WheelsOff` String,
+  `Div5TailNum` String
+) ENGINE = MergeTree(FlightDate, (Year, FlightDate), 8192)
+
+ + +

Loading data:

+
for i in *.zip; do echo $i; unzip -cq $i '*.csv' | sed 's/\.00//g' | clickhouse-client --host=example-perftest01j --query="INSERT INTO ontime FORMAT CSVWithNames"; done
+
+ + +

Queries:

+

Q0.

+
select avg(c1) from (select Year, Month, count(*) as c1 from ontime group by Year, Month);
+
+ + +

Q1. The number of flights per day from the year 2000 to 2008

+
SELECT DayOfWeek, count(*) AS c FROM ontime WHERE Year >= 2000 AND Year <= 2008 GROUP BY DayOfWeek ORDER BY c DESC;
+
+ + +

Q2. The number of flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008

+
SELECT DayOfWeek, count(*) AS c FROM ontime WHERE DepDelay>10 AND Year >= 2000 AND Year <= 2008 GROUP BY DayOfWeek ORDER BY c DESC
+
+ + +

Q3. The number of delays by airport for 2000-2008

+
SELECT Origin, count(*) AS c FROM ontime WHERE DepDelay>10 AND Year >= 2000 AND Year <= 2008 GROUP BY Origin ORDER BY c DESC LIMIT 10
+
+ + +

Q4. The number of delays by carrier for 2007

+
SELECT Carrier, count(*) FROM ontime WHERE DepDelay>10  AND Year = 2007 GROUP BY Carrier ORDER BY count(*) DESC
+
+ + +

Q5. The percentage of delays by carrier for 2007

+
SELECT Carrier, c, c2, c*1000/c2 as c3
+FROM
+(
+    SELECT
+        Carrier,
+        count(*) AS c
+    FROM ontime
+    WHERE DepDelay>10
+        AND Year=2007
+    GROUP BY Carrier
+)
+ANY INNER JOIN
+(
+    SELECT
+        Carrier,
+        count(*) AS c2
+    FROM ontime
+    WHERE Year=2007
+    GROUP BY Carrier
+) USING Carrier
+ORDER BY c3 DESC;
+
+ + +

Better version of the same query:

+
SELECT Carrier, avg(DepDelay > 10) * 1000 AS c3 FROM ontime WHERE Year = 2007 GROUP BY Carrier ORDER BY Carrier
+
+ + +

Q6. The previous request for a broader range of years, 2000-2008

+
SELECT Carrier, c, c2, c*1000/c2 as c3
+FROM
+(
+    SELECT
+        Carrier,
+        count(*) AS c
+    FROM ontime
+    WHERE DepDelay>10
+        AND Year >= 2000 AND Year <= 2008
+    GROUP BY Carrier
+)
+ANY INNER JOIN
+(
+    SELECT
+        Carrier,
+        count(*) AS c2
+    FROM ontime
+    WHERE Year >= 2000 AND Year <= 2008
+    GROUP BY Carrier
+) USING Carrier
+ORDER BY c3 DESC;
+
+ + +

Better version of the same query:

+
SELECT Carrier, avg(DepDelay > 10) * 1000 AS c3 FROM ontime WHERE Year >= 2000 AND Year <= 2008 GROUP BY Carrier ORDER BY Carrier
+
+ + +

Q7. Percentage of flights delayed for more than 10 minutes, by year

+
SELECT Year, c1/c2
+FROM
+(
+    select
+        Year,
+        count(*)*1000 as c1
+    from ontime
+    WHERE DepDelay>10
+    GROUP BY Year
+)
+ANY INNER JOIN
+(
+    select
+        Year,
+        count(*) as c2
+    from ontime
+    GROUP BY Year
+) USING (Year)
+ORDER BY Year
+
+ + +

Better version of the same query:

+
SELECT Year, avg(DepDelay > 10) FROM ontime GROUP BY Year ORDER BY Year
+
+ + +

Q8. The most popular destinations by the number of directly connected cities for various year ranges

+
SELECT DestCityName, uniqExact(OriginCityName) AS u FROM ontime WHERE Year >= 2000 and Year <= 2010 GROUP BY DestCityName ORDER BY u DESC LIMIT 10;
+
+ + +

Q9.

+
select Year, count(*) as c1 from ontime group by Year;
+
+ + +

Q10.

+
select
+   min(Year), max(Year), Carrier, count(*) as cnt,
+   sum(ArrDelayMinutes>30) as flights_delayed,
+   round(sum(ArrDelayMinutes>30)/count(*),2) as rate
+FROM ontime
+WHERE
+   DayOfWeek not in (6,7) and OriginState not in ('AK', 'HI', 'PR', 'VI')
+   and DestState not in ('AK', 'HI', 'PR', 'VI')
+   and FlightDate < '2010-01-01'
+GROUP by Carrier
+HAVING cnt > 100000 and max(Year) > 1990
+ORDER by rate DESC
+LIMIT 1000;
+
+ + +

Bonus:

+
SELECT avg(cnt) FROM (SELECT Year,Month,count(*) AS cnt FROM ontime WHERE DepDel15=1 GROUP BY Year,Month)
+
+select avg(c1) from (select Year,Month,count(*) as c1 from ontime group by Year,Month)
+
+SELECT DestCityName, uniqExact(OriginCityName) AS u FROM ontime GROUP BY DestCityName ORDER BY u DESC LIMIT 10;
+
+SELECT OriginCityName, DestCityName, count() AS c FROM ontime GROUP BY OriginCityName, DestCityName ORDER BY c DESC LIMIT 10;
+
+SELECT OriginCityName, count() AS c FROM ontime GROUP BY OriginCityName ORDER BY c DESC LIMIT 10;
+
+ + +

New York Taxi data

+

How to import the raw data

+

See https://github.com/toddwschneider/nyc-taxi-data and http://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html for the description of the dataset and instructions for downloading.

+

Downloading will result in about 227 GB of uncompressed data in CSV files. The download takes about an hour over a 1 Gbit connection (parallel downloading from s3.amazonaws.com recovers at least half of a 1 Gbit channel). +Some of the files might not download fully. Check the file sizes and re-download any that seem doubtful.

+

Some of the files might contain invalid rows. You can fix them as follows:

+
sed -E '/(.*,){18,}/d' data/yellow_tripdata_2010-02.csv > data/yellow_tripdata_2010-02.csv_
+sed -E '/(.*,){18,}/d' data/yellow_tripdata_2010-03.csv > data/yellow_tripdata_2010-03.csv_
+mv data/yellow_tripdata_2010-02.csv_ data/yellow_tripdata_2010-02.csv
+mv data/yellow_tripdata_2010-03.csv_ data/yellow_tripdata_2010-03.csv
+
+ + +

Then the data must be pre-processed in PostgreSQL. This will create selections of points in the polygons (to match points on the map with the boroughs of New York City) and combine all the data into a single denormalized flat table by using a JOIN. To do this, you will need to install PostgreSQL with PostGIS support.

+

Be careful when running initialize_database.sh and manually re-check that all the tables were created correctly.

+

It takes about 20-30 minutes to process each month's worth of data in PostgreSQL, for a total of about 48 hours.

+

You can check the number of downloaded rows as follows:

+
time psql nyc-taxi-data -c "SELECT count(*) FROM trips;"
+###    count
+ 1298979494
+(1 row)
+
+real    7m9.164s
+
+ + +

(This is slightly more than 1.1 billion rows reported by Mark Litwintschik in a series of blog posts.)

+

The data in PostgreSQL uses 370 GB of space.

+

Exporting the data from PostgreSQL:

+
COPY
+(
+    SELECT trips.id,
+           trips.vendor_id,
+           trips.pickup_datetime,
+           trips.dropoff_datetime,
+           trips.store_and_fwd_flag,
+           trips.rate_code_id,
+           trips.pickup_longitude,
+           trips.pickup_latitude,
+           trips.dropoff_longitude,
+           trips.dropoff_latitude,
+           trips.passenger_count,
+           trips.trip_distance,
+           trips.fare_amount,
+           trips.extra,
+           trips.mta_tax,
+           trips.tip_amount,
+           trips.tolls_amount,
+           trips.ehail_fee,
+           trips.improvement_surcharge,
+           trips.total_amount,
+           trips.payment_type,
+           trips.trip_type,
+           trips.pickup,
+           trips.dropoff,
+
+           cab_types.type cab_type,
+
+           weather.precipitation_tenths_of_mm rain,
+           weather.snow_depth_mm,
+           weather.snowfall_mm,
+           weather.max_temperature_tenths_degrees_celsius max_temp,
+           weather.min_temperature_tenths_degrees_celsius min_temp,
+           weather.average_wind_speed_tenths_of_meters_per_second wind,
+
+           pick_up.gid pickup_nyct2010_gid,
+           pick_up.ctlabel pickup_ctlabel,
+           pick_up.borocode pickup_borocode,
+           pick_up.boroname pickup_boroname,
+           pick_up.ct2010 pickup_ct2010,
+           pick_up.boroct2010 pickup_boroct2010,
+           pick_up.cdeligibil pickup_cdeligibil,
+           pick_up.ntacode pickup_ntacode,
+           pick_up.ntaname pickup_ntaname,
+           pick_up.puma pickup_puma,
+
+           drop_off.gid dropoff_nyct2010_gid,
+           drop_off.ctlabel dropoff_ctlabel,
+           drop_off.borocode dropoff_borocode,
+           drop_off.boroname dropoff_boroname,
+           drop_off.ct2010 dropoff_ct2010,
+           drop_off.boroct2010 dropoff_boroct2010,
+           drop_off.cdeligibil dropoff_cdeligibil,
+           drop_off.ntacode dropoff_ntacode,
+           drop_off.ntaname dropoff_ntaname,
+           drop_off.puma dropoff_puma
+    FROM trips
+    LEFT JOIN cab_types
+        ON trips.cab_type_id = cab_types.id
+    LEFT JOIN central_park_weather_observations_raw weather
+        ON weather.date = trips.pickup_datetime::date
+    LEFT JOIN nyct2010 pick_up
+        ON pick_up.gid = trips.pickup_nyct2010_gid
+    LEFT JOIN nyct2010 drop_off
+        ON drop_off.gid = trips.dropoff_nyct2010_gid
+) TO '/opt/milovidov/nyc-taxi-data/trips.tsv';
+
+ + +

The data snapshot is created at a speed of about 50 MB per second. While creating the snapshot, PostgreSQL reads from the disk at a speed of about 28 MB per second. +This takes about 5 hours. The resulting TSV file is 590612904969 bytes.

+

Create a temporary table in ClickHouse:

+
CREATE TABLE trips
+(
+trip_id                 UInt32,
+vendor_id               String,
+pickup_datetime         DateTime,
+dropoff_datetime        Nullable(DateTime),
+store_and_fwd_flag      Nullable(FixedString(1)),
+rate_code_id            Nullable(UInt8),
+pickup_longitude        Nullable(Float64),
+pickup_latitude         Nullable(Float64),
+dropoff_longitude       Nullable(Float64),
+dropoff_latitude        Nullable(Float64),
+passenger_count         Nullable(UInt8),
+trip_distance           Nullable(Float64),
+fare_amount             Nullable(Float32),
+extra                   Nullable(Float32),
+mta_tax                 Nullable(Float32),
+tip_amount              Nullable(Float32),
+tolls_amount            Nullable(Float32),
+ehail_fee               Nullable(Float32),
+improvement_surcharge   Nullable(Float32),
+total_amount            Nullable(Float32),
+payment_type            Nullable(String),
+trip_type               Nullable(UInt8),
+pickup                  Nullable(String),
+dropoff                 Nullable(String),
+cab_type                Nullable(String),
+precipitation           Nullable(UInt8),
+snow_depth              Nullable(UInt8),
+snowfall                Nullable(UInt8),
+max_temperature         Nullable(UInt8),
+min_temperature         Nullable(UInt8),
+average_wind_speed      Nullable(UInt8),
+pickup_nyct2010_gid     Nullable(UInt8),
+pickup_ctlabel          Nullable(String),
+pickup_borocode         Nullable(UInt8),
+pickup_boroname         Nullable(String),
+pickup_ct2010           Nullable(String),
+pickup_boroct2010       Nullable(String),
+pickup_cdeligibil       Nullable(FixedString(1)),
+pickup_ntacode          Nullable(String),
+pickup_ntaname          Nullable(String),
+pickup_puma             Nullable(String),
+dropoff_nyct2010_gid    Nullable(UInt8),
+dropoff_ctlabel         Nullable(String),
+dropoff_borocode        Nullable(UInt8),
+dropoff_boroname        Nullable(String),
+dropoff_ct2010          Nullable(String),
+dropoff_boroct2010      Nullable(String),
+dropoff_cdeligibil      Nullable(String),
+dropoff_ntacode         Nullable(String),
+dropoff_ntaname         Nullable(String),
+dropoff_puma            Nullable(String)
+) ENGINE = Log;
+
+ + +

It is needed for converting fields to more correct data types and, if possible, to eliminate NULLs.

+
time clickhouse-client --query="INSERT INTO trips FORMAT TabSeparated" < trips.tsv
+
+real    75m56.214s
+
+ + +

Data is read at a speed of 112-140 Mb/second. +Loading data into a Log type table in one stream took 76 minutes. +The data in this table uses 142 GB.

+

(Importing data directly from Postgres is also possible using COPY ... TO PROGRAM.)

+

Unfortunately, all the fields associated with the weather (precipitation...average_wind_speed) were filled with NULL. Because of this, we will remove them from the final data set.

+

To start, we'll create a table on a single server. Later we will make the table distributed.

+

Create and populate a summary table:

+
CREATE TABLE trips_mergetree
+ENGINE = MergeTree(pickup_date, pickup_datetime, 8192)
+AS SELECT
+
+trip_id,
+CAST(vendor_id AS Enum8('1' = 1, '2' = 2, 'CMT' = 3, 'VTS' = 4, 'DDS' = 5, 'B02512' = 10, 'B02598' = 11, 'B02617' = 12, 'B02682' = 13, 'B02764' = 14)) AS vendor_id,
+toDate(pickup_datetime) AS pickup_date,
+ifNull(pickup_datetime, toDateTime(0)) AS pickup_datetime,
+toDate(dropoff_datetime) AS dropoff_date,
+ifNull(dropoff_datetime, toDateTime(0)) AS dropoff_datetime,
+assumeNotNull(store_and_fwd_flag) IN ('Y', '1', '2') AS store_and_fwd_flag,
+assumeNotNull(rate_code_id) AS rate_code_id,
+assumeNotNull(pickup_longitude) AS pickup_longitude,
+assumeNotNull(pickup_latitude) AS pickup_latitude,
+assumeNotNull(dropoff_longitude) AS dropoff_longitude,
+assumeNotNull(dropoff_latitude) AS dropoff_latitude,
+assumeNotNull(passenger_count) AS passenger_count,
+assumeNotNull(trip_distance) AS trip_distance,
+assumeNotNull(fare_amount) AS fare_amount,
+assumeNotNull(extra) AS extra,
+assumeNotNull(mta_tax) AS mta_tax,
+assumeNotNull(tip_amount) AS tip_amount,
+assumeNotNull(tolls_amount) AS tolls_amount,
+assumeNotNull(ehail_fee) AS ehail_fee,
+assumeNotNull(improvement_surcharge) AS improvement_surcharge,
+assumeNotNull(total_amount) AS total_amount,
+CAST((assumeNotNull(payment_type) AS pt) IN ('CSH', 'CASH', 'Cash', 'CAS', 'Cas', '1') ? 'CSH' : (pt IN ('CRD', 'Credit', 'Cre', 'CRE', 'CREDIT', '2') ? 'CRE' : (pt IN ('NOC', 'No Charge', 'No', '3') ? 'NOC' : (pt IN ('DIS', 'Dispute', 'Dis', '4') ? 'DIS' : 'UNK'))) AS Enum8('CSH' = 1, 'CRE' = 2, 'UNK' = 0, 'NOC' = 3, 'DIS' = 4)) AS payment_type_,
+assumeNotNull(trip_type) AS trip_type,
+ifNull(toFixedString(unhex(pickup), 25), toFixedString('', 25)) AS pickup,
+ifNull(toFixedString(unhex(dropoff), 25), toFixedString('', 25)) AS dropoff,
+CAST(assumeNotNull(cab_type) AS Enum8('yellow' = 1, 'green' = 2, 'uber' = 3)) AS cab_type,
+
+assumeNotNull(pickup_nyct2010_gid) AS pickup_nyct2010_gid,
+toFloat32(ifNull(pickup_ctlabel, '0')) AS pickup_ctlabel,
+assumeNotNull(pickup_borocode) AS pickup_borocode,
+CAST(assumeNotNull(pickup_boroname) AS Enum8('Manhattan' = 1, 'Queens' = 4, 'Brooklyn' = 3, '' = 0, 'Bronx' = 2, 'Staten Island' = 5)) AS pickup_boroname,
+toFixedString(ifNull(pickup_ct2010, '000000'), 6) AS pickup_ct2010,
+toFixedString(ifNull(pickup_boroct2010, '0000000'), 7) AS pickup_boroct2010,
+CAST(assumeNotNull(ifNull(pickup_cdeligibil, ' ')) AS Enum8(' ' = 0, 'E' = 1, 'I' = 2)) AS pickup_cdeligibil,
+toFixedString(ifNull(pickup_ntacode, '0000'), 4) AS pickup_ntacode,
+
+CAST(assumeNotNull(pickup_ntaname) AS Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195)) AS pickup_ntaname,
+
+toUInt16(ifNull(pickup_puma, '0')) AS pickup_puma,
+
+assumeNotNull(dropoff_nyct2010_gid) AS dropoff_nyct2010_gid,
+toFloat32(ifNull(dropoff_ctlabel, '0')) AS dropoff_ctlabel,
+assumeNotNull(dropoff_borocode) AS dropoff_borocode,
+CAST(assumeNotNull(dropoff_boroname) AS Enum8('Manhattan' = 1, 'Queens' = 4, 'Brooklyn' = 3, '' = 0, 'Bronx' = 2, 'Staten Island' = 5)) AS dropoff_boroname,
+toFixedString(ifNull(dropoff_ct2010, '000000'), 6) AS dropoff_ct2010,
+toFixedString(ifNull(dropoff_boroct2010, '0000000'), 7) AS dropoff_boroct2010,
+CAST(assumeNotNull(ifNull(dropoff_cdeligibil, ' ')) AS Enum8(' ' = 0, 'E' = 1, 'I' = 2)) AS dropoff_cdeligibil,
+toFixedString(ifNull(dropoff_ntacode, '0000'), 4) AS dropoff_ntacode,
+
+CAST(assumeNotNull(dropoff_ntaname) AS Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195)) AS dropoff_ntaname,
+
+toUInt16(ifNull(dropoff_puma, '0')) AS dropoff_puma
+
+FROM trips
+
+ + +

This takes 3030 seconds at a speed of about 428,000 rows per second. +To load it faster, you can create the table with the Log engine instead of MergeTree. In this case, the download works faster than 200 seconds.

+

The table uses 126 GB of disk space.

+
:) SELECT formatReadableSize(sum(bytes)) FROM system.parts WHERE table = 'trips_mergetree' AND active
+
+SELECT formatReadableSize(sum(bytes))
+FROM system.parts
+WHERE (table = 'trips_mergetree') AND active
+
+┌─formatReadableSize(sum(bytes))─┐
+│ 126.18 GiB                     │
+└────────────────────────────────┘
+
+ + +

Among other things, you can run the OPTIMIZE query on MergeTree. But it's not required, since everything will be fine without it.

+

Results on single server

+

Q1:

+
SELECT cab_type, count(*) FROM trips_mergetree GROUP BY cab_type
+
+ + +

0.490 seconds.

+

Q2:

+
SELECT passenger_count, avg(total_amount) FROM trips_mergetree GROUP BY passenger_count
+
+ + +

1.224 seconds.

+

Q3:

+
SELECT passenger_count, toYear(pickup_date) AS year, count(*) FROM trips_mergetree GROUP BY passenger_count, year
+
+ + +

2.104 seconds.

+

Q4:

+
SELECT passenger_count, toYear(pickup_date) AS year, round(trip_distance) AS distance, count(*)
+FROM trips_mergetree
+GROUP BY passenger_count, year, distance
+ORDER BY year, count(*) DESC
+
+ + +

3.593 seconds.

+

The following server was used:

+

Two Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical kernels total, +128 GiB RAM, +8x6 TB HD on hardware RAID-5

+

Execution time is the best of three runsBut starting from the second run, queries read data from the file system cache. No further caching occurs: the data is read out and processed in each run.

+

Creating a table on three servers:

+

On each server:

+
CREATE TABLE default.trips_mergetree_third ( trip_id UInt32,  vendor_id Enum8('1' = 1, '2' = 2, 'CMT' = 3, 'VTS' = 4, 'DDS' = 5, 'B02512' = 10, 'B02598' = 11, 'B02617' = 12, 'B02682' = 13, 'B02764' = 14),  pickup_date Date,  pickup_datetime DateTime,  dropoff_date Date,  dropoff_datetime DateTime,  store_and_fwd_flag UInt8,  rate_code_id UInt8,  pickup_longitude Float64,  pickup_latitude Float64,  dropoff_longitude Float64,  dropoff_latitude Float64,  passenger_count UInt8,  trip_distance Float64,  fare_amount Float32,  extra Float32,  mta_tax Float32,  tip_amount Float32,  tolls_amount Float32,  ehail_fee Float32,  improvement_surcharge Float32,  total_amount Float32,  payment_type_ Enum8('UNK' = 0, 'CSH' = 1, 'CRE' = 2, 'NOC' = 3, 'DIS' = 4),  trip_type UInt8,  pickup FixedString(25),  dropoff FixedString(25),  cab_type Enum8('yellow' = 1, 'green' = 2, 'uber' = 3),  pickup_nyct2010_gid UInt8,  pickup_ctlabel Float32,  pickup_borocode UInt8,  pickup_boroname Enum8('' = 0, 'Manhattan' = 1, 'Bronx' = 2, 'Brooklyn' = 3, 'Queens' = 4, 'Staten Island' = 5),  pickup_ct2010 FixedString(6),  pickup_boroct2010 FixedString(7),  pickup_cdeligibil Enum8(' ' = 0, 'E' = 1, 'I' = 2),  pickup_ntacode FixedString(4),  pickup_ntaname Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195),  pickup_puma UInt16,  dropoff_nyct2010_gid UInt8,  dropoff_ctlabel Float32,  dropoff_borocode UInt8,  dropoff_boroname Enum8('' = 0, 'Manhattan' = 1, 'Bronx' = 2, 'Brooklyn' = 3, 'Queens' = 4, 'Staten Island' = 5),  dropoff_ct2010 FixedString(6),  dropoff_boroct2010 FixedString(7),  dropoff_cdeligibil Enum8(' ' = 0, 'E' = 1, 'I' = 2),  dropoff_ntacode FixedString(4),  dropoff_ntaname Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195),  dropoff_puma UInt16) ENGINE = MergeTree(pickup_date, pickup_datetime, 8192)
+
+ + +

On the source server:

+
CREATE TABLE trips_mergetree_x3 AS trips_mergetree_third ENGINE = Distributed(perftest, default, trips_mergetree_third, rand())
+
+ + +

The following query redistributes data:

+
INSERT INTO trips_mergetree_x3 SELECT * FROM trips_mergetree
+
+ + +

This takes 2454 seconds.

+

On three servers:

+

Q1: 0.212 seconds. +Q2: 0.438 seconds. +Q3: 0.733 seconds. +Q4: 1.241 seconds.

+

No surprises here, since the queries are scaled linearly.

+

We also have results from a cluster of 140 servers:

+

Q1: 0.028 sec. +Q2: 0.043 sec. +Q3: 0.051 sec. +Q4: 0.072 sec.

+

In this case, the query processing time is determined above all by network latency. +We ran queries using a client located in a Yandex datacenter in Finland on a cluster in Russia, which added about 20 ms of latency.

+

Summary

+
nodes   Q1     Q2     Q3     Q4
+  1  0.490  1.224  2.104  3.593
+  3  0.212  0.438  0.733  1.241
+140  0.028  0.043  0.051  0.072
+
+ + +

AMPLab Big Data Benchmark

+

See https://amplab.cs.berkeley.edu/benchmark/

+

Sign up for a free account at https://aws.amazon.com. You will need a credit card, email and phone number.Get a new access key at https://console.aws.amazon.com/iam/home?nc2=h_m_sc#security_credential

+

Run the following in the console:

+
sudo apt-get install s3cmd
+mkdir tiny; cd tiny;
+s3cmd sync s3://big-data-benchmark/pavlo/text-deflate/tiny/ .
+cd ..
+mkdir 1node; cd 1node;
+s3cmd sync s3://big-data-benchmark/pavlo/text-deflate/1node/ .
+cd ..
+mkdir 5nodes; cd 5nodes;
+s3cmd sync s3://big-data-benchmark/pavlo/text-deflate/5nodes/ .
+cd ..
+
+ + +

Run the following ClickHouse queries:

+
CREATE TABLE rankings_tiny
+(
+    pageURL String,
+    pageRank UInt32,
+    avgDuration UInt32
+) ENGINE = Log;
+
+CREATE TABLE uservisits_tiny
+(
+    sourceIP String,
+    destinationURL String,
+    visitDate Date,
+    adRevenue Float32,
+    UserAgent String,
+    cCode FixedString(3),
+    lCode FixedString(6),
+    searchWord String,
+    duration UInt32
+) ENGINE = MergeTree(visitDate, visitDate, 8192);
+
+CREATE TABLE rankings_1node
+(
+    pageURL String,
+    pageRank UInt32,
+    avgDuration UInt32
+) ENGINE = Log;
+
+CREATE TABLE uservisits_1node
+(
+    sourceIP String,
+    destinationURL String,
+    visitDate Date,
+    adRevenue Float32,
+    UserAgent String,
+    cCode FixedString(3),
+    lCode FixedString(6),
+    searchWord String,
+    duration UInt32
+) ENGINE = MergeTree(visitDate, visitDate, 8192);
+
+CREATE TABLE rankings_5nodes_on_single
+(
+    pageURL String,
+    pageRank UInt32,
+    avgDuration UInt32
+) ENGINE = Log;
+
+CREATE TABLE uservisits_5nodes_on_single
+(
+    sourceIP String,
+    destinationURL String,
+    visitDate Date,
+    adRevenue Float32,
+    UserAgent String,
+    cCode FixedString(3),
+    lCode FixedString(6),
+    searchWord String,
+    duration UInt32
+) ENGINE = MergeTree(visitDate, visitDate, 8192);
+
+ + +

Go back to the console:

+
for i in tiny/rankings/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO rankings_tiny FORMAT CSV"; done
+for i in tiny/uservisits/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO uservisits_tiny FORMAT CSV"; done
+for i in 1node/rankings/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO rankings_1node FORMAT CSV"; done
+for i in 1node/uservisits/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO uservisits_1node FORMAT CSV"; done
+for i in 5nodes/rankings/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO rankings_5nodes_on_single FORMAT CSV"; done
+for i in 5nodes/uservisits/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO uservisits_5nodes_on_single FORMAT CSV"; done
+
+ + +

Queries for obtaining data samples:

+
SELECT pageURL, pageRank FROM rankings_1node WHERE pageRank > 1000
+
+SELECT substring(sourceIP, 1, 8), sum(adRevenue) FROM uservisits_1node GROUP BY substring(sourceIP, 1, 8)
+
+SELECT
+    sourceIP,
+    sum(adRevenue) AS totalRevenue,
+    avg(pageRank) AS pageRank
+FROM rankings_1node ALL INNER JOIN
+(
+    SELECT
+        sourceIP,
+        destinationURL AS pageURL,
+        adRevenue
+    FROM uservisits_1node
+    WHERE (visitDate > '1980-01-01') AND (visitDate < '1980-04-01')
+) USING pageURL
+GROUP BY sourceIP
+ORDER BY totalRevenue DESC
+LIMIT 1
+
+ + +

WikiStat

+

See: http://dumps.wikimedia.org/other/pagecounts-raw/

+

Creating a table:

+
CREATE TABLE wikistat
+(
+    date Date,
+    time DateTime,
+    project String,
+    subproject String,
+    path String,
+    hits UInt64,
+    size UInt64
+) ENGINE = MergeTree(date, (path, time), 8192);
+
+ + +

Loading data:

+
for i in {2007..2016}; do for j in {01..12}; do echo $i-$j >&2; curl -sSL "http://dumps.wikimedia.org/other/pagecounts-raw/$i/$i-$j/" | grep -oE 'pagecounts-[0-9]+-[0-9]+\.gz'; done; done | sort | uniq | tee links.txt
+cat links.txt | while read link; do wget http://dumps.wikimedia.org/other/pagecounts-raw/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1/')/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1-\2/')/$link; done
+ls -1 /opt/wikistat/ | grep gz | while read i; do echo $i; gzip -cd /opt/wikistat/$i | ./wikistat-loader --time="$(echo -n $i | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\.gz/\1-\2-\3 \4-00-00/')" | clickhouse-client --query="INSERT INTO wikistat FORMAT TabSeparated"; done
+
+ + +

Terabyte of click logs from Criteo

+

Download the data from http://labs.criteo.com/downloads/download-terabyte-click-logs/

+

Create a table to import the log to:

+
CREATE TABLE criteo_log (date Date, clicked UInt8, int1 Int32, int2 Int32, int3 Int32, int4 Int32, int5 Int32, int6 Int32, int7 Int32, int8 Int32, int9 Int32, int10 Int32, int11 Int32, int12 Int32, int13 Int32, cat1 String, cat2 String, cat3 String, cat4 String, cat5 String, cat6 String, cat7 String, cat8 String, cat9 String, cat10 String, cat11 String, cat12 String, cat13 String, cat14 String, cat15 String, cat16 String, cat17 String, cat18 String, cat19 String, cat20 String, cat21 String, cat22 String, cat23 String, cat24 String, cat25 String, cat26 String) ENGINE = Log
+
+ + +

Download the data:

+
for i in {00..23}; do echo $i; zcat datasets/criteo/day_${i#0}.gz | sed -r 's/^/2000-01-'${i/00/24}'\t/' | clickhouse-client --host=example-perftest01j --query="INSERT INTO criteo_log FORMAT TabSeparated"; done
+
+ + +

Create a table for the converted data:

+
CREATE TABLE criteo
+(
+    date Date,
+    clicked UInt8,
+    int1 Int32,
+    int2 Int32,
+    int3 Int32,
+    int4 Int32,
+    int5 Int32,
+    int6 Int32,
+    int7 Int32,
+    int8 Int32,
+    int9 Int32,
+    int10 Int32,
+    int11 Int32,
+    int12 Int32,
+    int13 Int32,
+    icat1 UInt32,
+    icat2 UInt32,
+    icat3 UInt32,
+    icat4 UInt32,
+    icat5 UInt32,
+    icat6 UInt32,
+    icat7 UInt32,
+    icat8 UInt32,
+    icat9 UInt32,
+    icat10 UInt32,
+    icat11 UInt32,
+    icat12 UInt32,
+    icat13 UInt32,
+    icat14 UInt32,
+    icat15 UInt32,
+    icat16 UInt32,
+    icat17 UInt32,
+    icat18 UInt32,
+    icat19 UInt32,
+    icat20 UInt32,
+    icat21 UInt32,
+    icat22 UInt32,
+    icat23 UInt32,
+    icat24 UInt32,
+    icat25 UInt32,
+    icat26 UInt32
+) ENGINE = MergeTree(date, intHash32(icat1), (date, intHash32(icat1)), 8192)
+
+ + +

Transform data from the raw log and put it in the second table:

+
INSERT INTO criteo SELECT date, clicked, int1, int2, int3, int4, int5, int6, int7, int8, int9, int10, int11, int12, int13, reinterpretAsUInt32(unhex(cat1)) AS icat1, reinterpretAsUInt32(unhex(cat2)) AS icat2, reinterpretAsUInt32(unhex(cat3)) AS icat3, reinterpretAsUInt32(unhex(cat4)) AS icat4, reinterpretAsUInt32(unhex(cat5)) AS icat5, reinterpretAsUInt32(unhex(cat6)) AS icat6, reinterpretAsUInt32(unhex(cat7)) AS icat7, reinterpretAsUInt32(unhex(cat8)) AS icat8, reinterpretAsUInt32(unhex(cat9)) AS icat9, reinterpretAsUInt32(unhex(cat10)) AS icat10, reinterpretAsUInt32(unhex(cat11)) AS icat11, reinterpretAsUInt32(unhex(cat12)) AS icat12, reinterpretAsUInt32(unhex(cat13)) AS icat13, reinterpretAsUInt32(unhex(cat14)) AS icat14, reinterpretAsUInt32(unhex(cat15)) AS icat15, reinterpretAsUInt32(unhex(cat16)) AS icat16, reinterpretAsUInt32(unhex(cat17)) AS icat17, reinterpretAsUInt32(unhex(cat18)) AS icat18, reinterpretAsUInt32(unhex(cat19)) AS icat19, reinterpretAsUInt32(unhex(cat20)) AS icat20, reinterpretAsUInt32(unhex(cat21)) AS icat21, reinterpretAsUInt32(unhex(cat22)) AS icat22, reinterpretAsUInt32(unhex(cat23)) AS icat23, reinterpretAsUInt32(unhex(cat24)) AS icat24, reinterpretAsUInt32(unhex(cat25)) AS icat25, reinterpretAsUInt32(unhex(cat26)) AS icat26 FROM criteo_log;
+
+DROP TABLE criteo_log;
+
+ + +

Star Schema Benchmark

+

Compiling dbgen: https://github.com/vadimtk/ssb-dbgen

+
git clone git@github.com:vadimtk/ssb-dbgen.git
+cd ssb-dbgen
+make
+
+ + +

There will be some warnings during the process, but this is normal.

+

Place dbgen and dists.dss in any location with 800 GB of free disk space.

+

Generating data:

+
./dbgen -s 1000 -T c
+./dbgen -s 1000 -T l
+
+ + +

Creating tables in ClickHouse:

+
CREATE TABLE lineorder (
+        LO_ORDERKEY             UInt32,
+        LO_LINENUMBER           UInt8,
+        LO_CUSTKEY              UInt32,
+        LO_PARTKEY              UInt32,
+        LO_SUPPKEY              UInt32,
+        LO_ORDERDATE            Date,
+        LO_ORDERPRIORITY        String,
+        LO_SHIPPRIORITY         UInt8,
+        LO_QUANTITY             UInt8,
+        LO_EXTENDEDPRICE        UInt32,
+        LO_ORDTOTALPRICE        UInt32,
+        LO_DISCOUNT             UInt8,
+        LO_REVENUE              UInt32,
+        LO_SUPPLYCOST           UInt32,
+        LO_TAX                  UInt8,
+        LO_COMMITDATE           Date,
+        LO_SHIPMODE             String
+)Engine=MergeTree(LO_ORDERDATE,(LO_ORDERKEY,LO_LINENUMBER,LO_ORDERDATE),8192);
+
+CREATE TABLE customer (
+        C_CUSTKEY       UInt32,
+        C_NAME          String,
+        C_ADDRESS       String,
+        C_CITY          String,
+        C_NATION        String,
+        C_REGION        String,
+        C_PHONE         String,
+        C_MKTSEGMENT    String,
+        C_FAKEDATE      Date
+)Engine=MergeTree(C_FAKEDATE,(C_CUSTKEY,C_FAKEDATE),8192);
+
+CREATE TABLE part (
+        P_PARTKEY       UInt32,
+        P_NAME          String,
+        P_MFGR          String,
+        P_CATEGORY      String,
+        P_BRAND         String,
+        P_COLOR         String,
+        P_TYPE          String,
+        P_SIZE          UInt8,
+        P_CONTAINER     String,
+        P_FAKEDATE      Date
+)Engine=MergeTree(P_FAKEDATE,(P_PARTKEY,P_FAKEDATE),8192);
+
+CREATE TABLE lineorderd AS lineorder ENGINE = Distributed(perftest_3shards_1replicas, default, lineorder, rand());
+CREATE TABLE customerd AS customer ENGINE = Distributed(perftest_3shards_1replicas, default, customer, rand());
+CREATE TABLE partd AS part ENGINE = Distributed(perftest_3shards_1replicas, default, part, rand());
+
+ + +

For testing on a single server, just use MergeTree tables. +For distributed testing, you need to configure the perftest_3shards_1replicas cluster in the config file. +Next, create MergeTree tables on each server and a Distributed above them.

+

Downloading data (change 'customer' to 'customerd' in the distributed version):

+
cat customer.tbl | sed 's/$/2000-01-01/' | clickhouse-client --query "INSERT INTO customer FORMAT CSV"
+cat lineorder.tbl | clickhouse-client --query "INSERT INTO lineorder FORMAT CSV"
+
+ + +

+

Interfaces

+

To explore the system's capabilities, download data to tables, or make manual queries, use the clickhouse-client program.

+

Command-line client

+

To work from the command line, you can use clickhouse-client:

+
$ clickhouse-client
+ClickHouse client version 0.0.26176.
+Connecting to localhost:9000.
+Connected to ClickHouse server version 0.0.26176.
+
+:)
+
+ + +

The client supports command-line options and configuration files. For more information, see "Configuring".

+

Usage

+

The client can be used in interactive and non-interactive (batch) mode. +To use batch mode, specify the 'query' parameter, or send data to 'stdin' (it verifies that 'stdin' is not a terminal), or both. +Similar to the HTTP interface, when using the 'query' parameter and sending data to 'stdin', the request is a concatenation of the 'query' parameter, a line feed, and the data in 'stdin'. This is convenient for large INSERT queries.

+

Example of using the client to insert data:

+
echo -ne "1, 'some text', '2016-08-14 00:00:00'\n2, 'some more text', '2016-08-14 00:00:01'" | clickhouse-client --database=test --query="INSERT INTO test FORMAT CSV";
+
+cat <<_EOF | clickhouse-client --database=test --query="INSERT INTO test FORMAT CSV";
+3, 'some text', '2016-08-14 00:00:00'
+4, 'some more text', '2016-08-14 00:00:01'
+_EOF
+
+cat file.csv | clickhouse-client --database=test --query="INSERT INTO test FORMAT CSV";
+
+ + +

In batch mode, the default data format is TabSeparated. You can set the format in the FORMAT clause of the query.

+

By default, you can only process a single query in batch mode. To make multiple queries from a "script," use the --multiquery parameter. This works for all queries except INSERT. Query results are output consecutively without additional separators. +Similarly, to process a large number of queries, you can run 'clickhouse-client' for each query. Note that it may take tens of milliseconds to launch the 'clickhouse-client' program.

+

In interactive mode, you get a command line where you can enter queries.

+

If 'multiline' is not specified (the default):To run the query, press Enter. The semicolon is not necessary at the end of the query. To enter a multiline query, enter a backslash \ before the line feed. After you press Enter, you will be asked to enter the next line of the query.

+

If multiline is specified:To run a query, end it with a semicolon and press Enter. If the semicolon was omitted at the end of the entered line, you will be asked to enter the next line of the query.

+

Only a single query is run, so everything after the semicolon is ignored.

+

You can specify \G instead of or after the semicolon. This indicates Vertical format. In this format, each value is printed on a separate line, which is convenient for wide tables. This unusual feature was added for compatibility with the MySQL CLI.

+

The command line is based on 'readline' (and 'history' or 'libedit', or without a library, depending on the build). In other words, it uses the familiar keyboard shortcuts and keeps a history. +The history is written to ~/.clickhouse-client-history.

+

By default, the format used is PrettyCompact. You can change the format in the FORMAT clause of the query, or by specifying \G at the end of the query, using the --format or --vertical argument in the command line, or using the client configuration file.

+

To exit the client, press Ctrl+D (or Ctrl+C), or enter one of the following instead of a query:"exit", "quit", "logout", "учше", "йгше", "дщпщге", "exit;", "quit;", "logout;", "учшеж", "йгшеж", "дщпщгеж", "q", "й", "q", "Q", ":q", "й", "Й", "Жй"

+

When processing a query, the client shows:

+
    +
  1. Progress, which is updated no more than 10 times per second (by default). For quick queries, the progress might not have time to be displayed.
    2. +
  2. The formatted query after parsing, for debugging.
    3. +
  3. The result in the specified format.
    4. +
  4. The number of lines in the result, the time passed, and the average speed of query processing.
    5. +
+

You can cancel a long query by pressing Ctrl+C. However, you will still need to wait a little for the server to abort the request. It is not possible to cancel a query at certain stages. If you don't wait and press Ctrl+C a second time, the client will exit.

+

The command-line client allows passing external data (external temporary tables) for querying. For more information, see the section "External data for query processing".

+

+

Configuring

+

You can pass parameters to clickhouse-client (all parameters have a default value) using:

+
    +
  • From the Command Line
    • +
+

Command-line options override the default values and settings in configuration files.

+
    +
  • Configuration files.
    • +
+

Settings in the configuration files override the default values.

+

Command line options

+
    +
  • --host, -h -– The server name, 'localhost' by default. You can use either the name or the IPv4 or IPv6 address.
    • +
  • --port – The port to connect to. Default value: 9000. Note that the HTTP interface and the native interface use different ports.
    • +
  • --user, -u – The username. Default value: default.
    • +
  • --password – The password. Default value: empty string.
    • +
  • --query, -q – The query to process when using non-interactive mode.
    • +
  • --database, -d – Select the current default database. Default value: the current database from the server settings ('default' by default).
    • +
  • --multiline, -m – If specified, allow multiline queries (do not send the query on Enter).
    • +
  • --multiquery, -n – If specified, allow processing multiple queries separated by commas. Only works in non-interactive mode.
    • +
  • --format, -f – Use the specified default format to output the result.
    • +
  • --vertical, -E – If specified, use the Vertical format by default to output the result. This is the same as '--format=Vertical'. In this format, each value is printed on a separate line, which is helpful when displaying wide tables.
    • +
  • --time, -t – If specified, print the query execution time to 'stderr' in non-interactive mode.
    • +
  • --stacktrace – If specified, also print the stack trace if an exception occurs.
    • +
  • -config-file – The name of the configuration file.
    • +
+

Configuration files

+

clickhouse-client uses the first existing file of the following:

+
    +
  • Defined in the -config-file parameter.
    • +
  • ./clickhouse-client.xml
    • +
  • \~/.clickhouse-client/config.xml
    • +
  • /etc/clickhouse-client/config.xml
    • +
+

Example of a config file:

+
<config>
+    <user>username</user>
+    <password>password</password>
+</config>
+
+ + +

HTTP interface

+

The HTTP interface lets you use ClickHouse on any platform from any programming language. We use it for working from Java and Perl, as well as shell scripts. In other departments, the HTTP interface is used from Perl, Python, and Go. The HTTP interface is more limited than the native interface, but it has better compatibility.

+

By default, clickhouse-server listens for HTTP on port 8123 (this can be changed in the config). +If you make a GET / request without parameters, it returns the string "Ok" (with a line feed at the end). You can use this in health-check scripts.

+
$ curl 'http://localhost:8123/'
+Ok.
+
+ + +

Send the request as a URL 'query' parameter, or as a POST. Or send the beginning of the query in the 'query' parameter, and the rest in the POST (we'll explain later why this is necessary). The size of the URL is limited to 16 KB, so keep this in mind when sending large queries.

+

If successful, you receive the 200 response code and the result in the response body. +If an error occurs, you receive the 500 response code and an error description text in the response body.

+

When using the GET method, 'readonly' is set. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.

+

Examples:

+
$ curl 'http://localhost:8123/?query=SELECT%201'
+1
+
+$ wget -O- -q 'http://localhost:8123/?query=SELECT 1'
+1
+
+$ GET 'http://localhost:8123/?query=SELECT 1'
+1
+
+$ echo -ne 'GET /?query=SELECT%201 HTTP/1.0\r\n\r\n' | nc localhost 8123
+HTTP/1.0 200 OK
+Connection: Close
+Date: Fri, 16 Nov 2012 19:21:50 GMT
+
+1
+
+ + +

As you can see, curl is somewhat inconvenient in that spaces must be URL escaped.Although wget escapes everything itself, we don't recommend using it because it doesn't work well over HTTP 1.1 when using keep-alive and Transfer-Encoding: chunked.

+
$ echo 'SELECT 1' | curl 'http://localhost:8123/' --data-binary @-
+1
+
+$ echo 'SELECT 1' | curl 'http://localhost:8123/?query=' --data-binary @-
+1
+
+$ echo '1' | curl 'http://localhost:8123/?query=SELECT' --data-binary @-
+1
+
+ + +

If part of the query is sent in the parameter, and part in the POST, a line feed is inserted between these two data parts. +Example (this won't work):

+
$ echo 'ECT 1' | curl 'http://localhost:8123/?query=SEL' --data-binary @-
+Code: 59, e.displayText() = DB::Exception: Syntax error: failed at position 0: SEL
+ECT 1
+, expected One of: SHOW TABLES, SHOW DATABASES, SELECT, INSERT, CREATE, ATTACH, RENAME, DROP, DETACH, USE, SET, OPTIMIZE., e.what() = DB::Exception
+
+ + +

By default, data is returned in TabSeparated format (for more information, see the "Formats" section). +You use the FORMAT clause of the query to request any other format.

+
$ echo 'SELECT 1 FORMAT Pretty' | curl 'http://localhost:8123/?' --data-binary @-
+┏━━━┓
+┃ 1 ┃
+┡━━━┩
+│ 1 │
+└───┘
+
+ + +

The POST method of transmitting data is necessary for INSERT queries. In this case, you can write the beginning of the query in the URL parameter, and use POST to pass the data to insert. The data to insert could be, for example, a tab-separated dump from MySQL. In this way, the INSERT query replaces LOAD DATA LOCAL INFILE from MySQL.

+

Examples: Creating a table:

+
echo 'CREATE TABLE t (a UInt8) ENGINE = Memory' | POST 'http://localhost:8123/'
+
+ + +

Using the familiar INSERT query for data insertion:

+
echo 'INSERT INTO t VALUES (1),(2),(3)' | POST 'http://localhost:8123/'
+
+ + +

Data can be sent separately from the query:

+
echo '(4),(5),(6)' | POST 'http://localhost:8123/?query=INSERT INTO t VALUES'
+
+ + +

You can specify any data format. The 'Values' format is the same as what is used when writing INSERT INTO t VALUES:

+
echo '(7),(8),(9)' | POST 'http://localhost:8123/?query=INSERT INTO t FORMAT Values'
+
+ + +

To insert data from a tab-separated dump, specify the corresponding format:

+
echo -ne '10\n11\n12\n' | POST 'http://localhost:8123/?query=INSERT INTO t FORMAT TabSeparated'
+
+ + +

Reading the table contents. Data is output in random order due to parallel query processing:

+
$ GET 'http://localhost:8123/?query=SELECT a FROM t'
+7
+8
+9
+10
+11
+12
+1
+2
+3
+4
+5
+6
+
+ + +

Deleting the table.

+
POST 'http://localhost:8123/?query=DROP TABLE t'
+
+ + +

For successful requests that don't return a data table, an empty response body is returned.

+

You can use the internal ClickHouse compression format when transmitting data. The compressed data has a non-standard format, and you will need to use the special clickhouse-compressor program to work with it (it is installed with the clickhouse-client package).

+

If you specified 'compress=1' in the URL, the server will compress the data it sends you. +If you specified 'decompress=1' in the URL, the server will decompress the same data that you pass in the POST method.

+

It is also possible to use the standard gzip-based HTTP compression. To send a POST request compressed using gzip, append the request header Content-Encoding: gzip. +In order for ClickHouse to compress the response using gzip, you must append Accept-Encoding: gzip to the request headers, and enable the ClickHouse setting enable_http_compression.

+

You can use this to reduce network traffic when transmitting a large amount of data, or for creating dumps that are immediately compressed.

+

You can use the 'database' URL parameter to specify the default database.

+
$ echo 'SELECT number FROM numbers LIMIT 10' | curl 'http://localhost:8123/?database=system' --data-binary @-
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+
+ + +

By default, the database that is registered in the server settings is used as the default database. By default, this is the database called 'default'. Alternatively, you can always specify the database using a dot before the table name.

+

The username and password can be indicated in one of two ways:

+
    +
  1. Using HTTP Basic Authentication. Example:
    2. +
+
echo 'SELECT 1' | curl 'http://user:password@localhost:8123/' -d @-
+
+ + +
    +
  1. In the 'user' and 'password' URL parameters. Example:
    2. +
+
echo 'SELECT 1' | curl 'http://localhost:8123/?user=user&password=password' -d @-
+
+ + +

If the user name is not indicated, the username 'default' is used. If the password is not indicated, an empty password is used. +You can also use the URL parameters to specify any settings for processing a single query, or entire profiles of settings. Example: +http://localhost:8123/?profile=web&max_rows_to_read=1000000000&query=SELECT+1

+

For more information, see the section "Settings".

+
$ echo 'SELECT number FROM system.numbers LIMIT 10' | curl 'http://localhost:8123/?' --data-binary @-
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+
+ + +

For information about other parameters, see the section "SET".

+

Similarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to add the session_id GET parameter to the request. You can use any string as the session ID. By default, the session is terminated after 60 seconds of inactivity. To change this timeout, modify the default_session_timeout setting in the server configuration, or add the session_timeout GET parameter to the request. To check the session status, use the session_check=1 parameter. Only one query at a time can be executed within a single session.

+

You have the option to receive information about the progress of query execution in X-ClickHouse-Progress headers. To do this, enable the setting send_progress_in_http_headers.

+

Running requests don't stop automatically if the HTTP connection is lost. Parsing and data formatting are performed on the server side, and using the network might be ineffective. +The optional 'query_id' parameter can be passed as the query ID (any string). For more information, see the section "Settings, replace_running_query".

+

The optional 'quota_key' parameter can be passed as the quota key (any string). For more information, see the section "Quotas".

+

The HTTP interface allows passing external data (external temporary tables) for querying. For more information, see the section "External data for query processing".

+

Response buffering

+

You can enable response buffering on the server side. The buffer_size and wait_end_of_query URL parameters are provided for this purpose.

+

buffer_size determines the number of bytes in the result to buffer in the server memory. If the result body is larger than this threshold, the buffer is written to the HTTP channel, and the remaining data is sent directly to the HTTP channel.

+

To ensure that the entire response is buffered, set wait_end_of_query=1. In this case, the data that is not stored in memory will be buffered in a temporary server file.

+

Example:

+
curl -sS 'http://localhost:8123/?max_result_bytes=4000000&buffer_size=3000000&wait_end_of_query=1' -d 'SELECT toUInt8(number) FROM system.numbers LIMIT 9000000 FORMAT RowBinary'
+
+ + +

Use buffering to avoid situations where a query processing error occurred after the response code and HTTP headers were sent to the client. In this situation, an error message is written at the end of the response body, and on the client side, the error can only be detected at the parsing stage.

+

JDBC driver

+

There is an official JDBC driver for ClickHouse. See here .

+

Native interface (TCP)

+

The native interface is used in the "clickhouse-client" command-line client for interaction between servers with distributed query processing, and also in C++ programs. We will only cover the command-line client.

+

Libraries from third-party developers

+

There are libraries for working with ClickHouse for:

+ +

We have not tested these libraries. They are listed in random order.

+

Visual interfaces from third-party developers

+

Tabix

+

Web interface for ClickHouse in the Tabix project.

+

Features:

+
    +
  • Works with ClickHouse directly from the browser, without the need to install additional software.
    • +
  • Query editor with syntax highlighting.
    • +
  • Auto-completion of commands.
    • +
  • Tools for graphical analysis of query execution.
    • +
  • Color scheme options.
    • +
+

Tabix documentation.

+

HouseOps

+

HouseOps is a unique Desktop ClickHouse Ops UI / IDE for OSX, Linux and Windows.

+

Features:

+
    +
  • Query builder;
    • +
  • Database manangement (soon);
    • +
  • Users manangement (soon);
    • +
  • Real-Time Data Analytics (soon);
    • +
  • Cluster/Infra monitoring (soon);
    • +
  • Cluster manangement (soon);
    • +
  • Kafka and Replicated tables monitoring (soon);
    • +
  • And a lot of others features (soon) for you take a beautiful implementation of ClickHouse.
    • +
+

Query language

+

Queries

+

CREATE DATABASE

+

Creating db_name databases

+
CREATE DATABASE [IF NOT EXISTS] db_name
+
+ + +

A database is just a directory for tables. +If IF NOT EXISTS is included, the query won't return an error if the database already exists.

+

+

CREATE TABLE

+

The CREATE TABLE query can have several forms.

+
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db.]name [ON CLUSTER cluster]
+(
+    name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1],
+    name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2],
+    ...
+) ENGINE = engine
+
+ + +

Creates a table named 'name' in the 'db' database or the current database if 'db' is not set, with the structure specified in brackets and the 'engine' engine. +The structure of the table is a list of column descriptions. If indexes are supported by the engine, they are indicated as parameters for the table engine.

+

A column description is name type in the simplest case. Example: RegionID UInt32. +Expressions can also be defined for default values (see below).

+
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db.]name AS [db2.]name2 [ENGINE = engine]
+
+ + +

Creates a table with the same structure as another table. You can specify a different engine for the table. If the engine is not specified, the same engine will be used as for the db2.name2 table.

+
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db.]name ENGINE = engine AS SELECT ...
+
+ + +

Creates a table with a structure like the result of the SELECT query, with the 'engine' engine, and fills it with data from SELECT.

+

In all cases, if IF NOT EXISTS is specified, the query won't return an error if the table already exists. In this case, the query won't do anything.

+

Default values

+

The column description can specify an expression for a default value, in one of the following ways:DEFAULT expr, MATERIALIZED expr, ALIAS expr. +Example: URLDomain String DEFAULT domain(URL).

+

If an expression for the default value is not defined, the default values will be set to zeros for numbers, empty strings for strings, empty arrays for arrays, and 0000-00-00 for dates or 0000-00-00 00:00:00 for dates with time. NULLs are not supported.

+

If the default expression is defined, the column type is optional. If there isn't an explicitly defined type, the default expression type is used. Example: EventDate DEFAULT toDate(EventTime) – the 'Date' type will be used for the 'EventDate' column.

+

If the data type and default expression are defined explicitly, this expression will be cast to the specified type using type casting functions. Example: Hits UInt32 DEFAULT 0 means the same thing as Hits UInt32 DEFAULT toUInt32(0).

+

Default expressions may be defined as an arbitrary expression from table constants and columns. When creating and changing the table structure, it checks that expressions don't contain loops. For INSERT, it checks that expressions are resolvable – that all columns they can be calculated from have been passed.

+

DEFAULT expr

+

Normal default value. If the INSERT query doesn't specify the corresponding column, it will be filled in by computing the corresponding expression.

+

MATERIALIZED expr

+

Materialized expression. Such a column can't be specified for INSERT, because it is always calculated. +For an INSERT without a list of columns, these columns are not considered. +In addition, this column is not substituted when using an asterisk in a SELECT query. This is to preserve the invariant that the dump obtained using SELECT * can be inserted back into the table using INSERT without specifying the list of columns.

+

ALIAS expr

+

Synonym. Such a column isn't stored in the table at all. +Its values can't be inserted in a table, and it is not substituted when using an asterisk in a SELECT query. +It can be used in SELECTs if the alias is expanded during query parsing.

+

When using the ALTER query to add new columns, old data for these columns is not written. Instead, when reading old data that does not have values for the new columns, expressions are computed on the fly by default. However, if running the expressions requires different columns that are not indicated in the query, these columns will additionally be read, but only for the blocks of data that need it.

+

If you add a new column to a table but later change its default expression, the values used for old data will change (for data where values were not stored on the disk). Note that when running background merges, data for columns that are missing in one of the merging parts is written to the merged part.

+

It is not possible to set default values for elements in nested data structures.

+

Temporary tables

+

In all cases, if TEMPORARY is specified, a temporary table will be created. Temporary tables have the following characteristics:

+
    +
  • Temporary tables disappear when the session ends, including if the connection is lost.
    • +
  • A temporary table is created with the Memory engine. The other table engines are not supported.
    • +
  • The DB can't be specified for a temporary table. It is created outside of databases.
    • +
  • If a temporary table has the same name as another one and a query specifies the table name without specifying the DB, the temporary table will be used.
    • +
  • For distributed query processing, temporary tables used in a query are passed to remote servers.
    • +
+

In most cases, temporary tables are not created manually, but when using external data for a query, or for distributed (GLOBAL) IN. For more information, see the appropriate sections

+

Distributed DDL queries (ON CLUSTER clause)

+

The CREATE, DROP, ALTER, and RENAME queries support distributed execution on a cluster. +For example, the following query creates the all_hits Distributed table on each host in cluster:

+
CREATE TABLE IF NOT EXISTS all_hits ON CLUSTER cluster (p Date, i Int32) ENGINE = Distributed(cluster, default, hits)
+
+ + +

In order to run these queries correctly, each host must have the same cluster definition (to simplify syncing configs, you can use substitutions from ZooKeeper). They must also connect to the ZooKeeper servers. +The local version of the query will eventually be implemented on each host in the cluster, even if some hosts are currently not available. The order for executing queries within a single host is guaranteed. +ALTER queries are not yet supported for replicated tables.

+

CREATE VIEW

+
CREATE [MATERIALIZED] VIEW [IF NOT EXISTS] [db.]name [TO[db.]name] [ENGINE = engine] [POPULATE] AS SELECT ...
+
+ + +

Creates a view. There are two types of views: normal and MATERIALIZED.

+

When creating a materialized view, you must specify ENGINE – the table engine for storing data.

+

A materialized view works as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.

+

Normal views don't store any data, but just perform a read from another table. In other words, a normal view is nothing more than a saved query. When reading from a view, this saved query is used as a subquery in the FROM clause.

+

As an example, assume you've created a view:

+
CREATE VIEW view AS SELECT ...
+
+ + +

and written a query:

+
SELECT a, b, c FROM view
+
+ + +

This query is fully equivalent to using the subquery:

+
SELECT a, b, c FROM (SELECT ...)
+
+ + +

Materialized views store data transformed by the corresponding SELECT query.

+

When creating a materialized view, you must specify ENGINE – the table engine for storing data.

+

A materialized view is arranged as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.

+

If you specify POPULATE, the existing table data is inserted in the view when creating it, as if making a CREATE TABLE ... AS SELECT ... . Otherwise, the query contains only the data inserted in the table after creating the view. We don't recommend using POPULATE, since data inserted in the table during the view creation will not be inserted in it.

+

A SELECT query can contain DISTINCT, GROUP BY, ORDER BY, LIMIT... Note that the corresponding conversions are performed independently on each block of inserted data. For example, if GROUP BY is set, data is aggregated during insertion, but only within a single packet of inserted data. The data won't be further aggregated. The exception is when using an ENGINE that independently performs data aggregation, such as SummingMergeTree.

+

The execution of ALTER queries on materialized views has not been fully developed, so they might be inconvenient. If the materialized view uses the construction TO [db.]name, you can DETACH the view, run ALTER for the target table, and then ATTACH the previously detached (DETACH) view.

+

Views look the same as normal tables. For example, they are listed in the result of the SHOW TABLES query.

+

There isn't a separate query for deleting views. To delete a view, use DROP TABLE.

+

ATTACH

+

This query is exactly the same as CREATE, but

+
    +
  • instead of the word CREATE it uses the word ATTACH.
    • +
  • The query doesn't create data on the disk, but assumes that data is already in the appropriate places, and just adds information about the table to the server. +After executing an ATTACH query, the server will know about the existence of the table.
    • +
+

If the table was previously detached (DETACH), meaning that its structure is known, you can use shorthand without defining the structure.

+
ATTACH TABLE [IF NOT EXISTS] [db.]name
+
+ + +

This query is used when starting the server. The server stores table metadata as files with ATTACH queries, which it simply runs at launch (with the exception of system tables, which are explicitly created on the server).

+

DROP

+

This query has two types: DROP DATABASE and DROP TABLE.

+
DROP DATABASE [IF EXISTS] db [ON CLUSTER cluster]
+
+ + +

Deletes all tables inside the 'db' database, then deletes the 'db' database itself. +If IF EXISTS is specified, it doesn't return an error if the database doesn't exist.

+
DROP [TEMPORARY] TABLE [IF EXISTS] [db.]name [ON CLUSTER cluster]
+
+ + +

Deletes the table. +If IF EXISTS is specified, it doesn't return an error if the table doesn't exist or the database doesn't exist.

+

DETACH

+

Deletes information about the 'name' table from the server. The server stops knowing about the table's existence.

+
DETACH TABLE [IF EXISTS] [db.]name
+
+ + +

This does not delete the table's data or metadata. On the next server launch, the server will read the metadata and find out about the table again. +Similarly, a "detached" table can be re-attached using the ATTACH query (with the exception of system tables, which do not have metadata stored for them).

+

There is no DETACH DATABASE query.

+

RENAME

+

Renames one or more tables.

+
RENAME TABLE [db11.]name11 TO [db12.]name12, [db21.]name21 TO [db22.]name22, ... [ON CLUSTER cluster]
+
+ + +

All tables are renamed under global locking. Renaming tables is a light operation. If you indicated another database after TO, the table will be moved to this database. However, the directories with databases must reside in the same file system (otherwise, an error is returned).

+

+

ALTER

+

The ALTER query is only supported for *MergeTree tables, as well as MergeandDistributed. The query has several variations.

+

Column manipulations

+

Changing the table structure.

+
ALTER TABLE [db].name [ON CLUSTER cluster] ADD|DROP|MODIFY COLUMN ...
+
+ + +

In the query, specify a list of one or more comma-separated actions. +Each action is an operation on a column.

+

The following actions are supported:

+
ADD COLUMN name [type] [default_expr] [AFTER name_after]
+
+ + +

Adds a new column to the table with the specified name, type, and default_expr (see the section "Default expressions"). If you specify AFTER name_after (the name of another column), the column is added after the specified one in the list of table columns. Otherwise, the column is added to the end of the table. Note that there is no way to add a column to the beginning of a table. For a chain of actions, 'name_after' can be the name of a column that is added in one of the previous actions.

+

Adding a column just changes the table structure, without performing any actions with data. The data doesn't appear on the disk after ALTER. If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). The column appears on the disk after merging data parts (see MergeTree).

+

This approach allows us to complete the ALTER query instantly, without increasing the volume of old data.

+
DROP COLUMN name
+
+ + +

Deletes the column with the name 'name'. +Deletes data from the file system. Since this deletes entire files, the query is completed almost instantly.

+
MODIFY COLUMN name [type] [default_expr]
+
+ + +

Changes the 'name' column's type to 'type' and/or the default expression to 'default_expr'. When changing the type, values are converted as if the 'toType' function were applied to them.

+

If only the default expression is changed, the query doesn't do anything complex, and is completed almost instantly.

+

Changing the column type is the only complex action – it changes the contents of files with data. For large tables, this may take a long time.

+

There are several processing stages:

+
    +
  • Preparing temporary (new) files with modified data.
    • +
  • Renaming old files.
    • +
  • Renaming the temporary (new) files to the old names.
    • +
  • Deleting the old files.
    • +
+

Only the first stage takes time. If there is a failure at this stage, the data is not changed. +If there is a failure during one of the successive stages, data can be restored manually. The exception is if the old files were deleted from the file system but the data for the new files did not get written to the disk and was lost.

+

There is no support for changing the column type in arrays and nested data structures.

+

The ALTER query lets you create and delete separate elements (columns) in nested data structures, but not whole nested data structures. To add a nested data structure, you can add columns with a name like name.nested_name and the type Array(T). A nested data structure is equivalent to multiple array columns with a name that has the same prefix before the dot.

+

There is no support for deleting columns in the primary key or the sampling key (columns that are in the ENGINE expression). Changing the type for columns that are included in the primary key is only possible if this change does not cause the data to be modified (for example, it is allowed to add values to an Enum or change a type with DateTime to UInt32).

+

If the ALTER query is not sufficient for making the table changes you need, you can create a new table, copy the data to it using the INSERT SELECT query, then switch the tables using the RENAME query and delete the old table.

+

The ALTER query blocks all reads and writes for the table. In other words, if a long SELECT is running at the time of the ALTER query, the ALTER query will wait for it to complete. At the same time, all new queries to the same table will wait while this ALTER is running.

+

For tables that don't store data themselves (such as Merge and Distributed), ALTER just changes the table structure, and does not change the structure of subordinate tables. For example, when running ALTER for a Distributed table, you will also need to run ALTER for the tables on all remote servers.

+

The ALTER query for changing columns is replicated. The instructions are saved in ZooKeeper, then each replica applies them. All ALTER queries are run in the same order. The query waits for the appropriate actions to be completed on the other replicas. However, a query to change columns in a replicated table can be interrupted, and all actions will be performed asynchronously.

+

Manipulations with partitions and parts

+

It only works for tables in the MergeTree family. The following operations are available:

+
    +
  • DETACH PARTITION – Move a partition to the 'detached' directory and forget it.
    • +
  • DROP PARTITION – Delete a partition.
    • +
  • ATTACH PART|PARTITION – Add a new part or partition from the detached directory to the table.
    • +
  • FREEZE PARTITION – Create a backup of a partition.
    • +
  • FETCH PARTITION – Download a partition from another server.
    • +
+

Each type of query is covered separately below.

+

A partition in a table is data for a single calendar month. This is determined by the values of the date key specified in the table engine parameters. Each month's data is stored separately in order to simplify manipulations with this data.

+

A "part" in the table is part of the data from a single partition, sorted by the primary key.

+

You can use the system.parts table to view the set of table parts and partitions:

+
SELECT * FROM system.parts WHERE active
+
+ + +

active – Only count active parts. Inactive parts are, for example, source parts remaining after merging to a larger part – these parts are deleted approximately 10 minutes after merging.

+

Another way to view a set of parts and partitions is to go into the directory with table data. +Data directory: /var/lib/clickhouse/data/database/table/,where /var/lib/clickhouse/ is the path to the ClickHouse data, 'database' is the database name, and 'table' is the table name. Example:

+
$ ls -l /var/lib/clickhouse/data/test/visits/
+total 48
+drwxrwxrwx 2 clickhouse clickhouse 20480 May  5 02:58 20140317_20140323_2_2_0
+drwxrwxrwx 2 clickhouse clickhouse 20480 May  5 02:58 20140317_20140323_4_4_0
+drwxrwxrwx 2 clickhouse clickhouse  4096 May  5 02:55 detached
+-rw-rw-rw- 1 clickhouse clickhouse     2 May  5 02:58 increment.txt
+
+ + +

Here, 20140317_20140323_2_2_0 and 20140317_20140323_4_4_0 are the directories of data parts.

+

Let's break down the name of the first part: 20140317_20140323_2_2_0.

+
    +
  • 20140317 is the minimum date of the data in the chunk.
    • +
  • 20140323 is the maximum date of the data in the chunk.
    • +
  • 2 is the minimum number of the data block.
    • +
  • 2 is the maximum number of the data block.
    • +
  • 0 is the chunk level (the depth of the merge tree it is formed from).
    • +
+

Each piece relates to a single partition and contains data for just one month. +201403 is the name of the partition. A partition is a set of parts for a single month.

+

On an operating server, you can't manually change the set of parts or their data on the file system, since the server won't know about it. +For non-replicated tables, you can do this when the server is stopped, but we don't recommended it. +For replicated tables, the set of parts can't be changed in any case.

+

The detached directory contains parts that are not used by the server - detached from the table using the ALTER ... DETACH query. Parts that are damaged are also moved to this directory, instead of deleting them. You can add, delete, or modify the data in the 'detached' directory at any time – the server won't know about this until you make the ALTER TABLE ... ATTACH query.

+
ALTER TABLE [db.]table DETACH PARTITION 'name'
+
+ + +

Move all data for partitions named 'name' to the 'detached' directory and forget about them. +The partition name is specified in YYYYMM format. It can be indicated in single quotes or without them.

+

After the query is executed, you can do whatever you want with the data in the 'detached' directory — delete it from the file system, or just leave it.

+

The query is replicated – data will be moved to the 'detached' directory and forgotten on all replicas. The query can only be sent to a leader replica. To find out if a replica is a leader, perform SELECT to the 'system.replicas' system table. Alternatively, it is easier to make a query on all replicas, and all except one will throw an exception.

+
ALTER TABLE [db.]table DROP PARTITION 'name'
+
+ + +

The same as the DETACH operation. Deletes data from the table. Data parts will be tagged as inactive and will be completely deleted in approximately 10 minutes. The query is replicated – data will be deleted on all replicas.

+
ALTER TABLE [db.]table ATTACH PARTITION|PART 'name'
+
+ + +

Adds data to the table from the 'detached' directory.

+

It is possible to add data for an entire partition or a separate part. For a part, specify the full name of the part in single quotes.

+

The query is replicated. Each replica checks whether there is data in the 'detached' directory. If there is data, it checks the integrity, verifies that it matches the data on the server that initiated the query, and then adds it if everything is correct. If not, it downloads data from the query requestor replica, or from another replica where the data has already been added.

+

So you can put data in the 'detached' directory on one replica, and use the ALTER ... ATTACH query to add it to the table on all replicas.

+
ALTER TABLE [db.]table FREEZE PARTITION 'name'
+
+ + +

Creates a local backup of one or multiple partitions. The name can be the full name of the partition (for example, 201403), or its prefix (for example, 2014): then the backup will be created for all the corresponding partitions.

+

The query does the following: for a data snapshot at the time of execution, it creates hardlinks to table data in the directory /var/lib/clickhouse/shadow/N/...

+

/var/lib/clickhouse/ is the working ClickHouse directory from the config. +N is the incremental number of the backup.

+

The same structure of directories is created inside the backup as inside /var/lib/clickhouse/. +It also performs 'chmod' for all files, forbidding writes to them.

+

The backup is created almost instantly (but first it waits for current queries to the corresponding table to finish running). At first, the backup doesn't take any space on the disk. As the system works, the backup can take disk space, as data is modified. If the backup is made for old enough data, it won't take space on the disk.

+

After creating the backup, data from /var/lib/clickhouse/shadow/ can be copied to the remote server and then deleted on the local server. +The entire backup process is performed without stopping the server.

+

The ALTER ... FREEZE PARTITION query is not replicated. A local backup is only created on the local server.

+

As an alternative, you can manually copy data from the /var/lib/clickhouse/data/database/table directory. +But if you do this while the server is running, race conditions are possible when copying directories with files being added or changed, and the backup may be inconsistent. You can do this if the server isn't running – then the resulting data will be the same as after the ALTER TABLE t FREEZE PARTITION query.

+

ALTER TABLE ... FREEZE PARTITION only copies data, not table metadata. To make a backup of table metadata, copy the file /var/lib/clickhouse/metadata/database/table.sql

+

To restore from a backup:

+
+
    +
  • Use the CREATE query to create the table if it doesn't exist. The query can be taken from an .sql file (replace ATTACH in it with CREATE).
    • +
  • Copy the data from the data/database/table/ directory inside the backup to the /var/lib/clickhouse/data/database/table/detached/ directory.
    • +
  • Run ALTER TABLE ... ATTACH PARTITION YYYYMM queries, where YYYYMM is the month, for every month.
    • +
+
+

In this way, data from the backup will be added to the table. +Restoring from a backup doesn't require stopping the server.

+

Backups and replication

+

Replication provides protection from device failures. If all data disappeared on one of your replicas, follow the instructions in the "Restoration after failure" section to restore it.

+

For protection from device failures, you must use replication. For more information about replication, see the section "Data replication".

+

Backups protect against human error (accidentally deleting data, deleting the wrong data or in the wrong cluster, or corrupting data). +For high-volume databases, it can be difficult to copy backups to remote servers. In such cases, to protect from human error, you can keep a backup on the same server (it will reside in /var/lib/clickhouse/shadow/).

+
ALTER TABLE [db.]table FETCH PARTITION 'name' FROM 'path-in-zookeeper'
+
+ + +

This query only works for replicatable tables.

+

It downloads the specified partition from the shard that has its ZooKeeper path specified in the FROM clause, then puts it in the detached directory for the specified table.

+

Although the query is called ALTER TABLE, it does not change the table structure, and does not immediately change the data available in the table.

+

Data is placed in the detached directory. You can use the ALTER TABLE ... ATTACH query to attach the data.

+

The FROM clause specifies the path in ZooKeeper. For example, /clickhouse/tables/01-01/visits. +Before downloading, the system checks that the partition exists and the table structure matches. The most appropriate replica is selected automatically from the healthy replicas.

+

The ALTER ... FETCH PARTITION query is not replicated. The partition will be downloaded to the 'detached' directory only on the local server. Note that if after this you use the ALTER TABLE ... ATTACH query to add data to the table, the data will be added on all replicas (on one of the replicas it will be added from the 'detached' directory, and on the rest it will be loaded from neighboring replicas).

+

Synchronicity of ALTER queries

+

For non-replicatable tables, all ALTER queries are performed synchronously. For replicatable tables, the query just adds instructions for the appropriate actions to ZooKeeper, and the actions themselves are performed as soon as possible. However, the query can wait for these actions to be completed on all the replicas.

+

For ALTER ... ATTACH|DETACH|DROP queries, you can use the replication_alter_partitions_sync setting to set up waiting. +Possible values: 0 – do not wait; 1 – only wait for own execution (default); 2 – wait for all.

+

+

SHOW DATABASES

+
SHOW DATABASES [INTO OUTFILE filename] [FORMAT format]
+
+ + +

Prints a list of all databases. +This query is identical to SELECT name FROM system.databases [INTO OUTFILE filename] [FORMAT format].

+

See also the section "Formats".

+

SHOW TABLES

+
SHOW [TEMPORARY] TABLES [FROM db] [LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format]
+
+ + +

Displays a list of tables

+
    +
  • tables from the current database, or from the 'db' database if "FROM db" is specified.
    • +
  • all tables, or tables whose name matches the pattern, if "LIKE 'pattern'" is specified.
    • +
+

This query is identical to: SELECT name FROM system.tables WHERE database = 'db' [AND name LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format].

+

See also the section "LIKE operator".

+

SHOW PROCESSLIST

+
SHOW PROCESSLIST [INTO OUTFILE filename] [FORMAT format]
+
+ + +

Outputs a list of queries currently being processed, other than SHOW PROCESSLIST queries.

+

Prints a table containing the columns:

+

user – The user who made the query. Keep in mind that for distributed processing, queries are sent to remote servers under the 'default' user. SHOW PROCESSLIST shows the username for a specific query, not for a query that this query initiated.

+

address – The name of the host that the query was sent from. For distributed processing, on remote servers, this is the name of the query requestor host. To track where a distributed query was originally made from, look at SHOW PROCESSLIST on the query requestor server.

+

elapsed – The execution time, in seconds. Queries are output in order of decreasing execution time.

+

rows_read, bytes_read – How many rows and bytes of uncompressed data were read when processing the query. For distributed processing, data is totaled from all the remote servers. This is the data used for restrictions and quotas.

+

memory_usage – Current RAM usage in bytes. See the setting 'max_memory_usage'.

+

query – The query itself. In INSERT queries, the data for insertion is not output.

+

query_id – The query identifier. Non-empty only if it was explicitly defined by the user. For distributed processing, the query ID is not passed to remote servers.

+

This query is identical to: SELECT * FROM system.processes [INTO OUTFILE filename] [FORMAT format].

+

Tip (execute in the console):

+
watch -n1 "clickhouse-client --query='SHOW PROCESSLIST'"
+
+ + +

SHOW CREATE TABLE

+
SHOW CREATE [TEMPORARY] TABLE [db.]table [INTO OUTFILE filename] [FORMAT format]
+
+ + +

Returns a single String-type 'statement' column, which contains a single value – the CREATE query used for creating the specified table.

+

DESCRIBE TABLE

+
DESC|DESCRIBE TABLE [db.]table [INTO OUTFILE filename] [FORMAT format]
+
+ + +

Returns two String-type columns: name and type, which indicate the names and types of columns in the specified table.

+

Nested data structures are output in "expanded" format. Each column is shown separately, with the name after a dot.

+

EXISTS

+
EXISTS [TEMPORARY] TABLE [db.]name [INTO OUTFILE filename] [FORMAT format]
+
+ + +

Returns a single UInt8-type column, which contains the single value 0 if the table or database doesn't exist, or 1 if the table exists in the specified database.

+

USE

+
USE db
+
+ + +

Lets you set the current database for the session. +The current database is used for searching for tables if the database is not explicitly defined in the query with a dot before the table name. +This query can't be made when using the HTTP protocol, since there is no concept of a session.

+

SET

+
SET param = value
+
+ + +

Allows you to set param to value. You can also make all the settings from the specified settings profile in a single query. To do this, specify 'profile' as the setting name. For more information, see the section "Settings". +The setting is made for the session, or for the server (globally) if GLOBAL is specified. +When making a global setting, the setting is not applied to sessions already running, including the current session. It will only be used for new sessions.

+

When the server is restarted, global settings made using SET are lost. +To make settings that persist after a server restart, you can only use the server's config file.

+

OPTIMIZE

+
OPTIMIZE TABLE [db.]name [PARTITION partition] [FINAL]
+
+ + +

Asks the table engine to do something for optimization. +Supported only by *MergeTree engines, in which this query initializes a non-scheduled merge of data parts. +If you specify a PARTITION, only the specified partition will be optimized. +If you specify FINAL, optimization will be performed even when all the data is already in one part.

+

+

INSERT

+

Adding data.

+

Basic query format:

+
INSERT INTO [db.]table [(c1, c2, c3)] VALUES (v11, v12, v13), (v21, v22, v23), ...
+
+ + +

The query can specify a list of columns to insert [(c1, c2, c3)]. In this case, the rest of the columns are filled with:

+
    +
  • The values calculated from the DEFAULT expressions specified in the table definition.
    • +
  • Zeros and empty strings, if DEFAULT expressions are not defined.
    • +
+

If strict_insert_defaults=1, columns that do not have DEFAULT defined must be listed in the query.

+

Data can be passed to the INSERT in any format supported by ClickHouse. The format must be specified explicitly in the query:

+
INSERT INTO [db.]table [(c1, c2, c3)] FORMAT format_name data_set
+
+ + +

For example, the following query format is identical to the basic version of INSERT ... VALUES:

+
INSERT INTO [db.]table [(c1, c2, c3)] FORMAT Values (v11, v12, v13), (v21, v22, v23), ...
+
+ + +

ClickHouse removes all spaces and one line feed (if there is one) before the data. When forming a query, we recommend putting the data on a new line after the query operators (this is important if the data begins with spaces).

+

Example:

+
INSERT INTO t FORMAT TabSeparated
+11  Hello, world!
+22  Qwerty
+
+ + +

You can insert data separately from the query by using the command-line client or the HTTP interface. For more information, see the section "Interfaces".

+

Inserting the results of SELECT

+
INSERT INTO [db.]table [(c1, c2, c3)] SELECT ...
+
+ + +

Columns are mapped according to their position in the SELECT clause. However, their names in the SELECT expression and the table for INSERT may differ. If necessary, type casting is performed.

+

None of the data formats except Values allow setting values to expressions such as now(), 1 + 2, and so on. The Values format allows limited use of expressions, but this is not recommended, because in this case inefficient code is used for their execution.

+

Other queries for modifying data parts are not supported: UPDATE, DELETE, REPLACE, MERGE, UPSERT, INSERT UPDATE. +However, you can delete old data using ALTER TABLE ... DROP PARTITION.

+

Performance considerations

+

INSERT sorts the input data by primary key and splits them into partitions by month. If you insert data for mixed months, it can significantly reduce the performance of the INSERT query. To avoid this:

+
    +
  • Add data in fairly large batches, such as 100,000 rows at a time.
    • +
  • Group data by month before uploading it to ClickHouse.
    • +
+

Performance will not decrease if:

+
    +
  • Data is added in real time.
    • +
  • You upload data that is usually sorted by time.
    • +
+

SELECT

+

Data sampling.

+
SELECT [DISTINCT] expr_list
+    [FROM [db.]table | (subquery) | table_function] [FINAL]
+    [SAMPLE sample_coeff]
+    [ARRAY JOIN ...]
+    [GLOBAL] ANY|ALL INNER|LEFT JOIN (subquery)|table USING columns_list
+    [PREWHERE expr]
+    [WHERE expr]
+    [GROUP BY expr_list] [WITH TOTALS]
+    [HAVING expr]
+    [ORDER BY expr_list]
+    [LIMIT [n, ]m]
+    [UNION ALL ...]
+    [INTO OUTFILE filename]
+    [FORMAT format]
+    [LIMIT n BY columns]
+
+ + +

All the clauses are optional, except for the required list of expressions immediately after SELECT. +The clauses below are described in almost the same order as in the query execution conveyor.

+

If the query omits the DISTINCT, GROUP BY and ORDER BY clauses and the IN and JOIN subqueries, the query will be completely stream processed, using O(1) amount of RAM. +Otherwise, the query might consume a lot of RAM if the appropriate restrictions are not specified: max_memory_usage, max_rows_to_group_by, max_rows_to_sort, max_rows_in_distinct, max_bytes_in_distinct, max_rows_in_set, max_bytes_in_set, max_rows_in_join, max_bytes_in_join, max_bytes_before_external_sort, max_bytes_before_external_group_by. For more information, see the section "Settings". It is possible to use external sorting (saving temporary tables to a disk) and external aggregation. The system does not have "merge join".

+

FROM clause

+

If the FROM clause is omitted, data will be read from the system.one table. +The 'system.one' table contains exactly one row (this table fulfills the same purpose as the DUAL table found in other DBMSs).

+

The FROM clause specifies the table to read data from, or a subquery, or a table function; ARRAY JOIN and the regular JOIN may also be included (see below).

+

Instead of a table, the SELECT subquery may be specified in brackets. +In this case, the subquery processing pipeline will be built into the processing pipeline of an external query. +In contrast to standard SQL, a synonym does not need to be specified after a subquery. For compatibility, it is possible to write 'AS name' after a subquery, but the specified name isn't used anywhere.

+

A table function may be specified instead of a table. For more information, see the section "Table functions".

+

To execute a query, all the columns listed in the query are extracted from the appropriate table. Any columns not needed for the external query are thrown out of the subqueries. +If a query does not list any columns (for example, SELECT count() FROM t), some column is extracted from the table anyway (the smallest one is preferred), in order to calculate the number of rows.

+

The FINAL modifier can be used only for a SELECT from a CollapsingMergeTree table. When you specify FINAL, data is selected fully "collapsed". Keep in mind that using FINAL leads to a selection that includes columns related to the primary key, in addition to the columns specified in the SELECT. Additionally, the query will be executed in a single stream, and data will be merged during query execution. This means that when using FINAL, the query is processed more slowly. In most cases, you should avoid using FINAL. For more information, see the section "CollapsingMergeTree engine".

+

SAMPLE clause

+

The SAMPLE clause allows for approximated query processing. Approximated query processing is only supported by MergeTree* type tables, and only if the sampling expression was specified during table creation (see the section "MergeTree engine").

+

SAMPLE has the format SAMPLE k, where k is a decimal number from 0 to 1, or SAMPLE n, where 'n' is a sufficiently large integer.

+

In the first case, the query will be executed on 'k' percent of data. For example, SAMPLE 0.1 runs the query on 10% of data. +In the second case, the query will be executed on a sample of no more than 'n' rows. For example, SAMPLE 10000000 runs the query on a maximum of 10,000,000 rows.

+

Example:

+
SELECT
+    Title,
+    count() * 10 AS PageViews
+FROM hits_distributed
+SAMPLE 0.1
+WHERE
+    CounterID = 34
+    AND toDate(EventDate) >= toDate('2013-01-29')
+    AND toDate(EventDate) <= toDate('2013-02-04')
+    AND NOT DontCountHits
+    AND NOT Refresh
+    AND Title != ''
+GROUP BY Title
+ORDER BY PageViews DESC LIMIT 1000
+
+ + +

In this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value 'count()' is manually multiplied by 10.

+

When using something like SAMPLE 10000000, there isn't any information about which relative percent of data was processed or what the aggregate functions should be multiplied by, so this method of writing is not always appropriate to the situation.

+

A sample with a relative coefficient is "consistent": if we look at all possible data that could be in the table, a sample (when using a single sampling expression specified during table creation) with the same coefficient always selects the same subset of possible data. In other words, a sample from different tables on different servers at different times is made the same way.

+

For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This allows using the sample in subqueries in the IN clause, as well as for manually correlating results of different queries with samples.

+

ARRAY JOIN clause

+

Allows executing JOIN with an array or nested data structure. The intent is similar to the 'arrayJoin' function, but its functionality is broader.

+

ARRAY JOIN is essentially INNER JOIN with an array. Example:

+
:) CREATE TABLE arrays_test (s String, arr Array(UInt8)) ENGINE = Memory
+
+CREATE TABLE arrays_test
+(
+    s String,
+    arr Array(UInt8)
+) ENGINE = Memory
+
+Ok.
+
+0 rows in set. Elapsed: 0.001 sec.
+
+:) INSERT INTO arrays_test VALUES ('Hello', [1,2]), ('World', [3,4,5]), ('Goodbye', [])
+
+INSERT INTO arrays_test VALUES
+
+Ok.
+
+3 rows in set. Elapsed: 0.001 sec.
+
+:) SELECT * FROM arrays_test
+
+SELECT *
+FROM arrays_test
+
+┌─s───────┬─arr─────┐
+│ Hello   │ [1,2]   │
+│ World   │ [3,4,5] │
+│ Goodbye │ []      │
+└─────────┴─────────┘
+
+3 rows in set. Elapsed: 0.001 sec.
+
+:) SELECT s, arr FROM arrays_test ARRAY JOIN arr
+
+SELECT s, arr
+FROM arrays_test
+ARRAY JOIN arr
+
+┌─s─────┬─arr─┐
+│ Hello │   1 │
+│ Hello │   2 │
+│ World │   3 │
+│ World │   4 │
+│ World │   5 │
+└───────┴─────┘
+
+5 rows in set. Elapsed: 0.001 sec.
+
+ + +

An alias can be specified for an array in the ARRAY JOIN clause. In this case, an array item can be accessed by this alias, but the array itself by the original name. Example:

+
:) SELECT s, arr, a FROM arrays_test ARRAY JOIN arr AS a
+
+SELECT s, arr, a
+FROM arrays_test
+ARRAY JOIN arr AS a
+
+┌─s─────┬─arr─────┬─a─┐
+│ Hello │ [1,2]   │ 1 │
+│ Hello │ [1,2]   │ 2 │
+│ World │ [3,4,5] │ 3 │
+│ World │ [3,4,5] │ 4 │
+│ World │ [3,4,5] │ 5 │
+└───────┴─────────┴───┘
+
+5 rows in set. Elapsed: 0.001 sec.
+
+ + +

Multiple arrays of the same size can be comma-separated in the ARRAY JOIN clause. In this case, JOIN is performed with them simultaneously (the direct sum, not the direct product). Example:

+
:) SELECT s, arr, a, num, mapped FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(x -> x + 1, arr) AS mapped
+
+SELECT s, arr, a, num, mapped
+FROM arrays_test
+ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(lambda(tuple(x), plus(x, 1)), arr) AS mapped
+
+┌─s─────┬─arr─────┬─a─┬─num─┬─mapped─┐
+│ Hello │ [1,2]   │ 1 │   1 │      2 │
+│ Hello │ [1,2]   │ 2 │   2 │      3 │
+│ World │ [3,4,5] │ 3 │   1 │      4 │
+│ World │ [3,4,5] │ 4 │   2 │      5 │
+│ World │ [3,4,5] │ 5 │   3 │      6 │
+└───────┴─────────┴───┴─────┴────────┘
+
+5 rows in set. Elapsed: 0.002 sec.
+
+:) SELECT s, arr, a, num, arrayEnumerate(arr) FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num
+
+SELECT s, arr, a, num, arrayEnumerate(arr)
+FROM arrays_test
+ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num
+
+┌─s─────┬─arr─────┬─a─┬─num─┬─arrayEnumerate(arr)─┐
+│ Hello │ [1,2]   │ 1 │   1 │ [1,2]               │
+│ Hello │ [1,2]   │ 2 │   2 │ [1,2]               │
+│ World │ [3,4,5] │ 3 │   1 │ [1,2,3]             │
+│ World │ [3,4,5] │ 4 │   2 │ [1,2,3]             │
+│ World │ [3,4,5] │ 5 │   3 │ [1,2,3]             │
+└───────┴─────────┴───┴─────┴─────────────────────┘
+
+5 rows in set. Elapsed: 0.002 sec.
+
+ + +

ARRAY JOIN also works with nested data structures. Example:

+
:) CREATE TABLE nested_test (s String, nest Nested(x UInt8, y UInt32)) ENGINE = Memory
+
+CREATE TABLE nested_test
+(
+    s String,
+    nest Nested(
+    x UInt8,
+    y UInt32)
+) ENGINE = Memory
+
+Ok.
+
+0 rows in set. Elapsed: 0.006 sec.
+
+:) INSERT INTO nested_test VALUES ('Hello', [1,2], [10,20]), ('World', [3,4,5], [30,40,50]), ('Goodbye', [], [])
+
+INSERT INTO nested_test VALUES
+
+Ok.
+
+3 rows in set. Elapsed: 0.001 sec.
+
+:) SELECT * FROM nested_test
+
+SELECT *
+FROM nested_test
+
+┌─s───────┬─nest.x──┬─nest.y─────┐
+│ Hello   │ [1,2]   │ [10,20]    │
+│ World   │ [3,4,5] │ [30,40,50] │
+│ Goodbye │ []      │ []         │
+└─────────┴─────────┴────────────┘
+
+3 rows in set. Elapsed: 0.001 sec.
+
+:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest
+
+SELECT s, `nest.x`, `nest.y`
+FROM nested_test
+ARRAY JOIN nest
+
+┌─s─────┬─nest.x─┬─nest.y─┐
+│ Hello │      1 │     10 │
+│ Hello │      2 │     20 │
+│ World │      3 │     30 │
+│ World │      4 │     40 │
+│ World │      5 │     50 │
+└───────┴────────┴────────┘
+
+5 rows in set. Elapsed: 0.001 sec.
+
+ + +

When specifying names of nested data structures in ARRAY JOIN, the meaning is the same as ARRAY JOIN with all the array elements that it consists of. Example:

+
:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x, nest.y
+
+SELECT s, `nest.x`, `nest.y`
+FROM nested_test
+ARRAY JOIN `nest.x`, `nest.y`
+
+┌─s─────┬─nest.x─┬─nest.y─┐
+│ Hello │      1 │     10 │
+│ Hello │      2 │     20 │
+│ World │      3 │     30 │
+│ World │      4 │     40 │
+│ World │      5 │     50 │
+└───────┴────────┴────────┘
+
+5 rows in set. Elapsed: 0.001 sec.
+
+ + +

This variation also makes sense:

+
:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x
+
+SELECT s, `nest.x`, `nest.y`
+FROM nested_test
+ARRAY JOIN `nest.x`
+
+┌─s─────┬─nest.x─┬─nest.y─────┐
+│ Hello │      1 │ [10,20]    │
+│ Hello │      2 │ [10,20]    │
+│ World │      3 │ [30,40,50] │
+│ World │      4 │ [30,40,50] │
+│ World │      5 │ [30,40,50] │
+└───────┴────────┴────────────┘
+
+5 rows in set. Elapsed: 0.001 sec.
+
+ + +

An alias may be used for a nested data structure, in order to select either the JOIN result or the source array. Example:

+
:) SELECT s, n.x, n.y, nest.x, nest.y FROM nested_test ARRAY JOIN nest AS n
+
+SELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`
+FROM nested_test
+ARRAY JOIN nest AS n
+
+┌─s─────┬─n.x─┬─n.y─┬─nest.x──┬─nest.y─────┐
+│ Hello │   1 │  10 │ [1,2]   │ [10,20]    │
+│ Hello │   2 │  20 │ [1,2]   │ [10,20]    │
+│ World │   3 │  30 │ [3,4,5] │ [30,40,50] │
+│ World │   4 │  40 │ [3,4,5] │ [30,40,50] │
+│ World │   5 │  50 │ [3,4,5] │ [30,40,50] │
+└───────┴─────┴─────┴─────────┴────────────┘
+
+5 rows in set. Elapsed: 0.001 sec.
+
+ + +

Example of using the arrayEnumerate function:

+
:) SELECT s, n.x, n.y, nest.x, nest.y, num FROM nested_test ARRAY JOIN nest AS n, arrayEnumerate(nest.x) AS num
+
+SELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`, num
+FROM nested_test
+ARRAY JOIN nest AS n, arrayEnumerate(`nest.x`) AS num
+
+┌─s─────┬─n.x─┬─n.y─┬─nest.x──┬─nest.y─────┬─num─┐
+│ Hello │   1 │  10 │ [1,2]   │ [10,20]    │   1 │
+│ Hello │   2 │  20 │ [1,2]   │ [10,20]    │   2 │
+│ World │   3 │  30 │ [3,4,5] │ [30,40,50] │   1 │
+│ World │   4 │  40 │ [3,4,5] │ [30,40,50] │   2 │
+│ World │   5 │  50 │ [3,4,5] │ [30,40,50] │   3 │
+└───────┴─────┴─────┴─────────┴────────────┴─────┘
+
+5 rows in set. Elapsed: 0.002 sec.
+
+ + +

The query can only specify a single ARRAY JOIN clause.

+

The corresponding conversion can be performed before the WHERE/PREWHERE clause (if its result is needed in this clause), or after completing WHERE/PREWHERE (to reduce the volume of calculations).

+

JOIN clause

+

The normal JOIN, which is not related to ARRAY JOIN described above.

+
[GLOBAL] ANY|ALL INNER|LEFT [OUTER] JOIN (subquery)|table USING columns_list
+
+ + +

Performs joins with data from the subquery. At the beginning of query processing, the subquery specified after JOIN is run, and its result is saved in memory. Then it is read from the "left" table specified in the FROM clause, and while it is being read, for each of the read rows from the "left" table, rows are selected from the subquery results table (the "right" table) that meet the condition for matching the values of the columns specified in USING.

+

The table name can be specified instead of a subquery. This is equivalent to the SELECT * FROM table subquery, except in a special case when the table has the Join engine – an array prepared for joining.

+

All columns that are not needed for the JOIN are deleted from the subquery.

+

There are several types of JOINs:

+

INNER or LEFT type:If INNER is specified, the result will contain only those rows that have a matching row in the right table. +If LEFT is specified, any rows in the left table that don't have matching rows in the right table will be assigned the default value - zeros or empty rows. LEFT OUTER may be written instead of LEFT; the word OUTER does not affect anything.

+

ANY or ALL stringency:If ANY is specified and the right table has several matching rows, only the first one found is joined. +If ALL is specified and the right table has several matching rows, the data will be multiplied by the number of these rows.

+

Using ALL corresponds to the normal JOIN semantic from standard SQL. +Using ANY is optimal. If the right table has only one matching row, the results of ANY and ALL are the same. You must specify either ANY or ALL (neither of them is selected by default).

+

GLOBAL distribution:

+

When using a normal JOIN, the query is sent to remote servers. Subqueries are run on each of them in order to make the right table, and the join is performed with this table. In other words, the right table is formed on each server separately.

+

When using GLOBAL ... JOIN, first the requestor server runs a subquery to calculate the right table. This temporary table is passed to each remote server, and queries are run on them using the temporary data that was transmitted.

+

Be careful when using GLOBAL JOINs. For more information, see the section "Distributed subqueries".

+

Any combination of JOINs is possible. For example, GLOBAL ANY LEFT OUTER JOIN.

+

When running a JOIN, there is no optimization of the order of execution in relation to other stages of the query. The join (a search in the right table) is run before filtering in WHERE and before aggregation. In order to explicitly set the processing order, we recommend running a JOIN subquery with a subquery.

+

Example:

+
SELECT
+    CounterID,
+    hits,
+    visits
+FROM
+(
+    SELECT
+        CounterID,
+        count() AS hits
+    FROM test.hits
+    GROUP BY CounterID
+) ANY LEFT JOIN
+(
+    SELECT
+        CounterID,
+        sum(Sign) AS visits
+    FROM test.visits
+    GROUP BY CounterID
+) USING CounterID
+ORDER BY hits DESC
+LIMIT 10
+
+ + +
┌─CounterID─┬───hits─┬─visits─┐
+│   1143050 │ 523264 │  13665 │
+│    731962 │ 475698 │ 102716 │
+│    722545 │ 337212 │ 108187 │
+│    722889 │ 252197 │  10547 │
+│   2237260 │ 196036 │   9522 │
+│  23057320 │ 147211 │   7689 │
+│    722818 │  90109 │  17847 │
+│     48221 │  85379 │   4652 │
+│  19762435 │  77807 │   7026 │
+│    722884 │  77492 │  11056 │
+└───────────┴────────┴────────┘
+
+ + +

Subqueries don't allow you to set names or use them for referencing a column from a specific subquery. +The columns specified in USING must have the same names in both subqueries, and the other columns must be named differently. You can use aliases to change the names of columns in subqueries (the example uses the aliases 'hits' and 'visits').

+

The USING clause specifies one or more columns to join, which establishes the equality of these columns. The list of columns is set without brackets. More complex join conditions are not supported.

+

The right table (the subquery result) resides in RAM. If there isn't enough memory, you can't run a JOIN.

+

Only one JOIN can be specified in a query (on a single level). To run multiple JOINs, you can put them in subqueries.

+

Each time a query is run with the same JOIN, the subquery is run again – the result is not cached. To avoid this, use the special 'Join' table engine, which is a prepared array for joining that is always in RAM. For more information, see the section "Table engines, Join".

+

In some cases, it is more efficient to use IN instead of JOIN. +Among the various types of JOINs, the most efficient is ANY LEFT JOIN, then ANY INNER JOIN. The least efficient are ALL LEFT JOIN and ALL INNER JOIN.

+

If you need a JOIN for joining with dimension tables (these are relatively small tables that contain dimension properties, such as names for advertising campaigns), a JOIN might not be very convenient due to the bulky syntax and the fact that the right table is re-accessed for every query. For such cases, there is an "external dictionaries" feature that you should use instead of JOIN. For more information, see the section "External dictionaries".

+

WHERE clause

+

If there is a WHERE clause, it must contain an expression with the UInt8 type. This is usually an expression with comparison and logical operators. +This expression will be used for filtering data before all other transformations.

+

If indexes are supported by the database table engine, the expression is evaluated on the ability to use indexes.

+

PREWHERE clause

+

This clause has the same meaning as the WHERE clause. The difference is in which data is read from the table. +When using PREWHERE, first only the columns necessary for executing PREWHERE are read. Then the other columns are read that are needed for running the query, but only those blocks where the PREWHERE expression is true.

+

It makes sense to use PREWHERE if there are filtration conditions that are not suitable for indexes that are used by a minority of the columns in the query, but that provide strong data filtration. This reduces the volume of data to read.

+

For example, it is useful to write PREWHERE for queries that extract a large number of columns, but that only have filtration for a few columns.

+

PREWHERE is only supported by tables from the *MergeTree family.

+

A query may simultaneously specify PREWHERE and WHERE. In this case, PREWHERE precedes WHERE.

+

Keep in mind that it does not make much sense for PREWHERE to only specify those columns that have an index, because when using an index, only the data blocks that match the index are read.

+

If the 'optimize_move_to_prewhere' setting is set to 1 and PREWHERE is omitted, the system uses heuristics to automatically move parts of expressions from WHERE to PREWHERE.

+

GROUP BY clause

+

This is one of the most important parts of a column-oriented DBMS.

+

If there is a GROUP BY clause, it must contain a list of expressions. Each expression will be referred to here as a "key". +All the expressions in the SELECT, HAVING, and ORDER BY clauses must be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions.

+

If a query contains only table columns inside aggregate functions, the GROUP BY clause can be omitted, and aggregation by an empty set of keys is assumed.

+

Example:

+
SELECT
+    count(),
+    median(FetchTiming > 60 ? 60 : FetchTiming),
+    count() - sum(Refresh)
+FROM hits
+
+ + +

However, in contrast to standard SQL, if the table doesn't have any rows (either there aren't any at all, or there aren't any after using WHERE to filter), an empty result is returned, and not the result from one of the rows containing the initial values of aggregate functions.

+

As opposed to MySQL (and conforming to standard SQL), you can't get some value of some column that is not in a key or aggregate function (except constant expressions). To work around this, you can use the 'any' aggregate function (get the first encountered value) or 'min/max'.

+

Example:

+
SELECT
+    domainWithoutWWW(URL) AS domain,
+    count(),
+    any(Title) AS title -- getting the first occurred page header for each domain.
+FROM hits
+GROUP BY domain
+
+ + +

For every different key value encountered, GROUP BY calculates a set of aggregate function values.

+

GROUP BY is not supported for array columns.

+

A constant can't be specified as arguments for aggregate functions. Example: sum(1). Instead of this, you can get rid of the constant. Example: count().

+
WITH TOTALS modifier
+

If the WITH TOTALS modifier is specified, another row will be calculated. This row will have key columns containing default values (zeros or empty lines), and columns of aggregate functions with the values calculated across all the rows (the "total" values).

+

This extra row is output in JSON*, TabSeparated*, and Pretty* formats, separately from the other rows. In the other formats, this row is not output.

+

In JSON* formats, this row is output as a separate 'totals' field. In TabSeparated* formats, the row comes after the main result, preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result.

+

WITH TOTALS can be run in different ways when HAVING is present. The behavior depends on the 'totals_mode' setting. +By default, totals_mode = 'before_having'. In this case, 'totals' is calculated across all rows, including the ones that don't pass through HAVING and 'max_rows_to_group_by'.

+

The other alternatives include only the rows that pass through HAVING in 'totals', and behave differently with the setting max_rows_to_group_by and group_by_overflow_mode = 'any'.

+

after_having_exclusive – Don't include rows that didn't pass through max_rows_to_group_by. In other words, 'totals' will have less than or the same number of rows as it would if max_rows_to_group_by were omitted.

+

after_having_inclusive – Include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. In other words, 'totals' will have more than or the same number of rows as it would if max_rows_to_group_by were omitted.

+

after_having_auto – Count the number of rows that passed through HAVING. If it is more than a certain amount (by default, 50%), include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. Otherwise, do not include them.

+

totals_auto_threshold – By default, 0.5. The coefficient for after_having_auto.

+

If max_rows_to_group_by and group_by_overflow_mode = 'any' are not used, all variations of after_having are the same, and you can use any of them (for example, after_having_auto).

+

You can use WITH TOTALS in subqueries, including subqueries in the JOIN clause (in this case, the respective total values are combined).

+
GROUP BY in external memory
+

You can enable dumping temporary data to the disk to restrict memory usage during GROUP BY. +The max_bytes_before_external_group_by setting determines the threshold RAM consumption for dumping GROUP BY temporary data to the file system. If set to 0 (the default), it is disabled.

+

When using max_bytes_before_external_group_by, we recommend that you set max_memory_usage about twice as high. This is necessary because there are two stages to aggregation: reading the date and forming intermediate data (1) and merging the intermediate data (2). Dumping data to the file system can only occur during stage 1. If the temporary data wasn't dumped, then stage 2 might require up to the same amount of memory as in stage 1.

+

For example, if max_memory_usage was set to 10000000000 and you want to use external aggregation, it makes sense to set max_bytes_before_external_group_by to 10000000000, and max_memory_usage to 20000000000. When external aggregation is triggered (if there was at least one dump of temporary data), maximum consumption of RAM is only slightly more than max_bytes_before_external_group_by.

+

With distributed query processing, external aggregation is performed on remote servers. In order for the requestor server to use only a small amount of RAM, set distributed_aggregation_memory_efficient to 1.

+

When merging data flushed to the disk, as well as when merging results from remote servers when the distributed_aggregation_memory_efficient setting is enabled, consumes up to 1/256 * the number of threads from the total amount of RAM.

+

When external aggregation is enabled, if there was less than max_bytes_before_external_group_by of data (i.e. data was not flushed), the query runs just as fast as without external aggregation. If any temporary data was flushed, the run time will be several times longer (approximately three times).

+

If you have an ORDER BY with a small LIMIT after GROUP BY, then the ORDER BY CLAUSE will not use significant amounts of RAM. +But if the ORDER BY doesn't have LIMIT, don't forget to enable external sorting (max_bytes_before_external_sort).

+

LIMIT N BY clause

+

LIMIT N BY COLUMNS selects the top N rows for each group of COLUMNS. LIMIT N BY is not related to LIMIT; they can both be used in the same query. The key for LIMIT N BY can contain any number of columns or expressions.

+

Example:

+
SELECT
+    domainWithoutWWW(URL) AS domain,
+    domainWithoutWWW(REFERRER_URL) AS referrer,
+    device_type,
+    count() cnt
+FROM hits
+GROUP BY domain, referrer, device_type
+ORDER BY cnt DESC
+LIMIT 5 BY domain, device_type
+LIMIT 100
+
+ + +

The query will select the top 5 referrers for each domain, device_type pair, but not more than 100 rows (LIMIT n BY + LIMIT).

+

HAVING clause

+

Allows filtering the result received after GROUP BY, similar to the WHERE clause. +WHERE and HAVING differ in that WHERE is performed before aggregation (GROUP BY), while HAVING is performed after it. +If aggregation is not performed, HAVING can't be used.

+

+

ORDER BY clause

+

The ORDER BY clause contains a list of expressions, which can each be assigned DESC or ASC (the sorting direction). If the direction is not specified, ASC is assumed. ASC is sorted in ascending order, and DESC in descending order. The sorting direction applies to a single expression, not to the entire list. Example: ORDER BY Visits DESC, SearchPhrase

+

For sorting by String values, you can specify collation (comparison). Example: ORDER BY SearchPhrase COLLATE 'tr' - for sorting by keyword in ascending order, using the Turkish alphabet, case insensitive, assuming that strings are UTF-8 encoded. COLLATE can be specified or not for each expression in ORDER BY independently. If ASC or DESC is specified, COLLATE is specified after it. When using COLLATE, sorting is always case-insensitive.

+

We only recommend using COLLATE for final sorting of a small number of rows, since sorting with COLLATE is less efficient than normal sorting by bytes.

+

Rows that have identical values for the list of sorting expressions are output in an arbitrary order, which can also be nondeterministic (different each time). +If the ORDER BY clause is omitted, the order of the rows is also undefined, and may be nondeterministic as well.

+

When floating point numbers are sorted, NaNs are separate from the other values. Regardless of the sorting order, NaNs come at the end. In other words, for ascending sorting they are placed as if they are larger than all the other numbers, while for descending sorting they are placed as if they are smaller than the rest.

+

Less RAM is used if a small enough LIMIT is specified in addition to ORDER BY. Otherwise, the amount of memory spent is proportional to the volume of data for sorting. For distributed query processing, if GROUP BY is omitted, sorting is partially done on remote servers, and the results are merged on the requestor server. This means that for distributed sorting, the volume of data to sort can be greater than the amount of memory on a single server.

+

If there is not enough RAM, it is possible to perform sorting in external memory (creating temporary files on a disk). Use the setting max_bytes_before_external_sort for this purpose. If it is set to 0 (the default), external sorting is disabled. If it is enabled, when the volume of data to sort reaches the specified number of bytes, the collected data is sorted and dumped into a temporary file. After all data is read, all the sorted files are merged and the results are output. Files are written to the /var/lib/clickhouse/tmp/ directory in the config (by default, but you can use the 'tmp_path' parameter to change this setting).

+

Running a query may use more memory than 'max_bytes_before_external_sort'. For this reason, this setting must have a value significantly smaller than 'max_memory_usage'. As an example, if your server has 128 GB of RAM and you need to run a single query, set 'max_memory_usage' to 100 GB, and 'max_bytes_before_external_sort' to 80 GB.

+

External sorting works much less effectively than sorting in RAM.

+

SELECT clause

+

The expressions specified in the SELECT clause are analyzed after the calculations for all the clauses listed above are completed. +More specifically, expressions are analyzed that are above the aggregate functions, if there are any aggregate functions. +The aggregate functions and everything below them are calculated during aggregation (GROUP BY). +These expressions work as if they are applied to separate rows in the result.

+

DISTINCT clause

+

If DISTINCT is specified, only a single row will remain out of all the sets of fully matching rows in the result. +The result will be the same as if GROUP BY were specified across all the fields specified in SELECT without aggregate functions. But there are several differences from GROUP BY:

+
    +
  • DISTINCT can be applied together with GROUP BY.
    • +
  • When ORDER BY is omitted and LIMIT is defined, the query stops running immediately after the required number of different rows has been read.
    • +
  • Data blocks are output as they are processed, without waiting for the entire query to finish running.
    • +
+

DISTINCT is not supported if SELECT has at least one array column.

+

LIMIT clause

+

LIMIT m allows you to select the first 'm' rows from the result. +LIMIT n, m allows you to select the first 'm' rows from the result after skipping the first 'n' rows.

+

'n' and 'm' must be non-negative integers.

+

If there isn't an ORDER BY clause that explicitly sorts results, the result may be arbitrary and nondeterministic.

+

UNION ALL clause

+

You can use UNION ALL to combine any number of queries. Example:

+
SELECT CounterID, 1 AS table, toInt64(count()) AS c
+    FROM test.hits
+    GROUP BY CounterID
+
+UNION ALL
+
+SELECT CounterID, 2 AS table, sum(Sign) AS c
+    FROM test.visits
+    GROUP BY CounterID
+    HAVING c > 0
+
+ + +

Only UNION ALL is supported. The regular UNION (UNION DISTINCT) is not supported. If you need UNION DISTINCT, you can write SELECT DISTINCT from a subquery containing UNION ALL.

+

Queries that are parts of UNION ALL can be run simultaneously, and their results can be mixed together.

+

The structure of results (the number and type of columns) must match for the queries. But the column names can differ. In this case, the column names for the final result will be taken from the first query.

+

Queries that are parts of UNION ALL can't be enclosed in brackets. ORDER BY and LIMIT are applied to separate queries, not to the final result. If you need to apply a conversion to the final result, you can put all the queries with UNION ALL in a subquery in the FROM clause.

+

INTO OUTFILE clause

+

Add the INTO OUTFILE filename clause (where filename is a string literal) to redirect query output to the specified file. +In contrast to MySQL, the file is created on the client side. The query will fail if a file with the same filename already exists. +This functionality is available in the command-line client and clickhouse-local (a query sent via HTTP interface will fail).

+

The default output format is TabSeparated (the same as in the command-line client batch mode).

+

FORMAT clause

+

Specify 'FORMAT format' to get data in any specified format. +You can use this for convenience, or for creating dumps. +For more information, see the section "Formats". +If the FORMAT clause is omitted, the default format is used, which depends on both the settings and the interface used for accessing the DB. For the HTTP interface and the command-line client in batch mode, the default format is TabSeparated. For the command-line client in interactive mode, the default format is PrettyCompact (it has attractive and compact tables).

+

When using the command-line client, data is passed to the client in an internal efficient format. The client independently interprets the FORMAT clause of the query and formats the data itself (thus relieving the network and the server from the load).

+

IN operators

+

The IN, NOT IN, GLOBAL IN, and GLOBAL NOT IN operators are covered separately, since their functionality is quite rich.

+

The left side of the operator is either a single column or a tuple.

+

Examples:

+
SELECT UserID IN (123, 456) FROM ...
+SELECT (CounterID, UserID) IN ((34, 123), (101500, 456)) FROM ...
+
+ + +

If the left side is a single column that is in the index, and the right side is a set of constants, the system uses the index for processing the query.

+

Don't list too many values explicitly (i.e. millions). If a data set is large, put it in a temporary table (for example, see the section "External data for query processing"), then use a subquery.

+

The right side of the operator can be a set of constant expressions, a set of tuples with constant expressions (shown in the examples above), or the name of a database table or SELECT subquery in brackets.

+

If the right side of the operator is the name of a table (for example, UserID IN users), this is equivalent to the subquery UserID IN (SELECT * FROM users). Use this when working with external data that is sent along with the query. For example, the query can be sent together with a set of user IDs loaded to the 'users' temporary table, which should be filtered.

+

If the right side of the operator is a table name that has the Set engine (a prepared data set that is always in RAM), the data set will not be created over again for each query.

+

The subquery may specify more than one column for filtering tuples. +Example:

+
SELECT (CounterID, UserID) IN (SELECT CounterID, UserID FROM ...) FROM ...
+
+ + +

The columns to the left and right of the IN operator should have the same type.

+

The IN operator and subquery may occur in any part of the query, including in aggregate functions and lambda functions. +Example:

+
SELECT
+    EventDate,
+    avg(UserID IN
+    (
+        SELECT UserID
+        FROM test.hits
+        WHERE EventDate = toDate('2014-03-17')
+    )) AS ratio
+FROM test.hits
+GROUP BY EventDate
+ORDER BY EventDate ASC
+
+ + +
┌──EventDate─┬────ratio─┐
+│ 2014-03-17 │        1 │
+│ 2014-03-18 │ 0.807696 │
+│ 2014-03-19 │ 0.755406 │
+│ 2014-03-20 │ 0.723218 │
+│ 2014-03-21 │ 0.697021 │
+│ 2014-03-22 │ 0.647851 │
+│ 2014-03-23 │ 0.648416 │
+└────────────┴──────────┘
+
+ + +

For each day after March 17th, count the percentage of pageviews made by users who visited the site on March 17th. +A subquery in the IN clause is always run just one time on a single server. There are no dependent subqueries.

+

+
Distributed subqueries
+

There are two options for IN-s with subqueries (similar to JOINs): normal IN / OIN and IN GLOBAL / GLOBAL JOIN. They differ in how they are run for distributed query processing.

+
+ +Remember that the algorithms described below may work differently depending on the [settings](#settings-distributed_product_mode) `distributed_product_mode` setting. + +
+ +

When using the regular IN, the query is sent to remote servers, and each of them runs the subqueries in the IN or JOIN clause.

+

When using GLOBAL IN / GLOBAL JOINs, first all the subqueries are run for GLOBAL IN / GLOBAL JOINs, and the results are collected in temporary tables. Then the temporary tables are sent to each remote server, where the queries are run using this temporary data.

+

For a non-distributed query, use the regular IN / JOIN.

+

Be careful when using subqueries in the IN / JOIN clauses for distributed query processing.

+

Let's look at some examples. Assume that each server in the cluster has a normal local_table. Each server also has a distributed_table table with the Distributed type, which looks at all the servers in the cluster.

+

For a query to the distributed_table, the query will be sent to all the remote servers and run on them using the local_table.

+

For example, the query

+
SELECT uniq(UserID) FROM distributed_table
+
+ + +

will be sent to all remote servers as

+
SELECT uniq(UserID) FROM local_table
+
+ + +

and run on each of them in parallel, until it reaches the stage where intermediate results can be combined. Then the intermediate results will be returned to the requestor server and merged on it, and the final result will be sent to the client.

+

Now let's examine a query with IN:

+
SELECT uniq(UserID) FROM distributed_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM local_table WHERE CounterID = 34)
+
+ + +
    +
  • Calculation of the intersection of audiences of two sites.
    • +
+

This query will be sent to all remote servers as

+
SELECT uniq(UserID) FROM local_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM local_table WHERE CounterID = 34)
+
+ + +

In other words, the data set in the IN clause will be collected on each server independently, only across the data that is stored locally on each of the servers.

+

This will work correctly and optimally if you are prepared for this case and have spread data across the cluster servers such that the data for a single UserID resides entirely on a single server. In this case, all the necessary data will be available locally on each server. Otherwise, the result will be inaccurate. We refer to this variation of the query as "local IN".

+

To correct how the query works when data is spread randomly across the cluster servers, you could specify distributed_table inside a subquery. The query would look like this:

+
SELECT uniq(UserID) FROM distributed_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM distributed_table WHERE CounterID = 34)
+
+ + +

This query will be sent to all remote servers as

+
SELECT uniq(UserID) FROM local_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM distributed_table WHERE CounterID = 34)
+
+ + +

The subquery will begin running on each remote server. Since the subquery uses a distributed table, the subquery that is on each remote server will be resent to every remote server as

+
SELECT UserID FROM local_table WHERE CounterID = 34
+
+ + +

For example, if you have a cluster of 100 servers, executing the entire query will require 10,000 elementary requests, which is generally considered unacceptable.

+

In such cases, you should always use GLOBAL IN instead of IN. Let's look at how it works for the query

+
SELECT uniq(UserID) FROM distributed_table WHERE CounterID = 101500 AND UserID GLOBAL IN (SELECT UserID FROM distributed_table WHERE CounterID = 34)
+
+ + +

The requestor server will run the subquery

+
SELECT UserID FROM distributed_table WHERE CounterID = 34
+
+ + +

and the result will be put in a temporary table in RAM. Then the request will be sent to each remote server as

+
SELECT uniq(UserID) FROM local_table WHERE CounterID = 101500 AND UserID GLOBAL IN _data1
+
+ + +

and the temporary table _data1 will be sent to every remote server with the query (the name of the temporary table is implementation-defined).

+

This is more optimal than using the normal IN. However, keep the following points in mind:

+
    +
  1. When creating a temporary table, data is not made unique. To reduce the volume of data transmitted over the network, specify DISTINCT in the subquery. (You don't need to do this for a normal IN.)
    2. +
  2. The temporary table will be sent to all the remote servers. Transmission does not account for network topology. For example, if 10 remote servers reside in a datacenter that is very remote in relation to the requestor server, the data will be sent 10 times over the channel to the remote datacenter. Try to avoid large data sets when using GLOBAL IN.
    3. +
  3. When transmitting data to remote servers, restrictions on network bandwidth are not configurable. You might overload the network.
    4. +
  4. Try to distribute data across servers so that you don't need to use GLOBAL IN on a regular basis.
    5. +
  5. If you need to use GLOBAL IN often, plan the location of the ClickHouse cluster so that a single group of replicas resides in no more than one data center with a fast network between them, so that a query can be processed entirely within a single data center.
    6. +
+

It also makes sense to specify a local table in the GLOBAL IN clause, in case this local table is only available on the requestor server and you want to use data from it on remote servers.

+

Extreme values

+

In addition to results, you can also get minimum and maximum values for the results columns. To do this, set the extremes setting to 1. Minimums and maximums are calculated for numeric types, dates, and dates with times. For other columns, the default values are output.

+

An extra two rows are calculated – the minimums and maximums, respectively. These extra two rows are output in JSON*, TabSeparated*, and Pretty* formats, separate from the other rows. They are not output for other formats.

+

In JSON* formats, the extreme values are output in a separate 'extremes' field. In TabSeparated* formats, the row comes after the main result, and after 'totals' if present. It is preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result, and after 'totals' if present.

+

Extreme values are calculated for rows that have passed through LIMIT. However, when using 'LIMIT offset, size', the rows before 'offset' are included in 'extremes'. In stream requests, the result may also include a small number of rows that passed through LIMIT.

+

Notes

+

The GROUP BY and ORDER BY clauses do not support positional arguments. This contradicts MySQL, but conforms to standard SQL. +For example, GROUP BY 1, 2 will be interpreted as grouping by constants (i.e. aggregation of all rows into one).

+

You can use synonyms (AS aliases) in any part of a query.

+

You can put an asterisk in any part of a query instead of an expression. When the query is analyzed, the asterisk is expanded to a list of all table columns (excluding the MATERIALIZED and ALIAS columns). There are only a few cases when using an asterisk is justified:

+
    +
  • When creating a table dump.
    • +
  • For tables containing just a few columns, such as system tables.
    • +
  • For getting information about what columns are in a table. In this case, set LIMIT 1. But it is better to use the DESC TABLE query.
    • +
  • When there is strong filtration on a small number of columns using PREWHERE.
    • +
  • In subqueries (since columns that aren't needed for the external query are excluded from subqueries).
    • +
+

In all other cases, we don't recommend using the asterisk, since it only gives you the drawbacks of a columnar DBMS instead of the advantages. In other words using the asterisk is not recommended.

+

KILL QUERY

+
KILL QUERY
+  WHERE <where expression to SELECT FROM system.processes query>
+  [SYNC|ASYNC|TEST]
+  [FORMAT format]
+
+ + +

Attempts to forcibly terminate the currently running queries. +The queries to terminate are selected from the system.processes table using the criteria defined in the WHERE clause of the KILL query.

+

Examples:

+
-- Forcibly terminates all queries with the specified query_id:
+KILL QUERY WHERE query_id='2-857d-4a57-9ee0-327da5d60a90'
+
+-- Synchronously terminates all queries run by 'username':
+KILL QUERY WHERE user='username' SYNC
+
+ + +

Read-only users can only stop their own queries.

+

By default, the asynchronous version of queries is used (ASYNC), which doesn't wait for confirmation that queries have stopped.

+

The synchronous version (SYNC) waits for all queries to stop and displays information about each process as it stops. +The response contains the kill_status column, which can take the following values:

+
    +
  1. 'finished' – The query was terminated successfully.
    2. +
  2. 'waiting' – Waiting for the query to end after sending it a signal to terminate.
    3. +
  3. The other values ​​explain why the query can't be stopped.
    4. +
+

A test query (TEST) only checks the user's rights and displays a list of queries to stop.

+

Syntax

+

There are two types of parsers in the system: the full SQL parser (a recursive descent parser), and the data format parser (a fast stream parser). +In all cases except the INSERT query, only the full SQL parser is used. +The INSERT query uses both parsers:

+
INSERT INTO t VALUES (1, 'Hello, world'), (2, 'abc'), (3, 'def')
+
+ + +

The INSERT INTO t VALUES fragment is parsed by the full parser, and the data (1, 'Hello, world'), (2, 'abc'), (3, 'def') is parsed by the fast stream parser. +Data can have any format. When a query is received, the server calculates no more than max_query_size bytes of the request in RAM (by default, 1 MB), and the rest is stream parsed. +This means the system doesn't have problems with large INSERT queries, like MySQL does.

+

When using the Values format in an INSERT query, it may seem that data is parsed the same as expressions in a SELECT query, but this is not true. The Values format is much more limited.

+

Next we will cover the full parser. For more information about format parsers, see the section "Formats".

+

Spaces

+

There may be any number of space symbols between syntactical constructions (including the beginning and end of a query). Space symbols include the space, tab, line feed, CR, and form feed.

+

Comments

+

SQL-style and C-style comments are supported. +SQL-style comments: from -- to the end of the line. The space after -- can be omitted. +Comments in C-style: from /* to */. These comments can be multiline. Spaces are not required here, either.

+

Keywords

+

Keywords (such as SELECT) are not case-sensitive. Everything else (column names, functions, and so on), in contrast to standard SQL, is case-sensitive. Keywords are not reserved (they are just parsed as keywords in the corresponding context).

+

Identifiers

+

Identifiers (column names, functions, and data types) can be quoted or non-quoted. +Non-quoted identifiers start with a Latin letter or underscore, and continue with a Latin letter, underscore, or number. In other words, they must match the regex ^[a-zA-Z_][0-9a-zA-Z_]*$. Examples: x, _1, X_y__Z123_.

+

Quoted identifiers are placed in reversed quotation marks `id` (the same as in MySQL), and can indicate any set of bytes (non-empty). In addition, symbols (for example, the reverse quotation mark) inside this type of identifier can be backslash-escaped. Escaping rules are the same as for string literals (see below). +We recommend using identifiers that do not need to be quoted.

+

Literals

+

There are numeric literals, string literals, and compound literals.

+

Numeric literals

+

A numeric literal tries to be parsed:

+
    +
  • First as a 64-bit signed number, using the 'strtoull' function.
    • +
  • If unsuccessful, as a 64-bit unsigned number, using the 'strtoll' function.
    • +
  • If unsuccessful, as a floating-point number using the 'strtod' function.
    • +
  • Otherwise, an error is returned.
    • +
+

The corresponding value will have the smallest type that the value fits in. +For example, 1 is parsed as UInt8, but 256 is parsed as UInt16. For more information, see "Data types".

+

Examples: 1, 18446744073709551615, 0xDEADBEEF, 01, 0.1, 1e100, -1e-100, inf, nan.

+

String literals

+

Only string literals in single quotes are supported. The enclosed characters can be backslash-escaped. The following escape sequences have a corresponding special value: \b, \f, \r, \n, \t, \0, \a, \v, \xHH. In all other cases, escape sequences in the format \c, where "c" is any character, are converted to "c". This means that you can use the sequences \'and\\. The value will have the String type.

+

The minimum set of characters that you need to escape in string literals: ' and \.

+

Compound literals

+

Constructions are supported for arrays: [1, 2, 3] and tuples: (1, 'Hello, world!', 2).. +Actually, these are not literals, but expressions with the array creation operator and the tuple creation operator, respectively. +For more information, see the section "Operators2". +An array must consist of at least one item, and a tuple must have at least two items. +Tuples have a special purpose for use in the IN clause of a SELECT query. Tuples can be obtained as the result of a query, but they can't be saved to a database (with the exception of Memory-type tables).

+

Functions

+

Functions are written like an identifier with a list of arguments (possibly empty) in brackets. In contrast to standard SQL, the brackets are required, even for an empty arguments list. Example: now(). +There are regular and aggregate functions (see the section "Aggregate functions"). Some aggregate functions can contain two lists of arguments in brackets. Example: quantile (0.9) (x). These aggregate functions are called "parametric" functions, and the arguments in the first list are called "parameters". The syntax of aggregate functions without parameters is the same as for regular functions.

+

Operators

+

Operators are converted to their corresponding functions during query parsing, taking their priority and associativity into account. +For example, the expression 1 + 2 * 3 + 4 is transformed to plus(plus(1, multiply(2, 3)), 4). +For more information, see the section "Operators" below.

+

Data types and database table engines

+

Data types and table engines in the CREATE query are written the same way as identifiers or functions. In other words, they may or may not contain an arguments list in brackets. For more information, see the sections "Data types," "Table engines," and "CREATE".

+

Synonyms

+

In the SELECT query, expressions can specify synonyms using the AS keyword. Any expression is placed to the left of AS. The identifier name for the synonym is placed to the right of AS. As opposed to standard SQL, synonyms are not only declared on the top level of expressions:

+
SELECT (1 AS n) + 2, n
+
+ + +

In contrast to standard SQL, synonyms can be used in all parts of a query, not just SELECT.

+

Asterisk

+

In a SELECT query, an asterisk can replace the expression. For more information, see the section "SELECT".

+

Expressions

+

An expression is a function, identifier, literal, application of an operator, expression in brackets, subquery, or asterisk. It can also contain a synonym. +A list of expressions is one or more expressions separated by commas. +Functions and operators, in turn, can have expressions as arguments.

+

Table engines

+

The table engine (type of table) determines:

+
    +
  • How and where data is stored: where to write it to, and where to read it from.
    • +
  • Which queries are supported, and how.
    • +
  • Concurrent data access.
    • +
  • Use of indexes, if present.
    • +
  • Whether multithreaded request execution is possible.
    • +
  • Data replication.
    • +
+

When reading data, the engine is only required to extract the necessary set of columns. However, in some cases, the query may be partially processed inside the table engine.

+

Note that for most serious tasks, you should use engines from the MergeTree family.

+

TinyLog

+

The simplest table engine, which stores data on a disk. +Each column is stored in a separate compressed file. +When writing, data is appended to the end of files.

+

Concurrent data access is not restricted in any way:

+
    +
  • If you are simultaneously reading from a table and writing to it in a different query, the read operation will complete with an error.
    • +
  • If you are writing to a table in multiple queries simultaneously, the data will be broken.
    • +
+

The typical way to use this table is write-once: first just write the data one time, then read it as many times as needed. +Queries are executed in a single stream. In other words, this engine is intended for relatively small tables (recommended up to 1,000,000 rows). +It makes sense to use this table engine if you have many small tables, since it is simpler than the Log engine (fewer files need to be opened). +The situation when you have a large number of small tables guarantees poor productivity, but may already be used when working with another DBMS, and you may find it easier to switch to using TinyLog types of tables. +Indexes are not supported.

+

In Yandex.Metrica, TinyLog tables are used for intermediary data that is processed in small batches.

+

Log

+

Log differs from TinyLog in that a small file of "marks" resides with the column files. These marks are written on every data block and contain offsets that indicate where to start reading the file in order to skip the specified number of rows. This makes it possible to read table data in multiple threads. +For concurrent data access, the read operations can be performed simultaneously, while write operations block reads and each other. +The Log engine does not support indexes. Similarly, if writing to a table failed, the table is broken, and reading from it returns an error. The Log engine is appropriate for temporary data, write-once tables, and for testing or demonstration purposes.

+

Memory

+

The Memory engine stores data in RAM, in uncompressed form. Data is stored in exactly the same form as it is received when read. In other words, reading from this table is completely free. +Concurrent data access is synchronized. Locks are short: read and write operations don't block each other. +Indexes are not supported. Reading is parallelized. +Maximal productivity (over 10 GB/sec) is reached on simple queries, because there is no reading from the disk, decompressing, or deserializing data. (We should note that in many cases, the productivity of the MergeTree engine is almost as high.) +When restarting a server, data disappears from the table and the table becomes empty. +Normally, using this table engine is not justified. However, it can be used for tests, and for tasks where maximum speed is required on a relatively small number of rows (up to approximately 100,000,000).

+

The Memory engine is used by the system for temporary tables with external query data (see the section "External data for processing a query"), and for implementing GLOBAL IN (see the section "IN operators").

+

+

MergeTree

+

The MergeTree engine supports an index by primary key and by date, and provides the possibility to update data in real time. +This is the most advanced table engine in ClickHouse. Don't confuse it with the Merge engine.

+

The engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity.

+

Example without sampling support.

+
MergeTree(EventDate, (CounterID, EventDate), 8192)
+
+ + +

Example with sampling support.

+
MergeTree(EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID)), 8192)
+
+ + +

A MergeTree table must have a separate column containing the date. Here, it is the EventDate column. The date column must have the 'Date' type (not 'DateTime').

+

The primary key may be a tuple from any expressions (usually this is just a tuple of columns), or a single expression.

+

The sampling expression (optional) can be any expression. It must also be present in the primary key. The example uses a hash of user IDs to pseudo-randomly disperse data in the table for each CounterID and EventDate. In other words, when using the SAMPLE clause in a query, you get an evenly pseudo-random sample of data for a subset of users.

+

The table is implemented as a set of parts. Each part is sorted by the primary key. In addition, each part has the minimum and maximum date assigned. When inserting in the table, a new sorted part is created. The merge process is periodically initiated in the background. When merging, several parts are selected (usually the smallest ones) and then merged into one large sorted part.

+

In other words, incremental sorting occurs when inserting to the table. Merging is implemented so that the table always consists of a small number of sorted parts, and the merge itself doesn't do too much work.

+

During insertion, data belonging to different months is separated into different parts. The parts that correspond to different months are never combined. The purpose of this is to provide local data modification (for ease in backups).

+

Parts are combined up to a certain size threshold, so there aren't any merges that are too long.

+

For each part, an index file is also written. The index file contains the primary key value for every 'index_granularity' row in the table. In other words, this is an abbreviated index of sorted data.

+

For columns, "marks" are also written to each 'index_granularity' row so that data can be read in a specific range.

+

When reading from a table, the SELECT query is analyzed for whether indexes can be used. +An index can be used if the WHERE or PREWHERE clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has IN or LIKE with a fixed prefix on columns or expressions that are in the primary key or partitioning key, or on certain partially repetitive functions of these columns, or logical relationships of these expressions.

+

Thus, it is possible to quickly run queries on one or many ranges of the primary key. In this example, queries will be fast when run for a specific tracking tag; for a specific tag and date range; for a specific tag and date; for multiple tags with a date range, and so on.

+
SELECT count() FROM table WHERE EventDate = toDate(now()) AND CounterID = 34
+SELECT count() FROM table WHERE EventDate = toDate(now()) AND (CounterID = 34 OR CounterID = 42)
+SELECT count() FROM table WHERE ((EventDate >= toDate('2014-01-01') AND EventDate <= toDate('2014-01-31')) OR EventDate = toDate('2014-05-01')) AND CounterID IN (101500, 731962, 160656) AND (CounterID = 101500 OR EventDate != toDate('2014-05-01'))
+
+ + +

All of these cases will use the index by date and by primary key. The index is used even for complex expressions. Reading from the table is organized so that using the index can't be slower than a full scan.

+

In this example, the index can't be used.

+
SELECT count() FROM table WHERE CounterID = 34 OR URL LIKE '%upyachka%'
+
+ + +

To check whether ClickHouse can use the index when executing the query, use the settings force_index_by_dateandforce_primary_key.

+

The index by date only allows reading those parts that contain dates from the desired range. However, a data part may contain data for many dates (up to an entire month), while within a single part the data is ordered by the primary key, which might not contain the date as the first column. Because of this, using a query with only a date condition that does not specify the primary key prefix will cause more data to be read than for a single date.

+

For concurrent table access, we use multi-versioning. In other words, when a table is simultaneously read and updated, data is read from a set of parts that is current at the time of the query. There are no lengthy locks. Inserts do not get in the way of read operations.

+

Reading from a table is automatically parallelized.

+

The OPTIMIZE query is supported, which calls an extra merge step.

+

You can use a single large table and continually add data to it in small chunks – this is what MergeTree is intended for.

+

Data replication is possible for all types of tables in the MergeTree family (see the section "Data replication").

+

+

Custom partitioning key

+

Starting with version 1.1.54310, you can create tables in the MergeTree family with any partitioning expression (not only partitioning by month).

+

The partition key can be an expression from the table columns, or a tuple of such expressions (similar to the primary key). The partition key can be omitted. When creating a table, specify the partition key in the ENGINE description with the new syntax:

+
ENGINE [=] Name(...) [PARTITION BY expr] [ORDER BY expr] [SAMPLE BY expr] [SETTINGS name=value, ...]
+
+ + +

For MergeTree tables, the partition expression is specified after PARTITION BY, the primary key after ORDER BY, the sampling key after SAMPLE BY, and SETTINGS can specify index_granularity (optional; the default value is 8192), as well as other settings from MergeTreeSettings.h. The other engine parameters are specified in parentheses after the engine name, as previously. Example:

+
ENGINE = ReplicatedCollapsingMergeTree('/clickhouse/tables/name', 'replica1', Sign)
+    PARTITION BY (toMonday(StartDate), EventType)
+    ORDER BY (CounterID, StartDate, intHash32(UserID))
+    SAMPLE BY intHash32(UserID)
+
+ + +

The traditional partitioning by month is expressed as toYYYYMM(date_column).

+

You can't convert an old-style table to a table with custom partitions (only via INSERT SELECT).

+

After this table is created, merge will only work for data parts that have the same value for the partitioning expression. Note: This means that you shouldn't make overly granular partitions (more than about a thousand partitions), or SELECT will perform poorly.

+

To specify a partition in ALTER PARTITION commands, specify the value of the partition expression (or a tuple). Constants and constant expressions are supported. Example:

+
ALTER TABLE table DROP PARTITION (toMonday(today()), 1)
+
+ + +

Deletes the partition for the current week with event type 1. The same is true for the OPTIMIZE query. To specify the only partition in a non-partitioned table, specify PARTITION tuple().

+

Note: For old-style tables, the partition can be specified either as a number 201710 or a string '201710'. The syntax for the new style of tables is stricter with types (similar to the parser for the VALUES input format). In addition, ALTER TABLE FREEZE PARTITION uses exact match for new-style tables (not prefix match).

+

In the system.parts table, the partition column specifies the value of the partition expression to use in ALTER queries (if quotas are removed). The name column should specify the name of the data part that has a new format.

+

Was: 20140317_20140323_2_2_0 (minimum date - maximum date - minimum block number - maximum block number - level).

+

Now: 201403_2_2_0 (partition ID - minimum block number - maximum block number - level).

+

The partition ID is its string identifier (human-readable, if possible) that is used for the names of data parts in the file system and in ZooKeeper. You can specify it in ALTER queries in place of the partition key. Example: Partition key toYYYYMM(EventDate); ALTER can specify either PARTITION 201710 or PARTITION ID '201710'.

+

For more examples, see the tests 00502_custom_partitioning_local and 00502_custom_partitioning_replicated_zookeeper.

+

ReplacingMergeTree

+

This engine table differs from MergeTree in that it removes duplicate entries with the same primary key value.

+

The last optional parameter for the table engine is the version column. When merging, it reduces all rows with the same primary key value to just one row. If the version column is specified, it leaves the row with the highest version; otherwise, it leaves the last row.

+

The version column must have a type from the UInt family, Date, or DateTime.

+
ReplacingMergeTree(EventDate, (OrderID, EventDate, BannerID, ...), 8192, ver)
+
+ + +

Note that data is only deduplicated during merges. Merging occurs in the background at an unknown time, so you can't plan for it. Some of the data may remain unprocessed. Although you can run an unscheduled merge using the OPTIMIZE query, don't count on using it, because the OPTIMIZE query will read and write a large amount of data.

+

Thus, ReplacingMergeTree is suitable for clearing out duplicate data in the background in order to save space, but it doesn't guarantee the absence of duplicates.

+

This engine is not used in Yandex.Metrica, but it has been applied in other Yandex projects.

+

SummingMergeTree

+

This engine differs from MergeTree in that it totals data while merging.

+
SummingMergeTree(EventDate, (OrderID, EventDate, BannerID, ...), 8192)
+
+ + +

The columns to total are implicit. When merging, all rows with the same primary key value (in the example, OrderId, EventDate, BannerID, ...) have their values totaled in numeric columns that are not part of the primary key.

+
SummingMergeTree(EventDate, (OrderID, EventDate, BannerID, ...), 8192, (Shows, Clicks, Cost, ...))
+
+ + +

The columns to total are set explicitly (the last parameter – Shows, Clicks, Cost, ...). When merging, all rows with the same primary key value have their values totaled in the specified columns. The specified columns also must be numeric and must not be part of the primary key.

+

If the values were null in all of these columns, the row is deleted. (The exception is cases when the data part would not have any rows left in it.)

+

For the other rows that are not part of the primary key, the first value that occurs is selected when merging.

+

Summation is not performed for a read operation. If it is necessary, write the appropriate GROUP BY.

+

In addition, a table can have nested data structures that are processed in a special way. +If the name of a nested table ends in 'Map' and it contains at least two columns that meet the following criteria:

+
    +
  • The first table is numeric ((U)IntN, Date, DateTime), which we'll refer to as the 'key'.
    • +
  • The other columns are arithmetic ((U)IntN, Float32/64), which we'll refer to as '(values...)'. Then this nested table is interpreted as a mapping of key => (values...), and when merging its rows, the elements of two data sets are merged by 'key' with a summation of the corresponding (values...).
    • +
+

Examples:

+
[(1, 100)] + [(2, 150)] -> [(1, 100), (2, 150)]
+[(1, 100)] + [(1, 150)] -> [(1, 250)]
+[(1, 100)] + [(1, 150), (2, 150)] -> [(1, 250), (2, 150)]
+[(1, 100), (2, 150)] + [(1, -100)] -> [(2, 150)]
+
+ + +

For aggregation of Map, use the function sumMap(key, value).

+

For nested data structures, you don't need to specify the columns as a list of columns for totaling.

+

This table engine is not particularly useful. Remember that when saving just pre-aggregated data, you lose some of the system's advantages.

+

AggregatingMergeTree

+

This engine differs from MergeTree in that the merge combines the states of aggregate functions stored in the table for rows with the same primary key value.

+

For this to work, it uses the AggregateFunction data type, as well as -State and -Merge modifiers for aggregate functions. Let's examine it more closely.

+

There is an AggregateFunction data type. It is a parametric data type. As parameters, the name of the aggregate function is passed, then the types of its arguments.

+

Examples:

+
CREATE TABLE t
+(
+    column1 AggregateFunction(uniq, UInt64),
+    column2 AggregateFunction(anyIf, String, UInt8),
+    column3 AggregateFunction(quantiles(0.5, 0.9), UInt64)
+) ENGINE = ...
+
+ + +

This type of column stores the state of an aggregate function.

+

To get this type of value, use aggregate functions with the State suffix.

+

Example: +uniqState(UserID), quantilesState(0.5, 0.9)(SendTiming)

+

In contrast to the corresponding uniq and quantiles functions, these functions return the state, rather than the prepared value. In other words, they return an AggregateFunction type value.

+

An AggregateFunction type value can't be output in Pretty formats. In other formats, these types of values are output as implementation-specific binary data. The AggregateFunction type values are not intended for output or saving in a dump.

+

The only useful thing you can do with AggregateFunction type values is combine the states and get a result, which essentially means to finish aggregation. Aggregate functions with the 'Merge' suffix are used for this purpose. +Example: uniqMerge(UserIDState), where UserIDState has the AggregateFunction type.

+

In other words, an aggregate function with the 'Merge' suffix takes a set of states, combines them, and returns the result. +As an example, these two queries return the same result:

+
SELECT uniq(UserID) FROM table
+
+SELECT uniqMerge(state) FROM (SELECT uniqState(UserID) AS state FROM table GROUP BY RegionID)
+
+ + +

There is an AggregatingMergeTree engine. Its job during a merge is to combine the states of aggregate functions from different table rows with the same primary key value.

+

You can't use a normal INSERT to insert a row in a table containing AggregateFunction columns, because you can't explicitly define the AggregateFunction value. Instead, use INSERT SELECT with -State aggregate functions for inserting data.

+

With SELECT from an AggregatingMergeTree table, use GROUP BY and aggregate functions with the '-Merge' modifier in order to complete data aggregation.

+

You can use AggregatingMergeTree tables for incremental data aggregation, including for aggregated materialized views.

+

Example:

+

Create an AggregatingMergeTree materialized view that watches the test.visits table:

+
CREATE MATERIALIZED VIEW test.basic
+ENGINE = AggregatingMergeTree(StartDate, (CounterID, StartDate), 8192)
+AS SELECT
+    CounterID,
+    StartDate,
+    sumState(Sign)    AS Visits,
+    uniqState(UserID) AS Users
+FROM test.visits
+GROUP BY CounterID, StartDate;
+
+ + +

Insert data in the test.visits table. Data will also be inserted in the view, where it will be aggregated:

+
INSERT INTO test.visits ...
+
+ + +

Perform SELECT from the view using GROUP BY in order to complete data aggregation:

+
SELECT
+    StartDate,
+    sumMerge(Visits) AS Visits,
+    uniqMerge(Users) AS Users
+FROM test.basic
+GROUP BY StartDate
+ORDER BY StartDate;
+
+ + +

You can create a materialized view like this and assign a normal view to it that finishes data aggregation.

+

Note that in most cases, using AggregatingMergeTree is not justified, since queries can be run efficiently enough on non-aggregated data.

+

CollapsingMergeTree

+

This engine is used specifically for Yandex.Metrica.

+

It differs from MergeTree in that it allows automatic deletion, or "collapsing" certain pairs of rows when merging.

+

Yandex.Metrica has normal logs (such as hit logs) and change logs. Change logs are used for incrementally calculating statistics on data that is constantly changing. Examples are the log of session changes, or logs of changes to user histories. Sessions are constantly changing in Yandex.Metrica. For example, the number of hits per session increases. We refer to changes in any object as a pair (?old values, ?new values). Old values may be missing if the object was created. New values may be missing if the object was deleted. If the object was changed, but existed previously and was not deleted, both values are present. In the change log, one or two entries are made for each change. Each entry contains all the attributes that the object has, plus a special attribute for differentiating between the old and new values. When objects change, only the new entries are added to the change log, and the existing ones are not touched.

+

The change log makes it possible to incrementally calculate almost any statistics. To do this, we need to consider "new" rows with a plus sign, and "old" rows with a minus sign. In other words, incremental calculation is possible for all statistics whose algebraic structure contains an operation for taking the inverse of an element. This is true of most statistics. We can also calculate "idempotent" statistics, such as the number of unique visitors, since the unique visitors are not deleted when making changes to sessions.

+

This is the main concept that allows Yandex.Metrica to work in real time.

+

CollapsingMergeTree accepts an additional parameter - the name of an Int8-type column that contains the row's "sign". Example:

+
CollapsingMergeTree(EventDate, (CounterID, EventDate, intHash32(UniqID), VisitID), 8192, Sign)
+
+ + +

Here, Sign is a column containing -1 for "old" values and 1 for "new" values.

+

When merging, each group of consecutive identical primary key values (columns for sorting data) is reduced to no more than one row with the column value 'sign_column = -1' (the "negative row") and no more than one row with the column value 'sign_column = 1' (the "positive row"). In other words, entries from the change log are collapsed.

+

If the number of positive and negative rows matches, the first negative row and the last positive row are written. +If there is one more positive row than negative rows, only the last positive row is written. +If there is one more negative row than positive rows, only the first negative row is written. +Otherwise, there will be a logical error and none of the rows will be written. (A logical error can occur if the same section of the log was accidentally inserted more than once. The error is just recorded in the server log, and the merge continues.)

+

Thus, collapsing should not change the results of calculating statistics. +Changes are gradually collapsed so that in the end only the last value of almost every object is left. +Compared to MergeTree, the CollapsingMergeTree engine allows a multifold reduction of data volume.

+

There are several ways to get completely "collapsed" data from a CollapsingMergeTree table:

+
    +
  1. Write a query with GROUP BY and aggregate functions that accounts for the sign. For example, to calculate quantity, write 'sum(Sign)' instead of 'count()'. To calculate the sum of something, write 'sum(Sign * x)' instead of 'sum(x)', and so on, and also add 'HAVING sum(Sign) > 0'. Not all amounts can be calculated this way. For example, the aggregate functions 'min' and 'max' can't be rewritten.
    2. +
  2. If you must extract data without aggregation (for example, to check whether rows are present whose newest values match certain conditions), you can use the FINAL modifier for the FROM clause. This approach is significantly less efficient.
    3. +
+

+

GraphiteMergeTree

+

This engine is designed for rollup (thinning and aggregating/averaging) Graphite data. It may be helpful to developers who want to use ClickHouse as a data store for Graphite.

+

Graphite stores full data in ClickHouse, and data can be retrieved in the following ways:

+
    +
  • Without thinning.
    • +
+

Uses the MergeTree engine.

+
    +
  • With thinning.
    • +
+

Using the GraphiteMergeTree engine.

+

The engine inherits properties from MergeTree. The settings for thinning data are defined by the graphite_rollup parameter in the server configuration.

+

Using the engine

+

The Graphite data table must contain the following fields at minimum:

+
    +
  • Path – The metric name (Graphite sensor).
    • +
  • Time – The time for measuring the metric.
    • +
  • Value – The value of the metric at the time set in Time.
    • +
  • Version – Determines which value of the metric with the same Path and Time will remain in the database.
    • +
+

Rollup pattern:

+
pattern
+    regexp
+    function
+    age -> precision
+    ...
+pattern
+    ...
+default
+    function
+       age -> precision
+    ...
+
+ + +

When processing a record, ClickHouse will check the rules in the patternclause. If the metric name matches the regexp, the rules from pattern are applied; otherwise, the rules from default are used.

+

Fields in the pattern.

+
    +
  • age – The minimum age of the data in seconds.
    • +
  • function – The name of the aggregating function to apply to data whose age falls within the range [age, age + precision].
    • +
  • precision– How precisely to define the age of the data in seconds.
    • +
  • regexp– A pattern for the metric name.
    • +
+

Example of settings:

+
<graphite_rollup>
+    <pattern>
+        <regexp>click_cost</regexp>
+        <function>any</function>
+        <retention>
+            <age>0</age>
+            <precision>5</precision>
+        </retention>
+        <retention>
+            <age>86400</age>
+            <precision>60</precision>
+        </retention>
+    </pattern>
+    <default>
+        <function>max</function>
+        <retention>
+            <age>0</age>
+            <precision>60</precision>
+        </retention>
+        <retention>
+            <age>3600</age>
+            <precision>300</precision>
+        </retention>
+        <retention>
+            <age>86400</age>
+            <precision>3600</precision>
+        </retention>
+    </default>
+</graphite_rollup>
+
+ + +

+

Data replication

+

Replication is only supported for tables in the MergeTree family:

+
    +
  • ReplicatedMergeTree
    • +
  • ReplicatedSummingMergeTree
    • +
  • ReplicatedReplacingMergeTree
    • +
  • ReplicatedAggregatingMergeTree
    • +
  • ReplicatedCollapsingMergeTree
    • +
  • ReplicatedGraphiteMergeTree
    • +
+

Replication works at the level of an individual table, not the entire server. A server can store both replicated and non-replicated tables at the same time.

+

Replication does not depend on sharding. Each shard has its own independent replication.

+

Compressed data is replicated for INSERT and ALTER queries (see the description of the ALTER query).

+

CREATE, DROP, ATTACH, DETACH and RENAME queries are executed on a single server and are not replicated:

+
    +
  • The CREATE TABLE query creates a new replicatable table on the server where the query is run. If this table already exists on other servers, it adds a new replica.
    • +
  • The DROP TABLE query deletes the replica located on the server where the query is run.
    • +
  • The RENAME query renames the table on one of the replicas. In other words, replicated tables can have different names on different replicas.
    • +
+

To use replication, set the addresses of the ZooKeeper cluster in the config file. Example:

+
<zookeeper>
+    <node index="1">
+        <host>example1</host>
+        <port>2181</port>
+    </node>
+    <node index="2">
+        <host>example2</host>
+        <port>2181</port>
+    </node>
+    <node index="3">
+        <host>example3</host>
+        <port>2181</port>
+    </node>
+</zookeeper>
+
+ + +

Use ZooKeeper version 3.4.5 or later.

+

You can specify any existing ZooKeeper cluster and the system will use a directory on it for its own data (the directory is specified when creating a replicatable table).

+

If ZooKeeper isn't set in the config file, you can't create replicated tables, and any existing replicated tables will be read-only.

+

ZooKeeper is not used in SELECT queries because replication does not affect the performance of SELECT and queries run just as fast as they do for non-replicated tables. When querying distributed replicated tables, ClickHouse behavior is controlled by the settings max_replica_delay_for_distributed_queries and fallback_to_stale_replicas_for_distributed_queries.

+

For each INSERT query, approximately ten entries are added to ZooKeeper through several transactions. (To be more precise, this is for each inserted block of data; an INSERT query contains one block or one block per max_insert_block_size = 1048576 rows.) This leads to slightly longer latencies for INSERT compared to non-replicated tables. But if you follow the recommendations to insert data in batches of no more than one INSERT per second, it doesn't create any problems. The entire ClickHouse cluster used for coordinating one ZooKeeper cluster has a total of several hundred INSERTs per second. The throughput on data inserts (the number of rows per second) is just as high as for non-replicated data.

+

For very large clusters, you can use different ZooKeeper clusters for different shards. However, this hasn't proven necessary on the Yandex.Metrica cluster (approximately 300 servers).

+

Replication is asynchronous and multi-master. INSERT queries (as well as ALTER) can be sent to any available server. Data is inserted on the server where the query is run, and then it is copied to the other servers. Because it is asynchronous, recently inserted data appears on the other replicas with some latency. If part of the replicas are not available, the data is written when they become available. If a replica is available, the latency is the amount of time it takes to transfer the block of compressed data over the network.

+

By default, an INSERT query waits for confirmation of writing the data from only one replica. If the data was successfully written to only one replica and the server with this replica ceases to exist, the stored data will be lost. Tp enable getting confirmation of data writes from multiple replicas, use the insert_quorum option.

+

Each block of data is written atomically. The INSERT query is divided into blocks up to max_insert_block_size = 1048576 rows. In other words, if the INSERT query has less than 1048576 rows, it is made atomically.

+

Data blocks are deduplicated. For multiple writes of the same data block (data blocks of the same size containing the same rows in the same order), the block is only written once. The reason for this is in case of network failures when the client application doesn't know if the data was written to the DB, so the INSERT query can simply be repeated. It doesn't matter which replica INSERTs were sent to with identical data. INSERTs are idempotent. Deduplication parameters are controlled by merge_tree server settings.

+

During replication, only the source data to insert is transferred over the network. Further data transformation (merging) is coordinated and performed on all the replicas in the same way. This minimizes network usage, which means that replication works well when replicas reside in different datacenters. (Note that duplicating data in different datacenters is the main goal of replication.)

+

You can have any number of replicas of the same data. Yandex.Metrica uses double replication in production. Each server uses RAID-5 or RAID-6, and RAID-10 in some cases. This is a relatively reliable and convenient solution.

+

The system monitors data synchronicity on replicas and is able to recover after a failure. Failover is automatic (for small differences in data) or semi-automatic (when data differs too much, which may indicate a configuration error).

+

+

Creating replicated tables

+

The Replicated prefix is added to the table engine name. For example:ReplicatedMergeTree.

+

Two parameters are also added in the beginning of the parameters list – the path to the table in ZooKeeper, and the replica name in ZooKeeper.

+

Example:

+
ReplicatedMergeTree('/clickhouse/tables/{layer}-{shard}/hits', '{replica}', EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID), EventTime), 8192)
+
+ + +

As the example shows, these parameters can contain substitutions in curly brackets. The substituted values are taken from the 'macros' section of the config file. Example:

+
<macros>
+    <layer>05</layer>
+    <shard>02</shard>
+    <replica>example05-02-1.yandex.ru</replica>
+</macros>
+
+ + +

The path to the table in ZooKeeper should be unique for each replicated table. Tables on different shards should have different paths. +In this case, the path consists of the following parts:

+

/clickhouse/tables/ is the common prefix. We recommend using exactly this one.

+

{layer}-{shard} is the shard identifier. In this example it consists of two parts, since the Yandex.Metrica cluster uses bi-level sharding. For most tasks, you can leave just the {shard} substitution, which will be expanded to the shard identifier.

+

hits is the name of the node for the table in ZooKeeper. It is a good idea to make it the same as the table name. It is defined explicitly, because in contrast to the table name, it doesn't change after a RENAME query.

+

The replica name identifies different replicas of the same table. You can use the server name for this, as in the example. The name only needs to be unique within each shard.

+

You can define the parameters explicitly instead of using substitutions. This might be convenient for testing and for configuring small clusters. However, you can't use distributed DDL queries (ON CLUSTER) in this case.

+

When working with large clusters, we recommend using substitutions because they reduce the probability of error.

+

Run the CREATE TABLE query on each replica. This query creates a new replicated table, or adds a new replica to an existing one.

+

If you add a new replica after the table already contains some data on other replicas, the data will be copied from the other replicas to the new one after running the query. In other words, the new replica syncs itself with the others.

+

To delete a replica, run DROP TABLE. However, only one replica is deleted – the one that resides on the server where you run the query.

+

Recovery after failures

+

If ZooKeeper is unavailable when a server starts, replicated tables switch to read-only mode. The system periodically attempts to connect to ZooKeeper.

+

If ZooKeeper is unavailable during an INSERT, or an error occurs when interacting with ZooKeeper, an exception is thrown.

+

After connecting to ZooKeeper, the system checks whether the set of data in the local file system matches the expected set of data (ZooKeeper stores this information). If there are minor inconsistencies, the system resolves them by syncing data with the replicas.

+

If the system detects broken data parts (with the wrong size of files) or unrecognized parts (parts written to the file system but not recorded in ZooKeeper), it moves them to the 'detached' subdirectory (they are not deleted). Any missing parts are copied from the replicas.

+

Note that ClickHouse does not perform any destructive actions such as automatically deleting a large amount of data.

+

When the server starts (or establishes a new session with ZooKeeper), it only checks the quantity and sizes of all files. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a SELECT query. The query throws an exception about a non-matching checksum or size of a compressed block. In this case, data parts are added to the verification queue and copied from the replicas if necessary.

+

If the local set of data differs too much from the expected one, a safety mechanism is triggered. The server enters this in the log and refuses to launch. The reason for this is that this case may indicate a configuration error, such as if a replica on a shard was accidentally configured like a replica on a different shard. However, the thresholds for this mechanism are set fairly low, and this situation might occur during normal failure recovery. In this case, data is restored semi-automatically - by "pushing a button".

+

To start recovery, create the node /path_to_table/replica_name/flags/force_restore_data in ZooKeeper with any content, or run the command to restore all replicated tables:

+
sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data
+
+ + +

Then restart the server. On start, the server deletes these flags and starts recovery.

+

Recovery after complete data loss

+

If all data and metadata disappeared from one of the servers, follow these steps for recovery:

+
    +
  1. Install ClickHouse on the server. Define substitutions correctly in the config file that contains the shard identifier and replicas, if you use them.
    2. +
  2. If you had unreplicated tables that must be manually duplicated on the servers, copy their data from a replica (in the directory /var/lib/clickhouse/data/db_name/table_name/).
    3. +
  3. Copy table definitions located in /var/lib/clickhouse/metadata/ from a replica. If a shard or replica identifier is defined explicitly in the table definitions, correct it so that it corresponds to this replica. (Alternatively, start the server and make all the ATTACH TABLE queries that should have been in the .sql files in /var/lib/clickhouse/metadata/.)
    4. +
  4. To start recovery, create the ZooKeeper node /path_to_table/replica_name/flags/force_restore_data with any content, or run the command to restore all replicated tables: sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data
    5. +
+

Then start the server (restart, if it is already running). Data will be downloaded from replicas.

+

An alternative recovery option is to delete information about the lost replica from ZooKeeper (/path_to_table/replica_name), then create the replica again as described in "Creating replicatable tables".

+

There is no restriction on network bandwidth during recovery. Keep this in mind if you are restoring many replicas at once.

+

Converting from MergeTree to ReplicatedMergeTree

+

We use the term MergeTree to refer to all table engines in the MergeTree family, the same as for ReplicatedMergeTree.

+

If you had a MergeTree table that was manually replicated, you can convert it to a replicatable table. You might need to do this if you have already collected a large amount of data in a MergeTree table and now you want to enable replication.

+

If the data differs on various replicas, first sync it, or delete this data on all the replicas except one.

+

Rename the existing MergeTree table, then create a ReplicatedMergeTree table with the old name. +Move the data from the old table to the 'detached' subdirectory inside the directory with the new table data (/var/lib/clickhouse/data/db_name/table_name/). +Then run ALTER TABLE ATTACH PARTITION on one of the replicas to add these data parts to the working set.

+

Converting from ReplicatedMergeTree to MergeTree

+

Create a MergeTree table with a different name. Move all the data from the directory with the ReplicatedMergeTree table data to the new table's data directory. Then delete the ReplicatedMergeTree table and restart the server.

+

If you want to get rid of a ReplicatedMergeTree table without launching the server:

+
    +
  • Delete the corresponding .sql file in the metadata directory (/var/lib/clickhouse/metadata/).
    • +
  • Delete the corresponding path in ZooKeeper (/path_to_table/replica_name).
    • +
+

After this, you can launch the server, create a MergeTree table, move the data to its directory, and then restart the server.

+

Recovery when metadata in the ZooKeeper cluster is lost or damaged

+

If the data in ZooKeeper was lost or damaged, you can save data by moving it to an unreplicated table as described above.

+

If exactly the same parts exist on the other replicas, they are added to the working set on them. If not, the parts are downloaded from the replica that has them.

+

+

Distributed

+

The Distributed engine does not store data itself, but allows distributed query processing on multiple servers. +Reading is automatically parallelized. During a read, the table indexes on remote servers are used, if there are any. +The Distributed engine accepts parameters: the cluster name in the server's config file, the name of a remote database, the name of a remote table, and (optionally) a sharding key. +Example:

+
Distributed(logs, default, hits[, sharding_key])
+
+ + +

Data will be read from all servers in the 'logs' cluster, from the default.hits table located on every server in the cluster. +Data is not only read, but is partially processed on the remote servers (to the extent that this is possible). +For example, for a query with GROUP BY, data will be aggregated on remote servers, and the intermediate states of aggregate functions will be sent to the requestor server. Then data will be further aggregated.

+

Instead of the database name, you can use a constant expression that returns a string. For example: currentDatabase().

+

logs – The cluster name in the server's config file.

+

Clusters are set like this:

+
<remote_servers>
+    <logs>
+        <shard>
+            <!-- Optional. Shard weight when writing data. Default: 1. -->
+            <weight>1</weight>
+            <!-- Optional. Whether to write data to just one of the replicas. Default: false (write data to all replicas). -->
+            <internal_replication>false</internal_replication>
+            <replica>
+                <host>example01-01-1</host>
+                <port>9000</port>
+            </replica>
+            <replica>
+                <host>example01-01-2</host>
+                <port>9000</port>
+            </replica>
+        </shard>
+        <shard>
+            <weight>2</weight>
+            <internal_replication>false</internal_replication>
+            <replica>
+                <host>example01-02-1</host>
+                <port>9000</port>
+            </replica>
+            <replica>
+                <host>example01-02-2</host>
+                <port>9000</port>
+            </replica>
+        </shard>
+    </logs>
+</remote_servers>
+
+ + +

Here a cluster is defined with the name 'logs' that consists of two shards, each of which contains two replicas. +Shards refer to the servers that contain different parts of the data (in order to read all the data, you must access all the shards). +Replicas are duplicating servers (in order to read all the data, you can access the data on any one of the replicas).

+

The parameters host, port, and optionally user and password are specified for each server:

+

: - host – The address of the remote server. You can use either the domain or the IPv4 or IPv6 address. If you specify the domain, the server makes a DNS request when it starts, and the result is stored as long as the server is running. If the DNS request fails, the server doesn't start. If you change the DNS record, restart the server. +- port– The TCP port for messenger activity ('tcp_port' in the config, usually set to 9000). Do not confuse it with http_port. +- user– Name of the user for connecting to a remote server. Default value: default. This user must have access to connect to the specified server. Access is configured in the users.xml file. For more information, see the section "Access rights". +- password – The password for connecting to a remote server (not masked). Default value: empty string.

+

When specifying replicas, one of the available replicas will be selected for each of the shards when reading. You can configure the algorithm for load balancing (the preference for which replica to access) – see the 'load_balancing' setting. +If the connection with the server is not established, there will be an attempt to connect with a short timeout. If the connection failed, the next replica will be selected, and so on for all the replicas. If the connection attempt failed for all the replicas, the attempt will be repeated the same way, several times. +This works in favor of resiliency, but does not provide complete fault tolerance: a remote server might accept the connection, but might not work, or work poorly.

+

You can specify just one of the shards (in this case, query processing should be called remote, rather than distributed) or up to any number of shards. In each shard, you can specify from one to any number of replicas. You can specify a different number of replicas for each shard.

+

You can specify as many clusters as you wish in the configuration.

+

To view your clusters, use the 'system.clusters' table.

+

The Distributed engine allows working with a cluster like a local server. However, the cluster is inextensible: you must write its configuration in the server config file (even better, for all the cluster's servers).

+

There is no support for Distributed tables that look at other Distributed tables (except in cases when a Distributed table only has one shard). As an alternative, make the Distributed table look at the "final" tables.

+

The Distributed engine requires writing clusters to the config file. Clusters from the config file are updated on the fly, without restarting the server. If you need to send a query to an unknown set of shards and replicas each time, you don't need to create a Distributed table – use the 'remote' table function instead. See the section "Table functions".

+

There are two methods for writing data to a cluster:

+

First, you can define which servers to write which data to, and perform the write directly on each shard. In other words, perform INSERT in the tables that the distributed table "looks at". +This is the most flexible solution – you can use any sharding scheme, which could be non-trivial due to the requirements of the subject area. +This is also the most optimal solution, since data can be written to different shards completely independently.

+

Second, you can perform INSERT in a Distributed table. In this case, the table will distribute the inserted data across servers itself. +In order to write to a Distributed table, it must have a sharding key set (the last parameter). In addition, if there is only one shard, the write operation works without specifying the sharding key, since it doesn't have any meaning in this case.

+

Each shard can have a weight defined in the config file. By default, the weight is equal to one. Data is distributed across shards in the amount proportional to the shard weight. For example, if there are two shards and the first has a weight of 9 while the second has a weight of 10, the first will be sent 9 / 19 parts of the rows, and the second will be sent 10 / 19.

+

Each shard can have the 'internal_replication' parameter defined in the config file.

+

If this parameter is set to 'true', the write operation selects the first healthy replica and writes data to it. Use this alternative if the Distributed table "looks at" replicated tables. In other words, if the table where data will be written is going to replicate them itself.

+

If it is set to 'false' (the default), data is written to all replicas. In essence, this means that the Distributed table replicates data itself. This is worse than using replicated tables, because the consistency of replicas is not checked, and over time they will contain slightly different data.

+

To select the shard that a row of data is sent to, the sharding expression is analyzed, and its remainder is taken from dividing it by the total weight of the shards. The row is sent to the shard that corresponds to the half-interval of the remainders from 'prev_weight' to 'prev_weights + weight', where 'prev_weights' is the total weight of the shards with the smallest number, and 'weight' is the weight of this shard. For example, if there are two shards, and the first has a weight of 9 while the second has a weight of 10, the row will be sent to the first shard for the remainders from the range [0, 9), and to the second for the remainders from the range [9, 19).

+

The sharding expression can be any expression from constants and table columns that returns an integer. For example, you can use the expression 'rand()' for random distribution of data, or 'UserID' for distribution by the remainder from dividing the user's ID (then the data of a single user will reside on a single shard, which simplifies running IN and JOIN by users). If one of the columns is not distributed evenly enough, you can wrap it in a hash function: intHash64(UserID).

+

A simple remainder from division is a limited solution for sharding and isn't always appropriate. It works for medium and large volumes of data (dozens of servers), but not for very large volumes of data (hundreds of servers or more). In the latter case, use the sharding scheme required by the subject area, rather than using entries in Distributed tables.

+

SELECT queries are sent to all the shards, and work regardless of how data is distributed across the shards (they can be distributed completely randomly). When you add a new shard, you don't have to transfer the old data to it. You can write new data with a heavier weight – the data will be distributed slightly unevenly, but queries will work correctly and efficiently.

+

You should be concerned about the sharding scheme in the following cases:

+
    +
  • Queries are used that require joining data (IN or JOIN) by a specific key. If data is sharded by this key, you can use local IN or JOIN instead of GLOBAL IN or GLOBAL JOIN, which is much more efficient.
    • +
  • A large number of servers is used (hundreds or more) with a large number of small queries (queries of individual clients - websites, advertisers, or partners). In order for the small queries to not affect the entire cluster, it makes sense to locate data for a single client on a single shard. Alternatively, as we've done in Yandex.Metrica, you can set up bi-level sharding: divide the entire cluster into "layers", where a layer may consist of multiple shards. Data for a single client is located on a single layer, but shards can be added to a layer as necessary, and data is randomly distributed within them. Distributed tables are created for each layer, and a single shared distributed table is created for global queries.
    • +
+

Data is written asynchronously. For an INSERT to a Distributed table, the data block is just written to the local file system. The data is sent to the remote servers in the background as soon as possible. You should check whether data is sent successfully by checking the list of files (data waiting to be sent) in the table directory: /var/lib/clickhouse/data/database/table/.

+

If the server ceased to exist or had a rough restart (for example, after a device failure) after an INSERT to a Distributed table, the inserted data might be lost. If a damaged data part is detected in the table directory, it is transferred to the 'broken' subdirectory and no longer used.

+

When the max_parallel_replicas option is enabled, query processing is parallelized across all replicas within a single shard. For more information, see the section "Settings, max_parallel_replicas".

+

+

Dictionary

+

The Dictionary engine displays the dictionary data as a ClickHouse table.

+

As an example, consider a dictionary of products with the following configuration:

+
<dictionaries>
+<dictionary>
+        <name>products</name>
+        <source>
+            <odbc>
+                <table>products</table>
+                <connection_string>DSN=some-db-server</connection_string>
+            </odbc>
+        </source>
+        <lifetime>
+            <min>300</min>
+            <max>360</max>
+        </lifetime>
+        <layout>
+            <flat/>
+        </layout>
+        <structure>
+            <id>
+                <name>product_id</name>
+            </id>
+            <attribute>
+                <name>title</name>
+                <type>String</type>
+                <null_value></null_value>
+            </attribute>
+        </structure>
+</dictionary>
+</dictionaries>
+
+ + +

Query the dictionary data:

+
select name, type, key, attribute.names, attribute.types, bytes_allocated, element_count,source from system.dictionaries where name = 'products';                     
+
+SELECT
+    name,
+    type,
+    key,
+    attribute.names,
+    attribute.types,
+    bytes_allocated,
+    element_count,
+    source
+FROM system.dictionaries
+WHERE name = 'products'
+
+ + +
┌─name─────┬─type─┬─key────┬─attribute.names─┬─attribute.types─┬─bytes_allocated─┬─element_count─┬─source──────────┐
+│ products │ Flat │ UInt64 │ ['title']       │ ['String']      │        23065376 │        175032 │ ODBC: .products │
+└──────────┴──────┴────────┴─────────────────┴─────────────────┴─────────────────┴───────────────┴─────────────────┘
+
+ + +

You can use the dictGet* function to get the dictionary data in this format.

+

This view isn't helpful when you need to get raw data, or when performing a JOIN operation. For these cases, you can use the Dictionary engine, which displays the dictionary data in a table.

+

Syntax:

+
CREATE TABLE %table_name% (%fields%) engine = Dictionary(%dictionary_name%)`
+
+ + +

Usage example:

+
create table products (product_id UInt64, title String) Engine = Dictionary(products);
+
+CREATE TABLE products
+(
+    product_id UInt64,
+    title String,
+)
+ENGINE = Dictionary(products)
+
+ + +
Ok.
+
+0 rows in set. Elapsed: 0.004 sec.
+
+ + +

Take a look at what's in the table.

+
select * from products limit 1;
+
+SELECT *
+FROM products
+LIMIT 1
+
+ + +
┌────product_id─┬─title───────────┐
+│        152689 │ Some item       │
+└───────────────┴─────────────────┘
+
+1 rows in set. Elapsed: 0.006 sec.
+
+ + +

Merge

+

The Merge engine (not to be confused with MergeTree) does not store data itself, but allows reading from any number of other tables simultaneously. +Reading is automatically parallelized. Writing to a table is not supported. When reading, the indexes of tables that are actually being read are used, if they exist. +The Merge engine accepts parameters: the database name and a regular expression for tables.

+

Example:

+
Merge(hits, '^WatchLog')
+
+ + +

Data will be read from the tables in the 'hits' database that have names that match the regular expression '^WatchLog'.

+

Instead of the database name, you can use a constant expression that returns a string. For example, currentDatabase().

+

Regular expressions — re2 (supports a subset of PCRE), case-sensitive. +See the notes about escaping symbols in regular expressions in the "match" section.

+

When selecting tables to read, the Merge table itself will not be selected, even if it matches the regex. This is to avoid loops. +It is possible to create two Merge tables that will endlessly try to read each others' data, but this is not a good idea.

+

The typical way to use the Merge engine is for working with a large number of TinyLog tables as if with a single table.

+

Virtual columns

+

Virtual columns are columns that are provided by the table engine, regardless of the table definition. In other words, these columns are not specified in CREATE TABLE, but they are accessible for SELECT.

+

Virtual columns differ from normal columns in the following ways:

+
    +
  • They are not specified in table definitions.
    • +
  • Data can't be added to them with INSERT.
    • +
  • When using INSERT without specifying the list of columns, virtual columns are ignored.
    • +
  • They are not selected when using the asterisk (SELECT *).
    • +
  • Virtual columns are not shown in SHOW CREATE TABLE and DESC TABLE queries.
    • +
+

A Merge type table contains a virtual _table column with the String type. (If the table already has a _table column, the virtual column is named _table1, and if it already has _table1, it is named _table2, and so on.) It contains the name of the table that data was read from.

+

If the WHERE or PREWHERE clause contains conditions for the '_table' column that do not depend on other table columns (as one of the conjunction elements, or as an entire expression), these conditions are used as an index. The conditions are performed on a data set of table names to read data from, and the read operation will be performed from only those tables that the condition was triggered on.

+

Buffer

+

Buffers the data to write in RAM, periodically flushing it to another table. During the read operation, data is read from the buffer and the other table simultaneously.

+
Buffer(database, table, num_layers, min_time, max_time, min_rows, max_rows, min_bytes, max_bytes)
+
+ + +

Engine parameters:database, table – The table to flush data to. Instead of the database name, you can use a constant expression that returns a string.num_layers – Parallelism layer. Physically, the table will be represented as 'num_layers' of independent buffers. Recommended value: 16.min_time, max_time, min_rows, max_rows, min_bytes, and max_bytes are conditions for flushing data from the buffer.

+

Data is flushed from the buffer and written to the destination table if all the 'min' conditions or at least one 'max' condition are met.min_time, max_time – Condition for the time in seconds from the moment of the first write to the buffer.min_rows, max_rows – Condition for the number of rows in the buffer.min_bytes, max_bytes – Condition for the number of bytes in the buffer.

+

During the write operation, data is inserted to a 'num_layers' number of random buffers. Or, if the data part to insert is large enough (greater than 'max_rows' or 'max_bytes'), it is written directly to the destination table, omitting the buffer.

+

The conditions for flushing the data are calculated separately for each of the 'num_layers' buffers. For example, if num_layers = 16 and max_bytes = 100000000, the maximum RAM consumption is 1.6 GB.

+

Example:

+
CREATE TABLE merge.hits_buffer AS merge.hits ENGINE = Buffer(merge, hits, 16, 10, 100, 10000, 1000000, 10000000, 100000000)
+
+ + +

Creating a 'merge.hits_buffer' table with the same structure as 'merge.hits' and using the Buffer engine. When writing to this table, data is buffered in RAM and later written to the 'merge.hits' table. 16 buffers are created. The data in each of them is flushed if either 100 seconds have passed, or one million rows have been written, or 100 MB of data have been written; or if simultaneously 10 seconds have passed and 10,000 rows and 10 MB of data have been written. For example, if just one row has been written, after 100 seconds it will be flushed, no matter what. But if many rows have been written, the data will be flushed sooner.

+

When the server is stopped, with DROP TABLE or DETACH TABLE, buffer data is also flushed to the destination table.

+

You can set empty strings in single quotation marks for the database and table name. This indicates the absence of a destination table. In this case, when the data flush conditions are reached, the buffer is simply cleared. This may be useful for keeping a window of data in memory.

+

When reading from a Buffer table, data is processed both from the buffer and from the destination table (if there is one). +Note that the Buffer tables does not support an index. In other words, data in the buffer is fully scanned, which might be slow for large buffers. (For data in a subordinate table, the index that it supports will be used.)

+

If the set of columns in the Buffer table doesn't match the set of columns in a subordinate table, a subset of columns that exist in both tables is inserted.

+

If the types don't match for one of the columns in the Buffer table and a subordinate table, an error message is entered in the server log and the buffer is cleared. +The same thing happens if the subordinate table doesn't exist when the buffer is flushed.

+

If you need to run ALTER for a subordinate table and the Buffer table, we recommend first deleting the Buffer table, running ALTER for the subordinate table, then creating the Buffer table again.

+

If the server is restarted abnormally, the data in the buffer is lost.

+

PREWHERE, FINAL and SAMPLE do not work correctly for Buffer tables. These conditions are passed to the destination table, but are not used for processing data in the buffer. Because of this, we recommend only using the Buffer table for writing, while reading from the destination table.

+

When adding data to a Buffer, one of the buffers is locked. This causes delays if a read operation is simultaneously being performed from the table.

+

Data that is inserted to a Buffer table may end up in the subordinate table in a different order and in different blocks. Because of this, a Buffer table is difficult to use for writing to a CollapsingMergeTree correctly. To avoid problems, you can set 'num_layers' to 1.

+

If the destination table is replicated, some expected characteristics of replicated tables are lost when writing to a Buffer table. The random changes to the order of rows and sizes of data parts cause data deduplication to quit working, which means it is not possible to have a reliable 'exactly once' write to replicated tables.

+

Due to these disadvantages, we can only recommend using a Buffer table in rare cases.

+

A Buffer table is used when too many INSERTs are received from a large number of servers over a unit of time and data can't be buffered before insertion, which means the INSERTs can't run fast enough.

+

Note that it doesn't make sense to insert data one row at a time, even for Buffer tables. This will only produce a speed of a few thousand rows per second, while inserting larger blocks of data can produce over a million rows per second (see the section "Performance").

+

File(InputFormat)

+

The data source is a file that stores data in one of the supported input formats (TabSeparated, Native, etc.).

+

Null

+

When writing to a Null table, data is ignored. When reading from a Null table, the response is empty.

+

However, you can create a materialized view on a Null table. So the data written to the table will end up in the view.

+

Set

+

A data set that is always in RAM. It is intended for use on the right side of the IN operator (see the section "IN operators").

+

You can use INSERT to insert data in the table. New elements will be added to the data set, while duplicates will be ignored. +But you can't perform SELECT from the table. The only way to retrieve data is by using it in the right half of the IN operator.

+

Data is always located in RAM. For INSERT, the blocks of inserted data are also written to the directory of tables on the disk. When starting the server, this data is loaded to RAM. In other words, after restarting, the data remains in place.

+

For a rough server restart, the block of data on the disk might be lost or damaged. In the latter case, you may need to manually delete the file with damaged data.

+

Join

+

A prepared data structure for JOIN that is always located in RAM.

+
Join(ANY|ALL, LEFT|INNER, k1[, k2, ...])
+
+ + +

Engine parameters: ANY|ALL – strictness; LEFT|INNER – type. +These parameters are set without quotes and must match the JOIN that the table will be used for. k1, k2, ... are the key columns from the USING clause that the join will be made on.

+

The table can't be used for GLOBAL JOINs.

+

You can use INSERT to add data to the table, similar to the Set engine. For ANY, data for duplicated keys will be ignored. For ALL, it will be counted. You can't perform SELECT directly from the table. The only way to retrieve data is to use it as the "right-hand" table for JOIN.

+

Storing data on the disk is the same as for the Set engine.

+

View

+

Used for implementing views (for more information, see the CREATE VIEW query). It does not store data, but only stores the specified SELECT query. When reading from a table, it runs this query (and deletes all unnecessary columns from the query).

+

MaterializedView

+

Used for implementing materialized views (for more information, see the CREATE TABLE) query. For storing data, it uses a different engine that was specified when creating the view. When reading from a table, it just uses this engine.

+

Kafka

+

This engine works with Apache Kafka.

+

Kafka lets you:

+
    +
  • Publish or subscribe to data flows.
    • +
  • Organize fault-tolerant storage.
    • +
  • Process streams as they become available.
    • +
+
Kafka(broker_list, topic_list, group_name, format[, schema, num_consumers])
+
+ + +

Parameters:

+
    +
  • broker_list – A comma-separated list of brokers (localhost:9092).
    • +
  • topic_list – A list of Kafka topics (my_topic).
    • +
  • group_name – A group of Kafka consumers (group1). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere.
    • +
  • --format – Message format. Uses the same notation as the SQL FORMAT function, such as JSONEachRow. For more information, see the "Formats" section.
    • +
  • schema – An optional parameter that must be used if the format requires a schema definition. For example, Cap'n Proto requires the path to the schema file and the name of the root schema.capnp:Message object.
    • +
  • num_consumers – The number of consumers per table. Default: 1. Specify more consumers if the throughput of one consumer is insufficient. The total number of consumers should not exceed the number of partitions in the topic, since only one consumer can be assigned per partition.
    • +
+

Example:

+
  CREATE TABLE queue (
+    timestamp UInt64,
+    level String,
+    message String
+  ) ENGINE = Kafka('localhost:9092', 'topic', 'group1', 'JSONEachRow');
+
+  SELECT * FROM queue LIMIT 5;
+
+ + +

The delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name.

+

Groups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. Read more about this at http://kafka.apache.org/intro.

+

SELECT is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. To do this:

+
    +
  1. Use the engine to create a Kafka consumer and consider it a data stream.
    2. +
  2. Create a table with the desired structure.
    3. +
  3. Create a materialized view that converts data from the engine and puts it into a previously created table.
    4. +
+

When the MATERIALIZED VIEW joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using SELECT

+

Example:

+
  CREATE TABLE queue (
+    timestamp UInt64,
+    level String,
+    message String
+  ) ENGINE = Kafka('localhost:9092', 'topic', 'group1', 'JSONEachRow');
+
+  CREATE TABLE daily (
+    day Date,
+    level String,
+    total UInt64
+  ) ENGINE = SummingMergeTree(day, (day, level), 8192);
+
+  CREATE MATERIALIZED VIEW consumer TO daily
+    AS SELECT toDate(toDateTime(timestamp)) AS day, level, count() as total
+    FROM queue GROUP BY day, level;
+
+  SELECT level, sum(total) FROM daily GROUP BY level;
+
+ + +

To improve performance, received messages are grouped into blocks the size of max_insert_block_size. If the block wasn't formed within stream_flush_interval_ms milliseconds, the data will be flushed to the table regardless of the completeness of the block.

+

To stop receiving topic data or to change the conversion logic, detach the materialized view:

+
  DETACH TABLE consumer;
+  ATTACH MATERIALIZED VIEW consumer;
+
+ + +

If you want to change the target table by using ALTERmaterialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view.

+

Configuration

+

Similar to GraphiteMergeTree, the Kafka engine supports extended configuration using the ClickHouse config file. There are two configuration keys that you can use: global (kafka) and topic-level (kafka_topic_*). The global configuration is applied first, and the topic-level configuration is second (if it exists).

+
  <!--  Global configuration options for all tables of Kafka engine type -->
+  <kafka>
+    <debug>cgrp</debug>
+    <auto_offset_reset>smallest</auto_offset_reset>
+  </kafka>
+
+  <!-- Configuration specific for topic "logs" -->
+  <kafka_topic_logs>
+    <retry_backoff_ms>250</retry_backoff_ms>
+    <fetch_min_bytes>100000</fetch_min_bytes>
+  </kafka_topic_logs>
+
+ + +

For a list of possible configuration options, see the librdkafka configuration reference. Use the underscore (_) instead of a dot in the ClickHouse configuration. For example, check.crcs=true will be <check_crcs>true</check_crcs>.

+

+

MySQL

+

The MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server.

+

The engine takes 4 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example:

+
MySQL('host:port', 'database', 'table', 'user', 'password');
+
+ + +

At this time, simple WHERE clauses such as =, !=, >, >=, <, <= are executed on the MySQL server.

+

The rest of the conditions and the LIMIT sampling constraint are executed in ClickHouse only after the query to MySQL finishes.

+

External data for query processing

+

ClickHouse allows sending a server the data that is needed for processing a query, together with a SELECT query. This data is put in a temporary table (see the section "Temporary tables") and can be used in the query (for example, in IN operators).

+

For example, if you have a text file with important user identifiers, you can upload it to the server along with a query that uses filtration by this list.

+

If you need to run more than one query with a large volume of external data, don't use this feature. It is better to upload the data to the DB ahead of time.

+

External data can be uploaded using the command-line client (in non-interactive mode), or using the HTTP interface.

+

In the command-line client, you can specify a parameters section in the format

+
--external --file=... [--name=...] [--format=...] [--types=...|--structure=...]
+
+ + +

You may have multiple sections like this, for the number of tables being transmitted.

+

--external – Marks the beginning of a clause. +--file – Path to the file with the table dump, or -, which refers to stdin. +Only a single table can be retrieved from stdin.

+

The following parameters are optional: --name– Name of the table. If omitted, _data is used. +--format – Data format in the file. If omitted, TabSeparated is used.

+

One of the following parameters is required:--types – A list of comma-separated column types. For example: UInt64,String. The columns will be named _1, _2, ... +--structure– The table structure in the formatUserID UInt64, URL String. Defines the column names and types.

+

The files specified in 'file' will be parsed by the format specified in 'format', using the data types specified in 'types' or 'structure'. The table will be uploaded to the server and accessible there as a temporary table with the name in 'name'.

+

Examples:

+
echo -ne "1\n2\n3\n" | clickhouse-client --query="SELECT count() FROM test.visits WHERE TraficSourceID IN _data" --external --file=- --types=Int8
+849897
+cat /etc/passwd | sed 's/:/\t/g' | clickhouse-client --query="SELECT shell, count() AS c FROM passwd GROUP BY shell ORDER BY c DESC" --external --file=- --name=passwd --structure='login String, unused String, uid UInt16, gid UInt16, comment String, home String, shell String'
+/bin/sh 20
+/bin/false      5
+/bin/bash       4
+/usr/sbin/nologin       1
+/bin/sync       1
+
+ + +

When using the HTTP interface, external data is passed in the multipart/form-data format. Each table is transmitted as a separate file. The table name is taken from the file name. The 'query_string' is passed the parameters 'name_format', 'name_types', and 'name_structure', where 'name' is the name of the table that these parameters correspond to. The meaning of the parameters is the same as when using the command-line client.

+

Example:

+
cat /etc/passwd | sed 's/:/\t/g' > passwd.tsv
+
+curl -F 'passwd=@passwd.tsv;' 'http://localhost:8123/?query=SELECT+shell,+count()+AS+c+FROM+passwd+GROUP+BY+shell+ORDER+BY+c+DESC&passwd_structure=login+String,+unused+String,+uid+UInt16,+gid+UInt16,+comment+String,+home+String,+shell+String'
+/bin/sh 20
+/bin/false      5
+/bin/bash       4
+/usr/sbin/nologin       1
+/bin/sync       1
+
+ + +

For distributed query processing, the temporary tables are sent to all the remote servers.

+

System tables

+

System tables are used for implementing part of the system's functionality, and for providing access to information about how the system is working. +You can't delete a system table (but you can perform DETACH). +System tables don't have files with data on the disk or files with metadata. The server creates all the system tables when it starts. +System tables are read-only. +They are located in the 'system' database.

+

system.one

+

This table contains a single row with a single 'dummy' UInt8 column containing the value 0. +This table is used if a SELECT query doesn't specify the FROM clause. +This is similar to the DUAL table found in other DBMSs.

+

system.numbers

+

This table contains a single UInt64 column named 'number' that contains almost all the natural numbers starting from zero. +You can use this table for tests, or if you need to do a brute force search. +Reads from this table are not parallelized.

+

system.numbers_mt

+

The same as 'system.numbers' but reads are parallelized. The numbers can be returned in any order. +Used for tests.

+

system.databases

+

This table contains a single String column called 'name' – the name of a database. +Each database that the server knows about has a corresponding entry in the table. +This system table is used for implementing the SHOW DATABASES query.

+

system.tables

+

This table contains the String columns 'database', 'name', and 'engine'. +The table also contains three virtual columns: metadata_modification_time (DateTime type), create_table_query, and engine_full (String type). +Each table that the server knows about is entered in the 'system.tables' table. +This system table is used for implementing SHOW TABLES queries.

+

system.columns

+

Contains information about the columns in all tables. +You can use this table to get information similar to DESCRIBE TABLE, but for multiple tables at once.

+
database String           - Name of the database the table is located in.
+table String              - Table name.
+name String               - Column name.
+type String               - Column type.
+default_type String       - Expression type (DEFAULT, MATERIALIZED, ALIAS) for the default value, or an empty string if it is not defined.
+default_expression String - Expression for the default value, or an empty string if it is not defined.
+
+ + +

system.parts

+

Contains information about parts of a table in the MergeTree family.

+

Each row describes one part of the data.

+

Columns:

+
    +
  • partition (String) – The partition name. YYYYMM format. To learn what a partition is, see the description of the ALTER query.
    • +
  • name (String) – Name of the data part.
    • +
  • active (UInt8) – Indicates whether the part is active. If a part is active, it is used in a table; otherwise, it will be deleted. Inactive data parts remain after merging.
    • +
  • marks (UInt64) – The number of marks. To get the approximate number of rows in a data part, multiply marks by the index granularity (usually 8192).
    • +
  • marks_size (UInt64) – The size of the file with marks.
    • +
  • rows (UInt64) – The number of rows.
    • +
  • bytes (UInt64) – The number of bytes when compressed.
    • +
  • modification_time (DateTime) – The modification time of the directory with the data part. This usually corresponds to the time of data part creation.|
    • +
  • remove_time (DateTime) – The time when the data part became inactive.
    • +
  • refcount (UInt32) – The number of places where the data part is used. A value greater than 2 indicates that the data part is used in queries or merges.
    • +
  • min_date (Date) – The minimum value of the date key in the data part.
    • +
  • max_date (Date) – The maximum value of the date key in the data part.
    • +
  • min_block_number (UInt64) – The minimum number of data parts that make up the current part after merging.
    • +
  • max_block_number (UInt64) – The maximum number of data parts that make up the current part after merging.
    • +
  • level (UInt32) – Depth of the merge tree. If a merge was not performed, level=0.
    • +
  • primary_key_bytes_in_memory (UInt64) – The amount of memory (in bytes) used by primary key values.
    • +
  • primary_key_bytes_in_memory_allocated (UInt64) – The amount of memory (in bytes) reserved for primary key values.
    • +
  • database (String) – Name of the database.
    • +
  • table (String) – Name of the table.
    • +
  • engine (String) – Name of the table engine without parameters.
    • +
+

system.processes

+

This system table is used for implementing the SHOW PROCESSLIST query. +Columns:

+
user String              – Name of the user who made the request. For distributed query processing, this is the user who helped the requestor server send the query to this server, not the user who made the distributed request on the requestor server.
+
+address String           – The IP address that the query was made from. The same is true for distributed query processing.
+
+elapsed Float64          –  The time in seconds since request execution started.
+
+rows_read UInt64         – The number of rows read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.
+
+bytes_read UInt64        – The number of uncompressed bytes read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.
+
+UInt64 total_rows_approx – The approximate total number of rows that must be read. For distributed processing, on the requestor server, this is the total for all remote servers. It can be updated during request processing, when new sources to process become known.
+
+memory_usage UInt64 – Memory consumption by the query. It might not include some types of dedicated memory.
+
+query String – The query text. For INSERT, it doesn't include the data to insert.
+
+query_id – Query ID, if defined.
+
+ + +

system.merges

+

Contains information about merges currently in process for tables in the MergeTree family.

+

Columns:

+
    +
  • database String — Name of the database the table is located in.
    • +
  • table String — Name of the table.
    • +
  • elapsed Float64 — Time in seconds since the merge started.
    • +
  • progress Float64 — Percent of progress made, from 0 to 1.
    • +
  • num_parts UInt64 — Number of parts to merge.
    • +
  • result_part_name String — Name of the part that will be formed as the result of the merge.
    • +
  • total_size_bytes_compressed UInt64 — Total size of compressed data in the parts being merged.
    • +
  • total_size_marks UInt64 — Total number of marks in the parts being merged.
    • +
  • bytes_read_uncompressed UInt64 — Amount of bytes read, decompressed.
    • +
  • rows_read UInt64 — Number of rows read.
    • +
  • bytes_written_uncompressed UInt64 — Amount of bytes written, uncompressed.
    • +
  • rows_written UInt64 — Number of rows written.
    • +
+

+

system.events

+

Contains information about the number of events that have occurred in the system. This is used for profiling and monitoring purposes. +Example: The number of processed SELECT queries. +Columns: 'event String' – the event name, and 'value UInt64' – the quantity.

+

+

system.metrics

+

+

system.asynchronous_metrics

+

Contain metrics used for profiling and monitoring. +They usually reflect the number of events currently in the system, or the total resources consumed by the system. +Example: The number of SELECT queries currently running; the amount of memory in use.system.asynchronous_metricsandsystem.metrics differ in their sets of metrics and how they are calculated.

+

system.replicas

+

Contains information and status for replicated tables residing on the local server. +This table can be used for monitoring. The table contains a row for every Replicated* table.

+

Example:

+
SELECT *
+FROM system.replicas
+WHERE table = 'visits'
+FORMAT Vertical
+
+ + +
Row 1:
+──────
+database:           merge
+table:              visits
+engine:             ReplicatedCollapsingMergeTree
+is_leader:          1
+is_readonly:        0
+is_session_expired: 0
+future_parts:       1
+parts_to_check:     0
+zookeeper_path:     /clickhouse/tables/01-06/visits
+replica_name:       example01-06-1.yandex.ru
+replica_path:       /clickhouse/tables/01-06/visits/replicas/example01-06-1.yandex.ru
+columns_version:    9
+queue_size:         1
+inserts_in_queue:   0
+merges_in_queue:    1
+log_max_index:      596273
+log_pointer:        596274
+total_replicas:     2
+active_replicas:    2
+
+ + +

Columns:

+
database:           database name
+table:              table name
+engine:             table engine name
+
+is_leader:          whether the replica is the leader
+
+Only one replica at a time can be the leader. The leader is responsible for selecting background merges to perform.
+Note that writes can be performed to any replica that is available and has a session in ZK, regardless of whether it is a leader.
+
+is_readonly:        Whether the replica is in read-only mode.
+This mode is turned on if the config doesn't have sections with ZK, if an unknown error occurred when reinitializing sessions in ZK, and during session reinitialization in ZK.
+
+is_session_expired: Whether the ZK session expired.
+Basically, the same thing as is_readonly.
+
+future_parts: The number of data parts that will appear as the result of INSERTs or merges that haven't been done yet. 
+
+parts_to_check: The number of data parts in the queue for verification.
+A part is put in the verification queue if there is suspicion that it might be damaged.
+
+zookeeper_path: The path to the table data in ZK. 
+replica_name: Name of the replica in ZK. Different replicas of the same table have different names. 
+replica_path: The path to the replica data in ZK. The same as concatenating zookeeper_path/replicas/replica_path.
+
+columns_version: Version number of the table structure.
+Indicates how many times ALTER was performed. If replicas have different versions, it means some replicas haven't made all of the ALTERs yet.
+
+queue_size:         Size of the queue for operations waiting to be performed.
+Operations include inserting blocks of data, merges, and certain other actions.
+Normally coincides with future_parts.
+
+inserts_in_queue: Number of inserts of blocks of data that need to be made.
+Insertions are usually replicated fairly quickly. If the number is high, something is wrong.
+
+merges_in_queue: The number of merges waiting to be made. 
+Sometimes merges are lengthy, so this value may be greater than zero for a long time.
+
+The next 4 columns have a non-null value only if the ZK session is active.
+
+log_max_index:     Maximum entry number in the log of general activity.
+log_pointer:        Maximum entry number in the log of general activity that the replica copied to its execution queue, plus one.
+If log_pointer is much smaller than log_max_index, something is wrong.
+
+total_replicas:     Total number of known replicas of this table.
+active_replicas:    Number of replicas of this table that have a ZK session (the number of active replicas).
+
+ + +

If you request all the columns, the table may work a bit slowly, since several reads from ZK are made for each row. +If you don't request the last 4 columns (log_max_index, log_pointer, total_replicas, active_replicas), the table works quickly.

+

For example, you can check that everything is working correctly like this:

+
SELECT
+    database,
+    table,
+    is_leader,
+    is_readonly,
+    is_session_expired,
+    future_parts,
+    parts_to_check,
+    columns_version,
+    queue_size,
+    inserts_in_queue,
+    merges_in_queue,
+    log_max_index,
+    log_pointer,
+    total_replicas,
+    active_replicas
+FROM system.replicas
+WHERE
+       is_readonly
+    OR is_session_expired
+    OR future_parts > 20
+    OR parts_to_check > 10
+    OR queue_size > 20
+    OR inserts_in_queue > 10
+    OR log_max_index - log_pointer > 10
+    OR total_replicas < 2
+    OR active_replicas < total_replicas
+
+ + +

If this query doesn't return anything, it means that everything is fine.

+

system.dictionaries

+

Contains information about external dictionaries.

+

Columns:

+
    +
  • name String – Dictionary name.
    • +
  • type String – Dictionary type: Flat, Hashed, Cache.
    • +
  • origin String – Path to the config file where the dictionary is described.
    • +
  • attribute.names Array(String) – Array of attribute names provided by the dictionary.
    • +
  • attribute.types Array(String) – Corresponding array of attribute types provided by the dictionary.
    • +
  • has_hierarchy UInt8 – Whether the dictionary is hierarchical.
    • +
  • bytes_allocated UInt64 – The amount of RAM used by the dictionary.
    • +
  • hit_rate Float64 – For cache dictionaries, the percent of usage for which the value was in the cache.
    • +
  • element_count UInt64 – The number of items stored in the dictionary.
    • +
  • load_factor Float64 – The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table).
    • +
  • creation_time DateTime – Time spent for the creation or last successful reload of the dictionary.
    • +
  • last_exception String – Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created.
    • +
  • source String – Text describing the data source for the dictionary.
    • +
+

Note that the amount of memory used by the dictionary is not proportional to the number of items stored in it. So for flat and cached dictionaries, all the memory cells are pre-assigned, regardless of how full the dictionary actually is.

+

system.clusters

+

Contains information about clusters available in the config file and the servers in them. +Columns:

+
cluster String      – Cluster name.
+shard_num UInt32    – Number of a shard in the cluster, starting from 1.
+shard_weight UInt32 – Relative weight of a shard when writing data.
+replica_num UInt32  – Number of a replica in the shard, starting from 1.
+host_name String    – Host name as specified in the config.
+host_address String – Host's IP address obtained from DNS.
+port UInt16         – The port used to access the server.
+user String         – The username to use for connecting to the server.
+
+ + +

system.functions

+

Contains information about normal and aggregate functions.

+

Columns:

+
    +
  • name (String) – Function name.
    • +
  • is_aggregate (UInt8) – Whether it is an aggregate function.
    • +
+

system.settings

+

Contains information about settings that are currently in use. +I.e. used for executing the query you are using to read from the system.settings table).

+

Columns:

+
name String   – Setting name.
+value String  – Setting value.
+changed UInt8 - Whether the setting was explicitly defined in the config or explicitly changed.
+
+ + +

Example:

+
SELECT *
+FROM system.settings
+WHERE changed
+
+ + +
┌─name───────────────────┬─value───────┬─changed─┐
+│ max_threads            │ 8           │       1 │
+│ use_uncompressed_cache │ 0           │       1 │
+│ load_balancing         │ random      │       1 │
+│ max_memory_usage       │ 10000000000 │       1 │
+└────────────────────────┴─────────────┴─────────┘
+
+ + +

system.zookeeper

+

Allows reading data from the ZooKeeper cluster defined in the config. +The query must have a 'path' equality condition in the WHERE clause. This is the path in ZooKeeper for the children that you want to get data for.

+

The query SELECT * FROM system.zookeeper WHERE path = '/clickhouse' outputs data for all children on the /clickhouse node. +To output data for all root nodes, write path = '/'. +If the path specified in 'path' doesn't exist, an exception will be thrown.

+

Columns:

+
    +
  • name String — Name of the node.
    • +
  • path String — Path to the node.
    • +
  • value String — Value of the node.
    • +
  • dataLength Int32 — Size of the value.
    • +
  • numChildren Int32 — Number of children.
    • +
  • czxid Int64 — ID of the transaction that created the node.
    • +
  • mzxid Int64 — ID of the transaction that last changed the node.
    • +
  • pzxid Int64 — ID of the transaction that last added or removed children.
    • +
  • ctime DateTime — Time of node creation.
    • +
  • mtime DateTime — Time of the last node modification.
    • +
  • version Int32 — Node version - the number of times the node was changed.
    • +
  • cversion Int32 — Number of added or removed children.
    • +
  • aversion Int32 — Number of changes to ACL.
    • +
  • ephemeralOwner Int64 — For ephemeral nodes, the ID of the session that owns this node.
    • +
+

Example:

+
SELECT *
+FROM system.zookeeper
+WHERE path = '/clickhouse/tables/01-08/visits/replicas'
+FORMAT Vertical
+
+ + +
Row 1:
+──────
+name:           example01-08-1.yandex.ru
+value:
+czxid:          932998691229
+mzxid:          932998691229
+ctime:          2015-03-27 16:49:51
+mtime:          2015-03-27 16:49:51
+version:        0
+cversion:       47
+aversion:       0
+ephemeralOwner: 0
+dataLength:     0
+numChildren:    7
+pzxid:          987021031383
+path:           /clickhouse/tables/01-08/visits/replicas
+
+Row 2:
+──────
+name:           example01-08-2.yandex.ru
+value:
+czxid:          933002738135
+mzxid:          933002738135
+ctime:          2015-03-27 16:57:01
+mtime:          2015-03-27 16:57:01
+version:        0
+cversion:       37
+aversion:       0
+ephemeralOwner: 0
+dataLength:     0
+numChildren:    7
+pzxid:          987021252247
+path:           /clickhouse/tables/01-08/visits/replicas
+
+ + +

Table functions

+

Table functions can be specified in the FROM clause instead of the database and table names. +Table functions can only be used if 'readonly' is not set. +Table functions aren't related to other functions.

+

+

remote

+

Allows you to access remote servers without creating a Distributed table.

+

Signatures:

+
remote('addresses_expr', db, table[, 'user'[, 'password']])
+remote('addresses_expr', db.table[, 'user'[, 'password']])
+
+ + +

addresses_expr – An expression that generates addresses of remote servers. This may be just one server address. The server address is host:port, or just host. The host can be specified as the server name, or as the IPv4 or IPv6 address. An IPv6 address is specified in square brackets. The port is the TCP port on the remote server. If the port is omitted, it uses tcp_port from the server's config file (by default, 9000).

+
+ +The port is required for an IPv6 address. + +
+ +

Examples:

+
example01-01-1
+example01-01-1:9000
+localhost
+127.0.0.1
+[::]:9000
+[2a02:6b8:0:1111::11]:9000
+
+ + +

Multiple addresses can be comma-separated. In this case, ClickHouse will use distributed processing, so it will send the query to all specified addresses (like to shards with different data).

+

Example:

+
example01-01-1,example01-02-1
+
+ + +

Part of the expression can be specified in curly brackets. The previous example can be written as follows:

+
example01-0{1,2}-1
+
+ + +

Curly brackets can contain a range of numbers separated by two dots (non-negative integers). In this case, the range is expanded to a set of values that generate shard addresses. If the first number starts with zero, the values are formed with the same zero alignment. The previous example can be written as follows:

+
example01-{01..02}-1
+
+ + +

If you have multiple pairs of curly brackets, it generates the direct product of the corresponding sets.

+

Addresses and parts of addresses in curly brackets can be separated by the pipe symbol (|). In this case, the corresponding sets of addresses are interpreted as replicas, and the query will be sent to the first healthy replica. However, the replicas are iterated in the order currently set in the load_balancing setting.

+

Example:

+
example01-{01..02}-{1|2}
+
+ + +

This example specifies two shards that each have two replicas.

+

The number of addresses generated is limited by a constant. Right now this is 1000 addresses.

+

Using the remote table function is less optimal than creating a Distributed table, because in this case, the server connection is re-established for every request. In addition, if host names are set, the names are resolved, and errors are not counted when working with various replicas. When processing a large number of queries, always create the Distributed table ahead of time, and don't use the remote table function.

+

The remote table function can be useful in the following cases:

+
    +
  • Accessing a specific server for data comparison, debugging, and testing.
    • +
  • Queries between various ClickHouse clusters for research purposes.
    • +
  • Infrequent distributed requests that are made manually.
    • +
  • Distributed requests where the set of servers is re-defined each time.
    • +
+

If the user is not specified, default is used. +If the password is not specified, an empty password is used.

+

merge

+

merge(db_name, 'tables_regexp') – Creates a temporary Merge table. For more information, see the section "Table engines, Merge".

+

The table structure is taken from the first table encountered that matches the regular expression.

+

numbers

+

numbers(N) – Returns a table with the single 'number' column (UInt64) that contains integers from 0 to N-1.

+

Similar to the system.numbers table, it can be used for testing and generating successive values.

+

The following two queries are equivalent:

+
SELECT * FROM numbers(10);
+SELECT * FROM system.numbers LIMIT 10;
+
+ + +

Examples:

+
-- Generate a sequence of dates from 2010-01-01 to 2010-12-31
+select toDate('2010-01-01') + number as d FROM numbers(365);
+
+ + +

+

Formats

+

The format determines how data is returned to you after SELECTs (how it is written and formatted by the server), and how it is accepted for INSERTs (how it is read and parsed by the server).

+

TabSeparated

+

In TabSeparated format, data is written by row. Each row contains values separated by tabs. Each value is follow by a tab, except the last value in the row, which is followed by a line feed. Strictly Unix line feeds are assumed everywhere. The last row also must contain a line feed at the end. Values are written in text format, without enclosing quotation marks, and with special characters escaped.

+

Integer numbers are written in decimal form. Numbers can contain an extra "+" character at the beginning (ignored when parsing, and not recorded when formatting). Non-negative numbers can't contain the negative sign. When reading, it is allowed to parse an empty string as a zero, or (for signed types) a string consisting of just a minus sign as a zero. Numbers that do not fit into the corresponding data type may be parsed as a different number, without an error message.

+

Floating-point numbers are written in decimal form. The dot is used as the decimal separator. Exponential entries are supported, as are 'inf', '+inf', '-inf', and 'nan'. An entry of floating-point numbers may begin or end with a decimal point. +During formatting, accuracy may be lost on floating-point numbers. +During parsing, it is not strictly required to read the nearest machine-representable number.

+

Dates are written in YYYY-MM-DD format and parsed in the same format, but with any characters as separators. +Dates with times are written in the format YYYY-MM-DD hh:mm:ss and parsed in the same format, but with any characters as separators. +This all occurs in the system time zone at the time the client or server starts (depending on which one formats data). For dates with times, daylight saving time is not specified. So if a dump has times during daylight saving time, the dump does not unequivocally match the data, and parsing will select one of the two times. +During a read operation, incorrect dates and dates with times can be parsed with natural overflow or as null dates and times, without an error message.

+

As an exception, parsing dates with times is also supported in Unix timestamp format, if it consists of exactly 10 decimal digits. The result is not time zone-dependent. The formats YYYY-MM-DD hh:mm:ss and NNNNNNNNNN are differentiated automatically.

+

Strings are output with backslash-escaped special characters. The following escape sequences are used for output: \b, \f, \r, \n, \t, \0, \', \\. Parsing also supports the sequences \a, \v, and \xHH (hex escape sequences) and any \c sequences, where c is any character (these sequences are converted to c). Thus, reading data supports formats where a line feed can be written as \n or \, or as a line feed. For example, the string Hello world with a line feed between the words instead of a space can be parsed in any of the following variations:

+
Hello\nworld
+
+Hello\
+world
+
+ + +

The second variant is supported because MySQL uses it when writing tab-separated dumps.

+

The minimum set of characters that you need to escape when passing data in TabSeparated format: tab, line feed (LF) and backslash.

+

Only a small set of symbols are escaped. You can easily stumble onto a string value that your terminal will ruin in output.

+

Arrays are written as a list of comma-separated values in square brackets. Number items in the array are fomratted as normally, but dates, dates with times, and strings are written in single quotes with the same escaping rules as above.

+

The TabSeparated format is convenient for processing data using custom programs and scripts. It is used by default in the HTTP interface, and in the command-line client's batch mode. This format also allows transferring data between different DBMSs. For example, you can get a dump from MySQL and upload it to ClickHouse, or vice versa.

+

The TabSeparated format supports outputting total values (when using WITH TOTALS) and extreme values (when 'extremes' is set to 1). In these cases, the total values and extremes are output after the main data. The main result, total values, and extremes are separated from each other by an empty line. Example:

+
SELECT EventDate, count() AS c FROM test.hits GROUP BY EventDate WITH TOTALS ORDER BY EventDate FORMAT TabSeparated``
+
+ + +
2014-03-17      1406958
+2014-03-18      1383658
+2014-03-19      1405797
+2014-03-20      1353623
+2014-03-21      1245779
+2014-03-22      1031592
+2014-03-23      1046491
+
+0000-00-00      8873898
+
+2014-03-17      1031592
+2014-03-23      1406958
+
+ + +

This format is also available under the name TSV.

+

TabSeparatedRaw

+

Differs from TabSeparated format in that the rows are written without escaping. +This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

+

This format is also available under the name TSVRaw.

+

TabSeparatedWithNames

+

Differs from the TabSeparated format in that the column names are written in the first row. +During parsing, the first row is completely ignored. You can't use column names to determine their position or to check their correctness. +(Support for parsing the header row may be added in the future.)

+

This format is also available under the name TSVWithNames.

+

TabSeparatedWithNamesAndTypes

+

Differs from the TabSeparated format in that the column names are written to the first row, while the column types are in the second row. +During parsing, the first and second rows are completely ignored.

+

This format is also available under the name TSVWithNamesAndTypes.

+

CSV

+

Comma Separated Values format (RFC).

+

When formatting, rows are enclosed in double quotes. A double quote inside a string is output as two double quotes in a row. There are no other rules for escaping characters. Date and date-time are enclosed in double quotes. Numbers are output without quotes. Values ​​are separated by a delimiter*. Rows are separated using the Unix line feed (LF). Arrays are serialized in CSV as follows: first the array is serialized to a string as in TabSeparated format, and then the resulting string is output to CSV in double quotes. Tuples in CSV format are serialized as separate columns (that is, their nesting in the tuple is lost).

+

*By default — ,. See a format_csv_delimiter setting for additional info.

+

When parsing, all values can be parsed either with or without quotes. Both double and single quotes are supported. Rows can also be arranged without quotes. In this case, they are parsed up to a delimiter or line feed (CR or LF). In violation of the RFC, when parsing rows without quotes, the leading and trailing spaces and tabs are ignored. For the line feed, Unix (LF), Windows (CR LF) and Mac OS Classic (CR LF) are all supported.

+

The CSV format supports the output of totals and extremes the same way as TabSeparated.

+

CSVWithNames

+

Also prints the header row, similar to TabSeparatedWithNames.

+

Values

+

Prints every row in brackets. Rows are separated by commas. There is no comma after the last row. The values inside the brackets are also comma-separated. Numbers are output in decimal format without quotes. Arrays are output in square brackets. Strings, dates, and dates with times are output in quotes. Escaping rules and parsing are similar to the TabSeparated format. During formatting, extra spaces aren't inserted, but during parsing, they are allowed and skipped (except for spaces inside array values, which are not allowed).

+

The minimum set of characters that you need to escape when passing data in Values ​​format: single quotes and backslashes.

+

This is the format that is used in INSERT INTO t VALUES ..., but you can also use it for formatting query results.

+

Vertical

+

Prints each value on a separate line with the column name specified. This format is convenient for printing just one or a few rows, if each row consists of a large number of columns. +This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

+

VerticalRaw

+

Differs from Vertical format in that the rows are not escaped. +This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

+

Examples:

+
:) SHOW CREATE TABLE geonames FORMAT VerticalRaw;
+Row 1:
+──────
+statement: CREATE TABLE default.geonames ( geonameid UInt32, date Date DEFAULT CAST('2017-12-08' AS Date)) ENGINE = MergeTree(date, geonameid, 8192)
+
+:) SELECT 'string with \'quotes\' and \t with some special \n characters' AS test FORMAT VerticalRaw;
+Row 1:
+──────
+test: string with 'quotes' and   with some special
+ characters
+
+ + +

Compare with the Vertical format:

+
:) SELECT 'string with \'quotes\' and \t with some special \n characters' AS test FORMAT Vertical;
+Row 1:
+──────
+test: string with \'quotes\' and \t with some special \n characters
+
+ + +

JSON

+

Outputs data in JSON format. Besides data tables, it also outputs column names and types, along with some additional information: the total number of output rows, and the number of rows that could have been output if there weren't a LIMIT. Example:

+
SELECT SearchPhrase, count() AS c FROM test.hits GROUP BY SearchPhrase WITH TOTALS ORDER BY c DESC LIMIT 5 FORMAT JSON
+
+ + +
{
+        "meta":
+        [
+                {
+                        "name": "SearchPhrase",
+                        "type": "String"
+                },
+                {
+                        "name": "c",
+                        "type": "UInt64"
+                }
+        ],
+
+        "data":
+        [
+                {
+                        "SearchPhrase": "",
+                        "c": "8267016"
+                },
+                {
+                        "SearchPhrase": "bathroom interior design",
+                        "c": "2166"
+                },
+                {
+                        "SearchPhrase": "yandex",
+                        "c": "1655"
+                },
+                {
+                        "SearchPhrase": "spring 2014 fashion",
+                        "c": "1549"
+                },
+                {
+                        "SearchPhrase": "freeform photos",
+                        "c": "1480"
+                }
+        ],
+
+        "totals":
+        {
+                "SearchPhrase": "",
+                "c": "8873898"
+        },
+
+        "extremes":
+        {
+                "min":
+                {
+                        "SearchPhrase": "",
+                        "c": "1480"
+                },
+                "max":
+                {
+                        "SearchPhrase": "",
+                        "c": "8267016"
+                }
+        },
+
+        "rows": 5,
+
+        "rows_before_limit_at_least": 141137
+}
+
+ + +

The JSON is compatible with JavaScript. To ensure this, some characters are additionally escaped: the slash / is escaped as \/; alternative line breaks U+2028 and U+2029, which break some browsers, are escaped as \uXXXX. ASCII control characters are escaped: backspace, form feed, line feed, carriage return, and horizontal tab are replaced with \b, \f, \n, \r, \t , as well as the remaining bytes in the 00-1F range using \uXXXX sequences. Invalid UTF-8 sequences are changed to the replacement character � so the output text will consist of valid UTF-8 sequences. For compatibility with JavaScript, Int64 and UInt64 integers are enclosed in double quotes by default. To remove the quotes, you can set the configuration parameter output_format_json_quote_64bit_integers to 0.

+

rows – The total number of output rows.

+

rows_before_limit_at_least The minimal number of rows there would have been without LIMIT. Output only if the query contains LIMIT. +If the query contains GROUP BY, rows_before_limit_at_least is the exact number of rows there would have been without a LIMIT.

+

totals – Total values (when using WITH TOTALS).

+

extremes – Extreme values (when extremes is set to 1).

+

This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). +See also the JSONEachRow format.

+

JSONCompact

+

Differs from JSON only in that data rows are output in arrays, not in objects.

+

Example:

+
{
+        "meta":
+        [
+                {
+                        "name": "SearchPhrase",
+                        "type": "String"
+                },
+                {
+                        "name": "c",
+                        "type": "UInt64"
+                }
+        ],
+
+        "data":
+        [
+                ["", "8267016"],
+                ["bathroom interior design", "2166"],
+                ["yandex", "1655"],
+                ["spring 2014 fashion", "1549"],
+                ["freeform photos", "1480"]
+        ],
+
+        "totals": ["","8873898"],
+
+        "extremes":
+        {
+                "min": ["","1480"],
+                "max": ["","8267016"]
+        },
+
+        "rows": 5,
+
+        "rows_before_limit_at_least": 141137
+}
+
+ + +

This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). +See also the JSONEachRow format.

+

JSONEachRow

+

Outputs data as separate JSON objects for each row (newline delimited JSON).

+
{"SearchPhrase":"","count()":"8267016"}
+{"SearchPhrase":"bathroom interior design","count()":"2166"}
+{"SearchPhrase":"yandex","count()":"1655"}
+{"SearchPhrase":"spring 2014 fashion","count()":"1549"}
+{"SearchPhrase":"freeform photo","count()":"1480"}
+{"SearchPhrase":"angelina jolie","count()":"1245"}
+{"SearchPhrase":"omsk","count()":"1112"}
+{"SearchPhrase":"photos of dog breeds","count()":"1091"}
+{"SearchPhrase":"curtain design","count()":"1064"}
+{"SearchPhrase":"baku","count()":"1000"}
+
+ + +

Unlike the JSON format, there is no substitution of invalid UTF-8 sequences. Any set of bytes can be output in the rows. This is necessary so that data can be formatted without losing any information. Values are escaped in the same way as for JSON.

+

For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted – they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Whitespace between elements is ignored. If a comma is placed after the objects, it is ignored. Objects don't necessarily have to be separated by new lines.

+

TSKV

+

Similar to TabSeparated, but outputs a value in name=value format. Names are escaped the same way as in TabSeparated format, and the = symbol is also escaped.

+
SearchPhrase=   count()=8267016
+SearchPhrase=bathroom interior design    count()=2166
+SearchPhrase=yandex     count()=1655
+SearchPhrase=spring 2014 fashion    count()=1549
+SearchPhrase=freeform photos       count()=1480
+SearchPhrase=angelina jolia    count()=1245
+SearchPhrase=omsk       count()=1112
+SearchPhrase=photos of dog breeds    count()=1091
+SearchPhrase=curtain design        count()=1064
+SearchPhrase=baku       count()=1000
+
+ + +

When there is a large number of small columns, this format is ineffective, and there is generally no reason to use it. It is used in some departments of Yandex.

+

Both data output and parsing are supported in this format. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted – they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults.

+

Parsing allows the presence of the additional field tskv without the equal sign or a value. This field is ignored.

+

Pretty

+

Outputs data as Unicode-art tables, also using ANSI-escape sequences for setting colors in the terminal. +A full grid of the table is drawn, and each row occupies two lines in the terminal. +Each result block is output as a separate table. This is necessary so that blocks can be output without buffering results (buffering would be necessary in order to pre-calculate the visible width of all the values). +To avoid dumping too much data to the terminal, only the first 10,000 rows are printed. If the number of rows is greater than or equal to 10,000, the message "Showed first 10 000" is printed. +This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

+

The Pretty format supports outputting total values (when using WITH TOTALS) and extremes (when 'extremes' is set to 1). In these cases, total values and extreme values are output after the main data, in separate tables. Example (shown for the PrettyCompact format):

+
SELECT EventDate, count() AS c FROM test.hits GROUP BY EventDate WITH TOTALS ORDER BY EventDate FORMAT PrettyCompact
+
+ + +
┌──EventDate─┬───────c─┐
+│ 2014-03-17 │ 1406958 │
+│ 2014-03-18 │ 1383658 │
+│ 2014-03-19 │ 1405797 │
+│ 2014-03-20 │ 1353623 │
+│ 2014-03-21 │ 1245779 │
+│ 2014-03-22 │ 1031592 │
+│ 2014-03-23 │ 1046491 │
+└────────────┴─────────┘
+
+Totals:
+┌──EventDate─┬───────c─┐
+│ 0000-00-00 │ 8873898 │
+└────────────┴─────────┘
+
+Extremes:
+┌──EventDate─┬───────c─┐
+│ 2014-03-17 │ 1031592 │
+│ 2014-03-23 │ 1406958 │
+└────────────┴─────────┘
+
+ + +

PrettyCompact

+

Differs from Pretty in that the grid is drawn between rows and the result is more compact. +This format is used by default in the command-line client in interactive mode.

+

PrettyCompactMonoBlock

+

Differs from PrettyCompact in that up to 10,000 rows are buffered, then output as a single table, not by blocks.

+

PrettyNoEscapes

+

Differs from Pretty in that ANSI-escape sequences aren't used. This is necessary for displaying this format in a browser, as well as for using the 'watch' command-line utility.

+

Example:

+
watch -n1 "clickhouse-client --query='SELECT * FROM system.events FORMAT PrettyCompactNoEscapes'"
+
+ + +

You can use the HTTP interface for displaying in the browser.

+

PrettyCompactNoEscapes

+

The same as the previous setting.

+

PrettySpaceNoEscapes

+

The same as the previous setting.

+

PrettySpace

+

Differs from PrettyCompact in that whitespace (space characters) is used instead of the grid.

+

RowBinary

+

Formats and parses data by row in binary format. Rows and values are listed consecutively, without separators. +This format is less efficient than the Native format, since it is row-based.

+

Integers use fixed-length little endian representation. For example, UInt64 uses 8 bytes. +DateTime is represented as UInt32 containing the Unix timestamp as the value. +Date is represented as a UInt16 object that contains the number of days since 1970-01-01 as the value. +String is represented as a varint length (unsigned LEB128), followed by the bytes of the string. +FixedString is represented simply as a sequence of bytes.

+

Array is represented as a varint length (unsigned LEB128), followed by successive elements of the array.

+

Native

+

The most efficient format. Data is written and read by blocks in binary format. For each block, the number of rows, number of columns, column names and types, and parts of columns in this block are recorded one after another. In other words, this format is "columnar" – it doesn't convert columns to rows. This is the format used in the native interface for interaction between servers, for using the command-line client, and for C++ clients.

+

You can use this format to quickly generate dumps that can only be read by the ClickHouse DBMS. It doesn't make sense to work with this format yourself.

+

Null

+

Nothing is output. However, the query is processed, and when using the command-line client, data is transmitted to the client. This is used for tests, including productivity testing. +Obviously, this format is only appropriate for output, not for parsing.

+

XML

+

XML format is suitable only for output, not for parsing. Example:

+
<?xml version='1.0' encoding='UTF-8' ?>
+<result>
+        <meta>
+                <columns>
+                        <column>
+                                <name>SearchPhrase</name>
+                                <type>String</type>
+                        </column>
+                        <column>
+                                <name>count()</name>
+                                <type>UInt64</type>
+                        </column>
+                </columns>
+        </meta>
+        <data>
+                <row>
+                        <SearchPhrase></SearchPhrase>
+                        <field>8267016</field>
+                </row>
+                <row>
+                        <SearchPhrase>bathroom interior design</SearchPhrase>
+                        <field>2166</field>
+                </row>
+                <row>
+                        <SearchPhrase>yandex</SearchPhrase>
+                        <field>1655</field>
+                </row>
+                <row>
+                        <SearchPhrase>spring 2014 fashion</SearchPhrase>
+                        <field>1549</field>
+                </row>
+                <row>
+                        <SearchPhrase>freeform photos</SearchPhrase>
+                        <field>1480</field>
+                </row>
+                <row>
+                        <SearchPhrase>angelina jolie</SearchPhrase>
+                        <field>1245</field>
+                </row>
+                <row>
+                        <SearchPhrase>omsk</SearchPhrase>
+                        <field>1112</field>
+                </row>
+                <row>
+                        <SearchPhrase>photos of dog breeds</SearchPhrase>
+                        <field>1091</field>
+                </row>
+                <row>
+                        <SearchPhrase>curtain design</SearchPhrase>
+                        <field>1064</field>
+                </row>
+                <row>
+                        <SearchPhrase>baku</SearchPhrase>
+                        <field>1000</field>
+                </row>
+        </data>
+        <rows>10</rows>
+        <rows_before_limit_at_least>141137</rows_before_limit_at_least>
+</result>
+
+ + +

If the column name does not have an acceptable format, just 'field' is used as the element name. In general, the XML structure follows the JSON structure. +Just as for JSON, invalid UTF-8 sequences are changed to the replacement character � so the output text will consist of valid UTF-8 sequences.

+

In string values, the characters < and & are escaped as < and &.

+

Arrays are output as <array><elem>Hello</elem><elem>World</elem>...</array>, +and tuples as <tuple><elem>Hello</elem><elem>World</elem>...</tuple>.

+

+

CapnProto

+

Cap'n Proto is a binary message format similar to Protocol Buffers and Thrift, but not like JSON or MessagePack.

+

Cap'n Proto messages are strictly typed and not self-describing, meaning they need an external schema description. The schema is applied on the fly and cached for each query.

+
SELECT SearchPhrase, count() AS c FROM test.hits
+       GROUP BY SearchPhrase FORMAT CapnProto SETTINGS schema = 'schema:Message'
+
+ + +

Where schema.capnp looks like this:

+
struct Message {
+  SearchPhrase @0 :Text;
+  c @1 :Uint64;
+}
+
+ + +

Schema files are in the file that is located in the directory specified in format_schema_path in the server configuration.

+

Deserialization is effective and usually doesn't increase the system load.

+

+

Data types

+

ClickHouse can store various types of data in table cells.

+

This section describes the supported data types and special considerations when using and/or implementing them, if any.

+

UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64

+

Fixed-length integers, with or without a sign.

+

Int ranges

+
    +
  • Int8 - [-128 : 127]
    • +
  • Int16 - [-32768 : 32767]
    • +
  • Int32 - [-2147483648 : 2147483647]
    • +
  • Int64 - [-9223372036854775808 : 9223372036854775807]
    • +
+

Uint ranges

+
    +
  • UInt8 - [0 : 255]
    • +
  • UInt16 - [0 : 65535]
    • +
  • UInt32 - [0 : 4294967295]
    • +
  • UInt64 - [0 : 18446744073709551615]
    • +
+

Float32, Float64

+

Floating point numbers.

+

Types are equivalent to types of C:

+
    +
  • Float32 - float
    • +
  • Float64 - double
    • +
+

We recommend that you store data in integer form whenever possible. For example, convert fixed precision numbers to integer values, such as monetary amounts or page load times in milliseconds.

+

Using floating-point numbers

+
    +
  • Computations with floating-point numbers might produce a rounding error.
    • +
+
SELECT 1 - 0.9
+
+ + +
┌───────minus(1, 0.9)─┐
+│ 0.09999999999999998 │
+└─────────────────────┘
+
+ + +
    +
  • The result of the calculation depends on the calculation method (the processor type and architecture of the computer system).
    • +
  • Floating-point calculations might result in numbers such as infinity (Inf) and "not-a-number" (NaN). This should be taken into account when processing the results of calculations.
    • +
  • When reading floating point numbers from rows, the result might not be the nearest machine-representable number.
    • +
+

NaN and Inf

+

In contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers:

+
    +
  • Inf – Infinity.
    • +
+
SELECT 0.5 / 0
+
+ + +
┌─divide(0.5, 0)─┐
+│            inf │
+└────────────────┘
+
+ + +
    +
  • -Inf – Negative infinity.
    • +
+
SELECT -0.5 / 0
+
+ + +
┌─divide(-0.5, 0)─┐
+│            -inf │
+└─────────────────┘
+
+ + +
    +
  • NaN – Not a number.
    • +
+
SELECT 0 / 0
+
+ + +
┌─divide(0, 0)─┐
+│          nan │
+└──────────────┘
+
+ + +

See the rules for NaN sorting in the section ORDER BY clause.

+

Boolean values

+

There isn't a separate type for boolean values. They use the UInt8 type, restricted to the values 0 or 1.

+

String

+

Strings of an arbitrary length. The length is not limited. The value can contain an arbitrary set of bytes, including null bytes. +The String type replaces the types VARCHAR, BLOB, CLOB, and others from other DBMSs.

+

Encodings

+

ClickHouse doesn't have the concept of encodings. Strings can contain an arbitrary set of bytes, which are stored and output as-is. +If you need to store texts, we recommend using UTF-8 encoding. At the very least, if your terminal uses UTF-8 (as recommended), you can read and write your values without making conversions. +Similarly, certain functions for working with strings have separate variations that work under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. +For example, the 'length' function calculates the string length in bytes, while the 'lengthUTF8' function calculates the string length in Unicode code points, assuming that the value is UTF-8 encoded.

+

FixedString(N)

+

A fixed-length string of N bytes (not characters or code points). N must be a strictly positive natural number. +When the server reads a string that contains fewer bytes (such as when parsing INSERT data), the string is padded to N bytes by appending null bytes at the right. +When the server reads a string that contains more bytes, an error message is returned. +When the server writes a string (such as when outputting the result of a SELECT query), null bytes are not trimmed off of the end of the string, but are output. +Note that this behavior differs from MySQL behavior for the CHAR type (where strings are padded with spaces, and the spaces are removed for output).

+

Fewer functions can work with the FixedString(N) type than with String, so it is less convenient to use.

+

Date

+

A date. Stored in two bytes as the number of days since 1970-01-01 (unsigned). Allows storing values from just after the beginning of the Unix Epoch to the upper threshold defined by a constant at the compilation stage (currently, this is until the year 2106, but the final fully-supported year is 2105). +The minimum value is output as 0000-00-00.

+

The date is stored without the time zone.

+

DateTime

+

Date with time. Stored in four bytes as a Unix timestamp (unsigned). Allows storing values in the same range as for the Date type. The minimal value is output as 0000-00-00 00:00:00. +The time is stored with accuracy up to one second (without leap seconds).

+

Time zones

+

The date with time is converted from text (divided into component parts) to binary and back, using the system's time zone at the time the client or server starts. In text format, information about daylight savings is lost.

+

By default, the client switches to the timezone of the server when it connects. You can change this behavior by enabling the client command-line option --use_client_time_zone.

+

Supports only those time zones that never had the time differ from UTC for a partial number of hours (without leap seconds) over the entire time range you will be working with.

+

So when working with a textual date (for example, when saving text dumps), keep in mind that there may be ambiguity during changes for daylight savings time, and there may be problems matching data if the time zone changed.

+

Enum

+

Enum8 or Enum16. A finite set of string values that can be stored more efficiently than the String data type.

+

Example:

+
Enum8('hello' = 1, 'world' = 2)
+
+ + +
    +
  • A data type with two possible values: 'hello' and 'world'.
    • +
+

Each of the values is assigned a number in the range -128 ... 127 for Enum8 or in the range -32768 ... 32767 for Enum16. All the strings and numbers must be different. An empty string is allowed. If this type is specified (in a table definition), numbers can be in an arbitrary order. However, the order does not matter.

+

In RAM, this type of column is stored in the same way as Int8 or Int16 of the corresponding numerical values. +When reading in text form, ClickHouse parses the value as a string and searches for the corresponding string from the set of Enum values. If it is not found, an exception is thrown. When reading in text format, the string is read and the corresponding numeric value is looked up. An exception will be thrown if it is not found. +When writing in text form, it writes the value as the corresponding string. If column data contains garbage (numbers that are not from the valid set), an exception is thrown. When reading and writing in binary form, it works the same way as for Int8 and Int16 data types. +The implicit default value is the value with the lowest number.

+

During ORDER BY, GROUP BY, IN, DISTINCT and so on, Enums behave the same way as the corresponding numbers. For example, ORDER BY sorts them numerically. Equality and comparison operators work the same way on Enums as they do on the underlying numeric values.

+

Enum values cannot be compared with numbers. Enums can be compared to a constant string. If the string compared to is not a valid value for the Enum, an exception will be thrown. The IN operator is supported with the Enum on the left hand side and a set of strings on the right hand side. The strings are the values of the corresponding Enum.

+

Most numeric and string operations are not defined for Enum values, e.g. adding a number to an Enum or concatenating a string to an Enum. +However, the Enum has a natural toString function that returns its string value.

+

Enum values are also convertible to numeric types using the toT function, where T is a numeric type. When T corresponds to the enum’s underlying numeric type, this conversion is zero-cost. +The Enum type can be changed without cost using ALTER, if only the set of values is changed. It is possible to both add and remove members of the Enum using ALTER (removing is safe only if the removed value has never been used in the table). As a safeguard, changing the numeric value of a previously defined Enum member will throw an exception.

+

Using ALTER, it is possible to change an Enum8 to an Enum16 or vice versa, just like changing an Int8 to Int16.

+

Array(T)

+

An array of elements of type T. The T type can be any type, including an array. +We don't recommend using multidimensional arrays, because they are not well supported (for example, you can't store multidimensional arrays in tables with a MergeTree engine).

+

AggregateFunction(name, types_of_arguments...)

+

The intermediate state of an aggregate function. To get it, use aggregate functions with the '-State' suffix. For more information, see "AggregatingMergeTree".

+

Tuple(T1, T2, ...)

+

Tuples can't be written to tables (other than Memory tables). They are used for temporary column grouping. Columns can be grouped when an IN expression is used in a query, and for specifying certain formal parameters of lambda functions. For more information, see "IN operators" and "Higher order functions".

+

Tuples can be output as the result of running a query. In this case, for text formats other than JSON*, values are comma-separated in brackets. In JSON* formats, tuples are output as arrays (in square brackets).

+

Nested data structures

+

Nested(Name1 Type1, Name2 Type2, ...)

+

A nested data structure is like a nested table. The parameters of a nested data structure – the column names and types – are specified the same way as in a CREATE query. Each table row can correspond to any number of rows in a nested data structure.

+

Example:

+
CREATE TABLE test.visits
+(
+    CounterID UInt32,
+    StartDate Date,
+    Sign Int8,
+    IsNew UInt8,
+    VisitID UInt64,
+    UserID UInt64,
+    ...
+    Goals Nested
+    (
+        ID UInt32,
+        Serial UInt32,
+        EventTime DateTime,
+        Price Int64,
+        OrderID String,
+        CurrencyID UInt32
+    ),
+    ...
+) ENGINE = CollapsingMergeTree(StartDate, intHash32(UserID), (CounterID, StartDate, intHash32(UserID), VisitID), 8192, Sign)
+
+ + +

This example declares the Goals nested data structure, which contains data about conversions (goals reached). Each row in the 'visits' table can correspond to zero or any number of conversions.

+

Only a single nesting level is supported. Columns of nested structures containing arrays are equivalent to multidimensional arrays, so they have limited support (there is no support for storing these columns in tables with the MergeTree engine).

+

In most cases, when working with a nested data structure, its individual columns are specified. To do this, the column names are separated by a dot. These columns make up an array of matching types. All the column arrays of a single nested data structure have the same length.

+

Example:

+
SELECT
+    Goals.ID,
+    Goals.EventTime
+FROM test.visits
+WHERE CounterID = 101500 AND length(Goals.ID) < 5
+LIMIT 10
+
+ + +
┌─Goals.ID───────────────────────┬─Goals.EventTime───────────────────────────────────────────────────────────────────────────┐
+│ [1073752,591325,591325]        │ ['2014-03-17 16:38:10','2014-03-17 16:38:48','2014-03-17 16:42:27']                       │
+│ [1073752]                      │ ['2014-03-17 00:28:25']                                                                   │
+│ [1073752]                      │ ['2014-03-17 10:46:20']                                                                   │
+│ [1073752,591325,591325,591325] │ ['2014-03-17 13:59:20','2014-03-17 22:17:55','2014-03-17 22:18:07','2014-03-17 22:18:51'] │
+│ []                             │ []                                                                                        │
+│ [1073752,591325,591325]        │ ['2014-03-17 11:37:06','2014-03-17 14:07:47','2014-03-17 14:36:21']                       │
+│ []                             │ []                                                                                        │
+│ []                             │ []                                                                                        │
+│ [591325,1073752]               │ ['2014-03-17 00:46:05','2014-03-17 00:46:05']                                             │
+│ [1073752,591325,591325,591325] │ ['2014-03-17 13:28:33','2014-03-17 13:30:26','2014-03-17 18:51:21','2014-03-17 18:51:45'] │
+└────────────────────────────────┴───────────────────────────────────────────────────────────────────────────────────────────┘
+
+ + +

It is easiest to think of a nested data structure as a set of multiple column arrays of the same length.

+

The only place where a SELECT query can specify the name of an entire nested data structure instead of individual columns is the ARRAY JOIN clause. For more information, see "ARRAY JOIN clause". Example:

+
SELECT
+    Goal.ID,
+    Goal.EventTime
+FROM test.visits
+ARRAY JOIN Goals AS Goal
+WHERE CounterID = 101500 AND length(Goals.ID) < 5
+LIMIT 10
+
+ + +
┌─Goal.ID─┬──────Goal.EventTime─┐
+│ 1073752 │ 2014-03-17 16:38:10 │
+│  591325 │ 2014-03-17 16:38:48 │
+│  591325 │ 2014-03-17 16:42:27 │
+│ 1073752 │ 2014-03-17 00:28:25 │
+│ 1073752 │ 2014-03-17 10:46:20 │
+│ 1073752 │ 2014-03-17 13:59:20 │
+│  591325 │ 2014-03-17 22:17:55 │
+│  591325 │ 2014-03-17 22:18:07 │
+│  591325 │ 2014-03-17 22:18:51 │
+│ 1073752 │ 2014-03-17 11:37:06 │
+└─────────┴─────────────────────┘
+
+ + +

You can't perform SELECT for an entire nested data structure. You can only explicitly list individual columns that are part of it.

+

For an INSERT query, you should pass all the component column arrays of a nested data structure separately (as if they were individual column arrays). During insertion, the system checks that they have the same length.

+

For a DESCRIBE query, the columns in a nested data structure are listed separately in the same way.

+

The ALTER query is very limited for elements in a nested data structure.

+

Special data types

+

Special data type values can't be saved to a table or output in results, but are used as the intermediate result of running a query.

+

Expression

+

Used for representing lambda expressions in high-order functions.

+

Set

+

Used for the right half of an IN expression.

+

Operators

+

All operators are transformed to the corresponding functions at the query parsing stage, in accordance with their precedence and associativity. +Groups of operators are listed in order of priority (the higher it is in the list, the earlier the operator is connected to its arguments).

+

Access operators

+

a[N] Access to an element of an array; arrayElement(a, N) function.

+

a.N – Access to a tuble element; tupleElement(a, N) function.

+

Numeric negation operator

+

-a – The negate (a) function.

+

Multiplication and division operators

+

a * b – The multiply (a, b) function.

+

a / b – The divide(a, b) function.

+

a % b – The modulo(a, b) function.

+

Addition and subtraction operators

+

a + b – The plus(a, b) function.

+

a - b – The minus(a, b) function.

+

Comparison operators

+

a = b – The equals(a, b) function.

+

a == b – The equals(a, b) function.

+

a != b – The notEquals(a, b) function.

+

a <> b – The notEquals(a, b) function.

+

a <= b – The lessOrEquals(a, b) function.

+

a >= b – The greaterOrEquals(a, b) function.

+

a < b – The less(a, b) function.

+

a > b – The greater(a, b) function.

+

a LIKE s – The like(a, b) function.

+

a NOT LIKE s – The notLike(a, b) function.

+

a BETWEEN b AND c – The same as a >= b AND a <= c.

+

Operators for working with data sets

+

See the section "IN operators".

+

a IN ... – The in(a, b) function

+

a NOT IN ... – The notIn(a, b) function.

+

a GLOBAL IN ... – The globalIn(a, b) function.

+

a GLOBAL NOT IN ... – The globalNotIn(a, b) function.

+

Logical negation operator

+

NOT a The not(a) function.

+

Logical AND operator

+

a AND b – Theand(a, b) function.

+

Logical OR operator

+

a OR b – The or(a, b) function.

+

Conditional operator

+

a ? b : c – The if(a, b, c) function.

+

Note:

+

The conditional operator calculates the values of b and c, then checks whether condition a is met, and then returns the corresponding value. If "b" or "c" is an arrayJoin() function, each row will be replicated regardless of the "a" condition.

+

Conditional expression

+
CASE [x]
+    WHEN a THEN b
+    [WHEN ... THEN ...]
+    ELSE c
+END
+
+ + +

If "x" is specified, then transform(x, [a, ...], [b, ...], c). Otherwise – multiIf(a, b, ..., c).

+

Concatenation operator

+

s1 || s2 – The concat(s1, s2) function.

+

Lambda creation operator

+

x -> expr – The lambda(x, expr) function.

+

The following operators do not have a priority, since they are brackets:

+

Array creation operator

+

[x1, ...] – The array(x1, ...) function.

+

Tuple creation operator

+

(x1, x2, ...) – The tuple(x2, x2, ...) function.

+

Associativity

+

All binary operators have left associativity. For example, 1 + 2 + 3 is transformed to plus(plus(1, 2), 3). +Sometimes this doesn't work the way you expect. For example, SELECT 4 > 2 > 3 will result in 0.

+

For efficiency, the and and or functions accept any number of arguments. The corresponding chains of AND and OR operators are transformed to a single call of these functions.

+

Functions

+

There are at least* two types of functions - regular functions (they are just called "functions") and aggregate functions. These are completely different concepts. Regular functions work as if they are applied to each row separately (for each row, the result of the function doesn't depend on the other rows). Aggregate functions accumulate a set of values from various rows (i.e. they depend on the entire set of rows).

+

In this section we discuss regular functions. For aggregate functions, see the section "Aggregate functions".

+

* - There is a third type of function that the 'arrayJoin' function belongs to; table functions can also be mentioned separately.*

+

Strong typing

+

In contrast to standard SQL, ClickHouse has strong typing. In other words, it doesn't make implicit conversions between types. Each function works for a specific set of types. This means that sometimes you need to use type conversion functions.

+

Common subexpression elimination

+

All expressions in a query that have the same AST (the same record or same result of syntactic parsing) are considered to have identical values. Such expressions are concatenated and executed once. Identical subqueries are also eliminated this way.

+

Types of results

+

All functions return a single return as the result (not several values, and not zero values). The type of result is usually defined only by the types of arguments, not by the values. Exceptions are the tupleElement function (the a.N operator), and the toFixedString function.

+

Constants

+

For simplicity, certain functions can only work with constants for some arguments. For example, the right argument of the LIKE operator must be a constant. +Almost all functions return a constant for constant arguments. The exception is functions that generate random numbers. +The 'now' function returns different values for queries that were run at different times, but the result is considered a constant, since constancy is only important within a single query. +A constant expression is also considered a constant (for example, the right half of the LIKE operator can be constructed from multiple constants).

+

Functions can be implemented in different ways for constant and non-constant arguments (different code is executed). But the results for a constant and for a true column containing only the same value should match each other.

+

Constancy

+

Functions can't change the values of their arguments – any changes are returned as the result. Thus, the result of calculating separate functions does not depend on the order in which the functions are written in the query.

+

Error handling

+

Some functions might throw an exception if the data is invalid. In this case, the query is canceled and an error text is returned to the client. For distributed processing, when an exception occurs on one of the servers, the other servers also attempt to abort the query.

+

Evaluation of argument expressions

+

In almost all programming languages, one of the arguments might not be evaluated for certain operators. This is usually the operators &&, ||, and ?:. +But in ClickHouse, arguments of functions (operators) are always evaluated. This is because entire parts of columns are evaluated at once, instead of calculating each row separately.

+

Performing functions for distributed query processing

+

For distributed query processing, as many stages of query processing as possible are performed on remote servers, and the rest of the stages (merging intermediate results and everything after that) are performed on the requestor server.

+

This means that functions can be performed on different servers. +For example, in the query SELECT f(sum(g(x))) FROM distributed_table GROUP BY h(y),

+
    +
  • if a distributed_table has at least two shards, the functions 'g' and 'h' are performed on remote servers, and the function 'f' is performed on the requestor server.
    • +
  • if a distributed_table has only one shard, all the 'f', 'g', and 'h' functions are performed on this shard's server.
    • +
+

The result of a function usually doesn't depend on which server it is performed on. However, sometimes this is important. +For example, functions that work with dictionaries use the dictionary that exists on the server they are running on. +Another example is the hostName function, which returns the name of the server it is running on in order to make GROUP BY by servers in a SELECT query.

+

If a function in a query is performed on the requestor server, but you need to perform it on remote servers, you can wrap it in an 'any' aggregate function or add it to a key in GROUP BY.

+

Arithmetic functions

+

For all arithmetic functions, the result type is calculated as the smallest number type that the result fits in, if there is such a type. The minimum is taken simultaneously based on the number of bits, whether it is signed, and whether it floats. If there are not enough bits, the highest bit type is taken.

+

Example:

+
SELECT toTypeName(0), toTypeName(0 + 0), toTypeName(0 + 0 + 0), toTypeName(0 + 0 + 0 + 0)
+
+ + +
┌─toTypeName(0)─┬─toTypeName(plus(0, 0))─┬─toTypeName(plus(plus(0, 0), 0))─┬─toTypeName(plus(plus(plus(0, 0), 0), 0))─┐
+│ UInt8         │ UInt16                 │ UInt32                          │ UInt64                                   │
+└───────────────┴────────────────────────┴─────────────────────────────────┴──────────────────────────────────────────┘
+
+ + +

Arithmetic functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.

+

Overflow is produced the same way as in C++.

+

plus(a, b), a + b operator

+

Calculates the sum of the numbers. +You can also add integer numbers with a date or date and time. In the case of a date, adding an integer means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds.

+

minus(a, b), a - b operator

+

Calculates the difference. The result is always signed.

+

You can also calculate integer numbers from a date or date with time. The idea is the same – see above for 'plus'.

+

multiply(a, b), a * b operator

+

Calculates the product of the numbers.

+

divide(a, b), a / b operator

+

Calculates the quotient of the numbers. The result type is always a floating-point type. +It is not integer division. For integer division, use the 'intDiv' function. +When dividing by zero you get 'inf', '-inf', or 'nan'.

+

intDiv(a, b)

+

Calculates the quotient of the numbers. Divides into integers, rounding down (by the absolute value). +An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

+

intDivOrZero(a, b)

+

Differs from 'intDiv' in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one.

+

modulo(a, b), a % b operator

+

Calculates the remainder after division. +If arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion. +The remainder is taken in the same sense as in C++. Truncated division is used for negative numbers. +An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

+

negate(a), -a operator

+

Calculates a number with the reverse sign. The result is always signed.

+

abs(a)

+

Calculates the absolute value of the number (a). That is, if a < 0, it returns -a. For unsigned types it doesn't do anything. For signed integer types, it returns an unsigned number.

+

gcd(a, b)

+

Returns the greatest common divisor of the numbers. +An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

+

lcm(a, b)

+

Returns the least common multiple of the numbers. +An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

+

Comparison functions

+

Comparison functions always return 0 or 1 (Uint8).

+

The following types can be compared:

+
    +
  • numbers
    • +
  • strings and fixed strings
    • +
  • dates
    • +
  • dates with times
    • +
+

within each group, but not between different groups.

+

For example, you can't compare a date with a string. You have to use a function to convert the string to a date, or vice versa.

+

Strings are compared by bytes. A shorter string is smaller than all strings that start with it and that contain at least one more character.

+

Note. Up until version 1.1.54134, signed and unsigned numbers were compared the same way as in C++. In other words, you could get an incorrect result in cases like SELECT 9223372036854775807 > -1. This behavior changed in version 1.1.54134 and is now mathematically correct.

+

equals, a = b and a == b operator

+

notEquals, a ! operator= b and a <> b

+

less, < operator

+

greater, > operator

+

lessOrEquals, <= operator

+

greaterOrEquals, >= operator

+

Logical functions

+

Logical functions accept any numeric types, but return a UInt8 number equal to 0 or 1.

+

Zero as an argument is considered "false," while any non-zero value is considered "true".

+

and, AND operator

+

or, OR operator

+

not, NOT operator

+

xor

+

+

Type conversion functions

+

toUInt8, toUInt16, toUInt32, toUInt64

+

toInt8, toInt16, toInt32, toInt64

+

toFloat32, toFloat64

+

toUInt8OrZero, toUInt16OrZero, toUInt32OrZero, toUInt64OrZero, toInt8OrZero, toInt16OrZero, toInt32OrZero, toInt64OrZero, toFloat32OrZero, toFloat64OrZero

+

toDate, toDateTime

+

toString

+

Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. +All these functions accept one argument.

+

When converting to or from a string, the value is formatted or parsed using the same rules as for the TabSeparated format (and almost all other text formats). If the string can't be parsed, an exception is thrown and the request is canceled.

+

When converting dates to numbers or vice versa, the date corresponds to the number of days since the beginning of the Unix epoch. +When converting dates with times to numbers or vice versa, the date with time corresponds to the number of seconds since the beginning of the Unix epoch.

+

The date and date-with-time formats for the toDate/toDateTime functions are defined as follows:

+
YYYY-MM-DD
+YYYY-MM-DD hh:mm:ss
+
+ + +

As an exception, if converting from UInt32, Int32, UInt64, or Int64 numeric types to Date, and if the number is greater than or equal to 65536, the number is interpreted as a Unix timestamp (and not as the number of days) and is rounded to the date. This allows support for the common occurrence of writing 'toDate(unix_timestamp)', which otherwise would be an error and would require writing the more cumbersome 'toDate(toDateTime(unix_timestamp))'.

+

Conversion between a date and date with time is performed the natural way: by adding a null time or dropping the time.

+

Conversion between numeric types uses the same rules as assignments between different numeric types in C++.

+

Additionally, the toString function of the DateTime argument can take a second String argument containing the name of the time zone. Example: Asia/Yekaterinburg In this case, the time is formatted according to the specified time zone.

+
SELECT
+    now() AS now_local,
+    toString(now(), 'Asia/Yekaterinburg') AS now_yekat
+
+ + +
┌───────────now_local─┬─now_yekat───────────┐
+│ 2016-06-15 00:11:21 │ 2016-06-15 02:11:21 │
+└─────────────────────┴─────────────────────┘
+
+ + +

Also see the toUnixTimestamp function.

+

toFixedString(s, N)

+

Converts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant. +If the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown.

+

toStringCutToZero(s)

+

Accepts a String or FixedString argument. Returns the String with the content truncated at the first zero byte found.

+

Example:

+
SELECT toFixedString('foo', 8) AS s, toStringCutToZero(s) AS s_cut
+
+ + +
┌─s─────────────┬─s_cut─┐
+│ foo\0\0\0\0\0 │ foo   │
+└───────────────┴───────┘
+
+ + +
SELECT toFixedString('foo\0bar', 8) AS s, toStringCutToZero(s) AS s_cut
+
+ + +
┌─s──────────┬─s_cut─┐
+│ foo\0bar\0 │ foo   │
+└────────────┴───────┘
+
+ + +

reinterpretAsUInt8, reinterpretAsUInt16, reinterpretAsUInt32, reinterpretAsUInt64

+

reinterpretAsInt8, reinterpretAsInt16, reinterpretAsInt32, reinterpretAsInt64

+

reinterpretAsFloat32, reinterpretAsFloat64

+

reinterpretAsDate, reinterpretAsDateTime

+

These functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.

+

reinterpretAsString

+

This function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long.

+

CAST(x, t)

+

Converts 'x' to the 't' data type. The syntax CAST(x AS t) is also supported.

+

Example:

+
SELECT
+    '2016-06-15 23:00:00' AS timestamp,
+    CAST(timestamp AS DateTime) AS datetime,
+    CAST(timestamp AS Date) AS date,
+    CAST(timestamp, 'String') AS string,
+    CAST(timestamp, 'FixedString(22)') AS fixed_string
+
+ + +
┌─timestamp───────────┬────────────datetime─┬───────date─┬─string──────────────┬─fixed_string──────────────┐
+│ 2016-06-15 23:00:00 │ 2016-06-15 23:00:00 │ 2016-06-15 │ 2016-06-15 23:00:00 │ 2016-06-15 23:00:00\0\0\0 │
+└─────────────────────┴─────────────────────┴────────────┴─────────────────────┴───────────────────────────┘
+
+ + +

Conversion to FixedString (N) only works for arguments of type String or FixedString (N).

+

Functions for working with dates and times

+

Support for time zones

+

All functions for working with the date and time that have a logical use for the time zone can accept a second optional time zone argument. Example: Asia/Yekaterinburg. In this case, they use the specified time zone instead of the local (default) one.

+
SELECT
+    toDateTime('2016-06-15 23:00:00') AS time,
+    toDate(time) AS date_local,
+    toDate(time, 'Asia/Yekaterinburg') AS date_yekat,
+    toString(time, 'US/Samoa') AS time_samoa
+
+ + +
┌────────────────time─┬─date_local─┬─date_yekat─┬─time_samoa──────────┐
+│ 2016-06-15 23:00:00 │ 2016-06-15 │ 2016-06-16 │ 2016-06-15 09:00:00 │
+└─────────────────────┴────────────┴────────────┴─────────────────────┘
+
+ + +

Only time zones that differ from UTC by a whole number of hours are supported.

+

toYear

+

Converts a date or date with time to a UInt16 number containing the year number (AD).

+

toMonth

+

Converts a date or date with time to a UInt8 number containing the month number (1-12).

+

toDayOfMonth

+

-Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31).

+

toDayOfWeek

+

Converts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7).

+

toHour

+

Converts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23). +This function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true – even in Moscow the clocks were twice changed at a different time).

+

toMinute

+

Converts a date with time to a UInt8 number containing the number of the minute of the hour (0-59).

+

toSecond

+

Converts a date with time to a UInt8 number containing the number of the second in the minute (0-59). +Leap seconds are not accounted for.

+

toMonday

+

Rounds down a date or date with time to the nearest Monday. +Returns the date.

+

toStartOfMonth

+

Rounds down a date or date with time to the first day of the month. +Returns the date.

+

toStartOfQuarter

+

Rounds down a date or date with time to the first day of the quarter. +The first day of the quarter is either 1 January, 1 April, 1 July, or 1 October. +Returns the date.

+

toStartOfYear

+

Rounds down a date or date with time to the first day of the year. +Returns the date.

+

toStartOfMinute

+

Rounds down a date with time to the start of the minute.

+

toStartOfFiveMinute

+

Rounds down a date with time to the start of the hour.

+

toStartOfFifteenMinutes

+

Rounds down the date with time to the start of the fifteen-minute interval.

+

Note: If you need to round a date with time to any other number of seconds, minutes, or hours, you can convert it into a number by using the toUInt32 function, then round the number using intDiv and multiplication, and convert it back using the toDateTime function.

+

toStartOfHour

+

Rounds down a date with time to the start of the hour.

+

toStartOfDay

+

Rounds down a date with time to the start of the day.

+

toTime

+

Converts a date with time to a certain fixed date, while preserving the time.

+

toRelativeYearNum

+

Converts a date with time or date to the number of the year, starting from a certain fixed point in the past.

+

toRelativeMonthNum

+

Converts a date with time or date to the number of the month, starting from a certain fixed point in the past.

+

toRelativeWeekNum

+

Converts a date with time or date to the number of the week, starting from a certain fixed point in the past.

+

toRelativeDayNum

+

Converts a date with time or date to the number of the day, starting from a certain fixed point in the past.

+

toRelativeHourNum

+

Converts a date with time or date to the number of the hour, starting from a certain fixed point in the past.

+

toRelativeMinuteNum

+

Converts a date with time or date to the number of the minute, starting from a certain fixed point in the past.

+

toRelativeSecondNum

+

Converts a date with time or date to the number of the second, starting from a certain fixed point in the past.

+

now

+

Accepts zero arguments and returns the current time at one of the moments of request execution. +This function returns a constant, even if the request took a long time to complete.

+

today

+

Accepts zero arguments and returns the current date at one of the moments of request execution. +The same as 'toDate(now())'.

+

yesterday

+

Accepts zero arguments and returns yesterday's date at one of the moments of request execution. +The same as 'today() - 1'.

+

timeSlot

+

Rounds the time to the half hour. +This function is specific to Yandex.Metrica, since half an hour is the minimum amount of time for breaking a session into two sessions if a tracking tag shows a single user's consecutive pageviews that differ in time by strictly more than this amount. This means that tuples (the tag ID, user ID, and time slot) can be used to search for pageviews that are included in the corresponding session.

+

timeSlots(StartTime, Duration)

+

For a time interval starting at 'StartTime' and continuing for 'Duration' seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour. +For example, timeSlots(toDateTime('2012-01-01 12:20:00'), 600) = [toDateTime('2012-01-01 12:00:00'), toDateTime('2012-01-01 12:30:00')]. +This is necessary for searching for pageviews in the corresponding session.

+

Functions for working with strings

+

empty

+

Returns 1 for an empty string or 0 for a non-empty string. +The result type is UInt8. +A string is considered non-empty if it contains at least one byte, even if this is a space or a null byte. +The function also works for arrays.

+

notEmpty

+

Returns 0 for an empty string or 1 for a non-empty string. +The result type is UInt8. +The function also works for arrays.

+

length

+

Returns the length of a string in bytes (not in characters, and not in code points). +The result type is UInt64. +The function also works for arrays.

+

lengthUTF8

+

Returns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception). +The result type is UInt64.

+

lower

+

Converts ASCII Latin symbols in a string to lowercase.

+

upper

+

Converts ASCII Latin symbols in a string to uppercase.

+

lowerUTF8

+

Converts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. +It doesn't detect the language. So for Turkish the result might not be exactly correct. +If the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point. +If the string contains a set of bytes that is not UTF-8, then the behavior is undefined.

+

upperUTF8

+

Converts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. +It doesn't detect the language. So for Turkish the result might not be exactly correct. +If the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point. +If the string contains a set of bytes that is not UTF-8, then the behavior is undefined.

+

reverse

+

Reverses the string (as a sequence of bytes).

+

reverseUTF8

+

Reverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn't throw an exception).

+

concat(s1, s2, ...)

+

Concatenates the strings listed in the arguments, without a separator.

+

substring(s, offset, length)

+

Returns a substring starting with the byte from the 'offset' index that is 'length' bytes long. Character indexing starts from one (as in standard SQL). The 'offset' and 'length' arguments must be constants.

+

substringUTF8(s, offset, length)

+

The same as 'substring', but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).

+

appendTrailingCharIfAbsent(s, c)

+

If the 's' string is non-empty and does not contain the 'c' character at the end, it appends the 'c' character to the end.

+

convertCharset(s, from, to)

+

Returns the string 's' that was converted from the encoding in 'from' to the encoding in 'to'.

+

Functions for searching strings

+

The search is case-sensitive in all these functions. +The search substring or regular expression must be a constant in all these functions.

+

position(haystack, needle)

+

Search for the needle substring in the haystack string. +Returns the position (in bytes) of the found substring, starting from 1, or returns 0 if the substring was not found.

+

For case-insensitive search use positionCaseInsensitive function.

+

positionUTF8(haystack, needle)

+

The same as position, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).

+

For case-insensitive search use positionCaseInsensitiveUTF8 function.

+

match(haystack, pattern)

+

Checks whether the string matches the 'pattern' regular expression. A re2 regular expression. +Returns 0 if it doesn't match, or 1 if it matches.

+

Note that the backslash symbol (\) is used for escaping in the regular expression. The same symbol is used for escaping in string literals. So in order to escape the symbol in a regular expression, you must write two backslashes (\) in a string literal.

+

The regular expression works with the string as if it is a set of bytes. The regular expression can't contain null bytes. +For patterns to search for substrings in a string, it is better to use LIKE or 'position', since they work much faster.

+

extract(haystack, pattern)

+

Extracts a fragment of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. If the regex doesn't contain subpatterns, it takes the fragment that matches the entire regex. Otherwise, it takes the fragment that matches the first subpattern.

+

extractAll(haystack, pattern)

+

Extracts all the fragments of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex. In general, the behavior is the same as the 'extract' function (it takes the first subpattern, or the entire expression if there isn't a subpattern).

+

like(haystack, pattern), haystack LIKE pattern operator

+

Checks whether a string matches a simple regular expression. +The regular expression can contain the metasymbols % and _.

+

``% indicates any quantity of any bytes (including zero characters).

+

_ indicates any one byte.

+

Use the backslash (\) for escaping metasymbols. See the note on escaping in the description of the 'match' function.

+

For regular expressions like %needle%, the code is more optimal and works as fast as the position function. +For other regular expressions, the code is the same as for the 'match' function.

+

notLike(haystack, pattern), haystack NOT LIKE pattern operator

+

The same thing as 'like', but negative.

+

Functions for searching and replacing in strings

+

replaceOne(haystack, pattern, replacement)

+

Replaces the first occurrence, if it exists, of the 'pattern' substring in 'haystack' with the 'replacement' substring. +Hereafter, 'pattern' and 'replacement' must be constants.

+

replaceAll(haystack, pattern, replacement)

+

Replaces all occurrences of the 'pattern' substring in 'haystack' with the 'replacement' substring.

+

replaceRegexpOne(haystack, pattern, replacement)

+

Replacement using the 'pattern' regular expression. A re2 regular expression. +Replaces only the first occurrence, if it exists. +A pattern can be specified as 'replacement'. This pattern can include substitutions \0-\9. +The substitution \0 includes the entire regular expression. Substitutions \1-\9 correspond to the subpattern numbers.To use the \ character in a template, escape it using \. +Also keep in mind that a string literal requires an extra escape.

+

Example 1. Converting the date to American format:

+
SELECT DISTINCT
+    EventDate,
+    replaceRegexpOne(toString(EventDate), '(\\d{4})-(\\d{2})-(\\d{2})', '\\2/\\3/\\1') AS res
+FROM test.hits
+LIMIT 7
+FORMAT TabSeparated
+
+ + +
2014-03-17      03/17/2014
+2014-03-18      03/18/2014
+2014-03-19      03/19/2014
+2014-03-20      03/20/2014
+2014-03-21      03/21/2014
+2014-03-22      03/22/2014
+2014-03-23      03/23/2014
+
+ + +

Example 2. Copying a string ten times:

+
SELECT replaceRegexpOne('Hello, World!', '.*', '\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0') AS res
+
+ + +
┌─res────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
+│ Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World! │
+└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
+
+ + +

replaceRegexpAll(haystack, pattern, replacement)

+

This does the same thing, but replaces all the occurrences. Example:

+
SELECT replaceRegexpAll('Hello, World!', '.', '\\0\\0') AS res
+
+ + +
┌─res────────────────────────┐
+│ HHeelllloo,,  WWoorrlldd!! │
+└────────────────────────────┘
+
+ + +

As an exception, if a regular expression worked on an empty substring, the replacement is not made more than once. +Example:

+
SELECT replaceRegexpAll('Hello, World!', '^', 'here: ') AS res
+
+ + +
┌─res─────────────────┐
+│ here: Hello, World! │
+└─────────────────────┘
+
+ + +

Conditional functions

+

if(cond, then, else), cond ? operator then : else

+

Returns 'then' if cond !or 'else' if cond = 0.'cond' must be UInt 8, and 'then' and 'else' must be a type that has the smallest common type.

+

Mathematical functions

+

All the functions return a Float64 number. The accuracy of the result is close to the maximum precision possible, but the result might not coincide with the machine representable number nearest to the corresponding real number.

+

e()

+

Returns a Float64 number close to the e number.

+

pi()

+

Returns a Float64 number close to π.

+

exp(x)

+

Accepts a numeric argument and returns a Float64 number close to the exponent of the argument.

+

log(x)

+

Accepts a numeric argument and returns a Float64 number close to the natural logarithm of the argument.

+

exp2(x)

+

Accepts a numeric argument and returns a Float64 number close to 2^x.

+

log2(x)

+

Accepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument.

+

exp10(x)

+

Accepts a numeric argument and returns a Float64 number close to 10^x.

+

log10(x)

+

Accepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument.

+

sqrt(x)

+

Accepts a numeric argument and returns a Float64 number close to the square root of the argument.

+

cbrt(x)

+

Accepts a numeric argument and returns a Float64 number close to the cubic root of the argument.

+

erf(x)

+

If 'x' is non-negative, then erf(x / σ√2) is the probability that a random variable having a normal distribution with standard deviation 'σ' takes the value that is separated from the expected value by more than 'x'.

+

Example (three sigma rule):

+
SELECT erf(3 / sqrt(2))
+
+ + +
┌─erf(divide(3, sqrt(2)))─┐
+│      0.9973002039367398 │
+└─────────────────────────┘
+
+ + +

erfc(x)

+

Accepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large 'x' values.

+

lgamma(x)

+

The logarithm of the gamma function.

+

tgamma(x)

+

Gamma function.

+

sin(x)

+

The sine.

+

cos(x)

+

The cosine.

+

tan(x)

+

The tangent.

+

asin(x)

+

The arc sine.

+

acos(x)

+

The arc cosine.

+

atan(x)

+

The arc tangent.

+

pow(x, y)

+

Accepts two numeric arguments and returns a Float64 number close to x^y.

+

Rounding functions

+

floor(x[, N])

+

Returns the largest round number that is less than or equal to x. A round number is a multiple of 1/10N, or the nearest number of the appropriate data type if 1 / 10N isn't exact. +'N' is an integer constant, optional parameter. By default it is zero, which means to round to an integer. +'N' may be negative.

+

Examples: floor(123.45, 1) = 123.4, floor(123.45, -1) = 120.

+

x is any numeric type. The result is a number of the same type. +For integer arguments, it makes sense to round with a negative 'N' value (for non-negative 'N', the function doesn't do anything). +If rounding causes overflow (for example, floor(-128, -1)), an implementation-specific result is returned.

+

ceil(x[, N])

+

Returns the smallest round number that is greater than or equal to 'x'. In every other way, it is the same as the 'floor' function (see above).

+

round(x[, N])

+

Returns the round number nearest to 'num', which may be less than, greater than, or equal to 'x'.If 'x' is exactly in the middle between the nearest round numbers, one of them is returned (implementation-specific). +The number '-0.' may or may not be considered round (implementation-specific). +In every other way, this function is the same as 'floor' and 'ceil' described above.

+

roundToExp2(num)

+

Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two.

+

roundDuration(num)

+

Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length

+

roundAge(num)

+

Accepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to a number from the set: 18, 25, 35, 45, 55. This function is specific to Yandex.Metrica and used for implementing the report on user age.

+

Functions for working with arrays

+

empty

+

Returns 1 for an empty array, or 0 for a non-empty array. +The result type is UInt8. +The function also works for strings.

+

notEmpty

+

Returns 0 for an empty array, or 1 for a non-empty array. +The result type is UInt8. +The function also works for strings.

+

length

+

Returns the number of items in the array. +The result type is UInt64. +The function also works for strings.

+

emptyArrayUInt8, emptyArrayUInt16, emptyArrayUInt32, emptyArrayUInt64

+

emptyArrayInt8, emptyArrayInt16, emptyArrayInt32, emptyArrayInt64

+

emptyArrayFloat32, emptyArrayFloat64

+

emptyArrayDate, emptyArrayDateTime

+

emptyArrayString

+

Accepts zero arguments and returns an empty array of the appropriate type.

+

emptyArrayToSingle

+

Accepts an empty array and returns a one-element array that is equal to the default value.

+

range(N)

+

Returns an array of numbers from 0 to N-1. +Just in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block.

+

array(x1, ...), operator [x1, ...]

+

Creates an array from the function arguments. +The arguments must be constants and have types that have the smallest common type. At least one argument must be passed, because otherwise it isn't clear which type of array to create. That is, you can't use this function to create an empty array (to do that, use the 'emptyArray*' function described above). +Returns an 'Array(T)' type result, where 'T' is the smallest common type out of the passed arguments.

+

arrayConcat

+

Combines arrays passed as arguments.

+
arrayConcat(arrays)
+
+ + +

Arguments

+
    +
  • arrays – Arrays of comma-separated [values].
    • +
+

Example

+
SELECT arrayConcat([1, 2], [3, 4], [5, 6]) AS res
+
+ + +
┌─res───────────┐
+│ [1,2,3,4,5,6] │
+└───────────────┘
+
+ + +

arrayElement(arr, n), operator arr[n]

+

Get the element with the index 'n' from the array 'arr'.'n' must be any integer type. +Indexes in an array begin from one. +Negative indexes are supported. In this case, it selects the corresponding element numbered from the end. For example, 'arr[-1]' is the last item in the array.

+

If the index falls outside of the bounds of an array, it returns some default value (0 for numbers, an empty string for strings, etc.).

+

has(arr, elem)

+

Checks whether the 'arr' array has the 'elem' element. +Returns 0 if the the element is not in the array, or 1 if it is.

+

indexOf(arr, x)

+

Returns the index of the 'x' element (starting from 1) if it is in the array, or 0 if it is not.

+

countEqual(arr, x)

+

Returns the number of elements in the array equal to x. Equivalent to arrayCount (elem-> elem = x, arr).

+

arrayEnumerate(arr)

+

Returns the array [1, 2, 3, ..., length (arr) ]

+

This function is normally used with ARRAY JOIN. It allows counting something just once for each array after applying ARRAY JOIN. Example:

+
SELECT
+    count() AS Reaches,
+    countIf(num = 1) AS Hits
+FROM test.hits
+ARRAY JOIN
+    GoalsReached,
+    arrayEnumerate(GoalsReached) AS num
+WHERE CounterID = 160656
+LIMIT 10
+
+ + +
┌─Reaches─┬──Hits─┐
+│   95606 │ 31406 │
+└─────────┴───────┘
+
+ + +

In this example, Reaches is the number of conversions (the strings received after applying ARRAY JOIN), and Hits is the number of pageviews (strings before ARRAY JOIN). In this particular case, you can get the same result in an easier way:

+
SELECT
+    sum(length(GoalsReached)) AS Reaches,
+    count() AS Hits
+FROM test.hits
+WHERE (CounterID = 160656) AND notEmpty(GoalsReached)
+
+ + +
┌─Reaches─┬──Hits─┐
+│   95606 │ 31406 │
+└─────────┴───────┘
+
+ + +

This function can also be used in higher-order functions. For example, you can use it to get array indexes for elements that match a condition.

+

arrayEnumerateUniq(arr, ...)

+

Returns an array the same size as the source array, indicating for each element what its position is among elements with the same value. +For example: arrayEnumerateUniq([10, 20, 10, 30]) = [1, 1, 2, 1].

+

This function is useful when using ARRAY JOIN and aggregation of array elements. +Example:

+
SELECT
+    Goals.ID AS GoalID,
+    sum(Sign) AS Reaches,
+    sumIf(Sign, num = 1) AS Visits
+FROM test.visits
+ARRAY JOIN
+    Goals,
+    arrayEnumerateUniq(Goals.ID) AS num
+WHERE CounterID = 160656
+GROUP BY GoalID
+ORDER BY Reaches DESC
+LIMIT 10
+
+ + +
┌──GoalID─┬─Reaches─┬─Visits─┐
+│   53225 │    3214 │   1097 │
+│ 2825062 │    3188 │   1097 │
+│   56600 │    2803 │    488 │
+│ 1989037 │    2401 │    365 │
+│ 2830064 │    2396 │    910 │
+│ 1113562 │    2372 │    373 │
+│ 3270895 │    2262 │    812 │
+│ 1084657 │    2262 │    345 │
+│   56599 │    2260 │    799 │
+│ 3271094 │    2256 │    812 │
+└─────────┴─────────┴────────┘
+
+ + +

In this example, each goal ID has a calculation of the number of conversions (each element in the Goals nested data structure is a goal that was reached, which we refer to as a conversion) and the number of sessions. Without ARRAY JOIN, we would have counted the number of sessions as sum(Sign). But in this particular case, the rows were multiplied by the nested Goals structure, so in order to count each session one time after this, we apply a condition to the value of the arrayEnumerateUniq(Goals.ID) function.

+

The arrayEnumerateUniq function can take multiple arrays of the same size as arguments. In this case, uniqueness is considered for tuples of elements in the same positions in all the arrays.

+
SELECT arrayEnumerateUniq([1, 1, 1, 2, 2, 2], [1, 1, 2, 1, 1, 2]) AS res
+
+ + +
┌─res───────────┐
+│ [1,2,1,1,2,1] │
+└───────────────┘
+
+ + +

This is necessary when using ARRAY JOIN with a nested data structure and further aggregation across multiple elements in this structure.

+

arrayPopBack

+

Removes the last item from the array.

+
arrayPopBack(array)
+
+ + +

Arguments

+
    +
  • array – Array.
    • +
+

Example

+
SELECT arrayPopBack([1, 2, 3]) AS res
+
+ + +
┌─res───┐
+│ [1,2] │
+└───────┘
+
+ + +

arrayPopFront

+

Removes the first item from the array.

+
arrayPopFront(array)
+
+ + +

Arguments

+
    +
  • array – Array.
    • +
+

Example

+
SELECT arrayPopFront([1, 2, 3]) AS res
+
+ + +
┌─res───┐
+│ [2,3] │
+└───────┘
+
+ + +

arrayPushBack

+

Adds one item to the end of the array.

+
arrayPushBack(array, single_value)
+
+ + +

Arguments

+
    +
  • array – Array.
    • +
  • single_value – A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section "Data types".
    • +
+

Example

+
SELECT arrayPushBack(['a'], 'b') AS res
+
+ + +
┌─res───────┐
+│ ['a','b'] │
+└───────────┘
+
+ + +

arrayPushFront

+

Adds one element to the beginning of the array.

+
arrayPushFront(array, single_value)
+
+ + +

Arguments

+
    +
  • array – Array.
    • +
  • single_value – A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section "Data types".
    • +
+

Example

+
SELECT arrayPushBack(['b'], 'a') AS res
+
+ + +
┌─res───────┐
+│ ['a','b'] │
+└───────────┘
+
+ + +

arraySlice

+

Returns a slice of the array.

+
arraySlice(array, offset[, length])
+
+ + +

Arguments

+
    +
  • array – Array of data.
    • +
  • offset – Indent from the edge of the array. A positive value indicates an offset on the left, and a negative value is an indent on the right. Numbering of the array items begins with 1.
    • +
  • length - The length of the required slice. If you specify a negative value, the function returns an open slice [offset, array_length - length). If you omit the value, the function returns the slice [offset, the_end_of_array].
    • +
+

Example

+
SELECT arraySlice([1, 2, 3, 4, 5], 2, 3) AS res
+
+ + +
┌─res─────┐
+│ [2,3,4] │
+└─────────┘
+
+ + +

arrayUniq(arr, ...)

+

If one argument is passed, it counts the number of different elements in the array. +If multiple arguments are passed, it counts the number of different tuples of elements at corresponding positions in multiple arrays.

+

If you want to get a list of unique items in an array, you can use arrayReduce('groupUniqArray', arr).

+

arrayJoin(arr)

+

A special function. See the section "ArrayJoin function".

+

Functions for splitting and merging strings and arrays

+

splitByChar(separator, s)

+

Splits a string into substrings separated by 'separator'.'separator' must be a string constant consisting of exactly one character. +Returns an array of selected substrings. Empty substrings may be selected if the separator occurs at the beginning or end of the string, or if there are multiple consecutive separators.

+

splitByString(separator, s)

+

The same as above, but it uses a string of multiple characters as the separator. The string must be non-empty.

+

arrayStringConcat(arr[, separator])

+

Concatenates the strings listed in the array with the separator.'separator' is an optional parameter: a constant string, set to an empty string by default. +Returns the string.

+

alphaTokens(s)

+

Selects substrings of consecutive bytes from the ranges a-z and A-Z.Returns an array of substrings.

+

Bit functions

+

Bit functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.

+

The result type is an integer with bits equal to the maximum bits of its arguments. If at least one of the arguments is signed, the result is a signed number. If an argument is a floating-point number, it is cast to Int64.

+

bitAnd(a, b)

+

bitOr(a, b)

+

bitXor(a, b)

+

bitNot(a)

+

bitShiftLeft(a, b)

+

bitShiftRight(a, b)

+

Hash functions

+

Hash functions can be used for deterministic pseudo-random shuffling of elements.

+

halfMD5

+

Calculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian. +Accepts a String-type argument. Returns UInt64. +This function works fairly slowly (5 million short strings per second per processor core). +If you don't need MD5 in particular, use the 'sipHash64' function instead.

+

MD5

+

Calculates the MD5 from a string and returns the resulting set of bytes as FixedString(16). +If you don't need MD5 in particular, but you need a decent cryptographic 128-bit hash, use the 'sipHash128' function instead. +If you want to get the same result as output by the md5sum utility, use lower(hex(MD5(s))).

+

sipHash64

+

Calculates SipHash from a string. +Accepts a String-type argument. Returns UInt64. +SipHash is a cryptographic hash function. It works at least three times faster than MD5. +For more information, see the link: https://131002.net/siphash/

+

sipHash128

+

Calculates SipHash from a string. +Accepts a String-type argument. Returns FixedString(16). +Differs from sipHash64 in that the final xor-folding state is only done up to 128 bytes.

+

cityHash64

+

Calculates CityHash64 from a string or a similar hash function for any number of any type of arguments. +For String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality. +For other types of arguments, a decent implementation-specific fast non-cryptographic hash function is used. +If multiple arguments are passed, the function is calculated using the same rules and chain combinations using the CityHash combinator. +For example, you can compute the checksum of an entire table with accuracy up to the row order: SELECT sum(cityHash64(*)) FROM table.

+

intHash32

+

Calculates a 32-bit hash code from any type of integer. +This is a relatively fast non-cryptographic hash function of average quality for numbers.

+

intHash64

+

Calculates a 64-bit hash code from any type of integer. +It works faster than intHash32. Average quality.

+

SHA1

+

SHA224

+

SHA256

+

Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32). +The function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million). +We recommend using this function only in cases when you need a specific hash function and you can't select it. +Even in these cases, we recommend applying the function offline and pre-calculating values when inserting them into the table, instead of applying it in SELECTS.

+

URLHash(url[, N])

+

A fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization. +URLHash(s) – Calculates a hash from a string without one of the trailing symbols /,? or # at the end, if present. +URLHash(s, N) – Calculates a hash from a string up to the N level in the URL hierarchy, without one of the trailing symbols /,? or # at the end, if present. +Levels are the same as in URLHierarchy. This function is specific to Yandex.Metrica.

+

Functions for generating pseudo-random numbers

+

Non-cryptographic generators of pseudo-random numbers are used.

+

All the functions accept zero arguments or one argument. +If an argument is passed, it can be any type, and its value is not used for anything. +The only purpose of this argument is to prevent common subexpression elimination, so that two different instances of the same function return different columns with different random numbers.

+

rand

+

Returns a pseudo-random UInt32 number, evenly distributed among all UInt32-type numbers. +Uses a linear congruential generator.

+

rand64

+

Returns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers. +Uses a linear congruential generator.

+

Encoding functions

+

hex

+

Accepts arguments of types: String, unsigned integer, Date, or DateTime. Returns a string containing the argument's hexadecimal representation. Uses uppercase letters A-F. Does not use 0x prefixes or h suffixes. For strings, all bytes are simply encoded as two hexadecimal numbers. Numbers are converted to big endian ("human readable") format. For numbers, older zeros are trimmed, but only by entire bytes. For example, hex (1) = '01'. Date is encoded as the number of days since the beginning of the Unix epoch. DateTime is encoded as the number of seconds since the beginning of the Unix epoch.

+

unhex(str)

+

Accepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits does not have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn't thrown). +If you want to convert the result to a number, you can use the 'reverse' and 'reinterpretAsType' functions.

+

UUIDStringToNum(str)

+

Accepts a string containing 36 characters in the format 123e4567-e89b-12d3-a456-426655440000, and returns it as a set of bytes in a FixedString(16).

+

UUIDNumToString(str)

+

Accepts a FixedString(16) value. Returns a string containing 36 characters in text format.

+

bitmaskToList(num)

+

Accepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order.

+

bitmaskToArray(num)

+

Accepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order.

+

Functions for working with URLs

+

All these functions don't follow the RFC. They are maximally simplified for improved performance.

+

Functions that extract part of a URL

+

If there isn't anything similar in a URL, an empty string is returned.

+

protocol

+

Returns the protocol. Examples: http, ftp, mailto, magnet...

+

domain

+

Gets the domain.

+

domainWithoutWWW

+

Returns the domain and removes no more than one 'www.' from the beginning of it, if present.

+

topLevelDomain

+

Returns the top-level domain. Example: .ru.

+

firstSignificantSubdomain

+

Returns the "first significant subdomain". This is a non-standard concept specific to Yandex.Metrica. The first significant subdomain is a second-level domain if it is 'com', 'net', 'org', or 'co'. Otherwise, it is a third-level domain. For example, firstSignificantSubdomain ('https://news.yandex.ru/') = 'yandex ', firstSignificantSubdomain ('https://news.yandex.com.tr/') = 'yandex '. The list of "insignificant" second-level domains and other implementation details may change in the future.

+

cutToFirstSignificantSubdomain

+

Returns the part of the domain that includes top-level subdomains up to the "first significant subdomain" (see the explanation above).

+

For example, cutToFirstSignificantSubdomain('https://news.yandex.com.tr/') = 'yandex.com.tr'.

+

path

+

Returns the path. Example: /top/news.html The path does not include the query string.

+

pathFull

+

The same as above, but including query string and fragment. Example: /top/news.html?page=2#comments

+

queryString

+

Returns the query string. Example: page=1&lr=213. query-string does not include the initial question mark, as well as # and everything after #.

+

fragment

+

Returns the fragment identifier. fragment does not include the initial hash symbol.

+

queryStringAndFragment

+

Returns the query string and fragment identifier. Example: page=1#29390.

+

extractURLParameter(URL, name)

+

Returns the value of the 'name' parameter in the URL, if present. Otherwise, an empty string. If there are many parameters with this name, it returns the first occurrence. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.

+

extractURLParameters(URL)

+

Returns an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way.

+

extractURLParameterNames(URL)

+

Returns an array of name strings corresponding to the names of URL parameters. The values are not decoded in any way.

+

URLHierarchy(URL)

+

Returns an array containing the URL, truncated at the end by the symbols /,? in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:

+

URLPathHierarchy(URL)

+

The same as above, but without the protocol and host in the result. The / element (root) is not included. Example: the function is used to implement tree reports the URL in Yandex. Metric.

+
URLPathHierarchy('https://example.com/browse/CONV-6788') =
+[
+    '/browse/',
+    '/browse/CONV-6788'
+]
+
+ + +

decodeURLComponent(URL)

+

Returns the decoded URL. +Example:

+
SELECT decodeURLComponent('http://127.0.0.1:8123/?query=SELECT%201%3B') AS DecodedURL;
+
+ + +
┌─DecodedURL─────────────────────────────┐
+│ http://127.0.0.1:8123/?query=SELECT 1; │
+└────────────────────────────────────────┘
+
+ + +

Functions that remove part of a URL.

+

If the URL doesn't have anything similar, the URL remains unchanged.

+

cutWWW

+

Removes no more than one 'www.' from the beginning of the URL's domain, if present.

+

cutQueryString

+

Removes query string. The question mark is also removed.

+

cutFragment

+

Removes the fragment identifier. The number sign is also removed.

+

cutQueryStringAndFragment

+

Removes the query string and fragment identifier. The question mark and number sign are also removed.

+

cutURLParameter(URL, name)

+

Removes the 'name' URL parameter, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.

+

Functions for working with IP addresses

+

IPv4NumToString(num)

+

Takes a UInt32 number. Interprets it as an IPv4 address in big endian. Returns a string containing the corresponding IPv4 address in the format A.B.C.d (dot-separated numbers in decimal form).

+

IPv4StringToNum(s)

+

The reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0.

+

IPv4NumToStringClassC(num)

+

Similar to IPv4NumToString, but using xxx instead of the last octet.

+

Example:

+
SELECT
+    IPv4NumToStringClassC(ClientIP) AS k,
+    count() AS c
+FROM test.hits
+GROUP BY k
+ORDER BY c DESC
+LIMIT 10
+
+ + +
┌─k──────────────┬─────c─┐
+│ 83.149.9.xxx   │ 26238 │
+│ 217.118.81.xxx │ 26074 │
+│ 213.87.129.xxx │ 25481 │
+│ 83.149.8.xxx   │ 24984 │
+│ 217.118.83.xxx │ 22797 │
+│ 78.25.120.xxx  │ 22354 │
+│ 213.87.131.xxx │ 21285 │
+│ 78.25.121.xxx  │ 20887 │
+│ 188.162.65.xxx │ 19694 │
+│ 83.149.48.xxx  │ 17406 │
+└────────────────┴───────┘
+
+ + +

Since using 'xxx' is highly unusual, this may be changed in the future. We recommend that you don't rely on the exact format of this fragment.

+

IPv6NumToString(x)

+

Accepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format. +IPv6-mapped IPv4 addresses are output in the format ::ffff:111.222.33.44. Examples:

+
SELECT IPv6NumToString(toFixedString(unhex('2A0206B8000000000000000000000011'), 16)) AS addr
+
+ + +
┌─addr─────────┐
+│ 2a02:6b8::11 │
+└──────────────┘
+
+ + +
SELECT
+    IPv6NumToString(ClientIP6 AS k),
+    count() AS c
+FROM hits_all
+WHERE EventDate = today() AND substring(ClientIP6, 1, 12) != unhex('00000000000000000000FFFF')
+GROUP BY k
+ORDER BY c DESC
+LIMIT 10
+
+ + +
┌─IPv6NumToString(ClientIP6)──────────────┬─────c─┐
+│ 2a02:2168:aaa:bbbb::2                   │ 24695 │
+│ 2a02:2698:abcd:abcd:abcd:abcd:8888:5555 │ 22408 │
+│ 2a02:6b8:0:fff::ff                      │ 16389 │
+│ 2a01:4f8:111:6666::2                    │ 16016 │
+│ 2a02:2168:888:222::1                    │ 15896 │
+│ 2a01:7e00::ffff:ffff:ffff:222           │ 14774 │
+│ 2a02:8109:eee:ee:eeee:eeee:eeee:eeee    │ 14443 │
+│ 2a02:810b:8888:888:8888:8888:8888:8888  │ 14345 │
+│ 2a02:6b8:0:444:4444:4444:4444:4444      │ 14279 │
+│ 2a01:7e00::ffff:ffff:ffff:ffff          │ 13880 │
+└─────────────────────────────────────────┴───────┘
+
+ + +
SELECT
+    IPv6NumToString(ClientIP6 AS k),
+    count() AS c
+FROM hits_all
+WHERE EventDate = today()
+GROUP BY k
+ORDER BY c DESC
+LIMIT 10
+
+ + +
┌─IPv6NumToString(ClientIP6)─┬──────c─┐
+│ ::ffff:94.26.111.111       │ 747440 │
+│ ::ffff:37.143.222.4        │ 529483 │
+│ ::ffff:5.166.111.99        │ 317707 │
+│ ::ffff:46.38.11.77         │ 263086 │
+│ ::ffff:79.105.111.111      │ 186611 │
+│ ::ffff:93.92.111.88        │ 176773 │
+│ ::ffff:84.53.111.33        │ 158709 │
+│ ::ffff:217.118.11.22       │ 154004 │
+│ ::ffff:217.118.11.33       │ 148449 │
+│ ::ffff:217.118.11.44       │ 148243 │
+└────────────────────────────┴────────┘
+
+ + +

IPv6StringToNum(s)

+

The reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes. +HEX can be uppercase or lowercase.

+

Functions for working with JSON

+

In Yandex.Metrica, JSON is transmitted by users as session parameters. There are some special functions for working with this JSON. (Although in most of the cases, the JSONs are additionally pre-processed, and the resulting values are put in separate columns in their processed format.) All these functions are based on strong assumptions about what the JSON can be, but they try to do as little as possible to get the job done.

+

The following assumptions are made:

+
    +
  1. The field name (function argument) must be a constant.
    2. +
  2. The field name is somehow canonically encoded in JSON. For example: visitParamHas('{"abc":"def"}', 'abc') = 1, but visitParamHas('{"\\u0061\\u0062\\u0063":"def"}', 'abc') = 0
    3. +
  3. Fields are searched for on any nesting level, indiscriminately. If there are multiple matching fields, the first occurrence is used.
    4. +
  4. The JSON doesn't have space characters outside of string literals.
    5. +
+

visitParamHas(params, name)

+

Checks whether there is a field with the 'name' name.

+

visitParamExtractUInt(params, name)

+

Parses UInt64 from the value of the field named 'name'. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn't exist, or it exists but doesn't contain a number, it returns 0.

+

visitParamExtractInt(params, name)

+

The same as for Int64.

+

visitParamExtractFloat(params, name)

+

The same as for Float64.

+

visitParamExtractBool(params, name)

+

Parses a true/false value. The result is UInt8.

+

visitParamExtractRaw(params, name)

+

Returns the value of a field, including separators.

+

Examples:

+
visitParamExtractRaw('{"abc":"\\n\\u0000"}', 'abc') = '"\\n\\u0000"'
+visitParamExtractRaw('{"abc":{"def":[1,2,3]}}', 'abc') = '{"def":[1,2,3]}'
+
+ + +

visitParamExtractString(params, name)

+

Parses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string.

+

Examples:

+
visitParamExtractString('{"abc":"\\n\\u0000"}', 'abc') = '\n\0'
+visitParamExtractString('{"abc":"\\u263a"}', 'abc') = '☺'
+visitParamExtractString('{"abc":"\\u263"}', 'abc') = ''
+visitParamExtractString('{"abc":"hello}', 'abc') = ''
+
+ + +

There is currently no support for code points in the format \uXXXX\uYYYY that are not from the basic multilingual plane (they are converted to CESU-8 instead of UTF-8).

+

Higher-order functions

+

-> operator, lambda(params, expr) function

+

Allows describing a lambda function for passing to a higher-order function. The left side of the arrow has a formal parameter, which is any ID, or multiple formal parameters – any IDs in a tuple. The right side of the arrow has an expression that can use these formal parameters, as well as any table columns.

+

Examples: x -> 2 * x, str -> str != Referer.

+

Higher-order functions can only accept lambda functions as their functional argument.

+

A lambda function that accepts multiple arguments can be passed to a higher-order function. In this case, the higher-order function is passed several arrays of identical length that these arguments will correspond to.

+

For all functions other than 'arrayMap' and 'arrayFilter', the first argument (the lambda function) can be omitted. In this case, identical mapping is assumed.

+

arrayMap(func, arr1, ...)

+

Returns an array obtained from the original application of the 'func' function to each element in the 'arr' array.

+

arrayFilter(func, arr1, ...)

+

Returns an array containing only the elements in 'arr1' for which 'func' returns something other than 0.

+

Examples:

+
SELECT arrayFilter(x -> x LIKE '%World%', ['Hello', 'abc World']) AS res
+
+ + +
┌─res───────────┐
+│ ['abc World'] │
+└───────────────┘
+
+ + +
SELECT
+    arrayFilter(
+        (i, x) -> x LIKE '%World%',
+        arrayEnumerate(arr),
+        ['Hello', 'abc World'] AS arr)
+    AS res
+
+ + +
┌─res─┐
+│ [2] │
+└─────┘
+
+ + +

arrayCount([func,] arr1, ...)

+

Returns the number of elements in the arr array for which func returns something other than 0. If 'func' is not specified, it returns the number of non-zero elements in the array.

+

arrayExists([func,] arr1, ...)

+

Returns 1 if there is at least one element in 'arr' for which 'func' returns something other than 0. Otherwise, it returns 0.

+

arrayAll([func,] arr1, ...)

+

Returns 1 if 'func' returns something other than 0 for all the elements in 'arr'. Otherwise, it returns 0.

+

arraySum([func,] arr1, ...)

+

Returns the sum of the 'func' values. If the function is omitted, it just returns the sum of the array elements.

+

arrayFirst(func, arr1, ...)

+

Returns the first element in the 'arr1' array for which 'func' returns something other than 0.

+

arrayFirstIndex(func, arr1, ...)

+

Returns the index of the first element in the 'arr1' array for which 'func' returns something other than 0.

+

arrayCumSum([func,] arr1, ...)

+

Returns an array of partial sums of elements in the source array (a running sum). If the func function is specified, then the values of the array elements are converted by this function before summing.

+

Example:

+
SELECT arrayCumSum([1, 1, 1, 1]) AS res
+
+ + +
┌─res──────────┐
+│ [1, 2, 3, 4] │
+└──────────────┘
+
+ + +

arraySort([func,] arr1, ...)

+

Returns an array as result of sorting the elements of arr1 in ascending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays)

+

The Schwartzian transform is used to impove sorting efficiency.

+

Example:

+
SELECT arraySort((x, y) -> y, ['hello', 'world'], [2, 1]);
+
+ + +
┌─res────────────────┐
+│ ['world', 'hello'] │
+└────────────────────┘
+
+ + +

arrayReverseSort([func,] arr1, ...)

+

Returns an array as result of sorting the elements of arr1 in descending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays)

+

Other functions

+

hostName()

+

Returns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server.

+

visibleWidth(x)

+

Calculates the approximate width when outputting values to the console in text format (tab-separated). +This function is used by the system for implementing Pretty formats.

+

toTypeName(x)

+

Returns a string containing the type name of the passed argument.

+

blockSize()

+

Gets the size of the block. +In ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for.

+

materialize(x)

+

Turns a constant into a full column containing just one value. +In ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior.

+

ignore(...)

+

Accepts any arguments and always returns 0. +However, the argument is still evaluated. This can be used for benchmarks.

+

sleep(seconds)

+

Sleeps 'seconds' seconds on each data block. You can specify an integer or a floating-point number.

+

currentDatabase()

+

Returns the name of the current database. +You can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database.

+

isFinite(x)

+

Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0.

+

isInfinite(x)

+

Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. Note that 0 is returned for a NaN.

+

isNaN(x)

+

Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0.

+

hasColumnInTable(['hostname'[, 'username'[, 'password']],] 'database', 'table', 'column')

+

Accepts constant strings: database name, table name, and column name. Returns a UInt8 constant expression equal to 1 if there is a column, otherwise 0. If the hostname parameter is set, the test will run on a remote server. +The function throws an exception if the table does not exist. +For elements in a nested data structure, the function checks for the existence of a column. For the nested data structure itself, the function returns 0.

+

bar

+

Allows building a unicode-art diagram.

+

bar (x, min, max, width) draws a band with a width proportional to (x - min) and equal to width characters when x = max.

+

Parameters:

+
    +
  • x – Value to display.
    • +
  • min, max – Integer constants. The value must fit in Int64.
    • +
  • width – Constant, positive number, may be a fraction.
    • +
+

The band is drawn with accuracy to one eighth of a symbol.

+

Example:

+
SELECT
+    toHour(EventTime) AS h,
+    count() AS c,
+    bar(c, 0, 600000, 20) AS bar
+FROM test.hits
+GROUP BY h
+ORDER BY h ASC
+
+ + +
┌──h─┬──────c─┬─bar────────────────┐
+│  0 │ 292907 │ █████████▋         │
+│  1 │ 180563 │ ██████             │
+│  2 │ 114861 │ ███▋               │
+│  3 │  85069 │ ██▋                │
+│  4 │  68543 │ ██▎                │
+│  5 │  78116 │ ██▌                │
+│  6 │ 113474 │ ███▋               │
+│  7 │ 170678 │ █████▋             │
+│  8 │ 278380 │ █████████▎         │
+│  9 │ 391053 │ █████████████      │
+│ 10 │ 457681 │ ███████████████▎   │
+│ 11 │ 493667 │ ████████████████▍  │
+│ 12 │ 509641 │ ████████████████▊  │
+│ 13 │ 522947 │ █████████████████▍ │
+│ 14 │ 539954 │ █████████████████▊ │
+│ 15 │ 528460 │ █████████████████▌ │
+│ 16 │ 539201 │ █████████████████▊ │
+│ 17 │ 523539 │ █████████████████▍ │
+│ 18 │ 506467 │ ████████████████▊  │
+│ 19 │ 520915 │ █████████████████▎ │
+│ 20 │ 521665 │ █████████████████▍ │
+│ 21 │ 542078 │ ██████████████████ │
+│ 22 │ 493642 │ ████████████████▍  │
+│ 23 │ 400397 │ █████████████▎     │
+└────┴────────┴────────────────────┘
+
+ + +

+

transform

+

Transforms a value according to the explicitly defined mapping of some elements to other ones. +There are two variations of this function:

+
    +
  1. transform(x, array_from, array_to, default)
    2. +
+

x – What to transform.

+

array_from – Constant array of values for converting.

+

array_to – Constant array of values to convert the values in 'from' to.

+

default – Which value to use if 'x' is not equal to any of the values in 'from'.

+

array_from and array_to – Arrays of the same size.

+

Types:

+

transform(T, Array(T), Array(U), U) -> U

+

T and U can be numeric, string, or Date or DateTime types. +Where the same letter is indicated (T or U), for numeric types these might not be matching types, but types that have a common type. +For example, the first argument can have the Int64 type, while the second has the Array(Uint16) type.

+

If the 'x' value is equal to one of the elements in the 'array_from' array, it returns the existing element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'default'. If there are multiple matching elements in 'array_from', it returns one of the matches.

+

Example:

+
SELECT
+    transform(SearchEngineID, [2, 3], ['Yandex', 'Google'], 'Other') AS title,
+    count() AS c
+FROM test.hits
+WHERE SearchEngineID != 0
+GROUP BY title
+ORDER BY c DESC
+
+ + +
┌─title─────┬──────c─┐
+│ Yandex    │ 498635 │
+│ Google    │ 229872 │
+│ Other     │ 104472 │
+└───────────┴────────┘
+
+ + +
    +
  1. transform(x, array_from, array_to)
    2. +
+

Differs from the first variation in that the 'default' argument is omitted. +If the 'x' value is equal to one of the elements in the 'array_from' array, it returns the matching element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'x'.

+

Types:

+

transform(T, Array(T), Array(T)) -> T

+

Example:

+
SELECT
+    transform(domain(Referer), ['yandex.ru', 'google.ru', 'vk.com'], ['www.yandex', 'example.com']) AS s,
+    count() AS c
+FROM test.hits
+GROUP BY domain(Referer)
+ORDER BY count() DESC
+LIMIT 10
+
+ + +
┌─s──────────────┬───────c─┐
+│                │ 2906259 │
+│ www.yandex     │  867767 │
+│ ███████.ru     │  313599 │
+│ mail.yandex.ru │  107147 │
+│ ██████.ru      │  100355 │
+│ █████████.ru   │   65040 │
+│ news.yandex.ru │   64515 │
+│ ██████.net     │   59141 │
+│ example.com    │   57316 │
+└────────────────┴─────────┘
+
+ + +

formatReadableSize(x)

+

Accepts the size (number of bytes). Returns a rounded size with a suffix (KiB, MiB, etc.) as a string.

+

Example:

+
SELECT
+    arrayJoin([1, 1024, 1024*1024, 192851925]) AS filesize_bytes,
+    formatReadableSize(filesize_bytes) AS filesize
+
+ + +
┌─filesize_bytes─┬─filesize───┐
+│              1 │ 1.00 B     │
+│           1024 │ 1.00 KiB   │
+│        1048576 │ 1.00 MiB   │
+│      192851925 │ 183.92 MiB │
+└────────────────┴────────────┘
+
+ + +

least(a, b)

+

Returns the smallest value from a and b.

+

greatest(a, b)

+

Returns the largest value of a and b.

+

uptime()

+

Returns the server's uptime in seconds.

+

version()

+

Returns the version of the server as a string.

+

rowNumberInAllBlocks()

+

Returns the ordinal number of the row in the data block. This function only considers the affected data blocks.

+

runningDifference(x)

+

Calculates the difference between successive row values ​​in the data block. +Returns 0 for the first row and the difference from the previous row for each subsequent row.

+

The result of the function depends on the affected data blocks and the order of data in the block. +If you make a subquery with ORDER BY and call the function from outside the subquery, you can get the expected result.

+

Example:

+
SELECT
+    EventID,
+    EventTime,
+    runningDifference(EventTime) AS delta
+FROM
+(
+    SELECT
+        EventID,
+        EventTime
+    FROM events
+    WHERE EventDate = '2016-11-24'
+    ORDER BY EventTime ASC
+    LIMIT 5
+)
+
+ + +
┌─EventID─┬───────────EventTime─┬─delta─┐
+│    1106 │ 2016-11-24 00:00:04 │     0 │
+│    1107 │ 2016-11-24 00:00:05 │     1 │
+│    1108 │ 2016-11-24 00:00:05 │     0 │
+│    1109 │ 2016-11-24 00:00:09 │     4 │
+│    1110 │ 2016-11-24 00:00:10 │     1 │
+└─────────┴─────────────────────┴───────┘
+
+ + +

MACNumToString(num)

+

Accepts a UInt64 number. Interprets it as a MAC address in big endian. Returns a string containing the corresponding MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form).

+

MACStringToNum(s)

+

The inverse function of MACNumToString. If the MAC address has an invalid format, it returns 0.

+

MACStringToOUI(s)

+

Accepts a MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form). Returns the first three octets as a UInt64 number. If the MAC address has an invalid format, it returns 0.

+

+

Functions for working with external dictionaries

+

For information on connecting and configuring external dictionaries, see "External dictionaries".

+

dictGetUInt8, dictGetUInt16, dictGetUInt32, dictGetUInt64

+

dictGetInt8, dictGetInt16, dictGetInt32, dictGetInt64

+

dictGetFloat32, dictGetFloat64

+

dictGetDate, dictGetDateTime

+

dictGetUUID

+

dictGetString

+

dictGetT('dict_name', 'attr_name', id)

+
    +
  • Get the value of the attr_name attribute from the dict_name dictionary using the 'id' key.dict_name and attr_name are constant strings.idmust be UInt64. +If there is no id key in the dictionary, it returns the default value specified in the dictionary description.
    • +
+

dictGetTOrDefault

+

dictGetT('dict_name', 'attr_name', id, default)

+

The same as the dictGetT functions, but the default value is taken from the function's last argument.

+

dictIsIn

+

dictIsIn('dict_name', child_id, ancestor_id)

+
    +
  • For the 'dict_name' hierarchical dictionary, finds out whether the 'child_id' key is located inside 'ancestor_id' (or matches 'ancestor_id'). Returns UInt8.
    • +
+

dictGetHierarchy

+

dictGetHierarchy('dict_name', id)

+
    +
  • For the 'dict_name' hierarchical dictionary, returns an array of dictionary keys starting from 'id' and continuing along the chain of parent elements. Returns Array(UInt64).
    • +
+

dictHas

+

dictHas('dict_name', id)

+
    +
  • Check whether the dictionary has the key. Returns a UInt8 value equal to 0 if there is no key and 1 if there is a key.
    • +
+

Functions for working with Yandex.Metrica dictionaries

+

In order for the functions below to work, the server config must specify the paths and addresses for getting all the Yandex.Metrica dictionaries. The dictionaries are loaded at the first call of any of these functions. If the reference lists can't be loaded, an exception is thrown.

+

For information about creating reference lists, see the section "Dictionaries".

+

Multiple geobases

+

ClickHouse supports working with multiple alternative geobases (regional hierarchies) simultaneously, in order to support various perspectives on which countries certain regions belong to.

+

The 'clickhouse-server' config specifies the file with the regional hierarchy::<path_to_regions_hierarchy_file>/opt/geo/regions_hierarchy.txt</path_to_regions_hierarchy_file>

+

Besides this file, it also searches for files nearby that have the _ symbol and any suffix appended to the name (before the file extension). +For example, it will also find the file /opt/geo/regions_hierarchy_ua.txt, if present.

+

ua is called the dictionary key. For a dictionary without a suffix, the key is an empty string.

+

All the dictionaries are re-loaded in runtime (once every certain number of seconds, as defined in the builtin_dictionaries_reload_interval config parameter, or once an hour by default). However, the list of available dictionaries is defined one time, when the server starts.

+

All functions for working with regions have an optional argument at the end – the dictionary key. It is referred to as the geobase. +Example:

+
regionToCountry(RegionID) – Uses the default dictionary: /opt/geo/regions_hierarchy.txt
+regionToCountry(RegionID, '') – Uses the default dictionary: /opt/geo/regions_hierarchy.txt
+regionToCountry(RegionID, 'ua') – Uses the dictionary for the 'ua' key: /opt/geo/regions_hierarchy_ua.txt
+
+ + +

regionToCity(id[, geobase])

+

Accepts a UInt32 number – the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0.

+

regionToArea(id[, geobase])

+

Converts a region to an area (type 5 in the geobase). In every other way, this function is the same as 'regionToCity'.

+
SELECT DISTINCT regionToName(regionToArea(toUInt32(number), 'ua'))
+FROM system.numbers
+LIMIT 15
+
+ + +
┌─regionToName(regionToArea(toUInt32(number), \'ua\'))─┐
+│                                                      │
+│ Moscow and Moscow region                             │
+│ St. Petersburg and Leningrad region                  │
+│ Belgorod region                                      │
+│ Ivanovsk region                                      │
+│ Kaluga region                                        │
+│ Kostroma region                                      │
+│ Kursk region                                         │
+│ Lipetsk region                                       │
+│ Orlov region                                         │
+│ Ryazan region                                        │
+│ Smolensk region                                      │
+│ Tambov region                                        │
+│ Tver region                                          │
+│ Tula region                                          │
+└──────────────────────────────────────────────────────┘
+
+ + +

regionToDistrict(id[, geobase])

+

Converts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as 'regionToCity'.

+
SELECT DISTINCT regionToName(regionToDistrict(toUInt32(number), 'ua'))
+FROM system.numbers
+LIMIT 15
+
+ + +
┌─regionToName(regionToDistrict(toUInt32(number), \'ua\'))─┐
+│                                                          │
+│ Central federal district                                 │
+│ Northwest federal district                               │
+│ South federal district                                   │
+│ North Caucases federal district                          │
+│ Privolga federal district                                │
+│ Ural federal district                                    │
+│ Siberian federal district                                │
+│ Far East federal district                                │
+│ Scotland                                                 │
+│ Faroe Islands                                            │
+│ Flemish region                                           │
+│ Brussels capital region                                  │
+│ Wallonia                                                 │
+│ Federation of Bosnia and Herzegovina                     │
+└──────────────────────────────────────────────────────────┘
+
+ + +

regionToCountry(id[, geobase])

+

Converts a region to a country. In every other way, this function is the same as 'regionToCity'. +Example: regionToCountry(toUInt32(213)) = 225 converts Moscow (213) to Russia (225).

+

regionToContinent(id[, geobase])

+

Converts a region to a continent. In every other way, this function is the same as 'regionToCity'. +Example: regionToContinent(toUInt32(213)) = 10001 converts Moscow (213) to Eurasia (10001).

+

regionToPopulation(id[, geobase])

+

Gets the population for a region. +The population can be recorded in files with the geobase. See the section "External dictionaries". +If the population is not recorded for the region, it returns 0. +In the Yandex geobase, the population might be recorded for child regions, but not for parent regions.

+

regionIn(lhs, rhs[, geobase])

+

Checks whether a 'lhs' region belongs to a 'rhs' region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn't belong. +The relationship is reflexive – any region also belongs to itself.

+

regionHierarchy(id[, geobase])

+

Accepts a UInt32 number – the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain. +Example: regionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000].

+

regionToName(id[, lang])

+

Accepts a UInt32 number – the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language 'ru' is used. If the language is not supported, an exception is thrown. Returns a string – the name of the region in the corresponding language. If the region with the specified ID doesn't exist, an empty string is returned.

+

ua and uk both mean Ukrainian.

+

Functions for implementing the IN operator

+

in, notIn, globalIn, globalNotIn

+

See the section "IN operators".

+

tuple(x, y, ...), operator (x, y, ...)

+

A function that allows grouping multiple columns. +For columns with the types T1, T2, ..., it returns a Tuple(T1, T2, ...) type tuple containing these columns. There is no cost to execute the function. +Tuples are normally used as intermediate values for an argument of IN operators, or for creating a list of formal parameters of lambda functions. Tuples can't be written to a table.

+

tupleElement(tuple, n), operator x.N

+

A function that allows getting a column from a tuple. +'N' is the column index, starting from 1. N must be a constant. 'N' must be a constant. 'N' must be a strict postive integer no greater than the size of the tuple. +There is no cost to execute the function.

+

+

arrayJoin function

+

This is a very unusual function.

+

Normal functions don't change a set of rows, but just change the values in each row (map). +Aggregate functions compress a set of rows (fold or reduce). +The 'arrayJoin' function takes each row and generates a set of rows (unfold).

+

This function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array. +All the values in columns are simply copied, except the values in the column where this function is applied; it is replaced with the corresponding array value.

+

A query can use multiple arrayJoin functions. In this case, the transformation is performed multiple times.

+

Note the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities.

+

Example:

+
SELECT arrayJoin([1, 2, 3] AS src) AS dst, 'Hello', src
+
+ + +
┌─dst─┬─\'Hello\'─┬─src─────┐
+│   1 │ Hello     │ [1,2,3] │
+│   2 │ Hello     │ [1,2,3] │
+│   3 │ Hello     │ [1,2,3] │
+└─────┴───────────┴─────────┘
+
+ + +

+

Aggregate functions

+

Aggregate functions work in the normal way as expected by database experts.

+

ClickHouse also supports:

+ +

+

Function reference

+

count()

+

Counts the number of rows. Accepts zero arguments and returns UInt64. +The syntax COUNT(DISTINCT x) is not supported. The separate uniq aggregate function exists for this purpose.

+

A SELECT count() FROM table query is not optimized, because the number of entries in the table is not stored separately. It will select some small column from the table and count the number of values in it.

+

any(x)

+

Selects the first encountered value. +The query can be executed in any order and even in a different order each time, so the result of this function is indeterminate. +To get a determinate result, you can use the 'min' or 'max' function instead of 'any'.

+

In some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.

+

When a SELECT query has the GROUP BY clause or at least one aggregate function, ClickHouse (in contrast to MySQL) requires that all expressions in the SELECT, HAVING, and ORDER BY clauses be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. To get behavior like in MySQL, you can put the other columns in the any aggregate function.

+

anyHeavy(x)

+

Selects a frequently occurring value using the heavy hitters algorithm. If there is a value that occurs more than in half the cases in each of the query's execution threads, this value is returned. Normally, the result is nondeterministic.

+
anyHeavy(column)
+
+ + +

Arguments +- column – The column name.

+

Example

+

Take the OnTime data set and select any frequently occurring value in the AirlineID column.

+
SELECT anyHeavy(AirlineID) AS res
+FROM ontime
+
+ + +
┌───res─┐
+│ 19690 │
+└───────┘
+
+ + +

anyLast(x)

+

Selects the last value encountered. +The result is just as indeterminate as for the any function.

+

min(x)

+

Calculates the minimum.

+

max(x)

+

Calculates the maximum.

+

argMin(arg, val)

+

Calculates the 'arg' value for a minimal 'val' value. If there are several different values of 'arg' for minimal values of 'val', the first of these values encountered is output.

+

argMax(arg, val)

+

Calculates the 'arg' value for a maximum 'val' value. If there are several different values of 'arg' for maximum values of 'val', the first of these values encountered is output.

+

sum(x)

+

Calculates the sum. +Only works for numbers.

+

sumWithOverflow(x)

+

Computes the sum of the numbers, using the same data type for the result as for the input parameters. If the sum exceeds the maximum value for this data type, the function returns an error.

+

Only works for numbers.

+

sumMap(key, value)

+

Totals the 'value' array according to the keys specified in the 'key' array. +The number of elements in 'key' and 'value' must be the same for each row that is totaled. +Returns a tuple of two arrays: keys in sorted order, and values ​​summed for the corresponding keys.

+

Example:

+
CREATE TABLE sum_map(
+    date Date,
+    timeslot DateTime,
+    statusMap Nested(
+        status UInt16,
+        requests UInt64
+    )
+) ENGINE = Log;
+INSERT INTO sum_map VALUES
+    ('2000-01-01', '2000-01-01 00:00:00', [1, 2, 3], [10, 10, 10]),
+    ('2000-01-01', '2000-01-01 00:00:00', [3, 4, 5], [10, 10, 10]),
+    ('2000-01-01', '2000-01-01 00:01:00', [4, 5, 6], [10, 10, 10]),
+    ('2000-01-01', '2000-01-01 00:01:00', [6, 7, 8], [10, 10, 10]);
+SELECT
+    timeslot,
+    sumMap(statusMap.status, statusMap.requests)
+FROM sum_map
+GROUP BY timeslot
+
+ + +
┌────────────timeslot─┬─sumMap(statusMap.status, statusMap.requests)─┐
+│ 2000-01-01 00:00:00 │ ([1,2,3,4,5],[10,10,20,10,10])               │
+│ 2000-01-01 00:01:00 │ ([4,5,6,7,8],[10,10,20,10,10])               │
+└─────────────────────┴──────────────────────────────────────────────┘
+
+ + +

avg(x)

+

Calculates the average. +Only works for numbers. +The result is always Float64.

+

uniq(x)

+

Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.

+

Uses an adaptive sampling algorithm: for the calculation state, it uses a sample of element hash values with a size up to 65536. +This algorithm is also very accurate for data sets with low cardinality (up to 65536) and very efficient on CPU (when computing not too many of these functions, using uniq is almost as fast as using other aggregate functions).

+

The result is determinate (it doesn't depend on the order of query processing).

+

This function provides excellent accuracy even for data sets with extremely high cardinality (over 10 billion elements). It is recommended for default use.

+

uniqCombined(x)

+

Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.

+

A combination of three algorithms is used: array, hash table and HyperLogLog with an error correction table. The memory consumption is several times smaller than for the uniq function, and the accuracy is several times higher. Performance is slightly lower than for the uniq function, but sometimes it can be even higher than it, such as with distributed queries that transmit a large number of aggregation states over the network. The maximum state size is 96 KiB (HyperLogLog of 217 6-bit cells).

+

The result is determinate (it doesn't depend on the order of query processing).

+

The uniqCombined function is a good default choice for calculating the number of different values, but keep in mind that the estimation error will increase for high-cardinality data sets (200M+ elements), and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).

+

uniqHLL12(x)

+

Uses the HyperLogLog algorithm to approximate the number of different values of the argument. +212 5-bit cells are used. The size of the state is slightly more than 2.5 KB. The result is not very accurate (up to ~10% error) for small data sets (<10K elements). However, the result is fairly accurate for high-cardinality data sets (10K-100M), with a maximum error of ~1.6%. Starting from 100M, the estimation error increases, and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).

+

The result is determinate (it doesn't depend on the order of query processing).

+

We don't recommend using this function. In most cases, use the uniq or uniqCombined function.

+

uniqExact(x)

+

Calculates the number of different values of the argument, exactly. +There is no reason to fear approximations. It's better to use the uniq function. +Use the uniqExact function if you definitely need an exact result.

+

The uniqExact function uses more memory than the uniq function, because the size of the state has unbounded growth as the number of different values increases.

+

groupArray(x), groupArray(max_size)(x)

+

Creates an array of argument values. +Values can be added to the array in any (indeterminate) order.

+

The second version (with the max_size parameter) limits the size of the resulting array to max_size elements. +For example, groupArray (1) (x) is equivalent to [any (x)].

+

In some cases, you can still rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.

+

+

groupArrayInsertAt(x)

+

Inserts a value into the array in the specified position.

+

Accepts the value and position as input. If several values ​​are inserted into the same position, any of them might end up in the resulting array (the first one will be used in the case of single-threaded execution). If no value is inserted into a position, the position is assigned the default value.

+

Optional parameters:

+
    +
  • The default value for substituting in empty positions.
    • +
  • The length of the resulting array. This allows you to receive arrays of the same size for all the aggregate keys. When using this parameter, the default value must be specified.
    • +
+

groupUniqArray(x)

+

Creates an array from different argument values. Memory consumption is the same as for the uniqExact function.

+

quantile(level)(x)

+

Approximates the 'level' quantile. 'level' is a constant, a floating-point number from 0 to 1. +We recommend using a 'level' value in the range of 0.01..0.99 +Don't use a 'level' value equal to 0 or 1 – use the 'min' and 'max' functions for these cases.

+

In this function, as well as in all functions for calculating quantiles, the 'level' parameter can be omitted. In this case, it is assumed to be equal to 0.5 (in other words, the function will calculate the median).

+

Works for numbers, dates, and dates with times. +Returns: for numbers – Float64; for dates – a date; for dates with times – a date with time.

+

Uses reservoir sampling with a reservoir size up to 8192. +If necessary, the result is output with linear approximation from the two neighboring values. +This algorithm provides very low accuracy. See also: quantileTiming, quantileTDigest, quantileExact.

+

The result depends on the order of running the query, and is nondeterministic.

+

When using multiple quantile (and similar) functions with different levels in a query, the internal states are not combined (that is, the query works less efficiently than it could). In this case, use the quantiles (and similar) functions.

+

quantileDeterministic(level)(x, determinator)

+

Works the same way as the quantile function, but the result is deterministic and does not depend on the order of query execution.

+

To achieve this, the function takes a second argument – the "determinator". This is a number whose hash is used instead of a random number generator in the reservoir sampling algorithm. For the function to work correctly, the same determinator value should not occur too often. For the determinator, you can use an event ID, user ID, and so on.

+

Don't use this function for calculating timings. There is a more suitable function for this purpose: quantileTiming.

+

quantileTiming(level)(x)

+

Computes the quantile of 'level' with a fixed precision. +Works for numbers. Intended for calculating quantiles of page loading time in milliseconds.

+

If the value is greater than 30,000 (a page loading time of more than 30 seconds), the result is equated to 30,000.

+

If the total value is not more than about 5670, then the calculation is accurate.

+

Otherwise:

+
    +
  • if the time is less than 1024 ms, then the calculation is accurate.
    • +
  • otherwise the calculation is rounded to a multiple of 16 ms.
    • +
+

When passing negative values to the function, the behavior is undefined.

+

The returned value has the Float32 type. If no values were passed to the function (when using quantileTimingIf), 'nan' is returned. The purpose of this is to differentiate these instances from zeros. See the note on sorting NaNs in "ORDER BY clause".

+

The result is determinate (it doesn't depend on the order of query processing).

+

For its purpose (calculating quantiles of page loading times), using this function is more effective and the result is more accurate than for the quantile function.

+

quantileTimingWeighted(level)(x, weight)

+

Differs from the quantileTiming function in that it has a second argument, "weights". Weight is a non-negative integer. +The result is calculated as if the x value were passed weight number of times to the quantileTiming function.

+

quantileExact(level)(x)

+

Computes the quantile of 'level' exactly. To do this, all the passed values ​​are combined into an array, which is then partially sorted. Therefore, the function consumes O(n) memory, where 'n' is the number of values that were passed. However, for a small number of values, the function is very effective.

+

quantileExactWeighted(level)(x, weight)

+

Computes the quantile of 'level' exactly. In addition, each value is counted with its weight, as if it is present 'weight' times. The arguments of the function can be considered as histograms, where the value 'x' corresponds to a histogram "column" of the height 'weight', and the function itself can be considered as a summation of histograms.

+

A hash table is used as the algorithm. Because of this, if the passed values ​​are frequently repeated, the function consumes less RAM than quantileExact. You can use this function instead of quantileExact and specify the weight as 1.

+

quantileTDigest(level)(x)

+

Approximates the quantile level using the t-digest algorithm. The maximum error is 1%. Memory consumption by State is proportional to the logarithm of the number of passed values.

+

The performance of the function is lower than for quantile, quantileTiming. In terms of the ratio of State size to precision, this function is much better than quantile.

+

The result depends on the order of running the query, and is nondeterministic.

+

median(x)

+

All the quantile functions have corresponding median functions: median, medianDeterministic, medianTiming, medianTimingWeighted, medianExact, medianExactWeighted, medianTDigest. They are synonyms and their behavior is identical.

+

quantiles(level1, level2, ...)(x)

+

All the quantile functions also have corresponding quantiles functions: quantiles, quantilesDeterministic, quantilesTiming, quantilesTimingWeighted, quantilesExact, quantilesExactWeighted, quantilesTDigest. These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.

+

varSamp(x)

+

Calculates the amount Σ((x - x̅)^2) / (n - 1), where n is the sample size and x̅is the average value of x.

+

It represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount.

+

Returns Float64. When n <= 1, returns +∞.

+

varPop(x)

+

Calculates the amount Σ((x - x̅)^2) / (n - 1), where n is the sample size and x̅is the average value of x.

+

In other words, dispersion for a set of values. Returns Float64.

+

stddevSamp(x)

+

The result is equal to the square root of varSamp(x).

+

stddevPop(x)

+

The result is equal to the square root of varPop(x).

+

topK(N)(column)

+

Returns an array of the most frequent values in the specified column. The resulting array is sorted in descending order of frequency of values (not by the values themselves).

+

Implements the Filtered Space-Saving algorithm for analyzing TopK, based on the reduce-and-combine algorithm from Parallel Space Saving.

+
topK(N)(column)
+
+ + +

This function doesn't provide a guaranteed result. In certain situations, errors might occur and it might return frequent values that aren't the most frequent values.

+

We recommend using the N < 10 value; performance is reduced with large N values. Maximum value of N = 65536.

+

Arguments +- 'N' is the number of values. +- ' x ' – The column.

+

Example

+

Take the OnTime data set and select the three most frequently occurring values in the AirlineID column.

+
SELECT topK(3)(AirlineID) AS res
+FROM ontime
+
+ + +
┌─res─────────────────┐
+│ [19393,19790,19805] │
+└─────────────────────┘
+
+ + +

covarSamp(x, y)

+

Calculates the value of Σ((x - x̅)(y - y̅)) / (n - 1).

+

Returns Float64. When n <= 1, returns +∞.

+

covarPop(x, y)

+

Calculates the value of Σ((x - x̅)(y - y̅)) / n.

+

corr(x, y)

+

Calculates the Pearson correlation coefficient: Σ((x - x̅)(y - y̅)) / sqrt(Σ((x - x̅)^2) * Σ((y - y̅)^2)).

+

+

Aggregate function combinators

+

The name of an aggregate function can have a suffix appended to it. This changes the way the aggregate function works.

+

-If

+

The suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument – a condition (Uint8 type). The aggregate function processes only the rows that trigger the condition. If the condition was not triggered even once, it returns a default value (usually zeros or empty strings).

+

Examples: sumIf(column, cond), countIf(cond), avgIf(x, cond), quantilesTimingIf(level1, level2)(x, cond), argMinIf(arg, val, cond) and so on.

+

With conditional aggregate functions, you can calculate aggregates for several conditions at once, without using subqueries and JOINs. For example, in Yandex.Metrica, conditional aggregate functions are used to implement the segment comparison functionality.

+

-Array

+

The -Array suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the 'Array(T)' type (arrays) instead of 'T' type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. When processing arrays, the aggregate function works like the original aggregate function across all array elements.

+

Example 1: sumArray(arr) - Totals all the elements of all 'arr' arrays. In this example, it could have been written more simply: sum(arraySum(arr)).

+

Example 2: uniqArray(arr) – Count the number of unique elements in all 'arr' arrays. This could be done an easier way: uniq(arrayJoin(arr)), but it's not always possible to add 'arrayJoin' to a query.

+

-If and -Array can be combined. However, 'Array' must come first, then 'If'. Examples: uniqArrayIf(arr, cond), quantilesTimingArrayIf(level1, level2)(arr, cond). Due to this order, the 'cond' argument can't be an array.

+

-State

+

If you apply this combinator, the aggregate function doesn't return the resulting value (such as the number of unique values for the 'uniq' function), but an intermediate state of the aggregation (for uniq, this is the hash table for calculating the number of unique values). This is an AggregateFunction(...) that can be used for further processing or stored in a table to finish aggregating later. See the sections "AggregatingMergeTree" and "Functions for working with intermediate aggregation states".

+

-Merge

+

If you apply this combinator, the aggregate function takes the intermediate aggregation state as an argument, combines the states to finish aggregation, and returns the resulting value.

+

-MergeState.

+

Merges the intermediate aggregation states in the same way as the -Merge combinator. However, it doesn't return the resulting value, but an intermediate aggregation state, similar to the -State combinator.

+

-ForEach

+

Converts an aggregate function for tables into an aggregate function for arrays that aggregates the corresponding array items and returns an array of results. For example, sumForEach for the arrays [1, 2], [3, 4, 5]and[6, 7]returns the result [10, 13, 5] after adding together the corresponding array items.

+

+

Parametric aggregate functions

+

Some aggregate functions can accept not only argument columns (used for compression), but a set of parameters – constants for initialization. The syntax is two pairs of brackets instead of one. The first is for parameters, and the second is for arguments.

+

sequenceMatch(pattern)(time, cond1, cond2, ...)

+

Pattern matching for event chains.

+

pattern is a string containing a pattern to match. The pattern is similar to a regular expression.

+

time is the time of the event with the DateTime type.

+

cond1, cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event.

+

The function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern. +It returns UInt8: 0 if the pattern isn't matched, or 1 if it matches.

+

Example: sequenceMatch ('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%')

+
    +
  • whether there was a chain of events in which a pageview with 'company' in the address occurred earlier than a pageview with 'cart' in the address.
    • +
+

This is a singular example. You could write it using other aggregate functions:

+
minIf(EventTime, URL LIKE '%company%') < maxIf(EventTime, URL LIKE '%cart%').
+
+ + +

However, there is no such solution for more complex situations.

+

Pattern syntax:

+

(?1) refers to the condition (any number can be used in place of 1).

+

.* is any number of any events.

+

(?t>=1800) is a time condition.

+

Any quantity of any type of events is allowed over the specified time.

+

Instead of >=, the following operators can be used:<, >, <=.

+

Any number may be specified in place of 1800.

+

Events that occur during the same second can be put in the chain in any order. This may affect the result of the function.

+

sequenceCount(pattern)(time, cond1, cond2, ...)

+

Works the same way as the sequenceMatch function, but instead of returning whether there is an event chain, it returns UInt64 with the number of event chains found. +Chains are searched for without overlapping. In other words, the next chain can start only after the end of the previous one.

+

windowFunnel(window)(timestamp, cond1, cond2, cond3, ....)

+

Window funnel matching for event chains, calculates the max event level in a sliding window.

+

window is the timestamp window value, such as 3600.

+

timestamp is the time of the event with the DateTime type or UInt32 type.

+

cond1, cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event

+

Example:

+

Consider you are doing a website analytics, intend to find out the user counts clicked login button( event = 1001 ), then the user counts followed by searched the phones( event = 1003 and product = 'phone' ) , then the user counts followed by made an order ( event = 1009 ). And all event chains must be in a 3600 seconds sliding window.

+

This could be easily calculate by windowFunnel

+
SELECT
+    level,
+    count() AS c
+FROM
+(
+    SELECT
+        user_id,
+        windowFunnel(3600)(timestamp, event_id = 1001, event_id = 1003 AND product = 'phone', event_id = 1009) AS level
+    FROM trend_event
+    WHERE (event_date >= '2017-01-01') AND (event_date <= '2017-01-31')
+    GROUP BY user_id
+)
+GROUP BY level
+ORDER BY level
+
+ + +

Simply, the level could only be 0,1,2,3, it means the maxium event action stage that one user could reach.

+

uniqUpTo(N)(x)

+

Calculates the number of different argument values ​​if it is less than or equal to N. If the number of different argument values is greater than N, it returns N + 1.

+

Recommended for use with small Ns, up to 10. The maximum value of N is 100.

+

For the state of an aggregate function, it uses the amount of memory equal to 1 + N * the size of one value of bytes. +For strings, it stores a non-cryptographic hash of 8 bytes. That is, the calculation is approximated for strings.

+

The function also works for several arguments.

+

It works as fast as possible, except for cases when a large N value is used and the number of unique values is slightly less than N.

+

Usage example:

+
Problem: Generate a report that shows only keywords that produced at least 5 unique users.
+Solution: Write in the GROUP BY query SearchPhrase HAVING uniqUpTo(4)(UserID) >= 5
+
+ + +

Dictionaries

+

A dictionary is a mapping (key -> attributes) that can be used in a query as functions. +You can think of this as a more convenient and efficient type of JOIN with dimension tables.

+

There are built-in (internal) and add-on (external) dictionaries.

+

+

External dictionaries

+

You can add your own dictionaries from various data sources. The data source for a dictionary can be a local text or executable file, an HTTP(s) resource, or another DBMS. For more information, see "Sources for external dictionaries".

+

ClickHouse:

+
+
    +
  • Fully or partially stores dictionaries in RAM.
    • +
  • Periodically updates dictionaries and dynamically loads missing values. In other words, dictionaries can be loaded dynamically.
    • +
+
+

The configuration of external dictionaries is located in one or more files. The path to the configuration is specified in the dictionaries_config parameter.

+

Dictionaries can be loaded at server startup or at first use, depending on the dictionaries_lazy_load setting.

+

The dictionary config file has the following format:

+
<yandex>
+    <comment>An optional element with any content. Ignored by the ClickHouse server.</comment>
+
+    <!--Optional element. File name with substitutions-->
+    <include_from>/etc/metrika.xml</include_from>
+
+
+    <dictionary>
+        <!-- Dictionary configuration -->
+    </dictionary>
+
+    ...
+
+    <dictionary>
+        <!-- Dictionary configuration -->
+    </dictionary>
+</yandex>
+
+ + +

You can configure any number of dictionaries in the same file. The file format is preserved even if there is only one dictionary (i.e. <yandex><dictionary> <!--configuration -> </dictionary></yandex> ).

+

See also "Functions for working with external dictionaries".

+
+ +You can convert values ​​for a small dictionary by describing it in a `SELECT` query (see the [transform](#other_functions-transform) function). This functionality is not related to external dictionaries. + +
+ +

+

Configuring an external dictionary

+

The dictionary configuration has the following structure:

+
<dictionary>
+    <name>dict_name</name>
+
+    <source>
+      <!-- Source configuration -->
+    </source>
+
+    <layout>
+      <!-- Memory layout configuration -->
+    </layout>
+
+    <structure>
+      <!-- Complex key configuration -->
+    </structure>
+
+    <lifetime>
+      <!-- Lifetime of dictionary in memory -->
+    </lifetime>
+</dictionary>
+
+ + +
    +
  • name – The identifier that can be used to access the dictionary. Use the characters [a-zA-Z0-9_\-].
    • +
  • source — Source of the dictionary.
    • +
  • layout — Dictionary layout in memory.
    • +
  • structure — Structure of the dictionary . A key and attributes that can be retrieved by this key.
    • +
  • lifetime — Frequency of dictionary updates.
    • +
+

+

Storing dictionaries in memory

+

There are a variety of ways to store dictionaries in memory.

+

We recommend flat, hashedandcomplex_key_hashed. which provide optimal processing speed.

+

Caching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section "cache".

+

There are several ways to improve dictionary performance:

+
    +
  • Call the function for working with the dictionary after GROUP BY.
    • +
  • Mark attributes to extract as injective. An attribute is called injective if different attribute values correspond to different keys. So when GROUP BY uses a function that fetches an attribute value by the key, this function is automatically taken out of GROUP BY.
    • +
+

ClickHouse generates an exception for errors with dictionaries. Examples of errors:

+
    +
  • The dictionary being accessed could not be loaded.
    • +
  • Error querying a cached dictionary.
    • +
+

You can view the list of external dictionaries and their statuses in the system.dictionaries table.

+

The configuration looks like this:

+
<yandex>
+    <dictionary>
+        ...
+        <layout>
+            <layout_type>
+                <!-- layout settings -->
+            </layout_type>
+        </layout>
+        ...
+    </dictionary>
+</yandex>
+
+ + +

+

Ways to store dictionaries in memory

+ +

+

flat

+

The dictionary is completely stored in memory in the form of flat arrays. How much memory does the dictionary use? The amount is proportional to the size of the largest key (in space used).

+

The dictionary key has the UInt64 type and the value is limited to 500,000. If a larger key is discovered when creating the dictionary, ClickHouse throws an exception and does not create the dictionary.

+

All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.

+

This method provides the best performance among all available methods of storing the dictionary.

+

Configuration example:

+
<layout>
+  <flat />
+</layout>
+
+ + +

+

hashed

+

The dictionary is completely stored in memory in the form of a hash table. The dictionary can contain any number of elements with any identifiers In practice, the number of keys can reach tens of millions of items.

+

All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.

+

Configuration example:

+
<layout>
+  <hashed />
+</layout>
+
+ + +

+

complex_key_hashed

+

This type of storage is for use with composite keys. Similar to hashed.

+

Configuration example:

+
<layout>
+  <complex_key_hashed />
+</layout>
+
+ + +

+

range_hashed

+

The dictionary is stored in memory in the form of a hash table with an ordered array of ranges and their corresponding values.

+

This storage method works the same way as hashed and allows using date/time ranges in addition to the key, if they appear in the dictionary.

+

Example: The table contains discounts for each advertiser in the format:

+
+---------------+---------------------+-------------------+--------+
+| advertiser id | discount start date | discount end date | amount |
++===============+=====================+===================+========+
+| 123           | 2015-01-01          | 2015-01-15        | 0.15   |
++---------------+---------------------+-------------------+--------+
+| 123           | 2015-01-16          | 2015-01-31        | 0.25   |
++---------------+---------------------+-------------------+--------+
+| 456           | 2015-01-01          | 2015-01-15        | 0.05   |
++---------------+---------------------+-------------------+--------+
+
+ + +

To use a sample for date ranges, define the range_min and range_max elements in the structure.

+

Example:

+
<structure>
+    <id>
+        <name>Id</name>
+    </id>
+    <range_min>
+        <name>first</name>
+    </range_min>
+    <range_max>
+        <name>last</name>
+    </range_max>
+    ...
+
+ + +

To work with these dictionaries, you need to pass an additional date argument to the dictGetT function:

+
dictGetT('dict_name', 'attr_name', id, date)
+
+ + +

This function returns the value for the specified ids and the date range that includes the passed date.

+

Details of the algorithm:

+
    +
  • If the id is not found or a range is not found for the id, it returns the default value for the dictionary.
    • +
  • If there are overlapping ranges, you can use any.
    • +
  • If the range delimiter is NULL or an invalid date (such as 1900-01-01 or 2039-01-01), the range is left open. The range can be open on both sides.
    • +
+

Configuration example:

+
<yandex>
+        <dictionary>
+
+                ...
+
+                <layout>
+                        <range_hashed />
+                </layout>
+
+                <structure>
+                        <id>
+                                <name>Abcdef</name>
+                        </id>
+                        <range_min>
+                                <name>StartDate</name>
+                        </range_min>
+                        <range_max>
+                                <name>EndDate</name>
+                        </range_max>
+                        <attribute>
+                                <name>XXXType</name>
+                                <type>String</type>
+                                <null_value />
+                        </attribute>
+                </structure>
+
+        </dictionary>
+</yandex>
+
+ + +

+

cache

+

The dictionary is stored in a cache that has a fixed number of cells. These cells contain frequently used elements.

+

When searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using SELECT attrs... FROM db.table WHERE id IN (k1, k2, ...). The received data is then written to the cache.

+

For cache dictionaries, the expiration lifetime of data in the cache can be set. If more time than lifetime has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used.

+

This is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the system.dictionaries table.

+

To improve cache performance, use a subquery with LIMIT, and call the function with the dictionary externally.

+

Supported sources: MySQL, ClickHouse, executable, HTTP.

+

Example of settings:

+
<layout>
+    <cache>
+        <!-- The size of the cache, in number of cells. Rounded up to a power of two. -->
+        <size_in_cells>1000000000</size_in_cells>
+    </cache>
+</layout>
+
+ + +

Set a large enough cache size. You need to experiment to select the number of cells:

+
    +
  1. Set some value.
    2. +
  2. Run queries until the cache is completely full.
    3. +
  3. Assess memory consumption using the system.dictionaries table.
    4. +
  4. Increase or decrease the number of cells until the required memory consumption is reached.
    5. +
+
+ +Do not use ClickHouse as a source, because it is slow to process queries with random reads. + +
+ +

+

complex_key_cache

+

This type of storage is for use with composite keys. Similar to cache.

+

+

ip_trie

+

This type of storage is for mapping network prefixes (IP addresses) to metadata such as ASN.

+

Example: The table contains network prefixes and their corresponding AS number and country code:

+
  +-----------------+-------+--------+
+  | prefix          | asn   | cca2   |
+  +=================+=======+========+
+  | 202.79.32.0/20  | 17501 | NP     |
+  +-----------------+-------+--------+
+  | 2620:0:870::/48 | 3856  | US     |
+  +-----------------+-------+--------+
+  | 2a02:6b8:1::/48 | 13238 | RU     |
+  +-----------------+-------+--------+
+  | 2001:db8::/32   | 65536 | ZZ     |
+  +-----------------+-------+--------+
+
+ + +

When using this type of layout, the structure must have a composite key.

+

Example:

+
<structure>
+    <key>
+        <attribute>
+            <name>prefix</name>
+            <type>String</type>
+        </attribute>
+    </key>
+    <attribute>
+            <name>asn</name>
+            <type>UInt32</type>
+            <null_value />
+    </attribute>
+    <attribute>
+            <name>cca2</name>
+            <type>String</type>
+            <null_value>??</null_value>
+    </attribute>
+    ...
+
+ + +

The key must have only one String type attribute that contains an allowed IP prefix. Other types are not supported yet.

+

For queries, you must use the same functions (dictGetT with a tuple) as for dictionaries with composite keys:

+
dictGetT('dict_name', 'attr_name', tuple(ip))
+
+ + +

The function takes either UInt32 for IPv4, or FixedString(16) for IPv6:

+
dictGetString('prefix', 'asn', tuple(IPv6StringToNum('2001:db8::1')))
+
+ + +

Other types are not supported yet. The function returns the attribute for the prefix that corresponds to this IP address. If there are overlapping prefixes, the most specific one is returned.

+

Data is stored in a trie. It must completely fit into RAM.

+

+

Dictionary updates

+

ClickHouse periodically updates the dictionaries. The update interval for fully downloaded dictionaries and the invalidation interval for cached dictionaries are defined in the <lifetime> tag in seconds.

+

Dictionary updates (other than loading for first use) do not block queries. During updates, the old version of a dictionary is used. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.

+

Example of settings:

+
<dictionary>
+    ...
+    <lifetime>300</lifetime>
+    ...
+</dictionary>
+
+ + +

Setting <lifetime> 0</lifetime> prevents updating dictionaries.

+

You can set a time interval for upgrades, and ClickHouse will choose a uniformly random time within this range. This is necessary in order to distribute the load on the dictionary source when upgrading on a large number of servers.

+

Example of settings:

+
<dictionary>
+    ...
+    <lifetime>
+        <min>300</min>
+        <max>360</max>
+    </lifetime>
+    ...
+</dictionary>
+
+ + +

When upgrading the dictionaries, the ClickHouse server applies different logic depending on the type of source:

+
+
    +
  • For a text file, it checks the time of modification. If the time differs from the previously recorded time, the dictionary is updated.
    • +
  • For MyISAM tables, the time of modification is checked using a SHOW TABLE STATUS query.
    • +
  • Dictionaries from other sources are updated every time by default.
    • +
+
+

For MySQL (InnoDB) and ODBC sources, you can set up a query that will update the dictionaries only if they really changed, rather than each time. To do this, follow these steps:

+
+
    +
  • The dictionary table must have a field that always changes when the source data is updated.
    • +
  • The settings of the source must specify a query that retrieves the changing field. The ClickHouse server interprets the query result as a row, and if this row has changed relative to its previous state, the dictionary is updated. Specify the query in the <invalidate_query> field in the settings for the source.
    • +
+
+

Example of settings:

+
<dictionary>
+    ...
+    <odbc>
+      ...
+      <invalidate_query>SELECT update_time FROM dictionary_source where id = 1</invalidate_query>
+    </odbc>
+    ...
+</dictionary>
+
+ + +

+

Sources of external dictionaries

+

An external dictionary can be connected from many different sources.

+

The configuration looks like this:

+
<yandex>
+  <dictionary>
+    ...
+    <source>
+      <source_type>
+        <!-- Source configuration -->
+      </source_type>
+    </source>
+    ...
+  </dictionary>
+  ...
+</yandex>
+
+ + +

The source is configured in the source section.

+

Types of sources (source_type):

+ +

+

Local file

+

Example of settings:

+
<source>
+  <file>
+    <path>/opt/dictionaries/os.tsv</path>
+    <format>TabSeparated</format>
+  </file>
+</source>
+
+ + +

Setting fields:

+
    +
  • path – The absolute path to the file.
    • +
  • format – The file format. All the formats described in "Formats" are supported.
    • +
+

+

Executable file

+

Working with executable files depends on how the dictionary is stored in memory. If the dictionary is stored using cache and complex_key_cache, ClickHouse requests the necessary keys by sending a request to the executable file's STDIN.

+

Example of settings:

+
<source>
+    <executable>
+        <command>cat /opt/dictionaries/os.tsv</command>
+        <format>TabSeparated</format>
+    </executable>
+</source>
+
+ + +

Setting fields:

+
    +
  • command – The absolute path to the executable file, or the file name (if the program directory is written to PATH).
    • +
  • format – The file format. All the formats described in "Formats" are supported.
    • +
+

+

HTTP(s)

+

Working with an HTTP(s) server depends on how the dictionary is stored in memory. If the dictionary is stored using cache and complex_key_cache, ClickHouse requests the necessary keys by sending a request via the POST method.

+

Example of settings:

+
<source>
+    <http>
+        <url>http://[::1]/os.tsv</url>
+        <format>TabSeparated</format>
+    </http>
+</source>
+
+ + +

In order for ClickHouse to access an HTTPS resource, you must configure openSSL in the server configuration.

+

Setting fields:

+
    +
  • url – The source URL.
    • +
  • format – The file format. All the formats described in "Formats" are supported.
    • +
+

+

ODBC

+

You can use this method to connect any database that has an ODBC driver.

+

Example of settings:

+
<odbc>
+    <db>DatabaseName</db>
+    <table>TableName</table>
+    <connection_string>DSN=some_parameters</connection_string>
+    <invalidate_query>SQL_QUERY</invalidate_query>
+</odbc>
+
+ + +

Setting fields:

+
    +
  • db – Name of the database. Omit it if the database name is set in the <connection_string> parameters.
    • +
  • table – Name of the table.
    • +
  • connection_string – Connection string.
    • +
  • invalidate_query – Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries.
    • +
+

Example of connecting PostgreSQL

+

Ubuntu OS.

+

Installing unixODBC and the ODBC driver for PostgreSQL:

+
sudo apt-get install -y unixodbc odbcinst odbc-postgresql
+
+ + +

Configuring /etc/odbc.ini (or ~/.odbc.ini):

+
    [DEFAULT]
+    Driver = myconnection
+
+    [myconnection]
+    Description         = PostgreSQL connection to my_db
+    Driver              = PostgreSQL Unicode
+    Database            = my_db
+    Servername          = 127.0.0.1
+    UserName            = username
+    Password            = password
+    Port                = 5432
+    Protocol            = 9.3
+    ReadOnly            = No
+    RowVersioning       = No
+    ShowSystemTables    = No
+    ConnSettings        =
+
+ + +

The dictionary configuration in ClickHouse:

+
<dictionary>
+    <name>table_name</name>
+    <source>
+    <odbc>
+        <!-- You can specifiy the following parameters in connection_string: -->
+        <!-- DSN=myconnection;UID=username;PWD=password;HOST=127.0.0.1;PORT=5432;DATABASE=my_db -->
+            <connection_string>DSN=myconnection</connection_string>
+            <table>postgresql_table</table>
+        </odbc>
+    </source>
+    <lifetime>
+        <min>300</min>
+        <max>360</max>
+    </lifetime>
+    <layout>
+        <hashed/>
+    </layout>
+    <structure>
+        <id>
+            <name>id</name>
+        </id>
+        <attribute>
+            <name>some_column</name>
+            <type>UInt64</type>
+            <null_value>0</null_value>
+        </attribute>
+    </structure>
+</dictionary>
+
+ + +

You may need to edit odbc.ini to specify the full path to the library with the driver DRIVER=/usr/local/lib/psqlodbcw.so.

+

Example of connecting MS SQL Server

+

Ubuntu OS.

+

Installing the driver: :

+
    sudo apt-get install tdsodbc freetds-bin sqsh
+
+ + +

Configuring the driver: :

+
    $ cat /etc/freetds/freetds.conf 
+    ...
+
+    [MSSQL]
+    host = 192.168.56.101
+    port = 1433
+    tds version = 7.0
+    client charset = UTF-8
+
+    $ cat /etc/odbcinst.ini 
+    ...
+
+    [FreeTDS]
+    Description     = FreeTDS
+    Driver          = /usr/lib/x86_64-linux-gnu/odbc/libtdsodbc.so
+    Setup           = /usr/lib/x86_64-linux-gnu/odbc/libtdsS.so
+    FileUsage       = 1
+    UsageCount      = 5
+
+    $ cat ~/.odbc.ini 
+    ...
+
+    [MSSQL]
+    Description     = FreeTDS
+    Driver          = FreeTDS
+    Servername      = MSSQL
+    Database        = test
+    UID             = test
+    PWD             = test
+    Port            = 1433
+
+ + +

Configuring the dictionary in ClickHouse:

+
<yandex>
+    <dictionary>
+        <name>test</name>
+        <source>
+            <odbc>
+                <table>dict</table>
+                <connection_string>DSN=MSSQL;UID=test;PWD=test</connection_string>
+            </odbc>
+        </source>
+
+        <lifetime>
+            <min>300</min>
+            <max>360</max>
+        </lifetime>
+
+        <layout>
+            <flat />
+        </layout>
+
+        <structure>
+            <id>
+                <name>k</name>
+            </id>
+            <attribute>
+                <name>s</name>
+                <type>String</type>
+                <null_value></null_value>
+            </attribute>
+        </structure>
+    </dictionary>
+</yandex>
+
+ + +

DBMS

+

+

MySQL

+

Example of settings:

+
<source>
+  <mysql>
+      <port>3306</port>
+      <user>clickhouse</user>
+      <password>qwerty</password>
+      <replica>
+          <host>example01-1</host>
+          <priority>1</priority>
+      </replica>
+      <replica>
+          <host>example01-2</host>
+          <priority>1</priority>
+      </replica>
+      <db>db_name</db>
+      <table>table_name</table>
+      <where>id=10</where>
+      <invalidate_query>SQL_QUERY</invalidate_query>
+  </mysql>
+</source>
+
+ + +

Setting fields:

+
    +
  • +

    port – The port on the MySQL server. You can specify it for all replicas, or for each one individually (inside <replica>).

    +
    • +
  • +

    user – Name of the MySQL user. You can specify it for all replicas, or for each one individually (inside <replica>).

    +
    • +
  • +

    password – Password of the MySQL user. You can specify it for all replicas, or for each one individually (inside <replica>).

    +
    • +
  • +

    replica – Section of replica configurations. There can be multiple sections.

    +
    • +
  • replica/host – The MySQL host.
    • +
+

* replica/priority – The replica priority. When attempting to connect, ClickHouse traverses the replicas in order of priority. The lower the number, the higher the priority.

+
    +
  • +

    db – Name of the database.

    +
    • +
  • +

    table – Name of the table.

    +
    • +
  • +

    where – The selection criteria. Optional parameter.

    +
    • +
  • +

    invalidate_query – Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries.

    +
    • +
+

MySQL can be connected on a local host via sockets. To do this, set host and socket.

+

Example of settings:

+
<source>
+  <mysql>
+      <host>localhost</host>
+      <socket>/path/to/socket/file.sock</socket>
+      <user>clickhouse</user>
+      <password>qwerty</password>
+      <db>db_name</db>
+      <table>table_name</table>
+      <where>id=10</where>
+      <invalidate_query>SQL_QUERY</invalidate_query>
+  </mysql>
+</source>
+
+ + +

+

ClickHouse

+

Example of settings:

+
<source>
+    <clickhouse>
+        <host>example01-01-1</host>
+        <port>9000</port>
+        <user>default</user>
+        <password></password>
+        <db>default</db>
+        <table>ids</table>
+        <where>id=10</where>
+    </clickhouse>
+</source>
+
+ + +

Setting fields:

+
    +
  • host – The ClickHouse host. If it is a local host, the query is processed without any network activity. To improve fault tolerance, you can create a Distributed table and enter it in subsequent configurations.
    • +
  • port – The port on the ClickHouse server.
    • +
  • user – Name of the ClickHouse user.
    • +
  • password – Password of the ClickHouse user.
    • +
  • db – Name of the database.
    • +
  • table – Name of the table.
    • +
  • where – The selection criteria. May be omitted.
    • +
+

+

MongoDB

+

Example of settings:

+
<source>
+    <mongodb>
+        <host>localhost</host>
+        <port>27017</port>
+        <user></user>
+        <password></password>
+        <db>test</db>
+        <collection>dictionary_source</collection>
+    </mongodb>
+</source>
+
+ + +

Setting fields:

+
    +
  • host – The MongoDB host.
    • +
  • port – The port on the MongoDB server.
    • +
  • user – Name of the MongoDB user.
    • +
  • password – Password of the MongoDB user.
    • +
  • db – Name of the database.
    • +
  • collection – Name of the collection.
    • +
+

+

Dictionary key and fields

+

The <structure> clause describes the dictionary key and fields available for queries.

+

Overall structure:

+
<dictionary>
+    <structure>
+        <id>
+            <name>Id</name>
+        </id>
+
+        <attribute>
+            <!-- Attribute parameters -->
+        </attribute>
+
+        ...
+
+    </structure>
+</dictionary>
+
+ + +

Columns are described in the structure:

+ +

+

Key

+

ClickHouse supports the following types of keys:

+
    +
  • Numeric key. UInt64. Defined in the tag <id> .
    • +
  • Composite key. Set of values of different types. Defined in the tag <key> .
    • +
+

A structure can contain either <id> or <key> .

+
+ +The key doesn't need to be defined separately in attributes. + +
+ +

Numeric key

+

Format: UInt64.

+

Configuration example:

+
<id>
+    <name>Id</name>
+</id>
+
+ + +

Configuration fields:

+
    +
  • name – The name of the column with keys.
    • +
+

Composite key

+

The key can be a tuple from any types of fields. The layout in this case must be complex_key_hashed or complex_key_cache.

+
+A composite key can consist of a single element. This makes it possible to use a string as the key, for instance. +
+ +

The key structure is set in the element <key>. Key fields are specified in the same format as the dictionary attributes. Example:

+
<structure>
+    <key>
+        <attribute>
+            <name>field1</name>
+            <type>String</type>
+        </attribute>
+        <attribute>
+            <name>field2</name>
+            <type>UInt32</type>
+        </attribute>
+        ...
+    </key>
+...
+
+ + +

For a query to the dictGet* function, a tuple is passed as the key. Example: dictGetString('dict_name', 'attr_name', tuple('string for field1', num_for_field2)).

+

+

Attributes

+

Configuration example:

+
<structure>
+    ...
+    <attribute>
+        <name>Name</name>
+        <type>Type</type>
+        <null_value></null_value>
+        <expression>rand64()</expression>
+        <hierarchical>true</hierarchical>
+        <injective>true</injective>
+        <is_object_id>true</is_object_id>
+    </attribute>
+</structure>
+
+ + +

Configuration fields:

+
    +
  • name – The column name.
    • +
  • type – The column type. Sets the method for interpreting data in the source. For example, for MySQL, the field might be TEXT, VARCHAR, or BLOB in the source table, but it can be uploaded as String.
    • +
  • null_value – The default value for a non-existing element. In the example, it is an empty string.
    • +
  • expression – The attribute can be an expression. The tag is not required.
    • +
  • hierarchical – Hierarchical support. Mirrored to the parent identifier. By default, false.
    • +
  • injective – Whether the id -> attribute image is injective. If true, then you can optimize the GROUP BY clause. By default, false.
    • +
  • is_object_id – Whether the query is executed for a MongoDB document by ObjectID.
    • +
+

Internal dictionaries

+

ClickHouse contains a built-in feature for working with a geobase.

+

This allows you to:

+
    +
  • Use a region's ID to get its name in the desired language.
    • +
  • Use a region's ID to get the ID of a city, area, federal district, country, or continent.
    • +
  • Check whether a region is part of another region.
    • +
  • Get a chain of parent regions.
    • +
+

All the functions support "translocality," the ability to simultaneously use different perspectives on region ownership. For more information, see the section "Functions for working with Yandex.Metrica dictionaries".

+

The internal dictionaries are disabled in the default package. +To enable them, uncomment the parameters path_to_regions_hierarchy_file and path_to_regions_names_files in the server configuration file.

+

The geobase is loaded from text files. +If you work at Yandex, you can follow these instructions to create them: +https://github.yandex-team.ru/raw/Metrika/ClickHouse_private/master/doc/create_embedded_geobase_dictionaries.txt

+

Put the regions_hierarchy*.txt files in the path_to_regions_hierarchy_file directory. This configuration parameter must contain the path to the regions_hierarchy.txt file (the default regional hierarchy), and the other files (regions_hierarchy_ua.txt) must be located in the same directory.

+

Put the regions_names_*.txt files in the path_to_regions_names_files directory.

+

You can also create these files yourself. The file format is as follows:

+

regions_hierarchy*.txt: TabSeparated (no header), columns:

+
    +
  • Region ID (UInt32)
    • +
  • Parent region ID (UInt32)
    • +
  • Region type (UInt8): 1 - continent, 3 - country, 4 - federal district, 5 - region, 6 - city; other types don't have values.
    • +
  • Population (UInt32) - Optional column.
    • +
+

regions_names_*.txt: TabSeparated (no header), columns:

+
    +
  • Region ID (UInt32)
    • +
  • Region name (String) - Can't contain tabs or line feeds, even escaped ones.
    • +
+

A flat array is used for storing in RAM. For this reason, IDs shouldn't be more than a million.

+

Dictionaries can be updated without restarting the server. However, the set of available dictionaries is not updated. +For updates, the file modification times are checked. If a file has changed, the dictionary is updated. +The interval to check for changes is configured in the 'builtin_dictionaries_reload_interval' parameter. +Dictionary updates (other than loading at first use) do not block queries. During updates, queries use the old versions of dictionaries. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.

+

We recommend periodically updating the dictionaries with the geobase. During an update, generate new files and write them to a separate location. When everything is ready, rename them to the files used by the server.

+

There are also functions for working with OS identifiers and Yandex.Metrica search engines, but they shouldn't be used.

+

Usage

+

Access rights

+

Users and access rights are set up in the user config. This is usually users.xml.

+

Users are recorded in the users section. Here is a fragment of the users.xml file:

+
<!-- Users and ACL. -->
+<users>
+    <!-- If the user name is not specified, the 'default' user is used. -->
+    <default>
+        <!-- Password could be specified in plaintext or in SHA256 (in hex format).
+
+             If you want to specify the password in plain text (not recommended), place it in the 'password' element.
+             Example: <password>qwerty</password>.
+             Password can be empty.
+
+             If you want to specify SHA256, place it in the 'password_sha256_hex' element.
+                          Example: <password_sha256_hex>65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5</password_sha256_hex>
+
+             How to generate decent password:
+             Execute: PASSWORD=$(base64 < /dev/urandom | head -c8); echo "$PASSWORD"; echo -n "$PASSWORD" | sha256sum | tr -d '-'
+             In first line will be password and in second - corresponding SHA256.
+        -->
+        <password></password>
+        <!-- A list of networks that access is allowed from.
+            Each list item has one of the following forms:
+            <ip>IP address or subnet mask. For example: 198.51.100.0/24 or 2001:DB8::/32.
+            <host> Host name. For example: example01. A DNS query is made for verification, and all addresses obtained are compared with the address of the customer.
+            <host_regexp> Regular expression for host names. For example: ^example\d\d-\d\d-\d\.yandex\.ru$
+                For verification, a DNS PTR query is made for the customer's address and a regular expression is applied to the result.
+                Then another DNS query is made for the result of the PTR query, and all received address are compared to the client address.
+                We strongly recommend that the regex ends with \.yandex\.ru$.
+
+            If you are installing ClickHouse yourself, enter:
+                <networks>
+                        <ip>::/0</ip>
+                </networks>
+        -->
+        <networks incl="networks" />
+
+        <!-- Settings profile for the user. -->
+        <profile>default</profile>
+
+        <!-- Quota for the user. -->
+        <quota>default</quota>
+    </default>
+
+    <!-- For requests from the Yandex.Metrica user interface via the API for data on specific counters. -->
+    <web>
+        <password></password>
+        <networks incl="networks" />
+        <profile>web</profile>
+        <quota>default</quota>
+        <allow_databases>
+        <database>test</database>
+        </allow_databases>
+    </web>
+</users>
+
+ + +

You can see a declaration from two users: default and web. We added the web user separately.

+

The default user is chosen in cases when the username is not passed. The default user is also used for distributed query processing, if the configuration of the server or cluster doesn't specify the user and password (see the section on the Distributed engine).

+

The user that is used for exchanging information between servers combined in a cluster must not have substantial restrictions or quotas – otherwise, distributed queries will fail.

+

The password is specified in open format (not recommended) or in SHA-256. The hash isn't salted. In this regard, you should not consider these passwords as providing security against potential malicious attacks. Rather, they are necessary for protection from employees.

+

A list of networks is specified that access is allowed from. In this example, the list of networks for both users is loaded from a separate file (/etc/metrika.xml) containing the 'networks' substitution. Here is a fragment of it:

+
<yandex>
+    ...
+    <networks>
+        <ip>::/64</ip>
+        <ip>203.0.113.0/24</ip>
+        <ip>2001:DB8::/32</ip>
+        ...
+    </networks>
+</yandex>
+
+ + +

We could have defined this list of networks directly in 'users.xml', or in a file in the 'users.d' directory (for more information, see the section "Configuration files").

+

The config includes comments explaining how to open access from everywhere.

+

For use in production, only specify IP elements (IP addresses and their masks), since using 'host' and 'hoost_regexp' might cause extra latency.

+

Next the user settings profile is specified (see the section "Settings profiles"). You can specify the default profile, default. The profile can have any name. You can specify the same profile for different users. The most important thing you can write in the settings profile is 'readonly' set to 1, which provides read-only access.

+

After this, the quota is defined (see the section "Quotas"). You can specify the default quota, default. It is set in the config by default so that it only counts resource usage, but does not restrict it. The quota can have any name. You can specify the same quota for different users – in this case, resource usage is calculated for each user individually.

+

In the optional <allow_databases> section, you can also specify a list of databases that the user can access. By default, all databases are available to the user. You can specify the default database. In this case, the user will receive access to the database by default.

+

Access to the system database is always allowed (since this database is used for processing queries).

+

The user can get a list of all databases and tables in them by using SHOW queries or system tables, even if access to individual databases isn't allowed.

+

Database access is not related to the readonly setting. You can't grant full access to one database and readonly access to another one.

+

+

Configuration files

+

The main server config file is config.xml. It resides in the /etc/clickhouse-server/ directory.

+

Individual settings can be overridden in the *.xmland*.conf files in the conf.d and config.d directories next to the config file.

+

The replace or remove attributes can be specified for the elements of these config files.

+

If neither is specified, it combines the contents of elements recursively, replacing values of duplicate children.

+

If replace is specified, it replaces the entire element with the specified one.

+

If remove is specified, it deletes the element.

+

The config can also define "substitutions". If an element has the incl attribute, the corresponding substitution from the file will be used as the value. By default, the path to the file with substitutions is /etc/metrika.xml. This can be changed in the include_from element in the server config. The substitution values are specified in /yandex/substitution_name elements in this file. If a substitution specified in incl does not exist, it is recorded in the log. To prevent ClickHouse from logging missing substitutions, specify the optional="true" attribute (for example, settings for macros).

+

Substitutions can also be performed from ZooKeeper. To do this, specify the attribute from_zk = "/path/to/node". The element value is replaced with the contents of the node at /path/to/node in ZooKeeper. You can also put an entire XML subtree on the ZooKeeper node and it will be fully inserted into the source element.

+

The config.xml file can specify a separate config with user settings, profiles, and quotas. The relative path to this config is set in the 'users_config' element. By default, it is users.xml. If users_config is omitted, the user settings, profiles, and quotas are specified directly in config.xml.

+

In addition, users_config may have overrides in files from the users_config.d directory (for example, users.d) and substitutions.

+

For each config file, the server also generates file-preprocessed.xml files when starting. These files contain all the completed substitutions and overrides, and they are intended for informational use. If ZooKeeper substitutions were used in the config files but ZooKeeper is not available on the server start, the server loads the configuration from the preprocessed file.

+

The server tracks changes in config files, as well as files and ZooKeeper nodes that were used when performing substitutions and overrides, and reloads the settings for users and clusters on the fly. This means that you can modify the cluster, users, and their settings without restarting the server.

+

Quotas

+

Quotas allow you to limit resource usage over a period of time, or simply track the use of resources. +Quotas are set up in the user config. This is usually 'users.xml'.

+

The system also has a feature for limiting the complexity of a single query. See the section "Restrictions on query complexity").

+

In contrast to query complexity restrictions, quotas:

+
    +
  • Place restrictions on a set of queries that can be run over a period of time, instead of limiting a single query.
    • +
  • Account for resources spent on all remote servers for distributed query processing.
    • +
+

Let's look at the section of the 'users.xml' file that defines quotas.

+
<!-- Quotas. -->
+<quotas>
+    <!-- Quota name. -->
+    <default>
+        <!-- Restrictions for a time period. You can set many intervals with different restrictions. -->
+        <interval>
+            <!-- Length of the interval. -->
+            <duration>3600</duration>
+
+            <!-- Unlimited. Just collect data for the specified time interval. -->
+            <queries>0</queries>
+            <errors>0</errors>
+            <result_rows>0</result_rows>
+            <read_rows>0</read_rows>
+            <execution_time>0</execution_time>
+        </interval>
+    </default>
+
+ + +

By default, the quota just tracks resource consumption for each hour, without limiting usage. +The resource consumption calculated for each interval is output to the server log after each request.

+
<statbox>
+    <!-- Restrictions for a time period. You can set many intervals with different restrictions. -->
+    <interval>
+        <!-- Length of the interval. -->
+        <duration>3600</duration>
+
+        <queries>1000</queries>
+        <errors>100</errors>
+        <result_rows>1000000000</result_rows>
+        <read_rows>100000000000</read_rows>
+        <execution_time>900</execution_time>
+    </interval>
+
+    <interval>
+        <duration>86400</duration>
+
+        <queries>10000</queries>
+        <errors>1000</errors>
+        <result_rows>5000000000</result_rows>
+        <read_rows>500000000000</read_rows>
+        <execution_time>7200</execution_time>
+    </interval>
+</statbox>
+
+ + +

For the 'statbox' quota, restrictions are set for every hour and for every 24 hours (86,400 seconds). The time interval is counted starting from an implementation-defined fixed moment in time. In other words, the 24-hour interval doesn't necessarily begin at midnight.

+

When the interval ends, all collected values are cleared. For the next hour, the quota calculation starts over.

+

Here are the amounts that can be restricted:

+

queries – The total number of requests.

+

errors – The number of queries that threw an exception.

+

result_rows – The total number of rows given as the result.

+

read_rows – The total number of source rows read from tables for running the query, on all remote servers.

+

execution_time – The total query execution time, in seconds (wall time).

+

If the limit is exceeded for at least one time interval, an exception is thrown with a text about which restriction was exceeded, for which interval, and when the new interval begins (when queries can be sent again).

+

Quotas can use the "quota key" feature in order to report on resources for multiple keys independently. Here is an example of this:

+
<!-- For the global reports designer. -->
+<web_global>
+    <!-- keyed - The quota_key "key" is passed in the query parameter,
+            and the quota is tracked separately for each key value.
+        For example, you can pass a Yandex.Metrica username as the key,
+            so the quota will be counted separately for each username.
+        Using keys makes sense only if quota_key is transmitted by the program, not by a user.
+
+        You can also write <keyed_by_ip /> so the IP address is used as the quota key.
+        (But keep in mind that users can change the IPv6 address fairly easily.)
+    -->
+    <keyed />
+
+ + +

The quota is assigned to users in the 'users' section of the config. See the section "Access rights".

+

For distributed query processing, the accumulated amounts are stored on the requestor server. So if the user goes to another server, the quota there will "start over".

+

When the server is restarted, quotas are reset.

+

Usage recommendations

+

CPU

+

The SSE 4.2 instruction set must be supported. Modern processors (since 2008) support it.

+

When choosing a processor, prefer a large number of cores and slightly slower clock rate over fewer cores and a higher clock rate. +For example, 16 cores with 2600 MHz is better than 8 cores with 3600 MHz.

+

Hyper-threading

+

Don't disable hyper-threading. It helps for some queries, but not for others.

+

Turbo Boost

+

Turbo Boost is highly recommended. It significantly improves performance with a typical load. +You can use turbostat to view the CPU's actual clock rate under a load.

+

CPU scaling governor

+

Always use the performance scaling governor. The on-demand scaling governor works much worse with constantly high demand.

+
sudo echo 'performance' | tee /sys/devices/system/cpu/cpu\*/cpufreq/scaling_governor
+
+ + +

CPU limitations

+

Processors can overheat. Use dmesg to see if the CPU's clock rate was limited due to overheating. +The restriction can also be set externally at the datacenter level. You can use turbostat to monitor it under a load.

+

RAM

+

For small amounts of data (up to \~200 GB compressed), it is best to use as much memory as the volume of data. +For large amounts of data and when processing interactive (online) queries, you should use a reasonable amount of RAM (128 GB or more) so the hot data subset will fit in the cache of pages. +Even for data volumes of \~50 TB per server, using 128 GB of RAM significantly improves query performance compared to 64 GB.

+

Swap file

+

Always disable the swap file. The only reason for not doing this is if you are using ClickHouse on your personal laptop.

+

Huge pages

+

Always disable transparent huge pages. It interferes with memory allocators, which leads to significant performance degradation.

+
echo 'never' | sudo tee /sys/kernel/mm/transparent_hugepage/enabled
+
+ + +

Use perf top to watch the time spent in the kernel for memory management. +Permanent huge pages also do not need to be allocated.

+

Storage subsystem

+

If your budget allows you to use SSD, use SSD. +If not, use HDD. SATA HDDs 7200 RPM will do.

+

Give preference to a lot of servers with local hard drives over a smaller number of servers with attached disk shelves. +But for storing archives with rare queries, shelves will work.

+

RAID

+

When using HDD, you can combine their RAID-10, RAID-5, RAID-6 or RAID-50. +For Linux, software RAID is better (with mdadm). We don't recommend using LVM. +When creating RAID-10, select the far layout. +If your budget allows, choose RAID-10.

+

If you have more than 4 disks, use RAID-6 (preferred) or RAID-50, instead of RAID-5. +When using RAID-5, RAID-6 or RAID-50, always increase stripe_cache_size, since the default value is usually not the best choice.

+
echo 4096 | sudo tee /sys/block/md2/md/stripe_cache_size
+
+ + +

Calculate the exact number from the number of devices and the block size, using the formula: 2 * num_devices * chunk_size_in_bytes / 4096.

+

A block size of 1025 KB is sufficient for all RAID configurations. +Never set the block size too small or too large.

+

You can use RAID-0 on SSD. +Regardless of RAID use, always use replication for data security.

+

Enable NCQ with a long queue. For HDD, choose the CFQ scheduler, and for SSD, choose noop. Don't reduce the 'readahead' setting. +For HDD, enable the write cache.

+

File system

+

Ext4 is the most reliable option. Set the mount options noatime, nobarrier. +XFS is also suitable, but it hasn't been as thoroughly tested with ClickHouse. +Most other file systems should also work fine. File systems with delayed allocation work better.

+

Linux kernel

+

Don't use an outdated Linux kernel. In 2015, 3.18.19 was new enough. +Consider using the kernel build from Yandex:https://github.com/yandex/smart – it provides at least a 5% performance increase.

+

Network

+

If you are using IPv6, increase the size of the route cache. +The Linux kernel prior to 3.2 had a multitude of problems with IPv6 implementation.

+

Use at least a 10 GB network, if possible. 1 Gb will also work, but it will be much worse for patching replicas with tens of terabytes of data, or for processing distributed queries with a large amount of intermediate data.

+

ZooKeeper

+

You are probably already using ZooKeeper for other purposes. You can use the same installation of ZooKeeper, if it isn't already overloaded.

+

It's best to use a fresh version of ZooKeeper – 3.4.9 or later. The version in stable Linux distributions may be outdated.

+

With the default settings, ZooKeeper is a time bomb:

+
+

The ZooKeeper server won't delete files from old snapshots and logs when using the default configuration (see autopurge), and this is the responsibility of the operator.

+
+

This bomb must be defused.

+

The ZooKeeper (3.5.1) configuration below is used in the Yandex.Metrica production environment as of May 20, 2017:

+

zoo.cfg:

+
## http://hadoop.apache.org/zookeeper/docs/current/zookeeperAdmin.html
+
+## The number of milliseconds of each tick
+tickTime=2000
+## The number of ticks that the initial
+## synchronization phase can take
+initLimit=30000
+## The number of ticks that can pass between
+## sending a request and getting an acknowledgement
+syncLimit=10
+
+maxClientCnxns=2000
+
+maxSessionTimeout=60000000
+## the directory where the snapshot is stored.
+dataDir=/opt/zookeeper/{{ cluster['name'] }}/data
+## Place the dataLogDir to a separate physical disc for better performance
+dataLogDir=/opt/zookeeper/{{ cluster['name'] }}/logs
+
+autopurge.snapRetainCount=10
+autopurge.purgeInterval=1
+
+
+## To avoid seeks ZooKeeper allocates space in the transaction log file in
+## blocks of preAllocSize kilobytes. The default block size is 64M. One reason
+## for changing the size of the blocks is to reduce the block size if snapshots
+## are taken more often. (Also, see snapCount).
+preAllocSize=131072
+
+## Clients can submit requests faster than ZooKeeper can process them,
+## especially if there are a lot of clients. To prevent ZooKeeper from running
+## out of memory due to queued requests, ZooKeeper will throttle clients so that
+## there is no more than globalOutstandingLimit outstanding requests in the
+## system. The default limit is 1,000.ZooKeeper logs transactions to a
+## transaction log. After snapCount transactions are written to a log file a
+## snapshot is started and a new transaction log file is started. The default
+## snapCount is 10,000.
+snapCount=3000000
+
+## If this option is defined, requests will be will logged to a trace file named
+## traceFile.year.month.day.
+##traceFile=
+
+## Leader accepts client connections. Default value is "yes". The leader machine
+## coordinates updates. For higher update throughput at thes slight expense of
+## read throughput the leader can be configured to not accept clients and focus
+## on coordination.
+leaderServes=yes
+
+standaloneEnabled=false
+dynamicConfigFile=/etc/zookeeper-{{ cluster['name'] }}/conf/zoo.cfg.dynamic
+
+ + +

Java version:

+
Java(TM) SE Runtime Environment (build 1.8.0_25-b17)
+Java HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode)
+
+ + +

JVM parameters:

+
NAME=zookeeper-{{ cluster['name'] }}
+ZOOCFGDIR=/etc/$NAME/conf
+
+## TODO this is really ugly
+## How to find out, which jars are needed?
+## seems, that log4j requires the log4j.properties file to be in the classpath
+CLASSPATH="$ZOOCFGDIR:/usr/build/classes:/usr/build/lib/*.jar:/usr/share/zookeeper/zookeeper-3.5.1-metrika.jar:/usr/share/zookeeper/slf4j-log4j12-1.7.5.jar:/usr/share/zookeeper/slf4j-api-1.7.5.jar:/usr/share/zookeeper/servlet-api-2.5-20081211.jar:/usr/share/zookeeper/netty-3.7.0.Final.jar:/usr/share/zookeeper/log4j-1.2.16.jar:/usr/share/zookeeper/jline-2.11.jar:/usr/share/zookeeper/jetty-util-6.1.26.jar:/usr/share/zookeeper/jetty-6.1.26.jar:/usr/share/zookeeper/javacc.jar:/usr/share/zookeeper/jackson-mapper-asl-1.9.11.jar:/usr/share/zookeeper/jackson-core-asl-1.9.11.jar:/usr/share/zookeeper/commons-cli-1.2.jar:/usr/src/java/lib/*.jar:/usr/etc/zookeeper"
+
+ZOOCFG="$ZOOCFGDIR/zoo.cfg"
+ZOO_LOG_DIR=/var/log/$NAME
+USER=zookeeper
+GROUP=zookeeper
+PIDDIR=/var/run/$NAME
+PIDFILE=$PIDDIR/$NAME.pid
+SCRIPTNAME=/etc/init.d/$NAME
+JAVA=/usr/bin/java
+ZOOMAIN="org.apache.zookeeper.server.quorum.QuorumPeerMain"
+ZOO_LOG4J_PROP="INFO,ROLLINGFILE"
+JMXLOCALONLY=false
+JAVA_OPTS="-Xms{{ cluster.get('xms','128M') }} \
+    -Xmx{{ cluster.get('xmx','1G') }} \
+    -Xloggc:/var/log/$NAME/zookeeper-gc.log \
+    -XX:+UseGCLogFileRotation \
+    -XX:NumberOfGCLogFiles=16 \
+    -XX:GCLogFileSize=16M \
+    -verbose:gc \
+    -XX:+PrintGCTimeStamps \
+    -XX:+PrintGCDateStamps \
+    -XX:+PrintGCDetails
+    -XX:+PrintTenuringDistribution \
+    -XX:+PrintGCApplicationStoppedTime \
+    -XX:+PrintGCApplicationConcurrentTime \
+    -XX:+PrintSafepointStatistics \
+    -XX:+UseParNewGC \
+    -XX:+UseConcMarkSweepGC \
+-XX:+CMSParallelRemarkEnabled"
+
+ + +

Salt init:

+
description "zookeeper-{{ cluster['name'] }} centralized coordination service"
+
+start on runlevel [2345]
+stop on runlevel [!2345]
+
+respawn
+
+limit nofile 8192 8192
+
+pre-start script
+    [ -r "/etc/zookeeper-{{ cluster['name'] }}/conf/environment" ] || exit 0
+    . /etc/zookeeper-{{ cluster['name'] }}/conf/environment
+    [ -d $ZOO_LOG_DIR ] || mkdir -p $ZOO_LOG_DIR
+    chown $USER:$GROUP $ZOO_LOG_DIR
+end script
+
+script
+    . /etc/zookeeper-{{ cluster['name'] }}/conf/environment
+    [ -r /etc/default/zookeeper ] && . /etc/default/zookeeper
+    if [ -z "$JMXDISABLE" ]; then
+        JAVA_OPTS="$JAVA_OPTS -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.local.only=$JMXLOCALONLY"
+    fi
+    exec start-stop-daemon --start -c $USER --exec $JAVA --name zookeeper-{{ cluster['name'] }} \
+        -- -cp $CLASSPATH $JAVA_OPTS -Dzookeeper.log.dir=${ZOO_LOG_DIR} \
+        -Dzookeeper.root.logger=${ZOO_LOG4J_PROP} $ZOOMAIN $ZOOCFG
+end script
+
+ + +

+

Server configuration parameters

+

This section contains descriptions of server settings that cannot be changed at the session or query level.

+

These settings are stored in the config.xml file on the ClickHouse server.

+

Other settings are described in the "Settings" section.

+

Before studying the settings, read the Configuration files section and note the use of substitutions (the incl and optional attributes).

+

Server settings

+

+

builtin_dictionaries_reload_interval

+

The interval in seconds before reloading built-in dictionaries.

+

ClickHouse reloads built-in dictionaries every x seconds. This makes it possible to edit dictionaries "on the fly" without restarting the server.

+

Default value: 3600.

+

Example

+
<builtin_dictionaries_reload_interval>3600</builtin_dictionaries_reload_interval>
+
+ + +

+

compression

+

Data compression settings.

+
+ +Don't use it if you have just started using ClickHouse. + +
+ +

The configuration looks like this:

+
<compression>
+    <case>
+      <parameters/>
+    </case>
+    ...
+</compression>
+
+ + +

You can configure multiple sections <case>.

+

Block field <case>:

+
    +
  • min_part_size – The minimum size of a table part.
    • +
  • min_part_size_ratio – The ratio of the minimum size of a table part to the full size of the table.
    • +
  • method – Compression method. Acceptable values ​: lz4 or zstd(experimental).
    • +
+

ClickHouse checks min_part_size and min_part_size_ratio and processes the case blocks that match these conditions. If none of the <case> matches, ClickHouse applies the lz4 compression algorithm.

+

Example

+
<compression incl="clickhouse_compression">
+    <case>
+        <min_part_size>10000000000</min_part_size>
+        <min_part_size_ratio>0.01</min_part_size_ratio>
+        <method>zstd</method>
+    </case>
+</compression>
+
+ + +

+

default_database

+

The default database.

+

To get a list of databases, use the SHOW DATABASES.

+

Example

+
<default_database>default</default_database>
+
+ + +

+

default_profile

+

Default settings profile.

+

Settings profiles are located in the file specified in the parameter user_config.

+

Example

+
<default_profile>default</default_profile>
+
+ + +

+

dictionaries_config

+

The path to the config file for external dictionaries.

+

Path:

+
    +
  • Specify the absolute path or the path relative to the server config file.
    • +
  • The path can contain wildcards * and ?.
    • +
+

See also "External dictionaries".

+

Example

+
<dictionaries_config>*_dictionary.xml</dictionaries_config>
+
+ + +

+

dictionaries_lazy_load

+

Lazy loading of dictionaries.

+

If true, then each dictionary is created on first use. If dictionary creation failed, the function that was using the dictionary throws an exception.

+

If false, all dictionaries are created when the server starts, and if there is an error, the server shuts down.

+

The default is true.

+

Example

+
<dictionaries_lazy_load>true</dictionaries_lazy_load>
+
+ + +

+

format_schema_path

+

The path to the directory with the schemes for the input data, such as schemas for the CapnProto format.

+

Example

+
  <!-- Directory containing schema files for various input formats. -->
+  <format_schema_path>format_schemas/</format_schema_path>
+
+ + +

+

graphite

+

Sending data to Graphite.

+

Settings:

+
    +
  • host – The Graphite server.
    • +
  • port – The port on the Graphite server.
    • +
  • interval – The interval for sending, in seconds.
    • +
  • timeout – The timeout for sending data, in seconds.
    • +
  • root_path – Prefix for keys.
    • +
  • metrics – Sending data from a :ref:system_tables-system.metrics table.
    • +
  • events – Sending data from a :ref:system_tables-system.events table.
    • +
  • asynchronous_metrics – Sending data from a :ref:system_tables-system.asynchronous_metrics table.
    • +
+

You can configure multiple <graphite> clauses. For instance, you can use this for sending different data at different intervals.

+

Example

+
<graphite>
+    <host>localhost</host>
+    <port>42000</port>
+    <timeout>0.1</timeout>
+    <interval>60</interval>
+    <root_path>one_min</root_path>
+    <metrics>true</metrics>
+    <events>true</events>
+    <asynchronous_metrics>true</asynchronous_metrics>
+</graphite>
+
+ + +

+

graphite_rollup

+

Settings for thinning data for Graphite.

+

For more information, see GraphiteMergeTree.

+

Example

+
<graphite_rollup_example>
+    <default>
+        <function>max</function>
+        <retention>
+            <age>0</age>
+            <precision>60</precision>
+        </retention>
+        <retention>
+            <age>3600</age>
+            <precision>300</precision>
+        </retention>
+        <retention>
+            <age>86400</age>
+            <precision>3600</precision>
+        </retention>
+    </default>
+</graphite_rollup_example>
+
+ + +

+

http_port/https_port

+

The port for connecting to the server over HTTP(s).

+

If https_port is specified, openSSL must be configured.

+

If http_port is specified, the openSSL configuration is ignored even if it is set.

+

Example

+
<https>0000</https>
+
+ + +

+

http_server_default_response

+

The page that is shown by default when you access the ClickHouse HTTP(s) server.

+

Example

+

Opens https://tabix.io/ when accessing http://localhost: http_port.

+
<http_server_default_response>
+  <![CDATA[<html ng-app="SMI2"><head><base href="http://ui.tabix.io/"></head><body><div ui-view="" class="content-ui"></div><script src="http://loader.tabix.io/master.js"></script></body></html>]]>
+</http_server_default_response>
+
+ + +

+

include_from

+

The path to the file with substitutions.

+

For more information, see the section "Configuration files".

+

Example

+
<include_from>/etc/metrica.xml</include_from>
+
+ + +

+

interserver_http_port

+

Port for exchanging data between ClickHouse servers.

+

Example

+
<interserver_http_port>9009</interserver_http_port>
+
+ + +

+

interserver_http_host

+

The host name that can be used by other servers to access this server.

+

If omitted, it is defined in the same way as the hostname-f command.

+

Useful for breaking away from a specific network interface.

+

Example

+
<interserver_http_host>example.yandex.ru</interserver_http_host>
+
+ + +

+

keep_alive_timeout

+

The number of milliseconds that ClickHouse waits for incoming requests before closing the connection.

+

Example

+
<keep_alive_timeout>3</keep_alive_timeout>
+
+ + +

+

listen_host

+

Restriction on hosts that requests can come from. If you want the server to answer all of them, specify ::.

+

Examples:

+
<listen_host>::1</listen_host>
+<listen_host>127.0.0.1</listen_host>
+
+ + +

+

logger

+

Logging settings.

+

Keys:

+
    +
  • level – Logging level. Acceptable values: trace, debug, information, warning, error.
    • +
  • log – The log file. Contains all the entries according to level.
    • +
  • errorlog – Error log file.
    • +
  • size – Size of the file. Applies to loganderrorlog. Once the file reaches size, ClickHouse archives and renames it, and creates a new log file in its place.
    • +
  • count – The number of archived log files that ClickHouse stores.
    • +
+

Example

+
<logger>
+    <level>trace</level>
+    <log>/var/log/clickhouse-server/clickhouse-server.log</log>
+    <errorlog>/var/log/clickhouse-server/clickhouse-server.err.log</errorlog>
+    <size>1000M</size>
+    <count>10</count>
+</logger>
+
+ + +

+

macros

+

Parameter substitutions for replicated tables.

+

Can be omitted if replicated tables are not used.

+

For more information, see the section "Creating replicated tables".

+

Example

+
<macros incl="macros" optional="true" />
+
+ + +

+

mark_cache_size

+

Approximate size (in bytes) of the cache of "marks" used by MergeTree engines.

+

The cache is shared for the server and memory is allocated as needed. The cache size must be at least 5368709120.

+

Example

+
<mark_cache_size>5368709120</mark_cache_size>
+
+ + +

+

max_concurrent_queries

+

The maximum number of simultaneously processed requests.

+

Example

+
<max_concurrent_queries>100</max_concurrent_queries>
+
+ + +

+

max_connections

+

The maximum number of inbound connections.

+

Example

+
<max_connections>4096</max_connections>
+
+ + +

+

max_open_files

+

The maximum number of open files.

+

By default: maximum.

+

We recommend using this option in Mac OS X, since the getrlimit() function returns an incorrect value.

+

Example

+
<max_open_files>262144</max_open_files>
+
+ + +

+

max_table_size_to_drop

+

Restriction on deleting tables.

+

If the size of a MergeTree type table exceeds max_table_size_to_drop (in bytes), you can't delete it using a DROP query.

+

If you still need to delete the table without restarting the ClickHouse server, create the <clickhouse-path>/flags/force_drop_table file and run the DROP query.

+

Default value: 50 GB.

+

The value 0 means that you can delete all tables without any restrictions.

+

Example

+
<max_table_size_to_drop>0</max_table_size_to_drop>
+
+ + +

+

merge_tree

+

Fine tuning for tables in the MergeTree family.

+

For more information, see the MergeTreeSettings.h header file.

+

Example

+
<merge_tree>
+    <max_suspicious_broken_parts>5</max_suspicious_broken_parts>
+</merge_tree>
+
+ + +

+

openSSL

+

SSL client/server configuration.

+

Support for SSL is provided by the libpoco library. The interface is described in the file SSLManager.h

+

Keys for server/client settings:

+
    +
  • privateKeyFile – The path to the file with the secret key of the PEM certificate. The file may contain a key and certificate at the same time.
    • +
  • certificateFile – The path to the client/server certificate file in PEM format. You can omit it if privateKeyFile contains the certificate.
    • +
  • caConfig – The path to the file or directory that contains trusted root certificates.
    • +
  • verificationMode – The method for checking the node's certificates. Details are in the description of the Context class. Possible values: none, relaxed, strict, once.
    • +
  • verificationDepth – The maximum length of the verification chain. Verification will fail if the certificate chain length exceeds the set value.
    • +
  • loadDefaultCAFile – Indicates that built-in CA certificates for OpenSSL will be used. Acceptable values: true, false. |
    • +
  • cipherList – Supported OpenSSL encryptions. For example: ALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH.
    • +
  • cacheSessions – Enables or disables caching sessions. Must be used in combination with sessionIdContext. Acceptable values: true, false.
    • +
  • sessionIdContext – A unique set of random characters that the server appends to each generated identifier. The length of the string must not exceed SSL_MAX_SSL_SESSION_ID_LENGTH. This parameter is always recommended, since it helps avoid problems both if the server caches the session and if the client requested caching. Default value: ${application.name}.
    • +
  • sessionCacheSize – The maximum number of sessions that the server caches. Default value: 1024*20. 0 – Unlimited sessions.
    • +
  • sessionTimeout – Time for caching the session on the server.
    • +
  • extendedVerification – Automatically extended verification of certificates after the session ends. Acceptable values: true, false.
    • +
  • requireTLSv1 – Require a TLSv1 connection. Acceptable values: true, false.
    • +
  • requireTLSv1_1 – Require a TLSv1.1 connection. Acceptable values: true, false.
    • +
  • requireTLSv1 – Require a TLSv1.2 connection. Acceptable values: true, false.
    • +
  • fips – Activates OpenSSL FIPS mode. Supported if the library's OpenSSL version supports FIPS.
    • +
  • privateKeyPassphraseHandler – Class (PrivateKeyPassphraseHandler subclass) that requests the passphrase for accessing the private key. For example: <privateKeyPassphraseHandler>, <name>KeyFileHandler</name>, <options><password>test</password></options>, </privateKeyPassphraseHandler>.
    • +
  • invalidCertificateHandler – Class (subclass of CertificateHandler) for verifying invalid certificates. For example: <invalidCertificateHandler> <name>ConsoleCertificateHandler</name> </invalidCertificateHandler> .
    • +
  • disableProtocols – Protocols that are not allowed to use.
    • +
  • preferServerCiphers – Preferred server ciphers on the client.
    • +
+

Example of settings:

+
<openSSL>
+    <server>
+        <!-- openssl req -subj "/CN=localhost" -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout /etc/clickhouse-server/server.key -out /etc/clickhouse-server/server.crt -->
+        <certificateFile>/etc/clickhouse-server/server.crt</certificateFile>
+        <privateKeyFile>/etc/clickhouse-server/server.key</privateKeyFile>
+        <!-- openssl dhparam -out /etc/clickhouse-server/dhparam.pem 4096 -->
+        <dhParamsFile>/etc/clickhouse-server/dhparam.pem</dhParamsFile>
+        <verificationMode>none</verificationMode>
+        <loadDefaultCAFile>true</loadDefaultCAFile>
+        <cacheSessions>true</cacheSessions>
+        <disableProtocols>sslv2,sslv3</disableProtocols>
+        <preferServerCiphers>true</preferServerCiphers>
+    </server>
+    <client>
+        <loadDefaultCAFile>true</loadDefaultCAFile>
+        <cacheSessions>true</cacheSessions>
+        <disableProtocols>sslv2,sslv3</disableProtocols>
+        <preferServerCiphers>true</preferServerCiphers>
+        <!-- Use for self-signed: <verificationMode>none</verificationMode> -->
+        <invalidCertificateHandler>
+            <!-- Use for self-signed: <name>AcceptCertificateHandler</name> -->
+            <name>RejectCertificateHandler</name>
+        </invalidCertificateHandler>
+    </client>
+</openSSL>
+
+ + +

+

part_log

+

Logging events that are associated with MergeTree data. For instance, adding or merging data. You can use the log to simulate merge algorithms and compare their characteristics. You can visualize the merge process.

+

Queries are logged in the ClickHouse table, not in a separate file.

+

Columns in the log:

+
    +
  • event_time – Date of the event.
    • +
  • duration_ms – Duration of the event.
    • +
  • event_type – Type of event. 1 – new data part; 2 – merge result; 3 – data part downloaded from replica; 4 – data part deleted.
    • +
  • database_name – The name of the database.
    • +
  • table_name – Name of the table.
    • +
  • part_name – Name of the data part.
    • +
  • size_in_bytes – Size of the data part in bytes.
    • +
  • merged_from – An array of names of data parts that make up the merge (also used when downloading a merged part).
    • +
  • merge_time_ms – Time spent on the merge.
    • +
+

Use the following parameters to configure logging:

+
    +
  • database – Name of the database.
    • +
  • table – Name of the table.
    • +
  • partition_by – Sets a custom partitioning key.
    • +
  • flush_interval_milliseconds – Interval for flushing data from memory to the disk.
    • +
+

Example

+
<part_log>
+    <database>system</database>
+    <table>part_log</table>
+    <partition_by>toMonday(event_date)</partition_by>
+    <flush_interval_milliseconds>7500</flush_interval_milliseconds>
+</part_log>
+
+ + +

+

path

+

The path to the directory containing data.

+
+ +The end slash is mandatory. + +
+ +

Example

+
<path>/var/lib/clickhouse/</path>
+
+ + +

+

query_log

+

Setting for logging queries received with the log_queries=1 setting.

+

Queries are logged in the ClickHouse table, not in a separate file.

+

Use the following parameters to configure logging:

+
    +
  • database – Name of the database.
    • +
  • table – Name of the table.
    • +
  • partition_by – Sets a custom partitioning key.
    • +
  • flush_interval_milliseconds – Interval for flushing data from memory to the disk.
    • +
+

If the table doesn't exist, ClickHouse will create it. If the structure of the query log changed when the ClickHouse server was updated, the table with the old structure is renamed, and a new table is created automatically.

+

Example

+
<query_log>
+    <database>system</database>
+    <table>query_log</table>
+    <partition_by>toMonday(event_date)</partition_by>
+    <flush_interval_milliseconds>7500</flush_interval_milliseconds>
+</query_log>
+
+ + +

+

remote_servers

+

Configuration of clusters used by the Distributed table engine.

+

For more information, see the section "Table engines/Distributed".

+

Example

+
<remote_servers incl="clickhouse_remote_servers" />
+
+ + +

For the value of the incl attribute, see the section "Configuration files".

+

+

timezone

+

The server's time zone.

+

Specified as an IANA identifier for the UTC time zone or geographic location (for example, Africa/Abidjan).

+

The time zone is necessary for conversions between String and DateTime formats when DateTime fields are output to text format (printed on the screen or in a file), and when getting DateTime from a string. In addition, the time zone is used in functions that work with the time and date if they didn't receive the time zone in the input parameters.

+

Example

+
<timezone>Europe/Moscow</timezone>
+
+ + +

+

tcp_port

+

Port for communicating with clients over the TCP protocol.

+

Example

+
<tcp_port>9000</tcp_port>
+
+ + +

+

tmp_path

+

Path to temporary data for processing large queries.

+
+ +The end slash is mandatory. + +
+ +

Example

+
<tmp_path>/var/lib/clickhouse/tmp/</tmp_path>
+
+ + +

+

uncompressed_cache_size

+

Cache size (in bytes) for uncompressed data used by table engines from the MergeTree family.

+

There is one shared cache for the server. Memory is allocated on demand. The cache is used if the option use_uncompressed_cache is enabled.

+

The uncompressed cache is advantageous for very short queries in individual cases.

+

Example

+
<uncompressed_cache_size>8589934592</uncompressed_cache_size>
+
+ + +

+

users_config

+

Path to the file that contains:

+
    +
  • User configurations.
    • +
  • Access rights.
    • +
  • Settings profiles.
    • +
  • Quota settings.
    • +
+

Example

+
<users_config>users.xml</users_config>
+
+ + +

+

zookeeper

+

Configuration of ZooKeeper servers.

+

ClickHouse uses ZooKeeper for storing replica metadata when using replicated tables.

+

This parameter can be omitted if replicated tables are not used.

+

For more information, see the section "Replication".

+

Example

+
<zookeeper incl="zookeeper-servers" optional="true" />
+
+ + +

+

Settings

+

There are multiple ways to make all the settings described below. +Settings are configured in layers, so each subsequent layer redefines the previous settings.

+

Ways to configure settings, in order of priority:

+
    +
  • Settings in the server config file.
    • +
+

Settings from user profiles.

+
    +
  • Session settings.
    • +
+

Send SET setting=value from the ClickHouse console client in interactive mode. +Similarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to specify the session_id HTTP parameter.

+
    +
  • For a query.
    • +
  • When starting the ClickHouse console client in non-interactive mode, set the startup parameter --setting=value.
    • +
  • When using the HTTP API, pass CGI parameters (URL?setting_1=value&setting_2=value...).
    • +
+

Settings that can only be made in the server config file are not covered in this section.

+

Restrictions on query complexity

+

Restrictions on query complexity are part of the settings. +They are used in order to provide safer execution from the user interface. +Almost all the restrictions only apply to SELECTs.For distributed query processing, restrictions are applied on each server separately.

+

Restrictions on the "maximum amount of something" can take the value 0, which means "unrestricted". +Most restrictions also have an 'overflow_mode' setting, meaning what to do when the limit is exceeded. +It can take one of two values: throw or break. Restrictions on aggregation (group_by_overflow_mode) also have the value any.

+

throw – Throw an exception (default).

+

break – Stop executing the query and return the partial result, as if the source data ran out.

+

any (only for group_by_overflow_mode) – Continuing aggregation for the keys that got into the set, but don't add new keys to the set.

+

+

readonly

+

With a value of 0, you can execute any queries. +With a value of 1, you can only execute read requests (such as SELECT and SHOW). Requests for writing and changing settings (INSERT, SET) are prohibited. +With a value of 2, you can process read queries (SELECT, SHOW) and change settings (SET).

+

After enabling readonly mode, you can't disable it in the current session.

+

When using the GET method in the HTTP interface, 'readonly = 1' is set automatically. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.

+

+

max_memory_usage

+

The maximum amount of RAM to use for running a query on a single server.

+

In the default configuration file, the maximum is 10 GB.

+

The setting doesn't consider the volume of available memory or the total volume of memory on the machine. +The restriction applies to a single query within a single server. +You can use SHOW PROCESSLIST to see the current memory consumption for each query. +In addition, the peak memory consumption is tracked for each query and written to the log.

+

Memory usage is not monitored for the states of certain aggregate functions.

+

Memory usage is not fully tracked for states of the aggregate functions min, max, any, anyLast, argMin, argMax from String and Array arguments.

+

Memory consumption is also restricted by the parameters max_memory_usage_for_user and max_memory_usage_for_all_queries.

+

max_memory_usage_for_user

+

The maximum amount of RAM to use for running a user's queries on a single server.

+

Default values are defined in Settings.h. By default, the amount is not restricted (max_memory_usage_for_user = 0).

+

See also the description of max_memory_usage.

+

max_memory_usage_for_all_queries

+

The maximum amount of RAM to use for running all queries on a single server.

+

Default values are defined in Settings.h. By default, the amount is not restricted (max_memory_usage_for_all_queries = 0).

+

See also the description of max_memory_usage.

+

max_rows_to_read

+

The following restrictions can be checked on each block (instead of on each row). That is, the restrictions can be broken a little. +When running a query in multiple threads, the following restrictions apply to each thread separately.

+

Maximum number of rows that can be read from a table when running a query.

+

max_bytes_to_read

+

Maximum number of bytes (uncompressed data) that can be read from a table when running a query.

+

read_overflow_mode

+

What to do when the volume of data read exceeds one of the limits: 'throw' or 'break'. By default, throw.

+

max_rows_to_group_by

+

Maximum number of unique keys received from aggregation. This setting lets you limit memory consumption when aggregating.

+

group_by_overflow_mode

+

What to do when the number of unique keys for aggregation exceeds the limit: 'throw', 'break', or 'any'. By default, throw. +Using the 'any' value lets you run an approximation of GROUP BY. The quality of this approximation depends on the statistical nature of the data.

+

max_rows_to_sort

+

Maximum number of rows before sorting. This allows you to limit memory consumption when sorting.

+

max_bytes_to_sort

+

Maximum number of bytes before sorting.

+

sort_overflow_mode

+

What to do if the number of rows received before sorting exceeds one of the limits: 'throw' or 'break'. By default, throw.

+

max_result_rows

+

Limit on the number of rows in the result. Also checked for subqueries, and on remote servers when running parts of a distributed query.

+

max_result_bytes

+

Limit on the number of bytes in the result. The same as the previous setting.

+

result_overflow_mode

+

What to do if the volume of the result exceeds one of the limits: 'throw' or 'break'. By default, throw. +Using 'break' is similar to using LIMIT.

+

max_execution_time

+

Maximum query execution time in seconds. +At this time, it is not checked for one of the sorting stages, or when merging and finalizing aggregate functions.

+

timeout_overflow_mode

+

What to do if the query is run longer than 'max_execution_time': 'throw' or 'break'. By default, throw.

+

min_execution_speed

+

Minimal execution speed in rows per second. Checked on every data block when 'timeout_before_checking_execution_speed' expires. If the execution speed is lower, an exception is thrown.

+

timeout_before_checking_execution_speed

+

Checks that execution speed is not too slow (no less than 'min_execution_speed'), after the specified time in seconds has expired.

+

max_columns_to_read

+

Maximum number of columns that can be read from a table in a single query. If a query requires reading a greater number of columns, it throws an exception.

+

max_temporary_columns

+

Maximum number of temporary columns that must be kept in RAM at the same time when running a query, including constant columns. If there are more temporary columns than this, it throws an exception.

+

max_temporary_non_const_columns

+

The same thing as 'max_temporary_columns', but without counting constant columns. +Note that constant columns are formed fairly often when running a query, but they require approximately zero computing resources.

+

max_subquery_depth

+

Maximum nesting depth of subqueries. If subqueries are deeper, an exception is thrown. By default, 100.

+

max_pipeline_depth

+

Maximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000.

+

max_ast_depth

+

Maximum nesting depth of a query syntactic tree. If exceeded, an exception is thrown. +At this time, it isn't checked during parsing, but only after parsing the query. That is, a syntactic tree that is too deep can be created during parsing, but the query will fail. By default, 1000.

+

max_ast_elements

+

Maximum number of elements in a query syntactic tree. If exceeded, an exception is thrown. +In the same way as the previous setting, it is checked only after parsing the query. By default, 10,000.

+

max_rows_in_set

+

Maximum number of rows for a data set in the IN clause created from a subquery.

+

max_bytes_in_set

+

Maximum number of bytes (uncompressed data) used by a set in the IN clause created from a subquery.

+

set_overflow_mode

+

What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.

+

max_rows_in_distinct

+

Maximum number of different rows when using DISTINCT.

+

max_bytes_in_distinct

+

Maximum number of bytes used by a hash table when using DISTINCT.

+

distinct_overflow_mode

+

What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.

+

max_rows_to_transfer

+

Maximum number of rows that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.

+

max_bytes_to_transfer

+

Maximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.

+

transfer_overflow_mode

+

What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.

+

Settings

+

+

distributed_product_mode

+

Changes the behavior of distributed subqueries, i.e. in cases when the query contains the product of distributed tables.

+

ClickHouse applies the configuration if the subqueries on any level have a distributed table that exists on the local server and has more than one shard.

+

Restrictions:

+
    +
  • Only applied for IN and JOIN subqueries.
    • +
  • Used only if a distributed table is used in the FROM clause.
    • +
  • Not used for a table-valued remote function.
    • +
+

The possible values ​​are:

+

+

fallback_to_stale_replicas_for_distributed_queries

+

Forces a query to an out-of-date replica if updated data is not available. See "Replication".

+

ClickHouse selects the most relevant from the outdated replicas of the table.

+

Used when performing SELECT from a distributed table that points to replicated tables.

+

By default, 1 (enabled).

+

+

force_index_by_date

+

Disables query execution if the index can't be used by date.

+

Works with tables in the MergeTree family.

+

If force_index_by_date=1, ClickHouse checks whether the query has a date key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For example, the condition Date != ' 2000-01-01 ' is acceptable even when it matches all the data in the table (i.e., running the query requires a full scan). For more information about ranges of data in MergeTree tables, see "MergeTree".

+

+

force_primary_key

+

Disables query execution if indexing by the primary key is not possible.

+

Works with tables in the MergeTree family.

+

If force_primary_key=1, ClickHouse checks to see if the query has a primary key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For more information about data ranges in MergeTree tables, see "MergeTree".

+

+

fsync_metadata

+

Enable or disable fsync when writing .sql files. By default, it is enabled.

+

It makes sense to disable it if the server has millions of tiny table chunks that are constantly being created and destroyed.

+

input_format_allow_errors_num

+

Sets the maximum number of acceptable errors when reading from text formats (CSV, TSV, etc.).

+

The default value is 0.

+

Always pair it with input_format_allow_errors_ratio. To skip errors, both settings must be greater than 0.

+

If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_num, ClickHouse ignores the row and moves on to the next one.

+

If input_format_allow_errors_numis exceeded, ClickHouse throws an exception.

+

input_format_allow_errors_ratio

+

Sets the maximum percentage of errors allowed when reading from text formats (CSV, TSV, etc.). +The percentage of errors is set as a floating-point number between 0 and 1.

+

The default value is 0.

+

Always pair it with input_format_allow_errors_num. To skip errors, both settings must be greater than 0.

+

If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_ratio, ClickHouse ignores the row and moves on to the next one.

+

If input_format_allow_errors_ratio is exceeded, ClickHouse throws an exception.

+

max_block_size

+

In ClickHouse, data is processed by blocks (sets of column parts). The internal processing cycles for a single block are efficient enough, but there are noticeable expenditures on each block. max_block_size is a recommendation for what size of block (in number of rows) to load from tables. The block size shouldn't be too small, so that the expenditures on each block are still noticeable, but not too large, so that the query with LIMIT that is completed after the first block is processed quickly, so that too much memory isn't consumed when extracting a large number of columns in multiple threads, and so that at least some cache locality is preserved.

+

By default, 65,536.

+

Blocks the size of max_block_size are not always loaded from the table. If it is obvious that less data needs to be retrieved, a smaller block is processed.

+

preferred_block_size_bytes

+

Used for the same purpose as max_block_size, but it sets the recommended block size in bytes by adapting it to the number of rows in the block. +However, the block size cannot be more than max_block_size rows. +Disabled by default (set to 0). It only works when reading from MergeTree engines.

+

+

log_queries

+

Setting up query the logging.

+

Queries sent to ClickHouse with this setup are logged according to the rules in the query_log server configuration parameter.

+

Example:

+
log_queries=1
+
+ + +

+

max_insert_block_size

+

The size of blocks to form for insertion into a table. +This setting only applies in cases when the server forms the blocks. +For example, for an INSERT via the HTTP interface, the server parses the data format and forms blocks of the specified size. +But when using clickhouse-client, the client parses the data itself, and the 'max_insert_block_size' setting on the server doesn't affect the size of the inserted blocks. +The setting also doesn't have a purpose when using INSERT SELECT, since data is inserted using the same blocks that are formed after SELECT.

+

By default, it is 1,048,576.

+

This is slightly more than max_block_size. The reason for this is because certain table engines (*MergeTree) form a data part on the disk for each inserted block, which is a fairly large entity. Similarly, *MergeTree tables sort data during insertion, and a large enough block size allows sorting more data in RAM.

+

+

max_replica_delay_for_distributed_queries

+

Disables lagging replicas for distributed queries. See "Replication".

+

Sets the time in seconds. If a replica lags more than the set value, this replica is not used.

+

Default value: 0 (off).

+

Used when performing SELECT from a distributed table that points to replicated tables.

+

max_threads

+

The maximum number of query processing threads

+
    +
  • excluding threads for retrieving data from remote servers (see the 'max_distributed_connections' parameter).
    • +
+

This parameter applies to threads that perform the same stages of the query processing pipeline in parallel. +For example, if reading from a table, evaluating expressions with functions, filtering with WHERE and pre-aggregating for GROUP BY can all be done in parallel using at least 'max_threads' number of threads, then 'max_threads' are used.

+

By default, 8.

+

If less than one SELECT query is normally run on a server at a time, set this parameter to a value slightly less than the actual number of processor cores.

+

For queries that are completed quickly because of a LIMIT, you can set a lower 'max_threads'. For example, if the necessary number of entries are located in every block and max_threads = 8, 8 blocks are retrieved, although it would have been enough to read just one.

+

The smaller the max_threads value, the less memory is consumed.

+

max_compress_block_size

+

The maximum size of blocks of uncompressed data before compressing for writing to a table. By default, 1,048,576 (1 MiB). If the size is reduced, the compression rate is significantly reduced, the compression and decompression speed increases slightly due to cache locality, and memory consumption is reduced. There usually isn't any reason to change this setting.

+

Don't confuse blocks for compression (a chunk of memory consisting of bytes) and blocks for query processing (a set of rows from a table).

+

min_compress_block_size

+

For MergeTree" tables. In order to reduce latency when processing queries, a block is compressed when writing the next mark if its size is at least 'min_compress_block_size'. By default, 65,536.

+

The actual size of the block, if the uncompressed data is less than 'max_compress_block_size', is no less than this value and no less than the volume of data for one mark.

+

Let's look at an example. Assume that 'index_granularity' was set to 8192 during table creation.

+

We are writing a UInt32-type column (4 bytes per value). When writing 8192 rows, the total will be 32 KB of data. Since min_compress_block_size = 65,536, a compressed block will be formed for every two marks.

+

We are writing a URL column with the String type (average size of 60 bytes per value). When writing 8192 rows, the average will be slightly less than 500 KB of data. Since this is more than 65,536, a compressed block will be formed for each mark. In this case, when reading data from the disk in the range of a single mark, extra data won't be decompressed.

+

There usually isn't any reason to change this setting.

+

max_query_size

+

The maximum part of a query that can be taken to RAM for parsing with the SQL parser. +The INSERT query also contains data for INSERT that is processed by a separate stream parser (that consumes O(1) RAM), which is not included in this restriction.

+

The default is 256 KiB.

+

interactive_delay

+

The interval in microseconds for checking whether request execution has been canceled and sending the progress.

+

By default, 100,000 (check for canceling and send progress ten times per second).

+

connect_timeout

+

receive_timeout

+

send_timeout

+

Timeouts in seconds on the socket used for communicating with the client.

+

By default, 10, 300, 300.

+

poll_interval

+

Lock in a wait loop for the specified number of seconds.

+

By default, 10.

+

max_distributed_connections

+

The maximum number of simultaneous connections with remote servers for distributed processing of a single query to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.

+

By default, 100.

+

The following parameters are only used when creating Distributed tables (and when launching a server), so there is no reason to change them at runtime.

+

distributed_connections_pool_size

+

The maximum number of simultaneous connections with remote servers for distributed processing of all queries to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.

+

By default, 128.

+

connect_timeout_with_failover_ms

+

The timeout in milliseconds for connecting to a remote server for a Distributed table engine, if the 'shard' and 'replica' sections are used in the cluster definition. +If unsuccessful, several attempts are made to connect to various replicas.

+

By default, 50.

+

connections_with_failover_max_tries

+

The maximum number of connection attempts with each replica, for the Distributed table engine.

+

By default, 3.

+

extremes

+

Whether to count extreme values (the minimums and maximums in columns of a query result). Accepts 0 or 1. By default, 0 (disabled). +For more information, see the section "Extreme values".

+

+

use_uncompressed_cache

+

Whether to use a cache of uncompressed blocks. Accepts 0 or 1. By default, 0 (disabled). +The uncompressed cache (only for tables in the MergeTree family) allows significantly reducing latency and increasing throughput when working with a large number of short queries. Enable this setting for users who send frequent short requests. Also pay attention to the 'uncompressed_cache_size' configuration parameter (only set in the config file) – the size of uncompressed cache blocks. By default, it is 8 GiB. The uncompressed cache is filled in as needed; the least-used data is automatically deleted.

+

For queries that read at least a somewhat large volume of data (one million rows or more), the uncompressed cache is disabled automatically in order to save space for truly small queries. So you can keep the 'use_uncompressed_cache' setting always set to 1.

+

replace_running_query

+

When using the HTTP interface, the 'query_id' parameter can be passed. This is any string that serves as the query identifier. +If a query from the same user with the same 'query_id' already exists at this time, the behavior depends on the 'replace_running_query' parameter.

+

0 (default) – Throw an exception (don't allow the query to run if a query with the same 'query_id' is already running).

+

1 – Cancel the old query and start running the new one.

+

Yandex.Metrica uses this parameter set to 1 for implementing suggestions for segmentation conditions. After entering the next character, if the old query hasn't finished yet, it should be canceled.

+

schema

+

This parameter is useful when you are using formats that require a schema definition, such as Cap'n Proto. The value depends on the format.

+

+

stream_flush_interval_ms

+

Works for tables with streaming in the case of a timeout, or when a thread generatesmax_insert_block_size rows.

+

The default value is 7500.

+

The smaller the value, the more often data is flushed into the table. Setting the value too low leads to poor performance.

+

+

load_balancing

+

Which replicas (among healthy replicas) to preferably send a query to (on the first attempt) for distributed processing.

+

random (default)

+

The number of errors is counted for each replica. The query is sent to the replica with the fewest errors, and if there are several of these, to any one of them. +Disadvantages: Server proximity is not accounted for; if the replicas have different data, you will also get different data.

+

nearest_hostname

+

The number of errors is counted for each replica. Every 5 minutes, the number of errors is integrally divided by 2. Thus, the number of errors is calculated for a recent time with exponential smoothing. If there is one replica with a minimal number of errors (i.e. errors occurred recently on the other replicas), the query is sent to it. If there are multiple replicas with the same minimal number of errors, the query is sent to the replica with a host name that is most similar to the server's host name in the config file (for the number of different characters in identical positions, up to the minimum length of both host names).

+

For instance, example01-01-1 and example01-01-2.yandex.ru are different in one position, while example01-01-1 and example01-02-2 differ in two places. +This method might seem a little stupid, but it doesn't use external data about network topology, and it doesn't compare IP addresses, which would be complicated for our IPv6 addresses.

+

Thus, if there are equivalent replicas, the closest one by name is preferred. +We can also assume that when sending a query to the same server, in the absence of failures, a distributed query will also go to the same servers. So even if different data is placed on the replicas, the query will return mostly the same results.

+

in_order

+

Replicas are accessed in the same order as they are specified. The number of errors does not matter. +This method is appropriate when you know exactly which replica is preferable.

+

totals_mode

+

How to calculate TOTALS when HAVING is present, as well as when max_rows_to_group_by and group_by_overflow_mode = 'any' are present. +See the section "WITH TOTALS modifier".

+

totals_auto_threshold

+

The threshold for totals_mode = 'auto'. +See the section "WITH TOTALS modifier".

+

default_sample

+

Floating-point number from 0 to 1. By default, 1. +Allows you to set the default sampling ratio for all SELECT queries. +(For tables that do not support sampling, it throws an exception.) +If set to 1, sampling is not performed by default.

+

max_parallel_replicas

+

The maximum number of replicas for each shard when executing a query. +For consistency (to get different parts of the same data split), this option only works when the sampling key is set. +Replica lag is not controlled.

+

compile

+

Enable compilation of queries. By default, 0 (disabled).

+

Compilation is only used for part of the query-processing pipeline: for the first stage of aggregation (GROUP BY). +If this portion of the pipeline was compiled, the query may run faster due to deployment of short cycles and inlining aggregate function calls. The maximum performance improvement (up to four times faster in rare cases) is seen for queries with multiple simple aggregate functions. Typically, the performance gain is insignificant. In very rare cases, it may slow down query execution.

+

min_count_to_compile

+

How many times to potentially use a compiled chunk of code before running compilation. By default, 3. +If the value is zero, then compilation runs synchronously and the query waits for the end of the compilation process before continuing execution. This can be used for testing; otherwise, use values ​​starting with 1. Compilation normally takes about 5-10 seconds. +If the value is 1 or more, compilation occurs asynchronously in a separate thread. The result will be used as soon as it is ready, including by queries that are currently running.

+

Compiled code is required for each different combination of aggregate functions used in the query and the type of keys in the GROUP BY clause. +The results of compilation are saved in the build directory in the form of .so files. There is no restriction on the number of compilation results, since they don't use very much space. Old results will be used after server restarts, except in the case of a server upgrade – in this case, the old results are deleted.

+

input_format_skip_unknown_fields

+

If the value is true, running INSERT skips input data from columns with unknown names. Otherwise, this situation will generate an exception. +It works for JSONEachRow and TSKV formats.

+

output_format_json_quote_64bit_integers

+

If the value is true, integers appear in quotes when using JSON* Int64 and UInt64 formats (for compatibility with most JavaScript implementations); otherwise, integers are output without the quotes.

+

+

format_csv_delimiter

+

The character to be considered as a delimiter in CSV data. By default, ,.

+

Settings profiles

+

A settings profile is a collection of settings grouped under the same name. Each ClickHouse user has a profile. +To apply all the settings in a profile, set profile.

+

Example:

+

Setting web profile.

+
SET profile = 'web'
+
+ + +

Settings profiles are declared in the user config file. This is usually users.xml.

+

Example:

+
<!-- Settings profiles -->
+<profiles>
+    <!-- Default settings -->
+    <default>
+        <!-- The maximum number of threads when running a single query. -->
+        <max_threads>8</max_threads>
+    </default>
+
+    <!-- Settings for quries from the user interface -->
+    <web>
+        <max_rows_to_read>1000000000</max_rows_to_read>
+        <max_bytes_to_read>100000000000</max_bytes_to_read>
+
+        <max_rows_to_group_by>1000000</max_rows_to_group_by>
+        <group_by_overflow_mode>any</group_by_overflow_mode>
+
+        <max_rows_to_sort>1000000</max_rows_to_sort>
+        <max_bytes_to_sort>1000000000</max_bytes_to_sort>
+
+        <max_result_rows>100000</max_result_rows>
+        <max_result_bytes>100000000</max_result_bytes>
+        <result_overflow_mode>break</result_overflow_mode>
+
+        <max_execution_time>600</max_execution_time>
+        <min_execution_speed>1000000</min_execution_speed>
+        <timeout_before_checking_execution_speed>15</timeout_before_checking_execution_speed>
+
+        <max_columns_to_read>25</max_columns_to_read>
+        <max_temporary_columns>100</max_temporary_columns>
+        <max_temporary_non_const_columns>50</max_temporary_non_const_columns>
+
+        <max_subquery_depth>2</max_subquery_depth>
+        <max_pipeline_depth>25</max_pipeline_depth>
+        <max_ast_depth>50</max_ast_depth>
+        <max_ast_elements>100</max_ast_elements>
+
+        <readonly>1</readonly>
+    </web>
+</profiles>
+
+ + +

The example specifies two profiles: default and web. The default profile has a special purpose: it must always be present and is applied when starting the server. In other words, the default profile contains default settings. The web profile is a regular profile that can be set using the SET query or using a URL parameter in an HTTP query.

+

Settings profiles can inherit from each other. To use inheritance, indicate the profile setting before the other settings that are listed in the profile.

+

ClickHouse utility

+
    +
  • clickhouse-local — Allows running SQL queries on data without stopping the ClickHouse server, similar to how awk does this.
    • +
  • clickhouse-copier — Copies (and reshards) data from one cluster to another cluster.
    • +
+

+

clickhouse-copier

+

Copies data from the tables in one cluster to tables in another (or the same) cluster.

+

You can run multiple clickhouse-copier instances on different servers to perform the same job. ZooKeeper is used for syncing the processes.

+

After starting, clickhouse-copier:

+
    +
  • Connects to ZooKeeper and receives:
    • +
  • Copying jobs.
    • +
  • +

    The state of the copying jobs.

    +
    • +
  • +

    It performs the jobs.

    +
    • +
+

Each running process chooses the "closest" shard of the source cluster and copies the data into the destination cluster, resharding the data if necessary.

+

clickhouse-copier tracks the changes in ZooKeeper and applies them on the fly.

+

To reduce network traffic, we recommend running clickhouse-copier on the same server where the source data is located.

+

Running clickhouse-copier

+

The utility should be run manually:

+
clickhouse-copier copier --daemon --config zookeeper.xml --task-path /task/path --base-dir /path/to/dir
+
+ + +

Parameters:

+
    +
  • daemon — Starts clickhouse-copier in daemon mode.
    • +
  • config — The path to the zookeeper.xml file with the parameters for the connection to ZooKeeper.
    • +
  • task-path — The path to the ZooKeeper node. This node is used for syncing clickhouse-copier processes and storing tasks. Tasks are stored in $task-path/description.
    • +
  • base-dir — The path to logs and auxiliary files. When it starts, clickhouse-copier creates clickhouse-copier_YYYYMMHHSS_<PID> subdirectories in $base-dir. If this parameter is omitted, the directories are created in the directory where clickhouse-copier was launched.
    • +
+

Format of zookeeper.xml

+
<yandex>
+    <zookeeper>
+        <node index="1">
+            <host>127.0.0.1</host>
+            <port>2181</port>
+        </node>
+    </zookeeper>
+</yandex>
+
+ + +

Configuration of copying tasks

+
<yandex>
+    <!-- Configuration of clusters as in an ordinary server config -->
+    <remote_servers>
+        <source_cluster>
+            <shard>
+                <internal_replication>false</internal_replication>
+                    <replica>
+                        <host>127.0.0.1</host>
+                        <port>9000</port>
+                    </replica>
+            </shard>
+            ...
+        </source_cluster>
+
+        <destination_cluster>
+        ...
+        </destination_cluster>
+    </remote_servers>
+
+    <!-- How many simultaneously active workers are possible. If you run more workers superfluous workers will sleep. -->
+    <max_workers>2</max_workers>
+
+    <!-- Setting used to fetch (pull) data from source cluster tables -->
+    <settings_pull>
+        <readonly>1</readonly>
+    </settings_pull>
+
+    <!-- Setting used to insert (push) data to destination cluster tables -->
+    <settings_push>
+        <readonly>0</readonly>
+    </settings_push>
+
+    <!-- Common setting for fetch (pull) and insert (push) operations. The copier process context also uses it.
+         They are overlaid by <settings_pull/> and <settings_push/> respectively. -->
+    <settings>
+        <connect_timeout>3</connect_timeout>
+        <!-- Sync insert is set forcibly, leave it here just in case. -->
+        <insert_distributed_sync>1</insert_distributed_sync>
+    </settings>
+
+    <!-- Copying description of tasks.
+         You can specify several table tasks in the same task description (in the same ZooKeeper node), and they will be performed         sequentially.
+    -->
+    <tables>
+        <!-- A table task that copies one table. -->
+        <table_hits>
+            <!-- Source cluster name (from the <remote_servers/> section) and tables in it that should be copied -->
+            <cluster_pull>source_cluster</cluster_pull>
+            <database_pull>test</database_pull>
+            <table_pull>hits</table_pull>
+
+            <!-- Destination cluster name and tables in which the data should be inserted -->
+            <cluster_push>destination_cluster</cluster_push>
+            <database_push>test</database_push>
+            <table_push>hits2</table_push>
+
+            <!-- Engine of destination tables.
+                 If the destination tables have not been created yet, workers create them using column definitions from source tables and the engine                 definition from here.
+
+                 NOTE: If the first worker starts to insert data and detects that the destination partition is not empty, then the partition will
+                 be dropped and refilled. Take this into account if you already have some data in destination tables. You can directly 
+                 specify partitions that should be copied in <enabled_partitions/>. They should be in quoted format like the partition column in the                 
+                 system.parts table.
+            -->
+            <engine>
+            ENGINE=ReplicatedMergeTree('/clickhouse/tables/{cluster}/{shard}/hits2', '{replica}')
+            PARTITION BY toMonday(date)
+            ORDER BY (CounterID, EventDate)
+            </engine>
+
+            <!-- Sharding key used to insert data to destination cluster -->
+            <sharding_key>jumpConsistentHash(intHash64(UserID), 2)</sharding_key>
+
+            <!-- Optional expression that filter data while pull them from source servers -->
+            <where_condition>CounterID != 0</where_condition>
+
+            <!-- This section specifies partitions that should be copied, other partition will be ignored.
+                 Partition names should have the same format as
+                 partition column of system.parts table (i.e. a quoted text).
+                 Since partition key of source and destination cluster could be different,
+                 these partition names specify destination partitions.
+
+                 Note: Although this section is optional (if it omitted, all partitions will be copied), 
+                 it is strongly recommended to specify the partitions explicitly.
+                 If you already have some partitions ready on the destination cluster, they                 
+                 will be removed at the start of the copying, because they will be interpreted                 
+                 as unfinished data from the previous copying.
+            -->
+            <enabled_partitions>
+                <partition>'2018-02-26'</partition>
+                <partition>'2018-03-05'</partition>
+                ...
+            </enabled_partitions>
+        </table_hits>
+
+        <!-- Next table to copy. It is not copied until the previous table is copying. -->
+        </table_visits>
+        ...
+        </table_visits>
+        ...
+    </tables>
+</yandex>
+
+ + +

clickhouse-copier tracks the changes in /task/path/description and applies them on the fly. For instance, if you change the value of max_workers, the number of processes running tasks will also change.

+

+

clickhouse-local

+

The clickhouse-local program enables you to perform fast processing on local files that store tables, without having to deploy and configure the ClickHouse server.

+

ClickHouse Development

+

Overview of ClickHouse architecture

+

ClickHouse is a true column-oriented DBMS. Data is stored by columns, and during the execution of arrays (vectors or chunks of columns). Whenever possible, operations are dispatched on arrays, rather than on individual values. This is called "vectorized query execution," and it helps lower the cost of actual data processing.

+
+

This idea is nothing new. It dates back to the APL programming language and its descendants: A +, J, K, and Q. Array programming is used in scientific data processing. Neither is this idea something new in relational databases: for example, it is used in the Vectorwise system.

+
+

There are two different approaches for speeding up the query processing: vectorized query execution and runtime code generation. In the latter, the code is generated for every kind of query on the fly, removing all indirection and dynamic dispatch. Neither of these approaches is strictly better than the other. Runtime code generation can be better when it's fuses many operations together, thus fully utilizing CPU execution units and the pipeline. Vectorized query execution can be less practical, because it involves the temporary vectors that must be written to the cache and read back. If the temporary data does not fit in the L2 cache, this becomes an issue. But vectorized query execution more easily utilizes the SIMD capabilities of the CPU. A research paper written by our friends shows that it is better to combine both approaches. ClickHouse uses vectorized query execution and has limited initial support for runtime code.

+

Columns

+

To represent columns in memory (actually, chunks of columns), the IColumn interface is used. This interface provides helper methods for implementation of various relational operators. Almost all operations are immutable: they do not modify the original column, but create a new modified one. For example, the IColumn :: filter method accepts a filter byte mask. It is used for the WHERE and HAVING relational operators. Additional examples: the IColumn :: permute method to support ORDER BY, the IColumn :: cut method to support LIMIT, and so on.

+

Various IColumn implementations (ColumnUInt8, ColumnString and so on) are responsible for the memory layout of columns. Memory layout is usually a contiguous array. For the integer type of columns it is just one contiguous array, like std :: vector. For String and Array columns, it is two vectors: one for all array elements, placed contiguously, and a second one for offsets to the beginning of each array. There is also ColumnConst that stores just one value in memory, but looks like a column.

+

Field

+

Nevertheless, it is possible to work with individual values as well. To represent an individual value, the Field is used. Field is just a discriminated union of UInt64, Int64, Float64, String and Array. IColumn has the operator[] method to get the n-th value as a Field, and the insert method to append a Field to the end of a column. These methods are not very efficient, because they require dealing with temporary Field objects representing an individual value. There are more efficient methods, such as insertFrom, insertRangeFrom, and so on.

+

Field doesn't have enough information about a specific data type for a table. For example, UInt8, UInt16, UInt32, and UInt64 are all represented as UInt64 in a Field.

+

Leaky abstractions

+

IColumn has methods for common relational transformations of data, but they don't meet all needs. For example, ColumnUInt64 doesn't have a method to calculate the sum of two columns, and ColumnString doesn't have a method to run a substring search. These countless routines are implemented outside of IColumn.

+

Various functions on columns can be implemented in a generic, non-efficient way using IColumn methods to extract Field values, or in a specialized way using knowledge of inner memory layout of data in a specific IColumn implementation. To do this, functions are cast to a specific IColumn type and deal with internal representation directly. For example, ColumnUInt64 has the getData method that returns a reference to an internal array, then a separate routine reads or fills that array directly. In fact, we have "leaky abstractions" to allow efficient specializations of various routines.

+

Data types

+

IDataType is responsible for serialization and deserialization: for reading and writing chunks of columns or individual values in binary or text form. +IDataType directly corresponds to data types in tables. For example, there are DataTypeUInt32, DataTypeDateTime, DataTypeString and so on.

+

IDataType and IColumn are only loosely related to each other. Different data types can be represented in memory by the same IColumn implementations. For example, DataTypeUInt32 and DataTypeDateTime are both represented by ColumnUInt32 or ColumnConstUInt32. In addition, the same data type can be represented by different IColumn implementations. For example, DataTypeUInt8 can be represented by ColumnUInt8 or ColumnConstUInt8.

+

IDataType only stores metadata. For instance, DataTypeUInt8 doesn't store anything at all (except vptr) and DataTypeFixedString stores just N (the size of fixed-size strings).

+

IDataType has helper methods for various data formats. Examples are methods to serialize a value with possible quoting, to serialize a value for JSON, and to serialize a value as part of XML format. There is no direct correspondence to data formats. For example, the different data formats Pretty and TabSeparated can use the same serializeTextEscaped helper method from the IDataType interface.

+

Block

+

A Block is a container that represents a subset (chunk) of a table in memory. It is just a set of triples: (IColumn, IDataType, column name). During query execution, data is processed by Blocks. If we have a Block, we have data (in the IColumn object), we have information about its type (in IDataType) that tells us how to deal with that column, and we have the column name (either the original column name from the table, or some artificial name assigned for getting temporary results of calculations).

+

When we calculate some function over columns in a block, we add another column with its result to the block, and we don't touch columns for arguments of the function because operations are immutable. Later, unneeded columns can be removed from the block, but not modified. This is convenient for elimination of common subexpressions.

+

Blocks are created for every processed chunk of data. Note that for the same type of calculation, the column names and types remain the same for different blocks, and only column data changes. It is better to split block data from the block header, because small block sizes will have a high overhead of temporary strings for copying shared_ptrs and column names.

+

Block Streams

+

Block streams are for processing data. We use streams of blocks to read data from somewhere, perform data transformations, or write data to somewhere. IBlockInputStream has the read method to fetch the next block while available. IBlockOutputStream has the write method to push the block somewhere.

+

Streams are responsible for:

+
    +
  1. Reading or writing to a table. The table just returns a stream for reading or writing blocks.
    2. +
  2. Implementing data formats. For example, if you want to output data to a terminal in Pretty format, you create a block output stream where you push blocks, and it formats them.
    3. +
  3. Performing data transformations. Let's say you have IBlockInputStream and want to create a filtered stream. You create FilterBlockInputStream and initialize it with your stream. Then when you pull a block from FilterBlockInputStream, it pulls a block from your stream, filters it, and returns the filtered block to you. Query execution pipelines are represented this way.
    4. +
+

There are more sophisticated transformations. For example, when you pull from AggregatingBlockInputStream, it reads all data from its source, aggregates it, and then returns a stream of aggregated data for you. Another example: UnionBlockInputStream accepts many input sources in the constructor and also a number of threads. It launches multiple threads and reads from multiple sources in parallel.

+
+

Block streams use the "pull" approach to control flow: when you pull a block from the first stream, it consequently pulls the required blocks from nested streams, and the entire execution pipeline will work. Neither "pull" nor "push" is the best solution, because control flow is implicit, and that limits implementation of various features like simultaneous execution of multiple queries (merging many pipelines together). This limitation could be overcome with coroutines or just running extra threads that wait for each other. We may have more possibilities if we make control flow explicit: if we locate the logic for passing data from one calculation unit to another outside of those calculation units. Read this article for more thoughts.

+
+

We should note that the query execution pipeline creates temporary data at each step. We try to keep block size small enough so that temporary data fits in the CPU cache. With that assumption, writing and reading temporary data is almost free in comparison with other calculations. We could consider an alternative, which is to fuse many operations in the pipeline together, to make the pipeline as short as possible and remove much of the temporary data. This could be an advantage, but it also has drawbacks. For example, a split pipeline makes it easy to implement caching intermediate data, stealing intermediate data from similar queries running at the same time, and merging pipelines for similar queries.

+

Formats

+

Data formats are implemented with block streams. There are "presentational" formats only suitable for output of data to the client, such as Pretty format, which provides only IBlockOutputStream. And there are input/output formats, such as TabSeparated or JSONEachRow.

+

There are also row streams: IRowInputStream and IRowOutputStream. They allow you to pull/push data by individual rows, not by blocks. And they are only needed to simplify implementation of row-oriented formats. The wrappers BlockInputStreamFromRowInputStream and BlockOutputStreamFromRowOutputStream allow you to convert row-oriented streams to regular block-oriented streams.

+

I/O

+

For byte-oriented input/output, there are ReadBuffer and WriteBuffer abstract classes. They are used instead of C++ iostream's. Don't worry: every mature C++ project is using something other than iostream's for good reasons.

+

ReadBuffer and WriteBuffer are just a contiguous buffer and a cursor pointing to the position in that buffer. Implementations may own or not own the memory for the buffer. There is a virtual method to fill the buffer with the following data (for ReadBuffer) or to flush the buffer somewhere (for WriteBuffer). The virtual methods are rarely called.

+

Implementations of ReadBuffer/WriteBuffer are used for working with files and file descriptors and network sockets, for implementing compression (CompressedWriteBuffer is initialized with another WriteBuffer and performs compression before writing data to it), and for other purposes – the names ConcatReadBuffer, LimitReadBuffer, and HashingWriteBuffer speak for themselves.

+

Read/WriteBuffers only deal with bytes. To help with formatted input/output (for instance, to write a number in decimal format), there are functions from ReadHelpers and WriteHelpers header files.

+

Let's look at what happens when you want to write a result set in JSON format to stdout. You have a result set ready to be fetched from IBlockInputStream. You create WriteBufferFromFileDescriptor(STDOUT_FILENO) to write bytes to stdout. You create JSONRowOutputStream, initialized with that WriteBuffer, to write rows in JSON to stdout. You create BlockOutputStreamFromRowOutputStream on top of it, to represent it as IBlockOutputStream. Then you call copyData to transfer data from IBlockInputStream to IBlockOutputStream, and everything works. Internally, JSONRowOutputStream will write various JSON delimiters and call the IDataType::serializeTextJSON method with a reference to IColumn and the row number as arguments. Consequently, IDataType::serializeTextJSON will call a method from WriteHelpers.h: for example, writeText for numeric types and writeJSONString for DataTypeString.

+

Tables

+

Tables are represented by the IStorage interface. Different implementations of that interface are different table engines. Examples are StorageMergeTree, StorageMemory, and so on. Instances of these classes are just tables.

+

The most important IStorage methods are read and write. There are also alter, rename, drop, and so on. The read method accepts the following arguments: the set of columns to read from a table, the AST query to consider, and the desired number of streams to return. It returns one or multiple IBlockInputStream objects and information about the stage of data processing that was completed inside a table engine during query execution.

+

In most cases, the read method is only responsible for reading the specified columns from a table, not for any further data processing. All further data processing is done by the query interpreter and is outside the responsibility of IStorage.

+

But there are notable exceptions:

+
    +
  • The AST query is passed to the read method and the table engine can use it to derive index usage and to read less data from a table.
    • +
  • Sometimes the table engine can process data itself to a specific stage. For example, StorageDistributed can send a query to remote servers, ask them to process data to a stage where data from different remote servers can be merged, and return that preprocessed data. +The query interpreter then finishes processing the data.
    • +
+

The table's read method can return multiple IBlockInputStream objects to allow parallel data processing. These multiple block input streams can read from a table in parallel. Then you can wrap these streams with various transformations (such as expression evaluation or filtering) that can be calculated independently and create a UnionBlockInputStream on top of them, to read from multiple streams in parallel.

+

There are also TableFunctions. These are functions that return a temporary IStorage object to use in the FROM clause of a query.

+

To get a quick idea of how to implement your own table engine, look at something simple, like StorageMemory or StorageTinyLog.

+
+

As the result of the read method, IStorage returns QueryProcessingStage – information about what parts of the query were already calculated inside storage. Currently we have only very coarse granularity for that information. There is no way for the storage to say "I have already processed this part of the expression in WHERE, for this range of data". We need to work on that.

+
+

Parsers

+

A query is parsed by a hand-written recursive descent parser. For example, ParserSelectQuery just recursively calls the underlying parsers for various parts of the query. Parsers create an AST. The AST is represented by nodes, which are instances of IAST.

+
+

Parser generators are not used for historical reasons.

+
+

Interpreters

+

Interpreters are responsible for creating the query execution pipeline from an AST. There are simple interpreters, such as InterpreterExistsQueryand InterpreterDropQuery, or the more sophisticated InterpreterSelectQuery. The query execution pipeline is a combination of block input or output streams. For example, the result of interpreting the SELECT query is the IBlockInputStream to read the result set from; the result of the INSERT query is the IBlockOutputStream to write data for insertion to; and the result of interpreting the INSERT SELECT query is the IBlockInputStream that returns an empty result set on the first read, but that copies data from SELECT to INSERT at the same time.

+

InterpreterSelectQuery uses ExpressionAnalyzer and ExpressionActions machinery for query analysis and transformations. This is where most rule-based query optimizations are done. ExpressionAnalyzer is quite messy and should be rewritten: various query transformations and optimizations should be extracted to separate classes to allow modular transformations or query.

+

Functions

+

There are ordinary functions and aggregate functions. For aggregate functions, see the next section.

+

Ordinary functions don't change the number of rows – they work as if they are processing each row independently. In fact, functions are not called for individual rows, but for Block's of data to implement vectorized query execution.

+

There are some miscellaneous functions, like blockSize, rowNumberInBlock, and runningAccumulate, that exploit block processing and violate the independence of rows.

+

ClickHouse has strong typing, so implicit type conversion doesn't occur. If a function doesn't support a specific combination of types, an exception will be thrown. But functions can work (be overloaded) for many different combinations of types. For example, the plus function (to implement the + operator) works for any combination of numeric types: UInt8 + Float32, UInt16 + Int8, and so on. Also, some variadic functions can accept any number of arguments, such as the concat function.

+

Implementing a function may be slightly inconvenient because a function explicitly dispatches supported data types and supported IColumns. For example, the plus function has code generated by instantiation of a C++ template for each combination of numeric types, and for constant or non-constant left and right arguments.

+
+

This is a nice place to implement runtime code generation to avoid template code bloat. Also, it will make it possible to add fused functions like fused multiply-add, or to make multiple comparisons in one loop iteration.

+
+

Due to vectorized query execution, functions are not short-circuit. For example, if you write WHERE f(x) AND g(y), both sides will be calculated, even for rows, when f(x) is zero (except when f(x) is a zero constant expression). But if selectivity of the f(x) condition is high, and calculation of f(x) is much cheaper than g(y), it's better to implement multi-pass calculation: first calculate f(x), then filter columns by the result, and then calculate g(y) only for smaller, filtered chunks of data.

+

Aggregate Functions

+

Aggregate functions are stateful functions. They accumulate passed values into some state, and allow you to get results from that state. They are managed with the IAggregateFunction interface. States can be rather simple (the state for AggregateFunctionCount is just a single UInt64 value) or quite complex (the state of AggregateFunctionUniqCombined is a combination of a linear array, a hash table and a HyperLogLog probabilistic data structure).

+

To deal with multiple states while executing a high-cardinality GROUP BY query, states are allocated in Arena (a memory pool), or they could be allocated in any suitable piece of memory. States can have a non-trivial constructor and destructor: for example, complex aggregation states can allocate additional memory themselves. This requires some attention to creating and destroying states and properly passing their ownership, to keep track of who and when will destroy states.

+

Aggregation states can be serialized and deserialized to pass over the network during distributed query execution or to write them on disk where there is not enough RAM. They can even be stored in a table with the DataTypeAggregateFunction to allow incremental aggregation of data.

+
+

The serialized data format for aggregate function states is not versioned right now. This is ok if aggregate states are only stored temporarily. But we have the AggregatingMergeTree table engine for incremental aggregation, and people are already using it in production. This is why we should add support for backward compatibility when changing the serialized format for any aggregate function in the future.

+
+

Server

+

The server implements several different interfaces:

+
    +
  • An HTTP interface for any foreign clients.
    • +
  • A TCP interface for the native ClickHouse client and for cross-server communication during distributed query execution.
    • +
  • An interface for transferring data for replication.
    • +
+

Internally, it is just a basic multithreaded server without coroutines, fibers, etc. Since the server is not designed to process a high rate of simple queries but is intended to process a relatively low rate of complex queries, each of them can process a vast amount of data for analytics.

+

The server initializes the Context class with the necessary environment for query execution: the list of available databases, users and access rights, settings, clusters, the process list, the query log, and so on. This environment is used by interpreters.

+

We maintain full backward and forward compatibility for the server TCP protocol: old clients can talk to new servers and new clients can talk to old servers. But we don't want to maintain it eternally, and we are removing support for old versions after about one year.

+
+

For all external applications, we recommend using the HTTP interface because it is simple and easy to use. The TCP protocol is more tightly linked to internal data structures: it uses an internal format for passing blocks of data and it uses custom framing for compressed data. We haven't released a C library for that protocol because it requires linking most of the ClickHouse codebase, which is not practical.

+
+

Distributed query execution

+

Servers in a cluster setup are mostly independent. You can create a Distributed table on one or all servers in a cluster. The Distributed table does not store data itself – it only provides a "view" to all local tables on multiple nodes of a cluster. When you SELECT from a Distributed table, it rewrites that query, chooses remote nodes according to load balancing settings, and sends the query to them. The Distributed table requests remote servers to process a query just up to a stage where intermediate results from different servers can be merged. Then it receives the intermediate results and merges them. The distributed table tries to distribute as much work as possible to remote servers, and does not send much intermediate data over the network.

+
+

Things become more complicated when you have subqueries in IN or JOIN clauses and each of them uses a Distributed table. We have different strategies for execution of these queries.

+
+

There is no global query plan for distributed query execution. Each node has its own local query plan for its part of the job. We only have simple one-pass distributed query execution: we send queries for remote nodes and then merge the results. But this is not feasible for difficult queries with high cardinality GROUP BYs or with a large amount of temporary data for JOIN: in such cases, we need to "reshuffle" data between servers, which requires additional coordination. ClickHouse does not support that kind of query execution, and we need to work on it.

+

Merge Tree

+

MergeTree is a family of storage engines that supports indexing by primary key. The primary key can be an arbitary tuple of columns or expressions. Data in a MergeTree table is stored in "parts". Each part stores data in the primary key order (data is ordered lexicographically by the primary key tuple). All the table columns are stored in separate column.bin files in these parts. The files consist of compressed blocks. Each block is usually from 64 KB to 1 MB of uncompressed data, depending on the average value size. The blocks consist of column values placed contiguously one after the other. Column values are in the same order for each column (the order is defined by the primary key), so when you iterate by many columns, you get values for the corresponding rows.

+

The primary key itself is "sparse". It doesn't address each single row, but only some ranges of data. A separate primary.idx file has the value of the primary key for each N-th row, where N is called index_granularity (usually, N = 8192). Also, for each column, we have column.mrk files with "marks," which are offsets to each N-th row in the data file. Each mark is a pair: the offset in the file to the beginning of the compressed block, and the offset in the decompressed block to the beginning of data. Usually compressed blocks are aligned by marks, and the offset in the decompressed block is zero. Data for primary.idx always resides in memory and data for column.mrk files is cached.

+

When we are going to read something from a part in MergeTree, we look at primary.idx data and locate ranges that could possibly contain requested data, then look at column.mrk data and calculate offsets for where to start reading those ranges. Because of sparseness, excess data may be read. ClickHouse is not suitable for a high load of simple point queries, because the entire range with index_granularity rows must be read for each key, and the entire compressed block must be decompressed for each column. We made the index sparse because we must be able to maintain trillions of rows per single server without noticeable memory consumption for the index. Also, because the primary key is sparse, it is not unique: it cannot check the existence of the key in the table at INSERT time. You could have many rows with the same key in a table.

+

When you INSERT a bunch of data into MergeTree, that bunch is sorted by primary key order and forms a new part. To keep the number of parts relatively low, there are background threads that periodically select some parts and merge them to a single sorted part. That's why it is called MergeTree. Of course, merging leads to "write amplification". All parts are immutable: they are only created and deleted, but not modified. When SELECT is run, it holds a snapshot of the table (a set of parts). After merging, we also keep old parts for some time to make recovery after failure easier, so if we see that some merged part is probably broken, we can replace it with its source parts.

+

MergeTree is not an LSM tree because it doesn't contain "memtable" and "log": inserted data is written directly to the filesystem. This makes it suitable only to INSERT data in batches, not by individual row and not very frequently – about once per second is ok, but a thousand times a second is not. We did it this way for simplicity's sake, and because we are already inserting data in batches in our applications.

+
+

MergeTree tables can only have one (primary) index: there aren't any secondary indices. It would be nice to allow multiple physical representations under one logical table, for example, to store data in more than one physical order or even to allow representations with pre-aggregated data along with original data.

+
+

There are MergeTree engines that are doing additional work during background merges. Examples are CollapsingMergeTree and AggregatingMergeTree. This could be treated as special support for updates. Keep in mind that these are not real updates because users usually have no control over the time when background merges will be executed, and data in a MergeTree table is almost always stored in more than one part, not in completely merged form.

+

Replication

+

Replication in ClickHouse is implemented on a per-table basis. You could have some replicated and some non-replicated tables on the same server. You could also have tables replicated in different ways, such as one table with two-factor replication and another with three-factor.

+

Replication is implemented in the ReplicatedMergeTree storage engine. The path in ZooKeeper is specified as a parameter for the storage engine. All tables with the same path in ZooKeeper become replicas of each other: they synchronize their data and maintain consistency. Replicas can be added and removed dynamically simply by creating or dropping a table.

+

Replication uses an asynchronous multi-master scheme. You can insert data into any replica that has a session with ZooKeeper, and data is replicated to all other replicas asynchronously. Because ClickHouse doesn't support UPDATEs, replication is conflict-free. As there is no quorum acknowledgment of inserts, just-inserted data might be lost if one node fails.

+

Metadata for replication is stored in ZooKeeper. There is a replication log that lists what actions to do. Actions are: get part; merge parts; drop partition, etc. Each replica copies the replication log to its queue and then executes the actions from the queue. For example, on insertion, the "get part" action is created in the log, and every replica downloads that part. Merges are coordinated between replicas to get byte-identical results. All parts are merged in the same way on all replicas. To achieve this, one replica is elected as the leader, and that replica initiates merges and writes "merge parts" actions to the log.

+

Replication is physical: only compressed parts are transferred between nodes, not queries. To lower the network cost (to avoid network amplification), merges are processed on each replica independently in most cases. Large merged parts are sent over the network only in cases of significant replication lag.

+

In addition, each replica stores its state in ZooKeeper as the set of parts and its checksums. When the state on the local filesystem diverges from the reference state in ZooKeeper, the replica restores its consistency by downloading missing and broken parts from other replicas. When there is some unexpected or broken data in the local filesystem, ClickHouse does not remove it, but moves it to a separate directory and forgets it.

+
+

The ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is not elastic, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load will be uneven. This implementation gives you more control, and it is fine for relatively small clusters such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that will span its data across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically.

+
+

How to build ClickHouse on Linux

+

Build should work on Linux Ubuntu 12.04, 14.04 or newer. +With appropriate changes, it should also work on any other Linux distribution. +The build process is not intended to work on Mac OS X. +Only x86_64 with SSE 4.2 is supported. Support for AArch64 is experimental.

+

To test for SSE 4.2, do

+
grep -q sse4_2 /proc/cpuinfo && echo "SSE 4.2 supported" || echo "SSE 4.2 not supported"
+
+ + +

Install Git and CMake

+
sudo apt-get install git cmake
+
+ + +

Or cmake3 instead of cmake on older systems.

+

Detect the number of threads

+
export THREADS=$(grep -c ^processor /proc/cpuinfo)
+
+ + +

Install GCC 7

+

There are several ways to do this.

+

Install from a PPA package

+
sudo apt-get install software-properties-common
+sudo apt-add-repository ppa:ubuntu-toolchain-r/test
+sudo apt-get update
+sudo apt-get install gcc-7 g++-7
+
+ + +

Install from sources

+

Look at [https://github.com/yandex/ClickHouse/blob/master/utils/prepare-environment/install-gcc.sh]

+

Use GCC 7 for builds

+
export CC=gcc-7
+export CXX=g++-7
+
+ + +

Install required libraries from packages

+
sudo apt-get install libicu-dev libreadline-dev libmysqlclient-dev libssl-dev unixodbc-dev ninja-build
+
+ + +

Checkout ClickHouse sources

+

To get the latest stable version:

+
git clone -b stable --recursive git@github.com:yandex/ClickHouse.git
+## or: git clone -b stable --recursive https://github.com/yandex/ClickHouse.git
+
+cd ClickHouse
+
+ + +

For development, switch to the master branch. +For the latest release candidate, switch to the testing branch.

+

Build ClickHouse

+

There are two build variants.

+

Build release package

+

Install prerequisites to build Debian packages.

+
sudo apt-get install devscripts dupload fakeroot debhelper
+
+ + +

Install the most recent version of Clang.

+

Clang is embedded into the ClickHouse package and used at runtime. The minimum version is 5.0. It is optional.

+

To install clang, see utils/prepare-environment/install-clang.sh

+

You may also build ClickHouse with Clang for development purposes. +For production releases, GCC is used.

+

Run the release script:

+
rm -f ../clickhouse*.deb
+./release
+
+ + +

You will find built packages in the parent directory:

+
ls -l ../clickhouse*.deb
+
+ + +

Note that usage of debian packages is not required. +ClickHouse has no runtime dependencies except libc, so it could work on almost any Linux.

+

Installing freshly built packages on a development server:

+
sudo dpkg -i ../clickhouse*.deb
+sudo service clickhouse-server start
+
+ + +

Build to work with code

+
mkdir build
+cd build
+cmake ..
+make -j $THREADS
+cd ..
+
+ + +

To create an executable, run make clickhouse. +This will create the dbms/src/Server/clickhouse executable, which can be used with client or server arguments.

+

How to build ClickHouse on Mac OS X

+

Build should work on Mac OS X 10.12. If you're using earlier version, you can try to build ClickHouse using Gentoo Prefix and clang sl in this instruction. +With appropriate changes, it should also work on any other Linux distribution.

+

Install Homebrew

+
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
+
+ + +

Install required compilers, tools, and libraries

+
brew install cmake gcc icu4c mysql openssl unixodbc libtool gettext zlib readline boost --cc=gcc-7
+
+ + +

Checkout ClickHouse sources

+

To get the latest stable version:

+
git clone -b stable --recursive --depth=10 git@github.com:yandex/ClickHouse.git
+## or: git clone -b stable --recursive --depth=10 https://github.com/yandex/ClickHouse.git
+
+cd ClickHouse
+
+ + +

For development, switch to the master branch. +For the latest release candidate, switch to the testing branch.

+

Build ClickHouse

+
mkdir build
+cd build
+cmake .. -DCMAKE_CXX_COMPILER=`which g++-7` -DCMAKE_C_COMPILER=`which gcc-7`
+make -j `sysctl -n hw.ncpu`
+cd ..
+
+ + +

Caveats

+

If you intend to run clickhouse-server, make sure to increase the system's maxfiles variable. See MacOS.md for more details.

+

How to write C++ code

+

General recommendations

+

1. The following are recommendations, not requirements.

+

2. If you are editing code, it makes sense to follow the formatting of the existing code.

+

3. Code style is needed for consistency. Consistency makes it easier to read the code, and it also makes it easier to search the code.

+

4. Many of the rules do not have logical reasons; they are dictated by established practices.

+

Formatting

+

1. Most of the formatting will be done automatically by clang-format.

+

2. Indents are 4 spaces. Configure your development environment so that a tab adds four spaces.

+

3. A left curly bracket must be separated on a new line. (And the right one, as well.)

+
inline void readBoolText(bool & x, ReadBuffer & buf)
+{
+    char tmp = '0';
+    readChar(tmp, buf);
+    x = tmp != '0';
+}
+
+ + +

4. +But if the entire function body is quite short (a single statement), you can place it entirely on one line if you wish. Place spaces around curly braces (besides the space at the end of the line).

+
inline size_t mask() const                { return buf_size() - 1; }
+inline size_t place(HashValue x) const    { return x & mask(); }
+
+ + +

5. For functions, don't put spaces around brackets.

+
void reinsert(const Value & x)
+memcpy(&buf[place_value], &x, sizeof(x));
+
+ + +

6. When using statements such as if, for, and while (unlike function calls), put a space before the opening bracket.

+

cpp + for (size_t i = 0; i < rows; i += storage.index_granularity)

+

7. Put spaces around binary operators (+, -, *, /, %, ...), as well as the ternary operator ?:.

+
UInt16 year = (s[0] - '0') * 1000 + (s[1] - '0') * 100 + (s[2] - '0') * 10 + (s[3] - '0');
+UInt8 month = (s[5] - '0') * 10 + (s[6] - '0');
+UInt8 day = (s[8] - '0') * 10 + (s[9] - '0');
+
+ + +

8. If a line feed is entered, put the operator on a new line and increase the indent before it.

+
if (elapsed_ns)
+    message << " ("
+         << rows_read_on_server * 1000000000 / elapsed_ns << " rows/s., "
+        << bytes_read_on_server * 1000.0 / elapsed_ns << " MB/s.) ";
+
+ + +

9. You can use spaces for alignment within a line, if desired.

+
dst.ClickLogID         = click.LogID;
+dst.ClickEventID       = click.EventID;
+dst.ClickGoodEvent     = click.GoodEvent;
+
+ + +

10. Don't use spaces around the operators ., -> .

+

If necessary, the operator can be wrapped to the next line. In this case, the offset in front of it is increased.

+

11. Do not use a space to separate unary operators (-, +, *, &, ...) from the argument.

+

12. Put a space after a comma, but not before it. The same rule goes for a semicolon inside a for expression.

+

13. Do not use spaces to separate the [] operator.

+

14. In a template <...> expression, use a space between template and <. No spaces after < or before >.

+
template <typename TKey, typename TValue>
+struct AggregatedStatElement
+{}
+
+ + +

15. In classes and structures, public, private, and protected are written on the same level as the class/struct, but all other internal elements should be deeper.

+
template <typename T>
+class MultiVersion
+{
+public:
+    /// Version of object for usage. shared_ptr manage lifetime of version.
+    using Version = std::shared_ptr<const T>;
+    ...
+}
+
+ + +

16. If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace.

+

17. If the block for if, for, while... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets.

+
/// Finish write.
+for (auto & stream : streams)
+    stream.second->finalize();
+
+ + +

18. There should be any spaces at the ends of lines.

+

19. Sources are UTF-8 encoded.

+

20. Non-ASCII characters can be used in string literals.

+
<< ", " << (timer.elapsed() / chunks_stats.hits) << " μsec/hit.";
+
+ + +

21. Do not write multiple expressions in a single line.

+

22. Group sections of code inside functions and separate them with no more than one empty line.

+

23. Separate functions, classes, and so on with one or two empty lines.

+

24. A const (related to a value) must be written before the type name.

+
//correct
+const char * pos
+const std::string & s
+//incorrect
+char const * pos
+
+ + +

25. When declaring a pointer or reference, the * and & symbols should be separated by spaces on both sides.

+
//correct
+const char * pos
+//incorrect
+const char* pos
+const char *pos
+
+ + +

26. When using template types, alias them with the using keyword (except in the simplest cases).

+

In other words, the template parameters are specified only in using and aren't repeated in the code.

+

using can be declared locally, such as inside a function.

+
//correct
+using FileStreams = std::map<std::string, std::shared_ptr<Stream>>;
+FileStreams streams;
+//incorrect
+std::map<std::string, std::shared_ptr<Stream>> streams;
+
+ + +

27. Do not declare several variables of different types in one statement.

+
//incorrect
+int x, *y;
+
+ + +

28. Do not use C-style casts.

+
//incorrect
+std::cerr << (int)c <<; std::endl;
+//correct
+std::cerr << static_cast<int>(c) << std::endl;
+
+ + +

29. In classes and structs, group members and functions separately inside each visibility scope.

+

30. For small classes and structs, it is not necessary to separate the method declaration from the implementation.

+

The same is true for small methods in any classes or structs.

+

For templated classes and structs, don't separate the method declarations from the implementation (because otherwise they must be defined in the same translation unit).

+

31. You can wrap lines at 140 characters, instead of 80.

+

32. Always use the prefix increment/decrement operators if postfix is not required.

+
for (Names::const_iterator it = column_names.begin(); it != column_names.end(); ++it)
+
+ + +

Comments

+

1. Be sure to add comments for all non-trivial parts of code.

+

This is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong.

+
/** Part of piece of memory, that can be used.
+  * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading,
+  * then working_buffer will have size of only 10 bytes
+  * (working_buffer.end() will point to the position right after those 10 bytes available for read).
+*/
+
+ + +

2. Comments can be as detailed as necessary.

+

3. Place comments before the code they describe. In rare cases, comments can come after the code, on the same line.

+
/** Parses and executes the query.
+*/
+void executeQuery(
+    ReadBuffer & istr, /// Where to read the query from (and data for INSERT, if applicable)
+    WriteBuffer & ostr, /// Where to write the result
+    Context & context, /// DB, tables, data types, engines, functions, aggregate functions...
+    BlockInputStreamPtr & query_plan, /// A description of query processing can be included here
+    QueryProcessingStage::Enum stage = QueryProcessingStage::Complete /// The last stage to process the SELECT query to
+    )
+
+ + +

4. Comments should be written in English only.

+

5. If you are writing a library, include detailed comments explaining it in the main header file.

+

6. Do not add comments that do not provide additional information. In particular, do not leave empty comments like this:

+
/*
+* Procedure Name:
+* Original procedure name:
+* Author:
+* Date of creation:
+* Dates of modification:
+* Modification authors:
+* Original file name:
+* Purpose:
+* Intent:
+* Designation:
+* Classes used:
+* Constants:
+* Local variables:
+* Parameters:
+* Date of creation:
+* Purpose:
+*/
+
+ + +

The example is borrowed from http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/.

+

7. Do not write garbage comments (author, creation date ..) at the beginning of each file.

+

8. Single-line comments begin with three slashes: /// and multi-line comments begin with /**. These comments are considered "documentation".

+

Note: You can use Doxygen to generate documentation from these comments. But Doxygen is not generally used because it is more convenient to navigate the code in the IDE.

+

9. Multi-line comments must not have empty lines at the beginning and end (except the line that closes a multi-line comment).

+

10. For commenting out code, use basic comments, not "documenting" comments.

+

11. Delete the commented out parts of the code before commiting.

+

12. Do not use profanity in comments or code.

+

13. Do not use uppercase letters. Do not use excessive punctuation.

+
/// WHAT THE FAIL???
+
+ + +

14. Do not make delimeters from comments.

+
///******************************************************
+
+ + +

15. Do not start discussions in comments.

+
/// Why did you do this stuff?
+
+ + +

16. There's no need to write a comment at the end of a block describing what it was about.

+
/// for
+
+ + +

Names

+

1. The names of variables and class members use lowercase letters with underscores.

+
size_t max_block_size;
+
+ + +

2. The names of functions (methods) use camelCase beginning with a lowercase letter.

+
std::string getName() const override { return "Memory"; }
+
+ + +

3. The names of classes (structures) use CamelCase beginning with an uppercase letter. Prefixes other than I are not used for interfaces.

+
class StorageMemory : public IStorage
+
+ + +

4. The names of usings follow the same rules as classes, or you can add _t at the end.

+

5. Names of template type arguments for simple cases: T; T, U; T1, T2.

+

For more complex cases, either follow the rules for class names, or add the prefix T.

+
template <typename TKey, typename TValue>
+struct AggregatedStatElement
+
+ + +

6. Names of template constant arguments: either follow the rules for variable names, or use N in simple cases.

+
template <bool without_www>
+struct ExtractDomain
+
+ + +

7. For abstract classes (interfaces) you can add the I prefix.

+
class IBlockInputStream
+
+ + +

8. If you use a variable locally, you can use the short name.

+

In other cases, use a descriptive name that conveys the meaning.

+
bool info_successfully_loaded = false;
+
+ + +

9. define‘s should be in ALL_CAPS with underscores. The same is true for global constants.

+
##define MAX_SRC_TABLE_NAMES_TO_STORE 1000
+
+ + +

10. File names should use the same style as their contents.

+

If a file contains a single class, name the file the same way as the class, in CamelCase.

+

If the file contains a single function, name the file the same way as the function, in camelCase.

+

11. If the name contains an abbreviation, then:

+
    +
  • For variable names, the abbreviation should use lowercase letters mysql_connection (not mySQL_connection).
    • +
  • For names of classes and functions, keep the uppercase letters in the abbreviation MySQLConnection (not MySqlConnection).
    • +
+

12. Constructor arguments that are used just to initialize the class members should be named the same way as the class members, but with an underscore at the end.

+
FileQueueProcessor(
+    const std::string & path_,
+    const std::string & prefix_,
+    std::shared_ptr<FileHandler> handler_)
+    : path(path_),
+    prefix(prefix_),
+    handler(handler_),
+    log(&Logger::get("FileQueueProcessor"))
+{
+}
+
+ + +

The underscore suffix can be omitted if the argument is not used in the constructor body.

+

13. There is no difference in the names of local variables and class members (no prefixes required).

+
timer (not m_timer)
+
+ + +

14. Constants in enums use CamelCase beginning with an uppercase letter. ALL_CAPS is also allowed. If the enum is not local, use enum class.

+
enum class CompressionMethod
+{
+    QuickLZ = 0,
+    LZ4     = 1,
+};
+
+ + +

15. All names must be in English. Transliteration of Russian words is not allowed.

+
not Stroka
+
+ + +

16. Abbreviations are acceptable if they are well known (when you can easily find the meaning of the abbreviation in Wikipedia or in a search engine).

+
`AST`, `SQL`.
+
+Not `NVDH` (some random letters)
+
+ + +

Incomplete words are acceptable if the shortened version is common use.

+

You can also use an abbreviation if the full name is included next to it in the comments.

+

17. File names with C++ source code must have the .cpp extension. Header files must have the .h extension.

+

How to write code

+

1. Memory management.

+

Manual memory deallocation (delete) can only be used in library code.

+

In library code, the delete operator can only be used in destructors.

+

In application code, memory must be freed by the object that owns it.

+

Examples:

+
    +
  • The easiest way is to place an object on the stack, or make it a member of another class.
    • +
  • For a large number of small objects, use containers.
    • +
  • For automatic deallocation of a small number of objects that reside in the heap, use shared_ptr/unique_ptr.
    • +
+

2. Resource management.

+

Use RAII and see the previous point.

+

3. Error handling.

+

Use exceptions. In most cases, you only need to throw an exception, and don't need to catch it (because of RAII).

+

In offline data processing applications, it's often acceptable to not catch exceptions.

+

In servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler.

+
/// If there were no other calculations yet, do it synchronously
+if (!started)
+{
+    calculate();
+    started = true;
+}
+else    /// If the calculations are already in progress, wait for results
+    pool.wait();
+
+if (exception)
+    exception->rethrow();
+
+ + +

Never hide exceptions without handling. Never just blindly put all exceptions to log.

+

Not catch (...) {}.

+

If you need to ignore some exceptions, do so only for specific ones and rethrow the rest.

+
catch (const DB::Exception & e)
+{
+    if (e.code() == ErrorCodes::UNKNOWN_AGGREGATE_FUNCTION)
+        return nullptr;
+    else
+        throw;
+}
+
+ + +

When using functions with response codes or errno, always check the result and throw an exception in case of error.

+
if (0 != close(fd))
+    throwFromErrno("Cannot close file " + file_name, ErrorCodes::CANNOT_CLOSE_FILE);
+
+ + +

Asserts are not used.

+

4. Exception types.

+

There is no need to use complex exception hierarchy in application code. The exception text should be understandable to a system administrator.

+

5. Throwing exceptions from destructors.

+

This is not recommended, but it is allowed.

+

Use the following options:

+
    +
  • Create a (done() or finalize()) function that will do all the work in advance that might lead to an exception. If that function was called, there should be no exceptions in the destructor later.
    • +
  • Tasks that are too complex (such as sending messages over the network) can be put in separate method that the class user will have to call before destruction.
    • +
  • If there is an exception in the destructor, it’s better to log it than to hide it (if the logger is available).
    • +
  • In simple applications, it is acceptable to rely on std::terminate (for cases of noexcept by default in C++11) to handle exceptions.
    • +
+

6. Anonymous code blocks.

+

You can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block.

+
Block block = data.in->read();
+
+{
+    std::lock_guard<std::mutex> lock(mutex);
+    data.ready = true;
+    data.block = block;
+}
+
+ready_any.set();
+
+ + +

7. Multithreading.

+

For offline data processing applications:

+
    +
  • Try to get the best possible performance on a single CPU core. You can then parallelize your code if necessary.
    • +
+

In server applications:

+
    +
  • Use the thread pool to process requests. At this point, we haven't had any tasks that required userspace context switching.
    • +
+

Fork is not used for parallelization.

+

8. Synchronizing threads.

+

Often it is possible to make different threads use different memory cells (even better: different cache lines,) and to not use any thread synchronization (except joinAll).

+

If synchronization is required, in most cases, it is sufficient to use mutex under lock_guard.

+

In other cases use system synchronization primitives. Do not use busy wait.

+

Atomic operations should be used only in the simplest cases.

+

Do not try to implement lock-free data structures unless it is your primary area of expertise.

+

9. Pointers vs references.

+

In most cases, prefer references.

+

10. const.

+

Use constant references, pointers to constants, const_iterator, const methods.

+

Consider const to be default and use non-const only when necessary.

+

When passing variable by value, using const usually does not make sense.

+

11. unsigned.

+

Use unsigned, if needed.

+

12. Numeric types

+

Use UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, and size_t, ssize_t, ptrdiff_t.

+

Don't use signed/unsigned long, long long, short, signed char, unsigned char, or char types for numbers.

+

13. Passing arguments.

+

Pass complex values by reference (including std::string).

+

If a function captures ownership of an objected created in the heap, make the argument type shared_ptr or unique_ptr.

+

14. Returning values.

+

In most cases, just use return. Do not write [return std::move(res)]{.strike}.

+

If the function allocates an object on heap and returns it, use shared_ptr or unique_ptr.

+

In rare cases you might need to return the value via an argument. In this case, the argument should be a reference.

+
using AggregateFunctionPtr = std::shared_ptr<IAggregateFunction>;
+
+/** Creates an aggregate function by name.
+ */
+class AggregateFunctionFactory
+{
+public:
+   AggregateFunctionFactory();
+   AggregateFunctionPtr get(const String & name, const DataTypes & argument_types) const;
+
+ + +

15. namespace.

+

There is no need to use a separate namespace for application code or small libraries.

+

or small libraries.

+

For medium to large libraries, put everything in the namespace.

+

You can use the additional detail namespace in a library's .h file to hide implementation details.

+

In a .cpp file, you can use the static or anonymous namespace to hide symbols.

+

You can also use namespace for enums to prevent its names from polluting the outer namespace, but it’s better to use the enum class.

+

16. Delayed initialization.

+

If arguments are required for initialization then do not write a default constructor.

+

If later you’ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use shared_ptr/unique_ptr.

+
Loader(DB::Connection * connection_, const std::string & query, size_t max_block_size_);
+
+/// For delayed initialization
+Loader() {}
+
+ + +

17. Virtual functions.

+

If the class is not intended for polymorphic use, you do not need to make functions virtual. This also applies to the destructor.

+

18. Encodings.

+

Use UTF-8 everywhere. Use std::stringandchar *. Do not use std::wstringandwchar_t.

+

19. Logging.

+

See the examples everywhere in the code.

+

Before committing, delete all meaningless and debug logging, and any other types of debug output.

+

Logging in cycles should be avoided, even on the Trace level.

+

Logs must be readable at any logging level.

+

Logging should only be used in application code, for the most part.

+

Log messages must be written in English.

+

The log should preferably be understandable for the system administrator.

+

Do not use profanity in the log.

+

Use UTF-8 encoding in the log. In rare cases you can use non-ASCII characters in the log.

+

20. I/O.

+

Don't use iostreams in internal cycles that are critical for application performance (and never use stringstream).

+

Use the DB/IO library instead.

+

21. Date and time.

+

See the DateLUT library.

+

22. include.

+

Always use #pragma once instead of include guards.

+

23. using.

+

The using namespace is not used.

+

It's fine if you are 'using' something specific, but make it local inside a class or function.

+

24. Do not use trailing return type for functions unless necessary.

+
[auto f() -&gt; void;]{.strike}
+
+ + +

25. Do not declare and init variables like this:

+
auto s = std::string{"Hello"};
+
+ + +

Do it like this:

+
std::string s = "Hello";
+std::string s{"Hello"};
+
+ + +

26. For virtual functions, write virtual in the base class, but write override in descendent classes.

+

Unused features of C++

+

1. Virtual inheritance is not used.

+

2. Exception specifiers from C++03 are not used.

+

3. Function try block is not used, except for the main function in tests.

+

Platform

+

1. We write code for a specific platform.

+

But other things being equal, cross-platform or portable code is preferred.

+

2. The language is C++17.

+

3. The compiler is gcc. At this time (December 2017), the code is compiled using version 7.2. (It can also be compiled using clang 5.)

+

The standard library is used (implementation of libstdc++ or libc++).

+

4. OS: Linux Ubuntu, not older than Precise.

+

5. Code is written for x86_64 CPU architecture.

+

The CPU instruction set is the minimum supported set among our servers. Currently, it is SSE 4.2.

+

6. Use -Wall -Wextra -Werror compilation flags.

+

7. Use static linking with all libraries except those that are difficult to connect to statically (see the output of the ldd command).

+

8. Code is developed and debugged with release settings.

+

Tools

+

1. KDevelop is a good IDE.

+

2. For debugging, use gdb, valgrind (memcheck), strace, -fsanitize=, ..., tcmalloc_minimal_debug.

+

3. For profiling, use Linux Perf valgrind (callgrind), strace-cf.

+

4. Sources are in Git.

+

5. Compilation is managed by CMake.

+

6. Releases are in deb packages.

+

7. Commits to master must not break the build.

+

Though only selected revisions are considered workable.

+

8. Make commits as often as possible, even if the code is only partially ready.

+

Use branches for this purpose.

+

If your code is not buildable yet, exclude it from the build before pushing to master. You'll need to finish it or remove it from master within a few days.

+

9. For non-trivial changes, used branches and publish them on the server.

+

10. Unused code is removed from the repository.

+

Libraries

+

1. The C++14 standard library is used (experimental extensions are fine), as well as boost and Poco frameworks.

+

2. If necessary, you can use any well-known libraries available in the OS package.

+

If there is a good solution already available, then use it, even if it means you have to install another library.

+

(But be prepared to remove bad libraries from code.)

+

3. You can install a library that isn't in the packages, if the packages don't have what you need or have an outdated version or the wrong type of compilation.

+

4. If the library is small and doesn't have its own complex build system, put the source files in the contrib folder.

+

5. Preference is always given to libraries that are already used.

+

General recommendations

+

1. Write as little code as possible.

+

2. Try the simplest solution.

+

3. Don't write code until you know how it's going to work and how the inner loop will function.

+

4. In the simplest cases, use 'using' instead of classes or structs.

+

5. If possible, do not write copy constructors, assignment operators, destructors (other than a virtual one, if the class contains at least one virtual function), mpve-constructors and move assignment operators. In other words, the compiler-generated functions must work correctly. You can use 'default'.

+

6. Code simplification is encouraged. Reduce the size of your code where possible.

+

Additional recommendations

+

1. Explicit std:: for types from stddef.h is not recommended.

+

We recommend writing size_t instead std::size_t because it's shorter.

+

But if you prefer, std:: is acceptable.

+

2. Explicit std:: for functions from the standard C library is not recommended.

+

Write memcpy instead of std::memcpy.

+

The reason is that there are similar non-standard functions, such as memmem. We do use these functions on occasion. These functions do not exist in namespace std.

+

If you write std::memcpy instead of memcpy everywhere, then memmem without std:: will look awkward.

+

Nevertheless, std:: is allowed if you prefer it.

+

3. Using functions from C when the ones are available in the standard C++ library.

+

This is acceptable if it is more efficient.

+

For example, use memcpy instead of std::copy for copying large chunks of memory.

+

4. Multiline function arguments.

+

Any of the following wrapping styles are allowed:

+
function(
+    T1 x1,
+    T2 x2)
+
+ + +
function(
+    size_t left, size_t right,
+    const & RangesInDataParts ranges,
+    size_t limit)
+
+ + +
function(size_t left, size_t right,
+    const & RangesInDataParts ranges,
+    size_t limit)
+
+ + +
function(size_t left, size_t right,
+        const & RangesInDataParts ranges,
+        size_t limit)
+
+ + +
function(
+        size_t left,
+        size_t right,
+        const & RangesInDataParts ranges,
+        size_t limit)
+
+ + +

How to run ClickHouse tests

+

The clickhouse-test utility that is used for functional testing is written using Python 2.x.It also requires you to have some third-party packages:

+
$ pip install lxml termcolor
+
+ + +

In a nutshell:

+
    +
  • Put the clickhouse program to /usr/bin (or PATH)
    • +
  • Create a clickhouse-client symlink in /usr/bin pointing to clickhouse
    • +
  • Start the clickhouse server
    • +
  • cd dbms/tests/
    • +
  • Run ./clickhouse-test
    • +
+

Example usage

+

Run ./clickhouse-test --help to see available options.

+

To run tests without having to create a symlink or mess with PATH:

+
./clickhouse-test -c "../../build/dbms/src/Server/clickhouse --client"
+
+ + +

To run a single test, i.e. 00395_nullable:

+
./clickhouse-test 00395
+
+ + +

Roadmap

+

Q1 2018

+

New fuctionality

+
    +
  • +

    Support for UPDATE and DELETE.

    +
    • +
  • +

    Multidimensional and nested arrays.

    +
    • +
+

It can look something like this:

+
CREATE TABLE t
+(
+    x Array(Array(String)),
+    z Nested(
+        x Array(String),
+        y Nested(...))
+)
+ENGINE = MergeTree ORDER BY x
+
+ + +
    +
  • External MySQL and ODBC tables.
    • +
+

External tables can be integrated into ClickHouse using external dictionaries. This new functionality is a convenient alternative to connecting external tables.

+
SELECT ...
+FROM mysql('host:port', 'db', 'table', 'user', 'password')`
+
+ + +

Improvements

+
    +
  • Effective data copying between ClickHouse clusters.
    • +
+

Now you can copy data with the remote() function. For example: INSERT INTO t SELECT * FROM remote(...).

+

This operation will have improved performance.

+
    +
  • O_DIRECT for merges.
    • +
+

This will improve the performance of the OS cache and "hot" queries.

+

Q2 2018

+

New functionality

+
    +
  • +

    UPDATE/DELETE conform to the EU GDPR.

    +
    • +
  • +

    Protobuf and Parquet input and output formats.

    +
    • +
  • +

    Creating dictionaries using DDL queries.

    +
    • +
+

Currently, dictionaries that are part of the database schema are defined in external XML files. This is inconvenient and counter-intuitive. The new approach should fix it.

+
    +
  • +

    Integration with LDAP.

    +
    • +
  • +

    WITH ROLLUP and WITH CUBE for GROUP BY.

    +
    • +
  • +

    Custom encoding and compression for each column individually.

    +
    • +
+

As of now, ClickHouse supports LZ4 and ZSTD compression of columns, and compression settings are global (see the article Compression in ClickHouse). Per-column compression and encoding will provide more efficient data storage, which in turn will speed up queries.

+
    +
  • Storing data on multiple disks on the same server.
    • +
+

This functionality will make it easier to extend the disk space, since different disk systems can be used for different databases or tables. Currently, users are forced to use symbolic links if the databases and tables must be stored on a different disk.

+

Improvements

+

Many improvements and fixes are planned for the query execution system. For example:

+
    +
  • Using an index for in (subquery).
    • +
+

The index is not used right now, which reduces performance.

+
    +
  • Passing predicates from where to subqueries, and passing predicates to views.
    • +
+

The predicates must be passed, since the view is changed by the subquery. Performance is still low for view filters, and views can't use the primary key of the original table, which makes views useless for large tables.

+
    +
  • Optimizing branching operations (ternary operator, if, multiIf).
    • +
+

ClickHouse currently performs all branches, even if they aren't necessary.

+
    +
  • Using a primary key for GROUP BY and ORDER BY.
    • +
+

This will speed up certain types of queries with partially sorted data.

+

Q3-Q4 2018

+

We don't have any set plans yet, but the main projects will be:

+
    +
  • Resource pools for executing queries.
    • +
+

This will make load management more efficient.

+
    +
  • ANSI SQL JOIN syntax.
    • +
+

Improve ClickHouse compatibility with many SQL tools.

+ + + + + + + +
+
+
+
+ + +
+ +
+
+
+ +
+ ©2016–2018 Yandex LLC +
+ +
+ + + +
+
+
+ +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/single/search/search_index.json b/docs/build/docs/en/single/search/search_index.json new file mode 100644 index 00000000000..df30bbf064d --- /dev/null +++ b/docs/build/docs/en/single/search/search_index.json @@ -0,0 +1,4179 @@ +{ + "docs": [ + { + "location": "/index.html", + "text": "What is ClickHouse?\n\n\nClickHouse is a columnar DBMS for OLAP.\n\n\nIn a \"normal\" row-oriented DBMS, data is stored in this order:\n\n\n5123456789123456789 1 Eurobasket - Greece - Bosnia and Herzegovina - example.com 1 2011-09-01 01:03:02 6274717 1294101174 11409 612345678912345678 0 33 6 http://www.example.com/basketball/team/123/match/456789.html http://www.example.com/basketball/team/123/match/987654.html 0 1366 768 32 10 3183 0 0 13 0\\0 1 1 0 0 2011142 -1 0 0 01321 613 660 2011-09-01 08:01:17 0 0 0 0 utf-8 1466 0 0 0 5678901234567890123 277789954 0 0 0 0 0\n5234985259563631958 0 Consulting, Tax assessment, Accounting, Law 1 2011-09-01 01:03:02 6320881 2111222333 213 6458937489576391093 0 3 2 http://www.example.ru/ 0 800 600 16 10 2 153.1 0 0 10 63 1 1 0 0 2111678 000 0 588 368 240 2011-09-01 01:03:17 4 0 60310 0 windows-1251 1466 0 000 778899001 0 0 0 0 0\n...\n\n\n\n\n\nIn order words, all the values related to a row are stored next to each other.\nExamples of a row-oriented DBMS are MySQL, Postgres, MS SQL Server, and others.\n\n\nIn a column-oriented DBMS, data is stored like this:\n\n\nWatchID: 5385521489354350662 5385521490329509958 5385521489953706054 5385521490476781638 5385521490583269446 5385521490218868806 5385521491437850694 5385521491090174022 5385521490792669254 5385521490420695110 5385521491532181574 5385521491559694406 5385521491459625030 5385521492275175494 5385521492781318214 5385521492710027334 5385521492955615302 5385521493708759110 5385521494506434630 5385521493104611398\nJavaEnable: 1 0 1 0 0 0 1 0 1 1 1 1 1 1 0 1 0 0 1 1\nTitle: Yandex Announcements - Investor Relations - Yandex Yandex \u2014 Contact us \u2014 Moscow Yandex \u2014 Mission Ru Yandex \u2014 History \u2014 History of Yandex Yandex Financial Releases - Investor Relations - Yandex Yandex \u2014 Locations Yandex Board of Directors - Corporate Governance - Yandex Yandex \u2014 Technologies\nGoodEvent: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\nEventTime: 2016-05-18 05:19:20 2016-05-18 08:10:20 2016-05-18 07:38:00 2016-05-18 01:13:08 2016-05-18 00:04:06 2016-05-18 04:21:30 2016-05-18 00:34:16 2016-05-18 07:35:49 2016-05-18 11:41:59 2016-05-18 01:13:32\n\n\n\n\n\nThese examples only show the order that data is arranged in.\nThe values from different columns are stored separately, and data from the same column is stored together.\n\n\nExamples of column-oriented DBMSs: \nVertica\n, \nParaccel (Actian Matrix) (Amazon Redshift)\n, \nSybase IQ\n, \nExasol\n, \nInfobright\n, \nInfiniDB\n, \nMonetDB (VectorWise) (Actian Vector)\n, \nLucidDB\n, \nSAP HANA\n, \nGoogle Dremel\n, \nGoogle PowerDrill\n, \nDruid\n, \nkdb+\n, and so on.\n\n\nDifferent orders for storing data are better suited to different scenarios.\nThe data access scenario refers to what queries are made, how often, and in what proportion; how much data is read for each type of query \u2013 rows, columns, and bytes; the relationship between reading and updating data; the working size of the data and how locally it is used; whether transactions are used, and how isolated they are; requirements for data replication and logical integrity; requirements for latency and throughput for each type of query, and so on.\n\n\nThe higher the load on the system, the more important it is to customize the system to the scenario, and the more specific this customization becomes. There is no system that is equally well-suited to significantly different scenarios. If a system is adaptable to a wide set of scenarios, under a high load, the system will handle all the scenarios equally poorly, or will work well for just one of the scenarios.\n\n\nWe'll say that the following is true for the OLAP (online analytical processing) scenario:\n\n\n\n\nThe vast majority of requests are for read access.\n\n\nData is updated in fairly large batches (\n 1000 rows), not by single rows; or it is not updated at all.\n\n\nData is added to the DB but is not modified.\n\n\nFor reads, quite a large number of rows are extracted from the DB, but only a small subset of columns.\n\n\nTables are \"wide,\" meaning they contain a large number of columns.\n\n\nQueries are relatively rare (usually hundreds of queries per server or less per second).\n\n\nFor simple queries, latencies around 50 ms are allowed.\n\n\nColumn values are fairly small: numbers and short strings (for example, 60 bytes per URL).\n\n\nRequires high throughput when processing a single query (up to billions of rows per second per server).\n\n\nThere are no transactions.\n\n\nLow requirements for data consistency.\n\n\nThere is one large table per query. All tables are small, except for one.\n\n\nA query result is significantly smaller than the source data. In other words, data is filtered or aggregated. The result fits in a single server's RAM.\n\n\n\n\nIt is easy to see that the OLAP scenario is very different from other popular scenarios (such as OLTP or Key-Value access). So it doesn't make sense to try to use OLTP or a Key-Value DB for processing analytical queries if you want to get decent performance. For example, if you try to use MongoDB or Elliptics for analytics, you will get very poor performance compared to OLAP databases.\n\n\nColumnar-oriented databases are better suited to OLAP scenarios (at least 100 times better in processing speed for most queries), for the following reasons:\n\n\n\n\nFor I/O.\n\n\nFor an analytical query, only a small number of table columns need to be read. In a column-oriented database, you can read just the data you need. For example, if you need 5 columns out of 100, you can expect a 20-fold reduction in I/O.\n\n\nSince data is read in packets, it is easier to compress. Data in columns is also easier to compress. This further reduces the I/O volume.\n\n\nDue to the reduced I/O, more data fits in the system cache.\n\n\n\n\nFor example, the query \"count the number of records for each advertising platform\" requires reading one \"advertising platform ID\" column, which takes up 1 byte uncompressed. If most of the traffic was not from advertising platforms, you can expect at least 10-fold compression of this column. When using a quick compression algorithm, data decompression is possible at a speed of at least several gigabytes of uncompressed data per second. In other words, this query can be processed at a speed of approximately several billion rows per second on a single server. This speed is actually achieved in practice.\n\n\nExample:\n\n\nmilovidov@hostname:~$ clickhouse-client\nClickHouse client version \n0\n.0.52053.\nConnecting to localhost:9000.\nConnected to ClickHouse server version \n0\n.0.52053.\n\n:\n)\n SELECT CounterID, count\n()\n FROM hits GROUP BY CounterID ORDER BY count\n()\n DESC LIMIT \n20\n\n\nSELECT\n CounterID,\n count\n()\n\nFROM hits\nGROUP BY CounterID\nORDER BY count\n()\n DESC\nLIMIT \n20\n\n\n\u250c\u2500CounterID\u2500\u252c\u2500\u2500count\n()\n\u2500\u2510\n\u2502 \n114208\n \u2502 \n56057344\n \u2502\n\u2502 \n115080\n \u2502 \n51619590\n \u2502\n\u2502 \n3228\n \u2502 \n44658301\n \u2502\n\u2502 \n38230\n \u2502 \n42045932\n \u2502\n\u2502 \n145263\n \u2502 \n42042158\n \u2502\n\u2502 \n91244\n \u2502 \n38297270\n \u2502\n\u2502 \n154139\n \u2502 \n26647572\n \u2502\n\u2502 \n150748\n \u2502 \n24112755\n \u2502\n\u2502 \n242232\n \u2502 \n21302571\n \u2502\n\u2502 \n338158\n \u2502 \n13507087\n \u2502\n\u2502 \n62180\n \u2502 \n12229491\n \u2502\n\u2502 \n82264\n \u2502 \n12187441\n \u2502\n\u2502 \n232261\n \u2502 \n12148031\n \u2502\n\u2502 \n146272\n \u2502 \n11438516\n \u2502\n\u2502 \n168777\n \u2502 \n11403636\n \u2502\n\u2502 \n4120072\n \u2502 \n11227824\n \u2502\n\u2502 \n10938808\n \u2502 \n10519739\n \u2502\n\u2502 \n74088\n \u2502 \n9047015\n \u2502\n\u2502 \n115079\n \u2502 \n8837972\n \u2502\n\u2502 \n337234\n \u2502 \n8205961\n \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n20\n rows in set. Elapsed: \n0\n.153 sec. Processed \n1\n.00 billion rows, \n4\n.00 GB \n(\n6\n.53 billion rows/s., \n26\n.10 GB/s.\n)\n\n\n:\n)\n\n\n\n\n\n\n\n\nFor CPU.\n\n\n\n\nSince executing a query requires processing a large number of rows, it helps to dispatch all operations for entire vectors instead of for separate rows, or to implement the query engine so that there is almost no dispatching cost. If you don't do this, with any half-decent disk subsystem, the query interpreter inevitably stalls the CPU.\nIt makes sense to both store data in columns and process it, when possible, by columns.\n\n\nThere are two ways to do this:\n\n\n\n\n\n\nA vector engine. All operations are written for vectors, instead of for separate values. This means you don't need to call operations very often, and dispatching costs are negligible. Operation code contains an optimized internal cycle.\n\n\n\n\n\n\nCode generation. The code generated for the query has all the indirect calls in it.\n\n\n\n\n\n\nThis is not done in \"normal\" databases, because it doesn't make sense when running simple queries. However, there are exceptions. For example, MemSQL uses code generation to reduce latency when processing SQL queries. (For comparison, analytical DBMSs require optimization of throughput, not latency.)\n\n\nNote that for CPU efficiency, the query language must be declarative (SQL or MDX), or at least a vector (J, K). The query should only contain implicit loops, allowing for optimization.\n\n\nIntroduction\n\n\nDistinctive features of ClickHouse\n\n\nTrue column-oriented DBMS\n\n\nIn a true column-oriented DBMS, there isn't any \"garbage\" stored with the values. Among other things, this means that constant-length values must be supported, to avoid storing their length \"number\" next to the values. As an example, a billion UInt8-type values should actually consume around 1 GB uncompressed, or this will strongly affect the CPU use. It is very important to store data compactly (without any \"garbage\") even when uncompressed, since the speed of decompression (CPU usage) depends mainly on the volume of uncompressed data.\n\n\nThis is worth noting because there are systems that can store values of separate columns separately, but that can't effectively process analytical queries due to their optimization for other scenarios. Examples are HBase, BigTable, Cassandra, and HyperTable. In these systems, you will get throughput around a hundred thousand rows per second, but not hundreds of millions of rows per second.\n\n\nAlso note that ClickHouse is a DBMS, not a single database. ClickHouse allows creating tables and databases in runtime, loading data, and running queries without reconfiguring and restarting the server.\n\n\nData compression\n\n\nSome column-oriented DBMSs (InfiniDB CE and MonetDB) do not use data compression. However, data compression really improves performance.\n\n\nDisk storage of data\n\n\nMany column-oriented DBMSs (such as SAP HANA and Google PowerDrill) can only work in RAM. But even on thousands of servers, the RAM is too small for storing all the pageviews and sessions in Yandex.Metrica.\n\n\nParallel processing on multiple cores\n\n\nLarge queries are parallelized in a natural way.\n\n\nDistributed processing on multiple servers\n\n\nAlmost none of the columnar DBMSs listed above have support for distributed processing.\nIn ClickHouse, data can reside on different shards. Each shard can be a group of replicas that are used for fault tolerance. The query is processed on all the shards in parallel. This is transparent for the user.\n\n\nSQL support\n\n\nIf you are familiar with standard SQL, we can't really talk about SQL support.\nAll the functions have different names.\nHowever, this is a declarative query language based on SQL that can't be differentiated from SQL in many instances.\nJOINs are supported. Subqueries are supported in FROM, IN, and JOIN clauses, as well as scalar subqueries.\nDependent subqueries are not supported.\n\n\nVector engine\n\n\nData is not only stored by columns, but is processed by vectors (parts of columns). This allows us to achieve high CPU performance.\n\n\nReal-time data updates\n\n\nClickHouse supports primary key tables. In order to quickly perform queries on the range of the primary key, the data is sorted incrementally using the merge tree. Due to this, data can continually be added to the table. There is no locking when adding data.\n\n\nIndexes\n\n\nHaving a primary key makes it possible to extract data for specific clients (for instance, Yandex.Metrica tracking tags) for a specific time range, with low latency less than several dozen milliseconds.\n\n\nSuitable for online queries\n\n\nThis lets us use the system as the back-end for a web interface. Low latency means queries can be processed without delay, while the Yandex.Metrica interface page is loading. In other words, in online mode.\n\n\nSupport for approximated calculations\n\n\n\n\nThe system contains aggregate functions for approximated calculation of the number of various values, medians, and quantiles.\n\n\nSupports running a query based on a part (sample) of data and getting an approximated result. In this case, proportionally less data is retrieved from the disk.\n\n\nSupports running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reasonably accurate result while using fewer resources.\n\n\n\n\nData replication and support for data integrity on replicas\n\n\nUses asynchronous multimaster replication. After being written to any available replica, data is distributed to all the remaining replicas. The system maintains identical data on different replicas. Data is restored automatically after a failure, or using a \"button\" for complex cases.\nFor more information, see the section \nData replication\n.\n\n\nClickHouse features that can be considered disadvantages\n\n\n\n\nNo transactions.\n\n\nFor aggregation, query results must fit in the RAM on a single server. However, the volume of source data for a query may be indefinitely large.\n\n\nLack of full-fledged UPDATE/DELETE implementation.\n\n\n\n\nYandex.Metrica use case\n\n\nClickHouse currently powers \nYandex.Metrica\n, \nthe second largest web analytics platform in the world\n. With more than 13 trillion records in the database and more than 20 billion events daily, ClickHouse allows you generating custom reports on the fly directly from non-aggregated data.\n\n\nWe need to get custom reports based on hits and sessions, with custom segments set by the user. Data for the reports is updated in real-time. Queries must be run immediately (in online mode). We must be able to build reports for any time period. Complex aggregates must be calculated, such as the number of unique visitors.\nAt this time (April 2014), Yandex.Metrica receives approximately 12 billion events (pageviews and mouse clicks) daily. All these events must be stored in order to build custom reports. A single query may require scanning hundreds of millions of rows over a few seconds, or millions of rows in no more than a few hundred milliseconds.\n\n\nUsage in Yandex.Metrica and other Yandex services\n\n\nClickHouse is used for multiple purposes in Yandex.Metrica.\nIts main task is to build reports in online mode using non-aggregated data. It uses a cluster of 374 servers, which store over 20.3 trillion rows in the database. The volume of compressed data, without counting duplication and replication, is about 2 PB. The volume of uncompressed data (in TSV format) would be approximately 17 PB.\n\n\nClickHouse is also used for:\n\n\n\n\nStoring data for Session Replay from Yandex.Metrica.\n\n\nProcessing intermediate data.\n\n\nBuilding global reports with Analytics.\n\n\nRunning queries for debugging the Yandex.Metrica engine.\n\n\nAnalyzing logs from the API and the user interface.\n\n\n\n\nClickHouse has at least a dozen installations in other Yandex services: in search verticals, Market, Direct, business analytics, mobile development, AdFox, personal services, and others.\n\n\nAggregated and non-aggregated data\n\n\nThere is a popular opinion that in order to effectively calculate statistics, you must aggregate data, since this reduces the volume of data.\n\n\nBut data aggregation is a very limited solution, for the following reasons:\n\n\n\n\nYou must have a pre-defined list of reports the user will need.\n\n\nThe user can't make custom reports.\n\n\nWhen aggregating a large quantity of keys, the volume of data is not reduced, and aggregation is useless.\n\n\nFor a large number of reports, there are too many aggregation variations (combinatorial explosion).\n\n\nWhen aggregating keys with high cardinality (such as URLs), the volume of data is not reduced by much (less than twofold).\n\n\nFor this reason, the volume of data with aggregation might grow instead of shrink.\n\n\nUsers do not view all the reports we generate for them. A large portion of calculations are useless.\n\n\nThe logical integrity of data may be violated for various aggregations.\n\n\n\n\nIf we do not aggregate anything and work with non-aggregated data, this might actually reduce the volume of calculations.\n\n\nHowever, with aggregation, a significant part of the work is taken offline and completed relatively calmly. In contrast, online calculations require calculating as fast as possible, since the user is waiting for the result.\n\n\nYandex.Metrica has a specialized system for aggregating data called Metrage, which is used for the majority of reports.\nStarting in 2009, Yandex.Metrica also used a specialized OLAP database for non-aggregated data called OLAPServer, which was previously used for the report builder.\nOLAPServer worked well for non-aggregated data, but it had many restrictions that did not allow it to be used for all reports as desired. These included the lack of support for data types (only numbers), and the inability to incrementally update data in real-time (it could only be done by rewriting data daily). OLAPServer is not a DBMS, but a specialized DB.\n\n\nTo remove the limitations of OLAPServer and solve the problem of working with non-aggregated data for all reports, we developed the ClickHouse DBMS.\n\n\nQuestions you were afraid to ask\n\n\nWhy not use something like MapReduce?\n\n\nWe can refer to systems like map-reduce as distributed computing systems in which the reduce operation is based on distributed sorting. In this sense, they include Hadoop, and YT (YT is developed at Yandex for internal use).\n\n\nThese systems aren't appropriate for online queries due to their high latency. In other words, they can't be used as the back-end for a web interface.\nThese types of systems aren't useful for real-time data updates.\nDistributed sorting isn't the best way to perform reduce operations if the result of the operation and all the intermediate results (if there are any) are located in the RAM of a single server, which is usually the case for online queries. In such a case, a hash table is the optimal way to perform reduce operations. A common approach to optimizing map-reduce tasks is pre-aggregation (partial reduce) using a hash table in RAM. The user performs this optimization manually.\nDistributed sorting is one of the main causes of reduced performance when running simple map-reduce tasks.\n\n\nSystems like map-reduce allow executing any code on the cluster. But a declarative query language is better suited to OLAP in order to run experiments quickly. For example, Hadoop has Hive and Pig. Also consider Cloudera Impala, Shark (outdated) for Spark, and Spark SQL, Presto, and Apache Drill. Performance when running such tasks is highly sub-optimal compared to specialized systems, but relatively high latency makes it unrealistic to use these systems as the backend for a web interface.\n\n\nYT allows storing groups of columns separately. But YT can't be considered a true column-based system because it doesn't have fixed-length data types (for efficiently storing numbers without extra \"garbage\"), and also due to its lack of a vector engine. Tasks are performed in YT using custom code in streaming mode, so they cannot be optimized enough (up to hundreds of millions of rows per second per server). \"Dynamic table sorting\" is under development in YT using MergeTree, strict value typing, and a query language similar to SQL. Dynamically sorted tables are not appropriate for OLAP tasks because the data is stored by row. The YT query language is still under development, so we can't yet rely on this functionality. YT developers are considering using dynamically sorted tables in OLTP and Key-Value scenarios.\n\n\nPerformance\n\n\nAccording to internal testing results, ClickHouse shows the best performance for comparable operating scenarios among systems of its class that were available for testing. This includes the highest throughput for long queries, and the lowest latency on short queries. Testing results are shown on a separate page.\n\n\nThroughput for a single large query\n\n\nThroughput can be measured in rows per second or in megabytes per second. If the data is placed in the page cache, a query that is not too complex is processed on modern hardware at a speed of approximately 2-10 GB/s of uncompressed data on a single server (for the simplest cases, the speed may reach 30 GB/s). If data is not placed in the page cache, the speed depends on the disk subsystem and the data compression rate. For example, if the disk subsystem allows reading data at 400 MB/s, and the data compression rate is 3, the speed will be around 1.2 GB/s. To get the speed in rows per second, divide the speed in bytes per second by the total size of the columns used in the query. For example, if 10 bytes of columns are extracted, the speed will be around 100-200 million rows per second.\n\n\nThe processing speed increases almost linearly for distributed processing, but only if the number of rows resulting from aggregation or sorting is not too large.\n\n\nLatency when processing short queries\n\n\nIf a query uses a primary key and does not select too many rows to process (hundreds of thousands), and does not use too many columns, we can expect less than 50 milliseconds of latency (single digits of milliseconds in the best case) if data is placed in the page cache. Otherwise, latency is calculated from the number of seeks. If you use rotating drives, for a system that is not overloaded, the latency is calculated by this formula: seek time (10 ms) * number of columns queried * number of data parts.\n\n\nThroughput when processing a large quantity of short queries\n\n\nUnder the same conditions, ClickHouse can handle several hundred queries per second on a single server (up to several thousand in the best case). Since this scenario is not typical for analytical DBMSs, we recommend expecting a maximum of 100 queries per second.\n\n\nPerformance when inserting data\n\n\nWe recommend inserting data in packets of at least 1000 rows, or no more than a single request per second. When inserting to a MergeTree table from a tab-separated dump, the insertion speed will be from 50 to 200 MB/s. If the inserted rows are around 1 Kb in size, the speed will be from 50,000 to 200,000 rows per second. If the rows are small, the performance will be higher in rows per second (on Banner System data -\n 500,000 rows per second; on Graphite data -\n 1,000,000 rows per second). To improve performance, you can make multiple INSERT queries in parallel, and performance will increase linearly.\n\n\nGetting started\n\n\nSystem requirements\n\n\nThis is not a cross-platform system. It requires Linux Ubuntu Precise (12.04) or newer, with x86_64 architecture and support for the SSE 4.2 instruction set.\nTo check for SSE 4.2:\n\n\ngrep -q sse4_2 /proc/cpuinfo \n \necho\n \nSSE 4.2 supported\n \n||\n \necho\n \nSSE 4.2 not supported\n\n\n\n\n\n\nWe recommend using Ubuntu Trusty, Ubuntu Xenial, or Ubuntu Precise.\nThe terminal must use UTF-8 encoding (the default in Ubuntu).\n\n\nInstallation\n\n\nFor testing and development, the system can be installed on a single server or on a desktop computer.\n\n\nInstalling from packages for Debian/Ubuntu\n\n\nIn \n/etc/apt/sources.list\n (or in a separate \n/etc/apt/sources.list.d/clickhouse.list\n file), add the repository:\n\n\ndeb http://repo.yandex.ru/clickhouse/deb/stable/ main/\n\n\n\n\n\nIf you want to use the most recent test version, replace 'stable' with 'testing'.\n\n\nThen run:\n\n\nsudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4 \n# optional\n\nsudo apt-get update\nsudo apt-get install clickhouse-client clickhouse-server\n\n\n\n\n\nYou can also download and install packages manually from here: \nhttps://repo.yandex.ru/clickhouse/deb/stable/main/\n.\n\n\nClickHouse contains access restriction settings. They are located in the 'users.xml' file (next to 'config.xml').\nBy default, access is allowed from anywhere for the 'default' user, without a password. See 'user/default/networks'.\nFor more information, see the section \"Configuration files\".\n\n\nInstalling from sources\n\n\nTo compile, follow the instructions: build.md\n\n\nYou can compile packages and install them.\nYou can also use programs without installing packages.\n\n\nClient: dbms/src/Client/\nServer: dbms/src/Server/\n\n\n\n\n\nFor the server, create a catalog with data, such as:\n\n\n/opt/clickhouse/data/default/\n/opt/clickhouse/metadata/default/\n\n\n\n\n\n(Configurable in the server config.)\nRun 'chown' for the desired user.\n\n\nNote the path to logs in the server config (src/dbms/src/Server/config.xml).\n\n\nOther installation methods\n\n\nDocker image: \nhttps://hub.docker.com/r/yandex/clickhouse-server/\n\n\nRPM packages for CentOS or RHEL: \nhttps://github.com/Altinity/clickhouse-rpm-install\n\n\nGentoo overlay: \nhttps://github.com/kmeaw/clickhouse-overlay\n\n\nLaunch\n\n\nTo start the server (as a daemon), run:\n\n\nsudo service clickhouse-server start\n\n\n\n\n\nSee the logs in the \n/var/log/clickhouse-server/ directory.\n\n\nIf the server doesn't start, check the configurations in the file \n/etc/clickhouse-server/config.xml.\n\n\nYou can also launch the server from the console:\n\n\nclickhouse-server --config-file\n=\n/etc/clickhouse-server/config.xml\n\n\n\n\n\nIn this case, the log will be printed to the console, which is convenient during development.\nIf the configuration file is in the current directory, you don't need to specify the '--config-file' parameter. By default, it uses './config.xml'.\n\n\nYou can use the command-line client to connect to the server:\n\n\nclickhouse-client\n\n\n\n\n\nThe default parameters indicate connecting with localhost:9000 on behalf of the user 'default' without a password.\nThe client can be used for connecting to a remote server. Example:\n\n\nclickhouse-client --host\n=\nexample.com\n\n\n\n\n\nFor more information, see the section \"Command-line client\".\n\n\nChecking the system:\n\n\nmilovidov@hostname:~/work/metrica/src/dbms/src/Client$ ./clickhouse-client\nClickHouse client version \n0\n.0.18749.\nConnecting to localhost:9000.\nConnected to ClickHouse server version \n0\n.0.18749.\n\n:\n)\n SELECT \n1\n\n\nSELECT \n1\n\n\n\u250c\u25001\u2500\u2510\n\u2502 \n1\n \u2502\n\u2514\u2500\u2500\u2500\u2518\n\n\n1\n rows in set. Elapsed: \n0\n.003 sec.\n\n:\n)\n\n\n\n\n\n\nCongratulations, the system works!\n\n\nTo continue experimenting, you can try to download from the test data sets.\n\n\n\n\nOnTime\n\n\nThis performance test was created by Vadim Tkachenko. See:\n\n\n\n\nhttps://www.percona.com/blog/2009/10/02/analyzing-air-traffic-performance-with-infobright-and-monetdb/\n\n\nhttps://www.percona.com/blog/2009/10/26/air-traffic-queries-in-luciddb/\n\n\nhttps://www.percona.com/blog/2009/11/02/air-traffic-queries-in-infinidb-early-alpha/\n\n\nhttps://www.percona.com/blog/2014/04/21/using-apache-hadoop-and-impala-together-with-mysql-for-data-analysis/\n\n\nhttps://www.percona.com/blog/2016/01/07/apache-spark-with-air-ontime-performance-data/\n\n\nhttp://nickmakos.blogspot.ru/2012/08/analyzing-air-traffic-performance-with.html\n\n\n\n\nDownloading data:\n\n\nfor\n s in \n`\nseq \n1987\n \n2017\n`\n\n\ndo\n\n\nfor\n m in \n`\nseq \n1\n \n12\n`\n\n\ndo\n\nwget http://transtats.bts.gov/PREZIP/On_Time_On_Time_Performance_\n${\ns\n}\n_\n${\nm\n}\n.zip\n\ndone\n\n\ndone\n\n\n\n\n\n\n(from \nhttps://github.com/Percona-Lab/ontime-airline-performance/blob/master/download.sh\n )\n\n\nCreating a table:\n\n\nCREATE\n \nTABLE\n \n`\nontime\n`\n \n(\n\n \n`\nYear\n`\n \nUInt16\n,\n\n \n`\nQuarter\n`\n \nUInt8\n,\n\n \n`\nMonth\n`\n \nUInt8\n,\n\n \n`\nDayofMonth\n`\n \nUInt8\n,\n\n \n`\nDayOfWeek\n`\n \nUInt8\n,\n\n \n`\nFlightDate\n`\n \nDate\n,\n\n \n`\nUniqueCarrier\n`\n \nFixedString\n(\n7\n),\n\n \n`\nAirlineID\n`\n \nInt32\n,\n\n \n`\nCarrier\n`\n \nFixedString\n(\n2\n),\n\n \n`\nTailNum\n`\n \nString\n,\n\n \n`\nFlightNum\n`\n \nString\n,\n\n \n`\nOriginAirportID\n`\n \nInt32\n,\n\n \n`\nOriginAirportSeqID\n`\n \nInt32\n,\n\n \n`\nOriginCityMarketID\n`\n \nInt32\n,\n\n \n`\nOrigin\n`\n \nFixedString\n(\n5\n),\n\n \n`\nOriginCityName\n`\n \nString\n,\n\n \n`\nOriginState\n`\n \nFixedString\n(\n2\n),\n\n \n`\nOriginStateFips\n`\n \nString\n,\n\n \n`\nOriginStateName\n`\n \nString\n,\n\n \n`\nOriginWac\n`\n \nInt32\n,\n\n \n`\nDestAirportID\n`\n \nInt32\n,\n\n \n`\nDestAirportSeqID\n`\n \nInt32\n,\n\n \n`\nDestCityMarketID\n`\n \nInt32\n,\n\n \n`\nDest\n`\n \nFixedString\n(\n5\n),\n\n \n`\nDestCityName\n`\n \nString\n,\n\n \n`\nDestState\n`\n \nFixedString\n(\n2\n),\n\n \n`\nDestStateFips\n`\n \nString\n,\n\n \n`\nDestStateName\n`\n \nString\n,\n\n \n`\nDestWac\n`\n \nInt32\n,\n\n \n`\nCRSDepTime\n`\n \nInt32\n,\n\n \n`\nDepTime\n`\n \nInt32\n,\n\n \n`\nDepDelay\n`\n \nInt32\n,\n\n \n`\nDepDelayMinutes\n`\n \nInt32\n,\n\n \n`\nDepDel15\n`\n \nInt32\n,\n\n \n`\nDepartureDelayGroups\n`\n \nString\n,\n\n \n`\nDepTimeBlk\n`\n \nString\n,\n\n \n`\nTaxiOut\n`\n \nInt32\n,\n\n \n`\nWheelsOff\n`\n \nInt32\n,\n\n \n`\nWheelsOn\n`\n \nInt32\n,\n\n \n`\nTaxiIn\n`\n \nInt32\n,\n\n \n`\nCRSArrTime\n`\n \nInt32\n,\n\n \n`\nArrTime\n`\n \nInt32\n,\n\n \n`\nArrDelay\n`\n \nInt32\n,\n\n \n`\nArrDelayMinutes\n`\n \nInt32\n,\n\n \n`\nArrDel15\n`\n \nInt32\n,\n\n \n`\nArrivalDelayGroups\n`\n \nInt32\n,\n\n \n`\nArrTimeBlk\n`\n \nString\n,\n\n \n`\nCancelled\n`\n \nUInt8\n,\n\n \n`\nCancellationCode\n`\n \nFixedString\n(\n1\n),\n\n \n`\nDiverted\n`\n \nUInt8\n,\n\n \n`\nCRSElapsedTime\n`\n \nInt32\n,\n\n \n`\nActualElapsedTime\n`\n \nInt32\n,\n\n \n`\nAirTime\n`\n \nInt32\n,\n\n \n`\nFlights\n`\n \nInt32\n,\n\n \n`\nDistance\n`\n \nInt32\n,\n\n \n`\nDistanceGroup\n`\n \nUInt8\n,\n\n \n`\nCarrierDelay\n`\n \nInt32\n,\n\n \n`\nWeatherDelay\n`\n \nInt32\n,\n\n \n`\nNASDelay\n`\n \nInt32\n,\n\n \n`\nSecurityDelay\n`\n \nInt32\n,\n\n \n`\nLateAircraftDelay\n`\n \nInt32\n,\n\n \n`\nFirstDepTime\n`\n \nString\n,\n\n \n`\nTotalAddGTime\n`\n \nString\n,\n\n \n`\nLongestAddGTime\n`\n \nString\n,\n\n \n`\nDivAirportLandings\n`\n \nString\n,\n\n \n`\nDivReachedDest\n`\n \nString\n,\n\n \n`\nDivActualElapsedTime\n`\n \nString\n,\n\n \n`\nDivArrDelay\n`\n \nString\n,\n\n \n`\nDivDistance\n`\n \nString\n,\n\n \n`\nDiv1Airport\n`\n \nString\n,\n\n \n`\nDiv1AirportID\n`\n \nInt32\n,\n\n \n`\nDiv1AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv1WheelsOn\n`\n \nString\n,\n\n \n`\nDiv1TotalGTime\n`\n \nString\n,\n\n \n`\nDiv1LongestGTime\n`\n \nString\n,\n\n \n`\nDiv1WheelsOff\n`\n \nString\n,\n\n \n`\nDiv1TailNum\n`\n \nString\n,\n\n \n`\nDiv2Airport\n`\n \nString\n,\n\n \n`\nDiv2AirportID\n`\n \nInt32\n,\n\n \n`\nDiv2AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv2WheelsOn\n`\n \nString\n,\n\n \n`\nDiv2TotalGTime\n`\n \nString\n,\n\n \n`\nDiv2LongestGTime\n`\n \nString\n,\n\n \n`\nDiv2WheelsOff\n`\n \nString\n,\n\n \n`\nDiv2TailNum\n`\n \nString\n,\n\n \n`\nDiv3Airport\n`\n \nString\n,\n\n \n`\nDiv3AirportID\n`\n \nInt32\n,\n\n \n`\nDiv3AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv3WheelsOn\n`\n \nString\n,\n\n \n`\nDiv3TotalGTime\n`\n \nString\n,\n\n \n`\nDiv3LongestGTime\n`\n \nString\n,\n\n \n`\nDiv3WheelsOff\n`\n \nString\n,\n\n \n`\nDiv3TailNum\n`\n \nString\n,\n\n \n`\nDiv4Airport\n`\n \nString\n,\n\n \n`\nDiv4AirportID\n`\n \nInt32\n,\n\n \n`\nDiv4AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv4WheelsOn\n`\n \nString\n,\n\n \n`\nDiv4TotalGTime\n`\n \nString\n,\n\n \n`\nDiv4LongestGTime\n`\n \nString\n,\n\n \n`\nDiv4WheelsOff\n`\n \nString\n,\n\n \n`\nDiv4TailNum\n`\n \nString\n,\n\n \n`\nDiv5Airport\n`\n \nString\n,\n\n \n`\nDiv5AirportID\n`\n \nInt32\n,\n\n \n`\nDiv5AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv5WheelsOn\n`\n \nString\n,\n\n \n`\nDiv5TotalGTime\n`\n \nString\n,\n\n \n`\nDiv5LongestGTime\n`\n \nString\n,\n\n \n`\nDiv5WheelsOff\n`\n \nString\n,\n\n \n`\nDiv5TailNum\n`\n \nString\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nFlightDate\n,\n \n(\nYear\n,\n \nFlightDate\n),\n \n8192\n)\n\n\n\n\n\n\nLoading data:\n\n\nfor\n i in *.zip\n;\n \ndo\n \necho\n \n$i\n;\n unzip -cq \n$i\n \n*.csv\n \n|\n sed \ns/\\.00//g\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO ontime FORMAT CSVWithNames\n;\n \ndone\n\n\n\n\n\n\nQueries:\n\n\nQ0.\n\n\nselect\n \navg\n(\nc1\n)\n \nfrom\n \n(\nselect\n \nYear\n,\n \nMonth\n,\n \ncount\n(\n*\n)\n \nas\n \nc1\n \nfrom\n \nontime\n \ngroup\n \nby\n \nYear\n,\n \nMonth\n);\n\n\n\n\n\n\nQ1. The number of flights per day from the year 2000 to 2008\n\n\nSELECT\n \nDayOfWeek\n,\n \ncount\n(\n*\n)\n \nAS\n \nc\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nDayOfWeek\n \nORDER\n \nBY\n \nc\n \nDESC\n;\n\n\n\n\n\n\nQ2. The number of flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008\n\n\nSELECT\n \nDayOfWeek\n,\n \ncount\n(\n*\n)\n \nAS\n \nc\n \nFROM\n \nontime\n \nWHERE\n \nDepDelay\n10\n \nAND\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nDayOfWeek\n \nORDER\n \nBY\n \nc\n \nDESC\n\n\n\n\n\n\nQ3. The number of delays by airport for 2000-2008\n\n\nSELECT\n \nOrigin\n,\n \ncount\n(\n*\n)\n \nAS\n \nc\n \nFROM\n \nontime\n \nWHERE\n \nDepDelay\n10\n \nAND\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nOrigin\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n10\n\n\n\n\n\n\nQ4. The number of delays by carrier for 2007\n\n\nSELECT\n \nCarrier\n,\n \ncount\n(\n*\n)\n \nFROM\n \nontime\n \nWHERE\n \nDepDelay\n10\n \nAND\n \nYear\n \n=\n \n2007\n \nGROUP\n \nBY\n \nCarrier\n \nORDER\n \nBY\n \ncount\n(\n*\n)\n \nDESC\n\n\n\n\n\n\nQ5. The percentage of delays by carrier for 2007\n\n\nSELECT\n \nCarrier\n,\n \nc\n,\n \nc2\n,\n \nc\n*\n1000\n/\nc2\n \nas\n \nc3\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc\n\n \nFROM\n \nontime\n\n \nWHERE\n \nDepDelay\n10\n\n \nAND\n \nYear\n=\n2007\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n\n\nANY\n \nINNER\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc2\n\n \nFROM\n \nontime\n\n \nWHERE\n \nYear\n=\n2007\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n \nUSING\n \nCarrier\n\n\nORDER\n \nBY\n \nc3\n \nDESC\n;\n\n\n\n\n\n\nBetter version of the same query:\n\n\nSELECT\n \nCarrier\n,\n \navg\n(\nDepDelay\n \n \n10\n)\n \n*\n \n1000\n \nAS\n \nc3\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2007\n \nGROUP\n \nBY\n \nCarrier\n \nORDER\n \nBY\n \nCarrier\n\n\n\n\n\n\nQ6. The previous request for a broader range of years, 2000-2008\n\n\nSELECT\n \nCarrier\n,\n \nc\n,\n \nc2\n,\n \nc\n*\n1000\n/\nc2\n \nas\n \nc3\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc\n\n \nFROM\n \nontime\n\n \nWHERE\n \nDepDelay\n10\n\n \nAND\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n\n\nANY\n \nINNER\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc2\n\n \nFROM\n \nontime\n\n \nWHERE\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n \nUSING\n \nCarrier\n\n\nORDER\n \nBY\n \nc3\n \nDESC\n;\n\n\n\n\n\n\nBetter version of the same query:\n\n\nSELECT\n \nCarrier\n,\n \navg\n(\nDepDelay\n \n \n10\n)\n \n*\n \n1000\n \nAS\n \nc3\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nCarrier\n \nORDER\n \nBY\n \nCarrier\n\n\n\n\n\n\nQ7. Percentage of flights delayed for more than 10 minutes, by year\n\n\nSELECT\n \nYear\n,\n \nc1\n/\nc2\n\n\nFROM\n\n\n(\n\n \nselect\n\n \nYear\n,\n\n \ncount\n(\n*\n)\n*\n1000\n \nas\n \nc1\n\n \nfrom\n \nontime\n\n \nWHERE\n \nDepDelay\n10\n\n \nGROUP\n \nBY\n \nYear\n\n\n)\n\n\nANY\n \nINNER\n \nJOIN\n\n\n(\n\n \nselect\n\n \nYear\n,\n\n \ncount\n(\n*\n)\n \nas\n \nc2\n\n \nfrom\n \nontime\n\n \nGROUP\n \nBY\n \nYear\n\n\n)\n \nUSING\n \n(\nYear\n)\n\n\nORDER\n \nBY\n \nYear\n\n\n\n\n\n\nBetter version of the same query:\n\n\nSELECT\n \nYear\n,\n \navg\n(\nDepDelay\n \n \n10\n)\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nYear\n \nORDER\n \nBY\n \nYear\n\n\n\n\n\n\nQ8. The most popular destinations by the number of directly connected cities for various year ranges\n\n\nSELECT\n \nDestCityName\n,\n \nuniqExact\n(\nOriginCityName\n)\n \nAS\n \nu\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2000\n \nand\n \nYear\n \n=\n \n2010\n \nGROUP\n \nBY\n \nDestCityName\n \nORDER\n \nBY\n \nu\n \nDESC\n \nLIMIT\n \n10\n;\n\n\n\n\n\n\nQ9.\n\n\nselect\n \nYear\n,\n \ncount\n(\n*\n)\n \nas\n \nc1\n \nfrom\n \nontime\n \ngroup\n \nby\n \nYear\n;\n\n\n\n\n\n\nQ10.\n\n\nselect\n\n \nmin\n(\nYear\n),\n \nmax\n(\nYear\n),\n \nCarrier\n,\n \ncount\n(\n*\n)\n \nas\n \ncnt\n,\n\n \nsum\n(\nArrDelayMinutes\n30\n)\n \nas\n \nflights_delayed\n,\n\n \nround\n(\nsum\n(\nArrDelayMinutes\n30\n)\n/\ncount\n(\n*\n),\n2\n)\n \nas\n \nrate\n\n\nFROM\n \nontime\n\n\nWHERE\n\n \nDayOfWeek\n \nnot\n \nin\n \n(\n6\n,\n7\n)\n \nand\n \nOriginState\n \nnot\n \nin\n \n(\nAK\n,\n \nHI\n,\n \nPR\n,\n \nVI\n)\n\n \nand\n \nDestState\n \nnot\n \nin\n \n(\nAK\n,\n \nHI\n,\n \nPR\n,\n \nVI\n)\n\n \nand\n \nFlightDate\n \n \n2010-01-01\n\n\nGROUP\n \nby\n \nCarrier\n\n\nHAVING\n \ncnt\n \n \n100000\n \nand\n \nmax\n(\nYear\n)\n \n \n1990\n\n\nORDER\n \nby\n \nrate\n \nDESC\n\n\nLIMIT\n \n1000\n;\n\n\n\n\n\n\nBonus:\n\n\nSELECT\n \navg\n(\ncnt\n)\n \nFROM\n \n(\nSELECT\n \nYear\n,\nMonth\n,\ncount\n(\n*\n)\n \nAS\n \ncnt\n \nFROM\n \nontime\n \nWHERE\n \nDepDel15\n=\n1\n \nGROUP\n \nBY\n \nYear\n,\nMonth\n)\n\n\n\nselect\n \navg\n(\nc1\n)\n \nfrom\n \n(\nselect\n \nYear\n,\nMonth\n,\ncount\n(\n*\n)\n \nas\n \nc1\n \nfrom\n \nontime\n \ngroup\n \nby\n \nYear\n,\nMonth\n)\n\n\n\nSELECT\n \nDestCityName\n,\n \nuniqExact\n(\nOriginCityName\n)\n \nAS\n \nu\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nDestCityName\n \nORDER\n \nBY\n \nu\n \nDESC\n \nLIMIT\n \n10\n;\n\n\n\nSELECT\n \nOriginCityName\n,\n \nDestCityName\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nOriginCityName\n,\n \nDestCityName\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n10\n;\n\n\n\nSELECT\n \nOriginCityName\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nOriginCityName\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n10\n;\n\n\n\n\n\n\nNew York Taxi data\n\n\nHow to import the raw data\n\n\nSee \nhttps://github.com/toddwschneider/nyc-taxi-data\n and \nhttp://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html\n for the description of the dataset and instructions for downloading.\n\n\nDownloading will result in about 227 GB of uncompressed data in CSV files. The download takes about an hour over a 1 Gbit connection (parallel downloading from s3.amazonaws.com recovers at least half of a 1 Gbit channel).\nSome of the files might not download fully. Check the file sizes and re-download any that seem doubtful.\n\n\nSome of the files might contain invalid rows. You can fix them as follows:\n\n\nsed -E \n/(.*,){18,}/d\n data/yellow_tripdata_2010-02.csv \n data/yellow_tripdata_2010-02.csv_\nsed -E \n/(.*,){18,}/d\n data/yellow_tripdata_2010-03.csv \n data/yellow_tripdata_2010-03.csv_\nmv data/yellow_tripdata_2010-02.csv_ data/yellow_tripdata_2010-02.csv\nmv data/yellow_tripdata_2010-03.csv_ data/yellow_tripdata_2010-03.csv\n\n\n\n\n\nThen the data must be pre-processed in PostgreSQL. This will create selections of points in the polygons (to match points on the map with the boroughs of New York City) and combine all the data into a single denormalized flat table by using a JOIN. To do this, you will need to install PostgreSQL with PostGIS support.\n\n\nBe careful when running \ninitialize_database.sh\n and manually re-check that all the tables were created correctly.\n\n\nIt takes about 20-30 minutes to process each month's worth of data in PostgreSQL, for a total of about 48 hours.\n\n\nYou can check the number of downloaded rows as follows:\n\n\ntime psql nyc-taxi-data -c \nSELECT count(*) FROM trips;\n\n### count\n 1298979494\n(1 row)\n\nreal 7m9.164s\n\n\n\n\n\n(This is slightly more than 1.1 billion rows reported by Mark Litwintschik in a series of blog posts.)\n\n\nThe data in PostgreSQL uses 370 GB of space.\n\n\nExporting the data from PostgreSQL:\n\n\nCOPY\n\n\n(\n\n \nSELECT\n \ntrips\n.\nid\n,\n\n \ntrips\n.\nvendor_id\n,\n\n \ntrips\n.\npickup_datetime\n,\n\n \ntrips\n.\ndropoff_datetime\n,\n\n \ntrips\n.\nstore_and_fwd_flag\n,\n\n \ntrips\n.\nrate_code_id\n,\n\n \ntrips\n.\npickup_longitude\n,\n\n \ntrips\n.\npickup_latitude\n,\n\n \ntrips\n.\ndropoff_longitude\n,\n\n \ntrips\n.\ndropoff_latitude\n,\n\n \ntrips\n.\npassenger_count\n,\n\n \ntrips\n.\ntrip_distance\n,\n\n \ntrips\n.\nfare_amount\n,\n\n \ntrips\n.\nextra\n,\n\n \ntrips\n.\nmta_tax\n,\n\n \ntrips\n.\ntip_amount\n,\n\n \ntrips\n.\ntolls_amount\n,\n\n \ntrips\n.\nehail_fee\n,\n\n \ntrips\n.\nimprovement_surcharge\n,\n\n \ntrips\n.\ntotal_amount\n,\n\n \ntrips\n.\npayment_type\n,\n\n \ntrips\n.\ntrip_type\n,\n\n \ntrips\n.\npickup\n,\n\n \ntrips\n.\ndropoff\n,\n\n\n \ncab_types\n.\ntype\n \ncab_type\n,\n\n\n \nweather\n.\nprecipitation_tenths_of_mm\n \nrain\n,\n\n \nweather\n.\nsnow_depth_mm\n,\n\n \nweather\n.\nsnowfall_mm\n,\n\n \nweather\n.\nmax_temperature_tenths_degrees_celsius\n \nmax_temp\n,\n\n \nweather\n.\nmin_temperature_tenths_degrees_celsius\n \nmin_temp\n,\n\n \nweather\n.\naverage_wind_speed_tenths_of_meters_per_second\n \nwind\n,\n\n\n \npick_up\n.\ngid\n \npickup_nyct2010_gid\n,\n\n \npick_up\n.\nctlabel\n \npickup_ctlabel\n,\n\n \npick_up\n.\nborocode\n \npickup_borocode\n,\n\n \npick_up\n.\nboroname\n \npickup_boroname\n,\n\n \npick_up\n.\nct2010\n \npickup_ct2010\n,\n\n \npick_up\n.\nboroct2010\n \npickup_boroct2010\n,\n\n \npick_up\n.\ncdeligibil\n \npickup_cdeligibil\n,\n\n \npick_up\n.\nntacode\n \npickup_ntacode\n,\n\n \npick_up\n.\nntaname\n \npickup_ntaname\n,\n\n \npick_up\n.\npuma\n \npickup_puma\n,\n\n\n \ndrop_off\n.\ngid\n \ndropoff_nyct2010_gid\n,\n\n \ndrop_off\n.\nctlabel\n \ndropoff_ctlabel\n,\n\n \ndrop_off\n.\nborocode\n \ndropoff_borocode\n,\n\n \ndrop_off\n.\nboroname\n \ndropoff_boroname\n,\n\n \ndrop_off\n.\nct2010\n \ndropoff_ct2010\n,\n\n \ndrop_off\n.\nboroct2010\n \ndropoff_boroct2010\n,\n\n \ndrop_off\n.\ncdeligibil\n \ndropoff_cdeligibil\n,\n\n \ndrop_off\n.\nntacode\n \ndropoff_ntacode\n,\n\n \ndrop_off\n.\nntaname\n \ndropoff_ntaname\n,\n\n \ndrop_off\n.\npuma\n \ndropoff_puma\n\n \nFROM\n \ntrips\n\n \nLEFT\n \nJOIN\n \ncab_types\n\n \nON\n \ntrips\n.\ncab_type_id\n \n=\n \ncab_types\n.\nid\n\n \nLEFT\n \nJOIN\n \ncentral_park_weather_observations_raw\n \nweather\n\n \nON\n \nweather\n.\ndate\n \n=\n \ntrips\n.\npickup_datetime\n::\ndate\n\n \nLEFT\n \nJOIN\n \nnyct2010\n \npick_up\n\n \nON\n \npick_up\n.\ngid\n \n=\n \ntrips\n.\npickup_nyct2010_gid\n\n \nLEFT\n \nJOIN\n \nnyct2010\n \ndrop_off\n\n \nON\n \ndrop_off\n.\ngid\n \n=\n \ntrips\n.\ndropoff_nyct2010_gid\n\n\n)\n \nTO\n \n/opt/milovidov/nyc-taxi-data/trips.tsv\n;\n\n\n\n\n\n\nThe data snapshot is created at a speed of about 50 MB per second. While creating the snapshot, PostgreSQL reads from the disk at a speed of about 28 MB per second.\nThis takes about 5 hours. The resulting TSV file is 590612904969 bytes.\n\n\nCreate a temporary table in ClickHouse:\n\n\nCREATE\n \nTABLE\n \ntrips\n\n\n(\n\n\ntrip_id\n \nUInt32\n,\n\n\nvendor_id\n \nString\n,\n\n\npickup_datetime\n \nDateTime\n,\n\n\ndropoff_datetime\n \nNullable\n(\nDateTime\n),\n\n\nstore_and_fwd_flag\n \nNullable\n(\nFixedString\n(\n1\n)),\n\n\nrate_code_id\n \nNullable\n(\nUInt8\n),\n\n\npickup_longitude\n \nNullable\n(\nFloat64\n),\n\n\npickup_latitude\n \nNullable\n(\nFloat64\n),\n\n\ndropoff_longitude\n \nNullable\n(\nFloat64\n),\n\n\ndropoff_latitude\n \nNullable\n(\nFloat64\n),\n\n\npassenger_count\n \nNullable\n(\nUInt8\n),\n\n\ntrip_distance\n \nNullable\n(\nFloat64\n),\n\n\nfare_amount\n \nNullable\n(\nFloat32\n),\n\n\nextra\n \nNullable\n(\nFloat32\n),\n\n\nmta_tax\n \nNullable\n(\nFloat32\n),\n\n\ntip_amount\n \nNullable\n(\nFloat32\n),\n\n\ntolls_amount\n \nNullable\n(\nFloat32\n),\n\n\nehail_fee\n \nNullable\n(\nFloat32\n),\n\n\nimprovement_surcharge\n \nNullable\n(\nFloat32\n),\n\n\ntotal_amount\n \nNullable\n(\nFloat32\n),\n\n\npayment_type\n \nNullable\n(\nString\n),\n\n\ntrip_type\n \nNullable\n(\nUInt8\n),\n\n\npickup\n \nNullable\n(\nString\n),\n\n\ndropoff\n \nNullable\n(\nString\n),\n\n\ncab_type\n \nNullable\n(\nString\n),\n\n\nprecipitation\n \nNullable\n(\nUInt8\n),\n\n\nsnow_depth\n \nNullable\n(\nUInt8\n),\n\n\nsnowfall\n \nNullable\n(\nUInt8\n),\n\n\nmax_temperature\n \nNullable\n(\nUInt8\n),\n\n\nmin_temperature\n \nNullable\n(\nUInt8\n),\n\n\naverage_wind_speed\n \nNullable\n(\nUInt8\n),\n\n\npickup_nyct2010_gid\n \nNullable\n(\nUInt8\n),\n\n\npickup_ctlabel\n \nNullable\n(\nString\n),\n\n\npickup_borocode\n \nNullable\n(\nUInt8\n),\n\n\npickup_boroname\n \nNullable\n(\nString\n),\n\n\npickup_ct2010\n \nNullable\n(\nString\n),\n\n\npickup_boroct2010\n \nNullable\n(\nString\n),\n\n\npickup_cdeligibil\n \nNullable\n(\nFixedString\n(\n1\n)),\n\n\npickup_ntacode\n \nNullable\n(\nString\n),\n\n\npickup_ntaname\n \nNullable\n(\nString\n),\n\n\npickup_puma\n \nNullable\n(\nString\n),\n\n\ndropoff_nyct2010_gid\n \nNullable\n(\nUInt8\n),\n\n\ndropoff_ctlabel\n \nNullable\n(\nString\n),\n\n\ndropoff_borocode\n \nNullable\n(\nUInt8\n),\n\n\ndropoff_boroname\n \nNullable\n(\nString\n),\n\n\ndropoff_ct2010\n \nNullable\n(\nString\n),\n\n\ndropoff_boroct2010\n \nNullable\n(\nString\n),\n\n\ndropoff_cdeligibil\n \nNullable\n(\nString\n),\n\n\ndropoff_ntacode\n \nNullable\n(\nString\n),\n\n\ndropoff_ntaname\n \nNullable\n(\nString\n),\n\n\ndropoff_puma\n \nNullable\n(\nString\n)\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\n\n\n\nIt is needed for converting fields to more correct data types and, if possible, to eliminate NULLs.\n\n\ntime clickhouse-client --query=\nINSERT INTO trips FORMAT TabSeparated\n \n trips.tsv\n\nreal 75m56.214s\n\n\n\n\n\nData is read at a speed of 112-140 Mb/second.\nLoading data into a Log type table in one stream took 76 minutes.\nThe data in this table uses 142 GB.\n\n\n(Importing data directly from Postgres is also possible using \nCOPY ... TO PROGRAM\n.)\n\n\nUnfortunately, all the fields associated with the weather (precipitation...average_wind_speed) were filled with NULL. Because of this, we will remove them from the final data set.\n\n\nTo start, we'll create a table on a single server. Later we will make the table distributed.\n\n\nCreate and populate a summary table:\n\n\nCREATE TABLE trips_mergetree\nENGINE = MergeTree(pickup_date, pickup_datetime, 8192)\nAS SELECT\n\ntrip_id,\nCAST(vendor_id AS Enum8(\n1\n = 1, \n2\n = 2, \nCMT\n = 3, \nVTS\n = 4, \nDDS\n = 5, \nB02512\n = 10, \nB02598\n = 11, \nB02617\n = 12, \nB02682\n = 13, \nB02764\n = 14)) AS vendor_id,\ntoDate(pickup_datetime) AS pickup_date,\nifNull(pickup_datetime, toDateTime(0)) AS pickup_datetime,\ntoDate(dropoff_datetime) AS dropoff_date,\nifNull(dropoff_datetime, toDateTime(0)) AS dropoff_datetime,\nassumeNotNull(store_and_fwd_flag) IN (\nY\n, \n1\n, \n2\n) AS store_and_fwd_flag,\nassumeNotNull(rate_code_id) AS rate_code_id,\nassumeNotNull(pickup_longitude) AS pickup_longitude,\nassumeNotNull(pickup_latitude) AS pickup_latitude,\nassumeNotNull(dropoff_longitude) AS dropoff_longitude,\nassumeNotNull(dropoff_latitude) AS dropoff_latitude,\nassumeNotNull(passenger_count) AS passenger_count,\nassumeNotNull(trip_distance) AS trip_distance,\nassumeNotNull(fare_amount) AS fare_amount,\nassumeNotNull(extra) AS extra,\nassumeNotNull(mta_tax) AS mta_tax,\nassumeNotNull(tip_amount) AS tip_amount,\nassumeNotNull(tolls_amount) AS tolls_amount,\nassumeNotNull(ehail_fee) AS ehail_fee,\nassumeNotNull(improvement_surcharge) AS improvement_surcharge,\nassumeNotNull(total_amount) AS total_amount,\nCAST((assumeNotNull(payment_type) AS pt) IN (\nCSH\n, \nCASH\n, \nCash\n, \nCAS\n, \nCas\n, \n1\n) ? \nCSH\n : (pt IN (\nCRD\n, \nCredit\n, \nCre\n, \nCRE\n, \nCREDIT\n, \n2\n) ? \nCRE\n : (pt IN (\nNOC\n, \nNo Charge\n, \nNo\n, \n3\n) ? \nNOC\n : (pt IN (\nDIS\n, \nDispute\n, \nDis\n, \n4\n) ? \nDIS\n : \nUNK\n))) AS Enum8(\nCSH\n = 1, \nCRE\n = 2, \nUNK\n = 0, \nNOC\n = 3, \nDIS\n = 4)) AS payment_type_,\nassumeNotNull(trip_type) AS trip_type,\nifNull(toFixedString(unhex(pickup), 25), toFixedString(\n, 25)) AS pickup,\nifNull(toFixedString(unhex(dropoff), 25), toFixedString(\n, 25)) AS dropoff,\nCAST(assumeNotNull(cab_type) AS Enum8(\nyellow\n = 1, \ngreen\n = 2, \nuber\n = 3)) AS cab_type,\n\nassumeNotNull(pickup_nyct2010_gid) AS pickup_nyct2010_gid,\ntoFloat32(ifNull(pickup_ctlabel, \n0\n)) AS pickup_ctlabel,\nassumeNotNull(pickup_borocode) AS pickup_borocode,\nCAST(assumeNotNull(pickup_boroname) AS Enum8(\nManhattan\n = 1, \nQueens\n = 4, \nBrooklyn\n = 3, \n = 0, \nBronx\n = 2, \nStaten Island\n = 5)) AS pickup_boroname,\ntoFixedString(ifNull(pickup_ct2010, \n000000\n), 6) AS pickup_ct2010,\ntoFixedString(ifNull(pickup_boroct2010, \n0000000\n), 7) AS pickup_boroct2010,\nCAST(assumeNotNull(ifNull(pickup_cdeligibil, \n \n)) AS Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2)) AS pickup_cdeligibil,\ntoFixedString(ifNull(pickup_ntacode, \n0000\n), 4) AS pickup_ntacode,\n\nCAST(assumeNotNull(pickup_ntaname) AS Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195)) AS pickup_ntaname,\n\ntoUInt16(ifNull(pickup_puma, \n0\n)) AS pickup_puma,\n\nassumeNotNull(dropoff_nyct2010_gid) AS dropoff_nyct2010_gid,\ntoFloat32(ifNull(dropoff_ctlabel, \n0\n)) AS dropoff_ctlabel,\nassumeNotNull(dropoff_borocode) AS dropoff_borocode,\nCAST(assumeNotNull(dropoff_boroname) AS Enum8(\nManhattan\n = 1, \nQueens\n = 4, \nBrooklyn\n = 3, \n = 0, \nBronx\n = 2, \nStaten Island\n = 5)) AS dropoff_boroname,\ntoFixedString(ifNull(dropoff_ct2010, \n000000\n), 6) AS dropoff_ct2010,\ntoFixedString(ifNull(dropoff_boroct2010, \n0000000\n), 7) AS dropoff_boroct2010,\nCAST(assumeNotNull(ifNull(dropoff_cdeligibil, \n \n)) AS Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2)) AS dropoff_cdeligibil,\ntoFixedString(ifNull(dropoff_ntacode, \n0000\n), 4) AS dropoff_ntacode,\n\nCAST(assumeNotNull(dropoff_ntaname) AS Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195)) AS dropoff_ntaname,\n\ntoUInt16(ifNull(dropoff_puma, \n0\n)) AS dropoff_puma\n\nFROM trips\n\n\n\n\n\nThis takes 3030 seconds at a speed of about 428,000 rows per second.\nTo load it faster, you can create the table with the \nLog\n engine instead of \nMergeTree\n. In this case, the download works faster than 200 seconds.\n\n\nThe table uses 126 GB of disk space.\n\n\n:) SELECT formatReadableSize(sum(bytes)) FROM system.parts WHERE table = \ntrips_mergetree\n AND active\n\nSELECT formatReadableSize(sum(bytes))\nFROM system.parts\nWHERE (table = \ntrips_mergetree\n) AND active\n\n\u250c\u2500formatReadableSize(sum(bytes))\u2500\u2510\n\u2502 126.18 GiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nAmong other things, you can run the OPTIMIZE query on MergeTree. But it's not required, since everything will be fine without it.\n\n\nResults on single server\n\n\nQ1:\n\n\nSELECT\n \ncab_type\n,\n \ncount\n(\n*\n)\n \nFROM\n \ntrips_mergetree\n \nGROUP\n \nBY\n \ncab_type\n\n\n\n\n\n\n0.490 seconds.\n\n\nQ2:\n\n\nSELECT\n \npassenger_count\n,\n \navg\n(\ntotal_amount\n)\n \nFROM\n \ntrips_mergetree\n \nGROUP\n \nBY\n \npassenger_count\n\n\n\n\n\n\n1.224 seconds.\n\n\nQ3:\n\n\nSELECT\n \npassenger_count\n,\n \ntoYear\n(\npickup_date\n)\n \nAS\n \nyear\n,\n \ncount\n(\n*\n)\n \nFROM\n \ntrips_mergetree\n \nGROUP\n \nBY\n \npassenger_count\n,\n \nyear\n\n\n\n\n\n\n2.104 seconds.\n\n\nQ4:\n\n\nSELECT\n \npassenger_count\n,\n \ntoYear\n(\npickup_date\n)\n \nAS\n \nyear\n,\n \nround\n(\ntrip_distance\n)\n \nAS\n \ndistance\n,\n \ncount\n(\n*\n)\n\n\nFROM\n \ntrips_mergetree\n\n\nGROUP\n \nBY\n \npassenger_count\n,\n \nyear\n,\n \ndistance\n\n\nORDER\n \nBY\n \nyear\n,\n \ncount\n(\n*\n)\n \nDESC\n\n\n\n\n\n\n3.593 seconds.\n\n\nThe following server was used:\n\n\nTwo Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical kernels total,\n128 GiB RAM,\n8x6 TB HD on hardware RAID-5\n\n\nExecution time is the best of three runsBut starting from the second run, queries read data from the file system cache. No further caching occurs: the data is read out and processed in each run.\n\n\nCreating a table on three servers:\n\n\nOn each server:\n\n\nCREATE TABLE default.trips_mergetree_third ( trip_id UInt32, vendor_id Enum8(\n1\n = 1, \n2\n = 2, \nCMT\n = 3, \nVTS\n = 4, \nDDS\n = 5, \nB02512\n = 10, \nB02598\n = 11, \nB02617\n = 12, \nB02682\n = 13, \nB02764\n = 14), pickup_date Date, pickup_datetime DateTime, dropoff_date Date, dropoff_datetime DateTime, store_and_fwd_flag UInt8, rate_code_id UInt8, pickup_longitude Float64, pickup_latitude Float64, dropoff_longitude Float64, dropoff_latitude Float64, passenger_count UInt8, trip_distance Float64, fare_amount Float32, extra Float32, mta_tax Float32, tip_amount Float32, tolls_amount Float32, ehail_fee Float32, improvement_surcharge Float32, total_amount Float32, payment_type_ Enum8(\nUNK\n = 0, \nCSH\n = 1, \nCRE\n = 2, \nNOC\n = 3, \nDIS\n = 4), trip_type UInt8, pickup FixedString(25), dropoff FixedString(25), cab_type Enum8(\nyellow\n = 1, \ngreen\n = 2, \nuber\n = 3), pickup_nyct2010_gid UInt8, pickup_ctlabel Float32, pickup_borocode UInt8, pickup_boroname Enum8(\n = 0, \nManhattan\n = 1, \nBronx\n = 2, \nBrooklyn\n = 3, \nQueens\n = 4, \nStaten Island\n = 5), pickup_ct2010 FixedString(6), pickup_boroct2010 FixedString(7), pickup_cdeligibil Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2), pickup_ntacode FixedString(4), pickup_ntaname Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195), pickup_puma UInt16, dropoff_nyct2010_gid UInt8, dropoff_ctlabel Float32, dropoff_borocode UInt8, dropoff_boroname Enum8(\n = 0, \nManhattan\n = 1, \nBronx\n = 2, \nBrooklyn\n = 3, \nQueens\n = 4, \nStaten Island\n = 5), dropoff_ct2010 FixedString(6), dropoff_boroct2010 FixedString(7), dropoff_cdeligibil Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2), dropoff_ntacode FixedString(4), dropoff_ntaname Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195), dropoff_puma UInt16) ENGINE = MergeTree(pickup_date, pickup_datetime, 8192)\n\n\n\n\n\nOn the source server:\n\n\nCREATE\n \nTABLE\n \ntrips_mergetree_x3\n \nAS\n \ntrips_mergetree_third\n \nENGINE\n \n=\n \nDistributed\n(\nperftest\n,\n \ndefault\n,\n \ntrips_mergetree_third\n,\n \nrand\n())\n\n\n\n\n\n\nThe following query redistributes data:\n\n\nINSERT\n \nINTO\n \ntrips_mergetree_x3\n \nSELECT\n \n*\n \nFROM\n \ntrips_mergetree\n\n\n\n\n\n\nThis takes 2454 seconds.\n\n\nOn three servers:\n\n\nQ1: 0.212 seconds.\nQ2: 0.438 seconds.\nQ3: 0.733 seconds.\nQ4: 1.241 seconds.\n\n\nNo surprises here, since the queries are scaled linearly.\n\n\nWe also have results from a cluster of 140 servers:\n\n\nQ1: 0.028 sec.\nQ2: 0.043 sec.\nQ3: 0.051 sec.\nQ4: 0.072 sec.\n\n\nIn this case, the query processing time is determined above all by network latency.\nWe ran queries using a client located in a Yandex datacenter in Finland on a cluster in Russia, which added about 20 ms of latency.\n\n\nSummary\n\n\nnodes Q1 Q2 Q3 Q4\n 1 0.490 1.224 2.104 3.593\n 3 0.212 0.438 0.733 1.241\n140 0.028 0.043 0.051 0.072\n\n\n\n\n\nAMPLab Big Data Benchmark\n\n\nSee \nhttps://amplab.cs.berkeley.edu/benchmark/\n\n\nSign up for a free account at \nhttps://aws.amazon.com\n. You will need a credit card, email and phone number.Get a new access key at \nhttps://console.aws.amazon.com/iam/home?nc2=h_m_sc#security_credential\n\n\nRun the following in the console:\n\n\nsudo apt-get install s3cmd\nmkdir tiny\n;\n \ncd\n tiny\n;\n\ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/tiny/ .\n\ncd\n ..\nmkdir 1node\n;\n \ncd\n 1node\n;\n\ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/1node/ .\n\ncd\n ..\nmkdir 5nodes\n;\n \ncd\n 5nodes\n;\n\ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/5nodes/ .\n\ncd\n ..\n\n\n\n\n\nRun the following ClickHouse queries:\n\n\nCREATE\n \nTABLE\n \nrankings_tiny\n\n\n(\n\n \npageURL\n \nString\n,\n\n \npageRank\n \nUInt32\n,\n\n \navgDuration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\nCREATE\n \nTABLE\n \nuservisits_tiny\n\n\n(\n\n \nsourceIP\n \nString\n,\n\n \ndestinationURL\n \nString\n,\n\n \nvisitDate\n \nDate\n,\n\n \nadRevenue\n \nFloat32\n,\n\n \nUserAgent\n \nString\n,\n\n \ncCode\n \nFixedString\n(\n3\n),\n\n \nlCode\n \nFixedString\n(\n6\n),\n\n \nsearchWord\n \nString\n,\n\n \nduration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nvisitDate\n,\n \nvisitDate\n,\n \n8192\n);\n\n\n\nCREATE\n \nTABLE\n \nrankings_1node\n\n\n(\n\n \npageURL\n \nString\n,\n\n \npageRank\n \nUInt32\n,\n\n \navgDuration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\nCREATE\n \nTABLE\n \nuservisits_1node\n\n\n(\n\n \nsourceIP\n \nString\n,\n\n \ndestinationURL\n \nString\n,\n\n \nvisitDate\n \nDate\n,\n\n \nadRevenue\n \nFloat32\n,\n\n \nUserAgent\n \nString\n,\n\n \ncCode\n \nFixedString\n(\n3\n),\n\n \nlCode\n \nFixedString\n(\n6\n),\n\n \nsearchWord\n \nString\n,\n\n \nduration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nvisitDate\n,\n \nvisitDate\n,\n \n8192\n);\n\n\n\nCREATE\n \nTABLE\n \nrankings_5nodes_on_single\n\n\n(\n\n \npageURL\n \nString\n,\n\n \npageRank\n \nUInt32\n,\n\n \navgDuration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\nCREATE\n \nTABLE\n \nuservisits_5nodes_on_single\n\n\n(\n\n \nsourceIP\n \nString\n,\n\n \ndestinationURL\n \nString\n,\n\n \nvisitDate\n \nDate\n,\n\n \nadRevenue\n \nFloat32\n,\n\n \nUserAgent\n \nString\n,\n\n \ncCode\n \nFixedString\n(\n3\n),\n\n \nlCode\n \nFixedString\n(\n6\n),\n\n \nsearchWord\n \nString\n,\n\n \nduration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nvisitDate\n,\n \nvisitDate\n,\n \n8192\n);\n\n\n\n\n\n\nGo back to the console:\n\n\nfor\n i in tiny/rankings/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO rankings_tiny FORMAT CSV\n;\n \ndone\n\n\nfor\n i in tiny/uservisits/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO uservisits_tiny FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 1node/rankings/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO rankings_1node FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 1node/uservisits/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO uservisits_1node FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 5nodes/rankings/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO rankings_5nodes_on_single FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 5nodes/uservisits/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO uservisits_5nodes_on_single FORMAT CSV\n;\n \ndone\n\n\n\n\n\n\nQueries for obtaining data samples:\n\n\nSELECT\n \npageURL\n,\n \npageRank\n \nFROM\n \nrankings_1node\n \nWHERE\n \npageRank\n \n \n1000\n\n\n\nSELECT\n \nsubstring\n(\nsourceIP\n,\n \n1\n,\n \n8\n),\n \nsum\n(\nadRevenue\n)\n \nFROM\n \nuservisits_1node\n \nGROUP\n \nBY\n \nsubstring\n(\nsourceIP\n,\n \n1\n,\n \n8\n)\n\n\n\nSELECT\n\n \nsourceIP\n,\n\n \nsum\n(\nadRevenue\n)\n \nAS\n \ntotalRevenue\n,\n\n \navg\n(\npageRank\n)\n \nAS\n \npageRank\n\n\nFROM\n \nrankings_1node\n \nALL\n \nINNER\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nsourceIP\n,\n\n \ndestinationURL\n \nAS\n \npageURL\n,\n\n \nadRevenue\n\n \nFROM\n \nuservisits_1node\n\n \nWHERE\n \n(\nvisitDate\n \n \n1980-01-01\n)\n \nAND\n \n(\nvisitDate\n \n \n1980-04-01\n)\n\n\n)\n \nUSING\n \npageURL\n\n\nGROUP\n \nBY\n \nsourceIP\n\n\nORDER\n \nBY\n \ntotalRevenue\n \nDESC\n\n\nLIMIT\n \n1\n\n\n\n\n\n\nWikiStat\n\n\nSee: \nhttp://dumps.wikimedia.org/other/pagecounts-raw/\n\n\nCreating a table:\n\n\nCREATE\n \nTABLE\n \nwikistat\n\n\n(\n\n \ndate\n \nDate\n,\n\n \ntime\n \nDateTime\n,\n\n \nproject\n \nString\n,\n\n \nsubproject\n \nString\n,\n\n \npath\n \nString\n,\n\n \nhits\n \nUInt64\n,\n\n \nsize\n \nUInt64\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\ndate\n,\n \n(\npath\n,\n \ntime\n),\n \n8192\n);\n\n\n\n\n\n\nLoading data:\n\n\nfor\n i in \n{\n2007\n..2016\n}\n;\n \ndo\n \nfor\n j in \n{\n01\n..12\n}\n;\n \ndo\n \necho\n \n$i\n-\n$j\n \n2\n;\n curl -sSL \nhttp://dumps.wikimedia.org/other/pagecounts-raw/\n$i\n/\n$i\n-\n$j\n/\n \n|\n grep -oE \npagecounts-[0-9]+-[0-9]+\\.gz\n;\n \ndone\n;\n \ndone\n \n|\n sort \n|\n uniq \n|\n tee links.txt\ncat links.txt \n|\n \nwhile\n \nread\n link\n;\n \ndo\n wget http://dumps.wikimedia.org/other/pagecounts-raw/\n$(\necho\n \n$link\n \n|\n sed -r \ns/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1/\n)\n/\n$(\necho\n \n$link\n \n|\n sed -r \ns/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1-\\2/\n)\n/\n$link\n;\n \ndone\n\nls -1 /opt/wikistat/ \n|\n grep gz \n|\n \nwhile\n \nread\n i\n;\n \ndo\n \necho\n \n$i\n;\n gzip -cd /opt/wikistat/\n$i\n \n|\n ./wikistat-loader --time\n=\n$(\necho\n -n \n$i\n \n|\n sed -r \ns/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\\.gz/\\1-\\2-\\3 \\4-00-00/\n)\n \n|\n clickhouse-client --query\n=\nINSERT INTO wikistat FORMAT TabSeparated\n;\n \ndone\n\n\n\n\n\n\nTerabyte of click logs from Criteo\n\n\nDownload the data from \nhttp://labs.criteo.com/downloads/download-terabyte-click-logs/\n\n\nCreate a table to import the log to:\n\n\nCREATE\n \nTABLE\n \ncriteo_log\n \n(\ndate\n \nDate\n,\n \nclicked\n \nUInt8\n,\n \nint1\n \nInt32\n,\n \nint2\n \nInt32\n,\n \nint3\n \nInt32\n,\n \nint4\n \nInt32\n,\n \nint5\n \nInt32\n,\n \nint6\n \nInt32\n,\n \nint7\n \nInt32\n,\n \nint8\n \nInt32\n,\n \nint9\n \nInt32\n,\n \nint10\n \nInt32\n,\n \nint11\n \nInt32\n,\n \nint12\n \nInt32\n,\n \nint13\n \nInt32\n,\n \ncat1\n \nString\n,\n \ncat2\n \nString\n,\n \ncat3\n \nString\n,\n \ncat4\n \nString\n,\n \ncat5\n \nString\n,\n \ncat6\n \nString\n,\n \ncat7\n \nString\n,\n \ncat8\n \nString\n,\n \ncat9\n \nString\n,\n \ncat10\n \nString\n,\n \ncat11\n \nString\n,\n \ncat12\n \nString\n,\n \ncat13\n \nString\n,\n \ncat14\n \nString\n,\n \ncat15\n \nString\n,\n \ncat16\n \nString\n,\n \ncat17\n \nString\n,\n \ncat18\n \nString\n,\n \ncat19\n \nString\n,\n \ncat20\n \nString\n,\n \ncat21\n \nString\n,\n \ncat22\n \nString\n,\n \ncat23\n \nString\n,\n \ncat24\n \nString\n,\n \ncat25\n \nString\n,\n \ncat26\n \nString\n)\n \nENGINE\n \n=\n \nLog\n\n\n\n\n\n\nDownload the data:\n\n\nfor\n i in \n{\n00\n..23\n}\n;\n \ndo\n \necho\n \n$i\n;\n zcat datasets/criteo/day_\n${\ni\n#0\n}\n.gz \n|\n sed -r \ns/^/2000-01-\n${\ni\n/00/24\n}\n\\t/\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO criteo_log FORMAT TabSeparated\n;\n \ndone\n\n\n\n\n\n\nCreate a table for the converted data:\n\n\nCREATE\n \nTABLE\n \ncriteo\n\n\n(\n\n \ndate\n \nDate\n,\n\n \nclicked\n \nUInt8\n,\n\n \nint1\n \nInt32\n,\n\n \nint2\n \nInt32\n,\n\n \nint3\n \nInt32\n,\n\n \nint4\n \nInt32\n,\n\n \nint5\n \nInt32\n,\n\n \nint6\n \nInt32\n,\n\n \nint7\n \nInt32\n,\n\n \nint8\n \nInt32\n,\n\n \nint9\n \nInt32\n,\n\n \nint10\n \nInt32\n,\n\n \nint11\n \nInt32\n,\n\n \nint12\n \nInt32\n,\n\n \nint13\n \nInt32\n,\n\n \nicat1\n \nUInt32\n,\n\n \nicat2\n \nUInt32\n,\n\n \nicat3\n \nUInt32\n,\n\n \nicat4\n \nUInt32\n,\n\n \nicat5\n \nUInt32\n,\n\n \nicat6\n \nUInt32\n,\n\n \nicat7\n \nUInt32\n,\n\n \nicat8\n \nUInt32\n,\n\n \nicat9\n \nUInt32\n,\n\n \nicat10\n \nUInt32\n,\n\n \nicat11\n \nUInt32\n,\n\n \nicat12\n \nUInt32\n,\n\n \nicat13\n \nUInt32\n,\n\n \nicat14\n \nUInt32\n,\n\n \nicat15\n \nUInt32\n,\n\n \nicat16\n \nUInt32\n,\n\n \nicat17\n \nUInt32\n,\n\n \nicat18\n \nUInt32\n,\n\n \nicat19\n \nUInt32\n,\n\n \nicat20\n \nUInt32\n,\n\n \nicat21\n \nUInt32\n,\n\n \nicat22\n \nUInt32\n,\n\n \nicat23\n \nUInt32\n,\n\n \nicat24\n \nUInt32\n,\n\n \nicat25\n \nUInt32\n,\n\n \nicat26\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\ndate\n,\n \nintHash32\n(\nicat1\n),\n \n(\ndate\n,\n \nintHash32\n(\nicat1\n)),\n \n8192\n)\n\n\n\n\n\n\nTransform data from the raw log and put it in the second table:\n\n\nINSERT\n \nINTO\n \ncriteo\n \nSELECT\n \ndate\n,\n \nclicked\n,\n \nint1\n,\n \nint2\n,\n \nint3\n,\n \nint4\n,\n \nint5\n,\n \nint6\n,\n \nint7\n,\n \nint8\n,\n \nint9\n,\n \nint10\n,\n \nint11\n,\n \nint12\n,\n \nint13\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat1\n))\n \nAS\n \nicat1\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat2\n))\n \nAS\n \nicat2\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat3\n))\n \nAS\n \nicat3\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat4\n))\n \nAS\n \nicat4\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat5\n))\n \nAS\n \nicat5\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat6\n))\n \nAS\n \nicat6\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat7\n))\n \nAS\n \nicat7\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat8\n))\n \nAS\n \nicat8\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat9\n))\n \nAS\n \nicat9\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat10\n))\n \nAS\n \nicat10\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat11\n))\n \nAS\n \nicat11\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat12\n))\n \nAS\n \nicat12\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat13\n))\n \nAS\n \nicat13\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat14\n))\n \nAS\n \nicat14\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat15\n))\n \nAS\n \nicat15\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat16\n))\n \nAS\n \nicat16\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat17\n))\n \nAS\n \nicat17\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat18\n))\n \nAS\n \nicat18\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat19\n))\n \nAS\n \nicat19\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat20\n))\n \nAS\n \nicat20\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat21\n))\n \nAS\n \nicat21\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat22\n))\n \nAS\n \nicat22\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat23\n))\n \nAS\n \nicat23\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat24\n))\n \nAS\n \nicat24\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat25\n))\n \nAS\n \nicat25\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat26\n))\n \nAS\n \nicat26\n \nFROM\n \ncriteo_log\n;\n\n\n\nDROP\n \nTABLE\n \ncriteo_log\n;\n\n\n\n\n\n\nStar Schema Benchmark\n\n\nCompiling dbgen: \nhttps://github.com/vadimtk/ssb-dbgen\n\n\ngit clone git@github.com:vadimtk/ssb-dbgen.git\n\ncd\n ssb-dbgen\nmake\n\n\n\n\n\nThere will be some warnings during the process, but this is normal.\n\n\nPlace \ndbgen\n and \ndists.dss\n in any location with 800 GB of free disk space.\n\n\nGenerating data:\n\n\n./dbgen -s \n1000\n -T c\n./dbgen -s \n1000\n -T l\n\n\n\n\n\nCreating tables in ClickHouse:\n\n\nCREATE\n \nTABLE\n \nlineorder\n \n(\n\n \nLO_ORDERKEY\n \nUInt32\n,\n\n \nLO_LINENUMBER\n \nUInt8\n,\n\n \nLO_CUSTKEY\n \nUInt32\n,\n\n \nLO_PARTKEY\n \nUInt32\n,\n\n \nLO_SUPPKEY\n \nUInt32\n,\n\n \nLO_ORDERDATE\n \nDate\n,\n\n \nLO_ORDERPRIORITY\n \nString\n,\n\n \nLO_SHIPPRIORITY\n \nUInt8\n,\n\n \nLO_QUANTITY\n \nUInt8\n,\n\n \nLO_EXTENDEDPRICE\n \nUInt32\n,\n\n \nLO_ORDTOTALPRICE\n \nUInt32\n,\n\n \nLO_DISCOUNT\n \nUInt8\n,\n\n \nLO_REVENUE\n \nUInt32\n,\n\n \nLO_SUPPLYCOST\n \nUInt32\n,\n\n \nLO_TAX\n \nUInt8\n,\n\n \nLO_COMMITDATE\n \nDate\n,\n\n \nLO_SHIPMODE\n \nString\n\n\n)\nEngine\n=\nMergeTree\n(\nLO_ORDERDATE\n,(\nLO_ORDERKEY\n,\nLO_LINENUMBER\n,\nLO_ORDERDATE\n),\n8192\n);\n\n\n\nCREATE\n \nTABLE\n \ncustomer\n \n(\n\n \nC_CUSTKEY\n \nUInt32\n,\n\n \nC_NAME\n \nString\n,\n\n \nC_ADDRESS\n \nString\n,\n\n \nC_CITY\n \nString\n,\n\n \nC_NATION\n \nString\n,\n\n \nC_REGION\n \nString\n,\n\n \nC_PHONE\n \nString\n,\n\n \nC_MKTSEGMENT\n \nString\n,\n\n \nC_FAKEDATE\n \nDate\n\n\n)\nEngine\n=\nMergeTree\n(\nC_FAKEDATE\n,(\nC_CUSTKEY\n,\nC_FAKEDATE\n),\n8192\n);\n\n\n\nCREATE\n \nTABLE\n \npart\n \n(\n\n \nP_PARTKEY\n \nUInt32\n,\n\n \nP_NAME\n \nString\n,\n\n \nP_MFGR\n \nString\n,\n\n \nP_CATEGORY\n \nString\n,\n\n \nP_BRAND\n \nString\n,\n\n \nP_COLOR\n \nString\n,\n\n \nP_TYPE\n \nString\n,\n\n \nP_SIZE\n \nUInt8\n,\n\n \nP_CONTAINER\n \nString\n,\n\n \nP_FAKEDATE\n \nDate\n\n\n)\nEngine\n=\nMergeTree\n(\nP_FAKEDATE\n,(\nP_PARTKEY\n,\nP_FAKEDATE\n),\n8192\n);\n\n\n\nCREATE\n \nTABLE\n \nlineorderd\n \nAS\n \nlineorder\n \nENGINE\n \n=\n \nDistributed\n(\nperftest_3shards_1replicas\n,\n \ndefault\n,\n \nlineorder\n,\n \nrand\n());\n\n\nCREATE\n \nTABLE\n \ncustomerd\n \nAS\n \ncustomer\n \nENGINE\n \n=\n \nDistributed\n(\nperftest_3shards_1replicas\n,\n \ndefault\n,\n \ncustomer\n,\n \nrand\n());\n\n\nCREATE\n \nTABLE\n \npartd\n \nAS\n \npart\n \nENGINE\n \n=\n \nDistributed\n(\nperftest_3shards_1replicas\n,\n \ndefault\n,\n \npart\n,\n \nrand\n());\n\n\n\n\n\n\nFor testing on a single server, just use MergeTree tables.\nFor distributed testing, you need to configure the \nperftest_3shards_1replicas\n cluster in the config file.\nNext, create MergeTree tables on each server and a Distributed above them.\n\n\nDownloading data (change 'customer' to 'customerd' in the distributed version):\n\n\ncat customer.tbl \n|\n sed \ns/$/2000-01-01/\n \n|\n clickhouse-client --query \nINSERT INTO customer FORMAT CSV\n\ncat lineorder.tbl \n|\n clickhouse-client --query \nINSERT INTO lineorder FORMAT CSV\n\n\n\n\n\n\n\n\nInterfaces\n\n\nTo explore the system's capabilities, download data to tables, or make manual queries, use the clickhouse-client program.\n\n\nCommand-line client\n\n\nTo work from the command line, you can use \nclickhouse-client\n:\n\n\n$ clickhouse-client\nClickHouse client version \n0\n.0.26176.\nConnecting to localhost:9000.\nConnected to ClickHouse server version \n0\n.0.26176.\n\n:\n)\n\n\n\n\n\n\nThe client supports command-line options and configuration files. For more information, see \"\nConfiguring\n\".\n\n\nUsage\n\n\nThe client can be used in interactive and non-interactive (batch) mode.\nTo use batch mode, specify the 'query' parameter, or send data to 'stdin' (it verifies that 'stdin' is not a terminal), or both.\nSimilar to the HTTP interface, when using the 'query' parameter and sending data to 'stdin', the request is a concatenation of the 'query' parameter, a line feed, and the data in 'stdin'. This is convenient for large INSERT queries.\n\n\nExample of using the client to insert data:\n\n\necho\n -ne \n1, \nsome text\n, \n2016-08-14 00:00:00\n\\n2, \nsome more text\n, \n2016-08-14 00:00:01\n \n|\n clickhouse-client --database\n=\ntest\n --query\n=\nINSERT INTO test FORMAT CSV\n;\n\n\ncat \n_EOF | clickhouse-client --database=test --query=\nINSERT INTO test FORMAT CSV\n;\n\n\n3, \nsome text\n, \n2016-08-14 00:00:00\n\n\n4, \nsome more text\n, \n2016-08-14 00:00:01\n\n\n_EOF\n\n\ncat file.csv \n|\n clickhouse-client --database\n=\ntest\n --query\n=\nINSERT INTO test FORMAT CSV\n;\n\n\n\n\n\n\nIn batch mode, the default data format is TabSeparated. You can set the format in the FORMAT clause of the query.\n\n\nBy default, you can only process a single query in batch mode. To make multiple queries from a \"script,\" use the --multiquery parameter. This works for all queries except INSERT. Query results are output consecutively without additional separators.\nSimilarly, to process a large number of queries, you can run 'clickhouse-client' for each query. Note that it may take tens of milliseconds to launch the 'clickhouse-client' program.\n\n\nIn interactive mode, you get a command line where you can enter queries.\n\n\nIf 'multiline' is not specified (the default):To run the query, press Enter. The semicolon is not necessary at the end of the query. To enter a multiline query, enter a backslash \n\\\n before the line feed. After you press Enter, you will be asked to enter the next line of the query.\n\n\nIf multiline is specified:To run a query, end it with a semicolon and press Enter. If the semicolon was omitted at the end of the entered line, you will be asked to enter the next line of the query.\n\n\nOnly a single query is run, so everything after the semicolon is ignored.\n\n\nYou can specify \n\\G\n instead of or after the semicolon. This indicates Vertical format. In this format, each value is printed on a separate line, which is convenient for wide tables. This unusual feature was added for compatibility with the MySQL CLI.\n\n\nThe command line is based on 'readline' (and 'history' or 'libedit', or without a library, depending on the build). In other words, it uses the familiar keyboard shortcuts and keeps a history.\nThe history is written to \n~/.clickhouse-client-history\n.\n\n\nBy default, the format used is PrettyCompact. You can change the format in the FORMAT clause of the query, or by specifying \n\\G\n at the end of the query, using the \n--format\n or \n--vertical\n argument in the command line, or using the client configuration file.\n\n\nTo exit the client, press Ctrl+D (or Ctrl+C), or enter one of the following instead of a query:\"exit\", \"quit\", \"logout\", \"\u0443\u0447\u0448\u0435\", \"\u0439\u0433\u0448\u0435\", \"\u0434\u0449\u043f\u0449\u0433\u0435\", \"exit;\", \"quit;\", \"logout;\", \"\u0443\u0447\u0448\u0435\u0436\", \"\u0439\u0433\u0448\u0435\u0436\", \"\u0434\u0449\u043f\u0449\u0433\u0435\u0436\", \"q\", \"\u0439\", \"q\", \"Q\", \":q\", \"\u0439\", \"\u0419\", \"\u0416\u0439\"\n\n\nWhen processing a query, the client shows:\n\n\n\n\nProgress, which is updated no more than 10 times per second (by default). For quick queries, the progress might not have time to be displayed.\n\n\nThe formatted query after parsing, for debugging.\n\n\nThe result in the specified format.\n\n\nThe number of lines in the result, the time passed, and the average speed of query processing.\n\n\n\n\nYou can cancel a long query by pressing Ctrl+C. However, you will still need to wait a little for the server to abort the request. It is not possible to cancel a query at certain stages. If you don't wait and press Ctrl+C a second time, the client will exit.\n\n\nThe command-line client allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".\n\n\n\n\nConfiguring\n\n\nYou can pass parameters to \nclickhouse-client\n (all parameters have a default value) using:\n\n\n\n\nFrom the Command Line\n\n\n\n\nCommand-line options override the default values and settings in configuration files.\n\n\n\n\nConfiguration files.\n\n\n\n\nSettings in the configuration files override the default values.\n\n\nCommand line options\n\n\n\n\n--host, -h\n -\u2013 The server name, 'localhost' by default. You can use either the name or the IPv4 or IPv6 address.\n\n\n--port\n \u2013 The port to connect to. Default value: 9000. Note that the HTTP interface and the native interface use different ports.\n\n\n--user, -u\n \u2013 The username. Default value: default.\n\n\n--password\n \u2013 The password. Default value: empty string.\n\n\n--query, -q\n \u2013 The query to process when using non-interactive mode.\n\n\n--database, -d\n \u2013 Select the current default database. Default value: the current database from the server settings ('default' by default).\n\n\n--multiline, -m\n \u2013 If specified, allow multiline queries (do not send the query on Enter).\n\n\n--multiquery, -n\n \u2013 If specified, allow processing multiple queries separated by commas. Only works in non-interactive mode.\n\n\n--format, -f\n \u2013 Use the specified default format to output the result.\n\n\n--vertical, -E\n \u2013 If specified, use the Vertical format by default to output the result. This is the same as '--format=Vertical'. In this format, each value is printed on a separate line, which is helpful when displaying wide tables.\n\n\n--time, -t\n \u2013 If specified, print the query execution time to 'stderr' in non-interactive mode.\n\n\n--stacktrace\n \u2013 If specified, also print the stack trace if an exception occurs.\n\n\n-config-file\n \u2013 The name of the configuration file.\n\n\n\n\nConfiguration files\n\n\nclickhouse-client\n uses the first existing file of the following:\n\n\n\n\nDefined in the \n-config-file\n parameter.\n\n\n./clickhouse-client.xml\n\n\n\\~/.clickhouse-client/config.xml\n\n\n/etc/clickhouse-client/config.xml\n\n\n\n\nExample of a config file:\n\n\nconfig\n\n \nuser\nusername\n/user\n\n \npassword\npassword\n/password\n\n\n/config\n\n\n\n\n\n\nHTTP interface\n\n\nThe HTTP interface lets you use ClickHouse on any platform from any programming language. We use it for working from Java and Perl, as well as shell scripts. In other departments, the HTTP interface is used from Perl, Python, and Go. The HTTP interface is more limited than the native interface, but it has better compatibility.\n\n\nBy default, clickhouse-server listens for HTTP on port 8123 (this can be changed in the config).\nIf you make a GET / request without parameters, it returns the string \"Ok\" (with a line feed at the end). You can use this in health-check scripts.\n\n\n$ curl \nhttp://localhost:8123/\n\nOk.\n\n\n\n\n\nSend the request as a URL 'query' parameter, or as a POST. Or send the beginning of the query in the 'query' parameter, and the rest in the POST (we'll explain later why this is necessary). The size of the URL is limited to 16 KB, so keep this in mind when sending large queries.\n\n\nIf successful, you receive the 200 response code and the result in the response body.\nIf an error occurs, you receive the 500 response code and an error description text in the response body.\n\n\nWhen using the GET method, 'readonly' is set. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.\n\n\nExamples:\n\n\n$ curl \nhttp://localhost:8123/?query=SELECT%201\n\n\n1\n\n\n$ wget -O- -q \nhttp://localhost:8123/?query=SELECT 1\n\n\n1\n\n\n$ GET \nhttp://localhost:8123/?query=SELECT 1\n\n\n1\n\n\n$ \necho\n -ne \nGET /?query=SELECT%201 HTTP/1.0\\r\\n\\r\\n\n \n|\n nc localhost \n8123\n\nHTTP/1.0 \n200\n OK\nConnection: Close\nDate: Fri, \n16\n Nov \n2012\n \n19\n:21:50 GMT\n\n\n1\n\n\n\n\n\n\nAs you can see, curl is somewhat inconvenient in that spaces must be URL escaped.Although wget escapes everything itself, we don't recommend using it because it doesn't work well over HTTP 1.1 when using keep-alive and Transfer-Encoding: chunked.\n\n\n$ \necho\n \nSELECT 1\n \n|\n curl \nhttp://localhost:8123/\n --data-binary @-\n\n1\n\n\n$ \necho\n \nSELECT 1\n \n|\n curl \nhttp://localhost:8123/?query=\n --data-binary @-\n\n1\n\n\n$ \necho\n \n1\n \n|\n curl \nhttp://localhost:8123/?query=SELECT\n --data-binary @-\n\n1\n\n\n\n\n\n\nIf part of the query is sent in the parameter, and part in the POST, a line feed is inserted between these two data parts.\nExample (this won't work):\n\n\n$ \necho\n \nECT 1\n \n|\n curl \nhttp://localhost:8123/?query=SEL\n --data-binary @-\nCode: \n59\n, e.displayText\n()\n \n=\n DB::Exception: Syntax error: failed at position \n0\n: SEL\nECT \n1\n\n, expected One of: SHOW TABLES, SHOW DATABASES, SELECT, INSERT, CREATE, ATTACH, RENAME, DROP, DETACH, USE, SET, OPTIMIZE., e.what\n()\n \n=\n DB::Exception\n\n\n\n\n\nBy default, data is returned in TabSeparated format (for more information, see the \"Formats\" section).\nYou use the FORMAT clause of the query to request any other format.\n\n\n$ \necho\n \nSELECT 1 FORMAT Pretty\n \n|\n curl \nhttp://localhost:8123/?\n --data-binary @-\n\u250f\u2501\u2501\u2501\u2513\n\u2503 \n1\n \u2503\n\u2521\u2501\u2501\u2501\u2529\n\u2502 \n1\n \u2502\n\u2514\u2500\u2500\u2500\u2518\n\n\n\n\n\nThe POST method of transmitting data is necessary for INSERT queries. In this case, you can write the beginning of the query in the URL parameter, and use POST to pass the data to insert. The data to insert could be, for example, a tab-separated dump from MySQL. In this way, the INSERT query replaces LOAD DATA LOCAL INFILE from MySQL.\n\n\nExamples: Creating a table:\n\n\necho\n \nCREATE TABLE t (a UInt8) ENGINE = Memory\n \n|\n POST \nhttp://localhost:8123/\n\n\n\n\n\n\nUsing the familiar INSERT query for data insertion:\n\n\necho\n \nINSERT INTO t VALUES (1),(2),(3)\n \n|\n POST \nhttp://localhost:8123/\n\n\n\n\n\n\nData can be sent separately from the query:\n\n\necho\n \n(4),(5),(6)\n \n|\n POST \nhttp://localhost:8123/?query=INSERT INTO t VALUES\n\n\n\n\n\n\nYou can specify any data format. The 'Values' format is the same as what is used when writing INSERT INTO t VALUES:\n\n\necho\n \n(7),(8),(9)\n \n|\n POST \nhttp://localhost:8123/?query=INSERT INTO t FORMAT Values\n\n\n\n\n\n\nTo insert data from a tab-separated dump, specify the corresponding format:\n\n\necho\n -ne \n10\\n11\\n12\\n\n \n|\n POST \nhttp://localhost:8123/?query=INSERT INTO t FORMAT TabSeparated\n\n\n\n\n\n\nReading the table contents. Data is output in random order due to parallel query processing:\n\n\n$ GET \nhttp://localhost:8123/?query=SELECT a FROM t\n\n\n7\n\n\n8\n\n\n9\n\n\n10\n\n\n11\n\n\n12\n\n\n1\n\n\n2\n\n\n3\n\n\n4\n\n\n5\n\n\n6\n\n\n\n\n\n\nDeleting the table.\n\n\nPOST \nhttp://localhost:8123/?query=DROP TABLE t\n\n\n\n\n\n\nFor successful requests that don't return a data table, an empty response body is returned.\n\n\nYou can use the internal ClickHouse compression format when transmitting data. The compressed data has a non-standard format, and you will need to use the special clickhouse-compressor program to work with it (it is installed with the clickhouse-client package).\n\n\nIf you specified 'compress=1' in the URL, the server will compress the data it sends you.\nIf you specified 'decompress=1' in the URL, the server will decompress the same data that you pass in the POST method.\n\n\nIt is also possible to use the standard gzip-based HTTP compression. To send a POST request compressed using gzip, append the request header \nContent-Encoding: gzip\n.\nIn order for ClickHouse to compress the response using gzip, you must append \nAccept-Encoding: gzip\n to the request headers, and enable the ClickHouse setting \nenable_http_compression\n.\n\n\nYou can use this to reduce network traffic when transmitting a large amount of data, or for creating dumps that are immediately compressed.\n\n\nYou can use the 'database' URL parameter to specify the default database.\n\n\n$ \necho\n \nSELECT number FROM numbers LIMIT 10\n \n|\n curl \nhttp://localhost:8123/?database=system\n --data-binary @-\n\n0\n\n\n1\n\n\n2\n\n\n3\n\n\n4\n\n\n5\n\n\n6\n\n\n7\n\n\n8\n\n\n9\n\n\n\n\n\n\nBy default, the database that is registered in the server settings is used as the default database. By default, this is the database called 'default'. Alternatively, you can always specify the database using a dot before the table name.\n\n\nThe username and password can be indicated in one of two ways:\n\n\n\n\nUsing HTTP Basic Authentication. Example:\n\n\n\n\necho\n \nSELECT 1\n \n|\n curl \nhttp://user:password@localhost:8123/\n -d @-\n\n\n\n\n\n\n\nIn the 'user' and 'password' URL parameters. Example:\n\n\n\n\necho\n \nSELECT 1\n \n|\n curl \nhttp://localhost:8123/?user=user\npassword=password\n -d @-\n\n\n\n\n\nIf the user name is not indicated, the username 'default' is used. If the password is not indicated, an empty password is used.\nYou can also use the URL parameters to specify any settings for processing a single query, or entire profiles of settings. Example:\nhttp://localhost:8123/?profile=web\nmax_rows_to_read=1000000000\nquery=SELECT+1\n\n\nFor more information, see the section \"Settings\".\n\n\n$ \necho\n \nSELECT number FROM system.numbers LIMIT 10\n \n|\n curl \nhttp://localhost:8123/?\n --data-binary @-\n\n0\n\n\n1\n\n\n2\n\n\n3\n\n\n4\n\n\n5\n\n\n6\n\n\n7\n\n\n8\n\n\n9\n\n\n\n\n\n\nFor information about other parameters, see the section \"SET\".\n\n\nSimilarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to add the \nsession_id\n GET parameter to the request. You can use any string as the session ID. By default, the session is terminated after 60 seconds of inactivity. To change this timeout, modify the \ndefault_session_timeout\n setting in the server configuration, or add the \nsession_timeout\n GET parameter to the request. To check the session status, use the \nsession_check=1\n parameter. Only one query at a time can be executed within a single session.\n\n\nYou have the option to receive information about the progress of query execution in X-ClickHouse-Progress headers. To do this, enable the setting send_progress_in_http_headers.\n\n\nRunning requests don't stop automatically if the HTTP connection is lost. Parsing and data formatting are performed on the server side, and using the network might be ineffective.\nThe optional 'query_id' parameter can be passed as the query ID (any string). For more information, see the section \"Settings, replace_running_query\".\n\n\nThe optional 'quota_key' parameter can be passed as the quota key (any string). For more information, see the section \"Quotas\".\n\n\nThe HTTP interface allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".\n\n\nResponse buffering\n\n\nYou can enable response buffering on the server side. The \nbuffer_size\n and \nwait_end_of_query\n URL parameters are provided for this purpose.\n\n\nbuffer_size\n determines the number of bytes in the result to buffer in the server memory. If the result body is larger than this threshold, the buffer is written to the HTTP channel, and the remaining data is sent directly to the HTTP channel.\n\n\nTo ensure that the entire response is buffered, set \nwait_end_of_query=1\n. In this case, the data that is not stored in memory will be buffered in a temporary server file.\n\n\nExample:\n\n\ncurl -sS \nhttp://localhost:8123/?max_result_bytes=4000000\nbuffer_size=3000000\nwait_end_of_query=1\n -d \nSELECT toUInt8(number) FROM system.numbers LIMIT 9000000 FORMAT RowBinary\n\n\n\n\n\n\nUse buffering to avoid situations where a query processing error occurred after the response code and HTTP headers were sent to the client. In this situation, an error message is written at the end of the response body, and on the client side, the error can only be detected at the parsing stage.\n\n\nJDBC driver\n\n\nThere is an official JDBC driver for ClickHouse. See \nhere\n .\n\n\nNative interface (TCP)\n\n\nThe native interface is used in the \"clickhouse-client\" command-line client for interaction between servers with distributed query processing, and also in C++ programs. We will only cover the command-line client.\n\n\nLibraries from third-party developers\n\n\nThere are libraries for working with ClickHouse for:\n\n\n\n\nPython\n\n\ninfi.clickhouse_orm\n\n\nsqlalchemy-clickhouse\n\n\nclickhouse-driver\n\n\nclickhouse-client\n\n\n\n\n\n\nPHP\n\n\nclickhouse-php-client\n\n\nPhpClickHouseClient\n\n\nphpClickHouse\n\n\nclickhouse-client\n\n\n\n\n\n\nGo\n\n\nclickhouse\n\n\ngo-clickhouse\n\n\nmailrugo-clickhouse\n\n\ngolang-clickhouse\n\n\n\n\n\n\nNodeJs\n\n\nclickhouse (NodeJs)\n\n\nnode-clickhouse\n\n\n\n\n\n\nPerl\n\n\nperl-DBD-ClickHouse\n\n\nHTTP-ClickHouse\n\n\nAnyEvent-ClickHouse\n\n\n\n\n\n\nRuby\n\n\nclickhouse (Ruby)\n\n\n\n\n\n\nR\n\n\nclickhouse-r\n\n\nRClickhouse\n\n\n\n\n\n\n.NET\n\n\nClickHouse-Net\n\n\n\n\n\n\nC++\n\n\nclickhouse-cpp\n\n\n\n\n\n\nElixir\n\n\nclickhousex\n\n\nclickhouse_ecto\n\n\n\n\n\n\nJava\n\n\nclickhouse-client-java\n\n\n\n\n\n\n\n\nWe have not tested these libraries. They are listed in random order.\n\n\nVisual interfaces from third-party developers\n\n\nTabix\n\n\nWeb interface for ClickHouse in the \nTabix\n project.\n\n\nFeatures:\n\n\n\n\nWorks with ClickHouse directly from the browser, without the need to install additional software.\n\n\nQuery editor with syntax highlighting.\n\n\nAuto-completion of commands.\n\n\nTools for graphical analysis of query execution.\n\n\nColor scheme options.\n\n\n\n\nTabix documentation\n.\n\n\nHouseOps\n\n\nHouseOps\n is a unique Desktop ClickHouse Ops UI / IDE for OSX, Linux and Windows.\n\n\nFeatures:\n\n\n\n\nQuery builder;\n\n\nDatabase manangement (soon);\n\n\nUsers manangement (soon);\n\n\nReal-Time Data Analytics (soon);\n\n\nCluster/Infra monitoring (soon);\n\n\nCluster manangement (soon);\n\n\nKafka and Replicated tables monitoring (soon);\n\n\nAnd a lot of others features (soon) for you take a beautiful implementation of ClickHouse.\n\n\n\n\nQuery language\n\n\nQueries\n\n\nCREATE DATABASE\n\n\nCreating db_name databases\n\n\nCREATE\n \nDATABASE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \ndb_name\n\n\n\n\n\n\nA database\n is just a directory for tables.\nIf \nIF NOT EXISTS\n is included, the query won't return an error if the database already exists.\n\n\n\n\nCREATE TABLE\n\n\nThe \nCREATE TABLE\n query can have several forms.\n\n\nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n(\n\n \nname1\n \n[\ntype1\n]\n \n[\nDEFAULT\n|\nMATERIALIZED\n|\nALIAS\n \nexpr1\n],\n\n \nname2\n \n[\ntype2\n]\n \n[\nDEFAULT\n|\nMATERIALIZED\n|\nALIAS\n \nexpr2\n],\n\n \n...\n\n\n)\n \nENGINE\n \n=\n \nengine\n\n\n\n\n\n\nCreates a table named 'name' in the 'db' database or the current database if 'db' is not set, with the structure specified in brackets and the 'engine' engine.\nThe structure of the table is a list of column descriptions. If indexes are supported by the engine, they are indicated as parameters for the table engine.\n\n\nA column description is \nname type\n in the simplest case. Example: \nRegionID UInt32\n.\nExpressions can also be defined for default values (see below).\n\n\nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \nAS\n \n[\ndb2\n.]\nname2\n \n[\nENGINE\n \n=\n \nengine\n]\n\n\n\n\n\n\nCreates a table with the same structure as another table. You can specify a different engine for the table. If the engine is not specified, the same engine will be used as for the \ndb2.name2\n table.\n\n\nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \nENGINE\n \n=\n \nengine\n \nAS\n \nSELECT\n \n...\n\n\n\n\n\n\nCreates a table with a structure like the result of the \nSELECT\n query, with the 'engine' engine, and fills it with data from SELECT.\n\n\nIn all cases, if \nIF NOT EXISTS\n is specified, the query won't return an error if the table already exists. In this case, the query won't do anything.\n\n\nDefault values\n\n\nThe column description can specify an expression for a default value, in one of the following ways:\nDEFAULT expr\n, \nMATERIALIZED expr\n, \nALIAS expr\n.\nExample: \nURLDomain String DEFAULT domain(URL)\n.\n\n\nIf an expression for the default value is not defined, the default values will be set to zeros for numbers, empty strings for strings, empty arrays for arrays, and \n0000-00-00\n for dates or \n0000-00-00 00:00:00\n for dates with time. NULLs are not supported.\n\n\nIf the default expression is defined, the column type is optional. If there isn't an explicitly defined type, the default expression type is used. Example: \nEventDate DEFAULT toDate(EventTime)\n \u2013 the 'Date' type will be used for the 'EventDate' column.\n\n\nIf the data type and default expression are defined explicitly, this expression will be cast to the specified type using type casting functions. Example: \nHits UInt32 DEFAULT 0\n means the same thing as \nHits UInt32 DEFAULT toUInt32(0)\n.\n\n\nDefault expressions may be defined as an arbitrary expression from table constants and columns. When creating and changing the table structure, it checks that expressions don't contain loops. For INSERT, it checks that expressions are resolvable \u2013 that all columns they can be calculated from have been passed.\n\n\nDEFAULT expr\n\n\nNormal default value. If the INSERT query doesn't specify the corresponding column, it will be filled in by computing the corresponding expression.\n\n\nMATERIALIZED expr\n\n\nMaterialized expression. Such a column can't be specified for INSERT, because it is always calculated.\nFor an INSERT without a list of columns, these columns are not considered.\nIn addition, this column is not substituted when using an asterisk in a SELECT query. This is to preserve the invariant that the dump obtained using \nSELECT *\n can be inserted back into the table using INSERT without specifying the list of columns.\n\n\nALIAS expr\n\n\nSynonym. Such a column isn't stored in the table at all.\nIts values can't be inserted in a table, and it is not substituted when using an asterisk in a SELECT query.\nIt can be used in SELECTs if the alias is expanded during query parsing.\n\n\nWhen using the ALTER query to add new columns, old data for these columns is not written. Instead, when reading old data that does not have values for the new columns, expressions are computed on the fly by default. However, if running the expressions requires different columns that are not indicated in the query, these columns will additionally be read, but only for the blocks of data that need it.\n\n\nIf you add a new column to a table but later change its default expression, the values used for old data will change (for data where values were not stored on the disk). Note that when running background merges, data for columns that are missing in one of the merging parts is written to the merged part.\n\n\nIt is not possible to set default values for elements in nested data structures.\n\n\nTemporary tables\n\n\nIn all cases, if \nTEMPORARY\n is specified, a temporary table will be created. Temporary tables have the following characteristics:\n\n\n\n\nTemporary tables disappear when the session ends, including if the connection is lost.\n\n\nA temporary table is created with the Memory engine. The other table engines are not supported.\n\n\nThe DB can't be specified for a temporary table. It is created outside of databases.\n\n\nIf a temporary table has the same name as another one and a query specifies the table name without specifying the DB, the temporary table will be used.\n\n\nFor distributed query processing, temporary tables used in a query are passed to remote servers.\n\n\n\n\nIn most cases, temporary tables are not created manually, but when using external data for a query, or for distributed \n(GLOBAL) IN\n. For more information, see the appropriate sections\n\n\nDistributed DDL queries (ON CLUSTER clause)\n\n\nThe \nCREATE\n, \nDROP\n, \nALTER\n, and \nRENAME\n queries support distributed execution on a cluster.\nFor example, the following query creates the \nall_hits\n \nDistributed\n table on each host in \ncluster\n:\n\n\nCREATE\n \nTABLE\n \nIF\n \nNOT\n \nEXISTS\n \nall_hits\n \nON\n \nCLUSTER\n \ncluster\n \n(\np\n \nDate\n,\n \ni\n \nInt32\n)\n \nENGINE\n \n=\n \nDistributed\n(\ncluster\n,\n \ndefault\n,\n \nhits\n)\n\n\n\n\n\n\nIn order to run these queries correctly, each host must have the same cluster definition (to simplify syncing configs, you can use substitutions from ZooKeeper). They must also connect to the ZooKeeper servers.\nThe local version of the query will eventually be implemented on each host in the cluster, even if some hosts are currently not available. The order for executing queries within a single host is guaranteed.\n\nALTER\n queries are not yet supported for replicated tables.\n\n\nCREATE VIEW\n\n\nCREATE\n \n[\nMATERIALIZED\n]\n \nVIEW\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \n[\nTO\n[\ndb\n.]\nname\n]\n \n[\nENGINE\n \n=\n \nengine\n]\n \n[\nPOPULATE\n]\n \nAS\n \nSELECT\n \n...\n\n\n\n\n\n\nCreates a view. There are two types of views: normal and MATERIALIZED.\n\n\nWhen creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data.\n\n\nA materialized view works as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.\n\n\nNormal views don't store any data, but just perform a read from another table. In other words, a normal view is nothing more than a saved query. When reading from a view, this saved query is used as a subquery in the FROM clause.\n\n\nAs an example, assume you've created a view:\n\n\nCREATE\n \nVIEW\n \nview\n \nAS\n \nSELECT\n \n...\n\n\n\n\n\n\nand written a query:\n\n\nSELECT\n \na\n,\n \nb\n,\n \nc\n \nFROM\n \nview\n\n\n\n\n\n\nThis query is fully equivalent to using the subquery:\n\n\nSELECT\n \na\n,\n \nb\n,\n \nc\n \nFROM\n \n(\nSELECT\n \n...)\n\n\n\n\n\n\nMaterialized views store data transformed by the corresponding SELECT query.\n\n\nWhen creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data.\n\n\nA materialized view is arranged as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.\n\n\nIf you specify POPULATE, the existing table data is inserted in the view when creating it, as if making a \nCREATE TABLE ... AS SELECT ...\n . Otherwise, the query contains only the data inserted in the table after creating the view. We don't recommend using POPULATE, since data inserted in the table during the view creation will not be inserted in it.\n\n\nA \nSELECT\n query can contain \nDISTINCT\n, \nGROUP BY\n, \nORDER BY\n, \nLIMIT\n... Note that the corresponding conversions are performed independently on each block of inserted data. For example, if \nGROUP BY\n is set, data is aggregated during insertion, but only within a single packet of inserted data. The data won't be further aggregated. The exception is when using an ENGINE that independently performs data aggregation, such as \nSummingMergeTree\n.\n\n\nThe execution of \nALTER\n queries on materialized views has not been fully developed, so they might be inconvenient. If the materialized view uses the construction \nTO [db.]name\n, you can \nDETACH\n the view, run \nALTER\n for the target table, and then \nATTACH\n the previously detached (\nDETACH\n) view.\n\n\nViews look the same as normal tables. For example, they are listed in the result of the \nSHOW TABLES\n query.\n\n\nThere isn't a separate query for deleting views. To delete a view, use \nDROP TABLE\n.\n\n\nATTACH\n\n\nThis query is exactly the same as \nCREATE\n, but\n\n\n\n\ninstead of the word \nCREATE\n it uses the word \nATTACH\n.\n\n\nThe query doesn't create data on the disk, but assumes that data is already in the appropriate places, and just adds information about the table to the server.\nAfter executing an ATTACH query, the server will know about the existence of the table.\n\n\n\n\nIf the table was previously detached (\nDETACH\n), meaning that its structure is known, you can use shorthand without defining the structure.\n\n\nATTACH\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n\n\n\n\n\n\nThis query is used when starting the server. The server stores table metadata as files with \nATTACH\n queries, which it simply runs at launch (with the exception of system tables, which are explicitly created on the server).\n\n\nDROP\n\n\nThis query has two types: \nDROP DATABASE\n and \nDROP TABLE\n.\n\n\nDROP\n \nDATABASE\n \n[\nIF\n \nEXISTS\n]\n \ndb\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n\n\n\n\nDeletes all tables inside the 'db' database, then deletes the 'db' database itself.\nIf \nIF EXISTS\n is specified, it doesn't return an error if the database doesn't exist.\n\n\nDROP\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n\n\n\n\nDeletes the table.\nIf \nIF EXISTS\n is specified, it doesn't return an error if the table doesn't exist or the database doesn't exist.\n\n\nDETACH\n\n\nDeletes information about the 'name' table from the server. The server stops knowing about the table's existence.\n\n\nDETACH\n \nTABLE\n \n[\nIF\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n\n\n\n\n\n\nThis does not delete the table's data or metadata. On the next server launch, the server will read the metadata and find out about the table again.\nSimilarly, a \"detached\" table can be re-attached using the \nATTACH\n query (with the exception of system tables, which do not have metadata stored for them).\n\n\nThere is no \nDETACH DATABASE\n query.\n\n\nRENAME\n\n\nRenames one or more tables.\n\n\nRENAME\n \nTABLE\n \n[\ndb11\n.]\nname11\n \nTO\n \n[\ndb12\n.]\nname12\n,\n \n[\ndb21\n.]\nname21\n \nTO\n \n[\ndb22\n.]\nname22\n,\n \n...\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n\n\n\n\nAll tables are renamed under global locking. Renaming tables is a light operation. If you indicated another database after TO, the table will be moved to this database. However, the directories with databases must reside in the same file system (otherwise, an error is returned).\n\n\n\n\nALTER\n\n\nThe \nALTER\n query is only supported for \n*MergeTree\n tables, as well as \nMerge\nand\nDistributed\n. The query has several variations.\n\n\nColumn manipulations\n\n\nChanging the table structure.\n\n\nALTER\n \nTABLE\n \n[\ndb\n].\nname\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n \nADD\n|\nDROP\n|\nMODIFY\n \nCOLUMN\n \n...\n\n\n\n\n\n\nIn the query, specify a list of one or more comma-separated actions.\nEach action is an operation on a column.\n\n\nThe following actions are supported:\n\n\nADD\n \nCOLUMN\n \nname\n \n[\ntype\n]\n \n[\ndefault_expr\n]\n \n[\nAFTER\n \nname_after\n]\n\n\n\n\n\n\nAdds a new column to the table with the specified name, type, and \ndefault_expr\n (see the section \"Default expressions\"). If you specify \nAFTER name_after\n (the name of another column), the column is added after the specified one in the list of table columns. Otherwise, the column is added to the end of the table. Note that there is no way to add a column to the beginning of a table. For a chain of actions, 'name_after' can be the name of a column that is added in one of the previous actions.\n\n\nAdding a column just changes the table structure, without performing any actions with data. The data doesn't appear on the disk after ALTER. If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). The column appears on the disk after merging data parts (see MergeTree).\n\n\nThis approach allows us to complete the ALTER query instantly, without increasing the volume of old data.\n\n\nDROP\n \nCOLUMN\n \nname\n\n\n\n\n\n\nDeletes the column with the name 'name'.\nDeletes data from the file system. Since this deletes entire files, the query is completed almost instantly.\n\n\nMODIFY\n \nCOLUMN\n \nname\n \n[\ntype\n]\n \n[\ndefault_expr\n]\n\n\n\n\n\n\nChanges the 'name' column's type to 'type' and/or the default expression to 'default_expr'. When changing the type, values are converted as if the 'toType' function were applied to them.\n\n\nIf only the default expression is changed, the query doesn't do anything complex, and is completed almost instantly.\n\n\nChanging the column type is the only complex action \u2013 it changes the contents of files with data. For large tables, this may take a long time.\n\n\nThere are several processing stages:\n\n\n\n\nPreparing temporary (new) files with modified data.\n\n\nRenaming old files.\n\n\nRenaming the temporary (new) files to the old names.\n\n\nDeleting the old files.\n\n\n\n\nOnly the first stage takes time. If there is a failure at this stage, the data is not changed.\nIf there is a failure during one of the successive stages, data can be restored manually. The exception is if the old files were deleted from the file system but the data for the new files did not get written to the disk and was lost.\n\n\nThere is no support for changing the column type in arrays and nested data structures.\n\n\nThe \nALTER\n query lets you create and delete separate elements (columns) in nested data structures, but not whole nested data structures. To add a nested data structure, you can add columns with a name like \nname.nested_name\n and the type \nArray(T)\n. A nested data structure is equivalent to multiple array columns with a name that has the same prefix before the dot.\n\n\nThere is no support for deleting columns in the primary key or the sampling key (columns that are in the \nENGINE\n expression). Changing the type for columns that are included in the primary key is only possible if this change does not cause the data to be modified (for example, it is allowed to add values to an Enum or change a type with \nDateTime\n to \nUInt32\n).\n\n\nIf the \nALTER\n query is not sufficient for making the table changes you need, you can create a new table, copy the data to it using the \nINSERT SELECT\n query, then switch the tables using the \nRENAME\n query and delete the old table.\n\n\nThe \nALTER\n query blocks all reads and writes for the table. In other words, if a long \nSELECT\n is running at the time of the \nALTER\n query, the \nALTER\n query will wait for it to complete. At the same time, all new queries to the same table will wait while this \nALTER\n is running.\n\n\nFor tables that don't store data themselves (such as \nMerge\n and \nDistributed\n), \nALTER\n just changes the table structure, and does not change the structure of subordinate tables. For example, when running ALTER for a \nDistributed\n table, you will also need to run \nALTER\n for the tables on all remote servers.\n\n\nThe \nALTER\n query for changing columns is replicated. The instructions are saved in ZooKeeper, then each replica applies them. All \nALTER\n queries are run in the same order. The query waits for the appropriate actions to be completed on the other replicas. However, a query to change columns in a replicated table can be interrupted, and all actions will be performed asynchronously.\n\n\nManipulations with partitions and parts\n\n\nIt only works for tables in the \nMergeTree\n family. The following operations are available:\n\n\n\n\nDETACH PARTITION\n \u2013 Move a partition to the 'detached' directory and forget it.\n\n\nDROP PARTITION\n \u2013 Delete a partition.\n\n\nATTACH PART|PARTITION\n \u2013 Add a new part or partition from the \ndetached\n directory to the table.\n\n\nFREEZE PARTITION\n \u2013 Create a backup of a partition.\n\n\nFETCH PARTITION\n \u2013 Download a partition from another server.\n\n\n\n\nEach type of query is covered separately below.\n\n\nA partition in a table is data for a single calendar month. This is determined by the values of the date key specified in the table engine parameters. Each month's data is stored separately in order to simplify manipulations with this data.\n\n\nA \"part\" in the table is part of the data from a single partition, sorted by the primary key.\n\n\nYou can use the \nsystem.parts\n table to view the set of table parts and partitions:\n\n\nSELECT\n \n*\n \nFROM\n \nsystem\n.\nparts\n \nWHERE\n \nactive\n\n\n\n\n\n\nactive\n \u2013 Only count active parts. Inactive parts are, for example, source parts remaining after merging to a larger part \u2013 these parts are deleted approximately 10 minutes after merging.\n\n\nAnother way to view a set of parts and partitions is to go into the directory with table data.\nData directory: \n/var/lib/clickhouse/data/database/table/\n,where \n/var/lib/clickhouse/\n is the path to the ClickHouse data, 'database' is the database name, and 'table' is the table name. Example:\n\n\n$ ls -l /var/lib/clickhouse/data/test/visits/\ntotal \n48\n\ndrwxrwxrwx \n2\n clickhouse clickhouse \n20480\n May \n5\n \n02\n:58 20140317_20140323_2_2_0\ndrwxrwxrwx \n2\n clickhouse clickhouse \n20480\n May \n5\n \n02\n:58 20140317_20140323_4_4_0\ndrwxrwxrwx \n2\n clickhouse clickhouse \n4096\n May \n5\n \n02\n:55 detached\n-rw-rw-rw- \n1\n clickhouse clickhouse \n2\n May \n5\n \n02\n:58 increment.txt\n\n\n\n\n\nHere, \n20140317_20140323_2_2_0\n and \n20140317_20140323_4_4_0\n are the directories of data parts.\n\n\nLet's break down the name of the first part: \n20140317_20140323_2_2_0\n.\n\n\n\n\n20140317\n is the minimum date of the data in the chunk.\n\n\n20140323\n is the maximum date of the data in the chunk.\n\n\n2\n is the minimum number of the data block.\n\n\n2\n is the maximum number of the data block.\n\n\n0\n is the chunk level (the depth of the merge tree it is formed from).\n\n\n\n\nEach piece relates to a single partition and contains data for just one month.\n\n201403\n is the name of the partition. A partition is a set of parts for a single month.\n\n\nOn an operating server, you can't manually change the set of parts or their data on the file system, since the server won't know about it.\nFor non-replicated tables, you can do this when the server is stopped, but we don't recommended it.\nFor replicated tables, the set of parts can't be changed in any case.\n\n\nThe \ndetached\n directory contains parts that are not used by the server - detached from the table using the \nALTER ... DETACH\n query. Parts that are damaged are also moved to this directory, instead of deleting them. You can add, delete, or modify the data in the 'detached' directory at any time \u2013 the server won't know about this until you make the \nALTER TABLE ... ATTACH\n query.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nDETACH\n \nPARTITION\n \nname\n\n\n\n\n\n\nMove all data for partitions named 'name' to the 'detached' directory and forget about them.\nThe partition name is specified in YYYYMM format. It can be indicated in single quotes or without them.\n\n\nAfter the query is executed, you can do whatever you want with the data in the 'detached' directory \u2014 delete it from the file system, or just leave it.\n\n\nThe query is replicated \u2013 data will be moved to the 'detached' directory and forgotten on all replicas. The query can only be sent to a leader replica. To find out if a replica is a leader, perform SELECT to the 'system.replicas' system table. Alternatively, it is easier to make a query on all replicas, and all except one will throw an exception.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nDROP\n \nPARTITION\n \nname\n\n\n\n\n\n\nThe same as the \nDETACH\n operation. Deletes data from the table. Data parts will be tagged as inactive and will be completely deleted in approximately 10 minutes. The query is replicated \u2013 data will be deleted on all replicas.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nATTACH\n \nPARTITION\n|\nPART\n \nname\n\n\n\n\n\n\nAdds data to the table from the 'detached' directory.\n\n\nIt is possible to add data for an entire partition or a separate part. For a part, specify the full name of the part in single quotes.\n\n\nThe query is replicated. Each replica checks whether there is data in the 'detached' directory. If there is data, it checks the integrity, verifies that it matches the data on the server that initiated the query, and then adds it if everything is correct. If not, it downloads data from the query requestor replica, or from another replica where the data has already been added.\n\n\nSo you can put data in the 'detached' directory on one replica, and use the ALTER ... ATTACH query to add it to the table on all replicas.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nFREEZE\n \nPARTITION\n \nname\n\n\n\n\n\n\nCreates a local backup of one or multiple partitions. The name can be the full name of the partition (for example, 201403), or its prefix (for example, 2014): then the backup will be created for all the corresponding partitions.\n\n\nThe query does the following: for a data snapshot at the time of execution, it creates hardlinks to table data in the directory \n/var/lib/clickhouse/shadow/N/...\n\n\n/var/lib/clickhouse/\n is the working ClickHouse directory from the config.\n\nN\n is the incremental number of the backup.\n\n\nThe same structure of directories is created inside the backup as inside \n/var/lib/clickhouse/\n.\nIt also performs 'chmod' for all files, forbidding writes to them.\n\n\nThe backup is created almost instantly (but first it waits for current queries to the corresponding table to finish running). At first, the backup doesn't take any space on the disk. As the system works, the backup can take disk space, as data is modified. If the backup is made for old enough data, it won't take space on the disk.\n\n\nAfter creating the backup, data from \n/var/lib/clickhouse/shadow/\n can be copied to the remote server and then deleted on the local server.\nThe entire backup process is performed without stopping the server.\n\n\nThe \nALTER ... FREEZE PARTITION\n query is not replicated. A local backup is only created on the local server.\n\n\nAs an alternative, you can manually copy data from the \n/var/lib/clickhouse/data/database/table\n directory.\nBut if you do this while the server is running, race conditions are possible when copying directories with files being added or changed, and the backup may be inconsistent. You can do this if the server isn't running \u2013 then the resulting data will be the same as after the \nALTER TABLE t FREEZE PARTITION\n query.\n\n\nALTER TABLE ... FREEZE PARTITION\n only copies data, not table metadata. To make a backup of table metadata, copy the file \n/var/lib/clickhouse/metadata/database/table.sql\n\n\nTo restore from a backup:\n\n\n\n\n\n\nUse the CREATE query to create the table if it doesn't exist. The query can be taken from an .sql file (replace \nATTACH\n in it with \nCREATE\n).\n\n\nCopy the data from the data/database/table/ directory inside the backup to the \n/var/lib/clickhouse/data/database/table/detached/ directory.\n\n\nRun \nALTER TABLE ... ATTACH PARTITION YYYYMM\n queries, where \nYYYYMM\n is the month, for every month.\n\n\n\n\n\n\nIn this way, data from the backup will be added to the table.\nRestoring from a backup doesn't require stopping the server.\n\n\nBackups and replication\n\n\nReplication provides protection from device failures. If all data disappeared on one of your replicas, follow the instructions in the \"Restoration after failure\" section to restore it.\n\n\nFor protection from device failures, you must use replication. For more information about replication, see the section \"Data replication\".\n\n\nBackups protect against human error (accidentally deleting data, deleting the wrong data or in the wrong cluster, or corrupting data).\nFor high-volume databases, it can be difficult to copy backups to remote servers. In such cases, to protect from human error, you can keep a backup on the same server (it will reside in \n/var/lib/clickhouse/shadow/\n).\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nFETCH\n \nPARTITION\n \nname\n \nFROM\n \npath-in-zookeeper\n\n\n\n\n\n\nThis query only works for replicatable tables.\n\n\nIt downloads the specified partition from the shard that has its \nZooKeeper path\n specified in the \nFROM\n clause, then puts it in the \ndetached\n directory for the specified table.\n\n\nAlthough the query is called \nALTER TABLE\n, it does not change the table structure, and does not immediately change the data available in the table.\n\n\nData is placed in the \ndetached\n directory. You can use the \nALTER TABLE ... ATTACH\n query to attach the data.\n\n\nThe \nFROM\n clause specifies the path in \nZooKeeper\n. For example, \n/clickhouse/tables/01-01/visits\n.\nBefore downloading, the system checks that the partition exists and the table structure matches. The most appropriate replica is selected automatically from the healthy replicas.\n\n\nThe \nALTER ... FETCH PARTITION\n query is not replicated. The partition will be downloaded to the 'detached' directory only on the local server. Note that if after this you use the \nALTER TABLE ... ATTACH\n query to add data to the table, the data will be added on all replicas (on one of the replicas it will be added from the 'detached' directory, and on the rest it will be loaded from neighboring replicas).\n\n\nSynchronicity of ALTER queries\n\n\nFor non-replicatable tables, all \nALTER\n queries are performed synchronously. For replicatable tables, the query just adds instructions for the appropriate actions to \nZooKeeper\n, and the actions themselves are performed as soon as possible. However, the query can wait for these actions to be completed on all the replicas.\n\n\nFor \nALTER ... ATTACH|DETACH|DROP\n queries, you can use the \nreplication_alter_partitions_sync\n setting to set up waiting.\nPossible values: \n0\n \u2013 do not wait; \n1\n \u2013 only wait for own execution (default); \n2\n \u2013 wait for all.\n\n\n\n\nSHOW DATABASES\n\n\nSHOW\n \nDATABASES\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nPrints a list of all databases.\nThis query is identical to \nSELECT name FROM system.databases [INTO OUTFILE filename] [FORMAT format]\n.\n\n\nSee also the section \"Formats\".\n\n\nSHOW TABLES\n\n\nSHOW\n \n[\nTEMPORARY\n]\n \nTABLES\n \n[\nFROM\n \ndb\n]\n \n[\nLIKE\n \npattern\n]\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nDisplays a list of tables\n\n\n\n\ntables from the current database, or from the 'db' database if \"FROM db\" is specified.\n\n\nall tables, or tables whose name matches the pattern, if \"LIKE 'pattern'\" is specified.\n\n\n\n\nThis query is identical to: \nSELECT name FROM system.tables WHERE database = 'db' [AND name LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format]\n.\n\n\nSee also the section \"LIKE operator\".\n\n\nSHOW PROCESSLIST\n\n\nSHOW\n \nPROCESSLIST\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nOutputs a list of queries currently being processed, other than \nSHOW PROCESSLIST\n queries.\n\n\nPrints a table containing the columns:\n\n\nuser\n \u2013 The user who made the query. Keep in mind that for distributed processing, queries are sent to remote servers under the 'default' user. SHOW PROCESSLIST shows the username for a specific query, not for a query that this query initiated.\n\n\naddress\n \u2013 The name of the host that the query was sent from. For distributed processing, on remote servers, this is the name of the query requestor host. To track where a distributed query was originally made from, look at SHOW PROCESSLIST on the query requestor server.\n\n\nelapsed\n \u2013 The execution time, in seconds. Queries are output in order of decreasing execution time.\n\n\nrows_read\n, \nbytes_read\n \u2013 How many rows and bytes of uncompressed data were read when processing the query. For distributed processing, data is totaled from all the remote servers. This is the data used for restrictions and quotas.\n\n\nmemory_usage\n \u2013 Current RAM usage in bytes. See the setting 'max_memory_usage'.\n\n\nquery\n \u2013 The query itself. In INSERT queries, the data for insertion is not output.\n\n\nquery_id\n \u2013 The query identifier. Non-empty only if it was explicitly defined by the user. For distributed processing, the query ID is not passed to remote servers.\n\n\nThis query is identical to: \nSELECT * FROM system.processes [INTO OUTFILE filename] [FORMAT format]\n.\n\n\nTip (execute in the console):\n\n\nwatch -n1 \nclickhouse-client --query=\nSHOW PROCESSLIST\n\n\n\n\n\n\nSHOW CREATE TABLE\n\n\nSHOW\n \nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\ndb\n.]\ntable\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nReturns a single \nString\n-type 'statement' column, which contains a single value \u2013 the \nCREATE\n query used for creating the specified table.\n\n\nDESCRIBE TABLE\n\n\nDESC\n|\nDESCRIBE\n \nTABLE\n \n[\ndb\n.]\ntable\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nReturns two \nString\n-type columns: \nname\n and \ntype\n, which indicate the names and types of columns in the specified table.\n\n\nNested data structures are output in \"expanded\" format. Each column is shown separately, with the name after a dot.\n\n\nEXISTS\n\n\nEXISTS\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\ndb\n.]\nname\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nReturns a single \nUInt8\n-type column, which contains the single value \n0\n if the table or database doesn't exist, or \n1\n if the table exists in the specified database.\n\n\nUSE\n\n\nUSE\n \ndb\n\n\n\n\n\n\nLets you set the current database for the session.\nThe current database is used for searching for tables if the database is not explicitly defined in the query with a dot before the table name.\nThis query can't be made when using the HTTP protocol, since there is no concept of a session.\n\n\nSET\n\n\nSET\n \nparam\n \n=\n \nvalue\n\n\n\n\n\n\nAllows you to set \nparam\n to \nvalue\n. You can also make all the settings from the specified settings profile in a single query. To do this, specify 'profile' as the setting name. For more information, see the section \"Settings\".\nThe setting is made for the session, or for the server (globally) if \nGLOBAL\n is specified.\nWhen making a global setting, the setting is not applied to sessions already running, including the current session. It will only be used for new sessions.\n\n\nWhen the server is restarted, global settings made using \nSET\n are lost.\nTo make settings that persist after a server restart, you can only use the server's config file.\n\n\nOPTIMIZE\n\n\nOPTIMIZE\n \nTABLE\n \n[\ndb\n.]\nname\n \n[\nPARTITION\n \npartition\n]\n \n[\nFINAL\n]\n\n\n\n\n\n\nAsks the table engine to do something for optimization.\nSupported only by \n*MergeTree\n engines, in which this query initializes a non-scheduled merge of data parts.\nIf you specify a \nPARTITION\n, only the specified partition will be optimized.\nIf you specify \nFINAL\n, optimization will be performed even when all the data is already in one part.\n\n\n\n\nINSERT\n\n\nAdding data.\n\n\nBasic query format:\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nVALUES\n \n(\nv11\n,\n \nv12\n,\n \nv13\n),\n \n(\nv21\n,\n \nv22\n,\n \nv23\n),\n \n...\n\n\n\n\n\n\nThe query can specify a list of columns to insert \n[(c1, c2, c3)]\n. In this case, the rest of the columns are filled with:\n\n\n\n\nThe values calculated from the \nDEFAULT\n expressions specified in the table definition.\n\n\nZeros and empty strings, if \nDEFAULT\n expressions are not defined.\n\n\n\n\nIf \nstrict_insert_defaults=1\n, columns that do not have \nDEFAULT\n defined must be listed in the query.\n\n\nData can be passed to the INSERT in any \nformat\n supported by ClickHouse. The format must be specified explicitly in the query:\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nFORMAT\n \nformat_name\n \ndata_set\n\n\n\n\n\n\nFor example, the following query format is identical to the basic version of INSERT ... VALUES:\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nFORMAT\n \nValues\n \n(\nv11\n,\n \nv12\n,\n \nv13\n),\n \n(\nv21\n,\n \nv22\n,\n \nv23\n),\n \n...\n\n\n\n\n\n\nClickHouse removes all spaces and one line feed (if there is one) before the data. When forming a query, we recommend putting the data on a new line after the query operators (this is important if the data begins with spaces).\n\n\nExample:\n\n\nINSERT\n \nINTO\n \nt\n \nFORMAT\n \nTabSeparated\n\n\n11\n \nHello\n,\n \nworld\n!\n\n\n22\n \nQwerty\n\n\n\n\n\n\nYou can insert data separately from the query by using the command-line client or the HTTP interface. For more information, see the section \"\nInterfaces\n\".\n\n\nInserting the results of \nSELECT\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nSELECT\n \n...\n\n\n\n\n\n\nColumns are mapped according to their position in the SELECT clause. However, their names in the SELECT expression and the table for INSERT may differ. If necessary, type casting is performed.\n\n\nNone of the data formats except Values allow setting values to expressions such as \nnow()\n, \n1 + 2\n, and so on. The Values format allows limited use of expressions, but this is not recommended, because in this case inefficient code is used for their execution.\n\n\nOther queries for modifying data parts are not supported: \nUPDATE\n, \nDELETE\n, \nREPLACE\n, \nMERGE\n, \nUPSERT\n, \nINSERT UPDATE\n.\nHowever, you can delete old data using \nALTER TABLE ... DROP PARTITION\n.\n\n\nPerformance considerations\n\n\nINSERT\n sorts the input data by primary key and splits them into partitions by month. If you insert data for mixed months, it can significantly reduce the performance of the \nINSERT\n query. To avoid this:\n\n\n\n\nAdd data in fairly large batches, such as 100,000 rows at a time.\n\n\nGroup data by month before uploading it to ClickHouse.\n\n\n\n\nPerformance will not decrease if:\n\n\n\n\nData is added in real time.\n\n\nYou upload data that is usually sorted by time.\n\n\n\n\nSELECT\n\n\nData sampling.\n\n\nSELECT\n \n[\nDISTINCT\n]\n \nexpr_list\n\n \n[\nFROM\n \n[\ndb\n.]\ntable\n \n|\n \n(\nsubquery\n)\n \n|\n \ntable_function\n]\n \n[\nFINAL\n]\n\n \n[\nSAMPLE\n \nsample_coeff\n]\n\n \n[\nARRAY\n \nJOIN\n \n...]\n\n \n[\nGLOBAL\n]\n \nANY\n|\nALL\n \nINNER\n|\nLEFT\n \nJOIN\n \n(\nsubquery\n)\n|\ntable\n \nUSING\n \ncolumns_list\n\n \n[\nPREWHERE\n \nexpr\n]\n\n \n[\nWHERE\n \nexpr\n]\n\n \n[\nGROUP\n \nBY\n \nexpr_list\n]\n \n[\nWITH\n \nTOTALS\n]\n\n \n[\nHAVING\n \nexpr\n]\n\n \n[\nORDER\n \nBY\n \nexpr_list\n]\n\n \n[\nLIMIT\n \n[\nn\n,\n \n]\nm\n]\n\n \n[\nUNION\n \nALL\n \n...]\n\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n\n \n[\nFORMAT\n \nformat\n]\n\n \n[\nLIMIT\n \nn\n \nBY\n \ncolumns\n]\n\n\n\n\n\n\nAll the clauses are optional, except for the required list of expressions immediately after SELECT.\nThe clauses below are described in almost the same order as in the query execution conveyor.\n\n\nIf the query omits the \nDISTINCT\n, \nGROUP BY\n and \nORDER BY\n clauses and the \nIN\n and \nJOIN\n subqueries, the query will be completely stream processed, using O(1) amount of RAM.\nOtherwise, the query might consume a lot of RAM if the appropriate restrictions are not specified: \nmax_memory_usage\n, \nmax_rows_to_group_by\n, \nmax_rows_to_sort\n, \nmax_rows_in_distinct\n, \nmax_bytes_in_distinct\n, \nmax_rows_in_set\n, \nmax_bytes_in_set\n, \nmax_rows_in_join\n, \nmax_bytes_in_join\n, \nmax_bytes_before_external_sort\n, \nmax_bytes_before_external_group_by\n. For more information, see the section \"Settings\". It is possible to use external sorting (saving temporary tables to a disk) and external aggregation. \nThe system does not have \"merge join\"\n.\n\n\nFROM clause\n\n\nIf the FROM clause is omitted, data will be read from the \nsystem.one\n table.\nThe 'system.one' table contains exactly one row (this table fulfills the same purpose as the DUAL table found in other DBMSs).\n\n\nThe FROM clause specifies the table to read data from, or a subquery, or a table function; ARRAY JOIN and the regular JOIN may also be included (see below).\n\n\nInstead of a table, the SELECT subquery may be specified in brackets.\nIn this case, the subquery processing pipeline will be built into the processing pipeline of an external query.\nIn contrast to standard SQL, a synonym does not need to be specified after a subquery. For compatibility, it is possible to write 'AS name' after a subquery, but the specified name isn't used anywhere.\n\n\nA table function may be specified instead of a table. For more information, see the section \"Table functions\".\n\n\nTo execute a query, all the columns listed in the query are extracted from the appropriate table. Any columns not needed for the external query are thrown out of the subqueries.\nIf a query does not list any columns (for example, SELECT count() FROM t), some column is extracted from the table anyway (the smallest one is preferred), in order to calculate the number of rows.\n\n\nThe FINAL modifier can be used only for a SELECT from a CollapsingMergeTree table. When you specify FINAL, data is selected fully \"collapsed\". Keep in mind that using FINAL leads to a selection that includes columns related to the primary key, in addition to the columns specified in the SELECT. Additionally, the query will be executed in a single stream, and data will be merged during query execution. This means that when using FINAL, the query is processed more slowly. In most cases, you should avoid using FINAL. For more information, see the section \"CollapsingMergeTree engine\".\n\n\nSAMPLE clause\n\n\nThe SAMPLE clause allows for approximated query processing. Approximated query processing is only supported by MergeTree* type tables, and only if the sampling expression was specified during table creation (see the section \"MergeTree engine\").\n\n\nSAMPLE\n has the \nformat SAMPLE k\n, where \nk\n is a decimal number from 0 to 1, or \nSAMPLE n\n, where 'n' is a sufficiently large integer.\n\n\nIn the first case, the query will be executed on 'k' percent of data. For example, \nSAMPLE 0.1\n runs the query on 10% of data.\nIn the second case, the query will be executed on a sample of no more than 'n' rows. For example, \nSAMPLE 10000000\n runs the query on a maximum of 10,000,000 rows.\n\n\nExample:\n\n\nSELECT\n\n \nTitle\n,\n\n \ncount\n()\n \n*\n \n10\n \nAS\n \nPageViews\n\n\nFROM\n \nhits_distributed\n\n\nSAMPLE\n \n0\n.\n1\n\n\nWHERE\n\n \nCounterID\n \n=\n \n34\n\n \nAND\n \ntoDate\n(\nEventDate\n)\n \n=\n \ntoDate\n(\n2013-01-29\n)\n\n \nAND\n \ntoDate\n(\nEventDate\n)\n \n=\n \ntoDate\n(\n2013-02-04\n)\n\n \nAND\n \nNOT\n \nDontCountHits\n\n \nAND\n \nNOT\n \nRefresh\n\n \nAND\n \nTitle\n \n!=\n \n\n\nGROUP\n \nBY\n \nTitle\n\n\nORDER\n \nBY\n \nPageViews\n \nDESC\n \nLIMIT\n \n1000\n\n\n\n\n\n\nIn this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value 'count()' is manually multiplied by 10.\n\n\nWhen using something like \nSAMPLE 10000000\n, there isn't any information about which relative percent of data was processed or what the aggregate functions should be multiplied by, so this method of writing is not always appropriate to the situation.\n\n\nA sample with a relative coefficient is \"consistent\": if we look at all possible data that could be in the table, a sample (when using a single sampling expression specified during table creation) with the same coefficient always selects the same subset of possible data. In other words, a sample from different tables on different servers at different times is made the same way.\n\n\nFor example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This allows using the sample in subqueries in the IN clause, as well as for manually correlating results of different queries with samples.\n\n\nARRAY JOIN clause\n\n\nAllows executing JOIN with an array or nested data structure. The intent is similar to the 'arrayJoin' function, but its functionality is broader.\n\n\nARRAY JOIN\n is essentially \nINNER JOIN\n with an array. Example:\n\n\n:) CREATE TABLE arrays_test (s String, arr Array(UInt8)) ENGINE = Memory\n\nCREATE TABLE arrays_test\n(\n s String,\n arr Array(UInt8)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.001 sec.\n\n:) INSERT INTO arrays_test VALUES (\nHello\n, [1,2]), (\nWorld\n, [3,4,5]), (\nGoodbye\n, [])\n\nINSERT INTO arrays_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM arrays_test\n\nSELECT *\nFROM arrays_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502\n\u2502 Goodbye \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, arr FROM arrays_test ARRAY JOIN arr\n\nSELECT s, arr\nFROM arrays_test\nARRAY JOIN arr\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2510\n\u2502 Hello \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502\n\u2502 World \u2502 4 \u2502\n\u2502 World \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nAn alias can be specified for an array in the ARRAY JOIN clause. In this case, an array item can be accessed by this alias, but the array itself by the original name. Example:\n\n\n:) SELECT s, arr, a FROM arrays_test ARRAY JOIN arr AS a\n\nSELECT s, arr, a\nFROM arrays_test\nARRAY JOIN arr AS a\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nMultiple arrays of the same size can be comma-separated in the ARRAY JOIN clause. In this case, JOIN is performed with them simultaneously (the direct sum, not the direct product). Example:\n\n\n:) SELECT s, arr, a, num, mapped FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(x -\n x + 1, arr) AS mapped\n\nSELECT s, arr, a, num, mapped\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(lambda(tuple(x), plus(x, 1)), arr) AS mapped\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500mapped\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 2 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 5 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 6 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n:) SELECT s, arr, a, num, arrayEnumerate(arr) FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\nSELECT s, arr, a, num, arrayEnumerate(arr)\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500arrayEnumerate(arr)\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 [1,2] \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n\n\n\n\nARRAY JOIN also works with nested data structures. Example:\n\n\n:) CREATE TABLE nested_test (s String, nest Nested(x UInt8, y UInt32)) ENGINE = Memory\n\nCREATE TABLE nested_test\n(\n s String,\n nest Nested(\n x UInt8,\n y UInt32)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.006 sec.\n\n:) INSERT INTO nested_test VALUES (\nHello\n, [1,2], [10,20]), (\nWorld\n, [3,4,5], [30,40,50]), (\nGoodbye\n, [], [])\n\nINSERT INTO nested_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM nested_test\n\nSELECT *\nFROM nested_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 Goodbye \u2502 [] \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nWhen specifying names of nested data structures in ARRAY JOIN, the meaning is the same as ARRAY JOIN with all the array elements that it consists of. Example:\n\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x, nest.y\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`, `nest.y`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nThis variation also makes sense:\n\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nAn alias may be used for a nested data structure, in order to select either the JOIN result or the source array. Example:\n\n\n:) SELECT s, n.x, n.y, nest.x, nest.y FROM nested_test ARRAY JOIN nest AS n\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest AS n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nExample of using the arrayEnumerate function:\n\n\n:) SELECT s, n.x, n.y, nest.x, nest.y, num FROM nested_test ARRAY JOIN nest AS n, arrayEnumerate(nest.x) AS num\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`, num\nFROM nested_test\nARRAY JOIN nest AS n, arrayEnumerate(`nest.x`) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u252c\u2500num\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 1 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 2 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 3 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n\n\n\n\nThe query can only specify a single ARRAY JOIN clause.\n\n\nThe corresponding conversion can be performed before the WHERE/PREWHERE clause (if its result is needed in this clause), or after completing WHERE/PREWHERE (to reduce the volume of calculations).\n\n\nJOIN clause\n\n\nThe normal JOIN, which is not related to ARRAY JOIN described above.\n\n\n[\nGLOBAL\n]\n \nANY\n|\nALL\n \nINNER\n|\nLEFT\n \n[\nOUTER\n]\n \nJOIN\n \n(\nsubquery\n)\n|\ntable\n \nUSING\n \ncolumns_list\n\n\n\n\n\n\nPerforms joins with data from the subquery. At the beginning of query processing, the subquery specified after JOIN is run, and its result is saved in memory. Then it is read from the \"left\" table specified in the FROM clause, and while it is being read, for each of the read rows from the \"left\" table, rows are selected from the subquery results table (the \"right\" table) that meet the condition for matching the values of the columns specified in USING.\n\n\nThe table name can be specified instead of a subquery. This is equivalent to the \nSELECT * FROM table\n subquery, except in a special case when the table has the Join engine \u2013 an array prepared for joining.\n\n\nAll columns that are not needed for the JOIN are deleted from the subquery.\n\n\nThere are several types of JOINs:\n\n\nINNER\n or \nLEFT\n type:If INNER is specified, the result will contain only those rows that have a matching row in the right table.\nIf LEFT is specified, any rows in the left table that don't have matching rows in the right table will be assigned the default value - zeros or empty rows. LEFT OUTER may be written instead of LEFT; the word OUTER does not affect anything.\n\n\nANY\n or \nALL\n stringency:If \nANY\n is specified and the right table has several matching rows, only the first one found is joined.\nIf \nALL\n is specified and the right table has several matching rows, the data will be multiplied by the number of these rows.\n\n\nUsing ALL corresponds to the normal JOIN semantic from standard SQL.\nUsing ANY is optimal. If the right table has only one matching row, the results of ANY and ALL are the same. You must specify either ANY or ALL (neither of them is selected by default).\n\n\nGLOBAL\n distribution:\n\n\nWhen using a normal JOIN, the query is sent to remote servers. Subqueries are run on each of them in order to make the right table, and the join is performed with this table. In other words, the right table is formed on each server separately.\n\n\nWhen using \nGLOBAL ... JOIN\n, first the requestor server runs a subquery to calculate the right table. This temporary table is passed to each remote server, and queries are run on them using the temporary data that was transmitted.\n\n\nBe careful when using GLOBAL JOINs. For more information, see the section \"Distributed subqueries\".\n\n\nAny combination of JOINs is possible. For example, \nGLOBAL ANY LEFT OUTER JOIN\n.\n\n\nWhen running a JOIN, there is no optimization of the order of execution in relation to other stages of the query. The join (a search in the right table) is run before filtering in WHERE and before aggregation. In order to explicitly set the processing order, we recommend running a JOIN subquery with a subquery.\n\n\nExample:\n\n\nSELECT\n\n \nCounterID\n,\n\n \nhits\n,\n\n \nvisits\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nCounterID\n,\n\n \ncount\n()\n \nAS\n \nhits\n\n \nFROM\n \ntest\n.\nhits\n\n \nGROUP\n \nBY\n \nCounterID\n\n\n)\n \nANY\n \nLEFT\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nCounterID\n,\n\n \nsum\n(\nSign\n)\n \nAS\n \nvisits\n\n \nFROM\n \ntest\n.\nvisits\n\n \nGROUP\n \nBY\n \nCounterID\n\n\n)\n \nUSING\n \nCounterID\n\n\nORDER\n \nBY\n \nhits\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500CounterID\u2500\u252c\u2500\u2500\u2500hits\u2500\u252c\u2500visits\u2500\u2510\n\u2502 1143050 \u2502 523264 \u2502 13665 \u2502\n\u2502 731962 \u2502 475698 \u2502 102716 \u2502\n\u2502 722545 \u2502 337212 \u2502 108187 \u2502\n\u2502 722889 \u2502 252197 \u2502 10547 \u2502\n\u2502 2237260 \u2502 196036 \u2502 9522 \u2502\n\u2502 23057320 \u2502 147211 \u2502 7689 \u2502\n\u2502 722818 \u2502 90109 \u2502 17847 \u2502\n\u2502 48221 \u2502 85379 \u2502 4652 \u2502\n\u2502 19762435 \u2502 77807 \u2502 7026 \u2502\n\u2502 722884 \u2502 77492 \u2502 11056 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSubqueries don't allow you to set names or use them for referencing a column from a specific subquery.\nThe columns specified in USING must have the same names in both subqueries, and the other columns must be named differently. You can use aliases to change the names of columns in subqueries (the example uses the aliases 'hits' and 'visits').\n\n\nThe USING clause specifies one or more columns to join, which establishes the equality of these columns. The list of columns is set without brackets. More complex join conditions are not supported.\n\n\nThe right table (the subquery result) resides in RAM. If there isn't enough memory, you can't run a JOIN.\n\n\nOnly one JOIN can be specified in a query (on a single level). To run multiple JOINs, you can put them in subqueries.\n\n\nEach time a query is run with the same JOIN, the subquery is run again \u2013 the result is not cached. To avoid this, use the special 'Join' table engine, which is a prepared array for joining that is always in RAM. For more information, see the section \"Table engines, Join\".\n\n\nIn some cases, it is more efficient to use IN instead of JOIN.\nAmong the various types of JOINs, the most efficient is ANY LEFT JOIN, then ANY INNER JOIN. The least efficient are ALL LEFT JOIN and ALL INNER JOIN.\n\n\nIf you need a JOIN for joining with dimension tables (these are relatively small tables that contain dimension properties, such as names for advertising campaigns), a JOIN might not be very convenient due to the bulky syntax and the fact that the right table is re-accessed for every query. For such cases, there is an \"external dictionaries\" feature that you should use instead of JOIN. For more information, see the section \"External dictionaries\".\n\n\nWHERE clause\n\n\nIf there is a WHERE clause, it must contain an expression with the UInt8 type. This is usually an expression with comparison and logical operators.\nThis expression will be used for filtering data before all other transformations.\n\n\nIf indexes are supported by the database table engine, the expression is evaluated on the ability to use indexes.\n\n\nPREWHERE clause\n\n\nThis clause has the same meaning as the WHERE clause. The difference is in which data is read from the table.\nWhen using PREWHERE, first only the columns necessary for executing PREWHERE are read. Then the other columns are read that are needed for running the query, but only those blocks where the PREWHERE expression is true.\n\n\nIt makes sense to use PREWHERE if there are filtration conditions that are not suitable for indexes that are used by a minority of the columns in the query, but that provide strong data filtration. This reduces the volume of data to read.\n\n\nFor example, it is useful to write PREWHERE for queries that extract a large number of columns, but that only have filtration for a few columns.\n\n\nPREWHERE is only supported by tables from the \n*MergeTree\n family.\n\n\nA query may simultaneously specify PREWHERE and WHERE. In this case, PREWHERE precedes WHERE.\n\n\nKeep in mind that it does not make much sense for PREWHERE to only specify those columns that have an index, because when using an index, only the data blocks that match the index are read.\n\n\nIf the 'optimize_move_to_prewhere' setting is set to 1 and PREWHERE is omitted, the system uses heuristics to automatically move parts of expressions from WHERE to PREWHERE.\n\n\nGROUP BY clause\n\n\nThis is one of the most important parts of a column-oriented DBMS.\n\n\nIf there is a GROUP BY clause, it must contain a list of expressions. Each expression will be referred to here as a \"key\".\nAll the expressions in the SELECT, HAVING, and ORDER BY clauses must be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions.\n\n\nIf a query contains only table columns inside aggregate functions, the GROUP BY clause can be omitted, and aggregation by an empty set of keys is assumed.\n\n\nExample:\n\n\nSELECT\n\n \ncount\n(),\n\n \nmedian\n(\nFetchTiming\n \n \n60\n \n?\n \n60\n \n:\n \nFetchTiming\n),\n\n \ncount\n()\n \n-\n \nsum\n(\nRefresh\n)\n\n\nFROM\n \nhits\n\n\n\n\n\n\nHowever, in contrast to standard SQL, if the table doesn't have any rows (either there aren't any at all, or there aren't any after using WHERE to filter), an empty result is returned, and not the result from one of the rows containing the initial values of aggregate functions.\n\n\nAs opposed to MySQL (and conforming to standard SQL), you can't get some value of some column that is not in a key or aggregate function (except constant expressions). To work around this, you can use the 'any' aggregate function (get the first encountered value) or 'min/max'.\n\n\nExample:\n\n\nSELECT\n\n \ndomainWithoutWWW\n(\nURL\n)\n \nAS\n \ndomain\n,\n\n \ncount\n(),\n\n \nany\n(\nTitle\n)\n \nAS\n \ntitle\n \n-- getting the first occurred page header for each domain.\n\n\nFROM\n \nhits\n\n\nGROUP\n \nBY\n \ndomain\n\n\n\n\n\n\nFor every different key value encountered, GROUP BY calculates a set of aggregate function values.\n\n\nGROUP BY is not supported for array columns.\n\n\nA constant can't be specified as arguments for aggregate functions. Example: sum(1). Instead of this, you can get rid of the constant. Example: \ncount()\n.\n\n\nWITH TOTALS modifier\n\n\nIf the WITH TOTALS modifier is specified, another row will be calculated. This row will have key columns containing default values (zeros or empty lines), and columns of aggregate functions with the values calculated across all the rows (the \"total\" values).\n\n\nThis extra row is output in JSON*, TabSeparated*, and Pretty* formats, separately from the other rows. In the other formats, this row is not output.\n\n\nIn JSON* formats, this row is output as a separate 'totals' field. In TabSeparated* formats, the row comes after the main result, preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result.\n\n\nWITH TOTALS\n can be run in different ways when HAVING is present. The behavior depends on the 'totals_mode' setting.\nBy default, \ntotals_mode = 'before_having'\n. In this case, 'totals' is calculated across all rows, including the ones that don't pass through HAVING and 'max_rows_to_group_by'.\n\n\nThe other alternatives include only the rows that pass through HAVING in 'totals', and behave differently with the setting \nmax_rows_to_group_by\n and \ngroup_by_overflow_mode = 'any'\n.\n\n\nafter_having_exclusive\n \u2013 Don't include rows that didn't pass through \nmax_rows_to_group_by\n. In other words, 'totals' will have less than or the same number of rows as it would if \nmax_rows_to_group_by\n were omitted.\n\n\nafter_having_inclusive\n \u2013 Include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. In other words, 'totals' will have more than or the same number of rows as it would if \nmax_rows_to_group_by\n were omitted.\n\n\nafter_having_auto\n \u2013 Count the number of rows that passed through HAVING. If it is more than a certain amount (by default, 50%), include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. Otherwise, do not include them.\n\n\ntotals_auto_threshold\n \u2013 By default, 0.5. The coefficient for \nafter_having_auto\n.\n\n\nIf \nmax_rows_to_group_by\n and \ngroup_by_overflow_mode = 'any'\n are not used, all variations of \nafter_having\n are the same, and you can use any of them (for example, \nafter_having_auto\n).\n\n\nYou can use WITH TOTALS in subqueries, including subqueries in the JOIN clause (in this case, the respective total values are combined).\n\n\nGROUP BY in external memory\n\n\nYou can enable dumping temporary data to the disk to restrict memory usage during GROUP BY.\nThe \nmax_bytes_before_external_group_by\n setting determines the threshold RAM consumption for dumping GROUP BY temporary data to the file system. If set to 0 (the default), it is disabled.\n\n\nWhen using \nmax_bytes_before_external_group_by\n, we recommend that you set max_memory_usage about twice as high. This is necessary because there are two stages to aggregation: reading the date and forming intermediate data (1) and merging the intermediate data (2). Dumping data to the file system can only occur during stage 1. If the temporary data wasn't dumped, then stage 2 might require up to the same amount of memory as in stage 1.\n\n\nFor example, if \nmax_memory_usage\n was set to 10000000000 and you want to use external aggregation, it makes sense to set \nmax_bytes_before_external_group_by\n to 10000000000, and max_memory_usage to 20000000000. When external aggregation is triggered (if there was at least one dump of temporary data), maximum consumption of RAM is only slightly more than \nmax_bytes_before_external_group_by\n.\n\n\nWith distributed query processing, external aggregation is performed on remote servers. In order for the requestor server to use only a small amount of RAM, set \ndistributed_aggregation_memory_efficient\n to 1.\n\n\nWhen merging data flushed to the disk, as well as when merging results from remote servers when the \ndistributed_aggregation_memory_efficient\n setting is enabled, consumes up to 1/256 * the number of threads from the total amount of RAM.\n\n\nWhen external aggregation is enabled, if there was less than \nmax_bytes_before_external_group_by\n of data (i.e. data was not flushed), the query runs just as fast as without external aggregation. If any temporary data was flushed, the run time will be several times longer (approximately three times).\n\n\nIf you have an ORDER BY with a small LIMIT after GROUP BY, then the ORDER BY CLAUSE will not use significant amounts of RAM.\nBut if the ORDER BY doesn't have LIMIT, don't forget to enable external sorting (\nmax_bytes_before_external_sort\n).\n\n\nLIMIT N BY clause\n\n\nLIMIT N BY COLUMNS selects the top N rows for each group of COLUMNS. LIMIT N BY is not related to LIMIT; they can both be used in the same query. The key for LIMIT N BY can contain any number of columns or expressions.\n\n\nExample:\n\n\nSELECT\n\n \ndomainWithoutWWW\n(\nURL\n)\n \nAS\n \ndomain\n,\n\n \ndomainWithoutWWW\n(\nREFERRER_URL\n)\n \nAS\n \nreferrer\n,\n\n \ndevice_type\n,\n\n \ncount\n()\n \ncnt\n\n\nFROM\n \nhits\n\n\nGROUP\n \nBY\n \ndomain\n,\n \nreferrer\n,\n \ndevice_type\n\n\nORDER\n \nBY\n \ncnt\n \nDESC\n\n\nLIMIT\n \n5\n \nBY\n \ndomain\n,\n \ndevice_type\n\n\nLIMIT\n \n100\n\n\n\n\n\n\nThe query will select the top 5 referrers for each \ndomain, device_type\n pair, but not more than 100 rows (\nLIMIT n BY + LIMIT\n).\n\n\nHAVING clause\n\n\nAllows filtering the result received after GROUP BY, similar to the WHERE clause.\nWHERE and HAVING differ in that WHERE is performed before aggregation (GROUP BY), while HAVING is performed after it.\nIf aggregation is not performed, HAVING can't be used.\n\n\n\n\nORDER BY clause\n\n\nThe ORDER BY clause contains a list of expressions, which can each be assigned DESC or ASC (the sorting direction). If the direction is not specified, ASC is assumed. ASC is sorted in ascending order, and DESC in descending order. The sorting direction applies to a single expression, not to the entire list. Example: \nORDER BY Visits DESC, SearchPhrase\n\n\nFor sorting by String values, you can specify collation (comparison). Example: \nORDER BY SearchPhrase COLLATE 'tr'\n - for sorting by keyword in ascending order, using the Turkish alphabet, case insensitive, assuming that strings are UTF-8 encoded. COLLATE can be specified or not for each expression in ORDER BY independently. If ASC or DESC is specified, COLLATE is specified after it. When using COLLATE, sorting is always case-insensitive.\n\n\nWe only recommend using COLLATE for final sorting of a small number of rows, since sorting with COLLATE is less efficient than normal sorting by bytes.\n\n\nRows that have identical values for the list of sorting expressions are output in an arbitrary order, which can also be nondeterministic (different each time).\nIf the ORDER BY clause is omitted, the order of the rows is also undefined, and may be nondeterministic as well.\n\n\nWhen floating point numbers are sorted, NaNs are separate from the other values. Regardless of the sorting order, NaNs come at the end. In other words, for ascending sorting they are placed as if they are larger than all the other numbers, while for descending sorting they are placed as if they are smaller than the rest.\n\n\nLess RAM is used if a small enough LIMIT is specified in addition to ORDER BY. Otherwise, the amount of memory spent is proportional to the volume of data for sorting. For distributed query processing, if GROUP BY is omitted, sorting is partially done on remote servers, and the results are merged on the requestor server. This means that for distributed sorting, the volume of data to sort can be greater than the amount of memory on a single server.\n\n\nIf there is not enough RAM, it is possible to perform sorting in external memory (creating temporary files on a disk). Use the setting \nmax_bytes_before_external_sort\n for this purpose. If it is set to 0 (the default), external sorting is disabled. If it is enabled, when the volume of data to sort reaches the specified number of bytes, the collected data is sorted and dumped into a temporary file. After all data is read, all the sorted files are merged and the results are output. Files are written to the /var/lib/clickhouse/tmp/ directory in the config (by default, but you can use the 'tmp_path' parameter to change this setting).\n\n\nRunning a query may use more memory than 'max_bytes_before_external_sort'. For this reason, this setting must have a value significantly smaller than 'max_memory_usage'. As an example, if your server has 128 GB of RAM and you need to run a single query, set 'max_memory_usage' to 100 GB, and 'max_bytes_before_external_sort' to 80 GB.\n\n\nExternal sorting works much less effectively than sorting in RAM.\n\n\nSELECT clause\n\n\nThe expressions specified in the SELECT clause are analyzed after the calculations for all the clauses listed above are completed.\nMore specifically, expressions are analyzed that are above the aggregate functions, if there are any aggregate functions.\nThe aggregate functions and everything below them are calculated during aggregation (GROUP BY).\nThese expressions work as if they are applied to separate rows in the result.\n\n\nDISTINCT clause\n\n\nIf DISTINCT is specified, only a single row will remain out of all the sets of fully matching rows in the result.\nThe result will be the same as if GROUP BY were specified across all the fields specified in SELECT without aggregate functions. But there are several differences from GROUP BY:\n\n\n\n\nDISTINCT can be applied together with GROUP BY.\n\n\nWhen ORDER BY is omitted and LIMIT is defined, the query stops running immediately after the required number of different rows has been read.\n\n\nData blocks are output as they are processed, without waiting for the entire query to finish running.\n\n\n\n\nDISTINCT is not supported if SELECT has at least one array column.\n\n\nLIMIT clause\n\n\nLIMIT m allows you to select the first 'm' rows from the result.\nLIMIT n, m allows you to select the first 'm' rows from the result after skipping the first 'n' rows.\n\n\n'n' and 'm' must be non-negative integers.\n\n\nIf there isn't an ORDER BY clause that explicitly sorts results, the result may be arbitrary and nondeterministic.\n\n\nUNION ALL clause\n\n\nYou can use UNION ALL to combine any number of queries. Example:\n\n\nSELECT\n \nCounterID\n,\n \n1\n \nAS\n \ntable\n,\n \ntoInt64\n(\ncount\n())\n \nAS\n \nc\n\n \nFROM\n \ntest\n.\nhits\n\n \nGROUP\n \nBY\n \nCounterID\n\n\n\nUNION\n \nALL\n\n\n\nSELECT\n \nCounterID\n,\n \n2\n \nAS\n \ntable\n,\n \nsum\n(\nSign\n)\n \nAS\n \nc\n\n \nFROM\n \ntest\n.\nvisits\n\n \nGROUP\n \nBY\n \nCounterID\n\n \nHAVING\n \nc\n \n \n0\n\n\n\n\n\n\nOnly UNION ALL is supported. The regular UNION (UNION DISTINCT) is not supported. If you need UNION DISTINCT, you can write SELECT DISTINCT from a subquery containing UNION ALL.\n\n\nQueries that are parts of UNION ALL can be run simultaneously, and their results can be mixed together.\n\n\nThe structure of results (the number and type of columns) must match for the queries. But the column names can differ. In this case, the column names for the final result will be taken from the first query.\n\n\nQueries that are parts of UNION ALL can't be enclosed in brackets. ORDER BY and LIMIT are applied to separate queries, not to the final result. If you need to apply a conversion to the final result, you can put all the queries with UNION ALL in a subquery in the FROM clause.\n\n\nINTO OUTFILE clause\n\n\nAdd the \nINTO OUTFILE filename\n clause (where filename is a string literal) to redirect query output to the specified file.\nIn contrast to MySQL, the file is created on the client side. The query will fail if a file with the same filename already exists.\nThis functionality is available in the command-line client and clickhouse-local (a query sent via HTTP interface will fail).\n\n\nThe default output format is TabSeparated (the same as in the command-line client batch mode).\n\n\nFORMAT clause\n\n\nSpecify 'FORMAT format' to get data in any specified format.\nYou can use this for convenience, or for creating dumps.\nFor more information, see the section \"Formats\".\nIf the FORMAT clause is omitted, the default format is used, which depends on both the settings and the interface used for accessing the DB. For the HTTP interface and the command-line client in batch mode, the default format is TabSeparated. For the command-line client in interactive mode, the default format is PrettyCompact (it has attractive and compact tables).\n\n\nWhen using the command-line client, data is passed to the client in an internal efficient format. The client independently interprets the FORMAT clause of the query and formats the data itself (thus relieving the network and the server from the load).\n\n\nIN operators\n\n\nThe \nIN\n, \nNOT IN\n, \nGLOBAL IN\n, and \nGLOBAL NOT IN\n operators are covered separately, since their functionality is quite rich.\n\n\nThe left side of the operator is either a single column or a tuple.\n\n\nExamples:\n\n\nSELECT\n \nUserID\n \nIN\n \n(\n123\n,\n \n456\n)\n \nFROM\n \n...\n\n\nSELECT\n \n(\nCounterID\n,\n \nUserID\n)\n \nIN\n \n((\n34\n,\n \n123\n),\n \n(\n101500\n,\n \n456\n))\n \nFROM\n \n...\n\n\n\n\n\n\nIf the left side is a single column that is in the index, and the right side is a set of constants, the system uses the index for processing the query.\n\n\nDon't list too many values explicitly (i.e. millions). If a data set is large, put it in a temporary table (for example, see the section \"External data for query processing\"), then use a subquery.\n\n\nThe right side of the operator can be a set of constant expressions, a set of tuples with constant expressions (shown in the examples above), or the name of a database table or SELECT subquery in brackets.\n\n\nIf the right side of the operator is the name of a table (for example, \nUserID IN users\n), this is equivalent to the subquery \nUserID IN (SELECT * FROM users)\n. Use this when working with external data that is sent along with the query. For example, the query can be sent together with a set of user IDs loaded to the 'users' temporary table, which should be filtered.\n\n\nIf the right side of the operator is a table name that has the Set engine (a prepared data set that is always in RAM), the data set will not be created over again for each query.\n\n\nThe subquery may specify more than one column for filtering tuples.\nExample:\n\n\nSELECT\n \n(\nCounterID\n,\n \nUserID\n)\n \nIN\n \n(\nSELECT\n \nCounterID\n,\n \nUserID\n \nFROM\n \n...)\n \nFROM\n \n...\n\n\n\n\n\n\nThe columns to the left and right of the IN operator should have the same type.\n\n\nThe IN operator and subquery may occur in any part of the query, including in aggregate functions and lambda functions.\nExample:\n\n\nSELECT\n\n \nEventDate\n,\n\n \navg\n(\nUserID\n \nIN\n\n \n(\n\n \nSELECT\n \nUserID\n\n \nFROM\n \ntest\n.\nhits\n\n \nWHERE\n \nEventDate\n \n=\n \ntoDate\n(\n2014-03-17\n)\n\n \n))\n \nAS\n \nratio\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \nEventDate\n\n\nORDER\n \nBY\n \nEventDate\n \nASC\n\n\n\n\n\n\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500ratio\u2500\u2510\n\u2502 2014-03-17 \u2502 1 \u2502\n\u2502 2014-03-18 \u2502 0.807696 \u2502\n\u2502 2014-03-19 \u2502 0.755406 \u2502\n\u2502 2014-03-20 \u2502 0.723218 \u2502\n\u2502 2014-03-21 \u2502 0.697021 \u2502\n\u2502 2014-03-22 \u2502 0.647851 \u2502\n\u2502 2014-03-23 \u2502 0.648416 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nFor each day after March 17th, count the percentage of pageviews made by users who visited the site on March 17th.\nA subquery in the IN clause is always run just one time on a single server. There are no dependent subqueries.\n\n\n\n\nDistributed subqueries\n\n\nThere are two options for IN-s with subqueries (similar to JOINs): normal \nIN\n / \nOIN\n and \nIN GLOBAL\n / \nGLOBAL JOIN\n. They differ in how they are run for distributed query processing.\n\n\n\n\nRemember that the algorithms described below may work differently depending on the [settings](#settings-distributed_product_mode) `distributed_product_mode` setting.\n\n\n\n\n\nWhen using the regular IN, the query is sent to remote servers, and each of them runs the subqueries in the \nIN\n or \nJOIN\n clause.\n\n\nWhen using \nGLOBAL IN\n / \nGLOBAL JOINs\n, first all the subqueries are run for \nGLOBAL IN\n / \nGLOBAL JOINs\n, and the results are collected in temporary tables. Then the temporary tables are sent to each remote server, where the queries are run using this temporary data.\n\n\nFor a non-distributed query, use the regular \nIN\n / \nJOIN\n.\n\n\nBe careful when using subqueries in the \nIN\n / \nJOIN\n clauses for distributed query processing.\n\n\nLet's look at some examples. Assume that each server in the cluster has a normal \nlocal_table\n. Each server also has a \ndistributed_table\n table with the \nDistributed\n type, which looks at all the servers in the cluster.\n\n\nFor a query to the \ndistributed_table\n, the query will be sent to all the remote servers and run on them using the \nlocal_table\n.\n\n\nFor example, the query\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n\n\n\n\n\n\nwill be sent to all remote servers as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n\n\n\n\n\n\nand run on each of them in parallel, until it reaches the stage where intermediate results can be combined. Then the intermediate results will be returned to the requestor server and merged on it, and the final result will be sent to the client.\n\n\nNow let's examine a query with IN:\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\n\n\nCalculation of the intersection of audiences of two sites.\n\n\n\n\nThis query will be sent to all remote servers as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nIn other words, the data set in the IN clause will be collected on each server independently, only across the data that is stored locally on each of the servers.\n\n\nThis will work correctly and optimally if you are prepared for this case and have spread data across the cluster servers such that the data for a single UserID resides entirely on a single server. In this case, all the necessary data will be available locally on each server. Otherwise, the result will be inaccurate. We refer to this variation of the query as \"local IN\".\n\n\nTo correct how the query works when data is spread randomly across the cluster servers, you could specify \ndistributed_table\n inside a subquery. The query would look like this:\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nThis query will be sent to all remote servers as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nThe subquery will begin running on each remote server. Since the subquery uses a distributed table, the subquery that is on each remote server will be resent to every remote server as\n\n\nSELECT\n \nUserID\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n\n\n\n\n\n\nFor example, if you have a cluster of 100 servers, executing the entire query will require 10,000 elementary requests, which is generally considered unacceptable.\n\n\nIn such cases, you should always use GLOBAL IN instead of IN. Let's look at how it works for the query\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nGLOBAL\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nThe requestor server will run the subquery\n\n\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n\n\n\n\n\n\nand the result will be put in a temporary table in RAM. Then the request will be sent to each remote server as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nGLOBAL\n \nIN\n \n_data1\n\n\n\n\n\n\nand the temporary table \n_data1\n will be sent to every remote server with the query (the name of the temporary table is implementation-defined).\n\n\nThis is more optimal than using the normal IN. However, keep the following points in mind:\n\n\n\n\nWhen creating a temporary table, data is not made unique. To reduce the volume of data transmitted over the network, specify DISTINCT in the subquery. (You don't need to do this for a normal IN.)\n\n\nThe temporary table will be sent to all the remote servers. Transmission does not account for network topology. For example, if 10 remote servers reside in a datacenter that is very remote in relation to the requestor server, the data will be sent 10 times over the channel to the remote datacenter. Try to avoid large data sets when using GLOBAL IN.\n\n\nWhen transmitting data to remote servers, restrictions on network bandwidth are not configurable. You might overload the network.\n\n\nTry to distribute data across servers so that you don't need to use GLOBAL IN on a regular basis.\n\n\nIf you need to use GLOBAL IN often, plan the location of the ClickHouse cluster so that a single group of replicas resides in no more than one data center with a fast network between them, so that a query can be processed entirely within a single data center.\n\n\n\n\nIt also makes sense to specify a local table in the \nGLOBAL IN\n clause, in case this local table is only available on the requestor server and you want to use data from it on remote servers.\n\n\nExtreme values\n\n\nIn addition to results, you can also get minimum and maximum values for the results columns. To do this, set the \nextremes\n setting to 1. Minimums and maximums are calculated for numeric types, dates, and dates with times. For other columns, the default values are output.\n\n\nAn extra two rows are calculated \u2013 the minimums and maximums, respectively. These extra two rows are output in JSON*, TabSeparated*, and Pretty* formats, separate from the other rows. They are not output for other formats.\n\n\nIn JSON* formats, the extreme values are output in a separate 'extremes' field. In TabSeparated* formats, the row comes after the main result, and after 'totals' if present. It is preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result, and after 'totals' if present.\n\n\nExtreme values are calculated for rows that have passed through LIMIT. However, when using 'LIMIT offset, size', the rows before 'offset' are included in 'extremes'. In stream requests, the result may also include a small number of rows that passed through LIMIT.\n\n\nNotes\n\n\nThe \nGROUP BY\n and \nORDER BY\n clauses do not support positional arguments. This contradicts MySQL, but conforms to standard SQL.\nFor example, \nGROUP BY 1, 2\n will be interpreted as grouping by constants (i.e. aggregation of all rows into one).\n\n\nYou can use synonyms (\nAS\n aliases) in any part of a query.\n\n\nYou can put an asterisk in any part of a query instead of an expression. When the query is analyzed, the asterisk is expanded to a list of all table columns (excluding the \nMATERIALIZED\n and \nALIAS\n columns). There are only a few cases when using an asterisk is justified:\n\n\n\n\nWhen creating a table dump.\n\n\nFor tables containing just a few columns, such as system tables.\n\n\nFor getting information about what columns are in a table. In this case, set \nLIMIT 1\n. But it is better to use the \nDESC TABLE\n query.\n\n\nWhen there is strong filtration on a small number of columns using \nPREWHERE\n.\n\n\nIn subqueries (since columns that aren't needed for the external query are excluded from subqueries).\n\n\n\n\nIn all other cases, we don't recommend using the asterisk, since it only gives you the drawbacks of a columnar DBMS instead of the advantages. In other words using the asterisk is not recommended.\n\n\nKILL QUERY\n\n\nKILL\n \nQUERY\n\n \nWHERE\n \nwhere\n \nexpression\n \nto\n \nSELECT\n \nFROM\n \nsystem\n.\nprocesses\n \nquery\n\n \n[\nSYNC\n|\nASYNC\n|\nTEST\n]\n\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nAttempts to forcibly terminate the currently running queries.\nThe queries to terminate are selected from the system.processes table using the criteria defined in the \nWHERE\n clause of the \nKILL\n query.\n\n\nExamples:\n\n\n-- Forcibly terminates all queries with the specified query_id:\n\n\nKILL\n \nQUERY\n \nWHERE\n \nquery_id\n=\n2-857d-4a57-9ee0-327da5d60a90\n\n\n\n-- Synchronously terminates all queries run by \nusername\n:\n\n\nKILL\n \nQUERY\n \nWHERE\n \nuser\n=\nusername\n \nSYNC\n\n\n\n\n\n\nRead-only users can only stop their own queries.\n\n\nBy default, the asynchronous version of queries is used (\nASYNC\n), which doesn't wait for confirmation that queries have stopped.\n\n\nThe synchronous version (\nSYNC\n) waits for all queries to stop and displays information about each process as it stops.\nThe response contains the \nkill_status\n column, which can take the following values:\n\n\n\n\n'finished' \u2013 The query was terminated successfully.\n\n\n'waiting' \u2013 Waiting for the query to end after sending it a signal to terminate.\n\n\nThe other values \u200b\u200bexplain why the query can't be stopped.\n\n\n\n\nA test query (\nTEST\n) only checks the user's rights and displays a list of queries to stop.\n\n\nSyntax\n\n\nThere are two types of parsers in the system: the full SQL parser (a recursive descent parser), and the data format parser (a fast stream parser).\nIn all cases except the INSERT query, only the full SQL parser is used.\nThe INSERT query uses both parsers:\n\n\nINSERT\n \nINTO\n \nt\n \nVALUES\n \n(\n1\n,\n \nHello, world\n),\n \n(\n2\n,\n \nabc\n),\n \n(\n3\n,\n \ndef\n)\n\n\n\n\n\n\nThe \nINSERT INTO t VALUES\n fragment is parsed by the full parser, and the data \n(1, 'Hello, world'), (2, 'abc'), (3, 'def')\n is parsed by the fast stream parser.\nData can have any format. When a query is received, the server calculates no more than \nmax_query_size\n bytes of the request in RAM (by default, 1 MB), and the rest is stream parsed.\nThis means the system doesn't have problems with large INSERT queries, like MySQL does.\n\n\nWhen using the Values format in an INSERT query, it may seem that data is parsed the same as expressions in a SELECT query, but this is not true. The Values format is much more limited.\n\n\nNext we will cover the full parser. For more information about format parsers, see the section \"Formats\".\n\n\nSpaces\n\n\nThere may be any number of space symbols between syntactical constructions (including the beginning and end of a query). Space symbols include the space, tab, line feed, CR, and form feed.\n\n\nComments\n\n\nSQL-style and C-style comments are supported.\nSQL-style comments: from \n--\n to the end of the line. The space after \n--\n can be omitted.\nComments in C-style: from \n/*\n to \n*/\n. These comments can be multiline. Spaces are not required here, either.\n\n\nKeywords\n\n\nKeywords (such as \nSELECT\n) are not case-sensitive. Everything else (column names, functions, and so on), in contrast to standard SQL, is case-sensitive. Keywords are not reserved (they are just parsed as keywords in the corresponding context).\n\n\nIdentifiers\n\n\nIdentifiers (column names, functions, and data types) can be quoted or non-quoted.\nNon-quoted identifiers start with a Latin letter or underscore, and continue with a Latin letter, underscore, or number. In other words, they must match the regex \n^[a-zA-Z_][0-9a-zA-Z_]*$\n. Examples: \nx, _1, X_y__Z123_.\n\n\nQuoted identifiers are placed in reversed quotation marks \n`id`\n (the same as in MySQL), and can indicate any set of bytes (non-empty). In addition, symbols (for example, the reverse quotation mark) inside this type of identifier can be backslash-escaped. Escaping rules are the same as for string literals (see below).\nWe recommend using identifiers that do not need to be quoted.\n\n\nLiterals\n\n\nThere are numeric literals, string literals, and compound literals.\n\n\nNumeric literals\n\n\nA numeric literal tries to be parsed:\n\n\n\n\nFirst as a 64-bit signed number, using the 'strtoull' function.\n\n\nIf unsuccessful, as a 64-bit unsigned number, using the 'strtoll' function.\n\n\nIf unsuccessful, as a floating-point number using the 'strtod' function.\n\n\nOtherwise, an error is returned.\n\n\n\n\nThe corresponding value will have the smallest type that the value fits in.\nFor example, 1 is parsed as UInt8, but 256 is parsed as UInt16. For more information, see \"Data types\".\n\n\nExamples: \n1\n, \n18446744073709551615\n, \n0xDEADBEEF\n, \n01\n, \n0.1\n, \n1e100\n, \n-1e-100\n, \ninf\n, \nnan\n.\n\n\nString literals\n\n\nOnly string literals in single quotes are supported. The enclosed characters can be backslash-escaped. The following escape sequences have a corresponding special value: \n\\b\n, \n\\f\n, \n\\r\n, \n\\n\n, \n\\t\n, \n\\0\n, \n\\a\n, \n\\v\n, \n\\xHH\n. In all other cases, escape sequences in the format \n\\c\n, where \"c\" is any character, are converted to \"c\". This means that you can use the sequences \n\\'\nand\n\\\\\n. The value will have the String type.\n\n\nThe minimum set of characters that you need to escape in string literals: \n'\n and \n\\\n.\n\n\nCompound literals\n\n\nConstructions are supported for arrays: \n[1, 2, 3]\n and tuples: \n(1, 'Hello, world!', 2)\n..\nActually, these are not literals, but expressions with the array creation operator and the tuple creation operator, respectively.\nFor more information, see the section \"Operators2\".\nAn array must consist of at least one item, and a tuple must have at least two items.\nTuples have a special purpose for use in the IN clause of a SELECT query. Tuples can be obtained as the result of a query, but they can't be saved to a database (with the exception of Memory-type tables).\n\n\nFunctions\n\n\nFunctions are written like an identifier with a list of arguments (possibly empty) in brackets. In contrast to standard SQL, the brackets are required, even for an empty arguments list. Example: \nnow()\n.\nThere are regular and aggregate functions (see the section \"Aggregate functions\"). Some aggregate functions can contain two lists of arguments in brackets. Example: \nquantile (0.9) (x)\n. These aggregate functions are called \"parametric\" functions, and the arguments in the first list are called \"parameters\". The syntax of aggregate functions without parameters is the same as for regular functions.\n\n\nOperators\n\n\nOperators are converted to their corresponding functions during query parsing, taking their priority and associativity into account.\nFor example, the expression \n1 + 2 * 3 + 4\n is transformed to \nplus(plus(1, multiply(2, 3)), 4)\n.\nFor more information, see the section \"Operators\" below.\n\n\nData types and database table engines\n\n\nData types and table engines in the \nCREATE\n query are written the same way as identifiers or functions. In other words, they may or may not contain an arguments list in brackets. For more information, see the sections \"Data types,\" \"Table engines,\" and \"CREATE\".\n\n\nSynonyms\n\n\nIn the SELECT query, expressions can specify synonyms using the AS keyword. Any expression is placed to the left of AS. The identifier name for the synonym is placed to the right of AS. As opposed to standard SQL, synonyms are not only declared on the top level of expressions:\n\n\nSELECT\n \n(\n1\n \nAS\n \nn\n)\n \n+\n \n2\n,\n \nn\n\n\n\n\n\n\nIn contrast to standard SQL, synonyms can be used in all parts of a query, not just \nSELECT\n.\n\n\nAsterisk\n\n\nIn a \nSELECT\n query, an asterisk can replace the expression. For more information, see the section \"SELECT\".\n\n\nExpressions\n\n\nAn expression is a function, identifier, literal, application of an operator, expression in brackets, subquery, or asterisk. It can also contain a synonym.\nA list of expressions is one or more expressions separated by commas.\nFunctions and operators, in turn, can have expressions as arguments.\n\n\nTable engines\n\n\nThe table engine (type of table) determines:\n\n\n\n\nHow and where data is stored: where to write it to, and where to read it from.\n\n\nWhich queries are supported, and how.\n\n\nConcurrent data access.\n\n\nUse of indexes, if present.\n\n\nWhether multithreaded request execution is possible.\n\n\nData replication.\n\n\n\n\nWhen reading data, the engine is only required to extract the necessary set of columns. However, in some cases, the query may be partially processed inside the table engine.\n\n\nNote that for most serious tasks, you should use engines from the \nMergeTree\n family.\n\n\nTinyLog\n\n\nThe simplest table engine, which stores data on a disk.\nEach column is stored in a separate compressed file.\nWhen writing, data is appended to the end of files.\n\n\nConcurrent data access is not restricted in any way:\n\n\n\n\nIf you are simultaneously reading from a table and writing to it in a different query, the read operation will complete with an error.\n\n\nIf you are writing to a table in multiple queries simultaneously, the data will be broken.\n\n\n\n\nThe typical way to use this table is write-once: first just write the data one time, then read it as many times as needed.\nQueries are executed in a single stream. In other words, this engine is intended for relatively small tables (recommended up to 1,000,000 rows).\nIt makes sense to use this table engine if you have many small tables, since it is simpler than the Log engine (fewer files need to be opened).\nThe situation when you have a large number of small tables guarantees poor productivity, but may already be used when working with another DBMS, and you may find it easier to switch to using TinyLog types of tables.\n\nIndexes are not supported.\n\n\nIn Yandex.Metrica, TinyLog tables are used for intermediary data that is processed in small batches.\n\n\nLog\n\n\nLog differs from TinyLog in that a small file of \"marks\" resides with the column files. These marks are written on every data block and contain offsets that indicate where to start reading the file in order to skip the specified number of rows. This makes it possible to read table data in multiple threads.\nFor concurrent data access, the read operations can be performed simultaneously, while write operations block reads and each other.\nThe Log engine does not support indexes. Similarly, if writing to a table failed, the table is broken, and reading from it returns an error. The Log engine is appropriate for temporary data, write-once tables, and for testing or demonstration purposes.\n\n\nMemory\n\n\nThe Memory engine stores data in RAM, in uncompressed form. Data is stored in exactly the same form as it is received when read. In other words, reading from this table is completely free.\nConcurrent data access is synchronized. Locks are short: read and write operations don't block each other.\nIndexes are not supported. Reading is parallelized.\nMaximal productivity (over 10 GB/sec) is reached on simple queries, because there is no reading from the disk, decompressing, or deserializing data. (We should note that in many cases, the productivity of the MergeTree engine is almost as high.)\nWhen restarting a server, data disappears from the table and the table becomes empty.\nNormally, using this table engine is not justified. However, it can be used for tests, and for tasks where maximum speed is required on a relatively small number of rows (up to approximately 100,000,000).\n\n\nThe Memory engine is used by the system for temporary tables with external query data (see the section \"External data for processing a query\"), and for implementing GLOBAL IN (see the section \"IN operators\").\n\n\n\n\nMergeTree\n\n\nThe MergeTree engine supports an index by primary key and by date, and provides the possibility to update data in real time.\nThis is the most advanced table engine in ClickHouse. Don't confuse it with the Merge engine.\n\n\nThe engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity.\n\n\nExample without sampling support.\n\n\nMergeTree(EventDate, (CounterID, EventDate), 8192)\n\n\n\n\n\nExample with sampling support.\n\n\nMergeTree(EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID)), 8192)\n\n\n\n\n\nA MergeTree table must have a separate column containing the date. Here, it is the EventDate column. The date column must have the 'Date' type (not 'DateTime').\n\n\nThe primary key may be a tuple from any expressions (usually this is just a tuple of columns), or a single expression.\n\n\nThe sampling expression (optional) can be any expression. It must also be present in the primary key. The example uses a hash of user IDs to pseudo-randomly disperse data in the table for each CounterID and EventDate. In other words, when using the SAMPLE clause in a query, you get an evenly pseudo-random sample of data for a subset of users.\n\n\nThe table is implemented as a set of parts. Each part is sorted by the primary key. In addition, each part has the minimum and maximum date assigned. When inserting in the table, a new sorted part is created. The merge process is periodically initiated in the background. When merging, several parts are selected (usually the smallest ones) and then merged into one large sorted part.\n\n\nIn other words, incremental sorting occurs when inserting to the table. Merging is implemented so that the table always consists of a small number of sorted parts, and the merge itself doesn't do too much work.\n\n\nDuring insertion, data belonging to different months is separated into different parts. The parts that correspond to different months are never combined. The purpose of this is to provide local data modification (for ease in backups).\n\n\nParts are combined up to a certain size threshold, so there aren't any merges that are too long.\n\n\nFor each part, an index file is also written. The index file contains the primary key value for every 'index_granularity' row in the table. In other words, this is an abbreviated index of sorted data.\n\n\nFor columns, \"marks\" are also written to each 'index_granularity' row so that data can be read in a specific range.\n\n\nWhen reading from a table, the SELECT query is analyzed for whether indexes can be used.\nAn index can be used if the WHERE or PREWHERE clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has IN or LIKE with a fixed prefix on columns or expressions that are in the primary key or partitioning key, or on certain partially repetitive functions of these columns, or logical relationships of these expressions.\n\n\nThus, it is possible to quickly run queries on one or many ranges of the primary key. In this example, queries will be fast when run for a specific tracking tag; for a specific tag and date range; for a specific tag and date; for multiple tags with a date range, and so on.\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \nEventDate\n \n=\n \ntoDate\n(\nnow\n())\n \nAND\n \nCounterID\n \n=\n \n34\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \nEventDate\n \n=\n \ntoDate\n(\nnow\n())\n \nAND\n \n(\nCounterID\n \n=\n \n34\n \nOR\n \nCounterID\n \n=\n \n42\n)\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \n((\nEventDate\n \n=\n \ntoDate\n(\n2014-01-01\n)\n \nAND\n \nEventDate\n \n=\n \ntoDate\n(\n2014-01-31\n))\n \nOR\n \nEventDate\n \n=\n \ntoDate\n(\n2014-05-01\n))\n \nAND\n \nCounterID\n \nIN\n \n(\n101500\n,\n \n731962\n,\n \n160656\n)\n \nAND\n \n(\nCounterID\n \n=\n \n101500\n \nOR\n \nEventDate\n \n!=\n \ntoDate\n(\n2014-05-01\n))\n\n\n\n\n\n\nAll of these cases will use the index by date and by primary key. The index is used even for complex expressions. Reading from the table is organized so that using the index can't be slower than a full scan.\n\n\nIn this example, the index can't be used.\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \nCounterID\n \n=\n \n34\n \nOR\n \nURL\n \nLIKE\n \n%upyachka%\n\n\n\n\n\n\nTo check whether ClickHouse can use the index when executing the query, use the settings \nforce_index_by_date\nand\nforce_primary_key\n.\n\n\nThe index by date only allows reading those parts that contain dates from the desired range. However, a data part may contain data for many dates (up to an entire month), while within a single part the data is ordered by the primary key, which might not contain the date as the first column. Because of this, using a query with only a date condition that does not specify the primary key prefix will cause more data to be read than for a single date.\n\n\nFor concurrent table access, we use multi-versioning. In other words, when a table is simultaneously read and updated, data is read from a set of parts that is current at the time of the query. There are no lengthy locks. Inserts do not get in the way of read operations.\n\n\nReading from a table is automatically parallelized.\n\n\nThe \nOPTIMIZE\n query is supported, which calls an extra merge step.\n\n\nYou can use a single large table and continually add data to it in small chunks \u2013 this is what MergeTree is intended for.\n\n\nData replication is possible for all types of tables in the MergeTree family (see the section \"Data replication\").\n\n\n\n\nCustom partitioning key\n\n\nStarting with version 1.1.54310, you can create tables in the MergeTree family with any partitioning expression (not only partitioning by month).\n\n\nThe partition key can be an expression from the table columns, or a tuple of such expressions (similar to the primary key). The partition key can be omitted. When creating a table, specify the partition key in the ENGINE description with the new syntax:\n\n\nENGINE [=] Name(...) [PARTITION BY expr] [ORDER BY expr] [SAMPLE BY expr] [SETTINGS name=value, ...]\n\n\n\n\n\nFor MergeTree tables, the partition expression is specified after \nPARTITION BY\n, the primary key after \nORDER BY\n, the sampling key after \nSAMPLE BY\n, and \nSETTINGS\n can specify \nindex_granularity\n (optional; the default value is 8192), as well as other settings from \nMergeTreeSettings.h\n. The other engine parameters are specified in parentheses after the engine name, as previously. Example:\n\n\nENGINE\n \n=\n \nReplicatedCollapsingMergeTree\n(\n/clickhouse/tables/name\n,\n \nreplica1\n,\n \nSign\n)\n\n \nPARTITION\n \nBY\n \n(\ntoMonday\n(\nStartDate\n),\n \nEventType\n)\n\n \nORDER\n \nBY\n \n(\nCounterID\n,\n \nStartDate\n,\n \nintHash32\n(\nUserID\n))\n\n \nSAMPLE\n \nBY\n \nintHash32\n(\nUserID\n)\n\n\n\n\n\n\nThe traditional partitioning by month is expressed as \ntoYYYYMM(date_column)\n.\n\n\nYou can't convert an old-style table to a table with custom partitions (only via INSERT SELECT).\n\n\nAfter this table is created, merge will only work for data parts that have the same value for the partitioning expression. Note: This means that you shouldn't make overly granular partitions (more than about a thousand partitions), or SELECT will perform poorly.\n\n\nTo specify a partition in ALTER PARTITION commands, specify the value of the partition expression (or a tuple). Constants and constant expressions are supported. Example:\n\n\nALTER\n \nTABLE\n \ntable\n \nDROP\n \nPARTITION\n \n(\ntoMonday\n(\ntoday\n()),\n \n1\n)\n\n\n\n\n\n\nDeletes the partition for the current week with event type 1. The same is true for the OPTIMIZE query. To specify the only partition in a non-partitioned table, specify \nPARTITION tuple()\n.\n\n\nNote: For old-style tables, the partition can be specified either as a number \n201710\n or a string \n'201710'\n. The syntax for the new style of tables is stricter with types (similar to the parser for the VALUES input format). In addition, ALTER TABLE FREEZE PARTITION uses exact match for new-style tables (not prefix match).\n\n\nIn the \nsystem.parts\n table, the \npartition\n column specifies the value of the partition expression to use in ALTER queries (if quotas are removed). The \nname\n column should specify the name of the data part that has a new format.\n\n\nWas: \n20140317_20140323_2_2_0\n (minimum date - maximum date - minimum block number - maximum block number - level).\n\n\nNow: \n201403_2_2_0\n (partition ID - minimum block number - maximum block number - level).\n\n\nThe partition ID is its string identifier (human-readable, if possible) that is used for the names of data parts in the file system and in ZooKeeper. You can specify it in ALTER queries in place of the partition key. Example: Partition key \ntoYYYYMM(EventDate)\n; ALTER can specify either \nPARTITION 201710\n or \nPARTITION ID '201710'\n.\n\n\nFor more examples, see the tests \n00502_custom_partitioning_local\n and \n00502_custom_partitioning_replicated_zookeeper\n.\n\n\nReplacingMergeTree\n\n\nThis engine table differs from \nMergeTree\n in that it removes duplicate entries with the same primary key value.\n\n\nThe last optional parameter for the table engine is the version column. When merging, it reduces all rows with the same primary key value to just one row. If the version column is specified, it leaves the row with the highest version; otherwise, it leaves the last row.\n\n\nThe version column must have a type from the \nUInt\n family, \nDate\n, or \nDateTime\n.\n\n\nReplacingMergeTree\n(\nEventDate\n,\n \n(\nOrderID\n,\n \nEventDate\n,\n \nBannerID\n,\n \n...),\n \n8192\n,\n \nver\n)\n\n\n\n\n\n\nNote that data is only deduplicated during merges. Merging occurs in the background at an unknown time, so you can't plan for it. Some of the data may remain unprocessed. Although you can run an unscheduled merge using the OPTIMIZE query, don't count on using it, because the OPTIMIZE query will read and write a large amount of data.\n\n\nThus, \nReplacingMergeTree\n is suitable for clearing out duplicate data in the background in order to save space, but it doesn't guarantee the absence of duplicates.\n\n\nThis engine is not used in Yandex.Metrica, but it has been applied in other Yandex projects.\n\n\nSummingMergeTree\n\n\nThis engine differs from \nMergeTree\n in that it totals data while merging.\n\n\nSummingMergeTree\n(\nEventDate\n,\n \n(\nOrderID\n,\n \nEventDate\n,\n \nBannerID\n,\n \n...),\n \n8192\n)\n\n\n\n\n\n\nThe columns to total are implicit. When merging, all rows with the same primary key value (in the example, OrderId, EventDate, BannerID, ...) have their values totaled in numeric columns that are not part of the primary key.\n\n\nSummingMergeTree\n(\nEventDate\n,\n \n(\nOrderID\n,\n \nEventDate\n,\n \nBannerID\n,\n \n...),\n \n8192\n,\n \n(\nShows\n,\n \nClicks\n,\n \nCost\n,\n \n...))\n\n\n\n\n\n\nThe columns to total are set explicitly (the last parameter \u2013 Shows, Clicks, Cost, ...). When merging, all rows with the same primary key value have their values totaled in the specified columns. The specified columns also must be numeric and must not be part of the primary key.\n\n\nIf the values were null in all of these columns, the row is deleted. (The exception is cases when the data part would not have any rows left in it.)\n\n\nFor the other rows that are not part of the primary key, the first value that occurs is selected when merging.\n\n\nSummation is not performed for a read operation. If it is necessary, write the appropriate GROUP BY.\n\n\nIn addition, a table can have nested data structures that are processed in a special way.\nIf the name of a nested table ends in 'Map' and it contains at least two columns that meet the following criteria:\n\n\n\n\nThe first table is numeric ((U)IntN, Date, DateTime), which we'll refer to as the 'key'.\n\n\nThe other columns are arithmetic ((U)IntN, Float32/64), which we'll refer to as '(values...)'. Then this nested table is interpreted as a mapping of key =\n (values...), and when merging its rows, the elements of two data sets are merged by 'key' with a summation of the corresponding (values...).\n\n\n\n\nExamples:\n\n\n[(1, 100)] + [(2, 150)] -\n [(1, 100), (2, 150)]\n[(1, 100)] + [(1, 150)] -\n [(1, 250)]\n[(1, 100)] + [(1, 150), (2, 150)] -\n [(1, 250), (2, 150)]\n[(1, 100), (2, 150)] + [(1, -100)] -\n [(2, 150)]\n\n\n\n\n\nFor aggregation of Map, use the function sumMap(key, value).\n\n\nFor nested data structures, you don't need to specify the columns as a list of columns for totaling.\n\n\nThis table engine is not particularly useful. Remember that when saving just pre-aggregated data, you lose some of the system's advantages.\n\n\nAggregatingMergeTree\n\n\nThis engine differs from \nMergeTree\n in that the merge combines the states of aggregate functions stored in the table for rows with the same primary key value.\n\n\nFor this to work, it uses the \nAggregateFunction\n data type, as well as \n-State\n and \n-Merge\n modifiers for aggregate functions. Let's examine it more closely.\n\n\nThere is an \nAggregateFunction\n data type. It is a parametric data type. As parameters, the name of the aggregate function is passed, then the types of its arguments.\n\n\nExamples:\n\n\nCREATE\n \nTABLE\n \nt\n\n\n(\n\n \ncolumn1\n \nAggregateFunction\n(\nuniq\n,\n \nUInt64\n),\n\n \ncolumn2\n \nAggregateFunction\n(\nanyIf\n,\n \nString\n,\n \nUInt8\n),\n\n \ncolumn3\n \nAggregateFunction\n(\nquantiles\n(\n0\n.\n5\n,\n \n0\n.\n9\n),\n \nUInt64\n)\n\n\n)\n \nENGINE\n \n=\n \n...\n\n\n\n\n\n\nThis type of column stores the state of an aggregate function.\n\n\nTo get this type of value, use aggregate functions with the \nState\n suffix.\n\n\nExample:\n\nuniqState(UserID), quantilesState(0.5, 0.9)(SendTiming)\n\n\nIn contrast to the corresponding \nuniq\n and \nquantiles\n functions, these functions return the state, rather than the prepared value. In other words, they return an \nAggregateFunction\n type value.\n\n\nAn \nAggregateFunction\n type value can't be output in Pretty formats. In other formats, these types of values are output as implementation-specific binary data. The \nAggregateFunction\n type values are not intended for output or saving in a dump.\n\n\nThe only useful thing you can do with \nAggregateFunction\n type values is combine the states and get a result, which essentially means to finish aggregation. Aggregate functions with the 'Merge' suffix are used for this purpose.\nExample: \nuniqMerge(UserIDState), where UserIDState has the AggregateFunction\n type.\n\n\nIn other words, an aggregate function with the 'Merge' suffix takes a set of states, combines them, and returns the result.\nAs an example, these two queries return the same result:\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ntable\n\n\n\nSELECT\n \nuniqMerge\n(\nstate\n)\n \nFROM\n \n(\nSELECT\n \nuniqState\n(\nUserID\n)\n \nAS\n \nstate\n \nFROM\n \ntable\n \nGROUP\n \nBY\n \nRegionID\n)\n\n\n\n\n\n\nThere is an \nAggregatingMergeTree\n engine. Its job during a merge is to combine the states of aggregate functions from different table rows with the same primary key value.\n\n\nYou can't use a normal INSERT to insert a row in a table containing \nAggregateFunction\n columns, because you can't explicitly define the \nAggregateFunction\n value. Instead, use \nINSERT SELECT\n with \n-State\n aggregate functions for inserting data.\n\n\nWith SELECT from an \nAggregatingMergeTree\n table, use GROUP BY and aggregate functions with the '-Merge' modifier in order to complete data aggregation.\n\n\nYou can use \nAggregatingMergeTree\n tables for incremental data aggregation, including for aggregated materialized views.\n\n\nExample:\n\n\nCreate an \nAggregatingMergeTree\n materialized view that watches the \ntest.visits\n table:\n\n\nCREATE\n \nMATERIALIZED\n \nVIEW\n \ntest\n.\nbasic\n\n\nENGINE\n \n=\n \nAggregatingMergeTree\n(\nStartDate\n,\n \n(\nCounterID\n,\n \nStartDate\n),\n \n8192\n)\n\n\nAS\n \nSELECT\n\n \nCounterID\n,\n\n \nStartDate\n,\n\n \nsumState\n(\nSign\n)\n \nAS\n \nVisits\n,\n\n \nuniqState\n(\nUserID\n)\n \nAS\n \nUsers\n\n\nFROM\n \ntest\n.\nvisits\n\n\nGROUP\n \nBY\n \nCounterID\n,\n \nStartDate\n;\n\n\n\n\n\n\nInsert data in the \ntest.visits\n table. Data will also be inserted in the view, where it will be aggregated:\n\n\nINSERT\n \nINTO\n \ntest\n.\nvisits\n \n...\n\n\n\n\n\n\nPerform \nSELECT\n from the view using \nGROUP BY\n in order to complete data aggregation:\n\n\nSELECT\n\n \nStartDate\n,\n\n \nsumMerge\n(\nVisits\n)\n \nAS\n \nVisits\n,\n\n \nuniqMerge\n(\nUsers\n)\n \nAS\n \nUsers\n\n\nFROM\n \ntest\n.\nbasic\n\n\nGROUP\n \nBY\n \nStartDate\n\n\nORDER\n \nBY\n \nStartDate\n;\n\n\n\n\n\n\nYou can create a materialized view like this and assign a normal view to it that finishes data aggregation.\n\n\nNote that in most cases, using \nAggregatingMergeTree\n is not justified, since queries can be run efficiently enough on non-aggregated data.\n\n\nCollapsingMergeTree\n\n\nThis engine is used specifically for Yandex.Metrica.\n\n\nIt differs from \nMergeTree\n in that it allows automatic deletion, or \"collapsing\" certain pairs of rows when merging.\n\n\nYandex.Metrica has normal logs (such as hit logs) and change logs. Change logs are used for incrementally calculating statistics on data that is constantly changing. Examples are the log of session changes, or logs of changes to user histories. Sessions are constantly changing in Yandex.Metrica. For example, the number of hits per session increases. We refer to changes in any object as a pair (?old values, ?new values). Old values may be missing if the object was created. New values may be missing if the object was deleted. If the object was changed, but existed previously and was not deleted, both values are present. In the change log, one or two entries are made for each change. Each entry contains all the attributes that the object has, plus a special attribute for differentiating between the old and new values. When objects change, only the new entries are added to the change log, and the existing ones are not touched.\n\n\nThe change log makes it possible to incrementally calculate almost any statistics. To do this, we need to consider \"new\" rows with a plus sign, and \"old\" rows with a minus sign. In other words, incremental calculation is possible for all statistics whose algebraic structure contains an operation for taking the inverse of an element. This is true of most statistics. We can also calculate \"idempotent\" statistics, such as the number of unique visitors, since the unique visitors are not deleted when making changes to sessions.\n\n\nThis is the main concept that allows Yandex.Metrica to work in real time.\n\n\nCollapsingMergeTree accepts an additional parameter - the name of an Int8-type column that contains the row's \"sign\". Example:\n\n\nCollapsingMergeTree\n(\nEventDate\n,\n \n(\nCounterID\n,\n \nEventDate\n,\n \nintHash32\n(\nUniqID\n),\n \nVisitID\n),\n \n8192\n,\n \nSign\n)\n\n\n\n\n\n\nHere, \nSign\n is a column containing -1 for \"old\" values and 1 for \"new\" values.\n\n\nWhen merging, each group of consecutive identical primary key values (columns for sorting data) is reduced to no more than one row with the column value 'sign_column = -1' (the \"negative row\") and no more than one row with the column value 'sign_column = 1' (the \"positive row\"). In other words, entries from the change log are collapsed.\n\n\nIf the number of positive and negative rows matches, the first negative row and the last positive row are written.\nIf there is one more positive row than negative rows, only the last positive row is written.\nIf there is one more negative row than positive rows, only the first negative row is written.\nOtherwise, there will be a logical error and none of the rows will be written. (A logical error can occur if the same section of the log was accidentally inserted more than once. The error is just recorded in the server log, and the merge continues.)\n\n\nThus, collapsing should not change the results of calculating statistics.\nChanges are gradually collapsed so that in the end only the last value of almost every object is left.\nCompared to MergeTree, the CollapsingMergeTree engine allows a multifold reduction of data volume.\n\n\nThere are several ways to get completely \"collapsed\" data from a \nCollapsingMergeTree\n table:\n\n\n\n\nWrite a query with GROUP BY and aggregate functions that accounts for the sign. For example, to calculate quantity, write 'sum(Sign)' instead of 'count()'. To calculate the sum of something, write 'sum(Sign * x)' instead of 'sum(x)', and so on, and also add 'HAVING sum(Sign) \n 0'. Not all amounts can be calculated this way. For example, the aggregate functions 'min' and 'max' can't be rewritten.\n\n\nIf you must extract data without aggregation (for example, to check whether rows are present whose newest values match certain conditions), you can use the FINAL modifier for the FROM clause. This approach is significantly less efficient.\n\n\n\n\n\n\nGraphiteMergeTree\n\n\nThis engine is designed for rollup (thinning and aggregating/averaging) \nGraphite\n data. It may be helpful to developers who want to use ClickHouse as a data store for Graphite.\n\n\nGraphite stores full data in ClickHouse, and data can be retrieved in the following ways:\n\n\n\n\nWithout thinning.\n\n\n\n\nUses the \nMergeTree\n engine.\n\n\n\n\nWith thinning.\n\n\n\n\nUsing the \nGraphiteMergeTree\n engine.\n\n\nThe engine inherits properties from MergeTree. The settings for thinning data are defined by the \ngraphite_rollup\n parameter in the server configuration.\n\n\nUsing the engine\n\n\nThe Graphite data table must contain the following fields at minimum:\n\n\n\n\nPath\n \u2013 The metric name (Graphite sensor).\n\n\nTime\n \u2013 The time for measuring the metric.\n\n\nValue\n \u2013 The value of the metric at the time set in Time.\n\n\nVersion\n \u2013 Determines which value of the metric with the same Path and Time will remain in the database.\n\n\n\n\nRollup pattern:\n\n\npattern\n regexp\n function\n age -\n precision\n ...\npattern\n ...\ndefault\n function\n age -\n precision\n ...\n\n\n\n\n\nWhen processing a record, ClickHouse will check the rules in the \npattern\nclause. If the metric name matches the \nregexp\n, the rules from \npattern\n are applied; otherwise, the rules from \ndefault\n are used.\n\n\nFields in the pattern.\n\n\n\n\nage\n \u2013 The minimum age of the data in seconds.\n\n\nfunction\n \u2013 The name of the aggregating function to apply to data whose age falls within the range \n[age, age + precision]\n.\n\n\nprecision\n\u2013 How precisely to define the age of the data in seconds.\n\n\nregexp\n\u2013 A pattern for the metric name.\n\n\n\n\nExample of settings:\n\n\ngraphite_rollup\n\n \npattern\n\n \nregexp\nclick_cost\n/regexp\n\n \nfunction\nany\n/function\n\n \nretention\n\n \nage\n0\n/age\n\n \nprecision\n5\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n86400\n/age\n\n \nprecision\n60\n/precision\n\n \n/retention\n\n \n/pattern\n\n \ndefault\n\n \nfunction\nmax\n/function\n\n \nretention\n\n \nage\n0\n/age\n\n \nprecision\n60\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n3600\n/age\n\n \nprecision\n300\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n86400\n/age\n\n \nprecision\n3600\n/precision\n\n \n/retention\n\n \n/default\n\n\n/graphite_rollup\n\n\n\n\n\n\n\n\nData replication\n\n\nReplication is only supported for tables in the MergeTree family:\n\n\n\n\nReplicatedMergeTree\n\n\nReplicatedSummingMergeTree\n\n\nReplicatedReplacingMergeTree\n\n\nReplicatedAggregatingMergeTree\n\n\nReplicatedCollapsingMergeTree\n\n\nReplicatedGraphiteMergeTree\n\n\n\n\nReplication works at the level of an individual table, not the entire server. A server can store both replicated and non-replicated tables at the same time.\n\n\nReplication does not depend on sharding. Each shard has its own independent replication.\n\n\nCompressed data is replicated for \nINSERT\n and \nALTER\n queries (see the description of the \nALTER\n query).\n\n\nCREATE\n, \nDROP\n, \nATTACH\n, \nDETACH\n and \nRENAME\n queries are executed on a single server and are not replicated:\n\n\n\n\nThe CREATE TABLE\n query creates a new replicatable table on the server where the query is run. If this table already exists on other servers, it adds a new replica.\n\n\nThe DROP TABLE\n query deletes the replica located on the server where the query is run.\n\n\nThe RENAME\n query renames the table on one of the replicas. In other words, replicated tables can have different names on different replicas.\n\n\n\n\nTo use replication, set the addresses of the ZooKeeper cluster in the config file. Example:\n\n\nzookeeper\n\n \nnode\n \nindex=\n1\n\n \nhost\nexample1\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n \nnode\n \nindex=\n2\n\n \nhost\nexample2\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n \nnode\n \nindex=\n3\n\n \nhost\nexample3\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n\n/zookeeper\n\n\n\n\n\n\nUse ZooKeeper version 3.4.5 or later.\n\n\nYou can specify any existing ZooKeeper cluster and the system will use a directory on it for its own data (the directory is specified when creating a replicatable table).\n\n\nIf ZooKeeper isn't set in the config file, you can't create replicated tables, and any existing replicated tables will be read-only.\n\n\nZooKeeper is not used in \nSELECT\n queries because replication does not affect the performance of \nSELECT\n and queries run just as fast as they do for non-replicated tables. When querying distributed replicated tables, ClickHouse behavior is controlled by the settings \nmax_replica_delay_for_distributed_queries\n and \nfallback_to_stale_replicas_for_distributed_queries\n.\n\n\nFor each \nINSERT\n query, approximately ten entries are added to ZooKeeper through several transactions. (To be more precise, this is for each inserted block of data; an INSERT query contains one block or one block per \nmax_insert_block_size = 1048576\n rows.) This leads to slightly longer latencies for \nINSERT\n compared to non-replicated tables. But if you follow the recommendations to insert data in batches of no more than one \nINSERT\n per second, it doesn't create any problems. The entire ClickHouse cluster used for coordinating one ZooKeeper cluster has a total of several hundred \nINSERTs\n per second. The throughput on data inserts (the number of rows per second) is just as high as for non-replicated data.\n\n\nFor very large clusters, you can use different ZooKeeper clusters for different shards. However, this hasn't proven necessary on the Yandex.Metrica cluster (approximately 300 servers).\n\n\nReplication is asynchronous and multi-master. \nINSERT\n queries (as well as \nALTER\n) can be sent to any available server. Data is inserted on the server where the query is run, and then it is copied to the other servers. Because it is asynchronous, recently inserted data appears on the other replicas with some latency. If part of the replicas are not available, the data is written when they become available. If a replica is available, the latency is the amount of time it takes to transfer the block of compressed data over the network.\n\n\nBy default, an INSERT query waits for confirmation of writing the data from only one replica. If the data was successfully written to only one replica and the server with this replica ceases to exist, the stored data will be lost. Tp enable getting confirmation of data writes from multiple replicas, use the \ninsert_quorum\n option.\n\n\nEach block of data is written atomically. The INSERT query is divided into blocks up to \nmax_insert_block_size = 1048576\n rows. In other words, if the \nINSERT\n query has less than 1048576 rows, it is made atomically.\n\n\nData blocks are deduplicated. For multiple writes of the same data block (data blocks of the same size containing the same rows in the same order), the block is only written once. The reason for this is in case of network failures when the client application doesn't know if the data was written to the DB, so the \nINSERT\n query can simply be repeated. It doesn't matter which replica INSERTs were sent to with identical data. \nINSERTs\n are idempotent. Deduplication parameters are controlled by \nmerge_tree\n server settings.\n\n\nDuring replication, only the source data to insert is transferred over the network. Further data transformation (merging) is coordinated and performed on all the replicas in the same way. This minimizes network usage, which means that replication works well when replicas reside in different datacenters. (Note that duplicating data in different datacenters is the main goal of replication.)\n\n\nYou can have any number of replicas of the same data. Yandex.Metrica uses double replication in production. Each server uses RAID-5 or RAID-6, and RAID-10 in some cases. This is a relatively reliable and convenient solution.\n\n\nThe system monitors data synchronicity on replicas and is able to recover after a failure. Failover is automatic (for small differences in data) or semi-automatic (when data differs too much, which may indicate a configuration error).\n\n\n\n\nCreating replicated tables\n\n\nThe \nReplicated\n prefix is added to the table engine name. For example:\nReplicatedMergeTree\n.\n\n\nTwo parameters are also added in the beginning of the parameters list \u2013 the path to the table in ZooKeeper, and the replica name in ZooKeeper.\n\n\nExample:\n\n\nReplicatedMergeTree(\n/clickhouse/tables/{layer}-{shard}/hits\n, \n{replica}\n, EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID), EventTime), 8192)\n\n\n\n\n\nAs the example shows, these parameters can contain substitutions in curly brackets. The substituted values are taken from the 'macros' section of the config file. Example:\n\n\nmacros\n\n \nlayer\n05\n/layer\n\n \nshard\n02\n/shard\n\n \nreplica\nexample05-02-1.yandex.ru\n/replica\n\n\n/macros\n\n\n\n\n\n\nThe path to the table in ZooKeeper should be unique for each replicated table. Tables on different shards should have different paths.\nIn this case, the path consists of the following parts:\n\n\n/clickhouse/tables/\n is the common prefix. We recommend using exactly this one.\n\n\n{layer}-{shard}\n is the shard identifier. In this example it consists of two parts, since the Yandex.Metrica cluster uses bi-level sharding. For most tasks, you can leave just the {shard} substitution, which will be expanded to the shard identifier.\n\n\nhits\n is the name of the node for the table in ZooKeeper. It is a good idea to make it the same as the table name. It is defined explicitly, because in contrast to the table name, it doesn't change after a RENAME query.\n\n\nThe replica name identifies different replicas of the same table. You can use the server name for this, as in the example. The name only needs to be unique within each shard.\n\n\nYou can define the parameters explicitly instead of using substitutions. This might be convenient for testing and for configuring small clusters. However, you can't use distributed DDL queries (\nON CLUSTER\n) in this case.\n\n\nWhen working with large clusters, we recommend using substitutions because they reduce the probability of error.\n\n\nRun the \nCREATE TABLE\n query on each replica. This query creates a new replicated table, or adds a new replica to an existing one.\n\n\nIf you add a new replica after the table already contains some data on other replicas, the data will be copied from the other replicas to the new one after running the query. In other words, the new replica syncs itself with the others.\n\n\nTo delete a replica, run \nDROP TABLE\n. However, only one replica is deleted \u2013 the one that resides on the server where you run the query.\n\n\nRecovery after failures\n\n\nIf ZooKeeper is unavailable when a server starts, replicated tables switch to read-only mode. The system periodically attempts to connect to ZooKeeper.\n\n\nIf ZooKeeper is unavailable during an \nINSERT\n, or an error occurs when interacting with ZooKeeper, an exception is thrown.\n\n\nAfter connecting to ZooKeeper, the system checks whether the set of data in the local file system matches the expected set of data (ZooKeeper stores this information). If there are minor inconsistencies, the system resolves them by syncing data with the replicas.\n\n\nIf the system detects broken data parts (with the wrong size of files) or unrecognized parts (parts written to the file system but not recorded in ZooKeeper), it moves them to the 'detached' subdirectory (they are not deleted). Any missing parts are copied from the replicas.\n\n\nNote that ClickHouse does not perform any destructive actions such as automatically deleting a large amount of data.\n\n\nWhen the server starts (or establishes a new session with ZooKeeper), it only checks the quantity and sizes of all files. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a \nSELECT\n query. The query throws an exception about a non-matching checksum or size of a compressed block. In this case, data parts are added to the verification queue and copied from the replicas if necessary.\n\n\nIf the local set of data differs too much from the expected one, a safety mechanism is triggered. The server enters this in the log and refuses to launch. The reason for this is that this case may indicate a configuration error, such as if a replica on a shard was accidentally configured like a replica on a different shard. However, the thresholds for this mechanism are set fairly low, and this situation might occur during normal failure recovery. In this case, data is restored semi-automatically - by \"pushing a button\".\n\n\nTo start recovery, create the node \n/path_to_table/replica_name/flags/force_restore_data\n in ZooKeeper with any content, or run the command to restore all replicated tables:\n\n\nsudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data\n\n\n\n\n\nThen restart the server. On start, the server deletes these flags and starts recovery.\n\n\nRecovery after complete data loss\n\n\nIf all data and metadata disappeared from one of the servers, follow these steps for recovery:\n\n\n\n\nInstall ClickHouse on the server. Define substitutions correctly in the config file that contains the shard identifier and replicas, if you use them.\n\n\nIf you had unreplicated tables that must be manually duplicated on the servers, copy their data from a replica (in the directory \n/var/lib/clickhouse/data/db_name/table_name/\n).\n\n\nCopy table definitions located in \n/var/lib/clickhouse/metadata/\n from a replica. If a shard or replica identifier is defined explicitly in the table definitions, correct it so that it corresponds to this replica. (Alternatively, start the server and make all the \nATTACH TABLE\n queries that should have been in the .sql files in \n/var/lib/clickhouse/metadata/\n.)\n\n\nTo start recovery, create the ZooKeeper node \n/path_to_table/replica_name/flags/force_restore_data\n with any content, or run the command to restore all replicated tables: \nsudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data\n\n\n\n\nThen start the server (restart, if it is already running). Data will be downloaded from replicas.\n\n\nAn alternative recovery option is to delete information about the lost replica from ZooKeeper (\n/path_to_table/replica_name\n), then create the replica again as described in \"\nCreating replicatable tables\n\".\n\n\nThere is no restriction on network bandwidth during recovery. Keep this in mind if you are restoring many replicas at once.\n\n\nConverting from MergeTree to ReplicatedMergeTree\n\n\nWe use the term \nMergeTree\n to refer to all table engines in the \nMergeTree family\n, the same as for \nReplicatedMergeTree\n.\n\n\nIf you had a \nMergeTree\n table that was manually replicated, you can convert it to a replicatable table. You might need to do this if you have already collected a large amount of data in a \nMergeTree\n table and now you want to enable replication.\n\n\nIf the data differs on various replicas, first sync it, or delete this data on all the replicas except one.\n\n\nRename the existing MergeTree table, then create a \nReplicatedMergeTree\n table with the old name.\nMove the data from the old table to the 'detached' subdirectory inside the directory with the new table data (\n/var/lib/clickhouse/data/db_name/table_name/\n).\nThen run \nALTER TABLE ATTACH PARTITION\n on one of the replicas to add these data parts to the working set.\n\n\nConverting from ReplicatedMergeTree to MergeTree\n\n\nCreate a MergeTree table with a different name. Move all the data from the directory with the \nReplicatedMergeTree\n table data to the new table's data directory. Then delete the \nReplicatedMergeTree\n table and restart the server.\n\n\nIf you want to get rid of a \nReplicatedMergeTree\n table without launching the server:\n\n\n\n\nDelete the corresponding \n.sql\n file in the metadata directory (\n/var/lib/clickhouse/metadata/\n).\n\n\nDelete the corresponding path in ZooKeeper (\n/path_to_table/replica_name\n).\n\n\n\n\nAfter this, you can launch the server, create a \nMergeTree\n table, move the data to its directory, and then restart the server.\n\n\nRecovery when metadata in the ZooKeeper cluster is lost or damaged\n\n\nIf the data in ZooKeeper was lost or damaged, you can save data by moving it to an unreplicated table as described above.\n\n\nIf exactly the same parts exist on the other replicas, they are added to the working set on them. If not, the parts are downloaded from the replica that has them.\n\n\n\n\nDistributed\n\n\nThe Distributed engine does not store data itself\n, but allows distributed query processing on multiple servers.\nReading is automatically parallelized. During a read, the table indexes on remote servers are used, if there are any.\nThe Distributed engine accepts parameters: the cluster name in the server's config file, the name of a remote database, the name of a remote table, and (optionally) a sharding key.\nExample:\n\n\nDistributed(logs, default, hits[, sharding_key])\n\n\n\n\n\nData will be read from all servers in the 'logs' cluster, from the default.hits table located on every server in the cluster.\nData is not only read, but is partially processed on the remote servers (to the extent that this is possible).\nFor example, for a query with GROUP BY, data will be aggregated on remote servers, and the intermediate states of aggregate functions will be sent to the requestor server. Then data will be further aggregated.\n\n\nInstead of the database name, you can use a constant expression that returns a string. For example: currentDatabase().\n\n\nlogs \u2013 The cluster name in the server's config file.\n\n\nClusters are set like this:\n\n\nremote_servers\n\n \nlogs\n\n \nshard\n\n \n!-- Optional. Shard weight when writing data. Default: 1. --\n\n \nweight\n1\n/weight\n\n \n!-- Optional. Whether to write data to just one of the replicas. Default: false (write data to all replicas). --\n\n \ninternal_replication\nfalse\n/internal_replication\n\n \nreplica\n\n \nhost\nexample01-01-1\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \nreplica\n\n \nhost\nexample01-01-2\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \n/shard\n\n \nshard\n\n \nweight\n2\n/weight\n\n \ninternal_replication\nfalse\n/internal_replication\n\n \nreplica\n\n \nhost\nexample01-02-1\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \nreplica\n\n \nhost\nexample01-02-2\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \n/shard\n\n \n/logs\n\n\n/remote_servers\n\n\n\n\n\n\nHere a cluster is defined with the name 'logs' that consists of two shards, each of which contains two replicas.\nShards refer to the servers that contain different parts of the data (in order to read all the data, you must access all the shards).\nReplicas are duplicating servers (in order to read all the data, you can access the data on any one of the replicas).\n\n\nThe parameters \nhost\n, \nport\n, and optionally \nuser\n and \npassword\n are specified for each server:\n\n\n: - \nhost\n \u2013 The address of the remote server. You can use either the domain or the IPv4 or IPv6 address. If you specify the domain, the server makes a DNS request when it starts, and the result is stored as long as the server is running. If the DNS request fails, the server doesn't start. If you change the DNS record, restart the server.\n- \nport\n\u2013 The TCP port for messenger activity ('tcp_port' in the config, usually set to 9000). Do not confuse it with http_port.\n- \nuser\n\u2013 Name of the user for connecting to a remote server. Default value: default. This user must have access to connect to the specified server. Access is configured in the users.xml file. For more information, see the section \"Access rights\".\n- \npassword\n \u2013 The password for connecting to a remote server (not masked). Default value: empty string.\n\n\nWhen specifying replicas, one of the available replicas will be selected for each of the shards when reading. You can configure the algorithm for load balancing (the preference for which replica to access) \u2013 see the 'load_balancing' setting.\nIf the connection with the server is not established, there will be an attempt to connect with a short timeout. If the connection failed, the next replica will be selected, and so on for all the replicas. If the connection attempt failed for all the replicas, the attempt will be repeated the same way, several times.\nThis works in favor of resiliency, but does not provide complete fault tolerance: a remote server might accept the connection, but might not work, or work poorly.\n\n\nYou can specify just one of the shards (in this case, query processing should be called remote, rather than distributed) or up to any number of shards. In each shard, you can specify from one to any number of replicas. You can specify a different number of replicas for each shard.\n\n\nYou can specify as many clusters as you wish in the configuration.\n\n\nTo view your clusters, use the 'system.clusters' table.\n\n\nThe Distributed engine allows working with a cluster like a local server. However, the cluster is inextensible: you must write its configuration in the server config file (even better, for all the cluster's servers).\n\n\nThere is no support for Distributed tables that look at other Distributed tables (except in cases when a Distributed table only has one shard). As an alternative, make the Distributed table look at the \"final\" tables.\n\n\nThe Distributed engine requires writing clusters to the config file. Clusters from the config file are updated on the fly, without restarting the server. If you need to send a query to an unknown set of shards and replicas each time, you don't need to create a Distributed table \u2013 use the 'remote' table function instead. See the section \"Table functions\".\n\n\nThere are two methods for writing data to a cluster:\n\n\nFirst, you can define which servers to write which data to, and perform the write directly on each shard. In other words, perform INSERT in the tables that the distributed table \"looks at\".\nThis is the most flexible solution \u2013 you can use any sharding scheme, which could be non-trivial due to the requirements of the subject area.\nThis is also the most optimal solution, since data can be written to different shards completely independently.\n\n\nSecond, you can perform INSERT in a Distributed table. In this case, the table will distribute the inserted data across servers itself.\nIn order to write to a Distributed table, it must have a sharding key set (the last parameter). In addition, if there is only one shard, the write operation works without specifying the sharding key, since it doesn't have any meaning in this case.\n\n\nEach shard can have a weight defined in the config file. By default, the weight is equal to one. Data is distributed across shards in the amount proportional to the shard weight. For example, if there are two shards and the first has a weight of 9 while the second has a weight of 10, the first will be sent 9 / 19 parts of the rows, and the second will be sent 10 / 19.\n\n\nEach shard can have the 'internal_replication' parameter defined in the config file.\n\n\nIf this parameter is set to 'true', the write operation selects the first healthy replica and writes data to it. Use this alternative if the Distributed table \"looks at\" replicated tables. In other words, if the table where data will be written is going to replicate them itself.\n\n\nIf it is set to 'false' (the default), data is written to all replicas. In essence, this means that the Distributed table replicates data itself. This is worse than using replicated tables, because the consistency of replicas is not checked, and over time they will contain slightly different data.\n\n\nTo select the shard that a row of data is sent to, the sharding expression is analyzed, and its remainder is taken from dividing it by the total weight of the shards. The row is sent to the shard that corresponds to the half-interval of the remainders from 'prev_weight' to 'prev_weights + weight', where 'prev_weights' is the total weight of the shards with the smallest number, and 'weight' is the weight of this shard. For example, if there are two shards, and the first has a weight of 9 while the second has a weight of 10, the row will be sent to the first shard for the remainders from the range [0, 9), and to the second for the remainders from the range [9, 19).\n\n\nThe sharding expression can be any expression from constants and table columns that returns an integer. For example, you can use the expression 'rand()' for random distribution of data, or 'UserID' for distribution by the remainder from dividing the user's ID (then the data of a single user will reside on a single shard, which simplifies running IN and JOIN by users). If one of the columns is not distributed evenly enough, you can wrap it in a hash function: intHash64(UserID).\n\n\nA simple remainder from division is a limited solution for sharding and isn't always appropriate. It works for medium and large volumes of data (dozens of servers), but not for very large volumes of data (hundreds of servers or more). In the latter case, use the sharding scheme required by the subject area, rather than using entries in Distributed tables.\n\n\nSELECT queries are sent to all the shards, and work regardless of how data is distributed across the shards (they can be distributed completely randomly). When you add a new shard, you don't have to transfer the old data to it. You can write new data with a heavier weight \u2013 the data will be distributed slightly unevenly, but queries will work correctly and efficiently.\n\n\nYou should be concerned about the sharding scheme in the following cases:\n\n\n\n\nQueries are used that require joining data (IN or JOIN) by a specific key. If data is sharded by this key, you can use local IN or JOIN instead of GLOBAL IN or GLOBAL JOIN, which is much more efficient.\n\n\nA large number of servers is used (hundreds or more) with a large number of small queries (queries of individual clients - websites, advertisers, or partners). In order for the small queries to not affect the entire cluster, it makes sense to locate data for a single client on a single shard. Alternatively, as we've done in Yandex.Metrica, you can set up bi-level sharding: divide the entire cluster into \"layers\", where a layer may consist of multiple shards. Data for a single client is located on a single layer, but shards can be added to a layer as necessary, and data is randomly distributed within them. Distributed tables are created for each layer, and a single shared distributed table is created for global queries.\n\n\n\n\nData is written asynchronously. For an INSERT to a Distributed table, the data block is just written to the local file system. The data is sent to the remote servers in the background as soon as possible. You should check whether data is sent successfully by checking the list of files (data waiting to be sent) in the table directory: /var/lib/clickhouse/data/database/table/.\n\n\nIf the server ceased to exist or had a rough restart (for example, after a device failure) after an INSERT to a Distributed table, the inserted data might be lost. If a damaged data part is detected in the table directory, it is transferred to the 'broken' subdirectory and no longer used.\n\n\nWhen the max_parallel_replicas option is enabled, query processing is parallelized across all replicas within a single shard. For more information, see the section \"Settings, max_parallel_replicas\".\n\n\n\n\nDictionary\n\n\nThe \nDictionary\n engine displays the dictionary data as a ClickHouse table.\n\n\nAs an example, consider a dictionary of \nproducts\n with the following configuration:\n\n\ndictionaries\n\n\ndictionary\n\n \nname\nproducts\n/name\n\n \nsource\n\n \nodbc\n\n \ntable\nproducts\n/table\n\n \nconnection_string\nDSN=some-db-server\n/connection_string\n\n \n/odbc\n\n \n/source\n\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n \nlayout\n\n \nflat/\n\n \n/layout\n\n \nstructure\n\n \nid\n\n \nname\nproduct_id\n/name\n\n \n/id\n\n \nattribute\n\n \nname\ntitle\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n/null_value\n\n \n/attribute\n\n \n/structure\n\n\n/dictionary\n\n\n/dictionaries\n\n\n\n\n\n\nQuery the dictionary data:\n\n\nselect\n \nname\n,\n \ntype\n,\n \nkey\n,\n \nattribute\n.\nnames\n,\n \nattribute\n.\ntypes\n,\n \nbytes_allocated\n,\n \nelement_count\n,\nsource\n \nfrom\n \nsystem\n.\ndictionaries\n \nwhere\n \nname\n \n=\n \nproducts\n;\n \n\n\nSELECT\n\n \nname\n,\n\n \ntype\n,\n\n \nkey\n,\n\n \nattribute\n.\nnames\n,\n\n \nattribute\n.\ntypes\n,\n\n \nbytes_allocated\n,\n\n \nelement_count\n,\n\n \nsource\n\n\nFROM\n \nsystem\n.\ndictionaries\n\n\nWHERE\n \nname\n \n=\n \nproducts\n\n\n\n\n\n\n\u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u252c\u2500type\u2500\u252c\u2500key\u2500\u2500\u2500\u2500\u252c\u2500attribute.names\u2500\u252c\u2500attribute.types\u2500\u252c\u2500bytes_allocated\u2500\u252c\u2500element_count\u2500\u252c\u2500source\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 products \u2502 Flat \u2502 UInt64 \u2502 [\ntitle\n] \u2502 [\nString\n] \u2502 23065376 \u2502 175032 \u2502 ODBC: .products \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nYou can use the \ndictGet*\n function to get the dictionary data in this format.\n\n\nThis view isn't helpful when you need to get raw data, or when performing a \nJOIN\n operation. For these cases, you can use the \nDictionary\n engine, which displays the dictionary data in a table.\n\n\nSyntax:\n\n\nCREATE TABLE %table_name% (%fields%) engine = Dictionary(%dictionary_name%)`\n\n\n\n\n\nUsage example:\n\n\ncreate\n \ntable\n \nproducts\n \n(\nproduct_id\n \nUInt64\n,\n \ntitle\n \nString\n)\n \nEngine\n \n=\n \nDictionary\n(\nproducts\n);\n\n\n\nCREATE\n \nTABLE\n \nproducts\n\n\n(\n\n \nproduct_id\n \nUInt64\n,\n\n \ntitle\n \nString\n,\n\n\n)\n\n\nENGINE\n \n=\n \nDictionary\n(\nproducts\n)\n\n\n\n\n\n\nOk.\n\n0 rows in set. Elapsed: 0.004 sec.\n\n\n\n\n\nTake a look at what's in the table.\n\n\nselect\n \n*\n \nfrom\n \nproducts\n \nlimit\n \n1\n;\n\n\n\nSELECT\n \n*\n\n\nFROM\n \nproducts\n\n\nLIMIT\n \n1\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500product_id\u2500\u252c\u2500title\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 152689 \u2502 Some item \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n1 rows in set. Elapsed: 0.006 sec.\n\n\n\n\n\nMerge\n\n\nThe Merge engine (not to be confused with \nMergeTree\n) does not store data itself, but allows reading from any number of other tables simultaneously.\nReading is automatically parallelized. Writing to a table is not supported. When reading, the indexes of tables that are actually being read are used, if they exist.\nThe Merge engine accepts parameters: the database name and a regular expression for tables.\n\n\nExample:\n\n\nMerge(hits, \n^WatchLog\n)\n\n\n\n\n\nData will be read from the tables in the 'hits' database that have names that match the regular expression '\n^WatchLog\n'.\n\n\nInstead of the database name, you can use a constant expression that returns a string. For example, \ncurrentDatabase()\n.\n\n\nRegular expressions \u2014 \nre2\n (supports a subset of PCRE), case-sensitive.\nSee the notes about escaping symbols in regular expressions in the \"match\" section.\n\n\nWhen selecting tables to read, the Merge table itself will not be selected, even if it matches the regex. This is to avoid loops.\nIt is possible to create two Merge tables that will endlessly try to read each others' data, but this is not a good idea.\n\n\nThe typical way to use the Merge engine is for working with a large number of TinyLog tables as if with a single table.\n\n\nVirtual columns\n\n\nVirtual columns are columns that are provided by the table engine, regardless of the table definition. In other words, these columns are not specified in CREATE TABLE, but they are accessible for SELECT.\n\n\nVirtual columns differ from normal columns in the following ways:\n\n\n\n\nThey are not specified in table definitions.\n\n\nData can't be added to them with INSERT.\n\n\nWhen using INSERT without specifying the list of columns, virtual columns are ignored.\n\n\nThey are not selected when using the asterisk (\nSELECT *\n).\n\n\nVirtual columns are not shown in \nSHOW CREATE TABLE\n and \nDESC TABLE\n queries.\n\n\n\n\nA Merge type table contains a virtual _table column with the String type. (If the table already has a _table column, the virtual column is named _table1, and if it already has _table1, it is named _table2, and so on.) It contains the name of the table that data was read from.\n\n\nIf the WHERE or PREWHERE clause contains conditions for the '_table' column that do not depend on other table columns (as one of the conjunction elements, or as an entire expression), these conditions are used as an index. The conditions are performed on a data set of table names to read data from, and the read operation will be performed from only those tables that the condition was triggered on.\n\n\nBuffer\n\n\nBuffers the data to write in RAM, periodically flushing it to another table. During the read operation, data is read from the buffer and the other table simultaneously.\n\n\nBuffer(database, table, num_layers, min_time, max_time, min_rows, max_rows, min_bytes, max_bytes)\n\n\n\n\n\nEngine parameters:database, table \u2013 The table to flush data to. Instead of the database name, you can use a constant expression that returns a string.num_layers \u2013 Parallelism layer. Physically, the table will be represented as 'num_layers' of independent buffers. Recommended value: 16.min_time, max_time, min_rows, max_rows, min_bytes, and max_bytes are conditions for flushing data from the buffer.\n\n\nData is flushed from the buffer and written to the destination table if all the 'min' conditions or at least one 'max' condition are met.min_time, max_time \u2013 Condition for the time in seconds from the moment of the first write to the buffer.min_rows, max_rows \u2013 Condition for the number of rows in the buffer.min_bytes, max_bytes \u2013 Condition for the number of bytes in the buffer.\n\n\nDuring the write operation, data is inserted to a 'num_layers' number of random buffers. Or, if the data part to insert is large enough (greater than 'max_rows' or 'max_bytes'), it is written directly to the destination table, omitting the buffer.\n\n\nThe conditions for flushing the data are calculated separately for each of the 'num_layers' buffers. For example, if num_layers = 16 and max_bytes = 100000000, the maximum RAM consumption is 1.6 GB.\n\n\nExample:\n\n\nCREATE\n \nTABLE\n \nmerge\n.\nhits_buffer\n \nAS\n \nmerge\n.\nhits\n \nENGINE\n \n=\n \nBuffer\n(\nmerge\n,\n \nhits\n,\n \n16\n,\n \n10\n,\n \n100\n,\n \n10000\n,\n \n1000000\n,\n \n10000000\n,\n \n100000000\n)\n\n\n\n\n\n\nCreating a 'merge.hits_buffer' table with the same structure as 'merge.hits' and using the Buffer engine. When writing to this table, data is buffered in RAM and later written to the 'merge.hits' table. 16 buffers are created. The data in each of them is flushed if either 100 seconds have passed, or one million rows have been written, or 100 MB of data have been written; or if simultaneously 10 seconds have passed and 10,000 rows and 10 MB of data have been written. For example, if just one row has been written, after 100 seconds it will be flushed, no matter what. But if many rows have been written, the data will be flushed sooner.\n\n\nWhen the server is stopped, with DROP TABLE or DETACH TABLE, buffer data is also flushed to the destination table.\n\n\nYou can set empty strings in single quotation marks for the database and table name. This indicates the absence of a destination table. In this case, when the data flush conditions are reached, the buffer is simply cleared. This may be useful for keeping a window of data in memory.\n\n\nWhen reading from a Buffer table, data is processed both from the buffer and from the destination table (if there is one).\nNote that the Buffer tables does not support an index. In other words, data in the buffer is fully scanned, which might be slow for large buffers. (For data in a subordinate table, the index that it supports will be used.)\n\n\nIf the set of columns in the Buffer table doesn't match the set of columns in a subordinate table, a subset of columns that exist in both tables is inserted.\n\n\nIf the types don't match for one of the columns in the Buffer table and a subordinate table, an error message is entered in the server log and the buffer is cleared.\nThe same thing happens if the subordinate table doesn't exist when the buffer is flushed.\n\n\nIf you need to run ALTER for a subordinate table and the Buffer table, we recommend first deleting the Buffer table, running ALTER for the subordinate table, then creating the Buffer table again.\n\n\nIf the server is restarted abnormally, the data in the buffer is lost.\n\n\nPREWHERE, FINAL and SAMPLE do not work correctly for Buffer tables. These conditions are passed to the destination table, but are not used for processing data in the buffer. Because of this, we recommend only using the Buffer table for writing, while reading from the destination table.\n\n\nWhen adding data to a Buffer, one of the buffers is locked. This causes delays if a read operation is simultaneously being performed from the table.\n\n\nData that is inserted to a Buffer table may end up in the subordinate table in a different order and in different blocks. Because of this, a Buffer table is difficult to use for writing to a CollapsingMergeTree correctly. To avoid problems, you can set 'num_layers' to 1.\n\n\nIf the destination table is replicated, some expected characteristics of replicated tables are lost when writing to a Buffer table. The random changes to the order of rows and sizes of data parts cause data deduplication to quit working, which means it is not possible to have a reliable 'exactly once' write to replicated tables.\n\n\nDue to these disadvantages, we can only recommend using a Buffer table in rare cases.\n\n\nA Buffer table is used when too many INSERTs are received from a large number of servers over a unit of time and data can't be buffered before insertion, which means the INSERTs can't run fast enough.\n\n\nNote that it doesn't make sense to insert data one row at a time, even for Buffer tables. This will only produce a speed of a few thousand rows per second, while inserting larger blocks of data can produce over a million rows per second (see the section \"Performance\").\n\n\nFile(InputFormat)\n\n\nThe data source is a file that stores data in one of the supported input formats (TabSeparated, Native, etc.).\n\n\nNull\n\n\nWhen writing to a Null table, data is ignored. When reading from a Null table, the response is empty.\n\n\nHowever, you can create a materialized view on a Null table. So the data written to the table will end up in the view.\n\n\nSet\n\n\nA data set that is always in RAM. It is intended for use on the right side of the IN operator (see the section \"IN operators\").\n\n\nYou can use INSERT to insert data in the table. New elements will be added to the data set, while duplicates will be ignored.\nBut you can't perform SELECT from the table. The only way to retrieve data is by using it in the right half of the IN operator.\n\n\nData is always located in RAM. For INSERT, the blocks of inserted data are also written to the directory of tables on the disk. When starting the server, this data is loaded to RAM. In other words, after restarting, the data remains in place.\n\n\nFor a rough server restart, the block of data on the disk might be lost or damaged. In the latter case, you may need to manually delete the file with damaged data.\n\n\nJoin\n\n\nA prepared data structure for JOIN that is always located in RAM.\n\n\nJoin(ANY|ALL, LEFT|INNER, k1[, k2, ...])\n\n\n\n\n\nEngine parameters: \nANY|ALL\n \u2013 strictness; \nLEFT|INNER\n \u2013 type.\nThese parameters are set without quotes and must match the JOIN that the table will be used for. k1, k2, ... are the key columns from the USING clause that the join will be made on.\n\n\nThe table can't be used for GLOBAL JOINs.\n\n\nYou can use INSERT to add data to the table, similar to the Set engine. For ANY, data for duplicated keys will be ignored. For ALL, it will be counted. You can't perform SELECT directly from the table. The only way to retrieve data is to use it as the \"right-hand\" table for JOIN.\n\n\nStoring data on the disk is the same as for the Set engine.\n\n\nView\n\n\nUsed for implementing views (for more information, see the \nCREATE VIEW query\n). It does not store data, but only stores the specified \nSELECT\n query. When reading from a table, it runs this query (and deletes all unnecessary columns from the query).\n\n\nMaterializedView\n\n\nUsed for implementing materialized views (for more information, see the \nCREATE TABLE\n) query. For storing data, it uses a different engine that was specified when creating the view. When reading from a table, it just uses this engine.\n\n\nKafka\n\n\nThis engine works with \nApache Kafka\n.\n\n\nKafka lets you:\n\n\n\n\nPublish or subscribe to data flows.\n\n\nOrganize fault-tolerant storage.\n\n\nProcess streams as they become available.\n\n\n\n\nKafka(broker_list, topic_list, group_name, format[, schema, num_consumers])\n\n\n\n\n\nParameters:\n\n\n\n\nbroker_list\n \u2013 A comma-separated list of brokers (\nlocalhost:9092\n).\n\n\ntopic_list\n \u2013 A list of Kafka topics (\nmy_topic\n).\n\n\ngroup_name\n \u2013 A group of Kafka consumers (\ngroup1\n). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere.\n\n\n--format\n \u2013 Message format. Uses the same notation as the SQL \nFORMAT\n function, such as \nJSONEachRow\n. For more information, see the \"Formats\" section.\n\n\nschema\n \u2013 An optional parameter that must be used if the format requires a schema definition. For example, \nCap'n Proto\n requires the path to the schema file and the name of the root \nschema.capnp:Message\n object.\n\n\nnum_consumers\n \u2013 The number of consumers per table. Default: \n1\n. Specify more consumers if the throughput of one consumer is insufficient. The total number of consumers should not exceed the number of partitions in the topic, since only one consumer can be assigned per partition.\n\n\n\n\nExample:\n\n\n \nCREATE\n \nTABLE\n \nqueue\n \n(\n\n \ntimestamp\n \nUInt64\n,\n\n \nlevel\n \nString\n,\n\n \nmessage\n \nString\n\n \n)\n \nENGINE\n \n=\n \nKafka\n(\nlocalhost:9092\n,\n \ntopic\n,\n \ngroup1\n,\n \nJSONEachRow\n);\n\n\n \nSELECT\n \n*\n \nFROM\n \nqueue\n \nLIMIT\n \n5\n;\n\n\n\n\n\n\nThe delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name.\n\n\nGroups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. Read more about this at \nhttp://kafka.apache.org/intro\n.\n\n\nSELECT\n is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. To do this:\n\n\n\n\nUse the engine to create a Kafka consumer and consider it a data stream.\n\n\nCreate a table with the desired structure.\n\n\nCreate a materialized view that converts data from the engine and puts it into a previously created table.\n\n\n\n\nWhen the \nMATERIALIZED VIEW\n joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using \nSELECT\n\n\nExample:\n\n\n \nCREATE\n \nTABLE\n \nqueue\n \n(\n\n \ntimestamp\n \nUInt64\n,\n\n \nlevel\n \nString\n,\n\n \nmessage\n \nString\n\n \n)\n \nENGINE\n \n=\n \nKafka\n(\nlocalhost:9092\n,\n \ntopic\n,\n \ngroup1\n,\n \nJSONEachRow\n);\n\n\n \nCREATE\n \nTABLE\n \ndaily\n \n(\n\n \nday\n \nDate\n,\n\n \nlevel\n \nString\n,\n\n \ntotal\n \nUInt64\n\n \n)\n \nENGINE\n \n=\n \nSummingMergeTree\n(\nday\n,\n \n(\nday\n,\n \nlevel\n),\n \n8192\n);\n\n\n \nCREATE\n \nMATERIALIZED\n \nVIEW\n \nconsumer\n \nTO\n \ndaily\n\n \nAS\n \nSELECT\n \ntoDate\n(\ntoDateTime\n(\ntimestamp\n))\n \nAS\n \nday\n,\n \nlevel\n,\n \ncount\n()\n \nas\n \ntotal\n\n \nFROM\n \nqueue\n \nGROUP\n \nBY\n \nday\n,\n \nlevel\n;\n\n\n \nSELECT\n \nlevel\n,\n \nsum\n(\ntotal\n)\n \nFROM\n \ndaily\n \nGROUP\n \nBY\n \nlevel\n;\n\n\n\n\n\n\nTo improve performance, received messages are grouped into blocks the size of \nmax_insert_block_size\n. If the block wasn't formed within \nstream_flush_interval_ms\n milliseconds, the data will be flushed to the table regardless of the completeness of the block.\n\n\nTo stop receiving topic data or to change the conversion logic, detach the materialized view:\n\n\n DETACH TABLE consumer;\n ATTACH MATERIALIZED VIEW consumer;\n\n\n\n\n\nIf you want to change the target table by using \nALTER\nmaterialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view.\n\n\nConfiguration\n\n\nSimilar to GraphiteMergeTree, the Kafka engine supports extended configuration using the ClickHouse config file. There are two configuration keys that you can use: global (\nkafka\n) and topic-level (\nkafka_topic_*\n). The global configuration is applied first, and the topic-level configuration is second (if it exists).\n\n\n \n!-- Global configuration options for all tables of Kafka engine type --\n\n \nkafka\n\n \ndebug\ncgrp\n/debug\n\n \nauto_offset_reset\nsmallest\n/auto_offset_reset\n\n \n/kafka\n\n\n \n!-- Configuration specific for topic \nlogs\n --\n\n \nkafka_topic_logs\n\n \nretry_backoff_ms\n250\n/retry_backoff_ms\n\n \nfetch_min_bytes\n100000\n/fetch_min_bytes\n\n \n/kafka_topic_logs\n\n\n\n\n\n\nFor a list of possible configuration options, see the \nlibrdkafka configuration reference\n. Use the underscore (\n_\n) instead of a dot in the ClickHouse configuration. For example, \ncheck.crcs=true\n will be \ncheck_crcs\ntrue\n/check_crcs\n.\n\n\n\n\nMySQL\n\n\nThe MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server.\n\n\nThe engine takes 4 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example:\n\n\nMySQL(\nhost:port\n, \ndatabase\n, \ntable\n, \nuser\n, \npassword\n);\n\n\n\n\n\nAt this time, simple WHERE clauses such as \n=, !=, \n, \n=, \n, \n=\n are executed on the MySQL server.\n\n\nThe rest of the conditions and the LIMIT sampling constraint are executed in ClickHouse only after the query to MySQL finishes.\n\n\nExternal data for query processing\n\n\nClickHouse allows sending a server the data that is needed for processing a query, together with a SELECT query. This data is put in a temporary table (see the section \"Temporary tables\") and can be used in the query (for example, in IN operators).\n\n\nFor example, if you have a text file with important user identifiers, you can upload it to the server along with a query that uses filtration by this list.\n\n\nIf you need to run more than one query with a large volume of external data, don't use this feature. It is better to upload the data to the DB ahead of time.\n\n\nExternal data can be uploaded using the command-line client (in non-interactive mode), or using the HTTP interface.\n\n\nIn the command-line client, you can specify a parameters section in the format\n\n\n--external --file\n=\n... \n[\n--name\n=\n...\n]\n \n[\n--format\n=\n...\n]\n \n[\n--types\n=\n...\n|\n--structure\n=\n...\n]\n\n\n\n\n\n\nYou may have multiple sections like this, for the number of tables being transmitted.\n\n\n--external\n \u2013 Marks the beginning of a clause.\n\n--file\n \u2013 Path to the file with the table dump, or -, which refers to stdin.\nOnly a single table can be retrieved from stdin.\n\n\nThe following parameters are optional: \n--name\n\u2013 Name of the table. If omitted, _data is used.\n\n--format\n \u2013 Data format in the file. If omitted, TabSeparated is used.\n\n\nOne of the following parameters is required:\n--types\n \u2013 A list of comma-separated column types. For example: \nUInt64,String\n. The columns will be named _1, _2, ...\n\n--structure\n\u2013 The table structure in the format\nUserID UInt64\n, \nURL String\n. Defines the column names and types.\n\n\nThe files specified in 'file' will be parsed by the format specified in 'format', using the data types specified in 'types' or 'structure'. The table will be uploaded to the server and accessible there as a temporary table with the name in 'name'.\n\n\nExamples:\n\n\necho\n -ne \n1\\n2\\n3\\n\n \n|\n clickhouse-client --query\n=\nSELECT count() FROM test.visits WHERE TraficSourceID IN _data\n --external --file\n=\n- --types\n=\nInt8\n\n849897\n\ncat /etc/passwd \n|\n sed \ns/:/\\t/g\n \n|\n clickhouse-client --query\n=\nSELECT shell, count() AS c FROM passwd GROUP BY shell ORDER BY c DESC\n --external --file\n=\n- --name\n=\npasswd --structure\n=\nlogin String, unused String, uid UInt16, gid UInt16, comment String, home String, shell String\n\n/bin/sh \n20\n\n/bin/false \n5\n\n/bin/bash \n4\n\n/usr/sbin/nologin \n1\n\n/bin/sync \n1\n\n\n\n\n\n\nWhen using the HTTP interface, external data is passed in the multipart/form-data format. Each table is transmitted as a separate file. The table name is taken from the file name. The 'query_string' is passed the parameters 'name_format', 'name_types', and 'name_structure', where 'name' is the name of the table that these parameters correspond to. The meaning of the parameters is the same as when using the command-line client.\n\n\nExample:\n\n\ncat /etc/passwd \n|\n sed \ns/:/\\t/g\n \n passwd.tsv\n\ncurl -F \npasswd=@passwd.tsv;\n \nhttp://localhost:8123/?query=SELECT+shell,+count()+AS+c+FROM+passwd+GROUP+BY+shell+ORDER+BY+c+DESC\npasswd_structure=login+String,+unused+String,+uid+UInt16,+gid+UInt16,+comment+String,+home+String,+shell+String\n\n/bin/sh \n20\n\n/bin/false \n5\n\n/bin/bash \n4\n\n/usr/sbin/nologin \n1\n\n/bin/sync \n1\n\n\n\n\n\n\nFor distributed query processing, the temporary tables are sent to all the remote servers.\n\n\nSystem tables\n\n\nSystem tables are used for implementing part of the system's functionality, and for providing access to information about how the system is working.\nYou can't delete a system table (but you can perform DETACH).\nSystem tables don't have files with data on the disk or files with metadata. The server creates all the system tables when it starts.\nSystem tables are read-only.\nThey are located in the 'system' database.\n\n\nsystem.one\n\n\nThis table contains a single row with a single 'dummy' UInt8 column containing the value 0.\nThis table is used if a SELECT query doesn't specify the FROM clause.\nThis is similar to the DUAL table found in other DBMSs.\n\n\nsystem.numbers\n\n\nThis table contains a single UInt64 column named 'number' that contains almost all the natural numbers starting from zero.\nYou can use this table for tests, or if you need to do a brute force search.\nReads from this table are not parallelized.\n\n\nsystem.numbers_mt\n\n\nThe same as 'system.numbers' but reads are parallelized. The numbers can be returned in any order.\nUsed for tests.\n\n\nsystem.databases\n\n\nThis table contains a single String column called 'name' \u2013 the name of a database.\nEach database that the server knows about has a corresponding entry in the table.\nThis system table is used for implementing the \nSHOW DATABASES\n query.\n\n\nsystem.tables\n\n\nThis table contains the String columns 'database', 'name', and 'engine'.\nThe table also contains three virtual columns: metadata_modification_time (DateTime type), create_table_query, and engine_full (String type).\nEach table that the server knows about is entered in the 'system.tables' table.\nThis system table is used for implementing SHOW TABLES queries.\n\n\nsystem.columns\n\n\nContains information about the columns in all tables.\nYou can use this table to get information similar to \nDESCRIBE TABLE\n, but for multiple tables at once.\n\n\ndatabase String - Name of the database the table is located in.\ntable String - Table name.\nname String - Column name.\ntype String - Column type.\ndefault_type String - Expression type (DEFAULT, MATERIALIZED, ALIAS) for the default value, or an empty string if it is not defined.\ndefault_expression String - Expression for the default value, or an empty string if it is not defined.\n\n\n\n\n\nsystem.parts\n\n\nContains information about parts of a table in the \nMergeTree\n family.\n\n\nEach row describes one part of the data.\n\n\nColumns:\n\n\n\n\npartition (String) \u2013 The partition name. YYYYMM format. To learn what a partition is, see the description of the \nALTER\n query.\n\n\nname (String) \u2013 Name of the data part.\n\n\nactive (UInt8) \u2013 Indicates whether the part is active. If a part is active, it is used in a table; otherwise, it will be deleted. Inactive data parts remain after merging.\n\n\nmarks (UInt64) \u2013 The number of marks. To get the approximate number of rows in a data part, multiply \nmarks\n by the index granularity (usually 8192).\n\n\nmarks_size (UInt64) \u2013 The size of the file with marks.\n\n\nrows (UInt64) \u2013 The number of rows.\n\n\nbytes (UInt64) \u2013 The number of bytes when compressed.\n\n\nmodification_time (DateTime) \u2013 The modification time of the directory with the data part. This usually corresponds to the time of data part creation.|\n\n\nremove_time (DateTime) \u2013 The time when the data part became inactive.\n\n\nrefcount (UInt32) \u2013 The number of places where the data part is used. A value greater than 2 indicates that the data part is used in queries or merges.\n\n\nmin_date (Date) \u2013 The minimum value of the date key in the data part.\n\n\nmax_date (Date) \u2013 The maximum value of the date key in the data part.\n\n\nmin_block_number (UInt64) \u2013 The minimum number of data parts that make up the current part after merging.\n\n\nmax_block_number (UInt64) \u2013 The maximum number of data parts that make up the current part after merging.\n\n\nlevel (UInt32) \u2013 Depth of the merge tree. If a merge was not performed, \nlevel=0\n.\n\n\nprimary_key_bytes_in_memory (UInt64) \u2013 The amount of memory (in bytes) used by primary key values.\n\n\nprimary_key_bytes_in_memory_allocated (UInt64) \u2013 The amount of memory (in bytes) reserved for primary key values.\n\n\ndatabase (String) \u2013 Name of the database.\n\n\ntable (String) \u2013 Name of the table.\n\n\nengine (String) \u2013 Name of the table engine without parameters.\n\n\n\n\nsystem.processes\n\n\nThis system table is used for implementing the \nSHOW PROCESSLIST\n query.\nColumns:\n\n\nuser String \u2013 Name of the user who made the request. For distributed query processing, this is the user who helped the requestor server send the query to this server, not the user who made the distributed request on the requestor server.\n\naddress String \u2013 The IP address that the query was made from. The same is true for distributed query processing.\n\nelapsed Float64 \u2013 The time in seconds since request execution started.\n\nrows_read UInt64 \u2013 The number of rows read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nbytes_read UInt64 \u2013 The number of uncompressed bytes read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nUInt64 total_rows_approx \u2013 The approximate total number of rows that must be read. For distributed processing, on the requestor server, this is the total for all remote servers. It can be updated during request processing, when new sources to process become known.\n\nmemory_usage UInt64 \u2013 Memory consumption by the query. It might not include some types of dedicated memory.\n\nquery String \u2013 The query text. For INSERT, it doesn\nt include the data to insert.\n\nquery_id \u2013 Query ID, if defined.\n\n\n\n\n\nsystem.merges\n\n\nContains information about merges currently in process for tables in the MergeTree family.\n\n\nColumns:\n\n\n\n\ndatabase String\n \u2014 Name of the database the table is located in.\n\n\ntable String\n \u2014 Name of the table.\n\n\nelapsed Float64\n \u2014 Time in seconds since the merge started.\n\n\nprogress Float64\n \u2014 Percent of progress made, from 0 to 1.\n\n\nnum_parts UInt64\n \u2014 Number of parts to merge.\n\n\nresult_part_name String\n \u2014 Name of the part that will be formed as the result of the merge.\n\n\ntotal_size_bytes_compressed UInt64\n \u2014 Total size of compressed data in the parts being merged.\n\n\ntotal_size_marks UInt64\n \u2014 Total number of marks in the parts being merged.\n\n\nbytes_read_uncompressed UInt64\n \u2014 Amount of bytes read, decompressed.\n\n\nrows_read UInt64\n \u2014 Number of rows read.\n\n\nbytes_written_uncompressed UInt64\n \u2014 Amount of bytes written, uncompressed.\n\n\nrows_written UInt64\n \u2014 Number of rows written.\n\n\n\n\n\n\nsystem.events\n\n\nContains information about the number of events that have occurred in the system. This is used for profiling and monitoring purposes.\nExample: The number of processed SELECT queries.\nColumns: 'event String' \u2013 the event name, and 'value UInt64' \u2013 the quantity.\n\n\n\n\nsystem.metrics\n\n\n\n\nsystem.asynchronous_metrics\n\n\nContain metrics used for profiling and monitoring.\nThey usually reflect the number of events currently in the system, or the total resources consumed by the system.\nExample: The number of SELECT queries currently running; the amount of memory in use.\nsystem.asynchronous_metrics\nand\nsystem.metrics\n differ in their sets of metrics and how they are calculated.\n\n\nsystem.replicas\n\n\nContains information and status for replicated tables residing on the local server.\nThis table can be used for monitoring. The table contains a row for every Replicated* table.\n\n\nExample:\n\n\nSELECT\n \n*\n\n\nFROM\n \nsystem\n.\nreplicas\n\n\nWHERE\n \ntable\n \n=\n \nvisits\n\n\nFORMAT\n \nVertical\n\n\n\n\n\n\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ndatabase: merge\ntable: visits\nengine: ReplicatedCollapsingMergeTree\nis_leader: 1\nis_readonly: 0\nis_session_expired: 0\nfuture_parts: 1\nparts_to_check: 0\nzookeeper_path: /clickhouse/tables/01-06/visits\nreplica_name: example01-06-1.yandex.ru\nreplica_path: /clickhouse/tables/01-06/visits/replicas/example01-06-1.yandex.ru\ncolumns_version: 9\nqueue_size: 1\ninserts_in_queue: 0\nmerges_in_queue: 1\nlog_max_index: 596273\nlog_pointer: 596274\ntotal_replicas: 2\nactive_replicas: 2\n\n\n\n\n\nColumns:\n\n\ndatabase: database name\ntable: table name\nengine: table engine name\n\nis_leader: whether the replica is the leader\n\nOnly one replica at a time can be the leader. The leader is responsible for selecting background merges to perform.\nNote that writes can be performed to any replica that is available and has a session in ZK, regardless of whether it is a leader.\n\nis_readonly: Whether the replica is in read-only mode.\nThis mode is turned on if the config doesn\nt have sections with ZK, if an unknown error occurred when reinitializing sessions in ZK, and during session reinitialization in ZK.\n\nis_session_expired: Whether the ZK session expired.\nBasically, the same thing as is_readonly.\n\nfuture_parts: The number of data parts that will appear as the result of INSERTs or merges that haven\nt been done yet. \n\nparts_to_check: The number of data parts in the queue for verification.\nA part is put in the verification queue if there is suspicion that it might be damaged.\n\nzookeeper_path: The path to the table data in ZK. \nreplica_name: Name of the replica in ZK. Different replicas of the same table have different names. \nreplica_path: The path to the replica data in ZK. The same as concatenating zookeeper_path/replicas/replica_path.\n\ncolumns_version: Version number of the table structure.\nIndicates how many times ALTER was performed. If replicas have different versions, it means some replicas haven\nt made all of the ALTERs yet.\n\nqueue_size: Size of the queue for operations waiting to be performed.\nOperations include inserting blocks of data, merges, and certain other actions.\nNormally coincides with future_parts.\n\ninserts_in_queue: Number of inserts of blocks of data that need to be made.\nInsertions are usually replicated fairly quickly. If the number is high, something is wrong.\n\nmerges_in_queue: The number of merges waiting to be made. \nSometimes merges are lengthy, so this value may be greater than zero for a long time.\n\nThe next 4 columns have a non-null value only if the ZK session is active.\n\nlog_max_index: Maximum entry number in the log of general activity.\nlog_pointer: Maximum entry number in the log of general activity that the replica copied to its execution queue, plus one.\nIf log_pointer is much smaller than log_max_index, something is wrong.\n\ntotal_replicas: Total number of known replicas of this table.\nactive_replicas: Number of replicas of this table that have a ZK session (the number of active replicas).\n\n\n\n\n\nIf you request all the columns, the table may work a bit slowly, since several reads from ZK are made for each row.\nIf you don't request the last 4 columns (log_max_index, log_pointer, total_replicas, active_replicas), the table works quickly.\n\n\nFor example, you can check that everything is working correctly like this:\n\n\nSELECT\n\n \ndatabase\n,\n\n \ntable\n,\n\n \nis_leader\n,\n\n \nis_readonly\n,\n\n \nis_session_expired\n,\n\n \nfuture_parts\n,\n\n \nparts_to_check\n,\n\n \ncolumns_version\n,\n\n \nqueue_size\n,\n\n \ninserts_in_queue\n,\n\n \nmerges_in_queue\n,\n\n \nlog_max_index\n,\n\n \nlog_pointer\n,\n\n \ntotal_replicas\n,\n\n \nactive_replicas\n\n\nFROM\n \nsystem\n.\nreplicas\n\n\nWHERE\n\n \nis_readonly\n\n \nOR\n \nis_session_expired\n\n \nOR\n \nfuture_parts\n \n \n20\n\n \nOR\n \nparts_to_check\n \n \n10\n\n \nOR\n \nqueue_size\n \n \n20\n\n \nOR\n \ninserts_in_queue\n \n \n10\n\n \nOR\n \nlog_max_index\n \n-\n \nlog_pointer\n \n \n10\n\n \nOR\n \ntotal_replicas\n \n \n2\n\n \nOR\n \nactive_replicas\n \n \ntotal_replicas\n\n\n\n\n\n\nIf this query doesn't return anything, it means that everything is fine.\n\n\nsystem.dictionaries\n\n\nContains information about external dictionaries.\n\n\nColumns:\n\n\n\n\nname String\n \u2013 Dictionary name.\n\n\ntype String\n \u2013 Dictionary type: Flat, Hashed, Cache.\n\n\norigin String\n \u2013 Path to the config file where the dictionary is described.\n\n\nattribute.names Array(String)\n \u2013 Array of attribute names provided by the dictionary.\n\n\nattribute.types Array(String)\n \u2013 Corresponding array of attribute types provided by the dictionary.\n\n\nhas_hierarchy UInt8\n \u2013 Whether the dictionary is hierarchical.\n\n\nbytes_allocated UInt64\n \u2013 The amount of RAM used by the dictionary.\n\n\nhit_rate Float64\n \u2013 For cache dictionaries, the percent of usage for which the value was in the cache.\n\n\nelement_count UInt64\n \u2013 The number of items stored in the dictionary.\n\n\nload_factor Float64\n \u2013 The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table).\n\n\ncreation_time DateTime\n \u2013 Time spent for the creation or last successful reload of the dictionary.\n\n\nlast_exception String\n \u2013 Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created.\n\n\nsource String\n \u2013 Text describing the data source for the dictionary.\n\n\n\n\nNote that the amount of memory used by the dictionary is not proportional to the number of items stored in it. So for flat and cached dictionaries, all the memory cells are pre-assigned, regardless of how full the dictionary actually is.\n\n\nsystem.clusters\n\n\nContains information about clusters available in the config file and the servers in them.\nColumns:\n\n\ncluster String \u2013 Cluster name.\nshard_num UInt32 \u2013 Number of a shard in the cluster, starting from 1.\nshard_weight UInt32 \u2013 Relative weight of a shard when writing data.\nreplica_num UInt32 \u2013 Number of a replica in the shard, starting from 1.\nhost_name String \u2013 Host name as specified in the config.\nhost_address String \u2013 Host\ns IP address obtained from DNS.\nport UInt16 \u2013 The port used to access the server.\nuser String \u2013 The username to use for connecting to the server.\n\n\n\n\n\nsystem.functions\n\n\nContains information about normal and aggregate functions.\n\n\nColumns:\n\n\n\n\nname\n (\nString\n) \u2013 Function name.\n\n\nis_aggregate\n (\nUInt8\n) \u2013 Whether it is an aggregate function.\n\n\n\n\nsystem.settings\n\n\nContains information about settings that are currently in use.\nI.e. used for executing the query you are using to read from the system.settings table).\n\n\nColumns:\n\n\nname String \u2013 Setting name.\nvalue String \u2013 Setting value.\nchanged UInt8 - Whether the setting was explicitly defined in the config or explicitly changed.\n\n\n\n\n\nExample:\n\n\nSELECT\n \n*\n\n\nFROM\n \nsystem\n.\nsettings\n\n\nWHERE\n \nchanged\n\n\n\n\n\n\n\u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500value\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500changed\u2500\u2510\n\u2502 max_threads \u2502 8 \u2502 1 \u2502\n\u2502 use_uncompressed_cache \u2502 0 \u2502 1 \u2502\n\u2502 load_balancing \u2502 random \u2502 1 \u2502\n\u2502 max_memory_usage \u2502 10000000000 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nsystem.zookeeper\n\n\nAllows reading data from the ZooKeeper cluster defined in the config.\nThe query must have a 'path' equality condition in the WHERE clause. This is the path in ZooKeeper for the children that you want to get data for.\n\n\nThe query \nSELECT * FROM system.zookeeper WHERE path = '/clickhouse'\n outputs data for all children on the \n/clickhouse\n node.\nTo output data for all root nodes, write path = '/'.\nIf the path specified in 'path' doesn't exist, an exception will be thrown.\n\n\nColumns:\n\n\n\n\nname String\n \u2014 Name of the node.\n\n\npath String\n \u2014 Path to the node.\n\n\nvalue String\n \u2014 Value of the node.\n\n\ndataLength Int32\n \u2014 Size of the value.\n\n\nnumChildren Int32\n \u2014 Number of children.\n\n\nczxid Int64\n \u2014 ID of the transaction that created the node.\n\n\nmzxid Int64\n \u2014 ID of the transaction that last changed the node.\n\n\npzxid Int64\n \u2014 ID of the transaction that last added or removed children.\n\n\nctime DateTime\n \u2014 Time of node creation.\n\n\nmtime DateTime\n \u2014 Time of the last node modification.\n\n\nversion Int32\n \u2014 Node version - the number of times the node was changed.\n\n\ncversion Int32\n \u2014 Number of added or removed children.\n\n\naversion Int32\n \u2014 Number of changes to ACL.\n\n\nephemeralOwner Int64\n \u2014 For ephemeral nodes, the ID of the session that owns this node.\n\n\n\n\nExample:\n\n\nSELECT\n \n*\n\n\nFROM\n \nsystem\n.\nzookeeper\n\n\nWHERE\n \npath\n \n=\n \n/clickhouse/tables/01-08/visits/replicas\n\n\nFORMAT\n \nVertical\n\n\n\n\n\n\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-1.yandex.ru\nvalue:\nczxid: 932998691229\nmzxid: 932998691229\nctime: 2015-03-27 16:49:51\nmtime: 2015-03-27 16:49:51\nversion: 0\ncversion: 47\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021031383\npath: /clickhouse/tables/01-08/visits/replicas\n\nRow 2:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-2.yandex.ru\nvalue:\nczxid: 933002738135\nmzxid: 933002738135\nctime: 2015-03-27 16:57:01\nmtime: 2015-03-27 16:57:01\nversion: 0\ncversion: 37\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021252247\npath: /clickhouse/tables/01-08/visits/replicas\n\n\n\n\n\nTable functions\n\n\nTable functions can be specified in the FROM clause instead of the database and table names.\nTable functions can only be used if 'readonly' is not set.\nTable functions aren't related to other functions.\n\n\n\n\nremote\n\n\nAllows you to access remote servers without creating a \nDistributed\n table.\n\n\nSignatures:\n\n\nremote\n(\naddresses_expr\n,\n \ndb\n,\n \ntable\n[,\n \nuser\n[,\n \npassword\n]])\n\n\nremote\n(\naddresses_expr\n,\n \ndb\n.\ntable\n[,\n \nuser\n[,\n \npassword\n]])\n\n\n\n\n\n\naddresses_expr\n \u2013 An expression that generates addresses of remote servers. This may be just one server address. The server address is \nhost:port\n, or just \nhost\n. The host can be specified as the server name, or as the IPv4 or IPv6 address. An IPv6 address is specified in square brackets. The port is the TCP port on the remote server. If the port is omitted, it uses \ntcp_port\n from the server's config file (by default, 9000).\n\n\n\n\nThe port is required for an IPv6 address.\n\n\n\n\n\nExamples:\n\n\nexample01-01-1\nexample01-01-1:9000\nlocalhost\n127.0.0.1\n[::]:9000\n[2a02:6b8:0:1111::11]:9000\n\n\n\n\n\nMultiple addresses can be comma-separated. In this case, ClickHouse will use distributed processing, so it will send the query to all specified addresses (like to shards with different data).\n\n\nExample:\n\n\nexample01-01-1,example01-02-1\n\n\n\n\n\nPart of the expression can be specified in curly brackets. The previous example can be written as follows:\n\n\nexample01-0{1,2}-1\n\n\n\n\n\nCurly brackets can contain a range of numbers separated by two dots (non-negative integers). In this case, the range is expanded to a set of values that generate shard addresses. If the first number starts with zero, the values are formed with the same zero alignment. The previous example can be written as follows:\n\n\nexample01-{01..02}-1\n\n\n\n\n\nIf you have multiple pairs of curly brackets, it generates the direct product of the corresponding sets.\n\n\nAddresses and parts of addresses in curly brackets can be separated by the pipe symbol (|). In this case, the corresponding sets of addresses are interpreted as replicas, and the query will be sent to the first healthy replica. However, the replicas are iterated in the order currently set in the \nload_balancing\n setting.\n\n\nExample:\n\n\nexample01-{01..02}-{1|2}\n\n\n\n\n\nThis example specifies two shards that each have two replicas.\n\n\nThe number of addresses generated is limited by a constant. Right now this is 1000 addresses.\n\n\nUsing the \nremote\n table function is less optimal than creating a \nDistributed\n table, because in this case, the server connection is re-established for every request. In addition, if host names are set, the names are resolved, and errors are not counted when working with various replicas. When processing a large number of queries, always create the \nDistributed\n table ahead of time, and don't use the \nremote\n table function.\n\n\nThe \nremote\n table function can be useful in the following cases:\n\n\n\n\nAccessing a specific server for data comparison, debugging, and testing.\n\n\nQueries between various ClickHouse clusters for research purposes.\n\n\nInfrequent distributed requests that are made manually.\n\n\nDistributed requests where the set of servers is re-defined each time.\n\n\n\n\nIf the user is not specified, \ndefault\n is used.\nIf the password is not specified, an empty password is used.\n\n\nmerge\n\n\nmerge(db_name, 'tables_regexp')\n \u2013 Creates a temporary Merge table. For more information, see the section \"Table engines, Merge\".\n\n\nThe table structure is taken from the first table encountered that matches the regular expression.\n\n\nnumbers\n\n\nnumbers(N)\n \u2013 Returns a table with the single 'number' column (UInt64) that contains integers from 0 to N-1.\n\n\nSimilar to the \nsystem.numbers\n table, it can be used for testing and generating successive values.\n\n\nThe following two queries are equivalent:\n\n\nSELECT\n \n*\n \nFROM\n \nnumbers\n(\n10\n);\n\n\nSELECT\n \n*\n \nFROM\n \nsystem\n.\nnumbers\n \nLIMIT\n \n10\n;\n\n\n\n\n\n\nExamples:\n\n\n-- Generate a sequence of dates from 2010-01-01 to 2010-12-31\n\n\nselect\n \ntoDate\n(\n2010-01-01\n)\n \n+\n \nnumber\n \nas\n \nd\n \nFROM\n \nnumbers\n(\n365\n);\n\n\n\n\n\n\n\n\nFormats\n\n\nThe format determines how data is returned to you after SELECTs (how it is written and formatted by the server), and how it is accepted for INSERTs (how it is read and parsed by the server).\n\n\nTabSeparated\n\n\nIn TabSeparated format, data is written by row. Each row contains values separated by tabs. Each value is follow by a tab, except the last value in the row, which is followed by a line feed. Strictly Unix line feeds are assumed everywhere. The last row also must contain a line feed at the end. Values are written in text format, without enclosing quotation marks, and with special characters escaped.\n\n\nInteger numbers are written in decimal form. Numbers can contain an extra \"+\" character at the beginning (ignored when parsing, and not recorded when formatting). Non-negative numbers can't contain the negative sign. When reading, it is allowed to parse an empty string as a zero, or (for signed types) a string consisting of just a minus sign as a zero. Numbers that do not fit into the corresponding data type may be parsed as a different number, without an error message.\n\n\nFloating-point numbers are written in decimal form. The dot is used as the decimal separator. Exponential entries are supported, as are 'inf', '+inf', '-inf', and 'nan'. An entry of floating-point numbers may begin or end with a decimal point.\nDuring formatting, accuracy may be lost on floating-point numbers.\nDuring parsing, it is not strictly required to read the nearest machine-representable number.\n\n\nDates are written in YYYY-MM-DD format and parsed in the same format, but with any characters as separators.\nDates with times are written in the format YYYY-MM-DD hh:mm:ss and parsed in the same format, but with any characters as separators.\nThis all occurs in the system time zone at the time the client or server starts (depending on which one formats data). For dates with times, daylight saving time is not specified. So if a dump has times during daylight saving time, the dump does not unequivocally match the data, and parsing will select one of the two times.\nDuring a read operation, incorrect dates and dates with times can be parsed with natural overflow or as null dates and times, without an error message.\n\n\nAs an exception, parsing dates with times is also supported in Unix timestamp format, if it consists of exactly 10 decimal digits. The result is not time zone-dependent. The formats YYYY-MM-DD hh:mm:ss and NNNNNNNNNN are differentiated automatically.\n\n\nStrings are output with backslash-escaped special characters. The following escape sequences are used for output: \n\\b\n, \n\\f\n, \n\\r\n, \n\\n\n, \n\\t\n, \n\\0\n, \n\\'\n, \n\\\\\n. Parsing also supports the sequences \n\\a\n, \n\\v\n, and \n\\xHH\n (hex escape sequences) and any \n\\c\n sequences, where \nc\n is any character (these sequences are converted to \nc\n). Thus, reading data supports formats where a line feed can be written as \n\\n\n or \n\\\n, or as a line feed. For example, the string \nHello world\n with a line feed between the words instead of a space can be parsed in any of the following variations:\n\n\nHello\\nworld\n\nHello\\\nworld\n\n\n\n\n\nThe second variant is supported because MySQL uses it when writing tab-separated dumps.\n\n\nThe minimum set of characters that you need to escape when passing data in TabSeparated format: tab, line feed (LF) and backslash.\n\n\nOnly a small set of symbols are escaped. You can easily stumble onto a string value that your terminal will ruin in output.\n\n\nArrays are written as a list of comma-separated values in square brackets. Number items in the array are fomratted as normally, but dates, dates with times, and strings are written in single quotes with the same escaping rules as above.\n\n\nThe TabSeparated format is convenient for processing data using custom programs and scripts. It is used by default in the HTTP interface, and in the command-line client's batch mode. This format also allows transferring data between different DBMSs. For example, you can get a dump from MySQL and upload it to ClickHouse, or vice versa.\n\n\nThe TabSeparated format supports outputting total values (when using WITH TOTALS) and extreme values (when 'extremes' is set to 1). In these cases, the total values and extremes are output after the main data. The main result, total values, and extremes are separated from each other by an empty line. Example:\n\n\nSELECT\n \nEventDate\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n \nGROUP\n \nBY\n \nEventDate\n \nWITH\n \nTOTALS\n \nORDER\n \nBY\n \nEventDate\n \nFORMAT\n \nTabSeparated\n``\n\n\n\n\n\n\n2014-03-17 1406958\n2014-03-18 1383658\n2014-03-19 1405797\n2014-03-20 1353623\n2014-03-21 1245779\n2014-03-22 1031592\n2014-03-23 1046491\n\n0000-00-00 8873898\n\n2014-03-17 1031592\n2014-03-23 1406958\n\n\n\n\n\nThis format is also available under the name \nTSV\n.\n\n\nTabSeparatedRaw\n\n\nDiffers from \nTabSeparated\n format in that the rows are written without escaping.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\n\n\nThis format is also available under the name \nTSVRaw\n.\n\n\nTabSeparatedWithNames\n\n\nDiffers from the \nTabSeparated\n format in that the column names are written in the first row.\nDuring parsing, the first row is completely ignored. You can't use column names to determine their position or to check their correctness.\n(Support for parsing the header row may be added in the future.)\n\n\nThis format is also available under the name \nTSVWithNames\n.\n\n\nTabSeparatedWithNamesAndTypes\n\n\nDiffers from the \nTabSeparated\n format in that the column names are written to the first row, while the column types are in the second row.\nDuring parsing, the first and second rows are completely ignored.\n\n\nThis format is also available under the name \nTSVWithNamesAndTypes\n.\n\n\nCSV\n\n\nComma Separated Values format (\nRFC\n).\n\n\nWhen formatting, rows are enclosed in double quotes. A double quote inside a string is output as two double quotes in a row. There are no other rules for escaping characters. Date and date-time are enclosed in double quotes. Numbers are output without quotes. Values \u200b\u200bare separated by a delimiter\n. Rows are separated using the Unix line feed (LF). Arrays are serialized in CSV as follows: first the array is serialized to a string as in TabSeparated format, and then the resulting string is output to CSV in double quotes. Tuples in CSV format are serialized as separate columns (that is, their nesting in the tuple is lost).\n\n\nBy default \u2014 \n,\n. See a \nformat_csv_delimiter\n setting for additional info.\n\n\nWhen parsing, all values can be parsed either with or without quotes. Both double and single quotes are supported. Rows can also be arranged without quotes. In this case, they are parsed up to a delimiter or line feed (CR or LF). In violation of the RFC, when parsing rows without quotes, the leading and trailing spaces and tabs are ignored. For the line feed, Unix (LF), Windows (CR LF) and Mac OS Classic (CR LF) are all supported.\n\n\nThe CSV format supports the output of totals and extremes the same way as \nTabSeparated\n.\n\n\nCSVWithNames\n\n\nAlso prints the header row, similar to \nTabSeparatedWithNames\n.\n\n\nValues\n\n\nPrints every row in brackets. Rows are separated by commas. There is no comma after the last row. The values inside the brackets are also comma-separated. Numbers are output in decimal format without quotes. Arrays are output in square brackets. Strings, dates, and dates with times are output in quotes. Escaping rules and parsing are similar to the TabSeparated format. During formatting, extra spaces aren't inserted, but during parsing, they are allowed and skipped (except for spaces inside array values, which are not allowed).\n\n\nThe minimum set of characters that you need to escape when passing data in Values \u200b\u200bformat: single quotes and backslashes.\n\n\nThis is the format that is used in \nINSERT INTO t VALUES ...\n, but you can also use it for formatting query results.\n\n\nVertical\n\n\nPrints each value on a separate line with the column name specified. This format is convenient for printing just one or a few rows, if each row consists of a large number of columns.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\n\n\nVerticalRaw\n\n\nDiffers from \nVertical\n format in that the rows are not escaped.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\n\n\nExamples:\n\n\n:) SHOW CREATE TABLE geonames FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nstatement: CREATE TABLE default.geonames ( geonameid UInt32, date Date DEFAULT CAST(\n2017-12-08\n AS Date)) ENGINE = MergeTree(date, geonameid, 8192)\n\n:) SELECT \nstring with \\\nquotes\\\n and \\t with some special \\n characters\n AS test FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with \nquotes\n and with some special\n characters\n\n\n\n\n\nCompare with the Vertical format:\n\n\n:) SELECT \nstring with \\\nquotes\\\n and \\t with some special \\n characters\n AS test FORMAT Vertical;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with \\\nquotes\\\n and \\t with some special \\n characters\n\n\n\n\n\nJSON\n\n\nOutputs data in JSON format. Besides data tables, it also outputs column names and types, along with some additional information: the total number of output rows, and the number of rows that could have been output if there weren't a LIMIT. Example:\n\n\nSELECT\n \nSearchPhrase\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n \nGROUP\n \nBY\n \nSearchPhrase\n \nWITH\n \nTOTALS\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n5\n \nFORMAT\n \nJSON\n\n\n\n\n\n\n{\n\n \nmeta\n:\n\n \n[\n\n \n{\n\n \nname\n:\n \nSearchPhrase\n,\n\n \ntype\n:\n \nString\n\n \n},\n\n \n{\n\n \nname\n:\n \nc\n,\n\n \ntype\n:\n \nUInt64\n\n \n}\n\n \n],\n\n\n \ndata\n:\n\n \n[\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n8267016\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nbathroom interior design\n,\n\n \nc\n:\n \n2166\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nyandex\n,\n\n \nc\n:\n \n1655\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nspring 2014 fashion\n,\n\n \nc\n:\n \n1549\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nfreeform photos\n,\n\n \nc\n:\n \n1480\n\n \n}\n\n \n],\n\n\n \ntotals\n:\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n8873898\n\n \n},\n\n\n \nextremes\n:\n\n \n{\n\n \nmin\n:\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n1480\n\n \n},\n\n \nmax\n:\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n8267016\n\n \n}\n\n \n},\n\n\n \nrows\n:\n \n5\n,\n\n\n \nrows_before_limit_at_least\n:\n \n141137\n\n\n}\n\n\n\n\n\n\nThe JSON is compatible with JavaScript. To ensure this, some characters are additionally escaped: the slash \n/\n is escaped as \n\\/\n; alternative line breaks \nU+2028\n and \nU+2029\n, which break some browsers, are escaped as \n\\uXXXX\n. ASCII control characters are escaped: backspace, form feed, line feed, carriage return, and horizontal tab are replaced with \n\\b\n, \n\\f\n, \n\\n\n, \n\\r\n, \n\\t\n , as well as the remaining bytes in the 00-1F range using \n\\uXXXX\n sequences. Invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences. For compatibility with JavaScript, Int64 and UInt64 integers are enclosed in double quotes by default. To remove the quotes, you can set the configuration parameter output_format_json_quote_64bit_integers to 0.\n\n\nrows\n \u2013 The total number of output rows.\n\n\nrows_before_limit_at_least\n The minimal number of rows there would have been without LIMIT. Output only if the query contains LIMIT.\nIf the query contains GROUP BY, rows_before_limit_at_least is the exact number of rows there would have been without a LIMIT.\n\n\ntotals\n \u2013 Total values (when using WITH TOTALS).\n\n\nextremes\n \u2013 Extreme values (when extremes is set to 1).\n\n\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the JSONEachRow format.\n\n\nJSONCompact\n\n\nDiffers from JSON only in that data rows are output in arrays, not in objects.\n\n\nExample:\n\n\n{\n\n \nmeta\n:\n\n \n[\n\n \n{\n\n \nname\n:\n \nSearchPhrase\n,\n\n \ntype\n:\n \nString\n\n \n},\n\n \n{\n\n \nname\n:\n \nc\n,\n\n \ntype\n:\n \nUInt64\n\n \n}\n\n \n],\n\n\n \ndata\n:\n\n \n[\n\n \n[\n,\n \n8267016\n],\n\n \n[\nbathroom interior design\n,\n \n2166\n],\n\n \n[\nyandex\n,\n \n1655\n],\n\n \n[\nspring 2014 fashion\n,\n \n1549\n],\n\n \n[\nfreeform photos\n,\n \n1480\n]\n\n \n],\n\n\n \ntotals\n:\n \n[\n,\n8873898\n],\n\n\n \nextremes\n:\n\n \n{\n\n \nmin\n:\n \n[\n,\n1480\n],\n\n \nmax\n:\n \n[\n,\n8267016\n]\n\n \n},\n\n\n \nrows\n:\n \n5\n,\n\n\n \nrows_before_limit_at_least\n:\n \n141137\n\n\n}\n\n\n\n\n\n\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the \nJSONEachRow\n format.\n\n\nJSONEachRow\n\n\nOutputs data as separate JSON objects for each row (newline delimited JSON).\n\n\n{\nSearchPhrase\n:\n,\ncount()\n:\n8267016\n}\n\n\n{\nSearchPhrase\n:\nbathroom interior design\n,\ncount()\n:\n2166\n}\n\n\n{\nSearchPhrase\n:\nyandex\n,\ncount()\n:\n1655\n}\n\n\n{\nSearchPhrase\n:\nspring 2014 fashion\n,\ncount()\n:\n1549\n}\n\n\n{\nSearchPhrase\n:\nfreeform photo\n,\ncount()\n:\n1480\n}\n\n\n{\nSearchPhrase\n:\nangelina jolie\n,\ncount()\n:\n1245\n}\n\n\n{\nSearchPhrase\n:\nomsk\n,\ncount()\n:\n1112\n}\n\n\n{\nSearchPhrase\n:\nphotos of dog breeds\n,\ncount()\n:\n1091\n}\n\n\n{\nSearchPhrase\n:\ncurtain design\n,\ncount()\n:\n1064\n}\n\n\n{\nSearchPhrase\n:\nbaku\n,\ncount()\n:\n1000\n}\n\n\n\n\n\n\nUnlike the JSON format, there is no substitution of invalid UTF-8 sequences. Any set of bytes can be output in the rows. This is necessary so that data can be formatted without losing any information. Values are escaped in the same way as for JSON.\n\n\nFor parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Whitespace between elements is ignored. If a comma is placed after the objects, it is ignored. Objects don't necessarily have to be separated by new lines.\n\n\nTSKV\n\n\nSimilar to TabSeparated, but outputs a value in name=value format. Names are escaped the same way as in TabSeparated format, and the = symbol is also escaped.\n\n\nSearchPhrase= count()=8267016\nSearchPhrase=bathroom interior design count()=2166\nSearchPhrase=yandex count()=1655\nSearchPhrase=spring 2014 fashion count()=1549\nSearchPhrase=freeform photos count()=1480\nSearchPhrase=angelina jolia count()=1245\nSearchPhrase=omsk count()=1112\nSearchPhrase=photos of dog breeds count()=1091\nSearchPhrase=curtain design count()=1064\nSearchPhrase=baku count()=1000\n\n\n\n\n\nWhen there is a large number of small columns, this format is ineffective, and there is generally no reason to use it. It is used in some departments of Yandex.\n\n\nBoth data output and parsing are supported in this format. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults.\n\n\nParsing allows the presence of the additional field \ntskv\n without the equal sign or a value. This field is ignored.\n\n\nPretty\n\n\nOutputs data as Unicode-art tables, also using ANSI-escape sequences for setting colors in the terminal.\nA full grid of the table is drawn, and each row occupies two lines in the terminal.\nEach result block is output as a separate table. This is necessary so that blocks can be output without buffering results (buffering would be necessary in order to pre-calculate the visible width of all the values).\nTo avoid dumping too much data to the terminal, only the first 10,000 rows are printed. If the number of rows is greater than or equal to 10,000, the message \"Showed first 10 000\" is printed.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\n\n\nThe Pretty format supports outputting total values (when using WITH TOTALS) and extremes (when 'extremes' is set to 1). In these cases, total values and extreme values are output after the main data, in separate tables. Example (shown for the PrettyCompact format):\n\n\nSELECT\n \nEventDate\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n \nGROUP\n \nBY\n \nEventDate\n \nWITH\n \nTOTALS\n \nORDER\n \nBY\n \nEventDate\n \nFORMAT\n \nPrettyCompact\n\n\n\n\n\n\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1406958 \u2502\n\u2502 2014-03-18 \u2502 1383658 \u2502\n\u2502 2014-03-19 \u2502 1405797 \u2502\n\u2502 2014-03-20 \u2502 1353623 \u2502\n\u2502 2014-03-21 \u2502 1245779 \u2502\n\u2502 2014-03-22 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1046491 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nTotals:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 0000-00-00 \u2502 8873898 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nExtremes:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1406958 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nPrettyCompact\n\n\nDiffers from \nPretty\n in that the grid is drawn between rows and the result is more compact.\nThis format is used by default in the command-line client in interactive mode.\n\n\nPrettyCompactMonoBlock\n\n\nDiffers from \nPrettyCompact\n in that up to 10,000 rows are buffered, then output as a single table, not by blocks.\n\n\nPrettyNoEscapes\n\n\nDiffers from Pretty in that ANSI-escape sequences aren't used. This is necessary for displaying this format in a browser, as well as for using the 'watch' command-line utility.\n\n\nExample:\n\n\nwatch -n1 \nclickhouse-client --query=\nSELECT * FROM system.events FORMAT PrettyCompactNoEscapes\n\n\n\n\n\n\nYou can use the HTTP interface for displaying in the browser.\n\n\nPrettyCompactNoEscapes\n\n\nThe same as the previous setting.\n\n\nPrettySpaceNoEscapes\n\n\nThe same as the previous setting.\n\n\nPrettySpace\n\n\nDiffers from \nPrettyCompact\n in that whitespace (space characters) is used instead of the grid.\n\n\nRowBinary\n\n\nFormats and parses data by row in binary format. Rows and values are listed consecutively, without separators.\nThis format is less efficient than the Native format, since it is row-based.\n\n\nIntegers use fixed-length little endian representation. For example, UInt64 uses 8 bytes.\nDateTime is represented as UInt32 containing the Unix timestamp as the value.\nDate is represented as a UInt16 object that contains the number of days since 1970-01-01 as the value.\nString is represented as a varint length (unsigned \nLEB128\n), followed by the bytes of the string.\nFixedString is represented simply as a sequence of bytes.\n\n\nArray is represented as a varint length (unsigned \nLEB128\n), followed by successive elements of the array.\n\n\nNative\n\n\nThe most efficient format. Data is written and read by blocks in binary format. For each block, the number of rows, number of columns, column names and types, and parts of columns in this block are recorded one after another. In other words, this format is \"columnar\" \u2013 it doesn't convert columns to rows. This is the format used in the native interface for interaction between servers, for using the command-line client, and for C++ clients.\n\n\nYou can use this format to quickly generate dumps that can only be read by the ClickHouse DBMS. It doesn't make sense to work with this format yourself.\n\n\nNull\n\n\nNothing is output. However, the query is processed, and when using the command-line client, data is transmitted to the client. This is used for tests, including productivity testing.\nObviously, this format is only appropriate for output, not for parsing.\n\n\nXML\n\n\nXML format is suitable only for output, not for parsing. Example:\n\n\n?xml version=\n1.0\n encoding=\nUTF-8\n ?\n\n\nresult\n\n \nmeta\n\n \ncolumns\n\n \ncolumn\n\n \nname\nSearchPhrase\n/name\n\n \ntype\nString\n/type\n\n \n/column\n\n \ncolumn\n\n \nname\ncount()\n/name\n\n \ntype\nUInt64\n/type\n\n \n/column\n\n \n/columns\n\n \n/meta\n\n \ndata\n\n \nrow\n\n \nSearchPhrase\n/SearchPhrase\n\n \nfield\n8267016\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nbathroom interior design\n/SearchPhrase\n\n \nfield\n2166\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nyandex\n/SearchPhrase\n\n \nfield\n1655\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nspring 2014 fashion\n/SearchPhrase\n\n \nfield\n1549\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nfreeform photos\n/SearchPhrase\n\n \nfield\n1480\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nangelina jolie\n/SearchPhrase\n\n \nfield\n1245\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nomsk\n/SearchPhrase\n\n \nfield\n1112\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nphotos of dog breeds\n/SearchPhrase\n\n \nfield\n1091\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\ncurtain design\n/SearchPhrase\n\n \nfield\n1064\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nbaku\n/SearchPhrase\n\n \nfield\n1000\n/field\n\n \n/row\n\n \n/data\n\n \nrows\n10\n/rows\n\n \nrows_before_limit_at_least\n141137\n/rows_before_limit_at_least\n\n\n/result\n\n\n\n\n\n\nIf the column name does not have an acceptable format, just 'field' is used as the element name. In general, the XML structure follows the JSON structure.\nJust as for JSON, invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences.\n\n\nIn string values, the characters \n and \n are escaped as \n and \n.\n\n\nArrays are output as \narray\nelem\nHello\n/elem\nelem\nWorld\n/elem\n...\n/array\n,\nand tuples as \ntuple\nelem\nHello\n/elem\nelem\nWorld\n/elem\n...\n/tuple\n.\n\n\n\n\nCapnProto\n\n\nCap'n Proto is a binary message format similar to Protocol Buffers and Thrift, but not like JSON or MessagePack.\n\n\nCap'n Proto messages are strictly typed and not self-describing, meaning they need an external schema description. The schema is applied on the fly and cached for each query.\n\n\nSELECT\n \nSearchPhrase\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n\n \nGROUP\n \nBY\n \nSearchPhrase\n \nFORMAT\n \nCapnProto\n \nSETTINGS\n \nschema\n \n=\n \nschema:Message\n\n\n\n\n\n\nWhere \nschema.capnp\n looks like this:\n\n\nstruct\n \nMessage\n \n{\n\n \nSearchPhrase\n \n@0\n \n:\nText\n;\n\n \nc\n \n@1\n \n:\nUint64\n;\n\n\n}\n\n\n\n\n\n\nSchema files are in the file that is located in the directory specified in \n format_schema_path\n in the server configuration.\n\n\nDeserialization is effective and usually doesn't increase the system load.\n\n\n\n\nData types\n\n\nClickHouse can store various types of data in table cells.\n\n\nThis section describes the supported data types and special considerations when using and/or implementing them, if any.\n\n\nUInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64\n\n\nFixed-length integers, with or without a sign.\n\n\nInt ranges\n\n\n\n\nInt8 - [-128 : 127]\n\n\nInt16 - [-32768 : 32767]\n\n\nInt32 - [-2147483648 : 2147483647]\n\n\nInt64 - [-9223372036854775808 : 9223372036854775807]\n\n\n\n\nUint ranges\n\n\n\n\nUInt8 - [0 : 255]\n\n\nUInt16 - [0 : 65535]\n\n\nUInt32 - [0 : 4294967295]\n\n\nUInt64 - [0 : 18446744073709551615]\n\n\n\n\nFloat32, Float64\n\n\nFloating point numbers\n.\n\n\nTypes are equivalent to types of C:\n\n\n\n\nFloat32\n - \nfloat\n\n\nFloat64\n - \ndouble\n\n\n\n\nWe recommend that you store data in integer form whenever possible. For example, convert fixed precision numbers to integer values, such as monetary amounts or page load times in milliseconds.\n\n\nUsing floating-point numbers\n\n\n\n\nComputations with floating-point numbers might produce a rounding error.\n\n\n\n\nSELECT\n \n1\n \n-\n \n0\n.\n9\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500minus(1, 0.9)\u2500\u2510\n\u2502 0.09999999999999998 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\nThe result of the calculation depends on the calculation method (the processor type and architecture of the computer system).\n\n\nFloating-point calculations might result in numbers such as infinity (\nInf\n) and \"not-a-number\" (\nNaN\n). This should be taken into account when processing the results of calculations.\n\n\nWhen reading floating point numbers from rows, the result might not be the nearest machine-representable number.\n\n\n\n\nNaN and Inf\n\n\nIn contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers:\n\n\n\n\nInf\n \u2013 Infinity.\n\n\n\n\nSELECT\n \n0\n.\n5\n \n/\n \n0\n\n\n\n\n\n\n\u250c\u2500divide(0.5, 0)\u2500\u2510\n\u2502 inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\n-Inf\n \u2013 Negative infinity.\n\n\n\n\nSELECT\n \n-\n0\n.\n5\n \n/\n \n0\n\n\n\n\n\n\n\u250c\u2500divide(-0.5, 0)\u2500\u2510\n\u2502 -inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\nNaN\n \u2013 Not a number.\n\n\n\n\nSELECT 0 / 0\n\n\n\n\n\n\u250c\u2500divide(0, 0)\u2500\u2510\n\u2502 nan \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSee the rules for \nNaN\n sorting in the section \nORDER BY clause\n.\n\n\nBoolean values\n\n\nThere isn't a separate type for boolean values. They use the UInt8 type, restricted to the values 0 or 1.\n\n\nString\n\n\nStrings of an arbitrary length. The length is not limited. The value can contain an arbitrary set of bytes, including null bytes.\nThe String type replaces the types VARCHAR, BLOB, CLOB, and others from other DBMSs.\n\n\nEncodings\n\n\nClickHouse doesn't have the concept of encodings. Strings can contain an arbitrary set of bytes, which are stored and output as-is.\nIf you need to store texts, we recommend using UTF-8 encoding. At the very least, if your terminal uses UTF-8 (as recommended), you can read and write your values without making conversions.\nSimilarly, certain functions for working with strings have separate variations that work under the assumption that the string contains a set of bytes representing a UTF-8 encoded text.\nFor example, the 'length' function calculates the string length in bytes, while the 'lengthUTF8' function calculates the string length in Unicode code points, assuming that the value is UTF-8 encoded.\n\n\nFixedString(N)\n\n\nA fixed-length string of N bytes (not characters or code points). N must be a strictly positive natural number.\nWhen the server reads a string that contains fewer bytes (such as when parsing INSERT data), the string is padded to N bytes by appending null bytes at the right.\nWhen the server reads a string that contains more bytes, an error message is returned.\nWhen the server writes a string (such as when outputting the result of a SELECT query), null bytes are not trimmed off of the end of the string, but are output.\nNote that this behavior differs from MySQL behavior for the CHAR type (where strings are padded with spaces, and the spaces are removed for output).\n\n\nFewer functions can work with the FixedString(N) type than with String, so it is less convenient to use.\n\n\nDate\n\n\nA date. Stored in two bytes as the number of days since 1970-01-01 (unsigned). Allows storing values from just after the beginning of the Unix Epoch to the upper threshold defined by a constant at the compilation stage (currently, this is until the year 2106, but the final fully-supported year is 2105).\nThe minimum value is output as 0000-00-00.\n\n\nThe date is stored without the time zone.\n\n\nDateTime\n\n\nDate with time. Stored in four bytes as a Unix timestamp (unsigned). Allows storing values in the same range as for the Date type. The minimal value is output as 0000-00-00 00:00:00.\nThe time is stored with accuracy up to one second (without leap seconds).\n\n\nTime zones\n\n\nThe date with time is converted from text (divided into component parts) to binary and back, using the system's time zone at the time the client or server starts. In text format, information about daylight savings is lost.\n\n\nBy default, the client switches to the timezone of the server when it connects. You can change this behavior by enabling the client command-line option \n--use_client_time_zone\n.\n\n\nSupports only those time zones that never had the time differ from UTC for a partial number of hours (without leap seconds) over the entire time range you will be working with.\n\n\nSo when working with a textual date (for example, when saving text dumps), keep in mind that there may be ambiguity during changes for daylight savings time, and there may be problems matching data if the time zone changed.\n\n\nEnum\n\n\nEnum8 or Enum16. A finite set of string values that can be stored more efficiently than the \nString\n data type.\n\n\nExample:\n\n\nEnum8(\nhello\n = 1, \nworld\n = 2)\n\n\n\n\n\n\n\nA data type with two possible values: 'hello' and 'world'.\n\n\n\n\nEach of the values is assigned a number in the range \n-128 ... 127\n for \nEnum8\n or in the range \n-32768 ... 32767\n for \nEnum16\n. All the strings and numbers must be different. An empty string is allowed. If this type is specified (in a table definition), numbers can be in an arbitrary order. However, the order does not matter.\n\n\nIn RAM, this type of column is stored in the same way as \nInt8\n or \nInt16\n of the corresponding numerical values.\nWhen reading in text form, ClickHouse parses the value as a string and searches for the corresponding string from the set of Enum values. If it is not found, an exception is thrown. When reading in text format, the string is read and the corresponding numeric value is looked up. An exception will be thrown if it is not found.\nWhen writing in text form, it writes the value as the corresponding string. If column data contains garbage (numbers that are not from the valid set), an exception is thrown. When reading and writing in binary form, it works the same way as for Int8 and Int16 data types.\nThe implicit default value is the value with the lowest number.\n\n\nDuring \nORDER BY\n, \nGROUP BY\n, \nIN\n, \nDISTINCT\n and so on, Enums behave the same way as the corresponding numbers. For example, ORDER BY sorts them numerically. Equality and comparison operators work the same way on Enums as they do on the underlying numeric values.\n\n\nEnum values cannot be compared with numbers. Enums can be compared to a constant string. If the string compared to is not a valid value for the Enum, an exception will be thrown. The IN operator is supported with the Enum on the left hand side and a set of strings on the right hand side. The strings are the values of the corresponding Enum.\n\n\nMost numeric and string operations are not defined for Enum values, e.g. adding a number to an Enum or concatenating a string to an Enum.\nHowever, the Enum has a natural \ntoString\n function that returns its string value.\n\n\nEnum values are also convertible to numeric types using the \ntoT\n function, where T is a numeric type. When T corresponds to the enum\u2019s underlying numeric type, this conversion is zero-cost.\nThe Enum type can be changed without cost using ALTER, if only the set of values is changed. It is possible to both add and remove members of the Enum using ALTER (removing is safe only if the removed value has never been used in the table). As a safeguard, changing the numeric value of a previously defined Enum member will throw an exception.\n\n\nUsing ALTER, it is possible to change an Enum8 to an Enum16 or vice versa, just like changing an Int8 to Int16.\n\n\nArray(T)\n\n\nAn array of elements of type T. The T type can be any type, including an array.\nWe don't recommend using multidimensional arrays, because they are not well supported (for example, you can't store multidimensional arrays in tables with a MergeTree engine).\n\n\nAggregateFunction(name, types_of_arguments...)\n\n\nThe intermediate state of an aggregate function. To get it, use aggregate functions with the '-State' suffix. For more information, see \"AggregatingMergeTree\".\n\n\nTuple(T1, T2, ...)\n\n\nTuples can't be written to tables (other than Memory tables). They are used for temporary column grouping. Columns can be grouped when an IN expression is used in a query, and for specifying certain formal parameters of lambda functions. For more information, see \"IN operators\" and \"Higher order functions\".\n\n\nTuples can be output as the result of running a query. In this case, for text formats other than JSON*, values are comma-separated in brackets. In JSON* formats, tuples are output as arrays (in square brackets).\n\n\nNested data structures\n\n\nNested(Name1 Type1, Name2 Type2, ...)\n\n\nA nested data structure is like a nested table. The parameters of a nested data structure \u2013 the column names and types \u2013 are specified the same way as in a CREATE query. Each table row can correspond to any number of rows in a nested data structure.\n\n\nExample:\n\n\nCREATE\n \nTABLE\n \ntest\n.\nvisits\n\n\n(\n\n \nCounterID\n \nUInt32\n,\n\n \nStartDate\n \nDate\n,\n\n \nSign\n \nInt8\n,\n\n \nIsNew\n \nUInt8\n,\n\n \nVisitID\n \nUInt64\n,\n\n \nUserID\n \nUInt64\n,\n\n \n...\n\n \nGoals\n \nNested\n\n \n(\n\n \nID\n \nUInt32\n,\n\n \nSerial\n \nUInt32\n,\n\n \nEventTime\n \nDateTime\n,\n\n \nPrice\n \nInt64\n,\n\n \nOrderID\n \nString\n,\n\n \nCurrencyID\n \nUInt32\n\n \n),\n\n \n...\n\n\n)\n \nENGINE\n \n=\n \nCollapsingMergeTree\n(\nStartDate\n,\n \nintHash32\n(\nUserID\n),\n \n(\nCounterID\n,\n \nStartDate\n,\n \nintHash32\n(\nUserID\n),\n \nVisitID\n),\n \n8192\n,\n \nSign\n)\n\n\n\n\n\n\nThis example declares the \nGoals\n nested data structure, which contains data about conversions (goals reached). Each row in the 'visits' table can correspond to zero or any number of conversions.\n\n\nOnly a single nesting level is supported. Columns of nested structures containing arrays are equivalent to multidimensional arrays, so they have limited support (there is no support for storing these columns in tables with the MergeTree engine).\n\n\nIn most cases, when working with a nested data structure, its individual columns are specified. To do this, the column names are separated by a dot. These columns make up an array of matching types. All the column arrays of a single nested data structure have the same length.\n\n\nExample:\n\n\nSELECT\n\n \nGoals\n.\nID\n,\n\n \nGoals\n.\nEventTime\n\n\nFROM\n \ntest\n.\nvisits\n\n\nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nlength\n(\nGoals\n.\nID\n)\n \n \n5\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500Goals.ID\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500Goals.EventTime\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1073752,591325,591325] \u2502 [\n2014-03-17 16:38:10\n,\n2014-03-17 16:38:48\n,\n2014-03-17 16:42:27\n] \u2502\n\u2502 [1073752] \u2502 [\n2014-03-17 00:28:25\n] \u2502\n\u2502 [1073752] \u2502 [\n2014-03-17 10:46:20\n] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [\n2014-03-17 13:59:20\n,\n2014-03-17 22:17:55\n,\n2014-03-17 22:18:07\n,\n2014-03-17 22:18:51\n] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [1073752,591325,591325] \u2502 [\n2014-03-17 11:37:06\n,\n2014-03-17 14:07:47\n,\n2014-03-17 14:36:21\n] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [591325,1073752] \u2502 [\n2014-03-17 00:46:05\n,\n2014-03-17 00:46:05\n] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [\n2014-03-17 13:28:33\n,\n2014-03-17 13:30:26\n,\n2014-03-17 18:51:21\n,\n2014-03-17 18:51:45\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIt is easiest to think of a nested data structure as a set of multiple column arrays of the same length.\n\n\nThe only place where a SELECT query can specify the name of an entire nested data structure instead of individual columns is the ARRAY JOIN clause. For more information, see \"ARRAY JOIN clause\". Example:\n\n\nSELECT\n\n \nGoal\n.\nID\n,\n\n \nGoal\n.\nEventTime\n\n\nFROM\n \ntest\n.\nvisits\n\n\nARRAY\n \nJOIN\n \nGoals\n \nAS\n \nGoal\n\n\nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nlength\n(\nGoals\n.\nID\n)\n \n \n5\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500Goal.ID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500Goal.EventTime\u2500\u2510\n\u2502 1073752 \u2502 2014-03-17 16:38:10 \u2502\n\u2502 591325 \u2502 2014-03-17 16:38:48 \u2502\n\u2502 591325 \u2502 2014-03-17 16:42:27 \u2502\n\u2502 1073752 \u2502 2014-03-17 00:28:25 \u2502\n\u2502 1073752 \u2502 2014-03-17 10:46:20 \u2502\n\u2502 1073752 \u2502 2014-03-17 13:59:20 \u2502\n\u2502 591325 \u2502 2014-03-17 22:17:55 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:07 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:51 \u2502\n\u2502 1073752 \u2502 2014-03-17 11:37:06 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nYou can't perform SELECT for an entire nested data structure. You can only explicitly list individual columns that are part of it.\n\n\nFor an INSERT query, you should pass all the component column arrays of a nested data structure separately (as if they were individual column arrays). During insertion, the system checks that they have the same length.\n\n\nFor a DESCRIBE query, the columns in a nested data structure are listed separately in the same way.\n\n\nThe ALTER query is very limited for elements in a nested data structure.\n\n\nSpecial data types\n\n\nSpecial data type values can't be saved to a table or output in results, but are used as the intermediate result of running a query.\n\n\nExpression\n\n\nUsed for representing lambda expressions in high-order functions.\n\n\nSet\n\n\nUsed for the right half of an IN expression.\n\n\nOperators\n\n\nAll operators are transformed to the corresponding functions at the query parsing stage, in accordance with their precedence and associativity.\nGroups of operators are listed in order of priority (the higher it is in the list, the earlier the operator is connected to its arguments).\n\n\nAccess operators\n\n\na[N]\n Access to an element of an array; \narrayElement(a, N) function\n.\n\n\na.N\n \u2013 Access to a tuble element; \ntupleElement(a, N)\n function.\n\n\nNumeric negation operator\n\n\n-a\n \u2013 The \nnegate (a)\n function.\n\n\nMultiplication and division operators\n\n\na * b\n \u2013 The \nmultiply (a, b) function.\n\n\na / b\n \u2013 The \ndivide(a, b) function.\n\n\na % b\n \u2013 The \nmodulo(a, b) function.\n\n\nAddition and subtraction operators\n\n\na + b\n \u2013 The \nplus(a, b) function.\n\n\na - b\n \u2013 The \nminus(a, b) function.\n\n\nComparison operators\n\n\na = b\n \u2013 The \nequals(a, b) function.\n\n\na == b\n \u2013 The \nequals(a, b) function.\n\n\na != b\n \u2013 The \nnotEquals(a, b) function.\n\n\na \n b\n \u2013 The \nnotEquals(a, b) function.\n\n\na \n= b\n \u2013 The \nlessOrEquals(a, b) function.\n\n\na \n= b\n \u2013 The \ngreaterOrEquals(a, b) function.\n\n\na \n b\n \u2013 The \nless(a, b) function.\n\n\na \n b\n \u2013 The \ngreater(a, b) function.\n\n\na LIKE s\n \u2013 The \nlike(a, b) function.\n\n\na NOT LIKE s\n \u2013 The \nnotLike(a, b) function.\n\n\na BETWEEN b AND c\n \u2013 The same as \na \n= b AND a \n= c.\n\n\nOperators for working with data sets\n\n\nSee the section \"IN operators\".\n\n\na IN ...\n \u2013 The \nin(a, b) function\n\n\na NOT IN ...\n \u2013 The \nnotIn(a, b) function.\n\n\na GLOBAL IN ...\n \u2013 The \nglobalIn(a, b) function.\n\n\na GLOBAL NOT IN ...\n \u2013 The \nglobalNotIn(a, b) function.\n\n\nLogical negation operator\n\n\nNOT a\n The \nnot(a) function.\n\n\nLogical AND operator\n\n\na AND b\n \u2013 The\nand(a, b) function.\n\n\nLogical OR operator\n\n\na OR b\n \u2013 The \nor(a, b) function.\n\n\nConditional operator\n\n\na ? b : c\n \u2013 The \nif(a, b, c) function.\n\n\nNote:\n\n\nThe conditional operator calculates the values of b and c, then checks whether condition a is met, and then returns the corresponding value. If \"b\" or \"c\" is an arrayJoin() function, each row will be replicated regardless of the \"a\" condition.\n\n\nConditional expression\n\n\nCASE\n \n[\nx\n]\n\n \nWHEN\n \na\n \nTHEN\n \nb\n\n \n[\nWHEN\n \n...\n \nTHEN\n \n...]\n\n \nELSE\n \nc\n\n\nEND\n\n\n\n\n\n\nIf \"x\" is specified, then transform(x, [a, ...], [b, ...], c). Otherwise \u2013 multiIf(a, b, ..., c).\n\n\nConcatenation operator\n\n\ns1 || s2\n \u2013 The \nconcat(s1, s2) function.\n\n\nLambda creation operator\n\n\nx -\n expr\n \u2013 The \nlambda(x, expr) function.\n\n\nThe following operators do not have a priority, since they are brackets:\n\n\nArray creation operator\n\n\n[x1, ...]\n \u2013 The \narray(x1, ...) function.\n\n\nTuple creation operator\n\n\n(x1, x2, ...)\n \u2013 The \ntuple(x2, x2, ...) function.\n\n\nAssociativity\n\n\nAll binary operators have left associativity. For example, \n1 + 2 + 3\n is transformed to \nplus(plus(1, 2), 3)\n.\nSometimes this doesn't work the way you expect. For example, \nSELECT 4 \n 2 \n 3\n will result in 0.\n\n\nFor efficiency, the \nand\n and \nor\n functions accept any number of arguments. The corresponding chains of \nAND\n and \nOR\n operators are transformed to a single call of these functions.\n\n\nFunctions\n\n\nThere are at least* two types of functions - regular functions (they are just called \"functions\") and aggregate functions. These are completely different concepts. Regular functions work as if they are applied to each row separately (for each row, the result of the function doesn't depend on the other rows). Aggregate functions accumulate a set of values from various rows (i.e. they depend on the entire set of rows).\n\n\nIn this section we discuss regular functions. For aggregate functions, see the section \"Aggregate functions\".\n\n\n* - There is a third type of function that the 'arrayJoin' function belongs to; table functions can also be mentioned separately.*\n\n\nStrong typing\n\n\nIn contrast to standard SQL, ClickHouse has strong typing. In other words, it doesn't make implicit conversions between types. Each function works for a specific set of types. This means that sometimes you need to use type conversion functions.\n\n\nCommon subexpression elimination\n\n\nAll expressions in a query that have the same AST (the same record or same result of syntactic parsing) are considered to have identical values. Such expressions are concatenated and executed once. Identical subqueries are also eliminated this way.\n\n\nTypes of results\n\n\nAll functions return a single return as the result (not several values, and not zero values). The type of result is usually defined only by the types of arguments, not by the values. Exceptions are the tupleElement function (the a.N operator), and the toFixedString function.\n\n\nConstants\n\n\nFor simplicity, certain functions can only work with constants for some arguments. For example, the right argument of the LIKE operator must be a constant.\nAlmost all functions return a constant for constant arguments. The exception is functions that generate random numbers.\nThe 'now' function returns different values for queries that were run at different times, but the result is considered a constant, since constancy is only important within a single query.\nA constant expression is also considered a constant (for example, the right half of the LIKE operator can be constructed from multiple constants).\n\n\nFunctions can be implemented in different ways for constant and non-constant arguments (different code is executed). But the results for a constant and for a true column containing only the same value should match each other.\n\n\nConstancy\n\n\nFunctions can't change the values of their arguments \u2013 any changes are returned as the result. Thus, the result of calculating separate functions does not depend on the order in which the functions are written in the query.\n\n\nError handling\n\n\nSome functions might throw an exception if the data is invalid. In this case, the query is canceled and an error text is returned to the client. For distributed processing, when an exception occurs on one of the servers, the other servers also attempt to abort the query.\n\n\nEvaluation of argument expressions\n\n\nIn almost all programming languages, one of the arguments might not be evaluated for certain operators. This is usually the operators \n, \n||\n, and \n?:\n.\nBut in ClickHouse, arguments of functions (operators) are always evaluated. This is because entire parts of columns are evaluated at once, instead of calculating each row separately.\n\n\nPerforming functions for distributed query processing\n\n\nFor distributed query processing, as many stages of query processing as possible are performed on remote servers, and the rest of the stages (merging intermediate results and everything after that) are performed on the requestor server.\n\n\nThis means that functions can be performed on different servers.\nFor example, in the query \nSELECT f(sum(g(x))) FROM distributed_table GROUP BY h(y),\n\n\n\n\nif a \ndistributed_table\n has at least two shards, the functions 'g' and 'h' are performed on remote servers, and the function 'f' is performed on the requestor server.\n\n\nif a \ndistributed_table\n has only one shard, all the 'f', 'g', and 'h' functions are performed on this shard's server.\n\n\n\n\nThe result of a function usually doesn't depend on which server it is performed on. However, sometimes this is important.\nFor example, functions that work with dictionaries use the dictionary that exists on the server they are running on.\nAnother example is the \nhostName\n function, which returns the name of the server it is running on in order to make \nGROUP BY\n by servers in a \nSELECT\n query.\n\n\nIf a function in a query is performed on the requestor server, but you need to perform it on remote servers, you can wrap it in an 'any' aggregate function or add it to a key in \nGROUP BY\n.\n\n\nArithmetic functions\n\n\nFor all arithmetic functions, the result type is calculated as the smallest number type that the result fits in, if there is such a type. The minimum is taken simultaneously based on the number of bits, whether it is signed, and whether it floats. If there are not enough bits, the highest bit type is taken.\n\n\nExample:\n\n\nSELECT\n \ntoTypeName\n(\n0\n),\n \ntoTypeName\n(\n0\n \n+\n \n0\n),\n \ntoTypeName\n(\n0\n \n+\n \n0\n \n+\n \n0\n),\n \ntoTypeName\n(\n0\n \n+\n \n0\n \n+\n \n0\n \n+\n \n0\n)\n\n\n\n\n\n\n\u250c\u2500toTypeName(0)\u2500\u252c\u2500toTypeName(plus(0, 0))\u2500\u252c\u2500toTypeName(plus(plus(0, 0), 0))\u2500\u252c\u2500toTypeName(plus(plus(plus(0, 0), 0), 0))\u2500\u2510\n\u2502 UInt8 \u2502 UInt16 \u2502 UInt32 \u2502 UInt64 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nArithmetic functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.\n\n\nOverflow is produced the same way as in C++.\n\n\nplus(a, b), a + b operator\n\n\nCalculates the sum of the numbers.\nYou can also add integer numbers with a date or date and time. In the case of a date, adding an integer means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds.\n\n\nminus(a, b), a - b operator\n\n\nCalculates the difference. The result is always signed.\n\n\nYou can also calculate integer numbers from a date or date with time. The idea is the same \u2013 see above for 'plus'.\n\n\nmultiply(a, b), a * b operator\n\n\nCalculates the product of the numbers.\n\n\ndivide(a, b), a / b operator\n\n\nCalculates the quotient of the numbers. The result type is always a floating-point type.\nIt is not integer division. For integer division, use the 'intDiv' function.\nWhen dividing by zero you get 'inf', '-inf', or 'nan'.\n\n\nintDiv(a, b)\n\n\nCalculates the quotient of the numbers. Divides into integers, rounding down (by the absolute value).\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nintDivOrZero(a, b)\n\n\nDiffers from 'intDiv' in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nmodulo(a, b), a % b operator\n\n\nCalculates the remainder after division.\nIf arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion.\nThe remainder is taken in the same sense as in C++. Truncated division is used for negative numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nnegate(a), -a operator\n\n\nCalculates a number with the reverse sign. The result is always signed.\n\n\nabs(a)\n\n\nCalculates the absolute value of the number (a). That is, if a \n 0, it returns -a. For unsigned types it doesn't do anything. For signed integer types, it returns an unsigned number.\n\n\ngcd(a, b)\n\n\nReturns the greatest common divisor of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nlcm(a, b)\n\n\nReturns the least common multiple of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nComparison functions\n\n\nComparison functions always return 0 or 1 (Uint8).\n\n\nThe following types can be compared:\n\n\n\n\nnumbers\n\n\nstrings and fixed strings\n\n\ndates\n\n\ndates with times\n\n\n\n\nwithin each group, but not between different groups.\n\n\nFor example, you can't compare a date with a string. You have to use a function to convert the string to a date, or vice versa.\n\n\nStrings are compared by bytes. A shorter string is smaller than all strings that start with it and that contain at least one more character.\n\n\nNote. Up until version 1.1.54134, signed and unsigned numbers were compared the same way as in C++. In other words, you could get an incorrect result in cases like SELECT 9223372036854775807 \n -1. This behavior changed in version 1.1.54134 and is now mathematically correct.\n\n\nequals, a = b and a == b operator\n\n\nnotEquals, a ! operator= b and a \n b\n\n\nless, \n operator\n\n\ngreater, \n operator\n\n\nlessOrEquals, \n= operator\n\n\ngreaterOrEquals, \n= operator\n\n\nLogical functions\n\n\nLogical functions accept any numeric types, but return a UInt8 number equal to 0 or 1.\n\n\nZero as an argument is considered \"false,\" while any non-zero value is considered \"true\".\n\n\nand, AND operator\n\n\nor, OR operator\n\n\nnot, NOT operator\n\n\nxor\n\n\n\n\nType conversion functions\n\n\ntoUInt8, toUInt16, toUInt32, toUInt64\n\n\ntoInt8, toInt16, toInt32, toInt64\n\n\ntoFloat32, toFloat64\n\n\ntoUInt8OrZero, toUInt16OrZero, toUInt32OrZero, toUInt64OrZero, toInt8OrZero, toInt16OrZero, toInt32OrZero, toInt64OrZero, toFloat32OrZero, toFloat64OrZero\n\n\ntoDate, toDateTime\n\n\ntoString\n\n\nFunctions for converting between numbers, strings (but not fixed strings), dates, and dates with times.\nAll these functions accept one argument.\n\n\nWhen converting to or from a string, the value is formatted or parsed using the same rules as for the TabSeparated format (and almost all other text formats). If the string can't be parsed, an exception is thrown and the request is canceled.\n\n\nWhen converting dates to numbers or vice versa, the date corresponds to the number of days since the beginning of the Unix epoch.\nWhen converting dates with times to numbers or vice versa, the date with time corresponds to the number of seconds since the beginning of the Unix epoch.\n\n\nThe date and date-with-time formats for the toDate/toDateTime functions are defined as follows:\n\n\nYYYY-MM-DD\nYYYY-MM-DD hh:mm:ss\n\n\n\n\n\nAs an exception, if converting from UInt32, Int32, UInt64, or Int64 numeric types to Date, and if the number is greater than or equal to 65536, the number is interpreted as a Unix timestamp (and not as the number of days) and is rounded to the date. This allows support for the common occurrence of writing 'toDate(unix_timestamp)', which otherwise would be an error and would require writing the more cumbersome 'toDate(toDateTime(unix_timestamp))'.\n\n\nConversion between a date and date with time is performed the natural way: by adding a null time or dropping the time.\n\n\nConversion between numeric types uses the same rules as assignments between different numeric types in C++.\n\n\nAdditionally, the toString function of the DateTime argument can take a second String argument containing the name of the time zone. Example: \nAsia/Yekaterinburg\n In this case, the time is formatted according to the specified time zone.\n\n\nSELECT\n\n \nnow\n()\n \nAS\n \nnow_local\n,\n\n \ntoString\n(\nnow\n(),\n \nAsia/Yekaterinburg\n)\n \nAS\n \nnow_yekat\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500now_local\u2500\u252c\u2500now_yekat\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 00:11:21 \u2502 2016-06-15 02:11:21 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nAlso see the \ntoUnixTimestamp\n function.\n\n\ntoFixedString(s, N)\n\n\nConverts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant.\nIf the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown.\n\n\ntoStringCutToZero(s)\n\n\nAccepts a String or FixedString argument. Returns the String with the content truncated at the first zero byte found.\n\n\nExample:\n\n\nSELECT\n \ntoFixedString\n(\nfoo\n,\n \n8\n)\n \nAS\n \ns\n,\n \ntoStringCutToZero\n(\ns\n)\n \nAS\n \ns_cut\n\n\n\n\n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0\\0\\0\\0\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n \ntoFixedString\n(\nfoo\\0bar\n,\n \n8\n)\n \nAS\n \ns\n,\n \ntoStringCutToZero\n(\ns\n)\n \nAS\n \ns_cut\n\n\n\n\n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0bar\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nreinterpretAsUInt8, reinterpretAsUInt16, reinterpretAsUInt32, reinterpretAsUInt64\n\n\nreinterpretAsInt8, reinterpretAsInt16, reinterpretAsInt32, reinterpretAsInt64\n\n\nreinterpretAsFloat32, reinterpretAsFloat64\n\n\nreinterpretAsDate, reinterpretAsDateTime\n\n\nThese functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\n\n\nreinterpretAsString\n\n\nThis function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long.\n\n\nCAST(x, t)\n\n\nConverts 'x' to the 't' data type. The syntax CAST(x AS t) is also supported.\n\n\nExample:\n\n\nSELECT\n\n \n2016-06-15 23:00:00\n \nAS\n \ntimestamp\n,\n\n \nCAST\n(\ntimestamp\n \nAS\n \nDateTime\n)\n \nAS\n \ndatetime\n,\n\n \nCAST\n(\ntimestamp\n \nAS\n \nDate\n)\n \nAS\n \ndate\n,\n\n \nCAST\n(\ntimestamp\n,\n \nString\n)\n \nAS\n \nstring\n,\n\n \nCAST\n(\ntimestamp\n,\n \nFixedString(22)\n)\n \nAS\n \nfixed_string\n\n\n\n\n\n\n\u250c\u2500timestamp\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500datetime\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500date\u2500\u252c\u2500string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500fixed_string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00\\0\\0\\0 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nConversion to FixedString (N) only works for arguments of type String or FixedString (N).\n\n\nFunctions for working with dates and times\n\n\nSupport for time zones\n\n\nAll functions for working with the date and time that have a logical use for the time zone can accept a second optional time zone argument. Example: Asia/Yekaterinburg. In this case, they use the specified time zone instead of the local (default) one.\n\n\nSELECT\n\n \ntoDateTime\n(\n2016-06-15 23:00:00\n)\n \nAS\n \ntime\n,\n\n \ntoDate\n(\ntime\n)\n \nAS\n \ndate_local\n,\n\n \ntoDate\n(\ntime\n,\n \nAsia/Yekaterinburg\n)\n \nAS\n \ndate_yekat\n,\n\n \ntoString\n(\ntime\n,\n \nUS/Samoa\n)\n \nAS\n \ntime_samoa\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500time\u2500\u252c\u2500date_local\u2500\u252c\u2500date_yekat\u2500\u252c\u2500time_samoa\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-16 \u2502 2016-06-15 09:00:00 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nOnly time zones that differ from UTC by a whole number of hours are supported.\n\n\ntoYear\n\n\nConverts a date or date with time to a UInt16 number containing the year number (AD).\n\n\ntoMonth\n\n\nConverts a date or date with time to a UInt8 number containing the month number (1-12).\n\n\ntoDayOfMonth\n\n\n-Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31).\n\n\ntoDayOfWeek\n\n\nConverts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7).\n\n\ntoHour\n\n\nConverts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23).\nThis function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true \u2013 even in Moscow the clocks were twice changed at a different time).\n\n\ntoMinute\n\n\nConverts a date with time to a UInt8 number containing the number of the minute of the hour (0-59).\n\n\ntoSecond\n\n\nConverts a date with time to a UInt8 number containing the number of the second in the minute (0-59).\nLeap seconds are not accounted for.\n\n\ntoMonday\n\n\nRounds down a date or date with time to the nearest Monday.\nReturns the date.\n\n\ntoStartOfMonth\n\n\nRounds down a date or date with time to the first day of the month.\nReturns the date.\n\n\ntoStartOfQuarter\n\n\nRounds down a date or date with time to the first day of the quarter.\nThe first day of the quarter is either 1 January, 1 April, 1 July, or 1 October.\nReturns the date.\n\n\ntoStartOfYear\n\n\nRounds down a date or date with time to the first day of the year.\nReturns the date.\n\n\ntoStartOfMinute\n\n\nRounds down a date with time to the start of the minute.\n\n\ntoStartOfFiveMinute\n\n\nRounds down a date with time to the start of the hour.\n\n\ntoStartOfFifteenMinutes\n\n\nRounds down the date with time to the start of the fifteen-minute interval.\n\n\nNote: If you need to round a date with time to any other number of seconds, minutes, or hours, you can convert it into a number by using the toUInt32 function, then round the number using intDiv and multiplication, and convert it back using the toDateTime function.\n\n\ntoStartOfHour\n\n\nRounds down a date with time to the start of the hour.\n\n\ntoStartOfDay\n\n\nRounds down a date with time to the start of the day.\n\n\ntoTime\n\n\nConverts a date with time to a certain fixed date, while preserving the time.\n\n\ntoRelativeYearNum\n\n\nConverts a date with time or date to the number of the year, starting from a certain fixed point in the past.\n\n\ntoRelativeMonthNum\n\n\nConverts a date with time or date to the number of the month, starting from a certain fixed point in the past.\n\n\ntoRelativeWeekNum\n\n\nConverts a date with time or date to the number of the week, starting from a certain fixed point in the past.\n\n\ntoRelativeDayNum\n\n\nConverts a date with time or date to the number of the day, starting from a certain fixed point in the past.\n\n\ntoRelativeHourNum\n\n\nConverts a date with time or date to the number of the hour, starting from a certain fixed point in the past.\n\n\ntoRelativeMinuteNum\n\n\nConverts a date with time or date to the number of the minute, starting from a certain fixed point in the past.\n\n\ntoRelativeSecondNum\n\n\nConverts a date with time or date to the number of the second, starting from a certain fixed point in the past.\n\n\nnow\n\n\nAccepts zero arguments and returns the current time at one of the moments of request execution.\nThis function returns a constant, even if the request took a long time to complete.\n\n\ntoday\n\n\nAccepts zero arguments and returns the current date at one of the moments of request execution.\nThe same as 'toDate(now())'.\n\n\nyesterday\n\n\nAccepts zero arguments and returns yesterday's date at one of the moments of request execution.\nThe same as 'today() - 1'.\n\n\ntimeSlot\n\n\nRounds the time to the half hour.\nThis function is specific to Yandex.Metrica, since half an hour is the minimum amount of time for breaking a session into two sessions if a tracking tag shows a single user's consecutive pageviews that differ in time by strictly more than this amount. This means that tuples (the tag ID, user ID, and time slot) can be used to search for pageviews that are included in the corresponding session.\n\n\ntimeSlots(StartTime, Duration)\n\n\nFor a time interval starting at 'StartTime' and continuing for 'Duration' seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour.\nFor example, \ntimeSlots(toDateTime('2012-01-01 12:20:00'), 600) = [toDateTime('2012-01-01 12:00:00'), toDateTime('2012-01-01 12:30:00')]\n.\nThis is necessary for searching for pageviews in the corresponding session.\n\n\nFunctions for working with strings\n\n\nempty\n\n\nReturns 1 for an empty string or 0 for a non-empty string.\nThe result type is UInt8.\nA string is considered non-empty if it contains at least one byte, even if this is a space or a null byte.\nThe function also works for arrays.\n\n\nnotEmpty\n\n\nReturns 0 for an empty string or 1 for a non-empty string.\nThe result type is UInt8.\nThe function also works for arrays.\n\n\nlength\n\n\nReturns the length of a string in bytes (not in characters, and not in code points).\nThe result type is UInt64.\nThe function also works for arrays.\n\n\nlengthUTF8\n\n\nReturns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\nThe result type is UInt64.\n\n\nlower\n\n\nConverts ASCII Latin symbols in a string to lowercase.\n\n\nupper\n\n\nConverts ASCII Latin symbols in a string to uppercase.\n\n\nlowerUTF8\n\n\nConverts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.\n\n\nupperUTF8\n\n\nConverts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.\n\n\nreverse\n\n\nReverses the string (as a sequence of bytes).\n\n\nreverseUTF8\n\n\nReverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn't throw an exception).\n\n\nconcat(s1, s2, ...)\n\n\nConcatenates the strings listed in the arguments, without a separator.\n\n\nsubstring(s, offset, length)\n\n\nReturns a substring starting with the byte from the 'offset' index that is 'length' bytes long. Character indexing starts from one (as in standard SQL). The 'offset' and 'length' arguments must be constants.\n\n\nsubstringUTF8(s, offset, length)\n\n\nThe same as 'substring', but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\n\n\nappendTrailingCharIfAbsent(s, c)\n\n\nIf the 's' string is non-empty and does not contain the 'c' character at the end, it appends the 'c' character to the end.\n\n\nconvertCharset(s, from, to)\n\n\nReturns the string 's' that was converted from the encoding in 'from' to the encoding in 'to'.\n\n\nFunctions for searching strings\n\n\nThe search is case-sensitive in all these functions.\nThe search substring or regular expression must be a constant in all these functions.\n\n\nposition(haystack, needle)\n\n\nSearch for the \nneedle\n substring in the \nhaystack\n string.\nReturns the position (in bytes) of the found substring, starting from 1, or returns 0 if the substring was not found.\n\n\nFor case-insensitive search use \npositionCaseInsensitive\n function.\n\n\npositionUTF8(haystack, needle)\n\n\nThe same as \nposition\n, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\n\n\nFor case-insensitive search use \npositionCaseInsensitiveUTF8\n function.\n\n\nmatch(haystack, pattern)\n\n\nChecks whether the string matches the 'pattern' regular expression. A re2 regular expression.\nReturns 0 if it doesn't match, or 1 if it matches.\n\n\nNote that the backslash symbol (\n\\\n) is used for escaping in the regular expression. The same symbol is used for escaping in string literals. So in order to escape the symbol in a regular expression, you must write two backslashes (\\) in a string literal.\n\n\nThe regular expression works with the string as if it is a set of bytes. The regular expression can't contain null bytes.\nFor patterns to search for substrings in a string, it is better to use LIKE or 'position', since they work much faster.\n\n\nextract(haystack, pattern)\n\n\nExtracts a fragment of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. If the regex doesn't contain subpatterns, it takes the fragment that matches the entire regex. Otherwise, it takes the fragment that matches the first subpattern.\n\n\nextractAll(haystack, pattern)\n\n\nExtracts all the fragments of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex. In general, the behavior is the same as the 'extract' function (it takes the first subpattern, or the entire expression if there isn't a subpattern).\n\n\nlike(haystack, pattern), haystack LIKE pattern operator\n\n\nChecks whether a string matches a simple regular expression.\nThe regular expression can contain the metasymbols \n%\n and \n_\n.\n\n\n``% indicates any quantity of any bytes (including zero characters).\n\n\n_\n indicates any one byte.\n\n\nUse the backslash (\n\\\n) for escaping metasymbols. See the note on escaping in the description of the 'match' function.\n\n\nFor regular expressions like \n%needle%\n, the code is more optimal and works as fast as the \nposition\n function.\nFor other regular expressions, the code is the same as for the 'match' function.\n\n\nnotLike(haystack, pattern), haystack NOT LIKE pattern operator\n\n\nThe same thing as 'like', but negative.\n\n\nFunctions for searching and replacing in strings\n\n\nreplaceOne(haystack, pattern, replacement)\n\n\nReplaces the first occurrence, if it exists, of the 'pattern' substring in 'haystack' with the 'replacement' substring.\nHereafter, 'pattern' and 'replacement' must be constants.\n\n\nreplaceAll(haystack, pattern, replacement)\n\n\nReplaces all occurrences of the 'pattern' substring in 'haystack' with the 'replacement' substring.\n\n\nreplaceRegexpOne(haystack, pattern, replacement)\n\n\nReplacement using the 'pattern' regular expression. A re2 regular expression.\nReplaces only the first occurrence, if it exists.\nA pattern can be specified as 'replacement'. This pattern can include substitutions \n\\0-\\9\n.\nThe substitution \n\\0\n includes the entire regular expression. Substitutions \n\\1-\\9\n correspond to the subpattern numbers.To use the \n\\\n character in a template, escape it using \n\\\n.\nAlso keep in mind that a string literal requires an extra escape.\n\n\nExample 1. Converting the date to American format:\n\n\nSELECT\n \nDISTINCT\n\n \nEventDate\n,\n\n \nreplaceRegexpOne\n(\ntoString\n(\nEventDate\n),\n \n(\\\\d{4})-(\\\\d{2})-(\\\\d{2})\n,\n \n\\\\2/\\\\3/\\\\1\n)\n \nAS\n \nres\n\n\nFROM\n \ntest\n.\nhits\n\n\nLIMIT\n \n7\n\n\nFORMAT\n \nTabSeparated\n\n\n\n\n\n\n2014-03-17 03/17/2014\n2014-03-18 03/18/2014\n2014-03-19 03/19/2014\n2014-03-20 03/20/2014\n2014-03-21 03/21/2014\n2014-03-22 03/22/2014\n2014-03-23 03/23/2014\n\n\n\n\n\nExample 2. Copying a string ten times:\n\n\nSELECT\n \nreplaceRegexpOne\n(\nHello, World!\n,\n \n.*\n,\n \n\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nreplaceRegexpAll(haystack, pattern, replacement)\n\n\nThis does the same thing, but replaces all the occurrences. Example:\n\n\nSELECT\n \nreplaceRegexpAll\n(\nHello, World!\n,\n \n.\n,\n \n\\\\0\\\\0\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 HHeelllloo,, WWoorrlldd!! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nAs an exception, if a regular expression worked on an empty substring, the replacement is not made more than once.\nExample:\n\n\nSELECT\n \nreplaceRegexpAll\n(\nHello, World!\n,\n \n^\n,\n \nhere: \n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 here: Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nConditional functions\n\n\nif(cond, then, else), cond ? operator then : else\n\n\nReturns 'then' if cond !or 'else' if cond = 0.'cond' must be UInt 8, and 'then' and 'else' must be a type that has the smallest common type.\n\n\nMathematical functions\n\n\nAll the functions return a Float64 number. The accuracy of the result is close to the maximum precision possible, but the result might not coincide with the machine representable number nearest to the corresponding real number.\n\n\ne()\n\n\nReturns a Float64 number close to the e number.\n\n\npi()\n\n\nReturns a Float64 number close to \u03c0.\n\n\nexp(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the exponent of the argument.\n\n\nlog(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the natural logarithm of the argument.\n\n\nexp2(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to 2^x.\n\n\nlog2(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument.\n\n\nexp10(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to 10^x.\n\n\nlog10(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument.\n\n\nsqrt(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the square root of the argument.\n\n\ncbrt(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the cubic root of the argument.\n\n\nerf(x)\n\n\nIf 'x' is non-negative, then erf(x / \u03c3\u221a2)\n is the probability that a random variable having a normal distribution with standard deviation '\u03c3' takes the value that is separated from the expected value by more than 'x'.\n\n\nExample (three sigma rule):\n\n\nSELECT\n \nerf\n(\n3\n \n/\n \nsqrt\n(\n2\n))\n\n\n\n\n\n\n\u250c\u2500erf(divide(3, sqrt(2)))\u2500\u2510\n\u2502 0.9973002039367398 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nerfc(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large 'x' values.\n\n\nlgamma(x)\n\n\nThe logarithm of the gamma function.\n\n\ntgamma(x)\n\n\nGamma function.\n\n\nsin(x)\n\n\nThe sine.\n\n\ncos(x)\n\n\nThe cosine.\n\n\ntan(x)\n\n\nThe tangent.\n\n\nasin(x)\n\n\nThe arc sine.\n\n\nacos(x)\n\n\nThe arc cosine.\n\n\natan(x)\n\n\nThe arc tangent.\n\n\npow(x, y)\n\n\nAccepts two numeric arguments and returns a Float64 number close to x^y.\n\n\nRounding functions\n\n\nfloor(x[, N])\n\n\nReturns the largest round number that is less than or equal to x. A round number is a multiple of 1/10N, or the nearest number of the appropriate data type if 1 / 10N isn't exact.\n'N' is an integer constant, optional parameter. By default it is zero, which means to round to an integer.\n'N' may be negative.\n\n\nExamples: \nfloor(123.45, 1) = 123.4, floor(123.45, -1) = 120.\n\n\nx\n is any numeric type. The result is a number of the same type.\nFor integer arguments, it makes sense to round with a negative 'N' value (for non-negative 'N', the function doesn't do anything).\nIf rounding causes overflow (for example, floor(-128, -1)), an implementation-specific result is returned.\n\n\nceil(x[, N])\n\n\nReturns the smallest round number that is greater than or equal to 'x'. In every other way, it is the same as the 'floor' function (see above).\n\n\nround(x[, N])\n\n\nReturns the round number nearest to 'num', which may be less than, greater than, or equal to 'x'.If 'x' is exactly in the middle between the nearest round numbers, one of them is returned (implementation-specific).\nThe number '-0.' may or may not be considered round (implementation-specific).\nIn every other way, this function is the same as 'floor' and 'ceil' described above.\n\n\nroundToExp2(num)\n\n\nAccepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two.\n\n\nroundDuration(num)\n\n\nAccepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length\n\n\nroundAge(num)\n\n\nAccepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to a number from the set: 18, 25, 35, 45, 55. This function is specific to Yandex.Metrica and used for implementing the report on user age.\n\n\nFunctions for working with arrays\n\n\nempty\n\n\nReturns 1 for an empty array, or 0 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.\n\n\nnotEmpty\n\n\nReturns 0 for an empty array, or 1 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.\n\n\nlength\n\n\nReturns the number of items in the array.\nThe result type is UInt64.\nThe function also works for strings.\n\n\nemptyArrayUInt8, emptyArrayUInt16, emptyArrayUInt32, emptyArrayUInt64\n\n\nemptyArrayInt8, emptyArrayInt16, emptyArrayInt32, emptyArrayInt64\n\n\nemptyArrayFloat32, emptyArrayFloat64\n\n\nemptyArrayDate, emptyArrayDateTime\n\n\nemptyArrayString\n\n\nAccepts zero arguments and returns an empty array of the appropriate type.\n\n\nemptyArrayToSingle\n\n\nAccepts an empty array and returns a one-element array that is equal to the default value.\n\n\nrange(N)\n\n\nReturns an array of numbers from 0 to N-1.\nJust in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block.\n\n\narray(x1, ...), operator [x1, ...]\n\n\nCreates an array from the function arguments.\nThe arguments must be constants and have types that have the smallest common type. At least one argument must be passed, because otherwise it isn't clear which type of array to create. That is, you can't use this function to create an empty array (to do that, use the 'emptyArray*' function described above).\nReturns an 'Array(T)' type result, where 'T' is the smallest common type out of the passed arguments.\n\n\narrayConcat\n\n\nCombines arrays passed as arguments.\n\n\narrayConcat(arrays)\n\n\n\n\n\nArguments\n\n\n\n\narrays\n \u2013 Arrays of comma-separated \n[values]\n.\n\n\n\n\nExample\n\n\nSELECT\n \narrayConcat\n([\n1\n,\n \n2\n],\n \n[\n3\n,\n \n4\n],\n \n[\n5\n,\n \n6\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,3,4,5,6] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayElement(arr, n), operator arr[n]\n\n\nGet the element with the index 'n' from the array 'arr'.'n' must be any integer type.\nIndexes in an array begin from one.\nNegative indexes are supported. In this case, it selects the corresponding element numbered from the end. For example, 'arr[-1]' is the last item in the array.\n\n\nIf the index falls outside of the bounds of an array, it returns some default value (0 for numbers, an empty string for strings, etc.).\n\n\nhas(arr, elem)\n\n\nChecks whether the 'arr' array has the 'elem' element.\nReturns 0 if the the element is not in the array, or 1 if it is.\n\n\nindexOf(arr, x)\n\n\nReturns the index of the 'x' element (starting from 1) if it is in the array, or 0 if it is not.\n\n\ncountEqual(arr, x)\n\n\nReturns the number of elements in the array equal to x. Equivalent to arrayCount (elem-\n elem = x, arr).\n\n\narrayEnumerate(arr)\n\n\nReturns the array [1, 2, 3, ..., length (arr) ]\n\n\nThis function is normally used with ARRAY JOIN. It allows counting something just once for each array after applying ARRAY JOIN. Example:\n\n\nSELECT\n\n \ncount\n()\n \nAS\n \nReaches\n,\n\n \ncountIf\n(\nnum\n \n=\n \n1\n)\n \nAS\n \nHits\n\n\nFROM\n \ntest\n.\nhits\n\n\nARRAY\n \nJOIN\n\n \nGoalsReached\n,\n\n \narrayEnumerate\n(\nGoalsReached\n)\n \nAS\n \nnum\n\n\nWHERE\n \nCounterID\n \n=\n \n160656\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIn this example, Reaches is the number of conversions (the strings received after applying ARRAY JOIN), and Hits is the number of pageviews (strings before ARRAY JOIN). In this particular case, you can get the same result in an easier way:\n\n\nSELECT\n\n \nsum\n(\nlength\n(\nGoalsReached\n))\n \nAS\n \nReaches\n,\n\n \ncount\n()\n \nAS\n \nHits\n\n\nFROM\n \ntest\n.\nhits\n\n\nWHERE\n \n(\nCounterID\n \n=\n \n160656\n)\n \nAND\n \nnotEmpty\n(\nGoalsReached\n)\n\n\n\n\n\n\n\u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nThis function can also be used in higher-order functions. For example, you can use it to get array indexes for elements that match a condition.\n\n\narrayEnumerateUniq(arr, ...)\n\n\nReturns an array the same size as the source array, indicating for each element what its position is among elements with the same value.\nFor example: arrayEnumerateUniq([10, 20, 10, 30]) = [1, 1, 2, 1].\n\n\nThis function is useful when using ARRAY JOIN and aggregation of array elements.\nExample:\n\n\nSELECT\n\n \nGoals\n.\nID\n \nAS\n \nGoalID\n,\n\n \nsum\n(\nSign\n)\n \nAS\n \nReaches\n,\n\n \nsumIf\n(\nSign\n,\n \nnum\n \n=\n \n1\n)\n \nAS\n \nVisits\n\n\nFROM\n \ntest\n.\nvisits\n\n\nARRAY\n \nJOIN\n\n \nGoals\n,\n\n \narrayEnumerateUniq\n(\nGoals\n.\nID\n)\n \nAS\n \nnum\n\n\nWHERE\n \nCounterID\n \n=\n \n160656\n\n\nGROUP\n \nBY\n \nGoalID\n\n\nORDER\n \nBY\n \nReaches\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500\u2500GoalID\u2500\u252c\u2500Reaches\u2500\u252c\u2500Visits\u2500\u2510\n\u2502 53225 \u2502 3214 \u2502 1097 \u2502\n\u2502 2825062 \u2502 3188 \u2502 1097 \u2502\n\u2502 56600 \u2502 2803 \u2502 488 \u2502\n\u2502 1989037 \u2502 2401 \u2502 365 \u2502\n\u2502 2830064 \u2502 2396 \u2502 910 \u2502\n\u2502 1113562 \u2502 2372 \u2502 373 \u2502\n\u2502 3270895 \u2502 2262 \u2502 812 \u2502\n\u2502 1084657 \u2502 2262 \u2502 345 \u2502\n\u2502 56599 \u2502 2260 \u2502 799 \u2502\n\u2502 3271094 \u2502 2256 \u2502 812 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIn this example, each goal ID has a calculation of the number of conversions (each element in the Goals nested data structure is a goal that was reached, which we refer to as a conversion) and the number of sessions. Without ARRAY JOIN, we would have counted the number of sessions as sum(Sign). But in this particular case, the rows were multiplied by the nested Goals structure, so in order to count each session one time after this, we apply a condition to the value of the arrayEnumerateUniq(Goals.ID) function.\n\n\nThe arrayEnumerateUniq function can take multiple arrays of the same size as arguments. In this case, uniqueness is considered for tuples of elements in the same positions in all the arrays.\n\n\nSELECT\n \narrayEnumerateUniq\n([\n1\n,\n \n1\n,\n \n1\n,\n \n2\n,\n \n2\n,\n \n2\n],\n \n[\n1\n,\n \n1\n,\n \n2\n,\n \n1\n,\n \n1\n,\n \n2\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,1,1,2,1] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nThis is necessary when using ARRAY JOIN with a nested data structure and further aggregation across multiple elements in this structure.\n\n\narrayPopBack\n\n\nRemoves the last item from the array.\n\n\narrayPopBack(array)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\n\n\nExample\n\n\nSELECT\n \narrayPopBack\n([\n1\n,\n \n2\n,\n \n3\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [1,2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayPopFront\n\n\nRemoves the first item from the array.\n\n\narrayPopFront(array)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\n\n\nExample\n\n\nSELECT\n \narrayPopFront\n([\n1\n,\n \n2\n,\n \n3\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayPushBack\n\n\nAdds one item to the end of the array.\n\n\narrayPushBack(array, single_value)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\nsingle_value\n \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the \nsingle_value\n type for the data type of the array. For more information about ClickHouse data types, read the section \"\nData types\n\".\n\n\n\n\nExample\n\n\nSELECT\n \narrayPushBack\n([\na\n],\n \nb\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\na\n,\nb\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayPushFront\n\n\nAdds one element to the beginning of the array.\n\n\narrayPushFront(array, single_value)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\nsingle_value\n \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the \nsingle_value\n type for the data type of the array. For more information about ClickHouse data types, read the section \"\nData types\n\".\n\n\n\n\nExample\n\n\nSELECT\n \narrayPushBack\n([\nb\n],\n \na\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\na\n,\nb\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narraySlice\n\n\nReturns a slice of the array.\n\n\narraySlice(array, offset[, length])\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array of data.\n\n\noffset\n \u2013 Indent from the edge of the array. A positive value indicates an offset on the left, and a negative value is an indent on the right. Numbering of the array items begins with 1.\n\n\nlength\n - The length of the required slice. If you specify a negative value, the function returns an open slice \n[offset, array_length - length)\n. If you omit the value, the function returns the slice \n[offset, the_end_of_array]\n.\n\n\n\n\nExample\n\n\nSELECT\n \narraySlice\n([\n1\n,\n \n2\n,\n \n3\n,\n \n4\n,\n \n5\n],\n \n2\n,\n \n3\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [2,3,4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayUniq(arr, ...)\n\n\nIf one argument is passed, it counts the number of different elements in the array.\nIf multiple arguments are passed, it counts the number of different tuples of elements at corresponding positions in multiple arrays.\n\n\nIf you want to get a list of unique items in an array, you can use arrayReduce('groupUniqArray', arr).\n\n\narrayJoin(arr)\n\n\nA special function. See the section \n\"ArrayJoin function\"\n.\n\n\nFunctions for splitting and merging strings and arrays\n\n\nsplitByChar(separator, s)\n\n\nSplits a string into substrings separated by 'separator'.'separator' must be a string constant consisting of exactly one character.\nReturns an array of selected substrings. Empty substrings may be selected if the separator occurs at the beginning or end of the string, or if there are multiple consecutive separators.\n\n\nsplitByString(separator, s)\n\n\nThe same as above, but it uses a string of multiple characters as the separator. The string must be non-empty.\n\n\narrayStringConcat(arr[, separator])\n\n\nConcatenates the strings listed in the array with the separator.'separator' is an optional parameter: a constant string, set to an empty string by default.\nReturns the string.\n\n\nalphaTokens(s)\n\n\nSelects substrings of consecutive bytes from the ranges a-z and A-Z.Returns an array of substrings.\n\n\nBit functions\n\n\nBit functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.\n\n\nThe result type is an integer with bits equal to the maximum bits of its arguments. If at least one of the arguments is signed, the result is a signed number. If an argument is a floating-point number, it is cast to Int64.\n\n\nbitAnd(a, b)\n\n\nbitOr(a, b)\n\n\nbitXor(a, b)\n\n\nbitNot(a)\n\n\nbitShiftLeft(a, b)\n\n\nbitShiftRight(a, b)\n\n\nHash functions\n\n\nHash functions can be used for deterministic pseudo-random shuffling of elements.\n\n\nhalfMD5\n\n\nCalculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian.\nAccepts a String-type argument. Returns UInt64.\nThis function works fairly slowly (5 million short strings per second per processor core).\nIf you don't need MD5 in particular, use the 'sipHash64' function instead.\n\n\nMD5\n\n\nCalculates the MD5 from a string and returns the resulting set of bytes as FixedString(16).\nIf you don't need MD5 in particular, but you need a decent cryptographic 128-bit hash, use the 'sipHash128' function instead.\nIf you want to get the same result as output by the md5sum utility, use lower(hex(MD5(s))).\n\n\nsipHash64\n\n\nCalculates SipHash from a string.\nAccepts a String-type argument. Returns UInt64.\nSipHash is a cryptographic hash function. It works at least three times faster than MD5.\nFor more information, see the link: \nhttps://131002.net/siphash/\n\n\nsipHash128\n\n\nCalculates SipHash from a string.\nAccepts a String-type argument. Returns FixedString(16).\nDiffers from sipHash64 in that the final xor-folding state is only done up to 128 bytes.\n\n\ncityHash64\n\n\nCalculates CityHash64 from a string or a similar hash function for any number of any type of arguments.\nFor String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality.\nFor other types of arguments, a decent implementation-specific fast non-cryptographic hash function is used.\nIf multiple arguments are passed, the function is calculated using the same rules and chain combinations using the CityHash combinator.\nFor example, you can compute the checksum of an entire table with accuracy up to the row order: \nSELECT sum(cityHash64(*)) FROM table\n.\n\n\nintHash32\n\n\nCalculates a 32-bit hash code from any type of integer.\nThis is a relatively fast non-cryptographic hash function of average quality for numbers.\n\n\nintHash64\n\n\nCalculates a 64-bit hash code from any type of integer.\nIt works faster than intHash32. Average quality.\n\n\nSHA1\n\n\nSHA224\n\n\nSHA256\n\n\nCalculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32).\nThe function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million).\nWe recommend using this function only in cases when you need a specific hash function and you can't select it.\nEven in these cases, we recommend applying the function offline and pre-calculating values when inserting them into the table, instead of applying it in SELECTS.\n\n\nURLHash(url[, N])\n\n\nA fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization.\n\nURLHash(s)\n \u2013 Calculates a hash from a string without one of the trailing symbols \n/\n,\n?\n or \n#\n at the end, if present.\n\nURLHash(s, N)\n \u2013 Calculates a hash from a string up to the N level in the URL hierarchy, without one of the trailing symbols \n/\n,\n?\n or \n#\n at the end, if present.\nLevels are the same as in URLHierarchy. This function is specific to Yandex.Metrica.\n\n\nFunctions for generating pseudo-random numbers\n\n\nNon-cryptographic generators of pseudo-random numbers are used.\n\n\nAll the functions accept zero arguments or one argument.\nIf an argument is passed, it can be any type, and its value is not used for anything.\nThe only purpose of this argument is to prevent common subexpression elimination, so that two different instances of the same function return different columns with different random numbers.\n\n\nrand\n\n\nReturns a pseudo-random UInt32 number, evenly distributed among all UInt32-type numbers.\nUses a linear congruential generator.\n\n\nrand64\n\n\nReturns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers.\nUses a linear congruential generator.\n\n\nEncoding functions\n\n\nhex\n\n\nAccepts arguments of types: \nString\n, \nunsigned integer\n, \nDate\n, or \nDateTime\n. Returns a string containing the argument's hexadecimal representation. Uses uppercase letters \nA-F\n. Does not use \n0x\n prefixes or \nh\n suffixes. For strings, all bytes are simply encoded as two hexadecimal numbers. Numbers are converted to big endian (\"human readable\") format. For numbers, older zeros are trimmed, but only by entire bytes. For example, \nhex (1) = '01'\n. \nDate\n is encoded as the number of days since the beginning of the Unix epoch. \nDateTime\n is encoded as the number of seconds since the beginning of the Unix epoch.\n\n\nunhex(str)\n\n\nAccepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits does not have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn't thrown).\nIf you want to convert the result to a number, you can use the 'reverse' and 'reinterpretAsType' functions.\n\n\nUUIDStringToNum(str)\n\n\nAccepts a string containing 36 characters in the format \n123e4567-e89b-12d3-a456-426655440000\n, and returns it as a set of bytes in a FixedString(16).\n\n\nUUIDNumToString(str)\n\n\nAccepts a FixedString(16) value. Returns a string containing 36 characters in text format.\n\n\nbitmaskToList(num)\n\n\nAccepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order.\n\n\nbitmaskToArray(num)\n\n\nAccepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order.\n\n\nFunctions for working with URLs\n\n\nAll these functions don't follow the RFC. They are maximally simplified for improved performance.\n\n\nFunctions that extract part of a URL\n\n\nIf there isn't anything similar in a URL, an empty string is returned.\n\n\nprotocol\n\n\nReturns the protocol. Examples: http, ftp, mailto, magnet...\n\n\ndomain\n\n\nGets the domain.\n\n\ndomainWithoutWWW\n\n\nReturns the domain and removes no more than one 'www.' from the beginning of it, if present.\n\n\ntopLevelDomain\n\n\nReturns the top-level domain. Example: .ru.\n\n\nfirstSignificantSubdomain\n\n\nReturns the \"first significant subdomain\". This is a non-standard concept specific to Yandex.Metrica. The first significant subdomain is a second-level domain if it is 'com', 'net', 'org', or 'co'. Otherwise, it is a third-level domain. For example, firstSignificantSubdomain ('\nhttps://news.yandex.ru/\n') = 'yandex ', firstSignificantSubdomain ('\nhttps://news.yandex.com.tr/\n') = 'yandex '. The list of \"insignificant\" second-level domains and other implementation details may change in the future.\n\n\ncutToFirstSignificantSubdomain\n\n\nReturns the part of the domain that includes top-level subdomains up to the \"first significant subdomain\" (see the explanation above).\n\n\nFor example, \ncutToFirstSignificantSubdomain('https://news.yandex.com.tr/') = 'yandex.com.tr'\n.\n\n\npath\n\n\nReturns the path. Example: \n/top/news.html\n The path does not include the query string.\n\n\npathFull\n\n\nThe same as above, but including query string and fragment. Example: /top/news.html?page=2#comments\n\n\nqueryString\n\n\nReturns the query string. Example: page=1\nlr=213. query-string does not include the initial question mark, as well as # and everything after #.\n\n\nfragment\n\n\nReturns the fragment identifier. fragment does not include the initial hash symbol.\n\n\nqueryStringAndFragment\n\n\nReturns the query string and fragment identifier. Example: page=1#29390.\n\n\nextractURLParameter(URL, name)\n\n\nReturns the value of the 'name' parameter in the URL, if present. Otherwise, an empty string. If there are many parameters with this name, it returns the first occurrence. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.\n\n\nextractURLParameters(URL)\n\n\nReturns an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way.\n\n\nextractURLParameterNames(URL)\n\n\nReturns an array of name strings corresponding to the names of URL parameters. The values are not decoded in any way.\n\n\nURLHierarchy(URL)\n\n\nReturns an array containing the URL, truncated at the end by the symbols /,? in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:\n\n\nURLPathHierarchy(URL)\n\n\nThe same as above, but without the protocol and host in the result. The / element (root) is not included. Example: the function is used to implement tree reports the URL in Yandex. Metric.\n\n\nURLPathHierarchy(\nhttps://example.com/browse/CONV-6788\n) =\n[\n \n/browse/\n,\n \n/browse/CONV-6788\n\n]\n\n\n\n\n\ndecodeURLComponent(URL)\n\n\nReturns the decoded URL.\nExample:\n\n\nSELECT\n \ndecodeURLComponent\n(\nhttp://127.0.0.1:8123/?query=SELECT%201%3B\n)\n \nAS\n \nDecodedURL\n;\n\n\n\n\n\n\n\u250c\u2500DecodedURL\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 http://127.0.0.1:8123/?query=SELECT 1; \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nFunctions that remove part of a URL.\n\n\nIf the URL doesn't have anything similar, the URL remains unchanged.\n\n\ncutWWW\n\n\nRemoves no more than one 'www.' from the beginning of the URL's domain, if present.\n\n\ncutQueryString\n\n\nRemoves query string. The question mark is also removed.\n\n\ncutFragment\n\n\nRemoves the fragment identifier. The number sign is also removed.\n\n\ncutQueryStringAndFragment\n\n\nRemoves the query string and fragment identifier. The question mark and number sign are also removed.\n\n\ncutURLParameter(URL, name)\n\n\nRemoves the 'name' URL parameter, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.\n\n\nFunctions for working with IP addresses\n\n\nIPv4NumToString(num)\n\n\nTakes a UInt32 number. Interprets it as an IPv4 address in big endian. Returns a string containing the corresponding IPv4 address in the format A.B.C.d (dot-separated numbers in decimal form).\n\n\nIPv4StringToNum(s)\n\n\nThe reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0.\n\n\nIPv4NumToStringClassC(num)\n\n\nSimilar to IPv4NumToString, but using xxx instead of the last octet.\n\n\nExample:\n\n\nSELECT\n\n \nIPv4NumToStringClassC\n(\nClientIP\n)\n \nAS\n \nk\n,\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \nk\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500k\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 83.149.9.xxx \u2502 26238 \u2502\n\u2502 217.118.81.xxx \u2502 26074 \u2502\n\u2502 213.87.129.xxx \u2502 25481 \u2502\n\u2502 83.149.8.xxx \u2502 24984 \u2502\n\u2502 217.118.83.xxx \u2502 22797 \u2502\n\u2502 78.25.120.xxx \u2502 22354 \u2502\n\u2502 213.87.131.xxx \u2502 21285 \u2502\n\u2502 78.25.121.xxx \u2502 20887 \u2502\n\u2502 188.162.65.xxx \u2502 19694 \u2502\n\u2502 83.149.48.xxx \u2502 17406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSince using 'xxx' is highly unusual, this may be changed in the future. We recommend that you don't rely on the exact format of this fragment.\n\n\nIPv6NumToString(x)\n\n\nAccepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format.\nIPv6-mapped IPv4 addresses are output in the format ::ffff:111.222.33.44. Examples:\n\n\nSELECT\n \nIPv6NumToString\n(\ntoFixedString\n(\nunhex\n(\n2A0206B8000000000000000000000011\n),\n \n16\n))\n \nAS\n \naddr\n\n\n\n\n\n\n\u250c\u2500addr\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2a02:6b8::11 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n\n \nIPv6NumToString\n(\nClientIP6\n \nAS\n \nk\n),\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \nhits_all\n\n\nWHERE\n \nEventDate\n \n=\n \ntoday\n()\n \nAND\n \nsubstring\n(\nClientIP6\n,\n \n1\n,\n \n12\n)\n \n!=\n \nunhex\n(\n00000000000000000000FFFF\n)\n\n\nGROUP\n \nBY\n \nk\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500IPv6NumToString(ClientIP6)\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2a02:2168:aaa:bbbb::2 \u2502 24695 \u2502\n\u2502 2a02:2698:abcd:abcd:abcd:abcd:8888:5555 \u2502 22408 \u2502\n\u2502 2a02:6b8:0:fff::ff \u2502 16389 \u2502\n\u2502 2a01:4f8:111:6666::2 \u2502 16016 \u2502\n\u2502 2a02:2168:888:222::1 \u2502 15896 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:222 \u2502 14774 \u2502\n\u2502 2a02:8109:eee:ee:eeee:eeee:eeee:eeee \u2502 14443 \u2502\n\u2502 2a02:810b:8888:888:8888:8888:8888:8888 \u2502 14345 \u2502\n\u2502 2a02:6b8:0:444:4444:4444:4444:4444 \u2502 14279 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:ffff \u2502 13880 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n\n \nIPv6NumToString\n(\nClientIP6\n \nAS\n \nk\n),\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \nhits_all\n\n\nWHERE\n \nEventDate\n \n=\n \ntoday\n()\n\n\nGROUP\n \nBY\n \nk\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500IPv6NumToString(ClientIP6)\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 ::ffff:94.26.111.111 \u2502 747440 \u2502\n\u2502 ::ffff:37.143.222.4 \u2502 529483 \u2502\n\u2502 ::ffff:5.166.111.99 \u2502 317707 \u2502\n\u2502 ::ffff:46.38.11.77 \u2502 263086 \u2502\n\u2502 ::ffff:79.105.111.111 \u2502 186611 \u2502\n\u2502 ::ffff:93.92.111.88 \u2502 176773 \u2502\n\u2502 ::ffff:84.53.111.33 \u2502 158709 \u2502\n\u2502 ::ffff:217.118.11.22 \u2502 154004 \u2502\n\u2502 ::ffff:217.118.11.33 \u2502 148449 \u2502\n\u2502 ::ffff:217.118.11.44 \u2502 148243 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIPv6StringToNum(s)\n\n\nThe reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes.\nHEX can be uppercase or lowercase.\n\n\nFunctions for working with JSON\n\n\nIn Yandex.Metrica, JSON is transmitted by users as session parameters. There are some special functions for working with this JSON. (Although in most of the cases, the JSONs are additionally pre-processed, and the resulting values are put in separate columns in their processed format.) All these functions are based on strong assumptions about what the JSON can be, but they try to do as little as possible to get the job done.\n\n\nThe following assumptions are made:\n\n\n\n\nThe field name (function argument) must be a constant.\n\n\nThe field name is somehow canonically encoded in JSON. For example: \nvisitParamHas('{\"abc\":\"def\"}', 'abc') = 1\n, but \nvisitParamHas('{\"\\\\u0061\\\\u0062\\\\u0063\":\"def\"}', 'abc') = 0\n\n\nFields are searched for on any nesting level, indiscriminately. If there are multiple matching fields, the first occurrence is used.\n\n\nThe JSON doesn't have space characters outside of string literals.\n\n\n\n\nvisitParamHas(params, name)\n\n\nChecks whether there is a field with the 'name' name.\n\n\nvisitParamExtractUInt(params, name)\n\n\nParses UInt64 from the value of the field named 'name'. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn't exist, or it exists but doesn't contain a number, it returns 0.\n\n\nvisitParamExtractInt(params, name)\n\n\nThe same as for Int64.\n\n\nvisitParamExtractFloat(params, name)\n\n\nThe same as for Float64.\n\n\nvisitParamExtractBool(params, name)\n\n\nParses a true/false value. The result is UInt8.\n\n\nvisitParamExtractRaw(params, name)\n\n\nReturns the value of a field, including separators.\n\n\nExamples:\n\n\nvisitParamExtractRaw(\n{\nabc\n:\n\\\\n\\\\u0000\n}\n, \nabc\n) = \n\\\\n\\\\u0000\n\nvisitParamExtractRaw(\n{\nabc\n:{\ndef\n:[1,2,3]}}\n, \nabc\n) = \n{\ndef\n:[1,2,3]}\n\n\n\n\n\n\nvisitParamExtractString(params, name)\n\n\nParses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string.\n\n\nExamples:\n\n\nvisitParamExtractString(\n{\nabc\n:\n\\\\n\\\\u0000\n}\n, \nabc\n) = \n\\n\\0\n\nvisitParamExtractString(\n{\nabc\n:\n\\\\u263a\n}\n, \nabc\n) = \n\u263a\n\nvisitParamExtractString(\n{\nabc\n:\n\\\\u263\n}\n, \nabc\n) = \n\nvisitParamExtractString(\n{\nabc\n:\nhello}\n, \nabc\n) = \n\n\n\n\n\n\nThere is currently no support for code points in the format \n\\uXXXX\\uYYYY\n that are not from the basic multilingual plane (they are converted to CESU-8 instead of UTF-8).\n\n\nHigher-order functions\n\n\n-\n operator, lambda(params, expr) function\n\n\nAllows describing a lambda function for passing to a higher-order function. The left side of the arrow has a formal parameter, which is any ID, or multiple formal parameters \u2013 any IDs in a tuple. The right side of the arrow has an expression that can use these formal parameters, as well as any table columns.\n\n\nExamples: \nx -\n 2 * x, str -\n str != Referer.\n\n\nHigher-order functions can only accept lambda functions as their functional argument.\n\n\nA lambda function that accepts multiple arguments can be passed to a higher-order function. In this case, the higher-order function is passed several arrays of identical length that these arguments will correspond to.\n\n\nFor all functions other than 'arrayMap' and 'arrayFilter', the first argument (the lambda function) can be omitted. In this case, identical mapping is assumed.\n\n\narrayMap(func, arr1, ...)\n\n\nReturns an array obtained from the original application of the 'func' function to each element in the 'arr' array.\n\n\narrayFilter(func, arr1, ...)\n\n\nReturns an array containing only the elements in 'arr1' for which 'func' returns something other than 0.\n\n\nExamples:\n\n\nSELECT\n \narrayFilter\n(\nx\n \n-\n \nx\n \nLIKE\n \n%World%\n,\n \n[\nHello\n,\n \nabc World\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\nabc World\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n\n \narrayFilter\n(\n\n \n(\ni\n,\n \nx\n)\n \n-\n \nx\n \nLIKE\n \n%World%\n,\n\n \narrayEnumerate\n(\narr\n),\n\n \n[\nHello\n,\n \nabc World\n]\n \nAS\n \narr\n)\n\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2510\n\u2502 [2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayCount([func,] arr1, ...)\n\n\nReturns the number of elements in the arr array for which func returns something other than 0. If 'func' is not specified, it returns the number of non-zero elements in the array.\n\n\narrayExists([func,] arr1, ...)\n\n\nReturns 1 if there is at least one element in 'arr' for which 'func' returns something other than 0. Otherwise, it returns 0.\n\n\narrayAll([func,] arr1, ...)\n\n\nReturns 1 if 'func' returns something other than 0 for all the elements in 'arr'. Otherwise, it returns 0.\n\n\narraySum([func,] arr1, ...)\n\n\nReturns the sum of the 'func' values. If the function is omitted, it just returns the sum of the array elements.\n\n\narrayFirst(func, arr1, ...)\n\n\nReturns the first element in the 'arr1' array for which 'func' returns something other than 0.\n\n\narrayFirstIndex(func, arr1, ...)\n\n\nReturns the index of the first element in the 'arr1' array for which 'func' returns something other than 0.\n\n\narrayCumSum([func,] arr1, ...)\n\n\nReturns an array of partial sums of elements in the source array (a running sum). If the \nfunc\n function is specified, then the values of the array elements are converted by this function before summing.\n\n\nExample:\n\n\nSELECT\n \narrayCumSum\n([\n1\n,\n \n1\n,\n \n1\n,\n \n1\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1, 2, 3, 4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narraySort([func,] arr1, ...)\n\n\nReturns an array as result of sorting the elements of \narr1\n in ascending order. If the \nfunc\n function is specified, sorting order is determined by the result of the function \nfunc\n applied to the elements of array (arrays) \n\n\nThe \nSchwartzian transform\n is used to impove sorting efficiency.\n\n\nExample:\n\n\nSELECT\n \narraySort\n((\nx\n,\n \ny\n)\n \n-\n \ny\n,\n \n[\nhello\n,\n \nworld\n],\n \n[\n2\n,\n \n1\n]);\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\nworld\n, \nhello\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayReverseSort([func,] arr1, ...)\n\n\nReturns an array as result of sorting the elements of \narr1\n in descending order. If the \nfunc\n function is specified, sorting order is determined by the result of the function \nfunc\n applied to the elements of array (arrays) \n\n\nOther functions\n\n\nhostName()\n\n\nReturns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server.\n\n\nvisibleWidth(x)\n\n\nCalculates the approximate width when outputting values to the console in text format (tab-separated).\nThis function is used by the system for implementing Pretty formats.\n\n\ntoTypeName(x)\n\n\nReturns a string containing the type name of the passed argument.\n\n\nblockSize()\n\n\nGets the size of the block.\nIn ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for.\n\n\nmaterialize(x)\n\n\nTurns a constant into a full column containing just one value.\nIn ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior.\n\n\nignore(...)\n\n\nAccepts any arguments and always returns 0.\nHowever, the argument is still evaluated. This can be used for benchmarks.\n\n\nsleep(seconds)\n\n\nSleeps 'seconds' seconds on each data block. You can specify an integer or a floating-point number.\n\n\ncurrentDatabase()\n\n\nReturns the name of the current database.\nYou can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database.\n\n\nisFinite(x)\n\n\nAccepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0.\n\n\nisInfinite(x)\n\n\nAccepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. Note that 0 is returned for a NaN.\n\n\nisNaN(x)\n\n\nAccepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0.\n\n\nhasColumnInTable(['hostname'[, 'username'[, 'password']],] 'database', 'table', 'column')\n\n\nAccepts constant strings: database name, table name, and column name. Returns a UInt8 constant expression equal to 1 if there is a column, otherwise 0. If the hostname parameter is set, the test will run on a remote server.\nThe function throws an exception if the table does not exist.\nFor elements in a nested data structure, the function checks for the existence of a column. For the nested data structure itself, the function returns 0.\n\n\nbar\n\n\nAllows building a unicode-art diagram.\n\n\nbar (x, min, max, width)\n draws a band with a width proportional to \n(x - min)\n and equal to \nwidth\n characters when \nx = max\n.\n\n\nParameters:\n\n\n\n\nx\n \u2013 Value to display.\n\n\nmin, max\n \u2013 Integer constants. The value must fit in Int64.\n\n\nwidth\n \u2013 Constant, positive number, may be a fraction.\n\n\n\n\nThe band is drawn with accuracy to one eighth of a symbol.\n\n\nExample:\n\n\nSELECT\n\n \ntoHour\n(\nEventTime\n)\n \nAS\n \nh\n,\n\n \ncount\n()\n \nAS\n \nc\n,\n\n \nbar\n(\nc\n,\n \n0\n,\n \n600000\n,\n \n20\n)\n \nAS\n \nbar\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \nh\n\n\nORDER\n \nBY\n \nh\n \nASC\n\n\n\n\n\n\n\u250c\u2500\u2500h\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u252c\u2500bar\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 0 \u2502 292907 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 1 \u2502 180563 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 2 \u2502 114861 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 3 \u2502 85069 \u2502 \u2588\u2588\u258b \u2502\n\u2502 4 \u2502 68543 \u2502 \u2588\u2588\u258e \u2502\n\u2502 5 \u2502 78116 \u2502 \u2588\u2588\u258c \u2502\n\u2502 6 \u2502 113474 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 7 \u2502 170678 \u2502 \u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 8 \u2502 278380 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 9 \u2502 391053 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 10 \u2502 457681 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 11 \u2502 493667 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 12 \u2502 509641 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 13 \u2502 522947 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 14 \u2502 539954 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 15 \u2502 528460 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c \u2502\n\u2502 16 \u2502 539201 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 17 \u2502 523539 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 18 \u2502 506467 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 19 \u2502 520915 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 20 \u2502 521665 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 21 \u2502 542078 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 22 \u2502 493642 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 23 \u2502 400397 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2514\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\ntransform\n\n\nTransforms a value according to the explicitly defined mapping of some elements to other ones.\nThere are two variations of this function:\n\n\n\n\ntransform(x, array_from, array_to, default)\n\n\n\n\nx\n \u2013 What to transform.\n\n\narray_from\n \u2013 Constant array of values for converting.\n\n\narray_to\n \u2013 Constant array of values to convert the values in 'from' to.\n\n\ndefault\n \u2013 Which value to use if 'x' is not equal to any of the values in 'from'.\n\n\narray_from\n and \narray_to\n \u2013 Arrays of the same size.\n\n\nTypes:\n\n\ntransform(T, Array(T), Array(U), U) -\n U\n\n\nT\n and \nU\n can be numeric, string, or Date or DateTime types.\nWhere the same letter is indicated (T or U), for numeric types these might not be matching types, but types that have a common type.\nFor example, the first argument can have the Int64 type, while the second has the Array(Uint16) type.\n\n\nIf the 'x' value is equal to one of the elements in the 'array_from' array, it returns the existing element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'default'. If there are multiple matching elements in 'array_from', it returns one of the matches.\n\n\nExample:\n\n\nSELECT\n\n \ntransform\n(\nSearchEngineID\n,\n \n[\n2\n,\n \n3\n],\n \n[\nYandex\n,\n \nGoogle\n],\n \nOther\n)\n \nAS\n \ntitle\n,\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \ntest\n.\nhits\n\n\nWHERE\n \nSearchEngineID\n \n!=\n \n0\n\n\nGROUP\n \nBY\n \ntitle\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\n\n\n\n\n\u250c\u2500title\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 Yandex \u2502 498635 \u2502\n\u2502 Google \u2502 229872 \u2502\n\u2502 Other \u2502 104472 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\ntransform(x, array_from, array_to)\n\n\n\n\nDiffers from the first variation in that the 'default' argument is omitted.\nIf the 'x' value is equal to one of the elements in the 'array_from' array, it returns the matching element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'x'.\n\n\nTypes:\n\n\ntransform(T, Array(T), Array(T)) -\n T\n\n\nExample:\n\n\nSELECT\n\n \ntransform\n(\ndomain\n(\nReferer\n),\n \n[\nyandex.ru\n,\n \ngoogle.ru\n,\n \nvk.com\n],\n \n[\nwww.yandex\n,\n \nexample.com\n])\n \nAS\n \ns\n,\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \ndomain\n(\nReferer\n)\n\n\nORDER\n \nBY\n \ncount\n()\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 \u2502 2906259 \u2502\n\u2502 www.yandex \u2502 867767 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 313599 \u2502\n\u2502 mail.yandex.ru \u2502 107147 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 100355 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 65040 \u2502\n\u2502 news.yandex.ru \u2502 64515 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.net \u2502 59141 \u2502\n\u2502 example.com \u2502 57316 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nformatReadableSize(x)\n\n\nAccepts the size (number of bytes). Returns a rounded size with a suffix (KiB, MiB, etc.) as a string.\n\n\nExample:\n\n\nSELECT\n\n \narrayJoin\n([\n1\n,\n \n1024\n,\n \n1024\n*\n1024\n,\n \n192851925\n])\n \nAS\n \nfilesize_bytes\n,\n\n \nformatReadableSize\n(\nfilesize_bytes\n)\n \nAS\n \nfilesize\n\n\n\n\n\n\n\u250c\u2500filesize_bytes\u2500\u252c\u2500filesize\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 1.00 B \u2502\n\u2502 1024 \u2502 1.00 KiB \u2502\n\u2502 1048576 \u2502 1.00 MiB \u2502\n\u2502 192851925 \u2502 183.92 MiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nleast(a, b)\n\n\nReturns the smallest value from a and b.\n\n\ngreatest(a, b)\n\n\nReturns the largest value of a and b.\n\n\nuptime()\n\n\nReturns the server's uptime in seconds.\n\n\nversion()\n\n\nReturns the version of the server as a string.\n\n\nrowNumberInAllBlocks()\n\n\nReturns the ordinal number of the row in the data block. This function only considers the affected data blocks.\n\n\nrunningDifference(x)\n\n\nCalculates the difference between successive row values \u200b\u200bin the data block.\nReturns 0 for the first row and the difference from the previous row for each subsequent row.\n\n\nThe result of the function depends on the affected data blocks and the order of data in the block.\nIf you make a subquery with ORDER BY and call the function from outside the subquery, you can get the expected result.\n\n\nExample:\n\n\nSELECT\n\n \nEventID\n,\n\n \nEventTime\n,\n\n \nrunningDifference\n(\nEventTime\n)\n \nAS\n \ndelta\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nEventID\n,\n\n \nEventTime\n\n \nFROM\n \nevents\n\n \nWHERE\n \nEventDate\n \n=\n \n2016-11-24\n\n \nORDER\n \nBY\n \nEventTime\n \nASC\n\n \nLIMIT\n \n5\n\n\n)\n\n\n\n\n\n\n\u250c\u2500EventID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500EventTime\u2500\u252c\u2500delta\u2500\u2510\n\u2502 1106 \u2502 2016-11-24 00:00:04 \u2502 0 \u2502\n\u2502 1107 \u2502 2016-11-24 00:00:05 \u2502 1 \u2502\n\u2502 1108 \u2502 2016-11-24 00:00:05 \u2502 0 \u2502\n\u2502 1109 \u2502 2016-11-24 00:00:09 \u2502 4 \u2502\n\u2502 1110 \u2502 2016-11-24 00:00:10 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nMACNumToString(num)\n\n\nAccepts a UInt64 number. Interprets it as a MAC address in big endian. Returns a string containing the corresponding MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form).\n\n\nMACStringToNum(s)\n\n\nThe inverse function of MACNumToString. If the MAC address has an invalid format, it returns 0.\n\n\nMACStringToOUI(s)\n\n\nAccepts a MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form). Returns the first three octets as a UInt64 number. If the MAC address has an invalid format, it returns 0.\n\n\n\n\nFunctions for working with external dictionaries\n\n\nFor information on connecting and configuring external dictionaries, see \"\nExternal dictionaries\n\".\n\n\ndictGetUInt8, dictGetUInt16, dictGetUInt32, dictGetUInt64\n\n\ndictGetInt8, dictGetInt16, dictGetInt32, dictGetInt64\n\n\ndictGetFloat32, dictGetFloat64\n\n\ndictGetDate, dictGetDateTime\n\n\ndictGetUUID\n\n\ndictGetString\n\n\ndictGetT('dict_name', 'attr_name', id)\n\n\n\n\nGet the value of the attr_name attribute from the dict_name dictionary using the 'id' key.\ndict_name\n and \nattr_name\n are constant strings.\nid\nmust be UInt64.\nIf there is no \nid\n key in the dictionary, it returns the default value specified in the dictionary description.\n\n\n\n\ndictGetTOrDefault\n\n\ndictGetT('dict_name', 'attr_name', id, default)\n\n\nThe same as the \ndictGetT\n functions, but the default value is taken from the function's last argument.\n\n\ndictIsIn\n\n\ndictIsIn('dict_name', child_id, ancestor_id)\n\n\n\n\nFor the 'dict_name' hierarchical dictionary, finds out whether the 'child_id' key is located inside 'ancestor_id' (or matches 'ancestor_id'). Returns UInt8.\n\n\n\n\ndictGetHierarchy\n\n\ndictGetHierarchy('dict_name', id)\n\n\n\n\nFor the 'dict_name' hierarchical dictionary, returns an array of dictionary keys starting from 'id' and continuing along the chain of parent elements. Returns Array(UInt64).\n\n\n\n\ndictHas\n\n\ndictHas('dict_name', id)\n\n\n\n\nCheck whether the dictionary has the key. Returns a UInt8 value equal to 0 if there is no key and 1 if there is a key.\n\n\n\n\nFunctions for working with Yandex.Metrica dictionaries\n\n\nIn order for the functions below to work, the server config must specify the paths and addresses for getting all the Yandex.Metrica dictionaries. The dictionaries are loaded at the first call of any of these functions. If the reference lists can't be loaded, an exception is thrown.\n\n\nFor information about creating reference lists, see the section \"Dictionaries\".\n\n\nMultiple geobases\n\n\nClickHouse supports working with multiple alternative geobases (regional hierarchies) simultaneously, in order to support various perspectives on which countries certain regions belong to.\n\n\nThe 'clickhouse-server' config specifies the file with the regional hierarchy::\npath_to_regions_hierarchy_file\n/opt/geo/regions_hierarchy.txt\n/path_to_regions_hierarchy_file\n\n\nBesides this file, it also searches for files nearby that have the _ symbol and any suffix appended to the name (before the file extension).\nFor example, it will also find the file \n/opt/geo/regions_hierarchy_ua.txt\n, if present.\n\n\nua\n is called the dictionary key. For a dictionary without a suffix, the key is an empty string.\n\n\nAll the dictionaries are re-loaded in runtime (once every certain number of seconds, as defined in the builtin_dictionaries_reload_interval config parameter, or once an hour by default). However, the list of available dictionaries is defined one time, when the server starts.\n\n\nAll functions for working with regions have an optional argument at the end \u2013 the dictionary key. It is referred to as the geobase.\nExample:\n\n\nregionToCountry(RegionID) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, \n) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, \nua\n) \u2013 Uses the dictionary for the \nua\n key: /opt/geo/regions_hierarchy_ua.txt\n\n\n\n\n\nregionToCity(id[, geobase])\n\n\nAccepts a UInt32 number \u2013 the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0.\n\n\nregionToArea(id[, geobase])\n\n\nConverts a region to an area (type 5 in the geobase). In every other way, this function is the same as 'regionToCity'.\n\n\nSELECT\n \nDISTINCT\n \nregionToName\n(\nregionToArea\n(\ntoUInt32\n(\nnumber\n),\n \nua\n))\n\n\nFROM\n \nsystem\n.\nnumbers\n\n\nLIMIT\n \n15\n\n\n\n\n\n\n\u250c\u2500regionToName(regionToArea(toUInt32(number), \\\nua\\\n))\u2500\u2510\n\u2502 \u2502\n\u2502 Moscow and Moscow region \u2502\n\u2502 St. Petersburg and Leningrad region \u2502\n\u2502 Belgorod region \u2502\n\u2502 Ivanovsk region \u2502\n\u2502 Kaluga region \u2502\n\u2502 Kostroma region \u2502\n\u2502 Kursk region \u2502\n\u2502 Lipetsk region \u2502\n\u2502 Orlov region \u2502\n\u2502 Ryazan region \u2502\n\u2502 Smolensk region \u2502\n\u2502 Tambov region \u2502\n\u2502 Tver region \u2502\n\u2502 Tula region \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nregionToDistrict(id[, geobase])\n\n\nConverts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as 'regionToCity'.\n\n\nSELECT\n \nDISTINCT\n \nregionToName\n(\nregionToDistrict\n(\ntoUInt32\n(\nnumber\n),\n \nua\n))\n\n\nFROM\n \nsystem\n.\nnumbers\n\n\nLIMIT\n \n15\n\n\n\n\n\n\n\u250c\u2500regionToName(regionToDistrict(toUInt32(number), \\\nua\\\n))\u2500\u2510\n\u2502 \u2502\n\u2502 Central federal district \u2502\n\u2502 Northwest federal district \u2502\n\u2502 South federal district \u2502\n\u2502 North Caucases federal district \u2502\n\u2502 Privolga federal district \u2502\n\u2502 Ural federal district \u2502\n\u2502 Siberian federal district \u2502\n\u2502 Far East federal district \u2502\n\u2502 Scotland \u2502\n\u2502 Faroe Islands \u2502\n\u2502 Flemish region \u2502\n\u2502 Brussels capital region \u2502\n\u2502 Wallonia \u2502\n\u2502 Federation of Bosnia and Herzegovina \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nregionToCountry(id[, geobase])\n\n\nConverts a region to a country. In every other way, this function is the same as 'regionToCity'.\nExample: \nregionToCountry(toUInt32(213)) = 225\n converts Moscow (213) to Russia (225).\n\n\nregionToContinent(id[, geobase])\n\n\nConverts a region to a continent. In every other way, this function is the same as 'regionToCity'.\nExample: \nregionToContinent(toUInt32(213)) = 10001\n converts Moscow (213) to Eurasia (10001).\n\n\nregionToPopulation(id[, geobase])\n\n\nGets the population for a region.\nThe population can be recorded in files with the geobase. See the section \"External dictionaries\".\nIf the population is not recorded for the region, it returns 0.\nIn the Yandex geobase, the population might be recorded for child regions, but not for parent regions.\n\n\nregionIn(lhs, rhs[, geobase])\n\n\nChecks whether a 'lhs' region belongs to a 'rhs' region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn't belong.\nThe relationship is reflexive \u2013 any region also belongs to itself.\n\n\nregionHierarchy(id[, geobase])\n\n\nAccepts a UInt32 number \u2013 the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain.\nExample: \nregionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000]\n.\n\n\nregionToName(id[, lang])\n\n\nAccepts a UInt32 number \u2013 the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language 'ru' is used. If the language is not supported, an exception is thrown. Returns a string \u2013 the name of the region in the corresponding language. If the region with the specified ID doesn't exist, an empty string is returned.\n\n\nua\n and \nuk\n both mean Ukrainian.\n\n\nFunctions for implementing the IN operator\n\n\nin, notIn, globalIn, globalNotIn\n\n\nSee the section \"IN operators\".\n\n\ntuple(x, y, ...), operator (x, y, ...)\n\n\nA function that allows grouping multiple columns.\nFor columns with the types T1, T2, ..., it returns a Tuple(T1, T2, ...) type tuple containing these columns. There is no cost to execute the function.\nTuples are normally used as intermediate values for an argument of IN operators, or for creating a list of formal parameters of lambda functions. Tuples can't be written to a table.\n\n\ntupleElement(tuple, n), operator x.N\n\n\nA function that allows getting a column from a tuple.\n'N' is the column index, starting from 1. N must be a constant. 'N' must be a constant. 'N' must be a strict postive integer no greater than the size of the tuple.\nThere is no cost to execute the function.\n\n\n\n\narrayJoin function\n\n\nThis is a very unusual function.\n\n\nNormal functions don't change a set of rows, but just change the values in each row (map).\nAggregate functions compress a set of rows (fold or reduce).\nThe 'arrayJoin' function takes each row and generates a set of rows (unfold).\n\n\nThis function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array.\nAll the values in columns are simply copied, except the values in the column where this function is applied; it is replaced with the corresponding array value.\n\n\nA query can use multiple \narrayJoin\n functions. In this case, the transformation is performed multiple times.\n\n\nNote the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities.\n\n\nExample:\n\n\nSELECT\n \narrayJoin\n([\n1\n,\n \n2\n,\n \n3\n]\n \nAS\n \nsrc\n)\n \nAS\n \ndst\n,\n \nHello\n,\n \nsrc\n\n\n\n\n\n\n\u250c\u2500dst\u2500\u252c\u2500\\\nHello\\\n\u2500\u252c\u2500src\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 2 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 3 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\nAggregate functions\n\n\nAggregate functions work in the \nnormal\n way as expected by database experts.\n\n\nClickHouse also supports:\n\n\n\n\nParametric aggregate functions\n, which accept other parameters in addition to columns.\n\n\nCombinators\n, which change the behavior of aggregate functions.\n\n\n\n\n\n\nFunction reference\n\n\ncount()\n\n\nCounts the number of rows. Accepts zero arguments and returns UInt64.\nThe syntax \nCOUNT(DISTINCT x)\n is not supported. The separate \nuniq\n aggregate function exists for this purpose.\n\n\nA \nSELECT count() FROM table\n query is not optimized, because the number of entries in the table is not stored separately. It will select some small column from the table and count the number of values in it.\n\n\nany(x)\n\n\nSelects the first encountered value.\nThe query can be executed in any order and even in a different order each time, so the result of this function is indeterminate.\nTo get a determinate result, you can use the 'min' or 'max' function instead of 'any'.\n\n\nIn some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.\n\n\nWhen a \nSELECT\n query has the \nGROUP BY\n clause or at least one aggregate function, ClickHouse (in contrast to MySQL) requires that all expressions in the \nSELECT\n, \nHAVING\n, and \nORDER BY\n clauses be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. To get behavior like in MySQL, you can put the other columns in the \nany\n aggregate function.\n\n\nanyHeavy(x)\n\n\nSelects a frequently occurring value using the \nheavy hitters\n algorithm. If there is a value that occurs more than in half the cases in each of the query's execution threads, this value is returned. Normally, the result is nondeterministic.\n\n\nanyHeavy(column)\n\n\n\n\n\nArguments\n\n- \ncolumn\n \u2013 The column name.\n\n\nExample\n\n\nTake the \nOnTime\n data set and select any frequently occurring value in the \nAirlineID\n column.\n\n\nSELECT\n \nanyHeavy\n(\nAirlineID\n)\n \nAS\n \nres\n\n\nFROM\n \nontime\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500res\u2500\u2510\n\u2502 19690 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nanyLast(x)\n\n\nSelects the last value encountered.\nThe result is just as indeterminate as for the \nany\n function.\n\n\nmin(x)\n\n\nCalculates the minimum.\n\n\nmax(x)\n\n\nCalculates the maximum.\n\n\nargMin(arg, val)\n\n\nCalculates the 'arg' value for a minimal 'val' value. If there are several different values of 'arg' for minimal values of 'val', the first of these values encountered is output.\n\n\nargMax(arg, val)\n\n\nCalculates the 'arg' value for a maximum 'val' value. If there are several different values of 'arg' for maximum values of 'val', the first of these values encountered is output.\n\n\nsum(x)\n\n\nCalculates the sum.\nOnly works for numbers.\n\n\nsumWithOverflow(x)\n\n\nComputes the sum of the numbers, using the same data type for the result as for the input parameters. If the sum exceeds the maximum value for this data type, the function returns an error.\n\n\nOnly works for numbers.\n\n\nsumMap(key, value)\n\n\nTotals the 'value' array according to the keys specified in the 'key' array.\nThe number of elements in 'key' and 'value' must be the same for each row that is totaled.\nReturns a tuple of two arrays: keys in sorted order, and values \u200b\u200bsummed for the corresponding keys.\n\n\nExample:\n\n\nCREATE\n \nTABLE\n \nsum_map\n(\n\n \ndate\n \nDate\n,\n\n \ntimeslot\n \nDateTime\n,\n\n \nstatusMap\n \nNested\n(\n\n \nstatus\n \nUInt16\n,\n\n \nrequests\n \nUInt64\n\n \n)\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\nINSERT\n \nINTO\n \nsum_map\n \nVALUES\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:00:00\n,\n \n[\n1\n,\n \n2\n,\n \n3\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]),\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:00:00\n,\n \n[\n3\n,\n \n4\n,\n \n5\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]),\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:01:00\n,\n \n[\n4\n,\n \n5\n,\n \n6\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]),\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:01:00\n,\n \n[\n6\n,\n \n7\n,\n \n8\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]);\n\n\nSELECT\n\n \ntimeslot\n,\n\n \nsumMap\n(\nstatusMap\n.\nstatus\n,\n \nstatusMap\n.\nrequests\n)\n\n\nFROM\n \nsum_map\n\n\nGROUP\n \nBY\n \ntimeslot\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500timeslot\u2500\u252c\u2500sumMap(statusMap.status, statusMap.requests)\u2500\u2510\n\u2502 2000-01-01 00:00:00 \u2502 ([1,2,3,4,5],[10,10,20,10,10]) \u2502\n\u2502 2000-01-01 00:01:00 \u2502 ([4,5,6,7,8],[10,10,20,10,10]) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\navg(x)\n\n\nCalculates the average.\nOnly works for numbers.\nThe result is always Float64.\n\n\nuniq(x)\n\n\nCalculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.\n\n\nUses an adaptive sampling algorithm: for the calculation state, it uses a sample of element hash values with a size up to 65536.\nThis algorithm is also very accurate for data sets with low cardinality (up to 65536) and very efficient on CPU (when computing not too many of these functions, using \nuniq\n is almost as fast as using other aggregate functions).\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nThis function provides excellent accuracy even for data sets with extremely high cardinality (over 10 billion elements). It is recommended for default use.\n\n\nuniqCombined(x)\n\n\nCalculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.\n\n\nA combination of three algorithms is used: array, hash table and \nHyperLogLog\n with an error correction table. The memory consumption is several times smaller than for the \nuniq\n function, and the accuracy is several times higher. Performance is slightly lower than for the \nuniq\n function, but sometimes it can be even higher than it, such as with distributed queries that transmit a large number of aggregation states over the network. The maximum state size is 96 KiB (HyperLogLog of 217 6-bit cells).\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nThe \nuniqCombined\n function is a good default choice for calculating the number of different values, but keep in mind that the estimation error will increase for high-cardinality data sets (200M+ elements), and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).\n\n\nuniqHLL12(x)\n\n\nUses the \nHyperLogLog\n algorithm to approximate the number of different values of the argument.\n212 5-bit cells are used. The size of the state is slightly more than 2.5 KB. The result is not very accurate (up to ~10% error) for small data sets (\n10K elements). However, the result is fairly accurate for high-cardinality data sets (10K-100M), with a maximum error of ~1.6%. Starting from 100M, the estimation error increases, and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nWe don't recommend using this function. In most cases, use the \nuniq\n or \nuniqCombined\n function.\n\n\nuniqExact(x)\n\n\nCalculates the number of different values of the argument, exactly.\nThere is no reason to fear approximations. It's better to use the \nuniq\n function.\nUse the \nuniqExact\n function if you definitely need an exact result.\n\n\nThe \nuniqExact\n function uses more memory than the \nuniq\n function, because the size of the state has unbounded growth as the number of different values increases.\n\n\ngroupArray(x), groupArray(max_size)(x)\n\n\nCreates an array of argument values.\nValues can be added to the array in any (indeterminate) order.\n\n\nThe second version (with the \nmax_size\n parameter) limits the size of the resulting array to \nmax_size\n elements.\nFor example, \ngroupArray (1) (x)\n is equivalent to \n[any (x)]\n.\n\n\nIn some cases, you can still rely on the order of execution. This applies to cases when \nSELECT\n comes from a subquery that uses \nORDER BY\n.\n\n\n\n\ngroupArrayInsertAt(x)\n\n\nInserts a value into the array in the specified position.\n\n\nAccepts the value and position as input. If several values \u200b\u200bare inserted into the same position, any of them might end up in the resulting array (the first one will be used in the case of single-threaded execution). If no value is inserted into a position, the position is assigned the default value.\n\n\nOptional parameters:\n\n\n\n\nThe default value for substituting in empty positions.\n\n\nThe length of the resulting array. This allows you to receive arrays of the same size for all the aggregate keys. When using this parameter, the default value must be specified.\n\n\n\n\ngroupUniqArray(x)\n\n\nCreates an array from different argument values. Memory consumption is the same as for the \nuniqExact\n function.\n\n\nquantile(level)(x)\n\n\nApproximates the 'level' quantile. 'level' is a constant, a floating-point number from 0 to 1.\nWe recommend using a 'level' value in the range of 0.01..0.99\nDon't use a 'level' value equal to 0 or 1 \u2013 use the 'min' and 'max' functions for these cases.\n\n\nIn this function, as well as in all functions for calculating quantiles, the 'level' parameter can be omitted. In this case, it is assumed to be equal to 0.5 (in other words, the function will calculate the median).\n\n\nWorks for numbers, dates, and dates with times.\nReturns: for numbers \u2013 Float64; for dates \u2013 a date; for dates with times \u2013 a date with time.\n\n\nUses \nreservoir sampling\n with a reservoir size up to 8192.\nIf necessary, the result is output with linear approximation from the two neighboring values.\nThis algorithm provides very low accuracy. See also: \nquantileTiming\n, \nquantileTDigest\n, \nquantileExact\n.\n\n\nThe result depends on the order of running the query, and is nondeterministic.\n\n\nWhen using multiple \nquantile\n (and similar) functions with different levels in a query, the internal states are not combined (that is, the query works less efficiently than it could). In this case, use the \nquantiles\n (and similar) functions.\n\n\nquantileDeterministic(level)(x, determinator)\n\n\nWorks the same way as the \nquantile\n function, but the result is deterministic and does not depend on the order of query execution.\n\n\nTo achieve this, the function takes a second argument \u2013 the \"determinator\". This is a number whose hash is used instead of a random number generator in the reservoir sampling algorithm. For the function to work correctly, the same determinator value should not occur too often. For the determinator, you can use an event ID, user ID, and so on.\n\n\nDon't use this function for calculating timings. There is a more suitable function for this purpose: \nquantileTiming\n.\n\n\nquantileTiming(level)(x)\n\n\nComputes the quantile of 'level' with a fixed precision.\nWorks for numbers. Intended for calculating quantiles of page loading time in milliseconds.\n\n\nIf the value is greater than 30,000 (a page loading time of more than 30 seconds), the result is equated to 30,000.\n\n\nIf the total value is not more than about 5670, then the calculation is accurate.\n\n\nOtherwise:\n\n\n\n\nif the time is less than 1024 ms, then the calculation is accurate.\n\n\notherwise the calculation is rounded to a multiple of 16 ms.\n\n\n\n\nWhen passing negative values to the function, the behavior is undefined.\n\n\nThe returned value has the Float32 type. If no values were passed to the function (when using \nquantileTimingIf\n), 'nan' is returned. The purpose of this is to differentiate these instances from zeros. See the note on sorting NaNs in \"ORDER BY clause\".\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nFor its purpose (calculating quantiles of page loading times), using this function is more effective and the result is more accurate than for the \nquantile\n function.\n\n\nquantileTimingWeighted(level)(x, weight)\n\n\nDiffers from the \nquantileTiming\n function in that it has a second argument, \"weights\". Weight is a non-negative integer.\nThe result is calculated as if the \nx\n value were passed \nweight\n number of times to the \nquantileTiming\n function.\n\n\nquantileExact(level)(x)\n\n\nComputes the quantile of 'level' exactly. To do this, all the passed values \u200b\u200bare combined into an array, which is then partially sorted. Therefore, the function consumes O(n) memory, where 'n' is the number of values that were passed. However, for a small number of values, the function is very effective.\n\n\nquantileExactWeighted(level)(x, weight)\n\n\nComputes the quantile of 'level' exactly. In addition, each value is counted with its weight, as if it is present 'weight' times. The arguments of the function can be considered as histograms, where the value 'x' corresponds to a histogram \"column\" of the height 'weight', and the function itself can be considered as a summation of histograms.\n\n\nA hash table is used as the algorithm. Because of this, if the passed values \u200b\u200bare frequently repeated, the function consumes less RAM than \nquantileExact\n. You can use this function instead of \nquantileExact\n and specify the weight as 1.\n\n\nquantileTDigest(level)(x)\n\n\nApproximates the quantile level using the \nt-digest\n algorithm. The maximum error is 1%. Memory consumption by State is proportional to the logarithm of the number of passed values.\n\n\nThe performance of the function is lower than for \nquantile\n, \nquantileTiming\n. In terms of the ratio of State size to precision, this function is much better than \nquantile\n.\n\n\nThe result depends on the order of running the query, and is nondeterministic.\n\n\nmedian(x)\n\n\nAll the quantile functions have corresponding median functions: \nmedian\n, \nmedianDeterministic\n, \nmedianTiming\n, \nmedianTimingWeighted\n, \nmedianExact\n, \nmedianExactWeighted\n, \nmedianTDigest\n. They are synonyms and their behavior is identical.\n\n\nquantiles(level1, level2, ...)(x)\n\n\nAll the quantile functions also have corresponding quantiles functions: \nquantiles\n, \nquantilesDeterministic\n, \nquantilesTiming\n, \nquantilesTimingWeighted\n, \nquantilesExact\n, \nquantilesExactWeighted\n, \nquantilesTDigest\n. These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.\n\n\nvarSamp(x)\n\n\nCalculates the amount \n\u03a3((x - x\u0305)^2) / (n - 1)\n, where \nn\n is the sample size and \nx\u0305\nis the average value of \nx\n.\n\n\nIt represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount.\n\n\nReturns \nFloat64\n. When \nn \n= 1\n, returns \n+\u221e\n.\n\n\nvarPop(x)\n\n\nCalculates the amount \n\u03a3((x - x\u0305)^2) / (n - 1)\n, where \nn\n is the sample size and \nx\u0305\nis the average value of \nx\n.\n\n\nIn other words, dispersion for a set of values. Returns \nFloat64\n.\n\n\nstddevSamp(x)\n\n\nThe result is equal to the square root of \nvarSamp(x)\n.\n\n\nstddevPop(x)\n\n\nThe result is equal to the square root of \nvarPop(x)\n.\n\n\ntopK(N)(column)\n\n\nReturns an array of the most frequent values in the specified column. The resulting array is sorted in descending order of frequency of values (not by the values themselves).\n\n\nImplements the \nFiltered Space-Saving\n algorithm for analyzing TopK, based on the reduce-and-combine algorithm from \nParallel Space Saving\n.\n\n\ntopK(N)(column)\n\n\n\n\n\nThis function doesn't provide a guaranteed result. In certain situations, errors might occur and it might return frequent values that aren't the most frequent values.\n\n\nWe recommend using the \nN \n 10\n value; performance is reduced with large \nN\n values. Maximum value of \nN = 65536\n.\n\n\nArguments\n\n- 'N' is the number of values.\n- ' x ' \u2013 The column.\n\n\nExample\n\n\nTake the \nOnTime\n data set and select the three most frequently occurring values in the \nAirlineID\n column.\n\n\nSELECT\n \ntopK\n(\n3\n)(\nAirlineID\n)\n \nAS\n \nres\n\n\nFROM\n \nontime\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [19393,19790,19805] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\ncovarSamp(x, y)\n\n\nCalculates the value of \n\u03a3((x - x\u0305)(y - y\u0305)) / (n - 1)\n.\n\n\nReturns Float64. When \nn \n= 1\n, returns +\u221e.\n\n\ncovarPop(x, y)\n\n\nCalculates the value of \n\u03a3((x - x\u0305)(y - y\u0305)) / n\n.\n\n\ncorr(x, y)\n\n\nCalculates the Pearson correlation coefficient: \n\u03a3((x - x\u0305)(y - y\u0305)) / sqrt(\u03a3((x - x\u0305)^2) * \u03a3((y - y\u0305)^2))\n.\n\n\n\n\nAggregate function combinators\n\n\nThe name of an aggregate function can have a suffix appended to it. This changes the way the aggregate function works.\n\n\n-If\n\n\nThe suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument \u2013 a condition (Uint8 type). The aggregate function processes only the rows that trigger the condition. If the condition was not triggered even once, it returns a default value (usually zeros or empty strings).\n\n\nExamples: \nsumIf(column, cond)\n, \ncountIf(cond)\n, \navgIf(x, cond)\n, \nquantilesTimingIf(level1, level2)(x, cond)\n, \nargMinIf(arg, val, cond)\n and so on.\n\n\nWith conditional aggregate functions, you can calculate aggregates for several conditions at once, without using subqueries and \nJOIN\ns. For example, in Yandex.Metrica, conditional aggregate functions are used to implement the segment comparison functionality.\n\n\n-Array\n\n\nThe -Array suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the 'Array(T)' type (arrays) instead of 'T' type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. When processing arrays, the aggregate function works like the original aggregate function across all array elements.\n\n\nExample 1: \nsumArray(arr)\n - Totals all the elements of all 'arr' arrays. In this example, it could have been written more simply: \nsum(arraySum(arr))\n.\n\n\nExample 2: \nuniqArray(arr)\n \u2013 Count the number of unique elements in all 'arr' arrays. This could be done an easier way: \nuniq(arrayJoin(arr))\n, but it's not always possible to add 'arrayJoin' to a query.\n\n\n-If and -Array can be combined. However, 'Array' must come first, then 'If'. Examples: \nuniqArrayIf(arr, cond)\n, \nquantilesTimingArrayIf(level1, level2)(arr, cond)\n. Due to this order, the 'cond' argument can't be an array.\n\n\n-State\n\n\nIf you apply this combinator, the aggregate function doesn't return the resulting value (such as the number of unique values for the 'uniq' function), but an intermediate state of the aggregation (for \nuniq\n, this is the hash table for calculating the number of unique values). This is an AggregateFunction(...) that can be used for further processing or stored in a table to finish aggregating later. See the sections \"AggregatingMergeTree\" and \"Functions for working with intermediate aggregation states\".\n\n\n-Merge\n\n\nIf you apply this combinator, the aggregate function takes the intermediate aggregation state as an argument, combines the states to finish aggregation, and returns the resulting value.\n\n\n-MergeState.\n\n\nMerges the intermediate aggregation states in the same way as the -Merge combinator. However, it doesn't return the resulting value, but an intermediate aggregation state, similar to the -State combinator.\n\n\n-ForEach\n\n\nConverts an aggregate function for tables into an aggregate function for arrays that aggregates the corresponding array items and returns an array of results. For example, \nsumForEach\n for the arrays \n[1, 2]\n, \n[3, 4, 5]\nand\n[6, 7]\nreturns the result \n[10, 13, 5]\n after adding together the corresponding array items.\n\n\n\n\nParametric aggregate functions\n\n\nSome aggregate functions can accept not only argument columns (used for compression), but a set of parameters \u2013 constants for initialization. The syntax is two pairs of brackets instead of one. The first is for parameters, and the second is for arguments.\n\n\nsequenceMatch(pattern)(time, cond1, cond2, ...)\n\n\nPattern matching for event chains.\n\n\npattern\n is a string containing a pattern to match. The pattern is similar to a regular expression.\n\n\ntime\n is the time of the event with the DateTime type.\n\n\ncond1\n, \ncond2\n ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event.\n\n\nThe function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern.\nIt returns UInt8: 0 if the pattern isn't matched, or 1 if it matches.\n\n\nExample: \nsequenceMatch ('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%')\n\n\n\n\nwhether there was a chain of events in which a pageview with 'company' in the address occurred earlier than a pageview with 'cart' in the address.\n\n\n\n\nThis is a singular example. You could write it using other aggregate functions:\n\n\nminIf(EventTime, URL LIKE \n%company%\n) \n maxIf(EventTime, URL LIKE \n%cart%\n).\n\n\n\n\n\nHowever, there is no such solution for more complex situations.\n\n\nPattern syntax:\n\n\n(?1)\n refers to the condition (any number can be used in place of 1).\n\n\n.*\n is any number of any events.\n\n\n(?t\n=1800)\n is a time condition.\n\n\nAny quantity of any type of events is allowed over the specified time.\n\n\nInstead of \n=\n, the following operators can be used:\n, \n, \n=\n.\n\n\nAny number may be specified in place of 1800.\n\n\nEvents that occur during the same second can be put in the chain in any order. This may affect the result of the function.\n\n\nsequenceCount(pattern)(time, cond1, cond2, ...)\n\n\nWorks the same way as the sequenceMatch function, but instead of returning whether there is an event chain, it returns UInt64 with the number of event chains found.\nChains are searched for without overlapping. In other words, the next chain can start only after the end of the previous one.\n\n\nwindowFunnel(window)(timestamp, cond1, cond2, cond3, ....)\n\n\nWindow funnel matching for event chains, calculates the max event level in a sliding window.\n\n\nwindow\n is the timestamp window value, such as 3600.\n\n\ntimestamp\n is the time of the event with the DateTime type or UInt32 type.\n\n\ncond1\n, \ncond2\n ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event\n\n\nExample: \n\n\nConsider you are doing a website analytics, intend to find out the user counts clicked login button( event = 1001 ), then the user counts followed by searched the phones( event = 1003 and product = 'phone' ) , then the user counts followed by made an order ( event = 1009 ). And all event chains must be in a 3600 seconds sliding window. \n\n\nThis could be easily calculate by \nwindowFunnel\n\n\nSELECT\n level,\n count() AS c\nFROM\n(\n SELECT\n user_id,\n windowFunnel(3600)(timestamp, event_id = 1001, event_id = 1003 AND product = \nphone\n, event_id = 1009) AS level\n FROM trend_event\n WHERE (event_date \n= \n2017-01-01\n) AND (event_date \n= \n2017-01-31\n)\n GROUP BY user_id\n)\nGROUP BY level\nORDER BY level\n\n\n\n\n\nSimply, the level could only be 0,1,2,3, it means the maxium event action stage that one user could reach.\n\n\nuniqUpTo(N)(x)\n\n\nCalculates the number of different argument values \u200b\u200bif it is less than or equal to N. If the number of different argument values is greater than N, it returns N + 1.\n\n\nRecommended for use with small Ns, up to 10. The maximum value of N is 100.\n\n\nFor the state of an aggregate function, it uses the amount of memory equal to 1 + N * the size of one value of bytes.\nFor strings, it stores a non-cryptographic hash of 8 bytes. That is, the calculation is approximated for strings.\n\n\nThe function also works for several arguments.\n\n\nIt works as fast as possible, except for cases when a large N value is used and the number of unique values is slightly less than N.\n\n\nUsage example:\n\n\nProblem: Generate a report that shows only keywords that produced at least 5 unique users.\nSolution: Write in the GROUP BY query SearchPhrase HAVING uniqUpTo(4)(UserID) \n= 5\n\n\n\n\n\nDictionaries\n\n\nA dictionary\n is a mapping (key \n-\n attributes) that can be used in a query as functions.\nYou can think of this as a more convenient and efficient type of JOIN with dimension tables.\n\n\nThere are built-in (internal) and add-on (external) dictionaries.\n\n\n\n\nExternal dictionaries\n\n\nYou can add your own dictionaries from various data sources. The data source for a dictionary can be a local text or executable file, an HTTP(s) resource, or another DBMS. For more information, see \"\nSources for external dictionaries\n\".\n\n\nClickHouse:\n\n\n\n\n\n\nFully or partially stores dictionaries in RAM.\n\n\nPeriodically updates dictionaries and dynamically loads missing values. In other words, dictionaries can be loaded dynamically.\n\n\n\n\n\n\nThe configuration of external dictionaries is located in one or more files. The path to the configuration is specified in the \ndictionaries_config\n parameter.\n\n\nDictionaries can be loaded at server startup or at first use, depending on the \ndictionaries_lazy_load\n setting.\n\n\nThe dictionary config file has the following format:\n\n\nyandex\n\n \ncomment\nAn optional element with any content. Ignored by the ClickHouse server.\n/comment\n\n\n \n!--Optional element. File name with substitutions--\n\n \ninclude_from\n/etc/metrika.xml\n/include_from\n\n\n\n \ndictionary\n\n \n!-- Dictionary configuration --\n\n \n/dictionary\n\n\n ...\n\n \ndictionary\n\n \n!-- Dictionary configuration --\n\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\nYou can \nconfigure\n any number of dictionaries in the same file. The file format is preserved even if there is only one dictionary (i.e. \nyandex\ndictionary\n \n!--configuration -\n \n/dictionary\n/yandex\n ).\n\n\nSee also \"\nFunctions for working with external dictionaries\n\".\n\n\n\n\nYou can convert values \u200b\u200bfor a small dictionary by describing it in a `SELECT` query (see the [transform](#other_functions-transform) function). This functionality is not related to external dictionaries.\n\n\n\n\n\n\n\nConfiguring an external dictionary\n\n\nThe dictionary configuration has the following structure:\n\n\ndictionary\n\n \nname\ndict_name\n/name\n\n\n \nsource\n\n \n!-- Source configuration --\n\n \n/source\n\n\n \nlayout\n\n \n!-- Memory layout configuration --\n\n \n/layout\n\n\n \nstructure\n\n \n!-- Complex key configuration --\n\n \n/structure\n\n\n \nlifetime\n\n \n!-- Lifetime of dictionary in memory --\n\n \n/lifetime\n\n\n/dictionary\n\n\n\n\n\n\n\n\nname \u2013 The identifier that can be used to access the dictionary. Use the characters \n[a-zA-Z0-9_\\-]\n.\n\n\nsource\n \u2014 Source of the dictionary.\n\n\nlayout\n \u2014 Dictionary layout in memory.\n\n\nstructure\n \u2014 Structure of the dictionary . A key and attributes that can be retrieved by this key.\n\n\nlifetime\n \u2014 Frequency of dictionary updates.\n\n\n\n\n\n\nStoring dictionaries in memory\n\n\nThere are a \nvariety of ways\n to store dictionaries in memory.\n\n\nWe recommend \nflat\n, \nhashed\nand\ncomplex_key_hashed\n. which provide optimal processing speed.\n\n\nCaching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section \"\ncache\n\".\n\n\nThere are several ways to improve dictionary performance:\n\n\n\n\nCall the function for working with the dictionary after \nGROUP BY\n.\n\n\nMark attributes to extract as injective. An attribute is called injective if different attribute values correspond to different keys. So when \nGROUP BY\n uses a function that fetches an attribute value by the key, this function is automatically taken out of \nGROUP BY\n.\n\n\n\n\nClickHouse generates an exception for errors with dictionaries. Examples of errors:\n\n\n\n\nThe dictionary being accessed could not be loaded.\n\n\nError querying a \ncached\n dictionary.\n\n\n\n\nYou can view the list of external dictionaries and their statuses in the \nsystem.dictionaries\n table.\n\n\nThe configuration looks like this:\n\n\nyandex\n\n \ndictionary\n\n ...\n \nlayout\n\n \nlayout_type\n\n \n!-- layout settings --\n\n \n/layout_type\n\n \n/layout\n\n ...\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\n\n\nWays to store dictionaries in memory\n\n\n\n\nflat\n\n\nhashed\n\n\ncache\n\n\nrange_hashed\n\n\ncomplex_key_hashed\n\n\ncomplex_key_cache\n\n\nip_trie\n\n\n\n\n\n\nflat\n\n\nThe dictionary is completely stored in memory in the form of flat arrays. How much memory does the dictionary use? The amount is proportional to the size of the largest key (in space used).\n\n\nThe dictionary key has the \nUInt64\n type and the value is limited to 500,000. If a larger key is discovered when creating the dictionary, ClickHouse throws an exception and does not create the dictionary.\n\n\nAll types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.\n\n\nThis method provides the best performance among all available methods of storing the dictionary.\n\n\nConfiguration example:\n\n\nlayout\n\n \nflat\n \n/\n\n\n/layout\n\n\n\n\n\n\n\n\nhashed\n\n\nThe dictionary is completely stored in memory in the form of a hash table. The dictionary can contain any number of elements with any identifiers In practice, the number of keys can reach tens of millions of items.\n\n\nAll types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.\n\n\nConfiguration example:\n\n\nlayout\n\n \nhashed\n \n/\n\n\n/layout\n\n\n\n\n\n\n\n\ncomplex_key_hashed\n\n\nThis type of storage is for use with composite \nkeys\n. Similar to \nhashed\n.\n\n\nConfiguration example:\n\n\nlayout\n\n \ncomplex_key_hashed\n \n/\n\n\n/layout\n\n\n\n\n\n\n\n\nrange_hashed\n\n\nThe dictionary is stored in memory in the form of a hash table with an ordered array of ranges and their corresponding values.\n\n\nThis storage method works the same way as hashed and allows using date/time ranges in addition to the key, if they appear in the dictionary.\n\n\nExample: The table contains discounts for each advertiser in the format:\n\n\n+---------------+---------------------+-------------------+--------+\n| advertiser id | discount start date | discount end date | amount |\n+===============+=====================+===================+========+\n| 123 | 2015-01-01 | 2015-01-15 | 0.15 |\n+---------------+---------------------+-------------------+--------+\n| 123 | 2015-01-16 | 2015-01-31 | 0.25 |\n+---------------+---------------------+-------------------+--------+\n| 456 | 2015-01-01 | 2015-01-15 | 0.05 |\n+---------------+---------------------+-------------------+--------+\n\n\n\n\n\nTo use a sample for date ranges, define the \nrange_min\n and \nrange_max\n elements in the \nstructure\n.\n\n\nExample:\n\n\nstructure\n\n \nid\n\n \nname\nId\n/name\n\n \n/id\n\n \nrange_min\n\n \nname\nfirst\n/name\n\n \n/range_min\n\n \nrange_max\n\n \nname\nlast\n/name\n\n \n/range_max\n\n ...\n\n\n\n\n\nTo work with these dictionaries, you need to pass an additional date argument to the \ndictGetT\n function:\n\n\ndictGetT(\ndict_name\n, \nattr_name\n, id, date)\n\n\n\n\n\nThis function returns the value for the specified \nid\ns and the date range that includes the passed date.\n\n\nDetails of the algorithm:\n\n\n\n\nIf the \nid\n is not found or a range is not found for the \nid\n, it returns the default value for the dictionary.\n\n\nIf there are overlapping ranges, you can use any.\n\n\nIf the range delimiter is \nNULL\n or an invalid date (such as 1900-01-01 or 2039-01-01), the range is left open. The range can be open on both sides.\n\n\n\n\nConfiguration example:\n\n\nyandex\n\n \ndictionary\n\n\n ...\n\n \nlayout\n\n \nrange_hashed\n \n/\n\n \n/layout\n\n\n \nstructure\n\n \nid\n\n \nname\nAbcdef\n/name\n\n \n/id\n\n \nrange_min\n\n \nname\nStartDate\n/name\n\n \n/range_min\n\n \nrange_max\n\n \nname\nEndDate\n/name\n\n \n/range_max\n\n \nattribute\n\n \nname\nXXXType\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n \n/\n\n \n/attribute\n\n \n/structure\n\n\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\n\n\ncache\n\n\nThe dictionary is stored in a cache that has a fixed number of cells. These cells contain frequently used elements.\n\n\nWhen searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using \nSELECT attrs... FROM db.table WHERE id IN (k1, k2, ...)\n. The received data is then written to the cache.\n\n\nFor cache dictionaries, the expiration \nlifetime\n of data in the cache can be set. If more time than \nlifetime\n has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used.\n\n\nThis is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the \nsystem.dictionaries\n table.\n\n\nTo improve cache performance, use a subquery with \nLIMIT\n, and call the function with the dictionary externally.\n\n\nSupported \nsources\n: MySQL, ClickHouse, executable, HTTP.\n\n\nExample of settings:\n\n\nlayout\n\n \ncache\n\n \n!-- The size of the cache, in number of cells. Rounded up to a power of two. --\n\n \nsize_in_cells\n1000000000\n/size_in_cells\n\n \n/cache\n\n\n/layout\n\n\n\n\n\n\nSet a large enough cache size. You need to experiment to select the number of cells:\n\n\n\n\nSet some value.\n\n\nRun queries until the cache is completely full.\n\n\nAssess memory consumption using the \nsystem.dictionaries\n table.\n\n\nIncrease or decrease the number of cells until the required memory consumption is reached.\n\n\n\n\n\n\nDo not use ClickHouse as a source, because it is slow to process queries with random reads.\n\n\n\n\n\n\n\ncomplex_key_cache\n\n\nThis type of storage is for use with composite \nkeys\n. Similar to \ncache\n.\n\n\n\n\nip_trie\n\n\nThis type of storage is for mapping network prefixes (IP addresses) to metadata such as ASN.\n\n\nExample: The table contains network prefixes and their corresponding AS number and country code:\n\n\n +-----------------+-------+--------+\n | prefix | asn | cca2 |\n +=================+=======+========+\n | 202.79.32.0/20 | 17501 | NP |\n +-----------------+-------+--------+\n | 2620:0:870::/48 | 3856 | US |\n +-----------------+-------+--------+\n | 2a02:6b8:1::/48 | 13238 | RU |\n +-----------------+-------+--------+\n | 2001:db8::/32 | 65536 | ZZ |\n +-----------------+-------+--------+\n\n\n\n\n\nWhen using this type of layout, the structure must have a composite key.\n\n\nExample:\n\n\nstructure\n\n \nkey\n\n \nattribute\n\n \nname\nprefix\n/name\n\n \ntype\nString\n/type\n\n \n/attribute\n\n \n/key\n\n \nattribute\n\n \nname\nasn\n/name\n\n \ntype\nUInt32\n/type\n\n \nnull_value\n \n/\n\n \n/attribute\n\n \nattribute\n\n \nname\ncca2\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n??\n/null_value\n\n \n/attribute\n\n ...\n\n\n\n\n\nThe key must have only one String type attribute that contains an allowed IP prefix. Other types are not supported yet.\n\n\nFor queries, you must use the same functions (\ndictGetT\n with a tuple) as for dictionaries with composite keys:\n\n\ndictGetT(\ndict_name\n, \nattr_name\n, tuple(ip))\n\n\n\n\n\nThe function takes either \nUInt32\n for IPv4, or \nFixedString(16)\n for IPv6:\n\n\ndictGetString(\nprefix\n, \nasn\n, tuple(IPv6StringToNum(\n2001:db8::1\n)))\n\n\n\n\n\nOther types are not supported yet. The function returns the attribute for the prefix that corresponds to this IP address. If there are overlapping prefixes, the most specific one is returned.\n\n\nData is stored in a \ntrie\n. It must completely fit into RAM.\n\n\n\n\nDictionary updates\n\n\nClickHouse periodically updates the dictionaries. The update interval for fully downloaded dictionaries and the invalidation interval for cached dictionaries are defined in the \nlifetime\n tag in seconds.\n\n\nDictionary updates (other than loading for first use) do not block queries. During updates, the old version of a dictionary is used. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.\n\n\nExample of settings:\n\n\ndictionary\n\n ...\n \nlifetime\n300\n/lifetime\n\n ...\n\n/dictionary\n\n\n\n\n\n\nSetting \nlifetime\n 0\n/lifetime\n prevents updating dictionaries.\n\n\nYou can set a time interval for upgrades, and ClickHouse will choose a uniformly random time within this range. This is necessary in order to distribute the load on the dictionary source when upgrading on a large number of servers.\n\n\nExample of settings:\n\n\ndictionary\n\n ...\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n ...\n\n/dictionary\n\n\n\n\n\n\nWhen upgrading the dictionaries, the ClickHouse server applies different logic depending on the type of \n source\n:\n\n\n\n\n\n\nFor a text file, it checks the time of modification. If the time differs from the previously recorded time, the dictionary is updated.\n\n\nFor MyISAM tables, the time of modification is checked using a \nSHOW TABLE STATUS\n query.\n\n\nDictionaries from other sources are updated every time by default.\n\n\n\n\n\n\nFor MySQL (InnoDB) and ODBC sources, you can set up a query that will update the dictionaries only if they really changed, rather than each time. To do this, follow these steps:\n\n\n\n\n\n\nThe dictionary table must have a field that always changes when the source data is updated.\n\n\nThe settings of the source must specify a query that retrieves the changing field. The ClickHouse server interprets the query result as a row, and if this row has changed relative to its previous state, the dictionary is updated. Specify the query in the \ninvalidate_query\n field in the settings for the \nsource\n.\n\n\n\n\n\n\nExample of settings:\n\n\ndictionary\n\n ...\n \nodbc\n\n ...\n \ninvalidate_query\nSELECT update_time FROM dictionary_source where id = 1\n/invalidate_query\n\n \n/odbc\n\n ...\n\n/dictionary\n\n\n\n\n\n\n\n\nSources of external dictionaries\n\n\nAn external dictionary can be connected from many different sources.\n\n\nThe configuration looks like this:\n\n\nyandex\n\n \ndictionary\n\n ...\n \nsource\n\n \nsource_type\n\n \n!-- Source configuration --\n\n \n/source_type\n\n \n/source\n\n ...\n \n/dictionary\n\n ...\n\n/yandex\n\n\n\n\n\n\nThe source is configured in the \nsource\n section.\n\n\nTypes of sources (\nsource_type\n):\n\n\n\n\nLocal file\n\n\nExecutable file\n\n\nHTTP(s)\n\n\nODBC\n\n\nDBMS\n\n\nMySQL\n\n\nClickHouse\n\n\nMongoDB\n\n\n\n\n\n\nLocal file\n\n\nExample of settings:\n\n\nsource\n\n \nfile\n\n \npath\n/opt/dictionaries/os.tsv\n/path\n\n \nformat\nTabSeparated\n/format\n\n \n/file\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\npath\n \u2013 The absolute path to the file.\n\n\nformat\n \u2013 The file format. All the formats described in \"\nFormats\n\" are supported.\n\n\n\n\n\n\nExecutable file\n\n\nWorking with executable files depends on \nhow the dictionary is stored in memory\n. If the dictionary is stored using \ncache\n and \ncomplex_key_cache\n, ClickHouse requests the necessary keys by sending a request to the executable file's \nSTDIN\n.\n\n\nExample of settings:\n\n\nsource\n\n \nexecutable\n\n \ncommand\ncat /opt/dictionaries/os.tsv\n/command\n\n \nformat\nTabSeparated\n/format\n\n \n/executable\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\ncommand\n \u2013 The absolute path to the executable file, or the file name (if the program directory is written to \nPATH\n).\n\n\nformat\n \u2013 The file format. All the formats described in \"\nFormats\n\" are supported.\n\n\n\n\n\n\nHTTP(s)\n\n\nWorking with an HTTP(s) server depends on \nhow the dictionary is stored in memory\n. If the dictionary is stored using \ncache\n and \ncomplex_key_cache\n, ClickHouse requests the necessary keys by sending a request via the \nPOST\n method.\n\n\nExample of settings:\n\n\nsource\n\n \nhttp\n\n \nurl\nhttp://[::1]/os.tsv\n/url\n\n \nformat\nTabSeparated\n/format\n\n \n/http\n\n\n/source\n\n\n\n\n\n\nIn order for ClickHouse to access an HTTPS resource, you must \nconfigure openSSL\n in the server configuration.\n\n\nSetting fields:\n\n\n\n\nurl\n \u2013 The source URL.\n\n\nformat\n \u2013 The file format. All the formats described in \"\nFormats\n\" are supported.\n\n\n\n\n\n\nODBC\n\n\nYou can use this method to connect any database that has an ODBC driver.\n\n\nExample of settings:\n\n\nodbc\n\n \ndb\nDatabaseName\n/db\n\n \ntable\nTableName\n/table\n\n \nconnection_string\nDSN=some_parameters\n/connection_string\n\n \ninvalidate_query\nSQL_QUERY\n/invalidate_query\n\n\n/odbc\n\n\n\n\n\n\nSetting fields:\n\n\n\n\ndb\n \u2013 Name of the database. Omit it if the database name is set in the \nconnection_string\n parameters.\n\n\ntable\n \u2013 Name of the table.\n\n\nconnection_string\n \u2013 Connection string.\n\n\ninvalidate_query\n \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section \nUpdating dictionaries\n.\n\n\n\n\nExample of connecting PostgreSQL\n\n\nUbuntu OS.\n\n\nInstalling unixODBC and the ODBC driver for PostgreSQL:\n\n\nsudo apt-get install -y unixodbc odbcinst odbc-postgresql\n\n\n\n\n\nConfiguring \n/etc/odbc.ini\n (or \n~/.odbc.ini\n):\n\n\n [DEFAULT]\n Driver = myconnection\n\n [myconnection]\n Description = PostgreSQL connection to my_db\n Driver = PostgreSQL Unicode\n Database = my_db\n Servername = 127.0.0.1\n UserName = username\n Password = password\n Port = 5432\n Protocol = 9.3\n ReadOnly = No\n RowVersioning = No\n ShowSystemTables = No\n ConnSettings =\n\n\n\n\n\nThe dictionary configuration in ClickHouse:\n\n\ndictionary\n\n \nname\ntable_name\n/name\n\n \nsource\n\n \nodbc\n\n \n!-- You can specifiy the following parameters in connection_string: --\n\n \n!-- DSN=myconnection;UID=username;PWD=password;HOST=127.0.0.1;PORT=5432;DATABASE=my_db --\n\n \nconnection_string\nDSN=myconnection\n/connection_string\n\n \ntable\npostgresql_table\n/table\n\n \n/odbc\n\n \n/source\n\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n \nlayout\n\n \nhashed/\n\n \n/layout\n\n \nstructure\n\n \nid\n\n \nname\nid\n/name\n\n \n/id\n\n \nattribute\n\n \nname\nsome_column\n/name\n\n \ntype\nUInt64\n/type\n\n \nnull_value\n0\n/null_value\n\n \n/attribute\n\n \n/structure\n\n\n/dictionary\n\n\n\n\n\n\nYou may need to edit \nodbc.ini\n to specify the full path to the library with the driver \nDRIVER=/usr/local/lib/psqlodbcw.so\n.\n\n\nExample of connecting MS SQL Server\n\n\nUbuntu OS.\n\n\nInstalling the driver: :\n\n\n sudo apt-get install tdsodbc freetds-bin sqsh\n\n\n\n\n\nConfiguring the driver: :\n\n\n $ cat /etc/freetds/freetds.conf \n ...\n\n [MSSQL]\n host = 192.168.56.101\n port = 1433\n tds version = 7.0\n client charset = UTF-8\n\n $ cat /etc/odbcinst.ini \n ...\n\n [FreeTDS]\n Description = FreeTDS\n Driver = /usr/lib/x86_64-linux-gnu/odbc/libtdsodbc.so\n Setup = /usr/lib/x86_64-linux-gnu/odbc/libtdsS.so\n FileUsage = 1\n UsageCount = 5\n\n $ cat ~/.odbc.ini \n ...\n\n [MSSQL]\n Description = FreeTDS\n Driver = FreeTDS\n Servername = MSSQL\n Database = test\n UID = test\n PWD = test\n Port = 1433\n\n\n\n\n\nConfiguring the dictionary in ClickHouse:\n\n\nyandex\n\n \ndictionary\n\n \nname\ntest\n/name\n\n \nsource\n\n \nodbc\n\n \ntable\ndict\n/table\n\n \nconnection_string\nDSN=MSSQL;UID=test;PWD=test\n/connection_string\n\n \n/odbc\n\n \n/source\n\n\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n\n \nlayout\n\n \nflat\n \n/\n\n \n/layout\n\n\n \nstructure\n\n \nid\n\n \nname\nk\n/name\n\n \n/id\n\n \nattribute\n\n \nname\ns\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n/null_value\n\n \n/attribute\n\n \n/structure\n\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\nDBMS\n\n\n\n\nMySQL\n\n\nExample of settings:\n\n\nsource\n\n \nmysql\n\n \nport\n3306\n/port\n\n \nuser\nclickhouse\n/user\n\n \npassword\nqwerty\n/password\n\n \nreplica\n\n \nhost\nexample01-1\n/host\n\n \npriority\n1\n/priority\n\n \n/replica\n\n \nreplica\n\n \nhost\nexample01-2\n/host\n\n \npriority\n1\n/priority\n\n \n/replica\n\n \ndb\ndb_name\n/db\n\n \ntable\ntable_name\n/table\n\n \nwhere\nid=10\n/where\n\n \ninvalidate_query\nSQL_QUERY\n/invalidate_query\n\n \n/mysql\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\n\n\nport\n \u2013 The port on the MySQL server. You can specify it for all replicas, or for each one individually (inside \nreplica\n).\n\n\n\n\n\n\nuser\n \u2013 Name of the MySQL user. You can specify it for all replicas, or for each one individually (inside \nreplica\n).\n\n\n\n\n\n\npassword\n \u2013 Password of the MySQL user. You can specify it for all replicas, or for each one individually (inside \nreplica\n).\n\n\n\n\n\n\nreplica\n \u2013 Section of replica configurations. There can be multiple sections.\n\n\n\n\nreplica/host\n \u2013 The MySQL host.\n\n\n\n\n* \nreplica/priority\n \u2013 The replica priority. When attempting to connect, ClickHouse traverses the replicas in order of priority. The lower the number, the higher the priority.\n\n\n\n\n\n\ndb\n \u2013 Name of the database.\n\n\n\n\n\n\ntable\n \u2013 Name of the table.\n\n\n\n\n\n\nwhere\n \u2013 The selection criteria. Optional parameter.\n\n\n\n\n\n\ninvalidate_query\n \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section \nUpdating dictionaries\n.\n\n\n\n\n\n\nMySQL can be connected on a local host via sockets. To do this, set \nhost\n and \nsocket\n.\n\n\nExample of settings:\n\n\nsource\n\n \nmysql\n\n \nhost\nlocalhost\n/host\n\n \nsocket\n/path/to/socket/file.sock\n/socket\n\n \nuser\nclickhouse\n/user\n\n \npassword\nqwerty\n/password\n\n \ndb\ndb_name\n/db\n\n \ntable\ntable_name\n/table\n\n \nwhere\nid=10\n/where\n\n \ninvalidate_query\nSQL_QUERY\n/invalidate_query\n\n \n/mysql\n\n\n/source\n\n\n\n\n\n\n\n\nClickHouse\n\n\nExample of settings:\n\n\nsource\n\n \nclickhouse\n\n \nhost\nexample01-01-1\n/host\n\n \nport\n9000\n/port\n\n \nuser\ndefault\n/user\n\n \npassword\n/password\n\n \ndb\ndefault\n/db\n\n \ntable\nids\n/table\n\n \nwhere\nid=10\n/where\n\n \n/clickhouse\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\nhost\n \u2013 The ClickHouse host. If it is a local host, the query is processed without any network activity. To improve fault tolerance, you can create a \nDistributed\n table and enter it in subsequent configurations.\n\n\nport\n \u2013 The port on the ClickHouse server.\n\n\nuser\n \u2013 Name of the ClickHouse user.\n\n\npassword\n \u2013 Password of the ClickHouse user.\n\n\ndb\n \u2013 Name of the database.\n\n\ntable\n \u2013 Name of the table.\n\n\nwhere\n \u2013 The selection criteria. May be omitted.\n\n\n\n\n\n\nMongoDB\n\n\nExample of settings:\n\n\nsource\n\n \nmongodb\n\n \nhost\nlocalhost\n/host\n\n \nport\n27017\n/port\n\n \nuser\n/user\n\n \npassword\n/password\n\n \ndb\ntest\n/db\n\n \ncollection\ndictionary_source\n/collection\n\n \n/mongodb\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\nhost\n \u2013 The MongoDB host.\n\n\nport\n \u2013 The port on the MongoDB server.\n\n\nuser\n \u2013 Name of the MongoDB user.\n\n\npassword\n \u2013 Password of the MongoDB user.\n\n\ndb\n \u2013 Name of the database.\n\n\ncollection\n \u2013 Name of the collection.\n\n\n\n\n\n\nDictionary key and fields\n\n\nThe \nstructure\n clause describes the dictionary key and fields available for queries.\n\n\nOverall structure:\n\n\ndictionary\n\n \nstructure\n\n \nid\n\n \nname\nId\n/name\n\n \n/id\n\n\n \nattribute\n\n \n!-- Attribute parameters --\n\n \n/attribute\n\n\n ...\n\n \n/structure\n\n\n/dictionary\n\n\n\n\n\n\nColumns are described in the structure:\n\n\n\n\nid\n - \nkey column\n.\n\n\nattribute\n - \ndata column\n. There can be a large number of columns.\n\n\n\n\n\n\nKey\n\n\nClickHouse supports the following types of keys:\n\n\n\n\nNumeric key. UInt64. Defined in the tag \nid\n .\n\n\nComposite key. Set of values of different types. Defined in the tag \nkey\n .\n\n\n\n\nA structure can contain either \nid\n or \nkey\n .\n\n\n\n\nThe key doesn't need to be defined separately in attributes.\n\n\n\n\n\nNumeric key\n\n\nFormat: \nUInt64\n.\n\n\nConfiguration example:\n\n\nid\n\n \nname\nId\n/name\n\n\n/id\n\n\n\n\n\n\nConfiguration fields:\n\n\n\n\nname \u2013 The name of the column with keys.\n\n\n\n\nComposite key\n\n\nThe key can be a \ntuple\n from any types of fields. The \nlayout\n in this case must be \ncomplex_key_hashed\n or \ncomplex_key_cache\n.\n\n\n\nA composite key can consist of a single element. This makes it possible to use a string as the key, for instance.\n\n\n\n\nThe key structure is set in the element \nkey\n. Key fields are specified in the same format as the dictionary \nattributes\n. Example:\n\n\nstructure\n\n \nkey\n\n \nattribute\n\n \nname\nfield1\n/name\n\n \ntype\nString\n/type\n\n \n/attribute\n\n \nattribute\n\n \nname\nfield2\n/name\n\n \ntype\nUInt32\n/type\n\n \n/attribute\n\n ...\n \n/key\n\n...\n\n\n\n\n\nFor a query to the \ndictGet*\n function, a tuple is passed as the key. Example: \ndictGetString('dict_name', 'attr_name', tuple('string for field1', num_for_field2))\n.\n\n\n\n\nAttributes\n\n\nConfiguration example:\n\n\nstructure\n\n ...\n \nattribute\n\n \nname\nName\n/name\n\n \ntype\nType\n/type\n\n \nnull_value\n/null_value\n\n \nexpression\nrand64()\n/expression\n\n \nhierarchical\ntrue\n/hierarchical\n\n \ninjective\ntrue\n/injective\n\n \nis_object_id\ntrue\n/is_object_id\n\n \n/attribute\n\n\n/structure\n\n\n\n\n\n\nConfiguration fields:\n\n\n\n\nname\n \u2013 The column name.\n\n\ntype\n \u2013 The column type. Sets the method for interpreting data in the source. For example, for MySQL, the field might be \nTEXT\n, \nVARCHAR\n, or \nBLOB\n in the source table, but it can be uploaded as \nString\n.\n\n\nnull_value\n \u2013 The default value for a non-existing element. In the example, it is an empty string.\n\n\nexpression\n \u2013 The attribute can be an expression. The tag is not required.\n\n\nhierarchical\n \u2013 Hierarchical support. Mirrored to the parent identifier. By default, \nfalse\n.\n\n\ninjective\n \u2013 Whether the \nid -\n attribute\n image is injective. If \ntrue\n, then you can optimize the \nGROUP BY\n clause. By default, \nfalse\n.\n\n\nis_object_id\n \u2013 Whether the query is executed for a MongoDB document by \nObjectID\n.\n\n\n\n\nInternal dictionaries\n\n\nClickHouse contains a built-in feature for working with a geobase.\n\n\nThis allows you to:\n\n\n\n\nUse a region's ID to get its name in the desired language.\n\n\nUse a region's ID to get the ID of a city, area, federal district, country, or continent.\n\n\nCheck whether a region is part of another region.\n\n\nGet a chain of parent regions.\n\n\n\n\nAll the functions support \"translocality,\" the ability to simultaneously use different perspectives on region ownership. For more information, see the section \"Functions for working with Yandex.Metrica dictionaries\".\n\n\nThe internal dictionaries are disabled in the default package.\nTo enable them, uncomment the parameters \npath_to_regions_hierarchy_file\n and \npath_to_regions_names_files\n in the server configuration file.\n\n\nThe geobase is loaded from text files.\nIf you work at Yandex, you can follow these instructions to create them:\n\nhttps://github.yandex-team.ru/raw/Metrika/ClickHouse_private/master/doc/create_embedded_geobase_dictionaries.txt\n\n\nPut the regions_hierarchy*.txt files in the path_to_regions_hierarchy_file directory. This configuration parameter must contain the path to the regions_hierarchy.txt file (the default regional hierarchy), and the other files (regions_hierarchy_ua.txt) must be located in the same directory.\n\n\nPut the \nregions_names_*.txt\n files in the path_to_regions_names_files directory.\n\n\nYou can also create these files yourself. The file format is as follows:\n\n\nregions_hierarchy*.txt\n: TabSeparated (no header), columns:\n\n\n\n\nRegion ID (UInt32)\n\n\nParent region ID (UInt32)\n\n\nRegion type (UInt8): 1 - continent, 3 - country, 4 - federal district, 5 - region, 6 - city; other types don't have values.\n\n\nPopulation (UInt32) - Optional column.\n\n\n\n\nregions_names_*.txt\n: TabSeparated (no header), columns:\n\n\n\n\nRegion ID (UInt32)\n\n\nRegion name (String) - Can't contain tabs or line feeds, even escaped ones.\n\n\n\n\nA flat array is used for storing in RAM. For this reason, IDs shouldn't be more than a million.\n\n\nDictionaries can be updated without restarting the server. However, the set of available dictionaries is not updated.\nFor updates, the file modification times are checked. If a file has changed, the dictionary is updated.\nThe interval to check for changes is configured in the 'builtin_dictionaries_reload_interval' parameter.\nDictionary updates (other than loading at first use) do not block queries. During updates, queries use the old versions of dictionaries. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.\n\n\nWe recommend periodically updating the dictionaries with the geobase. During an update, generate new files and write them to a separate location. When everything is ready, rename them to the files used by the server.\n\n\nThere are also functions for working with OS identifiers and Yandex.Metrica search engines, but they shouldn't be used.\n\n\nUsage\n\n\nAccess rights\n\n\nUsers and access rights are set up in the user config. This is usually \nusers.xml\n.\n\n\nUsers are recorded in the \nusers\n section. Here is a fragment of the \nusers.xml\n file:\n\n\n!-- Users and ACL. --\n\n\nusers\n\n \n!-- If the user name is not specified, the \ndefault\n user is used. --\n\n \ndefault\n\n \n!-- Password could be specified in plaintext or in SHA256 (in hex format).\n\n\n\n If you want to specify the password in plain text (not recommended), place it in the \npassword\n element.\n\n\n Example: \npassword\nqwerty\n/password\n.\n\n\n Password can be empty.\n\n\n\n If you want to specify SHA256, place it in the \npassword_sha256_hex\n element.\n\n\n Example: \npassword_sha256_hex\n65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5\n/password_sha256_hex\n\n\n\n How to generate decent password:\n\n\n Execute: PASSWORD=$(base64 \n /dev/urandom | head -c8); echo \n$PASSWORD\n; echo -n \n$PASSWORD\n | sha256sum | tr -d \n-\n\n\n In first line will be password and in second - corresponding SHA256.\n\n\n --\n\n \npassword\n/password\n\n \n!-- A list of networks that access is allowed from.\n\n\n Each list item has one of the following forms:\n\n\n \nip\nIP address or subnet mask. For example: 198.51.100.0/24 or 2001:DB8::/32.\n\n\n \nhost\n Host name. For example: example01. A DNS query is made for verification, and all addresses obtained are compared with the address of the customer.\n\n\n \nhost_regexp\n Regular expression for host names. For example: ^example\\d\\d-\\d\\d-\\d\\.yandex\\.ru$\n\n\n For verification, a DNS PTR query is made for the customer\ns address and a regular expression is applied to the result.\n\n\n Then another DNS query is made for the result of the PTR query, and all received address are compared to the client address.\n\n\n We strongly recommend that the regex ends with \\.yandex\\.ru$.\n\n\n\n If you are installing ClickHouse yourself, enter:\n\n\n \nnetworks\n\n\n \nip\n::/0\n/ip\n\n\n \n/networks\n\n\n --\n\n \nnetworks\n \nincl=\nnetworks\n \n/\n\n\n \n!-- Settings profile for the user. --\n\n \nprofile\ndefault\n/profile\n\n\n \n!-- Quota for the user. --\n\n \nquota\ndefault\n/quota\n\n \n/default\n\n\n \n!-- For requests from the Yandex.Metrica user interface via the API for data on specific counters. --\n\n \nweb\n\n \npassword\n/password\n\n \nnetworks\n \nincl=\nnetworks\n \n/\n\n \nprofile\nweb\n/profile\n\n \nquota\ndefault\n/quota\n\n \nallow_databases\n\n \ndatabase\ntest\n/database\n\n \n/allow_databases\n\n \n/web\n\n\n/users\n\n\n\n\n\n\nYou can see a declaration from two users: \ndefault\n and \nweb\n. We added the \nweb\n user separately.\n\n\nThe \ndefault\n user is chosen in cases when the username is not passed. The \ndefault\n user is also used for distributed query processing, if the configuration of the server or cluster doesn't specify the \nuser\n and \npassword\n (see the section on the \nDistributed\n engine).\n\n\nThe user that is used for exchanging information between servers combined in a cluster must not have substantial restrictions or quotas \u2013 otherwise, distributed queries will fail.\n\n\nThe password is specified in open format (not recommended) or in SHA-256. The hash isn't salted. In this regard, you should not consider these passwords as providing security against potential malicious attacks. Rather, they are necessary for protection from employees.\n\n\nA list of networks is specified that access is allowed from. In this example, the list of networks for both users is loaded from a separate file (/etc/metrika.xml) containing the 'networks' substitution. Here is a fragment of it:\n\n\nyandex\n\n ...\n \nnetworks\n\n \nip\n::/64\n/ip\n\n \nip\n203.0.113.0/24\n/ip\n\n \nip\n2001:DB8::/32\n/ip\n\n ...\n \n/networks\n\n\n/yandex\n\n\n\n\n\n\nWe could have defined this list of networks directly in 'users.xml', or in a file in the 'users.d' directory (for more information, see the section \"Configuration files\").\n\n\nThe config includes comments explaining how to open access from everywhere.\n\n\nFor use in production, only specify IP elements (IP addresses and their masks), since using 'host' and 'hoost_regexp' might cause extra latency.\n\n\nNext the user settings profile is specified (see the section \"Settings profiles\"). You can specify the default profile, \ndefault\n. The profile can have any name. You can specify the same profile for different users. The most important thing you can write in the settings profile is 'readonly' set to 1, which provides read-only access.\n\n\nAfter this, the quota is defined (see the section \"Quotas\"). You can specify the default quota, \ndefault\n. It is set in the config by default so that it only counts resource usage, but does not restrict it. The quota can have any name. You can specify the same quota for different users \u2013 in this case, resource usage is calculated for each user individually.\n\n\nIn the optional \nallow_databases\n section, you can also specify a list of databases that the user can access. By default, all databases are available to the user. You can specify the \ndefault\n database. In this case, the user will receive access to the database by default.\n\n\nAccess to the \nsystem\n database is always allowed (since this database is used for processing queries).\n\n\nThe user can get a list of all databases and tables in them by using \nSHOW\n queries or system tables, even if access to individual databases isn't allowed.\n\n\nDatabase access is not related to the \nreadonly\n setting. You can't grant full access to one database and \nreadonly\n access to another one.\n\n\n\n\nConfiguration files\n\n\nThe main server config file is \nconfig.xml\n. It resides in the \n/etc/clickhouse-server/\n directory.\n\n\nIndividual settings can be overridden in the \n*.xml\nand\n*.conf\n files in the \nconf.d\n and \nconfig.d\n directories next to the config file.\n\n\nThe \nreplace\n or \nremove\n attributes can be specified for the elements of these config files.\n\n\nIf neither is specified, it combines the contents of elements recursively, replacing values of duplicate children.\n\n\nIf \nreplace\n is specified, it replaces the entire element with the specified one.\n\n\nIf \nremove\n is specified, it deletes the element.\n\n\nThe config can also define \"substitutions\". If an element has the \nincl\n attribute, the corresponding substitution from the file will be used as the value. By default, the path to the file with substitutions is \n/etc/metrika.xml\n. This can be changed in the \ninclude_from\n element in the server config. The substitution values are specified in \n/yandex/substitution_name\n elements in this file. If a substitution specified in \nincl\n does not exist, it is recorded in the log. To prevent ClickHouse from logging missing substitutions, specify the \noptional=\"true\"\n attribute (for example, settings for \nmacros\n).\n\n\nSubstitutions can also be performed from ZooKeeper. To do this, specify the attribute \nfrom_zk = \"/path/to/node\"\n. The element value is replaced with the contents of the node at \n/path/to/node\n in ZooKeeper. You can also put an entire XML subtree on the ZooKeeper node and it will be fully inserted into the source element.\n\n\nThe \nconfig.xml\n file can specify a separate config with user settings, profiles, and quotas. The relative path to this config is set in the 'users_config' element. By default, it is \nusers.xml\n. If \nusers_config\n is omitted, the user settings, profiles, and quotas are specified directly in \nconfig.xml\n.\n\n\nIn addition, \nusers_config\n may have overrides in files from the \nusers_config.d\n directory (for example, \nusers.d\n) and substitutions.\n\n\nFor each config file, the server also generates \nfile-preprocessed.xml\n files when starting. These files contain all the completed substitutions and overrides, and they are intended for informational use. If ZooKeeper substitutions were used in the config files but ZooKeeper is not available on the server start, the server loads the configuration from the preprocessed file.\n\n\nThe server tracks changes in config files, as well as files and ZooKeeper nodes that were used when performing substitutions and overrides, and reloads the settings for users and clusters on the fly. This means that you can modify the cluster, users, and their settings without restarting the server.\n\n\nQuotas\n\n\nQuotas allow you to limit resource usage over a period of time, or simply track the use of resources.\nQuotas are set up in the user config. This is usually 'users.xml'.\n\n\nThe system also has a feature for limiting the complexity of a single query. See the section \"Restrictions on query complexity\").\n\n\nIn contrast to query complexity restrictions, quotas:\n\n\n\n\nPlace restrictions on a set of queries that can be run over a period of time, instead of limiting a single query.\n\n\nAccount for resources spent on all remote servers for distributed query processing.\n\n\n\n\nLet's look at the section of the 'users.xml' file that defines quotas.\n\n\n!-- Quotas. --\n\n\nquotas\n\n \n!-- Quota name. --\n\n \ndefault\n\n \n!-- Restrictions for a time period. You can set many intervals with different restrictions. --\n\n \ninterval\n\n \n!-- Length of the interval. --\n\n \nduration\n3600\n/duration\n\n\n \n!-- Unlimited. Just collect data for the specified time interval. --\n\n \nqueries\n0\n/queries\n\n \nerrors\n0\n/errors\n\n \nresult_rows\n0\n/result_rows\n\n \nread_rows\n0\n/read_rows\n\n \nexecution_time\n0\n/execution_time\n\n \n/interval\n\n \n/default\n\n\n\n\n\n\nBy default, the quota just tracks resource consumption for each hour, without limiting usage.\nThe resource consumption calculated for each interval is output to the server log after each request.\n\n\nstatbox\n\n \n!-- Restrictions for a time period. You can set many intervals with different restrictions. --\n\n \ninterval\n\n \n!-- Length of the interval. --\n\n \nduration\n3600\n/duration\n\n\n \nqueries\n1000\n/queries\n\n \nerrors\n100\n/errors\n\n \nresult_rows\n1000000000\n/result_rows\n\n \nread_rows\n100000000000\n/read_rows\n\n \nexecution_time\n900\n/execution_time\n\n \n/interval\n\n\n \ninterval\n\n \nduration\n86400\n/duration\n\n\n \nqueries\n10000\n/queries\n\n \nerrors\n1000\n/errors\n\n \nresult_rows\n5000000000\n/result_rows\n\n \nread_rows\n500000000000\n/read_rows\n\n \nexecution_time\n7200\n/execution_time\n\n \n/interval\n\n\n/statbox\n\n\n\n\n\n\nFor the 'statbox' quota, restrictions are set for every hour and for every 24 hours (86,400 seconds). The time interval is counted starting from an implementation-defined fixed moment in time. In other words, the 24-hour interval doesn't necessarily begin at midnight.\n\n\nWhen the interval ends, all collected values are cleared. For the next hour, the quota calculation starts over.\n\n\nHere are the amounts that can be restricted:\n\n\nqueries\n \u2013 The total number of requests.\n\n\nerrors\n \u2013 The number of queries that threw an exception.\n\n\nresult_rows\n \u2013 The total number of rows given as the result.\n\n\nread_rows\n \u2013 The total number of source rows read from tables for running the query, on all remote servers.\n\n\nexecution_time\n \u2013 The total query execution time, in seconds (wall time).\n\n\nIf the limit is exceeded for at least one time interval, an exception is thrown with a text about which restriction was exceeded, for which interval, and when the new interval begins (when queries can be sent again).\n\n\nQuotas can use the \"quota key\" feature in order to report on resources for multiple keys independently. Here is an example of this:\n\n\n!-- For the global reports designer. --\n\n\nweb_global\n\n \n!-- keyed - The quota_key \nkey\n is passed in the query parameter,\n\n\n and the quota is tracked separately for each key value.\n\n\n For example, you can pass a Yandex.Metrica username as the key,\n\n\n so the quota will be counted separately for each username.\n\n\n Using keys makes sense only if quota_key is transmitted by the program, not by a user.\n\n\n\n You can also write \nkeyed_by_ip /\n so the IP address is used as the quota key.\n\n\n (But keep in mind that users can change the IPv6 address fairly easily.)\n\n\n --\n\n \nkeyed\n \n/\n\n\n\n\n\n\nThe quota is assigned to users in the 'users' section of the config. See the section \"Access rights\".\n\n\nFor distributed query processing, the accumulated amounts are stored on the requestor server. So if the user goes to another server, the quota there will \"start over\".\n\n\nWhen the server is restarted, quotas are reset.\n\n\nUsage recommendations\n\n\nCPU\n\n\nThe SSE 4.2 instruction set must be supported. Modern processors (since 2008) support it.\n\n\nWhen choosing a processor, prefer a large number of cores and slightly slower clock rate over fewer cores and a higher clock rate.\nFor example, 16 cores with 2600 MHz is better than 8 cores with 3600 MHz.\n\n\nHyper-threading\n\n\nDon't disable hyper-threading. It helps for some queries, but not for others.\n\n\nTurbo Boost\n\n\nTurbo Boost is highly recommended. It significantly improves performance with a typical load.\nYou can use \nturbostat\n to view the CPU's actual clock rate under a load.\n\n\nCPU scaling governor\n\n\nAlways use the \nperformance\n scaling governor. The \non-demand\n scaling governor works much worse with constantly high demand.\n\n\nsudo \necho\n \nperformance\n \n|\n tee /sys/devices/system/cpu/cpu\n\\*\n/cpufreq/scaling_governor\n\n\n\n\n\nCPU limitations\n\n\nProcessors can overheat. Use \ndmesg\n to see if the CPU's clock rate was limited due to overheating.\nThe restriction can also be set externally at the datacenter level. You can use \nturbostat\n to monitor it under a load.\n\n\nRAM\n\n\nFor small amounts of data (up to \\~200 GB compressed), it is best to use as much memory as the volume of data.\nFor large amounts of data and when processing interactive (online) queries, you should use a reasonable amount of RAM (128 GB or more) so the hot data subset will fit in the cache of pages.\nEven for data volumes of \\~50 TB per server, using 128 GB of RAM significantly improves query performance compared to 64 GB.\n\n\nSwap file\n\n\nAlways disable the swap file. The only reason for not doing this is if you are using ClickHouse on your personal laptop.\n\n\nHuge pages\n\n\nAlways disable transparent huge pages. It interferes with memory allocators, which leads to significant performance degradation.\n\n\necho\n \nnever\n \n|\n sudo tee /sys/kernel/mm/transparent_hugepage/enabled\n\n\n\n\n\nUse \nperf top\n to watch the time spent in the kernel for memory management.\nPermanent huge pages also do not need to be allocated.\n\n\nStorage subsystem\n\n\nIf your budget allows you to use SSD, use SSD.\nIf not, use HDD. SATA HDDs 7200 RPM will do.\n\n\nGive preference to a lot of servers with local hard drives over a smaller number of servers with attached disk shelves.\nBut for storing archives with rare queries, shelves will work.\n\n\nRAID\n\n\nWhen using HDD, you can combine their RAID-10, RAID-5, RAID-6 or RAID-50.\nFor Linux, software RAID is better (with \nmdadm\n). We don't recommend using LVM.\nWhen creating RAID-10, select the \nfar\n layout.\nIf your budget allows, choose RAID-10.\n\n\nIf you have more than 4 disks, use RAID-6 (preferred) or RAID-50, instead of RAID-5.\nWhen using RAID-5, RAID-6 or RAID-50, always increase stripe_cache_size, since the default value is usually not the best choice.\n\n\necho\n \n4096\n \n|\n sudo tee /sys/block/md2/md/stripe_cache_size\n\n\n\n\n\nCalculate the exact number from the number of devices and the block size, using the formula: \n2 * num_devices * chunk_size_in_bytes / 4096\n.\n\n\nA block size of 1025 KB is sufficient for all RAID configurations.\nNever set the block size too small or too large.\n\n\nYou can use RAID-0 on SSD.\nRegardless of RAID use, always use replication for data security.\n\n\nEnable NCQ with a long queue. For HDD, choose the CFQ scheduler, and for SSD, choose noop. Don't reduce the 'readahead' setting.\nFor HDD, enable the write cache.\n\n\nFile system\n\n\nExt4 is the most reliable option. Set the mount options \nnoatime, nobarrier\n.\nXFS is also suitable, but it hasn't been as thoroughly tested with ClickHouse.\nMost other file systems should also work fine. File systems with delayed allocation work better.\n\n\nLinux kernel\n\n\nDon't use an outdated Linux kernel. In 2015, 3.18.19 was new enough.\nConsider using the kernel build from Yandex:\nhttps://github.com/yandex/smart\n \u2013 it provides at least a 5% performance increase.\n\n\nNetwork\n\n\nIf you are using IPv6, increase the size of the route cache.\nThe Linux kernel prior to 3.2 had a multitude of problems with IPv6 implementation.\n\n\nUse at least a 10 GB network, if possible. 1 Gb will also work, but it will be much worse for patching replicas with tens of terabytes of data, or for processing distributed queries with a large amount of intermediate data.\n\n\nZooKeeper\n\n\nYou are probably already using ZooKeeper for other purposes. You can use the same installation of ZooKeeper, if it isn't already overloaded.\n\n\nIt's best to use a fresh version of ZooKeeper \u2013 3.4.9 or later. The version in stable Linux distributions may be outdated.\n\n\nWith the default settings, ZooKeeper is a time bomb:\n\n\n\n\nThe ZooKeeper server won't delete files from old snapshots and logs when using the default configuration (see autopurge), and this is the responsibility of the operator.\n\n\n\n\nThis bomb must be defused.\n\n\nThe ZooKeeper (3.5.1) configuration below is used in the Yandex.Metrica production environment as of May 20, 2017:\n\n\nzoo.cfg:\n\n\n## http://hadoop.apache.org/zookeeper/docs/current/zookeeperAdmin.html\n\n\n\n## The number of milliseconds of each tick\n\n\ntickTime\n=\n2000\n\n\n## The number of ticks that the initial\n\n\n## synchronization phase can take\n\n\ninitLimit\n=\n30000\n\n\n## The number of ticks that can pass between\n\n\n## sending a request and getting an acknowledgement\n\n\nsyncLimit\n=\n10\n\n\n\nmaxClientCnxns\n=\n2000\n\n\n\nmaxSessionTimeout\n=\n60000000\n\n\n## the directory where the snapshot is stored.\n\n\ndataDir\n=\n/opt/zookeeper/\n{{\n cluster\n[\nname\n]\n \n}}\n/data\n\n## Place the dataLogDir to a separate physical disc for better performance\n\n\ndataLogDir\n=\n/opt/zookeeper/\n{{\n cluster\n[\nname\n]\n \n}}\n/logs\n\nautopurge.snapRetainCount\n=\n10\n\nautopurge.purgeInterval\n=\n1\n\n\n\n\n## To avoid seeks ZooKeeper allocates space in the transaction log file in\n\n\n## blocks of preAllocSize kilobytes. The default block size is 64M. One reason\n\n\n## for changing the size of the blocks is to reduce the block size if snapshots\n\n\n## are taken more often. (Also, see snapCount).\n\n\npreAllocSize\n=\n131072\n\n\n\n## Clients can submit requests faster than ZooKeeper can process them,\n\n\n## especially if there are a lot of clients. To prevent ZooKeeper from running\n\n\n## out of memory due to queued requests, ZooKeeper will throttle clients so that\n\n\n## there is no more than globalOutstandingLimit outstanding requests in the\n\n\n## system. The default limit is 1,000.ZooKeeper logs transactions to a\n\n\n## transaction log. After snapCount transactions are written to a log file a\n\n\n## snapshot is started and a new transaction log file is started. The default\n\n\n## snapCount is 10,000.\n\n\nsnapCount\n=\n3000000\n\n\n\n## If this option is defined, requests will be will logged to a trace file named\n\n\n## traceFile.year.month.day.\n\n\n##traceFile=\n\n\n\n## Leader accepts client connections. Default value is \nyes\n. The leader machine\n\n\n## coordinates updates. For higher update throughput at thes slight expense of\n\n\n## read throughput the leader can be configured to not accept clients and focus\n\n\n## on coordination.\n\n\nleaderServes\n=\nyes\n\n\nstandaloneEnabled\n=\nfalse\n\n\ndynamicConfigFile\n=\n/etc/zookeeper-\n{{\n cluster\n[\nname\n]\n \n}}\n/conf/zoo.cfg.dynamic\n\n\n\n\n\nJava version:\n\n\nJava(TM) SE Runtime Environment (build 1.8.0_25-b17)\nJava HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode)\n\n\n\n\n\nJVM parameters:\n\n\nNAME\n=\nzookeeper-\n{{\n cluster\n[\nname\n]\n \n}}\n\n\nZOOCFGDIR\n=\n/etc/\n$NAME\n/conf\n\n\n## TODO this is really ugly\n\n\n## How to find out, which jars are needed?\n\n\n## seems, that log4j requires the log4j.properties file to be in the classpath\n\n\nCLASSPATH\n=\n$ZOOCFGDIR\n:/usr/build/classes:/usr/build/lib/*.jar:/usr/share/zookeeper/zookeeper-3.5.1-metrika.jar:/usr/share/zookeeper/slf4j-log4j12-1.7.5.jar:/usr/share/zookeeper/slf4j-api-1.7.5.jar:/usr/share/zookeeper/servlet-api-2.5-20081211.jar:/usr/share/zookeeper/netty-3.7.0.Final.jar:/usr/share/zookeeper/log4j-1.2.16.jar:/usr/share/zookeeper/jline-2.11.jar:/usr/share/zookeeper/jetty-util-6.1.26.jar:/usr/share/zookeeper/jetty-6.1.26.jar:/usr/share/zookeeper/javacc.jar:/usr/share/zookeeper/jackson-mapper-asl-1.9.11.jar:/usr/share/zookeeper/jackson-core-asl-1.9.11.jar:/usr/share/zookeeper/commons-cli-1.2.jar:/usr/src/java/lib/*.jar:/usr/etc/zookeeper\n\n\n\nZOOCFG\n=\n$ZOOCFGDIR\n/zoo.cfg\n\n\nZOO_LOG_DIR\n=\n/var/log/\n$NAME\n\n\nUSER\n=\nzookeeper\n\nGROUP\n=\nzookeeper\n\nPIDDIR\n=\n/var/run/\n$NAME\n\n\nPIDFILE\n=\n$PIDDIR\n/\n$NAME\n.pid\n\nSCRIPTNAME\n=\n/etc/init.d/\n$NAME\n\n\nJAVA\n=\n/usr/bin/java\n\nZOOMAIN\n=\norg.apache.zookeeper.server.quorum.QuorumPeerMain\n\n\nZOO_LOG4J_PROP\n=\nINFO,ROLLINGFILE\n\n\nJMXLOCALONLY\n=\nfalse\n\n\nJAVA_OPTS\n=\n-Xms{{ cluster.get(\nxms\n,\n128M\n) }} \\\n\n\n -Xmx{{ cluster.get(\nxmx\n,\n1G\n) }} \\\n\n\n -Xloggc:/var/log/\n$NAME\n/zookeeper-gc.log \\\n\n\n -XX:+UseGCLogFileRotation \\\n\n\n -XX:NumberOfGCLogFiles=16 \\\n\n\n -XX:GCLogFileSize=16M \\\n\n\n -verbose:gc \\\n\n\n -XX:+PrintGCTimeStamps \\\n\n\n -XX:+PrintGCDateStamps \\\n\n\n -XX:+PrintGCDetails\n\n\n -XX:+PrintTenuringDistribution \\\n\n\n -XX:+PrintGCApplicationStoppedTime \\\n\n\n -XX:+PrintGCApplicationConcurrentTime \\\n\n\n -XX:+PrintSafepointStatistics \\\n\n\n -XX:+UseParNewGC \\\n\n\n -XX:+UseConcMarkSweepGC \\\n\n\n-XX:+CMSParallelRemarkEnabled\n\n\n\n\n\n\nSalt init:\n\n\ndescription \nzookeeper-{{ cluster[\nname\n] }} centralized coordination service\n\n\nstart on runlevel [2345]\nstop on runlevel [!2345]\n\nrespawn\n\nlimit nofile 8192 8192\n\npre-start script\n [ -r \n/etc/zookeeper-{{ cluster[\nname\n] }}/conf/environment\n ] || exit 0\n . /etc/zookeeper-{{ cluster[\nname\n] }}/conf/environment\n [ -d $ZOO_LOG_DIR ] || mkdir -p $ZOO_LOG_DIR\n chown $USER:$GROUP $ZOO_LOG_DIR\nend script\n\nscript\n . /etc/zookeeper-{{ cluster[\nname\n] }}/conf/environment\n [ -r /etc/default/zookeeper ] \n . /etc/default/zookeeper\n if [ -z \n$JMXDISABLE\n ]; then\n JAVA_OPTS=\n$JAVA_OPTS -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.local.only=$JMXLOCALONLY\n\n fi\n exec start-stop-daemon --start -c $USER --exec $JAVA --name zookeeper-{{ cluster[\nname\n] }} \\\n -- -cp $CLASSPATH $JAVA_OPTS -Dzookeeper.log.dir=${ZOO_LOG_DIR} \\\n -Dzookeeper.root.logger=${ZOO_LOG4J_PROP} $ZOOMAIN $ZOOCFG\nend script\n\n\n\n\n\n\n\nServer configuration parameters\n\n\nThis section contains descriptions of server settings that cannot be changed at the session or query level.\n\n\nThese settings are stored in the \nconfig.xml\n file on the ClickHouse server.\n\n\nOther settings are described in the \"\nSettings\n\" section.\n\n\nBefore studying the settings, read the \nConfiguration files\n section and note the use of substitutions (the \nincl\n and \noptional\n attributes).\n\n\nServer settings\n\n\n\n\nbuiltin_dictionaries_reload_interval\n\n\nThe interval in seconds before reloading built-in dictionaries.\n\n\nClickHouse reloads built-in dictionaries every x seconds. This makes it possible to edit dictionaries \"on the fly\" without restarting the server.\n\n\nDefault value: 3600.\n\n\nExample\n\n\nbuiltin_dictionaries_reload_interval\n3600\n/builtin_dictionaries_reload_interval\n\n\n\n\n\n\n\n\ncompression\n\n\nData compression settings.\n\n\n\n\nDon't use it if you have just started using ClickHouse.\n\n\n\n\n\nThe configuration looks like this:\n\n\ncompression\n\n \ncase\n\n \nparameters/\n\n \n/case\n\n ...\n\n/compression\n\n\n\n\n\n\nYou can configure multiple sections \ncase\n.\n\n\nBlock field \ncase\n:\n\n\n\n\nmin_part_size\n \u2013 The minimum size of a table part.\n\n\nmin_part_size_ratio\n \u2013 The ratio of the minimum size of a table part to the full size of the table.\n\n\nmethod\n \u2013 Compression method. Acceptable values \u200b: \nlz4\n or \nzstd\n(experimental).\n\n\n\n\nClickHouse checks \nmin_part_size\n and \nmin_part_size_ratio\n and processes the \ncase\n blocks that match these conditions. If none of the \ncase\n matches, ClickHouse applies the \nlz4\n compression algorithm.\n\n\nExample\n\n\ncompression\n \nincl=\nclickhouse_compression\n\n \ncase\n\n \nmin_part_size\n10000000000\n/min_part_size\n\n \nmin_part_size_ratio\n0.01\n/min_part_size_ratio\n\n \nmethod\nzstd\n/method\n\n \n/case\n\n\n/compression\n\n\n\n\n\n\n\n\ndefault_database\n\n\nThe default database.\n\n\nTo get a list of databases, use the \nSHOW DATABASES\n.\n\n\nExample\n\n\ndefault_database\ndefault\n/default_database\n\n\n\n\n\n\n\n\ndefault_profile\n\n\nDefault settings profile.\n\n\nSettings profiles are located in the file specified in the parameter \nuser_config\n.\n\n\nExample\n\n\ndefault_profile\ndefault\n/default_profile\n\n\n\n\n\n\n\n\ndictionaries_config\n\n\nThe path to the config file for external dictionaries.\n\n\nPath:\n\n\n\n\nSpecify the absolute path or the path relative to the server config file.\n\n\nThe path can contain wildcards * and ?.\n\n\n\n\nSee also \"\nExternal dictionaries\n\".\n\n\nExample\n\n\ndictionaries_config\n*_dictionary.xml\n/dictionaries_config\n\n\n\n\n\n\n\n\ndictionaries_lazy_load\n\n\nLazy loading of dictionaries.\n\n\nIf \ntrue\n, then each dictionary is created on first use. If dictionary creation failed, the function that was using the dictionary throws an exception.\n\n\nIf \nfalse\n, all dictionaries are created when the server starts, and if there is an error, the server shuts down.\n\n\nThe default is \ntrue\n.\n\n\nExample\n\n\ndictionaries_lazy_load\ntrue\n/dictionaries_lazy_load\n\n\n\n\n\n\n\n\nformat_schema_path\n\n\nThe path to the directory with the schemes for the input data, such as schemas for the \nCapnProto\n format.\n\n\nExample\n\n\n \n!-- Directory containing schema files for various input formats. --\n\n \nformat_schema_path\nformat_schemas/\n/format_schema_path\n\n\n\n\n\n\n\n\ngraphite\n\n\nSending data to \nGraphite\n.\n\n\nSettings:\n\n\n\n\nhost \u2013 The Graphite server.\n\n\nport \u2013 The port on the Graphite server.\n\n\ninterval \u2013 The interval for sending, in seconds.\n\n\ntimeout \u2013 The timeout for sending data, in seconds.\n\n\nroot_path \u2013 Prefix for keys.\n\n\nmetrics \u2013 Sending data from a :ref:\nsystem_tables-system.metrics\n table.\n\n\nevents \u2013 Sending data from a :ref:\nsystem_tables-system.events\n table.\n\n\nasynchronous_metrics \u2013 Sending data from a :ref:\nsystem_tables-system.asynchronous_metrics\n table.\n\n\n\n\nYou can configure multiple \ngraphite\n clauses. For instance, you can use this for sending different data at different intervals.\n\n\nExample\n\n\ngraphite\n\n \nhost\nlocalhost\n/host\n\n \nport\n42000\n/port\n\n \ntimeout\n0.1\n/timeout\n\n \ninterval\n60\n/interval\n\n \nroot_path\none_min\n/root_path\n\n \nmetrics\ntrue\n/metrics\n\n \nevents\ntrue\n/events\n\n \nasynchronous_metrics\ntrue\n/asynchronous_metrics\n\n\n/graphite\n\n\n\n\n\n\n\n\ngraphite_rollup\n\n\nSettings for thinning data for Graphite.\n\n\nFor more information, see \nGraphiteMergeTree\n.\n\n\nExample\n\n\ngraphite_rollup_example\n\n \ndefault\n\n \nfunction\nmax\n/function\n\n \nretention\n\n \nage\n0\n/age\n\n \nprecision\n60\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n3600\n/age\n\n \nprecision\n300\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n86400\n/age\n\n \nprecision\n3600\n/precision\n\n \n/retention\n\n \n/default\n\n\n/graphite_rollup_example\n\n\n\n\n\n\n\n\nhttp_port/https_port\n\n\nThe port for connecting to the server over HTTP(s).\n\n\nIf \nhttps_port\n is specified, \nopenSSL\n must be configured.\n\n\nIf \nhttp_port\n is specified, the openSSL configuration is ignored even if it is set.\n\n\nExample\n\n\nhttps\n0000\n/https\n\n\n\n\n\n\n\n\nhttp_server_default_response\n\n\nThe page that is shown by default when you access the ClickHouse HTTP(s) server.\n\n\nExample\n\n\nOpens \nhttps://tabix.io/\n when accessing \nhttp://localhost: http_port\n.\n\n\nhttp_server_default_response\n\n \n![CDATA[\nhtml ng-app=\nSMI2\nhead\nbase href=\nhttp://ui.tabix.io/\n/head\nbody\ndiv ui-view=\n class=\ncontent-ui\n/div\nscript src=\nhttp://loader.tabix.io/master.js\n/script\n/body\n/html\n]]\n\n\n/http_server_default_response\n\n\n\n\n\n\n\n\ninclude_from\n\n\nThe path to the file with substitutions.\n\n\nFor more information, see the section \"\nConfiguration files\n\".\n\n\nExample\n\n\ninclude_from\n/etc/metrica.xml\n/include_from\n\n\n\n\n\n\n\n\ninterserver_http_port\n\n\nPort for exchanging data between ClickHouse servers.\n\n\nExample\n\n\ninterserver_http_port\n9009\n/interserver_http_port\n\n\n\n\n\n\n\n\ninterserver_http_host\n\n\nThe host name that can be used by other servers to access this server.\n\n\nIf omitted, it is defined in the same way as the \nhostname-f\n command.\n\n\nUseful for breaking away from a specific network interface.\n\n\nExample\n\n\ninterserver_http_host\nexample.yandex.ru\n/interserver_http_host\n\n\n\n\n\n\n\n\nkeep_alive_timeout\n\n\nThe number of milliseconds that ClickHouse waits for incoming requests before closing the connection.\n\n\nExample\n\n\nkeep_alive_timeout\n3\n/keep_alive_timeout\n\n\n\n\n\n\n\n\nlisten_host\n\n\nRestriction on hosts that requests can come from. If you want the server to answer all of them, specify \n::\n.\n\n\nExamples:\n\n\nlisten_host\n::1\n/listen_host\n\n\nlisten_host\n127.0.0.1\n/listen_host\n\n\n\n\n\n\n\n\nlogger\n\n\nLogging settings.\n\n\nKeys:\n\n\n\n\nlevel \u2013 Logging level. Acceptable values: \ntrace\n, \ndebug\n, \ninformation\n, \nwarning\n, \nerror\n.\n\n\nlog \u2013 The log file. Contains all the entries according to \nlevel\n.\n\n\nerrorlog \u2013 Error log file.\n\n\nsize \u2013 Size of the file. Applies to \nlog\nand\nerrorlog\n. Once the file reaches \nsize\n, ClickHouse archives and renames it, and creates a new log file in its place.\n\n\ncount \u2013 The number of archived log files that ClickHouse stores.\n\n\n\n\nExample\n\n\nlogger\n\n \nlevel\ntrace\n/level\n\n \nlog\n/var/log/clickhouse-server/clickhouse-server.log\n/log\n\n \nerrorlog\n/var/log/clickhouse-server/clickhouse-server.err.log\n/errorlog\n\n \nsize\n1000M\n/size\n\n \ncount\n10\n/count\n\n\n/logger\n\n\n\n\n\n\n\n\nmacros\n\n\nParameter substitutions for replicated tables.\n\n\nCan be omitted if replicated tables are not used.\n\n\nFor more information, see the section \"\nCreating replicated tables\n\".\n\n\nExample\n\n\nmacros\n \nincl=\nmacros\n \noptional=\ntrue\n \n/\n\n\n\n\n\n\n\n\nmark_cache_size\n\n\nApproximate size (in bytes) of the cache of \"marks\" used by \nMergeTree\n engines.\n\n\nThe cache is shared for the server and memory is allocated as needed. The cache size must be at least 5368709120.\n\n\nExample\n\n\nmark_cache_size\n5368709120\n/mark_cache_size\n\n\n\n\n\n\n\n\nmax_concurrent_queries\n\n\nThe maximum number of simultaneously processed requests.\n\n\nExample\n\n\nmax_concurrent_queries\n100\n/max_concurrent_queries\n\n\n\n\n\n\n\n\nmax_connections\n\n\nThe maximum number of inbound connections.\n\n\nExample\n\n\nmax_connections\n4096\n/max_connections\n\n\n\n\n\n\n\n\nmax_open_files\n\n\nThe maximum number of open files.\n\n\nBy default: \nmaximum\n.\n\n\nWe recommend using this option in Mac OS X, since the \ngetrlimit()\n function returns an incorrect value.\n\n\nExample\n\n\nmax_open_files\n262144\n/max_open_files\n\n\n\n\n\n\n\n\nmax_table_size_to_drop\n\n\nRestriction on deleting tables.\n\n\nIf the size of a \nMergeTree\n type table exceeds \nmax_table_size_to_drop\n (in bytes), you can't delete it using a DROP query.\n\n\nIf you still need to delete the table without restarting the ClickHouse server, create the \nclickhouse-path\n/flags/force_drop_table\n file and run the DROP query.\n\n\nDefault value: 50 GB.\n\n\nThe value 0 means that you can delete all tables without any restrictions.\n\n\nExample\n\n\nmax_table_size_to_drop\n0\n/max_table_size_to_drop\n\n\n\n\n\n\n\n\nmerge_tree\n\n\nFine tuning for tables in the \n MergeTree\n family.\n\n\nFor more information, see the MergeTreeSettings.h header file.\n\n\nExample\n\n\nmerge_tree\n\n \nmax_suspicious_broken_parts\n5\n/max_suspicious_broken_parts\n\n\n/merge_tree\n\n\n\n\n\n\n\n\nopenSSL\n\n\nSSL client/server configuration.\n\n\nSupport for SSL is provided by the \nlibpoco\n library. The interface is described in the file \nSSLManager.h\n\n\nKeys for server/client settings:\n\n\n\n\nprivateKeyFile \u2013 The path to the file with the secret key of the PEM certificate. The file may contain a key and certificate at the same time.\n\n\ncertificateFile \u2013 The path to the client/server certificate file in PEM format. You can omit it if \nprivateKeyFile\n contains the certificate.\n\n\ncaConfig \u2013 The path to the file or directory that contains trusted root certificates.\n\n\nverificationMode \u2013 The method for checking the node's certificates. Details are in the description of the \nContext\n class. Possible values: \nnone\n, \nrelaxed\n, \nstrict\n, \nonce\n.\n\n\nverificationDepth \u2013 The maximum length of the verification chain. Verification will fail if the certificate chain length exceeds the set value.\n\n\nloadDefaultCAFile \u2013 Indicates that built-in CA certificates for OpenSSL will be used. Acceptable values: \ntrue\n, \nfalse\n. |\n\n\ncipherList \u2013 Supported OpenSSL encryptions. For example: \nALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH\n.\n\n\ncacheSessions \u2013 Enables or disables caching sessions. Must be used in combination with \nsessionIdContext\n. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nsessionIdContext \u2013 A unique set of random characters that the server appends to each generated identifier. The length of the string must not exceed \nSSL_MAX_SSL_SESSION_ID_LENGTH\n. This parameter is always recommended, since it helps avoid problems both if the server caches the session and if the client requested caching. Default value: \n${application.name}\n.\n\n\nsessionCacheSize \u2013 The maximum number of sessions that the server caches. Default value: 1024*20. 0 \u2013 Unlimited sessions.\n\n\nsessionTimeout \u2013 Time for caching the session on the server.\n\n\nextendedVerification \u2013 Automatically extended verification of certificates after the session ends. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nrequireTLSv1 \u2013 Require a TLSv1 connection. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nrequireTLSv1_1 \u2013 Require a TLSv1.1 connection. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nrequireTLSv1 \u2013 Require a TLSv1.2 connection. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nfips \u2013 Activates OpenSSL FIPS mode. Supported if the library's OpenSSL version supports FIPS.\n\n\nprivateKeyPassphraseHandler \u2013 Class (PrivateKeyPassphraseHandler subclass) that requests the passphrase for accessing the private key. For example: \nprivateKeyPassphraseHandler\n, \nname\nKeyFileHandler\n/name\n, \noptions\npassword\ntest\n/password\n/options\n, \n/privateKeyPassphraseHandler\n.\n\n\ninvalidCertificateHandler \u2013 Class (subclass of CertificateHandler) for verifying invalid certificates. For example: \ninvalidCertificateHandler\n \nname\nConsoleCertificateHandler\n/name\n \n/invalidCertificateHandler\n .\n\n\ndisableProtocols \u2013 Protocols that are not allowed to use.\n\n\npreferServerCiphers \u2013 Preferred server ciphers on the client.\n\n\n\n\nExample of settings:\n\n\nopenSSL\n\n \nserver\n\n \n!-- openssl req -subj \n/CN=localhost\n -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout /etc/clickhouse-server/server.key -out /etc/clickhouse-server/server.crt --\n\n \ncertificateFile\n/etc/clickhouse-server/server.crt\n/certificateFile\n\n \nprivateKeyFile\n/etc/clickhouse-server/server.key\n/privateKeyFile\n\n \n!-- openssl dhparam -out /etc/clickhouse-server/dhparam.pem 4096 --\n\n \ndhParamsFile\n/etc/clickhouse-server/dhparam.pem\n/dhParamsFile\n\n \nverificationMode\nnone\n/verificationMode\n\n \nloadDefaultCAFile\ntrue\n/loadDefaultCAFile\n\n \ncacheSessions\ntrue\n/cacheSessions\n\n \ndisableProtocols\nsslv2,sslv3\n/disableProtocols\n\n \npreferServerCiphers\ntrue\n/preferServerCiphers\n\n \n/server\n\n \nclient\n\n \nloadDefaultCAFile\ntrue\n/loadDefaultCAFile\n\n \ncacheSessions\ntrue\n/cacheSessions\n\n \ndisableProtocols\nsslv2,sslv3\n/disableProtocols\n\n \npreferServerCiphers\ntrue\n/preferServerCiphers\n\n \n!-- Use for self-signed: \nverificationMode\nnone\n/verificationMode\n --\n\n \ninvalidCertificateHandler\n\n \n!-- Use for self-signed: \nname\nAcceptCertificateHandler\n/name\n --\n\n \nname\nRejectCertificateHandler\n/name\n\n \n/invalidCertificateHandler\n\n \n/client\n\n\n/openSSL\n\n\n\n\n\n\n\n\npart_log\n\n\nLogging events that are associated with \nMergeTree\n data. For instance, adding or merging data. You can use the log to simulate merge algorithms and compare their characteristics. You can visualize the merge process.\n\n\nQueries are logged in the ClickHouse table, not in a separate file.\n\n\nColumns in the log:\n\n\n\n\nevent_time \u2013 Date of the event.\n\n\nduration_ms \u2013 Duration of the event.\n\n\nevent_type \u2013 Type of event. 1 \u2013 new data part; 2 \u2013 merge result; 3 \u2013 data part downloaded from replica; 4 \u2013 data part deleted.\n\n\ndatabase_name \u2013 The name of the database.\n\n\ntable_name \u2013 Name of the table.\n\n\npart_name \u2013 Name of the data part.\n\n\nsize_in_bytes \u2013 Size of the data part in bytes.\n\n\nmerged_from \u2013 An array of names of data parts that make up the merge (also used when downloading a merged part).\n\n\nmerge_time_ms \u2013 Time spent on the merge.\n\n\n\n\nUse the following parameters to configure logging:\n\n\n\n\ndatabase \u2013 Name of the database.\n\n\ntable \u2013 Name of the table.\n\n\npartition_by \u2013 Sets a \ncustom partitioning key\n.\n\n\nflush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk.\n\n\n\n\nExample\n\n\npart_log\n\n \ndatabase\nsystem\n/database\n\n \ntable\npart_log\n/table\n\n \npartition_by\ntoMonday(event_date)\n/partition_by\n\n \nflush_interval_milliseconds\n7500\n/flush_interval_milliseconds\n\n\n/part_log\n\n\n\n\n\n\n\n\npath\n\n\nThe path to the directory containing data.\n\n\n\n\nThe end slash is mandatory.\n\n\n\n\n\nExample\n\n\npath\n/var/lib/clickhouse/\n/path\n\n\n\n\n\n\n\n\nquery_log\n\n\nSetting for logging queries received with the \nlog_queries=1\n setting.\n\n\nQueries are logged in the ClickHouse table, not in a separate file.\n\n\nUse the following parameters to configure logging:\n\n\n\n\ndatabase \u2013 Name of the database.\n\n\ntable \u2013 Name of the table.\n\n\npartition_by \u2013 Sets a \ncustom partitioning key\n.\n\n\nflush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk.\n\n\n\n\nIf the table doesn't exist, ClickHouse will create it. If the structure of the query log changed when the ClickHouse server was updated, the table with the old structure is renamed, and a new table is created automatically.\n\n\nExample\n\n\nquery_log\n\n \ndatabase\nsystem\n/database\n\n \ntable\nquery_log\n/table\n\n \npartition_by\ntoMonday(event_date)\n/partition_by\n\n \nflush_interval_milliseconds\n7500\n/flush_interval_milliseconds\n\n\n/query_log\n\n\n\n\n\n\n\n\nremote_servers\n\n\nConfiguration of clusters used by the Distributed table engine.\n\n\nFor more information, see the section \"\nTable engines/Distributed\n\".\n\n\nExample\n\n\nremote_servers\n \nincl=\nclickhouse_remote_servers\n \n/\n\n\n\n\n\n\nFor the value of the \nincl\n attribute, see the section \"\nConfiguration files\n\".\n\n\n\n\ntimezone\n\n\nThe server's time zone.\n\n\nSpecified as an IANA identifier for the UTC time zone or geographic location (for example, Africa/Abidjan).\n\n\nThe time zone is necessary for conversions between String and DateTime formats when DateTime fields are output to text format (printed on the screen or in a file), and when getting DateTime from a string. In addition, the time zone is used in functions that work with the time and date if they didn't receive the time zone in the input parameters.\n\n\nExample\n\n\ntimezone\nEurope/Moscow\n/timezone\n\n\n\n\n\n\n\n\ntcp_port\n\n\nPort for communicating with clients over the TCP protocol.\n\n\nExample\n\n\ntcp_port\n9000\n/tcp_port\n\n\n\n\n\n\n\n\ntmp_path\n\n\nPath to temporary data for processing large queries.\n\n\n\n\nThe end slash is mandatory.\n\n\n\n\n\nExample\n\n\ntmp_path\n/var/lib/clickhouse/tmp/\n/tmp_path\n\n\n\n\n\n\n\n\nuncompressed_cache_size\n\n\nCache size (in bytes) for uncompressed data used by table engines from the \nMergeTree\n family.\n\n\nThere is one shared cache for the server. Memory is allocated on demand. The cache is used if the option \nuse_uncompressed_cache\n is enabled.\n\n\nThe uncompressed cache is advantageous for very short queries in individual cases.\n\n\nExample\n\n\nuncompressed_cache_size\n8589934592\n/uncompressed_cache_size\n\n\n\n\n\n\n\n\nusers_config\n\n\nPath to the file that contains:\n\n\n\n\nUser configurations.\n\n\nAccess rights.\n\n\nSettings profiles.\n\n\nQuota settings.\n\n\n\n\nExample\n\n\nusers_config\nusers.xml\n/users_config\n\n\n\n\n\n\n\n\nzookeeper\n\n\nConfiguration of ZooKeeper servers.\n\n\nClickHouse uses ZooKeeper for storing replica metadata when using replicated tables.\n\n\nThis parameter can be omitted if replicated tables are not used.\n\n\nFor more information, see the section \"\nReplication\n\".\n\n\nExample\n\n\nzookeeper\n \nincl=\nzookeeper-servers\n \noptional=\ntrue\n \n/\n\n\n\n\n\n\n\n\nSettings\n\n\nThere are multiple ways to make all the settings described below.\nSettings are configured in layers, so each subsequent layer redefines the previous settings.\n\n\nWays to configure settings, in order of priority:\n\n\n\n\nSettings in the server config file.\n\n\n\n\nSettings from user profiles.\n\n\n\n\nSession settings.\n\n\n\n\nSend \nSET setting=value\n from the ClickHouse console client in interactive mode.\nSimilarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to specify the \nsession_id\n HTTP parameter.\n\n\n\n\nFor a query.\n\n\nWhen starting the ClickHouse console client in non-interactive mode, set the startup parameter \n--setting=value\n.\n\n\nWhen using the HTTP API, pass CGI parameters (\nURL?setting_1=value\nsetting_2=value...\n).\n\n\n\n\nSettings that can only be made in the server config file are not covered in this section.\n\n\nRestrictions on query complexity\n\n\nRestrictions on query complexity are part of the settings.\nThey are used in order to provide safer execution from the user interface.\nAlmost all the restrictions only apply to SELECTs.For distributed query processing, restrictions are applied on each server separately.\n\n\nRestrictions on the \"maximum amount of something\" can take the value 0, which means \"unrestricted\".\nMost restrictions also have an 'overflow_mode' setting, meaning what to do when the limit is exceeded.\nIt can take one of two values: \nthrow\n or \nbreak\n. Restrictions on aggregation (group_by_overflow_mode) also have the value \nany\n.\n\n\nthrow\n \u2013 Throw an exception (default).\n\n\nbreak\n \u2013 Stop executing the query and return the partial result, as if the source data ran out.\n\n\nany (only for group_by_overflow_mode)\n \u2013 Continuing aggregation for the keys that got into the set, but don't add new keys to the set.\n\n\n\n\nreadonly\n\n\nWith a value of 0, you can execute any queries.\nWith a value of 1, you can only execute read requests (such as SELECT and SHOW). Requests for writing and changing settings (INSERT, SET) are prohibited.\nWith a value of 2, you can process read queries (SELECT, SHOW) and change settings (SET).\n\n\nAfter enabling readonly mode, you can't disable it in the current session.\n\n\nWhen using the GET method in the HTTP interface, 'readonly = 1' is set automatically. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.\n\n\n\n\nmax_memory_usage\n\n\nThe maximum amount of RAM to use for running a query on a single server.\n\n\nIn the default configuration file, the maximum is 10 GB.\n\n\nThe setting doesn't consider the volume of available memory or the total volume of memory on the machine.\nThe restriction applies to a single query within a single server.\nYou can use \nSHOW PROCESSLIST\n to see the current memory consumption for each query.\nIn addition, the peak memory consumption is tracked for each query and written to the log.\n\n\nMemory usage is not monitored for the states of certain aggregate functions.\n\n\nMemory usage is not fully tracked for states of the aggregate functions \nmin\n, \nmax\n, \nany\n, \nanyLast\n, \nargMin\n, \nargMax\n from \nString\n and \nArray\n arguments.\n\n\nMemory consumption is also restricted by the parameters \nmax_memory_usage_for_user\n and \nmax_memory_usage_for_all_queries\n.\n\n\nmax_memory_usage_for_user\n\n\nThe maximum amount of RAM to use for running a user's queries on a single server.\n\n\nDefault values are defined in \nSettings.h\n. By default, the amount is not restricted (\nmax_memory_usage_for_user = 0\n).\n\n\nSee also the description of \nmax_memory_usage\n.\n\n\nmax_memory_usage_for_all_queries\n\n\nThe maximum amount of RAM to use for running all queries on a single server.\n\n\nDefault values are defined in \nSettings.h\n. By default, the amount is not restricted (\nmax_memory_usage_for_all_queries = 0\n).\n\n\nSee also the description of \nmax_memory_usage\n.\n\n\nmax_rows_to_read\n\n\nThe following restrictions can be checked on each block (instead of on each row). That is, the restrictions can be broken a little.\nWhen running a query in multiple threads, the following restrictions apply to each thread separately.\n\n\nMaximum number of rows that can be read from a table when running a query.\n\n\nmax_bytes_to_read\n\n\nMaximum number of bytes (uncompressed data) that can be read from a table when running a query.\n\n\nread_overflow_mode\n\n\nWhat to do when the volume of data read exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_rows_to_group_by\n\n\nMaximum number of unique keys received from aggregation. This setting lets you limit memory consumption when aggregating.\n\n\ngroup_by_overflow_mode\n\n\nWhat to do when the number of unique keys for aggregation exceeds the limit: 'throw', 'break', or 'any'. By default, throw.\nUsing the 'any' value lets you run an approximation of GROUP BY. The quality of this approximation depends on the statistical nature of the data.\n\n\nmax_rows_to_sort\n\n\nMaximum number of rows before sorting. This allows you to limit memory consumption when sorting.\n\n\nmax_bytes_to_sort\n\n\nMaximum number of bytes before sorting.\n\n\nsort_overflow_mode\n\n\nWhat to do if the number of rows received before sorting exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_result_rows\n\n\nLimit on the number of rows in the result. Also checked for subqueries, and on remote servers when running parts of a distributed query.\n\n\nmax_result_bytes\n\n\nLimit on the number of bytes in the result. The same as the previous setting.\n\n\nresult_overflow_mode\n\n\nWhat to do if the volume of the result exceeds one of the limits: 'throw' or 'break'. By default, throw.\nUsing 'break' is similar to using LIMIT.\n\n\nmax_execution_time\n\n\nMaximum query execution time in seconds.\nAt this time, it is not checked for one of the sorting stages, or when merging and finalizing aggregate functions.\n\n\ntimeout_overflow_mode\n\n\nWhat to do if the query is run longer than 'max_execution_time': 'throw' or 'break'. By default, throw.\n\n\nmin_execution_speed\n\n\nMinimal execution speed in rows per second. Checked on every data block when 'timeout_before_checking_execution_speed' expires. If the execution speed is lower, an exception is thrown.\n\n\ntimeout_before_checking_execution_speed\n\n\nChecks that execution speed is not too slow (no less than 'min_execution_speed'), after the specified time in seconds has expired.\n\n\nmax_columns_to_read\n\n\nMaximum number of columns that can be read from a table in a single query. If a query requires reading a greater number of columns, it throws an exception.\n\n\nmax_temporary_columns\n\n\nMaximum number of temporary columns that must be kept in RAM at the same time when running a query, including constant columns. If there are more temporary columns than this, it throws an exception.\n\n\nmax_temporary_non_const_columns\n\n\nThe same thing as 'max_temporary_columns', but without counting constant columns.\nNote that constant columns are formed fairly often when running a query, but they require approximately zero computing resources.\n\n\nmax_subquery_depth\n\n\nMaximum nesting depth of subqueries. If subqueries are deeper, an exception is thrown. By default, 100.\n\n\nmax_pipeline_depth\n\n\nMaximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000.\n\n\nmax_ast_depth\n\n\nMaximum nesting depth of a query syntactic tree. If exceeded, an exception is thrown.\nAt this time, it isn't checked during parsing, but only after parsing the query. That is, a syntactic tree that is too deep can be created during parsing, but the query will fail. By default, 1000.\n\n\nmax_ast_elements\n\n\nMaximum number of elements in a query syntactic tree. If exceeded, an exception is thrown.\nIn the same way as the previous setting, it is checked only after parsing the query. By default, 10,000.\n\n\nmax_rows_in_set\n\n\nMaximum number of rows for a data set in the IN clause created from a subquery.\n\n\nmax_bytes_in_set\n\n\nMaximum number of bytes (uncompressed data) used by a set in the IN clause created from a subquery.\n\n\nset_overflow_mode\n\n\nWhat to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_rows_in_distinct\n\n\nMaximum number of different rows when using DISTINCT.\n\n\nmax_bytes_in_distinct\n\n\nMaximum number of bytes used by a hash table when using DISTINCT.\n\n\ndistinct_overflow_mode\n\n\nWhat to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_rows_to_transfer\n\n\nMaximum number of rows that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.\n\n\nmax_bytes_to_transfer\n\n\nMaximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.\n\n\ntransfer_overflow_mode\n\n\nWhat to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nSettings\n\n\n\n\ndistributed_product_mode\n\n\nChanges the behavior of \ndistributed subqueries\n, i.e. in cases when the query contains the product of distributed tables.\n\n\nClickHouse applies the configuration if the subqueries on any level have a distributed table that exists on the local server and has more than one shard.\n\n\nRestrictions:\n\n\n\n\nOnly applied for IN and JOIN subqueries.\n\n\nUsed only if a distributed table is used in the FROM clause.\n\n\nNot used for a table-valued \n remote\n function.\n\n\n\n\nThe possible values \u200b\u200bare:\n\n\n\n\nfallback_to_stale_replicas_for_distributed_queries\n\n\nForces a query to an out-of-date replica if updated data is not available. See \"\nReplication\n\".\n\n\nClickHouse selects the most relevant from the outdated replicas of the table.\n\n\nUsed when performing \nSELECT\n from a distributed table that points to replicated tables.\n\n\nBy default, 1 (enabled).\n\n\n\n\nforce_index_by_date\n\n\nDisables query execution if the index can't be used by date.\n\n\nWorks with tables in the MergeTree family.\n\n\nIf \nforce_index_by_date=1\n, ClickHouse checks whether the query has a date key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For example, the condition \nDate != ' 2000-01-01 '\n is acceptable even when it matches all the data in the table (i.e., running the query requires a full scan). For more information about ranges of data in MergeTree tables, see \"\nMergeTree\n\".\n\n\n\n\nforce_primary_key\n\n\nDisables query execution if indexing by the primary key is not possible.\n\n\nWorks with tables in the MergeTree family.\n\n\nIf \nforce_primary_key=1\n, ClickHouse checks to see if the query has a primary key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For more information about data ranges in MergeTree tables, see \"\nMergeTree\n\".\n\n\n\n\nfsync_metadata\n\n\nEnable or disable fsync when writing .sql files. By default, it is enabled.\n\n\nIt makes sense to disable it if the server has millions of tiny table chunks that are constantly being created and destroyed.\n\n\ninput_format_allow_errors_num\n\n\nSets the maximum number of acceptable errors when reading from text formats (CSV, TSV, etc.).\n\n\nThe default value is 0.\n\n\nAlways pair it with \ninput_format_allow_errors_ratio\n. To skip errors, both settings must be greater than 0.\n\n\nIf an error occurred while reading rows but the error counter is still less than \ninput_format_allow_errors_num\n, ClickHouse ignores the row and moves on to the next one.\n\n\nIf \ninput_format_allow_errors_num\nis exceeded, ClickHouse throws an exception.\n\n\ninput_format_allow_errors_ratio\n\n\nSets the maximum percentage of errors allowed when reading from text formats (CSV, TSV, etc.).\nThe percentage of errors is set as a floating-point number between 0 and 1.\n\n\nThe default value is 0.\n\n\nAlways pair it with \ninput_format_allow_errors_num\n. To skip errors, both settings must be greater than 0.\n\n\nIf an error occurred while reading rows but the error counter is still less than \ninput_format_allow_errors_ratio\n, ClickHouse ignores the row and moves on to the next one.\n\n\nIf \ninput_format_allow_errors_ratio\n is exceeded, ClickHouse throws an exception.\n\n\nmax_block_size\n\n\nIn ClickHouse, data is processed by blocks (sets of column parts). The internal processing cycles for a single block are efficient enough, but there are noticeable expenditures on each block. \nmax_block_size\n is a recommendation for what size of block (in number of rows) to load from tables. The block size shouldn't be too small, so that the expenditures on each block are still noticeable, but not too large, so that the query with LIMIT that is completed after the first block is processed quickly, so that too much memory isn't consumed when extracting a large number of columns in multiple threads, and so that at least some cache locality is preserved.\n\n\nBy default, 65,536.\n\n\nBlocks the size of \nmax_block_size\n are not always loaded from the table. If it is obvious that less data needs to be retrieved, a smaller block is processed.\n\n\npreferred_block_size_bytes\n\n\nUsed for the same purpose as \nmax_block_size\n, but it sets the recommended block size in bytes by adapting it to the number of rows in the block.\nHowever, the block size cannot be more than \nmax_block_size\n rows.\nDisabled by default (set to 0). It only works when reading from MergeTree engines.\n\n\n\n\nlog_queries\n\n\nSetting up query the logging.\n\n\nQueries sent to ClickHouse with this setup are logged according to the rules in the \nquery_log\n server configuration parameter.\n\n\nExample\n:\n\n\nlog_queries=1\n\n\n\n\n\n\n\nmax_insert_block_size\n\n\nThe size of blocks to form for insertion into a table.\nThis setting only applies in cases when the server forms the blocks.\nFor example, for an INSERT via the HTTP interface, the server parses the data format and forms blocks of the specified size.\nBut when using clickhouse-client, the client parses the data itself, and the 'max_insert_block_size' setting on the server doesn't affect the size of the inserted blocks.\nThe setting also doesn't have a purpose when using INSERT SELECT, since data is inserted using the same blocks that are formed after SELECT.\n\n\nBy default, it is 1,048,576.\n\n\nThis is slightly more than \nmax_block_size\n. The reason for this is because certain table engines (\n*MergeTree\n) form a data part on the disk for each inserted block, which is a fairly large entity. Similarly, \n*MergeTree\n tables sort data during insertion, and a large enough block size allows sorting more data in RAM.\n\n\n\n\nmax_replica_delay_for_distributed_queries\n\n\nDisables lagging replicas for distributed queries. See \"\nReplication\n\".\n\n\nSets the time in seconds. If a replica lags more than the set value, this replica is not used.\n\n\nDefault value: 0 (off).\n\n\nUsed when performing \nSELECT\n from a distributed table that points to replicated tables.\n\n\nmax_threads\n\n\nThe maximum number of query processing threads\n\n\n\n\nexcluding threads for retrieving data from remote servers (see the 'max_distributed_connections' parameter).\n\n\n\n\nThis parameter applies to threads that perform the same stages of the query processing pipeline in parallel.\nFor example, if reading from a table, evaluating expressions with functions, filtering with WHERE and pre-aggregating for GROUP BY can all be done in parallel using at least 'max_threads' number of threads, then 'max_threads' are used.\n\n\nBy default, 8.\n\n\nIf less than one SELECT query is normally run on a server at a time, set this parameter to a value slightly less than the actual number of processor cores.\n\n\nFor queries that are completed quickly because of a LIMIT, you can set a lower 'max_threads'. For example, if the necessary number of entries are located in every block and max_threads = 8, 8 blocks are retrieved, although it would have been enough to read just one.\n\n\nThe smaller the \nmax_threads\n value, the less memory is consumed.\n\n\nmax_compress_block_size\n\n\nThe maximum size of blocks of uncompressed data before compressing for writing to a table. By default, 1,048,576 (1 MiB). If the size is reduced, the compression rate is significantly reduced, the compression and decompression speed increases slightly due to cache locality, and memory consumption is reduced. There usually isn't any reason to change this setting.\n\n\nDon't confuse blocks for compression (a chunk of memory consisting of bytes) and blocks for query processing (a set of rows from a table).\n\n\nmin_compress_block_size\n\n\nFor \nMergeTree\n\" tables. In order to reduce latency when processing queries, a block is compressed when writing the next mark if its size is at least 'min_compress_block_size'. By default, 65,536.\n\n\nThe actual size of the block, if the uncompressed data is less than 'max_compress_block_size', is no less than this value and no less than the volume of data for one mark.\n\n\nLet's look at an example. Assume that 'index_granularity' was set to 8192 during table creation.\n\n\nWe are writing a UInt32-type column (4 bytes per value). When writing 8192 rows, the total will be 32 KB of data. Since min_compress_block_size = 65,536, a compressed block will be formed for every two marks.\n\n\nWe are writing a URL column with the String type (average size of 60 bytes per value). When writing 8192 rows, the average will be slightly less than 500 KB of data. Since this is more than 65,536, a compressed block will be formed for each mark. In this case, when reading data from the disk in the range of a single mark, extra data won't be decompressed.\n\n\nThere usually isn't any reason to change this setting.\n\n\nmax_query_size\n\n\nThe maximum part of a query that can be taken to RAM for parsing with the SQL parser.\nThe INSERT query also contains data for INSERT that is processed by a separate stream parser (that consumes O(1) RAM), which is not included in this restriction.\n\n\nThe default is 256 KiB.\n\n\ninteractive_delay\n\n\nThe interval in microseconds for checking whether request execution has been canceled and sending the progress.\n\n\nBy default, 100,000 (check for canceling and send progress ten times per second).\n\n\nconnect_timeout\n\n\nreceive_timeout\n\n\nsend_timeout\n\n\nTimeouts in seconds on the socket used for communicating with the client.\n\n\nBy default, 10, 300, 300.\n\n\npoll_interval\n\n\nLock in a wait loop for the specified number of seconds.\n\n\nBy default, 10.\n\n\nmax_distributed_connections\n\n\nThe maximum number of simultaneous connections with remote servers for distributed processing of a single query to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.\n\n\nBy default, 100.\n\n\nThe following parameters are only used when creating Distributed tables (and when launching a server), so there is no reason to change them at runtime.\n\n\ndistributed_connections_pool_size\n\n\nThe maximum number of simultaneous connections with remote servers for distributed processing of all queries to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.\n\n\nBy default, 128.\n\n\nconnect_timeout_with_failover_ms\n\n\nThe timeout in milliseconds for connecting to a remote server for a Distributed table engine, if the 'shard' and 'replica' sections are used in the cluster definition.\nIf unsuccessful, several attempts are made to connect to various replicas.\n\n\nBy default, 50.\n\n\nconnections_with_failover_max_tries\n\n\nThe maximum number of connection attempts with each replica, for the Distributed table engine.\n\n\nBy default, 3.\n\n\nextremes\n\n\nWhether to count extreme values (the minimums and maximums in columns of a query result). Accepts 0 or 1. By default, 0 (disabled).\nFor more information, see the section \"Extreme values\".\n\n\n\n\nuse_uncompressed_cache\n\n\nWhether to use a cache of uncompressed blocks. Accepts 0 or 1. By default, 0 (disabled).\nThe uncompressed cache (only for tables in the MergeTree family) allows significantly reducing latency and increasing throughput when working with a large number of short queries. Enable this setting for users who send frequent short requests. Also pay attention to the 'uncompressed_cache_size' configuration parameter (only set in the config file) \u2013 the size of uncompressed cache blocks. By default, it is 8 GiB. The uncompressed cache is filled in as needed; the least-used data is automatically deleted.\n\n\nFor queries that read at least a somewhat large volume of data (one million rows or more), the uncompressed cache is disabled automatically in order to save space for truly small queries. So you can keep the 'use_uncompressed_cache' setting always set to 1.\n\n\nreplace_running_query\n\n\nWhen using the HTTP interface, the 'query_id' parameter can be passed. This is any string that serves as the query identifier.\nIf a query from the same user with the same 'query_id' already exists at this time, the behavior depends on the 'replace_running_query' parameter.\n\n\n0\n (default) \u2013 Throw an exception (don't allow the query to run if a query with the same 'query_id' is already running).\n\n\n1\n \u2013 Cancel the old query and start running the new one.\n\n\nYandex.Metrica uses this parameter set to 1 for implementing suggestions for segmentation conditions. After entering the next character, if the old query hasn't finished yet, it should be canceled.\n\n\nschema\n\n\nThis parameter is useful when you are using formats that require a schema definition, such as \nCap'n Proto\n. The value depends on the format.\n\n\n\n\nstream_flush_interval_ms\n\n\nWorks for tables with streaming in the case of a timeout, or when a thread generates\nmax_insert_block_size\n rows.\n\n\nThe default value is 7500.\n\n\nThe smaller the value, the more often data is flushed into the table. Setting the value too low leads to poor performance.\n\n\n\n\nload_balancing\n\n\nWhich replicas (among healthy replicas) to preferably send a query to (on the first attempt) for distributed processing.\n\n\nrandom (default)\n\n\nThe number of errors is counted for each replica. The query is sent to the replica with the fewest errors, and if there are several of these, to any one of them.\nDisadvantages: Server proximity is not accounted for; if the replicas have different data, you will also get different data.\n\n\nnearest_hostname\n\n\nThe number of errors is counted for each replica. Every 5 minutes, the number of errors is integrally divided by 2. Thus, the number of errors is calculated for a recent time with exponential smoothing. If there is one replica with a minimal number of errors (i.e. errors occurred recently on the other replicas), the query is sent to it. If there are multiple replicas with the same minimal number of errors, the query is sent to the replica with a host name that is most similar to the server's host name in the config file (for the number of different characters in identical positions, up to the minimum length of both host names).\n\n\nFor instance, example01-01-1 and example01-01-2.yandex.ru are different in one position, while example01-01-1 and example01-02-2 differ in two places.\nThis method might seem a little stupid, but it doesn't use external data about network topology, and it doesn't compare IP addresses, which would be complicated for our IPv6 addresses.\n\n\nThus, if there are equivalent replicas, the closest one by name is preferred.\nWe can also assume that when sending a query to the same server, in the absence of failures, a distributed query will also go to the same servers. So even if different data is placed on the replicas, the query will return mostly the same results.\n\n\nin_order\n\n\nReplicas are accessed in the same order as they are specified. The number of errors does not matter.\nThis method is appropriate when you know exactly which replica is preferable.\n\n\ntotals_mode\n\n\nHow to calculate TOTALS when HAVING is present, as well as when max_rows_to_group_by and group_by_overflow_mode = 'any' are present.\nSee the section \"WITH TOTALS modifier\".\n\n\ntotals_auto_threshold\n\n\nThe threshold for \ntotals_mode = 'auto'\n.\nSee the section \"WITH TOTALS modifier\".\n\n\ndefault_sample\n\n\nFloating-point number from 0 to 1. By default, 1.\nAllows you to set the default sampling ratio for all SELECT queries.\n(For tables that do not support sampling, it throws an exception.)\nIf set to 1, sampling is not performed by default.\n\n\nmax_parallel_replicas\n\n\nThe maximum number of replicas for each shard when executing a query.\nFor consistency (to get different parts of the same data split), this option only works when the sampling key is set.\nReplica lag is not controlled.\n\n\ncompile\n\n\nEnable compilation of queries. By default, 0 (disabled).\n\n\nCompilation is only used for part of the query-processing pipeline: for the first stage of aggregation (GROUP BY).\nIf this portion of the pipeline was compiled, the query may run faster due to deployment of short cycles and inlining aggregate function calls. The maximum performance improvement (up to four times faster in rare cases) is seen for queries with multiple simple aggregate functions. Typically, the performance gain is insignificant. In very rare cases, it may slow down query execution.\n\n\nmin_count_to_compile\n\n\nHow many times to potentially use a compiled chunk of code before running compilation. By default, 3.\nIf the value is zero, then compilation runs synchronously and the query waits for the end of the compilation process before continuing execution. This can be used for testing; otherwise, use values \u200b\u200bstarting with 1. Compilation normally takes about 5-10 seconds.\nIf the value is 1 or more, compilation occurs asynchronously in a separate thread. The result will be used as soon as it is ready, including by queries that are currently running.\n\n\nCompiled code is required for each different combination of aggregate functions used in the query and the type of keys in the GROUP BY clause.\nThe results of compilation are saved in the build directory in the form of .so files. There is no restriction on the number of compilation results, since they don't use very much space. Old results will be used after server restarts, except in the case of a server upgrade \u2013 in this case, the old results are deleted.\n\n\ninput_format_skip_unknown_fields\n\n\nIf the value is true, running INSERT skips input data from columns with unknown names. Otherwise, this situation will generate an exception.\nIt works for JSONEachRow and TSKV formats.\n\n\noutput_format_json_quote_64bit_integers\n\n\nIf the value is true, integers appear in quotes when using JSON* Int64 and UInt64 formats (for compatibility with most JavaScript implementations); otherwise, integers are output without the quotes.\n\n\n\n\nformat_csv_delimiter\n\n\nThe character to be considered as a delimiter in CSV data. By default, \n,\n.\n\n\nSettings profiles\n\n\nA settings profile is a collection of settings grouped under the same name. Each ClickHouse user has a profile.\nTo apply all the settings in a profile, set \nprofile\n.\n\n\nExample:\n\n\nSetting \nweb\n profile.\n\n\nSET\n \nprofile\n \n=\n \nweb\n\n\n\n\n\n\nSettings profiles are declared in the user config file. This is usually \nusers.xml\n.\n\n\nExample:\n\n\n!-- Settings profiles --\n\n\nprofiles\n\n \n!-- Default settings --\n\n \ndefault\n\n \n!-- The maximum number of threads when running a single query. --\n\n \nmax_threads\n8\n/max_threads\n\n \n/default\n\n\n \n!-- Settings for quries from the user interface --\n\n \nweb\n\n \nmax_rows_to_read\n1000000000\n/max_rows_to_read\n\n \nmax_bytes_to_read\n100000000000\n/max_bytes_to_read\n\n\n \nmax_rows_to_group_by\n1000000\n/max_rows_to_group_by\n\n \ngroup_by_overflow_mode\nany\n/group_by_overflow_mode\n\n\n \nmax_rows_to_sort\n1000000\n/max_rows_to_sort\n\n \nmax_bytes_to_sort\n1000000000\n/max_bytes_to_sort\n\n\n \nmax_result_rows\n100000\n/max_result_rows\n\n \nmax_result_bytes\n100000000\n/max_result_bytes\n\n \nresult_overflow_mode\nbreak\n/result_overflow_mode\n\n\n \nmax_execution_time\n600\n/max_execution_time\n\n \nmin_execution_speed\n1000000\n/min_execution_speed\n\n \ntimeout_before_checking_execution_speed\n15\n/timeout_before_checking_execution_speed\n\n\n \nmax_columns_to_read\n25\n/max_columns_to_read\n\n \nmax_temporary_columns\n100\n/max_temporary_columns\n\n \nmax_temporary_non_const_columns\n50\n/max_temporary_non_const_columns\n\n\n \nmax_subquery_depth\n2\n/max_subquery_depth\n\n \nmax_pipeline_depth\n25\n/max_pipeline_depth\n\n \nmax_ast_depth\n50\n/max_ast_depth\n\n \nmax_ast_elements\n100\n/max_ast_elements\n\n\n \nreadonly\n1\n/readonly\n\n \n/web\n\n\n/profiles\n\n\n\n\n\n\nThe example specifies two profiles: \ndefault\n and \nweb\n. The \ndefault\n profile has a special purpose: it must always be present and is applied when starting the server. In other words, the \ndefault\n profile contains default settings. The \nweb\n profile is a regular profile that can be set using the \nSET\n query or using a URL parameter in an HTTP query.\n\n\nSettings profiles can inherit from each other. To use inheritance, indicate the \nprofile\n setting before the other settings that are listed in the profile.\n\n\nClickHouse utility\n\n\n\n\nclickhouse-local\n \u2014 Allows running SQL queries on data without stopping the ClickHouse server, similar to how \nawk\n does this.\n\n\nclickhouse-copier\n \u2014 Copies (and reshards) data from one cluster to another cluster.\n\n\n\n\n\n\nclickhouse-copier\n\n\nCopies data from the tables in one cluster to tables in another (or the same) cluster.\n\n\nYou can run multiple \nclickhouse-copier\n instances on different servers to perform the same job. ZooKeeper is used for syncing the processes.\n\n\nAfter starting, \nclickhouse-copier\n:\n\n\n\n\nConnects to ZooKeeper and receives:\n\n\nCopying jobs.\n\n\n\n\nThe state of the copying jobs.\n\n\n\n\n\n\nIt performs the jobs.\n\n\n\n\n\n\nEach running process chooses the \"closest\" shard of the source cluster and copies the data into the destination cluster, resharding the data if necessary.\n\n\nclickhouse-copier\n tracks the changes in ZooKeeper and applies them on the fly.\n\n\nTo reduce network traffic, we recommend running \nclickhouse-copier\n on the same server where the source data is located.\n\n\nRunning clickhouse-copier\n\n\nThe utility should be run manually:\n\n\nclickhouse-copier copier --daemon --config zookeeper.xml --task-path /task/path --base-dir /path/to/dir\n\n\n\n\n\nParameters:\n\n\n\n\ndaemon\n \u2014 Starts \nclickhouse-copier\n in daemon mode.\n\n\nconfig\n \u2014 The path to the \nzookeeper.xml\n file with the parameters for the connection to ZooKeeper.\n\n\ntask-path\n \u2014 The path to the ZooKeeper node. This node is used for syncing \nclickhouse-copier\n processes and storing tasks. Tasks are stored in \n$task-path/description\n.\n\n\nbase-dir\n \u2014 The path to logs and auxiliary files. When it starts, \nclickhouse-copier\n creates \nclickhouse-copier_YYYYMMHHSS_\nPID\n subdirectories in \n$base-dir\n. If this parameter is omitted, the directories are created in the directory where \nclickhouse-copier\n was launched.\n\n\n\n\nFormat of zookeeper.xml\n\n\nyandex\n\n \nzookeeper\n\n \nnode\n \nindex=\n1\n\n \nhost\n127.0.0.1\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n \n/zookeeper\n\n\n/yandex\n\n\n\n\n\n\nConfiguration of copying tasks\n\n\nyandex\n\n \n!-- Configuration of clusters as in an ordinary server config --\n\n \nremote_servers\n\n \nsource_cluster\n\n \nshard\n\n \ninternal_replication\nfalse\n/internal_replication\n\n \nreplica\n\n \nhost\n127.0.0.1\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \n/shard\n\n ...\n \n/source_cluster\n\n\n \ndestination_cluster\n\n ...\n \n/destination_cluster\n\n \n/remote_servers\n\n\n \n!-- How many simultaneously active workers are possible. If you run more workers superfluous workers will sleep. --\n\n \nmax_workers\n2\n/max_workers\n\n\n \n!-- Setting used to fetch (pull) data from source cluster tables --\n\n \nsettings_pull\n\n \nreadonly\n1\n/readonly\n\n \n/settings_pull\n\n\n \n!-- Setting used to insert (push) data to destination cluster tables --\n\n \nsettings_push\n\n \nreadonly\n0\n/readonly\n\n \n/settings_push\n\n\n \n!-- Common setting for fetch (pull) and insert (push) operations. The copier process context also uses it.\n\n\n They are overlaid by \nsettings_pull/\n and \nsettings_push/\n respectively. --\n\n \nsettings\n\n \nconnect_timeout\n3\n/connect_timeout\n\n \n!-- Sync insert is set forcibly, leave it here just in case. --\n\n \ninsert_distributed_sync\n1\n/insert_distributed_sync\n\n \n/settings\n\n\n \n!-- Copying description of tasks.\n\n\n You can specify several table tasks in the same task description (in the same ZooKeeper node), and they will be performed sequentially.\n\n\n --\n\n \ntables\n\n \n!-- A table task that copies one table. --\n\n \ntable_hits\n\n \n!-- Source cluster name (from the \nremote_servers/\n section) and tables in it that should be copied --\n\n \ncluster_pull\nsource_cluster\n/cluster_pull\n\n \ndatabase_pull\ntest\n/database_pull\n\n \ntable_pull\nhits\n/table_pull\n\n\n \n!-- Destination cluster name and tables in which the data should be inserted --\n\n \ncluster_push\ndestination_cluster\n/cluster_push\n\n \ndatabase_push\ntest\n/database_push\n\n \ntable_push\nhits2\n/table_push\n\n\n \n!-- Engine of destination tables.\n\n\n If the destination tables have not been created yet, workers create them using column definitions from source tables and the engine definition from here.\n\n\n\n NOTE: If the first worker starts to insert data and detects that the destination partition is not empty, then the partition will\n\n\n be dropped and refilled. Take this into account if you already have some data in destination tables. You can directly \n\n\n specify partitions that should be copied in \nenabled_partitions/\n. They should be in quoted format like the partition column in the \n\n\n system.parts table.\n\n\n --\n\n \nengine\n\n ENGINE=ReplicatedMergeTree(\n/clickhouse/tables/{cluster}/{shard}/hits2\n, \n{replica}\n)\n PARTITION BY toMonday(date)\n ORDER BY (CounterID, EventDate)\n \n/engine\n\n\n \n!-- Sharding key used to insert data to destination cluster --\n\n \nsharding_key\njumpConsistentHash(intHash64(UserID), 2)\n/sharding_key\n\n\n \n!-- Optional expression that filter data while pull them from source servers --\n\n \nwhere_condition\nCounterID != 0\n/where_condition\n\n\n \n!-- This section specifies partitions that should be copied, other partition will be ignored.\n\n\n Partition names should have the same format as\n\n\n partition column of system.parts table (i.e. a quoted text).\n\n\n Since partition key of source and destination cluster could be different,\n\n\n these partition names specify destination partitions.\n\n\n\n Note: Although this section is optional (if it omitted, all partitions will be copied), \n\n\n it is strongly recommended to specify the partitions explicitly.\n\n\n If you already have some partitions ready on the destination cluster, they \n\n\n will be removed at the start of the copying, because they will be interpreted \n\n\n as unfinished data from the previous copying.\n\n\n --\n\n \nenabled_partitions\n\n \npartition\n2018-02-26\n/partition\n\n \npartition\n2018-03-05\n/partition\n\n ...\n \n/enabled_partitions\n\n \n/table_hits\n\n\n \n!-- Next table to copy. It is not copied until the previous table is copying. --\n\n \n/table_visits\n\n ...\n \n/table_visits\n\n ...\n \n/tables\n\n\n/yandex\n\n\n\n\n\n\nclickhouse-copier\n tracks the changes in \n/task/path/description\n and applies them on the fly. For instance, if you change the value of \nmax_workers\n, the number of processes running tasks will also change.\n\n\n\n\nclickhouse-local\n\n\nThe \nclickhouse-local\n program enables you to perform fast processing on local files that store tables, without having to deploy and configure the ClickHouse server.\n\n\nClickHouse Development\n\n\nOverview of ClickHouse architecture\n\n\nClickHouse is a true column-oriented DBMS. Data is stored by columns, and during the execution of arrays (vectors or chunks of columns). Whenever possible, operations are dispatched on arrays, rather than on individual values. This is called \"vectorized query execution,\" and it helps lower the cost of actual data processing.\n\n\n\n\nThis idea is nothing new. It dates back to the \nAPL\n programming language and its descendants: \nA +\n, \nJ\n, \nK\n, and \nQ\n. Array programming is used in scientific data processing. Neither is this idea something new in relational databases: for example, it is used in the \nVectorwise\n system.\n\n\n\n\nThere are two different approaches for speeding up the query processing: vectorized query execution and runtime code generation. In the latter, the code is generated for every kind of query on the fly, removing all indirection and dynamic dispatch. Neither of these approaches is strictly better than the other. Runtime code generation can be better when it's fuses many operations together, thus fully utilizing CPU execution units and the pipeline. Vectorized query execution can be less practical, because it involves the temporary vectors that must be written to the cache and read back. If the temporary data does not fit in the L2 cache, this becomes an issue. But vectorized query execution more easily utilizes the SIMD capabilities of the CPU. A \nresearch paper\n written by our friends shows that it is better to combine both approaches. ClickHouse uses vectorized query execution and has limited initial support for runtime code.\n\n\nColumns\n\n\nTo represent columns in memory (actually, chunks of columns), the \nIColumn\n interface is used. This interface provides helper methods for implementation of various relational operators. Almost all operations are immutable: they do not modify the original column, but create a new modified one. For example, the \nIColumn :: filter\n method accepts a filter byte mask. It is used for the \nWHERE\n and \nHAVING\n relational operators. Additional examples: the \nIColumn :: permute\n method to support \nORDER BY\n, the \nIColumn :: cut\n method to support \nLIMIT\n, and so on.\n\n\nVarious \nIColumn\n implementations (\nColumnUInt8\n, \nColumnString\n and so on) are responsible for the memory layout of columns. Memory layout is usually a contiguous array. For the integer type of columns it is just one contiguous array, like \nstd :: vector\n. For \nString\n and \nArray\n columns, it is two vectors: one for all array elements, placed contiguously, and a second one for offsets to the beginning of each array. There is also \nColumnConst\n that stores just one value in memory, but looks like a column.\n\n\nField\n\n\nNevertheless, it is possible to work with individual values as well. To represent an individual value, the \nField\n is used. \nField\n is just a discriminated union of \nUInt64\n, \nInt64\n, \nFloat64\n, \nString\n and \nArray\n. \nIColumn\n has the \noperator[]\n method to get the n-th value as a \nField\n, and the \ninsert\n method to append a \nField\n to the end of a column. These methods are not very efficient, because they require dealing with temporary \nField\n objects representing an individual value. There are more efficient methods, such as \ninsertFrom\n, \ninsertRangeFrom\n, and so on.\n\n\nField\n doesn't have enough information about a specific data type for a table. For example, \nUInt8\n, \nUInt16\n, \nUInt32\n, and \nUInt64\n are all represented as \nUInt64\n in a \nField\n.\n\n\nLeaky abstractions\n\n\nIColumn\n has methods for common relational transformations of data, but they don't meet all needs. For example, \nColumnUInt64\n doesn't have a method to calculate the sum of two columns, and \nColumnString\n doesn't have a method to run a substring search. These countless routines are implemented outside of \nIColumn\n.\n\n\nVarious functions on columns can be implemented in a generic, non-efficient way using \nIColumn\n methods to extract \nField\n values, or in a specialized way using knowledge of inner memory layout of data in a specific \nIColumn\n implementation. To do this, functions are cast to a specific \nIColumn\n type and deal with internal representation directly. For example, \nColumnUInt64\n has the \ngetData\n method that returns a reference to an internal array, then a separate routine reads or fills that array directly. In fact, we have \"leaky abstractions\" to allow efficient specializations of various routines.\n\n\nData types\n\n\nIDataType\n is responsible for serialization and deserialization: for reading and writing chunks of columns or individual values in binary or text form.\n\nIDataType\n directly corresponds to data types in tables. For example, there are \nDataTypeUInt32\n, \nDataTypeDateTime\n, \nDataTypeString\n and so on.\n\n\nIDataType\n and \nIColumn\n are only loosely related to each other. Different data types can be represented in memory by the same \nIColumn\n implementations. For example, \nDataTypeUInt32\n and \nDataTypeDateTime\n are both represented by \nColumnUInt32\n or \nColumnConstUInt32\n. In addition, the same data type can be represented by different \nIColumn\n implementations. For example, \nDataTypeUInt8\n can be represented by \nColumnUInt8\n or \nColumnConstUInt8\n.\n\n\nIDataType\n only stores metadata. For instance, \nDataTypeUInt8\n doesn't store anything at all (except vptr) and \nDataTypeFixedString\n stores just \nN\n (the size of fixed-size strings).\n\n\nIDataType\n has helper methods for various data formats. Examples are methods to serialize a value with possible quoting, to serialize a value for JSON, and to serialize a value as part of XML format. There is no direct correspondence to data formats. For example, the different data formats \nPretty\n and \nTabSeparated\n can use the same \nserializeTextEscaped\n helper method from the \nIDataType\n interface.\n\n\nBlock\n\n\nA \nBlock\n is a container that represents a subset (chunk) of a table in memory. It is just a set of triples: \n(IColumn, IDataType, column name)\n. During query execution, data is processed by \nBlock\ns. If we have a \nBlock\n, we have data (in the \nIColumn\n object), we have information about its type (in \nIDataType\n) that tells us how to deal with that column, and we have the column name (either the original column name from the table, or some artificial name assigned for getting temporary results of calculations).\n\n\nWhen we calculate some function over columns in a block, we add another column with its result to the block, and we don't touch columns for arguments of the function because operations are immutable. Later, unneeded columns can be removed from the block, but not modified. This is convenient for elimination of common subexpressions.\n\n\nBlocks are created for every processed chunk of data. Note that for the same type of calculation, the column names and types remain the same for different blocks, and only column data changes. It is better to split block data from the block header, because small block sizes will have a high overhead of temporary strings for copying shared_ptrs and column names.\n\n\nBlock Streams\n\n\nBlock streams are for processing data. We use streams of blocks to read data from somewhere, perform data transformations, or write data to somewhere. \nIBlockInputStream\n has the \nread\n method to fetch the next block while available. \nIBlockOutputStream\n has the \nwrite\n method to push the block somewhere.\n\n\nStreams are responsible for:\n\n\n\n\nReading or writing to a table. The table just returns a stream for reading or writing blocks.\n\n\nImplementing data formats. For example, if you want to output data to a terminal in \nPretty\n format, you create a block output stream where you push blocks, and it formats them.\n\n\nPerforming data transformations. Let's say you have \nIBlockInputStream\n and want to create a filtered stream. You create \nFilterBlockInputStream\n and initialize it with your stream. Then when you pull a block from \nFilterBlockInputStream\n, it pulls a block from your stream, filters it, and returns the filtered block to you. Query execution pipelines are represented this way.\n\n\n\n\nThere are more sophisticated transformations. For example, when you pull from \nAggregatingBlockInputStream\n, it reads all data from its source, aggregates it, and then returns a stream of aggregated data for you. Another example: \nUnionBlockInputStream\n accepts many input sources in the constructor and also a number of threads. It launches multiple threads and reads from multiple sources in parallel.\n\n\n\n\nBlock streams use the \"pull\" approach to control flow: when you pull a block from the first stream, it consequently pulls the required blocks from nested streams, and the entire execution pipeline will work. Neither \"pull\" nor \"push\" is the best solution, because control flow is implicit, and that limits implementation of various features like simultaneous execution of multiple queries (merging many pipelines together). This limitation could be overcome with coroutines or just running extra threads that wait for each other. We may have more possibilities if we make control flow explicit: if we locate the logic for passing data from one calculation unit to another outside of those calculation units. Read this \narticle\n for more thoughts.\n\n\n\n\nWe should note that the query execution pipeline creates temporary data at each step. We try to keep block size small enough so that temporary data fits in the CPU cache. With that assumption, writing and reading temporary data is almost free in comparison with other calculations. We could consider an alternative, which is to fuse many operations in the pipeline together, to make the pipeline as short as possible and remove much of the temporary data. This could be an advantage, but it also has drawbacks. For example, a split pipeline makes it easy to implement caching intermediate data, stealing intermediate data from similar queries running at the same time, and merging pipelines for similar queries.\n\n\nFormats\n\n\nData formats are implemented with block streams. There are \"presentational\" formats only suitable for output of data to the client, such as \nPretty\n format, which provides only \nIBlockOutputStream\n. And there are input/output formats, such as \nTabSeparated\n or \nJSONEachRow\n.\n\n\nThere are also row streams: \nIRowInputStream\n and \nIRowOutputStream\n. They allow you to pull/push data by individual rows, not by blocks. And they are only needed to simplify implementation of row-oriented formats. The wrappers \nBlockInputStreamFromRowInputStream\n and \nBlockOutputStreamFromRowOutputStream\n allow you to convert row-oriented streams to regular block-oriented streams.\n\n\nI/O\n\n\nFor byte-oriented input/output, there are \nReadBuffer\n and \nWriteBuffer\n abstract classes. They are used instead of C++ \niostream\n's. Don't worry: every mature C++ project is using something other than \niostream\n's for good reasons.\n\n\nReadBuffer\n and \nWriteBuffer\n are just a contiguous buffer and a cursor pointing to the position in that buffer. Implementations may own or not own the memory for the buffer. There is a virtual method to fill the buffer with the following data (for \nReadBuffer\n) or to flush the buffer somewhere (for \nWriteBuffer\n). The virtual methods are rarely called.\n\n\nImplementations of \nReadBuffer\n/\nWriteBuffer\n are used for working with files and file descriptors and network sockets, for implementing compression (\nCompressedWriteBuffer\n is initialized with another WriteBuffer and performs compression before writing data to it), and for other purposes \u2013 the names \nConcatReadBuffer\n, \nLimitReadBuffer\n, and \nHashingWriteBuffer\n speak for themselves.\n\n\nRead/WriteBuffers only deal with bytes. To help with formatted input/output (for instance, to write a number in decimal format), there are functions from \nReadHelpers\n and \nWriteHelpers\n header files.\n\n\nLet's look at what happens when you want to write a result set in \nJSON\n format to stdout. You have a result set ready to be fetched from \nIBlockInputStream\n. You create \nWriteBufferFromFileDescriptor(STDOUT_FILENO)\n to write bytes to stdout. You create \nJSONRowOutputStream\n, initialized with that \nWriteBuffer\n, to write rows in \nJSON\n to stdout. You create \nBlockOutputStreamFromRowOutputStream\n on top of it, to represent it as \nIBlockOutputStream\n. Then you call \ncopyData\n to transfer data from \nIBlockInputStream\n to \nIBlockOutputStream\n, and everything works. Internally, \nJSONRowOutputStream\n will write various JSON delimiters and call the \nIDataType::serializeTextJSON\n method with a reference to \nIColumn\n and the row number as arguments. Consequently, \nIDataType::serializeTextJSON\n will call a method from \nWriteHelpers.h\n: for example, \nwriteText\n for numeric types and \nwriteJSONString\n for \nDataTypeString\n.\n\n\nTables\n\n\nTables are represented by the \nIStorage\n interface. Different implementations of that interface are different table engines. Examples are \nStorageMergeTree\n, \nStorageMemory\n, and so on. Instances of these classes are just tables.\n\n\nThe most important \nIStorage\n methods are \nread\n and \nwrite\n. There are also \nalter\n, \nrename\n, \ndrop\n, and so on. The \nread\n method accepts the following arguments: the set of columns to read from a table, the \nAST\n query to consider, and the desired number of streams to return. It returns one or multiple \nIBlockInputStream\n objects and information about the stage of data processing that was completed inside a table engine during query execution.\n\n\nIn most cases, the read method is only responsible for reading the specified columns from a table, not for any further data processing. All further data processing is done by the query interpreter and is outside the responsibility of \nIStorage\n.\n\n\nBut there are notable exceptions:\n\n\n\n\nThe AST query is passed to the \nread\n method and the table engine can use it to derive index usage and to read less data from a table.\n\n\nSometimes the table engine can process data itself to a specific stage. For example, \nStorageDistributed\n can send a query to remote servers, ask them to process data to a stage where data from different remote servers can be merged, and return that preprocessed data.\nThe query interpreter then finishes processing the data.\n\n\n\n\nThe table's \nread\n method can return multiple \nIBlockInputStream\n objects to allow parallel data processing. These multiple block input streams can read from a table in parallel. Then you can wrap these streams with various transformations (such as expression evaluation or filtering) that can be calculated independently and create a \nUnionBlockInputStream\n on top of them, to read from multiple streams in parallel.\n\n\nThere are also \nTableFunction\ns. These are functions that return a temporary \nIStorage\n object to use in the \nFROM\n clause of a query.\n\n\nTo get a quick idea of how to implement your own table engine, look at something simple, like \nStorageMemory\n or \nStorageTinyLog\n.\n\n\n\n\nAs the result of the \nread\n method, \nIStorage\n returns \nQueryProcessingStage\n \u2013 information about what parts of the query were already calculated inside storage. Currently we have only very coarse granularity for that information. There is no way for the storage to say \"I have already processed this part of the expression in WHERE, for this range of data\". We need to work on that.\n\n\n\n\nParsers\n\n\nA query is parsed by a hand-written recursive descent parser. For example, \nParserSelectQuery\n just recursively calls the underlying parsers for various parts of the query. Parsers create an \nAST\n. The \nAST\n is represented by nodes, which are instances of \nIAST\n.\n\n\n\n\nParser generators are not used for historical reasons.\n\n\n\n\nInterpreters\n\n\nInterpreters are responsible for creating the query execution pipeline from an \nAST\n. There are simple interpreters, such as \nInterpreterExistsQuery\nand \nInterpreterDropQuery\n, or the more sophisticated \nInterpreterSelectQuery\n. The query execution pipeline is a combination of block input or output streams. For example, the result of interpreting the \nSELECT\n query is the \nIBlockInputStream\n to read the result set from; the result of the INSERT query is the \nIBlockOutputStream\n to write data for insertion to; and the result of interpreting the \nINSERT SELECT\n query is the \nIBlockInputStream\n that returns an empty result set on the first read, but that copies data from \nSELECT\n to \nINSERT\n at the same time.\n\n\nInterpreterSelectQuery\n uses \nExpressionAnalyzer\n and \nExpressionActions\n machinery for query analysis and transformations. This is where most rule-based query optimizations are done. \nExpressionAnalyzer\n is quite messy and should be rewritten: various query transformations and optimizations should be extracted to separate classes to allow modular transformations or query.\n\n\nFunctions\n\n\nThere are ordinary functions and aggregate functions. For aggregate functions, see the next section.\n\n\nOrdinary functions don't change the number of rows \u2013 they work as if they are processing each row independently. In fact, functions are not called for individual rows, but for \nBlock\n's of data to implement vectorized query execution.\n\n\nThere are some miscellaneous functions, like \nblockSize\n, \nrowNumberInBlock\n, and \nrunningAccumulate\n, that exploit block processing and violate the independence of rows.\n\n\nClickHouse has strong typing, so implicit type conversion doesn't occur. If a function doesn't support a specific combination of types, an exception will be thrown. But functions can work (be overloaded) for many different combinations of types. For example, the \nplus\n function (to implement the \n+\n operator) works for any combination of numeric types: \nUInt8\n + \nFloat32\n, \nUInt16\n + \nInt8\n, and so on. Also, some variadic functions can accept any number of arguments, such as the \nconcat\n function.\n\n\nImplementing a function may be slightly inconvenient because a function explicitly dispatches supported data types and supported \nIColumns\n. For example, the \nplus\n function has code generated by instantiation of a C++ template for each combination of numeric types, and for constant or non-constant left and right arguments.\n\n\n\n\nThis is a nice place to implement runtime code generation to avoid template code bloat. Also, it will make it possible to add fused functions like fused multiply-add, or to make multiple comparisons in one loop iteration.\n\n\n\n\nDue to vectorized query execution, functions are not short-circuit. For example, if you write \nWHERE f(x) AND g(y)\n, both sides will be calculated, even for rows, when \nf(x)\n is zero (except when \nf(x)\n is a zero constant expression). But if selectivity of the \nf(x)\n condition is high, and calculation of \nf(x)\n is much cheaper than \ng(y)\n, it's better to implement multi-pass calculation: first calculate \nf(x)\n, then filter columns by the result, and then calculate \ng(y)\n only for smaller, filtered chunks of data.\n\n\nAggregate Functions\n\n\nAggregate functions are stateful functions. They accumulate passed values into some state, and allow you to get results from that state. They are managed with the \nIAggregateFunction\n interface. States can be rather simple (the state for \nAggregateFunctionCount\n is just a single \nUInt64\n value) or quite complex (the state of \nAggregateFunctionUniqCombined\n is a combination of a linear array, a hash table and a \nHyperLogLog\n probabilistic data structure).\n\n\nTo deal with multiple states while executing a high-cardinality \nGROUP BY\n query, states are allocated in \nArena\n (a memory pool), or they could be allocated in any suitable piece of memory. States can have a non-trivial constructor and destructor: for example, complex aggregation states can allocate additional memory themselves. This requires some attention to creating and destroying states and properly passing their ownership, to keep track of who and when will destroy states.\n\n\nAggregation states can be serialized and deserialized to pass over the network during distributed query execution or to write them on disk where there is not enough RAM. They can even be stored in a table with the \nDataTypeAggregateFunction\n to allow incremental aggregation of data.\n\n\n\n\nThe serialized data format for aggregate function states is not versioned right now. This is ok if aggregate states are only stored temporarily. But we have the \nAggregatingMergeTree\n table engine for incremental aggregation, and people are already using it in production. This is why we should add support for backward compatibility when changing the serialized format for any aggregate function in the future.\n\n\n\n\nServer\n\n\nThe server implements several different interfaces:\n\n\n\n\nAn HTTP interface for any foreign clients.\n\n\nA TCP interface for the native ClickHouse client and for cross-server communication during distributed query execution.\n\n\nAn interface for transferring data for replication.\n\n\n\n\nInternally, it is just a basic multithreaded server without coroutines, fibers, etc. Since the server is not designed to process a high rate of simple queries but is intended to process a relatively low rate of complex queries, each of them can process a vast amount of data for analytics.\n\n\nThe server initializes the \nContext\n class with the necessary environment for query execution: the list of available databases, users and access rights, settings, clusters, the process list, the query log, and so on. This environment is used by interpreters.\n\n\nWe maintain full backward and forward compatibility for the server TCP protocol: old clients can talk to new servers and new clients can talk to old servers. But we don't want to maintain it eternally, and we are removing support for old versions after about one year.\n\n\n\n\nFor all external applications, we recommend using the HTTP interface because it is simple and easy to use. The TCP protocol is more tightly linked to internal data structures: it uses an internal format for passing blocks of data and it uses custom framing for compressed data. We haven't released a C library for that protocol because it requires linking most of the ClickHouse codebase, which is not practical.\n\n\n\n\nDistributed query execution\n\n\nServers in a cluster setup are mostly independent. You can create a \nDistributed\n table on one or all servers in a cluster. The \nDistributed\n table does not store data itself \u2013 it only provides a \"view\" to all local tables on multiple nodes of a cluster. When you SELECT from a \nDistributed\n table, it rewrites that query, chooses remote nodes according to load balancing settings, and sends the query to them. The \nDistributed\n table requests remote servers to process a query just up to a stage where intermediate results from different servers can be merged. Then it receives the intermediate results and merges them. The distributed table tries to distribute as much work as possible to remote servers, and does not send much intermediate data over the network.\n\n\n\n\nThings become more complicated when you have subqueries in IN or JOIN clauses and each of them uses a \nDistributed\n table. We have different strategies for execution of these queries.\n\n\n\n\nThere is no global query plan for distributed query execution. Each node has its own local query plan for its part of the job. We only have simple one-pass distributed query execution: we send queries for remote nodes and then merge the results. But this is not feasible for difficult queries with high cardinality GROUP BYs or with a large amount of temporary data for JOIN: in such cases, we need to \"reshuffle\" data between servers, which requires additional coordination. ClickHouse does not support that kind of query execution, and we need to work on it.\n\n\nMerge Tree\n\n\nMergeTree\n is a family of storage engines that supports indexing by primary key. The primary key can be an arbitary tuple of columns or expressions. Data in a \nMergeTree\n table is stored in \"parts\". Each part stores data in the primary key order (data is ordered lexicographically by the primary key tuple). All the table columns are stored in separate \ncolumn.bin\n files in these parts. The files consist of compressed blocks. Each block is usually from 64 KB to 1 MB of uncompressed data, depending on the average value size. The blocks consist of column values placed contiguously one after the other. Column values are in the same order for each column (the order is defined by the primary key), so when you iterate by many columns, you get values for the corresponding rows.\n\n\nThe primary key itself is \"sparse\". It doesn't address each single row, but only some ranges of data. A separate \nprimary.idx\n file has the value of the primary key for each N-th row, where N is called \nindex_granularity\n (usually, N = 8192). Also, for each column, we have \ncolumn.mrk\n files with \"marks,\" which are offsets to each N-th row in the data file. Each mark is a pair: the offset in the file to the beginning of the compressed block, and the offset in the decompressed block to the beginning of data. Usually compressed blocks are aligned by marks, and the offset in the decompressed block is zero. Data for \nprimary.idx\n always resides in memory and data for \ncolumn.mrk\n files is cached.\n\n\nWhen we are going to read something from a part in \nMergeTree\n, we look at \nprimary.idx\n data and locate ranges that could possibly contain requested data, then look at \ncolumn.mrk\n data and calculate offsets for where to start reading those ranges. Because of sparseness, excess data may be read. ClickHouse is not suitable for a high load of simple point queries, because the entire range with \nindex_granularity\n rows must be read for each key, and the entire compressed block must be decompressed for each column. We made the index sparse because we must be able to maintain trillions of rows per single server without noticeable memory consumption for the index. Also, because the primary key is sparse, it is not unique: it cannot check the existence of the key in the table at INSERT time. You could have many rows with the same key in a table.\n\n\nWhen you \nINSERT\n a bunch of data into \nMergeTree\n, that bunch is sorted by primary key order and forms a new part. To keep the number of parts relatively low, there are background threads that periodically select some parts and merge them to a single sorted part. That's why it is called \nMergeTree\n. Of course, merging leads to \"write amplification\". All parts are immutable: they are only created and deleted, but not modified. When SELECT is run, it holds a snapshot of the table (a set of parts). After merging, we also keep old parts for some time to make recovery after failure easier, so if we see that some merged part is probably broken, we can replace it with its source parts.\n\n\nMergeTree\n is not an LSM tree because it doesn't contain \"memtable\" and \"log\": inserted data is written directly to the filesystem. This makes it suitable only to INSERT data in batches, not by individual row and not very frequently \u2013 about once per second is ok, but a thousand times a second is not. We did it this way for simplicity's sake, and because we are already inserting data in batches in our applications.\n\n\n\n\nMergeTree tables can only have one (primary) index: there aren't any secondary indices. It would be nice to allow multiple physical representations under one logical table, for example, to store data in more than one physical order or even to allow representations with pre-aggregated data along with original data.\n\n\n\n\nThere are MergeTree engines that are doing additional work during background merges. Examples are \nCollapsingMergeTree\n and \nAggregatingMergeTree\n. This could be treated as special support for updates. Keep in mind that these are not real updates because users usually have no control over the time when background merges will be executed, and data in a \nMergeTree\n table is almost always stored in more than one part, not in completely merged form.\n\n\nReplication\n\n\nReplication in ClickHouse is implemented on a per-table basis. You could have some replicated and some non-replicated tables on the same server. You could also have tables replicated in different ways, such as one table with two-factor replication and another with three-factor.\n\n\nReplication is implemented in the \nReplicatedMergeTree\n storage engine. The path in \nZooKeeper\n is specified as a parameter for the storage engine. All tables with the same path in \nZooKeeper\n become replicas of each other: they synchronize their data and maintain consistency. Replicas can be added and removed dynamically simply by creating or dropping a table.\n\n\nReplication uses an asynchronous multi-master scheme. You can insert data into any replica that has a session with \nZooKeeper\n, and data is replicated to all other replicas asynchronously. Because ClickHouse doesn't support UPDATEs, replication is conflict-free. As there is no quorum acknowledgment of inserts, just-inserted data might be lost if one node fails.\n\n\nMetadata for replication is stored in ZooKeeper. There is a replication log that lists what actions to do. Actions are: get part; merge parts; drop partition, etc. Each replica copies the replication log to its queue and then executes the actions from the queue. For example, on insertion, the \"get part\" action is created in the log, and every replica downloads that part. Merges are coordinated between replicas to get byte-identical results. All parts are merged in the same way on all replicas. To achieve this, one replica is elected as the leader, and that replica initiates merges and writes \"merge parts\" actions to the log.\n\n\nReplication is physical: only compressed parts are transferred between nodes, not queries. To lower the network cost (to avoid network amplification), merges are processed on each replica independently in most cases. Large merged parts are sent over the network only in cases of significant replication lag.\n\n\nIn addition, each replica stores its state in ZooKeeper as the set of parts and its checksums. When the state on the local filesystem diverges from the reference state in ZooKeeper, the replica restores its consistency by downloading missing and broken parts from other replicas. When there is some unexpected or broken data in the local filesystem, ClickHouse does not remove it, but moves it to a separate directory and forgets it.\n\n\n\n\nThe ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is not elastic, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load will be uneven. This implementation gives you more control, and it is fine for relatively small clusters such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that will span its data across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically.\n\n\n\n\nHow to build ClickHouse on Linux\n\n\nBuild should work on Linux Ubuntu 12.04, 14.04 or newer.\nWith appropriate changes, it should also work on any other Linux distribution.\nThe build process is not intended to work on Mac OS X.\nOnly x86_64 with SSE 4.2 is supported. Support for AArch64 is experimental.\n\n\nTo test for SSE 4.2, do\n\n\ngrep -q sse4_2 /proc/cpuinfo \n \necho\n \nSSE 4.2 supported\n \n||\n \necho\n \nSSE 4.2 not supported\n\n\n\n\n\n\nInstall Git and CMake\n\n\nsudo apt-get install git cmake\n\n\n\n\n\nOr cmake3 instead of cmake on older systems.\n\n\nDetect the number of threads\n\n\nexport\n \nTHREADS\n=\n$(\ngrep -c ^processor /proc/cpuinfo\n)\n\n\n\n\n\n\nInstall GCC 7\n\n\nThere are several ways to do this.\n\n\nInstall from a PPA package\n\n\nsudo apt-get install software-properties-common\nsudo apt-add-repository ppa:ubuntu-toolchain-r/test\nsudo apt-get update\nsudo apt-get install gcc-7 g++-7\n\n\n\n\n\nInstall from sources\n\n\nLook at [https://github.com/yandex/ClickHouse/blob/master/utils/prepare-environment/install-gcc.sh]\n\n\nUse GCC 7 for builds\n\n\nexport\n \nCC\n=\ngcc-7\n\nexport\n \nCXX\n=\ng++-7\n\n\n\n\n\nInstall required libraries from packages\n\n\nsudo apt-get install libicu-dev libreadline-dev libmysqlclient-dev libssl-dev unixodbc-dev ninja-build\n\n\n\n\n\nCheckout ClickHouse sources\n\n\nTo get the latest stable version:\n\n\ngit clone -b stable --recursive git@github.com:yandex/ClickHouse.git\n\n## or: git clone -b stable --recursive https://github.com/yandex/ClickHouse.git\n\n\n\ncd\n ClickHouse\n\n\n\n\n\nFor development, switch to the \nmaster\n branch.\nFor the latest release candidate, switch to the \ntesting\n branch.\n\n\nBuild ClickHouse\n\n\nThere are two build variants.\n\n\nBuild release package\n\n\nInstall prerequisites to build Debian packages.\n\n\nsudo apt-get install devscripts dupload fakeroot debhelper\n\n\n\n\n\nInstall the most recent version of Clang.\n\n\nClang is embedded into the ClickHouse package and used at runtime. The minimum version is 5.0. It is optional.\n\n\nTo install clang, see \nutils/prepare-environment/install-clang.sh\n\n\nYou may also build ClickHouse with Clang for development purposes.\nFor production releases, GCC is used.\n\n\nRun the release script:\n\n\nrm -f ../clickhouse*.deb\n./release\n\n\n\n\n\nYou will find built packages in the parent directory:\n\n\nls -l ../clickhouse*.deb\n\n\n\n\n\nNote that usage of debian packages is not required.\nClickHouse has no runtime dependencies except libc, so it could work on almost any Linux.\n\n\nInstalling freshly built packages on a development server:\n\n\nsudo dpkg -i ../clickhouse*.deb\nsudo service clickhouse-server start\n\n\n\n\n\nBuild to work with code\n\n\nmkdir build\n\ncd\n build\ncmake ..\nmake -j \n$THREADS\n\n\ncd\n ..\n\n\n\n\n\nTo create an executable, run \nmake clickhouse\n.\nThis will create the \ndbms/src/Server/clickhouse\n executable, which can be used with \nclient\n or \nserver\n arguments.\n\n\nHow to build ClickHouse on Mac OS X\n\n\nBuild should work on Mac OS X 10.12. If you're using earlier version, you can try to build ClickHouse using Gentoo Prefix and clang sl in this instruction.\nWith appropriate changes, it should also work on any other Linux distribution.\n\n\nInstall Homebrew\n\n\n/usr/bin/ruby -e \n$(\ncurl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install\n)\n\n\n\n\n\n\nInstall required compilers, tools, and libraries\n\n\nbrew install cmake gcc icu4c mysql openssl unixodbc libtool gettext zlib readline boost --cc\n=\ngcc-7\n\n\n\n\n\nCheckout ClickHouse sources\n\n\nTo get the latest stable version:\n\n\ngit clone -b stable --recursive --depth\n=\n10\n git@github.com:yandex/ClickHouse.git\n\n## or: git clone -b stable --recursive --depth=10 https://github.com/yandex/ClickHouse.git\n\n\n\ncd\n ClickHouse\n\n\n\n\n\nFor development, switch to the \nmaster\n branch.\nFor the latest release candidate, switch to the \ntesting\n branch.\n\n\nBuild ClickHouse\n\n\nmkdir build\n\ncd\n build\ncmake .. -DCMAKE_CXX_COMPILER\n=\n`\nwhich g++-7\n`\n -DCMAKE_C_COMPILER\n=\n`\nwhich gcc-7\n`\n\nmake -j \n`\nsysctl -n hw.ncpu\n`\n\n\ncd\n ..\n\n\n\n\n\nCaveats\n\n\nIf you intend to run clickhouse-server, make sure to increase the system's maxfiles variable. See \nMacOS.md\n for more details.\n\n\nHow to write C++ code\n\n\nGeneral recommendations\n\n\n1.\n The following are recommendations, not requirements.\n\n\n2.\n If you are editing code, it makes sense to follow the formatting of the existing code.\n\n\n3.\n Code style is needed for consistency. Consistency makes it easier to read the code, and it also makes it easier to search the code.\n\n\n4.\n Many of the rules do not have logical reasons; they are dictated by established practices.\n\n\nFormatting\n\n\n1.\n Most of the formatting will be done automatically by \nclang-format\n.\n\n\n2.\n Indents are 4 spaces. Configure your development environment so that a tab adds four spaces.\n\n\n3.\n A left curly bracket must be separated on a new line. (And the right one, as well.)\n\n\ninline\n \nvoid\n \nreadBoolText\n(\nbool\n \n \nx\n,\n \nReadBuffer\n \n \nbuf\n)\n\n\n{\n\n \nchar\n \ntmp\n \n=\n \n0\n;\n\n \nreadChar\n(\ntmp\n,\n \nbuf\n);\n\n \nx\n \n=\n \ntmp\n \n!=\n \n0\n;\n\n\n}\n\n\n\n\n\n\n4.\n\nBut if the entire function body is quite short (a single statement), you can place it entirely on one line if you wish. Place spaces around curly braces (besides the space at the end of the line).\n\n\ninline\n \nsize_t\n \nmask\n()\n \nconst\n \n{\n \nreturn\n \nbuf_size\n()\n \n-\n \n1\n;\n \n}\n\n\ninline\n \nsize_t\n \nplace\n(\nHashValue\n \nx\n)\n \nconst\n \n{\n \nreturn\n \nx\n \n \nmask\n();\n \n}\n\n\n\n\n\n\n5.\n For functions, don't put spaces around brackets.\n\n\nvoid\n \nreinsert\n(\nconst\n \nValue\n \n \nx\n)\n\n\nmemcpy\n(\nbuf\n[\nplace_value\n],\n \nx\n,\n \nsizeof\n(\nx\n));\n\n\n\n\n\n\n6.\n When using statements such as \nif\n, \nfor\n, and \nwhile\n (unlike function calls), put a space before the opening bracket.\n\n\ncpp\n for (size_t i = 0; i \n rows; i += storage.index_granularity)\n\n\n7.\n Put spaces around binary operators (\n+\n, \n-\n, \n*\n, \n/\n, \n%\n, ...), as well as the ternary operator \n?:\n.\n\n\nUInt16\n \nyear\n \n=\n \n(\ns\n[\n0\n]\n \n-\n \n0\n)\n \n*\n \n1000\n \n+\n \n(\ns\n[\n1\n]\n \n-\n \n0\n)\n \n*\n \n100\n \n+\n \n(\ns\n[\n2\n]\n \n-\n \n0\n)\n \n*\n \n10\n \n+\n \n(\ns\n[\n3\n]\n \n-\n \n0\n);\n\n\nUInt8\n \nmonth\n \n=\n \n(\ns\n[\n5\n]\n \n-\n \n0\n)\n \n*\n \n10\n \n+\n \n(\ns\n[\n6\n]\n \n-\n \n0\n);\n\n\nUInt8\n \nday\n \n=\n \n(\ns\n[\n8\n]\n \n-\n \n0\n)\n \n*\n \n10\n \n+\n \n(\ns\n[\n9\n]\n \n-\n \n0\n);\n\n\n\n\n\n\n8.\n If a line feed is entered, put the operator on a new line and increase the indent before it.\n\n\nif\n \n(\nelapsed_ns\n)\n\n \nmessage\n \n \n (\n\n \n \nrows_read_on_server\n \n*\n \n1000000000\n \n/\n \nelapsed_ns\n \n \n rows/s., \n\n \n \nbytes_read_on_server\n \n*\n \n1000.0\n \n/\n \nelapsed_ns\n \n \n MB/s.) \n;\n\n\n\n\n\n\n9.\n You can use spaces for alignment within a line, if desired.\n\n\ndst\n.\nClickLogID\n \n=\n \nclick\n.\nLogID\n;\n\n\ndst\n.\nClickEventID\n \n=\n \nclick\n.\nEventID\n;\n\n\ndst\n.\nClickGoodEvent\n \n=\n \nclick\n.\nGoodEvent\n;\n\n\n\n\n\n\n10.\n Don't use spaces around the operators \n.\n, \n-\n .\n\n\nIf necessary, the operator can be wrapped to the next line. In this case, the offset in front of it is increased.\n\n\n11.\n Do not use a space to separate unary operators (\n-\n, \n+\n, \n*\n, \n, ...) from the argument.\n\n\n12.\n Put a space after a comma, but not before it. The same rule goes for a semicolon inside a for expression.\n\n\n13.\n Do not use spaces to separate the \n[]\n operator.\n\n\n14.\n In a \ntemplate \n...\n expression, use a space between \ntemplate\n and \n. No spaces after \n or before \n.\n\n\ntemplate\n \ntypename\n \nTKey\n,\n \ntypename\n \nTValue\n\n\nstruct\n \nAggregatedStatElement\n\n\n{}\n\n\n\n\n\n\n15.\n In classes and structures, public, private, and protected are written on the same level as the \nclass/struct\n, but all other internal elements should be deeper.\n\n\ntemplate\n \ntypename\n \nT\n\n\nclass\n \nMultiVersion\n\n\n{\n\n\npublic\n:\n\n \n/// Version of object for usage. shared_ptr manage lifetime of version.\n\n \nusing\n \nVersion\n \n=\n \nstd\n::\nshared_ptr\nconst\n \nT\n;\n\n \n...\n\n\n}\n\n\n\n\n\n\n16.\n If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace.\n\n\n17.\n If the block for \nif\n, \nfor\n, \nwhile\n... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets.\n\n\n/// Finish write.\n\n\nfor\n \n(\nauto\n \n \nstream\n \n:\n \nstreams\n)\n\n \nstream\n.\nsecond\n-\nfinalize\n();\n\n\n\n\n\n\n18.\n There should be any spaces at the ends of lines.\n\n\n19.\n Sources are UTF-8 encoded.\n\n\n20.\n Non-ASCII characters can be used in string literals.\n\n\n \n, \n \n \n(\ntimer\n.\nelapsed\n()\n \n/\n \nchunks_stats\n.\nhits\n)\n \n \n \u03bcsec/hit.\n;\n\n\n\n\n\n\n21.\n Do not write multiple expressions in a single line.\n\n\n22.\n Group sections of code inside functions and separate them with no more than one empty line.\n\n\n23.\n Separate functions, classes, and so on with one or two empty lines.\n\n\n24.\n A \nconst\n (related to a value) must be written before the type name.\n\n\n//correct\n\n\nconst\n \nchar\n \n*\n \npos\n\n\nconst\n \nstd\n::\nstring\n \n \ns\n\n\n//incorrect\n\n\nchar\n \nconst\n \n*\n \npos\n\n\n\n\n\n\n25.\n When declaring a pointer or reference, the \n*\n and \n symbols should be separated by spaces on both sides.\n\n\n//correct\n\n\nconst\n \nchar\n \n*\n \npos\n\n\n//incorrect\n\n\nconst\n \nchar\n*\n \npos\n\n\nconst\n \nchar\n \n*\npos\n\n\n\n\n\n\n26.\n When using template types, alias them with the \nusing\n keyword (except in the simplest cases).\n\n\nIn other words, the template parameters are specified only in \nusing\n and aren't repeated in the code.\n\n\nusing\n can be declared locally, such as inside a function.\n\n\n//correct\n\n\nusing\n \nFileStreams\n \n=\n \nstd\n::\nmap\nstd\n::\nstring\n,\n \nstd\n::\nshared_ptr\nStream\n;\n\n\nFileStreams\n \nstreams\n;\n\n\n//incorrect\n\n\nstd\n::\nmap\nstd\n::\nstring\n,\n \nstd\n::\nshared_ptr\nStream\n \nstreams\n;\n\n\n\n\n\n\n27.\n Do not declare several variables of different types in one statement.\n\n\n//incorrect\n\n\nint\n \nx\n,\n \n*\ny\n;\n\n\n\n\n\n\n28.\n Do not use C-style casts.\n\n\n//incorrect\n\n\nstd\n::\ncerr\n \n \n(\nint\n)\nc\n \n;\n \nstd\n::\nendl\n;\n\n\n//correct\n\n\nstd\n::\ncerr\n \n \nstatic_cast\nint\n(\nc\n)\n \n \nstd\n::\nendl\n;\n\n\n\n\n\n\n29.\n In classes and structs, group members and functions separately inside each visibility scope.\n\n\n30.\n For small classes and structs, it is not necessary to separate the method declaration from the implementation.\n\n\nThe same is true for small methods in any classes or structs.\n\n\nFor templated classes and structs, don't separate the method declarations from the implementation (because otherwise they must be defined in the same translation unit).\n\n\n31.\n You can wrap lines at 140 characters, instead of 80.\n\n\n32.\n Always use the prefix increment/decrement operators if postfix is not required.\n\n\nfor\n \n(\nNames\n::\nconst_iterator\n \nit\n \n=\n \ncolumn_names\n.\nbegin\n();\n \nit\n \n!=\n \ncolumn_names\n.\nend\n();\n \n++\nit\n)\n\n\n\n\n\n\nComments\n\n\n1.\n Be sure to add comments for all non-trivial parts of code.\n\n\nThis is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong.\n\n\n/** Part of piece of memory, that can be used.\n\n\n * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading,\n\n\n * then working_buffer will have size of only 10 bytes\n\n\n * (working_buffer.end() will point to the position right after those 10 bytes available for read).\n\n\n*/\n\n\n\n\n\n\n2.\n Comments can be as detailed as necessary.\n\n\n3.\n Place comments before the code they describe. In rare cases, comments can come after the code, on the same line.\n\n\n/** Parses and executes the query.\n\n\n*/\n\n\nvoid\n \nexecuteQuery\n(\n\n \nReadBuffer\n \n \nistr\n,\n \n/// Where to read the query from (and data for INSERT, if applicable)\n\n \nWriteBuffer\n \n \nostr\n,\n \n/// Where to write the result\n\n \nContext\n \n \ncontext\n,\n \n/// DB, tables, data types, engines, functions, aggregate functions...\n\n \nBlockInputStreamPtr\n \n \nquery_plan\n,\n \n/// A description of query processing can be included here\n\n \nQueryProcessingStage\n::\nEnum\n \nstage\n \n=\n \nQueryProcessingStage\n::\nComplete\n \n/// The last stage to process the SELECT query to\n\n \n)\n\n\n\n\n\n\n4.\n Comments should be written in English only.\n\n\n5.\n If you are writing a library, include detailed comments explaining it in the main header file.\n\n\n6.\n Do not add comments that do not provide additional information. In particular, do not leave empty comments like this:\n\n\n/*\n\n\n* Procedure Name:\n\n\n* Original procedure name:\n\n\n* Author:\n\n\n* Date of creation:\n\n\n* Dates of modification:\n\n\n* Modification authors:\n\n\n* Original file name:\n\n\n* Purpose:\n\n\n* Intent:\n\n\n* Designation:\n\n\n* Classes used:\n\n\n* Constants:\n\n\n* Local variables:\n\n\n* Parameters:\n\n\n* Date of creation:\n\n\n* Purpose:\n\n\n*/\n\n\n\n\n\n\nThe example is borrowed from \nhttp://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/\n.\n\n\n7.\n Do not write garbage comments (author, creation date ..) at the beginning of each file.\n\n\n8.\n Single-line comments begin with three slashes: \n///\n and multi-line comments begin with \n/**\n. These comments are considered \"documentation\".\n\n\nNote: You can use Doxygen to generate documentation from these comments. But Doxygen is not generally used because it is more convenient to navigate the code in the IDE.\n\n\n9.\n Multi-line comments must not have empty lines at the beginning and end (except the line that closes a multi-line comment).\n\n\n10.\n For commenting out code, use basic comments, not \"documenting\" comments.\n\n\n11.\n Delete the commented out parts of the code before commiting.\n\n\n12.\n Do not use profanity in comments or code.\n\n\n13.\n Do not use uppercase letters. Do not use excessive punctuation.\n\n\n/// WHAT THE FAIL???\n\n\n\n\n\n\n14.\n Do not make delimeters from comments.\n\n\n///******************************************************\n\n\n\n\n\n15.\n Do not start discussions in comments.\n\n\n/// Why did you do this stuff?\n\n\n\n\n\n16.\n There's no need to write a comment at the end of a block describing what it was about.\n\n\n/// for\n\n\n\n\n\nNames\n\n\n1.\n The names of variables and class members use lowercase letters with underscores.\n\n\nsize_t\n \nmax_block_size\n;\n\n\n\n\n\n\n2.\n The names of functions (methods) use camelCase beginning with a lowercase letter.\n\n\nstd\n::\nstring\n \ngetName\n()\n \nconst\n \noverride\n \n{\n \nreturn\n \nMemory\n;\n \n}\n\n\n\n\n\n\n3.\n The names of classes (structures) use CamelCase beginning with an uppercase letter. Prefixes other than I are not used for interfaces.\n\n\nclass\n \nStorageMemory\n \n:\n \npublic\n \nIStorage\n\n\n\n\n\n\n4.\n The names of usings follow the same rules as classes, or you can add _t at the end.\n\n\n5.\n Names of template type arguments for simple cases: T; T, U; T1, T2.\n\n\nFor more complex cases, either follow the rules for class names, or add the prefix T.\n\n\ntemplate\n \ntypename\n \nTKey\n,\n \ntypename\n \nTValue\n\n\nstruct\n \nAggregatedStatElement\n\n\n\n\n\n\n6.\n Names of template constant arguments: either follow the rules for variable names, or use N in simple cases.\n\n\ntemplate\n \nbool\n \nwithout_www\n\n\nstruct\n \nExtractDomain\n\n\n\n\n\n\n7.\n For abstract classes (interfaces) you can add the I prefix.\n\n\nclass\n \nIBlockInputStream\n\n\n\n\n\n\n8.\n If you use a variable locally, you can use the short name.\n\n\nIn other cases, use a descriptive name that conveys the meaning.\n\n\nbool\n \ninfo_successfully_loaded\n \n=\n \nfalse\n;\n\n\n\n\n\n\n9.\n \ndefine\n\u2018s should be in ALL_CAPS with underscores. The same is true for global constants.\n\n\n##define MAX_SRC_TABLE_NAMES_TO_STORE 1000\n\n\n\n\n\n\n10.\n File names should use the same style as their contents.\n\n\nIf a file contains a single class, name the file the same way as the class, in CamelCase.\n\n\nIf the file contains a single function, name the file the same way as the function, in camelCase.\n\n\n11.\n If the name contains an abbreviation, then:\n\n\n\n\nFor variable names, the abbreviation should use lowercase letters \nmysql_connection\n (not \nmySQL_connection\n).\n\n\nFor names of classes and functions, keep the uppercase letters in the abbreviation \nMySQLConnection\n (not \nMySqlConnection\n).\n\n\n\n\n12.\n Constructor arguments that are used just to initialize the class members should be named the same way as the class members, but with an underscore at the end.\n\n\nFileQueueProcessor\n(\n\n \nconst\n \nstd\n::\nstring\n \n \npath_\n,\n\n \nconst\n \nstd\n::\nstring\n \n \nprefix_\n,\n\n \nstd\n::\nshared_ptr\nFileHandler\n \nhandler_\n)\n\n \n:\n \npath\n(\npath_\n),\n\n \nprefix\n(\nprefix_\n),\n\n \nhandler\n(\nhandler_\n),\n\n \nlog\n(\nLogger\n::\nget\n(\nFileQueueProcessor\n))\n\n\n{\n\n\n}\n\n\n\n\n\n\nThe underscore suffix can be omitted if the argument is not used in the constructor body.\n\n\n13.\n There is no difference in the names of local variables and class members (no prefixes required).\n\n\ntimer\n \n(\nnot\n \nm_timer\n)\n\n\n\n\n\n\n14.\n Constants in enums use CamelCase beginning with an uppercase letter. ALL_CAPS is also allowed. If the enum is not local, use enum class.\n\n\nenum\n \nclass\n \nCompressionMethod\n\n\n{\n\n \nQuickLZ\n \n=\n \n0\n,\n\n \nLZ4\n \n=\n \n1\n,\n\n\n};\n\n\n\n\n\n\n15.\n All names must be in English. Transliteration of Russian words is not allowed.\n\n\nnot\n \nStroka\n\n\n\n\n\n\n16.\n Abbreviations are acceptable if they are well known (when you can easily find the meaning of the abbreviation in Wikipedia or in a search engine).\n\n\n`AST`, `SQL`.\n\nNot `NVDH` (some random letters)\n\n\n\n\n\nIncomplete words are acceptable if the shortened version is common use.\n\n\nYou can also use an abbreviation if the full name is included next to it in the comments.\n\n\n17.\n File names with C++ source code must have the \n.cpp\n extension. Header files must have the \n.h\n extension.\n\n\nHow to write code\n\n\n1.\n Memory management.\n\n\nManual memory deallocation (delete) can only be used in library code.\n\n\nIn library code, the delete operator can only be used in destructors.\n\n\nIn application code, memory must be freed by the object that owns it.\n\n\nExamples:\n\n\n\n\nThe easiest way is to place an object on the stack, or make it a member of another class.\n\n\nFor a large number of small objects, use containers.\n\n\nFor automatic deallocation of a small number of objects that reside in the heap, use shared_ptr/unique_ptr.\n\n\n\n\n2.\n Resource management.\n\n\nUse RAII and see the previous point.\n\n\n3.\n Error handling.\n\n\nUse exceptions. In most cases, you only need to throw an exception, and don't need to catch it (because of RAII).\n\n\nIn offline data processing applications, it's often acceptable to not catch exceptions.\n\n\nIn servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler.\n\n\n/// If there were no other calculations yet, do it synchronously\n\n\nif\n \n(\n!\nstarted\n)\n\n\n{\n\n \ncalculate\n();\n\n \nstarted\n \n=\n \ntrue\n;\n\n\n}\n\n\nelse\n \n/// If the calculations are already in progress, wait for results\n\n \npool\n.\nwait\n();\n\n\n\nif\n \n(\nexception\n)\n\n \nexception\n-\nrethrow\n();\n\n\n\n\n\n\nNever hide exceptions without handling. Never just blindly put all exceptions to log.\n\n\nNot \ncatch (...) {}\n.\n\n\nIf you need to ignore some exceptions, do so only for specific ones and rethrow the rest.\n\n\ncatch\n \n(\nconst\n \nDB\n::\nException\n \n \ne\n)\n\n\n{\n\n \nif\n \n(\ne\n.\ncode\n()\n \n==\n \nErrorCodes\n::\nUNKNOWN_AGGREGATE_FUNCTION\n)\n\n \nreturn\n \nnullptr\n;\n\n \nelse\n\n \nthrow\n;\n\n\n}\n\n\n\n\n\n\nWhen using functions with response codes or errno, always check the result and throw an exception in case of error.\n\n\nif\n \n(\n0\n \n!=\n \nclose\n(\nfd\n))\n\n \nthrowFromErrno\n(\nCannot close file \n \n+\n \nfile_name\n,\n \nErrorCodes\n::\nCANNOT_CLOSE_FILE\n);\n\n\n\n\n\n\nAsserts are not used.\n\n\n4.\n Exception types.\n\n\nThere is no need to use complex exception hierarchy in application code. The exception text should be understandable to a system administrator.\n\n\n5.\n Throwing exceptions from destructors.\n\n\nThis is not recommended, but it is allowed.\n\n\nUse the following options:\n\n\n\n\nCreate a (done() or finalize()) function that will do all the work in advance that might lead to an exception. If that function was called, there should be no exceptions in the destructor later.\n\n\nTasks that are too complex (such as sending messages over the network) can be put in separate method that the class user will have to call before destruction.\n\n\nIf there is an exception in the destructor, it\u2019s better to log it than to hide it (if the logger is available).\n\n\nIn simple applications, it is acceptable to rely on std::terminate (for cases of noexcept by default in C++11) to handle exceptions.\n\n\n\n\n6.\n Anonymous code blocks.\n\n\nYou can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block.\n\n\nBlock\n \nblock\n \n=\n \ndata\n.\nin\n-\nread\n();\n\n\n\n{\n\n \nstd\n::\nlock_guard\nstd\n::\nmutex\n \nlock\n(\nmutex\n);\n\n \ndata\n.\nready\n \n=\n \ntrue\n;\n\n \ndata\n.\nblock\n \n=\n \nblock\n;\n\n\n}\n\n\n\nready_any\n.\nset\n();\n\n\n\n\n\n\n7.\n Multithreading.\n\n\nFor offline data processing applications:\n\n\n\n\nTry to get the best possible performance on a single CPU core. You can then parallelize your code if necessary.\n\n\n\n\nIn server applications:\n\n\n\n\nUse the thread pool to process requests. At this point, we haven't had any tasks that required userspace context switching.\n\n\n\n\nFork is not used for parallelization.\n\n\n8.\n Synchronizing threads.\n\n\nOften it is possible to make different threads use different memory cells (even better: different cache lines,) and to not use any thread synchronization (except joinAll).\n\n\nIf synchronization is required, in most cases, it is sufficient to use mutex under lock_guard.\n\n\nIn other cases use system synchronization primitives. Do not use busy wait.\n\n\nAtomic operations should be used only in the simplest cases.\n\n\nDo not try to implement lock-free data structures unless it is your primary area of expertise.\n\n\n9.\n Pointers vs references.\n\n\nIn most cases, prefer references.\n\n\n10.\n const.\n\n\nUse constant references, pointers to constants, \nconst_iterator\n, \nconst\n methods.\n\n\nConsider \nconst\n to be default and use non-const only when necessary.\n\n\nWhen passing variable by value, using \nconst\n usually does not make sense.\n\n\n11.\n unsigned.\n\n\nUse \nunsigned\n, if needed.\n\n\n12.\n Numeric types\n\n\nUse \nUInt8\n, \nUInt16\n, \nUInt32\n, \nUInt64\n, \nInt8\n, \nInt16\n, \nInt32\n, \nInt64\n, and \nsize_t\n, \nssize_t\n, \nptrdiff_t\n.\n\n\nDon't use \nsigned/unsigned long\n, \nlong long\n, \nshort\n, \nsigned char\n, \nunsigned char\n, or \nchar\n types for numbers.\n\n\n13.\n Passing arguments.\n\n\nPass complex values by reference (including \nstd::string\n).\n\n\nIf a function captures ownership of an objected created in the heap, make the argument type \nshared_ptr\n or \nunique_ptr\n.\n\n\n14.\n Returning values.\n\n\nIn most cases, just use return. Do not write \n[return std::move(res)]{.strike}\n.\n\n\nIf the function allocates an object on heap and returns it, use \nshared_ptr\n or \nunique_ptr\n.\n\n\nIn rare cases you might need to return the value via an argument. In this case, the argument should be a reference.\n\n\nusing\n \nAggregateFunctionPtr\n \n=\n \nstd\n::\nshared_ptr\nIAggregateFunction\n;\n\n\n\n/** Creates an aggregate function by name.\n\n\n */\n\n\nclass\n \nAggregateFunctionFactory\n\n\n{\n\n\npublic\n:\n\n \nAggregateFunctionFactory\n();\n\n \nAggregateFunctionPtr\n \nget\n(\nconst\n \nString\n \n \nname\n,\n \nconst\n \nDataTypes\n \n \nargument_types\n)\n \nconst\n;\n\n\n\n\n\n\n15.\n namespace.\n\n\nThere is no need to use a separate namespace for application code or small libraries.\n\n\nor small libraries.\n\n\nFor medium to large libraries, put everything in the namespace.\n\n\nYou can use the additional detail namespace in a library's \n.h\n file to hide implementation details.\n\n\nIn a \n.cpp\n file, you can use the static or anonymous namespace to hide symbols.\n\n\nYou can also use namespace for enums to prevent its names from polluting the outer namespace, but it\u2019s better to use the enum class.\n\n\n16.\n Delayed initialization.\n\n\nIf arguments are required for initialization then do not write a default constructor.\n\n\nIf later you\u2019ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use \nshared_ptr/unique_ptr\n.\n\n\nLoader\n(\nDB\n::\nConnection\n \n*\n \nconnection_\n,\n \nconst\n \nstd\n::\nstring\n \n \nquery\n,\n \nsize_t\n \nmax_block_size_\n);\n\n\n\n/// For delayed initialization\n\n\nLoader\n()\n \n{}\n\n\n\n\n\n\n17.\n Virtual functions.\n\n\nIf the class is not intended for polymorphic use, you do not need to make functions virtual. This also applies to the destructor.\n\n\n18.\n Encodings.\n\n\nUse UTF-8 everywhere. Use \nstd::string\nand\nchar *\n. Do not use \nstd::wstring\nand\nwchar_t\n.\n\n\n19.\n Logging.\n\n\nSee the examples everywhere in the code.\n\n\nBefore committing, delete all meaningless and debug logging, and any other types of debug output.\n\n\nLogging in cycles should be avoided, even on the Trace level.\n\n\nLogs must be readable at any logging level.\n\n\nLogging should only be used in application code, for the most part.\n\n\nLog messages must be written in English.\n\n\nThe log should preferably be understandable for the system administrator.\n\n\nDo not use profanity in the log.\n\n\nUse UTF-8 encoding in the log. In rare cases you can use non-ASCII characters in the log.\n\n\n20.\n I/O.\n\n\nDon't use iostreams in internal cycles that are critical for application performance (and never use stringstream).\n\n\nUse the DB/IO library instead.\n\n\n21.\n Date and time.\n\n\nSee the \nDateLUT\n library.\n\n\n22.\n include.\n\n\nAlways use \n#pragma once\n instead of include guards.\n\n\n23.\n using.\n\n\nThe \nusing namespace\n is not used.\n\n\nIt's fine if you are 'using' something specific, but make it local inside a class or function.\n\n\n24.\n Do not use trailing return type for functions unless necessary.\n\n\n[auto f() -\ngt; void;]{.strike}\n\n\n\n\n\n25.\n Do not declare and init variables like this:\n\n\nauto\n \ns\n \n=\n \nstd\n::\nstring\n{\nHello\n};\n\n\n\n\n\n\nDo it like this:\n\n\nstd\n::\nstring\n \ns\n \n=\n \nHello\n;\n\n\nstd\n::\nstring\n \ns\n{\nHello\n};\n\n\n\n\n\n\n26.\n For virtual functions, write \nvirtual\n in the base class, but write \noverride\n in descendent classes.\n\n\nUnused features of C++\n\n\n1.\n Virtual inheritance is not used.\n\n\n2.\n Exception specifiers from C++03 are not used.\n\n\n3.\n Function try block is not used, except for the main function in tests.\n\n\nPlatform\n\n\n1.\n We write code for a specific platform.\n\n\nBut other things being equal, cross-platform or portable code is preferred.\n\n\n2.\n The language is C++17.\n\n\n3.\n The compiler is \ngcc\n. At this time (December 2017), the code is compiled using version 7.2. (It can also be compiled using clang 5.)\n\n\nThe standard library is used (implementation of \nlibstdc++\n or \nlibc++\n).\n\n\n4.\n OS: Linux Ubuntu, not older than Precise.\n\n\n5.\n Code is written for x86_64 CPU architecture.\n\n\nThe CPU instruction set is the minimum supported set among our servers. Currently, it is SSE 4.2.\n\n\n6.\n Use \n-Wall -Wextra -Werror\n compilation flags.\n\n\n7.\n Use static linking with all libraries except those that are difficult to connect to statically (see the output of the \nldd\n command).\n\n\n8.\n Code is developed and debugged with release settings.\n\n\nTools\n\n\n1.\n \nKDevelop\n is a good IDE.\n\n\n2.\n For debugging, use \ngdb\n, \nvalgrind\n (\nmemcheck\n), \nstrace\n, \n-fsanitize=\n, ..., \ntcmalloc_minimal_debug\n.\n\n\n3.\n For profiling, use Linux Perf \nvalgrind\n (\ncallgrind\n), \nstrace-cf\n.\n\n\n4.\n Sources are in Git.\n\n\n5.\n Compilation is managed by \nCMake\n.\n\n\n6.\n Releases are in \ndeb\n packages.\n\n\n7.\n Commits to master must not break the build.\n\n\nThough only selected revisions are considered workable.\n\n\n8.\n Make commits as often as possible, even if the code is only partially ready.\n\n\nUse branches for this purpose.\n\n\nIf your code is not buildable yet, exclude it from the build before pushing to master. You'll need to finish it or remove it from master within a few days.\n\n\n9.\n For non-trivial changes, used branches and publish them on the server.\n\n\n10.\n Unused code is removed from the repository.\n\n\nLibraries\n\n\n1.\n The C++14 standard library is used (experimental extensions are fine), as well as boost and Poco frameworks.\n\n\n2.\n If necessary, you can use any well-known libraries available in the OS package.\n\n\nIf there is a good solution already available, then use it, even if it means you have to install another library.\n\n\n(But be prepared to remove bad libraries from code.)\n\n\n3.\n You can install a library that isn't in the packages, if the packages don't have what you need or have an outdated version or the wrong type of compilation.\n\n\n4.\n If the library is small and doesn't have its own complex build system, put the source files in the contrib folder.\n\n\n5.\n Preference is always given to libraries that are already used.\n\n\nGeneral recommendations\n\n\n1.\n Write as little code as possible.\n\n\n2.\n Try the simplest solution.\n\n\n3.\n Don't write code until you know how it's going to work and how the inner loop will function.\n\n\n4.\n In the simplest cases, use 'using' instead of classes or structs.\n\n\n5.\n If possible, do not write copy constructors, assignment operators, destructors (other than a virtual one, if the class contains at least one virtual function), mpve-constructors and move assignment operators. In other words, the compiler-generated functions must work correctly. You can use 'default'.\n\n\n6.\n Code simplification is encouraged. Reduce the size of your code where possible.\n\n\nAdditional recommendations\n\n\n1.\n Explicit \nstd::\n for types from \nstddef.h\n is not recommended.\n\n\nWe recommend writing \nsize_t\n instead \nstd::size_t\n because it's shorter.\n\n\nBut if you prefer, \nstd::\n is acceptable.\n\n\n2.\n Explicit \nstd::\n for functions from the standard C library is not recommended.\n\n\nWrite \nmemcpy\n instead of \nstd::memcpy\n.\n\n\nThe reason is that there are similar non-standard functions, such as \nmemmem\n. We do use these functions on occasion. These functions do not exist in namespace \nstd\n.\n\n\nIf you write \nstd::memcpy\n instead of \nmemcpy\n everywhere, then \nmemmem\n without \nstd::\n will look awkward.\n\n\nNevertheless, \nstd::\n is allowed if you prefer it.\n\n\n3.\n Using functions from C when the ones are available in the standard C++ library.\n\n\nThis is acceptable if it is more efficient.\n\n\nFor example, use \nmemcpy\n instead of \nstd::copy\n for copying large chunks of memory.\n\n\n4.\n Multiline function arguments.\n\n\nAny of the following wrapping styles are allowed:\n\n\nfunction\n(\n\n \nT1\n \nx1\n,\n\n \nT2\n \nx2\n)\n\n\n\n\n\n\nfunction\n(\n\n \nsize_t\n \nleft\n,\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)\n\n\n\n\n\n\nfunction\n(\nsize_t\n \nleft\n,\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)\n\n\n\n\n\n\nfunction\n(\nsize_t\n \nleft\n,\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)\n\n\n\n\n\n\nfunction\n(\n\n \nsize_t\n \nleft\n,\n\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)\n\n\n\n\n\n\nHow to run ClickHouse tests\n\n\nThe \nclickhouse-test\n utility that is used for functional testing is written using Python 2.x.It also requires you to have some third-party packages:\n\n\n$ pip install lxml termcolor\n\n\n\n\n\nIn a nutshell:\n\n\n\n\nPut the \nclickhouse\n program to \n/usr/bin\n (or \nPATH\n)\n\n\nCreate a \nclickhouse-client\n symlink in \n/usr/bin\n pointing to \nclickhouse\n\n\nStart the \nclickhouse\n server\n\n\ncd dbms/tests/\n\n\nRun \n./clickhouse-test\n\n\n\n\nExample usage\n\n\nRun \n./clickhouse-test --help\n to see available options.\n\n\nTo run tests without having to create a symlink or mess with \nPATH\n:\n\n\n./clickhouse-test -c \n../../build/dbms/src/Server/clickhouse --client\n\n\n\n\n\n\nTo run a single test, i.e. \n00395_nullable\n:\n\n\n./clickhouse-test \n00395\n\n\n\n\n\n\nRoadmap\n\n\nQ1 2018\n\n\nNew fuctionality\n\n\n\n\n\n\nSupport for \nUPDATE\n and \nDELETE\n.\n\n\n\n\n\n\nMultidimensional and nested arrays.\n\n\n\n\n\n\nIt can look something like this:\n\n\nCREATE\n \nTABLE\n \nt\n\n\n(\n\n \nx\n \nArray\n(\nArray\n(\nString\n)),\n\n \nz\n \nNested\n(\n\n \nx\n \nArray\n(\nString\n),\n\n \ny\n \nNested\n(...))\n\n\n)\n\n\nENGINE\n \n=\n \nMergeTree\n \nORDER\n \nBY\n \nx\n\n\n\n\n\n\n\n\nExternal MySQL and ODBC tables.\n\n\n\n\nExternal tables can be integrated into ClickHouse using external dictionaries. This new functionality is a convenient alternative to connecting external tables.\n\n\nSELECT\n \n...\n\n\nFROM\n \nmysql\n(\nhost:port\n,\n \ndb\n,\n \ntable\n,\n \nuser\n,\n \npassword\n)\n`\n\n\n\n\n\n\nImprovements\n\n\n\n\nEffective data copying between ClickHouse clusters.\n\n\n\n\nNow you can copy data with the remote() function. For example: \nINSERT INTO t SELECT * FROM remote(...)\n.\n\n\nThis operation will have improved performance.\n\n\n\n\nO_DIRECT for merges.\n\n\n\n\nThis will improve the performance of the OS cache and \"hot\" queries.\n\n\nQ2 2018\n\n\nNew functionality\n\n\n\n\n\n\nUPDATE/DELETE conform to the EU GDPR.\n\n\n\n\n\n\nProtobuf and Parquet input and output formats.\n\n\n\n\n\n\nCreating dictionaries using DDL queries.\n\n\n\n\n\n\nCurrently, dictionaries that are part of the database schema are defined in external XML files. This is inconvenient and counter-intuitive. The new approach should fix it.\n\n\n\n\n\n\nIntegration with LDAP.\n\n\n\n\n\n\nWITH ROLLUP and WITH CUBE for GROUP BY.\n\n\n\n\n\n\nCustom encoding and compression for each column individually.\n\n\n\n\n\n\nAs of now, ClickHouse supports LZ4 and ZSTD compression of columns, and compression settings are global (see the article \nCompression in ClickHouse\n). Per-column compression and encoding will provide more efficient data storage, which in turn will speed up queries.\n\n\n\n\nStoring data on multiple disks on the same server.\n\n\n\n\nThis functionality will make it easier to extend the disk space, since different disk systems can be used for different databases or tables. Currently, users are forced to use symbolic links if the databases and tables must be stored on a different disk.\n\n\nImprovements\n\n\nMany improvements and fixes are planned for the query execution system. For example:\n\n\n\n\nUsing an index for \nin (subquery)\n.\n\n\n\n\nThe index is not used right now, which reduces performance.\n\n\n\n\nPassing predicates from \nwhere\n to subqueries, and passing predicates to views.\n\n\n\n\nThe predicates must be passed, since the view is changed by the subquery. Performance is still low for view filters, and views can't use the primary key of the original table, which makes views useless for large tables.\n\n\n\n\nOptimizing branching operations (ternary operator, if, multiIf).\n\n\n\n\nClickHouse currently performs all branches, even if they aren't necessary.\n\n\n\n\nUsing a primary key for GROUP BY and ORDER BY.\n\n\n\n\nThis will speed up certain types of queries with partially sorted data.\n\n\nQ3-Q4 2018\n\n\nWe don't have any set plans yet, but the main projects will be:\n\n\n\n\nResource pools for executing queries.\n\n\n\n\nThis will make load management more efficient.\n\n\n\n\nANSI SQL JOIN syntax.\n\n\n\n\nImprove ClickHouse compatibility with many SQL tools.", + "title": "Documentation" + }, + { + "location": "/index.html#what-is-clickhouse", + "text": "ClickHouse is a columnar DBMS for OLAP. In a \"normal\" row-oriented DBMS, data is stored in this order: 5123456789123456789 1 Eurobasket - Greece - Bosnia and Herzegovina - example.com 1 2011-09-01 01:03:02 6274717 1294101174 11409 612345678912345678 0 33 6 http://www.example.com/basketball/team/123/match/456789.html http://www.example.com/basketball/team/123/match/987654.html 0 1366 768 32 10 3183 0 0 13 0\\0 1 1 0 0 2011142 -1 0 0 01321 613 660 2011-09-01 08:01:17 0 0 0 0 utf-8 1466 0 0 0 5678901234567890123 277789954 0 0 0 0 0\n5234985259563631958 0 Consulting, Tax assessment, Accounting, Law 1 2011-09-01 01:03:02 6320881 2111222333 213 6458937489576391093 0 3 2 http://www.example.ru/ 0 800 600 16 10 2 153.1 0 0 10 63 1 1 0 0 2111678 000 0 588 368 240 2011-09-01 01:03:17 4 0 60310 0 windows-1251 1466 0 000 778899001 0 0 0 0 0\n... In order words, all the values related to a row are stored next to each other.\nExamples of a row-oriented DBMS are MySQL, Postgres, MS SQL Server, and others. In a column-oriented DBMS, data is stored like this: WatchID: 5385521489354350662 5385521490329509958 5385521489953706054 5385521490476781638 5385521490583269446 5385521490218868806 5385521491437850694 5385521491090174022 5385521490792669254 5385521490420695110 5385521491532181574 5385521491559694406 5385521491459625030 5385521492275175494 5385521492781318214 5385521492710027334 5385521492955615302 5385521493708759110 5385521494506434630 5385521493104611398\nJavaEnable: 1 0 1 0 0 0 1 0 1 1 1 1 1 1 0 1 0 0 1 1\nTitle: Yandex Announcements - Investor Relations - Yandex Yandex \u2014 Contact us \u2014 Moscow Yandex \u2014 Mission Ru Yandex \u2014 History \u2014 History of Yandex Yandex Financial Releases - Investor Relations - Yandex Yandex \u2014 Locations Yandex Board of Directors - Corporate Governance - Yandex Yandex \u2014 Technologies\nGoodEvent: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\nEventTime: 2016-05-18 05:19:20 2016-05-18 08:10:20 2016-05-18 07:38:00 2016-05-18 01:13:08 2016-05-18 00:04:06 2016-05-18 04:21:30 2016-05-18 00:34:16 2016-05-18 07:35:49 2016-05-18 11:41:59 2016-05-18 01:13:32 These examples only show the order that data is arranged in.\nThe values from different columns are stored separately, and data from the same column is stored together. Examples of column-oriented DBMSs: Vertica , Paraccel (Actian Matrix) (Amazon Redshift) , Sybase IQ , Exasol , Infobright , InfiniDB , MonetDB (VectorWise) (Actian Vector) , LucidDB , SAP HANA , Google Dremel , Google PowerDrill , Druid , kdb+ , and so on. Different orders for storing data are better suited to different scenarios.\nThe data access scenario refers to what queries are made, how often, and in what proportion; how much data is read for each type of query \u2013 rows, columns, and bytes; the relationship between reading and updating data; the working size of the data and how locally it is used; whether transactions are used, and how isolated they are; requirements for data replication and logical integrity; requirements for latency and throughput for each type of query, and so on. The higher the load on the system, the more important it is to customize the system to the scenario, and the more specific this customization becomes. There is no system that is equally well-suited to significantly different scenarios. If a system is adaptable to a wide set of scenarios, under a high load, the system will handle all the scenarios equally poorly, or will work well for just one of the scenarios. We'll say that the following is true for the OLAP (online analytical processing) scenario: The vast majority of requests are for read access. Data is updated in fairly large batches ( 1000 rows), not by single rows; or it is not updated at all. Data is added to the DB but is not modified. For reads, quite a large number of rows are extracted from the DB, but only a small subset of columns. Tables are \"wide,\" meaning they contain a large number of columns. Queries are relatively rare (usually hundreds of queries per server or less per second). For simple queries, latencies around 50 ms are allowed. Column values are fairly small: numbers and short strings (for example, 60 bytes per URL). Requires high throughput when processing a single query (up to billions of rows per second per server). There are no transactions. Low requirements for data consistency. There is one large table per query. All tables are small, except for one. A query result is significantly smaller than the source data. In other words, data is filtered or aggregated. The result fits in a single server's RAM. It is easy to see that the OLAP scenario is very different from other popular scenarios (such as OLTP or Key-Value access). So it doesn't make sense to try to use OLTP or a Key-Value DB for processing analytical queries if you want to get decent performance. For example, if you try to use MongoDB or Elliptics for analytics, you will get very poor performance compared to OLAP databases. Columnar-oriented databases are better suited to OLAP scenarios (at least 100 times better in processing speed for most queries), for the following reasons: For I/O. For an analytical query, only a small number of table columns need to be read. In a column-oriented database, you can read just the data you need. For example, if you need 5 columns out of 100, you can expect a 20-fold reduction in I/O. Since data is read in packets, it is easier to compress. Data in columns is also easier to compress. This further reduces the I/O volume. Due to the reduced I/O, more data fits in the system cache. For example, the query \"count the number of records for each advertising platform\" requires reading one \"advertising platform ID\" column, which takes up 1 byte uncompressed. If most of the traffic was not from advertising platforms, you can expect at least 10-fold compression of this column. When using a quick compression algorithm, data decompression is possible at a speed of at least several gigabytes of uncompressed data per second. In other words, this query can be processed at a speed of approximately several billion rows per second on a single server. This speed is actually achieved in practice. Example: milovidov@hostname:~$ clickhouse-client\nClickHouse client version 0 .0.52053.\nConnecting to localhost:9000.\nConnected to ClickHouse server version 0 .0.52053.\n\n: ) SELECT CounterID, count () FROM hits GROUP BY CounterID ORDER BY count () DESC LIMIT 20 \n\nSELECT\n CounterID,\n count () \nFROM hits\nGROUP BY CounterID\nORDER BY count () DESC\nLIMIT 20 \n\n\u250c\u2500CounterID\u2500\u252c\u2500\u2500count () \u2500\u2510\n\u2502 114208 \u2502 56057344 \u2502\n\u2502 115080 \u2502 51619590 \u2502\n\u2502 3228 \u2502 44658301 \u2502\n\u2502 38230 \u2502 42045932 \u2502\n\u2502 145263 \u2502 42042158 \u2502\n\u2502 91244 \u2502 38297270 \u2502\n\u2502 154139 \u2502 26647572 \u2502\n\u2502 150748 \u2502 24112755 \u2502\n\u2502 242232 \u2502 21302571 \u2502\n\u2502 338158 \u2502 13507087 \u2502\n\u2502 62180 \u2502 12229491 \u2502\n\u2502 82264 \u2502 12187441 \u2502\n\u2502 232261 \u2502 12148031 \u2502\n\u2502 146272 \u2502 11438516 \u2502\n\u2502 168777 \u2502 11403636 \u2502\n\u2502 4120072 \u2502 11227824 \u2502\n\u2502 10938808 \u2502 10519739 \u2502\n\u2502 74088 \u2502 9047015 \u2502\n\u2502 115079 \u2502 8837972 \u2502\n\u2502 337234 \u2502 8205961 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 20 rows in set. Elapsed: 0 .153 sec. Processed 1 .00 billion rows, 4 .00 GB ( 6 .53 billion rows/s., 26 .10 GB/s. ) \n\n: ) For CPU. Since executing a query requires processing a large number of rows, it helps to dispatch all operations for entire vectors instead of for separate rows, or to implement the query engine so that there is almost no dispatching cost. If you don't do this, with any half-decent disk subsystem, the query interpreter inevitably stalls the CPU.\nIt makes sense to both store data in columns and process it, when possible, by columns. There are two ways to do this: A vector engine. All operations are written for vectors, instead of for separate values. This means you don't need to call operations very often, and dispatching costs are negligible. Operation code contains an optimized internal cycle. Code generation. The code generated for the query has all the indirect calls in it. This is not done in \"normal\" databases, because it doesn't make sense when running simple queries. However, there are exceptions. For example, MemSQL uses code generation to reduce latency when processing SQL queries. (For comparison, analytical DBMSs require optimization of throughput, not latency.) Note that for CPU efficiency, the query language must be declarative (SQL or MDX), or at least a vector (J, K). The query should only contain implicit loops, allowing for optimization.", + "title": "What is ClickHouse?" + }, + { + "location": "/index.html#introduction", + "text": "", + "title": "Introduction" + }, + { + "location": "/index.html#distinctive-features-of-clickhouse", + "text": "", + "title": "Distinctive features of ClickHouse" + }, + { + "location": "/index.html#true-column-oriented-dbms", + "text": "In a true column-oriented DBMS, there isn't any \"garbage\" stored with the values. Among other things, this means that constant-length values must be supported, to avoid storing their length \"number\" next to the values. As an example, a billion UInt8-type values should actually consume around 1 GB uncompressed, or this will strongly affect the CPU use. It is very important to store data compactly (without any \"garbage\") even when uncompressed, since the speed of decompression (CPU usage) depends mainly on the volume of uncompressed data. This is worth noting because there are systems that can store values of separate columns separately, but that can't effectively process analytical queries due to their optimization for other scenarios. Examples are HBase, BigTable, Cassandra, and HyperTable. In these systems, you will get throughput around a hundred thousand rows per second, but not hundreds of millions of rows per second. Also note that ClickHouse is a DBMS, not a single database. ClickHouse allows creating tables and databases in runtime, loading data, and running queries without reconfiguring and restarting the server.", + "title": "True column-oriented DBMS" + }, + { + "location": "/index.html#data-compression", + "text": "Some column-oriented DBMSs (InfiniDB CE and MonetDB) do not use data compression. However, data compression really improves performance.", + "title": "Data compression" + }, + { + "location": "/index.html#disk-storage-of-data", + "text": "Many column-oriented DBMSs (such as SAP HANA and Google PowerDrill) can only work in RAM. But even on thousands of servers, the RAM is too small for storing all the pageviews and sessions in Yandex.Metrica.", + "title": "Disk storage of data" + }, + { + "location": "/index.html#parallel-processing-on-multiple-cores", + "text": "Large queries are parallelized in a natural way.", + "title": "Parallel processing on multiple cores" + }, + { + "location": "/index.html#distributed-processing-on-multiple-servers", + "text": "Almost none of the columnar DBMSs listed above have support for distributed processing.\nIn ClickHouse, data can reside on different shards. Each shard can be a group of replicas that are used for fault tolerance. The query is processed on all the shards in parallel. This is transparent for the user.", + "title": "Distributed processing on multiple servers" + }, + { + "location": "/index.html#sql-support", + "text": "If you are familiar with standard SQL, we can't really talk about SQL support.\nAll the functions have different names.\nHowever, this is a declarative query language based on SQL that can't be differentiated from SQL in many instances.\nJOINs are supported. Subqueries are supported in FROM, IN, and JOIN clauses, as well as scalar subqueries.\nDependent subqueries are not supported.", + "title": "SQL support" + }, + { + "location": "/index.html#vector-engine", + "text": "Data is not only stored by columns, but is processed by vectors (parts of columns). This allows us to achieve high CPU performance.", + "title": "Vector engine" + }, + { + "location": "/index.html#real-time-data-updates", + "text": "ClickHouse supports primary key tables. In order to quickly perform queries on the range of the primary key, the data is sorted incrementally using the merge tree. Due to this, data can continually be added to the table. There is no locking when adding data.", + "title": "Real-time data updates" + }, + { + "location": "/index.html#indexes", + "text": "Having a primary key makes it possible to extract data for specific clients (for instance, Yandex.Metrica tracking tags) for a specific time range, with low latency less than several dozen milliseconds.", + "title": "Indexes" + }, + { + "location": "/index.html#suitable-for-online-queries", + "text": "This lets us use the system as the back-end for a web interface. Low latency means queries can be processed without delay, while the Yandex.Metrica interface page is loading. In other words, in online mode.", + "title": "Suitable for online queries" + }, + { + "location": "/index.html#support-for-approximated-calculations", + "text": "The system contains aggregate functions for approximated calculation of the number of various values, medians, and quantiles. Supports running a query based on a part (sample) of data and getting an approximated result. In this case, proportionally less data is retrieved from the disk. Supports running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reasonably accurate result while using fewer resources.", + "title": "Support for approximated calculations" + }, + { + "location": "/index.html#data-replication-and-support-for-data-integrity-on-replicas", + "text": "Uses asynchronous multimaster replication. After being written to any available replica, data is distributed to all the remaining replicas. The system maintains identical data on different replicas. Data is restored automatically after a failure, or using a \"button\" for complex cases.\nFor more information, see the section Data replication .", + "title": "Data replication and support for data integrity on replicas" + }, + { + "location": "/index.html#clickhouse-features-that-can-be-considered-disadvantages", + "text": "No transactions. For aggregation, query results must fit in the RAM on a single server. However, the volume of source data for a query may be indefinitely large. Lack of full-fledged UPDATE/DELETE implementation.", + "title": "ClickHouse features that can be considered disadvantages" + }, + { + "location": "/index.html#yandexmetrica-use-case", + "text": "ClickHouse currently powers Yandex.Metrica , the second largest web analytics platform in the world . With more than 13 trillion records in the database and more than 20 billion events daily, ClickHouse allows you generating custom reports on the fly directly from non-aggregated data. We need to get custom reports based on hits and sessions, with custom segments set by the user. Data for the reports is updated in real-time. Queries must be run immediately (in online mode). We must be able to build reports for any time period. Complex aggregates must be calculated, such as the number of unique visitors.\nAt this time (April 2014), Yandex.Metrica receives approximately 12 billion events (pageviews and mouse clicks) daily. All these events must be stored in order to build custom reports. A single query may require scanning hundreds of millions of rows over a few seconds, or millions of rows in no more than a few hundred milliseconds.", + "title": "Yandex.Metrica use case" + }, + { + "location": "/index.html#usage-in-yandexmetrica-and-other-yandex-services", + "text": "ClickHouse is used for multiple purposes in Yandex.Metrica.\nIts main task is to build reports in online mode using non-aggregated data. It uses a cluster of 374 servers, which store over 20.3 trillion rows in the database. The volume of compressed data, without counting duplication and replication, is about 2 PB. The volume of uncompressed data (in TSV format) would be approximately 17 PB. ClickHouse is also used for: Storing data for Session Replay from Yandex.Metrica. Processing intermediate data. Building global reports with Analytics. Running queries for debugging the Yandex.Metrica engine. Analyzing logs from the API and the user interface. ClickHouse has at least a dozen installations in other Yandex services: in search verticals, Market, Direct, business analytics, mobile development, AdFox, personal services, and others.", + "title": "Usage in Yandex.Metrica and other Yandex services" + }, + { + "location": "/index.html#aggregated-and-non-aggregated-data", + "text": "There is a popular opinion that in order to effectively calculate statistics, you must aggregate data, since this reduces the volume of data. But data aggregation is a very limited solution, for the following reasons: You must have a pre-defined list of reports the user will need. The user can't make custom reports. When aggregating a large quantity of keys, the volume of data is not reduced, and aggregation is useless. For a large number of reports, there are too many aggregation variations (combinatorial explosion). When aggregating keys with high cardinality (such as URLs), the volume of data is not reduced by much (less than twofold). For this reason, the volume of data with aggregation might grow instead of shrink. Users do not view all the reports we generate for them. A large portion of calculations are useless. The logical integrity of data may be violated for various aggregations. If we do not aggregate anything and work with non-aggregated data, this might actually reduce the volume of calculations. However, with aggregation, a significant part of the work is taken offline and completed relatively calmly. In contrast, online calculations require calculating as fast as possible, since the user is waiting for the result. Yandex.Metrica has a specialized system for aggregating data called Metrage, which is used for the majority of reports.\nStarting in 2009, Yandex.Metrica also used a specialized OLAP database for non-aggregated data called OLAPServer, which was previously used for the report builder.\nOLAPServer worked well for non-aggregated data, but it had many restrictions that did not allow it to be used for all reports as desired. These included the lack of support for data types (only numbers), and the inability to incrementally update data in real-time (it could only be done by rewriting data daily). OLAPServer is not a DBMS, but a specialized DB. To remove the limitations of OLAPServer and solve the problem of working with non-aggregated data for all reports, we developed the ClickHouse DBMS.", + "title": "Aggregated and non-aggregated data" + }, + { + "location": "/index.html#questions-you-were-afraid-to-ask", + "text": "", + "title": "Questions you were afraid to ask" + }, + { + "location": "/index.html#why-not-use-something-like-mapreduce", + "text": "We can refer to systems like map-reduce as distributed computing systems in which the reduce operation is based on distributed sorting. In this sense, they include Hadoop, and YT (YT is developed at Yandex for internal use). These systems aren't appropriate for online queries due to their high latency. In other words, they can't be used as the back-end for a web interface.\nThese types of systems aren't useful for real-time data updates.\nDistributed sorting isn't the best way to perform reduce operations if the result of the operation and all the intermediate results (if there are any) are located in the RAM of a single server, which is usually the case for online queries. In such a case, a hash table is the optimal way to perform reduce operations. A common approach to optimizing map-reduce tasks is pre-aggregation (partial reduce) using a hash table in RAM. The user performs this optimization manually.\nDistributed sorting is one of the main causes of reduced performance when running simple map-reduce tasks. Systems like map-reduce allow executing any code on the cluster. But a declarative query language is better suited to OLAP in order to run experiments quickly. For example, Hadoop has Hive and Pig. Also consider Cloudera Impala, Shark (outdated) for Spark, and Spark SQL, Presto, and Apache Drill. Performance when running such tasks is highly sub-optimal compared to specialized systems, but relatively high latency makes it unrealistic to use these systems as the backend for a web interface. YT allows storing groups of columns separately. But YT can't be considered a true column-based system because it doesn't have fixed-length data types (for efficiently storing numbers without extra \"garbage\"), and also due to its lack of a vector engine. Tasks are performed in YT using custom code in streaming mode, so they cannot be optimized enough (up to hundreds of millions of rows per second per server). \"Dynamic table sorting\" is under development in YT using MergeTree, strict value typing, and a query language similar to SQL. Dynamically sorted tables are not appropriate for OLAP tasks because the data is stored by row. The YT query language is still under development, so we can't yet rely on this functionality. YT developers are considering using dynamically sorted tables in OLTP and Key-Value scenarios.", + "title": "Why not use something like MapReduce?" + }, + { + "location": "/index.html#performance", + "text": "According to internal testing results, ClickHouse shows the best performance for comparable operating scenarios among systems of its class that were available for testing. This includes the highest throughput for long queries, and the lowest latency on short queries. Testing results are shown on a separate page.", + "title": "Performance" + }, + { + "location": "/index.html#throughput-for-a-single-large-query", + "text": "Throughput can be measured in rows per second or in megabytes per second. If the data is placed in the page cache, a query that is not too complex is processed on modern hardware at a speed of approximately 2-10 GB/s of uncompressed data on a single server (for the simplest cases, the speed may reach 30 GB/s). If data is not placed in the page cache, the speed depends on the disk subsystem and the data compression rate. For example, if the disk subsystem allows reading data at 400 MB/s, and the data compression rate is 3, the speed will be around 1.2 GB/s. To get the speed in rows per second, divide the speed in bytes per second by the total size of the columns used in the query. For example, if 10 bytes of columns are extracted, the speed will be around 100-200 million rows per second. The processing speed increases almost linearly for distributed processing, but only if the number of rows resulting from aggregation or sorting is not too large.", + "title": "Throughput for a single large query" + }, + { + "location": "/index.html#latency-when-processing-short-queries", + "text": "If a query uses a primary key and does not select too many rows to process (hundreds of thousands), and does not use too many columns, we can expect less than 50 milliseconds of latency (single digits of milliseconds in the best case) if data is placed in the page cache. Otherwise, latency is calculated from the number of seeks. If you use rotating drives, for a system that is not overloaded, the latency is calculated by this formula: seek time (10 ms) * number of columns queried * number of data parts.", + "title": "Latency when processing short queries" + }, + { + "location": "/index.html#throughput-when-processing-a-large-quantity-of-short-queries", + "text": "Under the same conditions, ClickHouse can handle several hundred queries per second on a single server (up to several thousand in the best case). Since this scenario is not typical for analytical DBMSs, we recommend expecting a maximum of 100 queries per second.", + "title": "Throughput when processing a large quantity of short queries" + }, + { + "location": "/index.html#performance-when-inserting-data", + "text": "We recommend inserting data in packets of at least 1000 rows, or no more than a single request per second. When inserting to a MergeTree table from a tab-separated dump, the insertion speed will be from 50 to 200 MB/s. If the inserted rows are around 1 Kb in size, the speed will be from 50,000 to 200,000 rows per second. If the rows are small, the performance will be higher in rows per second (on Banner System data - 500,000 rows per second; on Graphite data - 1,000,000 rows per second). To improve performance, you can make multiple INSERT queries in parallel, and performance will increase linearly.", + "title": "Performance when inserting data" + }, + { + "location": "/index.html#getting-started", + "text": "", + "title": "Getting started" + }, + { + "location": "/index.html#system-requirements", + "text": "This is not a cross-platform system. It requires Linux Ubuntu Precise (12.04) or newer, with x86_64 architecture and support for the SSE 4.2 instruction set.\nTo check for SSE 4.2: grep -q sse4_2 /proc/cpuinfo echo SSE 4.2 supported || echo SSE 4.2 not supported We recommend using Ubuntu Trusty, Ubuntu Xenial, or Ubuntu Precise.\nThe terminal must use UTF-8 encoding (the default in Ubuntu).", + "title": "System requirements" + }, + { + "location": "/index.html#installation", + "text": "For testing and development, the system can be installed on a single server or on a desktop computer.", + "title": "Installation" + }, + { + "location": "/index.html#installing-from-packages-for-debianubuntu", + "text": "In /etc/apt/sources.list (or in a separate /etc/apt/sources.list.d/clickhouse.list file), add the repository: deb http://repo.yandex.ru/clickhouse/deb/stable/ main/ If you want to use the most recent test version, replace 'stable' with 'testing'. Then run: sudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4 # optional \nsudo apt-get update\nsudo apt-get install clickhouse-client clickhouse-server You can also download and install packages manually from here: https://repo.yandex.ru/clickhouse/deb/stable/main/ . ClickHouse contains access restriction settings. They are located in the 'users.xml' file (next to 'config.xml').\nBy default, access is allowed from anywhere for the 'default' user, without a password. See 'user/default/networks'.\nFor more information, see the section \"Configuration files\".", + "title": "Installing from packages for Debian/Ubuntu" + }, + { + "location": "/index.html#installing-from-sources", + "text": "To compile, follow the instructions: build.md You can compile packages and install them.\nYou can also use programs without installing packages. Client: dbms/src/Client/\nServer: dbms/src/Server/ For the server, create a catalog with data, such as: /opt/clickhouse/data/default/\n/opt/clickhouse/metadata/default/ (Configurable in the server config.)\nRun 'chown' for the desired user. Note the path to logs in the server config (src/dbms/src/Server/config.xml).", + "title": "Installing from sources" + }, + { + "location": "/index.html#other-installation-methods", + "text": "Docker image: https://hub.docker.com/r/yandex/clickhouse-server/ RPM packages for CentOS or RHEL: https://github.com/Altinity/clickhouse-rpm-install Gentoo overlay: https://github.com/kmeaw/clickhouse-overlay", + "title": "Other installation methods" + }, + { + "location": "/index.html#launch", + "text": "To start the server (as a daemon), run: sudo service clickhouse-server start See the logs in the /var/log/clickhouse-server/ directory. If the server doesn't start, check the configurations in the file /etc/clickhouse-server/config.xml. You can also launch the server from the console: clickhouse-server --config-file = /etc/clickhouse-server/config.xml In this case, the log will be printed to the console, which is convenient during development.\nIf the configuration file is in the current directory, you don't need to specify the '--config-file' parameter. By default, it uses './config.xml'. You can use the command-line client to connect to the server: clickhouse-client The default parameters indicate connecting with localhost:9000 on behalf of the user 'default' without a password.\nThe client can be used for connecting to a remote server. Example: clickhouse-client --host = example.com For more information, see the section \"Command-line client\". Checking the system: milovidov@hostname:~/work/metrica/src/dbms/src/Client$ ./clickhouse-client\nClickHouse client version 0 .0.18749.\nConnecting to localhost:9000.\nConnected to ClickHouse server version 0 .0.18749.\n\n: ) SELECT 1 \n\nSELECT 1 \n\n\u250c\u25001\u2500\u2510\n\u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2518 1 rows in set. Elapsed: 0 .003 sec.\n\n: ) Congratulations, the system works! To continue experimenting, you can try to download from the test data sets.", + "title": "Launch" + }, + { + "location": "/index.html#ontime", + "text": "This performance test was created by Vadim Tkachenko. See: https://www.percona.com/blog/2009/10/02/analyzing-air-traffic-performance-with-infobright-and-monetdb/ https://www.percona.com/blog/2009/10/26/air-traffic-queries-in-luciddb/ https://www.percona.com/blog/2009/11/02/air-traffic-queries-in-infinidb-early-alpha/ https://www.percona.com/blog/2014/04/21/using-apache-hadoop-and-impala-together-with-mysql-for-data-analysis/ https://www.percona.com/blog/2016/01/07/apache-spark-with-air-ontime-performance-data/ http://nickmakos.blogspot.ru/2012/08/analyzing-air-traffic-performance-with.html Downloading data: for s in ` seq 1987 2017 ` do for m in ` seq 1 12 ` do \nwget http://transtats.bts.gov/PREZIP/On_Time_On_Time_Performance_ ${ s } _ ${ m } .zip done done (from https://github.com/Percona-Lab/ontime-airline-performance/blob/master/download.sh ) Creating a table: CREATE TABLE ` ontime ` ( \n ` Year ` UInt16 , \n ` Quarter ` UInt8 , \n ` Month ` UInt8 , \n ` DayofMonth ` UInt8 , \n ` DayOfWeek ` UInt8 , \n ` FlightDate ` Date , \n ` UniqueCarrier ` FixedString ( 7 ), \n ` AirlineID ` Int32 , \n ` Carrier ` FixedString ( 2 ), \n ` TailNum ` String , \n ` FlightNum ` String , \n ` OriginAirportID ` Int32 , \n ` OriginAirportSeqID ` Int32 , \n ` OriginCityMarketID ` Int32 , \n ` Origin ` FixedString ( 5 ), \n ` OriginCityName ` String , \n ` OriginState ` FixedString ( 2 ), \n ` OriginStateFips ` String , \n ` OriginStateName ` String , \n ` OriginWac ` Int32 , \n ` DestAirportID ` Int32 , \n ` DestAirportSeqID ` Int32 , \n ` DestCityMarketID ` Int32 , \n ` Dest ` FixedString ( 5 ), \n ` DestCityName ` String , \n ` DestState ` FixedString ( 2 ), \n ` DestStateFips ` String , \n ` DestStateName ` String , \n ` DestWac ` Int32 , \n ` CRSDepTime ` Int32 , \n ` DepTime ` Int32 , \n ` DepDelay ` Int32 , \n ` DepDelayMinutes ` Int32 , \n ` DepDel15 ` Int32 , \n ` DepartureDelayGroups ` String , \n ` DepTimeBlk ` String , \n ` TaxiOut ` Int32 , \n ` WheelsOff ` Int32 , \n ` WheelsOn ` Int32 , \n ` TaxiIn ` Int32 , \n ` CRSArrTime ` Int32 , \n ` ArrTime ` Int32 , \n ` ArrDelay ` Int32 , \n ` ArrDelayMinutes ` Int32 , \n ` ArrDel15 ` Int32 , \n ` ArrivalDelayGroups ` Int32 , \n ` ArrTimeBlk ` String , \n ` Cancelled ` UInt8 , \n ` CancellationCode ` FixedString ( 1 ), \n ` Diverted ` UInt8 , \n ` CRSElapsedTime ` Int32 , \n ` ActualElapsedTime ` Int32 , \n ` AirTime ` Int32 , \n ` Flights ` Int32 , \n ` Distance ` Int32 , \n ` DistanceGroup ` UInt8 , \n ` CarrierDelay ` Int32 , \n ` WeatherDelay ` Int32 , \n ` NASDelay ` Int32 , \n ` SecurityDelay ` Int32 , \n ` LateAircraftDelay ` Int32 , \n ` FirstDepTime ` String , \n ` TotalAddGTime ` String , \n ` LongestAddGTime ` String , \n ` DivAirportLandings ` String , \n ` DivReachedDest ` String , \n ` DivActualElapsedTime ` String , \n ` DivArrDelay ` String , \n ` DivDistance ` String , \n ` Div1Airport ` String , \n ` Div1AirportID ` Int32 , \n ` Div1AirportSeqID ` Int32 , \n ` Div1WheelsOn ` String , \n ` Div1TotalGTime ` String , \n ` Div1LongestGTime ` String , \n ` Div1WheelsOff ` String , \n ` Div1TailNum ` String , \n ` Div2Airport ` String , \n ` Div2AirportID ` Int32 , \n ` Div2AirportSeqID ` Int32 , \n ` Div2WheelsOn ` String , \n ` Div2TotalGTime ` String , \n ` Div2LongestGTime ` String , \n ` Div2WheelsOff ` String , \n ` Div2TailNum ` String , \n ` Div3Airport ` String , \n ` Div3AirportID ` Int32 , \n ` Div3AirportSeqID ` Int32 , \n ` Div3WheelsOn ` String , \n ` Div3TotalGTime ` String , \n ` Div3LongestGTime ` String , \n ` Div3WheelsOff ` String , \n ` Div3TailNum ` String , \n ` Div4Airport ` String , \n ` Div4AirportID ` Int32 , \n ` Div4AirportSeqID ` Int32 , \n ` Div4WheelsOn ` String , \n ` Div4TotalGTime ` String , \n ` Div4LongestGTime ` String , \n ` Div4WheelsOff ` String , \n ` Div4TailNum ` String , \n ` Div5Airport ` String , \n ` Div5AirportID ` Int32 , \n ` Div5AirportSeqID ` Int32 , \n ` Div5WheelsOn ` String , \n ` Div5TotalGTime ` String , \n ` Div5LongestGTime ` String , \n ` Div5WheelsOff ` String , \n ` Div5TailNum ` String ) ENGINE = MergeTree ( FlightDate , ( Year , FlightDate ), 8192 ) Loading data: for i in *.zip ; do echo $i ; unzip -cq $i *.csv | sed s/\\.00//g | clickhouse-client --host = example-perftest01j --query = INSERT INTO ontime FORMAT CSVWithNames ; done Queries: Q0. select avg ( c1 ) from ( select Year , Month , count ( * ) as c1 from ontime group by Year , Month ); Q1. The number of flights per day from the year 2000 to 2008 SELECT DayOfWeek , count ( * ) AS c FROM ontime WHERE Year = 2000 AND Year = 2008 GROUP BY DayOfWeek ORDER BY c DESC ; Q2. The number of flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008 SELECT DayOfWeek , count ( * ) AS c FROM ontime WHERE DepDelay 10 AND Year = 2000 AND Year = 2008 GROUP BY DayOfWeek ORDER BY c DESC Q3. The number of delays by airport for 2000-2008 SELECT Origin , count ( * ) AS c FROM ontime WHERE DepDelay 10 AND Year = 2000 AND Year = 2008 GROUP BY Origin ORDER BY c DESC LIMIT 10 Q4. The number of delays by carrier for 2007 SELECT Carrier , count ( * ) FROM ontime WHERE DepDelay 10 AND Year = 2007 GROUP BY Carrier ORDER BY count ( * ) DESC Q5. The percentage of delays by carrier for 2007 SELECT Carrier , c , c2 , c * 1000 / c2 as c3 FROM ( \n SELECT \n Carrier , \n count ( * ) AS c \n FROM ontime \n WHERE DepDelay 10 \n AND Year = 2007 \n GROUP BY Carrier ) ANY INNER JOIN ( \n SELECT \n Carrier , \n count ( * ) AS c2 \n FROM ontime \n WHERE Year = 2007 \n GROUP BY Carrier ) USING Carrier ORDER BY c3 DESC ; Better version of the same query: SELECT Carrier , avg ( DepDelay 10 ) * 1000 AS c3 FROM ontime WHERE Year = 2007 GROUP BY Carrier ORDER BY Carrier Q6. The previous request for a broader range of years, 2000-2008 SELECT Carrier , c , c2 , c * 1000 / c2 as c3 FROM ( \n SELECT \n Carrier , \n count ( * ) AS c \n FROM ontime \n WHERE DepDelay 10 \n AND Year = 2000 AND Year = 2008 \n GROUP BY Carrier ) ANY INNER JOIN ( \n SELECT \n Carrier , \n count ( * ) AS c2 \n FROM ontime \n WHERE Year = 2000 AND Year = 2008 \n GROUP BY Carrier ) USING Carrier ORDER BY c3 DESC ; Better version of the same query: SELECT Carrier , avg ( DepDelay 10 ) * 1000 AS c3 FROM ontime WHERE Year = 2000 AND Year = 2008 GROUP BY Carrier ORDER BY Carrier Q7. Percentage of flights delayed for more than 10 minutes, by year SELECT Year , c1 / c2 FROM ( \n select \n Year , \n count ( * ) * 1000 as c1 \n from ontime \n WHERE DepDelay 10 \n GROUP BY Year ) ANY INNER JOIN ( \n select \n Year , \n count ( * ) as c2 \n from ontime \n GROUP BY Year ) USING ( Year ) ORDER BY Year Better version of the same query: SELECT Year , avg ( DepDelay 10 ) FROM ontime GROUP BY Year ORDER BY Year Q8. The most popular destinations by the number of directly connected cities for various year ranges SELECT DestCityName , uniqExact ( OriginCityName ) AS u FROM ontime WHERE Year = 2000 and Year = 2010 GROUP BY DestCityName ORDER BY u DESC LIMIT 10 ; Q9. select Year , count ( * ) as c1 from ontime group by Year ; Q10. select \n min ( Year ), max ( Year ), Carrier , count ( * ) as cnt , \n sum ( ArrDelayMinutes 30 ) as flights_delayed , \n round ( sum ( ArrDelayMinutes 30 ) / count ( * ), 2 ) as rate FROM ontime WHERE \n DayOfWeek not in ( 6 , 7 ) and OriginState not in ( AK , HI , PR , VI ) \n and DestState not in ( AK , HI , PR , VI ) \n and FlightDate 2010-01-01 GROUP by Carrier HAVING cnt 100000 and max ( Year ) 1990 ORDER by rate DESC LIMIT 1000 ; Bonus: SELECT avg ( cnt ) FROM ( SELECT Year , Month , count ( * ) AS cnt FROM ontime WHERE DepDel15 = 1 GROUP BY Year , Month ) select avg ( c1 ) from ( select Year , Month , count ( * ) as c1 from ontime group by Year , Month ) SELECT DestCityName , uniqExact ( OriginCityName ) AS u FROM ontime GROUP BY DestCityName ORDER BY u DESC LIMIT 10 ; SELECT OriginCityName , DestCityName , count () AS c FROM ontime GROUP BY OriginCityName , DestCityName ORDER BY c DESC LIMIT 10 ; SELECT OriginCityName , count () AS c FROM ontime GROUP BY OriginCityName ORDER BY c DESC LIMIT 10 ;", + "title": "OnTime" + }, + { + "location": "/index.html#new-york-taxi-data", + "text": "", + "title": "New York Taxi data" + }, + { + "location": "/index.html#how-to-import-the-raw-data", + "text": "See https://github.com/toddwschneider/nyc-taxi-data and http://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html for the description of the dataset and instructions for downloading. Downloading will result in about 227 GB of uncompressed data in CSV files. The download takes about an hour over a 1 Gbit connection (parallel downloading from s3.amazonaws.com recovers at least half of a 1 Gbit channel).\nSome of the files might not download fully. Check the file sizes and re-download any that seem doubtful. Some of the files might contain invalid rows. You can fix them as follows: sed -E /(.*,){18,}/d data/yellow_tripdata_2010-02.csv data/yellow_tripdata_2010-02.csv_\nsed -E /(.*,){18,}/d data/yellow_tripdata_2010-03.csv data/yellow_tripdata_2010-03.csv_\nmv data/yellow_tripdata_2010-02.csv_ data/yellow_tripdata_2010-02.csv\nmv data/yellow_tripdata_2010-03.csv_ data/yellow_tripdata_2010-03.csv Then the data must be pre-processed in PostgreSQL. This will create selections of points in the polygons (to match points on the map with the boroughs of New York City) and combine all the data into a single denormalized flat table by using a JOIN. To do this, you will need to install PostgreSQL with PostGIS support. Be careful when running initialize_database.sh and manually re-check that all the tables were created correctly. It takes about 20-30 minutes to process each month's worth of data in PostgreSQL, for a total of about 48 hours. You can check the number of downloaded rows as follows: time psql nyc-taxi-data -c SELECT count(*) FROM trips; \n### count\n 1298979494\n(1 row)\n\nreal 7m9.164s (This is slightly more than 1.1 billion rows reported by Mark Litwintschik in a series of blog posts.) The data in PostgreSQL uses 370 GB of space. Exporting the data from PostgreSQL: COPY ( \n SELECT trips . id , \n trips . vendor_id , \n trips . pickup_datetime , \n trips . dropoff_datetime , \n trips . store_and_fwd_flag , \n trips . rate_code_id , \n trips . pickup_longitude , \n trips . pickup_latitude , \n trips . dropoff_longitude , \n trips . dropoff_latitude , \n trips . passenger_count , \n trips . trip_distance , \n trips . fare_amount , \n trips . extra , \n trips . mta_tax , \n trips . tip_amount , \n trips . tolls_amount , \n trips . ehail_fee , \n trips . improvement_surcharge , \n trips . total_amount , \n trips . payment_type , \n trips . trip_type , \n trips . pickup , \n trips . dropoff , \n\n cab_types . type cab_type , \n\n weather . precipitation_tenths_of_mm rain , \n weather . snow_depth_mm , \n weather . snowfall_mm , \n weather . max_temperature_tenths_degrees_celsius max_temp , \n weather . min_temperature_tenths_degrees_celsius min_temp , \n weather . average_wind_speed_tenths_of_meters_per_second wind , \n\n pick_up . gid pickup_nyct2010_gid , \n pick_up . ctlabel pickup_ctlabel , \n pick_up . borocode pickup_borocode , \n pick_up . boroname pickup_boroname , \n pick_up . ct2010 pickup_ct2010 , \n pick_up . boroct2010 pickup_boroct2010 , \n pick_up . cdeligibil pickup_cdeligibil , \n pick_up . ntacode pickup_ntacode , \n pick_up . ntaname pickup_ntaname , \n pick_up . puma pickup_puma , \n\n drop_off . gid dropoff_nyct2010_gid , \n drop_off . ctlabel dropoff_ctlabel , \n drop_off . borocode dropoff_borocode , \n drop_off . boroname dropoff_boroname , \n drop_off . ct2010 dropoff_ct2010 , \n drop_off . boroct2010 dropoff_boroct2010 , \n drop_off . cdeligibil dropoff_cdeligibil , \n drop_off . ntacode dropoff_ntacode , \n drop_off . ntaname dropoff_ntaname , \n drop_off . puma dropoff_puma \n FROM trips \n LEFT JOIN cab_types \n ON trips . cab_type_id = cab_types . id \n LEFT JOIN central_park_weather_observations_raw weather \n ON weather . date = trips . pickup_datetime :: date \n LEFT JOIN nyct2010 pick_up \n ON pick_up . gid = trips . pickup_nyct2010_gid \n LEFT JOIN nyct2010 drop_off \n ON drop_off . gid = trips . dropoff_nyct2010_gid ) TO /opt/milovidov/nyc-taxi-data/trips.tsv ; The data snapshot is created at a speed of about 50 MB per second. While creating the snapshot, PostgreSQL reads from the disk at a speed of about 28 MB per second.\nThis takes about 5 hours. The resulting TSV file is 590612904969 bytes. Create a temporary table in ClickHouse: CREATE TABLE trips ( trip_id UInt32 , vendor_id String , pickup_datetime DateTime , dropoff_datetime Nullable ( DateTime ), store_and_fwd_flag Nullable ( FixedString ( 1 )), rate_code_id Nullable ( UInt8 ), pickup_longitude Nullable ( Float64 ), pickup_latitude Nullable ( Float64 ), dropoff_longitude Nullable ( Float64 ), dropoff_latitude Nullable ( Float64 ), passenger_count Nullable ( UInt8 ), trip_distance Nullable ( Float64 ), fare_amount Nullable ( Float32 ), extra Nullable ( Float32 ), mta_tax Nullable ( Float32 ), tip_amount Nullable ( Float32 ), tolls_amount Nullable ( Float32 ), ehail_fee Nullable ( Float32 ), improvement_surcharge Nullable ( Float32 ), total_amount Nullable ( Float32 ), payment_type Nullable ( String ), trip_type Nullable ( UInt8 ), pickup Nullable ( String ), dropoff Nullable ( String ), cab_type Nullable ( String ), precipitation Nullable ( UInt8 ), snow_depth Nullable ( UInt8 ), snowfall Nullable ( UInt8 ), max_temperature Nullable ( UInt8 ), min_temperature Nullable ( UInt8 ), average_wind_speed Nullable ( UInt8 ), pickup_nyct2010_gid Nullable ( UInt8 ), pickup_ctlabel Nullable ( String ), pickup_borocode Nullable ( UInt8 ), pickup_boroname Nullable ( String ), pickup_ct2010 Nullable ( String ), pickup_boroct2010 Nullable ( String ), pickup_cdeligibil Nullable ( FixedString ( 1 )), pickup_ntacode Nullable ( String ), pickup_ntaname Nullable ( String ), pickup_puma Nullable ( String ), dropoff_nyct2010_gid Nullable ( UInt8 ), dropoff_ctlabel Nullable ( String ), dropoff_borocode Nullable ( UInt8 ), dropoff_boroname Nullable ( String ), dropoff_ct2010 Nullable ( String ), dropoff_boroct2010 Nullable ( String ), dropoff_cdeligibil Nullable ( String ), dropoff_ntacode Nullable ( String ), dropoff_ntaname Nullable ( String ), dropoff_puma Nullable ( String ) ) ENGINE = Log ; It is needed for converting fields to more correct data types and, if possible, to eliminate NULLs. time clickhouse-client --query= INSERT INTO trips FORMAT TabSeparated trips.tsv\n\nreal 75m56.214s Data is read at a speed of 112-140 Mb/second.\nLoading data into a Log type table in one stream took 76 minutes.\nThe data in this table uses 142 GB. (Importing data directly from Postgres is also possible using COPY ... TO PROGRAM .) Unfortunately, all the fields associated with the weather (precipitation...average_wind_speed) were filled with NULL. Because of this, we will remove them from the final data set. To start, we'll create a table on a single server. Later we will make the table distributed. Create and populate a summary table: CREATE TABLE trips_mergetree\nENGINE = MergeTree(pickup_date, pickup_datetime, 8192)\nAS SELECT\n\ntrip_id,\nCAST(vendor_id AS Enum8( 1 = 1, 2 = 2, CMT = 3, VTS = 4, DDS = 5, B02512 = 10, B02598 = 11, B02617 = 12, B02682 = 13, B02764 = 14)) AS vendor_id,\ntoDate(pickup_datetime) AS pickup_date,\nifNull(pickup_datetime, toDateTime(0)) AS pickup_datetime,\ntoDate(dropoff_datetime) AS dropoff_date,\nifNull(dropoff_datetime, toDateTime(0)) AS dropoff_datetime,\nassumeNotNull(store_and_fwd_flag) IN ( Y , 1 , 2 ) AS store_and_fwd_flag,\nassumeNotNull(rate_code_id) AS rate_code_id,\nassumeNotNull(pickup_longitude) AS pickup_longitude,\nassumeNotNull(pickup_latitude) AS pickup_latitude,\nassumeNotNull(dropoff_longitude) AS dropoff_longitude,\nassumeNotNull(dropoff_latitude) AS dropoff_latitude,\nassumeNotNull(passenger_count) AS passenger_count,\nassumeNotNull(trip_distance) AS trip_distance,\nassumeNotNull(fare_amount) AS fare_amount,\nassumeNotNull(extra) AS extra,\nassumeNotNull(mta_tax) AS mta_tax,\nassumeNotNull(tip_amount) AS tip_amount,\nassumeNotNull(tolls_amount) AS tolls_amount,\nassumeNotNull(ehail_fee) AS ehail_fee,\nassumeNotNull(improvement_surcharge) AS improvement_surcharge,\nassumeNotNull(total_amount) AS total_amount,\nCAST((assumeNotNull(payment_type) AS pt) IN ( CSH , CASH , Cash , CAS , Cas , 1 ) ? CSH : (pt IN ( CRD , Credit , Cre , CRE , CREDIT , 2 ) ? CRE : (pt IN ( NOC , No Charge , No , 3 ) ? NOC : (pt IN ( DIS , Dispute , Dis , 4 ) ? DIS : UNK ))) AS Enum8( CSH = 1, CRE = 2, UNK = 0, NOC = 3, DIS = 4)) AS payment_type_,\nassumeNotNull(trip_type) AS trip_type,\nifNull(toFixedString(unhex(pickup), 25), toFixedString( , 25)) AS pickup,\nifNull(toFixedString(unhex(dropoff), 25), toFixedString( , 25)) AS dropoff,\nCAST(assumeNotNull(cab_type) AS Enum8( yellow = 1, green = 2, uber = 3)) AS cab_type,\n\nassumeNotNull(pickup_nyct2010_gid) AS pickup_nyct2010_gid,\ntoFloat32(ifNull(pickup_ctlabel, 0 )) AS pickup_ctlabel,\nassumeNotNull(pickup_borocode) AS pickup_borocode,\nCAST(assumeNotNull(pickup_boroname) AS Enum8( Manhattan = 1, Queens = 4, Brooklyn = 3, = 0, Bronx = 2, Staten Island = 5)) AS pickup_boroname,\ntoFixedString(ifNull(pickup_ct2010, 000000 ), 6) AS pickup_ct2010,\ntoFixedString(ifNull(pickup_boroct2010, 0000000 ), 7) AS pickup_boroct2010,\nCAST(assumeNotNull(ifNull(pickup_cdeligibil, )) AS Enum8( = 0, E = 1, I = 2)) AS pickup_cdeligibil,\ntoFixedString(ifNull(pickup_ntacode, 0000 ), 4) AS pickup_ntacode,\n\nCAST(assumeNotNull(pickup_ntaname) AS Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195)) AS pickup_ntaname,\n\ntoUInt16(ifNull(pickup_puma, 0 )) AS pickup_puma,\n\nassumeNotNull(dropoff_nyct2010_gid) AS dropoff_nyct2010_gid,\ntoFloat32(ifNull(dropoff_ctlabel, 0 )) AS dropoff_ctlabel,\nassumeNotNull(dropoff_borocode) AS dropoff_borocode,\nCAST(assumeNotNull(dropoff_boroname) AS Enum8( Manhattan = 1, Queens = 4, Brooklyn = 3, = 0, Bronx = 2, Staten Island = 5)) AS dropoff_boroname,\ntoFixedString(ifNull(dropoff_ct2010, 000000 ), 6) AS dropoff_ct2010,\ntoFixedString(ifNull(dropoff_boroct2010, 0000000 ), 7) AS dropoff_boroct2010,\nCAST(assumeNotNull(ifNull(dropoff_cdeligibil, )) AS Enum8( = 0, E = 1, I = 2)) AS dropoff_cdeligibil,\ntoFixedString(ifNull(dropoff_ntacode, 0000 ), 4) AS dropoff_ntacode,\n\nCAST(assumeNotNull(dropoff_ntaname) AS Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195)) AS dropoff_ntaname,\n\ntoUInt16(ifNull(dropoff_puma, 0 )) AS dropoff_puma\n\nFROM trips This takes 3030 seconds at a speed of about 428,000 rows per second.\nTo load it faster, you can create the table with the Log engine instead of MergeTree . In this case, the download works faster than 200 seconds. The table uses 126 GB of disk space. :) SELECT formatReadableSize(sum(bytes)) FROM system.parts WHERE table = trips_mergetree AND active\n\nSELECT formatReadableSize(sum(bytes))\nFROM system.parts\nWHERE (table = trips_mergetree ) AND active\n\n\u250c\u2500formatReadableSize(sum(bytes))\u2500\u2510\n\u2502 126.18 GiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Among other things, you can run the OPTIMIZE query on MergeTree. But it's not required, since everything will be fine without it.", + "title": "How to import the raw data" + }, + { + "location": "/index.html#results-on-single-server", + "text": "Q1: SELECT cab_type , count ( * ) FROM trips_mergetree GROUP BY cab_type 0.490 seconds. Q2: SELECT passenger_count , avg ( total_amount ) FROM trips_mergetree GROUP BY passenger_count 1.224 seconds. Q3: SELECT passenger_count , toYear ( pickup_date ) AS year , count ( * ) FROM trips_mergetree GROUP BY passenger_count , year 2.104 seconds. Q4: SELECT passenger_count , toYear ( pickup_date ) AS year , round ( trip_distance ) AS distance , count ( * ) FROM trips_mergetree GROUP BY passenger_count , year , distance ORDER BY year , count ( * ) DESC 3.593 seconds. The following server was used: Two Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical kernels total,\n128 GiB RAM,\n8x6 TB HD on hardware RAID-5 Execution time is the best of three runsBut starting from the second run, queries read data from the file system cache. No further caching occurs: the data is read out and processed in each run. Creating a table on three servers: On each server: CREATE TABLE default.trips_mergetree_third ( trip_id UInt32, vendor_id Enum8( 1 = 1, 2 = 2, CMT = 3, VTS = 4, DDS = 5, B02512 = 10, B02598 = 11, B02617 = 12, B02682 = 13, B02764 = 14), pickup_date Date, pickup_datetime DateTime, dropoff_date Date, dropoff_datetime DateTime, store_and_fwd_flag UInt8, rate_code_id UInt8, pickup_longitude Float64, pickup_latitude Float64, dropoff_longitude Float64, dropoff_latitude Float64, passenger_count UInt8, trip_distance Float64, fare_amount Float32, extra Float32, mta_tax Float32, tip_amount Float32, tolls_amount Float32, ehail_fee Float32, improvement_surcharge Float32, total_amount Float32, payment_type_ Enum8( UNK = 0, CSH = 1, CRE = 2, NOC = 3, DIS = 4), trip_type UInt8, pickup FixedString(25), dropoff FixedString(25), cab_type Enum8( yellow = 1, green = 2, uber = 3), pickup_nyct2010_gid UInt8, pickup_ctlabel Float32, pickup_borocode UInt8, pickup_boroname Enum8( = 0, Manhattan = 1, Bronx = 2, Brooklyn = 3, Queens = 4, Staten Island = 5), pickup_ct2010 FixedString(6), pickup_boroct2010 FixedString(7), pickup_cdeligibil Enum8( = 0, E = 1, I = 2), pickup_ntacode FixedString(4), pickup_ntaname Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195), pickup_puma UInt16, dropoff_nyct2010_gid UInt8, dropoff_ctlabel Float32, dropoff_borocode UInt8, dropoff_boroname Enum8( = 0, Manhattan = 1, Bronx = 2, Brooklyn = 3, Queens = 4, Staten Island = 5), dropoff_ct2010 FixedString(6), dropoff_boroct2010 FixedString(7), dropoff_cdeligibil Enum8( = 0, E = 1, I = 2), dropoff_ntacode FixedString(4), dropoff_ntaname Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195), dropoff_puma UInt16) ENGINE = MergeTree(pickup_date, pickup_datetime, 8192) On the source server: CREATE TABLE trips_mergetree_x3 AS trips_mergetree_third ENGINE = Distributed ( perftest , default , trips_mergetree_third , rand ()) The following query redistributes data: INSERT INTO trips_mergetree_x3 SELECT * FROM trips_mergetree This takes 2454 seconds. On three servers: Q1: 0.212 seconds.\nQ2: 0.438 seconds.\nQ3: 0.733 seconds.\nQ4: 1.241 seconds. No surprises here, since the queries are scaled linearly. We also have results from a cluster of 140 servers: Q1: 0.028 sec.\nQ2: 0.043 sec.\nQ3: 0.051 sec.\nQ4: 0.072 sec. In this case, the query processing time is determined above all by network latency.\nWe ran queries using a client located in a Yandex datacenter in Finland on a cluster in Russia, which added about 20 ms of latency.", + "title": "Results on single server" + }, + { + "location": "/index.html#summary", + "text": "nodes Q1 Q2 Q3 Q4\n 1 0.490 1.224 2.104 3.593\n 3 0.212 0.438 0.733 1.241\n140 0.028 0.043 0.051 0.072", + "title": "Summary" + }, + { + "location": "/index.html#amplab-big-data-benchmark", + "text": "See https://amplab.cs.berkeley.edu/benchmark/ Sign up for a free account at https://aws.amazon.com . You will need a credit card, email and phone number.Get a new access key at https://console.aws.amazon.com/iam/home?nc2=h_m_sc#security_credential Run the following in the console: sudo apt-get install s3cmd\nmkdir tiny ; cd tiny ; \ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/tiny/ . cd ..\nmkdir 1node ; cd 1node ; \ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/1node/ . cd ..\nmkdir 5nodes ; cd 5nodes ; \ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/5nodes/ . cd .. Run the following ClickHouse queries: CREATE TABLE rankings_tiny ( \n pageURL String , \n pageRank UInt32 , \n avgDuration UInt32 ) ENGINE = Log ; CREATE TABLE uservisits_tiny ( \n sourceIP String , \n destinationURL String , \n visitDate Date , \n adRevenue Float32 , \n UserAgent String , \n cCode FixedString ( 3 ), \n lCode FixedString ( 6 ), \n searchWord String , \n duration UInt32 ) ENGINE = MergeTree ( visitDate , visitDate , 8192 ); CREATE TABLE rankings_1node ( \n pageURL String , \n pageRank UInt32 , \n avgDuration UInt32 ) ENGINE = Log ; CREATE TABLE uservisits_1node ( \n sourceIP String , \n destinationURL String , \n visitDate Date , \n adRevenue Float32 , \n UserAgent String , \n cCode FixedString ( 3 ), \n lCode FixedString ( 6 ), \n searchWord String , \n duration UInt32 ) ENGINE = MergeTree ( visitDate , visitDate , 8192 ); CREATE TABLE rankings_5nodes_on_single ( \n pageURL String , \n pageRank UInt32 , \n avgDuration UInt32 ) ENGINE = Log ; CREATE TABLE uservisits_5nodes_on_single ( \n sourceIP String , \n destinationURL String , \n visitDate Date , \n adRevenue Float32 , \n UserAgent String , \n cCode FixedString ( 3 ), \n lCode FixedString ( 6 ), \n searchWord String , \n duration UInt32 ) ENGINE = MergeTree ( visitDate , visitDate , 8192 ); Go back to the console: for i in tiny/rankings/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO rankings_tiny FORMAT CSV ; done for i in tiny/uservisits/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO uservisits_tiny FORMAT CSV ; done for i in 1node/rankings/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO rankings_1node FORMAT CSV ; done for i in 1node/uservisits/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO uservisits_1node FORMAT CSV ; done for i in 5nodes/rankings/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO rankings_5nodes_on_single FORMAT CSV ; done for i in 5nodes/uservisits/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO uservisits_5nodes_on_single FORMAT CSV ; done Queries for obtaining data samples: SELECT pageURL , pageRank FROM rankings_1node WHERE pageRank 1000 SELECT substring ( sourceIP , 1 , 8 ), sum ( adRevenue ) FROM uservisits_1node GROUP BY substring ( sourceIP , 1 , 8 ) SELECT \n sourceIP , \n sum ( adRevenue ) AS totalRevenue , \n avg ( pageRank ) AS pageRank FROM rankings_1node ALL INNER JOIN ( \n SELECT \n sourceIP , \n destinationURL AS pageURL , \n adRevenue \n FROM uservisits_1node \n WHERE ( visitDate 1980-01-01 ) AND ( visitDate 1980-04-01 ) ) USING pageURL GROUP BY sourceIP ORDER BY totalRevenue DESC LIMIT 1", + "title": "AMPLab Big Data Benchmark" + }, + { + "location": "/index.html#wikistat", + "text": "See: http://dumps.wikimedia.org/other/pagecounts-raw/ Creating a table: CREATE TABLE wikistat ( \n date Date , \n time DateTime , \n project String , \n subproject String , \n path String , \n hits UInt64 , \n size UInt64 ) ENGINE = MergeTree ( date , ( path , time ), 8192 ); Loading data: for i in { 2007 ..2016 } ; do for j in { 01 ..12 } ; do echo $i - $j 2 ; curl -sSL http://dumps.wikimedia.org/other/pagecounts-raw/ $i / $i - $j / | grep -oE pagecounts-[0-9]+-[0-9]+\\.gz ; done ; done | sort | uniq | tee links.txt\ncat links.txt | while read link ; do wget http://dumps.wikimedia.org/other/pagecounts-raw/ $( echo $link | sed -r s/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1/ ) / $( echo $link | sed -r s/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1-\\2/ ) / $link ; done \nls -1 /opt/wikistat/ | grep gz | while read i ; do echo $i ; gzip -cd /opt/wikistat/ $i | ./wikistat-loader --time = $( echo -n $i | sed -r s/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\\.gz/\\1-\\2-\\3 \\4-00-00/ ) | clickhouse-client --query = INSERT INTO wikistat FORMAT TabSeparated ; done", + "title": "WikiStat" + }, + { + "location": "/index.html#terabyte-of-click-logs-from-criteo", + "text": "Download the data from http://labs.criteo.com/downloads/download-terabyte-click-logs/ Create a table to import the log to: CREATE TABLE criteo_log ( date Date , clicked UInt8 , int1 Int32 , int2 Int32 , int3 Int32 , int4 Int32 , int5 Int32 , int6 Int32 , int7 Int32 , int8 Int32 , int9 Int32 , int10 Int32 , int11 Int32 , int12 Int32 , int13 Int32 , cat1 String , cat2 String , cat3 String , cat4 String , cat5 String , cat6 String , cat7 String , cat8 String , cat9 String , cat10 String , cat11 String , cat12 String , cat13 String , cat14 String , cat15 String , cat16 String , cat17 String , cat18 String , cat19 String , cat20 String , cat21 String , cat22 String , cat23 String , cat24 String , cat25 String , cat26 String ) ENGINE = Log Download the data: for i in { 00 ..23 } ; do echo $i ; zcat datasets/criteo/day_ ${ i #0 } .gz | sed -r s/^/2000-01- ${ i /00/24 } \\t/ | clickhouse-client --host = example-perftest01j --query = INSERT INTO criteo_log FORMAT TabSeparated ; done Create a table for the converted data: CREATE TABLE criteo ( \n date Date , \n clicked UInt8 , \n int1 Int32 , \n int2 Int32 , \n int3 Int32 , \n int4 Int32 , \n int5 Int32 , \n int6 Int32 , \n int7 Int32 , \n int8 Int32 , \n int9 Int32 , \n int10 Int32 , \n int11 Int32 , \n int12 Int32 , \n int13 Int32 , \n icat1 UInt32 , \n icat2 UInt32 , \n icat3 UInt32 , \n icat4 UInt32 , \n icat5 UInt32 , \n icat6 UInt32 , \n icat7 UInt32 , \n icat8 UInt32 , \n icat9 UInt32 , \n icat10 UInt32 , \n icat11 UInt32 , \n icat12 UInt32 , \n icat13 UInt32 , \n icat14 UInt32 , \n icat15 UInt32 , \n icat16 UInt32 , \n icat17 UInt32 , \n icat18 UInt32 , \n icat19 UInt32 , \n icat20 UInt32 , \n icat21 UInt32 , \n icat22 UInt32 , \n icat23 UInt32 , \n icat24 UInt32 , \n icat25 UInt32 , \n icat26 UInt32 ) ENGINE = MergeTree ( date , intHash32 ( icat1 ), ( date , intHash32 ( icat1 )), 8192 ) Transform data from the raw log and put it in the second table: INSERT INTO criteo SELECT date , clicked , int1 , int2 , int3 , int4 , int5 , int6 , int7 , int8 , int9 , int10 , int11 , int12 , int13 , reinterpretAsUInt32 ( unhex ( cat1 )) AS icat1 , reinterpretAsUInt32 ( unhex ( cat2 )) AS icat2 , reinterpretAsUInt32 ( unhex ( cat3 )) AS icat3 , reinterpretAsUInt32 ( unhex ( cat4 )) AS icat4 , reinterpretAsUInt32 ( unhex ( cat5 )) AS icat5 , reinterpretAsUInt32 ( unhex ( cat6 )) AS icat6 , reinterpretAsUInt32 ( unhex ( cat7 )) AS icat7 , reinterpretAsUInt32 ( unhex ( cat8 )) AS icat8 , reinterpretAsUInt32 ( unhex ( cat9 )) AS icat9 , reinterpretAsUInt32 ( unhex ( cat10 )) AS icat10 , reinterpretAsUInt32 ( unhex ( cat11 )) AS icat11 , reinterpretAsUInt32 ( unhex ( cat12 )) AS icat12 , reinterpretAsUInt32 ( unhex ( cat13 )) AS icat13 , reinterpretAsUInt32 ( unhex ( cat14 )) AS icat14 , reinterpretAsUInt32 ( unhex ( cat15 )) AS icat15 , reinterpretAsUInt32 ( unhex ( cat16 )) AS icat16 , reinterpretAsUInt32 ( unhex ( cat17 )) AS icat17 , reinterpretAsUInt32 ( unhex ( cat18 )) AS icat18 , reinterpretAsUInt32 ( unhex ( cat19 )) AS icat19 , reinterpretAsUInt32 ( unhex ( cat20 )) AS icat20 , reinterpretAsUInt32 ( unhex ( cat21 )) AS icat21 , reinterpretAsUInt32 ( unhex ( cat22 )) AS icat22 , reinterpretAsUInt32 ( unhex ( cat23 )) AS icat23 , reinterpretAsUInt32 ( unhex ( cat24 )) AS icat24 , reinterpretAsUInt32 ( unhex ( cat25 )) AS icat25 , reinterpretAsUInt32 ( unhex ( cat26 )) AS icat26 FROM criteo_log ; DROP TABLE criteo_log ;", + "title": "Terabyte of click logs from Criteo" + }, + { + "location": "/index.html#star-schema-benchmark", + "text": "Compiling dbgen: https://github.com/vadimtk/ssb-dbgen git clone git@github.com:vadimtk/ssb-dbgen.git cd ssb-dbgen\nmake There will be some warnings during the process, but this is normal. Place dbgen and dists.dss in any location with 800 GB of free disk space. Generating data: ./dbgen -s 1000 -T c\n./dbgen -s 1000 -T l Creating tables in ClickHouse: CREATE TABLE lineorder ( \n LO_ORDERKEY UInt32 , \n LO_LINENUMBER UInt8 , \n LO_CUSTKEY UInt32 , \n LO_PARTKEY UInt32 , \n LO_SUPPKEY UInt32 , \n LO_ORDERDATE Date , \n LO_ORDERPRIORITY String , \n LO_SHIPPRIORITY UInt8 , \n LO_QUANTITY UInt8 , \n LO_EXTENDEDPRICE UInt32 , \n LO_ORDTOTALPRICE UInt32 , \n LO_DISCOUNT UInt8 , \n LO_REVENUE UInt32 , \n LO_SUPPLYCOST UInt32 , \n LO_TAX UInt8 , \n LO_COMMITDATE Date , \n LO_SHIPMODE String ) Engine = MergeTree ( LO_ORDERDATE ,( LO_ORDERKEY , LO_LINENUMBER , LO_ORDERDATE ), 8192 ); CREATE TABLE customer ( \n C_CUSTKEY UInt32 , \n C_NAME String , \n C_ADDRESS String , \n C_CITY String , \n C_NATION String , \n C_REGION String , \n C_PHONE String , \n C_MKTSEGMENT String , \n C_FAKEDATE Date ) Engine = MergeTree ( C_FAKEDATE ,( C_CUSTKEY , C_FAKEDATE ), 8192 ); CREATE TABLE part ( \n P_PARTKEY UInt32 , \n P_NAME String , \n P_MFGR String , \n P_CATEGORY String , \n P_BRAND String , \n P_COLOR String , \n P_TYPE String , \n P_SIZE UInt8 , \n P_CONTAINER String , \n P_FAKEDATE Date ) Engine = MergeTree ( P_FAKEDATE ,( P_PARTKEY , P_FAKEDATE ), 8192 ); CREATE TABLE lineorderd AS lineorder ENGINE = Distributed ( perftest_3shards_1replicas , default , lineorder , rand ()); CREATE TABLE customerd AS customer ENGINE = Distributed ( perftest_3shards_1replicas , default , customer , rand ()); CREATE TABLE partd AS part ENGINE = Distributed ( perftest_3shards_1replicas , default , part , rand ()); For testing on a single server, just use MergeTree tables.\nFor distributed testing, you need to configure the perftest_3shards_1replicas cluster in the config file.\nNext, create MergeTree tables on each server and a Distributed above them. Downloading data (change 'customer' to 'customerd' in the distributed version): cat customer.tbl | sed s/$/2000-01-01/ | clickhouse-client --query INSERT INTO customer FORMAT CSV \ncat lineorder.tbl | clickhouse-client --query INSERT INTO lineorder FORMAT CSV", + "title": "Star Schema Benchmark" + }, + { + "location": "/index.html#interfaces", + "text": "To explore the system's capabilities, download data to tables, or make manual queries, use the clickhouse-client program.", + "title": "Interfaces" + }, + { + "location": "/index.html#command-line-client", + "text": "To work from the command line, you can use clickhouse-client : $ clickhouse-client\nClickHouse client version 0 .0.26176.\nConnecting to localhost:9000.\nConnected to ClickHouse server version 0 .0.26176.\n\n: ) The client supports command-line options and configuration files. For more information, see \" Configuring \".", + "title": "Command-line client" + }, + { + "location": "/index.html#usage", + "text": "The client can be used in interactive and non-interactive (batch) mode.\nTo use batch mode, specify the 'query' parameter, or send data to 'stdin' (it verifies that 'stdin' is not a terminal), or both.\nSimilar to the HTTP interface, when using the 'query' parameter and sending data to 'stdin', the request is a concatenation of the 'query' parameter, a line feed, and the data in 'stdin'. This is convenient for large INSERT queries. Example of using the client to insert data: echo -ne 1, some text , 2016-08-14 00:00:00 \\n2, some more text , 2016-08-14 00:00:01 | clickhouse-client --database = test --query = INSERT INTO test FORMAT CSV ; \n\ncat _EOF | clickhouse-client --database=test --query= INSERT INTO test FORMAT CSV ; 3, some text , 2016-08-14 00:00:00 4, some more text , 2016-08-14 00:00:01 _EOF \n\ncat file.csv | clickhouse-client --database = test --query = INSERT INTO test FORMAT CSV ; In batch mode, the default data format is TabSeparated. You can set the format in the FORMAT clause of the query. By default, you can only process a single query in batch mode. To make multiple queries from a \"script,\" use the --multiquery parameter. This works for all queries except INSERT. Query results are output consecutively without additional separators.\nSimilarly, to process a large number of queries, you can run 'clickhouse-client' for each query. Note that it may take tens of milliseconds to launch the 'clickhouse-client' program. In interactive mode, you get a command line where you can enter queries. If 'multiline' is not specified (the default):To run the query, press Enter. The semicolon is not necessary at the end of the query. To enter a multiline query, enter a backslash \\ before the line feed. After you press Enter, you will be asked to enter the next line of the query. If multiline is specified:To run a query, end it with a semicolon and press Enter. If the semicolon was omitted at the end of the entered line, you will be asked to enter the next line of the query. Only a single query is run, so everything after the semicolon is ignored. You can specify \\G instead of or after the semicolon. This indicates Vertical format. In this format, each value is printed on a separate line, which is convenient for wide tables. This unusual feature was added for compatibility with the MySQL CLI. The command line is based on 'readline' (and 'history' or 'libedit', or without a library, depending on the build). In other words, it uses the familiar keyboard shortcuts and keeps a history.\nThe history is written to ~/.clickhouse-client-history . By default, the format used is PrettyCompact. You can change the format in the FORMAT clause of the query, or by specifying \\G at the end of the query, using the --format or --vertical argument in the command line, or using the client configuration file. To exit the client, press Ctrl+D (or Ctrl+C), or enter one of the following instead of a query:\"exit\", \"quit\", \"logout\", \"\u0443\u0447\u0448\u0435\", \"\u0439\u0433\u0448\u0435\", \"\u0434\u0449\u043f\u0449\u0433\u0435\", \"exit;\", \"quit;\", \"logout;\", \"\u0443\u0447\u0448\u0435\u0436\", \"\u0439\u0433\u0448\u0435\u0436\", \"\u0434\u0449\u043f\u0449\u0433\u0435\u0436\", \"q\", \"\u0439\", \"q\", \"Q\", \":q\", \"\u0439\", \"\u0419\", \"\u0416\u0439\" When processing a query, the client shows: Progress, which is updated no more than 10 times per second (by default). For quick queries, the progress might not have time to be displayed. The formatted query after parsing, for debugging. The result in the specified format. The number of lines in the result, the time passed, and the average speed of query processing. You can cancel a long query by pressing Ctrl+C. However, you will still need to wait a little for the server to abort the request. It is not possible to cancel a query at certain stages. If you don't wait and press Ctrl+C a second time, the client will exit. The command-line client allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".", + "title": "Usage" + }, + { + "location": "/index.html#configuring", + "text": "You can pass parameters to clickhouse-client (all parameters have a default value) using: From the Command Line Command-line options override the default values and settings in configuration files. Configuration files. Settings in the configuration files override the default values.", + "title": "Configuring" + }, + { + "location": "/index.html#command-line-options", + "text": "--host, -h -\u2013 The server name, 'localhost' by default. You can use either the name or the IPv4 or IPv6 address. --port \u2013 The port to connect to. Default value: 9000. Note that the HTTP interface and the native interface use different ports. --user, -u \u2013 The username. Default value: default. --password \u2013 The password. Default value: empty string. --query, -q \u2013 The query to process when using non-interactive mode. --database, -d \u2013 Select the current default database. Default value: the current database from the server settings ('default' by default). --multiline, -m \u2013 If specified, allow multiline queries (do not send the query on Enter). --multiquery, -n \u2013 If specified, allow processing multiple queries separated by commas. Only works in non-interactive mode. --format, -f \u2013 Use the specified default format to output the result. --vertical, -E \u2013 If specified, use the Vertical format by default to output the result. This is the same as '--format=Vertical'. In this format, each value is printed on a separate line, which is helpful when displaying wide tables. --time, -t \u2013 If specified, print the query execution time to 'stderr' in non-interactive mode. --stacktrace \u2013 If specified, also print the stack trace if an exception occurs. -config-file \u2013 The name of the configuration file.", + "title": "Command line options" + }, + { + "location": "/index.html#configuration-files", + "text": "clickhouse-client uses the first existing file of the following: Defined in the -config-file parameter. ./clickhouse-client.xml \\~/.clickhouse-client/config.xml /etc/clickhouse-client/config.xml Example of a config file: config \n user username /user \n password password /password /config", + "title": "Configuration files" + }, + { + "location": "/index.html#http-interface", + "text": "The HTTP interface lets you use ClickHouse on any platform from any programming language. We use it for working from Java and Perl, as well as shell scripts. In other departments, the HTTP interface is used from Perl, Python, and Go. The HTTP interface is more limited than the native interface, but it has better compatibility. By default, clickhouse-server listens for HTTP on port 8123 (this can be changed in the config).\nIf you make a GET / request without parameters, it returns the string \"Ok\" (with a line feed at the end). You can use this in health-check scripts. $ curl http://localhost:8123/ \nOk. Send the request as a URL 'query' parameter, or as a POST. Or send the beginning of the query in the 'query' parameter, and the rest in the POST (we'll explain later why this is necessary). The size of the URL is limited to 16 KB, so keep this in mind when sending large queries. If successful, you receive the 200 response code and the result in the response body.\nIf an error occurs, you receive the 500 response code and an error description text in the response body. When using the GET method, 'readonly' is set. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter. Examples: $ curl http://localhost:8123/?query=SELECT%201 1 \n\n$ wget -O- -q http://localhost:8123/?query=SELECT 1 1 \n\n$ GET http://localhost:8123/?query=SELECT 1 1 \n\n$ echo -ne GET /?query=SELECT%201 HTTP/1.0\\r\\n\\r\\n | nc localhost 8123 \nHTTP/1.0 200 OK\nConnection: Close\nDate: Fri, 16 Nov 2012 19 :21:50 GMT 1 As you can see, curl is somewhat inconvenient in that spaces must be URL escaped.Although wget escapes everything itself, we don't recommend using it because it doesn't work well over HTTP 1.1 when using keep-alive and Transfer-Encoding: chunked. $ echo SELECT 1 | curl http://localhost:8123/ --data-binary @- 1 \n\n$ echo SELECT 1 | curl http://localhost:8123/?query= --data-binary @- 1 \n\n$ echo 1 | curl http://localhost:8123/?query=SELECT --data-binary @- 1 If part of the query is sent in the parameter, and part in the POST, a line feed is inserted between these two data parts.\nExample (this won't work): $ echo ECT 1 | curl http://localhost:8123/?query=SEL --data-binary @-\nCode: 59 , e.displayText () = DB::Exception: Syntax error: failed at position 0 : SEL\nECT 1 \n, expected One of: SHOW TABLES, SHOW DATABASES, SELECT, INSERT, CREATE, ATTACH, RENAME, DROP, DETACH, USE, SET, OPTIMIZE., e.what () = DB::Exception By default, data is returned in TabSeparated format (for more information, see the \"Formats\" section).\nYou use the FORMAT clause of the query to request any other format. $ echo SELECT 1 FORMAT Pretty | curl http://localhost:8123/? --data-binary @-\n\u250f\u2501\u2501\u2501\u2513\n\u2503 1 \u2503\n\u2521\u2501\u2501\u2501\u2529\n\u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2518 The POST method of transmitting data is necessary for INSERT queries. In this case, you can write the beginning of the query in the URL parameter, and use POST to pass the data to insert. The data to insert could be, for example, a tab-separated dump from MySQL. In this way, the INSERT query replaces LOAD DATA LOCAL INFILE from MySQL. Examples: Creating a table: echo CREATE TABLE t (a UInt8) ENGINE = Memory | POST http://localhost:8123/ Using the familiar INSERT query for data insertion: echo INSERT INTO t VALUES (1),(2),(3) | POST http://localhost:8123/ Data can be sent separately from the query: echo (4),(5),(6) | POST http://localhost:8123/?query=INSERT INTO t VALUES You can specify any data format. The 'Values' format is the same as what is used when writing INSERT INTO t VALUES: echo (7),(8),(9) | POST http://localhost:8123/?query=INSERT INTO t FORMAT Values To insert data from a tab-separated dump, specify the corresponding format: echo -ne 10\\n11\\n12\\n | POST http://localhost:8123/?query=INSERT INTO t FORMAT TabSeparated Reading the table contents. Data is output in random order due to parallel query processing: $ GET http://localhost:8123/?query=SELECT a FROM t 7 8 9 10 11 12 1 2 3 4 5 6 Deleting the table. POST http://localhost:8123/?query=DROP TABLE t For successful requests that don't return a data table, an empty response body is returned. You can use the internal ClickHouse compression format when transmitting data. The compressed data has a non-standard format, and you will need to use the special clickhouse-compressor program to work with it (it is installed with the clickhouse-client package). If you specified 'compress=1' in the URL, the server will compress the data it sends you.\nIf you specified 'decompress=1' in the URL, the server will decompress the same data that you pass in the POST method. It is also possible to use the standard gzip-based HTTP compression. To send a POST request compressed using gzip, append the request header Content-Encoding: gzip .\nIn order for ClickHouse to compress the response using gzip, you must append Accept-Encoding: gzip to the request headers, and enable the ClickHouse setting enable_http_compression . You can use this to reduce network traffic when transmitting a large amount of data, or for creating dumps that are immediately compressed. You can use the 'database' URL parameter to specify the default database. $ echo SELECT number FROM numbers LIMIT 10 | curl http://localhost:8123/?database=system --data-binary @- 0 1 2 3 4 5 6 7 8 9 By default, the database that is registered in the server settings is used as the default database. By default, this is the database called 'default'. Alternatively, you can always specify the database using a dot before the table name. The username and password can be indicated in one of two ways: Using HTTP Basic Authentication. Example: echo SELECT 1 | curl http://user:password@localhost:8123/ -d @- In the 'user' and 'password' URL parameters. Example: echo SELECT 1 | curl http://localhost:8123/?user=user password=password -d @- If the user name is not indicated, the username 'default' is used. If the password is not indicated, an empty password is used.\nYou can also use the URL parameters to specify any settings for processing a single query, or entire profiles of settings. Example:\nhttp://localhost:8123/?profile=web max_rows_to_read=1000000000 query=SELECT+1 For more information, see the section \"Settings\". $ echo SELECT number FROM system.numbers LIMIT 10 | curl http://localhost:8123/? --data-binary @- 0 1 2 3 4 5 6 7 8 9 For information about other parameters, see the section \"SET\". Similarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to add the session_id GET parameter to the request. You can use any string as the session ID. By default, the session is terminated after 60 seconds of inactivity. To change this timeout, modify the default_session_timeout setting in the server configuration, or add the session_timeout GET parameter to the request. To check the session status, use the session_check=1 parameter. Only one query at a time can be executed within a single session. You have the option to receive information about the progress of query execution in X-ClickHouse-Progress headers. To do this, enable the setting send_progress_in_http_headers. Running requests don't stop automatically if the HTTP connection is lost. Parsing and data formatting are performed on the server side, and using the network might be ineffective.\nThe optional 'query_id' parameter can be passed as the query ID (any string). For more information, see the section \"Settings, replace_running_query\". The optional 'quota_key' parameter can be passed as the quota key (any string). For more information, see the section \"Quotas\". The HTTP interface allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".", + "title": "HTTP interface" + }, + { + "location": "/index.html#response-buffering", + "text": "You can enable response buffering on the server side. The buffer_size and wait_end_of_query URL parameters are provided for this purpose. buffer_size determines the number of bytes in the result to buffer in the server memory. If the result body is larger than this threshold, the buffer is written to the HTTP channel, and the remaining data is sent directly to the HTTP channel. To ensure that the entire response is buffered, set wait_end_of_query=1 . In this case, the data that is not stored in memory will be buffered in a temporary server file. Example: curl -sS http://localhost:8123/?max_result_bytes=4000000 buffer_size=3000000 wait_end_of_query=1 -d SELECT toUInt8(number) FROM system.numbers LIMIT 9000000 FORMAT RowBinary Use buffering to avoid situations where a query processing error occurred after the response code and HTTP headers were sent to the client. In this situation, an error message is written at the end of the response body, and on the client side, the error can only be detected at the parsing stage.", + "title": "Response buffering" + }, + { + "location": "/index.html#jdbc-driver", + "text": "There is an official JDBC driver for ClickHouse. See here .", + "title": "JDBC driver" + }, + { + "location": "/index.html#native-interface-tcp", + "text": "The native interface is used in the \"clickhouse-client\" command-line client for interaction between servers with distributed query processing, and also in C++ programs. We will only cover the command-line client.", + "title": "Native interface (TCP)" + }, + { + "location": "/index.html#libraries-from-third-party-developers", + "text": "There are libraries for working with ClickHouse for: Python infi.clickhouse_orm sqlalchemy-clickhouse clickhouse-driver clickhouse-client PHP clickhouse-php-client PhpClickHouseClient phpClickHouse clickhouse-client Go clickhouse go-clickhouse mailrugo-clickhouse golang-clickhouse NodeJs clickhouse (NodeJs) node-clickhouse Perl perl-DBD-ClickHouse HTTP-ClickHouse AnyEvent-ClickHouse Ruby clickhouse (Ruby) R clickhouse-r RClickhouse .NET ClickHouse-Net C++ clickhouse-cpp Elixir clickhousex clickhouse_ecto Java clickhouse-client-java We have not tested these libraries. They are listed in random order.", + "title": "Libraries from third-party developers" + }, + { + "location": "/index.html#visual-interfaces-from-third-party-developers", + "text": "", + "title": "Visual interfaces from third-party developers" + }, + { + "location": "/index.html#tabix", + "text": "Web interface for ClickHouse in the Tabix project.", + "title": "Tabix" + }, + { + "location": "/index.html#features", + "text": "Works with ClickHouse directly from the browser, without the need to install additional software. Query editor with syntax highlighting. Auto-completion of commands. Tools for graphical analysis of query execution. Color scheme options. Tabix documentation .", + "title": "Features:" + }, + { + "location": "/index.html#houseops", + "text": "HouseOps is a unique Desktop ClickHouse Ops UI / IDE for OSX, Linux and Windows.", + "title": "HouseOps" + }, + { + "location": "/index.html#features_1", + "text": "Query builder; Database manangement (soon); Users manangement (soon); Real-Time Data Analytics (soon); Cluster/Infra monitoring (soon); Cluster manangement (soon); Kafka and Replicated tables monitoring (soon); And a lot of others features (soon) for you take a beautiful implementation of ClickHouse.", + "title": "Features:" + }, + { + "location": "/index.html#query-language", + "text": "", + "title": "Query language" + }, + { + "location": "/index.html#queries", + "text": "", + "title": "Queries" + }, + { + "location": "/index.html#create-database", + "text": "Creating db_name databases CREATE DATABASE [ IF NOT EXISTS ] db_name A database is just a directory for tables.\nIf IF NOT EXISTS is included, the query won't return an error if the database already exists.", + "title": "CREATE DATABASE" + }, + { + "location": "/index.html#create-table", + "text": "The CREATE TABLE query can have several forms. CREATE [ TEMPORARY ] TABLE [ IF NOT EXISTS ] [ db .] name [ ON CLUSTER cluster ] ( \n name1 [ type1 ] [ DEFAULT | MATERIALIZED | ALIAS expr1 ], \n name2 [ type2 ] [ DEFAULT | MATERIALIZED | ALIAS expr2 ], \n ... ) ENGINE = engine Creates a table named 'name' in the 'db' database or the current database if 'db' is not set, with the structure specified in brackets and the 'engine' engine.\nThe structure of the table is a list of column descriptions. If indexes are supported by the engine, they are indicated as parameters for the table engine. A column description is name type in the simplest case. Example: RegionID UInt32 .\nExpressions can also be defined for default values (see below). CREATE [ TEMPORARY ] TABLE [ IF NOT EXISTS ] [ db .] name AS [ db2 .] name2 [ ENGINE = engine ] Creates a table with the same structure as another table. You can specify a different engine for the table. If the engine is not specified, the same engine will be used as for the db2.name2 table. CREATE [ TEMPORARY ] TABLE [ IF NOT EXISTS ] [ db .] name ENGINE = engine AS SELECT ... Creates a table with a structure like the result of the SELECT query, with the 'engine' engine, and fills it with data from SELECT. In all cases, if IF NOT EXISTS is specified, the query won't return an error if the table already exists. In this case, the query won't do anything.", + "title": "CREATE TABLE" + }, + { + "location": "/index.html#default-values", + "text": "The column description can specify an expression for a default value, in one of the following ways: DEFAULT expr , MATERIALIZED expr , ALIAS expr .\nExample: URLDomain String DEFAULT domain(URL) . If an expression for the default value is not defined, the default values will be set to zeros for numbers, empty strings for strings, empty arrays for arrays, and 0000-00-00 for dates or 0000-00-00 00:00:00 for dates with time. NULLs are not supported. If the default expression is defined, the column type is optional. If there isn't an explicitly defined type, the default expression type is used. Example: EventDate DEFAULT toDate(EventTime) \u2013 the 'Date' type will be used for the 'EventDate' column. If the data type and default expression are defined explicitly, this expression will be cast to the specified type using type casting functions. Example: Hits UInt32 DEFAULT 0 means the same thing as Hits UInt32 DEFAULT toUInt32(0) . Default expressions may be defined as an arbitrary expression from table constants and columns. When creating and changing the table structure, it checks that expressions don't contain loops. For INSERT, it checks that expressions are resolvable \u2013 that all columns they can be calculated from have been passed. DEFAULT expr Normal default value. If the INSERT query doesn't specify the corresponding column, it will be filled in by computing the corresponding expression. MATERIALIZED expr Materialized expression. Such a column can't be specified for INSERT, because it is always calculated.\nFor an INSERT without a list of columns, these columns are not considered.\nIn addition, this column is not substituted when using an asterisk in a SELECT query. This is to preserve the invariant that the dump obtained using SELECT * can be inserted back into the table using INSERT without specifying the list of columns. ALIAS expr Synonym. Such a column isn't stored in the table at all.\nIts values can't be inserted in a table, and it is not substituted when using an asterisk in a SELECT query.\nIt can be used in SELECTs if the alias is expanded during query parsing. When using the ALTER query to add new columns, old data for these columns is not written. Instead, when reading old data that does not have values for the new columns, expressions are computed on the fly by default. However, if running the expressions requires different columns that are not indicated in the query, these columns will additionally be read, but only for the blocks of data that need it. If you add a new column to a table but later change its default expression, the values used for old data will change (for data where values were not stored on the disk). Note that when running background merges, data for columns that are missing in one of the merging parts is written to the merged part. It is not possible to set default values for elements in nested data structures.", + "title": "Default values" + }, + { + "location": "/index.html#temporary-tables", + "text": "In all cases, if TEMPORARY is specified, a temporary table will be created. Temporary tables have the following characteristics: Temporary tables disappear when the session ends, including if the connection is lost. A temporary table is created with the Memory engine. The other table engines are not supported. The DB can't be specified for a temporary table. It is created outside of databases. If a temporary table has the same name as another one and a query specifies the table name without specifying the DB, the temporary table will be used. For distributed query processing, temporary tables used in a query are passed to remote servers. In most cases, temporary tables are not created manually, but when using external data for a query, or for distributed (GLOBAL) IN . For more information, see the appropriate sections", + "title": "Temporary tables" + }, + { + "location": "/index.html#distributed-ddl-queries-on-cluster-clause", + "text": "The CREATE , DROP , ALTER , and RENAME queries support distributed execution on a cluster.\nFor example, the following query creates the all_hits Distributed table on each host in cluster : CREATE TABLE IF NOT EXISTS all_hits ON CLUSTER cluster ( p Date , i Int32 ) ENGINE = Distributed ( cluster , default , hits ) In order to run these queries correctly, each host must have the same cluster definition (to simplify syncing configs, you can use substitutions from ZooKeeper). They must also connect to the ZooKeeper servers.\nThe local version of the query will eventually be implemented on each host in the cluster, even if some hosts are currently not available. The order for executing queries within a single host is guaranteed. ALTER queries are not yet supported for replicated tables.", + "title": "Distributed DDL queries (ON CLUSTER clause)" + }, + { + "location": "/index.html#create-view", + "text": "CREATE [ MATERIALIZED ] VIEW [ IF NOT EXISTS ] [ db .] name [ TO [ db .] name ] [ ENGINE = engine ] [ POPULATE ] AS SELECT ... Creates a view. There are two types of views: normal and MATERIALIZED. When creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data. A materialized view works as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view. Normal views don't store any data, but just perform a read from another table. In other words, a normal view is nothing more than a saved query. When reading from a view, this saved query is used as a subquery in the FROM clause. As an example, assume you've created a view: CREATE VIEW view AS SELECT ... and written a query: SELECT a , b , c FROM view This query is fully equivalent to using the subquery: SELECT a , b , c FROM ( SELECT ...) Materialized views store data transformed by the corresponding SELECT query. When creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data. A materialized view is arranged as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view. If you specify POPULATE, the existing table data is inserted in the view when creating it, as if making a CREATE TABLE ... AS SELECT ... . Otherwise, the query contains only the data inserted in the table after creating the view. We don't recommend using POPULATE, since data inserted in the table during the view creation will not be inserted in it. A SELECT query can contain DISTINCT , GROUP BY , ORDER BY , LIMIT ... Note that the corresponding conversions are performed independently on each block of inserted data. For example, if GROUP BY is set, data is aggregated during insertion, but only within a single packet of inserted data. The data won't be further aggregated. The exception is when using an ENGINE that independently performs data aggregation, such as SummingMergeTree . The execution of ALTER queries on materialized views has not been fully developed, so they might be inconvenient. If the materialized view uses the construction TO [db.]name , you can DETACH the view, run ALTER for the target table, and then ATTACH the previously detached ( DETACH ) view. Views look the same as normal tables. For example, they are listed in the result of the SHOW TABLES query. There isn't a separate query for deleting views. To delete a view, use DROP TABLE .", + "title": "CREATE VIEW" + }, + { + "location": "/index.html#attach", + "text": "This query is exactly the same as CREATE , but instead of the word CREATE it uses the word ATTACH . The query doesn't create data on the disk, but assumes that data is already in the appropriate places, and just adds information about the table to the server.\nAfter executing an ATTACH query, the server will know about the existence of the table. If the table was previously detached ( DETACH ), meaning that its structure is known, you can use shorthand without defining the structure. ATTACH TABLE [ IF NOT EXISTS ] [ db .] name This query is used when starting the server. The server stores table metadata as files with ATTACH queries, which it simply runs at launch (with the exception of system tables, which are explicitly created on the server).", + "title": "ATTACH" + }, + { + "location": "/index.html#drop", + "text": "This query has two types: DROP DATABASE and DROP TABLE . DROP DATABASE [ IF EXISTS ] db [ ON CLUSTER cluster ] Deletes all tables inside the 'db' database, then deletes the 'db' database itself.\nIf IF EXISTS is specified, it doesn't return an error if the database doesn't exist. DROP [ TEMPORARY ] TABLE [ IF EXISTS ] [ db .] name [ ON CLUSTER cluster ] Deletes the table.\nIf IF EXISTS is specified, it doesn't return an error if the table doesn't exist or the database doesn't exist.", + "title": "DROP" + }, + { + "location": "/index.html#detach", + "text": "Deletes information about the 'name' table from the server. The server stops knowing about the table's existence. DETACH TABLE [ IF EXISTS ] [ db .] name This does not delete the table's data or metadata. On the next server launch, the server will read the metadata and find out about the table again.\nSimilarly, a \"detached\" table can be re-attached using the ATTACH query (with the exception of system tables, which do not have metadata stored for them). There is no DETACH DATABASE query.", + "title": "DETACH" + }, + { + "location": "/index.html#rename", + "text": "Renames one or more tables. RENAME TABLE [ db11 .] name11 TO [ db12 .] name12 , [ db21 .] name21 TO [ db22 .] name22 , ... [ ON CLUSTER cluster ] All tables are renamed under global locking. Renaming tables is a light operation. If you indicated another database after TO, the table will be moved to this database. However, the directories with databases must reside in the same file system (otherwise, an error is returned).", + "title": "RENAME" + }, + { + "location": "/index.html#alter", + "text": "The ALTER query is only supported for *MergeTree tables, as well as Merge and Distributed . The query has several variations.", + "title": "ALTER" + }, + { + "location": "/index.html#column-manipulations", + "text": "Changing the table structure. ALTER TABLE [ db ]. name [ ON CLUSTER cluster ] ADD | DROP | MODIFY COLUMN ... In the query, specify a list of one or more comma-separated actions.\nEach action is an operation on a column. The following actions are supported: ADD COLUMN name [ type ] [ default_expr ] [ AFTER name_after ] Adds a new column to the table with the specified name, type, and default_expr (see the section \"Default expressions\"). If you specify AFTER name_after (the name of another column), the column is added after the specified one in the list of table columns. Otherwise, the column is added to the end of the table. Note that there is no way to add a column to the beginning of a table. For a chain of actions, 'name_after' can be the name of a column that is added in one of the previous actions. Adding a column just changes the table structure, without performing any actions with data. The data doesn't appear on the disk after ALTER. If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). The column appears on the disk after merging data parts (see MergeTree). This approach allows us to complete the ALTER query instantly, without increasing the volume of old data. DROP COLUMN name Deletes the column with the name 'name'.\nDeletes data from the file system. Since this deletes entire files, the query is completed almost instantly. MODIFY COLUMN name [ type ] [ default_expr ] Changes the 'name' column's type to 'type' and/or the default expression to 'default_expr'. When changing the type, values are converted as if the 'toType' function were applied to them. If only the default expression is changed, the query doesn't do anything complex, and is completed almost instantly. Changing the column type is the only complex action \u2013 it changes the contents of files with data. For large tables, this may take a long time. There are several processing stages: Preparing temporary (new) files with modified data. Renaming old files. Renaming the temporary (new) files to the old names. Deleting the old files. Only the first stage takes time. If there is a failure at this stage, the data is not changed.\nIf there is a failure during one of the successive stages, data can be restored manually. The exception is if the old files were deleted from the file system but the data for the new files did not get written to the disk and was lost. There is no support for changing the column type in arrays and nested data structures. The ALTER query lets you create and delete separate elements (columns) in nested data structures, but not whole nested data structures. To add a nested data structure, you can add columns with a name like name.nested_name and the type Array(T) . A nested data structure is equivalent to multiple array columns with a name that has the same prefix before the dot. There is no support for deleting columns in the primary key or the sampling key (columns that are in the ENGINE expression). Changing the type for columns that are included in the primary key is only possible if this change does not cause the data to be modified (for example, it is allowed to add values to an Enum or change a type with DateTime to UInt32 ). If the ALTER query is not sufficient for making the table changes you need, you can create a new table, copy the data to it using the INSERT SELECT query, then switch the tables using the RENAME query and delete the old table. The ALTER query blocks all reads and writes for the table. In other words, if a long SELECT is running at the time of the ALTER query, the ALTER query will wait for it to complete. At the same time, all new queries to the same table will wait while this ALTER is running. For tables that don't store data themselves (such as Merge and Distributed ), ALTER just changes the table structure, and does not change the structure of subordinate tables. For example, when running ALTER for a Distributed table, you will also need to run ALTER for the tables on all remote servers. The ALTER query for changing columns is replicated. The instructions are saved in ZooKeeper, then each replica applies them. All ALTER queries are run in the same order. The query waits for the appropriate actions to be completed on the other replicas. However, a query to change columns in a replicated table can be interrupted, and all actions will be performed asynchronously.", + "title": "Column manipulations" + }, + { + "location": "/index.html#manipulations-with-partitions-and-parts", + "text": "It only works for tables in the MergeTree family. The following operations are available: DETACH PARTITION \u2013 Move a partition to the 'detached' directory and forget it. DROP PARTITION \u2013 Delete a partition. ATTACH PART|PARTITION \u2013 Add a new part or partition from the detached directory to the table. FREEZE PARTITION \u2013 Create a backup of a partition. FETCH PARTITION \u2013 Download a partition from another server. Each type of query is covered separately below. A partition in a table is data for a single calendar month. This is determined by the values of the date key specified in the table engine parameters. Each month's data is stored separately in order to simplify manipulations with this data. A \"part\" in the table is part of the data from a single partition, sorted by the primary key. You can use the system.parts table to view the set of table parts and partitions: SELECT * FROM system . parts WHERE active active \u2013 Only count active parts. Inactive parts are, for example, source parts remaining after merging to a larger part \u2013 these parts are deleted approximately 10 minutes after merging. Another way to view a set of parts and partitions is to go into the directory with table data.\nData directory: /var/lib/clickhouse/data/database/table/ ,where /var/lib/clickhouse/ is the path to the ClickHouse data, 'database' is the database name, and 'table' is the table name. Example: $ ls -l /var/lib/clickhouse/data/test/visits/\ntotal 48 \ndrwxrwxrwx 2 clickhouse clickhouse 20480 May 5 02 :58 20140317_20140323_2_2_0\ndrwxrwxrwx 2 clickhouse clickhouse 20480 May 5 02 :58 20140317_20140323_4_4_0\ndrwxrwxrwx 2 clickhouse clickhouse 4096 May 5 02 :55 detached\n-rw-rw-rw- 1 clickhouse clickhouse 2 May 5 02 :58 increment.txt Here, 20140317_20140323_2_2_0 and 20140317_20140323_4_4_0 are the directories of data parts. Let's break down the name of the first part: 20140317_20140323_2_2_0 . 20140317 is the minimum date of the data in the chunk. 20140323 is the maximum date of the data in the chunk. 2 is the minimum number of the data block. 2 is the maximum number of the data block. 0 is the chunk level (the depth of the merge tree it is formed from). Each piece relates to a single partition and contains data for just one month. 201403 is the name of the partition. A partition is a set of parts for a single month. On an operating server, you can't manually change the set of parts or their data on the file system, since the server won't know about it.\nFor non-replicated tables, you can do this when the server is stopped, but we don't recommended it.\nFor replicated tables, the set of parts can't be changed in any case. The detached directory contains parts that are not used by the server - detached from the table using the ALTER ... DETACH query. Parts that are damaged are also moved to this directory, instead of deleting them. You can add, delete, or modify the data in the 'detached' directory at any time \u2013 the server won't know about this until you make the ALTER TABLE ... ATTACH query. ALTER TABLE [ db .] table DETACH PARTITION name Move all data for partitions named 'name' to the 'detached' directory and forget about them.\nThe partition name is specified in YYYYMM format. It can be indicated in single quotes or without them. After the query is executed, you can do whatever you want with the data in the 'detached' directory \u2014 delete it from the file system, or just leave it. The query is replicated \u2013 data will be moved to the 'detached' directory and forgotten on all replicas. The query can only be sent to a leader replica. To find out if a replica is a leader, perform SELECT to the 'system.replicas' system table. Alternatively, it is easier to make a query on all replicas, and all except one will throw an exception. ALTER TABLE [ db .] table DROP PARTITION name The same as the DETACH operation. Deletes data from the table. Data parts will be tagged as inactive and will be completely deleted in approximately 10 minutes. The query is replicated \u2013 data will be deleted on all replicas. ALTER TABLE [ db .] table ATTACH PARTITION | PART name Adds data to the table from the 'detached' directory. It is possible to add data for an entire partition or a separate part. For a part, specify the full name of the part in single quotes. The query is replicated. Each replica checks whether there is data in the 'detached' directory. If there is data, it checks the integrity, verifies that it matches the data on the server that initiated the query, and then adds it if everything is correct. If not, it downloads data from the query requestor replica, or from another replica where the data has already been added. So you can put data in the 'detached' directory on one replica, and use the ALTER ... ATTACH query to add it to the table on all replicas. ALTER TABLE [ db .] table FREEZE PARTITION name Creates a local backup of one or multiple partitions. The name can be the full name of the partition (for example, 201403), or its prefix (for example, 2014): then the backup will be created for all the corresponding partitions. The query does the following: for a data snapshot at the time of execution, it creates hardlinks to table data in the directory /var/lib/clickhouse/shadow/N/... /var/lib/clickhouse/ is the working ClickHouse directory from the config. N is the incremental number of the backup. The same structure of directories is created inside the backup as inside /var/lib/clickhouse/ .\nIt also performs 'chmod' for all files, forbidding writes to them. The backup is created almost instantly (but first it waits for current queries to the corresponding table to finish running). At first, the backup doesn't take any space on the disk. As the system works, the backup can take disk space, as data is modified. If the backup is made for old enough data, it won't take space on the disk. After creating the backup, data from /var/lib/clickhouse/shadow/ can be copied to the remote server and then deleted on the local server.\nThe entire backup process is performed without stopping the server. The ALTER ... FREEZE PARTITION query is not replicated. A local backup is only created on the local server. As an alternative, you can manually copy data from the /var/lib/clickhouse/data/database/table directory.\nBut if you do this while the server is running, race conditions are possible when copying directories with files being added or changed, and the backup may be inconsistent. You can do this if the server isn't running \u2013 then the resulting data will be the same as after the ALTER TABLE t FREEZE PARTITION query. ALTER TABLE ... FREEZE PARTITION only copies data, not table metadata. To make a backup of table metadata, copy the file /var/lib/clickhouse/metadata/database/table.sql To restore from a backup: Use the CREATE query to create the table if it doesn't exist. The query can be taken from an .sql file (replace ATTACH in it with CREATE ). Copy the data from the data/database/table/ directory inside the backup to the /var/lib/clickhouse/data/database/table/detached/ directory. Run ALTER TABLE ... ATTACH PARTITION YYYYMM queries, where YYYYMM is the month, for every month. In this way, data from the backup will be added to the table.\nRestoring from a backup doesn't require stopping the server.", + "title": "Manipulations with partitions and parts" + }, + { + "location": "/index.html#backups-and-replication", + "text": "Replication provides protection from device failures. If all data disappeared on one of your replicas, follow the instructions in the \"Restoration after failure\" section to restore it. For protection from device failures, you must use replication. For more information about replication, see the section \"Data replication\". Backups protect against human error (accidentally deleting data, deleting the wrong data or in the wrong cluster, or corrupting data).\nFor high-volume databases, it can be difficult to copy backups to remote servers. In such cases, to protect from human error, you can keep a backup on the same server (it will reside in /var/lib/clickhouse/shadow/ ). ALTER TABLE [ db .] table FETCH PARTITION name FROM path-in-zookeeper This query only works for replicatable tables. It downloads the specified partition from the shard that has its ZooKeeper path specified in the FROM clause, then puts it in the detached directory for the specified table. Although the query is called ALTER TABLE , it does not change the table structure, and does not immediately change the data available in the table. Data is placed in the detached directory. You can use the ALTER TABLE ... ATTACH query to attach the data. The FROM clause specifies the path in ZooKeeper . For example, /clickhouse/tables/01-01/visits .\nBefore downloading, the system checks that the partition exists and the table structure matches. The most appropriate replica is selected automatically from the healthy replicas. The ALTER ... FETCH PARTITION query is not replicated. The partition will be downloaded to the 'detached' directory only on the local server. Note that if after this you use the ALTER TABLE ... ATTACH query to add data to the table, the data will be added on all replicas (on one of the replicas it will be added from the 'detached' directory, and on the rest it will be loaded from neighboring replicas).", + "title": "Backups and replication" + }, + { + "location": "/index.html#synchronicity-of-alter-queries", + "text": "For non-replicatable tables, all ALTER queries are performed synchronously. For replicatable tables, the query just adds instructions for the appropriate actions to ZooKeeper , and the actions themselves are performed as soon as possible. However, the query can wait for these actions to be completed on all the replicas. For ALTER ... ATTACH|DETACH|DROP queries, you can use the replication_alter_partitions_sync setting to set up waiting.\nPossible values: 0 \u2013 do not wait; 1 \u2013 only wait for own execution (default); 2 \u2013 wait for all.", + "title": "Synchronicity of ALTER queries" + }, + { + "location": "/index.html#show-databases", + "text": "SHOW DATABASES [ INTO OUTFILE filename ] [ FORMAT format ] Prints a list of all databases.\nThis query is identical to SELECT name FROM system.databases [INTO OUTFILE filename] [FORMAT format] . See also the section \"Formats\".", + "title": "SHOW DATABASES" + }, + { + "location": "/index.html#show-tables", + "text": "SHOW [ TEMPORARY ] TABLES [ FROM db ] [ LIKE pattern ] [ INTO OUTFILE filename ] [ FORMAT format ] Displays a list of tables tables from the current database, or from the 'db' database if \"FROM db\" is specified. all tables, or tables whose name matches the pattern, if \"LIKE 'pattern'\" is specified. This query is identical to: SELECT name FROM system.tables WHERE database = 'db' [AND name LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format] . See also the section \"LIKE operator\".", + "title": "SHOW TABLES" + }, + { + "location": "/index.html#show-processlist", + "text": "SHOW PROCESSLIST [ INTO OUTFILE filename ] [ FORMAT format ] Outputs a list of queries currently being processed, other than SHOW PROCESSLIST queries. Prints a table containing the columns: user \u2013 The user who made the query. Keep in mind that for distributed processing, queries are sent to remote servers under the 'default' user. SHOW PROCESSLIST shows the username for a specific query, not for a query that this query initiated. address \u2013 The name of the host that the query was sent from. For distributed processing, on remote servers, this is the name of the query requestor host. To track where a distributed query was originally made from, look at SHOW PROCESSLIST on the query requestor server. elapsed \u2013 The execution time, in seconds. Queries are output in order of decreasing execution time. rows_read , bytes_read \u2013 How many rows and bytes of uncompressed data were read when processing the query. For distributed processing, data is totaled from all the remote servers. This is the data used for restrictions and quotas. memory_usage \u2013 Current RAM usage in bytes. See the setting 'max_memory_usage'. query \u2013 The query itself. In INSERT queries, the data for insertion is not output. query_id \u2013 The query identifier. Non-empty only if it was explicitly defined by the user. For distributed processing, the query ID is not passed to remote servers. This query is identical to: SELECT * FROM system.processes [INTO OUTFILE filename] [FORMAT format] . Tip (execute in the console): watch -n1 clickhouse-client --query= SHOW PROCESSLIST", + "title": "SHOW PROCESSLIST" + }, + { + "location": "/index.html#show-create-table", + "text": "SHOW CREATE [ TEMPORARY ] TABLE [ db .] table [ INTO OUTFILE filename ] [ FORMAT format ] Returns a single String -type 'statement' column, which contains a single value \u2013 the CREATE query used for creating the specified table.", + "title": "SHOW CREATE TABLE" + }, + { + "location": "/index.html#describe-table", + "text": "DESC | DESCRIBE TABLE [ db .] table [ INTO OUTFILE filename ] [ FORMAT format ] Returns two String -type columns: name and type , which indicate the names and types of columns in the specified table. Nested data structures are output in \"expanded\" format. Each column is shown separately, with the name after a dot.", + "title": "DESCRIBE TABLE" + }, + { + "location": "/index.html#exists", + "text": "EXISTS [ TEMPORARY ] TABLE [ db .] name [ INTO OUTFILE filename ] [ FORMAT format ] Returns a single UInt8 -type column, which contains the single value 0 if the table or database doesn't exist, or 1 if the table exists in the specified database.", + "title": "EXISTS" + }, + { + "location": "/index.html#use", + "text": "USE db Lets you set the current database for the session.\nThe current database is used for searching for tables if the database is not explicitly defined in the query with a dot before the table name.\nThis query can't be made when using the HTTP protocol, since there is no concept of a session.", + "title": "USE" + }, + { + "location": "/index.html#set", + "text": "SET param = value Allows you to set param to value . You can also make all the settings from the specified settings profile in a single query. To do this, specify 'profile' as the setting name. For more information, see the section \"Settings\".\nThe setting is made for the session, or for the server (globally) if GLOBAL is specified.\nWhen making a global setting, the setting is not applied to sessions already running, including the current session. It will only be used for new sessions. When the server is restarted, global settings made using SET are lost.\nTo make settings that persist after a server restart, you can only use the server's config file.", + "title": "SET" + }, + { + "location": "/index.html#optimize", + "text": "OPTIMIZE TABLE [ db .] name [ PARTITION partition ] [ FINAL ] Asks the table engine to do something for optimization.\nSupported only by *MergeTree engines, in which this query initializes a non-scheduled merge of data parts.\nIf you specify a PARTITION , only the specified partition will be optimized.\nIf you specify FINAL , optimization will be performed even when all the data is already in one part.", + "title": "OPTIMIZE" + }, + { + "location": "/index.html#insert", + "text": "Adding data. Basic query format: INSERT INTO [ db .] table [( c1 , c2 , c3 )] VALUES ( v11 , v12 , v13 ), ( v21 , v22 , v23 ), ... The query can specify a list of columns to insert [(c1, c2, c3)] . In this case, the rest of the columns are filled with: The values calculated from the DEFAULT expressions specified in the table definition. Zeros and empty strings, if DEFAULT expressions are not defined. If strict_insert_defaults=1 , columns that do not have DEFAULT defined must be listed in the query. Data can be passed to the INSERT in any format supported by ClickHouse. The format must be specified explicitly in the query: INSERT INTO [ db .] table [( c1 , c2 , c3 )] FORMAT format_name data_set For example, the following query format is identical to the basic version of INSERT ... VALUES: INSERT INTO [ db .] table [( c1 , c2 , c3 )] FORMAT Values ( v11 , v12 , v13 ), ( v21 , v22 , v23 ), ... ClickHouse removes all spaces and one line feed (if there is one) before the data. When forming a query, we recommend putting the data on a new line after the query operators (this is important if the data begins with spaces). Example: INSERT INTO t FORMAT TabSeparated 11 Hello , world ! 22 Qwerty You can insert data separately from the query by using the command-line client or the HTTP interface. For more information, see the section \" Interfaces \".", + "title": "INSERT" + }, + { + "location": "/index.html#inserting-the-results-of-select", + "text": "INSERT INTO [ db .] table [( c1 , c2 , c3 )] SELECT ... Columns are mapped according to their position in the SELECT clause. However, their names in the SELECT expression and the table for INSERT may differ. If necessary, type casting is performed. None of the data formats except Values allow setting values to expressions such as now() , 1 + 2 , and so on. The Values format allows limited use of expressions, but this is not recommended, because in this case inefficient code is used for their execution. Other queries for modifying data parts are not supported: UPDATE , DELETE , REPLACE , MERGE , UPSERT , INSERT UPDATE .\nHowever, you can delete old data using ALTER TABLE ... DROP PARTITION .", + "title": "Inserting the results of SELECT" + }, + { + "location": "/index.html#performance-considerations", + "text": "INSERT sorts the input data by primary key and splits them into partitions by month. If you insert data for mixed months, it can significantly reduce the performance of the INSERT query. To avoid this: Add data in fairly large batches, such as 100,000 rows at a time. Group data by month before uploading it to ClickHouse. Performance will not decrease if: Data is added in real time. You upload data that is usually sorted by time.", + "title": "Performance considerations" + }, + { + "location": "/index.html#select", + "text": "Data sampling. SELECT [ DISTINCT ] expr_list \n [ FROM [ db .] table | ( subquery ) | table_function ] [ FINAL ] \n [ SAMPLE sample_coeff ] \n [ ARRAY JOIN ...] \n [ GLOBAL ] ANY | ALL INNER | LEFT JOIN ( subquery ) | table USING columns_list \n [ PREWHERE expr ] \n [ WHERE expr ] \n [ GROUP BY expr_list ] [ WITH TOTALS ] \n [ HAVING expr ] \n [ ORDER BY expr_list ] \n [ LIMIT [ n , ] m ] \n [ UNION ALL ...] \n [ INTO OUTFILE filename ] \n [ FORMAT format ] \n [ LIMIT n BY columns ] All the clauses are optional, except for the required list of expressions immediately after SELECT.\nThe clauses below are described in almost the same order as in the query execution conveyor. If the query omits the DISTINCT , GROUP BY and ORDER BY clauses and the IN and JOIN subqueries, the query will be completely stream processed, using O(1) amount of RAM.\nOtherwise, the query might consume a lot of RAM if the appropriate restrictions are not specified: max_memory_usage , max_rows_to_group_by , max_rows_to_sort , max_rows_in_distinct , max_bytes_in_distinct , max_rows_in_set , max_bytes_in_set , max_rows_in_join , max_bytes_in_join , max_bytes_before_external_sort , max_bytes_before_external_group_by . For more information, see the section \"Settings\". It is possible to use external sorting (saving temporary tables to a disk) and external aggregation. The system does not have \"merge join\" .", + "title": "SELECT" + }, + { + "location": "/index.html#from-clause", + "text": "If the FROM clause is omitted, data will be read from the system.one table.\nThe 'system.one' table contains exactly one row (this table fulfills the same purpose as the DUAL table found in other DBMSs). The FROM clause specifies the table to read data from, or a subquery, or a table function; ARRAY JOIN and the regular JOIN may also be included (see below). Instead of a table, the SELECT subquery may be specified in brackets.\nIn this case, the subquery processing pipeline will be built into the processing pipeline of an external query.\nIn contrast to standard SQL, a synonym does not need to be specified after a subquery. For compatibility, it is possible to write 'AS name' after a subquery, but the specified name isn't used anywhere. A table function may be specified instead of a table. For more information, see the section \"Table functions\". To execute a query, all the columns listed in the query are extracted from the appropriate table. Any columns not needed for the external query are thrown out of the subqueries.\nIf a query does not list any columns (for example, SELECT count() FROM t), some column is extracted from the table anyway (the smallest one is preferred), in order to calculate the number of rows. The FINAL modifier can be used only for a SELECT from a CollapsingMergeTree table. When you specify FINAL, data is selected fully \"collapsed\". Keep in mind that using FINAL leads to a selection that includes columns related to the primary key, in addition to the columns specified in the SELECT. Additionally, the query will be executed in a single stream, and data will be merged during query execution. This means that when using FINAL, the query is processed more slowly. In most cases, you should avoid using FINAL. For more information, see the section \"CollapsingMergeTree engine\".", + "title": "FROM clause" + }, + { + "location": "/index.html#sample-clause", + "text": "The SAMPLE clause allows for approximated query processing. Approximated query processing is only supported by MergeTree* type tables, and only if the sampling expression was specified during table creation (see the section \"MergeTree engine\"). SAMPLE has the format SAMPLE k , where k is a decimal number from 0 to 1, or SAMPLE n , where 'n' is a sufficiently large integer. In the first case, the query will be executed on 'k' percent of data. For example, SAMPLE 0.1 runs the query on 10% of data.\nIn the second case, the query will be executed on a sample of no more than 'n' rows. For example, SAMPLE 10000000 runs the query on a maximum of 10,000,000 rows. Example: SELECT \n Title , \n count () * 10 AS PageViews FROM hits_distributed SAMPLE 0 . 1 WHERE \n CounterID = 34 \n AND toDate ( EventDate ) = toDate ( 2013-01-29 ) \n AND toDate ( EventDate ) = toDate ( 2013-02-04 ) \n AND NOT DontCountHits \n AND NOT Refresh \n AND Title != GROUP BY Title ORDER BY PageViews DESC LIMIT 1000 In this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value 'count()' is manually multiplied by 10. When using something like SAMPLE 10000000 , there isn't any information about which relative percent of data was processed or what the aggregate functions should be multiplied by, so this method of writing is not always appropriate to the situation. A sample with a relative coefficient is \"consistent\": if we look at all possible data that could be in the table, a sample (when using a single sampling expression specified during table creation) with the same coefficient always selects the same subset of possible data. In other words, a sample from different tables on different servers at different times is made the same way. For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This allows using the sample in subqueries in the IN clause, as well as for manually correlating results of different queries with samples.", + "title": "SAMPLE clause" + }, + { + "location": "/index.html#array-join-clause", + "text": "Allows executing JOIN with an array or nested data structure. The intent is similar to the 'arrayJoin' function, but its functionality is broader. ARRAY JOIN is essentially INNER JOIN with an array. Example: :) CREATE TABLE arrays_test (s String, arr Array(UInt8)) ENGINE = Memory\n\nCREATE TABLE arrays_test\n(\n s String,\n arr Array(UInt8)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.001 sec.\n\n:) INSERT INTO arrays_test VALUES ( Hello , [1,2]), ( World , [3,4,5]), ( Goodbye , [])\n\nINSERT INTO arrays_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM arrays_test\n\nSELECT *\nFROM arrays_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502\n\u2502 Goodbye \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, arr FROM arrays_test ARRAY JOIN arr\n\nSELECT s, arr\nFROM arrays_test\nARRAY JOIN arr\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2510\n\u2502 Hello \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502\n\u2502 World \u2502 4 \u2502\n\u2502 World \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. An alias can be specified for an array in the ARRAY JOIN clause. In this case, an array item can be accessed by this alias, but the array itself by the original name. Example: :) SELECT s, arr, a FROM arrays_test ARRAY JOIN arr AS a\n\nSELECT s, arr, a\nFROM arrays_test\nARRAY JOIN arr AS a\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. Multiple arrays of the same size can be comma-separated in the ARRAY JOIN clause. In this case, JOIN is performed with them simultaneously (the direct sum, not the direct product). Example: :) SELECT s, arr, a, num, mapped FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(x - x + 1, arr) AS mapped\n\nSELECT s, arr, a, num, mapped\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(lambda(tuple(x), plus(x, 1)), arr) AS mapped\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500mapped\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 2 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 5 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 6 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n:) SELECT s, arr, a, num, arrayEnumerate(arr) FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\nSELECT s, arr, a, num, arrayEnumerate(arr)\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500arrayEnumerate(arr)\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 [1,2] \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec. ARRAY JOIN also works with nested data structures. Example: :) CREATE TABLE nested_test (s String, nest Nested(x UInt8, y UInt32)) ENGINE = Memory\n\nCREATE TABLE nested_test\n(\n s String,\n nest Nested(\n x UInt8,\n y UInt32)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.006 sec.\n\n:) INSERT INTO nested_test VALUES ( Hello , [1,2], [10,20]), ( World , [3,4,5], [30,40,50]), ( Goodbye , [], [])\n\nINSERT INTO nested_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM nested_test\n\nSELECT *\nFROM nested_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 Goodbye \u2502 [] \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. When specifying names of nested data structures in ARRAY JOIN, the meaning is the same as ARRAY JOIN with all the array elements that it consists of. Example: :) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x, nest.y\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`, `nest.y`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. This variation also makes sense: :) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. An alias may be used for a nested data structure, in order to select either the JOIN result or the source array. Example: :) SELECT s, n.x, n.y, nest.x, nest.y FROM nested_test ARRAY JOIN nest AS n\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest AS n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. Example of using the arrayEnumerate function: :) SELECT s, n.x, n.y, nest.x, nest.y, num FROM nested_test ARRAY JOIN nest AS n, arrayEnumerate(nest.x) AS num\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`, num\nFROM nested_test\nARRAY JOIN nest AS n, arrayEnumerate(`nest.x`) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u252c\u2500num\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 1 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 2 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 3 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec. The query can only specify a single ARRAY JOIN clause. The corresponding conversion can be performed before the WHERE/PREWHERE clause (if its result is needed in this clause), or after completing WHERE/PREWHERE (to reduce the volume of calculations).", + "title": "ARRAY JOIN clause" + }, + { + "location": "/index.html#join-clause", + "text": "The normal JOIN, which is not related to ARRAY JOIN described above. [ GLOBAL ] ANY | ALL INNER | LEFT [ OUTER ] JOIN ( subquery ) | table USING columns_list Performs joins with data from the subquery. At the beginning of query processing, the subquery specified after JOIN is run, and its result is saved in memory. Then it is read from the \"left\" table specified in the FROM clause, and while it is being read, for each of the read rows from the \"left\" table, rows are selected from the subquery results table (the \"right\" table) that meet the condition for matching the values of the columns specified in USING. The table name can be specified instead of a subquery. This is equivalent to the SELECT * FROM table subquery, except in a special case when the table has the Join engine \u2013 an array prepared for joining. All columns that are not needed for the JOIN are deleted from the subquery. There are several types of JOINs: INNER or LEFT type:If INNER is specified, the result will contain only those rows that have a matching row in the right table.\nIf LEFT is specified, any rows in the left table that don't have matching rows in the right table will be assigned the default value - zeros or empty rows. LEFT OUTER may be written instead of LEFT; the word OUTER does not affect anything. ANY or ALL stringency:If ANY is specified and the right table has several matching rows, only the first one found is joined.\nIf ALL is specified and the right table has several matching rows, the data will be multiplied by the number of these rows. Using ALL corresponds to the normal JOIN semantic from standard SQL.\nUsing ANY is optimal. If the right table has only one matching row, the results of ANY and ALL are the same. You must specify either ANY or ALL (neither of them is selected by default). GLOBAL distribution: When using a normal JOIN, the query is sent to remote servers. Subqueries are run on each of them in order to make the right table, and the join is performed with this table. In other words, the right table is formed on each server separately. When using GLOBAL ... JOIN , first the requestor server runs a subquery to calculate the right table. This temporary table is passed to each remote server, and queries are run on them using the temporary data that was transmitted. Be careful when using GLOBAL JOINs. For more information, see the section \"Distributed subqueries\". Any combination of JOINs is possible. For example, GLOBAL ANY LEFT OUTER JOIN . When running a JOIN, there is no optimization of the order of execution in relation to other stages of the query. The join (a search in the right table) is run before filtering in WHERE and before aggregation. In order to explicitly set the processing order, we recommend running a JOIN subquery with a subquery. Example: SELECT \n CounterID , \n hits , \n visits FROM ( \n SELECT \n CounterID , \n count () AS hits \n FROM test . hits \n GROUP BY CounterID ) ANY LEFT JOIN ( \n SELECT \n CounterID , \n sum ( Sign ) AS visits \n FROM test . visits \n GROUP BY CounterID ) USING CounterID ORDER BY hits DESC LIMIT 10 \u250c\u2500CounterID\u2500\u252c\u2500\u2500\u2500hits\u2500\u252c\u2500visits\u2500\u2510\n\u2502 1143050 \u2502 523264 \u2502 13665 \u2502\n\u2502 731962 \u2502 475698 \u2502 102716 \u2502\n\u2502 722545 \u2502 337212 \u2502 108187 \u2502\n\u2502 722889 \u2502 252197 \u2502 10547 \u2502\n\u2502 2237260 \u2502 196036 \u2502 9522 \u2502\n\u2502 23057320 \u2502 147211 \u2502 7689 \u2502\n\u2502 722818 \u2502 90109 \u2502 17847 \u2502\n\u2502 48221 \u2502 85379 \u2502 4652 \u2502\n\u2502 19762435 \u2502 77807 \u2502 7026 \u2502\n\u2502 722884 \u2502 77492 \u2502 11056 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Subqueries don't allow you to set names or use them for referencing a column from a specific subquery.\nThe columns specified in USING must have the same names in both subqueries, and the other columns must be named differently. You can use aliases to change the names of columns in subqueries (the example uses the aliases 'hits' and 'visits'). The USING clause specifies one or more columns to join, which establishes the equality of these columns. The list of columns is set without brackets. More complex join conditions are not supported. The right table (the subquery result) resides in RAM. If there isn't enough memory, you can't run a JOIN. Only one JOIN can be specified in a query (on a single level). To run multiple JOINs, you can put them in subqueries. Each time a query is run with the same JOIN, the subquery is run again \u2013 the result is not cached. To avoid this, use the special 'Join' table engine, which is a prepared array for joining that is always in RAM. For more information, see the section \"Table engines, Join\". In some cases, it is more efficient to use IN instead of JOIN.\nAmong the various types of JOINs, the most efficient is ANY LEFT JOIN, then ANY INNER JOIN. The least efficient are ALL LEFT JOIN and ALL INNER JOIN. If you need a JOIN for joining with dimension tables (these are relatively small tables that contain dimension properties, such as names for advertising campaigns), a JOIN might not be very convenient due to the bulky syntax and the fact that the right table is re-accessed for every query. For such cases, there is an \"external dictionaries\" feature that you should use instead of JOIN. For more information, see the section \"External dictionaries\".", + "title": "JOIN clause" + }, + { + "location": "/index.html#where-clause", + "text": "If there is a WHERE clause, it must contain an expression with the UInt8 type. This is usually an expression with comparison and logical operators.\nThis expression will be used for filtering data before all other transformations. If indexes are supported by the database table engine, the expression is evaluated on the ability to use indexes.", + "title": "WHERE clause" + }, + { + "location": "/index.html#prewhere-clause", + "text": "This clause has the same meaning as the WHERE clause. The difference is in which data is read from the table.\nWhen using PREWHERE, first only the columns necessary for executing PREWHERE are read. Then the other columns are read that are needed for running the query, but only those blocks where the PREWHERE expression is true. It makes sense to use PREWHERE if there are filtration conditions that are not suitable for indexes that are used by a minority of the columns in the query, but that provide strong data filtration. This reduces the volume of data to read. For example, it is useful to write PREWHERE for queries that extract a large number of columns, but that only have filtration for a few columns. PREWHERE is only supported by tables from the *MergeTree family. A query may simultaneously specify PREWHERE and WHERE. In this case, PREWHERE precedes WHERE. Keep in mind that it does not make much sense for PREWHERE to only specify those columns that have an index, because when using an index, only the data blocks that match the index are read. If the 'optimize_move_to_prewhere' setting is set to 1 and PREWHERE is omitted, the system uses heuristics to automatically move parts of expressions from WHERE to PREWHERE.", + "title": "PREWHERE clause" + }, + { + "location": "/index.html#group-by-clause", + "text": "This is one of the most important parts of a column-oriented DBMS. If there is a GROUP BY clause, it must contain a list of expressions. Each expression will be referred to here as a \"key\".\nAll the expressions in the SELECT, HAVING, and ORDER BY clauses must be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. If a query contains only table columns inside aggregate functions, the GROUP BY clause can be omitted, and aggregation by an empty set of keys is assumed. Example: SELECT \n count (), \n median ( FetchTiming 60 ? 60 : FetchTiming ), \n count () - sum ( Refresh ) FROM hits However, in contrast to standard SQL, if the table doesn't have any rows (either there aren't any at all, or there aren't any after using WHERE to filter), an empty result is returned, and not the result from one of the rows containing the initial values of aggregate functions. As opposed to MySQL (and conforming to standard SQL), you can't get some value of some column that is not in a key or aggregate function (except constant expressions). To work around this, you can use the 'any' aggregate function (get the first encountered value) or 'min/max'. Example: SELECT \n domainWithoutWWW ( URL ) AS domain , \n count (), \n any ( Title ) AS title -- getting the first occurred page header for each domain. FROM hits GROUP BY domain For every different key value encountered, GROUP BY calculates a set of aggregate function values. GROUP BY is not supported for array columns. A constant can't be specified as arguments for aggregate functions. Example: sum(1). Instead of this, you can get rid of the constant. Example: count() .", + "title": "GROUP BY clause" + }, + { + "location": "/index.html#with-totals-modifier", + "text": "If the WITH TOTALS modifier is specified, another row will be calculated. This row will have key columns containing default values (zeros or empty lines), and columns of aggregate functions with the values calculated across all the rows (the \"total\" values). This extra row is output in JSON*, TabSeparated*, and Pretty* formats, separately from the other rows. In the other formats, this row is not output. In JSON* formats, this row is output as a separate 'totals' field. In TabSeparated* formats, the row comes after the main result, preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result. WITH TOTALS can be run in different ways when HAVING is present. The behavior depends on the 'totals_mode' setting.\nBy default, totals_mode = 'before_having' . In this case, 'totals' is calculated across all rows, including the ones that don't pass through HAVING and 'max_rows_to_group_by'. The other alternatives include only the rows that pass through HAVING in 'totals', and behave differently with the setting max_rows_to_group_by and group_by_overflow_mode = 'any' . after_having_exclusive \u2013 Don't include rows that didn't pass through max_rows_to_group_by . In other words, 'totals' will have less than or the same number of rows as it would if max_rows_to_group_by were omitted. after_having_inclusive \u2013 Include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. In other words, 'totals' will have more than or the same number of rows as it would if max_rows_to_group_by were omitted. after_having_auto \u2013 Count the number of rows that passed through HAVING. If it is more than a certain amount (by default, 50%), include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. Otherwise, do not include them. totals_auto_threshold \u2013 By default, 0.5. The coefficient for after_having_auto . If max_rows_to_group_by and group_by_overflow_mode = 'any' are not used, all variations of after_having are the same, and you can use any of them (for example, after_having_auto ). You can use WITH TOTALS in subqueries, including subqueries in the JOIN clause (in this case, the respective total values are combined).", + "title": "WITH TOTALS modifier" + }, + { + "location": "/index.html#group-by-in-external-memory", + "text": "You can enable dumping temporary data to the disk to restrict memory usage during GROUP BY.\nThe max_bytes_before_external_group_by setting determines the threshold RAM consumption for dumping GROUP BY temporary data to the file system. If set to 0 (the default), it is disabled. When using max_bytes_before_external_group_by , we recommend that you set max_memory_usage about twice as high. This is necessary because there are two stages to aggregation: reading the date and forming intermediate data (1) and merging the intermediate data (2). Dumping data to the file system can only occur during stage 1. If the temporary data wasn't dumped, then stage 2 might require up to the same amount of memory as in stage 1. For example, if max_memory_usage was set to 10000000000 and you want to use external aggregation, it makes sense to set max_bytes_before_external_group_by to 10000000000, and max_memory_usage to 20000000000. When external aggregation is triggered (if there was at least one dump of temporary data), maximum consumption of RAM is only slightly more than max_bytes_before_external_group_by . With distributed query processing, external aggregation is performed on remote servers. In order for the requestor server to use only a small amount of RAM, set distributed_aggregation_memory_efficient to 1. When merging data flushed to the disk, as well as when merging results from remote servers when the distributed_aggregation_memory_efficient setting is enabled, consumes up to 1/256 * the number of threads from the total amount of RAM. When external aggregation is enabled, if there was less than max_bytes_before_external_group_by of data (i.e. data was not flushed), the query runs just as fast as without external aggregation. If any temporary data was flushed, the run time will be several times longer (approximately three times). If you have an ORDER BY with a small LIMIT after GROUP BY, then the ORDER BY CLAUSE will not use significant amounts of RAM.\nBut if the ORDER BY doesn't have LIMIT, don't forget to enable external sorting ( max_bytes_before_external_sort ).", + "title": "GROUP BY in external memory" + }, + { + "location": "/index.html#limit-n-by-clause", + "text": "LIMIT N BY COLUMNS selects the top N rows for each group of COLUMNS. LIMIT N BY is not related to LIMIT; they can both be used in the same query. The key for LIMIT N BY can contain any number of columns or expressions. Example: SELECT \n domainWithoutWWW ( URL ) AS domain , \n domainWithoutWWW ( REFERRER_URL ) AS referrer , \n device_type , \n count () cnt FROM hits GROUP BY domain , referrer , device_type ORDER BY cnt DESC LIMIT 5 BY domain , device_type LIMIT 100 The query will select the top 5 referrers for each domain, device_type pair, but not more than 100 rows ( LIMIT n BY + LIMIT ).", + "title": "LIMIT N BY clause" + }, + { + "location": "/index.html#having-clause", + "text": "Allows filtering the result received after GROUP BY, similar to the WHERE clause.\nWHERE and HAVING differ in that WHERE is performed before aggregation (GROUP BY), while HAVING is performed after it.\nIf aggregation is not performed, HAVING can't be used.", + "title": "HAVING clause" + }, + { + "location": "/index.html#order-by-clause", + "text": "The ORDER BY clause contains a list of expressions, which can each be assigned DESC or ASC (the sorting direction). If the direction is not specified, ASC is assumed. ASC is sorted in ascending order, and DESC in descending order. The sorting direction applies to a single expression, not to the entire list. Example: ORDER BY Visits DESC, SearchPhrase For sorting by String values, you can specify collation (comparison). Example: ORDER BY SearchPhrase COLLATE 'tr' - for sorting by keyword in ascending order, using the Turkish alphabet, case insensitive, assuming that strings are UTF-8 encoded. COLLATE can be specified or not for each expression in ORDER BY independently. If ASC or DESC is specified, COLLATE is specified after it. When using COLLATE, sorting is always case-insensitive. We only recommend using COLLATE for final sorting of a small number of rows, since sorting with COLLATE is less efficient than normal sorting by bytes. Rows that have identical values for the list of sorting expressions are output in an arbitrary order, which can also be nondeterministic (different each time).\nIf the ORDER BY clause is omitted, the order of the rows is also undefined, and may be nondeterministic as well. When floating point numbers are sorted, NaNs are separate from the other values. Regardless of the sorting order, NaNs come at the end. In other words, for ascending sorting they are placed as if they are larger than all the other numbers, while for descending sorting they are placed as if they are smaller than the rest. Less RAM is used if a small enough LIMIT is specified in addition to ORDER BY. Otherwise, the amount of memory spent is proportional to the volume of data for sorting. For distributed query processing, if GROUP BY is omitted, sorting is partially done on remote servers, and the results are merged on the requestor server. This means that for distributed sorting, the volume of data to sort can be greater than the amount of memory on a single server. If there is not enough RAM, it is possible to perform sorting in external memory (creating temporary files on a disk). Use the setting max_bytes_before_external_sort for this purpose. If it is set to 0 (the default), external sorting is disabled. If it is enabled, when the volume of data to sort reaches the specified number of bytes, the collected data is sorted and dumped into a temporary file. After all data is read, all the sorted files are merged and the results are output. Files are written to the /var/lib/clickhouse/tmp/ directory in the config (by default, but you can use the 'tmp_path' parameter to change this setting). Running a query may use more memory than 'max_bytes_before_external_sort'. For this reason, this setting must have a value significantly smaller than 'max_memory_usage'. As an example, if your server has 128 GB of RAM and you need to run a single query, set 'max_memory_usage' to 100 GB, and 'max_bytes_before_external_sort' to 80 GB. External sorting works much less effectively than sorting in RAM.", + "title": "ORDER BY clause" + }, + { + "location": "/index.html#select-clause", + "text": "The expressions specified in the SELECT clause are analyzed after the calculations for all the clauses listed above are completed.\nMore specifically, expressions are analyzed that are above the aggregate functions, if there are any aggregate functions.\nThe aggregate functions and everything below them are calculated during aggregation (GROUP BY).\nThese expressions work as if they are applied to separate rows in the result.", + "title": "SELECT clause" + }, + { + "location": "/index.html#distinct-clause", + "text": "If DISTINCT is specified, only a single row will remain out of all the sets of fully matching rows in the result.\nThe result will be the same as if GROUP BY were specified across all the fields specified in SELECT without aggregate functions. But there are several differences from GROUP BY: DISTINCT can be applied together with GROUP BY. When ORDER BY is omitted and LIMIT is defined, the query stops running immediately after the required number of different rows has been read. Data blocks are output as they are processed, without waiting for the entire query to finish running. DISTINCT is not supported if SELECT has at least one array column.", + "title": "DISTINCT clause" + }, + { + "location": "/index.html#limit-clause", + "text": "LIMIT m allows you to select the first 'm' rows from the result.\nLIMIT n, m allows you to select the first 'm' rows from the result after skipping the first 'n' rows. 'n' and 'm' must be non-negative integers. If there isn't an ORDER BY clause that explicitly sorts results, the result may be arbitrary and nondeterministic.", + "title": "LIMIT clause" + }, + { + "location": "/index.html#union-all-clause", + "text": "You can use UNION ALL to combine any number of queries. Example: SELECT CounterID , 1 AS table , toInt64 ( count ()) AS c \n FROM test . hits \n GROUP BY CounterID UNION ALL SELECT CounterID , 2 AS table , sum ( Sign ) AS c \n FROM test . visits \n GROUP BY CounterID \n HAVING c 0 Only UNION ALL is supported. The regular UNION (UNION DISTINCT) is not supported. If you need UNION DISTINCT, you can write SELECT DISTINCT from a subquery containing UNION ALL. Queries that are parts of UNION ALL can be run simultaneously, and their results can be mixed together. The structure of results (the number and type of columns) must match for the queries. But the column names can differ. In this case, the column names for the final result will be taken from the first query. Queries that are parts of UNION ALL can't be enclosed in brackets. ORDER BY and LIMIT are applied to separate queries, not to the final result. If you need to apply a conversion to the final result, you can put all the queries with UNION ALL in a subquery in the FROM clause.", + "title": "UNION ALL clause" + }, + { + "location": "/index.html#into-outfile-clause", + "text": "Add the INTO OUTFILE filename clause (where filename is a string literal) to redirect query output to the specified file.\nIn contrast to MySQL, the file is created on the client side. The query will fail if a file with the same filename already exists.\nThis functionality is available in the command-line client and clickhouse-local (a query sent via HTTP interface will fail). The default output format is TabSeparated (the same as in the command-line client batch mode).", + "title": "INTO OUTFILE clause" + }, + { + "location": "/index.html#format-clause", + "text": "Specify 'FORMAT format' to get data in any specified format.\nYou can use this for convenience, or for creating dumps.\nFor more information, see the section \"Formats\".\nIf the FORMAT clause is omitted, the default format is used, which depends on both the settings and the interface used for accessing the DB. For the HTTP interface and the command-line client in batch mode, the default format is TabSeparated. For the command-line client in interactive mode, the default format is PrettyCompact (it has attractive and compact tables). When using the command-line client, data is passed to the client in an internal efficient format. The client independently interprets the FORMAT clause of the query and formats the data itself (thus relieving the network and the server from the load).", + "title": "FORMAT clause" + }, + { + "location": "/index.html#in-operators", + "text": "The IN , NOT IN , GLOBAL IN , and GLOBAL NOT IN operators are covered separately, since their functionality is quite rich. The left side of the operator is either a single column or a tuple. Examples: SELECT UserID IN ( 123 , 456 ) FROM ... SELECT ( CounterID , UserID ) IN (( 34 , 123 ), ( 101500 , 456 )) FROM ... If the left side is a single column that is in the index, and the right side is a set of constants, the system uses the index for processing the query. Don't list too many values explicitly (i.e. millions). If a data set is large, put it in a temporary table (for example, see the section \"External data for query processing\"), then use a subquery. The right side of the operator can be a set of constant expressions, a set of tuples with constant expressions (shown in the examples above), or the name of a database table or SELECT subquery in brackets. If the right side of the operator is the name of a table (for example, UserID IN users ), this is equivalent to the subquery UserID IN (SELECT * FROM users) . Use this when working with external data that is sent along with the query. For example, the query can be sent together with a set of user IDs loaded to the 'users' temporary table, which should be filtered. If the right side of the operator is a table name that has the Set engine (a prepared data set that is always in RAM), the data set will not be created over again for each query. The subquery may specify more than one column for filtering tuples.\nExample: SELECT ( CounterID , UserID ) IN ( SELECT CounterID , UserID FROM ...) FROM ... The columns to the left and right of the IN operator should have the same type. The IN operator and subquery may occur in any part of the query, including in aggregate functions and lambda functions.\nExample: SELECT \n EventDate , \n avg ( UserID IN \n ( \n SELECT UserID \n FROM test . hits \n WHERE EventDate = toDate ( 2014-03-17 ) \n )) AS ratio FROM test . hits GROUP BY EventDate ORDER BY EventDate ASC \u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500ratio\u2500\u2510\n\u2502 2014-03-17 \u2502 1 \u2502\n\u2502 2014-03-18 \u2502 0.807696 \u2502\n\u2502 2014-03-19 \u2502 0.755406 \u2502\n\u2502 2014-03-20 \u2502 0.723218 \u2502\n\u2502 2014-03-21 \u2502 0.697021 \u2502\n\u2502 2014-03-22 \u2502 0.647851 \u2502\n\u2502 2014-03-23 \u2502 0.648416 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 For each day after March 17th, count the percentage of pageviews made by users who visited the site on March 17th.\nA subquery in the IN clause is always run just one time on a single server. There are no dependent subqueries.", + "title": "IN operators" + }, + { + "location": "/index.html#distributed-subqueries", + "text": "There are two options for IN-s with subqueries (similar to JOINs): normal IN / OIN and IN GLOBAL / GLOBAL JOIN . They differ in how they are run for distributed query processing. \n\nRemember that the algorithms described below may work differently depending on the [settings](#settings-distributed_product_mode) `distributed_product_mode` setting. When using the regular IN, the query is sent to remote servers, and each of them runs the subqueries in the IN or JOIN clause. When using GLOBAL IN / GLOBAL JOINs , first all the subqueries are run for GLOBAL IN / GLOBAL JOINs , and the results are collected in temporary tables. Then the temporary tables are sent to each remote server, where the queries are run using this temporary data. For a non-distributed query, use the regular IN / JOIN . Be careful when using subqueries in the IN / JOIN clauses for distributed query processing. Let's look at some examples. Assume that each server in the cluster has a normal local_table . Each server also has a distributed_table table with the Distributed type, which looks at all the servers in the cluster. For a query to the distributed_table , the query will be sent to all the remote servers and run on them using the local_table . For example, the query SELECT uniq ( UserID ) FROM distributed_table will be sent to all remote servers as SELECT uniq ( UserID ) FROM local_table and run on each of them in parallel, until it reaches the stage where intermediate results can be combined. Then the intermediate results will be returned to the requestor server and merged on it, and the final result will be sent to the client. Now let's examine a query with IN: SELECT uniq ( UserID ) FROM distributed_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM local_table WHERE CounterID = 34 ) Calculation of the intersection of audiences of two sites. This query will be sent to all remote servers as SELECT uniq ( UserID ) FROM local_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM local_table WHERE CounterID = 34 ) In other words, the data set in the IN clause will be collected on each server independently, only across the data that is stored locally on each of the servers. This will work correctly and optimally if you are prepared for this case and have spread data across the cluster servers such that the data for a single UserID resides entirely on a single server. In this case, all the necessary data will be available locally on each server. Otherwise, the result will be inaccurate. We refer to this variation of the query as \"local IN\". To correct how the query works when data is spread randomly across the cluster servers, you could specify distributed_table inside a subquery. The query would look like this: SELECT uniq ( UserID ) FROM distributed_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM distributed_table WHERE CounterID = 34 ) This query will be sent to all remote servers as SELECT uniq ( UserID ) FROM local_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM distributed_table WHERE CounterID = 34 ) The subquery will begin running on each remote server. Since the subquery uses a distributed table, the subquery that is on each remote server will be resent to every remote server as SELECT UserID FROM local_table WHERE CounterID = 34 For example, if you have a cluster of 100 servers, executing the entire query will require 10,000 elementary requests, which is generally considered unacceptable. In such cases, you should always use GLOBAL IN instead of IN. Let's look at how it works for the query SELECT uniq ( UserID ) FROM distributed_table WHERE CounterID = 101500 AND UserID GLOBAL IN ( SELECT UserID FROM distributed_table WHERE CounterID = 34 ) The requestor server will run the subquery SELECT UserID FROM distributed_table WHERE CounterID = 34 and the result will be put in a temporary table in RAM. Then the request will be sent to each remote server as SELECT uniq ( UserID ) FROM local_table WHERE CounterID = 101500 AND UserID GLOBAL IN _data1 and the temporary table _data1 will be sent to every remote server with the query (the name of the temporary table is implementation-defined). This is more optimal than using the normal IN. However, keep the following points in mind: When creating a temporary table, data is not made unique. To reduce the volume of data transmitted over the network, specify DISTINCT in the subquery. (You don't need to do this for a normal IN.) The temporary table will be sent to all the remote servers. Transmission does not account for network topology. For example, if 10 remote servers reside in a datacenter that is very remote in relation to the requestor server, the data will be sent 10 times over the channel to the remote datacenter. Try to avoid large data sets when using GLOBAL IN. When transmitting data to remote servers, restrictions on network bandwidth are not configurable. You might overload the network. Try to distribute data across servers so that you don't need to use GLOBAL IN on a regular basis. If you need to use GLOBAL IN often, plan the location of the ClickHouse cluster so that a single group of replicas resides in no more than one data center with a fast network between them, so that a query can be processed entirely within a single data center. It also makes sense to specify a local table in the GLOBAL IN clause, in case this local table is only available on the requestor server and you want to use data from it on remote servers.", + "title": "Distributed subqueries" + }, + { + "location": "/index.html#extreme-values", + "text": "In addition to results, you can also get minimum and maximum values for the results columns. To do this, set the extremes setting to 1. Minimums and maximums are calculated for numeric types, dates, and dates with times. For other columns, the default values are output. An extra two rows are calculated \u2013 the minimums and maximums, respectively. These extra two rows are output in JSON*, TabSeparated*, and Pretty* formats, separate from the other rows. They are not output for other formats. In JSON* formats, the extreme values are output in a separate 'extremes' field. In TabSeparated* formats, the row comes after the main result, and after 'totals' if present. It is preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result, and after 'totals' if present. Extreme values are calculated for rows that have passed through LIMIT. However, when using 'LIMIT offset, size', the rows before 'offset' are included in 'extremes'. In stream requests, the result may also include a small number of rows that passed through LIMIT.", + "title": "Extreme values" + }, + { + "location": "/index.html#notes", + "text": "The GROUP BY and ORDER BY clauses do not support positional arguments. This contradicts MySQL, but conforms to standard SQL.\nFor example, GROUP BY 1, 2 will be interpreted as grouping by constants (i.e. aggregation of all rows into one). You can use synonyms ( AS aliases) in any part of a query. You can put an asterisk in any part of a query instead of an expression. When the query is analyzed, the asterisk is expanded to a list of all table columns (excluding the MATERIALIZED and ALIAS columns). There are only a few cases when using an asterisk is justified: When creating a table dump. For tables containing just a few columns, such as system tables. For getting information about what columns are in a table. In this case, set LIMIT 1 . But it is better to use the DESC TABLE query. When there is strong filtration on a small number of columns using PREWHERE . In subqueries (since columns that aren't needed for the external query are excluded from subqueries). In all other cases, we don't recommend using the asterisk, since it only gives you the drawbacks of a columnar DBMS instead of the advantages. In other words using the asterisk is not recommended.", + "title": "Notes" + }, + { + "location": "/index.html#kill-query", + "text": "KILL QUERY \n WHERE where expression to SELECT FROM system . processes query \n [ SYNC | ASYNC | TEST ] \n [ FORMAT format ] Attempts to forcibly terminate the currently running queries.\nThe queries to terminate are selected from the system.processes table using the criteria defined in the WHERE clause of the KILL query. Examples: -- Forcibly terminates all queries with the specified query_id: KILL QUERY WHERE query_id = 2-857d-4a57-9ee0-327da5d60a90 -- Synchronously terminates all queries run by username : KILL QUERY WHERE user = username SYNC Read-only users can only stop their own queries. By default, the asynchronous version of queries is used ( ASYNC ), which doesn't wait for confirmation that queries have stopped. The synchronous version ( SYNC ) waits for all queries to stop and displays information about each process as it stops.\nThe response contains the kill_status column, which can take the following values: 'finished' \u2013 The query was terminated successfully. 'waiting' \u2013 Waiting for the query to end after sending it a signal to terminate. The other values \u200b\u200bexplain why the query can't be stopped. A test query ( TEST ) only checks the user's rights and displays a list of queries to stop.", + "title": "KILL QUERY" + }, + { + "location": "/index.html#syntax", + "text": "There are two types of parsers in the system: the full SQL parser (a recursive descent parser), and the data format parser (a fast stream parser).\nIn all cases except the INSERT query, only the full SQL parser is used.\nThe INSERT query uses both parsers: INSERT INTO t VALUES ( 1 , Hello, world ), ( 2 , abc ), ( 3 , def ) The INSERT INTO t VALUES fragment is parsed by the full parser, and the data (1, 'Hello, world'), (2, 'abc'), (3, 'def') is parsed by the fast stream parser.\nData can have any format. When a query is received, the server calculates no more than max_query_size bytes of the request in RAM (by default, 1 MB), and the rest is stream parsed.\nThis means the system doesn't have problems with large INSERT queries, like MySQL does. When using the Values format in an INSERT query, it may seem that data is parsed the same as expressions in a SELECT query, but this is not true. The Values format is much more limited. Next we will cover the full parser. For more information about format parsers, see the section \"Formats\".", + "title": "Syntax" + }, + { + "location": "/index.html#spaces", + "text": "There may be any number of space symbols between syntactical constructions (including the beginning and end of a query). Space symbols include the space, tab, line feed, CR, and form feed.", + "title": "Spaces" + }, + { + "location": "/index.html#comments", + "text": "SQL-style and C-style comments are supported.\nSQL-style comments: from -- to the end of the line. The space after -- can be omitted.\nComments in C-style: from /* to */ . These comments can be multiline. Spaces are not required here, either.", + "title": "Comments" + }, + { + "location": "/index.html#keywords", + "text": "Keywords (such as SELECT ) are not case-sensitive. Everything else (column names, functions, and so on), in contrast to standard SQL, is case-sensitive. Keywords are not reserved (they are just parsed as keywords in the corresponding context).", + "title": "Keywords" + }, + { + "location": "/index.html#identifiers", + "text": "Identifiers (column names, functions, and data types) can be quoted or non-quoted.\nNon-quoted identifiers start with a Latin letter or underscore, and continue with a Latin letter, underscore, or number. In other words, they must match the regex ^[a-zA-Z_][0-9a-zA-Z_]*$ . Examples: x, _1, X_y__Z123_. Quoted identifiers are placed in reversed quotation marks `id` (the same as in MySQL), and can indicate any set of bytes (non-empty). In addition, symbols (for example, the reverse quotation mark) inside this type of identifier can be backslash-escaped. Escaping rules are the same as for string literals (see below).\nWe recommend using identifiers that do not need to be quoted.", + "title": "Identifiers" + }, + { + "location": "/index.html#literals", + "text": "There are numeric literals, string literals, and compound literals.", + "title": "Literals" + }, + { + "location": "/index.html#numeric-literals", + "text": "A numeric literal tries to be parsed: First as a 64-bit signed number, using the 'strtoull' function. If unsuccessful, as a 64-bit unsigned number, using the 'strtoll' function. If unsuccessful, as a floating-point number using the 'strtod' function. Otherwise, an error is returned. The corresponding value will have the smallest type that the value fits in.\nFor example, 1 is parsed as UInt8, but 256 is parsed as UInt16. For more information, see \"Data types\". Examples: 1 , 18446744073709551615 , 0xDEADBEEF , 01 , 0.1 , 1e100 , -1e-100 , inf , nan .", + "title": "Numeric literals" + }, + { + "location": "/index.html#string-literals", + "text": "Only string literals in single quotes are supported. The enclosed characters can be backslash-escaped. The following escape sequences have a corresponding special value: \\b , \\f , \\r , \\n , \\t , \\0 , \\a , \\v , \\xHH . In all other cases, escape sequences in the format \\c , where \"c\" is any character, are converted to \"c\". This means that you can use the sequences \\' and \\\\ . The value will have the String type. The minimum set of characters that you need to escape in string literals: ' and \\ .", + "title": "String literals" + }, + { + "location": "/index.html#compound-literals", + "text": "Constructions are supported for arrays: [1, 2, 3] and tuples: (1, 'Hello, world!', 2) ..\nActually, these are not literals, but expressions with the array creation operator and the tuple creation operator, respectively.\nFor more information, see the section \"Operators2\".\nAn array must consist of at least one item, and a tuple must have at least two items.\nTuples have a special purpose for use in the IN clause of a SELECT query. Tuples can be obtained as the result of a query, but they can't be saved to a database (with the exception of Memory-type tables).", + "title": "Compound literals" + }, + { + "location": "/index.html#functions", + "text": "Functions are written like an identifier with a list of arguments (possibly empty) in brackets. In contrast to standard SQL, the brackets are required, even for an empty arguments list. Example: now() .\nThere are regular and aggregate functions (see the section \"Aggregate functions\"). Some aggregate functions can contain two lists of arguments in brackets. Example: quantile (0.9) (x) . These aggregate functions are called \"parametric\" functions, and the arguments in the first list are called \"parameters\". The syntax of aggregate functions without parameters is the same as for regular functions.", + "title": "Functions" + }, + { + "location": "/index.html#operators", + "text": "Operators are converted to their corresponding functions during query parsing, taking their priority and associativity into account.\nFor example, the expression 1 + 2 * 3 + 4 is transformed to plus(plus(1, multiply(2, 3)), 4) .\nFor more information, see the section \"Operators\" below.", + "title": "Operators" + }, + { + "location": "/index.html#data-types-and-database-table-engines", + "text": "Data types and table engines in the CREATE query are written the same way as identifiers or functions. In other words, they may or may not contain an arguments list in brackets. For more information, see the sections \"Data types,\" \"Table engines,\" and \"CREATE\".", + "title": "Data types and database table engines" + }, + { + "location": "/index.html#synonyms", + "text": "In the SELECT query, expressions can specify synonyms using the AS keyword. Any expression is placed to the left of AS. The identifier name for the synonym is placed to the right of AS. As opposed to standard SQL, synonyms are not only declared on the top level of expressions: SELECT ( 1 AS n ) + 2 , n In contrast to standard SQL, synonyms can be used in all parts of a query, not just SELECT .", + "title": "Synonyms" + }, + { + "location": "/index.html#asterisk", + "text": "In a SELECT query, an asterisk can replace the expression. For more information, see the section \"SELECT\".", + "title": "Asterisk" + }, + { + "location": "/index.html#expressions", + "text": "An expression is a function, identifier, literal, application of an operator, expression in brackets, subquery, or asterisk. It can also contain a synonym.\nA list of expressions is one or more expressions separated by commas.\nFunctions and operators, in turn, can have expressions as arguments.", + "title": "Expressions" + }, + { + "location": "/index.html#table-engines", + "text": "The table engine (type of table) determines: How and where data is stored: where to write it to, and where to read it from. Which queries are supported, and how. Concurrent data access. Use of indexes, if present. Whether multithreaded request execution is possible. Data replication. When reading data, the engine is only required to extract the necessary set of columns. However, in some cases, the query may be partially processed inside the table engine. Note that for most serious tasks, you should use engines from the MergeTree family.", + "title": "Table engines" + }, + { + "location": "/index.html#tinylog", + "text": "The simplest table engine, which stores data on a disk.\nEach column is stored in a separate compressed file.\nWhen writing, data is appended to the end of files. Concurrent data access is not restricted in any way: If you are simultaneously reading from a table and writing to it in a different query, the read operation will complete with an error. If you are writing to a table in multiple queries simultaneously, the data will be broken. The typical way to use this table is write-once: first just write the data one time, then read it as many times as needed.\nQueries are executed in a single stream. In other words, this engine is intended for relatively small tables (recommended up to 1,000,000 rows).\nIt makes sense to use this table engine if you have many small tables, since it is simpler than the Log engine (fewer files need to be opened).\nThe situation when you have a large number of small tables guarantees poor productivity, but may already be used when working with another DBMS, and you may find it easier to switch to using TinyLog types of tables. Indexes are not supported. In Yandex.Metrica, TinyLog tables are used for intermediary data that is processed in small batches.", + "title": "TinyLog" + }, + { + "location": "/index.html#log", + "text": "Log differs from TinyLog in that a small file of \"marks\" resides with the column files. These marks are written on every data block and contain offsets that indicate where to start reading the file in order to skip the specified number of rows. This makes it possible to read table data in multiple threads.\nFor concurrent data access, the read operations can be performed simultaneously, while write operations block reads and each other.\nThe Log engine does not support indexes. Similarly, if writing to a table failed, the table is broken, and reading from it returns an error. The Log engine is appropriate for temporary data, write-once tables, and for testing or demonstration purposes.", + "title": "Log" + }, + { + "location": "/index.html#memory", + "text": "The Memory engine stores data in RAM, in uncompressed form. Data is stored in exactly the same form as it is received when read. In other words, reading from this table is completely free.\nConcurrent data access is synchronized. Locks are short: read and write operations don't block each other.\nIndexes are not supported. Reading is parallelized.\nMaximal productivity (over 10 GB/sec) is reached on simple queries, because there is no reading from the disk, decompressing, or deserializing data. (We should note that in many cases, the productivity of the MergeTree engine is almost as high.)\nWhen restarting a server, data disappears from the table and the table becomes empty.\nNormally, using this table engine is not justified. However, it can be used for tests, and for tasks where maximum speed is required on a relatively small number of rows (up to approximately 100,000,000). The Memory engine is used by the system for temporary tables with external query data (see the section \"External data for processing a query\"), and for implementing GLOBAL IN (see the section \"IN operators\").", + "title": "Memory" + }, + { + "location": "/index.html#mergetree", + "text": "The MergeTree engine supports an index by primary key and by date, and provides the possibility to update data in real time.\nThis is the most advanced table engine in ClickHouse. Don't confuse it with the Merge engine. The engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity. Example without sampling support. MergeTree(EventDate, (CounterID, EventDate), 8192) Example with sampling support. MergeTree(EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID)), 8192) A MergeTree table must have a separate column containing the date. Here, it is the EventDate column. The date column must have the 'Date' type (not 'DateTime'). The primary key may be a tuple from any expressions (usually this is just a tuple of columns), or a single expression. The sampling expression (optional) can be any expression. It must also be present in the primary key. The example uses a hash of user IDs to pseudo-randomly disperse data in the table for each CounterID and EventDate. In other words, when using the SAMPLE clause in a query, you get an evenly pseudo-random sample of data for a subset of users. The table is implemented as a set of parts. Each part is sorted by the primary key. In addition, each part has the minimum and maximum date assigned. When inserting in the table, a new sorted part is created. The merge process is periodically initiated in the background. When merging, several parts are selected (usually the smallest ones) and then merged into one large sorted part. In other words, incremental sorting occurs when inserting to the table. Merging is implemented so that the table always consists of a small number of sorted parts, and the merge itself doesn't do too much work. During insertion, data belonging to different months is separated into different parts. The parts that correspond to different months are never combined. The purpose of this is to provide local data modification (for ease in backups). Parts are combined up to a certain size threshold, so there aren't any merges that are too long. For each part, an index file is also written. The index file contains the primary key value for every 'index_granularity' row in the table. In other words, this is an abbreviated index of sorted data. For columns, \"marks\" are also written to each 'index_granularity' row so that data can be read in a specific range. When reading from a table, the SELECT query is analyzed for whether indexes can be used.\nAn index can be used if the WHERE or PREWHERE clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has IN or LIKE with a fixed prefix on columns or expressions that are in the primary key or partitioning key, or on certain partially repetitive functions of these columns, or logical relationships of these expressions. Thus, it is possible to quickly run queries on one or many ranges of the primary key. In this example, queries will be fast when run for a specific tracking tag; for a specific tag and date range; for a specific tag and date; for multiple tags with a date range, and so on. SELECT count () FROM table WHERE EventDate = toDate ( now ()) AND CounterID = 34 SELECT count () FROM table WHERE EventDate = toDate ( now ()) AND ( CounterID = 34 OR CounterID = 42 ) SELECT count () FROM table WHERE (( EventDate = toDate ( 2014-01-01 ) AND EventDate = toDate ( 2014-01-31 )) OR EventDate = toDate ( 2014-05-01 )) AND CounterID IN ( 101500 , 731962 , 160656 ) AND ( CounterID = 101500 OR EventDate != toDate ( 2014-05-01 )) All of these cases will use the index by date and by primary key. The index is used even for complex expressions. Reading from the table is organized so that using the index can't be slower than a full scan. In this example, the index can't be used. SELECT count () FROM table WHERE CounterID = 34 OR URL LIKE %upyachka% To check whether ClickHouse can use the index when executing the query, use the settings force_index_by_date and force_primary_key . The index by date only allows reading those parts that contain dates from the desired range. However, a data part may contain data for many dates (up to an entire month), while within a single part the data is ordered by the primary key, which might not contain the date as the first column. Because of this, using a query with only a date condition that does not specify the primary key prefix will cause more data to be read than for a single date. For concurrent table access, we use multi-versioning. In other words, when a table is simultaneously read and updated, data is read from a set of parts that is current at the time of the query. There are no lengthy locks. Inserts do not get in the way of read operations. Reading from a table is automatically parallelized. The OPTIMIZE query is supported, which calls an extra merge step. You can use a single large table and continually add data to it in small chunks \u2013 this is what MergeTree is intended for. Data replication is possible for all types of tables in the MergeTree family (see the section \"Data replication\").", + "title": "MergeTree" + }, + { + "location": "/index.html#custom-partitioning-key", + "text": "Starting with version 1.1.54310, you can create tables in the MergeTree family with any partitioning expression (not only partitioning by month). The partition key can be an expression from the table columns, or a tuple of such expressions (similar to the primary key). The partition key can be omitted. When creating a table, specify the partition key in the ENGINE description with the new syntax: ENGINE [=] Name(...) [PARTITION BY expr] [ORDER BY expr] [SAMPLE BY expr] [SETTINGS name=value, ...] For MergeTree tables, the partition expression is specified after PARTITION BY , the primary key after ORDER BY , the sampling key after SAMPLE BY , and SETTINGS can specify index_granularity (optional; the default value is 8192), as well as other settings from MergeTreeSettings.h . The other engine parameters are specified in parentheses after the engine name, as previously. Example: ENGINE = ReplicatedCollapsingMergeTree ( /clickhouse/tables/name , replica1 , Sign ) \n PARTITION BY ( toMonday ( StartDate ), EventType ) \n ORDER BY ( CounterID , StartDate , intHash32 ( UserID )) \n SAMPLE BY intHash32 ( UserID ) The traditional partitioning by month is expressed as toYYYYMM(date_column) . You can't convert an old-style table to a table with custom partitions (only via INSERT SELECT). After this table is created, merge will only work for data parts that have the same value for the partitioning expression. Note: This means that you shouldn't make overly granular partitions (more than about a thousand partitions), or SELECT will perform poorly. To specify a partition in ALTER PARTITION commands, specify the value of the partition expression (or a tuple). Constants and constant expressions are supported. Example: ALTER TABLE table DROP PARTITION ( toMonday ( today ()), 1 ) Deletes the partition for the current week with event type 1. The same is true for the OPTIMIZE query. To specify the only partition in a non-partitioned table, specify PARTITION tuple() . Note: For old-style tables, the partition can be specified either as a number 201710 or a string '201710' . The syntax for the new style of tables is stricter with types (similar to the parser for the VALUES input format). In addition, ALTER TABLE FREEZE PARTITION uses exact match for new-style tables (not prefix match). In the system.parts table, the partition column specifies the value of the partition expression to use in ALTER queries (if quotas are removed). The name column should specify the name of the data part that has a new format. Was: 20140317_20140323_2_2_0 (minimum date - maximum date - minimum block number - maximum block number - level). Now: 201403_2_2_0 (partition ID - minimum block number - maximum block number - level). The partition ID is its string identifier (human-readable, if possible) that is used for the names of data parts in the file system and in ZooKeeper. You can specify it in ALTER queries in place of the partition key. Example: Partition key toYYYYMM(EventDate) ; ALTER can specify either PARTITION 201710 or PARTITION ID '201710' . For more examples, see the tests 00502_custom_partitioning_local and 00502_custom_partitioning_replicated_zookeeper .", + "title": "Custom partitioning key" + }, + { + "location": "/index.html#replacingmergetree", + "text": "This engine table differs from MergeTree in that it removes duplicate entries with the same primary key value. The last optional parameter for the table engine is the version column. When merging, it reduces all rows with the same primary key value to just one row. If the version column is specified, it leaves the row with the highest version; otherwise, it leaves the last row. The version column must have a type from the UInt family, Date , or DateTime . ReplacingMergeTree ( EventDate , ( OrderID , EventDate , BannerID , ...), 8192 , ver ) Note that data is only deduplicated during merges. Merging occurs in the background at an unknown time, so you can't plan for it. Some of the data may remain unprocessed. Although you can run an unscheduled merge using the OPTIMIZE query, don't count on using it, because the OPTIMIZE query will read and write a large amount of data. Thus, ReplacingMergeTree is suitable for clearing out duplicate data in the background in order to save space, but it doesn't guarantee the absence of duplicates. This engine is not used in Yandex.Metrica, but it has been applied in other Yandex projects.", + "title": "ReplacingMergeTree" + }, + { + "location": "/index.html#summingmergetree", + "text": "This engine differs from MergeTree in that it totals data while merging. SummingMergeTree ( EventDate , ( OrderID , EventDate , BannerID , ...), 8192 ) The columns to total are implicit. When merging, all rows with the same primary key value (in the example, OrderId, EventDate, BannerID, ...) have their values totaled in numeric columns that are not part of the primary key. SummingMergeTree ( EventDate , ( OrderID , EventDate , BannerID , ...), 8192 , ( Shows , Clicks , Cost , ...)) The columns to total are set explicitly (the last parameter \u2013 Shows, Clicks, Cost, ...). When merging, all rows with the same primary key value have their values totaled in the specified columns. The specified columns also must be numeric and must not be part of the primary key. If the values were null in all of these columns, the row is deleted. (The exception is cases when the data part would not have any rows left in it.) For the other rows that are not part of the primary key, the first value that occurs is selected when merging. Summation is not performed for a read operation. If it is necessary, write the appropriate GROUP BY. In addition, a table can have nested data structures that are processed in a special way.\nIf the name of a nested table ends in 'Map' and it contains at least two columns that meet the following criteria: The first table is numeric ((U)IntN, Date, DateTime), which we'll refer to as the 'key'. The other columns are arithmetic ((U)IntN, Float32/64), which we'll refer to as '(values...)'. Then this nested table is interpreted as a mapping of key = (values...), and when merging its rows, the elements of two data sets are merged by 'key' with a summation of the corresponding (values...). Examples: [(1, 100)] + [(2, 150)] - [(1, 100), (2, 150)]\n[(1, 100)] + [(1, 150)] - [(1, 250)]\n[(1, 100)] + [(1, 150), (2, 150)] - [(1, 250), (2, 150)]\n[(1, 100), (2, 150)] + [(1, -100)] - [(2, 150)] For aggregation of Map, use the function sumMap(key, value). For nested data structures, you don't need to specify the columns as a list of columns for totaling. This table engine is not particularly useful. Remember that when saving just pre-aggregated data, you lose some of the system's advantages.", + "title": "SummingMergeTree" + }, + { + "location": "/index.html#aggregatingmergetree", + "text": "This engine differs from MergeTree in that the merge combines the states of aggregate functions stored in the table for rows with the same primary key value. For this to work, it uses the AggregateFunction data type, as well as -State and -Merge modifiers for aggregate functions. Let's examine it more closely. There is an AggregateFunction data type. It is a parametric data type. As parameters, the name of the aggregate function is passed, then the types of its arguments. Examples: CREATE TABLE t ( \n column1 AggregateFunction ( uniq , UInt64 ), \n column2 AggregateFunction ( anyIf , String , UInt8 ), \n column3 AggregateFunction ( quantiles ( 0 . 5 , 0 . 9 ), UInt64 ) ) ENGINE = ... This type of column stores the state of an aggregate function. To get this type of value, use aggregate functions with the State suffix. Example: uniqState(UserID), quantilesState(0.5, 0.9)(SendTiming) In contrast to the corresponding uniq and quantiles functions, these functions return the state, rather than the prepared value. In other words, they return an AggregateFunction type value. An AggregateFunction type value can't be output in Pretty formats. In other formats, these types of values are output as implementation-specific binary data. The AggregateFunction type values are not intended for output or saving in a dump. The only useful thing you can do with AggregateFunction type values is combine the states and get a result, which essentially means to finish aggregation. Aggregate functions with the 'Merge' suffix are used for this purpose.\nExample: uniqMerge(UserIDState), where UserIDState has the AggregateFunction type. In other words, an aggregate function with the 'Merge' suffix takes a set of states, combines them, and returns the result.\nAs an example, these two queries return the same result: SELECT uniq ( UserID ) FROM table SELECT uniqMerge ( state ) FROM ( SELECT uniqState ( UserID ) AS state FROM table GROUP BY RegionID ) There is an AggregatingMergeTree engine. Its job during a merge is to combine the states of aggregate functions from different table rows with the same primary key value. You can't use a normal INSERT to insert a row in a table containing AggregateFunction columns, because you can't explicitly define the AggregateFunction value. Instead, use INSERT SELECT with -State aggregate functions for inserting data. With SELECT from an AggregatingMergeTree table, use GROUP BY and aggregate functions with the '-Merge' modifier in order to complete data aggregation. You can use AggregatingMergeTree tables for incremental data aggregation, including for aggregated materialized views. Example: Create an AggregatingMergeTree materialized view that watches the test.visits table: CREATE MATERIALIZED VIEW test . basic ENGINE = AggregatingMergeTree ( StartDate , ( CounterID , StartDate ), 8192 ) AS SELECT \n CounterID , \n StartDate , \n sumState ( Sign ) AS Visits , \n uniqState ( UserID ) AS Users FROM test . visits GROUP BY CounterID , StartDate ; Insert data in the test.visits table. Data will also be inserted in the view, where it will be aggregated: INSERT INTO test . visits ... Perform SELECT from the view using GROUP BY in order to complete data aggregation: SELECT \n StartDate , \n sumMerge ( Visits ) AS Visits , \n uniqMerge ( Users ) AS Users FROM test . basic GROUP BY StartDate ORDER BY StartDate ; You can create a materialized view like this and assign a normal view to it that finishes data aggregation. Note that in most cases, using AggregatingMergeTree is not justified, since queries can be run efficiently enough on non-aggregated data.", + "title": "AggregatingMergeTree" + }, + { + "location": "/index.html#collapsingmergetree", + "text": "This engine is used specifically for Yandex.Metrica. It differs from MergeTree in that it allows automatic deletion, or \"collapsing\" certain pairs of rows when merging. Yandex.Metrica has normal logs (such as hit logs) and change logs. Change logs are used for incrementally calculating statistics on data that is constantly changing. Examples are the log of session changes, or logs of changes to user histories. Sessions are constantly changing in Yandex.Metrica. For example, the number of hits per session increases. We refer to changes in any object as a pair (?old values, ?new values). Old values may be missing if the object was created. New values may be missing if the object was deleted. If the object was changed, but existed previously and was not deleted, both values are present. In the change log, one or two entries are made for each change. Each entry contains all the attributes that the object has, plus a special attribute for differentiating between the old and new values. When objects change, only the new entries are added to the change log, and the existing ones are not touched. The change log makes it possible to incrementally calculate almost any statistics. To do this, we need to consider \"new\" rows with a plus sign, and \"old\" rows with a minus sign. In other words, incremental calculation is possible for all statistics whose algebraic structure contains an operation for taking the inverse of an element. This is true of most statistics. We can also calculate \"idempotent\" statistics, such as the number of unique visitors, since the unique visitors are not deleted when making changes to sessions. This is the main concept that allows Yandex.Metrica to work in real time. CollapsingMergeTree accepts an additional parameter - the name of an Int8-type column that contains the row's \"sign\". Example: CollapsingMergeTree ( EventDate , ( CounterID , EventDate , intHash32 ( UniqID ), VisitID ), 8192 , Sign ) Here, Sign is a column containing -1 for \"old\" values and 1 for \"new\" values. When merging, each group of consecutive identical primary key values (columns for sorting data) is reduced to no more than one row with the column value 'sign_column = -1' (the \"negative row\") and no more than one row with the column value 'sign_column = 1' (the \"positive row\"). In other words, entries from the change log are collapsed. If the number of positive and negative rows matches, the first negative row and the last positive row are written.\nIf there is one more positive row than negative rows, only the last positive row is written.\nIf there is one more negative row than positive rows, only the first negative row is written.\nOtherwise, there will be a logical error and none of the rows will be written. (A logical error can occur if the same section of the log was accidentally inserted more than once. The error is just recorded in the server log, and the merge continues.) Thus, collapsing should not change the results of calculating statistics.\nChanges are gradually collapsed so that in the end only the last value of almost every object is left.\nCompared to MergeTree, the CollapsingMergeTree engine allows a multifold reduction of data volume. There are several ways to get completely \"collapsed\" data from a CollapsingMergeTree table: Write a query with GROUP BY and aggregate functions that accounts for the sign. For example, to calculate quantity, write 'sum(Sign)' instead of 'count()'. To calculate the sum of something, write 'sum(Sign * x)' instead of 'sum(x)', and so on, and also add 'HAVING sum(Sign) 0'. Not all amounts can be calculated this way. For example, the aggregate functions 'min' and 'max' can't be rewritten. If you must extract data without aggregation (for example, to check whether rows are present whose newest values match certain conditions), you can use the FINAL modifier for the FROM clause. This approach is significantly less efficient.", + "title": "CollapsingMergeTree" + }, + { + "location": "/index.html#graphitemergetree", + "text": "This engine is designed for rollup (thinning and aggregating/averaging) Graphite data. It may be helpful to developers who want to use ClickHouse as a data store for Graphite. Graphite stores full data in ClickHouse, and data can be retrieved in the following ways: Without thinning. Uses the MergeTree engine. With thinning. Using the GraphiteMergeTree engine. The engine inherits properties from MergeTree. The settings for thinning data are defined by the graphite_rollup parameter in the server configuration.", + "title": "GraphiteMergeTree" + }, + { + "location": "/index.html#using-the-engine", + "text": "The Graphite data table must contain the following fields at minimum: Path \u2013 The metric name (Graphite sensor). Time \u2013 The time for measuring the metric. Value \u2013 The value of the metric at the time set in Time. Version \u2013 Determines which value of the metric with the same Path and Time will remain in the database. Rollup pattern: pattern\n regexp\n function\n age - precision\n ...\npattern\n ...\ndefault\n function\n age - precision\n ... When processing a record, ClickHouse will check the rules in the pattern clause. If the metric name matches the regexp , the rules from pattern are applied; otherwise, the rules from default are used. Fields in the pattern. age \u2013 The minimum age of the data in seconds. function \u2013 The name of the aggregating function to apply to data whose age falls within the range [age, age + precision] . precision \u2013 How precisely to define the age of the data in seconds. regexp \u2013 A pattern for the metric name. Example of settings: graphite_rollup \n pattern \n regexp click_cost /regexp \n function any /function \n retention \n age 0 /age \n precision 5 /precision \n /retention \n retention \n age 86400 /age \n precision 60 /precision \n /retention \n /pattern \n default \n function max /function \n retention \n age 0 /age \n precision 60 /precision \n /retention \n retention \n age 3600 /age \n precision 300 /precision \n /retention \n retention \n age 86400 /age \n precision 3600 /precision \n /retention \n /default /graphite_rollup", + "title": "Using the engine" + }, + { + "location": "/index.html#data-replication", + "text": "Replication is only supported for tables in the MergeTree family: ReplicatedMergeTree ReplicatedSummingMergeTree ReplicatedReplacingMergeTree ReplicatedAggregatingMergeTree ReplicatedCollapsingMergeTree ReplicatedGraphiteMergeTree Replication works at the level of an individual table, not the entire server. A server can store both replicated and non-replicated tables at the same time. Replication does not depend on sharding. Each shard has its own independent replication. Compressed data is replicated for INSERT and ALTER queries (see the description of the ALTER query). CREATE , DROP , ATTACH , DETACH and RENAME queries are executed on a single server and are not replicated: The CREATE TABLE query creates a new replicatable table on the server where the query is run. If this table already exists on other servers, it adds a new replica. The DROP TABLE query deletes the replica located on the server where the query is run. The RENAME query renames the table on one of the replicas. In other words, replicated tables can have different names on different replicas. To use replication, set the addresses of the ZooKeeper cluster in the config file. Example: zookeeper \n node index= 1 \n host example1 /host \n port 2181 /port \n /node \n node index= 2 \n host example2 /host \n port 2181 /port \n /node \n node index= 3 \n host example3 /host \n port 2181 /port \n /node /zookeeper Use ZooKeeper version 3.4.5 or later. You can specify any existing ZooKeeper cluster and the system will use a directory on it for its own data (the directory is specified when creating a replicatable table). If ZooKeeper isn't set in the config file, you can't create replicated tables, and any existing replicated tables will be read-only. ZooKeeper is not used in SELECT queries because replication does not affect the performance of SELECT and queries run just as fast as they do for non-replicated tables. When querying distributed replicated tables, ClickHouse behavior is controlled by the settings max_replica_delay_for_distributed_queries and fallback_to_stale_replicas_for_distributed_queries . For each INSERT query, approximately ten entries are added to ZooKeeper through several transactions. (To be more precise, this is for each inserted block of data; an INSERT query contains one block or one block per max_insert_block_size = 1048576 rows.) This leads to slightly longer latencies for INSERT compared to non-replicated tables. But if you follow the recommendations to insert data in batches of no more than one INSERT per second, it doesn't create any problems. The entire ClickHouse cluster used for coordinating one ZooKeeper cluster has a total of several hundred INSERTs per second. The throughput on data inserts (the number of rows per second) is just as high as for non-replicated data. For very large clusters, you can use different ZooKeeper clusters for different shards. However, this hasn't proven necessary on the Yandex.Metrica cluster (approximately 300 servers). Replication is asynchronous and multi-master. INSERT queries (as well as ALTER ) can be sent to any available server. Data is inserted on the server where the query is run, and then it is copied to the other servers. Because it is asynchronous, recently inserted data appears on the other replicas with some latency. If part of the replicas are not available, the data is written when they become available. If a replica is available, the latency is the amount of time it takes to transfer the block of compressed data over the network. By default, an INSERT query waits for confirmation of writing the data from only one replica. If the data was successfully written to only one replica and the server with this replica ceases to exist, the stored data will be lost. Tp enable getting confirmation of data writes from multiple replicas, use the insert_quorum option. Each block of data is written atomically. The INSERT query is divided into blocks up to max_insert_block_size = 1048576 rows. In other words, if the INSERT query has less than 1048576 rows, it is made atomically. Data blocks are deduplicated. For multiple writes of the same data block (data blocks of the same size containing the same rows in the same order), the block is only written once. The reason for this is in case of network failures when the client application doesn't know if the data was written to the DB, so the INSERT query can simply be repeated. It doesn't matter which replica INSERTs were sent to with identical data. INSERTs are idempotent. Deduplication parameters are controlled by merge_tree server settings. During replication, only the source data to insert is transferred over the network. Further data transformation (merging) is coordinated and performed on all the replicas in the same way. This minimizes network usage, which means that replication works well when replicas reside in different datacenters. (Note that duplicating data in different datacenters is the main goal of replication.) You can have any number of replicas of the same data. Yandex.Metrica uses double replication in production. Each server uses RAID-5 or RAID-6, and RAID-10 in some cases. This is a relatively reliable and convenient solution. The system monitors data synchronicity on replicas and is able to recover after a failure. Failover is automatic (for small differences in data) or semi-automatic (when data differs too much, which may indicate a configuration error).", + "title": "Data replication" + }, + { + "location": "/index.html#creating-replicated-tables", + "text": "The Replicated prefix is added to the table engine name. For example: ReplicatedMergeTree . Two parameters are also added in the beginning of the parameters list \u2013 the path to the table in ZooKeeper, and the replica name in ZooKeeper. Example: ReplicatedMergeTree( /clickhouse/tables/{layer}-{shard}/hits , {replica} , EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID), EventTime), 8192) As the example shows, these parameters can contain substitutions in curly brackets. The substituted values are taken from the 'macros' section of the config file. Example: macros \n layer 05 /layer \n shard 02 /shard \n replica example05-02-1.yandex.ru /replica /macros The path to the table in ZooKeeper should be unique for each replicated table. Tables on different shards should have different paths.\nIn this case, the path consists of the following parts: /clickhouse/tables/ is the common prefix. We recommend using exactly this one. {layer}-{shard} is the shard identifier. In this example it consists of two parts, since the Yandex.Metrica cluster uses bi-level sharding. For most tasks, you can leave just the {shard} substitution, which will be expanded to the shard identifier. hits is the name of the node for the table in ZooKeeper. It is a good idea to make it the same as the table name. It is defined explicitly, because in contrast to the table name, it doesn't change after a RENAME query. The replica name identifies different replicas of the same table. You can use the server name for this, as in the example. The name only needs to be unique within each shard. You can define the parameters explicitly instead of using substitutions. This might be convenient for testing and for configuring small clusters. However, you can't use distributed DDL queries ( ON CLUSTER ) in this case. When working with large clusters, we recommend using substitutions because they reduce the probability of error. Run the CREATE TABLE query on each replica. This query creates a new replicated table, or adds a new replica to an existing one. If you add a new replica after the table already contains some data on other replicas, the data will be copied from the other replicas to the new one after running the query. In other words, the new replica syncs itself with the others. To delete a replica, run DROP TABLE . However, only one replica is deleted \u2013 the one that resides on the server where you run the query.", + "title": "Creating replicated tables" + }, + { + "location": "/index.html#recovery-after-failures", + "text": "If ZooKeeper is unavailable when a server starts, replicated tables switch to read-only mode. The system periodically attempts to connect to ZooKeeper. If ZooKeeper is unavailable during an INSERT , or an error occurs when interacting with ZooKeeper, an exception is thrown. After connecting to ZooKeeper, the system checks whether the set of data in the local file system matches the expected set of data (ZooKeeper stores this information). If there are minor inconsistencies, the system resolves them by syncing data with the replicas. If the system detects broken data parts (with the wrong size of files) or unrecognized parts (parts written to the file system but not recorded in ZooKeeper), it moves them to the 'detached' subdirectory (they are not deleted). Any missing parts are copied from the replicas. Note that ClickHouse does not perform any destructive actions such as automatically deleting a large amount of data. When the server starts (or establishes a new session with ZooKeeper), it only checks the quantity and sizes of all files. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a SELECT query. The query throws an exception about a non-matching checksum or size of a compressed block. In this case, data parts are added to the verification queue and copied from the replicas if necessary. If the local set of data differs too much from the expected one, a safety mechanism is triggered. The server enters this in the log and refuses to launch. The reason for this is that this case may indicate a configuration error, such as if a replica on a shard was accidentally configured like a replica on a different shard. However, the thresholds for this mechanism are set fairly low, and this situation might occur during normal failure recovery. In this case, data is restored semi-automatically - by \"pushing a button\". To start recovery, create the node /path_to_table/replica_name/flags/force_restore_data in ZooKeeper with any content, or run the command to restore all replicated tables: sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data Then restart the server. On start, the server deletes these flags and starts recovery.", + "title": "Recovery after failures" + }, + { + "location": "/index.html#recovery-after-complete-data-loss", + "text": "If all data and metadata disappeared from one of the servers, follow these steps for recovery: Install ClickHouse on the server. Define substitutions correctly in the config file that contains the shard identifier and replicas, if you use them. If you had unreplicated tables that must be manually duplicated on the servers, copy their data from a replica (in the directory /var/lib/clickhouse/data/db_name/table_name/ ). Copy table definitions located in /var/lib/clickhouse/metadata/ from a replica. If a shard or replica identifier is defined explicitly in the table definitions, correct it so that it corresponds to this replica. (Alternatively, start the server and make all the ATTACH TABLE queries that should have been in the .sql files in /var/lib/clickhouse/metadata/ .) To start recovery, create the ZooKeeper node /path_to_table/replica_name/flags/force_restore_data with any content, or run the command to restore all replicated tables: sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data Then start the server (restart, if it is already running). Data will be downloaded from replicas. An alternative recovery option is to delete information about the lost replica from ZooKeeper ( /path_to_table/replica_name ), then create the replica again as described in \" Creating replicatable tables \". There is no restriction on network bandwidth during recovery. Keep this in mind if you are restoring many replicas at once.", + "title": "Recovery after complete data loss" + }, + { + "location": "/index.html#converting-from-mergetree-to-replicatedmergetree", + "text": "We use the term MergeTree to refer to all table engines in the MergeTree family , the same as for ReplicatedMergeTree . If you had a MergeTree table that was manually replicated, you can convert it to a replicatable table. You might need to do this if you have already collected a large amount of data in a MergeTree table and now you want to enable replication. If the data differs on various replicas, first sync it, or delete this data on all the replicas except one. Rename the existing MergeTree table, then create a ReplicatedMergeTree table with the old name.\nMove the data from the old table to the 'detached' subdirectory inside the directory with the new table data ( /var/lib/clickhouse/data/db_name/table_name/ ).\nThen run ALTER TABLE ATTACH PARTITION on one of the replicas to add these data parts to the working set.", + "title": "Converting from MergeTree to ReplicatedMergeTree" + }, + { + "location": "/index.html#converting-from-replicatedmergetree-to-mergetree", + "text": "Create a MergeTree table with a different name. Move all the data from the directory with the ReplicatedMergeTree table data to the new table's data directory. Then delete the ReplicatedMergeTree table and restart the server. If you want to get rid of a ReplicatedMergeTree table without launching the server: Delete the corresponding .sql file in the metadata directory ( /var/lib/clickhouse/metadata/ ). Delete the corresponding path in ZooKeeper ( /path_to_table/replica_name ). After this, you can launch the server, create a MergeTree table, move the data to its directory, and then restart the server.", + "title": "Converting from ReplicatedMergeTree to MergeTree" + }, + { + "location": "/index.html#recovery-when-metadata-in-the-zookeeper-cluster-is-lost-or-damaged", + "text": "If the data in ZooKeeper was lost or damaged, you can save data by moving it to an unreplicated table as described above. If exactly the same parts exist on the other replicas, they are added to the working set on them. If not, the parts are downloaded from the replica that has them.", + "title": "Recovery when metadata in the ZooKeeper cluster is lost or damaged" + }, + { + "location": "/index.html#distributed", + "text": "The Distributed engine does not store data itself , but allows distributed query processing on multiple servers.\nReading is automatically parallelized. During a read, the table indexes on remote servers are used, if there are any.\nThe Distributed engine accepts parameters: the cluster name in the server's config file, the name of a remote database, the name of a remote table, and (optionally) a sharding key.\nExample: Distributed(logs, default, hits[, sharding_key]) Data will be read from all servers in the 'logs' cluster, from the default.hits table located on every server in the cluster.\nData is not only read, but is partially processed on the remote servers (to the extent that this is possible).\nFor example, for a query with GROUP BY, data will be aggregated on remote servers, and the intermediate states of aggregate functions will be sent to the requestor server. Then data will be further aggregated. Instead of the database name, you can use a constant expression that returns a string. For example: currentDatabase(). logs \u2013 The cluster name in the server's config file. Clusters are set like this: remote_servers \n logs \n shard \n !-- Optional. Shard weight when writing data. Default: 1. -- \n weight 1 /weight \n !-- Optional. Whether to write data to just one of the replicas. Default: false (write data to all replicas). -- \n internal_replication false /internal_replication \n replica \n host example01-01-1 /host \n port 9000 /port \n /replica \n replica \n host example01-01-2 /host \n port 9000 /port \n /replica \n /shard \n shard \n weight 2 /weight \n internal_replication false /internal_replication \n replica \n host example01-02-1 /host \n port 9000 /port \n /replica \n replica \n host example01-02-2 /host \n port 9000 /port \n /replica \n /shard \n /logs /remote_servers Here a cluster is defined with the name 'logs' that consists of two shards, each of which contains two replicas.\nShards refer to the servers that contain different parts of the data (in order to read all the data, you must access all the shards).\nReplicas are duplicating servers (in order to read all the data, you can access the data on any one of the replicas). The parameters host , port , and optionally user and password are specified for each server: : - host \u2013 The address of the remote server. You can use either the domain or the IPv4 or IPv6 address. If you specify the domain, the server makes a DNS request when it starts, and the result is stored as long as the server is running. If the DNS request fails, the server doesn't start. If you change the DNS record, restart the server.\n- port \u2013 The TCP port for messenger activity ('tcp_port' in the config, usually set to 9000). Do not confuse it with http_port.\n- user \u2013 Name of the user for connecting to a remote server. Default value: default. This user must have access to connect to the specified server. Access is configured in the users.xml file. For more information, see the section \"Access rights\".\n- password \u2013 The password for connecting to a remote server (not masked). Default value: empty string. When specifying replicas, one of the available replicas will be selected for each of the shards when reading. You can configure the algorithm for load balancing (the preference for which replica to access) \u2013 see the 'load_balancing' setting.\nIf the connection with the server is not established, there will be an attempt to connect with a short timeout. If the connection failed, the next replica will be selected, and so on for all the replicas. If the connection attempt failed for all the replicas, the attempt will be repeated the same way, several times.\nThis works in favor of resiliency, but does not provide complete fault tolerance: a remote server might accept the connection, but might not work, or work poorly. You can specify just one of the shards (in this case, query processing should be called remote, rather than distributed) or up to any number of shards. In each shard, you can specify from one to any number of replicas. You can specify a different number of replicas for each shard. You can specify as many clusters as you wish in the configuration. To view your clusters, use the 'system.clusters' table. The Distributed engine allows working with a cluster like a local server. However, the cluster is inextensible: you must write its configuration in the server config file (even better, for all the cluster's servers). There is no support for Distributed tables that look at other Distributed tables (except in cases when a Distributed table only has one shard). As an alternative, make the Distributed table look at the \"final\" tables. The Distributed engine requires writing clusters to the config file. Clusters from the config file are updated on the fly, without restarting the server. If you need to send a query to an unknown set of shards and replicas each time, you don't need to create a Distributed table \u2013 use the 'remote' table function instead. See the section \"Table functions\". There are two methods for writing data to a cluster: First, you can define which servers to write which data to, and perform the write directly on each shard. In other words, perform INSERT in the tables that the distributed table \"looks at\".\nThis is the most flexible solution \u2013 you can use any sharding scheme, which could be non-trivial due to the requirements of the subject area.\nThis is also the most optimal solution, since data can be written to different shards completely independently. Second, you can perform INSERT in a Distributed table. In this case, the table will distribute the inserted data across servers itself.\nIn order to write to a Distributed table, it must have a sharding key set (the last parameter). In addition, if there is only one shard, the write operation works without specifying the sharding key, since it doesn't have any meaning in this case. Each shard can have a weight defined in the config file. By default, the weight is equal to one. Data is distributed across shards in the amount proportional to the shard weight. For example, if there are two shards and the first has a weight of 9 while the second has a weight of 10, the first will be sent 9 / 19 parts of the rows, and the second will be sent 10 / 19. Each shard can have the 'internal_replication' parameter defined in the config file. If this parameter is set to 'true', the write operation selects the first healthy replica and writes data to it. Use this alternative if the Distributed table \"looks at\" replicated tables. In other words, if the table where data will be written is going to replicate them itself. If it is set to 'false' (the default), data is written to all replicas. In essence, this means that the Distributed table replicates data itself. This is worse than using replicated tables, because the consistency of replicas is not checked, and over time they will contain slightly different data. To select the shard that a row of data is sent to, the sharding expression is analyzed, and its remainder is taken from dividing it by the total weight of the shards. The row is sent to the shard that corresponds to the half-interval of the remainders from 'prev_weight' to 'prev_weights + weight', where 'prev_weights' is the total weight of the shards with the smallest number, and 'weight' is the weight of this shard. For example, if there are two shards, and the first has a weight of 9 while the second has a weight of 10, the row will be sent to the first shard for the remainders from the range [0, 9), and to the second for the remainders from the range [9, 19). The sharding expression can be any expression from constants and table columns that returns an integer. For example, you can use the expression 'rand()' for random distribution of data, or 'UserID' for distribution by the remainder from dividing the user's ID (then the data of a single user will reside on a single shard, which simplifies running IN and JOIN by users). If one of the columns is not distributed evenly enough, you can wrap it in a hash function: intHash64(UserID). A simple remainder from division is a limited solution for sharding and isn't always appropriate. It works for medium and large volumes of data (dozens of servers), but not for very large volumes of data (hundreds of servers or more). In the latter case, use the sharding scheme required by the subject area, rather than using entries in Distributed tables. SELECT queries are sent to all the shards, and work regardless of how data is distributed across the shards (they can be distributed completely randomly). When you add a new shard, you don't have to transfer the old data to it. You can write new data with a heavier weight \u2013 the data will be distributed slightly unevenly, but queries will work correctly and efficiently. You should be concerned about the sharding scheme in the following cases: Queries are used that require joining data (IN or JOIN) by a specific key. If data is sharded by this key, you can use local IN or JOIN instead of GLOBAL IN or GLOBAL JOIN, which is much more efficient. A large number of servers is used (hundreds or more) with a large number of small queries (queries of individual clients - websites, advertisers, or partners). In order for the small queries to not affect the entire cluster, it makes sense to locate data for a single client on a single shard. Alternatively, as we've done in Yandex.Metrica, you can set up bi-level sharding: divide the entire cluster into \"layers\", where a layer may consist of multiple shards. Data for a single client is located on a single layer, but shards can be added to a layer as necessary, and data is randomly distributed within them. Distributed tables are created for each layer, and a single shared distributed table is created for global queries. Data is written asynchronously. For an INSERT to a Distributed table, the data block is just written to the local file system. The data is sent to the remote servers in the background as soon as possible. You should check whether data is sent successfully by checking the list of files (data waiting to be sent) in the table directory: /var/lib/clickhouse/data/database/table/. If the server ceased to exist or had a rough restart (for example, after a device failure) after an INSERT to a Distributed table, the inserted data might be lost. If a damaged data part is detected in the table directory, it is transferred to the 'broken' subdirectory and no longer used. When the max_parallel_replicas option is enabled, query processing is parallelized across all replicas within a single shard. For more information, see the section \"Settings, max_parallel_replicas\".", + "title": "Distributed" + }, + { + "location": "/index.html#dictionary", + "text": "The Dictionary engine displays the dictionary data as a ClickHouse table. As an example, consider a dictionary of products with the following configuration: dictionaries dictionary \n name products /name \n source \n odbc \n table products /table \n connection_string DSN=some-db-server /connection_string \n /odbc \n /source \n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n layout \n flat/ \n /layout \n structure \n id \n name product_id /name \n /id \n attribute \n name title /name \n type String /type \n null_value /null_value \n /attribute \n /structure /dictionary /dictionaries Query the dictionary data: select name , type , key , attribute . names , attribute . types , bytes_allocated , element_count , source from system . dictionaries where name = products ; SELECT \n name , \n type , \n key , \n attribute . names , \n attribute . types , \n bytes_allocated , \n element_count , \n source FROM system . dictionaries WHERE name = products \u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u252c\u2500type\u2500\u252c\u2500key\u2500\u2500\u2500\u2500\u252c\u2500attribute.names\u2500\u252c\u2500attribute.types\u2500\u252c\u2500bytes_allocated\u2500\u252c\u2500element_count\u2500\u252c\u2500source\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 products \u2502 Flat \u2502 UInt64 \u2502 [ title ] \u2502 [ String ] \u2502 23065376 \u2502 175032 \u2502 ODBC: .products \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 You can use the dictGet* function to get the dictionary data in this format. This view isn't helpful when you need to get raw data, or when performing a JOIN operation. For these cases, you can use the Dictionary engine, which displays the dictionary data in a table. Syntax: CREATE TABLE %table_name% (%fields%) engine = Dictionary(%dictionary_name%)` Usage example: create table products ( product_id UInt64 , title String ) Engine = Dictionary ( products ); CREATE TABLE products ( \n product_id UInt64 , \n title String , ) ENGINE = Dictionary ( products ) Ok.\n\n0 rows in set. Elapsed: 0.004 sec. Take a look at what's in the table. select * from products limit 1 ; SELECT * FROM products LIMIT 1 \u250c\u2500\u2500\u2500\u2500product_id\u2500\u252c\u2500title\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 152689 \u2502 Some item \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n1 rows in set. Elapsed: 0.006 sec.", + "title": "Dictionary" + }, + { + "location": "/index.html#merge", + "text": "The Merge engine (not to be confused with MergeTree ) does not store data itself, but allows reading from any number of other tables simultaneously.\nReading is automatically parallelized. Writing to a table is not supported. When reading, the indexes of tables that are actually being read are used, if they exist.\nThe Merge engine accepts parameters: the database name and a regular expression for tables. Example: Merge(hits, ^WatchLog ) Data will be read from the tables in the 'hits' database that have names that match the regular expression ' ^WatchLog '. Instead of the database name, you can use a constant expression that returns a string. For example, currentDatabase() . Regular expressions \u2014 re2 (supports a subset of PCRE), case-sensitive.\nSee the notes about escaping symbols in regular expressions in the \"match\" section. When selecting tables to read, the Merge table itself will not be selected, even if it matches the regex. This is to avoid loops.\nIt is possible to create two Merge tables that will endlessly try to read each others' data, but this is not a good idea. The typical way to use the Merge engine is for working with a large number of TinyLog tables as if with a single table.", + "title": "Merge" + }, + { + "location": "/index.html#virtual-columns", + "text": "Virtual columns are columns that are provided by the table engine, regardless of the table definition. In other words, these columns are not specified in CREATE TABLE, but they are accessible for SELECT. Virtual columns differ from normal columns in the following ways: They are not specified in table definitions. Data can't be added to them with INSERT. When using INSERT without specifying the list of columns, virtual columns are ignored. They are not selected when using the asterisk ( SELECT * ). Virtual columns are not shown in SHOW CREATE TABLE and DESC TABLE queries. A Merge type table contains a virtual _table column with the String type. (If the table already has a _table column, the virtual column is named _table1, and if it already has _table1, it is named _table2, and so on.) It contains the name of the table that data was read from. If the WHERE or PREWHERE clause contains conditions for the '_table' column that do not depend on other table columns (as one of the conjunction elements, or as an entire expression), these conditions are used as an index. The conditions are performed on a data set of table names to read data from, and the read operation will be performed from only those tables that the condition was triggered on.", + "title": "Virtual columns" + }, + { + "location": "/index.html#buffer", + "text": "Buffers the data to write in RAM, periodically flushing it to another table. During the read operation, data is read from the buffer and the other table simultaneously. Buffer(database, table, num_layers, min_time, max_time, min_rows, max_rows, min_bytes, max_bytes) Engine parameters:database, table \u2013 The table to flush data to. Instead of the database name, you can use a constant expression that returns a string.num_layers \u2013 Parallelism layer. Physically, the table will be represented as 'num_layers' of independent buffers. Recommended value: 16.min_time, max_time, min_rows, max_rows, min_bytes, and max_bytes are conditions for flushing data from the buffer. Data is flushed from the buffer and written to the destination table if all the 'min' conditions or at least one 'max' condition are met.min_time, max_time \u2013 Condition for the time in seconds from the moment of the first write to the buffer.min_rows, max_rows \u2013 Condition for the number of rows in the buffer.min_bytes, max_bytes \u2013 Condition for the number of bytes in the buffer. During the write operation, data is inserted to a 'num_layers' number of random buffers. Or, if the data part to insert is large enough (greater than 'max_rows' or 'max_bytes'), it is written directly to the destination table, omitting the buffer. The conditions for flushing the data are calculated separately for each of the 'num_layers' buffers. For example, if num_layers = 16 and max_bytes = 100000000, the maximum RAM consumption is 1.6 GB. Example: CREATE TABLE merge . hits_buffer AS merge . hits ENGINE = Buffer ( merge , hits , 16 , 10 , 100 , 10000 , 1000000 , 10000000 , 100000000 ) Creating a 'merge.hits_buffer' table with the same structure as 'merge.hits' and using the Buffer engine. When writing to this table, data is buffered in RAM and later written to the 'merge.hits' table. 16 buffers are created. The data in each of them is flushed if either 100 seconds have passed, or one million rows have been written, or 100 MB of data have been written; or if simultaneously 10 seconds have passed and 10,000 rows and 10 MB of data have been written. For example, if just one row has been written, after 100 seconds it will be flushed, no matter what. But if many rows have been written, the data will be flushed sooner. When the server is stopped, with DROP TABLE or DETACH TABLE, buffer data is also flushed to the destination table. You can set empty strings in single quotation marks for the database and table name. This indicates the absence of a destination table. In this case, when the data flush conditions are reached, the buffer is simply cleared. This may be useful for keeping a window of data in memory. When reading from a Buffer table, data is processed both from the buffer and from the destination table (if there is one).\nNote that the Buffer tables does not support an index. In other words, data in the buffer is fully scanned, which might be slow for large buffers. (For data in a subordinate table, the index that it supports will be used.) If the set of columns in the Buffer table doesn't match the set of columns in a subordinate table, a subset of columns that exist in both tables is inserted. If the types don't match for one of the columns in the Buffer table and a subordinate table, an error message is entered in the server log and the buffer is cleared.\nThe same thing happens if the subordinate table doesn't exist when the buffer is flushed. If you need to run ALTER for a subordinate table and the Buffer table, we recommend first deleting the Buffer table, running ALTER for the subordinate table, then creating the Buffer table again. If the server is restarted abnormally, the data in the buffer is lost. PREWHERE, FINAL and SAMPLE do not work correctly for Buffer tables. These conditions are passed to the destination table, but are not used for processing data in the buffer. Because of this, we recommend only using the Buffer table for writing, while reading from the destination table. When adding data to a Buffer, one of the buffers is locked. This causes delays if a read operation is simultaneously being performed from the table. Data that is inserted to a Buffer table may end up in the subordinate table in a different order and in different blocks. Because of this, a Buffer table is difficult to use for writing to a CollapsingMergeTree correctly. To avoid problems, you can set 'num_layers' to 1. If the destination table is replicated, some expected characteristics of replicated tables are lost when writing to a Buffer table. The random changes to the order of rows and sizes of data parts cause data deduplication to quit working, which means it is not possible to have a reliable 'exactly once' write to replicated tables. Due to these disadvantages, we can only recommend using a Buffer table in rare cases. A Buffer table is used when too many INSERTs are received from a large number of servers over a unit of time and data can't be buffered before insertion, which means the INSERTs can't run fast enough. Note that it doesn't make sense to insert data one row at a time, even for Buffer tables. This will only produce a speed of a few thousand rows per second, while inserting larger blocks of data can produce over a million rows per second (see the section \"Performance\").", + "title": "Buffer" + }, + { + "location": "/index.html#fileinputformat", + "text": "The data source is a file that stores data in one of the supported input formats (TabSeparated, Native, etc.).", + "title": "File(InputFormat)" + }, + { + "location": "/index.html#null", + "text": "When writing to a Null table, data is ignored. When reading from a Null table, the response is empty. However, you can create a materialized view on a Null table. So the data written to the table will end up in the view.", + "title": "Null" + }, + { + "location": "/index.html#set_1", + "text": "A data set that is always in RAM. It is intended for use on the right side of the IN operator (see the section \"IN operators\"). You can use INSERT to insert data in the table. New elements will be added to the data set, while duplicates will be ignored.\nBut you can't perform SELECT from the table. The only way to retrieve data is by using it in the right half of the IN operator. Data is always located in RAM. For INSERT, the blocks of inserted data are also written to the directory of tables on the disk. When starting the server, this data is loaded to RAM. In other words, after restarting, the data remains in place. For a rough server restart, the block of data on the disk might be lost or damaged. In the latter case, you may need to manually delete the file with damaged data.", + "title": "Set" + }, + { + "location": "/index.html#join", + "text": "A prepared data structure for JOIN that is always located in RAM. Join(ANY|ALL, LEFT|INNER, k1[, k2, ...]) Engine parameters: ANY|ALL \u2013 strictness; LEFT|INNER \u2013 type.\nThese parameters are set without quotes and must match the JOIN that the table will be used for. k1, k2, ... are the key columns from the USING clause that the join will be made on. The table can't be used for GLOBAL JOINs. You can use INSERT to add data to the table, similar to the Set engine. For ANY, data for duplicated keys will be ignored. For ALL, it will be counted. You can't perform SELECT directly from the table. The only way to retrieve data is to use it as the \"right-hand\" table for JOIN. Storing data on the disk is the same as for the Set engine.", + "title": "Join" + }, + { + "location": "/index.html#view", + "text": "Used for implementing views (for more information, see the CREATE VIEW query ). It does not store data, but only stores the specified SELECT query. When reading from a table, it runs this query (and deletes all unnecessary columns from the query).", + "title": "View" + }, + { + "location": "/index.html#materializedview", + "text": "Used for implementing materialized views (for more information, see the CREATE TABLE ) query. For storing data, it uses a different engine that was specified when creating the view. When reading from a table, it just uses this engine.", + "title": "MaterializedView" + }, + { + "location": "/index.html#kafka", + "text": "This engine works with Apache Kafka . Kafka lets you: Publish or subscribe to data flows. Organize fault-tolerant storage. Process streams as they become available. Kafka(broker_list, topic_list, group_name, format[, schema, num_consumers]) Parameters: broker_list \u2013 A comma-separated list of brokers ( localhost:9092 ). topic_list \u2013 A list of Kafka topics ( my_topic ). group_name \u2013 A group of Kafka consumers ( group1 ). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere. --format \u2013 Message format. Uses the same notation as the SQL FORMAT function, such as JSONEachRow . For more information, see the \"Formats\" section. schema \u2013 An optional parameter that must be used if the format requires a schema definition. For example, Cap'n Proto requires the path to the schema file and the name of the root schema.capnp:Message object. num_consumers \u2013 The number of consumers per table. Default: 1 . Specify more consumers if the throughput of one consumer is insufficient. The total number of consumers should not exceed the number of partitions in the topic, since only one consumer can be assigned per partition. Example: CREATE TABLE queue ( \n timestamp UInt64 , \n level String , \n message String \n ) ENGINE = Kafka ( localhost:9092 , topic , group1 , JSONEachRow ); \n\n SELECT * FROM queue LIMIT 5 ; The delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name. Groups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. Read more about this at http://kafka.apache.org/intro . SELECT is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. To do this: Use the engine to create a Kafka consumer and consider it a data stream. Create a table with the desired structure. Create a materialized view that converts data from the engine and puts it into a previously created table. When the MATERIALIZED VIEW joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using SELECT Example: CREATE TABLE queue ( \n timestamp UInt64 , \n level String , \n message String \n ) ENGINE = Kafka ( localhost:9092 , topic , group1 , JSONEachRow ); \n\n CREATE TABLE daily ( \n day Date , \n level String , \n total UInt64 \n ) ENGINE = SummingMergeTree ( day , ( day , level ), 8192 ); \n\n CREATE MATERIALIZED VIEW consumer TO daily \n AS SELECT toDate ( toDateTime ( timestamp )) AS day , level , count () as total \n FROM queue GROUP BY day , level ; \n\n SELECT level , sum ( total ) FROM daily GROUP BY level ; To improve performance, received messages are grouped into blocks the size of max_insert_block_size . If the block wasn't formed within stream_flush_interval_ms milliseconds, the data will be flushed to the table regardless of the completeness of the block. To stop receiving topic data or to change the conversion logic, detach the materialized view: DETACH TABLE consumer;\n ATTACH MATERIALIZED VIEW consumer; If you want to change the target table by using ALTER materialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view.", + "title": "Kafka" + }, + { + "location": "/index.html#configuration", + "text": "Similar to GraphiteMergeTree, the Kafka engine supports extended configuration using the ClickHouse config file. There are two configuration keys that you can use: global ( kafka ) and topic-level ( kafka_topic_* ). The global configuration is applied first, and the topic-level configuration is second (if it exists). !-- Global configuration options for all tables of Kafka engine type -- \n kafka \n debug cgrp /debug \n auto_offset_reset smallest /auto_offset_reset \n /kafka \n\n !-- Configuration specific for topic logs -- \n kafka_topic_logs \n retry_backoff_ms 250 /retry_backoff_ms \n fetch_min_bytes 100000 /fetch_min_bytes \n /kafka_topic_logs For a list of possible configuration options, see the librdkafka configuration reference . Use the underscore ( _ ) instead of a dot in the ClickHouse configuration. For example, check.crcs=true will be check_crcs true /check_crcs .", + "title": "Configuration" + }, + { + "location": "/index.html#mysql", + "text": "The MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server. The engine takes 4 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example: MySQL( host:port , database , table , user , password ); At this time, simple WHERE clauses such as =, !=, , =, , = are executed on the MySQL server. The rest of the conditions and the LIMIT sampling constraint are executed in ClickHouse only after the query to MySQL finishes.", + "title": "MySQL" + }, + { + "location": "/index.html#external-data-for-query-processing", + "text": "ClickHouse allows sending a server the data that is needed for processing a query, together with a SELECT query. This data is put in a temporary table (see the section \"Temporary tables\") and can be used in the query (for example, in IN operators). For example, if you have a text file with important user identifiers, you can upload it to the server along with a query that uses filtration by this list. If you need to run more than one query with a large volume of external data, don't use this feature. It is better to upload the data to the DB ahead of time. External data can be uploaded using the command-line client (in non-interactive mode), or using the HTTP interface. In the command-line client, you can specify a parameters section in the format --external --file = ... [ --name = ... ] [ --format = ... ] [ --types = ... | --structure = ... ] You may have multiple sections like this, for the number of tables being transmitted. --external \u2013 Marks the beginning of a clause. --file \u2013 Path to the file with the table dump, or -, which refers to stdin.\nOnly a single table can be retrieved from stdin. The following parameters are optional: --name \u2013 Name of the table. If omitted, _data is used. --format \u2013 Data format in the file. If omitted, TabSeparated is used. One of the following parameters is required: --types \u2013 A list of comma-separated column types. For example: UInt64,String . The columns will be named _1, _2, ... --structure \u2013 The table structure in the format UserID UInt64 , URL String . Defines the column names and types. The files specified in 'file' will be parsed by the format specified in 'format', using the data types specified in 'types' or 'structure'. The table will be uploaded to the server and accessible there as a temporary table with the name in 'name'. Examples: echo -ne 1\\n2\\n3\\n | clickhouse-client --query = SELECT count() FROM test.visits WHERE TraficSourceID IN _data --external --file = - --types = Int8 849897 \ncat /etc/passwd | sed s/:/\\t/g | clickhouse-client --query = SELECT shell, count() AS c FROM passwd GROUP BY shell ORDER BY c DESC --external --file = - --name = passwd --structure = login String, unused String, uid UInt16, gid UInt16, comment String, home String, shell String \n/bin/sh 20 \n/bin/false 5 \n/bin/bash 4 \n/usr/sbin/nologin 1 \n/bin/sync 1 When using the HTTP interface, external data is passed in the multipart/form-data format. Each table is transmitted as a separate file. The table name is taken from the file name. The 'query_string' is passed the parameters 'name_format', 'name_types', and 'name_structure', where 'name' is the name of the table that these parameters correspond to. The meaning of the parameters is the same as when using the command-line client. Example: cat /etc/passwd | sed s/:/\\t/g passwd.tsv\n\ncurl -F passwd=@passwd.tsv; http://localhost:8123/?query=SELECT+shell,+count()+AS+c+FROM+passwd+GROUP+BY+shell+ORDER+BY+c+DESC passwd_structure=login+String,+unused+String,+uid+UInt16,+gid+UInt16,+comment+String,+home+String,+shell+String \n/bin/sh 20 \n/bin/false 5 \n/bin/bash 4 \n/usr/sbin/nologin 1 \n/bin/sync 1 For distributed query processing, the temporary tables are sent to all the remote servers.", + "title": "External data for query processing" + }, + { + "location": "/index.html#system-tables", + "text": "System tables are used for implementing part of the system's functionality, and for providing access to information about how the system is working.\nYou can't delete a system table (but you can perform DETACH).\nSystem tables don't have files with data on the disk or files with metadata. The server creates all the system tables when it starts.\nSystem tables are read-only.\nThey are located in the 'system' database.", + "title": "System tables" + }, + { + "location": "/index.html#systemone", + "text": "This table contains a single row with a single 'dummy' UInt8 column containing the value 0.\nThis table is used if a SELECT query doesn't specify the FROM clause.\nThis is similar to the DUAL table found in other DBMSs.", + "title": "system.one" + }, + { + "location": "/index.html#systemnumbers", + "text": "This table contains a single UInt64 column named 'number' that contains almost all the natural numbers starting from zero.\nYou can use this table for tests, or if you need to do a brute force search.\nReads from this table are not parallelized.", + "title": "system.numbers" + }, + { + "location": "/index.html#systemnumbers_mt", + "text": "The same as 'system.numbers' but reads are parallelized. The numbers can be returned in any order.\nUsed for tests.", + "title": "system.numbers_mt" + }, + { + "location": "/index.html#systemdatabases", + "text": "This table contains a single String column called 'name' \u2013 the name of a database.\nEach database that the server knows about has a corresponding entry in the table.\nThis system table is used for implementing the SHOW DATABASES query.", + "title": "system.databases" + }, + { + "location": "/index.html#systemtables", + "text": "This table contains the String columns 'database', 'name', and 'engine'.\nThe table also contains three virtual columns: metadata_modification_time (DateTime type), create_table_query, and engine_full (String type).\nEach table that the server knows about is entered in the 'system.tables' table.\nThis system table is used for implementing SHOW TABLES queries.", + "title": "system.tables" + }, + { + "location": "/index.html#systemcolumns", + "text": "Contains information about the columns in all tables.\nYou can use this table to get information similar to DESCRIBE TABLE , but for multiple tables at once. database String - Name of the database the table is located in.\ntable String - Table name.\nname String - Column name.\ntype String - Column type.\ndefault_type String - Expression type (DEFAULT, MATERIALIZED, ALIAS) for the default value, or an empty string if it is not defined.\ndefault_expression String - Expression for the default value, or an empty string if it is not defined.", + "title": "system.columns" + }, + { + "location": "/index.html#systemparts", + "text": "Contains information about parts of a table in the MergeTree family. Each row describes one part of the data. Columns: partition (String) \u2013 The partition name. YYYYMM format. To learn what a partition is, see the description of the ALTER query. name (String) \u2013 Name of the data part. active (UInt8) \u2013 Indicates whether the part is active. If a part is active, it is used in a table; otherwise, it will be deleted. Inactive data parts remain after merging. marks (UInt64) \u2013 The number of marks. To get the approximate number of rows in a data part, multiply marks by the index granularity (usually 8192). marks_size (UInt64) \u2013 The size of the file with marks. rows (UInt64) \u2013 The number of rows. bytes (UInt64) \u2013 The number of bytes when compressed. modification_time (DateTime) \u2013 The modification time of the directory with the data part. This usually corresponds to the time of data part creation.| remove_time (DateTime) \u2013 The time when the data part became inactive. refcount (UInt32) \u2013 The number of places where the data part is used. A value greater than 2 indicates that the data part is used in queries or merges. min_date (Date) \u2013 The minimum value of the date key in the data part. max_date (Date) \u2013 The maximum value of the date key in the data part. min_block_number (UInt64) \u2013 The minimum number of data parts that make up the current part after merging. max_block_number (UInt64) \u2013 The maximum number of data parts that make up the current part after merging. level (UInt32) \u2013 Depth of the merge tree. If a merge was not performed, level=0 . primary_key_bytes_in_memory (UInt64) \u2013 The amount of memory (in bytes) used by primary key values. primary_key_bytes_in_memory_allocated (UInt64) \u2013 The amount of memory (in bytes) reserved for primary key values. database (String) \u2013 Name of the database. table (String) \u2013 Name of the table. engine (String) \u2013 Name of the table engine without parameters.", + "title": "system.parts" + }, + { + "location": "/index.html#systemprocesses", + "text": "This system table is used for implementing the SHOW PROCESSLIST query.\nColumns: user String \u2013 Name of the user who made the request. For distributed query processing, this is the user who helped the requestor server send the query to this server, not the user who made the distributed request on the requestor server.\n\naddress String \u2013 The IP address that the query was made from. The same is true for distributed query processing.\n\nelapsed Float64 \u2013 The time in seconds since request execution started.\n\nrows_read UInt64 \u2013 The number of rows read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nbytes_read UInt64 \u2013 The number of uncompressed bytes read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nUInt64 total_rows_approx \u2013 The approximate total number of rows that must be read. For distributed processing, on the requestor server, this is the total for all remote servers. It can be updated during request processing, when new sources to process become known.\n\nmemory_usage UInt64 \u2013 Memory consumption by the query. It might not include some types of dedicated memory.\n\nquery String \u2013 The query text. For INSERT, it doesn t include the data to insert.\n\nquery_id \u2013 Query ID, if defined.", + "title": "system.processes" + }, + { + "location": "/index.html#systemmerges", + "text": "Contains information about merges currently in process for tables in the MergeTree family. Columns: database String \u2014 Name of the database the table is located in. table String \u2014 Name of the table. elapsed Float64 \u2014 Time in seconds since the merge started. progress Float64 \u2014 Percent of progress made, from 0 to 1. num_parts UInt64 \u2014 Number of parts to merge. result_part_name String \u2014 Name of the part that will be formed as the result of the merge. total_size_bytes_compressed UInt64 \u2014 Total size of compressed data in the parts being merged. total_size_marks UInt64 \u2014 Total number of marks in the parts being merged. bytes_read_uncompressed UInt64 \u2014 Amount of bytes read, decompressed. rows_read UInt64 \u2014 Number of rows read. bytes_written_uncompressed UInt64 \u2014 Amount of bytes written, uncompressed. rows_written UInt64 \u2014 Number of rows written.", + "title": "system.merges" + }, + { + "location": "/index.html#systemevents", + "text": "Contains information about the number of events that have occurred in the system. This is used for profiling and monitoring purposes.\nExample: The number of processed SELECT queries.\nColumns: 'event String' \u2013 the event name, and 'value UInt64' \u2013 the quantity.", + "title": "system.events" + }, + { + "location": "/index.html#systemmetrics", + "text": "", + "title": "system.metrics" + }, + { + "location": "/index.html#systemasynchronous_metrics", + "text": "Contain metrics used for profiling and monitoring.\nThey usually reflect the number of events currently in the system, or the total resources consumed by the system.\nExample: The number of SELECT queries currently running; the amount of memory in use. system.asynchronous_metrics and system.metrics differ in their sets of metrics and how they are calculated.", + "title": "system.asynchronous_metrics" + }, + { + "location": "/index.html#systemreplicas", + "text": "Contains information and status for replicated tables residing on the local server.\nThis table can be used for monitoring. The table contains a row for every Replicated* table. Example: SELECT * FROM system . replicas WHERE table = visits FORMAT Vertical Row 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ndatabase: merge\ntable: visits\nengine: ReplicatedCollapsingMergeTree\nis_leader: 1\nis_readonly: 0\nis_session_expired: 0\nfuture_parts: 1\nparts_to_check: 0\nzookeeper_path: /clickhouse/tables/01-06/visits\nreplica_name: example01-06-1.yandex.ru\nreplica_path: /clickhouse/tables/01-06/visits/replicas/example01-06-1.yandex.ru\ncolumns_version: 9\nqueue_size: 1\ninserts_in_queue: 0\nmerges_in_queue: 1\nlog_max_index: 596273\nlog_pointer: 596274\ntotal_replicas: 2\nactive_replicas: 2 Columns: database: database name\ntable: table name\nengine: table engine name\n\nis_leader: whether the replica is the leader\n\nOnly one replica at a time can be the leader. The leader is responsible for selecting background merges to perform.\nNote that writes can be performed to any replica that is available and has a session in ZK, regardless of whether it is a leader.\n\nis_readonly: Whether the replica is in read-only mode.\nThis mode is turned on if the config doesn t have sections with ZK, if an unknown error occurred when reinitializing sessions in ZK, and during session reinitialization in ZK.\n\nis_session_expired: Whether the ZK session expired.\nBasically, the same thing as is_readonly.\n\nfuture_parts: The number of data parts that will appear as the result of INSERTs or merges that haven t been done yet. \n\nparts_to_check: The number of data parts in the queue for verification.\nA part is put in the verification queue if there is suspicion that it might be damaged.\n\nzookeeper_path: The path to the table data in ZK. \nreplica_name: Name of the replica in ZK. Different replicas of the same table have different names. \nreplica_path: The path to the replica data in ZK. The same as concatenating zookeeper_path/replicas/replica_path.\n\ncolumns_version: Version number of the table structure.\nIndicates how many times ALTER was performed. If replicas have different versions, it means some replicas haven t made all of the ALTERs yet.\n\nqueue_size: Size of the queue for operations waiting to be performed.\nOperations include inserting blocks of data, merges, and certain other actions.\nNormally coincides with future_parts.\n\ninserts_in_queue: Number of inserts of blocks of data that need to be made.\nInsertions are usually replicated fairly quickly. If the number is high, something is wrong.\n\nmerges_in_queue: The number of merges waiting to be made. \nSometimes merges are lengthy, so this value may be greater than zero for a long time.\n\nThe next 4 columns have a non-null value only if the ZK session is active.\n\nlog_max_index: Maximum entry number in the log of general activity.\nlog_pointer: Maximum entry number in the log of general activity that the replica copied to its execution queue, plus one.\nIf log_pointer is much smaller than log_max_index, something is wrong.\n\ntotal_replicas: Total number of known replicas of this table.\nactive_replicas: Number of replicas of this table that have a ZK session (the number of active replicas). If you request all the columns, the table may work a bit slowly, since several reads from ZK are made for each row.\nIf you don't request the last 4 columns (log_max_index, log_pointer, total_replicas, active_replicas), the table works quickly. For example, you can check that everything is working correctly like this: SELECT \n database , \n table , \n is_leader , \n is_readonly , \n is_session_expired , \n future_parts , \n parts_to_check , \n columns_version , \n queue_size , \n inserts_in_queue , \n merges_in_queue , \n log_max_index , \n log_pointer , \n total_replicas , \n active_replicas FROM system . replicas WHERE \n is_readonly \n OR is_session_expired \n OR future_parts 20 \n OR parts_to_check 10 \n OR queue_size 20 \n OR inserts_in_queue 10 \n OR log_max_index - log_pointer 10 \n OR total_replicas 2 \n OR active_replicas total_replicas If this query doesn't return anything, it means that everything is fine.", + "title": "system.replicas" + }, + { + "location": "/index.html#systemdictionaries", + "text": "Contains information about external dictionaries. Columns: name String \u2013 Dictionary name. type String \u2013 Dictionary type: Flat, Hashed, Cache. origin String \u2013 Path to the config file where the dictionary is described. attribute.names Array(String) \u2013 Array of attribute names provided by the dictionary. attribute.types Array(String) \u2013 Corresponding array of attribute types provided by the dictionary. has_hierarchy UInt8 \u2013 Whether the dictionary is hierarchical. bytes_allocated UInt64 \u2013 The amount of RAM used by the dictionary. hit_rate Float64 \u2013 For cache dictionaries, the percent of usage for which the value was in the cache. element_count UInt64 \u2013 The number of items stored in the dictionary. load_factor Float64 \u2013 The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table). creation_time DateTime \u2013 Time spent for the creation or last successful reload of the dictionary. last_exception String \u2013 Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created. source String \u2013 Text describing the data source for the dictionary. Note that the amount of memory used by the dictionary is not proportional to the number of items stored in it. So for flat and cached dictionaries, all the memory cells are pre-assigned, regardless of how full the dictionary actually is.", + "title": "system.dictionaries" + }, + { + "location": "/index.html#systemclusters", + "text": "Contains information about clusters available in the config file and the servers in them.\nColumns: cluster String \u2013 Cluster name.\nshard_num UInt32 \u2013 Number of a shard in the cluster, starting from 1.\nshard_weight UInt32 \u2013 Relative weight of a shard when writing data.\nreplica_num UInt32 \u2013 Number of a replica in the shard, starting from 1.\nhost_name String \u2013 Host name as specified in the config.\nhost_address String \u2013 Host s IP address obtained from DNS.\nport UInt16 \u2013 The port used to access the server.\nuser String \u2013 The username to use for connecting to the server.", + "title": "system.clusters" + }, + { + "location": "/index.html#systemfunctions", + "text": "Contains information about normal and aggregate functions. Columns: name ( String ) \u2013 Function name. is_aggregate ( UInt8 ) \u2013 Whether it is an aggregate function.", + "title": "system.functions" + }, + { + "location": "/index.html#systemsettings", + "text": "Contains information about settings that are currently in use.\nI.e. used for executing the query you are using to read from the system.settings table). Columns: name String \u2013 Setting name.\nvalue String \u2013 Setting value.\nchanged UInt8 - Whether the setting was explicitly defined in the config or explicitly changed. Example: SELECT * FROM system . settings WHERE changed \u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500value\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500changed\u2500\u2510\n\u2502 max_threads \u2502 8 \u2502 1 \u2502\n\u2502 use_uncompressed_cache \u2502 0 \u2502 1 \u2502\n\u2502 load_balancing \u2502 random \u2502 1 \u2502\n\u2502 max_memory_usage \u2502 10000000000 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "system.settings" + }, + { + "location": "/index.html#systemzookeeper", + "text": "Allows reading data from the ZooKeeper cluster defined in the config.\nThe query must have a 'path' equality condition in the WHERE clause. This is the path in ZooKeeper for the children that you want to get data for. The query SELECT * FROM system.zookeeper WHERE path = '/clickhouse' outputs data for all children on the /clickhouse node.\nTo output data for all root nodes, write path = '/'.\nIf the path specified in 'path' doesn't exist, an exception will be thrown. Columns: name String \u2014 Name of the node. path String \u2014 Path to the node. value String \u2014 Value of the node. dataLength Int32 \u2014 Size of the value. numChildren Int32 \u2014 Number of children. czxid Int64 \u2014 ID of the transaction that created the node. mzxid Int64 \u2014 ID of the transaction that last changed the node. pzxid Int64 \u2014 ID of the transaction that last added or removed children. ctime DateTime \u2014 Time of node creation. mtime DateTime \u2014 Time of the last node modification. version Int32 \u2014 Node version - the number of times the node was changed. cversion Int32 \u2014 Number of added or removed children. aversion Int32 \u2014 Number of changes to ACL. ephemeralOwner Int64 \u2014 For ephemeral nodes, the ID of the session that owns this node. Example: SELECT * FROM system . zookeeper WHERE path = /clickhouse/tables/01-08/visits/replicas FORMAT Vertical Row 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-1.yandex.ru\nvalue:\nczxid: 932998691229\nmzxid: 932998691229\nctime: 2015-03-27 16:49:51\nmtime: 2015-03-27 16:49:51\nversion: 0\ncversion: 47\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021031383\npath: /clickhouse/tables/01-08/visits/replicas\n\nRow 2:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-2.yandex.ru\nvalue:\nczxid: 933002738135\nmzxid: 933002738135\nctime: 2015-03-27 16:57:01\nmtime: 2015-03-27 16:57:01\nversion: 0\ncversion: 37\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021252247\npath: /clickhouse/tables/01-08/visits/replicas", + "title": "system.zookeeper" + }, + { + "location": "/index.html#table-functions", + "text": "Table functions can be specified in the FROM clause instead of the database and table names.\nTable functions can only be used if 'readonly' is not set.\nTable functions aren't related to other functions.", + "title": "Table functions" + }, + { + "location": "/index.html#remote", + "text": "Allows you to access remote servers without creating a Distributed table. Signatures: remote ( addresses_expr , db , table [, user [, password ]]) remote ( addresses_expr , db . table [, user [, password ]]) addresses_expr \u2013 An expression that generates addresses of remote servers. This may be just one server address. The server address is host:port , or just host . The host can be specified as the server name, or as the IPv4 or IPv6 address. An IPv6 address is specified in square brackets. The port is the TCP port on the remote server. If the port is omitted, it uses tcp_port from the server's config file (by default, 9000). \n\nThe port is required for an IPv6 address. Examples: example01-01-1\nexample01-01-1:9000\nlocalhost\n127.0.0.1\n[::]:9000\n[2a02:6b8:0:1111::11]:9000 Multiple addresses can be comma-separated. In this case, ClickHouse will use distributed processing, so it will send the query to all specified addresses (like to shards with different data). Example: example01-01-1,example01-02-1 Part of the expression can be specified in curly brackets. The previous example can be written as follows: example01-0{1,2}-1 Curly brackets can contain a range of numbers separated by two dots (non-negative integers). In this case, the range is expanded to a set of values that generate shard addresses. If the first number starts with zero, the values are formed with the same zero alignment. The previous example can be written as follows: example01-{01..02}-1 If you have multiple pairs of curly brackets, it generates the direct product of the corresponding sets. Addresses and parts of addresses in curly brackets can be separated by the pipe symbol (|). In this case, the corresponding sets of addresses are interpreted as replicas, and the query will be sent to the first healthy replica. However, the replicas are iterated in the order currently set in the load_balancing setting. Example: example01-{01..02}-{1|2} This example specifies two shards that each have two replicas. The number of addresses generated is limited by a constant. Right now this is 1000 addresses. Using the remote table function is less optimal than creating a Distributed table, because in this case, the server connection is re-established for every request. In addition, if host names are set, the names are resolved, and errors are not counted when working with various replicas. When processing a large number of queries, always create the Distributed table ahead of time, and don't use the remote table function. The remote table function can be useful in the following cases: Accessing a specific server for data comparison, debugging, and testing. Queries between various ClickHouse clusters for research purposes. Infrequent distributed requests that are made manually. Distributed requests where the set of servers is re-defined each time. If the user is not specified, default is used.\nIf the password is not specified, an empty password is used.", + "title": "remote" + }, + { + "location": "/index.html#merge_1", + "text": "merge(db_name, 'tables_regexp') \u2013 Creates a temporary Merge table. For more information, see the section \"Table engines, Merge\". The table structure is taken from the first table encountered that matches the regular expression.", + "title": "merge" + }, + { + "location": "/index.html#numbers", + "text": "numbers(N) \u2013 Returns a table with the single 'number' column (UInt64) that contains integers from 0 to N-1. Similar to the system.numbers table, it can be used for testing and generating successive values. The following two queries are equivalent: SELECT * FROM numbers ( 10 ); SELECT * FROM system . numbers LIMIT 10 ; Examples: -- Generate a sequence of dates from 2010-01-01 to 2010-12-31 select toDate ( 2010-01-01 ) + number as d FROM numbers ( 365 );", + "title": "numbers" + }, + { + "location": "/index.html#formats", + "text": "The format determines how data is returned to you after SELECTs (how it is written and formatted by the server), and how it is accepted for INSERTs (how it is read and parsed by the server).", + "title": "Formats" + }, + { + "location": "/index.html#tabseparated", + "text": "In TabSeparated format, data is written by row. Each row contains values separated by tabs. Each value is follow by a tab, except the last value in the row, which is followed by a line feed. Strictly Unix line feeds are assumed everywhere. The last row also must contain a line feed at the end. Values are written in text format, without enclosing quotation marks, and with special characters escaped. Integer numbers are written in decimal form. Numbers can contain an extra \"+\" character at the beginning (ignored when parsing, and not recorded when formatting). Non-negative numbers can't contain the negative sign. When reading, it is allowed to parse an empty string as a zero, or (for signed types) a string consisting of just a minus sign as a zero. Numbers that do not fit into the corresponding data type may be parsed as a different number, without an error message. Floating-point numbers are written in decimal form. The dot is used as the decimal separator. Exponential entries are supported, as are 'inf', '+inf', '-inf', and 'nan'. An entry of floating-point numbers may begin or end with a decimal point.\nDuring formatting, accuracy may be lost on floating-point numbers.\nDuring parsing, it is not strictly required to read the nearest machine-representable number. Dates are written in YYYY-MM-DD format and parsed in the same format, but with any characters as separators.\nDates with times are written in the format YYYY-MM-DD hh:mm:ss and parsed in the same format, but with any characters as separators.\nThis all occurs in the system time zone at the time the client or server starts (depending on which one formats data). For dates with times, daylight saving time is not specified. So if a dump has times during daylight saving time, the dump does not unequivocally match the data, and parsing will select one of the two times.\nDuring a read operation, incorrect dates and dates with times can be parsed with natural overflow or as null dates and times, without an error message. As an exception, parsing dates with times is also supported in Unix timestamp format, if it consists of exactly 10 decimal digits. The result is not time zone-dependent. The formats YYYY-MM-DD hh:mm:ss and NNNNNNNNNN are differentiated automatically. Strings are output with backslash-escaped special characters. The following escape sequences are used for output: \\b , \\f , \\r , \\n , \\t , \\0 , \\' , \\\\ . Parsing also supports the sequences \\a , \\v , and \\xHH (hex escape sequences) and any \\c sequences, where c is any character (these sequences are converted to c ). Thus, reading data supports formats where a line feed can be written as \\n or \\ , or as a line feed. For example, the string Hello world with a line feed between the words instead of a space can be parsed in any of the following variations: Hello\\nworld\n\nHello\\\nworld The second variant is supported because MySQL uses it when writing tab-separated dumps. The minimum set of characters that you need to escape when passing data in TabSeparated format: tab, line feed (LF) and backslash. Only a small set of symbols are escaped. You can easily stumble onto a string value that your terminal will ruin in output. Arrays are written as a list of comma-separated values in square brackets. Number items in the array are fomratted as normally, but dates, dates with times, and strings are written in single quotes with the same escaping rules as above. The TabSeparated format is convenient for processing data using custom programs and scripts. It is used by default in the HTTP interface, and in the command-line client's batch mode. This format also allows transferring data between different DBMSs. For example, you can get a dump from MySQL and upload it to ClickHouse, or vice versa. The TabSeparated format supports outputting total values (when using WITH TOTALS) and extreme values (when 'extremes' is set to 1). In these cases, the total values and extremes are output after the main data. The main result, total values, and extremes are separated from each other by an empty line. Example: SELECT EventDate , count () AS c FROM test . hits GROUP BY EventDate WITH TOTALS ORDER BY EventDate FORMAT TabSeparated `` 2014-03-17 1406958\n2014-03-18 1383658\n2014-03-19 1405797\n2014-03-20 1353623\n2014-03-21 1245779\n2014-03-22 1031592\n2014-03-23 1046491\n\n0000-00-00 8873898\n\n2014-03-17 1031592\n2014-03-23 1406958 This format is also available under the name TSV .", + "title": "TabSeparated" + }, + { + "location": "/index.html#tabseparatedraw", + "text": "Differs from TabSeparated format in that the rows are written without escaping.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). This format is also available under the name TSVRaw .", + "title": "TabSeparatedRaw" + }, + { + "location": "/index.html#tabseparatedwithnames", + "text": "Differs from the TabSeparated format in that the column names are written in the first row.\nDuring parsing, the first row is completely ignored. You can't use column names to determine their position or to check their correctness.\n(Support for parsing the header row may be added in the future.) This format is also available under the name TSVWithNames .", + "title": "TabSeparatedWithNames" + }, + { + "location": "/index.html#tabseparatedwithnamesandtypes", + "text": "Differs from the TabSeparated format in that the column names are written to the first row, while the column types are in the second row.\nDuring parsing, the first and second rows are completely ignored. This format is also available under the name TSVWithNamesAndTypes .", + "title": "TabSeparatedWithNamesAndTypes" + }, + { + "location": "/index.html#csv", + "text": "Comma Separated Values format ( RFC ). When formatting, rows are enclosed in double quotes. A double quote inside a string is output as two double quotes in a row. There are no other rules for escaping characters. Date and date-time are enclosed in double quotes. Numbers are output without quotes. Values \u200b\u200bare separated by a delimiter . Rows are separated using the Unix line feed (LF). Arrays are serialized in CSV as follows: first the array is serialized to a string as in TabSeparated format, and then the resulting string is output to CSV in double quotes. Tuples in CSV format are serialized as separate columns (that is, their nesting in the tuple is lost). By default \u2014 , . See a format_csv_delimiter setting for additional info. When parsing, all values can be parsed either with or without quotes. Both double and single quotes are supported. Rows can also be arranged without quotes. In this case, they are parsed up to a delimiter or line feed (CR or LF). In violation of the RFC, when parsing rows without quotes, the leading and trailing spaces and tabs are ignored. For the line feed, Unix (LF), Windows (CR LF) and Mac OS Classic (CR LF) are all supported. The CSV format supports the output of totals and extremes the same way as TabSeparated .", + "title": "CSV" + }, + { + "location": "/index.html#csvwithnames", + "text": "Also prints the header row, similar to TabSeparatedWithNames .", + "title": "CSVWithNames" + }, + { + "location": "/index.html#values", + "text": "Prints every row in brackets. Rows are separated by commas. There is no comma after the last row. The values inside the brackets are also comma-separated. Numbers are output in decimal format without quotes. Arrays are output in square brackets. Strings, dates, and dates with times are output in quotes. Escaping rules and parsing are similar to the TabSeparated format. During formatting, extra spaces aren't inserted, but during parsing, they are allowed and skipped (except for spaces inside array values, which are not allowed). The minimum set of characters that you need to escape when passing data in Values \u200b\u200bformat: single quotes and backslashes. This is the format that is used in INSERT INTO t VALUES ... , but you can also use it for formatting query results.", + "title": "Values" + }, + { + "location": "/index.html#vertical", + "text": "Prints each value on a separate line with the column name specified. This format is convenient for printing just one or a few rows, if each row consists of a large number of columns.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).", + "title": "Vertical" + }, + { + "location": "/index.html#verticalraw", + "text": "Differs from Vertical format in that the rows are not escaped.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). Examples: :) SHOW CREATE TABLE geonames FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nstatement: CREATE TABLE default.geonames ( geonameid UInt32, date Date DEFAULT CAST( 2017-12-08 AS Date)) ENGINE = MergeTree(date, geonameid, 8192)\n\n:) SELECT string with \\ quotes\\ and \\t with some special \\n characters AS test FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with quotes and with some special\n characters Compare with the Vertical format: :) SELECT string with \\ quotes\\ and \\t with some special \\n characters AS test FORMAT Vertical;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with \\ quotes\\ and \\t with some special \\n characters", + "title": "VerticalRaw" + }, + { + "location": "/index.html#json", + "text": "Outputs data in JSON format. Besides data tables, it also outputs column names and types, along with some additional information: the total number of output rows, and the number of rows that could have been output if there weren't a LIMIT. Example: SELECT SearchPhrase , count () AS c FROM test . hits GROUP BY SearchPhrase WITH TOTALS ORDER BY c DESC LIMIT 5 FORMAT JSON { \n meta : \n [ \n { \n name : SearchPhrase , \n type : String \n }, \n { \n name : c , \n type : UInt64 \n } \n ], \n\n data : \n [ \n { \n SearchPhrase : , \n c : 8267016 \n }, \n { \n SearchPhrase : bathroom interior design , \n c : 2166 \n }, \n { \n SearchPhrase : yandex , \n c : 1655 \n }, \n { \n SearchPhrase : spring 2014 fashion , \n c : 1549 \n }, \n { \n SearchPhrase : freeform photos , \n c : 1480 \n } \n ], \n\n totals : \n { \n SearchPhrase : , \n c : 8873898 \n }, \n\n extremes : \n { \n min : \n { \n SearchPhrase : , \n c : 1480 \n }, \n max : \n { \n SearchPhrase : , \n c : 8267016 \n } \n }, \n\n rows : 5 , \n\n rows_before_limit_at_least : 141137 } The JSON is compatible with JavaScript. To ensure this, some characters are additionally escaped: the slash / is escaped as \\/ ; alternative line breaks U+2028 and U+2029 , which break some browsers, are escaped as \\uXXXX . ASCII control characters are escaped: backspace, form feed, line feed, carriage return, and horizontal tab are replaced with \\b , \\f , \\n , \\r , \\t , as well as the remaining bytes in the 00-1F range using \\uXXXX sequences. Invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences. For compatibility with JavaScript, Int64 and UInt64 integers are enclosed in double quotes by default. To remove the quotes, you can set the configuration parameter output_format_json_quote_64bit_integers to 0. rows \u2013 The total number of output rows. rows_before_limit_at_least The minimal number of rows there would have been without LIMIT. Output only if the query contains LIMIT.\nIf the query contains GROUP BY, rows_before_limit_at_least is the exact number of rows there would have been without a LIMIT. totals \u2013 Total values (when using WITH TOTALS). extremes \u2013 Extreme values (when extremes is set to 1). This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the JSONEachRow format.", + "title": "JSON" + }, + { + "location": "/index.html#jsoncompact", + "text": "Differs from JSON only in that data rows are output in arrays, not in objects. Example: { \n meta : \n [ \n { \n name : SearchPhrase , \n type : String \n }, \n { \n name : c , \n type : UInt64 \n } \n ], \n\n data : \n [ \n [ , 8267016 ], \n [ bathroom interior design , 2166 ], \n [ yandex , 1655 ], \n [ spring 2014 fashion , 1549 ], \n [ freeform photos , 1480 ] \n ], \n\n totals : [ , 8873898 ], \n\n extremes : \n { \n min : [ , 1480 ], \n max : [ , 8267016 ] \n }, \n\n rows : 5 , \n\n rows_before_limit_at_least : 141137 } This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the JSONEachRow format.", + "title": "JSONCompact" + }, + { + "location": "/index.html#jsoneachrow", + "text": "Outputs data as separate JSON objects for each row (newline delimited JSON). { SearchPhrase : , count() : 8267016 } { SearchPhrase : bathroom interior design , count() : 2166 } { SearchPhrase : yandex , count() : 1655 } { SearchPhrase : spring 2014 fashion , count() : 1549 } { SearchPhrase : freeform photo , count() : 1480 } { SearchPhrase : angelina jolie , count() : 1245 } { SearchPhrase : omsk , count() : 1112 } { SearchPhrase : photos of dog breeds , count() : 1091 } { SearchPhrase : curtain design , count() : 1064 } { SearchPhrase : baku , count() : 1000 } Unlike the JSON format, there is no substitution of invalid UTF-8 sequences. Any set of bytes can be output in the rows. This is necessary so that data can be formatted without losing any information. Values are escaped in the same way as for JSON. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Whitespace between elements is ignored. If a comma is placed after the objects, it is ignored. Objects don't necessarily have to be separated by new lines.", + "title": "JSONEachRow" + }, + { + "location": "/index.html#tskv", + "text": "Similar to TabSeparated, but outputs a value in name=value format. Names are escaped the same way as in TabSeparated format, and the = symbol is also escaped. SearchPhrase= count()=8267016\nSearchPhrase=bathroom interior design count()=2166\nSearchPhrase=yandex count()=1655\nSearchPhrase=spring 2014 fashion count()=1549\nSearchPhrase=freeform photos count()=1480\nSearchPhrase=angelina jolia count()=1245\nSearchPhrase=omsk count()=1112\nSearchPhrase=photos of dog breeds count()=1091\nSearchPhrase=curtain design count()=1064\nSearchPhrase=baku count()=1000 When there is a large number of small columns, this format is ineffective, and there is generally no reason to use it. It is used in some departments of Yandex. Both data output and parsing are supported in this format. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Parsing allows the presence of the additional field tskv without the equal sign or a value. This field is ignored.", + "title": "TSKV" + }, + { + "location": "/index.html#pretty", + "text": "Outputs data as Unicode-art tables, also using ANSI-escape sequences for setting colors in the terminal.\nA full grid of the table is drawn, and each row occupies two lines in the terminal.\nEach result block is output as a separate table. This is necessary so that blocks can be output without buffering results (buffering would be necessary in order to pre-calculate the visible width of all the values).\nTo avoid dumping too much data to the terminal, only the first 10,000 rows are printed. If the number of rows is greater than or equal to 10,000, the message \"Showed first 10 000\" is printed.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). The Pretty format supports outputting total values (when using WITH TOTALS) and extremes (when 'extremes' is set to 1). In these cases, total values and extreme values are output after the main data, in separate tables. Example (shown for the PrettyCompact format): SELECT EventDate , count () AS c FROM test . hits GROUP BY EventDate WITH TOTALS ORDER BY EventDate FORMAT PrettyCompact \u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1406958 \u2502\n\u2502 2014-03-18 \u2502 1383658 \u2502\n\u2502 2014-03-19 \u2502 1405797 \u2502\n\u2502 2014-03-20 \u2502 1353623 \u2502\n\u2502 2014-03-21 \u2502 1245779 \u2502\n\u2502 2014-03-22 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1046491 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nTotals:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 0000-00-00 \u2502 8873898 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nExtremes:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1406958 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "Pretty" + }, + { + "location": "/index.html#prettycompact", + "text": "Differs from Pretty in that the grid is drawn between rows and the result is more compact.\nThis format is used by default in the command-line client in interactive mode.", + "title": "PrettyCompact" + }, + { + "location": "/index.html#prettycompactmonoblock", + "text": "Differs from PrettyCompact in that up to 10,000 rows are buffered, then output as a single table, not by blocks.", + "title": "PrettyCompactMonoBlock" + }, + { + "location": "/index.html#prettynoescapes", + "text": "Differs from Pretty in that ANSI-escape sequences aren't used. This is necessary for displaying this format in a browser, as well as for using the 'watch' command-line utility. Example: watch -n1 clickhouse-client --query= SELECT * FROM system.events FORMAT PrettyCompactNoEscapes You can use the HTTP interface for displaying in the browser.", + "title": "PrettyNoEscapes" + }, + { + "location": "/index.html#prettycompactnoescapes", + "text": "The same as the previous setting.", + "title": "PrettyCompactNoEscapes" + }, + { + "location": "/index.html#prettyspacenoescapes", + "text": "The same as the previous setting.", + "title": "PrettySpaceNoEscapes" + }, + { + "location": "/index.html#prettyspace", + "text": "Differs from PrettyCompact in that whitespace (space characters) is used instead of the grid.", + "title": "PrettySpace" + }, + { + "location": "/index.html#rowbinary", + "text": "Formats and parses data by row in binary format. Rows and values are listed consecutively, without separators.\nThis format is less efficient than the Native format, since it is row-based. Integers use fixed-length little endian representation. For example, UInt64 uses 8 bytes.\nDateTime is represented as UInt32 containing the Unix timestamp as the value.\nDate is represented as a UInt16 object that contains the number of days since 1970-01-01 as the value.\nString is represented as a varint length (unsigned LEB128 ), followed by the bytes of the string.\nFixedString is represented simply as a sequence of bytes. Array is represented as a varint length (unsigned LEB128 ), followed by successive elements of the array.", + "title": "RowBinary" + }, + { + "location": "/index.html#native", + "text": "The most efficient format. Data is written and read by blocks in binary format. For each block, the number of rows, number of columns, column names and types, and parts of columns in this block are recorded one after another. In other words, this format is \"columnar\" \u2013 it doesn't convert columns to rows. This is the format used in the native interface for interaction between servers, for using the command-line client, and for C++ clients. You can use this format to quickly generate dumps that can only be read by the ClickHouse DBMS. It doesn't make sense to work with this format yourself.", + "title": "Native" + }, + { + "location": "/index.html#null_1", + "text": "Nothing is output. However, the query is processed, and when using the command-line client, data is transmitted to the client. This is used for tests, including productivity testing.\nObviously, this format is only appropriate for output, not for parsing.", + "title": "Null" + }, + { + "location": "/index.html#xml", + "text": "XML format is suitable only for output, not for parsing. Example: ?xml version= 1.0 encoding= UTF-8 ? result \n meta \n columns \n column \n name SearchPhrase /name \n type String /type \n /column \n column \n name count() /name \n type UInt64 /type \n /column \n /columns \n /meta \n data \n row \n SearchPhrase /SearchPhrase \n field 8267016 /field \n /row \n row \n SearchPhrase bathroom interior design /SearchPhrase \n field 2166 /field \n /row \n row \n SearchPhrase yandex /SearchPhrase \n field 1655 /field \n /row \n row \n SearchPhrase spring 2014 fashion /SearchPhrase \n field 1549 /field \n /row \n row \n SearchPhrase freeform photos /SearchPhrase \n field 1480 /field \n /row \n row \n SearchPhrase angelina jolie /SearchPhrase \n field 1245 /field \n /row \n row \n SearchPhrase omsk /SearchPhrase \n field 1112 /field \n /row \n row \n SearchPhrase photos of dog breeds /SearchPhrase \n field 1091 /field \n /row \n row \n SearchPhrase curtain design /SearchPhrase \n field 1064 /field \n /row \n row \n SearchPhrase baku /SearchPhrase \n field 1000 /field \n /row \n /data \n rows 10 /rows \n rows_before_limit_at_least 141137 /rows_before_limit_at_least /result If the column name does not have an acceptable format, just 'field' is used as the element name. In general, the XML structure follows the JSON structure.\nJust as for JSON, invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences. In string values, the characters and are escaped as and . Arrays are output as array elem Hello /elem elem World /elem ... /array ,\nand tuples as tuple elem Hello /elem elem World /elem ... /tuple .", + "title": "XML" + }, + { + "location": "/index.html#capnproto", + "text": "Cap'n Proto is a binary message format similar to Protocol Buffers and Thrift, but not like JSON or MessagePack. Cap'n Proto messages are strictly typed and not self-describing, meaning they need an external schema description. The schema is applied on the fly and cached for each query. SELECT SearchPhrase , count () AS c FROM test . hits \n GROUP BY SearchPhrase FORMAT CapnProto SETTINGS schema = schema:Message Where schema.capnp looks like this: struct Message { \n SearchPhrase @0 : Text ; \n c @1 : Uint64 ; } Schema files are in the file that is located in the directory specified in format_schema_path in the server configuration. Deserialization is effective and usually doesn't increase the system load.", + "title": "CapnProto" + }, + { + "location": "/index.html#data-types", + "text": "ClickHouse can store various types of data in table cells. This section describes the supported data types and special considerations when using and/or implementing them, if any.", + "title": "Data types" + }, + { + "location": "/index.html#uint8-uint16-uint32-uint64-int8-int16-int32-int64", + "text": "Fixed-length integers, with or without a sign.", + "title": "UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64" + }, + { + "location": "/index.html#int-ranges", + "text": "Int8 - [-128 : 127] Int16 - [-32768 : 32767] Int32 - [-2147483648 : 2147483647] Int64 - [-9223372036854775808 : 9223372036854775807]", + "title": "Int ranges" + }, + { + "location": "/index.html#uint-ranges", + "text": "UInt8 - [0 : 255] UInt16 - [0 : 65535] UInt32 - [0 : 4294967295] UInt64 - [0 : 18446744073709551615]", + "title": "Uint ranges" + }, + { + "location": "/index.html#float32-float64", + "text": "Floating point numbers . Types are equivalent to types of C: Float32 - float Float64 - double We recommend that you store data in integer form whenever possible. For example, convert fixed precision numbers to integer values, such as monetary amounts or page load times in milliseconds.", + "title": "Float32, Float64" + }, + { + "location": "/index.html#using-floating-point-numbers", + "text": "Computations with floating-point numbers might produce a rounding error. SELECT 1 - 0 . 9 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500minus(1, 0.9)\u2500\u2510\n\u2502 0.09999999999999998 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 The result of the calculation depends on the calculation method (the processor type and architecture of the computer system). Floating-point calculations might result in numbers such as infinity ( Inf ) and \"not-a-number\" ( NaN ). This should be taken into account when processing the results of calculations. When reading floating point numbers from rows, the result might not be the nearest machine-representable number.", + "title": "Using floating-point numbers" + }, + { + "location": "/index.html#nan-and-inf", + "text": "In contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers: Inf \u2013 Infinity. SELECT 0 . 5 / 0 \u250c\u2500divide(0.5, 0)\u2500\u2510\n\u2502 inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 -Inf \u2013 Negative infinity. SELECT - 0 . 5 / 0 \u250c\u2500divide(-0.5, 0)\u2500\u2510\n\u2502 -inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 NaN \u2013 Not a number. SELECT 0 / 0 \u250c\u2500divide(0, 0)\u2500\u2510\n\u2502 nan \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 See the rules for NaN sorting in the section ORDER BY clause .", + "title": "NaN and Inf" + }, + { + "location": "/index.html#boolean-values", + "text": "There isn't a separate type for boolean values. They use the UInt8 type, restricted to the values 0 or 1.", + "title": "Boolean values" + }, + { + "location": "/index.html#string", + "text": "Strings of an arbitrary length. The length is not limited. The value can contain an arbitrary set of bytes, including null bytes.\nThe String type replaces the types VARCHAR, BLOB, CLOB, and others from other DBMSs.", + "title": "String" + }, + { + "location": "/index.html#encodings", + "text": "ClickHouse doesn't have the concept of encodings. Strings can contain an arbitrary set of bytes, which are stored and output as-is.\nIf you need to store texts, we recommend using UTF-8 encoding. At the very least, if your terminal uses UTF-8 (as recommended), you can read and write your values without making conversions.\nSimilarly, certain functions for working with strings have separate variations that work under the assumption that the string contains a set of bytes representing a UTF-8 encoded text.\nFor example, the 'length' function calculates the string length in bytes, while the 'lengthUTF8' function calculates the string length in Unicode code points, assuming that the value is UTF-8 encoded.", + "title": "Encodings" + }, + { + "location": "/index.html#fixedstringn", + "text": "A fixed-length string of N bytes (not characters or code points). N must be a strictly positive natural number.\nWhen the server reads a string that contains fewer bytes (such as when parsing INSERT data), the string is padded to N bytes by appending null bytes at the right.\nWhen the server reads a string that contains more bytes, an error message is returned.\nWhen the server writes a string (such as when outputting the result of a SELECT query), null bytes are not trimmed off of the end of the string, but are output.\nNote that this behavior differs from MySQL behavior for the CHAR type (where strings are padded with spaces, and the spaces are removed for output). Fewer functions can work with the FixedString(N) type than with String, so it is less convenient to use.", + "title": "FixedString(N)" + }, + { + "location": "/index.html#date", + "text": "A date. Stored in two bytes as the number of days since 1970-01-01 (unsigned). Allows storing values from just after the beginning of the Unix Epoch to the upper threshold defined by a constant at the compilation stage (currently, this is until the year 2106, but the final fully-supported year is 2105).\nThe minimum value is output as 0000-00-00. The date is stored without the time zone.", + "title": "Date" + }, + { + "location": "/index.html#datetime", + "text": "Date with time. Stored in four bytes as a Unix timestamp (unsigned). Allows storing values in the same range as for the Date type. The minimal value is output as 0000-00-00 00:00:00.\nThe time is stored with accuracy up to one second (without leap seconds).", + "title": "DateTime" + }, + { + "location": "/index.html#time-zones", + "text": "The date with time is converted from text (divided into component parts) to binary and back, using the system's time zone at the time the client or server starts. In text format, information about daylight savings is lost. By default, the client switches to the timezone of the server when it connects. You can change this behavior by enabling the client command-line option --use_client_time_zone . Supports only those time zones that never had the time differ from UTC for a partial number of hours (without leap seconds) over the entire time range you will be working with. So when working with a textual date (for example, when saving text dumps), keep in mind that there may be ambiguity during changes for daylight savings time, and there may be problems matching data if the time zone changed.", + "title": "Time zones" + }, + { + "location": "/index.html#enum", + "text": "Enum8 or Enum16. A finite set of string values that can be stored more efficiently than the String data type. Example: Enum8( hello = 1, world = 2) A data type with two possible values: 'hello' and 'world'. Each of the values is assigned a number in the range -128 ... 127 for Enum8 or in the range -32768 ... 32767 for Enum16 . All the strings and numbers must be different. An empty string is allowed. If this type is specified (in a table definition), numbers can be in an arbitrary order. However, the order does not matter. In RAM, this type of column is stored in the same way as Int8 or Int16 of the corresponding numerical values.\nWhen reading in text form, ClickHouse parses the value as a string and searches for the corresponding string from the set of Enum values. If it is not found, an exception is thrown. When reading in text format, the string is read and the corresponding numeric value is looked up. An exception will be thrown if it is not found.\nWhen writing in text form, it writes the value as the corresponding string. If column data contains garbage (numbers that are not from the valid set), an exception is thrown. When reading and writing in binary form, it works the same way as for Int8 and Int16 data types.\nThe implicit default value is the value with the lowest number. During ORDER BY , GROUP BY , IN , DISTINCT and so on, Enums behave the same way as the corresponding numbers. For example, ORDER BY sorts them numerically. Equality and comparison operators work the same way on Enums as they do on the underlying numeric values. Enum values cannot be compared with numbers. Enums can be compared to a constant string. If the string compared to is not a valid value for the Enum, an exception will be thrown. The IN operator is supported with the Enum on the left hand side and a set of strings on the right hand side. The strings are the values of the corresponding Enum. Most numeric and string operations are not defined for Enum values, e.g. adding a number to an Enum or concatenating a string to an Enum.\nHowever, the Enum has a natural toString function that returns its string value. Enum values are also convertible to numeric types using the toT function, where T is a numeric type. When T corresponds to the enum\u2019s underlying numeric type, this conversion is zero-cost.\nThe Enum type can be changed without cost using ALTER, if only the set of values is changed. It is possible to both add and remove members of the Enum using ALTER (removing is safe only if the removed value has never been used in the table). As a safeguard, changing the numeric value of a previously defined Enum member will throw an exception. Using ALTER, it is possible to change an Enum8 to an Enum16 or vice versa, just like changing an Int8 to Int16.", + "title": "Enum" + }, + { + "location": "/index.html#arrayt", + "text": "An array of elements of type T. The T type can be any type, including an array.\nWe don't recommend using multidimensional arrays, because they are not well supported (for example, you can't store multidimensional arrays in tables with a MergeTree engine).", + "title": "Array(T)" + }, + { + "location": "/index.html#aggregatefunctionname-types_of_arguments", + "text": "The intermediate state of an aggregate function. To get it, use aggregate functions with the '-State' suffix. For more information, see \"AggregatingMergeTree\".", + "title": "AggregateFunction(name, types_of_arguments...)" + }, + { + "location": "/index.html#tuplet1-t2", + "text": "Tuples can't be written to tables (other than Memory tables). They are used for temporary column grouping. Columns can be grouped when an IN expression is used in a query, and for specifying certain formal parameters of lambda functions. For more information, see \"IN operators\" and \"Higher order functions\". Tuples can be output as the result of running a query. In this case, for text formats other than JSON*, values are comma-separated in brackets. In JSON* formats, tuples are output as arrays (in square brackets).", + "title": "Tuple(T1, T2, ...)" + }, + { + "location": "/index.html#nested-data-structures", + "text": "", + "title": "Nested data structures" + }, + { + "location": "/index.html#nestedname1-type1-name2-type2", + "text": "A nested data structure is like a nested table. The parameters of a nested data structure \u2013 the column names and types \u2013 are specified the same way as in a CREATE query. Each table row can correspond to any number of rows in a nested data structure. Example: CREATE TABLE test . visits ( \n CounterID UInt32 , \n StartDate Date , \n Sign Int8 , \n IsNew UInt8 , \n VisitID UInt64 , \n UserID UInt64 , \n ... \n Goals Nested \n ( \n ID UInt32 , \n Serial UInt32 , \n EventTime DateTime , \n Price Int64 , \n OrderID String , \n CurrencyID UInt32 \n ), \n ... ) ENGINE = CollapsingMergeTree ( StartDate , intHash32 ( UserID ), ( CounterID , StartDate , intHash32 ( UserID ), VisitID ), 8192 , Sign ) This example declares the Goals nested data structure, which contains data about conversions (goals reached). Each row in the 'visits' table can correspond to zero or any number of conversions. Only a single nesting level is supported. Columns of nested structures containing arrays are equivalent to multidimensional arrays, so they have limited support (there is no support for storing these columns in tables with the MergeTree engine). In most cases, when working with a nested data structure, its individual columns are specified. To do this, the column names are separated by a dot. These columns make up an array of matching types. All the column arrays of a single nested data structure have the same length. Example: SELECT \n Goals . ID , \n Goals . EventTime FROM test . visits WHERE CounterID = 101500 AND length ( Goals . ID ) 5 LIMIT 10 \u250c\u2500Goals.ID\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500Goals.EventTime\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1073752,591325,591325] \u2502 [ 2014-03-17 16:38:10 , 2014-03-17 16:38:48 , 2014-03-17 16:42:27 ] \u2502\n\u2502 [1073752] \u2502 [ 2014-03-17 00:28:25 ] \u2502\n\u2502 [1073752] \u2502 [ 2014-03-17 10:46:20 ] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [ 2014-03-17 13:59:20 , 2014-03-17 22:17:55 , 2014-03-17 22:18:07 , 2014-03-17 22:18:51 ] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [1073752,591325,591325] \u2502 [ 2014-03-17 11:37:06 , 2014-03-17 14:07:47 , 2014-03-17 14:36:21 ] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [591325,1073752] \u2502 [ 2014-03-17 00:46:05 , 2014-03-17 00:46:05 ] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [ 2014-03-17 13:28:33 , 2014-03-17 13:30:26 , 2014-03-17 18:51:21 , 2014-03-17 18:51:45 ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 It is easiest to think of a nested data structure as a set of multiple column arrays of the same length. The only place where a SELECT query can specify the name of an entire nested data structure instead of individual columns is the ARRAY JOIN clause. For more information, see \"ARRAY JOIN clause\". Example: SELECT \n Goal . ID , \n Goal . EventTime FROM test . visits ARRAY JOIN Goals AS Goal WHERE CounterID = 101500 AND length ( Goals . ID ) 5 LIMIT 10 \u250c\u2500Goal.ID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500Goal.EventTime\u2500\u2510\n\u2502 1073752 \u2502 2014-03-17 16:38:10 \u2502\n\u2502 591325 \u2502 2014-03-17 16:38:48 \u2502\n\u2502 591325 \u2502 2014-03-17 16:42:27 \u2502\n\u2502 1073752 \u2502 2014-03-17 00:28:25 \u2502\n\u2502 1073752 \u2502 2014-03-17 10:46:20 \u2502\n\u2502 1073752 \u2502 2014-03-17 13:59:20 \u2502\n\u2502 591325 \u2502 2014-03-17 22:17:55 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:07 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:51 \u2502\n\u2502 1073752 \u2502 2014-03-17 11:37:06 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 You can't perform SELECT for an entire nested data structure. You can only explicitly list individual columns that are part of it. For an INSERT query, you should pass all the component column arrays of a nested data structure separately (as if they were individual column arrays). During insertion, the system checks that they have the same length. For a DESCRIBE query, the columns in a nested data structure are listed separately in the same way. The ALTER query is very limited for elements in a nested data structure.", + "title": "Nested(Name1 Type1, Name2 Type2, ...)" + }, + { + "location": "/index.html#special-data-types", + "text": "Special data type values can't be saved to a table or output in results, but are used as the intermediate result of running a query.", + "title": "Special data types" + }, + { + "location": "/index.html#expression", + "text": "Used for representing lambda expressions in high-order functions.", + "title": "Expression" + }, + { + "location": "/index.html#set_2", + "text": "Used for the right half of an IN expression.", + "title": "Set" + }, + { + "location": "/index.html#operators_1", + "text": "All operators are transformed to the corresponding functions at the query parsing stage, in accordance with their precedence and associativity.\nGroups of operators are listed in order of priority (the higher it is in the list, the earlier the operator is connected to its arguments).", + "title": "Operators" + }, + { + "location": "/index.html#access-operators", + "text": "a[N] Access to an element of an array; arrayElement(a, N) function . a.N \u2013 Access to a tuble element; tupleElement(a, N) function.", + "title": "Access operators" + }, + { + "location": "/index.html#numeric-negation-operator", + "text": "-a \u2013 The negate (a) function.", + "title": "Numeric negation operator" + }, + { + "location": "/index.html#multiplication-and-division-operators", + "text": "a * b \u2013 The multiply (a, b) function. a / b \u2013 The divide(a, b) function. a % b \u2013 The modulo(a, b) function.", + "title": "Multiplication and division operators" + }, + { + "location": "/index.html#addition-and-subtraction-operators", + "text": "a + b \u2013 The plus(a, b) function. a - b \u2013 The minus(a, b) function.", + "title": "Addition and subtraction operators" + }, + { + "location": "/index.html#comparison-operators", + "text": "a = b \u2013 The equals(a, b) function. a == b \u2013 The equals(a, b) function. a != b \u2013 The notEquals(a, b) function. a b \u2013 The notEquals(a, b) function. a = b \u2013 The lessOrEquals(a, b) function. a = b \u2013 The greaterOrEquals(a, b) function. a b \u2013 The less(a, b) function. a b \u2013 The greater(a, b) function. a LIKE s \u2013 The like(a, b) function. a NOT LIKE s \u2013 The notLike(a, b) function. a BETWEEN b AND c \u2013 The same as a = b AND a = c.", + "title": "Comparison operators" + }, + { + "location": "/index.html#operators-for-working-with-data-sets", + "text": "See the section \"IN operators\". a IN ... \u2013 The in(a, b) function a NOT IN ... \u2013 The notIn(a, b) function. a GLOBAL IN ... \u2013 The globalIn(a, b) function. a GLOBAL NOT IN ... \u2013 The globalNotIn(a, b) function.", + "title": "Operators for working with data sets" + }, + { + "location": "/index.html#logical-negation-operator", + "text": "NOT a The not(a) function.", + "title": "Logical negation operator" + }, + { + "location": "/index.html#logical-and-operator", + "text": "a AND b \u2013 The and(a, b) function.", + "title": "Logical AND operator" + }, + { + "location": "/index.html#logical-or-operator", + "text": "a OR b \u2013 The or(a, b) function.", + "title": "Logical OR operator" + }, + { + "location": "/index.html#conditional-operator", + "text": "a ? b : c \u2013 The if(a, b, c) function. Note: The conditional operator calculates the values of b and c, then checks whether condition a is met, and then returns the corresponding value. If \"b\" or \"c\" is an arrayJoin() function, each row will be replicated regardless of the \"a\" condition.", + "title": "Conditional operator" + }, + { + "location": "/index.html#conditional-expression", + "text": "CASE [ x ] \n WHEN a THEN b \n [ WHEN ... THEN ...] \n ELSE c END If \"x\" is specified, then transform(x, [a, ...], [b, ...], c). Otherwise \u2013 multiIf(a, b, ..., c).", + "title": "Conditional expression" + }, + { + "location": "/index.html#concatenation-operator", + "text": "s1 || s2 \u2013 The concat(s1, s2) function.", + "title": "Concatenation operator" + }, + { + "location": "/index.html#lambda-creation-operator", + "text": "x - expr \u2013 The lambda(x, expr) function. The following operators do not have a priority, since they are brackets:", + "title": "Lambda creation operator" + }, + { + "location": "/index.html#array-creation-operator", + "text": "[x1, ...] \u2013 The array(x1, ...) function.", + "title": "Array creation operator" + }, + { + "location": "/index.html#tuple-creation-operator", + "text": "(x1, x2, ...) \u2013 The tuple(x2, x2, ...) function.", + "title": "Tuple creation operator" + }, + { + "location": "/index.html#associativity", + "text": "All binary operators have left associativity. For example, 1 + 2 + 3 is transformed to plus(plus(1, 2), 3) .\nSometimes this doesn't work the way you expect. For example, SELECT 4 2 3 will result in 0. For efficiency, the and and or functions accept any number of arguments. The corresponding chains of AND and OR operators are transformed to a single call of these functions.", + "title": "Associativity" + }, + { + "location": "/index.html#functions_1", + "text": "There are at least* two types of functions - regular functions (they are just called \"functions\") and aggregate functions. These are completely different concepts. Regular functions work as if they are applied to each row separately (for each row, the result of the function doesn't depend on the other rows). Aggregate functions accumulate a set of values from various rows (i.e. they depend on the entire set of rows). In this section we discuss regular functions. For aggregate functions, see the section \"Aggregate functions\". * - There is a third type of function that the 'arrayJoin' function belongs to; table functions can also be mentioned separately.*", + "title": "Functions" + }, + { + "location": "/index.html#strong-typing", + "text": "In contrast to standard SQL, ClickHouse has strong typing. In other words, it doesn't make implicit conversions between types. Each function works for a specific set of types. This means that sometimes you need to use type conversion functions.", + "title": "Strong typing" + }, + { + "location": "/index.html#common-subexpression-elimination", + "text": "All expressions in a query that have the same AST (the same record or same result of syntactic parsing) are considered to have identical values. Such expressions are concatenated and executed once. Identical subqueries are also eliminated this way.", + "title": "Common subexpression elimination" + }, + { + "location": "/index.html#types-of-results", + "text": "All functions return a single return as the result (not several values, and not zero values). The type of result is usually defined only by the types of arguments, not by the values. Exceptions are the tupleElement function (the a.N operator), and the toFixedString function.", + "title": "Types of results" + }, + { + "location": "/index.html#constants", + "text": "For simplicity, certain functions can only work with constants for some arguments. For example, the right argument of the LIKE operator must be a constant.\nAlmost all functions return a constant for constant arguments. The exception is functions that generate random numbers.\nThe 'now' function returns different values for queries that were run at different times, but the result is considered a constant, since constancy is only important within a single query.\nA constant expression is also considered a constant (for example, the right half of the LIKE operator can be constructed from multiple constants). Functions can be implemented in different ways for constant and non-constant arguments (different code is executed). But the results for a constant and for a true column containing only the same value should match each other.", + "title": "Constants" + }, + { + "location": "/index.html#constancy", + "text": "Functions can't change the values of their arguments \u2013 any changes are returned as the result. Thus, the result of calculating separate functions does not depend on the order in which the functions are written in the query.", + "title": "Constancy" + }, + { + "location": "/index.html#error-handling", + "text": "Some functions might throw an exception if the data is invalid. In this case, the query is canceled and an error text is returned to the client. For distributed processing, when an exception occurs on one of the servers, the other servers also attempt to abort the query.", + "title": "Error handling" + }, + { + "location": "/index.html#evaluation-of-argument-expressions", + "text": "In almost all programming languages, one of the arguments might not be evaluated for certain operators. This is usually the operators , || , and ?: .\nBut in ClickHouse, arguments of functions (operators) are always evaluated. This is because entire parts of columns are evaluated at once, instead of calculating each row separately.", + "title": "Evaluation of argument expressions" + }, + { + "location": "/index.html#performing-functions-for-distributed-query-processing", + "text": "For distributed query processing, as many stages of query processing as possible are performed on remote servers, and the rest of the stages (merging intermediate results and everything after that) are performed on the requestor server. This means that functions can be performed on different servers.\nFor example, in the query SELECT f(sum(g(x))) FROM distributed_table GROUP BY h(y), if a distributed_table has at least two shards, the functions 'g' and 'h' are performed on remote servers, and the function 'f' is performed on the requestor server. if a distributed_table has only one shard, all the 'f', 'g', and 'h' functions are performed on this shard's server. The result of a function usually doesn't depend on which server it is performed on. However, sometimes this is important.\nFor example, functions that work with dictionaries use the dictionary that exists on the server they are running on.\nAnother example is the hostName function, which returns the name of the server it is running on in order to make GROUP BY by servers in a SELECT query. If a function in a query is performed on the requestor server, but you need to perform it on remote servers, you can wrap it in an 'any' aggregate function or add it to a key in GROUP BY .", + "title": "Performing functions for distributed query processing" + }, + { + "location": "/index.html#arithmetic-functions", + "text": "For all arithmetic functions, the result type is calculated as the smallest number type that the result fits in, if there is such a type. The minimum is taken simultaneously based on the number of bits, whether it is signed, and whether it floats. If there are not enough bits, the highest bit type is taken. Example: SELECT toTypeName ( 0 ), toTypeName ( 0 + 0 ), toTypeName ( 0 + 0 + 0 ), toTypeName ( 0 + 0 + 0 + 0 ) \u250c\u2500toTypeName(0)\u2500\u252c\u2500toTypeName(plus(0, 0))\u2500\u252c\u2500toTypeName(plus(plus(0, 0), 0))\u2500\u252c\u2500toTypeName(plus(plus(plus(0, 0), 0), 0))\u2500\u2510\n\u2502 UInt8 \u2502 UInt16 \u2502 UInt32 \u2502 UInt64 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Arithmetic functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64. Overflow is produced the same way as in C++.", + "title": "Arithmetic functions" + }, + { + "location": "/index.html#plusa-b-a-b-operator", + "text": "Calculates the sum of the numbers.\nYou can also add integer numbers with a date or date and time. In the case of a date, adding an integer means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds.", + "title": "plus(a, b), a + b operator" + }, + { + "location": "/index.html#minusa-b-a-b-operator", + "text": "Calculates the difference. The result is always signed. You can also calculate integer numbers from a date or date with time. The idea is the same \u2013 see above for 'plus'.", + "title": "minus(a, b), a - b operator" + }, + { + "location": "/index.html#multiplya-b-a-42-b-operator", + "text": "Calculates the product of the numbers.", + "title": "multiply(a, b), a * b operator" + }, + { + "location": "/index.html#dividea-b-a-b-operator", + "text": "Calculates the quotient of the numbers. The result type is always a floating-point type.\nIt is not integer division. For integer division, use the 'intDiv' function.\nWhen dividing by zero you get 'inf', '-inf', or 'nan'.", + "title": "divide(a, b), a / b operator" + }, + { + "location": "/index.html#intdiva-b", + "text": "Calculates the quotient of the numbers. Divides into integers, rounding down (by the absolute value).\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", + "title": "intDiv(a, b)" + }, + { + "location": "/index.html#intdivorzeroa-b", + "text": "Differs from 'intDiv' in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one.", + "title": "intDivOrZero(a, b)" + }, + { + "location": "/index.html#moduloa-b-a-b-operator", + "text": "Calculates the remainder after division.\nIf arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion.\nThe remainder is taken in the same sense as in C++. Truncated division is used for negative numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", + "title": "modulo(a, b), a % b operator" + }, + { + "location": "/index.html#negatea-a-operator", + "text": "Calculates a number with the reverse sign. The result is always signed.", + "title": "negate(a), -a operator" + }, + { + "location": "/index.html#absa", + "text": "Calculates the absolute value of the number (a). That is, if a 0, it returns -a. For unsigned types it doesn't do anything. For signed integer types, it returns an unsigned number.", + "title": "abs(a)" + }, + { + "location": "/index.html#gcda-b", + "text": "Returns the greatest common divisor of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", + "title": "gcd(a, b)" + }, + { + "location": "/index.html#lcma-b", + "text": "Returns the least common multiple of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", + "title": "lcm(a, b)" + }, + { + "location": "/index.html#comparison-functions", + "text": "Comparison functions always return 0 or 1 (Uint8). The following types can be compared: numbers strings and fixed strings dates dates with times within each group, but not between different groups. For example, you can't compare a date with a string. You have to use a function to convert the string to a date, or vice versa. Strings are compared by bytes. A shorter string is smaller than all strings that start with it and that contain at least one more character. Note. Up until version 1.1.54134, signed and unsigned numbers were compared the same way as in C++. In other words, you could get an incorrect result in cases like SELECT 9223372036854775807 -1. This behavior changed in version 1.1.54134 and is now mathematically correct.", + "title": "Comparison functions" + }, + { + "location": "/index.html#equals-a-b-and-a-b-operator", + "text": "", + "title": "equals, a = b and a == b operator" + }, + { + "location": "/index.html#notequals-a-operator-b-and-a-b", + "text": "", + "title": "notEquals, a ! operator= b and a <> b" + }, + { + "location": "/index.html#less-operator", + "text": "", + "title": "less, < operator" + }, + { + "location": "/index.html#greater-operator", + "text": "", + "title": "greater, > operator" + }, + { + "location": "/index.html#lessorequals-operator", + "text": "", + "title": "lessOrEquals, <= operator" + }, + { + "location": "/index.html#greaterorequals-operator", + "text": "", + "title": "greaterOrEquals, >= operator" + }, + { + "location": "/index.html#logical-functions", + "text": "Logical functions accept any numeric types, but return a UInt8 number equal to 0 or 1. Zero as an argument is considered \"false,\" while any non-zero value is considered \"true\".", + "title": "Logical functions" + }, + { + "location": "/index.html#and-and-operator", + "text": "", + "title": "and, AND operator" + }, + { + "location": "/index.html#or-or-operator", + "text": "", + "title": "or, OR operator" + }, + { + "location": "/index.html#not-not-operator", + "text": "", + "title": "not, NOT operator" + }, + { + "location": "/index.html#xor", + "text": "", + "title": "xor" + }, + { + "location": "/index.html#type-conversion-functions", + "text": "", + "title": "Type conversion functions" + }, + { + "location": "/index.html#touint8-touint16-touint32-touint64", + "text": "", + "title": "toUInt8, toUInt16, toUInt32, toUInt64" + }, + { + "location": "/index.html#toint8-toint16-toint32-toint64", + "text": "", + "title": "toInt8, toInt16, toInt32, toInt64" + }, + { + "location": "/index.html#tofloat32-tofloat64", + "text": "", + "title": "toFloat32, toFloat64" + }, + { + "location": "/index.html#touint8orzero-touint16orzero-touint32orzero-touint64orzero-toint8orzero-toint16orzero-toint32orzero-toint64orzero-tofloat32orzero-tofloat64orzero", + "text": "", + "title": "toUInt8OrZero, toUInt16OrZero, toUInt32OrZero, toUInt64OrZero, toInt8OrZero, toInt16OrZero, toInt32OrZero, toInt64OrZero, toFloat32OrZero, toFloat64OrZero" + }, + { + "location": "/index.html#todate-todatetime", + "text": "", + "title": "toDate, toDateTime" + }, + { + "location": "/index.html#tostring", + "text": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times.\nAll these functions accept one argument. When converting to or from a string, the value is formatted or parsed using the same rules as for the TabSeparated format (and almost all other text formats). If the string can't be parsed, an exception is thrown and the request is canceled. When converting dates to numbers or vice versa, the date corresponds to the number of days since the beginning of the Unix epoch.\nWhen converting dates with times to numbers or vice versa, the date with time corresponds to the number of seconds since the beginning of the Unix epoch. The date and date-with-time formats for the toDate/toDateTime functions are defined as follows: YYYY-MM-DD\nYYYY-MM-DD hh:mm:ss As an exception, if converting from UInt32, Int32, UInt64, or Int64 numeric types to Date, and if the number is greater than or equal to 65536, the number is interpreted as a Unix timestamp (and not as the number of days) and is rounded to the date. This allows support for the common occurrence of writing 'toDate(unix_timestamp)', which otherwise would be an error and would require writing the more cumbersome 'toDate(toDateTime(unix_timestamp))'. Conversion between a date and date with time is performed the natural way: by adding a null time or dropping the time. Conversion between numeric types uses the same rules as assignments between different numeric types in C++. Additionally, the toString function of the DateTime argument can take a second String argument containing the name of the time zone. Example: Asia/Yekaterinburg In this case, the time is formatted according to the specified time zone. SELECT \n now () AS now_local , \n toString ( now (), Asia/Yekaterinburg ) AS now_yekat \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500now_local\u2500\u252c\u2500now_yekat\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 00:11:21 \u2502 2016-06-15 02:11:21 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Also see the toUnixTimestamp function.", + "title": "toString" + }, + { + "location": "/index.html#tofixedstrings-n", + "text": "Converts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant.\nIf the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown.", + "title": "toFixedString(s, N)" + }, + { + "location": "/index.html#tostringcuttozeros", + "text": "Accepts a String or FixedString argument. Returns the String with the content truncated at the first zero byte found. Example: SELECT toFixedString ( foo , 8 ) AS s , toStringCutToZero ( s ) AS s_cut \u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0\\0\\0\\0\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT toFixedString ( foo\\0bar , 8 ) AS s , toStringCutToZero ( s ) AS s_cut \u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0bar\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "toStringCutToZero(s)" + }, + { + "location": "/index.html#reinterpretasuint8-reinterpretasuint16-reinterpretasuint32-reinterpretasuint64", + "text": "", + "title": "reinterpretAsUInt8, reinterpretAsUInt16, reinterpretAsUInt32, reinterpretAsUInt64" + }, + { + "location": "/index.html#reinterpretasint8-reinterpretasint16-reinterpretasint32-reinterpretasint64", + "text": "", + "title": "reinterpretAsInt8, reinterpretAsInt16, reinterpretAsInt32, reinterpretAsInt64" + }, + { + "location": "/index.html#reinterpretasfloat32-reinterpretasfloat64", + "text": "", + "title": "reinterpretAsFloat32, reinterpretAsFloat64" + }, + { + "location": "/index.html#reinterpretasdate-reinterpretasdatetime", + "text": "These functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.", + "title": "reinterpretAsDate, reinterpretAsDateTime" + }, + { + "location": "/index.html#reinterpretasstring", + "text": "This function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long.", + "title": "reinterpretAsString" + }, + { + "location": "/index.html#castx-t", + "text": "Converts 'x' to the 't' data type. The syntax CAST(x AS t) is also supported. Example: SELECT \n 2016-06-15 23:00:00 AS timestamp , \n CAST ( timestamp AS DateTime ) AS datetime , \n CAST ( timestamp AS Date ) AS date , \n CAST ( timestamp , String ) AS string , \n CAST ( timestamp , FixedString(22) ) AS fixed_string \u250c\u2500timestamp\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500datetime\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500date\u2500\u252c\u2500string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500fixed_string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00\\0\\0\\0 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Conversion to FixedString (N) only works for arguments of type String or FixedString (N).", + "title": "CAST(x, t)" + }, + { + "location": "/index.html#functions-for-working-with-dates-and-times", + "text": "Support for time zones All functions for working with the date and time that have a logical use for the time zone can accept a second optional time zone argument. Example: Asia/Yekaterinburg. In this case, they use the specified time zone instead of the local (default) one. SELECT \n toDateTime ( 2016-06-15 23:00:00 ) AS time , \n toDate ( time ) AS date_local , \n toDate ( time , Asia/Yekaterinburg ) AS date_yekat , \n toString ( time , US/Samoa ) AS time_samoa \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500time\u2500\u252c\u2500date_local\u2500\u252c\u2500date_yekat\u2500\u252c\u2500time_samoa\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-16 \u2502 2016-06-15 09:00:00 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Only time zones that differ from UTC by a whole number of hours are supported.", + "title": "Functions for working with dates and times" + }, + { + "location": "/index.html#toyear", + "text": "Converts a date or date with time to a UInt16 number containing the year number (AD).", + "title": "toYear" + }, + { + "location": "/index.html#tomonth", + "text": "Converts a date or date with time to a UInt8 number containing the month number (1-12).", + "title": "toMonth" + }, + { + "location": "/index.html#todayofmonth", + "text": "-Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31).", + "title": "toDayOfMonth" + }, + { + "location": "/index.html#todayofweek", + "text": "Converts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7).", + "title": "toDayOfWeek" + }, + { + "location": "/index.html#tohour", + "text": "Converts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23).\nThis function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true \u2013 even in Moscow the clocks were twice changed at a different time).", + "title": "toHour" + }, + { + "location": "/index.html#tominute", + "text": "Converts a date with time to a UInt8 number containing the number of the minute of the hour (0-59).", + "title": "toMinute" + }, + { + "location": "/index.html#tosecond", + "text": "Converts a date with time to a UInt8 number containing the number of the second in the minute (0-59).\nLeap seconds are not accounted for.", + "title": "toSecond" + }, + { + "location": "/index.html#tomonday", + "text": "Rounds down a date or date with time to the nearest Monday.\nReturns the date.", + "title": "toMonday" + }, + { + "location": "/index.html#tostartofmonth", + "text": "Rounds down a date or date with time to the first day of the month.\nReturns the date.", + "title": "toStartOfMonth" + }, + { + "location": "/index.html#tostartofquarter", + "text": "Rounds down a date or date with time to the first day of the quarter.\nThe first day of the quarter is either 1 January, 1 April, 1 July, or 1 October.\nReturns the date.", + "title": "toStartOfQuarter" + }, + { + "location": "/index.html#tostartofyear", + "text": "Rounds down a date or date with time to the first day of the year.\nReturns the date.", + "title": "toStartOfYear" + }, + { + "location": "/index.html#tostartofminute", + "text": "Rounds down a date with time to the start of the minute.", + "title": "toStartOfMinute" + }, + { + "location": "/index.html#tostartoffiveminute", + "text": "Rounds down a date with time to the start of the hour.", + "title": "toStartOfFiveMinute" + }, + { + "location": "/index.html#tostartoffifteenminutes", + "text": "Rounds down the date with time to the start of the fifteen-minute interval. Note: If you need to round a date with time to any other number of seconds, minutes, or hours, you can convert it into a number by using the toUInt32 function, then round the number using intDiv and multiplication, and convert it back using the toDateTime function.", + "title": "toStartOfFifteenMinutes" + }, + { + "location": "/index.html#tostartofhour", + "text": "Rounds down a date with time to the start of the hour.", + "title": "toStartOfHour" + }, + { + "location": "/index.html#tostartofday", + "text": "Rounds down a date with time to the start of the day.", + "title": "toStartOfDay" + }, + { + "location": "/index.html#totime", + "text": "Converts a date with time to a certain fixed date, while preserving the time.", + "title": "toTime" + }, + { + "location": "/index.html#torelativeyearnum", + "text": "Converts a date with time or date to the number of the year, starting from a certain fixed point in the past.", + "title": "toRelativeYearNum" + }, + { + "location": "/index.html#torelativemonthnum", + "text": "Converts a date with time or date to the number of the month, starting from a certain fixed point in the past.", + "title": "toRelativeMonthNum" + }, + { + "location": "/index.html#torelativeweeknum", + "text": "Converts a date with time or date to the number of the week, starting from a certain fixed point in the past.", + "title": "toRelativeWeekNum" + }, + { + "location": "/index.html#torelativedaynum", + "text": "Converts a date with time or date to the number of the day, starting from a certain fixed point in the past.", + "title": "toRelativeDayNum" + }, + { + "location": "/index.html#torelativehournum", + "text": "Converts a date with time or date to the number of the hour, starting from a certain fixed point in the past.", + "title": "toRelativeHourNum" + }, + { + "location": "/index.html#torelativeminutenum", + "text": "Converts a date with time or date to the number of the minute, starting from a certain fixed point in the past.", + "title": "toRelativeMinuteNum" + }, + { + "location": "/index.html#torelativesecondnum", + "text": "Converts a date with time or date to the number of the second, starting from a certain fixed point in the past.", + "title": "toRelativeSecondNum" + }, + { + "location": "/index.html#now", + "text": "Accepts zero arguments and returns the current time at one of the moments of request execution.\nThis function returns a constant, even if the request took a long time to complete.", + "title": "now" + }, + { + "location": "/index.html#today", + "text": "Accepts zero arguments and returns the current date at one of the moments of request execution.\nThe same as 'toDate(now())'.", + "title": "today" + }, + { + "location": "/index.html#yesterday", + "text": "Accepts zero arguments and returns yesterday's date at one of the moments of request execution.\nThe same as 'today() - 1'.", + "title": "yesterday" + }, + { + "location": "/index.html#timeslot", + "text": "Rounds the time to the half hour.\nThis function is specific to Yandex.Metrica, since half an hour is the minimum amount of time for breaking a session into two sessions if a tracking tag shows a single user's consecutive pageviews that differ in time by strictly more than this amount. This means that tuples (the tag ID, user ID, and time slot) can be used to search for pageviews that are included in the corresponding session.", + "title": "timeSlot" + }, + { + "location": "/index.html#timeslotsstarttime-duration", + "text": "For a time interval starting at 'StartTime' and continuing for 'Duration' seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour.\nFor example, timeSlots(toDateTime('2012-01-01 12:20:00'), 600) = [toDateTime('2012-01-01 12:00:00'), toDateTime('2012-01-01 12:30:00')] .\nThis is necessary for searching for pageviews in the corresponding session.", + "title": "timeSlots(StartTime, Duration)" + }, + { + "location": "/index.html#functions-for-working-with-strings", + "text": "", + "title": "Functions for working with strings" + }, + { + "location": "/index.html#empty", + "text": "Returns 1 for an empty string or 0 for a non-empty string.\nThe result type is UInt8.\nA string is considered non-empty if it contains at least one byte, even if this is a space or a null byte.\nThe function also works for arrays.", + "title": "empty" + }, + { + "location": "/index.html#notempty", + "text": "Returns 0 for an empty string or 1 for a non-empty string.\nThe result type is UInt8.\nThe function also works for arrays.", + "title": "notEmpty" + }, + { + "location": "/index.html#length", + "text": "Returns the length of a string in bytes (not in characters, and not in code points).\nThe result type is UInt64.\nThe function also works for arrays.", + "title": "length" + }, + { + "location": "/index.html#lengthutf8", + "text": "Returns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\nThe result type is UInt64.", + "title": "lengthUTF8" + }, + { + "location": "/index.html#lower", + "text": "Converts ASCII Latin symbols in a string to lowercase.", + "title": "lower" + }, + { + "location": "/index.html#upper", + "text": "Converts ASCII Latin symbols in a string to uppercase.", + "title": "upper" + }, + { + "location": "/index.html#lowerutf8", + "text": "Converts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.", + "title": "lowerUTF8" + }, + { + "location": "/index.html#upperutf8", + "text": "Converts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.", + "title": "upperUTF8" + }, + { + "location": "/index.html#reverse", + "text": "Reverses the string (as a sequence of bytes).", + "title": "reverse" + }, + { + "location": "/index.html#reverseutf8", + "text": "Reverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn't throw an exception).", + "title": "reverseUTF8" + }, + { + "location": "/index.html#concats1-s2", + "text": "Concatenates the strings listed in the arguments, without a separator.", + "title": "concat(s1, s2, ...)" + }, + { + "location": "/index.html#substrings-offset-length", + "text": "Returns a substring starting with the byte from the 'offset' index that is 'length' bytes long. Character indexing starts from one (as in standard SQL). The 'offset' and 'length' arguments must be constants.", + "title": "substring(s, offset, length)" + }, + { + "location": "/index.html#substringutf8s-offset-length", + "text": "The same as 'substring', but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).", + "title": "substringUTF8(s, offset, length)" + }, + { + "location": "/index.html#appendtrailingcharifabsents-c", + "text": "If the 's' string is non-empty and does not contain the 'c' character at the end, it appends the 'c' character to the end.", + "title": "appendTrailingCharIfAbsent(s, c)" + }, + { + "location": "/index.html#convertcharsets-from-to", + "text": "Returns the string 's' that was converted from the encoding in 'from' to the encoding in 'to'.", + "title": "convertCharset(s, from, to)" + }, + { + "location": "/index.html#functions-for-searching-strings", + "text": "The search is case-sensitive in all these functions.\nThe search substring or regular expression must be a constant in all these functions.", + "title": "Functions for searching strings" + }, + { + "location": "/index.html#positionhaystack-needle", + "text": "Search for the needle substring in the haystack string.\nReturns the position (in bytes) of the found substring, starting from 1, or returns 0 if the substring was not found. For case-insensitive search use positionCaseInsensitive function.", + "title": "position(haystack, needle)" + }, + { + "location": "/index.html#positionutf8haystack-needle", + "text": "The same as position , but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception). For case-insensitive search use positionCaseInsensitiveUTF8 function.", + "title": "positionUTF8(haystack, needle)" + }, + { + "location": "/index.html#matchhaystack-pattern", + "text": "Checks whether the string matches the 'pattern' regular expression. A re2 regular expression.\nReturns 0 if it doesn't match, or 1 if it matches. Note that the backslash symbol ( \\ ) is used for escaping in the regular expression. The same symbol is used for escaping in string literals. So in order to escape the symbol in a regular expression, you must write two backslashes (\\) in a string literal. The regular expression works with the string as if it is a set of bytes. The regular expression can't contain null bytes.\nFor patterns to search for substrings in a string, it is better to use LIKE or 'position', since they work much faster.", + "title": "match(haystack, pattern)" + }, + { + "location": "/index.html#extracthaystack-pattern", + "text": "Extracts a fragment of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. If the regex doesn't contain subpatterns, it takes the fragment that matches the entire regex. Otherwise, it takes the fragment that matches the first subpattern.", + "title": "extract(haystack, pattern)" + }, + { + "location": "/index.html#extractallhaystack-pattern", + "text": "Extracts all the fragments of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex. In general, the behavior is the same as the 'extract' function (it takes the first subpattern, or the entire expression if there isn't a subpattern).", + "title": "extractAll(haystack, pattern)" + }, + { + "location": "/index.html#likehaystack-pattern-haystack-like-pattern-operator", + "text": "Checks whether a string matches a simple regular expression.\nThe regular expression can contain the metasymbols % and _ . ``% indicates any quantity of any bytes (including zero characters). _ indicates any one byte. Use the backslash ( \\ ) for escaping metasymbols. See the note on escaping in the description of the 'match' function. For regular expressions like %needle% , the code is more optimal and works as fast as the position function.\nFor other regular expressions, the code is the same as for the 'match' function.", + "title": "like(haystack, pattern), haystack LIKE pattern operator" + }, + { + "location": "/index.html#notlikehaystack-pattern-haystack-not-like-pattern-operator", + "text": "The same thing as 'like', but negative.", + "title": "notLike(haystack, pattern), haystack NOT LIKE pattern operator" + }, + { + "location": "/index.html#functions-for-searching-and-replacing-in-strings", + "text": "", + "title": "Functions for searching and replacing in strings" + }, + { + "location": "/index.html#replaceonehaystack-pattern-replacement", + "text": "Replaces the first occurrence, if it exists, of the 'pattern' substring in 'haystack' with the 'replacement' substring.\nHereafter, 'pattern' and 'replacement' must be constants.", + "title": "replaceOne(haystack, pattern, replacement)" + }, + { + "location": "/index.html#replaceallhaystack-pattern-replacement", + "text": "Replaces all occurrences of the 'pattern' substring in 'haystack' with the 'replacement' substring.", + "title": "replaceAll(haystack, pattern, replacement)" + }, + { + "location": "/index.html#replaceregexponehaystack-pattern-replacement", + "text": "Replacement using the 'pattern' regular expression. A re2 regular expression.\nReplaces only the first occurrence, if it exists.\nA pattern can be specified as 'replacement'. This pattern can include substitutions \\0-\\9 .\nThe substitution \\0 includes the entire regular expression. Substitutions \\1-\\9 correspond to the subpattern numbers.To use the \\ character in a template, escape it using \\ .\nAlso keep in mind that a string literal requires an extra escape. Example 1. Converting the date to American format: SELECT DISTINCT \n EventDate , \n replaceRegexpOne ( toString ( EventDate ), (\\\\d{4})-(\\\\d{2})-(\\\\d{2}) , \\\\2/\\\\3/\\\\1 ) AS res FROM test . hits LIMIT 7 FORMAT TabSeparated 2014-03-17 03/17/2014\n2014-03-18 03/18/2014\n2014-03-19 03/19/2014\n2014-03-20 03/20/2014\n2014-03-21 03/21/2014\n2014-03-22 03/22/2014\n2014-03-23 03/23/2014 Example 2. Copying a string ten times: SELECT replaceRegexpOne ( Hello, World! , .* , \\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0 ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "replaceRegexpOne(haystack, pattern, replacement)" + }, + { + "location": "/index.html#replaceregexpallhaystack-pattern-replacement", + "text": "This does the same thing, but replaces all the occurrences. Example: SELECT replaceRegexpAll ( Hello, World! , . , \\\\0\\\\0 ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 HHeelllloo,, WWoorrlldd!! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 As an exception, if a regular expression worked on an empty substring, the replacement is not made more than once.\nExample: SELECT replaceRegexpAll ( Hello, World! , ^ , here: ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 here: Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "replaceRegexpAll(haystack, pattern, replacement)" + }, + { + "location": "/index.html#conditional-functions", + "text": "", + "title": "Conditional functions" + }, + { + "location": "/index.html#ifcond-then-else-cond-operator-then-else", + "text": "Returns 'then' if cond !or 'else' if cond = 0.'cond' must be UInt 8, and 'then' and 'else' must be a type that has the smallest common type.", + "title": "if(cond, then, else), cond ? operator then : else" + }, + { + "location": "/index.html#mathematical-functions", + "text": "All the functions return a Float64 number. The accuracy of the result is close to the maximum precision possible, but the result might not coincide with the machine representable number nearest to the corresponding real number.", + "title": "Mathematical functions" + }, + { + "location": "/index.html#e", + "text": "Returns a Float64 number close to the e number.", + "title": "e()" + }, + { + "location": "/index.html#pi", + "text": "Returns a Float64 number close to \u03c0.", + "title": "pi()" + }, + { + "location": "/index.html#expx", + "text": "Accepts a numeric argument and returns a Float64 number close to the exponent of the argument.", + "title": "exp(x)" + }, + { + "location": "/index.html#logx", + "text": "Accepts a numeric argument and returns a Float64 number close to the natural logarithm of the argument.", + "title": "log(x)" + }, + { + "location": "/index.html#exp2x", + "text": "Accepts a numeric argument and returns a Float64 number close to 2^x.", + "title": "exp2(x)" + }, + { + "location": "/index.html#log2x", + "text": "Accepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument.", + "title": "log2(x)" + }, + { + "location": "/index.html#exp10x", + "text": "Accepts a numeric argument and returns a Float64 number close to 10^x.", + "title": "exp10(x)" + }, + { + "location": "/index.html#log10x", + "text": "Accepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument.", + "title": "log10(x)" + }, + { + "location": "/index.html#sqrtx", + "text": "Accepts a numeric argument and returns a Float64 number close to the square root of the argument.", + "title": "sqrt(x)" + }, + { + "location": "/index.html#cbrtx", + "text": "Accepts a numeric argument and returns a Float64 number close to the cubic root of the argument.", + "title": "cbrt(x)" + }, + { + "location": "/index.html#erfx", + "text": "If 'x' is non-negative, then erf(x / \u03c3\u221a2) is the probability that a random variable having a normal distribution with standard deviation '\u03c3' takes the value that is separated from the expected value by more than 'x'. Example (three sigma rule): SELECT erf ( 3 / sqrt ( 2 )) \u250c\u2500erf(divide(3, sqrt(2)))\u2500\u2510\n\u2502 0.9973002039367398 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "erf(x)" + }, + { + "location": "/index.html#erfcx", + "text": "Accepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large 'x' values.", + "title": "erfc(x)" + }, + { + "location": "/index.html#lgammax", + "text": "The logarithm of the gamma function.", + "title": "lgamma(x)" + }, + { + "location": "/index.html#tgammax", + "text": "Gamma function.", + "title": "tgamma(x)" + }, + { + "location": "/index.html#sinx", + "text": "The sine.", + "title": "sin(x)" + }, + { + "location": "/index.html#cosx", + "text": "The cosine.", + "title": "cos(x)" + }, + { + "location": "/index.html#tanx", + "text": "The tangent.", + "title": "tan(x)" + }, + { + "location": "/index.html#asinx", + "text": "The arc sine.", + "title": "asin(x)" + }, + { + "location": "/index.html#acosx", + "text": "The arc cosine.", + "title": "acos(x)" + }, + { + "location": "/index.html#atanx", + "text": "The arc tangent.", + "title": "atan(x)" + }, + { + "location": "/index.html#powx-y", + "text": "Accepts two numeric arguments and returns a Float64 number close to x^y.", + "title": "pow(x, y)" + }, + { + "location": "/index.html#rounding-functions", + "text": "", + "title": "Rounding functions" + }, + { + "location": "/index.html#floorx91-n93", + "text": "Returns the largest round number that is less than or equal to x. A round number is a multiple of 1/10N, or the nearest number of the appropriate data type if 1 / 10N isn't exact.\n'N' is an integer constant, optional parameter. By default it is zero, which means to round to an integer.\n'N' may be negative. Examples: floor(123.45, 1) = 123.4, floor(123.45, -1) = 120. x is any numeric type. The result is a number of the same type.\nFor integer arguments, it makes sense to round with a negative 'N' value (for non-negative 'N', the function doesn't do anything).\nIf rounding causes overflow (for example, floor(-128, -1)), an implementation-specific result is returned.", + "title": "floor(x[, N])" + }, + { + "location": "/index.html#ceilx91-n93", + "text": "Returns the smallest round number that is greater than or equal to 'x'. In every other way, it is the same as the 'floor' function (see above).", + "title": "ceil(x[, N])" + }, + { + "location": "/index.html#roundx91-n93", + "text": "Returns the round number nearest to 'num', which may be less than, greater than, or equal to 'x'.If 'x' is exactly in the middle between the nearest round numbers, one of them is returned (implementation-specific).\nThe number '-0.' may or may not be considered round (implementation-specific).\nIn every other way, this function is the same as 'floor' and 'ceil' described above.", + "title": "round(x[, N])" + }, + { + "location": "/index.html#roundtoexp2num", + "text": "Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two.", + "title": "roundToExp2(num)" + }, + { + "location": "/index.html#rounddurationnum", + "text": "Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length", + "title": "roundDuration(num)" + }, + { + "location": "/index.html#roundagenum", + "text": "Accepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to a number from the set: 18, 25, 35, 45, 55. This function is specific to Yandex.Metrica and used for implementing the report on user age.", + "title": "roundAge(num)" + }, + { + "location": "/index.html#functions-for-working-with-arrays", + "text": "", + "title": "Functions for working with arrays" + }, + { + "location": "/index.html#empty_1", + "text": "Returns 1 for an empty array, or 0 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.", + "title": "empty" + }, + { + "location": "/index.html#notempty_1", + "text": "Returns 0 for an empty array, or 1 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.", + "title": "notEmpty" + }, + { + "location": "/index.html#length_1", + "text": "Returns the number of items in the array.\nThe result type is UInt64.\nThe function also works for strings.", + "title": "length" + }, + { + "location": "/index.html#emptyarrayuint8-emptyarrayuint16-emptyarrayuint32-emptyarrayuint64", + "text": "", + "title": "emptyArrayUInt8, emptyArrayUInt16, emptyArrayUInt32, emptyArrayUInt64" + }, + { + "location": "/index.html#emptyarrayint8-emptyarrayint16-emptyarrayint32-emptyarrayint64", + "text": "", + "title": "emptyArrayInt8, emptyArrayInt16, emptyArrayInt32, emptyArrayInt64" + }, + { + "location": "/index.html#emptyarrayfloat32-emptyarrayfloat64", + "text": "", + "title": "emptyArrayFloat32, emptyArrayFloat64" + }, + { + "location": "/index.html#emptyarraydate-emptyarraydatetime", + "text": "", + "title": "emptyArrayDate, emptyArrayDateTime" + }, + { + "location": "/index.html#emptyarraystring", + "text": "Accepts zero arguments and returns an empty array of the appropriate type.", + "title": "emptyArrayString" + }, + { + "location": "/index.html#emptyarraytosingle", + "text": "Accepts an empty array and returns a one-element array that is equal to the default value.", + "title": "emptyArrayToSingle" + }, + { + "location": "/index.html#rangen", + "text": "Returns an array of numbers from 0 to N-1.\nJust in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block.", + "title": "range(N)" + }, + { + "location": "/index.html#arrayx1-operator-91x1-93", + "text": "Creates an array from the function arguments.\nThe arguments must be constants and have types that have the smallest common type. At least one argument must be passed, because otherwise it isn't clear which type of array to create. That is, you can't use this function to create an empty array (to do that, use the 'emptyArray*' function described above).\nReturns an 'Array(T)' type result, where 'T' is the smallest common type out of the passed arguments.", + "title": "array(x1, ...), operator [x1, ...]" + }, + { + "location": "/index.html#arrayconcat", + "text": "Combines arrays passed as arguments. arrayConcat(arrays) Arguments arrays \u2013 Arrays of comma-separated [values] . Example SELECT arrayConcat ([ 1 , 2 ], [ 3 , 4 ], [ 5 , 6 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,3,4,5,6] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayConcat" + }, + { + "location": "/index.html#arrayelementarr-n-operator-arrn", + "text": "Get the element with the index 'n' from the array 'arr'.'n' must be any integer type.\nIndexes in an array begin from one.\nNegative indexes are supported. In this case, it selects the corresponding element numbered from the end. For example, 'arr[-1]' is the last item in the array. If the index falls outside of the bounds of an array, it returns some default value (0 for numbers, an empty string for strings, etc.).", + "title": "arrayElement(arr, n), operator arr[n]" + }, + { + "location": "/index.html#hasarr-elem", + "text": "Checks whether the 'arr' array has the 'elem' element.\nReturns 0 if the the element is not in the array, or 1 if it is.", + "title": "has(arr, elem)" + }, + { + "location": "/index.html#indexofarr-x", + "text": "Returns the index of the 'x' element (starting from 1) if it is in the array, or 0 if it is not.", + "title": "indexOf(arr, x)" + }, + { + "location": "/index.html#countequalarr-x", + "text": "Returns the number of elements in the array equal to x. Equivalent to arrayCount (elem- elem = x, arr).", + "title": "countEqual(arr, x)" + }, + { + "location": "/index.html#arrayenumeratearr", + "text": "Returns the array [1, 2, 3, ..., length (arr) ] This function is normally used with ARRAY JOIN. It allows counting something just once for each array after applying ARRAY JOIN. Example: SELECT \n count () AS Reaches , \n countIf ( num = 1 ) AS Hits FROM test . hits ARRAY JOIN \n GoalsReached , \n arrayEnumerate ( GoalsReached ) AS num WHERE CounterID = 160656 LIMIT 10 \u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 In this example, Reaches is the number of conversions (the strings received after applying ARRAY JOIN), and Hits is the number of pageviews (strings before ARRAY JOIN). In this particular case, you can get the same result in an easier way: SELECT \n sum ( length ( GoalsReached )) AS Reaches , \n count () AS Hits FROM test . hits WHERE ( CounterID = 160656 ) AND notEmpty ( GoalsReached ) \u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 This function can also be used in higher-order functions. For example, you can use it to get array indexes for elements that match a condition.", + "title": "arrayEnumerate(arr)" + }, + { + "location": "/index.html#arrayenumerateuniqarr", + "text": "Returns an array the same size as the source array, indicating for each element what its position is among elements with the same value.\nFor example: arrayEnumerateUniq([10, 20, 10, 30]) = [1, 1, 2, 1]. This function is useful when using ARRAY JOIN and aggregation of array elements.\nExample: SELECT \n Goals . ID AS GoalID , \n sum ( Sign ) AS Reaches , \n sumIf ( Sign , num = 1 ) AS Visits FROM test . visits ARRAY JOIN \n Goals , \n arrayEnumerateUniq ( Goals . ID ) AS num WHERE CounterID = 160656 GROUP BY GoalID ORDER BY Reaches DESC LIMIT 10 \u250c\u2500\u2500GoalID\u2500\u252c\u2500Reaches\u2500\u252c\u2500Visits\u2500\u2510\n\u2502 53225 \u2502 3214 \u2502 1097 \u2502\n\u2502 2825062 \u2502 3188 \u2502 1097 \u2502\n\u2502 56600 \u2502 2803 \u2502 488 \u2502\n\u2502 1989037 \u2502 2401 \u2502 365 \u2502\n\u2502 2830064 \u2502 2396 \u2502 910 \u2502\n\u2502 1113562 \u2502 2372 \u2502 373 \u2502\n\u2502 3270895 \u2502 2262 \u2502 812 \u2502\n\u2502 1084657 \u2502 2262 \u2502 345 \u2502\n\u2502 56599 \u2502 2260 \u2502 799 \u2502\n\u2502 3271094 \u2502 2256 \u2502 812 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 In this example, each goal ID has a calculation of the number of conversions (each element in the Goals nested data structure is a goal that was reached, which we refer to as a conversion) and the number of sessions. Without ARRAY JOIN, we would have counted the number of sessions as sum(Sign). But in this particular case, the rows were multiplied by the nested Goals structure, so in order to count each session one time after this, we apply a condition to the value of the arrayEnumerateUniq(Goals.ID) function. The arrayEnumerateUniq function can take multiple arrays of the same size as arguments. In this case, uniqueness is considered for tuples of elements in the same positions in all the arrays. SELECT arrayEnumerateUniq ([ 1 , 1 , 1 , 2 , 2 , 2 ], [ 1 , 1 , 2 , 1 , 1 , 2 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,1,1,2,1] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 This is necessary when using ARRAY JOIN with a nested data structure and further aggregation across multiple elements in this structure.", + "title": "arrayEnumerateUniq(arr, ...)" + }, + { + "location": "/index.html#arraypopback", + "text": "Removes the last item from the array. arrayPopBack(array) Arguments array \u2013 Array. Example SELECT arrayPopBack ([ 1 , 2 , 3 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [1,2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayPopBack" + }, + { + "location": "/index.html#arraypopfront", + "text": "Removes the first item from the array. arrayPopFront(array) Arguments array \u2013 Array. Example SELECT arrayPopFront ([ 1 , 2 , 3 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayPopFront" + }, + { + "location": "/index.html#arraypushback", + "text": "Adds one item to the end of the array. arrayPushBack(array, single_value) Arguments array \u2013 Array. single_value \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section \" Data types \". Example SELECT arrayPushBack ([ a ], b ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ a , b ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayPushBack" + }, + { + "location": "/index.html#arraypushfront", + "text": "Adds one element to the beginning of the array. arrayPushFront(array, single_value) Arguments array \u2013 Array. single_value \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section \" Data types \". Example SELECT arrayPushBack ([ b ], a ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ a , b ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayPushFront" + }, + { + "location": "/index.html#arrayslice", + "text": "Returns a slice of the array. arraySlice(array, offset[, length]) Arguments array \u2013 Array of data. offset \u2013 Indent from the edge of the array. A positive value indicates an offset on the left, and a negative value is an indent on the right. Numbering of the array items begins with 1. length - The length of the required slice. If you specify a negative value, the function returns an open slice [offset, array_length - length) . If you omit the value, the function returns the slice [offset, the_end_of_array] . Example SELECT arraySlice ([ 1 , 2 , 3 , 4 , 5 ], 2 , 3 ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [2,3,4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arraySlice" + }, + { + "location": "/index.html#arrayuniqarr", + "text": "If one argument is passed, it counts the number of different elements in the array.\nIf multiple arguments are passed, it counts the number of different tuples of elements at corresponding positions in multiple arrays. If you want to get a list of unique items in an array, you can use arrayReduce('groupUniqArray', arr).", + "title": "arrayUniq(arr, ...)" + }, + { + "location": "/index.html#arrayjoinarr", + "text": "A special function. See the section \"ArrayJoin function\" .", + "title": "arrayJoin(arr)" + }, + { + "location": "/index.html#functions-for-splitting-and-merging-strings-and-arrays", + "text": "", + "title": "Functions for splitting and merging strings and arrays" + }, + { + "location": "/index.html#splitbycharseparator-s", + "text": "Splits a string into substrings separated by 'separator'.'separator' must be a string constant consisting of exactly one character.\nReturns an array of selected substrings. Empty substrings may be selected if the separator occurs at the beginning or end of the string, or if there are multiple consecutive separators.", + "title": "splitByChar(separator, s)" + }, + { + "location": "/index.html#splitbystringseparator-s", + "text": "The same as above, but it uses a string of multiple characters as the separator. The string must be non-empty.", + "title": "splitByString(separator, s)" + }, + { + "location": "/index.html#arraystringconcatarr91-separator93", + "text": "Concatenates the strings listed in the array with the separator.'separator' is an optional parameter: a constant string, set to an empty string by default.\nReturns the string.", + "title": "arrayStringConcat(arr[, separator])" + }, + { + "location": "/index.html#alphatokenss", + "text": "Selects substrings of consecutive bytes from the ranges a-z and A-Z.Returns an array of substrings.", + "title": "alphaTokens(s)" + }, + { + "location": "/index.html#bit-functions", + "text": "Bit functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64. The result type is an integer with bits equal to the maximum bits of its arguments. If at least one of the arguments is signed, the result is a signed number. If an argument is a floating-point number, it is cast to Int64.", + "title": "Bit functions" + }, + { + "location": "/index.html#bitanda-b", + "text": "", + "title": "bitAnd(a, b)" + }, + { + "location": "/index.html#bitora-b", + "text": "", + "title": "bitOr(a, b)" + }, + { + "location": "/index.html#bitxora-b", + "text": "", + "title": "bitXor(a, b)" + }, + { + "location": "/index.html#bitnota", + "text": "", + "title": "bitNot(a)" + }, + { + "location": "/index.html#bitshiftlefta-b", + "text": "", + "title": "bitShiftLeft(a, b)" + }, + { + "location": "/index.html#bitshiftrighta-b", + "text": "", + "title": "bitShiftRight(a, b)" + }, + { + "location": "/index.html#hash-functions", + "text": "Hash functions can be used for deterministic pseudo-random shuffling of elements.", + "title": "Hash functions" + }, + { + "location": "/index.html#halfmd5", + "text": "Calculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian.\nAccepts a String-type argument. Returns UInt64.\nThis function works fairly slowly (5 million short strings per second per processor core).\nIf you don't need MD5 in particular, use the 'sipHash64' function instead.", + "title": "halfMD5" + }, + { + "location": "/index.html#md5", + "text": "Calculates the MD5 from a string and returns the resulting set of bytes as FixedString(16).\nIf you don't need MD5 in particular, but you need a decent cryptographic 128-bit hash, use the 'sipHash128' function instead.\nIf you want to get the same result as output by the md5sum utility, use lower(hex(MD5(s))).", + "title": "MD5" + }, + { + "location": "/index.html#siphash64", + "text": "Calculates SipHash from a string.\nAccepts a String-type argument. Returns UInt64.\nSipHash is a cryptographic hash function. It works at least three times faster than MD5.\nFor more information, see the link: https://131002.net/siphash/", + "title": "sipHash64" + }, + { + "location": "/index.html#siphash128", + "text": "Calculates SipHash from a string.\nAccepts a String-type argument. Returns FixedString(16).\nDiffers from sipHash64 in that the final xor-folding state is only done up to 128 bytes.", + "title": "sipHash128" + }, + { + "location": "/index.html#cityhash64", + "text": "Calculates CityHash64 from a string or a similar hash function for any number of any type of arguments.\nFor String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality.\nFor other types of arguments, a decent implementation-specific fast non-cryptographic hash function is used.\nIf multiple arguments are passed, the function is calculated using the same rules and chain combinations using the CityHash combinator.\nFor example, you can compute the checksum of an entire table with accuracy up to the row order: SELECT sum(cityHash64(*)) FROM table .", + "title": "cityHash64" + }, + { + "location": "/index.html#inthash32", + "text": "Calculates a 32-bit hash code from any type of integer.\nThis is a relatively fast non-cryptographic hash function of average quality for numbers.", + "title": "intHash32" + }, + { + "location": "/index.html#inthash64", + "text": "Calculates a 64-bit hash code from any type of integer.\nIt works faster than intHash32. Average quality.", + "title": "intHash64" + }, + { + "location": "/index.html#sha1", + "text": "", + "title": "SHA1" + }, + { + "location": "/index.html#sha224", + "text": "", + "title": "SHA224" + }, + { + "location": "/index.html#sha256", + "text": "Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32).\nThe function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million).\nWe recommend using this function only in cases when you need a specific hash function and you can't select it.\nEven in these cases, we recommend applying the function offline and pre-calculating values when inserting them into the table, instead of applying it in SELECTS.", + "title": "SHA256" + }, + { + "location": "/index.html#urlhashurl91-n93", + "text": "A fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization. URLHash(s) \u2013 Calculates a hash from a string without one of the trailing symbols / , ? or # at the end, if present. URLHash(s, N) \u2013 Calculates a hash from a string up to the N level in the URL hierarchy, without one of the trailing symbols / , ? or # at the end, if present.\nLevels are the same as in URLHierarchy. This function is specific to Yandex.Metrica.", + "title": "URLHash(url[, N])" + }, + { + "location": "/index.html#functions-for-generating-pseudo-random-numbers", + "text": "Non-cryptographic generators of pseudo-random numbers are used. All the functions accept zero arguments or one argument.\nIf an argument is passed, it can be any type, and its value is not used for anything.\nThe only purpose of this argument is to prevent common subexpression elimination, so that two different instances of the same function return different columns with different random numbers.", + "title": "Functions for generating pseudo-random numbers" + }, + { + "location": "/index.html#rand", + "text": "Returns a pseudo-random UInt32 number, evenly distributed among all UInt32-type numbers.\nUses a linear congruential generator.", + "title": "rand" + }, + { + "location": "/index.html#rand64", + "text": "Returns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers.\nUses a linear congruential generator.", + "title": "rand64" + }, + { + "location": "/index.html#encoding-functions", + "text": "", + "title": "Encoding functions" + }, + { + "location": "/index.html#hex", + "text": "Accepts arguments of types: String , unsigned integer , Date , or DateTime . Returns a string containing the argument's hexadecimal representation. Uses uppercase letters A-F . Does not use 0x prefixes or h suffixes. For strings, all bytes are simply encoded as two hexadecimal numbers. Numbers are converted to big endian (\"human readable\") format. For numbers, older zeros are trimmed, but only by entire bytes. For example, hex (1) = '01' . Date is encoded as the number of days since the beginning of the Unix epoch. DateTime is encoded as the number of seconds since the beginning of the Unix epoch.", + "title": "hex" + }, + { + "location": "/index.html#unhexstr", + "text": "Accepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits does not have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn't thrown).\nIf you want to convert the result to a number, you can use the 'reverse' and 'reinterpretAsType' functions.", + "title": "unhex(str)" + }, + { + "location": "/index.html#uuidstringtonumstr", + "text": "Accepts a string containing 36 characters in the format 123e4567-e89b-12d3-a456-426655440000 , and returns it as a set of bytes in a FixedString(16).", + "title": "UUIDStringToNum(str)" + }, + { + "location": "/index.html#uuidnumtostringstr", + "text": "Accepts a FixedString(16) value. Returns a string containing 36 characters in text format.", + "title": "UUIDNumToString(str)" + }, + { + "location": "/index.html#bitmasktolistnum", + "text": "Accepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order.", + "title": "bitmaskToList(num)" + }, + { + "location": "/index.html#bitmasktoarraynum", + "text": "Accepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order.", + "title": "bitmaskToArray(num)" + }, + { + "location": "/index.html#functions-for-working-with-urls", + "text": "All these functions don't follow the RFC. They are maximally simplified for improved performance.", + "title": "Functions for working with URLs" + }, + { + "location": "/index.html#functions-that-extract-part-of-a-url", + "text": "If there isn't anything similar in a URL, an empty string is returned.", + "title": "Functions that extract part of a URL" + }, + { + "location": "/index.html#protocol", + "text": "Returns the protocol. Examples: http, ftp, mailto, magnet...", + "title": "protocol" + }, + { + "location": "/index.html#domain", + "text": "Gets the domain.", + "title": "domain" + }, + { + "location": "/index.html#domainwithoutwww", + "text": "Returns the domain and removes no more than one 'www.' from the beginning of it, if present.", + "title": "domainWithoutWWW" + }, + { + "location": "/index.html#topleveldomain", + "text": "Returns the top-level domain. Example: .ru.", + "title": "topLevelDomain" + }, + { + "location": "/index.html#firstsignificantsubdomain", + "text": "Returns the \"first significant subdomain\". This is a non-standard concept specific to Yandex.Metrica. The first significant subdomain is a second-level domain if it is 'com', 'net', 'org', or 'co'. Otherwise, it is a third-level domain. For example, firstSignificantSubdomain (' https://news.yandex.ru/ ') = 'yandex ', firstSignificantSubdomain (' https://news.yandex.com.tr/ ') = 'yandex '. The list of \"insignificant\" second-level domains and other implementation details may change in the future.", + "title": "firstSignificantSubdomain" + }, + { + "location": "/index.html#cuttofirstsignificantsubdomain", + "text": "Returns the part of the domain that includes top-level subdomains up to the \"first significant subdomain\" (see the explanation above). For example, cutToFirstSignificantSubdomain('https://news.yandex.com.tr/') = 'yandex.com.tr' .", + "title": "cutToFirstSignificantSubdomain" + }, + { + "location": "/index.html#path", + "text": "Returns the path. Example: /top/news.html The path does not include the query string.", + "title": "path" + }, + { + "location": "/index.html#pathfull", + "text": "The same as above, but including query string and fragment. Example: /top/news.html?page=2#comments", + "title": "pathFull" + }, + { + "location": "/index.html#querystring", + "text": "Returns the query string. Example: page=1 lr=213. query-string does not include the initial question mark, as well as # and everything after #.", + "title": "queryString" + }, + { + "location": "/index.html#fragment", + "text": "Returns the fragment identifier. fragment does not include the initial hash symbol.", + "title": "fragment" + }, + { + "location": "/index.html#querystringandfragment", + "text": "Returns the query string and fragment identifier. Example: page=1#29390.", + "title": "queryStringAndFragment" + }, + { + "location": "/index.html#extracturlparameterurl-name", + "text": "Returns the value of the 'name' parameter in the URL, if present. Otherwise, an empty string. If there are many parameters with this name, it returns the first occurrence. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.", + "title": "extractURLParameter(URL, name)" + }, + { + "location": "/index.html#extracturlparametersurl", + "text": "Returns an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way.", + "title": "extractURLParameters(URL)" + }, + { + "location": "/index.html#extracturlparameternamesurl", + "text": "Returns an array of name strings corresponding to the names of URL parameters. The values are not decoded in any way.", + "title": "extractURLParameterNames(URL)" + }, + { + "location": "/index.html#urlhierarchyurl", + "text": "Returns an array containing the URL, truncated at the end by the symbols /,? in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:", + "title": "URLHierarchy(URL)" + }, + { + "location": "/index.html#urlpathhierarchyurl", + "text": "The same as above, but without the protocol and host in the result. The / element (root) is not included. Example: the function is used to implement tree reports the URL in Yandex. Metric. URLPathHierarchy( https://example.com/browse/CONV-6788 ) =\n[\n /browse/ ,\n /browse/CONV-6788 \n]", + "title": "URLPathHierarchy(URL)" + }, + { + "location": "/index.html#decodeurlcomponenturl", + "text": "Returns the decoded URL.\nExample: SELECT decodeURLComponent ( http://127.0.0.1:8123/?query=SELECT%201%3B ) AS DecodedURL ; \u250c\u2500DecodedURL\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 http://127.0.0.1:8123/?query=SELECT 1; \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "decodeURLComponent(URL)" + }, + { + "location": "/index.html#functions-that-remove-part-of-a-url", + "text": "If the URL doesn't have anything similar, the URL remains unchanged.", + "title": "Functions that remove part of a URL." + }, + { + "location": "/index.html#cutwww", + "text": "Removes no more than one 'www.' from the beginning of the URL's domain, if present.", + "title": "cutWWW" + }, + { + "location": "/index.html#cutquerystring", + "text": "Removes query string. The question mark is also removed.", + "title": "cutQueryString" + }, + { + "location": "/index.html#cutfragment", + "text": "Removes the fragment identifier. The number sign is also removed.", + "title": "cutFragment" + }, + { + "location": "/index.html#cutquerystringandfragment", + "text": "Removes the query string and fragment identifier. The question mark and number sign are also removed.", + "title": "cutQueryStringAndFragment" + }, + { + "location": "/index.html#cuturlparameterurl-name", + "text": "Removes the 'name' URL parameter, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.", + "title": "cutURLParameter(URL, name)" + }, + { + "location": "/index.html#functions-for-working-with-ip-addresses", + "text": "", + "title": "Functions for working with IP addresses" + }, + { + "location": "/index.html#ipv4numtostringnum", + "text": "Takes a UInt32 number. Interprets it as an IPv4 address in big endian. Returns a string containing the corresponding IPv4 address in the format A.B.C.d (dot-separated numbers in decimal form).", + "title": "IPv4NumToString(num)" + }, + { + "location": "/index.html#ipv4stringtonums", + "text": "The reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0.", + "title": "IPv4StringToNum(s)" + }, + { + "location": "/index.html#ipv4numtostringclasscnum", + "text": "Similar to IPv4NumToString, but using xxx instead of the last octet. Example: SELECT \n IPv4NumToStringClassC ( ClientIP ) AS k , \n count () AS c FROM test . hits GROUP BY k ORDER BY c DESC LIMIT 10 \u250c\u2500k\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 83.149.9.xxx \u2502 26238 \u2502\n\u2502 217.118.81.xxx \u2502 26074 \u2502\n\u2502 213.87.129.xxx \u2502 25481 \u2502\n\u2502 83.149.8.xxx \u2502 24984 \u2502\n\u2502 217.118.83.xxx \u2502 22797 \u2502\n\u2502 78.25.120.xxx \u2502 22354 \u2502\n\u2502 213.87.131.xxx \u2502 21285 \u2502\n\u2502 78.25.121.xxx \u2502 20887 \u2502\n\u2502 188.162.65.xxx \u2502 19694 \u2502\n\u2502 83.149.48.xxx \u2502 17406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Since using 'xxx' is highly unusual, this may be changed in the future. We recommend that you don't rely on the exact format of this fragment.", + "title": "IPv4NumToStringClassC(num)" + }, + { + "location": "/index.html#ipv6numtostringx", + "text": "Accepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format.\nIPv6-mapped IPv4 addresses are output in the format ::ffff:111.222.33.44. Examples: SELECT IPv6NumToString ( toFixedString ( unhex ( 2A0206B8000000000000000000000011 ), 16 )) AS addr \u250c\u2500addr\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2a02:6b8::11 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT \n IPv6NumToString ( ClientIP6 AS k ), \n count () AS c FROM hits_all WHERE EventDate = today () AND substring ( ClientIP6 , 1 , 12 ) != unhex ( 00000000000000000000FFFF ) GROUP BY k ORDER BY c DESC LIMIT 10 \u250c\u2500IPv6NumToString(ClientIP6)\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2a02:2168:aaa:bbbb::2 \u2502 24695 \u2502\n\u2502 2a02:2698:abcd:abcd:abcd:abcd:8888:5555 \u2502 22408 \u2502\n\u2502 2a02:6b8:0:fff::ff \u2502 16389 \u2502\n\u2502 2a01:4f8:111:6666::2 \u2502 16016 \u2502\n\u2502 2a02:2168:888:222::1 \u2502 15896 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:222 \u2502 14774 \u2502\n\u2502 2a02:8109:eee:ee:eeee:eeee:eeee:eeee \u2502 14443 \u2502\n\u2502 2a02:810b:8888:888:8888:8888:8888:8888 \u2502 14345 \u2502\n\u2502 2a02:6b8:0:444:4444:4444:4444:4444 \u2502 14279 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:ffff \u2502 13880 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT \n IPv6NumToString ( ClientIP6 AS k ), \n count () AS c FROM hits_all WHERE EventDate = today () GROUP BY k ORDER BY c DESC LIMIT 10 \u250c\u2500IPv6NumToString(ClientIP6)\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 ::ffff:94.26.111.111 \u2502 747440 \u2502\n\u2502 ::ffff:37.143.222.4 \u2502 529483 \u2502\n\u2502 ::ffff:5.166.111.99 \u2502 317707 \u2502\n\u2502 ::ffff:46.38.11.77 \u2502 263086 \u2502\n\u2502 ::ffff:79.105.111.111 \u2502 186611 \u2502\n\u2502 ::ffff:93.92.111.88 \u2502 176773 \u2502\n\u2502 ::ffff:84.53.111.33 \u2502 158709 \u2502\n\u2502 ::ffff:217.118.11.22 \u2502 154004 \u2502\n\u2502 ::ffff:217.118.11.33 \u2502 148449 \u2502\n\u2502 ::ffff:217.118.11.44 \u2502 148243 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "IPv6NumToString(x)" + }, + { + "location": "/index.html#ipv6stringtonums", + "text": "The reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes.\nHEX can be uppercase or lowercase.", + "title": "IPv6StringToNum(s)" + }, + { + "location": "/index.html#functions-for-working-with-json", + "text": "In Yandex.Metrica, JSON is transmitted by users as session parameters. There are some special functions for working with this JSON. (Although in most of the cases, the JSONs are additionally pre-processed, and the resulting values are put in separate columns in their processed format.) All these functions are based on strong assumptions about what the JSON can be, but they try to do as little as possible to get the job done. The following assumptions are made: The field name (function argument) must be a constant. The field name is somehow canonically encoded in JSON. For example: visitParamHas('{\"abc\":\"def\"}', 'abc') = 1 , but visitParamHas('{\"\\\\u0061\\\\u0062\\\\u0063\":\"def\"}', 'abc') = 0 Fields are searched for on any nesting level, indiscriminately. If there are multiple matching fields, the first occurrence is used. The JSON doesn't have space characters outside of string literals.", + "title": "Functions for working with JSON" + }, + { + "location": "/index.html#visitparamhasparams-name", + "text": "Checks whether there is a field with the 'name' name.", + "title": "visitParamHas(params, name)" + }, + { + "location": "/index.html#visitparamextractuintparams-name", + "text": "Parses UInt64 from the value of the field named 'name'. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn't exist, or it exists but doesn't contain a number, it returns 0.", + "title": "visitParamExtractUInt(params, name)" + }, + { + "location": "/index.html#visitparamextractintparams-name", + "text": "The same as for Int64.", + "title": "visitParamExtractInt(params, name)" + }, + { + "location": "/index.html#visitparamextractfloatparams-name", + "text": "The same as for Float64.", + "title": "visitParamExtractFloat(params, name)" + }, + { + "location": "/index.html#visitparamextractboolparams-name", + "text": "Parses a true/false value. The result is UInt8.", + "title": "visitParamExtractBool(params, name)" + }, + { + "location": "/index.html#visitparamextractrawparams-name", + "text": "Returns the value of a field, including separators. Examples: visitParamExtractRaw( { abc : \\\\n\\\\u0000 } , abc ) = \\\\n\\\\u0000 \nvisitParamExtractRaw( { abc :{ def :[1,2,3]}} , abc ) = { def :[1,2,3]}", + "title": "visitParamExtractRaw(params, name)" + }, + { + "location": "/index.html#visitparamextractstringparams-name", + "text": "Parses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string. Examples: visitParamExtractString( { abc : \\\\n\\\\u0000 } , abc ) = \\n\\0 \nvisitParamExtractString( { abc : \\\\u263a } , abc ) = \u263a \nvisitParamExtractString( { abc : \\\\u263 } , abc ) = \nvisitParamExtractString( { abc : hello} , abc ) = There is currently no support for code points in the format \\uXXXX\\uYYYY that are not from the basic multilingual plane (they are converted to CESU-8 instead of UTF-8).", + "title": "visitParamExtractString(params, name)" + }, + { + "location": "/index.html#higher-order-functions", + "text": "", + "title": "Higher-order functions" + }, + { + "location": "/index.html#-operator-lambdaparams-expr-function", + "text": "Allows describing a lambda function for passing to a higher-order function. The left side of the arrow has a formal parameter, which is any ID, or multiple formal parameters \u2013 any IDs in a tuple. The right side of the arrow has an expression that can use these formal parameters, as well as any table columns. Examples: x - 2 * x, str - str != Referer. Higher-order functions can only accept lambda functions as their functional argument. A lambda function that accepts multiple arguments can be passed to a higher-order function. In this case, the higher-order function is passed several arrays of identical length that these arguments will correspond to. For all functions other than 'arrayMap' and 'arrayFilter', the first argument (the lambda function) can be omitted. In this case, identical mapping is assumed.", + "title": "-> operator, lambda(params, expr) function" + }, + { + "location": "/index.html#arraymapfunc-arr1", + "text": "Returns an array obtained from the original application of the 'func' function to each element in the 'arr' array.", + "title": "arrayMap(func, arr1, ...)" + }, + { + "location": "/index.html#arrayfilterfunc-arr1", + "text": "Returns an array containing only the elements in 'arr1' for which 'func' returns something other than 0. Examples: SELECT arrayFilter ( x - x LIKE %World% , [ Hello , abc World ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ abc World ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT \n arrayFilter ( \n ( i , x ) - x LIKE %World% , \n arrayEnumerate ( arr ), \n [ Hello , abc World ] AS arr ) \n AS res \u250c\u2500res\u2500\u2510\n\u2502 [2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayFilter(func, arr1, ...)" + }, + { + "location": "/index.html#arraycount91func93-arr1", + "text": "Returns the number of elements in the arr array for which func returns something other than 0. If 'func' is not specified, it returns the number of non-zero elements in the array.", + "title": "arrayCount([func,] arr1, ...)" + }, + { + "location": "/index.html#arrayexists91func93-arr1", + "text": "Returns 1 if there is at least one element in 'arr' for which 'func' returns something other than 0. Otherwise, it returns 0.", + "title": "arrayExists([func,] arr1, ...)" + }, + { + "location": "/index.html#arrayall91func93-arr1", + "text": "Returns 1 if 'func' returns something other than 0 for all the elements in 'arr'. Otherwise, it returns 0.", + "title": "arrayAll([func,] arr1, ...)" + }, + { + "location": "/index.html#arraysum91func93-arr1", + "text": "Returns the sum of the 'func' values. If the function is omitted, it just returns the sum of the array elements.", + "title": "arraySum([func,] arr1, ...)" + }, + { + "location": "/index.html#arrayfirstfunc-arr1", + "text": "Returns the first element in the 'arr1' array for which 'func' returns something other than 0.", + "title": "arrayFirst(func, arr1, ...)" + }, + { + "location": "/index.html#arrayfirstindexfunc-arr1", + "text": "Returns the index of the first element in the 'arr1' array for which 'func' returns something other than 0.", + "title": "arrayFirstIndex(func, arr1, ...)" + }, + { + "location": "/index.html#arraycumsum91func93-arr1", + "text": "Returns an array of partial sums of elements in the source array (a running sum). If the func function is specified, then the values of the array elements are converted by this function before summing. Example: SELECT arrayCumSum ([ 1 , 1 , 1 , 1 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1, 2, 3, 4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayCumSum([func,] arr1, ...)" + }, + { + "location": "/index.html#arraysort91func93-arr1", + "text": "Returns an array as result of sorting the elements of arr1 in ascending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays) The Schwartzian transform is used to impove sorting efficiency. Example: SELECT arraySort (( x , y ) - y , [ hello , world ], [ 2 , 1 ]); \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ world , hello ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arraySort([func,] arr1, ...)" + }, + { + "location": "/index.html#arrayreversesort91func93-arr1", + "text": "Returns an array as result of sorting the elements of arr1 in descending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays)", + "title": "arrayReverseSort([func,] arr1, ...)" + }, + { + "location": "/index.html#other-functions", + "text": "", + "title": "Other functions" + }, + { + "location": "/index.html#hostname", + "text": "Returns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server.", + "title": "hostName()" + }, + { + "location": "/index.html#visiblewidthx", + "text": "Calculates the approximate width when outputting values to the console in text format (tab-separated).\nThis function is used by the system for implementing Pretty formats.", + "title": "visibleWidth(x)" + }, + { + "location": "/index.html#totypenamex", + "text": "Returns a string containing the type name of the passed argument.", + "title": "toTypeName(x)" + }, + { + "location": "/index.html#blocksize", + "text": "Gets the size of the block.\nIn ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for.", + "title": "blockSize()" + }, + { + "location": "/index.html#materializex", + "text": "Turns a constant into a full column containing just one value.\nIn ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior.", + "title": "materialize(x)" + }, + { + "location": "/index.html#ignore", + "text": "Accepts any arguments and always returns 0.\nHowever, the argument is still evaluated. This can be used for benchmarks.", + "title": "ignore(...)" + }, + { + "location": "/index.html#sleepseconds", + "text": "Sleeps 'seconds' seconds on each data block. You can specify an integer or a floating-point number.", + "title": "sleep(seconds)" + }, + { + "location": "/index.html#currentdatabase", + "text": "Returns the name of the current database.\nYou can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database.", + "title": "currentDatabase()" + }, + { + "location": "/index.html#isfinitex", + "text": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0.", + "title": "isFinite(x)" + }, + { + "location": "/index.html#isinfinitex", + "text": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. Note that 0 is returned for a NaN.", + "title": "isInfinite(x)" + }, + { + "location": "/index.html#isnanx", + "text": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0.", + "title": "isNaN(x)" + }, + { + "location": "/index.html#hascolumnintable91hostname91-username91-password939393-database-table-column", + "text": "Accepts constant strings: database name, table name, and column name. Returns a UInt8 constant expression equal to 1 if there is a column, otherwise 0. If the hostname parameter is set, the test will run on a remote server.\nThe function throws an exception if the table does not exist.\nFor elements in a nested data structure, the function checks for the existence of a column. For the nested data structure itself, the function returns 0.", + "title": "hasColumnInTable(['hostname'[, 'username'[, 'password']],] 'database', 'table', 'column')" + }, + { + "location": "/index.html#bar", + "text": "Allows building a unicode-art diagram. bar (x, min, max, width) draws a band with a width proportional to (x - min) and equal to width characters when x = max . Parameters: x \u2013 Value to display. min, max \u2013 Integer constants. The value must fit in Int64. width \u2013 Constant, positive number, may be a fraction. The band is drawn with accuracy to one eighth of a symbol. Example: SELECT \n toHour ( EventTime ) AS h , \n count () AS c , \n bar ( c , 0 , 600000 , 20 ) AS bar FROM test . hits GROUP BY h ORDER BY h ASC \u250c\u2500\u2500h\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u252c\u2500bar\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 0 \u2502 292907 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 1 \u2502 180563 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 2 \u2502 114861 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 3 \u2502 85069 \u2502 \u2588\u2588\u258b \u2502\n\u2502 4 \u2502 68543 \u2502 \u2588\u2588\u258e \u2502\n\u2502 5 \u2502 78116 \u2502 \u2588\u2588\u258c \u2502\n\u2502 6 \u2502 113474 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 7 \u2502 170678 \u2502 \u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 8 \u2502 278380 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 9 \u2502 391053 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 10 \u2502 457681 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 11 \u2502 493667 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 12 \u2502 509641 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 13 \u2502 522947 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 14 \u2502 539954 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 15 \u2502 528460 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c \u2502\n\u2502 16 \u2502 539201 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 17 \u2502 523539 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 18 \u2502 506467 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 19 \u2502 520915 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 20 \u2502 521665 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 21 \u2502 542078 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 22 \u2502 493642 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 23 \u2502 400397 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2514\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "bar" + }, + { + "location": "/index.html#transform", + "text": "Transforms a value according to the explicitly defined mapping of some elements to other ones.\nThere are two variations of this function: transform(x, array_from, array_to, default) x \u2013 What to transform. array_from \u2013 Constant array of values for converting. array_to \u2013 Constant array of values to convert the values in 'from' to. default \u2013 Which value to use if 'x' is not equal to any of the values in 'from'. array_from and array_to \u2013 Arrays of the same size. Types: transform(T, Array(T), Array(U), U) - U T and U can be numeric, string, or Date or DateTime types.\nWhere the same letter is indicated (T or U), for numeric types these might not be matching types, but types that have a common type.\nFor example, the first argument can have the Int64 type, while the second has the Array(Uint16) type. If the 'x' value is equal to one of the elements in the 'array_from' array, it returns the existing element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'default'. If there are multiple matching elements in 'array_from', it returns one of the matches. Example: SELECT \n transform ( SearchEngineID , [ 2 , 3 ], [ Yandex , Google ], Other ) AS title , \n count () AS c FROM test . hits WHERE SearchEngineID != 0 GROUP BY title ORDER BY c DESC \u250c\u2500title\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 Yandex \u2502 498635 \u2502\n\u2502 Google \u2502 229872 \u2502\n\u2502 Other \u2502 104472 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 transform(x, array_from, array_to) Differs from the first variation in that the 'default' argument is omitted.\nIf the 'x' value is equal to one of the elements in the 'array_from' array, it returns the matching element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'x'. Types: transform(T, Array(T), Array(T)) - T Example: SELECT \n transform ( domain ( Referer ), [ yandex.ru , google.ru , vk.com ], [ www.yandex , example.com ]) AS s , \n count () AS c FROM test . hits GROUP BY domain ( Referer ) ORDER BY count () DESC LIMIT 10 \u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 \u2502 2906259 \u2502\n\u2502 www.yandex \u2502 867767 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 313599 \u2502\n\u2502 mail.yandex.ru \u2502 107147 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 100355 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 65040 \u2502\n\u2502 news.yandex.ru \u2502 64515 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.net \u2502 59141 \u2502\n\u2502 example.com \u2502 57316 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "transform" + }, + { + "location": "/index.html#formatreadablesizex", + "text": "Accepts the size (number of bytes). Returns a rounded size with a suffix (KiB, MiB, etc.) as a string. Example: SELECT \n arrayJoin ([ 1 , 1024 , 1024 * 1024 , 192851925 ]) AS filesize_bytes , \n formatReadableSize ( filesize_bytes ) AS filesize \u250c\u2500filesize_bytes\u2500\u252c\u2500filesize\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 1.00 B \u2502\n\u2502 1024 \u2502 1.00 KiB \u2502\n\u2502 1048576 \u2502 1.00 MiB \u2502\n\u2502 192851925 \u2502 183.92 MiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "formatReadableSize(x)" + }, + { + "location": "/index.html#leasta-b", + "text": "Returns the smallest value from a and b.", + "title": "least(a, b)" + }, + { + "location": "/index.html#greatesta-b", + "text": "Returns the largest value of a and b.", + "title": "greatest(a, b)" + }, + { + "location": "/index.html#uptime", + "text": "Returns the server's uptime in seconds.", + "title": "uptime()" + }, + { + "location": "/index.html#version", + "text": "Returns the version of the server as a string.", + "title": "version()" + }, + { + "location": "/index.html#rownumberinallblocks", + "text": "Returns the ordinal number of the row in the data block. This function only considers the affected data blocks.", + "title": "rowNumberInAllBlocks()" + }, + { + "location": "/index.html#runningdifferencex", + "text": "Calculates the difference between successive row values \u200b\u200bin the data block.\nReturns 0 for the first row and the difference from the previous row for each subsequent row. The result of the function depends on the affected data blocks and the order of data in the block.\nIf you make a subquery with ORDER BY and call the function from outside the subquery, you can get the expected result. Example: SELECT \n EventID , \n EventTime , \n runningDifference ( EventTime ) AS delta FROM ( \n SELECT \n EventID , \n EventTime \n FROM events \n WHERE EventDate = 2016-11-24 \n ORDER BY EventTime ASC \n LIMIT 5 ) \u250c\u2500EventID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500EventTime\u2500\u252c\u2500delta\u2500\u2510\n\u2502 1106 \u2502 2016-11-24 00:00:04 \u2502 0 \u2502\n\u2502 1107 \u2502 2016-11-24 00:00:05 \u2502 1 \u2502\n\u2502 1108 \u2502 2016-11-24 00:00:05 \u2502 0 \u2502\n\u2502 1109 \u2502 2016-11-24 00:00:09 \u2502 4 \u2502\n\u2502 1110 \u2502 2016-11-24 00:00:10 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "runningDifference(x)" + }, + { + "location": "/index.html#macnumtostringnum", + "text": "Accepts a UInt64 number. Interprets it as a MAC address in big endian. Returns a string containing the corresponding MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form).", + "title": "MACNumToString(num)" + }, + { + "location": "/index.html#macstringtonums", + "text": "The inverse function of MACNumToString. If the MAC address has an invalid format, it returns 0.", + "title": "MACStringToNum(s)" + }, + { + "location": "/index.html#macstringtoouis", + "text": "Accepts a MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form). Returns the first three octets as a UInt64 number. If the MAC address has an invalid format, it returns 0.", + "title": "MACStringToOUI(s)" + }, + { + "location": "/index.html#functions-for-working-with-external-dictionaries", + "text": "For information on connecting and configuring external dictionaries, see \" External dictionaries \".", + "title": "Functions for working with external dictionaries" + }, + { + "location": "/index.html#dictgetuint8-dictgetuint16-dictgetuint32-dictgetuint64", + "text": "", + "title": "dictGetUInt8, dictGetUInt16, dictGetUInt32, dictGetUInt64" + }, + { + "location": "/index.html#dictgetint8-dictgetint16-dictgetint32-dictgetint64", + "text": "", + "title": "dictGetInt8, dictGetInt16, dictGetInt32, dictGetInt64" + }, + { + "location": "/index.html#dictgetfloat32-dictgetfloat64", + "text": "", + "title": "dictGetFloat32, dictGetFloat64" + }, + { + "location": "/index.html#dictgetdate-dictgetdatetime", + "text": "", + "title": "dictGetDate, dictGetDateTime" + }, + { + "location": "/index.html#dictgetuuid", + "text": "", + "title": "dictGetUUID" + }, + { + "location": "/index.html#dictgetstring", + "text": "dictGetT('dict_name', 'attr_name', id) Get the value of the attr_name attribute from the dict_name dictionary using the 'id' key. dict_name and attr_name are constant strings. id must be UInt64.\nIf there is no id key in the dictionary, it returns the default value specified in the dictionary description.", + "title": "dictGetString" + }, + { + "location": "/index.html#dictgettordefault", + "text": "dictGetT('dict_name', 'attr_name', id, default) The same as the dictGetT functions, but the default value is taken from the function's last argument.", + "title": "dictGetTOrDefault" + }, + { + "location": "/index.html#dictisin", + "text": "dictIsIn('dict_name', child_id, ancestor_id) For the 'dict_name' hierarchical dictionary, finds out whether the 'child_id' key is located inside 'ancestor_id' (or matches 'ancestor_id'). Returns UInt8.", + "title": "dictIsIn" + }, + { + "location": "/index.html#dictgethierarchy", + "text": "dictGetHierarchy('dict_name', id) For the 'dict_name' hierarchical dictionary, returns an array of dictionary keys starting from 'id' and continuing along the chain of parent elements. Returns Array(UInt64).", + "title": "dictGetHierarchy" + }, + { + "location": "/index.html#dicthas", + "text": "dictHas('dict_name', id) Check whether the dictionary has the key. Returns a UInt8 value equal to 0 if there is no key and 1 if there is a key.", + "title": "dictHas" + }, + { + "location": "/index.html#functions-for-working-with-yandexmetrica-dictionaries", + "text": "In order for the functions below to work, the server config must specify the paths and addresses for getting all the Yandex.Metrica dictionaries. The dictionaries are loaded at the first call of any of these functions. If the reference lists can't be loaded, an exception is thrown. For information about creating reference lists, see the section \"Dictionaries\".", + "title": "Functions for working with Yandex.Metrica dictionaries" + }, + { + "location": "/index.html#multiple-geobases", + "text": "ClickHouse supports working with multiple alternative geobases (regional hierarchies) simultaneously, in order to support various perspectives on which countries certain regions belong to. The 'clickhouse-server' config specifies the file with the regional hierarchy:: path_to_regions_hierarchy_file /opt/geo/regions_hierarchy.txt /path_to_regions_hierarchy_file Besides this file, it also searches for files nearby that have the _ symbol and any suffix appended to the name (before the file extension).\nFor example, it will also find the file /opt/geo/regions_hierarchy_ua.txt , if present. ua is called the dictionary key. For a dictionary without a suffix, the key is an empty string. All the dictionaries are re-loaded in runtime (once every certain number of seconds, as defined in the builtin_dictionaries_reload_interval config parameter, or once an hour by default). However, the list of available dictionaries is defined one time, when the server starts. All functions for working with regions have an optional argument at the end \u2013 the dictionary key. It is referred to as the geobase.\nExample: regionToCountry(RegionID) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, ) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, ua ) \u2013 Uses the dictionary for the ua key: /opt/geo/regions_hierarchy_ua.txt", + "title": "Multiple geobases" + }, + { + "location": "/index.html#regiontocityid-geobase", + "text": "Accepts a UInt32 number \u2013 the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0.", + "title": "regionToCity(id[, geobase])" + }, + { + "location": "/index.html#regiontoareaid91-geobase93", + "text": "Converts a region to an area (type 5 in the geobase). In every other way, this function is the same as 'regionToCity'. SELECT DISTINCT regionToName ( regionToArea ( toUInt32 ( number ), ua )) FROM system . numbers LIMIT 15 \u250c\u2500regionToName(regionToArea(toUInt32(number), \\ ua\\ ))\u2500\u2510\n\u2502 \u2502\n\u2502 Moscow and Moscow region \u2502\n\u2502 St. Petersburg and Leningrad region \u2502\n\u2502 Belgorod region \u2502\n\u2502 Ivanovsk region \u2502\n\u2502 Kaluga region \u2502\n\u2502 Kostroma region \u2502\n\u2502 Kursk region \u2502\n\u2502 Lipetsk region \u2502\n\u2502 Orlov region \u2502\n\u2502 Ryazan region \u2502\n\u2502 Smolensk region \u2502\n\u2502 Tambov region \u2502\n\u2502 Tver region \u2502\n\u2502 Tula region \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "regionToArea(id[, geobase])" + }, + { + "location": "/index.html#regiontodistrictid-geobase", + "text": "Converts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as 'regionToCity'. SELECT DISTINCT regionToName ( regionToDistrict ( toUInt32 ( number ), ua )) FROM system . numbers LIMIT 15 \u250c\u2500regionToName(regionToDistrict(toUInt32(number), \\ ua\\ ))\u2500\u2510\n\u2502 \u2502\n\u2502 Central federal district \u2502\n\u2502 Northwest federal district \u2502\n\u2502 South federal district \u2502\n\u2502 North Caucases federal district \u2502\n\u2502 Privolga federal district \u2502\n\u2502 Ural federal district \u2502\n\u2502 Siberian federal district \u2502\n\u2502 Far East federal district \u2502\n\u2502 Scotland \u2502\n\u2502 Faroe Islands \u2502\n\u2502 Flemish region \u2502\n\u2502 Brussels capital region \u2502\n\u2502 Wallonia \u2502\n\u2502 Federation of Bosnia and Herzegovina \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "regionToDistrict(id[, geobase])" + }, + { + "location": "/index.html#regiontocountryid-geobase", + "text": "Converts a region to a country. In every other way, this function is the same as 'regionToCity'.\nExample: regionToCountry(toUInt32(213)) = 225 converts Moscow (213) to Russia (225).", + "title": "regionToCountry(id[, geobase])" + }, + { + "location": "/index.html#regiontocontinentid-geobase", + "text": "Converts a region to a continent. In every other way, this function is the same as 'regionToCity'.\nExample: regionToContinent(toUInt32(213)) = 10001 converts Moscow (213) to Eurasia (10001).", + "title": "regionToContinent(id[, geobase])" + }, + { + "location": "/index.html#regiontopopulationid-geobase", + "text": "Gets the population for a region.\nThe population can be recorded in files with the geobase. See the section \"External dictionaries\".\nIf the population is not recorded for the region, it returns 0.\nIn the Yandex geobase, the population might be recorded for child regions, but not for parent regions.", + "title": "regionToPopulation(id[, geobase])" + }, + { + "location": "/index.html#regioninlhs-rhs-geobase", + "text": "Checks whether a 'lhs' region belongs to a 'rhs' region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn't belong.\nThe relationship is reflexive \u2013 any region also belongs to itself.", + "title": "regionIn(lhs, rhs[, geobase])" + }, + { + "location": "/index.html#regionhierarchyid91-geobase93", + "text": "Accepts a UInt32 number \u2013 the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain.\nExample: regionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000] .", + "title": "regionHierarchy(id[, geobase])" + }, + { + "location": "/index.html#regiontonameid91-lang93", + "text": "Accepts a UInt32 number \u2013 the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language 'ru' is used. If the language is not supported, an exception is thrown. Returns a string \u2013 the name of the region in the corresponding language. If the region with the specified ID doesn't exist, an empty string is returned. ua and uk both mean Ukrainian.", + "title": "regionToName(id[, lang])" + }, + { + "location": "/index.html#functions-for-implementing-the-in-operator", + "text": "", + "title": "Functions for implementing the IN operator" + }, + { + "location": "/index.html#in-notin-globalin-globalnotin", + "text": "See the section \"IN operators\".", + "title": "in, notIn, globalIn, globalNotIn" + }, + { + "location": "/index.html#tuplex-y-operator-x-y", + "text": "A function that allows grouping multiple columns.\nFor columns with the types T1, T2, ..., it returns a Tuple(T1, T2, ...) type tuple containing these columns. There is no cost to execute the function.\nTuples are normally used as intermediate values for an argument of IN operators, or for creating a list of formal parameters of lambda functions. Tuples can't be written to a table.", + "title": "tuple(x, y, ...), operator (x, y, ...)" + }, + { + "location": "/index.html#tupleelementtuple-n-operator-xn", + "text": "A function that allows getting a column from a tuple.\n'N' is the column index, starting from 1. N must be a constant. 'N' must be a constant. 'N' must be a strict postive integer no greater than the size of the tuple.\nThere is no cost to execute the function.", + "title": "tupleElement(tuple, n), operator x.N" + }, + { + "location": "/index.html#arrayjoin-function", + "text": "This is a very unusual function. Normal functions don't change a set of rows, but just change the values in each row (map).\nAggregate functions compress a set of rows (fold or reduce).\nThe 'arrayJoin' function takes each row and generates a set of rows (unfold). This function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array.\nAll the values in columns are simply copied, except the values in the column where this function is applied; it is replaced with the corresponding array value. A query can use multiple arrayJoin functions. In this case, the transformation is performed multiple times. Note the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities. Example: SELECT arrayJoin ([ 1 , 2 , 3 ] AS src ) AS dst , Hello , src \u250c\u2500dst\u2500\u252c\u2500\\ Hello\\ \u2500\u252c\u2500src\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 2 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 3 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "arrayJoin function" + }, + { + "location": "/index.html#aggregate-functions", + "text": "Aggregate functions work in the normal way as expected by database experts. ClickHouse also supports: Parametric aggregate functions , which accept other parameters in addition to columns. Combinators , which change the behavior of aggregate functions.", + "title": "Aggregate functions" + }, + { + "location": "/index.html#function-reference", + "text": "", + "title": "Function reference" + }, + { + "location": "/index.html#count", + "text": "Counts the number of rows. Accepts zero arguments and returns UInt64.\nThe syntax COUNT(DISTINCT x) is not supported. The separate uniq aggregate function exists for this purpose. A SELECT count() FROM table query is not optimized, because the number of entries in the table is not stored separately. It will select some small column from the table and count the number of values in it.", + "title": "count()" + }, + { + "location": "/index.html#anyx", + "text": "Selects the first encountered value.\nThe query can be executed in any order and even in a different order each time, so the result of this function is indeterminate.\nTo get a determinate result, you can use the 'min' or 'max' function instead of 'any'. In some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY. When a SELECT query has the GROUP BY clause or at least one aggregate function, ClickHouse (in contrast to MySQL) requires that all expressions in the SELECT , HAVING , and ORDER BY clauses be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. To get behavior like in MySQL, you can put the other columns in the any aggregate function.", + "title": "any(x)" + }, + { + "location": "/index.html#anyheavyx", + "text": "Selects a frequently occurring value using the heavy hitters algorithm. If there is a value that occurs more than in half the cases in each of the query's execution threads, this value is returned. Normally, the result is nondeterministic. anyHeavy(column) Arguments \n- column \u2013 The column name. Example Take the OnTime data set and select any frequently occurring value in the AirlineID column. SELECT anyHeavy ( AirlineID ) AS res FROM ontime \u250c\u2500\u2500\u2500res\u2500\u2510\n\u2502 19690 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "anyHeavy(x)" + }, + { + "location": "/index.html#anylastx", + "text": "Selects the last value encountered.\nThe result is just as indeterminate as for the any function.", + "title": "anyLast(x)" + }, + { + "location": "/index.html#minx", + "text": "Calculates the minimum.", + "title": "min(x)" + }, + { + "location": "/index.html#maxx", + "text": "Calculates the maximum.", + "title": "max(x)" + }, + { + "location": "/index.html#argminarg-val", + "text": "Calculates the 'arg' value for a minimal 'val' value. If there are several different values of 'arg' for minimal values of 'val', the first of these values encountered is output.", + "title": "argMin(arg, val)" + }, + { + "location": "/index.html#argmaxarg-val", + "text": "Calculates the 'arg' value for a maximum 'val' value. If there are several different values of 'arg' for maximum values of 'val', the first of these values encountered is output.", + "title": "argMax(arg, val)" + }, + { + "location": "/index.html#sumx", + "text": "Calculates the sum.\nOnly works for numbers.", + "title": "sum(x)" + }, + { + "location": "/index.html#sumwithoverflowx", + "text": "Computes the sum of the numbers, using the same data type for the result as for the input parameters. If the sum exceeds the maximum value for this data type, the function returns an error. Only works for numbers.", + "title": "sumWithOverflow(x)" + }, + { + "location": "/index.html#summapkey-value", + "text": "Totals the 'value' array according to the keys specified in the 'key' array.\nThe number of elements in 'key' and 'value' must be the same for each row that is totaled.\nReturns a tuple of two arrays: keys in sorted order, and values \u200b\u200bsummed for the corresponding keys. Example: CREATE TABLE sum_map ( \n date Date , \n timeslot DateTime , \n statusMap Nested ( \n status UInt16 , \n requests UInt64 \n ) ) ENGINE = Log ; INSERT INTO sum_map VALUES \n ( 2000-01-01 , 2000-01-01 00:00:00 , [ 1 , 2 , 3 ], [ 10 , 10 , 10 ]), \n ( 2000-01-01 , 2000-01-01 00:00:00 , [ 3 , 4 , 5 ], [ 10 , 10 , 10 ]), \n ( 2000-01-01 , 2000-01-01 00:01:00 , [ 4 , 5 , 6 ], [ 10 , 10 , 10 ]), \n ( 2000-01-01 , 2000-01-01 00:01:00 , [ 6 , 7 , 8 ], [ 10 , 10 , 10 ]); SELECT \n timeslot , \n sumMap ( statusMap . status , statusMap . requests ) FROM sum_map GROUP BY timeslot \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500timeslot\u2500\u252c\u2500sumMap(statusMap.status, statusMap.requests)\u2500\u2510\n\u2502 2000-01-01 00:00:00 \u2502 ([1,2,3,4,5],[10,10,20,10,10]) \u2502\n\u2502 2000-01-01 00:01:00 \u2502 ([4,5,6,7,8],[10,10,20,10,10]) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "sumMap(key, value)" + }, + { + "location": "/index.html#avgx", + "text": "Calculates the average.\nOnly works for numbers.\nThe result is always Float64.", + "title": "avg(x)" + }, + { + "location": "/index.html#uniqx", + "text": "Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments. Uses an adaptive sampling algorithm: for the calculation state, it uses a sample of element hash values with a size up to 65536.\nThis algorithm is also very accurate for data sets with low cardinality (up to 65536) and very efficient on CPU (when computing not too many of these functions, using uniq is almost as fast as using other aggregate functions). The result is determinate (it doesn't depend on the order of query processing). This function provides excellent accuracy even for data sets with extremely high cardinality (over 10 billion elements). It is recommended for default use.", + "title": "uniq(x)" + }, + { + "location": "/index.html#uniqcombinedx", + "text": "Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments. A combination of three algorithms is used: array, hash table and HyperLogLog with an error correction table. The memory consumption is several times smaller than for the uniq function, and the accuracy is several times higher. Performance is slightly lower than for the uniq function, but sometimes it can be even higher than it, such as with distributed queries that transmit a large number of aggregation states over the network. The maximum state size is 96 KiB (HyperLogLog of 217 6-bit cells). The result is determinate (it doesn't depend on the order of query processing). The uniqCombined function is a good default choice for calculating the number of different values, but keep in mind that the estimation error will increase for high-cardinality data sets (200M+ elements), and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).", + "title": "uniqCombined(x)" + }, + { + "location": "/index.html#uniqhll12x", + "text": "Uses the HyperLogLog algorithm to approximate the number of different values of the argument.\n212 5-bit cells are used. The size of the state is slightly more than 2.5 KB. The result is not very accurate (up to ~10% error) for small data sets ( 10K elements). However, the result is fairly accurate for high-cardinality data sets (10K-100M), with a maximum error of ~1.6%. Starting from 100M, the estimation error increases, and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements). The result is determinate (it doesn't depend on the order of query processing). We don't recommend using this function. In most cases, use the uniq or uniqCombined function.", + "title": "uniqHLL12(x)" + }, + { + "location": "/index.html#uniqexactx", + "text": "Calculates the number of different values of the argument, exactly.\nThere is no reason to fear approximations. It's better to use the uniq function.\nUse the uniqExact function if you definitely need an exact result. The uniqExact function uses more memory than the uniq function, because the size of the state has unbounded growth as the number of different values increases.", + "title": "uniqExact(x)" + }, + { + "location": "/index.html#grouparrayx-grouparraymax_sizex", + "text": "Creates an array of argument values.\nValues can be added to the array in any (indeterminate) order. The second version (with the max_size parameter) limits the size of the resulting array to max_size elements.\nFor example, groupArray (1) (x) is equivalent to [any (x)] . In some cases, you can still rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY .", + "title": "groupArray(x), groupArray(max_size)(x)" + }, + { + "location": "/index.html#grouparrayinsertatx", + "text": "Inserts a value into the array in the specified position. Accepts the value and position as input. If several values \u200b\u200bare inserted into the same position, any of them might end up in the resulting array (the first one will be used in the case of single-threaded execution). If no value is inserted into a position, the position is assigned the default value. Optional parameters: The default value for substituting in empty positions. The length of the resulting array. This allows you to receive arrays of the same size for all the aggregate keys. When using this parameter, the default value must be specified.", + "title": "groupArrayInsertAt(x)" + }, + { + "location": "/index.html#groupuniqarrayx", + "text": "Creates an array from different argument values. Memory consumption is the same as for the uniqExact function.", + "title": "groupUniqArray(x)" + }, + { + "location": "/index.html#quantilelevelx", + "text": "Approximates the 'level' quantile. 'level' is a constant, a floating-point number from 0 to 1.\nWe recommend using a 'level' value in the range of 0.01..0.99\nDon't use a 'level' value equal to 0 or 1 \u2013 use the 'min' and 'max' functions for these cases. In this function, as well as in all functions for calculating quantiles, the 'level' parameter can be omitted. In this case, it is assumed to be equal to 0.5 (in other words, the function will calculate the median). Works for numbers, dates, and dates with times.\nReturns: for numbers \u2013 Float64; for dates \u2013 a date; for dates with times \u2013 a date with time. Uses reservoir sampling with a reservoir size up to 8192.\nIf necessary, the result is output with linear approximation from the two neighboring values.\nThis algorithm provides very low accuracy. See also: quantileTiming , quantileTDigest , quantileExact . The result depends on the order of running the query, and is nondeterministic. When using multiple quantile (and similar) functions with different levels in a query, the internal states are not combined (that is, the query works less efficiently than it could). In this case, use the quantiles (and similar) functions.", + "title": "quantile(level)(x)" + }, + { + "location": "/index.html#quantiledeterministiclevelx-determinator", + "text": "Works the same way as the quantile function, but the result is deterministic and does not depend on the order of query execution. To achieve this, the function takes a second argument \u2013 the \"determinator\". This is a number whose hash is used instead of a random number generator in the reservoir sampling algorithm. For the function to work correctly, the same determinator value should not occur too often. For the determinator, you can use an event ID, user ID, and so on. Don't use this function for calculating timings. There is a more suitable function for this purpose: quantileTiming .", + "title": "quantileDeterministic(level)(x, determinator)" + }, + { + "location": "/index.html#quantiletiminglevelx", + "text": "Computes the quantile of 'level' with a fixed precision.\nWorks for numbers. Intended for calculating quantiles of page loading time in milliseconds. If the value is greater than 30,000 (a page loading time of more than 30 seconds), the result is equated to 30,000. If the total value is not more than about 5670, then the calculation is accurate. Otherwise: if the time is less than 1024 ms, then the calculation is accurate. otherwise the calculation is rounded to a multiple of 16 ms. When passing negative values to the function, the behavior is undefined. The returned value has the Float32 type. If no values were passed to the function (when using quantileTimingIf ), 'nan' is returned. The purpose of this is to differentiate these instances from zeros. See the note on sorting NaNs in \"ORDER BY clause\". The result is determinate (it doesn't depend on the order of query processing). For its purpose (calculating quantiles of page loading times), using this function is more effective and the result is more accurate than for the quantile function.", + "title": "quantileTiming(level)(x)" + }, + { + "location": "/index.html#quantiletimingweightedlevelx-weight", + "text": "Differs from the quantileTiming function in that it has a second argument, \"weights\". Weight is a non-negative integer.\nThe result is calculated as if the x value were passed weight number of times to the quantileTiming function.", + "title": "quantileTimingWeighted(level)(x, weight)" + }, + { + "location": "/index.html#quantileexactlevelx", + "text": "Computes the quantile of 'level' exactly. To do this, all the passed values \u200b\u200bare combined into an array, which is then partially sorted. Therefore, the function consumes O(n) memory, where 'n' is the number of values that were passed. However, for a small number of values, the function is very effective.", + "title": "quantileExact(level)(x)" + }, + { + "location": "/index.html#quantileexactweightedlevelx-weight", + "text": "Computes the quantile of 'level' exactly. In addition, each value is counted with its weight, as if it is present 'weight' times. The arguments of the function can be considered as histograms, where the value 'x' corresponds to a histogram \"column\" of the height 'weight', and the function itself can be considered as a summation of histograms. A hash table is used as the algorithm. Because of this, if the passed values \u200b\u200bare frequently repeated, the function consumes less RAM than quantileExact . You can use this function instead of quantileExact and specify the weight as 1.", + "title": "quantileExactWeighted(level)(x, weight)" + }, + { + "location": "/index.html#quantiletdigestlevelx", + "text": "Approximates the quantile level using the t-digest algorithm. The maximum error is 1%. Memory consumption by State is proportional to the logarithm of the number of passed values. The performance of the function is lower than for quantile , quantileTiming . In terms of the ratio of State size to precision, this function is much better than quantile . The result depends on the order of running the query, and is nondeterministic.", + "title": "quantileTDigest(level)(x)" + }, + { + "location": "/index.html#medianx", + "text": "All the quantile functions have corresponding median functions: median , medianDeterministic , medianTiming , medianTimingWeighted , medianExact , medianExactWeighted , medianTDigest . They are synonyms and their behavior is identical.", + "title": "median(x)" + }, + { + "location": "/index.html#quantileslevel1-level2-x", + "text": "All the quantile functions also have corresponding quantiles functions: quantiles , quantilesDeterministic , quantilesTiming , quantilesTimingWeighted , quantilesExact , quantilesExactWeighted , quantilesTDigest . These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.", + "title": "quantiles(level1, level2, ...)(x)" + }, + { + "location": "/index.html#varsampx", + "text": "Calculates the amount \u03a3((x - x\u0305)^2) / (n - 1) , where n is the sample size and x\u0305 is the average value of x . It represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount. Returns Float64 . When n = 1 , returns +\u221e .", + "title": "varSamp(x)" + }, + { + "location": "/index.html#varpopx", + "text": "Calculates the amount \u03a3((x - x\u0305)^2) / (n - 1) , where n is the sample size and x\u0305 is the average value of x . In other words, dispersion for a set of values. Returns Float64 .", + "title": "varPop(x)" + }, + { + "location": "/index.html#stddevsampx", + "text": "The result is equal to the square root of varSamp(x) .", + "title": "stddevSamp(x)" + }, + { + "location": "/index.html#stddevpopx", + "text": "The result is equal to the square root of varPop(x) .", + "title": "stddevPop(x)" + }, + { + "location": "/index.html#topkncolumn", + "text": "Returns an array of the most frequent values in the specified column. The resulting array is sorted in descending order of frequency of values (not by the values themselves). Implements the Filtered Space-Saving algorithm for analyzing TopK, based on the reduce-and-combine algorithm from Parallel Space Saving . topK(N)(column) This function doesn't provide a guaranteed result. In certain situations, errors might occur and it might return frequent values that aren't the most frequent values. We recommend using the N 10 value; performance is reduced with large N values. Maximum value of N = 65536 . Arguments \n- 'N' is the number of values.\n- ' x ' \u2013 The column. Example Take the OnTime data set and select the three most frequently occurring values in the AirlineID column. SELECT topK ( 3 )( AirlineID ) AS res FROM ontime \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [19393,19790,19805] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", + "title": "topK(N)(column)" + }, + { + "location": "/index.html#covarsampx-y", + "text": "Calculates the value of \u03a3((x - x\u0305)(y - y\u0305)) / (n - 1) . Returns Float64. When n = 1 , returns +\u221e.", + "title": "covarSamp(x, y)" + }, + { + "location": "/index.html#covarpopx-y", + "text": "Calculates the value of \u03a3((x - x\u0305)(y - y\u0305)) / n .", + "title": "covarPop(x, y)" + }, + { + "location": "/index.html#corrx-y", + "text": "Calculates the Pearson correlation coefficient: \u03a3((x - x\u0305)(y - y\u0305)) / sqrt(\u03a3((x - x\u0305)^2) * \u03a3((y - y\u0305)^2)) .", + "title": "corr(x, y)" + }, + { + "location": "/index.html#aggregate-function-combinators", + "text": "The name of an aggregate function can have a suffix appended to it. This changes the way the aggregate function works.", + "title": "Aggregate function combinators" + }, + { + "location": "/index.html#-if", + "text": "The suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument \u2013 a condition (Uint8 type). The aggregate function processes only the rows that trigger the condition. If the condition was not triggered even once, it returns a default value (usually zeros or empty strings). Examples: sumIf(column, cond) , countIf(cond) , avgIf(x, cond) , quantilesTimingIf(level1, level2)(x, cond) , argMinIf(arg, val, cond) and so on. With conditional aggregate functions, you can calculate aggregates for several conditions at once, without using subqueries and JOIN s. For example, in Yandex.Metrica, conditional aggregate functions are used to implement the segment comparison functionality.", + "title": "-If" + }, + { + "location": "/index.html#-array", + "text": "The -Array suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the 'Array(T)' type (arrays) instead of 'T' type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. When processing arrays, the aggregate function works like the original aggregate function across all array elements. Example 1: sumArray(arr) - Totals all the elements of all 'arr' arrays. In this example, it could have been written more simply: sum(arraySum(arr)) . Example 2: uniqArray(arr) \u2013 Count the number of unique elements in all 'arr' arrays. This could be done an easier way: uniq(arrayJoin(arr)) , but it's not always possible to add 'arrayJoin' to a query. -If and -Array can be combined. However, 'Array' must come first, then 'If'. Examples: uniqArrayIf(arr, cond) , quantilesTimingArrayIf(level1, level2)(arr, cond) . Due to this order, the 'cond' argument can't be an array.", + "title": "-Array" + }, + { + "location": "/index.html#-state", + "text": "If you apply this combinator, the aggregate function doesn't return the resulting value (such as the number of unique values for the 'uniq' function), but an intermediate state of the aggregation (for uniq , this is the hash table for calculating the number of unique values). This is an AggregateFunction(...) that can be used for further processing or stored in a table to finish aggregating later. See the sections \"AggregatingMergeTree\" and \"Functions for working with intermediate aggregation states\".", + "title": "-State" + }, + { + "location": "/index.html#-merge", + "text": "If you apply this combinator, the aggregate function takes the intermediate aggregation state as an argument, combines the states to finish aggregation, and returns the resulting value.", + "title": "-Merge" + }, + { + "location": "/index.html#-mergestate", + "text": "Merges the intermediate aggregation states in the same way as the -Merge combinator. However, it doesn't return the resulting value, but an intermediate aggregation state, similar to the -State combinator.", + "title": "-MergeState." + }, + { + "location": "/index.html#-foreach", + "text": "Converts an aggregate function for tables into an aggregate function for arrays that aggregates the corresponding array items and returns an array of results. For example, sumForEach for the arrays [1, 2] , [3, 4, 5] and [6, 7] returns the result [10, 13, 5] after adding together the corresponding array items.", + "title": "-ForEach" + }, + { + "location": "/index.html#parametric-aggregate-functions", + "text": "Some aggregate functions can accept not only argument columns (used for compression), but a set of parameters \u2013 constants for initialization. The syntax is two pairs of brackets instead of one. The first is for parameters, and the second is for arguments.", + "title": "Parametric aggregate functions" + }, + { + "location": "/index.html#sequencematchpatterntime-cond1-cond2", + "text": "Pattern matching for event chains. pattern is a string containing a pattern to match. The pattern is similar to a regular expression. time is the time of the event with the DateTime type. cond1 , cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event. The function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern.\nIt returns UInt8: 0 if the pattern isn't matched, or 1 if it matches. Example: sequenceMatch ('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%') whether there was a chain of events in which a pageview with 'company' in the address occurred earlier than a pageview with 'cart' in the address. This is a singular example. You could write it using other aggregate functions: minIf(EventTime, URL LIKE %company% ) maxIf(EventTime, URL LIKE %cart% ). However, there is no such solution for more complex situations. Pattern syntax: (?1) refers to the condition (any number can be used in place of 1). .* is any number of any events. (?t =1800) is a time condition. Any quantity of any type of events is allowed over the specified time. Instead of = , the following operators can be used: , , = . Any number may be specified in place of 1800. Events that occur during the same second can be put in the chain in any order. This may affect the result of the function.", + "title": "sequenceMatch(pattern)(time, cond1, cond2, ...)" + }, + { + "location": "/index.html#sequencecountpatterntime-cond1-cond2", + "text": "Works the same way as the sequenceMatch function, but instead of returning whether there is an event chain, it returns UInt64 with the number of event chains found.\nChains are searched for without overlapping. In other words, the next chain can start only after the end of the previous one.", + "title": "sequenceCount(pattern)(time, cond1, cond2, ...)" + }, + { + "location": "/index.html#windowfunnelwindowtimestamp-cond1-cond2-cond3", + "text": "Window funnel matching for event chains, calculates the max event level in a sliding window. window is the timestamp window value, such as 3600. timestamp is the time of the event with the DateTime type or UInt32 type. cond1 , cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event Example: Consider you are doing a website analytics, intend to find out the user counts clicked login button( event = 1001 ), then the user counts followed by searched the phones( event = 1003 and product = 'phone' ) , then the user counts followed by made an order ( event = 1009 ). And all event chains must be in a 3600 seconds sliding window. This could be easily calculate by windowFunnel SELECT\n level,\n count() AS c\nFROM\n(\n SELECT\n user_id,\n windowFunnel(3600)(timestamp, event_id = 1001, event_id = 1003 AND product = phone , event_id = 1009) AS level\n FROM trend_event\n WHERE (event_date = 2017-01-01 ) AND (event_date = 2017-01-31 )\n GROUP BY user_id\n)\nGROUP BY level\nORDER BY level Simply, the level could only be 0,1,2,3, it means the maxium event action stage that one user could reach.", + "title": "windowFunnel(window)(timestamp, cond1, cond2, cond3, ....)" + }, + { + "location": "/index.html#uniquptonx", + "text": "Calculates the number of different argument values \u200b\u200bif it is less than or equal to N. If the number of different argument values is greater than N, it returns N + 1. Recommended for use with small Ns, up to 10. The maximum value of N is 100. For the state of an aggregate function, it uses the amount of memory equal to 1 + N * the size of one value of bytes.\nFor strings, it stores a non-cryptographic hash of 8 bytes. That is, the calculation is approximated for strings. The function also works for several arguments. It works as fast as possible, except for cases when a large N value is used and the number of unique values is slightly less than N. Usage example: Problem: Generate a report that shows only keywords that produced at least 5 unique users.\nSolution: Write in the GROUP BY query SearchPhrase HAVING uniqUpTo(4)(UserID) = 5", + "title": "uniqUpTo(N)(x)" + }, + { + "location": "/index.html#dictionaries", + "text": "A dictionary is a mapping (key - attributes) that can be used in a query as functions.\nYou can think of this as a more convenient and efficient type of JOIN with dimension tables. There are built-in (internal) and add-on (external) dictionaries.", + "title": "Dictionaries" + }, + { + "location": "/index.html#external-dictionaries", + "text": "You can add your own dictionaries from various data sources. The data source for a dictionary can be a local text or executable file, an HTTP(s) resource, or another DBMS. For more information, see \" Sources for external dictionaries \". ClickHouse: Fully or partially stores dictionaries in RAM. Periodically updates dictionaries and dynamically loads missing values. In other words, dictionaries can be loaded dynamically. The configuration of external dictionaries is located in one or more files. The path to the configuration is specified in the dictionaries_config parameter. Dictionaries can be loaded at server startup or at first use, depending on the dictionaries_lazy_load setting. The dictionary config file has the following format: yandex \n comment An optional element with any content. Ignored by the ClickHouse server. /comment \n\n !--Optional element. File name with substitutions-- \n include_from /etc/metrika.xml /include_from \n\n\n dictionary \n !-- Dictionary configuration -- \n /dictionary \n\n ...\n\n dictionary \n !-- Dictionary configuration -- \n /dictionary /yandex You can configure any number of dictionaries in the same file. The file format is preserved even if there is only one dictionary (i.e. yandex dictionary !--configuration - /dictionary /yandex ). See also \" Functions for working with external dictionaries \". \n\nYou can convert values \u200b\u200bfor a small dictionary by describing it in a `SELECT` query (see the [transform](#other_functions-transform) function). This functionality is not related to external dictionaries.", + "title": "External dictionaries" + }, + { + "location": "/index.html#configuring-an-external-dictionary", + "text": "The dictionary configuration has the following structure: dictionary \n name dict_name /name \n\n source \n !-- Source configuration -- \n /source \n\n layout \n !-- Memory layout configuration -- \n /layout \n\n structure \n !-- Complex key configuration -- \n /structure \n\n lifetime \n !-- Lifetime of dictionary in memory -- \n /lifetime /dictionary name \u2013 The identifier that can be used to access the dictionary. Use the characters [a-zA-Z0-9_\\-] . source \u2014 Source of the dictionary. layout \u2014 Dictionary layout in memory. structure \u2014 Structure of the dictionary . A key and attributes that can be retrieved by this key. lifetime \u2014 Frequency of dictionary updates.", + "title": "Configuring an external dictionary" + }, + { + "location": "/index.html#storing-dictionaries-in-memory", + "text": "There are a variety of ways to store dictionaries in memory. We recommend flat , hashed and complex_key_hashed . which provide optimal processing speed. Caching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section \" cache \". There are several ways to improve dictionary performance: Call the function for working with the dictionary after GROUP BY . Mark attributes to extract as injective. An attribute is called injective if different attribute values correspond to different keys. So when GROUP BY uses a function that fetches an attribute value by the key, this function is automatically taken out of GROUP BY . ClickHouse generates an exception for errors with dictionaries. Examples of errors: The dictionary being accessed could not be loaded. Error querying a cached dictionary. You can view the list of external dictionaries and their statuses in the system.dictionaries table. The configuration looks like this: yandex \n dictionary \n ...\n layout \n layout_type \n !-- layout settings -- \n /layout_type \n /layout \n ...\n /dictionary /yandex", + "title": "Storing dictionaries in memory" + }, + { + "location": "/index.html#ways-to-store-dictionaries-in-memory", + "text": "flat hashed cache range_hashed complex_key_hashed complex_key_cache ip_trie", + "title": "Ways to store dictionaries in memory" + }, + { + "location": "/index.html#flat", + "text": "The dictionary is completely stored in memory in the form of flat arrays. How much memory does the dictionary use? The amount is proportional to the size of the largest key (in space used). The dictionary key has the UInt64 type and the value is limited to 500,000. If a larger key is discovered when creating the dictionary, ClickHouse throws an exception and does not create the dictionary. All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety. This method provides the best performance among all available methods of storing the dictionary. Configuration example: layout \n flat / /layout", + "title": "flat" + }, + { + "location": "/index.html#hashed", + "text": "The dictionary is completely stored in memory in the form of a hash table. The dictionary can contain any number of elements with any identifiers In practice, the number of keys can reach tens of millions of items. All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety. Configuration example: layout \n hashed / /layout", + "title": "hashed" + }, + { + "location": "/index.html#complex_key_hashed", + "text": "This type of storage is for use with composite keys . Similar to hashed . Configuration example: layout \n complex_key_hashed / /layout", + "title": "complex_key_hashed" + }, + { + "location": "/index.html#range_hashed", + "text": "The dictionary is stored in memory in the form of a hash table with an ordered array of ranges and their corresponding values. This storage method works the same way as hashed and allows using date/time ranges in addition to the key, if they appear in the dictionary. Example: The table contains discounts for each advertiser in the format: +---------------+---------------------+-------------------+--------+\n| advertiser id | discount start date | discount end date | amount |\n+===============+=====================+===================+========+\n| 123 | 2015-01-01 | 2015-01-15 | 0.15 |\n+---------------+---------------------+-------------------+--------+\n| 123 | 2015-01-16 | 2015-01-31 | 0.25 |\n+---------------+---------------------+-------------------+--------+\n| 456 | 2015-01-01 | 2015-01-15 | 0.05 |\n+---------------+---------------------+-------------------+--------+ To use a sample for date ranges, define the range_min and range_max elements in the structure . Example: structure \n id \n name Id /name \n /id \n range_min \n name first /name \n /range_min \n range_max \n name last /name \n /range_max \n ... To work with these dictionaries, you need to pass an additional date argument to the dictGetT function: dictGetT( dict_name , attr_name , id, date) This function returns the value for the specified id s and the date range that includes the passed date. Details of the algorithm: If the id is not found or a range is not found for the id , it returns the default value for the dictionary. If there are overlapping ranges, you can use any. If the range delimiter is NULL or an invalid date (such as 1900-01-01 or 2039-01-01), the range is left open. The range can be open on both sides. Configuration example: yandex \n dictionary \n\n ...\n\n layout \n range_hashed / \n /layout \n\n structure \n id \n name Abcdef /name \n /id \n range_min \n name StartDate /name \n /range_min \n range_max \n name EndDate /name \n /range_max \n attribute \n name XXXType /name \n type String /type \n null_value / \n /attribute \n /structure \n\n /dictionary /yandex", + "title": "range_hashed" + }, + { + "location": "/index.html#cache", + "text": "The dictionary is stored in a cache that has a fixed number of cells. These cells contain frequently used elements. When searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using SELECT attrs... FROM db.table WHERE id IN (k1, k2, ...) . The received data is then written to the cache. For cache dictionaries, the expiration lifetime of data in the cache can be set. If more time than lifetime has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used. This is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the system.dictionaries table. To improve cache performance, use a subquery with LIMIT , and call the function with the dictionary externally. Supported sources : MySQL, ClickHouse, executable, HTTP. Example of settings: layout \n cache \n !-- The size of the cache, in number of cells. Rounded up to a power of two. -- \n size_in_cells 1000000000 /size_in_cells \n /cache /layout Set a large enough cache size. You need to experiment to select the number of cells: Set some value. Run queries until the cache is completely full. Assess memory consumption using the system.dictionaries table. Increase or decrease the number of cells until the required memory consumption is reached. \n\nDo not use ClickHouse as a source, because it is slow to process queries with random reads.", + "title": "cache" + }, + { + "location": "/index.html#complex_key_cache", + "text": "This type of storage is for use with composite keys . Similar to cache .", + "title": "complex_key_cache" + }, + { + "location": "/index.html#ip_trie", + "text": "This type of storage is for mapping network prefixes (IP addresses) to metadata such as ASN. Example: The table contains network prefixes and their corresponding AS number and country code: +-----------------+-------+--------+\n | prefix | asn | cca2 |\n +=================+=======+========+\n | 202.79.32.0/20 | 17501 | NP |\n +-----------------+-------+--------+\n | 2620:0:870::/48 | 3856 | US |\n +-----------------+-------+--------+\n | 2a02:6b8:1::/48 | 13238 | RU |\n +-----------------+-------+--------+\n | 2001:db8::/32 | 65536 | ZZ |\n +-----------------+-------+--------+ When using this type of layout, the structure must have a composite key. Example: structure \n key \n attribute \n name prefix /name \n type String /type \n /attribute \n /key \n attribute \n name asn /name \n type UInt32 /type \n null_value / \n /attribute \n attribute \n name cca2 /name \n type String /type \n null_value ?? /null_value \n /attribute \n ... The key must have only one String type attribute that contains an allowed IP prefix. Other types are not supported yet. For queries, you must use the same functions ( dictGetT with a tuple) as for dictionaries with composite keys: dictGetT( dict_name , attr_name , tuple(ip)) The function takes either UInt32 for IPv4, or FixedString(16) for IPv6: dictGetString( prefix , asn , tuple(IPv6StringToNum( 2001:db8::1 ))) Other types are not supported yet. The function returns the attribute for the prefix that corresponds to this IP address. If there are overlapping prefixes, the most specific one is returned. Data is stored in a trie . It must completely fit into RAM.", + "title": "ip_trie" + }, + { + "location": "/index.html#dictionary-updates", + "text": "ClickHouse periodically updates the dictionaries. The update interval for fully downloaded dictionaries and the invalidation interval for cached dictionaries are defined in the lifetime tag in seconds. Dictionary updates (other than loading for first use) do not block queries. During updates, the old version of a dictionary is used. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries. Example of settings: dictionary \n ...\n lifetime 300 /lifetime \n ... /dictionary Setting lifetime 0 /lifetime prevents updating dictionaries. You can set a time interval for upgrades, and ClickHouse will choose a uniformly random time within this range. This is necessary in order to distribute the load on the dictionary source when upgrading on a large number of servers. Example of settings: dictionary \n ...\n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n ... /dictionary When upgrading the dictionaries, the ClickHouse server applies different logic depending on the type of source : For a text file, it checks the time of modification. If the time differs from the previously recorded time, the dictionary is updated. For MyISAM tables, the time of modification is checked using a SHOW TABLE STATUS query. Dictionaries from other sources are updated every time by default. For MySQL (InnoDB) and ODBC sources, you can set up a query that will update the dictionaries only if they really changed, rather than each time. To do this, follow these steps: The dictionary table must have a field that always changes when the source data is updated. The settings of the source must specify a query that retrieves the changing field. The ClickHouse server interprets the query result as a row, and if this row has changed relative to its previous state, the dictionary is updated. Specify the query in the invalidate_query field in the settings for the source . Example of settings: dictionary \n ...\n odbc \n ...\n invalidate_query SELECT update_time FROM dictionary_source where id = 1 /invalidate_query \n /odbc \n ... /dictionary", + "title": "Dictionary updates" + }, + { + "location": "/index.html#sources-of-external-dictionaries", + "text": "An external dictionary can be connected from many different sources. The configuration looks like this: yandex \n dictionary \n ...\n source \n source_type \n !-- Source configuration -- \n /source_type \n /source \n ...\n /dictionary \n ... /yandex The source is configured in the source section. Types of sources ( source_type ): Local file Executable file HTTP(s) ODBC DBMS MySQL ClickHouse MongoDB", + "title": "Sources of external dictionaries" + }, + { + "location": "/index.html#local-file", + "text": "Example of settings: source \n file \n path /opt/dictionaries/os.tsv /path \n format TabSeparated /format \n /file /source Setting fields: path \u2013 The absolute path to the file. format \u2013 The file format. All the formats described in \" Formats \" are supported.", + "title": "Local file" + }, + { + "location": "/index.html#executable-file", + "text": "Working with executable files depends on how the dictionary is stored in memory . If the dictionary is stored using cache and complex_key_cache , ClickHouse requests the necessary keys by sending a request to the executable file's STDIN . Example of settings: source \n executable \n command cat /opt/dictionaries/os.tsv /command \n format TabSeparated /format \n /executable /source Setting fields: command \u2013 The absolute path to the executable file, or the file name (if the program directory is written to PATH ). format \u2013 The file format. All the formats described in \" Formats \" are supported.", + "title": "Executable file" + }, + { + "location": "/index.html#https", + "text": "Working with an HTTP(s) server depends on how the dictionary is stored in memory . If the dictionary is stored using cache and complex_key_cache , ClickHouse requests the necessary keys by sending a request via the POST method. Example of settings: source \n http \n url http://[::1]/os.tsv /url \n format TabSeparated /format \n /http /source In order for ClickHouse to access an HTTPS resource, you must configure openSSL in the server configuration. Setting fields: url \u2013 The source URL. format \u2013 The file format. All the formats described in \" Formats \" are supported.", + "title": "HTTP(s)" + }, + { + "location": "/index.html#odbc", + "text": "You can use this method to connect any database that has an ODBC driver. Example of settings: odbc \n db DatabaseName /db \n table TableName /table \n connection_string DSN=some_parameters /connection_string \n invalidate_query SQL_QUERY /invalidate_query /odbc Setting fields: db \u2013 Name of the database. Omit it if the database name is set in the connection_string parameters. table \u2013 Name of the table. connection_string \u2013 Connection string. invalidate_query \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries .", + "title": "ODBC" + }, + { + "location": "/index.html#example-of-connecting-postgresql", + "text": "Ubuntu OS. Installing unixODBC and the ODBC driver for PostgreSQL: sudo apt-get install -y unixodbc odbcinst odbc-postgresql Configuring /etc/odbc.ini (or ~/.odbc.ini ): [DEFAULT]\n Driver = myconnection\n\n [myconnection]\n Description = PostgreSQL connection to my_db\n Driver = PostgreSQL Unicode\n Database = my_db\n Servername = 127.0.0.1\n UserName = username\n Password = password\n Port = 5432\n Protocol = 9.3\n ReadOnly = No\n RowVersioning = No\n ShowSystemTables = No\n ConnSettings = The dictionary configuration in ClickHouse: dictionary \n name table_name /name \n source \n odbc \n !-- You can specifiy the following parameters in connection_string: -- \n !-- DSN=myconnection;UID=username;PWD=password;HOST=127.0.0.1;PORT=5432;DATABASE=my_db -- \n connection_string DSN=myconnection /connection_string \n table postgresql_table /table \n /odbc \n /source \n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n layout \n hashed/ \n /layout \n structure \n id \n name id /name \n /id \n attribute \n name some_column /name \n type UInt64 /type \n null_value 0 /null_value \n /attribute \n /structure /dictionary You may need to edit odbc.ini to specify the full path to the library with the driver DRIVER=/usr/local/lib/psqlodbcw.so .", + "title": "Example of connecting PostgreSQL" + }, + { + "location": "/index.html#example-of-connecting-ms-sql-server", + "text": "Ubuntu OS. Installing the driver: : sudo apt-get install tdsodbc freetds-bin sqsh Configuring the driver: : $ cat /etc/freetds/freetds.conf \n ...\n\n [MSSQL]\n host = 192.168.56.101\n port = 1433\n tds version = 7.0\n client charset = UTF-8\n\n $ cat /etc/odbcinst.ini \n ...\n\n [FreeTDS]\n Description = FreeTDS\n Driver = /usr/lib/x86_64-linux-gnu/odbc/libtdsodbc.so\n Setup = /usr/lib/x86_64-linux-gnu/odbc/libtdsS.so\n FileUsage = 1\n UsageCount = 5\n\n $ cat ~/.odbc.ini \n ...\n\n [MSSQL]\n Description = FreeTDS\n Driver = FreeTDS\n Servername = MSSQL\n Database = test\n UID = test\n PWD = test\n Port = 1433 Configuring the dictionary in ClickHouse: yandex \n dictionary \n name test /name \n source \n odbc \n table dict /table \n connection_string DSN=MSSQL;UID=test;PWD=test /connection_string \n /odbc \n /source \n\n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n\n layout \n flat / \n /layout \n\n structure \n id \n name k /name \n /id \n attribute \n name s /name \n type String /type \n null_value /null_value \n /attribute \n /structure \n /dictionary /yandex", + "title": "Example of connecting MS SQL Server" + }, + { + "location": "/index.html#dbms", + "text": "", + "title": "DBMS" + }, + { + "location": "/index.html#mysql_1", + "text": "Example of settings: source \n mysql \n port 3306 /port \n user clickhouse /user \n password qwerty /password \n replica \n host example01-1 /host \n priority 1 /priority \n /replica \n replica \n host example01-2 /host \n priority 1 /priority \n /replica \n db db_name /db \n table table_name /table \n where id=10 /where \n invalidate_query SQL_QUERY /invalidate_query \n /mysql /source Setting fields: port \u2013 The port on the MySQL server. You can specify it for all replicas, or for each one individually (inside replica ). user \u2013 Name of the MySQL user. You can specify it for all replicas, or for each one individually (inside replica ). password \u2013 Password of the MySQL user. You can specify it for all replicas, or for each one individually (inside replica ). replica \u2013 Section of replica configurations. There can be multiple sections. replica/host \u2013 The MySQL host. * replica/priority \u2013 The replica priority. When attempting to connect, ClickHouse traverses the replicas in order of priority. The lower the number, the higher the priority. db \u2013 Name of the database. table \u2013 Name of the table. where \u2013 The selection criteria. Optional parameter. invalidate_query \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries . MySQL can be connected on a local host via sockets. To do this, set host and socket . Example of settings: source \n mysql \n host localhost /host \n socket /path/to/socket/file.sock /socket \n user clickhouse /user \n password qwerty /password \n db db_name /db \n table table_name /table \n where id=10 /where \n invalidate_query SQL_QUERY /invalidate_query \n /mysql /source", + "title": "MySQL" + }, + { + "location": "/index.html#clickhouse", + "text": "Example of settings: source \n clickhouse \n host example01-01-1 /host \n port 9000 /port \n user default /user \n password /password \n db default /db \n table ids /table \n where id=10 /where \n /clickhouse /source Setting fields: host \u2013 The ClickHouse host. If it is a local host, the query is processed without any network activity. To improve fault tolerance, you can create a Distributed table and enter it in subsequent configurations. port \u2013 The port on the ClickHouse server. user \u2013 Name of the ClickHouse user. password \u2013 Password of the ClickHouse user. db \u2013 Name of the database. table \u2013 Name of the table. where \u2013 The selection criteria. May be omitted.", + "title": "ClickHouse" + }, + { + "location": "/index.html#mongodb", + "text": "Example of settings: source \n mongodb \n host localhost /host \n port 27017 /port \n user /user \n password /password \n db test /db \n collection dictionary_source /collection \n /mongodb /source Setting fields: host \u2013 The MongoDB host. port \u2013 The port on the MongoDB server. user \u2013 Name of the MongoDB user. password \u2013 Password of the MongoDB user. db \u2013 Name of the database. collection \u2013 Name of the collection.", + "title": "MongoDB" + }, + { + "location": "/index.html#dictionary-key-and-fields", + "text": "The structure clause describes the dictionary key and fields available for queries. Overall structure: dictionary \n structure \n id \n name Id /name \n /id \n\n attribute \n !-- Attribute parameters -- \n /attribute \n\n ...\n\n /structure /dictionary Columns are described in the structure: id - key column . attribute - data column . There can be a large number of columns.", + "title": "Dictionary key and fields" + }, + { + "location": "/index.html#key", + "text": "ClickHouse supports the following types of keys: Numeric key. UInt64. Defined in the tag id . Composite key. Set of values of different types. Defined in the tag key . A structure can contain either id or key . \n\nThe key doesn't need to be defined separately in attributes.", + "title": "Key" + }, + { + "location": "/index.html#numeric-key", + "text": "Format: UInt64 . Configuration example: id \n name Id /name /id Configuration fields: name \u2013 The name of the column with keys.", + "title": "Numeric key" + }, + { + "location": "/index.html#composite-key", + "text": "The key can be a tuple from any types of fields. The layout in this case must be complex_key_hashed or complex_key_cache . \nA composite key can consist of a single element. This makes it possible to use a string as the key, for instance. The key structure is set in the element key . Key fields are specified in the same format as the dictionary attributes . Example: structure \n key \n attribute \n name field1 /name \n type String /type \n /attribute \n attribute \n name field2 /name \n type UInt32 /type \n /attribute \n ...\n /key \n... For a query to the dictGet* function, a tuple is passed as the key. Example: dictGetString('dict_name', 'attr_name', tuple('string for field1', num_for_field2)) .", + "title": "Composite key" + }, + { + "location": "/index.html#attributes", + "text": "Configuration example: structure \n ...\n attribute \n name Name /name \n type Type /type \n null_value /null_value \n expression rand64() /expression \n hierarchical true /hierarchical \n injective true /injective \n is_object_id true /is_object_id \n /attribute /structure Configuration fields: name \u2013 The column name. type \u2013 The column type. Sets the method for interpreting data in the source. For example, for MySQL, the field might be TEXT , VARCHAR , or BLOB in the source table, but it can be uploaded as String . null_value \u2013 The default value for a non-existing element. In the example, it is an empty string. expression \u2013 The attribute can be an expression. The tag is not required. hierarchical \u2013 Hierarchical support. Mirrored to the parent identifier. By default, false . injective \u2013 Whether the id - attribute image is injective. If true , then you can optimize the GROUP BY clause. By default, false . is_object_id \u2013 Whether the query is executed for a MongoDB document by ObjectID .", + "title": "Attributes" + }, + { + "location": "/index.html#internal-dictionaries", + "text": "ClickHouse contains a built-in feature for working with a geobase. This allows you to: Use a region's ID to get its name in the desired language. Use a region's ID to get the ID of a city, area, federal district, country, or continent. Check whether a region is part of another region. Get a chain of parent regions. All the functions support \"translocality,\" the ability to simultaneously use different perspectives on region ownership. For more information, see the section \"Functions for working with Yandex.Metrica dictionaries\". The internal dictionaries are disabled in the default package.\nTo enable them, uncomment the parameters path_to_regions_hierarchy_file and path_to_regions_names_files in the server configuration file. The geobase is loaded from text files.\nIf you work at Yandex, you can follow these instructions to create them: https://github.yandex-team.ru/raw/Metrika/ClickHouse_private/master/doc/create_embedded_geobase_dictionaries.txt Put the regions_hierarchy*.txt files in the path_to_regions_hierarchy_file directory. This configuration parameter must contain the path to the regions_hierarchy.txt file (the default regional hierarchy), and the other files (regions_hierarchy_ua.txt) must be located in the same directory. Put the regions_names_*.txt files in the path_to_regions_names_files directory. You can also create these files yourself. The file format is as follows: regions_hierarchy*.txt : TabSeparated (no header), columns: Region ID (UInt32) Parent region ID (UInt32) Region type (UInt8): 1 - continent, 3 - country, 4 - federal district, 5 - region, 6 - city; other types don't have values. Population (UInt32) - Optional column. regions_names_*.txt : TabSeparated (no header), columns: Region ID (UInt32) Region name (String) - Can't contain tabs or line feeds, even escaped ones. A flat array is used for storing in RAM. For this reason, IDs shouldn't be more than a million. Dictionaries can be updated without restarting the server. However, the set of available dictionaries is not updated.\nFor updates, the file modification times are checked. If a file has changed, the dictionary is updated.\nThe interval to check for changes is configured in the 'builtin_dictionaries_reload_interval' parameter.\nDictionary updates (other than loading at first use) do not block queries. During updates, queries use the old versions of dictionaries. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries. We recommend periodically updating the dictionaries with the geobase. During an update, generate new files and write them to a separate location. When everything is ready, rename them to the files used by the server. There are also functions for working with OS identifiers and Yandex.Metrica search engines, but they shouldn't be used.", + "title": "Internal dictionaries" + }, + { + "location": "/index.html#usage_1", + "text": "", + "title": "Usage" + }, + { + "location": "/index.html#access-rights", + "text": "Users and access rights are set up in the user config. This is usually users.xml . Users are recorded in the users section. Here is a fragment of the users.xml file: !-- Users and ACL. -- users \n !-- If the user name is not specified, the default user is used. -- \n default \n !-- Password could be specified in plaintext or in SHA256 (in hex format). If you want to specify the password in plain text (not recommended), place it in the password element. Example: password qwerty /password . Password can be empty. If you want to specify SHA256, place it in the password_sha256_hex element. Example: password_sha256_hex 65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5 /password_sha256_hex How to generate decent password: Execute: PASSWORD=$(base64 /dev/urandom | head -c8); echo $PASSWORD ; echo -n $PASSWORD | sha256sum | tr -d - In first line will be password and in second - corresponding SHA256. -- \n password /password \n !-- A list of networks that access is allowed from. Each list item has one of the following forms: ip IP address or subnet mask. For example: 198.51.100.0/24 or 2001:DB8::/32. host Host name. For example: example01. A DNS query is made for verification, and all addresses obtained are compared with the address of the customer. host_regexp Regular expression for host names. For example: ^example\\d\\d-\\d\\d-\\d\\.yandex\\.ru$ For verification, a DNS PTR query is made for the customer s address and a regular expression is applied to the result. Then another DNS query is made for the result of the PTR query, and all received address are compared to the client address. We strongly recommend that the regex ends with \\.yandex\\.ru$. If you are installing ClickHouse yourself, enter: networks ip ::/0 /ip /networks -- \n networks incl= networks / \n\n !-- Settings profile for the user. -- \n profile default /profile \n\n !-- Quota for the user. -- \n quota default /quota \n /default \n\n !-- For requests from the Yandex.Metrica user interface via the API for data on specific counters. -- \n web \n password /password \n networks incl= networks / \n profile web /profile \n quota default /quota \n allow_databases \n database test /database \n /allow_databases \n /web /users You can see a declaration from two users: default and web . We added the web user separately. The default user is chosen in cases when the username is not passed. The default user is also used for distributed query processing, if the configuration of the server or cluster doesn't specify the user and password (see the section on the Distributed engine). The user that is used for exchanging information between servers combined in a cluster must not have substantial restrictions or quotas \u2013 otherwise, distributed queries will fail. The password is specified in open format (not recommended) or in SHA-256. The hash isn't salted. In this regard, you should not consider these passwords as providing security against potential malicious attacks. Rather, they are necessary for protection from employees. A list of networks is specified that access is allowed from. In this example, the list of networks for both users is loaded from a separate file (/etc/metrika.xml) containing the 'networks' substitution. Here is a fragment of it: yandex \n ...\n networks \n ip ::/64 /ip \n ip 203.0.113.0/24 /ip \n ip 2001:DB8::/32 /ip \n ...\n /networks /yandex We could have defined this list of networks directly in 'users.xml', or in a file in the 'users.d' directory (for more information, see the section \"Configuration files\"). The config includes comments explaining how to open access from everywhere. For use in production, only specify IP elements (IP addresses and their masks), since using 'host' and 'hoost_regexp' might cause extra latency. Next the user settings profile is specified (see the section \"Settings profiles\"). You can specify the default profile, default . The profile can have any name. You can specify the same profile for different users. The most important thing you can write in the settings profile is 'readonly' set to 1, which provides read-only access. After this, the quota is defined (see the section \"Quotas\"). You can specify the default quota, default . It is set in the config by default so that it only counts resource usage, but does not restrict it. The quota can have any name. You can specify the same quota for different users \u2013 in this case, resource usage is calculated for each user individually. In the optional allow_databases section, you can also specify a list of databases that the user can access. By default, all databases are available to the user. You can specify the default database. In this case, the user will receive access to the database by default. Access to the system database is always allowed (since this database is used for processing queries). The user can get a list of all databases and tables in them by using SHOW queries or system tables, even if access to individual databases isn't allowed. Database access is not related to the readonly setting. You can't grant full access to one database and readonly access to another one.", + "title": "Access rights" + }, + { + "location": "/index.html#configuration-files_1", + "text": "The main server config file is config.xml . It resides in the /etc/clickhouse-server/ directory. Individual settings can be overridden in the *.xml and *.conf files in the conf.d and config.d directories next to the config file. The replace or remove attributes can be specified for the elements of these config files. If neither is specified, it combines the contents of elements recursively, replacing values of duplicate children. If replace is specified, it replaces the entire element with the specified one. If remove is specified, it deletes the element. The config can also define \"substitutions\". If an element has the incl attribute, the corresponding substitution from the file will be used as the value. By default, the path to the file with substitutions is /etc/metrika.xml . This can be changed in the include_from element in the server config. The substitution values are specified in /yandex/substitution_name elements in this file. If a substitution specified in incl does not exist, it is recorded in the log. To prevent ClickHouse from logging missing substitutions, specify the optional=\"true\" attribute (for example, settings for macros ). Substitutions can also be performed from ZooKeeper. To do this, specify the attribute from_zk = \"/path/to/node\" . The element value is replaced with the contents of the node at /path/to/node in ZooKeeper. You can also put an entire XML subtree on the ZooKeeper node and it will be fully inserted into the source element. The config.xml file can specify a separate config with user settings, profiles, and quotas. The relative path to this config is set in the 'users_config' element. By default, it is users.xml . If users_config is omitted, the user settings, profiles, and quotas are specified directly in config.xml . In addition, users_config may have overrides in files from the users_config.d directory (for example, users.d ) and substitutions. For each config file, the server also generates file-preprocessed.xml files when starting. These files contain all the completed substitutions and overrides, and they are intended for informational use. If ZooKeeper substitutions were used in the config files but ZooKeeper is not available on the server start, the server loads the configuration from the preprocessed file. The server tracks changes in config files, as well as files and ZooKeeper nodes that were used when performing substitutions and overrides, and reloads the settings for users and clusters on the fly. This means that you can modify the cluster, users, and their settings without restarting the server.", + "title": "Configuration files" + }, + { + "location": "/index.html#quotas", + "text": "Quotas allow you to limit resource usage over a period of time, or simply track the use of resources.\nQuotas are set up in the user config. This is usually 'users.xml'. The system also has a feature for limiting the complexity of a single query. See the section \"Restrictions on query complexity\"). In contrast to query complexity restrictions, quotas: Place restrictions on a set of queries that can be run over a period of time, instead of limiting a single query. Account for resources spent on all remote servers for distributed query processing. Let's look at the section of the 'users.xml' file that defines quotas. !-- Quotas. -- quotas \n !-- Quota name. -- \n default \n !-- Restrictions for a time period. You can set many intervals with different restrictions. -- \n interval \n !-- Length of the interval. -- \n duration 3600 /duration \n\n !-- Unlimited. Just collect data for the specified time interval. -- \n queries 0 /queries \n errors 0 /errors \n result_rows 0 /result_rows \n read_rows 0 /read_rows \n execution_time 0 /execution_time \n /interval \n /default By default, the quota just tracks resource consumption for each hour, without limiting usage.\nThe resource consumption calculated for each interval is output to the server log after each request. statbox \n !-- Restrictions for a time period. You can set many intervals with different restrictions. -- \n interval \n !-- Length of the interval. -- \n duration 3600 /duration \n\n queries 1000 /queries \n errors 100 /errors \n result_rows 1000000000 /result_rows \n read_rows 100000000000 /read_rows \n execution_time 900 /execution_time \n /interval \n\n interval \n duration 86400 /duration \n\n queries 10000 /queries \n errors 1000 /errors \n result_rows 5000000000 /result_rows \n read_rows 500000000000 /read_rows \n execution_time 7200 /execution_time \n /interval /statbox For the 'statbox' quota, restrictions are set for every hour and for every 24 hours (86,400 seconds). The time interval is counted starting from an implementation-defined fixed moment in time. In other words, the 24-hour interval doesn't necessarily begin at midnight. When the interval ends, all collected values are cleared. For the next hour, the quota calculation starts over. Here are the amounts that can be restricted: queries \u2013 The total number of requests. errors \u2013 The number of queries that threw an exception. result_rows \u2013 The total number of rows given as the result. read_rows \u2013 The total number of source rows read from tables for running the query, on all remote servers. execution_time \u2013 The total query execution time, in seconds (wall time). If the limit is exceeded for at least one time interval, an exception is thrown with a text about which restriction was exceeded, for which interval, and when the new interval begins (when queries can be sent again). Quotas can use the \"quota key\" feature in order to report on resources for multiple keys independently. Here is an example of this: !-- For the global reports designer. -- web_global \n !-- keyed - The quota_key key is passed in the query parameter, and the quota is tracked separately for each key value. For example, you can pass a Yandex.Metrica username as the key, so the quota will be counted separately for each username. Using keys makes sense only if quota_key is transmitted by the program, not by a user. You can also write keyed_by_ip / so the IP address is used as the quota key. (But keep in mind that users can change the IPv6 address fairly easily.) -- \n keyed / The quota is assigned to users in the 'users' section of the config. See the section \"Access rights\". For distributed query processing, the accumulated amounts are stored on the requestor server. So if the user goes to another server, the quota there will \"start over\". When the server is restarted, quotas are reset.", + "title": "Quotas" + }, + { + "location": "/index.html#usage-recommendations", + "text": "", + "title": "Usage recommendations" + }, + { + "location": "/index.html#cpu", + "text": "The SSE 4.2 instruction set must be supported. Modern processors (since 2008) support it. When choosing a processor, prefer a large number of cores and slightly slower clock rate over fewer cores and a higher clock rate.\nFor example, 16 cores with 2600 MHz is better than 8 cores with 3600 MHz.", + "title": "CPU" + }, + { + "location": "/index.html#hyper-threading", + "text": "Don't disable hyper-threading. It helps for some queries, but not for others.", + "title": "Hyper-threading" + }, + { + "location": "/index.html#turbo-boost", + "text": "Turbo Boost is highly recommended. It significantly improves performance with a typical load.\nYou can use turbostat to view the CPU's actual clock rate under a load.", + "title": "Turbo Boost" + }, + { + "location": "/index.html#cpu-scaling-governor", + "text": "Always use the performance scaling governor. The on-demand scaling governor works much worse with constantly high demand. sudo echo performance | tee /sys/devices/system/cpu/cpu \\* /cpufreq/scaling_governor", + "title": "CPU scaling governor" + }, + { + "location": "/index.html#cpu-limitations", + "text": "Processors can overheat. Use dmesg to see if the CPU's clock rate was limited due to overheating.\nThe restriction can also be set externally at the datacenter level. You can use turbostat to monitor it under a load.", + "title": "CPU limitations" + }, + { + "location": "/index.html#ram", + "text": "For small amounts of data (up to \\~200 GB compressed), it is best to use as much memory as the volume of data.\nFor large amounts of data and when processing interactive (online) queries, you should use a reasonable amount of RAM (128 GB or more) so the hot data subset will fit in the cache of pages.\nEven for data volumes of \\~50 TB per server, using 128 GB of RAM significantly improves query performance compared to 64 GB.", + "title": "RAM" + }, + { + "location": "/index.html#swap-file", + "text": "Always disable the swap file. The only reason for not doing this is if you are using ClickHouse on your personal laptop.", + "title": "Swap file" + }, + { + "location": "/index.html#huge-pages", + "text": "Always disable transparent huge pages. It interferes with memory allocators, which leads to significant performance degradation. echo never | sudo tee /sys/kernel/mm/transparent_hugepage/enabled Use perf top to watch the time spent in the kernel for memory management.\nPermanent huge pages also do not need to be allocated.", + "title": "Huge pages" + }, + { + "location": "/index.html#storage-subsystem", + "text": "If your budget allows you to use SSD, use SSD.\nIf not, use HDD. SATA HDDs 7200 RPM will do. Give preference to a lot of servers with local hard drives over a smaller number of servers with attached disk shelves.\nBut for storing archives with rare queries, shelves will work.", + "title": "Storage subsystem" + }, + { + "location": "/index.html#raid", + "text": "When using HDD, you can combine their RAID-10, RAID-5, RAID-6 or RAID-50.\nFor Linux, software RAID is better (with mdadm ). We don't recommend using LVM.\nWhen creating RAID-10, select the far layout.\nIf your budget allows, choose RAID-10. If you have more than 4 disks, use RAID-6 (preferred) or RAID-50, instead of RAID-5.\nWhen using RAID-5, RAID-6 or RAID-50, always increase stripe_cache_size, since the default value is usually not the best choice. echo 4096 | sudo tee /sys/block/md2/md/stripe_cache_size Calculate the exact number from the number of devices and the block size, using the formula: 2 * num_devices * chunk_size_in_bytes / 4096 . A block size of 1025 KB is sufficient for all RAID configurations.\nNever set the block size too small or too large. You can use RAID-0 on SSD.\nRegardless of RAID use, always use replication for data security. Enable NCQ with a long queue. For HDD, choose the CFQ scheduler, and for SSD, choose noop. Don't reduce the 'readahead' setting.\nFor HDD, enable the write cache.", + "title": "RAID" + }, + { + "location": "/index.html#file-system", + "text": "Ext4 is the most reliable option. Set the mount options noatime, nobarrier .\nXFS is also suitable, but it hasn't been as thoroughly tested with ClickHouse.\nMost other file systems should also work fine. File systems with delayed allocation work better.", + "title": "File system" + }, + { + "location": "/index.html#linux-kernel", + "text": "Don't use an outdated Linux kernel. In 2015, 3.18.19 was new enough.\nConsider using the kernel build from Yandex: https://github.com/yandex/smart \u2013 it provides at least a 5% performance increase.", + "title": "Linux kernel" + }, + { + "location": "/index.html#network", + "text": "If you are using IPv6, increase the size of the route cache.\nThe Linux kernel prior to 3.2 had a multitude of problems with IPv6 implementation. Use at least a 10 GB network, if possible. 1 Gb will also work, but it will be much worse for patching replicas with tens of terabytes of data, or for processing distributed queries with a large amount of intermediate data.", + "title": "Network" + }, + { + "location": "/index.html#zookeeper", + "text": "You are probably already using ZooKeeper for other purposes. You can use the same installation of ZooKeeper, if it isn't already overloaded. It's best to use a fresh version of ZooKeeper \u2013 3.4.9 or later. The version in stable Linux distributions may be outdated. With the default settings, ZooKeeper is a time bomb: The ZooKeeper server won't delete files from old snapshots and logs when using the default configuration (see autopurge), and this is the responsibility of the operator. This bomb must be defused. The ZooKeeper (3.5.1) configuration below is used in the Yandex.Metrica production environment as of May 20, 2017: zoo.cfg: ## http://hadoop.apache.org/zookeeper/docs/current/zookeeperAdmin.html ## The number of milliseconds of each tick tickTime = 2000 ## The number of ticks that the initial ## synchronization phase can take initLimit = 30000 ## The number of ticks that can pass between ## sending a request and getting an acknowledgement syncLimit = 10 maxClientCnxns = 2000 maxSessionTimeout = 60000000 ## the directory where the snapshot is stored. dataDir = /opt/zookeeper/ {{ cluster [ name ] }} /data ## Place the dataLogDir to a separate physical disc for better performance dataLogDir = /opt/zookeeper/ {{ cluster [ name ] }} /logs\n\nautopurge.snapRetainCount = 10 \nautopurge.purgeInterval = 1 ## To avoid seeks ZooKeeper allocates space in the transaction log file in ## blocks of preAllocSize kilobytes. The default block size is 64M. One reason ## for changing the size of the blocks is to reduce the block size if snapshots ## are taken more often. (Also, see snapCount). preAllocSize = 131072 ## Clients can submit requests faster than ZooKeeper can process them, ## especially if there are a lot of clients. To prevent ZooKeeper from running ## out of memory due to queued requests, ZooKeeper will throttle clients so that ## there is no more than globalOutstandingLimit outstanding requests in the ## system. The default limit is 1,000.ZooKeeper logs transactions to a ## transaction log. After snapCount transactions are written to a log file a ## snapshot is started and a new transaction log file is started. The default ## snapCount is 10,000. snapCount = 3000000 ## If this option is defined, requests will be will logged to a trace file named ## traceFile.year.month.day. ##traceFile= ## Leader accepts client connections. Default value is yes . The leader machine ## coordinates updates. For higher update throughput at thes slight expense of ## read throughput the leader can be configured to not accept clients and focus ## on coordination. leaderServes = yes standaloneEnabled = false dynamicConfigFile = /etc/zookeeper- {{ cluster [ name ] }} /conf/zoo.cfg.dynamic Java version: Java(TM) SE Runtime Environment (build 1.8.0_25-b17)\nJava HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode) JVM parameters: NAME = zookeeper- {{ cluster [ name ] }} ZOOCFGDIR = /etc/ $NAME /conf ## TODO this is really ugly ## How to find out, which jars are needed? ## seems, that log4j requires the log4j.properties file to be in the classpath CLASSPATH = $ZOOCFGDIR :/usr/build/classes:/usr/build/lib/*.jar:/usr/share/zookeeper/zookeeper-3.5.1-metrika.jar:/usr/share/zookeeper/slf4j-log4j12-1.7.5.jar:/usr/share/zookeeper/slf4j-api-1.7.5.jar:/usr/share/zookeeper/servlet-api-2.5-20081211.jar:/usr/share/zookeeper/netty-3.7.0.Final.jar:/usr/share/zookeeper/log4j-1.2.16.jar:/usr/share/zookeeper/jline-2.11.jar:/usr/share/zookeeper/jetty-util-6.1.26.jar:/usr/share/zookeeper/jetty-6.1.26.jar:/usr/share/zookeeper/javacc.jar:/usr/share/zookeeper/jackson-mapper-asl-1.9.11.jar:/usr/share/zookeeper/jackson-core-asl-1.9.11.jar:/usr/share/zookeeper/commons-cli-1.2.jar:/usr/src/java/lib/*.jar:/usr/etc/zookeeper ZOOCFG = $ZOOCFGDIR /zoo.cfg ZOO_LOG_DIR = /var/log/ $NAME USER = zookeeper GROUP = zookeeper PIDDIR = /var/run/ $NAME PIDFILE = $PIDDIR / $NAME .pid SCRIPTNAME = /etc/init.d/ $NAME JAVA = /usr/bin/java ZOOMAIN = org.apache.zookeeper.server.quorum.QuorumPeerMain ZOO_LOG4J_PROP = INFO,ROLLINGFILE JMXLOCALONLY = false JAVA_OPTS = -Xms{{ cluster.get( xms , 128M ) }} \\ -Xmx{{ cluster.get( xmx , 1G ) }} \\ -Xloggc:/var/log/ $NAME /zookeeper-gc.log \\ -XX:+UseGCLogFileRotation \\ -XX:NumberOfGCLogFiles=16 \\ -XX:GCLogFileSize=16M \\ -verbose:gc \\ -XX:+PrintGCTimeStamps \\ -XX:+PrintGCDateStamps \\ -XX:+PrintGCDetails -XX:+PrintTenuringDistribution \\ -XX:+PrintGCApplicationStoppedTime \\ -XX:+PrintGCApplicationConcurrentTime \\ -XX:+PrintSafepointStatistics \\ -XX:+UseParNewGC \\ -XX:+UseConcMarkSweepGC \\ -XX:+CMSParallelRemarkEnabled Salt init: description zookeeper-{{ cluster[ name ] }} centralized coordination service \n\nstart on runlevel [2345]\nstop on runlevel [!2345]\n\nrespawn\n\nlimit nofile 8192 8192\n\npre-start script\n [ -r /etc/zookeeper-{{ cluster[ name ] }}/conf/environment ] || exit 0\n . /etc/zookeeper-{{ cluster[ name ] }}/conf/environment\n [ -d $ZOO_LOG_DIR ] || mkdir -p $ZOO_LOG_DIR\n chown $USER:$GROUP $ZOO_LOG_DIR\nend script\n\nscript\n . /etc/zookeeper-{{ cluster[ name ] }}/conf/environment\n [ -r /etc/default/zookeeper ] . /etc/default/zookeeper\n if [ -z $JMXDISABLE ]; then\n JAVA_OPTS= $JAVA_OPTS -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.local.only=$JMXLOCALONLY \n fi\n exec start-stop-daemon --start -c $USER --exec $JAVA --name zookeeper-{{ cluster[ name ] }} \\\n -- -cp $CLASSPATH $JAVA_OPTS -Dzookeeper.log.dir=${ZOO_LOG_DIR} \\\n -Dzookeeper.root.logger=${ZOO_LOG4J_PROP} $ZOOMAIN $ZOOCFG\nend script", + "title": "ZooKeeper" + }, + { + "location": "/index.html#server-configuration-parameters", + "text": "This section contains descriptions of server settings that cannot be changed at the session or query level. These settings are stored in the config.xml file on the ClickHouse server. Other settings are described in the \" Settings \" section. Before studying the settings, read the Configuration files section and note the use of substitutions (the incl and optional attributes).", + "title": "Server configuration parameters" + }, + { + "location": "/index.html#server-settings", + "text": "", + "title": "Server settings" + }, + { + "location": "/index.html#builtin_dictionaries_reload_interval", + "text": "The interval in seconds before reloading built-in dictionaries. ClickHouse reloads built-in dictionaries every x seconds. This makes it possible to edit dictionaries \"on the fly\" without restarting the server. Default value: 3600. Example builtin_dictionaries_reload_interval 3600 /builtin_dictionaries_reload_interval", + "title": "builtin_dictionaries_reload_interval" + }, + { + "location": "/index.html#compression", + "text": "Data compression settings. \n\nDon't use it if you have just started using ClickHouse. The configuration looks like this: compression \n case \n parameters/ \n /case \n ... /compression You can configure multiple sections case . Block field case : min_part_size \u2013 The minimum size of a table part. min_part_size_ratio \u2013 The ratio of the minimum size of a table part to the full size of the table. method \u2013 Compression method. Acceptable values \u200b: lz4 or zstd (experimental). ClickHouse checks min_part_size and min_part_size_ratio and processes the case blocks that match these conditions. If none of the case matches, ClickHouse applies the lz4 compression algorithm. Example compression incl= clickhouse_compression \n case \n min_part_size 10000000000 /min_part_size \n min_part_size_ratio 0.01 /min_part_size_ratio \n method zstd /method \n /case /compression", + "title": "compression" + }, + { + "location": "/index.html#default_database", + "text": "The default database. To get a list of databases, use the SHOW DATABASES . Example default_database default /default_database", + "title": "default_database" + }, + { + "location": "/index.html#default_profile", + "text": "Default settings profile. Settings profiles are located in the file specified in the parameter user_config . Example default_profile default /default_profile", + "title": "default_profile" + }, + { + "location": "/index.html#dictionaries_config", + "text": "The path to the config file for external dictionaries. Path: Specify the absolute path or the path relative to the server config file. The path can contain wildcards * and ?. See also \" External dictionaries \". Example dictionaries_config *_dictionary.xml /dictionaries_config", + "title": "dictionaries_config" + }, + { + "location": "/index.html#dictionaries_lazy_load", + "text": "Lazy loading of dictionaries. If true , then each dictionary is created on first use. If dictionary creation failed, the function that was using the dictionary throws an exception. If false , all dictionaries are created when the server starts, and if there is an error, the server shuts down. The default is true . Example dictionaries_lazy_load true /dictionaries_lazy_load", + "title": "dictionaries_lazy_load" + }, + { + "location": "/index.html#format_schema_path", + "text": "The path to the directory with the schemes for the input data, such as schemas for the CapnProto format. Example !-- Directory containing schema files for various input formats. -- \n format_schema_path format_schemas/ /format_schema_path", + "title": "format_schema_path" + }, + { + "location": "/index.html#graphite", + "text": "Sending data to Graphite . Settings: host \u2013 The Graphite server. port \u2013 The port on the Graphite server. interval \u2013 The interval for sending, in seconds. timeout \u2013 The timeout for sending data, in seconds. root_path \u2013 Prefix for keys. metrics \u2013 Sending data from a :ref: system_tables-system.metrics table. events \u2013 Sending data from a :ref: system_tables-system.events table. asynchronous_metrics \u2013 Sending data from a :ref: system_tables-system.asynchronous_metrics table. You can configure multiple graphite clauses. For instance, you can use this for sending different data at different intervals. Example graphite \n host localhost /host \n port 42000 /port \n timeout 0.1 /timeout \n interval 60 /interval \n root_path one_min /root_path \n metrics true /metrics \n events true /events \n asynchronous_metrics true /asynchronous_metrics /graphite", + "title": "graphite" + }, + { + "location": "/index.html#graphite_rollup", + "text": "Settings for thinning data for Graphite. For more information, see GraphiteMergeTree . Example graphite_rollup_example \n default \n function max /function \n retention \n age 0 /age \n precision 60 /precision \n /retention \n retention \n age 3600 /age \n precision 300 /precision \n /retention \n retention \n age 86400 /age \n precision 3600 /precision \n /retention \n /default /graphite_rollup_example", + "title": "graphite_rollup" + }, + { + "location": "/index.html#http_porthttps_port", + "text": "The port for connecting to the server over HTTP(s). If https_port is specified, openSSL must be configured. If http_port is specified, the openSSL configuration is ignored even if it is set. Example https 0000 /https", + "title": "http_port/https_port" + }, + { + "location": "/index.html#http_server_default_response", + "text": "The page that is shown by default when you access the ClickHouse HTTP(s) server. Example Opens https://tabix.io/ when accessing http://localhost: http_port . http_server_default_response \n ![CDATA[ html ng-app= SMI2 head base href= http://ui.tabix.io/ /head body div ui-view= class= content-ui /div script src= http://loader.tabix.io/master.js /script /body /html ]] /http_server_default_response", + "title": "http_server_default_response" + }, + { + "location": "/index.html#include_from", + "text": "The path to the file with substitutions. For more information, see the section \" Configuration files \". Example include_from /etc/metrica.xml /include_from", + "title": "include_from" + }, + { + "location": "/index.html#interserver_http_port", + "text": "Port for exchanging data between ClickHouse servers. Example interserver_http_port 9009 /interserver_http_port", + "title": "interserver_http_port" + }, + { + "location": "/index.html#interserver_http_host", + "text": "The host name that can be used by other servers to access this server. If omitted, it is defined in the same way as the hostname-f command. Useful for breaking away from a specific network interface. Example interserver_http_host example.yandex.ru /interserver_http_host", + "title": "interserver_http_host" + }, + { + "location": "/index.html#keep_alive_timeout", + "text": "The number of milliseconds that ClickHouse waits for incoming requests before closing the connection. Example keep_alive_timeout 3 /keep_alive_timeout", + "title": "keep_alive_timeout" + }, + { + "location": "/index.html#listen_host", + "text": "Restriction on hosts that requests can come from. If you want the server to answer all of them, specify :: . Examples: listen_host ::1 /listen_host listen_host 127.0.0.1 /listen_host", + "title": "listen_host" + }, + { + "location": "/index.html#logger", + "text": "Logging settings. Keys: level \u2013 Logging level. Acceptable values: trace , debug , information , warning , error . log \u2013 The log file. Contains all the entries according to level . errorlog \u2013 Error log file. size \u2013 Size of the file. Applies to log and errorlog . Once the file reaches size , ClickHouse archives and renames it, and creates a new log file in its place. count \u2013 The number of archived log files that ClickHouse stores. Example logger \n level trace /level \n log /var/log/clickhouse-server/clickhouse-server.log /log \n errorlog /var/log/clickhouse-server/clickhouse-server.err.log /errorlog \n size 1000M /size \n count 10 /count /logger", + "title": "logger" + }, + { + "location": "/index.html#macros", + "text": "Parameter substitutions for replicated tables. Can be omitted if replicated tables are not used. For more information, see the section \" Creating replicated tables \". Example macros incl= macros optional= true /", + "title": "macros" + }, + { + "location": "/index.html#mark_cache_size", + "text": "Approximate size (in bytes) of the cache of \"marks\" used by MergeTree engines. The cache is shared for the server and memory is allocated as needed. The cache size must be at least 5368709120. Example mark_cache_size 5368709120 /mark_cache_size", + "title": "mark_cache_size" + }, + { + "location": "/index.html#max_concurrent_queries", + "text": "The maximum number of simultaneously processed requests. Example max_concurrent_queries 100 /max_concurrent_queries", + "title": "max_concurrent_queries" + }, + { + "location": "/index.html#max_connections", + "text": "The maximum number of inbound connections. Example max_connections 4096 /max_connections", + "title": "max_connections" + }, + { + "location": "/index.html#max_open_files", + "text": "The maximum number of open files. By default: maximum . We recommend using this option in Mac OS X, since the getrlimit() function returns an incorrect value. Example max_open_files 262144 /max_open_files", + "title": "max_open_files" + }, + { + "location": "/index.html#max_table_size_to_drop", + "text": "Restriction on deleting tables. If the size of a MergeTree type table exceeds max_table_size_to_drop (in bytes), you can't delete it using a DROP query. If you still need to delete the table without restarting the ClickHouse server, create the clickhouse-path /flags/force_drop_table file and run the DROP query. Default value: 50 GB. The value 0 means that you can delete all tables without any restrictions. Example max_table_size_to_drop 0 /max_table_size_to_drop", + "title": "max_table_size_to_drop" + }, + { + "location": "/index.html#merge_tree", + "text": "Fine tuning for tables in the MergeTree family. For more information, see the MergeTreeSettings.h header file. Example merge_tree \n max_suspicious_broken_parts 5 /max_suspicious_broken_parts /merge_tree", + "title": "merge_tree" + }, + { + "location": "/index.html#openssl", + "text": "SSL client/server configuration. Support for SSL is provided by the libpoco library. The interface is described in the file SSLManager.h Keys for server/client settings: privateKeyFile \u2013 The path to the file with the secret key of the PEM certificate. The file may contain a key and certificate at the same time. certificateFile \u2013 The path to the client/server certificate file in PEM format. You can omit it if privateKeyFile contains the certificate. caConfig \u2013 The path to the file or directory that contains trusted root certificates. verificationMode \u2013 The method for checking the node's certificates. Details are in the description of the Context class. Possible values: none , relaxed , strict , once . verificationDepth \u2013 The maximum length of the verification chain. Verification will fail if the certificate chain length exceeds the set value. loadDefaultCAFile \u2013 Indicates that built-in CA certificates for OpenSSL will be used. Acceptable values: true , false . | cipherList \u2013 Supported OpenSSL encryptions. For example: ALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH . cacheSessions \u2013 Enables or disables caching sessions. Must be used in combination with sessionIdContext . Acceptable values: true , false . sessionIdContext \u2013 A unique set of random characters that the server appends to each generated identifier. The length of the string must not exceed SSL_MAX_SSL_SESSION_ID_LENGTH . This parameter is always recommended, since it helps avoid problems both if the server caches the session and if the client requested caching. Default value: ${application.name} . sessionCacheSize \u2013 The maximum number of sessions that the server caches. Default value: 1024*20. 0 \u2013 Unlimited sessions. sessionTimeout \u2013 Time for caching the session on the server. extendedVerification \u2013 Automatically extended verification of certificates after the session ends. Acceptable values: true , false . requireTLSv1 \u2013 Require a TLSv1 connection. Acceptable values: true , false . requireTLSv1_1 \u2013 Require a TLSv1.1 connection. Acceptable values: true , false . requireTLSv1 \u2013 Require a TLSv1.2 connection. Acceptable values: true , false . fips \u2013 Activates OpenSSL FIPS mode. Supported if the library's OpenSSL version supports FIPS. privateKeyPassphraseHandler \u2013 Class (PrivateKeyPassphraseHandler subclass) that requests the passphrase for accessing the private key. For example: privateKeyPassphraseHandler , name KeyFileHandler /name , options password test /password /options , /privateKeyPassphraseHandler . invalidCertificateHandler \u2013 Class (subclass of CertificateHandler) for verifying invalid certificates. For example: invalidCertificateHandler name ConsoleCertificateHandler /name /invalidCertificateHandler . disableProtocols \u2013 Protocols that are not allowed to use. preferServerCiphers \u2013 Preferred server ciphers on the client. Example of settings: openSSL \n server \n !-- openssl req -subj /CN=localhost -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout /etc/clickhouse-server/server.key -out /etc/clickhouse-server/server.crt -- \n certificateFile /etc/clickhouse-server/server.crt /certificateFile \n privateKeyFile /etc/clickhouse-server/server.key /privateKeyFile \n !-- openssl dhparam -out /etc/clickhouse-server/dhparam.pem 4096 -- \n dhParamsFile /etc/clickhouse-server/dhparam.pem /dhParamsFile \n verificationMode none /verificationMode \n loadDefaultCAFile true /loadDefaultCAFile \n cacheSessions true /cacheSessions \n disableProtocols sslv2,sslv3 /disableProtocols \n preferServerCiphers true /preferServerCiphers \n /server \n client \n loadDefaultCAFile true /loadDefaultCAFile \n cacheSessions true /cacheSessions \n disableProtocols sslv2,sslv3 /disableProtocols \n preferServerCiphers true /preferServerCiphers \n !-- Use for self-signed: verificationMode none /verificationMode -- \n invalidCertificateHandler \n !-- Use for self-signed: name AcceptCertificateHandler /name -- \n name RejectCertificateHandler /name \n /invalidCertificateHandler \n /client /openSSL", + "title": "openSSL" + }, + { + "location": "/index.html#part_log", + "text": "Logging events that are associated with MergeTree data. For instance, adding or merging data. You can use the log to simulate merge algorithms and compare their characteristics. You can visualize the merge process. Queries are logged in the ClickHouse table, not in a separate file. Columns in the log: event_time \u2013 Date of the event. duration_ms \u2013 Duration of the event. event_type \u2013 Type of event. 1 \u2013 new data part; 2 \u2013 merge result; 3 \u2013 data part downloaded from replica; 4 \u2013 data part deleted. database_name \u2013 The name of the database. table_name \u2013 Name of the table. part_name \u2013 Name of the data part. size_in_bytes \u2013 Size of the data part in bytes. merged_from \u2013 An array of names of data parts that make up the merge (also used when downloading a merged part). merge_time_ms \u2013 Time spent on the merge. Use the following parameters to configure logging: database \u2013 Name of the database. table \u2013 Name of the table. partition_by \u2013 Sets a custom partitioning key . flush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk. Example part_log \n database system /database \n table part_log /table \n partition_by toMonday(event_date) /partition_by \n flush_interval_milliseconds 7500 /flush_interval_milliseconds /part_log", + "title": "part_log" + }, + { + "location": "/index.html#path_1", + "text": "The path to the directory containing data. \n\nThe end slash is mandatory. Example path /var/lib/clickhouse/ /path", + "title": "path" + }, + { + "location": "/index.html#query_log", + "text": "Setting for logging queries received with the log_queries=1 setting. Queries are logged in the ClickHouse table, not in a separate file. Use the following parameters to configure logging: database \u2013 Name of the database. table \u2013 Name of the table. partition_by \u2013 Sets a custom partitioning key . flush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk. If the table doesn't exist, ClickHouse will create it. If the structure of the query log changed when the ClickHouse server was updated, the table with the old structure is renamed, and a new table is created automatically. Example query_log \n database system /database \n table query_log /table \n partition_by toMonday(event_date) /partition_by \n flush_interval_milliseconds 7500 /flush_interval_milliseconds /query_log", + "title": "query_log" + }, + { + "location": "/index.html#remote_servers", + "text": "Configuration of clusters used by the Distributed table engine. For more information, see the section \" Table engines/Distributed \". Example remote_servers incl= clickhouse_remote_servers / For the value of the incl attribute, see the section \" Configuration files \".", + "title": "remote_servers" + }, + { + "location": "/index.html#timezone", + "text": "The server's time zone. Specified as an IANA identifier for the UTC time zone or geographic location (for example, Africa/Abidjan). The time zone is necessary for conversions between String and DateTime formats when DateTime fields are output to text format (printed on the screen or in a file), and when getting DateTime from a string. In addition, the time zone is used in functions that work with the time and date if they didn't receive the time zone in the input parameters. Example timezone Europe/Moscow /timezone", + "title": "timezone" + }, + { + "location": "/index.html#tcp_port", + "text": "Port for communicating with clients over the TCP protocol. Example tcp_port 9000 /tcp_port", + "title": "tcp_port" + }, + { + "location": "/index.html#tmp_path", + "text": "Path to temporary data for processing large queries. \n\nThe end slash is mandatory. Example tmp_path /var/lib/clickhouse/tmp/ /tmp_path", + "title": "tmp_path" + }, + { + "location": "/index.html#uncompressed_cache_size", + "text": "Cache size (in bytes) for uncompressed data used by table engines from the MergeTree family. There is one shared cache for the server. Memory is allocated on demand. The cache is used if the option use_uncompressed_cache is enabled. The uncompressed cache is advantageous for very short queries in individual cases. Example uncompressed_cache_size 8589934592 /uncompressed_cache_size", + "title": "uncompressed_cache_size" + }, + { + "location": "/index.html#users_config", + "text": "Path to the file that contains: User configurations. Access rights. Settings profiles. Quota settings. Example users_config users.xml /users_config", + "title": "users_config" + }, + { + "location": "/index.html#zookeeper_1", + "text": "Configuration of ZooKeeper servers. ClickHouse uses ZooKeeper for storing replica metadata when using replicated tables. This parameter can be omitted if replicated tables are not used. For more information, see the section \" Replication \". Example zookeeper incl= zookeeper-servers optional= true /", + "title": "zookeeper" + }, + { + "location": "/index.html#settings", + "text": "There are multiple ways to make all the settings described below.\nSettings are configured in layers, so each subsequent layer redefines the previous settings. Ways to configure settings, in order of priority: Settings in the server config file. Settings from user profiles. Session settings. Send SET setting=value from the ClickHouse console client in interactive mode.\nSimilarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to specify the session_id HTTP parameter. For a query. When starting the ClickHouse console client in non-interactive mode, set the startup parameter --setting=value . When using the HTTP API, pass CGI parameters ( URL?setting_1=value setting_2=value... ). Settings that can only be made in the server config file are not covered in this section.", + "title": "Settings" + }, + { + "location": "/index.html#restrictions-on-query-complexity", + "text": "Restrictions on query complexity are part of the settings.\nThey are used in order to provide safer execution from the user interface.\nAlmost all the restrictions only apply to SELECTs.For distributed query processing, restrictions are applied on each server separately. Restrictions on the \"maximum amount of something\" can take the value 0, which means \"unrestricted\".\nMost restrictions also have an 'overflow_mode' setting, meaning what to do when the limit is exceeded.\nIt can take one of two values: throw or break . Restrictions on aggregation (group_by_overflow_mode) also have the value any . throw \u2013 Throw an exception (default). break \u2013 Stop executing the query and return the partial result, as if the source data ran out. any (only for group_by_overflow_mode) \u2013 Continuing aggregation for the keys that got into the set, but don't add new keys to the set.", + "title": "Restrictions on query complexity" + }, + { + "location": "/index.html#readonly", + "text": "With a value of 0, you can execute any queries.\nWith a value of 1, you can only execute read requests (such as SELECT and SHOW). Requests for writing and changing settings (INSERT, SET) are prohibited.\nWith a value of 2, you can process read queries (SELECT, SHOW) and change settings (SET). After enabling readonly mode, you can't disable it in the current session. When using the GET method in the HTTP interface, 'readonly = 1' is set automatically. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.", + "title": "readonly" + }, + { + "location": "/index.html#max_memory_usage", + "text": "The maximum amount of RAM to use for running a query on a single server. In the default configuration file, the maximum is 10 GB. The setting doesn't consider the volume of available memory or the total volume of memory on the machine.\nThe restriction applies to a single query within a single server.\nYou can use SHOW PROCESSLIST to see the current memory consumption for each query.\nIn addition, the peak memory consumption is tracked for each query and written to the log. Memory usage is not monitored for the states of certain aggregate functions. Memory usage is not fully tracked for states of the aggregate functions min , max , any , anyLast , argMin , argMax from String and Array arguments. Memory consumption is also restricted by the parameters max_memory_usage_for_user and max_memory_usage_for_all_queries .", + "title": "max_memory_usage" + }, + { + "location": "/index.html#max_memory_usage_for_user", + "text": "The maximum amount of RAM to use for running a user's queries on a single server. Default values are defined in Settings.h . By default, the amount is not restricted ( max_memory_usage_for_user = 0 ). See also the description of max_memory_usage .", + "title": "max_memory_usage_for_user" + }, + { + "location": "/index.html#max_memory_usage_for_all_queries", + "text": "The maximum amount of RAM to use for running all queries on a single server. Default values are defined in Settings.h . By default, the amount is not restricted ( max_memory_usage_for_all_queries = 0 ). See also the description of max_memory_usage .", + "title": "max_memory_usage_for_all_queries" + }, + { + "location": "/index.html#max_rows_to_read", + "text": "The following restrictions can be checked on each block (instead of on each row). That is, the restrictions can be broken a little.\nWhen running a query in multiple threads, the following restrictions apply to each thread separately. Maximum number of rows that can be read from a table when running a query.", + "title": "max_rows_to_read" + }, + { + "location": "/index.html#max_bytes_to_read", + "text": "Maximum number of bytes (uncompressed data) that can be read from a table when running a query.", + "title": "max_bytes_to_read" + }, + { + "location": "/index.html#read_overflow_mode", + "text": "What to do when the volume of data read exceeds one of the limits: 'throw' or 'break'. By default, throw.", + "title": "read_overflow_mode" + }, + { + "location": "/index.html#max_rows_to_group_by", + "text": "Maximum number of unique keys received from aggregation. This setting lets you limit memory consumption when aggregating.", + "title": "max_rows_to_group_by" + }, + { + "location": "/index.html#group_by_overflow_mode", + "text": "What to do when the number of unique keys for aggregation exceeds the limit: 'throw', 'break', or 'any'. By default, throw.\nUsing the 'any' value lets you run an approximation of GROUP BY. The quality of this approximation depends on the statistical nature of the data.", + "title": "group_by_overflow_mode" + }, + { + "location": "/index.html#max_rows_to_sort", + "text": "Maximum number of rows before sorting. This allows you to limit memory consumption when sorting.", + "title": "max_rows_to_sort" + }, + { + "location": "/index.html#max_bytes_to_sort", + "text": "Maximum number of bytes before sorting.", + "title": "max_bytes_to_sort" + }, + { + "location": "/index.html#sort_overflow_mode", + "text": "What to do if the number of rows received before sorting exceeds one of the limits: 'throw' or 'break'. By default, throw.", + "title": "sort_overflow_mode" + }, + { + "location": "/index.html#max_result_rows", + "text": "Limit on the number of rows in the result. Also checked for subqueries, and on remote servers when running parts of a distributed query.", + "title": "max_result_rows" + }, + { + "location": "/index.html#max_result_bytes", + "text": "Limit on the number of bytes in the result. The same as the previous setting.", + "title": "max_result_bytes" + }, + { + "location": "/index.html#result_overflow_mode", + "text": "What to do if the volume of the result exceeds one of the limits: 'throw' or 'break'. By default, throw.\nUsing 'break' is similar to using LIMIT.", + "title": "result_overflow_mode" + }, + { + "location": "/index.html#max_execution_time", + "text": "Maximum query execution time in seconds.\nAt this time, it is not checked for one of the sorting stages, or when merging and finalizing aggregate functions.", + "title": "max_execution_time" + }, + { + "location": "/index.html#timeout_overflow_mode", + "text": "What to do if the query is run longer than 'max_execution_time': 'throw' or 'break'. By default, throw.", + "title": "timeout_overflow_mode" + }, + { + "location": "/index.html#min_execution_speed", + "text": "Minimal execution speed in rows per second. Checked on every data block when 'timeout_before_checking_execution_speed' expires. If the execution speed is lower, an exception is thrown.", + "title": "min_execution_speed" + }, + { + "location": "/index.html#timeout_before_checking_execution_speed", + "text": "Checks that execution speed is not too slow (no less than 'min_execution_speed'), after the specified time in seconds has expired.", + "title": "timeout_before_checking_execution_speed" + }, + { + "location": "/index.html#max_columns_to_read", + "text": "Maximum number of columns that can be read from a table in a single query. If a query requires reading a greater number of columns, it throws an exception.", + "title": "max_columns_to_read" + }, + { + "location": "/index.html#max_temporary_columns", + "text": "Maximum number of temporary columns that must be kept in RAM at the same time when running a query, including constant columns. If there are more temporary columns than this, it throws an exception.", + "title": "max_temporary_columns" + }, + { + "location": "/index.html#max_temporary_non_const_columns", + "text": "The same thing as 'max_temporary_columns', but without counting constant columns.\nNote that constant columns are formed fairly often when running a query, but they require approximately zero computing resources.", + "title": "max_temporary_non_const_columns" + }, + { + "location": "/index.html#max_subquery_depth", + "text": "Maximum nesting depth of subqueries. If subqueries are deeper, an exception is thrown. By default, 100.", + "title": "max_subquery_depth" + }, + { + "location": "/index.html#max_pipeline_depth", + "text": "Maximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000.", + "title": "max_pipeline_depth" + }, + { + "location": "/index.html#max_ast_depth", + "text": "Maximum nesting depth of a query syntactic tree. If exceeded, an exception is thrown.\nAt this time, it isn't checked during parsing, but only after parsing the query. That is, a syntactic tree that is too deep can be created during parsing, but the query will fail. By default, 1000.", + "title": "max_ast_depth" + }, + { + "location": "/index.html#max_ast_elements", + "text": "Maximum number of elements in a query syntactic tree. If exceeded, an exception is thrown.\nIn the same way as the previous setting, it is checked only after parsing the query. By default, 10,000.", + "title": "max_ast_elements" + }, + { + "location": "/index.html#max_rows_in_set", + "text": "Maximum number of rows for a data set in the IN clause created from a subquery.", + "title": "max_rows_in_set" + }, + { + "location": "/index.html#max_bytes_in_set", + "text": "Maximum number of bytes (uncompressed data) used by a set in the IN clause created from a subquery.", + "title": "max_bytes_in_set" + }, + { + "location": "/index.html#set_overflow_mode", + "text": "What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.", + "title": "set_overflow_mode" + }, + { + "location": "/index.html#max_rows_in_distinct", + "text": "Maximum number of different rows when using DISTINCT.", + "title": "max_rows_in_distinct" + }, + { + "location": "/index.html#max_bytes_in_distinct", + "text": "Maximum number of bytes used by a hash table when using DISTINCT.", + "title": "max_bytes_in_distinct" + }, + { + "location": "/index.html#distinct_overflow_mode", + "text": "What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.", + "title": "distinct_overflow_mode" + }, + { + "location": "/index.html#max_rows_to_transfer", + "text": "Maximum number of rows that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.", + "title": "max_rows_to_transfer" + }, + { + "location": "/index.html#max_bytes_to_transfer", + "text": "Maximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.", + "title": "max_bytes_to_transfer" + }, + { + "location": "/index.html#transfer_overflow_mode", + "text": "What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.", + "title": "transfer_overflow_mode" + }, + { + "location": "/index.html#settings_1", + "text": "", + "title": "Settings" + }, + { + "location": "/index.html#distributed_product_mode", + "text": "Changes the behavior of distributed subqueries , i.e. in cases when the query contains the product of distributed tables. ClickHouse applies the configuration if the subqueries on any level have a distributed table that exists on the local server and has more than one shard. Restrictions: Only applied for IN and JOIN subqueries. Used only if a distributed table is used in the FROM clause. Not used for a table-valued remote function. The possible values \u200b\u200bare:", + "title": "distributed_product_mode" + }, + { + "location": "/index.html#fallback_to_stale_replicas_for_distributed_queries", + "text": "Forces a query to an out-of-date replica if updated data is not available. See \" Replication \". ClickHouse selects the most relevant from the outdated replicas of the table. Used when performing SELECT from a distributed table that points to replicated tables. By default, 1 (enabled).", + "title": "fallback_to_stale_replicas_for_distributed_queries" + }, + { + "location": "/index.html#force_index_by_date", + "text": "Disables query execution if the index can't be used by date. Works with tables in the MergeTree family. If force_index_by_date=1 , ClickHouse checks whether the query has a date key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For example, the condition Date != ' 2000-01-01 ' is acceptable even when it matches all the data in the table (i.e., running the query requires a full scan). For more information about ranges of data in MergeTree tables, see \" MergeTree \".", + "title": "force_index_by_date" + }, + { + "location": "/index.html#force_primary_key", + "text": "Disables query execution if indexing by the primary key is not possible. Works with tables in the MergeTree family. If force_primary_key=1 , ClickHouse checks to see if the query has a primary key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For more information about data ranges in MergeTree tables, see \" MergeTree \".", + "title": "force_primary_key" + }, + { + "location": "/index.html#fsync_metadata", + "text": "Enable or disable fsync when writing .sql files. By default, it is enabled. It makes sense to disable it if the server has millions of tiny table chunks that are constantly being created and destroyed.", + "title": "fsync_metadata" + }, + { + "location": "/index.html#input_format_allow_errors_num", + "text": "Sets the maximum number of acceptable errors when reading from text formats (CSV, TSV, etc.). The default value is 0. Always pair it with input_format_allow_errors_ratio . To skip errors, both settings must be greater than 0. If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_num , ClickHouse ignores the row and moves on to the next one. If input_format_allow_errors_num is exceeded, ClickHouse throws an exception.", + "title": "input_format_allow_errors_num" + }, + { + "location": "/index.html#input_format_allow_errors_ratio", + "text": "Sets the maximum percentage of errors allowed when reading from text formats (CSV, TSV, etc.).\nThe percentage of errors is set as a floating-point number between 0 and 1. The default value is 0. Always pair it with input_format_allow_errors_num . To skip errors, both settings must be greater than 0. If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_ratio , ClickHouse ignores the row and moves on to the next one. If input_format_allow_errors_ratio is exceeded, ClickHouse throws an exception.", + "title": "input_format_allow_errors_ratio" + }, + { + "location": "/index.html#max_block_size", + "text": "In ClickHouse, data is processed by blocks (sets of column parts). The internal processing cycles for a single block are efficient enough, but there are noticeable expenditures on each block. max_block_size is a recommendation for what size of block (in number of rows) to load from tables. The block size shouldn't be too small, so that the expenditures on each block are still noticeable, but not too large, so that the query with LIMIT that is completed after the first block is processed quickly, so that too much memory isn't consumed when extracting a large number of columns in multiple threads, and so that at least some cache locality is preserved. By default, 65,536. Blocks the size of max_block_size are not always loaded from the table. If it is obvious that less data needs to be retrieved, a smaller block is processed.", + "title": "max_block_size" + }, + { + "location": "/index.html#preferred_block_size_bytes", + "text": "Used for the same purpose as max_block_size , but it sets the recommended block size in bytes by adapting it to the number of rows in the block.\nHowever, the block size cannot be more than max_block_size rows.\nDisabled by default (set to 0). It only works when reading from MergeTree engines.", + "title": "preferred_block_size_bytes" + }, + { + "location": "/index.html#log_queries", + "text": "Setting up query the logging. Queries sent to ClickHouse with this setup are logged according to the rules in the query_log server configuration parameter. Example : log_queries=1", + "title": "log_queries" + }, + { + "location": "/index.html#max_insert_block_size", + "text": "The size of blocks to form for insertion into a table.\nThis setting only applies in cases when the server forms the blocks.\nFor example, for an INSERT via the HTTP interface, the server parses the data format and forms blocks of the specified size.\nBut when using clickhouse-client, the client parses the data itself, and the 'max_insert_block_size' setting on the server doesn't affect the size of the inserted blocks.\nThe setting also doesn't have a purpose when using INSERT SELECT, since data is inserted using the same blocks that are formed after SELECT. By default, it is 1,048,576. This is slightly more than max_block_size . The reason for this is because certain table engines ( *MergeTree ) form a data part on the disk for each inserted block, which is a fairly large entity. Similarly, *MergeTree tables sort data during insertion, and a large enough block size allows sorting more data in RAM.", + "title": "max_insert_block_size" + }, + { + "location": "/index.html#max_replica_delay_for_distributed_queries", + "text": "Disables lagging replicas for distributed queries. See \" Replication \". Sets the time in seconds. If a replica lags more than the set value, this replica is not used. Default value: 0 (off). Used when performing SELECT from a distributed table that points to replicated tables.", + "title": "max_replica_delay_for_distributed_queries" + }, + { + "location": "/index.html#max_threads", + "text": "The maximum number of query processing threads excluding threads for retrieving data from remote servers (see the 'max_distributed_connections' parameter). This parameter applies to threads that perform the same stages of the query processing pipeline in parallel.\nFor example, if reading from a table, evaluating expressions with functions, filtering with WHERE and pre-aggregating for GROUP BY can all be done in parallel using at least 'max_threads' number of threads, then 'max_threads' are used. By default, 8. If less than one SELECT query is normally run on a server at a time, set this parameter to a value slightly less than the actual number of processor cores. For queries that are completed quickly because of a LIMIT, you can set a lower 'max_threads'. For example, if the necessary number of entries are located in every block and max_threads = 8, 8 blocks are retrieved, although it would have been enough to read just one. The smaller the max_threads value, the less memory is consumed.", + "title": "max_threads" + }, + { + "location": "/index.html#max_compress_block_size", + "text": "The maximum size of blocks of uncompressed data before compressing for writing to a table. By default, 1,048,576 (1 MiB). If the size is reduced, the compression rate is significantly reduced, the compression and decompression speed increases slightly due to cache locality, and memory consumption is reduced. There usually isn't any reason to change this setting. Don't confuse blocks for compression (a chunk of memory consisting of bytes) and blocks for query processing (a set of rows from a table).", + "title": "max_compress_block_size" + }, + { + "location": "/index.html#min_compress_block_size", + "text": "For MergeTree \" tables. In order to reduce latency when processing queries, a block is compressed when writing the next mark if its size is at least 'min_compress_block_size'. By default, 65,536. The actual size of the block, if the uncompressed data is less than 'max_compress_block_size', is no less than this value and no less than the volume of data for one mark. Let's look at an example. Assume that 'index_granularity' was set to 8192 during table creation. We are writing a UInt32-type column (4 bytes per value). When writing 8192 rows, the total will be 32 KB of data. Since min_compress_block_size = 65,536, a compressed block will be formed for every two marks. We are writing a URL column with the String type (average size of 60 bytes per value). When writing 8192 rows, the average will be slightly less than 500 KB of data. Since this is more than 65,536, a compressed block will be formed for each mark. In this case, when reading data from the disk in the range of a single mark, extra data won't be decompressed. There usually isn't any reason to change this setting.", + "title": "min_compress_block_size" + }, + { + "location": "/index.html#max_query_size", + "text": "The maximum part of a query that can be taken to RAM for parsing with the SQL parser.\nThe INSERT query also contains data for INSERT that is processed by a separate stream parser (that consumes O(1) RAM), which is not included in this restriction. The default is 256 KiB.", + "title": "max_query_size" + }, + { + "location": "/index.html#interactive_delay", + "text": "The interval in microseconds for checking whether request execution has been canceled and sending the progress. By default, 100,000 (check for canceling and send progress ten times per second).", + "title": "interactive_delay" + }, + { + "location": "/index.html#connect_timeout", + "text": "", + "title": "connect_timeout" + }, + { + "location": "/index.html#receive_timeout", + "text": "", + "title": "receive_timeout" + }, + { + "location": "/index.html#send_timeout", + "text": "Timeouts in seconds on the socket used for communicating with the client. By default, 10, 300, 300.", + "title": "send_timeout" + }, + { + "location": "/index.html#poll_interval", + "text": "Lock in a wait loop for the specified number of seconds. By default, 10.", + "title": "poll_interval" + }, + { + "location": "/index.html#max_distributed_connections", + "text": "The maximum number of simultaneous connections with remote servers for distributed processing of a single query to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster. By default, 100. The following parameters are only used when creating Distributed tables (and when launching a server), so there is no reason to change them at runtime.", + "title": "max_distributed_connections" + }, + { + "location": "/index.html#distributed_connections_pool_size", + "text": "The maximum number of simultaneous connections with remote servers for distributed processing of all queries to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster. By default, 128.", + "title": "distributed_connections_pool_size" + }, + { + "location": "/index.html#connect_timeout_with_failover_ms", + "text": "The timeout in milliseconds for connecting to a remote server for a Distributed table engine, if the 'shard' and 'replica' sections are used in the cluster definition.\nIf unsuccessful, several attempts are made to connect to various replicas. By default, 50.", + "title": "connect_timeout_with_failover_ms" + }, + { + "location": "/index.html#connections_with_failover_max_tries", + "text": "The maximum number of connection attempts with each replica, for the Distributed table engine. By default, 3.", + "title": "connections_with_failover_max_tries" + }, + { + "location": "/index.html#extremes", + "text": "Whether to count extreme values (the minimums and maximums in columns of a query result). Accepts 0 or 1. By default, 0 (disabled).\nFor more information, see the section \"Extreme values\".", + "title": "extremes" + }, + { + "location": "/index.html#use_uncompressed_cache", + "text": "Whether to use a cache of uncompressed blocks. Accepts 0 or 1. By default, 0 (disabled).\nThe uncompressed cache (only for tables in the MergeTree family) allows significantly reducing latency and increasing throughput when working with a large number of short queries. Enable this setting for users who send frequent short requests. Also pay attention to the 'uncompressed_cache_size' configuration parameter (only set in the config file) \u2013 the size of uncompressed cache blocks. By default, it is 8 GiB. The uncompressed cache is filled in as needed; the least-used data is automatically deleted. For queries that read at least a somewhat large volume of data (one million rows or more), the uncompressed cache is disabled automatically in order to save space for truly small queries. So you can keep the 'use_uncompressed_cache' setting always set to 1.", + "title": "use_uncompressed_cache" + }, + { + "location": "/index.html#replace_running_query", + "text": "When using the HTTP interface, the 'query_id' parameter can be passed. This is any string that serves as the query identifier.\nIf a query from the same user with the same 'query_id' already exists at this time, the behavior depends on the 'replace_running_query' parameter. 0 (default) \u2013 Throw an exception (don't allow the query to run if a query with the same 'query_id' is already running). 1 \u2013 Cancel the old query and start running the new one. Yandex.Metrica uses this parameter set to 1 for implementing suggestions for segmentation conditions. After entering the next character, if the old query hasn't finished yet, it should be canceled.", + "title": "replace_running_query" + }, + { + "location": "/index.html#schema", + "text": "This parameter is useful when you are using formats that require a schema definition, such as Cap'n Proto . The value depends on the format.", + "title": "schema" + }, + { + "location": "/index.html#stream_flush_interval_ms", + "text": "Works for tables with streaming in the case of a timeout, or when a thread generates max_insert_block_size rows. The default value is 7500. The smaller the value, the more often data is flushed into the table. Setting the value too low leads to poor performance.", + "title": "stream_flush_interval_ms" + }, + { + "location": "/index.html#load_balancing", + "text": "Which replicas (among healthy replicas) to preferably send a query to (on the first attempt) for distributed processing.", + "title": "load_balancing" + }, + { + "location": "/index.html#random-default", + "text": "The number of errors is counted for each replica. The query is sent to the replica with the fewest errors, and if there are several of these, to any one of them.\nDisadvantages: Server proximity is not accounted for; if the replicas have different data, you will also get different data.", + "title": "random (default)" + }, + { + "location": "/index.html#nearest_hostname", + "text": "The number of errors is counted for each replica. Every 5 minutes, the number of errors is integrally divided by 2. Thus, the number of errors is calculated for a recent time with exponential smoothing. If there is one replica with a minimal number of errors (i.e. errors occurred recently on the other replicas), the query is sent to it. If there are multiple replicas with the same minimal number of errors, the query is sent to the replica with a host name that is most similar to the server's host name in the config file (for the number of different characters in identical positions, up to the minimum length of both host names). For instance, example01-01-1 and example01-01-2.yandex.ru are different in one position, while example01-01-1 and example01-02-2 differ in two places.\nThis method might seem a little stupid, but it doesn't use external data about network topology, and it doesn't compare IP addresses, which would be complicated for our IPv6 addresses. Thus, if there are equivalent replicas, the closest one by name is preferred.\nWe can also assume that when sending a query to the same server, in the absence of failures, a distributed query will also go to the same servers. So even if different data is placed on the replicas, the query will return mostly the same results.", + "title": "nearest_hostname" + }, + { + "location": "/index.html#in_order", + "text": "Replicas are accessed in the same order as they are specified. The number of errors does not matter.\nThis method is appropriate when you know exactly which replica is preferable.", + "title": "in_order" + }, + { + "location": "/index.html#totals_mode", + "text": "How to calculate TOTALS when HAVING is present, as well as when max_rows_to_group_by and group_by_overflow_mode = 'any' are present.\nSee the section \"WITH TOTALS modifier\".", + "title": "totals_mode" + }, + { + "location": "/index.html#totals_auto_threshold", + "text": "The threshold for totals_mode = 'auto' .\nSee the section \"WITH TOTALS modifier\".", + "title": "totals_auto_threshold" + }, + { + "location": "/index.html#default_sample", + "text": "Floating-point number from 0 to 1. By default, 1.\nAllows you to set the default sampling ratio for all SELECT queries.\n(For tables that do not support sampling, it throws an exception.)\nIf set to 1, sampling is not performed by default.", + "title": "default_sample" + }, + { + "location": "/index.html#max_parallel_replicas", + "text": "The maximum number of replicas for each shard when executing a query.\nFor consistency (to get different parts of the same data split), this option only works when the sampling key is set.\nReplica lag is not controlled.", + "title": "max_parallel_replicas" + }, + { + "location": "/index.html#compile", + "text": "Enable compilation of queries. By default, 0 (disabled). Compilation is only used for part of the query-processing pipeline: for the first stage of aggregation (GROUP BY).\nIf this portion of the pipeline was compiled, the query may run faster due to deployment of short cycles and inlining aggregate function calls. The maximum performance improvement (up to four times faster in rare cases) is seen for queries with multiple simple aggregate functions. Typically, the performance gain is insignificant. In very rare cases, it may slow down query execution.", + "title": "compile" + }, + { + "location": "/index.html#min_count_to_compile", + "text": "How many times to potentially use a compiled chunk of code before running compilation. By default, 3.\nIf the value is zero, then compilation runs synchronously and the query waits for the end of the compilation process before continuing execution. This can be used for testing; otherwise, use values \u200b\u200bstarting with 1. Compilation normally takes about 5-10 seconds.\nIf the value is 1 or more, compilation occurs asynchronously in a separate thread. The result will be used as soon as it is ready, including by queries that are currently running. Compiled code is required for each different combination of aggregate functions used in the query and the type of keys in the GROUP BY clause.\nThe results of compilation are saved in the build directory in the form of .so files. There is no restriction on the number of compilation results, since they don't use very much space. Old results will be used after server restarts, except in the case of a server upgrade \u2013 in this case, the old results are deleted.", + "title": "min_count_to_compile" + }, + { + "location": "/index.html#input_format_skip_unknown_fields", + "text": "If the value is true, running INSERT skips input data from columns with unknown names. Otherwise, this situation will generate an exception.\nIt works for JSONEachRow and TSKV formats.", + "title": "input_format_skip_unknown_fields" + }, + { + "location": "/index.html#output_format_json_quote_64bit_integers", + "text": "If the value is true, integers appear in quotes when using JSON* Int64 and UInt64 formats (for compatibility with most JavaScript implementations); otherwise, integers are output without the quotes.", + "title": "output_format_json_quote_64bit_integers" + }, + { + "location": "/index.html#format_csv_delimiter", + "text": "The character to be considered as a delimiter in CSV data. By default, , .", + "title": "format_csv_delimiter" + }, + { + "location": "/index.html#settings-profiles", + "text": "A settings profile is a collection of settings grouped under the same name. Each ClickHouse user has a profile.\nTo apply all the settings in a profile, set profile . Example: Setting web profile. SET profile = web Settings profiles are declared in the user config file. This is usually users.xml . Example: !-- Settings profiles -- profiles \n !-- Default settings -- \n default \n !-- The maximum number of threads when running a single query. -- \n max_threads 8 /max_threads \n /default \n\n !-- Settings for quries from the user interface -- \n web \n max_rows_to_read 1000000000 /max_rows_to_read \n max_bytes_to_read 100000000000 /max_bytes_to_read \n\n max_rows_to_group_by 1000000 /max_rows_to_group_by \n group_by_overflow_mode any /group_by_overflow_mode \n\n max_rows_to_sort 1000000 /max_rows_to_sort \n max_bytes_to_sort 1000000000 /max_bytes_to_sort \n\n max_result_rows 100000 /max_result_rows \n max_result_bytes 100000000 /max_result_bytes \n result_overflow_mode break /result_overflow_mode \n\n max_execution_time 600 /max_execution_time \n min_execution_speed 1000000 /min_execution_speed \n timeout_before_checking_execution_speed 15 /timeout_before_checking_execution_speed \n\n max_columns_to_read 25 /max_columns_to_read \n max_temporary_columns 100 /max_temporary_columns \n max_temporary_non_const_columns 50 /max_temporary_non_const_columns \n\n max_subquery_depth 2 /max_subquery_depth \n max_pipeline_depth 25 /max_pipeline_depth \n max_ast_depth 50 /max_ast_depth \n max_ast_elements 100 /max_ast_elements \n\n readonly 1 /readonly \n /web /profiles The example specifies two profiles: default and web . The default profile has a special purpose: it must always be present and is applied when starting the server. In other words, the default profile contains default settings. The web profile is a regular profile that can be set using the SET query or using a URL parameter in an HTTP query. Settings profiles can inherit from each other. To use inheritance, indicate the profile setting before the other settings that are listed in the profile.", + "title": "Settings profiles" + }, + { + "location": "/index.html#clickhouse-utility", + "text": "clickhouse-local \u2014 Allows running SQL queries on data without stopping the ClickHouse server, similar to how awk does this. clickhouse-copier \u2014 Copies (and reshards) data from one cluster to another cluster.", + "title": "ClickHouse utility" + }, + { + "location": "/index.html#clickhouse-copier", + "text": "Copies data from the tables in one cluster to tables in another (or the same) cluster. You can run multiple clickhouse-copier instances on different servers to perform the same job. ZooKeeper is used for syncing the processes. After starting, clickhouse-copier : Connects to ZooKeeper and receives: Copying jobs. The state of the copying jobs. It performs the jobs. Each running process chooses the \"closest\" shard of the source cluster and copies the data into the destination cluster, resharding the data if necessary. clickhouse-copier tracks the changes in ZooKeeper and applies them on the fly. To reduce network traffic, we recommend running clickhouse-copier on the same server where the source data is located.", + "title": "clickhouse-copier" + }, + { + "location": "/index.html#running-clickhouse-copier", + "text": "The utility should be run manually: clickhouse-copier copier --daemon --config zookeeper.xml --task-path /task/path --base-dir /path/to/dir Parameters: daemon \u2014 Starts clickhouse-copier in daemon mode. config \u2014 The path to the zookeeper.xml file with the parameters for the connection to ZooKeeper. task-path \u2014 The path to the ZooKeeper node. This node is used for syncing clickhouse-copier processes and storing tasks. Tasks are stored in $task-path/description . base-dir \u2014 The path to logs and auxiliary files. When it starts, clickhouse-copier creates clickhouse-copier_YYYYMMHHSS_ PID subdirectories in $base-dir . If this parameter is omitted, the directories are created in the directory where clickhouse-copier was launched.", + "title": "Running clickhouse-copier" + }, + { + "location": "/index.html#format-of-zookeeperxml", + "text": "yandex \n zookeeper \n node index= 1 \n host 127.0.0.1 /host \n port 2181 /port \n /node \n /zookeeper /yandex", + "title": "Format of zookeeper.xml" + }, + { + "location": "/index.html#configuration-of-copying-tasks", + "text": "yandex \n !-- Configuration of clusters as in an ordinary server config -- \n remote_servers \n source_cluster \n shard \n internal_replication false /internal_replication \n replica \n host 127.0.0.1 /host \n port 9000 /port \n /replica \n /shard \n ...\n /source_cluster \n\n destination_cluster \n ...\n /destination_cluster \n /remote_servers \n\n !-- How many simultaneously active workers are possible. If you run more workers superfluous workers will sleep. -- \n max_workers 2 /max_workers \n\n !-- Setting used to fetch (pull) data from source cluster tables -- \n settings_pull \n readonly 1 /readonly \n /settings_pull \n\n !-- Setting used to insert (push) data to destination cluster tables -- \n settings_push \n readonly 0 /readonly \n /settings_push \n\n !-- Common setting for fetch (pull) and insert (push) operations. The copier process context also uses it. They are overlaid by settings_pull/ and settings_push/ respectively. -- \n settings \n connect_timeout 3 /connect_timeout \n !-- Sync insert is set forcibly, leave it here just in case. -- \n insert_distributed_sync 1 /insert_distributed_sync \n /settings \n\n !-- Copying description of tasks. You can specify several table tasks in the same task description (in the same ZooKeeper node), and they will be performed sequentially. -- \n tables \n !-- A table task that copies one table. -- \n table_hits \n !-- Source cluster name (from the remote_servers/ section) and tables in it that should be copied -- \n cluster_pull source_cluster /cluster_pull \n database_pull test /database_pull \n table_pull hits /table_pull \n\n !-- Destination cluster name and tables in which the data should be inserted -- \n cluster_push destination_cluster /cluster_push \n database_push test /database_push \n table_push hits2 /table_push \n\n !-- Engine of destination tables. If the destination tables have not been created yet, workers create them using column definitions from source tables and the engine definition from here. NOTE: If the first worker starts to insert data and detects that the destination partition is not empty, then the partition will be dropped and refilled. Take this into account if you already have some data in destination tables. You can directly specify partitions that should be copied in enabled_partitions/ . They should be in quoted format like the partition column in the system.parts table. -- \n engine \n ENGINE=ReplicatedMergeTree( /clickhouse/tables/{cluster}/{shard}/hits2 , {replica} )\n PARTITION BY toMonday(date)\n ORDER BY (CounterID, EventDate)\n /engine \n\n !-- Sharding key used to insert data to destination cluster -- \n sharding_key jumpConsistentHash(intHash64(UserID), 2) /sharding_key \n\n !-- Optional expression that filter data while pull them from source servers -- \n where_condition CounterID != 0 /where_condition \n\n !-- This section specifies partitions that should be copied, other partition will be ignored. Partition names should have the same format as partition column of system.parts table (i.e. a quoted text). Since partition key of source and destination cluster could be different, these partition names specify destination partitions. Note: Although this section is optional (if it omitted, all partitions will be copied), it is strongly recommended to specify the partitions explicitly. If you already have some partitions ready on the destination cluster, they will be removed at the start of the copying, because they will be interpreted as unfinished data from the previous copying. -- \n enabled_partitions \n partition 2018-02-26 /partition \n partition 2018-03-05 /partition \n ...\n /enabled_partitions \n /table_hits \n\n !-- Next table to copy. It is not copied until the previous table is copying. -- \n /table_visits \n ...\n /table_visits \n ...\n /tables /yandex clickhouse-copier tracks the changes in /task/path/description and applies them on the fly. For instance, if you change the value of max_workers , the number of processes running tasks will also change.", + "title": "Configuration of copying tasks" + }, + { + "location": "/index.html#clickhouse-local", + "text": "The clickhouse-local program enables you to perform fast processing on local files that store tables, without having to deploy and configure the ClickHouse server.", + "title": "clickhouse-local" + }, + { + "location": "/index.html#clickhouse-development", + "text": "", + "title": "ClickHouse Development" + }, + { + "location": "/index.html#overview-of-clickhouse-architecture", + "text": "ClickHouse is a true column-oriented DBMS. Data is stored by columns, and during the execution of arrays (vectors or chunks of columns). Whenever possible, operations are dispatched on arrays, rather than on individual values. This is called \"vectorized query execution,\" and it helps lower the cost of actual data processing. This idea is nothing new. It dates back to the APL programming language and its descendants: A + , J , K , and Q . Array programming is used in scientific data processing. Neither is this idea something new in relational databases: for example, it is used in the Vectorwise system. There are two different approaches for speeding up the query processing: vectorized query execution and runtime code generation. In the latter, the code is generated for every kind of query on the fly, removing all indirection and dynamic dispatch. Neither of these approaches is strictly better than the other. Runtime code generation can be better when it's fuses many operations together, thus fully utilizing CPU execution units and the pipeline. Vectorized query execution can be less practical, because it involves the temporary vectors that must be written to the cache and read back. If the temporary data does not fit in the L2 cache, this becomes an issue. But vectorized query execution more easily utilizes the SIMD capabilities of the CPU. A research paper written by our friends shows that it is better to combine both approaches. ClickHouse uses vectorized query execution and has limited initial support for runtime code.", + "title": "Overview of ClickHouse architecture" + }, + { + "location": "/index.html#columns", + "text": "To represent columns in memory (actually, chunks of columns), the IColumn interface is used. This interface provides helper methods for implementation of various relational operators. Almost all operations are immutable: they do not modify the original column, but create a new modified one. For example, the IColumn :: filter method accepts a filter byte mask. It is used for the WHERE and HAVING relational operators. Additional examples: the IColumn :: permute method to support ORDER BY , the IColumn :: cut method to support LIMIT , and so on. Various IColumn implementations ( ColumnUInt8 , ColumnString and so on) are responsible for the memory layout of columns. Memory layout is usually a contiguous array. For the integer type of columns it is just one contiguous array, like std :: vector . For String and Array columns, it is two vectors: one for all array elements, placed contiguously, and a second one for offsets to the beginning of each array. There is also ColumnConst that stores just one value in memory, but looks like a column.", + "title": "Columns" + }, + { + "location": "/index.html#field", + "text": "Nevertheless, it is possible to work with individual values as well. To represent an individual value, the Field is used. Field is just a discriminated union of UInt64 , Int64 , Float64 , String and Array . IColumn has the operator[] method to get the n-th value as a Field , and the insert method to append a Field to the end of a column. These methods are not very efficient, because they require dealing with temporary Field objects representing an individual value. There are more efficient methods, such as insertFrom , insertRangeFrom , and so on. Field doesn't have enough information about a specific data type for a table. For example, UInt8 , UInt16 , UInt32 , and UInt64 are all represented as UInt64 in a Field .", + "title": "Field" + }, + { + "location": "/index.html#leaky-abstractions", + "text": "IColumn has methods for common relational transformations of data, but they don't meet all needs. For example, ColumnUInt64 doesn't have a method to calculate the sum of two columns, and ColumnString doesn't have a method to run a substring search. These countless routines are implemented outside of IColumn . Various functions on columns can be implemented in a generic, non-efficient way using IColumn methods to extract Field values, or in a specialized way using knowledge of inner memory layout of data in a specific IColumn implementation. To do this, functions are cast to a specific IColumn type and deal with internal representation directly. For example, ColumnUInt64 has the getData method that returns a reference to an internal array, then a separate routine reads or fills that array directly. In fact, we have \"leaky abstractions\" to allow efficient specializations of various routines.", + "title": "Leaky abstractions" + }, + { + "location": "/index.html#data-types_1", + "text": "IDataType is responsible for serialization and deserialization: for reading and writing chunks of columns or individual values in binary or text form. IDataType directly corresponds to data types in tables. For example, there are DataTypeUInt32 , DataTypeDateTime , DataTypeString and so on. IDataType and IColumn are only loosely related to each other. Different data types can be represented in memory by the same IColumn implementations. For example, DataTypeUInt32 and DataTypeDateTime are both represented by ColumnUInt32 or ColumnConstUInt32 . In addition, the same data type can be represented by different IColumn implementations. For example, DataTypeUInt8 can be represented by ColumnUInt8 or ColumnConstUInt8 . IDataType only stores metadata. For instance, DataTypeUInt8 doesn't store anything at all (except vptr) and DataTypeFixedString stores just N (the size of fixed-size strings). IDataType has helper methods for various data formats. Examples are methods to serialize a value with possible quoting, to serialize a value for JSON, and to serialize a value as part of XML format. There is no direct correspondence to data formats. For example, the different data formats Pretty and TabSeparated can use the same serializeTextEscaped helper method from the IDataType interface.", + "title": "Data types" + }, + { + "location": "/index.html#block", + "text": "A Block is a container that represents a subset (chunk) of a table in memory. It is just a set of triples: (IColumn, IDataType, column name) . During query execution, data is processed by Block s. If we have a Block , we have data (in the IColumn object), we have information about its type (in IDataType ) that tells us how to deal with that column, and we have the column name (either the original column name from the table, or some artificial name assigned for getting temporary results of calculations). When we calculate some function over columns in a block, we add another column with its result to the block, and we don't touch columns for arguments of the function because operations are immutable. Later, unneeded columns can be removed from the block, but not modified. This is convenient for elimination of common subexpressions. Blocks are created for every processed chunk of data. Note that for the same type of calculation, the column names and types remain the same for different blocks, and only column data changes. It is better to split block data from the block header, because small block sizes will have a high overhead of temporary strings for copying shared_ptrs and column names.", + "title": "Block" + }, + { + "location": "/index.html#block-streams", + "text": "Block streams are for processing data. We use streams of blocks to read data from somewhere, perform data transformations, or write data to somewhere. IBlockInputStream has the read method to fetch the next block while available. IBlockOutputStream has the write method to push the block somewhere. Streams are responsible for: Reading or writing to a table. The table just returns a stream for reading or writing blocks. Implementing data formats. For example, if you want to output data to a terminal in Pretty format, you create a block output stream where you push blocks, and it formats them. Performing data transformations. Let's say you have IBlockInputStream and want to create a filtered stream. You create FilterBlockInputStream and initialize it with your stream. Then when you pull a block from FilterBlockInputStream , it pulls a block from your stream, filters it, and returns the filtered block to you. Query execution pipelines are represented this way. There are more sophisticated transformations. For example, when you pull from AggregatingBlockInputStream , it reads all data from its source, aggregates it, and then returns a stream of aggregated data for you. Another example: UnionBlockInputStream accepts many input sources in the constructor and also a number of threads. It launches multiple threads and reads from multiple sources in parallel. Block streams use the \"pull\" approach to control flow: when you pull a block from the first stream, it consequently pulls the required blocks from nested streams, and the entire execution pipeline will work. Neither \"pull\" nor \"push\" is the best solution, because control flow is implicit, and that limits implementation of various features like simultaneous execution of multiple queries (merging many pipelines together). This limitation could be overcome with coroutines or just running extra threads that wait for each other. We may have more possibilities if we make control flow explicit: if we locate the logic for passing data from one calculation unit to another outside of those calculation units. Read this article for more thoughts. We should note that the query execution pipeline creates temporary data at each step. We try to keep block size small enough so that temporary data fits in the CPU cache. With that assumption, writing and reading temporary data is almost free in comparison with other calculations. We could consider an alternative, which is to fuse many operations in the pipeline together, to make the pipeline as short as possible and remove much of the temporary data. This could be an advantage, but it also has drawbacks. For example, a split pipeline makes it easy to implement caching intermediate data, stealing intermediate data from similar queries running at the same time, and merging pipelines for similar queries.", + "title": "Block Streams" + }, + { + "location": "/index.html#formats_1", + "text": "Data formats are implemented with block streams. There are \"presentational\" formats only suitable for output of data to the client, such as Pretty format, which provides only IBlockOutputStream . And there are input/output formats, such as TabSeparated or JSONEachRow . There are also row streams: IRowInputStream and IRowOutputStream . They allow you to pull/push data by individual rows, not by blocks. And they are only needed to simplify implementation of row-oriented formats. The wrappers BlockInputStreamFromRowInputStream and BlockOutputStreamFromRowOutputStream allow you to convert row-oriented streams to regular block-oriented streams.", + "title": "Formats" + }, + { + "location": "/index.html#io", + "text": "For byte-oriented input/output, there are ReadBuffer and WriteBuffer abstract classes. They are used instead of C++ iostream 's. Don't worry: every mature C++ project is using something other than iostream 's for good reasons. ReadBuffer and WriteBuffer are just a contiguous buffer and a cursor pointing to the position in that buffer. Implementations may own or not own the memory for the buffer. There is a virtual method to fill the buffer with the following data (for ReadBuffer ) or to flush the buffer somewhere (for WriteBuffer ). The virtual methods are rarely called. Implementations of ReadBuffer / WriteBuffer are used for working with files and file descriptors and network sockets, for implementing compression ( CompressedWriteBuffer is initialized with another WriteBuffer and performs compression before writing data to it), and for other purposes \u2013 the names ConcatReadBuffer , LimitReadBuffer , and HashingWriteBuffer speak for themselves. Read/WriteBuffers only deal with bytes. To help with formatted input/output (for instance, to write a number in decimal format), there are functions from ReadHelpers and WriteHelpers header files. Let's look at what happens when you want to write a result set in JSON format to stdout. You have a result set ready to be fetched from IBlockInputStream . You create WriteBufferFromFileDescriptor(STDOUT_FILENO) to write bytes to stdout. You create JSONRowOutputStream , initialized with that WriteBuffer , to write rows in JSON to stdout. You create BlockOutputStreamFromRowOutputStream on top of it, to represent it as IBlockOutputStream . Then you call copyData to transfer data from IBlockInputStream to IBlockOutputStream , and everything works. Internally, JSONRowOutputStream will write various JSON delimiters and call the IDataType::serializeTextJSON method with a reference to IColumn and the row number as arguments. Consequently, IDataType::serializeTextJSON will call a method from WriteHelpers.h : for example, writeText for numeric types and writeJSONString for DataTypeString .", + "title": "I/O" + }, + { + "location": "/index.html#tables", + "text": "Tables are represented by the IStorage interface. Different implementations of that interface are different table engines. Examples are StorageMergeTree , StorageMemory , and so on. Instances of these classes are just tables. The most important IStorage methods are read and write . There are also alter , rename , drop , and so on. The read method accepts the following arguments: the set of columns to read from a table, the AST query to consider, and the desired number of streams to return. It returns one or multiple IBlockInputStream objects and information about the stage of data processing that was completed inside a table engine during query execution. In most cases, the read method is only responsible for reading the specified columns from a table, not for any further data processing. All further data processing is done by the query interpreter and is outside the responsibility of IStorage . But there are notable exceptions: The AST query is passed to the read method and the table engine can use it to derive index usage and to read less data from a table. Sometimes the table engine can process data itself to a specific stage. For example, StorageDistributed can send a query to remote servers, ask them to process data to a stage where data from different remote servers can be merged, and return that preprocessed data.\nThe query interpreter then finishes processing the data. The table's read method can return multiple IBlockInputStream objects to allow parallel data processing. These multiple block input streams can read from a table in parallel. Then you can wrap these streams with various transformations (such as expression evaluation or filtering) that can be calculated independently and create a UnionBlockInputStream on top of them, to read from multiple streams in parallel. There are also TableFunction s. These are functions that return a temporary IStorage object to use in the FROM clause of a query. To get a quick idea of how to implement your own table engine, look at something simple, like StorageMemory or StorageTinyLog . As the result of the read method, IStorage returns QueryProcessingStage \u2013 information about what parts of the query were already calculated inside storage. Currently we have only very coarse granularity for that information. There is no way for the storage to say \"I have already processed this part of the expression in WHERE, for this range of data\". We need to work on that.", + "title": "Tables" + }, + { + "location": "/index.html#parsers", + "text": "A query is parsed by a hand-written recursive descent parser. For example, ParserSelectQuery just recursively calls the underlying parsers for various parts of the query. Parsers create an AST . The AST is represented by nodes, which are instances of IAST . Parser generators are not used for historical reasons.", + "title": "Parsers" + }, + { + "location": "/index.html#interpreters", + "text": "Interpreters are responsible for creating the query execution pipeline from an AST . There are simple interpreters, such as InterpreterExistsQuery and InterpreterDropQuery , or the more sophisticated InterpreterSelectQuery . The query execution pipeline is a combination of block input or output streams. For example, the result of interpreting the SELECT query is the IBlockInputStream to read the result set from; the result of the INSERT query is the IBlockOutputStream to write data for insertion to; and the result of interpreting the INSERT SELECT query is the IBlockInputStream that returns an empty result set on the first read, but that copies data from SELECT to INSERT at the same time. InterpreterSelectQuery uses ExpressionAnalyzer and ExpressionActions machinery for query analysis and transformations. This is where most rule-based query optimizations are done. ExpressionAnalyzer is quite messy and should be rewritten: various query transformations and optimizations should be extracted to separate classes to allow modular transformations or query.", + "title": "Interpreters" + }, + { + "location": "/index.html#functions_2", + "text": "There are ordinary functions and aggregate functions. For aggregate functions, see the next section. Ordinary functions don't change the number of rows \u2013 they work as if they are processing each row independently. In fact, functions are not called for individual rows, but for Block 's of data to implement vectorized query execution. There are some miscellaneous functions, like blockSize , rowNumberInBlock , and runningAccumulate , that exploit block processing and violate the independence of rows. ClickHouse has strong typing, so implicit type conversion doesn't occur. If a function doesn't support a specific combination of types, an exception will be thrown. But functions can work (be overloaded) for many different combinations of types. For example, the plus function (to implement the + operator) works for any combination of numeric types: UInt8 + Float32 , UInt16 + Int8 , and so on. Also, some variadic functions can accept any number of arguments, such as the concat function. Implementing a function may be slightly inconvenient because a function explicitly dispatches supported data types and supported IColumns . For example, the plus function has code generated by instantiation of a C++ template for each combination of numeric types, and for constant or non-constant left and right arguments. This is a nice place to implement runtime code generation to avoid template code bloat. Also, it will make it possible to add fused functions like fused multiply-add, or to make multiple comparisons in one loop iteration. Due to vectorized query execution, functions are not short-circuit. For example, if you write WHERE f(x) AND g(y) , both sides will be calculated, even for rows, when f(x) is zero (except when f(x) is a zero constant expression). But if selectivity of the f(x) condition is high, and calculation of f(x) is much cheaper than g(y) , it's better to implement multi-pass calculation: first calculate f(x) , then filter columns by the result, and then calculate g(y) only for smaller, filtered chunks of data.", + "title": "Functions" + }, + { + "location": "/index.html#aggregate-functions_1", + "text": "Aggregate functions are stateful functions. They accumulate passed values into some state, and allow you to get results from that state. They are managed with the IAggregateFunction interface. States can be rather simple (the state for AggregateFunctionCount is just a single UInt64 value) or quite complex (the state of AggregateFunctionUniqCombined is a combination of a linear array, a hash table and a HyperLogLog probabilistic data structure). To deal with multiple states while executing a high-cardinality GROUP BY query, states are allocated in Arena (a memory pool), or they could be allocated in any suitable piece of memory. States can have a non-trivial constructor and destructor: for example, complex aggregation states can allocate additional memory themselves. This requires some attention to creating and destroying states and properly passing their ownership, to keep track of who and when will destroy states. Aggregation states can be serialized and deserialized to pass over the network during distributed query execution or to write them on disk where there is not enough RAM. They can even be stored in a table with the DataTypeAggregateFunction to allow incremental aggregation of data. The serialized data format for aggregate function states is not versioned right now. This is ok if aggregate states are only stored temporarily. But we have the AggregatingMergeTree table engine for incremental aggregation, and people are already using it in production. This is why we should add support for backward compatibility when changing the serialized format for any aggregate function in the future.", + "title": "Aggregate Functions" + }, + { + "location": "/index.html#server", + "text": "The server implements several different interfaces: An HTTP interface for any foreign clients. A TCP interface for the native ClickHouse client and for cross-server communication during distributed query execution. An interface for transferring data for replication. Internally, it is just a basic multithreaded server without coroutines, fibers, etc. Since the server is not designed to process a high rate of simple queries but is intended to process a relatively low rate of complex queries, each of them can process a vast amount of data for analytics. The server initializes the Context class with the necessary environment for query execution: the list of available databases, users and access rights, settings, clusters, the process list, the query log, and so on. This environment is used by interpreters. We maintain full backward and forward compatibility for the server TCP protocol: old clients can talk to new servers and new clients can talk to old servers. But we don't want to maintain it eternally, and we are removing support for old versions after about one year. For all external applications, we recommend using the HTTP interface because it is simple and easy to use. The TCP protocol is more tightly linked to internal data structures: it uses an internal format for passing blocks of data and it uses custom framing for compressed data. We haven't released a C library for that protocol because it requires linking most of the ClickHouse codebase, which is not practical.", + "title": "Server" + }, + { + "location": "/index.html#distributed-query-execution", + "text": "Servers in a cluster setup are mostly independent. You can create a Distributed table on one or all servers in a cluster. The Distributed table does not store data itself \u2013 it only provides a \"view\" to all local tables on multiple nodes of a cluster. When you SELECT from a Distributed table, it rewrites that query, chooses remote nodes according to load balancing settings, and sends the query to them. The Distributed table requests remote servers to process a query just up to a stage where intermediate results from different servers can be merged. Then it receives the intermediate results and merges them. The distributed table tries to distribute as much work as possible to remote servers, and does not send much intermediate data over the network. Things become more complicated when you have subqueries in IN or JOIN clauses and each of them uses a Distributed table. We have different strategies for execution of these queries. There is no global query plan for distributed query execution. Each node has its own local query plan for its part of the job. We only have simple one-pass distributed query execution: we send queries for remote nodes and then merge the results. But this is not feasible for difficult queries with high cardinality GROUP BYs or with a large amount of temporary data for JOIN: in such cases, we need to \"reshuffle\" data between servers, which requires additional coordination. ClickHouse does not support that kind of query execution, and we need to work on it.", + "title": "Distributed query execution" + }, + { + "location": "/index.html#merge-tree", + "text": "MergeTree is a family of storage engines that supports indexing by primary key. The primary key can be an arbitary tuple of columns or expressions. Data in a MergeTree table is stored in \"parts\". Each part stores data in the primary key order (data is ordered lexicographically by the primary key tuple). All the table columns are stored in separate column.bin files in these parts. The files consist of compressed blocks. Each block is usually from 64 KB to 1 MB of uncompressed data, depending on the average value size. The blocks consist of column values placed contiguously one after the other. Column values are in the same order for each column (the order is defined by the primary key), so when you iterate by many columns, you get values for the corresponding rows. The primary key itself is \"sparse\". It doesn't address each single row, but only some ranges of data. A separate primary.idx file has the value of the primary key for each N-th row, where N is called index_granularity (usually, N = 8192). Also, for each column, we have column.mrk files with \"marks,\" which are offsets to each N-th row in the data file. Each mark is a pair: the offset in the file to the beginning of the compressed block, and the offset in the decompressed block to the beginning of data. Usually compressed blocks are aligned by marks, and the offset in the decompressed block is zero. Data for primary.idx always resides in memory and data for column.mrk files is cached. When we are going to read something from a part in MergeTree , we look at primary.idx data and locate ranges that could possibly contain requested data, then look at column.mrk data and calculate offsets for where to start reading those ranges. Because of sparseness, excess data may be read. ClickHouse is not suitable for a high load of simple point queries, because the entire range with index_granularity rows must be read for each key, and the entire compressed block must be decompressed for each column. We made the index sparse because we must be able to maintain trillions of rows per single server without noticeable memory consumption for the index. Also, because the primary key is sparse, it is not unique: it cannot check the existence of the key in the table at INSERT time. You could have many rows with the same key in a table. When you INSERT a bunch of data into MergeTree , that bunch is sorted by primary key order and forms a new part. To keep the number of parts relatively low, there are background threads that periodically select some parts and merge them to a single sorted part. That's why it is called MergeTree . Of course, merging leads to \"write amplification\". All parts are immutable: they are only created and deleted, but not modified. When SELECT is run, it holds a snapshot of the table (a set of parts). After merging, we also keep old parts for some time to make recovery after failure easier, so if we see that some merged part is probably broken, we can replace it with its source parts. MergeTree is not an LSM tree because it doesn't contain \"memtable\" and \"log\": inserted data is written directly to the filesystem. This makes it suitable only to INSERT data in batches, not by individual row and not very frequently \u2013 about once per second is ok, but a thousand times a second is not. We did it this way for simplicity's sake, and because we are already inserting data in batches in our applications. MergeTree tables can only have one (primary) index: there aren't any secondary indices. It would be nice to allow multiple physical representations under one logical table, for example, to store data in more than one physical order or even to allow representations with pre-aggregated data along with original data. There are MergeTree engines that are doing additional work during background merges. Examples are CollapsingMergeTree and AggregatingMergeTree . This could be treated as special support for updates. Keep in mind that these are not real updates because users usually have no control over the time when background merges will be executed, and data in a MergeTree table is almost always stored in more than one part, not in completely merged form.", + "title": "Merge Tree" + }, + { + "location": "/index.html#replication", + "text": "Replication in ClickHouse is implemented on a per-table basis. You could have some replicated and some non-replicated tables on the same server. You could also have tables replicated in different ways, such as one table with two-factor replication and another with three-factor. Replication is implemented in the ReplicatedMergeTree storage engine. The path in ZooKeeper is specified as a parameter for the storage engine. All tables with the same path in ZooKeeper become replicas of each other: they synchronize their data and maintain consistency. Replicas can be added and removed dynamically simply by creating or dropping a table. Replication uses an asynchronous multi-master scheme. You can insert data into any replica that has a session with ZooKeeper , and data is replicated to all other replicas asynchronously. Because ClickHouse doesn't support UPDATEs, replication is conflict-free. As there is no quorum acknowledgment of inserts, just-inserted data might be lost if one node fails. Metadata for replication is stored in ZooKeeper. There is a replication log that lists what actions to do. Actions are: get part; merge parts; drop partition, etc. Each replica copies the replication log to its queue and then executes the actions from the queue. For example, on insertion, the \"get part\" action is created in the log, and every replica downloads that part. Merges are coordinated between replicas to get byte-identical results. All parts are merged in the same way on all replicas. To achieve this, one replica is elected as the leader, and that replica initiates merges and writes \"merge parts\" actions to the log. Replication is physical: only compressed parts are transferred between nodes, not queries. To lower the network cost (to avoid network amplification), merges are processed on each replica independently in most cases. Large merged parts are sent over the network only in cases of significant replication lag. In addition, each replica stores its state in ZooKeeper as the set of parts and its checksums. When the state on the local filesystem diverges from the reference state in ZooKeeper, the replica restores its consistency by downloading missing and broken parts from other replicas. When there is some unexpected or broken data in the local filesystem, ClickHouse does not remove it, but moves it to a separate directory and forgets it. The ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is not elastic, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load will be uneven. This implementation gives you more control, and it is fine for relatively small clusters such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that will span its data across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically.", + "title": "Replication" + }, + { + "location": "/index.html#how-to-build-clickhouse-on-linux", + "text": "Build should work on Linux Ubuntu 12.04, 14.04 or newer.\nWith appropriate changes, it should also work on any other Linux distribution.\nThe build process is not intended to work on Mac OS X.\nOnly x86_64 with SSE 4.2 is supported. Support for AArch64 is experimental. To test for SSE 4.2, do grep -q sse4_2 /proc/cpuinfo echo SSE 4.2 supported || echo SSE 4.2 not supported", + "title": "How to build ClickHouse on Linux" + }, + { + "location": "/index.html#install-git-and-cmake", + "text": "sudo apt-get install git cmake Or cmake3 instead of cmake on older systems.", + "title": "Install Git and CMake" + }, + { + "location": "/index.html#detect-the-number-of-threads", + "text": "export THREADS = $( grep -c ^processor /proc/cpuinfo )", + "title": "Detect the number of threads" + }, + { + "location": "/index.html#install-gcc-7", + "text": "There are several ways to do this.", + "title": "Install GCC 7" + }, + { + "location": "/index.html#install-from-a-ppa-package", + "text": "sudo apt-get install software-properties-common\nsudo apt-add-repository ppa:ubuntu-toolchain-r/test\nsudo apt-get update\nsudo apt-get install gcc-7 g++-7", + "title": "Install from a PPA package" + }, + { + "location": "/index.html#install-from-sources", + "text": "Look at [https://github.com/yandex/ClickHouse/blob/master/utils/prepare-environment/install-gcc.sh]", + "title": "Install from sources" + }, + { + "location": "/index.html#use-gcc-7-for-builds", + "text": "export CC = gcc-7 export CXX = g++-7", + "title": "Use GCC 7 for builds" + }, + { + "location": "/index.html#install-required-libraries-from-packages", + "text": "sudo apt-get install libicu-dev libreadline-dev libmysqlclient-dev libssl-dev unixodbc-dev ninja-build", + "title": "Install required libraries from packages" + }, + { + "location": "/index.html#checkout-clickhouse-sources", + "text": "To get the latest stable version: git clone -b stable --recursive git@github.com:yandex/ClickHouse.git ## or: git clone -b stable --recursive https://github.com/yandex/ClickHouse.git cd ClickHouse For development, switch to the master branch.\nFor the latest release candidate, switch to the testing branch.", + "title": "Checkout ClickHouse sources" + }, + { + "location": "/index.html#build-clickhouse", + "text": "There are two build variants.", + "title": "Build ClickHouse" + }, + { + "location": "/index.html#build-release-package", + "text": "Install prerequisites to build Debian packages. sudo apt-get install devscripts dupload fakeroot debhelper Install the most recent version of Clang. Clang is embedded into the ClickHouse package and used at runtime. The minimum version is 5.0. It is optional. To install clang, see utils/prepare-environment/install-clang.sh You may also build ClickHouse with Clang for development purposes.\nFor production releases, GCC is used. Run the release script: rm -f ../clickhouse*.deb\n./release You will find built packages in the parent directory: ls -l ../clickhouse*.deb Note that usage of debian packages is not required.\nClickHouse has no runtime dependencies except libc, so it could work on almost any Linux. Installing freshly built packages on a development server: sudo dpkg -i ../clickhouse*.deb\nsudo service clickhouse-server start", + "title": "Build release package" + }, + { + "location": "/index.html#build-to-work-with-code", + "text": "mkdir build cd build\ncmake ..\nmake -j $THREADS cd .. To create an executable, run make clickhouse .\nThis will create the dbms/src/Server/clickhouse executable, which can be used with client or server arguments.", + "title": "Build to work with code" + }, + { + "location": "/index.html#how-to-build-clickhouse-on-mac-os-x", + "text": "Build should work on Mac OS X 10.12. If you're using earlier version, you can try to build ClickHouse using Gentoo Prefix and clang sl in this instruction.\nWith appropriate changes, it should also work on any other Linux distribution.", + "title": "How to build ClickHouse on Mac OS X" + }, + { + "location": "/index.html#install-homebrew", + "text": "/usr/bin/ruby -e $( curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install )", + "title": "Install Homebrew" + }, + { + "location": "/index.html#install-required-compilers-tools-and-libraries", + "text": "brew install cmake gcc icu4c mysql openssl unixodbc libtool gettext zlib readline boost --cc = gcc-7", + "title": "Install required compilers, tools, and libraries" + }, + { + "location": "/index.html#checkout-clickhouse-sources_1", + "text": "To get the latest stable version: git clone -b stable --recursive --depth = 10 git@github.com:yandex/ClickHouse.git ## or: git clone -b stable --recursive --depth=10 https://github.com/yandex/ClickHouse.git cd ClickHouse For development, switch to the master branch.\nFor the latest release candidate, switch to the testing branch.", + "title": "Checkout ClickHouse sources" + }, + { + "location": "/index.html#build-clickhouse_1", + "text": "mkdir build cd build\ncmake .. -DCMAKE_CXX_COMPILER = ` which g++-7 ` -DCMAKE_C_COMPILER = ` which gcc-7 ` \nmake -j ` sysctl -n hw.ncpu ` cd ..", + "title": "Build ClickHouse" + }, + { + "location": "/index.html#caveats", + "text": "If you intend to run clickhouse-server, make sure to increase the system's maxfiles variable. See MacOS.md for more details.", + "title": "Caveats" + }, + { + "location": "/index.html#how-to-write-c-code", + "text": "", + "title": "How to write C++ code" + }, + { + "location": "/index.html#general-recommendations", + "text": "1. The following are recommendations, not requirements. 2. If you are editing code, it makes sense to follow the formatting of the existing code. 3. Code style is needed for consistency. Consistency makes it easier to read the code, and it also makes it easier to search the code. 4. Many of the rules do not have logical reasons; they are dictated by established practices.", + "title": "General recommendations" + }, + { + "location": "/index.html#formatting", + "text": "1. Most of the formatting will be done automatically by clang-format . 2. Indents are 4 spaces. Configure your development environment so that a tab adds four spaces. 3. A left curly bracket must be separated on a new line. (And the right one, as well.) inline void readBoolText ( bool x , ReadBuffer buf ) { \n char tmp = 0 ; \n readChar ( tmp , buf ); \n x = tmp != 0 ; } 4. \nBut if the entire function body is quite short (a single statement), you can place it entirely on one line if you wish. Place spaces around curly braces (besides the space at the end of the line). inline size_t mask () const { return buf_size () - 1 ; } inline size_t place ( HashValue x ) const { return x mask (); } 5. For functions, don't put spaces around brackets. void reinsert ( const Value x ) memcpy ( buf [ place_value ], x , sizeof ( x )); 6. When using statements such as if , for , and while (unlike function calls), put a space before the opening bracket. cpp\n for (size_t i = 0; i rows; i += storage.index_granularity) 7. Put spaces around binary operators ( + , - , * , / , % , ...), as well as the ternary operator ?: . UInt16 year = ( s [ 0 ] - 0 ) * 1000 + ( s [ 1 ] - 0 ) * 100 + ( s [ 2 ] - 0 ) * 10 + ( s [ 3 ] - 0 ); UInt8 month = ( s [ 5 ] - 0 ) * 10 + ( s [ 6 ] - 0 ); UInt8 day = ( s [ 8 ] - 0 ) * 10 + ( s [ 9 ] - 0 ); 8. If a line feed is entered, put the operator on a new line and increase the indent before it. if ( elapsed_ns ) \n message ( \n rows_read_on_server * 1000000000 / elapsed_ns rows/s., \n bytes_read_on_server * 1000.0 / elapsed_ns MB/s.) ; 9. You can use spaces for alignment within a line, if desired. dst . ClickLogID = click . LogID ; dst . ClickEventID = click . EventID ; dst . ClickGoodEvent = click . GoodEvent ; 10. Don't use spaces around the operators . , - . If necessary, the operator can be wrapped to the next line. In this case, the offset in front of it is increased. 11. Do not use a space to separate unary operators ( - , + , * , , ...) from the argument. 12. Put a space after a comma, but not before it. The same rule goes for a semicolon inside a for expression. 13. Do not use spaces to separate the [] operator. 14. In a template ... expression, use a space between template and . No spaces after or before . template typename TKey , typename TValue struct AggregatedStatElement {} 15. In classes and structures, public, private, and protected are written on the same level as the class/struct , but all other internal elements should be deeper. template typename T class MultiVersion { public : \n /// Version of object for usage. shared_ptr manage lifetime of version. \n using Version = std :: shared_ptr const T ; \n ... } 16. If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace. 17. If the block for if , for , while ... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets. /// Finish write. for ( auto stream : streams ) \n stream . second - finalize (); 18. There should be any spaces at the ends of lines. 19. Sources are UTF-8 encoded. 20. Non-ASCII characters can be used in string literals. , ( timer . elapsed () / chunks_stats . hits ) \u03bcsec/hit. ; 21. Do not write multiple expressions in a single line. 22. Group sections of code inside functions and separate them with no more than one empty line. 23. Separate functions, classes, and so on with one or two empty lines. 24. A const (related to a value) must be written before the type name. //correct const char * pos const std :: string s //incorrect char const * pos 25. When declaring a pointer or reference, the * and symbols should be separated by spaces on both sides. //correct const char * pos //incorrect const char * pos const char * pos 26. When using template types, alias them with the using keyword (except in the simplest cases). In other words, the template parameters are specified only in using and aren't repeated in the code. using can be declared locally, such as inside a function. //correct using FileStreams = std :: map std :: string , std :: shared_ptr Stream ; FileStreams streams ; //incorrect std :: map std :: string , std :: shared_ptr Stream streams ; 27. Do not declare several variables of different types in one statement. //incorrect int x , * y ; 28. Do not use C-style casts. //incorrect std :: cerr ( int ) c ; std :: endl ; //correct std :: cerr static_cast int ( c ) std :: endl ; 29. In classes and structs, group members and functions separately inside each visibility scope. 30. For small classes and structs, it is not necessary to separate the method declaration from the implementation. The same is true for small methods in any classes or structs. For templated classes and structs, don't separate the method declarations from the implementation (because otherwise they must be defined in the same translation unit). 31. You can wrap lines at 140 characters, instead of 80. 32. Always use the prefix increment/decrement operators if postfix is not required. for ( Names :: const_iterator it = column_names . begin (); it != column_names . end (); ++ it )", + "title": "Formatting" + }, + { + "location": "/index.html#comments_1", + "text": "1. Be sure to add comments for all non-trivial parts of code. This is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong. /** Part of piece of memory, that can be used. * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading, * then working_buffer will have size of only 10 bytes * (working_buffer.end() will point to the position right after those 10 bytes available for read). */ 2. Comments can be as detailed as necessary. 3. Place comments before the code they describe. In rare cases, comments can come after the code, on the same line. /** Parses and executes the query. */ void executeQuery ( \n ReadBuffer istr , /// Where to read the query from (and data for INSERT, if applicable) \n WriteBuffer ostr , /// Where to write the result \n Context context , /// DB, tables, data types, engines, functions, aggregate functions... \n BlockInputStreamPtr query_plan , /// A description of query processing can be included here \n QueryProcessingStage :: Enum stage = QueryProcessingStage :: Complete /// The last stage to process the SELECT query to \n ) 4. Comments should be written in English only. 5. If you are writing a library, include detailed comments explaining it in the main header file. 6. Do not add comments that do not provide additional information. In particular, do not leave empty comments like this: /* * Procedure Name: * Original procedure name: * Author: * Date of creation: * Dates of modification: * Modification authors: * Original file name: * Purpose: * Intent: * Designation: * Classes used: * Constants: * Local variables: * Parameters: * Date of creation: * Purpose: */ The example is borrowed from http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/ . 7. Do not write garbage comments (author, creation date ..) at the beginning of each file. 8. Single-line comments begin with three slashes: /// and multi-line comments begin with /** . These comments are considered \"documentation\". Note: You can use Doxygen to generate documentation from these comments. But Doxygen is not generally used because it is more convenient to navigate the code in the IDE. 9. Multi-line comments must not have empty lines at the beginning and end (except the line that closes a multi-line comment). 10. For commenting out code, use basic comments, not \"documenting\" comments. 11. Delete the commented out parts of the code before commiting. 12. Do not use profanity in comments or code. 13. Do not use uppercase letters. Do not use excessive punctuation. /// WHAT THE FAIL??? 14. Do not make delimeters from comments. ///****************************************************** 15. Do not start discussions in comments. /// Why did you do this stuff? 16. There's no need to write a comment at the end of a block describing what it was about. /// for", + "title": "Comments" + }, + { + "location": "/index.html#names", + "text": "1. The names of variables and class members use lowercase letters with underscores. size_t max_block_size ; 2. The names of functions (methods) use camelCase beginning with a lowercase letter. std :: string getName () const override { return Memory ; } 3. The names of classes (structures) use CamelCase beginning with an uppercase letter. Prefixes other than I are not used for interfaces. class StorageMemory : public IStorage 4. The names of usings follow the same rules as classes, or you can add _t at the end. 5. Names of template type arguments for simple cases: T; T, U; T1, T2. For more complex cases, either follow the rules for class names, or add the prefix T. template typename TKey , typename TValue struct AggregatedStatElement 6. Names of template constant arguments: either follow the rules for variable names, or use N in simple cases. template bool without_www struct ExtractDomain 7. For abstract classes (interfaces) you can add the I prefix. class IBlockInputStream 8. If you use a variable locally, you can use the short name. In other cases, use a descriptive name that conveys the meaning. bool info_successfully_loaded = false ; 9. define \u2018s should be in ALL_CAPS with underscores. The same is true for global constants. ##define MAX_SRC_TABLE_NAMES_TO_STORE 1000 10. File names should use the same style as their contents. If a file contains a single class, name the file the same way as the class, in CamelCase. If the file contains a single function, name the file the same way as the function, in camelCase. 11. If the name contains an abbreviation, then: For variable names, the abbreviation should use lowercase letters mysql_connection (not mySQL_connection ). For names of classes and functions, keep the uppercase letters in the abbreviation MySQLConnection (not MySqlConnection ). 12. Constructor arguments that are used just to initialize the class members should be named the same way as the class members, but with an underscore at the end. FileQueueProcessor ( \n const std :: string path_ , \n const std :: string prefix_ , \n std :: shared_ptr FileHandler handler_ ) \n : path ( path_ ), \n prefix ( prefix_ ), \n handler ( handler_ ), \n log ( Logger :: get ( FileQueueProcessor )) { } The underscore suffix can be omitted if the argument is not used in the constructor body. 13. There is no difference in the names of local variables and class members (no prefixes required). timer ( not m_timer ) 14. Constants in enums use CamelCase beginning with an uppercase letter. ALL_CAPS is also allowed. If the enum is not local, use enum class. enum class CompressionMethod { \n QuickLZ = 0 , \n LZ4 = 1 , }; 15. All names must be in English. Transliteration of Russian words is not allowed. not Stroka 16. Abbreviations are acceptable if they are well known (when you can easily find the meaning of the abbreviation in Wikipedia or in a search engine). `AST`, `SQL`.\n\nNot `NVDH` (some random letters) Incomplete words are acceptable if the shortened version is common use. You can also use an abbreviation if the full name is included next to it in the comments. 17. File names with C++ source code must have the .cpp extension. Header files must have the .h extension.", + "title": "Names" + }, + { + "location": "/index.html#how-to-write-code", + "text": "1. Memory management. Manual memory deallocation (delete) can only be used in library code. In library code, the delete operator can only be used in destructors. In application code, memory must be freed by the object that owns it. Examples: The easiest way is to place an object on the stack, or make it a member of another class. For a large number of small objects, use containers. For automatic deallocation of a small number of objects that reside in the heap, use shared_ptr/unique_ptr. 2. Resource management. Use RAII and see the previous point. 3. Error handling. Use exceptions. In most cases, you only need to throw an exception, and don't need to catch it (because of RAII). In offline data processing applications, it's often acceptable to not catch exceptions. In servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler. /// If there were no other calculations yet, do it synchronously if ( ! started ) { \n calculate (); \n started = true ; } else /// If the calculations are already in progress, wait for results \n pool . wait (); if ( exception ) \n exception - rethrow (); Never hide exceptions without handling. Never just blindly put all exceptions to log. Not catch (...) {} . If you need to ignore some exceptions, do so only for specific ones and rethrow the rest. catch ( const DB :: Exception e ) { \n if ( e . code () == ErrorCodes :: UNKNOWN_AGGREGATE_FUNCTION ) \n return nullptr ; \n else \n throw ; } When using functions with response codes or errno, always check the result and throw an exception in case of error. if ( 0 != close ( fd )) \n throwFromErrno ( Cannot close file + file_name , ErrorCodes :: CANNOT_CLOSE_FILE ); Asserts are not used. 4. Exception types. There is no need to use complex exception hierarchy in application code. The exception text should be understandable to a system administrator. 5. Throwing exceptions from destructors. This is not recommended, but it is allowed. Use the following options: Create a (done() or finalize()) function that will do all the work in advance that might lead to an exception. If that function was called, there should be no exceptions in the destructor later. Tasks that are too complex (such as sending messages over the network) can be put in separate method that the class user will have to call before destruction. If there is an exception in the destructor, it\u2019s better to log it than to hide it (if the logger is available). In simple applications, it is acceptable to rely on std::terminate (for cases of noexcept by default in C++11) to handle exceptions. 6. Anonymous code blocks. You can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block. Block block = data . in - read (); { \n std :: lock_guard std :: mutex lock ( mutex ); \n data . ready = true ; \n data . block = block ; } ready_any . set (); 7. Multithreading. For offline data processing applications: Try to get the best possible performance on a single CPU core. You can then parallelize your code if necessary. In server applications: Use the thread pool to process requests. At this point, we haven't had any tasks that required userspace context switching. Fork is not used for parallelization. 8. Synchronizing threads. Often it is possible to make different threads use different memory cells (even better: different cache lines,) and to not use any thread synchronization (except joinAll). If synchronization is required, in most cases, it is sufficient to use mutex under lock_guard. In other cases use system synchronization primitives. Do not use busy wait. Atomic operations should be used only in the simplest cases. Do not try to implement lock-free data structures unless it is your primary area of expertise. 9. Pointers vs references. In most cases, prefer references. 10. const. Use constant references, pointers to constants, const_iterator , const methods. Consider const to be default and use non-const only when necessary. When passing variable by value, using const usually does not make sense. 11. unsigned. Use unsigned , if needed. 12. Numeric types Use UInt8 , UInt16 , UInt32 , UInt64 , Int8 , Int16 , Int32 , Int64 , and size_t , ssize_t , ptrdiff_t . Don't use signed/unsigned long , long long , short , signed char , unsigned char , or char types for numbers. 13. Passing arguments. Pass complex values by reference (including std::string ). If a function captures ownership of an objected created in the heap, make the argument type shared_ptr or unique_ptr . 14. Returning values. In most cases, just use return. Do not write [return std::move(res)]{.strike} . If the function allocates an object on heap and returns it, use shared_ptr or unique_ptr . In rare cases you might need to return the value via an argument. In this case, the argument should be a reference. using AggregateFunctionPtr = std :: shared_ptr IAggregateFunction ; /** Creates an aggregate function by name. */ class AggregateFunctionFactory { public : \n AggregateFunctionFactory (); \n AggregateFunctionPtr get ( const String name , const DataTypes argument_types ) const ; 15. namespace. There is no need to use a separate namespace for application code or small libraries. or small libraries. For medium to large libraries, put everything in the namespace. You can use the additional detail namespace in a library's .h file to hide implementation details. In a .cpp file, you can use the static or anonymous namespace to hide symbols. You can also use namespace for enums to prevent its names from polluting the outer namespace, but it\u2019s better to use the enum class. 16. Delayed initialization. If arguments are required for initialization then do not write a default constructor. If later you\u2019ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use shared_ptr/unique_ptr . Loader ( DB :: Connection * connection_ , const std :: string query , size_t max_block_size_ ); /// For delayed initialization Loader () {} 17. Virtual functions. If the class is not intended for polymorphic use, you do not need to make functions virtual. This also applies to the destructor. 18. Encodings. Use UTF-8 everywhere. Use std::string and char * . Do not use std::wstring and wchar_t . 19. Logging. See the examples everywhere in the code. Before committing, delete all meaningless and debug logging, and any other types of debug output. Logging in cycles should be avoided, even on the Trace level. Logs must be readable at any logging level. Logging should only be used in application code, for the most part. Log messages must be written in English. The log should preferably be understandable for the system administrator. Do not use profanity in the log. Use UTF-8 encoding in the log. In rare cases you can use non-ASCII characters in the log. 20. I/O. Don't use iostreams in internal cycles that are critical for application performance (and never use stringstream). Use the DB/IO library instead. 21. Date and time. See the DateLUT library. 22. include. Always use #pragma once instead of include guards. 23. using. The using namespace is not used. It's fine if you are 'using' something specific, but make it local inside a class or function. 24. Do not use trailing return type for functions unless necessary. [auto f() - gt; void;]{.strike} 25. Do not declare and init variables like this: auto s = std :: string { Hello }; Do it like this: std :: string s = Hello ; std :: string s { Hello }; 26. For virtual functions, write virtual in the base class, but write override in descendent classes.", + "title": "How to write code" + }, + { + "location": "/index.html#unused-features-of-c", + "text": "1. Virtual inheritance is not used. 2. Exception specifiers from C++03 are not used. 3. Function try block is not used, except for the main function in tests.", + "title": "Unused features of C++" + }, + { + "location": "/index.html#platform", + "text": "1. We write code for a specific platform. But other things being equal, cross-platform or portable code is preferred. 2. The language is C++17. 3. The compiler is gcc . At this time (December 2017), the code is compiled using version 7.2. (It can also be compiled using clang 5.) The standard library is used (implementation of libstdc++ or libc++ ). 4. OS: Linux Ubuntu, not older than Precise. 5. Code is written for x86_64 CPU architecture. The CPU instruction set is the minimum supported set among our servers. Currently, it is SSE 4.2. 6. Use -Wall -Wextra -Werror compilation flags. 7. Use static linking with all libraries except those that are difficult to connect to statically (see the output of the ldd command). 8. Code is developed and debugged with release settings.", + "title": "Platform" + }, + { + "location": "/index.html#tools", + "text": "1. KDevelop is a good IDE. 2. For debugging, use gdb , valgrind ( memcheck ), strace , -fsanitize= , ..., tcmalloc_minimal_debug . 3. For profiling, use Linux Perf valgrind ( callgrind ), strace-cf . 4. Sources are in Git. 5. Compilation is managed by CMake . 6. Releases are in deb packages. 7. Commits to master must not break the build. Though only selected revisions are considered workable. 8. Make commits as often as possible, even if the code is only partially ready. Use branches for this purpose. If your code is not buildable yet, exclude it from the build before pushing to master. You'll need to finish it or remove it from master within a few days. 9. For non-trivial changes, used branches and publish them on the server. 10. Unused code is removed from the repository.", + "title": "Tools" + }, + { + "location": "/index.html#libraries", + "text": "1. The C++14 standard library is used (experimental extensions are fine), as well as boost and Poco frameworks. 2. If necessary, you can use any well-known libraries available in the OS package. If there is a good solution already available, then use it, even if it means you have to install another library. (But be prepared to remove bad libraries from code.) 3. You can install a library that isn't in the packages, if the packages don't have what you need or have an outdated version or the wrong type of compilation. 4. If the library is small and doesn't have its own complex build system, put the source files in the contrib folder. 5. Preference is always given to libraries that are already used.", + "title": "Libraries" + }, + { + "location": "/index.html#general-recommendations_1", + "text": "1. Write as little code as possible. 2. Try the simplest solution. 3. Don't write code until you know how it's going to work and how the inner loop will function. 4. In the simplest cases, use 'using' instead of classes or structs. 5. If possible, do not write copy constructors, assignment operators, destructors (other than a virtual one, if the class contains at least one virtual function), mpve-constructors and move assignment operators. In other words, the compiler-generated functions must work correctly. You can use 'default'. 6. Code simplification is encouraged. Reduce the size of your code where possible.", + "title": "General recommendations" + }, + { + "location": "/index.html#additional-recommendations", + "text": "1. Explicit std:: for types from stddef.h is not recommended. We recommend writing size_t instead std::size_t because it's shorter. But if you prefer, std:: is acceptable. 2. Explicit std:: for functions from the standard C library is not recommended. Write memcpy instead of std::memcpy . The reason is that there are similar non-standard functions, such as memmem . We do use these functions on occasion. These functions do not exist in namespace std . If you write std::memcpy instead of memcpy everywhere, then memmem without std:: will look awkward. Nevertheless, std:: is allowed if you prefer it. 3. Using functions from C when the ones are available in the standard C++ library. This is acceptable if it is more efficient. For example, use memcpy instead of std::copy for copying large chunks of memory. 4. Multiline function arguments. Any of the following wrapping styles are allowed: function ( \n T1 x1 , \n T2 x2 ) function ( \n size_t left , size_t right , \n const RangesInDataParts ranges , \n size_t limit ) function ( size_t left , size_t right , \n const RangesInDataParts ranges , \n size_t limit ) function ( size_t left , size_t right , \n const RangesInDataParts ranges , \n size_t limit ) function ( \n size_t left , \n size_t right , \n const RangesInDataParts ranges , \n size_t limit )", + "title": "Additional recommendations" + }, + { + "location": "/index.html#how-to-run-clickhouse-tests", + "text": "The clickhouse-test utility that is used for functional testing is written using Python 2.x.It also requires you to have some third-party packages: $ pip install lxml termcolor In a nutshell: Put the clickhouse program to /usr/bin (or PATH ) Create a clickhouse-client symlink in /usr/bin pointing to clickhouse Start the clickhouse server cd dbms/tests/ Run ./clickhouse-test", + "title": "How to run ClickHouse tests" + }, + { + "location": "/index.html#example-usage", + "text": "Run ./clickhouse-test --help to see available options. To run tests without having to create a symlink or mess with PATH : ./clickhouse-test -c ../../build/dbms/src/Server/clickhouse --client To run a single test, i.e. 00395_nullable : ./clickhouse-test 00395", + "title": "Example usage" + }, + { + "location": "/index.html#roadmap", + "text": "", + "title": "Roadmap" + }, + { + "location": "/index.html#q1-2018", + "text": "", + "title": "Q1 2018" + }, + { + "location": "/index.html#new-fuctionality", + "text": "Support for UPDATE and DELETE . Multidimensional and nested arrays. It can look something like this: CREATE TABLE t ( \n x Array ( Array ( String )), \n z Nested ( \n x Array ( String ), \n y Nested (...)) ) ENGINE = MergeTree ORDER BY x External MySQL and ODBC tables. External tables can be integrated into ClickHouse using external dictionaries. This new functionality is a convenient alternative to connecting external tables. SELECT ... FROM mysql ( host:port , db , table , user , password ) `", + "title": "New fuctionality" + }, + { + "location": "/index.html#improvements", + "text": "Effective data copying between ClickHouse clusters. Now you can copy data with the remote() function. For example: INSERT INTO t SELECT * FROM remote(...) . This operation will have improved performance. O_DIRECT for merges. This will improve the performance of the OS cache and \"hot\" queries.", + "title": "Improvements" + }, + { + "location": "/index.html#q2-2018", + "text": "", + "title": "Q2 2018" + }, + { + "location": "/index.html#new-functionality", + "text": "UPDATE/DELETE conform to the EU GDPR. Protobuf and Parquet input and output formats. Creating dictionaries using DDL queries. Currently, dictionaries that are part of the database schema are defined in external XML files. This is inconvenient and counter-intuitive. The new approach should fix it. Integration with LDAP. WITH ROLLUP and WITH CUBE for GROUP BY. Custom encoding and compression for each column individually. As of now, ClickHouse supports LZ4 and ZSTD compression of columns, and compression settings are global (see the article Compression in ClickHouse ). Per-column compression and encoding will provide more efficient data storage, which in turn will speed up queries. Storing data on multiple disks on the same server. This functionality will make it easier to extend the disk space, since different disk systems can be used for different databases or tables. Currently, users are forced to use symbolic links if the databases and tables must be stored on a different disk.", + "title": "New functionality" + }, + { + "location": "/index.html#improvements_1", + "text": "Many improvements and fixes are planned for the query execution system. For example: Using an index for in (subquery) . The index is not used right now, which reduces performance. Passing predicates from where to subqueries, and passing predicates to views. The predicates must be passed, since the view is changed by the subquery. Performance is still low for view filters, and views can't use the primary key of the original table, which makes views useless for large tables. Optimizing branching operations (ternary operator, if, multiIf). ClickHouse currently performs all branches, even if they aren't necessary. Using a primary key for GROUP BY and ORDER BY. This will speed up certain types of queries with partially sorted data.", + "title": "Improvements" + }, + { + "location": "/index.html#q3-q4-2018", + "text": "We don't have any set plans yet, but the main projects will be: Resource pools for executing queries. This will make load management more efficient. ANSI SQL JOIN syntax. Improve ClickHouse compatibility with many SQL tools.", + "title": "Q3-Q4 2018" + } + ] +} \ No newline at end of file diff --git a/docs/build/docs/en/single/sitemap.xml b/docs/build/docs/en/single/sitemap.xml new file mode 100644 index 00000000000..f9f9a9431a8 --- /dev/null +++ b/docs/build/docs/en/single/sitemap.xml @@ -0,0 +1,12 @@ + + + + + + /index.html + 2018-05-13 + daily + + + + \ No newline at end of file diff --git a/docs/build/docs/en/sitemap.xml b/docs/build/docs/en/sitemap.xml new file mode 100644 index 00000000000..72393e66e2f --- /dev/null +++ b/docs/build/docs/en/sitemap.xml @@ -0,0 +1,988 @@ + + + + + + / + 2018-05-13 + daily + + + + + + + /introduction/distinctive_features/ + 2018-05-13 + daily + + + + /introduction/features_considered_disadvantages/ + 2018-05-13 + daily + + + + /introduction/ya_metrika_task/ + 2018-05-13 + daily + + + + /introduction/possible_silly_questions/ + 2018-05-13 + daily + + + + /introduction/performance/ + 2018-05-13 + daily + + + + + + + + /getting_started/ + 2018-05-13 + daily + + + + + + daily + + + + + + + + /interfaces/ + 2018-05-13 + daily + + + + /interfaces/cli/ + 2018-05-13 + daily + + + + /interfaces/http_interface/ + 2018-05-13 + daily + + + + /interfaces/jdbc/ + 2018-05-13 + daily + + + + /interfaces/tcp/ + 2018-05-13 + daily + + + + /interfaces/third-party_client_libraries/ + 2018-05-13 + daily + + + + /interfaces/third-party_gui/ + 2018-05-13 + daily + + + + + + + + /query_language/queries/ + 2018-05-13 + daily + + + + /query_language/syntax/ + 2018-05-13 + daily + + + + + + + + /table_engines/ + 2018-05-13 + daily + + + + /table_engines/tinylog/ + 2018-05-13 + daily + + + + /table_engines/log/ + 2018-05-13 + daily + + + + /table_engines/memory/ + 2018-05-13 + daily + + + + /table_engines/mergetree/ + 2018-05-13 + daily + + + + /table_engines/custom_partitioning_key/ + 2018-05-13 + daily + + + + /table_engines/replacingmergetree/ + 2018-05-13 + daily + + + + /table_engines/summingmergetree/ + 2018-05-13 + daily + + + + /table_engines/aggregatingmergetree/ + 2018-05-13 + daily + + + + /table_engines/collapsingmergetree/ + 2018-05-13 + daily + + + + /table_engines/graphitemergetree/ + 2018-05-13 + daily + + + + /table_engines/replication/ + 2018-05-13 + daily + + + + /table_engines/distributed/ + 2018-05-13 + daily + + + + /table_engines/dictionary/ + 2018-05-13 + daily + + + + /table_engines/merge/ + 2018-05-13 + daily + + + + /table_engines/buffer/ + 2018-05-13 + daily + + + + /table_engines/file/ + 2018-05-13 + daily + + + + /table_engines/null/ + 2018-05-13 + daily + + + + /table_engines/set/ + 2018-05-13 + daily + + + + /table_engines/join/ + 2018-05-13 + daily + + + + /table_engines/view/ + 2018-05-13 + daily + + + + /table_engines/materializedview/ + 2018-05-13 + daily + + + + /table_engines/kafka/ + 2018-05-13 + daily + + + + /table_engines/mysql/ + 2018-05-13 + daily + + + + /table_engines/external_data/ + 2018-05-13 + daily + + + + + + + + /system_tables/ + 2018-05-13 + daily + + + + /system_tables/system.one/ + 2018-05-13 + daily + + + + /system_tables/system.numbers/ + 2018-05-13 + daily + + + + /system_tables/system.numbers_mt/ + 2018-05-13 + daily + + + + /system_tables/system.databases/ + 2018-05-13 + daily + + + + /system_tables/system.tables/ + 2018-05-13 + daily + + + + /system_tables/system.columns/ + 2018-05-13 + daily + + + + /system_tables/system.parts/ + 2018-05-13 + daily + + + + /system_tables/system.processes/ + 2018-05-13 + daily + + + + /system_tables/system.merges/ + 2018-05-13 + daily + + + + /system_tables/system.events/ + 2018-05-13 + daily + + + + /system_tables/system.metrics/ + 2018-05-13 + daily + + + + /system_tables/system.asynchronous_metrics/ + 2018-05-13 + daily + + + + /system_tables/system.replicas/ + 2018-05-13 + daily + + + + /system_tables/system.dictionaries/ + 2018-05-13 + daily + + + + /system_tables/system.clusters/ + 2018-05-13 + daily + + + + /system_tables/system.functions/ + 2018-05-13 + daily + + + + /system_tables/system.settings/ + 2018-05-13 + daily + + + + /system_tables/system.zookeeper/ + 2018-05-13 + daily + + + + + + + + /table_functions/ + 2018-05-13 + daily + + + + /table_functions/remote/ + 2018-05-13 + daily + + + + /table_functions/merge/ + 2018-05-13 + daily + + + + /table_functions/numbers/ + 2018-05-13 + daily + + + + + + + + /formats/ + 2018-05-13 + daily + + + + /formats/tabseparated/ + 2018-05-13 + daily + + + + /formats/tabseparatedraw/ + 2018-05-13 + daily + + + + /formats/tabseparatedwithnames/ + 2018-05-13 + daily + + + + /formats/tabseparatedwithnamesandtypes/ + 2018-05-13 + daily + + + + /formats/csv/ + 2018-05-13 + daily + + + + /formats/csvwithnames/ + 2018-05-13 + daily + + + + /formats/values/ + 2018-05-13 + daily + + + + /formats/vertical/ + 2018-05-13 + daily + + + + /formats/verticalraw/ + 2018-05-13 + daily + + + + /formats/json/ + 2018-05-13 + daily + + + + /formats/jsoncompact/ + 2018-05-13 + daily + + + + /formats/jsoneachrow/ + 2018-05-13 + daily + + + + /formats/tskv/ + 2018-05-13 + daily + + + + /formats/pretty/ + 2018-05-13 + daily + + + + /formats/prettycompact/ + 2018-05-13 + daily + + + + /formats/prettycompactmonoblock/ + 2018-05-13 + daily + + + + /formats/prettynoescapes/ + 2018-05-13 + daily + + + + /formats/prettyspace/ + 2018-05-13 + daily + + + + /formats/rowbinary/ + 2018-05-13 + daily + + + + /formats/native/ + 2018-05-13 + daily + + + + /formats/null/ + 2018-05-13 + daily + + + + /formats/xml/ + 2018-05-13 + daily + + + + /formats/capnproto/ + 2018-05-13 + daily + + + + + + + + /data_types/ + 2018-05-13 + daily + + + + /data_types/int_uint/ + 2018-05-13 + daily + + + + /data_types/float/ + 2018-05-13 + daily + + + + /data_types/boolean/ + 2018-05-13 + daily + + + + /data_types/string/ + 2018-05-13 + daily + + + + /data_types/fixedstring/ + 2018-05-13 + daily + + + + /data_types/date/ + 2018-05-13 + daily + + + + /data_types/datetime/ + 2018-05-13 + daily + + + + /data_types/enum/ + 2018-05-13 + daily + + + + /data_types/array/ + 2018-05-13 + daily + + + + /data_types/nested_data_structures/aggregatefunction/ + 2018-05-13 + daily + + + + /data_types/tuple/ + 2018-05-13 + daily + + + + + + daily + + + + + + daily + + + + + + + /operators/ + 2018-05-13 + daily + + + + + + + /functions/ + 2018-05-13 + daily + + + + /functions/arithmetic_functions/ + 2018-05-13 + daily + + + + /functions/comparison_functions/ + 2018-05-13 + daily + + + + /functions/logical_functions/ + 2018-05-13 + daily + + + + /functions/type_conversion_functions/ + 2018-05-13 + daily + + + + /functions/date_time_functions/ + 2018-05-13 + daily + + + + /functions/string_functions/ + 2018-05-13 + daily + + + + /functions/string_search_functions/ + 2018-05-13 + daily + + + + /functions/string_replace_functions/ + 2018-05-13 + daily + + + + /functions/conditional_functions/ + 2018-05-13 + daily + + + + /functions/math_functions/ + 2018-05-13 + daily + + + + /functions/rounding_functions/ + 2018-05-13 + daily + + + + /functions/array_functions/ + 2018-05-13 + daily + + + + /functions/splitting_merging_functions/ + 2018-05-13 + daily + + + + /functions/bit_functions/ + 2018-05-13 + daily + + + + /functions/hash_functions/ + 2018-05-13 + daily + + + + /functions/random_functions/ + 2018-05-13 + daily + + + + /functions/encoding_functions/ + 2018-05-13 + daily + + + + /functions/url_functions/ + 2018-05-13 + daily + + + + /functions/ip_address_functions/ + 2018-05-13 + daily + + + + /functions/json_functions/ + 2018-05-13 + daily + + + + /functions/higher_order_functions/ + 2018-05-13 + daily + + + + /functions/other_functions/ + 2018-05-13 + daily + + + + /functions/ext_dict_functions/ + 2018-05-13 + daily + + + + /functions/ym_dict_functions/ + 2018-05-13 + daily + + + + /functions/in_functions/ + 2018-05-13 + daily + + + + /functions/array_join/ + 2018-05-13 + daily + + + + + + + + /agg_functions/ + 2018-05-13 + daily + + + + /agg_functions/reference/ + 2018-05-13 + daily + + + + /agg_functions/combinators/ + 2018-05-13 + daily + + + + /agg_functions/parametric_functions/ + 2018-05-13 + daily + + + + + + + + /dicts/ + 2018-05-13 + daily + + + + + + daily + + + + /dicts/internal_dicts/ + 2018-05-13 + daily + + + + + + + + /operations/access_rights/ + 2018-05-13 + daily + + + + /operations/configuration_files/ + 2018-05-13 + daily + + + + /operations/quotas/ + 2018-05-13 + daily + + + + /operations/tips/ + 2018-05-13 + daily + + + + + + daily + + + + + + daily + + + + + + + + /utils/ + 2018-05-13 + daily + + + + /utils/clickhouse-copier/ + 2018-05-13 + daily + + + + /utils/clickhouse-local/ + 2018-05-13 + daily + + + + + + + + /development/architecture/ + 2018-05-13 + daily + + + + /development/build/ + 2018-05-13 + daily + + + + /development/build_osx/ + 2018-05-13 + daily + + + + /development/style/ + 2018-05-13 + daily + + + + /development/tests/ + 2018-05-13 + daily + + + + + + + /roadmap/ + 2018-05-13 + daily + + + + \ No newline at end of file diff --git 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System tables

+

System tables are used for implementing part of the system's functionality, and for providing access to information about how the system is working. +You can't delete a system table (but you can perform DETACH). +System tables don't have files with data on the disk or files with metadata. The server creates all the system tables when it starts. +System tables are read-only. +They are located in the 'system' database.

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system.asynchronous_metrics

+

Contain metrics used for profiling and monitoring. +They usually reflect the number of events currently in the system, or the total resources consumed by the system. +Example: The number of SELECT queries currently running; the amount of memory in use.system.asynchronous_metricsandsystem.metrics differ in their sets of metrics and how they are calculated.

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/system_tables/system.clusters/index.html b/docs/build/docs/en/system_tables/system.clusters/index.html new file mode 100644 index 00000000000..658cf1e8ac2 --- /dev/null +++ b/docs/build/docs/en/system_tables/system.clusters/index.html @@ -0,0 +1,2898 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + system.clusters - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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system.clusters

+

Contains information about clusters available in the config file and the servers in them. +Columns:

+
cluster String      – Cluster name.
+shard_num UInt32    – Number of a shard in the cluster, starting from 1.
+shard_weight UInt32 – Relative weight of a shard when writing data.
+replica_num UInt32  – Number of a replica in the shard, starting from 1.
+host_name String    – Host name as specified in the config.
+host_address String – Host's IP address obtained from DNS.
+port UInt16         – The port used to access the server.
+user String         – The username to use for connecting to the server.
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system.columns

+

Contains information about the columns in all tables. +You can use this table to get information similar to DESCRIBE TABLE, but for multiple tables at once.

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database String           - Name of the database the table is located in.
+table String              - Table name.
+name String               - Column name.
+type String               - Column type.
+default_type String       - Expression type (DEFAULT, MATERIALIZED, ALIAS) for the default value, or an empty string if it is not defined.
+default_expression String - Expression for the default value, or an empty string if it is not defined.
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system.databases

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This table contains a single String column called 'name' – the name of a database. +Each database that the server knows about has a corresponding entry in the table. +This system table is used for implementing the SHOW DATABASES query.

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system.dictionaries

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Contains information about external dictionaries.

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Columns:

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    +
  • name String – Dictionary name.
    • +
  • type String – Dictionary type: Flat, Hashed, Cache.
    • +
  • origin String – Path to the config file where the dictionary is described.
    • +
  • attribute.names Array(String) – Array of attribute names provided by the dictionary.
    • +
  • attribute.types Array(String) – Corresponding array of attribute types provided by the dictionary.
    • +
  • has_hierarchy UInt8 – Whether the dictionary is hierarchical.
    • +
  • bytes_allocated UInt64 – The amount of RAM used by the dictionary.
    • +
  • hit_rate Float64 – For cache dictionaries, the percent of usage for which the value was in the cache.
    • +
  • element_count UInt64 – The number of items stored in the dictionary.
    • +
  • load_factor Float64 – The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table).
    • +
  • creation_time DateTime – Time spent for the creation or last successful reload of the dictionary.
    • +
  • last_exception String – Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created.
    • +
  • source String – Text describing the data source for the dictionary.
    • +
+

Note that the amount of memory used by the dictionary is not proportional to the number of items stored in it. So for flat and cached dictionaries, all the memory cells are pre-assigned, regardless of how full the dictionary actually is.

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/system_tables/system.events/index.html b/docs/build/docs/en/system_tables/system.events/index.html new file mode 100644 index 00000000000..b263f5323b0 --- /dev/null +++ b/docs/build/docs/en/system_tables/system.events/index.html @@ -0,0 +1,2891 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + system.events - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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system.events

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Contains information about the number of events that have occurred in the system. This is used for profiling and monitoring purposes. +Example: The number of processed SELECT queries. +Columns: 'event String' – the event name, and 'value UInt64' – the quantity.

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/system_tables/system.functions/index.html b/docs/build/docs/en/system_tables/system.functions/index.html new file mode 100644 index 00000000000..bf4614bdde1 --- /dev/null +++ b/docs/build/docs/en/system_tables/system.functions/index.html @@ -0,0 +1,2893 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + system.functions - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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system.functions

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Contains information about normal and aggregate functions.

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Columns:

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  • name (String) – Function name.
    • +
  • is_aggregate (UInt8) – Whether it is an aggregate function.
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/system_tables/system.merges/index.html b/docs/build/docs/en/system_tables/system.merges/index.html new file mode 100644 index 00000000000..6af62af9b2e --- /dev/null +++ b/docs/build/docs/en/system_tables/system.merges/index.html @@ -0,0 +1,2903 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + system.merges - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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system.merges

+

Contains information about merges currently in process for tables in the MergeTree family.

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Columns:

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    +
  • database String — Name of the database the table is located in.
    • +
  • table String — Name of the table.
    • +
  • elapsed Float64 — Time in seconds since the merge started.
    • +
  • progress Float64 — Percent of progress made, from 0 to 1.
    • +
  • num_parts UInt64 — Number of parts to merge.
    • +
  • result_part_name String — Name of the part that will be formed as the result of the merge.
    • +
  • total_size_bytes_compressed UInt64 — Total size of compressed data in the parts being merged.
    • +
  • total_size_marks UInt64 — Total number of marks in the parts being merged.
    • +
  • bytes_read_uncompressed UInt64 — Amount of bytes read, decompressed.
    • +
  • rows_read UInt64 — Number of rows read.
    • +
  • bytes_written_uncompressed UInt64 — Amount of bytes written, uncompressed.
    • +
  • rows_written UInt64 — Number of rows written.
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/system_tables/system.numbers/index.html b/docs/build/docs/en/system_tables/system.numbers/index.html new file mode 100644 index 00000000000..06015d0e24b --- /dev/null +++ b/docs/build/docs/en/system_tables/system.numbers/index.html @@ -0,0 +1,2890 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + system.numbers - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
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+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

system.numbers

+

This table contains a single UInt64 column named 'number' that contains almost all the natural numbers starting from zero. +You can use this table for tests, or if you need to do a brute force search. +Reads from this table are not parallelized.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/system_tables/system.numbers_mt/index.html b/docs/build/docs/en/system_tables/system.numbers_mt/index.html new file mode 100644 index 00000000000..68ab395280d --- /dev/null +++ b/docs/build/docs/en/system_tables/system.numbers_mt/index.html @@ -0,0 +1,2889 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + system.numbers_mt - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

system.numbers_mt

+

The same as 'system.numbers' but reads are parallelized. The numbers can be returned in any order. +Used for tests.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/system_tables/system.one/index.html b/docs/build/docs/en/system_tables/system.one/index.html new file mode 100644 index 00000000000..80a3b41c796 --- /dev/null +++ b/docs/build/docs/en/system_tables/system.one/index.html @@ -0,0 +1,2890 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + system.one - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

system.one

+

This table contains a single row with a single 'dummy' UInt8 column containing the value 0. +This table is used if a SELECT query doesn't specify the FROM clause. +This is similar to the DUAL table found in other DBMSs.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/system_tables/system.parts/index.html b/docs/build/docs/en/system_tables/system.parts/index.html new file mode 100644 index 00000000000..308f3d051c6 --- /dev/null +++ b/docs/build/docs/en/system_tables/system.parts/index.html @@ -0,0 +1,2912 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + system.parts - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

system.parts

+

Contains information about parts of a table in the MergeTree family.

+

Each row describes one part of the data.

+

Columns:

+
    +
  • partition (String) – The partition name. YYYYMM format. To learn what a partition is, see the description of the ALTER query.
    • +
  • name (String) – Name of the data part.
    • +
  • active (UInt8) – Indicates whether the part is active. If a part is active, it is used in a table; otherwise, it will be deleted. Inactive data parts remain after merging.
    • +
  • marks (UInt64) – The number of marks. To get the approximate number of rows in a data part, multiply marks by the index granularity (usually 8192).
    • +
  • marks_size (UInt64) – The size of the file with marks.
    • +
  • rows (UInt64) – The number of rows.
    • +
  • bytes (UInt64) – The number of bytes when compressed.
    • +
  • modification_time (DateTime) – The modification time of the directory with the data part. This usually corresponds to the time of data part creation.|
    • +
  • remove_time (DateTime) – The time when the data part became inactive.
    • +
  • refcount (UInt32) – The number of places where the data part is used. A value greater than 2 indicates that the data part is used in queries or merges.
    • +
  • min_date (Date) – The minimum value of the date key in the data part.
    • +
  • max_date (Date) – The maximum value of the date key in the data part.
    • +
  • min_block_number (UInt64) – The minimum number of data parts that make up the current part after merging.
    • +
  • max_block_number (UInt64) – The maximum number of data parts that make up the current part after merging.
    • +
  • level (UInt32) – Depth of the merge tree. If a merge was not performed, level=0.
    • +
  • primary_key_bytes_in_memory (UInt64) – The amount of memory (in bytes) used by primary key values.
    • +
  • primary_key_bytes_in_memory_allocated (UInt64) – The amount of memory (in bytes) reserved for primary key values.
    • +
  • database (String) – Name of the database.
    • +
  • table (String) – Name of the table.
    • +
  • engine (String) – Name of the table engine without parameters.
    • +
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/system_tables/system.processes/index.html b/docs/build/docs/en/system_tables/system.processes/index.html new file mode 100644 index 00000000000..75225f9a3f9 --- /dev/null +++ b/docs/build/docs/en/system_tables/system.processes/index.html @@ -0,0 +1,2907 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + system.processes - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
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+ +
+
+
+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

system.processes

+

This system table is used for implementing the SHOW PROCESSLIST query. +Columns:

+
user String              – Name of the user who made the request. For distributed query processing, this is the user who helped the requestor server send the query to this server, not the user who made the distributed request on the requestor server.
+
+address String           – The IP address that the query was made from. The same is true for distributed query processing.
+
+elapsed Float64          –  The time in seconds since request execution started.
+
+rows_read UInt64         – The number of rows read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.
+
+bytes_read UInt64        – The number of uncompressed bytes read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.
+
+UInt64 total_rows_approx – The approximate total number of rows that must be read. For distributed processing, on the requestor server, this is the total for all remote servers. It can be updated during request processing, when new sources to process become known.
+
+memory_usage UInt64 – Memory consumption by the query. It might not include some types of dedicated memory.
+
+query String – The query text. For INSERT, it doesn't include the data to insert.
+
+query_id – Query ID, if defined.
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/system_tables/system.replicas/index.html b/docs/build/docs/en/system_tables/system.replicas/index.html new file mode 100644 index 00000000000..33f2d0cc349 --- /dev/null +++ b/docs/build/docs/en/system_tables/system.replicas/index.html @@ -0,0 +1,3004 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + system.replicas - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
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+ +
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+ + +
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+
+ + +
+
+
+ + +
+
+ + + + + + + +

system.replicas

+

Contains information and status for replicated tables residing on the local server. +This table can be used for monitoring. The table contains a row for every Replicated* table.

+

Example:

+
SELECT *
+FROM system.replicas
+WHERE table = 'visits'
+FORMAT Vertical
+
+ + +
Row 1:
+──────
+database:           merge
+table:              visits
+engine:             ReplicatedCollapsingMergeTree
+is_leader:          1
+is_readonly:        0
+is_session_expired: 0
+future_parts:       1
+parts_to_check:     0
+zookeeper_path:     /clickhouse/tables/01-06/visits
+replica_name:       example01-06-1.yandex.ru
+replica_path:       /clickhouse/tables/01-06/visits/replicas/example01-06-1.yandex.ru
+columns_version:    9
+queue_size:         1
+inserts_in_queue:   0
+merges_in_queue:    1
+log_max_index:      596273
+log_pointer:        596274
+total_replicas:     2
+active_replicas:    2
+
+ + +

Columns:

+
database:           database name
+table:              table name
+engine:             table engine name
+
+is_leader:          whether the replica is the leader
+
+Only one replica at a time can be the leader. The leader is responsible for selecting background merges to perform.
+Note that writes can be performed to any replica that is available and has a session in ZK, regardless of whether it is a leader.
+
+is_readonly:        Whether the replica is in read-only mode.
+This mode is turned on if the config doesn't have sections with ZK, if an unknown error occurred when reinitializing sessions in ZK, and during session reinitialization in ZK.
+
+is_session_expired: Whether the ZK session expired.
+Basically, the same thing as is_readonly.
+
+future_parts: The number of data parts that will appear as the result of INSERTs or merges that haven't been done yet. 
+
+parts_to_check: The number of data parts in the queue for verification.
+A part is put in the verification queue if there is suspicion that it might be damaged.
+
+zookeeper_path: The path to the table data in ZK. 
+replica_name: Name of the replica in ZK. Different replicas of the same table have different names. 
+replica_path: The path to the replica data in ZK. The same as concatenating zookeeper_path/replicas/replica_path.
+
+columns_version: Version number of the table structure.
+Indicates how many times ALTER was performed. If replicas have different versions, it means some replicas haven't made all of the ALTERs yet.
+
+queue_size:         Size of the queue for operations waiting to be performed.
+Operations include inserting blocks of data, merges, and certain other actions.
+Normally coincides with future_parts.
+
+inserts_in_queue: Number of inserts of blocks of data that need to be made.
+Insertions are usually replicated fairly quickly. If the number is high, something is wrong.
+
+merges_in_queue: The number of merges waiting to be made. 
+Sometimes merges are lengthy, so this value may be greater than zero for a long time.
+
+The next 4 columns have a non-null value only if the ZK session is active.
+
+log_max_index:     Maximum entry number in the log of general activity.
+log_pointer:        Maximum entry number in the log of general activity that the replica copied to its execution queue, plus one.
+If log_pointer is much smaller than log_max_index, something is wrong.
+
+total_replicas:     Total number of known replicas of this table.
+active_replicas:    Number of replicas of this table that have a ZK session (the number of active replicas).
+
+ + +

If you request all the columns, the table may work a bit slowly, since several reads from ZK are made for each row. +If you don't request the last 4 columns (log_max_index, log_pointer, total_replicas, active_replicas), the table works quickly.

+

For example, you can check that everything is working correctly like this:

+
SELECT
+    database,
+    table,
+    is_leader,
+    is_readonly,
+    is_session_expired,
+    future_parts,
+    parts_to_check,
+    columns_version,
+    queue_size,
+    inserts_in_queue,
+    merges_in_queue,
+    log_max_index,
+    log_pointer,
+    total_replicas,
+    active_replicas
+FROM system.replicas
+WHERE
+       is_readonly
+    OR is_session_expired
+    OR future_parts > 20
+    OR parts_to_check > 10
+    OR queue_size > 20
+    OR inserts_in_queue > 10
+    OR log_max_index - log_pointer > 10
+    OR total_replicas < 2
+    OR active_replicas < total_replicas
+
+ + +

If this query doesn't return anything, it means that everything is fine.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/system_tables/system.settings/index.html b/docs/build/docs/en/system_tables/system.settings/index.html new file mode 100644 index 00000000000..7fdab9a4316 --- /dev/null +++ b/docs/build/docs/en/system_tables/system.settings/index.html @@ -0,0 +1,2910 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + system.settings - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
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+ + +
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+ +
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+ + +
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+ + +
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+
+ + +
+
+ + + + + + + +

system.settings

+

Contains information about settings that are currently in use. +I.e. used for executing the query you are using to read from the system.settings table).

+

Columns:

+
name String   – Setting name.
+value String  – Setting value.
+changed UInt8 - Whether the setting was explicitly defined in the config or explicitly changed.
+
+ + +

Example:

+
SELECT *
+FROM system.settings
+WHERE changed
+
+ + +
┌─name───────────────────┬─value───────┬─changed─┐
+│ max_threads            │ 8           │       1 │
+│ use_uncompressed_cache │ 0           │       1 │
+│ load_balancing         │ random      │       1 │
+│ max_memory_usage       │ 10000000000 │       1 │
+└────────────────────────┴─────────────┴─────────┘
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/system_tables/system.tables/index.html b/docs/build/docs/en/system_tables/system.tables/index.html new file mode 100644 index 00000000000..a5c73dceae4 --- /dev/null +++ b/docs/build/docs/en/system_tables/system.tables/index.html @@ -0,0 +1,2891 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + system.tables - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
+
+
+ +
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+ + +
+
+
+ + +
+
+
+ + +
+
+ + + + + + + +

system.tables

+

This table contains the String columns 'database', 'name', and 'engine'. +The table also contains three virtual columns: metadata_modification_time (DateTime type), create_table_query, and engine_full (String type). +Each table that the server knows about is entered in the 'system.tables' table. +This system table is used for implementing SHOW TABLES queries.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/system_tables/system.zookeeper/index.html b/docs/build/docs/en/system_tables/system.zookeeper/index.html new file mode 100644 index 00000000000..af2db0248e4 --- /dev/null +++ b/docs/build/docs/en/system_tables/system.zookeeper/index.html @@ -0,0 +1,2951 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + system.zookeeper - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
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+ +
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+ + +
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+ + +
+
+ + + + + + + +

system.zookeeper

+

Allows reading data from the ZooKeeper cluster defined in the config. +The query must have a 'path' equality condition in the WHERE clause. This is the path in ZooKeeper for the children that you want to get data for.

+

The query SELECT * FROM system.zookeeper WHERE path = '/clickhouse' outputs data for all children on the /clickhouse node. +To output data for all root nodes, write path = '/'. +If the path specified in 'path' doesn't exist, an exception will be thrown.

+

Columns:

+
    +
  • name String — Name of the node.
    • +
  • path String — Path to the node.
    • +
  • value String — Value of the node.
    • +
  • dataLength Int32 — Size of the value.
    • +
  • numChildren Int32 — Number of children.
    • +
  • czxid Int64 — ID of the transaction that created the node.
    • +
  • mzxid Int64 — ID of the transaction that last changed the node.
    • +
  • pzxid Int64 — ID of the transaction that last added or removed children.
    • +
  • ctime DateTime — Time of node creation.
    • +
  • mtime DateTime — Time of the last node modification.
    • +
  • version Int32 — Node version - the number of times the node was changed.
    • +
  • cversion Int32 — Number of added or removed children.
    • +
  • aversion Int32 — Number of changes to ACL.
    • +
  • ephemeralOwner Int64 — For ephemeral nodes, the ID of the session that owns this node.
    • +
+

Example:

+
SELECT *
+FROM system.zookeeper
+WHERE path = '/clickhouse/tables/01-08/visits/replicas'
+FORMAT Vertical
+
+ + +
Row 1:
+──────
+name:           example01-08-1.yandex.ru
+value:
+czxid:          932998691229
+mzxid:          932998691229
+ctime:          2015-03-27 16:49:51
+mtime:          2015-03-27 16:49:51
+version:        0
+cversion:       47
+aversion:       0
+ephemeralOwner: 0
+dataLength:     0
+numChildren:    7
+pzxid:          987021031383
+path:           /clickhouse/tables/01-08/visits/replicas
+
+Row 2:
+──────
+name:           example01-08-2.yandex.ru
+value:
+czxid:          933002738135
+mzxid:          933002738135
+ctime:          2015-03-27 16:57:01
+mtime:          2015-03-27 16:57:01
+version:        0
+cversion:       37
+aversion:       0
+ephemeralOwner: 0
+dataLength:     0
+numChildren:    7
+pzxid:          987021252247
+path:           /clickhouse/tables/01-08/visits/replicas
+
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/aggregatingmergetree/index.html b/docs/build/docs/en/table_engines/aggregatingmergetree/index.html new file mode 100644 index 00000000000..5ce2def8f05 --- /dev/null +++ b/docs/build/docs/en/table_engines/aggregatingmergetree/index.html @@ -0,0 +1,2952 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + AggregatingMergeTree - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ +
+ + + + +
+
+ + +
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+ +
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+ + +
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+ + +
+
+ + + + + + + +

AggregatingMergeTree

+

This engine differs from MergeTree in that the merge combines the states of aggregate functions stored in the table for rows with the same primary key value.

+

For this to work, it uses the AggregateFunction data type, as well as -State and -Merge modifiers for aggregate functions. Let's examine it more closely.

+

There is an AggregateFunction data type. It is a parametric data type. As parameters, the name of the aggregate function is passed, then the types of its arguments.

+

Examples:

+
CREATE TABLE t
+(
+    column1 AggregateFunction(uniq, UInt64),
+    column2 AggregateFunction(anyIf, String, UInt8),
+    column3 AggregateFunction(quantiles(0.5, 0.9), UInt64)
+) ENGINE = ...
+
+ + +

This type of column stores the state of an aggregate function.

+

To get this type of value, use aggregate functions with the State suffix.

+

Example: +uniqState(UserID), quantilesState(0.5, 0.9)(SendTiming)

+

In contrast to the corresponding uniq and quantiles functions, these functions return the state, rather than the prepared value. In other words, they return an AggregateFunction type value.

+

An AggregateFunction type value can't be output in Pretty formats. In other formats, these types of values are output as implementation-specific binary data. The AggregateFunction type values are not intended for output or saving in a dump.

+

The only useful thing you can do with AggregateFunction type values is combine the states and get a result, which essentially means to finish aggregation. Aggregate functions with the 'Merge' suffix are used for this purpose. +Example: uniqMerge(UserIDState), where UserIDState has the AggregateFunction type.

+

In other words, an aggregate function with the 'Merge' suffix takes a set of states, combines them, and returns the result. +As an example, these two queries return the same result:

+
SELECT uniq(UserID) FROM table
+
+SELECT uniqMerge(state) FROM (SELECT uniqState(UserID) AS state FROM table GROUP BY RegionID)
+
+ + +

There is an AggregatingMergeTree engine. Its job during a merge is to combine the states of aggregate functions from different table rows with the same primary key value.

+

You can't use a normal INSERT to insert a row in a table containing AggregateFunction columns, because you can't explicitly define the AggregateFunction value. Instead, use INSERT SELECT with -State aggregate functions for inserting data.

+

With SELECT from an AggregatingMergeTree table, use GROUP BY and aggregate functions with the '-Merge' modifier in order to complete data aggregation.

+

You can use AggregatingMergeTree tables for incremental data aggregation, including for aggregated materialized views.

+

Example:

+

Create an AggregatingMergeTree materialized view that watches the test.visits table:

+
CREATE MATERIALIZED VIEW test.basic
+ENGINE = AggregatingMergeTree(StartDate, (CounterID, StartDate), 8192)
+AS SELECT
+    CounterID,
+    StartDate,
+    sumState(Sign)    AS Visits,
+    uniqState(UserID) AS Users
+FROM test.visits
+GROUP BY CounterID, StartDate;
+
+ + +

Insert data in the test.visits table. Data will also be inserted in the view, where it will be aggregated:

+
INSERT INTO test.visits ...
+
+ + +

Perform SELECT from the view using GROUP BY in order to complete data aggregation:

+
SELECT
+    StartDate,
+    sumMerge(Visits) AS Visits,
+    uniqMerge(Users) AS Users
+FROM test.basic
+GROUP BY StartDate
+ORDER BY StartDate;
+
+ + +

You can create a materialized view like this and assign a normal view to it that finishes data aggregation.

+

Note that in most cases, using AggregatingMergeTree is not justified, since queries can be run efficiently enough on non-aggregated data.

+ + + + + + + +
+
+
+
+ + + + +
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Buffer

+

Buffers the data to write in RAM, periodically flushing it to another table. During the read operation, data is read from the buffer and the other table simultaneously.

+
Buffer(database, table, num_layers, min_time, max_time, min_rows, max_rows, min_bytes, max_bytes)
+
+ + +

Engine parameters:database, table – The table to flush data to. Instead of the database name, you can use a constant expression that returns a string.num_layers – Parallelism layer. Physically, the table will be represented as 'num_layers' of independent buffers. Recommended value: 16.min_time, max_time, min_rows, max_rows, min_bytes, and max_bytes are conditions for flushing data from the buffer.

+

Data is flushed from the buffer and written to the destination table if all the 'min' conditions or at least one 'max' condition are met.min_time, max_time – Condition for the time in seconds from the moment of the first write to the buffer.min_rows, max_rows – Condition for the number of rows in the buffer.min_bytes, max_bytes – Condition for the number of bytes in the buffer.

+

During the write operation, data is inserted to a 'num_layers' number of random buffers. Or, if the data part to insert is large enough (greater than 'max_rows' or 'max_bytes'), it is written directly to the destination table, omitting the buffer.

+

The conditions for flushing the data are calculated separately for each of the 'num_layers' buffers. For example, if num_layers = 16 and max_bytes = 100000000, the maximum RAM consumption is 1.6 GB.

+

Example:

+
CREATE TABLE merge.hits_buffer AS merge.hits ENGINE = Buffer(merge, hits, 16, 10, 100, 10000, 1000000, 10000000, 100000000)
+
+ + +

Creating a 'merge.hits_buffer' table with the same structure as 'merge.hits' and using the Buffer engine. When writing to this table, data is buffered in RAM and later written to the 'merge.hits' table. 16 buffers are created. The data in each of them is flushed if either 100 seconds have passed, or one million rows have been written, or 100 MB of data have been written; or if simultaneously 10 seconds have passed and 10,000 rows and 10 MB of data have been written. For example, if just one row has been written, after 100 seconds it will be flushed, no matter what. But if many rows have been written, the data will be flushed sooner.

+

When the server is stopped, with DROP TABLE or DETACH TABLE, buffer data is also flushed to the destination table.

+

You can set empty strings in single quotation marks for the database and table name. This indicates the absence of a destination table. In this case, when the data flush conditions are reached, the buffer is simply cleared. This may be useful for keeping a window of data in memory.

+

When reading from a Buffer table, data is processed both from the buffer and from the destination table (if there is one). +Note that the Buffer tables does not support an index. In other words, data in the buffer is fully scanned, which might be slow for large buffers. (For data in a subordinate table, the index that it supports will be used.)

+

If the set of columns in the Buffer table doesn't match the set of columns in a subordinate table, a subset of columns that exist in both tables is inserted.

+

If the types don't match for one of the columns in the Buffer table and a subordinate table, an error message is entered in the server log and the buffer is cleared. +The same thing happens if the subordinate table doesn't exist when the buffer is flushed.

+

If you need to run ALTER for a subordinate table and the Buffer table, we recommend first deleting the Buffer table, running ALTER for the subordinate table, then creating the Buffer table again.

+

If the server is restarted abnormally, the data in the buffer is lost.

+

PREWHERE, FINAL and SAMPLE do not work correctly for Buffer tables. These conditions are passed to the destination table, but are not used for processing data in the buffer. Because of this, we recommend only using the Buffer table for writing, while reading from the destination table.

+

When adding data to a Buffer, one of the buffers is locked. This causes delays if a read operation is simultaneously being performed from the table.

+

Data that is inserted to a Buffer table may end up in the subordinate table in a different order and in different blocks. Because of this, a Buffer table is difficult to use for writing to a CollapsingMergeTree correctly. To avoid problems, you can set 'num_layers' to 1.

+

If the destination table is replicated, some expected characteristics of replicated tables are lost when writing to a Buffer table. The random changes to the order of rows and sizes of data parts cause data deduplication to quit working, which means it is not possible to have a reliable 'exactly once' write to replicated tables.

+

Due to these disadvantages, we can only recommend using a Buffer table in rare cases.

+

A Buffer table is used when too many INSERTs are received from a large number of servers over a unit of time and data can't be buffered before insertion, which means the INSERTs can't run fast enough.

+

Note that it doesn't make sense to insert data one row at a time, even for Buffer tables. This will only produce a speed of a few thousand rows per second, while inserting larger blocks of data can produce over a million rows per second (see the section "Performance").

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/collapsingmergetree/index.html b/docs/build/docs/en/table_engines/collapsingmergetree/index.html new file mode 100644 index 00000000000..aac17907338 --- /dev/null +++ b/docs/build/docs/en/table_engines/collapsingmergetree/index.html @@ -0,0 +1,2911 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + CollapsingMergeTree - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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CollapsingMergeTree

+

This engine is used specifically for Yandex.Metrica.

+

It differs from MergeTree in that it allows automatic deletion, or "collapsing" certain pairs of rows when merging.

+

Yandex.Metrica has normal logs (such as hit logs) and change logs. Change logs are used for incrementally calculating statistics on data that is constantly changing. Examples are the log of session changes, or logs of changes to user histories. Sessions are constantly changing in Yandex.Metrica. For example, the number of hits per session increases. We refer to changes in any object as a pair (?old values, ?new values). Old values may be missing if the object was created. New values may be missing if the object was deleted. If the object was changed, but existed previously and was not deleted, both values are present. In the change log, one or two entries are made for each change. Each entry contains all the attributes that the object has, plus a special attribute for differentiating between the old and new values. When objects change, only the new entries are added to the change log, and the existing ones are not touched.

+

The change log makes it possible to incrementally calculate almost any statistics. To do this, we need to consider "new" rows with a plus sign, and "old" rows with a minus sign. In other words, incremental calculation is possible for all statistics whose algebraic structure contains an operation for taking the inverse of an element. This is true of most statistics. We can also calculate "idempotent" statistics, such as the number of unique visitors, since the unique visitors are not deleted when making changes to sessions.

+

This is the main concept that allows Yandex.Metrica to work in real time.

+

CollapsingMergeTree accepts an additional parameter - the name of an Int8-type column that contains the row's "sign". Example:

+
CollapsingMergeTree(EventDate, (CounterID, EventDate, intHash32(UniqID), VisitID), 8192, Sign)
+
+ + +

Here, Sign is a column containing -1 for "old" values and 1 for "new" values.

+

When merging, each group of consecutive identical primary key values (columns for sorting data) is reduced to no more than one row with the column value 'sign_column = -1' (the "negative row") and no more than one row with the column value 'sign_column = 1' (the "positive row"). In other words, entries from the change log are collapsed.

+

If the number of positive and negative rows matches, the first negative row and the last positive row are written. +If there is one more positive row than negative rows, only the last positive row is written. +If there is one more negative row than positive rows, only the first negative row is written. +Otherwise, there will be a logical error and none of the rows will be written. (A logical error can occur if the same section of the log was accidentally inserted more than once. The error is just recorded in the server log, and the merge continues.)

+

Thus, collapsing should not change the results of calculating statistics. +Changes are gradually collapsed so that in the end only the last value of almost every object is left. +Compared to MergeTree, the CollapsingMergeTree engine allows a multifold reduction of data volume.

+

There are several ways to get completely "collapsed" data from a CollapsingMergeTree table:

+
    +
  1. Write a query with GROUP BY and aggregate functions that accounts for the sign. For example, to calculate quantity, write 'sum(Sign)' instead of 'count()'. To calculate the sum of something, write 'sum(Sign * x)' instead of 'sum(x)', and so on, and also add 'HAVING sum(Sign) > 0'. Not all amounts can be calculated this way. For example, the aggregate functions 'min' and 'max' can't be rewritten.
    2. +
  2. If you must extract data without aggregation (for example, to check whether rows are present whose newest values match certain conditions), you can use the FINAL modifier for the FROM clause. This approach is significantly less efficient.
    3. +
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Custom partitioning key

+

Starting with version 1.1.54310, you can create tables in the MergeTree family with any partitioning expression (not only partitioning by month).

+

The partition key can be an expression from the table columns, or a tuple of such expressions (similar to the primary key). The partition key can be omitted. When creating a table, specify the partition key in the ENGINE description with the new syntax:

+
ENGINE [=] Name(...) [PARTITION BY expr] [ORDER BY expr] [SAMPLE BY expr] [SETTINGS name=value, ...]
+
+ + +

For MergeTree tables, the partition expression is specified after PARTITION BY, the primary key after ORDER BY, the sampling key after SAMPLE BY, and SETTINGS can specify index_granularity (optional; the default value is 8192), as well as other settings from MergeTreeSettings.h. The other engine parameters are specified in parentheses after the engine name, as previously. Example:

+
ENGINE = ReplicatedCollapsingMergeTree('/clickhouse/tables/name', 'replica1', Sign)
+    PARTITION BY (toMonday(StartDate), EventType)
+    ORDER BY (CounterID, StartDate, intHash32(UserID))
+    SAMPLE BY intHash32(UserID)
+
+ + +

The traditional partitioning by month is expressed as toYYYYMM(date_column).

+

You can't convert an old-style table to a table with custom partitions (only via INSERT SELECT).

+

After this table is created, merge will only work for data parts that have the same value for the partitioning expression. Note: This means that you shouldn't make overly granular partitions (more than about a thousand partitions), or SELECT will perform poorly.

+

To specify a partition in ALTER PARTITION commands, specify the value of the partition expression (or a tuple). Constants and constant expressions are supported. Example:

+
ALTER TABLE table DROP PARTITION (toMonday(today()), 1)
+
+ + +

Deletes the partition for the current week with event type 1. The same is true for the OPTIMIZE query. To specify the only partition in a non-partitioned table, specify PARTITION tuple().

+

Note: For old-style tables, the partition can be specified either as a number 201710 or a string '201710'. The syntax for the new style of tables is stricter with types (similar to the parser for the VALUES input format). In addition, ALTER TABLE FREEZE PARTITION uses exact match for new-style tables (not prefix match).

+

In the system.parts table, the partition column specifies the value of the partition expression to use in ALTER queries (if quotas are removed). The name column should specify the name of the data part that has a new format.

+

Was: 20140317_20140323_2_2_0 (minimum date - maximum date - minimum block number - maximum block number - level).

+

Now: 201403_2_2_0 (partition ID - minimum block number - maximum block number - level).

+

The partition ID is its string identifier (human-readable, if possible) that is used for the names of data parts in the file system and in ZooKeeper. You can specify it in ALTER queries in place of the partition key. Example: Partition key toYYYYMM(EventDate); ALTER can specify either PARTITION 201710 or PARTITION ID '201710'.

+

For more examples, see the tests 00502_custom_partitioning_local and 00502_custom_partitioning_replicated_zookeeper.

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/dictionary/index.html b/docs/build/docs/en/table_engines/dictionary/index.html new file mode 100644 index 00000000000..288f1c03e49 --- /dev/null +++ b/docs/build/docs/en/table_engines/dictionary/index.html @@ -0,0 +1,2984 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Dictionary - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Dictionary

+

The Dictionary engine displays the dictionary data as a ClickHouse table.

+

As an example, consider a dictionary of products with the following configuration:

+
<dictionaries>
+<dictionary>
+        <name>products</name>
+        <source>
+            <odbc>
+                <table>products</table>
+                <connection_string>DSN=some-db-server</connection_string>
+            </odbc>
+        </source>
+        <lifetime>
+            <min>300</min>
+            <max>360</max>
+        </lifetime>
+        <layout>
+            <flat/>
+        </layout>
+        <structure>
+            <id>
+                <name>product_id</name>
+            </id>
+            <attribute>
+                <name>title</name>
+                <type>String</type>
+                <null_value></null_value>
+            </attribute>
+        </structure>
+</dictionary>
+</dictionaries>
+
+ + +

Query the dictionary data:

+
select name, type, key, attribute.names, attribute.types, bytes_allocated, element_count,source from system.dictionaries where name = 'products';                     
+
+SELECT
+    name,
+    type,
+    key,
+    attribute.names,
+    attribute.types,
+    bytes_allocated,
+    element_count,
+    source
+FROM system.dictionaries
+WHERE name = 'products'
+
+ + +
┌─name─────┬─type─┬─key────┬─attribute.names─┬─attribute.types─┬─bytes_allocated─┬─element_count─┬─source──────────┐
+│ products │ Flat │ UInt64 │ ['title']       │ ['String']      │        23065376 │        175032 │ ODBC: .products │
+└──────────┴──────┴────────┴─────────────────┴─────────────────┴─────────────────┴───────────────┴─────────────────┘
+
+ + +

You can use the dictGet* function to get the dictionary data in this format.

+

This view isn't helpful when you need to get raw data, or when performing a JOIN operation. For these cases, you can use the Dictionary engine, which displays the dictionary data in a table.

+

Syntax:

+
CREATE TABLE %table_name% (%fields%) engine = Dictionary(%dictionary_name%)`
+
+ + +

Usage example:

+
create table products (product_id UInt64, title String) Engine = Dictionary(products);
+
+CREATE TABLE products
+(
+    product_id UInt64,
+    title String,
+)
+ENGINE = Dictionary(products)
+
+ + +
Ok.
+
+0 rows in set. Elapsed: 0.004 sec.
+
+ + +

Take a look at what's in the table.

+
select * from products limit 1;
+
+SELECT *
+FROM products
+LIMIT 1
+
+ + +
┌────product_id─┬─title───────────┐
+│        152689 │ Some item       │
+└───────────────┴─────────────────┘
+
+1 rows in set. Elapsed: 0.006 sec.
+
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Distributed

+

The Distributed engine does not store data itself, but allows distributed query processing on multiple servers. +Reading is automatically parallelized. During a read, the table indexes on remote servers are used, if there are any. +The Distributed engine accepts parameters: the cluster name in the server's config file, the name of a remote database, the name of a remote table, and (optionally) a sharding key. +Example:

+
Distributed(logs, default, hits[, sharding_key])
+
+ + +

Data will be read from all servers in the 'logs' cluster, from the default.hits table located on every server in the cluster. +Data is not only read, but is partially processed on the remote servers (to the extent that this is possible). +For example, for a query with GROUP BY, data will be aggregated on remote servers, and the intermediate states of aggregate functions will be sent to the requestor server. Then data will be further aggregated.

+

Instead of the database name, you can use a constant expression that returns a string. For example: currentDatabase().

+

logs – The cluster name in the server's config file.

+

Clusters are set like this:

+
<remote_servers>
+    <logs>
+        <shard>
+            <!-- Optional. Shard weight when writing data. Default: 1. -->
+            <weight>1</weight>
+            <!-- Optional. Whether to write data to just one of the replicas. Default: false (write data to all replicas). -->
+            <internal_replication>false</internal_replication>
+            <replica>
+                <host>example01-01-1</host>
+                <port>9000</port>
+            </replica>
+            <replica>
+                <host>example01-01-2</host>
+                <port>9000</port>
+            </replica>
+        </shard>
+        <shard>
+            <weight>2</weight>
+            <internal_replication>false</internal_replication>
+            <replica>
+                <host>example01-02-1</host>
+                <port>9000</port>
+            </replica>
+            <replica>
+                <host>example01-02-2</host>
+                <port>9000</port>
+            </replica>
+        </shard>
+    </logs>
+</remote_servers>
+
+ + +

Here a cluster is defined with the name 'logs' that consists of two shards, each of which contains two replicas. +Shards refer to the servers that contain different parts of the data (in order to read all the data, you must access all the shards). +Replicas are duplicating servers (in order to read all the data, you can access the data on any one of the replicas).

+

The parameters host, port, and optionally user and password are specified for each server:

+

: - host – The address of the remote server. You can use either the domain or the IPv4 or IPv6 address. If you specify the domain, the server makes a DNS request when it starts, and the result is stored as long as the server is running. If the DNS request fails, the server doesn't start. If you change the DNS record, restart the server. +- port– The TCP port for messenger activity ('tcp_port' in the config, usually set to 9000). Do not confuse it with http_port. +- user– Name of the user for connecting to a remote server. Default value: default. This user must have access to connect to the specified server. Access is configured in the users.xml file. For more information, see the section "Access rights". +- password – The password for connecting to a remote server (not masked). Default value: empty string.

+

When specifying replicas, one of the available replicas will be selected for each of the shards when reading. You can configure the algorithm for load balancing (the preference for which replica to access) – see the 'load_balancing' setting. +If the connection with the server is not established, there will be an attempt to connect with a short timeout. If the connection failed, the next replica will be selected, and so on for all the replicas. If the connection attempt failed for all the replicas, the attempt will be repeated the same way, several times. +This works in favor of resiliency, but does not provide complete fault tolerance: a remote server might accept the connection, but might not work, or work poorly.

+

You can specify just one of the shards (in this case, query processing should be called remote, rather than distributed) or up to any number of shards. In each shard, you can specify from one to any number of replicas. You can specify a different number of replicas for each shard.

+

You can specify as many clusters as you wish in the configuration.

+

To view your clusters, use the 'system.clusters' table.

+

The Distributed engine allows working with a cluster like a local server. However, the cluster is inextensible: you must write its configuration in the server config file (even better, for all the cluster's servers).

+

There is no support for Distributed tables that look at other Distributed tables (except in cases when a Distributed table only has one shard). As an alternative, make the Distributed table look at the "final" tables.

+

The Distributed engine requires writing clusters to the config file. Clusters from the config file are updated on the fly, without restarting the server. If you need to send a query to an unknown set of shards and replicas each time, you don't need to create a Distributed table – use the 'remote' table function instead. See the section "Table functions".

+

There are two methods for writing data to a cluster:

+

First, you can define which servers to write which data to, and perform the write directly on each shard. In other words, perform INSERT in the tables that the distributed table "looks at". +This is the most flexible solution – you can use any sharding scheme, which could be non-trivial due to the requirements of the subject area. +This is also the most optimal solution, since data can be written to different shards completely independently.

+

Second, you can perform INSERT in a Distributed table. In this case, the table will distribute the inserted data across servers itself. +In order to write to a Distributed table, it must have a sharding key set (the last parameter). In addition, if there is only one shard, the write operation works without specifying the sharding key, since it doesn't have any meaning in this case.

+

Each shard can have a weight defined in the config file. By default, the weight is equal to one. Data is distributed across shards in the amount proportional to the shard weight. For example, if there are two shards and the first has a weight of 9 while the second has a weight of 10, the first will be sent 9 / 19 parts of the rows, and the second will be sent 10 / 19.

+

Each shard can have the 'internal_replication' parameter defined in the config file.

+

If this parameter is set to 'true', the write operation selects the first healthy replica and writes data to it. Use this alternative if the Distributed table "looks at" replicated tables. In other words, if the table where data will be written is going to replicate them itself.

+

If it is set to 'false' (the default), data is written to all replicas. In essence, this means that the Distributed table replicates data itself. This is worse than using replicated tables, because the consistency of replicas is not checked, and over time they will contain slightly different data.

+

To select the shard that a row of data is sent to, the sharding expression is analyzed, and its remainder is taken from dividing it by the total weight of the shards. The row is sent to the shard that corresponds to the half-interval of the remainders from 'prev_weight' to 'prev_weights + weight', where 'prev_weights' is the total weight of the shards with the smallest number, and 'weight' is the weight of this shard. For example, if there are two shards, and the first has a weight of 9 while the second has a weight of 10, the row will be sent to the first shard for the remainders from the range [0, 9), and to the second for the remainders from the range [9, 19).

+

The sharding expression can be any expression from constants and table columns that returns an integer. For example, you can use the expression 'rand()' for random distribution of data, or 'UserID' for distribution by the remainder from dividing the user's ID (then the data of a single user will reside on a single shard, which simplifies running IN and JOIN by users). If one of the columns is not distributed evenly enough, you can wrap it in a hash function: intHash64(UserID).

+

A simple remainder from division is a limited solution for sharding and isn't always appropriate. It works for medium and large volumes of data (dozens of servers), but not for very large volumes of data (hundreds of servers or more). In the latter case, use the sharding scheme required by the subject area, rather than using entries in Distributed tables.

+

SELECT queries are sent to all the shards, and work regardless of how data is distributed across the shards (they can be distributed completely randomly). When you add a new shard, you don't have to transfer the old data to it. You can write new data with a heavier weight – the data will be distributed slightly unevenly, but queries will work correctly and efficiently.

+

You should be concerned about the sharding scheme in the following cases:

+
    +
  • Queries are used that require joining data (IN or JOIN) by a specific key. If data is sharded by this key, you can use local IN or JOIN instead of GLOBAL IN or GLOBAL JOIN, which is much more efficient.
    • +
  • A large number of servers is used (hundreds or more) with a large number of small queries (queries of individual clients - websites, advertisers, or partners). In order for the small queries to not affect the entire cluster, it makes sense to locate data for a single client on a single shard. Alternatively, as we've done in Yandex.Metrica, you can set up bi-level sharding: divide the entire cluster into "layers", where a layer may consist of multiple shards. Data for a single client is located on a single layer, but shards can be added to a layer as necessary, and data is randomly distributed within them. Distributed tables are created for each layer, and a single shared distributed table is created for global queries.
    • +
+

Data is written asynchronously. For an INSERT to a Distributed table, the data block is just written to the local file system. The data is sent to the remote servers in the background as soon as possible. You should check whether data is sent successfully by checking the list of files (data waiting to be sent) in the table directory: /var/lib/clickhouse/data/database/table/.

+

If the server ceased to exist or had a rough restart (for example, after a device failure) after an INSERT to a Distributed table, the inserted data might be lost. If a damaged data part is detected in the table directory, it is transferred to the 'broken' subdirectory and no longer used.

+

When the max_parallel_replicas option is enabled, query processing is parallelized across all replicas within a single shard. For more information, see the section "Settings, max_parallel_replicas".

+ + + + + + + +
+
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/external_data/index.html b/docs/build/docs/en/table_engines/external_data/index.html new file mode 100644 index 00000000000..e89e8a896ce --- /dev/null +++ b/docs/build/docs/en/table_engines/external_data/index.html @@ -0,0 +1,2931 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + External data for query processing - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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External data for query processing

+

ClickHouse allows sending a server the data that is needed for processing a query, together with a SELECT query. This data is put in a temporary table (see the section "Temporary tables") and can be used in the query (for example, in IN operators).

+

For example, if you have a text file with important user identifiers, you can upload it to the server along with a query that uses filtration by this list.

+

If you need to run more than one query with a large volume of external data, don't use this feature. It is better to upload the data to the DB ahead of time.

+

External data can be uploaded using the command-line client (in non-interactive mode), or using the HTTP interface.

+

In the command-line client, you can specify a parameters section in the format

+
--external --file=... [--name=...] [--format=...] [--types=...|--structure=...]
+
+ + +

You may have multiple sections like this, for the number of tables being transmitted.

+

--external – Marks the beginning of a clause. +--file – Path to the file with the table dump, or -, which refers to stdin. +Only a single table can be retrieved from stdin.

+

The following parameters are optional: --name– Name of the table. If omitted, _data is used. +--format – Data format in the file. If omitted, TabSeparated is used.

+

One of the following parameters is required:--types – A list of comma-separated column types. For example: UInt64,String. The columns will be named _1, _2, ... +--structure– The table structure in the formatUserID UInt64, URL String. Defines the column names and types.

+

The files specified in 'file' will be parsed by the format specified in 'format', using the data types specified in 'types' or 'structure'. The table will be uploaded to the server and accessible there as a temporary table with the name in 'name'.

+

Examples:

+
echo -ne "1\n2\n3\n" | clickhouse-client --query="SELECT count() FROM test.visits WHERE TraficSourceID IN _data" --external --file=- --types=Int8
+849897
+cat /etc/passwd | sed 's/:/\t/g' | clickhouse-client --query="SELECT shell, count() AS c FROM passwd GROUP BY shell ORDER BY c DESC" --external --file=- --name=passwd --structure='login String, unused String, uid UInt16, gid UInt16, comment String, home String, shell String'
+/bin/sh 20
+/bin/false      5
+/bin/bash       4
+/usr/sbin/nologin       1
+/bin/sync       1
+
+ + +

When using the HTTP interface, external data is passed in the multipart/form-data format. Each table is transmitted as a separate file. The table name is taken from the file name. The 'query_string' is passed the parameters 'name_format', 'name_types', and 'name_structure', where 'name' is the name of the table that these parameters correspond to. The meaning of the parameters is the same as when using the command-line client.

+

Example:

+
cat /etc/passwd | sed 's/:/\t/g' > passwd.tsv
+
+curl -F 'passwd=@passwd.tsv;' 'http://localhost:8123/?query=SELECT+shell,+count()+AS+c+FROM+passwd+GROUP+BY+shell+ORDER+BY+c+DESC&passwd_structure=login+String,+unused+String,+uid+UInt16,+gid+UInt16,+comment+String,+home+String,+shell+String'
+/bin/sh 20
+/bin/false      5
+/bin/bash       4
+/usr/sbin/nologin       1
+/bin/sync       1
+
+ + +

For distributed query processing, the temporary tables are sent to all the remote servers.

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File(InputFormat)

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The data source is a file that stores data in one of the supported input formats (TabSeparated, Native, etc.).

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/graphitemergetree/index.html b/docs/build/docs/en/table_engines/graphitemergetree/index.html new file mode 100644 index 00000000000..eec681c83b6 --- /dev/null +++ b/docs/build/docs/en/table_engines/graphitemergetree/index.html @@ -0,0 +1,3002 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + GraphiteMergeTree - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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GraphiteMergeTree

+

This engine is designed for rollup (thinning and aggregating/averaging) Graphite data. It may be helpful to developers who want to use ClickHouse as a data store for Graphite.

+

Graphite stores full data in ClickHouse, and data can be retrieved in the following ways:

+
    +
  • Without thinning.
    • +
+

Uses the MergeTree engine.

+
    +
  • With thinning.
    • +
+

Using the GraphiteMergeTree engine.

+

The engine inherits properties from MergeTree. The settings for thinning data are defined by the graphite_rollup parameter in the server configuration.

+

Using the engine

+

The Graphite data table must contain the following fields at minimum:

+
    +
  • Path – The metric name (Graphite sensor).
    • +
  • Time – The time for measuring the metric.
    • +
  • Value – The value of the metric at the time set in Time.
    • +
  • Version – Determines which value of the metric with the same Path and Time will remain in the database.
    • +
+

Rollup pattern:

+
pattern
+    regexp
+    function
+    age -> precision
+    ...
+pattern
+    ...
+default
+    function
+       age -> precision
+    ...
+
+ + +

When processing a record, ClickHouse will check the rules in the patternclause. If the metric name matches the regexp, the rules from pattern are applied; otherwise, the rules from default are used.

+

Fields in the pattern.

+
    +
  • age – The minimum age of the data in seconds.
    • +
  • function – The name of the aggregating function to apply to data whose age falls within the range [age, age + precision].
    • +
  • precision– How precisely to define the age of the data in seconds.
    • +
  • regexp– A pattern for the metric name.
    • +
+

Example of settings:

+
<graphite_rollup>
+    <pattern>
+        <regexp>click_cost</regexp>
+        <function>any</function>
+        <retention>
+            <age>0</age>
+            <precision>5</precision>
+        </retention>
+        <retention>
+            <age>86400</age>
+            <precision>60</precision>
+        </retention>
+    </pattern>
+    <default>
+        <function>max</function>
+        <retention>
+            <age>0</age>
+            <precision>60</precision>
+        </retention>
+        <retention>
+            <age>3600</age>
+            <precision>300</precision>
+        </retention>
+        <retention>
+            <age>86400</age>
+            <precision>3600</precision>
+        </retention>
+    </default>
+</graphite_rollup>
+
+ + + + + + + +
+
+
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+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/index.html b/docs/build/docs/en/table_engines/index.html new file mode 100644 index 00000000000..1012bf334ee --- /dev/null +++ b/docs/build/docs/en/table_engines/index.html @@ -0,0 +1,2898 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Introduction - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Table engines

+

The table engine (type of table) determines:

+
    +
  • How and where data is stored: where to write it to, and where to read it from.
    • +
  • Which queries are supported, and how.
    • +
  • Concurrent data access.
    • +
  • Use of indexes, if present.
    • +
  • Whether multithreaded request execution is possible.
    • +
  • Data replication.
    • +
+

When reading data, the engine is only required to extract the necessary set of columns. However, in some cases, the query may be partially processed inside the table engine.

+

Note that for most serious tasks, you should use engines from the MergeTree family.

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/join/index.html b/docs/build/docs/en/table_engines/join/index.html new file mode 100644 index 00000000000..127262abb22 --- /dev/null +++ b/docs/build/docs/en/table_engines/join/index.html @@ -0,0 +1,2897 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Join - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Join

+

A prepared data structure for JOIN that is always located in RAM.

+
Join(ANY|ALL, LEFT|INNER, k1[, k2, ...])
+
+ + +

Engine parameters: ANY|ALL – strictness; LEFT|INNER – type. +These parameters are set without quotes and must match the JOIN that the table will be used for. k1, k2, ... are the key columns from the USING clause that the join will be made on.

+

The table can't be used for GLOBAL JOINs.

+

You can use INSERT to add data to the table, similar to the Set engine. For ANY, data for duplicated keys will be ignored. For ALL, it will be counted. You can't perform SELECT directly from the table. The only way to retrieve data is to use it as the "right-hand" table for JOIN.

+

Storing data on the disk is the same as for the Set engine.

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/kafka/index.html b/docs/build/docs/en/table_engines/kafka/index.html new file mode 100644 index 00000000000..ac06d3099b9 --- /dev/null +++ b/docs/build/docs/en/table_engines/kafka/index.html @@ -0,0 +1,3014 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Kafka - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Kafka

+

This engine works with Apache Kafka.

+

Kafka lets you:

+
    +
  • Publish or subscribe to data flows.
    • +
  • Organize fault-tolerant storage.
    • +
  • Process streams as they become available.
    • +
+
Kafka(broker_list, topic_list, group_name, format[, schema, num_consumers])
+
+ + +

Parameters:

+
    +
  • broker_list – A comma-separated list of brokers (localhost:9092).
    • +
  • topic_list – A list of Kafka topics (my_topic).
    • +
  • group_name – A group of Kafka consumers (group1). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere.
    • +
  • --format – Message format. Uses the same notation as the SQL FORMAT function, such as JSONEachRow. For more information, see the "Formats" section.
    • +
  • schema – An optional parameter that must be used if the format requires a schema definition. For example, Cap'n Proto requires the path to the schema file and the name of the root schema.capnp:Message object.
    • +
  • num_consumers – The number of consumers per table. Default: 1. Specify more consumers if the throughput of one consumer is insufficient. The total number of consumers should not exceed the number of partitions in the topic, since only one consumer can be assigned per partition.
    • +
+

Example:

+
  CREATE TABLE queue (
+    timestamp UInt64,
+    level String,
+    message String
+  ) ENGINE = Kafka('localhost:9092', 'topic', 'group1', 'JSONEachRow');
+
+  SELECT * FROM queue LIMIT 5;
+
+ + +

The delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name.

+

Groups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. Read more about this at http://kafka.apache.org/intro.

+

SELECT is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. To do this:

+
    +
  1. Use the engine to create a Kafka consumer and consider it a data stream.
    2. +
  2. Create a table with the desired structure.
    3. +
  3. Create a materialized view that converts data from the engine and puts it into a previously created table.
    4. +
+

When the MATERIALIZED VIEW joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using SELECT

+

Example:

+
  CREATE TABLE queue (
+    timestamp UInt64,
+    level String,
+    message String
+  ) ENGINE = Kafka('localhost:9092', 'topic', 'group1', 'JSONEachRow');
+
+  CREATE TABLE daily (
+    day Date,
+    level String,
+    total UInt64
+  ) ENGINE = SummingMergeTree(day, (day, level), 8192);
+
+  CREATE MATERIALIZED VIEW consumer TO daily
+    AS SELECT toDate(toDateTime(timestamp)) AS day, level, count() as total
+    FROM queue GROUP BY day, level;
+
+  SELECT level, sum(total) FROM daily GROUP BY level;
+
+ + +

To improve performance, received messages are grouped into blocks the size of max_insert_block_size. If the block wasn't formed within stream_flush_interval_ms milliseconds, the data will be flushed to the table regardless of the completeness of the block.

+

To stop receiving topic data or to change the conversion logic, detach the materialized view:

+
  DETACH TABLE consumer;
+  ATTACH MATERIALIZED VIEW consumer;
+
+ + +

If you want to change the target table by using ALTERmaterialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view.

+

Configuration

+

Similar to GraphiteMergeTree, the Kafka engine supports extended configuration using the ClickHouse config file. There are two configuration keys that you can use: global (kafka) and topic-level (kafka_topic_*). The global configuration is applied first, and the topic-level configuration is second (if it exists).

+
  <!--  Global configuration options for all tables of Kafka engine type -->
+  <kafka>
+    <debug>cgrp</debug>
+    <auto_offset_reset>smallest</auto_offset_reset>
+  </kafka>
+
+  <!-- Configuration specific for topic "logs" -->
+  <kafka_topic_logs>
+    <retry_backoff_ms>250</retry_backoff_ms>
+    <fetch_min_bytes>100000</fetch_min_bytes>
+  </kafka_topic_logs>
+
+ + +

For a list of possible configuration options, see the librdkafka configuration reference. Use the underscore (_) instead of a dot in the ClickHouse configuration. For example, check.crcs=true will be <check_crcs>true</check_crcs>.

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/log/index.html b/docs/build/docs/en/table_engines/log/index.html new file mode 100644 index 00000000000..37a0df6940b --- /dev/null +++ b/docs/build/docs/en/table_engines/log/index.html @@ -0,0 +1,2890 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Log - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Log

+

Log differs from TinyLog in that a small file of "marks" resides with the column files. These marks are written on every data block and contain offsets that indicate where to start reading the file in order to skip the specified number of rows. This makes it possible to read table data in multiple threads. +For concurrent data access, the read operations can be performed simultaneously, while write operations block reads and each other. +The Log engine does not support indexes. Similarly, if writing to a table failed, the table is broken, and reading from it returns an error. The Log engine is appropriate for temporary data, write-once tables, and for testing or demonstration purposes.

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/materializedview/index.html b/docs/build/docs/en/table_engines/materializedview/index.html new file mode 100644 index 00000000000..cb4988f7d01 --- /dev/null +++ b/docs/build/docs/en/table_engines/materializedview/index.html @@ -0,0 +1,2888 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + MaterializedView - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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MaterializedView

+

Used for implementing materialized views (for more information, see the CREATE TABLE) query. For storing data, it uses a different engine that was specified when creating the view. When reading from a table, it just uses this engine.

+ + + + + + + +
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Memory

+

The Memory engine stores data in RAM, in uncompressed form. Data is stored in exactly the same form as it is received when read. In other words, reading from this table is completely free. +Concurrent data access is synchronized. Locks are short: read and write operations don't block each other. +Indexes are not supported. Reading is parallelized. +Maximal productivity (over 10 GB/sec) is reached on simple queries, because there is no reading from the disk, decompressing, or deserializing data. (We should note that in many cases, the productivity of the MergeTree engine is almost as high.) +When restarting a server, data disappears from the table and the table becomes empty. +Normally, using this table engine is not justified. However, it can be used for tests, and for tasks where maximum speed is required on a relatively small number of rows (up to approximately 100,000,000).

+

The Memory engine is used by the system for temporary tables with external query data (see the section "External data for processing a query"), and for implementing GLOBAL IN (see the section "IN operators").

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/merge/index.html b/docs/build/docs/en/table_engines/merge/index.html new file mode 100644 index 00000000000..b262a05c88e --- /dev/null +++ b/docs/build/docs/en/table_engines/merge/index.html @@ -0,0 +1,2955 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Merge - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Merge

+

The Merge engine (not to be confused with MergeTree) does not store data itself, but allows reading from any number of other tables simultaneously. +Reading is automatically parallelized. Writing to a table is not supported. When reading, the indexes of tables that are actually being read are used, if they exist. +The Merge engine accepts parameters: the database name and a regular expression for tables.

+

Example:

+
Merge(hits, '^WatchLog')
+
+ + +

Data will be read from the tables in the 'hits' database that have names that match the regular expression '^WatchLog'.

+

Instead of the database name, you can use a constant expression that returns a string. For example, currentDatabase().

+

Regular expressions — re2 (supports a subset of PCRE), case-sensitive. +See the notes about escaping symbols in regular expressions in the "match" section.

+

When selecting tables to read, the Merge table itself will not be selected, even if it matches the regex. This is to avoid loops. +It is possible to create two Merge tables that will endlessly try to read each others' data, but this is not a good idea.

+

The typical way to use the Merge engine is for working with a large number of TinyLog tables as if with a single table.

+

Virtual columns

+

Virtual columns are columns that are provided by the table engine, regardless of the table definition. In other words, these columns are not specified in CREATE TABLE, but they are accessible for SELECT.

+

Virtual columns differ from normal columns in the following ways:

+
    +
  • They are not specified in table definitions.
    • +
  • Data can't be added to them with INSERT.
    • +
  • When using INSERT without specifying the list of columns, virtual columns are ignored.
    • +
  • They are not selected when using the asterisk (SELECT *).
    • +
  • Virtual columns are not shown in SHOW CREATE TABLE and DESC TABLE queries.
    • +
+

A Merge type table contains a virtual _table column with the String type. (If the table already has a _table column, the virtual column is named _table1, and if it already has _table1, it is named _table2, and so on.) It contains the name of the table that data was read from.

+

If the WHERE or PREWHERE clause contains conditions for the '_table' column that do not depend on other table columns (as one of the conjunction elements, or as an entire expression), these conditions are used as an index. The conditions are performed on a data set of table names to read data from, and the read operation will be performed from only those tables that the condition was triggered on.

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/mergetree/index.html b/docs/build/docs/en/table_engines/mergetree/index.html new file mode 100644 index 00000000000..297760f5d3f --- /dev/null +++ b/docs/build/docs/en/table_engines/mergetree/index.html @@ -0,0 +1,2932 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + MergeTree - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

MergeTree

+

The MergeTree engine supports an index by primary key and by date, and provides the possibility to update data in real time. +This is the most advanced table engine in ClickHouse. Don't confuse it with the Merge engine.

+

The engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity.

+

Example without sampling support.

+
MergeTree(EventDate, (CounterID, EventDate), 8192)
+
+ + +

Example with sampling support.

+
MergeTree(EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID)), 8192)
+
+ + +

A MergeTree table must have a separate column containing the date. Here, it is the EventDate column. The date column must have the 'Date' type (not 'DateTime').

+

The primary key may be a tuple from any expressions (usually this is just a tuple of columns), or a single expression.

+

The sampling expression (optional) can be any expression. It must also be present in the primary key. The example uses a hash of user IDs to pseudo-randomly disperse data in the table for each CounterID and EventDate. In other words, when using the SAMPLE clause in a query, you get an evenly pseudo-random sample of data for a subset of users.

+

The table is implemented as a set of parts. Each part is sorted by the primary key. In addition, each part has the minimum and maximum date assigned. When inserting in the table, a new sorted part is created. The merge process is periodically initiated in the background. When merging, several parts are selected (usually the smallest ones) and then merged into one large sorted part.

+

In other words, incremental sorting occurs when inserting to the table. Merging is implemented so that the table always consists of a small number of sorted parts, and the merge itself doesn't do too much work.

+

During insertion, data belonging to different months is separated into different parts. The parts that correspond to different months are never combined. The purpose of this is to provide local data modification (for ease in backups).

+

Parts are combined up to a certain size threshold, so there aren't any merges that are too long.

+

For each part, an index file is also written. The index file contains the primary key value for every 'index_granularity' row in the table. In other words, this is an abbreviated index of sorted data.

+

For columns, "marks" are also written to each 'index_granularity' row so that data can be read in a specific range.

+

When reading from a table, the SELECT query is analyzed for whether indexes can be used. +An index can be used if the WHERE or PREWHERE clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has IN or LIKE with a fixed prefix on columns or expressions that are in the primary key or partitioning key, or on certain partially repetitive functions of these columns, or logical relationships of these expressions.

+

Thus, it is possible to quickly run queries on one or many ranges of the primary key. In this example, queries will be fast when run for a specific tracking tag; for a specific tag and date range; for a specific tag and date; for multiple tags with a date range, and so on.

+
SELECT count() FROM table WHERE EventDate = toDate(now()) AND CounterID = 34
+SELECT count() FROM table WHERE EventDate = toDate(now()) AND (CounterID = 34 OR CounterID = 42)
+SELECT count() FROM table WHERE ((EventDate >= toDate('2014-01-01') AND EventDate <= toDate('2014-01-31')) OR EventDate = toDate('2014-05-01')) AND CounterID IN (101500, 731962, 160656) AND (CounterID = 101500 OR EventDate != toDate('2014-05-01'))
+
+ + +

All of these cases will use the index by date and by primary key. The index is used even for complex expressions. Reading from the table is organized so that using the index can't be slower than a full scan.

+

In this example, the index can't be used.

+
SELECT count() FROM table WHERE CounterID = 34 OR URL LIKE '%upyachka%'
+
+ + +

To check whether ClickHouse can use the index when executing the query, use the settings force_index_by_dateandforce_primary_key.

+

The index by date only allows reading those parts that contain dates from the desired range. However, a data part may contain data for many dates (up to an entire month), while within a single part the data is ordered by the primary key, which might not contain the date as the first column. Because of this, using a query with only a date condition that does not specify the primary key prefix will cause more data to be read than for a single date.

+

For concurrent table access, we use multi-versioning. In other words, when a table is simultaneously read and updated, data is read from a set of parts that is current at the time of the query. There are no lengthy locks. Inserts do not get in the way of read operations.

+

Reading from a table is automatically parallelized.

+

The OPTIMIZE query is supported, which calls an extra merge step.

+

You can use a single large table and continually add data to it in small chunks – this is what MergeTree is intended for.

+

Data replication is possible for all types of tables in the MergeTree family (see the section "Data replication").

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MySQL

+

The MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server.

+

The engine takes 4 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example:

+
MySQL('host:port', 'database', 'table', 'user', 'password');
+
+ + +

At this time, simple WHERE clauses such as =, !=, >, >=, <, <= are executed on the MySQL server.

+

The rest of the conditions and the LIMIT sampling constraint are executed in ClickHouse only after the query to MySQL finishes.

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Null

+

When writing to a Null table, data is ignored. When reading from a Null table, the response is empty.

+

However, you can create a materialized view on a Null table. So the data written to the table will end up in the view.

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/replacingmergetree/index.html b/docs/build/docs/en/table_engines/replacingmergetree/index.html new file mode 100644 index 00000000000..41a6d95c36a --- /dev/null +++ b/docs/build/docs/en/table_engines/replacingmergetree/index.html @@ -0,0 +1,2897 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ReplacingMergeTree - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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ReplacingMergeTree

+

This engine table differs from MergeTree in that it removes duplicate entries with the same primary key value.

+

The last optional parameter for the table engine is the version column. When merging, it reduces all rows with the same primary key value to just one row. If the version column is specified, it leaves the row with the highest version; otherwise, it leaves the last row.

+

The version column must have a type from the UInt family, Date, or DateTime.

+
ReplacingMergeTree(EventDate, (OrderID, EventDate, BannerID, ...), 8192, ver)
+
+ + +

Note that data is only deduplicated during merges. Merging occurs in the background at an unknown time, so you can't plan for it. Some of the data may remain unprocessed. Although you can run an unscheduled merge using the OPTIMIZE query, don't count on using it, because the OPTIMIZE query will read and write a large amount of data.

+

Thus, ReplacingMergeTree is suitable for clearing out duplicate data in the background in order to save space, but it doesn't guarantee the absence of duplicates.

+

This engine is not used in Yandex.Metrica, but it has been applied in other Yandex projects.

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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/replication/index.html b/docs/build/docs/en/table_engines/replication/index.html new file mode 100644 index 00000000000..1f6416eb6e4 --- /dev/null +++ b/docs/build/docs/en/table_engines/replication/index.html @@ -0,0 +1,3120 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Data replication - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Data replication

+

Replication is only supported for tables in the MergeTree family:

+
    +
  • ReplicatedMergeTree
    • +
  • ReplicatedSummingMergeTree
    • +
  • ReplicatedReplacingMergeTree
    • +
  • ReplicatedAggregatingMergeTree
    • +
  • ReplicatedCollapsingMergeTree
    • +
  • ReplicatedGraphiteMergeTree
    • +
+

Replication works at the level of an individual table, not the entire server. A server can store both replicated and non-replicated tables at the same time.

+

Replication does not depend on sharding. Each shard has its own independent replication.

+

Compressed data is replicated for INSERT and ALTER queries (see the description of the ALTER query).

+

CREATE, DROP, ATTACH, DETACH and RENAME queries are executed on a single server and are not replicated:

+
    +
  • The CREATE TABLE query creates a new replicatable table on the server where the query is run. If this table already exists on other servers, it adds a new replica.
    • +
  • The DROP TABLE query deletes the replica located on the server where the query is run.
    • +
  • The RENAME query renames the table on one of the replicas. In other words, replicated tables can have different names on different replicas.
    • +
+

To use replication, set the addresses of the ZooKeeper cluster in the config file. Example:

+
<zookeeper>
+    <node index="1">
+        <host>example1</host>
+        <port>2181</port>
+    </node>
+    <node index="2">
+        <host>example2</host>
+        <port>2181</port>
+    </node>
+    <node index="3">
+        <host>example3</host>
+        <port>2181</port>
+    </node>
+</zookeeper>
+
+ + +

Use ZooKeeper version 3.4.5 or later.

+

You can specify any existing ZooKeeper cluster and the system will use a directory on it for its own data (the directory is specified when creating a replicatable table).

+

If ZooKeeper isn't set in the config file, you can't create replicated tables, and any existing replicated tables will be read-only.

+

ZooKeeper is not used in SELECT queries because replication does not affect the performance of SELECT and queries run just as fast as they do for non-replicated tables. When querying distributed replicated tables, ClickHouse behavior is controlled by the settings max_replica_delay_for_distributed_queries and fallback_to_stale_replicas_for_distributed_queries.

+

For each INSERT query, approximately ten entries are added to ZooKeeper through several transactions. (To be more precise, this is for each inserted block of data; an INSERT query contains one block or one block per max_insert_block_size = 1048576 rows.) This leads to slightly longer latencies for INSERT compared to non-replicated tables. But if you follow the recommendations to insert data in batches of no more than one INSERT per second, it doesn't create any problems. The entire ClickHouse cluster used for coordinating one ZooKeeper cluster has a total of several hundred INSERTs per second. The throughput on data inserts (the number of rows per second) is just as high as for non-replicated data.

+

For very large clusters, you can use different ZooKeeper clusters for different shards. However, this hasn't proven necessary on the Yandex.Metrica cluster (approximately 300 servers).

+

Replication is asynchronous and multi-master. INSERT queries (as well as ALTER) can be sent to any available server. Data is inserted on the server where the query is run, and then it is copied to the other servers. Because it is asynchronous, recently inserted data appears on the other replicas with some latency. If part of the replicas are not available, the data is written when they become available. If a replica is available, the latency is the amount of time it takes to transfer the block of compressed data over the network.

+

By default, an INSERT query waits for confirmation of writing the data from only one replica. If the data was successfully written to only one replica and the server with this replica ceases to exist, the stored data will be lost. Tp enable getting confirmation of data writes from multiple replicas, use the insert_quorum option.

+

Each block of data is written atomically. The INSERT query is divided into blocks up to max_insert_block_size = 1048576 rows. In other words, if the INSERT query has less than 1048576 rows, it is made atomically.

+

Data blocks are deduplicated. For multiple writes of the same data block (data blocks of the same size containing the same rows in the same order), the block is only written once. The reason for this is in case of network failures when the client application doesn't know if the data was written to the DB, so the INSERT query can simply be repeated. It doesn't matter which replica INSERTs were sent to with identical data. INSERTs are idempotent. Deduplication parameters are controlled by merge_tree server settings.

+

During replication, only the source data to insert is transferred over the network. Further data transformation (merging) is coordinated and performed on all the replicas in the same way. This minimizes network usage, which means that replication works well when replicas reside in different datacenters. (Note that duplicating data in different datacenters is the main goal of replication.)

+

You can have any number of replicas of the same data. Yandex.Metrica uses double replication in production. Each server uses RAID-5 or RAID-6, and RAID-10 in some cases. This is a relatively reliable and convenient solution.

+

The system monitors data synchronicity on replicas and is able to recover after a failure. Failover is automatic (for small differences in data) or semi-automatic (when data differs too much, which may indicate a configuration error).

+

+

Creating replicated tables

+

The Replicated prefix is added to the table engine name. For example:ReplicatedMergeTree.

+

Two parameters are also added in the beginning of the parameters list – the path to the table in ZooKeeper, and the replica name in ZooKeeper.

+

Example:

+
ReplicatedMergeTree('/clickhouse/tables/{layer}-{shard}/hits', '{replica}', EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID), EventTime), 8192)
+
+ + +

As the example shows, these parameters can contain substitutions in curly brackets. The substituted values are taken from the 'macros' section of the config file. Example:

+
<macros>
+    <layer>05</layer>
+    <shard>02</shard>
+    <replica>example05-02-1.yandex.ru</replica>
+</macros>
+
+ + +

The path to the table in ZooKeeper should be unique for each replicated table. Tables on different shards should have different paths. +In this case, the path consists of the following parts:

+

/clickhouse/tables/ is the common prefix. We recommend using exactly this one.

+

{layer}-{shard} is the shard identifier. In this example it consists of two parts, since the Yandex.Metrica cluster uses bi-level sharding. For most tasks, you can leave just the {shard} substitution, which will be expanded to the shard identifier.

+

hits is the name of the node for the table in ZooKeeper. It is a good idea to make it the same as the table name. It is defined explicitly, because in contrast to the table name, it doesn't change after a RENAME query.

+

The replica name identifies different replicas of the same table. You can use the server name for this, as in the example. The name only needs to be unique within each shard.

+

You can define the parameters explicitly instead of using substitutions. This might be convenient for testing and for configuring small clusters. However, you can't use distributed DDL queries (ON CLUSTER) in this case.

+

When working with large clusters, we recommend using substitutions because they reduce the probability of error.

+

Run the CREATE TABLE query on each replica. This query creates a new replicated table, or adds a new replica to an existing one.

+

If you add a new replica after the table already contains some data on other replicas, the data will be copied from the other replicas to the new one after running the query. In other words, the new replica syncs itself with the others.

+

To delete a replica, run DROP TABLE. However, only one replica is deleted – the one that resides on the server where you run the query.

+

Recovery after failures

+

If ZooKeeper is unavailable when a server starts, replicated tables switch to read-only mode. The system periodically attempts to connect to ZooKeeper.

+

If ZooKeeper is unavailable during an INSERT, or an error occurs when interacting with ZooKeeper, an exception is thrown.

+

After connecting to ZooKeeper, the system checks whether the set of data in the local file system matches the expected set of data (ZooKeeper stores this information). If there are minor inconsistencies, the system resolves them by syncing data with the replicas.

+

If the system detects broken data parts (with the wrong size of files) or unrecognized parts (parts written to the file system but not recorded in ZooKeeper), it moves them to the 'detached' subdirectory (they are not deleted). Any missing parts are copied from the replicas.

+

Note that ClickHouse does not perform any destructive actions such as automatically deleting a large amount of data.

+

When the server starts (or establishes a new session with ZooKeeper), it only checks the quantity and sizes of all files. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a SELECT query. The query throws an exception about a non-matching checksum or size of a compressed block. In this case, data parts are added to the verification queue and copied from the replicas if necessary.

+

If the local set of data differs too much from the expected one, a safety mechanism is triggered. The server enters this in the log and refuses to launch. The reason for this is that this case may indicate a configuration error, such as if a replica on a shard was accidentally configured like a replica on a different shard. However, the thresholds for this mechanism are set fairly low, and this situation might occur during normal failure recovery. In this case, data is restored semi-automatically - by "pushing a button".

+

To start recovery, create the node /path_to_table/replica_name/flags/force_restore_data in ZooKeeper with any content, or run the command to restore all replicated tables:

+
sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data
+
+ + +

Then restart the server. On start, the server deletes these flags and starts recovery.

+

Recovery after complete data loss

+

If all data and metadata disappeared from one of the servers, follow these steps for recovery:

+
    +
  1. Install ClickHouse on the server. Define substitutions correctly in the config file that contains the shard identifier and replicas, if you use them.
    2. +
  2. If you had unreplicated tables that must be manually duplicated on the servers, copy their data from a replica (in the directory /var/lib/clickhouse/data/db_name/table_name/).
    3. +
  3. Copy table definitions located in /var/lib/clickhouse/metadata/ from a replica. If a shard or replica identifier is defined explicitly in the table definitions, correct it so that it corresponds to this replica. (Alternatively, start the server and make all the ATTACH TABLE queries that should have been in the .sql files in /var/lib/clickhouse/metadata/.)
    4. +
  4. To start recovery, create the ZooKeeper node /path_to_table/replica_name/flags/force_restore_data with any content, or run the command to restore all replicated tables: sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data
    5. +
+

Then start the server (restart, if it is already running). Data will be downloaded from replicas.

+

An alternative recovery option is to delete information about the lost replica from ZooKeeper (/path_to_table/replica_name), then create the replica again as described in "Creating replicatable tables".

+

There is no restriction on network bandwidth during recovery. Keep this in mind if you are restoring many replicas at once.

+

Converting from MergeTree to ReplicatedMergeTree

+

We use the term MergeTree to refer to all table engines in the MergeTree family, the same as for ReplicatedMergeTree.

+

If you had a MergeTree table that was manually replicated, you can convert it to a replicatable table. You might need to do this if you have already collected a large amount of data in a MergeTree table and now you want to enable replication.

+

If the data differs on various replicas, first sync it, or delete this data on all the replicas except one.

+

Rename the existing MergeTree table, then create a ReplicatedMergeTree table with the old name. +Move the data from the old table to the 'detached' subdirectory inside the directory with the new table data (/var/lib/clickhouse/data/db_name/table_name/). +Then run ALTER TABLE ATTACH PARTITION on one of the replicas to add these data parts to the working set.

+

Converting from ReplicatedMergeTree to MergeTree

+

Create a MergeTree table with a different name. Move all the data from the directory with the ReplicatedMergeTree table data to the new table's data directory. Then delete the ReplicatedMergeTree table and restart the server.

+

If you want to get rid of a ReplicatedMergeTree table without launching the server:

+
    +
  • Delete the corresponding .sql file in the metadata directory (/var/lib/clickhouse/metadata/).
    • +
  • Delete the corresponding path in ZooKeeper (/path_to_table/replica_name).
    • +
+

After this, you can launch the server, create a MergeTree table, move the data to its directory, and then restart the server.

+

Recovery when metadata in the ZooKeeper cluster is lost or damaged

+

If the data in ZooKeeper was lost or damaged, you can save data by moving it to an unreplicated table as described above.

+

If exactly the same parts exist on the other replicas, they are added to the working set on them. If not, the parts are downloaded from the replica that has them.

+ + + + + + + +
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Set

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A data set that is always in RAM. It is intended for use on the right side of the IN operator (see the section "IN operators").

+

You can use INSERT to insert data in the table. New elements will be added to the data set, while duplicates will be ignored. +But you can't perform SELECT from the table. The only way to retrieve data is by using it in the right half of the IN operator.

+

Data is always located in RAM. For INSERT, the blocks of inserted data are also written to the directory of tables on the disk. When starting the server, this data is loaded to RAM. In other words, after restarting, the data remains in place.

+

For a rough server restart, the block of data on the disk might be lost or damaged. In the latter case, you may need to manually delete the file with damaged data.

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SummingMergeTree

+

This engine differs from MergeTree in that it totals data while merging.

+
SummingMergeTree(EventDate, (OrderID, EventDate, BannerID, ...), 8192)
+
+ + +

The columns to total are implicit. When merging, all rows with the same primary key value (in the example, OrderId, EventDate, BannerID, ...) have their values totaled in numeric columns that are not part of the primary key.

+
SummingMergeTree(EventDate, (OrderID, EventDate, BannerID, ...), 8192, (Shows, Clicks, Cost, ...))
+
+ + +

The columns to total are set explicitly (the last parameter – Shows, Clicks, Cost, ...). When merging, all rows with the same primary key value have their values totaled in the specified columns. The specified columns also must be numeric and must not be part of the primary key.

+

If the values were null in all of these columns, the row is deleted. (The exception is cases when the data part would not have any rows left in it.)

+

For the other rows that are not part of the primary key, the first value that occurs is selected when merging.

+

Summation is not performed for a read operation. If it is necessary, write the appropriate GROUP BY.

+

In addition, a table can have nested data structures that are processed in a special way. +If the name of a nested table ends in 'Map' and it contains at least two columns that meet the following criteria:

+
    +
  • The first table is numeric ((U)IntN, Date, DateTime), which we'll refer to as the 'key'.
    • +
  • The other columns are arithmetic ((U)IntN, Float32/64), which we'll refer to as '(values...)'. Then this nested table is interpreted as a mapping of key => (values...), and when merging its rows, the elements of two data sets are merged by 'key' with a summation of the corresponding (values...).
    • +
+

Examples:

+
[(1, 100)] + [(2, 150)] -> [(1, 100), (2, 150)]
+[(1, 100)] + [(1, 150)] -> [(1, 250)]
+[(1, 100)] + [(1, 150), (2, 150)] -> [(1, 250), (2, 150)]
+[(1, 100), (2, 150)] + [(1, -100)] -> [(2, 150)]
+
+ + +

For aggregation of Map, use the function sumMap(key, value).

+

For nested data structures, you don't need to specify the columns as a list of columns for totaling.

+

This table engine is not particularly useful. Remember that when saving just pre-aggregated data, you lose some of the system's advantages.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/tinylog/index.html b/docs/build/docs/en/table_engines/tinylog/index.html new file mode 100644 index 00000000000..1db1255677f --- /dev/null +++ b/docs/build/docs/en/table_engines/tinylog/index.html @@ -0,0 +1,2901 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + TinyLog - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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TinyLog

+

The simplest table engine, which stores data on a disk. +Each column is stored in a separate compressed file. +When writing, data is appended to the end of files.

+

Concurrent data access is not restricted in any way:

+
    +
  • If you are simultaneously reading from a table and writing to it in a different query, the read operation will complete with an error.
    • +
  • If you are writing to a table in multiple queries simultaneously, the data will be broken.
    • +
+

The typical way to use this table is write-once: first just write the data one time, then read it as many times as needed. +Queries are executed in a single stream. In other words, this engine is intended for relatively small tables (recommended up to 1,000,000 rows). +It makes sense to use this table engine if you have many small tables, since it is simpler than the Log engine (fewer files need to be opened). +The situation when you have a large number of small tables guarantees poor productivity, but may already be used when working with another DBMS, and you may find it easier to switch to using TinyLog types of tables. +Indexes are not supported.

+

In Yandex.Metrica, TinyLog tables are used for intermediary data that is processed in small batches.

+ + + + + + + +
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+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_engines/view/index.html b/docs/build/docs/en/table_engines/view/index.html new file mode 100644 index 00000000000..c54d5a945b1 --- /dev/null +++ b/docs/build/docs/en/table_engines/view/index.html @@ -0,0 +1,2888 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + View - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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View

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Used for implementing views (for more information, see the CREATE VIEW query). It does not store data, but only stores the specified SELECT query. When reading from a table, it runs this query (and deletes all unnecessary columns from the query).

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_functions/index.html b/docs/build/docs/en/table_functions/index.html new file mode 100644 index 00000000000..0bbcd4ef306 --- /dev/null +++ b/docs/build/docs/en/table_functions/index.html @@ -0,0 +1,2890 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Introduction - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Table functions

+

Table functions can be specified in the FROM clause instead of the database and table names. +Table functions can only be used if 'readonly' is not set. +Table functions aren't related to other functions.

+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_functions/merge/index.html b/docs/build/docs/en/table_functions/merge/index.html new file mode 100644 index 00000000000..c64e9093d8d --- /dev/null +++ b/docs/build/docs/en/table_functions/merge/index.html @@ -0,0 +1,2889 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + merge - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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merge

+

merge(db_name, 'tables_regexp') – Creates a temporary Merge table. For more information, see the section "Table engines, Merge".

+

The table structure is taken from the first table encountered that matches the regular expression.

+ + + + + + + +
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+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_functions/numbers/index.html b/docs/build/docs/en/table_functions/numbers/index.html new file mode 100644 index 00000000000..9fa44fb01f8 --- /dev/null +++ b/docs/build/docs/en/table_functions/numbers/index.html @@ -0,0 +1,2899 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + numbers - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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numbers

+

numbers(N) – Returns a table with the single 'number' column (UInt64) that contains integers from 0 to N-1.

+

Similar to the system.numbers table, it can be used for testing and generating successive values.

+

The following two queries are equivalent:

+
SELECT * FROM numbers(10);
+SELECT * FROM system.numbers LIMIT 10;
+
+ + +

Examples:

+
-- Generate a sequence of dates from 2010-01-01 to 2010-12-31
+select toDate('2010-01-01') + number as d FROM numbers(365);
+
+ + + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/table_functions/remote/index.html b/docs/build/docs/en/table_functions/remote/index.html new file mode 100644 index 00000000000..1d4f9588700 --- /dev/null +++ b/docs/build/docs/en/table_functions/remote/index.html @@ -0,0 +1,2947 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + remote - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

remote

+

Allows you to access remote servers without creating a Distributed table.

+

Signatures:

+
remote('addresses_expr', db, table[, 'user'[, 'password']])
+remote('addresses_expr', db.table[, 'user'[, 'password']])
+
+ + +

addresses_expr – An expression that generates addresses of remote servers. This may be just one server address. The server address is host:port, or just host. The host can be specified as the server name, or as the IPv4 or IPv6 address. An IPv6 address is specified in square brackets. The port is the TCP port on the remote server. If the port is omitted, it uses tcp_port from the server's config file (by default, 9000).

+
+ +The port is required for an IPv6 address. + +
+ +

Examples:

+
example01-01-1
+example01-01-1:9000
+localhost
+127.0.0.1
+[::]:9000
+[2a02:6b8:0:1111::11]:9000
+
+ + +

Multiple addresses can be comma-separated. In this case, ClickHouse will use distributed processing, so it will send the query to all specified addresses (like to shards with different data).

+

Example:

+
example01-01-1,example01-02-1
+
+ + +

Part of the expression can be specified in curly brackets. The previous example can be written as follows:

+
example01-0{1,2}-1
+
+ + +

Curly brackets can contain a range of numbers separated by two dots (non-negative integers). In this case, the range is expanded to a set of values that generate shard addresses. If the first number starts with zero, the values are formed with the same zero alignment. The previous example can be written as follows:

+
example01-{01..02}-1
+
+ + +

If you have multiple pairs of curly brackets, it generates the direct product of the corresponding sets.

+

Addresses and parts of addresses in curly brackets can be separated by the pipe symbol (|). In this case, the corresponding sets of addresses are interpreted as replicas, and the query will be sent to the first healthy replica. However, the replicas are iterated in the order currently set in the load_balancing setting.

+

Example:

+
example01-{01..02}-{1|2}
+
+ + +

This example specifies two shards that each have two replicas.

+

The number of addresses generated is limited by a constant. Right now this is 1000 addresses.

+

Using the remote table function is less optimal than creating a Distributed table, because in this case, the server connection is re-established for every request. In addition, if host names are set, the names are resolved, and errors are not counted when working with various replicas. When processing a large number of queries, always create the Distributed table ahead of time, and don't use the remote table function.

+

The remote table function can be useful in the following cases:

+
    +
  • Accessing a specific server for data comparison, debugging, and testing.
    • +
  • Queries between various ClickHouse clusters for research purposes.
    • +
  • Infrequent distributed requests that are made manually.
    • +
  • Distributed requests where the set of servers is re-defined each time.
    • +
+

If the user is not specified, default is used. +If the password is not specified, an empty password is used.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/utils/clickhouse-copier/index.html b/docs/build/docs/en/utils/clickhouse-copier/index.html new file mode 100644 index 00000000000..8552c9cba95 --- /dev/null +++ b/docs/build/docs/en/utils/clickhouse-copier/index.html @@ -0,0 +1,3105 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + clickhouse-copier - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

clickhouse-copier

+

Copies data from the tables in one cluster to tables in another (or the same) cluster.

+

You can run multiple clickhouse-copier instances on different servers to perform the same job. ZooKeeper is used for syncing the processes.

+

After starting, clickhouse-copier:

+
    +
  • Connects to ZooKeeper and receives:
    • +
  • Copying jobs.
    • +
  • +

    The state of the copying jobs.

    +
    • +
  • +

    It performs the jobs.

    +
    • +
+

Each running process chooses the "closest" shard of the source cluster and copies the data into the destination cluster, resharding the data if necessary.

+

clickhouse-copier tracks the changes in ZooKeeper and applies them on the fly.

+

To reduce network traffic, we recommend running clickhouse-copier on the same server where the source data is located.

+

Running clickhouse-copier

+

The utility should be run manually:

+
clickhouse-copier copier --daemon --config zookeeper.xml --task-path /task/path --base-dir /path/to/dir
+
+ + +

Parameters:

+
    +
  • daemon — Starts clickhouse-copier in daemon mode.
    • +
  • config — The path to the zookeeper.xml file with the parameters for the connection to ZooKeeper.
    • +
  • task-path — The path to the ZooKeeper node. This node is used for syncing clickhouse-copier processes and storing tasks. Tasks are stored in $task-path/description.
    • +
  • base-dir — The path to logs and auxiliary files. When it starts, clickhouse-copier creates clickhouse-copier_YYYYMMHHSS_<PID> subdirectories in $base-dir. If this parameter is omitted, the directories are created in the directory where clickhouse-copier was launched.
    • +
+

Format of zookeeper.xml

+
<yandex>
+    <zookeeper>
+        <node index="1">
+            <host>127.0.0.1</host>
+            <port>2181</port>
+        </node>
+    </zookeeper>
+</yandex>
+
+ + +

Configuration of copying tasks

+
<yandex>
+    <!-- Configuration of clusters as in an ordinary server config -->
+    <remote_servers>
+        <source_cluster>
+            <shard>
+                <internal_replication>false</internal_replication>
+                    <replica>
+                        <host>127.0.0.1</host>
+                        <port>9000</port>
+                    </replica>
+            </shard>
+            ...
+        </source_cluster>
+
+        <destination_cluster>
+        ...
+        </destination_cluster>
+    </remote_servers>
+
+    <!-- How many simultaneously active workers are possible. If you run more workers superfluous workers will sleep. -->
+    <max_workers>2</max_workers>
+
+    <!-- Setting used to fetch (pull) data from source cluster tables -->
+    <settings_pull>
+        <readonly>1</readonly>
+    </settings_pull>
+
+    <!-- Setting used to insert (push) data to destination cluster tables -->
+    <settings_push>
+        <readonly>0</readonly>
+    </settings_push>
+
+    <!-- Common setting for fetch (pull) and insert (push) operations. The copier process context also uses it.
+         They are overlaid by <settings_pull/> and <settings_push/> respectively. -->
+    <settings>
+        <connect_timeout>3</connect_timeout>
+        <!-- Sync insert is set forcibly, leave it here just in case. -->
+        <insert_distributed_sync>1</insert_distributed_sync>
+    </settings>
+
+    <!-- Copying description of tasks.
+         You can specify several table tasks in the same task description (in the same ZooKeeper node), and they will be performed         sequentially.
+    -->
+    <tables>
+        <!-- A table task that copies one table. -->
+        <table_hits>
+            <!-- Source cluster name (from the <remote_servers/> section) and tables in it that should be copied -->
+            <cluster_pull>source_cluster</cluster_pull>
+            <database_pull>test</database_pull>
+            <table_pull>hits</table_pull>
+
+            <!-- Destination cluster name and tables in which the data should be inserted -->
+            <cluster_push>destination_cluster</cluster_push>
+            <database_push>test</database_push>
+            <table_push>hits2</table_push>
+
+            <!-- Engine of destination tables.
+                 If the destination tables have not been created yet, workers create them using column definitions from source tables and the engine                 definition from here.
+
+                 NOTE: If the first worker starts to insert data and detects that the destination partition is not empty, then the partition will
+                 be dropped and refilled. Take this into account if you already have some data in destination tables. You can directly 
+                 specify partitions that should be copied in <enabled_partitions/>. They should be in quoted format like the partition column in the                 
+                 system.parts table.
+            -->
+            <engine>
+            ENGINE=ReplicatedMergeTree('/clickhouse/tables/{cluster}/{shard}/hits2', '{replica}')
+            PARTITION BY toMonday(date)
+            ORDER BY (CounterID, EventDate)
+            </engine>
+
+            <!-- Sharding key used to insert data to destination cluster -->
+            <sharding_key>jumpConsistentHash(intHash64(UserID), 2)</sharding_key>
+
+            <!-- Optional expression that filter data while pull them from source servers -->
+            <where_condition>CounterID != 0</where_condition>
+
+            <!-- This section specifies partitions that should be copied, other partition will be ignored.
+                 Partition names should have the same format as
+                 partition column of system.parts table (i.e. a quoted text).
+                 Since partition key of source and destination cluster could be different,
+                 these partition names specify destination partitions.
+
+                 Note: Although this section is optional (if it omitted, all partitions will be copied), 
+                 it is strongly recommended to specify the partitions explicitly.
+                 If you already have some partitions ready on the destination cluster, they                 
+                 will be removed at the start of the copying, because they will be interpreted                 
+                 as unfinished data from the previous copying.
+            -->
+            <enabled_partitions>
+                <partition>'2018-02-26'</partition>
+                <partition>'2018-03-05'</partition>
+                ...
+            </enabled_partitions>
+        </table_hits>
+
+        <!-- Next table to copy. It is not copied until the previous table is copying. -->
+        </table_visits>
+        ...
+        </table_visits>
+        ...
+    </tables>
+</yandex>
+
+ + +

clickhouse-copier tracks the changes in /task/path/description and applies them on the fly. For instance, if you change the value of max_workers, the number of processes running tasks will also change.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/utils/clickhouse-local/index.html b/docs/build/docs/en/utils/clickhouse-local/index.html new file mode 100644 index 00000000000..12cf5e7ef2a --- /dev/null +++ b/docs/build/docs/en/utils/clickhouse-local/index.html @@ -0,0 +1,2889 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + clickhouse-local - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

clickhouse-local

+

The clickhouse-local program enables you to perform fast processing on local files that store tables, without having to deploy and configure the ClickHouse server.

+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/docs/en/utils/index.html b/docs/build/docs/en/utils/index.html new file mode 100644 index 00000000000..5d3d67aa420 --- /dev/null +++ b/docs/build/docs/en/utils/index.html @@ -0,0 +1,2891 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Introduction - ClickHouse Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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ClickHouse utility

+
    +
  • clickhouse-local — Allows running SQL queries on data without stopping the ClickHouse server, similar to how awk does this.
    • +
  • clickhouse-copier — Copies (and reshards) data from one cluster to another cluster.
    • +
+ + + + + + + +
+
+
+
+ + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/en/agg_functions/parametric_functions.md b/docs/en/agg_functions/parametric_functions.md index 8539312c9a1..05ea5c9a642 100644 --- a/docs/en/agg_functions/parametric_functions.md +++ b/docs/en/agg_functions/parametric_functions.md @@ -50,6 +50,42 @@ Events that occur during the same second can be put in the chain in any order. T Works the same way as the sequenceMatch function, but instead of returning whether there is an event chain, it returns UInt64 with the number of event chains found. Chains are searched for without overlapping. In other words, the next chain can start only after the end of the previous one. +## windowFunnel(window)(timestamp, cond1, cond2, cond3, ....) + +Window funnel matching for event chains, calculates the max event level in a sliding window. + +`window` is the timestamp window value, such as 3600. + +`timestamp` is the time of the event with the DateTime type or UInt32 type. + +`cond1`, `cond2` ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event + +Example: + +Consider you are doing a website analytics, intend to find out the user counts clicked login button( event = 1001 ), then the user counts followed by searched the phones( event = 1003 and product = 'phone' ) , then the user counts followed by made an order ( event = 1009 ). And all event chains must be in a 3600 seconds sliding window. + +This could be easily calculate by `windowFunnel` + +``` +SELECT + level, + count() AS c +FROM +( + SELECT + user_id, + windowFunnel(3600)(timestamp, event_id = 1001, event_id = 1003 AND product = 'phone', event_id = 1009) AS level + FROM trend_event + WHERE (event_date >= '2017-01-01') AND (event_date <= '2017-01-31') + GROUP BY user_id +) +GROUP BY level +ORDER BY level +``` + +Simply, the level value could only be 0,1,2,3, it means the maxium event action stage that one user could reach. + + ## uniqUpTo(N)(x) Calculates the number of different argument values ​​if it is less than or equal to N. If the number of different argument values is greater than N, it returns N + 1. 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Aggregate function combinators

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The name of an aggregate function can have a suffix appended to it. This changes the way the aggregate function works.

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-If

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The suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument – a condition (Uint8 type). The aggregate function processes only the rows that trigger the condition. If the condition was not triggered even once, it returns a default value (usually zeros or empty strings).

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Examples: sumIf(column, cond), countIf(cond), avgIf(x, cond), quantilesTimingIf(level1, level2)(x, cond), argMinIf(arg, val, cond) and so on.

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With conditional aggregate functions, you can calculate aggregates for several conditions at once, without using subqueries and JOINs. For example, in Yandex.Metrica, conditional aggregate functions are used to implement the segment comparison functionality.

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-Array

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The -Array suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the 'Array(T)' type (arrays) instead of 'T' type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. When processing arrays, the aggregate function works like the original aggregate function across all array elements.

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Example 1: sumArray(arr) - Totals all the elements of all 'arr' arrays. In this example, it could have been written more simply: sum(arraySum(arr)).

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Example 2: uniqArray(arr) – Count the number of unique elements in all 'arr' arrays. This could be done an easier way: uniq(arrayJoin(arr)), but it's not always possible to add 'arrayJoin' to a query.

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-If and -Array can be combined. However, 'Array' must come first, then 'If'. Examples: uniqArrayIf(arr, cond), quantilesTimingArrayIf(level1, level2)(arr, cond). Due to this order, the 'cond' argument can't be an array.

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-State

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If you apply this combinator, the aggregate function doesn't return the resulting value (such as the number of unique values for the 'uniq' function), but an intermediate state of the aggregation (for uniq, this is the hash table for calculating the number of unique values). This is an AggregateFunction(...) that can be used for further processing or stored in a table to finish aggregating later. See the sections "AggregatingMergeTree" and "Functions for working with intermediate aggregation states".

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-Merge

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If you apply this combinator, the aggregate function takes the intermediate aggregation state as an argument, combines the states to finish aggregation, and returns the resulting value.

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-MergeState.

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Merges the intermediate aggregation states in the same way as the -Merge combinator. However, it doesn't return the resulting value, but an intermediate aggregation state, similar to the -State combinator.

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-ForEach

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Converts an aggregate function for tables into an aggregate function for arrays that aggregates the corresponding array items and returns an array of results. For example, sumForEach for the arrays [1, 2], [3, 4, 5]and[6, 7]returns the result [10, 13, 5] after adding together the corresponding array items.

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Aggregate functions

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Aggregate functions work in the normal way as expected by database experts.

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ClickHouse also supports:

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Parametric aggregate functions

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Some aggregate functions can accept not only argument columns (used for compression), but a set of parameters – constants for initialization. The syntax is two pairs of brackets instead of one. The first is for parameters, and the second is for arguments.

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sequenceMatch(pattern)(time, cond1, cond2, ...)

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Pattern matching for event chains.

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pattern is a string containing a pattern to match. The pattern is similar to a regular expression.

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time is the time of the event with the DateTime type.

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cond1, cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event.

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The function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern. -It returns UInt8: 0 if the pattern isn't matched, or 1 if it matches.

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Example: sequenceMatch ('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%')

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minIf(EventTime, URL LIKE '%company%') < maxIf(EventTime, URL LIKE '%cart%').
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However, there is no such solution for more complex situations.

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Pattern syntax:

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(?1) refers to the condition (any number can be used in place of 1).

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.* is any number of any events.

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(?t>=1800) is a time condition.

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Any quantity of any type of events is allowed over the specified time.

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Instead of >=, the following operators can be used:<, >, <=.

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Any number may be specified in place of 1800.

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Events that occur during the same second can be put in the chain in any order. This may affect the result of the function.

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sequenceCount(pattern)(time, cond1, cond2, ...)

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Works the same way as the sequenceMatch function, but instead of returning whether there is an event chain, it returns UInt64 with the number of event chains found. -Chains are searched for without overlapping. In other words, the next chain can start only after the end of the previous one.

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windowFunnel(window)(timestamp, cond1, cond2, cond3, ....)

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Window funnel matching for event chains, calculates the max event level in a sliding window.

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window is the timestamp window value, such as 3600.

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timestamp is the time of the event with the DateTime type or UInt32 type.

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cond1, cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event

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Example:

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Consider you are doing a website analytics, intend to find out the user counts clicked login button( event = 1001 ), then the user counts followed by searched the phones( event = 1003 and product = 'phone' ) , then the user counts followed by made an order ( event = 1009 ). And all event chains must be in a 3600 seconds sliding window.

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This could be easily calculate by windowFunnel

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SELECT
-    level,
-    count() AS c
-FROM
-(
-    SELECT
-        user_id,
-        windowFunnel(3600)(timestamp, event_id = 1001, event_id = 1003 AND product = 'phone', event_id = 1009) AS level
-    FROM trend_event
-    WHERE (event_date >= '2017-01-01') AND (event_date <= '2017-01-31')
-    GROUP BY user_id
-)
-GROUP BY level
-ORDER BY level
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Simply, the level could only be 0,1,2,3, it means the maxium event action stage that one user could reach.

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uniqUpTo(N)(x)

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Calculates the number of different argument values ​​if it is less than or equal to N. If the number of different argument values is greater than N, it returns N + 1.

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Recommended for use with small Ns, up to 10. The maximum value of N is 100.

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For the state of an aggregate function, it uses the amount of memory equal to 1 + N * the size of one value of bytes. -For strings, it stores a non-cryptographic hash of 8 bytes. That is, the calculation is approximated for strings.

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The function also works for several arguments.

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It works as fast as possible, except for cases when a large N value is used and the number of unique values is slightly less than N.

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Usage example:

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Problem: Generate a report that shows only keywords that produced at least 5 unique users.
-Solution: Write in the GROUP BY query SearchPhrase HAVING uniqUpTo(4)(UserID) >= 5
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Function reference

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count()

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Counts the number of rows. Accepts zero arguments and returns UInt64. -The syntax COUNT(DISTINCT x) is not supported. The separate uniq aggregate function exists for this purpose.

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A SELECT count() FROM table query is not optimized, because the number of entries in the table is not stored separately. It will select some small column from the table and count the number of values in it.

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any(x)

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Selects the first encountered value. -The query can be executed in any order and even in a different order each time, so the result of this function is indeterminate. -To get a determinate result, you can use the 'min' or 'max' function instead of 'any'.

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In some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.

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When a SELECT query has the GROUP BY clause or at least one aggregate function, ClickHouse (in contrast to MySQL) requires that all expressions in the SELECT, HAVING, and ORDER BY clauses be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. To get behavior like in MySQL, you can put the other columns in the any aggregate function.

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anyHeavy(x)

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Selects a frequently occurring value using the heavy hitters algorithm. If there is a value that occurs more than in half the cases in each of the query's execution threads, this value is returned. Normally, the result is nondeterministic.

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anyHeavy(column)
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Arguments -- column – The column name.

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Example

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Take the OnTime data set and select any frequently occurring value in the AirlineID column.

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SELECT anyHeavy(AirlineID) AS res
-FROM ontime
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- - -
┌───res─┐
-│ 19690 │
-└───────┘
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anyLast(x)

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Selects the last value encountered. -The result is just as indeterminate as for the any function.

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min(x)

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Calculates the minimum.

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max(x)

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Calculates the maximum.

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argMin(arg, val)

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Calculates the 'arg' value for a minimal 'val' value. If there are several different values of 'arg' for minimal values of 'val', the first of these values encountered is output.

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argMax(arg, val)

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Calculates the 'arg' value for a maximum 'val' value. If there are several different values of 'arg' for maximum values of 'val', the first of these values encountered is output.

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sum(x)

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Calculates the sum. -Only works for numbers.

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sumWithOverflow(x)

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Computes the sum of the numbers, using the same data type for the result as for the input parameters. If the sum exceeds the maximum value for this data type, the function returns an error.

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Only works for numbers.

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sumMap(key, value)

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Totals the 'value' array according to the keys specified in the 'key' array. -The number of elements in 'key' and 'value' must be the same for each row that is totaled. -Returns a tuple of two arrays: keys in sorted order, and values ​​summed for the corresponding keys.

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Example:

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CREATE TABLE sum_map(
-    date Date,
-    timeslot DateTime,
-    statusMap Nested(
-        status UInt16,
-        requests UInt64
-    )
-) ENGINE = Log;
-INSERT INTO sum_map VALUES
-    ('2000-01-01', '2000-01-01 00:00:00', [1, 2, 3], [10, 10, 10]),
-    ('2000-01-01', '2000-01-01 00:00:00', [3, 4, 5], [10, 10, 10]),
-    ('2000-01-01', '2000-01-01 00:01:00', [4, 5, 6], [10, 10, 10]),
-    ('2000-01-01', '2000-01-01 00:01:00', [6, 7, 8], [10, 10, 10]);
-SELECT
-    timeslot,
-    sumMap(statusMap.status, statusMap.requests)
-FROM sum_map
-GROUP BY timeslot
-
- - -
┌────────────timeslot─┬─sumMap(statusMap.status, statusMap.requests)─┐
-│ 2000-01-01 00:00:00 │ ([1,2,3,4,5],[10,10,20,10,10])               │
-│ 2000-01-01 00:01:00 │ ([4,5,6,7,8],[10,10,20,10,10])               │
-└─────────────────────┴──────────────────────────────────────────────┘
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avg(x)

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Calculates the average. -Only works for numbers. -The result is always Float64.

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uniq(x)

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Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.

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Uses an adaptive sampling algorithm: for the calculation state, it uses a sample of element hash values with a size up to 65536. -This algorithm is also very accurate for data sets with low cardinality (up to 65536) and very efficient on CPU (when computing not too many of these functions, using uniq is almost as fast as using other aggregate functions).

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The result is determinate (it doesn't depend on the order of query processing).

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This function provides excellent accuracy even for data sets with extremely high cardinality (over 10 billion elements). It is recommended for default use.

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uniqCombined(x)

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Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.

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A combination of three algorithms is used: array, hash table and HyperLogLog with an error correction table. The memory consumption is several times smaller than for the uniq function, and the accuracy is several times higher. Performance is slightly lower than for the uniq function, but sometimes it can be even higher than it, such as with distributed queries that transmit a large number of aggregation states over the network. The maximum state size is 96 KiB (HyperLogLog of 217 6-bit cells).

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The result is determinate (it doesn't depend on the order of query processing).

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The uniqCombined function is a good default choice for calculating the number of different values, but keep in mind that the estimation error will increase for high-cardinality data sets (200M+ elements), and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).

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uniqHLL12(x)

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Uses the HyperLogLog algorithm to approximate the number of different values of the argument. -212 5-bit cells are used. The size of the state is slightly more than 2.5 KB. The result is not very accurate (up to ~10% error) for small data sets (<10K elements). However, the result is fairly accurate for high-cardinality data sets (10K-100M), with a maximum error of ~1.6%. Starting from 100M, the estimation error increases, and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).

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The result is determinate (it doesn't depend on the order of query processing).

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We don't recommend using this function. In most cases, use the uniq or uniqCombined function.

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uniqExact(x)

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Calculates the number of different values of the argument, exactly. -There is no reason to fear approximations. It's better to use the uniq function. -Use the uniqExact function if you definitely need an exact result.

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The uniqExact function uses more memory than the uniq function, because the size of the state has unbounded growth as the number of different values increases.

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groupArray(x), groupArray(max_size)(x)

-

Creates an array of argument values. -Values can be added to the array in any (indeterminate) order.

-

The second version (with the max_size parameter) limits the size of the resulting array to max_size elements. -For example, groupArray (1) (x) is equivalent to [any (x)].

-

In some cases, you can still rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.

-

-

groupArrayInsertAt(x)

-

Inserts a value into the array in the specified position.

-

Accepts the value and position as input. If several values ​​are inserted into the same position, any of them might end up in the resulting array (the first one will be used in the case of single-threaded execution). If no value is inserted into a position, the position is assigned the default value.

-

Optional parameters:

-
    -
  • The default value for substituting in empty positions.
    • -
  • The length of the resulting array. This allows you to receive arrays of the same size for all the aggregate keys. When using this parameter, the default value must be specified.
    • -
-

groupUniqArray(x)

-

Creates an array from different argument values. Memory consumption is the same as for the uniqExact function.

-

quantile(level)(x)

-

Approximates the 'level' quantile. 'level' is a constant, a floating-point number from 0 to 1. -We recommend using a 'level' value in the range of 0.01..0.99 -Don't use a 'level' value equal to 0 or 1 – use the 'min' and 'max' functions for these cases.

-

In this function, as well as in all functions for calculating quantiles, the 'level' parameter can be omitted. In this case, it is assumed to be equal to 0.5 (in other words, the function will calculate the median).

-

Works for numbers, dates, and dates with times. -Returns: for numbers – Float64; for dates – a date; for dates with times – a date with time.

-

Uses reservoir sampling with a reservoir size up to 8192. -If necessary, the result is output with linear approximation from the two neighboring values. -This algorithm provides very low accuracy. See also: quantileTiming, quantileTDigest, quantileExact.

-

The result depends on the order of running the query, and is nondeterministic.

-

When using multiple quantile (and similar) functions with different levels in a query, the internal states are not combined (that is, the query works less efficiently than it could). In this case, use the quantiles (and similar) functions.

-

quantileDeterministic(level)(x, determinator)

-

Works the same way as the quantile function, but the result is deterministic and does not depend on the order of query execution.

-

To achieve this, the function takes a second argument – the "determinator". This is a number whose hash is used instead of a random number generator in the reservoir sampling algorithm. For the function to work correctly, the same determinator value should not occur too often. For the determinator, you can use an event ID, user ID, and so on.

-

Don't use this function for calculating timings. There is a more suitable function for this purpose: quantileTiming.

-

quantileTiming(level)(x)

-

Computes the quantile of 'level' with a fixed precision. -Works for numbers. Intended for calculating quantiles of page loading time in milliseconds.

-

If the value is greater than 30,000 (a page loading time of more than 30 seconds), the result is equated to 30,000.

-

If the total value is not more than about 5670, then the calculation is accurate.

-

Otherwise:

-
    -
  • if the time is less than 1024 ms, then the calculation is accurate.
    • -
  • otherwise the calculation is rounded to a multiple of 16 ms.
    • -
-

When passing negative values to the function, the behavior is undefined.

-

The returned value has the Float32 type. If no values were passed to the function (when using quantileTimingIf), 'nan' is returned. The purpose of this is to differentiate these instances from zeros. See the note on sorting NaNs in "ORDER BY clause".

-

The result is determinate (it doesn't depend on the order of query processing).

-

For its purpose (calculating quantiles of page loading times), using this function is more effective and the result is more accurate than for the quantile function.

-

quantileTimingWeighted(level)(x, weight)

-

Differs from the quantileTiming function in that it has a second argument, "weights". Weight is a non-negative integer. -The result is calculated as if the x value were passed weight number of times to the quantileTiming function.

-

quantileExact(level)(x)

-

Computes the quantile of 'level' exactly. To do this, all the passed values ​​are combined into an array, which is then partially sorted. Therefore, the function consumes O(n) memory, where 'n' is the number of values that were passed. However, for a small number of values, the function is very effective.

-

quantileExactWeighted(level)(x, weight)

-

Computes the quantile of 'level' exactly. In addition, each value is counted with its weight, as if it is present 'weight' times. The arguments of the function can be considered as histograms, where the value 'x' corresponds to a histogram "column" of the height 'weight', and the function itself can be considered as a summation of histograms.

-

A hash table is used as the algorithm. Because of this, if the passed values ​​are frequently repeated, the function consumes less RAM than quantileExact. You can use this function instead of quantileExact and specify the weight as 1.

-

quantileTDigest(level)(x)

-

Approximates the quantile level using the t-digest algorithm. The maximum error is 1%. Memory consumption by State is proportional to the logarithm of the number of passed values.

-

The performance of the function is lower than for quantile, quantileTiming. In terms of the ratio of State size to precision, this function is much better than quantile.

-

The result depends on the order of running the query, and is nondeterministic.

-

median(x)

-

All the quantile functions have corresponding median functions: median, medianDeterministic, medianTiming, medianTimingWeighted, medianExact, medianExactWeighted, medianTDigest. They are synonyms and their behavior is identical.

-

quantiles(level1, level2, ...)(x)

-

All the quantile functions also have corresponding quantiles functions: quantiles, quantilesDeterministic, quantilesTiming, quantilesTimingWeighted, quantilesExact, quantilesExactWeighted, quantilesTDigest. These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.

-

varSamp(x)

-

Calculates the amount Σ((x - x̅)^2) / (n - 1), where n is the sample size and x̅is the average value of x.

-

It represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount.

-

Returns Float64. When n <= 1, returns +∞.

-

varPop(x)

-

Calculates the amount Σ((x - x̅)^2) / (n - 1), where n is the sample size and x̅is the average value of x.

-

In other words, dispersion for a set of values. Returns Float64.

-

stddevSamp(x)

-

The result is equal to the square root of varSamp(x).

-

stddevPop(x)

-

The result is equal to the square root of varPop(x).

-

topK(N)(column)

-

Returns an array of the most frequent values in the specified column. The resulting array is sorted in descending order of frequency of values (not by the values themselves).

-

Implements the Filtered Space-Saving algorithm for analyzing TopK, based on the reduce-and-combine algorithm from Parallel Space Saving.

-
topK(N)(column)
-
- - -

This function doesn't provide a guaranteed result. In certain situations, errors might occur and it might return frequent values that aren't the most frequent values.

-

We recommend using the N < 10 value; performance is reduced with large N values. Maximum value of N = 65536.

-

Arguments -- 'N' is the number of values. -- ' x ' – The column.

-

Example

-

Take the OnTime data set and select the three most frequently occurring values in the AirlineID column.

-
SELECT topK(3)(AirlineID) AS res
-FROM ontime
-
- - -
┌─res─────────────────┐
-│ [19393,19790,19805] │
-└─────────────────────┘
-
- - -

covarSamp(x, y)

-

Calculates the value of Σ((x - x̅)(y - y̅)) / (n - 1).

-

Returns Float64. When n <= 1, returns +∞.

-

covarPop(x, y)

-

Calculates the value of Σ((x - x̅)(y - y̅)) / n.

-

corr(x, y)

-

Calculates the Pearson correlation coefficient: Σ((x - x̅)(y - y̅)) / sqrt(Σ((x - x̅)^2) * Σ((y - y̅)^2)).

- - - - - - - -
-
-
-
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Please include / require Lunr stemmer support before this script.");e.da=function(){this.pipeline.reset(),this.pipeline.add(e.da.trimmer,e.da.stopWordFilter,e.da.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.da.stemmer))},e.da.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.da.trimmer=e.trimmerSupport.generateTrimmer(e.da.wordCharacters),e.Pipeline.registerFunction(e.da.trimmer,"trimmer-da"),e.da.stemmer=function(){var r=e.stemmerSupport.Among,i=e.stemmerSupport.SnowballProgram,n=new function(){function e(){var e,r=l.limit-l.cursor;l.cursor>=t&&(e=l.limit_backward,l.limit_backward=t,l.ket=l.cursor,l.find_among_b(a,4)?(l.bra=l.cursor,l.limit_backward=e,l.cursor=l.limit-r,l.cursor>l.limit_backward&&(l.cursor--,l.bra=l.cursor,l.slice_del())):l.limit_backward=e)}var n,t,s,o=[new r("hed",-1,1),new r("ethed",0,1),new r("ered",-1,1),new r("e",-1,1),new r("erede",3,1),new r("ende",3,1),new r("erende",5,1),new r("ene",3,1),new r("erne",3,1),new r("ere",3,1),new r("en",-1,1),new r("heden",10,1),new r("eren",10,1),new r("er",-1,1),new r("heder",13,1),new r("erer",13,1),new r("s",-1,2),new r("heds",16,1),new r("es",16,1),new r("endes",18,1),new r("erendes",19,1),new r("enes",18,1),new r("ernes",18,1),new r("eres",18,1),new r("ens",16,1),new r("hedens",24,1),new r("erens",24,1),new r("ers",16,1),new r("ets",16,1),new r("erets",28,1),new r("et",-1,1),new r("eret",30,1)],a=[new r("gd",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1)],d=[new r("ig",-1,1),new r("lig",0,1),new r("elig",1,1),new r("els",-1,1),new r("løst",-1,2)],u=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],c=[239,254,42,3,0,0,0,0,0,0,0,0,0,0,0,0,16],l=new i;this.setCurrent=function(e){l.setCurrent(e)},this.getCurrent=function(){return l.getCurrent()},this.stem=function(){var r=l.cursor;return function(){var e,r=l.cursor+3;if(t=l.limit,0<=r&&r<=l.limit){for(n=r;;){if(e=l.cursor,l.in_grouping(u,97,248)){l.cursor=e;break}if(l.cursor=e,e>=l.limit)return;l.cursor++}for(;!l.out_grouping(u,97,248);){if(l.cursor>=l.limit)return;l.cursor++}(t=l.cursor)=t&&(r=l.limit_backward,l.limit_backward=t,l.ket=l.cursor,e=l.find_among_b(o,32),l.limit_backward=r,e))switch(l.bra=l.cursor,e){case 1:l.slice_del();break;case 2:l.in_grouping_b(c,97,229)&&l.slice_del()}}(),l.cursor=l.limit,e(),l.cursor=l.limit,function(){var r,i,n,s=l.limit-l.cursor;if(l.ket=l.cursor,l.eq_s_b(2,"st")&&(l.bra=l.cursor,l.eq_s_b(2,"ig")&&l.slice_del()),l.cursor=l.limit-s,l.cursor>=t&&(i=l.limit_backward,l.limit_backward=t,l.ket=l.cursor,r=l.find_among_b(d,5),l.limit_backward=i,r))switch(l.bra=l.cursor,r){case 1:l.slice_del(),n=l.limit-l.cursor,e(),l.cursor=l.limit-n;break;case 2:l.slice_from("løs")}}(),l.cursor=l.limit,function(){var e;l.cursor>=t&&(e=l.limit_backward,l.limit_backward=t,l.ket=l.cursor,l.out_grouping_b(u,97,248)?(l.bra=l.cursor,s=l.slice_to(s),l.limit_backward=e,l.eq_v_b(s)&&l.slice_del()):l.limit_backward=e)}(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}}(),e.Pipeline.registerFunction(e.da.stemmer,"stemmer-da"),e.da.stopWordFilter=e.generateStopWordFilter("ad af alle alt anden at blev blive bliver da de dem den denne der deres det dette dig din disse dog du efter eller en end er et for fra ham han hans har havde have hende hendes her hos hun hvad hvis hvor i ikke ind jeg jer jo kunne man mange med meget men mig min mine mit mod ned noget nogle nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}}); \ No newline at end of file diff --git a/docs/build/docs/en/assets/javascripts/lunr/lunr.de.js b/docs/build/docs/en/assets/javascripts/lunr/lunr.de.js deleted file mode 100644 index 576a2192311..00000000000 --- a/docs/build/docs/en/assets/javascripts/lunr/lunr.de.js +++ /dev/null @@ -1 +0,0 @@ -!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.de=function(){this.pipeline.reset(),this.pipeline.add(e.de.trimmer,e.de.stopWordFilter,e.de.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.de.stemmer))},e.de.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.de.trimmer=e.trimmerSupport.generateTrimmer(e.de.wordCharacters),e.Pipeline.registerFunction(e.de.trimmer,"trimmer-de"),e.de.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){function e(e,r,n){return!(!_.eq_s(1,e)||(_.ket=_.cursor,!_.in_grouping(w,97,252)))&&(_.slice_from(r),_.cursor=n,!0)}function i(){for(;!_.in_grouping(w,97,252);){if(_.cursor>=_.limit)return!0;_.cursor++}for(;!_.out_grouping(w,97,252);){if(_.cursor>=_.limit)return!0;_.cursor++}return!1}function s(){return u<=_.cursor}function t(){return c<=_.cursor}var o,c,u,a=[new r("",-1,6),new r("U",0,2),new r("Y",0,1),new r("ä",0,3),new r("ö",0,4),new r("ü",0,5)],d=[new r("e",-1,2),new r("em",-1,1),new r("en",-1,2),new r("ern",-1,1),new r("er",-1,1),new r("s",-1,3),new r("es",5,2)],l=[new r("en",-1,1),new r("er",-1,1),new r("st",-1,2),new r("est",2,1)],m=[new r("ig",-1,1),new r("lich",-1,1)],h=[new r("end",-1,1),new r("ig",-1,2),new r("ung",-1,1),new r("lich",-1,3),new r("isch",-1,2),new r("ik",-1,2),new r("heit",-1,3),new r("keit",-1,4)],w=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,32,8],f=[117,30,5],b=[117,30,4],_=new n;this.setCurrent=function(e){_.setCurrent(e)},this.getCurrent=function(){return _.getCurrent()},this.stem=function(){var r=_.cursor;return function(){for(var r,n,i,s,t=_.cursor;;)if(r=_.cursor,_.bra=r,_.eq_s(1,"ß"))_.ket=_.cursor,_.slice_from("ss");else{if(r>=_.limit)break;_.cursor=r+1}for(_.cursor=t;;)for(n=_.cursor;;){if(i=_.cursor,_.in_grouping(w,97,252)){if(s=_.cursor,_.bra=s,e("u","U",i))break;if(_.cursor=s,e("y","Y",i))break}if(i>=_.limit)return void(_.cursor=n);_.cursor=i+1}}(),_.cursor=r,function(){u=_.limit,c=u;var e=_.cursor+3;0<=e&&e<=_.limit&&(o=e,i()||((u=_.cursor)=_.limit)return;_.cursor++}}}(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.de.stemmer,"stemmer-de"),e.de.stopWordFilter=e.generateStopWordFilter("aber alle allem allen aller alles als also am an ander andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines können könnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher solches soll sollte sondern sonst um und uns unse unsem unsen unser unses unter viel vom von vor war waren warst was weg weil weiter welche welchem welchen welcher welches wenn werde werden wie wieder will wir wird wirst wo wollen wollte während würde würden zu zum zur zwar zwischen über".split(" ")),e.Pipeline.registerFunction(e.de.stopWordFilter,"stopWordFilter-de")}}); \ No newline at end of file diff --git a/docs/build/docs/en/assets/javascripts/lunr/lunr.du.js b/docs/build/docs/en/assets/javascripts/lunr/lunr.du.js deleted file mode 100644 index c317652d6bd..00000000000 --- a/docs/build/docs/en/assets/javascripts/lunr/lunr.du.js +++ /dev/null @@ -1 +0,0 @@ -!function(r,e){"function"==typeof define&&define.amd?define(e):"object"==typeof exports?module.exports=e():e()(r.lunr)}(this,function(){return function(r){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");r.du=function(){this.pipeline.reset(),this.pipeline.add(r.du.trimmer,r.du.stopWordFilter,r.du.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(r.du.stemmer))},r.du.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",r.du.trimmer=r.trimmerSupport.generateTrimmer(r.du.wordCharacters),r.Pipeline.registerFunction(r.du.trimmer,"trimmer-du"),r.du.stemmer=function(){var e=r.stemmerSupport.Among,i=r.stemmerSupport.SnowballProgram,n=new function(){function r(r){return v.cursor=r,r>=v.limit||(v.cursor++,!1)}function n(){for(;!v.in_grouping(g,97,232);){if(v.cursor>=v.limit)return!0;v.cursor++}for(;!v.out_grouping(g,97,232);){if(v.cursor>=v.limit)return!0;v.cursor++}return!1}function o(){return l<=v.cursor}function t(){return a<=v.cursor}function s(){var r=v.limit-v.cursor;v.find_among_b(_,3)&&(v.cursor=v.limit-r,v.ket=v.cursor,v.cursor>v.limit_backward&&(v.cursor--,v.bra=v.cursor,v.slice_del()))}function u(){var r;m=!1,v.ket=v.cursor,v.eq_s_b(1,"e")&&(v.bra=v.cursor,o()&&(r=v.limit-v.cursor,v.out_grouping_b(g,97,232)&&(v.cursor=v.limit-r,v.slice_del(),m=!0,s())))}function c(){var r;o()&&(r=v.limit-v.cursor,v.out_grouping_b(g,97,232)&&(v.cursor=v.limit-r,v.eq_s_b(3,"gem")||(v.cursor=v.limit-r,v.slice_del(),s())))}var a,l,m,d=[new e("",-1,6),new e("á",0,1),new e("ä",0,1),new e("é",0,2),new e("ë",0,2),new e("í",0,3),new e("ï",0,3),new e("ó",0,4),new e("ö",0,4),new e("ú",0,5),new e("ü",0,5)],f=[new e("",-1,3),new e("I",0,2),new e("Y",0,1)],_=[new e("dd",-1,-1),new e("kk",-1,-1),new e("tt",-1,-1)],w=[new e("ene",-1,2),new e("se",-1,3),new e("en",-1,2),new e("heden",2,1),new e("s",-1,3)],b=[new e("end",-1,1),new e("ig",-1,2),new e("ing",-1,1),new e("lijk",-1,3),new e("baar",-1,4),new e("bar",-1,5)],p=[new e("aa",-1,-1),new e("ee",-1,-1),new e("oo",-1,-1),new e("uu",-1,-1)],g=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],h=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],k=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],v=new i;this.setCurrent=function(r){v.setCurrent(r)},this.getCurrent=function(){return v.getCurrent()},this.stem=function(){var e=v.cursor;return function(){for(var e,i,n,o=v.cursor;;){if(v.bra=v.cursor,e=v.find_among(d,11))switch(v.ket=v.cursor,e){case 1:v.slice_from("a");continue;case 2:v.slice_from("e");continue;case 3:v.slice_from("i");continue;case 4:v.slice_from("o");continue;case 5:v.slice_from("u");continue;case 6:if(v.cursor>=v.limit)break;v.cursor++;continue}break}for(v.cursor=o,v.bra=o,v.eq_s(1,"y")?(v.ket=v.cursor,v.slice_from("Y")):v.cursor=o;;)if(i=v.cursor,v.in_grouping(g,97,232)){if(n=v.cursor,v.bra=n,v.eq_s(1,"i"))v.ket=v.cursor,v.in_grouping(g,97,232)&&(v.slice_from("I"),v.cursor=i);else if(v.cursor=n,v.eq_s(1,"y"))v.ket=v.cursor,v.slice_from("Y"),v.cursor=i;else if(r(i))break}else if(r(i))break}(),v.cursor=e,l=v.limit,a=l,n()||((l=v.cursor)<3&&(l=3),n()||(a=v.cursor)),v.limit_backward=e,v.cursor=v.limit,function(){var r,e,i,n,a,l,d=v.limit-v.cursor;if(v.ket=v.cursor,r=v.find_among_b(w,5))switch(v.bra=v.cursor,r){case 1:o()&&v.slice_from("heid");break;case 2:c();break;case 3:o()&&v.out_grouping_b(k,97,232)&&v.slice_del()}if(v.cursor=v.limit-d,u(),v.cursor=v.limit-d,v.ket=v.cursor,v.eq_s_b(4,"heid")&&(v.bra=v.cursor,t()&&(e=v.limit-v.cursor,v.eq_s_b(1,"c")||(v.cursor=v.limit-e,v.slice_del(),v.ket=v.cursor,v.eq_s_b(2,"en")&&(v.bra=v.cursor,c())))),v.cursor=v.limit-d,v.ket=v.cursor,r=v.find_among_b(b,6))switch(v.bra=v.cursor,r){case 1:if(t()){if(v.slice_del(),i=v.limit-v.cursor,v.ket=v.cursor,v.eq_s_b(2,"ig")&&(v.bra=v.cursor,t()&&(n=v.limit-v.cursor,!v.eq_s_b(1,"e")))){v.cursor=v.limit-n,v.slice_del();break}v.cursor=v.limit-i,s()}break;case 2:t()&&(a=v.limit-v.cursor,v.eq_s_b(1,"e")||(v.cursor=v.limit-a,v.slice_del()));break;case 3:t()&&(v.slice_del(),u());break;case 4:t()&&v.slice_del();break;case 5:t()&&m&&v.slice_del()}v.cursor=v.limit-d,v.out_grouping_b(h,73,232)&&(l=v.limit-v.cursor,v.find_among_b(p,4)&&v.out_grouping_b(g,97,232)&&(v.cursor=v.limit-l,v.ket=v.cursor,v.cursor>v.limit_backward&&(v.cursor--,v.bra=v.cursor,v.slice_del())))}(),v.cursor=v.limit_backward,function(){for(var r;;)if(v.bra=v.cursor,r=v.find_among(f,3))switch(v.ket=v.cursor,r){case 1:v.slice_from("y");break;case 2:v.slice_from("i");break;case 3:if(v.cursor>=v.limit)return;v.cursor++}}(),!0}};return function(r){return"function"==typeof r.update?r.update(function(r){return n.setCurrent(r),n.stem(),n.getCurrent()}):(n.setCurrent(r),n.stem(),n.getCurrent())}}(),r.Pipeline.registerFunction(r.du.stemmer,"stemmer-du"),r.du.stopWordFilter=r.generateStopWordFilter(" aan al alles als altijd andere ben bij daar dan dat de der deze die dit doch doen door dus een eens en er ge geen geweest haar had heb hebben heeft hem het hier hij hoe hun iemand iets ik in is ja je kan kon kunnen maar me meer men met mij mijn moet na naar niet niets nog nu of om omdat onder ons ook op over reeds te tegen toch toen tot u uit uw van veel voor want waren was wat werd wezen wie wil worden wordt zal ze zelf zich zij zijn zo zonder zou".split(" ")),r.Pipeline.registerFunction(r.du.stopWordFilter,"stopWordFilter-du")}}); \ No newline at end of file diff --git a/docs/build/docs/en/assets/javascripts/lunr/lunr.es.js b/docs/build/docs/en/assets/javascripts/lunr/lunr.es.js deleted file mode 100644 index 5098feba48b..00000000000 --- a/docs/build/docs/en/assets/javascripts/lunr/lunr.es.js +++ /dev/null @@ -1 +0,0 @@ -!function(e,s){"function"==typeof define&&define.amd?define(s):"object"==typeof exports?module.exports=s():s()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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fie fiecare fii fim fiu fiţi frumos fără graţie halbă iar ieri la le li lor lui lângă lîngă mai mea mei mele mereu meu mi mie mine mult multă mulţi mulţumesc mâine mîine mă ne nevoie nici nicăieri nimeni nimeri nimic nişte noastre noastră noi noroc nostru nouă noştri nu opt ori oricare orice oricine oricum oricând oricât oricînd oricît oriunde patra patru patrulea pe pentru peste pic poate pot prea prima primul prin puţin puţina puţină până pînă rog sa sale sau se spate spre sub sunt suntem sunteţi sută sînt sîntem sînteţi să săi său ta tale te timp tine toate toată tot totuşi toţi trei treia treilea tu tăi tău un una unde undeva unei uneia unele uneori unii unor unora unu unui unuia unul vi voastre voastră voi vostru vouă voştri vreme vreo vreun vă zece zero zi zice îi îl îmi împotriva în înainte înaintea încotro încât încît între întrucât întrucît îţi ăla ălea ăsta ăstea ăştia şapte şase şi ştiu ţi ţie".split(" 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b=m.method();if(this.cursor=n-m.s_size,b)return m.result}if((s=m.substring_i)<0)return 0}},replace_s:function(t,i,s){var e=s.length-(i-t),n=r.substring(0,t),u=r.substring(i);return r=n+s+u,this.limit+=e,this.cursor>=i?this.cursor+=e:this.cursor>t&&(this.cursor=t),e},slice_check:function(){if(this.bra<0||this.bra>this.ket||this.ket>this.limit||this.limit>r.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),r.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}}); \ No newline at end of file diff --git a/docs/build/docs/en/assets/javascripts/lunr/lunr.sv.js b/docs/build/docs/en/assets/javascripts/lunr/lunr.sv.js deleted file mode 100644 index 70211fd77d9..00000000000 --- a/docs/build/docs/en/assets/javascripts/lunr/lunr.sv.js +++ /dev/null @@ -1 +0,0 @@ -!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sv=function(){this.pipeline.reset(),this.pipeline.add(e.sv.trimmer,e.sv.stopWordFilter,e.sv.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sv.stemmer))},e.sv.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.sv.trimmer=e.trimmerSupport.generateTrimmer(e.sv.wordCharacters),e.Pipeline.registerFunction(e.sv.trimmer,"trimmer-sv"),e.sv.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,t=new function(){var e,t,i=[new r("a",-1,1),new r("arna",0,1),new r("erna",0,1),new r("heterna",2,1),new r("orna",0,1),new r("ad",-1,1),new r("e",-1,1),new r("ade",6,1),new r("ande",6,1),new r("arne",6,1),new r("are",6,1),new r("aste",6,1),new r("en",-1,1),new r("anden",12,1),new r("aren",12,1),new r("heten",12,1),new r("ern",-1,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",18,1),new r("or",-1,1),new r("s",-1,2),new r("as",21,1),new r("arnas",22,1),new r("ernas",22,1),new r("ornas",22,1),new r("es",21,1),new r("ades",26,1),new r("andes",26,1),new r("ens",21,1),new r("arens",29,1),new r("hetens",29,1),new r("erns",21,1),new r("at",-1,1),new r("andet",-1,1),new r("het",-1,1),new r("ast",-1,1)],s=[new r("dd",-1,-1),new r("gd",-1,-1),new r("nn",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1),new r("tt",-1,-1)],a=[new r("ig",-1,1),new r("lig",0,1),new r("els",-1,1),new r("fullt",-1,3),new r("löst",-1,2)],o=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,24,0,32],u=[119,127,149],c=new n;this.setCurrent=function(e){c.setCurrent(e)},this.getCurrent=function(){return c.getCurrent()},this.stem=function(){var r=c.cursor;return function(){var r,n=c.cursor+3;if(t=c.limit,0<=n||n<=c.limit){for(e=n;;){if(r=c.cursor,c.in_grouping(o,97,246)){c.cursor=r;break}if(c.cursor=r,c.cursor>=c.limit)return;c.cursor++}for(;!c.out_grouping(o,97,246);){if(c.cursor>=c.limit)return;c.cursor++}(t=c.cursor)=t&&(c.limit_backward=t,c.cursor=c.limit,c.ket=c.cursor,e=c.find_among_b(i,37),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del();break;case 2:c.in_grouping_b(u,98,121)&&c.slice_del()}}(),c.cursor=c.limit,function(){var e=c.limit_backward;c.cursor>=t&&(c.limit_backward=t,c.cursor=c.limit,c.find_among_b(s,7)&&(c.cursor=c.limit,c.ket=c.cursor,c.cursor>c.limit_backward&&(c.bra=--c.cursor,c.slice_del())),c.limit_backward=e)}(),c.cursor=c.limit,function(){var e,r;if(c.cursor>=t){if(r=c.limit_backward,c.limit_backward=t,c.cursor=c.limit,c.ket=c.cursor,e=c.find_among_b(a,5))switch(c.bra=c.cursor,e){case 1:c.slice_del();break;case 2:c.slice_from("lös");break;case 3:c.slice_from("full")}c.limit_backward=r}}(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return t.setCurrent(e),t.stem(),t.getCurrent()}):(t.setCurrent(e),t.stem(),t.getCurrent())}}(),e.Pipeline.registerFunction(e.sv.stemmer,"stemmer-sv"),e.sv.stopWordFilter=e.generateStopWordFilter("alla allt att av blev bli blir blivit de dem den denna deras dess dessa det detta dig din dina ditt du där då efter ej eller en er era ert ett från för ha hade han hans har henne hennes hon honom hur här i icke ingen inom inte jag ju kan kunde man med mellan men mig min mina mitt mot mycket ni nu när någon något några och om oss på samma sedan sig sin sina sitta själv skulle som så sådan sådana sådant till under upp ut utan vad var vara varför varit varje vars vart vem vi vid vilka vilkas vilken vilket vår våra vårt än är åt över".split(" ")),e.Pipeline.registerFunction(e.sv.stopWordFilter,"stopWordFilter-sv")}}); \ No newline at end of file diff --git a/docs/build/docs/en/assets/javascripts/lunr/lunr.tr.js b/docs/build/docs/en/assets/javascripts/lunr/lunr.tr.js deleted file mode 100644 index db7c908a524..00000000000 --- a/docs/build/docs/en/assets/javascripts/lunr/lunr.tr.js +++ /dev/null @@ -1 +0,0 @@ -!function(r,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(r.lunr)}(this,function(){return function(r){if(void 0===r)throw new Error("Lunr is not present. 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.md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=light-blue]{background-color:#03a9f4}[data-md-color-primary=light-blue] .md-typeset a{color:#03a9f4}[data-md-color-primary=light-blue] .md-header,[data-md-color-primary=light-blue] .md-hero{background-color:#03a9f4}[data-md-color-primary=light-blue] .md-nav__link--active,[data-md-color-primary=light-blue] .md-nav__link:active{color:#03a9f4}[data-md-color-primary=light-blue] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=cyan]{background-color:#00bcd4}[data-md-color-primary=cyan] .md-typeset a{color:#00bcd4}[data-md-color-primary=cyan] .md-header,[data-md-color-primary=cyan] .md-hero{background-color:#00bcd4}[data-md-color-primary=cyan] .md-nav__link--active,[data-md-color-primary=cyan] .md-nav__link:active{color:#00bcd4}[data-md-color-primary=cyan] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=teal]{background-color:#009688}[data-md-color-primary=teal] .md-typeset a{color:#009688}[data-md-color-primary=teal] .md-header,[data-md-color-primary=teal] .md-hero{background-color:#009688}[data-md-color-primary=teal] .md-nav__link--active,[data-md-color-primary=teal] .md-nav__link:active{color:#009688}[data-md-color-primary=teal] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=green]{background-color:#4caf50}[data-md-color-primary=green] .md-typeset a{color:#4caf50}[data-md-color-primary=green] .md-header,[data-md-color-primary=green] .md-hero{background-color:#4caf50}[data-md-color-primary=green] .md-nav__link--active,[data-md-color-primary=green] .md-nav__link:active{color:#4caf50}[data-md-color-primary=green] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=light-green]{background-color:#7cb342}[data-md-color-primary=light-green] .md-typeset a{color:#7cb342}[data-md-color-primary=light-green] .md-header,[data-md-color-primary=light-green] .md-hero{background-color:#7cb342}[data-md-color-primary=light-green] .md-nav__link--active,[data-md-color-primary=light-green] .md-nav__link:active{color:#7cb342}[data-md-color-primary=light-green] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=lime]{background-color:#c0ca33}[data-md-color-primary=lime] .md-typeset a{color:#c0ca33}[data-md-color-primary=lime] .md-header,[data-md-color-primary=lime] .md-hero{background-color:#c0ca33}[data-md-color-primary=lime] .md-nav__link--active,[data-md-color-primary=lime] .md-nav__link:active{color:#c0ca33}[data-md-color-primary=lime] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=yellow]{background-color:#f9a825}[data-md-color-primary=yellow] .md-typeset a{color:#f9a825}[data-md-color-primary=yellow] .md-header,[data-md-color-primary=yellow] .md-hero{background-color:#f9a825}[data-md-color-primary=yellow] .md-nav__link--active,[data-md-color-primary=yellow] .md-nav__link:active{color:#f9a825}[data-md-color-primary=yellow] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=amber]{background-color:#ffa000}[data-md-color-primary=amber] .md-typeset a{color:#ffa000}[data-md-color-primary=amber] .md-header,[data-md-color-primary=amber] .md-hero{background-color:#ffa000}[data-md-color-primary=amber] .md-nav__link--active,[data-md-color-primary=amber] .md-nav__link:active{color:#ffa000}[data-md-color-primary=amber] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=orange]{background-color:#fb8c00}[data-md-color-primary=orange] .md-typeset a{color:#fb8c00}[data-md-color-primary=orange] .md-header,[data-md-color-primary=orange] .md-hero{background-color:#fb8c00}[data-md-color-primary=orange] .md-nav__link--active,[data-md-color-primary=orange] .md-nav__link:active{color:#fb8c00}[data-md-color-primary=orange] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=deep-orange]{background-color:#ff7043}[data-md-color-primary=deep-orange] .md-typeset a{color:#ff7043}[data-md-color-primary=deep-orange] .md-header,[data-md-color-primary=deep-orange] .md-hero{background-color:#ff7043}[data-md-color-primary=deep-orange] .md-nav__link--active,[data-md-color-primary=deep-orange] .md-nav__link:active{color:#ff7043}[data-md-color-primary=deep-orange] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=brown]{background-color:#795548}[data-md-color-primary=brown] .md-typeset a{color:#795548}[data-md-color-primary=brown] .md-header,[data-md-color-primary=brown] .md-hero{background-color:#795548}[data-md-color-primary=brown] .md-nav__link--active,[data-md-color-primary=brown] .md-nav__link:active{color:#795548}[data-md-color-primary=brown] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=grey]{background-color:#757575}[data-md-color-primary=grey] .md-typeset a{color:#757575}[data-md-color-primary=grey] .md-header,[data-md-color-primary=grey] .md-hero{background-color:#757575}[data-md-color-primary=grey] .md-nav__link--active,[data-md-color-primary=grey] .md-nav__link:active{color:#757575}[data-md-color-primary=grey] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=blue-grey]{background-color:#546e7a}[data-md-color-primary=blue-grey] .md-typeset a{color:#546e7a}[data-md-color-primary=blue-grey] .md-header,[data-md-color-primary=blue-grey] .md-hero{background-color:#546e7a}[data-md-color-primary=blue-grey] .md-nav__link--active,[data-md-color-primary=blue-grey] .md-nav__link:active{color:#546e7a}[data-md-color-primary=blue-grey] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=white]{-webkit-box-shadow:0 0 .1rem rgba(0,0,0,.54) inset;box-shadow:inset 0 0 .1rem rgba(0,0,0,.54)}[data-md-color-primary=white] .md-header,[data-md-color-primary=white] .md-hero,button[data-md-color-primary=white]{background-color:#fff;color:rgba(0,0,0,.87)}[data-md-color-primary=white] .md-hero--expand{border-bottom:.1rem solid rgba(0,0,0,.07)}button[data-md-color-accent=red]{background-color:#ff1744}[data-md-color-accent=red] .md-typeset a:active,[data-md-color-accent=red] .md-typeset a:hover{color:#ff1744}[data-md-color-accent=red] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=red] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-nav__link:focus,[data-md-color-accent=red] .md-nav__link:hover,[data-md-color-accent=red] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=red] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=red] .md-typeset .md-clipboard:active:before,[data-md-color-accent=red] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=red] .md-typeset [id] .headerlink:focus,[data-md-color-accent=red] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=red] .md-typeset [id]:target .headerlink{color:#ff1744}[data-md-color-accent=red] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-search-result__link:hover,[data-md-color-accent=red] .md-search-result__link[data-md-state=active]{background-color:rgba(255,23,68,.1)}[data-md-color-accent=red] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-source-file:hover:before{background-color:#ff1744}button[data-md-color-accent=pink]{background-color:#f50057}[data-md-color-accent=pink] .md-typeset a:active,[data-md-color-accent=pink] .md-typeset a:hover{color:#f50057}[data-md-color-accent=pink] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=pink] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-nav__link:focus,[data-md-color-accent=pink] .md-nav__link:hover,[data-md-color-accent=pink] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=pink] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=pink] .md-typeset .md-clipboard:active:before,[data-md-color-accent=pink] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=pink] .md-typeset [id] .headerlink:focus,[data-md-color-accent=pink] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=pink] .md-typeset [id]:target .headerlink{color:#f50057}[data-md-color-accent=pink] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-search-result__link:hover,[data-md-color-accent=pink] .md-search-result__link[data-md-state=active]{background-color:rgba(245,0,87,.1)}[data-md-color-accent=pink] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-source-file:hover:before{background-color:#f50057}button[data-md-color-accent=purple]{background-color:#e040fb}[data-md-color-accent=purple] .md-typeset a:active,[data-md-color-accent=purple] .md-typeset a:hover{color:#e040fb}[data-md-color-accent=purple] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=purple] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-nav__link:focus,[data-md-color-accent=purple] .md-nav__link:hover,[data-md-color-accent=purple] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=purple] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=purple] .md-typeset .md-clipboard:active:before,[data-md-color-accent=purple] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=purple] .md-typeset [id] .headerlink:focus,[data-md-color-accent=purple] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=purple] .md-typeset [id]:target .headerlink{color:#e040fb}[data-md-color-accent=purple] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-search-result__link:hover,[data-md-color-accent=purple] .md-search-result__link[data-md-state=active]{background-color:rgba(224,64,251,.1)}[data-md-color-accent=purple] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-source-file:hover:before{background-color:#e040fb}button[data-md-color-accent=deep-purple]{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-typeset a:active,[data-md-color-accent=deep-purple] .md-typeset a:hover{color:#7c4dff}[data-md-color-accent=deep-purple] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=deep-purple] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-nav__link:focus,[data-md-color-accent=deep-purple] .md-nav__link:hover,[data-md-color-accent=deep-purple] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=deep-purple] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=deep-purple] .md-typeset .md-clipboard:active:before,[data-md-color-accent=deep-purple] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=deep-purple] .md-typeset [id] .headerlink:focus,[data-md-color-accent=deep-purple] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=deep-purple] .md-typeset [id]:target .headerlink{color:#7c4dff}[data-md-color-accent=deep-purple] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-search-result__link:hover,[data-md-color-accent=deep-purple] .md-search-result__link[data-md-state=active]{background-color:rgba(124,77,255,.1)}[data-md-color-accent=deep-purple] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-source-file:hover:before{background-color:#7c4dff}button[data-md-color-accent=indigo]{background-color:#536dfe}[data-md-color-accent=indigo] .md-typeset a:active,[data-md-color-accent=indigo] .md-typeset a:hover{color:#536dfe}[data-md-color-accent=indigo] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=indigo] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-nav__link:focus,[data-md-color-accent=indigo] .md-nav__link:hover,[data-md-color-accent=indigo] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=indigo] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=indigo] .md-typeset .md-clipboard:active:before,[data-md-color-accent=indigo] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=indigo] .md-typeset [id] .headerlink:focus,[data-md-color-accent=indigo] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=indigo] .md-typeset [id]:target .headerlink{color:#536dfe}[data-md-color-accent=indigo] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-search-result__link:hover,[data-md-color-accent=indigo] .md-search-result__link[data-md-state=active]{background-color:rgba(83,109,254,.1)}[data-md-color-accent=indigo] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-source-file:hover:before{background-color:#536dfe}button[data-md-color-accent=blue]{background-color:#448aff}[data-md-color-accent=blue] .md-typeset a:active,[data-md-color-accent=blue] .md-typeset a:hover{color:#448aff}[data-md-color-accent=blue] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=blue] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-nav__link:focus,[data-md-color-accent=blue] .md-nav__link:hover,[data-md-color-accent=blue] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=blue] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=blue] .md-typeset .md-clipboard:active:before,[data-md-color-accent=blue] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=blue] .md-typeset [id] .headerlink:focus,[data-md-color-accent=blue] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=blue] .md-typeset [id]:target .headerlink{color:#448aff}[data-md-color-accent=blue] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-search-result__link:hover,[data-md-color-accent=blue] .md-search-result__link[data-md-state=active]{background-color:rgba(68,138,255,.1)}[data-md-color-accent=blue] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-source-file:hover:before{background-color:#448aff}button[data-md-color-accent=light-blue]{background-color:#0091ea}[data-md-color-accent=light-blue] .md-typeset a:active,[data-md-color-accent=light-blue] .md-typeset a:hover{color:#0091ea}[data-md-color-accent=light-blue] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=light-blue] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-nav__link:focus,[data-md-color-accent=light-blue] .md-nav__link:hover,[data-md-color-accent=light-blue] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=light-blue] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=light-blue] .md-typeset .md-clipboard:active:before,[data-md-color-accent=light-blue] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=light-blue] .md-typeset [id] .headerlink:focus,[data-md-color-accent=light-blue] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=light-blue] .md-typeset [id]:target .headerlink{color:#0091ea}[data-md-color-accent=light-blue] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-search-result__link:hover,[data-md-color-accent=light-blue] .md-search-result__link[data-md-state=active]{background-color:rgba(0,145,234,.1)}[data-md-color-accent=light-blue] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-source-file:hover:before{background-color:#0091ea}button[data-md-color-accent=cyan]{background-color:#00b8d4}[data-md-color-accent=cyan] .md-typeset a:active,[data-md-color-accent=cyan] .md-typeset a:hover{color:#00b8d4}[data-md-color-accent=cyan] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=cyan] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-nav__link:focus,[data-md-color-accent=cyan] .md-nav__link:hover,[data-md-color-accent=cyan] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=cyan] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=cyan] .md-typeset .md-clipboard:active:before,[data-md-color-accent=cyan] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=cyan] .md-typeset [id] .headerlink:focus,[data-md-color-accent=cyan] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=cyan] .md-typeset [id]:target .headerlink{color:#00b8d4}[data-md-color-accent=cyan] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-search-result__link:hover,[data-md-color-accent=cyan] .md-search-result__link[data-md-state=active]{background-color:rgba(0,184,212,.1)}[data-md-color-accent=cyan] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-source-file:hover:before{background-color:#00b8d4}button[data-md-color-accent=teal]{background-color:#00bfa5}[data-md-color-accent=teal] .md-typeset a:active,[data-md-color-accent=teal] .md-typeset a:hover{color:#00bfa5}[data-md-color-accent=teal] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=teal] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#00bfa5}[data-md-color-accent=teal] .md-nav__link:focus,[data-md-color-accent=teal] .md-nav__link:hover,[data-md-color-accent=teal] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=teal] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=teal] .md-typeset .md-clipboard:active:before,[data-md-color-accent=teal] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=teal] .md-typeset [id] .headerlink:focus,[data-md-color-accent=teal] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=teal] .md-typeset [id]:target .headerlink{color:#00bfa5}[data-md-color-accent=teal] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00bfa5}[data-md-color-accent=teal] .md-search-result__link:hover,[data-md-color-accent=teal] .md-search-result__link[data-md-state=active]{background-color:rgba(0,191,165,.1)}[data-md-color-accent=teal] 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- - - - - - - - - - - - - - - - - - - - - - Array(T) - ClickHouse Documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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An array of elements of type T. The T type can be any type, including an array. -We don't recommend using multidimensional arrays, because they are not well supported (for example, you can't store multidimensional arrays in tables with a MergeTree engine).

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There isn't a separate type for boolean values. They use the UInt8 type, restricted to the values 0 or 1.

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A date. Stored in two bytes as the number of days since 1970-01-01 (unsigned). Allows storing values from just after the beginning of the Unix Epoch to the upper threshold defined by a constant at the compilation stage (currently, this is until the year 2106, but the final fully-supported year is 2105). -The minimum value is output as 0000-00-00.

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Date with time. Stored in four bytes as a Unix timestamp (unsigned). Allows storing values in the same range as for the Date type. The minimal value is output as 0000-00-00 00:00:00. -The time is stored with accuracy up to one second (without leap seconds).

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The date with time is converted from text (divided into component parts) to binary and back, using the system's time zone at the time the client or server starts. In text format, information about daylight savings is lost.

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By default, the client switches to the timezone of the server when it connects. You can change this behavior by enabling the client command-line option --use_client_time_zone.

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So when working with a textual date (for example, when saving text dumps), keep in mind that there may be ambiguity during changes for daylight savings time, and there may be problems matching data if the time zone changed.

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Enum8 or Enum16. A finite set of string values that can be stored more efficiently than the String data type.

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  • A data type with two possible values: 'hello' and 'world'.
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Each of the values is assigned a number in the range -128 ... 127 for Enum8 or in the range -32768 ... 32767 for Enum16. All the strings and numbers must be different. An empty string is allowed. If this type is specified (in a table definition), numbers can be in an arbitrary order. However, the order does not matter.

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In RAM, this type of column is stored in the same way as Int8 or Int16 of the corresponding numerical values. -When reading in text form, ClickHouse parses the value as a string and searches for the corresponding string from the set of Enum values. If it is not found, an exception is thrown. When reading in text format, the string is read and the corresponding numeric value is looked up. An exception will be thrown if it is not found. -When writing in text form, it writes the value as the corresponding string. If column data contains garbage (numbers that are not from the valid set), an exception is thrown. When reading and writing in binary form, it works the same way as for Int8 and Int16 data types. -The implicit default value is the value with the lowest number.

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During ORDER BY, GROUP BY, IN, DISTINCT and so on, Enums behave the same way as the corresponding numbers. For example, ORDER BY sorts them numerically. Equality and comparison operators work the same way on Enums as they do on the underlying numeric values.

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Enum values cannot be compared with numbers. Enums can be compared to a constant string. If the string compared to is not a valid value for the Enum, an exception will be thrown. The IN operator is supported with the Enum on the left hand side and a set of strings on the right hand side. The strings are the values of the corresponding Enum.

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Most numeric and string operations are not defined for Enum values, e.g. adding a number to an Enum or concatenating a string to an Enum. -However, the Enum has a natural toString function that returns its string value.

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Enum values are also convertible to numeric types using the toT function, where T is a numeric type. When T corresponds to the enum’s underlying numeric type, this conversion is zero-cost. -The Enum type can be changed without cost using ALTER, if only the set of values is changed. It is possible to both add and remove members of the Enum using ALTER (removing is safe only if the removed value has never been used in the table). As a safeguard, changing the numeric value of a previously defined Enum member will throw an exception.

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Using ALTER, it is possible to change an Enum8 to an Enum16 or vice versa, just like changing an Int8 to Int16.

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A fixed-length string of N bytes (not characters or code points). N must be a strictly positive natural number. -When the server reads a string that contains fewer bytes (such as when parsing INSERT data), the string is padded to N bytes by appending null bytes at the right. -When the server reads a string that contains more bytes, an error message is returned. -When the server writes a string (such as when outputting the result of a SELECT query), null bytes are not trimmed off of the end of the string, but are output. -Note that this behavior differs from MySQL behavior for the CHAR type (where strings are padded with spaces, and the spaces are removed for output).

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Fewer functions can work with the FixedString(N) type than with String, so it is less convenient to use.

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Floating point numbers.

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We recommend that you store data in integer form whenever possible. For example, convert fixed precision numbers to integer values, such as monetary amounts or page load times in milliseconds.

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Using floating-point numbers

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┌───────minus(1, 0.9)─┐
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NaN and Inf

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In contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers:

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See the rules for NaN sorting in the section ORDER BY clause.

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Data types

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ClickHouse can store various types of data in table cells.

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This section describes the supported data types and special considerations when using and/or implementing them, if any.

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Fixed-length integers, with or without a sign.

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Int ranges

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Uint ranges

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AggregateFunction(name, types_of_arguments...)

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The intermediate state of an aggregate function. To get it, use aggregate functions with the '-State' suffix. For more information, see "AggregatingMergeTree".

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Nested(Name1 Type1, Name2 Type2, ...)

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A nested data structure is like a nested table. The parameters of a nested data structure – the column names and types – are specified the same way as in a CREATE query. Each table row can correspond to any number of rows in a nested data structure.

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Example:

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CREATE TABLE test.visits
-(
-    CounterID UInt32,
-    StartDate Date,
-    Sign Int8,
-    IsNew UInt8,
-    VisitID UInt64,
-    UserID UInt64,
-    ...
-    Goals Nested
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-        ID UInt32,
-        Serial UInt32,
-        EventTime DateTime,
-        Price Int64,
-        OrderID String,
-        CurrencyID UInt32
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-) ENGINE = CollapsingMergeTree(StartDate, intHash32(UserID), (CounterID, StartDate, intHash32(UserID), VisitID), 8192, Sign)
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This example declares the Goals nested data structure, which contains data about conversions (goals reached). Each row in the 'visits' table can correspond to zero or any number of conversions.

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Only a single nesting level is supported. Columns of nested structures containing arrays are equivalent to multidimensional arrays, so they have limited support (there is no support for storing these columns in tables with the MergeTree engine).

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In most cases, when working with a nested data structure, its individual columns are specified. To do this, the column names are separated by a dot. These columns make up an array of matching types. All the column arrays of a single nested data structure have the same length.

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Example:

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SELECT
-    Goals.ID,
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-WHERE CounterID = 101500 AND length(Goals.ID) < 5
-LIMIT 10
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- - -
┌─Goals.ID───────────────────────┬─Goals.EventTime───────────────────────────────────────────────────────────────────────────┐
-│ [1073752,591325,591325]        │ ['2014-03-17 16:38:10','2014-03-17 16:38:48','2014-03-17 16:42:27']                       │
-│ [1073752]                      │ ['2014-03-17 00:28:25']                                                                   │
-│ [1073752]                      │ ['2014-03-17 10:46:20']                                                                   │
-│ [1073752,591325,591325,591325] │ ['2014-03-17 13:59:20','2014-03-17 22:17:55','2014-03-17 22:18:07','2014-03-17 22:18:51'] │
-│ []                             │ []                                                                                        │
-│ [1073752,591325,591325]        │ ['2014-03-17 11:37:06','2014-03-17 14:07:47','2014-03-17 14:36:21']                       │
-│ []                             │ []                                                                                        │
-│ []                             │ []                                                                                        │
-│ [591325,1073752]               │ ['2014-03-17 00:46:05','2014-03-17 00:46:05']                                             │
-│ [1073752,591325,591325,591325] │ ['2014-03-17 13:28:33','2014-03-17 13:30:26','2014-03-17 18:51:21','2014-03-17 18:51:45'] │
-└────────────────────────────────┴───────────────────────────────────────────────────────────────────────────────────────────┘
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It is easiest to think of a nested data structure as a set of multiple column arrays of the same length.

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The only place where a SELECT query can specify the name of an entire nested data structure instead of individual columns is the ARRAY JOIN clause. For more information, see "ARRAY JOIN clause". Example:

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SELECT
-    Goal.ID,
-    Goal.EventTime
-FROM test.visits
-ARRAY JOIN Goals AS Goal
-WHERE CounterID = 101500 AND length(Goals.ID) < 5
-LIMIT 10
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- - -
┌─Goal.ID─┬──────Goal.EventTime─┐
-│ 1073752 │ 2014-03-17 16:38:10 │
-│  591325 │ 2014-03-17 16:38:48 │
-│  591325 │ 2014-03-17 16:42:27 │
-│ 1073752 │ 2014-03-17 00:28:25 │
-│ 1073752 │ 2014-03-17 10:46:20 │
-│ 1073752 │ 2014-03-17 13:59:20 │
-│  591325 │ 2014-03-17 22:17:55 │
-│  591325 │ 2014-03-17 22:18:07 │
-│  591325 │ 2014-03-17 22:18:51 │
-│ 1073752 │ 2014-03-17 11:37:06 │
-└─────────┴─────────────────────┘
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You can't perform SELECT for an entire nested data structure. You can only explicitly list individual columns that are part of it.

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For an INSERT query, you should pass all the component column arrays of a nested data structure separately (as if they were individual column arrays). During insertion, the system checks that they have the same length.

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For a DESCRIBE query, the columns in a nested data structure are listed separately in the same way.

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The ALTER query is very limited for elements in a nested data structure.

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Expression

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Used for representing lambda expressions in high-order functions.

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Set

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Used for the right half of an IN expression.

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String

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Strings of an arbitrary length. The length is not limited. The value can contain an arbitrary set of bytes, including null bytes. -The String type replaces the types VARCHAR, BLOB, CLOB, and others from other DBMSs.

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Encodings

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ClickHouse doesn't have the concept of encodings. Strings can contain an arbitrary set of bytes, which are stored and output as-is. -If you need to store texts, we recommend using UTF-8 encoding. At the very least, if your terminal uses UTF-8 (as recommended), you can read and write your values without making conversions. -Similarly, certain functions for working with strings have separate variations that work under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. -For example, the 'length' function calculates the string length in bytes, while the 'lengthUTF8' function calculates the string length in Unicode code points, assuming that the value is UTF-8 encoded.

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Tuple(T1, T2, ...)

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Tuples can't be written to tables (other than Memory tables). They are used for temporary column grouping. Columns can be grouped when an IN expression is used in a query, and for specifying certain formal parameters of lambda functions. For more information, see "IN operators" and "Higher order functions".

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Tuples can be output as the result of running a query. In this case, for text formats other than JSON*, values are comma-separated in brackets. In JSON* formats, tuples are output as arrays (in square brackets).

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Overview of ClickHouse architecture

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ClickHouse is a true column-oriented DBMS. Data is stored by columns, and during the execution of arrays (vectors or chunks of columns). Whenever possible, operations are dispatched on arrays, rather than on individual values. This is called "vectorized query execution," and it helps lower the cost of actual data processing.

-
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This idea is nothing new. It dates back to the APL programming language and its descendants: A +, J, K, and Q. Array programming is used in scientific data processing. Neither is this idea something new in relational databases: for example, it is used in the Vectorwise system.

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There are two different approaches for speeding up the query processing: vectorized query execution and runtime code generation. In the latter, the code is generated for every kind of query on the fly, removing all indirection and dynamic dispatch. Neither of these approaches is strictly better than the other. Runtime code generation can be better when it's fuses many operations together, thus fully utilizing CPU execution units and the pipeline. Vectorized query execution can be less practical, because it involves the temporary vectors that must be written to the cache and read back. If the temporary data does not fit in the L2 cache, this becomes an issue. But vectorized query execution more easily utilizes the SIMD capabilities of the CPU. A research paper written by our friends shows that it is better to combine both approaches. ClickHouse uses vectorized query execution and has limited initial support for runtime code.

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Columns

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To represent columns in memory (actually, chunks of columns), the IColumn interface is used. This interface provides helper methods for implementation of various relational operators. Almost all operations are immutable: they do not modify the original column, but create a new modified one. For example, the IColumn :: filter method accepts a filter byte mask. It is used for the WHERE and HAVING relational operators. Additional examples: the IColumn :: permute method to support ORDER BY, the IColumn :: cut method to support LIMIT, and so on.

-

Various IColumn implementations (ColumnUInt8, ColumnString and so on) are responsible for the memory layout of columns. Memory layout is usually a contiguous array. For the integer type of columns it is just one contiguous array, like std :: vector. For String and Array columns, it is two vectors: one for all array elements, placed contiguously, and a second one for offsets to the beginning of each array. There is also ColumnConst that stores just one value in memory, but looks like a column.

-

Field

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Nevertheless, it is possible to work with individual values as well. To represent an individual value, the Field is used. Field is just a discriminated union of UInt64, Int64, Float64, String and Array. IColumn has the operator[] method to get the n-th value as a Field, and the insert method to append a Field to the end of a column. These methods are not very efficient, because they require dealing with temporary Field objects representing an individual value. There are more efficient methods, such as insertFrom, insertRangeFrom, and so on.

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Field doesn't have enough information about a specific data type for a table. For example, UInt8, UInt16, UInt32, and UInt64 are all represented as UInt64 in a Field.

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Leaky abstractions

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IColumn has methods for common relational transformations of data, but they don't meet all needs. For example, ColumnUInt64 doesn't have a method to calculate the sum of two columns, and ColumnString doesn't have a method to run a substring search. These countless routines are implemented outside of IColumn.

-

Various functions on columns can be implemented in a generic, non-efficient way using IColumn methods to extract Field values, or in a specialized way using knowledge of inner memory layout of data in a specific IColumn implementation. To do this, functions are cast to a specific IColumn type and deal with internal representation directly. For example, ColumnUInt64 has the getData method that returns a reference to an internal array, then a separate routine reads or fills that array directly. In fact, we have "leaky abstractions" to allow efficient specializations of various routines.

-

Data types

-

IDataType is responsible for serialization and deserialization: for reading and writing chunks of columns or individual values in binary or text form. -IDataType directly corresponds to data types in tables. For example, there are DataTypeUInt32, DataTypeDateTime, DataTypeString and so on.

-

IDataType and IColumn are only loosely related to each other. Different data types can be represented in memory by the same IColumn implementations. For example, DataTypeUInt32 and DataTypeDateTime are both represented by ColumnUInt32 or ColumnConstUInt32. In addition, the same data type can be represented by different IColumn implementations. For example, DataTypeUInt8 can be represented by ColumnUInt8 or ColumnConstUInt8.

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IDataType only stores metadata. For instance, DataTypeUInt8 doesn't store anything at all (except vptr) and DataTypeFixedString stores just N (the size of fixed-size strings).

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IDataType has helper methods for various data formats. Examples are methods to serialize a value with possible quoting, to serialize a value for JSON, and to serialize a value as part of XML format. There is no direct correspondence to data formats. For example, the different data formats Pretty and TabSeparated can use the same serializeTextEscaped helper method from the IDataType interface.

-

Block

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A Block is a container that represents a subset (chunk) of a table in memory. It is just a set of triples: (IColumn, IDataType, column name). During query execution, data is processed by Blocks. If we have a Block, we have data (in the IColumn object), we have information about its type (in IDataType) that tells us how to deal with that column, and we have the column name (either the original column name from the table, or some artificial name assigned for getting temporary results of calculations).

-

When we calculate some function over columns in a block, we add another column with its result to the block, and we don't touch columns for arguments of the function because operations are immutable. Later, unneeded columns can be removed from the block, but not modified. This is convenient for elimination of common subexpressions.

-

Blocks are created for every processed chunk of data. Note that for the same type of calculation, the column names and types remain the same for different blocks, and only column data changes. It is better to split block data from the block header, because small block sizes will have a high overhead of temporary strings for copying shared_ptrs and column names.

-

Block Streams

-

Block streams are for processing data. We use streams of blocks to read data from somewhere, perform data transformations, or write data to somewhere. IBlockInputStream has the read method to fetch the next block while available. IBlockOutputStream has the write method to push the block somewhere.

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Streams are responsible for:

-
    -
  1. Reading or writing to a table. The table just returns a stream for reading or writing blocks.
    2. -
  2. Implementing data formats. For example, if you want to output data to a terminal in Pretty format, you create a block output stream where you push blocks, and it formats them.
    3. -
  3. Performing data transformations. Let's say you have IBlockInputStream and want to create a filtered stream. You create FilterBlockInputStream and initialize it with your stream. Then when you pull a block from FilterBlockInputStream, it pulls a block from your stream, filters it, and returns the filtered block to you. Query execution pipelines are represented this way.
    4. -
-

There are more sophisticated transformations. For example, when you pull from AggregatingBlockInputStream, it reads all data from its source, aggregates it, and then returns a stream of aggregated data for you. Another example: UnionBlockInputStream accepts many input sources in the constructor and also a number of threads. It launches multiple threads and reads from multiple sources in parallel.

-
-

Block streams use the "pull" approach to control flow: when you pull a block from the first stream, it consequently pulls the required blocks from nested streams, and the entire execution pipeline will work. Neither "pull" nor "push" is the best solution, because control flow is implicit, and that limits implementation of various features like simultaneous execution of multiple queries (merging many pipelines together). This limitation could be overcome with coroutines or just running extra threads that wait for each other. We may have more possibilities if we make control flow explicit: if we locate the logic for passing data from one calculation unit to another outside of those calculation units. Read this article for more thoughts.

-
-

We should note that the query execution pipeline creates temporary data at each step. We try to keep block size small enough so that temporary data fits in the CPU cache. With that assumption, writing and reading temporary data is almost free in comparison with other calculations. We could consider an alternative, which is to fuse many operations in the pipeline together, to make the pipeline as short as possible and remove much of the temporary data. This could be an advantage, but it also has drawbacks. For example, a split pipeline makes it easy to implement caching intermediate data, stealing intermediate data from similar queries running at the same time, and merging pipelines for similar queries.

-

Formats

-

Data formats are implemented with block streams. There are "presentational" formats only suitable for output of data to the client, such as Pretty format, which provides only IBlockOutputStream. And there are input/output formats, such as TabSeparated or JSONEachRow.

-

There are also row streams: IRowInputStream and IRowOutputStream. They allow you to pull/push data by individual rows, not by blocks. And they are only needed to simplify implementation of row-oriented formats. The wrappers BlockInputStreamFromRowInputStream and BlockOutputStreamFromRowOutputStream allow you to convert row-oriented streams to regular block-oriented streams.

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I/O

-

For byte-oriented input/output, there are ReadBuffer and WriteBuffer abstract classes. They are used instead of C++ iostream's. Don't worry: every mature C++ project is using something other than iostream's for good reasons.

-

ReadBuffer and WriteBuffer are just a contiguous buffer and a cursor pointing to the position in that buffer. Implementations may own or not own the memory for the buffer. There is a virtual method to fill the buffer with the following data (for ReadBuffer) or to flush the buffer somewhere (for WriteBuffer). The virtual methods are rarely called.

-

Implementations of ReadBuffer/WriteBuffer are used for working with files and file descriptors and network sockets, for implementing compression (CompressedWriteBuffer is initialized with another WriteBuffer and performs compression before writing data to it), and for other purposes – the names ConcatReadBuffer, LimitReadBuffer, and HashingWriteBuffer speak for themselves.

-

Read/WriteBuffers only deal with bytes. To help with formatted input/output (for instance, to write a number in decimal format), there are functions from ReadHelpers and WriteHelpers header files.

-

Let's look at what happens when you want to write a result set in JSON format to stdout. You have a result set ready to be fetched from IBlockInputStream. You create WriteBufferFromFileDescriptor(STDOUT_FILENO) to write bytes to stdout. You create JSONRowOutputStream, initialized with that WriteBuffer, to write rows in JSON to stdout. You create BlockOutputStreamFromRowOutputStream on top of it, to represent it as IBlockOutputStream. Then you call copyData to transfer data from IBlockInputStream to IBlockOutputStream, and everything works. Internally, JSONRowOutputStream will write various JSON delimiters and call the IDataType::serializeTextJSON method with a reference to IColumn and the row number as arguments. Consequently, IDataType::serializeTextJSON will call a method from WriteHelpers.h: for example, writeText for numeric types and writeJSONString for DataTypeString.

-

Tables

-

Tables are represented by the IStorage interface. Different implementations of that interface are different table engines. Examples are StorageMergeTree, StorageMemory, and so on. Instances of these classes are just tables.

-

The most important IStorage methods are read and write. There are also alter, rename, drop, and so on. The read method accepts the following arguments: the set of columns to read from a table, the AST query to consider, and the desired number of streams to return. It returns one or multiple IBlockInputStream objects and information about the stage of data processing that was completed inside a table engine during query execution.

-

In most cases, the read method is only responsible for reading the specified columns from a table, not for any further data processing. All further data processing is done by the query interpreter and is outside the responsibility of IStorage.

-

But there are notable exceptions:

-
    -
  • The AST query is passed to the read method and the table engine can use it to derive index usage and to read less data from a table.
    • -
  • Sometimes the table engine can process data itself to a specific stage. For example, StorageDistributed can send a query to remote servers, ask them to process data to a stage where data from different remote servers can be merged, and return that preprocessed data. -The query interpreter then finishes processing the data.
    • -
-

The table's read method can return multiple IBlockInputStream objects to allow parallel data processing. These multiple block input streams can read from a table in parallel. Then you can wrap these streams with various transformations (such as expression evaluation or filtering) that can be calculated independently and create a UnionBlockInputStream on top of them, to read from multiple streams in parallel.

-

There are also TableFunctions. These are functions that return a temporary IStorage object to use in the FROM clause of a query.

-

To get a quick idea of how to implement your own table engine, look at something simple, like StorageMemory or StorageTinyLog.

-
-

As the result of the read method, IStorage returns QueryProcessingStage – information about what parts of the query were already calculated inside storage. Currently we have only very coarse granularity for that information. There is no way for the storage to say "I have already processed this part of the expression in WHERE, for this range of data". We need to work on that.

-
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Parsers

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A query is parsed by a hand-written recursive descent parser. For example, ParserSelectQuery just recursively calls the underlying parsers for various parts of the query. Parsers create an AST. The AST is represented by nodes, which are instances of IAST.

-
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Parser generators are not used for historical reasons.

-
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Interpreters

-

Interpreters are responsible for creating the query execution pipeline from an AST. There are simple interpreters, such as InterpreterExistsQueryand InterpreterDropQuery, or the more sophisticated InterpreterSelectQuery. The query execution pipeline is a combination of block input or output streams. For example, the result of interpreting the SELECT query is the IBlockInputStream to read the result set from; the result of the INSERT query is the IBlockOutputStream to write data for insertion to; and the result of interpreting the INSERT SELECT query is the IBlockInputStream that returns an empty result set on the first read, but that copies data from SELECT to INSERT at the same time.

-

InterpreterSelectQuery uses ExpressionAnalyzer and ExpressionActions machinery for query analysis and transformations. This is where most rule-based query optimizations are done. ExpressionAnalyzer is quite messy and should be rewritten: various query transformations and optimizations should be extracted to separate classes to allow modular transformations or query.

-

Functions

-

There are ordinary functions and aggregate functions. For aggregate functions, see the next section.

-

Ordinary functions don't change the number of rows – they work as if they are processing each row independently. In fact, functions are not called for individual rows, but for Block's of data to implement vectorized query execution.

-

There are some miscellaneous functions, like blockSize, rowNumberInBlock, and runningAccumulate, that exploit block processing and violate the independence of rows.

-

ClickHouse has strong typing, so implicit type conversion doesn't occur. If a function doesn't support a specific combination of types, an exception will be thrown. But functions can work (be overloaded) for many different combinations of types. For example, the plus function (to implement the + operator) works for any combination of numeric types: UInt8 + Float32, UInt16 + Int8, and so on. Also, some variadic functions can accept any number of arguments, such as the concat function.

-

Implementing a function may be slightly inconvenient because a function explicitly dispatches supported data types and supported IColumns. For example, the plus function has code generated by instantiation of a C++ template for each combination of numeric types, and for constant or non-constant left and right arguments.

-
-

This is a nice place to implement runtime code generation to avoid template code bloat. Also, it will make it possible to add fused functions like fused multiply-add, or to make multiple comparisons in one loop iteration.

-
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Due to vectorized query execution, functions are not short-circuit. For example, if you write WHERE f(x) AND g(y), both sides will be calculated, even for rows, when f(x) is zero (except when f(x) is a zero constant expression). But if selectivity of the f(x) condition is high, and calculation of f(x) is much cheaper than g(y), it's better to implement multi-pass calculation: first calculate f(x), then filter columns by the result, and then calculate g(y) only for smaller, filtered chunks of data.

-

Aggregate Functions

-

Aggregate functions are stateful functions. They accumulate passed values into some state, and allow you to get results from that state. They are managed with the IAggregateFunction interface. States can be rather simple (the state for AggregateFunctionCount is just a single UInt64 value) or quite complex (the state of AggregateFunctionUniqCombined is a combination of a linear array, a hash table and a HyperLogLog probabilistic data structure).

-

To deal with multiple states while executing a high-cardinality GROUP BY query, states are allocated in Arena (a memory pool), or they could be allocated in any suitable piece of memory. States can have a non-trivial constructor and destructor: for example, complex aggregation states can allocate additional memory themselves. This requires some attention to creating and destroying states and properly passing their ownership, to keep track of who and when will destroy states.

-

Aggregation states can be serialized and deserialized to pass over the network during distributed query execution or to write them on disk where there is not enough RAM. They can even be stored in a table with the DataTypeAggregateFunction to allow incremental aggregation of data.

-
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The serialized data format for aggregate function states is not versioned right now. This is ok if aggregate states are only stored temporarily. But we have the AggregatingMergeTree table engine for incremental aggregation, and people are already using it in production. This is why we should add support for backward compatibility when changing the serialized format for any aggregate function in the future.

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Server

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The server implements several different interfaces:

-
    -
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    • -
  • A TCP interface for the native ClickHouse client and for cross-server communication during distributed query execution.
    • -
  • An interface for transferring data for replication.
    • -
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Internally, it is just a basic multithreaded server without coroutines, fibers, etc. Since the server is not designed to process a high rate of simple queries but is intended to process a relatively low rate of complex queries, each of them can process a vast amount of data for analytics.

-

The server initializes the Context class with the necessary environment for query execution: the list of available databases, users and access rights, settings, clusters, the process list, the query log, and so on. This environment is used by interpreters.

-

We maintain full backward and forward compatibility for the server TCP protocol: old clients can talk to new servers and new clients can talk to old servers. But we don't want to maintain it eternally, and we are removing support for old versions after about one year.

-
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For all external applications, we recommend using the HTTP interface because it is simple and easy to use. The TCP protocol is more tightly linked to internal data structures: it uses an internal format for passing blocks of data and it uses custom framing for compressed data. We haven't released a C library for that protocol because it requires linking most of the ClickHouse codebase, which is not practical.

-
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Distributed query execution

-

Servers in a cluster setup are mostly independent. You can create a Distributed table on one or all servers in a cluster. The Distributed table does not store data itself – it only provides a "view" to all local tables on multiple nodes of a cluster. When you SELECT from a Distributed table, it rewrites that query, chooses remote nodes according to load balancing settings, and sends the query to them. The Distributed table requests remote servers to process a query just up to a stage where intermediate results from different servers can be merged. Then it receives the intermediate results and merges them. The distributed table tries to distribute as much work as possible to remote servers, and does not send much intermediate data over the network.

-
-

Things become more complicated when you have subqueries in IN or JOIN clauses and each of them uses a Distributed table. We have different strategies for execution of these queries.

-
-

There is no global query plan for distributed query execution. Each node has its own local query plan for its part of the job. We only have simple one-pass distributed query execution: we send queries for remote nodes and then merge the results. But this is not feasible for difficult queries with high cardinality GROUP BYs or with a large amount of temporary data for JOIN: in such cases, we need to "reshuffle" data between servers, which requires additional coordination. ClickHouse does not support that kind of query execution, and we need to work on it.

-

Merge Tree

-

MergeTree is a family of storage engines that supports indexing by primary key. The primary key can be an arbitary tuple of columns or expressions. Data in a MergeTree table is stored in "parts". Each part stores data in the primary key order (data is ordered lexicographically by the primary key tuple). All the table columns are stored in separate column.bin files in these parts. The files consist of compressed blocks. Each block is usually from 64 KB to 1 MB of uncompressed data, depending on the average value size. The blocks consist of column values placed contiguously one after the other. Column values are in the same order for each column (the order is defined by the primary key), so when you iterate by many columns, you get values for the corresponding rows.

-

The primary key itself is "sparse". It doesn't address each single row, but only some ranges of data. A separate primary.idx file has the value of the primary key for each N-th row, where N is called index_granularity (usually, N = 8192). Also, for each column, we have column.mrk files with "marks," which are offsets to each N-th row in the data file. Each mark is a pair: the offset in the file to the beginning of the compressed block, and the offset in the decompressed block to the beginning of data. Usually compressed blocks are aligned by marks, and the offset in the decompressed block is zero. Data for primary.idx always resides in memory and data for column.mrk files is cached.

-

When we are going to read something from a part in MergeTree, we look at primary.idx data and locate ranges that could possibly contain requested data, then look at column.mrk data and calculate offsets for where to start reading those ranges. Because of sparseness, excess data may be read. ClickHouse is not suitable for a high load of simple point queries, because the entire range with index_granularity rows must be read for each key, and the entire compressed block must be decompressed for each column. We made the index sparse because we must be able to maintain trillions of rows per single server without noticeable memory consumption for the index. Also, because the primary key is sparse, it is not unique: it cannot check the existence of the key in the table at INSERT time. You could have many rows with the same key in a table.

-

When you INSERT a bunch of data into MergeTree, that bunch is sorted by primary key order and forms a new part. To keep the number of parts relatively low, there are background threads that periodically select some parts and merge them to a single sorted part. That's why it is called MergeTree. Of course, merging leads to "write amplification". All parts are immutable: they are only created and deleted, but not modified. When SELECT is run, it holds a snapshot of the table (a set of parts). After merging, we also keep old parts for some time to make recovery after failure easier, so if we see that some merged part is probably broken, we can replace it with its source parts.

-

MergeTree is not an LSM tree because it doesn't contain "memtable" and "log": inserted data is written directly to the filesystem. This makes it suitable only to INSERT data in batches, not by individual row and not very frequently – about once per second is ok, but a thousand times a second is not. We did it this way for simplicity's sake, and because we are already inserting data in batches in our applications.

-
-

MergeTree tables can only have one (primary) index: there aren't any secondary indices. It would be nice to allow multiple physical representations under one logical table, for example, to store data in more than one physical order or even to allow representations with pre-aggregated data along with original data.

-
-

There are MergeTree engines that are doing additional work during background merges. Examples are CollapsingMergeTree and AggregatingMergeTree. This could be treated as special support for updates. Keep in mind that these are not real updates because users usually have no control over the time when background merges will be executed, and data in a MergeTree table is almost always stored in more than one part, not in completely merged form.

-

Replication

-

Replication in ClickHouse is implemented on a per-table basis. You could have some replicated and some non-replicated tables on the same server. You could also have tables replicated in different ways, such as one table with two-factor replication and another with three-factor.

-

Replication is implemented in the ReplicatedMergeTree storage engine. The path in ZooKeeper is specified as a parameter for the storage engine. All tables with the same path in ZooKeeper become replicas of each other: they synchronize their data and maintain consistency. Replicas can be added and removed dynamically simply by creating or dropping a table.

-

Replication uses an asynchronous multi-master scheme. You can insert data into any replica that has a session with ZooKeeper, and data is replicated to all other replicas asynchronously. Because ClickHouse doesn't support UPDATEs, replication is conflict-free. As there is no quorum acknowledgment of inserts, just-inserted data might be lost if one node fails.

-

Metadata for replication is stored in ZooKeeper. There is a replication log that lists what actions to do. Actions are: get part; merge parts; drop partition, etc. Each replica copies the replication log to its queue and then executes the actions from the queue. For example, on insertion, the "get part" action is created in the log, and every replica downloads that part. Merges are coordinated between replicas to get byte-identical results. All parts are merged in the same way on all replicas. To achieve this, one replica is elected as the leader, and that replica initiates merges and writes "merge parts" actions to the log.

-

Replication is physical: only compressed parts are transferred between nodes, not queries. To lower the network cost (to avoid network amplification), merges are processed on each replica independently in most cases. Large merged parts are sent over the network only in cases of significant replication lag.

-

In addition, each replica stores its state in ZooKeeper as the set of parts and its checksums. When the state on the local filesystem diverges from the reference state in ZooKeeper, the replica restores its consistency by downloading missing and broken parts from other replicas. When there is some unexpected or broken data in the local filesystem, ClickHouse does not remove it, but moves it to a separate directory and forgets it.

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The ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is not elastic, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load will be uneven. This implementation gives you more control, and it is fine for relatively small clusters such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that will span its data across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically.

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How to build ClickHouse on Linux

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Build should work on Linux Ubuntu 12.04, 14.04 or newer. -With appropriate changes, it should also work on any other Linux distribution. -The build process is not intended to work on Mac OS X. -Only x86_64 with SSE 4.2 is supported. Support for AArch64 is experimental.

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To test for SSE 4.2, do

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Install Git and CMake

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Detect the number of threads

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Install GCC 7

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There are several ways to do this.

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Install from a PPA package

-
sudo apt-get install software-properties-common
-sudo apt-add-repository ppa:ubuntu-toolchain-r/test
-sudo apt-get update
-sudo apt-get install gcc-7 g++-7
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Install from sources

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Look at [https://github.com/yandex/ClickHouse/blob/master/utils/prepare-environment/install-gcc.sh]

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Use GCC 7 for builds

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export CC=gcc-7
-export CXX=g++-7
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Install required libraries from packages

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sudo apt-get install libicu-dev libreadline-dev libmysqlclient-dev libssl-dev unixodbc-dev ninja-build
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Checkout ClickHouse sources

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To get the latest stable version:

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git clone -b stable --recursive git@github.com:yandex/ClickHouse.git
-# or: git clone -b stable --recursive https://github.com/yandex/ClickHouse.git
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Build ClickHouse

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There are two build variants.

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Build release package

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Install prerequisites to build Debian packages.

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Install the most recent version of Clang.

-

Clang is embedded into the ClickHouse package and used at runtime. The minimum version is 5.0. It is optional.

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To install clang, see utils/prepare-environment/install-clang.sh

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You may also build ClickHouse with Clang for development purposes. -For production releases, GCC is used.

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Run the release script:

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rm -f ../clickhouse*.deb
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You will find built packages in the parent directory:

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ls -l ../clickhouse*.deb
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Note that usage of debian packages is not required. -ClickHouse has no runtime dependencies except libc, so it could work on almost any Linux.

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Installing freshly built packages on a development server:

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Build to work with code

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Build should work on Mac OS X 10.12. If you're using earlier version, you can try to build ClickHouse using Gentoo Prefix and clang sl in this instruction. -With appropriate changes, it should also work on any other Linux distribution.

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Install Homebrew

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Install required compilers, tools, and libraries

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Checkout ClickHouse sources

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To get the latest stable version:

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git clone -b stable --recursive --depth=10 git@github.com:yandex/ClickHouse.git
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Build ClickHouse

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mkdir build
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Caveats

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If you intend to run clickhouse-server, make sure to increase the system's maxfiles variable. See MacOS.md for more details.

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3. Code style is needed for consistency. Consistency makes it easier to read the code, and it also makes it easier to search the code.

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Formatting

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1. Most of the formatting will be done automatically by clang-format.

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2. Indents are 4 spaces. Configure your development environment so that a tab adds four spaces.

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3. A left curly bracket must be separated on a new line. (And the right one, as well.)

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inline size_t mask() const                { return buf_size() - 1; }
-inline size_t place(HashValue x) const    { return x & mask(); }
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cpp - for (size_t i = 0; i < rows; i += storage.index_granularity)

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7. Put spaces around binary operators (+, -, *, /, %, ...), as well as the ternary operator ?:.

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UInt16 year = (s[0] - '0') * 1000 + (s[1] - '0') * 100 + (s[2] - '0') * 10 + (s[3] - '0');
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8. If a line feed is entered, put the operator on a new line and increase the indent before it.

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if (elapsed_ns)
-    message << " ("
-         << rows_read_on_server * 1000000000 / elapsed_ns << " rows/s., "
-        << bytes_read_on_server * 1000.0 / elapsed_ns << " MB/s.) ";
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9. You can use spaces for alignment within a line, if desired.

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dst.ClickLogID         = click.LogID;
-dst.ClickEventID       = click.EventID;
-dst.ClickGoodEvent     = click.GoodEvent;
-
- - -

10. Don't use spaces around the operators ., -> .

-

If necessary, the operator can be wrapped to the next line. In this case, the offset in front of it is increased.

-

11. Do not use a space to separate unary operators (-, +, *, &, ...) from the argument.

-

12. Put a space after a comma, but not before it. The same rule goes for a semicolon inside a for expression.

-

13. Do not use spaces to separate the [] operator.

-

14. In a template <...> expression, use a space between template and <. No spaces after < or before >.

-
template <typename TKey, typename TValue>
-struct AggregatedStatElement
-{}
-
- - -

15. In classes and structures, public, private, and protected are written on the same level as the class/struct, but all other internal elements should be deeper.

-
template <typename T>
-class MultiVersion
-{
-public:
-    /// Version of object for usage. shared_ptr manage lifetime of version.
-    using Version = std::shared_ptr<const T>;
-    ...
-}
-
- - -

16. If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace.

-

17. If the block for if, for, while... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets.

-
/// Finish write.
-for (auto & stream : streams)
-    stream.second->finalize();
-
- - -

18. There should be any spaces at the ends of lines.

-

19. Sources are UTF-8 encoded.

-

20. Non-ASCII characters can be used in string literals.

-
<< ", " << (timer.elapsed() / chunks_stats.hits) << " μsec/hit.";
-
- - -

21. Do not write multiple expressions in a single line.

-

22. Group sections of code inside functions and separate them with no more than one empty line.

-

23. Separate functions, classes, and so on with one or two empty lines.

-

24. A const (related to a value) must be written before the type name.

-
//correct
-const char * pos
-const std::string & s
-//incorrect
-char const * pos
-
- - -

25. When declaring a pointer or reference, the * and & symbols should be separated by spaces on both sides.

-
//correct
-const char * pos
-//incorrect
-const char* pos
-const char *pos
-
- - -

26. When using template types, alias them with the using keyword (except in the simplest cases).

-

In other words, the template parameters are specified only in using and aren't repeated in the code.

-

using can be declared locally, such as inside a function.

-
//correct
-using FileStreams = std::map<std::string, std::shared_ptr<Stream>>;
-FileStreams streams;
-//incorrect
-std::map<std::string, std::shared_ptr<Stream>> streams;
-
- - -

27. Do not declare several variables of different types in one statement.

-
//incorrect
-int x, *y;
-
- - -

28. Do not use C-style casts.

-
//incorrect
-std::cerr << (int)c <<; std::endl;
-//correct
-std::cerr << static_cast<int>(c) << std::endl;
-
- - -

29. In classes and structs, group members and functions separately inside each visibility scope.

-

30. For small classes and structs, it is not necessary to separate the method declaration from the implementation.

-

The same is true for small methods in any classes or structs.

-

For templated classes and structs, don't separate the method declarations from the implementation (because otherwise they must be defined in the same translation unit).

-

31. You can wrap lines at 140 characters, instead of 80.

-

32. Always use the prefix increment/decrement operators if postfix is not required.

-
for (Names::const_iterator it = column_names.begin(); it != column_names.end(); ++it)
-
- - -

Comments

-

1. Be sure to add comments for all non-trivial parts of code.

-

This is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong.

-
/** Part of piece of memory, that can be used.
-  * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading,
-  * then working_buffer will have size of only 10 bytes
-  * (working_buffer.end() will point to the position right after those 10 bytes available for read).
-*/
-
- - -

2. Comments can be as detailed as necessary.

-

3. Place comments before the code they describe. In rare cases, comments can come after the code, on the same line.

-
/** Parses and executes the query.
-*/
-void executeQuery(
-    ReadBuffer & istr, /// Where to read the query from (and data for INSERT, if applicable)
-    WriteBuffer & ostr, /// Where to write the result
-    Context & context, /// DB, tables, data types, engines, functions, aggregate functions...
-    BlockInputStreamPtr & query_plan, /// A description of query processing can be included here
-    QueryProcessingStage::Enum stage = QueryProcessingStage::Complete /// The last stage to process the SELECT query to
-    )
-
- - -

4. Comments should be written in English only.

-

5. If you are writing a library, include detailed comments explaining it in the main header file.

-

6. Do not add comments that do not provide additional information. In particular, do not leave empty comments like this:

-
/*
-* Procedure Name:
-* Original procedure name:
-* Author:
-* Date of creation:
-* Dates of modification:
-* Modification authors:
-* Original file name:
-* Purpose:
-* Intent:
-* Designation:
-* Classes used:
-* Constants:
-* Local variables:
-* Parameters:
-* Date of creation:
-* Purpose:
-*/
-
- - -

The example is borrowed from http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/.

-

7. Do not write garbage comments (author, creation date ..) at the beginning of each file.

-

8. Single-line comments begin with three slashes: /// and multi-line comments begin with /**. These comments are considered "documentation".

-

Note: You can use Doxygen to generate documentation from these comments. But Doxygen is not generally used because it is more convenient to navigate the code in the IDE.

-

9. Multi-line comments must not have empty lines at the beginning and end (except the line that closes a multi-line comment).

-

10. For commenting out code, use basic comments, not "documenting" comments.

-

11. Delete the commented out parts of the code before commiting.

-

12. Do not use profanity in comments or code.

-

13. Do not use uppercase letters. Do not use excessive punctuation.

-
/// WHAT THE FAIL???
-
- - -

14. Do not make delimeters from comments.

-
///******************************************************
-
- - -

15. Do not start discussions in comments.

-
/// Why did you do this stuff?
-
- - -

16. There's no need to write a comment at the end of a block describing what it was about.

-
/// for
-
- - -

Names

-

1. The names of variables and class members use lowercase letters with underscores.

-
size_t max_block_size;
-
- - -

2. The names of functions (methods) use camelCase beginning with a lowercase letter.

-
std::string getName() const override { return "Memory"; }
-
- - -

3. The names of classes (structures) use CamelCase beginning with an uppercase letter. Prefixes other than I are not used for interfaces.

-
class StorageMemory : public IStorage
-
- - -

4. The names of usings follow the same rules as classes, or you can add _t at the end.

-

5. Names of template type arguments for simple cases: T; T, U; T1, T2.

-

For more complex cases, either follow the rules for class names, or add the prefix T.

-
template <typename TKey, typename TValue>
-struct AggregatedStatElement
-
- - -

6. Names of template constant arguments: either follow the rules for variable names, or use N in simple cases.

-
template <bool without_www>
-struct ExtractDomain
-
- - -

7. For abstract classes (interfaces) you can add the I prefix.

-
class IBlockInputStream
-
- - -

8. If you use a variable locally, you can use the short name.

-

In other cases, use a descriptive name that conveys the meaning.

-
bool info_successfully_loaded = false;
-
- - -

9. define‘s should be in ALL_CAPS with underscores. The same is true for global constants.

-
#define MAX_SRC_TABLE_NAMES_TO_STORE 1000
-
- - -

10. File names should use the same style as their contents.

-

If a file contains a single class, name the file the same way as the class, in CamelCase.

-

If the file contains a single function, name the file the same way as the function, in camelCase.

-

11. If the name contains an abbreviation, then:

-
    -
  • For variable names, the abbreviation should use lowercase letters mysql_connection (not mySQL_connection).
    • -
  • For names of classes and functions, keep the uppercase letters in the abbreviation MySQLConnection (not MySqlConnection).
    • -
-

12. Constructor arguments that are used just to initialize the class members should be named the same way as the class members, but with an underscore at the end.

-
FileQueueProcessor(
-    const std::string & path_,
-    const std::string & prefix_,
-    std::shared_ptr<FileHandler> handler_)
-    : path(path_),
-    prefix(prefix_),
-    handler(handler_),
-    log(&Logger::get("FileQueueProcessor"))
-{
-}
-
- - -

The underscore suffix can be omitted if the argument is not used in the constructor body.

-

13. There is no difference in the names of local variables and class members (no prefixes required).

-
timer (not m_timer)
-
- - -

14. Constants in enums use CamelCase beginning with an uppercase letter. ALL_CAPS is also allowed. If the enum is not local, use enum class.

-
enum class CompressionMethod
-{
-    QuickLZ = 0,
-    LZ4     = 1,
-};
-
- - -

15. All names must be in English. Transliteration of Russian words is not allowed.

-
not Stroka
-
- - -

16. Abbreviations are acceptable if they are well known (when you can easily find the meaning of the abbreviation in Wikipedia or in a search engine).

-
`AST`, `SQL`.
-
-Not `NVDH` (some random letters)
-
- - -

Incomplete words are acceptable if the shortened version is common use.

-

You can also use an abbreviation if the full name is included next to it in the comments.

-

17. File names with C++ source code must have the .cpp extension. Header files must have the .h extension.

-

How to write code

-

1. Memory management.

-

Manual memory deallocation (delete) can only be used in library code.

-

In library code, the delete operator can only be used in destructors.

-

In application code, memory must be freed by the object that owns it.

-

Examples:

-
    -
  • The easiest way is to place an object on the stack, or make it a member of another class.
    • -
  • For a large number of small objects, use containers.
    • -
  • For automatic deallocation of a small number of objects that reside in the heap, use shared_ptr/unique_ptr.
    • -
-

2. Resource management.

-

Use RAII and see the previous point.

-

3. Error handling.

-

Use exceptions. In most cases, you only need to throw an exception, and don't need to catch it (because of RAII).

-

In offline data processing applications, it's often acceptable to not catch exceptions.

-

In servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler.

-
/// If there were no other calculations yet, do it synchronously
-if (!started)
-{
-    calculate();
-    started = true;
-}
-else    /// If the calculations are already in progress, wait for results
-    pool.wait();
-
-if (exception)
-    exception->rethrow();
-
- - -

Never hide exceptions without handling. Never just blindly put all exceptions to log.

-

Not catch (...) {}.

-

If you need to ignore some exceptions, do so only for specific ones and rethrow the rest.

-
catch (const DB::Exception & e)
-{
-    if (e.code() == ErrorCodes::UNKNOWN_AGGREGATE_FUNCTION)
-        return nullptr;
-    else
-        throw;
-}
-
- - -

When using functions with response codes or errno, always check the result and throw an exception in case of error.

-
if (0 != close(fd))
-    throwFromErrno("Cannot close file " + file_name, ErrorCodes::CANNOT_CLOSE_FILE);
-
- - -

Asserts are not used.

-

4. Exception types.

-

There is no need to use complex exception hierarchy in application code. The exception text should be understandable to a system administrator.

-

5. Throwing exceptions from destructors.

-

This is not recommended, but it is allowed.

-

Use the following options:

-
    -
  • Create a (done() or finalize()) function that will do all the work in advance that might lead to an exception. If that function was called, there should be no exceptions in the destructor later.
    • -
  • Tasks that are too complex (such as sending messages over the network) can be put in separate method that the class user will have to call before destruction.
    • -
  • If there is an exception in the destructor, it’s better to log it than to hide it (if the logger is available).
    • -
  • In simple applications, it is acceptable to rely on std::terminate (for cases of noexcept by default in C++11) to handle exceptions.
    • -
-

6. Anonymous code blocks.

-

You can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block.

-
Block block = data.in->read();
-
-{
-    std::lock_guard<std::mutex> lock(mutex);
-    data.ready = true;
-    data.block = block;
-}
-
-ready_any.set();
-
- - -

7. Multithreading.

-

For offline data processing applications:

-
    -
  • Try to get the best possible performance on a single CPU core. You can then parallelize your code if necessary.
    • -
-

In server applications:

-
    -
  • Use the thread pool to process requests. At this point, we haven't had any tasks that required userspace context switching.
    • -
-

Fork is not used for parallelization.

-

8. Synchronizing threads.

-

Often it is possible to make different threads use different memory cells (even better: different cache lines,) and to not use any thread synchronization (except joinAll).

-

If synchronization is required, in most cases, it is sufficient to use mutex under lock_guard.

-

In other cases use system synchronization primitives. Do not use busy wait.

-

Atomic operations should be used only in the simplest cases.

-

Do not try to implement lock-free data structures unless it is your primary area of expertise.

-

9. Pointers vs references.

-

In most cases, prefer references.

-

10. const.

-

Use constant references, pointers to constants, const_iterator, const methods.

-

Consider const to be default and use non-const only when necessary.

-

When passing variable by value, using const usually does not make sense.

-

11. unsigned.

-

Use unsigned, if needed.

-

12. Numeric types

-

Use UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, and size_t, ssize_t, ptrdiff_t.

-

Don't use signed/unsigned long, long long, short, signed char, unsigned char, or char types for numbers.

-

13. Passing arguments.

-

Pass complex values by reference (including std::string).

-

If a function captures ownership of an objected created in the heap, make the argument type shared_ptr or unique_ptr.

-

14. Returning values.

-

In most cases, just use return. Do not write [return std::move(res)]{.strike}.

-

If the function allocates an object on heap and returns it, use shared_ptr or unique_ptr.

-

In rare cases you might need to return the value via an argument. In this case, the argument should be a reference.

-
using AggregateFunctionPtr = std::shared_ptr<IAggregateFunction>;
-
-/** Creates an aggregate function by name.
- */
-class AggregateFunctionFactory
-{
-public:
-   AggregateFunctionFactory();
-   AggregateFunctionPtr get(const String & name, const DataTypes & argument_types) const;
-
- - -

15. namespace.

-

There is no need to use a separate namespace for application code or small libraries.

-

or small libraries.

-

For medium to large libraries, put everything in the namespace.

-

You can use the additional detail namespace in a library's .h file to hide implementation details.

-

In a .cpp file, you can use the static or anonymous namespace to hide symbols.

-

You can also use namespace for enums to prevent its names from polluting the outer namespace, but it’s better to use the enum class.

-

16. Delayed initialization.

-

If arguments are required for initialization then do not write a default constructor.

-

If later you’ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use shared_ptr/unique_ptr.

-
Loader(DB::Connection * connection_, const std::string & query, size_t max_block_size_);
-
-/// For delayed initialization
-Loader() {}
-
- - -

17. Virtual functions.

-

If the class is not intended for polymorphic use, you do not need to make functions virtual. This also applies to the destructor.

-

18. Encodings.

-

Use UTF-8 everywhere. Use std::stringandchar *. Do not use std::wstringandwchar_t.

-

19. Logging.

-

See the examples everywhere in the code.

-

Before committing, delete all meaningless and debug logging, and any other types of debug output.

-

Logging in cycles should be avoided, even on the Trace level.

-

Logs must be readable at any logging level.

-

Logging should only be used in application code, for the most part.

-

Log messages must be written in English.

-

The log should preferably be understandable for the system administrator.

-

Do not use profanity in the log.

-

Use UTF-8 encoding in the log. In rare cases you can use non-ASCII characters in the log.

-

20. I/O.

-

Don't use iostreams in internal cycles that are critical for application performance (and never use stringstream).

-

Use the DB/IO library instead.

-

21. Date and time.

-

See the DateLUT library.

-

22. include.

-

Always use #pragma once instead of include guards.

-

23. using.

-

The using namespace is not used.

-

It's fine if you are 'using' something specific, but make it local inside a class or function.

-

24. Do not use trailing return type for functions unless necessary.

-
[auto f() -&gt; void;]{.strike}
-
- - -

25. Do not declare and init variables like this:

-
auto s = std::string{"Hello"};
-
- - -

Do it like this:

-
std::string s = "Hello";
-std::string s{"Hello"};
-
- - -

26. For virtual functions, write virtual in the base class, but write override in descendent classes.

-

Unused features of C++

-

1. Virtual inheritance is not used.

-

2. Exception specifiers from C++03 are not used.

-

3. Function try block is not used, except for the main function in tests.

-

Platform

-

1. We write code for a specific platform.

-

But other things being equal, cross-platform or portable code is preferred.

-

2. The language is C++17.

-

3. The compiler is gcc. At this time (December 2017), the code is compiled using version 7.2. (It can also be compiled using clang 5.)

-

The standard library is used (implementation of libstdc++ or libc++).

-

4. OS: Linux Ubuntu, not older than Precise.

-

5. Code is written for x86_64 CPU architecture.

-

The CPU instruction set is the minimum supported set among our servers. Currently, it is SSE 4.2.

-

6. Use -Wall -Wextra -Werror compilation flags.

-

7. Use static linking with all libraries except those that are difficult to connect to statically (see the output of the ldd command).

-

8. Code is developed and debugged with release settings.

-

Tools

-

1. KDevelop is a good IDE.

-

2. For debugging, use gdb, valgrind (memcheck), strace, -fsanitize=, ..., tcmalloc_minimal_debug.

-

3. For profiling, use Linux Perf valgrind (callgrind), strace-cf.

-

4. Sources are in Git.

-

5. Compilation is managed by CMake.

-

6. Releases are in deb packages.

-

7. Commits to master must not break the build.

-

Though only selected revisions are considered workable.

-

8. Make commits as often as possible, even if the code is only partially ready.

-

Use branches for this purpose.

-

If your code is not buildable yet, exclude it from the build before pushing to master. You'll need to finish it or remove it from master within a few days.

-

9. For non-trivial changes, used branches and publish them on the server.

-

10. Unused code is removed from the repository.

-

Libraries

-

1. The C++14 standard library is used (experimental extensions are fine), as well as boost and Poco frameworks.

-

2. If necessary, you can use any well-known libraries available in the OS package.

-

If there is a good solution already available, then use it, even if it means you have to install another library.

-

(But be prepared to remove bad libraries from code.)

-

3. You can install a library that isn't in the packages, if the packages don't have what you need or have an outdated version or the wrong type of compilation.

-

4. If the library is small and doesn't have its own complex build system, put the source files in the contrib folder.

-

5. Preference is always given to libraries that are already used.

-

General recommendations

-

1. Write as little code as possible.

-

2. Try the simplest solution.

-

3. Don't write code until you know how it's going to work and how the inner loop will function.

-

4. In the simplest cases, use 'using' instead of classes or structs.

-

5. If possible, do not write copy constructors, assignment operators, destructors (other than a virtual one, if the class contains at least one virtual function), mpve-constructors and move assignment operators. In other words, the compiler-generated functions must work correctly. You can use 'default'.

-

6. Code simplification is encouraged. Reduce the size of your code where possible.

-

Additional recommendations

-

1. Explicit std:: for types from stddef.h is not recommended.

-

We recommend writing size_t instead std::size_t because it's shorter.

-

But if you prefer, std:: is acceptable.

-

2. Explicit std:: for functions from the standard C library is not recommended.

-

Write memcpy instead of std::memcpy.

-

The reason is that there are similar non-standard functions, such as memmem. We do use these functions on occasion. These functions do not exist in namespace std.

-

If you write std::memcpy instead of memcpy everywhere, then memmem without std:: will look awkward.

-

Nevertheless, std:: is allowed if you prefer it.

-

3. Using functions from C when the ones are available in the standard C++ library.

-

This is acceptable if it is more efficient.

-

For example, use memcpy instead of std::copy for copying large chunks of memory.

-

4. Multiline function arguments.

-

Any of the following wrapping styles are allowed:

-
function(
-    T1 x1,
-    T2 x2)
-
- - -
function(
-    size_t left, size_t right,
-    const & RangesInDataParts ranges,
-    size_t limit)
-
- - -
function(size_t left, size_t right,
-    const & RangesInDataParts ranges,
-    size_t limit)
-
- - -
function(size_t left, size_t right,
-        const & RangesInDataParts ranges,
-        size_t limit)
-
- - -
function(
-        size_t left,
-        size_t right,
-        const & RangesInDataParts ranges,
-        size_t limit)
-
- - - - - - - -
-
-
-
- - - - -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/docs/en/development/tests/index.html b/docs/build/docs/en/development/tests/index.html deleted file mode 100644 index b161a82c24f..00000000000 --- a/docs/build/docs/en/development/tests/index.html +++ /dev/null @@ -1,2951 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - How to run ClickHouse tests - ClickHouse Documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - - -
- -
- - - - -
-
- - -
-
-
- -
-
-
- - -
-
-
- - -
-
-
- - -
-
- - - - - - - -

How to run ClickHouse tests

-

The clickhouse-test utility that is used for functional testing is written using Python 2.x.It also requires you to have some third-party packages:

-
$ pip install lxml termcolor
-
- - -

In a nutshell:

-
    -
  • Put the clickhouse program to /usr/bin (or PATH)
    • -
  • Create a clickhouse-client symlink in /usr/bin pointing to clickhouse
    • -
  • Start the clickhouse server
    • -
  • cd dbms/tests/
    • -
  • Run ./clickhouse-test
    • -
-

Example usage

-

Run ./clickhouse-test --help to see available options.

-

To run tests without having to create a symlink or mess with PATH:

-
./clickhouse-test -c "../../build/dbms/src/Server/clickhouse --client"
-
- - -

To run a single test, i.e. 00395_nullable:

-
./clickhouse-test 00395
-
- - - - - - - -
-
-
-
- - - - -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/docs/en/dicts/external_dicts/index.html b/docs/build/docs/en/dicts/external_dicts/index.html deleted file mode 100644 index 95a082ea7cc..00000000000 --- a/docs/build/docs/en/dicts/external_dicts/index.html +++ /dev/null @@ -1,2928 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - General desription - ClickHouse Documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - - -
- -
- - - - -
-
- - -
-
-
- -
-
-
- - -
-
-
- - -
-
-
- - -
-
- - - - - - - -

-

External dictionaries

-

You can add your own dictionaries from various data sources. The data source for a dictionary can be a local text or executable file, an HTTP(s) resource, or another DBMS. For more information, see "Sources for external dictionaries".

-

ClickHouse:

-
-
    -
  • Fully or partially stores dictionaries in RAM.
    • -
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    • -
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-

The configuration of external dictionaries is located in one or more files. The path to the configuration is specified in the dictionaries_config parameter.

-

Dictionaries can be loaded at server startup or at first use, depending on the dictionaries_lazy_load setting.

-

The dictionary config file has the following format:

-
<yandex>
-    <comment>An optional element with any content. Ignored by the ClickHouse server.</comment>
-
-    <!--Optional element. File name with substitutions-->
-    <include_from>/etc/metrika.xml</include_from>
-
-
-    <dictionary>
-        <!-- Dictionary configuration -->
-    </dictionary>
-
-    ...
-
-    <dictionary>
-        <!-- Dictionary configuration -->
-    </dictionary>
-</yandex>
-
- - -

You can configure any number of dictionaries in the same file. The file format is preserved even if there is only one dictionary (i.e. <yandex><dictionary> <!--configuration -> </dictionary></yandex> ).

-

See also "Functions for working with external dictionaries".

-
- -You can convert values ​​for a small dictionary by describing it in a `SELECT` query (see the [transform](../functions/other_functions.md#other_functions-transform) function). This functionality is not related to external dictionaries. - -
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Configuring an external dictionary

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The dictionary configuration has the following structure:

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<dictionary>
-    <name>dict_name</name>
-
-    <source>
-      <!-- Source configuration -->
-    </source>
-
-    <layout>
-      <!-- Memory layout configuration -->
-    </layout>
-
-    <structure>
-      <!-- Complex key configuration -->
-    </structure>
-
-    <lifetime>
-      <!-- Lifetime of dictionary in memory -->
-    </lifetime>
-</dictionary>
-
- - -
    -
  • name – The identifier that can be used to access the dictionary. Use the characters [a-zA-Z0-9_\-].
    • -
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    • -
  • layout — Dictionary layout in memory.
    • -
  • structure — Structure of the dictionary . A key and attributes that can be retrieved by this key.
    • -
  • lifetime — Frequency of dictionary updates.
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Storing dictionaries in memory

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There are a variety of ways to store dictionaries in memory.

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We recommend flat, hashedandcomplex_key_hashed. which provide optimal processing speed.

-

Caching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section "cache".

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There are several ways to improve dictionary performance:

-
    -
  • Call the function for working with the dictionary after GROUP BY.
    • -
  • Mark attributes to extract as injective. An attribute is called injective if different attribute values correspond to different keys. So when GROUP BY uses a function that fetches an attribute value by the key, this function is automatically taken out of GROUP BY.
    • -
-

ClickHouse generates an exception for errors with dictionaries. Examples of errors:

-
    -
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    • -
  • Error querying a cached dictionary.
    • -
-

You can view the list of external dictionaries and their statuses in the system.dictionaries table.

-

The configuration looks like this:

-
<yandex>
-    <dictionary>
-        ...
-        <layout>
-            <layout_type>
-                <!-- layout settings -->
-            </layout_type>
-        </layout>
-        ...
-    </dictionary>
-</yandex>
-
- - -

-

Ways to store dictionaries in memory

- -

-

flat

-

The dictionary is completely stored in memory in the form of flat arrays. How much memory does the dictionary use? The amount is proportional to the size of the largest key (in space used).

-

The dictionary key has the UInt64 type and the value is limited to 500,000. If a larger key is discovered when creating the dictionary, ClickHouse throws an exception and does not create the dictionary.

-

All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.

-

This method provides the best performance among all available methods of storing the dictionary.

-

Configuration example:

-
<layout>
-  <flat />
-</layout>
-
- - -

-

hashed

-

The dictionary is completely stored in memory in the form of a hash table. The dictionary can contain any number of elements with any identifiers In practice, the number of keys can reach tens of millions of items.

-

All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.

-

Configuration example:

-
<layout>
-  <hashed />
-</layout>
-
- - -

-

complex_key_hashed

-

This type of storage is for use with composite keys. Similar to hashed.

-

Configuration example:

-
<layout>
-  <complex_key_hashed />
-</layout>
-
- - -

-

range_hashed

-

The dictionary is stored in memory in the form of a hash table with an ordered array of ranges and their corresponding values.

-

This storage method works the same way as hashed and allows using date/time ranges in addition to the key, if they appear in the dictionary.

-

Example: The table contains discounts for each advertiser in the format:

-
+---------------+---------------------+-------------------+--------+
-| advertiser id | discount start date | discount end date | amount |
-+===============+=====================+===================+========+
-| 123           | 2015-01-01          | 2015-01-15        | 0.15   |
-+---------------+---------------------+-------------------+--------+
-| 123           | 2015-01-16          | 2015-01-31        | 0.25   |
-+---------------+---------------------+-------------------+--------+
-| 456           | 2015-01-01          | 2015-01-15        | 0.05   |
-+---------------+---------------------+-------------------+--------+
-
- - -

To use a sample for date ranges, define the range_min and range_max elements in the structure.

-

Example:

-
<structure>
-    <id>
-        <name>Id</name>
-    </id>
-    <range_min>
-        <name>first</name>
-    </range_min>
-    <range_max>
-        <name>last</name>
-    </range_max>
-    ...
-
- - -

To work with these dictionaries, you need to pass an additional date argument to the dictGetT function:

-
dictGetT('dict_name', 'attr_name', id, date)
-
- - -

This function returns the value for the specified ids and the date range that includes the passed date.

-

Details of the algorithm:

-
    -
  • If the id is not found or a range is not found for the id, it returns the default value for the dictionary.
    • -
  • If there are overlapping ranges, you can use any.
    • -
  • If the range delimiter is NULL or an invalid date (such as 1900-01-01 or 2039-01-01), the range is left open. The range can be open on both sides.
    • -
-

Configuration example:

-
<yandex>
-        <dictionary>
-
-                ...
-
-                <layout>
-                        <range_hashed />
-                </layout>
-
-                <structure>
-                        <id>
-                                <name>Abcdef</name>
-                        </id>
-                        <range_min>
-                                <name>StartDate</name>
-                        </range_min>
-                        <range_max>
-                                <name>EndDate</name>
-                        </range_max>
-                        <attribute>
-                                <name>XXXType</name>
-                                <type>String</type>
-                                <null_value />
-                        </attribute>
-                </structure>
-
-        </dictionary>
-</yandex>
-
- - -

-

cache

-

The dictionary is stored in a cache that has a fixed number of cells. These cells contain frequently used elements.

-

When searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using SELECT attrs... FROM db.table WHERE id IN (k1, k2, ...). The received data is then written to the cache.

-

For cache dictionaries, the expiration lifetime of data in the cache can be set. If more time than lifetime has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used.

-

This is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the system.dictionaries table.

-

To improve cache performance, use a subquery with LIMIT, and call the function with the dictionary externally.

-

Supported sources: MySQL, ClickHouse, executable, HTTP.

-

Example of settings:

-
<layout>
-    <cache>
-        <!-- The size of the cache, in number of cells. Rounded up to a power of two. -->
-        <size_in_cells>1000000000</size_in_cells>
-    </cache>
-</layout>
-
- - -

Set a large enough cache size. You need to experiment to select the number of cells:

-
    -
  1. Set some value.
    2. -
  2. Run queries until the cache is completely full.
    3. -
  3. Assess memory consumption using the system.dictionaries table.
    4. -
  4. Increase or decrease the number of cells until the required memory consumption is reached.
    5. -
-
- -Do not use ClickHouse as a source, because it is slow to process queries with random reads. - -
- -

-

complex_key_cache

-

This type of storage is for use with composite keys. Similar to cache.

-

-

ip_trie

-

This type of storage is for mapping network prefixes (IP addresses) to metadata such as ASN.

-

Example: The table contains network prefixes and their corresponding AS number and country code:

-
  +-----------------+-------+--------+
-  | prefix          | asn   | cca2   |
-  +=================+=======+========+
-  | 202.79.32.0/20  | 17501 | NP     |
-  +-----------------+-------+--------+
-  | 2620:0:870::/48 | 3856  | US     |
-  +-----------------+-------+--------+
-  | 2a02:6b8:1::/48 | 13238 | RU     |
-  +-----------------+-------+--------+
-  | 2001:db8::/32   | 65536 | ZZ     |
-  +-----------------+-------+--------+
-
- - -

When using this type of layout, the structure must have a composite key.

-

Example:

-
<structure>
-    <key>
-        <attribute>
-            <name>prefix</name>
-            <type>String</type>
-        </attribute>
-    </key>
-    <attribute>
-            <name>asn</name>
-            <type>UInt32</type>
-            <null_value />
-    </attribute>
-    <attribute>
-            <name>cca2</name>
-            <type>String</type>
-            <null_value>??</null_value>
-    </attribute>
-    ...
-
- - -

The key must have only one String type attribute that contains an allowed IP prefix. Other types are not supported yet.

-

For queries, you must use the same functions (dictGetT with a tuple) as for dictionaries with composite keys:

-
dictGetT('dict_name', 'attr_name', tuple(ip))
-
- - -

The function takes either UInt32 for IPv4, or FixedString(16) for IPv6:

-
dictGetString('prefix', 'asn', tuple(IPv6StringToNum('2001:db8::1')))
-
- - -

Other types are not supported yet. The function returns the attribute for the prefix that corresponds to this IP address. If there are overlapping prefixes, the most specific one is returned.

-

Data is stored in a trie. It must completely fit into RAM.

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Dictionary updates

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ClickHouse periodically updates the dictionaries. The update interval for fully downloaded dictionaries and the invalidation interval for cached dictionaries are defined in the <lifetime> tag in seconds.

-

Dictionary updates (other than loading for first use) do not block queries. During updates, the old version of a dictionary is used. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.

-

Example of settings:

-
<dictionary>
-    ...
-    <lifetime>300</lifetime>
-    ...
-</dictionary>
-
- - -

Setting <lifetime> 0</lifetime> prevents updating dictionaries.

-

You can set a time interval for upgrades, and ClickHouse will choose a uniformly random time within this range. This is necessary in order to distribute the load on the dictionary source when upgrading on a large number of servers.

-

Example of settings:

-
<dictionary>
-    ...
-    <lifetime>
-        <min>300</min>
-        <max>360</max>
-    </lifetime>
-    ...
-</dictionary>
-
- - -

When upgrading the dictionaries, the ClickHouse server applies different logic depending on the type of source:

-
-
    -
  • For a text file, it checks the time of modification. If the time differs from the previously recorded time, the dictionary is updated.
    • -
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    • -
  • Dictionaries from other sources are updated every time by default.
    • -
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-

For MySQL (InnoDB) and ODBC sources, you can set up a query that will update the dictionaries only if they really changed, rather than each time. To do this, follow these steps:

-
-
    -
  • The dictionary table must have a field that always changes when the source data is updated.
    • -
  • The settings of the source must specify a query that retrieves the changing field. The ClickHouse server interprets the query result as a row, and if this row has changed relative to its previous state, the dictionary is updated. Specify the query in the <invalidate_query> field in the settings for the source.
    • -
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-

Example of settings:

-
<dictionary>
-    ...
-    <odbc>
-      ...
-      <invalidate_query>SELECT update_time FROM dictionary_source where id = 1</invalidate_query>
-    </odbc>
-    ...
-</dictionary>
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An external dictionary can be connected from many different sources.

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The configuration looks like this:

-
<yandex>
-  <dictionary>
-    ...
-    <source>
-      <source_type>
-        <!-- Source configuration -->
-      </source_type>
-    </source>
-    ...
-  </dictionary>
-  ...
-</yandex>
-
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The source is configured in the source section.

-

Types of sources (source_type):

- -

-

Local file

-

Example of settings:

-
<source>
-  <file>
-    <path>/opt/dictionaries/os.tsv</path>
-    <format>TabSeparated</format>
-  </file>
-</source>
-
- - -

Setting fields:

-
    -
  • path – The absolute path to the file.
    • -
  • format – The file format. All the formats described in "Formats" are supported.
    • -
-

-

Executable file

-

Working with executable files depends on how the dictionary is stored in memory. If the dictionary is stored using cache and complex_key_cache, ClickHouse requests the necessary keys by sending a request to the executable file's STDIN.

-

Example of settings:

-
<source>
-    <executable>
-        <command>cat /opt/dictionaries/os.tsv</command>
-        <format>TabSeparated</format>
-    </executable>
-</source>
-
- - -

Setting fields:

-
    -
  • command – The absolute path to the executable file, or the file name (if the program directory is written to PATH).
    • -
  • format – The file format. All the formats described in "Formats" are supported.
    • -
-

-

HTTP(s)

-

Working with an HTTP(s) server depends on how the dictionary is stored in memory. If the dictionary is stored using cache and complex_key_cache, ClickHouse requests the necessary keys by sending a request via the POST method.

-

Example of settings:

-
<source>
-    <http>
-        <url>http://[::1]/os.tsv</url>
-        <format>TabSeparated</format>
-    </http>
-</source>
-
- - -

In order for ClickHouse to access an HTTPS resource, you must configure openSSL in the server configuration.

-

Setting fields:

-
    -
  • url – The source URL.
    • -
  • format – The file format. All the formats described in "Formats" are supported.
    • -
-

-

ODBC

-

You can use this method to connect any database that has an ODBC driver.

-

Example of settings:

-
<odbc>
-    <db>DatabaseName</db>
-    <table>TableName</table>
-    <connection_string>DSN=some_parameters</connection_string>
-    <invalidate_query>SQL_QUERY</invalidate_query>
-</odbc>
-
- - -

Setting fields:

-
    -
  • db – Name of the database. Omit it if the database name is set in the <connection_string> parameters.
    • -
  • table – Name of the table.
    • -
  • connection_string – Connection string.
    • -
  • invalidate_query – Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries.
    • -
-

Example of connecting PostgreSQL

-

Ubuntu OS.

-

Installing unixODBC and the ODBC driver for PostgreSQL:

-
sudo apt-get install -y unixodbc odbcinst odbc-postgresql
-
- - -

Configuring /etc/odbc.ini (or ~/.odbc.ini):

-
    [DEFAULT]
-    Driver = myconnection
-
-    [myconnection]
-    Description         = PostgreSQL connection to my_db
-    Driver              = PostgreSQL Unicode
-    Database            = my_db
-    Servername          = 127.0.0.1
-    UserName            = username
-    Password            = password
-    Port                = 5432
-    Protocol            = 9.3
-    ReadOnly            = No
-    RowVersioning       = No
-    ShowSystemTables    = No
-    ConnSettings        =
-
- - -

The dictionary configuration in ClickHouse:

-
<dictionary>
-    <name>table_name</name>
-    <source>
-    <odbc>
-        <!-- You can specifiy the following parameters in connection_string: -->
-        <!-- DSN=myconnection;UID=username;PWD=password;HOST=127.0.0.1;PORT=5432;DATABASE=my_db -->
-            <connection_string>DSN=myconnection</connection_string>
-            <table>postgresql_table</table>
-        </odbc>
-    </source>
-    <lifetime>
-        <min>300</min>
-        <max>360</max>
-    </lifetime>
-    <layout>
-        <hashed/>
-    </layout>
-    <structure>
-        <id>
-            <name>id</name>
-        </id>
-        <attribute>
-            <name>some_column</name>
-            <type>UInt64</type>
-            <null_value>0</null_value>
-        </attribute>
-    </structure>
-</dictionary>
-
- - -

You may need to edit odbc.ini to specify the full path to the library with the driver DRIVER=/usr/local/lib/psqlodbcw.so.

-

Example of connecting MS SQL Server

-

Ubuntu OS.

-

Installing the driver: :

-
    sudo apt-get install tdsodbc freetds-bin sqsh
-
- - -

Configuring the driver: :

-
    $ cat /etc/freetds/freetds.conf 
-    ...
-
-    [MSSQL]
-    host = 192.168.56.101
-    port = 1433
-    tds version = 7.0
-    client charset = UTF-8
-
-    $ cat /etc/odbcinst.ini 
-    ...
-
-    [FreeTDS]
-    Description     = FreeTDS
-    Driver          = /usr/lib/x86_64-linux-gnu/odbc/libtdsodbc.so
-    Setup           = /usr/lib/x86_64-linux-gnu/odbc/libtdsS.so
-    FileUsage       = 1
-    UsageCount      = 5
-
-    $ cat ~/.odbc.ini 
-    ...
-
-    [MSSQL]
-    Description     = FreeTDS
-    Driver          = FreeTDS
-    Servername      = MSSQL
-    Database        = test
-    UID             = test
-    PWD             = test
-    Port            = 1433
-
- - -

Configuring the dictionary in ClickHouse:

-
<yandex>
-    <dictionary>
-        <name>test</name>
-        <source>
-            <odbc>
-                <table>dict</table>
-                <connection_string>DSN=MSSQL;UID=test;PWD=test</connection_string>
-            </odbc>
-        </source>
-
-        <lifetime>
-            <min>300</min>
-            <max>360</max>
-        </lifetime>
-
-        <layout>
-            <flat />
-        </layout>
-
-        <structure>
-            <id>
-                <name>k</name>
-            </id>
-            <attribute>
-                <name>s</name>
-                <type>String</type>
-                <null_value></null_value>
-            </attribute>
-        </structure>
-    </dictionary>
-</yandex>
-
- - -

DBMS

-

-

MySQL

-

Example of settings:

-
<source>
-  <mysql>
-      <port>3306</port>
-      <user>clickhouse</user>
-      <password>qwerty</password>
-      <replica>
-          <host>example01-1</host>
-          <priority>1</priority>
-      </replica>
-      <replica>
-          <host>example01-2</host>
-          <priority>1</priority>
-      </replica>
-      <db>db_name</db>
-      <table>table_name</table>
-      <where>id=10</where>
-      <invalidate_query>SQL_QUERY</invalidate_query>
-  </mysql>
-</source>
-
- - -

Setting fields:

-
    -
  • -

    port – The port on the MySQL server. You can specify it for all replicas, or for each one individually (inside <replica>).

    -
    • -
  • -

    user – Name of the MySQL user. You can specify it for all replicas, or for each one individually (inside <replica>).

    -
    • -
  • -

    password – Password of the MySQL user. You can specify it for all replicas, or for each one individually (inside <replica>).

    -
    • -
  • -

    replica – Section of replica configurations. There can be multiple sections.

    -
    • -
  • replica/host – The MySQL host.
    • -
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    -
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MySQL can be connected on a local host via sockets. To do this, set host and socket.

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Example of settings:

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<source>
-  <mysql>
-      <host>localhost</host>
-      <socket>/path/to/socket/file.sock</socket>
-      <user>clickhouse</user>
-      <password>qwerty</password>
-      <db>db_name</db>
-      <table>table_name</table>
-      <where>id=10</where>
-      <invalidate_query>SQL_QUERY</invalidate_query>
-  </mysql>
-</source>
-
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ClickHouse

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Example of settings:

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<source>
-    <clickhouse>
-        <host>example01-01-1</host>
-        <port>9000</port>
-        <user>default</user>
-        <password></password>
-        <db>default</db>
-        <table>ids</table>
-        <where>id=10</where>
-    </clickhouse>
-</source>
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    -
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    • -
  • port – The port on the ClickHouse server.
    • -
  • user – Name of the ClickHouse user.
    • -
  • password – Password of the ClickHouse user.
    • -
  • db – Name of the database.
    • -
  • table – Name of the table.
    • -
  • where – The selection criteria. May be omitted.
    • -
-

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MongoDB

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Example of settings:

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<source>
-    <mongodb>
-        <host>localhost</host>
-        <port>27017</port>
-        <user></user>
-        <password></password>
-        <db>test</db>
-        <collection>dictionary_source</collection>
-    </mongodb>
-</source>
-
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Setting fields:

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    -
  • host – The MongoDB host.
    • -
  • port – The port on the MongoDB server.
    • -
  • user – Name of the MongoDB user.
    • -
  • password – Password of the MongoDB user.
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  • collection – Name of the collection.
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The <structure> clause describes the dictionary key and fields available for queries.

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Overall structure:

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<dictionary>
-    <structure>
-        <id>
-            <name>Id</name>
-        </id>
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-            <!-- Attribute parameters -->
-        </attribute>
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Columns are described in the structure:

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Key

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ClickHouse supports the following types of keys:

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Numeric key

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Format: UInt64.

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Configuration example:

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<id>
-    <name>Id</name>
-</id>
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    • -
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Composite key

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The key can be a tuple from any types of fields. The layout in this case must be complex_key_hashed or complex_key_cache.

-
-A composite key can consist of a single element. This makes it possible to use a string as the key, for instance. -
- -

The key structure is set in the element <key>. Key fields are specified in the same format as the dictionary attributes. Example:

-
<structure>
-    <key>
-        <attribute>
-            <name>field1</name>
-            <type>String</type>
-        </attribute>
-        <attribute>
-            <name>field2</name>
-            <type>UInt32</type>
-        </attribute>
-        ...
-    </key>
-...
-
- - -

For a query to the dictGet* function, a tuple is passed as the key. Example: dictGetString('dict_name', 'attr_name', tuple('string for field1', num_for_field2)).

-

-

Attributes

-

Configuration example:

-
<structure>
-    ...
-    <attribute>
-        <name>Name</name>
-        <type>Type</type>
-        <null_value></null_value>
-        <expression>rand64()</expression>
-        <hierarchical>true</hierarchical>
-        <injective>true</injective>
-        <is_object_id>true</is_object_id>
-    </attribute>
-</structure>
-
- - -

Configuration fields:

-
    -
  • name – The column name.
    • -
  • type – The column type. Sets the method for interpreting data in the source. For example, for MySQL, the field might be TEXT, VARCHAR, or BLOB in the source table, but it can be uploaded as String.
    • -
  • null_value – The default value for a non-existing element. In the example, it is an empty string.
    • -
  • expression – The attribute can be an expression. The tag is not required.
    • -
  • hierarchical – Hierarchical support. Mirrored to the parent identifier. By default, false.
    • -
  • injective – Whether the id -> attribute image is injective. If true, then you can optimize the GROUP BY clause. By default, false.
    • -
  • is_object_id – Whether the query is executed for a MongoDB document by ObjectID.
    • -
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Dictionaries

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A dictionary is a mapping (key -> attributes) that can be used in a query as functions. -You can think of this as a more convenient and efficient type of JOIN with dimension tables.

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There are built-in (internal) and add-on (external) dictionaries.

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Internal dictionaries

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ClickHouse contains a built-in feature for working with a geobase.

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This allows you to:

-
    -
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    • -
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    • -
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    • -
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    • -
-

All the functions support "translocality," the ability to simultaneously use different perspectives on region ownership. For more information, see the section "Functions for working with Yandex.Metrica dictionaries".

-

The internal dictionaries are disabled in the default package. -To enable them, uncomment the parameters path_to_regions_hierarchy_file and path_to_regions_names_files in the server configuration file.

-

The geobase is loaded from text files. -If you work at Yandex, you can follow these instructions to create them: -https://github.yandex-team.ru/raw/Metrika/ClickHouse_private/master/doc/create_embedded_geobase_dictionaries.txt

-

Put the regions_hierarchy*.txt files in the path_to_regions_hierarchy_file directory. This configuration parameter must contain the path to the regions_hierarchy.txt file (the default regional hierarchy), and the other files (regions_hierarchy_ua.txt) must be located in the same directory.

-

Put the regions_names_*.txt files in the path_to_regions_names_files directory.

-

You can also create these files yourself. The file format is as follows:

-

regions_hierarchy*.txt: TabSeparated (no header), columns:

-
    -
  • Region ID (UInt32)
    • -
  • Parent region ID (UInt32)
    • -
  • Region type (UInt8): 1 - continent, 3 - country, 4 - federal district, 5 - region, 6 - city; other types don't have values.
    • -
  • Population (UInt32) - Optional column.
    • -
-

regions_names_*.txt: TabSeparated (no header), columns:

-
    -
  • Region ID (UInt32)
    • -
  • Region name (String) - Can't contain tabs or line feeds, even escaped ones.
    • -
-

A flat array is used for storing in RAM. For this reason, IDs shouldn't be more than a million.

-

Dictionaries can be updated without restarting the server. However, the set of available dictionaries is not updated. -For updates, the file modification times are checked. If a file has changed, the dictionary is updated. -The interval to check for changes is configured in the 'builtin_dictionaries_reload_interval' parameter. -Dictionary updates (other than loading at first use) do not block queries. During updates, queries use the old versions of dictionaries. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.

-

We recommend periodically updating the dictionaries with the geobase. During an update, generate new files and write them to a separate location. When everything is ready, rename them to the files used by the server.

-

There are also functions for working with OS identifiers and Yandex.Metrica search engines, but they shouldn't be used.

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CapnProto

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Cap'n Proto is a binary message format similar to Protocol Buffers and Thrift, but not like JSON or MessagePack.

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Cap'n Proto messages are strictly typed and not self-describing, meaning they need an external schema description. The schema is applied on the fly and cached for each query.

-
SELECT SearchPhrase, count() AS c FROM test.hits
-       GROUP BY SearchPhrase FORMAT CapnProto SETTINGS schema = 'schema:Message'
-
- - -

Where schema.capnp looks like this:

-
struct Message {
-  SearchPhrase @0 :Text;
-  c @1 :Uint64;
-}
-
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Schema files are in the file that is located in the directory specified in format_schema_path in the server configuration.

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Deserialization is effective and usually doesn't increase the system load.

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CSV

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Comma Separated Values format (RFC).

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When formatting, rows are enclosed in double quotes. A double quote inside a string is output as two double quotes in a row. There are no other rules for escaping characters. Date and date-time are enclosed in double quotes. Numbers are output without quotes. Values ​​are separated by a delimiter*. Rows are separated using the Unix line feed (LF). Arrays are serialized in CSV as follows: first the array is serialized to a string as in TabSeparated format, and then the resulting string is output to CSV in double quotes. Tuples in CSV format are serialized as separate columns (that is, their nesting in the tuple is lost).

-

*By default — ,. See a format_csv_delimiter setting for additional info.

-

When parsing, all values can be parsed either with or without quotes. Both double and single quotes are supported. Rows can also be arranged without quotes. In this case, they are parsed up to a delimiter or line feed (CR or LF). In violation of the RFC, when parsing rows without quotes, the leading and trailing spaces and tabs are ignored. For the line feed, Unix (LF), Windows (CR LF) and Mac OS Classic (CR LF) are all supported.

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The CSV format supports the output of totals and extremes the same way as TabSeparated.

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Also prints the header row, similar to TabSeparatedWithNames.

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Formats

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The format determines how data is returned to you after SELECTs (how it is written and formatted by the server), and how it is accepted for INSERTs (how it is read and parsed by the server).

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JSON

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Outputs data in JSON format. Besides data tables, it also outputs column names and types, along with some additional information: the total number of output rows, and the number of rows that could have been output if there weren't a LIMIT. Example:

-
SELECT SearchPhrase, count() AS c FROM test.hits GROUP BY SearchPhrase WITH TOTALS ORDER BY c DESC LIMIT 5 FORMAT JSON
-
- - -
{
-        "meta":
-        [
-                {
-                        "name": "SearchPhrase",
-                        "type": "String"
-                },
-                {
-                        "name": "c",
-                        "type": "UInt64"
-                }
-        ],
-
-        "data":
-        [
-                {
-                        "SearchPhrase": "",
-                        "c": "8267016"
-                },
-                {
-                        "SearchPhrase": "bathroom interior design",
-                        "c": "2166"
-                },
-                {
-                        "SearchPhrase": "yandex",
-                        "c": "1655"
-                },
-                {
-                        "SearchPhrase": "spring 2014 fashion",
-                        "c": "1549"
-                },
-                {
-                        "SearchPhrase": "freeform photos",
-                        "c": "1480"
-                }
-        ],
-
-        "totals":
-        {
-                "SearchPhrase": "",
-                "c": "8873898"
-        },
-
-        "extremes":
-        {
-                "min":
-                {
-                        "SearchPhrase": "",
-                        "c": "1480"
-                },
-                "max":
-                {
-                        "SearchPhrase": "",
-                        "c": "8267016"
-                }
-        },
-
-        "rows": 5,
-
-        "rows_before_limit_at_least": 141137
-}
-
- - -

The JSON is compatible with JavaScript. To ensure this, some characters are additionally escaped: the slash / is escaped as \/; alternative line breaks U+2028 and U+2029, which break some browsers, are escaped as \uXXXX. ASCII control characters are escaped: backspace, form feed, line feed, carriage return, and horizontal tab are replaced with \b, \f, \n, \r, \t , as well as the remaining bytes in the 00-1F range using \uXXXX sequences. Invalid UTF-8 sequences are changed to the replacement character � so the output text will consist of valid UTF-8 sequences. For compatibility with JavaScript, Int64 and UInt64 integers are enclosed in double quotes by default. To remove the quotes, you can set the configuration parameter output_format_json_quote_64bit_integers to 0.

-

rows – The total number of output rows.

-

rows_before_limit_at_least The minimal number of rows there would have been without LIMIT. Output only if the query contains LIMIT. -If the query contains GROUP BY, rows_before_limit_at_least is the exact number of rows there would have been without a LIMIT.

-

totals – Total values (when using WITH TOTALS).

-

extremes – Extreme values (when extremes is set to 1).

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This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). -See also the JSONEachRow format.

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Differs from JSON only in that data rows are output in arrays, not in objects.

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Example:

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-        "meta":
-        [
-                {
-                        "name": "SearchPhrase",
-                        "type": "String"
-                },
-                {
-                        "name": "c",
-                        "type": "UInt64"
-                }
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-        "data":
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-                ["", "8267016"],
-                ["bathroom interior design", "2166"],
-                ["yandex", "1655"],
-                ["spring 2014 fashion", "1549"],
-                ["freeform photos", "1480"]
-        ],
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-        "totals": ["","8873898"],
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-        "extremes":
-        {
-                "min": ["","1480"],
-                "max": ["","8267016"]
-        },
-
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-
-        "rows_before_limit_at_least": 141137
-}
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This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). -See also the JSONEachRow format.

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JSONEachRow

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Outputs data as separate JSON objects for each row (newline delimited JSON).

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{"SearchPhrase":"","count()":"8267016"}
-{"SearchPhrase":"bathroom interior design","count()":"2166"}
-{"SearchPhrase":"yandex","count()":"1655"}
-{"SearchPhrase":"spring 2014 fashion","count()":"1549"}
-{"SearchPhrase":"freeform photo","count()":"1480"}
-{"SearchPhrase":"angelina jolie","count()":"1245"}
-{"SearchPhrase":"omsk","count()":"1112"}
-{"SearchPhrase":"photos of dog breeds","count()":"1091"}
-{"SearchPhrase":"curtain design","count()":"1064"}
-{"SearchPhrase":"baku","count()":"1000"}
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Unlike the JSON format, there is no substitution of invalid UTF-8 sequences. Any set of bytes can be output in the rows. This is necessary so that data can be formatted without losing any information. Values are escaped in the same way as for JSON.

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Native

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The most efficient format. Data is written and read by blocks in binary format. For each block, the number of rows, number of columns, column names and types, and parts of columns in this block are recorded one after another. In other words, this format is "columnar" – it doesn't convert columns to rows. This is the format used in the native interface for interaction between servers, for using the command-line client, and for C++ clients.

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Outputs data as Unicode-art tables, also using ANSI-escape sequences for setting colors in the terminal. -A full grid of the table is drawn, and each row occupies two lines in the terminal. -Each result block is output as a separate table. This is necessary so that blocks can be output without buffering results (buffering would be necessary in order to pre-calculate the visible width of all the values). -To avoid dumping too much data to the terminal, only the first 10,000 rows are printed. If the number of rows is greater than or equal to 10,000, the message "Showed first 10 000" is printed. -This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

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The Pretty format supports outputting total values (when using WITH TOTALS) and extremes (when 'extremes' is set to 1). In these cases, total values and extreme values are output after the main data, in separate tables. Example (shown for the PrettyCompact format):

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┌──EventDate─┬───────c─┐
-│ 2014-03-17 │ 1406958 │
-│ 2014-03-18 │ 1383658 │
-│ 2014-03-19 │ 1405797 │
-│ 2014-03-20 │ 1353623 │
-│ 2014-03-21 │ 1245779 │
-│ 2014-03-22 │ 1031592 │
-│ 2014-03-23 │ 1046491 │
-└────────────┴─────────┘
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-┌──EventDate─┬───────c─┐
-│ 0000-00-00 │ 8873898 │
-└────────────┴─────────┘
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-┌──EventDate─┬───────c─┐
-│ 2014-03-17 │ 1031592 │
-│ 2014-03-23 │ 1406958 │
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PrettyCompact

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Differs from Pretty in that the grid is drawn between rows and the result is more compact. -This format is used by default in the command-line client in interactive mode.

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Differs from PrettyCompact in that up to 10,000 rows are buffered, then output as a single table, not by blocks.

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PrettyNoEscapes

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Differs from Pretty in that ANSI-escape sequences aren't used. This is necessary for displaying this format in a browser, as well as for using the 'watch' command-line utility.

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PrettyCompactNoEscapes

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PrettySpaceNoEscapes

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Differs from PrettyCompact in that whitespace (space characters) is used instead of the grid.

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Formats and parses data by row in binary format. Rows and values are listed consecutively, without separators. -This format is less efficient than the Native format, since it is row-based.

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Integers use fixed-length little endian representation. For example, UInt64 uses 8 bytes. -DateTime is represented as UInt32 containing the Unix timestamp as the value. -Date is represented as a UInt16 object that contains the number of days since 1970-01-01 as the value. -String is represented as a varint length (unsigned LEB128), followed by the bytes of the string. -FixedString is represented simply as a sequence of bytes.

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In TabSeparated format, data is written by row. Each row contains values separated by tabs. Each value is follow by a tab, except the last value in the row, which is followed by a line feed. Strictly Unix line feeds are assumed everywhere. The last row also must contain a line feed at the end. Values are written in text format, without enclosing quotation marks, and with special characters escaped.

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Integer numbers are written in decimal form. Numbers can contain an extra "+" character at the beginning (ignored when parsing, and not recorded when formatting). Non-negative numbers can't contain the negative sign. When reading, it is allowed to parse an empty string as a zero, or (for signed types) a string consisting of just a minus sign as a zero. Numbers that do not fit into the corresponding data type may be parsed as a different number, without an error message.

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Floating-point numbers are written in decimal form. The dot is used as the decimal separator. Exponential entries are supported, as are 'inf', '+inf', '-inf', and 'nan'. An entry of floating-point numbers may begin or end with a decimal point. -During formatting, accuracy may be lost on floating-point numbers. -During parsing, it is not strictly required to read the nearest machine-representable number.

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Dates are written in YYYY-MM-DD format and parsed in the same format, but with any characters as separators. -Dates with times are written in the format YYYY-MM-DD hh:mm:ss and parsed in the same format, but with any characters as separators. -This all occurs in the system time zone at the time the client or server starts (depending on which one formats data). For dates with times, daylight saving time is not specified. So if a dump has times during daylight saving time, the dump does not unequivocally match the data, and parsing will select one of the two times. -During a read operation, incorrect dates and dates with times can be parsed with natural overflow or as null dates and times, without an error message.

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As an exception, parsing dates with times is also supported in Unix timestamp format, if it consists of exactly 10 decimal digits. The result is not time zone-dependent. The formats YYYY-MM-DD hh:mm:ss and NNNNNNNNNN are differentiated automatically.

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Strings are output with backslash-escaped special characters. The following escape sequences are used for output: \b, \f, \r, \n, \t, \0, \', \\. Parsing also supports the sequences \a, \v, and \xHH (hex escape sequences) and any \c sequences, where c is any character (these sequences are converted to c). Thus, reading data supports formats where a line feed can be written as \n or \, or as a line feed. For example, the string Hello world with a line feed between the words instead of a space can be parsed in any of the following variations:

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The second variant is supported because MySQL uses it when writing tab-separated dumps.

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The minimum set of characters that you need to escape when passing data in TabSeparated format: tab, line feed (LF) and backslash.

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Only a small set of symbols are escaped. You can easily stumble onto a string value that your terminal will ruin in output.

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The TabSeparated format is convenient for processing data using custom programs and scripts. It is used by default in the HTTP interface, and in the command-line client's batch mode. This format also allows transferring data between different DBMSs. For example, you can get a dump from MySQL and upload it to ClickHouse, or vice versa.

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The TabSeparated format supports outputting total values (when using WITH TOTALS) and extreme values (when 'extremes' is set to 1). In these cases, the total values and extremes are output after the main data. The main result, total values, and extremes are separated from each other by an empty line. Example:

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2014-03-17      1406958
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-2014-03-22      1031592
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-2014-03-23      1406958
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Differs from TabSeparated format in that the rows are written without escaping. -This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

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Differs from the TabSeparated format in that the column names are written to the first row, while the column types are in the second row. -During parsing, the first and second rows are completely ignored.

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Similar to TabSeparated, but outputs a value in name=value format. Names are escaped the same way as in TabSeparated format, and the = symbol is also escaped.

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SearchPhrase=   count()=8267016
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-SearchPhrase=freeform photos       count()=1480
-SearchPhrase=angelina jolia    count()=1245
-SearchPhrase=omsk       count()=1112
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Both data output and parsing are supported in this format. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted – they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults.

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Prints every row in brackets. Rows are separated by commas. There is no comma after the last row. The values inside the brackets are also comma-separated. Numbers are output in decimal format without quotes. Arrays are output in square brackets. Strings, dates, and dates with times are output in quotes. Escaping rules and parsing are similar to the TabSeparated format. During formatting, extra spaces aren't inserted, but during parsing, they are allowed and skipped (except for spaces inside array values, which are not allowed).

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The minimum set of characters that you need to escape when passing data in Values ​​format: single quotes and backslashes.

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This is the format that is used in INSERT INTO t VALUES ..., but you can also use it for formatting query results.

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Prints each value on a separate line with the column name specified. This format is convenient for printing just one or a few rows, if each row consists of a large number of columns. -This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

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VerticalRaw

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Differs from Vertical format in that the rows are not escaped. -This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

-

Examples:

-
:) SHOW CREATE TABLE geonames FORMAT VerticalRaw;
-Row 1:
-──────
-statement: CREATE TABLE default.geonames ( geonameid UInt32, date Date DEFAULT CAST('2017-12-08' AS Date)) ENGINE = MergeTree(date, geonameid, 8192)
-
-:) SELECT 'string with \'quotes\' and \t with some special \n characters' AS test FORMAT VerticalRaw;
-Row 1:
-──────
-test: string with 'quotes' and   with some special
- characters
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:) SELECT 'string with \'quotes\' and \t with some special \n characters' AS test FORMAT Vertical;
-Row 1:
-──────
-test: string with \'quotes\' and \t with some special \n characters
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XML format is suitable only for output, not for parsing. Example:

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<?xml version='1.0' encoding='UTF-8' ?>
-<result>
-        <meta>
-                <columns>
-                        <column>
-                                <name>SearchPhrase</name>
-                                <type>String</type>
-                        </column>
-                        <column>
-                                <name>count()</name>
-                                <type>UInt64</type>
-                        </column>
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-                <row>
-                        <SearchPhrase></SearchPhrase>
-                        <field>8267016</field>
-                </row>
-                <row>
-                        <SearchPhrase>bathroom interior design</SearchPhrase>
-                        <field>2166</field>
-                </row>
-                <row>
-                        <SearchPhrase>yandex</SearchPhrase>
-                        <field>1655</field>
-                </row>
-                <row>
-                        <SearchPhrase>spring 2014 fashion</SearchPhrase>
-                        <field>1549</field>
-                </row>
-                <row>
-                        <SearchPhrase>freeform photos</SearchPhrase>
-                        <field>1480</field>
-                </row>
-                <row>
-                        <SearchPhrase>angelina jolie</SearchPhrase>
-                        <field>1245</field>
-                </row>
-                <row>
-                        <SearchPhrase>omsk</SearchPhrase>
-                        <field>1112</field>
-                </row>
-                <row>
-                        <SearchPhrase>photos of dog breeds</SearchPhrase>
-                        <field>1091</field>
-                </row>
-                <row>
-                        <SearchPhrase>curtain design</SearchPhrase>
-                        <field>1064</field>
-                </row>
-                <row>
-                        <SearchPhrase>baku</SearchPhrase>
-                        <field>1000</field>
-                </row>
-        </data>
-        <rows>10</rows>
-        <rows_before_limit_at_least>141137</rows_before_limit_at_least>
-</result>
-
- - -

If the column name does not have an acceptable format, just 'field' is used as the element name. In general, the XML structure follows the JSON structure. -Just as for JSON, invalid UTF-8 sequences are changed to the replacement character � so the output text will consist of valid UTF-8 sequences.

-

In string values, the characters < and & are escaped as < and &.

-

Arrays are output as <array><elem>Hello</elem><elem>World</elem>...</array>, -and tuples as <tuple><elem>Hello</elem><elem>World</elem>...</tuple>.

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Arithmetic functions

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For all arithmetic functions, the result type is calculated as the smallest number type that the result fits in, if there is such a type. The minimum is taken simultaneously based on the number of bits, whether it is signed, and whether it floats. If there are not enough bits, the highest bit type is taken.

-

Example:

-
SELECT toTypeName(0), toTypeName(0 + 0), toTypeName(0 + 0 + 0), toTypeName(0 + 0 + 0 + 0)
-
- - -
┌─toTypeName(0)─┬─toTypeName(plus(0, 0))─┬─toTypeName(plus(plus(0, 0), 0))─┬─toTypeName(plus(plus(plus(0, 0), 0), 0))─┐
-│ UInt8         │ UInt16                 │ UInt32                          │ UInt64                                   │
-└───────────────┴────────────────────────┴─────────────────────────────────┴──────────────────────────────────────────┘
-
- - -

Arithmetic functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.

-

Overflow is produced the same way as in C++.

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plus(a, b), a + b operator

-

Calculates the sum of the numbers. -You can also add integer numbers with a date or date and time. In the case of a date, adding an integer means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds.

-

minus(a, b), a - b operator

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Calculates the difference. The result is always signed.

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You can also calculate integer numbers from a date or date with time. The idea is the same – see above for 'plus'.

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multiply(a, b), a * b operator

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Calculates the product of the numbers.

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divide(a, b), a / b operator

-

Calculates the quotient of the numbers. The result type is always a floating-point type. -It is not integer division. For integer division, use the 'intDiv' function. -When dividing by zero you get 'inf', '-inf', or 'nan'.

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intDiv(a, b)

-

Calculates the quotient of the numbers. Divides into integers, rounding down (by the absolute value). -An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

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intDivOrZero(a, b)

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Differs from 'intDiv' in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one.

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modulo(a, b), a % b operator

-

Calculates the remainder after division. -If arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion. -The remainder is taken in the same sense as in C++. Truncated division is used for negative numbers. -An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

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negate(a), -a operator

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Calculates a number with the reverse sign. The result is always signed.

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abs(a)

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Calculates the absolute value of the number (a). That is, if a < 0, it returns -a. For unsigned types it doesn't do anything. For signed integer types, it returns an unsigned number.

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gcd(a, b)

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Returns the greatest common divisor of the numbers. -An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

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lcm(a, b)

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Returns the least common multiple of the numbers. -An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

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Functions for working with arrays

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empty

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Returns 1 for an empty array, or 0 for a non-empty array. -The result type is UInt8. -The function also works for strings.

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notEmpty

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Returns 0 for an empty array, or 1 for a non-empty array. -The result type is UInt8. -The function also works for strings.

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length

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Returns the number of items in the array. -The result type is UInt64. -The function also works for strings.

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emptyArrayUInt8, emptyArrayUInt16, emptyArrayUInt32, emptyArrayUInt64

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emptyArrayInt8, emptyArrayInt16, emptyArrayInt32, emptyArrayInt64

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emptyArrayFloat32, emptyArrayFloat64

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emptyArrayDate, emptyArrayDateTime

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emptyArrayString

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Accepts zero arguments and returns an empty array of the appropriate type.

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emptyArrayToSingle

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Accepts an empty array and returns a one-element array that is equal to the default value.

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range(N)

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Returns an array of numbers from 0 to N-1. -Just in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block.

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array(x1, ...), operator [x1, ...]

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Creates an array from the function arguments. -The arguments must be constants and have types that have the smallest common type. At least one argument must be passed, because otherwise it isn't clear which type of array to create. That is, you can't use this function to create an empty array (to do that, use the 'emptyArray*' function described above). -Returns an 'Array(T)' type result, where 'T' is the smallest common type out of the passed arguments.

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arrayConcat

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Combines arrays passed as arguments.

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arrayConcat(arrays)
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    • -
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Example

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SELECT arrayConcat([1, 2], [3, 4], [5, 6]) AS res
-
- - -
┌─res───────────┐
-│ [1,2,3,4,5,6] │
-└───────────────┘
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arrayElement(arr, n), operator arr[n]

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Get the element with the index 'n' from the array 'arr'.'n' must be any integer type. -Indexes in an array begin from one. -Negative indexes are supported. In this case, it selects the corresponding element numbered from the end. For example, 'arr[-1]' is the last item in the array.

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If the index falls outside of the bounds of an array, it returns some default value (0 for numbers, an empty string for strings, etc.).

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has(arr, elem)

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Checks whether the 'arr' array has the 'elem' element. -Returns 0 if the the element is not in the array, or 1 if it is.

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indexOf(arr, x)

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Returns the index of the 'x' element (starting from 1) if it is in the array, or 0 if it is not.

-

countEqual(arr, x)

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Returns the number of elements in the array equal to x. Equivalent to arrayCount (elem-> elem = x, arr).

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arrayEnumerate(arr)

-

Returns the array [1, 2, 3, ..., length (arr) ]

-

This function is normally used with ARRAY JOIN. It allows counting something just once for each array after applying ARRAY JOIN. Example:

-
SELECT
-    count() AS Reaches,
-    countIf(num = 1) AS Hits
-FROM test.hits
-ARRAY JOIN
-    GoalsReached,
-    arrayEnumerate(GoalsReached) AS num
-WHERE CounterID = 160656
-LIMIT 10
-
- - -
┌─Reaches─┬──Hits─┐
-│   95606 │ 31406 │
-└─────────┴───────┘
-
- - -

In this example, Reaches is the number of conversions (the strings received after applying ARRAY JOIN), and Hits is the number of pageviews (strings before ARRAY JOIN). In this particular case, you can get the same result in an easier way:

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SELECT
-    sum(length(GoalsReached)) AS Reaches,
-    count() AS Hits
-FROM test.hits
-WHERE (CounterID = 160656) AND notEmpty(GoalsReached)
-
- - -
┌─Reaches─┬──Hits─┐
-│   95606 │ 31406 │
-└─────────┴───────┘
-
- - -

This function can also be used in higher-order functions. For example, you can use it to get array indexes for elements that match a condition.

-

arrayEnumerateUniq(arr, ...)

-

Returns an array the same size as the source array, indicating for each element what its position is among elements with the same value. -For example: arrayEnumerateUniq([10, 20, 10, 30]) = [1, 1, 2, 1].

-

This function is useful when using ARRAY JOIN and aggregation of array elements. -Example:

-
SELECT
-    Goals.ID AS GoalID,
-    sum(Sign) AS Reaches,
-    sumIf(Sign, num = 1) AS Visits
-FROM test.visits
-ARRAY JOIN
-    Goals,
-    arrayEnumerateUniq(Goals.ID) AS num
-WHERE CounterID = 160656
-GROUP BY GoalID
-ORDER BY Reaches DESC
-LIMIT 10
-
- - -
┌──GoalID─┬─Reaches─┬─Visits─┐
-│   53225 │    3214 │   1097 │
-│ 2825062 │    3188 │   1097 │
-│   56600 │    2803 │    488 │
-│ 1989037 │    2401 │    365 │
-│ 2830064 │    2396 │    910 │
-│ 1113562 │    2372 │    373 │
-│ 3270895 │    2262 │    812 │
-│ 1084657 │    2262 │    345 │
-│   56599 │    2260 │    799 │
-│ 3271094 │    2256 │    812 │
-└─────────┴─────────┴────────┘
-
- - -

In this example, each goal ID has a calculation of the number of conversions (each element in the Goals nested data structure is a goal that was reached, which we refer to as a conversion) and the number of sessions. Without ARRAY JOIN, we would have counted the number of sessions as sum(Sign). But in this particular case, the rows were multiplied by the nested Goals structure, so in order to count each session one time after this, we apply a condition to the value of the arrayEnumerateUniq(Goals.ID) function.

-

The arrayEnumerateUniq function can take multiple arrays of the same size as arguments. In this case, uniqueness is considered for tuples of elements in the same positions in all the arrays.

-
SELECT arrayEnumerateUniq([1, 1, 1, 2, 2, 2], [1, 1, 2, 1, 1, 2]) AS res
-
- - -
┌─res───────────┐
-│ [1,2,1,1,2,1] │
-└───────────────┘
-
- - -

This is necessary when using ARRAY JOIN with a nested data structure and further aggregation across multiple elements in this structure.

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arrayPopBack

-

Removes the last item from the array.

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arrayPopBack(array)
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  • array – Array.
    • -
-

Example

-
SELECT arrayPopBack([1, 2, 3]) AS res
-
- - -
┌─res───┐
-│ [1,2] │
-└───────┘
-
- - -

arrayPopFront

-

Removes the first item from the array.

-
arrayPopFront(array)
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Arguments

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    -
  • array – Array.
    • -
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Example

-
SELECT arrayPopFront([1, 2, 3]) AS res
-
- - -
┌─res───┐
-│ [2,3] │
-└───────┘
-
- - -

arrayPushBack

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Adds one item to the end of the array.

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arrayPushBack(array, single_value)
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Arguments

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  • array – Array.
    • -
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    • -
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Example

-
SELECT arrayPushBack(['a'], 'b') AS res
-
- - -
┌─res───────┐
-│ ['a','b'] │
-└───────────┘
-
- - -

arrayPushFront

-

Adds one element to the beginning of the array.

-
arrayPushFront(array, single_value)
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Arguments

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    -
  • array – Array.
    • -
  • single_value – A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section "Data types".
    • -
-

Example

-
SELECT arrayPushBack(['b'], 'a') AS res
-
- - -
┌─res───────┐
-│ ['a','b'] │
-└───────────┘
-
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arraySlice

-

Returns a slice of the array.

-
arraySlice(array, offset[, length])
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    • -
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    • -
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    • -
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Example

-
SELECT arraySlice([1, 2, 3, 4, 5], 2, 3) AS res
-
- - -
┌─res─────┐
-│ [2,3,4] │
-└─────────┘
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arrayUniq(arr, ...)

-

If one argument is passed, it counts the number of different elements in the array. -If multiple arguments are passed, it counts the number of different tuples of elements at corresponding positions in multiple arrays.

-

If you want to get a list of unique items in an array, you can use arrayReduce('groupUniqArray', arr).

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arrayJoin(arr)

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A special function. See the section "ArrayJoin function".

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arrayJoin function

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This is a very unusual function.

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Normal functions don't change a set of rows, but just change the values in each row (map). -Aggregate functions compress a set of rows (fold or reduce). -The 'arrayJoin' function takes each row and generates a set of rows (unfold).

-

This function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array. -All the values in columns are simply copied, except the values in the column where this function is applied; it is replaced with the corresponding array value.

-

A query can use multiple arrayJoin functions. In this case, the transformation is performed multiple times.

-

Note the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities.

-

Example:

-
SELECT arrayJoin([1, 2, 3] AS src) AS dst, 'Hello', src
-
- - -
┌─dst─┬─\'Hello\'─┬─src─────┐
-│   1 │ Hello     │ [1,2,3] │
-│   2 │ Hello     │ [1,2,3] │
-│   3 │ Hello     │ [1,2,3] │
-└─────┴───────────┴─────────┘
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Bit functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.

-

The result type is an integer with bits equal to the maximum bits of its arguments. If at least one of the arguments is signed, the result is a signed number. If an argument is a floating-point number, it is cast to Int64.

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bitAnd(a, b)

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bitOr(a, b)

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bitXor(a, b)

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bitNot(a)

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bitShiftRight(a, b)

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Comparison functions always return 0 or 1 (Uint8).

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within each group, but not between different groups.

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For example, you can't compare a date with a string. You have to use a function to convert the string to a date, or vice versa.

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Strings are compared by bytes. A shorter string is smaller than all strings that start with it and that contain at least one more character.

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Note. Up until version 1.1.54134, signed and unsigned numbers were compared the same way as in C++. In other words, you could get an incorrect result in cases like SELECT 9223372036854775807 > -1. This behavior changed in version 1.1.54134 and is now mathematically correct.

-

equals, a = b and a == b operator

-

notEquals, a ! operator= b and a <> b

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less, < operator

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greater, > operator

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lessOrEquals, <= operator

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greaterOrEquals, >= operator

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if(cond, then, else), cond ? operator then : else

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Returns 'then' if cond !or 'else' if cond = 0.'cond' must be UInt 8, and 'then' and 'else' must be a type that has the smallest common type.

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Functions for working with dates and times

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Support for time zones

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All functions for working with the date and time that have a logical use for the time zone can accept a second optional time zone argument. Example: Asia/Yekaterinburg. In this case, they use the specified time zone instead of the local (default) one.

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SELECT
-    toDateTime('2016-06-15 23:00:00') AS time,
-    toDate(time) AS date_local,
-    toDate(time, 'Asia/Yekaterinburg') AS date_yekat,
-    toString(time, 'US/Samoa') AS time_samoa
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┌────────────────time─┬─date_local─┬─date_yekat─┬─time_samoa──────────┐
-│ 2016-06-15 23:00:00 │ 2016-06-15 │ 2016-06-16 │ 2016-06-15 09:00:00 │
-└─────────────────────┴────────────┴────────────┴─────────────────────┘
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toYear

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Converts a date or date with time to a UInt16 number containing the year number (AD).

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toMonth

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Converts a date or date with time to a UInt8 number containing the month number (1-12).

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toDayOfMonth

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-Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31).

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toDayOfWeek

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Converts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7).

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toHour

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Converts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23). -This function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true – even in Moscow the clocks were twice changed at a different time).

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toMinute

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Converts a date with time to a UInt8 number containing the number of the minute of the hour (0-59).

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toSecond

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Converts a date with time to a UInt8 number containing the number of the second in the minute (0-59). -Leap seconds are not accounted for.

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toMonday

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Rounds down a date or date with time to the nearest Monday. -Returns the date.

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toStartOfMonth

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Rounds down a date or date with time to the first day of the month. -Returns the date.

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toStartOfQuarter

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Rounds down a date or date with time to the first day of the quarter. -The first day of the quarter is either 1 January, 1 April, 1 July, or 1 October. -Returns the date.

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toStartOfYear

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Rounds down a date or date with time to the first day of the year. -Returns the date.

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toStartOfMinute

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Rounds down a date with time to the start of the minute.

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toStartOfFiveMinute

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Rounds down a date with time to the start of the hour.

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toStartOfFifteenMinutes

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Rounds down the date with time to the start of the fifteen-minute interval.

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toStartOfHour

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Rounds down a date with time to the start of the hour.

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toStartOfDay

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Rounds down a date with time to the start of the day.

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toTime

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Converts a date with time to a certain fixed date, while preserving the time.

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toRelativeYearNum

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Converts a date with time or date to the number of the year, starting from a certain fixed point in the past.

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toRelativeMonthNum

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Converts a date with time or date to the number of the month, starting from a certain fixed point in the past.

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toRelativeWeekNum

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Converts a date with time or date to the number of the week, starting from a certain fixed point in the past.

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toRelativeDayNum

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Converts a date with time or date to the number of the day, starting from a certain fixed point in the past.

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toRelativeHourNum

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Converts a date with time or date to the number of the hour, starting from a certain fixed point in the past.

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toRelativeMinuteNum

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Converts a date with time or date to the number of the minute, starting from a certain fixed point in the past.

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toRelativeSecondNum

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Converts a date with time or date to the number of the second, starting from a certain fixed point in the past.

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now

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Accepts zero arguments and returns the current time at one of the moments of request execution. -This function returns a constant, even if the request took a long time to complete.

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today

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Accepts zero arguments and returns the current date at one of the moments of request execution. -The same as 'toDate(now())'.

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yesterday

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Accepts zero arguments and returns yesterday's date at one of the moments of request execution. -The same as 'today() - 1'.

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timeSlot

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Rounds the time to the half hour. -This function is specific to Yandex.Metrica, since half an hour is the minimum amount of time for breaking a session into two sessions if a tracking tag shows a single user's consecutive pageviews that differ in time by strictly more than this amount. This means that tuples (the tag ID, user ID, and time slot) can be used to search for pageviews that are included in the corresponding session.

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timeSlots(StartTime, Duration)

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For a time interval starting at 'StartTime' and continuing for 'Duration' seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour. -For example, timeSlots(toDateTime('2012-01-01 12:20:00'), 600) = [toDateTime('2012-01-01 12:00:00'), toDateTime('2012-01-01 12:30:00')]. -This is necessary for searching for pageviews in the corresponding session.

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Encoding functions

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hex

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Accepts arguments of types: String, unsigned integer, Date, or DateTime. Returns a string containing the argument's hexadecimal representation. Uses uppercase letters A-F. Does not use 0x prefixes or h suffixes. For strings, all bytes are simply encoded as two hexadecimal numbers. Numbers are converted to big endian ("human readable") format. For numbers, older zeros are trimmed, but only by entire bytes. For example, hex (1) = '01'. Date is encoded as the number of days since the beginning of the Unix epoch. DateTime is encoded as the number of seconds since the beginning of the Unix epoch.

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unhex(str)

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Accepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits does not have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn't thrown). -If you want to convert the result to a number, you can use the 'reverse' and 'reinterpretAsType' functions.

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UUIDStringToNum(str)

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Accepts a string containing 36 characters in the format 123e4567-e89b-12d3-a456-426655440000, and returns it as a set of bytes in a FixedString(16).

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UUIDNumToString(str)

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Accepts a FixedString(16) value. Returns a string containing 36 characters in text format.

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bitmaskToList(num)

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Accepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order.

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bitmaskToArray(num)

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Accepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order.

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Functions for working with external dictionaries

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For information on connecting and configuring external dictionaries, see "External dictionaries".

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dictGetUInt8, dictGetUInt16, dictGetUInt32, dictGetUInt64

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dictGetInt8, dictGetInt16, dictGetInt32, dictGetInt64

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dictGetFloat32, dictGetFloat64

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dictGetDate, dictGetDateTime

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dictGetUUID

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dictGetString

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dictGetT('dict_name', 'attr_name', id)

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dictGetTOrDefault

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dictGetT('dict_name', 'attr_name', id, default)

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dictIsIn

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dictIsIn('dict_name', child_id, ancestor_id)

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dictGetHierarchy

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dictGetHierarchy('dict_name', id)

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dictHas

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dictHas('dict_name', id)

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Hash functions

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Hash functions can be used for deterministic pseudo-random shuffling of elements.

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halfMD5

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Calculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian. -Accepts a String-type argument. Returns UInt64. -This function works fairly slowly (5 million short strings per second per processor core). -If you don't need MD5 in particular, use the 'sipHash64' function instead.

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MD5

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Calculates the MD5 from a string and returns the resulting set of bytes as FixedString(16). -If you don't need MD5 in particular, but you need a decent cryptographic 128-bit hash, use the 'sipHash128' function instead. -If you want to get the same result as output by the md5sum utility, use lower(hex(MD5(s))).

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sipHash64

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Calculates SipHash from a string. -Accepts a String-type argument. Returns UInt64. -SipHash is a cryptographic hash function. It works at least three times faster than MD5. -For more information, see the link: https://131002.net/siphash/

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sipHash128

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Calculates SipHash from a string. -Accepts a String-type argument. Returns FixedString(16). -Differs from sipHash64 in that the final xor-folding state is only done up to 128 bytes.

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cityHash64

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Calculates CityHash64 from a string or a similar hash function for any number of any type of arguments. -For String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality. -For other types of arguments, a decent implementation-specific fast non-cryptographic hash function is used. -If multiple arguments are passed, the function is calculated using the same rules and chain combinations using the CityHash combinator. -For example, you can compute the checksum of an entire table with accuracy up to the row order: SELECT sum(cityHash64(*)) FROM table.

-

intHash32

-

Calculates a 32-bit hash code from any type of integer. -This is a relatively fast non-cryptographic hash function of average quality for numbers.

-

intHash64

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Calculates a 64-bit hash code from any type of integer. -It works faster than intHash32. Average quality.

-

SHA1

-

SHA224

-

SHA256

-

Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32). -The function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million). -We recommend using this function only in cases when you need a specific hash function and you can't select it. -Even in these cases, we recommend applying the function offline and pre-calculating values when inserting them into the table, instead of applying it in SELECTS.

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URLHash(url[, N])

-

A fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization. -URLHash(s) – Calculates a hash from a string without one of the trailing symbols /,? or # at the end, if present. -URLHash(s, N) – Calculates a hash from a string up to the N level in the URL hierarchy, without one of the trailing symbols /,? or # at the end, if present. -Levels are the same as in URLHierarchy. This function is specific to Yandex.Metrica.

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Higher-order functions

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-> operator, lambda(params, expr) function

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Allows describing a lambda function for passing to a higher-order function. The left side of the arrow has a formal parameter, which is any ID, or multiple formal parameters – any IDs in a tuple. The right side of the arrow has an expression that can use these formal parameters, as well as any table columns.

-

Examples: x -> 2 * x, str -> str != Referer.

-

Higher-order functions can only accept lambda functions as their functional argument.

-

A lambda function that accepts multiple arguments can be passed to a higher-order function. In this case, the higher-order function is passed several arrays of identical length that these arguments will correspond to.

-

For all functions other than 'arrayMap' and 'arrayFilter', the first argument (the lambda function) can be omitted. In this case, identical mapping is assumed.

-

arrayMap(func, arr1, ...)

-

Returns an array obtained from the original application of the 'func' function to each element in the 'arr' array.

-

arrayFilter(func, arr1, ...)

-

Returns an array containing only the elements in 'arr1' for which 'func' returns something other than 0.

-

Examples:

-
SELECT arrayFilter(x -> x LIKE '%World%', ['Hello', 'abc World']) AS res
-
- - -
┌─res───────────┐
-│ ['abc World'] │
-└───────────────┘
-
- - -
SELECT
-    arrayFilter(
-        (i, x) -> x LIKE '%World%',
-        arrayEnumerate(arr),
-        ['Hello', 'abc World'] AS arr)
-    AS res
-
- - -
┌─res─┐
-│ [2] │
-└─────┘
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arrayCount([func,] arr1, ...)

-

Returns the number of elements in the arr array for which func returns something other than 0. If 'func' is not specified, it returns the number of non-zero elements in the array.

-

arrayExists([func,] arr1, ...)

-

Returns 1 if there is at least one element in 'arr' for which 'func' returns something other than 0. Otherwise, it returns 0.

-

arrayAll([func,] arr1, ...)

-

Returns 1 if 'func' returns something other than 0 for all the elements in 'arr'. Otherwise, it returns 0.

-

arraySum([func,] arr1, ...)

-

Returns the sum of the 'func' values. If the function is omitted, it just returns the sum of the array elements.

-

arrayFirst(func, arr1, ...)

-

Returns the first element in the 'arr1' array for which 'func' returns something other than 0.

-

arrayFirstIndex(func, arr1, ...)

-

Returns the index of the first element in the 'arr1' array for which 'func' returns something other than 0.

-

arrayCumSum([func,] arr1, ...)

-

Returns an array of partial sums of elements in the source array (a running sum). If the func function is specified, then the values of the array elements are converted by this function before summing.

-

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-
SELECT arrayCumSum([1, 1, 1, 1]) AS res
-
- - -
┌─res──────────┐
-│ [1, 2, 3, 4] │
-└──────────────┘
-
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arraySort([func,] arr1, ...)

-

Returns an array as result of sorting the elements of arr1 in ascending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays)

-

The Schwartzian transform is used to impove sorting efficiency.

-

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-
SELECT arraySort((x, y) -> y, ['hello', 'world'], [2, 1]);
-
- - -
┌─res────────────────┐
-│ ['world', 'hello'] │
-└────────────────────┘
-
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arrayReverseSort([func,] arr1, ...)

-

Returns an array as result of sorting the elements of arr1 in descending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays)

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Functions for implementing the IN operator

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in, notIn, globalIn, globalNotIn

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tuple(x, y, ...), operator (x, y, ...)

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A function that allows grouping multiple columns. -For columns with the types T1, T2, ..., it returns a Tuple(T1, T2, ...) type tuple containing these columns. There is no cost to execute the function. -Tuples are normally used as intermediate values for an argument of IN operators, or for creating a list of formal parameters of lambda functions. Tuples can't be written to a table.

-

tupleElement(tuple, n), operator x.N

-

A function that allows getting a column from a tuple. -'N' is the column index, starting from 1. N must be a constant. 'N' must be a constant. 'N' must be a strict postive integer no greater than the size of the tuple. -There is no cost to execute the function.

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There are at least* two types of functions - regular functions (they are just called "functions") and aggregate functions. These are completely different concepts. Regular functions work as if they are applied to each row separately (for each row, the result of the function doesn't depend on the other rows). Aggregate functions accumulate a set of values from various rows (i.e. they depend on the entire set of rows).

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In this section we discuss regular functions. For aggregate functions, see the section "Aggregate functions".

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* - There is a third type of function that the 'arrayJoin' function belongs to; table functions can also be mentioned separately.*

-

Strong typing

-

In contrast to standard SQL, ClickHouse has strong typing. In other words, it doesn't make implicit conversions between types. Each function works for a specific set of types. This means that sometimes you need to use type conversion functions.

-

Common subexpression elimination

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All expressions in a query that have the same AST (the same record or same result of syntactic parsing) are considered to have identical values. Such expressions are concatenated and executed once. Identical subqueries are also eliminated this way.

-

Types of results

-

All functions return a single return as the result (not several values, and not zero values). The type of result is usually defined only by the types of arguments, not by the values. Exceptions are the tupleElement function (the a.N operator), and the toFixedString function.

-

Constants

-

For simplicity, certain functions can only work with constants for some arguments. For example, the right argument of the LIKE operator must be a constant. -Almost all functions return a constant for constant arguments. The exception is functions that generate random numbers. -The 'now' function returns different values for queries that were run at different times, but the result is considered a constant, since constancy is only important within a single query. -A constant expression is also considered a constant (for example, the right half of the LIKE operator can be constructed from multiple constants).

-

Functions can be implemented in different ways for constant and non-constant arguments (different code is executed). But the results for a constant and for a true column containing only the same value should match each other.

-

Constancy

-

Functions can't change the values of their arguments – any changes are returned as the result. Thus, the result of calculating separate functions does not depend on the order in which the functions are written in the query.

-

Error handling

-

Some functions might throw an exception if the data is invalid. In this case, the query is canceled and an error text is returned to the client. For distributed processing, when an exception occurs on one of the servers, the other servers also attempt to abort the query.

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Evaluation of argument expressions

-

In almost all programming languages, one of the arguments might not be evaluated for certain operators. This is usually the operators &&, ||, and ?:. -But in ClickHouse, arguments of functions (operators) are always evaluated. This is because entire parts of columns are evaluated at once, instead of calculating each row separately.

-

Performing functions for distributed query processing

-

For distributed query processing, as many stages of query processing as possible are performed on remote servers, and the rest of the stages (merging intermediate results and everything after that) are performed on the requestor server.

-

This means that functions can be performed on different servers. -For example, in the query SELECT f(sum(g(x))) FROM distributed_table GROUP BY h(y),

-
    -
  • if a distributed_table has at least two shards, the functions 'g' and 'h' are performed on remote servers, and the function 'f' is performed on the requestor server.
    • -
  • if a distributed_table has only one shard, all the 'f', 'g', and 'h' functions are performed on this shard's server.
    • -
-

The result of a function usually doesn't depend on which server it is performed on. However, sometimes this is important. -For example, functions that work with dictionaries use the dictionary that exists on the server they are running on. -Another example is the hostName function, which returns the name of the server it is running on in order to make GROUP BY by servers in a SELECT query.

-

If a function in a query is performed on the requestor server, but you need to perform it on remote servers, you can wrap it in an 'any' aggregate function or add it to a key in GROUP BY.

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Functions for working with IP addresses

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IPv4NumToString(num)

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Takes a UInt32 number. Interprets it as an IPv4 address in big endian. Returns a string containing the corresponding IPv4 address in the format A.B.C.d (dot-separated numbers in decimal form).

-

IPv4StringToNum(s)

-

The reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0.

-

IPv4NumToStringClassC(num)

-

Similar to IPv4NumToString, but using xxx instead of the last octet.

-

Example:

-
SELECT
-    IPv4NumToStringClassC(ClientIP) AS k,
-    count() AS c
-FROM test.hits
-GROUP BY k
-ORDER BY c DESC
-LIMIT 10
-
- - -
┌─k──────────────┬─────c─┐
-│ 83.149.9.xxx   │ 26238 │
-│ 217.118.81.xxx │ 26074 │
-│ 213.87.129.xxx │ 25481 │
-│ 83.149.8.xxx   │ 24984 │
-│ 217.118.83.xxx │ 22797 │
-│ 78.25.120.xxx  │ 22354 │
-│ 213.87.131.xxx │ 21285 │
-│ 78.25.121.xxx  │ 20887 │
-│ 188.162.65.xxx │ 19694 │
-│ 83.149.48.xxx  │ 17406 │
-└────────────────┴───────┘
-
- - -

Since using 'xxx' is highly unusual, this may be changed in the future. We recommend that you don't rely on the exact format of this fragment.

-

IPv6NumToString(x)

-

Accepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format. -IPv6-mapped IPv4 addresses are output in the format ::ffff:111.222.33.44. Examples:

-
SELECT IPv6NumToString(toFixedString(unhex('2A0206B8000000000000000000000011'), 16)) AS addr
-
- - -
┌─addr─────────┐
-│ 2a02:6b8::11 │
-└──────────────┘
-
- - -
SELECT
-    IPv6NumToString(ClientIP6 AS k),
-    count() AS c
-FROM hits_all
-WHERE EventDate = today() AND substring(ClientIP6, 1, 12) != unhex('00000000000000000000FFFF')
-GROUP BY k
-ORDER BY c DESC
-LIMIT 10
-
- - -
┌─IPv6NumToString(ClientIP6)──────────────┬─────c─┐
-│ 2a02:2168:aaa:bbbb::2                   │ 24695 │
-│ 2a02:2698:abcd:abcd:abcd:abcd:8888:5555 │ 22408 │
-│ 2a02:6b8:0:fff::ff                      │ 16389 │
-│ 2a01:4f8:111:6666::2                    │ 16016 │
-│ 2a02:2168:888:222::1                    │ 15896 │
-│ 2a01:7e00::ffff:ffff:ffff:222           │ 14774 │
-│ 2a02:8109:eee:ee:eeee:eeee:eeee:eeee    │ 14443 │
-│ 2a02:810b:8888:888:8888:8888:8888:8888  │ 14345 │
-│ 2a02:6b8:0:444:4444:4444:4444:4444      │ 14279 │
-│ 2a01:7e00::ffff:ffff:ffff:ffff          │ 13880 │
-└─────────────────────────────────────────┴───────┘
-
- - -
SELECT
-    IPv6NumToString(ClientIP6 AS k),
-    count() AS c
-FROM hits_all
-WHERE EventDate = today()
-GROUP BY k
-ORDER BY c DESC
-LIMIT 10
-
- - -
┌─IPv6NumToString(ClientIP6)─┬──────c─┐
-│ ::ffff:94.26.111.111       │ 747440 │
-│ ::ffff:37.143.222.4        │ 529483 │
-│ ::ffff:5.166.111.99        │ 317707 │
-│ ::ffff:46.38.11.77         │ 263086 │
-│ ::ffff:79.105.111.111      │ 186611 │
-│ ::ffff:93.92.111.88        │ 176773 │
-│ ::ffff:84.53.111.33        │ 158709 │
-│ ::ffff:217.118.11.22       │ 154004 │
-│ ::ffff:217.118.11.33       │ 148449 │
-│ ::ffff:217.118.11.44       │ 148243 │
-└────────────────────────────┴────────┘
-
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IPv6StringToNum(s)

-

The reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes. -HEX can be uppercase or lowercase.

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Functions for working with JSON

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In Yandex.Metrica, JSON is transmitted by users as session parameters. There are some special functions for working with this JSON. (Although in most of the cases, the JSONs are additionally pre-processed, and the resulting values are put in separate columns in their processed format.) All these functions are based on strong assumptions about what the JSON can be, but they try to do as little as possible to get the job done.

-

The following assumptions are made:

-
    -
  1. The field name (function argument) must be a constant.
    2. -
  2. The field name is somehow canonically encoded in JSON. For example: visitParamHas('{"abc":"def"}', 'abc') = 1, but visitParamHas('{"\\u0061\\u0062\\u0063":"def"}', 'abc') = 0
    3. -
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    4. -
  4. The JSON doesn't have space characters outside of string literals.
    5. -
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visitParamHas(params, name)

-

Checks whether there is a field with the 'name' name.

-

visitParamExtractUInt(params, name)

-

Parses UInt64 from the value of the field named 'name'. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn't exist, or it exists but doesn't contain a number, it returns 0.

-

visitParamExtractInt(params, name)

-

The same as for Int64.

-

visitParamExtractFloat(params, name)

-

The same as for Float64.

-

visitParamExtractBool(params, name)

-

Parses a true/false value. The result is UInt8.

-

visitParamExtractRaw(params, name)

-

Returns the value of a field, including separators.

-

Examples:

-
visitParamExtractRaw('{"abc":"\\n\\u0000"}', 'abc') = '"\\n\\u0000"'
-visitParamExtractRaw('{"abc":{"def":[1,2,3]}}', 'abc') = '{"def":[1,2,3]}'
-
- - -

visitParamExtractString(params, name)

-

Parses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string.

-

Examples:

-
visitParamExtractString('{"abc":"\\n\\u0000"}', 'abc') = '\n\0'
-visitParamExtractString('{"abc":"\\u263a"}', 'abc') = '☺'
-visitParamExtractString('{"abc":"\\u263"}', 'abc') = ''
-visitParamExtractString('{"abc":"hello}', 'abc') = ''
-
- - -

There is currently no support for code points in the format \uXXXX\uYYYY that are not from the basic multilingual plane (they are converted to CESU-8 instead of UTF-8).

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Logical functions

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Logical functions accept any numeric types, but return a UInt8 number equal to 0 or 1.

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Zero as an argument is considered "false," while any non-zero value is considered "true".

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and, AND operator

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or, OR operator

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not, NOT operator

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xor

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Mathematical functions

-

All the functions return a Float64 number. The accuracy of the result is close to the maximum precision possible, but the result might not coincide with the machine representable number nearest to the corresponding real number.

-

e()

-

Returns a Float64 number close to the e number.

-

pi()

-

Returns a Float64 number close to π.

-

exp(x)

-

Accepts a numeric argument and returns a Float64 number close to the exponent of the argument.

-

log(x)

-

Accepts a numeric argument and returns a Float64 number close to the natural logarithm of the argument.

-

exp2(x)

-

Accepts a numeric argument and returns a Float64 number close to 2^x.

-

log2(x)

-

Accepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument.

-

exp10(x)

-

Accepts a numeric argument and returns a Float64 number close to 10^x.

-

log10(x)

-

Accepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument.

-

sqrt(x)

-

Accepts a numeric argument and returns a Float64 number close to the square root of the argument.

-

cbrt(x)

-

Accepts a numeric argument and returns a Float64 number close to the cubic root of the argument.

-

erf(x)

-

If 'x' is non-negative, then erf(x / σ√2) is the probability that a random variable having a normal distribution with standard deviation 'σ' takes the value that is separated from the expected value by more than 'x'.

-

Example (three sigma rule):

-
SELECT erf(3 / sqrt(2))
-
- - -
┌─erf(divide(3, sqrt(2)))─┐
-│      0.9973002039367398 │
-└─────────────────────────┘
-
- - -

erfc(x)

-

Accepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large 'x' values.

-

lgamma(x)

-

The logarithm of the gamma function.

-

tgamma(x)

-

Gamma function.

-

sin(x)

-

The sine.

-

cos(x)

-

The cosine.

-

tan(x)

-

The tangent.

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asin(x)

-

The arc sine.

-

acos(x)

-

The arc cosine.

-

atan(x)

-

The arc tangent.

-

pow(x, y)

-

Accepts two numeric arguments and returns a Float64 number close to x^y.

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Other functions

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hostName()

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Returns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server.

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visibleWidth(x)

-

Calculates the approximate width when outputting values to the console in text format (tab-separated). -This function is used by the system for implementing Pretty formats.

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toTypeName(x)

-

Returns a string containing the type name of the passed argument.

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blockSize()

-

Gets the size of the block. -In ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for.

-

materialize(x)

-

Turns a constant into a full column containing just one value. -In ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior.

-

ignore(...)

-

Accepts any arguments and always returns 0. -However, the argument is still evaluated. This can be used for benchmarks.

-

sleep(seconds)

-

Sleeps 'seconds' seconds on each data block. You can specify an integer or a floating-point number.

-

currentDatabase()

-

Returns the name of the current database. -You can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database.

-

isFinite(x)

-

Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0.

-

isInfinite(x)

-

Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. Note that 0 is returned for a NaN.

-

isNaN(x)

-

Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0.

-

hasColumnInTable(['hostname'[, 'username'[, 'password']],] 'database', 'table', 'column')

-

Accepts constant strings: database name, table name, and column name. Returns a UInt8 constant expression equal to 1 if there is a column, otherwise 0. If the hostname parameter is set, the test will run on a remote server. -The function throws an exception if the table does not exist. -For elements in a nested data structure, the function checks for the existence of a column. For the nested data structure itself, the function returns 0.

-

bar

-

Allows building a unicode-art diagram.

-

bar (x, min, max, width) draws a band with a width proportional to (x - min) and equal to width characters when x = max.

-

Parameters:

-
    -
  • x – Value to display.
    • -
  • min, max – Integer constants. The value must fit in Int64.
    • -
  • width – Constant, positive number, may be a fraction.
    • -
-

The band is drawn with accuracy to one eighth of a symbol.

-

Example:

-
SELECT
-    toHour(EventTime) AS h,
-    count() AS c,
-    bar(c, 0, 600000, 20) AS bar
-FROM test.hits
-GROUP BY h
-ORDER BY h ASC
-
- - -
┌──h─┬──────c─┬─bar────────────────┐
-│  0 │ 292907 │ █████████▋         │
-│  1 │ 180563 │ ██████             │
-│  2 │ 114861 │ ███▋               │
-│  3 │  85069 │ ██▋                │
-│  4 │  68543 │ ██▎                │
-│  5 │  78116 │ ██▌                │
-│  6 │ 113474 │ ███▋               │
-│  7 │ 170678 │ █████▋             │
-│  8 │ 278380 │ █████████▎         │
-│  9 │ 391053 │ █████████████      │
-│ 10 │ 457681 │ ███████████████▎   │
-│ 11 │ 493667 │ ████████████████▍  │
-│ 12 │ 509641 │ ████████████████▊  │
-│ 13 │ 522947 │ █████████████████▍ │
-│ 14 │ 539954 │ █████████████████▊ │
-│ 15 │ 528460 │ █████████████████▌ │
-│ 16 │ 539201 │ █████████████████▊ │
-│ 17 │ 523539 │ █████████████████▍ │
-│ 18 │ 506467 │ ████████████████▊  │
-│ 19 │ 520915 │ █████████████████▎ │
-│ 20 │ 521665 │ █████████████████▍ │
-│ 21 │ 542078 │ ██████████████████ │
-│ 22 │ 493642 │ ████████████████▍  │
-│ 23 │ 400397 │ █████████████▎     │
-└────┴────────┴────────────────────┘
-
- - -

-

transform

-

Transforms a value according to the explicitly defined mapping of some elements to other ones. -There are two variations of this function:

-
    -
  1. transform(x, array_from, array_to, default)
    2. -
-

x – What to transform.

-

array_from – Constant array of values for converting.

-

array_to – Constant array of values to convert the values in 'from' to.

-

default – Which value to use if 'x' is not equal to any of the values in 'from'.

-

array_from and array_to – Arrays of the same size.

-

Types:

-

transform(T, Array(T), Array(U), U) -> U

-

T and U can be numeric, string, or Date or DateTime types. -Where the same letter is indicated (T or U), for numeric types these might not be matching types, but types that have a common type. -For example, the first argument can have the Int64 type, while the second has the Array(Uint16) type.

-

If the 'x' value is equal to one of the elements in the 'array_from' array, it returns the existing element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'default'. If there are multiple matching elements in 'array_from', it returns one of the matches.

-

Example:

-
SELECT
-    transform(SearchEngineID, [2, 3], ['Yandex', 'Google'], 'Other') AS title,
-    count() AS c
-FROM test.hits
-WHERE SearchEngineID != 0
-GROUP BY title
-ORDER BY c DESC
-
- - -
┌─title─────┬──────c─┐
-│ Yandex    │ 498635 │
-│ Google    │ 229872 │
-│ Other     │ 104472 │
-└───────────┴────────┘
-
- - -
    -
  1. transform(x, array_from, array_to)
    2. -
-

Differs from the first variation in that the 'default' argument is omitted. -If the 'x' value is equal to one of the elements in the 'array_from' array, it returns the matching element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'x'.

-

Types:

-

transform(T, Array(T), Array(T)) -> T

-

Example:

-
SELECT
-    transform(domain(Referer), ['yandex.ru', 'google.ru', 'vk.com'], ['www.yandex', 'example.com']) AS s,
-    count() AS c
-FROM test.hits
-GROUP BY domain(Referer)
-ORDER BY count() DESC
-LIMIT 10
-
- - -
┌─s──────────────┬───────c─┐
-│                │ 2906259 │
-│ www.yandex     │  867767 │
-│ ███████.ru     │  313599 │
-│ mail.yandex.ru │  107147 │
-│ ██████.ru      │  100355 │
-│ █████████.ru   │   65040 │
-│ news.yandex.ru │   64515 │
-│ ██████.net     │   59141 │
-│ example.com    │   57316 │
-└────────────────┴─────────┘
-
- - -

formatReadableSize(x)

-

Accepts the size (number of bytes). Returns a rounded size with a suffix (KiB, MiB, etc.) as a string.

-

Example:

-
SELECT
-    arrayJoin([1, 1024, 1024*1024, 192851925]) AS filesize_bytes,
-    formatReadableSize(filesize_bytes) AS filesize
-
- - -
┌─filesize_bytes─┬─filesize───┐
-│              1 │ 1.00 B     │
-│           1024 │ 1.00 KiB   │
-│        1048576 │ 1.00 MiB   │
-│      192851925 │ 183.92 MiB │
-└────────────────┴────────────┘
-
- - -

least(a, b)

-

Returns the smallest value from a and b.

-

greatest(a, b)

-

Returns the largest value of a and b.

-

uptime()

-

Returns the server's uptime in seconds.

-

version()

-

Returns the version of the server as a string.

-

rowNumberInAllBlocks()

-

Returns the ordinal number of the row in the data block. This function only considers the affected data blocks.

-

runningDifference(x)

-

Calculates the difference between successive row values ​​in the data block. -Returns 0 for the first row and the difference from the previous row for each subsequent row.

-

The result of the function depends on the affected data blocks and the order of data in the block. -If you make a subquery with ORDER BY and call the function from outside the subquery, you can get the expected result.

-

Example:

-
SELECT
-    EventID,
-    EventTime,
-    runningDifference(EventTime) AS delta
-FROM
-(
-    SELECT
-        EventID,
-        EventTime
-    FROM events
-    WHERE EventDate = '2016-11-24'
-    ORDER BY EventTime ASC
-    LIMIT 5
-)
-
- - -
┌─EventID─┬───────────EventTime─┬─delta─┐
-│    1106 │ 2016-11-24 00:00:04 │     0 │
-│    1107 │ 2016-11-24 00:00:05 │     1 │
-│    1108 │ 2016-11-24 00:00:05 │     0 │
-│    1109 │ 2016-11-24 00:00:09 │     4 │
-│    1110 │ 2016-11-24 00:00:10 │     1 │
-└─────────┴─────────────────────┴───────┘
-
- - -

MACNumToString(num)

-

Accepts a UInt64 number. Interprets it as a MAC address in big endian. Returns a string containing the corresponding MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form).

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MACStringToNum(s)

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The inverse function of MACNumToString. If the MAC address has an invalid format, it returns 0.

-

MACStringToOUI(s)

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Accepts a MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form). Returns the first three octets as a UInt64 number. If the MAC address has an invalid format, it returns 0.

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Non-cryptographic generators of pseudo-random numbers are used.

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rand

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Returns a pseudo-random UInt32 number, evenly distributed among all UInt32-type numbers. -Uses a linear congruential generator.

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rand64

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Returns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers. -Uses a linear congruential generator.

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Rounding functions

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floor(x[, N])

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Returns the largest round number that is less than or equal to x. A round number is a multiple of 1/10N, or the nearest number of the appropriate data type if 1 / 10N isn't exact. -'N' is an integer constant, optional parameter. By default it is zero, which means to round to an integer. -'N' may be negative.

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Examples: floor(123.45, 1) = 123.4, floor(123.45, -1) = 120.

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x is any numeric type. The result is a number of the same type. -For integer arguments, it makes sense to round with a negative 'N' value (for non-negative 'N', the function doesn't do anything). -If rounding causes overflow (for example, floor(-128, -1)), an implementation-specific result is returned.

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ceil(x[, N])

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Returns the smallest round number that is greater than or equal to 'x'. In every other way, it is the same as the 'floor' function (see above).

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round(x[, N])

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Returns the round number nearest to 'num', which may be less than, greater than, or equal to 'x'.If 'x' is exactly in the middle between the nearest round numbers, one of them is returned (implementation-specific). -The number '-0.' may or may not be considered round (implementation-specific). -In every other way, this function is the same as 'floor' and 'ceil' described above.

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roundToExp2(num)

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Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two.

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roundDuration(num)

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Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length

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roundAge(num)

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Accepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to a number from the set: 18, 25, 35, 45, 55. This function is specific to Yandex.Metrica and used for implementing the report on user age.

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Functions for splitting and merging strings and arrays

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splitByChar(separator, s)

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Splits a string into substrings separated by 'separator'.'separator' must be a string constant consisting of exactly one character. -Returns an array of selected substrings. Empty substrings may be selected if the separator occurs at the beginning or end of the string, or if there are multiple consecutive separators.

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splitByString(separator, s)

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The same as above, but it uses a string of multiple characters as the separator. The string must be non-empty.

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arrayStringConcat(arr[, separator])

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Concatenates the strings listed in the array with the separator.'separator' is an optional parameter: a constant string, set to an empty string by default. -Returns the string.

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alphaTokens(s)

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Selects substrings of consecutive bytes from the ranges a-z and A-Z.Returns an array of substrings.

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Functions for working with strings

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empty

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Returns 1 for an empty string or 0 for a non-empty string. -The result type is UInt8. -A string is considered non-empty if it contains at least one byte, even if this is a space or a null byte. -The function also works for arrays.

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notEmpty

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Returns 0 for an empty string or 1 for a non-empty string. -The result type is UInt8. -The function also works for arrays.

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length

-

Returns the length of a string in bytes (not in characters, and not in code points). -The result type is UInt64. -The function also works for arrays.

-

lengthUTF8

-

Returns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception). -The result type is UInt64.

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lower

-

Converts ASCII Latin symbols in a string to lowercase.

-

upper

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Converts ASCII Latin symbols in a string to uppercase.

-

lowerUTF8

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Converts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. -It doesn't detect the language. So for Turkish the result might not be exactly correct. -If the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point. -If the string contains a set of bytes that is not UTF-8, then the behavior is undefined.

-

upperUTF8

-

Converts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. -It doesn't detect the language. So for Turkish the result might not be exactly correct. -If the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point. -If the string contains a set of bytes that is not UTF-8, then the behavior is undefined.

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reverse

-

Reverses the string (as a sequence of bytes).

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reverseUTF8

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Reverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn't throw an exception).

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concat(s1, s2, ...)

-

Concatenates the strings listed in the arguments, without a separator.

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substring(s, offset, length)

-

Returns a substring starting with the byte from the 'offset' index that is 'length' bytes long. Character indexing starts from one (as in standard SQL). The 'offset' and 'length' arguments must be constants.

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substringUTF8(s, offset, length)

-

The same as 'substring', but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).

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appendTrailingCharIfAbsent(s, c)

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If the 's' string is non-empty and does not contain the 'c' character at the end, it appends the 'c' character to the end.

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convertCharset(s, from, to)

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Returns the string 's' that was converted from the encoding in 'from' to the encoding in 'to'.

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Functions for searching and replacing in strings

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replaceOne(haystack, pattern, replacement)

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Replaces the first occurrence, if it exists, of the 'pattern' substring in 'haystack' with the 'replacement' substring. -Hereafter, 'pattern' and 'replacement' must be constants.

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replaceAll(haystack, pattern, replacement)

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Replaces all occurrences of the 'pattern' substring in 'haystack' with the 'replacement' substring.

-

replaceRegexpOne(haystack, pattern, replacement)

-

Replacement using the 'pattern' regular expression. A re2 regular expression. -Replaces only the first occurrence, if it exists. -A pattern can be specified as 'replacement'. This pattern can include substitutions \0-\9. -The substitution \0 includes the entire regular expression. Substitutions \1-\9 correspond to the subpattern numbers.To use the \ character in a template, escape it using \. -Also keep in mind that a string literal requires an extra escape.

-

Example 1. Converting the date to American format:

-
SELECT DISTINCT
-    EventDate,
-    replaceRegexpOne(toString(EventDate), '(\\d{4})-(\\d{2})-(\\d{2})', '\\2/\\3/\\1') AS res
-FROM test.hits
-LIMIT 7
-FORMAT TabSeparated
-
- - -
2014-03-17      03/17/2014
-2014-03-18      03/18/2014
-2014-03-19      03/19/2014
-2014-03-20      03/20/2014
-2014-03-21      03/21/2014
-2014-03-22      03/22/2014
-2014-03-23      03/23/2014
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Example 2. Copying a string ten times:

-
SELECT replaceRegexpOne('Hello, World!', '.*', '\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0') AS res
-
- - -
┌─res────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
-│ Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World! │
-└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
-
- - -

replaceRegexpAll(haystack, pattern, replacement)

-

This does the same thing, but replaces all the occurrences. Example:

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SELECT replaceRegexpAll('Hello, World!', '.', '\\0\\0') AS res
-
- - -
┌─res────────────────────────┐
-│ HHeelllloo,,  WWoorrlldd!! │
-└────────────────────────────┘
-
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As an exception, if a regular expression worked on an empty substring, the replacement is not made more than once. -Example:

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SELECT replaceRegexpAll('Hello, World!', '^', 'here: ') AS res
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┌─res─────────────────┐
-│ here: Hello, World! │
-└─────────────────────┘
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Functions for searching strings

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The search is case-sensitive in all these functions. -The search substring or regular expression must be a constant in all these functions.

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position(haystack, needle)

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Search for the needle substring in the haystack string. -Returns the position (in bytes) of the found substring, starting from 1, or returns 0 if the substring was not found.

-

For case-insensitive search use positionCaseInsensitive function.

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positionUTF8(haystack, needle)

-

The same as position, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).

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For case-insensitive search use positionCaseInsensitiveUTF8 function.

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match(haystack, pattern)

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Checks whether the string matches the 'pattern' regular expression. A re2 regular expression. -Returns 0 if it doesn't match, or 1 if it matches.

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Note that the backslash symbol (\) is used for escaping in the regular expression. The same symbol is used for escaping in string literals. So in order to escape the symbol in a regular expression, you must write two backslashes (\) in a string literal.

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The regular expression works with the string as if it is a set of bytes. The regular expression can't contain null bytes. -For patterns to search for substrings in a string, it is better to use LIKE or 'position', since they work much faster.

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extract(haystack, pattern)

-

Extracts a fragment of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. If the regex doesn't contain subpatterns, it takes the fragment that matches the entire regex. Otherwise, it takes the fragment that matches the first subpattern.

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extractAll(haystack, pattern)

-

Extracts all the fragments of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex. In general, the behavior is the same as the 'extract' function (it takes the first subpattern, or the entire expression if there isn't a subpattern).

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like(haystack, pattern), haystack LIKE pattern operator

-

Checks whether a string matches a simple regular expression. -The regular expression can contain the metasymbols % and _.

-

``% indicates any quantity of any bytes (including zero characters).

-

_ indicates any one byte.

-

Use the backslash (\) for escaping metasymbols. See the note on escaping in the description of the 'match' function.

-

For regular expressions like %needle%, the code is more optimal and works as fast as the position function. -For other regular expressions, the code is the same as for the 'match' function.

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notLike(haystack, pattern), haystack NOT LIKE pattern operator

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The same thing as 'like', but negative.

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Type conversion functions

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toUInt8, toUInt16, toUInt32, toUInt64

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toInt8, toInt16, toInt32, toInt64

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toFloat32, toFloat64

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toUInt8OrZero, toUInt16OrZero, toUInt32OrZero, toUInt64OrZero, toInt8OrZero, toInt16OrZero, toInt32OrZero, toInt64OrZero, toFloat32OrZero, toFloat64OrZero

-

toDate, toDateTime

-

toString

-

Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. -All these functions accept one argument.

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When converting to or from a string, the value is formatted or parsed using the same rules as for the TabSeparated format (and almost all other text formats). If the string can't be parsed, an exception is thrown and the request is canceled.

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When converting dates to numbers or vice versa, the date corresponds to the number of days since the beginning of the Unix epoch. -When converting dates with times to numbers or vice versa, the date with time corresponds to the number of seconds since the beginning of the Unix epoch.

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YYYY-MM-DD
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Conversion between a date and date with time is performed the natural way: by adding a null time or dropping the time.

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Conversion between numeric types uses the same rules as assignments between different numeric types in C++.

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Additionally, the toString function of the DateTime argument can take a second String argument containing the name of the time zone. Example: Asia/Yekaterinburg In this case, the time is formatted according to the specified time zone.

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SELECT
-    now() AS now_local,
-    toString(now(), 'Asia/Yekaterinburg') AS now_yekat
-
- - -
┌───────────now_local─┬─now_yekat───────────┐
-│ 2016-06-15 00:11:21 │ 2016-06-15 02:11:21 │
-└─────────────────────┴─────────────────────┘
-
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Also see the toUnixTimestamp function.

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toFixedString(s, N)

-

Converts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant. -If the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown.

-

toStringCutToZero(s)

-

Accepts a String or FixedString argument. Returns the String with the content truncated at the first zero byte found.

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Example:

-
SELECT toFixedString('foo', 8) AS s, toStringCutToZero(s) AS s_cut
-
- - -
┌─s─────────────┬─s_cut─┐
-│ foo\0\0\0\0\0 │ foo   │
-└───────────────┴───────┘
-
- - -
SELECT toFixedString('foo\0bar', 8) AS s, toStringCutToZero(s) AS s_cut
-
- - -
┌─s──────────┬─s_cut─┐
-│ foo\0bar\0 │ foo   │
-└────────────┴───────┘
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reinterpretAsUInt8, reinterpretAsUInt16, reinterpretAsUInt32, reinterpretAsUInt64

-

reinterpretAsInt8, reinterpretAsInt16, reinterpretAsInt32, reinterpretAsInt64

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reinterpretAsFloat32, reinterpretAsFloat64

-

reinterpretAsDate, reinterpretAsDateTime

-

These functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.

-

reinterpretAsString

-

This function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long.

-

CAST(x, t)

-

Converts 'x' to the 't' data type. The syntax CAST(x AS t) is also supported.

-

Example:

-
SELECT
-    '2016-06-15 23:00:00' AS timestamp,
-    CAST(timestamp AS DateTime) AS datetime,
-    CAST(timestamp AS Date) AS date,
-    CAST(timestamp, 'String') AS string,
-    CAST(timestamp, 'FixedString(22)') AS fixed_string
-
- - -
┌─timestamp───────────┬────────────datetime─┬───────date─┬─string──────────────┬─fixed_string──────────────┐
-│ 2016-06-15 23:00:00 │ 2016-06-15 23:00:00 │ 2016-06-15 │ 2016-06-15 23:00:00 │ 2016-06-15 23:00:00\0\0\0 │
-└─────────────────────┴─────────────────────┴────────────┴─────────────────────┴───────────────────────────┘
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All these functions don't follow the RFC. They are maximally simplified for improved performance.

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Functions that extract part of a URL

-

If there isn't anything similar in a URL, an empty string is returned.

-

protocol

-

Returns the protocol. Examples: http, ftp, mailto, magnet...

-

domain

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Gets the domain.

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domainWithoutWWW

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Returns the domain and removes no more than one 'www.' from the beginning of it, if present.

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topLevelDomain

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Returns the top-level domain. Example: .ru.

-

firstSignificantSubdomain

-

Returns the "first significant subdomain". This is a non-standard concept specific to Yandex.Metrica. The first significant subdomain is a second-level domain if it is 'com', 'net', 'org', or 'co'. Otherwise, it is a third-level domain. For example, firstSignificantSubdomain ('https://news.yandex.ru/') = 'yandex ', firstSignificantSubdomain ('https://news.yandex.com.tr/') = 'yandex '. The list of "insignificant" second-level domains and other implementation details may change in the future.

-

cutToFirstSignificantSubdomain

-

Returns the part of the domain that includes top-level subdomains up to the "first significant subdomain" (see the explanation above).

-

For example, cutToFirstSignificantSubdomain('https://news.yandex.com.tr/') = 'yandex.com.tr'.

-

path

-

Returns the path. Example: /top/news.html The path does not include the query string.

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pathFull

-

The same as above, but including query string and fragment. Example: /top/news.html?page=2#comments

-

queryString

-

Returns the query string. Example: page=1&lr=213. query-string does not include the initial question mark, as well as # and everything after #.

-

fragment

-

Returns the fragment identifier. fragment does not include the initial hash symbol.

-

queryStringAndFragment

-

Returns the query string and fragment identifier. Example: page=1#29390.

-

extractURLParameter(URL, name)

-

Returns the value of the 'name' parameter in the URL, if present. Otherwise, an empty string. If there are many parameters with this name, it returns the first occurrence. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.

-

extractURLParameters(URL)

-

Returns an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way.

-

extractURLParameterNames(URL)

-

Returns an array of name strings corresponding to the names of URL parameters. The values are not decoded in any way.

-

URLHierarchy(URL)

-

Returns an array containing the URL, truncated at the end by the symbols /,? in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:

-

URLPathHierarchy(URL)

-

The same as above, but without the protocol and host in the result. The / element (root) is not included. Example: the function is used to implement tree reports the URL in Yandex. Metric.

-
URLPathHierarchy('https://example.com/browse/CONV-6788') =
-[
-    '/browse/',
-    '/browse/CONV-6788'
-]
-
- - -

decodeURLComponent(URL)

-

Returns the decoded URL. -Example:

-
SELECT decodeURLComponent('http://127.0.0.1:8123/?query=SELECT%201%3B') AS DecodedURL;
-
- - -
┌─DecodedURL─────────────────────────────┐
-│ http://127.0.0.1:8123/?query=SELECT 1; │
-└────────────────────────────────────────┘
-
- - -

Functions that remove part of a URL.

-

If the URL doesn't have anything similar, the URL remains unchanged.

-

cutWWW

-

Removes no more than one 'www.' from the beginning of the URL's domain, if present.

-

cutQueryString

-

Removes query string. The question mark is also removed.

-

cutFragment

-

Removes the fragment identifier. The number sign is also removed.

-

cutQueryStringAndFragment

-

Removes the query string and fragment identifier. The question mark and number sign are also removed.

-

cutURLParameter(URL, name)

-

Removes the 'name' URL parameter, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.

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Functions for working with Yandex.Metrica dictionaries

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In order for the functions below to work, the server config must specify the paths and addresses for getting all the Yandex.Metrica dictionaries. The dictionaries are loaded at the first call of any of these functions. If the reference lists can't be loaded, an exception is thrown.

-

For information about creating reference lists, see the section "Dictionaries".

-

Multiple geobases

-

ClickHouse supports working with multiple alternative geobases (regional hierarchies) simultaneously, in order to support various perspectives on which countries certain regions belong to.

-

The 'clickhouse-server' config specifies the file with the regional hierarchy::<path_to_regions_hierarchy_file>/opt/geo/regions_hierarchy.txt</path_to_regions_hierarchy_file>

-

Besides this file, it also searches for files nearby that have the _ symbol and any suffix appended to the name (before the file extension). -For example, it will also find the file /opt/geo/regions_hierarchy_ua.txt, if present.

-

ua is called the dictionary key. For a dictionary without a suffix, the key is an empty string.

-

All the dictionaries are re-loaded in runtime (once every certain number of seconds, as defined in the builtin_dictionaries_reload_interval config parameter, or once an hour by default). However, the list of available dictionaries is defined one time, when the server starts.

-

All functions for working with regions have an optional argument at the end – the dictionary key. It is referred to as the geobase. -Example:

-
regionToCountry(RegionID) – Uses the default dictionary: /opt/geo/regions_hierarchy.txt
-regionToCountry(RegionID, '') – Uses the default dictionary: /opt/geo/regions_hierarchy.txt
-regionToCountry(RegionID, 'ua') – Uses the dictionary for the 'ua' key: /opt/geo/regions_hierarchy_ua.txt
-
- - -

regionToCity(id[, geobase])

-

Accepts a UInt32 number – the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0.

-

regionToArea(id[, geobase])

-

Converts a region to an area (type 5 in the geobase). In every other way, this function is the same as 'regionToCity'.

-
SELECT DISTINCT regionToName(regionToArea(toUInt32(number), 'ua'))
-FROM system.numbers
-LIMIT 15
-
- - -
┌─regionToName(regionToArea(toUInt32(number), \'ua\'))─┐
-│                                                      │
-│ Moscow and Moscow region                             │
-│ St. Petersburg and Leningrad region                  │
-│ Belgorod region                                      │
-│ Ivanovsk region                                      │
-│ Kaluga region                                        │
-│ Kostroma region                                      │
-│ Kursk region                                         │
-│ Lipetsk region                                       │
-│ Orlov region                                         │
-│ Ryazan region                                        │
-│ Smolensk region                                      │
-│ Tambov region                                        │
-│ Tver region                                          │
-│ Tula region                                          │
-└──────────────────────────────────────────────────────┘
-
- - -

regionToDistrict(id[, geobase])

-

Converts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as 'regionToCity'.

-
SELECT DISTINCT regionToName(regionToDistrict(toUInt32(number), 'ua'))
-FROM system.numbers
-LIMIT 15
-
- - -
┌─regionToName(regionToDistrict(toUInt32(number), \'ua\'))─┐
-│                                                          │
-│ Central federal district                                 │
-│ Northwest federal district                               │
-│ South federal district                                   │
-│ North Caucases federal district                          │
-│ Privolga federal district                                │
-│ Ural federal district                                    │
-│ Siberian federal district                                │
-│ Far East federal district                                │
-│ Scotland                                                 │
-│ Faroe Islands                                            │
-│ Flemish region                                           │
-│ Brussels capital region                                  │
-│ Wallonia                                                 │
-│ Federation of Bosnia and Herzegovina                     │
-└──────────────────────────────────────────────────────────┘
-
- - -

regionToCountry(id[, geobase])

-

Converts a region to a country. In every other way, this function is the same as 'regionToCity'. -Example: regionToCountry(toUInt32(213)) = 225 converts Moscow (213) to Russia (225).

-

regionToContinent(id[, geobase])

-

Converts a region to a continent. In every other way, this function is the same as 'regionToCity'. -Example: regionToContinent(toUInt32(213)) = 10001 converts Moscow (213) to Eurasia (10001).

-

regionToPopulation(id[, geobase])

-

Gets the population for a region. -The population can be recorded in files with the geobase. See the section "External dictionaries". -If the population is not recorded for the region, it returns 0. -In the Yandex geobase, the population might be recorded for child regions, but not for parent regions.

-

regionIn(lhs, rhs[, geobase])

-

Checks whether a 'lhs' region belongs to a 'rhs' region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn't belong. -The relationship is reflexive – any region also belongs to itself.

-

regionHierarchy(id[, geobase])

-

Accepts a UInt32 number – the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain. -Example: regionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000].

-

regionToName(id[, lang])

-

Accepts a UInt32 number – the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language 'ru' is used. If the language is not supported, an exception is thrown. Returns a string – the name of the region in the corresponding language. If the region with the specified ID doesn't exist, an empty string is returned.

-

ua and uk both mean Ukrainian.

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AMPLab Big Data Benchmark

-

See https://amplab.cs.berkeley.edu/benchmark/

-

Sign up for a free account at https://aws.amazon.com. You will need a credit card, email and phone number.Get a new access key at https://console.aws.amazon.com/iam/home?nc2=h_m_sc#security_credential

-

Run the following in the console:

-
sudo apt-get install s3cmd
-mkdir tiny; cd tiny;
-s3cmd sync s3://big-data-benchmark/pavlo/text-deflate/tiny/ .
-cd ..
-mkdir 1node; cd 1node;
-s3cmd sync s3://big-data-benchmark/pavlo/text-deflate/1node/ .
-cd ..
-mkdir 5nodes; cd 5nodes;
-s3cmd sync s3://big-data-benchmark/pavlo/text-deflate/5nodes/ .
-cd ..
-
- - -

Run the following ClickHouse queries:

-
CREATE TABLE rankings_tiny
-(
-    pageURL String,
-    pageRank UInt32,
-    avgDuration UInt32
-) ENGINE = Log;
-
-CREATE TABLE uservisits_tiny
-(
-    sourceIP String,
-    destinationURL String,
-    visitDate Date,
-    adRevenue Float32,
-    UserAgent String,
-    cCode FixedString(3),
-    lCode FixedString(6),
-    searchWord String,
-    duration UInt32
-) ENGINE = MergeTree(visitDate, visitDate, 8192);
-
-CREATE TABLE rankings_1node
-(
-    pageURL String,
-    pageRank UInt32,
-    avgDuration UInt32
-) ENGINE = Log;
-
-CREATE TABLE uservisits_1node
-(
-    sourceIP String,
-    destinationURL String,
-    visitDate Date,
-    adRevenue Float32,
-    UserAgent String,
-    cCode FixedString(3),
-    lCode FixedString(6),
-    searchWord String,
-    duration UInt32
-) ENGINE = MergeTree(visitDate, visitDate, 8192);
-
-CREATE TABLE rankings_5nodes_on_single
-(
-    pageURL String,
-    pageRank UInt32,
-    avgDuration UInt32
-) ENGINE = Log;
-
-CREATE TABLE uservisits_5nodes_on_single
-(
-    sourceIP String,
-    destinationURL String,
-    visitDate Date,
-    adRevenue Float32,
-    UserAgent String,
-    cCode FixedString(3),
-    lCode FixedString(6),
-    searchWord String,
-    duration UInt32
-) ENGINE = MergeTree(visitDate, visitDate, 8192);
-
- - -

Go back to the console:

-
for i in tiny/rankings/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO rankings_tiny FORMAT CSV"; done
-for i in tiny/uservisits/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO uservisits_tiny FORMAT CSV"; done
-for i in 1node/rankings/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO rankings_1node FORMAT CSV"; done
-for i in 1node/uservisits/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO uservisits_1node FORMAT CSV"; done
-for i in 5nodes/rankings/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO rankings_5nodes_on_single FORMAT CSV"; done
-for i in 5nodes/uservisits/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO uservisits_5nodes_on_single FORMAT CSV"; done
-
- - -

Queries for obtaining data samples:

-
SELECT pageURL, pageRank FROM rankings_1node WHERE pageRank > 1000
-
-SELECT substring(sourceIP, 1, 8), sum(adRevenue) FROM uservisits_1node GROUP BY substring(sourceIP, 1, 8)
-
-SELECT
-    sourceIP,
-    sum(adRevenue) AS totalRevenue,
-    avg(pageRank) AS pageRank
-FROM rankings_1node ALL INNER JOIN
-(
-    SELECT
-        sourceIP,
-        destinationURL AS pageURL,
-        adRevenue
-    FROM uservisits_1node
-    WHERE (visitDate > '1980-01-01') AND (visitDate < '1980-04-01')
-) USING pageURL
-GROUP BY sourceIP
-ORDER BY totalRevenue DESC
-LIMIT 1
-
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- - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/docs/en/getting_started/example_datasets/criteo/index.html b/docs/build/docs/en/getting_started/example_datasets/criteo/index.html deleted file mode 100644 index e05aaa979b6..00000000000 --- a/docs/build/docs/en/getting_started/example_datasets/criteo/index.html +++ /dev/null @@ -1,2953 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Terabyte click logs from Criteo - ClickHouse Documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Terabyte of click logs from Criteo

-

Download the data from http://labs.criteo.com/downloads/download-terabyte-click-logs/

-

Create a table to import the log to:

-
CREATE TABLE criteo_log (date Date, clicked UInt8, int1 Int32, int2 Int32, int3 Int32, int4 Int32, int5 Int32, int6 Int32, int7 Int32, int8 Int32, int9 Int32, int10 Int32, int11 Int32, int12 Int32, int13 Int32, cat1 String, cat2 String, cat3 String, cat4 String, cat5 String, cat6 String, cat7 String, cat8 String, cat9 String, cat10 String, cat11 String, cat12 String, cat13 String, cat14 String, cat15 String, cat16 String, cat17 String, cat18 String, cat19 String, cat20 String, cat21 String, cat22 String, cat23 String, cat24 String, cat25 String, cat26 String) ENGINE = Log
-
- - -

Download the data:

-
for i in {00..23}; do echo $i; zcat datasets/criteo/day_${i#0}.gz | sed -r 's/^/2000-01-'${i/00/24}'\t/' | clickhouse-client --host=example-perftest01j --query="INSERT INTO criteo_log FORMAT TabSeparated"; done
-
- - -

Create a table for the converted data:

-
CREATE TABLE criteo
-(
-    date Date,
-    clicked UInt8,
-    int1 Int32,
-    int2 Int32,
-    int3 Int32,
-    int4 Int32,
-    int5 Int32,
-    int6 Int32,
-    int7 Int32,
-    int8 Int32,
-    int9 Int32,
-    int10 Int32,
-    int11 Int32,
-    int12 Int32,
-    int13 Int32,
-    icat1 UInt32,
-    icat2 UInt32,
-    icat3 UInt32,
-    icat4 UInt32,
-    icat5 UInt32,
-    icat6 UInt32,
-    icat7 UInt32,
-    icat8 UInt32,
-    icat9 UInt32,
-    icat10 UInt32,
-    icat11 UInt32,
-    icat12 UInt32,
-    icat13 UInt32,
-    icat14 UInt32,
-    icat15 UInt32,
-    icat16 UInt32,
-    icat17 UInt32,
-    icat18 UInt32,
-    icat19 UInt32,
-    icat20 UInt32,
-    icat21 UInt32,
-    icat22 UInt32,
-    icat23 UInt32,
-    icat24 UInt32,
-    icat25 UInt32,
-    icat26 UInt32
-) ENGINE = MergeTree(date, intHash32(icat1), (date, intHash32(icat1)), 8192)
-
- - -

Transform data from the raw log and put it in the second table:

-
INSERT INTO criteo SELECT date, clicked, int1, int2, int3, int4, int5, int6, int7, int8, int9, int10, int11, int12, int13, reinterpretAsUInt32(unhex(cat1)) AS icat1, reinterpretAsUInt32(unhex(cat2)) AS icat2, reinterpretAsUInt32(unhex(cat3)) AS icat3, reinterpretAsUInt32(unhex(cat4)) AS icat4, reinterpretAsUInt32(unhex(cat5)) AS icat5, reinterpretAsUInt32(unhex(cat6)) AS icat6, reinterpretAsUInt32(unhex(cat7)) AS icat7, reinterpretAsUInt32(unhex(cat8)) AS icat8, reinterpretAsUInt32(unhex(cat9)) AS icat9, reinterpretAsUInt32(unhex(cat10)) AS icat10, reinterpretAsUInt32(unhex(cat11)) AS icat11, reinterpretAsUInt32(unhex(cat12)) AS icat12, reinterpretAsUInt32(unhex(cat13)) AS icat13, reinterpretAsUInt32(unhex(cat14)) AS icat14, reinterpretAsUInt32(unhex(cat15)) AS icat15, reinterpretAsUInt32(unhex(cat16)) AS icat16, reinterpretAsUInt32(unhex(cat17)) AS icat17, reinterpretAsUInt32(unhex(cat18)) AS icat18, reinterpretAsUInt32(unhex(cat19)) AS icat19, reinterpretAsUInt32(unhex(cat20)) AS icat20, reinterpretAsUInt32(unhex(cat21)) AS icat21, reinterpretAsUInt32(unhex(cat22)) AS icat22, reinterpretAsUInt32(unhex(cat23)) AS icat23, reinterpretAsUInt32(unhex(cat24)) AS icat24, reinterpretAsUInt32(unhex(cat25)) AS icat25, reinterpretAsUInt32(unhex(cat26)) AS icat26 FROM criteo_log;
-
-DROP TABLE criteo_log;
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New York Taxi data

-

How to import the raw data

-

See https://github.com/toddwschneider/nyc-taxi-data and http://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html for the description of the dataset and instructions for downloading.

-

Downloading will result in about 227 GB of uncompressed data in CSV files. The download takes about an hour over a 1 Gbit connection (parallel downloading from s3.amazonaws.com recovers at least half of a 1 Gbit channel). -Some of the files might not download fully. Check the file sizes and re-download any that seem doubtful.

-

Some of the files might contain invalid rows. You can fix them as follows:

-
sed -E '/(.*,){18,}/d' data/yellow_tripdata_2010-02.csv > data/yellow_tripdata_2010-02.csv_
-sed -E '/(.*,){18,}/d' data/yellow_tripdata_2010-03.csv > data/yellow_tripdata_2010-03.csv_
-mv data/yellow_tripdata_2010-02.csv_ data/yellow_tripdata_2010-02.csv
-mv data/yellow_tripdata_2010-03.csv_ data/yellow_tripdata_2010-03.csv
-
- - -

Then the data must be pre-processed in PostgreSQL. This will create selections of points in the polygons (to match points on the map with the boroughs of New York City) and combine all the data into a single denormalized flat table by using a JOIN. To do this, you will need to install PostgreSQL with PostGIS support.

-

Be careful when running initialize_database.sh and manually re-check that all the tables were created correctly.

-

It takes about 20-30 minutes to process each month's worth of data in PostgreSQL, for a total of about 48 hours.

-

You can check the number of downloaded rows as follows:

-
time psql nyc-taxi-data -c "SELECT count(*) FROM trips;"
-##    count
- 1298979494
-(1 row)
-
-real    7m9.164s
-
- - -

(This is slightly more than 1.1 billion rows reported by Mark Litwintschik in a series of blog posts.)

-

The data in PostgreSQL uses 370 GB of space.

-

Exporting the data from PostgreSQL:

-
COPY
-(
-    SELECT trips.id,
-           trips.vendor_id,
-           trips.pickup_datetime,
-           trips.dropoff_datetime,
-           trips.store_and_fwd_flag,
-           trips.rate_code_id,
-           trips.pickup_longitude,
-           trips.pickup_latitude,
-           trips.dropoff_longitude,
-           trips.dropoff_latitude,
-           trips.passenger_count,
-           trips.trip_distance,
-           trips.fare_amount,
-           trips.extra,
-           trips.mta_tax,
-           trips.tip_amount,
-           trips.tolls_amount,
-           trips.ehail_fee,
-           trips.improvement_surcharge,
-           trips.total_amount,
-           trips.payment_type,
-           trips.trip_type,
-           trips.pickup,
-           trips.dropoff,
-
-           cab_types.type cab_type,
-
-           weather.precipitation_tenths_of_mm rain,
-           weather.snow_depth_mm,
-           weather.snowfall_mm,
-           weather.max_temperature_tenths_degrees_celsius max_temp,
-           weather.min_temperature_tenths_degrees_celsius min_temp,
-           weather.average_wind_speed_tenths_of_meters_per_second wind,
-
-           pick_up.gid pickup_nyct2010_gid,
-           pick_up.ctlabel pickup_ctlabel,
-           pick_up.borocode pickup_borocode,
-           pick_up.boroname pickup_boroname,
-           pick_up.ct2010 pickup_ct2010,
-           pick_up.boroct2010 pickup_boroct2010,
-           pick_up.cdeligibil pickup_cdeligibil,
-           pick_up.ntacode pickup_ntacode,
-           pick_up.ntaname pickup_ntaname,
-           pick_up.puma pickup_puma,
-
-           drop_off.gid dropoff_nyct2010_gid,
-           drop_off.ctlabel dropoff_ctlabel,
-           drop_off.borocode dropoff_borocode,
-           drop_off.boroname dropoff_boroname,
-           drop_off.ct2010 dropoff_ct2010,
-           drop_off.boroct2010 dropoff_boroct2010,
-           drop_off.cdeligibil dropoff_cdeligibil,
-           drop_off.ntacode dropoff_ntacode,
-           drop_off.ntaname dropoff_ntaname,
-           drop_off.puma dropoff_puma
-    FROM trips
-    LEFT JOIN cab_types
-        ON trips.cab_type_id = cab_types.id
-    LEFT JOIN central_park_weather_observations_raw weather
-        ON weather.date = trips.pickup_datetime::date
-    LEFT JOIN nyct2010 pick_up
-        ON pick_up.gid = trips.pickup_nyct2010_gid
-    LEFT JOIN nyct2010 drop_off
-        ON drop_off.gid = trips.dropoff_nyct2010_gid
-) TO '/opt/milovidov/nyc-taxi-data/trips.tsv';
-
- - -

The data snapshot is created at a speed of about 50 MB per second. While creating the snapshot, PostgreSQL reads from the disk at a speed of about 28 MB per second. -This takes about 5 hours. The resulting TSV file is 590612904969 bytes.

-

Create a temporary table in ClickHouse:

-
CREATE TABLE trips
-(
-trip_id                 UInt32,
-vendor_id               String,
-pickup_datetime         DateTime,
-dropoff_datetime        Nullable(DateTime),
-store_and_fwd_flag      Nullable(FixedString(1)),
-rate_code_id            Nullable(UInt8),
-pickup_longitude        Nullable(Float64),
-pickup_latitude         Nullable(Float64),
-dropoff_longitude       Nullable(Float64),
-dropoff_latitude        Nullable(Float64),
-passenger_count         Nullable(UInt8),
-trip_distance           Nullable(Float64),
-fare_amount             Nullable(Float32),
-extra                   Nullable(Float32),
-mta_tax                 Nullable(Float32),
-tip_amount              Nullable(Float32),
-tolls_amount            Nullable(Float32),
-ehail_fee               Nullable(Float32),
-improvement_surcharge   Nullable(Float32),
-total_amount            Nullable(Float32),
-payment_type            Nullable(String),
-trip_type               Nullable(UInt8),
-pickup                  Nullable(String),
-dropoff                 Nullable(String),
-cab_type                Nullable(String),
-precipitation           Nullable(UInt8),
-snow_depth              Nullable(UInt8),
-snowfall                Nullable(UInt8),
-max_temperature         Nullable(UInt8),
-min_temperature         Nullable(UInt8),
-average_wind_speed      Nullable(UInt8),
-pickup_nyct2010_gid     Nullable(UInt8),
-pickup_ctlabel          Nullable(String),
-pickup_borocode         Nullable(UInt8),
-pickup_boroname         Nullable(String),
-pickup_ct2010           Nullable(String),
-pickup_boroct2010       Nullable(String),
-pickup_cdeligibil       Nullable(FixedString(1)),
-pickup_ntacode          Nullable(String),
-pickup_ntaname          Nullable(String),
-pickup_puma             Nullable(String),
-dropoff_nyct2010_gid    Nullable(UInt8),
-dropoff_ctlabel         Nullable(String),
-dropoff_borocode        Nullable(UInt8),
-dropoff_boroname        Nullable(String),
-dropoff_ct2010          Nullable(String),
-dropoff_boroct2010      Nullable(String),
-dropoff_cdeligibil      Nullable(String),
-dropoff_ntacode         Nullable(String),
-dropoff_ntaname         Nullable(String),
-dropoff_puma            Nullable(String)
-) ENGINE = Log;
-
- - -

It is needed for converting fields to more correct data types and, if possible, to eliminate NULLs.

-
time clickhouse-client --query="INSERT INTO trips FORMAT TabSeparated" < trips.tsv
-
-real    75m56.214s
-
- - -

Data is read at a speed of 112-140 Mb/second. -Loading data into a Log type table in one stream took 76 minutes. -The data in this table uses 142 GB.

-

(Importing data directly from Postgres is also possible using COPY ... TO PROGRAM.)

-

Unfortunately, all the fields associated with the weather (precipitation...average_wind_speed) were filled with NULL. Because of this, we will remove them from the final data set.

-

To start, we'll create a table on a single server. Later we will make the table distributed.

-

Create and populate a summary table:

-
CREATE TABLE trips_mergetree
-ENGINE = MergeTree(pickup_date, pickup_datetime, 8192)
-AS SELECT
-
-trip_id,
-CAST(vendor_id AS Enum8('1' = 1, '2' = 2, 'CMT' = 3, 'VTS' = 4, 'DDS' = 5, 'B02512' = 10, 'B02598' = 11, 'B02617' = 12, 'B02682' = 13, 'B02764' = 14)) AS vendor_id,
-toDate(pickup_datetime) AS pickup_date,
-ifNull(pickup_datetime, toDateTime(0)) AS pickup_datetime,
-toDate(dropoff_datetime) AS dropoff_date,
-ifNull(dropoff_datetime, toDateTime(0)) AS dropoff_datetime,
-assumeNotNull(store_and_fwd_flag) IN ('Y', '1', '2') AS store_and_fwd_flag,
-assumeNotNull(rate_code_id) AS rate_code_id,
-assumeNotNull(pickup_longitude) AS pickup_longitude,
-assumeNotNull(pickup_latitude) AS pickup_latitude,
-assumeNotNull(dropoff_longitude) AS dropoff_longitude,
-assumeNotNull(dropoff_latitude) AS dropoff_latitude,
-assumeNotNull(passenger_count) AS passenger_count,
-assumeNotNull(trip_distance) AS trip_distance,
-assumeNotNull(fare_amount) AS fare_amount,
-assumeNotNull(extra) AS extra,
-assumeNotNull(mta_tax) AS mta_tax,
-assumeNotNull(tip_amount) AS tip_amount,
-assumeNotNull(tolls_amount) AS tolls_amount,
-assumeNotNull(ehail_fee) AS ehail_fee,
-assumeNotNull(improvement_surcharge) AS improvement_surcharge,
-assumeNotNull(total_amount) AS total_amount,
-CAST((assumeNotNull(payment_type) AS pt) IN ('CSH', 'CASH', 'Cash', 'CAS', 'Cas', '1') ? 'CSH' : (pt IN ('CRD', 'Credit', 'Cre', 'CRE', 'CREDIT', '2') ? 'CRE' : (pt IN ('NOC', 'No Charge', 'No', '3') ? 'NOC' : (pt IN ('DIS', 'Dispute', 'Dis', '4') ? 'DIS' : 'UNK'))) AS Enum8('CSH' = 1, 'CRE' = 2, 'UNK' = 0, 'NOC' = 3, 'DIS' = 4)) AS payment_type_,
-assumeNotNull(trip_type) AS trip_type,
-ifNull(toFixedString(unhex(pickup), 25), toFixedString('', 25)) AS pickup,
-ifNull(toFixedString(unhex(dropoff), 25), toFixedString('', 25)) AS dropoff,
-CAST(assumeNotNull(cab_type) AS Enum8('yellow' = 1, 'green' = 2, 'uber' = 3)) AS cab_type,
-
-assumeNotNull(pickup_nyct2010_gid) AS pickup_nyct2010_gid,
-toFloat32(ifNull(pickup_ctlabel, '0')) AS pickup_ctlabel,
-assumeNotNull(pickup_borocode) AS pickup_borocode,
-CAST(assumeNotNull(pickup_boroname) AS Enum8('Manhattan' = 1, 'Queens' = 4, 'Brooklyn' = 3, '' = 0, 'Bronx' = 2, 'Staten Island' = 5)) AS pickup_boroname,
-toFixedString(ifNull(pickup_ct2010, '000000'), 6) AS pickup_ct2010,
-toFixedString(ifNull(pickup_boroct2010, '0000000'), 7) AS pickup_boroct2010,
-CAST(assumeNotNull(ifNull(pickup_cdeligibil, ' ')) AS Enum8(' ' = 0, 'E' = 1, 'I' = 2)) AS pickup_cdeligibil,
-toFixedString(ifNull(pickup_ntacode, '0000'), 4) AS pickup_ntacode,
-
-CAST(assumeNotNull(pickup_ntaname) AS Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195)) AS pickup_ntaname,
-
-toUInt16(ifNull(pickup_puma, '0')) AS pickup_puma,
-
-assumeNotNull(dropoff_nyct2010_gid) AS dropoff_nyct2010_gid,
-toFloat32(ifNull(dropoff_ctlabel, '0')) AS dropoff_ctlabel,
-assumeNotNull(dropoff_borocode) AS dropoff_borocode,
-CAST(assumeNotNull(dropoff_boroname) AS Enum8('Manhattan' = 1, 'Queens' = 4, 'Brooklyn' = 3, '' = 0, 'Bronx' = 2, 'Staten Island' = 5)) AS dropoff_boroname,
-toFixedString(ifNull(dropoff_ct2010, '000000'), 6) AS dropoff_ct2010,
-toFixedString(ifNull(dropoff_boroct2010, '0000000'), 7) AS dropoff_boroct2010,
-CAST(assumeNotNull(ifNull(dropoff_cdeligibil, ' ')) AS Enum8(' ' = 0, 'E' = 1, 'I' = 2)) AS dropoff_cdeligibil,
-toFixedString(ifNull(dropoff_ntacode, '0000'), 4) AS dropoff_ntacode,
-
-CAST(assumeNotNull(dropoff_ntaname) AS Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195)) AS dropoff_ntaname,
-
-toUInt16(ifNull(dropoff_puma, '0')) AS dropoff_puma
-
-FROM trips
-
- - -

This takes 3030 seconds at a speed of about 428,000 rows per second. -To load it faster, you can create the table with the Log engine instead of MergeTree. In this case, the download works faster than 200 seconds.

-

The table uses 126 GB of disk space.

-
:) SELECT formatReadableSize(sum(bytes)) FROM system.parts WHERE table = 'trips_mergetree' AND active
-
-SELECT formatReadableSize(sum(bytes))
-FROM system.parts
-WHERE (table = 'trips_mergetree') AND active
-
-┌─formatReadableSize(sum(bytes))─┐
-│ 126.18 GiB                     │
-└────────────────────────────────┘
-
- - -

Among other things, you can run the OPTIMIZE query on MergeTree. But it's not required, since everything will be fine without it.

-

Results on single server

-

Q1:

-
SELECT cab_type, count(*) FROM trips_mergetree GROUP BY cab_type
-
- - -

0.490 seconds.

-

Q2:

-
SELECT passenger_count, avg(total_amount) FROM trips_mergetree GROUP BY passenger_count
-
- - -

1.224 seconds.

-

Q3:

-
SELECT passenger_count, toYear(pickup_date) AS year, count(*) FROM trips_mergetree GROUP BY passenger_count, year
-
- - -

2.104 seconds.

-

Q4:

-
SELECT passenger_count, toYear(pickup_date) AS year, round(trip_distance) AS distance, count(*)
-FROM trips_mergetree
-GROUP BY passenger_count, year, distance
-ORDER BY year, count(*) DESC
-
- - -

3.593 seconds.

-

The following server was used:

-

Two Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical kernels total, -128 GiB RAM, -8x6 TB HD on hardware RAID-5

-

Execution time is the best of three runsBut starting from the second run, queries read data from the file system cache. No further caching occurs: the data is read out and processed in each run.

-

Creating a table on three servers:

-

On each server:

-
CREATE TABLE default.trips_mergetree_third ( trip_id UInt32,  vendor_id Enum8('1' = 1, '2' = 2, 'CMT' = 3, 'VTS' = 4, 'DDS' = 5, 'B02512' = 10, 'B02598' = 11, 'B02617' = 12, 'B02682' = 13, 'B02764' = 14),  pickup_date Date,  pickup_datetime DateTime,  dropoff_date Date,  dropoff_datetime DateTime,  store_and_fwd_flag UInt8,  rate_code_id UInt8,  pickup_longitude Float64,  pickup_latitude Float64,  dropoff_longitude Float64,  dropoff_latitude Float64,  passenger_count UInt8,  trip_distance Float64,  fare_amount Float32,  extra Float32,  mta_tax Float32,  tip_amount Float32,  tolls_amount Float32,  ehail_fee Float32,  improvement_surcharge Float32,  total_amount Float32,  payment_type_ Enum8('UNK' = 0, 'CSH' = 1, 'CRE' = 2, 'NOC' = 3, 'DIS' = 4),  trip_type UInt8,  pickup FixedString(25),  dropoff FixedString(25),  cab_type Enum8('yellow' = 1, 'green' = 2, 'uber' = 3),  pickup_nyct2010_gid UInt8,  pickup_ctlabel Float32,  pickup_borocode UInt8,  pickup_boroname Enum8('' = 0, 'Manhattan' = 1, 'Bronx' = 2, 'Brooklyn' = 3, 'Queens' = 4, 'Staten Island' = 5),  pickup_ct2010 FixedString(6),  pickup_boroct2010 FixedString(7),  pickup_cdeligibil Enum8(' ' = 0, 'E' = 1, 'I' = 2),  pickup_ntacode FixedString(4),  pickup_ntaname Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195),  pickup_puma UInt16,  dropoff_nyct2010_gid UInt8,  dropoff_ctlabel Float32,  dropoff_borocode UInt8,  dropoff_boroname Enum8('' = 0, 'Manhattan' = 1, 'Bronx' = 2, 'Brooklyn' = 3, 'Queens' = 4, 'Staten Island' = 5),  dropoff_ct2010 FixedString(6),  dropoff_boroct2010 FixedString(7),  dropoff_cdeligibil Enum8(' ' = 0, 'E' = 1, 'I' = 2),  dropoff_ntacode FixedString(4),  dropoff_ntaname Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195),  dropoff_puma UInt16) ENGINE = MergeTree(pickup_date, pickup_datetime, 8192)
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On the source server:

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CREATE TABLE trips_mergetree_x3 AS trips_mergetree_third ENGINE = Distributed(perftest, default, trips_mergetree_third, rand())
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The following query redistributes data:

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INSERT INTO trips_mergetree_x3 SELECT * FROM trips_mergetree
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This takes 2454 seconds.

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On three servers:

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Q1: 0.212 seconds. -Q2: 0.438 seconds. -Q3: 0.733 seconds. -Q4: 1.241 seconds.

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No surprises here, since the queries are scaled linearly.

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We also have results from a cluster of 140 servers:

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Q1: 0.028 sec. -Q2: 0.043 sec. -Q3: 0.051 sec. -Q4: 0.072 sec.

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In this case, the query processing time is determined above all by network latency. -We ran queries using a client located in a Yandex datacenter in Finland on a cluster in Russia, which added about 20 ms of latency.

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Summary

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nodes   Q1     Q2     Q3     Q4
-  1  0.490  1.224  2.104  3.593
-  3  0.212  0.438  0.733  1.241
-140  0.028  0.043  0.051  0.072
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OnTime

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This performance test was created by Vadim Tkachenko. See:

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Downloading data:

-
for s in `seq 1987 2017`
-do
-for m in `seq 1 12`
-do
-wget http://transtats.bts.gov/PREZIP/On_Time_On_Time_Performance_${s}_${m}.zip
-done
-done
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(from https://github.com/Percona-Lab/ontime-airline-performance/blob/master/download.sh )

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Creating a table:

-
CREATE TABLE `ontime` (
-  `Year` UInt16,
-  `Quarter` UInt8,
-  `Month` UInt8,
-  `DayofMonth` UInt8,
-  `DayOfWeek` UInt8,
-  `FlightDate` Date,
-  `UniqueCarrier` FixedString(7),
-  `AirlineID` Int32,
-  `Carrier` FixedString(2),
-  `TailNum` String,
-  `FlightNum` String,
-  `OriginAirportID` Int32,
-  `OriginAirportSeqID` Int32,
-  `OriginCityMarketID` Int32,
-  `Origin` FixedString(5),
-  `OriginCityName` String,
-  `OriginState` FixedString(2),
-  `OriginStateFips` String,
-  `OriginStateName` String,
-  `OriginWac` Int32,
-  `DestAirportID` Int32,
-  `DestAirportSeqID` Int32,
-  `DestCityMarketID` Int32,
-  `Dest` FixedString(5),
-  `DestCityName` String,
-  `DestState` FixedString(2),
-  `DestStateFips` String,
-  `DestStateName` String,
-  `DestWac` Int32,
-  `CRSDepTime` Int32,
-  `DepTime` Int32,
-  `DepDelay` Int32,
-  `DepDelayMinutes` Int32,
-  `DepDel15` Int32,
-  `DepartureDelayGroups` String,
-  `DepTimeBlk` String,
-  `TaxiOut` Int32,
-  `WheelsOff` Int32,
-  `WheelsOn` Int32,
-  `TaxiIn` Int32,
-  `CRSArrTime` Int32,
-  `ArrTime` Int32,
-  `ArrDelay` Int32,
-  `ArrDelayMinutes` Int32,
-  `ArrDel15` Int32,
-  `ArrivalDelayGroups` Int32,
-  `ArrTimeBlk` String,
-  `Cancelled` UInt8,
-  `CancellationCode` FixedString(1),
-  `Diverted` UInt8,
-  `CRSElapsedTime` Int32,
-  `ActualElapsedTime` Int32,
-  `AirTime` Int32,
-  `Flights` Int32,
-  `Distance` Int32,
-  `DistanceGroup` UInt8,
-  `CarrierDelay` Int32,
-  `WeatherDelay` Int32,
-  `NASDelay` Int32,
-  `SecurityDelay` Int32,
-  `LateAircraftDelay` Int32,
-  `FirstDepTime` String,
-  `TotalAddGTime` String,
-  `LongestAddGTime` String,
-  `DivAirportLandings` String,
-  `DivReachedDest` String,
-  `DivActualElapsedTime` String,
-  `DivArrDelay` String,
-  `DivDistance` String,
-  `Div1Airport` String,
-  `Div1AirportID` Int32,
-  `Div1AirportSeqID` Int32,
-  `Div1WheelsOn` String,
-  `Div1TotalGTime` String,
-  `Div1LongestGTime` String,
-  `Div1WheelsOff` String,
-  `Div1TailNum` String,
-  `Div2Airport` String,
-  `Div2AirportID` Int32,
-  `Div2AirportSeqID` Int32,
-  `Div2WheelsOn` String,
-  `Div2TotalGTime` String,
-  `Div2LongestGTime` String,
-  `Div2WheelsOff` String,
-  `Div2TailNum` String,
-  `Div3Airport` String,
-  `Div3AirportID` Int32,
-  `Div3AirportSeqID` Int32,
-  `Div3WheelsOn` String,
-  `Div3TotalGTime` String,
-  `Div3LongestGTime` String,
-  `Div3WheelsOff` String,
-  `Div3TailNum` String,
-  `Div4Airport` String,
-  `Div4AirportID` Int32,
-  `Div4AirportSeqID` Int32,
-  `Div4WheelsOn` String,
-  `Div4TotalGTime` String,
-  `Div4LongestGTime` String,
-  `Div4WheelsOff` String,
-  `Div4TailNum` String,
-  `Div5Airport` String,
-  `Div5AirportID` Int32,
-  `Div5AirportSeqID` Int32,
-  `Div5WheelsOn` String,
-  `Div5TotalGTime` String,
-  `Div5LongestGTime` String,
-  `Div5WheelsOff` String,
-  `Div5TailNum` String
-) ENGINE = MergeTree(FlightDate, (Year, FlightDate), 8192)
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Loading data:

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for i in *.zip; do echo $i; unzip -cq $i '*.csv' | sed 's/\.00//g' | clickhouse-client --host=example-perftest01j --query="INSERT INTO ontime FORMAT CSVWithNames"; done
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Queries:

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Q0.

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select avg(c1) from (select Year, Month, count(*) as c1 from ontime group by Year, Month);
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Q1. The number of flights per day from the year 2000 to 2008

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SELECT DayOfWeek, count(*) AS c FROM ontime WHERE Year >= 2000 AND Year <= 2008 GROUP BY DayOfWeek ORDER BY c DESC;
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Q2. The number of flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008

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SELECT DayOfWeek, count(*) AS c FROM ontime WHERE DepDelay>10 AND Year >= 2000 AND Year <= 2008 GROUP BY DayOfWeek ORDER BY c DESC
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Q3. The number of delays by airport for 2000-2008

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SELECT Origin, count(*) AS c FROM ontime WHERE DepDelay>10 AND Year >= 2000 AND Year <= 2008 GROUP BY Origin ORDER BY c DESC LIMIT 10
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Q4. The number of delays by carrier for 2007

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SELECT Carrier, count(*) FROM ontime WHERE DepDelay>10  AND Year = 2007 GROUP BY Carrier ORDER BY count(*) DESC
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Q5. The percentage of delays by carrier for 2007

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SELECT Carrier, c, c2, c*1000/c2 as c3
-FROM
-(
-    SELECT
-        Carrier,
-        count(*) AS c
-    FROM ontime
-    WHERE DepDelay>10
-        AND Year=2007
-    GROUP BY Carrier
-)
-ANY INNER JOIN
-(
-    SELECT
-        Carrier,
-        count(*) AS c2
-    FROM ontime
-    WHERE Year=2007
-    GROUP BY Carrier
-) USING Carrier
-ORDER BY c3 DESC;
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Better version of the same query:

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SELECT Carrier, avg(DepDelay > 10) * 1000 AS c3 FROM ontime WHERE Year = 2007 GROUP BY Carrier ORDER BY Carrier
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Q6. The previous request for a broader range of years, 2000-2008

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SELECT Carrier, c, c2, c*1000/c2 as c3
-FROM
-(
-    SELECT
-        Carrier,
-        count(*) AS c
-    FROM ontime
-    WHERE DepDelay>10
-        AND Year >= 2000 AND Year <= 2008
-    GROUP BY Carrier
-)
-ANY INNER JOIN
-(
-    SELECT
-        Carrier,
-        count(*) AS c2
-    FROM ontime
-    WHERE Year >= 2000 AND Year <= 2008
-    GROUP BY Carrier
-) USING Carrier
-ORDER BY c3 DESC;
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- - -

Better version of the same query:

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SELECT Carrier, avg(DepDelay > 10) * 1000 AS c3 FROM ontime WHERE Year >= 2000 AND Year <= 2008 GROUP BY Carrier ORDER BY Carrier
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Q7. Percentage of flights delayed for more than 10 minutes, by year

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SELECT Year, c1/c2
-FROM
-(
-    select
-        Year,
-        count(*)*1000 as c1
-    from ontime
-    WHERE DepDelay>10
-    GROUP BY Year
-)
-ANY INNER JOIN
-(
-    select
-        Year,
-        count(*) as c2
-    from ontime
-    GROUP BY Year
-) USING (Year)
-ORDER BY Year
-
- - -

Better version of the same query:

-
SELECT Year, avg(DepDelay > 10) FROM ontime GROUP BY Year ORDER BY Year
-
- - -

Q8. The most popular destinations by the number of directly connected cities for various year ranges

-
SELECT DestCityName, uniqExact(OriginCityName) AS u FROM ontime WHERE Year >= 2000 and Year <= 2010 GROUP BY DestCityName ORDER BY u DESC LIMIT 10;
-
- - -

Q9.

-
select Year, count(*) as c1 from ontime group by Year;
-
- - -

Q10.

-
select
-   min(Year), max(Year), Carrier, count(*) as cnt,
-   sum(ArrDelayMinutes>30) as flights_delayed,
-   round(sum(ArrDelayMinutes>30)/count(*),2) as rate
-FROM ontime
-WHERE
-   DayOfWeek not in (6,7) and OriginState not in ('AK', 'HI', 'PR', 'VI')
-   and DestState not in ('AK', 'HI', 'PR', 'VI')
-   and FlightDate < '2010-01-01'
-GROUP by Carrier
-HAVING cnt > 100000 and max(Year) > 1990
-ORDER by rate DESC
-LIMIT 1000;
-
- - -

Bonus:

-
SELECT avg(cnt) FROM (SELECT Year,Month,count(*) AS cnt FROM ontime WHERE DepDel15=1 GROUP BY Year,Month)
-
-select avg(c1) from (select Year,Month,count(*) as c1 from ontime group by Year,Month)
-
-SELECT DestCityName, uniqExact(OriginCityName) AS u FROM ontime GROUP BY DestCityName ORDER BY u DESC LIMIT 10;
-
-SELECT OriginCityName, DestCityName, count() AS c FROM ontime GROUP BY OriginCityName, DestCityName ORDER BY c DESC LIMIT 10;
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-SELECT OriginCityName, count() AS c FROM ontime GROUP BY OriginCityName ORDER BY c DESC LIMIT 10;
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Star Schema Benchmark

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Compiling dbgen: https://github.com/vadimtk/ssb-dbgen

-
git clone git@github.com:vadimtk/ssb-dbgen.git
-cd ssb-dbgen
-make
-
- - -

There will be some warnings during the process, but this is normal.

-

Place dbgen and dists.dss in any location with 800 GB of free disk space.

-

Generating data:

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./dbgen -s 1000 -T c
-./dbgen -s 1000 -T l
-
- - -

Creating tables in ClickHouse:

-
CREATE TABLE lineorder (
-        LO_ORDERKEY             UInt32,
-        LO_LINENUMBER           UInt8,
-        LO_CUSTKEY              UInt32,
-        LO_PARTKEY              UInt32,
-        LO_SUPPKEY              UInt32,
-        LO_ORDERDATE            Date,
-        LO_ORDERPRIORITY        String,
-        LO_SHIPPRIORITY         UInt8,
-        LO_QUANTITY             UInt8,
-        LO_EXTENDEDPRICE        UInt32,
-        LO_ORDTOTALPRICE        UInt32,
-        LO_DISCOUNT             UInt8,
-        LO_REVENUE              UInt32,
-        LO_SUPPLYCOST           UInt32,
-        LO_TAX                  UInt8,
-        LO_COMMITDATE           Date,
-        LO_SHIPMODE             String
-)Engine=MergeTree(LO_ORDERDATE,(LO_ORDERKEY,LO_LINENUMBER,LO_ORDERDATE),8192);
-
-CREATE TABLE customer (
-        C_CUSTKEY       UInt32,
-        C_NAME          String,
-        C_ADDRESS       String,
-        C_CITY          String,
-        C_NATION        String,
-        C_REGION        String,
-        C_PHONE         String,
-        C_MKTSEGMENT    String,
-        C_FAKEDATE      Date
-)Engine=MergeTree(C_FAKEDATE,(C_CUSTKEY,C_FAKEDATE),8192);
-
-CREATE TABLE part (
-        P_PARTKEY       UInt32,
-        P_NAME          String,
-        P_MFGR          String,
-        P_CATEGORY      String,
-        P_BRAND         String,
-        P_COLOR         String,
-        P_TYPE          String,
-        P_SIZE          UInt8,
-        P_CONTAINER     String,
-        P_FAKEDATE      Date
-)Engine=MergeTree(P_FAKEDATE,(P_PARTKEY,P_FAKEDATE),8192);
-
-CREATE TABLE lineorderd AS lineorder ENGINE = Distributed(perftest_3shards_1replicas, default, lineorder, rand());
-CREATE TABLE customerd AS customer ENGINE = Distributed(perftest_3shards_1replicas, default, customer, rand());
-CREATE TABLE partd AS part ENGINE = Distributed(perftest_3shards_1replicas, default, part, rand());
-
- - -

For testing on a single server, just use MergeTree tables. -For distributed testing, you need to configure the perftest_3shards_1replicas cluster in the config file. -Next, create MergeTree tables on each server and a Distributed above them.

-

Downloading data (change 'customer' to 'customerd' in the distributed version):

-
cat customer.tbl | sed 's/$/2000-01-01/' | clickhouse-client --query "INSERT INTO customer FORMAT CSV"
-cat lineorder.tbl | clickhouse-client --query "INSERT INTO lineorder FORMAT CSV"
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WikiStat

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See: http://dumps.wikimedia.org/other/pagecounts-raw/

-

Creating a table:

-
CREATE TABLE wikistat
-(
-    date Date,
-    time DateTime,
-    project String,
-    subproject String,
-    path String,
-    hits UInt64,
-    size UInt64
-) ENGINE = MergeTree(date, (path, time), 8192);
-
- - -

Loading data:

-
for i in {2007..2016}; do for j in {01..12}; do echo $i-$j >&2; curl -sSL "http://dumps.wikimedia.org/other/pagecounts-raw/$i/$i-$j/" | grep -oE 'pagecounts-[0-9]+-[0-9]+\.gz'; done; done | sort | uniq | tee links.txt
-cat links.txt | while read link; do wget http://dumps.wikimedia.org/other/pagecounts-raw/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1/')/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1-\2/')/$link; done
-ls -1 /opt/wikistat/ | grep gz | while read i; do echo $i; gzip -cd /opt/wikistat/$i | ./wikistat-loader --time="$(echo -n $i | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\.gz/\1-\2-\3 \4-00-00/')" | clickhouse-client --query="INSERT INTO wikistat FORMAT TabSeparated"; done
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Getting started

-

System requirements

-

This is not a cross-platform system. It requires Linux Ubuntu Precise (12.04) or newer, with x86_64 architecture and support for the SSE 4.2 instruction set. -To check for SSE 4.2:

-
grep -q sse4_2 /proc/cpuinfo && echo "SSE 4.2 supported" || echo "SSE 4.2 not supported"
-
- - -

We recommend using Ubuntu Trusty, Ubuntu Xenial, or Ubuntu Precise. -The terminal must use UTF-8 encoding (the default in Ubuntu).

-

Installation

-

For testing and development, the system can be installed on a single server or on a desktop computer.

-

Installing from packages for Debian/Ubuntu

-

In /etc/apt/sources.list (or in a separate /etc/apt/sources.list.d/clickhouse.list file), add the repository:

-
deb http://repo.yandex.ru/clickhouse/deb/stable/ main/
-
- - -

If you want to use the most recent test version, replace 'stable' with 'testing'.

-

Then run:

-
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4    # optional
-sudo apt-get update
-sudo apt-get install clickhouse-client clickhouse-server
-
- - -

You can also download and install packages manually from here: https://repo.yandex.ru/clickhouse/deb/stable/main/.

-

ClickHouse contains access restriction settings. They are located in the 'users.xml' file (next to 'config.xml'). -By default, access is allowed from anywhere for the 'default' user, without a password. See 'user/default/networks'. -For more information, see the section "Configuration files".

-

Installing from sources

-

To compile, follow the instructions: build.md

-

You can compile packages and install them. -You can also use programs without installing packages.

-
Client: dbms/src/Client/
-Server: dbms/src/Server/
-
- - -

For the server, create a catalog with data, such as:

-
/opt/clickhouse/data/default/
-/opt/clickhouse/metadata/default/
-
- - -

(Configurable in the server config.) -Run 'chown' for the desired user.

-

Note the path to logs in the server config (src/dbms/src/Server/config.xml).

-

Other installation methods

-

Docker image: https://hub.docker.com/r/yandex/clickhouse-server/

-

RPM packages for CentOS or RHEL: https://github.com/Altinity/clickhouse-rpm-install

-

Gentoo overlay: https://github.com/kmeaw/clickhouse-overlay

-

Launch

-

To start the server (as a daemon), run:

-
sudo service clickhouse-server start
-
- - -

See the logs in the /var/log/clickhouse-server/ directory.

-

If the server doesn't start, check the configurations in the file /etc/clickhouse-server/config.xml.

-

You can also launch the server from the console:

-
clickhouse-server --config-file=/etc/clickhouse-server/config.xml
-
- - -

In this case, the log will be printed to the console, which is convenient during development. -If the configuration file is in the current directory, you don't need to specify the '--config-file' parameter. By default, it uses './config.xml'.

-

You can use the command-line client to connect to the server:

-
clickhouse-client
-
- - -

The default parameters indicate connecting with localhost:9000 on behalf of the user 'default' without a password. -The client can be used for connecting to a remote server. Example:

-
clickhouse-client --host=example.com
-
- - -

For more information, see the section "Command-line client".

-

Checking the system:

-
milovidov@hostname:~/work/metrica/src/dbms/src/Client$ ./clickhouse-client
-ClickHouse client version 0.0.18749.
-Connecting to localhost:9000.
-Connected to ClickHouse server version 0.0.18749.
-
-:) SELECT 1
-
-SELECT 1
-
-┌─1─┐
-│ 1 │
-└───┘
-
-1 rows in set. Elapsed: 0.003 sec.
-
-:)
-
- - -

Congratulations, the system works!

-

To continue experimenting, you can try to download from the test data sets.

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What is ClickHouse?

-

ClickHouse is a columnar DBMS for OLAP.

-

In a "normal" row-oriented DBMS, data is stored in this order:

-
5123456789123456789     1       Eurobasket - Greece - Bosnia and Herzegovina - example.com      1       2011-09-01 01:03:02     6274717   1294101174      11409   612345678912345678      0       33      6       http://www.example.com/basketball/team/123/match/456789.html http://www.example.com/basketball/team/123/match/987654.html       0       1366    768     32      10      3183      0       0       13      0\0     1       1       0       0                       2011142 -1      0               0       01321     613     660     2011-09-01 08:01:17     0       0       0       0       utf-8   1466    0       0       0       5678901234567890123               277789954       0       0       0       0       0
-5234985259563631958     0       Consulting, Tax assessment, Accounting, Law       1       2011-09-01 01:03:02     6320881   2111222333      213     6458937489576391093     0       3       2       http://www.example.ru/         0       800     600       16      10      2       153.1   0       0       10      63      1       1       0       0                       2111678 000       0       588     368     240     2011-09-01 01:03:17     4       0       60310   0       windows-1251    1466    0       000               778899001       0       0       0       0       0
-...
-
- - -

In order words, all the values related to a row are stored next to each other. -Examples of a row-oriented DBMS are MySQL, Postgres, MS SQL Server, and others.

-

In a column-oriented DBMS, data is stored like this:

-
WatchID:    5385521489354350662     5385521490329509958     5385521489953706054     5385521490476781638     5385521490583269446     5385521490218868806     5385521491437850694   5385521491090174022      5385521490792669254     5385521490420695110     5385521491532181574     5385521491559694406     5385521491459625030     5385521492275175494   5385521492781318214      5385521492710027334     5385521492955615302     5385521493708759110     5385521494506434630     5385521493104611398
-JavaEnable: 1       0       1       0       0       0       1       0       1       1       1       1       1       1       0       1       0       0       1       1
-Title:      Yandex  Announcements - Investor Relations - Yandex     Yandex — Contact us — Moscow    Yandex — Mission        Ru      Yandex — History — History of Yandex    Yandex Financial Releases - Investor Relations - Yandex Yandex — Locations      Yandex Board of Directors - Corporate Governance - Yandex       Yandex — Technologies
-GoodEvent:  1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1
-EventTime:  2016-05-18 05:19:20     2016-05-18 08:10:20     2016-05-18 07:38:00     2016-05-18 01:13:08     2016-05-18 00:04:06     2016-05-18 04:21:30     2016-05-18 00:34:16     2016-05-18 07:35:49     2016-05-18 11:41:59     2016-05-18 01:13:32
-
- - -

These examples only show the order that data is arranged in. -The values from different columns are stored separately, and data from the same column is stored together.

-

Examples of column-oriented DBMSs: Vertica, Paraccel (Actian Matrix) (Amazon Redshift), Sybase IQ, Exasol, Infobright, InfiniDB, MonetDB (VectorWise) (Actian Vector), LucidDB, SAP HANA, Google Dremel, Google PowerDrill, Druid, kdb+, and so on.

-

Different orders for storing data are better suited to different scenarios. -The data access scenario refers to what queries are made, how often, and in what proportion; how much data is read for each type of query – rows, columns, and bytes; the relationship between reading and updating data; the working size of the data and how locally it is used; whether transactions are used, and how isolated they are; requirements for data replication and logical integrity; requirements for latency and throughput for each type of query, and so on.

-

The higher the load on the system, the more important it is to customize the system to the scenario, and the more specific this customization becomes. There is no system that is equally well-suited to significantly different scenarios. If a system is adaptable to a wide set of scenarios, under a high load, the system will handle all the scenarios equally poorly, or will work well for just one of the scenarios.

-

We'll say that the following is true for the OLAP (online analytical processing) scenario:

-
    -
  • The vast majority of requests are for read access.
    • -
  • Data is updated in fairly large batches (> 1000 rows), not by single rows; or it is not updated at all.
    • -
  • Data is added to the DB but is not modified.
    • -
  • For reads, quite a large number of rows are extracted from the DB, but only a small subset of columns.
    • -
  • Tables are "wide," meaning they contain a large number of columns.
    • -
  • Queries are relatively rare (usually hundreds of queries per server or less per second).
    • -
  • For simple queries, latencies around 50 ms are allowed.
    • -
  • Column values are fairly small: numbers and short strings (for example, 60 bytes per URL).
    • -
  • Requires high throughput when processing a single query (up to billions of rows per second per server).
    • -
  • There are no transactions.
    • -
  • Low requirements for data consistency.
    • -
  • There is one large table per query. All tables are small, except for one.
    • -
  • A query result is significantly smaller than the source data. In other words, data is filtered or aggregated. The result fits in a single server's RAM.
    • -
-

It is easy to see that the OLAP scenario is very different from other popular scenarios (such as OLTP or Key-Value access). So it doesn't make sense to try to use OLTP or a Key-Value DB for processing analytical queries if you want to get decent performance. For example, if you try to use MongoDB or Elliptics for analytics, you will get very poor performance compared to OLAP databases.

-

Columnar-oriented databases are better suited to OLAP scenarios (at least 100 times better in processing speed for most queries), for the following reasons:

-
    -
  1. For I/O.
    2. -
  2. For an analytical query, only a small number of table columns need to be read. In a column-oriented database, you can read just the data you need. For example, if you need 5 columns out of 100, you can expect a 20-fold reduction in I/O.
    3. -
  3. Since data is read in packets, it is easier to compress. Data in columns is also easier to compress. This further reduces the I/O volume.
    4. -
  4. Due to the reduced I/O, more data fits in the system cache.
    5. -
-

For example, the query "count the number of records for each advertising platform" requires reading one "advertising platform ID" column, which takes up 1 byte uncompressed. If most of the traffic was not from advertising platforms, you can expect at least 10-fold compression of this column. When using a quick compression algorithm, data decompression is possible at a speed of at least several gigabytes of uncompressed data per second. In other words, this query can be processed at a speed of approximately several billion rows per second on a single server. This speed is actually achieved in practice.

-

Example:

-
milovidov@hostname:~$ clickhouse-client
-ClickHouse client version 0.0.52053.
-Connecting to localhost:9000.
-Connected to ClickHouse server version 0.0.52053.
-
-:) SELECT CounterID, count() FROM hits GROUP BY CounterID ORDER BY count() DESC LIMIT 20
-
-SELECT
-    CounterID,
-    count()
-FROM hits
-GROUP BY CounterID
-ORDER BY count() DESC
-LIMIT 20
-
-┌─CounterID─┬──count()─┐
-│    114208 │ 56057344 │
-│    115080 │ 51619590 │
-│      3228 │ 44658301 │
-│     38230 │ 42045932 │
-│    145263 │ 42042158 │
-│     91244 │ 38297270 │
-│    154139 │ 26647572 │
-│    150748 │ 24112755 │
-│    242232 │ 21302571 │
-│    338158 │ 13507087 │
-│     62180 │ 12229491 │
-│     82264 │ 12187441 │
-│    232261 │ 12148031 │
-│    146272 │ 11438516 │
-│    168777 │ 11403636 │
-│   4120072 │ 11227824 │
-│  10938808 │ 10519739 │
-│     74088 │  9047015 │
-│    115079 │  8837972 │
-│    337234 │  8205961 │
-└───────────┴──────────┘
-
-20 rows in set. Elapsed: 0.153 sec. Processed 1.00 billion rows, 4.00 GB (6.53 billion rows/s., 26.10 GB/s.)
-
-:)
-
- - -
    -
  1. For CPU.
    2. -
-

Since executing a query requires processing a large number of rows, it helps to dispatch all operations for entire vectors instead of for separate rows, or to implement the query engine so that there is almost no dispatching cost. If you don't do this, with any half-decent disk subsystem, the query interpreter inevitably stalls the CPU. -It makes sense to both store data in columns and process it, when possible, by columns.

-

There are two ways to do this:

-
    -
  1. -

    A vector engine. All operations are written for vectors, instead of for separate values. This means you don't need to call operations very often, and dispatching costs are negligible. Operation code contains an optimized internal cycle.

    -
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  2. -

    Code generation. The code generated for the query has all the indirect calls in it.

    -
    3. -
-

This is not done in "normal" databases, because it doesn't make sense when running simple queries. However, there are exceptions. For example, MemSQL uses code generation to reduce latency when processing SQL queries. (For comparison, analytical DBMSs require optimization of throughput, not latency.)

-

Note that for CPU efficiency, the query language must be declarative (SQL or MDX), or at least a vector (J, K). The query should only contain implicit loops, allowing for optimization.

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Command-line client

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To work from the command line, you can use clickhouse-client:

-
$ clickhouse-client
-ClickHouse client version 0.0.26176.
-Connecting to localhost:9000.
-Connected to ClickHouse server version 0.0.26176.
-
-:)
-
- - -

The client supports command-line options and configuration files. For more information, see "Configuring".

-

Usage

-

The client can be used in interactive and non-interactive (batch) mode. -To use batch mode, specify the 'query' parameter, or send data to 'stdin' (it verifies that 'stdin' is not a terminal), or both. -Similar to the HTTP interface, when using the 'query' parameter and sending data to 'stdin', the request is a concatenation of the 'query' parameter, a line feed, and the data in 'stdin'. This is convenient for large INSERT queries.

-

Example of using the client to insert data:

-
echo -ne "1, 'some text', '2016-08-14 00:00:00'\n2, 'some more text', '2016-08-14 00:00:01'" | clickhouse-client --database=test --query="INSERT INTO test FORMAT CSV";
-
-cat <<_EOF | clickhouse-client --database=test --query="INSERT INTO test FORMAT CSV";
-3, 'some text', '2016-08-14 00:00:00'
-4, 'some more text', '2016-08-14 00:00:01'
-_EOF
-
-cat file.csv | clickhouse-client --database=test --query="INSERT INTO test FORMAT CSV";
-
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In batch mode, the default data format is TabSeparated. You can set the format in the FORMAT clause of the query.

-

By default, you can only process a single query in batch mode. To make multiple queries from a "script," use the --multiquery parameter. This works for all queries except INSERT. Query results are output consecutively without additional separators. -Similarly, to process a large number of queries, you can run 'clickhouse-client' for each query. Note that it may take tens of milliseconds to launch the 'clickhouse-client' program.

-

In interactive mode, you get a command line where you can enter queries.

-

If 'multiline' is not specified (the default):To run the query, press Enter. The semicolon is not necessary at the end of the query. To enter a multiline query, enter a backslash \ before the line feed. After you press Enter, you will be asked to enter the next line of the query.

-

If multiline is specified:To run a query, end it with a semicolon and press Enter. If the semicolon was omitted at the end of the entered line, you will be asked to enter the next line of the query.

-

Only a single query is run, so everything after the semicolon is ignored.

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You can specify \G instead of or after the semicolon. This indicates Vertical format. In this format, each value is printed on a separate line, which is convenient for wide tables. This unusual feature was added for compatibility with the MySQL CLI.

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The command line is based on 'readline' (and 'history' or 'libedit', or without a library, depending on the build). In other words, it uses the familiar keyboard shortcuts and keeps a history. -The history is written to ~/.clickhouse-client-history.

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By default, the format used is PrettyCompact. You can change the format in the FORMAT clause of the query, or by specifying \G at the end of the query, using the --format or --vertical argument in the command line, or using the client configuration file.

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To exit the client, press Ctrl+D (or Ctrl+C), or enter one of the following instead of a query:"exit", "quit", "logout", "учше", "йгше", "дщпщге", "exit;", "quit;", "logout;", "учшеж", "йгшеж", "дщпщгеж", "q", "й", "q", "Q", ":q", "й", "Й", "Жй"

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When processing a query, the client shows:

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    2. -
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    3. -
  3. The result in the specified format.
    4. -
  4. The number of lines in the result, the time passed, and the average speed of query processing.
    5. -
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You can cancel a long query by pressing Ctrl+C. However, you will still need to wait a little for the server to abort the request. It is not possible to cancel a query at certain stages. If you don't wait and press Ctrl+C a second time, the client will exit.

-

The command-line client allows passing external data (external temporary tables) for querying. For more information, see the section "External data for query processing".

-

-

Configuring

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You can pass parameters to clickhouse-client (all parameters have a default value) using:

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    -
  • From the Command Line
    • -
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Command-line options override the default values and settings in configuration files.

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    -
  • Configuration files.
    • -
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Settings in the configuration files override the default values.

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Command line options

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    -
  • --host, -h -– The server name, 'localhost' by default. You can use either the name or the IPv4 or IPv6 address.
    • -
  • --port – The port to connect to. Default value: 9000. Note that the HTTP interface and the native interface use different ports.
    • -
  • --user, -u – The username. Default value: default.
    • -
  • --password – The password. Default value: empty string.
    • -
  • --query, -q – The query to process when using non-interactive mode.
    • -
  • --database, -d – Select the current default database. Default value: the current database from the server settings ('default' by default).
    • -
  • --multiline, -m – If specified, allow multiline queries (do not send the query on Enter).
    • -
  • --multiquery, -n – If specified, allow processing multiple queries separated by commas. Only works in non-interactive mode.
    • -
  • --format, -f – Use the specified default format to output the result.
    • -
  • --vertical, -E – If specified, use the Vertical format by default to output the result. This is the same as '--format=Vertical'. In this format, each value is printed on a separate line, which is helpful when displaying wide tables.
    • -
  • --time, -t – If specified, print the query execution time to 'stderr' in non-interactive mode.
    • -
  • --stacktrace – If specified, also print the stack trace if an exception occurs.
    • -
  • -config-file – The name of the configuration file.
    • -
-

Configuration files

-

clickhouse-client uses the first existing file of the following:

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    -
  • Defined in the -config-file parameter.
    • -
  • ./clickhouse-client.xml
    • -
  • \~/.clickhouse-client/config.xml
    • -
  • /etc/clickhouse-client/config.xml
    • -
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Example of a config file:

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<config>
-    <user>username</user>
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The HTTP interface lets you use ClickHouse on any platform from any programming language. We use it for working from Java and Perl, as well as shell scripts. In other departments, the HTTP interface is used from Perl, Python, and Go. The HTTP interface is more limited than the native interface, but it has better compatibility.

-

By default, clickhouse-server listens for HTTP on port 8123 (this can be changed in the config). -If you make a GET / request without parameters, it returns the string "Ok" (with a line feed at the end). You can use this in health-check scripts.

-
$ curl 'http://localhost:8123/'
-Ok.
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Send the request as a URL 'query' parameter, or as a POST. Or send the beginning of the query in the 'query' parameter, and the rest in the POST (we'll explain later why this is necessary). The size of the URL is limited to 16 KB, so keep this in mind when sending large queries.

-

If successful, you receive the 200 response code and the result in the response body. -If an error occurs, you receive the 500 response code and an error description text in the response body.

-

When using the GET method, 'readonly' is set. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.

-

Examples:

-
$ curl 'http://localhost:8123/?query=SELECT%201'
-1
-
-$ wget -O- -q 'http://localhost:8123/?query=SELECT 1'
-1
-
-$ GET 'http://localhost:8123/?query=SELECT 1'
-1
-
-$ echo -ne 'GET /?query=SELECT%201 HTTP/1.0\r\n\r\n' | nc localhost 8123
-HTTP/1.0 200 OK
-Connection: Close
-Date: Fri, 16 Nov 2012 19:21:50 GMT
-
-1
-
- - -

As you can see, curl is somewhat inconvenient in that spaces must be URL escaped.Although wget escapes everything itself, we don't recommend using it because it doesn't work well over HTTP 1.1 when using keep-alive and Transfer-Encoding: chunked.

-
$ echo 'SELECT 1' | curl 'http://localhost:8123/' --data-binary @-
-1
-
-$ echo 'SELECT 1' | curl 'http://localhost:8123/?query=' --data-binary @-
-1
-
-$ echo '1' | curl 'http://localhost:8123/?query=SELECT' --data-binary @-
-1
-
- - -

If part of the query is sent in the parameter, and part in the POST, a line feed is inserted between these two data parts. -Example (this won't work):

-
$ echo 'ECT 1' | curl 'http://localhost:8123/?query=SEL' --data-binary @-
-Code: 59, e.displayText() = DB::Exception: Syntax error: failed at position 0: SEL
-ECT 1
-, expected One of: SHOW TABLES, SHOW DATABASES, SELECT, INSERT, CREATE, ATTACH, RENAME, DROP, DETACH, USE, SET, OPTIMIZE., e.what() = DB::Exception
-
- - -

By default, data is returned in TabSeparated format (for more information, see the "Formats" section). -You use the FORMAT clause of the query to request any other format.

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$ echo 'SELECT 1 FORMAT Pretty' | curl 'http://localhost:8123/?' --data-binary @-
-┏━━━┓
-┃ 1 ┃
-┡━━━┩
-│ 1 │
-└───┘
-
- - -

The POST method of transmitting data is necessary for INSERT queries. In this case, you can write the beginning of the query in the URL parameter, and use POST to pass the data to insert. The data to insert could be, for example, a tab-separated dump from MySQL. In this way, the INSERT query replaces LOAD DATA LOCAL INFILE from MySQL.

-

Examples: Creating a table:

-
echo 'CREATE TABLE t (a UInt8) ENGINE = Memory' | POST 'http://localhost:8123/'
-
- - -

Using the familiar INSERT query for data insertion:

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echo 'INSERT INTO t VALUES (1),(2),(3)' | POST 'http://localhost:8123/'
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Data can be sent separately from the query:

-
echo '(4),(5),(6)' | POST 'http://localhost:8123/?query=INSERT INTO t VALUES'
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You can specify any data format. The 'Values' format is the same as what is used when writing INSERT INTO t VALUES:

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echo '(7),(8),(9)' | POST 'http://localhost:8123/?query=INSERT INTO t FORMAT Values'
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- - -

To insert data from a tab-separated dump, specify the corresponding format:

-
echo -ne '10\n11\n12\n' | POST 'http://localhost:8123/?query=INSERT INTO t FORMAT TabSeparated'
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Reading the table contents. Data is output in random order due to parallel query processing:

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$ GET 'http://localhost:8123/?query=SELECT a FROM t'
-7
-8
-9
-10
-11
-12
-1
-2
-3
-4
-5
-6
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Deleting the table.

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POST 'http://localhost:8123/?query=DROP TABLE t'
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For successful requests that don't return a data table, an empty response body is returned.

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You can use the internal ClickHouse compression format when transmitting data. The compressed data has a non-standard format, and you will need to use the special clickhouse-compressor program to work with it (it is installed with the clickhouse-client package).

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If you specified 'compress=1' in the URL, the server will compress the data it sends you. -If you specified 'decompress=1' in the URL, the server will decompress the same data that you pass in the POST method.

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It is also possible to use the standard gzip-based HTTP compression. To send a POST request compressed using gzip, append the request header Content-Encoding: gzip. -In order for ClickHouse to compress the response using gzip, you must append Accept-Encoding: gzip to the request headers, and enable the ClickHouse setting enable_http_compression.

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You can use this to reduce network traffic when transmitting a large amount of data, or for creating dumps that are immediately compressed.

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You can use the 'database' URL parameter to specify the default database.

-
$ echo 'SELECT number FROM numbers LIMIT 10' | curl 'http://localhost:8123/?database=system' --data-binary @-
-0
-1
-2
-3
-4
-5
-6
-7
-8
-9
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- - -

By default, the database that is registered in the server settings is used as the default database. By default, this is the database called 'default'. Alternatively, you can always specify the database using a dot before the table name.

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The username and password can be indicated in one of two ways:

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  1. Using HTTP Basic Authentication. Example:
    2. -
-
echo 'SELECT 1' | curl 'http://user:password@localhost:8123/' -d @-
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    -
  1. In the 'user' and 'password' URL parameters. Example:
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echo 'SELECT 1' | curl 'http://localhost:8123/?user=user&password=password' -d @-
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If the user name is not indicated, the username 'default' is used. If the password is not indicated, an empty password is used. -You can also use the URL parameters to specify any settings for processing a single query, or entire profiles of settings. Example: -http://localhost:8123/?profile=web&max_rows_to_read=1000000000&query=SELECT+1

-

For more information, see the section "Settings".

-
$ echo 'SELECT number FROM system.numbers LIMIT 10' | curl 'http://localhost:8123/?' --data-binary @-
-0
-1
-2
-3
-4
-5
-6
-7
-8
-9
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For information about other parameters, see the section "SET".

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Similarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to add the session_id GET parameter to the request. You can use any string as the session ID. By default, the session is terminated after 60 seconds of inactivity. To change this timeout, modify the default_session_timeout setting in the server configuration, or add the session_timeout GET parameter to the request. To check the session status, use the session_check=1 parameter. Only one query at a time can be executed within a single session.

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You have the option to receive information about the progress of query execution in X-ClickHouse-Progress headers. To do this, enable the setting send_progress_in_http_headers.

-

Running requests don't stop automatically if the HTTP connection is lost. Parsing and data formatting are performed on the server side, and using the network might be ineffective. -The optional 'query_id' parameter can be passed as the query ID (any string). For more information, see the section "Settings, replace_running_query".

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The optional 'quota_key' parameter can be passed as the quota key (any string). For more information, see the section "Quotas".

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The HTTP interface allows passing external data (external temporary tables) for querying. For more information, see the section "External data for query processing".

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Response buffering

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You can enable response buffering on the server side. The buffer_size and wait_end_of_query URL parameters are provided for this purpose.

-

buffer_size determines the number of bytes in the result to buffer in the server memory. If the result body is larger than this threshold, the buffer is written to the HTTP channel, and the remaining data is sent directly to the HTTP channel.

-

To ensure that the entire response is buffered, set wait_end_of_query=1. In this case, the data that is not stored in memory will be buffered in a temporary server file.

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Example:

-
curl -sS 'http://localhost:8123/?max_result_bytes=4000000&buffer_size=3000000&wait_end_of_query=1' -d 'SELECT toUInt8(number) FROM system.numbers LIMIT 9000000 FORMAT RowBinary'
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Use buffering to avoid situations where a query processing error occurred after the response code and HTTP headers were sent to the client. In this situation, an error message is written at the end of the response body, and on the client side, the error can only be detected at the parsing stage.

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Interfaces

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To explore the system's capabilities, download data to tables, or make manual queries, use the clickhouse-client program.

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There is an official JDBC driver for ClickHouse. See here .

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Native interface (TCP)

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The native interface is used in the "clickhouse-client" command-line client for interaction between servers with distributed query processing, and also in C++ programs. We will only cover the command-line client.

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There are libraries for working with ClickHouse for:

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Tabix

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Web interface for ClickHouse in the Tabix project.

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Features:

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    • -
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Tabix documentation.

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HouseOps

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HouseOps is a unique Desktop ClickHouse Ops UI / IDE for OSX, Linux and Windows.

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True column-oriented DBMS

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In a true column-oriented DBMS, there isn't any "garbage" stored with the values. Among other things, this means that constant-length values must be supported, to avoid storing their length "number" next to the values. As an example, a billion UInt8-type values should actually consume around 1 GB uncompressed, or this will strongly affect the CPU use. It is very important to store data compactly (without any "garbage") even when uncompressed, since the speed of decompression (CPU usage) depends mainly on the volume of uncompressed data.

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This is worth noting because there are systems that can store values of separate columns separately, but that can't effectively process analytical queries due to their optimization for other scenarios. Examples are HBase, BigTable, Cassandra, and HyperTable. In these systems, you will get throughput around a hundred thousand rows per second, but not hundreds of millions of rows per second.

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Also note that ClickHouse is a DBMS, not a single database. ClickHouse allows creating tables and databases in runtime, loading data, and running queries without reconfiguring and restarting the server.

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Data compression

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Some column-oriented DBMSs (InfiniDB CE and MonetDB) do not use data compression. However, data compression really improves performance.

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Disk storage of data

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Many column-oriented DBMSs (such as SAP HANA and Google PowerDrill) can only work in RAM. But even on thousands of servers, the RAM is too small for storing all the pageviews and sessions in Yandex.Metrica.

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Parallel processing on multiple cores

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Large queries are parallelized in a natural way.

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Distributed processing on multiple servers

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Almost none of the columnar DBMSs listed above have support for distributed processing. -In ClickHouse, data can reside on different shards. Each shard can be a group of replicas that are used for fault tolerance. The query is processed on all the shards in parallel. This is transparent for the user.

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SQL support

-

If you are familiar with standard SQL, we can't really talk about SQL support. -All the functions have different names. -However, this is a declarative query language based on SQL that can't be differentiated from SQL in many instances. -JOINs are supported. Subqueries are supported in FROM, IN, and JOIN clauses, as well as scalar subqueries. -Dependent subqueries are not supported.

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Vector engine

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Data is not only stored by columns, but is processed by vectors (parts of columns). This allows us to achieve high CPU performance.

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Real-time data updates

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ClickHouse supports primary key tables. In order to quickly perform queries on the range of the primary key, the data is sorted incrementally using the merge tree. Due to this, data can continually be added to the table. There is no locking when adding data.

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Indexes

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Having a primary key makes it possible to extract data for specific clients (for instance, Yandex.Metrica tracking tags) for a specific time range, with low latency less than several dozen milliseconds.

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Suitable for online queries

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This lets us use the system as the back-end for a web interface. Low latency means queries can be processed without delay, while the Yandex.Metrica interface page is loading. In other words, in online mode.

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Support for approximated calculations

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    -
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    2. -
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    3. -
  3. Supports running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reasonably accurate result while using fewer resources.
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Data replication and support for data integrity on replicas

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Uses asynchronous multimaster replication. After being written to any available replica, data is distributed to all the remaining replicas. The system maintains identical data on different replicas. Data is restored automatically after a failure, or using a "button" for complex cases. -For more information, see the section Data replication.

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ClickHouse features that can be considered disadvantages

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Performance

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According to internal testing results, ClickHouse shows the best performance for comparable operating scenarios among systems of its class that were available for testing. This includes the highest throughput for long queries, and the lowest latency on short queries. Testing results are shown on a separate page.

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Throughput for a single large query

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Throughput can be measured in rows per second or in megabytes per second. If the data is placed in the page cache, a query that is not too complex is processed on modern hardware at a speed of approximately 2-10 GB/s of uncompressed data on a single server (for the simplest cases, the speed may reach 30 GB/s). If data is not placed in the page cache, the speed depends on the disk subsystem and the data compression rate. For example, if the disk subsystem allows reading data at 400 MB/s, and the data compression rate is 3, the speed will be around 1.2 GB/s. To get the speed in rows per second, divide the speed in bytes per second by the total size of the columns used in the query. For example, if 10 bytes of columns are extracted, the speed will be around 100-200 million rows per second.

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The processing speed increases almost linearly for distributed processing, but only if the number of rows resulting from aggregation or sorting is not too large.

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Latency when processing short queries

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If a query uses a primary key and does not select too many rows to process (hundreds of thousands), and does not use too many columns, we can expect less than 50 milliseconds of latency (single digits of milliseconds in the best case) if data is placed in the page cache. Otherwise, latency is calculated from the number of seeks. If you use rotating drives, for a system that is not overloaded, the latency is calculated by this formula: seek time (10 ms) * number of columns queried * number of data parts.

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Throughput when processing a large quantity of short queries

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Under the same conditions, ClickHouse can handle several hundred queries per second on a single server (up to several thousand in the best case). Since this scenario is not typical for analytical DBMSs, we recommend expecting a maximum of 100 queries per second.

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Performance when inserting data

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We recommend inserting data in packets of at least 1000 rows, or no more than a single request per second. When inserting to a MergeTree table from a tab-separated dump, the insertion speed will be from 50 to 200 MB/s. If the inserted rows are around 1 Kb in size, the speed will be from 50,000 to 200,000 rows per second. If the rows are small, the performance will be higher in rows per second (on Banner System data -> 500,000 rows per second; on Graphite data -> 1,000,000 rows per second). To improve performance, you can make multiple INSERT queries in parallel, and performance will increase linearly.

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Questions you were afraid to ask

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Why not use something like MapReduce?

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We can refer to systems like map-reduce as distributed computing systems in which the reduce operation is based on distributed sorting. In this sense, they include Hadoop, and YT (YT is developed at Yandex for internal use).

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These systems aren't appropriate for online queries due to their high latency. In other words, they can't be used as the back-end for a web interface. -These types of systems aren't useful for real-time data updates. -Distributed sorting isn't the best way to perform reduce operations if the result of the operation and all the intermediate results (if there are any) are located in the RAM of a single server, which is usually the case for online queries. In such a case, a hash table is the optimal way to perform reduce operations. A common approach to optimizing map-reduce tasks is pre-aggregation (partial reduce) using a hash table in RAM. The user performs this optimization manually. -Distributed sorting is one of the main causes of reduced performance when running simple map-reduce tasks.

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Systems like map-reduce allow executing any code on the cluster. But a declarative query language is better suited to OLAP in order to run experiments quickly. For example, Hadoop has Hive and Pig. Also consider Cloudera Impala, Shark (outdated) for Spark, and Spark SQL, Presto, and Apache Drill. Performance when running such tasks is highly sub-optimal compared to specialized systems, but relatively high latency makes it unrealistic to use these systems as the backend for a web interface.

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YT allows storing groups of columns separately. But YT can't be considered a true column-based system because it doesn't have fixed-length data types (for efficiently storing numbers without extra "garbage"), and also due to its lack of a vector engine. Tasks are performed in YT using custom code in streaming mode, so they cannot be optimized enough (up to hundreds of millions of rows per second per server). "Dynamic table sorting" is under development in YT using MergeTree, strict value typing, and a query language similar to SQL. Dynamically sorted tables are not appropriate for OLAP tasks because the data is stored by row. The YT query language is still under development, so we can't yet rely on this functionality. YT developers are considering using dynamically sorted tables in OLTP and Key-Value scenarios.

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Yandex.Metrica use case

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ClickHouse currently powers Yandex.Metrica, the second largest web analytics platform in the world. With more than 13 trillion records in the database and more than 20 billion events daily, ClickHouse allows you generating custom reports on the fly directly from non-aggregated data.

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We need to get custom reports based on hits and sessions, with custom segments set by the user. Data for the reports is updated in real-time. Queries must be run immediately (in online mode). We must be able to build reports for any time period. Complex aggregates must be calculated, such as the number of unique visitors. -At this time (April 2014), Yandex.Metrica receives approximately 12 billion events (pageviews and mouse clicks) daily. All these events must be stored in order to build custom reports. A single query may require scanning hundreds of millions of rows over a few seconds, or millions of rows in no more than a few hundred milliseconds.

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Usage in Yandex.Metrica and other Yandex services

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ClickHouse is used for multiple purposes in Yandex.Metrica. -Its main task is to build reports in online mode using non-aggregated data. It uses a cluster of 374 servers, which store over 20.3 trillion rows in the database. The volume of compressed data, without counting duplication and replication, is about 2 PB. The volume of uncompressed data (in TSV format) would be approximately 17 PB.

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ClickHouse is also used for:

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ClickHouse has at least a dozen installations in other Yandex services: in search verticals, Market, Direct, business analytics, mobile development, AdFox, personal services, and others.

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Aggregated and non-aggregated data

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There is a popular opinion that in order to effectively calculate statistics, you must aggregate data, since this reduces the volume of data.

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But data aggregation is a very limited solution, for the following reasons:

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  • When aggregating a large quantity of keys, the volume of data is not reduced, and aggregation is useless.
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  • For a large number of reports, there are too many aggregation variations (combinatorial explosion).
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  • For this reason, the volume of data with aggregation might grow instead of shrink.
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  • Users do not view all the reports we generate for them. A large portion of calculations are useless.
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If we do not aggregate anything and work with non-aggregated data, this might actually reduce the volume of calculations.

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However, with aggregation, a significant part of the work is taken offline and completed relatively calmly. In contrast, online calculations require calculating as fast as possible, since the user is waiting for the result.

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Yandex.Metrica has a specialized system for aggregating data called Metrage, which is used for the majority of reports. -Starting in 2009, Yandex.Metrica also used a specialized OLAP database for non-aggregated data called OLAPServer, which was previously used for the report builder. -OLAPServer worked well for non-aggregated data, but it had many restrictions that did not allow it to be used for all reports as desired. These included the lack of support for data types (only numbers), and the inability to incrementally update data in real-time (it could only be done by rewriting data daily). OLAPServer is not a DBMS, but a specialized DB.

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To remove the limitations of OLAPServer and solve the problem of working with non-aggregated data for all reports, we developed the ClickHouse DBMS.

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Access rights

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Users and access rights are set up in the user config. This is usually users.xml.

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Users are recorded in the users section. Here is a fragment of the users.xml file:

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<!-- Users and ACL. -->
-<users>
-    <!-- If the user name is not specified, the 'default' user is used. -->
-    <default>
-        <!-- Password could be specified in plaintext or in SHA256 (in hex format).
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-             If you want to specify the password in plain text (not recommended), place it in the 'password' element.
-             Example: <password>qwerty</password>.
-             Password can be empty.
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-             If you want to specify SHA256, place it in the 'password_sha256_hex' element.
-                          Example: <password_sha256_hex>65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5</password_sha256_hex>
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-             How to generate decent password:
-             Execute: PASSWORD=$(base64 < /dev/urandom | head -c8); echo "$PASSWORD"; echo -n "$PASSWORD" | sha256sum | tr -d '-'
-             In first line will be password and in second - corresponding SHA256.
-        -->
-        <password></password>
-        <!-- A list of networks that access is allowed from.
-            Each list item has one of the following forms:
-            <ip>IP address or subnet mask. For example: 198.51.100.0/24 or 2001:DB8::/32.
-            <host> Host name. For example: example01. A DNS query is made for verification, and all addresses obtained are compared with the address of the customer.
-            <host_regexp> Regular expression for host names. For example: ^example\d\d-\d\d-\d\.yandex\.ru$
-                For verification, a DNS PTR query is made for the customer's address and a regular expression is applied to the result.
-                Then another DNS query is made for the result of the PTR query, and all received address are compared to the client address.
-                We strongly recommend that the regex ends with \.yandex\.ru$.
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-            If you are installing ClickHouse yourself, enter:
-                <networks>
-                        <ip>::/0</ip>
-                </networks>
-        -->
-        <networks incl="networks" />
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-        <!-- Settings profile for the user. -->
-        <profile>default</profile>
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-        <!-- Quota for the user. -->
-        <quota>default</quota>
-    </default>
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-    <!-- For requests from the Yandex.Metrica user interface via the API for data on specific counters. -->
-    <web>
-        <password></password>
-        <networks incl="networks" />
-        <profile>web</profile>
-        <quota>default</quota>
-        <allow_databases>
-        <database>test</database>
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You can see a declaration from two users: default and web. We added the web user separately.

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The default user is chosen in cases when the username is not passed. The default user is also used for distributed query processing, if the configuration of the server or cluster doesn't specify the user and password (see the section on the Distributed engine).

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The user that is used for exchanging information between servers combined in a cluster must not have substantial restrictions or quotas – otherwise, distributed queries will fail.

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The password is specified in open format (not recommended) or in SHA-256. The hash isn't salted. In this regard, you should not consider these passwords as providing security against potential malicious attacks. Rather, they are necessary for protection from employees.

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A list of networks is specified that access is allowed from. In this example, the list of networks for both users is loaded from a separate file (/etc/metrika.xml) containing the 'networks' substitution. Here is a fragment of it:

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<yandex>
-    ...
-    <networks>
-        <ip>::/64</ip>
-        <ip>203.0.113.0/24</ip>
-        <ip>2001:DB8::/32</ip>
-        ...
-    </networks>
-</yandex>
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We could have defined this list of networks directly in 'users.xml', or in a file in the 'users.d' directory (for more information, see the section "Configuration files").

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The config includes comments explaining how to open access from everywhere.

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For use in production, only specify IP elements (IP addresses and their masks), since using 'host' and 'hoost_regexp' might cause extra latency.

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Next the user settings profile is specified (see the section "Settings profiles"). You can specify the default profile, default. The profile can have any name. You can specify the same profile for different users. The most important thing you can write in the settings profile is 'readonly' set to 1, which provides read-only access.

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After this, the quota is defined (see the section "Quotas"). You can specify the default quota, default. It is set in the config by default so that it only counts resource usage, but does not restrict it. The quota can have any name. You can specify the same quota for different users – in this case, resource usage is calculated for each user individually.

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In the optional <allow_databases> section, you can also specify a list of databases that the user can access. By default, all databases are available to the user. You can specify the default database. In this case, the user will receive access to the database by default.

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Access to the system database is always allowed (since this database is used for processing queries).

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The user can get a list of all databases and tables in them by using SHOW queries or system tables, even if access to individual databases isn't allowed.

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Database access is not related to the readonly setting. You can't grant full access to one database and readonly access to another one.

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The main server config file is config.xml. It resides in the /etc/clickhouse-server/ directory.

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Individual settings can be overridden in the *.xmland*.conf files in the conf.d and config.d directories next to the config file.

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The replace or remove attributes can be specified for the elements of these config files.

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If neither is specified, it combines the contents of elements recursively, replacing values of duplicate children.

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If replace is specified, it replaces the entire element with the specified one.

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If remove is specified, it deletes the element.

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The config can also define "substitutions". If an element has the incl attribute, the corresponding substitution from the file will be used as the value. By default, the path to the file with substitutions is /etc/metrika.xml. This can be changed in the include_from element in the server config. The substitution values are specified in /yandex/substitution_name elements in this file. If a substitution specified in incl does not exist, it is recorded in the log. To prevent ClickHouse from logging missing substitutions, specify the optional="true" attribute (for example, settings for macrosserver_settings/settings.md#server_settings-macros)).

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Substitutions can also be performed from ZooKeeper. To do this, specify the attribute from_zk = "/path/to/node". The element value is replaced with the contents of the node at /path/to/node in ZooKeeper. You can also put an entire XML subtree on the ZooKeeper node and it will be fully inserted into the source element.

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The config.xml file can specify a separate config with user settings, profiles, and quotas. The relative path to this config is set in the 'users_config' element. By default, it is users.xml. If users_config is omitted, the user settings, profiles, and quotas are specified directly in config.xml.

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In addition, users_config may have overrides in files from the users_config.d directory (for example, users.d) and substitutions.

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For each config file, the server also generates file-preprocessed.xml files when starting. These files contain all the completed substitutions and overrides, and they are intended for informational use. If ZooKeeper substitutions were used in the config files but ZooKeeper is not available on the server start, the server loads the configuration from the preprocessed file.

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The server tracks changes in config files, as well as files and ZooKeeper nodes that were used when performing substitutions and overrides, and reloads the settings for users and clusters on the fly. This means that you can modify the cluster, users, and their settings without restarting the server.

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Quotas allow you to limit resource usage over a period of time, or simply track the use of resources. -Quotas are set up in the user config. This is usually 'users.xml'.

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The system also has a feature for limiting the complexity of a single query. See the section "Restrictions on query complexity").

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In contrast to query complexity restrictions, quotas:

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Let's look at the section of the 'users.xml' file that defines quotas.

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<!-- Quotas. -->
-<quotas>
-    <!-- Quota name. -->
-    <default>
-        <!-- Restrictions for a time period. You can set many intervals with different restrictions. -->
-        <interval>
-            <!-- Length of the interval. -->
-            <duration>3600</duration>
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-            <!-- Unlimited. Just collect data for the specified time interval. -->
-            <queries>0</queries>
-            <errors>0</errors>
-            <result_rows>0</result_rows>
-            <read_rows>0</read_rows>
-            <execution_time>0</execution_time>
-        </interval>
-    </default>
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By default, the quota just tracks resource consumption for each hour, without limiting usage. -The resource consumption calculated for each interval is output to the server log after each request.

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<statbox>
-    <!-- Restrictions for a time period. You can set many intervals with different restrictions. -->
-    <interval>
-        <!-- Length of the interval. -->
-        <duration>3600</duration>
-
-        <queries>1000</queries>
-        <errors>100</errors>
-        <result_rows>1000000000</result_rows>
-        <read_rows>100000000000</read_rows>
-        <execution_time>900</execution_time>
-    </interval>
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-    <interval>
-        <duration>86400</duration>
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-        <queries>10000</queries>
-        <errors>1000</errors>
-        <result_rows>5000000000</result_rows>
-        <read_rows>500000000000</read_rows>
-        <execution_time>7200</execution_time>
-    </interval>
-</statbox>
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For the 'statbox' quota, restrictions are set for every hour and for every 24 hours (86,400 seconds). The time interval is counted starting from an implementation-defined fixed moment in time. In other words, the 24-hour interval doesn't necessarily begin at midnight.

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When the interval ends, all collected values are cleared. For the next hour, the quota calculation starts over.

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Here are the amounts that can be restricted:

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queries – The total number of requests.

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errors – The number of queries that threw an exception.

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result_rows – The total number of rows given as the result.

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read_rows – The total number of source rows read from tables for running the query, on all remote servers.

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execution_time – The total query execution time, in seconds (wall time).

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If the limit is exceeded for at least one time interval, an exception is thrown with a text about which restriction was exceeded, for which interval, and when the new interval begins (when queries can be sent again).

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Quotas can use the "quota key" feature in order to report on resources for multiple keys independently. Here is an example of this:

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<!-- For the global reports designer. -->
-<web_global>
-    <!-- keyed - The quota_key "key" is passed in the query parameter,
-            and the quota is tracked separately for each key value.
-        For example, you can pass a Yandex.Metrica username as the key,
-            so the quota will be counted separately for each username.
-        Using keys makes sense only if quota_key is transmitted by the program, not by a user.
-
-        You can also write <keyed_by_ip /> so the IP address is used as the quota key.
-        (But keep in mind that users can change the IPv6 address fairly easily.)
-    -->
-    <keyed />
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The quota is assigned to users in the 'users' section of the config. See the section "Access rights".

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For distributed query processing, the accumulated amounts are stored on the requestor server. So if the user goes to another server, the quota there will "start over".

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When the server is restarted, quotas are reset.

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Server configuration parameters

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This section contains descriptions of server settings that cannot be changed at the session or query level.

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These settings are stored in the config.xml file on the ClickHouse server.

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Other settings are described in the "Settings" section.

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Before studying the settings, read the Configuration files section and note the use of substitutions (the incl and optional attributes).

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Server settings

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builtin_dictionaries_reload_interval

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The interval in seconds before reloading built-in dictionaries.

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ClickHouse reloads built-in dictionaries every x seconds. This makes it possible to edit dictionaries "on the fly" without restarting the server.

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Default value: 3600.

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Example

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<builtin_dictionaries_reload_interval>3600</builtin_dictionaries_reload_interval>
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compression

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Data compression settings.

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- -Don't use it if you have just started using ClickHouse. - -
- -

The configuration looks like this:

-
<compression>
-    <case>
-      <parameters/>
-    </case>
-    ...
-</compression>
-
- - -

You can configure multiple sections <case>.

-

Block field <case>:

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    -
  • min_part_size – The minimum size of a table part.
    • -
  • min_part_size_ratio – The ratio of the minimum size of a table part to the full size of the table.
    • -
  • method – Compression method. Acceptable values ​: lz4 or zstd(experimental).
    • -
-

ClickHouse checks min_part_size and min_part_size_ratio and processes the case blocks that match these conditions. If none of the <case> matches, ClickHouse applies the lz4 compression algorithm.

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Example

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<compression incl="clickhouse_compression">
-    <case>
-        <min_part_size>10000000000</min_part_size>
-        <min_part_size_ratio>0.01</min_part_size_ratio>
-        <method>zstd</method>
-    </case>
-</compression>
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default_database

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The default database.

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To get a list of databases, use the SHOW DATABASES.

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Example

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<default_database>default</default_database>
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- - -

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default_profile

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Default settings profile.

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Settings profiles are located in the file specified in the parameter user_config.

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Example

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<default_profile>default</default_profile>
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dictionaries_config

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The path to the config file for external dictionaries.

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Path:

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    -
  • Specify the absolute path or the path relative to the server config file.
    • -
  • The path can contain wildcards * and ?.
    • -
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See also "External dictionaries".

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Example

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<dictionaries_config>*_dictionary.xml</dictionaries_config>
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dictionaries_lazy_load

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Lazy loading of dictionaries.

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If true, then each dictionary is created on first use. If dictionary creation failed, the function that was using the dictionary throws an exception.

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If false, all dictionaries are created when the server starts, and if there is an error, the server shuts down.

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The default is true.

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Example

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<dictionaries_lazy_load>true</dictionaries_lazy_load>
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format_schema_path

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The path to the directory with the schemes for the input data, such as schemas for the CapnProto format.

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Example

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  <!-- Directory containing schema files for various input formats. -->
-  <format_schema_path>format_schemas/</format_schema_path>
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graphite

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Sending data to Graphite.

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Settings:

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    -
  • host – The Graphite server.
    • -
  • port – The port on the Graphite server.
    • -
  • interval – The interval for sending, in seconds.
    • -
  • timeout – The timeout for sending data, in seconds.
    • -
  • root_path – Prefix for keys.
    • -
  • metrics – Sending data from a :ref:system_tables-system.metrics table.
    • -
  • events – Sending data from a :ref:system_tables-system.events table.
    • -
  • asynchronous_metrics – Sending data from a :ref:system_tables-system.asynchronous_metrics table.
    • -
-

You can configure multiple <graphite> clauses. For instance, you can use this for sending different data at different intervals.

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Example

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<graphite>
-    <host>localhost</host>
-    <port>42000</port>
-    <timeout>0.1</timeout>
-    <interval>60</interval>
-    <root_path>one_min</root_path>
-    <metrics>true</metrics>
-    <events>true</events>
-    <asynchronous_metrics>true</asynchronous_metrics>
-</graphite>
-
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graphite_rollup

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Settings for thinning data for Graphite.

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For more information, see GraphiteMergeTree.

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Example

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<graphite_rollup_example>
-    <default>
-        <function>max</function>
-        <retention>
-            <age>0</age>
-            <precision>60</precision>
-        </retention>
-        <retention>
-            <age>3600</age>
-            <precision>300</precision>
-        </retention>
-        <retention>
-            <age>86400</age>
-            <precision>3600</precision>
-        </retention>
-    </default>
-</graphite_rollup_example>
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http_port/https_port

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The port for connecting to the server over HTTP(s).

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If https_port is specified, openSSL must be configured.

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If http_port is specified, the openSSL configuration is ignored even if it is set.

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Example

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<https>0000</https>
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http_server_default_response

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The page that is shown by default when you access the ClickHouse HTTP(s) server.

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Example

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Opens https://tabix.io/ when accessing http://localhost: http_port.

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<http_server_default_response>
-  <![CDATA[<html ng-app="SMI2"><head><base href="http://ui.tabix.io/"></head><body><div ui-view="" class="content-ui"></div><script src="http://loader.tabix.io/master.js"></script></body></html>]]>
-</http_server_default_response>
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include_from

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The path to the file with substitutions.

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For more information, see the section "Configuration files".

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Example

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<include_from>/etc/metrica.xml</include_from>
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interserver_http_port

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Port for exchanging data between ClickHouse servers.

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Example

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<interserver_http_port>9009</interserver_http_port>
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interserver_http_host

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The host name that can be used by other servers to access this server.

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If omitted, it is defined in the same way as the hostname-f command.

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Useful for breaking away from a specific network interface.

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Example

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<interserver_http_host>example.yandex.ru</interserver_http_host>
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keep_alive_timeout

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The number of milliseconds that ClickHouse waits for incoming requests before closing the connection.

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Example

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<keep_alive_timeout>3</keep_alive_timeout>
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listen_host

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Restriction on hosts that requests can come from. If you want the server to answer all of them, specify ::.

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Examples:

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<listen_host>::1</listen_host>
-<listen_host>127.0.0.1</listen_host>
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logger

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Logging settings.

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Keys:

-
    -
  • level – Logging level. Acceptable values: trace, debug, information, warning, error.
    • -
  • log – The log file. Contains all the entries according to level.
    • -
  • errorlog – Error log file.
    • -
  • size – Size of the file. Applies to loganderrorlog. Once the file reaches size, ClickHouse archives and renames it, and creates a new log file in its place.
    • -
  • count – The number of archived log files that ClickHouse stores.
    • -
-

Example

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<logger>
-    <level>trace</level>
-    <log>/var/log/clickhouse-server/clickhouse-server.log</log>
-    <errorlog>/var/log/clickhouse-server/clickhouse-server.err.log</errorlog>
-    <size>1000M</size>
-    <count>10</count>
-</logger>
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macros

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Parameter substitutions for replicated tables.

-

Can be omitted if replicated tables are not used.

-

For more information, see the section "Creating replicated tables".

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Example

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<macros incl="macros" optional="true" />
-
- - -

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mark_cache_size

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Approximate size (in bytes) of the cache of "marks" used by MergeTree engines.

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The cache is shared for the server and memory is allocated as needed. The cache size must be at least 5368709120.

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Example

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<mark_cache_size>5368709120</mark_cache_size>
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- - -

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max_concurrent_queries

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The maximum number of simultaneously processed requests.

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Example

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<max_concurrent_queries>100</max_concurrent_queries>
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- - -

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max_connections

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The maximum number of inbound connections.

-

Example

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<max_connections>4096</max_connections>
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- - -

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max_open_files

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The maximum number of open files.

-

By default: maximum.

-

We recommend using this option in Mac OS X, since the getrlimit() function returns an incorrect value.

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Example

-
<max_open_files>262144</max_open_files>
-
- - -

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max_table_size_to_drop

-

Restriction on deleting tables.

-

If the size of a MergeTree type table exceeds max_table_size_to_drop (in bytes), you can't delete it using a DROP query.

-

If you still need to delete the table without restarting the ClickHouse server, create the <clickhouse-path>/flags/force_drop_table file and run the DROP query.

-

Default value: 50 GB.

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The value 0 means that you can delete all tables without any restrictions.

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Example

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<max_table_size_to_drop>0</max_table_size_to_drop>
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- - -

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merge_tree

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Fine tuning for tables in the MergeTree family.

-

For more information, see the MergeTreeSettings.h header file.

-

Example

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<merge_tree>
-    <max_suspicious_broken_parts>5</max_suspicious_broken_parts>
-</merge_tree>
-
- - -

-

openSSL

-

SSL client/server configuration.

-

Support for SSL is provided by the libpoco library. The interface is described in the file SSLManager.h

-

Keys for server/client settings:

-
    -
  • privateKeyFile – The path to the file with the secret key of the PEM certificate. The file may contain a key and certificate at the same time.
    • -
  • certificateFile – The path to the client/server certificate file in PEM format. You can omit it if privateKeyFile contains the certificate.
    • -
  • caConfig – The path to the file or directory that contains trusted root certificates.
    • -
  • verificationMode – The method for checking the node's certificates. Details are in the description of the Context class. Possible values: none, relaxed, strict, once.
    • -
  • verificationDepth – The maximum length of the verification chain. Verification will fail if the certificate chain length exceeds the set value.
    • -
  • loadDefaultCAFile – Indicates that built-in CA certificates for OpenSSL will be used. Acceptable values: true, false. |
    • -
  • cipherList – Supported OpenSSL encryptions. For example: ALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH.
    • -
  • cacheSessions – Enables or disables caching sessions. Must be used in combination with sessionIdContext. Acceptable values: true, false.
    • -
  • sessionIdContext – A unique set of random characters that the server appends to each generated identifier. The length of the string must not exceed SSL_MAX_SSL_SESSION_ID_LENGTH. This parameter is always recommended, since it helps avoid problems both if the server caches the session and if the client requested caching. Default value: ${application.name}.
    • -
  • sessionCacheSize – The maximum number of sessions that the server caches. Default value: 1024*20. 0 – Unlimited sessions.
    • -
  • sessionTimeout – Time for caching the session on the server.
    • -
  • extendedVerification – Automatically extended verification of certificates after the session ends. Acceptable values: true, false.
    • -
  • requireTLSv1 – Require a TLSv1 connection. Acceptable values: true, false.
    • -
  • requireTLSv1_1 – Require a TLSv1.1 connection. Acceptable values: true, false.
    • -
  • requireTLSv1 – Require a TLSv1.2 connection. Acceptable values: true, false.
    • -
  • fips – Activates OpenSSL FIPS mode. Supported if the library's OpenSSL version supports FIPS.
    • -
  • privateKeyPassphraseHandler – Class (PrivateKeyPassphraseHandler subclass) that requests the passphrase for accessing the private key. For example: <privateKeyPassphraseHandler>, <name>KeyFileHandler</name>, <options><password>test</password></options>, </privateKeyPassphraseHandler>.
    • -
  • invalidCertificateHandler – Class (subclass of CertificateHandler) for verifying invalid certificates. For example: <invalidCertificateHandler> <name>ConsoleCertificateHandler</name> </invalidCertificateHandler> .
    • -
  • disableProtocols – Protocols that are not allowed to use.
    • -
  • preferServerCiphers – Preferred server ciphers on the client.
    • -
-

Example of settings:

-
<openSSL>
-    <server>
-        <!-- openssl req -subj "/CN=localhost" -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout /etc/clickhouse-server/server.key -out /etc/clickhouse-server/server.crt -->
-        <certificateFile>/etc/clickhouse-server/server.crt</certificateFile>
-        <privateKeyFile>/etc/clickhouse-server/server.key</privateKeyFile>
-        <!-- openssl dhparam -out /etc/clickhouse-server/dhparam.pem 4096 -->
-        <dhParamsFile>/etc/clickhouse-server/dhparam.pem</dhParamsFile>
-        <verificationMode>none</verificationMode>
-        <loadDefaultCAFile>true</loadDefaultCAFile>
-        <cacheSessions>true</cacheSessions>
-        <disableProtocols>sslv2,sslv3</disableProtocols>
-        <preferServerCiphers>true</preferServerCiphers>
-    </server>
-    <client>
-        <loadDefaultCAFile>true</loadDefaultCAFile>
-        <cacheSessions>true</cacheSessions>
-        <disableProtocols>sslv2,sslv3</disableProtocols>
-        <preferServerCiphers>true</preferServerCiphers>
-        <!-- Use for self-signed: <verificationMode>none</verificationMode> -->
-        <invalidCertificateHandler>
-            <!-- Use for self-signed: <name>AcceptCertificateHandler</name> -->
-            <name>RejectCertificateHandler</name>
-        </invalidCertificateHandler>
-    </client>
-</openSSL>
-
- - -

-

part_log

-

Logging events that are associated with MergeTree data. For instance, adding or merging data. You can use the log to simulate merge algorithms and compare their characteristics. You can visualize the merge process.

-

Queries are logged in the ClickHouse table, not in a separate file.

-

Columns in the log:

-
    -
  • event_time – Date of the event.
    • -
  • duration_ms – Duration of the event.
    • -
  • event_type – Type of event. 1 – new data part; 2 – merge result; 3 – data part downloaded from replica; 4 – data part deleted.
    • -
  • database_name – The name of the database.
    • -
  • table_name – Name of the table.
    • -
  • part_name – Name of the data part.
    • -
  • size_in_bytes – Size of the data part in bytes.
    • -
  • merged_from – An array of names of data parts that make up the merge (also used when downloading a merged part).
    • -
  • merge_time_ms – Time spent on the merge.
    • -
-

Use the following parameters to configure logging:

-
    -
  • database – Name of the database.
    • -
  • table – Name of the table.
    • -
  • partition_by – Sets a custom partitioning key.
    • -
  • flush_interval_milliseconds – Interval for flushing data from memory to the disk.
    • -
-

Example

-
<part_log>
-    <database>system</database>
-    <table>part_log</table>
-    <partition_by>toMonday(event_date)</partition_by>
-    <flush_interval_milliseconds>7500</flush_interval_milliseconds>
-</part_log>
-
- - -

-

path

-

The path to the directory containing data.

-
- -The end slash is mandatory. - -
- -

Example

-
<path>/var/lib/clickhouse/</path>
-
- - -

-

query_log

-

Setting for logging queries received with the log_queries=1 setting.

-

Queries are logged in the ClickHouse table, not in a separate file.

-

Use the following parameters to configure logging:

-
    -
  • database – Name of the database.
    • -
  • table – Name of the table.
    • -
  • partition_by – Sets a custom partitioning key.
    • -
  • flush_interval_milliseconds – Interval for flushing data from memory to the disk.
    • -
-

If the table doesn't exist, ClickHouse will create it. If the structure of the query log changed when the ClickHouse server was updated, the table with the old structure is renamed, and a new table is created automatically.

-

Example

-
<query_log>
-    <database>system</database>
-    <table>query_log</table>
-    <partition_by>toMonday(event_date)</partition_by>
-    <flush_interval_milliseconds>7500</flush_interval_milliseconds>
-</query_log>
-
- - -

-

remote_servers

-

Configuration of clusters used by the Distributed table engine.

-

For more information, see the section "Table engines/Distributed".

-

Example

-
<remote_servers incl="clickhouse_remote_servers" />
-
- - -

For the value of the incl attribute, see the section "Configuration files".

-

-

timezone

-

The server's time zone.

-

Specified as an IANA identifier for the UTC time zone or geographic location (for example, Africa/Abidjan).

-

The time zone is necessary for conversions between String and DateTime formats when DateTime fields are output to text format (printed on the screen or in a file), and when getting DateTime from a string. In addition, the time zone is used in functions that work with the time and date if they didn't receive the time zone in the input parameters.

-

Example

-
<timezone>Europe/Moscow</timezone>
-
- - -

-

tcp_port

-

Port for communicating with clients over the TCP protocol.

-

Example

-
<tcp_port>9000</tcp_port>
-
- - -

-

tmp_path

-

Path to temporary data for processing large queries.

-
- -The end slash is mandatory. - -
- -

Example

-
<tmp_path>/var/lib/clickhouse/tmp/</tmp_path>
-
- - -

-

uncompressed_cache_size

-

Cache size (in bytes) for uncompressed data used by table engines from the MergeTree family.

-

There is one shared cache for the server. Memory is allocated on demand. The cache is used if the option use_uncompressed_cache is enabled.

-

The uncompressed cache is advantageous for very short queries in individual cases.

-

Example

-
<uncompressed_cache_size>8589934592</uncompressed_cache_size>
-
- - -

-

users_config

-

Path to the file that contains:

-
    -
  • User configurations.
    • -
  • Access rights.
    • -
  • Settings profiles.
    • -
  • Quota settings.
    • -
-

Example

-
<users_config>users.xml</users_config>
-
- - -

-

zookeeper

-

Configuration of ZooKeeper servers.

-

ClickHouse uses ZooKeeper for storing replica metadata when using replicated tables.

-

This parameter can be omitted if replicated tables are not used.

-

For more information, see the section "Replication".

-

Example

-
<zookeeper incl="zookeeper-servers" optional="true" />
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Settings

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There are multiple ways to make all the settings described below. -Settings are configured in layers, so each subsequent layer redefines the previous settings.

-

Ways to configure settings, in order of priority:

-
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  • Settings in the server config file.
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Settings from user profiles.

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  • Session settings.
    • -
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Send SET setting=value from the ClickHouse console client in interactive mode. -Similarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to specify the session_id HTTP parameter.

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  • For a query.
    • -
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    • -
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Settings that can only be made in the server config file are not covered in this section.

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Restrictions on query complexity

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Restrictions on query complexity are part of the settings. -They are used in order to provide safer execution from the user interface. -Almost all the restrictions only apply to SELECTs.For distributed query processing, restrictions are applied on each server separately.

-

Restrictions on the "maximum amount of something" can take the value 0, which means "unrestricted". -Most restrictions also have an 'overflow_mode' setting, meaning what to do when the limit is exceeded. -It can take one of two values: throw or break. Restrictions on aggregation (group_by_overflow_mode) also have the value any.

-

throw – Throw an exception (default).

-

break – Stop executing the query and return the partial result, as if the source data ran out.

-

any (only for group_by_overflow_mode) – Continuing aggregation for the keys that got into the set, but don't add new keys to the set.

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readonly

-

With a value of 0, you can execute any queries. -With a value of 1, you can only execute read requests (such as SELECT and SHOW). Requests for writing and changing settings (INSERT, SET) are prohibited. -With a value of 2, you can process read queries (SELECT, SHOW) and change settings (SET).

-

After enabling readonly mode, you can't disable it in the current session.

-

When using the GET method in the HTTP interface, 'readonly = 1' is set automatically. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.

-

-

max_memory_usage

-

The maximum amount of RAM to use for running a query on a single server.

-

In the default configuration file, the maximum is 10 GB.

-

The setting doesn't consider the volume of available memory or the total volume of memory on the machine. -The restriction applies to a single query within a single server. -You can use SHOW PROCESSLIST to see the current memory consumption for each query. -In addition, the peak memory consumption is tracked for each query and written to the log.

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Memory usage is not monitored for the states of certain aggregate functions.

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Memory usage is not fully tracked for states of the aggregate functions min, max, any, anyLast, argMin, argMax from String and Array arguments.

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Memory consumption is also restricted by the parameters max_memory_usage_for_user and max_memory_usage_for_all_queries.

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max_memory_usage_for_user

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The maximum amount of RAM to use for running a user's queries on a single server.

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Default values are defined in Settings.h. By default, the amount is not restricted (max_memory_usage_for_user = 0).

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See also the description of max_memory_usage.

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max_memory_usage_for_all_queries

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The maximum amount of RAM to use for running all queries on a single server.

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Default values are defined in Settings.h. By default, the amount is not restricted (max_memory_usage_for_all_queries = 0).

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See also the description of max_memory_usage.

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max_rows_to_read

-

The following restrictions can be checked on each block (instead of on each row). That is, the restrictions can be broken a little. -When running a query in multiple threads, the following restrictions apply to each thread separately.

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Maximum number of rows that can be read from a table when running a query.

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max_bytes_to_read

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Maximum number of bytes (uncompressed data) that can be read from a table when running a query.

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read_overflow_mode

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What to do when the volume of data read exceeds one of the limits: 'throw' or 'break'. By default, throw.

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max_rows_to_group_by

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Maximum number of unique keys received from aggregation. This setting lets you limit memory consumption when aggregating.

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group_by_overflow_mode

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What to do when the number of unique keys for aggregation exceeds the limit: 'throw', 'break', or 'any'. By default, throw. -Using the 'any' value lets you run an approximation of GROUP BY. The quality of this approximation depends on the statistical nature of the data.

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max_rows_to_sort

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Maximum number of rows before sorting. This allows you to limit memory consumption when sorting.

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max_bytes_to_sort

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Maximum number of bytes before sorting.

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sort_overflow_mode

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What to do if the number of rows received before sorting exceeds one of the limits: 'throw' or 'break'. By default, throw.

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max_result_rows

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Limit on the number of rows in the result. Also checked for subqueries, and on remote servers when running parts of a distributed query.

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max_result_bytes

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Limit on the number of bytes in the result. The same as the previous setting.

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result_overflow_mode

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What to do if the volume of the result exceeds one of the limits: 'throw' or 'break'. By default, throw. -Using 'break' is similar to using LIMIT.

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max_execution_time

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Maximum query execution time in seconds. -At this time, it is not checked for one of the sorting stages, or when merging and finalizing aggregate functions.

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timeout_overflow_mode

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What to do if the query is run longer than 'max_execution_time': 'throw' or 'break'. By default, throw.

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min_execution_speed

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Minimal execution speed in rows per second. Checked on every data block when 'timeout_before_checking_execution_speed' expires. If the execution speed is lower, an exception is thrown.

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timeout_before_checking_execution_speed

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Checks that execution speed is not too slow (no less than 'min_execution_speed'), after the specified time in seconds has expired.

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max_columns_to_read

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Maximum number of columns that can be read from a table in a single query. If a query requires reading a greater number of columns, it throws an exception.

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max_temporary_columns

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Maximum number of temporary columns that must be kept in RAM at the same time when running a query, including constant columns. If there are more temporary columns than this, it throws an exception.

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max_temporary_non_const_columns

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The same thing as 'max_temporary_columns', but without counting constant columns. -Note that constant columns are formed fairly often when running a query, but they require approximately zero computing resources.

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max_subquery_depth

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Maximum nesting depth of subqueries. If subqueries are deeper, an exception is thrown. By default, 100.

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max_pipeline_depth

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Maximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000.

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max_ast_depth

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Maximum nesting depth of a query syntactic tree. If exceeded, an exception is thrown. -At this time, it isn't checked during parsing, but only after parsing the query. That is, a syntactic tree that is too deep can be created during parsing, but the query will fail. By default, 1000.

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max_ast_elements

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Maximum number of elements in a query syntactic tree. If exceeded, an exception is thrown. -In the same way as the previous setting, it is checked only after parsing the query. By default, 10,000.

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max_rows_in_set

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Maximum number of rows for a data set in the IN clause created from a subquery.

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max_bytes_in_set

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Maximum number of bytes (uncompressed data) used by a set in the IN clause created from a subquery.

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set_overflow_mode

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What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.

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max_rows_in_distinct

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Maximum number of different rows when using DISTINCT.

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max_bytes_in_distinct

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Maximum number of bytes used by a hash table when using DISTINCT.

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distinct_overflow_mode

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What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.

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max_rows_to_transfer

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Maximum number of rows that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.

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max_bytes_to_transfer

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Maximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.

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transfer_overflow_mode

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What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.

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Settings

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distributed_product_mode

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Changes the behavior of distributed subqueries, i.e. in cases when the query contains the product of distributed tables.

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ClickHouse applies the configuration if the subqueries on any level have a distributed table that exists on the local server and has more than one shard.

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Restrictions:

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  • Only applied for IN and JOIN subqueries.
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The possible values ​​are:

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fallback_to_stale_replicas_for_distributed_queries

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Forces a query to an out-of-date replica if updated data is not available. See "Replication".

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ClickHouse selects the most relevant from the outdated replicas of the table.

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Used when performing SELECT from a distributed table that points to replicated tables.

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By default, 1 (enabled).

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force_index_by_date

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Disables query execution if the index can't be used by date.

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Works with tables in the MergeTree family.

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If force_index_by_date=1, ClickHouse checks whether the query has a date key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For example, the condition Date != ' 2000-01-01 ' is acceptable even when it matches all the data in the table (i.e., running the query requires a full scan). For more information about ranges of data in MergeTree tables, see "MergeTree".

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force_primary_key

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Disables query execution if indexing by the primary key is not possible.

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Works with tables in the MergeTree family.

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If force_primary_key=1, ClickHouse checks to see if the query has a primary key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For more information about data ranges in MergeTree tables, see "MergeTree".

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fsync_metadata

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Enable or disable fsync when writing .sql files. By default, it is enabled.

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It makes sense to disable it if the server has millions of tiny table chunks that are constantly being created and destroyed.

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input_format_allow_errors_num

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Sets the maximum number of acceptable errors when reading from text formats (CSV, TSV, etc.).

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The default value is 0.

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Always pair it with input_format_allow_errors_ratio. To skip errors, both settings must be greater than 0.

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If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_num, ClickHouse ignores the row and moves on to the next one.

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If input_format_allow_errors_numis exceeded, ClickHouse throws an exception.

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input_format_allow_errors_ratio

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Sets the maximum percentage of errors allowed when reading from text formats (CSV, TSV, etc.). -The percentage of errors is set as a floating-point number between 0 and 1.

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The default value is 0.

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Always pair it with input_format_allow_errors_num. To skip errors, both settings must be greater than 0.

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If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_ratio, ClickHouse ignores the row and moves on to the next one.

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If input_format_allow_errors_ratio is exceeded, ClickHouse throws an exception.

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max_block_size

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In ClickHouse, data is processed by blocks (sets of column parts). The internal processing cycles for a single block are efficient enough, but there are noticeable expenditures on each block. max_block_size is a recommendation for what size of block (in number of rows) to load from tables. The block size shouldn't be too small, so that the expenditures on each block are still noticeable, but not too large, so that the query with LIMIT that is completed after the first block is processed quickly, so that too much memory isn't consumed when extracting a large number of columns in multiple threads, and so that at least some cache locality is preserved.

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By default, 65,536.

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Blocks the size of max_block_size are not always loaded from the table. If it is obvious that less data needs to be retrieved, a smaller block is processed.

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preferred_block_size_bytes

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Used for the same purpose as max_block_size, but it sets the recommended block size in bytes by adapting it to the number of rows in the block. -However, the block size cannot be more than max_block_size rows. -Disabled by default (set to 0). It only works when reading from MergeTree engines.

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log_queries

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Setting up query the logging.

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Queries sent to ClickHouse with this setup are logged according to the rules in the query_log server configuration parameter.

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Example:

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log_queries=1
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max_insert_block_size

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The size of blocks to form for insertion into a table. -This setting only applies in cases when the server forms the blocks. -For example, for an INSERT via the HTTP interface, the server parses the data format and forms blocks of the specified size. -But when using clickhouse-client, the client parses the data itself, and the 'max_insert_block_size' setting on the server doesn't affect the size of the inserted blocks. -The setting also doesn't have a purpose when using INSERT SELECT, since data is inserted using the same blocks that are formed after SELECT.

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By default, it is 1,048,576.

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This is slightly more than max_block_size. The reason for this is because certain table engines (*MergeTree) form a data part on the disk for each inserted block, which is a fairly large entity. Similarly, *MergeTree tables sort data during insertion, and a large enough block size allows sorting more data in RAM.

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max_replica_delay_for_distributed_queries

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Disables lagging replicas for distributed queries. See "Replication".

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Sets the time in seconds. If a replica lags more than the set value, this replica is not used.

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Default value: 0 (off).

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Used when performing SELECT from a distributed table that points to replicated tables.

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max_threads

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The maximum number of query processing threads

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  • excluding threads for retrieving data from remote servers (see the 'max_distributed_connections' parameter).
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This parameter applies to threads that perform the same stages of the query processing pipeline in parallel. -For example, if reading from a table, evaluating expressions with functions, filtering with WHERE and pre-aggregating for GROUP BY can all be done in parallel using at least 'max_threads' number of threads, then 'max_threads' are used.

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By default, 8.

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If less than one SELECT query is normally run on a server at a time, set this parameter to a value slightly less than the actual number of processor cores.

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For queries that are completed quickly because of a LIMIT, you can set a lower 'max_threads'. For example, if the necessary number of entries are located in every block and max_threads = 8, 8 blocks are retrieved, although it would have been enough to read just one.

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The smaller the max_threads value, the less memory is consumed.

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max_compress_block_size

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The maximum size of blocks of uncompressed data before compressing for writing to a table. By default, 1,048,576 (1 MiB). If the size is reduced, the compression rate is significantly reduced, the compression and decompression speed increases slightly due to cache locality, and memory consumption is reduced. There usually isn't any reason to change this setting.

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Don't confuse blocks for compression (a chunk of memory consisting of bytes) and blocks for query processing (a set of rows from a table).

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min_compress_block_size

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For MergeTree" tables. In order to reduce latency when processing queries, a block is compressed when writing the next mark if its size is at least 'min_compress_block_size'. By default, 65,536.

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The actual size of the block, if the uncompressed data is less than 'max_compress_block_size', is no less than this value and no less than the volume of data for one mark.

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Let's look at an example. Assume that 'index_granularity' was set to 8192 during table creation.

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We are writing a UInt32-type column (4 bytes per value). When writing 8192 rows, the total will be 32 KB of data. Since min_compress_block_size = 65,536, a compressed block will be formed for every two marks.

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We are writing a URL column with the String type (average size of 60 bytes per value). When writing 8192 rows, the average will be slightly less than 500 KB of data. Since this is more than 65,536, a compressed block will be formed for each mark. In this case, when reading data from the disk in the range of a single mark, extra data won't be decompressed.

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There usually isn't any reason to change this setting.

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max_query_size

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The maximum part of a query that can be taken to RAM for parsing with the SQL parser. -The INSERT query also contains data for INSERT that is processed by a separate stream parser (that consumes O(1) RAM), which is not included in this restriction.

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The default is 256 KiB.

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interactive_delay

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The interval in microseconds for checking whether request execution has been canceled and sending the progress.

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By default, 100,000 (check for canceling and send progress ten times per second).

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connect_timeout

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receive_timeout

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send_timeout

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Timeouts in seconds on the socket used for communicating with the client.

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By default, 10, 300, 300.

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poll_interval

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Lock in a wait loop for the specified number of seconds.

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By default, 10.

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max_distributed_connections

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The maximum number of simultaneous connections with remote servers for distributed processing of a single query to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.

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By default, 100.

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The following parameters are only used when creating Distributed tables (and when launching a server), so there is no reason to change them at runtime.

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distributed_connections_pool_size

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The maximum number of simultaneous connections with remote servers for distributed processing of all queries to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.

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By default, 128.

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connect_timeout_with_failover_ms

-

The timeout in milliseconds for connecting to a remote server for a Distributed table engine, if the 'shard' and 'replica' sections are used in the cluster definition. -If unsuccessful, several attempts are made to connect to various replicas.

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By default, 50.

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connections_with_failover_max_tries

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The maximum number of connection attempts with each replica, for the Distributed table engine.

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By default, 3.

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extremes

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Whether to count extreme values (the minimums and maximums in columns of a query result). Accepts 0 or 1. By default, 0 (disabled). -For more information, see the section "Extreme values".

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use_uncompressed_cache

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Whether to use a cache of uncompressed blocks. Accepts 0 or 1. By default, 0 (disabled). -The uncompressed cache (only for tables in the MergeTree family) allows significantly reducing latency and increasing throughput when working with a large number of short queries. Enable this setting for users who send frequent short requests. Also pay attention to the 'uncompressed_cache_size' configuration parameter (only set in the config file) – the size of uncompressed cache blocks. By default, it is 8 GiB. The uncompressed cache is filled in as needed; the least-used data is automatically deleted.

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For queries that read at least a somewhat large volume of data (one million rows or more), the uncompressed cache is disabled automatically in order to save space for truly small queries. So you can keep the 'use_uncompressed_cache' setting always set to 1.

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replace_running_query

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When using the HTTP interface, the 'query_id' parameter can be passed. This is any string that serves as the query identifier. -If a query from the same user with the same 'query_id' already exists at this time, the behavior depends on the 'replace_running_query' parameter.

-

0 (default) – Throw an exception (don't allow the query to run if a query with the same 'query_id' is already running).

-

1 – Cancel the old query and start running the new one.

-

Yandex.Metrica uses this parameter set to 1 for implementing suggestions for segmentation conditions. After entering the next character, if the old query hasn't finished yet, it should be canceled.

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schema

-

This parameter is useful when you are using formats that require a schema definition, such as Cap'n Proto. The value depends on the format.

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stream_flush_interval_ms

-

Works for tables with streaming in the case of a timeout, or when a thread generatesmax_insert_block_size rows.

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The default value is 7500.

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The smaller the value, the more often data is flushed into the table. Setting the value too low leads to poor performance.

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load_balancing

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Which replicas (among healthy replicas) to preferably send a query to (on the first attempt) for distributed processing.

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random (default)

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The number of errors is counted for each replica. The query is sent to the replica with the fewest errors, and if there are several of these, to any one of them. -Disadvantages: Server proximity is not accounted for; if the replicas have different data, you will also get different data.

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nearest_hostname

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The number of errors is counted for each replica. Every 5 minutes, the number of errors is integrally divided by 2. Thus, the number of errors is calculated for a recent time with exponential smoothing. If there is one replica with a minimal number of errors (i.e. errors occurred recently on the other replicas), the query is sent to it. If there are multiple replicas with the same minimal number of errors, the query is sent to the replica with a host name that is most similar to the server's host name in the config file (for the number of different characters in identical positions, up to the minimum length of both host names).

-

For instance, example01-01-1 and example01-01-2.yandex.ru are different in one position, while example01-01-1 and example01-02-2 differ in two places. -This method might seem a little stupid, but it doesn't use external data about network topology, and it doesn't compare IP addresses, which would be complicated for our IPv6 addresses.

-

Thus, if there are equivalent replicas, the closest one by name is preferred. -We can also assume that when sending a query to the same server, in the absence of failures, a distributed query will also go to the same servers. So even if different data is placed on the replicas, the query will return mostly the same results.

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in_order

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Replicas are accessed in the same order as they are specified. The number of errors does not matter. -This method is appropriate when you know exactly which replica is preferable.

-

totals_mode

-

How to calculate TOTALS when HAVING is present, as well as when max_rows_to_group_by and group_by_overflow_mode = 'any' are present. -See the section "WITH TOTALS modifier".

-

totals_auto_threshold

-

The threshold for totals_mode = 'auto'. -See the section "WITH TOTALS modifier".

-

default_sample

-

Floating-point number from 0 to 1. By default, 1. -Allows you to set the default sampling ratio for all SELECT queries. -(For tables that do not support sampling, it throws an exception.) -If set to 1, sampling is not performed by default.

-

max_parallel_replicas

-

The maximum number of replicas for each shard when executing a query. -For consistency (to get different parts of the same data split), this option only works when the sampling key is set. -Replica lag is not controlled.

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compile

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Enable compilation of queries. By default, 0 (disabled).

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Compilation is only used for part of the query-processing pipeline: for the first stage of aggregation (GROUP BY). -If this portion of the pipeline was compiled, the query may run faster due to deployment of short cycles and inlining aggregate function calls. The maximum performance improvement (up to four times faster in rare cases) is seen for queries with multiple simple aggregate functions. Typically, the performance gain is insignificant. In very rare cases, it may slow down query execution.

-

min_count_to_compile

-

How many times to potentially use a compiled chunk of code before running compilation. By default, 3. -If the value is zero, then compilation runs synchronously and the query waits for the end of the compilation process before continuing execution. This can be used for testing; otherwise, use values ​​starting with 1. Compilation normally takes about 5-10 seconds. -If the value is 1 or more, compilation occurs asynchronously in a separate thread. The result will be used as soon as it is ready, including by queries that are currently running.

-

Compiled code is required for each different combination of aggregate functions used in the query and the type of keys in the GROUP BY clause. -The results of compilation are saved in the build directory in the form of .so files. There is no restriction on the number of compilation results, since they don't use very much space. Old results will be used after server restarts, except in the case of a server upgrade – in this case, the old results are deleted.

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input_format_skip_unknown_fields

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If the value is true, running INSERT skips input data from columns with unknown names. Otherwise, this situation will generate an exception. -It works for JSONEachRow and TSKV formats.

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output_format_json_quote_64bit_integers

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If the value is true, integers appear in quotes when using JSON* Int64 and UInt64 formats (for compatibility with most JavaScript implementations); otherwise, integers are output without the quotes.

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format_csv_delimiter

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The character to be considered as a delimiter in CSV data. By default, ,.

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Settings profiles

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A settings profile is a collection of settings grouped under the same name. Each ClickHouse user has a profile. -To apply all the settings in a profile, set profile.

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Example:

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Setting web profile.

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SET profile = 'web'
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Settings profiles are declared in the user config file. This is usually users.xml.

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Example:

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<!-- Settings profiles -->
-<profiles>
-    <!-- Default settings -->
-    <default>
-        <!-- The maximum number of threads when running a single query. -->
-        <max_threads>8</max_threads>
-    </default>
-
-    <!-- Settings for quries from the user interface -->
-    <web>
-        <max_rows_to_read>1000000000</max_rows_to_read>
-        <max_bytes_to_read>100000000000</max_bytes_to_read>
-
-        <max_rows_to_group_by>1000000</max_rows_to_group_by>
-        <group_by_overflow_mode>any</group_by_overflow_mode>
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-        <max_rows_to_sort>1000000</max_rows_to_sort>
-        <max_bytes_to_sort>1000000000</max_bytes_to_sort>
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-        <max_result_rows>100000</max_result_rows>
-        <max_result_bytes>100000000</max_result_bytes>
-        <result_overflow_mode>break</result_overflow_mode>
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-        <max_execution_time>600</max_execution_time>
-        <min_execution_speed>1000000</min_execution_speed>
-        <timeout_before_checking_execution_speed>15</timeout_before_checking_execution_speed>
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-        <max_columns_to_read>25</max_columns_to_read>
-        <max_temporary_columns>100</max_temporary_columns>
-        <max_temporary_non_const_columns>50</max_temporary_non_const_columns>
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-        <max_subquery_depth>2</max_subquery_depth>
-        <max_pipeline_depth>25</max_pipeline_depth>
-        <max_ast_depth>50</max_ast_depth>
-        <max_ast_elements>100</max_ast_elements>
-
-        <readonly>1</readonly>
-    </web>
-</profiles>
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The example specifies two profiles: default and web. The default profile has a special purpose: it must always be present and is applied when starting the server. In other words, the default profile contains default settings. The web profile is a regular profile that can be set using the SET query or using a URL parameter in an HTTP query.

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Settings profiles can inherit from each other. To use inheritance, indicate the profile setting before the other settings that are listed in the profile.

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Usage recommendations

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CPU

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The SSE 4.2 instruction set must be supported. Modern processors (since 2008) support it.

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When choosing a processor, prefer a large number of cores and slightly slower clock rate over fewer cores and a higher clock rate. -For example, 16 cores with 2600 MHz is better than 8 cores with 3600 MHz.

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Hyper-threading

-

Don't disable hyper-threading. It helps for some queries, but not for others.

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Turbo Boost

-

Turbo Boost is highly recommended. It significantly improves performance with a typical load. -You can use turbostat to view the CPU's actual clock rate under a load.

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CPU scaling governor

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Always use the performance scaling governor. The on-demand scaling governor works much worse with constantly high demand.

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sudo echo 'performance' | tee /sys/devices/system/cpu/cpu\*/cpufreq/scaling_governor
-
- - -

CPU limitations

-

Processors can overheat. Use dmesg to see if the CPU's clock rate was limited due to overheating. -The restriction can also be set externally at the datacenter level. You can use turbostat to monitor it under a load.

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RAM

-

For small amounts of data (up to \~200 GB compressed), it is best to use as much memory as the volume of data. -For large amounts of data and when processing interactive (online) queries, you should use a reasonable amount of RAM (128 GB or more) so the hot data subset will fit in the cache of pages. -Even for data volumes of \~50 TB per server, using 128 GB of RAM significantly improves query performance compared to 64 GB.

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Swap file

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Always disable the swap file. The only reason for not doing this is if you are using ClickHouse on your personal laptop.

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Huge pages

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Always disable transparent huge pages. It interferes with memory allocators, which leads to significant performance degradation.

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echo 'never' | sudo tee /sys/kernel/mm/transparent_hugepage/enabled
-
- - -

Use perf top to watch the time spent in the kernel for memory management. -Permanent huge pages also do not need to be allocated.

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Storage subsystem

-

If your budget allows you to use SSD, use SSD. -If not, use HDD. SATA HDDs 7200 RPM will do.

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Give preference to a lot of servers with local hard drives over a smaller number of servers with attached disk shelves. -But for storing archives with rare queries, shelves will work.

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RAID

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When using HDD, you can combine their RAID-10, RAID-5, RAID-6 or RAID-50. -For Linux, software RAID is better (with mdadm). We don't recommend using LVM. -When creating RAID-10, select the far layout. -If your budget allows, choose RAID-10.

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If you have more than 4 disks, use RAID-6 (preferred) or RAID-50, instead of RAID-5. -When using RAID-5, RAID-6 or RAID-50, always increase stripe_cache_size, since the default value is usually not the best choice.

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echo 4096 | sudo tee /sys/block/md2/md/stripe_cache_size
-
- - -

Calculate the exact number from the number of devices and the block size, using the formula: 2 * num_devices * chunk_size_in_bytes / 4096.

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A block size of 1025 KB is sufficient for all RAID configurations. -Never set the block size too small or too large.

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You can use RAID-0 on SSD. -Regardless of RAID use, always use replication for data security.

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Enable NCQ with a long queue. For HDD, choose the CFQ scheduler, and for SSD, choose noop. Don't reduce the 'readahead' setting. -For HDD, enable the write cache.

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File system

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Ext4 is the most reliable option. Set the mount options noatime, nobarrier. -XFS is also suitable, but it hasn't been as thoroughly tested with ClickHouse. -Most other file systems should also work fine. File systems with delayed allocation work better.

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Linux kernel

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Don't use an outdated Linux kernel. In 2015, 3.18.19 was new enough. -Consider using the kernel build from Yandex:https://github.com/yandex/smart – it provides at least a 5% performance increase.

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Network

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If you are using IPv6, increase the size of the route cache. -The Linux kernel prior to 3.2 had a multitude of problems with IPv6 implementation.

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Use at least a 10 GB network, if possible. 1 Gb will also work, but it will be much worse for patching replicas with tens of terabytes of data, or for processing distributed queries with a large amount of intermediate data.

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ZooKeeper

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You are probably already using ZooKeeper for other purposes. You can use the same installation of ZooKeeper, if it isn't already overloaded.

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It's best to use a fresh version of ZooKeeper – 3.4.9 or later. The version in stable Linux distributions may be outdated.

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With the default settings, ZooKeeper is a time bomb:

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The ZooKeeper server won't delete files from old snapshots and logs when using the default configuration (see autopurge), and this is the responsibility of the operator.

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This bomb must be defused.

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The ZooKeeper (3.5.1) configuration below is used in the Yandex.Metrica production environment as of May 20, 2017:

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zoo.cfg:

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# http://hadoop.apache.org/zookeeper/docs/current/zookeeperAdmin.html
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-# The number of milliseconds of each tick
-tickTime=2000
-# The number of ticks that the initial
-# synchronization phase can take
-initLimit=30000
-# The number of ticks that can pass between
-# sending a request and getting an acknowledgement
-syncLimit=10
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-maxClientCnxns=2000
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-maxSessionTimeout=60000000
-# the directory where the snapshot is stored.
-dataDir=/opt/zookeeper/{{ cluster['name'] }}/data
-# Place the dataLogDir to a separate physical disc for better performance
-dataLogDir=/opt/zookeeper/{{ cluster['name'] }}/logs
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-autopurge.snapRetainCount=10
-autopurge.purgeInterval=1
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-# To avoid seeks ZooKeeper allocates space in the transaction log file in
-# blocks of preAllocSize kilobytes. The default block size is 64M. One reason
-# for changing the size of the blocks is to reduce the block size if snapshots
-# are taken more often. (Also, see snapCount).
-preAllocSize=131072
-
-# Clients can submit requests faster than ZooKeeper can process them,
-# especially if there are a lot of clients. To prevent ZooKeeper from running
-# out of memory due to queued requests, ZooKeeper will throttle clients so that
-# there is no more than globalOutstandingLimit outstanding requests in the
-# system. The default limit is 1,000.ZooKeeper logs transactions to a
-# transaction log. After snapCount transactions are written to a log file a
-# snapshot is started and a new transaction log file is started. The default
-# snapCount is 10,000.
-snapCount=3000000
-
-# If this option is defined, requests will be will logged to a trace file named
-# traceFile.year.month.day.
-#traceFile=
-
-# Leader accepts client connections. Default value is "yes". The leader machine
-# coordinates updates. For higher update throughput at thes slight expense of
-# read throughput the leader can be configured to not accept clients and focus
-# on coordination.
-leaderServes=yes
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-standaloneEnabled=false
-dynamicConfigFile=/etc/zookeeper-{{ cluster['name'] }}/conf/zoo.cfg.dynamic
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Java version:

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Java(TM) SE Runtime Environment (build 1.8.0_25-b17)
-Java HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode)
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JVM parameters:

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NAME=zookeeper-{{ cluster['name'] }}
-ZOOCFGDIR=/etc/$NAME/conf
-
-# TODO this is really ugly
-# How to find out, which jars are needed?
-# seems, that log4j requires the log4j.properties file to be in the classpath
-CLASSPATH="$ZOOCFGDIR:/usr/build/classes:/usr/build/lib/*.jar:/usr/share/zookeeper/zookeeper-3.5.1-metrika.jar:/usr/share/zookeeper/slf4j-log4j12-1.7.5.jar:/usr/share/zookeeper/slf4j-api-1.7.5.jar:/usr/share/zookeeper/servlet-api-2.5-20081211.jar:/usr/share/zookeeper/netty-3.7.0.Final.jar:/usr/share/zookeeper/log4j-1.2.16.jar:/usr/share/zookeeper/jline-2.11.jar:/usr/share/zookeeper/jetty-util-6.1.26.jar:/usr/share/zookeeper/jetty-6.1.26.jar:/usr/share/zookeeper/javacc.jar:/usr/share/zookeeper/jackson-mapper-asl-1.9.11.jar:/usr/share/zookeeper/jackson-core-asl-1.9.11.jar:/usr/share/zookeeper/commons-cli-1.2.jar:/usr/src/java/lib/*.jar:/usr/etc/zookeeper"
-
-ZOOCFG="$ZOOCFGDIR/zoo.cfg"
-ZOO_LOG_DIR=/var/log/$NAME
-USER=zookeeper
-GROUP=zookeeper
-PIDDIR=/var/run/$NAME
-PIDFILE=$PIDDIR/$NAME.pid
-SCRIPTNAME=/etc/init.d/$NAME
-JAVA=/usr/bin/java
-ZOOMAIN="org.apache.zookeeper.server.quorum.QuorumPeerMain"
-ZOO_LOG4J_PROP="INFO,ROLLINGFILE"
-JMXLOCALONLY=false
-JAVA_OPTS="-Xms{{ cluster.get('xms','128M') }} \
-    -Xmx{{ cluster.get('xmx','1G') }} \
-    -Xloggc:/var/log/$NAME/zookeeper-gc.log \
-    -XX:+UseGCLogFileRotation \
-    -XX:NumberOfGCLogFiles=16 \
-    -XX:GCLogFileSize=16M \
-    -verbose:gc \
-    -XX:+PrintGCTimeStamps \
-    -XX:+PrintGCDateStamps \
-    -XX:+PrintGCDetails
-    -XX:+PrintTenuringDistribution \
-    -XX:+PrintGCApplicationStoppedTime \
-    -XX:+PrintGCApplicationConcurrentTime \
-    -XX:+PrintSafepointStatistics \
-    -XX:+UseParNewGC \
-    -XX:+UseConcMarkSweepGC \
--XX:+CMSParallelRemarkEnabled"
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Salt init:

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description "zookeeper-{{ cluster['name'] }} centralized coordination service"
-
-start on runlevel [2345]
-stop on runlevel [!2345]
-
-respawn
-
-limit nofile 8192 8192
-
-pre-start script
-    [ -r "/etc/zookeeper-{{ cluster['name'] }}/conf/environment" ] || exit 0
-    . /etc/zookeeper-{{ cluster['name'] }}/conf/environment
-    [ -d $ZOO_LOG_DIR ] || mkdir -p $ZOO_LOG_DIR
-    chown $USER:$GROUP $ZOO_LOG_DIR
-end script
-
-script
-    . /etc/zookeeper-{{ cluster['name'] }}/conf/environment
-    [ -r /etc/default/zookeeper ] && . /etc/default/zookeeper
-    if [ -z "$JMXDISABLE" ]; then
-        JAVA_OPTS="$JAVA_OPTS -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.local.only=$JMXLOCALONLY"
-    fi
-    exec start-stop-daemon --start -c $USER --exec $JAVA --name zookeeper-{{ cluster['name'] }} \
-        -- -cp $CLASSPATH $JAVA_OPTS -Dzookeeper.log.dir=${ZOO_LOG_DIR} \
-        -Dzookeeper.root.logger=${ZOO_LOG4J_PROP} $ZOOMAIN $ZOOCFG
-end script
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Operators

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All operators are transformed to the corresponding functions at the query parsing stage, in accordance with their precedence and associativity. -Groups of operators are listed in order of priority (the higher it is in the list, the earlier the operator is connected to its arguments).

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Access operators

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a[N] Access to an element of an array; arrayElement(a, N) function.

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a.N – Access to a tuble element; tupleElement(a, N) function.

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Numeric negation operator

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-a – The negate (a) function.

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Multiplication and division operators

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a * b – The multiply (a, b) function.

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a / b – The divide(a, b) function.

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a % b – The modulo(a, b) function.

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Addition and subtraction operators

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a + b – The plus(a, b) function.

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a - b – The minus(a, b) function.

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Comparison operators

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a = b – The equals(a, b) function.

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a == b – The equals(a, b) function.

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a != b – The notEquals(a, b) function.

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a <> b – The notEquals(a, b) function.

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a <= b – The lessOrEquals(a, b) function.

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a >= b – The greaterOrEquals(a, b) function.

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a < b – The less(a, b) function.

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a > b – The greater(a, b) function.

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a LIKE s – The like(a, b) function.

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a NOT LIKE s – The notLike(a, b) function.

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a BETWEEN b AND c – The same as a >= b AND a <= c.

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Operators for working with data sets

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See the section "IN operators".

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a IN ... – The in(a, b) function

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a NOT IN ... – The notIn(a, b) function.

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a GLOBAL IN ... – The globalIn(a, b) function.

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a GLOBAL NOT IN ... – The globalNotIn(a, b) function.

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Logical negation operator

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NOT a The not(a) function.

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Logical AND operator

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a AND b – Theand(a, b) function.

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Logical OR operator

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a OR b – The or(a, b) function.

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Conditional operator

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a ? b : c – The if(a, b, c) function.

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Note:

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The conditional operator calculates the values of b and c, then checks whether condition a is met, and then returns the corresponding value. If "b" or "c" is an arrayJoin() function, each row will be replicated regardless of the "a" condition.

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Conditional expression

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CASE [x]
-    WHEN a THEN b
-    [WHEN ... THEN ...]
-    ELSE c
-END
-
- - -

If "x" is specified, then transform(x, [a, ...], [b, ...], c). Otherwise – multiIf(a, b, ..., c).

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Concatenation operator

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s1 || s2 – The concat(s1, s2) function.

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Lambda creation operator

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x -> expr – The lambda(x, expr) function.

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The following operators do not have a priority, since they are brackets:

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Array creation operator

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[x1, ...] – The array(x1, ...) function.

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Tuple creation operator

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(x1, x2, ...) – The tuple(x2, x2, ...) function.

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Associativity

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All binary operators have left associativity. For example, 1 + 2 + 3 is transformed to plus(plus(1, 2), 3). -Sometimes this doesn't work the way you expect. For example, SELECT 4 > 2 > 3 will result in 0.

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For efficiency, the and and or functions accept any number of arguments. The corresponding chains of AND and OR operators are transformed to a single call of these functions.

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Queries

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CREATE DATABASE

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Creating db_name databases

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CREATE DATABASE [IF NOT EXISTS] db_name
-
- - -

A database is just a directory for tables. -If IF NOT EXISTS is included, the query won't return an error if the database already exists.

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CREATE TABLE

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The CREATE TABLE query can have several forms.

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CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db.]name [ON CLUSTER cluster]
-(
-    name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1],
-    name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2],
-    ...
-) ENGINE = engine
-
- - -

Creates a table named 'name' in the 'db' database or the current database if 'db' is not set, with the structure specified in brackets and the 'engine' engine. -The structure of the table is a list of column descriptions. If indexes are supported by the engine, they are indicated as parameters for the table engine.

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A column description is name type in the simplest case. Example: RegionID UInt32. -Expressions can also be defined for default values (see below).

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CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db.]name AS [db2.]name2 [ENGINE = engine]
-
- - -

Creates a table with the same structure as another table. You can specify a different engine for the table. If the engine is not specified, the same engine will be used as for the db2.name2 table.

-
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db.]name ENGINE = engine AS SELECT ...
-
- - -

Creates a table with a structure like the result of the SELECT query, with the 'engine' engine, and fills it with data from SELECT.

-

In all cases, if IF NOT EXISTS is specified, the query won't return an error if the table already exists. In this case, the query won't do anything.

-

Default values

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The column description can specify an expression for a default value, in one of the following ways:DEFAULT expr, MATERIALIZED expr, ALIAS expr. -Example: URLDomain String DEFAULT domain(URL).

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If an expression for the default value is not defined, the default values will be set to zeros for numbers, empty strings for strings, empty arrays for arrays, and 0000-00-00 for dates or 0000-00-00 00:00:00 for dates with time. NULLs are not supported.

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If the default expression is defined, the column type is optional. If there isn't an explicitly defined type, the default expression type is used. Example: EventDate DEFAULT toDate(EventTime) – the 'Date' type will be used for the 'EventDate' column.

-

If the data type and default expression are defined explicitly, this expression will be cast to the specified type using type casting functions. Example: Hits UInt32 DEFAULT 0 means the same thing as Hits UInt32 DEFAULT toUInt32(0).

-

Default expressions may be defined as an arbitrary expression from table constants and columns. When creating and changing the table structure, it checks that expressions don't contain loops. For INSERT, it checks that expressions are resolvable – that all columns they can be calculated from have been passed.

-

DEFAULT expr

-

Normal default value. If the INSERT query doesn't specify the corresponding column, it will be filled in by computing the corresponding expression.

-

MATERIALIZED expr

-

Materialized expression. Such a column can't be specified for INSERT, because it is always calculated. -For an INSERT without a list of columns, these columns are not considered. -In addition, this column is not substituted when using an asterisk in a SELECT query. This is to preserve the invariant that the dump obtained using SELECT * can be inserted back into the table using INSERT without specifying the list of columns.

-

ALIAS expr

-

Synonym. Such a column isn't stored in the table at all. -Its values can't be inserted in a table, and it is not substituted when using an asterisk in a SELECT query. -It can be used in SELECTs if the alias is expanded during query parsing.

-

When using the ALTER query to add new columns, old data for these columns is not written. Instead, when reading old data that does not have values for the new columns, expressions are computed on the fly by default. However, if running the expressions requires different columns that are not indicated in the query, these columns will additionally be read, but only for the blocks of data that need it.

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If you add a new column to a table but later change its default expression, the values used for old data will change (for data where values were not stored on the disk). Note that when running background merges, data for columns that are missing in one of the merging parts is written to the merged part.

-

It is not possible to set default values for elements in nested data structures.

-

Temporary tables

-

In all cases, if TEMPORARY is specified, a temporary table will be created. Temporary tables have the following characteristics:

-
    -
  • Temporary tables disappear when the session ends, including if the connection is lost.
    • -
  • A temporary table is created with the Memory engine. The other table engines are not supported.
    • -
  • The DB can't be specified for a temporary table. It is created outside of databases.
    • -
  • If a temporary table has the same name as another one and a query specifies the table name without specifying the DB, the temporary table will be used.
    • -
  • For distributed query processing, temporary tables used in a query are passed to remote servers.
    • -
-

In most cases, temporary tables are not created manually, but when using external data for a query, or for distributed (GLOBAL) IN. For more information, see the appropriate sections

-

Distributed DDL queries (ON CLUSTER clause)

-

The CREATE, DROP, ALTER, and RENAME queries support distributed execution on a cluster. -For example, the following query creates the all_hits Distributed table on each host in cluster:

-
CREATE TABLE IF NOT EXISTS all_hits ON CLUSTER cluster (p Date, i Int32) ENGINE = Distributed(cluster, default, hits)
-
- - -

In order to run these queries correctly, each host must have the same cluster definition (to simplify syncing configs, you can use substitutions from ZooKeeper). They must also connect to the ZooKeeper servers. -The local version of the query will eventually be implemented on each host in the cluster, even if some hosts are currently not available. The order for executing queries within a single host is guaranteed. -ALTER queries are not yet supported for replicated tables.

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CREATE VIEW

-
CREATE [MATERIALIZED] VIEW [IF NOT EXISTS] [db.]name [TO[db.]name] [ENGINE = engine] [POPULATE] AS SELECT ...
-
- - -

Creates a view. There are two types of views: normal and MATERIALIZED.

-

When creating a materialized view, you must specify ENGINE – the table engine for storing data.

-

A materialized view works as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.

-

Normal views don't store any data, but just perform a read from another table. In other words, a normal view is nothing more than a saved query. When reading from a view, this saved query is used as a subquery in the FROM clause.

-

As an example, assume you've created a view:

-
CREATE VIEW view AS SELECT ...
-
- - -

and written a query:

-
SELECT a, b, c FROM view
-
- - -

This query is fully equivalent to using the subquery:

-
SELECT a, b, c FROM (SELECT ...)
-
- - -

Materialized views store data transformed by the corresponding SELECT query.

-

When creating a materialized view, you must specify ENGINE – the table engine for storing data.

-

A materialized view is arranged as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.

-

If you specify POPULATE, the existing table data is inserted in the view when creating it, as if making a CREATE TABLE ... AS SELECT ... . Otherwise, the query contains only the data inserted in the table after creating the view. We don't recommend using POPULATE, since data inserted in the table during the view creation will not be inserted in it.

-

A SELECT query can contain DISTINCT, GROUP BY, ORDER BY, LIMIT... Note that the corresponding conversions are performed independently on each block of inserted data. For example, if GROUP BY is set, data is aggregated during insertion, but only within a single packet of inserted data. The data won't be further aggregated. The exception is when using an ENGINE that independently performs data aggregation, such as SummingMergeTree.

-

The execution of ALTER queries on materialized views has not been fully developed, so they might be inconvenient. If the materialized view uses the construction TO [db.]name, you can DETACH the view, run ALTER for the target table, and then ATTACH the previously detached (DETACH) view.

-

Views look the same as normal tables. For example, they are listed in the result of the SHOW TABLES query.

-

There isn't a separate query for deleting views. To delete a view, use DROP TABLE.

-

ATTACH

-

This query is exactly the same as CREATE, but

-
    -
  • instead of the word CREATE it uses the word ATTACH.
    • -
  • The query doesn't create data on the disk, but assumes that data is already in the appropriate places, and just adds information about the table to the server. -After executing an ATTACH query, the server will know about the existence of the table.
    • -
-

If the table was previously detached (DETACH), meaning that its structure is known, you can use shorthand without defining the structure.

-
ATTACH TABLE [IF NOT EXISTS] [db.]name
-
- - -

This query is used when starting the server. The server stores table metadata as files with ATTACH queries, which it simply runs at launch (with the exception of system tables, which are explicitly created on the server).

-

DROP

-

This query has two types: DROP DATABASE and DROP TABLE.

-
DROP DATABASE [IF EXISTS] db [ON CLUSTER cluster]
-
- - -

Deletes all tables inside the 'db' database, then deletes the 'db' database itself. -If IF EXISTS is specified, it doesn't return an error if the database doesn't exist.

-
DROP [TEMPORARY] TABLE [IF EXISTS] [db.]name [ON CLUSTER cluster]
-
- - -

Deletes the table. -If IF EXISTS is specified, it doesn't return an error if the table doesn't exist or the database doesn't exist.

-

DETACH

-

Deletes information about the 'name' table from the server. The server stops knowing about the table's existence.

-
DETACH TABLE [IF EXISTS] [db.]name
-
- - -

This does not delete the table's data or metadata. On the next server launch, the server will read the metadata and find out about the table again. -Similarly, a "detached" table can be re-attached using the ATTACH query (with the exception of system tables, which do not have metadata stored for them).

-

There is no DETACH DATABASE query.

-

RENAME

-

Renames one or more tables.

-
RENAME TABLE [db11.]name11 TO [db12.]name12, [db21.]name21 TO [db22.]name22, ... [ON CLUSTER cluster]
-
- - -

All tables are renamed under global locking. Renaming tables is a light operation. If you indicated another database after TO, the table will be moved to this database. However, the directories with databases must reside in the same file system (otherwise, an error is returned).

-

-

ALTER

-

The ALTER query is only supported for *MergeTree tables, as well as MergeandDistributed. The query has several variations.

-

Column manipulations

-

Changing the table structure.

-
ALTER TABLE [db].name [ON CLUSTER cluster] ADD|DROP|MODIFY COLUMN ...
-
- - -

In the query, specify a list of one or more comma-separated actions. -Each action is an operation on a column.

-

The following actions are supported:

-
ADD COLUMN name [type] [default_expr] [AFTER name_after]
-
- - -

Adds a new column to the table with the specified name, type, and default_expr (see the section "Default expressions"). If you specify AFTER name_after (the name of another column), the column is added after the specified one in the list of table columns. Otherwise, the column is added to the end of the table. Note that there is no way to add a column to the beginning of a table. For a chain of actions, 'name_after' can be the name of a column that is added in one of the previous actions.

-

Adding a column just changes the table structure, without performing any actions with data. The data doesn't appear on the disk after ALTER. If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). The column appears on the disk after merging data parts (see MergeTree).

-

This approach allows us to complete the ALTER query instantly, without increasing the volume of old data.

-
DROP COLUMN name
-
- - -

Deletes the column with the name 'name'. -Deletes data from the file system. Since this deletes entire files, the query is completed almost instantly.

-
MODIFY COLUMN name [type] [default_expr]
-
- - -

Changes the 'name' column's type to 'type' and/or the default expression to 'default_expr'. When changing the type, values are converted as if the 'toType' function were applied to them.

-

If only the default expression is changed, the query doesn't do anything complex, and is completed almost instantly.

-

Changing the column type is the only complex action – it changes the contents of files with data. For large tables, this may take a long time.

-

There are several processing stages:

-
    -
  • Preparing temporary (new) files with modified data.
    • -
  • Renaming old files.
    • -
  • Renaming the temporary (new) files to the old names.
    • -
  • Deleting the old files.
    • -
-

Only the first stage takes time. If there is a failure at this stage, the data is not changed. -If there is a failure during one of the successive stages, data can be restored manually. The exception is if the old files were deleted from the file system but the data for the new files did not get written to the disk and was lost.

-

There is no support for changing the column type in arrays and nested data structures.

-

The ALTER query lets you create and delete separate elements (columns) in nested data structures, but not whole nested data structures. To add a nested data structure, you can add columns with a name like name.nested_name and the type Array(T). A nested data structure is equivalent to multiple array columns with a name that has the same prefix before the dot.

-

There is no support for deleting columns in the primary key or the sampling key (columns that are in the ENGINE expression). Changing the type for columns that are included in the primary key is only possible if this change does not cause the data to be modified (for example, it is allowed to add values to an Enum or change a type with DateTime to UInt32).

-

If the ALTER query is not sufficient for making the table changes you need, you can create a new table, copy the data to it using the INSERT SELECT query, then switch the tables using the RENAME query and delete the old table.

-

The ALTER query blocks all reads and writes for the table. In other words, if a long SELECT is running at the time of the ALTER query, the ALTER query will wait for it to complete. At the same time, all new queries to the same table will wait while this ALTER is running.

-

For tables that don't store data themselves (such as Merge and Distributed), ALTER just changes the table structure, and does not change the structure of subordinate tables. For example, when running ALTER for a Distributed table, you will also need to run ALTER for the tables on all remote servers.

-

The ALTER query for changing columns is replicated. The instructions are saved in ZooKeeper, then each replica applies them. All ALTER queries are run in the same order. The query waits for the appropriate actions to be completed on the other replicas. However, a query to change columns in a replicated table can be interrupted, and all actions will be performed asynchronously.

-

Manipulations with partitions and parts

-

It only works for tables in the MergeTree family. The following operations are available:

-
    -
  • DETACH PARTITION – Move a partition to the 'detached' directory and forget it.
    • -
  • DROP PARTITION – Delete a partition.
    • -
  • ATTACH PART|PARTITION – Add a new part or partition from the detached directory to the table.
    • -
  • FREEZE PARTITION – Create a backup of a partition.
    • -
  • FETCH PARTITION – Download a partition from another server.
    • -
-

Each type of query is covered separately below.

-

A partition in a table is data for a single calendar month. This is determined by the values of the date key specified in the table engine parameters. Each month's data is stored separately in order to simplify manipulations with this data.

-

A "part" in the table is part of the data from a single partition, sorted by the primary key.

-

You can use the system.parts table to view the set of table parts and partitions:

-
SELECT * FROM system.parts WHERE active
-
- - -

active – Only count active parts. Inactive parts are, for example, source parts remaining after merging to a larger part – these parts are deleted approximately 10 minutes after merging.

-

Another way to view a set of parts and partitions is to go into the directory with table data. -Data directory: /var/lib/clickhouse/data/database/table/,where /var/lib/clickhouse/ is the path to the ClickHouse data, 'database' is the database name, and 'table' is the table name. Example:

-
$ ls -l /var/lib/clickhouse/data/test/visits/
-total 48
-drwxrwxrwx 2 clickhouse clickhouse 20480 May  5 02:58 20140317_20140323_2_2_0
-drwxrwxrwx 2 clickhouse clickhouse 20480 May  5 02:58 20140317_20140323_4_4_0
-drwxrwxrwx 2 clickhouse clickhouse  4096 May  5 02:55 detached
--rw-rw-rw- 1 clickhouse clickhouse     2 May  5 02:58 increment.txt
-
- - -

Here, 20140317_20140323_2_2_0 and 20140317_20140323_4_4_0 are the directories of data parts.

-

Let's break down the name of the first part: 20140317_20140323_2_2_0.

-
    -
  • 20140317 is the minimum date of the data in the chunk.
    • -
  • 20140323 is the maximum date of the data in the chunk.
    • -
  • 2 is the minimum number of the data block.
    • -
  • 2 is the maximum number of the data block.
    • -
  • 0 is the chunk level (the depth of the merge tree it is formed from).
    • -
-

Each piece relates to a single partition and contains data for just one month. -201403 is the name of the partition. A partition is a set of parts for a single month.

-

On an operating server, you can't manually change the set of parts or their data on the file system, since the server won't know about it. -For non-replicated tables, you can do this when the server is stopped, but we don't recommended it. -For replicated tables, the set of parts can't be changed in any case.

-

The detached directory contains parts that are not used by the server - detached from the table using the ALTER ... DETACH query. Parts that are damaged are also moved to this directory, instead of deleting them. You can add, delete, or modify the data in the 'detached' directory at any time – the server won't know about this until you make the ALTER TABLE ... ATTACH query.

-
ALTER TABLE [db.]table DETACH PARTITION 'name'
-
- - -

Move all data for partitions named 'name' to the 'detached' directory and forget about them. -The partition name is specified in YYYYMM format. It can be indicated in single quotes or without them.

-

After the query is executed, you can do whatever you want with the data in the 'detached' directory — delete it from the file system, or just leave it.

-

The query is replicated – data will be moved to the 'detached' directory and forgotten on all replicas. The query can only be sent to a leader replica. To find out if a replica is a leader, perform SELECT to the 'system.replicas' system table. Alternatively, it is easier to make a query on all replicas, and all except one will throw an exception.

-
ALTER TABLE [db.]table DROP PARTITION 'name'
-
- - -

The same as the DETACH operation. Deletes data from the table. Data parts will be tagged as inactive and will be completely deleted in approximately 10 minutes. The query is replicated – data will be deleted on all replicas.

-
ALTER TABLE [db.]table ATTACH PARTITION|PART 'name'
-
- - -

Adds data to the table from the 'detached' directory.

-

It is possible to add data for an entire partition or a separate part. For a part, specify the full name of the part in single quotes.

-

The query is replicated. Each replica checks whether there is data in the 'detached' directory. If there is data, it checks the integrity, verifies that it matches the data on the server that initiated the query, and then adds it if everything is correct. If not, it downloads data from the query requestor replica, or from another replica where the data has already been added.

-

So you can put data in the 'detached' directory on one replica, and use the ALTER ... ATTACH query to add it to the table on all replicas.

-
ALTER TABLE [db.]table FREEZE PARTITION 'name'
-
- - -

Creates a local backup of one or multiple partitions. The name can be the full name of the partition (for example, 201403), or its prefix (for example, 2014): then the backup will be created for all the corresponding partitions.

-

The query does the following: for a data snapshot at the time of execution, it creates hardlinks to table data in the directory /var/lib/clickhouse/shadow/N/...

-

/var/lib/clickhouse/ is the working ClickHouse directory from the config. -N is the incremental number of the backup.

-

The same structure of directories is created inside the backup as inside /var/lib/clickhouse/. -It also performs 'chmod' for all files, forbidding writes to them.

-

The backup is created almost instantly (but first it waits for current queries to the corresponding table to finish running). At first, the backup doesn't take any space on the disk. As the system works, the backup can take disk space, as data is modified. If the backup is made for old enough data, it won't take space on the disk.

-

After creating the backup, data from /var/lib/clickhouse/shadow/ can be copied to the remote server and then deleted on the local server. -The entire backup process is performed without stopping the server.

-

The ALTER ... FREEZE PARTITION query is not replicated. A local backup is only created on the local server.

-

As an alternative, you can manually copy data from the /var/lib/clickhouse/data/database/table directory. -But if you do this while the server is running, race conditions are possible when copying directories with files being added or changed, and the backup may be inconsistent. You can do this if the server isn't running – then the resulting data will be the same as after the ALTER TABLE t FREEZE PARTITION query.

-

ALTER TABLE ... FREEZE PARTITION only copies data, not table metadata. To make a backup of table metadata, copy the file /var/lib/clickhouse/metadata/database/table.sql

-

To restore from a backup:

-
-
    -
  • Use the CREATE query to create the table if it doesn't exist. The query can be taken from an .sql file (replace ATTACH in it with CREATE).
    • -
  • Copy the data from the data/database/table/ directory inside the backup to the /var/lib/clickhouse/data/database/table/detached/ directory.
    • -
  • Run ALTER TABLE ... ATTACH PARTITION YYYYMM queries, where YYYYMM is the month, for every month.
    • -
-
-

In this way, data from the backup will be added to the table. -Restoring from a backup doesn't require stopping the server.

-

Backups and replication

-

Replication provides protection from device failures. If all data disappeared on one of your replicas, follow the instructions in the "Restoration after failure" section to restore it.

-

For protection from device failures, you must use replication. For more information about replication, see the section "Data replication".

-

Backups protect against human error (accidentally deleting data, deleting the wrong data or in the wrong cluster, or corrupting data). -For high-volume databases, it can be difficult to copy backups to remote servers. In such cases, to protect from human error, you can keep a backup on the same server (it will reside in /var/lib/clickhouse/shadow/).

-
ALTER TABLE [db.]table FETCH PARTITION 'name' FROM 'path-in-zookeeper'
-
- - -

This query only works for replicatable tables.

-

It downloads the specified partition from the shard that has its ZooKeeper path specified in the FROM clause, then puts it in the detached directory for the specified table.

-

Although the query is called ALTER TABLE, it does not change the table structure, and does not immediately change the data available in the table.

-

Data is placed in the detached directory. You can use the ALTER TABLE ... ATTACH query to attach the data.

-

The FROM clause specifies the path in ZooKeeper. For example, /clickhouse/tables/01-01/visits. -Before downloading, the system checks that the partition exists and the table structure matches. The most appropriate replica is selected automatically from the healthy replicas.

-

The ALTER ... FETCH PARTITION query is not replicated. The partition will be downloaded to the 'detached' directory only on the local server. Note that if after this you use the ALTER TABLE ... ATTACH query to add data to the table, the data will be added on all replicas (on one of the replicas it will be added from the 'detached' directory, and on the rest it will be loaded from neighboring replicas).

-

Synchronicity of ALTER queries

-

For non-replicatable tables, all ALTER queries are performed synchronously. For replicatable tables, the query just adds instructions for the appropriate actions to ZooKeeper, and the actions themselves are performed as soon as possible. However, the query can wait for these actions to be completed on all the replicas.

-

For ALTER ... ATTACH|DETACH|DROP queries, you can use the replication_alter_partitions_sync setting to set up waiting. -Possible values: 0 – do not wait; 1 – only wait for own execution (default); 2 – wait for all.

-

-

SHOW DATABASES

-
SHOW DATABASES [INTO OUTFILE filename] [FORMAT format]
-
- - -

Prints a list of all databases. -This query is identical to SELECT name FROM system.databases [INTO OUTFILE filename] [FORMAT format].

-

See also the section "Formats".

-

SHOW TABLES

-
SHOW [TEMPORARY] TABLES [FROM db] [LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format]
-
- - -

Displays a list of tables

-
    -
  • tables from the current database, or from the 'db' database if "FROM db" is specified.
    • -
  • all tables, or tables whose name matches the pattern, if "LIKE 'pattern'" is specified.
    • -
-

This query is identical to: SELECT name FROM system.tables WHERE database = 'db' [AND name LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format].

-

See also the section "LIKE operator".

-

SHOW PROCESSLIST

-
SHOW PROCESSLIST [INTO OUTFILE filename] [FORMAT format]
-
- - -

Outputs a list of queries currently being processed, other than SHOW PROCESSLIST queries.

-

Prints a table containing the columns:

-

user – The user who made the query. Keep in mind that for distributed processing, queries are sent to remote servers under the 'default' user. SHOW PROCESSLIST shows the username for a specific query, not for a query that this query initiated.

-

address – The name of the host that the query was sent from. For distributed processing, on remote servers, this is the name of the query requestor host. To track where a distributed query was originally made from, look at SHOW PROCESSLIST on the query requestor server.

-

elapsed – The execution time, in seconds. Queries are output in order of decreasing execution time.

-

rows_read, bytes_read – How many rows and bytes of uncompressed data were read when processing the query. For distributed processing, data is totaled from all the remote servers. This is the data used for restrictions and quotas.

-

memory_usage – Current RAM usage in bytes. See the setting 'max_memory_usage'.

-

query – The query itself. In INSERT queries, the data for insertion is not output.

-

query_id – The query identifier. Non-empty only if it was explicitly defined by the user. For distributed processing, the query ID is not passed to remote servers.

-

This query is identical to: SELECT * FROM system.processes [INTO OUTFILE filename] [FORMAT format].

-

Tip (execute in the console):

-
watch -n1 "clickhouse-client --query='SHOW PROCESSLIST'"
-
- - -

SHOW CREATE TABLE

-
SHOW CREATE [TEMPORARY] TABLE [db.]table [INTO OUTFILE filename] [FORMAT format]
-
- - -

Returns a single String-type 'statement' column, which contains a single value – the CREATE query used for creating the specified table.

-

DESCRIBE TABLE

-
DESC|DESCRIBE TABLE [db.]table [INTO OUTFILE filename] [FORMAT format]
-
- - -

Returns two String-type columns: name and type, which indicate the names and types of columns in the specified table.

-

Nested data structures are output in "expanded" format. Each column is shown separately, with the name after a dot.

-

EXISTS

-
EXISTS [TEMPORARY] TABLE [db.]name [INTO OUTFILE filename] [FORMAT format]
-
- - -

Returns a single UInt8-type column, which contains the single value 0 if the table or database doesn't exist, or 1 if the table exists in the specified database.

-

USE

-
USE db
-
- - -

Lets you set the current database for the session. -The current database is used for searching for tables if the database is not explicitly defined in the query with a dot before the table name. -This query can't be made when using the HTTP protocol, since there is no concept of a session.

-

SET

-
SET param = value
-
- - -

Allows you to set param to value. You can also make all the settings from the specified settings profile in a single query. To do this, specify 'profile' as the setting name. For more information, see the section "Settings". -The setting is made for the session, or for the server (globally) if GLOBAL is specified. -When making a global setting, the setting is not applied to sessions already running, including the current session. It will only be used for new sessions.

-

When the server is restarted, global settings made using SET are lost. -To make settings that persist after a server restart, you can only use the server's config file.

-

OPTIMIZE

-
OPTIMIZE TABLE [db.]name [PARTITION partition] [FINAL]
-
- - -

Asks the table engine to do something for optimization. -Supported only by *MergeTree engines, in which this query initializes a non-scheduled merge of data parts. -If you specify a PARTITION, only the specified partition will be optimized. -If you specify FINAL, optimization will be performed even when all the data is already in one part.

-

-

INSERT

-

Adding data.

-

Basic query format:

-
INSERT INTO [db.]table [(c1, c2, c3)] VALUES (v11, v12, v13), (v21, v22, v23), ...
-
- - -

The query can specify a list of columns to insert [(c1, c2, c3)]. In this case, the rest of the columns are filled with:

-
    -
  • The values calculated from the DEFAULT expressions specified in the table definition.
    • -
  • Zeros and empty strings, if DEFAULT expressions are not defined.
    • -
-

If strict_insert_defaults=1, columns that do not have DEFAULT defined must be listed in the query.

-

Data can be passed to the INSERT in any format supported by ClickHouse. The format must be specified explicitly in the query:

-
INSERT INTO [db.]table [(c1, c2, c3)] FORMAT format_name data_set
-
- - -

For example, the following query format is identical to the basic version of INSERT ... VALUES:

-
INSERT INTO [db.]table [(c1, c2, c3)] FORMAT Values (v11, v12, v13), (v21, v22, v23), ...
-
- - -

ClickHouse removes all spaces and one line feed (if there is one) before the data. When forming a query, we recommend putting the data on a new line after the query operators (this is important if the data begins with spaces).

-

Example:

-
INSERT INTO t FORMAT TabSeparated
-11  Hello, world!
-22  Qwerty
-
- - -

You can insert data separately from the query by using the command-line client or the HTTP interface. For more information, see the section "Interfaces".

-

Inserting the results of SELECT

-
INSERT INTO [db.]table [(c1, c2, c3)] SELECT ...
-
- - -

Columns are mapped according to their position in the SELECT clause. However, their names in the SELECT expression and the table for INSERT may differ. If necessary, type casting is performed.

-

None of the data formats except Values allow setting values to expressions such as now(), 1 + 2, and so on. The Values format allows limited use of expressions, but this is not recommended, because in this case inefficient code is used for their execution.

-

Other queries for modifying data parts are not supported: UPDATE, DELETE, REPLACE, MERGE, UPSERT, INSERT UPDATE. -However, you can delete old data using ALTER TABLE ... DROP PARTITION.

-

Performance considerations

-

INSERT sorts the input data by primary key and splits them into partitions by month. If you insert data for mixed months, it can significantly reduce the performance of the INSERT query. To avoid this:

-
    -
  • Add data in fairly large batches, such as 100,000 rows at a time.
    • -
  • Group data by month before uploading it to ClickHouse.
    • -
-

Performance will not decrease if:

-
    -
  • Data is added in real time.
    • -
  • You upload data that is usually sorted by time.
    • -
-

SELECT

-

Data sampling.

-
SELECT [DISTINCT] expr_list
-    [FROM [db.]table | (subquery) | table_function] [FINAL]
-    [SAMPLE sample_coeff]
-    [ARRAY JOIN ...]
-    [GLOBAL] ANY|ALL INNER|LEFT JOIN (subquery)|table USING columns_list
-    [PREWHERE expr]
-    [WHERE expr]
-    [GROUP BY expr_list] [WITH TOTALS]
-    [HAVING expr]
-    [ORDER BY expr_list]
-    [LIMIT [n, ]m]
-    [UNION ALL ...]
-    [INTO OUTFILE filename]
-    [FORMAT format]
-    [LIMIT n BY columns]
-
- - -

All the clauses are optional, except for the required list of expressions immediately after SELECT. -The clauses below are described in almost the same order as in the query execution conveyor.

-

If the query omits the DISTINCT, GROUP BY and ORDER BY clauses and the IN and JOIN subqueries, the query will be completely stream processed, using O(1) amount of RAM. -Otherwise, the query might consume a lot of RAM if the appropriate restrictions are not specified: max_memory_usage, max_rows_to_group_by, max_rows_to_sort, max_rows_in_distinct, max_bytes_in_distinct, max_rows_in_set, max_bytes_in_set, max_rows_in_join, max_bytes_in_join, max_bytes_before_external_sort, max_bytes_before_external_group_by. For more information, see the section "Settings". It is possible to use external sorting (saving temporary tables to a disk) and external aggregation. The system does not have "merge join".

-

FROM clause

-

If the FROM clause is omitted, data will be read from the system.one table. -The 'system.one' table contains exactly one row (this table fulfills the same purpose as the DUAL table found in other DBMSs).

-

The FROM clause specifies the table to read data from, or a subquery, or a table function; ARRAY JOIN and the regular JOIN may also be included (see below).

-

Instead of a table, the SELECT subquery may be specified in brackets. -In this case, the subquery processing pipeline will be built into the processing pipeline of an external query. -In contrast to standard SQL, a synonym does not need to be specified after a subquery. For compatibility, it is possible to write 'AS name' after a subquery, but the specified name isn't used anywhere.

-

A table function may be specified instead of a table. For more information, see the section "Table functions".

-

To execute a query, all the columns listed in the query are extracted from the appropriate table. Any columns not needed for the external query are thrown out of the subqueries. -If a query does not list any columns (for example, SELECT count() FROM t), some column is extracted from the table anyway (the smallest one is preferred), in order to calculate the number of rows.

-

The FINAL modifier can be used only for a SELECT from a CollapsingMergeTree table. When you specify FINAL, data is selected fully "collapsed". Keep in mind that using FINAL leads to a selection that includes columns related to the primary key, in addition to the columns specified in the SELECT. Additionally, the query will be executed in a single stream, and data will be merged during query execution. This means that when using FINAL, the query is processed more slowly. In most cases, you should avoid using FINAL. For more information, see the section "CollapsingMergeTree engine".

-

SAMPLE clause

-

The SAMPLE clause allows for approximated query processing. Approximated query processing is only supported by MergeTree* type tables, and only if the sampling expression was specified during table creation (see the section "MergeTree engine").

-

SAMPLE has the format SAMPLE k, where k is a decimal number from 0 to 1, or SAMPLE n, where 'n' is a sufficiently large integer.

-

In the first case, the query will be executed on 'k' percent of data. For example, SAMPLE 0.1 runs the query on 10% of data. -In the second case, the query will be executed on a sample of no more than 'n' rows. For example, SAMPLE 10000000 runs the query on a maximum of 10,000,000 rows.

-

Example:

-
SELECT
-    Title,
-    count() * 10 AS PageViews
-FROM hits_distributed
-SAMPLE 0.1
-WHERE
-    CounterID = 34
-    AND toDate(EventDate) >= toDate('2013-01-29')
-    AND toDate(EventDate) <= toDate('2013-02-04')
-    AND NOT DontCountHits
-    AND NOT Refresh
-    AND Title != ''
-GROUP BY Title
-ORDER BY PageViews DESC LIMIT 1000
-
- - -

In this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value 'count()' is manually multiplied by 10.

-

When using something like SAMPLE 10000000, there isn't any information about which relative percent of data was processed or what the aggregate functions should be multiplied by, so this method of writing is not always appropriate to the situation.

-

A sample with a relative coefficient is "consistent": if we look at all possible data that could be in the table, a sample (when using a single sampling expression specified during table creation) with the same coefficient always selects the same subset of possible data. In other words, a sample from different tables on different servers at different times is made the same way.

-

For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This allows using the sample in subqueries in the IN clause, as well as for manually correlating results of different queries with samples.

-

ARRAY JOIN clause

-

Allows executing JOIN with an array or nested data structure. The intent is similar to the 'arrayJoin' function, but its functionality is broader.

-

ARRAY JOIN is essentially INNER JOIN with an array. Example:

-
:) CREATE TABLE arrays_test (s String, arr Array(UInt8)) ENGINE = Memory
-
-CREATE TABLE arrays_test
-(
-    s String,
-    arr Array(UInt8)
-) ENGINE = Memory
-
-Ok.
-
-0 rows in set. Elapsed: 0.001 sec.
-
-:) INSERT INTO arrays_test VALUES ('Hello', [1,2]), ('World', [3,4,5]), ('Goodbye', [])
-
-INSERT INTO arrays_test VALUES
-
-Ok.
-
-3 rows in set. Elapsed: 0.001 sec.
-
-:) SELECT * FROM arrays_test
-
-SELECT *
-FROM arrays_test
-
-┌─s───────┬─arr─────┐
-│ Hello   │ [1,2]   │
-│ World   │ [3,4,5] │
-│ Goodbye │ []      │
-└─────────┴─────────┘
-
-3 rows in set. Elapsed: 0.001 sec.
-
-:) SELECT s, arr FROM arrays_test ARRAY JOIN arr
-
-SELECT s, arr
-FROM arrays_test
-ARRAY JOIN arr
-
-┌─s─────┬─arr─┐
-│ Hello │   1 │
-│ Hello │   2 │
-│ World │   3 │
-│ World │   4 │
-│ World │   5 │
-└───────┴─────┘
-
-5 rows in set. Elapsed: 0.001 sec.
-
- - -

An alias can be specified for an array in the ARRAY JOIN clause. In this case, an array item can be accessed by this alias, but the array itself by the original name. Example:

-
:) SELECT s, arr, a FROM arrays_test ARRAY JOIN arr AS a
-
-SELECT s, arr, a
-FROM arrays_test
-ARRAY JOIN arr AS a
-
-┌─s─────┬─arr─────┬─a─┐
-│ Hello │ [1,2]   │ 1 │
-│ Hello │ [1,2]   │ 2 │
-│ World │ [3,4,5] │ 3 │
-│ World │ [3,4,5] │ 4 │
-│ World │ [3,4,5] │ 5 │
-└───────┴─────────┴───┘
-
-5 rows in set. Elapsed: 0.001 sec.
-
- - -

Multiple arrays of the same size can be comma-separated in the ARRAY JOIN clause. In this case, JOIN is performed with them simultaneously (the direct sum, not the direct product). Example:

-
:) SELECT s, arr, a, num, mapped FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(x -> x + 1, arr) AS mapped
-
-SELECT s, arr, a, num, mapped
-FROM arrays_test
-ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(lambda(tuple(x), plus(x, 1)), arr) AS mapped
-
-┌─s─────┬─arr─────┬─a─┬─num─┬─mapped─┐
-│ Hello │ [1,2]   │ 1 │   1 │      2 │
-│ Hello │ [1,2]   │ 2 │   2 │      3 │
-│ World │ [3,4,5] │ 3 │   1 │      4 │
-│ World │ [3,4,5] │ 4 │   2 │      5 │
-│ World │ [3,4,5] │ 5 │   3 │      6 │
-└───────┴─────────┴───┴─────┴────────┘
-
-5 rows in set. Elapsed: 0.002 sec.
-
-:) SELECT s, arr, a, num, arrayEnumerate(arr) FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num
-
-SELECT s, arr, a, num, arrayEnumerate(arr)
-FROM arrays_test
-ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num
-
-┌─s─────┬─arr─────┬─a─┬─num─┬─arrayEnumerate(arr)─┐
-│ Hello │ [1,2]   │ 1 │   1 │ [1,2]               │
-│ Hello │ [1,2]   │ 2 │   2 │ [1,2]               │
-│ World │ [3,4,5] │ 3 │   1 │ [1,2,3]             │
-│ World │ [3,4,5] │ 4 │   2 │ [1,2,3]             │
-│ World │ [3,4,5] │ 5 │   3 │ [1,2,3]             │
-└───────┴─────────┴───┴─────┴─────────────────────┘
-
-5 rows in set. Elapsed: 0.002 sec.
-
- - -

ARRAY JOIN also works with nested data structures. Example:

-
:) CREATE TABLE nested_test (s String, nest Nested(x UInt8, y UInt32)) ENGINE = Memory
-
-CREATE TABLE nested_test
-(
-    s String,
-    nest Nested(
-    x UInt8,
-    y UInt32)
-) ENGINE = Memory
-
-Ok.
-
-0 rows in set. Elapsed: 0.006 sec.
-
-:) INSERT INTO nested_test VALUES ('Hello', [1,2], [10,20]), ('World', [3,4,5], [30,40,50]), ('Goodbye', [], [])
-
-INSERT INTO nested_test VALUES
-
-Ok.
-
-3 rows in set. Elapsed: 0.001 sec.
-
-:) SELECT * FROM nested_test
-
-SELECT *
-FROM nested_test
-
-┌─s───────┬─nest.x──┬─nest.y─────┐
-│ Hello   │ [1,2]   │ [10,20]    │
-│ World   │ [3,4,5] │ [30,40,50] │
-│ Goodbye │ []      │ []         │
-└─────────┴─────────┴────────────┘
-
-3 rows in set. Elapsed: 0.001 sec.
-
-:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest
-
-SELECT s, `nest.x`, `nest.y`
-FROM nested_test
-ARRAY JOIN nest
-
-┌─s─────┬─nest.x─┬─nest.y─┐
-│ Hello │      1 │     10 │
-│ Hello │      2 │     20 │
-│ World │      3 │     30 │
-│ World │      4 │     40 │
-│ World │      5 │     50 │
-└───────┴────────┴────────┘
-
-5 rows in set. Elapsed: 0.001 sec.
-
- - -

When specifying names of nested data structures in ARRAY JOIN, the meaning is the same as ARRAY JOIN with all the array elements that it consists of. Example:

-
:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x, nest.y
-
-SELECT s, `nest.x`, `nest.y`
-FROM nested_test
-ARRAY JOIN `nest.x`, `nest.y`
-
-┌─s─────┬─nest.x─┬─nest.y─┐
-│ Hello │      1 │     10 │
-│ Hello │      2 │     20 │
-│ World │      3 │     30 │
-│ World │      4 │     40 │
-│ World │      5 │     50 │
-└───────┴────────┴────────┘
-
-5 rows in set. Elapsed: 0.001 sec.
-
- - -

This variation also makes sense:

-
:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x
-
-SELECT s, `nest.x`, `nest.y`
-FROM nested_test
-ARRAY JOIN `nest.x`
-
-┌─s─────┬─nest.x─┬─nest.y─────┐
-│ Hello │      1 │ [10,20]    │
-│ Hello │      2 │ [10,20]    │
-│ World │      3 │ [30,40,50] │
-│ World │      4 │ [30,40,50] │
-│ World │      5 │ [30,40,50] │
-└───────┴────────┴────────────┘
-
-5 rows in set. Elapsed: 0.001 sec.
-
- - -

An alias may be used for a nested data structure, in order to select either the JOIN result or the source array. Example:

-
:) SELECT s, n.x, n.y, nest.x, nest.y FROM nested_test ARRAY JOIN nest AS n
-
-SELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`
-FROM nested_test
-ARRAY JOIN nest AS n
-
-┌─s─────┬─n.x─┬─n.y─┬─nest.x──┬─nest.y─────┐
-│ Hello │   1 │  10 │ [1,2]   │ [10,20]    │
-│ Hello │   2 │  20 │ [1,2]   │ [10,20]    │
-│ World │   3 │  30 │ [3,4,5] │ [30,40,50] │
-│ World │   4 │  40 │ [3,4,5] │ [30,40,50] │
-│ World │   5 │  50 │ [3,4,5] │ [30,40,50] │
-└───────┴─────┴─────┴─────────┴────────────┘
-
-5 rows in set. Elapsed: 0.001 sec.
-
- - -

Example of using the arrayEnumerate function:

-
:) SELECT s, n.x, n.y, nest.x, nest.y, num FROM nested_test ARRAY JOIN nest AS n, arrayEnumerate(nest.x) AS num
-
-SELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`, num
-FROM nested_test
-ARRAY JOIN nest AS n, arrayEnumerate(`nest.x`) AS num
-
-┌─s─────┬─n.x─┬─n.y─┬─nest.x──┬─nest.y─────┬─num─┐
-│ Hello │   1 │  10 │ [1,2]   │ [10,20]    │   1 │
-│ Hello │   2 │  20 │ [1,2]   │ [10,20]    │   2 │
-│ World │   3 │  30 │ [3,4,5] │ [30,40,50] │   1 │
-│ World │   4 │  40 │ [3,4,5] │ [30,40,50] │   2 │
-│ World │   5 │  50 │ [3,4,5] │ [30,40,50] │   3 │
-└───────┴─────┴─────┴─────────┴────────────┴─────┘
-
-5 rows in set. Elapsed: 0.002 sec.
-
- - -

The query can only specify a single ARRAY JOIN clause.

-

The corresponding conversion can be performed before the WHERE/PREWHERE clause (if its result is needed in this clause), or after completing WHERE/PREWHERE (to reduce the volume of calculations).

-

JOIN clause

-

The normal JOIN, which is not related to ARRAY JOIN described above.

-
[GLOBAL] ANY|ALL INNER|LEFT [OUTER] JOIN (subquery)|table USING columns_list
-
- - -

Performs joins with data from the subquery. At the beginning of query processing, the subquery specified after JOIN is run, and its result is saved in memory. Then it is read from the "left" table specified in the FROM clause, and while it is being read, for each of the read rows from the "left" table, rows are selected from the subquery results table (the "right" table) that meet the condition for matching the values of the columns specified in USING.

-

The table name can be specified instead of a subquery. This is equivalent to the SELECT * FROM table subquery, except in a special case when the table has the Join engine – an array prepared for joining.

-

All columns that are not needed for the JOIN are deleted from the subquery.

-

There are several types of JOINs:

-

INNER or LEFT type:If INNER is specified, the result will contain only those rows that have a matching row in the right table. -If LEFT is specified, any rows in the left table that don't have matching rows in the right table will be assigned the default value - zeros or empty rows. LEFT OUTER may be written instead of LEFT; the word OUTER does not affect anything.

-

ANY or ALL stringency:If ANY is specified and the right table has several matching rows, only the first one found is joined. -If ALL is specified and the right table has several matching rows, the data will be multiplied by the number of these rows.

-

Using ALL corresponds to the normal JOIN semantic from standard SQL. -Using ANY is optimal. If the right table has only one matching row, the results of ANY and ALL are the same. You must specify either ANY or ALL (neither of them is selected by default).

-

GLOBAL distribution:

-

When using a normal JOIN, the query is sent to remote servers. Subqueries are run on each of them in order to make the right table, and the join is performed with this table. In other words, the right table is formed on each server separately.

-

When using GLOBAL ... JOIN, first the requestor server runs a subquery to calculate the right table. This temporary table is passed to each remote server, and queries are run on them using the temporary data that was transmitted.

-

Be careful when using GLOBAL JOINs. For more information, see the section "Distributed subqueries".

-

Any combination of JOINs is possible. For example, GLOBAL ANY LEFT OUTER JOIN.

-

When running a JOIN, there is no optimization of the order of execution in relation to other stages of the query. The join (a search in the right table) is run before filtering in WHERE and before aggregation. In order to explicitly set the processing order, we recommend running a JOIN subquery with a subquery.

-

Example:

-
SELECT
-    CounterID,
-    hits,
-    visits
-FROM
-(
-    SELECT
-        CounterID,
-        count() AS hits
-    FROM test.hits
-    GROUP BY CounterID
-) ANY LEFT JOIN
-(
-    SELECT
-        CounterID,
-        sum(Sign) AS visits
-    FROM test.visits
-    GROUP BY CounterID
-) USING CounterID
-ORDER BY hits DESC
-LIMIT 10
-
- - -
┌─CounterID─┬───hits─┬─visits─┐
-│   1143050 │ 523264 │  13665 │
-│    731962 │ 475698 │ 102716 │
-│    722545 │ 337212 │ 108187 │
-│    722889 │ 252197 │  10547 │
-│   2237260 │ 196036 │   9522 │
-│  23057320 │ 147211 │   7689 │
-│    722818 │  90109 │  17847 │
-│     48221 │  85379 │   4652 │
-│  19762435 │  77807 │   7026 │
-│    722884 │  77492 │  11056 │
-└───────────┴────────┴────────┘
-
- - -

Subqueries don't allow you to set names or use them for referencing a column from a specific subquery. -The columns specified in USING must have the same names in both subqueries, and the other columns must be named differently. You can use aliases to change the names of columns in subqueries (the example uses the aliases 'hits' and 'visits').

-

The USING clause specifies one or more columns to join, which establishes the equality of these columns. The list of columns is set without brackets. More complex join conditions are not supported.

-

The right table (the subquery result) resides in RAM. If there isn't enough memory, you can't run a JOIN.

-

Only one JOIN can be specified in a query (on a single level). To run multiple JOINs, you can put them in subqueries.

-

Each time a query is run with the same JOIN, the subquery is run again – the result is not cached. To avoid this, use the special 'Join' table engine, which is a prepared array for joining that is always in RAM. For more information, see the section "Table engines, Join".

-

In some cases, it is more efficient to use IN instead of JOIN. -Among the various types of JOINs, the most efficient is ANY LEFT JOIN, then ANY INNER JOIN. The least efficient are ALL LEFT JOIN and ALL INNER JOIN.

-

If you need a JOIN for joining with dimension tables (these are relatively small tables that contain dimension properties, such as names for advertising campaigns), a JOIN might not be very convenient due to the bulky syntax and the fact that the right table is re-accessed for every query. For such cases, there is an "external dictionaries" feature that you should use instead of JOIN. For more information, see the section "External dictionaries".

-

WHERE clause

-

If there is a WHERE clause, it must contain an expression with the UInt8 type. This is usually an expression with comparison and logical operators. -This expression will be used for filtering data before all other transformations.

-

If indexes are supported by the database table engine, the expression is evaluated on the ability to use indexes.

-

PREWHERE clause

-

This clause has the same meaning as the WHERE clause. The difference is in which data is read from the table. -When using PREWHERE, first only the columns necessary for executing PREWHERE are read. Then the other columns are read that are needed for running the query, but only those blocks where the PREWHERE expression is true.

-

It makes sense to use PREWHERE if there are filtration conditions that are not suitable for indexes that are used by a minority of the columns in the query, but that provide strong data filtration. This reduces the volume of data to read.

-

For example, it is useful to write PREWHERE for queries that extract a large number of columns, but that only have filtration for a few columns.

-

PREWHERE is only supported by tables from the *MergeTree family.

-

A query may simultaneously specify PREWHERE and WHERE. In this case, PREWHERE precedes WHERE.

-

Keep in mind that it does not make much sense for PREWHERE to only specify those columns that have an index, because when using an index, only the data blocks that match the index are read.

-

If the 'optimize_move_to_prewhere' setting is set to 1 and PREWHERE is omitted, the system uses heuristics to automatically move parts of expressions from WHERE to PREWHERE.

-

GROUP BY clause

-

This is one of the most important parts of a column-oriented DBMS.

-

If there is a GROUP BY clause, it must contain a list of expressions. Each expression will be referred to here as a "key". -All the expressions in the SELECT, HAVING, and ORDER BY clauses must be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions.

-

If a query contains only table columns inside aggregate functions, the GROUP BY clause can be omitted, and aggregation by an empty set of keys is assumed.

-

Example:

-
SELECT
-    count(),
-    median(FetchTiming > 60 ? 60 : FetchTiming),
-    count() - sum(Refresh)
-FROM hits
-
- - -

However, in contrast to standard SQL, if the table doesn't have any rows (either there aren't any at all, or there aren't any after using WHERE to filter), an empty result is returned, and not the result from one of the rows containing the initial values of aggregate functions.

-

As opposed to MySQL (and conforming to standard SQL), you can't get some value of some column that is not in a key or aggregate function (except constant expressions). To work around this, you can use the 'any' aggregate function (get the first encountered value) or 'min/max'.

-

Example:

-
SELECT
-    domainWithoutWWW(URL) AS domain,
-    count(),
-    any(Title) AS title -- getting the first occurred page header for each domain.
-FROM hits
-GROUP BY domain
-
- - -

For every different key value encountered, GROUP BY calculates a set of aggregate function values.

-

GROUP BY is not supported for array columns.

-

A constant can't be specified as arguments for aggregate functions. Example: sum(1). Instead of this, you can get rid of the constant. Example: count().

-

WITH TOTALS modifier

-

If the WITH TOTALS modifier is specified, another row will be calculated. This row will have key columns containing default values (zeros or empty lines), and columns of aggregate functions with the values calculated across all the rows (the "total" values).

-

This extra row is output in JSON*, TabSeparated*, and Pretty* formats, separately from the other rows. In the other formats, this row is not output.

-

In JSON* formats, this row is output as a separate 'totals' field. In TabSeparated* formats, the row comes after the main result, preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result.

-

WITH TOTALS can be run in different ways when HAVING is present. The behavior depends on the 'totals_mode' setting. -By default, totals_mode = 'before_having'. In this case, 'totals' is calculated across all rows, including the ones that don't pass through HAVING and 'max_rows_to_group_by'.

-

The other alternatives include only the rows that pass through HAVING in 'totals', and behave differently with the setting max_rows_to_group_by and group_by_overflow_mode = 'any'.

-

after_having_exclusive – Don't include rows that didn't pass through max_rows_to_group_by. In other words, 'totals' will have less than or the same number of rows as it would if max_rows_to_group_by were omitted.

-

after_having_inclusive – Include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. In other words, 'totals' will have more than or the same number of rows as it would if max_rows_to_group_by were omitted.

-

after_having_auto – Count the number of rows that passed through HAVING. If it is more than a certain amount (by default, 50%), include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. Otherwise, do not include them.

-

totals_auto_threshold – By default, 0.5. The coefficient for after_having_auto.

-

If max_rows_to_group_by and group_by_overflow_mode = 'any' are not used, all variations of after_having are the same, and you can use any of them (for example, after_having_auto).

-

You can use WITH TOTALS in subqueries, including subqueries in the JOIN clause (in this case, the respective total values are combined).

-

GROUP BY in external memory

-

You can enable dumping temporary data to the disk to restrict memory usage during GROUP BY. -The max_bytes_before_external_group_by setting determines the threshold RAM consumption for dumping GROUP BY temporary data to the file system. If set to 0 (the default), it is disabled.

-

When using max_bytes_before_external_group_by, we recommend that you set max_memory_usage about twice as high. This is necessary because there are two stages to aggregation: reading the date and forming intermediate data (1) and merging the intermediate data (2). Dumping data to the file system can only occur during stage 1. If the temporary data wasn't dumped, then stage 2 might require up to the same amount of memory as in stage 1.

-

For example, if max_memory_usage was set to 10000000000 and you want to use external aggregation, it makes sense to set max_bytes_before_external_group_by to 10000000000, and max_memory_usage to 20000000000. When external aggregation is triggered (if there was at least one dump of temporary data), maximum consumption of RAM is only slightly more than max_bytes_before_external_group_by.

-

With distributed query processing, external aggregation is performed on remote servers. In order for the requestor server to use only a small amount of RAM, set distributed_aggregation_memory_efficient to 1.

-

When merging data flushed to the disk, as well as when merging results from remote servers when the distributed_aggregation_memory_efficient setting is enabled, consumes up to 1/256 * the number of threads from the total amount of RAM.

-

When external aggregation is enabled, if there was less than max_bytes_before_external_group_by of data (i.e. data was not flushed), the query runs just as fast as without external aggregation. If any temporary data was flushed, the run time will be several times longer (approximately three times).

-

If you have an ORDER BY with a small LIMIT after GROUP BY, then the ORDER BY CLAUSE will not use significant amounts of RAM. -But if the ORDER BY doesn't have LIMIT, don't forget to enable external sorting (max_bytes_before_external_sort).

-

LIMIT N BY clause

-

LIMIT N BY COLUMNS selects the top N rows for each group of COLUMNS. LIMIT N BY is not related to LIMIT; they can both be used in the same query. The key for LIMIT N BY can contain any number of columns or expressions.

-

Example:

-
SELECT
-    domainWithoutWWW(URL) AS domain,
-    domainWithoutWWW(REFERRER_URL) AS referrer,
-    device_type,
-    count() cnt
-FROM hits
-GROUP BY domain, referrer, device_type
-ORDER BY cnt DESC
-LIMIT 5 BY domain, device_type
-LIMIT 100
-
- - -

The query will select the top 5 referrers for each domain, device_type pair, but not more than 100 rows (LIMIT n BY + LIMIT).

-

HAVING clause

-

Allows filtering the result received after GROUP BY, similar to the WHERE clause. -WHERE and HAVING differ in that WHERE is performed before aggregation (GROUP BY), while HAVING is performed after it. -If aggregation is not performed, HAVING can't be used.

-

-

ORDER BY clause

-

The ORDER BY clause contains a list of expressions, which can each be assigned DESC or ASC (the sorting direction). If the direction is not specified, ASC is assumed. ASC is sorted in ascending order, and DESC in descending order. The sorting direction applies to a single expression, not to the entire list. Example: ORDER BY Visits DESC, SearchPhrase

-

For sorting by String values, you can specify collation (comparison). Example: ORDER BY SearchPhrase COLLATE 'tr' - for sorting by keyword in ascending order, using the Turkish alphabet, case insensitive, assuming that strings are UTF-8 encoded. COLLATE can be specified or not for each expression in ORDER BY independently. If ASC or DESC is specified, COLLATE is specified after it. When using COLLATE, sorting is always case-insensitive.

-

We only recommend using COLLATE for final sorting of a small number of rows, since sorting with COLLATE is less efficient than normal sorting by bytes.

-

Rows that have identical values for the list of sorting expressions are output in an arbitrary order, which can also be nondeterministic (different each time). -If the ORDER BY clause is omitted, the order of the rows is also undefined, and may be nondeterministic as well.

-

When floating point numbers are sorted, NaNs are separate from the other values. Regardless of the sorting order, NaNs come at the end. In other words, for ascending sorting they are placed as if they are larger than all the other numbers, while for descending sorting they are placed as if they are smaller than the rest.

-

Less RAM is used if a small enough LIMIT is specified in addition to ORDER BY. Otherwise, the amount of memory spent is proportional to the volume of data for sorting. For distributed query processing, if GROUP BY is omitted, sorting is partially done on remote servers, and the results are merged on the requestor server. This means that for distributed sorting, the volume of data to sort can be greater than the amount of memory on a single server.

-

If there is not enough RAM, it is possible to perform sorting in external memory (creating temporary files on a disk). Use the setting max_bytes_before_external_sort for this purpose. If it is set to 0 (the default), external sorting is disabled. If it is enabled, when the volume of data to sort reaches the specified number of bytes, the collected data is sorted and dumped into a temporary file. After all data is read, all the sorted files are merged and the results are output. Files are written to the /var/lib/clickhouse/tmp/ directory in the config (by default, but you can use the 'tmp_path' parameter to change this setting).

-

Running a query may use more memory than 'max_bytes_before_external_sort'. For this reason, this setting must have a value significantly smaller than 'max_memory_usage'. As an example, if your server has 128 GB of RAM and you need to run a single query, set 'max_memory_usage' to 100 GB, and 'max_bytes_before_external_sort' to 80 GB.

-

External sorting works much less effectively than sorting in RAM.

-

SELECT clause

-

The expressions specified in the SELECT clause are analyzed after the calculations for all the clauses listed above are completed. -More specifically, expressions are analyzed that are above the aggregate functions, if there are any aggregate functions. -The aggregate functions and everything below them are calculated during aggregation (GROUP BY). -These expressions work as if they are applied to separate rows in the result.

-

DISTINCT clause

-

If DISTINCT is specified, only a single row will remain out of all the sets of fully matching rows in the result. -The result will be the same as if GROUP BY were specified across all the fields specified in SELECT without aggregate functions. But there are several differences from GROUP BY:

-
    -
  • DISTINCT can be applied together with GROUP BY.
    • -
  • When ORDER BY is omitted and LIMIT is defined, the query stops running immediately after the required number of different rows has been read.
    • -
  • Data blocks are output as they are processed, without waiting for the entire query to finish running.
    • -
-

DISTINCT is not supported if SELECT has at least one array column.

-

LIMIT clause

-

LIMIT m allows you to select the first 'm' rows from the result. -LIMIT n, m allows you to select the first 'm' rows from the result after skipping the first 'n' rows.

-

'n' and 'm' must be non-negative integers.

-

If there isn't an ORDER BY clause that explicitly sorts results, the result may be arbitrary and nondeterministic.

-

UNION ALL clause

-

You can use UNION ALL to combine any number of queries. Example:

-
SELECT CounterID, 1 AS table, toInt64(count()) AS c
-    FROM test.hits
-    GROUP BY CounterID
-
-UNION ALL
-
-SELECT CounterID, 2 AS table, sum(Sign) AS c
-    FROM test.visits
-    GROUP BY CounterID
-    HAVING c > 0
-
- - -

Only UNION ALL is supported. The regular UNION (UNION DISTINCT) is not supported. If you need UNION DISTINCT, you can write SELECT DISTINCT from a subquery containing UNION ALL.

-

Queries that are parts of UNION ALL can be run simultaneously, and their results can be mixed together.

-

The structure of results (the number and type of columns) must match for the queries. But the column names can differ. In this case, the column names for the final result will be taken from the first query.

-

Queries that are parts of UNION ALL can't be enclosed in brackets. ORDER BY and LIMIT are applied to separate queries, not to the final result. If you need to apply a conversion to the final result, you can put all the queries with UNION ALL in a subquery in the FROM clause.

-

INTO OUTFILE clause

-

Add the INTO OUTFILE filename clause (where filename is a string literal) to redirect query output to the specified file. -In contrast to MySQL, the file is created on the client side. The query will fail if a file with the same filename already exists. -This functionality is available in the command-line client and clickhouse-local (a query sent via HTTP interface will fail).

-

The default output format is TabSeparated (the same as in the command-line client batch mode).

-

FORMAT clause

-

Specify 'FORMAT format' to get data in any specified format. -You can use this for convenience, or for creating dumps. -For more information, see the section "Formats". -If the FORMAT clause is omitted, the default format is used, which depends on both the settings and the interface used for accessing the DB. For the HTTP interface and the command-line client in batch mode, the default format is TabSeparated. For the command-line client in interactive mode, the default format is PrettyCompact (it has attractive and compact tables).

-

When using the command-line client, data is passed to the client in an internal efficient format. The client independently interprets the FORMAT clause of the query and formats the data itself (thus relieving the network and the server from the load).

-

IN operators

-

The IN, NOT IN, GLOBAL IN, and GLOBAL NOT IN operators are covered separately, since their functionality is quite rich.

-

The left side of the operator is either a single column or a tuple.

-

Examples:

-
SELECT UserID IN (123, 456) FROM ...
-SELECT (CounterID, UserID) IN ((34, 123), (101500, 456)) FROM ...
-
- - -

If the left side is a single column that is in the index, and the right side is a set of constants, the system uses the index for processing the query.

-

Don't list too many values explicitly (i.e. millions). If a data set is large, put it in a temporary table (for example, see the section "External data for query processing"), then use a subquery.

-

The right side of the operator can be a set of constant expressions, a set of tuples with constant expressions (shown in the examples above), or the name of a database table or SELECT subquery in brackets.

-

If the right side of the operator is the name of a table (for example, UserID IN users), this is equivalent to the subquery UserID IN (SELECT * FROM users). Use this when working with external data that is sent along with the query. For example, the query can be sent together with a set of user IDs loaded to the 'users' temporary table, which should be filtered.

-

If the right side of the operator is a table name that has the Set engine (a prepared data set that is always in RAM), the data set will not be created over again for each query.

-

The subquery may specify more than one column for filtering tuples. -Example:

-
SELECT (CounterID, UserID) IN (SELECT CounterID, UserID FROM ...) FROM ...
-
- - -

The columns to the left and right of the IN operator should have the same type.

-

The IN operator and subquery may occur in any part of the query, including in aggregate functions and lambda functions. -Example:

-
SELECT
-    EventDate,
-    avg(UserID IN
-    (
-        SELECT UserID
-        FROM test.hits
-        WHERE EventDate = toDate('2014-03-17')
-    )) AS ratio
-FROM test.hits
-GROUP BY EventDate
-ORDER BY EventDate ASC
-
- - -
┌──EventDate─┬────ratio─┐
-│ 2014-03-17 │        1 │
-│ 2014-03-18 │ 0.807696 │
-│ 2014-03-19 │ 0.755406 │
-│ 2014-03-20 │ 0.723218 │
-│ 2014-03-21 │ 0.697021 │
-│ 2014-03-22 │ 0.647851 │
-│ 2014-03-23 │ 0.648416 │
-└────────────┴──────────┘
-
- - -

For each day after March 17th, count the percentage of pageviews made by users who visited the site on March 17th. -A subquery in the IN clause is always run just one time on a single server. There are no dependent subqueries.

-

-

Distributed subqueries

-

There are two options for IN-s with subqueries (similar to JOINs): normal IN / OIN and IN GLOBAL / GLOBAL JOIN. They differ in how they are run for distributed query processing.

-
- -Remember that the algorithms described below may work differently depending on the [settings](../operations/settings/settings.md#settings-distributed_product_mode) `distributed_product_mode` setting. - -
- -

When using the regular IN, the query is sent to remote servers, and each of them runs the subqueries in the IN or JOIN clause.

-

When using GLOBAL IN / GLOBAL JOINs, first all the subqueries are run for GLOBAL IN / GLOBAL JOINs, and the results are collected in temporary tables. Then the temporary tables are sent to each remote server, where the queries are run using this temporary data.

-

For a non-distributed query, use the regular IN / JOIN.

-

Be careful when using subqueries in the IN / JOIN clauses for distributed query processing.

-

Let's look at some examples. Assume that each server in the cluster has a normal local_table. Each server also has a distributed_table table with the Distributed type, which looks at all the servers in the cluster.

-

For a query to the distributed_table, the query will be sent to all the remote servers and run on them using the local_table.

-

For example, the query

-
SELECT uniq(UserID) FROM distributed_table
-
- - -

will be sent to all remote servers as

-
SELECT uniq(UserID) FROM local_table
-
- - -

and run on each of them in parallel, until it reaches the stage where intermediate results can be combined. Then the intermediate results will be returned to the requestor server and merged on it, and the final result will be sent to the client.

-

Now let's examine a query with IN:

-
SELECT uniq(UserID) FROM distributed_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM local_table WHERE CounterID = 34)
-
- - -
    -
  • Calculation of the intersection of audiences of two sites.
    • -
-

This query will be sent to all remote servers as

-
SELECT uniq(UserID) FROM local_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM local_table WHERE CounterID = 34)
-
- - -

In other words, the data set in the IN clause will be collected on each server independently, only across the data that is stored locally on each of the servers.

-

This will work correctly and optimally if you are prepared for this case and have spread data across the cluster servers such that the data for a single UserID resides entirely on a single server. In this case, all the necessary data will be available locally on each server. Otherwise, the result will be inaccurate. We refer to this variation of the query as "local IN".

-

To correct how the query works when data is spread randomly across the cluster servers, you could specify distributed_table inside a subquery. The query would look like this:

-
SELECT uniq(UserID) FROM distributed_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM distributed_table WHERE CounterID = 34)
-
- - -

This query will be sent to all remote servers as

-
SELECT uniq(UserID) FROM local_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM distributed_table WHERE CounterID = 34)
-
- - -

The subquery will begin running on each remote server. Since the subquery uses a distributed table, the subquery that is on each remote server will be resent to every remote server as

-
SELECT UserID FROM local_table WHERE CounterID = 34
-
- - -

For example, if you have a cluster of 100 servers, executing the entire query will require 10,000 elementary requests, which is generally considered unacceptable.

-

In such cases, you should always use GLOBAL IN instead of IN. Let's look at how it works for the query

-
SELECT uniq(UserID) FROM distributed_table WHERE CounterID = 101500 AND UserID GLOBAL IN (SELECT UserID FROM distributed_table WHERE CounterID = 34)
-
- - -

The requestor server will run the subquery

-
SELECT UserID FROM distributed_table WHERE CounterID = 34
-
- - -

and the result will be put in a temporary table in RAM. Then the request will be sent to each remote server as

-
SELECT uniq(UserID) FROM local_table WHERE CounterID = 101500 AND UserID GLOBAL IN _data1
-
- - -

and the temporary table _data1 will be sent to every remote server with the query (the name of the temporary table is implementation-defined).

-

This is more optimal than using the normal IN. However, keep the following points in mind:

-
    -
  1. When creating a temporary table, data is not made unique. To reduce the volume of data transmitted over the network, specify DISTINCT in the subquery. (You don't need to do this for a normal IN.)
    2. -
  2. The temporary table will be sent to all the remote servers. Transmission does not account for network topology. For example, if 10 remote servers reside in a datacenter that is very remote in relation to the requestor server, the data will be sent 10 times over the channel to the remote datacenter. Try to avoid large data sets when using GLOBAL IN.
    3. -
  3. When transmitting data to remote servers, restrictions on network bandwidth are not configurable. You might overload the network.
    4. -
  4. Try to distribute data across servers so that you don't need to use GLOBAL IN on a regular basis.
    5. -
  5. If you need to use GLOBAL IN often, plan the location of the ClickHouse cluster so that a single group of replicas resides in no more than one data center with a fast network between them, so that a query can be processed entirely within a single data center.
    6. -
-

It also makes sense to specify a local table in the GLOBAL IN clause, in case this local table is only available on the requestor server and you want to use data from it on remote servers.

-

Extreme values

-

In addition to results, you can also get minimum and maximum values for the results columns. To do this, set the extremes setting to 1. Minimums and maximums are calculated for numeric types, dates, and dates with times. For other columns, the default values are output.

-

An extra two rows are calculated – the minimums and maximums, respectively. These extra two rows are output in JSON*, TabSeparated*, and Pretty* formats, separate from the other rows. They are not output for other formats.

-

In JSON* formats, the extreme values are output in a separate 'extremes' field. In TabSeparated* formats, the row comes after the main result, and after 'totals' if present. It is preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result, and after 'totals' if present.

-

Extreme values are calculated for rows that have passed through LIMIT. However, when using 'LIMIT offset, size', the rows before 'offset' are included in 'extremes'. In stream requests, the result may also include a small number of rows that passed through LIMIT.

-

Notes

-

The GROUP BY and ORDER BY clauses do not support positional arguments. This contradicts MySQL, but conforms to standard SQL. -For example, GROUP BY 1, 2 will be interpreted as grouping by constants (i.e. aggregation of all rows into one).

-

You can use synonyms (AS aliases) in any part of a query.

-

You can put an asterisk in any part of a query instead of an expression. When the query is analyzed, the asterisk is expanded to a list of all table columns (excluding the MATERIALIZED and ALIAS columns). There are only a few cases when using an asterisk is justified:

-
    -
  • When creating a table dump.
    • -
  • For tables containing just a few columns, such as system tables.
    • -
  • For getting information about what columns are in a table. In this case, set LIMIT 1. But it is better to use the DESC TABLE query.
    • -
  • When there is strong filtration on a small number of columns using PREWHERE.
    • -
  • In subqueries (since columns that aren't needed for the external query are excluded from subqueries).
    • -
-

In all other cases, we don't recommend using the asterisk, since it only gives you the drawbacks of a columnar DBMS instead of the advantages. In other words using the asterisk is not recommended.

-

KILL QUERY

-
KILL QUERY
-  WHERE <where expression to SELECT FROM system.processes query>
-  [SYNC|ASYNC|TEST]
-  [FORMAT format]
-
- - -

Attempts to forcibly terminate the currently running queries. -The queries to terminate are selected from the system.processes table using the criteria defined in the WHERE clause of the KILL query.

-

Examples:

-
-- Forcibly terminates all queries with the specified query_id:
-KILL QUERY WHERE query_id='2-857d-4a57-9ee0-327da5d60a90'
-
--- Synchronously terminates all queries run by 'username':
-KILL QUERY WHERE user='username' SYNC
-
- - -

Read-only users can only stop their own queries.

-

By default, the asynchronous version of queries is used (ASYNC), which doesn't wait for confirmation that queries have stopped.

-

The synchronous version (SYNC) waits for all queries to stop and displays information about each process as it stops. -The response contains the kill_status column, which can take the following values:

-
    -
  1. 'finished' – The query was terminated successfully.
    2. -
  2. 'waiting' – Waiting for the query to end after sending it a signal to terminate.
    3. -
  3. The other values ​​explain why the query can't be stopped.
    4. -
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A test query (TEST) only checks the user's rights and displays a list of queries to stop.

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Syntax

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There are two types of parsers in the system: the full SQL parser (a recursive descent parser), and the data format parser (a fast stream parser). -In all cases except the INSERT query, only the full SQL parser is used. -The INSERT query uses both parsers:

-
INSERT INTO t VALUES (1, 'Hello, world'), (2, 'abc'), (3, 'def')
-
- - -

The INSERT INTO t VALUES fragment is parsed by the full parser, and the data (1, 'Hello, world'), (2, 'abc'), (3, 'def') is parsed by the fast stream parser. -Data can have any format. When a query is received, the server calculates no more than max_query_size bytes of the request in RAM (by default, 1 MB), and the rest is stream parsed. -This means the system doesn't have problems with large INSERT queries, like MySQL does.

-

When using the Values format in an INSERT query, it may seem that data is parsed the same as expressions in a SELECT query, but this is not true. The Values format is much more limited.

-

Next we will cover the full parser. For more information about format parsers, see the section "Formats".

-

Spaces

-

There may be any number of space symbols between syntactical constructions (including the beginning and end of a query). Space symbols include the space, tab, line feed, CR, and form feed.

-

Comments

-

SQL-style and C-style comments are supported. -SQL-style comments: from -- to the end of the line. The space after -- can be omitted. -Comments in C-style: from /* to */. These comments can be multiline. Spaces are not required here, either.

-

Keywords

-

Keywords (such as SELECT) are not case-sensitive. Everything else (column names, functions, and so on), in contrast to standard SQL, is case-sensitive. Keywords are not reserved (they are just parsed as keywords in the corresponding context).

-

Identifiers

-

Identifiers (column names, functions, and data types) can be quoted or non-quoted. -Non-quoted identifiers start with a Latin letter or underscore, and continue with a Latin letter, underscore, or number. In other words, they must match the regex ^[a-zA-Z_][0-9a-zA-Z_]*$. Examples: x, _1, X_y__Z123_.

-

Quoted identifiers are placed in reversed quotation marks `id` (the same as in MySQL), and can indicate any set of bytes (non-empty). In addition, symbols (for example, the reverse quotation mark) inside this type of identifier can be backslash-escaped. Escaping rules are the same as for string literals (see below). -We recommend using identifiers that do not need to be quoted.

-

Literals

-

There are numeric literals, string literals, and compound literals.

-

Numeric literals

-

A numeric literal tries to be parsed:

-
    -
  • First as a 64-bit signed number, using the 'strtoull' function.
    • -
  • If unsuccessful, as a 64-bit unsigned number, using the 'strtoll' function.
    • -
  • If unsuccessful, as a floating-point number using the 'strtod' function.
    • -
  • Otherwise, an error is returned.
    • -
-

The corresponding value will have the smallest type that the value fits in. -For example, 1 is parsed as UInt8, but 256 is parsed as UInt16. For more information, see "Data types".

-

Examples: 1, 18446744073709551615, 0xDEADBEEF, 01, 0.1, 1e100, -1e-100, inf, nan.

-

String literals

-

Only string literals in single quotes are supported. The enclosed characters can be backslash-escaped. The following escape sequences have a corresponding special value: \b, \f, \r, \n, \t, \0, \a, \v, \xHH. In all other cases, escape sequences in the format \c, where "c" is any character, are converted to "c". This means that you can use the sequences \'and\\. The value will have the String type.

-

The minimum set of characters that you need to escape in string literals: ' and \.

-

Compound literals

-

Constructions are supported for arrays: [1, 2, 3] and tuples: (1, 'Hello, world!', 2).. -Actually, these are not literals, but expressions with the array creation operator and the tuple creation operator, respectively. -For more information, see the section "Operators2". -An array must consist of at least one item, and a tuple must have at least two items. -Tuples have a special purpose for use in the IN clause of a SELECT query. Tuples can be obtained as the result of a query, but they can't be saved to a database (with the exception of Memory-type tables).

-

Functions

-

Functions are written like an identifier with a list of arguments (possibly empty) in brackets. In contrast to standard SQL, the brackets are required, even for an empty arguments list. Example: now(). -There are regular and aggregate functions (see the section "Aggregate functions"). Some aggregate functions can contain two lists of arguments in brackets. Example: quantile (0.9) (x). These aggregate functions are called "parametric" functions, and the arguments in the first list are called "parameters". The syntax of aggregate functions without parameters is the same as for regular functions.

-

Operators

-

Operators are converted to their corresponding functions during query parsing, taking their priority and associativity into account. -For example, the expression 1 + 2 * 3 + 4 is transformed to plus(plus(1, multiply(2, 3)), 4). -For more information, see the section "Operators" below.

-

Data types and database table engines

-

Data types and table engines in the CREATE query are written the same way as identifiers or functions. In other words, they may or may not contain an arguments list in brackets. For more information, see the sections "Data types," "Table engines," and "CREATE".

-

Synonyms

-

In the SELECT query, expressions can specify synonyms using the AS keyword. Any expression is placed to the left of AS. The identifier name for the synonym is placed to the right of AS. As opposed to standard SQL, synonyms are not only declared on the top level of expressions:

-
SELECT (1 AS n) + 2, n
-
- - -

In contrast to standard SQL, synonyms can be used in all parts of a query, not just SELECT.

-

Asterisk

-

In a SELECT query, an asterisk can replace the expression. For more information, see the section "SELECT".

-

Expressions

-

An expression is a function, identifier, literal, application of an operator, expression in brackets, subquery, or asterisk. It can also contain a synonym. -A list of expressions is one or more expressions separated by commas. -Functions and operators, in turn, can have expressions as arguments.

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- - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/docs/en/roadmap/index.html b/docs/build/docs/en/roadmap/index.html deleted file mode 100644 index fb7984d36eb..00000000000 --- a/docs/build/docs/en/roadmap/index.html +++ /dev/null @@ -1,3119 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Roadmap - ClickHouse Documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Q1 2018

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New fuctionality

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    -
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    Support for UPDATE and DELETE.

    -
    • -
  • -

    Multidimensional and nested arrays.

    -
    • -
-

It can look something like this:

-
CREATE TABLE t
-(
-    x Array(Array(String)),
-    z Nested(
-        x Array(String),
-        y Nested(...))
-)
-ENGINE = MergeTree ORDER BY x
-
- - -
    -
  • External MySQL and ODBC tables.
    • -
-

External tables can be integrated into ClickHouse using external dictionaries. This new functionality is a convenient alternative to connecting external tables.

-
SELECT ...
-FROM mysql('host:port', 'db', 'table', 'user', 'password')`
-
- - -

Improvements

-
    -
  • Effective data copying between ClickHouse clusters.
    • -
-

Now you can copy data with the remote() function. For example: INSERT INTO t SELECT * FROM remote(...).

-

This operation will have improved performance.

-
    -
  • O_DIRECT for merges.
    • -
-

This will improve the performance of the OS cache and "hot" queries.

-

Q2 2018

-

New functionality

-
    -
  • -

    UPDATE/DELETE conform to the EU GDPR.

    -
    • -
  • -

    Protobuf and Parquet input and output formats.

    -
    • -
  • -

    Creating dictionaries using DDL queries.

    -
    • -
-

Currently, dictionaries that are part of the database schema are defined in external XML files. This is inconvenient and counter-intuitive. The new approach should fix it.

-
    -
  • -

    Integration with LDAP.

    -
    • -
  • -

    WITH ROLLUP and WITH CUBE for GROUP BY.

    -
    • -
  • -

    Custom encoding and compression for each column individually.

    -
    • -
-

As of now, ClickHouse supports LZ4 and ZSTD compression of columns, and compression settings are global (see the article Compression in ClickHouse). Per-column compression and encoding will provide more efficient data storage, which in turn will speed up queries.

-
    -
  • Storing data on multiple disks on the same server.
    • -
-

This functionality will make it easier to extend the disk space, since different disk systems can be used for different databases or tables. Currently, users are forced to use symbolic links if the databases and tables must be stored on a different disk.

-

Improvements

-

Many improvements and fixes are planned for the query execution system. For example:

-
    -
  • Using an index for in (subquery).
    • -
-

The index is not used right now, which reduces performance.

-
    -
  • Passing predicates from where to subqueries, and passing predicates to views.
    • -
-

The predicates must be passed, since the view is changed by the subquery. Performance is still low for view filters, and views can't use the primary key of the original table, which makes views useless for large tables.

-
    -
  • Optimizing branching operations (ternary operator, if, multiIf).
    • -
-

ClickHouse currently performs all branches, even if they aren't necessary.

-
    -
  • Using a primary key for GROUP BY and ORDER BY.
    • -
-

This will speed up certain types of queries with partially sorted data.

-

Q3-Q4 2018

-

We don't have any set plans yet, but the main projects will be:

-
    -
  • Resource pools for executing queries.
    • -
-

This will make load management more efficient.

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    -
  • ANSI SQL JOIN syntax.
    • -
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Improve ClickHouse compatibility with many SQL tools.

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- - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/docs/en/search/search_index.json b/docs/build/docs/en/search/search_index.json deleted file mode 100644 index 6ae9746f5ad..00000000000 --- a/docs/build/docs/en/search/search_index.json +++ /dev/null @@ -1,4984 +0,0 @@ -{ - "docs": [ - { - "location": "/", - "text": "What is ClickHouse?\n\n\nClickHouse is a columnar DBMS for OLAP.\n\n\nIn a \"normal\" row-oriented DBMS, data is stored in this order:\n\n\n5123456789123456789 1 Eurobasket - Greece - Bosnia and Herzegovina - example.com 1 2011-09-01 01:03:02 6274717 1294101174 11409 612345678912345678 0 33 6 http://www.example.com/basketball/team/123/match/456789.html http://www.example.com/basketball/team/123/match/987654.html 0 1366 768 32 10 3183 0 0 13 0\\0 1 1 0 0 2011142 -1 0 0 01321 613 660 2011-09-01 08:01:17 0 0 0 0 utf-8 1466 0 0 0 5678901234567890123 277789954 0 0 0 0 0\n5234985259563631958 0 Consulting, Tax assessment, Accounting, Law 1 2011-09-01 01:03:02 6320881 2111222333 213 6458937489576391093 0 3 2 http://www.example.ru/ 0 800 600 16 10 2 153.1 0 0 10 63 1 1 0 0 2111678 000 0 588 368 240 2011-09-01 01:03:17 4 0 60310 0 windows-1251 1466 0 000 778899001 0 0 0 0 0\n...\n\n\n\n\n\nIn order words, all the values related to a row are stored next to each other.\nExamples of a row-oriented DBMS are MySQL, Postgres, MS SQL Server, and others.\n\n\nIn a column-oriented DBMS, data is stored like this:\n\n\nWatchID: 5385521489354350662 5385521490329509958 5385521489953706054 5385521490476781638 5385521490583269446 5385521490218868806 5385521491437850694 5385521491090174022 5385521490792669254 5385521490420695110 5385521491532181574 5385521491559694406 5385521491459625030 5385521492275175494 5385521492781318214 5385521492710027334 5385521492955615302 5385521493708759110 5385521494506434630 5385521493104611398\nJavaEnable: 1 0 1 0 0 0 1 0 1 1 1 1 1 1 0 1 0 0 1 1\nTitle: Yandex Announcements - Investor Relations - Yandex Yandex \u2014 Contact us \u2014 Moscow Yandex \u2014 Mission Ru Yandex \u2014 History \u2014 History of Yandex Yandex Financial Releases - Investor Relations - Yandex Yandex \u2014 Locations Yandex Board of Directors - Corporate Governance - Yandex Yandex \u2014 Technologies\nGoodEvent: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\nEventTime: 2016-05-18 05:19:20 2016-05-18 08:10:20 2016-05-18 07:38:00 2016-05-18 01:13:08 2016-05-18 00:04:06 2016-05-18 04:21:30 2016-05-18 00:34:16 2016-05-18 07:35:49 2016-05-18 11:41:59 2016-05-18 01:13:32\n\n\n\n\n\nThese examples only show the order that data is arranged in.\nThe values from different columns are stored separately, and data from the same column is stored together.\n\n\nExamples of column-oriented DBMSs: \nVertica\n, \nParaccel (Actian Matrix) (Amazon Redshift)\n, \nSybase IQ\n, \nExasol\n, \nInfobright\n, \nInfiniDB\n, \nMonetDB (VectorWise) (Actian Vector)\n, \nLucidDB\n, \nSAP HANA\n, \nGoogle Dremel\n, \nGoogle PowerDrill\n, \nDruid\n, \nkdb+\n, and so on.\n\n\nDifferent orders for storing data are better suited to different scenarios.\nThe data access scenario refers to what queries are made, how often, and in what proportion; how much data is read for each type of query \u2013 rows, columns, and bytes; the relationship between reading and updating data; the working size of the data and how locally it is used; whether transactions are used, and how isolated they are; requirements for data replication and logical integrity; requirements for latency and throughput for each type of query, and so on.\n\n\nThe higher the load on the system, the more important it is to customize the system to the scenario, and the more specific this customization becomes. There is no system that is equally well-suited to significantly different scenarios. If a system is adaptable to a wide set of scenarios, under a high load, the system will handle all the scenarios equally poorly, or will work well for just one of the scenarios.\n\n\nWe'll say that the following is true for the OLAP (online analytical processing) scenario:\n\n\n\n\nThe vast majority of requests are for read access.\n\n\nData is updated in fairly large batches (\n 1000 rows), not by single rows; or it is not updated at all.\n\n\nData is added to the DB but is not modified.\n\n\nFor reads, quite a large number of rows are extracted from the DB, but only a small subset of columns.\n\n\nTables are \"wide,\" meaning they contain a large number of columns.\n\n\nQueries are relatively rare (usually hundreds of queries per server or less per second).\n\n\nFor simple queries, latencies around 50 ms are allowed.\n\n\nColumn values are fairly small: numbers and short strings (for example, 60 bytes per URL).\n\n\nRequires high throughput when processing a single query (up to billions of rows per second per server).\n\n\nThere are no transactions.\n\n\nLow requirements for data consistency.\n\n\nThere is one large table per query. All tables are small, except for one.\n\n\nA query result is significantly smaller than the source data. In other words, data is filtered or aggregated. The result fits in a single server's RAM.\n\n\n\n\nIt is easy to see that the OLAP scenario is very different from other popular scenarios (such as OLTP or Key-Value access). So it doesn't make sense to try to use OLTP or a Key-Value DB for processing analytical queries if you want to get decent performance. For example, if you try to use MongoDB or Elliptics for analytics, you will get very poor performance compared to OLAP databases.\n\n\nColumnar-oriented databases are better suited to OLAP scenarios (at least 100 times better in processing speed for most queries), for the following reasons:\n\n\n\n\nFor I/O.\n\n\nFor an analytical query, only a small number of table columns need to be read. In a column-oriented database, you can read just the data you need. For example, if you need 5 columns out of 100, you can expect a 20-fold reduction in I/O.\n\n\nSince data is read in packets, it is easier to compress. Data in columns is also easier to compress. This further reduces the I/O volume.\n\n\nDue to the reduced I/O, more data fits in the system cache.\n\n\n\n\nFor example, the query \"count the number of records for each advertising platform\" requires reading one \"advertising platform ID\" column, which takes up 1 byte uncompressed. If most of the traffic was not from advertising platforms, you can expect at least 10-fold compression of this column. When using a quick compression algorithm, data decompression is possible at a speed of at least several gigabytes of uncompressed data per second. In other words, this query can be processed at a speed of approximately several billion rows per second on a single server. This speed is actually achieved in practice.\n\n\nExample:\n\n\nmilovidov@hostname:~$ clickhouse-client\nClickHouse client version \n0\n.0.52053.\nConnecting to localhost:9000.\nConnected to ClickHouse server version \n0\n.0.52053.\n\n:\n)\n SELECT CounterID, count\n()\n FROM hits GROUP BY CounterID ORDER BY count\n()\n DESC LIMIT \n20\n\n\nSELECT\n CounterID,\n count\n()\n\nFROM hits\nGROUP BY CounterID\nORDER BY count\n()\n DESC\nLIMIT \n20\n\n\n\u250c\u2500CounterID\u2500\u252c\u2500\u2500count\n()\n\u2500\u2510\n\u2502 \n114208\n \u2502 \n56057344\n \u2502\n\u2502 \n115080\n \u2502 \n51619590\n \u2502\n\u2502 \n3228\n \u2502 \n44658301\n \u2502\n\u2502 \n38230\n \u2502 \n42045932\n \u2502\n\u2502 \n145263\n \u2502 \n42042158\n \u2502\n\u2502 \n91244\n \u2502 \n38297270\n \u2502\n\u2502 \n154139\n \u2502 \n26647572\n \u2502\n\u2502 \n150748\n \u2502 \n24112755\n \u2502\n\u2502 \n242232\n \u2502 \n21302571\n \u2502\n\u2502 \n338158\n \u2502 \n13507087\n \u2502\n\u2502 \n62180\n \u2502 \n12229491\n \u2502\n\u2502 \n82264\n \u2502 \n12187441\n \u2502\n\u2502 \n232261\n \u2502 \n12148031\n \u2502\n\u2502 \n146272\n \u2502 \n11438516\n \u2502\n\u2502 \n168777\n \u2502 \n11403636\n \u2502\n\u2502 \n4120072\n \u2502 \n11227824\n \u2502\n\u2502 \n10938808\n \u2502 \n10519739\n \u2502\n\u2502 \n74088\n \u2502 \n9047015\n \u2502\n\u2502 \n115079\n \u2502 \n8837972\n \u2502\n\u2502 \n337234\n \u2502 \n8205961\n \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n20\n rows in set. Elapsed: \n0\n.153 sec. Processed \n1\n.00 billion rows, \n4\n.00 GB \n(\n6\n.53 billion rows/s., \n26\n.10 GB/s.\n)\n\n\n:\n)\n\n\n\n\n\n\n\n\nFor CPU.\n\n\n\n\nSince executing a query requires processing a large number of rows, it helps to dispatch all operations for entire vectors instead of for separate rows, or to implement the query engine so that there is almost no dispatching cost. If you don't do this, with any half-decent disk subsystem, the query interpreter inevitably stalls the CPU.\nIt makes sense to both store data in columns and process it, when possible, by columns.\n\n\nThere are two ways to do this:\n\n\n\n\n\n\nA vector engine. All operations are written for vectors, instead of for separate values. This means you don't need to call operations very often, and dispatching costs are negligible. Operation code contains an optimized internal cycle.\n\n\n\n\n\n\nCode generation. The code generated for the query has all the indirect calls in it.\n\n\n\n\n\n\nThis is not done in \"normal\" databases, because it doesn't make sense when running simple queries. However, there are exceptions. For example, MemSQL uses code generation to reduce latency when processing SQL queries. (For comparison, analytical DBMSs require optimization of throughput, not latency.)\n\n\nNote that for CPU efficiency, the query language must be declarative (SQL or MDX), or at least a vector (J, K). The query should only contain implicit loops, allowing for optimization.", - "title": "ClickHouse" - }, - { - "location": "/#what-is-clickhouse", - "text": "ClickHouse is a columnar DBMS for OLAP. In a \"normal\" row-oriented DBMS, data is stored in this order: 5123456789123456789 1 Eurobasket - Greece - Bosnia and Herzegovina - example.com 1 2011-09-01 01:03:02 6274717 1294101174 11409 612345678912345678 0 33 6 http://www.example.com/basketball/team/123/match/456789.html http://www.example.com/basketball/team/123/match/987654.html 0 1366 768 32 10 3183 0 0 13 0\\0 1 1 0 0 2011142 -1 0 0 01321 613 660 2011-09-01 08:01:17 0 0 0 0 utf-8 1466 0 0 0 5678901234567890123 277789954 0 0 0 0 0\n5234985259563631958 0 Consulting, Tax assessment, Accounting, Law 1 2011-09-01 01:03:02 6320881 2111222333 213 6458937489576391093 0 3 2 http://www.example.ru/ 0 800 600 16 10 2 153.1 0 0 10 63 1 1 0 0 2111678 000 0 588 368 240 2011-09-01 01:03:17 4 0 60310 0 windows-1251 1466 0 000 778899001 0 0 0 0 0\n... In order words, all the values related to a row are stored next to each other.\nExamples of a row-oriented DBMS are MySQL, Postgres, MS SQL Server, and others. In a column-oriented DBMS, data is stored like this: WatchID: 5385521489354350662 5385521490329509958 5385521489953706054 5385521490476781638 5385521490583269446 5385521490218868806 5385521491437850694 5385521491090174022 5385521490792669254 5385521490420695110 5385521491532181574 5385521491559694406 5385521491459625030 5385521492275175494 5385521492781318214 5385521492710027334 5385521492955615302 5385521493708759110 5385521494506434630 5385521493104611398\nJavaEnable: 1 0 1 0 0 0 1 0 1 1 1 1 1 1 0 1 0 0 1 1\nTitle: Yandex Announcements - Investor Relations - Yandex Yandex \u2014 Contact us \u2014 Moscow Yandex \u2014 Mission Ru Yandex \u2014 History \u2014 History of Yandex Yandex Financial Releases - Investor Relations - Yandex Yandex \u2014 Locations Yandex Board of Directors - Corporate Governance - Yandex Yandex \u2014 Technologies\nGoodEvent: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\nEventTime: 2016-05-18 05:19:20 2016-05-18 08:10:20 2016-05-18 07:38:00 2016-05-18 01:13:08 2016-05-18 00:04:06 2016-05-18 04:21:30 2016-05-18 00:34:16 2016-05-18 07:35:49 2016-05-18 11:41:59 2016-05-18 01:13:32 These examples only show the order that data is arranged in.\nThe values from different columns are stored separately, and data from the same column is stored together. Examples of column-oriented DBMSs: Vertica , Paraccel (Actian Matrix) (Amazon Redshift) , Sybase IQ , Exasol , Infobright , InfiniDB , MonetDB (VectorWise) (Actian Vector) , LucidDB , SAP HANA , Google Dremel , Google PowerDrill , Druid , kdb+ , and so on. Different orders for storing data are better suited to different scenarios.\nThe data access scenario refers to what queries are made, how often, and in what proportion; how much data is read for each type of query \u2013 rows, columns, and bytes; the relationship between reading and updating data; the working size of the data and how locally it is used; whether transactions are used, and how isolated they are; requirements for data replication and logical integrity; requirements for latency and throughput for each type of query, and so on. The higher the load on the system, the more important it is to customize the system to the scenario, and the more specific this customization becomes. There is no system that is equally well-suited to significantly different scenarios. If a system is adaptable to a wide set of scenarios, under a high load, the system will handle all the scenarios equally poorly, or will work well for just one of the scenarios. We'll say that the following is true for the OLAP (online analytical processing) scenario: The vast majority of requests are for read access. Data is updated in fairly large batches ( 1000 rows), not by single rows; or it is not updated at all. Data is added to the DB but is not modified. For reads, quite a large number of rows are extracted from the DB, but only a small subset of columns. Tables are \"wide,\" meaning they contain a large number of columns. Queries are relatively rare (usually hundreds of queries per server or less per second). For simple queries, latencies around 50 ms are allowed. Column values are fairly small: numbers and short strings (for example, 60 bytes per URL). Requires high throughput when processing a single query (up to billions of rows per second per server). There are no transactions. Low requirements for data consistency. There is one large table per query. All tables are small, except for one. A query result is significantly smaller than the source data. In other words, data is filtered or aggregated. The result fits in a single server's RAM. It is easy to see that the OLAP scenario is very different from other popular scenarios (such as OLTP or Key-Value access). So it doesn't make sense to try to use OLTP or a Key-Value DB for processing analytical queries if you want to get decent performance. For example, if you try to use MongoDB or Elliptics for analytics, you will get very poor performance compared to OLAP databases. Columnar-oriented databases are better suited to OLAP scenarios (at least 100 times better in processing speed for most queries), for the following reasons: For I/O. For an analytical query, only a small number of table columns need to be read. In a column-oriented database, you can read just the data you need. For example, if you need 5 columns out of 100, you can expect a 20-fold reduction in I/O. Since data is read in packets, it is easier to compress. Data in columns is also easier to compress. This further reduces the I/O volume. Due to the reduced I/O, more data fits in the system cache. For example, the query \"count the number of records for each advertising platform\" requires reading one \"advertising platform ID\" column, which takes up 1 byte uncompressed. If most of the traffic was not from advertising platforms, you can expect at least 10-fold compression of this column. When using a quick compression algorithm, data decompression is possible at a speed of at least several gigabytes of uncompressed data per second. In other words, this query can be processed at a speed of approximately several billion rows per second on a single server. This speed is actually achieved in practice. Example: milovidov@hostname:~$ clickhouse-client\nClickHouse client version 0 .0.52053.\nConnecting to localhost:9000.\nConnected to ClickHouse server version 0 .0.52053.\n\n: ) SELECT CounterID, count () FROM hits GROUP BY CounterID ORDER BY count () DESC LIMIT 20 \n\nSELECT\n CounterID,\n count () \nFROM hits\nGROUP BY CounterID\nORDER BY count () DESC\nLIMIT 20 \n\n\u250c\u2500CounterID\u2500\u252c\u2500\u2500count () \u2500\u2510\n\u2502 114208 \u2502 56057344 \u2502\n\u2502 115080 \u2502 51619590 \u2502\n\u2502 3228 \u2502 44658301 \u2502\n\u2502 38230 \u2502 42045932 \u2502\n\u2502 145263 \u2502 42042158 \u2502\n\u2502 91244 \u2502 38297270 \u2502\n\u2502 154139 \u2502 26647572 \u2502\n\u2502 150748 \u2502 24112755 \u2502\n\u2502 242232 \u2502 21302571 \u2502\n\u2502 338158 \u2502 13507087 \u2502\n\u2502 62180 \u2502 12229491 \u2502\n\u2502 82264 \u2502 12187441 \u2502\n\u2502 232261 \u2502 12148031 \u2502\n\u2502 146272 \u2502 11438516 \u2502\n\u2502 168777 \u2502 11403636 \u2502\n\u2502 4120072 \u2502 11227824 \u2502\n\u2502 10938808 \u2502 10519739 \u2502\n\u2502 74088 \u2502 9047015 \u2502\n\u2502 115079 \u2502 8837972 \u2502\n\u2502 337234 \u2502 8205961 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 20 rows in set. Elapsed: 0 .153 sec. Processed 1 .00 billion rows, 4 .00 GB ( 6 .53 billion rows/s., 26 .10 GB/s. ) \n\n: ) For CPU. Since executing a query requires processing a large number of rows, it helps to dispatch all operations for entire vectors instead of for separate rows, or to implement the query engine so that there is almost no dispatching cost. If you don't do this, with any half-decent disk subsystem, the query interpreter inevitably stalls the CPU.\nIt makes sense to both store data in columns and process it, when possible, by columns. There are two ways to do this: A vector engine. All operations are written for vectors, instead of for separate values. This means you don't need to call operations very often, and dispatching costs are negligible. Operation code contains an optimized internal cycle. Code generation. The code generated for the query has all the indirect calls in it. This is not done in \"normal\" databases, because it doesn't make sense when running simple queries. However, there are exceptions. For example, MemSQL uses code generation to reduce latency when processing SQL queries. (For comparison, analytical DBMSs require optimization of throughput, not latency.) Note that for CPU efficiency, the query language must be declarative (SQL or MDX), or at least a vector (J, K). The query should only contain implicit loops, allowing for optimization.", - "title": "What is ClickHouse?" - }, - { - "location": "/introduction/distinctive_features/", - "text": "Distinctive features of ClickHouse\n\n\nTrue column-oriented DBMS\n\n\nIn a true column-oriented DBMS, there isn't any \"garbage\" stored with the values. Among other things, this means that constant-length values must be supported, to avoid storing their length \"number\" next to the values. As an example, a billion UInt8-type values should actually consume around 1 GB uncompressed, or this will strongly affect the CPU use. It is very important to store data compactly (without any \"garbage\") even when uncompressed, since the speed of decompression (CPU usage) depends mainly on the volume of uncompressed data.\n\n\nThis is worth noting because there are systems that can store values of separate columns separately, but that can't effectively process analytical queries due to their optimization for other scenarios. Examples are HBase, BigTable, Cassandra, and HyperTable. In these systems, you will get throughput around a hundred thousand rows per second, but not hundreds of millions of rows per second.\n\n\nAlso note that ClickHouse is a DBMS, not a single database. ClickHouse allows creating tables and databases in runtime, loading data, and running queries without reconfiguring and restarting the server.\n\n\nData compression\n\n\nSome column-oriented DBMSs (InfiniDB CE and MonetDB) do not use data compression. However, data compression really improves performance.\n\n\nDisk storage of data\n\n\nMany column-oriented DBMSs (such as SAP HANA and Google PowerDrill) can only work in RAM. But even on thousands of servers, the RAM is too small for storing all the pageviews and sessions in Yandex.Metrica.\n\n\nParallel processing on multiple cores\n\n\nLarge queries are parallelized in a natural way.\n\n\nDistributed processing on multiple servers\n\n\nAlmost none of the columnar DBMSs listed above have support for distributed processing.\nIn ClickHouse, data can reside on different shards. Each shard can be a group of replicas that are used for fault tolerance. The query is processed on all the shards in parallel. This is transparent for the user.\n\n\nSQL support\n\n\nIf you are familiar with standard SQL, we can't really talk about SQL support.\nAll the functions have different names.\nHowever, this is a declarative query language based on SQL that can't be differentiated from SQL in many instances.\nJOINs are supported. Subqueries are supported in FROM, IN, and JOIN clauses, as well as scalar subqueries.\nDependent subqueries are not supported.\n\n\nVector engine\n\n\nData is not only stored by columns, but is processed by vectors (parts of columns). This allows us to achieve high CPU performance.\n\n\nReal-time data updates\n\n\nClickHouse supports primary key tables. In order to quickly perform queries on the range of the primary key, the data is sorted incrementally using the merge tree. Due to this, data can continually be added to the table. There is no locking when adding data.\n\n\nIndexes\n\n\nHaving a primary key makes it possible to extract data for specific clients (for instance, Yandex.Metrica tracking tags) for a specific time range, with low latency less than several dozen milliseconds.\n\n\nSuitable for online queries\n\n\nThis lets us use the system as the back-end for a web interface. Low latency means queries can be processed without delay, while the Yandex.Metrica interface page is loading. In other words, in online mode.\n\n\nSupport for approximated calculations\n\n\n\n\nThe system contains aggregate functions for approximated calculation of the number of various values, medians, and quantiles.\n\n\nSupports running a query based on a part (sample) of data and getting an approximated result. In this case, proportionally less data is retrieved from the disk.\n\n\nSupports running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reasonably accurate result while using fewer resources.\n\n\n\n\nData replication and support for data integrity on replicas\n\n\nUses asynchronous multimaster replication. After being written to any available replica, data is distributed to all the remaining replicas. The system maintains identical data on different replicas. Data is restored automatically after a failure, or using a \"button\" for complex cases.\nFor more information, see the section \nData replication\n.", - "title": "Distinctive features of ClickHouse" - }, - { - "location": "/introduction/distinctive_features/#distinctive-features-of-clickhouse", - "text": "", - "title": "Distinctive features of ClickHouse" - }, - { - "location": "/introduction/distinctive_features/#true-column-oriented-dbms", - "text": "In a true column-oriented DBMS, there isn't any \"garbage\" stored with the values. Among other things, this means that constant-length values must be supported, to avoid storing their length \"number\" next to the values. As an example, a billion UInt8-type values should actually consume around 1 GB uncompressed, or this will strongly affect the CPU use. It is very important to store data compactly (without any \"garbage\") even when uncompressed, since the speed of decompression (CPU usage) depends mainly on the volume of uncompressed data. This is worth noting because there are systems that can store values of separate columns separately, but that can't effectively process analytical queries due to their optimization for other scenarios. Examples are HBase, BigTable, Cassandra, and HyperTable. In these systems, you will get throughput around a hundred thousand rows per second, but not hundreds of millions of rows per second. Also note that ClickHouse is a DBMS, not a single database. ClickHouse allows creating tables and databases in runtime, loading data, and running queries without reconfiguring and restarting the server.", - "title": "True column-oriented DBMS" - }, - { - "location": "/introduction/distinctive_features/#data-compression", - "text": "Some column-oriented DBMSs (InfiniDB CE and MonetDB) do not use data compression. However, data compression really improves performance.", - "title": "Data compression" - }, - { - "location": "/introduction/distinctive_features/#disk-storage-of-data", - "text": "Many column-oriented DBMSs (such as SAP HANA and Google PowerDrill) can only work in RAM. But even on thousands of servers, the RAM is too small for storing all the pageviews and sessions in Yandex.Metrica.", - "title": "Disk storage of data" - }, - { - "location": "/introduction/distinctive_features/#parallel-processing-on-multiple-cores", - "text": "Large queries are parallelized in a natural way.", - "title": "Parallel processing on multiple cores" - }, - { - "location": "/introduction/distinctive_features/#distributed-processing-on-multiple-servers", - "text": "Almost none of the columnar DBMSs listed above have support for distributed processing.\nIn ClickHouse, data can reside on different shards. Each shard can be a group of replicas that are used for fault tolerance. The query is processed on all the shards in parallel. This is transparent for the user.", - "title": "Distributed processing on multiple servers" - }, - { - "location": "/introduction/distinctive_features/#sql-support", - "text": "If you are familiar with standard SQL, we can't really talk about SQL support.\nAll the functions have different names.\nHowever, this is a declarative query language based on SQL that can't be differentiated from SQL in many instances.\nJOINs are supported. Subqueries are supported in FROM, IN, and JOIN clauses, as well as scalar subqueries.\nDependent subqueries are not supported.", - "title": "SQL support" - }, - { - "location": "/introduction/distinctive_features/#vector-engine", - "text": "Data is not only stored by columns, but is processed by vectors (parts of columns). This allows us to achieve high CPU performance.", - "title": "Vector engine" - }, - { - "location": "/introduction/distinctive_features/#real-time-data-updates", - "text": "ClickHouse supports primary key tables. In order to quickly perform queries on the range of the primary key, the data is sorted incrementally using the merge tree. Due to this, data can continually be added to the table. There is no locking when adding data.", - "title": "Real-time data updates" - }, - { - "location": "/introduction/distinctive_features/#indexes", - "text": "Having a primary key makes it possible to extract data for specific clients (for instance, Yandex.Metrica tracking tags) for a specific time range, with low latency less than several dozen milliseconds.", - "title": "Indexes" - }, - { - "location": "/introduction/distinctive_features/#suitable-for-online-queries", - "text": "This lets us use the system as the back-end for a web interface. Low latency means queries can be processed without delay, while the Yandex.Metrica interface page is loading. In other words, in online mode.", - "title": "Suitable for online queries" - }, - { - "location": "/introduction/distinctive_features/#support-for-approximated-calculations", - "text": "The system contains aggregate functions for approximated calculation of the number of various values, medians, and quantiles. Supports running a query based on a part (sample) of data and getting an approximated result. In this case, proportionally less data is retrieved from the disk. Supports running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reasonably accurate result while using fewer resources.", - "title": "Support for approximated calculations" - }, - { - "location": "/introduction/distinctive_features/#data-replication-and-support-for-data-integrity-on-replicas", - "text": "Uses asynchronous multimaster replication. After being written to any available replica, data is distributed to all the remaining replicas. The system maintains identical data on different replicas. Data is restored automatically after a failure, or using a \"button\" for complex cases.\nFor more information, see the section Data replication .", - "title": "Data replication and support for data integrity on replicas" - }, - { - "location": "/introduction/features_considered_disadvantages/", - "text": "ClickHouse features that can be considered disadvantages\n\n\n\n\nNo transactions.\n\n\nFor aggregation, query results must fit in the RAM on a single server. However, the volume of source data for a query may be indefinitely large.\n\n\nLack of full-fledged UPDATE/DELETE implementation.", - "title": "ClickHouse features that can be considered disadvantages" - }, - { - "location": "/introduction/features_considered_disadvantages/#clickhouse-features-that-can-be-considered-disadvantages", - "text": "No transactions. For aggregation, query results must fit in the RAM on a single server. However, the volume of source data for a query may be indefinitely large. Lack of full-fledged UPDATE/DELETE implementation.", - "title": "ClickHouse features that can be considered disadvantages" - }, - { - "location": "/introduction/ya_metrika_task/", - "text": "Yandex.Metrica use case\n\n\nClickHouse currently powers \nYandex.Metrica\n, \nthe second largest web analytics platform in the world\n. With more than 13 trillion records in the database and more than 20 billion events daily, ClickHouse allows you generating custom reports on the fly directly from non-aggregated data.\n\n\nWe need to get custom reports based on hits and sessions, with custom segments set by the user. Data for the reports is updated in real-time. Queries must be run immediately (in online mode). We must be able to build reports for any time period. Complex aggregates must be calculated, such as the number of unique visitors.\nAt this time (April 2014), Yandex.Metrica receives approximately 12 billion events (pageviews and mouse clicks) daily. All these events must be stored in order to build custom reports. A single query may require scanning hundreds of millions of rows over a few seconds, or millions of rows in no more than a few hundred milliseconds.\n\n\nUsage in Yandex.Metrica and other Yandex services\n\n\nClickHouse is used for multiple purposes in Yandex.Metrica.\nIts main task is to build reports in online mode using non-aggregated data. It uses a cluster of 374 servers, which store over 20.3 trillion rows in the database. The volume of compressed data, without counting duplication and replication, is about 2 PB. The volume of uncompressed data (in TSV format) would be approximately 17 PB.\n\n\nClickHouse is also used for:\n\n\n\n\nStoring data for Session Replay from Yandex.Metrica.\n\n\nProcessing intermediate data.\n\n\nBuilding global reports with Analytics.\n\n\nRunning queries for debugging the Yandex.Metrica engine.\n\n\nAnalyzing logs from the API and the user interface.\n\n\n\n\nClickHouse has at least a dozen installations in other Yandex services: in search verticals, Market, Direct, business analytics, mobile development, AdFox, personal services, and others.\n\n\nAggregated and non-aggregated data\n\n\nThere is a popular opinion that in order to effectively calculate statistics, you must aggregate data, since this reduces the volume of data.\n\n\nBut data aggregation is a very limited solution, for the following reasons:\n\n\n\n\nYou must have a pre-defined list of reports the user will need.\n\n\nThe user can't make custom reports.\n\n\nWhen aggregating a large quantity of keys, the volume of data is not reduced, and aggregation is useless.\n\n\nFor a large number of reports, there are too many aggregation variations (combinatorial explosion).\n\n\nWhen aggregating keys with high cardinality (such as URLs), the volume of data is not reduced by much (less than twofold).\n\n\nFor this reason, the volume of data with aggregation might grow instead of shrink.\n\n\nUsers do not view all the reports we generate for them. A large portion of calculations are useless.\n\n\nThe logical integrity of data may be violated for various aggregations.\n\n\n\n\nIf we do not aggregate anything and work with non-aggregated data, this might actually reduce the volume of calculations.\n\n\nHowever, with aggregation, a significant part of the work is taken offline and completed relatively calmly. In contrast, online calculations require calculating as fast as possible, since the user is waiting for the result.\n\n\nYandex.Metrica has a specialized system for aggregating data called Metrage, which is used for the majority of reports.\nStarting in 2009, Yandex.Metrica also used a specialized OLAP database for non-aggregated data called OLAPServer, which was previously used for the report builder.\nOLAPServer worked well for non-aggregated data, but it had many restrictions that did not allow it to be used for all reports as desired. These included the lack of support for data types (only numbers), and the inability to incrementally update data in real-time (it could only be done by rewriting data daily). OLAPServer is not a DBMS, but a specialized DB.\n\n\nTo remove the limitations of OLAPServer and solve the problem of working with non-aggregated data for all reports, we developed the ClickHouse DBMS.", - "title": "The Yandex.Metrica task" - }, - { - "location": "/introduction/ya_metrika_task/#yandexmetrica-use-case", - "text": "ClickHouse currently powers Yandex.Metrica , the second largest web analytics platform in the world . With more than 13 trillion records in the database and more than 20 billion events daily, ClickHouse allows you generating custom reports on the fly directly from non-aggregated data. We need to get custom reports based on hits and sessions, with custom segments set by the user. Data for the reports is updated in real-time. Queries must be run immediately (in online mode). We must be able to build reports for any time period. Complex aggregates must be calculated, such as the number of unique visitors.\nAt this time (April 2014), Yandex.Metrica receives approximately 12 billion events (pageviews and mouse clicks) daily. All these events must be stored in order to build custom reports. A single query may require scanning hundreds of millions of rows over a few seconds, or millions of rows in no more than a few hundred milliseconds.", - "title": "Yandex.Metrica use case" - }, - { - "location": "/introduction/ya_metrika_task/#usage-in-yandexmetrica-and-other-yandex-services", - "text": "ClickHouse is used for multiple purposes in Yandex.Metrica.\nIts main task is to build reports in online mode using non-aggregated data. It uses a cluster of 374 servers, which store over 20.3 trillion rows in the database. The volume of compressed data, without counting duplication and replication, is about 2 PB. The volume of uncompressed data (in TSV format) would be approximately 17 PB. ClickHouse is also used for: Storing data for Session Replay from Yandex.Metrica. Processing intermediate data. Building global reports with Analytics. Running queries for debugging the Yandex.Metrica engine. Analyzing logs from the API and the user interface. ClickHouse has at least a dozen installations in other Yandex services: in search verticals, Market, Direct, business analytics, mobile development, AdFox, personal services, and others.", - "title": "Usage in Yandex.Metrica and other Yandex services" - }, - { - "location": "/introduction/ya_metrika_task/#aggregated-and-non-aggregated-data", - "text": "There is a popular opinion that in order to effectively calculate statistics, you must aggregate data, since this reduces the volume of data. But data aggregation is a very limited solution, for the following reasons: You must have a pre-defined list of reports the user will need. The user can't make custom reports. When aggregating a large quantity of keys, the volume of data is not reduced, and aggregation is useless. For a large number of reports, there are too many aggregation variations (combinatorial explosion). When aggregating keys with high cardinality (such as URLs), the volume of data is not reduced by much (less than twofold). For this reason, the volume of data with aggregation might grow instead of shrink. Users do not view all the reports we generate for them. A large portion of calculations are useless. The logical integrity of data may be violated for various aggregations. If we do not aggregate anything and work with non-aggregated data, this might actually reduce the volume of calculations. However, with aggregation, a significant part of the work is taken offline and completed relatively calmly. In contrast, online calculations require calculating as fast as possible, since the user is waiting for the result. Yandex.Metrica has a specialized system for aggregating data called Metrage, which is used for the majority of reports.\nStarting in 2009, Yandex.Metrica also used a specialized OLAP database for non-aggregated data called OLAPServer, which was previously used for the report builder.\nOLAPServer worked well for non-aggregated data, but it had many restrictions that did not allow it to be used for all reports as desired. These included the lack of support for data types (only numbers), and the inability to incrementally update data in real-time (it could only be done by rewriting data daily). OLAPServer is not a DBMS, but a specialized DB. To remove the limitations of OLAPServer and solve the problem of working with non-aggregated data for all reports, we developed the ClickHouse DBMS.", - "title": "Aggregated and non-aggregated data" - }, - { - "location": "/introduction/possible_silly_questions/", - "text": "Questions you were afraid to ask\n\n\nWhy not use something like MapReduce?\n\n\nWe can refer to systems like map-reduce as distributed computing systems in which the reduce operation is based on distributed sorting. In this sense, they include Hadoop, and YT (YT is developed at Yandex for internal use).\n\n\nThese systems aren't appropriate for online queries due to their high latency. In other words, they can't be used as the back-end for a web interface.\nThese types of systems aren't useful for real-time data updates.\nDistributed sorting isn't the best way to perform reduce operations if the result of the operation and all the intermediate results (if there are any) are located in the RAM of a single server, which is usually the case for online queries. In such a case, a hash table is the optimal way to perform reduce operations. A common approach to optimizing map-reduce tasks is pre-aggregation (partial reduce) using a hash table in RAM. The user performs this optimization manually.\nDistributed sorting is one of the main causes of reduced performance when running simple map-reduce tasks.\n\n\nSystems like map-reduce allow executing any code on the cluster. But a declarative query language is better suited to OLAP in order to run experiments quickly. For example, Hadoop has Hive and Pig. Also consider Cloudera Impala, Shark (outdated) for Spark, and Spark SQL, Presto, and Apache Drill. Performance when running such tasks is highly sub-optimal compared to specialized systems, but relatively high latency makes it unrealistic to use these systems as the backend for a web interface.\n\n\nYT allows storing groups of columns separately. But YT can't be considered a true column-based system because it doesn't have fixed-length data types (for efficiently storing numbers without extra \"garbage\"), and also due to its lack of a vector engine. Tasks are performed in YT using custom code in streaming mode, so they cannot be optimized enough (up to hundreds of millions of rows per second per server). \"Dynamic table sorting\" is under development in YT using MergeTree, strict value typing, and a query language similar to SQL. Dynamically sorted tables are not appropriate for OLAP tasks because the data is stored by row. The YT query language is still under development, so we can't yet rely on this functionality. YT developers are considering using dynamically sorted tables in OLTP and Key-Value scenarios.", - "title": "Everything you were afraid to ask" - }, - { - "location": "/introduction/possible_silly_questions/#questions-you-were-afraid-to-ask", - "text": "", - "title": "Questions you were afraid to ask" - }, - { - "location": "/introduction/possible_silly_questions/#why-not-use-something-like-mapreduce", - "text": "We can refer to systems like map-reduce as distributed computing systems in which the reduce operation is based on distributed sorting. In this sense, they include Hadoop, and YT (YT is developed at Yandex for internal use). These systems aren't appropriate for online queries due to their high latency. In other words, they can't be used as the back-end for a web interface.\nThese types of systems aren't useful for real-time data updates.\nDistributed sorting isn't the best way to perform reduce operations if the result of the operation and all the intermediate results (if there are any) are located in the RAM of a single server, which is usually the case for online queries. In such a case, a hash table is the optimal way to perform reduce operations. A common approach to optimizing map-reduce tasks is pre-aggregation (partial reduce) using a hash table in RAM. The user performs this optimization manually.\nDistributed sorting is one of the main causes of reduced performance when running simple map-reduce tasks. Systems like map-reduce allow executing any code on the cluster. But a declarative query language is better suited to OLAP in order to run experiments quickly. For example, Hadoop has Hive and Pig. Also consider Cloudera Impala, Shark (outdated) for Spark, and Spark SQL, Presto, and Apache Drill. Performance when running such tasks is highly sub-optimal compared to specialized systems, but relatively high latency makes it unrealistic to use these systems as the backend for a web interface. YT allows storing groups of columns separately. But YT can't be considered a true column-based system because it doesn't have fixed-length data types (for efficiently storing numbers without extra \"garbage\"), and also due to its lack of a vector engine. Tasks are performed in YT using custom code in streaming mode, so they cannot be optimized enough (up to hundreds of millions of rows per second per server). \"Dynamic table sorting\" is under development in YT using MergeTree, strict value typing, and a query language similar to SQL. Dynamically sorted tables are not appropriate for OLAP tasks because the data is stored by row. The YT query language is still under development, so we can't yet rely on this functionality. YT developers are considering using dynamically sorted tables in OLTP and Key-Value scenarios.", - "title": "Why not use something like MapReduce?" - }, - { - "location": "/introduction/performance/", - "text": "Performance\n\n\nAccording to internal testing results, ClickHouse shows the best performance for comparable operating scenarios among systems of its class that were available for testing. This includes the highest throughput for long queries, and the lowest latency on short queries. Testing results are shown on a separate page.\n\n\nThroughput for a single large query\n\n\nThroughput can be measured in rows per second or in megabytes per second. If the data is placed in the page cache, a query that is not too complex is processed on modern hardware at a speed of approximately 2-10 GB/s of uncompressed data on a single server (for the simplest cases, the speed may reach 30 GB/s). If data is not placed in the page cache, the speed depends on the disk subsystem and the data compression rate. For example, if the disk subsystem allows reading data at 400 MB/s, and the data compression rate is 3, the speed will be around 1.2 GB/s. To get the speed in rows per second, divide the speed in bytes per second by the total size of the columns used in the query. For example, if 10 bytes of columns are extracted, the speed will be around 100-200 million rows per second.\n\n\nThe processing speed increases almost linearly for distributed processing, but only if the number of rows resulting from aggregation or sorting is not too large.\n\n\nLatency when processing short queries\n\n\nIf a query uses a primary key and does not select too many rows to process (hundreds of thousands), and does not use too many columns, we can expect less than 50 milliseconds of latency (single digits of milliseconds in the best case) if data is placed in the page cache. Otherwise, latency is calculated from the number of seeks. If you use rotating drives, for a system that is not overloaded, the latency is calculated by this formula: seek time (10 ms) * number of columns queried * number of data parts.\n\n\nThroughput when processing a large quantity of short queries\n\n\nUnder the same conditions, ClickHouse can handle several hundred queries per second on a single server (up to several thousand in the best case). Since this scenario is not typical for analytical DBMSs, we recommend expecting a maximum of 100 queries per second.\n\n\nPerformance when inserting data\n\n\nWe recommend inserting data in packets of at least 1000 rows, or no more than a single request per second. When inserting to a MergeTree table from a tab-separated dump, the insertion speed will be from 50 to 200 MB/s. If the inserted rows are around 1 Kb in size, the speed will be from 50,000 to 200,000 rows per second. If the rows are small, the performance will be higher in rows per second (on Banner System data -\n 500,000 rows per second; on Graphite data -\n 1,000,000 rows per second). To improve performance, you can make multiple INSERT queries in parallel, and performance will increase linearly.", - "title": "Performance" - }, - { - "location": "/introduction/performance/#performance", - "text": "According to internal testing results, ClickHouse shows the best performance for comparable operating scenarios among systems of its class that were available for testing. This includes the highest throughput for long queries, and the lowest latency on short queries. Testing results are shown on a separate page.", - "title": "Performance" - }, - { - "location": "/introduction/performance/#throughput-for-a-single-large-query", - "text": "Throughput can be measured in rows per second or in megabytes per second. If the data is placed in the page cache, a query that is not too complex is processed on modern hardware at a speed of approximately 2-10 GB/s of uncompressed data on a single server (for the simplest cases, the speed may reach 30 GB/s). If data is not placed in the page cache, the speed depends on the disk subsystem and the data compression rate. For example, if the disk subsystem allows reading data at 400 MB/s, and the data compression rate is 3, the speed will be around 1.2 GB/s. To get the speed in rows per second, divide the speed in bytes per second by the total size of the columns used in the query. For example, if 10 bytes of columns are extracted, the speed will be around 100-200 million rows per second. The processing speed increases almost linearly for distributed processing, but only if the number of rows resulting from aggregation or sorting is not too large.", - "title": "Throughput for a single large query" - }, - { - "location": "/introduction/performance/#latency-when-processing-short-queries", - "text": "If a query uses a primary key and does not select too many rows to process (hundreds of thousands), and does not use too many columns, we can expect less than 50 milliseconds of latency (single digits of milliseconds in the best case) if data is placed in the page cache. Otherwise, latency is calculated from the number of seeks. If you use rotating drives, for a system that is not overloaded, the latency is calculated by this formula: seek time (10 ms) * number of columns queried * number of data parts.", - "title": "Latency when processing short queries" - }, - { - "location": "/introduction/performance/#throughput-when-processing-a-large-quantity-of-short-queries", - "text": "Under the same conditions, ClickHouse can handle several hundred queries per second on a single server (up to several thousand in the best case). Since this scenario is not typical for analytical DBMSs, we recommend expecting a maximum of 100 queries per second.", - "title": "Throughput when processing a large quantity of short queries" - }, - { - "location": "/introduction/performance/#performance-when-inserting-data", - "text": "We recommend inserting data in packets of at least 1000 rows, or no more than a single request per second. When inserting to a MergeTree table from a tab-separated dump, the insertion speed will be from 50 to 200 MB/s. If the inserted rows are around 1 Kb in size, the speed will be from 50,000 to 200,000 rows per second. If the rows are small, the performance will be higher in rows per second (on Banner System data - 500,000 rows per second; on Graphite data - 1,000,000 rows per second). To improve performance, you can make multiple INSERT queries in parallel, and performance will increase linearly.", - "title": "Performance when inserting data" - }, - { - "location": "/getting_started/", - "text": "Getting started\n\n\nSystem requirements\n\n\nThis is not a cross-platform system. It requires Linux Ubuntu Precise (12.04) or newer, with x86_64 architecture and support for the SSE 4.2 instruction set.\nTo check for SSE 4.2:\n\n\ngrep -q sse4_2 /proc/cpuinfo \n \necho\n \nSSE 4.2 supported\n \n||\n \necho\n \nSSE 4.2 not supported\n\n\n\n\n\n\nWe recommend using Ubuntu Trusty, Ubuntu Xenial, or Ubuntu Precise.\nThe terminal must use UTF-8 encoding (the default in Ubuntu).\n\n\nInstallation\n\n\nFor testing and development, the system can be installed on a single server or on a desktop computer.\n\n\nInstalling from packages for Debian/Ubuntu\n\n\nIn \n/etc/apt/sources.list\n (or in a separate \n/etc/apt/sources.list.d/clickhouse.list\n file), add the repository:\n\n\ndeb http://repo.yandex.ru/clickhouse/deb/stable/ main/\n\n\n\n\n\nIf you want to use the most recent test version, replace 'stable' with 'testing'.\n\n\nThen run:\n\n\nsudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4 \n# optional\n\nsudo apt-get update\nsudo apt-get install clickhouse-client clickhouse-server\n\n\n\n\n\nYou can also download and install packages manually from here: \nhttps://repo.yandex.ru/clickhouse/deb/stable/main/\n.\n\n\nClickHouse contains access restriction settings. They are located in the 'users.xml' file (next to 'config.xml').\nBy default, access is allowed from anywhere for the 'default' user, without a password. See 'user/default/networks'.\nFor more information, see the section \"Configuration files\".\n\n\nInstalling from sources\n\n\nTo compile, follow the instructions: build.md\n\n\nYou can compile packages and install them.\nYou can also use programs without installing packages.\n\n\nClient: dbms/src/Client/\nServer: dbms/src/Server/\n\n\n\n\n\nFor the server, create a catalog with data, such as:\n\n\n/opt/clickhouse/data/default/\n/opt/clickhouse/metadata/default/\n\n\n\n\n\n(Configurable in the server config.)\nRun 'chown' for the desired user.\n\n\nNote the path to logs in the server config (src/dbms/src/Server/config.xml).\n\n\nOther installation methods\n\n\nDocker image: \nhttps://hub.docker.com/r/yandex/clickhouse-server/\n\n\nRPM packages for CentOS or RHEL: \nhttps://github.com/Altinity/clickhouse-rpm-install\n\n\nGentoo overlay: \nhttps://github.com/kmeaw/clickhouse-overlay\n\n\nLaunch\n\n\nTo start the server (as a daemon), run:\n\n\nsudo service clickhouse-server start\n\n\n\n\n\nSee the logs in the \n/var/log/clickhouse-server/ directory.\n\n\nIf the server doesn't start, check the configurations in the file \n/etc/clickhouse-server/config.xml.\n\n\nYou can also launch the server from the console:\n\n\nclickhouse-server --config-file\n=\n/etc/clickhouse-server/config.xml\n\n\n\n\n\nIn this case, the log will be printed to the console, which is convenient during development.\nIf the configuration file is in the current directory, you don't need to specify the '--config-file' parameter. By default, it uses './config.xml'.\n\n\nYou can use the command-line client to connect to the server:\n\n\nclickhouse-client\n\n\n\n\n\nThe default parameters indicate connecting with localhost:9000 on behalf of the user 'default' without a password.\nThe client can be used for connecting to a remote server. Example:\n\n\nclickhouse-client --host\n=\nexample.com\n\n\n\n\n\nFor more information, see the section \"Command-line client\".\n\n\nChecking the system:\n\n\nmilovidov@hostname:~/work/metrica/src/dbms/src/Client$ ./clickhouse-client\nClickHouse client version \n0\n.0.18749.\nConnecting to localhost:9000.\nConnected to ClickHouse server version \n0\n.0.18749.\n\n:\n)\n SELECT \n1\n\n\nSELECT \n1\n\n\n\u250c\u25001\u2500\u2510\n\u2502 \n1\n \u2502\n\u2514\u2500\u2500\u2500\u2518\n\n\n1\n rows in set. Elapsed: \n0\n.003 sec.\n\n:\n)\n\n\n\n\n\n\nCongratulations, the system works!\n\n\nTo continue experimenting, you can try to download from the test data sets.", - "title": "Deploying and running" - }, - { - "location": "/getting_started/#getting-started", - "text": "", - "title": "Getting started" - }, - { - "location": "/getting_started/#system-requirements", - "text": "This is not a cross-platform system. It requires Linux Ubuntu Precise (12.04) or newer, with x86_64 architecture and support for the SSE 4.2 instruction set.\nTo check for SSE 4.2: grep -q sse4_2 /proc/cpuinfo echo SSE 4.2 supported || echo SSE 4.2 not supported We recommend using Ubuntu Trusty, Ubuntu Xenial, or Ubuntu Precise.\nThe terminal must use UTF-8 encoding (the default in Ubuntu).", - "title": "System requirements" - }, - { - "location": "/getting_started/#installation", - "text": "For testing and development, the system can be installed on a single server or on a desktop computer.", - "title": "Installation" - }, - { - "location": "/getting_started/#installing-from-packages-for-debianubuntu", - "text": "In /etc/apt/sources.list (or in a separate /etc/apt/sources.list.d/clickhouse.list file), add the repository: deb http://repo.yandex.ru/clickhouse/deb/stable/ main/ If you want to use the most recent test version, replace 'stable' with 'testing'. Then run: sudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4 # optional \nsudo apt-get update\nsudo apt-get install clickhouse-client clickhouse-server You can also download and install packages manually from here: https://repo.yandex.ru/clickhouse/deb/stable/main/ . ClickHouse contains access restriction settings. They are located in the 'users.xml' file (next to 'config.xml').\nBy default, access is allowed from anywhere for the 'default' user, without a password. See 'user/default/networks'.\nFor more information, see the section \"Configuration files\".", - "title": "Installing from packages for Debian/Ubuntu" - }, - { - "location": "/getting_started/#installing-from-sources", - "text": "To compile, follow the instructions: build.md You can compile packages and install them.\nYou can also use programs without installing packages. Client: dbms/src/Client/\nServer: dbms/src/Server/ For the server, create a catalog with data, such as: /opt/clickhouse/data/default/\n/opt/clickhouse/metadata/default/ (Configurable in the server config.)\nRun 'chown' for the desired user. Note the path to logs in the server config (src/dbms/src/Server/config.xml).", - "title": "Installing from sources" - }, - { - "location": "/getting_started/#other-installation-methods", - "text": "Docker image: https://hub.docker.com/r/yandex/clickhouse-server/ RPM packages for CentOS or RHEL: https://github.com/Altinity/clickhouse-rpm-install Gentoo overlay: https://github.com/kmeaw/clickhouse-overlay", - "title": "Other installation methods" - }, - { - "location": "/getting_started/#launch", - "text": "To start the server (as a daemon), run: sudo service clickhouse-server start See the logs in the /var/log/clickhouse-server/ directory. If the server doesn't start, check the configurations in the file /etc/clickhouse-server/config.xml. You can also launch the server from the console: clickhouse-server --config-file = /etc/clickhouse-server/config.xml In this case, the log will be printed to the console, which is convenient during development.\nIf the configuration file is in the current directory, you don't need to specify the '--config-file' parameter. By default, it uses './config.xml'. You can use the command-line client to connect to the server: clickhouse-client The default parameters indicate connecting with localhost:9000 on behalf of the user 'default' without a password.\nThe client can be used for connecting to a remote server. Example: clickhouse-client --host = example.com For more information, see the section \"Command-line client\". Checking the system: milovidov@hostname:~/work/metrica/src/dbms/src/Client$ ./clickhouse-client\nClickHouse client version 0 .0.18749.\nConnecting to localhost:9000.\nConnected to ClickHouse server version 0 .0.18749.\n\n: ) SELECT 1 \n\nSELECT 1 \n\n\u250c\u25001\u2500\u2510\n\u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2518 1 rows in set. Elapsed: 0 .003 sec.\n\n: ) Congratulations, the system works! To continue experimenting, you can try to download from the test data sets.", - "title": "Launch" - }, - { - "location": "/getting_started/example_datasets/ontime/", - "text": "OnTime\n\n\nThis performance test was created by Vadim Tkachenko. See:\n\n\n\n\nhttps://www.percona.com/blog/2009/10/02/analyzing-air-traffic-performance-with-infobright-and-monetdb/\n\n\nhttps://www.percona.com/blog/2009/10/26/air-traffic-queries-in-luciddb/\n\n\nhttps://www.percona.com/blog/2009/11/02/air-traffic-queries-in-infinidb-early-alpha/\n\n\nhttps://www.percona.com/blog/2014/04/21/using-apache-hadoop-and-impala-together-with-mysql-for-data-analysis/\n\n\nhttps://www.percona.com/blog/2016/01/07/apache-spark-with-air-ontime-performance-data/\n\n\nhttp://nickmakos.blogspot.ru/2012/08/analyzing-air-traffic-performance-with.html\n\n\n\n\nDownloading data:\n\n\nfor\n s in \n`\nseq \n1987\n \n2017\n`\n\n\ndo\n\n\nfor\n m in \n`\nseq \n1\n \n12\n`\n\n\ndo\n\nwget http://transtats.bts.gov/PREZIP/On_Time_On_Time_Performance_\n${\ns\n}\n_\n${\nm\n}\n.zip\n\ndone\n\n\ndone\n\n\n\n\n\n\n(from \nhttps://github.com/Percona-Lab/ontime-airline-performance/blob/master/download.sh\n )\n\n\nCreating a table:\n\n\nCREATE\n \nTABLE\n \n`\nontime\n`\n \n(\n\n \n`\nYear\n`\n \nUInt16\n,\n\n \n`\nQuarter\n`\n \nUInt8\n,\n\n \n`\nMonth\n`\n \nUInt8\n,\n\n \n`\nDayofMonth\n`\n \nUInt8\n,\n\n \n`\nDayOfWeek\n`\n \nUInt8\n,\n\n \n`\nFlightDate\n`\n \nDate\n,\n\n \n`\nUniqueCarrier\n`\n \nFixedString\n(\n7\n),\n\n \n`\nAirlineID\n`\n \nInt32\n,\n\n \n`\nCarrier\n`\n \nFixedString\n(\n2\n),\n\n \n`\nTailNum\n`\n \nString\n,\n\n \n`\nFlightNum\n`\n \nString\n,\n\n \n`\nOriginAirportID\n`\n \nInt32\n,\n\n \n`\nOriginAirportSeqID\n`\n \nInt32\n,\n\n \n`\nOriginCityMarketID\n`\n \nInt32\n,\n\n \n`\nOrigin\n`\n \nFixedString\n(\n5\n),\n\n \n`\nOriginCityName\n`\n \nString\n,\n\n \n`\nOriginState\n`\n \nFixedString\n(\n2\n),\n\n \n`\nOriginStateFips\n`\n \nString\n,\n\n \n`\nOriginStateName\n`\n \nString\n,\n\n \n`\nOriginWac\n`\n \nInt32\n,\n\n \n`\nDestAirportID\n`\n \nInt32\n,\n\n \n`\nDestAirportSeqID\n`\n \nInt32\n,\n\n \n`\nDestCityMarketID\n`\n \nInt32\n,\n\n \n`\nDest\n`\n \nFixedString\n(\n5\n),\n\n \n`\nDestCityName\n`\n \nString\n,\n\n \n`\nDestState\n`\n \nFixedString\n(\n2\n),\n\n \n`\nDestStateFips\n`\n \nString\n,\n\n \n`\nDestStateName\n`\n \nString\n,\n\n \n`\nDestWac\n`\n \nInt32\n,\n\n \n`\nCRSDepTime\n`\n \nInt32\n,\n\n \n`\nDepTime\n`\n \nInt32\n,\n\n \n`\nDepDelay\n`\n \nInt32\n,\n\n \n`\nDepDelayMinutes\n`\n \nInt32\n,\n\n \n`\nDepDel15\n`\n \nInt32\n,\n\n \n`\nDepartureDelayGroups\n`\n \nString\n,\n\n \n`\nDepTimeBlk\n`\n \nString\n,\n\n \n`\nTaxiOut\n`\n \nInt32\n,\n\n \n`\nWheelsOff\n`\n \nInt32\n,\n\n \n`\nWheelsOn\n`\n \nInt32\n,\n\n \n`\nTaxiIn\n`\n \nInt32\n,\n\n \n`\nCRSArrTime\n`\n \nInt32\n,\n\n \n`\nArrTime\n`\n \nInt32\n,\n\n \n`\nArrDelay\n`\n \nInt32\n,\n\n \n`\nArrDelayMinutes\n`\n \nInt32\n,\n\n \n`\nArrDel15\n`\n \nInt32\n,\n\n \n`\nArrivalDelayGroups\n`\n \nInt32\n,\n\n \n`\nArrTimeBlk\n`\n \nString\n,\n\n \n`\nCancelled\n`\n \nUInt8\n,\n\n \n`\nCancellationCode\n`\n \nFixedString\n(\n1\n),\n\n \n`\nDiverted\n`\n \nUInt8\n,\n\n \n`\nCRSElapsedTime\n`\n \nInt32\n,\n\n \n`\nActualElapsedTime\n`\n \nInt32\n,\n\n \n`\nAirTime\n`\n \nInt32\n,\n\n \n`\nFlights\n`\n \nInt32\n,\n\n \n`\nDistance\n`\n \nInt32\n,\n\n \n`\nDistanceGroup\n`\n \nUInt8\n,\n\n \n`\nCarrierDelay\n`\n \nInt32\n,\n\n \n`\nWeatherDelay\n`\n \nInt32\n,\n\n \n`\nNASDelay\n`\n \nInt32\n,\n\n \n`\nSecurityDelay\n`\n \nInt32\n,\n\n \n`\nLateAircraftDelay\n`\n \nInt32\n,\n\n \n`\nFirstDepTime\n`\n \nString\n,\n\n \n`\nTotalAddGTime\n`\n \nString\n,\n\n \n`\nLongestAddGTime\n`\n \nString\n,\n\n \n`\nDivAirportLandings\n`\n \nString\n,\n\n \n`\nDivReachedDest\n`\n \nString\n,\n\n \n`\nDivActualElapsedTime\n`\n \nString\n,\n\n \n`\nDivArrDelay\n`\n \nString\n,\n\n \n`\nDivDistance\n`\n \nString\n,\n\n \n`\nDiv1Airport\n`\n \nString\n,\n\n \n`\nDiv1AirportID\n`\n \nInt32\n,\n\n \n`\nDiv1AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv1WheelsOn\n`\n \nString\n,\n\n \n`\nDiv1TotalGTime\n`\n \nString\n,\n\n \n`\nDiv1LongestGTime\n`\n \nString\n,\n\n \n`\nDiv1WheelsOff\n`\n \nString\n,\n\n \n`\nDiv1TailNum\n`\n \nString\n,\n\n \n`\nDiv2Airport\n`\n \nString\n,\n\n \n`\nDiv2AirportID\n`\n \nInt32\n,\n\n \n`\nDiv2AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv2WheelsOn\n`\n \nString\n,\n\n \n`\nDiv2TotalGTime\n`\n \nString\n,\n\n \n`\nDiv2LongestGTime\n`\n \nString\n,\n\n \n`\nDiv2WheelsOff\n`\n \nString\n,\n\n \n`\nDiv2TailNum\n`\n \nString\n,\n\n \n`\nDiv3Airport\n`\n \nString\n,\n\n \n`\nDiv3AirportID\n`\n \nInt32\n,\n\n \n`\nDiv3AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv3WheelsOn\n`\n \nString\n,\n\n \n`\nDiv3TotalGTime\n`\n \nString\n,\n\n \n`\nDiv3LongestGTime\n`\n \nString\n,\n\n \n`\nDiv3WheelsOff\n`\n \nString\n,\n\n \n`\nDiv3TailNum\n`\n \nString\n,\n\n \n`\nDiv4Airport\n`\n \nString\n,\n\n \n`\nDiv4AirportID\n`\n \nInt32\n,\n\n \n`\nDiv4AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv4WheelsOn\n`\n \nString\n,\n\n \n`\nDiv4TotalGTime\n`\n \nString\n,\n\n \n`\nDiv4LongestGTime\n`\n \nString\n,\n\n \n`\nDiv4WheelsOff\n`\n \nString\n,\n\n \n`\nDiv4TailNum\n`\n \nString\n,\n\n \n`\nDiv5Airport\n`\n \nString\n,\n\n \n`\nDiv5AirportID\n`\n \nInt32\n,\n\n \n`\nDiv5AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv5WheelsOn\n`\n \nString\n,\n\n \n`\nDiv5TotalGTime\n`\n \nString\n,\n\n \n`\nDiv5LongestGTime\n`\n \nString\n,\n\n \n`\nDiv5WheelsOff\n`\n \nString\n,\n\n \n`\nDiv5TailNum\n`\n \nString\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nFlightDate\n,\n \n(\nYear\n,\n \nFlightDate\n),\n \n8192\n)\n\n\n\n\n\n\nLoading data:\n\n\nfor\n i in *.zip\n;\n \ndo\n \necho\n \n$i\n;\n unzip -cq \n$i\n \n*.csv\n \n|\n sed \ns/\\.00//g\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO ontime FORMAT CSVWithNames\n;\n \ndone\n\n\n\n\n\n\nQueries:\n\n\nQ0.\n\n\nselect\n \navg\n(\nc1\n)\n \nfrom\n \n(\nselect\n \nYear\n,\n \nMonth\n,\n \ncount\n(\n*\n)\n \nas\n \nc1\n \nfrom\n \nontime\n \ngroup\n \nby\n \nYear\n,\n \nMonth\n);\n\n\n\n\n\n\nQ1. The number of flights per day from the year 2000 to 2008\n\n\nSELECT\n \nDayOfWeek\n,\n \ncount\n(\n*\n)\n \nAS\n \nc\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nDayOfWeek\n \nORDER\n \nBY\n \nc\n \nDESC\n;\n\n\n\n\n\n\nQ2. The number of flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008\n\n\nSELECT\n \nDayOfWeek\n,\n \ncount\n(\n*\n)\n \nAS\n \nc\n \nFROM\n \nontime\n \nWHERE\n \nDepDelay\n10\n \nAND\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nDayOfWeek\n \nORDER\n \nBY\n \nc\n \nDESC\n\n\n\n\n\n\nQ3. The number of delays by airport for 2000-2008\n\n\nSELECT\n \nOrigin\n,\n \ncount\n(\n*\n)\n \nAS\n \nc\n \nFROM\n \nontime\n \nWHERE\n \nDepDelay\n10\n \nAND\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nOrigin\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n10\n\n\n\n\n\n\nQ4. The number of delays by carrier for 2007\n\n\nSELECT\n \nCarrier\n,\n \ncount\n(\n*\n)\n \nFROM\n \nontime\n \nWHERE\n \nDepDelay\n10\n \nAND\n \nYear\n \n=\n \n2007\n \nGROUP\n \nBY\n \nCarrier\n \nORDER\n \nBY\n \ncount\n(\n*\n)\n \nDESC\n\n\n\n\n\n\nQ5. The percentage of delays by carrier for 2007\n\n\nSELECT\n \nCarrier\n,\n \nc\n,\n \nc2\n,\n \nc\n*\n1000\n/\nc2\n \nas\n \nc3\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc\n\n \nFROM\n \nontime\n\n \nWHERE\n \nDepDelay\n10\n\n \nAND\n \nYear\n=\n2007\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n\n\nANY\n \nINNER\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc2\n\n \nFROM\n \nontime\n\n \nWHERE\n \nYear\n=\n2007\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n \nUSING\n \nCarrier\n\n\nORDER\n \nBY\n \nc3\n \nDESC\n;\n\n\n\n\n\n\nBetter version of the same query:\n\n\nSELECT\n \nCarrier\n,\n \navg\n(\nDepDelay\n \n \n10\n)\n \n*\n \n1000\n \nAS\n \nc3\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2007\n \nGROUP\n \nBY\n \nCarrier\n \nORDER\n \nBY\n \nCarrier\n\n\n\n\n\n\nQ6. The previous request for a broader range of years, 2000-2008\n\n\nSELECT\n \nCarrier\n,\n \nc\n,\n \nc2\n,\n \nc\n*\n1000\n/\nc2\n \nas\n \nc3\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc\n\n \nFROM\n \nontime\n\n \nWHERE\n \nDepDelay\n10\n\n \nAND\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n\n\nANY\n \nINNER\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc2\n\n \nFROM\n \nontime\n\n \nWHERE\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n \nUSING\n \nCarrier\n\n\nORDER\n \nBY\n \nc3\n \nDESC\n;\n\n\n\n\n\n\nBetter version of the same query:\n\n\nSELECT\n \nCarrier\n,\n \navg\n(\nDepDelay\n \n \n10\n)\n \n*\n \n1000\n \nAS\n \nc3\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nCarrier\n \nORDER\n \nBY\n \nCarrier\n\n\n\n\n\n\nQ7. Percentage of flights delayed for more than 10 minutes, by year\n\n\nSELECT\n \nYear\n,\n \nc1\n/\nc2\n\n\nFROM\n\n\n(\n\n \nselect\n\n \nYear\n,\n\n \ncount\n(\n*\n)\n*\n1000\n \nas\n \nc1\n\n \nfrom\n \nontime\n\n \nWHERE\n \nDepDelay\n10\n\n \nGROUP\n \nBY\n \nYear\n\n\n)\n\n\nANY\n \nINNER\n \nJOIN\n\n\n(\n\n \nselect\n\n \nYear\n,\n\n \ncount\n(\n*\n)\n \nas\n \nc2\n\n \nfrom\n \nontime\n\n \nGROUP\n \nBY\n \nYear\n\n\n)\n \nUSING\n \n(\nYear\n)\n\n\nORDER\n \nBY\n \nYear\n\n\n\n\n\n\nBetter version of the same query:\n\n\nSELECT\n \nYear\n,\n \navg\n(\nDepDelay\n \n \n10\n)\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nYear\n \nORDER\n \nBY\n \nYear\n\n\n\n\n\n\nQ8. The most popular destinations by the number of directly connected cities for various year ranges\n\n\nSELECT\n \nDestCityName\n,\n \nuniqExact\n(\nOriginCityName\n)\n \nAS\n \nu\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2000\n \nand\n \nYear\n \n=\n \n2010\n \nGROUP\n \nBY\n \nDestCityName\n \nORDER\n \nBY\n \nu\n \nDESC\n \nLIMIT\n \n10\n;\n\n\n\n\n\n\nQ9.\n\n\nselect\n \nYear\n,\n \ncount\n(\n*\n)\n \nas\n \nc1\n \nfrom\n \nontime\n \ngroup\n \nby\n \nYear\n;\n\n\n\n\n\n\nQ10.\n\n\nselect\n\n \nmin\n(\nYear\n),\n \nmax\n(\nYear\n),\n \nCarrier\n,\n \ncount\n(\n*\n)\n \nas\n \ncnt\n,\n\n \nsum\n(\nArrDelayMinutes\n30\n)\n \nas\n \nflights_delayed\n,\n\n \nround\n(\nsum\n(\nArrDelayMinutes\n30\n)\n/\ncount\n(\n*\n),\n2\n)\n \nas\n \nrate\n\n\nFROM\n \nontime\n\n\nWHERE\n\n \nDayOfWeek\n \nnot\n \nin\n \n(\n6\n,\n7\n)\n \nand\n \nOriginState\n \nnot\n \nin\n \n(\nAK\n,\n \nHI\n,\n \nPR\n,\n \nVI\n)\n\n \nand\n \nDestState\n \nnot\n \nin\n \n(\nAK\n,\n \nHI\n,\n \nPR\n,\n \nVI\n)\n\n \nand\n \nFlightDate\n \n \n2010-01-01\n\n\nGROUP\n \nby\n \nCarrier\n\n\nHAVING\n \ncnt\n \n \n100000\n \nand\n \nmax\n(\nYear\n)\n \n \n1990\n\n\nORDER\n \nby\n \nrate\n \nDESC\n\n\nLIMIT\n \n1000\n;\n\n\n\n\n\n\nBonus:\n\n\nSELECT\n \navg\n(\ncnt\n)\n \nFROM\n \n(\nSELECT\n \nYear\n,\nMonth\n,\ncount\n(\n*\n)\n \nAS\n \ncnt\n \nFROM\n \nontime\n \nWHERE\n \nDepDel15\n=\n1\n \nGROUP\n \nBY\n \nYear\n,\nMonth\n)\n\n\n\nselect\n \navg\n(\nc1\n)\n \nfrom\n \n(\nselect\n \nYear\n,\nMonth\n,\ncount\n(\n*\n)\n \nas\n \nc1\n \nfrom\n \nontime\n \ngroup\n \nby\n \nYear\n,\nMonth\n)\n\n\n\nSELECT\n \nDestCityName\n,\n \nuniqExact\n(\nOriginCityName\n)\n \nAS\n \nu\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nDestCityName\n \nORDER\n \nBY\n \nu\n \nDESC\n \nLIMIT\n \n10\n;\n\n\n\nSELECT\n \nOriginCityName\n,\n \nDestCityName\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nOriginCityName\n,\n \nDestCityName\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n10\n;\n\n\n\nSELECT\n \nOriginCityName\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nOriginCityName\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n10\n;", - "title": "OnTime" - }, - { - "location": "/getting_started/example_datasets/ontime/#ontime", - "text": "This performance test was created by Vadim Tkachenko. See: https://www.percona.com/blog/2009/10/02/analyzing-air-traffic-performance-with-infobright-and-monetdb/ https://www.percona.com/blog/2009/10/26/air-traffic-queries-in-luciddb/ https://www.percona.com/blog/2009/11/02/air-traffic-queries-in-infinidb-early-alpha/ https://www.percona.com/blog/2014/04/21/using-apache-hadoop-and-impala-together-with-mysql-for-data-analysis/ https://www.percona.com/blog/2016/01/07/apache-spark-with-air-ontime-performance-data/ http://nickmakos.blogspot.ru/2012/08/analyzing-air-traffic-performance-with.html Downloading data: for s in ` seq 1987 2017 ` do for m in ` seq 1 12 ` do \nwget http://transtats.bts.gov/PREZIP/On_Time_On_Time_Performance_ ${ s } _ ${ m } .zip done done (from https://github.com/Percona-Lab/ontime-airline-performance/blob/master/download.sh ) Creating a table: CREATE TABLE ` ontime ` ( \n ` Year ` UInt16 , \n ` Quarter ` UInt8 , \n ` Month ` UInt8 , \n ` DayofMonth ` UInt8 , \n ` DayOfWeek ` UInt8 , \n ` FlightDate ` Date , \n ` UniqueCarrier ` FixedString ( 7 ), \n ` AirlineID ` Int32 , \n ` Carrier ` FixedString ( 2 ), \n ` TailNum ` String , \n ` FlightNum ` String , \n ` OriginAirportID ` Int32 , \n ` OriginAirportSeqID ` Int32 , \n ` OriginCityMarketID ` Int32 , \n ` Origin ` FixedString ( 5 ), \n ` OriginCityName ` String , \n ` OriginState ` FixedString ( 2 ), \n ` OriginStateFips ` String , \n ` OriginStateName ` String , \n ` OriginWac ` Int32 , \n ` DestAirportID ` Int32 , \n ` DestAirportSeqID ` Int32 , \n ` DestCityMarketID ` Int32 , \n ` Dest ` FixedString ( 5 ), \n ` DestCityName ` String , \n ` DestState ` FixedString ( 2 ), \n ` DestStateFips ` String , \n ` DestStateName ` String , \n ` DestWac ` Int32 , \n ` CRSDepTime ` Int32 , \n ` DepTime ` Int32 , \n ` DepDelay ` Int32 , \n ` DepDelayMinutes ` Int32 , \n ` DepDel15 ` Int32 , \n ` DepartureDelayGroups ` String , \n ` DepTimeBlk ` String , \n ` TaxiOut ` Int32 , \n ` WheelsOff ` Int32 , \n ` WheelsOn ` Int32 , \n ` TaxiIn ` Int32 , \n ` CRSArrTime ` Int32 , \n ` ArrTime ` Int32 , \n ` ArrDelay ` Int32 , \n ` ArrDelayMinutes ` Int32 , \n ` ArrDel15 ` Int32 , \n ` ArrivalDelayGroups ` Int32 , \n ` ArrTimeBlk ` String , \n ` Cancelled ` UInt8 , \n ` CancellationCode ` FixedString ( 1 ), \n ` Diverted ` UInt8 , \n ` CRSElapsedTime ` Int32 , \n ` ActualElapsedTime ` Int32 , \n ` AirTime ` Int32 , \n ` Flights ` Int32 , \n ` Distance ` Int32 , \n ` DistanceGroup ` UInt8 , \n ` CarrierDelay ` Int32 , \n ` WeatherDelay ` Int32 , \n ` NASDelay ` Int32 , \n ` SecurityDelay ` Int32 , \n ` LateAircraftDelay ` Int32 , \n ` FirstDepTime ` String , \n ` TotalAddGTime ` String , \n ` LongestAddGTime ` String , \n ` DivAirportLandings ` String , \n ` DivReachedDest ` String , \n ` DivActualElapsedTime ` String , \n ` DivArrDelay ` String , \n ` DivDistance ` String , \n ` Div1Airport ` String , \n ` Div1AirportID ` Int32 , \n ` Div1AirportSeqID ` Int32 , \n ` Div1WheelsOn ` String , \n ` Div1TotalGTime ` String , \n ` Div1LongestGTime ` String , \n ` Div1WheelsOff ` String , \n ` Div1TailNum ` String , \n ` Div2Airport ` String , \n ` Div2AirportID ` Int32 , \n ` Div2AirportSeqID ` Int32 , \n ` Div2WheelsOn ` String , \n ` Div2TotalGTime ` String , \n ` Div2LongestGTime ` String , \n ` Div2WheelsOff ` String , \n ` Div2TailNum ` String , \n ` Div3Airport ` String , \n ` Div3AirportID ` Int32 , \n ` Div3AirportSeqID ` Int32 , \n ` Div3WheelsOn ` String , \n ` Div3TotalGTime ` String , \n ` Div3LongestGTime ` String , \n ` Div3WheelsOff ` String , \n ` Div3TailNum ` String , \n ` Div4Airport ` String , \n ` Div4AirportID ` Int32 , \n ` Div4AirportSeqID ` Int32 , \n ` Div4WheelsOn ` String , \n ` Div4TotalGTime ` String , \n ` Div4LongestGTime ` String , \n ` Div4WheelsOff ` String , \n ` Div4TailNum ` String , \n ` Div5Airport ` String , \n ` Div5AirportID ` Int32 , \n ` Div5AirportSeqID ` Int32 , \n ` Div5WheelsOn ` String , \n ` Div5TotalGTime ` String , \n ` Div5LongestGTime ` String , \n ` Div5WheelsOff ` String , \n ` Div5TailNum ` String ) ENGINE = MergeTree ( FlightDate , ( Year , FlightDate ), 8192 ) Loading data: for i in *.zip ; do echo $i ; unzip -cq $i *.csv | sed s/\\.00//g | clickhouse-client --host = example-perftest01j --query = INSERT INTO ontime FORMAT CSVWithNames ; done Queries: Q0. select avg ( c1 ) from ( select Year , Month , count ( * ) as c1 from ontime group by Year , Month ); Q1. The number of flights per day from the year 2000 to 2008 SELECT DayOfWeek , count ( * ) AS c FROM ontime WHERE Year = 2000 AND Year = 2008 GROUP BY DayOfWeek ORDER BY c DESC ; Q2. The number of flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008 SELECT DayOfWeek , count ( * ) AS c FROM ontime WHERE DepDelay 10 AND Year = 2000 AND Year = 2008 GROUP BY DayOfWeek ORDER BY c DESC Q3. The number of delays by airport for 2000-2008 SELECT Origin , count ( * ) AS c FROM ontime WHERE DepDelay 10 AND Year = 2000 AND Year = 2008 GROUP BY Origin ORDER BY c DESC LIMIT 10 Q4. The number of delays by carrier for 2007 SELECT Carrier , count ( * ) FROM ontime WHERE DepDelay 10 AND Year = 2007 GROUP BY Carrier ORDER BY count ( * ) DESC Q5. The percentage of delays by carrier for 2007 SELECT Carrier , c , c2 , c * 1000 / c2 as c3 FROM ( \n SELECT \n Carrier , \n count ( * ) AS c \n FROM ontime \n WHERE DepDelay 10 \n AND Year = 2007 \n GROUP BY Carrier ) ANY INNER JOIN ( \n SELECT \n Carrier , \n count ( * ) AS c2 \n FROM ontime \n WHERE Year = 2007 \n GROUP BY Carrier ) USING Carrier ORDER BY c3 DESC ; Better version of the same query: SELECT Carrier , avg ( DepDelay 10 ) * 1000 AS c3 FROM ontime WHERE Year = 2007 GROUP BY Carrier ORDER BY Carrier Q6. The previous request for a broader range of years, 2000-2008 SELECT Carrier , c , c2 , c * 1000 / c2 as c3 FROM ( \n SELECT \n Carrier , \n count ( * ) AS c \n FROM ontime \n WHERE DepDelay 10 \n AND Year = 2000 AND Year = 2008 \n GROUP BY Carrier ) ANY INNER JOIN ( \n SELECT \n Carrier , \n count ( * ) AS c2 \n FROM ontime \n WHERE Year = 2000 AND Year = 2008 \n GROUP BY Carrier ) USING Carrier ORDER BY c3 DESC ; Better version of the same query: SELECT Carrier , avg ( DepDelay 10 ) * 1000 AS c3 FROM ontime WHERE Year = 2000 AND Year = 2008 GROUP BY Carrier ORDER BY Carrier Q7. Percentage of flights delayed for more than 10 minutes, by year SELECT Year , c1 / c2 FROM ( \n select \n Year , \n count ( * ) * 1000 as c1 \n from ontime \n WHERE DepDelay 10 \n GROUP BY Year ) ANY INNER JOIN ( \n select \n Year , \n count ( * ) as c2 \n from ontime \n GROUP BY Year ) USING ( Year ) ORDER BY Year Better version of the same query: SELECT Year , avg ( DepDelay 10 ) FROM ontime GROUP BY Year ORDER BY Year Q8. The most popular destinations by the number of directly connected cities for various year ranges SELECT DestCityName , uniqExact ( OriginCityName ) AS u FROM ontime WHERE Year = 2000 and Year = 2010 GROUP BY DestCityName ORDER BY u DESC LIMIT 10 ; Q9. select Year , count ( * ) as c1 from ontime group by Year ; Q10. select \n min ( Year ), max ( Year ), Carrier , count ( * ) as cnt , \n sum ( ArrDelayMinutes 30 ) as flights_delayed , \n round ( sum ( ArrDelayMinutes 30 ) / count ( * ), 2 ) as rate FROM ontime WHERE \n DayOfWeek not in ( 6 , 7 ) and OriginState not in ( AK , HI , PR , VI ) \n and DestState not in ( AK , HI , PR , VI ) \n and FlightDate 2010-01-01 GROUP by Carrier HAVING cnt 100000 and max ( Year ) 1990 ORDER by rate DESC LIMIT 1000 ; Bonus: SELECT avg ( cnt ) FROM ( SELECT Year , Month , count ( * ) AS cnt FROM ontime WHERE DepDel15 = 1 GROUP BY Year , Month ) select avg ( c1 ) from ( select Year , Month , count ( * ) as c1 from ontime group by Year , Month ) SELECT DestCityName , uniqExact ( OriginCityName ) AS u FROM ontime GROUP BY DestCityName ORDER BY u DESC LIMIT 10 ; SELECT OriginCityName , DestCityName , count () AS c FROM ontime GROUP BY OriginCityName , DestCityName ORDER BY c DESC LIMIT 10 ; SELECT OriginCityName , count () AS c FROM ontime GROUP BY OriginCityName ORDER BY c DESC LIMIT 10 ;", - "title": "OnTime" - }, - { - "location": "/getting_started/example_datasets/nyc_taxi/", - "text": "New York Taxi data\n\n\nHow to import the raw data\n\n\nSee \nhttps://github.com/toddwschneider/nyc-taxi-data\n and \nhttp://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html\n for the description of the dataset and instructions for downloading.\n\n\nDownloading will result in about 227 GB of uncompressed data in CSV files. The download takes about an hour over a 1 Gbit connection (parallel downloading from s3.amazonaws.com recovers at least half of a 1 Gbit channel).\nSome of the files might not download fully. Check the file sizes and re-download any that seem doubtful.\n\n\nSome of the files might contain invalid rows. You can fix them as follows:\n\n\nsed -E \n/(.*,){18,}/d\n data/yellow_tripdata_2010-02.csv \n data/yellow_tripdata_2010-02.csv_\nsed -E \n/(.*,){18,}/d\n data/yellow_tripdata_2010-03.csv \n data/yellow_tripdata_2010-03.csv_\nmv data/yellow_tripdata_2010-02.csv_ data/yellow_tripdata_2010-02.csv\nmv data/yellow_tripdata_2010-03.csv_ data/yellow_tripdata_2010-03.csv\n\n\n\n\n\nThen the data must be pre-processed in PostgreSQL. This will create selections of points in the polygons (to match points on the map with the boroughs of New York City) and combine all the data into a single denormalized flat table by using a JOIN. To do this, you will need to install PostgreSQL with PostGIS support.\n\n\nBe careful when running \ninitialize_database.sh\n and manually re-check that all the tables were created correctly.\n\n\nIt takes about 20-30 minutes to process each month's worth of data in PostgreSQL, for a total of about 48 hours.\n\n\nYou can check the number of downloaded rows as follows:\n\n\ntime psql nyc-taxi-data -c \nSELECT count(*) FROM trips;\n\n## count\n 1298979494\n(1 row)\n\nreal 7m9.164s\n\n\n\n\n\n(This is slightly more than 1.1 billion rows reported by Mark Litwintschik in a series of blog posts.)\n\n\nThe data in PostgreSQL uses 370 GB of space.\n\n\nExporting the data from PostgreSQL:\n\n\nCOPY\n\n\n(\n\n \nSELECT\n \ntrips\n.\nid\n,\n\n \ntrips\n.\nvendor_id\n,\n\n \ntrips\n.\npickup_datetime\n,\n\n \ntrips\n.\ndropoff_datetime\n,\n\n \ntrips\n.\nstore_and_fwd_flag\n,\n\n \ntrips\n.\nrate_code_id\n,\n\n \ntrips\n.\npickup_longitude\n,\n\n \ntrips\n.\npickup_latitude\n,\n\n \ntrips\n.\ndropoff_longitude\n,\n\n \ntrips\n.\ndropoff_latitude\n,\n\n \ntrips\n.\npassenger_count\n,\n\n \ntrips\n.\ntrip_distance\n,\n\n \ntrips\n.\nfare_amount\n,\n\n \ntrips\n.\nextra\n,\n\n \ntrips\n.\nmta_tax\n,\n\n \ntrips\n.\ntip_amount\n,\n\n \ntrips\n.\ntolls_amount\n,\n\n \ntrips\n.\nehail_fee\n,\n\n \ntrips\n.\nimprovement_surcharge\n,\n\n \ntrips\n.\ntotal_amount\n,\n\n \ntrips\n.\npayment_type\n,\n\n \ntrips\n.\ntrip_type\n,\n\n \ntrips\n.\npickup\n,\n\n \ntrips\n.\ndropoff\n,\n\n\n \ncab_types\n.\ntype\n \ncab_type\n,\n\n\n \nweather\n.\nprecipitation_tenths_of_mm\n \nrain\n,\n\n \nweather\n.\nsnow_depth_mm\n,\n\n \nweather\n.\nsnowfall_mm\n,\n\n \nweather\n.\nmax_temperature_tenths_degrees_celsius\n \nmax_temp\n,\n\n \nweather\n.\nmin_temperature_tenths_degrees_celsius\n \nmin_temp\n,\n\n \nweather\n.\naverage_wind_speed_tenths_of_meters_per_second\n \nwind\n,\n\n\n \npick_up\n.\ngid\n \npickup_nyct2010_gid\n,\n\n \npick_up\n.\nctlabel\n \npickup_ctlabel\n,\n\n \npick_up\n.\nborocode\n \npickup_borocode\n,\n\n \npick_up\n.\nboroname\n \npickup_boroname\n,\n\n \npick_up\n.\nct2010\n \npickup_ct2010\n,\n\n \npick_up\n.\nboroct2010\n \npickup_boroct2010\n,\n\n \npick_up\n.\ncdeligibil\n \npickup_cdeligibil\n,\n\n \npick_up\n.\nntacode\n \npickup_ntacode\n,\n\n \npick_up\n.\nntaname\n \npickup_ntaname\n,\n\n \npick_up\n.\npuma\n \npickup_puma\n,\n\n\n \ndrop_off\n.\ngid\n \ndropoff_nyct2010_gid\n,\n\n \ndrop_off\n.\nctlabel\n \ndropoff_ctlabel\n,\n\n \ndrop_off\n.\nborocode\n \ndropoff_borocode\n,\n\n \ndrop_off\n.\nboroname\n \ndropoff_boroname\n,\n\n \ndrop_off\n.\nct2010\n \ndropoff_ct2010\n,\n\n \ndrop_off\n.\nboroct2010\n \ndropoff_boroct2010\n,\n\n \ndrop_off\n.\ncdeligibil\n \ndropoff_cdeligibil\n,\n\n \ndrop_off\n.\nntacode\n \ndropoff_ntacode\n,\n\n \ndrop_off\n.\nntaname\n \ndropoff_ntaname\n,\n\n \ndrop_off\n.\npuma\n \ndropoff_puma\n\n \nFROM\n \ntrips\n\n \nLEFT\n \nJOIN\n \ncab_types\n\n \nON\n \ntrips\n.\ncab_type_id\n \n=\n \ncab_types\n.\nid\n\n \nLEFT\n \nJOIN\n \ncentral_park_weather_observations_raw\n \nweather\n\n \nON\n \nweather\n.\ndate\n \n=\n \ntrips\n.\npickup_datetime\n::\ndate\n\n \nLEFT\n \nJOIN\n \nnyct2010\n \npick_up\n\n \nON\n \npick_up\n.\ngid\n \n=\n \ntrips\n.\npickup_nyct2010_gid\n\n \nLEFT\n \nJOIN\n \nnyct2010\n \ndrop_off\n\n \nON\n \ndrop_off\n.\ngid\n \n=\n \ntrips\n.\ndropoff_nyct2010_gid\n\n\n)\n \nTO\n \n/opt/milovidov/nyc-taxi-data/trips.tsv\n;\n\n\n\n\n\n\nThe data snapshot is created at a speed of about 50 MB per second. While creating the snapshot, PostgreSQL reads from the disk at a speed of about 28 MB per second.\nThis takes about 5 hours. The resulting TSV file is 590612904969 bytes.\n\n\nCreate a temporary table in ClickHouse:\n\n\nCREATE\n \nTABLE\n \ntrips\n\n\n(\n\n\ntrip_id\n \nUInt32\n,\n\n\nvendor_id\n \nString\n,\n\n\npickup_datetime\n \nDateTime\n,\n\n\ndropoff_datetime\n \nNullable\n(\nDateTime\n),\n\n\nstore_and_fwd_flag\n \nNullable\n(\nFixedString\n(\n1\n)),\n\n\nrate_code_id\n \nNullable\n(\nUInt8\n),\n\n\npickup_longitude\n \nNullable\n(\nFloat64\n),\n\n\npickup_latitude\n \nNullable\n(\nFloat64\n),\n\n\ndropoff_longitude\n \nNullable\n(\nFloat64\n),\n\n\ndropoff_latitude\n \nNullable\n(\nFloat64\n),\n\n\npassenger_count\n \nNullable\n(\nUInt8\n),\n\n\ntrip_distance\n \nNullable\n(\nFloat64\n),\n\n\nfare_amount\n \nNullable\n(\nFloat32\n),\n\n\nextra\n \nNullable\n(\nFloat32\n),\n\n\nmta_tax\n \nNullable\n(\nFloat32\n),\n\n\ntip_amount\n \nNullable\n(\nFloat32\n),\n\n\ntolls_amount\n \nNullable\n(\nFloat32\n),\n\n\nehail_fee\n \nNullable\n(\nFloat32\n),\n\n\nimprovement_surcharge\n \nNullable\n(\nFloat32\n),\n\n\ntotal_amount\n \nNullable\n(\nFloat32\n),\n\n\npayment_type\n \nNullable\n(\nString\n),\n\n\ntrip_type\n \nNullable\n(\nUInt8\n),\n\n\npickup\n \nNullable\n(\nString\n),\n\n\ndropoff\n \nNullable\n(\nString\n),\n\n\ncab_type\n \nNullable\n(\nString\n),\n\n\nprecipitation\n \nNullable\n(\nUInt8\n),\n\n\nsnow_depth\n \nNullable\n(\nUInt8\n),\n\n\nsnowfall\n \nNullable\n(\nUInt8\n),\n\n\nmax_temperature\n \nNullable\n(\nUInt8\n),\n\n\nmin_temperature\n \nNullable\n(\nUInt8\n),\n\n\naverage_wind_speed\n \nNullable\n(\nUInt8\n),\n\n\npickup_nyct2010_gid\n \nNullable\n(\nUInt8\n),\n\n\npickup_ctlabel\n \nNullable\n(\nString\n),\n\n\npickup_borocode\n \nNullable\n(\nUInt8\n),\n\n\npickup_boroname\n \nNullable\n(\nString\n),\n\n\npickup_ct2010\n \nNullable\n(\nString\n),\n\n\npickup_boroct2010\n \nNullable\n(\nString\n),\n\n\npickup_cdeligibil\n \nNullable\n(\nFixedString\n(\n1\n)),\n\n\npickup_ntacode\n \nNullable\n(\nString\n),\n\n\npickup_ntaname\n \nNullable\n(\nString\n),\n\n\npickup_puma\n \nNullable\n(\nString\n),\n\n\ndropoff_nyct2010_gid\n \nNullable\n(\nUInt8\n),\n\n\ndropoff_ctlabel\n \nNullable\n(\nString\n),\n\n\ndropoff_borocode\n \nNullable\n(\nUInt8\n),\n\n\ndropoff_boroname\n \nNullable\n(\nString\n),\n\n\ndropoff_ct2010\n \nNullable\n(\nString\n),\n\n\ndropoff_boroct2010\n \nNullable\n(\nString\n),\n\n\ndropoff_cdeligibil\n \nNullable\n(\nString\n),\n\n\ndropoff_ntacode\n \nNullable\n(\nString\n),\n\n\ndropoff_ntaname\n \nNullable\n(\nString\n),\n\n\ndropoff_puma\n \nNullable\n(\nString\n)\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\n\n\n\nIt is needed for converting fields to more correct data types and, if possible, to eliminate NULLs.\n\n\ntime clickhouse-client --query=\nINSERT INTO trips FORMAT TabSeparated\n \n trips.tsv\n\nreal 75m56.214s\n\n\n\n\n\nData is read at a speed of 112-140 Mb/second.\nLoading data into a Log type table in one stream took 76 minutes.\nThe data in this table uses 142 GB.\n\n\n(Importing data directly from Postgres is also possible using \nCOPY ... TO PROGRAM\n.)\n\n\nUnfortunately, all the fields associated with the weather (precipitation...average_wind_speed) were filled with NULL. Because of this, we will remove them from the final data set.\n\n\nTo start, we'll create a table on a single server. Later we will make the table distributed.\n\n\nCreate and populate a summary table:\n\n\nCREATE TABLE trips_mergetree\nENGINE = MergeTree(pickup_date, pickup_datetime, 8192)\nAS SELECT\n\ntrip_id,\nCAST(vendor_id AS Enum8(\n1\n = 1, \n2\n = 2, \nCMT\n = 3, \nVTS\n = 4, \nDDS\n = 5, \nB02512\n = 10, \nB02598\n = 11, \nB02617\n = 12, \nB02682\n = 13, \nB02764\n = 14)) AS vendor_id,\ntoDate(pickup_datetime) AS pickup_date,\nifNull(pickup_datetime, toDateTime(0)) AS pickup_datetime,\ntoDate(dropoff_datetime) AS dropoff_date,\nifNull(dropoff_datetime, toDateTime(0)) AS dropoff_datetime,\nassumeNotNull(store_and_fwd_flag) IN (\nY\n, \n1\n, \n2\n) AS store_and_fwd_flag,\nassumeNotNull(rate_code_id) AS rate_code_id,\nassumeNotNull(pickup_longitude) AS pickup_longitude,\nassumeNotNull(pickup_latitude) AS pickup_latitude,\nassumeNotNull(dropoff_longitude) AS dropoff_longitude,\nassumeNotNull(dropoff_latitude) AS dropoff_latitude,\nassumeNotNull(passenger_count) AS passenger_count,\nassumeNotNull(trip_distance) AS trip_distance,\nassumeNotNull(fare_amount) AS fare_amount,\nassumeNotNull(extra) AS extra,\nassumeNotNull(mta_tax) AS mta_tax,\nassumeNotNull(tip_amount) AS tip_amount,\nassumeNotNull(tolls_amount) AS tolls_amount,\nassumeNotNull(ehail_fee) AS ehail_fee,\nassumeNotNull(improvement_surcharge) AS improvement_surcharge,\nassumeNotNull(total_amount) AS total_amount,\nCAST((assumeNotNull(payment_type) AS pt) IN (\nCSH\n, \nCASH\n, \nCash\n, \nCAS\n, \nCas\n, \n1\n) ? \nCSH\n : (pt IN (\nCRD\n, \nCredit\n, \nCre\n, \nCRE\n, \nCREDIT\n, \n2\n) ? \nCRE\n : (pt IN (\nNOC\n, \nNo Charge\n, \nNo\n, \n3\n) ? \nNOC\n : (pt IN (\nDIS\n, \nDispute\n, \nDis\n, \n4\n) ? \nDIS\n : \nUNK\n))) AS Enum8(\nCSH\n = 1, \nCRE\n = 2, \nUNK\n = 0, \nNOC\n = 3, \nDIS\n = 4)) AS payment_type_,\nassumeNotNull(trip_type) AS trip_type,\nifNull(toFixedString(unhex(pickup), 25), toFixedString(\n, 25)) AS pickup,\nifNull(toFixedString(unhex(dropoff), 25), toFixedString(\n, 25)) AS dropoff,\nCAST(assumeNotNull(cab_type) AS Enum8(\nyellow\n = 1, \ngreen\n = 2, \nuber\n = 3)) AS cab_type,\n\nassumeNotNull(pickup_nyct2010_gid) AS pickup_nyct2010_gid,\ntoFloat32(ifNull(pickup_ctlabel, \n0\n)) AS pickup_ctlabel,\nassumeNotNull(pickup_borocode) AS pickup_borocode,\nCAST(assumeNotNull(pickup_boroname) AS Enum8(\nManhattan\n = 1, \nQueens\n = 4, \nBrooklyn\n = 3, \n = 0, \nBronx\n = 2, \nStaten Island\n = 5)) AS pickup_boroname,\ntoFixedString(ifNull(pickup_ct2010, \n000000\n), 6) AS pickup_ct2010,\ntoFixedString(ifNull(pickup_boroct2010, \n0000000\n), 7) AS pickup_boroct2010,\nCAST(assumeNotNull(ifNull(pickup_cdeligibil, \n \n)) AS Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2)) AS pickup_cdeligibil,\ntoFixedString(ifNull(pickup_ntacode, \n0000\n), 4) AS pickup_ntacode,\n\nCAST(assumeNotNull(pickup_ntaname) AS Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195)) AS pickup_ntaname,\n\ntoUInt16(ifNull(pickup_puma, \n0\n)) AS pickup_puma,\n\nassumeNotNull(dropoff_nyct2010_gid) AS dropoff_nyct2010_gid,\ntoFloat32(ifNull(dropoff_ctlabel, \n0\n)) AS dropoff_ctlabel,\nassumeNotNull(dropoff_borocode) AS dropoff_borocode,\nCAST(assumeNotNull(dropoff_boroname) AS Enum8(\nManhattan\n = 1, \nQueens\n = 4, \nBrooklyn\n = 3, \n = 0, \nBronx\n = 2, \nStaten Island\n = 5)) AS dropoff_boroname,\ntoFixedString(ifNull(dropoff_ct2010, \n000000\n), 6) AS dropoff_ct2010,\ntoFixedString(ifNull(dropoff_boroct2010, \n0000000\n), 7) AS dropoff_boroct2010,\nCAST(assumeNotNull(ifNull(dropoff_cdeligibil, \n \n)) AS Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2)) AS dropoff_cdeligibil,\ntoFixedString(ifNull(dropoff_ntacode, \n0000\n), 4) AS dropoff_ntacode,\n\nCAST(assumeNotNull(dropoff_ntaname) AS Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195)) AS dropoff_ntaname,\n\ntoUInt16(ifNull(dropoff_puma, \n0\n)) AS dropoff_puma\n\nFROM trips\n\n\n\n\n\nThis takes 3030 seconds at a speed of about 428,000 rows per second.\nTo load it faster, you can create the table with the \nLog\n engine instead of \nMergeTree\n. In this case, the download works faster than 200 seconds.\n\n\nThe table uses 126 GB of disk space.\n\n\n:) SELECT formatReadableSize(sum(bytes)) FROM system.parts WHERE table = \ntrips_mergetree\n AND active\n\nSELECT formatReadableSize(sum(bytes))\nFROM system.parts\nWHERE (table = \ntrips_mergetree\n) AND active\n\n\u250c\u2500formatReadableSize(sum(bytes))\u2500\u2510\n\u2502 126.18 GiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nAmong other things, you can run the OPTIMIZE query on MergeTree. But it's not required, since everything will be fine without it.\n\n\nResults on single server\n\n\nQ1:\n\n\nSELECT\n \ncab_type\n,\n \ncount\n(\n*\n)\n \nFROM\n \ntrips_mergetree\n \nGROUP\n \nBY\n \ncab_type\n\n\n\n\n\n\n0.490 seconds.\n\n\nQ2:\n\n\nSELECT\n \npassenger_count\n,\n \navg\n(\ntotal_amount\n)\n \nFROM\n \ntrips_mergetree\n \nGROUP\n \nBY\n \npassenger_count\n\n\n\n\n\n\n1.224 seconds.\n\n\nQ3:\n\n\nSELECT\n \npassenger_count\n,\n \ntoYear\n(\npickup_date\n)\n \nAS\n \nyear\n,\n \ncount\n(\n*\n)\n \nFROM\n \ntrips_mergetree\n \nGROUP\n \nBY\n \npassenger_count\n,\n \nyear\n\n\n\n\n\n\n2.104 seconds.\n\n\nQ4:\n\n\nSELECT\n \npassenger_count\n,\n \ntoYear\n(\npickup_date\n)\n \nAS\n \nyear\n,\n \nround\n(\ntrip_distance\n)\n \nAS\n \ndistance\n,\n \ncount\n(\n*\n)\n\n\nFROM\n \ntrips_mergetree\n\n\nGROUP\n \nBY\n \npassenger_count\n,\n \nyear\n,\n \ndistance\n\n\nORDER\n \nBY\n \nyear\n,\n \ncount\n(\n*\n)\n \nDESC\n\n\n\n\n\n\n3.593 seconds.\n\n\nThe following server was used:\n\n\nTwo Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical kernels total,\n128 GiB RAM,\n8x6 TB HD on hardware RAID-5\n\n\nExecution time is the best of three runsBut starting from the second run, queries read data from the file system cache. No further caching occurs: the data is read out and processed in each run.\n\n\nCreating a table on three servers:\n\n\nOn each server:\n\n\nCREATE TABLE default.trips_mergetree_third ( trip_id UInt32, vendor_id Enum8(\n1\n = 1, \n2\n = 2, \nCMT\n = 3, \nVTS\n = 4, \nDDS\n = 5, \nB02512\n = 10, \nB02598\n = 11, \nB02617\n = 12, \nB02682\n = 13, \nB02764\n = 14), pickup_date Date, pickup_datetime DateTime, dropoff_date Date, dropoff_datetime DateTime, store_and_fwd_flag UInt8, rate_code_id UInt8, pickup_longitude Float64, pickup_latitude Float64, dropoff_longitude Float64, dropoff_latitude Float64, passenger_count UInt8, trip_distance Float64, fare_amount Float32, extra Float32, mta_tax Float32, tip_amount Float32, tolls_amount Float32, ehail_fee Float32, improvement_surcharge Float32, total_amount Float32, payment_type_ Enum8(\nUNK\n = 0, \nCSH\n = 1, \nCRE\n = 2, \nNOC\n = 3, \nDIS\n = 4), trip_type UInt8, pickup FixedString(25), dropoff FixedString(25), cab_type Enum8(\nyellow\n = 1, \ngreen\n = 2, \nuber\n = 3), pickup_nyct2010_gid UInt8, pickup_ctlabel Float32, pickup_borocode UInt8, pickup_boroname Enum8(\n = 0, \nManhattan\n = 1, \nBronx\n = 2, \nBrooklyn\n = 3, \nQueens\n = 4, \nStaten Island\n = 5), pickup_ct2010 FixedString(6), pickup_boroct2010 FixedString(7), pickup_cdeligibil Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2), pickup_ntacode FixedString(4), pickup_ntaname Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195), pickup_puma UInt16, dropoff_nyct2010_gid UInt8, dropoff_ctlabel Float32, dropoff_borocode UInt8, dropoff_boroname Enum8(\n = 0, \nManhattan\n = 1, \nBronx\n = 2, \nBrooklyn\n = 3, \nQueens\n = 4, \nStaten Island\n = 5), dropoff_ct2010 FixedString(6), dropoff_boroct2010 FixedString(7), dropoff_cdeligibil Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2), dropoff_ntacode FixedString(4), dropoff_ntaname Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195), dropoff_puma UInt16) ENGINE = MergeTree(pickup_date, pickup_datetime, 8192)\n\n\n\n\n\nOn the source server:\n\n\nCREATE\n \nTABLE\n \ntrips_mergetree_x3\n \nAS\n \ntrips_mergetree_third\n \nENGINE\n \n=\n \nDistributed\n(\nperftest\n,\n \ndefault\n,\n \ntrips_mergetree_third\n,\n \nrand\n())\n\n\n\n\n\n\nThe following query redistributes data:\n\n\nINSERT\n \nINTO\n \ntrips_mergetree_x3\n \nSELECT\n \n*\n \nFROM\n \ntrips_mergetree\n\n\n\n\n\n\nThis takes 2454 seconds.\n\n\nOn three servers:\n\n\nQ1: 0.212 seconds.\nQ2: 0.438 seconds.\nQ3: 0.733 seconds.\nQ4: 1.241 seconds.\n\n\nNo surprises here, since the queries are scaled linearly.\n\n\nWe also have results from a cluster of 140 servers:\n\n\nQ1: 0.028 sec.\nQ2: 0.043 sec.\nQ3: 0.051 sec.\nQ4: 0.072 sec.\n\n\nIn this case, the query processing time is determined above all by network latency.\nWe ran queries using a client located in a Yandex datacenter in Finland on a cluster in Russia, which added about 20 ms of latency.\n\n\nSummary\n\n\nnodes Q1 Q2 Q3 Q4\n 1 0.490 1.224 2.104 3.593\n 3 0.212 0.438 0.733 1.241\n140 0.028 0.043 0.051 0.072", - "title": "New York Taxi data" - }, - { - "location": "/getting_started/example_datasets/nyc_taxi/#new-york-taxi-data", - "text": "", - "title": "New York Taxi data" - }, - { - "location": "/getting_started/example_datasets/nyc_taxi/#how-to-import-the-raw-data", - "text": "See https://github.com/toddwschneider/nyc-taxi-data and http://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html for the description of the dataset and instructions for downloading. Downloading will result in about 227 GB of uncompressed data in CSV files. The download takes about an hour over a 1 Gbit connection (parallel downloading from s3.amazonaws.com recovers at least half of a 1 Gbit channel).\nSome of the files might not download fully. Check the file sizes and re-download any that seem doubtful. Some of the files might contain invalid rows. You can fix them as follows: sed -E /(.*,){18,}/d data/yellow_tripdata_2010-02.csv data/yellow_tripdata_2010-02.csv_\nsed -E /(.*,){18,}/d data/yellow_tripdata_2010-03.csv data/yellow_tripdata_2010-03.csv_\nmv data/yellow_tripdata_2010-02.csv_ data/yellow_tripdata_2010-02.csv\nmv data/yellow_tripdata_2010-03.csv_ data/yellow_tripdata_2010-03.csv Then the data must be pre-processed in PostgreSQL. This will create selections of points in the polygons (to match points on the map with the boroughs of New York City) and combine all the data into a single denormalized flat table by using a JOIN. To do this, you will need to install PostgreSQL with PostGIS support. Be careful when running initialize_database.sh and manually re-check that all the tables were created correctly. It takes about 20-30 minutes to process each month's worth of data in PostgreSQL, for a total of about 48 hours. You can check the number of downloaded rows as follows: time psql nyc-taxi-data -c SELECT count(*) FROM trips; \n## count\n 1298979494\n(1 row)\n\nreal 7m9.164s (This is slightly more than 1.1 billion rows reported by Mark Litwintschik in a series of blog posts.) The data in PostgreSQL uses 370 GB of space. Exporting the data from PostgreSQL: COPY ( \n SELECT trips . id , \n trips . vendor_id , \n trips . pickup_datetime , \n trips . dropoff_datetime , \n trips . store_and_fwd_flag , \n trips . rate_code_id , \n trips . pickup_longitude , \n trips . pickup_latitude , \n trips . dropoff_longitude , \n trips . dropoff_latitude , \n trips . passenger_count , \n trips . trip_distance , \n trips . fare_amount , \n trips . extra , \n trips . mta_tax , \n trips . tip_amount , \n trips . tolls_amount , \n trips . ehail_fee , \n trips . improvement_surcharge , \n trips . total_amount , \n trips . payment_type , \n trips . trip_type , \n trips . pickup , \n trips . dropoff , \n\n cab_types . type cab_type , \n\n weather . precipitation_tenths_of_mm rain , \n weather . snow_depth_mm , \n weather . snowfall_mm , \n weather . max_temperature_tenths_degrees_celsius max_temp , \n weather . min_temperature_tenths_degrees_celsius min_temp , \n weather . average_wind_speed_tenths_of_meters_per_second wind , \n\n pick_up . gid pickup_nyct2010_gid , \n pick_up . ctlabel pickup_ctlabel , \n pick_up . borocode pickup_borocode , \n pick_up . boroname pickup_boroname , \n pick_up . ct2010 pickup_ct2010 , \n pick_up . boroct2010 pickup_boroct2010 , \n pick_up . cdeligibil pickup_cdeligibil , \n pick_up . ntacode pickup_ntacode , \n pick_up . ntaname pickup_ntaname , \n pick_up . puma pickup_puma , \n\n drop_off . gid dropoff_nyct2010_gid , \n drop_off . ctlabel dropoff_ctlabel , \n drop_off . borocode dropoff_borocode , \n drop_off . boroname dropoff_boroname , \n drop_off . ct2010 dropoff_ct2010 , \n drop_off . boroct2010 dropoff_boroct2010 , \n drop_off . cdeligibil dropoff_cdeligibil , \n drop_off . ntacode dropoff_ntacode , \n drop_off . ntaname dropoff_ntaname , \n drop_off . puma dropoff_puma \n FROM trips \n LEFT JOIN cab_types \n ON trips . cab_type_id = cab_types . id \n LEFT JOIN central_park_weather_observations_raw weather \n ON weather . date = trips . pickup_datetime :: date \n LEFT JOIN nyct2010 pick_up \n ON pick_up . gid = trips . pickup_nyct2010_gid \n LEFT JOIN nyct2010 drop_off \n ON drop_off . gid = trips . dropoff_nyct2010_gid ) TO /opt/milovidov/nyc-taxi-data/trips.tsv ; The data snapshot is created at a speed of about 50 MB per second. While creating the snapshot, PostgreSQL reads from the disk at a speed of about 28 MB per second.\nThis takes about 5 hours. The resulting TSV file is 590612904969 bytes. Create a temporary table in ClickHouse: CREATE TABLE trips ( trip_id UInt32 , vendor_id String , pickup_datetime DateTime , dropoff_datetime Nullable ( DateTime ), store_and_fwd_flag Nullable ( FixedString ( 1 )), rate_code_id Nullable ( UInt8 ), pickup_longitude Nullable ( Float64 ), pickup_latitude Nullable ( Float64 ), dropoff_longitude Nullable ( Float64 ), dropoff_latitude Nullable ( Float64 ), passenger_count Nullable ( UInt8 ), trip_distance Nullable ( Float64 ), fare_amount Nullable ( Float32 ), extra Nullable ( Float32 ), mta_tax Nullable ( Float32 ), tip_amount Nullable ( Float32 ), tolls_amount Nullable ( Float32 ), ehail_fee Nullable ( Float32 ), improvement_surcharge Nullable ( Float32 ), total_amount Nullable ( Float32 ), payment_type Nullable ( String ), trip_type Nullable ( UInt8 ), pickup Nullable ( String ), dropoff Nullable ( String ), cab_type Nullable ( String ), precipitation Nullable ( UInt8 ), snow_depth Nullable ( UInt8 ), snowfall Nullable ( UInt8 ), max_temperature Nullable ( UInt8 ), min_temperature Nullable ( UInt8 ), average_wind_speed Nullable ( UInt8 ), pickup_nyct2010_gid Nullable ( UInt8 ), pickup_ctlabel Nullable ( String ), pickup_borocode Nullable ( UInt8 ), pickup_boroname Nullable ( String ), pickup_ct2010 Nullable ( String ), pickup_boroct2010 Nullable ( String ), pickup_cdeligibil Nullable ( FixedString ( 1 )), pickup_ntacode Nullable ( String ), pickup_ntaname Nullable ( String ), pickup_puma Nullable ( String ), dropoff_nyct2010_gid Nullable ( UInt8 ), dropoff_ctlabel Nullable ( String ), dropoff_borocode Nullable ( UInt8 ), dropoff_boroname Nullable ( String ), dropoff_ct2010 Nullable ( String ), dropoff_boroct2010 Nullable ( String ), dropoff_cdeligibil Nullable ( String ), dropoff_ntacode Nullable ( String ), dropoff_ntaname Nullable ( String ), dropoff_puma Nullable ( String ) ) ENGINE = Log ; It is needed for converting fields to more correct data types and, if possible, to eliminate NULLs. time clickhouse-client --query= INSERT INTO trips FORMAT TabSeparated trips.tsv\n\nreal 75m56.214s Data is read at a speed of 112-140 Mb/second.\nLoading data into a Log type table in one stream took 76 minutes.\nThe data in this table uses 142 GB. (Importing data directly from Postgres is also possible using COPY ... TO PROGRAM .) Unfortunately, all the fields associated with the weather (precipitation...average_wind_speed) were filled with NULL. Because of this, we will remove them from the final data set. To start, we'll create a table on a single server. Later we will make the table distributed. Create and populate a summary table: CREATE TABLE trips_mergetree\nENGINE = MergeTree(pickup_date, pickup_datetime, 8192)\nAS SELECT\n\ntrip_id,\nCAST(vendor_id AS Enum8( 1 = 1, 2 = 2, CMT = 3, VTS = 4, DDS = 5, B02512 = 10, B02598 = 11, B02617 = 12, B02682 = 13, B02764 = 14)) AS vendor_id,\ntoDate(pickup_datetime) AS pickup_date,\nifNull(pickup_datetime, toDateTime(0)) AS pickup_datetime,\ntoDate(dropoff_datetime) AS dropoff_date,\nifNull(dropoff_datetime, toDateTime(0)) AS dropoff_datetime,\nassumeNotNull(store_and_fwd_flag) IN ( Y , 1 , 2 ) AS store_and_fwd_flag,\nassumeNotNull(rate_code_id) AS rate_code_id,\nassumeNotNull(pickup_longitude) AS pickup_longitude,\nassumeNotNull(pickup_latitude) AS pickup_latitude,\nassumeNotNull(dropoff_longitude) AS dropoff_longitude,\nassumeNotNull(dropoff_latitude) AS dropoff_latitude,\nassumeNotNull(passenger_count) AS passenger_count,\nassumeNotNull(trip_distance) AS trip_distance,\nassumeNotNull(fare_amount) AS fare_amount,\nassumeNotNull(extra) AS extra,\nassumeNotNull(mta_tax) AS mta_tax,\nassumeNotNull(tip_amount) AS tip_amount,\nassumeNotNull(tolls_amount) AS tolls_amount,\nassumeNotNull(ehail_fee) AS ehail_fee,\nassumeNotNull(improvement_surcharge) AS improvement_surcharge,\nassumeNotNull(total_amount) AS total_amount,\nCAST((assumeNotNull(payment_type) AS pt) IN ( CSH , CASH , Cash , CAS , Cas , 1 ) ? CSH : (pt IN ( CRD , Credit , Cre , CRE , CREDIT , 2 ) ? CRE : (pt IN ( NOC , No Charge , No , 3 ) ? NOC : (pt IN ( DIS , Dispute , Dis , 4 ) ? DIS : UNK ))) AS Enum8( CSH = 1, CRE = 2, UNK = 0, NOC = 3, DIS = 4)) AS payment_type_,\nassumeNotNull(trip_type) AS trip_type,\nifNull(toFixedString(unhex(pickup), 25), toFixedString( , 25)) AS pickup,\nifNull(toFixedString(unhex(dropoff), 25), toFixedString( , 25)) AS dropoff,\nCAST(assumeNotNull(cab_type) AS Enum8( yellow = 1, green = 2, uber = 3)) AS cab_type,\n\nassumeNotNull(pickup_nyct2010_gid) AS pickup_nyct2010_gid,\ntoFloat32(ifNull(pickup_ctlabel, 0 )) AS pickup_ctlabel,\nassumeNotNull(pickup_borocode) AS pickup_borocode,\nCAST(assumeNotNull(pickup_boroname) AS Enum8( Manhattan = 1, Queens = 4, Brooklyn = 3, = 0, Bronx = 2, Staten Island = 5)) AS pickup_boroname,\ntoFixedString(ifNull(pickup_ct2010, 000000 ), 6) AS pickup_ct2010,\ntoFixedString(ifNull(pickup_boroct2010, 0000000 ), 7) AS pickup_boroct2010,\nCAST(assumeNotNull(ifNull(pickup_cdeligibil, )) AS Enum8( = 0, E = 1, I = 2)) AS pickup_cdeligibil,\ntoFixedString(ifNull(pickup_ntacode, 0000 ), 4) AS pickup_ntacode,\n\nCAST(assumeNotNull(pickup_ntaname) AS Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195)) AS pickup_ntaname,\n\ntoUInt16(ifNull(pickup_puma, 0 )) AS pickup_puma,\n\nassumeNotNull(dropoff_nyct2010_gid) AS dropoff_nyct2010_gid,\ntoFloat32(ifNull(dropoff_ctlabel, 0 )) AS dropoff_ctlabel,\nassumeNotNull(dropoff_borocode) AS dropoff_borocode,\nCAST(assumeNotNull(dropoff_boroname) AS Enum8( Manhattan = 1, Queens = 4, Brooklyn = 3, = 0, Bronx = 2, Staten Island = 5)) AS dropoff_boroname,\ntoFixedString(ifNull(dropoff_ct2010, 000000 ), 6) AS dropoff_ct2010,\ntoFixedString(ifNull(dropoff_boroct2010, 0000000 ), 7) AS dropoff_boroct2010,\nCAST(assumeNotNull(ifNull(dropoff_cdeligibil, )) AS Enum8( = 0, E = 1, I = 2)) AS dropoff_cdeligibil,\ntoFixedString(ifNull(dropoff_ntacode, 0000 ), 4) AS dropoff_ntacode,\n\nCAST(assumeNotNull(dropoff_ntaname) AS Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195)) AS dropoff_ntaname,\n\ntoUInt16(ifNull(dropoff_puma, 0 )) AS dropoff_puma\n\nFROM trips This takes 3030 seconds at a speed of about 428,000 rows per second.\nTo load it faster, you can create the table with the Log engine instead of MergeTree . In this case, the download works faster than 200 seconds. The table uses 126 GB of disk space. :) SELECT formatReadableSize(sum(bytes)) FROM system.parts WHERE table = trips_mergetree AND active\n\nSELECT formatReadableSize(sum(bytes))\nFROM system.parts\nWHERE (table = trips_mergetree ) AND active\n\n\u250c\u2500formatReadableSize(sum(bytes))\u2500\u2510\n\u2502 126.18 GiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Among other things, you can run the OPTIMIZE query on MergeTree. But it's not required, since everything will be fine without it.", - "title": "How to import the raw data" - }, - { - "location": "/getting_started/example_datasets/nyc_taxi/#results-on-single-server", - "text": "Q1: SELECT cab_type , count ( * ) FROM trips_mergetree GROUP BY cab_type 0.490 seconds. Q2: SELECT passenger_count , avg ( total_amount ) FROM trips_mergetree GROUP BY passenger_count 1.224 seconds. Q3: SELECT passenger_count , toYear ( pickup_date ) AS year , count ( * ) FROM trips_mergetree GROUP BY passenger_count , year 2.104 seconds. Q4: SELECT passenger_count , toYear ( pickup_date ) AS year , round ( trip_distance ) AS distance , count ( * ) FROM trips_mergetree GROUP BY passenger_count , year , distance ORDER BY year , count ( * ) DESC 3.593 seconds. The following server was used: Two Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical kernels total,\n128 GiB RAM,\n8x6 TB HD on hardware RAID-5 Execution time is the best of three runsBut starting from the second run, queries read data from the file system cache. No further caching occurs: the data is read out and processed in each run. Creating a table on three servers: On each server: CREATE TABLE default.trips_mergetree_third ( trip_id UInt32, vendor_id Enum8( 1 = 1, 2 = 2, CMT = 3, VTS = 4, DDS = 5, B02512 = 10, B02598 = 11, B02617 = 12, B02682 = 13, B02764 = 14), pickup_date Date, pickup_datetime DateTime, dropoff_date Date, dropoff_datetime DateTime, store_and_fwd_flag UInt8, rate_code_id UInt8, pickup_longitude Float64, pickup_latitude Float64, dropoff_longitude Float64, dropoff_latitude Float64, passenger_count UInt8, trip_distance Float64, fare_amount Float32, extra Float32, mta_tax Float32, tip_amount Float32, tolls_amount Float32, ehail_fee Float32, improvement_surcharge Float32, total_amount Float32, payment_type_ Enum8( UNK = 0, CSH = 1, CRE = 2, NOC = 3, DIS = 4), trip_type UInt8, pickup FixedString(25), dropoff FixedString(25), cab_type Enum8( yellow = 1, green = 2, uber = 3), pickup_nyct2010_gid UInt8, pickup_ctlabel Float32, pickup_borocode UInt8, pickup_boroname Enum8( = 0, Manhattan = 1, Bronx = 2, Brooklyn = 3, Queens = 4, Staten Island = 5), pickup_ct2010 FixedString(6), pickup_boroct2010 FixedString(7), pickup_cdeligibil Enum8( = 0, E = 1, I = 2), pickup_ntacode FixedString(4), pickup_ntaname Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195), pickup_puma UInt16, dropoff_nyct2010_gid UInt8, dropoff_ctlabel Float32, dropoff_borocode UInt8, dropoff_boroname Enum8( = 0, Manhattan = 1, Bronx = 2, Brooklyn = 3, Queens = 4, Staten Island = 5), dropoff_ct2010 FixedString(6), dropoff_boroct2010 FixedString(7), dropoff_cdeligibil Enum8( = 0, E = 1, I = 2), dropoff_ntacode FixedString(4), dropoff_ntaname Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195), dropoff_puma UInt16) ENGINE = MergeTree(pickup_date, pickup_datetime, 8192) On the source server: CREATE TABLE trips_mergetree_x3 AS trips_mergetree_third ENGINE = Distributed ( perftest , default , trips_mergetree_third , rand ()) The following query redistributes data: INSERT INTO trips_mergetree_x3 SELECT * FROM trips_mergetree This takes 2454 seconds. On three servers: Q1: 0.212 seconds.\nQ2: 0.438 seconds.\nQ3: 0.733 seconds.\nQ4: 1.241 seconds. No surprises here, since the queries are scaled linearly. We also have results from a cluster of 140 servers: Q1: 0.028 sec.\nQ2: 0.043 sec.\nQ3: 0.051 sec.\nQ4: 0.072 sec. In this case, the query processing time is determined above all by network latency.\nWe ran queries using a client located in a Yandex datacenter in Finland on a cluster in Russia, which added about 20 ms of latency.", - "title": "Results on single server" - }, - { - "location": "/getting_started/example_datasets/nyc_taxi/#summary", - "text": "nodes Q1 Q2 Q3 Q4\n 1 0.490 1.224 2.104 3.593\n 3 0.212 0.438 0.733 1.241\n140 0.028 0.043 0.051 0.072", - "title": "Summary" - }, - { - "location": "/getting_started/example_datasets/amplab_benchmark/", - "text": "AMPLab Big Data Benchmark\n\n\nSee \nhttps://amplab.cs.berkeley.edu/benchmark/\n\n\nSign up for a free account at \nhttps://aws.amazon.com\n. You will need a credit card, email and phone number.Get a new access key at \nhttps://console.aws.amazon.com/iam/home?nc2=h_m_sc#security_credential\n\n\nRun the following in the console:\n\n\nsudo apt-get install s3cmd\nmkdir tiny\n;\n \ncd\n tiny\n;\n\ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/tiny/ .\n\ncd\n ..\nmkdir 1node\n;\n \ncd\n 1node\n;\n\ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/1node/ .\n\ncd\n ..\nmkdir 5nodes\n;\n \ncd\n 5nodes\n;\n\ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/5nodes/ .\n\ncd\n ..\n\n\n\n\n\nRun the following ClickHouse queries:\n\n\nCREATE\n \nTABLE\n \nrankings_tiny\n\n\n(\n\n \npageURL\n \nString\n,\n\n \npageRank\n \nUInt32\n,\n\n \navgDuration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\nCREATE\n \nTABLE\n \nuservisits_tiny\n\n\n(\n\n \nsourceIP\n \nString\n,\n\n \ndestinationURL\n \nString\n,\n\n \nvisitDate\n \nDate\n,\n\n \nadRevenue\n \nFloat32\n,\n\n \nUserAgent\n \nString\n,\n\n \ncCode\n \nFixedString\n(\n3\n),\n\n \nlCode\n \nFixedString\n(\n6\n),\n\n \nsearchWord\n \nString\n,\n\n \nduration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nvisitDate\n,\n \nvisitDate\n,\n \n8192\n);\n\n\n\nCREATE\n \nTABLE\n \nrankings_1node\n\n\n(\n\n \npageURL\n \nString\n,\n\n \npageRank\n \nUInt32\n,\n\n \navgDuration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\nCREATE\n \nTABLE\n \nuservisits_1node\n\n\n(\n\n \nsourceIP\n \nString\n,\n\n \ndestinationURL\n \nString\n,\n\n \nvisitDate\n \nDate\n,\n\n \nadRevenue\n \nFloat32\n,\n\n \nUserAgent\n \nString\n,\n\n \ncCode\n \nFixedString\n(\n3\n),\n\n \nlCode\n \nFixedString\n(\n6\n),\n\n \nsearchWord\n \nString\n,\n\n \nduration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nvisitDate\n,\n \nvisitDate\n,\n \n8192\n);\n\n\n\nCREATE\n \nTABLE\n \nrankings_5nodes_on_single\n\n\n(\n\n \npageURL\n \nString\n,\n\n \npageRank\n \nUInt32\n,\n\n \navgDuration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\nCREATE\n \nTABLE\n \nuservisits_5nodes_on_single\n\n\n(\n\n \nsourceIP\n \nString\n,\n\n \ndestinationURL\n \nString\n,\n\n \nvisitDate\n \nDate\n,\n\n \nadRevenue\n \nFloat32\n,\n\n \nUserAgent\n \nString\n,\n\n \ncCode\n \nFixedString\n(\n3\n),\n\n \nlCode\n \nFixedString\n(\n6\n),\n\n \nsearchWord\n \nString\n,\n\n \nduration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nvisitDate\n,\n \nvisitDate\n,\n \n8192\n);\n\n\n\n\n\n\nGo back to the console:\n\n\nfor\n i in tiny/rankings/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO rankings_tiny FORMAT CSV\n;\n \ndone\n\n\nfor\n i in tiny/uservisits/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO uservisits_tiny FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 1node/rankings/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO rankings_1node FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 1node/uservisits/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO uservisits_1node FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 5nodes/rankings/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO rankings_5nodes_on_single FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 5nodes/uservisits/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO uservisits_5nodes_on_single FORMAT CSV\n;\n \ndone\n\n\n\n\n\n\nQueries for obtaining data samples:\n\n\nSELECT\n \npageURL\n,\n \npageRank\n \nFROM\n \nrankings_1node\n \nWHERE\n \npageRank\n \n \n1000\n\n\n\nSELECT\n \nsubstring\n(\nsourceIP\n,\n \n1\n,\n \n8\n),\n \nsum\n(\nadRevenue\n)\n \nFROM\n \nuservisits_1node\n \nGROUP\n \nBY\n \nsubstring\n(\nsourceIP\n,\n \n1\n,\n \n8\n)\n\n\n\nSELECT\n\n \nsourceIP\n,\n\n \nsum\n(\nadRevenue\n)\n \nAS\n \ntotalRevenue\n,\n\n \navg\n(\npageRank\n)\n \nAS\n \npageRank\n\n\nFROM\n \nrankings_1node\n \nALL\n \nINNER\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nsourceIP\n,\n\n \ndestinationURL\n \nAS\n \npageURL\n,\n\n \nadRevenue\n\n \nFROM\n \nuservisits_1node\n\n \nWHERE\n \n(\nvisitDate\n \n \n1980-01-01\n)\n \nAND\n \n(\nvisitDate\n \n \n1980-04-01\n)\n\n\n)\n \nUSING\n \npageURL\n\n\nGROUP\n \nBY\n \nsourceIP\n\n\nORDER\n \nBY\n \ntotalRevenue\n \nDESC\n\n\nLIMIT\n \n1", - "title": "AMPLab Big Data Benchmark" - }, - { - "location": "/getting_started/example_datasets/amplab_benchmark/#amplab-big-data-benchmark", - "text": "See https://amplab.cs.berkeley.edu/benchmark/ Sign up for a free account at https://aws.amazon.com . You will need a credit card, email and phone number.Get a new access key at https://console.aws.amazon.com/iam/home?nc2=h_m_sc#security_credential Run the following in the console: sudo apt-get install s3cmd\nmkdir tiny ; cd tiny ; \ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/tiny/ . cd ..\nmkdir 1node ; cd 1node ; \ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/1node/ . cd ..\nmkdir 5nodes ; cd 5nodes ; \ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/5nodes/ . cd .. Run the following ClickHouse queries: CREATE TABLE rankings_tiny ( \n pageURL String , \n pageRank UInt32 , \n avgDuration UInt32 ) ENGINE = Log ; CREATE TABLE uservisits_tiny ( \n sourceIP String , \n destinationURL String , \n visitDate Date , \n adRevenue Float32 , \n UserAgent String , \n cCode FixedString ( 3 ), \n lCode FixedString ( 6 ), \n searchWord String , \n duration UInt32 ) ENGINE = MergeTree ( visitDate , visitDate , 8192 ); CREATE TABLE rankings_1node ( \n pageURL String , \n pageRank UInt32 , \n avgDuration UInt32 ) ENGINE = Log ; CREATE TABLE uservisits_1node ( \n sourceIP String , \n destinationURL String , \n visitDate Date , \n adRevenue Float32 , \n UserAgent String , \n cCode FixedString ( 3 ), \n lCode FixedString ( 6 ), \n searchWord String , \n duration UInt32 ) ENGINE = MergeTree ( visitDate , visitDate , 8192 ); CREATE TABLE rankings_5nodes_on_single ( \n pageURL String , \n pageRank UInt32 , \n avgDuration UInt32 ) ENGINE = Log ; CREATE TABLE uservisits_5nodes_on_single ( \n sourceIP String , \n destinationURL String , \n visitDate Date , \n adRevenue Float32 , \n UserAgent String , \n cCode FixedString ( 3 ), \n lCode FixedString ( 6 ), \n searchWord String , \n duration UInt32 ) ENGINE = MergeTree ( visitDate , visitDate , 8192 ); Go back to the console: for i in tiny/rankings/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO rankings_tiny FORMAT CSV ; done for i in tiny/uservisits/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO uservisits_tiny FORMAT CSV ; done for i in 1node/rankings/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO rankings_1node FORMAT CSV ; done for i in 1node/uservisits/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO uservisits_1node FORMAT CSV ; done for i in 5nodes/rankings/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO rankings_5nodes_on_single FORMAT CSV ; done for i in 5nodes/uservisits/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO uservisits_5nodes_on_single FORMAT CSV ; done Queries for obtaining data samples: SELECT pageURL , pageRank FROM rankings_1node WHERE pageRank 1000 SELECT substring ( sourceIP , 1 , 8 ), sum ( adRevenue ) FROM uservisits_1node GROUP BY substring ( sourceIP , 1 , 8 ) SELECT \n sourceIP , \n sum ( adRevenue ) AS totalRevenue , \n avg ( pageRank ) AS pageRank FROM rankings_1node ALL INNER JOIN ( \n SELECT \n sourceIP , \n destinationURL AS pageURL , \n adRevenue \n FROM uservisits_1node \n WHERE ( visitDate 1980-01-01 ) AND ( visitDate 1980-04-01 ) ) USING pageURL GROUP BY sourceIP ORDER BY totalRevenue DESC LIMIT 1", - "title": "AMPLab Big Data Benchmark" - }, - { - "location": "/getting_started/example_datasets/wikistat/", - "text": "WikiStat\n\n\nSee: \nhttp://dumps.wikimedia.org/other/pagecounts-raw/\n\n\nCreating a table:\n\n\nCREATE\n \nTABLE\n \nwikistat\n\n\n(\n\n \ndate\n \nDate\n,\n\n \ntime\n \nDateTime\n,\n\n \nproject\n \nString\n,\n\n \nsubproject\n \nString\n,\n\n \npath\n \nString\n,\n\n \nhits\n \nUInt64\n,\n\n \nsize\n \nUInt64\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\ndate\n,\n \n(\npath\n,\n \ntime\n),\n \n8192\n);\n\n\n\n\n\n\nLoading data:\n\n\nfor\n i in \n{\n2007\n..2016\n}\n;\n \ndo\n \nfor\n j in \n{\n01\n..12\n}\n;\n \ndo\n \necho\n \n$i\n-\n$j\n \n2\n;\n curl -sSL \nhttp://dumps.wikimedia.org/other/pagecounts-raw/\n$i\n/\n$i\n-\n$j\n/\n \n|\n grep -oE \npagecounts-[0-9]+-[0-9]+\\.gz\n;\n \ndone\n;\n \ndone\n \n|\n sort \n|\n uniq \n|\n tee links.txt\ncat links.txt \n|\n \nwhile\n \nread\n link\n;\n \ndo\n wget http://dumps.wikimedia.org/other/pagecounts-raw/\n$(\necho\n \n$link\n \n|\n sed -r \ns/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1/\n)\n/\n$(\necho\n \n$link\n \n|\n sed -r \ns/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1-\\2/\n)\n/\n$link\n;\n \ndone\n\nls -1 /opt/wikistat/ \n|\n grep gz \n|\n \nwhile\n \nread\n i\n;\n \ndo\n \necho\n \n$i\n;\n gzip -cd /opt/wikistat/\n$i\n \n|\n ./wikistat-loader --time\n=\n$(\necho\n -n \n$i\n \n|\n sed -r \ns/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\\.gz/\\1-\\2-\\3 \\4-00-00/\n)\n \n|\n clickhouse-client --query\n=\nINSERT INTO wikistat FORMAT TabSeparated\n;\n \ndone", - "title": "WikiStat" - }, - { - "location": "/getting_started/example_datasets/wikistat/#wikistat", - "text": "See: http://dumps.wikimedia.org/other/pagecounts-raw/ Creating a table: CREATE TABLE wikistat ( \n date Date , \n time DateTime , \n project String , \n subproject String , \n path String , \n hits UInt64 , \n size UInt64 ) ENGINE = MergeTree ( date , ( path , time ), 8192 ); Loading data: for i in { 2007 ..2016 } ; do for j in { 01 ..12 } ; do echo $i - $j 2 ; curl -sSL http://dumps.wikimedia.org/other/pagecounts-raw/ $i / $i - $j / | grep -oE pagecounts-[0-9]+-[0-9]+\\.gz ; done ; done | sort | uniq | tee links.txt\ncat links.txt | while read link ; do wget http://dumps.wikimedia.org/other/pagecounts-raw/ $( echo $link | sed -r s/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1/ ) / $( echo $link | sed -r s/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1-\\2/ ) / $link ; done \nls -1 /opt/wikistat/ | grep gz | while read i ; do echo $i ; gzip -cd /opt/wikistat/ $i | ./wikistat-loader --time = $( echo -n $i | sed -r s/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\\.gz/\\1-\\2-\\3 \\4-00-00/ ) | clickhouse-client --query = INSERT INTO wikistat FORMAT TabSeparated ; done", - "title": "WikiStat" - }, - { - "location": "/getting_started/example_datasets/criteo/", - "text": "Terabyte of click logs from Criteo\n\n\nDownload the data from \nhttp://labs.criteo.com/downloads/download-terabyte-click-logs/\n\n\nCreate a table to import the log to:\n\n\nCREATE\n \nTABLE\n \ncriteo_log\n \n(\ndate\n \nDate\n,\n \nclicked\n \nUInt8\n,\n \nint1\n \nInt32\n,\n \nint2\n \nInt32\n,\n \nint3\n \nInt32\n,\n \nint4\n \nInt32\n,\n \nint5\n \nInt32\n,\n \nint6\n \nInt32\n,\n \nint7\n \nInt32\n,\n \nint8\n \nInt32\n,\n \nint9\n \nInt32\n,\n \nint10\n \nInt32\n,\n \nint11\n \nInt32\n,\n \nint12\n \nInt32\n,\n \nint13\n \nInt32\n,\n \ncat1\n \nString\n,\n \ncat2\n \nString\n,\n \ncat3\n \nString\n,\n \ncat4\n \nString\n,\n \ncat5\n \nString\n,\n \ncat6\n \nString\n,\n \ncat7\n \nString\n,\n \ncat8\n \nString\n,\n \ncat9\n \nString\n,\n \ncat10\n \nString\n,\n \ncat11\n \nString\n,\n \ncat12\n \nString\n,\n \ncat13\n \nString\n,\n \ncat14\n \nString\n,\n \ncat15\n \nString\n,\n \ncat16\n \nString\n,\n \ncat17\n \nString\n,\n \ncat18\n \nString\n,\n \ncat19\n \nString\n,\n \ncat20\n \nString\n,\n \ncat21\n \nString\n,\n \ncat22\n \nString\n,\n \ncat23\n \nString\n,\n \ncat24\n \nString\n,\n \ncat25\n \nString\n,\n \ncat26\n \nString\n)\n \nENGINE\n \n=\n \nLog\n\n\n\n\n\n\nDownload the data:\n\n\nfor\n i in \n{\n00\n..23\n}\n;\n \ndo\n \necho\n \n$i\n;\n zcat datasets/criteo/day_\n${\ni\n#0\n}\n.gz \n|\n sed -r \ns/^/2000-01-\n${\ni\n/00/24\n}\n\\t/\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO criteo_log FORMAT TabSeparated\n;\n \ndone\n\n\n\n\n\n\nCreate a table for the converted data:\n\n\nCREATE\n \nTABLE\n \ncriteo\n\n\n(\n\n \ndate\n \nDate\n,\n\n \nclicked\n \nUInt8\n,\n\n \nint1\n \nInt32\n,\n\n \nint2\n \nInt32\n,\n\n \nint3\n \nInt32\n,\n\n \nint4\n \nInt32\n,\n\n \nint5\n \nInt32\n,\n\n \nint6\n \nInt32\n,\n\n \nint7\n \nInt32\n,\n\n \nint8\n \nInt32\n,\n\n \nint9\n \nInt32\n,\n\n \nint10\n \nInt32\n,\n\n \nint11\n \nInt32\n,\n\n \nint12\n \nInt32\n,\n\n \nint13\n \nInt32\n,\n\n \nicat1\n \nUInt32\n,\n\n \nicat2\n \nUInt32\n,\n\n \nicat3\n \nUInt32\n,\n\n \nicat4\n \nUInt32\n,\n\n \nicat5\n \nUInt32\n,\n\n \nicat6\n \nUInt32\n,\n\n \nicat7\n \nUInt32\n,\n\n \nicat8\n \nUInt32\n,\n\n \nicat9\n \nUInt32\n,\n\n \nicat10\n \nUInt32\n,\n\n \nicat11\n \nUInt32\n,\n\n \nicat12\n \nUInt32\n,\n\n \nicat13\n \nUInt32\n,\n\n \nicat14\n \nUInt32\n,\n\n \nicat15\n \nUInt32\n,\n\n \nicat16\n \nUInt32\n,\n\n \nicat17\n \nUInt32\n,\n\n \nicat18\n \nUInt32\n,\n\n \nicat19\n \nUInt32\n,\n\n \nicat20\n \nUInt32\n,\n\n \nicat21\n \nUInt32\n,\n\n \nicat22\n \nUInt32\n,\n\n \nicat23\n \nUInt32\n,\n\n \nicat24\n \nUInt32\n,\n\n \nicat25\n \nUInt32\n,\n\n \nicat26\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\ndate\n,\n \nintHash32\n(\nicat1\n),\n \n(\ndate\n,\n \nintHash32\n(\nicat1\n)),\n \n8192\n)\n\n\n\n\n\n\nTransform data from the raw log and put it in the second table:\n\n\nINSERT\n \nINTO\n \ncriteo\n \nSELECT\n \ndate\n,\n \nclicked\n,\n \nint1\n,\n \nint2\n,\n \nint3\n,\n \nint4\n,\n \nint5\n,\n \nint6\n,\n \nint7\n,\n \nint8\n,\n \nint9\n,\n \nint10\n,\n \nint11\n,\n \nint12\n,\n \nint13\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat1\n))\n \nAS\n \nicat1\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat2\n))\n \nAS\n \nicat2\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat3\n))\n \nAS\n \nicat3\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat4\n))\n \nAS\n \nicat4\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat5\n))\n \nAS\n \nicat5\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat6\n))\n \nAS\n \nicat6\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat7\n))\n \nAS\n \nicat7\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat8\n))\n \nAS\n \nicat8\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat9\n))\n \nAS\n \nicat9\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat10\n))\n \nAS\n \nicat10\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat11\n))\n \nAS\n \nicat11\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat12\n))\n \nAS\n \nicat12\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat13\n))\n \nAS\n \nicat13\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat14\n))\n \nAS\n \nicat14\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat15\n))\n \nAS\n \nicat15\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat16\n))\n \nAS\n \nicat16\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat17\n))\n \nAS\n \nicat17\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat18\n))\n \nAS\n \nicat18\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat19\n))\n \nAS\n \nicat19\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat20\n))\n \nAS\n \nicat20\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat21\n))\n \nAS\n \nicat21\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat22\n))\n \nAS\n \nicat22\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat23\n))\n \nAS\n \nicat23\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat24\n))\n \nAS\n \nicat24\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat25\n))\n \nAS\n \nicat25\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat26\n))\n \nAS\n \nicat26\n \nFROM\n \ncriteo_log\n;\n\n\n\nDROP\n \nTABLE\n \ncriteo_log\n;", - "title": "Terabyte click logs from Criteo" - }, - { - "location": "/getting_started/example_datasets/criteo/#terabyte-of-click-logs-from-criteo", - "text": "Download the data from http://labs.criteo.com/downloads/download-terabyte-click-logs/ Create a table to import the log to: CREATE TABLE criteo_log ( date Date , clicked UInt8 , int1 Int32 , int2 Int32 , int3 Int32 , int4 Int32 , int5 Int32 , int6 Int32 , int7 Int32 , int8 Int32 , int9 Int32 , int10 Int32 , int11 Int32 , int12 Int32 , int13 Int32 , cat1 String , cat2 String , cat3 String , cat4 String , cat5 String , cat6 String , cat7 String , cat8 String , cat9 String , cat10 String , cat11 String , cat12 String , cat13 String , cat14 String , cat15 String , cat16 String , cat17 String , cat18 String , cat19 String , cat20 String , cat21 String , cat22 String , cat23 String , cat24 String , cat25 String , cat26 String ) ENGINE = Log Download the data: for i in { 00 ..23 } ; do echo $i ; zcat datasets/criteo/day_ ${ i #0 } .gz | sed -r s/^/2000-01- ${ i /00/24 } \\t/ | clickhouse-client --host = example-perftest01j --query = INSERT INTO criteo_log FORMAT TabSeparated ; done Create a table for the converted data: CREATE TABLE criteo ( \n date Date , \n clicked UInt8 , \n int1 Int32 , \n int2 Int32 , \n int3 Int32 , \n int4 Int32 , \n int5 Int32 , \n int6 Int32 , \n int7 Int32 , \n int8 Int32 , \n int9 Int32 , \n int10 Int32 , \n int11 Int32 , \n int12 Int32 , \n int13 Int32 , \n icat1 UInt32 , \n icat2 UInt32 , \n icat3 UInt32 , \n icat4 UInt32 , \n icat5 UInt32 , \n icat6 UInt32 , \n icat7 UInt32 , \n icat8 UInt32 , \n icat9 UInt32 , \n icat10 UInt32 , \n icat11 UInt32 , \n icat12 UInt32 , \n icat13 UInt32 , \n icat14 UInt32 , \n icat15 UInt32 , \n icat16 UInt32 , \n icat17 UInt32 , \n icat18 UInt32 , \n icat19 UInt32 , \n icat20 UInt32 , \n icat21 UInt32 , \n icat22 UInt32 , \n icat23 UInt32 , \n icat24 UInt32 , \n icat25 UInt32 , \n icat26 UInt32 ) ENGINE = MergeTree ( date , intHash32 ( icat1 ), ( date , intHash32 ( icat1 )), 8192 ) Transform data from the raw log and put it in the second table: INSERT INTO criteo SELECT date , clicked , int1 , int2 , int3 , int4 , int5 , int6 , int7 , int8 , int9 , int10 , int11 , int12 , int13 , reinterpretAsUInt32 ( unhex ( cat1 )) AS icat1 , reinterpretAsUInt32 ( unhex ( cat2 )) AS icat2 , reinterpretAsUInt32 ( unhex ( cat3 )) AS icat3 , reinterpretAsUInt32 ( unhex ( cat4 )) AS icat4 , reinterpretAsUInt32 ( unhex ( cat5 )) AS icat5 , reinterpretAsUInt32 ( unhex ( cat6 )) AS icat6 , reinterpretAsUInt32 ( unhex ( cat7 )) AS icat7 , reinterpretAsUInt32 ( unhex ( cat8 )) AS icat8 , reinterpretAsUInt32 ( unhex ( cat9 )) AS icat9 , reinterpretAsUInt32 ( unhex ( cat10 )) AS icat10 , reinterpretAsUInt32 ( unhex ( cat11 )) AS icat11 , reinterpretAsUInt32 ( unhex ( cat12 )) AS icat12 , reinterpretAsUInt32 ( unhex ( cat13 )) AS icat13 , reinterpretAsUInt32 ( unhex ( cat14 )) AS icat14 , reinterpretAsUInt32 ( unhex ( cat15 )) AS icat15 , reinterpretAsUInt32 ( unhex ( cat16 )) AS icat16 , reinterpretAsUInt32 ( unhex ( cat17 )) AS icat17 , reinterpretAsUInt32 ( unhex ( cat18 )) AS icat18 , reinterpretAsUInt32 ( unhex ( cat19 )) AS icat19 , reinterpretAsUInt32 ( unhex ( cat20 )) AS icat20 , reinterpretAsUInt32 ( unhex ( cat21 )) AS icat21 , reinterpretAsUInt32 ( unhex ( cat22 )) AS icat22 , reinterpretAsUInt32 ( unhex ( cat23 )) AS icat23 , reinterpretAsUInt32 ( unhex ( cat24 )) AS icat24 , reinterpretAsUInt32 ( unhex ( cat25 )) AS icat25 , reinterpretAsUInt32 ( unhex ( cat26 )) AS icat26 FROM criteo_log ; DROP TABLE criteo_log ;", - "title": "Terabyte of click logs from Criteo" - }, - { - "location": "/getting_started/example_datasets/star_schema/", - "text": "Star Schema Benchmark\n\n\nCompiling dbgen: \nhttps://github.com/vadimtk/ssb-dbgen\n\n\ngit clone git@github.com:vadimtk/ssb-dbgen.git\n\ncd\n ssb-dbgen\nmake\n\n\n\n\n\nThere will be some warnings during the process, but this is normal.\n\n\nPlace \ndbgen\n and \ndists.dss\n in any location with 800 GB of free disk space.\n\n\nGenerating data:\n\n\n./dbgen -s \n1000\n -T c\n./dbgen -s \n1000\n -T l\n\n\n\n\n\nCreating tables in ClickHouse:\n\n\nCREATE\n \nTABLE\n \nlineorder\n \n(\n\n \nLO_ORDERKEY\n \nUInt32\n,\n\n \nLO_LINENUMBER\n \nUInt8\n,\n\n \nLO_CUSTKEY\n \nUInt32\n,\n\n \nLO_PARTKEY\n \nUInt32\n,\n\n \nLO_SUPPKEY\n \nUInt32\n,\n\n \nLO_ORDERDATE\n \nDate\n,\n\n \nLO_ORDERPRIORITY\n \nString\n,\n\n \nLO_SHIPPRIORITY\n \nUInt8\n,\n\n \nLO_QUANTITY\n \nUInt8\n,\n\n \nLO_EXTENDEDPRICE\n \nUInt32\n,\n\n \nLO_ORDTOTALPRICE\n \nUInt32\n,\n\n \nLO_DISCOUNT\n \nUInt8\n,\n\n \nLO_REVENUE\n \nUInt32\n,\n\n \nLO_SUPPLYCOST\n \nUInt32\n,\n\n \nLO_TAX\n \nUInt8\n,\n\n \nLO_COMMITDATE\n \nDate\n,\n\n \nLO_SHIPMODE\n \nString\n\n\n)\nEngine\n=\nMergeTree\n(\nLO_ORDERDATE\n,(\nLO_ORDERKEY\n,\nLO_LINENUMBER\n,\nLO_ORDERDATE\n),\n8192\n);\n\n\n\nCREATE\n \nTABLE\n \ncustomer\n \n(\n\n \nC_CUSTKEY\n \nUInt32\n,\n\n \nC_NAME\n \nString\n,\n\n \nC_ADDRESS\n \nString\n,\n\n \nC_CITY\n \nString\n,\n\n \nC_NATION\n \nString\n,\n\n \nC_REGION\n \nString\n,\n\n \nC_PHONE\n \nString\n,\n\n \nC_MKTSEGMENT\n \nString\n,\n\n \nC_FAKEDATE\n \nDate\n\n\n)\nEngine\n=\nMergeTree\n(\nC_FAKEDATE\n,(\nC_CUSTKEY\n,\nC_FAKEDATE\n),\n8192\n);\n\n\n\nCREATE\n \nTABLE\n \npart\n \n(\n\n \nP_PARTKEY\n \nUInt32\n,\n\n \nP_NAME\n \nString\n,\n\n \nP_MFGR\n \nString\n,\n\n \nP_CATEGORY\n \nString\n,\n\n \nP_BRAND\n \nString\n,\n\n \nP_COLOR\n \nString\n,\n\n \nP_TYPE\n \nString\n,\n\n \nP_SIZE\n \nUInt8\n,\n\n \nP_CONTAINER\n \nString\n,\n\n \nP_FAKEDATE\n \nDate\n\n\n)\nEngine\n=\nMergeTree\n(\nP_FAKEDATE\n,(\nP_PARTKEY\n,\nP_FAKEDATE\n),\n8192\n);\n\n\n\nCREATE\n \nTABLE\n \nlineorderd\n \nAS\n \nlineorder\n \nENGINE\n \n=\n \nDistributed\n(\nperftest_3shards_1replicas\n,\n \ndefault\n,\n \nlineorder\n,\n \nrand\n());\n\n\nCREATE\n \nTABLE\n \ncustomerd\n \nAS\n \ncustomer\n \nENGINE\n \n=\n \nDistributed\n(\nperftest_3shards_1replicas\n,\n \ndefault\n,\n \ncustomer\n,\n \nrand\n());\n\n\nCREATE\n \nTABLE\n \npartd\n \nAS\n \npart\n \nENGINE\n \n=\n \nDistributed\n(\nperftest_3shards_1replicas\n,\n \ndefault\n,\n \npart\n,\n \nrand\n());\n\n\n\n\n\n\nFor testing on a single server, just use MergeTree tables.\nFor distributed testing, you need to configure the \nperftest_3shards_1replicas\n cluster in the config file.\nNext, create MergeTree tables on each server and a Distributed above them.\n\n\nDownloading data (change 'customer' to 'customerd' in the distributed version):\n\n\ncat customer.tbl \n|\n sed \ns/$/2000-01-01/\n \n|\n clickhouse-client --query \nINSERT INTO customer FORMAT CSV\n\ncat lineorder.tbl \n|\n clickhouse-client --query \nINSERT INTO lineorder FORMAT CSV", - "title": "Star Schema Benchmark" - }, - { - "location": "/getting_started/example_datasets/star_schema/#star-schema-benchmark", - "text": "Compiling dbgen: https://github.com/vadimtk/ssb-dbgen git clone git@github.com:vadimtk/ssb-dbgen.git cd ssb-dbgen\nmake There will be some warnings during the process, but this is normal. Place dbgen and dists.dss in any location with 800 GB of free disk space. Generating data: ./dbgen -s 1000 -T c\n./dbgen -s 1000 -T l Creating tables in ClickHouse: CREATE TABLE lineorder ( \n LO_ORDERKEY UInt32 , \n LO_LINENUMBER UInt8 , \n LO_CUSTKEY UInt32 , \n LO_PARTKEY UInt32 , \n LO_SUPPKEY UInt32 , \n LO_ORDERDATE Date , \n LO_ORDERPRIORITY String , \n LO_SHIPPRIORITY UInt8 , \n LO_QUANTITY UInt8 , \n LO_EXTENDEDPRICE UInt32 , \n LO_ORDTOTALPRICE UInt32 , \n LO_DISCOUNT UInt8 , \n LO_REVENUE UInt32 , \n LO_SUPPLYCOST UInt32 , \n LO_TAX UInt8 , \n LO_COMMITDATE Date , \n LO_SHIPMODE String ) Engine = MergeTree ( LO_ORDERDATE ,( LO_ORDERKEY , LO_LINENUMBER , LO_ORDERDATE ), 8192 ); CREATE TABLE customer ( \n C_CUSTKEY UInt32 , \n C_NAME String , \n C_ADDRESS String , \n C_CITY String , \n C_NATION String , \n C_REGION String , \n C_PHONE String , \n C_MKTSEGMENT String , \n C_FAKEDATE Date ) Engine = MergeTree ( C_FAKEDATE ,( C_CUSTKEY , C_FAKEDATE ), 8192 ); CREATE TABLE part ( \n P_PARTKEY UInt32 , \n P_NAME String , \n P_MFGR String , \n P_CATEGORY String , \n P_BRAND String , \n P_COLOR String , \n P_TYPE String , \n P_SIZE UInt8 , \n P_CONTAINER String , \n P_FAKEDATE Date ) Engine = MergeTree ( P_FAKEDATE ,( P_PARTKEY , P_FAKEDATE ), 8192 ); CREATE TABLE lineorderd AS lineorder ENGINE = Distributed ( perftest_3shards_1replicas , default , lineorder , rand ()); CREATE TABLE customerd AS customer ENGINE = Distributed ( perftest_3shards_1replicas , default , customer , rand ()); CREATE TABLE partd AS part ENGINE = Distributed ( perftest_3shards_1replicas , default , part , rand ()); For testing on a single server, just use MergeTree tables.\nFor distributed testing, you need to configure the perftest_3shards_1replicas cluster in the config file.\nNext, create MergeTree tables on each server and a Distributed above them. Downloading data (change 'customer' to 'customerd' in the distributed version): cat customer.tbl | sed s/$/2000-01-01/ | clickhouse-client --query INSERT INTO customer FORMAT CSV \ncat lineorder.tbl | clickhouse-client --query INSERT INTO lineorder FORMAT CSV", - "title": "Star Schema Benchmark" - }, - { - "location": "/interfaces/", - "text": "Interfaces\n\n\nTo explore the system's capabilities, download data to tables, or make manual queries, use the clickhouse-client program.", - "title": "Introduction" - }, - { - "location": "/interfaces/#interfaces", - "text": "To explore the system's capabilities, download data to tables, or make manual queries, use the clickhouse-client program.", - "title": "Interfaces" - }, - { - "location": "/interfaces/cli/", - "text": "Command-line client\n\n\nTo work from the command line, you can use \nclickhouse-client\n:\n\n\n$ clickhouse-client\nClickHouse client version \n0\n.0.26176.\nConnecting to localhost:9000.\nConnected to ClickHouse server version \n0\n.0.26176.\n\n:\n)\n\n\n\n\n\n\nThe client supports command-line options and configuration files. For more information, see \"\nConfiguring\n\".\n\n\nUsage\n\n\nThe client can be used in interactive and non-interactive (batch) mode.\nTo use batch mode, specify the 'query' parameter, or send data to 'stdin' (it verifies that 'stdin' is not a terminal), or both.\nSimilar to the HTTP interface, when using the 'query' parameter and sending data to 'stdin', the request is a concatenation of the 'query' parameter, a line feed, and the data in 'stdin'. This is convenient for large INSERT queries.\n\n\nExample of using the client to insert data:\n\n\necho\n -ne \n1, \nsome text\n, \n2016-08-14 00:00:00\n\\n2, \nsome more text\n, \n2016-08-14 00:00:01\n \n|\n clickhouse-client --database\n=\ntest\n --query\n=\nINSERT INTO test FORMAT CSV\n;\n\n\ncat \n_EOF | clickhouse-client --database=test --query=\nINSERT INTO test FORMAT CSV\n;\n\n\n3, \nsome text\n, \n2016-08-14 00:00:00\n\n\n4, \nsome more text\n, \n2016-08-14 00:00:01\n\n\n_EOF\n\n\ncat file.csv \n|\n clickhouse-client --database\n=\ntest\n --query\n=\nINSERT INTO test FORMAT CSV\n;\n\n\n\n\n\n\nIn batch mode, the default data format is TabSeparated. You can set the format in the FORMAT clause of the query.\n\n\nBy default, you can only process a single query in batch mode. To make multiple queries from a \"script,\" use the --multiquery parameter. This works for all queries except INSERT. Query results are output consecutively without additional separators.\nSimilarly, to process a large number of queries, you can run 'clickhouse-client' for each query. Note that it may take tens of milliseconds to launch the 'clickhouse-client' program.\n\n\nIn interactive mode, you get a command line where you can enter queries.\n\n\nIf 'multiline' is not specified (the default):To run the query, press Enter. The semicolon is not necessary at the end of the query. To enter a multiline query, enter a backslash \n\\\n before the line feed. After you press Enter, you will be asked to enter the next line of the query.\n\n\nIf multiline is specified:To run a query, end it with a semicolon and press Enter. If the semicolon was omitted at the end of the entered line, you will be asked to enter the next line of the query.\n\n\nOnly a single query is run, so everything after the semicolon is ignored.\n\n\nYou can specify \n\\G\n instead of or after the semicolon. This indicates Vertical format. In this format, each value is printed on a separate line, which is convenient for wide tables. This unusual feature was added for compatibility with the MySQL CLI.\n\n\nThe command line is based on 'readline' (and 'history' or 'libedit', or without a library, depending on the build). In other words, it uses the familiar keyboard shortcuts and keeps a history.\nThe history is written to \n~/.clickhouse-client-history\n.\n\n\nBy default, the format used is PrettyCompact. You can change the format in the FORMAT clause of the query, or by specifying \n\\G\n at the end of the query, using the \n--format\n or \n--vertical\n argument in the command line, or using the client configuration file.\n\n\nTo exit the client, press Ctrl+D (or Ctrl+C), or enter one of the following instead of a query:\"exit\", \"quit\", \"logout\", \"\u0443\u0447\u0448\u0435\", \"\u0439\u0433\u0448\u0435\", \"\u0434\u0449\u043f\u0449\u0433\u0435\", \"exit;\", \"quit;\", \"logout;\", \"\u0443\u0447\u0448\u0435\u0436\", \"\u0439\u0433\u0448\u0435\u0436\", \"\u0434\u0449\u043f\u0449\u0433\u0435\u0436\", \"q\", \"\u0439\", \"q\", \"Q\", \":q\", \"\u0439\", \"\u0419\", \"\u0416\u0439\"\n\n\nWhen processing a query, the client shows:\n\n\n\n\nProgress, which is updated no more than 10 times per second (by default). For quick queries, the progress might not have time to be displayed.\n\n\nThe formatted query after parsing, for debugging.\n\n\nThe result in the specified format.\n\n\nThe number of lines in the result, the time passed, and the average speed of query processing.\n\n\n\n\nYou can cancel a long query by pressing Ctrl+C. However, you will still need to wait a little for the server to abort the request. It is not possible to cancel a query at certain stages. If you don't wait and press Ctrl+C a second time, the client will exit.\n\n\nThe command-line client allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".\n\n\n\n\nConfiguring\n\n\nYou can pass parameters to \nclickhouse-client\n (all parameters have a default value) using:\n\n\n\n\nFrom the Command Line\n\n\n\n\nCommand-line options override the default values and settings in configuration files.\n\n\n\n\nConfiguration files.\n\n\n\n\nSettings in the configuration files override the default values.\n\n\nCommand line options\n\n\n\n\n--host, -h\n -\u2013 The server name, 'localhost' by default. You can use either the name or the IPv4 or IPv6 address.\n\n\n--port\n \u2013 The port to connect to. Default value: 9000. Note that the HTTP interface and the native interface use different ports.\n\n\n--user, -u\n \u2013 The username. Default value: default.\n\n\n--password\n \u2013 The password. Default value: empty string.\n\n\n--query, -q\n \u2013 The query to process when using non-interactive mode.\n\n\n--database, -d\n \u2013 Select the current default database. Default value: the current database from the server settings ('default' by default).\n\n\n--multiline, -m\n \u2013 If specified, allow multiline queries (do not send the query on Enter).\n\n\n--multiquery, -n\n \u2013 If specified, allow processing multiple queries separated by commas. Only works in non-interactive mode.\n\n\n--format, -f\n \u2013 Use the specified default format to output the result.\n\n\n--vertical, -E\n \u2013 If specified, use the Vertical format by default to output the result. This is the same as '--format=Vertical'. In this format, each value is printed on a separate line, which is helpful when displaying wide tables.\n\n\n--time, -t\n \u2013 If specified, print the query execution time to 'stderr' in non-interactive mode.\n\n\n--stacktrace\n \u2013 If specified, also print the stack trace if an exception occurs.\n\n\n-config-file\n \u2013 The name of the configuration file.\n\n\n\n\nConfiguration files\n\n\nclickhouse-client\n uses the first existing file of the following:\n\n\n\n\nDefined in the \n-config-file\n parameter.\n\n\n./clickhouse-client.xml\n\n\n\\~/.clickhouse-client/config.xml\n\n\n/etc/clickhouse-client/config.xml\n\n\n\n\nExample of a config file:\n\n\nconfig\n\n \nuser\nusername\n/user\n\n \npassword\npassword\n/password\n\n\n/config", - "title": "Command-line client" - }, - { - "location": "/interfaces/cli/#command-line-client", - "text": "To work from the command line, you can use clickhouse-client : $ clickhouse-client\nClickHouse client version 0 .0.26176.\nConnecting to localhost:9000.\nConnected to ClickHouse server version 0 .0.26176.\n\n: ) The client supports command-line options and configuration files. For more information, see \" Configuring \".", - "title": "Command-line client" - }, - { - "location": "/interfaces/cli/#usage", - "text": "The client can be used in interactive and non-interactive (batch) mode.\nTo use batch mode, specify the 'query' parameter, or send data to 'stdin' (it verifies that 'stdin' is not a terminal), or both.\nSimilar to the HTTP interface, when using the 'query' parameter and sending data to 'stdin', the request is a concatenation of the 'query' parameter, a line feed, and the data in 'stdin'. This is convenient for large INSERT queries. Example of using the client to insert data: echo -ne 1, some text , 2016-08-14 00:00:00 \\n2, some more text , 2016-08-14 00:00:01 | clickhouse-client --database = test --query = INSERT INTO test FORMAT CSV ; \n\ncat _EOF | clickhouse-client --database=test --query= INSERT INTO test FORMAT CSV ; 3, some text , 2016-08-14 00:00:00 4, some more text , 2016-08-14 00:00:01 _EOF \n\ncat file.csv | clickhouse-client --database = test --query = INSERT INTO test FORMAT CSV ; In batch mode, the default data format is TabSeparated. You can set the format in the FORMAT clause of the query. By default, you can only process a single query in batch mode. To make multiple queries from a \"script,\" use the --multiquery parameter. This works for all queries except INSERT. Query results are output consecutively without additional separators.\nSimilarly, to process a large number of queries, you can run 'clickhouse-client' for each query. Note that it may take tens of milliseconds to launch the 'clickhouse-client' program. In interactive mode, you get a command line where you can enter queries. If 'multiline' is not specified (the default):To run the query, press Enter. The semicolon is not necessary at the end of the query. To enter a multiline query, enter a backslash \\ before the line feed. After you press Enter, you will be asked to enter the next line of the query. If multiline is specified:To run a query, end it with a semicolon and press Enter. If the semicolon was omitted at the end of the entered line, you will be asked to enter the next line of the query. Only a single query is run, so everything after the semicolon is ignored. You can specify \\G instead of or after the semicolon. This indicates Vertical format. In this format, each value is printed on a separate line, which is convenient for wide tables. This unusual feature was added for compatibility with the MySQL CLI. The command line is based on 'readline' (and 'history' or 'libedit', or without a library, depending on the build). In other words, it uses the familiar keyboard shortcuts and keeps a history.\nThe history is written to ~/.clickhouse-client-history . By default, the format used is PrettyCompact. You can change the format in the FORMAT clause of the query, or by specifying \\G at the end of the query, using the --format or --vertical argument in the command line, or using the client configuration file. To exit the client, press Ctrl+D (or Ctrl+C), or enter one of the following instead of a query:\"exit\", \"quit\", \"logout\", \"\u0443\u0447\u0448\u0435\", \"\u0439\u0433\u0448\u0435\", \"\u0434\u0449\u043f\u0449\u0433\u0435\", \"exit;\", \"quit;\", \"logout;\", \"\u0443\u0447\u0448\u0435\u0436\", \"\u0439\u0433\u0448\u0435\u0436\", \"\u0434\u0449\u043f\u0449\u0433\u0435\u0436\", \"q\", \"\u0439\", \"q\", \"Q\", \":q\", \"\u0439\", \"\u0419\", \"\u0416\u0439\" When processing a query, the client shows: Progress, which is updated no more than 10 times per second (by default). For quick queries, the progress might not have time to be displayed. The formatted query after parsing, for debugging. The result in the specified format. The number of lines in the result, the time passed, and the average speed of query processing. You can cancel a long query by pressing Ctrl+C. However, you will still need to wait a little for the server to abort the request. It is not possible to cancel a query at certain stages. If you don't wait and press Ctrl+C a second time, the client will exit. The command-line client allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".", - "title": "Usage" - }, - { - "location": "/interfaces/cli/#configuring", - "text": "You can pass parameters to clickhouse-client (all parameters have a default value) using: From the Command Line Command-line options override the default values and settings in configuration files. Configuration files. Settings in the configuration files override the default values.", - "title": "Configuring" - }, - { - "location": "/interfaces/cli/#command-line-options", - "text": "--host, -h -\u2013 The server name, 'localhost' by default. You can use either the name or the IPv4 or IPv6 address. --port \u2013 The port to connect to. Default value: 9000. Note that the HTTP interface and the native interface use different ports. --user, -u \u2013 The username. Default value: default. --password \u2013 The password. Default value: empty string. --query, -q \u2013 The query to process when using non-interactive mode. --database, -d \u2013 Select the current default database. Default value: the current database from the server settings ('default' by default). --multiline, -m \u2013 If specified, allow multiline queries (do not send the query on Enter). --multiquery, -n \u2013 If specified, allow processing multiple queries separated by commas. Only works in non-interactive mode. --format, -f \u2013 Use the specified default format to output the result. --vertical, -E \u2013 If specified, use the Vertical format by default to output the result. This is the same as '--format=Vertical'. In this format, each value is printed on a separate line, which is helpful when displaying wide tables. --time, -t \u2013 If specified, print the query execution time to 'stderr' in non-interactive mode. --stacktrace \u2013 If specified, also print the stack trace if an exception occurs. -config-file \u2013 The name of the configuration file.", - "title": "Command line options" - }, - { - "location": "/interfaces/cli/#configuration-files", - "text": "clickhouse-client uses the first existing file of the following: Defined in the -config-file parameter. ./clickhouse-client.xml \\~/.clickhouse-client/config.xml /etc/clickhouse-client/config.xml Example of a config file: config \n user username /user \n password password /password /config", - "title": "Configuration files" - }, - { - "location": "/interfaces/http_interface/", - "text": "HTTP interface\n\n\nThe HTTP interface lets you use ClickHouse on any platform from any programming language. We use it for working from Java and Perl, as well as shell scripts. In other departments, the HTTP interface is used from Perl, Python, and Go. The HTTP interface is more limited than the native interface, but it has better compatibility.\n\n\nBy default, clickhouse-server listens for HTTP on port 8123 (this can be changed in the config).\nIf you make a GET / request without parameters, it returns the string \"Ok\" (with a line feed at the end). You can use this in health-check scripts.\n\n\n$ curl \nhttp://localhost:8123/\n\nOk.\n\n\n\n\n\nSend the request as a URL 'query' parameter, or as a POST. Or send the beginning of the query in the 'query' parameter, and the rest in the POST (we'll explain later why this is necessary). The size of the URL is limited to 16 KB, so keep this in mind when sending large queries.\n\n\nIf successful, you receive the 200 response code and the result in the response body.\nIf an error occurs, you receive the 500 response code and an error description text in the response body.\n\n\nWhen using the GET method, 'readonly' is set. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.\n\n\nExamples:\n\n\n$ curl \nhttp://localhost:8123/?query=SELECT%201\n\n\n1\n\n\n$ wget -O- -q \nhttp://localhost:8123/?query=SELECT 1\n\n\n1\n\n\n$ GET \nhttp://localhost:8123/?query=SELECT 1\n\n\n1\n\n\n$ \necho\n -ne \nGET /?query=SELECT%201 HTTP/1.0\\r\\n\\r\\n\n \n|\n nc localhost \n8123\n\nHTTP/1.0 \n200\n OK\nConnection: Close\nDate: Fri, \n16\n Nov \n2012\n \n19\n:21:50 GMT\n\n\n1\n\n\n\n\n\n\nAs you can see, curl is somewhat inconvenient in that spaces must be URL escaped.Although wget escapes everything itself, we don't recommend using it because it doesn't work well over HTTP 1.1 when using keep-alive and Transfer-Encoding: chunked.\n\n\n$ \necho\n \nSELECT 1\n \n|\n curl \nhttp://localhost:8123/\n --data-binary @-\n\n1\n\n\n$ \necho\n \nSELECT 1\n \n|\n curl \nhttp://localhost:8123/?query=\n --data-binary @-\n\n1\n\n\n$ \necho\n \n1\n \n|\n curl \nhttp://localhost:8123/?query=SELECT\n --data-binary @-\n\n1\n\n\n\n\n\n\nIf part of the query is sent in the parameter, and part in the POST, a line feed is inserted between these two data parts.\nExample (this won't work):\n\n\n$ \necho\n \nECT 1\n \n|\n curl \nhttp://localhost:8123/?query=SEL\n --data-binary @-\nCode: \n59\n, e.displayText\n()\n \n=\n DB::Exception: Syntax error: failed at position \n0\n: SEL\nECT \n1\n\n, expected One of: SHOW TABLES, SHOW DATABASES, SELECT, INSERT, CREATE, ATTACH, RENAME, DROP, DETACH, USE, SET, OPTIMIZE., e.what\n()\n \n=\n DB::Exception\n\n\n\n\n\nBy default, data is returned in TabSeparated format (for more information, see the \"Formats\" section).\nYou use the FORMAT clause of the query to request any other format.\n\n\n$ \necho\n \nSELECT 1 FORMAT Pretty\n \n|\n curl \nhttp://localhost:8123/?\n --data-binary @-\n\u250f\u2501\u2501\u2501\u2513\n\u2503 \n1\n \u2503\n\u2521\u2501\u2501\u2501\u2529\n\u2502 \n1\n \u2502\n\u2514\u2500\u2500\u2500\u2518\n\n\n\n\n\nThe POST method of transmitting data is necessary for INSERT queries. In this case, you can write the beginning of the query in the URL parameter, and use POST to pass the data to insert. The data to insert could be, for example, a tab-separated dump from MySQL. In this way, the INSERT query replaces LOAD DATA LOCAL INFILE from MySQL.\n\n\nExamples: Creating a table:\n\n\necho\n \nCREATE TABLE t (a UInt8) ENGINE = Memory\n \n|\n POST \nhttp://localhost:8123/\n\n\n\n\n\n\nUsing the familiar INSERT query for data insertion:\n\n\necho\n \nINSERT INTO t VALUES (1),(2),(3)\n \n|\n POST \nhttp://localhost:8123/\n\n\n\n\n\n\nData can be sent separately from the query:\n\n\necho\n \n(4),(5),(6)\n \n|\n POST \nhttp://localhost:8123/?query=INSERT INTO t VALUES\n\n\n\n\n\n\nYou can specify any data format. The 'Values' format is the same as what is used when writing INSERT INTO t VALUES:\n\n\necho\n \n(7),(8),(9)\n \n|\n POST \nhttp://localhost:8123/?query=INSERT INTO t FORMAT Values\n\n\n\n\n\n\nTo insert data from a tab-separated dump, specify the corresponding format:\n\n\necho\n -ne \n10\\n11\\n12\\n\n \n|\n POST \nhttp://localhost:8123/?query=INSERT INTO t FORMAT TabSeparated\n\n\n\n\n\n\nReading the table contents. Data is output in random order due to parallel query processing:\n\n\n$ GET \nhttp://localhost:8123/?query=SELECT a FROM t\n\n\n7\n\n\n8\n\n\n9\n\n\n10\n\n\n11\n\n\n12\n\n\n1\n\n\n2\n\n\n3\n\n\n4\n\n\n5\n\n\n6\n\n\n\n\n\n\nDeleting the table.\n\n\nPOST \nhttp://localhost:8123/?query=DROP TABLE t\n\n\n\n\n\n\nFor successful requests that don't return a data table, an empty response body is returned.\n\n\nYou can use the internal ClickHouse compression format when transmitting data. The compressed data has a non-standard format, and you will need to use the special clickhouse-compressor program to work with it (it is installed with the clickhouse-client package).\n\n\nIf you specified 'compress=1' in the URL, the server will compress the data it sends you.\nIf you specified 'decompress=1' in the URL, the server will decompress the same data that you pass in the POST method.\n\n\nIt is also possible to use the standard gzip-based HTTP compression. To send a POST request compressed using gzip, append the request header \nContent-Encoding: gzip\n.\nIn order for ClickHouse to compress the response using gzip, you must append \nAccept-Encoding: gzip\n to the request headers, and enable the ClickHouse setting \nenable_http_compression\n.\n\n\nYou can use this to reduce network traffic when transmitting a large amount of data, or for creating dumps that are immediately compressed.\n\n\nYou can use the 'database' URL parameter to specify the default database.\n\n\n$ \necho\n \nSELECT number FROM numbers LIMIT 10\n \n|\n curl \nhttp://localhost:8123/?database=system\n --data-binary @-\n\n0\n\n\n1\n\n\n2\n\n\n3\n\n\n4\n\n\n5\n\n\n6\n\n\n7\n\n\n8\n\n\n9\n\n\n\n\n\n\nBy default, the database that is registered in the server settings is used as the default database. By default, this is the database called 'default'. Alternatively, you can always specify the database using a dot before the table name.\n\n\nThe username and password can be indicated in one of two ways:\n\n\n\n\nUsing HTTP Basic Authentication. Example:\n\n\n\n\necho\n \nSELECT 1\n \n|\n curl \nhttp://user:password@localhost:8123/\n -d @-\n\n\n\n\n\n\n\nIn the 'user' and 'password' URL parameters. Example:\n\n\n\n\necho\n \nSELECT 1\n \n|\n curl \nhttp://localhost:8123/?user=user\npassword=password\n -d @-\n\n\n\n\n\nIf the user name is not indicated, the username 'default' is used. If the password is not indicated, an empty password is used.\nYou can also use the URL parameters to specify any settings for processing a single query, or entire profiles of settings. Example:\nhttp://localhost:8123/?profile=web\nmax_rows_to_read=1000000000\nquery=SELECT+1\n\n\nFor more information, see the section \"Settings\".\n\n\n$ \necho\n \nSELECT number FROM system.numbers LIMIT 10\n \n|\n curl \nhttp://localhost:8123/?\n --data-binary @-\n\n0\n\n\n1\n\n\n2\n\n\n3\n\n\n4\n\n\n5\n\n\n6\n\n\n7\n\n\n8\n\n\n9\n\n\n\n\n\n\nFor information about other parameters, see the section \"SET\".\n\n\nSimilarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to add the \nsession_id\n GET parameter to the request. You can use any string as the session ID. By default, the session is terminated after 60 seconds of inactivity. To change this timeout, modify the \ndefault_session_timeout\n setting in the server configuration, or add the \nsession_timeout\n GET parameter to the request. To check the session status, use the \nsession_check=1\n parameter. Only one query at a time can be executed within a single session.\n\n\nYou have the option to receive information about the progress of query execution in X-ClickHouse-Progress headers. To do this, enable the setting send_progress_in_http_headers.\n\n\nRunning requests don't stop automatically if the HTTP connection is lost. Parsing and data formatting are performed on the server side, and using the network might be ineffective.\nThe optional 'query_id' parameter can be passed as the query ID (any string). For more information, see the section \"Settings, replace_running_query\".\n\n\nThe optional 'quota_key' parameter can be passed as the quota key (any string). For more information, see the section \"Quotas\".\n\n\nThe HTTP interface allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".\n\n\nResponse buffering\n\n\nYou can enable response buffering on the server side. The \nbuffer_size\n and \nwait_end_of_query\n URL parameters are provided for this purpose.\n\n\nbuffer_size\n determines the number of bytes in the result to buffer in the server memory. If the result body is larger than this threshold, the buffer is written to the HTTP channel, and the remaining data is sent directly to the HTTP channel.\n\n\nTo ensure that the entire response is buffered, set \nwait_end_of_query=1\n. In this case, the data that is not stored in memory will be buffered in a temporary server file.\n\n\nExample:\n\n\ncurl -sS \nhttp://localhost:8123/?max_result_bytes=4000000\nbuffer_size=3000000\nwait_end_of_query=1\n -d \nSELECT toUInt8(number) FROM system.numbers LIMIT 9000000 FORMAT RowBinary\n\n\n\n\n\n\nUse buffering to avoid situations where a query processing error occurred after the response code and HTTP headers were sent to the client. In this situation, an error message is written at the end of the response body, and on the client side, the error can only be detected at the parsing stage.", - "title": "HTTP interface" - }, - { - "location": "/interfaces/http_interface/#http-interface", - "text": "The HTTP interface lets you use ClickHouse on any platform from any programming language. We use it for working from Java and Perl, as well as shell scripts. In other departments, the HTTP interface is used from Perl, Python, and Go. The HTTP interface is more limited than the native interface, but it has better compatibility. By default, clickhouse-server listens for HTTP on port 8123 (this can be changed in the config).\nIf you make a GET / request without parameters, it returns the string \"Ok\" (with a line feed at the end). You can use this in health-check scripts. $ curl http://localhost:8123/ \nOk. Send the request as a URL 'query' parameter, or as a POST. Or send the beginning of the query in the 'query' parameter, and the rest in the POST (we'll explain later why this is necessary). The size of the URL is limited to 16 KB, so keep this in mind when sending large queries. If successful, you receive the 200 response code and the result in the response body.\nIf an error occurs, you receive the 500 response code and an error description text in the response body. When using the GET method, 'readonly' is set. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter. Examples: $ curl http://localhost:8123/?query=SELECT%201 1 \n\n$ wget -O- -q http://localhost:8123/?query=SELECT 1 1 \n\n$ GET http://localhost:8123/?query=SELECT 1 1 \n\n$ echo -ne GET /?query=SELECT%201 HTTP/1.0\\r\\n\\r\\n | nc localhost 8123 \nHTTP/1.0 200 OK\nConnection: Close\nDate: Fri, 16 Nov 2012 19 :21:50 GMT 1 As you can see, curl is somewhat inconvenient in that spaces must be URL escaped.Although wget escapes everything itself, we don't recommend using it because it doesn't work well over HTTP 1.1 when using keep-alive and Transfer-Encoding: chunked. $ echo SELECT 1 | curl http://localhost:8123/ --data-binary @- 1 \n\n$ echo SELECT 1 | curl http://localhost:8123/?query= --data-binary @- 1 \n\n$ echo 1 | curl http://localhost:8123/?query=SELECT --data-binary @- 1 If part of the query is sent in the parameter, and part in the POST, a line feed is inserted between these two data parts.\nExample (this won't work): $ echo ECT 1 | curl http://localhost:8123/?query=SEL --data-binary @-\nCode: 59 , e.displayText () = DB::Exception: Syntax error: failed at position 0 : SEL\nECT 1 \n, expected One of: SHOW TABLES, SHOW DATABASES, SELECT, INSERT, CREATE, ATTACH, RENAME, DROP, DETACH, USE, SET, OPTIMIZE., e.what () = DB::Exception By default, data is returned in TabSeparated format (for more information, see the \"Formats\" section).\nYou use the FORMAT clause of the query to request any other format. $ echo SELECT 1 FORMAT Pretty | curl http://localhost:8123/? --data-binary @-\n\u250f\u2501\u2501\u2501\u2513\n\u2503 1 \u2503\n\u2521\u2501\u2501\u2501\u2529\n\u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2518 The POST method of transmitting data is necessary for INSERT queries. In this case, you can write the beginning of the query in the URL parameter, and use POST to pass the data to insert. The data to insert could be, for example, a tab-separated dump from MySQL. In this way, the INSERT query replaces LOAD DATA LOCAL INFILE from MySQL. Examples: Creating a table: echo CREATE TABLE t (a UInt8) ENGINE = Memory | POST http://localhost:8123/ Using the familiar INSERT query for data insertion: echo INSERT INTO t VALUES (1),(2),(3) | POST http://localhost:8123/ Data can be sent separately from the query: echo (4),(5),(6) | POST http://localhost:8123/?query=INSERT INTO t VALUES You can specify any data format. The 'Values' format is the same as what is used when writing INSERT INTO t VALUES: echo (7),(8),(9) | POST http://localhost:8123/?query=INSERT INTO t FORMAT Values To insert data from a tab-separated dump, specify the corresponding format: echo -ne 10\\n11\\n12\\n | POST http://localhost:8123/?query=INSERT INTO t FORMAT TabSeparated Reading the table contents. Data is output in random order due to parallel query processing: $ GET http://localhost:8123/?query=SELECT a FROM t 7 8 9 10 11 12 1 2 3 4 5 6 Deleting the table. POST http://localhost:8123/?query=DROP TABLE t For successful requests that don't return a data table, an empty response body is returned. You can use the internal ClickHouse compression format when transmitting data. The compressed data has a non-standard format, and you will need to use the special clickhouse-compressor program to work with it (it is installed with the clickhouse-client package). If you specified 'compress=1' in the URL, the server will compress the data it sends you.\nIf you specified 'decompress=1' in the URL, the server will decompress the same data that you pass in the POST method. It is also possible to use the standard gzip-based HTTP compression. To send a POST request compressed using gzip, append the request header Content-Encoding: gzip .\nIn order for ClickHouse to compress the response using gzip, you must append Accept-Encoding: gzip to the request headers, and enable the ClickHouse setting enable_http_compression . You can use this to reduce network traffic when transmitting a large amount of data, or for creating dumps that are immediately compressed. You can use the 'database' URL parameter to specify the default database. $ echo SELECT number FROM numbers LIMIT 10 | curl http://localhost:8123/?database=system --data-binary @- 0 1 2 3 4 5 6 7 8 9 By default, the database that is registered in the server settings is used as the default database. By default, this is the database called 'default'. Alternatively, you can always specify the database using a dot before the table name. The username and password can be indicated in one of two ways: Using HTTP Basic Authentication. Example: echo SELECT 1 | curl http://user:password@localhost:8123/ -d @- In the 'user' and 'password' URL parameters. Example: echo SELECT 1 | curl http://localhost:8123/?user=user password=password -d @- If the user name is not indicated, the username 'default' is used. If the password is not indicated, an empty password is used.\nYou can also use the URL parameters to specify any settings for processing a single query, or entire profiles of settings. Example:\nhttp://localhost:8123/?profile=web max_rows_to_read=1000000000 query=SELECT+1 For more information, see the section \"Settings\". $ echo SELECT number FROM system.numbers LIMIT 10 | curl http://localhost:8123/? --data-binary @- 0 1 2 3 4 5 6 7 8 9 For information about other parameters, see the section \"SET\". Similarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to add the session_id GET parameter to the request. You can use any string as the session ID. By default, the session is terminated after 60 seconds of inactivity. To change this timeout, modify the default_session_timeout setting in the server configuration, or add the session_timeout GET parameter to the request. To check the session status, use the session_check=1 parameter. Only one query at a time can be executed within a single session. You have the option to receive information about the progress of query execution in X-ClickHouse-Progress headers. To do this, enable the setting send_progress_in_http_headers. Running requests don't stop automatically if the HTTP connection is lost. Parsing and data formatting are performed on the server side, and using the network might be ineffective.\nThe optional 'query_id' parameter can be passed as the query ID (any string). For more information, see the section \"Settings, replace_running_query\". The optional 'quota_key' parameter can be passed as the quota key (any string). For more information, see the section \"Quotas\". The HTTP interface allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".", - "title": "HTTP interface" - }, - { - "location": "/interfaces/http_interface/#response-buffering", - "text": "You can enable response buffering on the server side. The buffer_size and wait_end_of_query URL parameters are provided for this purpose. buffer_size determines the number of bytes in the result to buffer in the server memory. If the result body is larger than this threshold, the buffer is written to the HTTP channel, and the remaining data is sent directly to the HTTP channel. To ensure that the entire response is buffered, set wait_end_of_query=1 . In this case, the data that is not stored in memory will be buffered in a temporary server file. Example: curl -sS http://localhost:8123/?max_result_bytes=4000000 buffer_size=3000000 wait_end_of_query=1 -d SELECT toUInt8(number) FROM system.numbers LIMIT 9000000 FORMAT RowBinary Use buffering to avoid situations where a query processing error occurred after the response code and HTTP headers were sent to the client. In this situation, an error message is written at the end of the response body, and on the client side, the error can only be detected at the parsing stage.", - "title": "Response buffering" - }, - { - "location": "/interfaces/jdbc/", - "text": "JDBC driver\n\n\nThere is an official JDBC driver for ClickHouse. See \nhere\n .", - "title": "JDBC driver" - }, - { - "location": "/interfaces/jdbc/#jdbc-driver", - "text": "There is an official JDBC driver for ClickHouse. See here .", - "title": "JDBC driver" - }, - { - "location": "/interfaces/tcp/", - "text": "Native interface (TCP)\n\n\nThe native interface is used in the \"clickhouse-client\" command-line client for interaction between servers with distributed query processing, and also in C++ programs. We will only cover the command-line client.", - "title": "Native interface (TCP)" - }, - { - "location": "/interfaces/tcp/#native-interface-tcp", - "text": "The native interface is used in the \"clickhouse-client\" command-line client for interaction between servers with distributed query processing, and also in C++ programs. We will only cover the command-line client.", - "title": "Native interface (TCP)" - }, - { - "location": "/interfaces/third-party_client_libraries/", - "text": "Libraries from third-party developers\n\n\nThere are libraries for working with ClickHouse for:\n\n\n\n\nPython\n\n\ninfi.clickhouse_orm\n\n\nsqlalchemy-clickhouse\n\n\nclickhouse-driver\n\n\nclickhouse-client\n\n\n\n\n\n\nPHP\n\n\nclickhouse-php-client\n\n\nPhpClickHouseClient\n\n\nphpClickHouse\n\n\nclickhouse-client\n\n\n\n\n\n\nGo\n\n\nclickhouse\n\n\ngo-clickhouse\n\n\nmailrugo-clickhouse\n\n\ngolang-clickhouse\n\n\n\n\n\n\nNodeJs\n\n\nclickhouse (NodeJs)\n\n\nnode-clickhouse\n\n\n\n\n\n\nPerl\n\n\nperl-DBD-ClickHouse\n\n\nHTTP-ClickHouse\n\n\nAnyEvent-ClickHouse\n\n\n\n\n\n\nRuby\n\n\nclickhouse (Ruby)\n\n\n\n\n\n\nR\n\n\nclickhouse-r\n\n\nRClickhouse\n\n\n\n\n\n\n.NET\n\n\nClickHouse-Net\n\n\n\n\n\n\nC++\n\n\nclickhouse-cpp\n\n\n\n\n\n\nElixir\n\n\nclickhousex\n\n\nclickhouse_ecto\n\n\n\n\n\n\nJava\n\n\nclickhouse-client-java\n\n\n\n\n\n\n\n\nWe have not tested these libraries. They are listed in random order.", - "title": "Libraries from third-party developers" - }, - { - "location": "/interfaces/third-party_client_libraries/#libraries-from-third-party-developers", - "text": "There are libraries for working with ClickHouse for: Python infi.clickhouse_orm sqlalchemy-clickhouse clickhouse-driver clickhouse-client PHP clickhouse-php-client PhpClickHouseClient phpClickHouse clickhouse-client Go clickhouse go-clickhouse mailrugo-clickhouse golang-clickhouse NodeJs clickhouse (NodeJs) node-clickhouse Perl perl-DBD-ClickHouse HTTP-ClickHouse AnyEvent-ClickHouse Ruby clickhouse (Ruby) R clickhouse-r RClickhouse .NET ClickHouse-Net C++ clickhouse-cpp Elixir clickhousex clickhouse_ecto Java clickhouse-client-java We have not tested these libraries. They are listed in random order.", - "title": "Libraries from third-party developers" - }, - { - "location": "/interfaces/third-party_gui/", - "text": "Visual interfaces from third-party developers\n\n\nTabix\n\n\nWeb interface for ClickHouse in the \nTabix\n project.\n\n\nFeatures:\n\n\n\n\nWorks with ClickHouse directly from the browser, without the need to install additional software.\n\n\nQuery editor with syntax highlighting.\n\n\nAuto-completion of commands.\n\n\nTools for graphical analysis of query execution.\n\n\nColor scheme options.\n\n\n\n\nTabix documentation\n.\n\n\nHouseOps\n\n\nHouseOps\n is a unique Desktop ClickHouse Ops UI / IDE for OSX, Linux and Windows.\n\n\nFeatures:\n\n\n\n\nQuery builder;\n\n\nDatabase manangement (soon);\n\n\nUsers manangement (soon);\n\n\nReal-Time Data Analytics (soon);\n\n\nCluster/Infra monitoring (soon);\n\n\nCluster manangement (soon);\n\n\nKafka and Replicated tables monitoring (soon);\n\n\nAnd a lot of others features (soon) for you take a beautiful implementation of ClickHouse.", - "title": "Visual interfaces from third-party developers" - }, - { - "location": "/interfaces/third-party_gui/#visual-interfaces-from-third-party-developers", - "text": "", - "title": "Visual interfaces from third-party developers" - }, - { - "location": "/interfaces/third-party_gui/#tabix", - "text": "Web interface for ClickHouse in the Tabix project.", - "title": "Tabix" - }, - { - "location": "/interfaces/third-party_gui/#features", - "text": "Works with ClickHouse directly from the browser, without the need to install additional software. Query editor with syntax highlighting. Auto-completion of commands. Tools for graphical analysis of query execution. Color scheme options. Tabix documentation .", - "title": "Features:" - }, - { - "location": "/interfaces/third-party_gui/#houseops", - "text": "HouseOps is a unique Desktop ClickHouse Ops UI / IDE for OSX, Linux and Windows.", - "title": "HouseOps" - }, - { - "location": "/interfaces/third-party_gui/#features_1", - "text": "Query builder; Database manangement (soon); Users manangement (soon); Real-Time Data Analytics (soon); Cluster/Infra monitoring (soon); Cluster manangement (soon); Kafka and Replicated tables monitoring (soon); And a lot of others features (soon) for you take a beautiful implementation of ClickHouse.", - "title": "Features:" - }, - { - "location": "/query_language/queries/", - "text": "Queries\n\n\nCREATE DATABASE\n\n\nCreating db_name databases\n\n\nCREATE\n \nDATABASE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \ndb_name\n\n\n\n\n\n\nA database\n is just a directory for tables.\nIf \nIF NOT EXISTS\n is included, the query won't return an error if the database already exists.\n\n\n\n\nCREATE TABLE\n\n\nThe \nCREATE TABLE\n query can have several forms.\n\n\nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n(\n\n \nname1\n \n[\ntype1\n]\n \n[\nDEFAULT\n|\nMATERIALIZED\n|\nALIAS\n \nexpr1\n],\n\n \nname2\n \n[\ntype2\n]\n \n[\nDEFAULT\n|\nMATERIALIZED\n|\nALIAS\n \nexpr2\n],\n\n \n...\n\n\n)\n \nENGINE\n \n=\n \nengine\n\n\n\n\n\n\nCreates a table named 'name' in the 'db' database or the current database if 'db' is not set, with the structure specified in brackets and the 'engine' engine.\nThe structure of the table is a list of column descriptions. If indexes are supported by the engine, they are indicated as parameters for the table engine.\n\n\nA column description is \nname type\n in the simplest case. Example: \nRegionID UInt32\n.\nExpressions can also be defined for default values (see below).\n\n\nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \nAS\n \n[\ndb2\n.]\nname2\n \n[\nENGINE\n \n=\n \nengine\n]\n\n\n\n\n\n\nCreates a table with the same structure as another table. You can specify a different engine for the table. If the engine is not specified, the same engine will be used as for the \ndb2.name2\n table.\n\n\nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \nENGINE\n \n=\n \nengine\n \nAS\n \nSELECT\n \n...\n\n\n\n\n\n\nCreates a table with a structure like the result of the \nSELECT\n query, with the 'engine' engine, and fills it with data from SELECT.\n\n\nIn all cases, if \nIF NOT EXISTS\n is specified, the query won't return an error if the table already exists. In this case, the query won't do anything.\n\n\nDefault values\n\n\nThe column description can specify an expression for a default value, in one of the following ways:\nDEFAULT expr\n, \nMATERIALIZED expr\n, \nALIAS expr\n.\nExample: \nURLDomain String DEFAULT domain(URL)\n.\n\n\nIf an expression for the default value is not defined, the default values will be set to zeros for numbers, empty strings for strings, empty arrays for arrays, and \n0000-00-00\n for dates or \n0000-00-00 00:00:00\n for dates with time. NULLs are not supported.\n\n\nIf the default expression is defined, the column type is optional. If there isn't an explicitly defined type, the default expression type is used. Example: \nEventDate DEFAULT toDate(EventTime)\n \u2013 the 'Date' type will be used for the 'EventDate' column.\n\n\nIf the data type and default expression are defined explicitly, this expression will be cast to the specified type using type casting functions. Example: \nHits UInt32 DEFAULT 0\n means the same thing as \nHits UInt32 DEFAULT toUInt32(0)\n.\n\n\nDefault expressions may be defined as an arbitrary expression from table constants and columns. When creating and changing the table structure, it checks that expressions don't contain loops. For INSERT, it checks that expressions are resolvable \u2013 that all columns they can be calculated from have been passed.\n\n\nDEFAULT expr\n\n\nNormal default value. If the INSERT query doesn't specify the corresponding column, it will be filled in by computing the corresponding expression.\n\n\nMATERIALIZED expr\n\n\nMaterialized expression. Such a column can't be specified for INSERT, because it is always calculated.\nFor an INSERT without a list of columns, these columns are not considered.\nIn addition, this column is not substituted when using an asterisk in a SELECT query. This is to preserve the invariant that the dump obtained using \nSELECT *\n can be inserted back into the table using INSERT without specifying the list of columns.\n\n\nALIAS expr\n\n\nSynonym. Such a column isn't stored in the table at all.\nIts values can't be inserted in a table, and it is not substituted when using an asterisk in a SELECT query.\nIt can be used in SELECTs if the alias is expanded during query parsing.\n\n\nWhen using the ALTER query to add new columns, old data for these columns is not written. Instead, when reading old data that does not have values for the new columns, expressions are computed on the fly by default. However, if running the expressions requires different columns that are not indicated in the query, these columns will additionally be read, but only for the blocks of data that need it.\n\n\nIf you add a new column to a table but later change its default expression, the values used for old data will change (for data where values were not stored on the disk). Note that when running background merges, data for columns that are missing in one of the merging parts is written to the merged part.\n\n\nIt is not possible to set default values for elements in nested data structures.\n\n\nTemporary tables\n\n\nIn all cases, if \nTEMPORARY\n is specified, a temporary table will be created. Temporary tables have the following characteristics:\n\n\n\n\nTemporary tables disappear when the session ends, including if the connection is lost.\n\n\nA temporary table is created with the Memory engine. The other table engines are not supported.\n\n\nThe DB can't be specified for a temporary table. It is created outside of databases.\n\n\nIf a temporary table has the same name as another one and a query specifies the table name without specifying the DB, the temporary table will be used.\n\n\nFor distributed query processing, temporary tables used in a query are passed to remote servers.\n\n\n\n\nIn most cases, temporary tables are not created manually, but when using external data for a query, or for distributed \n(GLOBAL) IN\n. For more information, see the appropriate sections\n\n\nDistributed DDL queries (ON CLUSTER clause)\n\n\nThe \nCREATE\n, \nDROP\n, \nALTER\n, and \nRENAME\n queries support distributed execution on a cluster.\nFor example, the following query creates the \nall_hits\n \nDistributed\n table on each host in \ncluster\n:\n\n\nCREATE\n \nTABLE\n \nIF\n \nNOT\n \nEXISTS\n \nall_hits\n \nON\n \nCLUSTER\n \ncluster\n \n(\np\n \nDate\n,\n \ni\n \nInt32\n)\n \nENGINE\n \n=\n \nDistributed\n(\ncluster\n,\n \ndefault\n,\n \nhits\n)\n\n\n\n\n\n\nIn order to run these queries correctly, each host must have the same cluster definition (to simplify syncing configs, you can use substitutions from ZooKeeper). They must also connect to the ZooKeeper servers.\nThe local version of the query will eventually be implemented on each host in the cluster, even if some hosts are currently not available. The order for executing queries within a single host is guaranteed.\n\nALTER\n queries are not yet supported for replicated tables.\n\n\nCREATE VIEW\n\n\nCREATE\n \n[\nMATERIALIZED\n]\n \nVIEW\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \n[\nTO\n[\ndb\n.]\nname\n]\n \n[\nENGINE\n \n=\n \nengine\n]\n \n[\nPOPULATE\n]\n \nAS\n \nSELECT\n \n...\n\n\n\n\n\n\nCreates a view. There are two types of views: normal and MATERIALIZED.\n\n\nWhen creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data.\n\n\nA materialized view works as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.\n\n\nNormal views don't store any data, but just perform a read from another table. In other words, a normal view is nothing more than a saved query. When reading from a view, this saved query is used as a subquery in the FROM clause.\n\n\nAs an example, assume you've created a view:\n\n\nCREATE\n \nVIEW\n \nview\n \nAS\n \nSELECT\n \n...\n\n\n\n\n\n\nand written a query:\n\n\nSELECT\n \na\n,\n \nb\n,\n \nc\n \nFROM\n \nview\n\n\n\n\n\n\nThis query is fully equivalent to using the subquery:\n\n\nSELECT\n \na\n,\n \nb\n,\n \nc\n \nFROM\n \n(\nSELECT\n \n...)\n\n\n\n\n\n\nMaterialized views store data transformed by the corresponding SELECT query.\n\n\nWhen creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data.\n\n\nA materialized view is arranged as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.\n\n\nIf you specify POPULATE, the existing table data is inserted in the view when creating it, as if making a \nCREATE TABLE ... AS SELECT ...\n . Otherwise, the query contains only the data inserted in the table after creating the view. We don't recommend using POPULATE, since data inserted in the table during the view creation will not be inserted in it.\n\n\nA \nSELECT\n query can contain \nDISTINCT\n, \nGROUP BY\n, \nORDER BY\n, \nLIMIT\n... Note that the corresponding conversions are performed independently on each block of inserted data. For example, if \nGROUP BY\n is set, data is aggregated during insertion, but only within a single packet of inserted data. The data won't be further aggregated. The exception is when using an ENGINE that independently performs data aggregation, such as \nSummingMergeTree\n.\n\n\nThe execution of \nALTER\n queries on materialized views has not been fully developed, so they might be inconvenient. If the materialized view uses the construction \nTO [db.]name\n, you can \nDETACH\n the view, run \nALTER\n for the target table, and then \nATTACH\n the previously detached (\nDETACH\n) view.\n\n\nViews look the same as normal tables. For example, they are listed in the result of the \nSHOW TABLES\n query.\n\n\nThere isn't a separate query for deleting views. To delete a view, use \nDROP TABLE\n.\n\n\nATTACH\n\n\nThis query is exactly the same as \nCREATE\n, but\n\n\n\n\ninstead of the word \nCREATE\n it uses the word \nATTACH\n.\n\n\nThe query doesn't create data on the disk, but assumes that data is already in the appropriate places, and just adds information about the table to the server.\nAfter executing an ATTACH query, the server will know about the existence of the table.\n\n\n\n\nIf the table was previously detached (\nDETACH\n), meaning that its structure is known, you can use shorthand without defining the structure.\n\n\nATTACH\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n\n\n\n\n\n\nThis query is used when starting the server. The server stores table metadata as files with \nATTACH\n queries, which it simply runs at launch (with the exception of system tables, which are explicitly created on the server).\n\n\nDROP\n\n\nThis query has two types: \nDROP DATABASE\n and \nDROP TABLE\n.\n\n\nDROP\n \nDATABASE\n \n[\nIF\n \nEXISTS\n]\n \ndb\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n\n\n\n\nDeletes all tables inside the 'db' database, then deletes the 'db' database itself.\nIf \nIF EXISTS\n is specified, it doesn't return an error if the database doesn't exist.\n\n\nDROP\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n\n\n\n\nDeletes the table.\nIf \nIF EXISTS\n is specified, it doesn't return an error if the table doesn't exist or the database doesn't exist.\n\n\nDETACH\n\n\nDeletes information about the 'name' table from the server. The server stops knowing about the table's existence.\n\n\nDETACH\n \nTABLE\n \n[\nIF\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n\n\n\n\n\n\nThis does not delete the table's data or metadata. On the next server launch, the server will read the metadata and find out about the table again.\nSimilarly, a \"detached\" table can be re-attached using the \nATTACH\n query (with the exception of system tables, which do not have metadata stored for them).\n\n\nThere is no \nDETACH DATABASE\n query.\n\n\nRENAME\n\n\nRenames one or more tables.\n\n\nRENAME\n \nTABLE\n \n[\ndb11\n.]\nname11\n \nTO\n \n[\ndb12\n.]\nname12\n,\n \n[\ndb21\n.]\nname21\n \nTO\n \n[\ndb22\n.]\nname22\n,\n \n...\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n\n\n\n\nAll tables are renamed under global locking. Renaming tables is a light operation. If you indicated another database after TO, the table will be moved to this database. However, the directories with databases must reside in the same file system (otherwise, an error is returned).\n\n\n\n\nALTER\n\n\nThe \nALTER\n query is only supported for \n*MergeTree\n tables, as well as \nMerge\nand\nDistributed\n. The query has several variations.\n\n\nColumn manipulations\n\n\nChanging the table structure.\n\n\nALTER\n \nTABLE\n \n[\ndb\n].\nname\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n \nADD\n|\nDROP\n|\nMODIFY\n \nCOLUMN\n \n...\n\n\n\n\n\n\nIn the query, specify a list of one or more comma-separated actions.\nEach action is an operation on a column.\n\n\nThe following actions are supported:\n\n\nADD\n \nCOLUMN\n \nname\n \n[\ntype\n]\n \n[\ndefault_expr\n]\n \n[\nAFTER\n \nname_after\n]\n\n\n\n\n\n\nAdds a new column to the table with the specified name, type, and \ndefault_expr\n (see the section \"Default expressions\"). If you specify \nAFTER name_after\n (the name of another column), the column is added after the specified one in the list of table columns. Otherwise, the column is added to the end of the table. Note that there is no way to add a column to the beginning of a table. For a chain of actions, 'name_after' can be the name of a column that is added in one of the previous actions.\n\n\nAdding a column just changes the table structure, without performing any actions with data. The data doesn't appear on the disk after ALTER. If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). The column appears on the disk after merging data parts (see MergeTree).\n\n\nThis approach allows us to complete the ALTER query instantly, without increasing the volume of old data.\n\n\nDROP\n \nCOLUMN\n \nname\n\n\n\n\n\n\nDeletes the column with the name 'name'.\nDeletes data from the file system. Since this deletes entire files, the query is completed almost instantly.\n\n\nMODIFY\n \nCOLUMN\n \nname\n \n[\ntype\n]\n \n[\ndefault_expr\n]\n\n\n\n\n\n\nChanges the 'name' column's type to 'type' and/or the default expression to 'default_expr'. When changing the type, values are converted as if the 'toType' function were applied to them.\n\n\nIf only the default expression is changed, the query doesn't do anything complex, and is completed almost instantly.\n\n\nChanging the column type is the only complex action \u2013 it changes the contents of files with data. For large tables, this may take a long time.\n\n\nThere are several processing stages:\n\n\n\n\nPreparing temporary (new) files with modified data.\n\n\nRenaming old files.\n\n\nRenaming the temporary (new) files to the old names.\n\n\nDeleting the old files.\n\n\n\n\nOnly the first stage takes time. If there is a failure at this stage, the data is not changed.\nIf there is a failure during one of the successive stages, data can be restored manually. The exception is if the old files were deleted from the file system but the data for the new files did not get written to the disk and was lost.\n\n\nThere is no support for changing the column type in arrays and nested data structures.\n\n\nThe \nALTER\n query lets you create and delete separate elements (columns) in nested data structures, but not whole nested data structures. To add a nested data structure, you can add columns with a name like \nname.nested_name\n and the type \nArray(T)\n. A nested data structure is equivalent to multiple array columns with a name that has the same prefix before the dot.\n\n\nThere is no support for deleting columns in the primary key or the sampling key (columns that are in the \nENGINE\n expression). Changing the type for columns that are included in the primary key is only possible if this change does not cause the data to be modified (for example, it is allowed to add values to an Enum or change a type with \nDateTime\n to \nUInt32\n).\n\n\nIf the \nALTER\n query is not sufficient for making the table changes you need, you can create a new table, copy the data to it using the \nINSERT SELECT\n query, then switch the tables using the \nRENAME\n query and delete the old table.\n\n\nThe \nALTER\n query blocks all reads and writes for the table. In other words, if a long \nSELECT\n is running at the time of the \nALTER\n query, the \nALTER\n query will wait for it to complete. At the same time, all new queries to the same table will wait while this \nALTER\n is running.\n\n\nFor tables that don't store data themselves (such as \nMerge\n and \nDistributed\n), \nALTER\n just changes the table structure, and does not change the structure of subordinate tables. For example, when running ALTER for a \nDistributed\n table, you will also need to run \nALTER\n for the tables on all remote servers.\n\n\nThe \nALTER\n query for changing columns is replicated. The instructions are saved in ZooKeeper, then each replica applies them. All \nALTER\n queries are run in the same order. The query waits for the appropriate actions to be completed on the other replicas. However, a query to change columns in a replicated table can be interrupted, and all actions will be performed asynchronously.\n\n\nManipulations with partitions and parts\n\n\nIt only works for tables in the \nMergeTree\n family. The following operations are available:\n\n\n\n\nDETACH PARTITION\n \u2013 Move a partition to the 'detached' directory and forget it.\n\n\nDROP PARTITION\n \u2013 Delete a partition.\n\n\nATTACH PART|PARTITION\n \u2013 Add a new part or partition from the \ndetached\n directory to the table.\n\n\nFREEZE PARTITION\n \u2013 Create a backup of a partition.\n\n\nFETCH PARTITION\n \u2013 Download a partition from another server.\n\n\n\n\nEach type of query is covered separately below.\n\n\nA partition in a table is data for a single calendar month. This is determined by the values of the date key specified in the table engine parameters. Each month's data is stored separately in order to simplify manipulations with this data.\n\n\nA \"part\" in the table is part of the data from a single partition, sorted by the primary key.\n\n\nYou can use the \nsystem.parts\n table to view the set of table parts and partitions:\n\n\nSELECT\n \n*\n \nFROM\n \nsystem\n.\nparts\n \nWHERE\n \nactive\n\n\n\n\n\n\nactive\n \u2013 Only count active parts. Inactive parts are, for example, source parts remaining after merging to a larger part \u2013 these parts are deleted approximately 10 minutes after merging.\n\n\nAnother way to view a set of parts and partitions is to go into the directory with table data.\nData directory: \n/var/lib/clickhouse/data/database/table/\n,where \n/var/lib/clickhouse/\n is the path to the ClickHouse data, 'database' is the database name, and 'table' is the table name. Example:\n\n\n$ ls -l /var/lib/clickhouse/data/test/visits/\ntotal \n48\n\ndrwxrwxrwx \n2\n clickhouse clickhouse \n20480\n May \n5\n \n02\n:58 20140317_20140323_2_2_0\ndrwxrwxrwx \n2\n clickhouse clickhouse \n20480\n May \n5\n \n02\n:58 20140317_20140323_4_4_0\ndrwxrwxrwx \n2\n clickhouse clickhouse \n4096\n May \n5\n \n02\n:55 detached\n-rw-rw-rw- \n1\n clickhouse clickhouse \n2\n May \n5\n \n02\n:58 increment.txt\n\n\n\n\n\nHere, \n20140317_20140323_2_2_0\n and \n20140317_20140323_4_4_0\n are the directories of data parts.\n\n\nLet's break down the name of the first part: \n20140317_20140323_2_2_0\n.\n\n\n\n\n20140317\n is the minimum date of the data in the chunk.\n\n\n20140323\n is the maximum date of the data in the chunk.\n\n\n2\n is the minimum number of the data block.\n\n\n2\n is the maximum number of the data block.\n\n\n0\n is the chunk level (the depth of the merge tree it is formed from).\n\n\n\n\nEach piece relates to a single partition and contains data for just one month.\n\n201403\n is the name of the partition. A partition is a set of parts for a single month.\n\n\nOn an operating server, you can't manually change the set of parts or their data on the file system, since the server won't know about it.\nFor non-replicated tables, you can do this when the server is stopped, but we don't recommended it.\nFor replicated tables, the set of parts can't be changed in any case.\n\n\nThe \ndetached\n directory contains parts that are not used by the server - detached from the table using the \nALTER ... DETACH\n query. Parts that are damaged are also moved to this directory, instead of deleting them. You can add, delete, or modify the data in the 'detached' directory at any time \u2013 the server won't know about this until you make the \nALTER TABLE ... ATTACH\n query.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nDETACH\n \nPARTITION\n \nname\n\n\n\n\n\n\nMove all data for partitions named 'name' to the 'detached' directory and forget about them.\nThe partition name is specified in YYYYMM format. It can be indicated in single quotes or without them.\n\n\nAfter the query is executed, you can do whatever you want with the data in the 'detached' directory \u2014 delete it from the file system, or just leave it.\n\n\nThe query is replicated \u2013 data will be moved to the 'detached' directory and forgotten on all replicas. The query can only be sent to a leader replica. To find out if a replica is a leader, perform SELECT to the 'system.replicas' system table. Alternatively, it is easier to make a query on all replicas, and all except one will throw an exception.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nDROP\n \nPARTITION\n \nname\n\n\n\n\n\n\nThe same as the \nDETACH\n operation. Deletes data from the table. Data parts will be tagged as inactive and will be completely deleted in approximately 10 minutes. The query is replicated \u2013 data will be deleted on all replicas.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nATTACH\n \nPARTITION\n|\nPART\n \nname\n\n\n\n\n\n\nAdds data to the table from the 'detached' directory.\n\n\nIt is possible to add data for an entire partition or a separate part. For a part, specify the full name of the part in single quotes.\n\n\nThe query is replicated. Each replica checks whether there is data in the 'detached' directory. If there is data, it checks the integrity, verifies that it matches the data on the server that initiated the query, and then adds it if everything is correct. If not, it downloads data from the query requestor replica, or from another replica where the data has already been added.\n\n\nSo you can put data in the 'detached' directory on one replica, and use the ALTER ... ATTACH query to add it to the table on all replicas.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nFREEZE\n \nPARTITION\n \nname\n\n\n\n\n\n\nCreates a local backup of one or multiple partitions. The name can be the full name of the partition (for example, 201403), or its prefix (for example, 2014): then the backup will be created for all the corresponding partitions.\n\n\nThe query does the following: for a data snapshot at the time of execution, it creates hardlinks to table data in the directory \n/var/lib/clickhouse/shadow/N/...\n\n\n/var/lib/clickhouse/\n is the working ClickHouse directory from the config.\n\nN\n is the incremental number of the backup.\n\n\nThe same structure of directories is created inside the backup as inside \n/var/lib/clickhouse/\n.\nIt also performs 'chmod' for all files, forbidding writes to them.\n\n\nThe backup is created almost instantly (but first it waits for current queries to the corresponding table to finish running). At first, the backup doesn't take any space on the disk. As the system works, the backup can take disk space, as data is modified. If the backup is made for old enough data, it won't take space on the disk.\n\n\nAfter creating the backup, data from \n/var/lib/clickhouse/shadow/\n can be copied to the remote server and then deleted on the local server.\nThe entire backup process is performed without stopping the server.\n\n\nThe \nALTER ... FREEZE PARTITION\n query is not replicated. A local backup is only created on the local server.\n\n\nAs an alternative, you can manually copy data from the \n/var/lib/clickhouse/data/database/table\n directory.\nBut if you do this while the server is running, race conditions are possible when copying directories with files being added or changed, and the backup may be inconsistent. You can do this if the server isn't running \u2013 then the resulting data will be the same as after the \nALTER TABLE t FREEZE PARTITION\n query.\n\n\nALTER TABLE ... FREEZE PARTITION\n only copies data, not table metadata. To make a backup of table metadata, copy the file \n/var/lib/clickhouse/metadata/database/table.sql\n\n\nTo restore from a backup:\n\n\n\n\n\n\nUse the CREATE query to create the table if it doesn't exist. The query can be taken from an .sql file (replace \nATTACH\n in it with \nCREATE\n).\n\n\nCopy the data from the data/database/table/ directory inside the backup to the \n/var/lib/clickhouse/data/database/table/detached/ directory.\n\n\nRun \nALTER TABLE ... ATTACH PARTITION YYYYMM\n queries, where \nYYYYMM\n is the month, for every month.\n\n\n\n\n\n\nIn this way, data from the backup will be added to the table.\nRestoring from a backup doesn't require stopping the server.\n\n\nBackups and replication\n\n\nReplication provides protection from device failures. If all data disappeared on one of your replicas, follow the instructions in the \"Restoration after failure\" section to restore it.\n\n\nFor protection from device failures, you must use replication. For more information about replication, see the section \"Data replication\".\n\n\nBackups protect against human error (accidentally deleting data, deleting the wrong data or in the wrong cluster, or corrupting data).\nFor high-volume databases, it can be difficult to copy backups to remote servers. In such cases, to protect from human error, you can keep a backup on the same server (it will reside in \n/var/lib/clickhouse/shadow/\n).\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nFETCH\n \nPARTITION\n \nname\n \nFROM\n \npath-in-zookeeper\n\n\n\n\n\n\nThis query only works for replicatable tables.\n\n\nIt downloads the specified partition from the shard that has its \nZooKeeper path\n specified in the \nFROM\n clause, then puts it in the \ndetached\n directory for the specified table.\n\n\nAlthough the query is called \nALTER TABLE\n, it does not change the table structure, and does not immediately change the data available in the table.\n\n\nData is placed in the \ndetached\n directory. You can use the \nALTER TABLE ... ATTACH\n query to attach the data.\n\n\nThe \nFROM\n clause specifies the path in \nZooKeeper\n. For example, \n/clickhouse/tables/01-01/visits\n.\nBefore downloading, the system checks that the partition exists and the table structure matches. The most appropriate replica is selected automatically from the healthy replicas.\n\n\nThe \nALTER ... FETCH PARTITION\n query is not replicated. The partition will be downloaded to the 'detached' directory only on the local server. Note that if after this you use the \nALTER TABLE ... ATTACH\n query to add data to the table, the data will be added on all replicas (on one of the replicas it will be added from the 'detached' directory, and on the rest it will be loaded from neighboring replicas).\n\n\nSynchronicity of ALTER queries\n\n\nFor non-replicatable tables, all \nALTER\n queries are performed synchronously. For replicatable tables, the query just adds instructions for the appropriate actions to \nZooKeeper\n, and the actions themselves are performed as soon as possible. However, the query can wait for these actions to be completed on all the replicas.\n\n\nFor \nALTER ... ATTACH|DETACH|DROP\n queries, you can use the \nreplication_alter_partitions_sync\n setting to set up waiting.\nPossible values: \n0\n \u2013 do not wait; \n1\n \u2013 only wait for own execution (default); \n2\n \u2013 wait for all.\n\n\n\n\nSHOW DATABASES\n\n\nSHOW\n \nDATABASES\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nPrints a list of all databases.\nThis query is identical to \nSELECT name FROM system.databases [INTO OUTFILE filename] [FORMAT format]\n.\n\n\nSee also the section \"Formats\".\n\n\nSHOW TABLES\n\n\nSHOW\n \n[\nTEMPORARY\n]\n \nTABLES\n \n[\nFROM\n \ndb\n]\n \n[\nLIKE\n \npattern\n]\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nDisplays a list of tables\n\n\n\n\ntables from the current database, or from the 'db' database if \"FROM db\" is specified.\n\n\nall tables, or tables whose name matches the pattern, if \"LIKE 'pattern'\" is specified.\n\n\n\n\nThis query is identical to: \nSELECT name FROM system.tables WHERE database = 'db' [AND name LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format]\n.\n\n\nSee also the section \"LIKE operator\".\n\n\nSHOW PROCESSLIST\n\n\nSHOW\n \nPROCESSLIST\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nOutputs a list of queries currently being processed, other than \nSHOW PROCESSLIST\n queries.\n\n\nPrints a table containing the columns:\n\n\nuser\n \u2013 The user who made the query. Keep in mind that for distributed processing, queries are sent to remote servers under the 'default' user. SHOW PROCESSLIST shows the username for a specific query, not for a query that this query initiated.\n\n\naddress\n \u2013 The name of the host that the query was sent from. For distributed processing, on remote servers, this is the name of the query requestor host. To track where a distributed query was originally made from, look at SHOW PROCESSLIST on the query requestor server.\n\n\nelapsed\n \u2013 The execution time, in seconds. Queries are output in order of decreasing execution time.\n\n\nrows_read\n, \nbytes_read\n \u2013 How many rows and bytes of uncompressed data were read when processing the query. For distributed processing, data is totaled from all the remote servers. This is the data used for restrictions and quotas.\n\n\nmemory_usage\n \u2013 Current RAM usage in bytes. See the setting 'max_memory_usage'.\n\n\nquery\n \u2013 The query itself. In INSERT queries, the data for insertion is not output.\n\n\nquery_id\n \u2013 The query identifier. Non-empty only if it was explicitly defined by the user. For distributed processing, the query ID is not passed to remote servers.\n\n\nThis query is identical to: \nSELECT * FROM system.processes [INTO OUTFILE filename] [FORMAT format]\n.\n\n\nTip (execute in the console):\n\n\nwatch -n1 \nclickhouse-client --query=\nSHOW PROCESSLIST\n\n\n\n\n\n\nSHOW CREATE TABLE\n\n\nSHOW\n \nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\ndb\n.]\ntable\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nReturns a single \nString\n-type 'statement' column, which contains a single value \u2013 the \nCREATE\n query used for creating the specified table.\n\n\nDESCRIBE TABLE\n\n\nDESC\n|\nDESCRIBE\n \nTABLE\n \n[\ndb\n.]\ntable\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nReturns two \nString\n-type columns: \nname\n and \ntype\n, which indicate the names and types of columns in the specified table.\n\n\nNested data structures are output in \"expanded\" format. Each column is shown separately, with the name after a dot.\n\n\nEXISTS\n\n\nEXISTS\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\ndb\n.]\nname\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nReturns a single \nUInt8\n-type column, which contains the single value \n0\n if the table or database doesn't exist, or \n1\n if the table exists in the specified database.\n\n\nUSE\n\n\nUSE\n \ndb\n\n\n\n\n\n\nLets you set the current database for the session.\nThe current database is used for searching for tables if the database is not explicitly defined in the query with a dot before the table name.\nThis query can't be made when using the HTTP protocol, since there is no concept of a session.\n\n\nSET\n\n\nSET\n \nparam\n \n=\n \nvalue\n\n\n\n\n\n\nAllows you to set \nparam\n to \nvalue\n. You can also make all the settings from the specified settings profile in a single query. To do this, specify 'profile' as the setting name. For more information, see the section \"Settings\".\nThe setting is made for the session, or for the server (globally) if \nGLOBAL\n is specified.\nWhen making a global setting, the setting is not applied to sessions already running, including the current session. It will only be used for new sessions.\n\n\nWhen the server is restarted, global settings made using \nSET\n are lost.\nTo make settings that persist after a server restart, you can only use the server's config file.\n\n\nOPTIMIZE\n\n\nOPTIMIZE\n \nTABLE\n \n[\ndb\n.]\nname\n \n[\nPARTITION\n \npartition\n]\n \n[\nFINAL\n]\n\n\n\n\n\n\nAsks the table engine to do something for optimization.\nSupported only by \n*MergeTree\n engines, in which this query initializes a non-scheduled merge of data parts.\nIf you specify a \nPARTITION\n, only the specified partition will be optimized.\nIf you specify \nFINAL\n, optimization will be performed even when all the data is already in one part.\n\n\n\n\nINSERT\n\n\nAdding data.\n\n\nBasic query format:\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nVALUES\n \n(\nv11\n,\n \nv12\n,\n \nv13\n),\n \n(\nv21\n,\n \nv22\n,\n \nv23\n),\n \n...\n\n\n\n\n\n\nThe query can specify a list of columns to insert \n[(c1, c2, c3)]\n. In this case, the rest of the columns are filled with:\n\n\n\n\nThe values calculated from the \nDEFAULT\n expressions specified in the table definition.\n\n\nZeros and empty strings, if \nDEFAULT\n expressions are not defined.\n\n\n\n\nIf \nstrict_insert_defaults=1\n, columns that do not have \nDEFAULT\n defined must be listed in the query.\n\n\nData can be passed to the INSERT in any \nformat\n supported by ClickHouse. The format must be specified explicitly in the query:\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nFORMAT\n \nformat_name\n \ndata_set\n\n\n\n\n\n\nFor example, the following query format is identical to the basic version of INSERT ... VALUES:\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nFORMAT\n \nValues\n \n(\nv11\n,\n \nv12\n,\n \nv13\n),\n \n(\nv21\n,\n \nv22\n,\n \nv23\n),\n \n...\n\n\n\n\n\n\nClickHouse removes all spaces and one line feed (if there is one) before the data. When forming a query, we recommend putting the data on a new line after the query operators (this is important if the data begins with spaces).\n\n\nExample:\n\n\nINSERT\n \nINTO\n \nt\n \nFORMAT\n \nTabSeparated\n\n\n11\n \nHello\n,\n \nworld\n!\n\n\n22\n \nQwerty\n\n\n\n\n\n\nYou can insert data separately from the query by using the command-line client or the HTTP interface. For more information, see the section \"\nInterfaces\n\".\n\n\nInserting the results of \nSELECT\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nSELECT\n \n...\n\n\n\n\n\n\nColumns are mapped according to their position in the SELECT clause. However, their names in the SELECT expression and the table for INSERT may differ. If necessary, type casting is performed.\n\n\nNone of the data formats except Values allow setting values to expressions such as \nnow()\n, \n1 + 2\n, and so on. The Values format allows limited use of expressions, but this is not recommended, because in this case inefficient code is used for their execution.\n\n\nOther queries for modifying data parts are not supported: \nUPDATE\n, \nDELETE\n, \nREPLACE\n, \nMERGE\n, \nUPSERT\n, \nINSERT UPDATE\n.\nHowever, you can delete old data using \nALTER TABLE ... DROP PARTITION\n.\n\n\nPerformance considerations\n\n\nINSERT\n sorts the input data by primary key and splits them into partitions by month. If you insert data for mixed months, it can significantly reduce the performance of the \nINSERT\n query. To avoid this:\n\n\n\n\nAdd data in fairly large batches, such as 100,000 rows at a time.\n\n\nGroup data by month before uploading it to ClickHouse.\n\n\n\n\nPerformance will not decrease if:\n\n\n\n\nData is added in real time.\n\n\nYou upload data that is usually sorted by time.\n\n\n\n\nSELECT\n\n\nData sampling.\n\n\nSELECT\n \n[\nDISTINCT\n]\n \nexpr_list\n\n \n[\nFROM\n \n[\ndb\n.]\ntable\n \n|\n \n(\nsubquery\n)\n \n|\n \ntable_function\n]\n \n[\nFINAL\n]\n\n \n[\nSAMPLE\n \nsample_coeff\n]\n\n \n[\nARRAY\n \nJOIN\n \n...]\n\n \n[\nGLOBAL\n]\n \nANY\n|\nALL\n \nINNER\n|\nLEFT\n \nJOIN\n \n(\nsubquery\n)\n|\ntable\n \nUSING\n \ncolumns_list\n\n \n[\nPREWHERE\n \nexpr\n]\n\n \n[\nWHERE\n \nexpr\n]\n\n \n[\nGROUP\n \nBY\n \nexpr_list\n]\n \n[\nWITH\n \nTOTALS\n]\n\n \n[\nHAVING\n \nexpr\n]\n\n \n[\nORDER\n \nBY\n \nexpr_list\n]\n\n \n[\nLIMIT\n \n[\nn\n,\n \n]\nm\n]\n\n \n[\nUNION\n \nALL\n \n...]\n\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n\n \n[\nFORMAT\n \nformat\n]\n\n \n[\nLIMIT\n \nn\n \nBY\n \ncolumns\n]\n\n\n\n\n\n\nAll the clauses are optional, except for the required list of expressions immediately after SELECT.\nThe clauses below are described in almost the same order as in the query execution conveyor.\n\n\nIf the query omits the \nDISTINCT\n, \nGROUP BY\n and \nORDER BY\n clauses and the \nIN\n and \nJOIN\n subqueries, the query will be completely stream processed, using O(1) amount of RAM.\nOtherwise, the query might consume a lot of RAM if the appropriate restrictions are not specified: \nmax_memory_usage\n, \nmax_rows_to_group_by\n, \nmax_rows_to_sort\n, \nmax_rows_in_distinct\n, \nmax_bytes_in_distinct\n, \nmax_rows_in_set\n, \nmax_bytes_in_set\n, \nmax_rows_in_join\n, \nmax_bytes_in_join\n, \nmax_bytes_before_external_sort\n, \nmax_bytes_before_external_group_by\n. For more information, see the section \"Settings\". It is possible to use external sorting (saving temporary tables to a disk) and external aggregation. \nThe system does not have \"merge join\"\n.\n\n\nFROM clause\n\n\nIf the FROM clause is omitted, data will be read from the \nsystem.one\n table.\nThe 'system.one' table contains exactly one row (this table fulfills the same purpose as the DUAL table found in other DBMSs).\n\n\nThe FROM clause specifies the table to read data from, or a subquery, or a table function; ARRAY JOIN and the regular JOIN may also be included (see below).\n\n\nInstead of a table, the SELECT subquery may be specified in brackets.\nIn this case, the subquery processing pipeline will be built into the processing pipeline of an external query.\nIn contrast to standard SQL, a synonym does not need to be specified after a subquery. For compatibility, it is possible to write 'AS name' after a subquery, but the specified name isn't used anywhere.\n\n\nA table function may be specified instead of a table. For more information, see the section \"Table functions\".\n\n\nTo execute a query, all the columns listed in the query are extracted from the appropriate table. Any columns not needed for the external query are thrown out of the subqueries.\nIf a query does not list any columns (for example, SELECT count() FROM t), some column is extracted from the table anyway (the smallest one is preferred), in order to calculate the number of rows.\n\n\nThe FINAL modifier can be used only for a SELECT from a CollapsingMergeTree table. When you specify FINAL, data is selected fully \"collapsed\". Keep in mind that using FINAL leads to a selection that includes columns related to the primary key, in addition to the columns specified in the SELECT. Additionally, the query will be executed in a single stream, and data will be merged during query execution. This means that when using FINAL, the query is processed more slowly. In most cases, you should avoid using FINAL. For more information, see the section \"CollapsingMergeTree engine\".\n\n\nSAMPLE clause\n\n\nThe SAMPLE clause allows for approximated query processing. Approximated query processing is only supported by MergeTree* type tables, and only if the sampling expression was specified during table creation (see the section \"MergeTree engine\").\n\n\nSAMPLE\n has the \nformat SAMPLE k\n, where \nk\n is a decimal number from 0 to 1, or \nSAMPLE n\n, where 'n' is a sufficiently large integer.\n\n\nIn the first case, the query will be executed on 'k' percent of data. For example, \nSAMPLE 0.1\n runs the query on 10% of data.\nIn the second case, the query will be executed on a sample of no more than 'n' rows. For example, \nSAMPLE 10000000\n runs the query on a maximum of 10,000,000 rows.\n\n\nExample:\n\n\nSELECT\n\n \nTitle\n,\n\n \ncount\n()\n \n*\n \n10\n \nAS\n \nPageViews\n\n\nFROM\n \nhits_distributed\n\n\nSAMPLE\n \n0\n.\n1\n\n\nWHERE\n\n \nCounterID\n \n=\n \n34\n\n \nAND\n \ntoDate\n(\nEventDate\n)\n \n=\n \ntoDate\n(\n2013-01-29\n)\n\n \nAND\n \ntoDate\n(\nEventDate\n)\n \n=\n \ntoDate\n(\n2013-02-04\n)\n\n \nAND\n \nNOT\n \nDontCountHits\n\n \nAND\n \nNOT\n \nRefresh\n\n \nAND\n \nTitle\n \n!=\n \n\n\nGROUP\n \nBY\n \nTitle\n\n\nORDER\n \nBY\n \nPageViews\n \nDESC\n \nLIMIT\n \n1000\n\n\n\n\n\n\nIn this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value 'count()' is manually multiplied by 10.\n\n\nWhen using something like \nSAMPLE 10000000\n, there isn't any information about which relative percent of data was processed or what the aggregate functions should be multiplied by, so this method of writing is not always appropriate to the situation.\n\n\nA sample with a relative coefficient is \"consistent\": if we look at all possible data that could be in the table, a sample (when using a single sampling expression specified during table creation) with the same coefficient always selects the same subset of possible data. In other words, a sample from different tables on different servers at different times is made the same way.\n\n\nFor example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This allows using the sample in subqueries in the IN clause, as well as for manually correlating results of different queries with samples.\n\n\nARRAY JOIN clause\n\n\nAllows executing JOIN with an array or nested data structure. The intent is similar to the 'arrayJoin' function, but its functionality is broader.\n\n\nARRAY JOIN\n is essentially \nINNER JOIN\n with an array. Example:\n\n\n:) CREATE TABLE arrays_test (s String, arr Array(UInt8)) ENGINE = Memory\n\nCREATE TABLE arrays_test\n(\n s String,\n arr Array(UInt8)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.001 sec.\n\n:) INSERT INTO arrays_test VALUES (\nHello\n, [1,2]), (\nWorld\n, [3,4,5]), (\nGoodbye\n, [])\n\nINSERT INTO arrays_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM arrays_test\n\nSELECT *\nFROM arrays_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502\n\u2502 Goodbye \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, arr FROM arrays_test ARRAY JOIN arr\n\nSELECT s, arr\nFROM arrays_test\nARRAY JOIN arr\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2510\n\u2502 Hello \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502\n\u2502 World \u2502 4 \u2502\n\u2502 World \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nAn alias can be specified for an array in the ARRAY JOIN clause. In this case, an array item can be accessed by this alias, but the array itself by the original name. Example:\n\n\n:) SELECT s, arr, a FROM arrays_test ARRAY JOIN arr AS a\n\nSELECT s, arr, a\nFROM arrays_test\nARRAY JOIN arr AS a\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nMultiple arrays of the same size can be comma-separated in the ARRAY JOIN clause. In this case, JOIN is performed with them simultaneously (the direct sum, not the direct product). Example:\n\n\n:) SELECT s, arr, a, num, mapped FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(x -\n x + 1, arr) AS mapped\n\nSELECT s, arr, a, num, mapped\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(lambda(tuple(x), plus(x, 1)), arr) AS mapped\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500mapped\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 2 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 5 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 6 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n:) SELECT s, arr, a, num, arrayEnumerate(arr) FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\nSELECT s, arr, a, num, arrayEnumerate(arr)\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500arrayEnumerate(arr)\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 [1,2] \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n\n\n\n\nARRAY JOIN also works with nested data structures. Example:\n\n\n:) CREATE TABLE nested_test (s String, nest Nested(x UInt8, y UInt32)) ENGINE = Memory\n\nCREATE TABLE nested_test\n(\n s String,\n nest Nested(\n x UInt8,\n y UInt32)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.006 sec.\n\n:) INSERT INTO nested_test VALUES (\nHello\n, [1,2], [10,20]), (\nWorld\n, [3,4,5], [30,40,50]), (\nGoodbye\n, [], [])\n\nINSERT INTO nested_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM nested_test\n\nSELECT *\nFROM nested_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 Goodbye \u2502 [] \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nWhen specifying names of nested data structures in ARRAY JOIN, the meaning is the same as ARRAY JOIN with all the array elements that it consists of. Example:\n\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x, nest.y\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`, `nest.y`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nThis variation also makes sense:\n\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nAn alias may be used for a nested data structure, in order to select either the JOIN result or the source array. Example:\n\n\n:) SELECT s, n.x, n.y, nest.x, nest.y FROM nested_test ARRAY JOIN nest AS n\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest AS n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nExample of using the arrayEnumerate function:\n\n\n:) SELECT s, n.x, n.y, nest.x, nest.y, num FROM nested_test ARRAY JOIN nest AS n, arrayEnumerate(nest.x) AS num\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`, num\nFROM nested_test\nARRAY JOIN nest AS n, arrayEnumerate(`nest.x`) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u252c\u2500num\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 1 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 2 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 3 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n\n\n\n\nThe query can only specify a single ARRAY JOIN clause.\n\n\nThe corresponding conversion can be performed before the WHERE/PREWHERE clause (if its result is needed in this clause), or after completing WHERE/PREWHERE (to reduce the volume of calculations).\n\n\nJOIN clause\n\n\nThe normal JOIN, which is not related to ARRAY JOIN described above.\n\n\n[\nGLOBAL\n]\n \nANY\n|\nALL\n \nINNER\n|\nLEFT\n \n[\nOUTER\n]\n \nJOIN\n \n(\nsubquery\n)\n|\ntable\n \nUSING\n \ncolumns_list\n\n\n\n\n\n\nPerforms joins with data from the subquery. At the beginning of query processing, the subquery specified after JOIN is run, and its result is saved in memory. Then it is read from the \"left\" table specified in the FROM clause, and while it is being read, for each of the read rows from the \"left\" table, rows are selected from the subquery results table (the \"right\" table) that meet the condition for matching the values of the columns specified in USING.\n\n\nThe table name can be specified instead of a subquery. This is equivalent to the \nSELECT * FROM table\n subquery, except in a special case when the table has the Join engine \u2013 an array prepared for joining.\n\n\nAll columns that are not needed for the JOIN are deleted from the subquery.\n\n\nThere are several types of JOINs:\n\n\nINNER\n or \nLEFT\n type:If INNER is specified, the result will contain only those rows that have a matching row in the right table.\nIf LEFT is specified, any rows in the left table that don't have matching rows in the right table will be assigned the default value - zeros or empty rows. LEFT OUTER may be written instead of LEFT; the word OUTER does not affect anything.\n\n\nANY\n or \nALL\n stringency:If \nANY\n is specified and the right table has several matching rows, only the first one found is joined.\nIf \nALL\n is specified and the right table has several matching rows, the data will be multiplied by the number of these rows.\n\n\nUsing ALL corresponds to the normal JOIN semantic from standard SQL.\nUsing ANY is optimal. If the right table has only one matching row, the results of ANY and ALL are the same. You must specify either ANY or ALL (neither of them is selected by default).\n\n\nGLOBAL\n distribution:\n\n\nWhen using a normal JOIN, the query is sent to remote servers. Subqueries are run on each of them in order to make the right table, and the join is performed with this table. In other words, the right table is formed on each server separately.\n\n\nWhen using \nGLOBAL ... JOIN\n, first the requestor server runs a subquery to calculate the right table. This temporary table is passed to each remote server, and queries are run on them using the temporary data that was transmitted.\n\n\nBe careful when using GLOBAL JOINs. For more information, see the section \"Distributed subqueries\".\n\n\nAny combination of JOINs is possible. For example, \nGLOBAL ANY LEFT OUTER JOIN\n.\n\n\nWhen running a JOIN, there is no optimization of the order of execution in relation to other stages of the query. The join (a search in the right table) is run before filtering in WHERE and before aggregation. In order to explicitly set the processing order, we recommend running a JOIN subquery with a subquery.\n\n\nExample:\n\n\nSELECT\n\n \nCounterID\n,\n\n \nhits\n,\n\n \nvisits\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nCounterID\n,\n\n \ncount\n()\n \nAS\n \nhits\n\n \nFROM\n \ntest\n.\nhits\n\n \nGROUP\n \nBY\n \nCounterID\n\n\n)\n \nANY\n \nLEFT\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nCounterID\n,\n\n \nsum\n(\nSign\n)\n \nAS\n \nvisits\n\n \nFROM\n \ntest\n.\nvisits\n\n \nGROUP\n \nBY\n \nCounterID\n\n\n)\n \nUSING\n \nCounterID\n\n\nORDER\n \nBY\n \nhits\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500CounterID\u2500\u252c\u2500\u2500\u2500hits\u2500\u252c\u2500visits\u2500\u2510\n\u2502 1143050 \u2502 523264 \u2502 13665 \u2502\n\u2502 731962 \u2502 475698 \u2502 102716 \u2502\n\u2502 722545 \u2502 337212 \u2502 108187 \u2502\n\u2502 722889 \u2502 252197 \u2502 10547 \u2502\n\u2502 2237260 \u2502 196036 \u2502 9522 \u2502\n\u2502 23057320 \u2502 147211 \u2502 7689 \u2502\n\u2502 722818 \u2502 90109 \u2502 17847 \u2502\n\u2502 48221 \u2502 85379 \u2502 4652 \u2502\n\u2502 19762435 \u2502 77807 \u2502 7026 \u2502\n\u2502 722884 \u2502 77492 \u2502 11056 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSubqueries don't allow you to set names or use them for referencing a column from a specific subquery.\nThe columns specified in USING must have the same names in both subqueries, and the other columns must be named differently. You can use aliases to change the names of columns in subqueries (the example uses the aliases 'hits' and 'visits').\n\n\nThe USING clause specifies one or more columns to join, which establishes the equality of these columns. The list of columns is set without brackets. More complex join conditions are not supported.\n\n\nThe right table (the subquery result) resides in RAM. If there isn't enough memory, you can't run a JOIN.\n\n\nOnly one JOIN can be specified in a query (on a single level). To run multiple JOINs, you can put them in subqueries.\n\n\nEach time a query is run with the same JOIN, the subquery is run again \u2013 the result is not cached. To avoid this, use the special 'Join' table engine, which is a prepared array for joining that is always in RAM. For more information, see the section \"Table engines, Join\".\n\n\nIn some cases, it is more efficient to use IN instead of JOIN.\nAmong the various types of JOINs, the most efficient is ANY LEFT JOIN, then ANY INNER JOIN. The least efficient are ALL LEFT JOIN and ALL INNER JOIN.\n\n\nIf you need a JOIN for joining with dimension tables (these are relatively small tables that contain dimension properties, such as names for advertising campaigns), a JOIN might not be very convenient due to the bulky syntax and the fact that the right table is re-accessed for every query. For such cases, there is an \"external dictionaries\" feature that you should use instead of JOIN. For more information, see the section \"External dictionaries\".\n\n\nWHERE clause\n\n\nIf there is a WHERE clause, it must contain an expression with the UInt8 type. This is usually an expression with comparison and logical operators.\nThis expression will be used for filtering data before all other transformations.\n\n\nIf indexes are supported by the database table engine, the expression is evaluated on the ability to use indexes.\n\n\nPREWHERE clause\n\n\nThis clause has the same meaning as the WHERE clause. The difference is in which data is read from the table.\nWhen using PREWHERE, first only the columns necessary for executing PREWHERE are read. Then the other columns are read that are needed for running the query, but only those blocks where the PREWHERE expression is true.\n\n\nIt makes sense to use PREWHERE if there are filtration conditions that are not suitable for indexes that are used by a minority of the columns in the query, but that provide strong data filtration. This reduces the volume of data to read.\n\n\nFor example, it is useful to write PREWHERE for queries that extract a large number of columns, but that only have filtration for a few columns.\n\n\nPREWHERE is only supported by tables from the \n*MergeTree\n family.\n\n\nA query may simultaneously specify PREWHERE and WHERE. In this case, PREWHERE precedes WHERE.\n\n\nKeep in mind that it does not make much sense for PREWHERE to only specify those columns that have an index, because when using an index, only the data blocks that match the index are read.\n\n\nIf the 'optimize_move_to_prewhere' setting is set to 1 and PREWHERE is omitted, the system uses heuristics to automatically move parts of expressions from WHERE to PREWHERE.\n\n\nGROUP BY clause\n\n\nThis is one of the most important parts of a column-oriented DBMS.\n\n\nIf there is a GROUP BY clause, it must contain a list of expressions. Each expression will be referred to here as a \"key\".\nAll the expressions in the SELECT, HAVING, and ORDER BY clauses must be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions.\n\n\nIf a query contains only table columns inside aggregate functions, the GROUP BY clause can be omitted, and aggregation by an empty set of keys is assumed.\n\n\nExample:\n\n\nSELECT\n\n \ncount\n(),\n\n \nmedian\n(\nFetchTiming\n \n \n60\n \n?\n \n60\n \n:\n \nFetchTiming\n),\n\n \ncount\n()\n \n-\n \nsum\n(\nRefresh\n)\n\n\nFROM\n \nhits\n\n\n\n\n\n\nHowever, in contrast to standard SQL, if the table doesn't have any rows (either there aren't any at all, or there aren't any after using WHERE to filter), an empty result is returned, and not the result from one of the rows containing the initial values of aggregate functions.\n\n\nAs opposed to MySQL (and conforming to standard SQL), you can't get some value of some column that is not in a key or aggregate function (except constant expressions). To work around this, you can use the 'any' aggregate function (get the first encountered value) or 'min/max'.\n\n\nExample:\n\n\nSELECT\n\n \ndomainWithoutWWW\n(\nURL\n)\n \nAS\n \ndomain\n,\n\n \ncount\n(),\n\n \nany\n(\nTitle\n)\n \nAS\n \ntitle\n \n-- getting the first occurred page header for each domain.\n\n\nFROM\n \nhits\n\n\nGROUP\n \nBY\n \ndomain\n\n\n\n\n\n\nFor every different key value encountered, GROUP BY calculates a set of aggregate function values.\n\n\nGROUP BY is not supported for array columns.\n\n\nA constant can't be specified as arguments for aggregate functions. Example: sum(1). Instead of this, you can get rid of the constant. Example: \ncount()\n.\n\n\nWITH TOTALS modifier\n\n\nIf the WITH TOTALS modifier is specified, another row will be calculated. This row will have key columns containing default values (zeros or empty lines), and columns of aggregate functions with the values calculated across all the rows (the \"total\" values).\n\n\nThis extra row is output in JSON*, TabSeparated*, and Pretty* formats, separately from the other rows. In the other formats, this row is not output.\n\n\nIn JSON* formats, this row is output as a separate 'totals' field. In TabSeparated* formats, the row comes after the main result, preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result.\n\n\nWITH TOTALS\n can be run in different ways when HAVING is present. The behavior depends on the 'totals_mode' setting.\nBy default, \ntotals_mode = 'before_having'\n. In this case, 'totals' is calculated across all rows, including the ones that don't pass through HAVING and 'max_rows_to_group_by'.\n\n\nThe other alternatives include only the rows that pass through HAVING in 'totals', and behave differently with the setting \nmax_rows_to_group_by\n and \ngroup_by_overflow_mode = 'any'\n.\n\n\nafter_having_exclusive\n \u2013 Don't include rows that didn't pass through \nmax_rows_to_group_by\n. In other words, 'totals' will have less than or the same number of rows as it would if \nmax_rows_to_group_by\n were omitted.\n\n\nafter_having_inclusive\n \u2013 Include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. In other words, 'totals' will have more than or the same number of rows as it would if \nmax_rows_to_group_by\n were omitted.\n\n\nafter_having_auto\n \u2013 Count the number of rows that passed through HAVING. If it is more than a certain amount (by default, 50%), include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. Otherwise, do not include them.\n\n\ntotals_auto_threshold\n \u2013 By default, 0.5. The coefficient for \nafter_having_auto\n.\n\n\nIf \nmax_rows_to_group_by\n and \ngroup_by_overflow_mode = 'any'\n are not used, all variations of \nafter_having\n are the same, and you can use any of them (for example, \nafter_having_auto\n).\n\n\nYou can use WITH TOTALS in subqueries, including subqueries in the JOIN clause (in this case, the respective total values are combined).\n\n\nGROUP BY in external memory\n\n\nYou can enable dumping temporary data to the disk to restrict memory usage during GROUP BY.\nThe \nmax_bytes_before_external_group_by\n setting determines the threshold RAM consumption for dumping GROUP BY temporary data to the file system. If set to 0 (the default), it is disabled.\n\n\nWhen using \nmax_bytes_before_external_group_by\n, we recommend that you set max_memory_usage about twice as high. This is necessary because there are two stages to aggregation: reading the date and forming intermediate data (1) and merging the intermediate data (2). Dumping data to the file system can only occur during stage 1. If the temporary data wasn't dumped, then stage 2 might require up to the same amount of memory as in stage 1.\n\n\nFor example, if \nmax_memory_usage\n was set to 10000000000 and you want to use external aggregation, it makes sense to set \nmax_bytes_before_external_group_by\n to 10000000000, and max_memory_usage to 20000000000. When external aggregation is triggered (if there was at least one dump of temporary data), maximum consumption of RAM is only slightly more than \nmax_bytes_before_external_group_by\n.\n\n\nWith distributed query processing, external aggregation is performed on remote servers. In order for the requestor server to use only a small amount of RAM, set \ndistributed_aggregation_memory_efficient\n to 1.\n\n\nWhen merging data flushed to the disk, as well as when merging results from remote servers when the \ndistributed_aggregation_memory_efficient\n setting is enabled, consumes up to 1/256 * the number of threads from the total amount of RAM.\n\n\nWhen external aggregation is enabled, if there was less than \nmax_bytes_before_external_group_by\n of data (i.e. data was not flushed), the query runs just as fast as without external aggregation. If any temporary data was flushed, the run time will be several times longer (approximately three times).\n\n\nIf you have an ORDER BY with a small LIMIT after GROUP BY, then the ORDER BY CLAUSE will not use significant amounts of RAM.\nBut if the ORDER BY doesn't have LIMIT, don't forget to enable external sorting (\nmax_bytes_before_external_sort\n).\n\n\nLIMIT N BY clause\n\n\nLIMIT N BY COLUMNS selects the top N rows for each group of COLUMNS. LIMIT N BY is not related to LIMIT; they can both be used in the same query. The key for LIMIT N BY can contain any number of columns or expressions.\n\n\nExample:\n\n\nSELECT\n\n \ndomainWithoutWWW\n(\nURL\n)\n \nAS\n \ndomain\n,\n\n \ndomainWithoutWWW\n(\nREFERRER_URL\n)\n \nAS\n \nreferrer\n,\n\n \ndevice_type\n,\n\n \ncount\n()\n \ncnt\n\n\nFROM\n \nhits\n\n\nGROUP\n \nBY\n \ndomain\n,\n \nreferrer\n,\n \ndevice_type\n\n\nORDER\n \nBY\n \ncnt\n \nDESC\n\n\nLIMIT\n \n5\n \nBY\n \ndomain\n,\n \ndevice_type\n\n\nLIMIT\n \n100\n\n\n\n\n\n\nThe query will select the top 5 referrers for each \ndomain, device_type\n pair, but not more than 100 rows (\nLIMIT n BY + LIMIT\n).\n\n\nHAVING clause\n\n\nAllows filtering the result received after GROUP BY, similar to the WHERE clause.\nWHERE and HAVING differ in that WHERE is performed before aggregation (GROUP BY), while HAVING is performed after it.\nIf aggregation is not performed, HAVING can't be used.\n\n\n\n\nORDER BY clause\n\n\nThe ORDER BY clause contains a list of expressions, which can each be assigned DESC or ASC (the sorting direction). If the direction is not specified, ASC is assumed. ASC is sorted in ascending order, and DESC in descending order. The sorting direction applies to a single expression, not to the entire list. Example: \nORDER BY Visits DESC, SearchPhrase\n\n\nFor sorting by String values, you can specify collation (comparison). Example: \nORDER BY SearchPhrase COLLATE 'tr'\n - for sorting by keyword in ascending order, using the Turkish alphabet, case insensitive, assuming that strings are UTF-8 encoded. COLLATE can be specified or not for each expression in ORDER BY independently. If ASC or DESC is specified, COLLATE is specified after it. When using COLLATE, sorting is always case-insensitive.\n\n\nWe only recommend using COLLATE for final sorting of a small number of rows, since sorting with COLLATE is less efficient than normal sorting by bytes.\n\n\nRows that have identical values for the list of sorting expressions are output in an arbitrary order, which can also be nondeterministic (different each time).\nIf the ORDER BY clause is omitted, the order of the rows is also undefined, and may be nondeterministic as well.\n\n\nWhen floating point numbers are sorted, NaNs are separate from the other values. Regardless of the sorting order, NaNs come at the end. In other words, for ascending sorting they are placed as if they are larger than all the other numbers, while for descending sorting they are placed as if they are smaller than the rest.\n\n\nLess RAM is used if a small enough LIMIT is specified in addition to ORDER BY. Otherwise, the amount of memory spent is proportional to the volume of data for sorting. For distributed query processing, if GROUP BY is omitted, sorting is partially done on remote servers, and the results are merged on the requestor server. This means that for distributed sorting, the volume of data to sort can be greater than the amount of memory on a single server.\n\n\nIf there is not enough RAM, it is possible to perform sorting in external memory (creating temporary files on a disk). Use the setting \nmax_bytes_before_external_sort\n for this purpose. If it is set to 0 (the default), external sorting is disabled. If it is enabled, when the volume of data to sort reaches the specified number of bytes, the collected data is sorted and dumped into a temporary file. After all data is read, all the sorted files are merged and the results are output. Files are written to the /var/lib/clickhouse/tmp/ directory in the config (by default, but you can use the 'tmp_path' parameter to change this setting).\n\n\nRunning a query may use more memory than 'max_bytes_before_external_sort'. For this reason, this setting must have a value significantly smaller than 'max_memory_usage'. As an example, if your server has 128 GB of RAM and you need to run a single query, set 'max_memory_usage' to 100 GB, and 'max_bytes_before_external_sort' to 80 GB.\n\n\nExternal sorting works much less effectively than sorting in RAM.\n\n\nSELECT clause\n\n\nThe expressions specified in the SELECT clause are analyzed after the calculations for all the clauses listed above are completed.\nMore specifically, expressions are analyzed that are above the aggregate functions, if there are any aggregate functions.\nThe aggregate functions and everything below them are calculated during aggregation (GROUP BY).\nThese expressions work as if they are applied to separate rows in the result.\n\n\nDISTINCT clause\n\n\nIf DISTINCT is specified, only a single row will remain out of all the sets of fully matching rows in the result.\nThe result will be the same as if GROUP BY were specified across all the fields specified in SELECT without aggregate functions. But there are several differences from GROUP BY:\n\n\n\n\nDISTINCT can be applied together with GROUP BY.\n\n\nWhen ORDER BY is omitted and LIMIT is defined, the query stops running immediately after the required number of different rows has been read.\n\n\nData blocks are output as they are processed, without waiting for the entire query to finish running.\n\n\n\n\nDISTINCT is not supported if SELECT has at least one array column.\n\n\nLIMIT clause\n\n\nLIMIT m allows you to select the first 'm' rows from the result.\nLIMIT n, m allows you to select the first 'm' rows from the result after skipping the first 'n' rows.\n\n\n'n' and 'm' must be non-negative integers.\n\n\nIf there isn't an ORDER BY clause that explicitly sorts results, the result may be arbitrary and nondeterministic.\n\n\nUNION ALL clause\n\n\nYou can use UNION ALL to combine any number of queries. Example:\n\n\nSELECT\n \nCounterID\n,\n \n1\n \nAS\n \ntable\n,\n \ntoInt64\n(\ncount\n())\n \nAS\n \nc\n\n \nFROM\n \ntest\n.\nhits\n\n \nGROUP\n \nBY\n \nCounterID\n\n\n\nUNION\n \nALL\n\n\n\nSELECT\n \nCounterID\n,\n \n2\n \nAS\n \ntable\n,\n \nsum\n(\nSign\n)\n \nAS\n \nc\n\n \nFROM\n \ntest\n.\nvisits\n\n \nGROUP\n \nBY\n \nCounterID\n\n \nHAVING\n \nc\n \n \n0\n\n\n\n\n\n\nOnly UNION ALL is supported. The regular UNION (UNION DISTINCT) is not supported. If you need UNION DISTINCT, you can write SELECT DISTINCT from a subquery containing UNION ALL.\n\n\nQueries that are parts of UNION ALL can be run simultaneously, and their results can be mixed together.\n\n\nThe structure of results (the number and type of columns) must match for the queries. But the column names can differ. In this case, the column names for the final result will be taken from the first query.\n\n\nQueries that are parts of UNION ALL can't be enclosed in brackets. ORDER BY and LIMIT are applied to separate queries, not to the final result. If you need to apply a conversion to the final result, you can put all the queries with UNION ALL in a subquery in the FROM clause.\n\n\nINTO OUTFILE clause\n\n\nAdd the \nINTO OUTFILE filename\n clause (where filename is a string literal) to redirect query output to the specified file.\nIn contrast to MySQL, the file is created on the client side. The query will fail if a file with the same filename already exists.\nThis functionality is available in the command-line client and clickhouse-local (a query sent via HTTP interface will fail).\n\n\nThe default output format is TabSeparated (the same as in the command-line client batch mode).\n\n\nFORMAT clause\n\n\nSpecify 'FORMAT format' to get data in any specified format.\nYou can use this for convenience, or for creating dumps.\nFor more information, see the section \"Formats\".\nIf the FORMAT clause is omitted, the default format is used, which depends on both the settings and the interface used for accessing the DB. For the HTTP interface and the command-line client in batch mode, the default format is TabSeparated. For the command-line client in interactive mode, the default format is PrettyCompact (it has attractive and compact tables).\n\n\nWhen using the command-line client, data is passed to the client in an internal efficient format. The client independently interprets the FORMAT clause of the query and formats the data itself (thus relieving the network and the server from the load).\n\n\nIN operators\n\n\nThe \nIN\n, \nNOT IN\n, \nGLOBAL IN\n, and \nGLOBAL NOT IN\n operators are covered separately, since their functionality is quite rich.\n\n\nThe left side of the operator is either a single column or a tuple.\n\n\nExamples:\n\n\nSELECT\n \nUserID\n \nIN\n \n(\n123\n,\n \n456\n)\n \nFROM\n \n...\n\n\nSELECT\n \n(\nCounterID\n,\n \nUserID\n)\n \nIN\n \n((\n34\n,\n \n123\n),\n \n(\n101500\n,\n \n456\n))\n \nFROM\n \n...\n\n\n\n\n\n\nIf the left side is a single column that is in the index, and the right side is a set of constants, the system uses the index for processing the query.\n\n\nDon't list too many values explicitly (i.e. millions). If a data set is large, put it in a temporary table (for example, see the section \"External data for query processing\"), then use a subquery.\n\n\nThe right side of the operator can be a set of constant expressions, a set of tuples with constant expressions (shown in the examples above), or the name of a database table or SELECT subquery in brackets.\n\n\nIf the right side of the operator is the name of a table (for example, \nUserID IN users\n), this is equivalent to the subquery \nUserID IN (SELECT * FROM users)\n. Use this when working with external data that is sent along with the query. For example, the query can be sent together with a set of user IDs loaded to the 'users' temporary table, which should be filtered.\n\n\nIf the right side of the operator is a table name that has the Set engine (a prepared data set that is always in RAM), the data set will not be created over again for each query.\n\n\nThe subquery may specify more than one column for filtering tuples.\nExample:\n\n\nSELECT\n \n(\nCounterID\n,\n \nUserID\n)\n \nIN\n \n(\nSELECT\n \nCounterID\n,\n \nUserID\n \nFROM\n \n...)\n \nFROM\n \n...\n\n\n\n\n\n\nThe columns to the left and right of the IN operator should have the same type.\n\n\nThe IN operator and subquery may occur in any part of the query, including in aggregate functions and lambda functions.\nExample:\n\n\nSELECT\n\n \nEventDate\n,\n\n \navg\n(\nUserID\n \nIN\n\n \n(\n\n \nSELECT\n \nUserID\n\n \nFROM\n \ntest\n.\nhits\n\n \nWHERE\n \nEventDate\n \n=\n \ntoDate\n(\n2014-03-17\n)\n\n \n))\n \nAS\n \nratio\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \nEventDate\n\n\nORDER\n \nBY\n \nEventDate\n \nASC\n\n\n\n\n\n\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500ratio\u2500\u2510\n\u2502 2014-03-17 \u2502 1 \u2502\n\u2502 2014-03-18 \u2502 0.807696 \u2502\n\u2502 2014-03-19 \u2502 0.755406 \u2502\n\u2502 2014-03-20 \u2502 0.723218 \u2502\n\u2502 2014-03-21 \u2502 0.697021 \u2502\n\u2502 2014-03-22 \u2502 0.647851 \u2502\n\u2502 2014-03-23 \u2502 0.648416 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nFor each day after March 17th, count the percentage of pageviews made by users who visited the site on March 17th.\nA subquery in the IN clause is always run just one time on a single server. There are no dependent subqueries.\n\n\n\n\nDistributed subqueries\n\n\nThere are two options for IN-s with subqueries (similar to JOINs): normal \nIN\n / \nOIN\n and \nIN GLOBAL\n / \nGLOBAL JOIN\n. They differ in how they are run for distributed query processing.\n\n\n\n\nRemember that the algorithms described below may work differently depending on the [settings](../operations/settings/settings.md#settings-distributed_product_mode) `distributed_product_mode` setting.\n\n\n\n\n\nWhen using the regular IN, the query is sent to remote servers, and each of them runs the subqueries in the \nIN\n or \nJOIN\n clause.\n\n\nWhen using \nGLOBAL IN\n / \nGLOBAL JOINs\n, first all the subqueries are run for \nGLOBAL IN\n / \nGLOBAL JOINs\n, and the results are collected in temporary tables. Then the temporary tables are sent to each remote server, where the queries are run using this temporary data.\n\n\nFor a non-distributed query, use the regular \nIN\n / \nJOIN\n.\n\n\nBe careful when using subqueries in the \nIN\n / \nJOIN\n clauses for distributed query processing.\n\n\nLet's look at some examples. Assume that each server in the cluster has a normal \nlocal_table\n. Each server also has a \ndistributed_table\n table with the \nDistributed\n type, which looks at all the servers in the cluster.\n\n\nFor a query to the \ndistributed_table\n, the query will be sent to all the remote servers and run on them using the \nlocal_table\n.\n\n\nFor example, the query\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n\n\n\n\n\n\nwill be sent to all remote servers as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n\n\n\n\n\n\nand run on each of them in parallel, until it reaches the stage where intermediate results can be combined. Then the intermediate results will be returned to the requestor server and merged on it, and the final result will be sent to the client.\n\n\nNow let's examine a query with IN:\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\n\n\nCalculation of the intersection of audiences of two sites.\n\n\n\n\nThis query will be sent to all remote servers as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nIn other words, the data set in the IN clause will be collected on each server independently, only across the data that is stored locally on each of the servers.\n\n\nThis will work correctly and optimally if you are prepared for this case and have spread data across the cluster servers such that the data for a single UserID resides entirely on a single server. In this case, all the necessary data will be available locally on each server. Otherwise, the result will be inaccurate. We refer to this variation of the query as \"local IN\".\n\n\nTo correct how the query works when data is spread randomly across the cluster servers, you could specify \ndistributed_table\n inside a subquery. The query would look like this:\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nThis query will be sent to all remote servers as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nThe subquery will begin running on each remote server. Since the subquery uses a distributed table, the subquery that is on each remote server will be resent to every remote server as\n\n\nSELECT\n \nUserID\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n\n\n\n\n\n\nFor example, if you have a cluster of 100 servers, executing the entire query will require 10,000 elementary requests, which is generally considered unacceptable.\n\n\nIn such cases, you should always use GLOBAL IN instead of IN. Let's look at how it works for the query\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nGLOBAL\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nThe requestor server will run the subquery\n\n\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n\n\n\n\n\n\nand the result will be put in a temporary table in RAM. Then the request will be sent to each remote server as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nGLOBAL\n \nIN\n \n_data1\n\n\n\n\n\n\nand the temporary table \n_data1\n will be sent to every remote server with the query (the name of the temporary table is implementation-defined).\n\n\nThis is more optimal than using the normal IN. However, keep the following points in mind:\n\n\n\n\nWhen creating a temporary table, data is not made unique. To reduce the volume of data transmitted over the network, specify DISTINCT in the subquery. (You don't need to do this for a normal IN.)\n\n\nThe temporary table will be sent to all the remote servers. Transmission does not account for network topology. For example, if 10 remote servers reside in a datacenter that is very remote in relation to the requestor server, the data will be sent 10 times over the channel to the remote datacenter. Try to avoid large data sets when using GLOBAL IN.\n\n\nWhen transmitting data to remote servers, restrictions on network bandwidth are not configurable. You might overload the network.\n\n\nTry to distribute data across servers so that you don't need to use GLOBAL IN on a regular basis.\n\n\nIf you need to use GLOBAL IN often, plan the location of the ClickHouse cluster so that a single group of replicas resides in no more than one data center with a fast network between them, so that a query can be processed entirely within a single data center.\n\n\n\n\nIt also makes sense to specify a local table in the \nGLOBAL IN\n clause, in case this local table is only available on the requestor server and you want to use data from it on remote servers.\n\n\nExtreme values\n\n\nIn addition to results, you can also get minimum and maximum values for the results columns. To do this, set the \nextremes\n setting to 1. Minimums and maximums are calculated for numeric types, dates, and dates with times. For other columns, the default values are output.\n\n\nAn extra two rows are calculated \u2013 the minimums and maximums, respectively. These extra two rows are output in JSON*, TabSeparated*, and Pretty* formats, separate from the other rows. They are not output for other formats.\n\n\nIn JSON* formats, the extreme values are output in a separate 'extremes' field. In TabSeparated* formats, the row comes after the main result, and after 'totals' if present. It is preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result, and after 'totals' if present.\n\n\nExtreme values are calculated for rows that have passed through LIMIT. However, when using 'LIMIT offset, size', the rows before 'offset' are included in 'extremes'. In stream requests, the result may also include a small number of rows that passed through LIMIT.\n\n\nNotes\n\n\nThe \nGROUP BY\n and \nORDER BY\n clauses do not support positional arguments. This contradicts MySQL, but conforms to standard SQL.\nFor example, \nGROUP BY 1, 2\n will be interpreted as grouping by constants (i.e. aggregation of all rows into one).\n\n\nYou can use synonyms (\nAS\n aliases) in any part of a query.\n\n\nYou can put an asterisk in any part of a query instead of an expression. When the query is analyzed, the asterisk is expanded to a list of all table columns (excluding the \nMATERIALIZED\n and \nALIAS\n columns). There are only a few cases when using an asterisk is justified:\n\n\n\n\nWhen creating a table dump.\n\n\nFor tables containing just a few columns, such as system tables.\n\n\nFor getting information about what columns are in a table. In this case, set \nLIMIT 1\n. But it is better to use the \nDESC TABLE\n query.\n\n\nWhen there is strong filtration on a small number of columns using \nPREWHERE\n.\n\n\nIn subqueries (since columns that aren't needed for the external query are excluded from subqueries).\n\n\n\n\nIn all other cases, we don't recommend using the asterisk, since it only gives you the drawbacks of a columnar DBMS instead of the advantages. In other words using the asterisk is not recommended.\n\n\nKILL QUERY\n\n\nKILL\n \nQUERY\n\n \nWHERE\n \nwhere\n \nexpression\n \nto\n \nSELECT\n \nFROM\n \nsystem\n.\nprocesses\n \nquery\n\n \n[\nSYNC\n|\nASYNC\n|\nTEST\n]\n\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nAttempts to forcibly terminate the currently running queries.\nThe queries to terminate are selected from the system.processes table using the criteria defined in the \nWHERE\n clause of the \nKILL\n query.\n\n\nExamples:\n\n\n-- Forcibly terminates all queries with the specified query_id:\n\n\nKILL\n \nQUERY\n \nWHERE\n \nquery_id\n=\n2-857d-4a57-9ee0-327da5d60a90\n\n\n\n-- Synchronously terminates all queries run by \nusername\n:\n\n\nKILL\n \nQUERY\n \nWHERE\n \nuser\n=\nusername\n \nSYNC\n\n\n\n\n\n\nRead-only users can only stop their own queries.\n\n\nBy default, the asynchronous version of queries is used (\nASYNC\n), which doesn't wait for confirmation that queries have stopped.\n\n\nThe synchronous version (\nSYNC\n) waits for all queries to stop and displays information about each process as it stops.\nThe response contains the \nkill_status\n column, which can take the following values:\n\n\n\n\n'finished' \u2013 The query was terminated successfully.\n\n\n'waiting' \u2013 Waiting for the query to end after sending it a signal to terminate.\n\n\nThe other values \u200b\u200bexplain why the query can't be stopped.\n\n\n\n\nA test query (\nTEST\n) only checks the user's rights and displays a list of queries to stop.", - "title": "Queries" - }, - { - "location": "/query_language/queries/#queries", - "text": "", - "title": "Queries" - }, - { - "location": "/query_language/queries/#create-database", - "text": "Creating db_name databases CREATE DATABASE [ IF NOT EXISTS ] db_name A database is just a directory for tables.\nIf IF NOT EXISTS is included, the query won't return an error if the database already exists.", - "title": "CREATE DATABASE" - }, - { - "location": "/query_language/queries/#create-table", - "text": "The CREATE TABLE query can have several forms. CREATE [ TEMPORARY ] TABLE [ IF NOT EXISTS ] [ db .] name [ ON CLUSTER cluster ] ( \n name1 [ type1 ] [ DEFAULT | MATERIALIZED | ALIAS expr1 ], \n name2 [ type2 ] [ DEFAULT | MATERIALIZED | ALIAS expr2 ], \n ... ) ENGINE = engine Creates a table named 'name' in the 'db' database or the current database if 'db' is not set, with the structure specified in brackets and the 'engine' engine.\nThe structure of the table is a list of column descriptions. If indexes are supported by the engine, they are indicated as parameters for the table engine. A column description is name type in the simplest case. Example: RegionID UInt32 .\nExpressions can also be defined for default values (see below). CREATE [ TEMPORARY ] TABLE [ IF NOT EXISTS ] [ db .] name AS [ db2 .] name2 [ ENGINE = engine ] Creates a table with the same structure as another table. You can specify a different engine for the table. If the engine is not specified, the same engine will be used as for the db2.name2 table. CREATE [ TEMPORARY ] TABLE [ IF NOT EXISTS ] [ db .] name ENGINE = engine AS SELECT ... Creates a table with a structure like the result of the SELECT query, with the 'engine' engine, and fills it with data from SELECT. In all cases, if IF NOT EXISTS is specified, the query won't return an error if the table already exists. In this case, the query won't do anything.", - "title": "CREATE TABLE" - }, - { - "location": "/query_language/queries/#default-values", - "text": "The column description can specify an expression for a default value, in one of the following ways: DEFAULT expr , MATERIALIZED expr , ALIAS expr .\nExample: URLDomain String DEFAULT domain(URL) . If an expression for the default value is not defined, the default values will be set to zeros for numbers, empty strings for strings, empty arrays for arrays, and 0000-00-00 for dates or 0000-00-00 00:00:00 for dates with time. NULLs are not supported. If the default expression is defined, the column type is optional. If there isn't an explicitly defined type, the default expression type is used. Example: EventDate DEFAULT toDate(EventTime) \u2013 the 'Date' type will be used for the 'EventDate' column. If the data type and default expression are defined explicitly, this expression will be cast to the specified type using type casting functions. Example: Hits UInt32 DEFAULT 0 means the same thing as Hits UInt32 DEFAULT toUInt32(0) . Default expressions may be defined as an arbitrary expression from table constants and columns. When creating and changing the table structure, it checks that expressions don't contain loops. For INSERT, it checks that expressions are resolvable \u2013 that all columns they can be calculated from have been passed. DEFAULT expr Normal default value. If the INSERT query doesn't specify the corresponding column, it will be filled in by computing the corresponding expression. MATERIALIZED expr Materialized expression. Such a column can't be specified for INSERT, because it is always calculated.\nFor an INSERT without a list of columns, these columns are not considered.\nIn addition, this column is not substituted when using an asterisk in a SELECT query. This is to preserve the invariant that the dump obtained using SELECT * can be inserted back into the table using INSERT without specifying the list of columns. ALIAS expr Synonym. Such a column isn't stored in the table at all.\nIts values can't be inserted in a table, and it is not substituted when using an asterisk in a SELECT query.\nIt can be used in SELECTs if the alias is expanded during query parsing. When using the ALTER query to add new columns, old data for these columns is not written. Instead, when reading old data that does not have values for the new columns, expressions are computed on the fly by default. However, if running the expressions requires different columns that are not indicated in the query, these columns will additionally be read, but only for the blocks of data that need it. If you add a new column to a table but later change its default expression, the values used for old data will change (for data where values were not stored on the disk). Note that when running background merges, data for columns that are missing in one of the merging parts is written to the merged part. It is not possible to set default values for elements in nested data structures.", - "title": "Default values" - }, - { - "location": "/query_language/queries/#temporary-tables", - "text": "In all cases, if TEMPORARY is specified, a temporary table will be created. Temporary tables have the following characteristics: Temporary tables disappear when the session ends, including if the connection is lost. A temporary table is created with the Memory engine. The other table engines are not supported. The DB can't be specified for a temporary table. It is created outside of databases. If a temporary table has the same name as another one and a query specifies the table name without specifying the DB, the temporary table will be used. For distributed query processing, temporary tables used in a query are passed to remote servers. In most cases, temporary tables are not created manually, but when using external data for a query, or for distributed (GLOBAL) IN . For more information, see the appropriate sections", - "title": "Temporary tables" - }, - { - "location": "/query_language/queries/#distributed-ddl-queries-on-cluster-clause", - "text": "The CREATE , DROP , ALTER , and RENAME queries support distributed execution on a cluster.\nFor example, the following query creates the all_hits Distributed table on each host in cluster : CREATE TABLE IF NOT EXISTS all_hits ON CLUSTER cluster ( p Date , i Int32 ) ENGINE = Distributed ( cluster , default , hits ) In order to run these queries correctly, each host must have the same cluster definition (to simplify syncing configs, you can use substitutions from ZooKeeper). They must also connect to the ZooKeeper servers.\nThe local version of the query will eventually be implemented on each host in the cluster, even if some hosts are currently not available. The order for executing queries within a single host is guaranteed. ALTER queries are not yet supported for replicated tables.", - "title": "Distributed DDL queries (ON CLUSTER clause)" - }, - { - "location": "/query_language/queries/#create-view", - "text": "CREATE [ MATERIALIZED ] VIEW [ IF NOT EXISTS ] [ db .] name [ TO [ db .] name ] [ ENGINE = engine ] [ POPULATE ] AS SELECT ... Creates a view. There are two types of views: normal and MATERIALIZED. When creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data. A materialized view works as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view. Normal views don't store any data, but just perform a read from another table. In other words, a normal view is nothing more than a saved query. When reading from a view, this saved query is used as a subquery in the FROM clause. As an example, assume you've created a view: CREATE VIEW view AS SELECT ... and written a query: SELECT a , b , c FROM view This query is fully equivalent to using the subquery: SELECT a , b , c FROM ( SELECT ...) Materialized views store data transformed by the corresponding SELECT query. When creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data. A materialized view is arranged as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view. If you specify POPULATE, the existing table data is inserted in the view when creating it, as if making a CREATE TABLE ... AS SELECT ... . Otherwise, the query contains only the data inserted in the table after creating the view. We don't recommend using POPULATE, since data inserted in the table during the view creation will not be inserted in it. A SELECT query can contain DISTINCT , GROUP BY , ORDER BY , LIMIT ... Note that the corresponding conversions are performed independently on each block of inserted data. For example, if GROUP BY is set, data is aggregated during insertion, but only within a single packet of inserted data. The data won't be further aggregated. The exception is when using an ENGINE that independently performs data aggregation, such as SummingMergeTree . The execution of ALTER queries on materialized views has not been fully developed, so they might be inconvenient. If the materialized view uses the construction TO [db.]name , you can DETACH the view, run ALTER for the target table, and then ATTACH the previously detached ( DETACH ) view. Views look the same as normal tables. For example, they are listed in the result of the SHOW TABLES query. There isn't a separate query for deleting views. To delete a view, use DROP TABLE .", - "title": "CREATE VIEW" - }, - { - "location": "/query_language/queries/#attach", - "text": "This query is exactly the same as CREATE , but instead of the word CREATE it uses the word ATTACH . The query doesn't create data on the disk, but assumes that data is already in the appropriate places, and just adds information about the table to the server.\nAfter executing an ATTACH query, the server will know about the existence of the table. If the table was previously detached ( DETACH ), meaning that its structure is known, you can use shorthand without defining the structure. ATTACH TABLE [ IF NOT EXISTS ] [ db .] name This query is used when starting the server. The server stores table metadata as files with ATTACH queries, which it simply runs at launch (with the exception of system tables, which are explicitly created on the server).", - "title": "ATTACH" - }, - { - "location": "/query_language/queries/#drop", - "text": "This query has two types: DROP DATABASE and DROP TABLE . DROP DATABASE [ IF EXISTS ] db [ ON CLUSTER cluster ] Deletes all tables inside the 'db' database, then deletes the 'db' database itself.\nIf IF EXISTS is specified, it doesn't return an error if the database doesn't exist. DROP [ TEMPORARY ] TABLE [ IF EXISTS ] [ db .] name [ ON CLUSTER cluster ] Deletes the table.\nIf IF EXISTS is specified, it doesn't return an error if the table doesn't exist or the database doesn't exist.", - "title": "DROP" - }, - { - "location": "/query_language/queries/#detach", - "text": "Deletes information about the 'name' table from the server. The server stops knowing about the table's existence. DETACH TABLE [ IF EXISTS ] [ db .] name This does not delete the table's data or metadata. On the next server launch, the server will read the metadata and find out about the table again.\nSimilarly, a \"detached\" table can be re-attached using the ATTACH query (with the exception of system tables, which do not have metadata stored for them). There is no DETACH DATABASE query.", - "title": "DETACH" - }, - { - "location": "/query_language/queries/#rename", - "text": "Renames one or more tables. RENAME TABLE [ db11 .] name11 TO [ db12 .] name12 , [ db21 .] name21 TO [ db22 .] name22 , ... [ ON CLUSTER cluster ] All tables are renamed under global locking. Renaming tables is a light operation. If you indicated another database after TO, the table will be moved to this database. However, the directories with databases must reside in the same file system (otherwise, an error is returned).", - "title": "RENAME" - }, - { - "location": "/query_language/queries/#alter", - "text": "The ALTER query is only supported for *MergeTree tables, as well as Merge and Distributed . The query has several variations.", - "title": "ALTER" - }, - { - "location": "/query_language/queries/#column-manipulations", - "text": "Changing the table structure. ALTER TABLE [ db ]. name [ ON CLUSTER cluster ] ADD | DROP | MODIFY COLUMN ... In the query, specify a list of one or more comma-separated actions.\nEach action is an operation on a column. The following actions are supported: ADD COLUMN name [ type ] [ default_expr ] [ AFTER name_after ] Adds a new column to the table with the specified name, type, and default_expr (see the section \"Default expressions\"). If you specify AFTER name_after (the name of another column), the column is added after the specified one in the list of table columns. Otherwise, the column is added to the end of the table. Note that there is no way to add a column to the beginning of a table. For a chain of actions, 'name_after' can be the name of a column that is added in one of the previous actions. Adding a column just changes the table structure, without performing any actions with data. The data doesn't appear on the disk after ALTER. If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). The column appears on the disk after merging data parts (see MergeTree). This approach allows us to complete the ALTER query instantly, without increasing the volume of old data. DROP COLUMN name Deletes the column with the name 'name'.\nDeletes data from the file system. Since this deletes entire files, the query is completed almost instantly. MODIFY COLUMN name [ type ] [ default_expr ] Changes the 'name' column's type to 'type' and/or the default expression to 'default_expr'. When changing the type, values are converted as if the 'toType' function were applied to them. If only the default expression is changed, the query doesn't do anything complex, and is completed almost instantly. Changing the column type is the only complex action \u2013 it changes the contents of files with data. For large tables, this may take a long time. There are several processing stages: Preparing temporary (new) files with modified data. Renaming old files. Renaming the temporary (new) files to the old names. Deleting the old files. Only the first stage takes time. If there is a failure at this stage, the data is not changed.\nIf there is a failure during one of the successive stages, data can be restored manually. The exception is if the old files were deleted from the file system but the data for the new files did not get written to the disk and was lost. There is no support for changing the column type in arrays and nested data structures. The ALTER query lets you create and delete separate elements (columns) in nested data structures, but not whole nested data structures. To add a nested data structure, you can add columns with a name like name.nested_name and the type Array(T) . A nested data structure is equivalent to multiple array columns with a name that has the same prefix before the dot. There is no support for deleting columns in the primary key or the sampling key (columns that are in the ENGINE expression). Changing the type for columns that are included in the primary key is only possible if this change does not cause the data to be modified (for example, it is allowed to add values to an Enum or change a type with DateTime to UInt32 ). If the ALTER query is not sufficient for making the table changes you need, you can create a new table, copy the data to it using the INSERT SELECT query, then switch the tables using the RENAME query and delete the old table. The ALTER query blocks all reads and writes for the table. In other words, if a long SELECT is running at the time of the ALTER query, the ALTER query will wait for it to complete. At the same time, all new queries to the same table will wait while this ALTER is running. For tables that don't store data themselves (such as Merge and Distributed ), ALTER just changes the table structure, and does not change the structure of subordinate tables. For example, when running ALTER for a Distributed table, you will also need to run ALTER for the tables on all remote servers. The ALTER query for changing columns is replicated. The instructions are saved in ZooKeeper, then each replica applies them. All ALTER queries are run in the same order. The query waits for the appropriate actions to be completed on the other replicas. However, a query to change columns in a replicated table can be interrupted, and all actions will be performed asynchronously.", - "title": "Column manipulations" - }, - { - "location": "/query_language/queries/#manipulations-with-partitions-and-parts", - "text": "It only works for tables in the MergeTree family. The following operations are available: DETACH PARTITION \u2013 Move a partition to the 'detached' directory and forget it. DROP PARTITION \u2013 Delete a partition. ATTACH PART|PARTITION \u2013 Add a new part or partition from the detached directory to the table. FREEZE PARTITION \u2013 Create a backup of a partition. FETCH PARTITION \u2013 Download a partition from another server. Each type of query is covered separately below. A partition in a table is data for a single calendar month. This is determined by the values of the date key specified in the table engine parameters. Each month's data is stored separately in order to simplify manipulations with this data. A \"part\" in the table is part of the data from a single partition, sorted by the primary key. You can use the system.parts table to view the set of table parts and partitions: SELECT * FROM system . parts WHERE active active \u2013 Only count active parts. Inactive parts are, for example, source parts remaining after merging to a larger part \u2013 these parts are deleted approximately 10 minutes after merging. Another way to view a set of parts and partitions is to go into the directory with table data.\nData directory: /var/lib/clickhouse/data/database/table/ ,where /var/lib/clickhouse/ is the path to the ClickHouse data, 'database' is the database name, and 'table' is the table name. Example: $ ls -l /var/lib/clickhouse/data/test/visits/\ntotal 48 \ndrwxrwxrwx 2 clickhouse clickhouse 20480 May 5 02 :58 20140317_20140323_2_2_0\ndrwxrwxrwx 2 clickhouse clickhouse 20480 May 5 02 :58 20140317_20140323_4_4_0\ndrwxrwxrwx 2 clickhouse clickhouse 4096 May 5 02 :55 detached\n-rw-rw-rw- 1 clickhouse clickhouse 2 May 5 02 :58 increment.txt Here, 20140317_20140323_2_2_0 and 20140317_20140323_4_4_0 are the directories of data parts. Let's break down the name of the first part: 20140317_20140323_2_2_0 . 20140317 is the minimum date of the data in the chunk. 20140323 is the maximum date of the data in the chunk. 2 is the minimum number of the data block. 2 is the maximum number of the data block. 0 is the chunk level (the depth of the merge tree it is formed from). Each piece relates to a single partition and contains data for just one month. 201403 is the name of the partition. A partition is a set of parts for a single month. On an operating server, you can't manually change the set of parts or their data on the file system, since the server won't know about it.\nFor non-replicated tables, you can do this when the server is stopped, but we don't recommended it.\nFor replicated tables, the set of parts can't be changed in any case. The detached directory contains parts that are not used by the server - detached from the table using the ALTER ... DETACH query. Parts that are damaged are also moved to this directory, instead of deleting them. You can add, delete, or modify the data in the 'detached' directory at any time \u2013 the server won't know about this until you make the ALTER TABLE ... ATTACH query. ALTER TABLE [ db .] table DETACH PARTITION name Move all data for partitions named 'name' to the 'detached' directory and forget about them.\nThe partition name is specified in YYYYMM format. It can be indicated in single quotes or without them. After the query is executed, you can do whatever you want with the data in the 'detached' directory \u2014 delete it from the file system, or just leave it. The query is replicated \u2013 data will be moved to the 'detached' directory and forgotten on all replicas. The query can only be sent to a leader replica. To find out if a replica is a leader, perform SELECT to the 'system.replicas' system table. Alternatively, it is easier to make a query on all replicas, and all except one will throw an exception. ALTER TABLE [ db .] table DROP PARTITION name The same as the DETACH operation. Deletes data from the table. Data parts will be tagged as inactive and will be completely deleted in approximately 10 minutes. The query is replicated \u2013 data will be deleted on all replicas. ALTER TABLE [ db .] table ATTACH PARTITION | PART name Adds data to the table from the 'detached' directory. It is possible to add data for an entire partition or a separate part. For a part, specify the full name of the part in single quotes. The query is replicated. Each replica checks whether there is data in the 'detached' directory. If there is data, it checks the integrity, verifies that it matches the data on the server that initiated the query, and then adds it if everything is correct. If not, it downloads data from the query requestor replica, or from another replica where the data has already been added. So you can put data in the 'detached' directory on one replica, and use the ALTER ... ATTACH query to add it to the table on all replicas. ALTER TABLE [ db .] table FREEZE PARTITION name Creates a local backup of one or multiple partitions. The name can be the full name of the partition (for example, 201403), or its prefix (for example, 2014): then the backup will be created for all the corresponding partitions. The query does the following: for a data snapshot at the time of execution, it creates hardlinks to table data in the directory /var/lib/clickhouse/shadow/N/... /var/lib/clickhouse/ is the working ClickHouse directory from the config. N is the incremental number of the backup. The same structure of directories is created inside the backup as inside /var/lib/clickhouse/ .\nIt also performs 'chmod' for all files, forbidding writes to them. The backup is created almost instantly (but first it waits for current queries to the corresponding table to finish running). At first, the backup doesn't take any space on the disk. As the system works, the backup can take disk space, as data is modified. If the backup is made for old enough data, it won't take space on the disk. After creating the backup, data from /var/lib/clickhouse/shadow/ can be copied to the remote server and then deleted on the local server.\nThe entire backup process is performed without stopping the server. The ALTER ... FREEZE PARTITION query is not replicated. A local backup is only created on the local server. As an alternative, you can manually copy data from the /var/lib/clickhouse/data/database/table directory.\nBut if you do this while the server is running, race conditions are possible when copying directories with files being added or changed, and the backup may be inconsistent. You can do this if the server isn't running \u2013 then the resulting data will be the same as after the ALTER TABLE t FREEZE PARTITION query. ALTER TABLE ... FREEZE PARTITION only copies data, not table metadata. To make a backup of table metadata, copy the file /var/lib/clickhouse/metadata/database/table.sql To restore from a backup: Use the CREATE query to create the table if it doesn't exist. The query can be taken from an .sql file (replace ATTACH in it with CREATE ). Copy the data from the data/database/table/ directory inside the backup to the /var/lib/clickhouse/data/database/table/detached/ directory. Run ALTER TABLE ... ATTACH PARTITION YYYYMM queries, where YYYYMM is the month, for every month. In this way, data from the backup will be added to the table.\nRestoring from a backup doesn't require stopping the server.", - "title": "Manipulations with partitions and parts" - }, - { - "location": "/query_language/queries/#backups-and-replication", - "text": "Replication provides protection from device failures. If all data disappeared on one of your replicas, follow the instructions in the \"Restoration after failure\" section to restore it. For protection from device failures, you must use replication. For more information about replication, see the section \"Data replication\". Backups protect against human error (accidentally deleting data, deleting the wrong data or in the wrong cluster, or corrupting data).\nFor high-volume databases, it can be difficult to copy backups to remote servers. In such cases, to protect from human error, you can keep a backup on the same server (it will reside in /var/lib/clickhouse/shadow/ ). ALTER TABLE [ db .] table FETCH PARTITION name FROM path-in-zookeeper This query only works for replicatable tables. It downloads the specified partition from the shard that has its ZooKeeper path specified in the FROM clause, then puts it in the detached directory for the specified table. Although the query is called ALTER TABLE , it does not change the table structure, and does not immediately change the data available in the table. Data is placed in the detached directory. You can use the ALTER TABLE ... ATTACH query to attach the data. The FROM clause specifies the path in ZooKeeper . For example, /clickhouse/tables/01-01/visits .\nBefore downloading, the system checks that the partition exists and the table structure matches. The most appropriate replica is selected automatically from the healthy replicas. The ALTER ... FETCH PARTITION query is not replicated. The partition will be downloaded to the 'detached' directory only on the local server. Note that if after this you use the ALTER TABLE ... ATTACH query to add data to the table, the data will be added on all replicas (on one of the replicas it will be added from the 'detached' directory, and on the rest it will be loaded from neighboring replicas).", - "title": "Backups and replication" - }, - { - "location": "/query_language/queries/#synchronicity-of-alter-queries", - "text": "For non-replicatable tables, all ALTER queries are performed synchronously. For replicatable tables, the query just adds instructions for the appropriate actions to ZooKeeper , and the actions themselves are performed as soon as possible. However, the query can wait for these actions to be completed on all the replicas. For ALTER ... ATTACH|DETACH|DROP queries, you can use the replication_alter_partitions_sync setting to set up waiting.\nPossible values: 0 \u2013 do not wait; 1 \u2013 only wait for own execution (default); 2 \u2013 wait for all.", - "title": "Synchronicity of ALTER queries" - }, - { - "location": "/query_language/queries/#show-databases", - "text": "SHOW DATABASES [ INTO OUTFILE filename ] [ FORMAT format ] Prints a list of all databases.\nThis query is identical to SELECT name FROM system.databases [INTO OUTFILE filename] [FORMAT format] . See also the section \"Formats\".", - "title": "SHOW DATABASES" - }, - { - "location": "/query_language/queries/#show-tables", - "text": "SHOW [ TEMPORARY ] TABLES [ FROM db ] [ LIKE pattern ] [ INTO OUTFILE filename ] [ FORMAT format ] Displays a list of tables tables from the current database, or from the 'db' database if \"FROM db\" is specified. all tables, or tables whose name matches the pattern, if \"LIKE 'pattern'\" is specified. This query is identical to: SELECT name FROM system.tables WHERE database = 'db' [AND name LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format] . See also the section \"LIKE operator\".", - "title": "SHOW TABLES" - }, - { - "location": "/query_language/queries/#show-processlist", - "text": "SHOW PROCESSLIST [ INTO OUTFILE filename ] [ FORMAT format ] Outputs a list of queries currently being processed, other than SHOW PROCESSLIST queries. Prints a table containing the columns: user \u2013 The user who made the query. Keep in mind that for distributed processing, queries are sent to remote servers under the 'default' user. SHOW PROCESSLIST shows the username for a specific query, not for a query that this query initiated. address \u2013 The name of the host that the query was sent from. For distributed processing, on remote servers, this is the name of the query requestor host. To track where a distributed query was originally made from, look at SHOW PROCESSLIST on the query requestor server. elapsed \u2013 The execution time, in seconds. Queries are output in order of decreasing execution time. rows_read , bytes_read \u2013 How many rows and bytes of uncompressed data were read when processing the query. For distributed processing, data is totaled from all the remote servers. This is the data used for restrictions and quotas. memory_usage \u2013 Current RAM usage in bytes. See the setting 'max_memory_usage'. query \u2013 The query itself. In INSERT queries, the data for insertion is not output. query_id \u2013 The query identifier. Non-empty only if it was explicitly defined by the user. For distributed processing, the query ID is not passed to remote servers. This query is identical to: SELECT * FROM system.processes [INTO OUTFILE filename] [FORMAT format] . Tip (execute in the console): watch -n1 clickhouse-client --query= SHOW PROCESSLIST", - "title": "SHOW PROCESSLIST" - }, - { - "location": "/query_language/queries/#show-create-table", - "text": "SHOW CREATE [ TEMPORARY ] TABLE [ db .] table [ INTO OUTFILE filename ] [ FORMAT format ] Returns a single String -type 'statement' column, which contains a single value \u2013 the CREATE query used for creating the specified table.", - "title": "SHOW CREATE TABLE" - }, - { - "location": "/query_language/queries/#describe-table", - "text": "DESC | DESCRIBE TABLE [ db .] table [ INTO OUTFILE filename ] [ FORMAT format ] Returns two String -type columns: name and type , which indicate the names and types of columns in the specified table. Nested data structures are output in \"expanded\" format. Each column is shown separately, with the name after a dot.", - "title": "DESCRIBE TABLE" - }, - { - "location": "/query_language/queries/#exists", - "text": "EXISTS [ TEMPORARY ] TABLE [ db .] name [ INTO OUTFILE filename ] [ FORMAT format ] Returns a single UInt8 -type column, which contains the single value 0 if the table or database doesn't exist, or 1 if the table exists in the specified database.", - "title": "EXISTS" - }, - { - "location": "/query_language/queries/#use", - "text": "USE db Lets you set the current database for the session.\nThe current database is used for searching for tables if the database is not explicitly defined in the query with a dot before the table name.\nThis query can't be made when using the HTTP protocol, since there is no concept of a session.", - "title": "USE" - }, - { - "location": "/query_language/queries/#set", - "text": "SET param = value Allows you to set param to value . You can also make all the settings from the specified settings profile in a single query. To do this, specify 'profile' as the setting name. For more information, see the section \"Settings\".\nThe setting is made for the session, or for the server (globally) if GLOBAL is specified.\nWhen making a global setting, the setting is not applied to sessions already running, including the current session. It will only be used for new sessions. When the server is restarted, global settings made using SET are lost.\nTo make settings that persist after a server restart, you can only use the server's config file.", - "title": "SET" - }, - { - "location": "/query_language/queries/#optimize", - "text": "OPTIMIZE TABLE [ db .] name [ PARTITION partition ] [ FINAL ] Asks the table engine to do something for optimization.\nSupported only by *MergeTree engines, in which this query initializes a non-scheduled merge of data parts.\nIf you specify a PARTITION , only the specified partition will be optimized.\nIf you specify FINAL , optimization will be performed even when all the data is already in one part.", - "title": "OPTIMIZE" - }, - { - "location": "/query_language/queries/#insert", - "text": "Adding data. Basic query format: INSERT INTO [ db .] table [( c1 , c2 , c3 )] VALUES ( v11 , v12 , v13 ), ( v21 , v22 , v23 ), ... The query can specify a list of columns to insert [(c1, c2, c3)] . In this case, the rest of the columns are filled with: The values calculated from the DEFAULT expressions specified in the table definition. Zeros and empty strings, if DEFAULT expressions are not defined. If strict_insert_defaults=1 , columns that do not have DEFAULT defined must be listed in the query. Data can be passed to the INSERT in any format supported by ClickHouse. The format must be specified explicitly in the query: INSERT INTO [ db .] table [( c1 , c2 , c3 )] FORMAT format_name data_set For example, the following query format is identical to the basic version of INSERT ... VALUES: INSERT INTO [ db .] table [( c1 , c2 , c3 )] FORMAT Values ( v11 , v12 , v13 ), ( v21 , v22 , v23 ), ... ClickHouse removes all spaces and one line feed (if there is one) before the data. When forming a query, we recommend putting the data on a new line after the query operators (this is important if the data begins with spaces). Example: INSERT INTO t FORMAT TabSeparated 11 Hello , world ! 22 Qwerty You can insert data separately from the query by using the command-line client or the HTTP interface. For more information, see the section \" Interfaces \".", - "title": "INSERT" - }, - { - "location": "/query_language/queries/#inserting-the-results-of-select", - "text": "INSERT INTO [ db .] table [( c1 , c2 , c3 )] SELECT ... Columns are mapped according to their position in the SELECT clause. However, their names in the SELECT expression and the table for INSERT may differ. If necessary, type casting is performed. None of the data formats except Values allow setting values to expressions such as now() , 1 + 2 , and so on. The Values format allows limited use of expressions, but this is not recommended, because in this case inefficient code is used for their execution. Other queries for modifying data parts are not supported: UPDATE , DELETE , REPLACE , MERGE , UPSERT , INSERT UPDATE .\nHowever, you can delete old data using ALTER TABLE ... DROP PARTITION .", - "title": "Inserting the results of SELECT" - }, - { - "location": "/query_language/queries/#performance-considerations", - "text": "INSERT sorts the input data by primary key and splits them into partitions by month. If you insert data for mixed months, it can significantly reduce the performance of the INSERT query. To avoid this: Add data in fairly large batches, such as 100,000 rows at a time. Group data by month before uploading it to ClickHouse. Performance will not decrease if: Data is added in real time. You upload data that is usually sorted by time.", - "title": "Performance considerations" - }, - { - "location": "/query_language/queries/#select", - "text": "Data sampling. SELECT [ DISTINCT ] expr_list \n [ FROM [ db .] table | ( subquery ) | table_function ] [ FINAL ] \n [ SAMPLE sample_coeff ] \n [ ARRAY JOIN ...] \n [ GLOBAL ] ANY | ALL INNER | LEFT JOIN ( subquery ) | table USING columns_list \n [ PREWHERE expr ] \n [ WHERE expr ] \n [ GROUP BY expr_list ] [ WITH TOTALS ] \n [ HAVING expr ] \n [ ORDER BY expr_list ] \n [ LIMIT [ n , ] m ] \n [ UNION ALL ...] \n [ INTO OUTFILE filename ] \n [ FORMAT format ] \n [ LIMIT n BY columns ] All the clauses are optional, except for the required list of expressions immediately after SELECT.\nThe clauses below are described in almost the same order as in the query execution conveyor. If the query omits the DISTINCT , GROUP BY and ORDER BY clauses and the IN and JOIN subqueries, the query will be completely stream processed, using O(1) amount of RAM.\nOtherwise, the query might consume a lot of RAM if the appropriate restrictions are not specified: max_memory_usage , max_rows_to_group_by , max_rows_to_sort , max_rows_in_distinct , max_bytes_in_distinct , max_rows_in_set , max_bytes_in_set , max_rows_in_join , max_bytes_in_join , max_bytes_before_external_sort , max_bytes_before_external_group_by . For more information, see the section \"Settings\". It is possible to use external sorting (saving temporary tables to a disk) and external aggregation. The system does not have \"merge join\" .", - "title": "SELECT" - }, - { - "location": "/query_language/queries/#from-clause", - "text": "If the FROM clause is omitted, data will be read from the system.one table.\nThe 'system.one' table contains exactly one row (this table fulfills the same purpose as the DUAL table found in other DBMSs). The FROM clause specifies the table to read data from, or a subquery, or a table function; ARRAY JOIN and the regular JOIN may also be included (see below). Instead of a table, the SELECT subquery may be specified in brackets.\nIn this case, the subquery processing pipeline will be built into the processing pipeline of an external query.\nIn contrast to standard SQL, a synonym does not need to be specified after a subquery. For compatibility, it is possible to write 'AS name' after a subquery, but the specified name isn't used anywhere. A table function may be specified instead of a table. For more information, see the section \"Table functions\". To execute a query, all the columns listed in the query are extracted from the appropriate table. Any columns not needed for the external query are thrown out of the subqueries.\nIf a query does not list any columns (for example, SELECT count() FROM t), some column is extracted from the table anyway (the smallest one is preferred), in order to calculate the number of rows. The FINAL modifier can be used only for a SELECT from a CollapsingMergeTree table. When you specify FINAL, data is selected fully \"collapsed\". Keep in mind that using FINAL leads to a selection that includes columns related to the primary key, in addition to the columns specified in the SELECT. Additionally, the query will be executed in a single stream, and data will be merged during query execution. This means that when using FINAL, the query is processed more slowly. In most cases, you should avoid using FINAL. For more information, see the section \"CollapsingMergeTree engine\".", - "title": "FROM clause" - }, - { - "location": "/query_language/queries/#sample-clause", - "text": "The SAMPLE clause allows for approximated query processing. Approximated query processing is only supported by MergeTree* type tables, and only if the sampling expression was specified during table creation (see the section \"MergeTree engine\"). SAMPLE has the format SAMPLE k , where k is a decimal number from 0 to 1, or SAMPLE n , where 'n' is a sufficiently large integer. In the first case, the query will be executed on 'k' percent of data. For example, SAMPLE 0.1 runs the query on 10% of data.\nIn the second case, the query will be executed on a sample of no more than 'n' rows. For example, SAMPLE 10000000 runs the query on a maximum of 10,000,000 rows. Example: SELECT \n Title , \n count () * 10 AS PageViews FROM hits_distributed SAMPLE 0 . 1 WHERE \n CounterID = 34 \n AND toDate ( EventDate ) = toDate ( 2013-01-29 ) \n AND toDate ( EventDate ) = toDate ( 2013-02-04 ) \n AND NOT DontCountHits \n AND NOT Refresh \n AND Title != GROUP BY Title ORDER BY PageViews DESC LIMIT 1000 In this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value 'count()' is manually multiplied by 10. When using something like SAMPLE 10000000 , there isn't any information about which relative percent of data was processed or what the aggregate functions should be multiplied by, so this method of writing is not always appropriate to the situation. A sample with a relative coefficient is \"consistent\": if we look at all possible data that could be in the table, a sample (when using a single sampling expression specified during table creation) with the same coefficient always selects the same subset of possible data. In other words, a sample from different tables on different servers at different times is made the same way. For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This allows using the sample in subqueries in the IN clause, as well as for manually correlating results of different queries with samples.", - "title": "SAMPLE clause" - }, - { - "location": "/query_language/queries/#array-join-clause", - "text": "Allows executing JOIN with an array or nested data structure. The intent is similar to the 'arrayJoin' function, but its functionality is broader. ARRAY JOIN is essentially INNER JOIN with an array. Example: :) CREATE TABLE arrays_test (s String, arr Array(UInt8)) ENGINE = Memory\n\nCREATE TABLE arrays_test\n(\n s String,\n arr Array(UInt8)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.001 sec.\n\n:) INSERT INTO arrays_test VALUES ( Hello , [1,2]), ( World , [3,4,5]), ( Goodbye , [])\n\nINSERT INTO arrays_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM arrays_test\n\nSELECT *\nFROM arrays_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502\n\u2502 Goodbye \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, arr FROM arrays_test ARRAY JOIN arr\n\nSELECT s, arr\nFROM arrays_test\nARRAY JOIN arr\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2510\n\u2502 Hello \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502\n\u2502 World \u2502 4 \u2502\n\u2502 World \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. An alias can be specified for an array in the ARRAY JOIN clause. In this case, an array item can be accessed by this alias, but the array itself by the original name. Example: :) SELECT s, arr, a FROM arrays_test ARRAY JOIN arr AS a\n\nSELECT s, arr, a\nFROM arrays_test\nARRAY JOIN arr AS a\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. Multiple arrays of the same size can be comma-separated in the ARRAY JOIN clause. In this case, JOIN is performed with them simultaneously (the direct sum, not the direct product). Example: :) SELECT s, arr, a, num, mapped FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(x - x + 1, arr) AS mapped\n\nSELECT s, arr, a, num, mapped\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(lambda(tuple(x), plus(x, 1)), arr) AS mapped\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500mapped\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 2 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 5 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 6 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n:) SELECT s, arr, a, num, arrayEnumerate(arr) FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\nSELECT s, arr, a, num, arrayEnumerate(arr)\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500arrayEnumerate(arr)\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 [1,2] \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec. ARRAY JOIN also works with nested data structures. Example: :) CREATE TABLE nested_test (s String, nest Nested(x UInt8, y UInt32)) ENGINE = Memory\n\nCREATE TABLE nested_test\n(\n s String,\n nest Nested(\n x UInt8,\n y UInt32)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.006 sec.\n\n:) INSERT INTO nested_test VALUES ( Hello , [1,2], [10,20]), ( World , [3,4,5], [30,40,50]), ( Goodbye , [], [])\n\nINSERT INTO nested_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM nested_test\n\nSELECT *\nFROM nested_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 Goodbye \u2502 [] \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. When specifying names of nested data structures in ARRAY JOIN, the meaning is the same as ARRAY JOIN with all the array elements that it consists of. Example: :) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x, nest.y\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`, `nest.y`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. This variation also makes sense: :) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. An alias may be used for a nested data structure, in order to select either the JOIN result or the source array. Example: :) SELECT s, n.x, n.y, nest.x, nest.y FROM nested_test ARRAY JOIN nest AS n\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest AS n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. Example of using the arrayEnumerate function: :) SELECT s, n.x, n.y, nest.x, nest.y, num FROM nested_test ARRAY JOIN nest AS n, arrayEnumerate(nest.x) AS num\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`, num\nFROM nested_test\nARRAY JOIN nest AS n, arrayEnumerate(`nest.x`) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u252c\u2500num\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 1 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 2 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 3 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec. The query can only specify a single ARRAY JOIN clause. The corresponding conversion can be performed before the WHERE/PREWHERE clause (if its result is needed in this clause), or after completing WHERE/PREWHERE (to reduce the volume of calculations).", - "title": "ARRAY JOIN clause" - }, - { - "location": "/query_language/queries/#join-clause", - "text": "The normal JOIN, which is not related to ARRAY JOIN described above. [ GLOBAL ] ANY | ALL INNER | LEFT [ OUTER ] JOIN ( subquery ) | table USING columns_list Performs joins with data from the subquery. At the beginning of query processing, the subquery specified after JOIN is run, and its result is saved in memory. Then it is read from the \"left\" table specified in the FROM clause, and while it is being read, for each of the read rows from the \"left\" table, rows are selected from the subquery results table (the \"right\" table) that meet the condition for matching the values of the columns specified in USING. The table name can be specified instead of a subquery. This is equivalent to the SELECT * FROM table subquery, except in a special case when the table has the Join engine \u2013 an array prepared for joining. All columns that are not needed for the JOIN are deleted from the subquery. There are several types of JOINs: INNER or LEFT type:If INNER is specified, the result will contain only those rows that have a matching row in the right table.\nIf LEFT is specified, any rows in the left table that don't have matching rows in the right table will be assigned the default value - zeros or empty rows. LEFT OUTER may be written instead of LEFT; the word OUTER does not affect anything. ANY or ALL stringency:If ANY is specified and the right table has several matching rows, only the first one found is joined.\nIf ALL is specified and the right table has several matching rows, the data will be multiplied by the number of these rows. Using ALL corresponds to the normal JOIN semantic from standard SQL.\nUsing ANY is optimal. If the right table has only one matching row, the results of ANY and ALL are the same. You must specify either ANY or ALL (neither of them is selected by default). GLOBAL distribution: When using a normal JOIN, the query is sent to remote servers. Subqueries are run on each of them in order to make the right table, and the join is performed with this table. In other words, the right table is formed on each server separately. When using GLOBAL ... JOIN , first the requestor server runs a subquery to calculate the right table. This temporary table is passed to each remote server, and queries are run on them using the temporary data that was transmitted. Be careful when using GLOBAL JOINs. For more information, see the section \"Distributed subqueries\". Any combination of JOINs is possible. For example, GLOBAL ANY LEFT OUTER JOIN . When running a JOIN, there is no optimization of the order of execution in relation to other stages of the query. The join (a search in the right table) is run before filtering in WHERE and before aggregation. In order to explicitly set the processing order, we recommend running a JOIN subquery with a subquery. Example: SELECT \n CounterID , \n hits , \n visits FROM ( \n SELECT \n CounterID , \n count () AS hits \n FROM test . hits \n GROUP BY CounterID ) ANY LEFT JOIN ( \n SELECT \n CounterID , \n sum ( Sign ) AS visits \n FROM test . visits \n GROUP BY CounterID ) USING CounterID ORDER BY hits DESC LIMIT 10 \u250c\u2500CounterID\u2500\u252c\u2500\u2500\u2500hits\u2500\u252c\u2500visits\u2500\u2510\n\u2502 1143050 \u2502 523264 \u2502 13665 \u2502\n\u2502 731962 \u2502 475698 \u2502 102716 \u2502\n\u2502 722545 \u2502 337212 \u2502 108187 \u2502\n\u2502 722889 \u2502 252197 \u2502 10547 \u2502\n\u2502 2237260 \u2502 196036 \u2502 9522 \u2502\n\u2502 23057320 \u2502 147211 \u2502 7689 \u2502\n\u2502 722818 \u2502 90109 \u2502 17847 \u2502\n\u2502 48221 \u2502 85379 \u2502 4652 \u2502\n\u2502 19762435 \u2502 77807 \u2502 7026 \u2502\n\u2502 722884 \u2502 77492 \u2502 11056 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Subqueries don't allow you to set names or use them for referencing a column from a specific subquery.\nThe columns specified in USING must have the same names in both subqueries, and the other columns must be named differently. You can use aliases to change the names of columns in subqueries (the example uses the aliases 'hits' and 'visits'). The USING clause specifies one or more columns to join, which establishes the equality of these columns. The list of columns is set without brackets. More complex join conditions are not supported. The right table (the subquery result) resides in RAM. If there isn't enough memory, you can't run a JOIN. Only one JOIN can be specified in a query (on a single level). To run multiple JOINs, you can put them in subqueries. Each time a query is run with the same JOIN, the subquery is run again \u2013 the result is not cached. To avoid this, use the special 'Join' table engine, which is a prepared array for joining that is always in RAM. For more information, see the section \"Table engines, Join\". In some cases, it is more efficient to use IN instead of JOIN.\nAmong the various types of JOINs, the most efficient is ANY LEFT JOIN, then ANY INNER JOIN. The least efficient are ALL LEFT JOIN and ALL INNER JOIN. If you need a JOIN for joining with dimension tables (these are relatively small tables that contain dimension properties, such as names for advertising campaigns), a JOIN might not be very convenient due to the bulky syntax and the fact that the right table is re-accessed for every query. For such cases, there is an \"external dictionaries\" feature that you should use instead of JOIN. For more information, see the section \"External dictionaries\".", - "title": "JOIN clause" - }, - { - "location": "/query_language/queries/#where-clause", - "text": "If there is a WHERE clause, it must contain an expression with the UInt8 type. This is usually an expression with comparison and logical operators.\nThis expression will be used for filtering data before all other transformations. If indexes are supported by the database table engine, the expression is evaluated on the ability to use indexes.", - "title": "WHERE clause" - }, - { - "location": "/query_language/queries/#prewhere-clause", - "text": "This clause has the same meaning as the WHERE clause. The difference is in which data is read from the table.\nWhen using PREWHERE, first only the columns necessary for executing PREWHERE are read. Then the other columns are read that are needed for running the query, but only those blocks where the PREWHERE expression is true. It makes sense to use PREWHERE if there are filtration conditions that are not suitable for indexes that are used by a minority of the columns in the query, but that provide strong data filtration. This reduces the volume of data to read. For example, it is useful to write PREWHERE for queries that extract a large number of columns, but that only have filtration for a few columns. PREWHERE is only supported by tables from the *MergeTree family. A query may simultaneously specify PREWHERE and WHERE. In this case, PREWHERE precedes WHERE. Keep in mind that it does not make much sense for PREWHERE to only specify those columns that have an index, because when using an index, only the data blocks that match the index are read. If the 'optimize_move_to_prewhere' setting is set to 1 and PREWHERE is omitted, the system uses heuristics to automatically move parts of expressions from WHERE to PREWHERE.", - "title": "PREWHERE clause" - }, - { - "location": "/query_language/queries/#group-by-clause", - "text": "This is one of the most important parts of a column-oriented DBMS. If there is a GROUP BY clause, it must contain a list of expressions. Each expression will be referred to here as a \"key\".\nAll the expressions in the SELECT, HAVING, and ORDER BY clauses must be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. If a query contains only table columns inside aggregate functions, the GROUP BY clause can be omitted, and aggregation by an empty set of keys is assumed. Example: SELECT \n count (), \n median ( FetchTiming 60 ? 60 : FetchTiming ), \n count () - sum ( Refresh ) FROM hits However, in contrast to standard SQL, if the table doesn't have any rows (either there aren't any at all, or there aren't any after using WHERE to filter), an empty result is returned, and not the result from one of the rows containing the initial values of aggregate functions. As opposed to MySQL (and conforming to standard SQL), you can't get some value of some column that is not in a key or aggregate function (except constant expressions). To work around this, you can use the 'any' aggregate function (get the first encountered value) or 'min/max'. Example: SELECT \n domainWithoutWWW ( URL ) AS domain , \n count (), \n any ( Title ) AS title -- getting the first occurred page header for each domain. FROM hits GROUP BY domain For every different key value encountered, GROUP BY calculates a set of aggregate function values. GROUP BY is not supported for array columns. A constant can't be specified as arguments for aggregate functions. Example: sum(1). Instead of this, you can get rid of the constant. Example: count() .", - "title": "GROUP BY clause" - }, - { - "location": "/query_language/queries/#with-totals-modifier", - "text": "If the WITH TOTALS modifier is specified, another row will be calculated. This row will have key columns containing default values (zeros or empty lines), and columns of aggregate functions with the values calculated across all the rows (the \"total\" values). This extra row is output in JSON*, TabSeparated*, and Pretty* formats, separately from the other rows. In the other formats, this row is not output. In JSON* formats, this row is output as a separate 'totals' field. In TabSeparated* formats, the row comes after the main result, preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result. WITH TOTALS can be run in different ways when HAVING is present. The behavior depends on the 'totals_mode' setting.\nBy default, totals_mode = 'before_having' . In this case, 'totals' is calculated across all rows, including the ones that don't pass through HAVING and 'max_rows_to_group_by'. The other alternatives include only the rows that pass through HAVING in 'totals', and behave differently with the setting max_rows_to_group_by and group_by_overflow_mode = 'any' . after_having_exclusive \u2013 Don't include rows that didn't pass through max_rows_to_group_by . In other words, 'totals' will have less than or the same number of rows as it would if max_rows_to_group_by were omitted. after_having_inclusive \u2013 Include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. In other words, 'totals' will have more than or the same number of rows as it would if max_rows_to_group_by were omitted. after_having_auto \u2013 Count the number of rows that passed through HAVING. If it is more than a certain amount (by default, 50%), include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. Otherwise, do not include them. totals_auto_threshold \u2013 By default, 0.5. The coefficient for after_having_auto . If max_rows_to_group_by and group_by_overflow_mode = 'any' are not used, all variations of after_having are the same, and you can use any of them (for example, after_having_auto ). You can use WITH TOTALS in subqueries, including subqueries in the JOIN clause (in this case, the respective total values are combined).", - "title": "WITH TOTALS modifier" - }, - { - "location": "/query_language/queries/#group-by-in-external-memory", - "text": "You can enable dumping temporary data to the disk to restrict memory usage during GROUP BY.\nThe max_bytes_before_external_group_by setting determines the threshold RAM consumption for dumping GROUP BY temporary data to the file system. If set to 0 (the default), it is disabled. When using max_bytes_before_external_group_by , we recommend that you set max_memory_usage about twice as high. This is necessary because there are two stages to aggregation: reading the date and forming intermediate data (1) and merging the intermediate data (2). Dumping data to the file system can only occur during stage 1. If the temporary data wasn't dumped, then stage 2 might require up to the same amount of memory as in stage 1. For example, if max_memory_usage was set to 10000000000 and you want to use external aggregation, it makes sense to set max_bytes_before_external_group_by to 10000000000, and max_memory_usage to 20000000000. When external aggregation is triggered (if there was at least one dump of temporary data), maximum consumption of RAM is only slightly more than max_bytes_before_external_group_by . With distributed query processing, external aggregation is performed on remote servers. In order for the requestor server to use only a small amount of RAM, set distributed_aggregation_memory_efficient to 1. When merging data flushed to the disk, as well as when merging results from remote servers when the distributed_aggregation_memory_efficient setting is enabled, consumes up to 1/256 * the number of threads from the total amount of RAM. When external aggregation is enabled, if there was less than max_bytes_before_external_group_by of data (i.e. data was not flushed), the query runs just as fast as without external aggregation. If any temporary data was flushed, the run time will be several times longer (approximately three times). If you have an ORDER BY with a small LIMIT after GROUP BY, then the ORDER BY CLAUSE will not use significant amounts of RAM.\nBut if the ORDER BY doesn't have LIMIT, don't forget to enable external sorting ( max_bytes_before_external_sort ).", - "title": "GROUP BY in external memory" - }, - { - "location": "/query_language/queries/#limit-n-by-clause", - "text": "LIMIT N BY COLUMNS selects the top N rows for each group of COLUMNS. LIMIT N BY is not related to LIMIT; they can both be used in the same query. The key for LIMIT N BY can contain any number of columns or expressions. Example: SELECT \n domainWithoutWWW ( URL ) AS domain , \n domainWithoutWWW ( REFERRER_URL ) AS referrer , \n device_type , \n count () cnt FROM hits GROUP BY domain , referrer , device_type ORDER BY cnt DESC LIMIT 5 BY domain , device_type LIMIT 100 The query will select the top 5 referrers for each domain, device_type pair, but not more than 100 rows ( LIMIT n BY + LIMIT ).", - "title": "LIMIT N BY clause" - }, - { - "location": "/query_language/queries/#having-clause", - "text": "Allows filtering the result received after GROUP BY, similar to the WHERE clause.\nWHERE and HAVING differ in that WHERE is performed before aggregation (GROUP BY), while HAVING is performed after it.\nIf aggregation is not performed, HAVING can't be used.", - "title": "HAVING clause" - }, - { - "location": "/query_language/queries/#order-by-clause", - "text": "The ORDER BY clause contains a list of expressions, which can each be assigned DESC or ASC (the sorting direction). If the direction is not specified, ASC is assumed. ASC is sorted in ascending order, and DESC in descending order. The sorting direction applies to a single expression, not to the entire list. Example: ORDER BY Visits DESC, SearchPhrase For sorting by String values, you can specify collation (comparison). Example: ORDER BY SearchPhrase COLLATE 'tr' - for sorting by keyword in ascending order, using the Turkish alphabet, case insensitive, assuming that strings are UTF-8 encoded. COLLATE can be specified or not for each expression in ORDER BY independently. If ASC or DESC is specified, COLLATE is specified after it. When using COLLATE, sorting is always case-insensitive. We only recommend using COLLATE for final sorting of a small number of rows, since sorting with COLLATE is less efficient than normal sorting by bytes. Rows that have identical values for the list of sorting expressions are output in an arbitrary order, which can also be nondeterministic (different each time).\nIf the ORDER BY clause is omitted, the order of the rows is also undefined, and may be nondeterministic as well. When floating point numbers are sorted, NaNs are separate from the other values. Regardless of the sorting order, NaNs come at the end. In other words, for ascending sorting they are placed as if they are larger than all the other numbers, while for descending sorting they are placed as if they are smaller than the rest. Less RAM is used if a small enough LIMIT is specified in addition to ORDER BY. Otherwise, the amount of memory spent is proportional to the volume of data for sorting. For distributed query processing, if GROUP BY is omitted, sorting is partially done on remote servers, and the results are merged on the requestor server. This means that for distributed sorting, the volume of data to sort can be greater than the amount of memory on a single server. If there is not enough RAM, it is possible to perform sorting in external memory (creating temporary files on a disk). Use the setting max_bytes_before_external_sort for this purpose. If it is set to 0 (the default), external sorting is disabled. If it is enabled, when the volume of data to sort reaches the specified number of bytes, the collected data is sorted and dumped into a temporary file. After all data is read, all the sorted files are merged and the results are output. Files are written to the /var/lib/clickhouse/tmp/ directory in the config (by default, but you can use the 'tmp_path' parameter to change this setting). Running a query may use more memory than 'max_bytes_before_external_sort'. For this reason, this setting must have a value significantly smaller than 'max_memory_usage'. As an example, if your server has 128 GB of RAM and you need to run a single query, set 'max_memory_usage' to 100 GB, and 'max_bytes_before_external_sort' to 80 GB. External sorting works much less effectively than sorting in RAM.", - "title": "ORDER BY clause" - }, - { - "location": "/query_language/queries/#select-clause", - "text": "The expressions specified in the SELECT clause are analyzed after the calculations for all the clauses listed above are completed.\nMore specifically, expressions are analyzed that are above the aggregate functions, if there are any aggregate functions.\nThe aggregate functions and everything below them are calculated during aggregation (GROUP BY).\nThese expressions work as if they are applied to separate rows in the result.", - "title": "SELECT clause" - }, - { - "location": "/query_language/queries/#distinct-clause", - "text": "If DISTINCT is specified, only a single row will remain out of all the sets of fully matching rows in the result.\nThe result will be the same as if GROUP BY were specified across all the fields specified in SELECT without aggregate functions. But there are several differences from GROUP BY: DISTINCT can be applied together with GROUP BY. When ORDER BY is omitted and LIMIT is defined, the query stops running immediately after the required number of different rows has been read. Data blocks are output as they are processed, without waiting for the entire query to finish running. DISTINCT is not supported if SELECT has at least one array column.", - "title": "DISTINCT clause" - }, - { - "location": "/query_language/queries/#limit-clause", - "text": "LIMIT m allows you to select the first 'm' rows from the result.\nLIMIT n, m allows you to select the first 'm' rows from the result after skipping the first 'n' rows. 'n' and 'm' must be non-negative integers. If there isn't an ORDER BY clause that explicitly sorts results, the result may be arbitrary and nondeterministic.", - "title": "LIMIT clause" - }, - { - "location": "/query_language/queries/#union-all-clause", - "text": "You can use UNION ALL to combine any number of queries. Example: SELECT CounterID , 1 AS table , toInt64 ( count ()) AS c \n FROM test . hits \n GROUP BY CounterID UNION ALL SELECT CounterID , 2 AS table , sum ( Sign ) AS c \n FROM test . visits \n GROUP BY CounterID \n HAVING c 0 Only UNION ALL is supported. The regular UNION (UNION DISTINCT) is not supported. If you need UNION DISTINCT, you can write SELECT DISTINCT from a subquery containing UNION ALL. Queries that are parts of UNION ALL can be run simultaneously, and their results can be mixed together. The structure of results (the number and type of columns) must match for the queries. But the column names can differ. In this case, the column names for the final result will be taken from the first query. Queries that are parts of UNION ALL can't be enclosed in brackets. ORDER BY and LIMIT are applied to separate queries, not to the final result. If you need to apply a conversion to the final result, you can put all the queries with UNION ALL in a subquery in the FROM clause.", - "title": "UNION ALL clause" - }, - { - "location": "/query_language/queries/#into-outfile-clause", - "text": "Add the INTO OUTFILE filename clause (where filename is a string literal) to redirect query output to the specified file.\nIn contrast to MySQL, the file is created on the client side. The query will fail if a file with the same filename already exists.\nThis functionality is available in the command-line client and clickhouse-local (a query sent via HTTP interface will fail). The default output format is TabSeparated (the same as in the command-line client batch mode).", - "title": "INTO OUTFILE clause" - }, - { - "location": "/query_language/queries/#format-clause", - "text": "Specify 'FORMAT format' to get data in any specified format.\nYou can use this for convenience, or for creating dumps.\nFor more information, see the section \"Formats\".\nIf the FORMAT clause is omitted, the default format is used, which depends on both the settings and the interface used for accessing the DB. For the HTTP interface and the command-line client in batch mode, the default format is TabSeparated. For the command-line client in interactive mode, the default format is PrettyCompact (it has attractive and compact tables). When using the command-line client, data is passed to the client in an internal efficient format. The client independently interprets the FORMAT clause of the query and formats the data itself (thus relieving the network and the server from the load).", - "title": "FORMAT clause" - }, - { - "location": "/query_language/queries/#in-operators", - "text": "The IN , NOT IN , GLOBAL IN , and GLOBAL NOT IN operators are covered separately, since their functionality is quite rich. The left side of the operator is either a single column or a tuple. Examples: SELECT UserID IN ( 123 , 456 ) FROM ... SELECT ( CounterID , UserID ) IN (( 34 , 123 ), ( 101500 , 456 )) FROM ... If the left side is a single column that is in the index, and the right side is a set of constants, the system uses the index for processing the query. Don't list too many values explicitly (i.e. millions). If a data set is large, put it in a temporary table (for example, see the section \"External data for query processing\"), then use a subquery. The right side of the operator can be a set of constant expressions, a set of tuples with constant expressions (shown in the examples above), or the name of a database table or SELECT subquery in brackets. If the right side of the operator is the name of a table (for example, UserID IN users ), this is equivalent to the subquery UserID IN (SELECT * FROM users) . Use this when working with external data that is sent along with the query. For example, the query can be sent together with a set of user IDs loaded to the 'users' temporary table, which should be filtered. If the right side of the operator is a table name that has the Set engine (a prepared data set that is always in RAM), the data set will not be created over again for each query. The subquery may specify more than one column for filtering tuples.\nExample: SELECT ( CounterID , UserID ) IN ( SELECT CounterID , UserID FROM ...) FROM ... The columns to the left and right of the IN operator should have the same type. The IN operator and subquery may occur in any part of the query, including in aggregate functions and lambda functions.\nExample: SELECT \n EventDate , \n avg ( UserID IN \n ( \n SELECT UserID \n FROM test . hits \n WHERE EventDate = toDate ( 2014-03-17 ) \n )) AS ratio FROM test . hits GROUP BY EventDate ORDER BY EventDate ASC \u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500ratio\u2500\u2510\n\u2502 2014-03-17 \u2502 1 \u2502\n\u2502 2014-03-18 \u2502 0.807696 \u2502\n\u2502 2014-03-19 \u2502 0.755406 \u2502\n\u2502 2014-03-20 \u2502 0.723218 \u2502\n\u2502 2014-03-21 \u2502 0.697021 \u2502\n\u2502 2014-03-22 \u2502 0.647851 \u2502\n\u2502 2014-03-23 \u2502 0.648416 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 For each day after March 17th, count the percentage of pageviews made by users who visited the site on March 17th.\nA subquery in the IN clause is always run just one time on a single server. There are no dependent subqueries.", - "title": "IN operators" - }, - { - "location": "/query_language/queries/#distributed-subqueries", - "text": "There are two options for IN-s with subqueries (similar to JOINs): normal IN / OIN and IN GLOBAL / GLOBAL JOIN . They differ in how they are run for distributed query processing. \n\nRemember that the algorithms described below may work differently depending on the [settings](../operations/settings/settings.md#settings-distributed_product_mode) `distributed_product_mode` setting. When using the regular IN, the query is sent to remote servers, and each of them runs the subqueries in the IN or JOIN clause. When using GLOBAL IN / GLOBAL JOINs , first all the subqueries are run for GLOBAL IN / GLOBAL JOINs , and the results are collected in temporary tables. Then the temporary tables are sent to each remote server, where the queries are run using this temporary data. For a non-distributed query, use the regular IN / JOIN . Be careful when using subqueries in the IN / JOIN clauses for distributed query processing. Let's look at some examples. Assume that each server in the cluster has a normal local_table . Each server also has a distributed_table table with the Distributed type, which looks at all the servers in the cluster. For a query to the distributed_table , the query will be sent to all the remote servers and run on them using the local_table . For example, the query SELECT uniq ( UserID ) FROM distributed_table will be sent to all remote servers as SELECT uniq ( UserID ) FROM local_table and run on each of them in parallel, until it reaches the stage where intermediate results can be combined. Then the intermediate results will be returned to the requestor server and merged on it, and the final result will be sent to the client. Now let's examine a query with IN: SELECT uniq ( UserID ) FROM distributed_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM local_table WHERE CounterID = 34 ) Calculation of the intersection of audiences of two sites. This query will be sent to all remote servers as SELECT uniq ( UserID ) FROM local_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM local_table WHERE CounterID = 34 ) In other words, the data set in the IN clause will be collected on each server independently, only across the data that is stored locally on each of the servers. This will work correctly and optimally if you are prepared for this case and have spread data across the cluster servers such that the data for a single UserID resides entirely on a single server. In this case, all the necessary data will be available locally on each server. Otherwise, the result will be inaccurate. We refer to this variation of the query as \"local IN\". To correct how the query works when data is spread randomly across the cluster servers, you could specify distributed_table inside a subquery. The query would look like this: SELECT uniq ( UserID ) FROM distributed_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM distributed_table WHERE CounterID = 34 ) This query will be sent to all remote servers as SELECT uniq ( UserID ) FROM local_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM distributed_table WHERE CounterID = 34 ) The subquery will begin running on each remote server. Since the subquery uses a distributed table, the subquery that is on each remote server will be resent to every remote server as SELECT UserID FROM local_table WHERE CounterID = 34 For example, if you have a cluster of 100 servers, executing the entire query will require 10,000 elementary requests, which is generally considered unacceptable. In such cases, you should always use GLOBAL IN instead of IN. Let's look at how it works for the query SELECT uniq ( UserID ) FROM distributed_table WHERE CounterID = 101500 AND UserID GLOBAL IN ( SELECT UserID FROM distributed_table WHERE CounterID = 34 ) The requestor server will run the subquery SELECT UserID FROM distributed_table WHERE CounterID = 34 and the result will be put in a temporary table in RAM. Then the request will be sent to each remote server as SELECT uniq ( UserID ) FROM local_table WHERE CounterID = 101500 AND UserID GLOBAL IN _data1 and the temporary table _data1 will be sent to every remote server with the query (the name of the temporary table is implementation-defined). This is more optimal than using the normal IN. However, keep the following points in mind: When creating a temporary table, data is not made unique. To reduce the volume of data transmitted over the network, specify DISTINCT in the subquery. (You don't need to do this for a normal IN.) The temporary table will be sent to all the remote servers. Transmission does not account for network topology. For example, if 10 remote servers reside in a datacenter that is very remote in relation to the requestor server, the data will be sent 10 times over the channel to the remote datacenter. Try to avoid large data sets when using GLOBAL IN. When transmitting data to remote servers, restrictions on network bandwidth are not configurable. You might overload the network. Try to distribute data across servers so that you don't need to use GLOBAL IN on a regular basis. If you need to use GLOBAL IN often, plan the location of the ClickHouse cluster so that a single group of replicas resides in no more than one data center with a fast network between them, so that a query can be processed entirely within a single data center. It also makes sense to specify a local table in the GLOBAL IN clause, in case this local table is only available on the requestor server and you want to use data from it on remote servers.", - "title": "Distributed subqueries" - }, - { - "location": "/query_language/queries/#extreme-values", - "text": "In addition to results, you can also get minimum and maximum values for the results columns. To do this, set the extremes setting to 1. Minimums and maximums are calculated for numeric types, dates, and dates with times. For other columns, the default values are output. An extra two rows are calculated \u2013 the minimums and maximums, respectively. These extra two rows are output in JSON*, TabSeparated*, and Pretty* formats, separate from the other rows. They are not output for other formats. In JSON* formats, the extreme values are output in a separate 'extremes' field. In TabSeparated* formats, the row comes after the main result, and after 'totals' if present. It is preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result, and after 'totals' if present. Extreme values are calculated for rows that have passed through LIMIT. However, when using 'LIMIT offset, size', the rows before 'offset' are included in 'extremes'. In stream requests, the result may also include a small number of rows that passed through LIMIT.", - "title": "Extreme values" - }, - { - "location": "/query_language/queries/#notes", - "text": "The GROUP BY and ORDER BY clauses do not support positional arguments. This contradicts MySQL, but conforms to standard SQL.\nFor example, GROUP BY 1, 2 will be interpreted as grouping by constants (i.e. aggregation of all rows into one). You can use synonyms ( AS aliases) in any part of a query. You can put an asterisk in any part of a query instead of an expression. When the query is analyzed, the asterisk is expanded to a list of all table columns (excluding the MATERIALIZED and ALIAS columns). There are only a few cases when using an asterisk is justified: When creating a table dump. For tables containing just a few columns, such as system tables. For getting information about what columns are in a table. In this case, set LIMIT 1 . But it is better to use the DESC TABLE query. When there is strong filtration on a small number of columns using PREWHERE . In subqueries (since columns that aren't needed for the external query are excluded from subqueries). In all other cases, we don't recommend using the asterisk, since it only gives you the drawbacks of a columnar DBMS instead of the advantages. In other words using the asterisk is not recommended.", - "title": "Notes" - }, - { - "location": "/query_language/queries/#kill-query", - "text": "KILL QUERY \n WHERE where expression to SELECT FROM system . processes query \n [ SYNC | ASYNC | TEST ] \n [ FORMAT format ] Attempts to forcibly terminate the currently running queries.\nThe queries to terminate are selected from the system.processes table using the criteria defined in the WHERE clause of the KILL query. Examples: -- Forcibly terminates all queries with the specified query_id: KILL QUERY WHERE query_id = 2-857d-4a57-9ee0-327da5d60a90 -- Synchronously terminates all queries run by username : KILL QUERY WHERE user = username SYNC Read-only users can only stop their own queries. By default, the asynchronous version of queries is used ( ASYNC ), which doesn't wait for confirmation that queries have stopped. The synchronous version ( SYNC ) waits for all queries to stop and displays information about each process as it stops.\nThe response contains the kill_status column, which can take the following values: 'finished' \u2013 The query was terminated successfully. 'waiting' \u2013 Waiting for the query to end after sending it a signal to terminate. The other values \u200b\u200bexplain why the query can't be stopped. A test query ( TEST ) only checks the user's rights and displays a list of queries to stop.", - "title": "KILL QUERY" - }, - { - "location": "/query_language/syntax/", - "text": "Syntax\n\n\nThere are two types of parsers in the system: the full SQL parser (a recursive descent parser), and the data format parser (a fast stream parser).\nIn all cases except the INSERT query, only the full SQL parser is used.\nThe INSERT query uses both parsers:\n\n\nINSERT\n \nINTO\n \nt\n \nVALUES\n \n(\n1\n,\n \nHello, world\n),\n \n(\n2\n,\n \nabc\n),\n \n(\n3\n,\n \ndef\n)\n\n\n\n\n\n\nThe \nINSERT INTO t VALUES\n fragment is parsed by the full parser, and the data \n(1, 'Hello, world'), (2, 'abc'), (3, 'def')\n is parsed by the fast stream parser.\nData can have any format. When a query is received, the server calculates no more than \nmax_query_size\n bytes of the request in RAM (by default, 1 MB), and the rest is stream parsed.\nThis means the system doesn't have problems with large INSERT queries, like MySQL does.\n\n\nWhen using the Values format in an INSERT query, it may seem that data is parsed the same as expressions in a SELECT query, but this is not true. The Values format is much more limited.\n\n\nNext we will cover the full parser. For more information about format parsers, see the section \"Formats\".\n\n\nSpaces\n\n\nThere may be any number of space symbols between syntactical constructions (including the beginning and end of a query). Space symbols include the space, tab, line feed, CR, and form feed.\n\n\nComments\n\n\nSQL-style and C-style comments are supported.\nSQL-style comments: from \n--\n to the end of the line. The space after \n--\n can be omitted.\nComments in C-style: from \n/*\n to \n*/\n. These comments can be multiline. Spaces are not required here, either.\n\n\nKeywords\n\n\nKeywords (such as \nSELECT\n) are not case-sensitive. Everything else (column names, functions, and so on), in contrast to standard SQL, is case-sensitive. Keywords are not reserved (they are just parsed as keywords in the corresponding context).\n\n\nIdentifiers\n\n\nIdentifiers (column names, functions, and data types) can be quoted or non-quoted.\nNon-quoted identifiers start with a Latin letter or underscore, and continue with a Latin letter, underscore, or number. In other words, they must match the regex \n^[a-zA-Z_][0-9a-zA-Z_]*$\n. Examples: \nx, _1, X_y__Z123_.\n\n\nQuoted identifiers are placed in reversed quotation marks \n`id`\n (the same as in MySQL), and can indicate any set of bytes (non-empty). In addition, symbols (for example, the reverse quotation mark) inside this type of identifier can be backslash-escaped. Escaping rules are the same as for string literals (see below).\nWe recommend using identifiers that do not need to be quoted.\n\n\nLiterals\n\n\nThere are numeric literals, string literals, and compound literals.\n\n\nNumeric literals\n\n\nA numeric literal tries to be parsed:\n\n\n\n\nFirst as a 64-bit signed number, using the 'strtoull' function.\n\n\nIf unsuccessful, as a 64-bit unsigned number, using the 'strtoll' function.\n\n\nIf unsuccessful, as a floating-point number using the 'strtod' function.\n\n\nOtherwise, an error is returned.\n\n\n\n\nThe corresponding value will have the smallest type that the value fits in.\nFor example, 1 is parsed as UInt8, but 256 is parsed as UInt16. For more information, see \"Data types\".\n\n\nExamples: \n1\n, \n18446744073709551615\n, \n0xDEADBEEF\n, \n01\n, \n0.1\n, \n1e100\n, \n-1e-100\n, \ninf\n, \nnan\n.\n\n\nString literals\n\n\nOnly string literals in single quotes are supported. The enclosed characters can be backslash-escaped. The following escape sequences have a corresponding special value: \n\\b\n, \n\\f\n, \n\\r\n, \n\\n\n, \n\\t\n, \n\\0\n, \n\\a\n, \n\\v\n, \n\\xHH\n. In all other cases, escape sequences in the format \n\\c\n, where \"c\" is any character, are converted to \"c\". This means that you can use the sequences \n\\'\nand\n\\\\\n. The value will have the String type.\n\n\nThe minimum set of characters that you need to escape in string literals: \n'\n and \n\\\n.\n\n\nCompound literals\n\n\nConstructions are supported for arrays: \n[1, 2, 3]\n and tuples: \n(1, 'Hello, world!', 2)\n..\nActually, these are not literals, but expressions with the array creation operator and the tuple creation operator, respectively.\nFor more information, see the section \"Operators2\".\nAn array must consist of at least one item, and a tuple must have at least two items.\nTuples have a special purpose for use in the IN clause of a SELECT query. Tuples can be obtained as the result of a query, but they can't be saved to a database (with the exception of Memory-type tables).\n\n\nFunctions\n\n\nFunctions are written like an identifier with a list of arguments (possibly empty) in brackets. In contrast to standard SQL, the brackets are required, even for an empty arguments list. Example: \nnow()\n.\nThere are regular and aggregate functions (see the section \"Aggregate functions\"). Some aggregate functions can contain two lists of arguments in brackets. Example: \nquantile (0.9) (x)\n. These aggregate functions are called \"parametric\" functions, and the arguments in the first list are called \"parameters\". The syntax of aggregate functions without parameters is the same as for regular functions.\n\n\nOperators\n\n\nOperators are converted to their corresponding functions during query parsing, taking their priority and associativity into account.\nFor example, the expression \n1 + 2 * 3 + 4\n is transformed to \nplus(plus(1, multiply(2, 3)), 4)\n.\nFor more information, see the section \"Operators\" below.\n\n\nData types and database table engines\n\n\nData types and table engines in the \nCREATE\n query are written the same way as identifiers or functions. In other words, they may or may not contain an arguments list in brackets. For more information, see the sections \"Data types,\" \"Table engines,\" and \"CREATE\".\n\n\nSynonyms\n\n\nIn the SELECT query, expressions can specify synonyms using the AS keyword. Any expression is placed to the left of AS. The identifier name for the synonym is placed to the right of AS. As opposed to standard SQL, synonyms are not only declared on the top level of expressions:\n\n\nSELECT\n \n(\n1\n \nAS\n \nn\n)\n \n+\n \n2\n,\n \nn\n\n\n\n\n\n\nIn contrast to standard SQL, synonyms can be used in all parts of a query, not just \nSELECT\n.\n\n\nAsterisk\n\n\nIn a \nSELECT\n query, an asterisk can replace the expression. For more information, see the section \"SELECT\".\n\n\nExpressions\n\n\nAn expression is a function, identifier, literal, application of an operator, expression in brackets, subquery, or asterisk. It can also contain a synonym.\nA list of expressions is one or more expressions separated by commas.\nFunctions and operators, in turn, can have expressions as arguments.", - "title": "Syntax" - }, - { - "location": "/query_language/syntax/#syntax", - "text": "There are two types of parsers in the system: the full SQL parser (a recursive descent parser), and the data format parser (a fast stream parser).\nIn all cases except the INSERT query, only the full SQL parser is used.\nThe INSERT query uses both parsers: INSERT INTO t VALUES ( 1 , Hello, world ), ( 2 , abc ), ( 3 , def ) The INSERT INTO t VALUES fragment is parsed by the full parser, and the data (1, 'Hello, world'), (2, 'abc'), (3, 'def') is parsed by the fast stream parser.\nData can have any format. When a query is received, the server calculates no more than max_query_size bytes of the request in RAM (by default, 1 MB), and the rest is stream parsed.\nThis means the system doesn't have problems with large INSERT queries, like MySQL does. When using the Values format in an INSERT query, it may seem that data is parsed the same as expressions in a SELECT query, but this is not true. The Values format is much more limited. Next we will cover the full parser. For more information about format parsers, see the section \"Formats\".", - "title": "Syntax" - }, - { - "location": "/query_language/syntax/#spaces", - "text": "There may be any number of space symbols between syntactical constructions (including the beginning and end of a query). Space symbols include the space, tab, line feed, CR, and form feed.", - "title": "Spaces" - }, - { - "location": "/query_language/syntax/#comments", - "text": "SQL-style and C-style comments are supported.\nSQL-style comments: from -- to the end of the line. The space after -- can be omitted.\nComments in C-style: from /* to */ . These comments can be multiline. Spaces are not required here, either.", - "title": "Comments" - }, - { - "location": "/query_language/syntax/#keywords", - "text": "Keywords (such as SELECT ) are not case-sensitive. Everything else (column names, functions, and so on), in contrast to standard SQL, is case-sensitive. Keywords are not reserved (they are just parsed as keywords in the corresponding context).", - "title": "Keywords" - }, - { - "location": "/query_language/syntax/#identifiers", - "text": "Identifiers (column names, functions, and data types) can be quoted or non-quoted.\nNon-quoted identifiers start with a Latin letter or underscore, and continue with a Latin letter, underscore, or number. In other words, they must match the regex ^[a-zA-Z_][0-9a-zA-Z_]*$ . Examples: x, _1, X_y__Z123_. Quoted identifiers are placed in reversed quotation marks `id` (the same as in MySQL), and can indicate any set of bytes (non-empty). In addition, symbols (for example, the reverse quotation mark) inside this type of identifier can be backslash-escaped. Escaping rules are the same as for string literals (see below).\nWe recommend using identifiers that do not need to be quoted.", - "title": "Identifiers" - }, - { - "location": "/query_language/syntax/#literals", - "text": "There are numeric literals, string literals, and compound literals.", - "title": "Literals" - }, - { - "location": "/query_language/syntax/#numeric-literals", - "text": "A numeric literal tries to be parsed: First as a 64-bit signed number, using the 'strtoull' function. If unsuccessful, as a 64-bit unsigned number, using the 'strtoll' function. If unsuccessful, as a floating-point number using the 'strtod' function. Otherwise, an error is returned. The corresponding value will have the smallest type that the value fits in.\nFor example, 1 is parsed as UInt8, but 256 is parsed as UInt16. For more information, see \"Data types\". Examples: 1 , 18446744073709551615 , 0xDEADBEEF , 01 , 0.1 , 1e100 , -1e-100 , inf , nan .", - "title": "Numeric literals" - }, - { - "location": "/query_language/syntax/#string-literals", - "text": "Only string literals in single quotes are supported. The enclosed characters can be backslash-escaped. The following escape sequences have a corresponding special value: \\b , \\f , \\r , \\n , \\t , \\0 , \\a , \\v , \\xHH . In all other cases, escape sequences in the format \\c , where \"c\" is any character, are converted to \"c\". This means that you can use the sequences \\' and \\\\ . The value will have the String type. The minimum set of characters that you need to escape in string literals: ' and \\ .", - "title": "String literals" - }, - { - "location": "/query_language/syntax/#compound-literals", - "text": "Constructions are supported for arrays: [1, 2, 3] and tuples: (1, 'Hello, world!', 2) ..\nActually, these are not literals, but expressions with the array creation operator and the tuple creation operator, respectively.\nFor more information, see the section \"Operators2\".\nAn array must consist of at least one item, and a tuple must have at least two items.\nTuples have a special purpose for use in the IN clause of a SELECT query. Tuples can be obtained as the result of a query, but they can't be saved to a database (with the exception of Memory-type tables).", - "title": "Compound literals" - }, - { - "location": "/query_language/syntax/#functions", - "text": "Functions are written like an identifier with a list of arguments (possibly empty) in brackets. In contrast to standard SQL, the brackets are required, even for an empty arguments list. Example: now() .\nThere are regular and aggregate functions (see the section \"Aggregate functions\"). Some aggregate functions can contain two lists of arguments in brackets. Example: quantile (0.9) (x) . These aggregate functions are called \"parametric\" functions, and the arguments in the first list are called \"parameters\". The syntax of aggregate functions without parameters is the same as for regular functions.", - "title": "Functions" - }, - { - "location": "/query_language/syntax/#operators", - "text": "Operators are converted to their corresponding functions during query parsing, taking their priority and associativity into account.\nFor example, the expression 1 + 2 * 3 + 4 is transformed to plus(plus(1, multiply(2, 3)), 4) .\nFor more information, see the section \"Operators\" below.", - "title": "Operators" - }, - { - "location": "/query_language/syntax/#data-types-and-database-table-engines", - "text": "Data types and table engines in the CREATE query are written the same way as identifiers or functions. In other words, they may or may not contain an arguments list in brackets. For more information, see the sections \"Data types,\" \"Table engines,\" and \"CREATE\".", - "title": "Data types and database table engines" - }, - { - "location": "/query_language/syntax/#synonyms", - "text": "In the SELECT query, expressions can specify synonyms using the AS keyword. Any expression is placed to the left of AS. The identifier name for the synonym is placed to the right of AS. As opposed to standard SQL, synonyms are not only declared on the top level of expressions: SELECT ( 1 AS n ) + 2 , n In contrast to standard SQL, synonyms can be used in all parts of a query, not just SELECT .", - "title": "Synonyms" - }, - { - "location": "/query_language/syntax/#asterisk", - "text": "In a SELECT query, an asterisk can replace the expression. For more information, see the section \"SELECT\".", - "title": "Asterisk" - }, - { - "location": "/query_language/syntax/#expressions", - "text": "An expression is a function, identifier, literal, application of an operator, expression in brackets, subquery, or asterisk. It can also contain a synonym.\nA list of expressions is one or more expressions separated by commas.\nFunctions and operators, in turn, can have expressions as arguments.", - "title": "Expressions" - }, - { - "location": "/table_engines/", - "text": "Table engines\n\n\nThe table engine (type of table) determines:\n\n\n\n\nHow and where data is stored: where to write it to, and where to read it from.\n\n\nWhich queries are supported, and how.\n\n\nConcurrent data access.\n\n\nUse of indexes, if present.\n\n\nWhether multithreaded request execution is possible.\n\n\nData replication.\n\n\n\n\nWhen reading data, the engine is only required to extract the necessary set of columns. However, in some cases, the query may be partially processed inside the table engine.\n\n\nNote that for most serious tasks, you should use engines from the \nMergeTree\n family.", - "title": "Introduction" - }, - { - "location": "/table_engines/#table-engines", - "text": "The table engine (type of table) determines: How and where data is stored: where to write it to, and where to read it from. Which queries are supported, and how. Concurrent data access. Use of indexes, if present. Whether multithreaded request execution is possible. Data replication. When reading data, the engine is only required to extract the necessary set of columns. However, in some cases, the query may be partially processed inside the table engine. Note that for most serious tasks, you should use engines from the MergeTree family.", - "title": "Table engines" - }, - { - "location": "/table_engines/tinylog/", - "text": "TinyLog\n\n\nThe simplest table engine, which stores data on a disk.\nEach column is stored in a separate compressed file.\nWhen writing, data is appended to the end of files.\n\n\nConcurrent data access is not restricted in any way:\n\n\n\n\nIf you are simultaneously reading from a table and writing to it in a different query, the read operation will complete with an error.\n\n\nIf you are writing to a table in multiple queries simultaneously, the data will be broken.\n\n\n\n\nThe typical way to use this table is write-once: first just write the data one time, then read it as many times as needed.\nQueries are executed in a single stream. In other words, this engine is intended for relatively small tables (recommended up to 1,000,000 rows).\nIt makes sense to use this table engine if you have many small tables, since it is simpler than the Log engine (fewer files need to be opened).\nThe situation when you have a large number of small tables guarantees poor productivity, but may already be used when working with another DBMS, and you may find it easier to switch to using TinyLog types of tables.\n\nIndexes are not supported.\n\n\nIn Yandex.Metrica, TinyLog tables are used for intermediary data that is processed in small batches.", - "title": "TinyLog" - }, - { - "location": "/table_engines/tinylog/#tinylog", - "text": "The simplest table engine, which stores data on a disk.\nEach column is stored in a separate compressed file.\nWhen writing, data is appended to the end of files. Concurrent data access is not restricted in any way: If you are simultaneously reading from a table and writing to it in a different query, the read operation will complete with an error. If you are writing to a table in multiple queries simultaneously, the data will be broken. The typical way to use this table is write-once: first just write the data one time, then read it as many times as needed.\nQueries are executed in a single stream. In other words, this engine is intended for relatively small tables (recommended up to 1,000,000 rows).\nIt makes sense to use this table engine if you have many small tables, since it is simpler than the Log engine (fewer files need to be opened).\nThe situation when you have a large number of small tables guarantees poor productivity, but may already be used when working with another DBMS, and you may find it easier to switch to using TinyLog types of tables. Indexes are not supported. In Yandex.Metrica, TinyLog tables are used for intermediary data that is processed in small batches.", - "title": "TinyLog" - }, - { - "location": "/table_engines/log/", - "text": "Log\n\n\nLog differs from TinyLog in that a small file of \"marks\" resides with the column files. These marks are written on every data block and contain offsets that indicate where to start reading the file in order to skip the specified number of rows. This makes it possible to read table data in multiple threads.\nFor concurrent data access, the read operations can be performed simultaneously, while write operations block reads and each other.\nThe Log engine does not support indexes. Similarly, if writing to a table failed, the table is broken, and reading from it returns an error. The Log engine is appropriate for temporary data, write-once tables, and for testing or demonstration purposes.", - "title": "Log" - }, - { - "location": "/table_engines/log/#log", - "text": "Log differs from TinyLog in that a small file of \"marks\" resides with the column files. These marks are written on every data block and contain offsets that indicate where to start reading the file in order to skip the specified number of rows. This makes it possible to read table data in multiple threads.\nFor concurrent data access, the read operations can be performed simultaneously, while write operations block reads and each other.\nThe Log engine does not support indexes. Similarly, if writing to a table failed, the table is broken, and reading from it returns an error. The Log engine is appropriate for temporary data, write-once tables, and for testing or demonstration purposes.", - "title": "Log" - }, - { - "location": "/table_engines/memory/", - "text": "Memory\n\n\nThe Memory engine stores data in RAM, in uncompressed form. Data is stored in exactly the same form as it is received when read. In other words, reading from this table is completely free.\nConcurrent data access is synchronized. Locks are short: read and write operations don't block each other.\nIndexes are not supported. Reading is parallelized.\nMaximal productivity (over 10 GB/sec) is reached on simple queries, because there is no reading from the disk, decompressing, or deserializing data. (We should note that in many cases, the productivity of the MergeTree engine is almost as high.)\nWhen restarting a server, data disappears from the table and the table becomes empty.\nNormally, using this table engine is not justified. However, it can be used for tests, and for tasks where maximum speed is required on a relatively small number of rows (up to approximately 100,000,000).\n\n\nThe Memory engine is used by the system for temporary tables with external query data (see the section \"External data for processing a query\"), and for implementing GLOBAL IN (see the section \"IN operators\").", - "title": "Memory" - }, - { - "location": "/table_engines/memory/#memory", - "text": "The Memory engine stores data in RAM, in uncompressed form. Data is stored in exactly the same form as it is received when read. In other words, reading from this table is completely free.\nConcurrent data access is synchronized. Locks are short: read and write operations don't block each other.\nIndexes are not supported. Reading is parallelized.\nMaximal productivity (over 10 GB/sec) is reached on simple queries, because there is no reading from the disk, decompressing, or deserializing data. (We should note that in many cases, the productivity of the MergeTree engine is almost as high.)\nWhen restarting a server, data disappears from the table and the table becomes empty.\nNormally, using this table engine is not justified. However, it can be used for tests, and for tasks where maximum speed is required on a relatively small number of rows (up to approximately 100,000,000). The Memory engine is used by the system for temporary tables with external query data (see the section \"External data for processing a query\"), and for implementing GLOBAL IN (see the section \"IN operators\").", - "title": "Memory" - }, - { - "location": "/table_engines/mergetree/", - "text": "MergeTree\n\n\nThe MergeTree engine supports an index by primary key and by date, and provides the possibility to update data in real time.\nThis is the most advanced table engine in ClickHouse. Don't confuse it with the Merge engine.\n\n\nThe engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity.\n\n\nExample without sampling support.\n\n\nMergeTree(EventDate, (CounterID, EventDate), 8192)\n\n\n\n\n\nExample with sampling support.\n\n\nMergeTree(EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID)), 8192)\n\n\n\n\n\nA MergeTree table must have a separate column containing the date. Here, it is the EventDate column. The date column must have the 'Date' type (not 'DateTime').\n\n\nThe primary key may be a tuple from any expressions (usually this is just a tuple of columns), or a single expression.\n\n\nThe sampling expression (optional) can be any expression. It must also be present in the primary key. The example uses a hash of user IDs to pseudo-randomly disperse data in the table for each CounterID and EventDate. In other words, when using the SAMPLE clause in a query, you get an evenly pseudo-random sample of data for a subset of users.\n\n\nThe table is implemented as a set of parts. Each part is sorted by the primary key. In addition, each part has the minimum and maximum date assigned. When inserting in the table, a new sorted part is created. The merge process is periodically initiated in the background. When merging, several parts are selected (usually the smallest ones) and then merged into one large sorted part.\n\n\nIn other words, incremental sorting occurs when inserting to the table. Merging is implemented so that the table always consists of a small number of sorted parts, and the merge itself doesn't do too much work.\n\n\nDuring insertion, data belonging to different months is separated into different parts. The parts that correspond to different months are never combined. The purpose of this is to provide local data modification (for ease in backups).\n\n\nParts are combined up to a certain size threshold, so there aren't any merges that are too long.\n\n\nFor each part, an index file is also written. The index file contains the primary key value for every 'index_granularity' row in the table. In other words, this is an abbreviated index of sorted data.\n\n\nFor columns, \"marks\" are also written to each 'index_granularity' row so that data can be read in a specific range.\n\n\nWhen reading from a table, the SELECT query is analyzed for whether indexes can be used.\nAn index can be used if the WHERE or PREWHERE clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has IN or LIKE with a fixed prefix on columns or expressions that are in the primary key or partitioning key, or on certain partially repetitive functions of these columns, or logical relationships of these expressions.\n\n\nThus, it is possible to quickly run queries on one or many ranges of the primary key. In this example, queries will be fast when run for a specific tracking tag; for a specific tag and date range; for a specific tag and date; for multiple tags with a date range, and so on.\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \nEventDate\n \n=\n \ntoDate\n(\nnow\n())\n \nAND\n \nCounterID\n \n=\n \n34\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \nEventDate\n \n=\n \ntoDate\n(\nnow\n())\n \nAND\n \n(\nCounterID\n \n=\n \n34\n \nOR\n \nCounterID\n \n=\n \n42\n)\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \n((\nEventDate\n \n=\n \ntoDate\n(\n2014-01-01\n)\n \nAND\n \nEventDate\n \n=\n \ntoDate\n(\n2014-01-31\n))\n \nOR\n \nEventDate\n \n=\n \ntoDate\n(\n2014-05-01\n))\n \nAND\n \nCounterID\n \nIN\n \n(\n101500\n,\n \n731962\n,\n \n160656\n)\n \nAND\n \n(\nCounterID\n \n=\n \n101500\n \nOR\n \nEventDate\n \n!=\n \ntoDate\n(\n2014-05-01\n))\n\n\n\n\n\n\nAll of these cases will use the index by date and by primary key. The index is used even for complex expressions. Reading from the table is organized so that using the index can't be slower than a full scan.\n\n\nIn this example, the index can't be used.\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \nCounterID\n \n=\n \n34\n \nOR\n \nURL\n \nLIKE\n \n%upyachka%\n\n\n\n\n\n\nTo check whether ClickHouse can use the index when executing the query, use the settings \nforce_index_by_date\nand\nforce_primary_key\n.\n\n\nThe index by date only allows reading those parts that contain dates from the desired range. However, a data part may contain data for many dates (up to an entire month), while within a single part the data is ordered by the primary key, which might not contain the date as the first column. Because of this, using a query with only a date condition that does not specify the primary key prefix will cause more data to be read than for a single date.\n\n\nFor concurrent table access, we use multi-versioning. In other words, when a table is simultaneously read and updated, data is read from a set of parts that is current at the time of the query. There are no lengthy locks. Inserts do not get in the way of read operations.\n\n\nReading from a table is automatically parallelized.\n\n\nThe \nOPTIMIZE\n query is supported, which calls an extra merge step.\n\n\nYou can use a single large table and continually add data to it in small chunks \u2013 this is what MergeTree is intended for.\n\n\nData replication is possible for all types of tables in the MergeTree family (see the section \"Data replication\").", - "title": "MergeTree" - }, - { - "location": "/table_engines/mergetree/#mergetree", - "text": "The MergeTree engine supports an index by primary key and by date, and provides the possibility to update data in real time.\nThis is the most advanced table engine in ClickHouse. Don't confuse it with the Merge engine. The engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity. Example without sampling support. MergeTree(EventDate, (CounterID, EventDate), 8192) Example with sampling support. MergeTree(EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID)), 8192) A MergeTree table must have a separate column containing the date. Here, it is the EventDate column. The date column must have the 'Date' type (not 'DateTime'). The primary key may be a tuple from any expressions (usually this is just a tuple of columns), or a single expression. The sampling expression (optional) can be any expression. It must also be present in the primary key. The example uses a hash of user IDs to pseudo-randomly disperse data in the table for each CounterID and EventDate. In other words, when using the SAMPLE clause in a query, you get an evenly pseudo-random sample of data for a subset of users. The table is implemented as a set of parts. Each part is sorted by the primary key. In addition, each part has the minimum and maximum date assigned. When inserting in the table, a new sorted part is created. The merge process is periodically initiated in the background. When merging, several parts are selected (usually the smallest ones) and then merged into one large sorted part. In other words, incremental sorting occurs when inserting to the table. Merging is implemented so that the table always consists of a small number of sorted parts, and the merge itself doesn't do too much work. During insertion, data belonging to different months is separated into different parts. The parts that correspond to different months are never combined. The purpose of this is to provide local data modification (for ease in backups). Parts are combined up to a certain size threshold, so there aren't any merges that are too long. For each part, an index file is also written. The index file contains the primary key value for every 'index_granularity' row in the table. In other words, this is an abbreviated index of sorted data. For columns, \"marks\" are also written to each 'index_granularity' row so that data can be read in a specific range. When reading from a table, the SELECT query is analyzed for whether indexes can be used.\nAn index can be used if the WHERE or PREWHERE clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has IN or LIKE with a fixed prefix on columns or expressions that are in the primary key or partitioning key, or on certain partially repetitive functions of these columns, or logical relationships of these expressions. Thus, it is possible to quickly run queries on one or many ranges of the primary key. In this example, queries will be fast when run for a specific tracking tag; for a specific tag and date range; for a specific tag and date; for multiple tags with a date range, and so on. SELECT count () FROM table WHERE EventDate = toDate ( now ()) AND CounterID = 34 SELECT count () FROM table WHERE EventDate = toDate ( now ()) AND ( CounterID = 34 OR CounterID = 42 ) SELECT count () FROM table WHERE (( EventDate = toDate ( 2014-01-01 ) AND EventDate = toDate ( 2014-01-31 )) OR EventDate = toDate ( 2014-05-01 )) AND CounterID IN ( 101500 , 731962 , 160656 ) AND ( CounterID = 101500 OR EventDate != toDate ( 2014-05-01 )) All of these cases will use the index by date and by primary key. The index is used even for complex expressions. Reading from the table is organized so that using the index can't be slower than a full scan. In this example, the index can't be used. SELECT count () FROM table WHERE CounterID = 34 OR URL LIKE %upyachka% To check whether ClickHouse can use the index when executing the query, use the settings force_index_by_date and force_primary_key . The index by date only allows reading those parts that contain dates from the desired range. However, a data part may contain data for many dates (up to an entire month), while within a single part the data is ordered by the primary key, which might not contain the date as the first column. Because of this, using a query with only a date condition that does not specify the primary key prefix will cause more data to be read than for a single date. For concurrent table access, we use multi-versioning. In other words, when a table is simultaneously read and updated, data is read from a set of parts that is current at the time of the query. There are no lengthy locks. Inserts do not get in the way of read operations. Reading from a table is automatically parallelized. The OPTIMIZE query is supported, which calls an extra merge step. You can use a single large table and continually add data to it in small chunks \u2013 this is what MergeTree is intended for. Data replication is possible for all types of tables in the MergeTree family (see the section \"Data replication\").", - "title": "MergeTree" - }, - { - "location": "/table_engines/custom_partitioning_key/", - "text": "Custom partitioning key\n\n\nStarting with version 1.1.54310, you can create tables in the MergeTree family with any partitioning expression (not only partitioning by month).\n\n\nThe partition key can be an expression from the table columns, or a tuple of such expressions (similar to the primary key). The partition key can be omitted. When creating a table, specify the partition key in the ENGINE description with the new syntax:\n\n\nENGINE [=] Name(...) [PARTITION BY expr] [ORDER BY expr] [SAMPLE BY expr] [SETTINGS name=value, ...]\n\n\n\n\n\nFor MergeTree tables, the partition expression is specified after \nPARTITION BY\n, the primary key after \nORDER BY\n, the sampling key after \nSAMPLE BY\n, and \nSETTINGS\n can specify \nindex_granularity\n (optional; the default value is 8192), as well as other settings from \nMergeTreeSettings.h\n. The other engine parameters are specified in parentheses after the engine name, as previously. Example:\n\n\nENGINE\n \n=\n \nReplicatedCollapsingMergeTree\n(\n/clickhouse/tables/name\n,\n \nreplica1\n,\n \nSign\n)\n\n \nPARTITION\n \nBY\n \n(\ntoMonday\n(\nStartDate\n),\n \nEventType\n)\n\n \nORDER\n \nBY\n \n(\nCounterID\n,\n \nStartDate\n,\n \nintHash32\n(\nUserID\n))\n\n \nSAMPLE\n \nBY\n \nintHash32\n(\nUserID\n)\n\n\n\n\n\n\nThe traditional partitioning by month is expressed as \ntoYYYYMM(date_column)\n.\n\n\nYou can't convert an old-style table to a table with custom partitions (only via INSERT SELECT).\n\n\nAfter this table is created, merge will only work for data parts that have the same value for the partitioning expression. Note: This means that you shouldn't make overly granular partitions (more than about a thousand partitions), or SELECT will perform poorly.\n\n\nTo specify a partition in ALTER PARTITION commands, specify the value of the partition expression (or a tuple). Constants and constant expressions are supported. Example:\n\n\nALTER\n \nTABLE\n \ntable\n \nDROP\n \nPARTITION\n \n(\ntoMonday\n(\ntoday\n()),\n \n1\n)\n\n\n\n\n\n\nDeletes the partition for the current week with event type 1. The same is true for the OPTIMIZE query. To specify the only partition in a non-partitioned table, specify \nPARTITION tuple()\n.\n\n\nNote: For old-style tables, the partition can be specified either as a number \n201710\n or a string \n'201710'\n. The syntax for the new style of tables is stricter with types (similar to the parser for the VALUES input format). In addition, ALTER TABLE FREEZE PARTITION uses exact match for new-style tables (not prefix match).\n\n\nIn the \nsystem.parts\n table, the \npartition\n column specifies the value of the partition expression to use in ALTER queries (if quotas are removed). The \nname\n column should specify the name of the data part that has a new format.\n\n\nWas: \n20140317_20140323_2_2_0\n (minimum date - maximum date - minimum block number - maximum block number - level).\n\n\nNow: \n201403_2_2_0\n (partition ID - minimum block number - maximum block number - level).\n\n\nThe partition ID is its string identifier (human-readable, if possible) that is used for the names of data parts in the file system and in ZooKeeper. You can specify it in ALTER queries in place of the partition key. Example: Partition key \ntoYYYYMM(EventDate)\n; ALTER can specify either \nPARTITION 201710\n or \nPARTITION ID '201710'\n.\n\n\nFor more examples, see the tests \n00502_custom_partitioning_local\n and \n00502_custom_partitioning_replicated_zookeeper\n.", - "title": "Custom partitioning key" - }, - { - "location": "/table_engines/custom_partitioning_key/#custom-partitioning-key", - "text": "Starting with version 1.1.54310, you can create tables in the MergeTree family with any partitioning expression (not only partitioning by month). The partition key can be an expression from the table columns, or a tuple of such expressions (similar to the primary key). The partition key can be omitted. When creating a table, specify the partition key in the ENGINE description with the new syntax: ENGINE [=] Name(...) [PARTITION BY expr] [ORDER BY expr] [SAMPLE BY expr] [SETTINGS name=value, ...] For MergeTree tables, the partition expression is specified after PARTITION BY , the primary key after ORDER BY , the sampling key after SAMPLE BY , and SETTINGS can specify index_granularity (optional; the default value is 8192), as well as other settings from MergeTreeSettings.h . The other engine parameters are specified in parentheses after the engine name, as previously. Example: ENGINE = ReplicatedCollapsingMergeTree ( /clickhouse/tables/name , replica1 , Sign ) \n PARTITION BY ( toMonday ( StartDate ), EventType ) \n ORDER BY ( CounterID , StartDate , intHash32 ( UserID )) \n SAMPLE BY intHash32 ( UserID ) The traditional partitioning by month is expressed as toYYYYMM(date_column) . You can't convert an old-style table to a table with custom partitions (only via INSERT SELECT). After this table is created, merge will only work for data parts that have the same value for the partitioning expression. Note: This means that you shouldn't make overly granular partitions (more than about a thousand partitions), or SELECT will perform poorly. To specify a partition in ALTER PARTITION commands, specify the value of the partition expression (or a tuple). Constants and constant expressions are supported. Example: ALTER TABLE table DROP PARTITION ( toMonday ( today ()), 1 ) Deletes the partition for the current week with event type 1. The same is true for the OPTIMIZE query. To specify the only partition in a non-partitioned table, specify PARTITION tuple() . Note: For old-style tables, the partition can be specified either as a number 201710 or a string '201710' . The syntax for the new style of tables is stricter with types (similar to the parser for the VALUES input format). In addition, ALTER TABLE FREEZE PARTITION uses exact match for new-style tables (not prefix match). In the system.parts table, the partition column specifies the value of the partition expression to use in ALTER queries (if quotas are removed). The name column should specify the name of the data part that has a new format. Was: 20140317_20140323_2_2_0 (minimum date - maximum date - minimum block number - maximum block number - level). Now: 201403_2_2_0 (partition ID - minimum block number - maximum block number - level). The partition ID is its string identifier (human-readable, if possible) that is used for the names of data parts in the file system and in ZooKeeper. You can specify it in ALTER queries in place of the partition key. Example: Partition key toYYYYMM(EventDate) ; ALTER can specify either PARTITION 201710 or PARTITION ID '201710' . For more examples, see the tests 00502_custom_partitioning_local and 00502_custom_partitioning_replicated_zookeeper .", - "title": "Custom partitioning key" - }, - { - "location": "/table_engines/replacingmergetree/", - "text": "ReplacingMergeTree\n\n\nThis engine table differs from \nMergeTree\n in that it removes duplicate entries with the same primary key value.\n\n\nThe last optional parameter for the table engine is the version column. When merging, it reduces all rows with the same primary key value to just one row. If the version column is specified, it leaves the row with the highest version; otherwise, it leaves the last row.\n\n\nThe version column must have a type from the \nUInt\n family, \nDate\n, or \nDateTime\n.\n\n\nReplacingMergeTree\n(\nEventDate\n,\n \n(\nOrderID\n,\n \nEventDate\n,\n \nBannerID\n,\n \n...),\n \n8192\n,\n \nver\n)\n\n\n\n\n\n\nNote that data is only deduplicated during merges. Merging occurs in the background at an unknown time, so you can't plan for it. Some of the data may remain unprocessed. Although you can run an unscheduled merge using the OPTIMIZE query, don't count on using it, because the OPTIMIZE query will read and write a large amount of data.\n\n\nThus, \nReplacingMergeTree\n is suitable for clearing out duplicate data in the background in order to save space, but it doesn't guarantee the absence of duplicates.\n\n\nThis engine is not used in Yandex.Metrica, but it has been applied in other Yandex projects.", - "title": "ReplacingMergeTree" - }, - { - "location": "/table_engines/replacingmergetree/#replacingmergetree", - "text": "This engine table differs from MergeTree in that it removes duplicate entries with the same primary key value. The last optional parameter for the table engine is the version column. When merging, it reduces all rows with the same primary key value to just one row. If the version column is specified, it leaves the row with the highest version; otherwise, it leaves the last row. The version column must have a type from the UInt family, Date , or DateTime . ReplacingMergeTree ( EventDate , ( OrderID , EventDate , BannerID , ...), 8192 , ver ) Note that data is only deduplicated during merges. Merging occurs in the background at an unknown time, so you can't plan for it. Some of the data may remain unprocessed. Although you can run an unscheduled merge using the OPTIMIZE query, don't count on using it, because the OPTIMIZE query will read and write a large amount of data. Thus, ReplacingMergeTree is suitable for clearing out duplicate data in the background in order to save space, but it doesn't guarantee the absence of duplicates. This engine is not used in Yandex.Metrica, but it has been applied in other Yandex projects.", - "title": "ReplacingMergeTree" - }, - { - "location": "/table_engines/summingmergetree/", - "text": "SummingMergeTree\n\n\nThis engine differs from \nMergeTree\n in that it totals data while merging.\n\n\nSummingMergeTree\n(\nEventDate\n,\n \n(\nOrderID\n,\n \nEventDate\n,\n \nBannerID\n,\n \n...),\n \n8192\n)\n\n\n\n\n\n\nThe columns to total are implicit. When merging, all rows with the same primary key value (in the example, OrderId, EventDate, BannerID, ...) have their values totaled in numeric columns that are not part of the primary key.\n\n\nSummingMergeTree\n(\nEventDate\n,\n \n(\nOrderID\n,\n \nEventDate\n,\n \nBannerID\n,\n \n...),\n \n8192\n,\n \n(\nShows\n,\n \nClicks\n,\n \nCost\n,\n \n...))\n\n\n\n\n\n\nThe columns to total are set explicitly (the last parameter \u2013 Shows, Clicks, Cost, ...). When merging, all rows with the same primary key value have their values totaled in the specified columns. The specified columns also must be numeric and must not be part of the primary key.\n\n\nIf the values were null in all of these columns, the row is deleted. (The exception is cases when the data part would not have any rows left in it.)\n\n\nFor the other rows that are not part of the primary key, the first value that occurs is selected when merging.\n\n\nSummation is not performed for a read operation. If it is necessary, write the appropriate GROUP BY.\n\n\nIn addition, a table can have nested data structures that are processed in a special way.\nIf the name of a nested table ends in 'Map' and it contains at least two columns that meet the following criteria:\n\n\n\n\nThe first table is numeric ((U)IntN, Date, DateTime), which we'll refer to as the 'key'.\n\n\nThe other columns are arithmetic ((U)IntN, Float32/64), which we'll refer to as '(values...)'. Then this nested table is interpreted as a mapping of key =\n (values...), and when merging its rows, the elements of two data sets are merged by 'key' with a summation of the corresponding (values...).\n\n\n\n\nExamples:\n\n\n[(1, 100)] + [(2, 150)] -\n [(1, 100), (2, 150)]\n[(1, 100)] + [(1, 150)] -\n [(1, 250)]\n[(1, 100)] + [(1, 150), (2, 150)] -\n [(1, 250), (2, 150)]\n[(1, 100), (2, 150)] + [(1, -100)] -\n [(2, 150)]\n\n\n\n\n\nFor aggregation of Map, use the function sumMap(key, value).\n\n\nFor nested data structures, you don't need to specify the columns as a list of columns for totaling.\n\n\nThis table engine is not particularly useful. Remember that when saving just pre-aggregated data, you lose some of the system's advantages.", - "title": "SummingMergeTree" - }, - { - "location": "/table_engines/summingmergetree/#summingmergetree", - "text": "This engine differs from MergeTree in that it totals data while merging. SummingMergeTree ( EventDate , ( OrderID , EventDate , BannerID , ...), 8192 ) The columns to total are implicit. When merging, all rows with the same primary key value (in the example, OrderId, EventDate, BannerID, ...) have their values totaled in numeric columns that are not part of the primary key. SummingMergeTree ( EventDate , ( OrderID , EventDate , BannerID , ...), 8192 , ( Shows , Clicks , Cost , ...)) The columns to total are set explicitly (the last parameter \u2013 Shows, Clicks, Cost, ...). When merging, all rows with the same primary key value have their values totaled in the specified columns. The specified columns also must be numeric and must not be part of the primary key. If the values were null in all of these columns, the row is deleted. (The exception is cases when the data part would not have any rows left in it.) For the other rows that are not part of the primary key, the first value that occurs is selected when merging. Summation is not performed for a read operation. If it is necessary, write the appropriate GROUP BY. In addition, a table can have nested data structures that are processed in a special way.\nIf the name of a nested table ends in 'Map' and it contains at least two columns that meet the following criteria: The first table is numeric ((U)IntN, Date, DateTime), which we'll refer to as the 'key'. The other columns are arithmetic ((U)IntN, Float32/64), which we'll refer to as '(values...)'. Then this nested table is interpreted as a mapping of key = (values...), and when merging its rows, the elements of two data sets are merged by 'key' with a summation of the corresponding (values...). Examples: [(1, 100)] + [(2, 150)] - [(1, 100), (2, 150)]\n[(1, 100)] + [(1, 150)] - [(1, 250)]\n[(1, 100)] + [(1, 150), (2, 150)] - [(1, 250), (2, 150)]\n[(1, 100), (2, 150)] + [(1, -100)] - [(2, 150)] For aggregation of Map, use the function sumMap(key, value). For nested data structures, you don't need to specify the columns as a list of columns for totaling. This table engine is not particularly useful. Remember that when saving just pre-aggregated data, you lose some of the system's advantages.", - "title": "SummingMergeTree" - }, - { - "location": "/table_engines/aggregatingmergetree/", - "text": "AggregatingMergeTree\n\n\nThis engine differs from \nMergeTree\n in that the merge combines the states of aggregate functions stored in the table for rows with the same primary key value.\n\n\nFor this to work, it uses the \nAggregateFunction\n data type, as well as \n-State\n and \n-Merge\n modifiers for aggregate functions. Let's examine it more closely.\n\n\nThere is an \nAggregateFunction\n data type. It is a parametric data type. As parameters, the name of the aggregate function is passed, then the types of its arguments.\n\n\nExamples:\n\n\nCREATE\n \nTABLE\n \nt\n\n\n(\n\n \ncolumn1\n \nAggregateFunction\n(\nuniq\n,\n \nUInt64\n),\n\n \ncolumn2\n \nAggregateFunction\n(\nanyIf\n,\n \nString\n,\n \nUInt8\n),\n\n \ncolumn3\n \nAggregateFunction\n(\nquantiles\n(\n0\n.\n5\n,\n \n0\n.\n9\n),\n \nUInt64\n)\n\n\n)\n \nENGINE\n \n=\n \n...\n\n\n\n\n\n\nThis type of column stores the state of an aggregate function.\n\n\nTo get this type of value, use aggregate functions with the \nState\n suffix.\n\n\nExample:\n\nuniqState(UserID), quantilesState(0.5, 0.9)(SendTiming)\n\n\nIn contrast to the corresponding \nuniq\n and \nquantiles\n functions, these functions return the state, rather than the prepared value. In other words, they return an \nAggregateFunction\n type value.\n\n\nAn \nAggregateFunction\n type value can't be output in Pretty formats. In other formats, these types of values are output as implementation-specific binary data. The \nAggregateFunction\n type values are not intended for output or saving in a dump.\n\n\nThe only useful thing you can do with \nAggregateFunction\n type values is combine the states and get a result, which essentially means to finish aggregation. Aggregate functions with the 'Merge' suffix are used for this purpose.\nExample: \nuniqMerge(UserIDState), where UserIDState has the AggregateFunction\n type.\n\n\nIn other words, an aggregate function with the 'Merge' suffix takes a set of states, combines them, and returns the result.\nAs an example, these two queries return the same result:\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ntable\n\n\n\nSELECT\n \nuniqMerge\n(\nstate\n)\n \nFROM\n \n(\nSELECT\n \nuniqState\n(\nUserID\n)\n \nAS\n \nstate\n \nFROM\n \ntable\n \nGROUP\n \nBY\n \nRegionID\n)\n\n\n\n\n\n\nThere is an \nAggregatingMergeTree\n engine. Its job during a merge is to combine the states of aggregate functions from different table rows with the same primary key value.\n\n\nYou can't use a normal INSERT to insert a row in a table containing \nAggregateFunction\n columns, because you can't explicitly define the \nAggregateFunction\n value. Instead, use \nINSERT SELECT\n with \n-State\n aggregate functions for inserting data.\n\n\nWith SELECT from an \nAggregatingMergeTree\n table, use GROUP BY and aggregate functions with the '-Merge' modifier in order to complete data aggregation.\n\n\nYou can use \nAggregatingMergeTree\n tables for incremental data aggregation, including for aggregated materialized views.\n\n\nExample:\n\n\nCreate an \nAggregatingMergeTree\n materialized view that watches the \ntest.visits\n table:\n\n\nCREATE\n \nMATERIALIZED\n \nVIEW\n \ntest\n.\nbasic\n\n\nENGINE\n \n=\n \nAggregatingMergeTree\n(\nStartDate\n,\n \n(\nCounterID\n,\n \nStartDate\n),\n \n8192\n)\n\n\nAS\n \nSELECT\n\n \nCounterID\n,\n\n \nStartDate\n,\n\n \nsumState\n(\nSign\n)\n \nAS\n \nVisits\n,\n\n \nuniqState\n(\nUserID\n)\n \nAS\n \nUsers\n\n\nFROM\n \ntest\n.\nvisits\n\n\nGROUP\n \nBY\n \nCounterID\n,\n \nStartDate\n;\n\n\n\n\n\n\nInsert data in the \ntest.visits\n table. Data will also be inserted in the view, where it will be aggregated:\n\n\nINSERT\n \nINTO\n \ntest\n.\nvisits\n \n...\n\n\n\n\n\n\nPerform \nSELECT\n from the view using \nGROUP BY\n in order to complete data aggregation:\n\n\nSELECT\n\n \nStartDate\n,\n\n \nsumMerge\n(\nVisits\n)\n \nAS\n \nVisits\n,\n\n \nuniqMerge\n(\nUsers\n)\n \nAS\n \nUsers\n\n\nFROM\n \ntest\n.\nbasic\n\n\nGROUP\n \nBY\n \nStartDate\n\n\nORDER\n \nBY\n \nStartDate\n;\n\n\n\n\n\n\nYou can create a materialized view like this and assign a normal view to it that finishes data aggregation.\n\n\nNote that in most cases, using \nAggregatingMergeTree\n is not justified, since queries can be run efficiently enough on non-aggregated data.", - "title": "AggregatingMergeTree" - }, - { - "location": "/table_engines/aggregatingmergetree/#aggregatingmergetree", - "text": "This engine differs from MergeTree in that the merge combines the states of aggregate functions stored in the table for rows with the same primary key value. For this to work, it uses the AggregateFunction data type, as well as -State and -Merge modifiers for aggregate functions. Let's examine it more closely. There is an AggregateFunction data type. It is a parametric data type. As parameters, the name of the aggregate function is passed, then the types of its arguments. Examples: CREATE TABLE t ( \n column1 AggregateFunction ( uniq , UInt64 ), \n column2 AggregateFunction ( anyIf , String , UInt8 ), \n column3 AggregateFunction ( quantiles ( 0 . 5 , 0 . 9 ), UInt64 ) ) ENGINE = ... This type of column stores the state of an aggregate function. To get this type of value, use aggregate functions with the State suffix. Example: uniqState(UserID), quantilesState(0.5, 0.9)(SendTiming) In contrast to the corresponding uniq and quantiles functions, these functions return the state, rather than the prepared value. In other words, they return an AggregateFunction type value. An AggregateFunction type value can't be output in Pretty formats. In other formats, these types of values are output as implementation-specific binary data. The AggregateFunction type values are not intended for output or saving in a dump. The only useful thing you can do with AggregateFunction type values is combine the states and get a result, which essentially means to finish aggregation. Aggregate functions with the 'Merge' suffix are used for this purpose.\nExample: uniqMerge(UserIDState), where UserIDState has the AggregateFunction type. In other words, an aggregate function with the 'Merge' suffix takes a set of states, combines them, and returns the result.\nAs an example, these two queries return the same result: SELECT uniq ( UserID ) FROM table SELECT uniqMerge ( state ) FROM ( SELECT uniqState ( UserID ) AS state FROM table GROUP BY RegionID ) There is an AggregatingMergeTree engine. Its job during a merge is to combine the states of aggregate functions from different table rows with the same primary key value. You can't use a normal INSERT to insert a row in a table containing AggregateFunction columns, because you can't explicitly define the AggregateFunction value. Instead, use INSERT SELECT with -State aggregate functions for inserting data. With SELECT from an AggregatingMergeTree table, use GROUP BY and aggregate functions with the '-Merge' modifier in order to complete data aggregation. You can use AggregatingMergeTree tables for incremental data aggregation, including for aggregated materialized views. Example: Create an AggregatingMergeTree materialized view that watches the test.visits table: CREATE MATERIALIZED VIEW test . basic ENGINE = AggregatingMergeTree ( StartDate , ( CounterID , StartDate ), 8192 ) AS SELECT \n CounterID , \n StartDate , \n sumState ( Sign ) AS Visits , \n uniqState ( UserID ) AS Users FROM test . visits GROUP BY CounterID , StartDate ; Insert data in the test.visits table. Data will also be inserted in the view, where it will be aggregated: INSERT INTO test . visits ... Perform SELECT from the view using GROUP BY in order to complete data aggregation: SELECT \n StartDate , \n sumMerge ( Visits ) AS Visits , \n uniqMerge ( Users ) AS Users FROM test . basic GROUP BY StartDate ORDER BY StartDate ; You can create a materialized view like this and assign a normal view to it that finishes data aggregation. Note that in most cases, using AggregatingMergeTree is not justified, since queries can be run efficiently enough on non-aggregated data.", - "title": "AggregatingMergeTree" - }, - { - "location": "/table_engines/collapsingmergetree/", - "text": "CollapsingMergeTree\n\n\nThis engine is used specifically for Yandex.Metrica.\n\n\nIt differs from \nMergeTree\n in that it allows automatic deletion, or \"collapsing\" certain pairs of rows when merging.\n\n\nYandex.Metrica has normal logs (such as hit logs) and change logs. Change logs are used for incrementally calculating statistics on data that is constantly changing. Examples are the log of session changes, or logs of changes to user histories. Sessions are constantly changing in Yandex.Metrica. For example, the number of hits per session increases. We refer to changes in any object as a pair (?old values, ?new values). Old values may be missing if the object was created. New values may be missing if the object was deleted. If the object was changed, but existed previously and was not deleted, both values are present. In the change log, one or two entries are made for each change. Each entry contains all the attributes that the object has, plus a special attribute for differentiating between the old and new values. When objects change, only the new entries are added to the change log, and the existing ones are not touched.\n\n\nThe change log makes it possible to incrementally calculate almost any statistics. To do this, we need to consider \"new\" rows with a plus sign, and \"old\" rows with a minus sign. In other words, incremental calculation is possible for all statistics whose algebraic structure contains an operation for taking the inverse of an element. This is true of most statistics. We can also calculate \"idempotent\" statistics, such as the number of unique visitors, since the unique visitors are not deleted when making changes to sessions.\n\n\nThis is the main concept that allows Yandex.Metrica to work in real time.\n\n\nCollapsingMergeTree accepts an additional parameter - the name of an Int8-type column that contains the row's \"sign\". Example:\n\n\nCollapsingMergeTree\n(\nEventDate\n,\n \n(\nCounterID\n,\n \nEventDate\n,\n \nintHash32\n(\nUniqID\n),\n \nVisitID\n),\n \n8192\n,\n \nSign\n)\n\n\n\n\n\n\nHere, \nSign\n is a column containing -1 for \"old\" values and 1 for \"new\" values.\n\n\nWhen merging, each group of consecutive identical primary key values (columns for sorting data) is reduced to no more than one row with the column value 'sign_column = -1' (the \"negative row\") and no more than one row with the column value 'sign_column = 1' (the \"positive row\"). In other words, entries from the change log are collapsed.\n\n\nIf the number of positive and negative rows matches, the first negative row and the last positive row are written.\nIf there is one more positive row than negative rows, only the last positive row is written.\nIf there is one more negative row than positive rows, only the first negative row is written.\nOtherwise, there will be a logical error and none of the rows will be written. (A logical error can occur if the same section of the log was accidentally inserted more than once. The error is just recorded in the server log, and the merge continues.)\n\n\nThus, collapsing should not change the results of calculating statistics.\nChanges are gradually collapsed so that in the end only the last value of almost every object is left.\nCompared to MergeTree, the CollapsingMergeTree engine allows a multifold reduction of data volume.\n\n\nThere are several ways to get completely \"collapsed\" data from a \nCollapsingMergeTree\n table:\n\n\n\n\nWrite a query with GROUP BY and aggregate functions that accounts for the sign. For example, to calculate quantity, write 'sum(Sign)' instead of 'count()'. To calculate the sum of something, write 'sum(Sign * x)' instead of 'sum(x)', and so on, and also add 'HAVING sum(Sign) \n 0'. Not all amounts can be calculated this way. For example, the aggregate functions 'min' and 'max' can't be rewritten.\n\n\nIf you must extract data without aggregation (for example, to check whether rows are present whose newest values match certain conditions), you can use the FINAL modifier for the FROM clause. This approach is significantly less efficient.", - "title": "CollapsingMergeTree" - }, - { - "location": "/table_engines/collapsingmergetree/#collapsingmergetree", - "text": "This engine is used specifically for Yandex.Metrica. It differs from MergeTree in that it allows automatic deletion, or \"collapsing\" certain pairs of rows when merging. Yandex.Metrica has normal logs (such as hit logs) and change logs. Change logs are used for incrementally calculating statistics on data that is constantly changing. Examples are the log of session changes, or logs of changes to user histories. Sessions are constantly changing in Yandex.Metrica. For example, the number of hits per session increases. We refer to changes in any object as a pair (?old values, ?new values). Old values may be missing if the object was created. New values may be missing if the object was deleted. If the object was changed, but existed previously and was not deleted, both values are present. In the change log, one or two entries are made for each change. Each entry contains all the attributes that the object has, plus a special attribute for differentiating between the old and new values. When objects change, only the new entries are added to the change log, and the existing ones are not touched. The change log makes it possible to incrementally calculate almost any statistics. To do this, we need to consider \"new\" rows with a plus sign, and \"old\" rows with a minus sign. In other words, incremental calculation is possible for all statistics whose algebraic structure contains an operation for taking the inverse of an element. This is true of most statistics. We can also calculate \"idempotent\" statistics, such as the number of unique visitors, since the unique visitors are not deleted when making changes to sessions. This is the main concept that allows Yandex.Metrica to work in real time. CollapsingMergeTree accepts an additional parameter - the name of an Int8-type column that contains the row's \"sign\". Example: CollapsingMergeTree ( EventDate , ( CounterID , EventDate , intHash32 ( UniqID ), VisitID ), 8192 , Sign ) Here, Sign is a column containing -1 for \"old\" values and 1 for \"new\" values. When merging, each group of consecutive identical primary key values (columns for sorting data) is reduced to no more than one row with the column value 'sign_column = -1' (the \"negative row\") and no more than one row with the column value 'sign_column = 1' (the \"positive row\"). In other words, entries from the change log are collapsed. If the number of positive and negative rows matches, the first negative row and the last positive row are written.\nIf there is one more positive row than negative rows, only the last positive row is written.\nIf there is one more negative row than positive rows, only the first negative row is written.\nOtherwise, there will be a logical error and none of the rows will be written. (A logical error can occur if the same section of the log was accidentally inserted more than once. The error is just recorded in the server log, and the merge continues.) Thus, collapsing should not change the results of calculating statistics.\nChanges are gradually collapsed so that in the end only the last value of almost every object is left.\nCompared to MergeTree, the CollapsingMergeTree engine allows a multifold reduction of data volume. There are several ways to get completely \"collapsed\" data from a CollapsingMergeTree table: Write a query with GROUP BY and aggregate functions that accounts for the sign. For example, to calculate quantity, write 'sum(Sign)' instead of 'count()'. To calculate the sum of something, write 'sum(Sign * x)' instead of 'sum(x)', and so on, and also add 'HAVING sum(Sign) 0'. Not all amounts can be calculated this way. For example, the aggregate functions 'min' and 'max' can't be rewritten. If you must extract data without aggregation (for example, to check whether rows are present whose newest values match certain conditions), you can use the FINAL modifier for the FROM clause. This approach is significantly less efficient.", - "title": "CollapsingMergeTree" - }, - { - "location": "/table_engines/graphitemergetree/", - "text": "GraphiteMergeTree\n\n\nThis engine is designed for rollup (thinning and aggregating/averaging) \nGraphite\n data. It may be helpful to developers who want to use ClickHouse as a data store for Graphite.\n\n\nGraphite stores full data in ClickHouse, and data can be retrieved in the following ways:\n\n\n\n\nWithout thinning.\n\n\n\n\nUses the \nMergeTree\n engine.\n\n\n\n\nWith thinning.\n\n\n\n\nUsing the \nGraphiteMergeTree\n engine.\n\n\nThe engine inherits properties from MergeTree. The settings for thinning data are defined by the \ngraphite_rollup\n parameter in the server configuration.\n\n\nUsing the engine\n\n\nThe Graphite data table must contain the following fields at minimum:\n\n\n\n\nPath\n \u2013 The metric name (Graphite sensor).\n\n\nTime\n \u2013 The time for measuring the metric.\n\n\nValue\n \u2013 The value of the metric at the time set in Time.\n\n\nVersion\n \u2013 Determines which value of the metric with the same Path and Time will remain in the database.\n\n\n\n\nRollup pattern:\n\n\npattern\n regexp\n function\n age -\n precision\n ...\npattern\n ...\ndefault\n function\n age -\n precision\n ...\n\n\n\n\n\nWhen processing a record, ClickHouse will check the rules in the \npattern\nclause. If the metric name matches the \nregexp\n, the rules from \npattern\n are applied; otherwise, the rules from \ndefault\n are used.\n\n\nFields in the pattern.\n\n\n\n\nage\n \u2013 The minimum age of the data in seconds.\n\n\nfunction\n \u2013 The name of the aggregating function to apply to data whose age falls within the range \n[age, age + precision]\n.\n\n\nprecision\n\u2013 How precisely to define the age of the data in seconds.\n\n\nregexp\n\u2013 A pattern for the metric name.\n\n\n\n\nExample of settings:\n\n\ngraphite_rollup\n\n \npattern\n\n \nregexp\nclick_cost\n/regexp\n\n \nfunction\nany\n/function\n\n \nretention\n\n \nage\n0\n/age\n\n \nprecision\n5\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n86400\n/age\n\n \nprecision\n60\n/precision\n\n \n/retention\n\n \n/pattern\n\n \ndefault\n\n \nfunction\nmax\n/function\n\n \nretention\n\n \nage\n0\n/age\n\n \nprecision\n60\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n3600\n/age\n\n \nprecision\n300\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n86400\n/age\n\n \nprecision\n3600\n/precision\n\n \n/retention\n\n \n/default\n\n\n/graphite_rollup", - "title": "GraphiteMergeTree" - }, - { - "location": "/table_engines/graphitemergetree/#graphitemergetree", - "text": "This engine is designed for rollup (thinning and aggregating/averaging) Graphite data. It may be helpful to developers who want to use ClickHouse as a data store for Graphite. Graphite stores full data in ClickHouse, and data can be retrieved in the following ways: Without thinning. Uses the MergeTree engine. With thinning. Using the GraphiteMergeTree engine. The engine inherits properties from MergeTree. The settings for thinning data are defined by the graphite_rollup parameter in the server configuration.", - "title": "GraphiteMergeTree" - }, - { - "location": "/table_engines/graphitemergetree/#using-the-engine", - "text": "The Graphite data table must contain the following fields at minimum: Path \u2013 The metric name (Graphite sensor). Time \u2013 The time for measuring the metric. Value \u2013 The value of the metric at the time set in Time. Version \u2013 Determines which value of the metric with the same Path and Time will remain in the database. Rollup pattern: pattern\n regexp\n function\n age - precision\n ...\npattern\n ...\ndefault\n function\n age - precision\n ... When processing a record, ClickHouse will check the rules in the pattern clause. If the metric name matches the regexp , the rules from pattern are applied; otherwise, the rules from default are used. Fields in the pattern. age \u2013 The minimum age of the data in seconds. function \u2013 The name of the aggregating function to apply to data whose age falls within the range [age, age + precision] . precision \u2013 How precisely to define the age of the data in seconds. regexp \u2013 A pattern for the metric name. Example of settings: graphite_rollup \n pattern \n regexp click_cost /regexp \n function any /function \n retention \n age 0 /age \n precision 5 /precision \n /retention \n retention \n age 86400 /age \n precision 60 /precision \n /retention \n /pattern \n default \n function max /function \n retention \n age 0 /age \n precision 60 /precision \n /retention \n retention \n age 3600 /age \n precision 300 /precision \n /retention \n retention \n age 86400 /age \n precision 3600 /precision \n /retention \n /default /graphite_rollup", - "title": "Using the engine" - }, - { - "location": "/table_engines/replication/", - "text": "Data replication\n\n\nReplication is only supported for tables in the MergeTree family:\n\n\n\n\nReplicatedMergeTree\n\n\nReplicatedSummingMergeTree\n\n\nReplicatedReplacingMergeTree\n\n\nReplicatedAggregatingMergeTree\n\n\nReplicatedCollapsingMergeTree\n\n\nReplicatedGraphiteMergeTree\n\n\n\n\nReplication works at the level of an individual table, not the entire server. A server can store both replicated and non-replicated tables at the same time.\n\n\nReplication does not depend on sharding. Each shard has its own independent replication.\n\n\nCompressed data is replicated for \nINSERT\n and \nALTER\n queries (see the description of the \nALTER\n query).\n\n\nCREATE\n, \nDROP\n, \nATTACH\n, \nDETACH\n and \nRENAME\n queries are executed on a single server and are not replicated:\n\n\n\n\nThe CREATE TABLE\n query creates a new replicatable table on the server where the query is run. If this table already exists on other servers, it adds a new replica.\n\n\nThe DROP TABLE\n query deletes the replica located on the server where the query is run.\n\n\nThe RENAME\n query renames the table on one of the replicas. In other words, replicated tables can have different names on different replicas.\n\n\n\n\nTo use replication, set the addresses of the ZooKeeper cluster in the config file. Example:\n\n\nzookeeper\n\n \nnode\n \nindex=\n1\n\n \nhost\nexample1\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n \nnode\n \nindex=\n2\n\n \nhost\nexample2\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n \nnode\n \nindex=\n3\n\n \nhost\nexample3\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n\n/zookeeper\n\n\n\n\n\n\nUse ZooKeeper version 3.4.5 or later.\n\n\nYou can specify any existing ZooKeeper cluster and the system will use a directory on it for its own data (the directory is specified when creating a replicatable table).\n\n\nIf ZooKeeper isn't set in the config file, you can't create replicated tables, and any existing replicated tables will be read-only.\n\n\nZooKeeper is not used in \nSELECT\n queries because replication does not affect the performance of \nSELECT\n and queries run just as fast as they do for non-replicated tables. When querying distributed replicated tables, ClickHouse behavior is controlled by the settings \nmax_replica_delay_for_distributed_queries\n and \nfallback_to_stale_replicas_for_distributed_queries\n.\n\n\nFor each \nINSERT\n query, approximately ten entries are added to ZooKeeper through several transactions. (To be more precise, this is for each inserted block of data; an INSERT query contains one block or one block per \nmax_insert_block_size = 1048576\n rows.) This leads to slightly longer latencies for \nINSERT\n compared to non-replicated tables. But if you follow the recommendations to insert data in batches of no more than one \nINSERT\n per second, it doesn't create any problems. The entire ClickHouse cluster used for coordinating one ZooKeeper cluster has a total of several hundred \nINSERTs\n per second. The throughput on data inserts (the number of rows per second) is just as high as for non-replicated data.\n\n\nFor very large clusters, you can use different ZooKeeper clusters for different shards. However, this hasn't proven necessary on the Yandex.Metrica cluster (approximately 300 servers).\n\n\nReplication is asynchronous and multi-master. \nINSERT\n queries (as well as \nALTER\n) can be sent to any available server. Data is inserted on the server where the query is run, and then it is copied to the other servers. Because it is asynchronous, recently inserted data appears on the other replicas with some latency. If part of the replicas are not available, the data is written when they become available. If a replica is available, the latency is the amount of time it takes to transfer the block of compressed data over the network.\n\n\nBy default, an INSERT query waits for confirmation of writing the data from only one replica. If the data was successfully written to only one replica and the server with this replica ceases to exist, the stored data will be lost. Tp enable getting confirmation of data writes from multiple replicas, use the \ninsert_quorum\n option.\n\n\nEach block of data is written atomically. The INSERT query is divided into blocks up to \nmax_insert_block_size = 1048576\n rows. In other words, if the \nINSERT\n query has less than 1048576 rows, it is made atomically.\n\n\nData blocks are deduplicated. For multiple writes of the same data block (data blocks of the same size containing the same rows in the same order), the block is only written once. The reason for this is in case of network failures when the client application doesn't know if the data was written to the DB, so the \nINSERT\n query can simply be repeated. It doesn't matter which replica INSERTs were sent to with identical data. \nINSERTs\n are idempotent. Deduplication parameters are controlled by \nmerge_tree\n server settings.\n\n\nDuring replication, only the source data to insert is transferred over the network. Further data transformation (merging) is coordinated and performed on all the replicas in the same way. This minimizes network usage, which means that replication works well when replicas reside in different datacenters. (Note that duplicating data in different datacenters is the main goal of replication.)\n\n\nYou can have any number of replicas of the same data. Yandex.Metrica uses double replication in production. Each server uses RAID-5 or RAID-6, and RAID-10 in some cases. This is a relatively reliable and convenient solution.\n\n\nThe system monitors data synchronicity on replicas and is able to recover after a failure. Failover is automatic (for small differences in data) or semi-automatic (when data differs too much, which may indicate a configuration error).\n\n\n\n\nCreating replicated tables\n\n\nThe \nReplicated\n prefix is added to the table engine name. For example:\nReplicatedMergeTree\n.\n\n\nTwo parameters are also added in the beginning of the parameters list \u2013 the path to the table in ZooKeeper, and the replica name in ZooKeeper.\n\n\nExample:\n\n\nReplicatedMergeTree(\n/clickhouse/tables/{layer}-{shard}/hits\n, \n{replica}\n, EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID), EventTime), 8192)\n\n\n\n\n\nAs the example shows, these parameters can contain substitutions in curly brackets. The substituted values are taken from the 'macros' section of the config file. Example:\n\n\nmacros\n\n \nlayer\n05\n/layer\n\n \nshard\n02\n/shard\n\n \nreplica\nexample05-02-1.yandex.ru\n/replica\n\n\n/macros\n\n\n\n\n\n\nThe path to the table in ZooKeeper should be unique for each replicated table. Tables on different shards should have different paths.\nIn this case, the path consists of the following parts:\n\n\n/clickhouse/tables/\n is the common prefix. We recommend using exactly this one.\n\n\n{layer}-{shard}\n is the shard identifier. In this example it consists of two parts, since the Yandex.Metrica cluster uses bi-level sharding. For most tasks, you can leave just the {shard} substitution, which will be expanded to the shard identifier.\n\n\nhits\n is the name of the node for the table in ZooKeeper. It is a good idea to make it the same as the table name. It is defined explicitly, because in contrast to the table name, it doesn't change after a RENAME query.\n\n\nThe replica name identifies different replicas of the same table. You can use the server name for this, as in the example. The name only needs to be unique within each shard.\n\n\nYou can define the parameters explicitly instead of using substitutions. This might be convenient for testing and for configuring small clusters. However, you can't use distributed DDL queries (\nON CLUSTER\n) in this case.\n\n\nWhen working with large clusters, we recommend using substitutions because they reduce the probability of error.\n\n\nRun the \nCREATE TABLE\n query on each replica. This query creates a new replicated table, or adds a new replica to an existing one.\n\n\nIf you add a new replica after the table already contains some data on other replicas, the data will be copied from the other replicas to the new one after running the query. In other words, the new replica syncs itself with the others.\n\n\nTo delete a replica, run \nDROP TABLE\n. However, only one replica is deleted \u2013 the one that resides on the server where you run the query.\n\n\nRecovery after failures\n\n\nIf ZooKeeper is unavailable when a server starts, replicated tables switch to read-only mode. The system periodically attempts to connect to ZooKeeper.\n\n\nIf ZooKeeper is unavailable during an \nINSERT\n, or an error occurs when interacting with ZooKeeper, an exception is thrown.\n\n\nAfter connecting to ZooKeeper, the system checks whether the set of data in the local file system matches the expected set of data (ZooKeeper stores this information). If there are minor inconsistencies, the system resolves them by syncing data with the replicas.\n\n\nIf the system detects broken data parts (with the wrong size of files) or unrecognized parts (parts written to the file system but not recorded in ZooKeeper), it moves them to the 'detached' subdirectory (they are not deleted). Any missing parts are copied from the replicas.\n\n\nNote that ClickHouse does not perform any destructive actions such as automatically deleting a large amount of data.\n\n\nWhen the server starts (or establishes a new session with ZooKeeper), it only checks the quantity and sizes of all files. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a \nSELECT\n query. The query throws an exception about a non-matching checksum or size of a compressed block. In this case, data parts are added to the verification queue and copied from the replicas if necessary.\n\n\nIf the local set of data differs too much from the expected one, a safety mechanism is triggered. The server enters this in the log and refuses to launch. The reason for this is that this case may indicate a configuration error, such as if a replica on a shard was accidentally configured like a replica on a different shard. However, the thresholds for this mechanism are set fairly low, and this situation might occur during normal failure recovery. In this case, data is restored semi-automatically - by \"pushing a button\".\n\n\nTo start recovery, create the node \n/path_to_table/replica_name/flags/force_restore_data\n in ZooKeeper with any content, or run the command to restore all replicated tables:\n\n\nsudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data\n\n\n\n\n\nThen restart the server. On start, the server deletes these flags and starts recovery.\n\n\nRecovery after complete data loss\n\n\nIf all data and metadata disappeared from one of the servers, follow these steps for recovery:\n\n\n\n\nInstall ClickHouse on the server. Define substitutions correctly in the config file that contains the shard identifier and replicas, if you use them.\n\n\nIf you had unreplicated tables that must be manually duplicated on the servers, copy their data from a replica (in the directory \n/var/lib/clickhouse/data/db_name/table_name/\n).\n\n\nCopy table definitions located in \n/var/lib/clickhouse/metadata/\n from a replica. If a shard or replica identifier is defined explicitly in the table definitions, correct it so that it corresponds to this replica. (Alternatively, start the server and make all the \nATTACH TABLE\n queries that should have been in the .sql files in \n/var/lib/clickhouse/metadata/\n.)\n\n\nTo start recovery, create the ZooKeeper node \n/path_to_table/replica_name/flags/force_restore_data\n with any content, or run the command to restore all replicated tables: \nsudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data\n\n\n\n\nThen start the server (restart, if it is already running). Data will be downloaded from replicas.\n\n\nAn alternative recovery option is to delete information about the lost replica from ZooKeeper (\n/path_to_table/replica_name\n), then create the replica again as described in \"\nCreating replicatable tables\n\".\n\n\nThere is no restriction on network bandwidth during recovery. Keep this in mind if you are restoring many replicas at once.\n\n\nConverting from MergeTree to ReplicatedMergeTree\n\n\nWe use the term \nMergeTree\n to refer to all table engines in the \nMergeTree family\n, the same as for \nReplicatedMergeTree\n.\n\n\nIf you had a \nMergeTree\n table that was manually replicated, you can convert it to a replicatable table. You might need to do this if you have already collected a large amount of data in a \nMergeTree\n table and now you want to enable replication.\n\n\nIf the data differs on various replicas, first sync it, or delete this data on all the replicas except one.\n\n\nRename the existing MergeTree table, then create a \nReplicatedMergeTree\n table with the old name.\nMove the data from the old table to the 'detached' subdirectory inside the directory with the new table data (\n/var/lib/clickhouse/data/db_name/table_name/\n).\nThen run \nALTER TABLE ATTACH PARTITION\n on one of the replicas to add these data parts to the working set.\n\n\nConverting from ReplicatedMergeTree to MergeTree\n\n\nCreate a MergeTree table with a different name. Move all the data from the directory with the \nReplicatedMergeTree\n table data to the new table's data directory. Then delete the \nReplicatedMergeTree\n table and restart the server.\n\n\nIf you want to get rid of a \nReplicatedMergeTree\n table without launching the server:\n\n\n\n\nDelete the corresponding \n.sql\n file in the metadata directory (\n/var/lib/clickhouse/metadata/\n).\n\n\nDelete the corresponding path in ZooKeeper (\n/path_to_table/replica_name\n).\n\n\n\n\nAfter this, you can launch the server, create a \nMergeTree\n table, move the data to its directory, and then restart the server.\n\n\nRecovery when metadata in the ZooKeeper cluster is lost or damaged\n\n\nIf the data in ZooKeeper was lost or damaged, you can save data by moving it to an unreplicated table as described above.\n\n\nIf exactly the same parts exist on the other replicas, they are added to the working set on them. If not, the parts are downloaded from the replica that has them.", - "title": "Data replication" - }, - { - "location": "/table_engines/replication/#data-replication", - "text": "Replication is only supported for tables in the MergeTree family: ReplicatedMergeTree ReplicatedSummingMergeTree ReplicatedReplacingMergeTree ReplicatedAggregatingMergeTree ReplicatedCollapsingMergeTree ReplicatedGraphiteMergeTree Replication works at the level of an individual table, not the entire server. A server can store both replicated and non-replicated tables at the same time. Replication does not depend on sharding. Each shard has its own independent replication. Compressed data is replicated for INSERT and ALTER queries (see the description of the ALTER query). CREATE , DROP , ATTACH , DETACH and RENAME queries are executed on a single server and are not replicated: The CREATE TABLE query creates a new replicatable table on the server where the query is run. If this table already exists on other servers, it adds a new replica. The DROP TABLE query deletes the replica located on the server where the query is run. The RENAME query renames the table on one of the replicas. In other words, replicated tables can have different names on different replicas. To use replication, set the addresses of the ZooKeeper cluster in the config file. Example: zookeeper \n node index= 1 \n host example1 /host \n port 2181 /port \n /node \n node index= 2 \n host example2 /host \n port 2181 /port \n /node \n node index= 3 \n host example3 /host \n port 2181 /port \n /node /zookeeper Use ZooKeeper version 3.4.5 or later. You can specify any existing ZooKeeper cluster and the system will use a directory on it for its own data (the directory is specified when creating a replicatable table). If ZooKeeper isn't set in the config file, you can't create replicated tables, and any existing replicated tables will be read-only. ZooKeeper is not used in SELECT queries because replication does not affect the performance of SELECT and queries run just as fast as they do for non-replicated tables. When querying distributed replicated tables, ClickHouse behavior is controlled by the settings max_replica_delay_for_distributed_queries and fallback_to_stale_replicas_for_distributed_queries . For each INSERT query, approximately ten entries are added to ZooKeeper through several transactions. (To be more precise, this is for each inserted block of data; an INSERT query contains one block or one block per max_insert_block_size = 1048576 rows.) This leads to slightly longer latencies for INSERT compared to non-replicated tables. But if you follow the recommendations to insert data in batches of no more than one INSERT per second, it doesn't create any problems. The entire ClickHouse cluster used for coordinating one ZooKeeper cluster has a total of several hundred INSERTs per second. The throughput on data inserts (the number of rows per second) is just as high as for non-replicated data. For very large clusters, you can use different ZooKeeper clusters for different shards. However, this hasn't proven necessary on the Yandex.Metrica cluster (approximately 300 servers). Replication is asynchronous and multi-master. INSERT queries (as well as ALTER ) can be sent to any available server. Data is inserted on the server where the query is run, and then it is copied to the other servers. Because it is asynchronous, recently inserted data appears on the other replicas with some latency. If part of the replicas are not available, the data is written when they become available. If a replica is available, the latency is the amount of time it takes to transfer the block of compressed data over the network. By default, an INSERT query waits for confirmation of writing the data from only one replica. If the data was successfully written to only one replica and the server with this replica ceases to exist, the stored data will be lost. Tp enable getting confirmation of data writes from multiple replicas, use the insert_quorum option. Each block of data is written atomically. The INSERT query is divided into blocks up to max_insert_block_size = 1048576 rows. In other words, if the INSERT query has less than 1048576 rows, it is made atomically. Data blocks are deduplicated. For multiple writes of the same data block (data blocks of the same size containing the same rows in the same order), the block is only written once. The reason for this is in case of network failures when the client application doesn't know if the data was written to the DB, so the INSERT query can simply be repeated. It doesn't matter which replica INSERTs were sent to with identical data. INSERTs are idempotent. Deduplication parameters are controlled by merge_tree server settings. During replication, only the source data to insert is transferred over the network. Further data transformation (merging) is coordinated and performed on all the replicas in the same way. This minimizes network usage, which means that replication works well when replicas reside in different datacenters. (Note that duplicating data in different datacenters is the main goal of replication.) You can have any number of replicas of the same data. Yandex.Metrica uses double replication in production. Each server uses RAID-5 or RAID-6, and RAID-10 in some cases. This is a relatively reliable and convenient solution. The system monitors data synchronicity on replicas and is able to recover after a failure. Failover is automatic (for small differences in data) or semi-automatic (when data differs too much, which may indicate a configuration error).", - "title": "Data replication" - }, - { - "location": "/table_engines/replication/#creating-replicated-tables", - "text": "The Replicated prefix is added to the table engine name. For example: ReplicatedMergeTree . Two parameters are also added in the beginning of the parameters list \u2013 the path to the table in ZooKeeper, and the replica name in ZooKeeper. Example: ReplicatedMergeTree( /clickhouse/tables/{layer}-{shard}/hits , {replica} , EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID), EventTime), 8192) As the example shows, these parameters can contain substitutions in curly brackets. The substituted values are taken from the 'macros' section of the config file. Example: macros \n layer 05 /layer \n shard 02 /shard \n replica example05-02-1.yandex.ru /replica /macros The path to the table in ZooKeeper should be unique for each replicated table. Tables on different shards should have different paths.\nIn this case, the path consists of the following parts: /clickhouse/tables/ is the common prefix. We recommend using exactly this one. {layer}-{shard} is the shard identifier. In this example it consists of two parts, since the Yandex.Metrica cluster uses bi-level sharding. For most tasks, you can leave just the {shard} substitution, which will be expanded to the shard identifier. hits is the name of the node for the table in ZooKeeper. It is a good idea to make it the same as the table name. It is defined explicitly, because in contrast to the table name, it doesn't change after a RENAME query. The replica name identifies different replicas of the same table. You can use the server name for this, as in the example. The name only needs to be unique within each shard. You can define the parameters explicitly instead of using substitutions. This might be convenient for testing and for configuring small clusters. However, you can't use distributed DDL queries ( ON CLUSTER ) in this case. When working with large clusters, we recommend using substitutions because they reduce the probability of error. Run the CREATE TABLE query on each replica. This query creates a new replicated table, or adds a new replica to an existing one. If you add a new replica after the table already contains some data on other replicas, the data will be copied from the other replicas to the new one after running the query. In other words, the new replica syncs itself with the others. To delete a replica, run DROP TABLE . However, only one replica is deleted \u2013 the one that resides on the server where you run the query.", - "title": "Creating replicated tables" - }, - { - "location": "/table_engines/replication/#recovery-after-failures", - "text": "If ZooKeeper is unavailable when a server starts, replicated tables switch to read-only mode. The system periodically attempts to connect to ZooKeeper. If ZooKeeper is unavailable during an INSERT , or an error occurs when interacting with ZooKeeper, an exception is thrown. After connecting to ZooKeeper, the system checks whether the set of data in the local file system matches the expected set of data (ZooKeeper stores this information). If there are minor inconsistencies, the system resolves them by syncing data with the replicas. If the system detects broken data parts (with the wrong size of files) or unrecognized parts (parts written to the file system but not recorded in ZooKeeper), it moves them to the 'detached' subdirectory (they are not deleted). Any missing parts are copied from the replicas. Note that ClickHouse does not perform any destructive actions such as automatically deleting a large amount of data. When the server starts (or establishes a new session with ZooKeeper), it only checks the quantity and sizes of all files. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a SELECT query. The query throws an exception about a non-matching checksum or size of a compressed block. In this case, data parts are added to the verification queue and copied from the replicas if necessary. If the local set of data differs too much from the expected one, a safety mechanism is triggered. The server enters this in the log and refuses to launch. The reason for this is that this case may indicate a configuration error, such as if a replica on a shard was accidentally configured like a replica on a different shard. However, the thresholds for this mechanism are set fairly low, and this situation might occur during normal failure recovery. In this case, data is restored semi-automatically - by \"pushing a button\". To start recovery, create the node /path_to_table/replica_name/flags/force_restore_data in ZooKeeper with any content, or run the command to restore all replicated tables: sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data Then restart the server. On start, the server deletes these flags and starts recovery.", - "title": "Recovery after failures" - }, - { - "location": "/table_engines/replication/#recovery-after-complete-data-loss", - "text": "If all data and metadata disappeared from one of the servers, follow these steps for recovery: Install ClickHouse on the server. Define substitutions correctly in the config file that contains the shard identifier and replicas, if you use them. If you had unreplicated tables that must be manually duplicated on the servers, copy their data from a replica (in the directory /var/lib/clickhouse/data/db_name/table_name/ ). Copy table definitions located in /var/lib/clickhouse/metadata/ from a replica. If a shard or replica identifier is defined explicitly in the table definitions, correct it so that it corresponds to this replica. (Alternatively, start the server and make all the ATTACH TABLE queries that should have been in the .sql files in /var/lib/clickhouse/metadata/ .) To start recovery, create the ZooKeeper node /path_to_table/replica_name/flags/force_restore_data with any content, or run the command to restore all replicated tables: sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data Then start the server (restart, if it is already running). Data will be downloaded from replicas. An alternative recovery option is to delete information about the lost replica from ZooKeeper ( /path_to_table/replica_name ), then create the replica again as described in \" Creating replicatable tables \". There is no restriction on network bandwidth during recovery. Keep this in mind if you are restoring many replicas at once.", - "title": "Recovery after complete data loss" - }, - { - "location": "/table_engines/replication/#converting-from-mergetree-to-replicatedmergetree", - "text": "We use the term MergeTree to refer to all table engines in the MergeTree family , the same as for ReplicatedMergeTree . If you had a MergeTree table that was manually replicated, you can convert it to a replicatable table. You might need to do this if you have already collected a large amount of data in a MergeTree table and now you want to enable replication. If the data differs on various replicas, first sync it, or delete this data on all the replicas except one. Rename the existing MergeTree table, then create a ReplicatedMergeTree table with the old name.\nMove the data from the old table to the 'detached' subdirectory inside the directory with the new table data ( /var/lib/clickhouse/data/db_name/table_name/ ).\nThen run ALTER TABLE ATTACH PARTITION on one of the replicas to add these data parts to the working set.", - "title": "Converting from MergeTree to ReplicatedMergeTree" - }, - { - "location": "/table_engines/replication/#converting-from-replicatedmergetree-to-mergetree", - "text": "Create a MergeTree table with a different name. Move all the data from the directory with the ReplicatedMergeTree table data to the new table's data directory. Then delete the ReplicatedMergeTree table and restart the server. If you want to get rid of a ReplicatedMergeTree table without launching the server: Delete the corresponding .sql file in the metadata directory ( /var/lib/clickhouse/metadata/ ). Delete the corresponding path in ZooKeeper ( /path_to_table/replica_name ). After this, you can launch the server, create a MergeTree table, move the data to its directory, and then restart the server.", - "title": "Converting from ReplicatedMergeTree to MergeTree" - }, - { - "location": "/table_engines/replication/#recovery-when-metadata-in-the-zookeeper-cluster-is-lost-or-damaged", - "text": "If the data in ZooKeeper was lost or damaged, you can save data by moving it to an unreplicated table as described above. If exactly the same parts exist on the other replicas, they are added to the working set on them. If not, the parts are downloaded from the replica that has them.", - "title": "Recovery when metadata in the ZooKeeper cluster is lost or damaged" - }, - { - "location": "/table_engines/distributed/", - "text": "Distributed\n\n\nThe Distributed engine does not store data itself\n, but allows distributed query processing on multiple servers.\nReading is automatically parallelized. During a read, the table indexes on remote servers are used, if there are any.\nThe Distributed engine accepts parameters: the cluster name in the server's config file, the name of a remote database, the name of a remote table, and (optionally) a sharding key.\nExample:\n\n\nDistributed(logs, default, hits[, sharding_key])\n\n\n\n\n\nData will be read from all servers in the 'logs' cluster, from the default.hits table located on every server in the cluster.\nData is not only read, but is partially processed on the remote servers (to the extent that this is possible).\nFor example, for a query with GROUP BY, data will be aggregated on remote servers, and the intermediate states of aggregate functions will be sent to the requestor server. Then data will be further aggregated.\n\n\nInstead of the database name, you can use a constant expression that returns a string. For example: currentDatabase().\n\n\nlogs \u2013 The cluster name in the server's config file.\n\n\nClusters are set like this:\n\n\nremote_servers\n\n \nlogs\n\n \nshard\n\n \n!-- Optional. Shard weight when writing data. Default: 1. --\n\n \nweight\n1\n/weight\n\n \n!-- Optional. Whether to write data to just one of the replicas. Default: false (write data to all replicas). --\n\n \ninternal_replication\nfalse\n/internal_replication\n\n \nreplica\n\n \nhost\nexample01-01-1\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \nreplica\n\n \nhost\nexample01-01-2\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \n/shard\n\n \nshard\n\n \nweight\n2\n/weight\n\n \ninternal_replication\nfalse\n/internal_replication\n\n \nreplica\n\n \nhost\nexample01-02-1\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \nreplica\n\n \nhost\nexample01-02-2\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \n/shard\n\n \n/logs\n\n\n/remote_servers\n\n\n\n\n\n\nHere a cluster is defined with the name 'logs' that consists of two shards, each of which contains two replicas.\nShards refer to the servers that contain different parts of the data (in order to read all the data, you must access all the shards).\nReplicas are duplicating servers (in order to read all the data, you can access the data on any one of the replicas).\n\n\nThe parameters \nhost\n, \nport\n, and optionally \nuser\n and \npassword\n are specified for each server:\n\n\n: - \nhost\n \u2013 The address of the remote server. You can use either the domain or the IPv4 or IPv6 address. If you specify the domain, the server makes a DNS request when it starts, and the result is stored as long as the server is running. If the DNS request fails, the server doesn't start. If you change the DNS record, restart the server.\n- \nport\n\u2013 The TCP port for messenger activity ('tcp_port' in the config, usually set to 9000). Do not confuse it with http_port.\n- \nuser\n\u2013 Name of the user for connecting to a remote server. Default value: default. This user must have access to connect to the specified server. Access is configured in the users.xml file. For more information, see the section \"Access rights\".\n- \npassword\n \u2013 The password for connecting to a remote server (not masked). Default value: empty string.\n\n\nWhen specifying replicas, one of the available replicas will be selected for each of the shards when reading. You can configure the algorithm for load balancing (the preference for which replica to access) \u2013 see the 'load_balancing' setting.\nIf the connection with the server is not established, there will be an attempt to connect with a short timeout. If the connection failed, the next replica will be selected, and so on for all the replicas. If the connection attempt failed for all the replicas, the attempt will be repeated the same way, several times.\nThis works in favor of resiliency, but does not provide complete fault tolerance: a remote server might accept the connection, but might not work, or work poorly.\n\n\nYou can specify just one of the shards (in this case, query processing should be called remote, rather than distributed) or up to any number of shards. In each shard, you can specify from one to any number of replicas. You can specify a different number of replicas for each shard.\n\n\nYou can specify as many clusters as you wish in the configuration.\n\n\nTo view your clusters, use the 'system.clusters' table.\n\n\nThe Distributed engine allows working with a cluster like a local server. However, the cluster is inextensible: you must write its configuration in the server config file (even better, for all the cluster's servers).\n\n\nThere is no support for Distributed tables that look at other Distributed tables (except in cases when a Distributed table only has one shard). As an alternative, make the Distributed table look at the \"final\" tables.\n\n\nThe Distributed engine requires writing clusters to the config file. Clusters from the config file are updated on the fly, without restarting the server. If you need to send a query to an unknown set of shards and replicas each time, you don't need to create a Distributed table \u2013 use the 'remote' table function instead. See the section \"Table functions\".\n\n\nThere are two methods for writing data to a cluster:\n\n\nFirst, you can define which servers to write which data to, and perform the write directly on each shard. In other words, perform INSERT in the tables that the distributed table \"looks at\".\nThis is the most flexible solution \u2013 you can use any sharding scheme, which could be non-trivial due to the requirements of the subject area.\nThis is also the most optimal solution, since data can be written to different shards completely independently.\n\n\nSecond, you can perform INSERT in a Distributed table. In this case, the table will distribute the inserted data across servers itself.\nIn order to write to a Distributed table, it must have a sharding key set (the last parameter). In addition, if there is only one shard, the write operation works without specifying the sharding key, since it doesn't have any meaning in this case.\n\n\nEach shard can have a weight defined in the config file. By default, the weight is equal to one. Data is distributed across shards in the amount proportional to the shard weight. For example, if there are two shards and the first has a weight of 9 while the second has a weight of 10, the first will be sent 9 / 19 parts of the rows, and the second will be sent 10 / 19.\n\n\nEach shard can have the 'internal_replication' parameter defined in the config file.\n\n\nIf this parameter is set to 'true', the write operation selects the first healthy replica and writes data to it. Use this alternative if the Distributed table \"looks at\" replicated tables. In other words, if the table where data will be written is going to replicate them itself.\n\n\nIf it is set to 'false' (the default), data is written to all replicas. In essence, this means that the Distributed table replicates data itself. This is worse than using replicated tables, because the consistency of replicas is not checked, and over time they will contain slightly different data.\n\n\nTo select the shard that a row of data is sent to, the sharding expression is analyzed, and its remainder is taken from dividing it by the total weight of the shards. The row is sent to the shard that corresponds to the half-interval of the remainders from 'prev_weight' to 'prev_weights + weight', where 'prev_weights' is the total weight of the shards with the smallest number, and 'weight' is the weight of this shard. For example, if there are two shards, and the first has a weight of 9 while the second has a weight of 10, the row will be sent to the first shard for the remainders from the range [0, 9), and to the second for the remainders from the range [9, 19).\n\n\nThe sharding expression can be any expression from constants and table columns that returns an integer. For example, you can use the expression 'rand()' for random distribution of data, or 'UserID' for distribution by the remainder from dividing the user's ID (then the data of a single user will reside on a single shard, which simplifies running IN and JOIN by users). If one of the columns is not distributed evenly enough, you can wrap it in a hash function: intHash64(UserID).\n\n\nA simple remainder from division is a limited solution for sharding and isn't always appropriate. It works for medium and large volumes of data (dozens of servers), but not for very large volumes of data (hundreds of servers or more). In the latter case, use the sharding scheme required by the subject area, rather than using entries in Distributed tables.\n\n\nSELECT queries are sent to all the shards, and work regardless of how data is distributed across the shards (they can be distributed completely randomly). When you add a new shard, you don't have to transfer the old data to it. You can write new data with a heavier weight \u2013 the data will be distributed slightly unevenly, but queries will work correctly and efficiently.\n\n\nYou should be concerned about the sharding scheme in the following cases:\n\n\n\n\nQueries are used that require joining data (IN or JOIN) by a specific key. If data is sharded by this key, you can use local IN or JOIN instead of GLOBAL IN or GLOBAL JOIN, which is much more efficient.\n\n\nA large number of servers is used (hundreds or more) with a large number of small queries (queries of individual clients - websites, advertisers, or partners). In order for the small queries to not affect the entire cluster, it makes sense to locate data for a single client on a single shard. Alternatively, as we've done in Yandex.Metrica, you can set up bi-level sharding: divide the entire cluster into \"layers\", where a layer may consist of multiple shards. Data for a single client is located on a single layer, but shards can be added to a layer as necessary, and data is randomly distributed within them. Distributed tables are created for each layer, and a single shared distributed table is created for global queries.\n\n\n\n\nData is written asynchronously. For an INSERT to a Distributed table, the data block is just written to the local file system. The data is sent to the remote servers in the background as soon as possible. You should check whether data is sent successfully by checking the list of files (data waiting to be sent) in the table directory: /var/lib/clickhouse/data/database/table/.\n\n\nIf the server ceased to exist or had a rough restart (for example, after a device failure) after an INSERT to a Distributed table, the inserted data might be lost. If a damaged data part is detected in the table directory, it is transferred to the 'broken' subdirectory and no longer used.\n\n\nWhen the max_parallel_replicas option is enabled, query processing is parallelized across all replicas within a single shard. For more information, see the section \"Settings, max_parallel_replicas\".", - "title": "Distributed" - }, - { - "location": "/table_engines/distributed/#distributed", - "text": "The Distributed engine does not store data itself , but allows distributed query processing on multiple servers.\nReading is automatically parallelized. During a read, the table indexes on remote servers are used, if there are any.\nThe Distributed engine accepts parameters: the cluster name in the server's config file, the name of a remote database, the name of a remote table, and (optionally) a sharding key.\nExample: Distributed(logs, default, hits[, sharding_key]) Data will be read from all servers in the 'logs' cluster, from the default.hits table located on every server in the cluster.\nData is not only read, but is partially processed on the remote servers (to the extent that this is possible).\nFor example, for a query with GROUP BY, data will be aggregated on remote servers, and the intermediate states of aggregate functions will be sent to the requestor server. Then data will be further aggregated. Instead of the database name, you can use a constant expression that returns a string. For example: currentDatabase(). logs \u2013 The cluster name in the server's config file. Clusters are set like this: remote_servers \n logs \n shard \n !-- Optional. Shard weight when writing data. Default: 1. -- \n weight 1 /weight \n !-- Optional. Whether to write data to just one of the replicas. Default: false (write data to all replicas). -- \n internal_replication false /internal_replication \n replica \n host example01-01-1 /host \n port 9000 /port \n /replica \n replica \n host example01-01-2 /host \n port 9000 /port \n /replica \n /shard \n shard \n weight 2 /weight \n internal_replication false /internal_replication \n replica \n host example01-02-1 /host \n port 9000 /port \n /replica \n replica \n host example01-02-2 /host \n port 9000 /port \n /replica \n /shard \n /logs /remote_servers Here a cluster is defined with the name 'logs' that consists of two shards, each of which contains two replicas.\nShards refer to the servers that contain different parts of the data (in order to read all the data, you must access all the shards).\nReplicas are duplicating servers (in order to read all the data, you can access the data on any one of the replicas). The parameters host , port , and optionally user and password are specified for each server: : - host \u2013 The address of the remote server. You can use either the domain or the IPv4 or IPv6 address. If you specify the domain, the server makes a DNS request when it starts, and the result is stored as long as the server is running. If the DNS request fails, the server doesn't start. If you change the DNS record, restart the server.\n- port \u2013 The TCP port for messenger activity ('tcp_port' in the config, usually set to 9000). Do not confuse it with http_port.\n- user \u2013 Name of the user for connecting to a remote server. Default value: default. This user must have access to connect to the specified server. Access is configured in the users.xml file. For more information, see the section \"Access rights\".\n- password \u2013 The password for connecting to a remote server (not masked). Default value: empty string. When specifying replicas, one of the available replicas will be selected for each of the shards when reading. You can configure the algorithm for load balancing (the preference for which replica to access) \u2013 see the 'load_balancing' setting.\nIf the connection with the server is not established, there will be an attempt to connect with a short timeout. If the connection failed, the next replica will be selected, and so on for all the replicas. If the connection attempt failed for all the replicas, the attempt will be repeated the same way, several times.\nThis works in favor of resiliency, but does not provide complete fault tolerance: a remote server might accept the connection, but might not work, or work poorly. You can specify just one of the shards (in this case, query processing should be called remote, rather than distributed) or up to any number of shards. In each shard, you can specify from one to any number of replicas. You can specify a different number of replicas for each shard. You can specify as many clusters as you wish in the configuration. To view your clusters, use the 'system.clusters' table. The Distributed engine allows working with a cluster like a local server. However, the cluster is inextensible: you must write its configuration in the server config file (even better, for all the cluster's servers). There is no support for Distributed tables that look at other Distributed tables (except in cases when a Distributed table only has one shard). As an alternative, make the Distributed table look at the \"final\" tables. The Distributed engine requires writing clusters to the config file. Clusters from the config file are updated on the fly, without restarting the server. If you need to send a query to an unknown set of shards and replicas each time, you don't need to create a Distributed table \u2013 use the 'remote' table function instead. See the section \"Table functions\". There are two methods for writing data to a cluster: First, you can define which servers to write which data to, and perform the write directly on each shard. In other words, perform INSERT in the tables that the distributed table \"looks at\".\nThis is the most flexible solution \u2013 you can use any sharding scheme, which could be non-trivial due to the requirements of the subject area.\nThis is also the most optimal solution, since data can be written to different shards completely independently. Second, you can perform INSERT in a Distributed table. In this case, the table will distribute the inserted data across servers itself.\nIn order to write to a Distributed table, it must have a sharding key set (the last parameter). In addition, if there is only one shard, the write operation works without specifying the sharding key, since it doesn't have any meaning in this case. Each shard can have a weight defined in the config file. By default, the weight is equal to one. Data is distributed across shards in the amount proportional to the shard weight. For example, if there are two shards and the first has a weight of 9 while the second has a weight of 10, the first will be sent 9 / 19 parts of the rows, and the second will be sent 10 / 19. Each shard can have the 'internal_replication' parameter defined in the config file. If this parameter is set to 'true', the write operation selects the first healthy replica and writes data to it. Use this alternative if the Distributed table \"looks at\" replicated tables. In other words, if the table where data will be written is going to replicate them itself. If it is set to 'false' (the default), data is written to all replicas. In essence, this means that the Distributed table replicates data itself. This is worse than using replicated tables, because the consistency of replicas is not checked, and over time they will contain slightly different data. To select the shard that a row of data is sent to, the sharding expression is analyzed, and its remainder is taken from dividing it by the total weight of the shards. The row is sent to the shard that corresponds to the half-interval of the remainders from 'prev_weight' to 'prev_weights + weight', where 'prev_weights' is the total weight of the shards with the smallest number, and 'weight' is the weight of this shard. For example, if there are two shards, and the first has a weight of 9 while the second has a weight of 10, the row will be sent to the first shard for the remainders from the range [0, 9), and to the second for the remainders from the range [9, 19). The sharding expression can be any expression from constants and table columns that returns an integer. For example, you can use the expression 'rand()' for random distribution of data, or 'UserID' for distribution by the remainder from dividing the user's ID (then the data of a single user will reside on a single shard, which simplifies running IN and JOIN by users). If one of the columns is not distributed evenly enough, you can wrap it in a hash function: intHash64(UserID). A simple remainder from division is a limited solution for sharding and isn't always appropriate. It works for medium and large volumes of data (dozens of servers), but not for very large volumes of data (hundreds of servers or more). In the latter case, use the sharding scheme required by the subject area, rather than using entries in Distributed tables. SELECT queries are sent to all the shards, and work regardless of how data is distributed across the shards (they can be distributed completely randomly). When you add a new shard, you don't have to transfer the old data to it. You can write new data with a heavier weight \u2013 the data will be distributed slightly unevenly, but queries will work correctly and efficiently. You should be concerned about the sharding scheme in the following cases: Queries are used that require joining data (IN or JOIN) by a specific key. If data is sharded by this key, you can use local IN or JOIN instead of GLOBAL IN or GLOBAL JOIN, which is much more efficient. A large number of servers is used (hundreds or more) with a large number of small queries (queries of individual clients - websites, advertisers, or partners). In order for the small queries to not affect the entire cluster, it makes sense to locate data for a single client on a single shard. Alternatively, as we've done in Yandex.Metrica, you can set up bi-level sharding: divide the entire cluster into \"layers\", where a layer may consist of multiple shards. Data for a single client is located on a single layer, but shards can be added to a layer as necessary, and data is randomly distributed within them. Distributed tables are created for each layer, and a single shared distributed table is created for global queries. Data is written asynchronously. For an INSERT to a Distributed table, the data block is just written to the local file system. The data is sent to the remote servers in the background as soon as possible. You should check whether data is sent successfully by checking the list of files (data waiting to be sent) in the table directory: /var/lib/clickhouse/data/database/table/. If the server ceased to exist or had a rough restart (for example, after a device failure) after an INSERT to a Distributed table, the inserted data might be lost. If a damaged data part is detected in the table directory, it is transferred to the 'broken' subdirectory and no longer used. When the max_parallel_replicas option is enabled, query processing is parallelized across all replicas within a single shard. For more information, see the section \"Settings, max_parallel_replicas\".", - "title": "Distributed" - }, - { - "location": "/table_engines/dictionary/", - "text": "Dictionary\n\n\nThe \nDictionary\n engine displays the dictionary data as a ClickHouse table.\n\n\nAs an example, consider a dictionary of \nproducts\n with the following configuration:\n\n\ndictionaries\n\n\ndictionary\n\n \nname\nproducts\n/name\n\n \nsource\n\n \nodbc\n\n \ntable\nproducts\n/table\n\n \nconnection_string\nDSN=some-db-server\n/connection_string\n\n \n/odbc\n\n \n/source\n\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n \nlayout\n\n \nflat/\n\n \n/layout\n\n \nstructure\n\n \nid\n\n \nname\nproduct_id\n/name\n\n \n/id\n\n \nattribute\n\n \nname\ntitle\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n/null_value\n\n \n/attribute\n\n \n/structure\n\n\n/dictionary\n\n\n/dictionaries\n\n\n\n\n\n\nQuery the dictionary data:\n\n\nselect\n \nname\n,\n \ntype\n,\n \nkey\n,\n \nattribute\n.\nnames\n,\n \nattribute\n.\ntypes\n,\n \nbytes_allocated\n,\n \nelement_count\n,\nsource\n \nfrom\n \nsystem\n.\ndictionaries\n \nwhere\n \nname\n \n=\n \nproducts\n;\n \n\n\nSELECT\n\n \nname\n,\n\n \ntype\n,\n\n \nkey\n,\n\n \nattribute\n.\nnames\n,\n\n \nattribute\n.\ntypes\n,\n\n \nbytes_allocated\n,\n\n \nelement_count\n,\n\n \nsource\n\n\nFROM\n \nsystem\n.\ndictionaries\n\n\nWHERE\n \nname\n \n=\n \nproducts\n\n\n\n\n\n\n\u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u252c\u2500type\u2500\u252c\u2500key\u2500\u2500\u2500\u2500\u252c\u2500attribute.names\u2500\u252c\u2500attribute.types\u2500\u252c\u2500bytes_allocated\u2500\u252c\u2500element_count\u2500\u252c\u2500source\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 products \u2502 Flat \u2502 UInt64 \u2502 [\ntitle\n] \u2502 [\nString\n] \u2502 23065376 \u2502 175032 \u2502 ODBC: .products \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nYou can use the \ndictGet*\n function to get the dictionary data in this format.\n\n\nThis view isn't helpful when you need to get raw data, or when performing a \nJOIN\n operation. For these cases, you can use the \nDictionary\n engine, which displays the dictionary data in a table.\n\n\nSyntax:\n\n\nCREATE TABLE %table_name% (%fields%) engine = Dictionary(%dictionary_name%)`\n\n\n\n\n\nUsage example:\n\n\ncreate\n \ntable\n \nproducts\n \n(\nproduct_id\n \nUInt64\n,\n \ntitle\n \nString\n)\n \nEngine\n \n=\n \nDictionary\n(\nproducts\n);\n\n\n\nCREATE\n \nTABLE\n \nproducts\n\n\n(\n\n \nproduct_id\n \nUInt64\n,\n\n \ntitle\n \nString\n,\n\n\n)\n\n\nENGINE\n \n=\n \nDictionary\n(\nproducts\n)\n\n\n\n\n\n\nOk.\n\n0 rows in set. Elapsed: 0.004 sec.\n\n\n\n\n\nTake a look at what's in the table.\n\n\nselect\n \n*\n \nfrom\n \nproducts\n \nlimit\n \n1\n;\n\n\n\nSELECT\n \n*\n\n\nFROM\n \nproducts\n\n\nLIMIT\n \n1\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500product_id\u2500\u252c\u2500title\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 152689 \u2502 Some item \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n1 rows in set. Elapsed: 0.006 sec.", - "title": "Dictionary" - }, - { - "location": "/table_engines/dictionary/#dictionary", - "text": "The Dictionary engine displays the dictionary data as a ClickHouse table. As an example, consider a dictionary of products with the following configuration: dictionaries dictionary \n name products /name \n source \n odbc \n table products /table \n connection_string DSN=some-db-server /connection_string \n /odbc \n /source \n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n layout \n flat/ \n /layout \n structure \n id \n name product_id /name \n /id \n attribute \n name title /name \n type String /type \n null_value /null_value \n /attribute \n /structure /dictionary /dictionaries Query the dictionary data: select name , type , key , attribute . names , attribute . types , bytes_allocated , element_count , source from system . dictionaries where name = products ; SELECT \n name , \n type , \n key , \n attribute . names , \n attribute . types , \n bytes_allocated , \n element_count , \n source FROM system . dictionaries WHERE name = products \u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u252c\u2500type\u2500\u252c\u2500key\u2500\u2500\u2500\u2500\u252c\u2500attribute.names\u2500\u252c\u2500attribute.types\u2500\u252c\u2500bytes_allocated\u2500\u252c\u2500element_count\u2500\u252c\u2500source\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 products \u2502 Flat \u2502 UInt64 \u2502 [ title ] \u2502 [ String ] \u2502 23065376 \u2502 175032 \u2502 ODBC: .products \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 You can use the dictGet* function to get the dictionary data in this format. This view isn't helpful when you need to get raw data, or when performing a JOIN operation. For these cases, you can use the Dictionary engine, which displays the dictionary data in a table. Syntax: CREATE TABLE %table_name% (%fields%) engine = Dictionary(%dictionary_name%)` Usage example: create table products ( product_id UInt64 , title String ) Engine = Dictionary ( products ); CREATE TABLE products ( \n product_id UInt64 , \n title String , ) ENGINE = Dictionary ( products ) Ok.\n\n0 rows in set. Elapsed: 0.004 sec. Take a look at what's in the table. select * from products limit 1 ; SELECT * FROM products LIMIT 1 \u250c\u2500\u2500\u2500\u2500product_id\u2500\u252c\u2500title\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 152689 \u2502 Some item \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n1 rows in set. Elapsed: 0.006 sec.", - "title": "Dictionary" - }, - { - "location": "/table_engines/merge/", - "text": "Merge\n\n\nThe Merge engine (not to be confused with \nMergeTree\n) does not store data itself, but allows reading from any number of other tables simultaneously.\nReading is automatically parallelized. Writing to a table is not supported. When reading, the indexes of tables that are actually being read are used, if they exist.\nThe Merge engine accepts parameters: the database name and a regular expression for tables.\n\n\nExample:\n\n\nMerge(hits, \n^WatchLog\n)\n\n\n\n\n\nData will be read from the tables in the 'hits' database that have names that match the regular expression '\n^WatchLog\n'.\n\n\nInstead of the database name, you can use a constant expression that returns a string. For example, \ncurrentDatabase()\n.\n\n\nRegular expressions \u2014 \nre2\n (supports a subset of PCRE), case-sensitive.\nSee the notes about escaping symbols in regular expressions in the \"match\" section.\n\n\nWhen selecting tables to read, the Merge table itself will not be selected, even if it matches the regex. This is to avoid loops.\nIt is possible to create two Merge tables that will endlessly try to read each others' data, but this is not a good idea.\n\n\nThe typical way to use the Merge engine is for working with a large number of TinyLog tables as if with a single table.\n\n\nVirtual columns\n\n\nVirtual columns are columns that are provided by the table engine, regardless of the table definition. In other words, these columns are not specified in CREATE TABLE, but they are accessible for SELECT.\n\n\nVirtual columns differ from normal columns in the following ways:\n\n\n\n\nThey are not specified in table definitions.\n\n\nData can't be added to them with INSERT.\n\n\nWhen using INSERT without specifying the list of columns, virtual columns are ignored.\n\n\nThey are not selected when using the asterisk (\nSELECT *\n).\n\n\nVirtual columns are not shown in \nSHOW CREATE TABLE\n and \nDESC TABLE\n queries.\n\n\n\n\nA Merge type table contains a virtual _table column with the String type. (If the table already has a _table column, the virtual column is named _table1, and if it already has _table1, it is named _table2, and so on.) It contains the name of the table that data was read from.\n\n\nIf the WHERE or PREWHERE clause contains conditions for the '_table' column that do not depend on other table columns (as one of the conjunction elements, or as an entire expression), these conditions are used as an index. The conditions are performed on a data set of table names to read data from, and the read operation will be performed from only those tables that the condition was triggered on.", - "title": "Merge" - }, - { - "location": "/table_engines/merge/#merge", - "text": "The Merge engine (not to be confused with MergeTree ) does not store data itself, but allows reading from any number of other tables simultaneously.\nReading is automatically parallelized. Writing to a table is not supported. When reading, the indexes of tables that are actually being read are used, if they exist.\nThe Merge engine accepts parameters: the database name and a regular expression for tables. Example: Merge(hits, ^WatchLog ) Data will be read from the tables in the 'hits' database that have names that match the regular expression ' ^WatchLog '. Instead of the database name, you can use a constant expression that returns a string. For example, currentDatabase() . Regular expressions \u2014 re2 (supports a subset of PCRE), case-sensitive.\nSee the notes about escaping symbols in regular expressions in the \"match\" section. When selecting tables to read, the Merge table itself will not be selected, even if it matches the regex. This is to avoid loops.\nIt is possible to create two Merge tables that will endlessly try to read each others' data, but this is not a good idea. The typical way to use the Merge engine is for working with a large number of TinyLog tables as if with a single table.", - "title": "Merge" - }, - { - "location": "/table_engines/merge/#virtual-columns", - "text": "Virtual columns are columns that are provided by the table engine, regardless of the table definition. In other words, these columns are not specified in CREATE TABLE, but they are accessible for SELECT. Virtual columns differ from normal columns in the following ways: They are not specified in table definitions. Data can't be added to them with INSERT. When using INSERT without specifying the list of columns, virtual columns are ignored. They are not selected when using the asterisk ( SELECT * ). Virtual columns are not shown in SHOW CREATE TABLE and DESC TABLE queries. A Merge type table contains a virtual _table column with the String type. (If the table already has a _table column, the virtual column is named _table1, and if it already has _table1, it is named _table2, and so on.) It contains the name of the table that data was read from. If the WHERE or PREWHERE clause contains conditions for the '_table' column that do not depend on other table columns (as one of the conjunction elements, or as an entire expression), these conditions are used as an index. The conditions are performed on a data set of table names to read data from, and the read operation will be performed from only those tables that the condition was triggered on.", - "title": "Virtual columns" - }, - { - "location": "/table_engines/buffer/", - "text": "Buffer\n\n\nBuffers the data to write in RAM, periodically flushing it to another table. During the read operation, data is read from the buffer and the other table simultaneously.\n\n\nBuffer(database, table, num_layers, min_time, max_time, min_rows, max_rows, min_bytes, max_bytes)\n\n\n\n\n\nEngine parameters:database, table \u2013 The table to flush data to. Instead of the database name, you can use a constant expression that returns a string.num_layers \u2013 Parallelism layer. Physically, the table will be represented as 'num_layers' of independent buffers. Recommended value: 16.min_time, max_time, min_rows, max_rows, min_bytes, and max_bytes are conditions for flushing data from the buffer.\n\n\nData is flushed from the buffer and written to the destination table if all the 'min' conditions or at least one 'max' condition are met.min_time, max_time \u2013 Condition for the time in seconds from the moment of the first write to the buffer.min_rows, max_rows \u2013 Condition for the number of rows in the buffer.min_bytes, max_bytes \u2013 Condition for the number of bytes in the buffer.\n\n\nDuring the write operation, data is inserted to a 'num_layers' number of random buffers. Or, if the data part to insert is large enough (greater than 'max_rows' or 'max_bytes'), it is written directly to the destination table, omitting the buffer.\n\n\nThe conditions for flushing the data are calculated separately for each of the 'num_layers' buffers. For example, if num_layers = 16 and max_bytes = 100000000, the maximum RAM consumption is 1.6 GB.\n\n\nExample:\n\n\nCREATE\n \nTABLE\n \nmerge\n.\nhits_buffer\n \nAS\n \nmerge\n.\nhits\n \nENGINE\n \n=\n \nBuffer\n(\nmerge\n,\n \nhits\n,\n \n16\n,\n \n10\n,\n \n100\n,\n \n10000\n,\n \n1000000\n,\n \n10000000\n,\n \n100000000\n)\n\n\n\n\n\n\nCreating a 'merge.hits_buffer' table with the same structure as 'merge.hits' and using the Buffer engine. When writing to this table, data is buffered in RAM and later written to the 'merge.hits' table. 16 buffers are created. The data in each of them is flushed if either 100 seconds have passed, or one million rows have been written, or 100 MB of data have been written; or if simultaneously 10 seconds have passed and 10,000 rows and 10 MB of data have been written. For example, if just one row has been written, after 100 seconds it will be flushed, no matter what. But if many rows have been written, the data will be flushed sooner.\n\n\nWhen the server is stopped, with DROP TABLE or DETACH TABLE, buffer data is also flushed to the destination table.\n\n\nYou can set empty strings in single quotation marks for the database and table name. This indicates the absence of a destination table. In this case, when the data flush conditions are reached, the buffer is simply cleared. This may be useful for keeping a window of data in memory.\n\n\nWhen reading from a Buffer table, data is processed both from the buffer and from the destination table (if there is one).\nNote that the Buffer tables does not support an index. In other words, data in the buffer is fully scanned, which might be slow for large buffers. (For data in a subordinate table, the index that it supports will be used.)\n\n\nIf the set of columns in the Buffer table doesn't match the set of columns in a subordinate table, a subset of columns that exist in both tables is inserted.\n\n\nIf the types don't match for one of the columns in the Buffer table and a subordinate table, an error message is entered in the server log and the buffer is cleared.\nThe same thing happens if the subordinate table doesn't exist when the buffer is flushed.\n\n\nIf you need to run ALTER for a subordinate table and the Buffer table, we recommend first deleting the Buffer table, running ALTER for the subordinate table, then creating the Buffer table again.\n\n\nIf the server is restarted abnormally, the data in the buffer is lost.\n\n\nPREWHERE, FINAL and SAMPLE do not work correctly for Buffer tables. These conditions are passed to the destination table, but are not used for processing data in the buffer. Because of this, we recommend only using the Buffer table for writing, while reading from the destination table.\n\n\nWhen adding data to a Buffer, one of the buffers is locked. This causes delays if a read operation is simultaneously being performed from the table.\n\n\nData that is inserted to a Buffer table may end up in the subordinate table in a different order and in different blocks. Because of this, a Buffer table is difficult to use for writing to a CollapsingMergeTree correctly. To avoid problems, you can set 'num_layers' to 1.\n\n\nIf the destination table is replicated, some expected characteristics of replicated tables are lost when writing to a Buffer table. The random changes to the order of rows and sizes of data parts cause data deduplication to quit working, which means it is not possible to have a reliable 'exactly once' write to replicated tables.\n\n\nDue to these disadvantages, we can only recommend using a Buffer table in rare cases.\n\n\nA Buffer table is used when too many INSERTs are received from a large number of servers over a unit of time and data can't be buffered before insertion, which means the INSERTs can't run fast enough.\n\n\nNote that it doesn't make sense to insert data one row at a time, even for Buffer tables. This will only produce a speed of a few thousand rows per second, while inserting larger blocks of data can produce over a million rows per second (see the section \"Performance\").", - "title": "Buffer" - }, - { - "location": "/table_engines/buffer/#buffer", - "text": "Buffers the data to write in RAM, periodically flushing it to another table. During the read operation, data is read from the buffer and the other table simultaneously. Buffer(database, table, num_layers, min_time, max_time, min_rows, max_rows, min_bytes, max_bytes) Engine parameters:database, table \u2013 The table to flush data to. Instead of the database name, you can use a constant expression that returns a string.num_layers \u2013 Parallelism layer. Physically, the table will be represented as 'num_layers' of independent buffers. Recommended value: 16.min_time, max_time, min_rows, max_rows, min_bytes, and max_bytes are conditions for flushing data from the buffer. Data is flushed from the buffer and written to the destination table if all the 'min' conditions or at least one 'max' condition are met.min_time, max_time \u2013 Condition for the time in seconds from the moment of the first write to the buffer.min_rows, max_rows \u2013 Condition for the number of rows in the buffer.min_bytes, max_bytes \u2013 Condition for the number of bytes in the buffer. During the write operation, data is inserted to a 'num_layers' number of random buffers. Or, if the data part to insert is large enough (greater than 'max_rows' or 'max_bytes'), it is written directly to the destination table, omitting the buffer. The conditions for flushing the data are calculated separately for each of the 'num_layers' buffers. For example, if num_layers = 16 and max_bytes = 100000000, the maximum RAM consumption is 1.6 GB. Example: CREATE TABLE merge . hits_buffer AS merge . hits ENGINE = Buffer ( merge , hits , 16 , 10 , 100 , 10000 , 1000000 , 10000000 , 100000000 ) Creating a 'merge.hits_buffer' table with the same structure as 'merge.hits' and using the Buffer engine. When writing to this table, data is buffered in RAM and later written to the 'merge.hits' table. 16 buffers are created. The data in each of them is flushed if either 100 seconds have passed, or one million rows have been written, or 100 MB of data have been written; or if simultaneously 10 seconds have passed and 10,000 rows and 10 MB of data have been written. For example, if just one row has been written, after 100 seconds it will be flushed, no matter what. But if many rows have been written, the data will be flushed sooner. When the server is stopped, with DROP TABLE or DETACH TABLE, buffer data is also flushed to the destination table. You can set empty strings in single quotation marks for the database and table name. This indicates the absence of a destination table. In this case, when the data flush conditions are reached, the buffer is simply cleared. This may be useful for keeping a window of data in memory. When reading from a Buffer table, data is processed both from the buffer and from the destination table (if there is one).\nNote that the Buffer tables does not support an index. In other words, data in the buffer is fully scanned, which might be slow for large buffers. (For data in a subordinate table, the index that it supports will be used.) If the set of columns in the Buffer table doesn't match the set of columns in a subordinate table, a subset of columns that exist in both tables is inserted. If the types don't match for one of the columns in the Buffer table and a subordinate table, an error message is entered in the server log and the buffer is cleared.\nThe same thing happens if the subordinate table doesn't exist when the buffer is flushed. If you need to run ALTER for a subordinate table and the Buffer table, we recommend first deleting the Buffer table, running ALTER for the subordinate table, then creating the Buffer table again. If the server is restarted abnormally, the data in the buffer is lost. PREWHERE, FINAL and SAMPLE do not work correctly for Buffer tables. These conditions are passed to the destination table, but are not used for processing data in the buffer. Because of this, we recommend only using the Buffer table for writing, while reading from the destination table. When adding data to a Buffer, one of the buffers is locked. This causes delays if a read operation is simultaneously being performed from the table. Data that is inserted to a Buffer table may end up in the subordinate table in a different order and in different blocks. Because of this, a Buffer table is difficult to use for writing to a CollapsingMergeTree correctly. To avoid problems, you can set 'num_layers' to 1. If the destination table is replicated, some expected characteristics of replicated tables are lost when writing to a Buffer table. The random changes to the order of rows and sizes of data parts cause data deduplication to quit working, which means it is not possible to have a reliable 'exactly once' write to replicated tables. Due to these disadvantages, we can only recommend using a Buffer table in rare cases. A Buffer table is used when too many INSERTs are received from a large number of servers over a unit of time and data can't be buffered before insertion, which means the INSERTs can't run fast enough. Note that it doesn't make sense to insert data one row at a time, even for Buffer tables. This will only produce a speed of a few thousand rows per second, while inserting larger blocks of data can produce over a million rows per second (see the section \"Performance\").", - "title": "Buffer" - }, - { - "location": "/table_engines/file/", - "text": "File(InputFormat)\n\n\nThe data source is a file that stores data in one of the supported input formats (TabSeparated, Native, etc.).", - "title": "File" - }, - { - "location": "/table_engines/file/#fileinputformat", - "text": "The data source is a file that stores data in one of the supported input formats (TabSeparated, Native, etc.).", - "title": "File(InputFormat)" - }, - { - "location": "/table_engines/null/", - "text": "Null\n\n\nWhen writing to a Null table, data is ignored. When reading from a Null table, the response is empty.\n\n\nHowever, you can create a materialized view on a Null table. So the data written to the table will end up in the view.", - "title": "Null" - }, - { - "location": "/table_engines/null/#null", - "text": "When writing to a Null table, data is ignored. When reading from a Null table, the response is empty. However, you can create a materialized view on a Null table. So the data written to the table will end up in the view.", - "title": "Null" - }, - { - "location": "/table_engines/set/", - "text": "Set\n\n\nA data set that is always in RAM. It is intended for use on the right side of the IN operator (see the section \"IN operators\").\n\n\nYou can use INSERT to insert data in the table. New elements will be added to the data set, while duplicates will be ignored.\nBut you can't perform SELECT from the table. The only way to retrieve data is by using it in the right half of the IN operator.\n\n\nData is always located in RAM. For INSERT, the blocks of inserted data are also written to the directory of tables on the disk. When starting the server, this data is loaded to RAM. In other words, after restarting, the data remains in place.\n\n\nFor a rough server restart, the block of data on the disk might be lost or damaged. In the latter case, you may need to manually delete the file with damaged data.", - "title": "Set" - }, - { - "location": "/table_engines/set/#set", - "text": "A data set that is always in RAM. It is intended for use on the right side of the IN operator (see the section \"IN operators\"). You can use INSERT to insert data in the table. New elements will be added to the data set, while duplicates will be ignored.\nBut you can't perform SELECT from the table. The only way to retrieve data is by using it in the right half of the IN operator. Data is always located in RAM. For INSERT, the blocks of inserted data are also written to the directory of tables on the disk. When starting the server, this data is loaded to RAM. In other words, after restarting, the data remains in place. For a rough server restart, the block of data on the disk might be lost or damaged. In the latter case, you may need to manually delete the file with damaged data.", - "title": "Set" - }, - { - "location": "/table_engines/join/", - "text": "Join\n\n\nA prepared data structure for JOIN that is always located in RAM.\n\n\nJoin(ANY|ALL, LEFT|INNER, k1[, k2, ...])\n\n\n\n\n\nEngine parameters: \nANY|ALL\n \u2013 strictness; \nLEFT|INNER\n \u2013 type.\nThese parameters are set without quotes and must match the JOIN that the table will be used for. k1, k2, ... are the key columns from the USING clause that the join will be made on.\n\n\nThe table can't be used for GLOBAL JOINs.\n\n\nYou can use INSERT to add data to the table, similar to the Set engine. For ANY, data for duplicated keys will be ignored. For ALL, it will be counted. You can't perform SELECT directly from the table. The only way to retrieve data is to use it as the \"right-hand\" table for JOIN.\n\n\nStoring data on the disk is the same as for the Set engine.", - "title": "Join" - }, - { - "location": "/table_engines/join/#join", - "text": "A prepared data structure for JOIN that is always located in RAM. Join(ANY|ALL, LEFT|INNER, k1[, k2, ...]) Engine parameters: ANY|ALL \u2013 strictness; LEFT|INNER \u2013 type.\nThese parameters are set without quotes and must match the JOIN that the table will be used for. k1, k2, ... are the key columns from the USING clause that the join will be made on. The table can't be used for GLOBAL JOINs. You can use INSERT to add data to the table, similar to the Set engine. For ANY, data for duplicated keys will be ignored. For ALL, it will be counted. You can't perform SELECT directly from the table. The only way to retrieve data is to use it as the \"right-hand\" table for JOIN. Storing data on the disk is the same as for the Set engine.", - "title": "Join" - }, - { - "location": "/table_engines/view/", - "text": "View\n\n\nUsed for implementing views (for more information, see the \nCREATE VIEW query\n). It does not store data, but only stores the specified \nSELECT\n query. When reading from a table, it runs this query (and deletes all unnecessary columns from the query).", - "title": "View" - }, - { - "location": "/table_engines/view/#view", - "text": "Used for implementing views (for more information, see the CREATE VIEW query ). It does not store data, but only stores the specified SELECT query. When reading from a table, it runs this query (and deletes all unnecessary columns from the query).", - "title": "View" - }, - { - "location": "/table_engines/materializedview/", - "text": "MaterializedView\n\n\nUsed for implementing materialized views (for more information, see the \nCREATE TABLE\n) query. For storing data, it uses a different engine that was specified when creating the view. When reading from a table, it just uses this engine.", - "title": "MaterializedView" - }, - { - "location": "/table_engines/materializedview/#materializedview", - "text": "Used for implementing materialized views (for more information, see the CREATE TABLE ) query. For storing data, it uses a different engine that was specified when creating the view. When reading from a table, it just uses this engine.", - "title": "MaterializedView" - }, - { - "location": "/table_engines/kafka/", - "text": "Kafka\n\n\nThis engine works with \nApache Kafka\n.\n\n\nKafka lets you:\n\n\n\n\nPublish or subscribe to data flows.\n\n\nOrganize fault-tolerant storage.\n\n\nProcess streams as they become available.\n\n\n\n\nKafka(broker_list, topic_list, group_name, format[, schema, num_consumers])\n\n\n\n\n\nParameters:\n\n\n\n\nbroker_list\n \u2013 A comma-separated list of brokers (\nlocalhost:9092\n).\n\n\ntopic_list\n \u2013 A list of Kafka topics (\nmy_topic\n).\n\n\ngroup_name\n \u2013 A group of Kafka consumers (\ngroup1\n). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere.\n\n\n--format\n \u2013 Message format. Uses the same notation as the SQL \nFORMAT\n function, such as \nJSONEachRow\n. For more information, see the \"Formats\" section.\n\n\nschema\n \u2013 An optional parameter that must be used if the format requires a schema definition. For example, \nCap'n Proto\n requires the path to the schema file and the name of the root \nschema.capnp:Message\n object.\n\n\nnum_consumers\n \u2013 The number of consumers per table. Default: \n1\n. Specify more consumers if the throughput of one consumer is insufficient. The total number of consumers should not exceed the number of partitions in the topic, since only one consumer can be assigned per partition.\n\n\n\n\nExample:\n\n\n \nCREATE\n \nTABLE\n \nqueue\n \n(\n\n \ntimestamp\n \nUInt64\n,\n\n \nlevel\n \nString\n,\n\n \nmessage\n \nString\n\n \n)\n \nENGINE\n \n=\n \nKafka\n(\nlocalhost:9092\n,\n \ntopic\n,\n \ngroup1\n,\n \nJSONEachRow\n);\n\n\n \nSELECT\n \n*\n \nFROM\n \nqueue\n \nLIMIT\n \n5\n;\n\n\n\n\n\n\nThe delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name.\n\n\nGroups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. Read more about this at \nhttp://kafka.apache.org/intro\n.\n\n\nSELECT\n is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. To do this:\n\n\n\n\nUse the engine to create a Kafka consumer and consider it a data stream.\n\n\nCreate a table with the desired structure.\n\n\nCreate a materialized view that converts data from the engine and puts it into a previously created table.\n\n\n\n\nWhen the \nMATERIALIZED VIEW\n joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using \nSELECT\n\n\nExample:\n\n\n \nCREATE\n \nTABLE\n \nqueue\n \n(\n\n \ntimestamp\n \nUInt64\n,\n\n \nlevel\n \nString\n,\n\n \nmessage\n \nString\n\n \n)\n \nENGINE\n \n=\n \nKafka\n(\nlocalhost:9092\n,\n \ntopic\n,\n \ngroup1\n,\n \nJSONEachRow\n);\n\n\n \nCREATE\n \nTABLE\n \ndaily\n \n(\n\n \nday\n \nDate\n,\n\n \nlevel\n \nString\n,\n\n \ntotal\n \nUInt64\n\n \n)\n \nENGINE\n \n=\n \nSummingMergeTree\n(\nday\n,\n \n(\nday\n,\n \nlevel\n),\n \n8192\n);\n\n\n \nCREATE\n \nMATERIALIZED\n \nVIEW\n \nconsumer\n \nTO\n \ndaily\n\n \nAS\n \nSELECT\n \ntoDate\n(\ntoDateTime\n(\ntimestamp\n))\n \nAS\n \nday\n,\n \nlevel\n,\n \ncount\n()\n \nas\n \ntotal\n\n \nFROM\n \nqueue\n \nGROUP\n \nBY\n \nday\n,\n \nlevel\n;\n\n\n \nSELECT\n \nlevel\n,\n \nsum\n(\ntotal\n)\n \nFROM\n \ndaily\n \nGROUP\n \nBY\n \nlevel\n;\n\n\n\n\n\n\nTo improve performance, received messages are grouped into blocks the size of \nmax_insert_block_size\n. If the block wasn't formed within \nstream_flush_interval_ms\n milliseconds, the data will be flushed to the table regardless of the completeness of the block.\n\n\nTo stop receiving topic data or to change the conversion logic, detach the materialized view:\n\n\n DETACH TABLE consumer;\n ATTACH MATERIALIZED VIEW consumer;\n\n\n\n\n\nIf you want to change the target table by using \nALTER\nmaterialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view.\n\n\nConfiguration\n\n\nSimilar to GraphiteMergeTree, the Kafka engine supports extended configuration using the ClickHouse config file. There are two configuration keys that you can use: global (\nkafka\n) and topic-level (\nkafka_topic_*\n). The global configuration is applied first, and the topic-level configuration is second (if it exists).\n\n\n \n!-- Global configuration options for all tables of Kafka engine type --\n\n \nkafka\n\n \ndebug\ncgrp\n/debug\n\n \nauto_offset_reset\nsmallest\n/auto_offset_reset\n\n \n/kafka\n\n\n \n!-- Configuration specific for topic \nlogs\n --\n\n \nkafka_topic_logs\n\n \nretry_backoff_ms\n250\n/retry_backoff_ms\n\n \nfetch_min_bytes\n100000\n/fetch_min_bytes\n\n \n/kafka_topic_logs\n\n\n\n\n\n\nFor a list of possible configuration options, see the \nlibrdkafka configuration reference\n. Use the underscore (\n_\n) instead of a dot in the ClickHouse configuration. For example, \ncheck.crcs=true\n will be \ncheck_crcs\ntrue\n/check_crcs\n.", - "title": "Kafka" - }, - { - "location": "/table_engines/kafka/#kafka", - "text": "This engine works with Apache Kafka . Kafka lets you: Publish or subscribe to data flows. Organize fault-tolerant storage. Process streams as they become available. Kafka(broker_list, topic_list, group_name, format[, schema, num_consumers]) Parameters: broker_list \u2013 A comma-separated list of brokers ( localhost:9092 ). topic_list \u2013 A list of Kafka topics ( my_topic ). group_name \u2013 A group of Kafka consumers ( group1 ). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere. --format \u2013 Message format. Uses the same notation as the SQL FORMAT function, such as JSONEachRow . For more information, see the \"Formats\" section. schema \u2013 An optional parameter that must be used if the format requires a schema definition. For example, Cap'n Proto requires the path to the schema file and the name of the root schema.capnp:Message object. num_consumers \u2013 The number of consumers per table. Default: 1 . Specify more consumers if the throughput of one consumer is insufficient. The total number of consumers should not exceed the number of partitions in the topic, since only one consumer can be assigned per partition. Example: CREATE TABLE queue ( \n timestamp UInt64 , \n level String , \n message String \n ) ENGINE = Kafka ( localhost:9092 , topic , group1 , JSONEachRow ); \n\n SELECT * FROM queue LIMIT 5 ; The delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name. Groups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. Read more about this at http://kafka.apache.org/intro . SELECT is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. To do this: Use the engine to create a Kafka consumer and consider it a data stream. Create a table with the desired structure. Create a materialized view that converts data from the engine and puts it into a previously created table. When the MATERIALIZED VIEW joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using SELECT Example: CREATE TABLE queue ( \n timestamp UInt64 , \n level String , \n message String \n ) ENGINE = Kafka ( localhost:9092 , topic , group1 , JSONEachRow ); \n\n CREATE TABLE daily ( \n day Date , \n level String , \n total UInt64 \n ) ENGINE = SummingMergeTree ( day , ( day , level ), 8192 ); \n\n CREATE MATERIALIZED VIEW consumer TO daily \n AS SELECT toDate ( toDateTime ( timestamp )) AS day , level , count () as total \n FROM queue GROUP BY day , level ; \n\n SELECT level , sum ( total ) FROM daily GROUP BY level ; To improve performance, received messages are grouped into blocks the size of max_insert_block_size . If the block wasn't formed within stream_flush_interval_ms milliseconds, the data will be flushed to the table regardless of the completeness of the block. To stop receiving topic data or to change the conversion logic, detach the materialized view: DETACH TABLE consumer;\n ATTACH MATERIALIZED VIEW consumer; If you want to change the target table by using ALTER materialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view.", - "title": "Kafka" - }, - { - "location": "/table_engines/kafka/#configuration", - "text": "Similar to GraphiteMergeTree, the Kafka engine supports extended configuration using the ClickHouse config file. There are two configuration keys that you can use: global ( kafka ) and topic-level ( kafka_topic_* ). The global configuration is applied first, and the topic-level configuration is second (if it exists). !-- Global configuration options for all tables of Kafka engine type -- \n kafka \n debug cgrp /debug \n auto_offset_reset smallest /auto_offset_reset \n /kafka \n\n !-- Configuration specific for topic logs -- \n kafka_topic_logs \n retry_backoff_ms 250 /retry_backoff_ms \n fetch_min_bytes 100000 /fetch_min_bytes \n /kafka_topic_logs For a list of possible configuration options, see the librdkafka configuration reference . Use the underscore ( _ ) instead of a dot in the ClickHouse configuration. For example, check.crcs=true will be check_crcs true /check_crcs .", - "title": "Configuration" - }, - { - "location": "/table_engines/mysql/", - "text": "MySQL\n\n\nThe MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server.\n\n\nThe engine takes 4 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example:\n\n\nMySQL(\nhost:port\n, \ndatabase\n, \ntable\n, \nuser\n, \npassword\n);\n\n\n\n\n\nAt this time, simple WHERE clauses such as \n=, !=, \n, \n=, \n, \n=\n are executed on the MySQL server.\n\n\nThe rest of the conditions and the LIMIT sampling constraint are executed in ClickHouse only after the query to MySQL finishes.", - "title": "MySQL" - }, - { - "location": "/table_engines/mysql/#mysql", - "text": "The MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server. The engine takes 4 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example: MySQL( host:port , database , table , user , password ); At this time, simple WHERE clauses such as =, !=, , =, , = are executed on the MySQL server. The rest of the conditions and the LIMIT sampling constraint are executed in ClickHouse only after the query to MySQL finishes.", - "title": "MySQL" - }, - { - "location": "/table_engines/external_data/", - "text": "External data for query processing\n\n\nClickHouse allows sending a server the data that is needed for processing a query, together with a SELECT query. This data is put in a temporary table (see the section \"Temporary tables\") and can be used in the query (for example, in IN operators).\n\n\nFor example, if you have a text file with important user identifiers, you can upload it to the server along with a query that uses filtration by this list.\n\n\nIf you need to run more than one query with a large volume of external data, don't use this feature. It is better to upload the data to the DB ahead of time.\n\n\nExternal data can be uploaded using the command-line client (in non-interactive mode), or using the HTTP interface.\n\n\nIn the command-line client, you can specify a parameters section in the format\n\n\n--external --file\n=\n... \n[\n--name\n=\n...\n]\n \n[\n--format\n=\n...\n]\n \n[\n--types\n=\n...\n|\n--structure\n=\n...\n]\n\n\n\n\n\n\nYou may have multiple sections like this, for the number of tables being transmitted.\n\n\n--external\n \u2013 Marks the beginning of a clause.\n\n--file\n \u2013 Path to the file with the table dump, or -, which refers to stdin.\nOnly a single table can be retrieved from stdin.\n\n\nThe following parameters are optional: \n--name\n\u2013 Name of the table. If omitted, _data is used.\n\n--format\n \u2013 Data format in the file. If omitted, TabSeparated is used.\n\n\nOne of the following parameters is required:\n--types\n \u2013 A list of comma-separated column types. For example: \nUInt64,String\n. The columns will be named _1, _2, ...\n\n--structure\n\u2013 The table structure in the format\nUserID UInt64\n, \nURL String\n. Defines the column names and types.\n\n\nThe files specified in 'file' will be parsed by the format specified in 'format', using the data types specified in 'types' or 'structure'. The table will be uploaded to the server and accessible there as a temporary table with the name in 'name'.\n\n\nExamples:\n\n\necho\n -ne \n1\\n2\\n3\\n\n \n|\n clickhouse-client --query\n=\nSELECT count() FROM test.visits WHERE TraficSourceID IN _data\n --external --file\n=\n- --types\n=\nInt8\n\n849897\n\ncat /etc/passwd \n|\n sed \ns/:/\\t/g\n \n|\n clickhouse-client --query\n=\nSELECT shell, count() AS c FROM passwd GROUP BY shell ORDER BY c DESC\n --external --file\n=\n- --name\n=\npasswd --structure\n=\nlogin String, unused String, uid UInt16, gid UInt16, comment String, home String, shell String\n\n/bin/sh \n20\n\n/bin/false \n5\n\n/bin/bash \n4\n\n/usr/sbin/nologin \n1\n\n/bin/sync \n1\n\n\n\n\n\n\nWhen using the HTTP interface, external data is passed in the multipart/form-data format. Each table is transmitted as a separate file. The table name is taken from the file name. The 'query_string' is passed the parameters 'name_format', 'name_types', and 'name_structure', where 'name' is the name of the table that these parameters correspond to. The meaning of the parameters is the same as when using the command-line client.\n\n\nExample:\n\n\ncat /etc/passwd \n|\n sed \ns/:/\\t/g\n \n passwd.tsv\n\ncurl -F \npasswd=@passwd.tsv;\n \nhttp://localhost:8123/?query=SELECT+shell,+count()+AS+c+FROM+passwd+GROUP+BY+shell+ORDER+BY+c+DESC\npasswd_structure=login+String,+unused+String,+uid+UInt16,+gid+UInt16,+comment+String,+home+String,+shell+String\n\n/bin/sh \n20\n\n/bin/false \n5\n\n/bin/bash \n4\n\n/usr/sbin/nologin \n1\n\n/bin/sync \n1\n\n\n\n\n\n\nFor distributed query processing, the temporary tables are sent to all the remote servers.", - "title": "External data for query processing" - }, - { - "location": "/table_engines/external_data/#external-data-for-query-processing", - "text": "ClickHouse allows sending a server the data that is needed for processing a query, together with a SELECT query. This data is put in a temporary table (see the section \"Temporary tables\") and can be used in the query (for example, in IN operators). For example, if you have a text file with important user identifiers, you can upload it to the server along with a query that uses filtration by this list. If you need to run more than one query with a large volume of external data, don't use this feature. It is better to upload the data to the DB ahead of time. External data can be uploaded using the command-line client (in non-interactive mode), or using the HTTP interface. In the command-line client, you can specify a parameters section in the format --external --file = ... [ --name = ... ] [ --format = ... ] [ --types = ... | --structure = ... ] You may have multiple sections like this, for the number of tables being transmitted. --external \u2013 Marks the beginning of a clause. --file \u2013 Path to the file with the table dump, or -, which refers to stdin.\nOnly a single table can be retrieved from stdin. The following parameters are optional: --name \u2013 Name of the table. If omitted, _data is used. --format \u2013 Data format in the file. If omitted, TabSeparated is used. One of the following parameters is required: --types \u2013 A list of comma-separated column types. For example: UInt64,String . The columns will be named _1, _2, ... --structure \u2013 The table structure in the format UserID UInt64 , URL String . Defines the column names and types. The files specified in 'file' will be parsed by the format specified in 'format', using the data types specified in 'types' or 'structure'. The table will be uploaded to the server and accessible there as a temporary table with the name in 'name'. Examples: echo -ne 1\\n2\\n3\\n | clickhouse-client --query = SELECT count() FROM test.visits WHERE TraficSourceID IN _data --external --file = - --types = Int8 849897 \ncat /etc/passwd | sed s/:/\\t/g | clickhouse-client --query = SELECT shell, count() AS c FROM passwd GROUP BY shell ORDER BY c DESC --external --file = - --name = passwd --structure = login String, unused String, uid UInt16, gid UInt16, comment String, home String, shell String \n/bin/sh 20 \n/bin/false 5 \n/bin/bash 4 \n/usr/sbin/nologin 1 \n/bin/sync 1 When using the HTTP interface, external data is passed in the multipart/form-data format. Each table is transmitted as a separate file. The table name is taken from the file name. The 'query_string' is passed the parameters 'name_format', 'name_types', and 'name_structure', where 'name' is the name of the table that these parameters correspond to. The meaning of the parameters is the same as when using the command-line client. Example: cat /etc/passwd | sed s/:/\\t/g passwd.tsv\n\ncurl -F passwd=@passwd.tsv; http://localhost:8123/?query=SELECT+shell,+count()+AS+c+FROM+passwd+GROUP+BY+shell+ORDER+BY+c+DESC passwd_structure=login+String,+unused+String,+uid+UInt16,+gid+UInt16,+comment+String,+home+String,+shell+String \n/bin/sh 20 \n/bin/false 5 \n/bin/bash 4 \n/usr/sbin/nologin 1 \n/bin/sync 1 For distributed query processing, the temporary tables are sent to all the remote servers.", - "title": "External data for query processing" - }, - { - "location": "/system_tables/", - "text": "System tables\n\n\nSystem tables are used for implementing part of the system's functionality, and for providing access to information about how the system is working.\nYou can't delete a system table (but you can perform DETACH).\nSystem tables don't have files with data on the disk or files with metadata. The server creates all the system tables when it starts.\nSystem tables are read-only.\nThey are located in the 'system' database.", - "title": "Introduction" - }, - { - "location": "/system_tables/#system-tables", - "text": "System tables are used for implementing part of the system's functionality, and for providing access to information about how the system is working.\nYou can't delete a system table (but you can perform DETACH).\nSystem tables don't have files with data on the disk or files with metadata. The server creates all the system tables when it starts.\nSystem tables are read-only.\nThey are located in the 'system' database.", - "title": "System tables" - }, - { - "location": "/system_tables/system.one/", - "text": "system.one\n\n\nThis table contains a single row with a single 'dummy' UInt8 column containing the value 0.\nThis table is used if a SELECT query doesn't specify the FROM clause.\nThis is similar to the DUAL table found in other DBMSs.", - "title": "system.one" - }, - { - "location": "/system_tables/system.one/#systemone", - "text": "This table contains a single row with a single 'dummy' UInt8 column containing the value 0.\nThis table is used if a SELECT query doesn't specify the FROM clause.\nThis is similar to the DUAL table found in other DBMSs.", - "title": "system.one" - }, - { - "location": "/system_tables/system.numbers/", - "text": "system.numbers\n\n\nThis table contains a single UInt64 column named 'number' that contains almost all the natural numbers starting from zero.\nYou can use this table for tests, or if you need to do a brute force search.\nReads from this table are not parallelized.", - "title": "system.numbers" - }, - { - "location": "/system_tables/system.numbers/#systemnumbers", - "text": "This table contains a single UInt64 column named 'number' that contains almost all the natural numbers starting from zero.\nYou can use this table for tests, or if you need to do a brute force search.\nReads from this table are not parallelized.", - "title": "system.numbers" - }, - { - "location": "/system_tables/system.numbers_mt/", - "text": "system.numbers_mt\n\n\nThe same as 'system.numbers' but reads are parallelized. The numbers can be returned in any order.\nUsed for tests.", - "title": "system.numbers_mt" - }, - { - "location": "/system_tables/system.numbers_mt/#systemnumbers_mt", - "text": "The same as 'system.numbers' but reads are parallelized. The numbers can be returned in any order.\nUsed for tests.", - "title": "system.numbers_mt" - }, - { - "location": "/system_tables/system.databases/", - "text": "system.databases\n\n\nThis table contains a single String column called 'name' \u2013 the name of a database.\nEach database that the server knows about has a corresponding entry in the table.\nThis system table is used for implementing the \nSHOW DATABASES\n query.", - "title": "system.databases" - }, - { - "location": "/system_tables/system.databases/#systemdatabases", - "text": "This table contains a single String column called 'name' \u2013 the name of a database.\nEach database that the server knows about has a corresponding entry in the table.\nThis system table is used for implementing the SHOW DATABASES query.", - "title": "system.databases" - }, - { - "location": "/system_tables/system.tables/", - "text": "system.tables\n\n\nThis table contains the String columns 'database', 'name', and 'engine'.\nThe table also contains three virtual columns: metadata_modification_time (DateTime type), create_table_query, and engine_full (String type).\nEach table that the server knows about is entered in the 'system.tables' table.\nThis system table is used for implementing SHOW TABLES queries.", - "title": "system.tables" - }, - { - "location": "/system_tables/system.tables/#systemtables", - "text": "This table contains the String columns 'database', 'name', and 'engine'.\nThe table also contains three virtual columns: metadata_modification_time (DateTime type), create_table_query, and engine_full (String type).\nEach table that the server knows about is entered in the 'system.tables' table.\nThis system table is used for implementing SHOW TABLES queries.", - "title": "system.tables" - }, - { - "location": "/system_tables/system.columns/", - "text": "system.columns\n\n\nContains information about the columns in all tables.\nYou can use this table to get information similar to \nDESCRIBE TABLE\n, but for multiple tables at once.\n\n\ndatabase String - Name of the database the table is located in.\ntable String - Table name.\nname String - Column name.\ntype String - Column type.\ndefault_type String - Expression type (DEFAULT, MATERIALIZED, ALIAS) for the default value, or an empty string if it is not defined.\ndefault_expression String - Expression for the default value, or an empty string if it is not defined.", - "title": "system.columns" - }, - { - "location": "/system_tables/system.columns/#systemcolumns", - "text": "Contains information about the columns in all tables.\nYou can use this table to get information similar to DESCRIBE TABLE , but for multiple tables at once. database String - Name of the database the table is located in.\ntable String - Table name.\nname String - Column name.\ntype String - Column type.\ndefault_type String - Expression type (DEFAULT, MATERIALIZED, ALIAS) for the default value, or an empty string if it is not defined.\ndefault_expression String - Expression for the default value, or an empty string if it is not defined.", - "title": "system.columns" - }, - { - "location": "/system_tables/system.parts/", - "text": "system.parts\n\n\nContains information about parts of a table in the \nMergeTree\n family.\n\n\nEach row describes one part of the data.\n\n\nColumns:\n\n\n\n\npartition (String) \u2013 The partition name. YYYYMM format. To learn what a partition is, see the description of the \nALTER\n query.\n\n\nname (String) \u2013 Name of the data part.\n\n\nactive (UInt8) \u2013 Indicates whether the part is active. If a part is active, it is used in a table; otherwise, it will be deleted. Inactive data parts remain after merging.\n\n\nmarks (UInt64) \u2013 The number of marks. To get the approximate number of rows in a data part, multiply \nmarks\n by the index granularity (usually 8192).\n\n\nmarks_size (UInt64) \u2013 The size of the file with marks.\n\n\nrows (UInt64) \u2013 The number of rows.\n\n\nbytes (UInt64) \u2013 The number of bytes when compressed.\n\n\nmodification_time (DateTime) \u2013 The modification time of the directory with the data part. This usually corresponds to the time of data part creation.|\n\n\nremove_time (DateTime) \u2013 The time when the data part became inactive.\n\n\nrefcount (UInt32) \u2013 The number of places where the data part is used. A value greater than 2 indicates that the data part is used in queries or merges.\n\n\nmin_date (Date) \u2013 The minimum value of the date key in the data part.\n\n\nmax_date (Date) \u2013 The maximum value of the date key in the data part.\n\n\nmin_block_number (UInt64) \u2013 The minimum number of data parts that make up the current part after merging.\n\n\nmax_block_number (UInt64) \u2013 The maximum number of data parts that make up the current part after merging.\n\n\nlevel (UInt32) \u2013 Depth of the merge tree. If a merge was not performed, \nlevel=0\n.\n\n\nprimary_key_bytes_in_memory (UInt64) \u2013 The amount of memory (in bytes) used by primary key values.\n\n\nprimary_key_bytes_in_memory_allocated (UInt64) \u2013 The amount of memory (in bytes) reserved for primary key values.\n\n\ndatabase (String) \u2013 Name of the database.\n\n\ntable (String) \u2013 Name of the table.\n\n\nengine (String) \u2013 Name of the table engine without parameters.", - "title": "system.parts" - }, - { - "location": "/system_tables/system.parts/#systemparts", - "text": "Contains information about parts of a table in the MergeTree family. Each row describes one part of the data. Columns: partition (String) \u2013 The partition name. YYYYMM format. To learn what a partition is, see the description of the ALTER query. name (String) \u2013 Name of the data part. active (UInt8) \u2013 Indicates whether the part is active. If a part is active, it is used in a table; otherwise, it will be deleted. Inactive data parts remain after merging. marks (UInt64) \u2013 The number of marks. To get the approximate number of rows in a data part, multiply marks by the index granularity (usually 8192). marks_size (UInt64) \u2013 The size of the file with marks. rows (UInt64) \u2013 The number of rows. bytes (UInt64) \u2013 The number of bytes when compressed. modification_time (DateTime) \u2013 The modification time of the directory with the data part. This usually corresponds to the time of data part creation.| remove_time (DateTime) \u2013 The time when the data part became inactive. refcount (UInt32) \u2013 The number of places where the data part is used. A value greater than 2 indicates that the data part is used in queries or merges. min_date (Date) \u2013 The minimum value of the date key in the data part. max_date (Date) \u2013 The maximum value of the date key in the data part. min_block_number (UInt64) \u2013 The minimum number of data parts that make up the current part after merging. max_block_number (UInt64) \u2013 The maximum number of data parts that make up the current part after merging. level (UInt32) \u2013 Depth of the merge tree. If a merge was not performed, level=0 . primary_key_bytes_in_memory (UInt64) \u2013 The amount of memory (in bytes) used by primary key values. primary_key_bytes_in_memory_allocated (UInt64) \u2013 The amount of memory (in bytes) reserved for primary key values. database (String) \u2013 Name of the database. table (String) \u2013 Name of the table. engine (String) \u2013 Name of the table engine without parameters.", - "title": "system.parts" - }, - { - "location": "/system_tables/system.processes/", - "text": "system.processes\n\n\nThis system table is used for implementing the \nSHOW PROCESSLIST\n query.\nColumns:\n\n\nuser String \u2013 Name of the user who made the request. For distributed query processing, this is the user who helped the requestor server send the query to this server, not the user who made the distributed request on the requestor server.\n\naddress String \u2013 The IP address that the query was made from. The same is true for distributed query processing.\n\nelapsed Float64 \u2013 The time in seconds since request execution started.\n\nrows_read UInt64 \u2013 The number of rows read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nbytes_read UInt64 \u2013 The number of uncompressed bytes read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nUInt64 total_rows_approx \u2013 The approximate total number of rows that must be read. For distributed processing, on the requestor server, this is the total for all remote servers. It can be updated during request processing, when new sources to process become known.\n\nmemory_usage UInt64 \u2013 Memory consumption by the query. It might not include some types of dedicated memory.\n\nquery String \u2013 The query text. For INSERT, it doesn\nt include the data to insert.\n\nquery_id \u2013 Query ID, if defined.", - "title": "system.processes" - }, - { - "location": "/system_tables/system.processes/#systemprocesses", - "text": "This system table is used for implementing the SHOW PROCESSLIST query.\nColumns: user String \u2013 Name of the user who made the request. For distributed query processing, this is the user who helped the requestor server send the query to this server, not the user who made the distributed request on the requestor server.\n\naddress String \u2013 The IP address that the query was made from. The same is true for distributed query processing.\n\nelapsed Float64 \u2013 The time in seconds since request execution started.\n\nrows_read UInt64 \u2013 The number of rows read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nbytes_read UInt64 \u2013 The number of uncompressed bytes read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nUInt64 total_rows_approx \u2013 The approximate total number of rows that must be read. For distributed processing, on the requestor server, this is the total for all remote servers. It can be updated during request processing, when new sources to process become known.\n\nmemory_usage UInt64 \u2013 Memory consumption by the query. It might not include some types of dedicated memory.\n\nquery String \u2013 The query text. For INSERT, it doesn t include the data to insert.\n\nquery_id \u2013 Query ID, if defined.", - "title": "system.processes" - }, - { - "location": "/system_tables/system.merges/", - "text": "system.merges\n\n\nContains information about merges currently in process for tables in the MergeTree family.\n\n\nColumns:\n\n\n\n\ndatabase String\n \u2014 Name of the database the table is located in.\n\n\ntable String\n \u2014 Name of the table.\n\n\nelapsed Float64\n \u2014 Time in seconds since the merge started.\n\n\nprogress Float64\n \u2014 Percent of progress made, from 0 to 1.\n\n\nnum_parts UInt64\n \u2014 Number of parts to merge.\n\n\nresult_part_name String\n \u2014 Name of the part that will be formed as the result of the merge.\n\n\ntotal_size_bytes_compressed UInt64\n \u2014 Total size of compressed data in the parts being merged.\n\n\ntotal_size_marks UInt64\n \u2014 Total number of marks in the parts being merged.\n\n\nbytes_read_uncompressed UInt64\n \u2014 Amount of bytes read, decompressed.\n\n\nrows_read UInt64\n \u2014 Number of rows read.\n\n\nbytes_written_uncompressed UInt64\n \u2014 Amount of bytes written, uncompressed.\n\n\nrows_written UInt64\n \u2014 Number of rows written.", - "title": "system.merges" - }, - { - "location": "/system_tables/system.merges/#systemmerges", - "text": "Contains information about merges currently in process for tables in the MergeTree family. Columns: database String \u2014 Name of the database the table is located in. table String \u2014 Name of the table. elapsed Float64 \u2014 Time in seconds since the merge started. progress Float64 \u2014 Percent of progress made, from 0 to 1. num_parts UInt64 \u2014 Number of parts to merge. result_part_name String \u2014 Name of the part that will be formed as the result of the merge. total_size_bytes_compressed UInt64 \u2014 Total size of compressed data in the parts being merged. total_size_marks UInt64 \u2014 Total number of marks in the parts being merged. bytes_read_uncompressed UInt64 \u2014 Amount of bytes read, decompressed. rows_read UInt64 \u2014 Number of rows read. bytes_written_uncompressed UInt64 \u2014 Amount of bytes written, uncompressed. rows_written UInt64 \u2014 Number of rows written.", - "title": "system.merges" - }, - { - "location": "/system_tables/system.events/", - "text": "system.events\n\n\nContains information about the number of events that have occurred in the system. This is used for profiling and monitoring purposes.\nExample: The number of processed SELECT queries.\nColumns: 'event String' \u2013 the event name, and 'value UInt64' \u2013 the quantity.", - "title": "system.events" - }, - { - "location": "/system_tables/system.events/#systemevents", - "text": "Contains information about the number of events that have occurred in the system. This is used for profiling and monitoring purposes.\nExample: The number of processed SELECT queries.\nColumns: 'event String' \u2013 the event name, and 'value UInt64' \u2013 the quantity.", - "title": "system.events" - }, - { - "location": "/system_tables/system.metrics/", - "text": "system.metrics", - "title": "system.metrics" - }, - { - "location": "/system_tables/system.metrics/#systemmetrics", - "text": "", - "title": "system.metrics" - }, - { - "location": "/system_tables/system.asynchronous_metrics/", - "text": "system.asynchronous_metrics\n\n\nContain metrics used for profiling and monitoring.\nThey usually reflect the number of events currently in the system, or the total resources consumed by the system.\nExample: The number of SELECT queries currently running; the amount of memory in use.\nsystem.asynchronous_metrics\nand\nsystem.metrics\n differ in their sets of metrics and how they are calculated.", - "title": "system.asynchronous_metrics" - }, - { - "location": "/system_tables/system.asynchronous_metrics/#systemasynchronous_metrics", - "text": "Contain metrics used for profiling and monitoring.\nThey usually reflect the number of events currently in the system, or the total resources consumed by the system.\nExample: The number of SELECT queries currently running; the amount of memory in use. system.asynchronous_metrics and system.metrics differ in their sets of metrics and how they are calculated.", - "title": "system.asynchronous_metrics" - }, - { - "location": "/system_tables/system.replicas/", - "text": "system.replicas\n\n\nContains information and status for replicated tables residing on the local server.\nThis table can be used for monitoring. The table contains a row for every Replicated* table.\n\n\nExample:\n\n\nSELECT\n \n*\n\n\nFROM\n \nsystem\n.\nreplicas\n\n\nWHERE\n \ntable\n \n=\n \nvisits\n\n\nFORMAT\n \nVertical\n\n\n\n\n\n\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ndatabase: merge\ntable: visits\nengine: ReplicatedCollapsingMergeTree\nis_leader: 1\nis_readonly: 0\nis_session_expired: 0\nfuture_parts: 1\nparts_to_check: 0\nzookeeper_path: /clickhouse/tables/01-06/visits\nreplica_name: example01-06-1.yandex.ru\nreplica_path: /clickhouse/tables/01-06/visits/replicas/example01-06-1.yandex.ru\ncolumns_version: 9\nqueue_size: 1\ninserts_in_queue: 0\nmerges_in_queue: 1\nlog_max_index: 596273\nlog_pointer: 596274\ntotal_replicas: 2\nactive_replicas: 2\n\n\n\n\n\nColumns:\n\n\ndatabase: database name\ntable: table name\nengine: table engine name\n\nis_leader: whether the replica is the leader\n\nOnly one replica at a time can be the leader. The leader is responsible for selecting background merges to perform.\nNote that writes can be performed to any replica that is available and has a session in ZK, regardless of whether it is a leader.\n\nis_readonly: Whether the replica is in read-only mode.\nThis mode is turned on if the config doesn\nt have sections with ZK, if an unknown error occurred when reinitializing sessions in ZK, and during session reinitialization in ZK.\n\nis_session_expired: Whether the ZK session expired.\nBasically, the same thing as is_readonly.\n\nfuture_parts: The number of data parts that will appear as the result of INSERTs or merges that haven\nt been done yet. \n\nparts_to_check: The number of data parts in the queue for verification.\nA part is put in the verification queue if there is suspicion that it might be damaged.\n\nzookeeper_path: The path to the table data in ZK. \nreplica_name: Name of the replica in ZK. Different replicas of the same table have different names. \nreplica_path: The path to the replica data in ZK. The same as concatenating zookeeper_path/replicas/replica_path.\n\ncolumns_version: Version number of the table structure.\nIndicates how many times ALTER was performed. If replicas have different versions, it means some replicas haven\nt made all of the ALTERs yet.\n\nqueue_size: Size of the queue for operations waiting to be performed.\nOperations include inserting blocks of data, merges, and certain other actions.\nNormally coincides with future_parts.\n\ninserts_in_queue: Number of inserts of blocks of data that need to be made.\nInsertions are usually replicated fairly quickly. If the number is high, something is wrong.\n\nmerges_in_queue: The number of merges waiting to be made. \nSometimes merges are lengthy, so this value may be greater than zero for a long time.\n\nThe next 4 columns have a non-null value only if the ZK session is active.\n\nlog_max_index: Maximum entry number in the log of general activity.\nlog_pointer: Maximum entry number in the log of general activity that the replica copied to its execution queue, plus one.\nIf log_pointer is much smaller than log_max_index, something is wrong.\n\ntotal_replicas: Total number of known replicas of this table.\nactive_replicas: Number of replicas of this table that have a ZK session (the number of active replicas).\n\n\n\n\n\nIf you request all the columns, the table may work a bit slowly, since several reads from ZK are made for each row.\nIf you don't request the last 4 columns (log_max_index, log_pointer, total_replicas, active_replicas), the table works quickly.\n\n\nFor example, you can check that everything is working correctly like this:\n\n\nSELECT\n\n \ndatabase\n,\n\n \ntable\n,\n\n \nis_leader\n,\n\n \nis_readonly\n,\n\n \nis_session_expired\n,\n\n \nfuture_parts\n,\n\n \nparts_to_check\n,\n\n \ncolumns_version\n,\n\n \nqueue_size\n,\n\n \ninserts_in_queue\n,\n\n \nmerges_in_queue\n,\n\n \nlog_max_index\n,\n\n \nlog_pointer\n,\n\n \ntotal_replicas\n,\n\n \nactive_replicas\n\n\nFROM\n \nsystem\n.\nreplicas\n\n\nWHERE\n\n \nis_readonly\n\n \nOR\n \nis_session_expired\n\n \nOR\n \nfuture_parts\n \n \n20\n\n \nOR\n \nparts_to_check\n \n \n10\n\n \nOR\n \nqueue_size\n \n \n20\n\n \nOR\n \ninserts_in_queue\n \n \n10\n\n \nOR\n \nlog_max_index\n \n-\n \nlog_pointer\n \n \n10\n\n \nOR\n \ntotal_replicas\n \n \n2\n\n \nOR\n \nactive_replicas\n \n \ntotal_replicas\n\n\n\n\n\n\nIf this query doesn't return anything, it means that everything is fine.", - "title": "system.replicas" - }, - { - "location": "/system_tables/system.replicas/#systemreplicas", - "text": "Contains information and status for replicated tables residing on the local server.\nThis table can be used for monitoring. The table contains a row for every Replicated* table. Example: SELECT * FROM system . replicas WHERE table = visits FORMAT Vertical Row 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ndatabase: merge\ntable: visits\nengine: ReplicatedCollapsingMergeTree\nis_leader: 1\nis_readonly: 0\nis_session_expired: 0\nfuture_parts: 1\nparts_to_check: 0\nzookeeper_path: /clickhouse/tables/01-06/visits\nreplica_name: example01-06-1.yandex.ru\nreplica_path: /clickhouse/tables/01-06/visits/replicas/example01-06-1.yandex.ru\ncolumns_version: 9\nqueue_size: 1\ninserts_in_queue: 0\nmerges_in_queue: 1\nlog_max_index: 596273\nlog_pointer: 596274\ntotal_replicas: 2\nactive_replicas: 2 Columns: database: database name\ntable: table name\nengine: table engine name\n\nis_leader: whether the replica is the leader\n\nOnly one replica at a time can be the leader. The leader is responsible for selecting background merges to perform.\nNote that writes can be performed to any replica that is available and has a session in ZK, regardless of whether it is a leader.\n\nis_readonly: Whether the replica is in read-only mode.\nThis mode is turned on if the config doesn t have sections with ZK, if an unknown error occurred when reinitializing sessions in ZK, and during session reinitialization in ZK.\n\nis_session_expired: Whether the ZK session expired.\nBasically, the same thing as is_readonly.\n\nfuture_parts: The number of data parts that will appear as the result of INSERTs or merges that haven t been done yet. \n\nparts_to_check: The number of data parts in the queue for verification.\nA part is put in the verification queue if there is suspicion that it might be damaged.\n\nzookeeper_path: The path to the table data in ZK. \nreplica_name: Name of the replica in ZK. Different replicas of the same table have different names. \nreplica_path: The path to the replica data in ZK. The same as concatenating zookeeper_path/replicas/replica_path.\n\ncolumns_version: Version number of the table structure.\nIndicates how many times ALTER was performed. If replicas have different versions, it means some replicas haven t made all of the ALTERs yet.\n\nqueue_size: Size of the queue for operations waiting to be performed.\nOperations include inserting blocks of data, merges, and certain other actions.\nNormally coincides with future_parts.\n\ninserts_in_queue: Number of inserts of blocks of data that need to be made.\nInsertions are usually replicated fairly quickly. If the number is high, something is wrong.\n\nmerges_in_queue: The number of merges waiting to be made. \nSometimes merges are lengthy, so this value may be greater than zero for a long time.\n\nThe next 4 columns have a non-null value only if the ZK session is active.\n\nlog_max_index: Maximum entry number in the log of general activity.\nlog_pointer: Maximum entry number in the log of general activity that the replica copied to its execution queue, plus one.\nIf log_pointer is much smaller than log_max_index, something is wrong.\n\ntotal_replicas: Total number of known replicas of this table.\nactive_replicas: Number of replicas of this table that have a ZK session (the number of active replicas). If you request all the columns, the table may work a bit slowly, since several reads from ZK are made for each row.\nIf you don't request the last 4 columns (log_max_index, log_pointer, total_replicas, active_replicas), the table works quickly. For example, you can check that everything is working correctly like this: SELECT \n database , \n table , \n is_leader , \n is_readonly , \n is_session_expired , \n future_parts , \n parts_to_check , \n columns_version , \n queue_size , \n inserts_in_queue , \n merges_in_queue , \n log_max_index , \n log_pointer , \n total_replicas , \n active_replicas FROM system . replicas WHERE \n is_readonly \n OR is_session_expired \n OR future_parts 20 \n OR parts_to_check 10 \n OR queue_size 20 \n OR inserts_in_queue 10 \n OR log_max_index - log_pointer 10 \n OR total_replicas 2 \n OR active_replicas total_replicas If this query doesn't return anything, it means that everything is fine.", - "title": "system.replicas" - }, - { - "location": "/system_tables/system.dictionaries/", - "text": "system.dictionaries\n\n\nContains information about external dictionaries.\n\n\nColumns:\n\n\n\n\nname String\n \u2013 Dictionary name.\n\n\ntype String\n \u2013 Dictionary type: Flat, Hashed, Cache.\n\n\norigin String\n \u2013 Path to the config file where the dictionary is described.\n\n\nattribute.names Array(String)\n \u2013 Array of attribute names provided by the dictionary.\n\n\nattribute.types Array(String)\n \u2013 Corresponding array of attribute types provided by the dictionary.\n\n\nhas_hierarchy UInt8\n \u2013 Whether the dictionary is hierarchical.\n\n\nbytes_allocated UInt64\n \u2013 The amount of RAM used by the dictionary.\n\n\nhit_rate Float64\n \u2013 For cache dictionaries, the percent of usage for which the value was in the cache.\n\n\nelement_count UInt64\n \u2013 The number of items stored in the dictionary.\n\n\nload_factor Float64\n \u2013 The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table).\n\n\ncreation_time DateTime\n \u2013 Time spent for the creation or last successful reload of the dictionary.\n\n\nlast_exception String\n \u2013 Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created.\n\n\nsource String\n \u2013 Text describing the data source for the dictionary.\n\n\n\n\nNote that the amount of memory used by the dictionary is not proportional to the number of items stored in it. So for flat and cached dictionaries, all the memory cells are pre-assigned, regardless of how full the dictionary actually is.", - "title": "system.dictionaries" - }, - { - "location": "/system_tables/system.dictionaries/#systemdictionaries", - "text": "Contains information about external dictionaries. Columns: name String \u2013 Dictionary name. type String \u2013 Dictionary type: Flat, Hashed, Cache. origin String \u2013 Path to the config file where the dictionary is described. attribute.names Array(String) \u2013 Array of attribute names provided by the dictionary. attribute.types Array(String) \u2013 Corresponding array of attribute types provided by the dictionary. has_hierarchy UInt8 \u2013 Whether the dictionary is hierarchical. bytes_allocated UInt64 \u2013 The amount of RAM used by the dictionary. hit_rate Float64 \u2013 For cache dictionaries, the percent of usage for which the value was in the cache. element_count UInt64 \u2013 The number of items stored in the dictionary. load_factor Float64 \u2013 The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table). creation_time DateTime \u2013 Time spent for the creation or last successful reload of the dictionary. last_exception String \u2013 Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created. source String \u2013 Text describing the data source for the dictionary. Note that the amount of memory used by the dictionary is not proportional to the number of items stored in it. So for flat and cached dictionaries, all the memory cells are pre-assigned, regardless of how full the dictionary actually is.", - "title": "system.dictionaries" - }, - { - "location": "/system_tables/system.clusters/", - "text": "system.clusters\n\n\nContains information about clusters available in the config file and the servers in them.\nColumns:\n\n\ncluster String \u2013 Cluster name.\nshard_num UInt32 \u2013 Number of a shard in the cluster, starting from 1.\nshard_weight UInt32 \u2013 Relative weight of a shard when writing data.\nreplica_num UInt32 \u2013 Number of a replica in the shard, starting from 1.\nhost_name String \u2013 Host name as specified in the config.\nhost_address String \u2013 Host\ns IP address obtained from DNS.\nport UInt16 \u2013 The port used to access the server.\nuser String \u2013 The username to use for connecting to the server.", - "title": "system.clusters" - }, - { - "location": "/system_tables/system.clusters/#systemclusters", - "text": "Contains information about clusters available in the config file and the servers in them.\nColumns: cluster String \u2013 Cluster name.\nshard_num UInt32 \u2013 Number of a shard in the cluster, starting from 1.\nshard_weight UInt32 \u2013 Relative weight of a shard when writing data.\nreplica_num UInt32 \u2013 Number of a replica in the shard, starting from 1.\nhost_name String \u2013 Host name as specified in the config.\nhost_address String \u2013 Host s IP address obtained from DNS.\nport UInt16 \u2013 The port used to access the server.\nuser String \u2013 The username to use for connecting to the server.", - "title": "system.clusters" - }, - { - "location": "/system_tables/system.functions/", - "text": "system.functions\n\n\nContains information about normal and aggregate functions.\n\n\nColumns:\n\n\n\n\nname\n (\nString\n) \u2013 Function name.\n\n\nis_aggregate\n (\nUInt8\n) \u2013 Whether it is an aggregate function.", - "title": "system.functions" - }, - { - "location": "/system_tables/system.functions/#systemfunctions", - "text": "Contains information about normal and aggregate functions. Columns: name ( String ) \u2013 Function name. is_aggregate ( UInt8 ) \u2013 Whether it is an aggregate function.", - "title": "system.functions" - }, - { - "location": "/system_tables/system.settings/", - "text": "system.settings\n\n\nContains information about settings that are currently in use.\nI.e. used for executing the query you are using to read from the system.settings table).\n\n\nColumns:\n\n\nname String \u2013 Setting name.\nvalue String \u2013 Setting value.\nchanged UInt8 - Whether the setting was explicitly defined in the config or explicitly changed.\n\n\n\n\n\nExample:\n\n\nSELECT\n \n*\n\n\nFROM\n \nsystem\n.\nsettings\n\n\nWHERE\n \nchanged\n\n\n\n\n\n\n\u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500value\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500changed\u2500\u2510\n\u2502 max_threads \u2502 8 \u2502 1 \u2502\n\u2502 use_uncompressed_cache \u2502 0 \u2502 1 \u2502\n\u2502 load_balancing \u2502 random \u2502 1 \u2502\n\u2502 max_memory_usage \u2502 10000000000 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "system.settings" - }, - { - "location": "/system_tables/system.settings/#systemsettings", - "text": "Contains information about settings that are currently in use.\nI.e. used for executing the query you are using to read from the system.settings table). Columns: name String \u2013 Setting name.\nvalue String \u2013 Setting value.\nchanged UInt8 - Whether the setting was explicitly defined in the config or explicitly changed. Example: SELECT * FROM system . settings WHERE changed \u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500value\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500changed\u2500\u2510\n\u2502 max_threads \u2502 8 \u2502 1 \u2502\n\u2502 use_uncompressed_cache \u2502 0 \u2502 1 \u2502\n\u2502 load_balancing \u2502 random \u2502 1 \u2502\n\u2502 max_memory_usage \u2502 10000000000 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "system.settings" - }, - { - "location": "/system_tables/system.zookeeper/", - "text": "system.zookeeper\n\n\nAllows reading data from the ZooKeeper cluster defined in the config.\nThe query must have a 'path' equality condition in the WHERE clause. This is the path in ZooKeeper for the children that you want to get data for.\n\n\nThe query \nSELECT * FROM system.zookeeper WHERE path = '/clickhouse'\n outputs data for all children on the \n/clickhouse\n node.\nTo output data for all root nodes, write path = '/'.\nIf the path specified in 'path' doesn't exist, an exception will be thrown.\n\n\nColumns:\n\n\n\n\nname String\n \u2014 Name of the node.\n\n\npath String\n \u2014 Path to the node.\n\n\nvalue String\n \u2014 Value of the node.\n\n\ndataLength Int32\n \u2014 Size of the value.\n\n\nnumChildren Int32\n \u2014 Number of children.\n\n\nczxid Int64\n \u2014 ID of the transaction that created the node.\n\n\nmzxid Int64\n \u2014 ID of the transaction that last changed the node.\n\n\npzxid Int64\n \u2014 ID of the transaction that last added or removed children.\n\n\nctime DateTime\n \u2014 Time of node creation.\n\n\nmtime DateTime\n \u2014 Time of the last node modification.\n\n\nversion Int32\n \u2014 Node version - the number of times the node was changed.\n\n\ncversion Int32\n \u2014 Number of added or removed children.\n\n\naversion Int32\n \u2014 Number of changes to ACL.\n\n\nephemeralOwner Int64\n \u2014 For ephemeral nodes, the ID of the session that owns this node.\n\n\n\n\nExample:\n\n\nSELECT\n \n*\n\n\nFROM\n \nsystem\n.\nzookeeper\n\n\nWHERE\n \npath\n \n=\n \n/clickhouse/tables/01-08/visits/replicas\n\n\nFORMAT\n \nVertical\n\n\n\n\n\n\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-1.yandex.ru\nvalue:\nczxid: 932998691229\nmzxid: 932998691229\nctime: 2015-03-27 16:49:51\nmtime: 2015-03-27 16:49:51\nversion: 0\ncversion: 47\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021031383\npath: /clickhouse/tables/01-08/visits/replicas\n\nRow 2:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-2.yandex.ru\nvalue:\nczxid: 933002738135\nmzxid: 933002738135\nctime: 2015-03-27 16:57:01\nmtime: 2015-03-27 16:57:01\nversion: 0\ncversion: 37\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021252247\npath: /clickhouse/tables/01-08/visits/replicas", - "title": "system.zookeeper" - }, - { - "location": "/system_tables/system.zookeeper/#systemzookeeper", - "text": "Allows reading data from the ZooKeeper cluster defined in the config.\nThe query must have a 'path' equality condition in the WHERE clause. This is the path in ZooKeeper for the children that you want to get data for. The query SELECT * FROM system.zookeeper WHERE path = '/clickhouse' outputs data for all children on the /clickhouse node.\nTo output data for all root nodes, write path = '/'.\nIf the path specified in 'path' doesn't exist, an exception will be thrown. Columns: name String \u2014 Name of the node. path String \u2014 Path to the node. value String \u2014 Value of the node. dataLength Int32 \u2014 Size of the value. numChildren Int32 \u2014 Number of children. czxid Int64 \u2014 ID of the transaction that created the node. mzxid Int64 \u2014 ID of the transaction that last changed the node. pzxid Int64 \u2014 ID of the transaction that last added or removed children. ctime DateTime \u2014 Time of node creation. mtime DateTime \u2014 Time of the last node modification. version Int32 \u2014 Node version - the number of times the node was changed. cversion Int32 \u2014 Number of added or removed children. aversion Int32 \u2014 Number of changes to ACL. ephemeralOwner Int64 \u2014 For ephemeral nodes, the ID of the session that owns this node. Example: SELECT * FROM system . zookeeper WHERE path = /clickhouse/tables/01-08/visits/replicas FORMAT Vertical Row 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-1.yandex.ru\nvalue:\nczxid: 932998691229\nmzxid: 932998691229\nctime: 2015-03-27 16:49:51\nmtime: 2015-03-27 16:49:51\nversion: 0\ncversion: 47\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021031383\npath: /clickhouse/tables/01-08/visits/replicas\n\nRow 2:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-2.yandex.ru\nvalue:\nczxid: 933002738135\nmzxid: 933002738135\nctime: 2015-03-27 16:57:01\nmtime: 2015-03-27 16:57:01\nversion: 0\ncversion: 37\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021252247\npath: /clickhouse/tables/01-08/visits/replicas", - "title": "system.zookeeper" - }, - { - "location": "/table_functions/", - "text": "Table functions\n\n\nTable functions can be specified in the FROM clause instead of the database and table names.\nTable functions can only be used if 'readonly' is not set.\nTable functions aren't related to other functions.", - "title": "Introduction" - }, - { - "location": "/table_functions/#table-functions", - "text": "Table functions can be specified in the FROM clause instead of the database and table names.\nTable functions can only be used if 'readonly' is not set.\nTable functions aren't related to other functions.", - "title": "Table functions" - }, - { - "location": "/table_functions/remote/", - "text": "remote\n\n\nAllows you to access remote servers without creating a \nDistributed\n table.\n\n\nSignatures:\n\n\nremote\n(\naddresses_expr\n,\n \ndb\n,\n \ntable\n[,\n \nuser\n[,\n \npassword\n]])\n\n\nremote\n(\naddresses_expr\n,\n \ndb\n.\ntable\n[,\n \nuser\n[,\n \npassword\n]])\n\n\n\n\n\n\naddresses_expr\n \u2013 An expression that generates addresses of remote servers. This may be just one server address. The server address is \nhost:port\n, or just \nhost\n. The host can be specified as the server name, or as the IPv4 or IPv6 address. An IPv6 address is specified in square brackets. The port is the TCP port on the remote server. If the port is omitted, it uses \ntcp_port\n from the server's config file (by default, 9000).\n\n\n\n\nThe port is required for an IPv6 address.\n\n\n\n\n\nExamples:\n\n\nexample01-01-1\nexample01-01-1:9000\nlocalhost\n127.0.0.1\n[::]:9000\n[2a02:6b8:0:1111::11]:9000\n\n\n\n\n\nMultiple addresses can be comma-separated. In this case, ClickHouse will use distributed processing, so it will send the query to all specified addresses (like to shards with different data).\n\n\nExample:\n\n\nexample01-01-1,example01-02-1\n\n\n\n\n\nPart of the expression can be specified in curly brackets. The previous example can be written as follows:\n\n\nexample01-0{1,2}-1\n\n\n\n\n\nCurly brackets can contain a range of numbers separated by two dots (non-negative integers). In this case, the range is expanded to a set of values that generate shard addresses. If the first number starts with zero, the values are formed with the same zero alignment. The previous example can be written as follows:\n\n\nexample01-{01..02}-1\n\n\n\n\n\nIf you have multiple pairs of curly brackets, it generates the direct product of the corresponding sets.\n\n\nAddresses and parts of addresses in curly brackets can be separated by the pipe symbol (|). In this case, the corresponding sets of addresses are interpreted as replicas, and the query will be sent to the first healthy replica. However, the replicas are iterated in the order currently set in the \nload_balancing\n setting.\n\n\nExample:\n\n\nexample01-{01..02}-{1|2}\n\n\n\n\n\nThis example specifies two shards that each have two replicas.\n\n\nThe number of addresses generated is limited by a constant. Right now this is 1000 addresses.\n\n\nUsing the \nremote\n table function is less optimal than creating a \nDistributed\n table, because in this case, the server connection is re-established for every request. In addition, if host names are set, the names are resolved, and errors are not counted when working with various replicas. When processing a large number of queries, always create the \nDistributed\n table ahead of time, and don't use the \nremote\n table function.\n\n\nThe \nremote\n table function can be useful in the following cases:\n\n\n\n\nAccessing a specific server for data comparison, debugging, and testing.\n\n\nQueries between various ClickHouse clusters for research purposes.\n\n\nInfrequent distributed requests that are made manually.\n\n\nDistributed requests where the set of servers is re-defined each time.\n\n\n\n\nIf the user is not specified, \ndefault\n is used.\nIf the password is not specified, an empty password is used.", - "title": "remote" - }, - { - "location": "/table_functions/remote/#remote", - "text": "Allows you to access remote servers without creating a Distributed table. Signatures: remote ( addresses_expr , db , table [, user [, password ]]) remote ( addresses_expr , db . table [, user [, password ]]) addresses_expr \u2013 An expression that generates addresses of remote servers. This may be just one server address. The server address is host:port , or just host . The host can be specified as the server name, or as the IPv4 or IPv6 address. An IPv6 address is specified in square brackets. The port is the TCP port on the remote server. If the port is omitted, it uses tcp_port from the server's config file (by default, 9000). \n\nThe port is required for an IPv6 address. Examples: example01-01-1\nexample01-01-1:9000\nlocalhost\n127.0.0.1\n[::]:9000\n[2a02:6b8:0:1111::11]:9000 Multiple addresses can be comma-separated. In this case, ClickHouse will use distributed processing, so it will send the query to all specified addresses (like to shards with different data). Example: example01-01-1,example01-02-1 Part of the expression can be specified in curly brackets. The previous example can be written as follows: example01-0{1,2}-1 Curly brackets can contain a range of numbers separated by two dots (non-negative integers). In this case, the range is expanded to a set of values that generate shard addresses. If the first number starts with zero, the values are formed with the same zero alignment. The previous example can be written as follows: example01-{01..02}-1 If you have multiple pairs of curly brackets, it generates the direct product of the corresponding sets. Addresses and parts of addresses in curly brackets can be separated by the pipe symbol (|). In this case, the corresponding sets of addresses are interpreted as replicas, and the query will be sent to the first healthy replica. However, the replicas are iterated in the order currently set in the load_balancing setting. Example: example01-{01..02}-{1|2} This example specifies two shards that each have two replicas. The number of addresses generated is limited by a constant. Right now this is 1000 addresses. Using the remote table function is less optimal than creating a Distributed table, because in this case, the server connection is re-established for every request. In addition, if host names are set, the names are resolved, and errors are not counted when working with various replicas. When processing a large number of queries, always create the Distributed table ahead of time, and don't use the remote table function. The remote table function can be useful in the following cases: Accessing a specific server for data comparison, debugging, and testing. Queries between various ClickHouse clusters for research purposes. Infrequent distributed requests that are made manually. Distributed requests where the set of servers is re-defined each time. If the user is not specified, default is used.\nIf the password is not specified, an empty password is used.", - "title": "remote" - }, - { - "location": "/table_functions/merge/", - "text": "merge\n\n\nmerge(db_name, 'tables_regexp')\n \u2013 Creates a temporary Merge table. For more information, see the section \"Table engines, Merge\".\n\n\nThe table structure is taken from the first table encountered that matches the regular expression.", - "title": "merge" - }, - { - "location": "/table_functions/merge/#merge", - "text": "merge(db_name, 'tables_regexp') \u2013 Creates a temporary Merge table. For more information, see the section \"Table engines, Merge\". The table structure is taken from the first table encountered that matches the regular expression.", - "title": "merge" - }, - { - "location": "/table_functions/numbers/", - "text": "numbers\n\n\nnumbers(N)\n \u2013 Returns a table with the single 'number' column (UInt64) that contains integers from 0 to N-1.\n\n\nSimilar to the \nsystem.numbers\n table, it can be used for testing and generating successive values.\n\n\nThe following two queries are equivalent:\n\n\nSELECT\n \n*\n \nFROM\n \nnumbers\n(\n10\n);\n\n\nSELECT\n \n*\n \nFROM\n \nsystem\n.\nnumbers\n \nLIMIT\n \n10\n;\n\n\n\n\n\n\nExamples:\n\n\n-- Generate a sequence of dates from 2010-01-01 to 2010-12-31\n\n\nselect\n \ntoDate\n(\n2010-01-01\n)\n \n+\n \nnumber\n \nas\n \nd\n \nFROM\n \nnumbers\n(\n365\n);", - "title": "numbers" - }, - { - "location": "/table_functions/numbers/#numbers", - "text": "numbers(N) \u2013 Returns a table with the single 'number' column (UInt64) that contains integers from 0 to N-1. Similar to the system.numbers table, it can be used for testing and generating successive values. The following two queries are equivalent: SELECT * FROM numbers ( 10 ); SELECT * FROM system . numbers LIMIT 10 ; Examples: -- Generate a sequence of dates from 2010-01-01 to 2010-12-31 select toDate ( 2010-01-01 ) + number as d FROM numbers ( 365 );", - "title": "numbers" - }, - { - "location": "/formats/", - "text": "Formats\n\n\nThe format determines how data is returned to you after SELECTs (how it is written and formatted by the server), and how it is accepted for INSERTs (how it is read and parsed by the server).", - "title": "Introduction" - }, - { - "location": "/formats/#formats", - "text": "The format determines how data is returned to you after SELECTs (how it is written and formatted by the server), and how it is accepted for INSERTs (how it is read and parsed by the server).", - "title": "Formats" - }, - { - "location": "/formats/tabseparated/", - "text": "TabSeparated\n\n\nIn TabSeparated format, data is written by row. Each row contains values separated by tabs. Each value is follow by a tab, except the last value in the row, which is followed by a line feed. Strictly Unix line feeds are assumed everywhere. The last row also must contain a line feed at the end. Values are written in text format, without enclosing quotation marks, and with special characters escaped.\n\n\nInteger numbers are written in decimal form. Numbers can contain an extra \"+\" character at the beginning (ignored when parsing, and not recorded when formatting). Non-negative numbers can't contain the negative sign. When reading, it is allowed to parse an empty string as a zero, or (for signed types) a string consisting of just a minus sign as a zero. Numbers that do not fit into the corresponding data type may be parsed as a different number, without an error message.\n\n\nFloating-point numbers are written in decimal form. The dot is used as the decimal separator. Exponential entries are supported, as are 'inf', '+inf', '-inf', and 'nan'. An entry of floating-point numbers may begin or end with a decimal point.\nDuring formatting, accuracy may be lost on floating-point numbers.\nDuring parsing, it is not strictly required to read the nearest machine-representable number.\n\n\nDates are written in YYYY-MM-DD format and parsed in the same format, but with any characters as separators.\nDates with times are written in the format YYYY-MM-DD hh:mm:ss and parsed in the same format, but with any characters as separators.\nThis all occurs in the system time zone at the time the client or server starts (depending on which one formats data). For dates with times, daylight saving time is not specified. So if a dump has times during daylight saving time, the dump does not unequivocally match the data, and parsing will select one of the two times.\nDuring a read operation, incorrect dates and dates with times can be parsed with natural overflow or as null dates and times, without an error message.\n\n\nAs an exception, parsing dates with times is also supported in Unix timestamp format, if it consists of exactly 10 decimal digits. The result is not time zone-dependent. The formats YYYY-MM-DD hh:mm:ss and NNNNNNNNNN are differentiated automatically.\n\n\nStrings are output with backslash-escaped special characters. The following escape sequences are used for output: \n\\b\n, \n\\f\n, \n\\r\n, \n\\n\n, \n\\t\n, \n\\0\n, \n\\'\n, \n\\\\\n. Parsing also supports the sequences \n\\a\n, \n\\v\n, and \n\\xHH\n (hex escape sequences) and any \n\\c\n sequences, where \nc\n is any character (these sequences are converted to \nc\n). Thus, reading data supports formats where a line feed can be written as \n\\n\n or \n\\\n, or as a line feed. For example, the string \nHello world\n with a line feed between the words instead of a space can be parsed in any of the following variations:\n\n\nHello\\nworld\n\nHello\\\nworld\n\n\n\n\n\nThe second variant is supported because MySQL uses it when writing tab-separated dumps.\n\n\nThe minimum set of characters that you need to escape when passing data in TabSeparated format: tab, line feed (LF) and backslash.\n\n\nOnly a small set of symbols are escaped. You can easily stumble onto a string value that your terminal will ruin in output.\n\n\nArrays are written as a list of comma-separated values in square brackets. Number items in the array are fomratted as normally, but dates, dates with times, and strings are written in single quotes with the same escaping rules as above.\n\n\nThe TabSeparated format is convenient for processing data using custom programs and scripts. It is used by default in the HTTP interface, and in the command-line client's batch mode. This format also allows transferring data between different DBMSs. For example, you can get a dump from MySQL and upload it to ClickHouse, or vice versa.\n\n\nThe TabSeparated format supports outputting total values (when using WITH TOTALS) and extreme values (when 'extremes' is set to 1). In these cases, the total values and extremes are output after the main data. The main result, total values, and extremes are separated from each other by an empty line. Example:\n\n\nSELECT\n \nEventDate\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n \nGROUP\n \nBY\n \nEventDate\n \nWITH\n \nTOTALS\n \nORDER\n \nBY\n \nEventDate\n \nFORMAT\n \nTabSeparated\n``\n\n\n\n\n\n\n2014-03-17 1406958\n2014-03-18 1383658\n2014-03-19 1405797\n2014-03-20 1353623\n2014-03-21 1245779\n2014-03-22 1031592\n2014-03-23 1046491\n\n0000-00-00 8873898\n\n2014-03-17 1031592\n2014-03-23 1406958\n\n\n\n\n\nThis format is also available under the name \nTSV\n.", - "title": "TabSeparated" - }, - { - "location": "/formats/tabseparated/#tabseparated", - "text": "In TabSeparated format, data is written by row. Each row contains values separated by tabs. Each value is follow by a tab, except the last value in the row, which is followed by a line feed. Strictly Unix line feeds are assumed everywhere. The last row also must contain a line feed at the end. Values are written in text format, without enclosing quotation marks, and with special characters escaped. Integer numbers are written in decimal form. Numbers can contain an extra \"+\" character at the beginning (ignored when parsing, and not recorded when formatting). Non-negative numbers can't contain the negative sign. When reading, it is allowed to parse an empty string as a zero, or (for signed types) a string consisting of just a minus sign as a zero. Numbers that do not fit into the corresponding data type may be parsed as a different number, without an error message. Floating-point numbers are written in decimal form. The dot is used as the decimal separator. Exponential entries are supported, as are 'inf', '+inf', '-inf', and 'nan'. An entry of floating-point numbers may begin or end with a decimal point.\nDuring formatting, accuracy may be lost on floating-point numbers.\nDuring parsing, it is not strictly required to read the nearest machine-representable number. Dates are written in YYYY-MM-DD format and parsed in the same format, but with any characters as separators.\nDates with times are written in the format YYYY-MM-DD hh:mm:ss and parsed in the same format, but with any characters as separators.\nThis all occurs in the system time zone at the time the client or server starts (depending on which one formats data). For dates with times, daylight saving time is not specified. So if a dump has times during daylight saving time, the dump does not unequivocally match the data, and parsing will select one of the two times.\nDuring a read operation, incorrect dates and dates with times can be parsed with natural overflow or as null dates and times, without an error message. As an exception, parsing dates with times is also supported in Unix timestamp format, if it consists of exactly 10 decimal digits. The result is not time zone-dependent. The formats YYYY-MM-DD hh:mm:ss and NNNNNNNNNN are differentiated automatically. Strings are output with backslash-escaped special characters. The following escape sequences are used for output: \\b , \\f , \\r , \\n , \\t , \\0 , \\' , \\\\ . Parsing also supports the sequences \\a , \\v , and \\xHH (hex escape sequences) and any \\c sequences, where c is any character (these sequences are converted to c ). Thus, reading data supports formats where a line feed can be written as \\n or \\ , or as a line feed. For example, the string Hello world with a line feed between the words instead of a space can be parsed in any of the following variations: Hello\\nworld\n\nHello\\\nworld The second variant is supported because MySQL uses it when writing tab-separated dumps. The minimum set of characters that you need to escape when passing data in TabSeparated format: tab, line feed (LF) and backslash. Only a small set of symbols are escaped. You can easily stumble onto a string value that your terminal will ruin in output. Arrays are written as a list of comma-separated values in square brackets. Number items in the array are fomratted as normally, but dates, dates with times, and strings are written in single quotes with the same escaping rules as above. The TabSeparated format is convenient for processing data using custom programs and scripts. It is used by default in the HTTP interface, and in the command-line client's batch mode. This format also allows transferring data between different DBMSs. For example, you can get a dump from MySQL and upload it to ClickHouse, or vice versa. The TabSeparated format supports outputting total values (when using WITH TOTALS) and extreme values (when 'extremes' is set to 1). In these cases, the total values and extremes are output after the main data. The main result, total values, and extremes are separated from each other by an empty line. Example: SELECT EventDate , count () AS c FROM test . hits GROUP BY EventDate WITH TOTALS ORDER BY EventDate FORMAT TabSeparated `` 2014-03-17 1406958\n2014-03-18 1383658\n2014-03-19 1405797\n2014-03-20 1353623\n2014-03-21 1245779\n2014-03-22 1031592\n2014-03-23 1046491\n\n0000-00-00 8873898\n\n2014-03-17 1031592\n2014-03-23 1406958 This format is also available under the name TSV .", - "title": "TabSeparated" - }, - { - "location": "/formats/tabseparatedraw/", - "text": "TabSeparatedRaw\n\n\nDiffers from \nTabSeparated\n format in that the rows are written without escaping.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\n\n\nThis format is also available under the name \nTSVRaw\n.", - "title": "TabSeparatedRaw" - }, - { - "location": "/formats/tabseparatedraw/#tabseparatedraw", - "text": "Differs from TabSeparated format in that the rows are written without escaping.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). This format is also available under the name TSVRaw .", - "title": "TabSeparatedRaw" - }, - { - "location": "/formats/tabseparatedwithnames/", - "text": "TabSeparatedWithNames\n\n\nDiffers from the \nTabSeparated\n format in that the column names are written in the first row.\nDuring parsing, the first row is completely ignored. You can't use column names to determine their position or to check their correctness.\n(Support for parsing the header row may be added in the future.)\n\n\nThis format is also available under the name \nTSVWithNames\n.", - "title": "TabSeparatedWithNames" - }, - { - "location": "/formats/tabseparatedwithnames/#tabseparatedwithnames", - "text": "Differs from the TabSeparated format in that the column names are written in the first row.\nDuring parsing, the first row is completely ignored. You can't use column names to determine their position or to check their correctness.\n(Support for parsing the header row may be added in the future.) This format is also available under the name TSVWithNames .", - "title": "TabSeparatedWithNames" - }, - { - "location": "/formats/tabseparatedwithnamesandtypes/", - "text": "TabSeparatedWithNamesAndTypes\n\n\nDiffers from the \nTabSeparated\n format in that the column names are written to the first row, while the column types are in the second row.\nDuring parsing, the first and second rows are completely ignored.\n\n\nThis format is also available under the name \nTSVWithNamesAndTypes\n.", - "title": "TabSeparatedWithNamesAndTypes" - }, - { - "location": "/formats/tabseparatedwithnamesandtypes/#tabseparatedwithnamesandtypes", - "text": "Differs from the TabSeparated format in that the column names are written to the first row, while the column types are in the second row.\nDuring parsing, the first and second rows are completely ignored. This format is also available under the name TSVWithNamesAndTypes .", - "title": "TabSeparatedWithNamesAndTypes" - }, - { - "location": "/formats/csv/", - "text": "CSV\n\n\nComma Separated Values format (\nRFC\n).\n\n\nWhen formatting, rows are enclosed in double quotes. A double quote inside a string is output as two double quotes in a row. There are no other rules for escaping characters. Date and date-time are enclosed in double quotes. Numbers are output without quotes. Values \u200b\u200bare separated by a delimiter\n. Rows are separated using the Unix line feed (LF). Arrays are serialized in CSV as follows: first the array is serialized to a string as in TabSeparated format, and then the resulting string is output to CSV in double quotes. Tuples in CSV format are serialized as separate columns (that is, their nesting in the tuple is lost).\n\n\nBy default \u2014 \n,\n. See a \nformat_csv_delimiter\n setting for additional info.\n\n\nWhen parsing, all values can be parsed either with or without quotes. Both double and single quotes are supported. Rows can also be arranged without quotes. In this case, they are parsed up to a delimiter or line feed (CR or LF). In violation of the RFC, when parsing rows without quotes, the leading and trailing spaces and tabs are ignored. For the line feed, Unix (LF), Windows (CR LF) and Mac OS Classic (CR LF) are all supported.\n\n\nThe CSV format supports the output of totals and extremes the same way as \nTabSeparated\n.", - "title": "CSV" - }, - { - "location": "/formats/csv/#csv", - "text": "Comma Separated Values format ( RFC ). When formatting, rows are enclosed in double quotes. A double quote inside a string is output as two double quotes in a row. There are no other rules for escaping characters. Date and date-time are enclosed in double quotes. Numbers are output without quotes. Values \u200b\u200bare separated by a delimiter . Rows are separated using the Unix line feed (LF). Arrays are serialized in CSV as follows: first the array is serialized to a string as in TabSeparated format, and then the resulting string is output to CSV in double quotes. Tuples in CSV format are serialized as separate columns (that is, their nesting in the tuple is lost). By default \u2014 , . See a format_csv_delimiter setting for additional info. When parsing, all values can be parsed either with or without quotes. Both double and single quotes are supported. Rows can also be arranged without quotes. In this case, they are parsed up to a delimiter or line feed (CR or LF). In violation of the RFC, when parsing rows without quotes, the leading and trailing spaces and tabs are ignored. For the line feed, Unix (LF), Windows (CR LF) and Mac OS Classic (CR LF) are all supported. The CSV format supports the output of totals and extremes the same way as TabSeparated .", - "title": "CSV" - }, - { - "location": "/formats/csvwithnames/", - "text": "CSVWithNames\n\n\nAlso prints the header row, similar to \nTabSeparatedWithNames\n.", - "title": "CSVWithNames" - }, - { - "location": "/formats/csvwithnames/#csvwithnames", - "text": "Also prints the header row, similar to TabSeparatedWithNames .", - "title": "CSVWithNames" - }, - { - "location": "/formats/values/", - "text": "Values\n\n\nPrints every row in brackets. Rows are separated by commas. There is no comma after the last row. The values inside the brackets are also comma-separated. Numbers are output in decimal format without quotes. Arrays are output in square brackets. Strings, dates, and dates with times are output in quotes. Escaping rules and parsing are similar to the TabSeparated format. During formatting, extra spaces aren't inserted, but during parsing, they are allowed and skipped (except for spaces inside array values, which are not allowed).\n\n\nThe minimum set of characters that you need to escape when passing data in Values \u200b\u200bformat: single quotes and backslashes.\n\n\nThis is the format that is used in \nINSERT INTO t VALUES ...\n, but you can also use it for formatting query results.", - "title": "Values" - }, - { - "location": "/formats/values/#values", - "text": "Prints every row in brackets. Rows are separated by commas. There is no comma after the last row. The values inside the brackets are also comma-separated. Numbers are output in decimal format without quotes. Arrays are output in square brackets. Strings, dates, and dates with times are output in quotes. Escaping rules and parsing are similar to the TabSeparated format. During formatting, extra spaces aren't inserted, but during parsing, they are allowed and skipped (except for spaces inside array values, which are not allowed). The minimum set of characters that you need to escape when passing data in Values \u200b\u200bformat: single quotes and backslashes. This is the format that is used in INSERT INTO t VALUES ... , but you can also use it for formatting query results.", - "title": "Values" - }, - { - "location": "/formats/vertical/", - "text": "Vertical\n\n\nPrints each value on a separate line with the column name specified. This format is convenient for printing just one or a few rows, if each row consists of a large number of columns.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).", - "title": "Vertical" - }, - { - "location": "/formats/vertical/#vertical", - "text": "Prints each value on a separate line with the column name specified. This format is convenient for printing just one or a few rows, if each row consists of a large number of columns.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).", - "title": "Vertical" - }, - { - "location": "/formats/verticalraw/", - "text": "VerticalRaw\n\n\nDiffers from \nVertical\n format in that the rows are not escaped.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\n\n\nExamples:\n\n\n:) SHOW CREATE TABLE geonames FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nstatement: CREATE TABLE default.geonames ( geonameid UInt32, date Date DEFAULT CAST(\n2017-12-08\n AS Date)) ENGINE = MergeTree(date, geonameid, 8192)\n\n:) SELECT \nstring with \\\nquotes\\\n and \\t with some special \\n characters\n AS test FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with \nquotes\n and with some special\n characters\n\n\n\n\n\nCompare with the Vertical format:\n\n\n:) SELECT \nstring with \\\nquotes\\\n and \\t with some special \\n characters\n AS test FORMAT Vertical;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with \\\nquotes\\\n and \\t with some special \\n characters", - "title": "VerticalRaw" - }, - { - "location": "/formats/verticalraw/#verticalraw", - "text": "Differs from Vertical format in that the rows are not escaped.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). Examples: :) SHOW CREATE TABLE geonames FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nstatement: CREATE TABLE default.geonames ( geonameid UInt32, date Date DEFAULT CAST( 2017-12-08 AS Date)) ENGINE = MergeTree(date, geonameid, 8192)\n\n:) SELECT string with \\ quotes\\ and \\t with some special \\n characters AS test FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with quotes and with some special\n characters Compare with the Vertical format: :) SELECT string with \\ quotes\\ and \\t with some special \\n characters AS test FORMAT Vertical;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with \\ quotes\\ and \\t with some special \\n characters", - "title": "VerticalRaw" - }, - { - "location": "/formats/json/", - "text": "JSON\n\n\nOutputs data in JSON format. Besides data tables, it also outputs column names and types, along with some additional information: the total number of output rows, and the number of rows that could have been output if there weren't a LIMIT. Example:\n\n\nSELECT\n \nSearchPhrase\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n \nGROUP\n \nBY\n \nSearchPhrase\n \nWITH\n \nTOTALS\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n5\n \nFORMAT\n \nJSON\n\n\n\n\n\n\n{\n\n \nmeta\n:\n\n \n[\n\n \n{\n\n \nname\n:\n \nSearchPhrase\n,\n\n \ntype\n:\n \nString\n\n \n},\n\n \n{\n\n \nname\n:\n \nc\n,\n\n \ntype\n:\n \nUInt64\n\n \n}\n\n \n],\n\n\n \ndata\n:\n\n \n[\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n8267016\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nbathroom interior design\n,\n\n \nc\n:\n \n2166\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nyandex\n,\n\n \nc\n:\n \n1655\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nspring 2014 fashion\n,\n\n \nc\n:\n \n1549\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nfreeform photos\n,\n\n \nc\n:\n \n1480\n\n \n}\n\n \n],\n\n\n \ntotals\n:\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n8873898\n\n \n},\n\n\n \nextremes\n:\n\n \n{\n\n \nmin\n:\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n1480\n\n \n},\n\n \nmax\n:\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n8267016\n\n \n}\n\n \n},\n\n\n \nrows\n:\n \n5\n,\n\n\n \nrows_before_limit_at_least\n:\n \n141137\n\n\n}\n\n\n\n\n\n\nThe JSON is compatible with JavaScript. To ensure this, some characters are additionally escaped: the slash \n/\n is escaped as \n\\/\n; alternative line breaks \nU+2028\n and \nU+2029\n, which break some browsers, are escaped as \n\\uXXXX\n. ASCII control characters are escaped: backspace, form feed, line feed, carriage return, and horizontal tab are replaced with \n\\b\n, \n\\f\n, \n\\n\n, \n\\r\n, \n\\t\n , as well as the remaining bytes in the 00-1F range using \n\\uXXXX\n sequences. Invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences. For compatibility with JavaScript, Int64 and UInt64 integers are enclosed in double quotes by default. To remove the quotes, you can set the configuration parameter output_format_json_quote_64bit_integers to 0.\n\n\nrows\n \u2013 The total number of output rows.\n\n\nrows_before_limit_at_least\n The minimal number of rows there would have been without LIMIT. Output only if the query contains LIMIT.\nIf the query contains GROUP BY, rows_before_limit_at_least is the exact number of rows there would have been without a LIMIT.\n\n\ntotals\n \u2013 Total values (when using WITH TOTALS).\n\n\nextremes\n \u2013 Extreme values (when extremes is set to 1).\n\n\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the JSONEachRow format.", - "title": "JSON" - }, - { - "location": "/formats/json/#json", - "text": "Outputs data in JSON format. Besides data tables, it also outputs column names and types, along with some additional information: the total number of output rows, and the number of rows that could have been output if there weren't a LIMIT. Example: SELECT SearchPhrase , count () AS c FROM test . hits GROUP BY SearchPhrase WITH TOTALS ORDER BY c DESC LIMIT 5 FORMAT JSON { \n meta : \n [ \n { \n name : SearchPhrase , \n type : String \n }, \n { \n name : c , \n type : UInt64 \n } \n ], \n\n data : \n [ \n { \n SearchPhrase : , \n c : 8267016 \n }, \n { \n SearchPhrase : bathroom interior design , \n c : 2166 \n }, \n { \n SearchPhrase : yandex , \n c : 1655 \n }, \n { \n SearchPhrase : spring 2014 fashion , \n c : 1549 \n }, \n { \n SearchPhrase : freeform photos , \n c : 1480 \n } \n ], \n\n totals : \n { \n SearchPhrase : , \n c : 8873898 \n }, \n\n extremes : \n { \n min : \n { \n SearchPhrase : , \n c : 1480 \n }, \n max : \n { \n SearchPhrase : , \n c : 8267016 \n } \n }, \n\n rows : 5 , \n\n rows_before_limit_at_least : 141137 } The JSON is compatible with JavaScript. To ensure this, some characters are additionally escaped: the slash / is escaped as \\/ ; alternative line breaks U+2028 and U+2029 , which break some browsers, are escaped as \\uXXXX . ASCII control characters are escaped: backspace, form feed, line feed, carriage return, and horizontal tab are replaced with \\b , \\f , \\n , \\r , \\t , as well as the remaining bytes in the 00-1F range using \\uXXXX sequences. Invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences. For compatibility with JavaScript, Int64 and UInt64 integers are enclosed in double quotes by default. To remove the quotes, you can set the configuration parameter output_format_json_quote_64bit_integers to 0. rows \u2013 The total number of output rows. rows_before_limit_at_least The minimal number of rows there would have been without LIMIT. Output only if the query contains LIMIT.\nIf the query contains GROUP BY, rows_before_limit_at_least is the exact number of rows there would have been without a LIMIT. totals \u2013 Total values (when using WITH TOTALS). extremes \u2013 Extreme values (when extremes is set to 1). This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the JSONEachRow format.", - "title": "JSON" - }, - { - "location": "/formats/jsoncompact/", - "text": "JSONCompact\n\n\nDiffers from JSON only in that data rows are output in arrays, not in objects.\n\n\nExample:\n\n\n{\n\n \nmeta\n:\n\n \n[\n\n \n{\n\n \nname\n:\n \nSearchPhrase\n,\n\n \ntype\n:\n \nString\n\n \n},\n\n \n{\n\n \nname\n:\n \nc\n,\n\n \ntype\n:\n \nUInt64\n\n \n}\n\n \n],\n\n\n \ndata\n:\n\n \n[\n\n \n[\n,\n \n8267016\n],\n\n \n[\nbathroom interior design\n,\n \n2166\n],\n\n \n[\nyandex\n,\n \n1655\n],\n\n \n[\nspring 2014 fashion\n,\n \n1549\n],\n\n \n[\nfreeform photos\n,\n \n1480\n]\n\n \n],\n\n\n \ntotals\n:\n \n[\n,\n8873898\n],\n\n\n \nextremes\n:\n\n \n{\n\n \nmin\n:\n \n[\n,\n1480\n],\n\n \nmax\n:\n \n[\n,\n8267016\n]\n\n \n},\n\n\n \nrows\n:\n \n5\n,\n\n\n \nrows_before_limit_at_least\n:\n \n141137\n\n\n}\n\n\n\n\n\n\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the \nJSONEachRow\n format.", - "title": "JSONCompact" - }, - { - "location": "/formats/jsoncompact/#jsoncompact", - "text": "Differs from JSON only in that data rows are output in arrays, not in objects. Example: { \n meta : \n [ \n { \n name : SearchPhrase , \n type : String \n }, \n { \n name : c , \n type : UInt64 \n } \n ], \n\n data : \n [ \n [ , 8267016 ], \n [ bathroom interior design , 2166 ], \n [ yandex , 1655 ], \n [ spring 2014 fashion , 1549 ], \n [ freeform photos , 1480 ] \n ], \n\n totals : [ , 8873898 ], \n\n extremes : \n { \n min : [ , 1480 ], \n max : [ , 8267016 ] \n }, \n\n rows : 5 , \n\n rows_before_limit_at_least : 141137 } This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the JSONEachRow format.", - "title": "JSONCompact" - }, - { - "location": "/formats/jsoneachrow/", - "text": "JSONEachRow\n\n\nOutputs data as separate JSON objects for each row (newline delimited JSON).\n\n\n{\nSearchPhrase\n:\n,\ncount()\n:\n8267016\n}\n\n\n{\nSearchPhrase\n:\nbathroom interior design\n,\ncount()\n:\n2166\n}\n\n\n{\nSearchPhrase\n:\nyandex\n,\ncount()\n:\n1655\n}\n\n\n{\nSearchPhrase\n:\nspring 2014 fashion\n,\ncount()\n:\n1549\n}\n\n\n{\nSearchPhrase\n:\nfreeform photo\n,\ncount()\n:\n1480\n}\n\n\n{\nSearchPhrase\n:\nangelina jolie\n,\ncount()\n:\n1245\n}\n\n\n{\nSearchPhrase\n:\nomsk\n,\ncount()\n:\n1112\n}\n\n\n{\nSearchPhrase\n:\nphotos of dog breeds\n,\ncount()\n:\n1091\n}\n\n\n{\nSearchPhrase\n:\ncurtain design\n,\ncount()\n:\n1064\n}\n\n\n{\nSearchPhrase\n:\nbaku\n,\ncount()\n:\n1000\n}\n\n\n\n\n\n\nUnlike the JSON format, there is no substitution of invalid UTF-8 sequences. Any set of bytes can be output in the rows. This is necessary so that data can be formatted without losing any information. Values are escaped in the same way as for JSON.\n\n\nFor parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Whitespace between elements is ignored. If a comma is placed after the objects, it is ignored. Objects don't necessarily have to be separated by new lines.", - "title": "JSONEachRow" - }, - { - "location": "/formats/jsoneachrow/#jsoneachrow", - "text": "Outputs data as separate JSON objects for each row (newline delimited JSON). { SearchPhrase : , count() : 8267016 } { SearchPhrase : bathroom interior design , count() : 2166 } { SearchPhrase : yandex , count() : 1655 } { SearchPhrase : spring 2014 fashion , count() : 1549 } { SearchPhrase : freeform photo , count() : 1480 } { SearchPhrase : angelina jolie , count() : 1245 } { SearchPhrase : omsk , count() : 1112 } { SearchPhrase : photos of dog breeds , count() : 1091 } { SearchPhrase : curtain design , count() : 1064 } { SearchPhrase : baku , count() : 1000 } Unlike the JSON format, there is no substitution of invalid UTF-8 sequences. Any set of bytes can be output in the rows. This is necessary so that data can be formatted without losing any information. Values are escaped in the same way as for JSON. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Whitespace between elements is ignored. If a comma is placed after the objects, it is ignored. Objects don't necessarily have to be separated by new lines.", - "title": "JSONEachRow" - }, - { - "location": "/formats/tskv/", - "text": "TSKV\n\n\nSimilar to TabSeparated, but outputs a value in name=value format. Names are escaped the same way as in TabSeparated format, and the = symbol is also escaped.\n\n\nSearchPhrase= count()=8267016\nSearchPhrase=bathroom interior design count()=2166\nSearchPhrase=yandex count()=1655\nSearchPhrase=spring 2014 fashion count()=1549\nSearchPhrase=freeform photos count()=1480\nSearchPhrase=angelina jolia count()=1245\nSearchPhrase=omsk count()=1112\nSearchPhrase=photos of dog breeds count()=1091\nSearchPhrase=curtain design count()=1064\nSearchPhrase=baku count()=1000\n\n\n\n\n\nWhen there is a large number of small columns, this format is ineffective, and there is generally no reason to use it. It is used in some departments of Yandex.\n\n\nBoth data output and parsing are supported in this format. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults.\n\n\nParsing allows the presence of the additional field \ntskv\n without the equal sign or a value. This field is ignored.", - "title": "TSKV" - }, - { - "location": "/formats/tskv/#tskv", - "text": "Similar to TabSeparated, but outputs a value in name=value format. Names are escaped the same way as in TabSeparated format, and the = symbol is also escaped. SearchPhrase= count()=8267016\nSearchPhrase=bathroom interior design count()=2166\nSearchPhrase=yandex count()=1655\nSearchPhrase=spring 2014 fashion count()=1549\nSearchPhrase=freeform photos count()=1480\nSearchPhrase=angelina jolia count()=1245\nSearchPhrase=omsk count()=1112\nSearchPhrase=photos of dog breeds count()=1091\nSearchPhrase=curtain design count()=1064\nSearchPhrase=baku count()=1000 When there is a large number of small columns, this format is ineffective, and there is generally no reason to use it. It is used in some departments of Yandex. Both data output and parsing are supported in this format. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Parsing allows the presence of the additional field tskv without the equal sign or a value. This field is ignored.", - "title": "TSKV" - }, - { - "location": "/formats/pretty/", - "text": "Pretty\n\n\nOutputs data as Unicode-art tables, also using ANSI-escape sequences for setting colors in the terminal.\nA full grid of the table is drawn, and each row occupies two lines in the terminal.\nEach result block is output as a separate table. This is necessary so that blocks can be output without buffering results (buffering would be necessary in order to pre-calculate the visible width of all the values).\nTo avoid dumping too much data to the terminal, only the first 10,000 rows are printed. If the number of rows is greater than or equal to 10,000, the message \"Showed first 10 000\" is printed.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\n\n\nThe Pretty format supports outputting total values (when using WITH TOTALS) and extremes (when 'extremes' is set to 1). In these cases, total values and extreme values are output after the main data, in separate tables. Example (shown for the PrettyCompact format):\n\n\nSELECT\n \nEventDate\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n \nGROUP\n \nBY\n \nEventDate\n \nWITH\n \nTOTALS\n \nORDER\n \nBY\n \nEventDate\n \nFORMAT\n \nPrettyCompact\n\n\n\n\n\n\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1406958 \u2502\n\u2502 2014-03-18 \u2502 1383658 \u2502\n\u2502 2014-03-19 \u2502 1405797 \u2502\n\u2502 2014-03-20 \u2502 1353623 \u2502\n\u2502 2014-03-21 \u2502 1245779 \u2502\n\u2502 2014-03-22 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1046491 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nTotals:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 0000-00-00 \u2502 8873898 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nExtremes:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1406958 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "Pretty" - }, - { - "location": "/formats/pretty/#pretty", - "text": "Outputs data as Unicode-art tables, also using ANSI-escape sequences for setting colors in the terminal.\nA full grid of the table is drawn, and each row occupies two lines in the terminal.\nEach result block is output as a separate table. This is necessary so that blocks can be output without buffering results (buffering would be necessary in order to pre-calculate the visible width of all the values).\nTo avoid dumping too much data to the terminal, only the first 10,000 rows are printed. If the number of rows is greater than or equal to 10,000, the message \"Showed first 10 000\" is printed.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). The Pretty format supports outputting total values (when using WITH TOTALS) and extremes (when 'extremes' is set to 1). In these cases, total values and extreme values are output after the main data, in separate tables. Example (shown for the PrettyCompact format): SELECT EventDate , count () AS c FROM test . hits GROUP BY EventDate WITH TOTALS ORDER BY EventDate FORMAT PrettyCompact \u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1406958 \u2502\n\u2502 2014-03-18 \u2502 1383658 \u2502\n\u2502 2014-03-19 \u2502 1405797 \u2502\n\u2502 2014-03-20 \u2502 1353623 \u2502\n\u2502 2014-03-21 \u2502 1245779 \u2502\n\u2502 2014-03-22 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1046491 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nTotals:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 0000-00-00 \u2502 8873898 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nExtremes:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1406958 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "Pretty" - }, - { - "location": "/formats/prettycompact/", - "text": "PrettyCompact\n\n\nDiffers from \nPretty\n in that the grid is drawn between rows and the result is more compact.\nThis format is used by default in the command-line client in interactive mode.", - "title": "PrettyCompact" - }, - { - "location": "/formats/prettycompact/#prettycompact", - "text": "Differs from Pretty in that the grid is drawn between rows and the result is more compact.\nThis format is used by default in the command-line client in interactive mode.", - "title": "PrettyCompact" - }, - { - "location": "/formats/prettycompactmonoblock/", - "text": "PrettyCompactMonoBlock\n\n\nDiffers from \nPrettyCompact\n in that up to 10,000 rows are buffered, then output as a single table, not by blocks.", - "title": "PrettyCompactMonoBlock" - }, - { - "location": "/formats/prettycompactmonoblock/#prettycompactmonoblock", - "text": "Differs from PrettyCompact in that up to 10,000 rows are buffered, then output as a single table, not by blocks.", - "title": "PrettyCompactMonoBlock" - }, - { - "location": "/formats/prettynoescapes/", - "text": "PrettyNoEscapes\n\n\nDiffers from Pretty in that ANSI-escape sequences aren't used. This is necessary for displaying this format in a browser, as well as for using the 'watch' command-line utility.\n\n\nExample:\n\n\nwatch -n1 \nclickhouse-client --query=\nSELECT * FROM system.events FORMAT PrettyCompactNoEscapes\n\n\n\n\n\n\nYou can use the HTTP interface for displaying in the browser.\n\n\nPrettyCompactNoEscapes\n\n\nThe same as the previous setting.\n\n\nPrettySpaceNoEscapes\n\n\nThe same as the previous setting.", - "title": "PrettyNoEscapes" - }, - { - "location": "/formats/prettynoescapes/#prettynoescapes", - "text": "Differs from Pretty in that ANSI-escape sequences aren't used. This is necessary for displaying this format in a browser, as well as for using the 'watch' command-line utility. Example: watch -n1 clickhouse-client --query= SELECT * FROM system.events FORMAT PrettyCompactNoEscapes You can use the HTTP interface for displaying in the browser.", - "title": "PrettyNoEscapes" - }, - { - "location": "/formats/prettynoescapes/#prettycompactnoescapes", - "text": "The same as the previous setting.", - "title": "PrettyCompactNoEscapes" - }, - { - "location": "/formats/prettynoescapes/#prettyspacenoescapes", - "text": "The same as the previous setting.", - "title": "PrettySpaceNoEscapes" - }, - { - "location": "/formats/prettyspace/", - "text": "PrettySpace\n\n\nDiffers from \nPrettyCompact\n in that whitespace (space characters) is used instead of the grid.", - "title": "PrettySpace" - }, - { - "location": "/formats/prettyspace/#prettyspace", - "text": "Differs from PrettyCompact in that whitespace (space characters) is used instead of the grid.", - "title": "PrettySpace" - }, - { - "location": "/formats/rowbinary/", - "text": "RowBinary\n\n\nFormats and parses data by row in binary format. Rows and values are listed consecutively, without separators.\nThis format is less efficient than the Native format, since it is row-based.\n\n\nIntegers use fixed-length little endian representation. For example, UInt64 uses 8 bytes.\nDateTime is represented as UInt32 containing the Unix timestamp as the value.\nDate is represented as a UInt16 object that contains the number of days since 1970-01-01 as the value.\nString is represented as a varint length (unsigned \nLEB128\n), followed by the bytes of the string.\nFixedString is represented simply as a sequence of bytes.\n\n\nArray is represented as a varint length (unsigned \nLEB128\n), followed by successive elements of the array.", - "title": "RowBinary" - }, - { - "location": "/formats/rowbinary/#rowbinary", - "text": "Formats and parses data by row in binary format. Rows and values are listed consecutively, without separators.\nThis format is less efficient than the Native format, since it is row-based. Integers use fixed-length little endian representation. For example, UInt64 uses 8 bytes.\nDateTime is represented as UInt32 containing the Unix timestamp as the value.\nDate is represented as a UInt16 object that contains the number of days since 1970-01-01 as the value.\nString is represented as a varint length (unsigned LEB128 ), followed by the bytes of the string.\nFixedString is represented simply as a sequence of bytes. Array is represented as a varint length (unsigned LEB128 ), followed by successive elements of the array.", - "title": "RowBinary" - }, - { - "location": "/formats/native/", - "text": "Native\n\n\nThe most efficient format. Data is written and read by blocks in binary format. For each block, the number of rows, number of columns, column names and types, and parts of columns in this block are recorded one after another. In other words, this format is \"columnar\" \u2013 it doesn't convert columns to rows. This is the format used in the native interface for interaction between servers, for using the command-line client, and for C++ clients.\n\n\nYou can use this format to quickly generate dumps that can only be read by the ClickHouse DBMS. It doesn't make sense to work with this format yourself.", - "title": "Native" - }, - { - "location": "/formats/native/#native", - "text": "The most efficient format. Data is written and read by blocks in binary format. For each block, the number of rows, number of columns, column names and types, and parts of columns in this block are recorded one after another. In other words, this format is \"columnar\" \u2013 it doesn't convert columns to rows. This is the format used in the native interface for interaction between servers, for using the command-line client, and for C++ clients. You can use this format to quickly generate dumps that can only be read by the ClickHouse DBMS. It doesn't make sense to work with this format yourself.", - "title": "Native" - }, - { - "location": "/formats/null/", - "text": "Null\n\n\nNothing is output. However, the query is processed, and when using the command-line client, data is transmitted to the client. This is used for tests, including productivity testing.\nObviously, this format is only appropriate for output, not for parsing.", - "title": "Null" - }, - { - "location": "/formats/null/#null", - "text": "Nothing is output. However, the query is processed, and when using the command-line client, data is transmitted to the client. This is used for tests, including productivity testing.\nObviously, this format is only appropriate for output, not for parsing.", - "title": "Null" - }, - { - "location": "/formats/xml/", - "text": "XML\n\n\nXML format is suitable only for output, not for parsing. Example:\n\n\n?xml version=\n1.0\n encoding=\nUTF-8\n ?\n\n\nresult\n\n \nmeta\n\n \ncolumns\n\n \ncolumn\n\n \nname\nSearchPhrase\n/name\n\n \ntype\nString\n/type\n\n \n/column\n\n \ncolumn\n\n \nname\ncount()\n/name\n\n \ntype\nUInt64\n/type\n\n \n/column\n\n \n/columns\n\n \n/meta\n\n \ndata\n\n \nrow\n\n \nSearchPhrase\n/SearchPhrase\n\n \nfield\n8267016\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nbathroom interior design\n/SearchPhrase\n\n \nfield\n2166\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nyandex\n/SearchPhrase\n\n \nfield\n1655\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nspring 2014 fashion\n/SearchPhrase\n\n \nfield\n1549\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nfreeform photos\n/SearchPhrase\n\n \nfield\n1480\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nangelina jolie\n/SearchPhrase\n\n \nfield\n1245\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nomsk\n/SearchPhrase\n\n \nfield\n1112\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nphotos of dog breeds\n/SearchPhrase\n\n \nfield\n1091\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\ncurtain design\n/SearchPhrase\n\n \nfield\n1064\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nbaku\n/SearchPhrase\n\n \nfield\n1000\n/field\n\n \n/row\n\n \n/data\n\n \nrows\n10\n/rows\n\n \nrows_before_limit_at_least\n141137\n/rows_before_limit_at_least\n\n\n/result\n\n\n\n\n\n\nIf the column name does not have an acceptable format, just 'field' is used as the element name. In general, the XML structure follows the JSON structure.\nJust as for JSON, invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences.\n\n\nIn string values, the characters \n and \n are escaped as \n and \n.\n\n\nArrays are output as \narray\nelem\nHello\n/elem\nelem\nWorld\n/elem\n...\n/array\n,\nand tuples as \ntuple\nelem\nHello\n/elem\nelem\nWorld\n/elem\n...\n/tuple\n.", - "title": "XML" - }, - { - "location": "/formats/xml/#xml", - "text": "XML format is suitable only for output, not for parsing. Example: ?xml version= 1.0 encoding= UTF-8 ? result \n meta \n columns \n column \n name SearchPhrase /name \n type String /type \n /column \n column \n name count() /name \n type UInt64 /type \n /column \n /columns \n /meta \n data \n row \n SearchPhrase /SearchPhrase \n field 8267016 /field \n /row \n row \n SearchPhrase bathroom interior design /SearchPhrase \n field 2166 /field \n /row \n row \n SearchPhrase yandex /SearchPhrase \n field 1655 /field \n /row \n row \n SearchPhrase spring 2014 fashion /SearchPhrase \n field 1549 /field \n /row \n row \n SearchPhrase freeform photos /SearchPhrase \n field 1480 /field \n /row \n row \n SearchPhrase angelina jolie /SearchPhrase \n field 1245 /field \n /row \n row \n SearchPhrase omsk /SearchPhrase \n field 1112 /field \n /row \n row \n SearchPhrase photos of dog breeds /SearchPhrase \n field 1091 /field \n /row \n row \n SearchPhrase curtain design /SearchPhrase \n field 1064 /field \n /row \n row \n SearchPhrase baku /SearchPhrase \n field 1000 /field \n /row \n /data \n rows 10 /rows \n rows_before_limit_at_least 141137 /rows_before_limit_at_least /result If the column name does not have an acceptable format, just 'field' is used as the element name. In general, the XML structure follows the JSON structure.\nJust as for JSON, invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences. In string values, the characters and are escaped as and . Arrays are output as array elem Hello /elem elem World /elem ... /array ,\nand tuples as tuple elem Hello /elem elem World /elem ... /tuple .", - "title": "XML" - }, - { - "location": "/formats/capnproto/", - "text": "CapnProto\n\n\nCap'n Proto is a binary message format similar to Protocol Buffers and Thrift, but not like JSON or MessagePack.\n\n\nCap'n Proto messages are strictly typed and not self-describing, meaning they need an external schema description. The schema is applied on the fly and cached for each query.\n\n\nSELECT\n \nSearchPhrase\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n\n \nGROUP\n \nBY\n \nSearchPhrase\n \nFORMAT\n \nCapnProto\n \nSETTINGS\n \nschema\n \n=\n \nschema:Message\n\n\n\n\n\n\nWhere \nschema.capnp\n looks like this:\n\n\nstruct\n \nMessage\n \n{\n\n \nSearchPhrase\n \n@0\n \n:\nText\n;\n\n \nc\n \n@1\n \n:\nUint64\n;\n\n\n}\n\n\n\n\n\n\nSchema files are in the file that is located in the directory specified in \n format_schema_path\n in the server configuration.\n\n\nDeserialization is effective and usually doesn't increase the system load.", - "title": "CapnProto" - }, - { - "location": "/formats/capnproto/#capnproto", - "text": "Cap'n Proto is a binary message format similar to Protocol Buffers and Thrift, but not like JSON or MessagePack. Cap'n Proto messages are strictly typed and not self-describing, meaning they need an external schema description. The schema is applied on the fly and cached for each query. SELECT SearchPhrase , count () AS c FROM test . hits \n GROUP BY SearchPhrase FORMAT CapnProto SETTINGS schema = schema:Message Where schema.capnp looks like this: struct Message { \n SearchPhrase @0 : Text ; \n c @1 : Uint64 ; } Schema files are in the file that is located in the directory specified in format_schema_path in the server configuration. Deserialization is effective and usually doesn't increase the system load.", - "title": "CapnProto" - }, - { - "location": "/data_types/", - "text": "Data types\n\n\nClickHouse can store various types of data in table cells.\n\n\nThis section describes the supported data types and special considerations when using and/or implementing them, if any.", - "title": "Introduction" - }, - { - "location": "/data_types/#data-types", - "text": "ClickHouse can store various types of data in table cells. This section describes the supported data types and special considerations when using and/or implementing them, if any.", - "title": "Data types" - }, - { - "location": "/data_types/int_uint/", - "text": "UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64\n\n\nFixed-length integers, with or without a sign.\n\n\nInt ranges\n\n\n\n\nInt8 - [-128 : 127]\n\n\nInt16 - [-32768 : 32767]\n\n\nInt32 - [-2147483648 : 2147483647]\n\n\nInt64 - [-9223372036854775808 : 9223372036854775807]\n\n\n\n\nUint ranges\n\n\n\n\nUInt8 - [0 : 255]\n\n\nUInt16 - [0 : 65535]\n\n\nUInt32 - [0 : 4294967295]\n\n\nUInt64 - [0 : 18446744073709551615]", - "title": "UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64" - }, - { - "location": "/data_types/int_uint/#uint8-uint16-uint32-uint64-int8-int16-int32-int64", - "text": "Fixed-length integers, with or without a sign.", - "title": "UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64" - }, - { - "location": "/data_types/int_uint/#int-ranges", - "text": "Int8 - [-128 : 127] Int16 - [-32768 : 32767] Int32 - [-2147483648 : 2147483647] Int64 - [-9223372036854775808 : 9223372036854775807]", - "title": "Int ranges" - }, - { - "location": "/data_types/int_uint/#uint-ranges", - "text": "UInt8 - [0 : 255] UInt16 - [0 : 65535] UInt32 - [0 : 4294967295] UInt64 - [0 : 18446744073709551615]", - "title": "Uint ranges" - }, - { - "location": "/data_types/float/", - "text": "Float32, Float64\n\n\nFloating point numbers\n.\n\n\nTypes are equivalent to types of C:\n\n\n\n\nFloat32\n - \nfloat\n\n\nFloat64\n - \ndouble\n\n\n\n\nWe recommend that you store data in integer form whenever possible. For example, convert fixed precision numbers to integer values, such as monetary amounts or page load times in milliseconds.\n\n\nUsing floating-point numbers\n\n\n\n\nComputations with floating-point numbers might produce a rounding error.\n\n\n\n\nSELECT\n \n1\n \n-\n \n0\n.\n9\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500minus(1, 0.9)\u2500\u2510\n\u2502 0.09999999999999998 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\nThe result of the calculation depends on the calculation method (the processor type and architecture of the computer system).\n\n\nFloating-point calculations might result in numbers such as infinity (\nInf\n) and \"not-a-number\" (\nNaN\n). This should be taken into account when processing the results of calculations.\n\n\nWhen reading floating point numbers from rows, the result might not be the nearest machine-representable number.\n\n\n\n\nNaN and Inf\n\n\nIn contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers:\n\n\n\n\nInf\n \u2013 Infinity.\n\n\n\n\nSELECT\n \n0\n.\n5\n \n/\n \n0\n\n\n\n\n\n\n\u250c\u2500divide(0.5, 0)\u2500\u2510\n\u2502 inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\n-Inf\n \u2013 Negative infinity.\n\n\n\n\nSELECT\n \n-\n0\n.\n5\n \n/\n \n0\n\n\n\n\n\n\n\u250c\u2500divide(-0.5, 0)\u2500\u2510\n\u2502 -inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\nNaN\n \u2013 Not a number.\n\n\n\n\nSELECT 0 / 0\n\n\n\n\n\n\u250c\u2500divide(0, 0)\u2500\u2510\n\u2502 nan \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSee the rules for \nNaN\n sorting in the section \nORDER BY clause\n.", - "title": "Float32, Float64" - }, - { - "location": "/data_types/float/#float32-float64", - "text": "Floating point numbers . Types are equivalent to types of C: Float32 - float Float64 - double We recommend that you store data in integer form whenever possible. For example, convert fixed precision numbers to integer values, such as monetary amounts or page load times in milliseconds.", - "title": "Float32, Float64" - }, - { - "location": "/data_types/float/#using-floating-point-numbers", - "text": "Computations with floating-point numbers might produce a rounding error. SELECT 1 - 0 . 9 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500minus(1, 0.9)\u2500\u2510\n\u2502 0.09999999999999998 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 The result of the calculation depends on the calculation method (the processor type and architecture of the computer system). Floating-point calculations might result in numbers such as infinity ( Inf ) and \"not-a-number\" ( NaN ). This should be taken into account when processing the results of calculations. When reading floating point numbers from rows, the result might not be the nearest machine-representable number.", - "title": "Using floating-point numbers" - }, - { - "location": "/data_types/float/#nan-and-inf", - "text": "In contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers: Inf \u2013 Infinity. SELECT 0 . 5 / 0 \u250c\u2500divide(0.5, 0)\u2500\u2510\n\u2502 inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 -Inf \u2013 Negative infinity. SELECT - 0 . 5 / 0 \u250c\u2500divide(-0.5, 0)\u2500\u2510\n\u2502 -inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 NaN \u2013 Not a number. SELECT 0 / 0 \u250c\u2500divide(0, 0)\u2500\u2510\n\u2502 nan \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 See the rules for NaN sorting in the section ORDER BY clause .", - "title": "NaN and Inf" - }, - { - "location": "/data_types/boolean/", - "text": "Boolean values\n\n\nThere isn't a separate type for boolean values. They use the UInt8 type, restricted to the values 0 or 1.", - "title": "Boolean values" - }, - { - "location": "/data_types/boolean/#boolean-values", - "text": "There isn't a separate type for boolean values. They use the UInt8 type, restricted to the values 0 or 1.", - "title": "Boolean values" - }, - { - "location": "/data_types/string/", - "text": "String\n\n\nStrings of an arbitrary length. The length is not limited. The value can contain an arbitrary set of bytes, including null bytes.\nThe String type replaces the types VARCHAR, BLOB, CLOB, and others from other DBMSs.\n\n\nEncodings\n\n\nClickHouse doesn't have the concept of encodings. Strings can contain an arbitrary set of bytes, which are stored and output as-is.\nIf you need to store texts, we recommend using UTF-8 encoding. At the very least, if your terminal uses UTF-8 (as recommended), you can read and write your values without making conversions.\nSimilarly, certain functions for working with strings have separate variations that work under the assumption that the string contains a set of bytes representing a UTF-8 encoded text.\nFor example, the 'length' function calculates the string length in bytes, while the 'lengthUTF8' function calculates the string length in Unicode code points, assuming that the value is UTF-8 encoded.", - "title": "String" - }, - { - "location": "/data_types/string/#string", - "text": "Strings of an arbitrary length. The length is not limited. The value can contain an arbitrary set of bytes, including null bytes.\nThe String type replaces the types VARCHAR, BLOB, CLOB, and others from other DBMSs.", - "title": "String" - }, - { - "location": "/data_types/string/#encodings", - "text": "ClickHouse doesn't have the concept of encodings. Strings can contain an arbitrary set of bytes, which are stored and output as-is.\nIf you need to store texts, we recommend using UTF-8 encoding. At the very least, if your terminal uses UTF-8 (as recommended), you can read and write your values without making conversions.\nSimilarly, certain functions for working with strings have separate variations that work under the assumption that the string contains a set of bytes representing a UTF-8 encoded text.\nFor example, the 'length' function calculates the string length in bytes, while the 'lengthUTF8' function calculates the string length in Unicode code points, assuming that the value is UTF-8 encoded.", - "title": "Encodings" - }, - { - "location": "/data_types/fixedstring/", - "text": "FixedString(N)\n\n\nA fixed-length string of N bytes (not characters or code points). N must be a strictly positive natural number.\nWhen the server reads a string that contains fewer bytes (such as when parsing INSERT data), the string is padded to N bytes by appending null bytes at the right.\nWhen the server reads a string that contains more bytes, an error message is returned.\nWhen the server writes a string (such as when outputting the result of a SELECT query), null bytes are not trimmed off of the end of the string, but are output.\nNote that this behavior differs from MySQL behavior for the CHAR type (where strings are padded with spaces, and the spaces are removed for output).\n\n\nFewer functions can work with the FixedString(N) type than with String, so it is less convenient to use.", - "title": "FixedString(N)" - }, - { - "location": "/data_types/fixedstring/#fixedstringn", - "text": "A fixed-length string of N bytes (not characters or code points). N must be a strictly positive natural number.\nWhen the server reads a string that contains fewer bytes (such as when parsing INSERT data), the string is padded to N bytes by appending null bytes at the right.\nWhen the server reads a string that contains more bytes, an error message is returned.\nWhen the server writes a string (such as when outputting the result of a SELECT query), null bytes are not trimmed off of the end of the string, but are output.\nNote that this behavior differs from MySQL behavior for the CHAR type (where strings are padded with spaces, and the spaces are removed for output). Fewer functions can work with the FixedString(N) type than with String, so it is less convenient to use.", - "title": "FixedString(N)" - }, - { - "location": "/data_types/date/", - "text": "Date\n\n\nA date. Stored in two bytes as the number of days since 1970-01-01 (unsigned). Allows storing values from just after the beginning of the Unix Epoch to the upper threshold defined by a constant at the compilation stage (currently, this is until the year 2106, but the final fully-supported year is 2105).\nThe minimum value is output as 0000-00-00.\n\n\nThe date is stored without the time zone.", - "title": "Date" - }, - { - "location": "/data_types/date/#date", - "text": "A date. Stored in two bytes as the number of days since 1970-01-01 (unsigned). Allows storing values from just after the beginning of the Unix Epoch to the upper threshold defined by a constant at the compilation stage (currently, this is until the year 2106, but the final fully-supported year is 2105).\nThe minimum value is output as 0000-00-00. The date is stored without the time zone.", - "title": "Date" - }, - { - "location": "/data_types/datetime/", - "text": "DateTime\n\n\nDate with time. Stored in four bytes as a Unix timestamp (unsigned). Allows storing values in the same range as for the Date type. The minimal value is output as 0000-00-00 00:00:00.\nThe time is stored with accuracy up to one second (without leap seconds).\n\n\nTime zones\n\n\nThe date with time is converted from text (divided into component parts) to binary and back, using the system's time zone at the time the client or server starts. In text format, information about daylight savings is lost.\n\n\nBy default, the client switches to the timezone of the server when it connects. You can change this behavior by enabling the client command-line option \n--use_client_time_zone\n.\n\n\nSupports only those time zones that never had the time differ from UTC for a partial number of hours (without leap seconds) over the entire time range you will be working with.\n\n\nSo when working with a textual date (for example, when saving text dumps), keep in mind that there may be ambiguity during changes for daylight savings time, and there may be problems matching data if the time zone changed.", - "title": "DateTime" - }, - { - "location": "/data_types/datetime/#datetime", - "text": "Date with time. Stored in four bytes as a Unix timestamp (unsigned). Allows storing values in the same range as for the Date type. The minimal value is output as 0000-00-00 00:00:00.\nThe time is stored with accuracy up to one second (without leap seconds).", - "title": "DateTime" - }, - { - "location": "/data_types/datetime/#time-zones", - "text": "The date with time is converted from text (divided into component parts) to binary and back, using the system's time zone at the time the client or server starts. In text format, information about daylight savings is lost. By default, the client switches to the timezone of the server when it connects. You can change this behavior by enabling the client command-line option --use_client_time_zone . Supports only those time zones that never had the time differ from UTC for a partial number of hours (without leap seconds) over the entire time range you will be working with. So when working with a textual date (for example, when saving text dumps), keep in mind that there may be ambiguity during changes for daylight savings time, and there may be problems matching data if the time zone changed.", - "title": "Time zones" - }, - { - "location": "/data_types/enum/", - "text": "Enum\n\n\nEnum8 or Enum16. A finite set of string values that can be stored more efficiently than the \nString\n data type.\n\n\nExample:\n\n\nEnum8(\nhello\n = 1, \nworld\n = 2)\n\n\n\n\n\n\n\nA data type with two possible values: 'hello' and 'world'.\n\n\n\n\nEach of the values is assigned a number in the range \n-128 ... 127\n for \nEnum8\n or in the range \n-32768 ... 32767\n for \nEnum16\n. All the strings and numbers must be different. An empty string is allowed. If this type is specified (in a table definition), numbers can be in an arbitrary order. However, the order does not matter.\n\n\nIn RAM, this type of column is stored in the same way as \nInt8\n or \nInt16\n of the corresponding numerical values.\nWhen reading in text form, ClickHouse parses the value as a string and searches for the corresponding string from the set of Enum values. If it is not found, an exception is thrown. When reading in text format, the string is read and the corresponding numeric value is looked up. An exception will be thrown if it is not found.\nWhen writing in text form, it writes the value as the corresponding string. If column data contains garbage (numbers that are not from the valid set), an exception is thrown. When reading and writing in binary form, it works the same way as for Int8 and Int16 data types.\nThe implicit default value is the value with the lowest number.\n\n\nDuring \nORDER BY\n, \nGROUP BY\n, \nIN\n, \nDISTINCT\n and so on, Enums behave the same way as the corresponding numbers. For example, ORDER BY sorts them numerically. Equality and comparison operators work the same way on Enums as they do on the underlying numeric values.\n\n\nEnum values cannot be compared with numbers. Enums can be compared to a constant string. If the string compared to is not a valid value for the Enum, an exception will be thrown. The IN operator is supported with the Enum on the left hand side and a set of strings on the right hand side. The strings are the values of the corresponding Enum.\n\n\nMost numeric and string operations are not defined for Enum values, e.g. adding a number to an Enum or concatenating a string to an Enum.\nHowever, the Enum has a natural \ntoString\n function that returns its string value.\n\n\nEnum values are also convertible to numeric types using the \ntoT\n function, where T is a numeric type. When T corresponds to the enum\u2019s underlying numeric type, this conversion is zero-cost.\nThe Enum type can be changed without cost using ALTER, if only the set of values is changed. It is possible to both add and remove members of the Enum using ALTER (removing is safe only if the removed value has never been used in the table). As a safeguard, changing the numeric value of a previously defined Enum member will throw an exception.\n\n\nUsing ALTER, it is possible to change an Enum8 to an Enum16 or vice versa, just like changing an Int8 to Int16.", - "title": "Enum" - }, - { - "location": "/data_types/enum/#enum", - "text": "Enum8 or Enum16. A finite set of string values that can be stored more efficiently than the String data type. Example: Enum8( hello = 1, world = 2) A data type with two possible values: 'hello' and 'world'. Each of the values is assigned a number in the range -128 ... 127 for Enum8 or in the range -32768 ... 32767 for Enum16 . All the strings and numbers must be different. An empty string is allowed. If this type is specified (in a table definition), numbers can be in an arbitrary order. However, the order does not matter. In RAM, this type of column is stored in the same way as Int8 or Int16 of the corresponding numerical values.\nWhen reading in text form, ClickHouse parses the value as a string and searches for the corresponding string from the set of Enum values. If it is not found, an exception is thrown. When reading in text format, the string is read and the corresponding numeric value is looked up. An exception will be thrown if it is not found.\nWhen writing in text form, it writes the value as the corresponding string. If column data contains garbage (numbers that are not from the valid set), an exception is thrown. When reading and writing in binary form, it works the same way as for Int8 and Int16 data types.\nThe implicit default value is the value with the lowest number. During ORDER BY , GROUP BY , IN , DISTINCT and so on, Enums behave the same way as the corresponding numbers. For example, ORDER BY sorts them numerically. Equality and comparison operators work the same way on Enums as they do on the underlying numeric values. Enum values cannot be compared with numbers. Enums can be compared to a constant string. If the string compared to is not a valid value for the Enum, an exception will be thrown. The IN operator is supported with the Enum on the left hand side and a set of strings on the right hand side. The strings are the values of the corresponding Enum. Most numeric and string operations are not defined for Enum values, e.g. adding a number to an Enum or concatenating a string to an Enum.\nHowever, the Enum has a natural toString function that returns its string value. Enum values are also convertible to numeric types using the toT function, where T is a numeric type. When T corresponds to the enum\u2019s underlying numeric type, this conversion is zero-cost.\nThe Enum type can be changed without cost using ALTER, if only the set of values is changed. It is possible to both add and remove members of the Enum using ALTER (removing is safe only if the removed value has never been used in the table). As a safeguard, changing the numeric value of a previously defined Enum member will throw an exception. Using ALTER, it is possible to change an Enum8 to an Enum16 or vice versa, just like changing an Int8 to Int16.", - "title": "Enum" - }, - { - "location": "/data_types/array/", - "text": "Array(T)\n\n\nAn array of elements of type T. The T type can be any type, including an array.\nWe don't recommend using multidimensional arrays, because they are not well supported (for example, you can't store multidimensional arrays in tables with a MergeTree engine).", - "title": "Array(T)" - }, - { - "location": "/data_types/array/#arrayt", - "text": "An array of elements of type T. The T type can be any type, including an array.\nWe don't recommend using multidimensional arrays, because they are not well supported (for example, you can't store multidimensional arrays in tables with a MergeTree engine).", - "title": "Array(T)" - }, - { - "location": "/data_types/nested_data_structures/aggregatefunction/", - "text": "AggregateFunction(name, types_of_arguments...)\n\n\nThe intermediate state of an aggregate function. To get it, use aggregate functions with the '-State' suffix. For more information, see \"AggregatingMergeTree\".", - "title": "AggregateFunction(name, types_of_arguments...)" - }, - { - "location": "/data_types/nested_data_structures/aggregatefunction/#aggregatefunctionname-types_of_arguments", - "text": "The intermediate state of an aggregate function. To get it, use aggregate functions with the '-State' suffix. For more information, see \"AggregatingMergeTree\".", - "title": "AggregateFunction(name, types_of_arguments...)" - }, - { - "location": "/data_types/tuple/", - "text": "Tuple(T1, T2, ...)\n\n\nTuples can't be written to tables (other than Memory tables). They are used for temporary column grouping. Columns can be grouped when an IN expression is used in a query, and for specifying certain formal parameters of lambda functions. For more information, see \"IN operators\" and \"Higher order functions\".\n\n\nTuples can be output as the result of running a query. In this case, for text formats other than JSON*, values are comma-separated in brackets. In JSON* formats, tuples are output as arrays (in square brackets).", - "title": "Tuple(T1, T2, ...)" - }, - { - "location": "/data_types/tuple/#tuplet1-t2", - "text": "Tuples can't be written to tables (other than Memory tables). They are used for temporary column grouping. Columns can be grouped when an IN expression is used in a query, and for specifying certain formal parameters of lambda functions. For more information, see \"IN operators\" and \"Higher order functions\". Tuples can be output as the result of running a query. In this case, for text formats other than JSON*, values are comma-separated in brackets. In JSON* formats, tuples are output as arrays (in square brackets).", - "title": "Tuple(T1, T2, ...)" - }, - { - "location": "/data_types/nested_data_structures/nested/", - "text": "Nested(Name1 Type1, Name2 Type2, ...)\n\n\nA nested data structure is like a nested table. The parameters of a nested data structure \u2013 the column names and types \u2013 are specified the same way as in a CREATE query. Each table row can correspond to any number of rows in a nested data structure.\n\n\nExample:\n\n\nCREATE\n \nTABLE\n \ntest\n.\nvisits\n\n\n(\n\n \nCounterID\n \nUInt32\n,\n\n \nStartDate\n \nDate\n,\n\n \nSign\n \nInt8\n,\n\n \nIsNew\n \nUInt8\n,\n\n \nVisitID\n \nUInt64\n,\n\n \nUserID\n \nUInt64\n,\n\n \n...\n\n \nGoals\n \nNested\n\n \n(\n\n \nID\n \nUInt32\n,\n\n \nSerial\n \nUInt32\n,\n\n \nEventTime\n \nDateTime\n,\n\n \nPrice\n \nInt64\n,\n\n \nOrderID\n \nString\n,\n\n \nCurrencyID\n \nUInt32\n\n \n),\n\n \n...\n\n\n)\n \nENGINE\n \n=\n \nCollapsingMergeTree\n(\nStartDate\n,\n \nintHash32\n(\nUserID\n),\n \n(\nCounterID\n,\n \nStartDate\n,\n \nintHash32\n(\nUserID\n),\n \nVisitID\n),\n \n8192\n,\n \nSign\n)\n\n\n\n\n\n\nThis example declares the \nGoals\n nested data structure, which contains data about conversions (goals reached). Each row in the 'visits' table can correspond to zero or any number of conversions.\n\n\nOnly a single nesting level is supported. Columns of nested structures containing arrays are equivalent to multidimensional arrays, so they have limited support (there is no support for storing these columns in tables with the MergeTree engine).\n\n\nIn most cases, when working with a nested data structure, its individual columns are specified. To do this, the column names are separated by a dot. These columns make up an array of matching types. All the column arrays of a single nested data structure have the same length.\n\n\nExample:\n\n\nSELECT\n\n \nGoals\n.\nID\n,\n\n \nGoals\n.\nEventTime\n\n\nFROM\n \ntest\n.\nvisits\n\n\nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nlength\n(\nGoals\n.\nID\n)\n \n \n5\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500Goals.ID\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500Goals.EventTime\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1073752,591325,591325] \u2502 [\n2014-03-17 16:38:10\n,\n2014-03-17 16:38:48\n,\n2014-03-17 16:42:27\n] \u2502\n\u2502 [1073752] \u2502 [\n2014-03-17 00:28:25\n] \u2502\n\u2502 [1073752] \u2502 [\n2014-03-17 10:46:20\n] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [\n2014-03-17 13:59:20\n,\n2014-03-17 22:17:55\n,\n2014-03-17 22:18:07\n,\n2014-03-17 22:18:51\n] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [1073752,591325,591325] \u2502 [\n2014-03-17 11:37:06\n,\n2014-03-17 14:07:47\n,\n2014-03-17 14:36:21\n] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [591325,1073752] \u2502 [\n2014-03-17 00:46:05\n,\n2014-03-17 00:46:05\n] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [\n2014-03-17 13:28:33\n,\n2014-03-17 13:30:26\n,\n2014-03-17 18:51:21\n,\n2014-03-17 18:51:45\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIt is easiest to think of a nested data structure as a set of multiple column arrays of the same length.\n\n\nThe only place where a SELECT query can specify the name of an entire nested data structure instead of individual columns is the ARRAY JOIN clause. For more information, see \"ARRAY JOIN clause\". Example:\n\n\nSELECT\n\n \nGoal\n.\nID\n,\n\n \nGoal\n.\nEventTime\n\n\nFROM\n \ntest\n.\nvisits\n\n\nARRAY\n \nJOIN\n \nGoals\n \nAS\n \nGoal\n\n\nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nlength\n(\nGoals\n.\nID\n)\n \n \n5\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500Goal.ID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500Goal.EventTime\u2500\u2510\n\u2502 1073752 \u2502 2014-03-17 16:38:10 \u2502\n\u2502 591325 \u2502 2014-03-17 16:38:48 \u2502\n\u2502 591325 \u2502 2014-03-17 16:42:27 \u2502\n\u2502 1073752 \u2502 2014-03-17 00:28:25 \u2502\n\u2502 1073752 \u2502 2014-03-17 10:46:20 \u2502\n\u2502 1073752 \u2502 2014-03-17 13:59:20 \u2502\n\u2502 591325 \u2502 2014-03-17 22:17:55 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:07 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:51 \u2502\n\u2502 1073752 \u2502 2014-03-17 11:37:06 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nYou can't perform SELECT for an entire nested data structure. You can only explicitly list individual columns that are part of it.\n\n\nFor an INSERT query, you should pass all the component column arrays of a nested data structure separately (as if they were individual column arrays). During insertion, the system checks that they have the same length.\n\n\nFor a DESCRIBE query, the columns in a nested data structure are listed separately in the same way.\n\n\nThe ALTER query is very limited for elements in a nested data structure.", - "title": "Nested(Name1 Type1, Name2 Type2, ...)" - }, - { - "location": "/data_types/nested_data_structures/nested/#nestedname1-type1-name2-type2", - "text": "A nested data structure is like a nested table. The parameters of a nested data structure \u2013 the column names and types \u2013 are specified the same way as in a CREATE query. Each table row can correspond to any number of rows in a nested data structure. Example: CREATE TABLE test . visits ( \n CounterID UInt32 , \n StartDate Date , \n Sign Int8 , \n IsNew UInt8 , \n VisitID UInt64 , \n UserID UInt64 , \n ... \n Goals Nested \n ( \n ID UInt32 , \n Serial UInt32 , \n EventTime DateTime , \n Price Int64 , \n OrderID String , \n CurrencyID UInt32 \n ), \n ... ) ENGINE = CollapsingMergeTree ( StartDate , intHash32 ( UserID ), ( CounterID , StartDate , intHash32 ( UserID ), VisitID ), 8192 , Sign ) This example declares the Goals nested data structure, which contains data about conversions (goals reached). Each row in the 'visits' table can correspond to zero or any number of conversions. Only a single nesting level is supported. Columns of nested structures containing arrays are equivalent to multidimensional arrays, so they have limited support (there is no support for storing these columns in tables with the MergeTree engine). In most cases, when working with a nested data structure, its individual columns are specified. To do this, the column names are separated by a dot. These columns make up an array of matching types. All the column arrays of a single nested data structure have the same length. Example: SELECT \n Goals . ID , \n Goals . EventTime FROM test . visits WHERE CounterID = 101500 AND length ( Goals . ID ) 5 LIMIT 10 \u250c\u2500Goals.ID\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500Goals.EventTime\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1073752,591325,591325] \u2502 [ 2014-03-17 16:38:10 , 2014-03-17 16:38:48 , 2014-03-17 16:42:27 ] \u2502\n\u2502 [1073752] \u2502 [ 2014-03-17 00:28:25 ] \u2502\n\u2502 [1073752] \u2502 [ 2014-03-17 10:46:20 ] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [ 2014-03-17 13:59:20 , 2014-03-17 22:17:55 , 2014-03-17 22:18:07 , 2014-03-17 22:18:51 ] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [1073752,591325,591325] \u2502 [ 2014-03-17 11:37:06 , 2014-03-17 14:07:47 , 2014-03-17 14:36:21 ] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [591325,1073752] \u2502 [ 2014-03-17 00:46:05 , 2014-03-17 00:46:05 ] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [ 2014-03-17 13:28:33 , 2014-03-17 13:30:26 , 2014-03-17 18:51:21 , 2014-03-17 18:51:45 ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 It is easiest to think of a nested data structure as a set of multiple column arrays of the same length. The only place where a SELECT query can specify the name of an entire nested data structure instead of individual columns is the ARRAY JOIN clause. For more information, see \"ARRAY JOIN clause\". Example: SELECT \n Goal . ID , \n Goal . EventTime FROM test . visits ARRAY JOIN Goals AS Goal WHERE CounterID = 101500 AND length ( Goals . ID ) 5 LIMIT 10 \u250c\u2500Goal.ID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500Goal.EventTime\u2500\u2510\n\u2502 1073752 \u2502 2014-03-17 16:38:10 \u2502\n\u2502 591325 \u2502 2014-03-17 16:38:48 \u2502\n\u2502 591325 \u2502 2014-03-17 16:42:27 \u2502\n\u2502 1073752 \u2502 2014-03-17 00:28:25 \u2502\n\u2502 1073752 \u2502 2014-03-17 10:46:20 \u2502\n\u2502 1073752 \u2502 2014-03-17 13:59:20 \u2502\n\u2502 591325 \u2502 2014-03-17 22:17:55 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:07 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:51 \u2502\n\u2502 1073752 \u2502 2014-03-17 11:37:06 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 You can't perform SELECT for an entire nested data structure. You can only explicitly list individual columns that are part of it. For an INSERT query, you should pass all the component column arrays of a nested data structure separately (as if they were individual column arrays). During insertion, the system checks that they have the same length. For a DESCRIBE query, the columns in a nested data structure are listed separately in the same way. The ALTER query is very limited for elements in a nested data structure.", - "title": "Nested(Name1 Type1, Name2 Type2, ...)" - }, - { - "location": "/data_types/special_data_types/expression/", - "text": "Expression\n\n\nUsed for representing lambda expressions in high-order functions.", - "title": "Expression" - }, - { - "location": "/data_types/special_data_types/expression/#expression", - "text": "Used for representing lambda expressions in high-order functions.", - "title": "Expression" - }, - { - "location": "/data_types/special_data_types/set/", - "text": "Set\n\n\nUsed for the right half of an IN expression.", - "title": "Set" - }, - { - "location": "/data_types/special_data_types/set/#set", - "text": "Used for the right half of an IN expression.", - "title": "Set" - }, - { - "location": "/operators/", - "text": "Operators\n\n\nAll operators are transformed to the corresponding functions at the query parsing stage, in accordance with their precedence and associativity.\nGroups of operators are listed in order of priority (the higher it is in the list, the earlier the operator is connected to its arguments).\n\n\nAccess operators\n\n\na[N]\n Access to an element of an array; \narrayElement(a, N) function\n.\n\n\na.N\n \u2013 Access to a tuble element; \ntupleElement(a, N)\n function.\n\n\nNumeric negation operator\n\n\n-a\n \u2013 The \nnegate (a)\n function.\n\n\nMultiplication and division operators\n\n\na * b\n \u2013 The \nmultiply (a, b) function.\n\n\na / b\n \u2013 The \ndivide(a, b) function.\n\n\na % b\n \u2013 The \nmodulo(a, b) function.\n\n\nAddition and subtraction operators\n\n\na + b\n \u2013 The \nplus(a, b) function.\n\n\na - b\n \u2013 The \nminus(a, b) function.\n\n\nComparison operators\n\n\na = b\n \u2013 The \nequals(a, b) function.\n\n\na == b\n \u2013 The \nequals(a, b) function.\n\n\na != b\n \u2013 The \nnotEquals(a, b) function.\n\n\na \n b\n \u2013 The \nnotEquals(a, b) function.\n\n\na \n= b\n \u2013 The \nlessOrEquals(a, b) function.\n\n\na \n= b\n \u2013 The \ngreaterOrEquals(a, b) function.\n\n\na \n b\n \u2013 The \nless(a, b) function.\n\n\na \n b\n \u2013 The \ngreater(a, b) function.\n\n\na LIKE s\n \u2013 The \nlike(a, b) function.\n\n\na NOT LIKE s\n \u2013 The \nnotLike(a, b) function.\n\n\na BETWEEN b AND c\n \u2013 The same as \na \n= b AND a \n= c.\n\n\nOperators for working with data sets\n\n\nSee the section \"IN operators\".\n\n\na IN ...\n \u2013 The \nin(a, b) function\n\n\na NOT IN ...\n \u2013 The \nnotIn(a, b) function.\n\n\na GLOBAL IN ...\n \u2013 The \nglobalIn(a, b) function.\n\n\na GLOBAL NOT IN ...\n \u2013 The \nglobalNotIn(a, b) function.\n\n\nLogical negation operator\n\n\nNOT a\n The \nnot(a) function.\n\n\nLogical AND operator\n\n\na AND b\n \u2013 The\nand(a, b) function.\n\n\nLogical OR operator\n\n\na OR b\n \u2013 The \nor(a, b) function.\n\n\nConditional operator\n\n\na ? b : c\n \u2013 The \nif(a, b, c) function.\n\n\nNote:\n\n\nThe conditional operator calculates the values of b and c, then checks whether condition a is met, and then returns the corresponding value. If \"b\" or \"c\" is an arrayJoin() function, each row will be replicated regardless of the \"a\" condition.\n\n\nConditional expression\n\n\nCASE\n \n[\nx\n]\n\n \nWHEN\n \na\n \nTHEN\n \nb\n\n \n[\nWHEN\n \n...\n \nTHEN\n \n...]\n\n \nELSE\n \nc\n\n\nEND\n\n\n\n\n\n\nIf \"x\" is specified, then transform(x, [a, ...], [b, ...], c). Otherwise \u2013 multiIf(a, b, ..., c).\n\n\nConcatenation operator\n\n\ns1 || s2\n \u2013 The \nconcat(s1, s2) function.\n\n\nLambda creation operator\n\n\nx -\n expr\n \u2013 The \nlambda(x, expr) function.\n\n\nThe following operators do not have a priority, since they are brackets:\n\n\nArray creation operator\n\n\n[x1, ...]\n \u2013 The \narray(x1, ...) function.\n\n\nTuple creation operator\n\n\n(x1, x2, ...)\n \u2013 The \ntuple(x2, x2, ...) function.\n\n\nAssociativity\n\n\nAll binary operators have left associativity. For example, \n1 + 2 + 3\n is transformed to \nplus(plus(1, 2), 3)\n.\nSometimes this doesn't work the way you expect. For example, \nSELECT 4 \n 2 \n 3\n will result in 0.\n\n\nFor efficiency, the \nand\n and \nor\n functions accept any number of arguments. The corresponding chains of \nAND\n and \nOR\n operators are transformed to a single call of these functions.", - "title": "Operators" - }, - { - "location": "/operators/#operators", - "text": "All operators are transformed to the corresponding functions at the query parsing stage, in accordance with their precedence and associativity.\nGroups of operators are listed in order of priority (the higher it is in the list, the earlier the operator is connected to its arguments).", - "title": "Operators" - }, - { - "location": "/operators/#access-operators", - "text": "a[N] Access to an element of an array; arrayElement(a, N) function . a.N \u2013 Access to a tuble element; tupleElement(a, N) function.", - "title": "Access operators" - }, - { - "location": "/operators/#numeric-negation-operator", - "text": "-a \u2013 The negate (a) function.", - "title": "Numeric negation operator" - }, - { - "location": "/operators/#multiplication-and-division-operators", - "text": "a * b \u2013 The multiply (a, b) function. a / b \u2013 The divide(a, b) function. a % b \u2013 The modulo(a, b) function.", - "title": "Multiplication and division operators" - }, - { - "location": "/operators/#addition-and-subtraction-operators", - "text": "a + b \u2013 The plus(a, b) function. a - b \u2013 The minus(a, b) function.", - "title": "Addition and subtraction operators" - }, - { - "location": "/operators/#comparison-operators", - "text": "a = b \u2013 The equals(a, b) function. a == b \u2013 The equals(a, b) function. a != b \u2013 The notEquals(a, b) function. a b \u2013 The notEquals(a, b) function. a = b \u2013 The lessOrEquals(a, b) function. a = b \u2013 The greaterOrEquals(a, b) function. a b \u2013 The less(a, b) function. a b \u2013 The greater(a, b) function. a LIKE s \u2013 The like(a, b) function. a NOT LIKE s \u2013 The notLike(a, b) function. a BETWEEN b AND c \u2013 The same as a = b AND a = c.", - "title": "Comparison operators" - }, - { - "location": "/operators/#operators-for-working-with-data-sets", - "text": "See the section \"IN operators\". a IN ... \u2013 The in(a, b) function a NOT IN ... \u2013 The notIn(a, b) function. a GLOBAL IN ... \u2013 The globalIn(a, b) function. a GLOBAL NOT IN ... \u2013 The globalNotIn(a, b) function.", - "title": "Operators for working with data sets" - }, - { - "location": "/operators/#logical-negation-operator", - "text": "NOT a The not(a) function.", - "title": "Logical negation operator" - }, - { - "location": "/operators/#logical-and-operator", - "text": "a AND b \u2013 The and(a, b) function.", - "title": "Logical AND operator" - }, - { - "location": "/operators/#logical-or-operator", - "text": "a OR b \u2013 The or(a, b) function.", - "title": "Logical OR operator" - }, - { - "location": "/operators/#conditional-operator", - "text": "a ? b : c \u2013 The if(a, b, c) function. Note: The conditional operator calculates the values of b and c, then checks whether condition a is met, and then returns the corresponding value. If \"b\" or \"c\" is an arrayJoin() function, each row will be replicated regardless of the \"a\" condition.", - "title": "Conditional operator" - }, - { - "location": "/operators/#conditional-expression", - "text": "CASE [ x ] \n WHEN a THEN b \n [ WHEN ... THEN ...] \n ELSE c END If \"x\" is specified, then transform(x, [a, ...], [b, ...], c). Otherwise \u2013 multiIf(a, b, ..., c).", - "title": "Conditional expression" - }, - { - "location": "/operators/#concatenation-operator", - "text": "s1 || s2 \u2013 The concat(s1, s2) function.", - "title": "Concatenation operator" - }, - { - "location": "/operators/#lambda-creation-operator", - "text": "x - expr \u2013 The lambda(x, expr) function. The following operators do not have a priority, since they are brackets:", - "title": "Lambda creation operator" - }, - { - "location": "/operators/#array-creation-operator", - "text": "[x1, ...] \u2013 The array(x1, ...) function.", - "title": "Array creation operator" - }, - { - "location": "/operators/#tuple-creation-operator", - "text": "(x1, x2, ...) \u2013 The tuple(x2, x2, ...) function.", - "title": "Tuple creation operator" - }, - { - "location": "/operators/#associativity", - "text": "All binary operators have left associativity. For example, 1 + 2 + 3 is transformed to plus(plus(1, 2), 3) .\nSometimes this doesn't work the way you expect. For example, SELECT 4 2 3 will result in 0. For efficiency, the and and or functions accept any number of arguments. The corresponding chains of AND and OR operators are transformed to a single call of these functions.", - "title": "Associativity" - }, - { - "location": "/functions/", - "text": "Functions\n\n\nThere are at least* two types of functions - regular functions (they are just called \"functions\") and aggregate functions. These are completely different concepts. Regular functions work as if they are applied to each row separately (for each row, the result of the function doesn't depend on the other rows). Aggregate functions accumulate a set of values from various rows (i.e. they depend on the entire set of rows).\n\n\nIn this section we discuss regular functions. For aggregate functions, see the section \"Aggregate functions\".\n\n\n* - There is a third type of function that the 'arrayJoin' function belongs to; table functions can also be mentioned separately.*\n\n\nStrong typing\n\n\nIn contrast to standard SQL, ClickHouse has strong typing. In other words, it doesn't make implicit conversions between types. Each function works for a specific set of types. This means that sometimes you need to use type conversion functions.\n\n\nCommon subexpression elimination\n\n\nAll expressions in a query that have the same AST (the same record or same result of syntactic parsing) are considered to have identical values. Such expressions are concatenated and executed once. Identical subqueries are also eliminated this way.\n\n\nTypes of results\n\n\nAll functions return a single return as the result (not several values, and not zero values). The type of result is usually defined only by the types of arguments, not by the values. Exceptions are the tupleElement function (the a.N operator), and the toFixedString function.\n\n\nConstants\n\n\nFor simplicity, certain functions can only work with constants for some arguments. For example, the right argument of the LIKE operator must be a constant.\nAlmost all functions return a constant for constant arguments. The exception is functions that generate random numbers.\nThe 'now' function returns different values for queries that were run at different times, but the result is considered a constant, since constancy is only important within a single query.\nA constant expression is also considered a constant (for example, the right half of the LIKE operator can be constructed from multiple constants).\n\n\nFunctions can be implemented in different ways for constant and non-constant arguments (different code is executed). But the results for a constant and for a true column containing only the same value should match each other.\n\n\nConstancy\n\n\nFunctions can't change the values of their arguments \u2013 any changes are returned as the result. Thus, the result of calculating separate functions does not depend on the order in which the functions are written in the query.\n\n\nError handling\n\n\nSome functions might throw an exception if the data is invalid. In this case, the query is canceled and an error text is returned to the client. For distributed processing, when an exception occurs on one of the servers, the other servers also attempt to abort the query.\n\n\nEvaluation of argument expressions\n\n\nIn almost all programming languages, one of the arguments might not be evaluated for certain operators. This is usually the operators \n, \n||\n, and \n?:\n.\nBut in ClickHouse, arguments of functions (operators) are always evaluated. This is because entire parts of columns are evaluated at once, instead of calculating each row separately.\n\n\nPerforming functions for distributed query processing\n\n\nFor distributed query processing, as many stages of query processing as possible are performed on remote servers, and the rest of the stages (merging intermediate results and everything after that) are performed on the requestor server.\n\n\nThis means that functions can be performed on different servers.\nFor example, in the query \nSELECT f(sum(g(x))) FROM distributed_table GROUP BY h(y),\n\n\n\n\nif a \ndistributed_table\n has at least two shards, the functions 'g' and 'h' are performed on remote servers, and the function 'f' is performed on the requestor server.\n\n\nif a \ndistributed_table\n has only one shard, all the 'f', 'g', and 'h' functions are performed on this shard's server.\n\n\n\n\nThe result of a function usually doesn't depend on which server it is performed on. However, sometimes this is important.\nFor example, functions that work with dictionaries use the dictionary that exists on the server they are running on.\nAnother example is the \nhostName\n function, which returns the name of the server it is running on in order to make \nGROUP BY\n by servers in a \nSELECT\n query.\n\n\nIf a function in a query is performed on the requestor server, but you need to perform it on remote servers, you can wrap it in an 'any' aggregate function or add it to a key in \nGROUP BY\n.", - "title": "Introduction" - }, - { - "location": "/functions/#functions", - "text": "There are at least* two types of functions - regular functions (they are just called \"functions\") and aggregate functions. These are completely different concepts. Regular functions work as if they are applied to each row separately (for each row, the result of the function doesn't depend on the other rows). Aggregate functions accumulate a set of values from various rows (i.e. they depend on the entire set of rows). In this section we discuss regular functions. For aggregate functions, see the section \"Aggregate functions\". * - There is a third type of function that the 'arrayJoin' function belongs to; table functions can also be mentioned separately.*", - "title": "Functions" - }, - { - "location": "/functions/#strong-typing", - "text": "In contrast to standard SQL, ClickHouse has strong typing. In other words, it doesn't make implicit conversions between types. Each function works for a specific set of types. This means that sometimes you need to use type conversion functions.", - "title": "Strong typing" - }, - { - "location": "/functions/#common-subexpression-elimination", - "text": "All expressions in a query that have the same AST (the same record or same result of syntactic parsing) are considered to have identical values. Such expressions are concatenated and executed once. Identical subqueries are also eliminated this way.", - "title": "Common subexpression elimination" - }, - { - "location": "/functions/#types-of-results", - "text": "All functions return a single return as the result (not several values, and not zero values). The type of result is usually defined only by the types of arguments, not by the values. Exceptions are the tupleElement function (the a.N operator), and the toFixedString function.", - "title": "Types of results" - }, - { - "location": "/functions/#constants", - "text": "For simplicity, certain functions can only work with constants for some arguments. For example, the right argument of the LIKE operator must be a constant.\nAlmost all functions return a constant for constant arguments. The exception is functions that generate random numbers.\nThe 'now' function returns different values for queries that were run at different times, but the result is considered a constant, since constancy is only important within a single query.\nA constant expression is also considered a constant (for example, the right half of the LIKE operator can be constructed from multiple constants). Functions can be implemented in different ways for constant and non-constant arguments (different code is executed). But the results for a constant and for a true column containing only the same value should match each other.", - "title": "Constants" - }, - { - "location": "/functions/#constancy", - "text": "Functions can't change the values of their arguments \u2013 any changes are returned as the result. Thus, the result of calculating separate functions does not depend on the order in which the functions are written in the query.", - "title": "Constancy" - }, - { - "location": "/functions/#error-handling", - "text": "Some functions might throw an exception if the data is invalid. In this case, the query is canceled and an error text is returned to the client. For distributed processing, when an exception occurs on one of the servers, the other servers also attempt to abort the query.", - "title": "Error handling" - }, - { - "location": "/functions/#evaluation-of-argument-expressions", - "text": "In almost all programming languages, one of the arguments might not be evaluated for certain operators. This is usually the operators , || , and ?: .\nBut in ClickHouse, arguments of functions (operators) are always evaluated. This is because entire parts of columns are evaluated at once, instead of calculating each row separately.", - "title": "Evaluation of argument expressions" - }, - { - "location": "/functions/#performing-functions-for-distributed-query-processing", - "text": "For distributed query processing, as many stages of query processing as possible are performed on remote servers, and the rest of the stages (merging intermediate results and everything after that) are performed on the requestor server. This means that functions can be performed on different servers.\nFor example, in the query SELECT f(sum(g(x))) FROM distributed_table GROUP BY h(y), if a distributed_table has at least two shards, the functions 'g' and 'h' are performed on remote servers, and the function 'f' is performed on the requestor server. if a distributed_table has only one shard, all the 'f', 'g', and 'h' functions are performed on this shard's server. The result of a function usually doesn't depend on which server it is performed on. However, sometimes this is important.\nFor example, functions that work with dictionaries use the dictionary that exists on the server they are running on.\nAnother example is the hostName function, which returns the name of the server it is running on in order to make GROUP BY by servers in a SELECT query. If a function in a query is performed on the requestor server, but you need to perform it on remote servers, you can wrap it in an 'any' aggregate function or add it to a key in GROUP BY .", - "title": "Performing functions for distributed query processing" - }, - { - "location": "/functions/arithmetic_functions/", - "text": "Arithmetic functions\n\n\nFor all arithmetic functions, the result type is calculated as the smallest number type that the result fits in, if there is such a type. The minimum is taken simultaneously based on the number of bits, whether it is signed, and whether it floats. If there are not enough bits, the highest bit type is taken.\n\n\nExample:\n\n\nSELECT\n \ntoTypeName\n(\n0\n),\n \ntoTypeName\n(\n0\n \n+\n \n0\n),\n \ntoTypeName\n(\n0\n \n+\n \n0\n \n+\n \n0\n),\n \ntoTypeName\n(\n0\n \n+\n \n0\n \n+\n \n0\n \n+\n \n0\n)\n\n\n\n\n\n\n\u250c\u2500toTypeName(0)\u2500\u252c\u2500toTypeName(plus(0, 0))\u2500\u252c\u2500toTypeName(plus(plus(0, 0), 0))\u2500\u252c\u2500toTypeName(plus(plus(plus(0, 0), 0), 0))\u2500\u2510\n\u2502 UInt8 \u2502 UInt16 \u2502 UInt32 \u2502 UInt64 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nArithmetic functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.\n\n\nOverflow is produced the same way as in C++.\n\n\nplus(a, b), a + b operator\n\n\nCalculates the sum of the numbers.\nYou can also add integer numbers with a date or date and time. In the case of a date, adding an integer means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds.\n\n\nminus(a, b), a - b operator\n\n\nCalculates the difference. The result is always signed.\n\n\nYou can also calculate integer numbers from a date or date with time. The idea is the same \u2013 see above for 'plus'.\n\n\nmultiply(a, b), a * b operator\n\n\nCalculates the product of the numbers.\n\n\ndivide(a, b), a / b operator\n\n\nCalculates the quotient of the numbers. The result type is always a floating-point type.\nIt is not integer division. For integer division, use the 'intDiv' function.\nWhen dividing by zero you get 'inf', '-inf', or 'nan'.\n\n\nintDiv(a, b)\n\n\nCalculates the quotient of the numbers. Divides into integers, rounding down (by the absolute value).\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nintDivOrZero(a, b)\n\n\nDiffers from 'intDiv' in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nmodulo(a, b), a % b operator\n\n\nCalculates the remainder after division.\nIf arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion.\nThe remainder is taken in the same sense as in C++. Truncated division is used for negative numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nnegate(a), -a operator\n\n\nCalculates a number with the reverse sign. The result is always signed.\n\n\nabs(a)\n\n\nCalculates the absolute value of the number (a). That is, if a \n 0, it returns -a. For unsigned types it doesn't do anything. For signed integer types, it returns an unsigned number.\n\n\ngcd(a, b)\n\n\nReturns the greatest common divisor of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nlcm(a, b)\n\n\nReturns the least common multiple of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", - "title": "Arithmetic functions" - }, - { - "location": "/functions/arithmetic_functions/#arithmetic-functions", - "text": "For all arithmetic functions, the result type is calculated as the smallest number type that the result fits in, if there is such a type. The minimum is taken simultaneously based on the number of bits, whether it is signed, and whether it floats. If there are not enough bits, the highest bit type is taken. Example: SELECT toTypeName ( 0 ), toTypeName ( 0 + 0 ), toTypeName ( 0 + 0 + 0 ), toTypeName ( 0 + 0 + 0 + 0 ) \u250c\u2500toTypeName(0)\u2500\u252c\u2500toTypeName(plus(0, 0))\u2500\u252c\u2500toTypeName(plus(plus(0, 0), 0))\u2500\u252c\u2500toTypeName(plus(plus(plus(0, 0), 0), 0))\u2500\u2510\n\u2502 UInt8 \u2502 UInt16 \u2502 UInt32 \u2502 UInt64 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Arithmetic functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64. Overflow is produced the same way as in C++.", - "title": "Arithmetic functions" - }, - { - "location": "/functions/arithmetic_functions/#plusa-b-a-b-operator", - "text": "Calculates the sum of the numbers.\nYou can also add integer numbers with a date or date and time. In the case of a date, adding an integer means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds.", - "title": "plus(a, b), a + b operator" - }, - { - "location": "/functions/arithmetic_functions/#minusa-b-a-b-operator", - "text": "Calculates the difference. The result is always signed. You can also calculate integer numbers from a date or date with time. The idea is the same \u2013 see above for 'plus'.", - "title": "minus(a, b), a - b operator" - }, - { - "location": "/functions/arithmetic_functions/#multiplya-b-a-42-b-operator", - "text": "Calculates the product of the numbers.", - "title": "multiply(a, b), a * b operator" - }, - { - "location": "/functions/arithmetic_functions/#dividea-b-a-b-operator", - "text": "Calculates the quotient of the numbers. The result type is always a floating-point type.\nIt is not integer division. For integer division, use the 'intDiv' function.\nWhen dividing by zero you get 'inf', '-inf', or 'nan'.", - "title": "divide(a, b), a / b operator" - }, - { - "location": "/functions/arithmetic_functions/#intdiva-b", - "text": "Calculates the quotient of the numbers. Divides into integers, rounding down (by the absolute value).\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", - "title": "intDiv(a, b)" - }, - { - "location": "/functions/arithmetic_functions/#intdivorzeroa-b", - "text": "Differs from 'intDiv' in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one.", - "title": "intDivOrZero(a, b)" - }, - { - "location": "/functions/arithmetic_functions/#moduloa-b-a-b-operator", - "text": "Calculates the remainder after division.\nIf arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion.\nThe remainder is taken in the same sense as in C++. Truncated division is used for negative numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", - "title": "modulo(a, b), a % b operator" - }, - { - "location": "/functions/arithmetic_functions/#negatea-a-operator", - "text": "Calculates a number with the reverse sign. The result is always signed.", - "title": "negate(a), -a operator" - }, - { - "location": "/functions/arithmetic_functions/#absa", - "text": "Calculates the absolute value of the number (a). That is, if a 0, it returns -a. For unsigned types it doesn't do anything. For signed integer types, it returns an unsigned number.", - "title": "abs(a)" - }, - { - "location": "/functions/arithmetic_functions/#gcda-b", - "text": "Returns the greatest common divisor of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", - "title": "gcd(a, b)" - }, - { - "location": "/functions/arithmetic_functions/#lcma-b", - "text": "Returns the least common multiple of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", - "title": "lcm(a, b)" - }, - { - "location": "/functions/comparison_functions/", - "text": "Comparison functions\n\n\nComparison functions always return 0 or 1 (Uint8).\n\n\nThe following types can be compared:\n\n\n\n\nnumbers\n\n\nstrings and fixed strings\n\n\ndates\n\n\ndates with times\n\n\n\n\nwithin each group, but not between different groups.\n\n\nFor example, you can't compare a date with a string. You have to use a function to convert the string to a date, or vice versa.\n\n\nStrings are compared by bytes. A shorter string is smaller than all strings that start with it and that contain at least one more character.\n\n\nNote. Up until version 1.1.54134, signed and unsigned numbers were compared the same way as in C++. In other words, you could get an incorrect result in cases like SELECT 9223372036854775807 \n -1. This behavior changed in version 1.1.54134 and is now mathematically correct.\n\n\nequals, a = b and a == b operator\n\n\nnotEquals, a ! operator= b and a \n b\n\n\nless, \n operator\n\n\ngreater, \n operator\n\n\nlessOrEquals, \n= operator\n\n\ngreaterOrEquals, \n= operator", - "title": "Comparison functions" - }, - { - "location": "/functions/comparison_functions/#comparison-functions", - "text": "Comparison functions always return 0 or 1 (Uint8). The following types can be compared: numbers strings and fixed strings dates dates with times within each group, but not between different groups. For example, you can't compare a date with a string. You have to use a function to convert the string to a date, or vice versa. Strings are compared by bytes. A shorter string is smaller than all strings that start with it and that contain at least one more character. Note. Up until version 1.1.54134, signed and unsigned numbers were compared the same way as in C++. In other words, you could get an incorrect result in cases like SELECT 9223372036854775807 -1. This behavior changed in version 1.1.54134 and is now mathematically correct.", - "title": "Comparison functions" - }, - { - "location": "/functions/comparison_functions/#equals-a-b-and-a-b-operator", - "text": "", - "title": "equals, a = b and a == b operator" - }, - { - "location": "/functions/comparison_functions/#notequals-a-operator-b-and-a-b", - "text": "", - "title": "notEquals, a ! operator= b and a <> b" - }, - { - "location": "/functions/comparison_functions/#less-operator", - "text": "", - "title": "less, < operator" - }, - { - "location": "/functions/comparison_functions/#greater-operator", - "text": "", - "title": "greater, > operator" - }, - { - "location": "/functions/comparison_functions/#lessorequals-operator", - "text": "", - "title": "lessOrEquals, <= operator" - }, - { - "location": "/functions/comparison_functions/#greaterorequals-operator", - "text": "", - "title": "greaterOrEquals, >= operator" - }, - { - "location": "/functions/logical_functions/", - "text": "Logical functions\n\n\nLogical functions accept any numeric types, but return a UInt8 number equal to 0 or 1.\n\n\nZero as an argument is considered \"false,\" while any non-zero value is considered \"true\".\n\n\nand, AND operator\n\n\nor, OR operator\n\n\nnot, NOT operator\n\n\nxor", - "title": "Logical functions" - }, - { - "location": "/functions/logical_functions/#logical-functions", - "text": "Logical functions accept any numeric types, but return a UInt8 number equal to 0 or 1. Zero as an argument is considered \"false,\" while any non-zero value is considered \"true\".", - "title": "Logical functions" - }, - { - "location": "/functions/logical_functions/#and-and-operator", - "text": "", - "title": "and, AND operator" - }, - { - "location": "/functions/logical_functions/#or-or-operator", - "text": "", - "title": "or, OR operator" - }, - { - "location": "/functions/logical_functions/#not-not-operator", - "text": "", - "title": "not, NOT operator" - }, - { - "location": "/functions/logical_functions/#xor", - "text": "", - "title": "xor" - }, - { - "location": "/functions/type_conversion_functions/", - "text": "Type conversion functions\n\n\ntoUInt8, toUInt16, toUInt32, toUInt64\n\n\ntoInt8, toInt16, toInt32, toInt64\n\n\ntoFloat32, toFloat64\n\n\ntoUInt8OrZero, toUInt16OrZero, toUInt32OrZero, toUInt64OrZero, toInt8OrZero, toInt16OrZero, toInt32OrZero, toInt64OrZero, toFloat32OrZero, toFloat64OrZero\n\n\ntoDate, toDateTime\n\n\ntoString\n\n\nFunctions for converting between numbers, strings (but not fixed strings), dates, and dates with times.\nAll these functions accept one argument.\n\n\nWhen converting to or from a string, the value is formatted or parsed using the same rules as for the TabSeparated format (and almost all other text formats). If the string can't be parsed, an exception is thrown and the request is canceled.\n\n\nWhen converting dates to numbers or vice versa, the date corresponds to the number of days since the beginning of the Unix epoch.\nWhen converting dates with times to numbers or vice versa, the date with time corresponds to the number of seconds since the beginning of the Unix epoch.\n\n\nThe date and date-with-time formats for the toDate/toDateTime functions are defined as follows:\n\n\nYYYY-MM-DD\nYYYY-MM-DD hh:mm:ss\n\n\n\n\n\nAs an exception, if converting from UInt32, Int32, UInt64, or Int64 numeric types to Date, and if the number is greater than or equal to 65536, the number is interpreted as a Unix timestamp (and not as the number of days) and is rounded to the date. This allows support for the common occurrence of writing 'toDate(unix_timestamp)', which otherwise would be an error and would require writing the more cumbersome 'toDate(toDateTime(unix_timestamp))'.\n\n\nConversion between a date and date with time is performed the natural way: by adding a null time or dropping the time.\n\n\nConversion between numeric types uses the same rules as assignments between different numeric types in C++.\n\n\nAdditionally, the toString function of the DateTime argument can take a second String argument containing the name of the time zone. Example: \nAsia/Yekaterinburg\n In this case, the time is formatted according to the specified time zone.\n\n\nSELECT\n\n \nnow\n()\n \nAS\n \nnow_local\n,\n\n \ntoString\n(\nnow\n(),\n \nAsia/Yekaterinburg\n)\n \nAS\n \nnow_yekat\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500now_local\u2500\u252c\u2500now_yekat\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 00:11:21 \u2502 2016-06-15 02:11:21 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nAlso see the \ntoUnixTimestamp\n function.\n\n\ntoFixedString(s, N)\n\n\nConverts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant.\nIf the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown.\n\n\ntoStringCutToZero(s)\n\n\nAccepts a String or FixedString argument. Returns the String with the content truncated at the first zero byte found.\n\n\nExample:\n\n\nSELECT\n \ntoFixedString\n(\nfoo\n,\n \n8\n)\n \nAS\n \ns\n,\n \ntoStringCutToZero\n(\ns\n)\n \nAS\n \ns_cut\n\n\n\n\n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0\\0\\0\\0\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n \ntoFixedString\n(\nfoo\\0bar\n,\n \n8\n)\n \nAS\n \ns\n,\n \ntoStringCutToZero\n(\ns\n)\n \nAS\n \ns_cut\n\n\n\n\n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0bar\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nreinterpretAsUInt8, reinterpretAsUInt16, reinterpretAsUInt32, reinterpretAsUInt64\n\n\nreinterpretAsInt8, reinterpretAsInt16, reinterpretAsInt32, reinterpretAsInt64\n\n\nreinterpretAsFloat32, reinterpretAsFloat64\n\n\nreinterpretAsDate, reinterpretAsDateTime\n\n\nThese functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\n\n\nreinterpretAsString\n\n\nThis function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long.\n\n\nCAST(x, t)\n\n\nConverts 'x' to the 't' data type. The syntax CAST(x AS t) is also supported.\n\n\nExample:\n\n\nSELECT\n\n \n2016-06-15 23:00:00\n \nAS\n \ntimestamp\n,\n\n \nCAST\n(\ntimestamp\n \nAS\n \nDateTime\n)\n \nAS\n \ndatetime\n,\n\n \nCAST\n(\ntimestamp\n \nAS\n \nDate\n)\n \nAS\n \ndate\n,\n\n \nCAST\n(\ntimestamp\n,\n \nString\n)\n \nAS\n \nstring\n,\n\n \nCAST\n(\ntimestamp\n,\n \nFixedString(22)\n)\n \nAS\n \nfixed_string\n\n\n\n\n\n\n\u250c\u2500timestamp\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500datetime\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500date\u2500\u252c\u2500string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500fixed_string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00\\0\\0\\0 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nConversion to FixedString (N) only works for arguments of type String or FixedString (N).", - "title": "Type conversion functions" - }, - { - "location": "/functions/type_conversion_functions/#type-conversion-functions", - "text": "", - "title": "Type conversion functions" - }, - { - "location": "/functions/type_conversion_functions/#touint8-touint16-touint32-touint64", - "text": "", - "title": "toUInt8, toUInt16, toUInt32, toUInt64" - }, - { - "location": "/functions/type_conversion_functions/#toint8-toint16-toint32-toint64", - "text": "", - "title": "toInt8, toInt16, toInt32, toInt64" - }, - { - "location": "/functions/type_conversion_functions/#tofloat32-tofloat64", - "text": "", - "title": "toFloat32, toFloat64" - }, - { - "location": "/functions/type_conversion_functions/#touint8orzero-touint16orzero-touint32orzero-touint64orzero-toint8orzero-toint16orzero-toint32orzero-toint64orzero-tofloat32orzero-tofloat64orzero", - "text": "", - "title": "toUInt8OrZero, toUInt16OrZero, toUInt32OrZero, toUInt64OrZero, toInt8OrZero, toInt16OrZero, toInt32OrZero, toInt64OrZero, toFloat32OrZero, toFloat64OrZero" - }, - { - "location": "/functions/type_conversion_functions/#todate-todatetime", - "text": "", - "title": "toDate, toDateTime" - }, - { - "location": "/functions/type_conversion_functions/#tostring", - "text": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times.\nAll these functions accept one argument. When converting to or from a string, the value is formatted or parsed using the same rules as for the TabSeparated format (and almost all other text formats). If the string can't be parsed, an exception is thrown and the request is canceled. When converting dates to numbers or vice versa, the date corresponds to the number of days since the beginning of the Unix epoch.\nWhen converting dates with times to numbers or vice versa, the date with time corresponds to the number of seconds since the beginning of the Unix epoch. The date and date-with-time formats for the toDate/toDateTime functions are defined as follows: YYYY-MM-DD\nYYYY-MM-DD hh:mm:ss As an exception, if converting from UInt32, Int32, UInt64, or Int64 numeric types to Date, and if the number is greater than or equal to 65536, the number is interpreted as a Unix timestamp (and not as the number of days) and is rounded to the date. This allows support for the common occurrence of writing 'toDate(unix_timestamp)', which otherwise would be an error and would require writing the more cumbersome 'toDate(toDateTime(unix_timestamp))'. Conversion between a date and date with time is performed the natural way: by adding a null time or dropping the time. Conversion between numeric types uses the same rules as assignments between different numeric types in C++. Additionally, the toString function of the DateTime argument can take a second String argument containing the name of the time zone. Example: Asia/Yekaterinburg In this case, the time is formatted according to the specified time zone. SELECT \n now () AS now_local , \n toString ( now (), Asia/Yekaterinburg ) AS now_yekat \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500now_local\u2500\u252c\u2500now_yekat\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 00:11:21 \u2502 2016-06-15 02:11:21 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Also see the toUnixTimestamp function.", - "title": "toString" - }, - { - "location": "/functions/type_conversion_functions/#tofixedstrings-n", - "text": "Converts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant.\nIf the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown.", - "title": "toFixedString(s, N)" - }, - { - "location": "/functions/type_conversion_functions/#tostringcuttozeros", - "text": "Accepts a String or FixedString argument. Returns the String with the content truncated at the first zero byte found. Example: SELECT toFixedString ( foo , 8 ) AS s , toStringCutToZero ( s ) AS s_cut \u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0\\0\\0\\0\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT toFixedString ( foo\\0bar , 8 ) AS s , toStringCutToZero ( s ) AS s_cut \u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0bar\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "toStringCutToZero(s)" - }, - { - "location": "/functions/type_conversion_functions/#reinterpretasuint8-reinterpretasuint16-reinterpretasuint32-reinterpretasuint64", - "text": "", - "title": "reinterpretAsUInt8, reinterpretAsUInt16, reinterpretAsUInt32, reinterpretAsUInt64" - }, - { - "location": "/functions/type_conversion_functions/#reinterpretasint8-reinterpretasint16-reinterpretasint32-reinterpretasint64", - "text": "", - "title": "reinterpretAsInt8, reinterpretAsInt16, reinterpretAsInt32, reinterpretAsInt64" - }, - { - "location": "/functions/type_conversion_functions/#reinterpretasfloat32-reinterpretasfloat64", - "text": "", - "title": "reinterpretAsFloat32, reinterpretAsFloat64" - }, - { - "location": "/functions/type_conversion_functions/#reinterpretasdate-reinterpretasdatetime", - "text": "These functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.", - "title": "reinterpretAsDate, reinterpretAsDateTime" - }, - { - "location": "/functions/type_conversion_functions/#reinterpretasstring", - "text": "This function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long.", - "title": "reinterpretAsString" - }, - { - "location": "/functions/type_conversion_functions/#castx-t", - "text": "Converts 'x' to the 't' data type. The syntax CAST(x AS t) is also supported. Example: SELECT \n 2016-06-15 23:00:00 AS timestamp , \n CAST ( timestamp AS DateTime ) AS datetime , \n CAST ( timestamp AS Date ) AS date , \n CAST ( timestamp , String ) AS string , \n CAST ( timestamp , FixedString(22) ) AS fixed_string \u250c\u2500timestamp\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500datetime\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500date\u2500\u252c\u2500string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500fixed_string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00\\0\\0\\0 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Conversion to FixedString (N) only works for arguments of type String or FixedString (N).", - "title": "CAST(x, t)" - }, - { - "location": "/functions/date_time_functions/", - "text": "Functions for working with dates and times\n\n\nSupport for time zones\n\n\nAll functions for working with the date and time that have a logical use for the time zone can accept a second optional time zone argument. Example: Asia/Yekaterinburg. In this case, they use the specified time zone instead of the local (default) one.\n\n\nSELECT\n\n \ntoDateTime\n(\n2016-06-15 23:00:00\n)\n \nAS\n \ntime\n,\n\n \ntoDate\n(\ntime\n)\n \nAS\n \ndate_local\n,\n\n \ntoDate\n(\ntime\n,\n \nAsia/Yekaterinburg\n)\n \nAS\n \ndate_yekat\n,\n\n \ntoString\n(\ntime\n,\n \nUS/Samoa\n)\n \nAS\n \ntime_samoa\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500time\u2500\u252c\u2500date_local\u2500\u252c\u2500date_yekat\u2500\u252c\u2500time_samoa\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-16 \u2502 2016-06-15 09:00:00 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nOnly time zones that differ from UTC by a whole number of hours are supported.\n\n\ntoYear\n\n\nConverts a date or date with time to a UInt16 number containing the year number (AD).\n\n\ntoMonth\n\n\nConverts a date or date with time to a UInt8 number containing the month number (1-12).\n\n\ntoDayOfMonth\n\n\n-Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31).\n\n\ntoDayOfWeek\n\n\nConverts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7).\n\n\ntoHour\n\n\nConverts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23).\nThis function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true \u2013 even in Moscow the clocks were twice changed at a different time).\n\n\ntoMinute\n\n\nConverts a date with time to a UInt8 number containing the number of the minute of the hour (0-59).\n\n\ntoSecond\n\n\nConverts a date with time to a UInt8 number containing the number of the second in the minute (0-59).\nLeap seconds are not accounted for.\n\n\ntoMonday\n\n\nRounds down a date or date with time to the nearest Monday.\nReturns the date.\n\n\ntoStartOfMonth\n\n\nRounds down a date or date with time to the first day of the month.\nReturns the date.\n\n\ntoStartOfQuarter\n\n\nRounds down a date or date with time to the first day of the quarter.\nThe first day of the quarter is either 1 January, 1 April, 1 July, or 1 October.\nReturns the date.\n\n\ntoStartOfYear\n\n\nRounds down a date or date with time to the first day of the year.\nReturns the date.\n\n\ntoStartOfMinute\n\n\nRounds down a date with time to the start of the minute.\n\n\ntoStartOfFiveMinute\n\n\nRounds down a date with time to the start of the hour.\n\n\ntoStartOfFifteenMinutes\n\n\nRounds down the date with time to the start of the fifteen-minute interval.\n\n\nNote: If you need to round a date with time to any other number of seconds, minutes, or hours, you can convert it into a number by using the toUInt32 function, then round the number using intDiv and multiplication, and convert it back using the toDateTime function.\n\n\ntoStartOfHour\n\n\nRounds down a date with time to the start of the hour.\n\n\ntoStartOfDay\n\n\nRounds down a date with time to the start of the day.\n\n\ntoTime\n\n\nConverts a date with time to a certain fixed date, while preserving the time.\n\n\ntoRelativeYearNum\n\n\nConverts a date with time or date to the number of the year, starting from a certain fixed point in the past.\n\n\ntoRelativeMonthNum\n\n\nConverts a date with time or date to the number of the month, starting from a certain fixed point in the past.\n\n\ntoRelativeWeekNum\n\n\nConverts a date with time or date to the number of the week, starting from a certain fixed point in the past.\n\n\ntoRelativeDayNum\n\n\nConverts a date with time or date to the number of the day, starting from a certain fixed point in the past.\n\n\ntoRelativeHourNum\n\n\nConverts a date with time or date to the number of the hour, starting from a certain fixed point in the past.\n\n\ntoRelativeMinuteNum\n\n\nConverts a date with time or date to the number of the minute, starting from a certain fixed point in the past.\n\n\ntoRelativeSecondNum\n\n\nConverts a date with time or date to the number of the second, starting from a certain fixed point in the past.\n\n\nnow\n\n\nAccepts zero arguments and returns the current time at one of the moments of request execution.\nThis function returns a constant, even if the request took a long time to complete.\n\n\ntoday\n\n\nAccepts zero arguments and returns the current date at one of the moments of request execution.\nThe same as 'toDate(now())'.\n\n\nyesterday\n\n\nAccepts zero arguments and returns yesterday's date at one of the moments of request execution.\nThe same as 'today() - 1'.\n\n\ntimeSlot\n\n\nRounds the time to the half hour.\nThis function is specific to Yandex.Metrica, since half an hour is the minimum amount of time for breaking a session into two sessions if a tracking tag shows a single user's consecutive pageviews that differ in time by strictly more than this amount. This means that tuples (the tag ID, user ID, and time slot) can be used to search for pageviews that are included in the corresponding session.\n\n\ntimeSlots(StartTime, Duration)\n\n\nFor a time interval starting at 'StartTime' and continuing for 'Duration' seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour.\nFor example, \ntimeSlots(toDateTime('2012-01-01 12:20:00'), 600) = [toDateTime('2012-01-01 12:00:00'), toDateTime('2012-01-01 12:30:00')]\n.\nThis is necessary for searching for pageviews in the corresponding session.", - "title": "Functions for working with dates and times" - }, - { - "location": "/functions/date_time_functions/#functions-for-working-with-dates-and-times", - "text": "Support for time zones All functions for working with the date and time that have a logical use for the time zone can accept a second optional time zone argument. Example: Asia/Yekaterinburg. In this case, they use the specified time zone instead of the local (default) one. SELECT \n toDateTime ( 2016-06-15 23:00:00 ) AS time , \n toDate ( time ) AS date_local , \n toDate ( time , Asia/Yekaterinburg ) AS date_yekat , \n toString ( time , US/Samoa ) AS time_samoa \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500time\u2500\u252c\u2500date_local\u2500\u252c\u2500date_yekat\u2500\u252c\u2500time_samoa\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-16 \u2502 2016-06-15 09:00:00 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Only time zones that differ from UTC by a whole number of hours are supported.", - "title": "Functions for working with dates and times" - }, - { - "location": "/functions/date_time_functions/#toyear", - "text": "Converts a date or date with time to a UInt16 number containing the year number (AD).", - "title": "toYear" - }, - { - "location": "/functions/date_time_functions/#tomonth", - "text": "Converts a date or date with time to a UInt8 number containing the month number (1-12).", - "title": "toMonth" - }, - { - "location": "/functions/date_time_functions/#todayofmonth", - "text": "-Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31).", - "title": "toDayOfMonth" - }, - { - "location": "/functions/date_time_functions/#todayofweek", - "text": "Converts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7).", - "title": "toDayOfWeek" - }, - { - "location": "/functions/date_time_functions/#tohour", - "text": "Converts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23).\nThis function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true \u2013 even in Moscow the clocks were twice changed at a different time).", - "title": "toHour" - }, - { - "location": "/functions/date_time_functions/#tominute", - "text": "Converts a date with time to a UInt8 number containing the number of the minute of the hour (0-59).", - "title": "toMinute" - }, - { - "location": "/functions/date_time_functions/#tosecond", - "text": "Converts a date with time to a UInt8 number containing the number of the second in the minute (0-59).\nLeap seconds are not accounted for.", - "title": "toSecond" - }, - { - "location": "/functions/date_time_functions/#tomonday", - "text": "Rounds down a date or date with time to the nearest Monday.\nReturns the date.", - "title": "toMonday" - }, - { - "location": "/functions/date_time_functions/#tostartofmonth", - "text": "Rounds down a date or date with time to the first day of the month.\nReturns the date.", - "title": "toStartOfMonth" - }, - { - "location": "/functions/date_time_functions/#tostartofquarter", - "text": "Rounds down a date or date with time to the first day of the quarter.\nThe first day of the quarter is either 1 January, 1 April, 1 July, or 1 October.\nReturns the date.", - "title": "toStartOfQuarter" - }, - { - "location": "/functions/date_time_functions/#tostartofyear", - "text": "Rounds down a date or date with time to the first day of the year.\nReturns the date.", - "title": "toStartOfYear" - }, - { - "location": "/functions/date_time_functions/#tostartofminute", - "text": "Rounds down a date with time to the start of the minute.", - "title": "toStartOfMinute" - }, - { - "location": "/functions/date_time_functions/#tostartoffiveminute", - "text": "Rounds down a date with time to the start of the hour.", - "title": "toStartOfFiveMinute" - }, - { - "location": "/functions/date_time_functions/#tostartoffifteenminutes", - "text": "Rounds down the date with time to the start of the fifteen-minute interval. Note: If you need to round a date with time to any other number of seconds, minutes, or hours, you can convert it into a number by using the toUInt32 function, then round the number using intDiv and multiplication, and convert it back using the toDateTime function.", - "title": "toStartOfFifteenMinutes" - }, - { - "location": "/functions/date_time_functions/#tostartofhour", - "text": "Rounds down a date with time to the start of the hour.", - "title": "toStartOfHour" - }, - { - "location": "/functions/date_time_functions/#tostartofday", - "text": "Rounds down a date with time to the start of the day.", - "title": "toStartOfDay" - }, - { - "location": "/functions/date_time_functions/#totime", - "text": "Converts a date with time to a certain fixed date, while preserving the time.", - "title": "toTime" - }, - { - "location": "/functions/date_time_functions/#torelativeyearnum", - "text": "Converts a date with time or date to the number of the year, starting from a certain fixed point in the past.", - "title": "toRelativeYearNum" - }, - { - "location": "/functions/date_time_functions/#torelativemonthnum", - "text": "Converts a date with time or date to the number of the month, starting from a certain fixed point in the past.", - "title": "toRelativeMonthNum" - }, - { - "location": "/functions/date_time_functions/#torelativeweeknum", - "text": "Converts a date with time or date to the number of the week, starting from a certain fixed point in the past.", - "title": "toRelativeWeekNum" - }, - { - "location": "/functions/date_time_functions/#torelativedaynum", - "text": "Converts a date with time or date to the number of the day, starting from a certain fixed point in the past.", - "title": "toRelativeDayNum" - }, - { - "location": "/functions/date_time_functions/#torelativehournum", - "text": "Converts a date with time or date to the number of the hour, starting from a certain fixed point in the past.", - "title": "toRelativeHourNum" - }, - { - "location": "/functions/date_time_functions/#torelativeminutenum", - "text": "Converts a date with time or date to the number of the minute, starting from a certain fixed point in the past.", - "title": "toRelativeMinuteNum" - }, - { - "location": "/functions/date_time_functions/#torelativesecondnum", - "text": "Converts a date with time or date to the number of the second, starting from a certain fixed point in the past.", - "title": "toRelativeSecondNum" - }, - { - "location": "/functions/date_time_functions/#now", - "text": "Accepts zero arguments and returns the current time at one of the moments of request execution.\nThis function returns a constant, even if the request took a long time to complete.", - "title": "now" - }, - { - "location": "/functions/date_time_functions/#today", - "text": "Accepts zero arguments and returns the current date at one of the moments of request execution.\nThe same as 'toDate(now())'.", - "title": "today" - }, - { - "location": "/functions/date_time_functions/#yesterday", - "text": "Accepts zero arguments and returns yesterday's date at one of the moments of request execution.\nThe same as 'today() - 1'.", - "title": "yesterday" - }, - { - "location": "/functions/date_time_functions/#timeslot", - "text": "Rounds the time to the half hour.\nThis function is specific to Yandex.Metrica, since half an hour is the minimum amount of time for breaking a session into two sessions if a tracking tag shows a single user's consecutive pageviews that differ in time by strictly more than this amount. This means that tuples (the tag ID, user ID, and time slot) can be used to search for pageviews that are included in the corresponding session.", - "title": "timeSlot" - }, - { - "location": "/functions/date_time_functions/#timeslotsstarttime-duration", - "text": "For a time interval starting at 'StartTime' and continuing for 'Duration' seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour.\nFor example, timeSlots(toDateTime('2012-01-01 12:20:00'), 600) = [toDateTime('2012-01-01 12:00:00'), toDateTime('2012-01-01 12:30:00')] .\nThis is necessary for searching for pageviews in the corresponding session.", - "title": "timeSlots(StartTime, Duration)" - }, - { - "location": "/functions/string_functions/", - "text": "Functions for working with strings\n\n\nempty\n\n\nReturns 1 for an empty string or 0 for a non-empty string.\nThe result type is UInt8.\nA string is considered non-empty if it contains at least one byte, even if this is a space or a null byte.\nThe function also works for arrays.\n\n\nnotEmpty\n\n\nReturns 0 for an empty string or 1 for a non-empty string.\nThe result type is UInt8.\nThe function also works for arrays.\n\n\nlength\n\n\nReturns the length of a string in bytes (not in characters, and not in code points).\nThe result type is UInt64.\nThe function also works for arrays.\n\n\nlengthUTF8\n\n\nReturns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\nThe result type is UInt64.\n\n\nlower\n\n\nConverts ASCII Latin symbols in a string to lowercase.\n\n\nupper\n\n\nConverts ASCII Latin symbols in a string to uppercase.\n\n\nlowerUTF8\n\n\nConverts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.\n\n\nupperUTF8\n\n\nConverts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.\n\n\nreverse\n\n\nReverses the string (as a sequence of bytes).\n\n\nreverseUTF8\n\n\nReverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn't throw an exception).\n\n\nconcat(s1, s2, ...)\n\n\nConcatenates the strings listed in the arguments, without a separator.\n\n\nsubstring(s, offset, length)\n\n\nReturns a substring starting with the byte from the 'offset' index that is 'length' bytes long. Character indexing starts from one (as in standard SQL). The 'offset' and 'length' arguments must be constants.\n\n\nsubstringUTF8(s, offset, length)\n\n\nThe same as 'substring', but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\n\n\nappendTrailingCharIfAbsent(s, c)\n\n\nIf the 's' string is non-empty and does not contain the 'c' character at the end, it appends the 'c' character to the end.\n\n\nconvertCharset(s, from, to)\n\n\nReturns the string 's' that was converted from the encoding in 'from' to the encoding in 'to'.", - "title": "Functions for working with strings" - }, - { - "location": "/functions/string_functions/#functions-for-working-with-strings", - "text": "", - "title": "Functions for working with strings" - }, - { - "location": "/functions/string_functions/#empty", - "text": "Returns 1 for an empty string or 0 for a non-empty string.\nThe result type is UInt8.\nA string is considered non-empty if it contains at least one byte, even if this is a space or a null byte.\nThe function also works for arrays.", - "title": "empty" - }, - { - "location": "/functions/string_functions/#notempty", - "text": "Returns 0 for an empty string or 1 for a non-empty string.\nThe result type is UInt8.\nThe function also works for arrays.", - "title": "notEmpty" - }, - { - "location": "/functions/string_functions/#length", - "text": "Returns the length of a string in bytes (not in characters, and not in code points).\nThe result type is UInt64.\nThe function also works for arrays.", - "title": "length" - }, - { - "location": "/functions/string_functions/#lengthutf8", - "text": "Returns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\nThe result type is UInt64.", - "title": "lengthUTF8" - }, - { - "location": "/functions/string_functions/#lower", - "text": "Converts ASCII Latin symbols in a string to lowercase.", - "title": "lower" - }, - { - "location": "/functions/string_functions/#upper", - "text": "Converts ASCII Latin symbols in a string to uppercase.", - "title": "upper" - }, - { - "location": "/functions/string_functions/#lowerutf8", - "text": "Converts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.", - "title": "lowerUTF8" - }, - { - "location": "/functions/string_functions/#upperutf8", - "text": "Converts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.", - "title": "upperUTF8" - }, - { - "location": "/functions/string_functions/#reverse", - "text": "Reverses the string (as a sequence of bytes).", - "title": "reverse" - }, - { - "location": "/functions/string_functions/#reverseutf8", - "text": "Reverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn't throw an exception).", - "title": "reverseUTF8" - }, - { - "location": "/functions/string_functions/#concats1-s2", - "text": "Concatenates the strings listed in the arguments, without a separator.", - "title": "concat(s1, s2, ...)" - }, - { - "location": "/functions/string_functions/#substrings-offset-length", - "text": "Returns a substring starting with the byte from the 'offset' index that is 'length' bytes long. Character indexing starts from one (as in standard SQL). The 'offset' and 'length' arguments must be constants.", - "title": "substring(s, offset, length)" - }, - { - "location": "/functions/string_functions/#substringutf8s-offset-length", - "text": "The same as 'substring', but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).", - "title": "substringUTF8(s, offset, length)" - }, - { - "location": "/functions/string_functions/#appendtrailingcharifabsents-c", - "text": "If the 's' string is non-empty and does not contain the 'c' character at the end, it appends the 'c' character to the end.", - "title": "appendTrailingCharIfAbsent(s, c)" - }, - { - "location": "/functions/string_functions/#convertcharsets-from-to", - "text": "Returns the string 's' that was converted from the encoding in 'from' to the encoding in 'to'.", - "title": "convertCharset(s, from, to)" - }, - { - "location": "/functions/string_search_functions/", - "text": "Functions for searching strings\n\n\nThe search is case-sensitive in all these functions.\nThe search substring or regular expression must be a constant in all these functions.\n\n\nposition(haystack, needle)\n\n\nSearch for the \nneedle\n substring in the \nhaystack\n string.\nReturns the position (in bytes) of the found substring, starting from 1, or returns 0 if the substring was not found.\n\n\nFor case-insensitive search use \npositionCaseInsensitive\n function.\n\n\npositionUTF8(haystack, needle)\n\n\nThe same as \nposition\n, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\n\n\nFor case-insensitive search use \npositionCaseInsensitiveUTF8\n function.\n\n\nmatch(haystack, pattern)\n\n\nChecks whether the string matches the 'pattern' regular expression. A re2 regular expression.\nReturns 0 if it doesn't match, or 1 if it matches.\n\n\nNote that the backslash symbol (\n\\\n) is used for escaping in the regular expression. The same symbol is used for escaping in string literals. So in order to escape the symbol in a regular expression, you must write two backslashes (\\) in a string literal.\n\n\nThe regular expression works with the string as if it is a set of bytes. The regular expression can't contain null bytes.\nFor patterns to search for substrings in a string, it is better to use LIKE or 'position', since they work much faster.\n\n\nextract(haystack, pattern)\n\n\nExtracts a fragment of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. If the regex doesn't contain subpatterns, it takes the fragment that matches the entire regex. Otherwise, it takes the fragment that matches the first subpattern.\n\n\nextractAll(haystack, pattern)\n\n\nExtracts all the fragments of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex. In general, the behavior is the same as the 'extract' function (it takes the first subpattern, or the entire expression if there isn't a subpattern).\n\n\nlike(haystack, pattern), haystack LIKE pattern operator\n\n\nChecks whether a string matches a simple regular expression.\nThe regular expression can contain the metasymbols \n%\n and \n_\n.\n\n\n``% indicates any quantity of any bytes (including zero characters).\n\n\n_\n indicates any one byte.\n\n\nUse the backslash (\n\\\n) for escaping metasymbols. See the note on escaping in the description of the 'match' function.\n\n\nFor regular expressions like \n%needle%\n, the code is more optimal and works as fast as the \nposition\n function.\nFor other regular expressions, the code is the same as for the 'match' function.\n\n\nnotLike(haystack, pattern), haystack NOT LIKE pattern operator\n\n\nThe same thing as 'like', but negative.", - "title": "Functions for searching strings" - }, - { - "location": "/functions/string_search_functions/#functions-for-searching-strings", - "text": "The search is case-sensitive in all these functions.\nThe search substring or regular expression must be a constant in all these functions.", - "title": "Functions for searching strings" - }, - { - "location": "/functions/string_search_functions/#positionhaystack-needle", - "text": "Search for the needle substring in the haystack string.\nReturns the position (in bytes) of the found substring, starting from 1, or returns 0 if the substring was not found. For case-insensitive search use positionCaseInsensitive function.", - "title": "position(haystack, needle)" - }, - { - "location": "/functions/string_search_functions/#positionutf8haystack-needle", - "text": "The same as position , but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception). For case-insensitive search use positionCaseInsensitiveUTF8 function.", - "title": "positionUTF8(haystack, needle)" - }, - { - "location": "/functions/string_search_functions/#matchhaystack-pattern", - "text": "Checks whether the string matches the 'pattern' regular expression. A re2 regular expression.\nReturns 0 if it doesn't match, or 1 if it matches. Note that the backslash symbol ( \\ ) is used for escaping in the regular expression. The same symbol is used for escaping in string literals. So in order to escape the symbol in a regular expression, you must write two backslashes (\\) in a string literal. The regular expression works with the string as if it is a set of bytes. The regular expression can't contain null bytes.\nFor patterns to search for substrings in a string, it is better to use LIKE or 'position', since they work much faster.", - "title": "match(haystack, pattern)" - }, - { - "location": "/functions/string_search_functions/#extracthaystack-pattern", - "text": "Extracts a fragment of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. If the regex doesn't contain subpatterns, it takes the fragment that matches the entire regex. Otherwise, it takes the fragment that matches the first subpattern.", - "title": "extract(haystack, pattern)" - }, - { - "location": "/functions/string_search_functions/#extractallhaystack-pattern", - "text": "Extracts all the fragments of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex. In general, the behavior is the same as the 'extract' function (it takes the first subpattern, or the entire expression if there isn't a subpattern).", - "title": "extractAll(haystack, pattern)" - }, - { - "location": "/functions/string_search_functions/#likehaystack-pattern-haystack-like-pattern-operator", - "text": "Checks whether a string matches a simple regular expression.\nThe regular expression can contain the metasymbols % and _ . ``% indicates any quantity of any bytes (including zero characters). _ indicates any one byte. Use the backslash ( \\ ) for escaping metasymbols. See the note on escaping in the description of the 'match' function. For regular expressions like %needle% , the code is more optimal and works as fast as the position function.\nFor other regular expressions, the code is the same as for the 'match' function.", - "title": "like(haystack, pattern), haystack LIKE pattern operator" - }, - { - "location": "/functions/string_search_functions/#notlikehaystack-pattern-haystack-not-like-pattern-operator", - "text": "The same thing as 'like', but negative.", - "title": "notLike(haystack, pattern), haystack NOT LIKE pattern operator" - }, - { - "location": "/functions/string_replace_functions/", - "text": "Functions for searching and replacing in strings\n\n\nreplaceOne(haystack, pattern, replacement)\n\n\nReplaces the first occurrence, if it exists, of the 'pattern' substring in 'haystack' with the 'replacement' substring.\nHereafter, 'pattern' and 'replacement' must be constants.\n\n\nreplaceAll(haystack, pattern, replacement)\n\n\nReplaces all occurrences of the 'pattern' substring in 'haystack' with the 'replacement' substring.\n\n\nreplaceRegexpOne(haystack, pattern, replacement)\n\n\nReplacement using the 'pattern' regular expression. A re2 regular expression.\nReplaces only the first occurrence, if it exists.\nA pattern can be specified as 'replacement'. This pattern can include substitutions \n\\0-\\9\n.\nThe substitution \n\\0\n includes the entire regular expression. Substitutions \n\\1-\\9\n correspond to the subpattern numbers.To use the \n\\\n character in a template, escape it using \n\\\n.\nAlso keep in mind that a string literal requires an extra escape.\n\n\nExample 1. Converting the date to American format:\n\n\nSELECT\n \nDISTINCT\n\n \nEventDate\n,\n\n \nreplaceRegexpOne\n(\ntoString\n(\nEventDate\n),\n \n(\\\\d{4})-(\\\\d{2})-(\\\\d{2})\n,\n \n\\\\2/\\\\3/\\\\1\n)\n \nAS\n \nres\n\n\nFROM\n \ntest\n.\nhits\n\n\nLIMIT\n \n7\n\n\nFORMAT\n \nTabSeparated\n\n\n\n\n\n\n2014-03-17 03/17/2014\n2014-03-18 03/18/2014\n2014-03-19 03/19/2014\n2014-03-20 03/20/2014\n2014-03-21 03/21/2014\n2014-03-22 03/22/2014\n2014-03-23 03/23/2014\n\n\n\n\n\nExample 2. Copying a string ten times:\n\n\nSELECT\n \nreplaceRegexpOne\n(\nHello, World!\n,\n \n.*\n,\n \n\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nreplaceRegexpAll(haystack, pattern, replacement)\n\n\nThis does the same thing, but replaces all the occurrences. Example:\n\n\nSELECT\n \nreplaceRegexpAll\n(\nHello, World!\n,\n \n.\n,\n \n\\\\0\\\\0\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 HHeelllloo,, WWoorrlldd!! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nAs an exception, if a regular expression worked on an empty substring, the replacement is not made more than once.\nExample:\n\n\nSELECT\n \nreplaceRegexpAll\n(\nHello, World!\n,\n \n^\n,\n \nhere: \n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 here: Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "Functions for searching and replacing in strings" - }, - { - "location": "/functions/string_replace_functions/#functions-for-searching-and-replacing-in-strings", - "text": "", - "title": "Functions for searching and replacing in strings" - }, - { - "location": "/functions/string_replace_functions/#replaceonehaystack-pattern-replacement", - "text": "Replaces the first occurrence, if it exists, of the 'pattern' substring in 'haystack' with the 'replacement' substring.\nHereafter, 'pattern' and 'replacement' must be constants.", - "title": "replaceOne(haystack, pattern, replacement)" - }, - { - "location": "/functions/string_replace_functions/#replaceallhaystack-pattern-replacement", - "text": "Replaces all occurrences of the 'pattern' substring in 'haystack' with the 'replacement' substring.", - "title": "replaceAll(haystack, pattern, replacement)" - }, - { - "location": "/functions/string_replace_functions/#replaceregexponehaystack-pattern-replacement", - "text": "Replacement using the 'pattern' regular expression. A re2 regular expression.\nReplaces only the first occurrence, if it exists.\nA pattern can be specified as 'replacement'. This pattern can include substitutions \\0-\\9 .\nThe substitution \\0 includes the entire regular expression. Substitutions \\1-\\9 correspond to the subpattern numbers.To use the \\ character in a template, escape it using \\ .\nAlso keep in mind that a string literal requires an extra escape. Example 1. Converting the date to American format: SELECT DISTINCT \n EventDate , \n replaceRegexpOne ( toString ( EventDate ), (\\\\d{4})-(\\\\d{2})-(\\\\d{2}) , \\\\2/\\\\3/\\\\1 ) AS res FROM test . hits LIMIT 7 FORMAT TabSeparated 2014-03-17 03/17/2014\n2014-03-18 03/18/2014\n2014-03-19 03/19/2014\n2014-03-20 03/20/2014\n2014-03-21 03/21/2014\n2014-03-22 03/22/2014\n2014-03-23 03/23/2014 Example 2. Copying a string ten times: SELECT replaceRegexpOne ( Hello, World! , .* , \\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0 ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "replaceRegexpOne(haystack, pattern, replacement)" - }, - { - "location": "/functions/string_replace_functions/#replaceregexpallhaystack-pattern-replacement", - "text": "This does the same thing, but replaces all the occurrences. Example: SELECT replaceRegexpAll ( Hello, World! , . , \\\\0\\\\0 ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 HHeelllloo,, WWoorrlldd!! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 As an exception, if a regular expression worked on an empty substring, the replacement is not made more than once.\nExample: SELECT replaceRegexpAll ( Hello, World! , ^ , here: ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 here: Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "replaceRegexpAll(haystack, pattern, replacement)" - }, - { - "location": "/functions/conditional_functions/", - "text": "Conditional functions\n\n\nif(cond, then, else), cond ? operator then : else\n\n\nReturns 'then' if cond !or 'else' if cond = 0.'cond' must be UInt 8, and 'then' and 'else' must be a type that has the smallest common type.", - "title": "Conditional functions" - }, - { - "location": "/functions/conditional_functions/#conditional-functions", - "text": "", - "title": "Conditional functions" - }, - { - "location": "/functions/conditional_functions/#ifcond-then-else-cond-operator-then-else", - "text": "Returns 'then' if cond !or 'else' if cond = 0.'cond' must be UInt 8, and 'then' and 'else' must be a type that has the smallest common type.", - "title": "if(cond, then, else), cond ? operator then : else" - }, - { - "location": "/functions/math_functions/", - "text": "Mathematical functions\n\n\nAll the functions return a Float64 number. The accuracy of the result is close to the maximum precision possible, but the result might not coincide with the machine representable number nearest to the corresponding real number.\n\n\ne()\n\n\nReturns a Float64 number close to the e number.\n\n\npi()\n\n\nReturns a Float64 number close to \u03c0.\n\n\nexp(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the exponent of the argument.\n\n\nlog(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the natural logarithm of the argument.\n\n\nexp2(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to 2^x.\n\n\nlog2(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument.\n\n\nexp10(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to 10^x.\n\n\nlog10(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument.\n\n\nsqrt(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the square root of the argument.\n\n\ncbrt(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the cubic root of the argument.\n\n\nerf(x)\n\n\nIf 'x' is non-negative, then erf(x / \u03c3\u221a2)\n is the probability that a random variable having a normal distribution with standard deviation '\u03c3' takes the value that is separated from the expected value by more than 'x'.\n\n\nExample (three sigma rule):\n\n\nSELECT\n \nerf\n(\n3\n \n/\n \nsqrt\n(\n2\n))\n\n\n\n\n\n\n\u250c\u2500erf(divide(3, sqrt(2)))\u2500\u2510\n\u2502 0.9973002039367398 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nerfc(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large 'x' values.\n\n\nlgamma(x)\n\n\nThe logarithm of the gamma function.\n\n\ntgamma(x)\n\n\nGamma function.\n\n\nsin(x)\n\n\nThe sine.\n\n\ncos(x)\n\n\nThe cosine.\n\n\ntan(x)\n\n\nThe tangent.\n\n\nasin(x)\n\n\nThe arc sine.\n\n\nacos(x)\n\n\nThe arc cosine.\n\n\natan(x)\n\n\nThe arc tangent.\n\n\npow(x, y)\n\n\nAccepts two numeric arguments and returns a Float64 number close to x^y.", - "title": "Mathematical functions" - }, - { - "location": "/functions/math_functions/#mathematical-functions", - "text": "All the functions return a Float64 number. The accuracy of the result is close to the maximum precision possible, but the result might not coincide with the machine representable number nearest to the corresponding real number.", - "title": "Mathematical functions" - }, - { - "location": "/functions/math_functions/#e", - "text": "Returns a Float64 number close to the e number.", - "title": "e()" - }, - { - "location": "/functions/math_functions/#pi", - "text": "Returns a Float64 number close to \u03c0.", - "title": "pi()" - }, - { - "location": "/functions/math_functions/#expx", - "text": "Accepts a numeric argument and returns a Float64 number close to the exponent of the argument.", - "title": "exp(x)" - }, - { - "location": "/functions/math_functions/#logx", - "text": "Accepts a numeric argument and returns a Float64 number close to the natural logarithm of the argument.", - "title": "log(x)" - }, - { - "location": "/functions/math_functions/#exp2x", - "text": "Accepts a numeric argument and returns a Float64 number close to 2^x.", - "title": "exp2(x)" - }, - { - "location": "/functions/math_functions/#log2x", - "text": "Accepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument.", - "title": "log2(x)" - }, - { - "location": "/functions/math_functions/#exp10x", - "text": "Accepts a numeric argument and returns a Float64 number close to 10^x.", - "title": "exp10(x)" - }, - { - "location": "/functions/math_functions/#log10x", - "text": "Accepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument.", - "title": "log10(x)" - }, - { - "location": "/functions/math_functions/#sqrtx", - "text": "Accepts a numeric argument and returns a Float64 number close to the square root of the argument.", - "title": "sqrt(x)" - }, - { - "location": "/functions/math_functions/#cbrtx", - "text": "Accepts a numeric argument and returns a Float64 number close to the cubic root of the argument.", - "title": "cbrt(x)" - }, - { - "location": "/functions/math_functions/#erfx", - "text": "If 'x' is non-negative, then erf(x / \u03c3\u221a2) is the probability that a random variable having a normal distribution with standard deviation '\u03c3' takes the value that is separated from the expected value by more than 'x'. Example (three sigma rule): SELECT erf ( 3 / sqrt ( 2 )) \u250c\u2500erf(divide(3, sqrt(2)))\u2500\u2510\n\u2502 0.9973002039367398 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "erf(x)" - }, - { - "location": "/functions/math_functions/#erfcx", - "text": "Accepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large 'x' values.", - "title": "erfc(x)" - }, - { - "location": "/functions/math_functions/#lgammax", - "text": "The logarithm of the gamma function.", - "title": "lgamma(x)" - }, - { - "location": "/functions/math_functions/#tgammax", - "text": "Gamma function.", - "title": "tgamma(x)" - }, - { - "location": "/functions/math_functions/#sinx", - "text": "The sine.", - "title": "sin(x)" - }, - { - "location": "/functions/math_functions/#cosx", - "text": "The cosine.", - "title": "cos(x)" - }, - { - "location": "/functions/math_functions/#tanx", - "text": "The tangent.", - "title": "tan(x)" - }, - { - "location": "/functions/math_functions/#asinx", - "text": "The arc sine.", - "title": "asin(x)" - }, - { - "location": "/functions/math_functions/#acosx", - "text": "The arc cosine.", - "title": "acos(x)" - }, - { - "location": "/functions/math_functions/#atanx", - "text": "The arc tangent.", - "title": "atan(x)" - }, - { - "location": "/functions/math_functions/#powx-y", - "text": "Accepts two numeric arguments and returns a Float64 number close to x^y.", - "title": "pow(x, y)" - }, - { - "location": "/functions/rounding_functions/", - "text": "Rounding functions\n\n\nfloor(x[, N])\n\n\nReturns the largest round number that is less than or equal to x. A round number is a multiple of 1/10N, or the nearest number of the appropriate data type if 1 / 10N isn't exact.\n'N' is an integer constant, optional parameter. By default it is zero, which means to round to an integer.\n'N' may be negative.\n\n\nExamples: \nfloor(123.45, 1) = 123.4, floor(123.45, -1) = 120.\n\n\nx\n is any numeric type. The result is a number of the same type.\nFor integer arguments, it makes sense to round with a negative 'N' value (for non-negative 'N', the function doesn't do anything).\nIf rounding causes overflow (for example, floor(-128, -1)), an implementation-specific result is returned.\n\n\nceil(x[, N])\n\n\nReturns the smallest round number that is greater than or equal to 'x'. In every other way, it is the same as the 'floor' function (see above).\n\n\nround(x[, N])\n\n\nReturns the round number nearest to 'num', which may be less than, greater than, or equal to 'x'.If 'x' is exactly in the middle between the nearest round numbers, one of them is returned (implementation-specific).\nThe number '-0.' may or may not be considered round (implementation-specific).\nIn every other way, this function is the same as 'floor' and 'ceil' described above.\n\n\nroundToExp2(num)\n\n\nAccepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two.\n\n\nroundDuration(num)\n\n\nAccepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length\n\n\nroundAge(num)\n\n\nAccepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to a number from the set: 18, 25, 35, 45, 55. This function is specific to Yandex.Metrica and used for implementing the report on user age.", - "title": "Rounding functions" - }, - { - "location": "/functions/rounding_functions/#rounding-functions", - "text": "", - "title": "Rounding functions" - }, - { - "location": "/functions/rounding_functions/#floorx91-n93", - "text": "Returns the largest round number that is less than or equal to x. A round number is a multiple of 1/10N, or the nearest number of the appropriate data type if 1 / 10N isn't exact.\n'N' is an integer constant, optional parameter. By default it is zero, which means to round to an integer.\n'N' may be negative. Examples: floor(123.45, 1) = 123.4, floor(123.45, -1) = 120. x is any numeric type. The result is a number of the same type.\nFor integer arguments, it makes sense to round with a negative 'N' value (for non-negative 'N', the function doesn't do anything).\nIf rounding causes overflow (for example, floor(-128, -1)), an implementation-specific result is returned.", - "title": "floor(x[, N])" - }, - { - "location": "/functions/rounding_functions/#ceilx91-n93", - "text": "Returns the smallest round number that is greater than or equal to 'x'. In every other way, it is the same as the 'floor' function (see above).", - "title": "ceil(x[, N])" - }, - { - "location": "/functions/rounding_functions/#roundx91-n93", - "text": "Returns the round number nearest to 'num', which may be less than, greater than, or equal to 'x'.If 'x' is exactly in the middle between the nearest round numbers, one of them is returned (implementation-specific).\nThe number '-0.' may or may not be considered round (implementation-specific).\nIn every other way, this function is the same as 'floor' and 'ceil' described above.", - "title": "round(x[, N])" - }, - { - "location": "/functions/rounding_functions/#roundtoexp2num", - "text": "Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two.", - "title": "roundToExp2(num)" - }, - { - "location": "/functions/rounding_functions/#rounddurationnum", - "text": "Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length", - "title": "roundDuration(num)" - }, - { - "location": "/functions/rounding_functions/#roundagenum", - "text": "Accepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to a number from the set: 18, 25, 35, 45, 55. This function is specific to Yandex.Metrica and used for implementing the report on user age.", - "title": "roundAge(num)" - }, - { - "location": "/functions/array_functions/", - "text": "Functions for working with arrays\n\n\nempty\n\n\nReturns 1 for an empty array, or 0 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.\n\n\nnotEmpty\n\n\nReturns 0 for an empty array, or 1 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.\n\n\nlength\n\n\nReturns the number of items in the array.\nThe result type is UInt64.\nThe function also works for strings.\n\n\nemptyArrayUInt8, emptyArrayUInt16, emptyArrayUInt32, emptyArrayUInt64\n\n\nemptyArrayInt8, emptyArrayInt16, emptyArrayInt32, emptyArrayInt64\n\n\nemptyArrayFloat32, emptyArrayFloat64\n\n\nemptyArrayDate, emptyArrayDateTime\n\n\nemptyArrayString\n\n\nAccepts zero arguments and returns an empty array of the appropriate type.\n\n\nemptyArrayToSingle\n\n\nAccepts an empty array and returns a one-element array that is equal to the default value.\n\n\nrange(N)\n\n\nReturns an array of numbers from 0 to N-1.\nJust in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block.\n\n\narray(x1, ...), operator [x1, ...]\n\n\nCreates an array from the function arguments.\nThe arguments must be constants and have types that have the smallest common type. At least one argument must be passed, because otherwise it isn't clear which type of array to create. That is, you can't use this function to create an empty array (to do that, use the 'emptyArray*' function described above).\nReturns an 'Array(T)' type result, where 'T' is the smallest common type out of the passed arguments.\n\n\narrayConcat\n\n\nCombines arrays passed as arguments.\n\n\narrayConcat(arrays)\n\n\n\n\n\nArguments\n\n\n\n\narrays\n \u2013 Arrays of comma-separated \n[values]\n.\n\n\n\n\nExample\n\n\nSELECT\n \narrayConcat\n([\n1\n,\n \n2\n],\n \n[\n3\n,\n \n4\n],\n \n[\n5\n,\n \n6\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,3,4,5,6] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayElement(arr, n), operator arr[n]\n\n\nGet the element with the index 'n' from the array 'arr'.'n' must be any integer type.\nIndexes in an array begin from one.\nNegative indexes are supported. In this case, it selects the corresponding element numbered from the end. For example, 'arr[-1]' is the last item in the array.\n\n\nIf the index falls outside of the bounds of an array, it returns some default value (0 for numbers, an empty string for strings, etc.).\n\n\nhas(arr, elem)\n\n\nChecks whether the 'arr' array has the 'elem' element.\nReturns 0 if the the element is not in the array, or 1 if it is.\n\n\nindexOf(arr, x)\n\n\nReturns the index of the 'x' element (starting from 1) if it is in the array, or 0 if it is not.\n\n\ncountEqual(arr, x)\n\n\nReturns the number of elements in the array equal to x. Equivalent to arrayCount (elem-\n elem = x, arr).\n\n\narrayEnumerate(arr)\n\n\nReturns the array [1, 2, 3, ..., length (arr) ]\n\n\nThis function is normally used with ARRAY JOIN. It allows counting something just once for each array after applying ARRAY JOIN. Example:\n\n\nSELECT\n\n \ncount\n()\n \nAS\n \nReaches\n,\n\n \ncountIf\n(\nnum\n \n=\n \n1\n)\n \nAS\n \nHits\n\n\nFROM\n \ntest\n.\nhits\n\n\nARRAY\n \nJOIN\n\n \nGoalsReached\n,\n\n \narrayEnumerate\n(\nGoalsReached\n)\n \nAS\n \nnum\n\n\nWHERE\n \nCounterID\n \n=\n \n160656\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIn this example, Reaches is the number of conversions (the strings received after applying ARRAY JOIN), and Hits is the number of pageviews (strings before ARRAY JOIN). In this particular case, you can get the same result in an easier way:\n\n\nSELECT\n\n \nsum\n(\nlength\n(\nGoalsReached\n))\n \nAS\n \nReaches\n,\n\n \ncount\n()\n \nAS\n \nHits\n\n\nFROM\n \ntest\n.\nhits\n\n\nWHERE\n \n(\nCounterID\n \n=\n \n160656\n)\n \nAND\n \nnotEmpty\n(\nGoalsReached\n)\n\n\n\n\n\n\n\u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nThis function can also be used in higher-order functions. For example, you can use it to get array indexes for elements that match a condition.\n\n\narrayEnumerateUniq(arr, ...)\n\n\nReturns an array the same size as the source array, indicating for each element what its position is among elements with the same value.\nFor example: arrayEnumerateUniq([10, 20, 10, 30]) = [1, 1, 2, 1].\n\n\nThis function is useful when using ARRAY JOIN and aggregation of array elements.\nExample:\n\n\nSELECT\n\n \nGoals\n.\nID\n \nAS\n \nGoalID\n,\n\n \nsum\n(\nSign\n)\n \nAS\n \nReaches\n,\n\n \nsumIf\n(\nSign\n,\n \nnum\n \n=\n \n1\n)\n \nAS\n \nVisits\n\n\nFROM\n \ntest\n.\nvisits\n\n\nARRAY\n \nJOIN\n\n \nGoals\n,\n\n \narrayEnumerateUniq\n(\nGoals\n.\nID\n)\n \nAS\n \nnum\n\n\nWHERE\n \nCounterID\n \n=\n \n160656\n\n\nGROUP\n \nBY\n \nGoalID\n\n\nORDER\n \nBY\n \nReaches\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500\u2500GoalID\u2500\u252c\u2500Reaches\u2500\u252c\u2500Visits\u2500\u2510\n\u2502 53225 \u2502 3214 \u2502 1097 \u2502\n\u2502 2825062 \u2502 3188 \u2502 1097 \u2502\n\u2502 56600 \u2502 2803 \u2502 488 \u2502\n\u2502 1989037 \u2502 2401 \u2502 365 \u2502\n\u2502 2830064 \u2502 2396 \u2502 910 \u2502\n\u2502 1113562 \u2502 2372 \u2502 373 \u2502\n\u2502 3270895 \u2502 2262 \u2502 812 \u2502\n\u2502 1084657 \u2502 2262 \u2502 345 \u2502\n\u2502 56599 \u2502 2260 \u2502 799 \u2502\n\u2502 3271094 \u2502 2256 \u2502 812 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIn this example, each goal ID has a calculation of the number of conversions (each element in the Goals nested data structure is a goal that was reached, which we refer to as a conversion) and the number of sessions. Without ARRAY JOIN, we would have counted the number of sessions as sum(Sign). But in this particular case, the rows were multiplied by the nested Goals structure, so in order to count each session one time after this, we apply a condition to the value of the arrayEnumerateUniq(Goals.ID) function.\n\n\nThe arrayEnumerateUniq function can take multiple arrays of the same size as arguments. In this case, uniqueness is considered for tuples of elements in the same positions in all the arrays.\n\n\nSELECT\n \narrayEnumerateUniq\n([\n1\n,\n \n1\n,\n \n1\n,\n \n2\n,\n \n2\n,\n \n2\n],\n \n[\n1\n,\n \n1\n,\n \n2\n,\n \n1\n,\n \n1\n,\n \n2\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,1,1,2,1] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nThis is necessary when using ARRAY JOIN with a nested data structure and further aggregation across multiple elements in this structure.\n\n\narrayPopBack\n\n\nRemoves the last item from the array.\n\n\narrayPopBack(array)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\n\n\nExample\n\n\nSELECT\n \narrayPopBack\n([\n1\n,\n \n2\n,\n \n3\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [1,2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayPopFront\n\n\nRemoves the first item from the array.\n\n\narrayPopFront(array)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\n\n\nExample\n\n\nSELECT\n \narrayPopFront\n([\n1\n,\n \n2\n,\n \n3\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayPushBack\n\n\nAdds one item to the end of the array.\n\n\narrayPushBack(array, single_value)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\nsingle_value\n \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the \nsingle_value\n type for the data type of the array. For more information about ClickHouse data types, read the section \"\nData types\n\".\n\n\n\n\nExample\n\n\nSELECT\n \narrayPushBack\n([\na\n],\n \nb\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\na\n,\nb\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayPushFront\n\n\nAdds one element to the beginning of the array.\n\n\narrayPushFront(array, single_value)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\nsingle_value\n \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the \nsingle_value\n type for the data type of the array. For more information about ClickHouse data types, read the section \"\nData types\n\".\n\n\n\n\nExample\n\n\nSELECT\n \narrayPushBack\n([\nb\n],\n \na\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\na\n,\nb\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narraySlice\n\n\nReturns a slice of the array.\n\n\narraySlice(array, offset[, length])\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array of data.\n\n\noffset\n \u2013 Indent from the edge of the array. A positive value indicates an offset on the left, and a negative value is an indent on the right. Numbering of the array items begins with 1.\n\n\nlength\n - The length of the required slice. If you specify a negative value, the function returns an open slice \n[offset, array_length - length)\n. If you omit the value, the function returns the slice \n[offset, the_end_of_array]\n.\n\n\n\n\nExample\n\n\nSELECT\n \narraySlice\n([\n1\n,\n \n2\n,\n \n3\n,\n \n4\n,\n \n5\n],\n \n2\n,\n \n3\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [2,3,4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayUniq(arr, ...)\n\n\nIf one argument is passed, it counts the number of different elements in the array.\nIf multiple arguments are passed, it counts the number of different tuples of elements at corresponding positions in multiple arrays.\n\n\nIf you want to get a list of unique items in an array, you can use arrayReduce('groupUniqArray', arr).\n\n\narrayJoin(arr)\n\n\nA special function. See the section \n\"ArrayJoin function\"\n.", - "title": "Functions for working with arrays" - }, - { - "location": "/functions/array_functions/#functions-for-working-with-arrays", - "text": "", - "title": "Functions for working with arrays" - }, - { - "location": "/functions/array_functions/#empty", - "text": "Returns 1 for an empty array, or 0 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.", - "title": "empty" - }, - { - "location": "/functions/array_functions/#notempty", - "text": "Returns 0 for an empty array, or 1 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.", - "title": "notEmpty" - }, - { - "location": "/functions/array_functions/#length", - "text": "Returns the number of items in the array.\nThe result type is UInt64.\nThe function also works for strings.", - "title": "length" - }, - { - "location": "/functions/array_functions/#emptyarrayuint8-emptyarrayuint16-emptyarrayuint32-emptyarrayuint64", - "text": "", - "title": "emptyArrayUInt8, emptyArrayUInt16, emptyArrayUInt32, emptyArrayUInt64" - }, - { - "location": "/functions/array_functions/#emptyarrayint8-emptyarrayint16-emptyarrayint32-emptyarrayint64", - "text": "", - "title": "emptyArrayInt8, emptyArrayInt16, emptyArrayInt32, emptyArrayInt64" - }, - { - "location": "/functions/array_functions/#emptyarrayfloat32-emptyarrayfloat64", - "text": "", - "title": "emptyArrayFloat32, emptyArrayFloat64" - }, - { - "location": "/functions/array_functions/#emptyarraydate-emptyarraydatetime", - "text": "", - "title": "emptyArrayDate, emptyArrayDateTime" - }, - { - "location": "/functions/array_functions/#emptyarraystring", - "text": "Accepts zero arguments and returns an empty array of the appropriate type.", - "title": "emptyArrayString" - }, - { - "location": "/functions/array_functions/#emptyarraytosingle", - "text": "Accepts an empty array and returns a one-element array that is equal to the default value.", - "title": "emptyArrayToSingle" - }, - { - "location": "/functions/array_functions/#rangen", - "text": "Returns an array of numbers from 0 to N-1.\nJust in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block.", - "title": "range(N)" - }, - { - "location": "/functions/array_functions/#arrayx1-operator-91x1-93", - "text": "Creates an array from the function arguments.\nThe arguments must be constants and have types that have the smallest common type. At least one argument must be passed, because otherwise it isn't clear which type of array to create. That is, you can't use this function to create an empty array (to do that, use the 'emptyArray*' function described above).\nReturns an 'Array(T)' type result, where 'T' is the smallest common type out of the passed arguments.", - "title": "array(x1, ...), operator [x1, ...]" - }, - { - "location": "/functions/array_functions/#arrayconcat", - "text": "Combines arrays passed as arguments. arrayConcat(arrays) Arguments arrays \u2013 Arrays of comma-separated [values] . Example SELECT arrayConcat ([ 1 , 2 ], [ 3 , 4 ], [ 5 , 6 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,3,4,5,6] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayConcat" - }, - { - "location": "/functions/array_functions/#arrayelementarr-n-operator-arrn", - "text": "Get the element with the index 'n' from the array 'arr'.'n' must be any integer type.\nIndexes in an array begin from one.\nNegative indexes are supported. In this case, it selects the corresponding element numbered from the end. For example, 'arr[-1]' is the last item in the array. If the index falls outside of the bounds of an array, it returns some default value (0 for numbers, an empty string for strings, etc.).", - "title": "arrayElement(arr, n), operator arr[n]" - }, - { - "location": "/functions/array_functions/#hasarr-elem", - "text": "Checks whether the 'arr' array has the 'elem' element.\nReturns 0 if the the element is not in the array, or 1 if it is.", - "title": "has(arr, elem)" - }, - { - "location": "/functions/array_functions/#indexofarr-x", - "text": "Returns the index of the 'x' element (starting from 1) if it is in the array, or 0 if it is not.", - "title": "indexOf(arr, x)" - }, - { - "location": "/functions/array_functions/#countequalarr-x", - "text": "Returns the number of elements in the array equal to x. Equivalent to arrayCount (elem- elem = x, arr).", - "title": "countEqual(arr, x)" - }, - { - "location": "/functions/array_functions/#arrayenumeratearr", - "text": "Returns the array [1, 2, 3, ..., length (arr) ] This function is normally used with ARRAY JOIN. It allows counting something just once for each array after applying ARRAY JOIN. Example: SELECT \n count () AS Reaches , \n countIf ( num = 1 ) AS Hits FROM test . hits ARRAY JOIN \n GoalsReached , \n arrayEnumerate ( GoalsReached ) AS num WHERE CounterID = 160656 LIMIT 10 \u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 In this example, Reaches is the number of conversions (the strings received after applying ARRAY JOIN), and Hits is the number of pageviews (strings before ARRAY JOIN). In this particular case, you can get the same result in an easier way: SELECT \n sum ( length ( GoalsReached )) AS Reaches , \n count () AS Hits FROM test . hits WHERE ( CounterID = 160656 ) AND notEmpty ( GoalsReached ) \u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 This function can also be used in higher-order functions. For example, you can use it to get array indexes for elements that match a condition.", - "title": "arrayEnumerate(arr)" - }, - { - "location": "/functions/array_functions/#arrayenumerateuniqarr", - "text": "Returns an array the same size as the source array, indicating for each element what its position is among elements with the same value.\nFor example: arrayEnumerateUniq([10, 20, 10, 30]) = [1, 1, 2, 1]. This function is useful when using ARRAY JOIN and aggregation of array elements.\nExample: SELECT \n Goals . ID AS GoalID , \n sum ( Sign ) AS Reaches , \n sumIf ( Sign , num = 1 ) AS Visits FROM test . visits ARRAY JOIN \n Goals , \n arrayEnumerateUniq ( Goals . ID ) AS num WHERE CounterID = 160656 GROUP BY GoalID ORDER BY Reaches DESC LIMIT 10 \u250c\u2500\u2500GoalID\u2500\u252c\u2500Reaches\u2500\u252c\u2500Visits\u2500\u2510\n\u2502 53225 \u2502 3214 \u2502 1097 \u2502\n\u2502 2825062 \u2502 3188 \u2502 1097 \u2502\n\u2502 56600 \u2502 2803 \u2502 488 \u2502\n\u2502 1989037 \u2502 2401 \u2502 365 \u2502\n\u2502 2830064 \u2502 2396 \u2502 910 \u2502\n\u2502 1113562 \u2502 2372 \u2502 373 \u2502\n\u2502 3270895 \u2502 2262 \u2502 812 \u2502\n\u2502 1084657 \u2502 2262 \u2502 345 \u2502\n\u2502 56599 \u2502 2260 \u2502 799 \u2502\n\u2502 3271094 \u2502 2256 \u2502 812 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 In this example, each goal ID has a calculation of the number of conversions (each element in the Goals nested data structure is a goal that was reached, which we refer to as a conversion) and the number of sessions. Without ARRAY JOIN, we would have counted the number of sessions as sum(Sign). But in this particular case, the rows were multiplied by the nested Goals structure, so in order to count each session one time after this, we apply a condition to the value of the arrayEnumerateUniq(Goals.ID) function. The arrayEnumerateUniq function can take multiple arrays of the same size as arguments. In this case, uniqueness is considered for tuples of elements in the same positions in all the arrays. SELECT arrayEnumerateUniq ([ 1 , 1 , 1 , 2 , 2 , 2 ], [ 1 , 1 , 2 , 1 , 1 , 2 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,1,1,2,1] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 This is necessary when using ARRAY JOIN with a nested data structure and further aggregation across multiple elements in this structure.", - "title": "arrayEnumerateUniq(arr, ...)" - }, - { - "location": "/functions/array_functions/#arraypopback", - "text": "Removes the last item from the array. arrayPopBack(array) Arguments array \u2013 Array. Example SELECT arrayPopBack ([ 1 , 2 , 3 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [1,2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayPopBack" - }, - { - "location": "/functions/array_functions/#arraypopfront", - "text": "Removes the first item from the array. arrayPopFront(array) Arguments array \u2013 Array. Example SELECT arrayPopFront ([ 1 , 2 , 3 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayPopFront" - }, - { - "location": "/functions/array_functions/#arraypushback", - "text": "Adds one item to the end of the array. arrayPushBack(array, single_value) Arguments array \u2013 Array. single_value \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section \" Data types \". Example SELECT arrayPushBack ([ a ], b ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ a , b ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayPushBack" - }, - { - "location": "/functions/array_functions/#arraypushfront", - "text": "Adds one element to the beginning of the array. arrayPushFront(array, single_value) Arguments array \u2013 Array. single_value \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section \" Data types \". Example SELECT arrayPushBack ([ b ], a ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ a , b ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayPushFront" - }, - { - "location": "/functions/array_functions/#arrayslice", - "text": "Returns a slice of the array. arraySlice(array, offset[, length]) Arguments array \u2013 Array of data. offset \u2013 Indent from the edge of the array. A positive value indicates an offset on the left, and a negative value is an indent on the right. Numbering of the array items begins with 1. length - The length of the required slice. If you specify a negative value, the function returns an open slice [offset, array_length - length) . If you omit the value, the function returns the slice [offset, the_end_of_array] . Example SELECT arraySlice ([ 1 , 2 , 3 , 4 , 5 ], 2 , 3 ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [2,3,4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arraySlice" - }, - { - "location": "/functions/array_functions/#arrayuniqarr", - "text": "If one argument is passed, it counts the number of different elements in the array.\nIf multiple arguments are passed, it counts the number of different tuples of elements at corresponding positions in multiple arrays. If you want to get a list of unique items in an array, you can use arrayReduce('groupUniqArray', arr).", - "title": "arrayUniq(arr, ...)" - }, - { - "location": "/functions/array_functions/#arrayjoinarr", - "text": "A special function. See the section \"ArrayJoin function\" .", - "title": "arrayJoin(arr)" - }, - { - "location": "/functions/splitting_merging_functions/", - "text": "Functions for splitting and merging strings and arrays\n\n\nsplitByChar(separator, s)\n\n\nSplits a string into substrings separated by 'separator'.'separator' must be a string constant consisting of exactly one character.\nReturns an array of selected substrings. Empty substrings may be selected if the separator occurs at the beginning or end of the string, or if there are multiple consecutive separators.\n\n\nsplitByString(separator, s)\n\n\nThe same as above, but it uses a string of multiple characters as the separator. The string must be non-empty.\n\n\narrayStringConcat(arr[, separator])\n\n\nConcatenates the strings listed in the array with the separator.'separator' is an optional parameter: a constant string, set to an empty string by default.\nReturns the string.\n\n\nalphaTokens(s)\n\n\nSelects substrings of consecutive bytes from the ranges a-z and A-Z.Returns an array of substrings.", - "title": "Functions for splitting and merging strings and arrays" - }, - { - "location": "/functions/splitting_merging_functions/#functions-for-splitting-and-merging-strings-and-arrays", - "text": "", - "title": "Functions for splitting and merging strings and arrays" - }, - { - "location": "/functions/splitting_merging_functions/#splitbycharseparator-s", - "text": "Splits a string into substrings separated by 'separator'.'separator' must be a string constant consisting of exactly one character.\nReturns an array of selected substrings. Empty substrings may be selected if the separator occurs at the beginning or end of the string, or if there are multiple consecutive separators.", - "title": "splitByChar(separator, s)" - }, - { - "location": "/functions/splitting_merging_functions/#splitbystringseparator-s", - "text": "The same as above, but it uses a string of multiple characters as the separator. The string must be non-empty.", - "title": "splitByString(separator, s)" - }, - { - "location": "/functions/splitting_merging_functions/#arraystringconcatarr91-separator93", - "text": "Concatenates the strings listed in the array with the separator.'separator' is an optional parameter: a constant string, set to an empty string by default.\nReturns the string.", - "title": "arrayStringConcat(arr[, separator])" - }, - { - "location": "/functions/splitting_merging_functions/#alphatokenss", - "text": "Selects substrings of consecutive bytes from the ranges a-z and A-Z.Returns an array of substrings.", - "title": "alphaTokens(s)" - }, - { - "location": "/functions/bit_functions/", - "text": "Bit functions\n\n\nBit functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.\n\n\nThe result type is an integer with bits equal to the maximum bits of its arguments. If at least one of the arguments is signed, the result is a signed number. If an argument is a floating-point number, it is cast to Int64.\n\n\nbitAnd(a, b)\n\n\nbitOr(a, b)\n\n\nbitXor(a, b)\n\n\nbitNot(a)\n\n\nbitShiftLeft(a, b)\n\n\nbitShiftRight(a, b)", - "title": "Bit functions" - }, - { - "location": "/functions/bit_functions/#bit-functions", - "text": "Bit functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64. The result type is an integer with bits equal to the maximum bits of its arguments. If at least one of the arguments is signed, the result is a signed number. If an argument is a floating-point number, it is cast to Int64.", - "title": "Bit functions" - }, - { - "location": "/functions/bit_functions/#bitanda-b", - "text": "", - "title": "bitAnd(a, b)" - }, - { - "location": "/functions/bit_functions/#bitora-b", - "text": "", - "title": "bitOr(a, b)" - }, - { - "location": "/functions/bit_functions/#bitxora-b", - "text": "", - "title": "bitXor(a, b)" - }, - { - "location": "/functions/bit_functions/#bitnota", - "text": "", - "title": "bitNot(a)" - }, - { - "location": "/functions/bit_functions/#bitshiftlefta-b", - "text": "", - "title": "bitShiftLeft(a, b)" - }, - { - "location": "/functions/bit_functions/#bitshiftrighta-b", - "text": "", - "title": "bitShiftRight(a, b)" - }, - { - "location": "/functions/hash_functions/", - "text": "Hash functions\n\n\nHash functions can be used for deterministic pseudo-random shuffling of elements.\n\n\nhalfMD5\n\n\nCalculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian.\nAccepts a String-type argument. Returns UInt64.\nThis function works fairly slowly (5 million short strings per second per processor core).\nIf you don't need MD5 in particular, use the 'sipHash64' function instead.\n\n\nMD5\n\n\nCalculates the MD5 from a string and returns the resulting set of bytes as FixedString(16).\nIf you don't need MD5 in particular, but you need a decent cryptographic 128-bit hash, use the 'sipHash128' function instead.\nIf you want to get the same result as output by the md5sum utility, use lower(hex(MD5(s))).\n\n\nsipHash64\n\n\nCalculates SipHash from a string.\nAccepts a String-type argument. Returns UInt64.\nSipHash is a cryptographic hash function. It works at least three times faster than MD5.\nFor more information, see the link: \nhttps://131002.net/siphash/\n\n\nsipHash128\n\n\nCalculates SipHash from a string.\nAccepts a String-type argument. Returns FixedString(16).\nDiffers from sipHash64 in that the final xor-folding state is only done up to 128 bytes.\n\n\ncityHash64\n\n\nCalculates CityHash64 from a string or a similar hash function for any number of any type of arguments.\nFor String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality.\nFor other types of arguments, a decent implementation-specific fast non-cryptographic hash function is used.\nIf multiple arguments are passed, the function is calculated using the same rules and chain combinations using the CityHash combinator.\nFor example, you can compute the checksum of an entire table with accuracy up to the row order: \nSELECT sum(cityHash64(*)) FROM table\n.\n\n\nintHash32\n\n\nCalculates a 32-bit hash code from any type of integer.\nThis is a relatively fast non-cryptographic hash function of average quality for numbers.\n\n\nintHash64\n\n\nCalculates a 64-bit hash code from any type of integer.\nIt works faster than intHash32. Average quality.\n\n\nSHA1\n\n\nSHA224\n\n\nSHA256\n\n\nCalculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32).\nThe function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million).\nWe recommend using this function only in cases when you need a specific hash function and you can't select it.\nEven in these cases, we recommend applying the function offline and pre-calculating values when inserting them into the table, instead of applying it in SELECTS.\n\n\nURLHash(url[, N])\n\n\nA fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization.\n\nURLHash(s)\n \u2013 Calculates a hash from a string without one of the trailing symbols \n/\n,\n?\n or \n#\n at the end, if present.\n\nURLHash(s, N)\n \u2013 Calculates a hash from a string up to the N level in the URL hierarchy, without one of the trailing symbols \n/\n,\n?\n or \n#\n at the end, if present.\nLevels are the same as in URLHierarchy. This function is specific to Yandex.Metrica.", - "title": "Hash functions" - }, - { - "location": "/functions/hash_functions/#hash-functions", - "text": "Hash functions can be used for deterministic pseudo-random shuffling of elements.", - "title": "Hash functions" - }, - { - "location": "/functions/hash_functions/#halfmd5", - "text": "Calculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian.\nAccepts a String-type argument. Returns UInt64.\nThis function works fairly slowly (5 million short strings per second per processor core).\nIf you don't need MD5 in particular, use the 'sipHash64' function instead.", - "title": "halfMD5" - }, - { - "location": "/functions/hash_functions/#md5", - "text": "Calculates the MD5 from a string and returns the resulting set of bytes as FixedString(16).\nIf you don't need MD5 in particular, but you need a decent cryptographic 128-bit hash, use the 'sipHash128' function instead.\nIf you want to get the same result as output by the md5sum utility, use lower(hex(MD5(s))).", - "title": "MD5" - }, - { - "location": "/functions/hash_functions/#siphash64", - "text": "Calculates SipHash from a string.\nAccepts a String-type argument. Returns UInt64.\nSipHash is a cryptographic hash function. It works at least three times faster than MD5.\nFor more information, see the link: https://131002.net/siphash/", - "title": "sipHash64" - }, - { - "location": "/functions/hash_functions/#siphash128", - "text": "Calculates SipHash from a string.\nAccepts a String-type argument. Returns FixedString(16).\nDiffers from sipHash64 in that the final xor-folding state is only done up to 128 bytes.", - "title": "sipHash128" - }, - { - "location": "/functions/hash_functions/#cityhash64", - "text": "Calculates CityHash64 from a string or a similar hash function for any number of any type of arguments.\nFor String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality.\nFor other types of arguments, a decent implementation-specific fast non-cryptographic hash function is used.\nIf multiple arguments are passed, the function is calculated using the same rules and chain combinations using the CityHash combinator.\nFor example, you can compute the checksum of an entire table with accuracy up to the row order: SELECT sum(cityHash64(*)) FROM table .", - "title": "cityHash64" - }, - { - "location": "/functions/hash_functions/#inthash32", - "text": "Calculates a 32-bit hash code from any type of integer.\nThis is a relatively fast non-cryptographic hash function of average quality for numbers.", - "title": "intHash32" - }, - { - "location": "/functions/hash_functions/#inthash64", - "text": "Calculates a 64-bit hash code from any type of integer.\nIt works faster than intHash32. Average quality.", - "title": "intHash64" - }, - { - "location": "/functions/hash_functions/#sha1", - "text": "", - "title": "SHA1" - }, - { - "location": "/functions/hash_functions/#sha224", - "text": "", - "title": "SHA224" - }, - { - "location": "/functions/hash_functions/#sha256", - "text": "Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32).\nThe function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million).\nWe recommend using this function only in cases when you need a specific hash function and you can't select it.\nEven in these cases, we recommend applying the function offline and pre-calculating values when inserting them into the table, instead of applying it in SELECTS.", - "title": "SHA256" - }, - { - "location": "/functions/hash_functions/#urlhashurl91-n93", - "text": "A fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization. URLHash(s) \u2013 Calculates a hash from a string without one of the trailing symbols / , ? or # at the end, if present. URLHash(s, N) \u2013 Calculates a hash from a string up to the N level in the URL hierarchy, without one of the trailing symbols / , ? or # at the end, if present.\nLevels are the same as in URLHierarchy. This function is specific to Yandex.Metrica.", - "title": "URLHash(url[, N])" - }, - { - "location": "/functions/random_functions/", - "text": "Functions for generating pseudo-random numbers\n\n\nNon-cryptographic generators of pseudo-random numbers are used.\n\n\nAll the functions accept zero arguments or one argument.\nIf an argument is passed, it can be any type, and its value is not used for anything.\nThe only purpose of this argument is to prevent common subexpression elimination, so that two different instances of the same function return different columns with different random numbers.\n\n\nrand\n\n\nReturns a pseudo-random UInt32 number, evenly distributed among all UInt32-type numbers.\nUses a linear congruential generator.\n\n\nrand64\n\n\nReturns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers.\nUses a linear congruential generator.", - "title": "Functions for generating pseudo-random numbers" - }, - { - "location": "/functions/random_functions/#functions-for-generating-pseudo-random-numbers", - "text": "Non-cryptographic generators of pseudo-random numbers are used. All the functions accept zero arguments or one argument.\nIf an argument is passed, it can be any type, and its value is not used for anything.\nThe only purpose of this argument is to prevent common subexpression elimination, so that two different instances of the same function return different columns with different random numbers.", - "title": "Functions for generating pseudo-random numbers" - }, - { - "location": "/functions/random_functions/#rand", - "text": "Returns a pseudo-random UInt32 number, evenly distributed among all UInt32-type numbers.\nUses a linear congruential generator.", - "title": "rand" - }, - { - "location": "/functions/random_functions/#rand64", - "text": "Returns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers.\nUses a linear congruential generator.", - "title": "rand64" - }, - { - "location": "/functions/encoding_functions/", - "text": "Encoding functions\n\n\nhex\n\n\nAccepts arguments of types: \nString\n, \nunsigned integer\n, \nDate\n, or \nDateTime\n. Returns a string containing the argument's hexadecimal representation. Uses uppercase letters \nA-F\n. Does not use \n0x\n prefixes or \nh\n suffixes. For strings, all bytes are simply encoded as two hexadecimal numbers. Numbers are converted to big endian (\"human readable\") format. For numbers, older zeros are trimmed, but only by entire bytes. For example, \nhex (1) = '01'\n. \nDate\n is encoded as the number of days since the beginning of the Unix epoch. \nDateTime\n is encoded as the number of seconds since the beginning of the Unix epoch.\n\n\nunhex(str)\n\n\nAccepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits does not have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn't thrown).\nIf you want to convert the result to a number, you can use the 'reverse' and 'reinterpretAsType' functions.\n\n\nUUIDStringToNum(str)\n\n\nAccepts a string containing 36 characters in the format \n123e4567-e89b-12d3-a456-426655440000\n, and returns it as a set of bytes in a FixedString(16).\n\n\nUUIDNumToString(str)\n\n\nAccepts a FixedString(16) value. Returns a string containing 36 characters in text format.\n\n\nbitmaskToList(num)\n\n\nAccepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order.\n\n\nbitmaskToArray(num)\n\n\nAccepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order.", - "title": "Encoding functions" - }, - { - "location": "/functions/encoding_functions/#encoding-functions", - "text": "", - "title": "Encoding functions" - }, - { - "location": "/functions/encoding_functions/#hex", - "text": "Accepts arguments of types: String , unsigned integer , Date , or DateTime . Returns a string containing the argument's hexadecimal representation. Uses uppercase letters A-F . Does not use 0x prefixes or h suffixes. For strings, all bytes are simply encoded as two hexadecimal numbers. Numbers are converted to big endian (\"human readable\") format. For numbers, older zeros are trimmed, but only by entire bytes. For example, hex (1) = '01' . Date is encoded as the number of days since the beginning of the Unix epoch. DateTime is encoded as the number of seconds since the beginning of the Unix epoch.", - "title": "hex" - }, - { - "location": "/functions/encoding_functions/#unhexstr", - "text": "Accepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits does not have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn't thrown).\nIf you want to convert the result to a number, you can use the 'reverse' and 'reinterpretAsType' functions.", - "title": "unhex(str)" - }, - { - "location": "/functions/encoding_functions/#uuidstringtonumstr", - "text": "Accepts a string containing 36 characters in the format 123e4567-e89b-12d3-a456-426655440000 , and returns it as a set of bytes in a FixedString(16).", - "title": "UUIDStringToNum(str)" - }, - { - "location": "/functions/encoding_functions/#uuidnumtostringstr", - "text": "Accepts a FixedString(16) value. Returns a string containing 36 characters in text format.", - "title": "UUIDNumToString(str)" - }, - { - "location": "/functions/encoding_functions/#bitmasktolistnum", - "text": "Accepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order.", - "title": "bitmaskToList(num)" - }, - { - "location": "/functions/encoding_functions/#bitmasktoarraynum", - "text": "Accepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order.", - "title": "bitmaskToArray(num)" - }, - { - "location": "/functions/url_functions/", - "text": "Functions for working with URLs\n\n\nAll these functions don't follow the RFC. They are maximally simplified for improved performance.\n\n\nFunctions that extract part of a URL\n\n\nIf there isn't anything similar in a URL, an empty string is returned.\n\n\nprotocol\n\n\nReturns the protocol. Examples: http, ftp, mailto, magnet...\n\n\ndomain\n\n\nGets the domain.\n\n\ndomainWithoutWWW\n\n\nReturns the domain and removes no more than one 'www.' from the beginning of it, if present.\n\n\ntopLevelDomain\n\n\nReturns the top-level domain. Example: .ru.\n\n\nfirstSignificantSubdomain\n\n\nReturns the \"first significant subdomain\". This is a non-standard concept specific to Yandex.Metrica. The first significant subdomain is a second-level domain if it is 'com', 'net', 'org', or 'co'. Otherwise, it is a third-level domain. For example, firstSignificantSubdomain ('\nhttps://news.yandex.ru/\n') = 'yandex ', firstSignificantSubdomain ('\nhttps://news.yandex.com.tr/\n') = 'yandex '. The list of \"insignificant\" second-level domains and other implementation details may change in the future.\n\n\ncutToFirstSignificantSubdomain\n\n\nReturns the part of the domain that includes top-level subdomains up to the \"first significant subdomain\" (see the explanation above).\n\n\nFor example, \ncutToFirstSignificantSubdomain('https://news.yandex.com.tr/') = 'yandex.com.tr'\n.\n\n\npath\n\n\nReturns the path. Example: \n/top/news.html\n The path does not include the query string.\n\n\npathFull\n\n\nThe same as above, but including query string and fragment. Example: /top/news.html?page=2#comments\n\n\nqueryString\n\n\nReturns the query string. Example: page=1\nlr=213. query-string does not include the initial question mark, as well as # and everything after #.\n\n\nfragment\n\n\nReturns the fragment identifier. fragment does not include the initial hash symbol.\n\n\nqueryStringAndFragment\n\n\nReturns the query string and fragment identifier. Example: page=1#29390.\n\n\nextractURLParameter(URL, name)\n\n\nReturns the value of the 'name' parameter in the URL, if present. Otherwise, an empty string. If there are many parameters with this name, it returns the first occurrence. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.\n\n\nextractURLParameters(URL)\n\n\nReturns an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way.\n\n\nextractURLParameterNames(URL)\n\n\nReturns an array of name strings corresponding to the names of URL parameters. The values are not decoded in any way.\n\n\nURLHierarchy(URL)\n\n\nReturns an array containing the URL, truncated at the end by the symbols /,? in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:\n\n\nURLPathHierarchy(URL)\n\n\nThe same as above, but without the protocol and host in the result. The / element (root) is not included. Example: the function is used to implement tree reports the URL in Yandex. Metric.\n\n\nURLPathHierarchy(\nhttps://example.com/browse/CONV-6788\n) =\n[\n \n/browse/\n,\n \n/browse/CONV-6788\n\n]\n\n\n\n\n\ndecodeURLComponent(URL)\n\n\nReturns the decoded URL.\nExample:\n\n\nSELECT\n \ndecodeURLComponent\n(\nhttp://127.0.0.1:8123/?query=SELECT%201%3B\n)\n \nAS\n \nDecodedURL\n;\n\n\n\n\n\n\n\u250c\u2500DecodedURL\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 http://127.0.0.1:8123/?query=SELECT 1; \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nFunctions that remove part of a URL.\n\n\nIf the URL doesn't have anything similar, the URL remains unchanged.\n\n\ncutWWW\n\n\nRemoves no more than one 'www.' from the beginning of the URL's domain, if present.\n\n\ncutQueryString\n\n\nRemoves query string. The question mark is also removed.\n\n\ncutFragment\n\n\nRemoves the fragment identifier. The number sign is also removed.\n\n\ncutQueryStringAndFragment\n\n\nRemoves the query string and fragment identifier. The question mark and number sign are also removed.\n\n\ncutURLParameter(URL, name)\n\n\nRemoves the 'name' URL parameter, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.", - "title": "Functions for working with URLs" - }, - { - "location": "/functions/url_functions/#functions-for-working-with-urls", - "text": "All these functions don't follow the RFC. They are maximally simplified for improved performance.", - "title": "Functions for working with URLs" - }, - { - "location": "/functions/url_functions/#functions-that-extract-part-of-a-url", - "text": "If there isn't anything similar in a URL, an empty string is returned.", - "title": "Functions that extract part of a URL" - }, - { - "location": "/functions/url_functions/#protocol", - "text": "Returns the protocol. Examples: http, ftp, mailto, magnet...", - "title": "protocol" - }, - { - "location": "/functions/url_functions/#domain", - "text": "Gets the domain.", - "title": "domain" - }, - { - "location": "/functions/url_functions/#domainwithoutwww", - "text": "Returns the domain and removes no more than one 'www.' from the beginning of it, if present.", - "title": "domainWithoutWWW" - }, - { - "location": "/functions/url_functions/#topleveldomain", - "text": "Returns the top-level domain. Example: .ru.", - "title": "topLevelDomain" - }, - { - "location": "/functions/url_functions/#firstsignificantsubdomain", - "text": "Returns the \"first significant subdomain\". This is a non-standard concept specific to Yandex.Metrica. The first significant subdomain is a second-level domain if it is 'com', 'net', 'org', or 'co'. Otherwise, it is a third-level domain. For example, firstSignificantSubdomain (' https://news.yandex.ru/ ') = 'yandex ', firstSignificantSubdomain (' https://news.yandex.com.tr/ ') = 'yandex '. The list of \"insignificant\" second-level domains and other implementation details may change in the future.", - "title": "firstSignificantSubdomain" - }, - { - "location": "/functions/url_functions/#cuttofirstsignificantsubdomain", - "text": "Returns the part of the domain that includes top-level subdomains up to the \"first significant subdomain\" (see the explanation above). For example, cutToFirstSignificantSubdomain('https://news.yandex.com.tr/') = 'yandex.com.tr' .", - "title": "cutToFirstSignificantSubdomain" - }, - { - "location": "/functions/url_functions/#path", - "text": "Returns the path. Example: /top/news.html The path does not include the query string.", - "title": "path" - }, - { - "location": "/functions/url_functions/#pathfull", - "text": "The same as above, but including query string and fragment. Example: /top/news.html?page=2#comments", - "title": "pathFull" - }, - { - "location": "/functions/url_functions/#querystring", - "text": "Returns the query string. Example: page=1 lr=213. query-string does not include the initial question mark, as well as # and everything after #.", - "title": "queryString" - }, - { - "location": "/functions/url_functions/#fragment", - "text": "Returns the fragment identifier. fragment does not include the initial hash symbol.", - "title": "fragment" - }, - { - "location": "/functions/url_functions/#querystringandfragment", - "text": "Returns the query string and fragment identifier. Example: page=1#29390.", - "title": "queryStringAndFragment" - }, - { - "location": "/functions/url_functions/#extracturlparameterurl-name", - "text": "Returns the value of the 'name' parameter in the URL, if present. Otherwise, an empty string. If there are many parameters with this name, it returns the first occurrence. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.", - "title": "extractURLParameter(URL, name)" - }, - { - "location": "/functions/url_functions/#extracturlparametersurl", - "text": "Returns an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way.", - "title": "extractURLParameters(URL)" - }, - { - "location": "/functions/url_functions/#extracturlparameternamesurl", - "text": "Returns an array of name strings corresponding to the names of URL parameters. The values are not decoded in any way.", - "title": "extractURLParameterNames(URL)" - }, - { - "location": "/functions/url_functions/#urlhierarchyurl", - "text": "Returns an array containing the URL, truncated at the end by the symbols /,? in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:", - "title": "URLHierarchy(URL)" - }, - { - "location": "/functions/url_functions/#urlpathhierarchyurl", - "text": "The same as above, but without the protocol and host in the result. The / element (root) is not included. Example: the function is used to implement tree reports the URL in Yandex. Metric. URLPathHierarchy( https://example.com/browse/CONV-6788 ) =\n[\n /browse/ ,\n /browse/CONV-6788 \n]", - "title": "URLPathHierarchy(URL)" - }, - { - "location": "/functions/url_functions/#decodeurlcomponenturl", - "text": "Returns the decoded URL.\nExample: SELECT decodeURLComponent ( http://127.0.0.1:8123/?query=SELECT%201%3B ) AS DecodedURL ; \u250c\u2500DecodedURL\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 http://127.0.0.1:8123/?query=SELECT 1; \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "decodeURLComponent(URL)" - }, - { - "location": "/functions/url_functions/#functions-that-remove-part-of-a-url", - "text": "If the URL doesn't have anything similar, the URL remains unchanged.", - "title": "Functions that remove part of a URL." - }, - { - "location": "/functions/url_functions/#cutwww", - "text": "Removes no more than one 'www.' from the beginning of the URL's domain, if present.", - "title": "cutWWW" - }, - { - "location": "/functions/url_functions/#cutquerystring", - "text": "Removes query string. The question mark is also removed.", - "title": "cutQueryString" - }, - { - "location": "/functions/url_functions/#cutfragment", - "text": "Removes the fragment identifier. The number sign is also removed.", - "title": "cutFragment" - }, - { - "location": "/functions/url_functions/#cutquerystringandfragment", - "text": "Removes the query string and fragment identifier. The question mark and number sign are also removed.", - "title": "cutQueryStringAndFragment" - }, - { - "location": "/functions/url_functions/#cuturlparameterurl-name", - "text": "Removes the 'name' URL parameter, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.", - "title": "cutURLParameter(URL, name)" - }, - { - "location": "/functions/ip_address_functions/", - "text": "Functions for working with IP addresses\n\n\nIPv4NumToString(num)\n\n\nTakes a UInt32 number. Interprets it as an IPv4 address in big endian. Returns a string containing the corresponding IPv4 address in the format A.B.C.d (dot-separated numbers in decimal form).\n\n\nIPv4StringToNum(s)\n\n\nThe reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0.\n\n\nIPv4NumToStringClassC(num)\n\n\nSimilar to IPv4NumToString, but using xxx instead of the last octet.\n\n\nExample:\n\n\nSELECT\n\n \nIPv4NumToStringClassC\n(\nClientIP\n)\n \nAS\n \nk\n,\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \nk\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500k\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 83.149.9.xxx \u2502 26238 \u2502\n\u2502 217.118.81.xxx \u2502 26074 \u2502\n\u2502 213.87.129.xxx \u2502 25481 \u2502\n\u2502 83.149.8.xxx \u2502 24984 \u2502\n\u2502 217.118.83.xxx \u2502 22797 \u2502\n\u2502 78.25.120.xxx \u2502 22354 \u2502\n\u2502 213.87.131.xxx \u2502 21285 \u2502\n\u2502 78.25.121.xxx \u2502 20887 \u2502\n\u2502 188.162.65.xxx \u2502 19694 \u2502\n\u2502 83.149.48.xxx \u2502 17406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSince using 'xxx' is highly unusual, this may be changed in the future. We recommend that you don't rely on the exact format of this fragment.\n\n\nIPv6NumToString(x)\n\n\nAccepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format.\nIPv6-mapped IPv4 addresses are output in the format ::ffff:111.222.33.44. Examples:\n\n\nSELECT\n \nIPv6NumToString\n(\ntoFixedString\n(\nunhex\n(\n2A0206B8000000000000000000000011\n),\n \n16\n))\n \nAS\n \naddr\n\n\n\n\n\n\n\u250c\u2500addr\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2a02:6b8::11 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n\n \nIPv6NumToString\n(\nClientIP6\n \nAS\n \nk\n),\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \nhits_all\n\n\nWHERE\n \nEventDate\n \n=\n \ntoday\n()\n \nAND\n \nsubstring\n(\nClientIP6\n,\n \n1\n,\n \n12\n)\n \n!=\n \nunhex\n(\n00000000000000000000FFFF\n)\n\n\nGROUP\n \nBY\n \nk\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500IPv6NumToString(ClientIP6)\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2a02:2168:aaa:bbbb::2 \u2502 24695 \u2502\n\u2502 2a02:2698:abcd:abcd:abcd:abcd:8888:5555 \u2502 22408 \u2502\n\u2502 2a02:6b8:0:fff::ff \u2502 16389 \u2502\n\u2502 2a01:4f8:111:6666::2 \u2502 16016 \u2502\n\u2502 2a02:2168:888:222::1 \u2502 15896 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:222 \u2502 14774 \u2502\n\u2502 2a02:8109:eee:ee:eeee:eeee:eeee:eeee \u2502 14443 \u2502\n\u2502 2a02:810b:8888:888:8888:8888:8888:8888 \u2502 14345 \u2502\n\u2502 2a02:6b8:0:444:4444:4444:4444:4444 \u2502 14279 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:ffff \u2502 13880 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n\n \nIPv6NumToString\n(\nClientIP6\n \nAS\n \nk\n),\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \nhits_all\n\n\nWHERE\n \nEventDate\n \n=\n \ntoday\n()\n\n\nGROUP\n \nBY\n \nk\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500IPv6NumToString(ClientIP6)\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 ::ffff:94.26.111.111 \u2502 747440 \u2502\n\u2502 ::ffff:37.143.222.4 \u2502 529483 \u2502\n\u2502 ::ffff:5.166.111.99 \u2502 317707 \u2502\n\u2502 ::ffff:46.38.11.77 \u2502 263086 \u2502\n\u2502 ::ffff:79.105.111.111 \u2502 186611 \u2502\n\u2502 ::ffff:93.92.111.88 \u2502 176773 \u2502\n\u2502 ::ffff:84.53.111.33 \u2502 158709 \u2502\n\u2502 ::ffff:217.118.11.22 \u2502 154004 \u2502\n\u2502 ::ffff:217.118.11.33 \u2502 148449 \u2502\n\u2502 ::ffff:217.118.11.44 \u2502 148243 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIPv6StringToNum(s)\n\n\nThe reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes.\nHEX can be uppercase or lowercase.", - "title": "Functions for working with IP addresses" - }, - { - "location": "/functions/ip_address_functions/#functions-for-working-with-ip-addresses", - "text": "", - "title": "Functions for working with IP addresses" - }, - { - "location": "/functions/ip_address_functions/#ipv4numtostringnum", - "text": "Takes a UInt32 number. Interprets it as an IPv4 address in big endian. Returns a string containing the corresponding IPv4 address in the format A.B.C.d (dot-separated numbers in decimal form).", - "title": "IPv4NumToString(num)" - }, - { - "location": "/functions/ip_address_functions/#ipv4stringtonums", - "text": "The reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0.", - "title": "IPv4StringToNum(s)" - }, - { - "location": "/functions/ip_address_functions/#ipv4numtostringclasscnum", - "text": "Similar to IPv4NumToString, but using xxx instead of the last octet. Example: SELECT \n IPv4NumToStringClassC ( ClientIP ) AS k , \n count () AS c FROM test . hits GROUP BY k ORDER BY c DESC LIMIT 10 \u250c\u2500k\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 83.149.9.xxx \u2502 26238 \u2502\n\u2502 217.118.81.xxx \u2502 26074 \u2502\n\u2502 213.87.129.xxx \u2502 25481 \u2502\n\u2502 83.149.8.xxx \u2502 24984 \u2502\n\u2502 217.118.83.xxx \u2502 22797 \u2502\n\u2502 78.25.120.xxx \u2502 22354 \u2502\n\u2502 213.87.131.xxx \u2502 21285 \u2502\n\u2502 78.25.121.xxx \u2502 20887 \u2502\n\u2502 188.162.65.xxx \u2502 19694 \u2502\n\u2502 83.149.48.xxx \u2502 17406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Since using 'xxx' is highly unusual, this may be changed in the future. We recommend that you don't rely on the exact format of this fragment.", - "title": "IPv4NumToStringClassC(num)" - }, - { - "location": "/functions/ip_address_functions/#ipv6numtostringx", - "text": "Accepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format.\nIPv6-mapped IPv4 addresses are output in the format ::ffff:111.222.33.44. Examples: SELECT IPv6NumToString ( toFixedString ( unhex ( 2A0206B8000000000000000000000011 ), 16 )) AS addr \u250c\u2500addr\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2a02:6b8::11 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT \n IPv6NumToString ( ClientIP6 AS k ), \n count () AS c FROM hits_all WHERE EventDate = today () AND substring ( ClientIP6 , 1 , 12 ) != unhex ( 00000000000000000000FFFF ) GROUP BY k ORDER BY c DESC LIMIT 10 \u250c\u2500IPv6NumToString(ClientIP6)\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2a02:2168:aaa:bbbb::2 \u2502 24695 \u2502\n\u2502 2a02:2698:abcd:abcd:abcd:abcd:8888:5555 \u2502 22408 \u2502\n\u2502 2a02:6b8:0:fff::ff \u2502 16389 \u2502\n\u2502 2a01:4f8:111:6666::2 \u2502 16016 \u2502\n\u2502 2a02:2168:888:222::1 \u2502 15896 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:222 \u2502 14774 \u2502\n\u2502 2a02:8109:eee:ee:eeee:eeee:eeee:eeee \u2502 14443 \u2502\n\u2502 2a02:810b:8888:888:8888:8888:8888:8888 \u2502 14345 \u2502\n\u2502 2a02:6b8:0:444:4444:4444:4444:4444 \u2502 14279 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:ffff \u2502 13880 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT \n IPv6NumToString ( ClientIP6 AS k ), \n count () AS c FROM hits_all WHERE EventDate = today () GROUP BY k ORDER BY c DESC LIMIT 10 \u250c\u2500IPv6NumToString(ClientIP6)\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 ::ffff:94.26.111.111 \u2502 747440 \u2502\n\u2502 ::ffff:37.143.222.4 \u2502 529483 \u2502\n\u2502 ::ffff:5.166.111.99 \u2502 317707 \u2502\n\u2502 ::ffff:46.38.11.77 \u2502 263086 \u2502\n\u2502 ::ffff:79.105.111.111 \u2502 186611 \u2502\n\u2502 ::ffff:93.92.111.88 \u2502 176773 \u2502\n\u2502 ::ffff:84.53.111.33 \u2502 158709 \u2502\n\u2502 ::ffff:217.118.11.22 \u2502 154004 \u2502\n\u2502 ::ffff:217.118.11.33 \u2502 148449 \u2502\n\u2502 ::ffff:217.118.11.44 \u2502 148243 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "IPv6NumToString(x)" - }, - { - "location": "/functions/ip_address_functions/#ipv6stringtonums", - "text": "The reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes.\nHEX can be uppercase or lowercase.", - "title": "IPv6StringToNum(s)" - }, - { - "location": "/functions/json_functions/", - "text": "Functions for working with JSON\n\n\nIn Yandex.Metrica, JSON is transmitted by users as session parameters. There are some special functions for working with this JSON. (Although in most of the cases, the JSONs are additionally pre-processed, and the resulting values are put in separate columns in their processed format.) All these functions are based on strong assumptions about what the JSON can be, but they try to do as little as possible to get the job done.\n\n\nThe following assumptions are made:\n\n\n\n\nThe field name (function argument) must be a constant.\n\n\nThe field name is somehow canonically encoded in JSON. For example: \nvisitParamHas('{\"abc\":\"def\"}', 'abc') = 1\n, but \nvisitParamHas('{\"\\\\u0061\\\\u0062\\\\u0063\":\"def\"}', 'abc') = 0\n\n\nFields are searched for on any nesting level, indiscriminately. If there are multiple matching fields, the first occurrence is used.\n\n\nThe JSON doesn't have space characters outside of string literals.\n\n\n\n\nvisitParamHas(params, name)\n\n\nChecks whether there is a field with the 'name' name.\n\n\nvisitParamExtractUInt(params, name)\n\n\nParses UInt64 from the value of the field named 'name'. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn't exist, or it exists but doesn't contain a number, it returns 0.\n\n\nvisitParamExtractInt(params, name)\n\n\nThe same as for Int64.\n\n\nvisitParamExtractFloat(params, name)\n\n\nThe same as for Float64.\n\n\nvisitParamExtractBool(params, name)\n\n\nParses a true/false value. The result is UInt8.\n\n\nvisitParamExtractRaw(params, name)\n\n\nReturns the value of a field, including separators.\n\n\nExamples:\n\n\nvisitParamExtractRaw(\n{\nabc\n:\n\\\\n\\\\u0000\n}\n, \nabc\n) = \n\\\\n\\\\u0000\n\nvisitParamExtractRaw(\n{\nabc\n:{\ndef\n:[1,2,3]}}\n, \nabc\n) = \n{\ndef\n:[1,2,3]}\n\n\n\n\n\n\nvisitParamExtractString(params, name)\n\n\nParses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string.\n\n\nExamples:\n\n\nvisitParamExtractString(\n{\nabc\n:\n\\\\n\\\\u0000\n}\n, \nabc\n) = \n\\n\\0\n\nvisitParamExtractString(\n{\nabc\n:\n\\\\u263a\n}\n, \nabc\n) = \n\u263a\n\nvisitParamExtractString(\n{\nabc\n:\n\\\\u263\n}\n, \nabc\n) = \n\nvisitParamExtractString(\n{\nabc\n:\nhello}\n, \nabc\n) = \n\n\n\n\n\n\nThere is currently no support for code points in the format \n\\uXXXX\\uYYYY\n that are not from the basic multilingual plane (they are converted to CESU-8 instead of UTF-8).", - "title": "Functions for working with JSON." - }, - { - "location": "/functions/json_functions/#functions-for-working-with-json", - "text": "In Yandex.Metrica, JSON is transmitted by users as session parameters. There are some special functions for working with this JSON. (Although in most of the cases, the JSONs are additionally pre-processed, and the resulting values are put in separate columns in their processed format.) All these functions are based on strong assumptions about what the JSON can be, but they try to do as little as possible to get the job done. The following assumptions are made: The field name (function argument) must be a constant. The field name is somehow canonically encoded in JSON. For example: visitParamHas('{\"abc\":\"def\"}', 'abc') = 1 , but visitParamHas('{\"\\\\u0061\\\\u0062\\\\u0063\":\"def\"}', 'abc') = 0 Fields are searched for on any nesting level, indiscriminately. If there are multiple matching fields, the first occurrence is used. The JSON doesn't have space characters outside of string literals.", - "title": "Functions for working with JSON" - }, - { - "location": "/functions/json_functions/#visitparamhasparams-name", - "text": "Checks whether there is a field with the 'name' name.", - "title": "visitParamHas(params, name)" - }, - { - "location": "/functions/json_functions/#visitparamextractuintparams-name", - "text": "Parses UInt64 from the value of the field named 'name'. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn't exist, or it exists but doesn't contain a number, it returns 0.", - "title": "visitParamExtractUInt(params, name)" - }, - { - "location": "/functions/json_functions/#visitparamextractintparams-name", - "text": "The same as for Int64.", - "title": "visitParamExtractInt(params, name)" - }, - { - "location": "/functions/json_functions/#visitparamextractfloatparams-name", - "text": "The same as for Float64.", - "title": "visitParamExtractFloat(params, name)" - }, - { - "location": "/functions/json_functions/#visitparamextractboolparams-name", - "text": "Parses a true/false value. The result is UInt8.", - "title": "visitParamExtractBool(params, name)" - }, - { - "location": "/functions/json_functions/#visitparamextractrawparams-name", - "text": "Returns the value of a field, including separators. Examples: visitParamExtractRaw( { abc : \\\\n\\\\u0000 } , abc ) = \\\\n\\\\u0000 \nvisitParamExtractRaw( { abc :{ def :[1,2,3]}} , abc ) = { def :[1,2,3]}", - "title": "visitParamExtractRaw(params, name)" - }, - { - "location": "/functions/json_functions/#visitparamextractstringparams-name", - "text": "Parses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string. Examples: visitParamExtractString( { abc : \\\\n\\\\u0000 } , abc ) = \\n\\0 \nvisitParamExtractString( { abc : \\\\u263a } , abc ) = \u263a \nvisitParamExtractString( { abc : \\\\u263 } , abc ) = \nvisitParamExtractString( { abc : hello} , abc ) = There is currently no support for code points in the format \\uXXXX\\uYYYY that are not from the basic multilingual plane (they are converted to CESU-8 instead of UTF-8).", - "title": "visitParamExtractString(params, name)" - }, - { - "location": "/functions/higher_order_functions/", - "text": "Higher-order functions\n\n\n-\n operator, lambda(params, expr) function\n\n\nAllows describing a lambda function for passing to a higher-order function. The left side of the arrow has a formal parameter, which is any ID, or multiple formal parameters \u2013 any IDs in a tuple. The right side of the arrow has an expression that can use these formal parameters, as well as any table columns.\n\n\nExamples: \nx -\n 2 * x, str -\n str != Referer.\n\n\nHigher-order functions can only accept lambda functions as their functional argument.\n\n\nA lambda function that accepts multiple arguments can be passed to a higher-order function. In this case, the higher-order function is passed several arrays of identical length that these arguments will correspond to.\n\n\nFor all functions other than 'arrayMap' and 'arrayFilter', the first argument (the lambda function) can be omitted. In this case, identical mapping is assumed.\n\n\narrayMap(func, arr1, ...)\n\n\nReturns an array obtained from the original application of the 'func' function to each element in the 'arr' array.\n\n\narrayFilter(func, arr1, ...)\n\n\nReturns an array containing only the elements in 'arr1' for which 'func' returns something other than 0.\n\n\nExamples:\n\n\nSELECT\n \narrayFilter\n(\nx\n \n-\n \nx\n \nLIKE\n \n%World%\n,\n \n[\nHello\n,\n \nabc World\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\nabc World\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n\n \narrayFilter\n(\n\n \n(\ni\n,\n \nx\n)\n \n-\n \nx\n \nLIKE\n \n%World%\n,\n\n \narrayEnumerate\n(\narr\n),\n\n \n[\nHello\n,\n \nabc World\n]\n \nAS\n \narr\n)\n\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2510\n\u2502 [2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayCount([func,] arr1, ...)\n\n\nReturns the number of elements in the arr array for which func returns something other than 0. If 'func' is not specified, it returns the number of non-zero elements in the array.\n\n\narrayExists([func,] arr1, ...)\n\n\nReturns 1 if there is at least one element in 'arr' for which 'func' returns something other than 0. Otherwise, it returns 0.\n\n\narrayAll([func,] arr1, ...)\n\n\nReturns 1 if 'func' returns something other than 0 for all the elements in 'arr'. Otherwise, it returns 0.\n\n\narraySum([func,] arr1, ...)\n\n\nReturns the sum of the 'func' values. If the function is omitted, it just returns the sum of the array elements.\n\n\narrayFirst(func, arr1, ...)\n\n\nReturns the first element in the 'arr1' array for which 'func' returns something other than 0.\n\n\narrayFirstIndex(func, arr1, ...)\n\n\nReturns the index of the first element in the 'arr1' array for which 'func' returns something other than 0.\n\n\narrayCumSum([func,] arr1, ...)\n\n\nReturns an array of partial sums of elements in the source array (a running sum). If the \nfunc\n function is specified, then the values of the array elements are converted by this function before summing.\n\n\nExample:\n\n\nSELECT\n \narrayCumSum\n([\n1\n,\n \n1\n,\n \n1\n,\n \n1\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1, 2, 3, 4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narraySort([func,] arr1, ...)\n\n\nReturns an array as result of sorting the elements of \narr1\n in ascending order. If the \nfunc\n function is specified, sorting order is determined by the result of the function \nfunc\n applied to the elements of array (arrays) \n\n\nThe \nSchwartzian transform\n is used to impove sorting efficiency.\n\n\nExample:\n\n\nSELECT\n \narraySort\n((\nx\n,\n \ny\n)\n \n-\n \ny\n,\n \n[\nhello\n,\n \nworld\n],\n \n[\n2\n,\n \n1\n]);\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\nworld\n, \nhello\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayReverseSort([func,] arr1, ...)\n\n\nReturns an array as result of sorting the elements of \narr1\n in descending order. If the \nfunc\n function is specified, sorting order is determined by the result of the function \nfunc\n applied to the elements of array (arrays)", - "title": "Higher-order functions" - }, - { - "location": "/functions/higher_order_functions/#higher-order-functions", - "text": "", - "title": "Higher-order functions" - }, - { - "location": "/functions/higher_order_functions/#-operator-lambdaparams-expr-function", - "text": "Allows describing a lambda function for passing to a higher-order function. The left side of the arrow has a formal parameter, which is any ID, or multiple formal parameters \u2013 any IDs in a tuple. The right side of the arrow has an expression that can use these formal parameters, as well as any table columns. Examples: x - 2 * x, str - str != Referer. Higher-order functions can only accept lambda functions as their functional argument. A lambda function that accepts multiple arguments can be passed to a higher-order function. In this case, the higher-order function is passed several arrays of identical length that these arguments will correspond to. For all functions other than 'arrayMap' and 'arrayFilter', the first argument (the lambda function) can be omitted. In this case, identical mapping is assumed.", - "title": "-> operator, lambda(params, expr) function" - }, - { - "location": "/functions/higher_order_functions/#arraymapfunc-arr1", - "text": "Returns an array obtained from the original application of the 'func' function to each element in the 'arr' array.", - "title": "arrayMap(func, arr1, ...)" - }, - { - "location": "/functions/higher_order_functions/#arrayfilterfunc-arr1", - "text": "Returns an array containing only the elements in 'arr1' for which 'func' returns something other than 0. Examples: SELECT arrayFilter ( x - x LIKE %World% , [ Hello , abc World ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ abc World ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT \n arrayFilter ( \n ( i , x ) - x LIKE %World% , \n arrayEnumerate ( arr ), \n [ Hello , abc World ] AS arr ) \n AS res \u250c\u2500res\u2500\u2510\n\u2502 [2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayFilter(func, arr1, ...)" - }, - { - "location": "/functions/higher_order_functions/#arraycount91func93-arr1", - "text": "Returns the number of elements in the arr array for which func returns something other than 0. If 'func' is not specified, it returns the number of non-zero elements in the array.", - "title": "arrayCount([func,] arr1, ...)" - }, - { - "location": "/functions/higher_order_functions/#arrayexists91func93-arr1", - "text": "Returns 1 if there is at least one element in 'arr' for which 'func' returns something other than 0. Otherwise, it returns 0.", - "title": "arrayExists([func,] arr1, ...)" - }, - { - "location": "/functions/higher_order_functions/#arrayall91func93-arr1", - "text": "Returns 1 if 'func' returns something other than 0 for all the elements in 'arr'. Otherwise, it returns 0.", - "title": "arrayAll([func,] arr1, ...)" - }, - { - "location": "/functions/higher_order_functions/#arraysum91func93-arr1", - "text": "Returns the sum of the 'func' values. If the function is omitted, it just returns the sum of the array elements.", - "title": "arraySum([func,] arr1, ...)" - }, - { - "location": "/functions/higher_order_functions/#arrayfirstfunc-arr1", - "text": "Returns the first element in the 'arr1' array for which 'func' returns something other than 0.", - "title": "arrayFirst(func, arr1, ...)" - }, - { - "location": "/functions/higher_order_functions/#arrayfirstindexfunc-arr1", - "text": "Returns the index of the first element in the 'arr1' array for which 'func' returns something other than 0.", - "title": "arrayFirstIndex(func, arr1, ...)" - }, - { - "location": "/functions/higher_order_functions/#arraycumsum91func93-arr1", - "text": "Returns an array of partial sums of elements in the source array (a running sum). If the func function is specified, then the values of the array elements are converted by this function before summing. Example: SELECT arrayCumSum ([ 1 , 1 , 1 , 1 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1, 2, 3, 4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayCumSum([func,] arr1, ...)" - }, - { - "location": "/functions/higher_order_functions/#arraysort91func93-arr1", - "text": "Returns an array as result of sorting the elements of arr1 in ascending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays) The Schwartzian transform is used to impove sorting efficiency. Example: SELECT arraySort (( x , y ) - y , [ hello , world ], [ 2 , 1 ]); \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ world , hello ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arraySort([func,] arr1, ...)" - }, - { - "location": "/functions/higher_order_functions/#arrayreversesort91func93-arr1", - "text": "Returns an array as result of sorting the elements of arr1 in descending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays)", - "title": "arrayReverseSort([func,] arr1, ...)" - }, - { - "location": "/functions/other_functions/", - "text": "Other functions\n\n\nhostName()\n\n\nReturns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server.\n\n\nvisibleWidth(x)\n\n\nCalculates the approximate width when outputting values to the console in text format (tab-separated).\nThis function is used by the system for implementing Pretty formats.\n\n\ntoTypeName(x)\n\n\nReturns a string containing the type name of the passed argument.\n\n\nblockSize()\n\n\nGets the size of the block.\nIn ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for.\n\n\nmaterialize(x)\n\n\nTurns a constant into a full column containing just one value.\nIn ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior.\n\n\nignore(...)\n\n\nAccepts any arguments and always returns 0.\nHowever, the argument is still evaluated. This can be used for benchmarks.\n\n\nsleep(seconds)\n\n\nSleeps 'seconds' seconds on each data block. You can specify an integer or a floating-point number.\n\n\ncurrentDatabase()\n\n\nReturns the name of the current database.\nYou can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database.\n\n\nisFinite(x)\n\n\nAccepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0.\n\n\nisInfinite(x)\n\n\nAccepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. Note that 0 is returned for a NaN.\n\n\nisNaN(x)\n\n\nAccepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0.\n\n\nhasColumnInTable(['hostname'[, 'username'[, 'password']],] 'database', 'table', 'column')\n\n\nAccepts constant strings: database name, table name, and column name. Returns a UInt8 constant expression equal to 1 if there is a column, otherwise 0. If the hostname parameter is set, the test will run on a remote server.\nThe function throws an exception if the table does not exist.\nFor elements in a nested data structure, the function checks for the existence of a column. For the nested data structure itself, the function returns 0.\n\n\nbar\n\n\nAllows building a unicode-art diagram.\n\n\nbar (x, min, max, width)\n draws a band with a width proportional to \n(x - min)\n and equal to \nwidth\n characters when \nx = max\n.\n\n\nParameters:\n\n\n\n\nx\n \u2013 Value to display.\n\n\nmin, max\n \u2013 Integer constants. The value must fit in Int64.\n\n\nwidth\n \u2013 Constant, positive number, may be a fraction.\n\n\n\n\nThe band is drawn with accuracy to one eighth of a symbol.\n\n\nExample:\n\n\nSELECT\n\n \ntoHour\n(\nEventTime\n)\n \nAS\n \nh\n,\n\n \ncount\n()\n \nAS\n \nc\n,\n\n \nbar\n(\nc\n,\n \n0\n,\n \n600000\n,\n \n20\n)\n \nAS\n \nbar\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \nh\n\n\nORDER\n \nBY\n \nh\n \nASC\n\n\n\n\n\n\n\u250c\u2500\u2500h\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u252c\u2500bar\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 0 \u2502 292907 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 1 \u2502 180563 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 2 \u2502 114861 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 3 \u2502 85069 \u2502 \u2588\u2588\u258b \u2502\n\u2502 4 \u2502 68543 \u2502 \u2588\u2588\u258e \u2502\n\u2502 5 \u2502 78116 \u2502 \u2588\u2588\u258c \u2502\n\u2502 6 \u2502 113474 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 7 \u2502 170678 \u2502 \u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 8 \u2502 278380 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 9 \u2502 391053 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 10 \u2502 457681 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 11 \u2502 493667 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 12 \u2502 509641 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 13 \u2502 522947 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 14 \u2502 539954 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 15 \u2502 528460 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c \u2502\n\u2502 16 \u2502 539201 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 17 \u2502 523539 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 18 \u2502 506467 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 19 \u2502 520915 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 20 \u2502 521665 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 21 \u2502 542078 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 22 \u2502 493642 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 23 \u2502 400397 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2514\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\ntransform\n\n\nTransforms a value according to the explicitly defined mapping of some elements to other ones.\nThere are two variations of this function:\n\n\n\n\ntransform(x, array_from, array_to, default)\n\n\n\n\nx\n \u2013 What to transform.\n\n\narray_from\n \u2013 Constant array of values for converting.\n\n\narray_to\n \u2013 Constant array of values to convert the values in 'from' to.\n\n\ndefault\n \u2013 Which value to use if 'x' is not equal to any of the values in 'from'.\n\n\narray_from\n and \narray_to\n \u2013 Arrays of the same size.\n\n\nTypes:\n\n\ntransform(T, Array(T), Array(U), U) -\n U\n\n\nT\n and \nU\n can be numeric, string, or Date or DateTime types.\nWhere the same letter is indicated (T or U), for numeric types these might not be matching types, but types that have a common type.\nFor example, the first argument can have the Int64 type, while the second has the Array(Uint16) type.\n\n\nIf the 'x' value is equal to one of the elements in the 'array_from' array, it returns the existing element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'default'. If there are multiple matching elements in 'array_from', it returns one of the matches.\n\n\nExample:\n\n\nSELECT\n\n \ntransform\n(\nSearchEngineID\n,\n \n[\n2\n,\n \n3\n],\n \n[\nYandex\n,\n \nGoogle\n],\n \nOther\n)\n \nAS\n \ntitle\n,\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \ntest\n.\nhits\n\n\nWHERE\n \nSearchEngineID\n \n!=\n \n0\n\n\nGROUP\n \nBY\n \ntitle\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\n\n\n\n\n\u250c\u2500title\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 Yandex \u2502 498635 \u2502\n\u2502 Google \u2502 229872 \u2502\n\u2502 Other \u2502 104472 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\ntransform(x, array_from, array_to)\n\n\n\n\nDiffers from the first variation in that the 'default' argument is omitted.\nIf the 'x' value is equal to one of the elements in the 'array_from' array, it returns the matching element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'x'.\n\n\nTypes:\n\n\ntransform(T, Array(T), Array(T)) -\n T\n\n\nExample:\n\n\nSELECT\n\n \ntransform\n(\ndomain\n(\nReferer\n),\n \n[\nyandex.ru\n,\n \ngoogle.ru\n,\n \nvk.com\n],\n \n[\nwww.yandex\n,\n \nexample.com\n])\n \nAS\n \ns\n,\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \ndomain\n(\nReferer\n)\n\n\nORDER\n \nBY\n \ncount\n()\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 \u2502 2906259 \u2502\n\u2502 www.yandex \u2502 867767 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 313599 \u2502\n\u2502 mail.yandex.ru \u2502 107147 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 100355 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 65040 \u2502\n\u2502 news.yandex.ru \u2502 64515 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.net \u2502 59141 \u2502\n\u2502 example.com \u2502 57316 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nformatReadableSize(x)\n\n\nAccepts the size (number of bytes). Returns a rounded size with a suffix (KiB, MiB, etc.) as a string.\n\n\nExample:\n\n\nSELECT\n\n \narrayJoin\n([\n1\n,\n \n1024\n,\n \n1024\n*\n1024\n,\n \n192851925\n])\n \nAS\n \nfilesize_bytes\n,\n\n \nformatReadableSize\n(\nfilesize_bytes\n)\n \nAS\n \nfilesize\n\n\n\n\n\n\n\u250c\u2500filesize_bytes\u2500\u252c\u2500filesize\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 1.00 B \u2502\n\u2502 1024 \u2502 1.00 KiB \u2502\n\u2502 1048576 \u2502 1.00 MiB \u2502\n\u2502 192851925 \u2502 183.92 MiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nleast(a, b)\n\n\nReturns the smallest value from a and b.\n\n\ngreatest(a, b)\n\n\nReturns the largest value of a and b.\n\n\nuptime()\n\n\nReturns the server's uptime in seconds.\n\n\nversion()\n\n\nReturns the version of the server as a string.\n\n\nrowNumberInAllBlocks()\n\n\nReturns the ordinal number of the row in the data block. This function only considers the affected data blocks.\n\n\nrunningDifference(x)\n\n\nCalculates the difference between successive row values \u200b\u200bin the data block.\nReturns 0 for the first row and the difference from the previous row for each subsequent row.\n\n\nThe result of the function depends on the affected data blocks and the order of data in the block.\nIf you make a subquery with ORDER BY and call the function from outside the subquery, you can get the expected result.\n\n\nExample:\n\n\nSELECT\n\n \nEventID\n,\n\n \nEventTime\n,\n\n \nrunningDifference\n(\nEventTime\n)\n \nAS\n \ndelta\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nEventID\n,\n\n \nEventTime\n\n \nFROM\n \nevents\n\n \nWHERE\n \nEventDate\n \n=\n \n2016-11-24\n\n \nORDER\n \nBY\n \nEventTime\n \nASC\n\n \nLIMIT\n \n5\n\n\n)\n\n\n\n\n\n\n\u250c\u2500EventID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500EventTime\u2500\u252c\u2500delta\u2500\u2510\n\u2502 1106 \u2502 2016-11-24 00:00:04 \u2502 0 \u2502\n\u2502 1107 \u2502 2016-11-24 00:00:05 \u2502 1 \u2502\n\u2502 1108 \u2502 2016-11-24 00:00:05 \u2502 0 \u2502\n\u2502 1109 \u2502 2016-11-24 00:00:09 \u2502 4 \u2502\n\u2502 1110 \u2502 2016-11-24 00:00:10 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nMACNumToString(num)\n\n\nAccepts a UInt64 number. Interprets it as a MAC address in big endian. Returns a string containing the corresponding MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form).\n\n\nMACStringToNum(s)\n\n\nThe inverse function of MACNumToString. If the MAC address has an invalid format, it returns 0.\n\n\nMACStringToOUI(s)\n\n\nAccepts a MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form). Returns the first three octets as a UInt64 number. If the MAC address has an invalid format, it returns 0.", - "title": "Other functions" - }, - { - "location": "/functions/other_functions/#other-functions", - "text": "", - "title": "Other functions" - }, - { - "location": "/functions/other_functions/#hostname", - "text": "Returns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server.", - "title": "hostName()" - }, - { - "location": "/functions/other_functions/#visiblewidthx", - "text": "Calculates the approximate width when outputting values to the console in text format (tab-separated).\nThis function is used by the system for implementing Pretty formats.", - "title": "visibleWidth(x)" - }, - { - "location": "/functions/other_functions/#totypenamex", - "text": "Returns a string containing the type name of the passed argument.", - "title": "toTypeName(x)" - }, - { - "location": "/functions/other_functions/#blocksize", - "text": "Gets the size of the block.\nIn ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for.", - "title": "blockSize()" - }, - { - "location": "/functions/other_functions/#materializex", - "text": "Turns a constant into a full column containing just one value.\nIn ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior.", - "title": "materialize(x)" - }, - { - "location": "/functions/other_functions/#ignore", - "text": "Accepts any arguments and always returns 0.\nHowever, the argument is still evaluated. This can be used for benchmarks.", - "title": "ignore(...)" - }, - { - "location": "/functions/other_functions/#sleepseconds", - "text": "Sleeps 'seconds' seconds on each data block. You can specify an integer or a floating-point number.", - "title": "sleep(seconds)" - }, - { - "location": "/functions/other_functions/#currentdatabase", - "text": "Returns the name of the current database.\nYou can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database.", - "title": "currentDatabase()" - }, - { - "location": "/functions/other_functions/#isfinitex", - "text": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0.", - "title": "isFinite(x)" - }, - { - "location": "/functions/other_functions/#isinfinitex", - "text": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. Note that 0 is returned for a NaN.", - "title": "isInfinite(x)" - }, - { - "location": "/functions/other_functions/#isnanx", - "text": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0.", - "title": "isNaN(x)" - }, - { - "location": "/functions/other_functions/#hascolumnintable91hostname91-username91-password939393-database-table-column", - "text": "Accepts constant strings: database name, table name, and column name. Returns a UInt8 constant expression equal to 1 if there is a column, otherwise 0. If the hostname parameter is set, the test will run on a remote server.\nThe function throws an exception if the table does not exist.\nFor elements in a nested data structure, the function checks for the existence of a column. For the nested data structure itself, the function returns 0.", - "title": "hasColumnInTable(['hostname'[, 'username'[, 'password']],] 'database', 'table', 'column')" - }, - { - "location": "/functions/other_functions/#bar", - "text": "Allows building a unicode-art diagram. bar (x, min, max, width) draws a band with a width proportional to (x - min) and equal to width characters when x = max . Parameters: x \u2013 Value to display. min, max \u2013 Integer constants. The value must fit in Int64. width \u2013 Constant, positive number, may be a fraction. The band is drawn with accuracy to one eighth of a symbol. Example: SELECT \n toHour ( EventTime ) AS h , \n count () AS c , \n bar ( c , 0 , 600000 , 20 ) AS bar FROM test . hits GROUP BY h ORDER BY h ASC \u250c\u2500\u2500h\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u252c\u2500bar\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 0 \u2502 292907 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 1 \u2502 180563 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 2 \u2502 114861 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 3 \u2502 85069 \u2502 \u2588\u2588\u258b \u2502\n\u2502 4 \u2502 68543 \u2502 \u2588\u2588\u258e \u2502\n\u2502 5 \u2502 78116 \u2502 \u2588\u2588\u258c \u2502\n\u2502 6 \u2502 113474 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 7 \u2502 170678 \u2502 \u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 8 \u2502 278380 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 9 \u2502 391053 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 10 \u2502 457681 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 11 \u2502 493667 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 12 \u2502 509641 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 13 \u2502 522947 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 14 \u2502 539954 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 15 \u2502 528460 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c \u2502\n\u2502 16 \u2502 539201 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 17 \u2502 523539 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 18 \u2502 506467 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 19 \u2502 520915 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 20 \u2502 521665 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 21 \u2502 542078 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 22 \u2502 493642 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 23 \u2502 400397 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2514\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "bar" - }, - { - "location": "/functions/other_functions/#transform", - "text": "Transforms a value according to the explicitly defined mapping of some elements to other ones.\nThere are two variations of this function: transform(x, array_from, array_to, default) x \u2013 What to transform. array_from \u2013 Constant array of values for converting. array_to \u2013 Constant array of values to convert the values in 'from' to. default \u2013 Which value to use if 'x' is not equal to any of the values in 'from'. array_from and array_to \u2013 Arrays of the same size. Types: transform(T, Array(T), Array(U), U) - U T and U can be numeric, string, or Date or DateTime types.\nWhere the same letter is indicated (T or U), for numeric types these might not be matching types, but types that have a common type.\nFor example, the first argument can have the Int64 type, while the second has the Array(Uint16) type. If the 'x' value is equal to one of the elements in the 'array_from' array, it returns the existing element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'default'. If there are multiple matching elements in 'array_from', it returns one of the matches. Example: SELECT \n transform ( SearchEngineID , [ 2 , 3 ], [ Yandex , Google ], Other ) AS title , \n count () AS c FROM test . hits WHERE SearchEngineID != 0 GROUP BY title ORDER BY c DESC \u250c\u2500title\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 Yandex \u2502 498635 \u2502\n\u2502 Google \u2502 229872 \u2502\n\u2502 Other \u2502 104472 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 transform(x, array_from, array_to) Differs from the first variation in that the 'default' argument is omitted.\nIf the 'x' value is equal to one of the elements in the 'array_from' array, it returns the matching element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'x'. Types: transform(T, Array(T), Array(T)) - T Example: SELECT \n transform ( domain ( Referer ), [ yandex.ru , google.ru , vk.com ], [ www.yandex , example.com ]) AS s , \n count () AS c FROM test . hits GROUP BY domain ( Referer ) ORDER BY count () DESC LIMIT 10 \u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 \u2502 2906259 \u2502\n\u2502 www.yandex \u2502 867767 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 313599 \u2502\n\u2502 mail.yandex.ru \u2502 107147 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 100355 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 65040 \u2502\n\u2502 news.yandex.ru \u2502 64515 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.net \u2502 59141 \u2502\n\u2502 example.com \u2502 57316 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "transform" - }, - { - "location": "/functions/other_functions/#formatreadablesizex", - "text": "Accepts the size (number of bytes). Returns a rounded size with a suffix (KiB, MiB, etc.) as a string. Example: SELECT \n arrayJoin ([ 1 , 1024 , 1024 * 1024 , 192851925 ]) AS filesize_bytes , \n formatReadableSize ( filesize_bytes ) AS filesize \u250c\u2500filesize_bytes\u2500\u252c\u2500filesize\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 1.00 B \u2502\n\u2502 1024 \u2502 1.00 KiB \u2502\n\u2502 1048576 \u2502 1.00 MiB \u2502\n\u2502 192851925 \u2502 183.92 MiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "formatReadableSize(x)" - }, - { - "location": "/functions/other_functions/#leasta-b", - "text": "Returns the smallest value from a and b.", - "title": "least(a, b)" - }, - { - "location": "/functions/other_functions/#greatesta-b", - "text": "Returns the largest value of a and b.", - "title": "greatest(a, b)" - }, - { - "location": "/functions/other_functions/#uptime", - "text": "Returns the server's uptime in seconds.", - "title": "uptime()" - }, - { - "location": "/functions/other_functions/#version", - "text": "Returns the version of the server as a string.", - "title": "version()" - }, - { - "location": "/functions/other_functions/#rownumberinallblocks", - "text": "Returns the ordinal number of the row in the data block. This function only considers the affected data blocks.", - "title": "rowNumberInAllBlocks()" - }, - { - "location": "/functions/other_functions/#runningdifferencex", - "text": "Calculates the difference between successive row values \u200b\u200bin the data block.\nReturns 0 for the first row and the difference from the previous row for each subsequent row. The result of the function depends on the affected data blocks and the order of data in the block.\nIf you make a subquery with ORDER BY and call the function from outside the subquery, you can get the expected result. Example: SELECT \n EventID , \n EventTime , \n runningDifference ( EventTime ) AS delta FROM ( \n SELECT \n EventID , \n EventTime \n FROM events \n WHERE EventDate = 2016-11-24 \n ORDER BY EventTime ASC \n LIMIT 5 ) \u250c\u2500EventID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500EventTime\u2500\u252c\u2500delta\u2500\u2510\n\u2502 1106 \u2502 2016-11-24 00:00:04 \u2502 0 \u2502\n\u2502 1107 \u2502 2016-11-24 00:00:05 \u2502 1 \u2502\n\u2502 1108 \u2502 2016-11-24 00:00:05 \u2502 0 \u2502\n\u2502 1109 \u2502 2016-11-24 00:00:09 \u2502 4 \u2502\n\u2502 1110 \u2502 2016-11-24 00:00:10 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "runningDifference(x)" - }, - { - "location": "/functions/other_functions/#macnumtostringnum", - "text": "Accepts a UInt64 number. Interprets it as a MAC address in big endian. Returns a string containing the corresponding MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form).", - "title": "MACNumToString(num)" - }, - { - "location": "/functions/other_functions/#macstringtonums", - "text": "The inverse function of MACNumToString. If the MAC address has an invalid format, it returns 0.", - "title": "MACStringToNum(s)" - }, - { - "location": "/functions/other_functions/#macstringtoouis", - "text": "Accepts a MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form). Returns the first three octets as a UInt64 number. If the MAC address has an invalid format, it returns 0.", - "title": "MACStringToOUI(s)" - }, - { - "location": "/functions/ext_dict_functions/", - "text": "Functions for working with external dictionaries\n\n\nFor information on connecting and configuring external dictionaries, see \"\nExternal dictionaries\n\".\n\n\ndictGetUInt8, dictGetUInt16, dictGetUInt32, dictGetUInt64\n\n\ndictGetInt8, dictGetInt16, dictGetInt32, dictGetInt64\n\n\ndictGetFloat32, dictGetFloat64\n\n\ndictGetDate, dictGetDateTime\n\n\ndictGetUUID\n\n\ndictGetString\n\n\ndictGetT('dict_name', 'attr_name', id)\n\n\n\n\nGet the value of the attr_name attribute from the dict_name dictionary using the 'id' key.\ndict_name\n and \nattr_name\n are constant strings.\nid\nmust be UInt64.\nIf there is no \nid\n key in the dictionary, it returns the default value specified in the dictionary description.\n\n\n\n\ndictGetTOrDefault\n\n\ndictGetT('dict_name', 'attr_name', id, default)\n\n\nThe same as the \ndictGetT\n functions, but the default value is taken from the function's last argument.\n\n\ndictIsIn\n\n\ndictIsIn('dict_name', child_id, ancestor_id)\n\n\n\n\nFor the 'dict_name' hierarchical dictionary, finds out whether the 'child_id' key is located inside 'ancestor_id' (or matches 'ancestor_id'). Returns UInt8.\n\n\n\n\ndictGetHierarchy\n\n\ndictGetHierarchy('dict_name', id)\n\n\n\n\nFor the 'dict_name' hierarchical dictionary, returns an array of dictionary keys starting from 'id' and continuing along the chain of parent elements. Returns Array(UInt64).\n\n\n\n\ndictHas\n\n\ndictHas('dict_name', id)\n\n\n\n\nCheck whether the dictionary has the key. Returns a UInt8 value equal to 0 if there is no key and 1 if there is a key.", - "title": "Functions for working with external dictionaries" - }, - { - "location": "/functions/ext_dict_functions/#functions-for-working-with-external-dictionaries", - "text": "For information on connecting and configuring external dictionaries, see \" External dictionaries \".", - "title": "Functions for working with external dictionaries" - }, - { - "location": "/functions/ext_dict_functions/#dictgetuint8-dictgetuint16-dictgetuint32-dictgetuint64", - "text": "", - "title": "dictGetUInt8, dictGetUInt16, dictGetUInt32, dictGetUInt64" - }, - { - "location": "/functions/ext_dict_functions/#dictgetint8-dictgetint16-dictgetint32-dictgetint64", - "text": "", - "title": "dictGetInt8, dictGetInt16, dictGetInt32, dictGetInt64" - }, - { - "location": "/functions/ext_dict_functions/#dictgetfloat32-dictgetfloat64", - "text": "", - "title": "dictGetFloat32, dictGetFloat64" - }, - { - "location": "/functions/ext_dict_functions/#dictgetdate-dictgetdatetime", - "text": "", - "title": "dictGetDate, dictGetDateTime" - }, - { - "location": "/functions/ext_dict_functions/#dictgetuuid", - "text": "", - "title": "dictGetUUID" - }, - { - "location": "/functions/ext_dict_functions/#dictgetstring", - "text": "dictGetT('dict_name', 'attr_name', id) Get the value of the attr_name attribute from the dict_name dictionary using the 'id' key. dict_name and attr_name are constant strings. id must be UInt64.\nIf there is no id key in the dictionary, it returns the default value specified in the dictionary description.", - "title": "dictGetString" - }, - { - "location": "/functions/ext_dict_functions/#dictgettordefault", - "text": "dictGetT('dict_name', 'attr_name', id, default) The same as the dictGetT functions, but the default value is taken from the function's last argument.", - "title": "dictGetTOrDefault" - }, - { - "location": "/functions/ext_dict_functions/#dictisin", - "text": "dictIsIn('dict_name', child_id, ancestor_id) For the 'dict_name' hierarchical dictionary, finds out whether the 'child_id' key is located inside 'ancestor_id' (or matches 'ancestor_id'). Returns UInt8.", - "title": "dictIsIn" - }, - { - "location": "/functions/ext_dict_functions/#dictgethierarchy", - "text": "dictGetHierarchy('dict_name', id) For the 'dict_name' hierarchical dictionary, returns an array of dictionary keys starting from 'id' and continuing along the chain of parent elements. Returns Array(UInt64).", - "title": "dictGetHierarchy" - }, - { - "location": "/functions/ext_dict_functions/#dicthas", - "text": "dictHas('dict_name', id) Check whether the dictionary has the key. Returns a UInt8 value equal to 0 if there is no key and 1 if there is a key.", - "title": "dictHas" - }, - { - "location": "/functions/ym_dict_functions/", - "text": "Functions for working with Yandex.Metrica dictionaries\n\n\nIn order for the functions below to work, the server config must specify the paths and addresses for getting all the Yandex.Metrica dictionaries. The dictionaries are loaded at the first call of any of these functions. If the reference lists can't be loaded, an exception is thrown.\n\n\nFor information about creating reference lists, see the section \"Dictionaries\".\n\n\nMultiple geobases\n\n\nClickHouse supports working with multiple alternative geobases (regional hierarchies) simultaneously, in order to support various perspectives on which countries certain regions belong to.\n\n\nThe 'clickhouse-server' config specifies the file with the regional hierarchy::\npath_to_regions_hierarchy_file\n/opt/geo/regions_hierarchy.txt\n/path_to_regions_hierarchy_file\n\n\nBesides this file, it also searches for files nearby that have the _ symbol and any suffix appended to the name (before the file extension).\nFor example, it will also find the file \n/opt/geo/regions_hierarchy_ua.txt\n, if present.\n\n\nua\n is called the dictionary key. For a dictionary without a suffix, the key is an empty string.\n\n\nAll the dictionaries are re-loaded in runtime (once every certain number of seconds, as defined in the builtin_dictionaries_reload_interval config parameter, or once an hour by default). However, the list of available dictionaries is defined one time, when the server starts.\n\n\nAll functions for working with regions have an optional argument at the end \u2013 the dictionary key. It is referred to as the geobase.\nExample:\n\n\nregionToCountry(RegionID) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, \n) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, \nua\n) \u2013 Uses the dictionary for the \nua\n key: /opt/geo/regions_hierarchy_ua.txt\n\n\n\n\n\nregionToCity(id[, geobase])\n\n\nAccepts a UInt32 number \u2013 the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0.\n\n\nregionToArea(id[, geobase])\n\n\nConverts a region to an area (type 5 in the geobase). In every other way, this function is the same as 'regionToCity'.\n\n\nSELECT\n \nDISTINCT\n \nregionToName\n(\nregionToArea\n(\ntoUInt32\n(\nnumber\n),\n \nua\n))\n\n\nFROM\n \nsystem\n.\nnumbers\n\n\nLIMIT\n \n15\n\n\n\n\n\n\n\u250c\u2500regionToName(regionToArea(toUInt32(number), \\\nua\\\n))\u2500\u2510\n\u2502 \u2502\n\u2502 Moscow and Moscow region \u2502\n\u2502 St. Petersburg and Leningrad region \u2502\n\u2502 Belgorod region \u2502\n\u2502 Ivanovsk region \u2502\n\u2502 Kaluga region \u2502\n\u2502 Kostroma region \u2502\n\u2502 Kursk region \u2502\n\u2502 Lipetsk region \u2502\n\u2502 Orlov region \u2502\n\u2502 Ryazan region \u2502\n\u2502 Smolensk region \u2502\n\u2502 Tambov region \u2502\n\u2502 Tver region \u2502\n\u2502 Tula region \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nregionToDistrict(id[, geobase])\n\n\nConverts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as 'regionToCity'.\n\n\nSELECT\n \nDISTINCT\n \nregionToName\n(\nregionToDistrict\n(\ntoUInt32\n(\nnumber\n),\n \nua\n))\n\n\nFROM\n \nsystem\n.\nnumbers\n\n\nLIMIT\n \n15\n\n\n\n\n\n\n\u250c\u2500regionToName(regionToDistrict(toUInt32(number), \\\nua\\\n))\u2500\u2510\n\u2502 \u2502\n\u2502 Central federal district \u2502\n\u2502 Northwest federal district \u2502\n\u2502 South federal district \u2502\n\u2502 North Caucases federal district \u2502\n\u2502 Privolga federal district \u2502\n\u2502 Ural federal district \u2502\n\u2502 Siberian federal district \u2502\n\u2502 Far East federal district \u2502\n\u2502 Scotland \u2502\n\u2502 Faroe Islands \u2502\n\u2502 Flemish region \u2502\n\u2502 Brussels capital region \u2502\n\u2502 Wallonia \u2502\n\u2502 Federation of Bosnia and Herzegovina \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nregionToCountry(id[, geobase])\n\n\nConverts a region to a country. In every other way, this function is the same as 'regionToCity'.\nExample: \nregionToCountry(toUInt32(213)) = 225\n converts Moscow (213) to Russia (225).\n\n\nregionToContinent(id[, geobase])\n\n\nConverts a region to a continent. In every other way, this function is the same as 'regionToCity'.\nExample: \nregionToContinent(toUInt32(213)) = 10001\n converts Moscow (213) to Eurasia (10001).\n\n\nregionToPopulation(id[, geobase])\n\n\nGets the population for a region.\nThe population can be recorded in files with the geobase. See the section \"External dictionaries\".\nIf the population is not recorded for the region, it returns 0.\nIn the Yandex geobase, the population might be recorded for child regions, but not for parent regions.\n\n\nregionIn(lhs, rhs[, geobase])\n\n\nChecks whether a 'lhs' region belongs to a 'rhs' region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn't belong.\nThe relationship is reflexive \u2013 any region also belongs to itself.\n\n\nregionHierarchy(id[, geobase])\n\n\nAccepts a UInt32 number \u2013 the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain.\nExample: \nregionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000]\n.\n\n\nregionToName(id[, lang])\n\n\nAccepts a UInt32 number \u2013 the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language 'ru' is used. If the language is not supported, an exception is thrown. Returns a string \u2013 the name of the region in the corresponding language. If the region with the specified ID doesn't exist, an empty string is returned.\n\n\nua\n and \nuk\n both mean Ukrainian.", - "title": "Functions for working with Yandex.Metrica dictionaries" - }, - { - "location": "/functions/ym_dict_functions/#functions-for-working-with-yandexmetrica-dictionaries", - "text": "In order for the functions below to work, the server config must specify the paths and addresses for getting all the Yandex.Metrica dictionaries. The dictionaries are loaded at the first call of any of these functions. If the reference lists can't be loaded, an exception is thrown. For information about creating reference lists, see the section \"Dictionaries\".", - "title": "Functions for working with Yandex.Metrica dictionaries" - }, - { - "location": "/functions/ym_dict_functions/#multiple-geobases", - "text": "ClickHouse supports working with multiple alternative geobases (regional hierarchies) simultaneously, in order to support various perspectives on which countries certain regions belong to. The 'clickhouse-server' config specifies the file with the regional hierarchy:: path_to_regions_hierarchy_file /opt/geo/regions_hierarchy.txt /path_to_regions_hierarchy_file Besides this file, it also searches for files nearby that have the _ symbol and any suffix appended to the name (before the file extension).\nFor example, it will also find the file /opt/geo/regions_hierarchy_ua.txt , if present. ua is called the dictionary key. For a dictionary without a suffix, the key is an empty string. All the dictionaries are re-loaded in runtime (once every certain number of seconds, as defined in the builtin_dictionaries_reload_interval config parameter, or once an hour by default). However, the list of available dictionaries is defined one time, when the server starts. All functions for working with regions have an optional argument at the end \u2013 the dictionary key. It is referred to as the geobase.\nExample: regionToCountry(RegionID) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, ) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, ua ) \u2013 Uses the dictionary for the ua key: /opt/geo/regions_hierarchy_ua.txt", - "title": "Multiple geobases" - }, - { - "location": "/functions/ym_dict_functions/#regiontocityid-geobase", - "text": "Accepts a UInt32 number \u2013 the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0.", - "title": "regionToCity(id[, geobase])" - }, - { - "location": "/functions/ym_dict_functions/#regiontoareaid91-geobase93", - "text": "Converts a region to an area (type 5 in the geobase). In every other way, this function is the same as 'regionToCity'. SELECT DISTINCT regionToName ( regionToArea ( toUInt32 ( number ), ua )) FROM system . numbers LIMIT 15 \u250c\u2500regionToName(regionToArea(toUInt32(number), \\ ua\\ ))\u2500\u2510\n\u2502 \u2502\n\u2502 Moscow and Moscow region \u2502\n\u2502 St. Petersburg and Leningrad region \u2502\n\u2502 Belgorod region \u2502\n\u2502 Ivanovsk region \u2502\n\u2502 Kaluga region \u2502\n\u2502 Kostroma region \u2502\n\u2502 Kursk region \u2502\n\u2502 Lipetsk region \u2502\n\u2502 Orlov region \u2502\n\u2502 Ryazan region \u2502\n\u2502 Smolensk region \u2502\n\u2502 Tambov region \u2502\n\u2502 Tver region \u2502\n\u2502 Tula region \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "regionToArea(id[, geobase])" - }, - { - "location": "/functions/ym_dict_functions/#regiontodistrictid-geobase", - "text": "Converts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as 'regionToCity'. SELECT DISTINCT regionToName ( regionToDistrict ( toUInt32 ( number ), ua )) FROM system . numbers LIMIT 15 \u250c\u2500regionToName(regionToDistrict(toUInt32(number), \\ ua\\ ))\u2500\u2510\n\u2502 \u2502\n\u2502 Central federal district \u2502\n\u2502 Northwest federal district \u2502\n\u2502 South federal district \u2502\n\u2502 North Caucases federal district \u2502\n\u2502 Privolga federal district \u2502\n\u2502 Ural federal district \u2502\n\u2502 Siberian federal district \u2502\n\u2502 Far East federal district \u2502\n\u2502 Scotland \u2502\n\u2502 Faroe Islands \u2502\n\u2502 Flemish region \u2502\n\u2502 Brussels capital region \u2502\n\u2502 Wallonia \u2502\n\u2502 Federation of Bosnia and Herzegovina \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "regionToDistrict(id[, geobase])" - }, - { - "location": "/functions/ym_dict_functions/#regiontocountryid-geobase", - "text": "Converts a region to a country. In every other way, this function is the same as 'regionToCity'.\nExample: regionToCountry(toUInt32(213)) = 225 converts Moscow (213) to Russia (225).", - "title": "regionToCountry(id[, geobase])" - }, - { - "location": "/functions/ym_dict_functions/#regiontocontinentid-geobase", - "text": "Converts a region to a continent. In every other way, this function is the same as 'regionToCity'.\nExample: regionToContinent(toUInt32(213)) = 10001 converts Moscow (213) to Eurasia (10001).", - "title": "regionToContinent(id[, geobase])" - }, - { - "location": "/functions/ym_dict_functions/#regiontopopulationid-geobase", - "text": "Gets the population for a region.\nThe population can be recorded in files with the geobase. See the section \"External dictionaries\".\nIf the population is not recorded for the region, it returns 0.\nIn the Yandex geobase, the population might be recorded for child regions, but not for parent regions.", - "title": "regionToPopulation(id[, geobase])" - }, - { - "location": "/functions/ym_dict_functions/#regioninlhs-rhs-geobase", - "text": "Checks whether a 'lhs' region belongs to a 'rhs' region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn't belong.\nThe relationship is reflexive \u2013 any region also belongs to itself.", - "title": "regionIn(lhs, rhs[, geobase])" - }, - { - "location": "/functions/ym_dict_functions/#regionhierarchyid91-geobase93", - "text": "Accepts a UInt32 number \u2013 the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain.\nExample: regionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000] .", - "title": "regionHierarchy(id[, geobase])" - }, - { - "location": "/functions/ym_dict_functions/#regiontonameid91-lang93", - "text": "Accepts a UInt32 number \u2013 the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language 'ru' is used. If the language is not supported, an exception is thrown. Returns a string \u2013 the name of the region in the corresponding language. If the region with the specified ID doesn't exist, an empty string is returned. ua and uk both mean Ukrainian.", - "title": "regionToName(id[, lang])" - }, - { - "location": "/functions/in_functions/", - "text": "Functions for implementing the IN operator\n\n\nin, notIn, globalIn, globalNotIn\n\n\nSee the section \"IN operators\".\n\n\ntuple(x, y, ...), operator (x, y, ...)\n\n\nA function that allows grouping multiple columns.\nFor columns with the types T1, T2, ..., it returns a Tuple(T1, T2, ...) type tuple containing these columns. There is no cost to execute the function.\nTuples are normally used as intermediate values for an argument of IN operators, or for creating a list of formal parameters of lambda functions. Tuples can't be written to a table.\n\n\ntupleElement(tuple, n), operator x.N\n\n\nA function that allows getting a column from a tuple.\n'N' is the column index, starting from 1. N must be a constant. 'N' must be a constant. 'N' must be a strict postive integer no greater than the size of the tuple.\nThere is no cost to execute the function.", - "title": "Functions for implementing the IN operator" - }, - { - "location": "/functions/in_functions/#functions-for-implementing-the-in-operator", - "text": "", - "title": "Functions for implementing the IN operator" - }, - { - "location": "/functions/in_functions/#in-notin-globalin-globalnotin", - "text": "See the section \"IN operators\".", - "title": "in, notIn, globalIn, globalNotIn" - }, - { - "location": "/functions/in_functions/#tuplex-y-operator-x-y", - "text": "A function that allows grouping multiple columns.\nFor columns with the types T1, T2, ..., it returns a Tuple(T1, T2, ...) type tuple containing these columns. There is no cost to execute the function.\nTuples are normally used as intermediate values for an argument of IN operators, or for creating a list of formal parameters of lambda functions. Tuples can't be written to a table.", - "title": "tuple(x, y, ...), operator (x, y, ...)" - }, - { - "location": "/functions/in_functions/#tupleelementtuple-n-operator-xn", - "text": "A function that allows getting a column from a tuple.\n'N' is the column index, starting from 1. N must be a constant. 'N' must be a constant. 'N' must be a strict postive integer no greater than the size of the tuple.\nThere is no cost to execute the function.", - "title": "tupleElement(tuple, n), operator x.N" - }, - { - "location": "/functions/array_join/", - "text": "arrayJoin function\n\n\nThis is a very unusual function.\n\n\nNormal functions don't change a set of rows, but just change the values in each row (map).\nAggregate functions compress a set of rows (fold or reduce).\nThe 'arrayJoin' function takes each row and generates a set of rows (unfold).\n\n\nThis function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array.\nAll the values in columns are simply copied, except the values in the column where this function is applied; it is replaced with the corresponding array value.\n\n\nA query can use multiple \narrayJoin\n functions. In this case, the transformation is performed multiple times.\n\n\nNote the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities.\n\n\nExample:\n\n\nSELECT\n \narrayJoin\n([\n1\n,\n \n2\n,\n \n3\n]\n \nAS\n \nsrc\n)\n \nAS\n \ndst\n,\n \nHello\n,\n \nsrc\n\n\n\n\n\n\n\u250c\u2500dst\u2500\u252c\u2500\\\nHello\\\n\u2500\u252c\u2500src\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 2 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 3 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayJoin function" - }, - { - "location": "/functions/array_join/#arrayjoin-function", - "text": "This is a very unusual function. Normal functions don't change a set of rows, but just change the values in each row (map).\nAggregate functions compress a set of rows (fold or reduce).\nThe 'arrayJoin' function takes each row and generates a set of rows (unfold). This function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array.\nAll the values in columns are simply copied, except the values in the column where this function is applied; it is replaced with the corresponding array value. A query can use multiple arrayJoin functions. In this case, the transformation is performed multiple times. Note the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities. Example: SELECT arrayJoin ([ 1 , 2 , 3 ] AS src ) AS dst , Hello , src \u250c\u2500dst\u2500\u252c\u2500\\ Hello\\ \u2500\u252c\u2500src\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 2 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 3 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayJoin function" - }, - { - "location": "/agg_functions/", - "text": "Aggregate functions\n\n\nAggregate functions work in the \nnormal\n way as expected by database experts.\n\n\nClickHouse also supports:\n\n\n\n\nParametric aggregate functions\n, which accept other parameters in addition to columns.\n\n\nCombinators\n, which change the behavior of aggregate functions.", - "title": "Introduction" - }, - { - "location": "/agg_functions/#aggregate-functions", - "text": "Aggregate functions work in the normal way as expected by database experts. ClickHouse also supports: Parametric aggregate functions , which accept other parameters in addition to columns. Combinators , which change the behavior of aggregate functions.", - "title": "Aggregate functions" - }, - { - "location": "/agg_functions/reference/", - "text": "Function reference\n\n\ncount()\n\n\nCounts the number of rows. Accepts zero arguments and returns UInt64.\nThe syntax \nCOUNT(DISTINCT x)\n is not supported. The separate \nuniq\n aggregate function exists for this purpose.\n\n\nA \nSELECT count() FROM table\n query is not optimized, because the number of entries in the table is not stored separately. It will select some small column from the table and count the number of values in it.\n\n\nany(x)\n\n\nSelects the first encountered value.\nThe query can be executed in any order and even in a different order each time, so the result of this function is indeterminate.\nTo get a determinate result, you can use the 'min' or 'max' function instead of 'any'.\n\n\nIn some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.\n\n\nWhen a \nSELECT\n query has the \nGROUP BY\n clause or at least one aggregate function, ClickHouse (in contrast to MySQL) requires that all expressions in the \nSELECT\n, \nHAVING\n, and \nORDER BY\n clauses be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. To get behavior like in MySQL, you can put the other columns in the \nany\n aggregate function.\n\n\nanyHeavy(x)\n\n\nSelects a frequently occurring value using the \nheavy hitters\n algorithm. If there is a value that occurs more than in half the cases in each of the query's execution threads, this value is returned. Normally, the result is nondeterministic.\n\n\nanyHeavy(column)\n\n\n\n\n\nArguments\n\n- \ncolumn\n \u2013 The column name.\n\n\nExample\n\n\nTake the \nOnTime\n data set and select any frequently occurring value in the \nAirlineID\n column.\n\n\nSELECT\n \nanyHeavy\n(\nAirlineID\n)\n \nAS\n \nres\n\n\nFROM\n \nontime\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500res\u2500\u2510\n\u2502 19690 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nanyLast(x)\n\n\nSelects the last value encountered.\nThe result is just as indeterminate as for the \nany\n function.\n\n\nmin(x)\n\n\nCalculates the minimum.\n\n\nmax(x)\n\n\nCalculates the maximum.\n\n\nargMin(arg, val)\n\n\nCalculates the 'arg' value for a minimal 'val' value. If there are several different values of 'arg' for minimal values of 'val', the first of these values encountered is output.\n\n\nargMax(arg, val)\n\n\nCalculates the 'arg' value for a maximum 'val' value. If there are several different values of 'arg' for maximum values of 'val', the first of these values encountered is output.\n\n\nsum(x)\n\n\nCalculates the sum.\nOnly works for numbers.\n\n\nsumWithOverflow(x)\n\n\nComputes the sum of the numbers, using the same data type for the result as for the input parameters. If the sum exceeds the maximum value for this data type, the function returns an error.\n\n\nOnly works for numbers.\n\n\nsumMap(key, value)\n\n\nTotals the 'value' array according to the keys specified in the 'key' array.\nThe number of elements in 'key' and 'value' must be the same for each row that is totaled.\nReturns a tuple of two arrays: keys in sorted order, and values \u200b\u200bsummed for the corresponding keys.\n\n\nExample:\n\n\nCREATE\n \nTABLE\n \nsum_map\n(\n\n \ndate\n \nDate\n,\n\n \ntimeslot\n \nDateTime\n,\n\n \nstatusMap\n \nNested\n(\n\n \nstatus\n \nUInt16\n,\n\n \nrequests\n \nUInt64\n\n \n)\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\nINSERT\n \nINTO\n \nsum_map\n \nVALUES\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:00:00\n,\n \n[\n1\n,\n \n2\n,\n \n3\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]),\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:00:00\n,\n \n[\n3\n,\n \n4\n,\n \n5\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]),\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:01:00\n,\n \n[\n4\n,\n \n5\n,\n \n6\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]),\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:01:00\n,\n \n[\n6\n,\n \n7\n,\n \n8\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]);\n\n\nSELECT\n\n \ntimeslot\n,\n\n \nsumMap\n(\nstatusMap\n.\nstatus\n,\n \nstatusMap\n.\nrequests\n)\n\n\nFROM\n \nsum_map\n\n\nGROUP\n \nBY\n \ntimeslot\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500timeslot\u2500\u252c\u2500sumMap(statusMap.status, statusMap.requests)\u2500\u2510\n\u2502 2000-01-01 00:00:00 \u2502 ([1,2,3,4,5],[10,10,20,10,10]) \u2502\n\u2502 2000-01-01 00:01:00 \u2502 ([4,5,6,7,8],[10,10,20,10,10]) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\navg(x)\n\n\nCalculates the average.\nOnly works for numbers.\nThe result is always Float64.\n\n\nuniq(x)\n\n\nCalculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.\n\n\nUses an adaptive sampling algorithm: for the calculation state, it uses a sample of element hash values with a size up to 65536.\nThis algorithm is also very accurate for data sets with low cardinality (up to 65536) and very efficient on CPU (when computing not too many of these functions, using \nuniq\n is almost as fast as using other aggregate functions).\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nThis function provides excellent accuracy even for data sets with extremely high cardinality (over 10 billion elements). It is recommended for default use.\n\n\nuniqCombined(x)\n\n\nCalculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.\n\n\nA combination of three algorithms is used: array, hash table and \nHyperLogLog\n with an error correction table. The memory consumption is several times smaller than for the \nuniq\n function, and the accuracy is several times higher. Performance is slightly lower than for the \nuniq\n function, but sometimes it can be even higher than it, such as with distributed queries that transmit a large number of aggregation states over the network. The maximum state size is 96 KiB (HyperLogLog of 217 6-bit cells).\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nThe \nuniqCombined\n function is a good default choice for calculating the number of different values, but keep in mind that the estimation error will increase for high-cardinality data sets (200M+ elements), and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).\n\n\nuniqHLL12(x)\n\n\nUses the \nHyperLogLog\n algorithm to approximate the number of different values of the argument.\n212 5-bit cells are used. The size of the state is slightly more than 2.5 KB. The result is not very accurate (up to ~10% error) for small data sets (\n10K elements). However, the result is fairly accurate for high-cardinality data sets (10K-100M), with a maximum error of ~1.6%. Starting from 100M, the estimation error increases, and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nWe don't recommend using this function. In most cases, use the \nuniq\n or \nuniqCombined\n function.\n\n\nuniqExact(x)\n\n\nCalculates the number of different values of the argument, exactly.\nThere is no reason to fear approximations. It's better to use the \nuniq\n function.\nUse the \nuniqExact\n function if you definitely need an exact result.\n\n\nThe \nuniqExact\n function uses more memory than the \nuniq\n function, because the size of the state has unbounded growth as the number of different values increases.\n\n\ngroupArray(x), groupArray(max_size)(x)\n\n\nCreates an array of argument values.\nValues can be added to the array in any (indeterminate) order.\n\n\nThe second version (with the \nmax_size\n parameter) limits the size of the resulting array to \nmax_size\n elements.\nFor example, \ngroupArray (1) (x)\n is equivalent to \n[any (x)]\n.\n\n\nIn some cases, you can still rely on the order of execution. This applies to cases when \nSELECT\n comes from a subquery that uses \nORDER BY\n.\n\n\n\n\ngroupArrayInsertAt(x)\n\n\nInserts a value into the array in the specified position.\n\n\nAccepts the value and position as input. If several values \u200b\u200bare inserted into the same position, any of them might end up in the resulting array (the first one will be used in the case of single-threaded execution). If no value is inserted into a position, the position is assigned the default value.\n\n\nOptional parameters:\n\n\n\n\nThe default value for substituting in empty positions.\n\n\nThe length of the resulting array. This allows you to receive arrays of the same size for all the aggregate keys. When using this parameter, the default value must be specified.\n\n\n\n\ngroupUniqArray(x)\n\n\nCreates an array from different argument values. Memory consumption is the same as for the \nuniqExact\n function.\n\n\nquantile(level)(x)\n\n\nApproximates the 'level' quantile. 'level' is a constant, a floating-point number from 0 to 1.\nWe recommend using a 'level' value in the range of 0.01..0.99\nDon't use a 'level' value equal to 0 or 1 \u2013 use the 'min' and 'max' functions for these cases.\n\n\nIn this function, as well as in all functions for calculating quantiles, the 'level' parameter can be omitted. In this case, it is assumed to be equal to 0.5 (in other words, the function will calculate the median).\n\n\nWorks for numbers, dates, and dates with times.\nReturns: for numbers \u2013 Float64; for dates \u2013 a date; for dates with times \u2013 a date with time.\n\n\nUses \nreservoir sampling\n with a reservoir size up to 8192.\nIf necessary, the result is output with linear approximation from the two neighboring values.\nThis algorithm provides very low accuracy. See also: \nquantileTiming\n, \nquantileTDigest\n, \nquantileExact\n.\n\n\nThe result depends on the order of running the query, and is nondeterministic.\n\n\nWhen using multiple \nquantile\n (and similar) functions with different levels in a query, the internal states are not combined (that is, the query works less efficiently than it could). In this case, use the \nquantiles\n (and similar) functions.\n\n\nquantileDeterministic(level)(x, determinator)\n\n\nWorks the same way as the \nquantile\n function, but the result is deterministic and does not depend on the order of query execution.\n\n\nTo achieve this, the function takes a second argument \u2013 the \"determinator\". This is a number whose hash is used instead of a random number generator in the reservoir sampling algorithm. For the function to work correctly, the same determinator value should not occur too often. For the determinator, you can use an event ID, user ID, and so on.\n\n\nDon't use this function for calculating timings. There is a more suitable function for this purpose: \nquantileTiming\n.\n\n\nquantileTiming(level)(x)\n\n\nComputes the quantile of 'level' with a fixed precision.\nWorks for numbers. Intended for calculating quantiles of page loading time in milliseconds.\n\n\nIf the value is greater than 30,000 (a page loading time of more than 30 seconds), the result is equated to 30,000.\n\n\nIf the total value is not more than about 5670, then the calculation is accurate.\n\n\nOtherwise:\n\n\n\n\nif the time is less than 1024 ms, then the calculation is accurate.\n\n\notherwise the calculation is rounded to a multiple of 16 ms.\n\n\n\n\nWhen passing negative values to the function, the behavior is undefined.\n\n\nThe returned value has the Float32 type. If no values were passed to the function (when using \nquantileTimingIf\n), 'nan' is returned. The purpose of this is to differentiate these instances from zeros. See the note on sorting NaNs in \"ORDER BY clause\".\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nFor its purpose (calculating quantiles of page loading times), using this function is more effective and the result is more accurate than for the \nquantile\n function.\n\n\nquantileTimingWeighted(level)(x, weight)\n\n\nDiffers from the \nquantileTiming\n function in that it has a second argument, \"weights\". Weight is a non-negative integer.\nThe result is calculated as if the \nx\n value were passed \nweight\n number of times to the \nquantileTiming\n function.\n\n\nquantileExact(level)(x)\n\n\nComputes the quantile of 'level' exactly. To do this, all the passed values \u200b\u200bare combined into an array, which is then partially sorted. Therefore, the function consumes O(n) memory, where 'n' is the number of values that were passed. However, for a small number of values, the function is very effective.\n\n\nquantileExactWeighted(level)(x, weight)\n\n\nComputes the quantile of 'level' exactly. In addition, each value is counted with its weight, as if it is present 'weight' times. The arguments of the function can be considered as histograms, where the value 'x' corresponds to a histogram \"column\" of the height 'weight', and the function itself can be considered as a summation of histograms.\n\n\nA hash table is used as the algorithm. Because of this, if the passed values \u200b\u200bare frequently repeated, the function consumes less RAM than \nquantileExact\n. You can use this function instead of \nquantileExact\n and specify the weight as 1.\n\n\nquantileTDigest(level)(x)\n\n\nApproximates the quantile level using the \nt-digest\n algorithm. The maximum error is 1%. Memory consumption by State is proportional to the logarithm of the number of passed values.\n\n\nThe performance of the function is lower than for \nquantile\n, \nquantileTiming\n. In terms of the ratio of State size to precision, this function is much better than \nquantile\n.\n\n\nThe result depends on the order of running the query, and is nondeterministic.\n\n\nmedian(x)\n\n\nAll the quantile functions have corresponding median functions: \nmedian\n, \nmedianDeterministic\n, \nmedianTiming\n, \nmedianTimingWeighted\n, \nmedianExact\n, \nmedianExactWeighted\n, \nmedianTDigest\n. They are synonyms and their behavior is identical.\n\n\nquantiles(level1, level2, ...)(x)\n\n\nAll the quantile functions also have corresponding quantiles functions: \nquantiles\n, \nquantilesDeterministic\n, \nquantilesTiming\n, \nquantilesTimingWeighted\n, \nquantilesExact\n, \nquantilesExactWeighted\n, \nquantilesTDigest\n. These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.\n\n\nvarSamp(x)\n\n\nCalculates the amount \n\u03a3((x - x\u0305)^2) / (n - 1)\n, where \nn\n is the sample size and \nx\u0305\nis the average value of \nx\n.\n\n\nIt represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount.\n\n\nReturns \nFloat64\n. When \nn \n= 1\n, returns \n+\u221e\n.\n\n\nvarPop(x)\n\n\nCalculates the amount \n\u03a3((x - x\u0305)^2) / (n - 1)\n, where \nn\n is the sample size and \nx\u0305\nis the average value of \nx\n.\n\n\nIn other words, dispersion for a set of values. Returns \nFloat64\n.\n\n\nstddevSamp(x)\n\n\nThe result is equal to the square root of \nvarSamp(x)\n.\n\n\nstddevPop(x)\n\n\nThe result is equal to the square root of \nvarPop(x)\n.\n\n\ntopK(N)(column)\n\n\nReturns an array of the most frequent values in the specified column. The resulting array is sorted in descending order of frequency of values (not by the values themselves).\n\n\nImplements the \nFiltered Space-Saving\n algorithm for analyzing TopK, based on the reduce-and-combine algorithm from \nParallel Space Saving\n.\n\n\ntopK(N)(column)\n\n\n\n\n\nThis function doesn't provide a guaranteed result. In certain situations, errors might occur and it might return frequent values that aren't the most frequent values.\n\n\nWe recommend using the \nN \n 10\n value; performance is reduced with large \nN\n values. Maximum value of \nN = 65536\n.\n\n\nArguments\n\n- 'N' is the number of values.\n- ' x ' \u2013 The column.\n\n\nExample\n\n\nTake the \nOnTime\n data set and select the three most frequently occurring values in the \nAirlineID\n column.\n\n\nSELECT\n \ntopK\n(\n3\n)(\nAirlineID\n)\n \nAS\n \nres\n\n\nFROM\n \nontime\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [19393,19790,19805] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\ncovarSamp(x, y)\n\n\nCalculates the value of \n\u03a3((x - x\u0305)(y - y\u0305)) / (n - 1)\n.\n\n\nReturns Float64. When \nn \n= 1\n, returns +\u221e.\n\n\ncovarPop(x, y)\n\n\nCalculates the value of \n\u03a3((x - x\u0305)(y - y\u0305)) / n\n.\n\n\ncorr(x, y)\n\n\nCalculates the Pearson correlation coefficient: \n\u03a3((x - x\u0305)(y - y\u0305)) / sqrt(\u03a3((x - x\u0305)^2) * \u03a3((y - y\u0305)^2))\n.", - "title": "Function reference" - }, - { - "location": "/agg_functions/reference/#function-reference", - "text": "", - "title": "Function reference" - }, - { - "location": "/agg_functions/reference/#count", - "text": "Counts the number of rows. Accepts zero arguments and returns UInt64.\nThe syntax COUNT(DISTINCT x) is not supported. The separate uniq aggregate function exists for this purpose. A SELECT count() FROM table query is not optimized, because the number of entries in the table is not stored separately. It will select some small column from the table and count the number of values in it.", - "title": "count()" - }, - { - "location": "/agg_functions/reference/#anyx", - "text": "Selects the first encountered value.\nThe query can be executed in any order and even in a different order each time, so the result of this function is indeterminate.\nTo get a determinate result, you can use the 'min' or 'max' function instead of 'any'. In some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY. When a SELECT query has the GROUP BY clause or at least one aggregate function, ClickHouse (in contrast to MySQL) requires that all expressions in the SELECT , HAVING , and ORDER BY clauses be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. To get behavior like in MySQL, you can put the other columns in the any aggregate function.", - "title": "any(x)" - }, - { - "location": "/agg_functions/reference/#anyheavyx", - "text": "Selects a frequently occurring value using the heavy hitters algorithm. If there is a value that occurs more than in half the cases in each of the query's execution threads, this value is returned. Normally, the result is nondeterministic. anyHeavy(column) Arguments \n- column \u2013 The column name. Example Take the OnTime data set and select any frequently occurring value in the AirlineID column. SELECT anyHeavy ( AirlineID ) AS res FROM ontime \u250c\u2500\u2500\u2500res\u2500\u2510\n\u2502 19690 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "anyHeavy(x)" - }, - { - "location": "/agg_functions/reference/#anylastx", - "text": "Selects the last value encountered.\nThe result is just as indeterminate as for the any function.", - "title": "anyLast(x)" - }, - { - "location": "/agg_functions/reference/#minx", - "text": "Calculates the minimum.", - "title": "min(x)" - }, - { - "location": "/agg_functions/reference/#maxx", - "text": "Calculates the maximum.", - "title": "max(x)" - }, - { - "location": "/agg_functions/reference/#argminarg-val", - "text": "Calculates the 'arg' value for a minimal 'val' value. If there are several different values of 'arg' for minimal values of 'val', the first of these values encountered is output.", - "title": "argMin(arg, val)" - }, - { - "location": "/agg_functions/reference/#argmaxarg-val", - "text": "Calculates the 'arg' value for a maximum 'val' value. If there are several different values of 'arg' for maximum values of 'val', the first of these values encountered is output.", - "title": "argMax(arg, val)" - }, - { - "location": "/agg_functions/reference/#sumx", - "text": "Calculates the sum.\nOnly works for numbers.", - "title": "sum(x)" - }, - { - "location": "/agg_functions/reference/#sumwithoverflowx", - "text": "Computes the sum of the numbers, using the same data type for the result as for the input parameters. If the sum exceeds the maximum value for this data type, the function returns an error. Only works for numbers.", - "title": "sumWithOverflow(x)" - }, - { - "location": "/agg_functions/reference/#summapkey-value", - "text": "Totals the 'value' array according to the keys specified in the 'key' array.\nThe number of elements in 'key' and 'value' must be the same for each row that is totaled.\nReturns a tuple of two arrays: keys in sorted order, and values \u200b\u200bsummed for the corresponding keys. Example: CREATE TABLE sum_map ( \n date Date , \n timeslot DateTime , \n statusMap Nested ( \n status UInt16 , \n requests UInt64 \n ) ) ENGINE = Log ; INSERT INTO sum_map VALUES \n ( 2000-01-01 , 2000-01-01 00:00:00 , [ 1 , 2 , 3 ], [ 10 , 10 , 10 ]), \n ( 2000-01-01 , 2000-01-01 00:00:00 , [ 3 , 4 , 5 ], [ 10 , 10 , 10 ]), \n ( 2000-01-01 , 2000-01-01 00:01:00 , [ 4 , 5 , 6 ], [ 10 , 10 , 10 ]), \n ( 2000-01-01 , 2000-01-01 00:01:00 , [ 6 , 7 , 8 ], [ 10 , 10 , 10 ]); SELECT \n timeslot , \n sumMap ( statusMap . status , statusMap . requests ) FROM sum_map GROUP BY timeslot \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500timeslot\u2500\u252c\u2500sumMap(statusMap.status, statusMap.requests)\u2500\u2510\n\u2502 2000-01-01 00:00:00 \u2502 ([1,2,3,4,5],[10,10,20,10,10]) \u2502\n\u2502 2000-01-01 00:01:00 \u2502 ([4,5,6,7,8],[10,10,20,10,10]) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "sumMap(key, value)" - }, - { - "location": "/agg_functions/reference/#avgx", - "text": "Calculates the average.\nOnly works for numbers.\nThe result is always Float64.", - "title": "avg(x)" - }, - { - "location": "/agg_functions/reference/#uniqx", - "text": "Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments. Uses an adaptive sampling algorithm: for the calculation state, it uses a sample of element hash values with a size up to 65536.\nThis algorithm is also very accurate for data sets with low cardinality (up to 65536) and very efficient on CPU (when computing not too many of these functions, using uniq is almost as fast as using other aggregate functions). The result is determinate (it doesn't depend on the order of query processing). This function provides excellent accuracy even for data sets with extremely high cardinality (over 10 billion elements). It is recommended for default use.", - "title": "uniq(x)" - }, - { - "location": "/agg_functions/reference/#uniqcombinedx", - "text": "Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments. A combination of three algorithms is used: array, hash table and HyperLogLog with an error correction table. The memory consumption is several times smaller than for the uniq function, and the accuracy is several times higher. Performance is slightly lower than for the uniq function, but sometimes it can be even higher than it, such as with distributed queries that transmit a large number of aggregation states over the network. The maximum state size is 96 KiB (HyperLogLog of 217 6-bit cells). The result is determinate (it doesn't depend on the order of query processing). The uniqCombined function is a good default choice for calculating the number of different values, but keep in mind that the estimation error will increase for high-cardinality data sets (200M+ elements), and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).", - "title": "uniqCombined(x)" - }, - { - "location": "/agg_functions/reference/#uniqhll12x", - "text": "Uses the HyperLogLog algorithm to approximate the number of different values of the argument.\n212 5-bit cells are used. The size of the state is slightly more than 2.5 KB. The result is not very accurate (up to ~10% error) for small data sets ( 10K elements). However, the result is fairly accurate for high-cardinality data sets (10K-100M), with a maximum error of ~1.6%. Starting from 100M, the estimation error increases, and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements). The result is determinate (it doesn't depend on the order of query processing). We don't recommend using this function. In most cases, use the uniq or uniqCombined function.", - "title": "uniqHLL12(x)" - }, - { - "location": "/agg_functions/reference/#uniqexactx", - "text": "Calculates the number of different values of the argument, exactly.\nThere is no reason to fear approximations. It's better to use the uniq function.\nUse the uniqExact function if you definitely need an exact result. The uniqExact function uses more memory than the uniq function, because the size of the state has unbounded growth as the number of different values increases.", - "title": "uniqExact(x)" - }, - { - "location": "/agg_functions/reference/#grouparrayx-grouparraymax_sizex", - "text": "Creates an array of argument values.\nValues can be added to the array in any (indeterminate) order. The second version (with the max_size parameter) limits the size of the resulting array to max_size elements.\nFor example, groupArray (1) (x) is equivalent to [any (x)] . In some cases, you can still rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY .", - "title": "groupArray(x), groupArray(max_size)(x)" - }, - { - "location": "/agg_functions/reference/#grouparrayinsertatx", - "text": "Inserts a value into the array in the specified position. Accepts the value and position as input. If several values \u200b\u200bare inserted into the same position, any of them might end up in the resulting array (the first one will be used in the case of single-threaded execution). If no value is inserted into a position, the position is assigned the default value. Optional parameters: The default value for substituting in empty positions. The length of the resulting array. This allows you to receive arrays of the same size for all the aggregate keys. When using this parameter, the default value must be specified.", - "title": "groupArrayInsertAt(x)" - }, - { - "location": "/agg_functions/reference/#groupuniqarrayx", - "text": "Creates an array from different argument values. Memory consumption is the same as for the uniqExact function.", - "title": "groupUniqArray(x)" - }, - { - "location": "/agg_functions/reference/#quantilelevelx", - "text": "Approximates the 'level' quantile. 'level' is a constant, a floating-point number from 0 to 1.\nWe recommend using a 'level' value in the range of 0.01..0.99\nDon't use a 'level' value equal to 0 or 1 \u2013 use the 'min' and 'max' functions for these cases. In this function, as well as in all functions for calculating quantiles, the 'level' parameter can be omitted. In this case, it is assumed to be equal to 0.5 (in other words, the function will calculate the median). Works for numbers, dates, and dates with times.\nReturns: for numbers \u2013 Float64; for dates \u2013 a date; for dates with times \u2013 a date with time. Uses reservoir sampling with a reservoir size up to 8192.\nIf necessary, the result is output with linear approximation from the two neighboring values.\nThis algorithm provides very low accuracy. See also: quantileTiming , quantileTDigest , quantileExact . The result depends on the order of running the query, and is nondeterministic. When using multiple quantile (and similar) functions with different levels in a query, the internal states are not combined (that is, the query works less efficiently than it could). In this case, use the quantiles (and similar) functions.", - "title": "quantile(level)(x)" - }, - { - "location": "/agg_functions/reference/#quantiledeterministiclevelx-determinator", - "text": "Works the same way as the quantile function, but the result is deterministic and does not depend on the order of query execution. To achieve this, the function takes a second argument \u2013 the \"determinator\". This is a number whose hash is used instead of a random number generator in the reservoir sampling algorithm. For the function to work correctly, the same determinator value should not occur too often. For the determinator, you can use an event ID, user ID, and so on. Don't use this function for calculating timings. There is a more suitable function for this purpose: quantileTiming .", - "title": "quantileDeterministic(level)(x, determinator)" - }, - { - "location": "/agg_functions/reference/#quantiletiminglevelx", - "text": "Computes the quantile of 'level' with a fixed precision.\nWorks for numbers. Intended for calculating quantiles of page loading time in milliseconds. If the value is greater than 30,000 (a page loading time of more than 30 seconds), the result is equated to 30,000. If the total value is not more than about 5670, then the calculation is accurate. Otherwise: if the time is less than 1024 ms, then the calculation is accurate. otherwise the calculation is rounded to a multiple of 16 ms. When passing negative values to the function, the behavior is undefined. The returned value has the Float32 type. If no values were passed to the function (when using quantileTimingIf ), 'nan' is returned. The purpose of this is to differentiate these instances from zeros. See the note on sorting NaNs in \"ORDER BY clause\". The result is determinate (it doesn't depend on the order of query processing). For its purpose (calculating quantiles of page loading times), using this function is more effective and the result is more accurate than for the quantile function.", - "title": "quantileTiming(level)(x)" - }, - { - "location": "/agg_functions/reference/#quantiletimingweightedlevelx-weight", - "text": "Differs from the quantileTiming function in that it has a second argument, \"weights\". Weight is a non-negative integer.\nThe result is calculated as if the x value were passed weight number of times to the quantileTiming function.", - "title": "quantileTimingWeighted(level)(x, weight)" - }, - { - "location": "/agg_functions/reference/#quantileexactlevelx", - "text": "Computes the quantile of 'level' exactly. To do this, all the passed values \u200b\u200bare combined into an array, which is then partially sorted. Therefore, the function consumes O(n) memory, where 'n' is the number of values that were passed. However, for a small number of values, the function is very effective.", - "title": "quantileExact(level)(x)" - }, - { - "location": "/agg_functions/reference/#quantileexactweightedlevelx-weight", - "text": "Computes the quantile of 'level' exactly. In addition, each value is counted with its weight, as if it is present 'weight' times. The arguments of the function can be considered as histograms, where the value 'x' corresponds to a histogram \"column\" of the height 'weight', and the function itself can be considered as a summation of histograms. A hash table is used as the algorithm. Because of this, if the passed values \u200b\u200bare frequently repeated, the function consumes less RAM than quantileExact . You can use this function instead of quantileExact and specify the weight as 1.", - "title": "quantileExactWeighted(level)(x, weight)" - }, - { - "location": "/agg_functions/reference/#quantiletdigestlevelx", - "text": "Approximates the quantile level using the t-digest algorithm. The maximum error is 1%. Memory consumption by State is proportional to the logarithm of the number of passed values. The performance of the function is lower than for quantile , quantileTiming . In terms of the ratio of State size to precision, this function is much better than quantile . The result depends on the order of running the query, and is nondeterministic.", - "title": "quantileTDigest(level)(x)" - }, - { - "location": "/agg_functions/reference/#medianx", - "text": "All the quantile functions have corresponding median functions: median , medianDeterministic , medianTiming , medianTimingWeighted , medianExact , medianExactWeighted , medianTDigest . They are synonyms and their behavior is identical.", - "title": "median(x)" - }, - { - "location": "/agg_functions/reference/#quantileslevel1-level2-x", - "text": "All the quantile functions also have corresponding quantiles functions: quantiles , quantilesDeterministic , quantilesTiming , quantilesTimingWeighted , quantilesExact , quantilesExactWeighted , quantilesTDigest . These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.", - "title": "quantiles(level1, level2, ...)(x)" - }, - { - "location": "/agg_functions/reference/#varsampx", - "text": "Calculates the amount \u03a3((x - x\u0305)^2) / (n - 1) , where n is the sample size and x\u0305 is the average value of x . It represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount. Returns Float64 . When n = 1 , returns +\u221e .", - "title": "varSamp(x)" - }, - { - "location": "/agg_functions/reference/#varpopx", - "text": "Calculates the amount \u03a3((x - x\u0305)^2) / (n - 1) , where n is the sample size and x\u0305 is the average value of x . In other words, dispersion for a set of values. Returns Float64 .", - "title": "varPop(x)" - }, - { - "location": "/agg_functions/reference/#stddevsampx", - "text": "The result is equal to the square root of varSamp(x) .", - "title": "stddevSamp(x)" - }, - { - "location": "/agg_functions/reference/#stddevpopx", - "text": "The result is equal to the square root of varPop(x) .", - "title": "stddevPop(x)" - }, - { - "location": "/agg_functions/reference/#topkncolumn", - "text": "Returns an array of the most frequent values in the specified column. The resulting array is sorted in descending order of frequency of values (not by the values themselves). Implements the Filtered Space-Saving algorithm for analyzing TopK, based on the reduce-and-combine algorithm from Parallel Space Saving . topK(N)(column) This function doesn't provide a guaranteed result. In certain situations, errors might occur and it might return frequent values that aren't the most frequent values. We recommend using the N 10 value; performance is reduced with large N values. Maximum value of N = 65536 . Arguments \n- 'N' is the number of values.\n- ' x ' \u2013 The column. Example Take the OnTime data set and select the three most frequently occurring values in the AirlineID column. SELECT topK ( 3 )( AirlineID ) AS res FROM ontime \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [19393,19790,19805] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "topK(N)(column)" - }, - { - "location": "/agg_functions/reference/#covarsampx-y", - "text": "Calculates the value of \u03a3((x - x\u0305)(y - y\u0305)) / (n - 1) . Returns Float64. When n = 1 , returns +\u221e.", - "title": "covarSamp(x, y)" - }, - { - "location": "/agg_functions/reference/#covarpopx-y", - "text": "Calculates the value of \u03a3((x - x\u0305)(y - y\u0305)) / n .", - "title": "covarPop(x, y)" - }, - { - "location": "/agg_functions/reference/#corrx-y", - "text": "Calculates the Pearson correlation coefficient: \u03a3((x - x\u0305)(y - y\u0305)) / sqrt(\u03a3((x - x\u0305)^2) * \u03a3((y - y\u0305)^2)) .", - "title": "corr(x, y)" - }, - { - "location": "/agg_functions/combinators/", - "text": "Aggregate function combinators\n\n\nThe name of an aggregate function can have a suffix appended to it. This changes the way the aggregate function works.\n\n\n-If\n\n\nThe suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument \u2013 a condition (Uint8 type). The aggregate function processes only the rows that trigger the condition. If the condition was not triggered even once, it returns a default value (usually zeros or empty strings).\n\n\nExamples: \nsumIf(column, cond)\n, \ncountIf(cond)\n, \navgIf(x, cond)\n, \nquantilesTimingIf(level1, level2)(x, cond)\n, \nargMinIf(arg, val, cond)\n and so on.\n\n\nWith conditional aggregate functions, you can calculate aggregates for several conditions at once, without using subqueries and \nJOIN\ns. For example, in Yandex.Metrica, conditional aggregate functions are used to implement the segment comparison functionality.\n\n\n-Array\n\n\nThe -Array suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the 'Array(T)' type (arrays) instead of 'T' type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. When processing arrays, the aggregate function works like the original aggregate function across all array elements.\n\n\nExample 1: \nsumArray(arr)\n - Totals all the elements of all 'arr' arrays. In this example, it could have been written more simply: \nsum(arraySum(arr))\n.\n\n\nExample 2: \nuniqArray(arr)\n \u2013 Count the number of unique elements in all 'arr' arrays. This could be done an easier way: \nuniq(arrayJoin(arr))\n, but it's not always possible to add 'arrayJoin' to a query.\n\n\n-If and -Array can be combined. However, 'Array' must come first, then 'If'. Examples: \nuniqArrayIf(arr, cond)\n, \nquantilesTimingArrayIf(level1, level2)(arr, cond)\n. Due to this order, the 'cond' argument can't be an array.\n\n\n-State\n\n\nIf you apply this combinator, the aggregate function doesn't return the resulting value (such as the number of unique values for the 'uniq' function), but an intermediate state of the aggregation (for \nuniq\n, this is the hash table for calculating the number of unique values). This is an AggregateFunction(...) that can be used for further processing or stored in a table to finish aggregating later. See the sections \"AggregatingMergeTree\" and \"Functions for working with intermediate aggregation states\".\n\n\n-Merge\n\n\nIf you apply this combinator, the aggregate function takes the intermediate aggregation state as an argument, combines the states to finish aggregation, and returns the resulting value.\n\n\n-MergeState.\n\n\nMerges the intermediate aggregation states in the same way as the -Merge combinator. However, it doesn't return the resulting value, but an intermediate aggregation state, similar to the -State combinator.\n\n\n-ForEach\n\n\nConverts an aggregate function for tables into an aggregate function for arrays that aggregates the corresponding array items and returns an array of results. For example, \nsumForEach\n for the arrays \n[1, 2]\n, \n[3, 4, 5]\nand\n[6, 7]\nreturns the result \n[10, 13, 5]\n after adding together the corresponding array items.", - "title": "Aggregate function combinators" - }, - { - "location": "/agg_functions/combinators/#aggregate-function-combinators", - "text": "The name of an aggregate function can have a suffix appended to it. This changes the way the aggregate function works.", - "title": "Aggregate function combinators" - }, - { - "location": "/agg_functions/combinators/#-if", - "text": "The suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument \u2013 a condition (Uint8 type). The aggregate function processes only the rows that trigger the condition. If the condition was not triggered even once, it returns a default value (usually zeros or empty strings). Examples: sumIf(column, cond) , countIf(cond) , avgIf(x, cond) , quantilesTimingIf(level1, level2)(x, cond) , argMinIf(arg, val, cond) and so on. With conditional aggregate functions, you can calculate aggregates for several conditions at once, without using subqueries and JOIN s. For example, in Yandex.Metrica, conditional aggregate functions are used to implement the segment comparison functionality.", - "title": "-If" - }, - { - "location": "/agg_functions/combinators/#-array", - "text": "The -Array suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the 'Array(T)' type (arrays) instead of 'T' type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. When processing arrays, the aggregate function works like the original aggregate function across all array elements. Example 1: sumArray(arr) - Totals all the elements of all 'arr' arrays. In this example, it could have been written more simply: sum(arraySum(arr)) . Example 2: uniqArray(arr) \u2013 Count the number of unique elements in all 'arr' arrays. This could be done an easier way: uniq(arrayJoin(arr)) , but it's not always possible to add 'arrayJoin' to a query. -If and -Array can be combined. However, 'Array' must come first, then 'If'. Examples: uniqArrayIf(arr, cond) , quantilesTimingArrayIf(level1, level2)(arr, cond) . Due to this order, the 'cond' argument can't be an array.", - "title": "-Array" - }, - { - "location": "/agg_functions/combinators/#-state", - "text": "If you apply this combinator, the aggregate function doesn't return the resulting value (such as the number of unique values for the 'uniq' function), but an intermediate state of the aggregation (for uniq , this is the hash table for calculating the number of unique values). This is an AggregateFunction(...) that can be used for further processing or stored in a table to finish aggregating later. See the sections \"AggregatingMergeTree\" and \"Functions for working with intermediate aggregation states\".", - "title": "-State" - }, - { - "location": "/agg_functions/combinators/#-merge", - "text": "If you apply this combinator, the aggregate function takes the intermediate aggregation state as an argument, combines the states to finish aggregation, and returns the resulting value.", - "title": "-Merge" - }, - { - "location": "/agg_functions/combinators/#-mergestate", - "text": "Merges the intermediate aggregation states in the same way as the -Merge combinator. However, it doesn't return the resulting value, but an intermediate aggregation state, similar to the -State combinator.", - "title": "-MergeState." - }, - { - "location": "/agg_functions/combinators/#-foreach", - "text": "Converts an aggregate function for tables into an aggregate function for arrays that aggregates the corresponding array items and returns an array of results. For example, sumForEach for the arrays [1, 2] , [3, 4, 5] and [6, 7] returns the result [10, 13, 5] after adding together the corresponding array items.", - "title": "-ForEach" - }, - { - "location": "/agg_functions/parametric_functions/", - "text": "Parametric aggregate functions\n\n\nSome aggregate functions can accept not only argument columns (used for compression), but a set of parameters \u2013 constants for initialization. The syntax is two pairs of brackets instead of one. The first is for parameters, and the second is for arguments.\n\n\nsequenceMatch(pattern)(time, cond1, cond2, ...)\n\n\nPattern matching for event chains.\n\n\npattern\n is a string containing a pattern to match. The pattern is similar to a regular expression.\n\n\ntime\n is the time of the event with the DateTime type.\n\n\ncond1\n, \ncond2\n ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event.\n\n\nThe function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern.\nIt returns UInt8: 0 if the pattern isn't matched, or 1 if it matches.\n\n\nExample: \nsequenceMatch ('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%')\n\n\n\n\nwhether there was a chain of events in which a pageview with 'company' in the address occurred earlier than a pageview with 'cart' in the address.\n\n\n\n\nThis is a singular example. You could write it using other aggregate functions:\n\n\nminIf(EventTime, URL LIKE \n%company%\n) \n maxIf(EventTime, URL LIKE \n%cart%\n).\n\n\n\n\n\nHowever, there is no such solution for more complex situations.\n\n\nPattern syntax:\n\n\n(?1)\n refers to the condition (any number can be used in place of 1).\n\n\n.*\n is any number of any events.\n\n\n(?t\n=1800)\n is a time condition.\n\n\nAny quantity of any type of events is allowed over the specified time.\n\n\nInstead of \n=\n, the following operators can be used:\n, \n, \n=\n.\n\n\nAny number may be specified in place of 1800.\n\n\nEvents that occur during the same second can be put in the chain in any order. This may affect the result of the function.\n\n\nsequenceCount(pattern)(time, cond1, cond2, ...)\n\n\nWorks the same way as the sequenceMatch function, but instead of returning whether there is an event chain, it returns UInt64 with the number of event chains found.\nChains are searched for without overlapping. In other words, the next chain can start only after the end of the previous one.\n\n\nwindowFunnel(window)(timestamp, cond1, cond2, cond3, ....)\n\n\nWindow funnel matching for event chains, calculates the max event level in a sliding window.\n\n\nwindow\n is the timestamp window value, such as 3600.\n\n\ntimestamp\n is the time of the event with the DateTime type or UInt32 type.\n\n\ncond1\n, \ncond2\n ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event\n\n\nExample: \n\n\nConsider you are doing a website analytics, intend to find out the user counts clicked login button( event = 1001 ), then the user counts followed by searched the phones( event = 1003 and product = 'phone' ) , then the user counts followed by made an order ( event = 1009 ). And all event chains must be in a 3600 seconds sliding window. \n\n\nThis could be easily calculate by \nwindowFunnel\n\n\nSELECT\n level,\n count() AS c\nFROM\n(\n SELECT\n user_id,\n windowFunnel(3600)(timestamp, event_id = 1001, event_id = 1003 AND product = \nphone\n, event_id = 1009) AS level\n FROM trend_event\n WHERE (event_date \n= \n2017-01-01\n) AND (event_date \n= \n2017-01-31\n)\n GROUP BY user_id\n)\nGROUP BY level\nORDER BY level\n\n\n\n\n\nSimply, the level could only be 0,1,2,3, it means the maxium event action stage that one user could reach.\n\n\nuniqUpTo(N)(x)\n\n\nCalculates the number of different argument values \u200b\u200bif it is less than or equal to N. If the number of different argument values is greater than N, it returns N + 1.\n\n\nRecommended for use with small Ns, up to 10. The maximum value of N is 100.\n\n\nFor the state of an aggregate function, it uses the amount of memory equal to 1 + N * the size of one value of bytes.\nFor strings, it stores a non-cryptographic hash of 8 bytes. That is, the calculation is approximated for strings.\n\n\nThe function also works for several arguments.\n\n\nIt works as fast as possible, except for cases when a large N value is used and the number of unique values is slightly less than N.\n\n\nUsage example:\n\n\nProblem: Generate a report that shows only keywords that produced at least 5 unique users.\nSolution: Write in the GROUP BY query SearchPhrase HAVING uniqUpTo(4)(UserID) \n= 5", - "title": "Parametric aggregate functions" - }, - { - "location": "/agg_functions/parametric_functions/#parametric-aggregate-functions", - "text": "Some aggregate functions can accept not only argument columns (used for compression), but a set of parameters \u2013 constants for initialization. The syntax is two pairs of brackets instead of one. The first is for parameters, and the second is for arguments.", - "title": "Parametric aggregate functions" - }, - { - "location": "/agg_functions/parametric_functions/#sequencematchpatterntime-cond1-cond2", - "text": "Pattern matching for event chains. pattern is a string containing a pattern to match. The pattern is similar to a regular expression. time is the time of the event with the DateTime type. cond1 , cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event. The function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern.\nIt returns UInt8: 0 if the pattern isn't matched, or 1 if it matches. Example: sequenceMatch ('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%') whether there was a chain of events in which a pageview with 'company' in the address occurred earlier than a pageview with 'cart' in the address. This is a singular example. You could write it using other aggregate functions: minIf(EventTime, URL LIKE %company% ) maxIf(EventTime, URL LIKE %cart% ). However, there is no such solution for more complex situations. Pattern syntax: (?1) refers to the condition (any number can be used in place of 1). .* is any number of any events. (?t =1800) is a time condition. Any quantity of any type of events is allowed over the specified time. Instead of = , the following operators can be used: , , = . Any number may be specified in place of 1800. Events that occur during the same second can be put in the chain in any order. This may affect the result of the function.", - "title": "sequenceMatch(pattern)(time, cond1, cond2, ...)" - }, - { - "location": "/agg_functions/parametric_functions/#sequencecountpatterntime-cond1-cond2", - "text": "Works the same way as the sequenceMatch function, but instead of returning whether there is an event chain, it returns UInt64 with the number of event chains found.\nChains are searched for without overlapping. In other words, the next chain can start only after the end of the previous one.", - "title": "sequenceCount(pattern)(time, cond1, cond2, ...)" - }, - { - "location": "/agg_functions/parametric_functions/#windowfunnelwindowtimestamp-cond1-cond2-cond3", - "text": "Window funnel matching for event chains, calculates the max event level in a sliding window. window is the timestamp window value, such as 3600. timestamp is the time of the event with the DateTime type or UInt32 type. cond1 , cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event Example: Consider you are doing a website analytics, intend to find out the user counts clicked login button( event = 1001 ), then the user counts followed by searched the phones( event = 1003 and product = 'phone' ) , then the user counts followed by made an order ( event = 1009 ). And all event chains must be in a 3600 seconds sliding window. This could be easily calculate by windowFunnel SELECT\n level,\n count() AS c\nFROM\n(\n SELECT\n user_id,\n windowFunnel(3600)(timestamp, event_id = 1001, event_id = 1003 AND product = phone , event_id = 1009) AS level\n FROM trend_event\n WHERE (event_date = 2017-01-01 ) AND (event_date = 2017-01-31 )\n GROUP BY user_id\n)\nGROUP BY level\nORDER BY level Simply, the level could only be 0,1,2,3, it means the maxium event action stage that one user could reach.", - "title": "windowFunnel(window)(timestamp, cond1, cond2, cond3, ....)" - }, - { - "location": "/agg_functions/parametric_functions/#uniquptonx", - "text": "Calculates the number of different argument values \u200b\u200bif it is less than or equal to N. If the number of different argument values is greater than N, it returns N + 1. Recommended for use with small Ns, up to 10. The maximum value of N is 100. For the state of an aggregate function, it uses the amount of memory equal to 1 + N * the size of one value of bytes.\nFor strings, it stores a non-cryptographic hash of 8 bytes. That is, the calculation is approximated for strings. The function also works for several arguments. It works as fast as possible, except for cases when a large N value is used and the number of unique values is slightly less than N. Usage example: Problem: Generate a report that shows only keywords that produced at least 5 unique users.\nSolution: Write in the GROUP BY query SearchPhrase HAVING uniqUpTo(4)(UserID) = 5", - "title": "uniqUpTo(N)(x)" - }, - { - "location": "/dicts/", - "text": "Dictionaries\n\n\nA dictionary\n is a mapping (key \n-\n attributes) that can be used in a query as functions.\nYou can think of this as a more convenient and efficient type of JOIN with dimension tables.\n\n\nThere are built-in (internal) and add-on (external) dictionaries.", - "title": "Introduction" - }, - { - "location": "/dicts/#dictionaries", - "text": "A dictionary is a mapping (key - attributes) that can be used in a query as functions.\nYou can think of this as a more convenient and efficient type of JOIN with dimension tables. There are built-in (internal) and add-on (external) dictionaries.", - "title": "Dictionaries" - }, - { - "location": "/dicts/external_dicts/", - "text": "External dictionaries\n\n\nYou can add your own dictionaries from various data sources. The data source for a dictionary can be a local text or executable file, an HTTP(s) resource, or another DBMS. For more information, see \"\nSources for external dictionaries\n\".\n\n\nClickHouse:\n\n\n\n\n\n\nFully or partially stores dictionaries in RAM.\n\n\nPeriodically updates dictionaries and dynamically loads missing values. In other words, dictionaries can be loaded dynamically.\n\n\n\n\n\n\nThe configuration of external dictionaries is located in one or more files. The path to the configuration is specified in the \ndictionaries_config\n parameter.\n\n\nDictionaries can be loaded at server startup or at first use, depending on the \ndictionaries_lazy_load\n setting.\n\n\nThe dictionary config file has the following format:\n\n\nyandex\n\n \ncomment\nAn optional element with any content. Ignored by the ClickHouse server.\n/comment\n\n\n \n!--Optional element. File name with substitutions--\n\n \ninclude_from\n/etc/metrika.xml\n/include_from\n\n\n\n \ndictionary\n\n \n!-- Dictionary configuration --\n\n \n/dictionary\n\n\n ...\n\n \ndictionary\n\n \n!-- Dictionary configuration --\n\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\nYou can \nconfigure\n any number of dictionaries in the same file. The file format is preserved even if there is only one dictionary (i.e. \nyandex\ndictionary\n \n!--configuration -\n \n/dictionary\n/yandex\n ).\n\n\nSee also \"\nFunctions for working with external dictionaries\n\".\n\n\n\n\nYou can convert values \u200b\u200bfor a small dictionary by describing it in a `SELECT` query (see the [transform](../functions/other_functions.md#other_functions-transform) function). This functionality is not related to external dictionaries.", - "title": "General desription" - }, - { - "location": "/dicts/external_dicts/#external-dictionaries", - "text": "You can add your own dictionaries from various data sources. The data source for a dictionary can be a local text or executable file, an HTTP(s) resource, or another DBMS. For more information, see \" Sources for external dictionaries \". ClickHouse: Fully or partially stores dictionaries in RAM. Periodically updates dictionaries and dynamically loads missing values. In other words, dictionaries can be loaded dynamically. The configuration of external dictionaries is located in one or more files. The path to the configuration is specified in the dictionaries_config parameter. Dictionaries can be loaded at server startup or at first use, depending on the dictionaries_lazy_load setting. The dictionary config file has the following format: yandex \n comment An optional element with any content. Ignored by the ClickHouse server. /comment \n\n !--Optional element. File name with substitutions-- \n include_from /etc/metrika.xml /include_from \n\n\n dictionary \n !-- Dictionary configuration -- \n /dictionary \n\n ...\n\n dictionary \n !-- Dictionary configuration -- \n /dictionary /yandex You can configure any number of dictionaries in the same file. The file format is preserved even if there is only one dictionary (i.e. yandex dictionary !--configuration - /dictionary /yandex ). See also \" Functions for working with external dictionaries \". \n\nYou can convert values \u200b\u200bfor a small dictionary by describing it in a `SELECT` query (see the [transform](../functions/other_functions.md#other_functions-transform) function). This functionality is not related to external dictionaries.", - "title": "External dictionaries" - }, - { - "location": "/dicts/external_dicts_dict/", - "text": "Configuring an external dictionary\n\n\nThe dictionary configuration has the following structure:\n\n\ndictionary\n\n \nname\ndict_name\n/name\n\n\n \nsource\n\n \n!-- Source configuration --\n\n \n/source\n\n\n \nlayout\n\n \n!-- Memory layout configuration --\n\n \n/layout\n\n\n \nstructure\n\n \n!-- Complex key configuration --\n\n \n/structure\n\n\n \nlifetime\n\n \n!-- Lifetime of dictionary in memory --\n\n \n/lifetime\n\n\n/dictionary\n\n\n\n\n\n\n\n\nname \u2013 The identifier that can be used to access the dictionary. Use the characters \n[a-zA-Z0-9_\\-]\n.\n\n\nsource\n \u2014 Source of the dictionary.\n\n\nlayout\n \u2014 Dictionary layout in memory.\n\n\nstructure\n \u2014 Structure of the dictionary . A key and attributes that can be retrieved by this key.\n\n\nlifetime\n \u2014 Frequency of dictionary updates.", - "title": "Configuring an external dictionary" - }, - { - "location": "/dicts/external_dicts_dict/#configuring-an-external-dictionary", - "text": "The dictionary configuration has the following structure: dictionary \n name dict_name /name \n\n source \n !-- Source configuration -- \n /source \n\n layout \n !-- Memory layout configuration -- \n /layout \n\n structure \n !-- Complex key configuration -- \n /structure \n\n lifetime \n !-- Lifetime of dictionary in memory -- \n /lifetime /dictionary name \u2013 The identifier that can be used to access the dictionary. Use the characters [a-zA-Z0-9_\\-] . source \u2014 Source of the dictionary. layout \u2014 Dictionary layout in memory. structure \u2014 Structure of the dictionary . A key and attributes that can be retrieved by this key. lifetime \u2014 Frequency of dictionary updates.", - "title": "Configuring an external dictionary" - }, - { - "location": "/dicts/external_dicts_dict_layout/", - "text": "Storing dictionaries in memory\n\n\nThere are a \nvariety of ways\n to store dictionaries in memory.\n\n\nWe recommend \nflat\n, \nhashed\nand\ncomplex_key_hashed\n. which provide optimal processing speed.\n\n\nCaching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section \"\ncache\n\".\n\n\nThere are several ways to improve dictionary performance:\n\n\n\n\nCall the function for working with the dictionary after \nGROUP BY\n.\n\n\nMark attributes to extract as injective. An attribute is called injective if different attribute values correspond to different keys. So when \nGROUP BY\n uses a function that fetches an attribute value by the key, this function is automatically taken out of \nGROUP BY\n.\n\n\n\n\nClickHouse generates an exception for errors with dictionaries. Examples of errors:\n\n\n\n\nThe dictionary being accessed could not be loaded.\n\n\nError querying a \ncached\n dictionary.\n\n\n\n\nYou can view the list of external dictionaries and their statuses in the \nsystem.dictionaries\n table.\n\n\nThe configuration looks like this:\n\n\nyandex\n\n \ndictionary\n\n ...\n \nlayout\n\n \nlayout_type\n\n \n!-- layout settings --\n\n \n/layout_type\n\n \n/layout\n\n ...\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\n\n\nWays to store dictionaries in memory\n\n\n\n\nflat\n\n\nhashed\n\n\ncache\n\n\nrange_hashed\n\n\ncomplex_key_hashed\n\n\ncomplex_key_cache\n\n\nip_trie\n\n\n\n\n\n\nflat\n\n\nThe dictionary is completely stored in memory in the form of flat arrays. How much memory does the dictionary use? The amount is proportional to the size of the largest key (in space used).\n\n\nThe dictionary key has the \nUInt64\n type and the value is limited to 500,000. If a larger key is discovered when creating the dictionary, ClickHouse throws an exception and does not create the dictionary.\n\n\nAll types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.\n\n\nThis method provides the best performance among all available methods of storing the dictionary.\n\n\nConfiguration example:\n\n\nlayout\n\n \nflat\n \n/\n\n\n/layout\n\n\n\n\n\n\n\n\nhashed\n\n\nThe dictionary is completely stored in memory in the form of a hash table. The dictionary can contain any number of elements with any identifiers In practice, the number of keys can reach tens of millions of items.\n\n\nAll types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.\n\n\nConfiguration example:\n\n\nlayout\n\n \nhashed\n \n/\n\n\n/layout\n\n\n\n\n\n\n\n\ncomplex_key_hashed\n\n\nThis type of storage is for use with composite \nkeys\n. Similar to \nhashed\n.\n\n\nConfiguration example:\n\n\nlayout\n\n \ncomplex_key_hashed\n \n/\n\n\n/layout\n\n\n\n\n\n\n\n\nrange_hashed\n\n\nThe dictionary is stored in memory in the form of a hash table with an ordered array of ranges and their corresponding values.\n\n\nThis storage method works the same way as hashed and allows using date/time ranges in addition to the key, if they appear in the dictionary.\n\n\nExample: The table contains discounts for each advertiser in the format:\n\n\n+---------------+---------------------+-------------------+--------+\n| advertiser id | discount start date | discount end date | amount |\n+===============+=====================+===================+========+\n| 123 | 2015-01-01 | 2015-01-15 | 0.15 |\n+---------------+---------------------+-------------------+--------+\n| 123 | 2015-01-16 | 2015-01-31 | 0.25 |\n+---------------+---------------------+-------------------+--------+\n| 456 | 2015-01-01 | 2015-01-15 | 0.05 |\n+---------------+---------------------+-------------------+--------+\n\n\n\n\n\nTo use a sample for date ranges, define the \nrange_min\n and \nrange_max\n elements in the \nstructure\n.\n\n\nExample:\n\n\nstructure\n\n \nid\n\n \nname\nId\n/name\n\n \n/id\n\n \nrange_min\n\n \nname\nfirst\n/name\n\n \n/range_min\n\n \nrange_max\n\n \nname\nlast\n/name\n\n \n/range_max\n\n ...\n\n\n\n\n\nTo work with these dictionaries, you need to pass an additional date argument to the \ndictGetT\n function:\n\n\ndictGetT(\ndict_name\n, \nattr_name\n, id, date)\n\n\n\n\n\nThis function returns the value for the specified \nid\ns and the date range that includes the passed date.\n\n\nDetails of the algorithm:\n\n\n\n\nIf the \nid\n is not found or a range is not found for the \nid\n, it returns the default value for the dictionary.\n\n\nIf there are overlapping ranges, you can use any.\n\n\nIf the range delimiter is \nNULL\n or an invalid date (such as 1900-01-01 or 2039-01-01), the range is left open. The range can be open on both sides.\n\n\n\n\nConfiguration example:\n\n\nyandex\n\n \ndictionary\n\n\n ...\n\n \nlayout\n\n \nrange_hashed\n \n/\n\n \n/layout\n\n\n \nstructure\n\n \nid\n\n \nname\nAbcdef\n/name\n\n \n/id\n\n \nrange_min\n\n \nname\nStartDate\n/name\n\n \n/range_min\n\n \nrange_max\n\n \nname\nEndDate\n/name\n\n \n/range_max\n\n \nattribute\n\n \nname\nXXXType\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n \n/\n\n \n/attribute\n\n \n/structure\n\n\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\n\n\ncache\n\n\nThe dictionary is stored in a cache that has a fixed number of cells. These cells contain frequently used elements.\n\n\nWhen searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using \nSELECT attrs... FROM db.table WHERE id IN (k1, k2, ...)\n. The received data is then written to the cache.\n\n\nFor cache dictionaries, the expiration \nlifetime\n of data in the cache can be set. If more time than \nlifetime\n has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used.\n\n\nThis is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the \nsystem.dictionaries\n table.\n\n\nTo improve cache performance, use a subquery with \nLIMIT\n, and call the function with the dictionary externally.\n\n\nSupported \nsources\n: MySQL, ClickHouse, executable, HTTP.\n\n\nExample of settings:\n\n\nlayout\n\n \ncache\n\n \n!-- The size of the cache, in number of cells. Rounded up to a power of two. --\n\n \nsize_in_cells\n1000000000\n/size_in_cells\n\n \n/cache\n\n\n/layout\n\n\n\n\n\n\nSet a large enough cache size. You need to experiment to select the number of cells:\n\n\n\n\nSet some value.\n\n\nRun queries until the cache is completely full.\n\n\nAssess memory consumption using the \nsystem.dictionaries\n table.\n\n\nIncrease or decrease the number of cells until the required memory consumption is reached.\n\n\n\n\n\n\nDo not use ClickHouse as a source, because it is slow to process queries with random reads.\n\n\n\n\n\n\n\ncomplex_key_cache\n\n\nThis type of storage is for use with composite \nkeys\n. Similar to \ncache\n.\n\n\n\n\nip_trie\n\n\nThis type of storage is for mapping network prefixes (IP addresses) to metadata such as ASN.\n\n\nExample: The table contains network prefixes and their corresponding AS number and country code:\n\n\n +-----------------+-------+--------+\n | prefix | asn | cca2 |\n +=================+=======+========+\n | 202.79.32.0/20 | 17501 | NP |\n +-----------------+-------+--------+\n | 2620:0:870::/48 | 3856 | US |\n +-----------------+-------+--------+\n | 2a02:6b8:1::/48 | 13238 | RU |\n +-----------------+-------+--------+\n | 2001:db8::/32 | 65536 | ZZ |\n +-----------------+-------+--------+\n\n\n\n\n\nWhen using this type of layout, the structure must have a composite key.\n\n\nExample:\n\n\nstructure\n\n \nkey\n\n \nattribute\n\n \nname\nprefix\n/name\n\n \ntype\nString\n/type\n\n \n/attribute\n\n \n/key\n\n \nattribute\n\n \nname\nasn\n/name\n\n \ntype\nUInt32\n/type\n\n \nnull_value\n \n/\n\n \n/attribute\n\n \nattribute\n\n \nname\ncca2\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n??\n/null_value\n\n \n/attribute\n\n ...\n\n\n\n\n\nThe key must have only one String type attribute that contains an allowed IP prefix. Other types are not supported yet.\n\n\nFor queries, you must use the same functions (\ndictGetT\n with a tuple) as for dictionaries with composite keys:\n\n\ndictGetT(\ndict_name\n, \nattr_name\n, tuple(ip))\n\n\n\n\n\nThe function takes either \nUInt32\n for IPv4, or \nFixedString(16)\n for IPv6:\n\n\ndictGetString(\nprefix\n, \nasn\n, tuple(IPv6StringToNum(\n2001:db8::1\n)))\n\n\n\n\n\nOther types are not supported yet. The function returns the attribute for the prefix that corresponds to this IP address. If there are overlapping prefixes, the most specific one is returned.\n\n\nData is stored in a \ntrie\n. It must completely fit into RAM.", - "title": "Storing dictionaries in memory" - }, - { - "location": "/dicts/external_dicts_dict_layout/#storing-dictionaries-in-memory", - "text": "There are a variety of ways to store dictionaries in memory. We recommend flat , hashed and complex_key_hashed . which provide optimal processing speed. Caching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section \" cache \". There are several ways to improve dictionary performance: Call the function for working with the dictionary after GROUP BY . Mark attributes to extract as injective. An attribute is called injective if different attribute values correspond to different keys. So when GROUP BY uses a function that fetches an attribute value by the key, this function is automatically taken out of GROUP BY . ClickHouse generates an exception for errors with dictionaries. Examples of errors: The dictionary being accessed could not be loaded. Error querying a cached dictionary. You can view the list of external dictionaries and their statuses in the system.dictionaries table. The configuration looks like this: yandex \n dictionary \n ...\n layout \n layout_type \n !-- layout settings -- \n /layout_type \n /layout \n ...\n /dictionary /yandex", - "title": "Storing dictionaries in memory" - }, - { - "location": "/dicts/external_dicts_dict_layout/#ways-to-store-dictionaries-in-memory", - "text": "flat hashed cache range_hashed complex_key_hashed complex_key_cache ip_trie", - "title": "Ways to store dictionaries in memory" - }, - { - "location": "/dicts/external_dicts_dict_layout/#flat", - "text": "The dictionary is completely stored in memory in the form of flat arrays. How much memory does the dictionary use? The amount is proportional to the size of the largest key (in space used). The dictionary key has the UInt64 type and the value is limited to 500,000. If a larger key is discovered when creating the dictionary, ClickHouse throws an exception and does not create the dictionary. All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety. This method provides the best performance among all available methods of storing the dictionary. Configuration example: layout \n flat / /layout", - "title": "flat" - }, - { - "location": "/dicts/external_dicts_dict_layout/#hashed", - "text": "The dictionary is completely stored in memory in the form of a hash table. The dictionary can contain any number of elements with any identifiers In practice, the number of keys can reach tens of millions of items. All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety. Configuration example: layout \n hashed / /layout", - "title": "hashed" - }, - { - "location": "/dicts/external_dicts_dict_layout/#complex_key_hashed", - "text": "This type of storage is for use with composite keys . Similar to hashed . Configuration example: layout \n complex_key_hashed / /layout", - "title": "complex_key_hashed" - }, - { - "location": "/dicts/external_dicts_dict_layout/#range_hashed", - "text": "The dictionary is stored in memory in the form of a hash table with an ordered array of ranges and their corresponding values. This storage method works the same way as hashed and allows using date/time ranges in addition to the key, if they appear in the dictionary. Example: The table contains discounts for each advertiser in the format: +---------------+---------------------+-------------------+--------+\n| advertiser id | discount start date | discount end date | amount |\n+===============+=====================+===================+========+\n| 123 | 2015-01-01 | 2015-01-15 | 0.15 |\n+---------------+---------------------+-------------------+--------+\n| 123 | 2015-01-16 | 2015-01-31 | 0.25 |\n+---------------+---------------------+-------------------+--------+\n| 456 | 2015-01-01 | 2015-01-15 | 0.05 |\n+---------------+---------------------+-------------------+--------+ To use a sample for date ranges, define the range_min and range_max elements in the structure . Example: structure \n id \n name Id /name \n /id \n range_min \n name first /name \n /range_min \n range_max \n name last /name \n /range_max \n ... To work with these dictionaries, you need to pass an additional date argument to the dictGetT function: dictGetT( dict_name , attr_name , id, date) This function returns the value for the specified id s and the date range that includes the passed date. Details of the algorithm: If the id is not found or a range is not found for the id , it returns the default value for the dictionary. If there are overlapping ranges, you can use any. If the range delimiter is NULL or an invalid date (such as 1900-01-01 or 2039-01-01), the range is left open. The range can be open on both sides. Configuration example: yandex \n dictionary \n\n ...\n\n layout \n range_hashed / \n /layout \n\n structure \n id \n name Abcdef /name \n /id \n range_min \n name StartDate /name \n /range_min \n range_max \n name EndDate /name \n /range_max \n attribute \n name XXXType /name \n type String /type \n null_value / \n /attribute \n /structure \n\n /dictionary /yandex", - "title": "range_hashed" - }, - { - "location": "/dicts/external_dicts_dict_layout/#cache", - "text": "The dictionary is stored in a cache that has a fixed number of cells. These cells contain frequently used elements. When searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using SELECT attrs... FROM db.table WHERE id IN (k1, k2, ...) . The received data is then written to the cache. For cache dictionaries, the expiration lifetime of data in the cache can be set. If more time than lifetime has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used. This is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the system.dictionaries table. To improve cache performance, use a subquery with LIMIT , and call the function with the dictionary externally. Supported sources : MySQL, ClickHouse, executable, HTTP. Example of settings: layout \n cache \n !-- The size of the cache, in number of cells. Rounded up to a power of two. -- \n size_in_cells 1000000000 /size_in_cells \n /cache /layout Set a large enough cache size. You need to experiment to select the number of cells: Set some value. Run queries until the cache is completely full. Assess memory consumption using the system.dictionaries table. Increase or decrease the number of cells until the required memory consumption is reached. \n\nDo not use ClickHouse as a source, because it is slow to process queries with random reads.", - "title": "cache" - }, - { - "location": "/dicts/external_dicts_dict_layout/#complex_key_cache", - "text": "This type of storage is for use with composite keys . Similar to cache .", - "title": "complex_key_cache" - }, - { - "location": "/dicts/external_dicts_dict_layout/#ip_trie", - "text": "This type of storage is for mapping network prefixes (IP addresses) to metadata such as ASN. Example: The table contains network prefixes and their corresponding AS number and country code: +-----------------+-------+--------+\n | prefix | asn | cca2 |\n +=================+=======+========+\n | 202.79.32.0/20 | 17501 | NP |\n +-----------------+-------+--------+\n | 2620:0:870::/48 | 3856 | US |\n +-----------------+-------+--------+\n | 2a02:6b8:1::/48 | 13238 | RU |\n +-----------------+-------+--------+\n | 2001:db8::/32 | 65536 | ZZ |\n +-----------------+-------+--------+ When using this type of layout, the structure must have a composite key. Example: structure \n key \n attribute \n name prefix /name \n type String /type \n /attribute \n /key \n attribute \n name asn /name \n type UInt32 /type \n null_value / \n /attribute \n attribute \n name cca2 /name \n type String /type \n null_value ?? /null_value \n /attribute \n ... The key must have only one String type attribute that contains an allowed IP prefix. Other types are not supported yet. For queries, you must use the same functions ( dictGetT with a tuple) as for dictionaries with composite keys: dictGetT( dict_name , attr_name , tuple(ip)) The function takes either UInt32 for IPv4, or FixedString(16) for IPv6: dictGetString( prefix , asn , tuple(IPv6StringToNum( 2001:db8::1 ))) Other types are not supported yet. The function returns the attribute for the prefix that corresponds to this IP address. If there are overlapping prefixes, the most specific one is returned. Data is stored in a trie . It must completely fit into RAM.", - "title": "ip_trie" - }, - { - "location": "/dicts/external_dicts_dict_lifetime/", - "text": "Dictionary updates\n\n\nClickHouse periodically updates the dictionaries. The update interval for fully downloaded dictionaries and the invalidation interval for cached dictionaries are defined in the \nlifetime\n tag in seconds.\n\n\nDictionary updates (other than loading for first use) do not block queries. During updates, the old version of a dictionary is used. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.\n\n\nExample of settings:\n\n\ndictionary\n\n ...\n \nlifetime\n300\n/lifetime\n\n ...\n\n/dictionary\n\n\n\n\n\n\nSetting \nlifetime\n 0\n/lifetime\n prevents updating dictionaries.\n\n\nYou can set a time interval for upgrades, and ClickHouse will choose a uniformly random time within this range. This is necessary in order to distribute the load on the dictionary source when upgrading on a large number of servers.\n\n\nExample of settings:\n\n\ndictionary\n\n ...\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n ...\n\n/dictionary\n\n\n\n\n\n\nWhen upgrading the dictionaries, the ClickHouse server applies different logic depending on the type of \n source\n:\n\n\n\n\n\n\nFor a text file, it checks the time of modification. If the time differs from the previously recorded time, the dictionary is updated.\n\n\nFor MyISAM tables, the time of modification is checked using a \nSHOW TABLE STATUS\n query.\n\n\nDictionaries from other sources are updated every time by default.\n\n\n\n\n\n\nFor MySQL (InnoDB) and ODBC sources, you can set up a query that will update the dictionaries only if they really changed, rather than each time. To do this, follow these steps:\n\n\n\n\n\n\nThe dictionary table must have a field that always changes when the source data is updated.\n\n\nThe settings of the source must specify a query that retrieves the changing field. The ClickHouse server interprets the query result as a row, and if this row has changed relative to its previous state, the dictionary is updated. Specify the query in the \ninvalidate_query\n field in the settings for the \nsource\n.\n\n\n\n\n\n\nExample of settings:\n\n\ndictionary\n\n ...\n \nodbc\n\n ...\n \ninvalidate_query\nSELECT update_time FROM dictionary_source where id = 1\n/invalidate_query\n\n \n/odbc\n\n ...\n\n/dictionary", - "title": "Dictionary updates" - }, - { - "location": "/dicts/external_dicts_dict_lifetime/#dictionary-updates", - "text": "ClickHouse periodically updates the dictionaries. The update interval for fully downloaded dictionaries and the invalidation interval for cached dictionaries are defined in the lifetime tag in seconds. Dictionary updates (other than loading for first use) do not block queries. During updates, the old version of a dictionary is used. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries. Example of settings: dictionary \n ...\n lifetime 300 /lifetime \n ... /dictionary Setting lifetime 0 /lifetime prevents updating dictionaries. You can set a time interval for upgrades, and ClickHouse will choose a uniformly random time within this range. This is necessary in order to distribute the load on the dictionary source when upgrading on a large number of servers. Example of settings: dictionary \n ...\n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n ... /dictionary When upgrading the dictionaries, the ClickHouse server applies different logic depending on the type of source : For a text file, it checks the time of modification. If the time differs from the previously recorded time, the dictionary is updated. For MyISAM tables, the time of modification is checked using a SHOW TABLE STATUS query. Dictionaries from other sources are updated every time by default. For MySQL (InnoDB) and ODBC sources, you can set up a query that will update the dictionaries only if they really changed, rather than each time. To do this, follow these steps: The dictionary table must have a field that always changes when the source data is updated. The settings of the source must specify a query that retrieves the changing field. The ClickHouse server interprets the query result as a row, and if this row has changed relative to its previous state, the dictionary is updated. Specify the query in the invalidate_query field in the settings for the source . Example of settings: dictionary \n ...\n odbc \n ...\n invalidate_query SELECT update_time FROM dictionary_source where id = 1 /invalidate_query \n /odbc \n ... /dictionary", - "title": "Dictionary updates" - }, - { - "location": "/dicts/external_dicts_dict_sources/", - "text": "Sources of external dictionaries\n\n\nAn external dictionary can be connected from many different sources.\n\n\nThe configuration looks like this:\n\n\nyandex\n\n \ndictionary\n\n ...\n \nsource\n\n \nsource_type\n\n \n!-- Source configuration --\n\n \n/source_type\n\n \n/source\n\n ...\n \n/dictionary\n\n ...\n\n/yandex\n\n\n\n\n\n\nThe source is configured in the \nsource\n section.\n\n\nTypes of sources (\nsource_type\n):\n\n\n\n\nLocal file\n\n\nExecutable file\n\n\nHTTP(s)\n\n\nODBC\n\n\nDBMS\n\n\nMySQL\n\n\nClickHouse\n\n\nMongoDB\n\n\n\n\n\n\nLocal file\n\n\nExample of settings:\n\n\nsource\n\n \nfile\n\n \npath\n/opt/dictionaries/os.tsv\n/path\n\n \nformat\nTabSeparated\n/format\n\n \n/file\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\npath\n \u2013 The absolute path to the file.\n\n\nformat\n \u2013 The file format. All the formats described in \"\nFormats\n\" are supported.\n\n\n\n\n\n\nExecutable file\n\n\nWorking with executable files depends on \nhow the dictionary is stored in memory\n. If the dictionary is stored using \ncache\n and \ncomplex_key_cache\n, ClickHouse requests the necessary keys by sending a request to the executable file's \nSTDIN\n.\n\n\nExample of settings:\n\n\nsource\n\n \nexecutable\n\n \ncommand\ncat /opt/dictionaries/os.tsv\n/command\n\n \nformat\nTabSeparated\n/format\n\n \n/executable\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\ncommand\n \u2013 The absolute path to the executable file, or the file name (if the program directory is written to \nPATH\n).\n\n\nformat\n \u2013 The file format. All the formats described in \"\nFormats\n\" are supported.\n\n\n\n\n\n\nHTTP(s)\n\n\nWorking with an HTTP(s) server depends on \nhow the dictionary is stored in memory\n. If the dictionary is stored using \ncache\n and \ncomplex_key_cache\n, ClickHouse requests the necessary keys by sending a request via the \nPOST\n method.\n\n\nExample of settings:\n\n\nsource\n\n \nhttp\n\n \nurl\nhttp://[::1]/os.tsv\n/url\n\n \nformat\nTabSeparated\n/format\n\n \n/http\n\n\n/source\n\n\n\n\n\n\nIn order for ClickHouse to access an HTTPS resource, you must \nconfigure openSSL\n in the server configuration.\n\n\nSetting fields:\n\n\n\n\nurl\n \u2013 The source URL.\n\n\nformat\n \u2013 The file format. All the formats described in \"\nFormats\n\" are supported.\n\n\n\n\n\n\nODBC\n\n\nYou can use this method to connect any database that has an ODBC driver.\n\n\nExample of settings:\n\n\nodbc\n\n \ndb\nDatabaseName\n/db\n\n \ntable\nTableName\n/table\n\n \nconnection_string\nDSN=some_parameters\n/connection_string\n\n \ninvalidate_query\nSQL_QUERY\n/invalidate_query\n\n\n/odbc\n\n\n\n\n\n\nSetting fields:\n\n\n\n\ndb\n \u2013 Name of the database. Omit it if the database name is set in the \nconnection_string\n parameters.\n\n\ntable\n \u2013 Name of the table.\n\n\nconnection_string\n \u2013 Connection string.\n\n\ninvalidate_query\n \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section \nUpdating dictionaries\n.\n\n\n\n\nExample of connecting PostgreSQL\n\n\nUbuntu OS.\n\n\nInstalling unixODBC and the ODBC driver for PostgreSQL:\n\n\nsudo apt-get install -y unixodbc odbcinst odbc-postgresql\n\n\n\n\n\nConfiguring \n/etc/odbc.ini\n (or \n~/.odbc.ini\n):\n\n\n [DEFAULT]\n Driver = myconnection\n\n [myconnection]\n Description = PostgreSQL connection to my_db\n Driver = PostgreSQL Unicode\n Database = my_db\n Servername = 127.0.0.1\n UserName = username\n Password = password\n Port = 5432\n Protocol = 9.3\n ReadOnly = No\n RowVersioning = No\n ShowSystemTables = No\n ConnSettings =\n\n\n\n\n\nThe dictionary configuration in ClickHouse:\n\n\ndictionary\n\n \nname\ntable_name\n/name\n\n \nsource\n\n \nodbc\n\n \n!-- You can specifiy the following parameters in connection_string: --\n\n \n!-- DSN=myconnection;UID=username;PWD=password;HOST=127.0.0.1;PORT=5432;DATABASE=my_db --\n\n \nconnection_string\nDSN=myconnection\n/connection_string\n\n \ntable\npostgresql_table\n/table\n\n \n/odbc\n\n \n/source\n\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n \nlayout\n\n \nhashed/\n\n \n/layout\n\n \nstructure\n\n \nid\n\n \nname\nid\n/name\n\n \n/id\n\n \nattribute\n\n \nname\nsome_column\n/name\n\n \ntype\nUInt64\n/type\n\n \nnull_value\n0\n/null_value\n\n \n/attribute\n\n \n/structure\n\n\n/dictionary\n\n\n\n\n\n\nYou may need to edit \nodbc.ini\n to specify the full path to the library with the driver \nDRIVER=/usr/local/lib/psqlodbcw.so\n.\n\n\nExample of connecting MS SQL Server\n\n\nUbuntu OS.\n\n\nInstalling the driver: :\n\n\n sudo apt-get install tdsodbc freetds-bin sqsh\n\n\n\n\n\nConfiguring the driver: :\n\n\n $ cat /etc/freetds/freetds.conf \n ...\n\n [MSSQL]\n host = 192.168.56.101\n port = 1433\n tds version = 7.0\n client charset = UTF-8\n\n $ cat /etc/odbcinst.ini \n ...\n\n [FreeTDS]\n Description = FreeTDS\n Driver = /usr/lib/x86_64-linux-gnu/odbc/libtdsodbc.so\n Setup = /usr/lib/x86_64-linux-gnu/odbc/libtdsS.so\n FileUsage = 1\n UsageCount = 5\n\n $ cat ~/.odbc.ini \n ...\n\n [MSSQL]\n Description = FreeTDS\n Driver = FreeTDS\n Servername = MSSQL\n Database = test\n UID = test\n PWD = test\n Port = 1433\n\n\n\n\n\nConfiguring the dictionary in ClickHouse:\n\n\nyandex\n\n \ndictionary\n\n \nname\ntest\n/name\n\n \nsource\n\n \nodbc\n\n \ntable\ndict\n/table\n\n \nconnection_string\nDSN=MSSQL;UID=test;PWD=test\n/connection_string\n\n \n/odbc\n\n \n/source\n\n\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n\n \nlayout\n\n \nflat\n \n/\n\n \n/layout\n\n\n \nstructure\n\n \nid\n\n \nname\nk\n/name\n\n \n/id\n\n \nattribute\n\n \nname\ns\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n/null_value\n\n \n/attribute\n\n \n/structure\n\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\nDBMS\n\n\n\n\nMySQL\n\n\nExample of settings:\n\n\nsource\n\n \nmysql\n\n \nport\n3306\n/port\n\n \nuser\nclickhouse\n/user\n\n \npassword\nqwerty\n/password\n\n \nreplica\n\n \nhost\nexample01-1\n/host\n\n \npriority\n1\n/priority\n\n \n/replica\n\n \nreplica\n\n \nhost\nexample01-2\n/host\n\n \npriority\n1\n/priority\n\n \n/replica\n\n \ndb\ndb_name\n/db\n\n \ntable\ntable_name\n/table\n\n \nwhere\nid=10\n/where\n\n \ninvalidate_query\nSQL_QUERY\n/invalidate_query\n\n \n/mysql\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\n\n\nport\n \u2013 The port on the MySQL server. You can specify it for all replicas, or for each one individually (inside \nreplica\n).\n\n\n\n\n\n\nuser\n \u2013 Name of the MySQL user. You can specify it for all replicas, or for each one individually (inside \nreplica\n).\n\n\n\n\n\n\npassword\n \u2013 Password of the MySQL user. You can specify it for all replicas, or for each one individually (inside \nreplica\n).\n\n\n\n\n\n\nreplica\n \u2013 Section of replica configurations. There can be multiple sections.\n\n\n\n\nreplica/host\n \u2013 The MySQL host.\n\n\n\n\n* \nreplica/priority\n \u2013 The replica priority. When attempting to connect, ClickHouse traverses the replicas in order of priority. The lower the number, the higher the priority.\n\n\n\n\n\n\ndb\n \u2013 Name of the database.\n\n\n\n\n\n\ntable\n \u2013 Name of the table.\n\n\n\n\n\n\nwhere\n \u2013 The selection criteria. Optional parameter.\n\n\n\n\n\n\ninvalidate_query\n \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section \nUpdating dictionaries\n.\n\n\n\n\n\n\nMySQL can be connected on a local host via sockets. To do this, set \nhost\n and \nsocket\n.\n\n\nExample of settings:\n\n\nsource\n\n \nmysql\n\n \nhost\nlocalhost\n/host\n\n \nsocket\n/path/to/socket/file.sock\n/socket\n\n \nuser\nclickhouse\n/user\n\n \npassword\nqwerty\n/password\n\n \ndb\ndb_name\n/db\n\n \ntable\ntable_name\n/table\n\n \nwhere\nid=10\n/where\n\n \ninvalidate_query\nSQL_QUERY\n/invalidate_query\n\n \n/mysql\n\n\n/source\n\n\n\n\n\n\n\n\nClickHouse\n\n\nExample of settings:\n\n\nsource\n\n \nclickhouse\n\n \nhost\nexample01-01-1\n/host\n\n \nport\n9000\n/port\n\n \nuser\ndefault\n/user\n\n \npassword\n/password\n\n \ndb\ndefault\n/db\n\n \ntable\nids\n/table\n\n \nwhere\nid=10\n/where\n\n \n/clickhouse\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\nhost\n \u2013 The ClickHouse host. If it is a local host, the query is processed without any network activity. To improve fault tolerance, you can create a \nDistributed\n table and enter it in subsequent configurations.\n\n\nport\n \u2013 The port on the ClickHouse server.\n\n\nuser\n \u2013 Name of the ClickHouse user.\n\n\npassword\n \u2013 Password of the ClickHouse user.\n\n\ndb\n \u2013 Name of the database.\n\n\ntable\n \u2013 Name of the table.\n\n\nwhere\n \u2013 The selection criteria. May be omitted.\n\n\n\n\n\n\nMongoDB\n\n\nExample of settings:\n\n\nsource\n\n \nmongodb\n\n \nhost\nlocalhost\n/host\n\n \nport\n27017\n/port\n\n \nuser\n/user\n\n \npassword\n/password\n\n \ndb\ntest\n/db\n\n \ncollection\ndictionary_source\n/collection\n\n \n/mongodb\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\nhost\n \u2013 The MongoDB host.\n\n\nport\n \u2013 The port on the MongoDB server.\n\n\nuser\n \u2013 Name of the MongoDB user.\n\n\npassword\n \u2013 Password of the MongoDB user.\n\n\ndb\n \u2013 Name of the database.\n\n\ncollection\n \u2013 Name of the collection.", - "title": "Sources of external dictionaries" - }, - { - "location": "/dicts/external_dicts_dict_sources/#sources-of-external-dictionaries", - "text": "An external dictionary can be connected from many different sources. The configuration looks like this: yandex \n dictionary \n ...\n source \n source_type \n !-- Source configuration -- \n /source_type \n /source \n ...\n /dictionary \n ... /yandex The source is configured in the source section. Types of sources ( source_type ): Local file Executable file HTTP(s) ODBC DBMS MySQL ClickHouse MongoDB", - "title": "Sources of external dictionaries" - }, - { - "location": "/dicts/external_dicts_dict_sources/#local-file", - "text": "Example of settings: source \n file \n path /opt/dictionaries/os.tsv /path \n format TabSeparated /format \n /file /source Setting fields: path \u2013 The absolute path to the file. format \u2013 The file format. All the formats described in \" Formats \" are supported.", - "title": "Local file" - }, - { - "location": "/dicts/external_dicts_dict_sources/#executable-file", - "text": "Working with executable files depends on how the dictionary is stored in memory . If the dictionary is stored using cache and complex_key_cache , ClickHouse requests the necessary keys by sending a request to the executable file's STDIN . Example of settings: source \n executable \n command cat /opt/dictionaries/os.tsv /command \n format TabSeparated /format \n /executable /source Setting fields: command \u2013 The absolute path to the executable file, or the file name (if the program directory is written to PATH ). format \u2013 The file format. All the formats described in \" Formats \" are supported.", - "title": "Executable file" - }, - { - "location": "/dicts/external_dicts_dict_sources/#https", - "text": "Working with an HTTP(s) server depends on how the dictionary is stored in memory . If the dictionary is stored using cache and complex_key_cache , ClickHouse requests the necessary keys by sending a request via the POST method. Example of settings: source \n http \n url http://[::1]/os.tsv /url \n format TabSeparated /format \n /http /source In order for ClickHouse to access an HTTPS resource, you must configure openSSL in the server configuration. Setting fields: url \u2013 The source URL. format \u2013 The file format. All the formats described in \" Formats \" are supported.", - "title": "HTTP(s)" - }, - { - "location": "/dicts/external_dicts_dict_sources/#odbc", - "text": "You can use this method to connect any database that has an ODBC driver. Example of settings: odbc \n db DatabaseName /db \n table TableName /table \n connection_string DSN=some_parameters /connection_string \n invalidate_query SQL_QUERY /invalidate_query /odbc Setting fields: db \u2013 Name of the database. Omit it if the database name is set in the connection_string parameters. table \u2013 Name of the table. connection_string \u2013 Connection string. invalidate_query \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries .", - "title": "ODBC" - }, - { - "location": "/dicts/external_dicts_dict_sources/#example-of-connecting-postgresql", - "text": "Ubuntu OS. Installing unixODBC and the ODBC driver for PostgreSQL: sudo apt-get install -y unixodbc odbcinst odbc-postgresql Configuring /etc/odbc.ini (or ~/.odbc.ini ): [DEFAULT]\n Driver = myconnection\n\n [myconnection]\n Description = PostgreSQL connection to my_db\n Driver = PostgreSQL Unicode\n Database = my_db\n Servername = 127.0.0.1\n UserName = username\n Password = password\n Port = 5432\n Protocol = 9.3\n ReadOnly = No\n RowVersioning = No\n ShowSystemTables = No\n ConnSettings = The dictionary configuration in ClickHouse: dictionary \n name table_name /name \n source \n odbc \n !-- You can specifiy the following parameters in connection_string: -- \n !-- DSN=myconnection;UID=username;PWD=password;HOST=127.0.0.1;PORT=5432;DATABASE=my_db -- \n connection_string DSN=myconnection /connection_string \n table postgresql_table /table \n /odbc \n /source \n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n layout \n hashed/ \n /layout \n structure \n id \n name id /name \n /id \n attribute \n name some_column /name \n type UInt64 /type \n null_value 0 /null_value \n /attribute \n /structure /dictionary You may need to edit odbc.ini to specify the full path to the library with the driver DRIVER=/usr/local/lib/psqlodbcw.so .", - "title": "Example of connecting PostgreSQL" - }, - { - "location": "/dicts/external_dicts_dict_sources/#example-of-connecting-ms-sql-server", - "text": "Ubuntu OS. Installing the driver: : sudo apt-get install tdsodbc freetds-bin sqsh Configuring the driver: : $ cat /etc/freetds/freetds.conf \n ...\n\n [MSSQL]\n host = 192.168.56.101\n port = 1433\n tds version = 7.0\n client charset = UTF-8\n\n $ cat /etc/odbcinst.ini \n ...\n\n [FreeTDS]\n Description = FreeTDS\n Driver = /usr/lib/x86_64-linux-gnu/odbc/libtdsodbc.so\n Setup = /usr/lib/x86_64-linux-gnu/odbc/libtdsS.so\n FileUsage = 1\n UsageCount = 5\n\n $ cat ~/.odbc.ini \n ...\n\n [MSSQL]\n Description = FreeTDS\n Driver = FreeTDS\n Servername = MSSQL\n Database = test\n UID = test\n PWD = test\n Port = 1433 Configuring the dictionary in ClickHouse: yandex \n dictionary \n name test /name \n source \n odbc \n table dict /table \n connection_string DSN=MSSQL;UID=test;PWD=test /connection_string \n /odbc \n /source \n\n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n\n layout \n flat / \n /layout \n\n structure \n id \n name k /name \n /id \n attribute \n name s /name \n type String /type \n null_value /null_value \n /attribute \n /structure \n /dictionary /yandex", - "title": "Example of connecting MS SQL Server" - }, - { - "location": "/dicts/external_dicts_dict_sources/#dbms", - "text": "", - "title": "DBMS" - }, - { - "location": "/dicts/external_dicts_dict_sources/#mysql", - "text": "Example of settings: source \n mysql \n port 3306 /port \n user clickhouse /user \n password qwerty /password \n replica \n host example01-1 /host \n priority 1 /priority \n /replica \n replica \n host example01-2 /host \n priority 1 /priority \n /replica \n db db_name /db \n table table_name /table \n where id=10 /where \n invalidate_query SQL_QUERY /invalidate_query \n /mysql /source Setting fields: port \u2013 The port on the MySQL server. You can specify it for all replicas, or for each one individually (inside replica ). user \u2013 Name of the MySQL user. You can specify it for all replicas, or for each one individually (inside replica ). password \u2013 Password of the MySQL user. You can specify it for all replicas, or for each one individually (inside replica ). replica \u2013 Section of replica configurations. There can be multiple sections. replica/host \u2013 The MySQL host. * replica/priority \u2013 The replica priority. When attempting to connect, ClickHouse traverses the replicas in order of priority. The lower the number, the higher the priority. db \u2013 Name of the database. table \u2013 Name of the table. where \u2013 The selection criteria. Optional parameter. invalidate_query \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries . MySQL can be connected on a local host via sockets. To do this, set host and socket . Example of settings: source \n mysql \n host localhost /host \n socket /path/to/socket/file.sock /socket \n user clickhouse /user \n password qwerty /password \n db db_name /db \n table table_name /table \n where id=10 /where \n invalidate_query SQL_QUERY /invalidate_query \n /mysql /source", - "title": "MySQL" - }, - { - "location": "/dicts/external_dicts_dict_sources/#clickhouse", - "text": "Example of settings: source \n clickhouse \n host example01-01-1 /host \n port 9000 /port \n user default /user \n password /password \n db default /db \n table ids /table \n where id=10 /where \n /clickhouse /source Setting fields: host \u2013 The ClickHouse host. If it is a local host, the query is processed without any network activity. To improve fault tolerance, you can create a Distributed table and enter it in subsequent configurations. port \u2013 The port on the ClickHouse server. user \u2013 Name of the ClickHouse user. password \u2013 Password of the ClickHouse user. db \u2013 Name of the database. table \u2013 Name of the table. where \u2013 The selection criteria. May be omitted.", - "title": "ClickHouse" - }, - { - "location": "/dicts/external_dicts_dict_sources/#mongodb", - "text": "Example of settings: source \n mongodb \n host localhost /host \n port 27017 /port \n user /user \n password /password \n db test /db \n collection dictionary_source /collection \n /mongodb /source Setting fields: host \u2013 The MongoDB host. port \u2013 The port on the MongoDB server. user \u2013 Name of the MongoDB user. password \u2013 Password of the MongoDB user. db \u2013 Name of the database. collection \u2013 Name of the collection.", - "title": "MongoDB" - }, - { - "location": "/dicts/external_dicts_dict_structure/", - "text": "Dictionary key and fields\n\n\nThe \nstructure\n clause describes the dictionary key and fields available for queries.\n\n\nOverall structure:\n\n\ndictionary\n\n \nstructure\n\n \nid\n\n \nname\nId\n/name\n\n \n/id\n\n\n \nattribute\n\n \n!-- Attribute parameters --\n\n \n/attribute\n\n\n ...\n\n \n/structure\n\n\n/dictionary\n\n\n\n\n\n\nColumns are described in the structure:\n\n\n\n\nid\n - \nkey column\n.\n\n\nattribute\n - \ndata column\n. There can be a large number of columns.\n\n\n\n\n\n\nKey\n\n\nClickHouse supports the following types of keys:\n\n\n\n\nNumeric key. UInt64. Defined in the tag \nid\n .\n\n\nComposite key. Set of values of different types. Defined in the tag \nkey\n .\n\n\n\n\nA structure can contain either \nid\n or \nkey\n .\n\n\n\n\nThe key doesn't need to be defined separately in attributes.\n\n\n\n\n\nNumeric key\n\n\nFormat: \nUInt64\n.\n\n\nConfiguration example:\n\n\nid\n\n \nname\nId\n/name\n\n\n/id\n\n\n\n\n\n\nConfiguration fields:\n\n\n\n\nname \u2013 The name of the column with keys.\n\n\n\n\nComposite key\n\n\nThe key can be a \ntuple\n from any types of fields. The \nlayout\n in this case must be \ncomplex_key_hashed\n or \ncomplex_key_cache\n.\n\n\n\nA composite key can consist of a single element. This makes it possible to use a string as the key, for instance.\n\n\n\n\nThe key structure is set in the element \nkey\n. Key fields are specified in the same format as the dictionary \nattributes\n. Example:\n\n\nstructure\n\n \nkey\n\n \nattribute\n\n \nname\nfield1\n/name\n\n \ntype\nString\n/type\n\n \n/attribute\n\n \nattribute\n\n \nname\nfield2\n/name\n\n \ntype\nUInt32\n/type\n\n \n/attribute\n\n ...\n \n/key\n\n...\n\n\n\n\n\nFor a query to the \ndictGet*\n function, a tuple is passed as the key. Example: \ndictGetString('dict_name', 'attr_name', tuple('string for field1', num_for_field2))\n.\n\n\n\n\nAttributes\n\n\nConfiguration example:\n\n\nstructure\n\n ...\n \nattribute\n\n \nname\nName\n/name\n\n \ntype\nType\n/type\n\n \nnull_value\n/null_value\n\n \nexpression\nrand64()\n/expression\n\n \nhierarchical\ntrue\n/hierarchical\n\n \ninjective\ntrue\n/injective\n\n \nis_object_id\ntrue\n/is_object_id\n\n \n/attribute\n\n\n/structure\n\n\n\n\n\n\nConfiguration fields:\n\n\n\n\nname\n \u2013 The column name.\n\n\ntype\n \u2013 The column type. Sets the method for interpreting data in the source. For example, for MySQL, the field might be \nTEXT\n, \nVARCHAR\n, or \nBLOB\n in the source table, but it can be uploaded as \nString\n.\n\n\nnull_value\n \u2013 The default value for a non-existing element. In the example, it is an empty string.\n\n\nexpression\n \u2013 The attribute can be an expression. The tag is not required.\n\n\nhierarchical\n \u2013 Hierarchical support. Mirrored to the parent identifier. By default, \nfalse\n.\n\n\ninjective\n \u2013 Whether the \nid -\n attribute\n image is injective. If \ntrue\n, then you can optimize the \nGROUP BY\n clause. By default, \nfalse\n.\n\n\nis_object_id\n \u2013 Whether the query is executed for a MongoDB document by \nObjectID\n.", - "title": "Dictionary key and fields" - }, - { - "location": "/dicts/external_dicts_dict_structure/#dictionary-key-and-fields", - "text": "The structure clause describes the dictionary key and fields available for queries. Overall structure: dictionary \n structure \n id \n name Id /name \n /id \n\n attribute \n !-- Attribute parameters -- \n /attribute \n\n ...\n\n /structure /dictionary Columns are described in the structure: id - key column . attribute - data column . There can be a large number of columns.", - "title": "Dictionary key and fields" - }, - { - "location": "/dicts/external_dicts_dict_structure/#key", - "text": "ClickHouse supports the following types of keys: Numeric key. UInt64. Defined in the tag id . Composite key. Set of values of different types. Defined in the tag key . A structure can contain either id or key . \n\nThe key doesn't need to be defined separately in attributes.", - "title": "Key" - }, - { - "location": "/dicts/external_dicts_dict_structure/#numeric-key", - "text": "Format: UInt64 . Configuration example: id \n name Id /name /id Configuration fields: name \u2013 The name of the column with keys.", - "title": "Numeric key" - }, - { - "location": "/dicts/external_dicts_dict_structure/#composite-key", - "text": "The key can be a tuple from any types of fields. The layout in this case must be complex_key_hashed or complex_key_cache . \nA composite key can consist of a single element. This makes it possible to use a string as the key, for instance. The key structure is set in the element key . Key fields are specified in the same format as the dictionary attributes . Example: structure \n key \n attribute \n name field1 /name \n type String /type \n /attribute \n attribute \n name field2 /name \n type UInt32 /type \n /attribute \n ...\n /key \n... For a query to the dictGet* function, a tuple is passed as the key. Example: dictGetString('dict_name', 'attr_name', tuple('string for field1', num_for_field2)) .", - "title": "Composite key" - }, - { - "location": "/dicts/external_dicts_dict_structure/#attributes", - "text": "Configuration example: structure \n ...\n attribute \n name Name /name \n type Type /type \n null_value /null_value \n expression rand64() /expression \n hierarchical true /hierarchical \n injective true /injective \n is_object_id true /is_object_id \n /attribute /structure Configuration fields: name \u2013 The column name. type \u2013 The column type. Sets the method for interpreting data in the source. For example, for MySQL, the field might be TEXT , VARCHAR , or BLOB in the source table, but it can be uploaded as String . null_value \u2013 The default value for a non-existing element. In the example, it is an empty string. expression \u2013 The attribute can be an expression. The tag is not required. hierarchical \u2013 Hierarchical support. Mirrored to the parent identifier. By default, false . injective \u2013 Whether the id - attribute image is injective. If true , then you can optimize the GROUP BY clause. By default, false . is_object_id \u2013 Whether the query is executed for a MongoDB document by ObjectID .", - "title": "Attributes" - }, - { - "location": "/dicts/internal_dicts/", - "text": "Internal dictionaries\n\n\nClickHouse contains a built-in feature for working with a geobase.\n\n\nThis allows you to:\n\n\n\n\nUse a region's ID to get its name in the desired language.\n\n\nUse a region's ID to get the ID of a city, area, federal district, country, or continent.\n\n\nCheck whether a region is part of another region.\n\n\nGet a chain of parent regions.\n\n\n\n\nAll the functions support \"translocality,\" the ability to simultaneously use different perspectives on region ownership. For more information, see the section \"Functions for working with Yandex.Metrica dictionaries\".\n\n\nThe internal dictionaries are disabled in the default package.\nTo enable them, uncomment the parameters \npath_to_regions_hierarchy_file\n and \npath_to_regions_names_files\n in the server configuration file.\n\n\nThe geobase is loaded from text files.\nIf you work at Yandex, you can follow these instructions to create them:\n\nhttps://github.yandex-team.ru/raw/Metrika/ClickHouse_private/master/doc/create_embedded_geobase_dictionaries.txt\n\n\nPut the regions_hierarchy*.txt files in the path_to_regions_hierarchy_file directory. This configuration parameter must contain the path to the regions_hierarchy.txt file (the default regional hierarchy), and the other files (regions_hierarchy_ua.txt) must be located in the same directory.\n\n\nPut the \nregions_names_*.txt\n files in the path_to_regions_names_files directory.\n\n\nYou can also create these files yourself. The file format is as follows:\n\n\nregions_hierarchy*.txt\n: TabSeparated (no header), columns:\n\n\n\n\nRegion ID (UInt32)\n\n\nParent region ID (UInt32)\n\n\nRegion type (UInt8): 1 - continent, 3 - country, 4 - federal district, 5 - region, 6 - city; other types don't have values.\n\n\nPopulation (UInt32) - Optional column.\n\n\n\n\nregions_names_*.txt\n: TabSeparated (no header), columns:\n\n\n\n\nRegion ID (UInt32)\n\n\nRegion name (String) - Can't contain tabs or line feeds, even escaped ones.\n\n\n\n\nA flat array is used for storing in RAM. For this reason, IDs shouldn't be more than a million.\n\n\nDictionaries can be updated without restarting the server. However, the set of available dictionaries is not updated.\nFor updates, the file modification times are checked. If a file has changed, the dictionary is updated.\nThe interval to check for changes is configured in the 'builtin_dictionaries_reload_interval' parameter.\nDictionary updates (other than loading at first use) do not block queries. During updates, queries use the old versions of dictionaries. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.\n\n\nWe recommend periodically updating the dictionaries with the geobase. During an update, generate new files and write them to a separate location. When everything is ready, rename them to the files used by the server.\n\n\nThere are also functions for working with OS identifiers and Yandex.Metrica search engines, but they shouldn't be used.", - "title": "Internal dictionaries" - }, - { - "location": "/dicts/internal_dicts/#internal-dictionaries", - "text": "ClickHouse contains a built-in feature for working with a geobase. This allows you to: Use a region's ID to get its name in the desired language. Use a region's ID to get the ID of a city, area, federal district, country, or continent. Check whether a region is part of another region. Get a chain of parent regions. All the functions support \"translocality,\" the ability to simultaneously use different perspectives on region ownership. For more information, see the section \"Functions for working with Yandex.Metrica dictionaries\". The internal dictionaries are disabled in the default package.\nTo enable them, uncomment the parameters path_to_regions_hierarchy_file and path_to_regions_names_files in the server configuration file. The geobase is loaded from text files.\nIf you work at Yandex, you can follow these instructions to create them: https://github.yandex-team.ru/raw/Metrika/ClickHouse_private/master/doc/create_embedded_geobase_dictionaries.txt Put the regions_hierarchy*.txt files in the path_to_regions_hierarchy_file directory. This configuration parameter must contain the path to the regions_hierarchy.txt file (the default regional hierarchy), and the other files (regions_hierarchy_ua.txt) must be located in the same directory. Put the regions_names_*.txt files in the path_to_regions_names_files directory. You can also create these files yourself. The file format is as follows: regions_hierarchy*.txt : TabSeparated (no header), columns: Region ID (UInt32) Parent region ID (UInt32) Region type (UInt8): 1 - continent, 3 - country, 4 - federal district, 5 - region, 6 - city; other types don't have values. Population (UInt32) - Optional column. regions_names_*.txt : TabSeparated (no header), columns: Region ID (UInt32) Region name (String) - Can't contain tabs or line feeds, even escaped ones. A flat array is used for storing in RAM. For this reason, IDs shouldn't be more than a million. Dictionaries can be updated without restarting the server. However, the set of available dictionaries is not updated.\nFor updates, the file modification times are checked. If a file has changed, the dictionary is updated.\nThe interval to check for changes is configured in the 'builtin_dictionaries_reload_interval' parameter.\nDictionary updates (other than loading at first use) do not block queries. During updates, queries use the old versions of dictionaries. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries. We recommend periodically updating the dictionaries with the geobase. During an update, generate new files and write them to a separate location. When everything is ready, rename them to the files used by the server. There are also functions for working with OS identifiers and Yandex.Metrica search engines, but they shouldn't be used.", - "title": "Internal dictionaries" - }, - { - "location": "/operations/access_rights/", - "text": "Access rights\n\n\nUsers and access rights are set up in the user config. This is usually \nusers.xml\n.\n\n\nUsers are recorded in the \nusers\n section. Here is a fragment of the \nusers.xml\n file:\n\n\n!-- Users and ACL. --\n\n\nusers\n\n \n!-- If the user name is not specified, the \ndefault\n user is used. --\n\n \ndefault\n\n \n!-- Password could be specified in plaintext or in SHA256 (in hex format).\n\n\n\n If you want to specify the password in plain text (not recommended), place it in the \npassword\n element.\n\n\n Example: \npassword\nqwerty\n/password\n.\n\n\n Password can be empty.\n\n\n\n If you want to specify SHA256, place it in the \npassword_sha256_hex\n element.\n\n\n Example: \npassword_sha256_hex\n65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5\n/password_sha256_hex\n\n\n\n How to generate decent password:\n\n\n Execute: PASSWORD=$(base64 \n /dev/urandom | head -c8); echo \n$PASSWORD\n; echo -n \n$PASSWORD\n | sha256sum | tr -d \n-\n\n\n In first line will be password and in second - corresponding SHA256.\n\n\n --\n\n \npassword\n/password\n\n \n!-- A list of networks that access is allowed from.\n\n\n Each list item has one of the following forms:\n\n\n \nip\nIP address or subnet mask. For example: 198.51.100.0/24 or 2001:DB8::/32.\n\n\n \nhost\n Host name. For example: example01. A DNS query is made for verification, and all addresses obtained are compared with the address of the customer.\n\n\n \nhost_regexp\n Regular expression for host names. For example: ^example\\d\\d-\\d\\d-\\d\\.yandex\\.ru$\n\n\n For verification, a DNS PTR query is made for the customer\ns address and a regular expression is applied to the result.\n\n\n Then another DNS query is made for the result of the PTR query, and all received address are compared to the client address.\n\n\n We strongly recommend that the regex ends with \\.yandex\\.ru$.\n\n\n\n If you are installing ClickHouse yourself, enter:\n\n\n \nnetworks\n\n\n \nip\n::/0\n/ip\n\n\n \n/networks\n\n\n --\n\n \nnetworks\n \nincl=\nnetworks\n \n/\n\n\n \n!-- Settings profile for the user. --\n\n \nprofile\ndefault\n/profile\n\n\n \n!-- Quota for the user. --\n\n \nquota\ndefault\n/quota\n\n \n/default\n\n\n \n!-- For requests from the Yandex.Metrica user interface via the API for data on specific counters. --\n\n \nweb\n\n \npassword\n/password\n\n \nnetworks\n \nincl=\nnetworks\n \n/\n\n \nprofile\nweb\n/profile\n\n \nquota\ndefault\n/quota\n\n \nallow_databases\n\n \ndatabase\ntest\n/database\n\n \n/allow_databases\n\n \n/web\n\n\n/users\n\n\n\n\n\n\nYou can see a declaration from two users: \ndefault\n and \nweb\n. We added the \nweb\n user separately.\n\n\nThe \ndefault\n user is chosen in cases when the username is not passed. The \ndefault\n user is also used for distributed query processing, if the configuration of the server or cluster doesn't specify the \nuser\n and \npassword\n (see the section on the \nDistributed\n engine).\n\n\nThe user that is used for exchanging information between servers combined in a cluster must not have substantial restrictions or quotas \u2013 otherwise, distributed queries will fail.\n\n\nThe password is specified in open format (not recommended) or in SHA-256. The hash isn't salted. In this regard, you should not consider these passwords as providing security against potential malicious attacks. Rather, they are necessary for protection from employees.\n\n\nA list of networks is specified that access is allowed from. In this example, the list of networks for both users is loaded from a separate file (/etc/metrika.xml) containing the 'networks' substitution. Here is a fragment of it:\n\n\nyandex\n\n ...\n \nnetworks\n\n \nip\n::/64\n/ip\n\n \nip\n203.0.113.0/24\n/ip\n\n \nip\n2001:DB8::/32\n/ip\n\n ...\n \n/networks\n\n\n/yandex\n\n\n\n\n\n\nWe could have defined this list of networks directly in 'users.xml', or in a file in the 'users.d' directory (for more information, see the section \"Configuration files\").\n\n\nThe config includes comments explaining how to open access from everywhere.\n\n\nFor use in production, only specify IP elements (IP addresses and their masks), since using 'host' and 'hoost_regexp' might cause extra latency.\n\n\nNext the user settings profile is specified (see the section \"Settings profiles\"). You can specify the default profile, \ndefault\n. The profile can have any name. You can specify the same profile for different users. The most important thing you can write in the settings profile is 'readonly' set to 1, which provides read-only access.\n\n\nAfter this, the quota is defined (see the section \"Quotas\"). You can specify the default quota, \ndefault\n. It is set in the config by default so that it only counts resource usage, but does not restrict it. The quota can have any name. You can specify the same quota for different users \u2013 in this case, resource usage is calculated for each user individually.\n\n\nIn the optional \nallow_databases\n section, you can also specify a list of databases that the user can access. By default, all databases are available to the user. You can specify the \ndefault\n database. In this case, the user will receive access to the database by default.\n\n\nAccess to the \nsystem\n database is always allowed (since this database is used for processing queries).\n\n\nThe user can get a list of all databases and tables in them by using \nSHOW\n queries or system tables, even if access to individual databases isn't allowed.\n\n\nDatabase access is not related to the \nreadonly\n setting. You can't grant full access to one database and \nreadonly\n access to another one.", - "title": "Access rights" - }, - { - "location": "/operations/access_rights/#access-rights", - "text": "Users and access rights are set up in the user config. This is usually users.xml . Users are recorded in the users section. Here is a fragment of the users.xml file: !-- Users and ACL. -- users \n !-- If the user name is not specified, the default user is used. -- \n default \n !-- Password could be specified in plaintext or in SHA256 (in hex format). If you want to specify the password in plain text (not recommended), place it in the password element. Example: password qwerty /password . Password can be empty. If you want to specify SHA256, place it in the password_sha256_hex element. Example: password_sha256_hex 65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5 /password_sha256_hex How to generate decent password: Execute: PASSWORD=$(base64 /dev/urandom | head -c8); echo $PASSWORD ; echo -n $PASSWORD | sha256sum | tr -d - In first line will be password and in second - corresponding SHA256. -- \n password /password \n !-- A list of networks that access is allowed from. Each list item has one of the following forms: ip IP address or subnet mask. For example: 198.51.100.0/24 or 2001:DB8::/32. host Host name. For example: example01. A DNS query is made for verification, and all addresses obtained are compared with the address of the customer. host_regexp Regular expression for host names. For example: ^example\\d\\d-\\d\\d-\\d\\.yandex\\.ru$ For verification, a DNS PTR query is made for the customer s address and a regular expression is applied to the result. Then another DNS query is made for the result of the PTR query, and all received address are compared to the client address. We strongly recommend that the regex ends with \\.yandex\\.ru$. If you are installing ClickHouse yourself, enter: networks ip ::/0 /ip /networks -- \n networks incl= networks / \n\n !-- Settings profile for the user. -- \n profile default /profile \n\n !-- Quota for the user. -- \n quota default /quota \n /default \n\n !-- For requests from the Yandex.Metrica user interface via the API for data on specific counters. -- \n web \n password /password \n networks incl= networks / \n profile web /profile \n quota default /quota \n allow_databases \n database test /database \n /allow_databases \n /web /users You can see a declaration from two users: default and web . We added the web user separately. The default user is chosen in cases when the username is not passed. The default user is also used for distributed query processing, if the configuration of the server or cluster doesn't specify the user and password (see the section on the Distributed engine). The user that is used for exchanging information between servers combined in a cluster must not have substantial restrictions or quotas \u2013 otherwise, distributed queries will fail. The password is specified in open format (not recommended) or in SHA-256. The hash isn't salted. In this regard, you should not consider these passwords as providing security against potential malicious attacks. Rather, they are necessary for protection from employees. A list of networks is specified that access is allowed from. In this example, the list of networks for both users is loaded from a separate file (/etc/metrika.xml) containing the 'networks' substitution. Here is a fragment of it: yandex \n ...\n networks \n ip ::/64 /ip \n ip 203.0.113.0/24 /ip \n ip 2001:DB8::/32 /ip \n ...\n /networks /yandex We could have defined this list of networks directly in 'users.xml', or in a file in the 'users.d' directory (for more information, see the section \"Configuration files\"). The config includes comments explaining how to open access from everywhere. For use in production, only specify IP elements (IP addresses and their masks), since using 'host' and 'hoost_regexp' might cause extra latency. Next the user settings profile is specified (see the section \"Settings profiles\"). You can specify the default profile, default . The profile can have any name. You can specify the same profile for different users. The most important thing you can write in the settings profile is 'readonly' set to 1, which provides read-only access. After this, the quota is defined (see the section \"Quotas\"). You can specify the default quota, default . It is set in the config by default so that it only counts resource usage, but does not restrict it. The quota can have any name. You can specify the same quota for different users \u2013 in this case, resource usage is calculated for each user individually. In the optional allow_databases section, you can also specify a list of databases that the user can access. By default, all databases are available to the user. You can specify the default database. In this case, the user will receive access to the database by default. Access to the system database is always allowed (since this database is used for processing queries). The user can get a list of all databases and tables in them by using SHOW queries or system tables, even if access to individual databases isn't allowed. Database access is not related to the readonly setting. You can't grant full access to one database and readonly access to another one.", - "title": "Access rights" - }, - { - "location": "/operations/configuration_files/", - "text": "Configuration files\n\n\nThe main server config file is \nconfig.xml\n. It resides in the \n/etc/clickhouse-server/\n directory.\n\n\nIndividual settings can be overridden in the \n*.xml\nand\n*.conf\n files in the \nconf.d\n and \nconfig.d\n directories next to the config file.\n\n\nThe \nreplace\n or \nremove\n attributes can be specified for the elements of these config files.\n\n\nIf neither is specified, it combines the contents of elements recursively, replacing values of duplicate children.\n\n\nIf \nreplace\n is specified, it replaces the entire element with the specified one.\n\n\nIf \nremove\n is specified, it deletes the element.\n\n\nThe config can also define \"substitutions\". If an element has the \nincl\n attribute, the corresponding substitution from the file will be used as the value. By default, the path to the file with substitutions is \n/etc/metrika.xml\n. This can be changed in the \ninclude_from\n element in the server config. The substitution values are specified in \n/yandex/substitution_name\n elements in this file. If a substitution specified in \nincl\n does not exist, it is recorded in the log. To prevent ClickHouse from logging missing substitutions, specify the \noptional=\"true\"\n attribute (for example, settings for \nmacros\nserver_settings/settings.md#server_settings-macros)).\n\n\nSubstitutions can also be performed from ZooKeeper. To do this, specify the attribute \nfrom_zk = \"/path/to/node\"\n. The element value is replaced with the contents of the node at \n/path/to/node\n in ZooKeeper. You can also put an entire XML subtree on the ZooKeeper node and it will be fully inserted into the source element.\n\n\nThe \nconfig.xml\n file can specify a separate config with user settings, profiles, and quotas. The relative path to this config is set in the 'users_config' element. By default, it is \nusers.xml\n. If \nusers_config\n is omitted, the user settings, profiles, and quotas are specified directly in \nconfig.xml\n.\n\n\nIn addition, \nusers_config\n may have overrides in files from the \nusers_config.d\n directory (for example, \nusers.d\n) and substitutions.\n\n\nFor each config file, the server also generates \nfile-preprocessed.xml\n files when starting. These files contain all the completed substitutions and overrides, and they are intended for informational use. If ZooKeeper substitutions were used in the config files but ZooKeeper is not available on the server start, the server loads the configuration from the preprocessed file.\n\n\nThe server tracks changes in config files, as well as files and ZooKeeper nodes that were used when performing substitutions and overrides, and reloads the settings for users and clusters on the fly. This means that you can modify the cluster, users, and their settings without restarting the server.", - "title": "Configuration files" - }, - { - "location": "/operations/configuration_files/#configuration-files", - "text": "The main server config file is config.xml . It resides in the /etc/clickhouse-server/ directory. Individual settings can be overridden in the *.xml and *.conf files in the conf.d and config.d directories next to the config file. The replace or remove attributes can be specified for the elements of these config files. If neither is specified, it combines the contents of elements recursively, replacing values of duplicate children. If replace is specified, it replaces the entire element with the specified one. If remove is specified, it deletes the element. The config can also define \"substitutions\". If an element has the incl attribute, the corresponding substitution from the file will be used as the value. By default, the path to the file with substitutions is /etc/metrika.xml . This can be changed in the include_from element in the server config. The substitution values are specified in /yandex/substitution_name elements in this file. If a substitution specified in incl does not exist, it is recorded in the log. To prevent ClickHouse from logging missing substitutions, specify the optional=\"true\" attribute (for example, settings for macros server_settings/settings.md#server_settings-macros)). Substitutions can also be performed from ZooKeeper. To do this, specify the attribute from_zk = \"/path/to/node\" . The element value is replaced with the contents of the node at /path/to/node in ZooKeeper. You can also put an entire XML subtree on the ZooKeeper node and it will be fully inserted into the source element. The config.xml file can specify a separate config with user settings, profiles, and quotas. The relative path to this config is set in the 'users_config' element. By default, it is users.xml . If users_config is omitted, the user settings, profiles, and quotas are specified directly in config.xml . In addition, users_config may have overrides in files from the users_config.d directory (for example, users.d ) and substitutions. For each config file, the server also generates file-preprocessed.xml files when starting. These files contain all the completed substitutions and overrides, and they are intended for informational use. If ZooKeeper substitutions were used in the config files but ZooKeeper is not available on the server start, the server loads the configuration from the preprocessed file. The server tracks changes in config files, as well as files and ZooKeeper nodes that were used when performing substitutions and overrides, and reloads the settings for users and clusters on the fly. This means that you can modify the cluster, users, and their settings without restarting the server.", - "title": "Configuration files" - }, - { - "location": "/operations/quotas/", - "text": "Quotas\n\n\nQuotas allow you to limit resource usage over a period of time, or simply track the use of resources.\nQuotas are set up in the user config. This is usually 'users.xml'.\n\n\nThe system also has a feature for limiting the complexity of a single query. See the section \"Restrictions on query complexity\").\n\n\nIn contrast to query complexity restrictions, quotas:\n\n\n\n\nPlace restrictions on a set of queries that can be run over a period of time, instead of limiting a single query.\n\n\nAccount for resources spent on all remote servers for distributed query processing.\n\n\n\n\nLet's look at the section of the 'users.xml' file that defines quotas.\n\n\n!-- Quotas. --\n\n\nquotas\n\n \n!-- Quota name. --\n\n \ndefault\n\n \n!-- Restrictions for a time period. You can set many intervals with different restrictions. --\n\n \ninterval\n\n \n!-- Length of the interval. --\n\n \nduration\n3600\n/duration\n\n\n \n!-- Unlimited. Just collect data for the specified time interval. --\n\n \nqueries\n0\n/queries\n\n \nerrors\n0\n/errors\n\n \nresult_rows\n0\n/result_rows\n\n \nread_rows\n0\n/read_rows\n\n \nexecution_time\n0\n/execution_time\n\n \n/interval\n\n \n/default\n\n\n\n\n\n\nBy default, the quota just tracks resource consumption for each hour, without limiting usage.\nThe resource consumption calculated for each interval is output to the server log after each request.\n\n\nstatbox\n\n \n!-- Restrictions for a time period. You can set many intervals with different restrictions. --\n\n \ninterval\n\n \n!-- Length of the interval. --\n\n \nduration\n3600\n/duration\n\n\n \nqueries\n1000\n/queries\n\n \nerrors\n100\n/errors\n\n \nresult_rows\n1000000000\n/result_rows\n\n \nread_rows\n100000000000\n/read_rows\n\n \nexecution_time\n900\n/execution_time\n\n \n/interval\n\n\n \ninterval\n\n \nduration\n86400\n/duration\n\n\n \nqueries\n10000\n/queries\n\n \nerrors\n1000\n/errors\n\n \nresult_rows\n5000000000\n/result_rows\n\n \nread_rows\n500000000000\n/read_rows\n\n \nexecution_time\n7200\n/execution_time\n\n \n/interval\n\n\n/statbox\n\n\n\n\n\n\nFor the 'statbox' quota, restrictions are set for every hour and for every 24 hours (86,400 seconds). The time interval is counted starting from an implementation-defined fixed moment in time. In other words, the 24-hour interval doesn't necessarily begin at midnight.\n\n\nWhen the interval ends, all collected values are cleared. For the next hour, the quota calculation starts over.\n\n\nHere are the amounts that can be restricted:\n\n\nqueries\n \u2013 The total number of requests.\n\n\nerrors\n \u2013 The number of queries that threw an exception.\n\n\nresult_rows\n \u2013 The total number of rows given as the result.\n\n\nread_rows\n \u2013 The total number of source rows read from tables for running the query, on all remote servers.\n\n\nexecution_time\n \u2013 The total query execution time, in seconds (wall time).\n\n\nIf the limit is exceeded for at least one time interval, an exception is thrown with a text about which restriction was exceeded, for which interval, and when the new interval begins (when queries can be sent again).\n\n\nQuotas can use the \"quota key\" feature in order to report on resources for multiple keys independently. Here is an example of this:\n\n\n!-- For the global reports designer. --\n\n\nweb_global\n\n \n!-- keyed - The quota_key \nkey\n is passed in the query parameter,\n\n\n and the quota is tracked separately for each key value.\n\n\n For example, you can pass a Yandex.Metrica username as the key,\n\n\n so the quota will be counted separately for each username.\n\n\n Using keys makes sense only if quota_key is transmitted by the program, not by a user.\n\n\n\n You can also write \nkeyed_by_ip /\n so the IP address is used as the quota key.\n\n\n (But keep in mind that users can change the IPv6 address fairly easily.)\n\n\n --\n\n \nkeyed\n \n/\n\n\n\n\n\n\nThe quota is assigned to users in the 'users' section of the config. See the section \"Access rights\".\n\n\nFor distributed query processing, the accumulated amounts are stored on the requestor server. So if the user goes to another server, the quota there will \"start over\".\n\n\nWhen the server is restarted, quotas are reset.", - "title": "Quotas" - }, - { - "location": "/operations/quotas/#quotas", - "text": "Quotas allow you to limit resource usage over a period of time, or simply track the use of resources.\nQuotas are set up in the user config. This is usually 'users.xml'. The system also has a feature for limiting the complexity of a single query. See the section \"Restrictions on query complexity\"). In contrast to query complexity restrictions, quotas: Place restrictions on a set of queries that can be run over a period of time, instead of limiting a single query. Account for resources spent on all remote servers for distributed query processing. Let's look at the section of the 'users.xml' file that defines quotas. !-- Quotas. -- quotas \n !-- Quota name. -- \n default \n !-- Restrictions for a time period. You can set many intervals with different restrictions. -- \n interval \n !-- Length of the interval. -- \n duration 3600 /duration \n\n !-- Unlimited. Just collect data for the specified time interval. -- \n queries 0 /queries \n errors 0 /errors \n result_rows 0 /result_rows \n read_rows 0 /read_rows \n execution_time 0 /execution_time \n /interval \n /default By default, the quota just tracks resource consumption for each hour, without limiting usage.\nThe resource consumption calculated for each interval is output to the server log after each request. statbox \n !-- Restrictions for a time period. You can set many intervals with different restrictions. -- \n interval \n !-- Length of the interval. -- \n duration 3600 /duration \n\n queries 1000 /queries \n errors 100 /errors \n result_rows 1000000000 /result_rows \n read_rows 100000000000 /read_rows \n execution_time 900 /execution_time \n /interval \n\n interval \n duration 86400 /duration \n\n queries 10000 /queries \n errors 1000 /errors \n result_rows 5000000000 /result_rows \n read_rows 500000000000 /read_rows \n execution_time 7200 /execution_time \n /interval /statbox For the 'statbox' quota, restrictions are set for every hour and for every 24 hours (86,400 seconds). The time interval is counted starting from an implementation-defined fixed moment in time. In other words, the 24-hour interval doesn't necessarily begin at midnight. When the interval ends, all collected values are cleared. For the next hour, the quota calculation starts over. Here are the amounts that can be restricted: queries \u2013 The total number of requests. errors \u2013 The number of queries that threw an exception. result_rows \u2013 The total number of rows given as the result. read_rows \u2013 The total number of source rows read from tables for running the query, on all remote servers. execution_time \u2013 The total query execution time, in seconds (wall time). If the limit is exceeded for at least one time interval, an exception is thrown with a text about which restriction was exceeded, for which interval, and when the new interval begins (when queries can be sent again). Quotas can use the \"quota key\" feature in order to report on resources for multiple keys independently. Here is an example of this: !-- For the global reports designer. -- web_global \n !-- keyed - The quota_key key is passed in the query parameter, and the quota is tracked separately for each key value. For example, you can pass a Yandex.Metrica username as the key, so the quota will be counted separately for each username. Using keys makes sense only if quota_key is transmitted by the program, not by a user. You can also write keyed_by_ip / so the IP address is used as the quota key. (But keep in mind that users can change the IPv6 address fairly easily.) -- \n keyed / The quota is assigned to users in the 'users' section of the config. See the section \"Access rights\". For distributed query processing, the accumulated amounts are stored on the requestor server. So if the user goes to another server, the quota there will \"start over\". When the server is restarted, quotas are reset.", - "title": "Quotas" - }, - { - "location": "/operations/tips/", - "text": "Usage recommendations\n\n\nCPU\n\n\nThe SSE 4.2 instruction set must be supported. Modern processors (since 2008) support it.\n\n\nWhen choosing a processor, prefer a large number of cores and slightly slower clock rate over fewer cores and a higher clock rate.\nFor example, 16 cores with 2600 MHz is better than 8 cores with 3600 MHz.\n\n\nHyper-threading\n\n\nDon't disable hyper-threading. It helps for some queries, but not for others.\n\n\nTurbo Boost\n\n\nTurbo Boost is highly recommended. It significantly improves performance with a typical load.\nYou can use \nturbostat\n to view the CPU's actual clock rate under a load.\n\n\nCPU scaling governor\n\n\nAlways use the \nperformance\n scaling governor. The \non-demand\n scaling governor works much worse with constantly high demand.\n\n\nsudo \necho\n \nperformance\n \n|\n tee /sys/devices/system/cpu/cpu\n\\*\n/cpufreq/scaling_governor\n\n\n\n\n\nCPU limitations\n\n\nProcessors can overheat. Use \ndmesg\n to see if the CPU's clock rate was limited due to overheating.\nThe restriction can also be set externally at the datacenter level. You can use \nturbostat\n to monitor it under a load.\n\n\nRAM\n\n\nFor small amounts of data (up to \\~200 GB compressed), it is best to use as much memory as the volume of data.\nFor large amounts of data and when processing interactive (online) queries, you should use a reasonable amount of RAM (128 GB or more) so the hot data subset will fit in the cache of pages.\nEven for data volumes of \\~50 TB per server, using 128 GB of RAM significantly improves query performance compared to 64 GB.\n\n\nSwap file\n\n\nAlways disable the swap file. The only reason for not doing this is if you are using ClickHouse on your personal laptop.\n\n\nHuge pages\n\n\nAlways disable transparent huge pages. It interferes with memory allocators, which leads to significant performance degradation.\n\n\necho\n \nnever\n \n|\n sudo tee /sys/kernel/mm/transparent_hugepage/enabled\n\n\n\n\n\nUse \nperf top\n to watch the time spent in the kernel for memory management.\nPermanent huge pages also do not need to be allocated.\n\n\nStorage subsystem\n\n\nIf your budget allows you to use SSD, use SSD.\nIf not, use HDD. SATA HDDs 7200 RPM will do.\n\n\nGive preference to a lot of servers with local hard drives over a smaller number of servers with attached disk shelves.\nBut for storing archives with rare queries, shelves will work.\n\n\nRAID\n\n\nWhen using HDD, you can combine their RAID-10, RAID-5, RAID-6 or RAID-50.\nFor Linux, software RAID is better (with \nmdadm\n). We don't recommend using LVM.\nWhen creating RAID-10, select the \nfar\n layout.\nIf your budget allows, choose RAID-10.\n\n\nIf you have more than 4 disks, use RAID-6 (preferred) or RAID-50, instead of RAID-5.\nWhen using RAID-5, RAID-6 or RAID-50, always increase stripe_cache_size, since the default value is usually not the best choice.\n\n\necho\n \n4096\n \n|\n sudo tee /sys/block/md2/md/stripe_cache_size\n\n\n\n\n\nCalculate the exact number from the number of devices and the block size, using the formula: \n2 * num_devices * chunk_size_in_bytes / 4096\n.\n\n\nA block size of 1025 KB is sufficient for all RAID configurations.\nNever set the block size too small or too large.\n\n\nYou can use RAID-0 on SSD.\nRegardless of RAID use, always use replication for data security.\n\n\nEnable NCQ with a long queue. For HDD, choose the CFQ scheduler, and for SSD, choose noop. Don't reduce the 'readahead' setting.\nFor HDD, enable the write cache.\n\n\nFile system\n\n\nExt4 is the most reliable option. Set the mount options \nnoatime, nobarrier\n.\nXFS is also suitable, but it hasn't been as thoroughly tested with ClickHouse.\nMost other file systems should also work fine. File systems with delayed allocation work better.\n\n\nLinux kernel\n\n\nDon't use an outdated Linux kernel. In 2015, 3.18.19 was new enough.\nConsider using the kernel build from Yandex:\nhttps://github.com/yandex/smart\n \u2013 it provides at least a 5% performance increase.\n\n\nNetwork\n\n\nIf you are using IPv6, increase the size of the route cache.\nThe Linux kernel prior to 3.2 had a multitude of problems with IPv6 implementation.\n\n\nUse at least a 10 GB network, if possible. 1 Gb will also work, but it will be much worse for patching replicas with tens of terabytes of data, or for processing distributed queries with a large amount of intermediate data.\n\n\nZooKeeper\n\n\nYou are probably already using ZooKeeper for other purposes. You can use the same installation of ZooKeeper, if it isn't already overloaded.\n\n\nIt's best to use a fresh version of ZooKeeper \u2013 3.4.9 or later. The version in stable Linux distributions may be outdated.\n\n\nWith the default settings, ZooKeeper is a time bomb:\n\n\n\n\nThe ZooKeeper server won't delete files from old snapshots and logs when using the default configuration (see autopurge), and this is the responsibility of the operator.\n\n\n\n\nThis bomb must be defused.\n\n\nThe ZooKeeper (3.5.1) configuration below is used in the Yandex.Metrica production environment as of May 20, 2017:\n\n\nzoo.cfg:\n\n\n# http://hadoop.apache.org/zookeeper/docs/current/zookeeperAdmin.html\n\n\n\n# The number of milliseconds of each tick\n\n\ntickTime\n=\n2000\n\n\n# The number of ticks that the initial\n\n\n# synchronization phase can take\n\n\ninitLimit\n=\n30000\n\n\n# The number of ticks that can pass between\n\n\n# sending a request and getting an acknowledgement\n\n\nsyncLimit\n=\n10\n\n\n\nmaxClientCnxns\n=\n2000\n\n\n\nmaxSessionTimeout\n=\n60000000\n\n\n# the directory where the snapshot is stored.\n\n\ndataDir\n=\n/opt/zookeeper/\n{{\n cluster\n[\nname\n]\n \n}}\n/data\n\n# Place the dataLogDir to a separate physical disc for better performance\n\n\ndataLogDir\n=\n/opt/zookeeper/\n{{\n cluster\n[\nname\n]\n \n}}\n/logs\n\nautopurge.snapRetainCount\n=\n10\n\nautopurge.purgeInterval\n=\n1\n\n\n\n\n# To avoid seeks ZooKeeper allocates space in the transaction log file in\n\n\n# blocks of preAllocSize kilobytes. The default block size is 64M. One reason\n\n\n# for changing the size of the blocks is to reduce the block size if snapshots\n\n\n# are taken more often. (Also, see snapCount).\n\n\npreAllocSize\n=\n131072\n\n\n\n# Clients can submit requests faster than ZooKeeper can process them,\n\n\n# especially if there are a lot of clients. To prevent ZooKeeper from running\n\n\n# out of memory due to queued requests, ZooKeeper will throttle clients so that\n\n\n# there is no more than globalOutstandingLimit outstanding requests in the\n\n\n# system. The default limit is 1,000.ZooKeeper logs transactions to a\n\n\n# transaction log. After snapCount transactions are written to a log file a\n\n\n# snapshot is started and a new transaction log file is started. The default\n\n\n# snapCount is 10,000.\n\n\nsnapCount\n=\n3000000\n\n\n\n# If this option is defined, requests will be will logged to a trace file named\n\n\n# traceFile.year.month.day.\n\n\n#traceFile=\n\n\n\n# Leader accepts client connections. Default value is \nyes\n. The leader machine\n\n\n# coordinates updates. For higher update throughput at thes slight expense of\n\n\n# read throughput the leader can be configured to not accept clients and focus\n\n\n# on coordination.\n\n\nleaderServes\n=\nyes\n\n\nstandaloneEnabled\n=\nfalse\n\n\ndynamicConfigFile\n=\n/etc/zookeeper-\n{{\n cluster\n[\nname\n]\n \n}}\n/conf/zoo.cfg.dynamic\n\n\n\n\n\nJava version:\n\n\nJava(TM) SE Runtime Environment (build 1.8.0_25-b17)\nJava HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode)\n\n\n\n\n\nJVM parameters:\n\n\nNAME\n=\nzookeeper-\n{{\n cluster\n[\nname\n]\n \n}}\n\n\nZOOCFGDIR\n=\n/etc/\n$NAME\n/conf\n\n\n# TODO this is really ugly\n\n\n# How to find out, which jars are needed?\n\n\n# seems, that log4j requires the log4j.properties file to be in the classpath\n\n\nCLASSPATH\n=\n$ZOOCFGDIR\n:/usr/build/classes:/usr/build/lib/*.jar:/usr/share/zookeeper/zookeeper-3.5.1-metrika.jar:/usr/share/zookeeper/slf4j-log4j12-1.7.5.jar:/usr/share/zookeeper/slf4j-api-1.7.5.jar:/usr/share/zookeeper/servlet-api-2.5-20081211.jar:/usr/share/zookeeper/netty-3.7.0.Final.jar:/usr/share/zookeeper/log4j-1.2.16.jar:/usr/share/zookeeper/jline-2.11.jar:/usr/share/zookeeper/jetty-util-6.1.26.jar:/usr/share/zookeeper/jetty-6.1.26.jar:/usr/share/zookeeper/javacc.jar:/usr/share/zookeeper/jackson-mapper-asl-1.9.11.jar:/usr/share/zookeeper/jackson-core-asl-1.9.11.jar:/usr/share/zookeeper/commons-cli-1.2.jar:/usr/src/java/lib/*.jar:/usr/etc/zookeeper\n\n\n\nZOOCFG\n=\n$ZOOCFGDIR\n/zoo.cfg\n\n\nZOO_LOG_DIR\n=\n/var/log/\n$NAME\n\n\nUSER\n=\nzookeeper\n\nGROUP\n=\nzookeeper\n\nPIDDIR\n=\n/var/run/\n$NAME\n\n\nPIDFILE\n=\n$PIDDIR\n/\n$NAME\n.pid\n\nSCRIPTNAME\n=\n/etc/init.d/\n$NAME\n\n\nJAVA\n=\n/usr/bin/java\n\nZOOMAIN\n=\norg.apache.zookeeper.server.quorum.QuorumPeerMain\n\n\nZOO_LOG4J_PROP\n=\nINFO,ROLLINGFILE\n\n\nJMXLOCALONLY\n=\nfalse\n\n\nJAVA_OPTS\n=\n-Xms{{ cluster.get(\nxms\n,\n128M\n) }} \\\n\n\n -Xmx{{ cluster.get(\nxmx\n,\n1G\n) }} \\\n\n\n -Xloggc:/var/log/\n$NAME\n/zookeeper-gc.log \\\n\n\n -XX:+UseGCLogFileRotation \\\n\n\n -XX:NumberOfGCLogFiles=16 \\\n\n\n -XX:GCLogFileSize=16M \\\n\n\n -verbose:gc \\\n\n\n -XX:+PrintGCTimeStamps \\\n\n\n -XX:+PrintGCDateStamps \\\n\n\n -XX:+PrintGCDetails\n\n\n -XX:+PrintTenuringDistribution \\\n\n\n -XX:+PrintGCApplicationStoppedTime \\\n\n\n -XX:+PrintGCApplicationConcurrentTime \\\n\n\n -XX:+PrintSafepointStatistics \\\n\n\n -XX:+UseParNewGC \\\n\n\n -XX:+UseConcMarkSweepGC \\\n\n\n-XX:+CMSParallelRemarkEnabled\n\n\n\n\n\n\nSalt init:\n\n\ndescription \nzookeeper-{{ cluster[\nname\n] }} centralized coordination service\n\n\nstart on runlevel [2345]\nstop on runlevel [!2345]\n\nrespawn\n\nlimit nofile 8192 8192\n\npre-start script\n [ -r \n/etc/zookeeper-{{ cluster[\nname\n] }}/conf/environment\n ] || exit 0\n . /etc/zookeeper-{{ cluster[\nname\n] }}/conf/environment\n [ -d $ZOO_LOG_DIR ] || mkdir -p $ZOO_LOG_DIR\n chown $USER:$GROUP $ZOO_LOG_DIR\nend script\n\nscript\n . /etc/zookeeper-{{ cluster[\nname\n] }}/conf/environment\n [ -r /etc/default/zookeeper ] \n . /etc/default/zookeeper\n if [ -z \n$JMXDISABLE\n ]; then\n JAVA_OPTS=\n$JAVA_OPTS -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.local.only=$JMXLOCALONLY\n\n fi\n exec start-stop-daemon --start -c $USER --exec $JAVA --name zookeeper-{{ cluster[\nname\n] }} \\\n -- -cp $CLASSPATH $JAVA_OPTS -Dzookeeper.log.dir=${ZOO_LOG_DIR} \\\n -Dzookeeper.root.logger=${ZOO_LOG4J_PROP} $ZOOMAIN $ZOOCFG\nend script", - "title": "Usage recommendations" - }, - { - "location": "/operations/tips/#usage-recommendations", - "text": "", - "title": "Usage recommendations" - }, - { - "location": "/operations/tips/#cpu", - "text": "The SSE 4.2 instruction set must be supported. Modern processors (since 2008) support it. When choosing a processor, prefer a large number of cores and slightly slower clock rate over fewer cores and a higher clock rate.\nFor example, 16 cores with 2600 MHz is better than 8 cores with 3600 MHz.", - "title": "CPU" - }, - { - "location": "/operations/tips/#hyper-threading", - "text": "Don't disable hyper-threading. It helps for some queries, but not for others.", - "title": "Hyper-threading" - }, - { - "location": "/operations/tips/#turbo-boost", - "text": "Turbo Boost is highly recommended. It significantly improves performance with a typical load.\nYou can use turbostat to view the CPU's actual clock rate under a load.", - "title": "Turbo Boost" - }, - { - "location": "/operations/tips/#cpu-scaling-governor", - "text": "Always use the performance scaling governor. The on-demand scaling governor works much worse with constantly high demand. sudo echo performance | tee /sys/devices/system/cpu/cpu \\* /cpufreq/scaling_governor", - "title": "CPU scaling governor" - }, - { - "location": "/operations/tips/#cpu-limitations", - "text": "Processors can overheat. Use dmesg to see if the CPU's clock rate was limited due to overheating.\nThe restriction can also be set externally at the datacenter level. You can use turbostat to monitor it under a load.", - "title": "CPU limitations" - }, - { - "location": "/operations/tips/#ram", - "text": "For small amounts of data (up to \\~200 GB compressed), it is best to use as much memory as the volume of data.\nFor large amounts of data and when processing interactive (online) queries, you should use a reasonable amount of RAM (128 GB or more) so the hot data subset will fit in the cache of pages.\nEven for data volumes of \\~50 TB per server, using 128 GB of RAM significantly improves query performance compared to 64 GB.", - "title": "RAM" - }, - { - "location": "/operations/tips/#swap-file", - "text": "Always disable the swap file. The only reason for not doing this is if you are using ClickHouse on your personal laptop.", - "title": "Swap file" - }, - { - "location": "/operations/tips/#huge-pages", - "text": "Always disable transparent huge pages. It interferes with memory allocators, which leads to significant performance degradation. echo never | sudo tee /sys/kernel/mm/transparent_hugepage/enabled Use perf top to watch the time spent in the kernel for memory management.\nPermanent huge pages also do not need to be allocated.", - "title": "Huge pages" - }, - { - "location": "/operations/tips/#storage-subsystem", - "text": "If your budget allows you to use SSD, use SSD.\nIf not, use HDD. SATA HDDs 7200 RPM will do. Give preference to a lot of servers with local hard drives over a smaller number of servers with attached disk shelves.\nBut for storing archives with rare queries, shelves will work.", - "title": "Storage subsystem" - }, - { - "location": "/operations/tips/#raid", - "text": "When using HDD, you can combine their RAID-10, RAID-5, RAID-6 or RAID-50.\nFor Linux, software RAID is better (with mdadm ). We don't recommend using LVM.\nWhen creating RAID-10, select the far layout.\nIf your budget allows, choose RAID-10. If you have more than 4 disks, use RAID-6 (preferred) or RAID-50, instead of RAID-5.\nWhen using RAID-5, RAID-6 or RAID-50, always increase stripe_cache_size, since the default value is usually not the best choice. echo 4096 | sudo tee /sys/block/md2/md/stripe_cache_size Calculate the exact number from the number of devices and the block size, using the formula: 2 * num_devices * chunk_size_in_bytes / 4096 . A block size of 1025 KB is sufficient for all RAID configurations.\nNever set the block size too small or too large. You can use RAID-0 on SSD.\nRegardless of RAID use, always use replication for data security. Enable NCQ with a long queue. For HDD, choose the CFQ scheduler, and for SSD, choose noop. Don't reduce the 'readahead' setting.\nFor HDD, enable the write cache.", - "title": "RAID" - }, - { - "location": "/operations/tips/#file-system", - "text": "Ext4 is the most reliable option. Set the mount options noatime, nobarrier .\nXFS is also suitable, but it hasn't been as thoroughly tested with ClickHouse.\nMost other file systems should also work fine. File systems with delayed allocation work better.", - "title": "File system" - }, - { - "location": "/operations/tips/#linux-kernel", - "text": "Don't use an outdated Linux kernel. In 2015, 3.18.19 was new enough.\nConsider using the kernel build from Yandex: https://github.com/yandex/smart \u2013 it provides at least a 5% performance increase.", - "title": "Linux kernel" - }, - { - "location": "/operations/tips/#network", - "text": "If you are using IPv6, increase the size of the route cache.\nThe Linux kernel prior to 3.2 had a multitude of problems with IPv6 implementation. Use at least a 10 GB network, if possible. 1 Gb will also work, but it will be much worse for patching replicas with tens of terabytes of data, or for processing distributed queries with a large amount of intermediate data.", - "title": "Network" - }, - { - "location": "/operations/tips/#zookeeper", - "text": "You are probably already using ZooKeeper for other purposes. You can use the same installation of ZooKeeper, if it isn't already overloaded. It's best to use a fresh version of ZooKeeper \u2013 3.4.9 or later. The version in stable Linux distributions may be outdated. With the default settings, ZooKeeper is a time bomb: The ZooKeeper server won't delete files from old snapshots and logs when using the default configuration (see autopurge), and this is the responsibility of the operator. This bomb must be defused. The ZooKeeper (3.5.1) configuration below is used in the Yandex.Metrica production environment as of May 20, 2017: zoo.cfg: # http://hadoop.apache.org/zookeeper/docs/current/zookeeperAdmin.html # The number of milliseconds of each tick tickTime = 2000 # The number of ticks that the initial # synchronization phase can take initLimit = 30000 # The number of ticks that can pass between # sending a request and getting an acknowledgement syncLimit = 10 maxClientCnxns = 2000 maxSessionTimeout = 60000000 # the directory where the snapshot is stored. dataDir = /opt/zookeeper/ {{ cluster [ name ] }} /data # Place the dataLogDir to a separate physical disc for better performance dataLogDir = /opt/zookeeper/ {{ cluster [ name ] }} /logs\n\nautopurge.snapRetainCount = 10 \nautopurge.purgeInterval = 1 # To avoid seeks ZooKeeper allocates space in the transaction log file in # blocks of preAllocSize kilobytes. The default block size is 64M. One reason # for changing the size of the blocks is to reduce the block size if snapshots # are taken more often. (Also, see snapCount). preAllocSize = 131072 # Clients can submit requests faster than ZooKeeper can process them, # especially if there are a lot of clients. To prevent ZooKeeper from running # out of memory due to queued requests, ZooKeeper will throttle clients so that # there is no more than globalOutstandingLimit outstanding requests in the # system. The default limit is 1,000.ZooKeeper logs transactions to a # transaction log. After snapCount transactions are written to a log file a # snapshot is started and a new transaction log file is started. The default # snapCount is 10,000. snapCount = 3000000 # If this option is defined, requests will be will logged to a trace file named # traceFile.year.month.day. #traceFile= # Leader accepts client connections. Default value is yes . The leader machine # coordinates updates. For higher update throughput at thes slight expense of # read throughput the leader can be configured to not accept clients and focus # on coordination. leaderServes = yes standaloneEnabled = false dynamicConfigFile = /etc/zookeeper- {{ cluster [ name ] }} /conf/zoo.cfg.dynamic Java version: Java(TM) SE Runtime Environment (build 1.8.0_25-b17)\nJava HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode) JVM parameters: NAME = zookeeper- {{ cluster [ name ] }} ZOOCFGDIR = /etc/ $NAME /conf # TODO this is really ugly # How to find out, which jars are needed? # seems, that log4j requires the log4j.properties file to be in the classpath CLASSPATH = $ZOOCFGDIR :/usr/build/classes:/usr/build/lib/*.jar:/usr/share/zookeeper/zookeeper-3.5.1-metrika.jar:/usr/share/zookeeper/slf4j-log4j12-1.7.5.jar:/usr/share/zookeeper/slf4j-api-1.7.5.jar:/usr/share/zookeeper/servlet-api-2.5-20081211.jar:/usr/share/zookeeper/netty-3.7.0.Final.jar:/usr/share/zookeeper/log4j-1.2.16.jar:/usr/share/zookeeper/jline-2.11.jar:/usr/share/zookeeper/jetty-util-6.1.26.jar:/usr/share/zookeeper/jetty-6.1.26.jar:/usr/share/zookeeper/javacc.jar:/usr/share/zookeeper/jackson-mapper-asl-1.9.11.jar:/usr/share/zookeeper/jackson-core-asl-1.9.11.jar:/usr/share/zookeeper/commons-cli-1.2.jar:/usr/src/java/lib/*.jar:/usr/etc/zookeeper ZOOCFG = $ZOOCFGDIR /zoo.cfg ZOO_LOG_DIR = /var/log/ $NAME USER = zookeeper GROUP = zookeeper PIDDIR = /var/run/ $NAME PIDFILE = $PIDDIR / $NAME .pid SCRIPTNAME = /etc/init.d/ $NAME JAVA = /usr/bin/java ZOOMAIN = org.apache.zookeeper.server.quorum.QuorumPeerMain ZOO_LOG4J_PROP = INFO,ROLLINGFILE JMXLOCALONLY = false JAVA_OPTS = -Xms{{ cluster.get( xms , 128M ) }} \\ -Xmx{{ cluster.get( xmx , 1G ) }} \\ -Xloggc:/var/log/ $NAME /zookeeper-gc.log \\ -XX:+UseGCLogFileRotation \\ -XX:NumberOfGCLogFiles=16 \\ -XX:GCLogFileSize=16M \\ -verbose:gc \\ -XX:+PrintGCTimeStamps \\ -XX:+PrintGCDateStamps \\ -XX:+PrintGCDetails -XX:+PrintTenuringDistribution \\ -XX:+PrintGCApplicationStoppedTime \\ -XX:+PrintGCApplicationConcurrentTime \\ -XX:+PrintSafepointStatistics \\ -XX:+UseParNewGC \\ -XX:+UseConcMarkSweepGC \\ -XX:+CMSParallelRemarkEnabled Salt init: description zookeeper-{{ cluster[ name ] }} centralized coordination service \n\nstart on runlevel [2345]\nstop on runlevel [!2345]\n\nrespawn\n\nlimit nofile 8192 8192\n\npre-start script\n [ -r /etc/zookeeper-{{ cluster[ name ] }}/conf/environment ] || exit 0\n . /etc/zookeeper-{{ cluster[ name ] }}/conf/environment\n [ -d $ZOO_LOG_DIR ] || mkdir -p $ZOO_LOG_DIR\n chown $USER:$GROUP $ZOO_LOG_DIR\nend script\n\nscript\n . /etc/zookeeper-{{ cluster[ name ] }}/conf/environment\n [ -r /etc/default/zookeeper ] . /etc/default/zookeeper\n if [ -z $JMXDISABLE ]; then\n JAVA_OPTS= $JAVA_OPTS -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.local.only=$JMXLOCALONLY \n fi\n exec start-stop-daemon --start -c $USER --exec $JAVA --name zookeeper-{{ cluster[ name ] }} \\\n -- -cp $CLASSPATH $JAVA_OPTS -Dzookeeper.log.dir=${ZOO_LOG_DIR} \\\n -Dzookeeper.root.logger=${ZOO_LOG4J_PROP} $ZOOMAIN $ZOOCFG\nend script", - "title": "ZooKeeper" - }, - { - "location": "/operations/server_settings/", - "text": "Server configuration parameters\n\n\nThis section contains descriptions of server settings that cannot be changed at the session or query level.\n\n\nThese settings are stored in the \nconfig.xml\n file on the ClickHouse server.\n\n\nOther settings are described in the \"\nSettings\n\" section.\n\n\nBefore studying the settings, read the \nConfiguration files\n section and note the use of substitutions (the \nincl\n and \noptional\n attributes).", - "title": "Introduction" - }, - { - "location": "/operations/server_settings/#server-configuration-parameters", - "text": "This section contains descriptions of server settings that cannot be changed at the session or query level. These settings are stored in the config.xml file on the ClickHouse server. Other settings are described in the \" Settings \" section. Before studying the settings, read the Configuration files section and note the use of substitutions (the incl and optional attributes).", - "title": "Server configuration parameters" - }, - { - "location": "/operations/server_settings/settings/", - "text": "Server settings\n\n\n\n\nbuiltin_dictionaries_reload_interval\n\n\nThe interval in seconds before reloading built-in dictionaries.\n\n\nClickHouse reloads built-in dictionaries every x seconds. This makes it possible to edit dictionaries \"on the fly\" without restarting the server.\n\n\nDefault value: 3600.\n\n\nExample\n\n\nbuiltin_dictionaries_reload_interval\n3600\n/builtin_dictionaries_reload_interval\n\n\n\n\n\n\n\n\ncompression\n\n\nData compression settings.\n\n\n\n\nDon't use it if you have just started using ClickHouse.\n\n\n\n\n\nThe configuration looks like this:\n\n\ncompression\n\n \ncase\n\n \nparameters/\n\n \n/case\n\n ...\n\n/compression\n\n\n\n\n\n\nYou can configure multiple sections \ncase\n.\n\n\nBlock field \ncase\n:\n\n\n\n\nmin_part_size\n \u2013 The minimum size of a table part.\n\n\nmin_part_size_ratio\n \u2013 The ratio of the minimum size of a table part to the full size of the table.\n\n\nmethod\n \u2013 Compression method. Acceptable values \u200b: \nlz4\n or \nzstd\n(experimental).\n\n\n\n\nClickHouse checks \nmin_part_size\n and \nmin_part_size_ratio\n and processes the \ncase\n blocks that match these conditions. If none of the \ncase\n matches, ClickHouse applies the \nlz4\n compression algorithm.\n\n\nExample\n\n\ncompression\n \nincl=\nclickhouse_compression\n\n \ncase\n\n \nmin_part_size\n10000000000\n/min_part_size\n\n \nmin_part_size_ratio\n0.01\n/min_part_size_ratio\n\n \nmethod\nzstd\n/method\n\n \n/case\n\n\n/compression\n\n\n\n\n\n\n\n\ndefault_database\n\n\nThe default database.\n\n\nTo get a list of databases, use the \nSHOW DATABASES\n.\n\n\nExample\n\n\ndefault_database\ndefault\n/default_database\n\n\n\n\n\n\n\n\ndefault_profile\n\n\nDefault settings profile.\n\n\nSettings profiles are located in the file specified in the parameter \nuser_config\n.\n\n\nExample\n\n\ndefault_profile\ndefault\n/default_profile\n\n\n\n\n\n\n\n\ndictionaries_config\n\n\nThe path to the config file for external dictionaries.\n\n\nPath:\n\n\n\n\nSpecify the absolute path or the path relative to the server config file.\n\n\nThe path can contain wildcards * and ?.\n\n\n\n\nSee also \"\nExternal dictionaries\n\".\n\n\nExample\n\n\ndictionaries_config\n*_dictionary.xml\n/dictionaries_config\n\n\n\n\n\n\n\n\ndictionaries_lazy_load\n\n\nLazy loading of dictionaries.\n\n\nIf \ntrue\n, then each dictionary is created on first use. If dictionary creation failed, the function that was using the dictionary throws an exception.\n\n\nIf \nfalse\n, all dictionaries are created when the server starts, and if there is an error, the server shuts down.\n\n\nThe default is \ntrue\n.\n\n\nExample\n\n\ndictionaries_lazy_load\ntrue\n/dictionaries_lazy_load\n\n\n\n\n\n\n\n\nformat_schema_path\n\n\nThe path to the directory with the schemes for the input data, such as schemas for the \nCapnProto\n format.\n\n\nExample\n\n\n \n!-- Directory containing schema files for various input formats. --\n\n \nformat_schema_path\nformat_schemas/\n/format_schema_path\n\n\n\n\n\n\n\n\ngraphite\n\n\nSending data to \nGraphite\n.\n\n\nSettings:\n\n\n\n\nhost \u2013 The Graphite server.\n\n\nport \u2013 The port on the Graphite server.\n\n\ninterval \u2013 The interval for sending, in seconds.\n\n\ntimeout \u2013 The timeout for sending data, in seconds.\n\n\nroot_path \u2013 Prefix for keys.\n\n\nmetrics \u2013 Sending data from a :ref:\nsystem_tables-system.metrics\n table.\n\n\nevents \u2013 Sending data from a :ref:\nsystem_tables-system.events\n table.\n\n\nasynchronous_metrics \u2013 Sending data from a :ref:\nsystem_tables-system.asynchronous_metrics\n table.\n\n\n\n\nYou can configure multiple \ngraphite\n clauses. For instance, you can use this for sending different data at different intervals.\n\n\nExample\n\n\ngraphite\n\n \nhost\nlocalhost\n/host\n\n \nport\n42000\n/port\n\n \ntimeout\n0.1\n/timeout\n\n \ninterval\n60\n/interval\n\n \nroot_path\none_min\n/root_path\n\n \nmetrics\ntrue\n/metrics\n\n \nevents\ntrue\n/events\n\n \nasynchronous_metrics\ntrue\n/asynchronous_metrics\n\n\n/graphite\n\n\n\n\n\n\n\n\ngraphite_rollup\n\n\nSettings for thinning data for Graphite.\n\n\nFor more information, see \nGraphiteMergeTree\n.\n\n\nExample\n\n\ngraphite_rollup_example\n\n \ndefault\n\n \nfunction\nmax\n/function\n\n \nretention\n\n \nage\n0\n/age\n\n \nprecision\n60\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n3600\n/age\n\n \nprecision\n300\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n86400\n/age\n\n \nprecision\n3600\n/precision\n\n \n/retention\n\n \n/default\n\n\n/graphite_rollup_example\n\n\n\n\n\n\n\n\nhttp_port/https_port\n\n\nThe port for connecting to the server over HTTP(s).\n\n\nIf \nhttps_port\n is specified, \nopenSSL\n must be configured.\n\n\nIf \nhttp_port\n is specified, the openSSL configuration is ignored even if it is set.\n\n\nExample\n\n\nhttps\n0000\n/https\n\n\n\n\n\n\n\n\nhttp_server_default_response\n\n\nThe page that is shown by default when you access the ClickHouse HTTP(s) server.\n\n\nExample\n\n\nOpens \nhttps://tabix.io/\n when accessing \nhttp://localhost: http_port\n.\n\n\nhttp_server_default_response\n\n \n![CDATA[\nhtml ng-app=\nSMI2\nhead\nbase href=\nhttp://ui.tabix.io/\n/head\nbody\ndiv ui-view=\n class=\ncontent-ui\n/div\nscript src=\nhttp://loader.tabix.io/master.js\n/script\n/body\n/html\n]]\n\n\n/http_server_default_response\n\n\n\n\n\n\n\n\ninclude_from\n\n\nThe path to the file with substitutions.\n\n\nFor more information, see the section \"\nConfiguration files\n\".\n\n\nExample\n\n\ninclude_from\n/etc/metrica.xml\n/include_from\n\n\n\n\n\n\n\n\ninterserver_http_port\n\n\nPort for exchanging data between ClickHouse servers.\n\n\nExample\n\n\ninterserver_http_port\n9009\n/interserver_http_port\n\n\n\n\n\n\n\n\ninterserver_http_host\n\n\nThe host name that can be used by other servers to access this server.\n\n\nIf omitted, it is defined in the same way as the \nhostname-f\n command.\n\n\nUseful for breaking away from a specific network interface.\n\n\nExample\n\n\ninterserver_http_host\nexample.yandex.ru\n/interserver_http_host\n\n\n\n\n\n\n\n\nkeep_alive_timeout\n\n\nThe number of milliseconds that ClickHouse waits for incoming requests before closing the connection.\n\n\nExample\n\n\nkeep_alive_timeout\n3\n/keep_alive_timeout\n\n\n\n\n\n\n\n\nlisten_host\n\n\nRestriction on hosts that requests can come from. If you want the server to answer all of them, specify \n::\n.\n\n\nExamples:\n\n\nlisten_host\n::1\n/listen_host\n\n\nlisten_host\n127.0.0.1\n/listen_host\n\n\n\n\n\n\n\n\nlogger\n\n\nLogging settings.\n\n\nKeys:\n\n\n\n\nlevel \u2013 Logging level. Acceptable values: \ntrace\n, \ndebug\n, \ninformation\n, \nwarning\n, \nerror\n.\n\n\nlog \u2013 The log file. Contains all the entries according to \nlevel\n.\n\n\nerrorlog \u2013 Error log file.\n\n\nsize \u2013 Size of the file. Applies to \nlog\nand\nerrorlog\n. Once the file reaches \nsize\n, ClickHouse archives and renames it, and creates a new log file in its place.\n\n\ncount \u2013 The number of archived log files that ClickHouse stores.\n\n\n\n\nExample\n\n\nlogger\n\n \nlevel\ntrace\n/level\n\n \nlog\n/var/log/clickhouse-server/clickhouse-server.log\n/log\n\n \nerrorlog\n/var/log/clickhouse-server/clickhouse-server.err.log\n/errorlog\n\n \nsize\n1000M\n/size\n\n \ncount\n10\n/count\n\n\n/logger\n\n\n\n\n\n\n\n\nmacros\n\n\nParameter substitutions for replicated tables.\n\n\nCan be omitted if replicated tables are not used.\n\n\nFor more information, see the section \"\nCreating replicated tables\n\".\n\n\nExample\n\n\nmacros\n \nincl=\nmacros\n \noptional=\ntrue\n \n/\n\n\n\n\n\n\n\n\nmark_cache_size\n\n\nApproximate size (in bytes) of the cache of \"marks\" used by \nMergeTree\n engines.\n\n\nThe cache is shared for the server and memory is allocated as needed. The cache size must be at least 5368709120.\n\n\nExample\n\n\nmark_cache_size\n5368709120\n/mark_cache_size\n\n\n\n\n\n\n\n\nmax_concurrent_queries\n\n\nThe maximum number of simultaneously processed requests.\n\n\nExample\n\n\nmax_concurrent_queries\n100\n/max_concurrent_queries\n\n\n\n\n\n\n\n\nmax_connections\n\n\nThe maximum number of inbound connections.\n\n\nExample\n\n\nmax_connections\n4096\n/max_connections\n\n\n\n\n\n\n\n\nmax_open_files\n\n\nThe maximum number of open files.\n\n\nBy default: \nmaximum\n.\n\n\nWe recommend using this option in Mac OS X, since the \ngetrlimit()\n function returns an incorrect value.\n\n\nExample\n\n\nmax_open_files\n262144\n/max_open_files\n\n\n\n\n\n\n\n\nmax_table_size_to_drop\n\n\nRestriction on deleting tables.\n\n\nIf the size of a \nMergeTree\n type table exceeds \nmax_table_size_to_drop\n (in bytes), you can't delete it using a DROP query.\n\n\nIf you still need to delete the table without restarting the ClickHouse server, create the \nclickhouse-path\n/flags/force_drop_table\n file and run the DROP query.\n\n\nDefault value: 50 GB.\n\n\nThe value 0 means that you can delete all tables without any restrictions.\n\n\nExample\n\n\nmax_table_size_to_drop\n0\n/max_table_size_to_drop\n\n\n\n\n\n\n\n\nmerge_tree\n\n\nFine tuning for tables in the \n MergeTree\n family.\n\n\nFor more information, see the MergeTreeSettings.h header file.\n\n\nExample\n\n\nmerge_tree\n\n \nmax_suspicious_broken_parts\n5\n/max_suspicious_broken_parts\n\n\n/merge_tree\n\n\n\n\n\n\n\n\nopenSSL\n\n\nSSL client/server configuration.\n\n\nSupport for SSL is provided by the \nlibpoco\n library. The interface is described in the file \nSSLManager.h\n\n\nKeys for server/client settings:\n\n\n\n\nprivateKeyFile \u2013 The path to the file with the secret key of the PEM certificate. The file may contain a key and certificate at the same time.\n\n\ncertificateFile \u2013 The path to the client/server certificate file in PEM format. You can omit it if \nprivateKeyFile\n contains the certificate.\n\n\ncaConfig \u2013 The path to the file or directory that contains trusted root certificates.\n\n\nverificationMode \u2013 The method for checking the node's certificates. Details are in the description of the \nContext\n class. Possible values: \nnone\n, \nrelaxed\n, \nstrict\n, \nonce\n.\n\n\nverificationDepth \u2013 The maximum length of the verification chain. Verification will fail if the certificate chain length exceeds the set value.\n\n\nloadDefaultCAFile \u2013 Indicates that built-in CA certificates for OpenSSL will be used. Acceptable values: \ntrue\n, \nfalse\n. |\n\n\ncipherList \u2013 Supported OpenSSL encryptions. For example: \nALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH\n.\n\n\ncacheSessions \u2013 Enables or disables caching sessions. Must be used in combination with \nsessionIdContext\n. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nsessionIdContext \u2013 A unique set of random characters that the server appends to each generated identifier. The length of the string must not exceed \nSSL_MAX_SSL_SESSION_ID_LENGTH\n. This parameter is always recommended, since it helps avoid problems both if the server caches the session and if the client requested caching. Default value: \n${application.name}\n.\n\n\nsessionCacheSize \u2013 The maximum number of sessions that the server caches. Default value: 1024*20. 0 \u2013 Unlimited sessions.\n\n\nsessionTimeout \u2013 Time for caching the session on the server.\n\n\nextendedVerification \u2013 Automatically extended verification of certificates after the session ends. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nrequireTLSv1 \u2013 Require a TLSv1 connection. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nrequireTLSv1_1 \u2013 Require a TLSv1.1 connection. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nrequireTLSv1 \u2013 Require a TLSv1.2 connection. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nfips \u2013 Activates OpenSSL FIPS mode. Supported if the library's OpenSSL version supports FIPS.\n\n\nprivateKeyPassphraseHandler \u2013 Class (PrivateKeyPassphraseHandler subclass) that requests the passphrase for accessing the private key. For example: \nprivateKeyPassphraseHandler\n, \nname\nKeyFileHandler\n/name\n, \noptions\npassword\ntest\n/password\n/options\n, \n/privateKeyPassphraseHandler\n.\n\n\ninvalidCertificateHandler \u2013 Class (subclass of CertificateHandler) for verifying invalid certificates. For example: \ninvalidCertificateHandler\n \nname\nConsoleCertificateHandler\n/name\n \n/invalidCertificateHandler\n .\n\n\ndisableProtocols \u2013 Protocols that are not allowed to use.\n\n\npreferServerCiphers \u2013 Preferred server ciphers on the client.\n\n\n\n\nExample of settings:\n\n\nopenSSL\n\n \nserver\n\n \n!-- openssl req -subj \n/CN=localhost\n -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout /etc/clickhouse-server/server.key -out /etc/clickhouse-server/server.crt --\n\n \ncertificateFile\n/etc/clickhouse-server/server.crt\n/certificateFile\n\n \nprivateKeyFile\n/etc/clickhouse-server/server.key\n/privateKeyFile\n\n \n!-- openssl dhparam -out /etc/clickhouse-server/dhparam.pem 4096 --\n\n \ndhParamsFile\n/etc/clickhouse-server/dhparam.pem\n/dhParamsFile\n\n \nverificationMode\nnone\n/verificationMode\n\n \nloadDefaultCAFile\ntrue\n/loadDefaultCAFile\n\n \ncacheSessions\ntrue\n/cacheSessions\n\n \ndisableProtocols\nsslv2,sslv3\n/disableProtocols\n\n \npreferServerCiphers\ntrue\n/preferServerCiphers\n\n \n/server\n\n \nclient\n\n \nloadDefaultCAFile\ntrue\n/loadDefaultCAFile\n\n \ncacheSessions\ntrue\n/cacheSessions\n\n \ndisableProtocols\nsslv2,sslv3\n/disableProtocols\n\n \npreferServerCiphers\ntrue\n/preferServerCiphers\n\n \n!-- Use for self-signed: \nverificationMode\nnone\n/verificationMode\n --\n\n \ninvalidCertificateHandler\n\n \n!-- Use for self-signed: \nname\nAcceptCertificateHandler\n/name\n --\n\n \nname\nRejectCertificateHandler\n/name\n\n \n/invalidCertificateHandler\n\n \n/client\n\n\n/openSSL\n\n\n\n\n\n\n\n\npart_log\n\n\nLogging events that are associated with \nMergeTree\n data. For instance, adding or merging data. You can use the log to simulate merge algorithms and compare their characteristics. You can visualize the merge process.\n\n\nQueries are logged in the ClickHouse table, not in a separate file.\n\n\nColumns in the log:\n\n\n\n\nevent_time \u2013 Date of the event.\n\n\nduration_ms \u2013 Duration of the event.\n\n\nevent_type \u2013 Type of event. 1 \u2013 new data part; 2 \u2013 merge result; 3 \u2013 data part downloaded from replica; 4 \u2013 data part deleted.\n\n\ndatabase_name \u2013 The name of the database.\n\n\ntable_name \u2013 Name of the table.\n\n\npart_name \u2013 Name of the data part.\n\n\nsize_in_bytes \u2013 Size of the data part in bytes.\n\n\nmerged_from \u2013 An array of names of data parts that make up the merge (also used when downloading a merged part).\n\n\nmerge_time_ms \u2013 Time spent on the merge.\n\n\n\n\nUse the following parameters to configure logging:\n\n\n\n\ndatabase \u2013 Name of the database.\n\n\ntable \u2013 Name of the table.\n\n\npartition_by \u2013 Sets a \ncustom partitioning key\n.\n\n\nflush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk.\n\n\n\n\nExample\n\n\npart_log\n\n \ndatabase\nsystem\n/database\n\n \ntable\npart_log\n/table\n\n \npartition_by\ntoMonday(event_date)\n/partition_by\n\n \nflush_interval_milliseconds\n7500\n/flush_interval_milliseconds\n\n\n/part_log\n\n\n\n\n\n\n\n\npath\n\n\nThe path to the directory containing data.\n\n\n\n\nThe end slash is mandatory.\n\n\n\n\n\nExample\n\n\npath\n/var/lib/clickhouse/\n/path\n\n\n\n\n\n\n\n\nquery_log\n\n\nSetting for logging queries received with the \nlog_queries=1\n setting.\n\n\nQueries are logged in the ClickHouse table, not in a separate file.\n\n\nUse the following parameters to configure logging:\n\n\n\n\ndatabase \u2013 Name of the database.\n\n\ntable \u2013 Name of the table.\n\n\npartition_by \u2013 Sets a \ncustom partitioning key\n.\n\n\nflush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk.\n\n\n\n\nIf the table doesn't exist, ClickHouse will create it. If the structure of the query log changed when the ClickHouse server was updated, the table with the old structure is renamed, and a new table is created automatically.\n\n\nExample\n\n\nquery_log\n\n \ndatabase\nsystem\n/database\n\n \ntable\nquery_log\n/table\n\n \npartition_by\ntoMonday(event_date)\n/partition_by\n\n \nflush_interval_milliseconds\n7500\n/flush_interval_milliseconds\n\n\n/query_log\n\n\n\n\n\n\n\n\nremote_servers\n\n\nConfiguration of clusters used by the Distributed table engine.\n\n\nFor more information, see the section \"\nTable engines/Distributed\n\".\n\n\nExample\n\n\nremote_servers\n \nincl=\nclickhouse_remote_servers\n \n/\n\n\n\n\n\n\nFor the value of the \nincl\n attribute, see the section \"\nConfiguration files\n\".\n\n\n\n\ntimezone\n\n\nThe server's time zone.\n\n\nSpecified as an IANA identifier for the UTC time zone or geographic location (for example, Africa/Abidjan).\n\n\nThe time zone is necessary for conversions between String and DateTime formats when DateTime fields are output to text format (printed on the screen or in a file), and when getting DateTime from a string. In addition, the time zone is used in functions that work with the time and date if they didn't receive the time zone in the input parameters.\n\n\nExample\n\n\ntimezone\nEurope/Moscow\n/timezone\n\n\n\n\n\n\n\n\ntcp_port\n\n\nPort for communicating with clients over the TCP protocol.\n\n\nExample\n\n\ntcp_port\n9000\n/tcp_port\n\n\n\n\n\n\n\n\ntmp_path\n\n\nPath to temporary data for processing large queries.\n\n\n\n\nThe end slash is mandatory.\n\n\n\n\n\nExample\n\n\ntmp_path\n/var/lib/clickhouse/tmp/\n/tmp_path\n\n\n\n\n\n\n\n\nuncompressed_cache_size\n\n\nCache size (in bytes) for uncompressed data used by table engines from the \nMergeTree\n family.\n\n\nThere is one shared cache for the server. Memory is allocated on demand. The cache is used if the option \nuse_uncompressed_cache\n is enabled.\n\n\nThe uncompressed cache is advantageous for very short queries in individual cases.\n\n\nExample\n\n\nuncompressed_cache_size\n8589934592\n/uncompressed_cache_size\n\n\n\n\n\n\n\n\nusers_config\n\n\nPath to the file that contains:\n\n\n\n\nUser configurations.\n\n\nAccess rights.\n\n\nSettings profiles.\n\n\nQuota settings.\n\n\n\n\nExample\n\n\nusers_config\nusers.xml\n/users_config\n\n\n\n\n\n\n\n\nzookeeper\n\n\nConfiguration of ZooKeeper servers.\n\n\nClickHouse uses ZooKeeper for storing replica metadata when using replicated tables.\n\n\nThis parameter can be omitted if replicated tables are not used.\n\n\nFor more information, see the section \"\nReplication\n\".\n\n\nExample\n\n\nzookeeper\n \nincl=\nzookeeper-servers\n \noptional=\ntrue\n \n/", - "title": "Server settings" - }, - { - "location": "/operations/server_settings/settings/#server-settings", - "text": "", - "title": "Server settings" - }, - { - "location": "/operations/server_settings/settings/#builtin_dictionaries_reload_interval", - "text": "The interval in seconds before reloading built-in dictionaries. ClickHouse reloads built-in dictionaries every x seconds. This makes it possible to edit dictionaries \"on the fly\" without restarting the server. Default value: 3600. Example builtin_dictionaries_reload_interval 3600 /builtin_dictionaries_reload_interval", - "title": "builtin_dictionaries_reload_interval" - }, - { - "location": "/operations/server_settings/settings/#compression", - "text": "Data compression settings. \n\nDon't use it if you have just started using ClickHouse. The configuration looks like this: compression \n case \n parameters/ \n /case \n ... /compression You can configure multiple sections case . Block field case : min_part_size \u2013 The minimum size of a table part. min_part_size_ratio \u2013 The ratio of the minimum size of a table part to the full size of the table. method \u2013 Compression method. Acceptable values \u200b: lz4 or zstd (experimental). ClickHouse checks min_part_size and min_part_size_ratio and processes the case blocks that match these conditions. If none of the case matches, ClickHouse applies the lz4 compression algorithm. Example compression incl= clickhouse_compression \n case \n min_part_size 10000000000 /min_part_size \n min_part_size_ratio 0.01 /min_part_size_ratio \n method zstd /method \n /case /compression", - "title": "compression" - }, - { - "location": "/operations/server_settings/settings/#default_database", - "text": "The default database. To get a list of databases, use the SHOW DATABASES . Example default_database default /default_database", - "title": "default_database" - }, - { - "location": "/operations/server_settings/settings/#default_profile", - "text": "Default settings profile. Settings profiles are located in the file specified in the parameter user_config . Example default_profile default /default_profile", - "title": "default_profile" - }, - { - "location": "/operations/server_settings/settings/#dictionaries_config", - "text": "The path to the config file for external dictionaries. Path: Specify the absolute path or the path relative to the server config file. The path can contain wildcards * and ?. See also \" External dictionaries \". Example dictionaries_config *_dictionary.xml /dictionaries_config", - "title": "dictionaries_config" - }, - { - "location": "/operations/server_settings/settings/#dictionaries_lazy_load", - "text": "Lazy loading of dictionaries. If true , then each dictionary is created on first use. If dictionary creation failed, the function that was using the dictionary throws an exception. If false , all dictionaries are created when the server starts, and if there is an error, the server shuts down. The default is true . Example dictionaries_lazy_load true /dictionaries_lazy_load", - "title": "dictionaries_lazy_load" - }, - { - "location": "/operations/server_settings/settings/#format_schema_path", - "text": "The path to the directory with the schemes for the input data, such as schemas for the CapnProto format. Example !-- Directory containing schema files for various input formats. -- \n format_schema_path format_schemas/ /format_schema_path", - "title": "format_schema_path" - }, - { - "location": "/operations/server_settings/settings/#graphite", - "text": "Sending data to Graphite . Settings: host \u2013 The Graphite server. port \u2013 The port on the Graphite server. interval \u2013 The interval for sending, in seconds. timeout \u2013 The timeout for sending data, in seconds. root_path \u2013 Prefix for keys. metrics \u2013 Sending data from a :ref: system_tables-system.metrics table. events \u2013 Sending data from a :ref: system_tables-system.events table. asynchronous_metrics \u2013 Sending data from a :ref: system_tables-system.asynchronous_metrics table. You can configure multiple graphite clauses. For instance, you can use this for sending different data at different intervals. Example graphite \n host localhost /host \n port 42000 /port \n timeout 0.1 /timeout \n interval 60 /interval \n root_path one_min /root_path \n metrics true /metrics \n events true /events \n asynchronous_metrics true /asynchronous_metrics /graphite", - "title": "graphite" - }, - { - "location": "/operations/server_settings/settings/#graphite_rollup", - "text": "Settings for thinning data for Graphite. For more information, see GraphiteMergeTree . Example graphite_rollup_example \n default \n function max /function \n retention \n age 0 /age \n precision 60 /precision \n /retention \n retention \n age 3600 /age \n precision 300 /precision \n /retention \n retention \n age 86400 /age \n precision 3600 /precision \n /retention \n /default /graphite_rollup_example", - "title": "graphite_rollup" - }, - { - "location": "/operations/server_settings/settings/#http_porthttps_port", - "text": "The port for connecting to the server over HTTP(s). If https_port is specified, openSSL must be configured. If http_port is specified, the openSSL configuration is ignored even if it is set. Example https 0000 /https", - "title": "http_port/https_port" - }, - { - "location": "/operations/server_settings/settings/#http_server_default_response", - "text": "The page that is shown by default when you access the ClickHouse HTTP(s) server. Example Opens https://tabix.io/ when accessing http://localhost: http_port . http_server_default_response \n ![CDATA[ html ng-app= SMI2 head base href= http://ui.tabix.io/ /head body div ui-view= class= content-ui /div script src= http://loader.tabix.io/master.js /script /body /html ]] /http_server_default_response", - "title": "http_server_default_response" - }, - { - "location": "/operations/server_settings/settings/#include_from", - "text": "The path to the file with substitutions. For more information, see the section \" Configuration files \". Example include_from /etc/metrica.xml /include_from", - "title": "include_from" - }, - { - "location": "/operations/server_settings/settings/#interserver_http_port", - "text": "Port for exchanging data between ClickHouse servers. Example interserver_http_port 9009 /interserver_http_port", - "title": "interserver_http_port" - }, - { - "location": "/operations/server_settings/settings/#interserver_http_host", - "text": "The host name that can be used by other servers to access this server. If omitted, it is defined in the same way as the hostname-f command. Useful for breaking away from a specific network interface. Example interserver_http_host example.yandex.ru /interserver_http_host", - "title": "interserver_http_host" - }, - { - "location": "/operations/server_settings/settings/#keep_alive_timeout", - "text": "The number of milliseconds that ClickHouse waits for incoming requests before closing the connection. Example keep_alive_timeout 3 /keep_alive_timeout", - "title": "keep_alive_timeout" - }, - { - "location": "/operations/server_settings/settings/#listen_host", - "text": "Restriction on hosts that requests can come from. If you want the server to answer all of them, specify :: . Examples: listen_host ::1 /listen_host listen_host 127.0.0.1 /listen_host", - "title": "listen_host" - }, - { - "location": "/operations/server_settings/settings/#logger", - "text": "Logging settings. Keys: level \u2013 Logging level. Acceptable values: trace , debug , information , warning , error . log \u2013 The log file. Contains all the entries according to level . errorlog \u2013 Error log file. size \u2013 Size of the file. Applies to log and errorlog . Once the file reaches size , ClickHouse archives and renames it, and creates a new log file in its place. count \u2013 The number of archived log files that ClickHouse stores. Example logger \n level trace /level \n log /var/log/clickhouse-server/clickhouse-server.log /log \n errorlog /var/log/clickhouse-server/clickhouse-server.err.log /errorlog \n size 1000M /size \n count 10 /count /logger", - "title": "logger" - }, - { - "location": "/operations/server_settings/settings/#macros", - "text": "Parameter substitutions for replicated tables. Can be omitted if replicated tables are not used. For more information, see the section \" Creating replicated tables \". Example macros incl= macros optional= true /", - "title": "macros" - }, - { - "location": "/operations/server_settings/settings/#mark_cache_size", - "text": "Approximate size (in bytes) of the cache of \"marks\" used by MergeTree engines. The cache is shared for the server and memory is allocated as needed. The cache size must be at least 5368709120. Example mark_cache_size 5368709120 /mark_cache_size", - "title": "mark_cache_size" - }, - { - "location": "/operations/server_settings/settings/#max_concurrent_queries", - "text": "The maximum number of simultaneously processed requests. Example max_concurrent_queries 100 /max_concurrent_queries", - "title": "max_concurrent_queries" - }, - { - "location": "/operations/server_settings/settings/#max_connections", - "text": "The maximum number of inbound connections. Example max_connections 4096 /max_connections", - "title": "max_connections" - }, - { - "location": "/operations/server_settings/settings/#max_open_files", - "text": "The maximum number of open files. By default: maximum . We recommend using this option in Mac OS X, since the getrlimit() function returns an incorrect value. Example max_open_files 262144 /max_open_files", - "title": "max_open_files" - }, - { - "location": "/operations/server_settings/settings/#max_table_size_to_drop", - "text": "Restriction on deleting tables. If the size of a MergeTree type table exceeds max_table_size_to_drop (in bytes), you can't delete it using a DROP query. If you still need to delete the table without restarting the ClickHouse server, create the clickhouse-path /flags/force_drop_table file and run the DROP query. Default value: 50 GB. The value 0 means that you can delete all tables without any restrictions. Example max_table_size_to_drop 0 /max_table_size_to_drop", - "title": "max_table_size_to_drop" - }, - { - "location": "/operations/server_settings/settings/#merge_tree", - "text": "Fine tuning for tables in the MergeTree family. For more information, see the MergeTreeSettings.h header file. Example merge_tree \n max_suspicious_broken_parts 5 /max_suspicious_broken_parts /merge_tree", - "title": "merge_tree" - }, - { - "location": "/operations/server_settings/settings/#openssl", - "text": "SSL client/server configuration. Support for SSL is provided by the libpoco library. The interface is described in the file SSLManager.h Keys for server/client settings: privateKeyFile \u2013 The path to the file with the secret key of the PEM certificate. The file may contain a key and certificate at the same time. certificateFile \u2013 The path to the client/server certificate file in PEM format. You can omit it if privateKeyFile contains the certificate. caConfig \u2013 The path to the file or directory that contains trusted root certificates. verificationMode \u2013 The method for checking the node's certificates. Details are in the description of the Context class. Possible values: none , relaxed , strict , once . verificationDepth \u2013 The maximum length of the verification chain. Verification will fail if the certificate chain length exceeds the set value. loadDefaultCAFile \u2013 Indicates that built-in CA certificates for OpenSSL will be used. Acceptable values: true , false . | cipherList \u2013 Supported OpenSSL encryptions. For example: ALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH . cacheSessions \u2013 Enables or disables caching sessions. Must be used in combination with sessionIdContext . Acceptable values: true , false . sessionIdContext \u2013 A unique set of random characters that the server appends to each generated identifier. The length of the string must not exceed SSL_MAX_SSL_SESSION_ID_LENGTH . This parameter is always recommended, since it helps avoid problems both if the server caches the session and if the client requested caching. Default value: ${application.name} . sessionCacheSize \u2013 The maximum number of sessions that the server caches. Default value: 1024*20. 0 \u2013 Unlimited sessions. sessionTimeout \u2013 Time for caching the session on the server. extendedVerification \u2013 Automatically extended verification of certificates after the session ends. Acceptable values: true , false . requireTLSv1 \u2013 Require a TLSv1 connection. Acceptable values: true , false . requireTLSv1_1 \u2013 Require a TLSv1.1 connection. Acceptable values: true , false . requireTLSv1 \u2013 Require a TLSv1.2 connection. Acceptable values: true , false . fips \u2013 Activates OpenSSL FIPS mode. Supported if the library's OpenSSL version supports FIPS. privateKeyPassphraseHandler \u2013 Class (PrivateKeyPassphraseHandler subclass) that requests the passphrase for accessing the private key. For example: privateKeyPassphraseHandler , name KeyFileHandler /name , options password test /password /options , /privateKeyPassphraseHandler . invalidCertificateHandler \u2013 Class (subclass of CertificateHandler) for verifying invalid certificates. For example: invalidCertificateHandler name ConsoleCertificateHandler /name /invalidCertificateHandler . disableProtocols \u2013 Protocols that are not allowed to use. preferServerCiphers \u2013 Preferred server ciphers on the client. Example of settings: openSSL \n server \n !-- openssl req -subj /CN=localhost -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout /etc/clickhouse-server/server.key -out /etc/clickhouse-server/server.crt -- \n certificateFile /etc/clickhouse-server/server.crt /certificateFile \n privateKeyFile /etc/clickhouse-server/server.key /privateKeyFile \n !-- openssl dhparam -out /etc/clickhouse-server/dhparam.pem 4096 -- \n dhParamsFile /etc/clickhouse-server/dhparam.pem /dhParamsFile \n verificationMode none /verificationMode \n loadDefaultCAFile true /loadDefaultCAFile \n cacheSessions true /cacheSessions \n disableProtocols sslv2,sslv3 /disableProtocols \n preferServerCiphers true /preferServerCiphers \n /server \n client \n loadDefaultCAFile true /loadDefaultCAFile \n cacheSessions true /cacheSessions \n disableProtocols sslv2,sslv3 /disableProtocols \n preferServerCiphers true /preferServerCiphers \n !-- Use for self-signed: verificationMode none /verificationMode -- \n invalidCertificateHandler \n !-- Use for self-signed: name AcceptCertificateHandler /name -- \n name RejectCertificateHandler /name \n /invalidCertificateHandler \n /client /openSSL", - "title": "openSSL" - }, - { - "location": "/operations/server_settings/settings/#part_log", - "text": "Logging events that are associated with MergeTree data. For instance, adding or merging data. You can use the log to simulate merge algorithms and compare their characteristics. You can visualize the merge process. Queries are logged in the ClickHouse table, not in a separate file. Columns in the log: event_time \u2013 Date of the event. duration_ms \u2013 Duration of the event. event_type \u2013 Type of event. 1 \u2013 new data part; 2 \u2013 merge result; 3 \u2013 data part downloaded from replica; 4 \u2013 data part deleted. database_name \u2013 The name of the database. table_name \u2013 Name of the table. part_name \u2013 Name of the data part. size_in_bytes \u2013 Size of the data part in bytes. merged_from \u2013 An array of names of data parts that make up the merge (also used when downloading a merged part). merge_time_ms \u2013 Time spent on the merge. Use the following parameters to configure logging: database \u2013 Name of the database. table \u2013 Name of the table. partition_by \u2013 Sets a custom partitioning key . flush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk. Example part_log \n database system /database \n table part_log /table \n partition_by toMonday(event_date) /partition_by \n flush_interval_milliseconds 7500 /flush_interval_milliseconds /part_log", - "title": "part_log" - }, - { - "location": "/operations/server_settings/settings/#path", - "text": "The path to the directory containing data. \n\nThe end slash is mandatory. Example path /var/lib/clickhouse/ /path", - "title": "path" - }, - { - "location": "/operations/server_settings/settings/#query_log", - "text": "Setting for logging queries received with the log_queries=1 setting. Queries are logged in the ClickHouse table, not in a separate file. Use the following parameters to configure logging: database \u2013 Name of the database. table \u2013 Name of the table. partition_by \u2013 Sets a custom partitioning key . flush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk. If the table doesn't exist, ClickHouse will create it. If the structure of the query log changed when the ClickHouse server was updated, the table with the old structure is renamed, and a new table is created automatically. Example query_log \n database system /database \n table query_log /table \n partition_by toMonday(event_date) /partition_by \n flush_interval_milliseconds 7500 /flush_interval_milliseconds /query_log", - "title": "query_log" - }, - { - "location": "/operations/server_settings/settings/#remote_servers", - "text": "Configuration of clusters used by the Distributed table engine. For more information, see the section \" Table engines/Distributed \". Example remote_servers incl= clickhouse_remote_servers / For the value of the incl attribute, see the section \" Configuration files \".", - "title": "remote_servers" - }, - { - "location": "/operations/server_settings/settings/#timezone", - "text": "The server's time zone. Specified as an IANA identifier for the UTC time zone or geographic location (for example, Africa/Abidjan). The time zone is necessary for conversions between String and DateTime formats when DateTime fields are output to text format (printed on the screen or in a file), and when getting DateTime from a string. In addition, the time zone is used in functions that work with the time and date if they didn't receive the time zone in the input parameters. Example timezone Europe/Moscow /timezone", - "title": "timezone" - }, - { - "location": "/operations/server_settings/settings/#tcp_port", - "text": "Port for communicating with clients over the TCP protocol. Example tcp_port 9000 /tcp_port", - "title": "tcp_port" - }, - { - "location": "/operations/server_settings/settings/#tmp_path", - "text": "Path to temporary data for processing large queries. \n\nThe end slash is mandatory. Example tmp_path /var/lib/clickhouse/tmp/ /tmp_path", - "title": "tmp_path" - }, - { - "location": "/operations/server_settings/settings/#uncompressed_cache_size", - "text": "Cache size (in bytes) for uncompressed data used by table engines from the MergeTree family. There is one shared cache for the server. Memory is allocated on demand. The cache is used if the option use_uncompressed_cache is enabled. The uncompressed cache is advantageous for very short queries in individual cases. Example uncompressed_cache_size 8589934592 /uncompressed_cache_size", - "title": "uncompressed_cache_size" - }, - { - "location": "/operations/server_settings/settings/#users_config", - "text": "Path to the file that contains: User configurations. Access rights. Settings profiles. Quota settings. Example users_config users.xml /users_config", - "title": "users_config" - }, - { - "location": "/operations/server_settings/settings/#zookeeper", - "text": "Configuration of ZooKeeper servers. ClickHouse uses ZooKeeper for storing replica metadata when using replicated tables. This parameter can be omitted if replicated tables are not used. For more information, see the section \" Replication \". Example zookeeper incl= zookeeper-servers optional= true /", - "title": "zookeeper" - }, - { - "location": "/operations/settings/", - "text": "Settings\n\n\nThere are multiple ways to make all the settings described below.\nSettings are configured in layers, so each subsequent layer redefines the previous settings.\n\n\nWays to configure settings, in order of priority:\n\n\n\n\nSettings in the server config file.\n\n\n\n\nSettings from user profiles.\n\n\n\n\nSession settings.\n\n\n\n\nSend \nSET setting=value\n from the ClickHouse console client in interactive mode.\nSimilarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to specify the \nsession_id\n HTTP parameter.\n\n\n\n\nFor a query.\n\n\nWhen starting the ClickHouse console client in non-interactive mode, set the startup parameter \n--setting=value\n.\n\n\nWhen using the HTTP API, pass CGI parameters (\nURL?setting_1=value\nsetting_2=value...\n).\n\n\n\n\nSettings that can only be made in the server config file are not covered in this section.", - "title": "Introduction" - }, - { - "location": "/operations/settings/#settings", - "text": "There are multiple ways to make all the settings described below.\nSettings are configured in layers, so each subsequent layer redefines the previous settings. Ways to configure settings, in order of priority: Settings in the server config file. Settings from user profiles. Session settings. Send SET setting=value from the ClickHouse console client in interactive mode.\nSimilarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to specify the session_id HTTP parameter. For a query. When starting the ClickHouse console client in non-interactive mode, set the startup parameter --setting=value . When using the HTTP API, pass CGI parameters ( URL?setting_1=value setting_2=value... ). Settings that can only be made in the server config file are not covered in this section.", - "title": "Settings" - }, - { - "location": "/operations/settings/query_complexity/", - "text": "Restrictions on query complexity\n\n\nRestrictions on query complexity are part of the settings.\nThey are used in order to provide safer execution from the user interface.\nAlmost all the restrictions only apply to SELECTs.For distributed query processing, restrictions are applied on each server separately.\n\n\nRestrictions on the \"maximum amount of something\" can take the value 0, which means \"unrestricted\".\nMost restrictions also have an 'overflow_mode' setting, meaning what to do when the limit is exceeded.\nIt can take one of two values: \nthrow\n or \nbreak\n. Restrictions on aggregation (group_by_overflow_mode) also have the value \nany\n.\n\n\nthrow\n \u2013 Throw an exception (default).\n\n\nbreak\n \u2013 Stop executing the query and return the partial result, as if the source data ran out.\n\n\nany (only for group_by_overflow_mode)\n \u2013 Continuing aggregation for the keys that got into the set, but don't add new keys to the set.\n\n\n\n\nreadonly\n\n\nWith a value of 0, you can execute any queries.\nWith a value of 1, you can only execute read requests (such as SELECT and SHOW). Requests for writing and changing settings (INSERT, SET) are prohibited.\nWith a value of 2, you can process read queries (SELECT, SHOW) and change settings (SET).\n\n\nAfter enabling readonly mode, you can't disable it in the current session.\n\n\nWhen using the GET method in the HTTP interface, 'readonly = 1' is set automatically. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.\n\n\n\n\nmax_memory_usage\n\n\nThe maximum amount of RAM to use for running a query on a single server.\n\n\nIn the default configuration file, the maximum is 10 GB.\n\n\nThe setting doesn't consider the volume of available memory or the total volume of memory on the machine.\nThe restriction applies to a single query within a single server.\nYou can use \nSHOW PROCESSLIST\n to see the current memory consumption for each query.\nIn addition, the peak memory consumption is tracked for each query and written to the log.\n\n\nMemory usage is not monitored for the states of certain aggregate functions.\n\n\nMemory usage is not fully tracked for states of the aggregate functions \nmin\n, \nmax\n, \nany\n, \nanyLast\n, \nargMin\n, \nargMax\n from \nString\n and \nArray\n arguments.\n\n\nMemory consumption is also restricted by the parameters \nmax_memory_usage_for_user\n and \nmax_memory_usage_for_all_queries\n.\n\n\nmax_memory_usage_for_user\n\n\nThe maximum amount of RAM to use for running a user's queries on a single server.\n\n\nDefault values are defined in \nSettings.h\n. By default, the amount is not restricted (\nmax_memory_usage_for_user = 0\n).\n\n\nSee also the description of \nmax_memory_usage\n.\n\n\nmax_memory_usage_for_all_queries\n\n\nThe maximum amount of RAM to use for running all queries on a single server.\n\n\nDefault values are defined in \nSettings.h\n. By default, the amount is not restricted (\nmax_memory_usage_for_all_queries = 0\n).\n\n\nSee also the description of \nmax_memory_usage\n.\n\n\nmax_rows_to_read\n\n\nThe following restrictions can be checked on each block (instead of on each row). That is, the restrictions can be broken a little.\nWhen running a query in multiple threads, the following restrictions apply to each thread separately.\n\n\nMaximum number of rows that can be read from a table when running a query.\n\n\nmax_bytes_to_read\n\n\nMaximum number of bytes (uncompressed data) that can be read from a table when running a query.\n\n\nread_overflow_mode\n\n\nWhat to do when the volume of data read exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_rows_to_group_by\n\n\nMaximum number of unique keys received from aggregation. This setting lets you limit memory consumption when aggregating.\n\n\ngroup_by_overflow_mode\n\n\nWhat to do when the number of unique keys for aggregation exceeds the limit: 'throw', 'break', or 'any'. By default, throw.\nUsing the 'any' value lets you run an approximation of GROUP BY. The quality of this approximation depends on the statistical nature of the data.\n\n\nmax_rows_to_sort\n\n\nMaximum number of rows before sorting. This allows you to limit memory consumption when sorting.\n\n\nmax_bytes_to_sort\n\n\nMaximum number of bytes before sorting.\n\n\nsort_overflow_mode\n\n\nWhat to do if the number of rows received before sorting exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_result_rows\n\n\nLimit on the number of rows in the result. Also checked for subqueries, and on remote servers when running parts of a distributed query.\n\n\nmax_result_bytes\n\n\nLimit on the number of bytes in the result. The same as the previous setting.\n\n\nresult_overflow_mode\n\n\nWhat to do if the volume of the result exceeds one of the limits: 'throw' or 'break'. By default, throw.\nUsing 'break' is similar to using LIMIT.\n\n\nmax_execution_time\n\n\nMaximum query execution time in seconds.\nAt this time, it is not checked for one of the sorting stages, or when merging and finalizing aggregate functions.\n\n\ntimeout_overflow_mode\n\n\nWhat to do if the query is run longer than 'max_execution_time': 'throw' or 'break'. By default, throw.\n\n\nmin_execution_speed\n\n\nMinimal execution speed in rows per second. Checked on every data block when 'timeout_before_checking_execution_speed' expires. If the execution speed is lower, an exception is thrown.\n\n\ntimeout_before_checking_execution_speed\n\n\nChecks that execution speed is not too slow (no less than 'min_execution_speed'), after the specified time in seconds has expired.\n\n\nmax_columns_to_read\n\n\nMaximum number of columns that can be read from a table in a single query. If a query requires reading a greater number of columns, it throws an exception.\n\n\nmax_temporary_columns\n\n\nMaximum number of temporary columns that must be kept in RAM at the same time when running a query, including constant columns. If there are more temporary columns than this, it throws an exception.\n\n\nmax_temporary_non_const_columns\n\n\nThe same thing as 'max_temporary_columns', but without counting constant columns.\nNote that constant columns are formed fairly often when running a query, but they require approximately zero computing resources.\n\n\nmax_subquery_depth\n\n\nMaximum nesting depth of subqueries. If subqueries are deeper, an exception is thrown. By default, 100.\n\n\nmax_pipeline_depth\n\n\nMaximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000.\n\n\nmax_ast_depth\n\n\nMaximum nesting depth of a query syntactic tree. If exceeded, an exception is thrown.\nAt this time, it isn't checked during parsing, but only after parsing the query. That is, a syntactic tree that is too deep can be created during parsing, but the query will fail. By default, 1000.\n\n\nmax_ast_elements\n\n\nMaximum number of elements in a query syntactic tree. If exceeded, an exception is thrown.\nIn the same way as the previous setting, it is checked only after parsing the query. By default, 10,000.\n\n\nmax_rows_in_set\n\n\nMaximum number of rows for a data set in the IN clause created from a subquery.\n\n\nmax_bytes_in_set\n\n\nMaximum number of bytes (uncompressed data) used by a set in the IN clause created from a subquery.\n\n\nset_overflow_mode\n\n\nWhat to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_rows_in_distinct\n\n\nMaximum number of different rows when using DISTINCT.\n\n\nmax_bytes_in_distinct\n\n\nMaximum number of bytes used by a hash table when using DISTINCT.\n\n\ndistinct_overflow_mode\n\n\nWhat to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_rows_to_transfer\n\n\nMaximum number of rows that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.\n\n\nmax_bytes_to_transfer\n\n\nMaximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.\n\n\ntransfer_overflow_mode\n\n\nWhat to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.", - "title": "Restrictions on query complexity" - }, - { - "location": "/operations/settings/query_complexity/#restrictions-on-query-complexity", - "text": "Restrictions on query complexity are part of the settings.\nThey are used in order to provide safer execution from the user interface.\nAlmost all the restrictions only apply to SELECTs.For distributed query processing, restrictions are applied on each server separately. Restrictions on the \"maximum amount of something\" can take the value 0, which means \"unrestricted\".\nMost restrictions also have an 'overflow_mode' setting, meaning what to do when the limit is exceeded.\nIt can take one of two values: throw or break . Restrictions on aggregation (group_by_overflow_mode) also have the value any . throw \u2013 Throw an exception (default). break \u2013 Stop executing the query and return the partial result, as if the source data ran out. any (only for group_by_overflow_mode) \u2013 Continuing aggregation for the keys that got into the set, but don't add new keys to the set.", - "title": "Restrictions on query complexity" - }, - { - "location": "/operations/settings/query_complexity/#readonly", - "text": "With a value of 0, you can execute any queries.\nWith a value of 1, you can only execute read requests (such as SELECT and SHOW). Requests for writing and changing settings (INSERT, SET) are prohibited.\nWith a value of 2, you can process read queries (SELECT, SHOW) and change settings (SET). After enabling readonly mode, you can't disable it in the current session. When using the GET method in the HTTP interface, 'readonly = 1' is set automatically. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.", - "title": "readonly" - }, - { - "location": "/operations/settings/query_complexity/#max_memory_usage", - "text": "The maximum amount of RAM to use for running a query on a single server. In the default configuration file, the maximum is 10 GB. The setting doesn't consider the volume of available memory or the total volume of memory on the machine.\nThe restriction applies to a single query within a single server.\nYou can use SHOW PROCESSLIST to see the current memory consumption for each query.\nIn addition, the peak memory consumption is tracked for each query and written to the log. Memory usage is not monitored for the states of certain aggregate functions. Memory usage is not fully tracked for states of the aggregate functions min , max , any , anyLast , argMin , argMax from String and Array arguments. Memory consumption is also restricted by the parameters max_memory_usage_for_user and max_memory_usage_for_all_queries .", - "title": "max_memory_usage" - }, - { - "location": "/operations/settings/query_complexity/#max_memory_usage_for_user", - "text": "The maximum amount of RAM to use for running a user's queries on a single server. Default values are defined in Settings.h . By default, the amount is not restricted ( max_memory_usage_for_user = 0 ). See also the description of max_memory_usage .", - "title": "max_memory_usage_for_user" - }, - { - "location": "/operations/settings/query_complexity/#max_memory_usage_for_all_queries", - "text": "The maximum amount of RAM to use for running all queries on a single server. Default values are defined in Settings.h . By default, the amount is not restricted ( max_memory_usage_for_all_queries = 0 ). See also the description of max_memory_usage .", - "title": "max_memory_usage_for_all_queries" - }, - { - "location": "/operations/settings/query_complexity/#max_rows_to_read", - "text": "The following restrictions can be checked on each block (instead of on each row). That is, the restrictions can be broken a little.\nWhen running a query in multiple threads, the following restrictions apply to each thread separately. Maximum number of rows that can be read from a table when running a query.", - "title": "max_rows_to_read" - }, - { - "location": "/operations/settings/query_complexity/#max_bytes_to_read", - "text": "Maximum number of bytes (uncompressed data) that can be read from a table when running a query.", - "title": "max_bytes_to_read" - }, - { - "location": "/operations/settings/query_complexity/#read_overflow_mode", - "text": "What to do when the volume of data read exceeds one of the limits: 'throw' or 'break'. By default, throw.", - "title": "read_overflow_mode" - }, - { - "location": "/operations/settings/query_complexity/#max_rows_to_group_by", - "text": "Maximum number of unique keys received from aggregation. This setting lets you limit memory consumption when aggregating.", - "title": "max_rows_to_group_by" - }, - { - "location": "/operations/settings/query_complexity/#group_by_overflow_mode", - "text": "What to do when the number of unique keys for aggregation exceeds the limit: 'throw', 'break', or 'any'. By default, throw.\nUsing the 'any' value lets you run an approximation of GROUP BY. The quality of this approximation depends on the statistical nature of the data.", - "title": "group_by_overflow_mode" - }, - { - "location": "/operations/settings/query_complexity/#max_rows_to_sort", - "text": "Maximum number of rows before sorting. This allows you to limit memory consumption when sorting.", - "title": "max_rows_to_sort" - }, - { - "location": "/operations/settings/query_complexity/#max_bytes_to_sort", - "text": "Maximum number of bytes before sorting.", - "title": "max_bytes_to_sort" - }, - { - "location": "/operations/settings/query_complexity/#sort_overflow_mode", - "text": "What to do if the number of rows received before sorting exceeds one of the limits: 'throw' or 'break'. By default, throw.", - "title": "sort_overflow_mode" - }, - { - "location": "/operations/settings/query_complexity/#max_result_rows", - "text": "Limit on the number of rows in the result. Also checked for subqueries, and on remote servers when running parts of a distributed query.", - "title": "max_result_rows" - }, - { - "location": "/operations/settings/query_complexity/#max_result_bytes", - "text": "Limit on the number of bytes in the result. The same as the previous setting.", - "title": "max_result_bytes" - }, - { - "location": "/operations/settings/query_complexity/#result_overflow_mode", - "text": "What to do if the volume of the result exceeds one of the limits: 'throw' or 'break'. By default, throw.\nUsing 'break' is similar to using LIMIT.", - "title": "result_overflow_mode" - }, - { - "location": "/operations/settings/query_complexity/#max_execution_time", - "text": "Maximum query execution time in seconds.\nAt this time, it is not checked for one of the sorting stages, or when merging and finalizing aggregate functions.", - "title": "max_execution_time" - }, - { - "location": "/operations/settings/query_complexity/#timeout_overflow_mode", - "text": "What to do if the query is run longer than 'max_execution_time': 'throw' or 'break'. By default, throw.", - "title": "timeout_overflow_mode" - }, - { - "location": "/operations/settings/query_complexity/#min_execution_speed", - "text": "Minimal execution speed in rows per second. Checked on every data block when 'timeout_before_checking_execution_speed' expires. If the execution speed is lower, an exception is thrown.", - "title": "min_execution_speed" - }, - { - "location": "/operations/settings/query_complexity/#timeout_before_checking_execution_speed", - "text": "Checks that execution speed is not too slow (no less than 'min_execution_speed'), after the specified time in seconds has expired.", - "title": "timeout_before_checking_execution_speed" - }, - { - "location": "/operations/settings/query_complexity/#max_columns_to_read", - "text": "Maximum number of columns that can be read from a table in a single query. If a query requires reading a greater number of columns, it throws an exception.", - "title": "max_columns_to_read" - }, - { - "location": "/operations/settings/query_complexity/#max_temporary_columns", - "text": "Maximum number of temporary columns that must be kept in RAM at the same time when running a query, including constant columns. If there are more temporary columns than this, it throws an exception.", - "title": "max_temporary_columns" - }, - { - "location": "/operations/settings/query_complexity/#max_temporary_non_const_columns", - "text": "The same thing as 'max_temporary_columns', but without counting constant columns.\nNote that constant columns are formed fairly often when running a query, but they require approximately zero computing resources.", - "title": "max_temporary_non_const_columns" - }, - { - "location": "/operations/settings/query_complexity/#max_subquery_depth", - "text": "Maximum nesting depth of subqueries. If subqueries are deeper, an exception is thrown. By default, 100.", - "title": "max_subquery_depth" - }, - { - "location": "/operations/settings/query_complexity/#max_pipeline_depth", - "text": "Maximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000.", - "title": "max_pipeline_depth" - }, - { - "location": "/operations/settings/query_complexity/#max_ast_depth", - "text": "Maximum nesting depth of a query syntactic tree. If exceeded, an exception is thrown.\nAt this time, it isn't checked during parsing, but only after parsing the query. That is, a syntactic tree that is too deep can be created during parsing, but the query will fail. By default, 1000.", - "title": "max_ast_depth" - }, - { - "location": "/operations/settings/query_complexity/#max_ast_elements", - "text": "Maximum number of elements in a query syntactic tree. If exceeded, an exception is thrown.\nIn the same way as the previous setting, it is checked only after parsing the query. By default, 10,000.", - "title": "max_ast_elements" - }, - { - "location": "/operations/settings/query_complexity/#max_rows_in_set", - "text": "Maximum number of rows for a data set in the IN clause created from a subquery.", - "title": "max_rows_in_set" - }, - { - "location": "/operations/settings/query_complexity/#max_bytes_in_set", - "text": "Maximum number of bytes (uncompressed data) used by a set in the IN clause created from a subquery.", - "title": "max_bytes_in_set" - }, - { - "location": "/operations/settings/query_complexity/#set_overflow_mode", - "text": "What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.", - "title": "set_overflow_mode" - }, - { - "location": "/operations/settings/query_complexity/#max_rows_in_distinct", - "text": "Maximum number of different rows when using DISTINCT.", - "title": "max_rows_in_distinct" - }, - { - "location": "/operations/settings/query_complexity/#max_bytes_in_distinct", - "text": "Maximum number of bytes used by a hash table when using DISTINCT.", - "title": "max_bytes_in_distinct" - }, - { - "location": "/operations/settings/query_complexity/#distinct_overflow_mode", - "text": "What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.", - "title": "distinct_overflow_mode" - }, - { - "location": "/operations/settings/query_complexity/#max_rows_to_transfer", - "text": "Maximum number of rows that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.", - "title": "max_rows_to_transfer" - }, - { - "location": "/operations/settings/query_complexity/#max_bytes_to_transfer", - "text": "Maximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.", - "title": "max_bytes_to_transfer" - }, - { - "location": "/operations/settings/query_complexity/#transfer_overflow_mode", - "text": "What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.", - "title": "transfer_overflow_mode" - }, - { - "location": "/operations/settings/settings/", - "text": "Settings\n\n\n\n\ndistributed_product_mode\n\n\nChanges the behavior of \ndistributed subqueries\n, i.e. in cases when the query contains the product of distributed tables.\n\n\nClickHouse applies the configuration if the subqueries on any level have a distributed table that exists on the local server and has more than one shard.\n\n\nRestrictions:\n\n\n\n\nOnly applied for IN and JOIN subqueries.\n\n\nUsed only if a distributed table is used in the FROM clause.\n\n\nNot used for a table-valued \n remote\n function.\n\n\n\n\nThe possible values \u200b\u200bare:\n\n\n\n\nfallback_to_stale_replicas_for_distributed_queries\n\n\nForces a query to an out-of-date replica if updated data is not available. See \"\nReplication\n\".\n\n\nClickHouse selects the most relevant from the outdated replicas of the table.\n\n\nUsed when performing \nSELECT\n from a distributed table that points to replicated tables.\n\n\nBy default, 1 (enabled).\n\n\n\n\nforce_index_by_date\n\n\nDisables query execution if the index can't be used by date.\n\n\nWorks with tables in the MergeTree family.\n\n\nIf \nforce_index_by_date=1\n, ClickHouse checks whether the query has a date key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For example, the condition \nDate != ' 2000-01-01 '\n is acceptable even when it matches all the data in the table (i.e., running the query requires a full scan). For more information about ranges of data in MergeTree tables, see \"\nMergeTree\n\".\n\n\n\n\nforce_primary_key\n\n\nDisables query execution if indexing by the primary key is not possible.\n\n\nWorks with tables in the MergeTree family.\n\n\nIf \nforce_primary_key=1\n, ClickHouse checks to see if the query has a primary key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For more information about data ranges in MergeTree tables, see \"\nMergeTree\n\".\n\n\n\n\nfsync_metadata\n\n\nEnable or disable fsync when writing .sql files. By default, it is enabled.\n\n\nIt makes sense to disable it if the server has millions of tiny table chunks that are constantly being created and destroyed.\n\n\ninput_format_allow_errors_num\n\n\nSets the maximum number of acceptable errors when reading from text formats (CSV, TSV, etc.).\n\n\nThe default value is 0.\n\n\nAlways pair it with \ninput_format_allow_errors_ratio\n. To skip errors, both settings must be greater than 0.\n\n\nIf an error occurred while reading rows but the error counter is still less than \ninput_format_allow_errors_num\n, ClickHouse ignores the row and moves on to the next one.\n\n\nIf \ninput_format_allow_errors_num\nis exceeded, ClickHouse throws an exception.\n\n\ninput_format_allow_errors_ratio\n\n\nSets the maximum percentage of errors allowed when reading from text formats (CSV, TSV, etc.).\nThe percentage of errors is set as a floating-point number between 0 and 1.\n\n\nThe default value is 0.\n\n\nAlways pair it with \ninput_format_allow_errors_num\n. To skip errors, both settings must be greater than 0.\n\n\nIf an error occurred while reading rows but the error counter is still less than \ninput_format_allow_errors_ratio\n, ClickHouse ignores the row and moves on to the next one.\n\n\nIf \ninput_format_allow_errors_ratio\n is exceeded, ClickHouse throws an exception.\n\n\nmax_block_size\n\n\nIn ClickHouse, data is processed by blocks (sets of column parts). The internal processing cycles for a single block are efficient enough, but there are noticeable expenditures on each block. \nmax_block_size\n is a recommendation for what size of block (in number of rows) to load from tables. The block size shouldn't be too small, so that the expenditures on each block are still noticeable, but not too large, so that the query with LIMIT that is completed after the first block is processed quickly, so that too much memory isn't consumed when extracting a large number of columns in multiple threads, and so that at least some cache locality is preserved.\n\n\nBy default, 65,536.\n\n\nBlocks the size of \nmax_block_size\n are not always loaded from the table. If it is obvious that less data needs to be retrieved, a smaller block is processed.\n\n\npreferred_block_size_bytes\n\n\nUsed for the same purpose as \nmax_block_size\n, but it sets the recommended block size in bytes by adapting it to the number of rows in the block.\nHowever, the block size cannot be more than \nmax_block_size\n rows.\nDisabled by default (set to 0). It only works when reading from MergeTree engines.\n\n\n\n\nlog_queries\n\n\nSetting up query the logging.\n\n\nQueries sent to ClickHouse with this setup are logged according to the rules in the \nquery_log\n server configuration parameter.\n\n\nExample\n:\n\n\nlog_queries=1\n\n\n\n\n\n\n\nmax_insert_block_size\n\n\nThe size of blocks to form for insertion into a table.\nThis setting only applies in cases when the server forms the blocks.\nFor example, for an INSERT via the HTTP interface, the server parses the data format and forms blocks of the specified size.\nBut when using clickhouse-client, the client parses the data itself, and the 'max_insert_block_size' setting on the server doesn't affect the size of the inserted blocks.\nThe setting also doesn't have a purpose when using INSERT SELECT, since data is inserted using the same blocks that are formed after SELECT.\n\n\nBy default, it is 1,048,576.\n\n\nThis is slightly more than \nmax_block_size\n. The reason for this is because certain table engines (\n*MergeTree\n) form a data part on the disk for each inserted block, which is a fairly large entity. Similarly, \n*MergeTree\n tables sort data during insertion, and a large enough block size allows sorting more data in RAM.\n\n\n\n\nmax_replica_delay_for_distributed_queries\n\n\nDisables lagging replicas for distributed queries. See \"\nReplication\n\".\n\n\nSets the time in seconds. If a replica lags more than the set value, this replica is not used.\n\n\nDefault value: 0 (off).\n\n\nUsed when performing \nSELECT\n from a distributed table that points to replicated tables.\n\n\nmax_threads\n\n\nThe maximum number of query processing threads\n\n\n\n\nexcluding threads for retrieving data from remote servers (see the 'max_distributed_connections' parameter).\n\n\n\n\nThis parameter applies to threads that perform the same stages of the query processing pipeline in parallel.\nFor example, if reading from a table, evaluating expressions with functions, filtering with WHERE and pre-aggregating for GROUP BY can all be done in parallel using at least 'max_threads' number of threads, then 'max_threads' are used.\n\n\nBy default, 8.\n\n\nIf less than one SELECT query is normally run on a server at a time, set this parameter to a value slightly less than the actual number of processor cores.\n\n\nFor queries that are completed quickly because of a LIMIT, you can set a lower 'max_threads'. For example, if the necessary number of entries are located in every block and max_threads = 8, 8 blocks are retrieved, although it would have been enough to read just one.\n\n\nThe smaller the \nmax_threads\n value, the less memory is consumed.\n\n\nmax_compress_block_size\n\n\nThe maximum size of blocks of uncompressed data before compressing for writing to a table. By default, 1,048,576 (1 MiB). If the size is reduced, the compression rate is significantly reduced, the compression and decompression speed increases slightly due to cache locality, and memory consumption is reduced. There usually isn't any reason to change this setting.\n\n\nDon't confuse blocks for compression (a chunk of memory consisting of bytes) and blocks for query processing (a set of rows from a table).\n\n\nmin_compress_block_size\n\n\nFor \nMergeTree\n\" tables. In order to reduce latency when processing queries, a block is compressed when writing the next mark if its size is at least 'min_compress_block_size'. By default, 65,536.\n\n\nThe actual size of the block, if the uncompressed data is less than 'max_compress_block_size', is no less than this value and no less than the volume of data for one mark.\n\n\nLet's look at an example. Assume that 'index_granularity' was set to 8192 during table creation.\n\n\nWe are writing a UInt32-type column (4 bytes per value). When writing 8192 rows, the total will be 32 KB of data. Since min_compress_block_size = 65,536, a compressed block will be formed for every two marks.\n\n\nWe are writing a URL column with the String type (average size of 60 bytes per value). When writing 8192 rows, the average will be slightly less than 500 KB of data. Since this is more than 65,536, a compressed block will be formed for each mark. In this case, when reading data from the disk in the range of a single mark, extra data won't be decompressed.\n\n\nThere usually isn't any reason to change this setting.\n\n\nmax_query_size\n\n\nThe maximum part of a query that can be taken to RAM for parsing with the SQL parser.\nThe INSERT query also contains data for INSERT that is processed by a separate stream parser (that consumes O(1) RAM), which is not included in this restriction.\n\n\nThe default is 256 KiB.\n\n\ninteractive_delay\n\n\nThe interval in microseconds for checking whether request execution has been canceled and sending the progress.\n\n\nBy default, 100,000 (check for canceling and send progress ten times per second).\n\n\nconnect_timeout\n\n\nreceive_timeout\n\n\nsend_timeout\n\n\nTimeouts in seconds on the socket used for communicating with the client.\n\n\nBy default, 10, 300, 300.\n\n\npoll_interval\n\n\nLock in a wait loop for the specified number of seconds.\n\n\nBy default, 10.\n\n\nmax_distributed_connections\n\n\nThe maximum number of simultaneous connections with remote servers for distributed processing of a single query to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.\n\n\nBy default, 100.\n\n\nThe following parameters are only used when creating Distributed tables (and when launching a server), so there is no reason to change them at runtime.\n\n\ndistributed_connections_pool_size\n\n\nThe maximum number of simultaneous connections with remote servers for distributed processing of all queries to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.\n\n\nBy default, 128.\n\n\nconnect_timeout_with_failover_ms\n\n\nThe timeout in milliseconds for connecting to a remote server for a Distributed table engine, if the 'shard' and 'replica' sections are used in the cluster definition.\nIf unsuccessful, several attempts are made to connect to various replicas.\n\n\nBy default, 50.\n\n\nconnections_with_failover_max_tries\n\n\nThe maximum number of connection attempts with each replica, for the Distributed table engine.\n\n\nBy default, 3.\n\n\nextremes\n\n\nWhether to count extreme values (the minimums and maximums in columns of a query result). Accepts 0 or 1. By default, 0 (disabled).\nFor more information, see the section \"Extreme values\".\n\n\n\n\nuse_uncompressed_cache\n\n\nWhether to use a cache of uncompressed blocks. Accepts 0 or 1. By default, 0 (disabled).\nThe uncompressed cache (only for tables in the MergeTree family) allows significantly reducing latency and increasing throughput when working with a large number of short queries. Enable this setting for users who send frequent short requests. Also pay attention to the 'uncompressed_cache_size' configuration parameter (only set in the config file) \u2013 the size of uncompressed cache blocks. By default, it is 8 GiB. The uncompressed cache is filled in as needed; the least-used data is automatically deleted.\n\n\nFor queries that read at least a somewhat large volume of data (one million rows or more), the uncompressed cache is disabled automatically in order to save space for truly small queries. So you can keep the 'use_uncompressed_cache' setting always set to 1.\n\n\nreplace_running_query\n\n\nWhen using the HTTP interface, the 'query_id' parameter can be passed. This is any string that serves as the query identifier.\nIf a query from the same user with the same 'query_id' already exists at this time, the behavior depends on the 'replace_running_query' parameter.\n\n\n0\n (default) \u2013 Throw an exception (don't allow the query to run if a query with the same 'query_id' is already running).\n\n\n1\n \u2013 Cancel the old query and start running the new one.\n\n\nYandex.Metrica uses this parameter set to 1 for implementing suggestions for segmentation conditions. After entering the next character, if the old query hasn't finished yet, it should be canceled.\n\n\nschema\n\n\nThis parameter is useful when you are using formats that require a schema definition, such as \nCap'n Proto\n. The value depends on the format.\n\n\n\n\nstream_flush_interval_ms\n\n\nWorks for tables with streaming in the case of a timeout, or when a thread generates\nmax_insert_block_size\n rows.\n\n\nThe default value is 7500.\n\n\nThe smaller the value, the more often data is flushed into the table. Setting the value too low leads to poor performance.\n\n\n\n\nload_balancing\n\n\nWhich replicas (among healthy replicas) to preferably send a query to (on the first attempt) for distributed processing.\n\n\nrandom (default)\n\n\nThe number of errors is counted for each replica. The query is sent to the replica with the fewest errors, and if there are several of these, to any one of them.\nDisadvantages: Server proximity is not accounted for; if the replicas have different data, you will also get different data.\n\n\nnearest_hostname\n\n\nThe number of errors is counted for each replica. Every 5 minutes, the number of errors is integrally divided by 2. Thus, the number of errors is calculated for a recent time with exponential smoothing. If there is one replica with a minimal number of errors (i.e. errors occurred recently on the other replicas), the query is sent to it. If there are multiple replicas with the same minimal number of errors, the query is sent to the replica with a host name that is most similar to the server's host name in the config file (for the number of different characters in identical positions, up to the minimum length of both host names).\n\n\nFor instance, example01-01-1 and example01-01-2.yandex.ru are different in one position, while example01-01-1 and example01-02-2 differ in two places.\nThis method might seem a little stupid, but it doesn't use external data about network topology, and it doesn't compare IP addresses, which would be complicated for our IPv6 addresses.\n\n\nThus, if there are equivalent replicas, the closest one by name is preferred.\nWe can also assume that when sending a query to the same server, in the absence of failures, a distributed query will also go to the same servers. So even if different data is placed on the replicas, the query will return mostly the same results.\n\n\nin_order\n\n\nReplicas are accessed in the same order as they are specified. The number of errors does not matter.\nThis method is appropriate when you know exactly which replica is preferable.\n\n\ntotals_mode\n\n\nHow to calculate TOTALS when HAVING is present, as well as when max_rows_to_group_by and group_by_overflow_mode = 'any' are present.\nSee the section \"WITH TOTALS modifier\".\n\n\ntotals_auto_threshold\n\n\nThe threshold for \ntotals_mode = 'auto'\n.\nSee the section \"WITH TOTALS modifier\".\n\n\ndefault_sample\n\n\nFloating-point number from 0 to 1. By default, 1.\nAllows you to set the default sampling ratio for all SELECT queries.\n(For tables that do not support sampling, it throws an exception.)\nIf set to 1, sampling is not performed by default.\n\n\nmax_parallel_replicas\n\n\nThe maximum number of replicas for each shard when executing a query.\nFor consistency (to get different parts of the same data split), this option only works when the sampling key is set.\nReplica lag is not controlled.\n\n\ncompile\n\n\nEnable compilation of queries. By default, 0 (disabled).\n\n\nCompilation is only used for part of the query-processing pipeline: for the first stage of aggregation (GROUP BY).\nIf this portion of the pipeline was compiled, the query may run faster due to deployment of short cycles and inlining aggregate function calls. The maximum performance improvement (up to four times faster in rare cases) is seen for queries with multiple simple aggregate functions. Typically, the performance gain is insignificant. In very rare cases, it may slow down query execution.\n\n\nmin_count_to_compile\n\n\nHow many times to potentially use a compiled chunk of code before running compilation. By default, 3.\nIf the value is zero, then compilation runs synchronously and the query waits for the end of the compilation process before continuing execution. This can be used for testing; otherwise, use values \u200b\u200bstarting with 1. Compilation normally takes about 5-10 seconds.\nIf the value is 1 or more, compilation occurs asynchronously in a separate thread. The result will be used as soon as it is ready, including by queries that are currently running.\n\n\nCompiled code is required for each different combination of aggregate functions used in the query and the type of keys in the GROUP BY clause.\nThe results of compilation are saved in the build directory in the form of .so files. There is no restriction on the number of compilation results, since they don't use very much space. Old results will be used after server restarts, except in the case of a server upgrade \u2013 in this case, the old results are deleted.\n\n\ninput_format_skip_unknown_fields\n\n\nIf the value is true, running INSERT skips input data from columns with unknown names. Otherwise, this situation will generate an exception.\nIt works for JSONEachRow and TSKV formats.\n\n\noutput_format_json_quote_64bit_integers\n\n\nIf the value is true, integers appear in quotes when using JSON* Int64 and UInt64 formats (for compatibility with most JavaScript implementations); otherwise, integers are output without the quotes.\n\n\n\n\nformat_csv_delimiter\n\n\nThe character to be considered as a delimiter in CSV data. By default, \n,\n.", - "title": "Settings" - }, - { - "location": "/operations/settings/settings/#settings", - "text": "", - "title": "Settings" - }, - { - "location": "/operations/settings/settings/#distributed_product_mode", - "text": "Changes the behavior of distributed subqueries , i.e. in cases when the query contains the product of distributed tables. ClickHouse applies the configuration if the subqueries on any level have a distributed table that exists on the local server and has more than one shard. Restrictions: Only applied for IN and JOIN subqueries. Used only if a distributed table is used in the FROM clause. Not used for a table-valued remote function. The possible values \u200b\u200bare:", - "title": "distributed_product_mode" - }, - { - "location": "/operations/settings/settings/#fallback_to_stale_replicas_for_distributed_queries", - "text": "Forces a query to an out-of-date replica if updated data is not available. See \" Replication \". ClickHouse selects the most relevant from the outdated replicas of the table. Used when performing SELECT from a distributed table that points to replicated tables. By default, 1 (enabled).", - "title": "fallback_to_stale_replicas_for_distributed_queries" - }, - { - "location": "/operations/settings/settings/#force_index_by_date", - "text": "Disables query execution if the index can't be used by date. Works with tables in the MergeTree family. If force_index_by_date=1 , ClickHouse checks whether the query has a date key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For example, the condition Date != ' 2000-01-01 ' is acceptable even when it matches all the data in the table (i.e., running the query requires a full scan). For more information about ranges of data in MergeTree tables, see \" MergeTree \".", - "title": "force_index_by_date" - }, - { - "location": "/operations/settings/settings/#force_primary_key", - "text": "Disables query execution if indexing by the primary key is not possible. Works with tables in the MergeTree family. If force_primary_key=1 , ClickHouse checks to see if the query has a primary key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For more information about data ranges in MergeTree tables, see \" MergeTree \".", - "title": "force_primary_key" - }, - { - "location": "/operations/settings/settings/#fsync_metadata", - "text": "Enable or disable fsync when writing .sql files. By default, it is enabled. It makes sense to disable it if the server has millions of tiny table chunks that are constantly being created and destroyed.", - "title": "fsync_metadata" - }, - { - "location": "/operations/settings/settings/#input_format_allow_errors_num", - "text": "Sets the maximum number of acceptable errors when reading from text formats (CSV, TSV, etc.). The default value is 0. Always pair it with input_format_allow_errors_ratio . To skip errors, both settings must be greater than 0. If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_num , ClickHouse ignores the row and moves on to the next one. If input_format_allow_errors_num is exceeded, ClickHouse throws an exception.", - "title": "input_format_allow_errors_num" - }, - { - "location": "/operations/settings/settings/#input_format_allow_errors_ratio", - "text": "Sets the maximum percentage of errors allowed when reading from text formats (CSV, TSV, etc.).\nThe percentage of errors is set as a floating-point number between 0 and 1. The default value is 0. Always pair it with input_format_allow_errors_num . To skip errors, both settings must be greater than 0. If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_ratio , ClickHouse ignores the row and moves on to the next one. If input_format_allow_errors_ratio is exceeded, ClickHouse throws an exception.", - "title": "input_format_allow_errors_ratio" - }, - { - "location": "/operations/settings/settings/#max_block_size", - "text": "In ClickHouse, data is processed by blocks (sets of column parts). The internal processing cycles for a single block are efficient enough, but there are noticeable expenditures on each block. max_block_size is a recommendation for what size of block (in number of rows) to load from tables. The block size shouldn't be too small, so that the expenditures on each block are still noticeable, but not too large, so that the query with LIMIT that is completed after the first block is processed quickly, so that too much memory isn't consumed when extracting a large number of columns in multiple threads, and so that at least some cache locality is preserved. By default, 65,536. Blocks the size of max_block_size are not always loaded from the table. If it is obvious that less data needs to be retrieved, a smaller block is processed.", - "title": "max_block_size" - }, - { - "location": "/operations/settings/settings/#preferred_block_size_bytes", - "text": "Used for the same purpose as max_block_size , but it sets the recommended block size in bytes by adapting it to the number of rows in the block.\nHowever, the block size cannot be more than max_block_size rows.\nDisabled by default (set to 0). It only works when reading from MergeTree engines.", - "title": "preferred_block_size_bytes" - }, - { - "location": "/operations/settings/settings/#log_queries", - "text": "Setting up query the logging. Queries sent to ClickHouse with this setup are logged according to the rules in the query_log server configuration parameter. Example : log_queries=1", - "title": "log_queries" - }, - { - "location": "/operations/settings/settings/#max_insert_block_size", - "text": "The size of blocks to form for insertion into a table.\nThis setting only applies in cases when the server forms the blocks.\nFor example, for an INSERT via the HTTP interface, the server parses the data format and forms blocks of the specified size.\nBut when using clickhouse-client, the client parses the data itself, and the 'max_insert_block_size' setting on the server doesn't affect the size of the inserted blocks.\nThe setting also doesn't have a purpose when using INSERT SELECT, since data is inserted using the same blocks that are formed after SELECT. By default, it is 1,048,576. This is slightly more than max_block_size . The reason for this is because certain table engines ( *MergeTree ) form a data part on the disk for each inserted block, which is a fairly large entity. Similarly, *MergeTree tables sort data during insertion, and a large enough block size allows sorting more data in RAM.", - "title": "max_insert_block_size" - }, - { - "location": "/operations/settings/settings/#max_replica_delay_for_distributed_queries", - "text": "Disables lagging replicas for distributed queries. See \" Replication \". Sets the time in seconds. If a replica lags more than the set value, this replica is not used. Default value: 0 (off). Used when performing SELECT from a distributed table that points to replicated tables.", - "title": "max_replica_delay_for_distributed_queries" - }, - { - "location": "/operations/settings/settings/#max_threads", - "text": "The maximum number of query processing threads excluding threads for retrieving data from remote servers (see the 'max_distributed_connections' parameter). This parameter applies to threads that perform the same stages of the query processing pipeline in parallel.\nFor example, if reading from a table, evaluating expressions with functions, filtering with WHERE and pre-aggregating for GROUP BY can all be done in parallel using at least 'max_threads' number of threads, then 'max_threads' are used. By default, 8. If less than one SELECT query is normally run on a server at a time, set this parameter to a value slightly less than the actual number of processor cores. For queries that are completed quickly because of a LIMIT, you can set a lower 'max_threads'. For example, if the necessary number of entries are located in every block and max_threads = 8, 8 blocks are retrieved, although it would have been enough to read just one. The smaller the max_threads value, the less memory is consumed.", - "title": "max_threads" - }, - { - "location": "/operations/settings/settings/#max_compress_block_size", - "text": "The maximum size of blocks of uncompressed data before compressing for writing to a table. By default, 1,048,576 (1 MiB). If the size is reduced, the compression rate is significantly reduced, the compression and decompression speed increases slightly due to cache locality, and memory consumption is reduced. There usually isn't any reason to change this setting. Don't confuse blocks for compression (a chunk of memory consisting of bytes) and blocks for query processing (a set of rows from a table).", - "title": "max_compress_block_size" - }, - { - "location": "/operations/settings/settings/#min_compress_block_size", - "text": "For MergeTree \" tables. In order to reduce latency when processing queries, a block is compressed when writing the next mark if its size is at least 'min_compress_block_size'. By default, 65,536. The actual size of the block, if the uncompressed data is less than 'max_compress_block_size', is no less than this value and no less than the volume of data for one mark. Let's look at an example. Assume that 'index_granularity' was set to 8192 during table creation. We are writing a UInt32-type column (4 bytes per value). When writing 8192 rows, the total will be 32 KB of data. Since min_compress_block_size = 65,536, a compressed block will be formed for every two marks. We are writing a URL column with the String type (average size of 60 bytes per value). When writing 8192 rows, the average will be slightly less than 500 KB of data. Since this is more than 65,536, a compressed block will be formed for each mark. In this case, when reading data from the disk in the range of a single mark, extra data won't be decompressed. There usually isn't any reason to change this setting.", - "title": "min_compress_block_size" - }, - { - "location": "/operations/settings/settings/#max_query_size", - "text": "The maximum part of a query that can be taken to RAM for parsing with the SQL parser.\nThe INSERT query also contains data for INSERT that is processed by a separate stream parser (that consumes O(1) RAM), which is not included in this restriction. The default is 256 KiB.", - "title": "max_query_size" - }, - { - "location": "/operations/settings/settings/#interactive_delay", - "text": "The interval in microseconds for checking whether request execution has been canceled and sending the progress. By default, 100,000 (check for canceling and send progress ten times per second).", - "title": "interactive_delay" - }, - { - "location": "/operations/settings/settings/#connect_timeout", - "text": "", - "title": "connect_timeout" - }, - { - "location": "/operations/settings/settings/#receive_timeout", - "text": "", - "title": "receive_timeout" - }, - { - "location": "/operations/settings/settings/#send_timeout", - "text": "Timeouts in seconds on the socket used for communicating with the client. By default, 10, 300, 300.", - "title": "send_timeout" - }, - { - "location": "/operations/settings/settings/#poll_interval", - "text": "Lock in a wait loop for the specified number of seconds. By default, 10.", - "title": "poll_interval" - }, - { - "location": "/operations/settings/settings/#max_distributed_connections", - "text": "The maximum number of simultaneous connections with remote servers for distributed processing of a single query to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster. By default, 100. The following parameters are only used when creating Distributed tables (and when launching a server), so there is no reason to change them at runtime.", - "title": "max_distributed_connections" - }, - { - "location": "/operations/settings/settings/#distributed_connections_pool_size", - "text": "The maximum number of simultaneous connections with remote servers for distributed processing of all queries to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster. By default, 128.", - "title": "distributed_connections_pool_size" - }, - { - "location": "/operations/settings/settings/#connect_timeout_with_failover_ms", - "text": "The timeout in milliseconds for connecting to a remote server for a Distributed table engine, if the 'shard' and 'replica' sections are used in the cluster definition.\nIf unsuccessful, several attempts are made to connect to various replicas. By default, 50.", - "title": "connect_timeout_with_failover_ms" - }, - { - "location": "/operations/settings/settings/#connections_with_failover_max_tries", - "text": "The maximum number of connection attempts with each replica, for the Distributed table engine. By default, 3.", - "title": "connections_with_failover_max_tries" - }, - { - "location": "/operations/settings/settings/#extremes", - "text": "Whether to count extreme values (the minimums and maximums in columns of a query result). Accepts 0 or 1. By default, 0 (disabled).\nFor more information, see the section \"Extreme values\".", - "title": "extremes" - }, - { - "location": "/operations/settings/settings/#use_uncompressed_cache", - "text": "Whether to use a cache of uncompressed blocks. Accepts 0 or 1. By default, 0 (disabled).\nThe uncompressed cache (only for tables in the MergeTree family) allows significantly reducing latency and increasing throughput when working with a large number of short queries. Enable this setting for users who send frequent short requests. Also pay attention to the 'uncompressed_cache_size' configuration parameter (only set in the config file) \u2013 the size of uncompressed cache blocks. By default, it is 8 GiB. The uncompressed cache is filled in as needed; the least-used data is automatically deleted. For queries that read at least a somewhat large volume of data (one million rows or more), the uncompressed cache is disabled automatically in order to save space for truly small queries. So you can keep the 'use_uncompressed_cache' setting always set to 1.", - "title": "use_uncompressed_cache" - }, - { - "location": "/operations/settings/settings/#replace_running_query", - "text": "When using the HTTP interface, the 'query_id' parameter can be passed. This is any string that serves as the query identifier.\nIf a query from the same user with the same 'query_id' already exists at this time, the behavior depends on the 'replace_running_query' parameter. 0 (default) \u2013 Throw an exception (don't allow the query to run if a query with the same 'query_id' is already running). 1 \u2013 Cancel the old query and start running the new one. Yandex.Metrica uses this parameter set to 1 for implementing suggestions for segmentation conditions. After entering the next character, if the old query hasn't finished yet, it should be canceled.", - "title": "replace_running_query" - }, - { - "location": "/operations/settings/settings/#schema", - "text": "This parameter is useful when you are using formats that require a schema definition, such as Cap'n Proto . The value depends on the format.", - "title": "schema" - }, - { - "location": "/operations/settings/settings/#stream_flush_interval_ms", - "text": "Works for tables with streaming in the case of a timeout, or when a thread generates max_insert_block_size rows. The default value is 7500. The smaller the value, the more often data is flushed into the table. Setting the value too low leads to poor performance.", - "title": "stream_flush_interval_ms" - }, - { - "location": "/operations/settings/settings/#load_balancing", - "text": "Which replicas (among healthy replicas) to preferably send a query to (on the first attempt) for distributed processing.", - "title": "load_balancing" - }, - { - "location": "/operations/settings/settings/#random-default", - "text": "The number of errors is counted for each replica. The query is sent to the replica with the fewest errors, and if there are several of these, to any one of them.\nDisadvantages: Server proximity is not accounted for; if the replicas have different data, you will also get different data.", - "title": "random (default)" - }, - { - "location": "/operations/settings/settings/#nearest_hostname", - "text": "The number of errors is counted for each replica. Every 5 minutes, the number of errors is integrally divided by 2. Thus, the number of errors is calculated for a recent time with exponential smoothing. If there is one replica with a minimal number of errors (i.e. errors occurred recently on the other replicas), the query is sent to it. If there are multiple replicas with the same minimal number of errors, the query is sent to the replica with a host name that is most similar to the server's host name in the config file (for the number of different characters in identical positions, up to the minimum length of both host names). For instance, example01-01-1 and example01-01-2.yandex.ru are different in one position, while example01-01-1 and example01-02-2 differ in two places.\nThis method might seem a little stupid, but it doesn't use external data about network topology, and it doesn't compare IP addresses, which would be complicated for our IPv6 addresses. Thus, if there are equivalent replicas, the closest one by name is preferred.\nWe can also assume that when sending a query to the same server, in the absence of failures, a distributed query will also go to the same servers. So even if different data is placed on the replicas, the query will return mostly the same results.", - "title": "nearest_hostname" - }, - { - "location": "/operations/settings/settings/#in_order", - "text": "Replicas are accessed in the same order as they are specified. The number of errors does not matter.\nThis method is appropriate when you know exactly which replica is preferable.", - "title": "in_order" - }, - { - "location": "/operations/settings/settings/#totals_mode", - "text": "How to calculate TOTALS when HAVING is present, as well as when max_rows_to_group_by and group_by_overflow_mode = 'any' are present.\nSee the section \"WITH TOTALS modifier\".", - "title": "totals_mode" - }, - { - "location": "/operations/settings/settings/#totals_auto_threshold", - "text": "The threshold for totals_mode = 'auto' .\nSee the section \"WITH TOTALS modifier\".", - "title": "totals_auto_threshold" - }, - { - "location": "/operations/settings/settings/#default_sample", - "text": "Floating-point number from 0 to 1. By default, 1.\nAllows you to set the default sampling ratio for all SELECT queries.\n(For tables that do not support sampling, it throws an exception.)\nIf set to 1, sampling is not performed by default.", - "title": "default_sample" - }, - { - "location": "/operations/settings/settings/#max_parallel_replicas", - "text": "The maximum number of replicas for each shard when executing a query.\nFor consistency (to get different parts of the same data split), this option only works when the sampling key is set.\nReplica lag is not controlled.", - "title": "max_parallel_replicas" - }, - { - "location": "/operations/settings/settings/#compile", - "text": "Enable compilation of queries. By default, 0 (disabled). Compilation is only used for part of the query-processing pipeline: for the first stage of aggregation (GROUP BY).\nIf this portion of the pipeline was compiled, the query may run faster due to deployment of short cycles and inlining aggregate function calls. The maximum performance improvement (up to four times faster in rare cases) is seen for queries with multiple simple aggregate functions. Typically, the performance gain is insignificant. In very rare cases, it may slow down query execution.", - "title": "compile" - }, - { - "location": "/operations/settings/settings/#min_count_to_compile", - "text": "How many times to potentially use a compiled chunk of code before running compilation. By default, 3.\nIf the value is zero, then compilation runs synchronously and the query waits for the end of the compilation process before continuing execution. This can be used for testing; otherwise, use values \u200b\u200bstarting with 1. Compilation normally takes about 5-10 seconds.\nIf the value is 1 or more, compilation occurs asynchronously in a separate thread. The result will be used as soon as it is ready, including by queries that are currently running. Compiled code is required for each different combination of aggregate functions used in the query and the type of keys in the GROUP BY clause.\nThe results of compilation are saved in the build directory in the form of .so files. There is no restriction on the number of compilation results, since they don't use very much space. Old results will be used after server restarts, except in the case of a server upgrade \u2013 in this case, the old results are deleted.", - "title": "min_count_to_compile" - }, - { - "location": "/operations/settings/settings/#input_format_skip_unknown_fields", - "text": "If the value is true, running INSERT skips input data from columns with unknown names. Otherwise, this situation will generate an exception.\nIt works for JSONEachRow and TSKV formats.", - "title": "input_format_skip_unknown_fields" - }, - { - "location": "/operations/settings/settings/#output_format_json_quote_64bit_integers", - "text": "If the value is true, integers appear in quotes when using JSON* Int64 and UInt64 formats (for compatibility with most JavaScript implementations); otherwise, integers are output without the quotes.", - "title": "output_format_json_quote_64bit_integers" - }, - { - "location": "/operations/settings/settings/#format_csv_delimiter", - "text": "The character to be considered as a delimiter in CSV data. By default, , .", - "title": "format_csv_delimiter" - }, - { - "location": "/operations/settings/settings_profiles/", - "text": "Settings profiles\n\n\nA settings profile is a collection of settings grouped under the same name. Each ClickHouse user has a profile.\nTo apply all the settings in a profile, set \nprofile\n.\n\n\nExample:\n\n\nSetting \nweb\n profile.\n\n\nSET\n \nprofile\n \n=\n \nweb\n\n\n\n\n\n\nSettings profiles are declared in the user config file. This is usually \nusers.xml\n.\n\n\nExample:\n\n\n!-- Settings profiles --\n\n\nprofiles\n\n \n!-- Default settings --\n\n \ndefault\n\n \n!-- The maximum number of threads when running a single query. --\n\n \nmax_threads\n8\n/max_threads\n\n \n/default\n\n\n \n!-- Settings for quries from the user interface --\n\n \nweb\n\n \nmax_rows_to_read\n1000000000\n/max_rows_to_read\n\n \nmax_bytes_to_read\n100000000000\n/max_bytes_to_read\n\n\n \nmax_rows_to_group_by\n1000000\n/max_rows_to_group_by\n\n \ngroup_by_overflow_mode\nany\n/group_by_overflow_mode\n\n\n \nmax_rows_to_sort\n1000000\n/max_rows_to_sort\n\n \nmax_bytes_to_sort\n1000000000\n/max_bytes_to_sort\n\n\n \nmax_result_rows\n100000\n/max_result_rows\n\n \nmax_result_bytes\n100000000\n/max_result_bytes\n\n \nresult_overflow_mode\nbreak\n/result_overflow_mode\n\n\n \nmax_execution_time\n600\n/max_execution_time\n\n \nmin_execution_speed\n1000000\n/min_execution_speed\n\n \ntimeout_before_checking_execution_speed\n15\n/timeout_before_checking_execution_speed\n\n\n \nmax_columns_to_read\n25\n/max_columns_to_read\n\n \nmax_temporary_columns\n100\n/max_temporary_columns\n\n \nmax_temporary_non_const_columns\n50\n/max_temporary_non_const_columns\n\n\n \nmax_subquery_depth\n2\n/max_subquery_depth\n\n \nmax_pipeline_depth\n25\n/max_pipeline_depth\n\n \nmax_ast_depth\n50\n/max_ast_depth\n\n \nmax_ast_elements\n100\n/max_ast_elements\n\n\n \nreadonly\n1\n/readonly\n\n \n/web\n\n\n/profiles\n\n\n\n\n\n\nThe example specifies two profiles: \ndefault\n and \nweb\n. The \ndefault\n profile has a special purpose: it must always be present and is applied when starting the server. In other words, the \ndefault\n profile contains default settings. The \nweb\n profile is a regular profile that can be set using the \nSET\n query or using a URL parameter in an HTTP query.\n\n\nSettings profiles can inherit from each other. To use inheritance, indicate the \nprofile\n setting before the other settings that are listed in the profile.", - "title": "Settings profiles" - }, - { - "location": "/operations/settings/settings_profiles/#settings-profiles", - "text": "A settings profile is a collection of settings grouped under the same name. Each ClickHouse user has a profile.\nTo apply all the settings in a profile, set profile . Example: Setting web profile. SET profile = web Settings profiles are declared in the user config file. This is usually users.xml . Example: !-- Settings profiles -- profiles \n !-- Default settings -- \n default \n !-- The maximum number of threads when running a single query. -- \n max_threads 8 /max_threads \n /default \n\n !-- Settings for quries from the user interface -- \n web \n max_rows_to_read 1000000000 /max_rows_to_read \n max_bytes_to_read 100000000000 /max_bytes_to_read \n\n max_rows_to_group_by 1000000 /max_rows_to_group_by \n group_by_overflow_mode any /group_by_overflow_mode \n\n max_rows_to_sort 1000000 /max_rows_to_sort \n max_bytes_to_sort 1000000000 /max_bytes_to_sort \n\n max_result_rows 100000 /max_result_rows \n max_result_bytes 100000000 /max_result_bytes \n result_overflow_mode break /result_overflow_mode \n\n max_execution_time 600 /max_execution_time \n min_execution_speed 1000000 /min_execution_speed \n timeout_before_checking_execution_speed 15 /timeout_before_checking_execution_speed \n\n max_columns_to_read 25 /max_columns_to_read \n max_temporary_columns 100 /max_temporary_columns \n max_temporary_non_const_columns 50 /max_temporary_non_const_columns \n\n max_subquery_depth 2 /max_subquery_depth \n max_pipeline_depth 25 /max_pipeline_depth \n max_ast_depth 50 /max_ast_depth \n max_ast_elements 100 /max_ast_elements \n\n readonly 1 /readonly \n /web /profiles The example specifies two profiles: default and web . The default profile has a special purpose: it must always be present and is applied when starting the server. In other words, the default profile contains default settings. The web profile is a regular profile that can be set using the SET query or using a URL parameter in an HTTP query. Settings profiles can inherit from each other. To use inheritance, indicate the profile setting before the other settings that are listed in the profile.", - "title": "Settings profiles" - }, - { - "location": "/utils/", - "text": "ClickHouse utility\n\n\n\n\nclickhouse-local\n \u2014 Allows running SQL queries on data without stopping the ClickHouse server, similar to how \nawk\n does this.\n\n\nclickhouse-copier\n \u2014 Copies (and reshards) data from one cluster to another cluster.", - "title": "Introduction" - }, - { - "location": "/utils/#clickhouse-utility", - "text": "clickhouse-local \u2014 Allows running SQL queries on data without stopping the ClickHouse server, similar to how awk does this. clickhouse-copier \u2014 Copies (and reshards) data from one cluster to another cluster.", - "title": "ClickHouse utility" - }, - { - "location": "/utils/clickhouse-copier/", - "text": "clickhouse-copier\n\n\nCopies data from the tables in one cluster to tables in another (or the same) cluster.\n\n\nYou can run multiple \nclickhouse-copier\n instances on different servers to perform the same job. ZooKeeper is used for syncing the processes.\n\n\nAfter starting, \nclickhouse-copier\n:\n\n\n\n\nConnects to ZooKeeper and receives:\n\n\nCopying jobs.\n\n\n\n\nThe state of the copying jobs.\n\n\n\n\n\n\nIt performs the jobs.\n\n\n\n\n\n\nEach running process chooses the \"closest\" shard of the source cluster and copies the data into the destination cluster, resharding the data if necessary.\n\n\nclickhouse-copier\n tracks the changes in ZooKeeper and applies them on the fly.\n\n\nTo reduce network traffic, we recommend running \nclickhouse-copier\n on the same server where the source data is located.\n\n\nRunning clickhouse-copier\n\n\nThe utility should be run manually:\n\n\nclickhouse-copier copier --daemon --config zookeeper.xml --task-path /task/path --base-dir /path/to/dir\n\n\n\n\n\nParameters:\n\n\n\n\ndaemon\n \u2014 Starts \nclickhouse-copier\n in daemon mode.\n\n\nconfig\n \u2014 The path to the \nzookeeper.xml\n file with the parameters for the connection to ZooKeeper.\n\n\ntask-path\n \u2014 The path to the ZooKeeper node. This node is used for syncing \nclickhouse-copier\n processes and storing tasks. Tasks are stored in \n$task-path/description\n.\n\n\nbase-dir\n \u2014 The path to logs and auxiliary files. When it starts, \nclickhouse-copier\n creates \nclickhouse-copier_YYYYMMHHSS_\nPID\n subdirectories in \n$base-dir\n. If this parameter is omitted, the directories are created in the directory where \nclickhouse-copier\n was launched.\n\n\n\n\nFormat of zookeeper.xml\n\n\nyandex\n\n \nzookeeper\n\n \nnode\n \nindex=\n1\n\n \nhost\n127.0.0.1\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n \n/zookeeper\n\n\n/yandex\n\n\n\n\n\n\nConfiguration of copying tasks\n\n\nyandex\n\n \n!-- Configuration of clusters as in an ordinary server config --\n\n \nremote_servers\n\n \nsource_cluster\n\n \nshard\n\n \ninternal_replication\nfalse\n/internal_replication\n\n \nreplica\n\n \nhost\n127.0.0.1\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \n/shard\n\n ...\n \n/source_cluster\n\n\n \ndestination_cluster\n\n ...\n \n/destination_cluster\n\n \n/remote_servers\n\n\n \n!-- How many simultaneously active workers are possible. If you run more workers superfluous workers will sleep. --\n\n \nmax_workers\n2\n/max_workers\n\n\n \n!-- Setting used to fetch (pull) data from source cluster tables --\n\n \nsettings_pull\n\n \nreadonly\n1\n/readonly\n\n \n/settings_pull\n\n\n \n!-- Setting used to insert (push) data to destination cluster tables --\n\n \nsettings_push\n\n \nreadonly\n0\n/readonly\n\n \n/settings_push\n\n\n \n!-- Common setting for fetch (pull) and insert (push) operations. The copier process context also uses it.\n\n\n They are overlaid by \nsettings_pull/\n and \nsettings_push/\n respectively. --\n\n \nsettings\n\n \nconnect_timeout\n3\n/connect_timeout\n\n \n!-- Sync insert is set forcibly, leave it here just in case. --\n\n \ninsert_distributed_sync\n1\n/insert_distributed_sync\n\n \n/settings\n\n\n \n!-- Copying description of tasks.\n\n\n You can specify several table tasks in the same task description (in the same ZooKeeper node), and they will be performed sequentially.\n\n\n --\n\n \ntables\n\n \n!-- A table task that copies one table. --\n\n \ntable_hits\n\n \n!-- Source cluster name (from the \nremote_servers/\n section) and tables in it that should be copied --\n\n \ncluster_pull\nsource_cluster\n/cluster_pull\n\n \ndatabase_pull\ntest\n/database_pull\n\n \ntable_pull\nhits\n/table_pull\n\n\n \n!-- Destination cluster name and tables in which the data should be inserted --\n\n \ncluster_push\ndestination_cluster\n/cluster_push\n\n \ndatabase_push\ntest\n/database_push\n\n \ntable_push\nhits2\n/table_push\n\n\n \n!-- Engine of destination tables.\n\n\n If the destination tables have not been created yet, workers create them using column definitions from source tables and the engine definition from here.\n\n\n\n NOTE: If the first worker starts to insert data and detects that the destination partition is not empty, then the partition will\n\n\n be dropped and refilled. Take this into account if you already have some data in destination tables. You can directly \n\n\n specify partitions that should be copied in \nenabled_partitions/\n. They should be in quoted format like the partition column in the \n\n\n system.parts table.\n\n\n --\n\n \nengine\n\n ENGINE=ReplicatedMergeTree(\n/clickhouse/tables/{cluster}/{shard}/hits2\n, \n{replica}\n)\n PARTITION BY toMonday(date)\n ORDER BY (CounterID, EventDate)\n \n/engine\n\n\n \n!-- Sharding key used to insert data to destination cluster --\n\n \nsharding_key\njumpConsistentHash(intHash64(UserID), 2)\n/sharding_key\n\n\n \n!-- Optional expression that filter data while pull them from source servers --\n\n \nwhere_condition\nCounterID != 0\n/where_condition\n\n\n \n!-- This section specifies partitions that should be copied, other partition will be ignored.\n\n\n Partition names should have the same format as\n\n\n partition column of system.parts table (i.e. a quoted text).\n\n\n Since partition key of source and destination cluster could be different,\n\n\n these partition names specify destination partitions.\n\n\n\n Note: Although this section is optional (if it omitted, all partitions will be copied), \n\n\n it is strongly recommended to specify the partitions explicitly.\n\n\n If you already have some partitions ready on the destination cluster, they \n\n\n will be removed at the start of the copying, because they will be interpreted \n\n\n as unfinished data from the previous copying.\n\n\n --\n\n \nenabled_partitions\n\n \npartition\n2018-02-26\n/partition\n\n \npartition\n2018-03-05\n/partition\n\n ...\n \n/enabled_partitions\n\n \n/table_hits\n\n\n \n!-- Next table to copy. It is not copied until the previous table is copying. --\n\n \n/table_visits\n\n ...\n \n/table_visits\n\n ...\n \n/tables\n\n\n/yandex\n\n\n\n\n\n\nclickhouse-copier\n tracks the changes in \n/task/path/description\n and applies them on the fly. For instance, if you change the value of \nmax_workers\n, the number of processes running tasks will also change.", - "title": "clickhouse-copier" - }, - { - "location": "/utils/clickhouse-copier/#clickhouse-copier", - "text": "Copies data from the tables in one cluster to tables in another (or the same) cluster. You can run multiple clickhouse-copier instances on different servers to perform the same job. ZooKeeper is used for syncing the processes. After starting, clickhouse-copier : Connects to ZooKeeper and receives: Copying jobs. The state of the copying jobs. It performs the jobs. Each running process chooses the \"closest\" shard of the source cluster and copies the data into the destination cluster, resharding the data if necessary. clickhouse-copier tracks the changes in ZooKeeper and applies them on the fly. To reduce network traffic, we recommend running clickhouse-copier on the same server where the source data is located.", - "title": "clickhouse-copier" - }, - { - "location": "/utils/clickhouse-copier/#running-clickhouse-copier", - "text": "The utility should be run manually: clickhouse-copier copier --daemon --config zookeeper.xml --task-path /task/path --base-dir /path/to/dir Parameters: daemon \u2014 Starts clickhouse-copier in daemon mode. config \u2014 The path to the zookeeper.xml file with the parameters for the connection to ZooKeeper. task-path \u2014 The path to the ZooKeeper node. This node is used for syncing clickhouse-copier processes and storing tasks. Tasks are stored in $task-path/description . base-dir \u2014 The path to logs and auxiliary files. When it starts, clickhouse-copier creates clickhouse-copier_YYYYMMHHSS_ PID subdirectories in $base-dir . If this parameter is omitted, the directories are created in the directory where clickhouse-copier was launched.", - "title": "Running clickhouse-copier" - }, - { - "location": "/utils/clickhouse-copier/#format-of-zookeeperxml", - "text": "yandex \n zookeeper \n node index= 1 \n host 127.0.0.1 /host \n port 2181 /port \n /node \n /zookeeper /yandex", - "title": "Format of zookeeper.xml" - }, - { - "location": "/utils/clickhouse-copier/#configuration-of-copying-tasks", - "text": "yandex \n !-- Configuration of clusters as in an ordinary server config -- \n remote_servers \n source_cluster \n shard \n internal_replication false /internal_replication \n replica \n host 127.0.0.1 /host \n port 9000 /port \n /replica \n /shard \n ...\n /source_cluster \n\n destination_cluster \n ...\n /destination_cluster \n /remote_servers \n\n !-- How many simultaneously active workers are possible. If you run more workers superfluous workers will sleep. -- \n max_workers 2 /max_workers \n\n !-- Setting used to fetch (pull) data from source cluster tables -- \n settings_pull \n readonly 1 /readonly \n /settings_pull \n\n !-- Setting used to insert (push) data to destination cluster tables -- \n settings_push \n readonly 0 /readonly \n /settings_push \n\n !-- Common setting for fetch (pull) and insert (push) operations. The copier process context also uses it. They are overlaid by settings_pull/ and settings_push/ respectively. -- \n settings \n connect_timeout 3 /connect_timeout \n !-- Sync insert is set forcibly, leave it here just in case. -- \n insert_distributed_sync 1 /insert_distributed_sync \n /settings \n\n !-- Copying description of tasks. You can specify several table tasks in the same task description (in the same ZooKeeper node), and they will be performed sequentially. -- \n tables \n !-- A table task that copies one table. -- \n table_hits \n !-- Source cluster name (from the remote_servers/ section) and tables in it that should be copied -- \n cluster_pull source_cluster /cluster_pull \n database_pull test /database_pull \n table_pull hits /table_pull \n\n !-- Destination cluster name and tables in which the data should be inserted -- \n cluster_push destination_cluster /cluster_push \n database_push test /database_push \n table_push hits2 /table_push \n\n !-- Engine of destination tables. If the destination tables have not been created yet, workers create them using column definitions from source tables and the engine definition from here. NOTE: If the first worker starts to insert data and detects that the destination partition is not empty, then the partition will be dropped and refilled. Take this into account if you already have some data in destination tables. You can directly specify partitions that should be copied in enabled_partitions/ . They should be in quoted format like the partition column in the system.parts table. -- \n engine \n ENGINE=ReplicatedMergeTree( /clickhouse/tables/{cluster}/{shard}/hits2 , {replica} )\n PARTITION BY toMonday(date)\n ORDER BY (CounterID, EventDate)\n /engine \n\n !-- Sharding key used to insert data to destination cluster -- \n sharding_key jumpConsistentHash(intHash64(UserID), 2) /sharding_key \n\n !-- Optional expression that filter data while pull them from source servers -- \n where_condition CounterID != 0 /where_condition \n\n !-- This section specifies partitions that should be copied, other partition will be ignored. Partition names should have the same format as partition column of system.parts table (i.e. a quoted text). Since partition key of source and destination cluster could be different, these partition names specify destination partitions. Note: Although this section is optional (if it omitted, all partitions will be copied), it is strongly recommended to specify the partitions explicitly. If you already have some partitions ready on the destination cluster, they will be removed at the start of the copying, because they will be interpreted as unfinished data from the previous copying. -- \n enabled_partitions \n partition 2018-02-26 /partition \n partition 2018-03-05 /partition \n ...\n /enabled_partitions \n /table_hits \n\n !-- Next table to copy. It is not copied until the previous table is copying. -- \n /table_visits \n ...\n /table_visits \n ...\n /tables /yandex clickhouse-copier tracks the changes in /task/path/description and applies them on the fly. For instance, if you change the value of max_workers , the number of processes running tasks will also change.", - "title": "Configuration of copying tasks" - }, - { - "location": "/utils/clickhouse-local/", - "text": "clickhouse-local\n\n\nThe \nclickhouse-local\n program enables you to perform fast processing on local files that store tables, without having to deploy and configure the ClickHouse server.", - "title": "clickhouse-local" - }, - { - "location": "/utils/clickhouse-local/#clickhouse-local", - "text": "The clickhouse-local program enables you to perform fast processing on local files that store tables, without having to deploy and configure the ClickHouse server.", - "title": "clickhouse-local" - }, - { - "location": "/development/architecture/", - "text": "Overview of ClickHouse architecture\n\n\nClickHouse is a true column-oriented DBMS. Data is stored by columns, and during the execution of arrays (vectors or chunks of columns). Whenever possible, operations are dispatched on arrays, rather than on individual values. This is called \"vectorized query execution,\" and it helps lower the cost of actual data processing.\n\n\n\n\nThis idea is nothing new. It dates back to the \nAPL\n programming language and its descendants: \nA +\n, \nJ\n, \nK\n, and \nQ\n. Array programming is used in scientific data processing. Neither is this idea something new in relational databases: for example, it is used in the \nVectorwise\n system.\n\n\n\n\nThere are two different approaches for speeding up the query processing: vectorized query execution and runtime code generation. In the latter, the code is generated for every kind of query on the fly, removing all indirection and dynamic dispatch. Neither of these approaches is strictly better than the other. Runtime code generation can be better when it's fuses many operations together, thus fully utilizing CPU execution units and the pipeline. Vectorized query execution can be less practical, because it involves the temporary vectors that must be written to the cache and read back. If the temporary data does not fit in the L2 cache, this becomes an issue. But vectorized query execution more easily utilizes the SIMD capabilities of the CPU. A \nresearch paper\n written by our friends shows that it is better to combine both approaches. ClickHouse uses vectorized query execution and has limited initial support for runtime code.\n\n\nColumns\n\n\nTo represent columns in memory (actually, chunks of columns), the \nIColumn\n interface is used. This interface provides helper methods for implementation of various relational operators. Almost all operations are immutable: they do not modify the original column, but create a new modified one. For example, the \nIColumn :: filter\n method accepts a filter byte mask. It is used for the \nWHERE\n and \nHAVING\n relational operators. Additional examples: the \nIColumn :: permute\n method to support \nORDER BY\n, the \nIColumn :: cut\n method to support \nLIMIT\n, and so on.\n\n\nVarious \nIColumn\n implementations (\nColumnUInt8\n, \nColumnString\n and so on) are responsible for the memory layout of columns. Memory layout is usually a contiguous array. For the integer type of columns it is just one contiguous array, like \nstd :: vector\n. For \nString\n and \nArray\n columns, it is two vectors: one for all array elements, placed contiguously, and a second one for offsets to the beginning of each array. There is also \nColumnConst\n that stores just one value in memory, but looks like a column.\n\n\nField\n\n\nNevertheless, it is possible to work with individual values as well. To represent an individual value, the \nField\n is used. \nField\n is just a discriminated union of \nUInt64\n, \nInt64\n, \nFloat64\n, \nString\n and \nArray\n. \nIColumn\n has the \noperator[]\n method to get the n-th value as a \nField\n, and the \ninsert\n method to append a \nField\n to the end of a column. These methods are not very efficient, because they require dealing with temporary \nField\n objects representing an individual value. There are more efficient methods, such as \ninsertFrom\n, \ninsertRangeFrom\n, and so on.\n\n\nField\n doesn't have enough information about a specific data type for a table. For example, \nUInt8\n, \nUInt16\n, \nUInt32\n, and \nUInt64\n are all represented as \nUInt64\n in a \nField\n.\n\n\nLeaky abstractions\n\n\nIColumn\n has methods for common relational transformations of data, but they don't meet all needs. For example, \nColumnUInt64\n doesn't have a method to calculate the sum of two columns, and \nColumnString\n doesn't have a method to run a substring search. These countless routines are implemented outside of \nIColumn\n.\n\n\nVarious functions on columns can be implemented in a generic, non-efficient way using \nIColumn\n methods to extract \nField\n values, or in a specialized way using knowledge of inner memory layout of data in a specific \nIColumn\n implementation. To do this, functions are cast to a specific \nIColumn\n type and deal with internal representation directly. For example, \nColumnUInt64\n has the \ngetData\n method that returns a reference to an internal array, then a separate routine reads or fills that array directly. In fact, we have \"leaky abstractions\" to allow efficient specializations of various routines.\n\n\nData types\n\n\nIDataType\n is responsible for serialization and deserialization: for reading and writing chunks of columns or individual values in binary or text form.\n\nIDataType\n directly corresponds to data types in tables. For example, there are \nDataTypeUInt32\n, \nDataTypeDateTime\n, \nDataTypeString\n and so on.\n\n\nIDataType\n and \nIColumn\n are only loosely related to each other. Different data types can be represented in memory by the same \nIColumn\n implementations. For example, \nDataTypeUInt32\n and \nDataTypeDateTime\n are both represented by \nColumnUInt32\n or \nColumnConstUInt32\n. In addition, the same data type can be represented by different \nIColumn\n implementations. For example, \nDataTypeUInt8\n can be represented by \nColumnUInt8\n or \nColumnConstUInt8\n.\n\n\nIDataType\n only stores metadata. For instance, \nDataTypeUInt8\n doesn't store anything at all (except vptr) and \nDataTypeFixedString\n stores just \nN\n (the size of fixed-size strings).\n\n\nIDataType\n has helper methods for various data formats. Examples are methods to serialize a value with possible quoting, to serialize a value for JSON, and to serialize a value as part of XML format. There is no direct correspondence to data formats. For example, the different data formats \nPretty\n and \nTabSeparated\n can use the same \nserializeTextEscaped\n helper method from the \nIDataType\n interface.\n\n\nBlock\n\n\nA \nBlock\n is a container that represents a subset (chunk) of a table in memory. It is just a set of triples: \n(IColumn, IDataType, column name)\n. During query execution, data is processed by \nBlock\ns. If we have a \nBlock\n, we have data (in the \nIColumn\n object), we have information about its type (in \nIDataType\n) that tells us how to deal with that column, and we have the column name (either the original column name from the table, or some artificial name assigned for getting temporary results of calculations).\n\n\nWhen we calculate some function over columns in a block, we add another column with its result to the block, and we don't touch columns for arguments of the function because operations are immutable. Later, unneeded columns can be removed from the block, but not modified. This is convenient for elimination of common subexpressions.\n\n\nBlocks are created for every processed chunk of data. Note that for the same type of calculation, the column names and types remain the same for different blocks, and only column data changes. It is better to split block data from the block header, because small block sizes will have a high overhead of temporary strings for copying shared_ptrs and column names.\n\n\nBlock Streams\n\n\nBlock streams are for processing data. We use streams of blocks to read data from somewhere, perform data transformations, or write data to somewhere. \nIBlockInputStream\n has the \nread\n method to fetch the next block while available. \nIBlockOutputStream\n has the \nwrite\n method to push the block somewhere.\n\n\nStreams are responsible for:\n\n\n\n\nReading or writing to a table. The table just returns a stream for reading or writing blocks.\n\n\nImplementing data formats. For example, if you want to output data to a terminal in \nPretty\n format, you create a block output stream where you push blocks, and it formats them.\n\n\nPerforming data transformations. Let's say you have \nIBlockInputStream\n and want to create a filtered stream. You create \nFilterBlockInputStream\n and initialize it with your stream. Then when you pull a block from \nFilterBlockInputStream\n, it pulls a block from your stream, filters it, and returns the filtered block to you. Query execution pipelines are represented this way.\n\n\n\n\nThere are more sophisticated transformations. For example, when you pull from \nAggregatingBlockInputStream\n, it reads all data from its source, aggregates it, and then returns a stream of aggregated data for you. Another example: \nUnionBlockInputStream\n accepts many input sources in the constructor and also a number of threads. It launches multiple threads and reads from multiple sources in parallel.\n\n\n\n\nBlock streams use the \"pull\" approach to control flow: when you pull a block from the first stream, it consequently pulls the required blocks from nested streams, and the entire execution pipeline will work. Neither \"pull\" nor \"push\" is the best solution, because control flow is implicit, and that limits implementation of various features like simultaneous execution of multiple queries (merging many pipelines together). This limitation could be overcome with coroutines or just running extra threads that wait for each other. We may have more possibilities if we make control flow explicit: if we locate the logic for passing data from one calculation unit to another outside of those calculation units. Read this \narticle\n for more thoughts.\n\n\n\n\nWe should note that the query execution pipeline creates temporary data at each step. We try to keep block size small enough so that temporary data fits in the CPU cache. With that assumption, writing and reading temporary data is almost free in comparison with other calculations. We could consider an alternative, which is to fuse many operations in the pipeline together, to make the pipeline as short as possible and remove much of the temporary data. This could be an advantage, but it also has drawbacks. For example, a split pipeline makes it easy to implement caching intermediate data, stealing intermediate data from similar queries running at the same time, and merging pipelines for similar queries.\n\n\nFormats\n\n\nData formats are implemented with block streams. There are \"presentational\" formats only suitable for output of data to the client, such as \nPretty\n format, which provides only \nIBlockOutputStream\n. And there are input/output formats, such as \nTabSeparated\n or \nJSONEachRow\n.\n\n\nThere are also row streams: \nIRowInputStream\n and \nIRowOutputStream\n. They allow you to pull/push data by individual rows, not by blocks. And they are only needed to simplify implementation of row-oriented formats. The wrappers \nBlockInputStreamFromRowInputStream\n and \nBlockOutputStreamFromRowOutputStream\n allow you to convert row-oriented streams to regular block-oriented streams.\n\n\nI/O\n\n\nFor byte-oriented input/output, there are \nReadBuffer\n and \nWriteBuffer\n abstract classes. They are used instead of C++ \niostream\n's. Don't worry: every mature C++ project is using something other than \niostream\n's for good reasons.\n\n\nReadBuffer\n and \nWriteBuffer\n are just a contiguous buffer and a cursor pointing to the position in that buffer. Implementations may own or not own the memory for the buffer. There is a virtual method to fill the buffer with the following data (for \nReadBuffer\n) or to flush the buffer somewhere (for \nWriteBuffer\n). The virtual methods are rarely called.\n\n\nImplementations of \nReadBuffer\n/\nWriteBuffer\n are used for working with files and file descriptors and network sockets, for implementing compression (\nCompressedWriteBuffer\n is initialized with another WriteBuffer and performs compression before writing data to it), and for other purposes \u2013 the names \nConcatReadBuffer\n, \nLimitReadBuffer\n, and \nHashingWriteBuffer\n speak for themselves.\n\n\nRead/WriteBuffers only deal with bytes. To help with formatted input/output (for instance, to write a number in decimal format), there are functions from \nReadHelpers\n and \nWriteHelpers\n header files.\n\n\nLet's look at what happens when you want to write a result set in \nJSON\n format to stdout. You have a result set ready to be fetched from \nIBlockInputStream\n. You create \nWriteBufferFromFileDescriptor(STDOUT_FILENO)\n to write bytes to stdout. You create \nJSONRowOutputStream\n, initialized with that \nWriteBuffer\n, to write rows in \nJSON\n to stdout. You create \nBlockOutputStreamFromRowOutputStream\n on top of it, to represent it as \nIBlockOutputStream\n. Then you call \ncopyData\n to transfer data from \nIBlockInputStream\n to \nIBlockOutputStream\n, and everything works. Internally, \nJSONRowOutputStream\n will write various JSON delimiters and call the \nIDataType::serializeTextJSON\n method with a reference to \nIColumn\n and the row number as arguments. Consequently, \nIDataType::serializeTextJSON\n will call a method from \nWriteHelpers.h\n: for example, \nwriteText\n for numeric types and \nwriteJSONString\n for \nDataTypeString\n.\n\n\nTables\n\n\nTables are represented by the \nIStorage\n interface. Different implementations of that interface are different table engines. Examples are \nStorageMergeTree\n, \nStorageMemory\n, and so on. Instances of these classes are just tables.\n\n\nThe most important \nIStorage\n methods are \nread\n and \nwrite\n. There are also \nalter\n, \nrename\n, \ndrop\n, and so on. The \nread\n method accepts the following arguments: the set of columns to read from a table, the \nAST\n query to consider, and the desired number of streams to return. It returns one or multiple \nIBlockInputStream\n objects and information about the stage of data processing that was completed inside a table engine during query execution.\n\n\nIn most cases, the read method is only responsible for reading the specified columns from a table, not for any further data processing. All further data processing is done by the query interpreter and is outside the responsibility of \nIStorage\n.\n\n\nBut there are notable exceptions:\n\n\n\n\nThe AST query is passed to the \nread\n method and the table engine can use it to derive index usage and to read less data from a table.\n\n\nSometimes the table engine can process data itself to a specific stage. For example, \nStorageDistributed\n can send a query to remote servers, ask them to process data to a stage where data from different remote servers can be merged, and return that preprocessed data.\nThe query interpreter then finishes processing the data.\n\n\n\n\nThe table's \nread\n method can return multiple \nIBlockInputStream\n objects to allow parallel data processing. These multiple block input streams can read from a table in parallel. Then you can wrap these streams with various transformations (such as expression evaluation or filtering) that can be calculated independently and create a \nUnionBlockInputStream\n on top of them, to read from multiple streams in parallel.\n\n\nThere are also \nTableFunction\ns. These are functions that return a temporary \nIStorage\n object to use in the \nFROM\n clause of a query.\n\n\nTo get a quick idea of how to implement your own table engine, look at something simple, like \nStorageMemory\n or \nStorageTinyLog\n.\n\n\n\n\nAs the result of the \nread\n method, \nIStorage\n returns \nQueryProcessingStage\n \u2013 information about what parts of the query were already calculated inside storage. Currently we have only very coarse granularity for that information. There is no way for the storage to say \"I have already processed this part of the expression in WHERE, for this range of data\". We need to work on that.\n\n\n\n\nParsers\n\n\nA query is parsed by a hand-written recursive descent parser. For example, \nParserSelectQuery\n just recursively calls the underlying parsers for various parts of the query. Parsers create an \nAST\n. The \nAST\n is represented by nodes, which are instances of \nIAST\n.\n\n\n\n\nParser generators are not used for historical reasons.\n\n\n\n\nInterpreters\n\n\nInterpreters are responsible for creating the query execution pipeline from an \nAST\n. There are simple interpreters, such as \nInterpreterExistsQuery\nand \nInterpreterDropQuery\n, or the more sophisticated \nInterpreterSelectQuery\n. The query execution pipeline is a combination of block input or output streams. For example, the result of interpreting the \nSELECT\n query is the \nIBlockInputStream\n to read the result set from; the result of the INSERT query is the \nIBlockOutputStream\n to write data for insertion to; and the result of interpreting the \nINSERT SELECT\n query is the \nIBlockInputStream\n that returns an empty result set on the first read, but that copies data from \nSELECT\n to \nINSERT\n at the same time.\n\n\nInterpreterSelectQuery\n uses \nExpressionAnalyzer\n and \nExpressionActions\n machinery for query analysis and transformations. This is where most rule-based query optimizations are done. \nExpressionAnalyzer\n is quite messy and should be rewritten: various query transformations and optimizations should be extracted to separate classes to allow modular transformations or query.\n\n\nFunctions\n\n\nThere are ordinary functions and aggregate functions. For aggregate functions, see the next section.\n\n\nOrdinary functions don't change the number of rows \u2013 they work as if they are processing each row independently. In fact, functions are not called for individual rows, but for \nBlock\n's of data to implement vectorized query execution.\n\n\nThere are some miscellaneous functions, like \nblockSize\n, \nrowNumberInBlock\n, and \nrunningAccumulate\n, that exploit block processing and violate the independence of rows.\n\n\nClickHouse has strong typing, so implicit type conversion doesn't occur. If a function doesn't support a specific combination of types, an exception will be thrown. But functions can work (be overloaded) for many different combinations of types. For example, the \nplus\n function (to implement the \n+\n operator) works for any combination of numeric types: \nUInt8\n + \nFloat32\n, \nUInt16\n + \nInt8\n, and so on. Also, some variadic functions can accept any number of arguments, such as the \nconcat\n function.\n\n\nImplementing a function may be slightly inconvenient because a function explicitly dispatches supported data types and supported \nIColumns\n. For example, the \nplus\n function has code generated by instantiation of a C++ template for each combination of numeric types, and for constant or non-constant left and right arguments.\n\n\n\n\nThis is a nice place to implement runtime code generation to avoid template code bloat. Also, it will make it possible to add fused functions like fused multiply-add, or to make multiple comparisons in one loop iteration.\n\n\n\n\nDue to vectorized query execution, functions are not short-circuit. For example, if you write \nWHERE f(x) AND g(y)\n, both sides will be calculated, even for rows, when \nf(x)\n is zero (except when \nf(x)\n is a zero constant expression). But if selectivity of the \nf(x)\n condition is high, and calculation of \nf(x)\n is much cheaper than \ng(y)\n, it's better to implement multi-pass calculation: first calculate \nf(x)\n, then filter columns by the result, and then calculate \ng(y)\n only for smaller, filtered chunks of data.\n\n\nAggregate Functions\n\n\nAggregate functions are stateful functions. They accumulate passed values into some state, and allow you to get results from that state. They are managed with the \nIAggregateFunction\n interface. States can be rather simple (the state for \nAggregateFunctionCount\n is just a single \nUInt64\n value) or quite complex (the state of \nAggregateFunctionUniqCombined\n is a combination of a linear array, a hash table and a \nHyperLogLog\n probabilistic data structure).\n\n\nTo deal with multiple states while executing a high-cardinality \nGROUP BY\n query, states are allocated in \nArena\n (a memory pool), or they could be allocated in any suitable piece of memory. States can have a non-trivial constructor and destructor: for example, complex aggregation states can allocate additional memory themselves. This requires some attention to creating and destroying states and properly passing their ownership, to keep track of who and when will destroy states.\n\n\nAggregation states can be serialized and deserialized to pass over the network during distributed query execution or to write them on disk where there is not enough RAM. They can even be stored in a table with the \nDataTypeAggregateFunction\n to allow incremental aggregation of data.\n\n\n\n\nThe serialized data format for aggregate function states is not versioned right now. This is ok if aggregate states are only stored temporarily. But we have the \nAggregatingMergeTree\n table engine for incremental aggregation, and people are already using it in production. This is why we should add support for backward compatibility when changing the serialized format for any aggregate function in the future.\n\n\n\n\nServer\n\n\nThe server implements several different interfaces:\n\n\n\n\nAn HTTP interface for any foreign clients.\n\n\nA TCP interface for the native ClickHouse client and for cross-server communication during distributed query execution.\n\n\nAn interface for transferring data for replication.\n\n\n\n\nInternally, it is just a basic multithreaded server without coroutines, fibers, etc. Since the server is not designed to process a high rate of simple queries but is intended to process a relatively low rate of complex queries, each of them can process a vast amount of data for analytics.\n\n\nThe server initializes the \nContext\n class with the necessary environment for query execution: the list of available databases, users and access rights, settings, clusters, the process list, the query log, and so on. This environment is used by interpreters.\n\n\nWe maintain full backward and forward compatibility for the server TCP protocol: old clients can talk to new servers and new clients can talk to old servers. But we don't want to maintain it eternally, and we are removing support for old versions after about one year.\n\n\n\n\nFor all external applications, we recommend using the HTTP interface because it is simple and easy to use. The TCP protocol is more tightly linked to internal data structures: it uses an internal format for passing blocks of data and it uses custom framing for compressed data. We haven't released a C library for that protocol because it requires linking most of the ClickHouse codebase, which is not practical.\n\n\n\n\nDistributed query execution\n\n\nServers in a cluster setup are mostly independent. You can create a \nDistributed\n table on one or all servers in a cluster. The \nDistributed\n table does not store data itself \u2013 it only provides a \"view\" to all local tables on multiple nodes of a cluster. When you SELECT from a \nDistributed\n table, it rewrites that query, chooses remote nodes according to load balancing settings, and sends the query to them. The \nDistributed\n table requests remote servers to process a query just up to a stage where intermediate results from different servers can be merged. Then it receives the intermediate results and merges them. The distributed table tries to distribute as much work as possible to remote servers, and does not send much intermediate data over the network.\n\n\n\n\nThings become more complicated when you have subqueries in IN or JOIN clauses and each of them uses a \nDistributed\n table. We have different strategies for execution of these queries.\n\n\n\n\nThere is no global query plan for distributed query execution. Each node has its own local query plan for its part of the job. We only have simple one-pass distributed query execution: we send queries for remote nodes and then merge the results. But this is not feasible for difficult queries with high cardinality GROUP BYs or with a large amount of temporary data for JOIN: in such cases, we need to \"reshuffle\" data between servers, which requires additional coordination. ClickHouse does not support that kind of query execution, and we need to work on it.\n\n\nMerge Tree\n\n\nMergeTree\n is a family of storage engines that supports indexing by primary key. The primary key can be an arbitary tuple of columns or expressions. Data in a \nMergeTree\n table is stored in \"parts\". Each part stores data in the primary key order (data is ordered lexicographically by the primary key tuple). All the table columns are stored in separate \ncolumn.bin\n files in these parts. The files consist of compressed blocks. Each block is usually from 64 KB to 1 MB of uncompressed data, depending on the average value size. The blocks consist of column values placed contiguously one after the other. Column values are in the same order for each column (the order is defined by the primary key), so when you iterate by many columns, you get values for the corresponding rows.\n\n\nThe primary key itself is \"sparse\". It doesn't address each single row, but only some ranges of data. A separate \nprimary.idx\n file has the value of the primary key for each N-th row, where N is called \nindex_granularity\n (usually, N = 8192). Also, for each column, we have \ncolumn.mrk\n files with \"marks,\" which are offsets to each N-th row in the data file. Each mark is a pair: the offset in the file to the beginning of the compressed block, and the offset in the decompressed block to the beginning of data. Usually compressed blocks are aligned by marks, and the offset in the decompressed block is zero. Data for \nprimary.idx\n always resides in memory and data for \ncolumn.mrk\n files is cached.\n\n\nWhen we are going to read something from a part in \nMergeTree\n, we look at \nprimary.idx\n data and locate ranges that could possibly contain requested data, then look at \ncolumn.mrk\n data and calculate offsets for where to start reading those ranges. Because of sparseness, excess data may be read. ClickHouse is not suitable for a high load of simple point queries, because the entire range with \nindex_granularity\n rows must be read for each key, and the entire compressed block must be decompressed for each column. We made the index sparse because we must be able to maintain trillions of rows per single server without noticeable memory consumption for the index. Also, because the primary key is sparse, it is not unique: it cannot check the existence of the key in the table at INSERT time. You could have many rows with the same key in a table.\n\n\nWhen you \nINSERT\n a bunch of data into \nMergeTree\n, that bunch is sorted by primary key order and forms a new part. To keep the number of parts relatively low, there are background threads that periodically select some parts and merge them to a single sorted part. That's why it is called \nMergeTree\n. Of course, merging leads to \"write amplification\". All parts are immutable: they are only created and deleted, but not modified. When SELECT is run, it holds a snapshot of the table (a set of parts). After merging, we also keep old parts for some time to make recovery after failure easier, so if we see that some merged part is probably broken, we can replace it with its source parts.\n\n\nMergeTree\n is not an LSM tree because it doesn't contain \"memtable\" and \"log\": inserted data is written directly to the filesystem. This makes it suitable only to INSERT data in batches, not by individual row and not very frequently \u2013 about once per second is ok, but a thousand times a second is not. We did it this way for simplicity's sake, and because we are already inserting data in batches in our applications.\n\n\n\n\nMergeTree tables can only have one (primary) index: there aren't any secondary indices. It would be nice to allow multiple physical representations under one logical table, for example, to store data in more than one physical order or even to allow representations with pre-aggregated data along with original data.\n\n\n\n\nThere are MergeTree engines that are doing additional work during background merges. Examples are \nCollapsingMergeTree\n and \nAggregatingMergeTree\n. This could be treated as special support for updates. Keep in mind that these are not real updates because users usually have no control over the time when background merges will be executed, and data in a \nMergeTree\n table is almost always stored in more than one part, not in completely merged form.\n\n\nReplication\n\n\nReplication in ClickHouse is implemented on a per-table basis. You could have some replicated and some non-replicated tables on the same server. You could also have tables replicated in different ways, such as one table with two-factor replication and another with three-factor.\n\n\nReplication is implemented in the \nReplicatedMergeTree\n storage engine. The path in \nZooKeeper\n is specified as a parameter for the storage engine. All tables with the same path in \nZooKeeper\n become replicas of each other: they synchronize their data and maintain consistency. Replicas can be added and removed dynamically simply by creating or dropping a table.\n\n\nReplication uses an asynchronous multi-master scheme. You can insert data into any replica that has a session with \nZooKeeper\n, and data is replicated to all other replicas asynchronously. Because ClickHouse doesn't support UPDATEs, replication is conflict-free. As there is no quorum acknowledgment of inserts, just-inserted data might be lost if one node fails.\n\n\nMetadata for replication is stored in ZooKeeper. There is a replication log that lists what actions to do. Actions are: get part; merge parts; drop partition, etc. Each replica copies the replication log to its queue and then executes the actions from the queue. For example, on insertion, the \"get part\" action is created in the log, and every replica downloads that part. Merges are coordinated between replicas to get byte-identical results. All parts are merged in the same way on all replicas. To achieve this, one replica is elected as the leader, and that replica initiates merges and writes \"merge parts\" actions to the log.\n\n\nReplication is physical: only compressed parts are transferred between nodes, not queries. To lower the network cost (to avoid network amplification), merges are processed on each replica independently in most cases. Large merged parts are sent over the network only in cases of significant replication lag.\n\n\nIn addition, each replica stores its state in ZooKeeper as the set of parts and its checksums. When the state on the local filesystem diverges from the reference state in ZooKeeper, the replica restores its consistency by downloading missing and broken parts from other replicas. When there is some unexpected or broken data in the local filesystem, ClickHouse does not remove it, but moves it to a separate directory and forgets it.\n\n\n\n\nThe ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is not elastic, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load will be uneven. This implementation gives you more control, and it is fine for relatively small clusters such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that will span its data across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically.", - "title": "Overview of ClickHouse architecture" - }, - { - "location": "/development/architecture/#overview-of-clickhouse-architecture", - "text": "ClickHouse is a true column-oriented DBMS. Data is stored by columns, and during the execution of arrays (vectors or chunks of columns). Whenever possible, operations are dispatched on arrays, rather than on individual values. This is called \"vectorized query execution,\" and it helps lower the cost of actual data processing. This idea is nothing new. It dates back to the APL programming language and its descendants: A + , J , K , and Q . Array programming is used in scientific data processing. Neither is this idea something new in relational databases: for example, it is used in the Vectorwise system. There are two different approaches for speeding up the query processing: vectorized query execution and runtime code generation. In the latter, the code is generated for every kind of query on the fly, removing all indirection and dynamic dispatch. Neither of these approaches is strictly better than the other. Runtime code generation can be better when it's fuses many operations together, thus fully utilizing CPU execution units and the pipeline. Vectorized query execution can be less practical, because it involves the temporary vectors that must be written to the cache and read back. If the temporary data does not fit in the L2 cache, this becomes an issue. But vectorized query execution more easily utilizes the SIMD capabilities of the CPU. A research paper written by our friends shows that it is better to combine both approaches. ClickHouse uses vectorized query execution and has limited initial support for runtime code.", - "title": "Overview of ClickHouse architecture" - }, - { - "location": "/development/architecture/#columns", - "text": "To represent columns in memory (actually, chunks of columns), the IColumn interface is used. This interface provides helper methods for implementation of various relational operators. Almost all operations are immutable: they do not modify the original column, but create a new modified one. For example, the IColumn :: filter method accepts a filter byte mask. It is used for the WHERE and HAVING relational operators. Additional examples: the IColumn :: permute method to support ORDER BY , the IColumn :: cut method to support LIMIT , and so on. Various IColumn implementations ( ColumnUInt8 , ColumnString and so on) are responsible for the memory layout of columns. Memory layout is usually a contiguous array. For the integer type of columns it is just one contiguous array, like std :: vector . For String and Array columns, it is two vectors: one for all array elements, placed contiguously, and a second one for offsets to the beginning of each array. There is also ColumnConst that stores just one value in memory, but looks like a column.", - "title": "Columns" - }, - { - "location": "/development/architecture/#field", - "text": "Nevertheless, it is possible to work with individual values as well. To represent an individual value, the Field is used. Field is just a discriminated union of UInt64 , Int64 , Float64 , String and Array . IColumn has the operator[] method to get the n-th value as a Field , and the insert method to append a Field to the end of a column. These methods are not very efficient, because they require dealing with temporary Field objects representing an individual value. There are more efficient methods, such as insertFrom , insertRangeFrom , and so on. Field doesn't have enough information about a specific data type for a table. For example, UInt8 , UInt16 , UInt32 , and UInt64 are all represented as UInt64 in a Field .", - "title": "Field" - }, - { - "location": "/development/architecture/#leaky-abstractions", - "text": "IColumn has methods for common relational transformations of data, but they don't meet all needs. For example, ColumnUInt64 doesn't have a method to calculate the sum of two columns, and ColumnString doesn't have a method to run a substring search. These countless routines are implemented outside of IColumn . Various functions on columns can be implemented in a generic, non-efficient way using IColumn methods to extract Field values, or in a specialized way using knowledge of inner memory layout of data in a specific IColumn implementation. To do this, functions are cast to a specific IColumn type and deal with internal representation directly. For example, ColumnUInt64 has the getData method that returns a reference to an internal array, then a separate routine reads or fills that array directly. In fact, we have \"leaky abstractions\" to allow efficient specializations of various routines.", - "title": "Leaky abstractions" - }, - { - "location": "/development/architecture/#data-types", - "text": "IDataType is responsible for serialization and deserialization: for reading and writing chunks of columns or individual values in binary or text form. IDataType directly corresponds to data types in tables. For example, there are DataTypeUInt32 , DataTypeDateTime , DataTypeString and so on. IDataType and IColumn are only loosely related to each other. Different data types can be represented in memory by the same IColumn implementations. For example, DataTypeUInt32 and DataTypeDateTime are both represented by ColumnUInt32 or ColumnConstUInt32 . In addition, the same data type can be represented by different IColumn implementations. For example, DataTypeUInt8 can be represented by ColumnUInt8 or ColumnConstUInt8 . IDataType only stores metadata. For instance, DataTypeUInt8 doesn't store anything at all (except vptr) and DataTypeFixedString stores just N (the size of fixed-size strings). IDataType has helper methods for various data formats. Examples are methods to serialize a value with possible quoting, to serialize a value for JSON, and to serialize a value as part of XML format. There is no direct correspondence to data formats. For example, the different data formats Pretty and TabSeparated can use the same serializeTextEscaped helper method from the IDataType interface.", - "title": "Data types" - }, - { - "location": "/development/architecture/#block", - "text": "A Block is a container that represents a subset (chunk) of a table in memory. It is just a set of triples: (IColumn, IDataType, column name) . During query execution, data is processed by Block s. If we have a Block , we have data (in the IColumn object), we have information about its type (in IDataType ) that tells us how to deal with that column, and we have the column name (either the original column name from the table, or some artificial name assigned for getting temporary results of calculations). When we calculate some function over columns in a block, we add another column with its result to the block, and we don't touch columns for arguments of the function because operations are immutable. Later, unneeded columns can be removed from the block, but not modified. This is convenient for elimination of common subexpressions. Blocks are created for every processed chunk of data. Note that for the same type of calculation, the column names and types remain the same for different blocks, and only column data changes. It is better to split block data from the block header, because small block sizes will have a high overhead of temporary strings for copying shared_ptrs and column names.", - "title": "Block" - }, - { - "location": "/development/architecture/#block-streams", - "text": "Block streams are for processing data. We use streams of blocks to read data from somewhere, perform data transformations, or write data to somewhere. IBlockInputStream has the read method to fetch the next block while available. IBlockOutputStream has the write method to push the block somewhere. Streams are responsible for: Reading or writing to a table. The table just returns a stream for reading or writing blocks. Implementing data formats. For example, if you want to output data to a terminal in Pretty format, you create a block output stream where you push blocks, and it formats them. Performing data transformations. Let's say you have IBlockInputStream and want to create a filtered stream. You create FilterBlockInputStream and initialize it with your stream. Then when you pull a block from FilterBlockInputStream , it pulls a block from your stream, filters it, and returns the filtered block to you. Query execution pipelines are represented this way. There are more sophisticated transformations. For example, when you pull from AggregatingBlockInputStream , it reads all data from its source, aggregates it, and then returns a stream of aggregated data for you. Another example: UnionBlockInputStream accepts many input sources in the constructor and also a number of threads. It launches multiple threads and reads from multiple sources in parallel. Block streams use the \"pull\" approach to control flow: when you pull a block from the first stream, it consequently pulls the required blocks from nested streams, and the entire execution pipeline will work. Neither \"pull\" nor \"push\" is the best solution, because control flow is implicit, and that limits implementation of various features like simultaneous execution of multiple queries (merging many pipelines together). This limitation could be overcome with coroutines or just running extra threads that wait for each other. We may have more possibilities if we make control flow explicit: if we locate the logic for passing data from one calculation unit to another outside of those calculation units. Read this article for more thoughts. We should note that the query execution pipeline creates temporary data at each step. We try to keep block size small enough so that temporary data fits in the CPU cache. With that assumption, writing and reading temporary data is almost free in comparison with other calculations. We could consider an alternative, which is to fuse many operations in the pipeline together, to make the pipeline as short as possible and remove much of the temporary data. This could be an advantage, but it also has drawbacks. For example, a split pipeline makes it easy to implement caching intermediate data, stealing intermediate data from similar queries running at the same time, and merging pipelines for similar queries.", - "title": "Block Streams" - }, - { - "location": "/development/architecture/#formats", - "text": "Data formats are implemented with block streams. There are \"presentational\" formats only suitable for output of data to the client, such as Pretty format, which provides only IBlockOutputStream . And there are input/output formats, such as TabSeparated or JSONEachRow . There are also row streams: IRowInputStream and IRowOutputStream . They allow you to pull/push data by individual rows, not by blocks. And they are only needed to simplify implementation of row-oriented formats. The wrappers BlockInputStreamFromRowInputStream and BlockOutputStreamFromRowOutputStream allow you to convert row-oriented streams to regular block-oriented streams.", - "title": "Formats" - }, - { - "location": "/development/architecture/#io", - "text": "For byte-oriented input/output, there are ReadBuffer and WriteBuffer abstract classes. They are used instead of C++ iostream 's. Don't worry: every mature C++ project is using something other than iostream 's for good reasons. ReadBuffer and WriteBuffer are just a contiguous buffer and a cursor pointing to the position in that buffer. Implementations may own or not own the memory for the buffer. There is a virtual method to fill the buffer with the following data (for ReadBuffer ) or to flush the buffer somewhere (for WriteBuffer ). The virtual methods are rarely called. Implementations of ReadBuffer / WriteBuffer are used for working with files and file descriptors and network sockets, for implementing compression ( CompressedWriteBuffer is initialized with another WriteBuffer and performs compression before writing data to it), and for other purposes \u2013 the names ConcatReadBuffer , LimitReadBuffer , and HashingWriteBuffer speak for themselves. Read/WriteBuffers only deal with bytes. To help with formatted input/output (for instance, to write a number in decimal format), there are functions from ReadHelpers and WriteHelpers header files. Let's look at what happens when you want to write a result set in JSON format to stdout. You have a result set ready to be fetched from IBlockInputStream . You create WriteBufferFromFileDescriptor(STDOUT_FILENO) to write bytes to stdout. You create JSONRowOutputStream , initialized with that WriteBuffer , to write rows in JSON to stdout. You create BlockOutputStreamFromRowOutputStream on top of it, to represent it as IBlockOutputStream . Then you call copyData to transfer data from IBlockInputStream to IBlockOutputStream , and everything works. Internally, JSONRowOutputStream will write various JSON delimiters and call the IDataType::serializeTextJSON method with a reference to IColumn and the row number as arguments. Consequently, IDataType::serializeTextJSON will call a method from WriteHelpers.h : for example, writeText for numeric types and writeJSONString for DataTypeString .", - "title": "I/O" - }, - { - "location": "/development/architecture/#tables", - "text": "Tables are represented by the IStorage interface. Different implementations of that interface are different table engines. Examples are StorageMergeTree , StorageMemory , and so on. Instances of these classes are just tables. The most important IStorage methods are read and write . There are also alter , rename , drop , and so on. The read method accepts the following arguments: the set of columns to read from a table, the AST query to consider, and the desired number of streams to return. It returns one or multiple IBlockInputStream objects and information about the stage of data processing that was completed inside a table engine during query execution. In most cases, the read method is only responsible for reading the specified columns from a table, not for any further data processing. All further data processing is done by the query interpreter and is outside the responsibility of IStorage . But there are notable exceptions: The AST query is passed to the read method and the table engine can use it to derive index usage and to read less data from a table. Sometimes the table engine can process data itself to a specific stage. For example, StorageDistributed can send a query to remote servers, ask them to process data to a stage where data from different remote servers can be merged, and return that preprocessed data.\nThe query interpreter then finishes processing the data. The table's read method can return multiple IBlockInputStream objects to allow parallel data processing. These multiple block input streams can read from a table in parallel. Then you can wrap these streams with various transformations (such as expression evaluation or filtering) that can be calculated independently and create a UnionBlockInputStream on top of them, to read from multiple streams in parallel. There are also TableFunction s. These are functions that return a temporary IStorage object to use in the FROM clause of a query. To get a quick idea of how to implement your own table engine, look at something simple, like StorageMemory or StorageTinyLog . As the result of the read method, IStorage returns QueryProcessingStage \u2013 information about what parts of the query were already calculated inside storage. Currently we have only very coarse granularity for that information. There is no way for the storage to say \"I have already processed this part of the expression in WHERE, for this range of data\". We need to work on that.", - "title": "Tables" - }, - { - "location": "/development/architecture/#parsers", - "text": "A query is parsed by a hand-written recursive descent parser. For example, ParserSelectQuery just recursively calls the underlying parsers for various parts of the query. Parsers create an AST . The AST is represented by nodes, which are instances of IAST . Parser generators are not used for historical reasons.", - "title": "Parsers" - }, - { - "location": "/development/architecture/#interpreters", - "text": "Interpreters are responsible for creating the query execution pipeline from an AST . There are simple interpreters, such as InterpreterExistsQuery and InterpreterDropQuery , or the more sophisticated InterpreterSelectQuery . The query execution pipeline is a combination of block input or output streams. For example, the result of interpreting the SELECT query is the IBlockInputStream to read the result set from; the result of the INSERT query is the IBlockOutputStream to write data for insertion to; and the result of interpreting the INSERT SELECT query is the IBlockInputStream that returns an empty result set on the first read, but that copies data from SELECT to INSERT at the same time. InterpreterSelectQuery uses ExpressionAnalyzer and ExpressionActions machinery for query analysis and transformations. This is where most rule-based query optimizations are done. ExpressionAnalyzer is quite messy and should be rewritten: various query transformations and optimizations should be extracted to separate classes to allow modular transformations or query.", - "title": "Interpreters" - }, - { - "location": "/development/architecture/#functions", - "text": "There are ordinary functions and aggregate functions. For aggregate functions, see the next section. Ordinary functions don't change the number of rows \u2013 they work as if they are processing each row independently. In fact, functions are not called for individual rows, but for Block 's of data to implement vectorized query execution. There are some miscellaneous functions, like blockSize , rowNumberInBlock , and runningAccumulate , that exploit block processing and violate the independence of rows. ClickHouse has strong typing, so implicit type conversion doesn't occur. If a function doesn't support a specific combination of types, an exception will be thrown. But functions can work (be overloaded) for many different combinations of types. For example, the plus function (to implement the + operator) works for any combination of numeric types: UInt8 + Float32 , UInt16 + Int8 , and so on. Also, some variadic functions can accept any number of arguments, such as the concat function. Implementing a function may be slightly inconvenient because a function explicitly dispatches supported data types and supported IColumns . For example, the plus function has code generated by instantiation of a C++ template for each combination of numeric types, and for constant or non-constant left and right arguments. This is a nice place to implement runtime code generation to avoid template code bloat. Also, it will make it possible to add fused functions like fused multiply-add, or to make multiple comparisons in one loop iteration. Due to vectorized query execution, functions are not short-circuit. For example, if you write WHERE f(x) AND g(y) , both sides will be calculated, even for rows, when f(x) is zero (except when f(x) is a zero constant expression). But if selectivity of the f(x) condition is high, and calculation of f(x) is much cheaper than g(y) , it's better to implement multi-pass calculation: first calculate f(x) , then filter columns by the result, and then calculate g(y) only for smaller, filtered chunks of data.", - "title": "Functions" - }, - { - "location": "/development/architecture/#aggregate-functions", - "text": "Aggregate functions are stateful functions. They accumulate passed values into some state, and allow you to get results from that state. They are managed with the IAggregateFunction interface. States can be rather simple (the state for AggregateFunctionCount is just a single UInt64 value) or quite complex (the state of AggregateFunctionUniqCombined is a combination of a linear array, a hash table and a HyperLogLog probabilistic data structure). To deal with multiple states while executing a high-cardinality GROUP BY query, states are allocated in Arena (a memory pool), or they could be allocated in any suitable piece of memory. States can have a non-trivial constructor and destructor: for example, complex aggregation states can allocate additional memory themselves. This requires some attention to creating and destroying states and properly passing their ownership, to keep track of who and when will destroy states. Aggregation states can be serialized and deserialized to pass over the network during distributed query execution or to write them on disk where there is not enough RAM. They can even be stored in a table with the DataTypeAggregateFunction to allow incremental aggregation of data. The serialized data format for aggregate function states is not versioned right now. This is ok if aggregate states are only stored temporarily. But we have the AggregatingMergeTree table engine for incremental aggregation, and people are already using it in production. This is why we should add support for backward compatibility when changing the serialized format for any aggregate function in the future.", - "title": "Aggregate Functions" - }, - { - "location": "/development/architecture/#server", - "text": "The server implements several different interfaces: An HTTP interface for any foreign clients. A TCP interface for the native ClickHouse client and for cross-server communication during distributed query execution. An interface for transferring data for replication. Internally, it is just a basic multithreaded server without coroutines, fibers, etc. Since the server is not designed to process a high rate of simple queries but is intended to process a relatively low rate of complex queries, each of them can process a vast amount of data for analytics. The server initializes the Context class with the necessary environment for query execution: the list of available databases, users and access rights, settings, clusters, the process list, the query log, and so on. This environment is used by interpreters. We maintain full backward and forward compatibility for the server TCP protocol: old clients can talk to new servers and new clients can talk to old servers. But we don't want to maintain it eternally, and we are removing support for old versions after about one year. For all external applications, we recommend using the HTTP interface because it is simple and easy to use. The TCP protocol is more tightly linked to internal data structures: it uses an internal format for passing blocks of data and it uses custom framing for compressed data. We haven't released a C library for that protocol because it requires linking most of the ClickHouse codebase, which is not practical.", - "title": "Server" - }, - { - "location": "/development/architecture/#distributed-query-execution", - "text": "Servers in a cluster setup are mostly independent. You can create a Distributed table on one or all servers in a cluster. The Distributed table does not store data itself \u2013 it only provides a \"view\" to all local tables on multiple nodes of a cluster. When you SELECT from a Distributed table, it rewrites that query, chooses remote nodes according to load balancing settings, and sends the query to them. The Distributed table requests remote servers to process a query just up to a stage where intermediate results from different servers can be merged. Then it receives the intermediate results and merges them. The distributed table tries to distribute as much work as possible to remote servers, and does not send much intermediate data over the network. Things become more complicated when you have subqueries in IN or JOIN clauses and each of them uses a Distributed table. We have different strategies for execution of these queries. There is no global query plan for distributed query execution. Each node has its own local query plan for its part of the job. We only have simple one-pass distributed query execution: we send queries for remote nodes and then merge the results. But this is not feasible for difficult queries with high cardinality GROUP BYs or with a large amount of temporary data for JOIN: in such cases, we need to \"reshuffle\" data between servers, which requires additional coordination. ClickHouse does not support that kind of query execution, and we need to work on it.", - "title": "Distributed query execution" - }, - { - "location": "/development/architecture/#merge-tree", - "text": "MergeTree is a family of storage engines that supports indexing by primary key. The primary key can be an arbitary tuple of columns or expressions. Data in a MergeTree table is stored in \"parts\". Each part stores data in the primary key order (data is ordered lexicographically by the primary key tuple). All the table columns are stored in separate column.bin files in these parts. The files consist of compressed blocks. Each block is usually from 64 KB to 1 MB of uncompressed data, depending on the average value size. The blocks consist of column values placed contiguously one after the other. Column values are in the same order for each column (the order is defined by the primary key), so when you iterate by many columns, you get values for the corresponding rows. The primary key itself is \"sparse\". It doesn't address each single row, but only some ranges of data. A separate primary.idx file has the value of the primary key for each N-th row, where N is called index_granularity (usually, N = 8192). Also, for each column, we have column.mrk files with \"marks,\" which are offsets to each N-th row in the data file. Each mark is a pair: the offset in the file to the beginning of the compressed block, and the offset in the decompressed block to the beginning of data. Usually compressed blocks are aligned by marks, and the offset in the decompressed block is zero. Data for primary.idx always resides in memory and data for column.mrk files is cached. When we are going to read something from a part in MergeTree , we look at primary.idx data and locate ranges that could possibly contain requested data, then look at column.mrk data and calculate offsets for where to start reading those ranges. Because of sparseness, excess data may be read. ClickHouse is not suitable for a high load of simple point queries, because the entire range with index_granularity rows must be read for each key, and the entire compressed block must be decompressed for each column. We made the index sparse because we must be able to maintain trillions of rows per single server without noticeable memory consumption for the index. Also, because the primary key is sparse, it is not unique: it cannot check the existence of the key in the table at INSERT time. You could have many rows with the same key in a table. When you INSERT a bunch of data into MergeTree , that bunch is sorted by primary key order and forms a new part. To keep the number of parts relatively low, there are background threads that periodically select some parts and merge them to a single sorted part. That's why it is called MergeTree . Of course, merging leads to \"write amplification\". All parts are immutable: they are only created and deleted, but not modified. When SELECT is run, it holds a snapshot of the table (a set of parts). After merging, we also keep old parts for some time to make recovery after failure easier, so if we see that some merged part is probably broken, we can replace it with its source parts. MergeTree is not an LSM tree because it doesn't contain \"memtable\" and \"log\": inserted data is written directly to the filesystem. This makes it suitable only to INSERT data in batches, not by individual row and not very frequently \u2013 about once per second is ok, but a thousand times a second is not. We did it this way for simplicity's sake, and because we are already inserting data in batches in our applications. MergeTree tables can only have one (primary) index: there aren't any secondary indices. It would be nice to allow multiple physical representations under one logical table, for example, to store data in more than one physical order or even to allow representations with pre-aggregated data along with original data. There are MergeTree engines that are doing additional work during background merges. Examples are CollapsingMergeTree and AggregatingMergeTree . This could be treated as special support for updates. Keep in mind that these are not real updates because users usually have no control over the time when background merges will be executed, and data in a MergeTree table is almost always stored in more than one part, not in completely merged form.", - "title": "Merge Tree" - }, - { - "location": "/development/architecture/#replication", - "text": "Replication in ClickHouse is implemented on a per-table basis. You could have some replicated and some non-replicated tables on the same server. You could also have tables replicated in different ways, such as one table with two-factor replication and another with three-factor. Replication is implemented in the ReplicatedMergeTree storage engine. The path in ZooKeeper is specified as a parameter for the storage engine. All tables with the same path in ZooKeeper become replicas of each other: they synchronize their data and maintain consistency. Replicas can be added and removed dynamically simply by creating or dropping a table. Replication uses an asynchronous multi-master scheme. You can insert data into any replica that has a session with ZooKeeper , and data is replicated to all other replicas asynchronously. Because ClickHouse doesn't support UPDATEs, replication is conflict-free. As there is no quorum acknowledgment of inserts, just-inserted data might be lost if one node fails. Metadata for replication is stored in ZooKeeper. There is a replication log that lists what actions to do. Actions are: get part; merge parts; drop partition, etc. Each replica copies the replication log to its queue and then executes the actions from the queue. For example, on insertion, the \"get part\" action is created in the log, and every replica downloads that part. Merges are coordinated between replicas to get byte-identical results. All parts are merged in the same way on all replicas. To achieve this, one replica is elected as the leader, and that replica initiates merges and writes \"merge parts\" actions to the log. Replication is physical: only compressed parts are transferred between nodes, not queries. To lower the network cost (to avoid network amplification), merges are processed on each replica independently in most cases. Large merged parts are sent over the network only in cases of significant replication lag. In addition, each replica stores its state in ZooKeeper as the set of parts and its checksums. When the state on the local filesystem diverges from the reference state in ZooKeeper, the replica restores its consistency by downloading missing and broken parts from other replicas. When there is some unexpected or broken data in the local filesystem, ClickHouse does not remove it, but moves it to a separate directory and forgets it. The ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is not elastic, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load will be uneven. This implementation gives you more control, and it is fine for relatively small clusters such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that will span its data across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically.", - "title": "Replication" - }, - { - "location": "/development/build/", - "text": "How to build ClickHouse on Linux\n\n\nBuild should work on Linux Ubuntu 12.04, 14.04 or newer.\nWith appropriate changes, it should also work on any other Linux distribution.\nThe build process is not intended to work on Mac OS X.\nOnly x86_64 with SSE 4.2 is supported. Support for AArch64 is experimental.\n\n\nTo test for SSE 4.2, do\n\n\ngrep -q sse4_2 /proc/cpuinfo \n \necho\n \nSSE 4.2 supported\n \n||\n \necho\n \nSSE 4.2 not supported\n\n\n\n\n\n\nInstall Git and CMake\n\n\nsudo apt-get install git cmake\n\n\n\n\n\nOr cmake3 instead of cmake on older systems.\n\n\nDetect the number of threads\n\n\nexport\n \nTHREADS\n=\n$(\ngrep -c ^processor /proc/cpuinfo\n)\n\n\n\n\n\n\nInstall GCC 7\n\n\nThere are several ways to do this.\n\n\nInstall from a PPA package\n\n\nsudo apt-get install software-properties-common\nsudo apt-add-repository ppa:ubuntu-toolchain-r/test\nsudo apt-get update\nsudo apt-get install gcc-7 g++-7\n\n\n\n\n\nInstall from sources\n\n\nLook at [https://github.com/yandex/ClickHouse/blob/master/utils/prepare-environment/install-gcc.sh]\n\n\nUse GCC 7 for builds\n\n\nexport\n \nCC\n=\ngcc-7\n\nexport\n \nCXX\n=\ng++-7\n\n\n\n\n\nInstall required libraries from packages\n\n\nsudo apt-get install libicu-dev libreadline-dev libmysqlclient-dev libssl-dev unixodbc-dev ninja-build\n\n\n\n\n\nCheckout ClickHouse sources\n\n\nTo get the latest stable version:\n\n\ngit clone -b stable --recursive git@github.com:yandex/ClickHouse.git\n\n# or: git clone -b stable --recursive https://github.com/yandex/ClickHouse.git\n\n\n\ncd\n ClickHouse\n\n\n\n\n\nFor development, switch to the \nmaster\n branch.\nFor the latest release candidate, switch to the \ntesting\n branch.\n\n\nBuild ClickHouse\n\n\nThere are two build variants.\n\n\nBuild release package\n\n\nInstall prerequisites to build Debian packages.\n\n\nsudo apt-get install devscripts dupload fakeroot debhelper\n\n\n\n\n\nInstall the most recent version of Clang.\n\n\nClang is embedded into the ClickHouse package and used at runtime. The minimum version is 5.0. It is optional.\n\n\nTo install clang, see \nutils/prepare-environment/install-clang.sh\n\n\nYou may also build ClickHouse with Clang for development purposes.\nFor production releases, GCC is used.\n\n\nRun the release script:\n\n\nrm -f ../clickhouse*.deb\n./release\n\n\n\n\n\nYou will find built packages in the parent directory:\n\n\nls -l ../clickhouse*.deb\n\n\n\n\n\nNote that usage of debian packages is not required.\nClickHouse has no runtime dependencies except libc, so it could work on almost any Linux.\n\n\nInstalling freshly built packages on a development server:\n\n\nsudo dpkg -i ../clickhouse*.deb\nsudo service clickhouse-server start\n\n\n\n\n\nBuild to work with code\n\n\nmkdir build\n\ncd\n build\ncmake ..\nmake -j \n$THREADS\n\n\ncd\n ..\n\n\n\n\n\nTo create an executable, run \nmake clickhouse\n.\nThis will create the \ndbms/src/Server/clickhouse\n executable, which can be used with \nclient\n or \nserver\n arguments.", - "title": "How to build ClickHouse on Linux" - }, - { - "location": "/development/build/#how-to-build-clickhouse-on-linux", - "text": "Build should work on Linux Ubuntu 12.04, 14.04 or newer.\nWith appropriate changes, it should also work on any other Linux distribution.\nThe build process is not intended to work on Mac OS X.\nOnly x86_64 with SSE 4.2 is supported. Support for AArch64 is experimental. To test for SSE 4.2, do grep -q sse4_2 /proc/cpuinfo echo SSE 4.2 supported || echo SSE 4.2 not supported", - "title": "How to build ClickHouse on Linux" - }, - { - "location": "/development/build/#install-git-and-cmake", - "text": "sudo apt-get install git cmake Or cmake3 instead of cmake on older systems.", - "title": "Install Git and CMake" - }, - { - "location": "/development/build/#detect-the-number-of-threads", - "text": "export THREADS = $( grep -c ^processor /proc/cpuinfo )", - "title": "Detect the number of threads" - }, - { - "location": "/development/build/#install-gcc-7", - "text": "There are several ways to do this.", - "title": "Install GCC 7" - }, - { - "location": "/development/build/#install-from-a-ppa-package", - "text": "sudo apt-get install software-properties-common\nsudo apt-add-repository ppa:ubuntu-toolchain-r/test\nsudo apt-get update\nsudo apt-get install gcc-7 g++-7", - "title": "Install from a PPA package" - }, - { - "location": "/development/build/#install-from-sources", - "text": "Look at [https://github.com/yandex/ClickHouse/blob/master/utils/prepare-environment/install-gcc.sh]", - "title": "Install from sources" - }, - { - "location": "/development/build/#use-gcc-7-for-builds", - "text": "export CC = gcc-7 export CXX = g++-7", - "title": "Use GCC 7 for builds" - }, - { - "location": "/development/build/#install-required-libraries-from-packages", - "text": "sudo apt-get install libicu-dev libreadline-dev libmysqlclient-dev libssl-dev unixodbc-dev ninja-build", - "title": "Install required libraries from packages" - }, - { - "location": "/development/build/#checkout-clickhouse-sources", - "text": "To get the latest stable version: git clone -b stable --recursive git@github.com:yandex/ClickHouse.git # or: git clone -b stable --recursive https://github.com/yandex/ClickHouse.git cd ClickHouse For development, switch to the master branch.\nFor the latest release candidate, switch to the testing branch.", - "title": "Checkout ClickHouse sources" - }, - { - "location": "/development/build/#build-clickhouse", - "text": "There are two build variants.", - "title": "Build ClickHouse" - }, - { - "location": "/development/build/#build-release-package", - "text": "Install prerequisites to build Debian packages. sudo apt-get install devscripts dupload fakeroot debhelper Install the most recent version of Clang. Clang is embedded into the ClickHouse package and used at runtime. The minimum version is 5.0. It is optional. To install clang, see utils/prepare-environment/install-clang.sh You may also build ClickHouse with Clang for development purposes.\nFor production releases, GCC is used. Run the release script: rm -f ../clickhouse*.deb\n./release You will find built packages in the parent directory: ls -l ../clickhouse*.deb Note that usage of debian packages is not required.\nClickHouse has no runtime dependencies except libc, so it could work on almost any Linux. Installing freshly built packages on a development server: sudo dpkg -i ../clickhouse*.deb\nsudo service clickhouse-server start", - "title": "Build release package" - }, - { - "location": "/development/build/#build-to-work-with-code", - "text": "mkdir build cd build\ncmake ..\nmake -j $THREADS cd .. To create an executable, run make clickhouse .\nThis will create the dbms/src/Server/clickhouse executable, which can be used with client or server arguments.", - "title": "Build to work with code" - }, - { - "location": "/development/build_osx/", - "text": "How to build ClickHouse on Mac OS X\n\n\nBuild should work on Mac OS X 10.12. If you're using earlier version, you can try to build ClickHouse using Gentoo Prefix and clang sl in this instruction.\nWith appropriate changes, it should also work on any other Linux distribution.\n\n\nInstall Homebrew\n\n\n/usr/bin/ruby -e \n$(\ncurl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install\n)\n\n\n\n\n\n\nInstall required compilers, tools, and libraries\n\n\nbrew install cmake gcc icu4c mysql openssl unixodbc libtool gettext zlib readline boost --cc\n=\ngcc-7\n\n\n\n\n\nCheckout ClickHouse sources\n\n\nTo get the latest stable version:\n\n\ngit clone -b stable --recursive --depth\n=\n10\n git@github.com:yandex/ClickHouse.git\n\n# or: git clone -b stable --recursive --depth=10 https://github.com/yandex/ClickHouse.git\n\n\n\ncd\n ClickHouse\n\n\n\n\n\nFor development, switch to the \nmaster\n branch.\nFor the latest release candidate, switch to the \ntesting\n branch.\n\n\nBuild ClickHouse\n\n\nmkdir build\n\ncd\n build\ncmake .. -DCMAKE_CXX_COMPILER\n=\n`\nwhich g++-7\n`\n -DCMAKE_C_COMPILER\n=\n`\nwhich gcc-7\n`\n\nmake -j \n`\nsysctl -n hw.ncpu\n`\n\n\ncd\n ..\n\n\n\n\n\nCaveats\n\n\nIf you intend to run clickhouse-server, make sure to increase the system's maxfiles variable. See \nMacOS.md\n for more details.", - "title": "How to build ClickHouse on Mac OS X" - }, - { - "location": "/development/build_osx/#how-to-build-clickhouse-on-mac-os-x", - "text": "Build should work on Mac OS X 10.12. If you're using earlier version, you can try to build ClickHouse using Gentoo Prefix and clang sl in this instruction.\nWith appropriate changes, it should also work on any other Linux distribution.", - "title": "How to build ClickHouse on Mac OS X" - }, - { - "location": "/development/build_osx/#install-homebrew", - "text": "/usr/bin/ruby -e $( curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install )", - "title": "Install Homebrew" - }, - { - "location": "/development/build_osx/#install-required-compilers-tools-and-libraries", - "text": "brew install cmake gcc icu4c mysql openssl unixodbc libtool gettext zlib readline boost --cc = gcc-7", - "title": "Install required compilers, tools, and libraries" - }, - { - "location": "/development/build_osx/#checkout-clickhouse-sources", - "text": "To get the latest stable version: git clone -b stable --recursive --depth = 10 git@github.com:yandex/ClickHouse.git # or: git clone -b stable --recursive --depth=10 https://github.com/yandex/ClickHouse.git cd ClickHouse For development, switch to the master branch.\nFor the latest release candidate, switch to the testing branch.", - "title": "Checkout ClickHouse sources" - }, - { - "location": "/development/build_osx/#build-clickhouse", - "text": "mkdir build cd build\ncmake .. -DCMAKE_CXX_COMPILER = ` which g++-7 ` -DCMAKE_C_COMPILER = ` which gcc-7 ` \nmake -j ` sysctl -n hw.ncpu ` cd ..", - "title": "Build ClickHouse" - }, - { - "location": "/development/build_osx/#caveats", - "text": "If you intend to run clickhouse-server, make sure to increase the system's maxfiles variable. See MacOS.md for more details.", - "title": "Caveats" - }, - { - "location": "/development/style/", - "text": "How to write C++ code\n\n\nGeneral recommendations\n\n\n1.\n The following are recommendations, not requirements.\n\n\n2.\n If you are editing code, it makes sense to follow the formatting of the existing code.\n\n\n3.\n Code style is needed for consistency. Consistency makes it easier to read the code, and it also makes it easier to search the code.\n\n\n4.\n Many of the rules do not have logical reasons; they are dictated by established practices.\n\n\nFormatting\n\n\n1.\n Most of the formatting will be done automatically by \nclang-format\n.\n\n\n2.\n Indents are 4 spaces. Configure your development environment so that a tab adds four spaces.\n\n\n3.\n A left curly bracket must be separated on a new line. (And the right one, as well.)\n\n\ninline\n \nvoid\n \nreadBoolText\n(\nbool\n \n \nx\n,\n \nReadBuffer\n \n \nbuf\n)\n\n\n{\n\n \nchar\n \ntmp\n \n=\n \n0\n;\n\n \nreadChar\n(\ntmp\n,\n \nbuf\n);\n\n \nx\n \n=\n \ntmp\n \n!=\n \n0\n;\n\n\n}\n\n\n\n\n\n\n4.\n\nBut if the entire function body is quite short (a single statement), you can place it entirely on one line if you wish. Place spaces around curly braces (besides the space at the end of the line).\n\n\ninline\n \nsize_t\n \nmask\n()\n \nconst\n \n{\n \nreturn\n \nbuf_size\n()\n \n-\n \n1\n;\n \n}\n\n\ninline\n \nsize_t\n \nplace\n(\nHashValue\n \nx\n)\n \nconst\n \n{\n \nreturn\n \nx\n \n \nmask\n();\n \n}\n\n\n\n\n\n\n5.\n For functions, don't put spaces around brackets.\n\n\nvoid\n \nreinsert\n(\nconst\n \nValue\n \n \nx\n)\n\n\nmemcpy\n(\nbuf\n[\nplace_value\n],\n \nx\n,\n \nsizeof\n(\nx\n));\n\n\n\n\n\n\n6.\n When using statements such as \nif\n, \nfor\n, and \nwhile\n (unlike function calls), put a space before the opening bracket.\n\n\ncpp\n for (size_t i = 0; i \n rows; i += storage.index_granularity)\n\n\n7.\n Put spaces around binary operators (\n+\n, \n-\n, \n*\n, \n/\n, \n%\n, ...), as well as the ternary operator \n?:\n.\n\n\nUInt16\n \nyear\n \n=\n \n(\ns\n[\n0\n]\n \n-\n \n0\n)\n \n*\n \n1000\n \n+\n \n(\ns\n[\n1\n]\n \n-\n \n0\n)\n \n*\n \n100\n \n+\n \n(\ns\n[\n2\n]\n \n-\n \n0\n)\n \n*\n \n10\n \n+\n \n(\ns\n[\n3\n]\n \n-\n \n0\n);\n\n\nUInt8\n \nmonth\n \n=\n \n(\ns\n[\n5\n]\n \n-\n \n0\n)\n \n*\n \n10\n \n+\n \n(\ns\n[\n6\n]\n \n-\n \n0\n);\n\n\nUInt8\n \nday\n \n=\n \n(\ns\n[\n8\n]\n \n-\n \n0\n)\n \n*\n \n10\n \n+\n \n(\ns\n[\n9\n]\n \n-\n \n0\n);\n\n\n\n\n\n\n8.\n If a line feed is entered, put the operator on a new line and increase the indent before it.\n\n\nif\n \n(\nelapsed_ns\n)\n\n \nmessage\n \n \n (\n\n \n \nrows_read_on_server\n \n*\n \n1000000000\n \n/\n \nelapsed_ns\n \n \n rows/s., \n\n \n \nbytes_read_on_server\n \n*\n \n1000.0\n \n/\n \nelapsed_ns\n \n \n MB/s.) \n;\n\n\n\n\n\n\n9.\n You can use spaces for alignment within a line, if desired.\n\n\ndst\n.\nClickLogID\n \n=\n \nclick\n.\nLogID\n;\n\n\ndst\n.\nClickEventID\n \n=\n \nclick\n.\nEventID\n;\n\n\ndst\n.\nClickGoodEvent\n \n=\n \nclick\n.\nGoodEvent\n;\n\n\n\n\n\n\n10.\n Don't use spaces around the operators \n.\n, \n-\n .\n\n\nIf necessary, the operator can be wrapped to the next line. In this case, the offset in front of it is increased.\n\n\n11.\n Do not use a space to separate unary operators (\n-\n, \n+\n, \n*\n, \n, ...) from the argument.\n\n\n12.\n Put a space after a comma, but not before it. The same rule goes for a semicolon inside a for expression.\n\n\n13.\n Do not use spaces to separate the \n[]\n operator.\n\n\n14.\n In a \ntemplate \n...\n expression, use a space between \ntemplate\n and \n. No spaces after \n or before \n.\n\n\ntemplate\n \ntypename\n \nTKey\n,\n \ntypename\n \nTValue\n\n\nstruct\n \nAggregatedStatElement\n\n\n{}\n\n\n\n\n\n\n15.\n In classes and structures, public, private, and protected are written on the same level as the \nclass/struct\n, but all other internal elements should be deeper.\n\n\ntemplate\n \ntypename\n \nT\n\n\nclass\n \nMultiVersion\n\n\n{\n\n\npublic\n:\n\n \n/// Version of object for usage. shared_ptr manage lifetime of version.\n\n \nusing\n \nVersion\n \n=\n \nstd\n::\nshared_ptr\nconst\n \nT\n;\n\n \n...\n\n\n}\n\n\n\n\n\n\n16.\n If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace.\n\n\n17.\n If the block for \nif\n, \nfor\n, \nwhile\n... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets.\n\n\n/// Finish write.\n\n\nfor\n \n(\nauto\n \n \nstream\n \n:\n \nstreams\n)\n\n \nstream\n.\nsecond\n-\nfinalize\n();\n\n\n\n\n\n\n18.\n There should be any spaces at the ends of lines.\n\n\n19.\n Sources are UTF-8 encoded.\n\n\n20.\n Non-ASCII characters can be used in string literals.\n\n\n \n, \n \n \n(\ntimer\n.\nelapsed\n()\n \n/\n \nchunks_stats\n.\nhits\n)\n \n \n \u03bcsec/hit.\n;\n\n\n\n\n\n\n21.\n Do not write multiple expressions in a single line.\n\n\n22.\n Group sections of code inside functions and separate them with no more than one empty line.\n\n\n23.\n Separate functions, classes, and so on with one or two empty lines.\n\n\n24.\n A \nconst\n (related to a value) must be written before the type name.\n\n\n//correct\n\n\nconst\n \nchar\n \n*\n \npos\n\n\nconst\n \nstd\n::\nstring\n \n \ns\n\n\n//incorrect\n\n\nchar\n \nconst\n \n*\n \npos\n\n\n\n\n\n\n25.\n When declaring a pointer or reference, the \n*\n and \n symbols should be separated by spaces on both sides.\n\n\n//correct\n\n\nconst\n \nchar\n \n*\n \npos\n\n\n//incorrect\n\n\nconst\n \nchar\n*\n \npos\n\n\nconst\n \nchar\n \n*\npos\n\n\n\n\n\n\n26.\n When using template types, alias them with the \nusing\n keyword (except in the simplest cases).\n\n\nIn other words, the template parameters are specified only in \nusing\n and aren't repeated in the code.\n\n\nusing\n can be declared locally, such as inside a function.\n\n\n//correct\n\n\nusing\n \nFileStreams\n \n=\n \nstd\n::\nmap\nstd\n::\nstring\n,\n \nstd\n::\nshared_ptr\nStream\n;\n\n\nFileStreams\n \nstreams\n;\n\n\n//incorrect\n\n\nstd\n::\nmap\nstd\n::\nstring\n,\n \nstd\n::\nshared_ptr\nStream\n \nstreams\n;\n\n\n\n\n\n\n27.\n Do not declare several variables of different types in one statement.\n\n\n//incorrect\n\n\nint\n \nx\n,\n \n*\ny\n;\n\n\n\n\n\n\n28.\n Do not use C-style casts.\n\n\n//incorrect\n\n\nstd\n::\ncerr\n \n \n(\nint\n)\nc\n \n;\n \nstd\n::\nendl\n;\n\n\n//correct\n\n\nstd\n::\ncerr\n \n \nstatic_cast\nint\n(\nc\n)\n \n \nstd\n::\nendl\n;\n\n\n\n\n\n\n29.\n In classes and structs, group members and functions separately inside each visibility scope.\n\n\n30.\n For small classes and structs, it is not necessary to separate the method declaration from the implementation.\n\n\nThe same is true for small methods in any classes or structs.\n\n\nFor templated classes and structs, don't separate the method declarations from the implementation (because otherwise they must be defined in the same translation unit).\n\n\n31.\n You can wrap lines at 140 characters, instead of 80.\n\n\n32.\n Always use the prefix increment/decrement operators if postfix is not required.\n\n\nfor\n \n(\nNames\n::\nconst_iterator\n \nit\n \n=\n \ncolumn_names\n.\nbegin\n();\n \nit\n \n!=\n \ncolumn_names\n.\nend\n();\n \n++\nit\n)\n\n\n\n\n\n\nComments\n\n\n1.\n Be sure to add comments for all non-trivial parts of code.\n\n\nThis is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong.\n\n\n/** Part of piece of memory, that can be used.\n\n\n * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading,\n\n\n * then working_buffer will have size of only 10 bytes\n\n\n * (working_buffer.end() will point to the position right after those 10 bytes available for read).\n\n\n*/\n\n\n\n\n\n\n2.\n Comments can be as detailed as necessary.\n\n\n3.\n Place comments before the code they describe. In rare cases, comments can come after the code, on the same line.\n\n\n/** Parses and executes the query.\n\n\n*/\n\n\nvoid\n \nexecuteQuery\n(\n\n \nReadBuffer\n \n \nistr\n,\n \n/// Where to read the query from (and data for INSERT, if applicable)\n\n \nWriteBuffer\n \n \nostr\n,\n \n/// Where to write the result\n\n \nContext\n \n \ncontext\n,\n \n/// DB, tables, data types, engines, functions, aggregate functions...\n\n \nBlockInputStreamPtr\n \n \nquery_plan\n,\n \n/// A description of query processing can be included here\n\n \nQueryProcessingStage\n::\nEnum\n \nstage\n \n=\n \nQueryProcessingStage\n::\nComplete\n \n/// The last stage to process the SELECT query to\n\n \n)\n\n\n\n\n\n\n4.\n Comments should be written in English only.\n\n\n5.\n If you are writing a library, include detailed comments explaining it in the main header file.\n\n\n6.\n Do not add comments that do not provide additional information. In particular, do not leave empty comments like this:\n\n\n/*\n\n\n* Procedure Name:\n\n\n* Original procedure name:\n\n\n* Author:\n\n\n* Date of creation:\n\n\n* Dates of modification:\n\n\n* Modification authors:\n\n\n* Original file name:\n\n\n* Purpose:\n\n\n* Intent:\n\n\n* Designation:\n\n\n* Classes used:\n\n\n* Constants:\n\n\n* Local variables:\n\n\n* Parameters:\n\n\n* Date of creation:\n\n\n* Purpose:\n\n\n*/\n\n\n\n\n\n\nThe example is borrowed from \nhttp://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/\n.\n\n\n7.\n Do not write garbage comments (author, creation date ..) at the beginning of each file.\n\n\n8.\n Single-line comments begin with three slashes: \n///\n and multi-line comments begin with \n/**\n. These comments are considered \"documentation\".\n\n\nNote: You can use Doxygen to generate documentation from these comments. But Doxygen is not generally used because it is more convenient to navigate the code in the IDE.\n\n\n9.\n Multi-line comments must not have empty lines at the beginning and end (except the line that closes a multi-line comment).\n\n\n10.\n For commenting out code, use basic comments, not \"documenting\" comments.\n\n\n11.\n Delete the commented out parts of the code before commiting.\n\n\n12.\n Do not use profanity in comments or code.\n\n\n13.\n Do not use uppercase letters. Do not use excessive punctuation.\n\n\n/// WHAT THE FAIL???\n\n\n\n\n\n\n14.\n Do not make delimeters from comments.\n\n\n///******************************************************\n\n\n\n\n\n15.\n Do not start discussions in comments.\n\n\n/// Why did you do this stuff?\n\n\n\n\n\n16.\n There's no need to write a comment at the end of a block describing what it was about.\n\n\n/// for\n\n\n\n\n\nNames\n\n\n1.\n The names of variables and class members use lowercase letters with underscores.\n\n\nsize_t\n \nmax_block_size\n;\n\n\n\n\n\n\n2.\n The names of functions (methods) use camelCase beginning with a lowercase letter.\n\n\nstd\n::\nstring\n \ngetName\n()\n \nconst\n \noverride\n \n{\n \nreturn\n \nMemory\n;\n \n}\n\n\n\n\n\n\n3.\n The names of classes (structures) use CamelCase beginning with an uppercase letter. Prefixes other than I are not used for interfaces.\n\n\nclass\n \nStorageMemory\n \n:\n \npublic\n \nIStorage\n\n\n\n\n\n\n4.\n The names of usings follow the same rules as classes, or you can add _t at the end.\n\n\n5.\n Names of template type arguments for simple cases: T; T, U; T1, T2.\n\n\nFor more complex cases, either follow the rules for class names, or add the prefix T.\n\n\ntemplate\n \ntypename\n \nTKey\n,\n \ntypename\n \nTValue\n\n\nstruct\n \nAggregatedStatElement\n\n\n\n\n\n\n6.\n Names of template constant arguments: either follow the rules for variable names, or use N in simple cases.\n\n\ntemplate\n \nbool\n \nwithout_www\n\n\nstruct\n \nExtractDomain\n\n\n\n\n\n\n7.\n For abstract classes (interfaces) you can add the I prefix.\n\n\nclass\n \nIBlockInputStream\n\n\n\n\n\n\n8.\n If you use a variable locally, you can use the short name.\n\n\nIn other cases, use a descriptive name that conveys the meaning.\n\n\nbool\n \ninfo_successfully_loaded\n \n=\n \nfalse\n;\n\n\n\n\n\n\n9.\n \ndefine\n\u2018s should be in ALL_CAPS with underscores. The same is true for global constants.\n\n\n#define MAX_SRC_TABLE_NAMES_TO_STORE 1000\n\n\n\n\n\n\n10.\n File names should use the same style as their contents.\n\n\nIf a file contains a single class, name the file the same way as the class, in CamelCase.\n\n\nIf the file contains a single function, name the file the same way as the function, in camelCase.\n\n\n11.\n If the name contains an abbreviation, then:\n\n\n\n\nFor variable names, the abbreviation should use lowercase letters \nmysql_connection\n (not \nmySQL_connection\n).\n\n\nFor names of classes and functions, keep the uppercase letters in the abbreviation \nMySQLConnection\n (not \nMySqlConnection\n).\n\n\n\n\n12.\n Constructor arguments that are used just to initialize the class members should be named the same way as the class members, but with an underscore at the end.\n\n\nFileQueueProcessor\n(\n\n \nconst\n \nstd\n::\nstring\n \n \npath_\n,\n\n \nconst\n \nstd\n::\nstring\n \n \nprefix_\n,\n\n \nstd\n::\nshared_ptr\nFileHandler\n \nhandler_\n)\n\n \n:\n \npath\n(\npath_\n),\n\n \nprefix\n(\nprefix_\n),\n\n \nhandler\n(\nhandler_\n),\n\n \nlog\n(\nLogger\n::\nget\n(\nFileQueueProcessor\n))\n\n\n{\n\n\n}\n\n\n\n\n\n\nThe underscore suffix can be omitted if the argument is not used in the constructor body.\n\n\n13.\n There is no difference in the names of local variables and class members (no prefixes required).\n\n\ntimer\n \n(\nnot\n \nm_timer\n)\n\n\n\n\n\n\n14.\n Constants in enums use CamelCase beginning with an uppercase letter. ALL_CAPS is also allowed. If the enum is not local, use enum class.\n\n\nenum\n \nclass\n \nCompressionMethod\n\n\n{\n\n \nQuickLZ\n \n=\n \n0\n,\n\n \nLZ4\n \n=\n \n1\n,\n\n\n};\n\n\n\n\n\n\n15.\n All names must be in English. Transliteration of Russian words is not allowed.\n\n\nnot\n \nStroka\n\n\n\n\n\n\n16.\n Abbreviations are acceptable if they are well known (when you can easily find the meaning of the abbreviation in Wikipedia or in a search engine).\n\n\n`AST`, `SQL`.\n\nNot `NVDH` (some random letters)\n\n\n\n\n\nIncomplete words are acceptable if the shortened version is common use.\n\n\nYou can also use an abbreviation if the full name is included next to it in the comments.\n\n\n17.\n File names with C++ source code must have the \n.cpp\n extension. Header files must have the \n.h\n extension.\n\n\nHow to write code\n\n\n1.\n Memory management.\n\n\nManual memory deallocation (delete) can only be used in library code.\n\n\nIn library code, the delete operator can only be used in destructors.\n\n\nIn application code, memory must be freed by the object that owns it.\n\n\nExamples:\n\n\n\n\nThe easiest way is to place an object on the stack, or make it a member of another class.\n\n\nFor a large number of small objects, use containers.\n\n\nFor automatic deallocation of a small number of objects that reside in the heap, use shared_ptr/unique_ptr.\n\n\n\n\n2.\n Resource management.\n\n\nUse RAII and see the previous point.\n\n\n3.\n Error handling.\n\n\nUse exceptions. In most cases, you only need to throw an exception, and don't need to catch it (because of RAII).\n\n\nIn offline data processing applications, it's often acceptable to not catch exceptions.\n\n\nIn servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler.\n\n\n/// If there were no other calculations yet, do it synchronously\n\n\nif\n \n(\n!\nstarted\n)\n\n\n{\n\n \ncalculate\n();\n\n \nstarted\n \n=\n \ntrue\n;\n\n\n}\n\n\nelse\n \n/// If the calculations are already in progress, wait for results\n\n \npool\n.\nwait\n();\n\n\n\nif\n \n(\nexception\n)\n\n \nexception\n-\nrethrow\n();\n\n\n\n\n\n\nNever hide exceptions without handling. Never just blindly put all exceptions to log.\n\n\nNot \ncatch (...) {}\n.\n\n\nIf you need to ignore some exceptions, do so only for specific ones and rethrow the rest.\n\n\ncatch\n \n(\nconst\n \nDB\n::\nException\n \n \ne\n)\n\n\n{\n\n \nif\n \n(\ne\n.\ncode\n()\n \n==\n \nErrorCodes\n::\nUNKNOWN_AGGREGATE_FUNCTION\n)\n\n \nreturn\n \nnullptr\n;\n\n \nelse\n\n \nthrow\n;\n\n\n}\n\n\n\n\n\n\nWhen using functions with response codes or errno, always check the result and throw an exception in case of error.\n\n\nif\n \n(\n0\n \n!=\n \nclose\n(\nfd\n))\n\n \nthrowFromErrno\n(\nCannot close file \n \n+\n \nfile_name\n,\n \nErrorCodes\n::\nCANNOT_CLOSE_FILE\n);\n\n\n\n\n\n\nAsserts are not used.\n\n\n4.\n Exception types.\n\n\nThere is no need to use complex exception hierarchy in application code. The exception text should be understandable to a system administrator.\n\n\n5.\n Throwing exceptions from destructors.\n\n\nThis is not recommended, but it is allowed.\n\n\nUse the following options:\n\n\n\n\nCreate a (done() or finalize()) function that will do all the work in advance that might lead to an exception. If that function was called, there should be no exceptions in the destructor later.\n\n\nTasks that are too complex (such as sending messages over the network) can be put in separate method that the class user will have to call before destruction.\n\n\nIf there is an exception in the destructor, it\u2019s better to log it than to hide it (if the logger is available).\n\n\nIn simple applications, it is acceptable to rely on std::terminate (for cases of noexcept by default in C++11) to handle exceptions.\n\n\n\n\n6.\n Anonymous code blocks.\n\n\nYou can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block.\n\n\nBlock\n \nblock\n \n=\n \ndata\n.\nin\n-\nread\n();\n\n\n\n{\n\n \nstd\n::\nlock_guard\nstd\n::\nmutex\n \nlock\n(\nmutex\n);\n\n \ndata\n.\nready\n \n=\n \ntrue\n;\n\n \ndata\n.\nblock\n \n=\n \nblock\n;\n\n\n}\n\n\n\nready_any\n.\nset\n();\n\n\n\n\n\n\n7.\n Multithreading.\n\n\nFor offline data processing applications:\n\n\n\n\nTry to get the best possible performance on a single CPU core. You can then parallelize your code if necessary.\n\n\n\n\nIn server applications:\n\n\n\n\nUse the thread pool to process requests. At this point, we haven't had any tasks that required userspace context switching.\n\n\n\n\nFork is not used for parallelization.\n\n\n8.\n Synchronizing threads.\n\n\nOften it is possible to make different threads use different memory cells (even better: different cache lines,) and to not use any thread synchronization (except joinAll).\n\n\nIf synchronization is required, in most cases, it is sufficient to use mutex under lock_guard.\n\n\nIn other cases use system synchronization primitives. Do not use busy wait.\n\n\nAtomic operations should be used only in the simplest cases.\n\n\nDo not try to implement lock-free data structures unless it is your primary area of expertise.\n\n\n9.\n Pointers vs references.\n\n\nIn most cases, prefer references.\n\n\n10.\n const.\n\n\nUse constant references, pointers to constants, \nconst_iterator\n, \nconst\n methods.\n\n\nConsider \nconst\n to be default and use non-const only when necessary.\n\n\nWhen passing variable by value, using \nconst\n usually does not make sense.\n\n\n11.\n unsigned.\n\n\nUse \nunsigned\n, if needed.\n\n\n12.\n Numeric types\n\n\nUse \nUInt8\n, \nUInt16\n, \nUInt32\n, \nUInt64\n, \nInt8\n, \nInt16\n, \nInt32\n, \nInt64\n, and \nsize_t\n, \nssize_t\n, \nptrdiff_t\n.\n\n\nDon't use \nsigned/unsigned long\n, \nlong long\n, \nshort\n, \nsigned char\n, \nunsigned char\n, or \nchar\n types for numbers.\n\n\n13.\n Passing arguments.\n\n\nPass complex values by reference (including \nstd::string\n).\n\n\nIf a function captures ownership of an objected created in the heap, make the argument type \nshared_ptr\n or \nunique_ptr\n.\n\n\n14.\n Returning values.\n\n\nIn most cases, just use return. Do not write \n[return std::move(res)]{.strike}\n.\n\n\nIf the function allocates an object on heap and returns it, use \nshared_ptr\n or \nunique_ptr\n.\n\n\nIn rare cases you might need to return the value via an argument. In this case, the argument should be a reference.\n\n\nusing\n \nAggregateFunctionPtr\n \n=\n \nstd\n::\nshared_ptr\nIAggregateFunction\n;\n\n\n\n/** Creates an aggregate function by name.\n\n\n */\n\n\nclass\n \nAggregateFunctionFactory\n\n\n{\n\n\npublic\n:\n\n \nAggregateFunctionFactory\n();\n\n \nAggregateFunctionPtr\n \nget\n(\nconst\n \nString\n \n \nname\n,\n \nconst\n \nDataTypes\n \n \nargument_types\n)\n \nconst\n;\n\n\n\n\n\n\n15.\n namespace.\n\n\nThere is no need to use a separate namespace for application code or small libraries.\n\n\nor small libraries.\n\n\nFor medium to large libraries, put everything in the namespace.\n\n\nYou can use the additional detail namespace in a library's \n.h\n file to hide implementation details.\n\n\nIn a \n.cpp\n file, you can use the static or anonymous namespace to hide symbols.\n\n\nYou can also use namespace for enums to prevent its names from polluting the outer namespace, but it\u2019s better to use the enum class.\n\n\n16.\n Delayed initialization.\n\n\nIf arguments are required for initialization then do not write a default constructor.\n\n\nIf later you\u2019ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use \nshared_ptr/unique_ptr\n.\n\n\nLoader\n(\nDB\n::\nConnection\n \n*\n \nconnection_\n,\n \nconst\n \nstd\n::\nstring\n \n \nquery\n,\n \nsize_t\n \nmax_block_size_\n);\n\n\n\n/// For delayed initialization\n\n\nLoader\n()\n \n{}\n\n\n\n\n\n\n17.\n Virtual functions.\n\n\nIf the class is not intended for polymorphic use, you do not need to make functions virtual. This also applies to the destructor.\n\n\n18.\n Encodings.\n\n\nUse UTF-8 everywhere. Use \nstd::string\nand\nchar *\n. Do not use \nstd::wstring\nand\nwchar_t\n.\n\n\n19.\n Logging.\n\n\nSee the examples everywhere in the code.\n\n\nBefore committing, delete all meaningless and debug logging, and any other types of debug output.\n\n\nLogging in cycles should be avoided, even on the Trace level.\n\n\nLogs must be readable at any logging level.\n\n\nLogging should only be used in application code, for the most part.\n\n\nLog messages must be written in English.\n\n\nThe log should preferably be understandable for the system administrator.\n\n\nDo not use profanity in the log.\n\n\nUse UTF-8 encoding in the log. In rare cases you can use non-ASCII characters in the log.\n\n\n20.\n I/O.\n\n\nDon't use iostreams in internal cycles that are critical for application performance (and never use stringstream).\n\n\nUse the DB/IO library instead.\n\n\n21.\n Date and time.\n\n\nSee the \nDateLUT\n library.\n\n\n22.\n include.\n\n\nAlways use \n#pragma once\n instead of include guards.\n\n\n23.\n using.\n\n\nThe \nusing namespace\n is not used.\n\n\nIt's fine if you are 'using' something specific, but make it local inside a class or function.\n\n\n24.\n Do not use trailing return type for functions unless necessary.\n\n\n[auto f() -\ngt; void;]{.strike}\n\n\n\n\n\n25.\n Do not declare and init variables like this:\n\n\nauto\n \ns\n \n=\n \nstd\n::\nstring\n{\nHello\n};\n\n\n\n\n\n\nDo it like this:\n\n\nstd\n::\nstring\n \ns\n \n=\n \nHello\n;\n\n\nstd\n::\nstring\n \ns\n{\nHello\n};\n\n\n\n\n\n\n26.\n For virtual functions, write \nvirtual\n in the base class, but write \noverride\n in descendent classes.\n\n\nUnused features of C++\n\n\n1.\n Virtual inheritance is not used.\n\n\n2.\n Exception specifiers from C++03 are not used.\n\n\n3.\n Function try block is not used, except for the main function in tests.\n\n\nPlatform\n\n\n1.\n We write code for a specific platform.\n\n\nBut other things being equal, cross-platform or portable code is preferred.\n\n\n2.\n The language is C++17.\n\n\n3.\n The compiler is \ngcc\n. At this time (December 2017), the code is compiled using version 7.2. (It can also be compiled using clang 5.)\n\n\nThe standard library is used (implementation of \nlibstdc++\n or \nlibc++\n).\n\n\n4.\n OS: Linux Ubuntu, not older than Precise.\n\n\n5.\n Code is written for x86_64 CPU architecture.\n\n\nThe CPU instruction set is the minimum supported set among our servers. Currently, it is SSE 4.2.\n\n\n6.\n Use \n-Wall -Wextra -Werror\n compilation flags.\n\n\n7.\n Use static linking with all libraries except those that are difficult to connect to statically (see the output of the \nldd\n command).\n\n\n8.\n Code is developed and debugged with release settings.\n\n\nTools\n\n\n1.\n \nKDevelop\n is a good IDE.\n\n\n2.\n For debugging, use \ngdb\n, \nvalgrind\n (\nmemcheck\n), \nstrace\n, \n-fsanitize=\n, ..., \ntcmalloc_minimal_debug\n.\n\n\n3.\n For profiling, use Linux Perf \nvalgrind\n (\ncallgrind\n), \nstrace-cf\n.\n\n\n4.\n Sources are in Git.\n\n\n5.\n Compilation is managed by \nCMake\n.\n\n\n6.\n Releases are in \ndeb\n packages.\n\n\n7.\n Commits to master must not break the build.\n\n\nThough only selected revisions are considered workable.\n\n\n8.\n Make commits as often as possible, even if the code is only partially ready.\n\n\nUse branches for this purpose.\n\n\nIf your code is not buildable yet, exclude it from the build before pushing to master. You'll need to finish it or remove it from master within a few days.\n\n\n9.\n For non-trivial changes, used branches and publish them on the server.\n\n\n10.\n Unused code is removed from the repository.\n\n\nLibraries\n\n\n1.\n The C++14 standard library is used (experimental extensions are fine), as well as boost and Poco frameworks.\n\n\n2.\n If necessary, you can use any well-known libraries available in the OS package.\n\n\nIf there is a good solution already available, then use it, even if it means you have to install another library.\n\n\n(But be prepared to remove bad libraries from code.)\n\n\n3.\n You can install a library that isn't in the packages, if the packages don't have what you need or have an outdated version or the wrong type of compilation.\n\n\n4.\n If the library is small and doesn't have its own complex build system, put the source files in the contrib folder.\n\n\n5.\n Preference is always given to libraries that are already used.\n\n\nGeneral recommendations\n\n\n1.\n Write as little code as possible.\n\n\n2.\n Try the simplest solution.\n\n\n3.\n Don't write code until you know how it's going to work and how the inner loop will function.\n\n\n4.\n In the simplest cases, use 'using' instead of classes or structs.\n\n\n5.\n If possible, do not write copy constructors, assignment operators, destructors (other than a virtual one, if the class contains at least one virtual function), mpve-constructors and move assignment operators. In other words, the compiler-generated functions must work correctly. You can use 'default'.\n\n\n6.\n Code simplification is encouraged. Reduce the size of your code where possible.\n\n\nAdditional recommendations\n\n\n1.\n Explicit \nstd::\n for types from \nstddef.h\n is not recommended.\n\n\nWe recommend writing \nsize_t\n instead \nstd::size_t\n because it's shorter.\n\n\nBut if you prefer, \nstd::\n is acceptable.\n\n\n2.\n Explicit \nstd::\n for functions from the standard C library is not recommended.\n\n\nWrite \nmemcpy\n instead of \nstd::memcpy\n.\n\n\nThe reason is that there are similar non-standard functions, such as \nmemmem\n. We do use these functions on occasion. These functions do not exist in namespace \nstd\n.\n\n\nIf you write \nstd::memcpy\n instead of \nmemcpy\n everywhere, then \nmemmem\n without \nstd::\n will look awkward.\n\n\nNevertheless, \nstd::\n is allowed if you prefer it.\n\n\n3.\n Using functions from C when the ones are available in the standard C++ library.\n\n\nThis is acceptable if it is more efficient.\n\n\nFor example, use \nmemcpy\n instead of \nstd::copy\n for copying large chunks of memory.\n\n\n4.\n Multiline function arguments.\n\n\nAny of the following wrapping styles are allowed:\n\n\nfunction\n(\n\n \nT1\n \nx1\n,\n\n \nT2\n \nx2\n)\n\n\n\n\n\n\nfunction\n(\n\n \nsize_t\n \nleft\n,\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)\n\n\n\n\n\n\nfunction\n(\nsize_t\n \nleft\n,\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)\n\n\n\n\n\n\nfunction\n(\nsize_t\n \nleft\n,\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)\n\n\n\n\n\n\nfunction\n(\n\n \nsize_t\n \nleft\n,\n\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)", - "title": "How to write C++ code" - }, - { - "location": "/development/style/#how-to-write-c-code", - "text": "", - "title": "How to write C++ code" - }, - { - "location": "/development/style/#general-recommendations", - "text": "1. The following are recommendations, not requirements. 2. If you are editing code, it makes sense to follow the formatting of the existing code. 3. Code style is needed for consistency. Consistency makes it easier to read the code, and it also makes it easier to search the code. 4. Many of the rules do not have logical reasons; they are dictated by established practices.", - "title": "General recommendations" - }, - { - "location": "/development/style/#formatting", - "text": "1. Most of the formatting will be done automatically by clang-format . 2. Indents are 4 spaces. Configure your development environment so that a tab adds four spaces. 3. A left curly bracket must be separated on a new line. (And the right one, as well.) inline void readBoolText ( bool x , ReadBuffer buf ) { \n char tmp = 0 ; \n readChar ( tmp , buf ); \n x = tmp != 0 ; } 4. \nBut if the entire function body is quite short (a single statement), you can place it entirely on one line if you wish. Place spaces around curly braces (besides the space at the end of the line). inline size_t mask () const { return buf_size () - 1 ; } inline size_t place ( HashValue x ) const { return x mask (); } 5. For functions, don't put spaces around brackets. void reinsert ( const Value x ) memcpy ( buf [ place_value ], x , sizeof ( x )); 6. When using statements such as if , for , and while (unlike function calls), put a space before the opening bracket. cpp\n for (size_t i = 0; i rows; i += storage.index_granularity) 7. Put spaces around binary operators ( + , - , * , / , % , ...), as well as the ternary operator ?: . UInt16 year = ( s [ 0 ] - 0 ) * 1000 + ( s [ 1 ] - 0 ) * 100 + ( s [ 2 ] - 0 ) * 10 + ( s [ 3 ] - 0 ); UInt8 month = ( s [ 5 ] - 0 ) * 10 + ( s [ 6 ] - 0 ); UInt8 day = ( s [ 8 ] - 0 ) * 10 + ( s [ 9 ] - 0 ); 8. If a line feed is entered, put the operator on a new line and increase the indent before it. if ( elapsed_ns ) \n message ( \n rows_read_on_server * 1000000000 / elapsed_ns rows/s., \n bytes_read_on_server * 1000.0 / elapsed_ns MB/s.) ; 9. You can use spaces for alignment within a line, if desired. dst . ClickLogID = click . LogID ; dst . ClickEventID = click . EventID ; dst . ClickGoodEvent = click . GoodEvent ; 10. Don't use spaces around the operators . , - . If necessary, the operator can be wrapped to the next line. In this case, the offset in front of it is increased. 11. Do not use a space to separate unary operators ( - , + , * , , ...) from the argument. 12. Put a space after a comma, but not before it. The same rule goes for a semicolon inside a for expression. 13. Do not use spaces to separate the [] operator. 14. In a template ... expression, use a space between template and . No spaces after or before . template typename TKey , typename TValue struct AggregatedStatElement {} 15. In classes and structures, public, private, and protected are written on the same level as the class/struct , but all other internal elements should be deeper. template typename T class MultiVersion { public : \n /// Version of object for usage. shared_ptr manage lifetime of version. \n using Version = std :: shared_ptr const T ; \n ... } 16. If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace. 17. If the block for if , for , while ... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets. /// Finish write. for ( auto stream : streams ) \n stream . second - finalize (); 18. There should be any spaces at the ends of lines. 19. Sources are UTF-8 encoded. 20. Non-ASCII characters can be used in string literals. , ( timer . elapsed () / chunks_stats . hits ) \u03bcsec/hit. ; 21. Do not write multiple expressions in a single line. 22. Group sections of code inside functions and separate them with no more than one empty line. 23. Separate functions, classes, and so on with one or two empty lines. 24. A const (related to a value) must be written before the type name. //correct const char * pos const std :: string s //incorrect char const * pos 25. When declaring a pointer or reference, the * and symbols should be separated by spaces on both sides. //correct const char * pos //incorrect const char * pos const char * pos 26. When using template types, alias them with the using keyword (except in the simplest cases). In other words, the template parameters are specified only in using and aren't repeated in the code. using can be declared locally, such as inside a function. //correct using FileStreams = std :: map std :: string , std :: shared_ptr Stream ; FileStreams streams ; //incorrect std :: map std :: string , std :: shared_ptr Stream streams ; 27. Do not declare several variables of different types in one statement. //incorrect int x , * y ; 28. Do not use C-style casts. //incorrect std :: cerr ( int ) c ; std :: endl ; //correct std :: cerr static_cast int ( c ) std :: endl ; 29. In classes and structs, group members and functions separately inside each visibility scope. 30. For small classes and structs, it is not necessary to separate the method declaration from the implementation. The same is true for small methods in any classes or structs. For templated classes and structs, don't separate the method declarations from the implementation (because otherwise they must be defined in the same translation unit). 31. You can wrap lines at 140 characters, instead of 80. 32. Always use the prefix increment/decrement operators if postfix is not required. for ( Names :: const_iterator it = column_names . begin (); it != column_names . end (); ++ it )", - "title": "Formatting" - }, - { - "location": "/development/style/#comments", - "text": "1. Be sure to add comments for all non-trivial parts of code. This is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong. /** Part of piece of memory, that can be used. * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading, * then working_buffer will have size of only 10 bytes * (working_buffer.end() will point to the position right after those 10 bytes available for read). */ 2. Comments can be as detailed as necessary. 3. Place comments before the code they describe. In rare cases, comments can come after the code, on the same line. /** Parses and executes the query. */ void executeQuery ( \n ReadBuffer istr , /// Where to read the query from (and data for INSERT, if applicable) \n WriteBuffer ostr , /// Where to write the result \n Context context , /// DB, tables, data types, engines, functions, aggregate functions... \n BlockInputStreamPtr query_plan , /// A description of query processing can be included here \n QueryProcessingStage :: Enum stage = QueryProcessingStage :: Complete /// The last stage to process the SELECT query to \n ) 4. Comments should be written in English only. 5. If you are writing a library, include detailed comments explaining it in the main header file. 6. Do not add comments that do not provide additional information. In particular, do not leave empty comments like this: /* * Procedure Name: * Original procedure name: * Author: * Date of creation: * Dates of modification: * Modification authors: * Original file name: * Purpose: * Intent: * Designation: * Classes used: * Constants: * Local variables: * Parameters: * Date of creation: * Purpose: */ The example is borrowed from http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/ . 7. Do not write garbage comments (author, creation date ..) at the beginning of each file. 8. Single-line comments begin with three slashes: /// and multi-line comments begin with /** . These comments are considered \"documentation\". Note: You can use Doxygen to generate documentation from these comments. But Doxygen is not generally used because it is more convenient to navigate the code in the IDE. 9. Multi-line comments must not have empty lines at the beginning and end (except the line that closes a multi-line comment). 10. For commenting out code, use basic comments, not \"documenting\" comments. 11. Delete the commented out parts of the code before commiting. 12. Do not use profanity in comments or code. 13. Do not use uppercase letters. Do not use excessive punctuation. /// WHAT THE FAIL??? 14. Do not make delimeters from comments. ///****************************************************** 15. Do not start discussions in comments. /// Why did you do this stuff? 16. There's no need to write a comment at the end of a block describing what it was about. /// for", - "title": "Comments" - }, - { - "location": "/development/style/#names", - "text": "1. The names of variables and class members use lowercase letters with underscores. size_t max_block_size ; 2. The names of functions (methods) use camelCase beginning with a lowercase letter. std :: string getName () const override { return Memory ; } 3. The names of classes (structures) use CamelCase beginning with an uppercase letter. Prefixes other than I are not used for interfaces. class StorageMemory : public IStorage 4. The names of usings follow the same rules as classes, or you can add _t at the end. 5. Names of template type arguments for simple cases: T; T, U; T1, T2. For more complex cases, either follow the rules for class names, or add the prefix T. template typename TKey , typename TValue struct AggregatedStatElement 6. Names of template constant arguments: either follow the rules for variable names, or use N in simple cases. template bool without_www struct ExtractDomain 7. For abstract classes (interfaces) you can add the I prefix. class IBlockInputStream 8. If you use a variable locally, you can use the short name. In other cases, use a descriptive name that conveys the meaning. bool info_successfully_loaded = false ; 9. define \u2018s should be in ALL_CAPS with underscores. The same is true for global constants. #define MAX_SRC_TABLE_NAMES_TO_STORE 1000 10. File names should use the same style as their contents. If a file contains a single class, name the file the same way as the class, in CamelCase. If the file contains a single function, name the file the same way as the function, in camelCase. 11. If the name contains an abbreviation, then: For variable names, the abbreviation should use lowercase letters mysql_connection (not mySQL_connection ). For names of classes and functions, keep the uppercase letters in the abbreviation MySQLConnection (not MySqlConnection ). 12. Constructor arguments that are used just to initialize the class members should be named the same way as the class members, but with an underscore at the end. FileQueueProcessor ( \n const std :: string path_ , \n const std :: string prefix_ , \n std :: shared_ptr FileHandler handler_ ) \n : path ( path_ ), \n prefix ( prefix_ ), \n handler ( handler_ ), \n log ( Logger :: get ( FileQueueProcessor )) { } The underscore suffix can be omitted if the argument is not used in the constructor body. 13. There is no difference in the names of local variables and class members (no prefixes required). timer ( not m_timer ) 14. Constants in enums use CamelCase beginning with an uppercase letter. ALL_CAPS is also allowed. If the enum is not local, use enum class. enum class CompressionMethod { \n QuickLZ = 0 , \n LZ4 = 1 , }; 15. All names must be in English. Transliteration of Russian words is not allowed. not Stroka 16. Abbreviations are acceptable if they are well known (when you can easily find the meaning of the abbreviation in Wikipedia or in a search engine). `AST`, `SQL`.\n\nNot `NVDH` (some random letters) Incomplete words are acceptable if the shortened version is common use. You can also use an abbreviation if the full name is included next to it in the comments. 17. File names with C++ source code must have the .cpp extension. Header files must have the .h extension.", - "title": "Names" - }, - { - "location": "/development/style/#how-to-write-code", - "text": "1. Memory management. Manual memory deallocation (delete) can only be used in library code. In library code, the delete operator can only be used in destructors. In application code, memory must be freed by the object that owns it. Examples: The easiest way is to place an object on the stack, or make it a member of another class. For a large number of small objects, use containers. For automatic deallocation of a small number of objects that reside in the heap, use shared_ptr/unique_ptr. 2. Resource management. Use RAII and see the previous point. 3. Error handling. Use exceptions. In most cases, you only need to throw an exception, and don't need to catch it (because of RAII). In offline data processing applications, it's often acceptable to not catch exceptions. In servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler. /// If there were no other calculations yet, do it synchronously if ( ! started ) { \n calculate (); \n started = true ; } else /// If the calculations are already in progress, wait for results \n pool . wait (); if ( exception ) \n exception - rethrow (); Never hide exceptions without handling. Never just blindly put all exceptions to log. Not catch (...) {} . If you need to ignore some exceptions, do so only for specific ones and rethrow the rest. catch ( const DB :: Exception e ) { \n if ( e . code () == ErrorCodes :: UNKNOWN_AGGREGATE_FUNCTION ) \n return nullptr ; \n else \n throw ; } When using functions with response codes or errno, always check the result and throw an exception in case of error. if ( 0 != close ( fd )) \n throwFromErrno ( Cannot close file + file_name , ErrorCodes :: CANNOT_CLOSE_FILE ); Asserts are not used. 4. Exception types. There is no need to use complex exception hierarchy in application code. The exception text should be understandable to a system administrator. 5. Throwing exceptions from destructors. This is not recommended, but it is allowed. Use the following options: Create a (done() or finalize()) function that will do all the work in advance that might lead to an exception. If that function was called, there should be no exceptions in the destructor later. Tasks that are too complex (such as sending messages over the network) can be put in separate method that the class user will have to call before destruction. If there is an exception in the destructor, it\u2019s better to log it than to hide it (if the logger is available). In simple applications, it is acceptable to rely on std::terminate (for cases of noexcept by default in C++11) to handle exceptions. 6. Anonymous code blocks. You can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block. Block block = data . in - read (); { \n std :: lock_guard std :: mutex lock ( mutex ); \n data . ready = true ; \n data . block = block ; } ready_any . set (); 7. Multithreading. For offline data processing applications: Try to get the best possible performance on a single CPU core. You can then parallelize your code if necessary. In server applications: Use the thread pool to process requests. At this point, we haven't had any tasks that required userspace context switching. Fork is not used for parallelization. 8. Synchronizing threads. Often it is possible to make different threads use different memory cells (even better: different cache lines,) and to not use any thread synchronization (except joinAll). If synchronization is required, in most cases, it is sufficient to use mutex under lock_guard. In other cases use system synchronization primitives. Do not use busy wait. Atomic operations should be used only in the simplest cases. Do not try to implement lock-free data structures unless it is your primary area of expertise. 9. Pointers vs references. In most cases, prefer references. 10. const. Use constant references, pointers to constants, const_iterator , const methods. Consider const to be default and use non-const only when necessary. When passing variable by value, using const usually does not make sense. 11. unsigned. Use unsigned , if needed. 12. Numeric types Use UInt8 , UInt16 , UInt32 , UInt64 , Int8 , Int16 , Int32 , Int64 , and size_t , ssize_t , ptrdiff_t . Don't use signed/unsigned long , long long , short , signed char , unsigned char , or char types for numbers. 13. Passing arguments. Pass complex values by reference (including std::string ). If a function captures ownership of an objected created in the heap, make the argument type shared_ptr or unique_ptr . 14. Returning values. In most cases, just use return. Do not write [return std::move(res)]{.strike} . If the function allocates an object on heap and returns it, use shared_ptr or unique_ptr . In rare cases you might need to return the value via an argument. In this case, the argument should be a reference. using AggregateFunctionPtr = std :: shared_ptr IAggregateFunction ; /** Creates an aggregate function by name. */ class AggregateFunctionFactory { public : \n AggregateFunctionFactory (); \n AggregateFunctionPtr get ( const String name , const DataTypes argument_types ) const ; 15. namespace. There is no need to use a separate namespace for application code or small libraries. or small libraries. For medium to large libraries, put everything in the namespace. You can use the additional detail namespace in a library's .h file to hide implementation details. In a .cpp file, you can use the static or anonymous namespace to hide symbols. You can also use namespace for enums to prevent its names from polluting the outer namespace, but it\u2019s better to use the enum class. 16. Delayed initialization. If arguments are required for initialization then do not write a default constructor. If later you\u2019ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use shared_ptr/unique_ptr . Loader ( DB :: Connection * connection_ , const std :: string query , size_t max_block_size_ ); /// For delayed initialization Loader () {} 17. Virtual functions. If the class is not intended for polymorphic use, you do not need to make functions virtual. This also applies to the destructor. 18. Encodings. Use UTF-8 everywhere. Use std::string and char * . Do not use std::wstring and wchar_t . 19. Logging. See the examples everywhere in the code. Before committing, delete all meaningless and debug logging, and any other types of debug output. Logging in cycles should be avoided, even on the Trace level. Logs must be readable at any logging level. Logging should only be used in application code, for the most part. Log messages must be written in English. The log should preferably be understandable for the system administrator. Do not use profanity in the log. Use UTF-8 encoding in the log. In rare cases you can use non-ASCII characters in the log. 20. I/O. Don't use iostreams in internal cycles that are critical for application performance (and never use stringstream). Use the DB/IO library instead. 21. Date and time. See the DateLUT library. 22. include. Always use #pragma once instead of include guards. 23. using. The using namespace is not used. It's fine if you are 'using' something specific, but make it local inside a class or function. 24. Do not use trailing return type for functions unless necessary. [auto f() - gt; void;]{.strike} 25. Do not declare and init variables like this: auto s = std :: string { Hello }; Do it like this: std :: string s = Hello ; std :: string s { Hello }; 26. For virtual functions, write virtual in the base class, but write override in descendent classes.", - "title": "How to write code" - }, - { - "location": "/development/style/#unused-features-of-c", - "text": "1. Virtual inheritance is not used. 2. Exception specifiers from C++03 are not used. 3. Function try block is not used, except for the main function in tests.", - "title": "Unused features of C++" - }, - { - "location": "/development/style/#platform", - "text": "1. We write code for a specific platform. But other things being equal, cross-platform or portable code is preferred. 2. The language is C++17. 3. The compiler is gcc . At this time (December 2017), the code is compiled using version 7.2. (It can also be compiled using clang 5.) The standard library is used (implementation of libstdc++ or libc++ ). 4. OS: Linux Ubuntu, not older than Precise. 5. Code is written for x86_64 CPU architecture. The CPU instruction set is the minimum supported set among our servers. Currently, it is SSE 4.2. 6. Use -Wall -Wextra -Werror compilation flags. 7. Use static linking with all libraries except those that are difficult to connect to statically (see the output of the ldd command). 8. Code is developed and debugged with release settings.", - "title": "Platform" - }, - { - "location": "/development/style/#tools", - "text": "1. KDevelop is a good IDE. 2. For debugging, use gdb , valgrind ( memcheck ), strace , -fsanitize= , ..., tcmalloc_minimal_debug . 3. For profiling, use Linux Perf valgrind ( callgrind ), strace-cf . 4. Sources are in Git. 5. Compilation is managed by CMake . 6. Releases are in deb packages. 7. Commits to master must not break the build. Though only selected revisions are considered workable. 8. Make commits as often as possible, even if the code is only partially ready. Use branches for this purpose. If your code is not buildable yet, exclude it from the build before pushing to master. You'll need to finish it or remove it from master within a few days. 9. For non-trivial changes, used branches and publish them on the server. 10. Unused code is removed from the repository.", - "title": "Tools" - }, - { - "location": "/development/style/#libraries", - "text": "1. The C++14 standard library is used (experimental extensions are fine), as well as boost and Poco frameworks. 2. If necessary, you can use any well-known libraries available in the OS package. If there is a good solution already available, then use it, even if it means you have to install another library. (But be prepared to remove bad libraries from code.) 3. You can install a library that isn't in the packages, if the packages don't have what you need or have an outdated version or the wrong type of compilation. 4. If the library is small and doesn't have its own complex build system, put the source files in the contrib folder. 5. Preference is always given to libraries that are already used.", - "title": "Libraries" - }, - { - "location": "/development/style/#general-recommendations_1", - "text": "1. Write as little code as possible. 2. Try the simplest solution. 3. Don't write code until you know how it's going to work and how the inner loop will function. 4. In the simplest cases, use 'using' instead of classes or structs. 5. If possible, do not write copy constructors, assignment operators, destructors (other than a virtual one, if the class contains at least one virtual function), mpve-constructors and move assignment operators. In other words, the compiler-generated functions must work correctly. You can use 'default'. 6. Code simplification is encouraged. Reduce the size of your code where possible.", - "title": "General recommendations" - }, - { - "location": "/development/style/#additional-recommendations", - "text": "1. Explicit std:: for types from stddef.h is not recommended. We recommend writing size_t instead std::size_t because it's shorter. But if you prefer, std:: is acceptable. 2. Explicit std:: for functions from the standard C library is not recommended. Write memcpy instead of std::memcpy . The reason is that there are similar non-standard functions, such as memmem . We do use these functions on occasion. These functions do not exist in namespace std . If you write std::memcpy instead of memcpy everywhere, then memmem without std:: will look awkward. Nevertheless, std:: is allowed if you prefer it. 3. Using functions from C when the ones are available in the standard C++ library. This is acceptable if it is more efficient. For example, use memcpy instead of std::copy for copying large chunks of memory. 4. Multiline function arguments. Any of the following wrapping styles are allowed: function ( \n T1 x1 , \n T2 x2 ) function ( \n size_t left , size_t right , \n const RangesInDataParts ranges , \n size_t limit ) function ( size_t left , size_t right , \n const RangesInDataParts ranges , \n size_t limit ) function ( size_t left , size_t right , \n const RangesInDataParts ranges , \n size_t limit ) function ( \n size_t left , \n size_t right , \n const RangesInDataParts ranges , \n size_t limit )", - "title": "Additional recommendations" - }, - { - "location": "/development/tests/", - "text": "How to run ClickHouse tests\n\n\nThe \nclickhouse-test\n utility that is used for functional testing is written using Python 2.x.It also requires you to have some third-party packages:\n\n\n$ pip install lxml termcolor\n\n\n\n\n\nIn a nutshell:\n\n\n\n\nPut the \nclickhouse\n program to \n/usr/bin\n (or \nPATH\n)\n\n\nCreate a \nclickhouse-client\n symlink in \n/usr/bin\n pointing to \nclickhouse\n\n\nStart the \nclickhouse\n server\n\n\ncd dbms/tests/\n\n\nRun \n./clickhouse-test\n\n\n\n\nExample usage\n\n\nRun \n./clickhouse-test --help\n to see available options.\n\n\nTo run tests without having to create a symlink or mess with \nPATH\n:\n\n\n./clickhouse-test -c \n../../build/dbms/src/Server/clickhouse --client\n\n\n\n\n\n\nTo run a single test, i.e. \n00395_nullable\n:\n\n\n./clickhouse-test \n00395", - "title": "How to run ClickHouse tests" - }, - { - "location": "/development/tests/#how-to-run-clickhouse-tests", - "text": "The clickhouse-test utility that is used for functional testing is written using Python 2.x.It also requires you to have some third-party packages: $ pip install lxml termcolor In a nutshell: Put the clickhouse program to /usr/bin (or PATH ) Create a clickhouse-client symlink in /usr/bin pointing to clickhouse Start the clickhouse server cd dbms/tests/ Run ./clickhouse-test", - "title": "How to run ClickHouse tests" - }, - { - "location": "/development/tests/#example-usage", - "text": "Run ./clickhouse-test --help to see available options. To run tests without having to create a symlink or mess with PATH : ./clickhouse-test -c ../../build/dbms/src/Server/clickhouse --client To run a single test, i.e. 00395_nullable : ./clickhouse-test 00395", - "title": "Example usage" - }, - { - "location": "/roadmap/", - "text": "Roadmap\n\n\nQ1 2018\n\n\nNew fuctionality\n\n\n\n\n\n\nSupport for \nUPDATE\n and \nDELETE\n.\n\n\n\n\n\n\nMultidimensional and nested arrays.\n\n\n\n\n\n\nIt can look something like this:\n\n\nCREATE\n \nTABLE\n \nt\n\n\n(\n\n \nx\n \nArray\n(\nArray\n(\nString\n)),\n\n \nz\n \nNested\n(\n\n \nx\n \nArray\n(\nString\n),\n\n \ny\n \nNested\n(...))\n\n\n)\n\n\nENGINE\n \n=\n \nMergeTree\n \nORDER\n \nBY\n \nx\n\n\n\n\n\n\n\n\nExternal MySQL and ODBC tables.\n\n\n\n\nExternal tables can be integrated into ClickHouse using external dictionaries. This new functionality is a convenient alternative to connecting external tables.\n\n\nSELECT\n \n...\n\n\nFROM\n \nmysql\n(\nhost:port\n,\n \ndb\n,\n \ntable\n,\n \nuser\n,\n \npassword\n)\n`\n\n\n\n\n\n\nImprovements\n\n\n\n\nEffective data copying between ClickHouse clusters.\n\n\n\n\nNow you can copy data with the remote() function. For example: \nINSERT INTO t SELECT * FROM remote(...)\n.\n\n\nThis operation will have improved performance.\n\n\n\n\nO_DIRECT for merges.\n\n\n\n\nThis will improve the performance of the OS cache and \"hot\" queries.\n\n\nQ2 2018\n\n\nNew functionality\n\n\n\n\n\n\nUPDATE/DELETE conform to the EU GDPR.\n\n\n\n\n\n\nProtobuf and Parquet input and output formats.\n\n\n\n\n\n\nCreating dictionaries using DDL queries.\n\n\n\n\n\n\nCurrently, dictionaries that are part of the database schema are defined in external XML files. This is inconvenient and counter-intuitive. The new approach should fix it.\n\n\n\n\n\n\nIntegration with LDAP.\n\n\n\n\n\n\nWITH ROLLUP and WITH CUBE for GROUP BY.\n\n\n\n\n\n\nCustom encoding and compression for each column individually.\n\n\n\n\n\n\nAs of now, ClickHouse supports LZ4 and ZSTD compression of columns, and compression settings are global (see the article \nCompression in ClickHouse\n). Per-column compression and encoding will provide more efficient data storage, which in turn will speed up queries.\n\n\n\n\nStoring data on multiple disks on the same server.\n\n\n\n\nThis functionality will make it easier to extend the disk space, since different disk systems can be used for different databases or tables. Currently, users are forced to use symbolic links if the databases and tables must be stored on a different disk.\n\n\nImprovements\n\n\nMany improvements and fixes are planned for the query execution system. For example:\n\n\n\n\nUsing an index for \nin (subquery)\n.\n\n\n\n\nThe index is not used right now, which reduces performance.\n\n\n\n\nPassing predicates from \nwhere\n to subqueries, and passing predicates to views.\n\n\n\n\nThe predicates must be passed, since the view is changed by the subquery. Performance is still low for view filters, and views can't use the primary key of the original table, which makes views useless for large tables.\n\n\n\n\nOptimizing branching operations (ternary operator, if, multiIf).\n\n\n\n\nClickHouse currently performs all branches, even if they aren't necessary.\n\n\n\n\nUsing a primary key for GROUP BY and ORDER BY.\n\n\n\n\nThis will speed up certain types of queries with partially sorted data.\n\n\nQ3-Q4 2018\n\n\nWe don't have any set plans yet, but the main projects will be:\n\n\n\n\nResource pools for executing queries.\n\n\n\n\nThis will make load management more efficient.\n\n\n\n\nANSI SQL JOIN syntax.\n\n\n\n\nImprove ClickHouse compatibility with many SQL tools.", - "title": "Roadmap" - }, - { - "location": "/roadmap/#roadmap", - "text": "", - "title": "Roadmap" - }, - { - "location": "/roadmap/#q1-2018", - "text": "", - "title": "Q1 2018" - }, - { - "location": "/roadmap/#new-fuctionality", - "text": "Support for UPDATE and DELETE . Multidimensional and nested arrays. It can look something like this: CREATE TABLE t ( \n x Array ( Array ( String )), \n z Nested ( \n x Array ( String ), \n y Nested (...)) ) ENGINE = MergeTree ORDER BY x External MySQL and ODBC tables. External tables can be integrated into ClickHouse using external dictionaries. This new functionality is a convenient alternative to connecting external tables. SELECT ... FROM mysql ( host:port , db , table , user , password ) `", - "title": "New fuctionality" - }, - { - "location": "/roadmap/#improvements", - "text": "Effective data copying between ClickHouse clusters. Now you can copy data with the remote() function. For example: INSERT INTO t SELECT * FROM remote(...) . This operation will have improved performance. O_DIRECT for merges. This will improve the performance of the OS cache and \"hot\" queries.", - "title": "Improvements" - }, - { - "location": "/roadmap/#q2-2018", - "text": "", - "title": "Q2 2018" - }, - { - "location": "/roadmap/#new-functionality", - "text": "UPDATE/DELETE conform to the EU GDPR. Protobuf and Parquet input and output formats. Creating dictionaries using DDL queries. Currently, dictionaries that are part of the database schema are defined in external XML files. This is inconvenient and counter-intuitive. The new approach should fix it. Integration with LDAP. WITH ROLLUP and WITH CUBE for GROUP BY. Custom encoding and compression for each column individually. As of now, ClickHouse supports LZ4 and ZSTD compression of columns, and compression settings are global (see the article Compression in ClickHouse ). Per-column compression and encoding will provide more efficient data storage, which in turn will speed up queries. Storing data on multiple disks on the same server. This functionality will make it easier to extend the disk space, since different disk systems can be used for different databases or tables. Currently, users are forced to use symbolic links if the databases and tables must be stored on a different disk.", - "title": "New functionality" - }, - { - "location": "/roadmap/#improvements_1", - "text": "Many improvements and fixes are planned for the query execution system. For example: Using an index for in (subquery) . The index is not used right now, which reduces performance. Passing predicates from where to subqueries, and passing predicates to views. The predicates must be passed, since the view is changed by the subquery. Performance is still low for view filters, and views can't use the primary key of the original table, which makes views useless for large tables. Optimizing branching operations (ternary operator, if, multiIf). ClickHouse currently performs all branches, even if they aren't necessary. Using a primary key for GROUP BY and ORDER BY. This will speed up certain types of queries with partially sorted data.", - "title": "Improvements" - }, - { - "location": "/roadmap/#q3-q4-2018", - "text": "We don't have any set plans yet, but the main projects will be: Resource pools for executing queries. This will make load management more efficient. ANSI SQL JOIN syntax. Improve ClickHouse compatibility with many SQL tools.", - "title": "Q3-Q4 2018" - } - ] -} \ No newline at end of file diff --git a/docs/build/docs/en/single/404.html b/docs/build/docs/en/single/404.html deleted file mode 100644 index b7c03b8124c..00000000000 --- a/docs/build/docs/en/single/404.html +++ /dev/null @@ -1,293 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ClickHouse Documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Please include / require Lunr stemmer support before this script.");e.de=function(){this.pipeline.reset(),this.pipeline.add(e.de.trimmer,e.de.stopWordFilter,e.de.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.de.stemmer))},e.de.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.de.trimmer=e.trimmerSupport.generateTrimmer(e.de.wordCharacters),e.Pipeline.registerFunction(e.de.trimmer,"trimmer-de"),e.de.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){function e(e,r,n){return!(!_.eq_s(1,e)||(_.ket=_.cursor,!_.in_grouping(w,97,252)))&&(_.slice_from(r),_.cursor=n,!0)}function i(){for(;!_.in_grouping(w,97,252);){if(_.cursor>=_.limit)return!0;_.cursor++}for(;!_.out_grouping(w,97,252);){if(_.cursor>=_.limit)return!0;_.cursor++}return!1}function s(){return u<=_.cursor}function t(){return c<=_.cursor}var o,c,u,a=[new r("",-1,6),new r("U",0,2),new r("Y",0,1),new r("ä",0,3),new r("ö",0,4),new r("ü",0,5)],d=[new r("e",-1,2),new r("em",-1,1),new r("en",-1,2),new r("ern",-1,1),new r("er",-1,1),new r("s",-1,3),new r("es",5,2)],l=[new r("en",-1,1),new r("er",-1,1),new r("st",-1,2),new r("est",2,1)],m=[new r("ig",-1,1),new r("lich",-1,1)],h=[new r("end",-1,1),new r("ig",-1,2),new r("ung",-1,1),new r("lich",-1,3),new r("isch",-1,2),new r("ik",-1,2),new r("heit",-1,3),new r("keit",-1,4)],w=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,32,8],f=[117,30,5],b=[117,30,4],_=new n;this.setCurrent=function(e){_.setCurrent(e)},this.getCurrent=function(){return _.getCurrent()},this.stem=function(){var r=_.cursor;return function(){for(var r,n,i,s,t=_.cursor;;)if(r=_.cursor,_.bra=r,_.eq_s(1,"ß"))_.ket=_.cursor,_.slice_from("ss");else{if(r>=_.limit)break;_.cursor=r+1}for(_.cursor=t;;)for(n=_.cursor;;){if(i=_.cursor,_.in_grouping(w,97,252)){if(s=_.cursor,_.bra=s,e("u","U",i))break;if(_.cursor=s,e("y","Y",i))break}if(i>=_.limit)return void(_.cursor=n);_.cursor=i+1}}(),_.cursor=r,function(){u=_.limit,c=u;var e=_.cursor+3;0<=e&&e<=_.limit&&(o=e,i()||((u=_.cursor)=_.limit)return;_.cursor++}}}(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.de.stemmer,"stemmer-de"),e.de.stopWordFilter=e.generateStopWordFilter("aber alle allem allen aller alles als also am an ander andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines können könnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher solches soll sollte sondern sonst um und uns unse unsem unsen unser unses unter viel vom von vor war waren warst was weg weil weiter welche welchem welchen welcher welches wenn werde werden wie wieder will wir wird wirst wo wollen wollte während würde würden zu zum zur zwar zwischen über".split(" ")),e.Pipeline.registerFunction(e.de.stopWordFilter,"stopWordFilter-de")}}); \ No newline at end of file diff --git a/docs/build/docs/en/single/assets/javascripts/lunr/lunr.du.js b/docs/build/docs/en/single/assets/javascripts/lunr/lunr.du.js deleted file mode 100644 index c317652d6bd..00000000000 --- a/docs/build/docs/en/single/assets/javascripts/lunr/lunr.du.js +++ /dev/null @@ -1 +0,0 @@ -!function(r,e){"function"==typeof define&&define.amd?define(e):"object"==typeof exports?module.exports=e():e()(r.lunr)}(this,function(){return function(r){if(void 0===r)throw new Error("Lunr is not present. 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Please include / require Lunr stemmer support before this script.");r.du=function(){this.pipeline.reset(),this.pipeline.add(r.du.trimmer,r.du.stopWordFilter,r.du.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(r.du.stemmer))},r.du.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",r.du.trimmer=r.trimmerSupport.generateTrimmer(r.du.wordCharacters),r.Pipeline.registerFunction(r.du.trimmer,"trimmer-du"),r.du.stemmer=function(){var e=r.stemmerSupport.Among,i=r.stemmerSupport.SnowballProgram,n=new function(){function r(r){return v.cursor=r,r>=v.limit||(v.cursor++,!1)}function n(){for(;!v.in_grouping(g,97,232);){if(v.cursor>=v.limit)return!0;v.cursor++}for(;!v.out_grouping(g,97,232);){if(v.cursor>=v.limit)return!0;v.cursor++}return!1}function o(){return l<=v.cursor}function t(){return a<=v.cursor}function s(){var r=v.limit-v.cursor;v.find_among_b(_,3)&&(v.cursor=v.limit-r,v.ket=v.cursor,v.cursor>v.limit_backward&&(v.cursor--,v.bra=v.cursor,v.slice_del()))}function u(){var r;m=!1,v.ket=v.cursor,v.eq_s_b(1,"e")&&(v.bra=v.cursor,o()&&(r=v.limit-v.cursor,v.out_grouping_b(g,97,232)&&(v.cursor=v.limit-r,v.slice_del(),m=!0,s())))}function c(){var r;o()&&(r=v.limit-v.cursor,v.out_grouping_b(g,97,232)&&(v.cursor=v.limit-r,v.eq_s_b(3,"gem")||(v.cursor=v.limit-r,v.slice_del(),s())))}var a,l,m,d=[new e("",-1,6),new e("á",0,1),new e("ä",0,1),new e("é",0,2),new e("ë",0,2),new e("í",0,3),new e("ï",0,3),new e("ó",0,4),new e("ö",0,4),new e("ú",0,5),new e("ü",0,5)],f=[new e("",-1,3),new e("I",0,2),new e("Y",0,1)],_=[new e("dd",-1,-1),new e("kk",-1,-1),new e("tt",-1,-1)],w=[new e("ene",-1,2),new e("se",-1,3),new e("en",-1,2),new e("heden",2,1),new e("s",-1,3)],b=[new e("end",-1,1),new e("ig",-1,2),new e("ing",-1,1),new e("lijk",-1,3),new e("baar",-1,4),new e("bar",-1,5)],p=[new e("aa",-1,-1),new e("ee",-1,-1),new 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if(r(i))break}(),v.cursor=e,l=v.limit,a=l,n()||((l=v.cursor)<3&&(l=3),n()||(a=v.cursor)),v.limit_backward=e,v.cursor=v.limit,function(){var r,e,i,n,a,l,d=v.limit-v.cursor;if(v.ket=v.cursor,r=v.find_among_b(w,5))switch(v.bra=v.cursor,r){case 1:o()&&v.slice_from("heid");break;case 2:c();break;case 3:o()&&v.out_grouping_b(k,97,232)&&v.slice_del()}if(v.cursor=v.limit-d,u(),v.cursor=v.limit-d,v.ket=v.cursor,v.eq_s_b(4,"heid")&&(v.bra=v.cursor,t()&&(e=v.limit-v.cursor,v.eq_s_b(1,"c")||(v.cursor=v.limit-e,v.slice_del(),v.ket=v.cursor,v.eq_s_b(2,"en")&&(v.bra=v.cursor,c())))),v.cursor=v.limit-d,v.ket=v.cursor,r=v.find_among_b(b,6))switch(v.bra=v.cursor,r){case 1:if(t()){if(v.slice_del(),i=v.limit-v.cursor,v.ket=v.cursor,v.eq_s_b(2,"ig")&&(v.bra=v.cursor,t()&&(n=v.limit-v.cursor,!v.eq_s_b(1,"e")))){v.cursor=v.limit-n,v.slice_del();break}v.cursor=v.limit-i,s()}break;case 2:t()&&(a=v.limit-v.cursor,v.eq_s_b(1,"e")||(v.cursor=v.limit-a,v.slice_del()));break;case 3:t()&&(v.slice_del(),u());break;case 4:t()&&v.slice_del();break;case 5:t()&&m&&v.slice_del()}v.cursor=v.limit-d,v.out_grouping_b(h,73,232)&&(l=v.limit-v.cursor,v.find_among_b(p,4)&&v.out_grouping_b(g,97,232)&&(v.cursor=v.limit-l,v.ket=v.cursor,v.cursor>v.limit_backward&&(v.cursor--,v.bra=v.cursor,v.slice_del())))}(),v.cursor=v.limit_backward,function(){for(var r;;)if(v.bra=v.cursor,r=v.find_among(f,3))switch(v.ket=v.cursor,r){case 1:v.slice_from("y");break;case 2:v.slice_from("i");break;case 3:if(v.cursor>=v.limit)return;v.cursor++}}(),!0}};return function(r){return"function"==typeof r.update?r.update(function(r){return n.setCurrent(r),n.stem(),n.getCurrent()}):(n.setCurrent(r),n.stem(),n.getCurrent())}}(),r.Pipeline.registerFunction(r.du.stemmer,"stemmer-du"),r.du.stopWordFilter=r.generateStopWordFilter(" aan al alles als altijd andere ben bij daar dan dat de der deze die dit doch doen door dus een eens en er ge geen geweest haar had heb hebben heeft hem het hier hij hoe hun iemand iets ik in is ja je kan kon kunnen maar me meer men met mij mijn moet na naar niet niets nog nu of om omdat onder ons ook op over reeds te tegen toch toen tot u uit uw van veel voor want waren was wat werd wezen wie wil worden wordt zal ze zelf zich zij zijn zo zonder zou".split(" ")),r.Pipeline.registerFunction(r.du.stopWordFilter,"stopWordFilter-du")}}); \ No newline at end of file diff --git a/docs/build/docs/en/single/assets/javascripts/lunr/lunr.es.js b/docs/build/docs/en/single/assets/javascripts/lunr/lunr.es.js deleted file mode 100644 index 5098feba48b..00000000000 --- a/docs/build/docs/en/single/assets/javascripts/lunr/lunr.es.js +++ /dev/null @@ -1 +0,0 @@ -!function(e,s){"function"==typeof define&&define.amd?define(s):"object"==typeof exports?module.exports=s():s()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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niin niinä niissä niistä niitä noiden noihin noiksi noilla noille noilta noin noina noissa noista noita nuo nyt näiden näihin näiksi näille näillä näiltä näinä näissä näistä näitä nämä ole olemme olen olet olette oli olimme olin olisi olisimme olisin olisit olisitte olisivat olit olitte olivat olla olleet ollut on ovat poikki se sekä sen siihen siinä siitä siksi sille sillä sillä siltä sinua sinulla sinulle sinulta sinun sinussa sinusta sinut sinuun sinä sinä sitä tai te teidän teidät teihin teille teillä teiltä teissä teistä teitä tuo tuohon tuoksi tuolla tuolle tuolta tuon tuona tuossa tuosta tuota tähän täksi tälle tällä tältä tämä tämän tänä tässä tästä tätä vaan vai vaikka yli".split(" ")),i.Pipeline.registerFunction(i.fi.stopWordFilter,"stopWordFilter-fi")}}); \ No newline at end of file diff --git a/docs/build/docs/en/single/assets/javascripts/lunr/lunr.fr.js b/docs/build/docs/en/single/assets/javascripts/lunr/lunr.fr.js deleted file mode 100644 index ae9f8cf6b7d..00000000000 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s;this.setCurrent=function(e){j.setCurrent(e)},this.getCurrent=function(){return j.getCurrent()},this.stem=function(){var e=j.cursor;return function(){for(var e;;){if(j.bra=j.cursor,e=j.find_among(f,3))switch(j.ket=j.cursor,e){case 1:j.slice_from("a~");continue;case 2:j.slice_from("o~");continue;case 3:if(j.cursor>=j.limit)break;j.cursor++;continue}break}}(),j.cursor=e,function(){var e=j.cursor;l=j.limit,c=l,m=l,n(),j.cursor=e,i()&&(c=j.cursor,i()&&(m=j.cursor))}(),j.limit_backward=e,j.cursor=j.limit,w(),j.cursor=j.limit,function(){var e;if(j.ket=j.cursor,e=j.find_among_b(k,4))switch(j.bra=j.cursor,e){case 1:o()&&(j.slice_del(),j.ket=j.cursor,j.limit,j.cursor,u("u","g")&&u("i","c"));break;case 2:j.slice_from("c")}}(),j.cursor=j.limit_backward,function(){for(var e;;){if(j.bra=j.cursor,e=j.find_among(d,3))switch(j.ket=j.cursor,e){case 1:j.slice_from("ã");continue;case 2:j.slice_from("õ");continue;case 3:if(j.cursor>=j.limit)break;j.cursor++;continue}break}}(),!0}};return 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nas nem no nos nossa nossas nosso nossos num numa não nós o os ou para pela pelas pelo pelos por qual quando que quem se seja sejam sejamos sem serei seremos seria seriam será serão seríamos seu seus somos sou sua suas são só também te tem temos tenha tenham tenhamos tenho terei teremos teria teriam terá terão teríamos teu teus teve tinha tinham tive tivemos tiver tivera tiveram tiverem tivermos tivesse tivessem tivéramos tivéssemos tu tua tuas tém tínhamos um uma você vocês vos à às éramos".split(" ")),e.Pipeline.registerFunction(e.pt.stopWordFilter,"stopWordFilter-pt")}}); \ No newline at end of file diff --git a/docs/build/docs/en/single/assets/javascripts/lunr/lunr.ro.js b/docs/build/docs/en/single/assets/javascripts/lunr/lunr.ro.js deleted file mode 100644 index 9b5612891c5..00000000000 --- a/docs/build/docs/en/single/assets/javascripts/lunr/lunr.ro.js +++ /dev/null @@ -1 +0,0 @@ -!function(e,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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fie fiecare fii fim fiu fiţi frumos fără graţie halbă iar ieri la le li lor lui lângă lîngă mai mea mei mele mereu meu mi mie mine mult multă mulţi mulţumesc mâine mîine mă ne nevoie nici nicăieri nimeni nimeri nimic nişte noastre noastră noi noroc nostru nouă noştri nu opt ori oricare orice oricine oricum oricând oricât oricînd oricît oriunde patra patru patrulea pe pentru peste pic poate pot prea prima primul prin puţin puţina puţină până pînă rog sa sale sau se spate spre sub sunt suntem sunteţi sută sînt sîntem sînteţi să săi său ta tale te timp tine toate toată tot totuşi toţi trei treia treilea tu tăi tău un una unde undeva unei uneia unele uneori unii unor unora unu unui unuia unul vi voastre voastră voi vostru vouă voştri vreme vreo vreun vă zece zero zi zice îi îl îmi împotriva în înainte înaintea încotro încât încît între întrucât întrucît îţi ăla ălea ăsta ăstea ăştia şapte şase şi ştiu ţi ţie".split(" 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.md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=light-blue]{background-color:#03a9f4}[data-md-color-primary=light-blue] .md-typeset a{color:#03a9f4}[data-md-color-primary=light-blue] .md-header,[data-md-color-primary=light-blue] .md-hero{background-color:#03a9f4}[data-md-color-primary=light-blue] .md-nav__link--active,[data-md-color-primary=light-blue] .md-nav__link:active{color:#03a9f4}[data-md-color-primary=light-blue] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=cyan]{background-color:#00bcd4}[data-md-color-primary=cyan] .md-typeset a{color:#00bcd4}[data-md-color-primary=cyan] .md-header,[data-md-color-primary=cyan] .md-hero{background-color:#00bcd4}[data-md-color-primary=cyan] .md-nav__link--active,[data-md-color-primary=cyan] .md-nav__link:active{color:#00bcd4}[data-md-color-primary=cyan] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=teal]{background-color:#009688}[data-md-color-primary=teal] .md-typeset a{color:#009688}[data-md-color-primary=teal] .md-header,[data-md-color-primary=teal] .md-hero{background-color:#009688}[data-md-color-primary=teal] .md-nav__link--active,[data-md-color-primary=teal] .md-nav__link:active{color:#009688}[data-md-color-primary=teal] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=green]{background-color:#4caf50}[data-md-color-primary=green] .md-typeset a{color:#4caf50}[data-md-color-primary=green] .md-header,[data-md-color-primary=green] .md-hero{background-color:#4caf50}[data-md-color-primary=green] .md-nav__link--active,[data-md-color-primary=green] .md-nav__link:active{color:#4caf50}[data-md-color-primary=green] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=light-green]{background-color:#7cb342}[data-md-color-primary=light-green] .md-typeset a{color:#7cb342}[data-md-color-primary=light-green] .md-header,[data-md-color-primary=light-green] .md-hero{background-color:#7cb342}[data-md-color-primary=light-green] .md-nav__link--active,[data-md-color-primary=light-green] .md-nav__link:active{color:#7cb342}[data-md-color-primary=light-green] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=lime]{background-color:#c0ca33}[data-md-color-primary=lime] .md-typeset a{color:#c0ca33}[data-md-color-primary=lime] .md-header,[data-md-color-primary=lime] .md-hero{background-color:#c0ca33}[data-md-color-primary=lime] .md-nav__link--active,[data-md-color-primary=lime] .md-nav__link:active{color:#c0ca33}[data-md-color-primary=lime] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=yellow]{background-color:#f9a825}[data-md-color-primary=yellow] .md-typeset a{color:#f9a825}[data-md-color-primary=yellow] .md-header,[data-md-color-primary=yellow] .md-hero{background-color:#f9a825}[data-md-color-primary=yellow] .md-nav__link--active,[data-md-color-primary=yellow] .md-nav__link:active{color:#f9a825}[data-md-color-primary=yellow] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=amber]{background-color:#ffa000}[data-md-color-primary=amber] .md-typeset a{color:#ffa000}[data-md-color-primary=amber] .md-header,[data-md-color-primary=amber] .md-hero{background-color:#ffa000}[data-md-color-primary=amber] .md-nav__link--active,[data-md-color-primary=amber] .md-nav__link:active{color:#ffa000}[data-md-color-primary=amber] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=orange]{background-color:#fb8c00}[data-md-color-primary=orange] .md-typeset a{color:#fb8c00}[data-md-color-primary=orange] .md-header,[data-md-color-primary=orange] .md-hero{background-color:#fb8c00}[data-md-color-primary=orange] .md-nav__link--active,[data-md-color-primary=orange] .md-nav__link:active{color:#fb8c00}[data-md-color-primary=orange] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=deep-orange]{background-color:#ff7043}[data-md-color-primary=deep-orange] .md-typeset a{color:#ff7043}[data-md-color-primary=deep-orange] .md-header,[data-md-color-primary=deep-orange] .md-hero{background-color:#ff7043}[data-md-color-primary=deep-orange] .md-nav__link--active,[data-md-color-primary=deep-orange] .md-nav__link:active{color:#ff7043}[data-md-color-primary=deep-orange] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=brown]{background-color:#795548}[data-md-color-primary=brown] .md-typeset a{color:#795548}[data-md-color-primary=brown] .md-header,[data-md-color-primary=brown] .md-hero{background-color:#795548}[data-md-color-primary=brown] .md-nav__link--active,[data-md-color-primary=brown] .md-nav__link:active{color:#795548}[data-md-color-primary=brown] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=grey]{background-color:#757575}[data-md-color-primary=grey] .md-typeset a{color:#757575}[data-md-color-primary=grey] .md-header,[data-md-color-primary=grey] .md-hero{background-color:#757575}[data-md-color-primary=grey] .md-nav__link--active,[data-md-color-primary=grey] .md-nav__link:active{color:#757575}[data-md-color-primary=grey] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=blue-grey]{background-color:#546e7a}[data-md-color-primary=blue-grey] .md-typeset a{color:#546e7a}[data-md-color-primary=blue-grey] .md-header,[data-md-color-primary=blue-grey] .md-hero{background-color:#546e7a}[data-md-color-primary=blue-grey] .md-nav__link--active,[data-md-color-primary=blue-grey] .md-nav__link:active{color:#546e7a}[data-md-color-primary=blue-grey] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=white]{-webkit-box-shadow:0 0 .1rem rgba(0,0,0,.54) inset;box-shadow:inset 0 0 .1rem rgba(0,0,0,.54)}[data-md-color-primary=white] .md-header,[data-md-color-primary=white] .md-hero,button[data-md-color-primary=white]{background-color:#fff;color:rgba(0,0,0,.87)}[data-md-color-primary=white] .md-hero--expand{border-bottom:.1rem solid rgba(0,0,0,.07)}button[data-md-color-accent=red]{background-color:#ff1744}[data-md-color-accent=red] .md-typeset a:active,[data-md-color-accent=red] .md-typeset a:hover{color:#ff1744}[data-md-color-accent=red] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=red] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-nav__link:focus,[data-md-color-accent=red] .md-nav__link:hover,[data-md-color-accent=red] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=red] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=red] .md-typeset .md-clipboard:active:before,[data-md-color-accent=red] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=red] .md-typeset [id] .headerlink:focus,[data-md-color-accent=red] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=red] .md-typeset [id]:target .headerlink{color:#ff1744}[data-md-color-accent=red] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-search-result__link:hover,[data-md-color-accent=red] .md-search-result__link[data-md-state=active]{background-color:rgba(255,23,68,.1)}[data-md-color-accent=red] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-source-file:hover:before{background-color:#ff1744}button[data-md-color-accent=pink]{background-color:#f50057}[data-md-color-accent=pink] .md-typeset a:active,[data-md-color-accent=pink] .md-typeset a:hover{color:#f50057}[data-md-color-accent=pink] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=pink] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-nav__link:focus,[data-md-color-accent=pink] .md-nav__link:hover,[data-md-color-accent=pink] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=pink] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=pink] .md-typeset .md-clipboard:active:before,[data-md-color-accent=pink] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=pink] .md-typeset [id] .headerlink:focus,[data-md-color-accent=pink] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=pink] .md-typeset [id]:target .headerlink{color:#f50057}[data-md-color-accent=pink] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-search-result__link:hover,[data-md-color-accent=pink] .md-search-result__link[data-md-state=active]{background-color:rgba(245,0,87,.1)}[data-md-color-accent=pink] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-source-file:hover:before{background-color:#f50057}button[data-md-color-accent=purple]{background-color:#e040fb}[data-md-color-accent=purple] .md-typeset a:active,[data-md-color-accent=purple] .md-typeset a:hover{color:#e040fb}[data-md-color-accent=purple] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=purple] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-nav__link:focus,[data-md-color-accent=purple] .md-nav__link:hover,[data-md-color-accent=purple] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=purple] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=purple] .md-typeset .md-clipboard:active:before,[data-md-color-accent=purple] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=purple] .md-typeset [id] .headerlink:focus,[data-md-color-accent=purple] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=purple] .md-typeset [id]:target .headerlink{color:#e040fb}[data-md-color-accent=purple] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-search-result__link:hover,[data-md-color-accent=purple] .md-search-result__link[data-md-state=active]{background-color:rgba(224,64,251,.1)}[data-md-color-accent=purple] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-source-file:hover:before{background-color:#e040fb}button[data-md-color-accent=deep-purple]{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-typeset a:active,[data-md-color-accent=deep-purple] .md-typeset a:hover{color:#7c4dff}[data-md-color-accent=deep-purple] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=deep-purple] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-nav__link:focus,[data-md-color-accent=deep-purple] .md-nav__link:hover,[data-md-color-accent=deep-purple] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=deep-purple] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=deep-purple] .md-typeset .md-clipboard:active:before,[data-md-color-accent=deep-purple] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=deep-purple] .md-typeset [id] .headerlink:focus,[data-md-color-accent=deep-purple] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=deep-purple] .md-typeset [id]:target .headerlink{color:#7c4dff}[data-md-color-accent=deep-purple] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-search-result__link:hover,[data-md-color-accent=deep-purple] .md-search-result__link[data-md-state=active]{background-color:rgba(124,77,255,.1)}[data-md-color-accent=deep-purple] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-source-file:hover:before{background-color:#7c4dff}button[data-md-color-accent=indigo]{background-color:#536dfe}[data-md-color-accent=indigo] .md-typeset a:active,[data-md-color-accent=indigo] .md-typeset a:hover{color:#536dfe}[data-md-color-accent=indigo] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=indigo] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-nav__link:focus,[data-md-color-accent=indigo] .md-nav__link:hover,[data-md-color-accent=indigo] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=indigo] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=indigo] .md-typeset .md-clipboard:active:before,[data-md-color-accent=indigo] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=indigo] .md-typeset [id] .headerlink:focus,[data-md-color-accent=indigo] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=indigo] .md-typeset [id]:target .headerlink{color:#536dfe}[data-md-color-accent=indigo] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-search-result__link:hover,[data-md-color-accent=indigo] .md-search-result__link[data-md-state=active]{background-color:rgba(83,109,254,.1)}[data-md-color-accent=indigo] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-source-file:hover:before{background-color:#536dfe}button[data-md-color-accent=blue]{background-color:#448aff}[data-md-color-accent=blue] .md-typeset a:active,[data-md-color-accent=blue] .md-typeset a:hover{color:#448aff}[data-md-color-accent=blue] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=blue] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-nav__link:focus,[data-md-color-accent=blue] .md-nav__link:hover,[data-md-color-accent=blue] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=blue] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=blue] .md-typeset .md-clipboard:active:before,[data-md-color-accent=blue] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=blue] .md-typeset [id] .headerlink:focus,[data-md-color-accent=blue] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=blue] .md-typeset [id]:target .headerlink{color:#448aff}[data-md-color-accent=blue] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-search-result__link:hover,[data-md-color-accent=blue] .md-search-result__link[data-md-state=active]{background-color:rgba(68,138,255,.1)}[data-md-color-accent=blue] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-source-file:hover:before{background-color:#448aff}button[data-md-color-accent=light-blue]{background-color:#0091ea}[data-md-color-accent=light-blue] .md-typeset a:active,[data-md-color-accent=light-blue] .md-typeset a:hover{color:#0091ea}[data-md-color-accent=light-blue] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=light-blue] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-nav__link:focus,[data-md-color-accent=light-blue] .md-nav__link:hover,[data-md-color-accent=light-blue] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=light-blue] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=light-blue] .md-typeset .md-clipboard:active:before,[data-md-color-accent=light-blue] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=light-blue] .md-typeset [id] .headerlink:focus,[data-md-color-accent=light-blue] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=light-blue] .md-typeset [id]:target .headerlink{color:#0091ea}[data-md-color-accent=light-blue] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-search-result__link:hover,[data-md-color-accent=light-blue] .md-search-result__link[data-md-state=active]{background-color:rgba(0,145,234,.1)}[data-md-color-accent=light-blue] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-source-file:hover:before{background-color:#0091ea}button[data-md-color-accent=cyan]{background-color:#00b8d4}[data-md-color-accent=cyan] .md-typeset a:active,[data-md-color-accent=cyan] .md-typeset a:hover{color:#00b8d4}[data-md-color-accent=cyan] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=cyan] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-nav__link:focus,[data-md-color-accent=cyan] .md-nav__link:hover,[data-md-color-accent=cyan] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=cyan] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=cyan] .md-typeset .md-clipboard:active:before,[data-md-color-accent=cyan] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=cyan] .md-typeset [id] .headerlink:focus,[data-md-color-accent=cyan] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=cyan] .md-typeset [id]:target .headerlink{color:#00b8d4}[data-md-color-accent=cyan] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-search-result__link:hover,[data-md-color-accent=cyan] .md-search-result__link[data-md-state=active]{background-color:rgba(0,184,212,.1)}[data-md-color-accent=cyan] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-source-file:hover:before{background-color:#00b8d4}button[data-md-color-accent=teal]{background-color:#00bfa5}[data-md-color-accent=teal] .md-typeset a:active,[data-md-color-accent=teal] .md-typeset a:hover{color:#00bfa5}[data-md-color-accent=teal] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=teal] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#00bfa5}[data-md-color-accent=teal] .md-nav__link:focus,[data-md-color-accent=teal] .md-nav__link:hover,[data-md-color-accent=teal] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=teal] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=teal] .md-typeset .md-clipboard:active:before,[data-md-color-accent=teal] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=teal] .md-typeset [id] .headerlink:focus,[data-md-color-accent=teal] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=teal] .md-typeset [id]:target .headerlink{color:#00bfa5}[data-md-color-accent=teal] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00bfa5}[data-md-color-accent=teal] .md-search-result__link:hover,[data-md-color-accent=teal] .md-search-result__link[data-md-state=active]{background-color:rgba(0,191,165,.1)}[data-md-color-accent=teal] 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What is ClickHouse?

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ClickHouse is a columnar DBMS for OLAP.

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In a "normal" row-oriented DBMS, data is stored in this order:

-
5123456789123456789     1       Eurobasket - Greece - Bosnia and Herzegovina - example.com      1       2011-09-01 01:03:02     6274717   1294101174      11409   612345678912345678      0       33      6       http://www.example.com/basketball/team/123/match/456789.html http://www.example.com/basketball/team/123/match/987654.html       0       1366    768     32      10      3183      0       0       13      0\0     1       1       0       0                       2011142 -1      0               0       01321     613     660     2011-09-01 08:01:17     0       0       0       0       utf-8   1466    0       0       0       5678901234567890123               277789954       0       0       0       0       0
-5234985259563631958     0       Consulting, Tax assessment, Accounting, Law       1       2011-09-01 01:03:02     6320881   2111222333      213     6458937489576391093     0       3       2       http://www.example.ru/         0       800     600       16      10      2       153.1   0       0       10      63      1       1       0       0                       2111678 000       0       588     368     240     2011-09-01 01:03:17     4       0       60310   0       windows-1251    1466    0       000               778899001       0       0       0       0       0
-...
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In order words, all the values related to a row are stored next to each other. -Examples of a row-oriented DBMS are MySQL, Postgres, MS SQL Server, and others.

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In a column-oriented DBMS, data is stored like this:

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WatchID:    5385521489354350662     5385521490329509958     5385521489953706054     5385521490476781638     5385521490583269446     5385521490218868806     5385521491437850694   5385521491090174022      5385521490792669254     5385521490420695110     5385521491532181574     5385521491559694406     5385521491459625030     5385521492275175494   5385521492781318214      5385521492710027334     5385521492955615302     5385521493708759110     5385521494506434630     5385521493104611398
-JavaEnable: 1       0       1       0       0       0       1       0       1       1       1       1       1       1       0       1       0       0       1       1
-Title:      Yandex  Announcements - Investor Relations - Yandex     Yandex — Contact us — Moscow    Yandex — Mission        Ru      Yandex — History — History of Yandex    Yandex Financial Releases - Investor Relations - Yandex Yandex — Locations      Yandex Board of Directors - Corporate Governance - Yandex       Yandex — Technologies
-GoodEvent:  1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1       1
-EventTime:  2016-05-18 05:19:20     2016-05-18 08:10:20     2016-05-18 07:38:00     2016-05-18 01:13:08     2016-05-18 00:04:06     2016-05-18 04:21:30     2016-05-18 00:34:16     2016-05-18 07:35:49     2016-05-18 11:41:59     2016-05-18 01:13:32
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These examples only show the order that data is arranged in. -The values from different columns are stored separately, and data from the same column is stored together.

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Examples of column-oriented DBMSs: Vertica, Paraccel (Actian Matrix) (Amazon Redshift), Sybase IQ, Exasol, Infobright, InfiniDB, MonetDB (VectorWise) (Actian Vector), LucidDB, SAP HANA, Google Dremel, Google PowerDrill, Druid, kdb+, and so on.

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Different orders for storing data are better suited to different scenarios. -The data access scenario refers to what queries are made, how often, and in what proportion; how much data is read for each type of query – rows, columns, and bytes; the relationship between reading and updating data; the working size of the data and how locally it is used; whether transactions are used, and how isolated they are; requirements for data replication and logical integrity; requirements for latency and throughput for each type of query, and so on.

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The higher the load on the system, the more important it is to customize the system to the scenario, and the more specific this customization becomes. There is no system that is equally well-suited to significantly different scenarios. If a system is adaptable to a wide set of scenarios, under a high load, the system will handle all the scenarios equally poorly, or will work well for just one of the scenarios.

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We'll say that the following is true for the OLAP (online analytical processing) scenario:

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  • The vast majority of requests are for read access.
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  • Data is updated in fairly large batches (> 1000 rows), not by single rows; or it is not updated at all.
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  • Data is added to the DB but is not modified.
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  • For reads, quite a large number of rows are extracted from the DB, but only a small subset of columns.
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  • Tables are "wide," meaning they contain a large number of columns.
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  • Queries are relatively rare (usually hundreds of queries per server or less per second).
    • -
  • For simple queries, latencies around 50 ms are allowed.
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  • Column values are fairly small: numbers and short strings (for example, 60 bytes per URL).
    • -
  • Requires high throughput when processing a single query (up to billions of rows per second per server).
    • -
  • There are no transactions.
    • -
  • Low requirements for data consistency.
    • -
  • There is one large table per query. All tables are small, except for one.
    • -
  • A query result is significantly smaller than the source data. In other words, data is filtered or aggregated. The result fits in a single server's RAM.
    • -
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It is easy to see that the OLAP scenario is very different from other popular scenarios (such as OLTP or Key-Value access). So it doesn't make sense to try to use OLTP or a Key-Value DB for processing analytical queries if you want to get decent performance. For example, if you try to use MongoDB or Elliptics for analytics, you will get very poor performance compared to OLAP databases.

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Columnar-oriented databases are better suited to OLAP scenarios (at least 100 times better in processing speed for most queries), for the following reasons:

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    -
  1. For I/O.
    2. -
  2. For an analytical query, only a small number of table columns need to be read. In a column-oriented database, you can read just the data you need. For example, if you need 5 columns out of 100, you can expect a 20-fold reduction in I/O.
    3. -
  3. Since data is read in packets, it is easier to compress. Data in columns is also easier to compress. This further reduces the I/O volume.
    4. -
  4. Due to the reduced I/O, more data fits in the system cache.
    5. -
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For example, the query "count the number of records for each advertising platform" requires reading one "advertising platform ID" column, which takes up 1 byte uncompressed. If most of the traffic was not from advertising platforms, you can expect at least 10-fold compression of this column. When using a quick compression algorithm, data decompression is possible at a speed of at least several gigabytes of uncompressed data per second. In other words, this query can be processed at a speed of approximately several billion rows per second on a single server. This speed is actually achieved in practice.

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Example:

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milovidov@hostname:~$ clickhouse-client
-ClickHouse client version 0.0.52053.
-Connecting to localhost:9000.
-Connected to ClickHouse server version 0.0.52053.
-
-:) SELECT CounterID, count() FROM hits GROUP BY CounterID ORDER BY count() DESC LIMIT 20
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-SELECT
-    CounterID,
-    count()
-FROM hits
-GROUP BY CounterID
-ORDER BY count() DESC
-LIMIT 20
-
-┌─CounterID─┬──count()─┐
-│    114208 │ 56057344 │
-│    115080 │ 51619590 │
-│      3228 │ 44658301 │
-│     38230 │ 42045932 │
-│    145263 │ 42042158 │
-│     91244 │ 38297270 │
-│    154139 │ 26647572 │
-│    150748 │ 24112755 │
-│    242232 │ 21302571 │
-│    338158 │ 13507087 │
-│     62180 │ 12229491 │
-│     82264 │ 12187441 │
-│    232261 │ 12148031 │
-│    146272 │ 11438516 │
-│    168777 │ 11403636 │
-│   4120072 │ 11227824 │
-│  10938808 │ 10519739 │
-│     74088 │  9047015 │
-│    115079 │  8837972 │
-│    337234 │  8205961 │
-└───────────┴──────────┘
-
-20 rows in set. Elapsed: 0.153 sec. Processed 1.00 billion rows, 4.00 GB (6.53 billion rows/s., 26.10 GB/s.)
-
-:)
-
- - -
    -
  1. For CPU.
    2. -
-

Since executing a query requires processing a large number of rows, it helps to dispatch all operations for entire vectors instead of for separate rows, or to implement the query engine so that there is almost no dispatching cost. If you don't do this, with any half-decent disk subsystem, the query interpreter inevitably stalls the CPU. -It makes sense to both store data in columns and process it, when possible, by columns.

-

There are two ways to do this:

-
    -
  1. -

    A vector engine. All operations are written for vectors, instead of for separate values. This means you don't need to call operations very often, and dispatching costs are negligible. Operation code contains an optimized internal cycle.

    -
    2. -
  2. -

    Code generation. The code generated for the query has all the indirect calls in it.

    -
    3. -
-

This is not done in "normal" databases, because it doesn't make sense when running simple queries. However, there are exceptions. For example, MemSQL uses code generation to reduce latency when processing SQL queries. (For comparison, analytical DBMSs require optimization of throughput, not latency.)

-

Note that for CPU efficiency, the query language must be declarative (SQL or MDX), or at least a vector (J, K). The query should only contain implicit loops, allowing for optimization.

-

Introduction

-

Distinctive features of ClickHouse

-

True column-oriented DBMS

-

In a true column-oriented DBMS, there isn't any "garbage" stored with the values. Among other things, this means that constant-length values must be supported, to avoid storing their length "number" next to the values. As an example, a billion UInt8-type values should actually consume around 1 GB uncompressed, or this will strongly affect the CPU use. It is very important to store data compactly (without any "garbage") even when uncompressed, since the speed of decompression (CPU usage) depends mainly on the volume of uncompressed data.

-

This is worth noting because there are systems that can store values of separate columns separately, but that can't effectively process analytical queries due to their optimization for other scenarios. Examples are HBase, BigTable, Cassandra, and HyperTable. In these systems, you will get throughput around a hundred thousand rows per second, but not hundreds of millions of rows per second.

-

Also note that ClickHouse is a DBMS, not a single database. ClickHouse allows creating tables and databases in runtime, loading data, and running queries without reconfiguring and restarting the server.

-

Data compression

-

Some column-oriented DBMSs (InfiniDB CE and MonetDB) do not use data compression. However, data compression really improves performance.

-

Disk storage of data

-

Many column-oriented DBMSs (such as SAP HANA and Google PowerDrill) can only work in RAM. But even on thousands of servers, the RAM is too small for storing all the pageviews and sessions in Yandex.Metrica.

-

Parallel processing on multiple cores

-

Large queries are parallelized in a natural way.

-

Distributed processing on multiple servers

-

Almost none of the columnar DBMSs listed above have support for distributed processing. -In ClickHouse, data can reside on different shards. Each shard can be a group of replicas that are used for fault tolerance. The query is processed on all the shards in parallel. This is transparent for the user.

-

SQL support

-

If you are familiar with standard SQL, we can't really talk about SQL support. -All the functions have different names. -However, this is a declarative query language based on SQL that can't be differentiated from SQL in many instances. -JOINs are supported. Subqueries are supported in FROM, IN, and JOIN clauses, as well as scalar subqueries. -Dependent subqueries are not supported.

-

Vector engine

-

Data is not only stored by columns, but is processed by vectors (parts of columns). This allows us to achieve high CPU performance.

-

Real-time data updates

-

ClickHouse supports primary key tables. In order to quickly perform queries on the range of the primary key, the data is sorted incrementally using the merge tree. Due to this, data can continually be added to the table. There is no locking when adding data.

-

Indexes

-

Having a primary key makes it possible to extract data for specific clients (for instance, Yandex.Metrica tracking tags) for a specific time range, with low latency less than several dozen milliseconds.

-

Suitable for online queries

-

This lets us use the system as the back-end for a web interface. Low latency means queries can be processed without delay, while the Yandex.Metrica interface page is loading. In other words, in online mode.

-

Support for approximated calculations

-
    -
  1. The system contains aggregate functions for approximated calculation of the number of various values, medians, and quantiles.
    2. -
  2. Supports running a query based on a part (sample) of data and getting an approximated result. In this case, proportionally less data is retrieved from the disk.
    3. -
  3. Supports running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reasonably accurate result while using fewer resources.
    4. -
-

Data replication and support for data integrity on replicas

-

Uses asynchronous multimaster replication. After being written to any available replica, data is distributed to all the remaining replicas. The system maintains identical data on different replicas. Data is restored automatically after a failure, or using a "button" for complex cases. -For more information, see the section Data replication.

-

ClickHouse features that can be considered disadvantages

-
    -
  1. No transactions.
    2. -
  2. For aggregation, query results must fit in the RAM on a single server. However, the volume of source data for a query may be indefinitely large.
    3. -
  3. Lack of full-fledged UPDATE/DELETE implementation.
    4. -
-

Yandex.Metrica use case

-

ClickHouse currently powers Yandex.Metrica, the second largest web analytics platform in the world. With more than 13 trillion records in the database and more than 20 billion events daily, ClickHouse allows you generating custom reports on the fly directly from non-aggregated data.

-

We need to get custom reports based on hits and sessions, with custom segments set by the user. Data for the reports is updated in real-time. Queries must be run immediately (in online mode). We must be able to build reports for any time period. Complex aggregates must be calculated, such as the number of unique visitors. -At this time (April 2014), Yandex.Metrica receives approximately 12 billion events (pageviews and mouse clicks) daily. All these events must be stored in order to build custom reports. A single query may require scanning hundreds of millions of rows over a few seconds, or millions of rows in no more than a few hundred milliseconds.

-

Usage in Yandex.Metrica and other Yandex services

-

ClickHouse is used for multiple purposes in Yandex.Metrica. -Its main task is to build reports in online mode using non-aggregated data. It uses a cluster of 374 servers, which store over 20.3 trillion rows in the database. The volume of compressed data, without counting duplication and replication, is about 2 PB. The volume of uncompressed data (in TSV format) would be approximately 17 PB.

-

ClickHouse is also used for:

-
    -
  • Storing data for Session Replay from Yandex.Metrica.
    • -
  • Processing intermediate data.
    • -
  • Building global reports with Analytics.
    • -
  • Running queries for debugging the Yandex.Metrica engine.
    • -
  • Analyzing logs from the API and the user interface.
    • -
-

ClickHouse has at least a dozen installations in other Yandex services: in search verticals, Market, Direct, business analytics, mobile development, AdFox, personal services, and others.

-

Aggregated and non-aggregated data

-

There is a popular opinion that in order to effectively calculate statistics, you must aggregate data, since this reduces the volume of data.

-

But data aggregation is a very limited solution, for the following reasons:

-
    -
  • You must have a pre-defined list of reports the user will need.
    • -
  • The user can't make custom reports.
    • -
  • When aggregating a large quantity of keys, the volume of data is not reduced, and aggregation is useless.
    • -
  • For a large number of reports, there are too many aggregation variations (combinatorial explosion).
    • -
  • When aggregating keys with high cardinality (such as URLs), the volume of data is not reduced by much (less than twofold).
    • -
  • For this reason, the volume of data with aggregation might grow instead of shrink.
    • -
  • Users do not view all the reports we generate for them. A large portion of calculations are useless.
    • -
  • The logical integrity of data may be violated for various aggregations.
    • -
-

If we do not aggregate anything and work with non-aggregated data, this might actually reduce the volume of calculations.

-

However, with aggregation, a significant part of the work is taken offline and completed relatively calmly. In contrast, online calculations require calculating as fast as possible, since the user is waiting for the result.

-

Yandex.Metrica has a specialized system for aggregating data called Metrage, which is used for the majority of reports. -Starting in 2009, Yandex.Metrica also used a specialized OLAP database for non-aggregated data called OLAPServer, which was previously used for the report builder. -OLAPServer worked well for non-aggregated data, but it had many restrictions that did not allow it to be used for all reports as desired. These included the lack of support for data types (only numbers), and the inability to incrementally update data in real-time (it could only be done by rewriting data daily). OLAPServer is not a DBMS, but a specialized DB.

-

To remove the limitations of OLAPServer and solve the problem of working with non-aggregated data for all reports, we developed the ClickHouse DBMS.

-

Questions you were afraid to ask

-

Why not use something like MapReduce?

-

We can refer to systems like map-reduce as distributed computing systems in which the reduce operation is based on distributed sorting. In this sense, they include Hadoop, and YT (YT is developed at Yandex for internal use).

-

These systems aren't appropriate for online queries due to their high latency. In other words, they can't be used as the back-end for a web interface. -These types of systems aren't useful for real-time data updates. -Distributed sorting isn't the best way to perform reduce operations if the result of the operation and all the intermediate results (if there are any) are located in the RAM of a single server, which is usually the case for online queries. In such a case, a hash table is the optimal way to perform reduce operations. A common approach to optimizing map-reduce tasks is pre-aggregation (partial reduce) using a hash table in RAM. The user performs this optimization manually. -Distributed sorting is one of the main causes of reduced performance when running simple map-reduce tasks.

-

Systems like map-reduce allow executing any code on the cluster. But a declarative query language is better suited to OLAP in order to run experiments quickly. For example, Hadoop has Hive and Pig. Also consider Cloudera Impala, Shark (outdated) for Spark, and Spark SQL, Presto, and Apache Drill. Performance when running such tasks is highly sub-optimal compared to specialized systems, but relatively high latency makes it unrealistic to use these systems as the backend for a web interface.

-

YT allows storing groups of columns separately. But YT can't be considered a true column-based system because it doesn't have fixed-length data types (for efficiently storing numbers without extra "garbage"), and also due to its lack of a vector engine. Tasks are performed in YT using custom code in streaming mode, so they cannot be optimized enough (up to hundreds of millions of rows per second per server). "Dynamic table sorting" is under development in YT using MergeTree, strict value typing, and a query language similar to SQL. Dynamically sorted tables are not appropriate for OLAP tasks because the data is stored by row. The YT query language is still under development, so we can't yet rely on this functionality. YT developers are considering using dynamically sorted tables in OLTP and Key-Value scenarios.

-

Performance

-

According to internal testing results, ClickHouse shows the best performance for comparable operating scenarios among systems of its class that were available for testing. This includes the highest throughput for long queries, and the lowest latency on short queries. Testing results are shown on a separate page.

-

Throughput for a single large query

-

Throughput can be measured in rows per second or in megabytes per second. If the data is placed in the page cache, a query that is not too complex is processed on modern hardware at a speed of approximately 2-10 GB/s of uncompressed data on a single server (for the simplest cases, the speed may reach 30 GB/s). If data is not placed in the page cache, the speed depends on the disk subsystem and the data compression rate. For example, if the disk subsystem allows reading data at 400 MB/s, and the data compression rate is 3, the speed will be around 1.2 GB/s. To get the speed in rows per second, divide the speed in bytes per second by the total size of the columns used in the query. For example, if 10 bytes of columns are extracted, the speed will be around 100-200 million rows per second.

-

The processing speed increases almost linearly for distributed processing, but only if the number of rows resulting from aggregation or sorting is not too large.

-

Latency when processing short queries

-

If a query uses a primary key and does not select too many rows to process (hundreds of thousands), and does not use too many columns, we can expect less than 50 milliseconds of latency (single digits of milliseconds in the best case) if data is placed in the page cache. Otherwise, latency is calculated from the number of seeks. If you use rotating drives, for a system that is not overloaded, the latency is calculated by this formula: seek time (10 ms) * number of columns queried * number of data parts.

-

Throughput when processing a large quantity of short queries

-

Under the same conditions, ClickHouse can handle several hundred queries per second on a single server (up to several thousand in the best case). Since this scenario is not typical for analytical DBMSs, we recommend expecting a maximum of 100 queries per second.

-

Performance when inserting data

-

We recommend inserting data in packets of at least 1000 rows, or no more than a single request per second. When inserting to a MergeTree table from a tab-separated dump, the insertion speed will be from 50 to 200 MB/s. If the inserted rows are around 1 Kb in size, the speed will be from 50,000 to 200,000 rows per second. If the rows are small, the performance will be higher in rows per second (on Banner System data -> 500,000 rows per second; on Graphite data -> 1,000,000 rows per second). To improve performance, you can make multiple INSERT queries in parallel, and performance will increase linearly.

-

Getting started

-

System requirements

-

This is not a cross-platform system. It requires Linux Ubuntu Precise (12.04) or newer, with x86_64 architecture and support for the SSE 4.2 instruction set. -To check for SSE 4.2:

-
grep -q sse4_2 /proc/cpuinfo && echo "SSE 4.2 supported" || echo "SSE 4.2 not supported"
-
- - -

We recommend using Ubuntu Trusty, Ubuntu Xenial, or Ubuntu Precise. -The terminal must use UTF-8 encoding (the default in Ubuntu).

-

Installation

-

For testing and development, the system can be installed on a single server or on a desktop computer.

-

Installing from packages for Debian/Ubuntu

-

In /etc/apt/sources.list (or in a separate /etc/apt/sources.list.d/clickhouse.list file), add the repository:

-
deb http://repo.yandex.ru/clickhouse/deb/stable/ main/
-
- - -

If you want to use the most recent test version, replace 'stable' with 'testing'.

-

Then run:

-
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4    # optional
-sudo apt-get update
-sudo apt-get install clickhouse-client clickhouse-server
-
- - -

You can also download and install packages manually from here: https://repo.yandex.ru/clickhouse/deb/stable/main/.

-

ClickHouse contains access restriction settings. They are located in the 'users.xml' file (next to 'config.xml'). -By default, access is allowed from anywhere for the 'default' user, without a password. See 'user/default/networks'. -For more information, see the section "Configuration files".

-

Installing from sources

-

To compile, follow the instructions: build.md

-

You can compile packages and install them. -You can also use programs without installing packages.

-
Client: dbms/src/Client/
-Server: dbms/src/Server/
-
- - -

For the server, create a catalog with data, such as:

-
/opt/clickhouse/data/default/
-/opt/clickhouse/metadata/default/
-
- - -

(Configurable in the server config.) -Run 'chown' for the desired user.

-

Note the path to logs in the server config (src/dbms/src/Server/config.xml).

-

Other installation methods

-

Docker image: https://hub.docker.com/r/yandex/clickhouse-server/

-

RPM packages for CentOS or RHEL: https://github.com/Altinity/clickhouse-rpm-install

-

Gentoo overlay: https://github.com/kmeaw/clickhouse-overlay

-

Launch

-

To start the server (as a daemon), run:

-
sudo service clickhouse-server start
-
- - -

See the logs in the /var/log/clickhouse-server/ directory.

-

If the server doesn't start, check the configurations in the file /etc/clickhouse-server/config.xml.

-

You can also launch the server from the console:

-
clickhouse-server --config-file=/etc/clickhouse-server/config.xml
-
- - -

In this case, the log will be printed to the console, which is convenient during development. -If the configuration file is in the current directory, you don't need to specify the '--config-file' parameter. By default, it uses './config.xml'.

-

You can use the command-line client to connect to the server:

-
clickhouse-client
-
- - -

The default parameters indicate connecting with localhost:9000 on behalf of the user 'default' without a password. -The client can be used for connecting to a remote server. Example:

-
clickhouse-client --host=example.com
-
- - -

For more information, see the section "Command-line client".

-

Checking the system:

-
milovidov@hostname:~/work/metrica/src/dbms/src/Client$ ./clickhouse-client
-ClickHouse client version 0.0.18749.
-Connecting to localhost:9000.
-Connected to ClickHouse server version 0.0.18749.
-
-:) SELECT 1
-
-SELECT 1
-
-┌─1─┐
-│ 1 │
-└───┘
-
-1 rows in set. Elapsed: 0.003 sec.
-
-:)
-
- - -

Congratulations, the system works!

-

To continue experimenting, you can try to download from the test data sets.

-

-

OnTime

-

This performance test was created by Vadim Tkachenko. See:

- -

Downloading data:

-
for s in `seq 1987 2017`
-do
-for m in `seq 1 12`
-do
-wget http://transtats.bts.gov/PREZIP/On_Time_On_Time_Performance_${s}_${m}.zip
-done
-done
-
- - -

(from https://github.com/Percona-Lab/ontime-airline-performance/blob/master/download.sh )

-

Creating a table:

-
CREATE TABLE `ontime` (
-  `Year` UInt16,
-  `Quarter` UInt8,
-  `Month` UInt8,
-  `DayofMonth` UInt8,
-  `DayOfWeek` UInt8,
-  `FlightDate` Date,
-  `UniqueCarrier` FixedString(7),
-  `AirlineID` Int32,
-  `Carrier` FixedString(2),
-  `TailNum` String,
-  `FlightNum` String,
-  `OriginAirportID` Int32,
-  `OriginAirportSeqID` Int32,
-  `OriginCityMarketID` Int32,
-  `Origin` FixedString(5),
-  `OriginCityName` String,
-  `OriginState` FixedString(2),
-  `OriginStateFips` String,
-  `OriginStateName` String,
-  `OriginWac` Int32,
-  `DestAirportID` Int32,
-  `DestAirportSeqID` Int32,
-  `DestCityMarketID` Int32,
-  `Dest` FixedString(5),
-  `DestCityName` String,
-  `DestState` FixedString(2),
-  `DestStateFips` String,
-  `DestStateName` String,
-  `DestWac` Int32,
-  `CRSDepTime` Int32,
-  `DepTime` Int32,
-  `DepDelay` Int32,
-  `DepDelayMinutes` Int32,
-  `DepDel15` Int32,
-  `DepartureDelayGroups` String,
-  `DepTimeBlk` String,
-  `TaxiOut` Int32,
-  `WheelsOff` Int32,
-  `WheelsOn` Int32,
-  `TaxiIn` Int32,
-  `CRSArrTime` Int32,
-  `ArrTime` Int32,
-  `ArrDelay` Int32,
-  `ArrDelayMinutes` Int32,
-  `ArrDel15` Int32,
-  `ArrivalDelayGroups` Int32,
-  `ArrTimeBlk` String,
-  `Cancelled` UInt8,
-  `CancellationCode` FixedString(1),
-  `Diverted` UInt8,
-  `CRSElapsedTime` Int32,
-  `ActualElapsedTime` Int32,
-  `AirTime` Int32,
-  `Flights` Int32,
-  `Distance` Int32,
-  `DistanceGroup` UInt8,
-  `CarrierDelay` Int32,
-  `WeatherDelay` Int32,
-  `NASDelay` Int32,
-  `SecurityDelay` Int32,
-  `LateAircraftDelay` Int32,
-  `FirstDepTime` String,
-  `TotalAddGTime` String,
-  `LongestAddGTime` String,
-  `DivAirportLandings` String,
-  `DivReachedDest` String,
-  `DivActualElapsedTime` String,
-  `DivArrDelay` String,
-  `DivDistance` String,
-  `Div1Airport` String,
-  `Div1AirportID` Int32,
-  `Div1AirportSeqID` Int32,
-  `Div1WheelsOn` String,
-  `Div1TotalGTime` String,
-  `Div1LongestGTime` String,
-  `Div1WheelsOff` String,
-  `Div1TailNum` String,
-  `Div2Airport` String,
-  `Div2AirportID` Int32,
-  `Div2AirportSeqID` Int32,
-  `Div2WheelsOn` String,
-  `Div2TotalGTime` String,
-  `Div2LongestGTime` String,
-  `Div2WheelsOff` String,
-  `Div2TailNum` String,
-  `Div3Airport` String,
-  `Div3AirportID` Int32,
-  `Div3AirportSeqID` Int32,
-  `Div3WheelsOn` String,
-  `Div3TotalGTime` String,
-  `Div3LongestGTime` String,
-  `Div3WheelsOff` String,
-  `Div3TailNum` String,
-  `Div4Airport` String,
-  `Div4AirportID` Int32,
-  `Div4AirportSeqID` Int32,
-  `Div4WheelsOn` String,
-  `Div4TotalGTime` String,
-  `Div4LongestGTime` String,
-  `Div4WheelsOff` String,
-  `Div4TailNum` String,
-  `Div5Airport` String,
-  `Div5AirportID` Int32,
-  `Div5AirportSeqID` Int32,
-  `Div5WheelsOn` String,
-  `Div5TotalGTime` String,
-  `Div5LongestGTime` String,
-  `Div5WheelsOff` String,
-  `Div5TailNum` String
-) ENGINE = MergeTree(FlightDate, (Year, FlightDate), 8192)
-
- - -

Loading data:

-
for i in *.zip; do echo $i; unzip -cq $i '*.csv' | sed 's/\.00//g' | clickhouse-client --host=example-perftest01j --query="INSERT INTO ontime FORMAT CSVWithNames"; done
-
- - -

Queries:

-

Q0.

-
select avg(c1) from (select Year, Month, count(*) as c1 from ontime group by Year, Month);
-
- - -

Q1. The number of flights per day from the year 2000 to 2008

-
SELECT DayOfWeek, count(*) AS c FROM ontime WHERE Year >= 2000 AND Year <= 2008 GROUP BY DayOfWeek ORDER BY c DESC;
-
- - -

Q2. The number of flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008

-
SELECT DayOfWeek, count(*) AS c FROM ontime WHERE DepDelay>10 AND Year >= 2000 AND Year <= 2008 GROUP BY DayOfWeek ORDER BY c DESC
-
- - -

Q3. The number of delays by airport for 2000-2008

-
SELECT Origin, count(*) AS c FROM ontime WHERE DepDelay>10 AND Year >= 2000 AND Year <= 2008 GROUP BY Origin ORDER BY c DESC LIMIT 10
-
- - -

Q4. The number of delays by carrier for 2007

-
SELECT Carrier, count(*) FROM ontime WHERE DepDelay>10  AND Year = 2007 GROUP BY Carrier ORDER BY count(*) DESC
-
- - -

Q5. The percentage of delays by carrier for 2007

-
SELECT Carrier, c, c2, c*1000/c2 as c3
-FROM
-(
-    SELECT
-        Carrier,
-        count(*) AS c
-    FROM ontime
-    WHERE DepDelay>10
-        AND Year=2007
-    GROUP BY Carrier
-)
-ANY INNER JOIN
-(
-    SELECT
-        Carrier,
-        count(*) AS c2
-    FROM ontime
-    WHERE Year=2007
-    GROUP BY Carrier
-) USING Carrier
-ORDER BY c3 DESC;
-
- - -

Better version of the same query:

-
SELECT Carrier, avg(DepDelay > 10) * 1000 AS c3 FROM ontime WHERE Year = 2007 GROUP BY Carrier ORDER BY Carrier
-
- - -

Q6. The previous request for a broader range of years, 2000-2008

-
SELECT Carrier, c, c2, c*1000/c2 as c3
-FROM
-(
-    SELECT
-        Carrier,
-        count(*) AS c
-    FROM ontime
-    WHERE DepDelay>10
-        AND Year >= 2000 AND Year <= 2008
-    GROUP BY Carrier
-)
-ANY INNER JOIN
-(
-    SELECT
-        Carrier,
-        count(*) AS c2
-    FROM ontime
-    WHERE Year >= 2000 AND Year <= 2008
-    GROUP BY Carrier
-) USING Carrier
-ORDER BY c3 DESC;
-
- - -

Better version of the same query:

-
SELECT Carrier, avg(DepDelay > 10) * 1000 AS c3 FROM ontime WHERE Year >= 2000 AND Year <= 2008 GROUP BY Carrier ORDER BY Carrier
-
- - -

Q7. Percentage of flights delayed for more than 10 minutes, by year

-
SELECT Year, c1/c2
-FROM
-(
-    select
-        Year,
-        count(*)*1000 as c1
-    from ontime
-    WHERE DepDelay>10
-    GROUP BY Year
-)
-ANY INNER JOIN
-(
-    select
-        Year,
-        count(*) as c2
-    from ontime
-    GROUP BY Year
-) USING (Year)
-ORDER BY Year
-
- - -

Better version of the same query:

-
SELECT Year, avg(DepDelay > 10) FROM ontime GROUP BY Year ORDER BY Year
-
- - -

Q8. The most popular destinations by the number of directly connected cities for various year ranges

-
SELECT DestCityName, uniqExact(OriginCityName) AS u FROM ontime WHERE Year >= 2000 and Year <= 2010 GROUP BY DestCityName ORDER BY u DESC LIMIT 10;
-
- - -

Q9.

-
select Year, count(*) as c1 from ontime group by Year;
-
- - -

Q10.

-
select
-   min(Year), max(Year), Carrier, count(*) as cnt,
-   sum(ArrDelayMinutes>30) as flights_delayed,
-   round(sum(ArrDelayMinutes>30)/count(*),2) as rate
-FROM ontime
-WHERE
-   DayOfWeek not in (6,7) and OriginState not in ('AK', 'HI', 'PR', 'VI')
-   and DestState not in ('AK', 'HI', 'PR', 'VI')
-   and FlightDate < '2010-01-01'
-GROUP by Carrier
-HAVING cnt > 100000 and max(Year) > 1990
-ORDER by rate DESC
-LIMIT 1000;
-
- - -

Bonus:

-
SELECT avg(cnt) FROM (SELECT Year,Month,count(*) AS cnt FROM ontime WHERE DepDel15=1 GROUP BY Year,Month)
-
-select avg(c1) from (select Year,Month,count(*) as c1 from ontime group by Year,Month)
-
-SELECT DestCityName, uniqExact(OriginCityName) AS u FROM ontime GROUP BY DestCityName ORDER BY u DESC LIMIT 10;
-
-SELECT OriginCityName, DestCityName, count() AS c FROM ontime GROUP BY OriginCityName, DestCityName ORDER BY c DESC LIMIT 10;
-
-SELECT OriginCityName, count() AS c FROM ontime GROUP BY OriginCityName ORDER BY c DESC LIMIT 10;
-
- - -

New York Taxi data

-

How to import the raw data

-

See https://github.com/toddwschneider/nyc-taxi-data and http://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html for the description of the dataset and instructions for downloading.

-

Downloading will result in about 227 GB of uncompressed data in CSV files. The download takes about an hour over a 1 Gbit connection (parallel downloading from s3.amazonaws.com recovers at least half of a 1 Gbit channel). -Some of the files might not download fully. Check the file sizes and re-download any that seem doubtful.

-

Some of the files might contain invalid rows. You can fix them as follows:

-
sed -E '/(.*,){18,}/d' data/yellow_tripdata_2010-02.csv > data/yellow_tripdata_2010-02.csv_
-sed -E '/(.*,){18,}/d' data/yellow_tripdata_2010-03.csv > data/yellow_tripdata_2010-03.csv_
-mv data/yellow_tripdata_2010-02.csv_ data/yellow_tripdata_2010-02.csv
-mv data/yellow_tripdata_2010-03.csv_ data/yellow_tripdata_2010-03.csv
-
- - -

Then the data must be pre-processed in PostgreSQL. This will create selections of points in the polygons (to match points on the map with the boroughs of New York City) and combine all the data into a single denormalized flat table by using a JOIN. To do this, you will need to install PostgreSQL with PostGIS support.

-

Be careful when running initialize_database.sh and manually re-check that all the tables were created correctly.

-

It takes about 20-30 minutes to process each month's worth of data in PostgreSQL, for a total of about 48 hours.

-

You can check the number of downloaded rows as follows:

-
time psql nyc-taxi-data -c "SELECT count(*) FROM trips;"
-###    count
- 1298979494
-(1 row)
-
-real    7m9.164s
-
- - -

(This is slightly more than 1.1 billion rows reported by Mark Litwintschik in a series of blog posts.)

-

The data in PostgreSQL uses 370 GB of space.

-

Exporting the data from PostgreSQL:

-
COPY
-(
-    SELECT trips.id,
-           trips.vendor_id,
-           trips.pickup_datetime,
-           trips.dropoff_datetime,
-           trips.store_and_fwd_flag,
-           trips.rate_code_id,
-           trips.pickup_longitude,
-           trips.pickup_latitude,
-           trips.dropoff_longitude,
-           trips.dropoff_latitude,
-           trips.passenger_count,
-           trips.trip_distance,
-           trips.fare_amount,
-           trips.extra,
-           trips.mta_tax,
-           trips.tip_amount,
-           trips.tolls_amount,
-           trips.ehail_fee,
-           trips.improvement_surcharge,
-           trips.total_amount,
-           trips.payment_type,
-           trips.trip_type,
-           trips.pickup,
-           trips.dropoff,
-
-           cab_types.type cab_type,
-
-           weather.precipitation_tenths_of_mm rain,
-           weather.snow_depth_mm,
-           weather.snowfall_mm,
-           weather.max_temperature_tenths_degrees_celsius max_temp,
-           weather.min_temperature_tenths_degrees_celsius min_temp,
-           weather.average_wind_speed_tenths_of_meters_per_second wind,
-
-           pick_up.gid pickup_nyct2010_gid,
-           pick_up.ctlabel pickup_ctlabel,
-           pick_up.borocode pickup_borocode,
-           pick_up.boroname pickup_boroname,
-           pick_up.ct2010 pickup_ct2010,
-           pick_up.boroct2010 pickup_boroct2010,
-           pick_up.cdeligibil pickup_cdeligibil,
-           pick_up.ntacode pickup_ntacode,
-           pick_up.ntaname pickup_ntaname,
-           pick_up.puma pickup_puma,
-
-           drop_off.gid dropoff_nyct2010_gid,
-           drop_off.ctlabel dropoff_ctlabel,
-           drop_off.borocode dropoff_borocode,
-           drop_off.boroname dropoff_boroname,
-           drop_off.ct2010 dropoff_ct2010,
-           drop_off.boroct2010 dropoff_boroct2010,
-           drop_off.cdeligibil dropoff_cdeligibil,
-           drop_off.ntacode dropoff_ntacode,
-           drop_off.ntaname dropoff_ntaname,
-           drop_off.puma dropoff_puma
-    FROM trips
-    LEFT JOIN cab_types
-        ON trips.cab_type_id = cab_types.id
-    LEFT JOIN central_park_weather_observations_raw weather
-        ON weather.date = trips.pickup_datetime::date
-    LEFT JOIN nyct2010 pick_up
-        ON pick_up.gid = trips.pickup_nyct2010_gid
-    LEFT JOIN nyct2010 drop_off
-        ON drop_off.gid = trips.dropoff_nyct2010_gid
-) TO '/opt/milovidov/nyc-taxi-data/trips.tsv';
-
- - -

The data snapshot is created at a speed of about 50 MB per second. While creating the snapshot, PostgreSQL reads from the disk at a speed of about 28 MB per second. -This takes about 5 hours. The resulting TSV file is 590612904969 bytes.

-

Create a temporary table in ClickHouse:

-
CREATE TABLE trips
-(
-trip_id                 UInt32,
-vendor_id               String,
-pickup_datetime         DateTime,
-dropoff_datetime        Nullable(DateTime),
-store_and_fwd_flag      Nullable(FixedString(1)),
-rate_code_id            Nullable(UInt8),
-pickup_longitude        Nullable(Float64),
-pickup_latitude         Nullable(Float64),
-dropoff_longitude       Nullable(Float64),
-dropoff_latitude        Nullable(Float64),
-passenger_count         Nullable(UInt8),
-trip_distance           Nullable(Float64),
-fare_amount             Nullable(Float32),
-extra                   Nullable(Float32),
-mta_tax                 Nullable(Float32),
-tip_amount              Nullable(Float32),
-tolls_amount            Nullable(Float32),
-ehail_fee               Nullable(Float32),
-improvement_surcharge   Nullable(Float32),
-total_amount            Nullable(Float32),
-payment_type            Nullable(String),
-trip_type               Nullable(UInt8),
-pickup                  Nullable(String),
-dropoff                 Nullable(String),
-cab_type                Nullable(String),
-precipitation           Nullable(UInt8),
-snow_depth              Nullable(UInt8),
-snowfall                Nullable(UInt8),
-max_temperature         Nullable(UInt8),
-min_temperature         Nullable(UInt8),
-average_wind_speed      Nullable(UInt8),
-pickup_nyct2010_gid     Nullable(UInt8),
-pickup_ctlabel          Nullable(String),
-pickup_borocode         Nullable(UInt8),
-pickup_boroname         Nullable(String),
-pickup_ct2010           Nullable(String),
-pickup_boroct2010       Nullable(String),
-pickup_cdeligibil       Nullable(FixedString(1)),
-pickup_ntacode          Nullable(String),
-pickup_ntaname          Nullable(String),
-pickup_puma             Nullable(String),
-dropoff_nyct2010_gid    Nullable(UInt8),
-dropoff_ctlabel         Nullable(String),
-dropoff_borocode        Nullable(UInt8),
-dropoff_boroname        Nullable(String),
-dropoff_ct2010          Nullable(String),
-dropoff_boroct2010      Nullable(String),
-dropoff_cdeligibil      Nullable(String),
-dropoff_ntacode         Nullable(String),
-dropoff_ntaname         Nullable(String),
-dropoff_puma            Nullable(String)
-) ENGINE = Log;
-
- - -

It is needed for converting fields to more correct data types and, if possible, to eliminate NULLs.

-
time clickhouse-client --query="INSERT INTO trips FORMAT TabSeparated" < trips.tsv
-
-real    75m56.214s
-
- - -

Data is read at a speed of 112-140 Mb/second. -Loading data into a Log type table in one stream took 76 minutes. -The data in this table uses 142 GB.

-

(Importing data directly from Postgres is also possible using COPY ... TO PROGRAM.)

-

Unfortunately, all the fields associated with the weather (precipitation...average_wind_speed) were filled with NULL. Because of this, we will remove them from the final data set.

-

To start, we'll create a table on a single server. Later we will make the table distributed.

-

Create and populate a summary table:

-
CREATE TABLE trips_mergetree
-ENGINE = MergeTree(pickup_date, pickup_datetime, 8192)
-AS SELECT
-
-trip_id,
-CAST(vendor_id AS Enum8('1' = 1, '2' = 2, 'CMT' = 3, 'VTS' = 4, 'DDS' = 5, 'B02512' = 10, 'B02598' = 11, 'B02617' = 12, 'B02682' = 13, 'B02764' = 14)) AS vendor_id,
-toDate(pickup_datetime) AS pickup_date,
-ifNull(pickup_datetime, toDateTime(0)) AS pickup_datetime,
-toDate(dropoff_datetime) AS dropoff_date,
-ifNull(dropoff_datetime, toDateTime(0)) AS dropoff_datetime,
-assumeNotNull(store_and_fwd_flag) IN ('Y', '1', '2') AS store_and_fwd_flag,
-assumeNotNull(rate_code_id) AS rate_code_id,
-assumeNotNull(pickup_longitude) AS pickup_longitude,
-assumeNotNull(pickup_latitude) AS pickup_latitude,
-assumeNotNull(dropoff_longitude) AS dropoff_longitude,
-assumeNotNull(dropoff_latitude) AS dropoff_latitude,
-assumeNotNull(passenger_count) AS passenger_count,
-assumeNotNull(trip_distance) AS trip_distance,
-assumeNotNull(fare_amount) AS fare_amount,
-assumeNotNull(extra) AS extra,
-assumeNotNull(mta_tax) AS mta_tax,
-assumeNotNull(tip_amount) AS tip_amount,
-assumeNotNull(tolls_amount) AS tolls_amount,
-assumeNotNull(ehail_fee) AS ehail_fee,
-assumeNotNull(improvement_surcharge) AS improvement_surcharge,
-assumeNotNull(total_amount) AS total_amount,
-CAST((assumeNotNull(payment_type) AS pt) IN ('CSH', 'CASH', 'Cash', 'CAS', 'Cas', '1') ? 'CSH' : (pt IN ('CRD', 'Credit', 'Cre', 'CRE', 'CREDIT', '2') ? 'CRE' : (pt IN ('NOC', 'No Charge', 'No', '3') ? 'NOC' : (pt IN ('DIS', 'Dispute', 'Dis', '4') ? 'DIS' : 'UNK'))) AS Enum8('CSH' = 1, 'CRE' = 2, 'UNK' = 0, 'NOC' = 3, 'DIS' = 4)) AS payment_type_,
-assumeNotNull(trip_type) AS trip_type,
-ifNull(toFixedString(unhex(pickup), 25), toFixedString('', 25)) AS pickup,
-ifNull(toFixedString(unhex(dropoff), 25), toFixedString('', 25)) AS dropoff,
-CAST(assumeNotNull(cab_type) AS Enum8('yellow' = 1, 'green' = 2, 'uber' = 3)) AS cab_type,
-
-assumeNotNull(pickup_nyct2010_gid) AS pickup_nyct2010_gid,
-toFloat32(ifNull(pickup_ctlabel, '0')) AS pickup_ctlabel,
-assumeNotNull(pickup_borocode) AS pickup_borocode,
-CAST(assumeNotNull(pickup_boroname) AS Enum8('Manhattan' = 1, 'Queens' = 4, 'Brooklyn' = 3, '' = 0, 'Bronx' = 2, 'Staten Island' = 5)) AS pickup_boroname,
-toFixedString(ifNull(pickup_ct2010, '000000'), 6) AS pickup_ct2010,
-toFixedString(ifNull(pickup_boroct2010, '0000000'), 7) AS pickup_boroct2010,
-CAST(assumeNotNull(ifNull(pickup_cdeligibil, ' ')) AS Enum8(' ' = 0, 'E' = 1, 'I' = 2)) AS pickup_cdeligibil,
-toFixedString(ifNull(pickup_ntacode, '0000'), 4) AS pickup_ntacode,
-
-CAST(assumeNotNull(pickup_ntaname) AS Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195)) AS pickup_ntaname,
-
-toUInt16(ifNull(pickup_puma, '0')) AS pickup_puma,
-
-assumeNotNull(dropoff_nyct2010_gid) AS dropoff_nyct2010_gid,
-toFloat32(ifNull(dropoff_ctlabel, '0')) AS dropoff_ctlabel,
-assumeNotNull(dropoff_borocode) AS dropoff_borocode,
-CAST(assumeNotNull(dropoff_boroname) AS Enum8('Manhattan' = 1, 'Queens' = 4, 'Brooklyn' = 3, '' = 0, 'Bronx' = 2, 'Staten Island' = 5)) AS dropoff_boroname,
-toFixedString(ifNull(dropoff_ct2010, '000000'), 6) AS dropoff_ct2010,
-toFixedString(ifNull(dropoff_boroct2010, '0000000'), 7) AS dropoff_boroct2010,
-CAST(assumeNotNull(ifNull(dropoff_cdeligibil, ' ')) AS Enum8(' ' = 0, 'E' = 1, 'I' = 2)) AS dropoff_cdeligibil,
-toFixedString(ifNull(dropoff_ntacode, '0000'), 4) AS dropoff_ntacode,
-
-CAST(assumeNotNull(dropoff_ntaname) AS Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195)) AS dropoff_ntaname,
-
-toUInt16(ifNull(dropoff_puma, '0')) AS dropoff_puma
-
-FROM trips
-
- - -

This takes 3030 seconds at a speed of about 428,000 rows per second. -To load it faster, you can create the table with the Log engine instead of MergeTree. In this case, the download works faster than 200 seconds.

-

The table uses 126 GB of disk space.

-
:) SELECT formatReadableSize(sum(bytes)) FROM system.parts WHERE table = 'trips_mergetree' AND active
-
-SELECT formatReadableSize(sum(bytes))
-FROM system.parts
-WHERE (table = 'trips_mergetree') AND active
-
-┌─formatReadableSize(sum(bytes))─┐
-│ 126.18 GiB                     │
-└────────────────────────────────┘
-
- - -

Among other things, you can run the OPTIMIZE query on MergeTree. But it's not required, since everything will be fine without it.

-

Results on single server

-

Q1:

-
SELECT cab_type, count(*) FROM trips_mergetree GROUP BY cab_type
-
- - -

0.490 seconds.

-

Q2:

-
SELECT passenger_count, avg(total_amount) FROM trips_mergetree GROUP BY passenger_count
-
- - -

1.224 seconds.

-

Q3:

-
SELECT passenger_count, toYear(pickup_date) AS year, count(*) FROM trips_mergetree GROUP BY passenger_count, year
-
- - -

2.104 seconds.

-

Q4:

-
SELECT passenger_count, toYear(pickup_date) AS year, round(trip_distance) AS distance, count(*)
-FROM trips_mergetree
-GROUP BY passenger_count, year, distance
-ORDER BY year, count(*) DESC
-
- - -

3.593 seconds.

-

The following server was used:

-

Two Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical kernels total, -128 GiB RAM, -8x6 TB HD on hardware RAID-5

-

Execution time is the best of three runsBut starting from the second run, queries read data from the file system cache. No further caching occurs: the data is read out and processed in each run.

-

Creating a table on three servers:

-

On each server:

-
CREATE TABLE default.trips_mergetree_third ( trip_id UInt32,  vendor_id Enum8('1' = 1, '2' = 2, 'CMT' = 3, 'VTS' = 4, 'DDS' = 5, 'B02512' = 10, 'B02598' = 11, 'B02617' = 12, 'B02682' = 13, 'B02764' = 14),  pickup_date Date,  pickup_datetime DateTime,  dropoff_date Date,  dropoff_datetime DateTime,  store_and_fwd_flag UInt8,  rate_code_id UInt8,  pickup_longitude Float64,  pickup_latitude Float64,  dropoff_longitude Float64,  dropoff_latitude Float64,  passenger_count UInt8,  trip_distance Float64,  fare_amount Float32,  extra Float32,  mta_tax Float32,  tip_amount Float32,  tolls_amount Float32,  ehail_fee Float32,  improvement_surcharge Float32,  total_amount Float32,  payment_type_ Enum8('UNK' = 0, 'CSH' = 1, 'CRE' = 2, 'NOC' = 3, 'DIS' = 4),  trip_type UInt8,  pickup FixedString(25),  dropoff FixedString(25),  cab_type Enum8('yellow' = 1, 'green' = 2, 'uber' = 3),  pickup_nyct2010_gid UInt8,  pickup_ctlabel Float32,  pickup_borocode UInt8,  pickup_boroname Enum8('' = 0, 'Manhattan' = 1, 'Bronx' = 2, 'Brooklyn' = 3, 'Queens' = 4, 'Staten Island' = 5),  pickup_ct2010 FixedString(6),  pickup_boroct2010 FixedString(7),  pickup_cdeligibil Enum8(' ' = 0, 'E' = 1, 'I' = 2),  pickup_ntacode FixedString(4),  pickup_ntaname Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195),  pickup_puma UInt16,  dropoff_nyct2010_gid UInt8,  dropoff_ctlabel Float32,  dropoff_borocode UInt8,  dropoff_boroname Enum8('' = 0, 'Manhattan' = 1, 'Bronx' = 2, 'Brooklyn' = 3, 'Queens' = 4, 'Staten Island' = 5),  dropoff_ct2010 FixedString(6),  dropoff_boroct2010 FixedString(7),  dropoff_cdeligibil Enum8(' ' = 0, 'E' = 1, 'I' = 2),  dropoff_ntacode FixedString(4),  dropoff_ntaname Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195),  dropoff_puma UInt16) ENGINE = MergeTree(pickup_date, pickup_datetime, 8192)
-
- - -

On the source server:

-
CREATE TABLE trips_mergetree_x3 AS trips_mergetree_third ENGINE = Distributed(perftest, default, trips_mergetree_third, rand())
-
- - -

The following query redistributes data:

-
INSERT INTO trips_mergetree_x3 SELECT * FROM trips_mergetree
-
- - -

This takes 2454 seconds.

-

On three servers:

-

Q1: 0.212 seconds. -Q2: 0.438 seconds. -Q3: 0.733 seconds. -Q4: 1.241 seconds.

-

No surprises here, since the queries are scaled linearly.

-

We also have results from a cluster of 140 servers:

-

Q1: 0.028 sec. -Q2: 0.043 sec. -Q3: 0.051 sec. -Q4: 0.072 sec.

-

In this case, the query processing time is determined above all by network latency. -We ran queries using a client located in a Yandex datacenter in Finland on a cluster in Russia, which added about 20 ms of latency.

-

Summary

-
nodes   Q1     Q2     Q3     Q4
-  1  0.490  1.224  2.104  3.593
-  3  0.212  0.438  0.733  1.241
-140  0.028  0.043  0.051  0.072
-
- - -

AMPLab Big Data Benchmark

-

See https://amplab.cs.berkeley.edu/benchmark/

-

Sign up for a free account at https://aws.amazon.com. You will need a credit card, email and phone number.Get a new access key at https://console.aws.amazon.com/iam/home?nc2=h_m_sc#security_credential

-

Run the following in the console:

-
sudo apt-get install s3cmd
-mkdir tiny; cd tiny;
-s3cmd sync s3://big-data-benchmark/pavlo/text-deflate/tiny/ .
-cd ..
-mkdir 1node; cd 1node;
-s3cmd sync s3://big-data-benchmark/pavlo/text-deflate/1node/ .
-cd ..
-mkdir 5nodes; cd 5nodes;
-s3cmd sync s3://big-data-benchmark/pavlo/text-deflate/5nodes/ .
-cd ..
-
- - -

Run the following ClickHouse queries:

-
CREATE TABLE rankings_tiny
-(
-    pageURL String,
-    pageRank UInt32,
-    avgDuration UInt32
-) ENGINE = Log;
-
-CREATE TABLE uservisits_tiny
-(
-    sourceIP String,
-    destinationURL String,
-    visitDate Date,
-    adRevenue Float32,
-    UserAgent String,
-    cCode FixedString(3),
-    lCode FixedString(6),
-    searchWord String,
-    duration UInt32
-) ENGINE = MergeTree(visitDate, visitDate, 8192);
-
-CREATE TABLE rankings_1node
-(
-    pageURL String,
-    pageRank UInt32,
-    avgDuration UInt32
-) ENGINE = Log;
-
-CREATE TABLE uservisits_1node
-(
-    sourceIP String,
-    destinationURL String,
-    visitDate Date,
-    adRevenue Float32,
-    UserAgent String,
-    cCode FixedString(3),
-    lCode FixedString(6),
-    searchWord String,
-    duration UInt32
-) ENGINE = MergeTree(visitDate, visitDate, 8192);
-
-CREATE TABLE rankings_5nodes_on_single
-(
-    pageURL String,
-    pageRank UInt32,
-    avgDuration UInt32
-) ENGINE = Log;
-
-CREATE TABLE uservisits_5nodes_on_single
-(
-    sourceIP String,
-    destinationURL String,
-    visitDate Date,
-    adRevenue Float32,
-    UserAgent String,
-    cCode FixedString(3),
-    lCode FixedString(6),
-    searchWord String,
-    duration UInt32
-) ENGINE = MergeTree(visitDate, visitDate, 8192);
-
- - -

Go back to the console:

-
for i in tiny/rankings/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO rankings_tiny FORMAT CSV"; done
-for i in tiny/uservisits/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO uservisits_tiny FORMAT CSV"; done
-for i in 1node/rankings/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO rankings_1node FORMAT CSV"; done
-for i in 1node/uservisits/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO uservisits_1node FORMAT CSV"; done
-for i in 5nodes/rankings/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO rankings_5nodes_on_single FORMAT CSV"; done
-for i in 5nodes/uservisits/*.deflate; do echo $i; zlib-flate -uncompress < $i | clickhouse-client --host=example-perftest01j --query="INSERT INTO uservisits_5nodes_on_single FORMAT CSV"; done
-
- - -

Queries for obtaining data samples:

-
SELECT pageURL, pageRank FROM rankings_1node WHERE pageRank > 1000
-
-SELECT substring(sourceIP, 1, 8), sum(adRevenue) FROM uservisits_1node GROUP BY substring(sourceIP, 1, 8)
-
-SELECT
-    sourceIP,
-    sum(adRevenue) AS totalRevenue,
-    avg(pageRank) AS pageRank
-FROM rankings_1node ALL INNER JOIN
-(
-    SELECT
-        sourceIP,
-        destinationURL AS pageURL,
-        adRevenue
-    FROM uservisits_1node
-    WHERE (visitDate > '1980-01-01') AND (visitDate < '1980-04-01')
-) USING pageURL
-GROUP BY sourceIP
-ORDER BY totalRevenue DESC
-LIMIT 1
-
- - -

WikiStat

-

See: http://dumps.wikimedia.org/other/pagecounts-raw/

-

Creating a table:

-
CREATE TABLE wikistat
-(
-    date Date,
-    time DateTime,
-    project String,
-    subproject String,
-    path String,
-    hits UInt64,
-    size UInt64
-) ENGINE = MergeTree(date, (path, time), 8192);
-
- - -

Loading data:

-
for i in {2007..2016}; do for j in {01..12}; do echo $i-$j >&2; curl -sSL "http://dumps.wikimedia.org/other/pagecounts-raw/$i/$i-$j/" | grep -oE 'pagecounts-[0-9]+-[0-9]+\.gz'; done; done | sort | uniq | tee links.txt
-cat links.txt | while read link; do wget http://dumps.wikimedia.org/other/pagecounts-raw/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1/')/$(echo $link | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\.gz/\1-\2/')/$link; done
-ls -1 /opt/wikistat/ | grep gz | while read i; do echo $i; gzip -cd /opt/wikistat/$i | ./wikistat-loader --time="$(echo -n $i | sed -r 's/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\.gz/\1-\2-\3 \4-00-00/')" | clickhouse-client --query="INSERT INTO wikistat FORMAT TabSeparated"; done
-
- - -

Terabyte of click logs from Criteo

-

Download the data from http://labs.criteo.com/downloads/download-terabyte-click-logs/

-

Create a table to import the log to:

-
CREATE TABLE criteo_log (date Date, clicked UInt8, int1 Int32, int2 Int32, int3 Int32, int4 Int32, int5 Int32, int6 Int32, int7 Int32, int8 Int32, int9 Int32, int10 Int32, int11 Int32, int12 Int32, int13 Int32, cat1 String, cat2 String, cat3 String, cat4 String, cat5 String, cat6 String, cat7 String, cat8 String, cat9 String, cat10 String, cat11 String, cat12 String, cat13 String, cat14 String, cat15 String, cat16 String, cat17 String, cat18 String, cat19 String, cat20 String, cat21 String, cat22 String, cat23 String, cat24 String, cat25 String, cat26 String) ENGINE = Log
-
- - -

Download the data:

-
for i in {00..23}; do echo $i; zcat datasets/criteo/day_${i#0}.gz | sed -r 's/^/2000-01-'${i/00/24}'\t/' | clickhouse-client --host=example-perftest01j --query="INSERT INTO criteo_log FORMAT TabSeparated"; done
-
- - -

Create a table for the converted data:

-
CREATE TABLE criteo
-(
-    date Date,
-    clicked UInt8,
-    int1 Int32,
-    int2 Int32,
-    int3 Int32,
-    int4 Int32,
-    int5 Int32,
-    int6 Int32,
-    int7 Int32,
-    int8 Int32,
-    int9 Int32,
-    int10 Int32,
-    int11 Int32,
-    int12 Int32,
-    int13 Int32,
-    icat1 UInt32,
-    icat2 UInt32,
-    icat3 UInt32,
-    icat4 UInt32,
-    icat5 UInt32,
-    icat6 UInt32,
-    icat7 UInt32,
-    icat8 UInt32,
-    icat9 UInt32,
-    icat10 UInt32,
-    icat11 UInt32,
-    icat12 UInt32,
-    icat13 UInt32,
-    icat14 UInt32,
-    icat15 UInt32,
-    icat16 UInt32,
-    icat17 UInt32,
-    icat18 UInt32,
-    icat19 UInt32,
-    icat20 UInt32,
-    icat21 UInt32,
-    icat22 UInt32,
-    icat23 UInt32,
-    icat24 UInt32,
-    icat25 UInt32,
-    icat26 UInt32
-) ENGINE = MergeTree(date, intHash32(icat1), (date, intHash32(icat1)), 8192)
-
- - -

Transform data from the raw log and put it in the second table:

-
INSERT INTO criteo SELECT date, clicked, int1, int2, int3, int4, int5, int6, int7, int8, int9, int10, int11, int12, int13, reinterpretAsUInt32(unhex(cat1)) AS icat1, reinterpretAsUInt32(unhex(cat2)) AS icat2, reinterpretAsUInt32(unhex(cat3)) AS icat3, reinterpretAsUInt32(unhex(cat4)) AS icat4, reinterpretAsUInt32(unhex(cat5)) AS icat5, reinterpretAsUInt32(unhex(cat6)) AS icat6, reinterpretAsUInt32(unhex(cat7)) AS icat7, reinterpretAsUInt32(unhex(cat8)) AS icat8, reinterpretAsUInt32(unhex(cat9)) AS icat9, reinterpretAsUInt32(unhex(cat10)) AS icat10, reinterpretAsUInt32(unhex(cat11)) AS icat11, reinterpretAsUInt32(unhex(cat12)) AS icat12, reinterpretAsUInt32(unhex(cat13)) AS icat13, reinterpretAsUInt32(unhex(cat14)) AS icat14, reinterpretAsUInt32(unhex(cat15)) AS icat15, reinterpretAsUInt32(unhex(cat16)) AS icat16, reinterpretAsUInt32(unhex(cat17)) AS icat17, reinterpretAsUInt32(unhex(cat18)) AS icat18, reinterpretAsUInt32(unhex(cat19)) AS icat19, reinterpretAsUInt32(unhex(cat20)) AS icat20, reinterpretAsUInt32(unhex(cat21)) AS icat21, reinterpretAsUInt32(unhex(cat22)) AS icat22, reinterpretAsUInt32(unhex(cat23)) AS icat23, reinterpretAsUInt32(unhex(cat24)) AS icat24, reinterpretAsUInt32(unhex(cat25)) AS icat25, reinterpretAsUInt32(unhex(cat26)) AS icat26 FROM criteo_log;
-
-DROP TABLE criteo_log;
-
- - -

Star Schema Benchmark

-

Compiling dbgen: https://github.com/vadimtk/ssb-dbgen

-
git clone git@github.com:vadimtk/ssb-dbgen.git
-cd ssb-dbgen
-make
-
- - -

There will be some warnings during the process, but this is normal.

-

Place dbgen and dists.dss in any location with 800 GB of free disk space.

-

Generating data:

-
./dbgen -s 1000 -T c
-./dbgen -s 1000 -T l
-
- - -

Creating tables in ClickHouse:

-
CREATE TABLE lineorder (
-        LO_ORDERKEY             UInt32,
-        LO_LINENUMBER           UInt8,
-        LO_CUSTKEY              UInt32,
-        LO_PARTKEY              UInt32,
-        LO_SUPPKEY              UInt32,
-        LO_ORDERDATE            Date,
-        LO_ORDERPRIORITY        String,
-        LO_SHIPPRIORITY         UInt8,
-        LO_QUANTITY             UInt8,
-        LO_EXTENDEDPRICE        UInt32,
-        LO_ORDTOTALPRICE        UInt32,
-        LO_DISCOUNT             UInt8,
-        LO_REVENUE              UInt32,
-        LO_SUPPLYCOST           UInt32,
-        LO_TAX                  UInt8,
-        LO_COMMITDATE           Date,
-        LO_SHIPMODE             String
-)Engine=MergeTree(LO_ORDERDATE,(LO_ORDERKEY,LO_LINENUMBER,LO_ORDERDATE),8192);
-
-CREATE TABLE customer (
-        C_CUSTKEY       UInt32,
-        C_NAME          String,
-        C_ADDRESS       String,
-        C_CITY          String,
-        C_NATION        String,
-        C_REGION        String,
-        C_PHONE         String,
-        C_MKTSEGMENT    String,
-        C_FAKEDATE      Date
-)Engine=MergeTree(C_FAKEDATE,(C_CUSTKEY,C_FAKEDATE),8192);
-
-CREATE TABLE part (
-        P_PARTKEY       UInt32,
-        P_NAME          String,
-        P_MFGR          String,
-        P_CATEGORY      String,
-        P_BRAND         String,
-        P_COLOR         String,
-        P_TYPE          String,
-        P_SIZE          UInt8,
-        P_CONTAINER     String,
-        P_FAKEDATE      Date
-)Engine=MergeTree(P_FAKEDATE,(P_PARTKEY,P_FAKEDATE),8192);
-
-CREATE TABLE lineorderd AS lineorder ENGINE = Distributed(perftest_3shards_1replicas, default, lineorder, rand());
-CREATE TABLE customerd AS customer ENGINE = Distributed(perftest_3shards_1replicas, default, customer, rand());
-CREATE TABLE partd AS part ENGINE = Distributed(perftest_3shards_1replicas, default, part, rand());
-
- - -

For testing on a single server, just use MergeTree tables. -For distributed testing, you need to configure the perftest_3shards_1replicas cluster in the config file. -Next, create MergeTree tables on each server and a Distributed above them.

-

Downloading data (change 'customer' to 'customerd' in the distributed version):

-
cat customer.tbl | sed 's/$/2000-01-01/' | clickhouse-client --query "INSERT INTO customer FORMAT CSV"
-cat lineorder.tbl | clickhouse-client --query "INSERT INTO lineorder FORMAT CSV"
-
- - -

-

Interfaces

-

To explore the system's capabilities, download data to tables, or make manual queries, use the clickhouse-client program.

-

Command-line client

-

To work from the command line, you can use clickhouse-client:

-
$ clickhouse-client
-ClickHouse client version 0.0.26176.
-Connecting to localhost:9000.
-Connected to ClickHouse server version 0.0.26176.
-
-:)
-
- - -

The client supports command-line options and configuration files. For more information, see "Configuring".

-

Usage

-

The client can be used in interactive and non-interactive (batch) mode. -To use batch mode, specify the 'query' parameter, or send data to 'stdin' (it verifies that 'stdin' is not a terminal), or both. -Similar to the HTTP interface, when using the 'query' parameter and sending data to 'stdin', the request is a concatenation of the 'query' parameter, a line feed, and the data in 'stdin'. This is convenient for large INSERT queries.

-

Example of using the client to insert data:

-
echo -ne "1, 'some text', '2016-08-14 00:00:00'\n2, 'some more text', '2016-08-14 00:00:01'" | clickhouse-client --database=test --query="INSERT INTO test FORMAT CSV";
-
-cat <<_EOF | clickhouse-client --database=test --query="INSERT INTO test FORMAT CSV";
-3, 'some text', '2016-08-14 00:00:00'
-4, 'some more text', '2016-08-14 00:00:01'
-_EOF
-
-cat file.csv | clickhouse-client --database=test --query="INSERT INTO test FORMAT CSV";
-
- - -

In batch mode, the default data format is TabSeparated. You can set the format in the FORMAT clause of the query.

-

By default, you can only process a single query in batch mode. To make multiple queries from a "script," use the --multiquery parameter. This works for all queries except INSERT. Query results are output consecutively without additional separators. -Similarly, to process a large number of queries, you can run 'clickhouse-client' for each query. Note that it may take tens of milliseconds to launch the 'clickhouse-client' program.

-

In interactive mode, you get a command line where you can enter queries.

-

If 'multiline' is not specified (the default):To run the query, press Enter. The semicolon is not necessary at the end of the query. To enter a multiline query, enter a backslash \ before the line feed. After you press Enter, you will be asked to enter the next line of the query.

-

If multiline is specified:To run a query, end it with a semicolon and press Enter. If the semicolon was omitted at the end of the entered line, you will be asked to enter the next line of the query.

-

Only a single query is run, so everything after the semicolon is ignored.

-

You can specify \G instead of or after the semicolon. This indicates Vertical format. In this format, each value is printed on a separate line, which is convenient for wide tables. This unusual feature was added for compatibility with the MySQL CLI.

-

The command line is based on 'readline' (and 'history' or 'libedit', or without a library, depending on the build). In other words, it uses the familiar keyboard shortcuts and keeps a history. -The history is written to ~/.clickhouse-client-history.

-

By default, the format used is PrettyCompact. You can change the format in the FORMAT clause of the query, or by specifying \G at the end of the query, using the --format or --vertical argument in the command line, or using the client configuration file.

-

To exit the client, press Ctrl+D (or Ctrl+C), or enter one of the following instead of a query:"exit", "quit", "logout", "учше", "йгше", "дщпщге", "exit;", "quit;", "logout;", "учшеж", "йгшеж", "дщпщгеж", "q", "й", "q", "Q", ":q", "й", "Й", "Жй"

-

When processing a query, the client shows:

-
    -
  1. Progress, which is updated no more than 10 times per second (by default). For quick queries, the progress might not have time to be displayed.
    2. -
  2. The formatted query after parsing, for debugging.
    3. -
  3. The result in the specified format.
    4. -
  4. The number of lines in the result, the time passed, and the average speed of query processing.
    5. -
-

You can cancel a long query by pressing Ctrl+C. However, you will still need to wait a little for the server to abort the request. It is not possible to cancel a query at certain stages. If you don't wait and press Ctrl+C a second time, the client will exit.

-

The command-line client allows passing external data (external temporary tables) for querying. For more information, see the section "External data for query processing".

-

-

Configuring

-

You can pass parameters to clickhouse-client (all parameters have a default value) using:

-
    -
  • From the Command Line
    • -
-

Command-line options override the default values and settings in configuration files.

-
    -
  • Configuration files.
    • -
-

Settings in the configuration files override the default values.

-

Command line options

-
    -
  • --host, -h -– The server name, 'localhost' by default. You can use either the name or the IPv4 or IPv6 address.
    • -
  • --port – The port to connect to. Default value: 9000. Note that the HTTP interface and the native interface use different ports.
    • -
  • --user, -u – The username. Default value: default.
    • -
  • --password – The password. Default value: empty string.
    • -
  • --query, -q – The query to process when using non-interactive mode.
    • -
  • --database, -d – Select the current default database. Default value: the current database from the server settings ('default' by default).
    • -
  • --multiline, -m – If specified, allow multiline queries (do not send the query on Enter).
    • -
  • --multiquery, -n – If specified, allow processing multiple queries separated by commas. Only works in non-interactive mode.
    • -
  • --format, -f – Use the specified default format to output the result.
    • -
  • --vertical, -E – If specified, use the Vertical format by default to output the result. This is the same as '--format=Vertical'. In this format, each value is printed on a separate line, which is helpful when displaying wide tables.
    • -
  • --time, -t – If specified, print the query execution time to 'stderr' in non-interactive mode.
    • -
  • --stacktrace – If specified, also print the stack trace if an exception occurs.
    • -
  • -config-file – The name of the configuration file.
    • -
-

Configuration files

-

clickhouse-client uses the first existing file of the following:

-
    -
  • Defined in the -config-file parameter.
    • -
  • ./clickhouse-client.xml
    • -
  • \~/.clickhouse-client/config.xml
    • -
  • /etc/clickhouse-client/config.xml
    • -
-

Example of a config file:

-
<config>
-    <user>username</user>
-    <password>password</password>
-</config>
-
- - -

HTTP interface

-

The HTTP interface lets you use ClickHouse on any platform from any programming language. We use it for working from Java and Perl, as well as shell scripts. In other departments, the HTTP interface is used from Perl, Python, and Go. The HTTP interface is more limited than the native interface, but it has better compatibility.

-

By default, clickhouse-server listens for HTTP on port 8123 (this can be changed in the config). -If you make a GET / request without parameters, it returns the string "Ok" (with a line feed at the end). You can use this in health-check scripts.

-
$ curl 'http://localhost:8123/'
-Ok.
-
- - -

Send the request as a URL 'query' parameter, or as a POST. Or send the beginning of the query in the 'query' parameter, and the rest in the POST (we'll explain later why this is necessary). The size of the URL is limited to 16 KB, so keep this in mind when sending large queries.

-

If successful, you receive the 200 response code and the result in the response body. -If an error occurs, you receive the 500 response code and an error description text in the response body.

-

When using the GET method, 'readonly' is set. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.

-

Examples:

-
$ curl 'http://localhost:8123/?query=SELECT%201'
-1
-
-$ wget -O- -q 'http://localhost:8123/?query=SELECT 1'
-1
-
-$ GET 'http://localhost:8123/?query=SELECT 1'
-1
-
-$ echo -ne 'GET /?query=SELECT%201 HTTP/1.0\r\n\r\n' | nc localhost 8123
-HTTP/1.0 200 OK
-Connection: Close
-Date: Fri, 16 Nov 2012 19:21:50 GMT
-
-1
-
- - -

As you can see, curl is somewhat inconvenient in that spaces must be URL escaped.Although wget escapes everything itself, we don't recommend using it because it doesn't work well over HTTP 1.1 when using keep-alive and Transfer-Encoding: chunked.

-
$ echo 'SELECT 1' | curl 'http://localhost:8123/' --data-binary @-
-1
-
-$ echo 'SELECT 1' | curl 'http://localhost:8123/?query=' --data-binary @-
-1
-
-$ echo '1' | curl 'http://localhost:8123/?query=SELECT' --data-binary @-
-1
-
- - -

If part of the query is sent in the parameter, and part in the POST, a line feed is inserted between these two data parts. -Example (this won't work):

-
$ echo 'ECT 1' | curl 'http://localhost:8123/?query=SEL' --data-binary @-
-Code: 59, e.displayText() = DB::Exception: Syntax error: failed at position 0: SEL
-ECT 1
-, expected One of: SHOW TABLES, SHOW DATABASES, SELECT, INSERT, CREATE, ATTACH, RENAME, DROP, DETACH, USE, SET, OPTIMIZE., e.what() = DB::Exception
-
- - -

By default, data is returned in TabSeparated format (for more information, see the "Formats" section). -You use the FORMAT clause of the query to request any other format.

-
$ echo 'SELECT 1 FORMAT Pretty' | curl 'http://localhost:8123/?' --data-binary @-
-┏━━━┓
-┃ 1 ┃
-┡━━━┩
-│ 1 │
-└───┘
-
- - -

The POST method of transmitting data is necessary for INSERT queries. In this case, you can write the beginning of the query in the URL parameter, and use POST to pass the data to insert. The data to insert could be, for example, a tab-separated dump from MySQL. In this way, the INSERT query replaces LOAD DATA LOCAL INFILE from MySQL.

-

Examples: Creating a table:

-
echo 'CREATE TABLE t (a UInt8) ENGINE = Memory' | POST 'http://localhost:8123/'
-
- - -

Using the familiar INSERT query for data insertion:

-
echo 'INSERT INTO t VALUES (1),(2),(3)' | POST 'http://localhost:8123/'
-
- - -

Data can be sent separately from the query:

-
echo '(4),(5),(6)' | POST 'http://localhost:8123/?query=INSERT INTO t VALUES'
-
- - -

You can specify any data format. The 'Values' format is the same as what is used when writing INSERT INTO t VALUES:

-
echo '(7),(8),(9)' | POST 'http://localhost:8123/?query=INSERT INTO t FORMAT Values'
-
- - -

To insert data from a tab-separated dump, specify the corresponding format:

-
echo -ne '10\n11\n12\n' | POST 'http://localhost:8123/?query=INSERT INTO t FORMAT TabSeparated'
-
- - -

Reading the table contents. Data is output in random order due to parallel query processing:

-
$ GET 'http://localhost:8123/?query=SELECT a FROM t'
-7
-8
-9
-10
-11
-12
-1
-2
-3
-4
-5
-6
-
- - -

Deleting the table.

-
POST 'http://localhost:8123/?query=DROP TABLE t'
-
- - -

For successful requests that don't return a data table, an empty response body is returned.

-

You can use the internal ClickHouse compression format when transmitting data. The compressed data has a non-standard format, and you will need to use the special clickhouse-compressor program to work with it (it is installed with the clickhouse-client package).

-

If you specified 'compress=1' in the URL, the server will compress the data it sends you. -If you specified 'decompress=1' in the URL, the server will decompress the same data that you pass in the POST method.

-

It is also possible to use the standard gzip-based HTTP compression. To send a POST request compressed using gzip, append the request header Content-Encoding: gzip. -In order for ClickHouse to compress the response using gzip, you must append Accept-Encoding: gzip to the request headers, and enable the ClickHouse setting enable_http_compression.

-

You can use this to reduce network traffic when transmitting a large amount of data, or for creating dumps that are immediately compressed.

-

You can use the 'database' URL parameter to specify the default database.

-
$ echo 'SELECT number FROM numbers LIMIT 10' | curl 'http://localhost:8123/?database=system' --data-binary @-
-0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-
- - -

By default, the database that is registered in the server settings is used as the default database. By default, this is the database called 'default'. Alternatively, you can always specify the database using a dot before the table name.

-

The username and password can be indicated in one of two ways:

-
    -
  1. Using HTTP Basic Authentication. Example:
    2. -
-
echo 'SELECT 1' | curl 'http://user:password@localhost:8123/' -d @-
-
- - -
    -
  1. In the 'user' and 'password' URL parameters. Example:
    2. -
-
echo 'SELECT 1' | curl 'http://localhost:8123/?user=user&password=password' -d @-
-
- - -

If the user name is not indicated, the username 'default' is used. If the password is not indicated, an empty password is used. -You can also use the URL parameters to specify any settings for processing a single query, or entire profiles of settings. Example: -http://localhost:8123/?profile=web&max_rows_to_read=1000000000&query=SELECT+1

-

For more information, see the section "Settings".

-
$ echo 'SELECT number FROM system.numbers LIMIT 10' | curl 'http://localhost:8123/?' --data-binary @-
-0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-
- - -

For information about other parameters, see the section "SET".

-

Similarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to add the session_id GET parameter to the request. You can use any string as the session ID. By default, the session is terminated after 60 seconds of inactivity. To change this timeout, modify the default_session_timeout setting in the server configuration, or add the session_timeout GET parameter to the request. To check the session status, use the session_check=1 parameter. Only one query at a time can be executed within a single session.

-

You have the option to receive information about the progress of query execution in X-ClickHouse-Progress headers. To do this, enable the setting send_progress_in_http_headers.

-

Running requests don't stop automatically if the HTTP connection is lost. Parsing and data formatting are performed on the server side, and using the network might be ineffective. -The optional 'query_id' parameter can be passed as the query ID (any string). For more information, see the section "Settings, replace_running_query".

-

The optional 'quota_key' parameter can be passed as the quota key (any string). For more information, see the section "Quotas".

-

The HTTP interface allows passing external data (external temporary tables) for querying. For more information, see the section "External data for query processing".

-

Response buffering

-

You can enable response buffering on the server side. The buffer_size and wait_end_of_query URL parameters are provided for this purpose.

-

buffer_size determines the number of bytes in the result to buffer in the server memory. If the result body is larger than this threshold, the buffer is written to the HTTP channel, and the remaining data is sent directly to the HTTP channel.

-

To ensure that the entire response is buffered, set wait_end_of_query=1. In this case, the data that is not stored in memory will be buffered in a temporary server file.

-

Example:

-
curl -sS 'http://localhost:8123/?max_result_bytes=4000000&buffer_size=3000000&wait_end_of_query=1' -d 'SELECT toUInt8(number) FROM system.numbers LIMIT 9000000 FORMAT RowBinary'
-
- - -

Use buffering to avoid situations where a query processing error occurred after the response code and HTTP headers were sent to the client. In this situation, an error message is written at the end of the response body, and on the client side, the error can only be detected at the parsing stage.

-

JDBC driver

-

There is an official JDBC driver for ClickHouse. See here .

-

Native interface (TCP)

-

The native interface is used in the "clickhouse-client" command-line client for interaction between servers with distributed query processing, and also in C++ programs. We will only cover the command-line client.

-

Libraries from third-party developers

-

There are libraries for working with ClickHouse for:

- -

We have not tested these libraries. They are listed in random order.

-

Visual interfaces from third-party developers

-

Tabix

-

Web interface for ClickHouse in the Tabix project.

-

Features:

-
    -
  • Works with ClickHouse directly from the browser, without the need to install additional software.
    • -
  • Query editor with syntax highlighting.
    • -
  • Auto-completion of commands.
    • -
  • Tools for graphical analysis of query execution.
    • -
  • Color scheme options.
    • -
-

Tabix documentation.

-

HouseOps

-

HouseOps is a unique Desktop ClickHouse Ops UI / IDE for OSX, Linux and Windows.

-

Features:

-
    -
  • Query builder;
    • -
  • Database manangement (soon);
    • -
  • Users manangement (soon);
    • -
  • Real-Time Data Analytics (soon);
    • -
  • Cluster/Infra monitoring (soon);
    • -
  • Cluster manangement (soon);
    • -
  • Kafka and Replicated tables monitoring (soon);
    • -
  • And a lot of others features (soon) for you take a beautiful implementation of ClickHouse.
    • -
-

Query language

-

Queries

-

CREATE DATABASE

-

Creating db_name databases

-
CREATE DATABASE [IF NOT EXISTS] db_name
-
- - -

A database is just a directory for tables. -If IF NOT EXISTS is included, the query won't return an error if the database already exists.

-

-

CREATE TABLE

-

The CREATE TABLE query can have several forms.

-
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db.]name [ON CLUSTER cluster]
-(
-    name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1],
-    name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2],
-    ...
-) ENGINE = engine
-
- - -

Creates a table named 'name' in the 'db' database or the current database if 'db' is not set, with the structure specified in brackets and the 'engine' engine. -The structure of the table is a list of column descriptions. If indexes are supported by the engine, they are indicated as parameters for the table engine.

-

A column description is name type in the simplest case. Example: RegionID UInt32. -Expressions can also be defined for default values (see below).

-
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db.]name AS [db2.]name2 [ENGINE = engine]
-
- - -

Creates a table with the same structure as another table. You can specify a different engine for the table. If the engine is not specified, the same engine will be used as for the db2.name2 table.

-
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db.]name ENGINE = engine AS SELECT ...
-
- - -

Creates a table with a structure like the result of the SELECT query, with the 'engine' engine, and fills it with data from SELECT.

-

In all cases, if IF NOT EXISTS is specified, the query won't return an error if the table already exists. In this case, the query won't do anything.

-

Default values

-

The column description can specify an expression for a default value, in one of the following ways:DEFAULT expr, MATERIALIZED expr, ALIAS expr. -Example: URLDomain String DEFAULT domain(URL).

-

If an expression for the default value is not defined, the default values will be set to zeros for numbers, empty strings for strings, empty arrays for arrays, and 0000-00-00 for dates or 0000-00-00 00:00:00 for dates with time. NULLs are not supported.

-

If the default expression is defined, the column type is optional. If there isn't an explicitly defined type, the default expression type is used. Example: EventDate DEFAULT toDate(EventTime) – the 'Date' type will be used for the 'EventDate' column.

-

If the data type and default expression are defined explicitly, this expression will be cast to the specified type using type casting functions. Example: Hits UInt32 DEFAULT 0 means the same thing as Hits UInt32 DEFAULT toUInt32(0).

-

Default expressions may be defined as an arbitrary expression from table constants and columns. When creating and changing the table structure, it checks that expressions don't contain loops. For INSERT, it checks that expressions are resolvable – that all columns they can be calculated from have been passed.

-

DEFAULT expr

-

Normal default value. If the INSERT query doesn't specify the corresponding column, it will be filled in by computing the corresponding expression.

-

MATERIALIZED expr

-

Materialized expression. Such a column can't be specified for INSERT, because it is always calculated. -For an INSERT without a list of columns, these columns are not considered. -In addition, this column is not substituted when using an asterisk in a SELECT query. This is to preserve the invariant that the dump obtained using SELECT * can be inserted back into the table using INSERT without specifying the list of columns.

-

ALIAS expr

-

Synonym. Such a column isn't stored in the table at all. -Its values can't be inserted in a table, and it is not substituted when using an asterisk in a SELECT query. -It can be used in SELECTs if the alias is expanded during query parsing.

-

When using the ALTER query to add new columns, old data for these columns is not written. Instead, when reading old data that does not have values for the new columns, expressions are computed on the fly by default. However, if running the expressions requires different columns that are not indicated in the query, these columns will additionally be read, but only for the blocks of data that need it.

-

If you add a new column to a table but later change its default expression, the values used for old data will change (for data where values were not stored on the disk). Note that when running background merges, data for columns that are missing in one of the merging parts is written to the merged part.

-

It is not possible to set default values for elements in nested data structures.

-

Temporary tables

-

In all cases, if TEMPORARY is specified, a temporary table will be created. Temporary tables have the following characteristics:

-
    -
  • Temporary tables disappear when the session ends, including if the connection is lost.
    • -
  • A temporary table is created with the Memory engine. The other table engines are not supported.
    • -
  • The DB can't be specified for a temporary table. It is created outside of databases.
    • -
  • If a temporary table has the same name as another one and a query specifies the table name without specifying the DB, the temporary table will be used.
    • -
  • For distributed query processing, temporary tables used in a query are passed to remote servers.
    • -
-

In most cases, temporary tables are not created manually, but when using external data for a query, or for distributed (GLOBAL) IN. For more information, see the appropriate sections

-

Distributed DDL queries (ON CLUSTER clause)

-

The CREATE, DROP, ALTER, and RENAME queries support distributed execution on a cluster. -For example, the following query creates the all_hits Distributed table on each host in cluster:

-
CREATE TABLE IF NOT EXISTS all_hits ON CLUSTER cluster (p Date, i Int32) ENGINE = Distributed(cluster, default, hits)
-
- - -

In order to run these queries correctly, each host must have the same cluster definition (to simplify syncing configs, you can use substitutions from ZooKeeper). They must also connect to the ZooKeeper servers. -The local version of the query will eventually be implemented on each host in the cluster, even if some hosts are currently not available. The order for executing queries within a single host is guaranteed. -ALTER queries are not yet supported for replicated tables.

-

CREATE VIEW

-
CREATE [MATERIALIZED] VIEW [IF NOT EXISTS] [db.]name [TO[db.]name] [ENGINE = engine] [POPULATE] AS SELECT ...
-
- - -

Creates a view. There are two types of views: normal and MATERIALIZED.

-

When creating a materialized view, you must specify ENGINE – the table engine for storing data.

-

A materialized view works as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.

-

Normal views don't store any data, but just perform a read from another table. In other words, a normal view is nothing more than a saved query. When reading from a view, this saved query is used as a subquery in the FROM clause.

-

As an example, assume you've created a view:

-
CREATE VIEW view AS SELECT ...
-
- - -

and written a query:

-
SELECT a, b, c FROM view
-
- - -

This query is fully equivalent to using the subquery:

-
SELECT a, b, c FROM (SELECT ...)
-
- - -

Materialized views store data transformed by the corresponding SELECT query.

-

When creating a materialized view, you must specify ENGINE – the table engine for storing data.

-

A materialized view is arranged as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.

-

If you specify POPULATE, the existing table data is inserted in the view when creating it, as if making a CREATE TABLE ... AS SELECT ... . Otherwise, the query contains only the data inserted in the table after creating the view. We don't recommend using POPULATE, since data inserted in the table during the view creation will not be inserted in it.

-

A SELECT query can contain DISTINCT, GROUP BY, ORDER BY, LIMIT... Note that the corresponding conversions are performed independently on each block of inserted data. For example, if GROUP BY is set, data is aggregated during insertion, but only within a single packet of inserted data. The data won't be further aggregated. The exception is when using an ENGINE that independently performs data aggregation, such as SummingMergeTree.

-

The execution of ALTER queries on materialized views has not been fully developed, so they might be inconvenient. If the materialized view uses the construction TO [db.]name, you can DETACH the view, run ALTER for the target table, and then ATTACH the previously detached (DETACH) view.

-

Views look the same as normal tables. For example, they are listed in the result of the SHOW TABLES query.

-

There isn't a separate query for deleting views. To delete a view, use DROP TABLE.

-

ATTACH

-

This query is exactly the same as CREATE, but

-
    -
  • instead of the word CREATE it uses the word ATTACH.
    • -
  • The query doesn't create data on the disk, but assumes that data is already in the appropriate places, and just adds information about the table to the server. -After executing an ATTACH query, the server will know about the existence of the table.
    • -
-

If the table was previously detached (DETACH), meaning that its structure is known, you can use shorthand without defining the structure.

-
ATTACH TABLE [IF NOT EXISTS] [db.]name
-
- - -

This query is used when starting the server. The server stores table metadata as files with ATTACH queries, which it simply runs at launch (with the exception of system tables, which are explicitly created on the server).

-

DROP

-

This query has two types: DROP DATABASE and DROP TABLE.

-
DROP DATABASE [IF EXISTS] db [ON CLUSTER cluster]
-
- - -

Deletes all tables inside the 'db' database, then deletes the 'db' database itself. -If IF EXISTS is specified, it doesn't return an error if the database doesn't exist.

-
DROP [TEMPORARY] TABLE [IF EXISTS] [db.]name [ON CLUSTER cluster]
-
- - -

Deletes the table. -If IF EXISTS is specified, it doesn't return an error if the table doesn't exist or the database doesn't exist.

-

DETACH

-

Deletes information about the 'name' table from the server. The server stops knowing about the table's existence.

-
DETACH TABLE [IF EXISTS] [db.]name
-
- - -

This does not delete the table's data or metadata. On the next server launch, the server will read the metadata and find out about the table again. -Similarly, a "detached" table can be re-attached using the ATTACH query (with the exception of system tables, which do not have metadata stored for them).

-

There is no DETACH DATABASE query.

-

RENAME

-

Renames one or more tables.

-
RENAME TABLE [db11.]name11 TO [db12.]name12, [db21.]name21 TO [db22.]name22, ... [ON CLUSTER cluster]
-
- - -

All tables are renamed under global locking. Renaming tables is a light operation. If you indicated another database after TO, the table will be moved to this database. However, the directories with databases must reside in the same file system (otherwise, an error is returned).

-

-

ALTER

-

The ALTER query is only supported for *MergeTree tables, as well as MergeandDistributed. The query has several variations.

-

Column manipulations

-

Changing the table structure.

-
ALTER TABLE [db].name [ON CLUSTER cluster] ADD|DROP|MODIFY COLUMN ...
-
- - -

In the query, specify a list of one or more comma-separated actions. -Each action is an operation on a column.

-

The following actions are supported:

-
ADD COLUMN name [type] [default_expr] [AFTER name_after]
-
- - -

Adds a new column to the table with the specified name, type, and default_expr (see the section "Default expressions"). If you specify AFTER name_after (the name of another column), the column is added after the specified one in the list of table columns. Otherwise, the column is added to the end of the table. Note that there is no way to add a column to the beginning of a table. For a chain of actions, 'name_after' can be the name of a column that is added in one of the previous actions.

-

Adding a column just changes the table structure, without performing any actions with data. The data doesn't appear on the disk after ALTER. If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). The column appears on the disk after merging data parts (see MergeTree).

-

This approach allows us to complete the ALTER query instantly, without increasing the volume of old data.

-
DROP COLUMN name
-
- - -

Deletes the column with the name 'name'. -Deletes data from the file system. Since this deletes entire files, the query is completed almost instantly.

-
MODIFY COLUMN name [type] [default_expr]
-
- - -

Changes the 'name' column's type to 'type' and/or the default expression to 'default_expr'. When changing the type, values are converted as if the 'toType' function were applied to them.

-

If only the default expression is changed, the query doesn't do anything complex, and is completed almost instantly.

-

Changing the column type is the only complex action – it changes the contents of files with data. For large tables, this may take a long time.

-

There are several processing stages:

-
    -
  • Preparing temporary (new) files with modified data.
    • -
  • Renaming old files.
    • -
  • Renaming the temporary (new) files to the old names.
    • -
  • Deleting the old files.
    • -
-

Only the first stage takes time. If there is a failure at this stage, the data is not changed. -If there is a failure during one of the successive stages, data can be restored manually. The exception is if the old files were deleted from the file system but the data for the new files did not get written to the disk and was lost.

-

There is no support for changing the column type in arrays and nested data structures.

-

The ALTER query lets you create and delete separate elements (columns) in nested data structures, but not whole nested data structures. To add a nested data structure, you can add columns with a name like name.nested_name and the type Array(T). A nested data structure is equivalent to multiple array columns with a name that has the same prefix before the dot.

-

There is no support for deleting columns in the primary key or the sampling key (columns that are in the ENGINE expression). Changing the type for columns that are included in the primary key is only possible if this change does not cause the data to be modified (for example, it is allowed to add values to an Enum or change a type with DateTime to UInt32).

-

If the ALTER query is not sufficient for making the table changes you need, you can create a new table, copy the data to it using the INSERT SELECT query, then switch the tables using the RENAME query and delete the old table.

-

The ALTER query blocks all reads and writes for the table. In other words, if a long SELECT is running at the time of the ALTER query, the ALTER query will wait for it to complete. At the same time, all new queries to the same table will wait while this ALTER is running.

-

For tables that don't store data themselves (such as Merge and Distributed), ALTER just changes the table structure, and does not change the structure of subordinate tables. For example, when running ALTER for a Distributed table, you will also need to run ALTER for the tables on all remote servers.

-

The ALTER query for changing columns is replicated. The instructions are saved in ZooKeeper, then each replica applies them. All ALTER queries are run in the same order. The query waits for the appropriate actions to be completed on the other replicas. However, a query to change columns in a replicated table can be interrupted, and all actions will be performed asynchronously.

-

Manipulations with partitions and parts

-

It only works for tables in the MergeTree family. The following operations are available:

-
    -
  • DETACH PARTITION – Move a partition to the 'detached' directory and forget it.
    • -
  • DROP PARTITION – Delete a partition.
    • -
  • ATTACH PART|PARTITION – Add a new part or partition from the detached directory to the table.
    • -
  • FREEZE PARTITION – Create a backup of a partition.
    • -
  • FETCH PARTITION – Download a partition from another server.
    • -
-

Each type of query is covered separately below.

-

A partition in a table is data for a single calendar month. This is determined by the values of the date key specified in the table engine parameters. Each month's data is stored separately in order to simplify manipulations with this data.

-

A "part" in the table is part of the data from a single partition, sorted by the primary key.

-

You can use the system.parts table to view the set of table parts and partitions:

-
SELECT * FROM system.parts WHERE active
-
- - -

active – Only count active parts. Inactive parts are, for example, source parts remaining after merging to a larger part – these parts are deleted approximately 10 minutes after merging.

-

Another way to view a set of parts and partitions is to go into the directory with table data. -Data directory: /var/lib/clickhouse/data/database/table/,where /var/lib/clickhouse/ is the path to the ClickHouse data, 'database' is the database name, and 'table' is the table name. Example:

-
$ ls -l /var/lib/clickhouse/data/test/visits/
-total 48
-drwxrwxrwx 2 clickhouse clickhouse 20480 May  5 02:58 20140317_20140323_2_2_0
-drwxrwxrwx 2 clickhouse clickhouse 20480 May  5 02:58 20140317_20140323_4_4_0
-drwxrwxrwx 2 clickhouse clickhouse  4096 May  5 02:55 detached
--rw-rw-rw- 1 clickhouse clickhouse     2 May  5 02:58 increment.txt
-
- - -

Here, 20140317_20140323_2_2_0 and 20140317_20140323_4_4_0 are the directories of data parts.

-

Let's break down the name of the first part: 20140317_20140323_2_2_0.

-
    -
  • 20140317 is the minimum date of the data in the chunk.
    • -
  • 20140323 is the maximum date of the data in the chunk.
    • -
  • 2 is the minimum number of the data block.
    • -
  • 2 is the maximum number of the data block.
    • -
  • 0 is the chunk level (the depth of the merge tree it is formed from).
    • -
-

Each piece relates to a single partition and contains data for just one month. -201403 is the name of the partition. A partition is a set of parts for a single month.

-

On an operating server, you can't manually change the set of parts or their data on the file system, since the server won't know about it. -For non-replicated tables, you can do this when the server is stopped, but we don't recommended it. -For replicated tables, the set of parts can't be changed in any case.

-

The detached directory contains parts that are not used by the server - detached from the table using the ALTER ... DETACH query. Parts that are damaged are also moved to this directory, instead of deleting them. You can add, delete, or modify the data in the 'detached' directory at any time – the server won't know about this until you make the ALTER TABLE ... ATTACH query.

-
ALTER TABLE [db.]table DETACH PARTITION 'name'
-
- - -

Move all data for partitions named 'name' to the 'detached' directory and forget about them. -The partition name is specified in YYYYMM format. It can be indicated in single quotes or without them.

-

After the query is executed, you can do whatever you want with the data in the 'detached' directory — delete it from the file system, or just leave it.

-

The query is replicated – data will be moved to the 'detached' directory and forgotten on all replicas. The query can only be sent to a leader replica. To find out if a replica is a leader, perform SELECT to the 'system.replicas' system table. Alternatively, it is easier to make a query on all replicas, and all except one will throw an exception.

-
ALTER TABLE [db.]table DROP PARTITION 'name'
-
- - -

The same as the DETACH operation. Deletes data from the table. Data parts will be tagged as inactive and will be completely deleted in approximately 10 minutes. The query is replicated – data will be deleted on all replicas.

-
ALTER TABLE [db.]table ATTACH PARTITION|PART 'name'
-
- - -

Adds data to the table from the 'detached' directory.

-

It is possible to add data for an entire partition or a separate part. For a part, specify the full name of the part in single quotes.

-

The query is replicated. Each replica checks whether there is data in the 'detached' directory. If there is data, it checks the integrity, verifies that it matches the data on the server that initiated the query, and then adds it if everything is correct. If not, it downloads data from the query requestor replica, or from another replica where the data has already been added.

-

So you can put data in the 'detached' directory on one replica, and use the ALTER ... ATTACH query to add it to the table on all replicas.

-
ALTER TABLE [db.]table FREEZE PARTITION 'name'
-
- - -

Creates a local backup of one or multiple partitions. The name can be the full name of the partition (for example, 201403), or its prefix (for example, 2014): then the backup will be created for all the corresponding partitions.

-

The query does the following: for a data snapshot at the time of execution, it creates hardlinks to table data in the directory /var/lib/clickhouse/shadow/N/...

-

/var/lib/clickhouse/ is the working ClickHouse directory from the config. -N is the incremental number of the backup.

-

The same structure of directories is created inside the backup as inside /var/lib/clickhouse/. -It also performs 'chmod' for all files, forbidding writes to them.

-

The backup is created almost instantly (but first it waits for current queries to the corresponding table to finish running). At first, the backup doesn't take any space on the disk. As the system works, the backup can take disk space, as data is modified. If the backup is made for old enough data, it won't take space on the disk.

-

After creating the backup, data from /var/lib/clickhouse/shadow/ can be copied to the remote server and then deleted on the local server. -The entire backup process is performed without stopping the server.

-

The ALTER ... FREEZE PARTITION query is not replicated. A local backup is only created on the local server.

-

As an alternative, you can manually copy data from the /var/lib/clickhouse/data/database/table directory. -But if you do this while the server is running, race conditions are possible when copying directories with files being added or changed, and the backup may be inconsistent. You can do this if the server isn't running – then the resulting data will be the same as after the ALTER TABLE t FREEZE PARTITION query.

-

ALTER TABLE ... FREEZE PARTITION only copies data, not table metadata. To make a backup of table metadata, copy the file /var/lib/clickhouse/metadata/database/table.sql

-

To restore from a backup:

-
-
    -
  • Use the CREATE query to create the table if it doesn't exist. The query can be taken from an .sql file (replace ATTACH in it with CREATE).
    • -
  • Copy the data from the data/database/table/ directory inside the backup to the /var/lib/clickhouse/data/database/table/detached/ directory.
    • -
  • Run ALTER TABLE ... ATTACH PARTITION YYYYMM queries, where YYYYMM is the month, for every month.
    • -
-
-

In this way, data from the backup will be added to the table. -Restoring from a backup doesn't require stopping the server.

-

Backups and replication

-

Replication provides protection from device failures. If all data disappeared on one of your replicas, follow the instructions in the "Restoration after failure" section to restore it.

-

For protection from device failures, you must use replication. For more information about replication, see the section "Data replication".

-

Backups protect against human error (accidentally deleting data, deleting the wrong data or in the wrong cluster, or corrupting data). -For high-volume databases, it can be difficult to copy backups to remote servers. In such cases, to protect from human error, you can keep a backup on the same server (it will reside in /var/lib/clickhouse/shadow/).

-
ALTER TABLE [db.]table FETCH PARTITION 'name' FROM 'path-in-zookeeper'
-
- - -

This query only works for replicatable tables.

-

It downloads the specified partition from the shard that has its ZooKeeper path specified in the FROM clause, then puts it in the detached directory for the specified table.

-

Although the query is called ALTER TABLE, it does not change the table structure, and does not immediately change the data available in the table.

-

Data is placed in the detached directory. You can use the ALTER TABLE ... ATTACH query to attach the data.

-

The FROM clause specifies the path in ZooKeeper. For example, /clickhouse/tables/01-01/visits. -Before downloading, the system checks that the partition exists and the table structure matches. The most appropriate replica is selected automatically from the healthy replicas.

-

The ALTER ... FETCH PARTITION query is not replicated. The partition will be downloaded to the 'detached' directory only on the local server. Note that if after this you use the ALTER TABLE ... ATTACH query to add data to the table, the data will be added on all replicas (on one of the replicas it will be added from the 'detached' directory, and on the rest it will be loaded from neighboring replicas).

-

Synchronicity of ALTER queries

-

For non-replicatable tables, all ALTER queries are performed synchronously. For replicatable tables, the query just adds instructions for the appropriate actions to ZooKeeper, and the actions themselves are performed as soon as possible. However, the query can wait for these actions to be completed on all the replicas.

-

For ALTER ... ATTACH|DETACH|DROP queries, you can use the replication_alter_partitions_sync setting to set up waiting. -Possible values: 0 – do not wait; 1 – only wait for own execution (default); 2 – wait for all.

-

-

SHOW DATABASES

-
SHOW DATABASES [INTO OUTFILE filename] [FORMAT format]
-
- - -

Prints a list of all databases. -This query is identical to SELECT name FROM system.databases [INTO OUTFILE filename] [FORMAT format].

-

See also the section "Formats".

-

SHOW TABLES

-
SHOW [TEMPORARY] TABLES [FROM db] [LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format]
-
- - -

Displays a list of tables

-
    -
  • tables from the current database, or from the 'db' database if "FROM db" is specified.
    • -
  • all tables, or tables whose name matches the pattern, if "LIKE 'pattern'" is specified.
    • -
-

This query is identical to: SELECT name FROM system.tables WHERE database = 'db' [AND name LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format].

-

See also the section "LIKE operator".

-

SHOW PROCESSLIST

-
SHOW PROCESSLIST [INTO OUTFILE filename] [FORMAT format]
-
- - -

Outputs a list of queries currently being processed, other than SHOW PROCESSLIST queries.

-

Prints a table containing the columns:

-

user – The user who made the query. Keep in mind that for distributed processing, queries are sent to remote servers under the 'default' user. SHOW PROCESSLIST shows the username for a specific query, not for a query that this query initiated.

-

address – The name of the host that the query was sent from. For distributed processing, on remote servers, this is the name of the query requestor host. To track where a distributed query was originally made from, look at SHOW PROCESSLIST on the query requestor server.

-

elapsed – The execution time, in seconds. Queries are output in order of decreasing execution time.

-

rows_read, bytes_read – How many rows and bytes of uncompressed data were read when processing the query. For distributed processing, data is totaled from all the remote servers. This is the data used for restrictions and quotas.

-

memory_usage – Current RAM usage in bytes. See the setting 'max_memory_usage'.

-

query – The query itself. In INSERT queries, the data for insertion is not output.

-

query_id – The query identifier. Non-empty only if it was explicitly defined by the user. For distributed processing, the query ID is not passed to remote servers.

-

This query is identical to: SELECT * FROM system.processes [INTO OUTFILE filename] [FORMAT format].

-

Tip (execute in the console):

-
watch -n1 "clickhouse-client --query='SHOW PROCESSLIST'"
-
- - -

SHOW CREATE TABLE

-
SHOW CREATE [TEMPORARY] TABLE [db.]table [INTO OUTFILE filename] [FORMAT format]
-
- - -

Returns a single String-type 'statement' column, which contains a single value – the CREATE query used for creating the specified table.

-

DESCRIBE TABLE

-
DESC|DESCRIBE TABLE [db.]table [INTO OUTFILE filename] [FORMAT format]
-
- - -

Returns two String-type columns: name and type, which indicate the names and types of columns in the specified table.

-

Nested data structures are output in "expanded" format. Each column is shown separately, with the name after a dot.

-

EXISTS

-
EXISTS [TEMPORARY] TABLE [db.]name [INTO OUTFILE filename] [FORMAT format]
-
- - -

Returns a single UInt8-type column, which contains the single value 0 if the table or database doesn't exist, or 1 if the table exists in the specified database.

-

USE

-
USE db
-
- - -

Lets you set the current database for the session. -The current database is used for searching for tables if the database is not explicitly defined in the query with a dot before the table name. -This query can't be made when using the HTTP protocol, since there is no concept of a session.

-

SET

-
SET param = value
-
- - -

Allows you to set param to value. You can also make all the settings from the specified settings profile in a single query. To do this, specify 'profile' as the setting name. For more information, see the section "Settings". -The setting is made for the session, or for the server (globally) if GLOBAL is specified. -When making a global setting, the setting is not applied to sessions already running, including the current session. It will only be used for new sessions.

-

When the server is restarted, global settings made using SET are lost. -To make settings that persist after a server restart, you can only use the server's config file.

-

OPTIMIZE

-
OPTIMIZE TABLE [db.]name [PARTITION partition] [FINAL]
-
- - -

Asks the table engine to do something for optimization. -Supported only by *MergeTree engines, in which this query initializes a non-scheduled merge of data parts. -If you specify a PARTITION, only the specified partition will be optimized. -If you specify FINAL, optimization will be performed even when all the data is already in one part.

-

-

INSERT

-

Adding data.

-

Basic query format:

-
INSERT INTO [db.]table [(c1, c2, c3)] VALUES (v11, v12, v13), (v21, v22, v23), ...
-
- - -

The query can specify a list of columns to insert [(c1, c2, c3)]. In this case, the rest of the columns are filled with:

-
    -
  • The values calculated from the DEFAULT expressions specified in the table definition.
    • -
  • Zeros and empty strings, if DEFAULT expressions are not defined.
    • -
-

If strict_insert_defaults=1, columns that do not have DEFAULT defined must be listed in the query.

-

Data can be passed to the INSERT in any format supported by ClickHouse. The format must be specified explicitly in the query:

-
INSERT INTO [db.]table [(c1, c2, c3)] FORMAT format_name data_set
-
- - -

For example, the following query format is identical to the basic version of INSERT ... VALUES:

-
INSERT INTO [db.]table [(c1, c2, c3)] FORMAT Values (v11, v12, v13), (v21, v22, v23), ...
-
- - -

ClickHouse removes all spaces and one line feed (if there is one) before the data. When forming a query, we recommend putting the data on a new line after the query operators (this is important if the data begins with spaces).

-

Example:

-
INSERT INTO t FORMAT TabSeparated
-11  Hello, world!
-22  Qwerty
-
- - -

You can insert data separately from the query by using the command-line client or the HTTP interface. For more information, see the section "Interfaces".

-

Inserting the results of SELECT

-
INSERT INTO [db.]table [(c1, c2, c3)] SELECT ...
-
- - -

Columns are mapped according to their position in the SELECT clause. However, their names in the SELECT expression and the table for INSERT may differ. If necessary, type casting is performed.

-

None of the data formats except Values allow setting values to expressions such as now(), 1 + 2, and so on. The Values format allows limited use of expressions, but this is not recommended, because in this case inefficient code is used for their execution.

-

Other queries for modifying data parts are not supported: UPDATE, DELETE, REPLACE, MERGE, UPSERT, INSERT UPDATE. -However, you can delete old data using ALTER TABLE ... DROP PARTITION.

-

Performance considerations

-

INSERT sorts the input data by primary key and splits them into partitions by month. If you insert data for mixed months, it can significantly reduce the performance of the INSERT query. To avoid this:

-
    -
  • Add data in fairly large batches, such as 100,000 rows at a time.
    • -
  • Group data by month before uploading it to ClickHouse.
    • -
-

Performance will not decrease if:

-
    -
  • Data is added in real time.
    • -
  • You upload data that is usually sorted by time.
    • -
-

SELECT

-

Data sampling.

-
SELECT [DISTINCT] expr_list
-    [FROM [db.]table | (subquery) | table_function] [FINAL]
-    [SAMPLE sample_coeff]
-    [ARRAY JOIN ...]
-    [GLOBAL] ANY|ALL INNER|LEFT JOIN (subquery)|table USING columns_list
-    [PREWHERE expr]
-    [WHERE expr]
-    [GROUP BY expr_list] [WITH TOTALS]
-    [HAVING expr]
-    [ORDER BY expr_list]
-    [LIMIT [n, ]m]
-    [UNION ALL ...]
-    [INTO OUTFILE filename]
-    [FORMAT format]
-    [LIMIT n BY columns]
-
- - -

All the clauses are optional, except for the required list of expressions immediately after SELECT. -The clauses below are described in almost the same order as in the query execution conveyor.

-

If the query omits the DISTINCT, GROUP BY and ORDER BY clauses and the IN and JOIN subqueries, the query will be completely stream processed, using O(1) amount of RAM. -Otherwise, the query might consume a lot of RAM if the appropriate restrictions are not specified: max_memory_usage, max_rows_to_group_by, max_rows_to_sort, max_rows_in_distinct, max_bytes_in_distinct, max_rows_in_set, max_bytes_in_set, max_rows_in_join, max_bytes_in_join, max_bytes_before_external_sort, max_bytes_before_external_group_by. For more information, see the section "Settings". It is possible to use external sorting (saving temporary tables to a disk) and external aggregation. The system does not have "merge join".

-

FROM clause

-

If the FROM clause is omitted, data will be read from the system.one table. -The 'system.one' table contains exactly one row (this table fulfills the same purpose as the DUAL table found in other DBMSs).

-

The FROM clause specifies the table to read data from, or a subquery, or a table function; ARRAY JOIN and the regular JOIN may also be included (see below).

-

Instead of a table, the SELECT subquery may be specified in brackets. -In this case, the subquery processing pipeline will be built into the processing pipeline of an external query. -In contrast to standard SQL, a synonym does not need to be specified after a subquery. For compatibility, it is possible to write 'AS name' after a subquery, but the specified name isn't used anywhere.

-

A table function may be specified instead of a table. For more information, see the section "Table functions".

-

To execute a query, all the columns listed in the query are extracted from the appropriate table. Any columns not needed for the external query are thrown out of the subqueries. -If a query does not list any columns (for example, SELECT count() FROM t), some column is extracted from the table anyway (the smallest one is preferred), in order to calculate the number of rows.

-

The FINAL modifier can be used only for a SELECT from a CollapsingMergeTree table. When you specify FINAL, data is selected fully "collapsed". Keep in mind that using FINAL leads to a selection that includes columns related to the primary key, in addition to the columns specified in the SELECT. Additionally, the query will be executed in a single stream, and data will be merged during query execution. This means that when using FINAL, the query is processed more slowly. In most cases, you should avoid using FINAL. For more information, see the section "CollapsingMergeTree engine".

-

SAMPLE clause

-

The SAMPLE clause allows for approximated query processing. Approximated query processing is only supported by MergeTree* type tables, and only if the sampling expression was specified during table creation (see the section "MergeTree engine").

-

SAMPLE has the format SAMPLE k, where k is a decimal number from 0 to 1, or SAMPLE n, where 'n' is a sufficiently large integer.

-

In the first case, the query will be executed on 'k' percent of data. For example, SAMPLE 0.1 runs the query on 10% of data. -In the second case, the query will be executed on a sample of no more than 'n' rows. For example, SAMPLE 10000000 runs the query on a maximum of 10,000,000 rows.

-

Example:

-
SELECT
-    Title,
-    count() * 10 AS PageViews
-FROM hits_distributed
-SAMPLE 0.1
-WHERE
-    CounterID = 34
-    AND toDate(EventDate) >= toDate('2013-01-29')
-    AND toDate(EventDate) <= toDate('2013-02-04')
-    AND NOT DontCountHits
-    AND NOT Refresh
-    AND Title != ''
-GROUP BY Title
-ORDER BY PageViews DESC LIMIT 1000
-
- - -

In this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value 'count()' is manually multiplied by 10.

-

When using something like SAMPLE 10000000, there isn't any information about which relative percent of data was processed or what the aggregate functions should be multiplied by, so this method of writing is not always appropriate to the situation.

-

A sample with a relative coefficient is "consistent": if we look at all possible data that could be in the table, a sample (when using a single sampling expression specified during table creation) with the same coefficient always selects the same subset of possible data. In other words, a sample from different tables on different servers at different times is made the same way.

-

For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This allows using the sample in subqueries in the IN clause, as well as for manually correlating results of different queries with samples.

-

ARRAY JOIN clause

-

Allows executing JOIN with an array or nested data structure. The intent is similar to the 'arrayJoin' function, but its functionality is broader.

-

ARRAY JOIN is essentially INNER JOIN with an array. Example:

-
:) CREATE TABLE arrays_test (s String, arr Array(UInt8)) ENGINE = Memory
-
-CREATE TABLE arrays_test
-(
-    s String,
-    arr Array(UInt8)
-) ENGINE = Memory
-
-Ok.
-
-0 rows in set. Elapsed: 0.001 sec.
-
-:) INSERT INTO arrays_test VALUES ('Hello', [1,2]), ('World', [3,4,5]), ('Goodbye', [])
-
-INSERT INTO arrays_test VALUES
-
-Ok.
-
-3 rows in set. Elapsed: 0.001 sec.
-
-:) SELECT * FROM arrays_test
-
-SELECT *
-FROM arrays_test
-
-┌─s───────┬─arr─────┐
-│ Hello   │ [1,2]   │
-│ World   │ [3,4,5] │
-│ Goodbye │ []      │
-└─────────┴─────────┘
-
-3 rows in set. Elapsed: 0.001 sec.
-
-:) SELECT s, arr FROM arrays_test ARRAY JOIN arr
-
-SELECT s, arr
-FROM arrays_test
-ARRAY JOIN arr
-
-┌─s─────┬─arr─┐
-│ Hello │   1 │
-│ Hello │   2 │
-│ World │   3 │
-│ World │   4 │
-│ World │   5 │
-└───────┴─────┘
-
-5 rows in set. Elapsed: 0.001 sec.
-
- - -

An alias can be specified for an array in the ARRAY JOIN clause. In this case, an array item can be accessed by this alias, but the array itself by the original name. Example:

-
:) SELECT s, arr, a FROM arrays_test ARRAY JOIN arr AS a
-
-SELECT s, arr, a
-FROM arrays_test
-ARRAY JOIN arr AS a
-
-┌─s─────┬─arr─────┬─a─┐
-│ Hello │ [1,2]   │ 1 │
-│ Hello │ [1,2]   │ 2 │
-│ World │ [3,4,5] │ 3 │
-│ World │ [3,4,5] │ 4 │
-│ World │ [3,4,5] │ 5 │
-└───────┴─────────┴───┘
-
-5 rows in set. Elapsed: 0.001 sec.
-
- - -

Multiple arrays of the same size can be comma-separated in the ARRAY JOIN clause. In this case, JOIN is performed with them simultaneously (the direct sum, not the direct product). Example:

-
:) SELECT s, arr, a, num, mapped FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(x -> x + 1, arr) AS mapped
-
-SELECT s, arr, a, num, mapped
-FROM arrays_test
-ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(lambda(tuple(x), plus(x, 1)), arr) AS mapped
-
-┌─s─────┬─arr─────┬─a─┬─num─┬─mapped─┐
-│ Hello │ [1,2]   │ 1 │   1 │      2 │
-│ Hello │ [1,2]   │ 2 │   2 │      3 │
-│ World │ [3,4,5] │ 3 │   1 │      4 │
-│ World │ [3,4,5] │ 4 │   2 │      5 │
-│ World │ [3,4,5] │ 5 │   3 │      6 │
-└───────┴─────────┴───┴─────┴────────┘
-
-5 rows in set. Elapsed: 0.002 sec.
-
-:) SELECT s, arr, a, num, arrayEnumerate(arr) FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num
-
-SELECT s, arr, a, num, arrayEnumerate(arr)
-FROM arrays_test
-ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num
-
-┌─s─────┬─arr─────┬─a─┬─num─┬─arrayEnumerate(arr)─┐
-│ Hello │ [1,2]   │ 1 │   1 │ [1,2]               │
-│ Hello │ [1,2]   │ 2 │   2 │ [1,2]               │
-│ World │ [3,4,5] │ 3 │   1 │ [1,2,3]             │
-│ World │ [3,4,5] │ 4 │   2 │ [1,2,3]             │
-│ World │ [3,4,5] │ 5 │   3 │ [1,2,3]             │
-└───────┴─────────┴───┴─────┴─────────────────────┘
-
-5 rows in set. Elapsed: 0.002 sec.
-
- - -

ARRAY JOIN also works with nested data structures. Example:

-
:) CREATE TABLE nested_test (s String, nest Nested(x UInt8, y UInt32)) ENGINE = Memory
-
-CREATE TABLE nested_test
-(
-    s String,
-    nest Nested(
-    x UInt8,
-    y UInt32)
-) ENGINE = Memory
-
-Ok.
-
-0 rows in set. Elapsed: 0.006 sec.
-
-:) INSERT INTO nested_test VALUES ('Hello', [1,2], [10,20]), ('World', [3,4,5], [30,40,50]), ('Goodbye', [], [])
-
-INSERT INTO nested_test VALUES
-
-Ok.
-
-3 rows in set. Elapsed: 0.001 sec.
-
-:) SELECT * FROM nested_test
-
-SELECT *
-FROM nested_test
-
-┌─s───────┬─nest.x──┬─nest.y─────┐
-│ Hello   │ [1,2]   │ [10,20]    │
-│ World   │ [3,4,5] │ [30,40,50] │
-│ Goodbye │ []      │ []         │
-└─────────┴─────────┴────────────┘
-
-3 rows in set. Elapsed: 0.001 sec.
-
-:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest
-
-SELECT s, `nest.x`, `nest.y`
-FROM nested_test
-ARRAY JOIN nest
-
-┌─s─────┬─nest.x─┬─nest.y─┐
-│ Hello │      1 │     10 │
-│ Hello │      2 │     20 │
-│ World │      3 │     30 │
-│ World │      4 │     40 │
-│ World │      5 │     50 │
-└───────┴────────┴────────┘
-
-5 rows in set. Elapsed: 0.001 sec.
-
- - -

When specifying names of nested data structures in ARRAY JOIN, the meaning is the same as ARRAY JOIN with all the array elements that it consists of. Example:

-
:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x, nest.y
-
-SELECT s, `nest.x`, `nest.y`
-FROM nested_test
-ARRAY JOIN `nest.x`, `nest.y`
-
-┌─s─────┬─nest.x─┬─nest.y─┐
-│ Hello │      1 │     10 │
-│ Hello │      2 │     20 │
-│ World │      3 │     30 │
-│ World │      4 │     40 │
-│ World │      5 │     50 │
-└───────┴────────┴────────┘
-
-5 rows in set. Elapsed: 0.001 sec.
-
- - -

This variation also makes sense:

-
:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x
-
-SELECT s, `nest.x`, `nest.y`
-FROM nested_test
-ARRAY JOIN `nest.x`
-
-┌─s─────┬─nest.x─┬─nest.y─────┐
-│ Hello │      1 │ [10,20]    │
-│ Hello │      2 │ [10,20]    │
-│ World │      3 │ [30,40,50] │
-│ World │      4 │ [30,40,50] │
-│ World │      5 │ [30,40,50] │
-└───────┴────────┴────────────┘
-
-5 rows in set. Elapsed: 0.001 sec.
-
- - -

An alias may be used for a nested data structure, in order to select either the JOIN result or the source array. Example:

-
:) SELECT s, n.x, n.y, nest.x, nest.y FROM nested_test ARRAY JOIN nest AS n
-
-SELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`
-FROM nested_test
-ARRAY JOIN nest AS n
-
-┌─s─────┬─n.x─┬─n.y─┬─nest.x──┬─nest.y─────┐
-│ Hello │   1 │  10 │ [1,2]   │ [10,20]    │
-│ Hello │   2 │  20 │ [1,2]   │ [10,20]    │
-│ World │   3 │  30 │ [3,4,5] │ [30,40,50] │
-│ World │   4 │  40 │ [3,4,5] │ [30,40,50] │
-│ World │   5 │  50 │ [3,4,5] │ [30,40,50] │
-└───────┴─────┴─────┴─────────┴────────────┘
-
-5 rows in set. Elapsed: 0.001 sec.
-
- - -

Example of using the arrayEnumerate function:

-
:) SELECT s, n.x, n.y, nest.x, nest.y, num FROM nested_test ARRAY JOIN nest AS n, arrayEnumerate(nest.x) AS num
-
-SELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`, num
-FROM nested_test
-ARRAY JOIN nest AS n, arrayEnumerate(`nest.x`) AS num
-
-┌─s─────┬─n.x─┬─n.y─┬─nest.x──┬─nest.y─────┬─num─┐
-│ Hello │   1 │  10 │ [1,2]   │ [10,20]    │   1 │
-│ Hello │   2 │  20 │ [1,2]   │ [10,20]    │   2 │
-│ World │   3 │  30 │ [3,4,5] │ [30,40,50] │   1 │
-│ World │   4 │  40 │ [3,4,5] │ [30,40,50] │   2 │
-│ World │   5 │  50 │ [3,4,5] │ [30,40,50] │   3 │
-└───────┴─────┴─────┴─────────┴────────────┴─────┘
-
-5 rows in set. Elapsed: 0.002 sec.
-
- - -

The query can only specify a single ARRAY JOIN clause.

-

The corresponding conversion can be performed before the WHERE/PREWHERE clause (if its result is needed in this clause), or after completing WHERE/PREWHERE (to reduce the volume of calculations).

-

JOIN clause

-

The normal JOIN, which is not related to ARRAY JOIN described above.

-
[GLOBAL] ANY|ALL INNER|LEFT [OUTER] JOIN (subquery)|table USING columns_list
-
- - -

Performs joins with data from the subquery. At the beginning of query processing, the subquery specified after JOIN is run, and its result is saved in memory. Then it is read from the "left" table specified in the FROM clause, and while it is being read, for each of the read rows from the "left" table, rows are selected from the subquery results table (the "right" table) that meet the condition for matching the values of the columns specified in USING.

-

The table name can be specified instead of a subquery. This is equivalent to the SELECT * FROM table subquery, except in a special case when the table has the Join engine – an array prepared for joining.

-

All columns that are not needed for the JOIN are deleted from the subquery.

-

There are several types of JOINs:

-

INNER or LEFT type:If INNER is specified, the result will contain only those rows that have a matching row in the right table. -If LEFT is specified, any rows in the left table that don't have matching rows in the right table will be assigned the default value - zeros or empty rows. LEFT OUTER may be written instead of LEFT; the word OUTER does not affect anything.

-

ANY or ALL stringency:If ANY is specified and the right table has several matching rows, only the first one found is joined. -If ALL is specified and the right table has several matching rows, the data will be multiplied by the number of these rows.

-

Using ALL corresponds to the normal JOIN semantic from standard SQL. -Using ANY is optimal. If the right table has only one matching row, the results of ANY and ALL are the same. You must specify either ANY or ALL (neither of them is selected by default).

-

GLOBAL distribution:

-

When using a normal JOIN, the query is sent to remote servers. Subqueries are run on each of them in order to make the right table, and the join is performed with this table. In other words, the right table is formed on each server separately.

-

When using GLOBAL ... JOIN, first the requestor server runs a subquery to calculate the right table. This temporary table is passed to each remote server, and queries are run on them using the temporary data that was transmitted.

-

Be careful when using GLOBAL JOINs. For more information, see the section "Distributed subqueries".

-

Any combination of JOINs is possible. For example, GLOBAL ANY LEFT OUTER JOIN.

-

When running a JOIN, there is no optimization of the order of execution in relation to other stages of the query. The join (a search in the right table) is run before filtering in WHERE and before aggregation. In order to explicitly set the processing order, we recommend running a JOIN subquery with a subquery.

-

Example:

-
SELECT
-    CounterID,
-    hits,
-    visits
-FROM
-(
-    SELECT
-        CounterID,
-        count() AS hits
-    FROM test.hits
-    GROUP BY CounterID
-) ANY LEFT JOIN
-(
-    SELECT
-        CounterID,
-        sum(Sign) AS visits
-    FROM test.visits
-    GROUP BY CounterID
-) USING CounterID
-ORDER BY hits DESC
-LIMIT 10
-
- - -
┌─CounterID─┬───hits─┬─visits─┐
-│   1143050 │ 523264 │  13665 │
-│    731962 │ 475698 │ 102716 │
-│    722545 │ 337212 │ 108187 │
-│    722889 │ 252197 │  10547 │
-│   2237260 │ 196036 │   9522 │
-│  23057320 │ 147211 │   7689 │
-│    722818 │  90109 │  17847 │
-│     48221 │  85379 │   4652 │
-│  19762435 │  77807 │   7026 │
-│    722884 │  77492 │  11056 │
-└───────────┴────────┴────────┘
-
- - -

Subqueries don't allow you to set names or use them for referencing a column from a specific subquery. -The columns specified in USING must have the same names in both subqueries, and the other columns must be named differently. You can use aliases to change the names of columns in subqueries (the example uses the aliases 'hits' and 'visits').

-

The USING clause specifies one or more columns to join, which establishes the equality of these columns. The list of columns is set without brackets. More complex join conditions are not supported.

-

The right table (the subquery result) resides in RAM. If there isn't enough memory, you can't run a JOIN.

-

Only one JOIN can be specified in a query (on a single level). To run multiple JOINs, you can put them in subqueries.

-

Each time a query is run with the same JOIN, the subquery is run again – the result is not cached. To avoid this, use the special 'Join' table engine, which is a prepared array for joining that is always in RAM. For more information, see the section "Table engines, Join".

-

In some cases, it is more efficient to use IN instead of JOIN. -Among the various types of JOINs, the most efficient is ANY LEFT JOIN, then ANY INNER JOIN. The least efficient are ALL LEFT JOIN and ALL INNER JOIN.

-

If you need a JOIN for joining with dimension tables (these are relatively small tables that contain dimension properties, such as names for advertising campaigns), a JOIN might not be very convenient due to the bulky syntax and the fact that the right table is re-accessed for every query. For such cases, there is an "external dictionaries" feature that you should use instead of JOIN. For more information, see the section "External dictionaries".

-

WHERE clause

-

If there is a WHERE clause, it must contain an expression with the UInt8 type. This is usually an expression with comparison and logical operators. -This expression will be used for filtering data before all other transformations.

-

If indexes are supported by the database table engine, the expression is evaluated on the ability to use indexes.

-

PREWHERE clause

-

This clause has the same meaning as the WHERE clause. The difference is in which data is read from the table. -When using PREWHERE, first only the columns necessary for executing PREWHERE are read. Then the other columns are read that are needed for running the query, but only those blocks where the PREWHERE expression is true.

-

It makes sense to use PREWHERE if there are filtration conditions that are not suitable for indexes that are used by a minority of the columns in the query, but that provide strong data filtration. This reduces the volume of data to read.

-

For example, it is useful to write PREWHERE for queries that extract a large number of columns, but that only have filtration for a few columns.

-

PREWHERE is only supported by tables from the *MergeTree family.

-

A query may simultaneously specify PREWHERE and WHERE. In this case, PREWHERE precedes WHERE.

-

Keep in mind that it does not make much sense for PREWHERE to only specify those columns that have an index, because when using an index, only the data blocks that match the index are read.

-

If the 'optimize_move_to_prewhere' setting is set to 1 and PREWHERE is omitted, the system uses heuristics to automatically move parts of expressions from WHERE to PREWHERE.

-

GROUP BY clause

-

This is one of the most important parts of a column-oriented DBMS.

-

If there is a GROUP BY clause, it must contain a list of expressions. Each expression will be referred to here as a "key". -All the expressions in the SELECT, HAVING, and ORDER BY clauses must be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions.

-

If a query contains only table columns inside aggregate functions, the GROUP BY clause can be omitted, and aggregation by an empty set of keys is assumed.

-

Example:

-
SELECT
-    count(),
-    median(FetchTiming > 60 ? 60 : FetchTiming),
-    count() - sum(Refresh)
-FROM hits
-
- - -

However, in contrast to standard SQL, if the table doesn't have any rows (either there aren't any at all, or there aren't any after using WHERE to filter), an empty result is returned, and not the result from one of the rows containing the initial values of aggregate functions.

-

As opposed to MySQL (and conforming to standard SQL), you can't get some value of some column that is not in a key or aggregate function (except constant expressions). To work around this, you can use the 'any' aggregate function (get the first encountered value) or 'min/max'.

-

Example:

-
SELECT
-    domainWithoutWWW(URL) AS domain,
-    count(),
-    any(Title) AS title -- getting the first occurred page header for each domain.
-FROM hits
-GROUP BY domain
-
- - -

For every different key value encountered, GROUP BY calculates a set of aggregate function values.

-

GROUP BY is not supported for array columns.

-

A constant can't be specified as arguments for aggregate functions. Example: sum(1). Instead of this, you can get rid of the constant. Example: count().

-
WITH TOTALS modifier
-

If the WITH TOTALS modifier is specified, another row will be calculated. This row will have key columns containing default values (zeros or empty lines), and columns of aggregate functions with the values calculated across all the rows (the "total" values).

-

This extra row is output in JSON*, TabSeparated*, and Pretty* formats, separately from the other rows. In the other formats, this row is not output.

-

In JSON* formats, this row is output as a separate 'totals' field. In TabSeparated* formats, the row comes after the main result, preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result.

-

WITH TOTALS can be run in different ways when HAVING is present. The behavior depends on the 'totals_mode' setting. -By default, totals_mode = 'before_having'. In this case, 'totals' is calculated across all rows, including the ones that don't pass through HAVING and 'max_rows_to_group_by'.

-

The other alternatives include only the rows that pass through HAVING in 'totals', and behave differently with the setting max_rows_to_group_by and group_by_overflow_mode = 'any'.

-

after_having_exclusive – Don't include rows that didn't pass through max_rows_to_group_by. In other words, 'totals' will have less than or the same number of rows as it would if max_rows_to_group_by were omitted.

-

after_having_inclusive – Include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. In other words, 'totals' will have more than or the same number of rows as it would if max_rows_to_group_by were omitted.

-

after_having_auto – Count the number of rows that passed through HAVING. If it is more than a certain amount (by default, 50%), include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. Otherwise, do not include them.

-

totals_auto_threshold – By default, 0.5. The coefficient for after_having_auto.

-

If max_rows_to_group_by and group_by_overflow_mode = 'any' are not used, all variations of after_having are the same, and you can use any of them (for example, after_having_auto).

-

You can use WITH TOTALS in subqueries, including subqueries in the JOIN clause (in this case, the respective total values are combined).

-
GROUP BY in external memory
-

You can enable dumping temporary data to the disk to restrict memory usage during GROUP BY. -The max_bytes_before_external_group_by setting determines the threshold RAM consumption for dumping GROUP BY temporary data to the file system. If set to 0 (the default), it is disabled.

-

When using max_bytes_before_external_group_by, we recommend that you set max_memory_usage about twice as high. This is necessary because there are two stages to aggregation: reading the date and forming intermediate data (1) and merging the intermediate data (2). Dumping data to the file system can only occur during stage 1. If the temporary data wasn't dumped, then stage 2 might require up to the same amount of memory as in stage 1.

-

For example, if max_memory_usage was set to 10000000000 and you want to use external aggregation, it makes sense to set max_bytes_before_external_group_by to 10000000000, and max_memory_usage to 20000000000. When external aggregation is triggered (if there was at least one dump of temporary data), maximum consumption of RAM is only slightly more than max_bytes_before_external_group_by.

-

With distributed query processing, external aggregation is performed on remote servers. In order for the requestor server to use only a small amount of RAM, set distributed_aggregation_memory_efficient to 1.

-

When merging data flushed to the disk, as well as when merging results from remote servers when the distributed_aggregation_memory_efficient setting is enabled, consumes up to 1/256 * the number of threads from the total amount of RAM.

-

When external aggregation is enabled, if there was less than max_bytes_before_external_group_by of data (i.e. data was not flushed), the query runs just as fast as without external aggregation. If any temporary data was flushed, the run time will be several times longer (approximately three times).

-

If you have an ORDER BY with a small LIMIT after GROUP BY, then the ORDER BY CLAUSE will not use significant amounts of RAM. -But if the ORDER BY doesn't have LIMIT, don't forget to enable external sorting (max_bytes_before_external_sort).

-

LIMIT N BY clause

-

LIMIT N BY COLUMNS selects the top N rows for each group of COLUMNS. LIMIT N BY is not related to LIMIT; they can both be used in the same query. The key for LIMIT N BY can contain any number of columns or expressions.

-

Example:

-
SELECT
-    domainWithoutWWW(URL) AS domain,
-    domainWithoutWWW(REFERRER_URL) AS referrer,
-    device_type,
-    count() cnt
-FROM hits
-GROUP BY domain, referrer, device_type
-ORDER BY cnt DESC
-LIMIT 5 BY domain, device_type
-LIMIT 100
-
- - -

The query will select the top 5 referrers for each domain, device_type pair, but not more than 100 rows (LIMIT n BY + LIMIT).

-

HAVING clause

-

Allows filtering the result received after GROUP BY, similar to the WHERE clause. -WHERE and HAVING differ in that WHERE is performed before aggregation (GROUP BY), while HAVING is performed after it. -If aggregation is not performed, HAVING can't be used.

-

-

ORDER BY clause

-

The ORDER BY clause contains a list of expressions, which can each be assigned DESC or ASC (the sorting direction). If the direction is not specified, ASC is assumed. ASC is sorted in ascending order, and DESC in descending order. The sorting direction applies to a single expression, not to the entire list. Example: ORDER BY Visits DESC, SearchPhrase

-

For sorting by String values, you can specify collation (comparison). Example: ORDER BY SearchPhrase COLLATE 'tr' - for sorting by keyword in ascending order, using the Turkish alphabet, case insensitive, assuming that strings are UTF-8 encoded. COLLATE can be specified or not for each expression in ORDER BY independently. If ASC or DESC is specified, COLLATE is specified after it. When using COLLATE, sorting is always case-insensitive.

-

We only recommend using COLLATE for final sorting of a small number of rows, since sorting with COLLATE is less efficient than normal sorting by bytes.

-

Rows that have identical values for the list of sorting expressions are output in an arbitrary order, which can also be nondeterministic (different each time). -If the ORDER BY clause is omitted, the order of the rows is also undefined, and may be nondeterministic as well.

-

When floating point numbers are sorted, NaNs are separate from the other values. Regardless of the sorting order, NaNs come at the end. In other words, for ascending sorting they are placed as if they are larger than all the other numbers, while for descending sorting they are placed as if they are smaller than the rest.

-

Less RAM is used if a small enough LIMIT is specified in addition to ORDER BY. Otherwise, the amount of memory spent is proportional to the volume of data for sorting. For distributed query processing, if GROUP BY is omitted, sorting is partially done on remote servers, and the results are merged on the requestor server. This means that for distributed sorting, the volume of data to sort can be greater than the amount of memory on a single server.

-

If there is not enough RAM, it is possible to perform sorting in external memory (creating temporary files on a disk). Use the setting max_bytes_before_external_sort for this purpose. If it is set to 0 (the default), external sorting is disabled. If it is enabled, when the volume of data to sort reaches the specified number of bytes, the collected data is sorted and dumped into a temporary file. After all data is read, all the sorted files are merged and the results are output. Files are written to the /var/lib/clickhouse/tmp/ directory in the config (by default, but you can use the 'tmp_path' parameter to change this setting).

-

Running a query may use more memory than 'max_bytes_before_external_sort'. For this reason, this setting must have a value significantly smaller than 'max_memory_usage'. As an example, if your server has 128 GB of RAM and you need to run a single query, set 'max_memory_usage' to 100 GB, and 'max_bytes_before_external_sort' to 80 GB.

-

External sorting works much less effectively than sorting in RAM.

-

SELECT clause

-

The expressions specified in the SELECT clause are analyzed after the calculations for all the clauses listed above are completed. -More specifically, expressions are analyzed that are above the aggregate functions, if there are any aggregate functions. -The aggregate functions and everything below them are calculated during aggregation (GROUP BY). -These expressions work as if they are applied to separate rows in the result.

-

DISTINCT clause

-

If DISTINCT is specified, only a single row will remain out of all the sets of fully matching rows in the result. -The result will be the same as if GROUP BY were specified across all the fields specified in SELECT without aggregate functions. But there are several differences from GROUP BY:

-
    -
  • DISTINCT can be applied together with GROUP BY.
    • -
  • When ORDER BY is omitted and LIMIT is defined, the query stops running immediately after the required number of different rows has been read.
    • -
  • Data blocks are output as they are processed, without waiting for the entire query to finish running.
    • -
-

DISTINCT is not supported if SELECT has at least one array column.

-

LIMIT clause

-

LIMIT m allows you to select the first 'm' rows from the result. -LIMIT n, m allows you to select the first 'm' rows from the result after skipping the first 'n' rows.

-

'n' and 'm' must be non-negative integers.

-

If there isn't an ORDER BY clause that explicitly sorts results, the result may be arbitrary and nondeterministic.

-

UNION ALL clause

-

You can use UNION ALL to combine any number of queries. Example:

-
SELECT CounterID, 1 AS table, toInt64(count()) AS c
-    FROM test.hits
-    GROUP BY CounterID
-
-UNION ALL
-
-SELECT CounterID, 2 AS table, sum(Sign) AS c
-    FROM test.visits
-    GROUP BY CounterID
-    HAVING c > 0
-
- - -

Only UNION ALL is supported. The regular UNION (UNION DISTINCT) is not supported. If you need UNION DISTINCT, you can write SELECT DISTINCT from a subquery containing UNION ALL.

-

Queries that are parts of UNION ALL can be run simultaneously, and their results can be mixed together.

-

The structure of results (the number and type of columns) must match for the queries. But the column names can differ. In this case, the column names for the final result will be taken from the first query.

-

Queries that are parts of UNION ALL can't be enclosed in brackets. ORDER BY and LIMIT are applied to separate queries, not to the final result. If you need to apply a conversion to the final result, you can put all the queries with UNION ALL in a subquery in the FROM clause.

-

INTO OUTFILE clause

-

Add the INTO OUTFILE filename clause (where filename is a string literal) to redirect query output to the specified file. -In contrast to MySQL, the file is created on the client side. The query will fail if a file with the same filename already exists. -This functionality is available in the command-line client and clickhouse-local (a query sent via HTTP interface will fail).

-

The default output format is TabSeparated (the same as in the command-line client batch mode).

-

FORMAT clause

-

Specify 'FORMAT format' to get data in any specified format. -You can use this for convenience, or for creating dumps. -For more information, see the section "Formats". -If the FORMAT clause is omitted, the default format is used, which depends on both the settings and the interface used for accessing the DB. For the HTTP interface and the command-line client in batch mode, the default format is TabSeparated. For the command-line client in interactive mode, the default format is PrettyCompact (it has attractive and compact tables).

-

When using the command-line client, data is passed to the client in an internal efficient format. The client independently interprets the FORMAT clause of the query and formats the data itself (thus relieving the network and the server from the load).

-

IN operators

-

The IN, NOT IN, GLOBAL IN, and GLOBAL NOT IN operators are covered separately, since their functionality is quite rich.

-

The left side of the operator is either a single column or a tuple.

-

Examples:

-
SELECT UserID IN (123, 456) FROM ...
-SELECT (CounterID, UserID) IN ((34, 123), (101500, 456)) FROM ...
-
- - -

If the left side is a single column that is in the index, and the right side is a set of constants, the system uses the index for processing the query.

-

Don't list too many values explicitly (i.e. millions). If a data set is large, put it in a temporary table (for example, see the section "External data for query processing"), then use a subquery.

-

The right side of the operator can be a set of constant expressions, a set of tuples with constant expressions (shown in the examples above), or the name of a database table or SELECT subquery in brackets.

-

If the right side of the operator is the name of a table (for example, UserID IN users), this is equivalent to the subquery UserID IN (SELECT * FROM users). Use this when working with external data that is sent along with the query. For example, the query can be sent together with a set of user IDs loaded to the 'users' temporary table, which should be filtered.

-

If the right side of the operator is a table name that has the Set engine (a prepared data set that is always in RAM), the data set will not be created over again for each query.

-

The subquery may specify more than one column for filtering tuples. -Example:

-
SELECT (CounterID, UserID) IN (SELECT CounterID, UserID FROM ...) FROM ...
-
- - -

The columns to the left and right of the IN operator should have the same type.

-

The IN operator and subquery may occur in any part of the query, including in aggregate functions and lambda functions. -Example:

-
SELECT
-    EventDate,
-    avg(UserID IN
-    (
-        SELECT UserID
-        FROM test.hits
-        WHERE EventDate = toDate('2014-03-17')
-    )) AS ratio
-FROM test.hits
-GROUP BY EventDate
-ORDER BY EventDate ASC
-
- - -
┌──EventDate─┬────ratio─┐
-│ 2014-03-17 │        1 │
-│ 2014-03-18 │ 0.807696 │
-│ 2014-03-19 │ 0.755406 │
-│ 2014-03-20 │ 0.723218 │
-│ 2014-03-21 │ 0.697021 │
-│ 2014-03-22 │ 0.647851 │
-│ 2014-03-23 │ 0.648416 │
-└────────────┴──────────┘
-
- - -

For each day after March 17th, count the percentage of pageviews made by users who visited the site on March 17th. -A subquery in the IN clause is always run just one time on a single server. There are no dependent subqueries.

-

-
Distributed subqueries
-

There are two options for IN-s with subqueries (similar to JOINs): normal IN / OIN and IN GLOBAL / GLOBAL JOIN. They differ in how they are run for distributed query processing.

-
- -Remember that the algorithms described below may work differently depending on the [settings](#settings-distributed_product_mode) `distributed_product_mode` setting. - -
- -

When using the regular IN, the query is sent to remote servers, and each of them runs the subqueries in the IN or JOIN clause.

-

When using GLOBAL IN / GLOBAL JOINs, first all the subqueries are run for GLOBAL IN / GLOBAL JOINs, and the results are collected in temporary tables. Then the temporary tables are sent to each remote server, where the queries are run using this temporary data.

-

For a non-distributed query, use the regular IN / JOIN.

-

Be careful when using subqueries in the IN / JOIN clauses for distributed query processing.

-

Let's look at some examples. Assume that each server in the cluster has a normal local_table. Each server also has a distributed_table table with the Distributed type, which looks at all the servers in the cluster.

-

For a query to the distributed_table, the query will be sent to all the remote servers and run on them using the local_table.

-

For example, the query

-
SELECT uniq(UserID) FROM distributed_table
-
- - -

will be sent to all remote servers as

-
SELECT uniq(UserID) FROM local_table
-
- - -

and run on each of them in parallel, until it reaches the stage where intermediate results can be combined. Then the intermediate results will be returned to the requestor server and merged on it, and the final result will be sent to the client.

-

Now let's examine a query with IN:

-
SELECT uniq(UserID) FROM distributed_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM local_table WHERE CounterID = 34)
-
- - -
    -
  • Calculation of the intersection of audiences of two sites.
    • -
-

This query will be sent to all remote servers as

-
SELECT uniq(UserID) FROM local_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM local_table WHERE CounterID = 34)
-
- - -

In other words, the data set in the IN clause will be collected on each server independently, only across the data that is stored locally on each of the servers.

-

This will work correctly and optimally if you are prepared for this case and have spread data across the cluster servers such that the data for a single UserID resides entirely on a single server. In this case, all the necessary data will be available locally on each server. Otherwise, the result will be inaccurate. We refer to this variation of the query as "local IN".

-

To correct how the query works when data is spread randomly across the cluster servers, you could specify distributed_table inside a subquery. The query would look like this:

-
SELECT uniq(UserID) FROM distributed_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM distributed_table WHERE CounterID = 34)
-
- - -

This query will be sent to all remote servers as

-
SELECT uniq(UserID) FROM local_table WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM distributed_table WHERE CounterID = 34)
-
- - -

The subquery will begin running on each remote server. Since the subquery uses a distributed table, the subquery that is on each remote server will be resent to every remote server as

-
SELECT UserID FROM local_table WHERE CounterID = 34
-
- - -

For example, if you have a cluster of 100 servers, executing the entire query will require 10,000 elementary requests, which is generally considered unacceptable.

-

In such cases, you should always use GLOBAL IN instead of IN. Let's look at how it works for the query

-
SELECT uniq(UserID) FROM distributed_table WHERE CounterID = 101500 AND UserID GLOBAL IN (SELECT UserID FROM distributed_table WHERE CounterID = 34)
-
- - -

The requestor server will run the subquery

-
SELECT UserID FROM distributed_table WHERE CounterID = 34
-
- - -

and the result will be put in a temporary table in RAM. Then the request will be sent to each remote server as

-
SELECT uniq(UserID) FROM local_table WHERE CounterID = 101500 AND UserID GLOBAL IN _data1
-
- - -

and the temporary table _data1 will be sent to every remote server with the query (the name of the temporary table is implementation-defined).

-

This is more optimal than using the normal IN. However, keep the following points in mind:

-
    -
  1. When creating a temporary table, data is not made unique. To reduce the volume of data transmitted over the network, specify DISTINCT in the subquery. (You don't need to do this for a normal IN.)
    2. -
  2. The temporary table will be sent to all the remote servers. Transmission does not account for network topology. For example, if 10 remote servers reside in a datacenter that is very remote in relation to the requestor server, the data will be sent 10 times over the channel to the remote datacenter. Try to avoid large data sets when using GLOBAL IN.
    3. -
  3. When transmitting data to remote servers, restrictions on network bandwidth are not configurable. You might overload the network.
    4. -
  4. Try to distribute data across servers so that you don't need to use GLOBAL IN on a regular basis.
    5. -
  5. If you need to use GLOBAL IN often, plan the location of the ClickHouse cluster so that a single group of replicas resides in no more than one data center with a fast network between them, so that a query can be processed entirely within a single data center.
    6. -
-

It also makes sense to specify a local table in the GLOBAL IN clause, in case this local table is only available on the requestor server and you want to use data from it on remote servers.

-

Extreme values

-

In addition to results, you can also get minimum and maximum values for the results columns. To do this, set the extremes setting to 1. Minimums and maximums are calculated for numeric types, dates, and dates with times. For other columns, the default values are output.

-

An extra two rows are calculated – the minimums and maximums, respectively. These extra two rows are output in JSON*, TabSeparated*, and Pretty* formats, separate from the other rows. They are not output for other formats.

-

In JSON* formats, the extreme values are output in a separate 'extremes' field. In TabSeparated* formats, the row comes after the main result, and after 'totals' if present. It is preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result, and after 'totals' if present.

-

Extreme values are calculated for rows that have passed through LIMIT. However, when using 'LIMIT offset, size', the rows before 'offset' are included in 'extremes'. In stream requests, the result may also include a small number of rows that passed through LIMIT.

-

Notes

-

The GROUP BY and ORDER BY clauses do not support positional arguments. This contradicts MySQL, but conforms to standard SQL. -For example, GROUP BY 1, 2 will be interpreted as grouping by constants (i.e. aggregation of all rows into one).

-

You can use synonyms (AS aliases) in any part of a query.

-

You can put an asterisk in any part of a query instead of an expression. When the query is analyzed, the asterisk is expanded to a list of all table columns (excluding the MATERIALIZED and ALIAS columns). There are only a few cases when using an asterisk is justified:

-
    -
  • When creating a table dump.
    • -
  • For tables containing just a few columns, such as system tables.
    • -
  • For getting information about what columns are in a table. In this case, set LIMIT 1. But it is better to use the DESC TABLE query.
    • -
  • When there is strong filtration on a small number of columns using PREWHERE.
    • -
  • In subqueries (since columns that aren't needed for the external query are excluded from subqueries).
    • -
-

In all other cases, we don't recommend using the asterisk, since it only gives you the drawbacks of a columnar DBMS instead of the advantages. In other words using the asterisk is not recommended.

-

KILL QUERY

-
KILL QUERY
-  WHERE <where expression to SELECT FROM system.processes query>
-  [SYNC|ASYNC|TEST]
-  [FORMAT format]
-
- - -

Attempts to forcibly terminate the currently running queries. -The queries to terminate are selected from the system.processes table using the criteria defined in the WHERE clause of the KILL query.

-

Examples:

-
-- Forcibly terminates all queries with the specified query_id:
-KILL QUERY WHERE query_id='2-857d-4a57-9ee0-327da5d60a90'
-
--- Synchronously terminates all queries run by 'username':
-KILL QUERY WHERE user='username' SYNC
-
- - -

Read-only users can only stop their own queries.

-

By default, the asynchronous version of queries is used (ASYNC), which doesn't wait for confirmation that queries have stopped.

-

The synchronous version (SYNC) waits for all queries to stop and displays information about each process as it stops. -The response contains the kill_status column, which can take the following values:

-
    -
  1. 'finished' – The query was terminated successfully.
    2. -
  2. 'waiting' – Waiting for the query to end after sending it a signal to terminate.
    3. -
  3. The other values ​​explain why the query can't be stopped.
    4. -
-

A test query (TEST) only checks the user's rights and displays a list of queries to stop.

-

Syntax

-

There are two types of parsers in the system: the full SQL parser (a recursive descent parser), and the data format parser (a fast stream parser). -In all cases except the INSERT query, only the full SQL parser is used. -The INSERT query uses both parsers:

-
INSERT INTO t VALUES (1, 'Hello, world'), (2, 'abc'), (3, 'def')
-
- - -

The INSERT INTO t VALUES fragment is parsed by the full parser, and the data (1, 'Hello, world'), (2, 'abc'), (3, 'def') is parsed by the fast stream parser. -Data can have any format. When a query is received, the server calculates no more than max_query_size bytes of the request in RAM (by default, 1 MB), and the rest is stream parsed. -This means the system doesn't have problems with large INSERT queries, like MySQL does.

-

When using the Values format in an INSERT query, it may seem that data is parsed the same as expressions in a SELECT query, but this is not true. The Values format is much more limited.

-

Next we will cover the full parser. For more information about format parsers, see the section "Formats".

-

Spaces

-

There may be any number of space symbols between syntactical constructions (including the beginning and end of a query). Space symbols include the space, tab, line feed, CR, and form feed.

-

Comments

-

SQL-style and C-style comments are supported. -SQL-style comments: from -- to the end of the line. The space after -- can be omitted. -Comments in C-style: from /* to */. These comments can be multiline. Spaces are not required here, either.

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Keywords

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Keywords (such as SELECT) are not case-sensitive. Everything else (column names, functions, and so on), in contrast to standard SQL, is case-sensitive. Keywords are not reserved (they are just parsed as keywords in the corresponding context).

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Identifiers

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Identifiers (column names, functions, and data types) can be quoted or non-quoted. -Non-quoted identifiers start with a Latin letter or underscore, and continue with a Latin letter, underscore, or number. In other words, they must match the regex ^[a-zA-Z_][0-9a-zA-Z_]*$. Examples: x, _1, X_y__Z123_.

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Quoted identifiers are placed in reversed quotation marks `id` (the same as in MySQL), and can indicate any set of bytes (non-empty). In addition, symbols (for example, the reverse quotation mark) inside this type of identifier can be backslash-escaped. Escaping rules are the same as for string literals (see below). -We recommend using identifiers that do not need to be quoted.

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Literals

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There are numeric literals, string literals, and compound literals.

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Numeric literals

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A numeric literal tries to be parsed:

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    -
  • First as a 64-bit signed number, using the 'strtoull' function.
    • -
  • If unsuccessful, as a 64-bit unsigned number, using the 'strtoll' function.
    • -
  • If unsuccessful, as a floating-point number using the 'strtod' function.
    • -
  • Otherwise, an error is returned.
    • -
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The corresponding value will have the smallest type that the value fits in. -For example, 1 is parsed as UInt8, but 256 is parsed as UInt16. For more information, see "Data types".

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Examples: 1, 18446744073709551615, 0xDEADBEEF, 01, 0.1, 1e100, -1e-100, inf, nan.

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String literals

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Only string literals in single quotes are supported. The enclosed characters can be backslash-escaped. The following escape sequences have a corresponding special value: \b, \f, \r, \n, \t, \0, \a, \v, \xHH. In all other cases, escape sequences in the format \c, where "c" is any character, are converted to "c". This means that you can use the sequences \'and\\. The value will have the String type.

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The minimum set of characters that you need to escape in string literals: ' and \.

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Compound literals

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Constructions are supported for arrays: [1, 2, 3] and tuples: (1, 'Hello, world!', 2).. -Actually, these are not literals, but expressions with the array creation operator and the tuple creation operator, respectively. -For more information, see the section "Operators2". -An array must consist of at least one item, and a tuple must have at least two items. -Tuples have a special purpose for use in the IN clause of a SELECT query. Tuples can be obtained as the result of a query, but they can't be saved to a database (with the exception of Memory-type tables).

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Functions

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Functions are written like an identifier with a list of arguments (possibly empty) in brackets. In contrast to standard SQL, the brackets are required, even for an empty arguments list. Example: now(). -There are regular and aggregate functions (see the section "Aggregate functions"). Some aggregate functions can contain two lists of arguments in brackets. Example: quantile (0.9) (x). These aggregate functions are called "parametric" functions, and the arguments in the first list are called "parameters". The syntax of aggregate functions without parameters is the same as for regular functions.

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Operators

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Operators are converted to their corresponding functions during query parsing, taking their priority and associativity into account. -For example, the expression 1 + 2 * 3 + 4 is transformed to plus(plus(1, multiply(2, 3)), 4). -For more information, see the section "Operators" below.

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Data types and database table engines

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Data types and table engines in the CREATE query are written the same way as identifiers or functions. In other words, they may or may not contain an arguments list in brackets. For more information, see the sections "Data types," "Table engines," and "CREATE".

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Synonyms

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In the SELECT query, expressions can specify synonyms using the AS keyword. Any expression is placed to the left of AS. The identifier name for the synonym is placed to the right of AS. As opposed to standard SQL, synonyms are not only declared on the top level of expressions:

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SELECT (1 AS n) + 2, n
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- - -

In contrast to standard SQL, synonyms can be used in all parts of a query, not just SELECT.

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Asterisk

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In a SELECT query, an asterisk can replace the expression. For more information, see the section "SELECT".

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Expressions

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An expression is a function, identifier, literal, application of an operator, expression in brackets, subquery, or asterisk. It can also contain a synonym. -A list of expressions is one or more expressions separated by commas. -Functions and operators, in turn, can have expressions as arguments.

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Table engines

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The table engine (type of table) determines:

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    -
  • How and where data is stored: where to write it to, and where to read it from.
    • -
  • Which queries are supported, and how.
    • -
  • Concurrent data access.
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  • Use of indexes, if present.
    • -
  • Whether multithreaded request execution is possible.
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  • Data replication.
    • -
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When reading data, the engine is only required to extract the necessary set of columns. However, in some cases, the query may be partially processed inside the table engine.

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Note that for most serious tasks, you should use engines from the MergeTree family.

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TinyLog

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The simplest table engine, which stores data on a disk. -Each column is stored in a separate compressed file. -When writing, data is appended to the end of files.

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Concurrent data access is not restricted in any way:

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    -
  • If you are simultaneously reading from a table and writing to it in a different query, the read operation will complete with an error.
    • -
  • If you are writing to a table in multiple queries simultaneously, the data will be broken.
    • -
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The typical way to use this table is write-once: first just write the data one time, then read it as many times as needed. -Queries are executed in a single stream. In other words, this engine is intended for relatively small tables (recommended up to 1,000,000 rows). -It makes sense to use this table engine if you have many small tables, since it is simpler than the Log engine (fewer files need to be opened). -The situation when you have a large number of small tables guarantees poor productivity, but may already be used when working with another DBMS, and you may find it easier to switch to using TinyLog types of tables. -Indexes are not supported.

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In Yandex.Metrica, TinyLog tables are used for intermediary data that is processed in small batches.

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Log

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Log differs from TinyLog in that a small file of "marks" resides with the column files. These marks are written on every data block and contain offsets that indicate where to start reading the file in order to skip the specified number of rows. This makes it possible to read table data in multiple threads. -For concurrent data access, the read operations can be performed simultaneously, while write operations block reads and each other. -The Log engine does not support indexes. Similarly, if writing to a table failed, the table is broken, and reading from it returns an error. The Log engine is appropriate for temporary data, write-once tables, and for testing or demonstration purposes.

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Memory

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The Memory engine stores data in RAM, in uncompressed form. Data is stored in exactly the same form as it is received when read. In other words, reading from this table is completely free. -Concurrent data access is synchronized. Locks are short: read and write operations don't block each other. -Indexes are not supported. Reading is parallelized. -Maximal productivity (over 10 GB/sec) is reached on simple queries, because there is no reading from the disk, decompressing, or deserializing data. (We should note that in many cases, the productivity of the MergeTree engine is almost as high.) -When restarting a server, data disappears from the table and the table becomes empty. -Normally, using this table engine is not justified. However, it can be used for tests, and for tasks where maximum speed is required on a relatively small number of rows (up to approximately 100,000,000).

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The Memory engine is used by the system for temporary tables with external query data (see the section "External data for processing a query"), and for implementing GLOBAL IN (see the section "IN operators").

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MergeTree

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The MergeTree engine supports an index by primary key and by date, and provides the possibility to update data in real time. -This is the most advanced table engine in ClickHouse. Don't confuse it with the Merge engine.

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The engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity.

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Example without sampling support.

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MergeTree(EventDate, (CounterID, EventDate), 8192)
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- - -

Example with sampling support.

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MergeTree(EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID)), 8192)
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- - -

A MergeTree table must have a separate column containing the date. Here, it is the EventDate column. The date column must have the 'Date' type (not 'DateTime').

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The primary key may be a tuple from any expressions (usually this is just a tuple of columns), or a single expression.

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The sampling expression (optional) can be any expression. It must also be present in the primary key. The example uses a hash of user IDs to pseudo-randomly disperse data in the table for each CounterID and EventDate. In other words, when using the SAMPLE clause in a query, you get an evenly pseudo-random sample of data for a subset of users.

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The table is implemented as a set of parts. Each part is sorted by the primary key. In addition, each part has the minimum and maximum date assigned. When inserting in the table, a new sorted part is created. The merge process is periodically initiated in the background. When merging, several parts are selected (usually the smallest ones) and then merged into one large sorted part.

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In other words, incremental sorting occurs when inserting to the table. Merging is implemented so that the table always consists of a small number of sorted parts, and the merge itself doesn't do too much work.

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During insertion, data belonging to different months is separated into different parts. The parts that correspond to different months are never combined. The purpose of this is to provide local data modification (for ease in backups).

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Parts are combined up to a certain size threshold, so there aren't any merges that are too long.

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For each part, an index file is also written. The index file contains the primary key value for every 'index_granularity' row in the table. In other words, this is an abbreviated index of sorted data.

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For columns, "marks" are also written to each 'index_granularity' row so that data can be read in a specific range.

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When reading from a table, the SELECT query is analyzed for whether indexes can be used. -An index can be used if the WHERE or PREWHERE clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has IN or LIKE with a fixed prefix on columns or expressions that are in the primary key or partitioning key, or on certain partially repetitive functions of these columns, or logical relationships of these expressions.

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Thus, it is possible to quickly run queries on one or many ranges of the primary key. In this example, queries will be fast when run for a specific tracking tag; for a specific tag and date range; for a specific tag and date; for multiple tags with a date range, and so on.

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SELECT count() FROM table WHERE EventDate = toDate(now()) AND CounterID = 34
-SELECT count() FROM table WHERE EventDate = toDate(now()) AND (CounterID = 34 OR CounterID = 42)
-SELECT count() FROM table WHERE ((EventDate >= toDate('2014-01-01') AND EventDate <= toDate('2014-01-31')) OR EventDate = toDate('2014-05-01')) AND CounterID IN (101500, 731962, 160656) AND (CounterID = 101500 OR EventDate != toDate('2014-05-01'))
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- - -

All of these cases will use the index by date and by primary key. The index is used even for complex expressions. Reading from the table is organized so that using the index can't be slower than a full scan.

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In this example, the index can't be used.

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SELECT count() FROM table WHERE CounterID = 34 OR URL LIKE '%upyachka%'
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- - -

To check whether ClickHouse can use the index when executing the query, use the settings force_index_by_dateandforce_primary_key.

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The index by date only allows reading those parts that contain dates from the desired range. However, a data part may contain data for many dates (up to an entire month), while within a single part the data is ordered by the primary key, which might not contain the date as the first column. Because of this, using a query with only a date condition that does not specify the primary key prefix will cause more data to be read than for a single date.

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For concurrent table access, we use multi-versioning. In other words, when a table is simultaneously read and updated, data is read from a set of parts that is current at the time of the query. There are no lengthy locks. Inserts do not get in the way of read operations.

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Reading from a table is automatically parallelized.

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The OPTIMIZE query is supported, which calls an extra merge step.

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You can use a single large table and continually add data to it in small chunks – this is what MergeTree is intended for.

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Data replication is possible for all types of tables in the MergeTree family (see the section "Data replication").

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Custom partitioning key

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Starting with version 1.1.54310, you can create tables in the MergeTree family with any partitioning expression (not only partitioning by month).

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The partition key can be an expression from the table columns, or a tuple of such expressions (similar to the primary key). The partition key can be omitted. When creating a table, specify the partition key in the ENGINE description with the new syntax:

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ENGINE [=] Name(...) [PARTITION BY expr] [ORDER BY expr] [SAMPLE BY expr] [SETTINGS name=value, ...]
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- - -

For MergeTree tables, the partition expression is specified after PARTITION BY, the primary key after ORDER BY, the sampling key after SAMPLE BY, and SETTINGS can specify index_granularity (optional; the default value is 8192), as well as other settings from MergeTreeSettings.h. The other engine parameters are specified in parentheses after the engine name, as previously. Example:

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ENGINE = ReplicatedCollapsingMergeTree('/clickhouse/tables/name', 'replica1', Sign)
-    PARTITION BY (toMonday(StartDate), EventType)
-    ORDER BY (CounterID, StartDate, intHash32(UserID))
-    SAMPLE BY intHash32(UserID)
-
- - -

The traditional partitioning by month is expressed as toYYYYMM(date_column).

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You can't convert an old-style table to a table with custom partitions (only via INSERT SELECT).

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After this table is created, merge will only work for data parts that have the same value for the partitioning expression. Note: This means that you shouldn't make overly granular partitions (more than about a thousand partitions), or SELECT will perform poorly.

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To specify a partition in ALTER PARTITION commands, specify the value of the partition expression (or a tuple). Constants and constant expressions are supported. Example:

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ALTER TABLE table DROP PARTITION (toMonday(today()), 1)
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- - -

Deletes the partition for the current week with event type 1. The same is true for the OPTIMIZE query. To specify the only partition in a non-partitioned table, specify PARTITION tuple().

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Note: For old-style tables, the partition can be specified either as a number 201710 or a string '201710'. The syntax for the new style of tables is stricter with types (similar to the parser for the VALUES input format). In addition, ALTER TABLE FREEZE PARTITION uses exact match for new-style tables (not prefix match).

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In the system.parts table, the partition column specifies the value of the partition expression to use in ALTER queries (if quotas are removed). The name column should specify the name of the data part that has a new format.

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Was: 20140317_20140323_2_2_0 (minimum date - maximum date - minimum block number - maximum block number - level).

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Now: 201403_2_2_0 (partition ID - minimum block number - maximum block number - level).

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The partition ID is its string identifier (human-readable, if possible) that is used for the names of data parts in the file system and in ZooKeeper. You can specify it in ALTER queries in place of the partition key. Example: Partition key toYYYYMM(EventDate); ALTER can specify either PARTITION 201710 or PARTITION ID '201710'.

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For more examples, see the tests 00502_custom_partitioning_local and 00502_custom_partitioning_replicated_zookeeper.

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ReplacingMergeTree

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This engine table differs from MergeTree in that it removes duplicate entries with the same primary key value.

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The last optional parameter for the table engine is the version column. When merging, it reduces all rows with the same primary key value to just one row. If the version column is specified, it leaves the row with the highest version; otherwise, it leaves the last row.

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The version column must have a type from the UInt family, Date, or DateTime.

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ReplacingMergeTree(EventDate, (OrderID, EventDate, BannerID, ...), 8192, ver)
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- - -

Note that data is only deduplicated during merges. Merging occurs in the background at an unknown time, so you can't plan for it. Some of the data may remain unprocessed. Although you can run an unscheduled merge using the OPTIMIZE query, don't count on using it, because the OPTIMIZE query will read and write a large amount of data.

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Thus, ReplacingMergeTree is suitable for clearing out duplicate data in the background in order to save space, but it doesn't guarantee the absence of duplicates.

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This engine is not used in Yandex.Metrica, but it has been applied in other Yandex projects.

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SummingMergeTree

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This engine differs from MergeTree in that it totals data while merging.

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SummingMergeTree(EventDate, (OrderID, EventDate, BannerID, ...), 8192)
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- - -

The columns to total are implicit. When merging, all rows with the same primary key value (in the example, OrderId, EventDate, BannerID, ...) have their values totaled in numeric columns that are not part of the primary key.

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SummingMergeTree(EventDate, (OrderID, EventDate, BannerID, ...), 8192, (Shows, Clicks, Cost, ...))
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- - -

The columns to total are set explicitly (the last parameter – Shows, Clicks, Cost, ...). When merging, all rows with the same primary key value have their values totaled in the specified columns. The specified columns also must be numeric and must not be part of the primary key.

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If the values were null in all of these columns, the row is deleted. (The exception is cases when the data part would not have any rows left in it.)

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For the other rows that are not part of the primary key, the first value that occurs is selected when merging.

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Summation is not performed for a read operation. If it is necessary, write the appropriate GROUP BY.

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In addition, a table can have nested data structures that are processed in a special way. -If the name of a nested table ends in 'Map' and it contains at least two columns that meet the following criteria:

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    -
  • The first table is numeric ((U)IntN, Date, DateTime), which we'll refer to as the 'key'.
    • -
  • The other columns are arithmetic ((U)IntN, Float32/64), which we'll refer to as '(values...)'. Then this nested table is interpreted as a mapping of key => (values...), and when merging its rows, the elements of two data sets are merged by 'key' with a summation of the corresponding (values...).
    • -
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Examples:

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[(1, 100)] + [(2, 150)] -> [(1, 100), (2, 150)]
-[(1, 100)] + [(1, 150)] -> [(1, 250)]
-[(1, 100)] + [(1, 150), (2, 150)] -> [(1, 250), (2, 150)]
-[(1, 100), (2, 150)] + [(1, -100)] -> [(2, 150)]
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- - -

For aggregation of Map, use the function sumMap(key, value).

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For nested data structures, you don't need to specify the columns as a list of columns for totaling.

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This table engine is not particularly useful. Remember that when saving just pre-aggregated data, you lose some of the system's advantages.

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AggregatingMergeTree

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This engine differs from MergeTree in that the merge combines the states of aggregate functions stored in the table for rows with the same primary key value.

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For this to work, it uses the AggregateFunction data type, as well as -State and -Merge modifiers for aggregate functions. Let's examine it more closely.

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There is an AggregateFunction data type. It is a parametric data type. As parameters, the name of the aggregate function is passed, then the types of its arguments.

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Examples:

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CREATE TABLE t
-(
-    column1 AggregateFunction(uniq, UInt64),
-    column2 AggregateFunction(anyIf, String, UInt8),
-    column3 AggregateFunction(quantiles(0.5, 0.9), UInt64)
-) ENGINE = ...
-
- - -

This type of column stores the state of an aggregate function.

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To get this type of value, use aggregate functions with the State suffix.

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Example: -uniqState(UserID), quantilesState(0.5, 0.9)(SendTiming)

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In contrast to the corresponding uniq and quantiles functions, these functions return the state, rather than the prepared value. In other words, they return an AggregateFunction type value.

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An AggregateFunction type value can't be output in Pretty formats. In other formats, these types of values are output as implementation-specific binary data. The AggregateFunction type values are not intended for output or saving in a dump.

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The only useful thing you can do with AggregateFunction type values is combine the states and get a result, which essentially means to finish aggregation. Aggregate functions with the 'Merge' suffix are used for this purpose. -Example: uniqMerge(UserIDState), where UserIDState has the AggregateFunction type.

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In other words, an aggregate function with the 'Merge' suffix takes a set of states, combines them, and returns the result. -As an example, these two queries return the same result:

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SELECT uniq(UserID) FROM table
-
-SELECT uniqMerge(state) FROM (SELECT uniqState(UserID) AS state FROM table GROUP BY RegionID)
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- - -

There is an AggregatingMergeTree engine. Its job during a merge is to combine the states of aggregate functions from different table rows with the same primary key value.

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You can't use a normal INSERT to insert a row in a table containing AggregateFunction columns, because you can't explicitly define the AggregateFunction value. Instead, use INSERT SELECT with -State aggregate functions for inserting data.

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With SELECT from an AggregatingMergeTree table, use GROUP BY and aggregate functions with the '-Merge' modifier in order to complete data aggregation.

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You can use AggregatingMergeTree tables for incremental data aggregation, including for aggregated materialized views.

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Example:

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Create an AggregatingMergeTree materialized view that watches the test.visits table:

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CREATE MATERIALIZED VIEW test.basic
-ENGINE = AggregatingMergeTree(StartDate, (CounterID, StartDate), 8192)
-AS SELECT
-    CounterID,
-    StartDate,
-    sumState(Sign)    AS Visits,
-    uniqState(UserID) AS Users
-FROM test.visits
-GROUP BY CounterID, StartDate;
-
- - -

Insert data in the test.visits table. Data will also be inserted in the view, where it will be aggregated:

-
INSERT INTO test.visits ...
-
- - -

Perform SELECT from the view using GROUP BY in order to complete data aggregation:

-
SELECT
-    StartDate,
-    sumMerge(Visits) AS Visits,
-    uniqMerge(Users) AS Users
-FROM test.basic
-GROUP BY StartDate
-ORDER BY StartDate;
-
- - -

You can create a materialized view like this and assign a normal view to it that finishes data aggregation.

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Note that in most cases, using AggregatingMergeTree is not justified, since queries can be run efficiently enough on non-aggregated data.

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CollapsingMergeTree

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This engine is used specifically for Yandex.Metrica.

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It differs from MergeTree in that it allows automatic deletion, or "collapsing" certain pairs of rows when merging.

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Yandex.Metrica has normal logs (such as hit logs) and change logs. Change logs are used for incrementally calculating statistics on data that is constantly changing. Examples are the log of session changes, or logs of changes to user histories. Sessions are constantly changing in Yandex.Metrica. For example, the number of hits per session increases. We refer to changes in any object as a pair (?old values, ?new values). Old values may be missing if the object was created. New values may be missing if the object was deleted. If the object was changed, but existed previously and was not deleted, both values are present. In the change log, one or two entries are made for each change. Each entry contains all the attributes that the object has, plus a special attribute for differentiating between the old and new values. When objects change, only the new entries are added to the change log, and the existing ones are not touched.

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The change log makes it possible to incrementally calculate almost any statistics. To do this, we need to consider "new" rows with a plus sign, and "old" rows with a minus sign. In other words, incremental calculation is possible for all statistics whose algebraic structure contains an operation for taking the inverse of an element. This is true of most statistics. We can also calculate "idempotent" statistics, such as the number of unique visitors, since the unique visitors are not deleted when making changes to sessions.

-

This is the main concept that allows Yandex.Metrica to work in real time.

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CollapsingMergeTree accepts an additional parameter - the name of an Int8-type column that contains the row's "sign". Example:

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CollapsingMergeTree(EventDate, (CounterID, EventDate, intHash32(UniqID), VisitID), 8192, Sign)
-
- - -

Here, Sign is a column containing -1 for "old" values and 1 for "new" values.

-

When merging, each group of consecutive identical primary key values (columns for sorting data) is reduced to no more than one row with the column value 'sign_column = -1' (the "negative row") and no more than one row with the column value 'sign_column = 1' (the "positive row"). In other words, entries from the change log are collapsed.

-

If the number of positive and negative rows matches, the first negative row and the last positive row are written. -If there is one more positive row than negative rows, only the last positive row is written. -If there is one more negative row than positive rows, only the first negative row is written. -Otherwise, there will be a logical error and none of the rows will be written. (A logical error can occur if the same section of the log was accidentally inserted more than once. The error is just recorded in the server log, and the merge continues.)

-

Thus, collapsing should not change the results of calculating statistics. -Changes are gradually collapsed so that in the end only the last value of almost every object is left. -Compared to MergeTree, the CollapsingMergeTree engine allows a multifold reduction of data volume.

-

There are several ways to get completely "collapsed" data from a CollapsingMergeTree table:

-
    -
  1. Write a query with GROUP BY and aggregate functions that accounts for the sign. For example, to calculate quantity, write 'sum(Sign)' instead of 'count()'. To calculate the sum of something, write 'sum(Sign * x)' instead of 'sum(x)', and so on, and also add 'HAVING sum(Sign) > 0'. Not all amounts can be calculated this way. For example, the aggregate functions 'min' and 'max' can't be rewritten.
    2. -
  2. If you must extract data without aggregation (for example, to check whether rows are present whose newest values match certain conditions), you can use the FINAL modifier for the FROM clause. This approach is significantly less efficient.
    3. -
-

-

GraphiteMergeTree

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This engine is designed for rollup (thinning and aggregating/averaging) Graphite data. It may be helpful to developers who want to use ClickHouse as a data store for Graphite.

-

Graphite stores full data in ClickHouse, and data can be retrieved in the following ways:

-
    -
  • Without thinning.
    • -
-

Uses the MergeTree engine.

-
    -
  • With thinning.
    • -
-

Using the GraphiteMergeTree engine.

-

The engine inherits properties from MergeTree. The settings for thinning data are defined by the graphite_rollup parameter in the server configuration.

-

Using the engine

-

The Graphite data table must contain the following fields at minimum:

-
    -
  • Path – The metric name (Graphite sensor).
    • -
  • Time – The time for measuring the metric.
    • -
  • Value – The value of the metric at the time set in Time.
    • -
  • Version – Determines which value of the metric with the same Path and Time will remain in the database.
    • -
-

Rollup pattern:

-
pattern
-    regexp
-    function
-    age -> precision
-    ...
-pattern
-    ...
-default
-    function
-       age -> precision
-    ...
-
- - -

When processing a record, ClickHouse will check the rules in the patternclause. If the metric name matches the regexp, the rules from pattern are applied; otherwise, the rules from default are used.

-

Fields in the pattern.

-
    -
  • age – The minimum age of the data in seconds.
    • -
  • function – The name of the aggregating function to apply to data whose age falls within the range [age, age + precision].
    • -
  • precision– How precisely to define the age of the data in seconds.
    • -
  • regexp– A pattern for the metric name.
    • -
-

Example of settings:

-
<graphite_rollup>
-    <pattern>
-        <regexp>click_cost</regexp>
-        <function>any</function>
-        <retention>
-            <age>0</age>
-            <precision>5</precision>
-        </retention>
-        <retention>
-            <age>86400</age>
-            <precision>60</precision>
-        </retention>
-    </pattern>
-    <default>
-        <function>max</function>
-        <retention>
-            <age>0</age>
-            <precision>60</precision>
-        </retention>
-        <retention>
-            <age>3600</age>
-            <precision>300</precision>
-        </retention>
-        <retention>
-            <age>86400</age>
-            <precision>3600</precision>
-        </retention>
-    </default>
-</graphite_rollup>
-
- - -

-

Data replication

-

Replication is only supported for tables in the MergeTree family:

-
    -
  • ReplicatedMergeTree
    • -
  • ReplicatedSummingMergeTree
    • -
  • ReplicatedReplacingMergeTree
    • -
  • ReplicatedAggregatingMergeTree
    • -
  • ReplicatedCollapsingMergeTree
    • -
  • ReplicatedGraphiteMergeTree
    • -
-

Replication works at the level of an individual table, not the entire server. A server can store both replicated and non-replicated tables at the same time.

-

Replication does not depend on sharding. Each shard has its own independent replication.

-

Compressed data is replicated for INSERT and ALTER queries (see the description of the ALTER query).

-

CREATE, DROP, ATTACH, DETACH and RENAME queries are executed on a single server and are not replicated:

-
    -
  • The CREATE TABLE query creates a new replicatable table on the server where the query is run. If this table already exists on other servers, it adds a new replica.
    • -
  • The DROP TABLE query deletes the replica located on the server where the query is run.
    • -
  • The RENAME query renames the table on one of the replicas. In other words, replicated tables can have different names on different replicas.
    • -
-

To use replication, set the addresses of the ZooKeeper cluster in the config file. Example:

-
<zookeeper>
-    <node index="1">
-        <host>example1</host>
-        <port>2181</port>
-    </node>
-    <node index="2">
-        <host>example2</host>
-        <port>2181</port>
-    </node>
-    <node index="3">
-        <host>example3</host>
-        <port>2181</port>
-    </node>
-</zookeeper>
-
- - -

Use ZooKeeper version 3.4.5 or later.

-

You can specify any existing ZooKeeper cluster and the system will use a directory on it for its own data (the directory is specified when creating a replicatable table).

-

If ZooKeeper isn't set in the config file, you can't create replicated tables, and any existing replicated tables will be read-only.

-

ZooKeeper is not used in SELECT queries because replication does not affect the performance of SELECT and queries run just as fast as they do for non-replicated tables. When querying distributed replicated tables, ClickHouse behavior is controlled by the settings max_replica_delay_for_distributed_queries and fallback_to_stale_replicas_for_distributed_queries.

-

For each INSERT query, approximately ten entries are added to ZooKeeper through several transactions. (To be more precise, this is for each inserted block of data; an INSERT query contains one block or one block per max_insert_block_size = 1048576 rows.) This leads to slightly longer latencies for INSERT compared to non-replicated tables. But if you follow the recommendations to insert data in batches of no more than one INSERT per second, it doesn't create any problems. The entire ClickHouse cluster used for coordinating one ZooKeeper cluster has a total of several hundred INSERTs per second. The throughput on data inserts (the number of rows per second) is just as high as for non-replicated data.

-

For very large clusters, you can use different ZooKeeper clusters for different shards. However, this hasn't proven necessary on the Yandex.Metrica cluster (approximately 300 servers).

-

Replication is asynchronous and multi-master. INSERT queries (as well as ALTER) can be sent to any available server. Data is inserted on the server where the query is run, and then it is copied to the other servers. Because it is asynchronous, recently inserted data appears on the other replicas with some latency. If part of the replicas are not available, the data is written when they become available. If a replica is available, the latency is the amount of time it takes to transfer the block of compressed data over the network.

-

By default, an INSERT query waits for confirmation of writing the data from only one replica. If the data was successfully written to only one replica and the server with this replica ceases to exist, the stored data will be lost. Tp enable getting confirmation of data writes from multiple replicas, use the insert_quorum option.

-

Each block of data is written atomically. The INSERT query is divided into blocks up to max_insert_block_size = 1048576 rows. In other words, if the INSERT query has less than 1048576 rows, it is made atomically.

-

Data blocks are deduplicated. For multiple writes of the same data block (data blocks of the same size containing the same rows in the same order), the block is only written once. The reason for this is in case of network failures when the client application doesn't know if the data was written to the DB, so the INSERT query can simply be repeated. It doesn't matter which replica INSERTs were sent to with identical data. INSERTs are idempotent. Deduplication parameters are controlled by merge_tree server settings.

-

During replication, only the source data to insert is transferred over the network. Further data transformation (merging) is coordinated and performed on all the replicas in the same way. This minimizes network usage, which means that replication works well when replicas reside in different datacenters. (Note that duplicating data in different datacenters is the main goal of replication.)

-

You can have any number of replicas of the same data. Yandex.Metrica uses double replication in production. Each server uses RAID-5 or RAID-6, and RAID-10 in some cases. This is a relatively reliable and convenient solution.

-

The system monitors data synchronicity on replicas and is able to recover after a failure. Failover is automatic (for small differences in data) or semi-automatic (when data differs too much, which may indicate a configuration error).

-

-

Creating replicated tables

-

The Replicated prefix is added to the table engine name. For example:ReplicatedMergeTree.

-

Two parameters are also added in the beginning of the parameters list – the path to the table in ZooKeeper, and the replica name in ZooKeeper.

-

Example:

-
ReplicatedMergeTree('/clickhouse/tables/{layer}-{shard}/hits', '{replica}', EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID), EventTime), 8192)
-
- - -

As the example shows, these parameters can contain substitutions in curly brackets. The substituted values are taken from the 'macros' section of the config file. Example:

-
<macros>
-    <layer>05</layer>
-    <shard>02</shard>
-    <replica>example05-02-1.yandex.ru</replica>
-</macros>
-
- - -

The path to the table in ZooKeeper should be unique for each replicated table. Tables on different shards should have different paths. -In this case, the path consists of the following parts:

-

/clickhouse/tables/ is the common prefix. We recommend using exactly this one.

-

{layer}-{shard} is the shard identifier. In this example it consists of two parts, since the Yandex.Metrica cluster uses bi-level sharding. For most tasks, you can leave just the {shard} substitution, which will be expanded to the shard identifier.

-

hits is the name of the node for the table in ZooKeeper. It is a good idea to make it the same as the table name. It is defined explicitly, because in contrast to the table name, it doesn't change after a RENAME query.

-

The replica name identifies different replicas of the same table. You can use the server name for this, as in the example. The name only needs to be unique within each shard.

-

You can define the parameters explicitly instead of using substitutions. This might be convenient for testing and for configuring small clusters. However, you can't use distributed DDL queries (ON CLUSTER) in this case.

-

When working with large clusters, we recommend using substitutions because they reduce the probability of error.

-

Run the CREATE TABLE query on each replica. This query creates a new replicated table, or adds a new replica to an existing one.

-

If you add a new replica after the table already contains some data on other replicas, the data will be copied from the other replicas to the new one after running the query. In other words, the new replica syncs itself with the others.

-

To delete a replica, run DROP TABLE. However, only one replica is deleted – the one that resides on the server where you run the query.

-

Recovery after failures

-

If ZooKeeper is unavailable when a server starts, replicated tables switch to read-only mode. The system periodically attempts to connect to ZooKeeper.

-

If ZooKeeper is unavailable during an INSERT, or an error occurs when interacting with ZooKeeper, an exception is thrown.

-

After connecting to ZooKeeper, the system checks whether the set of data in the local file system matches the expected set of data (ZooKeeper stores this information). If there are minor inconsistencies, the system resolves them by syncing data with the replicas.

-

If the system detects broken data parts (with the wrong size of files) or unrecognized parts (parts written to the file system but not recorded in ZooKeeper), it moves them to the 'detached' subdirectory (they are not deleted). Any missing parts are copied from the replicas.

-

Note that ClickHouse does not perform any destructive actions such as automatically deleting a large amount of data.

-

When the server starts (or establishes a new session with ZooKeeper), it only checks the quantity and sizes of all files. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a SELECT query. The query throws an exception about a non-matching checksum or size of a compressed block. In this case, data parts are added to the verification queue and copied from the replicas if necessary.

-

If the local set of data differs too much from the expected one, a safety mechanism is triggered. The server enters this in the log and refuses to launch. The reason for this is that this case may indicate a configuration error, such as if a replica on a shard was accidentally configured like a replica on a different shard. However, the thresholds for this mechanism are set fairly low, and this situation might occur during normal failure recovery. In this case, data is restored semi-automatically - by "pushing a button".

-

To start recovery, create the node /path_to_table/replica_name/flags/force_restore_data in ZooKeeper with any content, or run the command to restore all replicated tables:

-
sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data
-
- - -

Then restart the server. On start, the server deletes these flags and starts recovery.

-

Recovery after complete data loss

-

If all data and metadata disappeared from one of the servers, follow these steps for recovery:

-
    -
  1. Install ClickHouse on the server. Define substitutions correctly in the config file that contains the shard identifier and replicas, if you use them.
    2. -
  2. If you had unreplicated tables that must be manually duplicated on the servers, copy their data from a replica (in the directory /var/lib/clickhouse/data/db_name/table_name/).
    3. -
  3. Copy table definitions located in /var/lib/clickhouse/metadata/ from a replica. If a shard or replica identifier is defined explicitly in the table definitions, correct it so that it corresponds to this replica. (Alternatively, start the server and make all the ATTACH TABLE queries that should have been in the .sql files in /var/lib/clickhouse/metadata/.)
    4. -
  4. To start recovery, create the ZooKeeper node /path_to_table/replica_name/flags/force_restore_data with any content, or run the command to restore all replicated tables: sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data
    5. -
-

Then start the server (restart, if it is already running). Data will be downloaded from replicas.

-

An alternative recovery option is to delete information about the lost replica from ZooKeeper (/path_to_table/replica_name), then create the replica again as described in "Creating replicatable tables".

-

There is no restriction on network bandwidth during recovery. Keep this in mind if you are restoring many replicas at once.

-

Converting from MergeTree to ReplicatedMergeTree

-

We use the term MergeTree to refer to all table engines in the MergeTree family, the same as for ReplicatedMergeTree.

-

If you had a MergeTree table that was manually replicated, you can convert it to a replicatable table. You might need to do this if you have already collected a large amount of data in a MergeTree table and now you want to enable replication.

-

If the data differs on various replicas, first sync it, or delete this data on all the replicas except one.

-

Rename the existing MergeTree table, then create a ReplicatedMergeTree table with the old name. -Move the data from the old table to the 'detached' subdirectory inside the directory with the new table data (/var/lib/clickhouse/data/db_name/table_name/). -Then run ALTER TABLE ATTACH PARTITION on one of the replicas to add these data parts to the working set.

-

Converting from ReplicatedMergeTree to MergeTree

-

Create a MergeTree table with a different name. Move all the data from the directory with the ReplicatedMergeTree table data to the new table's data directory. Then delete the ReplicatedMergeTree table and restart the server.

-

If you want to get rid of a ReplicatedMergeTree table without launching the server:

-
    -
  • Delete the corresponding .sql file in the metadata directory (/var/lib/clickhouse/metadata/).
    • -
  • Delete the corresponding path in ZooKeeper (/path_to_table/replica_name).
    • -
-

After this, you can launch the server, create a MergeTree table, move the data to its directory, and then restart the server.

-

Recovery when metadata in the ZooKeeper cluster is lost or damaged

-

If the data in ZooKeeper was lost or damaged, you can save data by moving it to an unreplicated table as described above.

-

If exactly the same parts exist on the other replicas, they are added to the working set on them. If not, the parts are downloaded from the replica that has them.

-

-

Distributed

-

The Distributed engine does not store data itself, but allows distributed query processing on multiple servers. -Reading is automatically parallelized. During a read, the table indexes on remote servers are used, if there are any. -The Distributed engine accepts parameters: the cluster name in the server's config file, the name of a remote database, the name of a remote table, and (optionally) a sharding key. -Example:

-
Distributed(logs, default, hits[, sharding_key])
-
- - -

Data will be read from all servers in the 'logs' cluster, from the default.hits table located on every server in the cluster. -Data is not only read, but is partially processed on the remote servers (to the extent that this is possible). -For example, for a query with GROUP BY, data will be aggregated on remote servers, and the intermediate states of aggregate functions will be sent to the requestor server. Then data will be further aggregated.

-

Instead of the database name, you can use a constant expression that returns a string. For example: currentDatabase().

-

logs – The cluster name in the server's config file.

-

Clusters are set like this:

-
<remote_servers>
-    <logs>
-        <shard>
-            <!-- Optional. Shard weight when writing data. Default: 1. -->
-            <weight>1</weight>
-            <!-- Optional. Whether to write data to just one of the replicas. Default: false (write data to all replicas). -->
-            <internal_replication>false</internal_replication>
-            <replica>
-                <host>example01-01-1</host>
-                <port>9000</port>
-            </replica>
-            <replica>
-                <host>example01-01-2</host>
-                <port>9000</port>
-            </replica>
-        </shard>
-        <shard>
-            <weight>2</weight>
-            <internal_replication>false</internal_replication>
-            <replica>
-                <host>example01-02-1</host>
-                <port>9000</port>
-            </replica>
-            <replica>
-                <host>example01-02-2</host>
-                <port>9000</port>
-            </replica>
-        </shard>
-    </logs>
-</remote_servers>
-
- - -

Here a cluster is defined with the name 'logs' that consists of two shards, each of which contains two replicas. -Shards refer to the servers that contain different parts of the data (in order to read all the data, you must access all the shards). -Replicas are duplicating servers (in order to read all the data, you can access the data on any one of the replicas).

-

The parameters host, port, and optionally user and password are specified for each server:

-

: - host – The address of the remote server. You can use either the domain or the IPv4 or IPv6 address. If you specify the domain, the server makes a DNS request when it starts, and the result is stored as long as the server is running. If the DNS request fails, the server doesn't start. If you change the DNS record, restart the server. -- port– The TCP port for messenger activity ('tcp_port' in the config, usually set to 9000). Do not confuse it with http_port. -- user– Name of the user for connecting to a remote server. Default value: default. This user must have access to connect to the specified server. Access is configured in the users.xml file. For more information, see the section "Access rights". -- password – The password for connecting to a remote server (not masked). Default value: empty string.

-

When specifying replicas, one of the available replicas will be selected for each of the shards when reading. You can configure the algorithm for load balancing (the preference for which replica to access) – see the 'load_balancing' setting. -If the connection with the server is not established, there will be an attempt to connect with a short timeout. If the connection failed, the next replica will be selected, and so on for all the replicas. If the connection attempt failed for all the replicas, the attempt will be repeated the same way, several times. -This works in favor of resiliency, but does not provide complete fault tolerance: a remote server might accept the connection, but might not work, or work poorly.

-

You can specify just one of the shards (in this case, query processing should be called remote, rather than distributed) or up to any number of shards. In each shard, you can specify from one to any number of replicas. You can specify a different number of replicas for each shard.

-

You can specify as many clusters as you wish in the configuration.

-

To view your clusters, use the 'system.clusters' table.

-

The Distributed engine allows working with a cluster like a local server. However, the cluster is inextensible: you must write its configuration in the server config file (even better, for all the cluster's servers).

-

There is no support for Distributed tables that look at other Distributed tables (except in cases when a Distributed table only has one shard). As an alternative, make the Distributed table look at the "final" tables.

-

The Distributed engine requires writing clusters to the config file. Clusters from the config file are updated on the fly, without restarting the server. If you need to send a query to an unknown set of shards and replicas each time, you don't need to create a Distributed table – use the 'remote' table function instead. See the section "Table functions".

-

There are two methods for writing data to a cluster:

-

First, you can define which servers to write which data to, and perform the write directly on each shard. In other words, perform INSERT in the tables that the distributed table "looks at". -This is the most flexible solution – you can use any sharding scheme, which could be non-trivial due to the requirements of the subject area. -This is also the most optimal solution, since data can be written to different shards completely independently.

-

Second, you can perform INSERT in a Distributed table. In this case, the table will distribute the inserted data across servers itself. -In order to write to a Distributed table, it must have a sharding key set (the last parameter). In addition, if there is only one shard, the write operation works without specifying the sharding key, since it doesn't have any meaning in this case.

-

Each shard can have a weight defined in the config file. By default, the weight is equal to one. Data is distributed across shards in the amount proportional to the shard weight. For example, if there are two shards and the first has a weight of 9 while the second has a weight of 10, the first will be sent 9 / 19 parts of the rows, and the second will be sent 10 / 19.

-

Each shard can have the 'internal_replication' parameter defined in the config file.

-

If this parameter is set to 'true', the write operation selects the first healthy replica and writes data to it. Use this alternative if the Distributed table "looks at" replicated tables. In other words, if the table where data will be written is going to replicate them itself.

-

If it is set to 'false' (the default), data is written to all replicas. In essence, this means that the Distributed table replicates data itself. This is worse than using replicated tables, because the consistency of replicas is not checked, and over time they will contain slightly different data.

-

To select the shard that a row of data is sent to, the sharding expression is analyzed, and its remainder is taken from dividing it by the total weight of the shards. The row is sent to the shard that corresponds to the half-interval of the remainders from 'prev_weight' to 'prev_weights + weight', where 'prev_weights' is the total weight of the shards with the smallest number, and 'weight' is the weight of this shard. For example, if there are two shards, and the first has a weight of 9 while the second has a weight of 10, the row will be sent to the first shard for the remainders from the range [0, 9), and to the second for the remainders from the range [9, 19).

-

The sharding expression can be any expression from constants and table columns that returns an integer. For example, you can use the expression 'rand()' for random distribution of data, or 'UserID' for distribution by the remainder from dividing the user's ID (then the data of a single user will reside on a single shard, which simplifies running IN and JOIN by users). If one of the columns is not distributed evenly enough, you can wrap it in a hash function: intHash64(UserID).

-

A simple remainder from division is a limited solution for sharding and isn't always appropriate. It works for medium and large volumes of data (dozens of servers), but not for very large volumes of data (hundreds of servers or more). In the latter case, use the sharding scheme required by the subject area, rather than using entries in Distributed tables.

-

SELECT queries are sent to all the shards, and work regardless of how data is distributed across the shards (they can be distributed completely randomly). When you add a new shard, you don't have to transfer the old data to it. You can write new data with a heavier weight – the data will be distributed slightly unevenly, but queries will work correctly and efficiently.

-

You should be concerned about the sharding scheme in the following cases:

-
    -
  • Queries are used that require joining data (IN or JOIN) by a specific key. If data is sharded by this key, you can use local IN or JOIN instead of GLOBAL IN or GLOBAL JOIN, which is much more efficient.
    • -
  • A large number of servers is used (hundreds or more) with a large number of small queries (queries of individual clients - websites, advertisers, or partners). In order for the small queries to not affect the entire cluster, it makes sense to locate data for a single client on a single shard. Alternatively, as we've done in Yandex.Metrica, you can set up bi-level sharding: divide the entire cluster into "layers", where a layer may consist of multiple shards. Data for a single client is located on a single layer, but shards can be added to a layer as necessary, and data is randomly distributed within them. Distributed tables are created for each layer, and a single shared distributed table is created for global queries.
    • -
-

Data is written asynchronously. For an INSERT to a Distributed table, the data block is just written to the local file system. The data is sent to the remote servers in the background as soon as possible. You should check whether data is sent successfully by checking the list of files (data waiting to be sent) in the table directory: /var/lib/clickhouse/data/database/table/.

-

If the server ceased to exist or had a rough restart (for example, after a device failure) after an INSERT to a Distributed table, the inserted data might be lost. If a damaged data part is detected in the table directory, it is transferred to the 'broken' subdirectory and no longer used.

-

When the max_parallel_replicas option is enabled, query processing is parallelized across all replicas within a single shard. For more information, see the section "Settings, max_parallel_replicas".

-

-

Dictionary

-

The Dictionary engine displays the dictionary data as a ClickHouse table.

-

As an example, consider a dictionary of products with the following configuration:

-
<dictionaries>
-<dictionary>
-        <name>products</name>
-        <source>
-            <odbc>
-                <table>products</table>
-                <connection_string>DSN=some-db-server</connection_string>
-            </odbc>
-        </source>
-        <lifetime>
-            <min>300</min>
-            <max>360</max>
-        </lifetime>
-        <layout>
-            <flat/>
-        </layout>
-        <structure>
-            <id>
-                <name>product_id</name>
-            </id>
-            <attribute>
-                <name>title</name>
-                <type>String</type>
-                <null_value></null_value>
-            </attribute>
-        </structure>
-</dictionary>
-</dictionaries>
-
- - -

Query the dictionary data:

-
select name, type, key, attribute.names, attribute.types, bytes_allocated, element_count,source from system.dictionaries where name = 'products';                     
-
-SELECT
-    name,
-    type,
-    key,
-    attribute.names,
-    attribute.types,
-    bytes_allocated,
-    element_count,
-    source
-FROM system.dictionaries
-WHERE name = 'products'
-
- - -
┌─name─────┬─type─┬─key────┬─attribute.names─┬─attribute.types─┬─bytes_allocated─┬─element_count─┬─source──────────┐
-│ products │ Flat │ UInt64 │ ['title']       │ ['String']      │        23065376 │        175032 │ ODBC: .products │
-└──────────┴──────┴────────┴─────────────────┴─────────────────┴─────────────────┴───────────────┴─────────────────┘
-
- - -

You can use the dictGet* function to get the dictionary data in this format.

-

This view isn't helpful when you need to get raw data, or when performing a JOIN operation. For these cases, you can use the Dictionary engine, which displays the dictionary data in a table.

-

Syntax:

-
CREATE TABLE %table_name% (%fields%) engine = Dictionary(%dictionary_name%)`
-
- - -

Usage example:

-
create table products (product_id UInt64, title String) Engine = Dictionary(products);
-
-CREATE TABLE products
-(
-    product_id UInt64,
-    title String,
-)
-ENGINE = Dictionary(products)
-
- - -
Ok.
-
-0 rows in set. Elapsed: 0.004 sec.
-
- - -

Take a look at what's in the table.

-
select * from products limit 1;
-
-SELECT *
-FROM products
-LIMIT 1
-
- - -
┌────product_id─┬─title───────────┐
-│        152689 │ Some item       │
-└───────────────┴─────────────────┘
-
-1 rows in set. Elapsed: 0.006 sec.
-
- - -

Merge

-

The Merge engine (not to be confused with MergeTree) does not store data itself, but allows reading from any number of other tables simultaneously. -Reading is automatically parallelized. Writing to a table is not supported. When reading, the indexes of tables that are actually being read are used, if they exist. -The Merge engine accepts parameters: the database name and a regular expression for tables.

-

Example:

-
Merge(hits, '^WatchLog')
-
- - -

Data will be read from the tables in the 'hits' database that have names that match the regular expression '^WatchLog'.

-

Instead of the database name, you can use a constant expression that returns a string. For example, currentDatabase().

-

Regular expressions — re2 (supports a subset of PCRE), case-sensitive. -See the notes about escaping symbols in regular expressions in the "match" section.

-

When selecting tables to read, the Merge table itself will not be selected, even if it matches the regex. This is to avoid loops. -It is possible to create two Merge tables that will endlessly try to read each others' data, but this is not a good idea.

-

The typical way to use the Merge engine is for working with a large number of TinyLog tables as if with a single table.

-

Virtual columns

-

Virtual columns are columns that are provided by the table engine, regardless of the table definition. In other words, these columns are not specified in CREATE TABLE, but they are accessible for SELECT.

-

Virtual columns differ from normal columns in the following ways:

-
    -
  • They are not specified in table definitions.
    • -
  • Data can't be added to them with INSERT.
    • -
  • When using INSERT without specifying the list of columns, virtual columns are ignored.
    • -
  • They are not selected when using the asterisk (SELECT *).
    • -
  • Virtual columns are not shown in SHOW CREATE TABLE and DESC TABLE queries.
    • -
-

A Merge type table contains a virtual _table column with the String type. (If the table already has a _table column, the virtual column is named _table1, and if it already has _table1, it is named _table2, and so on.) It contains the name of the table that data was read from.

-

If the WHERE or PREWHERE clause contains conditions for the '_table' column that do not depend on other table columns (as one of the conjunction elements, or as an entire expression), these conditions are used as an index. The conditions are performed on a data set of table names to read data from, and the read operation will be performed from only those tables that the condition was triggered on.

-

Buffer

-

Buffers the data to write in RAM, periodically flushing it to another table. During the read operation, data is read from the buffer and the other table simultaneously.

-
Buffer(database, table, num_layers, min_time, max_time, min_rows, max_rows, min_bytes, max_bytes)
-
- - -

Engine parameters:database, table – The table to flush data to. Instead of the database name, you can use a constant expression that returns a string.num_layers – Parallelism layer. Physically, the table will be represented as 'num_layers' of independent buffers. Recommended value: 16.min_time, max_time, min_rows, max_rows, min_bytes, and max_bytes are conditions for flushing data from the buffer.

-

Data is flushed from the buffer and written to the destination table if all the 'min' conditions or at least one 'max' condition are met.min_time, max_time – Condition for the time in seconds from the moment of the first write to the buffer.min_rows, max_rows – Condition for the number of rows in the buffer.min_bytes, max_bytes – Condition for the number of bytes in the buffer.

-

During the write operation, data is inserted to a 'num_layers' number of random buffers. Or, if the data part to insert is large enough (greater than 'max_rows' or 'max_bytes'), it is written directly to the destination table, omitting the buffer.

-

The conditions for flushing the data are calculated separately for each of the 'num_layers' buffers. For example, if num_layers = 16 and max_bytes = 100000000, the maximum RAM consumption is 1.6 GB.

-

Example:

-
CREATE TABLE merge.hits_buffer AS merge.hits ENGINE = Buffer(merge, hits, 16, 10, 100, 10000, 1000000, 10000000, 100000000)
-
- - -

Creating a 'merge.hits_buffer' table with the same structure as 'merge.hits' and using the Buffer engine. When writing to this table, data is buffered in RAM and later written to the 'merge.hits' table. 16 buffers are created. The data in each of them is flushed if either 100 seconds have passed, or one million rows have been written, or 100 MB of data have been written; or if simultaneously 10 seconds have passed and 10,000 rows and 10 MB of data have been written. For example, if just one row has been written, after 100 seconds it will be flushed, no matter what. But if many rows have been written, the data will be flushed sooner.

-

When the server is stopped, with DROP TABLE or DETACH TABLE, buffer data is also flushed to the destination table.

-

You can set empty strings in single quotation marks for the database and table name. This indicates the absence of a destination table. In this case, when the data flush conditions are reached, the buffer is simply cleared. This may be useful for keeping a window of data in memory.

-

When reading from a Buffer table, data is processed both from the buffer and from the destination table (if there is one). -Note that the Buffer tables does not support an index. In other words, data in the buffer is fully scanned, which might be slow for large buffers. (For data in a subordinate table, the index that it supports will be used.)

-

If the set of columns in the Buffer table doesn't match the set of columns in a subordinate table, a subset of columns that exist in both tables is inserted.

-

If the types don't match for one of the columns in the Buffer table and a subordinate table, an error message is entered in the server log and the buffer is cleared. -The same thing happens if the subordinate table doesn't exist when the buffer is flushed.

-

If you need to run ALTER for a subordinate table and the Buffer table, we recommend first deleting the Buffer table, running ALTER for the subordinate table, then creating the Buffer table again.

-

If the server is restarted abnormally, the data in the buffer is lost.

-

PREWHERE, FINAL and SAMPLE do not work correctly for Buffer tables. These conditions are passed to the destination table, but are not used for processing data in the buffer. Because of this, we recommend only using the Buffer table for writing, while reading from the destination table.

-

When adding data to a Buffer, one of the buffers is locked. This causes delays if a read operation is simultaneously being performed from the table.

-

Data that is inserted to a Buffer table may end up in the subordinate table in a different order and in different blocks. Because of this, a Buffer table is difficult to use for writing to a CollapsingMergeTree correctly. To avoid problems, you can set 'num_layers' to 1.

-

If the destination table is replicated, some expected characteristics of replicated tables are lost when writing to a Buffer table. The random changes to the order of rows and sizes of data parts cause data deduplication to quit working, which means it is not possible to have a reliable 'exactly once' write to replicated tables.

-

Due to these disadvantages, we can only recommend using a Buffer table in rare cases.

-

A Buffer table is used when too many INSERTs are received from a large number of servers over a unit of time and data can't be buffered before insertion, which means the INSERTs can't run fast enough.

-

Note that it doesn't make sense to insert data one row at a time, even for Buffer tables. This will only produce a speed of a few thousand rows per second, while inserting larger blocks of data can produce over a million rows per second (see the section "Performance").

-

File(InputFormat)

-

The data source is a file that stores data in one of the supported input formats (TabSeparated, Native, etc.).

-

Null

-

When writing to a Null table, data is ignored. When reading from a Null table, the response is empty.

-

However, you can create a materialized view on a Null table. So the data written to the table will end up in the view.

-

Set

-

A data set that is always in RAM. It is intended for use on the right side of the IN operator (see the section "IN operators").

-

You can use INSERT to insert data in the table. New elements will be added to the data set, while duplicates will be ignored. -But you can't perform SELECT from the table. The only way to retrieve data is by using it in the right half of the IN operator.

-

Data is always located in RAM. For INSERT, the blocks of inserted data are also written to the directory of tables on the disk. When starting the server, this data is loaded to RAM. In other words, after restarting, the data remains in place.

-

For a rough server restart, the block of data on the disk might be lost or damaged. In the latter case, you may need to manually delete the file with damaged data.

-

Join

-

A prepared data structure for JOIN that is always located in RAM.

-
Join(ANY|ALL, LEFT|INNER, k1[, k2, ...])
-
- - -

Engine parameters: ANY|ALL – strictness; LEFT|INNER – type. -These parameters are set without quotes and must match the JOIN that the table will be used for. k1, k2, ... are the key columns from the USING clause that the join will be made on.

-

The table can't be used for GLOBAL JOINs.

-

You can use INSERT to add data to the table, similar to the Set engine. For ANY, data for duplicated keys will be ignored. For ALL, it will be counted. You can't perform SELECT directly from the table. The only way to retrieve data is to use it as the "right-hand" table for JOIN.

-

Storing data on the disk is the same as for the Set engine.

-

View

-

Used for implementing views (for more information, see the CREATE VIEW query). It does not store data, but only stores the specified SELECT query. When reading from a table, it runs this query (and deletes all unnecessary columns from the query).

-

MaterializedView

-

Used for implementing materialized views (for more information, see the CREATE TABLE) query. For storing data, it uses a different engine that was specified when creating the view. When reading from a table, it just uses this engine.

-

Kafka

-

This engine works with Apache Kafka.

-

Kafka lets you:

-
    -
  • Publish or subscribe to data flows.
    • -
  • Organize fault-tolerant storage.
    • -
  • Process streams as they become available.
    • -
-
Kafka(broker_list, topic_list, group_name, format[, schema, num_consumers])
-
- - -

Parameters:

-
    -
  • broker_list – A comma-separated list of brokers (localhost:9092).
    • -
  • topic_list – A list of Kafka topics (my_topic).
    • -
  • group_name – A group of Kafka consumers (group1). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere.
    • -
  • --format – Message format. Uses the same notation as the SQL FORMAT function, such as JSONEachRow. For more information, see the "Formats" section.
    • -
  • schema – An optional parameter that must be used if the format requires a schema definition. For example, Cap'n Proto requires the path to the schema file and the name of the root schema.capnp:Message object.
    • -
  • num_consumers – The number of consumers per table. Default: 1. Specify more consumers if the throughput of one consumer is insufficient. The total number of consumers should not exceed the number of partitions in the topic, since only one consumer can be assigned per partition.
    • -
-

Example:

-
  CREATE TABLE queue (
-    timestamp UInt64,
-    level String,
-    message String
-  ) ENGINE = Kafka('localhost:9092', 'topic', 'group1', 'JSONEachRow');
-
-  SELECT * FROM queue LIMIT 5;
-
- - -

The delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name.

-

Groups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. Read more about this at http://kafka.apache.org/intro.

-

SELECT is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. To do this:

-
    -
  1. Use the engine to create a Kafka consumer and consider it a data stream.
    2. -
  2. Create a table with the desired structure.
    3. -
  3. Create a materialized view that converts data from the engine and puts it into a previously created table.
    4. -
-

When the MATERIALIZED VIEW joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using SELECT

-

Example:

-
  CREATE TABLE queue (
-    timestamp UInt64,
-    level String,
-    message String
-  ) ENGINE = Kafka('localhost:9092', 'topic', 'group1', 'JSONEachRow');
-
-  CREATE TABLE daily (
-    day Date,
-    level String,
-    total UInt64
-  ) ENGINE = SummingMergeTree(day, (day, level), 8192);
-
-  CREATE MATERIALIZED VIEW consumer TO daily
-    AS SELECT toDate(toDateTime(timestamp)) AS day, level, count() as total
-    FROM queue GROUP BY day, level;
-
-  SELECT level, sum(total) FROM daily GROUP BY level;
-
- - -

To improve performance, received messages are grouped into blocks the size of max_insert_block_size. If the block wasn't formed within stream_flush_interval_ms milliseconds, the data will be flushed to the table regardless of the completeness of the block.

-

To stop receiving topic data or to change the conversion logic, detach the materialized view:

-
  DETACH TABLE consumer;
-  ATTACH MATERIALIZED VIEW consumer;
-
- - -

If you want to change the target table by using ALTERmaterialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view.

-

Configuration

-

Similar to GraphiteMergeTree, the Kafka engine supports extended configuration using the ClickHouse config file. There are two configuration keys that you can use: global (kafka) and topic-level (kafka_topic_*). The global configuration is applied first, and the topic-level configuration is second (if it exists).

-
  <!--  Global configuration options for all tables of Kafka engine type -->
-  <kafka>
-    <debug>cgrp</debug>
-    <auto_offset_reset>smallest</auto_offset_reset>
-  </kafka>
-
-  <!-- Configuration specific for topic "logs" -->
-  <kafka_topic_logs>
-    <retry_backoff_ms>250</retry_backoff_ms>
-    <fetch_min_bytes>100000</fetch_min_bytes>
-  </kafka_topic_logs>
-
- - -

For a list of possible configuration options, see the librdkafka configuration reference. Use the underscore (_) instead of a dot in the ClickHouse configuration. For example, check.crcs=true will be <check_crcs>true</check_crcs>.

-

-

MySQL

-

The MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server.

-

The engine takes 4 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example:

-
MySQL('host:port', 'database', 'table', 'user', 'password');
-
- - -

At this time, simple WHERE clauses such as =, !=, >, >=, <, <= are executed on the MySQL server.

-

The rest of the conditions and the LIMIT sampling constraint are executed in ClickHouse only after the query to MySQL finishes.

-

External data for query processing

-

ClickHouse allows sending a server the data that is needed for processing a query, together with a SELECT query. This data is put in a temporary table (see the section "Temporary tables") and can be used in the query (for example, in IN operators).

-

For example, if you have a text file with important user identifiers, you can upload it to the server along with a query that uses filtration by this list.

-

If you need to run more than one query with a large volume of external data, don't use this feature. It is better to upload the data to the DB ahead of time.

-

External data can be uploaded using the command-line client (in non-interactive mode), or using the HTTP interface.

-

In the command-line client, you can specify a parameters section in the format

-
--external --file=... [--name=...] [--format=...] [--types=...|--structure=...]
-
- - -

You may have multiple sections like this, for the number of tables being transmitted.

-

--external – Marks the beginning of a clause. ---file – Path to the file with the table dump, or -, which refers to stdin. -Only a single table can be retrieved from stdin.

-

The following parameters are optional: --name– Name of the table. If omitted, _data is used. ---format – Data format in the file. If omitted, TabSeparated is used.

-

One of the following parameters is required:--types – A list of comma-separated column types. For example: UInt64,String. The columns will be named _1, _2, ... ---structure– The table structure in the formatUserID UInt64, URL String. Defines the column names and types.

-

The files specified in 'file' will be parsed by the format specified in 'format', using the data types specified in 'types' or 'structure'. The table will be uploaded to the server and accessible there as a temporary table with the name in 'name'.

-

Examples:

-
echo -ne "1\n2\n3\n" | clickhouse-client --query="SELECT count() FROM test.visits WHERE TraficSourceID IN _data" --external --file=- --types=Int8
-849897
-cat /etc/passwd | sed 's/:/\t/g' | clickhouse-client --query="SELECT shell, count() AS c FROM passwd GROUP BY shell ORDER BY c DESC" --external --file=- --name=passwd --structure='login String, unused String, uid UInt16, gid UInt16, comment String, home String, shell String'
-/bin/sh 20
-/bin/false      5
-/bin/bash       4
-/usr/sbin/nologin       1
-/bin/sync       1
-
- - -

When using the HTTP interface, external data is passed in the multipart/form-data format. Each table is transmitted as a separate file. The table name is taken from the file name. The 'query_string' is passed the parameters 'name_format', 'name_types', and 'name_structure', where 'name' is the name of the table that these parameters correspond to. The meaning of the parameters is the same as when using the command-line client.

-

Example:

-
cat /etc/passwd | sed 's/:/\t/g' > passwd.tsv
-
-curl -F 'passwd=@passwd.tsv;' 'http://localhost:8123/?query=SELECT+shell,+count()+AS+c+FROM+passwd+GROUP+BY+shell+ORDER+BY+c+DESC&passwd_structure=login+String,+unused+String,+uid+UInt16,+gid+UInt16,+comment+String,+home+String,+shell+String'
-/bin/sh 20
-/bin/false      5
-/bin/bash       4
-/usr/sbin/nologin       1
-/bin/sync       1
-
- - -

For distributed query processing, the temporary tables are sent to all the remote servers.

-

System tables

-

System tables are used for implementing part of the system's functionality, and for providing access to information about how the system is working. -You can't delete a system table (but you can perform DETACH). -System tables don't have files with data on the disk or files with metadata. The server creates all the system tables when it starts. -System tables are read-only. -They are located in the 'system' database.

-

system.one

-

This table contains a single row with a single 'dummy' UInt8 column containing the value 0. -This table is used if a SELECT query doesn't specify the FROM clause. -This is similar to the DUAL table found in other DBMSs.

-

system.numbers

-

This table contains a single UInt64 column named 'number' that contains almost all the natural numbers starting from zero. -You can use this table for tests, or if you need to do a brute force search. -Reads from this table are not parallelized.

-

system.numbers_mt

-

The same as 'system.numbers' but reads are parallelized. The numbers can be returned in any order. -Used for tests.

-

system.databases

-

This table contains a single String column called 'name' – the name of a database. -Each database that the server knows about has a corresponding entry in the table. -This system table is used for implementing the SHOW DATABASES query.

-

system.tables

-

This table contains the String columns 'database', 'name', and 'engine'. -The table also contains three virtual columns: metadata_modification_time (DateTime type), create_table_query, and engine_full (String type). -Each table that the server knows about is entered in the 'system.tables' table. -This system table is used for implementing SHOW TABLES queries.

-

system.columns

-

Contains information about the columns in all tables. -You can use this table to get information similar to DESCRIBE TABLE, but for multiple tables at once.

-
database String           - Name of the database the table is located in.
-table String              - Table name.
-name String               - Column name.
-type String               - Column type.
-default_type String       - Expression type (DEFAULT, MATERIALIZED, ALIAS) for the default value, or an empty string if it is not defined.
-default_expression String - Expression for the default value, or an empty string if it is not defined.
-
- - -

system.parts

-

Contains information about parts of a table in the MergeTree family.

-

Each row describes one part of the data.

-

Columns:

-
    -
  • partition (String) – The partition name. YYYYMM format. To learn what a partition is, see the description of the ALTER query.
    • -
  • name (String) – Name of the data part.
    • -
  • active (UInt8) – Indicates whether the part is active. If a part is active, it is used in a table; otherwise, it will be deleted. Inactive data parts remain after merging.
    • -
  • marks (UInt64) – The number of marks. To get the approximate number of rows in a data part, multiply marks by the index granularity (usually 8192).
    • -
  • marks_size (UInt64) – The size of the file with marks.
    • -
  • rows (UInt64) – The number of rows.
    • -
  • bytes (UInt64) – The number of bytes when compressed.
    • -
  • modification_time (DateTime) – The modification time of the directory with the data part. This usually corresponds to the time of data part creation.|
    • -
  • remove_time (DateTime) – The time when the data part became inactive.
    • -
  • refcount (UInt32) – The number of places where the data part is used. A value greater than 2 indicates that the data part is used in queries or merges.
    • -
  • min_date (Date) – The minimum value of the date key in the data part.
    • -
  • max_date (Date) – The maximum value of the date key in the data part.
    • -
  • min_block_number (UInt64) – The minimum number of data parts that make up the current part after merging.
    • -
  • max_block_number (UInt64) – The maximum number of data parts that make up the current part after merging.
    • -
  • level (UInt32) – Depth of the merge tree. If a merge was not performed, level=0.
    • -
  • primary_key_bytes_in_memory (UInt64) – The amount of memory (in bytes) used by primary key values.
    • -
  • primary_key_bytes_in_memory_allocated (UInt64) – The amount of memory (in bytes) reserved for primary key values.
    • -
  • database (String) – Name of the database.
    • -
  • table (String) – Name of the table.
    • -
  • engine (String) – Name of the table engine without parameters.
    • -
-

system.processes

-

This system table is used for implementing the SHOW PROCESSLIST query. -Columns:

-
user String              – Name of the user who made the request. For distributed query processing, this is the user who helped the requestor server send the query to this server, not the user who made the distributed request on the requestor server.
-
-address String           – The IP address that the query was made from. The same is true for distributed query processing.
-
-elapsed Float64          –  The time in seconds since request execution started.
-
-rows_read UInt64         – The number of rows read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.
-
-bytes_read UInt64        – The number of uncompressed bytes read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.
-
-UInt64 total_rows_approx – The approximate total number of rows that must be read. For distributed processing, on the requestor server, this is the total for all remote servers. It can be updated during request processing, when new sources to process become known.
-
-memory_usage UInt64 – Memory consumption by the query. It might not include some types of dedicated memory.
-
-query String – The query text. For INSERT, it doesn't include the data to insert.
-
-query_id – Query ID, if defined.
-
- - -

system.merges

-

Contains information about merges currently in process for tables in the MergeTree family.

-

Columns:

-
    -
  • database String — Name of the database the table is located in.
    • -
  • table String — Name of the table.
    • -
  • elapsed Float64 — Time in seconds since the merge started.
    • -
  • progress Float64 — Percent of progress made, from 0 to 1.
    • -
  • num_parts UInt64 — Number of parts to merge.
    • -
  • result_part_name String — Name of the part that will be formed as the result of the merge.
    • -
  • total_size_bytes_compressed UInt64 — Total size of compressed data in the parts being merged.
    • -
  • total_size_marks UInt64 — Total number of marks in the parts being merged.
    • -
  • bytes_read_uncompressed UInt64 — Amount of bytes read, decompressed.
    • -
  • rows_read UInt64 — Number of rows read.
    • -
  • bytes_written_uncompressed UInt64 — Amount of bytes written, uncompressed.
    • -
  • rows_written UInt64 — Number of rows written.
    • -
-

-

system.events

-

Contains information about the number of events that have occurred in the system. This is used for profiling and monitoring purposes. -Example: The number of processed SELECT queries. -Columns: 'event String' – the event name, and 'value UInt64' – the quantity.

-

-

system.metrics

-

-

system.asynchronous_metrics

-

Contain metrics used for profiling and monitoring. -They usually reflect the number of events currently in the system, or the total resources consumed by the system. -Example: The number of SELECT queries currently running; the amount of memory in use.system.asynchronous_metricsandsystem.metrics differ in their sets of metrics and how they are calculated.

-

system.replicas

-

Contains information and status for replicated tables residing on the local server. -This table can be used for monitoring. The table contains a row for every Replicated* table.

-

Example:

-
SELECT *
-FROM system.replicas
-WHERE table = 'visits'
-FORMAT Vertical
-
- - -
Row 1:
-──────
-database:           merge
-table:              visits
-engine:             ReplicatedCollapsingMergeTree
-is_leader:          1
-is_readonly:        0
-is_session_expired: 0
-future_parts:       1
-parts_to_check:     0
-zookeeper_path:     /clickhouse/tables/01-06/visits
-replica_name:       example01-06-1.yandex.ru
-replica_path:       /clickhouse/tables/01-06/visits/replicas/example01-06-1.yandex.ru
-columns_version:    9
-queue_size:         1
-inserts_in_queue:   0
-merges_in_queue:    1
-log_max_index:      596273
-log_pointer:        596274
-total_replicas:     2
-active_replicas:    2
-
- - -

Columns:

-
database:           database name
-table:              table name
-engine:             table engine name
-
-is_leader:          whether the replica is the leader
-
-Only one replica at a time can be the leader. The leader is responsible for selecting background merges to perform.
-Note that writes can be performed to any replica that is available and has a session in ZK, regardless of whether it is a leader.
-
-is_readonly:        Whether the replica is in read-only mode.
-This mode is turned on if the config doesn't have sections with ZK, if an unknown error occurred when reinitializing sessions in ZK, and during session reinitialization in ZK.
-
-is_session_expired: Whether the ZK session expired.
-Basically, the same thing as is_readonly.
-
-future_parts: The number of data parts that will appear as the result of INSERTs or merges that haven't been done yet. 
-
-parts_to_check: The number of data parts in the queue for verification.
-A part is put in the verification queue if there is suspicion that it might be damaged.
-
-zookeeper_path: The path to the table data in ZK. 
-replica_name: Name of the replica in ZK. Different replicas of the same table have different names. 
-replica_path: The path to the replica data in ZK. The same as concatenating zookeeper_path/replicas/replica_path.
-
-columns_version: Version number of the table structure.
-Indicates how many times ALTER was performed. If replicas have different versions, it means some replicas haven't made all of the ALTERs yet.
-
-queue_size:         Size of the queue for operations waiting to be performed.
-Operations include inserting blocks of data, merges, and certain other actions.
-Normally coincides with future_parts.
-
-inserts_in_queue: Number of inserts of blocks of data that need to be made.
-Insertions are usually replicated fairly quickly. If the number is high, something is wrong.
-
-merges_in_queue: The number of merges waiting to be made. 
-Sometimes merges are lengthy, so this value may be greater than zero for a long time.
-
-The next 4 columns have a non-null value only if the ZK session is active.
-
-log_max_index:     Maximum entry number in the log of general activity.
-log_pointer:        Maximum entry number in the log of general activity that the replica copied to its execution queue, plus one.
-If log_pointer is much smaller than log_max_index, something is wrong.
-
-total_replicas:     Total number of known replicas of this table.
-active_replicas:    Number of replicas of this table that have a ZK session (the number of active replicas).
-
- - -

If you request all the columns, the table may work a bit slowly, since several reads from ZK are made for each row. -If you don't request the last 4 columns (log_max_index, log_pointer, total_replicas, active_replicas), the table works quickly.

-

For example, you can check that everything is working correctly like this:

-
SELECT
-    database,
-    table,
-    is_leader,
-    is_readonly,
-    is_session_expired,
-    future_parts,
-    parts_to_check,
-    columns_version,
-    queue_size,
-    inserts_in_queue,
-    merges_in_queue,
-    log_max_index,
-    log_pointer,
-    total_replicas,
-    active_replicas
-FROM system.replicas
-WHERE
-       is_readonly
-    OR is_session_expired
-    OR future_parts > 20
-    OR parts_to_check > 10
-    OR queue_size > 20
-    OR inserts_in_queue > 10
-    OR log_max_index - log_pointer > 10
-    OR total_replicas < 2
-    OR active_replicas < total_replicas
-
- - -

If this query doesn't return anything, it means that everything is fine.

-

system.dictionaries

-

Contains information about external dictionaries.

-

Columns:

-
    -
  • name String – Dictionary name.
    • -
  • type String – Dictionary type: Flat, Hashed, Cache.
    • -
  • origin String – Path to the config file where the dictionary is described.
    • -
  • attribute.names Array(String) – Array of attribute names provided by the dictionary.
    • -
  • attribute.types Array(String) – Corresponding array of attribute types provided by the dictionary.
    • -
  • has_hierarchy UInt8 – Whether the dictionary is hierarchical.
    • -
  • bytes_allocated UInt64 – The amount of RAM used by the dictionary.
    • -
  • hit_rate Float64 – For cache dictionaries, the percent of usage for which the value was in the cache.
    • -
  • element_count UInt64 – The number of items stored in the dictionary.
    • -
  • load_factor Float64 – The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table).
    • -
  • creation_time DateTime – Time spent for the creation or last successful reload of the dictionary.
    • -
  • last_exception String – Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created.
    • -
  • source String – Text describing the data source for the dictionary.
    • -
-

Note that the amount of memory used by the dictionary is not proportional to the number of items stored in it. So for flat and cached dictionaries, all the memory cells are pre-assigned, regardless of how full the dictionary actually is.

-

system.clusters

-

Contains information about clusters available in the config file and the servers in them. -Columns:

-
cluster String      – Cluster name.
-shard_num UInt32    – Number of a shard in the cluster, starting from 1.
-shard_weight UInt32 – Relative weight of a shard when writing data.
-replica_num UInt32  – Number of a replica in the shard, starting from 1.
-host_name String    – Host name as specified in the config.
-host_address String – Host's IP address obtained from DNS.
-port UInt16         – The port used to access the server.
-user String         – The username to use for connecting to the server.
-
- - -

system.functions

-

Contains information about normal and aggregate functions.

-

Columns:

-
    -
  • name (String) – Function name.
    • -
  • is_aggregate (UInt8) – Whether it is an aggregate function.
    • -
-

system.settings

-

Contains information about settings that are currently in use. -I.e. used for executing the query you are using to read from the system.settings table).

-

Columns:

-
name String   – Setting name.
-value String  – Setting value.
-changed UInt8 - Whether the setting was explicitly defined in the config or explicitly changed.
-
- - -

Example:

-
SELECT *
-FROM system.settings
-WHERE changed
-
- - -
┌─name───────────────────┬─value───────┬─changed─┐
-│ max_threads            │ 8           │       1 │
-│ use_uncompressed_cache │ 0           │       1 │
-│ load_balancing         │ random      │       1 │
-│ max_memory_usage       │ 10000000000 │       1 │
-└────────────────────────┴─────────────┴─────────┘
-
- - -

system.zookeeper

-

Allows reading data from the ZooKeeper cluster defined in the config. -The query must have a 'path' equality condition in the WHERE clause. This is the path in ZooKeeper for the children that you want to get data for.

-

The query SELECT * FROM system.zookeeper WHERE path = '/clickhouse' outputs data for all children on the /clickhouse node. -To output data for all root nodes, write path = '/'. -If the path specified in 'path' doesn't exist, an exception will be thrown.

-

Columns:

-
    -
  • name String — Name of the node.
    • -
  • path String — Path to the node.
    • -
  • value String — Value of the node.
    • -
  • dataLength Int32 — Size of the value.
    • -
  • numChildren Int32 — Number of children.
    • -
  • czxid Int64 — ID of the transaction that created the node.
    • -
  • mzxid Int64 — ID of the transaction that last changed the node.
    • -
  • pzxid Int64 — ID of the transaction that last added or removed children.
    • -
  • ctime DateTime — Time of node creation.
    • -
  • mtime DateTime — Time of the last node modification.
    • -
  • version Int32 — Node version - the number of times the node was changed.
    • -
  • cversion Int32 — Number of added or removed children.
    • -
  • aversion Int32 — Number of changes to ACL.
    • -
  • ephemeralOwner Int64 — For ephemeral nodes, the ID of the session that owns this node.
    • -
-

Example:

-
SELECT *
-FROM system.zookeeper
-WHERE path = '/clickhouse/tables/01-08/visits/replicas'
-FORMAT Vertical
-
- - -
Row 1:
-──────
-name:           example01-08-1.yandex.ru
-value:
-czxid:          932998691229
-mzxid:          932998691229
-ctime:          2015-03-27 16:49:51
-mtime:          2015-03-27 16:49:51
-version:        0
-cversion:       47
-aversion:       0
-ephemeralOwner: 0
-dataLength:     0
-numChildren:    7
-pzxid:          987021031383
-path:           /clickhouse/tables/01-08/visits/replicas
-
-Row 2:
-──────
-name:           example01-08-2.yandex.ru
-value:
-czxid:          933002738135
-mzxid:          933002738135
-ctime:          2015-03-27 16:57:01
-mtime:          2015-03-27 16:57:01
-version:        0
-cversion:       37
-aversion:       0
-ephemeralOwner: 0
-dataLength:     0
-numChildren:    7
-pzxid:          987021252247
-path:           /clickhouse/tables/01-08/visits/replicas
-
- - -

Table functions

-

Table functions can be specified in the FROM clause instead of the database and table names. -Table functions can only be used if 'readonly' is not set. -Table functions aren't related to other functions.

-

-

remote

-

Allows you to access remote servers without creating a Distributed table.

-

Signatures:

-
remote('addresses_expr', db, table[, 'user'[, 'password']])
-remote('addresses_expr', db.table[, 'user'[, 'password']])
-
- - -

addresses_expr – An expression that generates addresses of remote servers. This may be just one server address. The server address is host:port, or just host. The host can be specified as the server name, or as the IPv4 or IPv6 address. An IPv6 address is specified in square brackets. The port is the TCP port on the remote server. If the port is omitted, it uses tcp_port from the server's config file (by default, 9000).

-
- -The port is required for an IPv6 address. - -
- -

Examples:

-
example01-01-1
-example01-01-1:9000
-localhost
-127.0.0.1
-[::]:9000
-[2a02:6b8:0:1111::11]:9000
-
- - -

Multiple addresses can be comma-separated. In this case, ClickHouse will use distributed processing, so it will send the query to all specified addresses (like to shards with different data).

-

Example:

-
example01-01-1,example01-02-1
-
- - -

Part of the expression can be specified in curly brackets. The previous example can be written as follows:

-
example01-0{1,2}-1
-
- - -

Curly brackets can contain a range of numbers separated by two dots (non-negative integers). In this case, the range is expanded to a set of values that generate shard addresses. If the first number starts with zero, the values are formed with the same zero alignment. The previous example can be written as follows:

-
example01-{01..02}-1
-
- - -

If you have multiple pairs of curly brackets, it generates the direct product of the corresponding sets.

-

Addresses and parts of addresses in curly brackets can be separated by the pipe symbol (|). In this case, the corresponding sets of addresses are interpreted as replicas, and the query will be sent to the first healthy replica. However, the replicas are iterated in the order currently set in the load_balancing setting.

-

Example:

-
example01-{01..02}-{1|2}
-
- - -

This example specifies two shards that each have two replicas.

-

The number of addresses generated is limited by a constant. Right now this is 1000 addresses.

-

Using the remote table function is less optimal than creating a Distributed table, because in this case, the server connection is re-established for every request. In addition, if host names are set, the names are resolved, and errors are not counted when working with various replicas. When processing a large number of queries, always create the Distributed table ahead of time, and don't use the remote table function.

-

The remote table function can be useful in the following cases:

-
    -
  • Accessing a specific server for data comparison, debugging, and testing.
    • -
  • Queries between various ClickHouse clusters for research purposes.
    • -
  • Infrequent distributed requests that are made manually.
    • -
  • Distributed requests where the set of servers is re-defined each time.
    • -
-

If the user is not specified, default is used. -If the password is not specified, an empty password is used.

-

merge

-

merge(db_name, 'tables_regexp') – Creates a temporary Merge table. For more information, see the section "Table engines, Merge".

-

The table structure is taken from the first table encountered that matches the regular expression.

-

numbers

-

numbers(N) – Returns a table with the single 'number' column (UInt64) that contains integers from 0 to N-1.

-

Similar to the system.numbers table, it can be used for testing and generating successive values.

-

The following two queries are equivalent:

-
SELECT * FROM numbers(10);
-SELECT * FROM system.numbers LIMIT 10;
-
- - -

Examples:

-
-- Generate a sequence of dates from 2010-01-01 to 2010-12-31
-select toDate('2010-01-01') + number as d FROM numbers(365);
-
- - -

-

Formats

-

The format determines how data is returned to you after SELECTs (how it is written and formatted by the server), and how it is accepted for INSERTs (how it is read and parsed by the server).

-

TabSeparated

-

In TabSeparated format, data is written by row. Each row contains values separated by tabs. Each value is follow by a tab, except the last value in the row, which is followed by a line feed. Strictly Unix line feeds are assumed everywhere. The last row also must contain a line feed at the end. Values are written in text format, without enclosing quotation marks, and with special characters escaped.

-

Integer numbers are written in decimal form. Numbers can contain an extra "+" character at the beginning (ignored when parsing, and not recorded when formatting). Non-negative numbers can't contain the negative sign. When reading, it is allowed to parse an empty string as a zero, or (for signed types) a string consisting of just a minus sign as a zero. Numbers that do not fit into the corresponding data type may be parsed as a different number, without an error message.

-

Floating-point numbers are written in decimal form. The dot is used as the decimal separator. Exponential entries are supported, as are 'inf', '+inf', '-inf', and 'nan'. An entry of floating-point numbers may begin or end with a decimal point. -During formatting, accuracy may be lost on floating-point numbers. -During parsing, it is not strictly required to read the nearest machine-representable number.

-

Dates are written in YYYY-MM-DD format and parsed in the same format, but with any characters as separators. -Dates with times are written in the format YYYY-MM-DD hh:mm:ss and parsed in the same format, but with any characters as separators. -This all occurs in the system time zone at the time the client or server starts (depending on which one formats data). For dates with times, daylight saving time is not specified. So if a dump has times during daylight saving time, the dump does not unequivocally match the data, and parsing will select one of the two times. -During a read operation, incorrect dates and dates with times can be parsed with natural overflow or as null dates and times, without an error message.

-

As an exception, parsing dates with times is also supported in Unix timestamp format, if it consists of exactly 10 decimal digits. The result is not time zone-dependent. The formats YYYY-MM-DD hh:mm:ss and NNNNNNNNNN are differentiated automatically.

-

Strings are output with backslash-escaped special characters. The following escape sequences are used for output: \b, \f, \r, \n, \t, \0, \', \\. Parsing also supports the sequences \a, \v, and \xHH (hex escape sequences) and any \c sequences, where c is any character (these sequences are converted to c). Thus, reading data supports formats where a line feed can be written as \n or \, or as a line feed. For example, the string Hello world with a line feed between the words instead of a space can be parsed in any of the following variations:

-
Hello\nworld
-
-Hello\
-world
-
- - -

The second variant is supported because MySQL uses it when writing tab-separated dumps.

-

The minimum set of characters that you need to escape when passing data in TabSeparated format: tab, line feed (LF) and backslash.

-

Only a small set of symbols are escaped. You can easily stumble onto a string value that your terminal will ruin in output.

-

Arrays are written as a list of comma-separated values in square brackets. Number items in the array are fomratted as normally, but dates, dates with times, and strings are written in single quotes with the same escaping rules as above.

-

The TabSeparated format is convenient for processing data using custom programs and scripts. It is used by default in the HTTP interface, and in the command-line client's batch mode. This format also allows transferring data between different DBMSs. For example, you can get a dump from MySQL and upload it to ClickHouse, or vice versa.

-

The TabSeparated format supports outputting total values (when using WITH TOTALS) and extreme values (when 'extremes' is set to 1). In these cases, the total values and extremes are output after the main data. The main result, total values, and extremes are separated from each other by an empty line. Example:

-
SELECT EventDate, count() AS c FROM test.hits GROUP BY EventDate WITH TOTALS ORDER BY EventDate FORMAT TabSeparated``
-
- - -
2014-03-17      1406958
-2014-03-18      1383658
-2014-03-19      1405797
-2014-03-20      1353623
-2014-03-21      1245779
-2014-03-22      1031592
-2014-03-23      1046491
-
-0000-00-00      8873898
-
-2014-03-17      1031592
-2014-03-23      1406958
-
- - -

This format is also available under the name TSV.

-

TabSeparatedRaw

-

Differs from TabSeparated format in that the rows are written without escaping. -This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

-

This format is also available under the name TSVRaw.

-

TabSeparatedWithNames

-

Differs from the TabSeparated format in that the column names are written in the first row. -During parsing, the first row is completely ignored. You can't use column names to determine their position or to check their correctness. -(Support for parsing the header row may be added in the future.)

-

This format is also available under the name TSVWithNames.

-

TabSeparatedWithNamesAndTypes

-

Differs from the TabSeparated format in that the column names are written to the first row, while the column types are in the second row. -During parsing, the first and second rows are completely ignored.

-

This format is also available under the name TSVWithNamesAndTypes.

-

CSV

-

Comma Separated Values format (RFC).

-

When formatting, rows are enclosed in double quotes. A double quote inside a string is output as two double quotes in a row. There are no other rules for escaping characters. Date and date-time are enclosed in double quotes. Numbers are output without quotes. Values ​​are separated by a delimiter*. Rows are separated using the Unix line feed (LF). Arrays are serialized in CSV as follows: first the array is serialized to a string as in TabSeparated format, and then the resulting string is output to CSV in double quotes. Tuples in CSV format are serialized as separate columns (that is, their nesting in the tuple is lost).

-

*By default — ,. See a format_csv_delimiter setting for additional info.

-

When parsing, all values can be parsed either with or without quotes. Both double and single quotes are supported. Rows can also be arranged without quotes. In this case, they are parsed up to a delimiter or line feed (CR or LF). In violation of the RFC, when parsing rows without quotes, the leading and trailing spaces and tabs are ignored. For the line feed, Unix (LF), Windows (CR LF) and Mac OS Classic (CR LF) are all supported.

-

The CSV format supports the output of totals and extremes the same way as TabSeparated.

-

CSVWithNames

-

Also prints the header row, similar to TabSeparatedWithNames.

-

Values

-

Prints every row in brackets. Rows are separated by commas. There is no comma after the last row. The values inside the brackets are also comma-separated. Numbers are output in decimal format without quotes. Arrays are output in square brackets. Strings, dates, and dates with times are output in quotes. Escaping rules and parsing are similar to the TabSeparated format. During formatting, extra spaces aren't inserted, but during parsing, they are allowed and skipped (except for spaces inside array values, which are not allowed).

-

The minimum set of characters that you need to escape when passing data in Values ​​format: single quotes and backslashes.

-

This is the format that is used in INSERT INTO t VALUES ..., but you can also use it for formatting query results.

-

Vertical

-

Prints each value on a separate line with the column name specified. This format is convenient for printing just one or a few rows, if each row consists of a large number of columns. -This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

-

VerticalRaw

-

Differs from Vertical format in that the rows are not escaped. -This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

-

Examples:

-
:) SHOW CREATE TABLE geonames FORMAT VerticalRaw;
-Row 1:
-──────
-statement: CREATE TABLE default.geonames ( geonameid UInt32, date Date DEFAULT CAST('2017-12-08' AS Date)) ENGINE = MergeTree(date, geonameid, 8192)
-
-:) SELECT 'string with \'quotes\' and \t with some special \n characters' AS test FORMAT VerticalRaw;
-Row 1:
-──────
-test: string with 'quotes' and   with some special
- characters
-
- - -

Compare with the Vertical format:

-
:) SELECT 'string with \'quotes\' and \t with some special \n characters' AS test FORMAT Vertical;
-Row 1:
-──────
-test: string with \'quotes\' and \t with some special \n characters
-
- - -

JSON

-

Outputs data in JSON format. Besides data tables, it also outputs column names and types, along with some additional information: the total number of output rows, and the number of rows that could have been output if there weren't a LIMIT. Example:

-
SELECT SearchPhrase, count() AS c FROM test.hits GROUP BY SearchPhrase WITH TOTALS ORDER BY c DESC LIMIT 5 FORMAT JSON
-
- - -
{
-        "meta":
-        [
-                {
-                        "name": "SearchPhrase",
-                        "type": "String"
-                },
-                {
-                        "name": "c",
-                        "type": "UInt64"
-                }
-        ],
-
-        "data":
-        [
-                {
-                        "SearchPhrase": "",
-                        "c": "8267016"
-                },
-                {
-                        "SearchPhrase": "bathroom interior design",
-                        "c": "2166"
-                },
-                {
-                        "SearchPhrase": "yandex",
-                        "c": "1655"
-                },
-                {
-                        "SearchPhrase": "spring 2014 fashion",
-                        "c": "1549"
-                },
-                {
-                        "SearchPhrase": "freeform photos",
-                        "c": "1480"
-                }
-        ],
-
-        "totals":
-        {
-                "SearchPhrase": "",
-                "c": "8873898"
-        },
-
-        "extremes":
-        {
-                "min":
-                {
-                        "SearchPhrase": "",
-                        "c": "1480"
-                },
-                "max":
-                {
-                        "SearchPhrase": "",
-                        "c": "8267016"
-                }
-        },
-
-        "rows": 5,
-
-        "rows_before_limit_at_least": 141137
-}
-
- - -

The JSON is compatible with JavaScript. To ensure this, some characters are additionally escaped: the slash / is escaped as \/; alternative line breaks U+2028 and U+2029, which break some browsers, are escaped as \uXXXX. ASCII control characters are escaped: backspace, form feed, line feed, carriage return, and horizontal tab are replaced with \b, \f, \n, \r, \t , as well as the remaining bytes in the 00-1F range using \uXXXX sequences. Invalid UTF-8 sequences are changed to the replacement character � so the output text will consist of valid UTF-8 sequences. For compatibility with JavaScript, Int64 and UInt64 integers are enclosed in double quotes by default. To remove the quotes, you can set the configuration parameter output_format_json_quote_64bit_integers to 0.

-

rows – The total number of output rows.

-

rows_before_limit_at_least The minimal number of rows there would have been without LIMIT. Output only if the query contains LIMIT. -If the query contains GROUP BY, rows_before_limit_at_least is the exact number of rows there would have been without a LIMIT.

-

totals – Total values (when using WITH TOTALS).

-

extremes – Extreme values (when extremes is set to 1).

-

This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). -See also the JSONEachRow format.

-

JSONCompact

-

Differs from JSON only in that data rows are output in arrays, not in objects.

-

Example:

-
{
-        "meta":
-        [
-                {
-                        "name": "SearchPhrase",
-                        "type": "String"
-                },
-                {
-                        "name": "c",
-                        "type": "UInt64"
-                }
-        ],
-
-        "data":
-        [
-                ["", "8267016"],
-                ["bathroom interior design", "2166"],
-                ["yandex", "1655"],
-                ["spring 2014 fashion", "1549"],
-                ["freeform photos", "1480"]
-        ],
-
-        "totals": ["","8873898"],
-
-        "extremes":
-        {
-                "min": ["","1480"],
-                "max": ["","8267016"]
-        },
-
-        "rows": 5,
-
-        "rows_before_limit_at_least": 141137
-}
-
- - -

This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). -See also the JSONEachRow format.

-

JSONEachRow

-

Outputs data as separate JSON objects for each row (newline delimited JSON).

-
{"SearchPhrase":"","count()":"8267016"}
-{"SearchPhrase":"bathroom interior design","count()":"2166"}
-{"SearchPhrase":"yandex","count()":"1655"}
-{"SearchPhrase":"spring 2014 fashion","count()":"1549"}
-{"SearchPhrase":"freeform photo","count()":"1480"}
-{"SearchPhrase":"angelina jolie","count()":"1245"}
-{"SearchPhrase":"omsk","count()":"1112"}
-{"SearchPhrase":"photos of dog breeds","count()":"1091"}
-{"SearchPhrase":"curtain design","count()":"1064"}
-{"SearchPhrase":"baku","count()":"1000"}
-
- - -

Unlike the JSON format, there is no substitution of invalid UTF-8 sequences. Any set of bytes can be output in the rows. This is necessary so that data can be formatted without losing any information. Values are escaped in the same way as for JSON.

-

For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted – they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Whitespace between elements is ignored. If a comma is placed after the objects, it is ignored. Objects don't necessarily have to be separated by new lines.

-

TSKV

-

Similar to TabSeparated, but outputs a value in name=value format. Names are escaped the same way as in TabSeparated format, and the = symbol is also escaped.

-
SearchPhrase=   count()=8267016
-SearchPhrase=bathroom interior design    count()=2166
-SearchPhrase=yandex     count()=1655
-SearchPhrase=spring 2014 fashion    count()=1549
-SearchPhrase=freeform photos       count()=1480
-SearchPhrase=angelina jolia    count()=1245
-SearchPhrase=omsk       count()=1112
-SearchPhrase=photos of dog breeds    count()=1091
-SearchPhrase=curtain design        count()=1064
-SearchPhrase=baku       count()=1000
-
- - -

When there is a large number of small columns, this format is ineffective, and there is generally no reason to use it. It is used in some departments of Yandex.

-

Both data output and parsing are supported in this format. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted – they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults.

-

Parsing allows the presence of the additional field tskv without the equal sign or a value. This field is ignored.

-

Pretty

-

Outputs data as Unicode-art tables, also using ANSI-escape sequences for setting colors in the terminal. -A full grid of the table is drawn, and each row occupies two lines in the terminal. -Each result block is output as a separate table. This is necessary so that blocks can be output without buffering results (buffering would be necessary in order to pre-calculate the visible width of all the values). -To avoid dumping too much data to the terminal, only the first 10,000 rows are printed. If the number of rows is greater than or equal to 10,000, the message "Showed first 10 000" is printed. -This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).

-

The Pretty format supports outputting total values (when using WITH TOTALS) and extremes (when 'extremes' is set to 1). In these cases, total values and extreme values are output after the main data, in separate tables. Example (shown for the PrettyCompact format):

-
SELECT EventDate, count() AS c FROM test.hits GROUP BY EventDate WITH TOTALS ORDER BY EventDate FORMAT PrettyCompact
-
- - -
┌──EventDate─┬───────c─┐
-│ 2014-03-17 │ 1406958 │
-│ 2014-03-18 │ 1383658 │
-│ 2014-03-19 │ 1405797 │
-│ 2014-03-20 │ 1353623 │
-│ 2014-03-21 │ 1245779 │
-│ 2014-03-22 │ 1031592 │
-│ 2014-03-23 │ 1046491 │
-└────────────┴─────────┘
-
-Totals:
-┌──EventDate─┬───────c─┐
-│ 0000-00-00 │ 8873898 │
-└────────────┴─────────┘
-
-Extremes:
-┌──EventDate─┬───────c─┐
-│ 2014-03-17 │ 1031592 │
-│ 2014-03-23 │ 1406958 │
-└────────────┴─────────┘
-
- - -

PrettyCompact

-

Differs from Pretty in that the grid is drawn between rows and the result is more compact. -This format is used by default in the command-line client in interactive mode.

-

PrettyCompactMonoBlock

-

Differs from PrettyCompact in that up to 10,000 rows are buffered, then output as a single table, not by blocks.

-

PrettyNoEscapes

-

Differs from Pretty in that ANSI-escape sequences aren't used. This is necessary for displaying this format in a browser, as well as for using the 'watch' command-line utility.

-

Example:

-
watch -n1 "clickhouse-client --query='SELECT * FROM system.events FORMAT PrettyCompactNoEscapes'"
-
- - -

You can use the HTTP interface for displaying in the browser.

-

PrettyCompactNoEscapes

-

The same as the previous setting.

-

PrettySpaceNoEscapes

-

The same as the previous setting.

-

PrettySpace

-

Differs from PrettyCompact in that whitespace (space characters) is used instead of the grid.

-

RowBinary

-

Formats and parses data by row in binary format. Rows and values are listed consecutively, without separators. -This format is less efficient than the Native format, since it is row-based.

-

Integers use fixed-length little endian representation. For example, UInt64 uses 8 bytes. -DateTime is represented as UInt32 containing the Unix timestamp as the value. -Date is represented as a UInt16 object that contains the number of days since 1970-01-01 as the value. -String is represented as a varint length (unsigned LEB128), followed by the bytes of the string. -FixedString is represented simply as a sequence of bytes.

-

Array is represented as a varint length (unsigned LEB128), followed by successive elements of the array.

-

Native

-

The most efficient format. Data is written and read by blocks in binary format. For each block, the number of rows, number of columns, column names and types, and parts of columns in this block are recorded one after another. In other words, this format is "columnar" – it doesn't convert columns to rows. This is the format used in the native interface for interaction between servers, for using the command-line client, and for C++ clients.

-

You can use this format to quickly generate dumps that can only be read by the ClickHouse DBMS. It doesn't make sense to work with this format yourself.

-

Null

-

Nothing is output. However, the query is processed, and when using the command-line client, data is transmitted to the client. This is used for tests, including productivity testing. -Obviously, this format is only appropriate for output, not for parsing.

-

XML

-

XML format is suitable only for output, not for parsing. Example:

-
<?xml version='1.0' encoding='UTF-8' ?>
-<result>
-        <meta>
-                <columns>
-                        <column>
-                                <name>SearchPhrase</name>
-                                <type>String</type>
-                        </column>
-                        <column>
-                                <name>count()</name>
-                                <type>UInt64</type>
-                        </column>
-                </columns>
-        </meta>
-        <data>
-                <row>
-                        <SearchPhrase></SearchPhrase>
-                        <field>8267016</field>
-                </row>
-                <row>
-                        <SearchPhrase>bathroom interior design</SearchPhrase>
-                        <field>2166</field>
-                </row>
-                <row>
-                        <SearchPhrase>yandex</SearchPhrase>
-                        <field>1655</field>
-                </row>
-                <row>
-                        <SearchPhrase>spring 2014 fashion</SearchPhrase>
-                        <field>1549</field>
-                </row>
-                <row>
-                        <SearchPhrase>freeform photos</SearchPhrase>
-                        <field>1480</field>
-                </row>
-                <row>
-                        <SearchPhrase>angelina jolie</SearchPhrase>
-                        <field>1245</field>
-                </row>
-                <row>
-                        <SearchPhrase>omsk</SearchPhrase>
-                        <field>1112</field>
-                </row>
-                <row>
-                        <SearchPhrase>photos of dog breeds</SearchPhrase>
-                        <field>1091</field>
-                </row>
-                <row>
-                        <SearchPhrase>curtain design</SearchPhrase>
-                        <field>1064</field>
-                </row>
-                <row>
-                        <SearchPhrase>baku</SearchPhrase>
-                        <field>1000</field>
-                </row>
-        </data>
-        <rows>10</rows>
-        <rows_before_limit_at_least>141137</rows_before_limit_at_least>
-</result>
-
- - -

If the column name does not have an acceptable format, just 'field' is used as the element name. In general, the XML structure follows the JSON structure. -Just as for JSON, invalid UTF-8 sequences are changed to the replacement character � so the output text will consist of valid UTF-8 sequences.

-

In string values, the characters < and & are escaped as < and &.

-

Arrays are output as <array><elem>Hello</elem><elem>World</elem>...</array>, -and tuples as <tuple><elem>Hello</elem><elem>World</elem>...</tuple>.

-

-

CapnProto

-

Cap'n Proto is a binary message format similar to Protocol Buffers and Thrift, but not like JSON or MessagePack.

-

Cap'n Proto messages are strictly typed and not self-describing, meaning they need an external schema description. The schema is applied on the fly and cached for each query.

-
SELECT SearchPhrase, count() AS c FROM test.hits
-       GROUP BY SearchPhrase FORMAT CapnProto SETTINGS schema = 'schema:Message'
-
- - -

Where schema.capnp looks like this:

-
struct Message {
-  SearchPhrase @0 :Text;
-  c @1 :Uint64;
-}
-
- - -

Schema files are in the file that is located in the directory specified in format_schema_path in the server configuration.

-

Deserialization is effective and usually doesn't increase the system load.

-

-

Data types

-

ClickHouse can store various types of data in table cells.

-

This section describes the supported data types and special considerations when using and/or implementing them, if any.

-

UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64

-

Fixed-length integers, with or without a sign.

-

Int ranges

-
    -
  • Int8 - [-128 : 127]
    • -
  • Int16 - [-32768 : 32767]
    • -
  • Int32 - [-2147483648 : 2147483647]
    • -
  • Int64 - [-9223372036854775808 : 9223372036854775807]
    • -
-

Uint ranges

-
    -
  • UInt8 - [0 : 255]
    • -
  • UInt16 - [0 : 65535]
    • -
  • UInt32 - [0 : 4294967295]
    • -
  • UInt64 - [0 : 18446744073709551615]
    • -
-

Float32, Float64

-

Floating point numbers.

-

Types are equivalent to types of C:

-
    -
  • Float32 - float
    • -
  • Float64 - double
    • -
-

We recommend that you store data in integer form whenever possible. For example, convert fixed precision numbers to integer values, such as monetary amounts or page load times in milliseconds.

-

Using floating-point numbers

-
    -
  • Computations with floating-point numbers might produce a rounding error.
    • -
-
SELECT 1 - 0.9
-
- - -
┌───────minus(1, 0.9)─┐
-│ 0.09999999999999998 │
-└─────────────────────┘
-
- - -
    -
  • The result of the calculation depends on the calculation method (the processor type and architecture of the computer system).
    • -
  • Floating-point calculations might result in numbers such as infinity (Inf) and "not-a-number" (NaN). This should be taken into account when processing the results of calculations.
    • -
  • When reading floating point numbers from rows, the result might not be the nearest machine-representable number.
    • -
-

NaN and Inf

-

In contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers:

-
    -
  • Inf – Infinity.
    • -
-
SELECT 0.5 / 0
-
- - -
┌─divide(0.5, 0)─┐
-│            inf │
-└────────────────┘
-
- - -
    -
  • -Inf – Negative infinity.
    • -
-
SELECT -0.5 / 0
-
- - -
┌─divide(-0.5, 0)─┐
-│            -inf │
-└─────────────────┘
-
- - -
    -
  • NaN – Not a number.
    • -
-
SELECT 0 / 0
-
- - -
┌─divide(0, 0)─┐
-│          nan │
-└──────────────┘
-
- - -

See the rules for NaN sorting in the section ORDER BY clause.

-

Boolean values

-

There isn't a separate type for boolean values. They use the UInt8 type, restricted to the values 0 or 1.

-

String

-

Strings of an arbitrary length. The length is not limited. The value can contain an arbitrary set of bytes, including null bytes. -The String type replaces the types VARCHAR, BLOB, CLOB, and others from other DBMSs.

-

Encodings

-

ClickHouse doesn't have the concept of encodings. Strings can contain an arbitrary set of bytes, which are stored and output as-is. -If you need to store texts, we recommend using UTF-8 encoding. At the very least, if your terminal uses UTF-8 (as recommended), you can read and write your values without making conversions. -Similarly, certain functions for working with strings have separate variations that work under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. -For example, the 'length' function calculates the string length in bytes, while the 'lengthUTF8' function calculates the string length in Unicode code points, assuming that the value is UTF-8 encoded.

-

FixedString(N)

-

A fixed-length string of N bytes (not characters or code points). N must be a strictly positive natural number. -When the server reads a string that contains fewer bytes (such as when parsing INSERT data), the string is padded to N bytes by appending null bytes at the right. -When the server reads a string that contains more bytes, an error message is returned. -When the server writes a string (such as when outputting the result of a SELECT query), null bytes are not trimmed off of the end of the string, but are output. -Note that this behavior differs from MySQL behavior for the CHAR type (where strings are padded with spaces, and the spaces are removed for output).

-

Fewer functions can work with the FixedString(N) type than with String, so it is less convenient to use.

-

Date

-

A date. Stored in two bytes as the number of days since 1970-01-01 (unsigned). Allows storing values from just after the beginning of the Unix Epoch to the upper threshold defined by a constant at the compilation stage (currently, this is until the year 2106, but the final fully-supported year is 2105). -The minimum value is output as 0000-00-00.

-

The date is stored without the time zone.

-

DateTime

-

Date with time. Stored in four bytes as a Unix timestamp (unsigned). Allows storing values in the same range as for the Date type. The minimal value is output as 0000-00-00 00:00:00. -The time is stored with accuracy up to one second (without leap seconds).

-

Time zones

-

The date with time is converted from text (divided into component parts) to binary and back, using the system's time zone at the time the client or server starts. In text format, information about daylight savings is lost.

-

By default, the client switches to the timezone of the server when it connects. You can change this behavior by enabling the client command-line option --use_client_time_zone.

-

Supports only those time zones that never had the time differ from UTC for a partial number of hours (without leap seconds) over the entire time range you will be working with.

-

So when working with a textual date (for example, when saving text dumps), keep in mind that there may be ambiguity during changes for daylight savings time, and there may be problems matching data if the time zone changed.

-

Enum

-

Enum8 or Enum16. A finite set of string values that can be stored more efficiently than the String data type.

-

Example:

-
Enum8('hello' = 1, 'world' = 2)
-
- - -
    -
  • A data type with two possible values: 'hello' and 'world'.
    • -
-

Each of the values is assigned a number in the range -128 ... 127 for Enum8 or in the range -32768 ... 32767 for Enum16. All the strings and numbers must be different. An empty string is allowed. If this type is specified (in a table definition), numbers can be in an arbitrary order. However, the order does not matter.

-

In RAM, this type of column is stored in the same way as Int8 or Int16 of the corresponding numerical values. -When reading in text form, ClickHouse parses the value as a string and searches for the corresponding string from the set of Enum values. If it is not found, an exception is thrown. When reading in text format, the string is read and the corresponding numeric value is looked up. An exception will be thrown if it is not found. -When writing in text form, it writes the value as the corresponding string. If column data contains garbage (numbers that are not from the valid set), an exception is thrown. When reading and writing in binary form, it works the same way as for Int8 and Int16 data types. -The implicit default value is the value with the lowest number.

-

During ORDER BY, GROUP BY, IN, DISTINCT and so on, Enums behave the same way as the corresponding numbers. For example, ORDER BY sorts them numerically. Equality and comparison operators work the same way on Enums as they do on the underlying numeric values.

-

Enum values cannot be compared with numbers. Enums can be compared to a constant string. If the string compared to is not a valid value for the Enum, an exception will be thrown. The IN operator is supported with the Enum on the left hand side and a set of strings on the right hand side. The strings are the values of the corresponding Enum.

-

Most numeric and string operations are not defined for Enum values, e.g. adding a number to an Enum or concatenating a string to an Enum. -However, the Enum has a natural toString function that returns its string value.

-

Enum values are also convertible to numeric types using the toT function, where T is a numeric type. When T corresponds to the enum’s underlying numeric type, this conversion is zero-cost. -The Enum type can be changed without cost using ALTER, if only the set of values is changed. It is possible to both add and remove members of the Enum using ALTER (removing is safe only if the removed value has never been used in the table). As a safeguard, changing the numeric value of a previously defined Enum member will throw an exception.

-

Using ALTER, it is possible to change an Enum8 to an Enum16 or vice versa, just like changing an Int8 to Int16.

-

Array(T)

-

An array of elements of type T. The T type can be any type, including an array. -We don't recommend using multidimensional arrays, because they are not well supported (for example, you can't store multidimensional arrays in tables with a MergeTree engine).

-

AggregateFunction(name, types_of_arguments...)

-

The intermediate state of an aggregate function. To get it, use aggregate functions with the '-State' suffix. For more information, see "AggregatingMergeTree".

-

Tuple(T1, T2, ...)

-

Tuples can't be written to tables (other than Memory tables). They are used for temporary column grouping. Columns can be grouped when an IN expression is used in a query, and for specifying certain formal parameters of lambda functions. For more information, see "IN operators" and "Higher order functions".

-

Tuples can be output as the result of running a query. In this case, for text formats other than JSON*, values are comma-separated in brackets. In JSON* formats, tuples are output as arrays (in square brackets).

-

Nested data structures

-

Nested(Name1 Type1, Name2 Type2, ...)

-

A nested data structure is like a nested table. The parameters of a nested data structure – the column names and types – are specified the same way as in a CREATE query. Each table row can correspond to any number of rows in a nested data structure.

-

Example:

-
CREATE TABLE test.visits
-(
-    CounterID UInt32,
-    StartDate Date,
-    Sign Int8,
-    IsNew UInt8,
-    VisitID UInt64,
-    UserID UInt64,
-    ...
-    Goals Nested
-    (
-        ID UInt32,
-        Serial UInt32,
-        EventTime DateTime,
-        Price Int64,
-        OrderID String,
-        CurrencyID UInt32
-    ),
-    ...
-) ENGINE = CollapsingMergeTree(StartDate, intHash32(UserID), (CounterID, StartDate, intHash32(UserID), VisitID), 8192, Sign)
-
- - -

This example declares the Goals nested data structure, which contains data about conversions (goals reached). Each row in the 'visits' table can correspond to zero or any number of conversions.

-

Only a single nesting level is supported. Columns of nested structures containing arrays are equivalent to multidimensional arrays, so they have limited support (there is no support for storing these columns in tables with the MergeTree engine).

-

In most cases, when working with a nested data structure, its individual columns are specified. To do this, the column names are separated by a dot. These columns make up an array of matching types. All the column arrays of a single nested data structure have the same length.

-

Example:

-
SELECT
-    Goals.ID,
-    Goals.EventTime
-FROM test.visits
-WHERE CounterID = 101500 AND length(Goals.ID) < 5
-LIMIT 10
-
- - -
┌─Goals.ID───────────────────────┬─Goals.EventTime───────────────────────────────────────────────────────────────────────────┐
-│ [1073752,591325,591325]        │ ['2014-03-17 16:38:10','2014-03-17 16:38:48','2014-03-17 16:42:27']                       │
-│ [1073752]                      │ ['2014-03-17 00:28:25']                                                                   │
-│ [1073752]                      │ ['2014-03-17 10:46:20']                                                                   │
-│ [1073752,591325,591325,591325] │ ['2014-03-17 13:59:20','2014-03-17 22:17:55','2014-03-17 22:18:07','2014-03-17 22:18:51'] │
-│ []                             │ []                                                                                        │
-│ [1073752,591325,591325]        │ ['2014-03-17 11:37:06','2014-03-17 14:07:47','2014-03-17 14:36:21']                       │
-│ []                             │ []                                                                                        │
-│ []                             │ []                                                                                        │
-│ [591325,1073752]               │ ['2014-03-17 00:46:05','2014-03-17 00:46:05']                                             │
-│ [1073752,591325,591325,591325] │ ['2014-03-17 13:28:33','2014-03-17 13:30:26','2014-03-17 18:51:21','2014-03-17 18:51:45'] │
-└────────────────────────────────┴───────────────────────────────────────────────────────────────────────────────────────────┘
-
- - -

It is easiest to think of a nested data structure as a set of multiple column arrays of the same length.

-

The only place where a SELECT query can specify the name of an entire nested data structure instead of individual columns is the ARRAY JOIN clause. For more information, see "ARRAY JOIN clause". Example:

-
SELECT
-    Goal.ID,
-    Goal.EventTime
-FROM test.visits
-ARRAY JOIN Goals AS Goal
-WHERE CounterID = 101500 AND length(Goals.ID) < 5
-LIMIT 10
-
- - -
┌─Goal.ID─┬──────Goal.EventTime─┐
-│ 1073752 │ 2014-03-17 16:38:10 │
-│  591325 │ 2014-03-17 16:38:48 │
-│  591325 │ 2014-03-17 16:42:27 │
-│ 1073752 │ 2014-03-17 00:28:25 │
-│ 1073752 │ 2014-03-17 10:46:20 │
-│ 1073752 │ 2014-03-17 13:59:20 │
-│  591325 │ 2014-03-17 22:17:55 │
-│  591325 │ 2014-03-17 22:18:07 │
-│  591325 │ 2014-03-17 22:18:51 │
-│ 1073752 │ 2014-03-17 11:37:06 │
-└─────────┴─────────────────────┘
-
- - -

You can't perform SELECT for an entire nested data structure. You can only explicitly list individual columns that are part of it.

-

For an INSERT query, you should pass all the component column arrays of a nested data structure separately (as if they were individual column arrays). During insertion, the system checks that they have the same length.

-

For a DESCRIBE query, the columns in a nested data structure are listed separately in the same way.

-

The ALTER query is very limited for elements in a nested data structure.

-

Special data types

-

Special data type values can't be saved to a table or output in results, but are used as the intermediate result of running a query.

-

Expression

-

Used for representing lambda expressions in high-order functions.

-

Set

-

Used for the right half of an IN expression.

-

Operators

-

All operators are transformed to the corresponding functions at the query parsing stage, in accordance with their precedence and associativity. -Groups of operators are listed in order of priority (the higher it is in the list, the earlier the operator is connected to its arguments).

-

Access operators

-

a[N] Access to an element of an array; arrayElement(a, N) function.

-

a.N – Access to a tuble element; tupleElement(a, N) function.

-

Numeric negation operator

-

-a – The negate (a) function.

-

Multiplication and division operators

-

a * b – The multiply (a, b) function.

-

a / b – The divide(a, b) function.

-

a % b – The modulo(a, b) function.

-

Addition and subtraction operators

-

a + b – The plus(a, b) function.

-

a - b – The minus(a, b) function.

-

Comparison operators

-

a = b – The equals(a, b) function.

-

a == b – The equals(a, b) function.

-

a != b – The notEquals(a, b) function.

-

a <> b – The notEquals(a, b) function.

-

a <= b – The lessOrEquals(a, b) function.

-

a >= b – The greaterOrEquals(a, b) function.

-

a < b – The less(a, b) function.

-

a > b – The greater(a, b) function.

-

a LIKE s – The like(a, b) function.

-

a NOT LIKE s – The notLike(a, b) function.

-

a BETWEEN b AND c – The same as a >= b AND a <= c.

-

Operators for working with data sets

-

See the section "IN operators".

-

a IN ... – The in(a, b) function

-

a NOT IN ... – The notIn(a, b) function.

-

a GLOBAL IN ... – The globalIn(a, b) function.

-

a GLOBAL NOT IN ... – The globalNotIn(a, b) function.

-

Logical negation operator

-

NOT a The not(a) function.

-

Logical AND operator

-

a AND b – Theand(a, b) function.

-

Logical OR operator

-

a OR b – The or(a, b) function.

-

Conditional operator

-

a ? b : c – The if(a, b, c) function.

-

Note:

-

The conditional operator calculates the values of b and c, then checks whether condition a is met, and then returns the corresponding value. If "b" or "c" is an arrayJoin() function, each row will be replicated regardless of the "a" condition.

-

Conditional expression

-
CASE [x]
-    WHEN a THEN b
-    [WHEN ... THEN ...]
-    ELSE c
-END
-
- - -

If "x" is specified, then transform(x, [a, ...], [b, ...], c). Otherwise – multiIf(a, b, ..., c).

-

Concatenation operator

-

s1 || s2 – The concat(s1, s2) function.

-

Lambda creation operator

-

x -> expr – The lambda(x, expr) function.

-

The following operators do not have a priority, since they are brackets:

-

Array creation operator

-

[x1, ...] – The array(x1, ...) function.

-

Tuple creation operator

-

(x1, x2, ...) – The tuple(x2, x2, ...) function.

-

Associativity

-

All binary operators have left associativity. For example, 1 + 2 + 3 is transformed to plus(plus(1, 2), 3). -Sometimes this doesn't work the way you expect. For example, SELECT 4 > 2 > 3 will result in 0.

-

For efficiency, the and and or functions accept any number of arguments. The corresponding chains of AND and OR operators are transformed to a single call of these functions.

-

Functions

-

There are at least* two types of functions - regular functions (they are just called "functions") and aggregate functions. These are completely different concepts. Regular functions work as if they are applied to each row separately (for each row, the result of the function doesn't depend on the other rows). Aggregate functions accumulate a set of values from various rows (i.e. they depend on the entire set of rows).

-

In this section we discuss regular functions. For aggregate functions, see the section "Aggregate functions".

-

* - There is a third type of function that the 'arrayJoin' function belongs to; table functions can also be mentioned separately.*

-

Strong typing

-

In contrast to standard SQL, ClickHouse has strong typing. In other words, it doesn't make implicit conversions between types. Each function works for a specific set of types. This means that sometimes you need to use type conversion functions.

-

Common subexpression elimination

-

All expressions in a query that have the same AST (the same record or same result of syntactic parsing) are considered to have identical values. Such expressions are concatenated and executed once. Identical subqueries are also eliminated this way.

-

Types of results

-

All functions return a single return as the result (not several values, and not zero values). The type of result is usually defined only by the types of arguments, not by the values. Exceptions are the tupleElement function (the a.N operator), and the toFixedString function.

-

Constants

-

For simplicity, certain functions can only work with constants for some arguments. For example, the right argument of the LIKE operator must be a constant. -Almost all functions return a constant for constant arguments. The exception is functions that generate random numbers. -The 'now' function returns different values for queries that were run at different times, but the result is considered a constant, since constancy is only important within a single query. -A constant expression is also considered a constant (for example, the right half of the LIKE operator can be constructed from multiple constants).

-

Functions can be implemented in different ways for constant and non-constant arguments (different code is executed). But the results for a constant and for a true column containing only the same value should match each other.

-

Constancy

-

Functions can't change the values of their arguments – any changes are returned as the result. Thus, the result of calculating separate functions does not depend on the order in which the functions are written in the query.

-

Error handling

-

Some functions might throw an exception if the data is invalid. In this case, the query is canceled and an error text is returned to the client. For distributed processing, when an exception occurs on one of the servers, the other servers also attempt to abort the query.

-

Evaluation of argument expressions

-

In almost all programming languages, one of the arguments might not be evaluated for certain operators. This is usually the operators &&, ||, and ?:. -But in ClickHouse, arguments of functions (operators) are always evaluated. This is because entire parts of columns are evaluated at once, instead of calculating each row separately.

-

Performing functions for distributed query processing

-

For distributed query processing, as many stages of query processing as possible are performed on remote servers, and the rest of the stages (merging intermediate results and everything after that) are performed on the requestor server.

-

This means that functions can be performed on different servers. -For example, in the query SELECT f(sum(g(x))) FROM distributed_table GROUP BY h(y),

-
    -
  • if a distributed_table has at least two shards, the functions 'g' and 'h' are performed on remote servers, and the function 'f' is performed on the requestor server.
    • -
  • if a distributed_table has only one shard, all the 'f', 'g', and 'h' functions are performed on this shard's server.
    • -
-

The result of a function usually doesn't depend on which server it is performed on. However, sometimes this is important. -For example, functions that work with dictionaries use the dictionary that exists on the server they are running on. -Another example is the hostName function, which returns the name of the server it is running on in order to make GROUP BY by servers in a SELECT query.

-

If a function in a query is performed on the requestor server, but you need to perform it on remote servers, you can wrap it in an 'any' aggregate function or add it to a key in GROUP BY.

-

Arithmetic functions

-

For all arithmetic functions, the result type is calculated as the smallest number type that the result fits in, if there is such a type. The minimum is taken simultaneously based on the number of bits, whether it is signed, and whether it floats. If there are not enough bits, the highest bit type is taken.

-

Example:

-
SELECT toTypeName(0), toTypeName(0 + 0), toTypeName(0 + 0 + 0), toTypeName(0 + 0 + 0 + 0)
-
- - -
┌─toTypeName(0)─┬─toTypeName(plus(0, 0))─┬─toTypeName(plus(plus(0, 0), 0))─┬─toTypeName(plus(plus(plus(0, 0), 0), 0))─┐
-│ UInt8         │ UInt16                 │ UInt32                          │ UInt64                                   │
-└───────────────┴────────────────────────┴─────────────────────────────────┴──────────────────────────────────────────┘
-
- - -

Arithmetic functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.

-

Overflow is produced the same way as in C++.

-

plus(a, b), a + b operator

-

Calculates the sum of the numbers. -You can also add integer numbers with a date or date and time. In the case of a date, adding an integer means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds.

-

minus(a, b), a - b operator

-

Calculates the difference. The result is always signed.

-

You can also calculate integer numbers from a date or date with time. The idea is the same – see above for 'plus'.

-

multiply(a, b), a * b operator

-

Calculates the product of the numbers.

-

divide(a, b), a / b operator

-

Calculates the quotient of the numbers. The result type is always a floating-point type. -It is not integer division. For integer division, use the 'intDiv' function. -When dividing by zero you get 'inf', '-inf', or 'nan'.

-

intDiv(a, b)

-

Calculates the quotient of the numbers. Divides into integers, rounding down (by the absolute value). -An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

-

intDivOrZero(a, b)

-

Differs from 'intDiv' in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one.

-

modulo(a, b), a % b operator

-

Calculates the remainder after division. -If arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion. -The remainder is taken in the same sense as in C++. Truncated division is used for negative numbers. -An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

-

negate(a), -a operator

-

Calculates a number with the reverse sign. The result is always signed.

-

abs(a)

-

Calculates the absolute value of the number (a). That is, if a < 0, it returns -a. For unsigned types it doesn't do anything. For signed integer types, it returns an unsigned number.

-

gcd(a, b)

-

Returns the greatest common divisor of the numbers. -An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

-

lcm(a, b)

-

Returns the least common multiple of the numbers. -An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.

-

Comparison functions

-

Comparison functions always return 0 or 1 (Uint8).

-

The following types can be compared:

-
    -
  • numbers
    • -
  • strings and fixed strings
    • -
  • dates
    • -
  • dates with times
    • -
-

within each group, but not between different groups.

-

For example, you can't compare a date with a string. You have to use a function to convert the string to a date, or vice versa.

-

Strings are compared by bytes. A shorter string is smaller than all strings that start with it and that contain at least one more character.

-

Note. Up until version 1.1.54134, signed and unsigned numbers were compared the same way as in C++. In other words, you could get an incorrect result in cases like SELECT 9223372036854775807 > -1. This behavior changed in version 1.1.54134 and is now mathematically correct.

-

equals, a = b and a == b operator

-

notEquals, a ! operator= b and a <> b

-

less, < operator

-

greater, > operator

-

lessOrEquals, <= operator

-

greaterOrEquals, >= operator

-

Logical functions

-

Logical functions accept any numeric types, but return a UInt8 number equal to 0 or 1.

-

Zero as an argument is considered "false," while any non-zero value is considered "true".

-

and, AND operator

-

or, OR operator

-

not, NOT operator

-

xor

-

-

Type conversion functions

-

toUInt8, toUInt16, toUInt32, toUInt64

-

toInt8, toInt16, toInt32, toInt64

-

toFloat32, toFloat64

-

toUInt8OrZero, toUInt16OrZero, toUInt32OrZero, toUInt64OrZero, toInt8OrZero, toInt16OrZero, toInt32OrZero, toInt64OrZero, toFloat32OrZero, toFloat64OrZero

-

toDate, toDateTime

-

toString

-

Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. -All these functions accept one argument.

-

When converting to or from a string, the value is formatted or parsed using the same rules as for the TabSeparated format (and almost all other text formats). If the string can't be parsed, an exception is thrown and the request is canceled.

-

When converting dates to numbers or vice versa, the date corresponds to the number of days since the beginning of the Unix epoch. -When converting dates with times to numbers or vice versa, the date with time corresponds to the number of seconds since the beginning of the Unix epoch.

-

The date and date-with-time formats for the toDate/toDateTime functions are defined as follows:

-
YYYY-MM-DD
-YYYY-MM-DD hh:mm:ss
-
- - -

As an exception, if converting from UInt32, Int32, UInt64, or Int64 numeric types to Date, and if the number is greater than or equal to 65536, the number is interpreted as a Unix timestamp (and not as the number of days) and is rounded to the date. This allows support for the common occurrence of writing 'toDate(unix_timestamp)', which otherwise would be an error and would require writing the more cumbersome 'toDate(toDateTime(unix_timestamp))'.

-

Conversion between a date and date with time is performed the natural way: by adding a null time or dropping the time.

-

Conversion between numeric types uses the same rules as assignments between different numeric types in C++.

-

Additionally, the toString function of the DateTime argument can take a second String argument containing the name of the time zone. Example: Asia/Yekaterinburg In this case, the time is formatted according to the specified time zone.

-
SELECT
-    now() AS now_local,
-    toString(now(), 'Asia/Yekaterinburg') AS now_yekat
-
- - -
┌───────────now_local─┬─now_yekat───────────┐
-│ 2016-06-15 00:11:21 │ 2016-06-15 02:11:21 │
-└─────────────────────┴─────────────────────┘
-
- - -

Also see the toUnixTimestamp function.

-

toFixedString(s, N)

-

Converts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant. -If the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown.

-

toStringCutToZero(s)

-

Accepts a String or FixedString argument. Returns the String with the content truncated at the first zero byte found.

-

Example:

-
SELECT toFixedString('foo', 8) AS s, toStringCutToZero(s) AS s_cut
-
- - -
┌─s─────────────┬─s_cut─┐
-│ foo\0\0\0\0\0 │ foo   │
-└───────────────┴───────┘
-
- - -
SELECT toFixedString('foo\0bar', 8) AS s, toStringCutToZero(s) AS s_cut
-
- - -
┌─s──────────┬─s_cut─┐
-│ foo\0bar\0 │ foo   │
-└────────────┴───────┘
-
- - -

reinterpretAsUInt8, reinterpretAsUInt16, reinterpretAsUInt32, reinterpretAsUInt64

-

reinterpretAsInt8, reinterpretAsInt16, reinterpretAsInt32, reinterpretAsInt64

-

reinterpretAsFloat32, reinterpretAsFloat64

-

reinterpretAsDate, reinterpretAsDateTime

-

These functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.

-

reinterpretAsString

-

This function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long.

-

CAST(x, t)

-

Converts 'x' to the 't' data type. The syntax CAST(x AS t) is also supported.

-

Example:

-
SELECT
-    '2016-06-15 23:00:00' AS timestamp,
-    CAST(timestamp AS DateTime) AS datetime,
-    CAST(timestamp AS Date) AS date,
-    CAST(timestamp, 'String') AS string,
-    CAST(timestamp, 'FixedString(22)') AS fixed_string
-
- - -
┌─timestamp───────────┬────────────datetime─┬───────date─┬─string──────────────┬─fixed_string──────────────┐
-│ 2016-06-15 23:00:00 │ 2016-06-15 23:00:00 │ 2016-06-15 │ 2016-06-15 23:00:00 │ 2016-06-15 23:00:00\0\0\0 │
-└─────────────────────┴─────────────────────┴────────────┴─────────────────────┴───────────────────────────┘
-
- - -

Conversion to FixedString (N) only works for arguments of type String or FixedString (N).

-

Functions for working with dates and times

-

Support for time zones

-

All functions for working with the date and time that have a logical use for the time zone can accept a second optional time zone argument. Example: Asia/Yekaterinburg. In this case, they use the specified time zone instead of the local (default) one.

-
SELECT
-    toDateTime('2016-06-15 23:00:00') AS time,
-    toDate(time) AS date_local,
-    toDate(time, 'Asia/Yekaterinburg') AS date_yekat,
-    toString(time, 'US/Samoa') AS time_samoa
-
- - -
┌────────────────time─┬─date_local─┬─date_yekat─┬─time_samoa──────────┐
-│ 2016-06-15 23:00:00 │ 2016-06-15 │ 2016-06-16 │ 2016-06-15 09:00:00 │
-└─────────────────────┴────────────┴────────────┴─────────────────────┘
-
- - -

Only time zones that differ from UTC by a whole number of hours are supported.

-

toYear

-

Converts a date or date with time to a UInt16 number containing the year number (AD).

-

toMonth

-

Converts a date or date with time to a UInt8 number containing the month number (1-12).

-

toDayOfMonth

-

-Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31).

-

toDayOfWeek

-

Converts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7).

-

toHour

-

Converts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23). -This function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true – even in Moscow the clocks were twice changed at a different time).

-

toMinute

-

Converts a date with time to a UInt8 number containing the number of the minute of the hour (0-59).

-

toSecond

-

Converts a date with time to a UInt8 number containing the number of the second in the minute (0-59). -Leap seconds are not accounted for.

-

toMonday

-

Rounds down a date or date with time to the nearest Monday. -Returns the date.

-

toStartOfMonth

-

Rounds down a date or date with time to the first day of the month. -Returns the date.

-

toStartOfQuarter

-

Rounds down a date or date with time to the first day of the quarter. -The first day of the quarter is either 1 January, 1 April, 1 July, or 1 October. -Returns the date.

-

toStartOfYear

-

Rounds down a date or date with time to the first day of the year. -Returns the date.

-

toStartOfMinute

-

Rounds down a date with time to the start of the minute.

-

toStartOfFiveMinute

-

Rounds down a date with time to the start of the hour.

-

toStartOfFifteenMinutes

-

Rounds down the date with time to the start of the fifteen-minute interval.

-

Note: If you need to round a date with time to any other number of seconds, minutes, or hours, you can convert it into a number by using the toUInt32 function, then round the number using intDiv and multiplication, and convert it back using the toDateTime function.

-

toStartOfHour

-

Rounds down a date with time to the start of the hour.

-

toStartOfDay

-

Rounds down a date with time to the start of the day.

-

toTime

-

Converts a date with time to a certain fixed date, while preserving the time.

-

toRelativeYearNum

-

Converts a date with time or date to the number of the year, starting from a certain fixed point in the past.

-

toRelativeMonthNum

-

Converts a date with time or date to the number of the month, starting from a certain fixed point in the past.

-

toRelativeWeekNum

-

Converts a date with time or date to the number of the week, starting from a certain fixed point in the past.

-

toRelativeDayNum

-

Converts a date with time or date to the number of the day, starting from a certain fixed point in the past.

-

toRelativeHourNum

-

Converts a date with time or date to the number of the hour, starting from a certain fixed point in the past.

-

toRelativeMinuteNum

-

Converts a date with time or date to the number of the minute, starting from a certain fixed point in the past.

-

toRelativeSecondNum

-

Converts a date with time or date to the number of the second, starting from a certain fixed point in the past.

-

now

-

Accepts zero arguments and returns the current time at one of the moments of request execution. -This function returns a constant, even if the request took a long time to complete.

-

today

-

Accepts zero arguments and returns the current date at one of the moments of request execution. -The same as 'toDate(now())'.

-

yesterday

-

Accepts zero arguments and returns yesterday's date at one of the moments of request execution. -The same as 'today() - 1'.

-

timeSlot

-

Rounds the time to the half hour. -This function is specific to Yandex.Metrica, since half an hour is the minimum amount of time for breaking a session into two sessions if a tracking tag shows a single user's consecutive pageviews that differ in time by strictly more than this amount. This means that tuples (the tag ID, user ID, and time slot) can be used to search for pageviews that are included in the corresponding session.

-

timeSlots(StartTime, Duration)

-

For a time interval starting at 'StartTime' and continuing for 'Duration' seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour. -For example, timeSlots(toDateTime('2012-01-01 12:20:00'), 600) = [toDateTime('2012-01-01 12:00:00'), toDateTime('2012-01-01 12:30:00')]. -This is necessary for searching for pageviews in the corresponding session.

-

Functions for working with strings

-

empty

-

Returns 1 for an empty string or 0 for a non-empty string. -The result type is UInt8. -A string is considered non-empty if it contains at least one byte, even if this is a space or a null byte. -The function also works for arrays.

-

notEmpty

-

Returns 0 for an empty string or 1 for a non-empty string. -The result type is UInt8. -The function also works for arrays.

-

length

-

Returns the length of a string in bytes (not in characters, and not in code points). -The result type is UInt64. -The function also works for arrays.

-

lengthUTF8

-

Returns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception). -The result type is UInt64.

-

lower

-

Converts ASCII Latin symbols in a string to lowercase.

-

upper

-

Converts ASCII Latin symbols in a string to uppercase.

-

lowerUTF8

-

Converts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. -It doesn't detect the language. So for Turkish the result might not be exactly correct. -If the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point. -If the string contains a set of bytes that is not UTF-8, then the behavior is undefined.

-

upperUTF8

-

Converts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. -It doesn't detect the language. So for Turkish the result might not be exactly correct. -If the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point. -If the string contains a set of bytes that is not UTF-8, then the behavior is undefined.

-

reverse

-

Reverses the string (as a sequence of bytes).

-

reverseUTF8

-

Reverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn't throw an exception).

-

concat(s1, s2, ...)

-

Concatenates the strings listed in the arguments, without a separator.

-

substring(s, offset, length)

-

Returns a substring starting with the byte from the 'offset' index that is 'length' bytes long. Character indexing starts from one (as in standard SQL). The 'offset' and 'length' arguments must be constants.

-

substringUTF8(s, offset, length)

-

The same as 'substring', but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).

-

appendTrailingCharIfAbsent(s, c)

-

If the 's' string is non-empty and does not contain the 'c' character at the end, it appends the 'c' character to the end.

-

convertCharset(s, from, to)

-

Returns the string 's' that was converted from the encoding in 'from' to the encoding in 'to'.

-

Functions for searching strings

-

The search is case-sensitive in all these functions. -The search substring or regular expression must be a constant in all these functions.

-

position(haystack, needle)

-

Search for the needle substring in the haystack string. -Returns the position (in bytes) of the found substring, starting from 1, or returns 0 if the substring was not found.

-

For case-insensitive search use positionCaseInsensitive function.

-

positionUTF8(haystack, needle)

-

The same as position, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).

-

For case-insensitive search use positionCaseInsensitiveUTF8 function.

-

match(haystack, pattern)

-

Checks whether the string matches the 'pattern' regular expression. A re2 regular expression. -Returns 0 if it doesn't match, or 1 if it matches.

-

Note that the backslash symbol (\) is used for escaping in the regular expression. The same symbol is used for escaping in string literals. So in order to escape the symbol in a regular expression, you must write two backslashes (\) in a string literal.

-

The regular expression works with the string as if it is a set of bytes. The regular expression can't contain null bytes. -For patterns to search for substrings in a string, it is better to use LIKE or 'position', since they work much faster.

-

extract(haystack, pattern)

-

Extracts a fragment of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. If the regex doesn't contain subpatterns, it takes the fragment that matches the entire regex. Otherwise, it takes the fragment that matches the first subpattern.

-

extractAll(haystack, pattern)

-

Extracts all the fragments of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex. In general, the behavior is the same as the 'extract' function (it takes the first subpattern, or the entire expression if there isn't a subpattern).

-

like(haystack, pattern), haystack LIKE pattern operator

-

Checks whether a string matches a simple regular expression. -The regular expression can contain the metasymbols % and _.

-

``% indicates any quantity of any bytes (including zero characters).

-

_ indicates any one byte.

-

Use the backslash (\) for escaping metasymbols. See the note on escaping in the description of the 'match' function.

-

For regular expressions like %needle%, the code is more optimal and works as fast as the position function. -For other regular expressions, the code is the same as for the 'match' function.

-

notLike(haystack, pattern), haystack NOT LIKE pattern operator

-

The same thing as 'like', but negative.

-

Functions for searching and replacing in strings

-

replaceOne(haystack, pattern, replacement)

-

Replaces the first occurrence, if it exists, of the 'pattern' substring in 'haystack' with the 'replacement' substring. -Hereafter, 'pattern' and 'replacement' must be constants.

-

replaceAll(haystack, pattern, replacement)

-

Replaces all occurrences of the 'pattern' substring in 'haystack' with the 'replacement' substring.

-

replaceRegexpOne(haystack, pattern, replacement)

-

Replacement using the 'pattern' regular expression. A re2 regular expression. -Replaces only the first occurrence, if it exists. -A pattern can be specified as 'replacement'. This pattern can include substitutions \0-\9. -The substitution \0 includes the entire regular expression. Substitutions \1-\9 correspond to the subpattern numbers.To use the \ character in a template, escape it using \. -Also keep in mind that a string literal requires an extra escape.

-

Example 1. Converting the date to American format:

-
SELECT DISTINCT
-    EventDate,
-    replaceRegexpOne(toString(EventDate), '(\\d{4})-(\\d{2})-(\\d{2})', '\\2/\\3/\\1') AS res
-FROM test.hits
-LIMIT 7
-FORMAT TabSeparated
-
- - -
2014-03-17      03/17/2014
-2014-03-18      03/18/2014
-2014-03-19      03/19/2014
-2014-03-20      03/20/2014
-2014-03-21      03/21/2014
-2014-03-22      03/22/2014
-2014-03-23      03/23/2014
-
- - -

Example 2. Copying a string ten times:

-
SELECT replaceRegexpOne('Hello, World!', '.*', '\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0') AS res
-
- - -
┌─res────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
-│ Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World! │
-└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
-
- - -

replaceRegexpAll(haystack, pattern, replacement)

-

This does the same thing, but replaces all the occurrences. Example:

-
SELECT replaceRegexpAll('Hello, World!', '.', '\\0\\0') AS res
-
- - -
┌─res────────────────────────┐
-│ HHeelllloo,,  WWoorrlldd!! │
-└────────────────────────────┘
-
- - -

As an exception, if a regular expression worked on an empty substring, the replacement is not made more than once. -Example:

-
SELECT replaceRegexpAll('Hello, World!', '^', 'here: ') AS res
-
- - -
┌─res─────────────────┐
-│ here: Hello, World! │
-└─────────────────────┘
-
- - -

Conditional functions

-

if(cond, then, else), cond ? operator then : else

-

Returns 'then' if cond !or 'else' if cond = 0.'cond' must be UInt 8, and 'then' and 'else' must be a type that has the smallest common type.

-

Mathematical functions

-

All the functions return a Float64 number. The accuracy of the result is close to the maximum precision possible, but the result might not coincide with the machine representable number nearest to the corresponding real number.

-

e()

-

Returns a Float64 number close to the e number.

-

pi()

-

Returns a Float64 number close to π.

-

exp(x)

-

Accepts a numeric argument and returns a Float64 number close to the exponent of the argument.

-

log(x)

-

Accepts a numeric argument and returns a Float64 number close to the natural logarithm of the argument.

-

exp2(x)

-

Accepts a numeric argument and returns a Float64 number close to 2^x.

-

log2(x)

-

Accepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument.

-

exp10(x)

-

Accepts a numeric argument and returns a Float64 number close to 10^x.

-

log10(x)

-

Accepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument.

-

sqrt(x)

-

Accepts a numeric argument and returns a Float64 number close to the square root of the argument.

-

cbrt(x)

-

Accepts a numeric argument and returns a Float64 number close to the cubic root of the argument.

-

erf(x)

-

If 'x' is non-negative, then erf(x / σ√2) is the probability that a random variable having a normal distribution with standard deviation 'σ' takes the value that is separated from the expected value by more than 'x'.

-

Example (three sigma rule):

-
SELECT erf(3 / sqrt(2))
-
- - -
┌─erf(divide(3, sqrt(2)))─┐
-│      0.9973002039367398 │
-└─────────────────────────┘
-
- - -

erfc(x)

-

Accepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large 'x' values.

-

lgamma(x)

-

The logarithm of the gamma function.

-

tgamma(x)

-

Gamma function.

-

sin(x)

-

The sine.

-

cos(x)

-

The cosine.

-

tan(x)

-

The tangent.

-

asin(x)

-

The arc sine.

-

acos(x)

-

The arc cosine.

-

atan(x)

-

The arc tangent.

-

pow(x, y)

-

Accepts two numeric arguments and returns a Float64 number close to x^y.

-

Rounding functions

-

floor(x[, N])

-

Returns the largest round number that is less than or equal to x. A round number is a multiple of 1/10N, or the nearest number of the appropriate data type if 1 / 10N isn't exact. -'N' is an integer constant, optional parameter. By default it is zero, which means to round to an integer. -'N' may be negative.

-

Examples: floor(123.45, 1) = 123.4, floor(123.45, -1) = 120.

-

x is any numeric type. The result is a number of the same type. -For integer arguments, it makes sense to round with a negative 'N' value (for non-negative 'N', the function doesn't do anything). -If rounding causes overflow (for example, floor(-128, -1)), an implementation-specific result is returned.

-

ceil(x[, N])

-

Returns the smallest round number that is greater than or equal to 'x'. In every other way, it is the same as the 'floor' function (see above).

-

round(x[, N])

-

Returns the round number nearest to 'num', which may be less than, greater than, or equal to 'x'.If 'x' is exactly in the middle between the nearest round numbers, one of them is returned (implementation-specific). -The number '-0.' may or may not be considered round (implementation-specific). -In every other way, this function is the same as 'floor' and 'ceil' described above.

-

roundToExp2(num)

-

Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two.

-

roundDuration(num)

-

Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length

-

roundAge(num)

-

Accepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to a number from the set: 18, 25, 35, 45, 55. This function is specific to Yandex.Metrica and used for implementing the report on user age.

-

Functions for working with arrays

-

empty

-

Returns 1 for an empty array, or 0 for a non-empty array. -The result type is UInt8. -The function also works for strings.

-

notEmpty

-

Returns 0 for an empty array, or 1 for a non-empty array. -The result type is UInt8. -The function also works for strings.

-

length

-

Returns the number of items in the array. -The result type is UInt64. -The function also works for strings.

-

emptyArrayUInt8, emptyArrayUInt16, emptyArrayUInt32, emptyArrayUInt64

-

emptyArrayInt8, emptyArrayInt16, emptyArrayInt32, emptyArrayInt64

-

emptyArrayFloat32, emptyArrayFloat64

-

emptyArrayDate, emptyArrayDateTime

-

emptyArrayString

-

Accepts zero arguments and returns an empty array of the appropriate type.

-

emptyArrayToSingle

-

Accepts an empty array and returns a one-element array that is equal to the default value.

-

range(N)

-

Returns an array of numbers from 0 to N-1. -Just in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block.

-

array(x1, ...), operator [x1, ...]

-

Creates an array from the function arguments. -The arguments must be constants and have types that have the smallest common type. At least one argument must be passed, because otherwise it isn't clear which type of array to create. That is, you can't use this function to create an empty array (to do that, use the 'emptyArray*' function described above). -Returns an 'Array(T)' type result, where 'T' is the smallest common type out of the passed arguments.

-

arrayConcat

-

Combines arrays passed as arguments.

-
arrayConcat(arrays)
-
- - -

Arguments

-
    -
  • arrays – Arrays of comma-separated [values].
    • -
-

Example

-
SELECT arrayConcat([1, 2], [3, 4], [5, 6]) AS res
-
- - -
┌─res───────────┐
-│ [1,2,3,4,5,6] │
-└───────────────┘
-
- - -

arrayElement(arr, n), operator arr[n]

-

Get the element with the index 'n' from the array 'arr'.'n' must be any integer type. -Indexes in an array begin from one. -Negative indexes are supported. In this case, it selects the corresponding element numbered from the end. For example, 'arr[-1]' is the last item in the array.

-

If the index falls outside of the bounds of an array, it returns some default value (0 for numbers, an empty string for strings, etc.).

-

has(arr, elem)

-

Checks whether the 'arr' array has the 'elem' element. -Returns 0 if the the element is not in the array, or 1 if it is.

-

indexOf(arr, x)

-

Returns the index of the 'x' element (starting from 1) if it is in the array, or 0 if it is not.

-

countEqual(arr, x)

-

Returns the number of elements in the array equal to x. Equivalent to arrayCount (elem-> elem = x, arr).

-

arrayEnumerate(arr)

-

Returns the array [1, 2, 3, ..., length (arr) ]

-

This function is normally used with ARRAY JOIN. It allows counting something just once for each array after applying ARRAY JOIN. Example:

-
SELECT
-    count() AS Reaches,
-    countIf(num = 1) AS Hits
-FROM test.hits
-ARRAY JOIN
-    GoalsReached,
-    arrayEnumerate(GoalsReached) AS num
-WHERE CounterID = 160656
-LIMIT 10
-
- - -
┌─Reaches─┬──Hits─┐
-│   95606 │ 31406 │
-└─────────┴───────┘
-
- - -

In this example, Reaches is the number of conversions (the strings received after applying ARRAY JOIN), and Hits is the number of pageviews (strings before ARRAY JOIN). In this particular case, you can get the same result in an easier way:

-
SELECT
-    sum(length(GoalsReached)) AS Reaches,
-    count() AS Hits
-FROM test.hits
-WHERE (CounterID = 160656) AND notEmpty(GoalsReached)
-
- - -
┌─Reaches─┬──Hits─┐
-│   95606 │ 31406 │
-└─────────┴───────┘
-
- - -

This function can also be used in higher-order functions. For example, you can use it to get array indexes for elements that match a condition.

-

arrayEnumerateUniq(arr, ...)

-

Returns an array the same size as the source array, indicating for each element what its position is among elements with the same value. -For example: arrayEnumerateUniq([10, 20, 10, 30]) = [1, 1, 2, 1].

-

This function is useful when using ARRAY JOIN and aggregation of array elements. -Example:

-
SELECT
-    Goals.ID AS GoalID,
-    sum(Sign) AS Reaches,
-    sumIf(Sign, num = 1) AS Visits
-FROM test.visits
-ARRAY JOIN
-    Goals,
-    arrayEnumerateUniq(Goals.ID) AS num
-WHERE CounterID = 160656
-GROUP BY GoalID
-ORDER BY Reaches DESC
-LIMIT 10
-
- - -
┌──GoalID─┬─Reaches─┬─Visits─┐
-│   53225 │    3214 │   1097 │
-│ 2825062 │    3188 │   1097 │
-│   56600 │    2803 │    488 │
-│ 1989037 │    2401 │    365 │
-│ 2830064 │    2396 │    910 │
-│ 1113562 │    2372 │    373 │
-│ 3270895 │    2262 │    812 │
-│ 1084657 │    2262 │    345 │
-│   56599 │    2260 │    799 │
-│ 3271094 │    2256 │    812 │
-└─────────┴─────────┴────────┘
-
- - -

In this example, each goal ID has a calculation of the number of conversions (each element in the Goals nested data structure is a goal that was reached, which we refer to as a conversion) and the number of sessions. Without ARRAY JOIN, we would have counted the number of sessions as sum(Sign). But in this particular case, the rows were multiplied by the nested Goals structure, so in order to count each session one time after this, we apply a condition to the value of the arrayEnumerateUniq(Goals.ID) function.

-

The arrayEnumerateUniq function can take multiple arrays of the same size as arguments. In this case, uniqueness is considered for tuples of elements in the same positions in all the arrays.

-
SELECT arrayEnumerateUniq([1, 1, 1, 2, 2, 2], [1, 1, 2, 1, 1, 2]) AS res
-
- - -
┌─res───────────┐
-│ [1,2,1,1,2,1] │
-└───────────────┘
-
- - -

This is necessary when using ARRAY JOIN with a nested data structure and further aggregation across multiple elements in this structure.

-

arrayPopBack

-

Removes the last item from the array.

-
arrayPopBack(array)
-
- - -

Arguments

-
    -
  • array – Array.
    • -
-

Example

-
SELECT arrayPopBack([1, 2, 3]) AS res
-
- - -
┌─res───┐
-│ [1,2] │
-└───────┘
-
- - -

arrayPopFront

-

Removes the first item from the array.

-
arrayPopFront(array)
-
- - -

Arguments

-
    -
  • array – Array.
    • -
-

Example

-
SELECT arrayPopFront([1, 2, 3]) AS res
-
- - -
┌─res───┐
-│ [2,3] │
-└───────┘
-
- - -

arrayPushBack

-

Adds one item to the end of the array.

-
arrayPushBack(array, single_value)
-
- - -

Arguments

-
    -
  • array – Array.
    • -
  • single_value – A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section "Data types".
    • -
-

Example

-
SELECT arrayPushBack(['a'], 'b') AS res
-
- - -
┌─res───────┐
-│ ['a','b'] │
-└───────────┘
-
- - -

arrayPushFront

-

Adds one element to the beginning of the array.

-
arrayPushFront(array, single_value)
-
- - -

Arguments

-
    -
  • array – Array.
    • -
  • single_value – A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section "Data types".
    • -
-

Example

-
SELECT arrayPushBack(['b'], 'a') AS res
-
- - -
┌─res───────┐
-│ ['a','b'] │
-└───────────┘
-
- - -

arraySlice

-

Returns a slice of the array.

-
arraySlice(array, offset[, length])
-
- - -

Arguments

-
    -
  • array – Array of data.
    • -
  • offset – Indent from the edge of the array. A positive value indicates an offset on the left, and a negative value is an indent on the right. Numbering of the array items begins with 1.
    • -
  • length - The length of the required slice. If you specify a negative value, the function returns an open slice [offset, array_length - length). If you omit the value, the function returns the slice [offset, the_end_of_array].
    • -
-

Example

-
SELECT arraySlice([1, 2, 3, 4, 5], 2, 3) AS res
-
- - -
┌─res─────┐
-│ [2,3,4] │
-└─────────┘
-
- - -

arrayUniq(arr, ...)

-

If one argument is passed, it counts the number of different elements in the array. -If multiple arguments are passed, it counts the number of different tuples of elements at corresponding positions in multiple arrays.

-

If you want to get a list of unique items in an array, you can use arrayReduce('groupUniqArray', arr).

-

arrayJoin(arr)

-

A special function. See the section "ArrayJoin function".

-

Functions for splitting and merging strings and arrays

-

splitByChar(separator, s)

-

Splits a string into substrings separated by 'separator'.'separator' must be a string constant consisting of exactly one character. -Returns an array of selected substrings. Empty substrings may be selected if the separator occurs at the beginning or end of the string, or if there are multiple consecutive separators.

-

splitByString(separator, s)

-

The same as above, but it uses a string of multiple characters as the separator. The string must be non-empty.

-

arrayStringConcat(arr[, separator])

-

Concatenates the strings listed in the array with the separator.'separator' is an optional parameter: a constant string, set to an empty string by default. -Returns the string.

-

alphaTokens(s)

-

Selects substrings of consecutive bytes from the ranges a-z and A-Z.Returns an array of substrings.

-

Bit functions

-

Bit functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.

-

The result type is an integer with bits equal to the maximum bits of its arguments. If at least one of the arguments is signed, the result is a signed number. If an argument is a floating-point number, it is cast to Int64.

-

bitAnd(a, b)

-

bitOr(a, b)

-

bitXor(a, b)

-

bitNot(a)

-

bitShiftLeft(a, b)

-

bitShiftRight(a, b)

-

Hash functions

-

Hash functions can be used for deterministic pseudo-random shuffling of elements.

-

halfMD5

-

Calculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian. -Accepts a String-type argument. Returns UInt64. -This function works fairly slowly (5 million short strings per second per processor core). -If you don't need MD5 in particular, use the 'sipHash64' function instead.

-

MD5

-

Calculates the MD5 from a string and returns the resulting set of bytes as FixedString(16). -If you don't need MD5 in particular, but you need a decent cryptographic 128-bit hash, use the 'sipHash128' function instead. -If you want to get the same result as output by the md5sum utility, use lower(hex(MD5(s))).

-

sipHash64

-

Calculates SipHash from a string. -Accepts a String-type argument. Returns UInt64. -SipHash is a cryptographic hash function. It works at least three times faster than MD5. -For more information, see the link: https://131002.net/siphash/

-

sipHash128

-

Calculates SipHash from a string. -Accepts a String-type argument. Returns FixedString(16). -Differs from sipHash64 in that the final xor-folding state is only done up to 128 bytes.

-

cityHash64

-

Calculates CityHash64 from a string or a similar hash function for any number of any type of arguments. -For String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality. -For other types of arguments, a decent implementation-specific fast non-cryptographic hash function is used. -If multiple arguments are passed, the function is calculated using the same rules and chain combinations using the CityHash combinator. -For example, you can compute the checksum of an entire table with accuracy up to the row order: SELECT sum(cityHash64(*)) FROM table.

-

intHash32

-

Calculates a 32-bit hash code from any type of integer. -This is a relatively fast non-cryptographic hash function of average quality for numbers.

-

intHash64

-

Calculates a 64-bit hash code from any type of integer. -It works faster than intHash32. Average quality.

-

SHA1

-

SHA224

-

SHA256

-

Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32). -The function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million). -We recommend using this function only in cases when you need a specific hash function and you can't select it. -Even in these cases, we recommend applying the function offline and pre-calculating values when inserting them into the table, instead of applying it in SELECTS.

-

URLHash(url[, N])

-

A fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization. -URLHash(s) – Calculates a hash from a string without one of the trailing symbols /,? or # at the end, if present. -URLHash(s, N) – Calculates a hash from a string up to the N level in the URL hierarchy, without one of the trailing symbols /,? or # at the end, if present. -Levels are the same as in URLHierarchy. This function is specific to Yandex.Metrica.

-

Functions for generating pseudo-random numbers

-

Non-cryptographic generators of pseudo-random numbers are used.

-

All the functions accept zero arguments or one argument. -If an argument is passed, it can be any type, and its value is not used for anything. -The only purpose of this argument is to prevent common subexpression elimination, so that two different instances of the same function return different columns with different random numbers.

-

rand

-

Returns a pseudo-random UInt32 number, evenly distributed among all UInt32-type numbers. -Uses a linear congruential generator.

-

rand64

-

Returns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers. -Uses a linear congruential generator.

-

Encoding functions

-

hex

-

Accepts arguments of types: String, unsigned integer, Date, or DateTime. Returns a string containing the argument's hexadecimal representation. Uses uppercase letters A-F. Does not use 0x prefixes or h suffixes. For strings, all bytes are simply encoded as two hexadecimal numbers. Numbers are converted to big endian ("human readable") format. For numbers, older zeros are trimmed, but only by entire bytes. For example, hex (1) = '01'. Date is encoded as the number of days since the beginning of the Unix epoch. DateTime is encoded as the number of seconds since the beginning of the Unix epoch.

-

unhex(str)

-

Accepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits does not have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn't thrown). -If you want to convert the result to a number, you can use the 'reverse' and 'reinterpretAsType' functions.

-

UUIDStringToNum(str)

-

Accepts a string containing 36 characters in the format 123e4567-e89b-12d3-a456-426655440000, and returns it as a set of bytes in a FixedString(16).

-

UUIDNumToString(str)

-

Accepts a FixedString(16) value. Returns a string containing 36 characters in text format.

-

bitmaskToList(num)

-

Accepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order.

-

bitmaskToArray(num)

-

Accepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order.

-

Functions for working with URLs

-

All these functions don't follow the RFC. They are maximally simplified for improved performance.

-

Functions that extract part of a URL

-

If there isn't anything similar in a URL, an empty string is returned.

-

protocol

-

Returns the protocol. Examples: http, ftp, mailto, magnet...

-

domain

-

Gets the domain.

-

domainWithoutWWW

-

Returns the domain and removes no more than one 'www.' from the beginning of it, if present.

-

topLevelDomain

-

Returns the top-level domain. Example: .ru.

-

firstSignificantSubdomain

-

Returns the "first significant subdomain". This is a non-standard concept specific to Yandex.Metrica. The first significant subdomain is a second-level domain if it is 'com', 'net', 'org', or 'co'. Otherwise, it is a third-level domain. For example, firstSignificantSubdomain ('https://news.yandex.ru/') = 'yandex ', firstSignificantSubdomain ('https://news.yandex.com.tr/') = 'yandex '. The list of "insignificant" second-level domains and other implementation details may change in the future.

-

cutToFirstSignificantSubdomain

-

Returns the part of the domain that includes top-level subdomains up to the "first significant subdomain" (see the explanation above).

-

For example, cutToFirstSignificantSubdomain('https://news.yandex.com.tr/') = 'yandex.com.tr'.

-

path

-

Returns the path. Example: /top/news.html The path does not include the query string.

-

pathFull

-

The same as above, but including query string and fragment. Example: /top/news.html?page=2#comments

-

queryString

-

Returns the query string. Example: page=1&lr=213. query-string does not include the initial question mark, as well as # and everything after #.

-

fragment

-

Returns the fragment identifier. fragment does not include the initial hash symbol.

-

queryStringAndFragment

-

Returns the query string and fragment identifier. Example: page=1#29390.

-

extractURLParameter(URL, name)

-

Returns the value of the 'name' parameter in the URL, if present. Otherwise, an empty string. If there are many parameters with this name, it returns the first occurrence. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.

-

extractURLParameters(URL)

-

Returns an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way.

-

extractURLParameterNames(URL)

-

Returns an array of name strings corresponding to the names of URL parameters. The values are not decoded in any way.

-

URLHierarchy(URL)

-

Returns an array containing the URL, truncated at the end by the symbols /,? in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:

-

URLPathHierarchy(URL)

-

The same as above, but without the protocol and host in the result. The / element (root) is not included. Example: the function is used to implement tree reports the URL in Yandex. Metric.

-
URLPathHierarchy('https://example.com/browse/CONV-6788') =
-[
-    '/browse/',
-    '/browse/CONV-6788'
-]
-
- - -

decodeURLComponent(URL)

-

Returns the decoded URL. -Example:

-
SELECT decodeURLComponent('http://127.0.0.1:8123/?query=SELECT%201%3B') AS DecodedURL;
-
- - -
┌─DecodedURL─────────────────────────────┐
-│ http://127.0.0.1:8123/?query=SELECT 1; │
-└────────────────────────────────────────┘
-
- - -

Functions that remove part of a URL.

-

If the URL doesn't have anything similar, the URL remains unchanged.

-

cutWWW

-

Removes no more than one 'www.' from the beginning of the URL's domain, if present.

-

cutQueryString

-

Removes query string. The question mark is also removed.

-

cutFragment

-

Removes the fragment identifier. The number sign is also removed.

-

cutQueryStringAndFragment

-

Removes the query string and fragment identifier. The question mark and number sign are also removed.

-

cutURLParameter(URL, name)

-

Removes the 'name' URL parameter, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.

-

Functions for working with IP addresses

-

IPv4NumToString(num)

-

Takes a UInt32 number. Interprets it as an IPv4 address in big endian. Returns a string containing the corresponding IPv4 address in the format A.B.C.d (dot-separated numbers in decimal form).

-

IPv4StringToNum(s)

-

The reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0.

-

IPv4NumToStringClassC(num)

-

Similar to IPv4NumToString, but using xxx instead of the last octet.

-

Example:

-
SELECT
-    IPv4NumToStringClassC(ClientIP) AS k,
-    count() AS c
-FROM test.hits
-GROUP BY k
-ORDER BY c DESC
-LIMIT 10
-
- - -
┌─k──────────────┬─────c─┐
-│ 83.149.9.xxx   │ 26238 │
-│ 217.118.81.xxx │ 26074 │
-│ 213.87.129.xxx │ 25481 │
-│ 83.149.8.xxx   │ 24984 │
-│ 217.118.83.xxx │ 22797 │
-│ 78.25.120.xxx  │ 22354 │
-│ 213.87.131.xxx │ 21285 │
-│ 78.25.121.xxx  │ 20887 │
-│ 188.162.65.xxx │ 19694 │
-│ 83.149.48.xxx  │ 17406 │
-└────────────────┴───────┘
-
- - -

Since using 'xxx' is highly unusual, this may be changed in the future. We recommend that you don't rely on the exact format of this fragment.

-

IPv6NumToString(x)

-

Accepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format. -IPv6-mapped IPv4 addresses are output in the format ::ffff:111.222.33.44. Examples:

-
SELECT IPv6NumToString(toFixedString(unhex('2A0206B8000000000000000000000011'), 16)) AS addr
-
- - -
┌─addr─────────┐
-│ 2a02:6b8::11 │
-└──────────────┘
-
- - -
SELECT
-    IPv6NumToString(ClientIP6 AS k),
-    count() AS c
-FROM hits_all
-WHERE EventDate = today() AND substring(ClientIP6, 1, 12) != unhex('00000000000000000000FFFF')
-GROUP BY k
-ORDER BY c DESC
-LIMIT 10
-
- - -
┌─IPv6NumToString(ClientIP6)──────────────┬─────c─┐
-│ 2a02:2168:aaa:bbbb::2                   │ 24695 │
-│ 2a02:2698:abcd:abcd:abcd:abcd:8888:5555 │ 22408 │
-│ 2a02:6b8:0:fff::ff                      │ 16389 │
-│ 2a01:4f8:111:6666::2                    │ 16016 │
-│ 2a02:2168:888:222::1                    │ 15896 │
-│ 2a01:7e00::ffff:ffff:ffff:222           │ 14774 │
-│ 2a02:8109:eee:ee:eeee:eeee:eeee:eeee    │ 14443 │
-│ 2a02:810b:8888:888:8888:8888:8888:8888  │ 14345 │
-│ 2a02:6b8:0:444:4444:4444:4444:4444      │ 14279 │
-│ 2a01:7e00::ffff:ffff:ffff:ffff          │ 13880 │
-└─────────────────────────────────────────┴───────┘
-
- - -
SELECT
-    IPv6NumToString(ClientIP6 AS k),
-    count() AS c
-FROM hits_all
-WHERE EventDate = today()
-GROUP BY k
-ORDER BY c DESC
-LIMIT 10
-
- - -
┌─IPv6NumToString(ClientIP6)─┬──────c─┐
-│ ::ffff:94.26.111.111       │ 747440 │
-│ ::ffff:37.143.222.4        │ 529483 │
-│ ::ffff:5.166.111.99        │ 317707 │
-│ ::ffff:46.38.11.77         │ 263086 │
-│ ::ffff:79.105.111.111      │ 186611 │
-│ ::ffff:93.92.111.88        │ 176773 │
-│ ::ffff:84.53.111.33        │ 158709 │
-│ ::ffff:217.118.11.22       │ 154004 │
-│ ::ffff:217.118.11.33       │ 148449 │
-│ ::ffff:217.118.11.44       │ 148243 │
-└────────────────────────────┴────────┘
-
- - -

IPv6StringToNum(s)

-

The reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes. -HEX can be uppercase or lowercase.

-

Functions for working with JSON

-

In Yandex.Metrica, JSON is transmitted by users as session parameters. There are some special functions for working with this JSON. (Although in most of the cases, the JSONs are additionally pre-processed, and the resulting values are put in separate columns in their processed format.) All these functions are based on strong assumptions about what the JSON can be, but they try to do as little as possible to get the job done.

-

The following assumptions are made:

-
    -
  1. The field name (function argument) must be a constant.
    2. -
  2. The field name is somehow canonically encoded in JSON. For example: visitParamHas('{"abc":"def"}', 'abc') = 1, but visitParamHas('{"\\u0061\\u0062\\u0063":"def"}', 'abc') = 0
    3. -
  3. Fields are searched for on any nesting level, indiscriminately. If there are multiple matching fields, the first occurrence is used.
    4. -
  4. The JSON doesn't have space characters outside of string literals.
    5. -
-

visitParamHas(params, name)

-

Checks whether there is a field with the 'name' name.

-

visitParamExtractUInt(params, name)

-

Parses UInt64 from the value of the field named 'name'. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn't exist, or it exists but doesn't contain a number, it returns 0.

-

visitParamExtractInt(params, name)

-

The same as for Int64.

-

visitParamExtractFloat(params, name)

-

The same as for Float64.

-

visitParamExtractBool(params, name)

-

Parses a true/false value. The result is UInt8.

-

visitParamExtractRaw(params, name)

-

Returns the value of a field, including separators.

-

Examples:

-
visitParamExtractRaw('{"abc":"\\n\\u0000"}', 'abc') = '"\\n\\u0000"'
-visitParamExtractRaw('{"abc":{"def":[1,2,3]}}', 'abc') = '{"def":[1,2,3]}'
-
- - -

visitParamExtractString(params, name)

-

Parses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string.

-

Examples:

-
visitParamExtractString('{"abc":"\\n\\u0000"}', 'abc') = '\n\0'
-visitParamExtractString('{"abc":"\\u263a"}', 'abc') = '☺'
-visitParamExtractString('{"abc":"\\u263"}', 'abc') = ''
-visitParamExtractString('{"abc":"hello}', 'abc') = ''
-
- - -

There is currently no support for code points in the format \uXXXX\uYYYY that are not from the basic multilingual plane (they are converted to CESU-8 instead of UTF-8).

-

Higher-order functions

-

-> operator, lambda(params, expr) function

-

Allows describing a lambda function for passing to a higher-order function. The left side of the arrow has a formal parameter, which is any ID, or multiple formal parameters – any IDs in a tuple. The right side of the arrow has an expression that can use these formal parameters, as well as any table columns.

-

Examples: x -> 2 * x, str -> str != Referer.

-

Higher-order functions can only accept lambda functions as their functional argument.

-

A lambda function that accepts multiple arguments can be passed to a higher-order function. In this case, the higher-order function is passed several arrays of identical length that these arguments will correspond to.

-

For all functions other than 'arrayMap' and 'arrayFilter', the first argument (the lambda function) can be omitted. In this case, identical mapping is assumed.

-

arrayMap(func, arr1, ...)

-

Returns an array obtained from the original application of the 'func' function to each element in the 'arr' array.

-

arrayFilter(func, arr1, ...)

-

Returns an array containing only the elements in 'arr1' for which 'func' returns something other than 0.

-

Examples:

-
SELECT arrayFilter(x -> x LIKE '%World%', ['Hello', 'abc World']) AS res
-
- - -
┌─res───────────┐
-│ ['abc World'] │
-└───────────────┘
-
- - -
SELECT
-    arrayFilter(
-        (i, x) -> x LIKE '%World%',
-        arrayEnumerate(arr),
-        ['Hello', 'abc World'] AS arr)
-    AS res
-
- - -
┌─res─┐
-│ [2] │
-└─────┘
-
- - -

arrayCount([func,] arr1, ...)

-

Returns the number of elements in the arr array for which func returns something other than 0. If 'func' is not specified, it returns the number of non-zero elements in the array.

-

arrayExists([func,] arr1, ...)

-

Returns 1 if there is at least one element in 'arr' for which 'func' returns something other than 0. Otherwise, it returns 0.

-

arrayAll([func,] arr1, ...)

-

Returns 1 if 'func' returns something other than 0 for all the elements in 'arr'. Otherwise, it returns 0.

-

arraySum([func,] arr1, ...)

-

Returns the sum of the 'func' values. If the function is omitted, it just returns the sum of the array elements.

-

arrayFirst(func, arr1, ...)

-

Returns the first element in the 'arr1' array for which 'func' returns something other than 0.

-

arrayFirstIndex(func, arr1, ...)

-

Returns the index of the first element in the 'arr1' array for which 'func' returns something other than 0.

-

arrayCumSum([func,] arr1, ...)

-

Returns an array of partial sums of elements in the source array (a running sum). If the func function is specified, then the values of the array elements are converted by this function before summing.

-

Example:

-
SELECT arrayCumSum([1, 1, 1, 1]) AS res
-
- - -
┌─res──────────┐
-│ [1, 2, 3, 4] │
-└──────────────┘
-
- - -

arraySort([func,] arr1, ...)

-

Returns an array as result of sorting the elements of arr1 in ascending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays)

-

The Schwartzian transform is used to impove sorting efficiency.

-

Example:

-
SELECT arraySort((x, y) -> y, ['hello', 'world'], [2, 1]);
-
- - -
┌─res────────────────┐
-│ ['world', 'hello'] │
-└────────────────────┘
-
- - -

arrayReverseSort([func,] arr1, ...)

-

Returns an array as result of sorting the elements of arr1 in descending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays)

-

Other functions

-

hostName()

-

Returns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server.

-

visibleWidth(x)

-

Calculates the approximate width when outputting values to the console in text format (tab-separated). -This function is used by the system for implementing Pretty formats.

-

toTypeName(x)

-

Returns a string containing the type name of the passed argument.

-

blockSize()

-

Gets the size of the block. -In ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for.

-

materialize(x)

-

Turns a constant into a full column containing just one value. -In ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior.

-

ignore(...)

-

Accepts any arguments and always returns 0. -However, the argument is still evaluated. This can be used for benchmarks.

-

sleep(seconds)

-

Sleeps 'seconds' seconds on each data block. You can specify an integer or a floating-point number.

-

currentDatabase()

-

Returns the name of the current database. -You can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database.

-

isFinite(x)

-

Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0.

-

isInfinite(x)

-

Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. Note that 0 is returned for a NaN.

-

isNaN(x)

-

Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0.

-

hasColumnInTable(['hostname'[, 'username'[, 'password']],] 'database', 'table', 'column')

-

Accepts constant strings: database name, table name, and column name. Returns a UInt8 constant expression equal to 1 if there is a column, otherwise 0. If the hostname parameter is set, the test will run on a remote server. -The function throws an exception if the table does not exist. -For elements in a nested data structure, the function checks for the existence of a column. For the nested data structure itself, the function returns 0.

-

bar

-

Allows building a unicode-art diagram.

-

bar (x, min, max, width) draws a band with a width proportional to (x - min) and equal to width characters when x = max.

-

Parameters:

-
    -
  • x – Value to display.
    • -
  • min, max – Integer constants. The value must fit in Int64.
    • -
  • width – Constant, positive number, may be a fraction.
    • -
-

The band is drawn with accuracy to one eighth of a symbol.

-

Example:

-
SELECT
-    toHour(EventTime) AS h,
-    count() AS c,
-    bar(c, 0, 600000, 20) AS bar
-FROM test.hits
-GROUP BY h
-ORDER BY h ASC
-
- - -
┌──h─┬──────c─┬─bar────────────────┐
-│  0 │ 292907 │ █████████▋         │
-│  1 │ 180563 │ ██████             │
-│  2 │ 114861 │ ███▋               │
-│  3 │  85069 │ ██▋                │
-│  4 │  68543 │ ██▎                │
-│  5 │  78116 │ ██▌                │
-│  6 │ 113474 │ ███▋               │
-│  7 │ 170678 │ █████▋             │
-│  8 │ 278380 │ █████████▎         │
-│  9 │ 391053 │ █████████████      │
-│ 10 │ 457681 │ ███████████████▎   │
-│ 11 │ 493667 │ ████████████████▍  │
-│ 12 │ 509641 │ ████████████████▊  │
-│ 13 │ 522947 │ █████████████████▍ │
-│ 14 │ 539954 │ █████████████████▊ │
-│ 15 │ 528460 │ █████████████████▌ │
-│ 16 │ 539201 │ █████████████████▊ │
-│ 17 │ 523539 │ █████████████████▍ │
-│ 18 │ 506467 │ ████████████████▊  │
-│ 19 │ 520915 │ █████████████████▎ │
-│ 20 │ 521665 │ █████████████████▍ │
-│ 21 │ 542078 │ ██████████████████ │
-│ 22 │ 493642 │ ████████████████▍  │
-│ 23 │ 400397 │ █████████████▎     │
-└────┴────────┴────────────────────┘
-
- - -

-

transform

-

Transforms a value according to the explicitly defined mapping of some elements to other ones. -There are two variations of this function:

-
    -
  1. transform(x, array_from, array_to, default)
    2. -
-

x – What to transform.

-

array_from – Constant array of values for converting.

-

array_to – Constant array of values to convert the values in 'from' to.

-

default – Which value to use if 'x' is not equal to any of the values in 'from'.

-

array_from and array_to – Arrays of the same size.

-

Types:

-

transform(T, Array(T), Array(U), U) -> U

-

T and U can be numeric, string, or Date or DateTime types. -Where the same letter is indicated (T or U), for numeric types these might not be matching types, but types that have a common type. -For example, the first argument can have the Int64 type, while the second has the Array(Uint16) type.

-

If the 'x' value is equal to one of the elements in the 'array_from' array, it returns the existing element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'default'. If there are multiple matching elements in 'array_from', it returns one of the matches.

-

Example:

-
SELECT
-    transform(SearchEngineID, [2, 3], ['Yandex', 'Google'], 'Other') AS title,
-    count() AS c
-FROM test.hits
-WHERE SearchEngineID != 0
-GROUP BY title
-ORDER BY c DESC
-
- - -
┌─title─────┬──────c─┐
-│ Yandex    │ 498635 │
-│ Google    │ 229872 │
-│ Other     │ 104472 │
-└───────────┴────────┘
-
- - -
    -
  1. transform(x, array_from, array_to)
    2. -
-

Differs from the first variation in that the 'default' argument is omitted. -If the 'x' value is equal to one of the elements in the 'array_from' array, it returns the matching element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'x'.

-

Types:

-

transform(T, Array(T), Array(T)) -> T

-

Example:

-
SELECT
-    transform(domain(Referer), ['yandex.ru', 'google.ru', 'vk.com'], ['www.yandex', 'example.com']) AS s,
-    count() AS c
-FROM test.hits
-GROUP BY domain(Referer)
-ORDER BY count() DESC
-LIMIT 10
-
- - -
┌─s──────────────┬───────c─┐
-│                │ 2906259 │
-│ www.yandex     │  867767 │
-│ ███████.ru     │  313599 │
-│ mail.yandex.ru │  107147 │
-│ ██████.ru      │  100355 │
-│ █████████.ru   │   65040 │
-│ news.yandex.ru │   64515 │
-│ ██████.net     │   59141 │
-│ example.com    │   57316 │
-└────────────────┴─────────┘
-
- - -

formatReadableSize(x)

-

Accepts the size (number of bytes). Returns a rounded size with a suffix (KiB, MiB, etc.) as a string.

-

Example:

-
SELECT
-    arrayJoin([1, 1024, 1024*1024, 192851925]) AS filesize_bytes,
-    formatReadableSize(filesize_bytes) AS filesize
-
- - -
┌─filesize_bytes─┬─filesize───┐
-│              1 │ 1.00 B     │
-│           1024 │ 1.00 KiB   │
-│        1048576 │ 1.00 MiB   │
-│      192851925 │ 183.92 MiB │
-└────────────────┴────────────┘
-
- - -

least(a, b)

-

Returns the smallest value from a and b.

-

greatest(a, b)

-

Returns the largest value of a and b.

-

uptime()

-

Returns the server's uptime in seconds.

-

version()

-

Returns the version of the server as a string.

-

rowNumberInAllBlocks()

-

Returns the ordinal number of the row in the data block. This function only considers the affected data blocks.

-

runningDifference(x)

-

Calculates the difference between successive row values ​​in the data block. -Returns 0 for the first row and the difference from the previous row for each subsequent row.

-

The result of the function depends on the affected data blocks and the order of data in the block. -If you make a subquery with ORDER BY and call the function from outside the subquery, you can get the expected result.

-

Example:

-
SELECT
-    EventID,
-    EventTime,
-    runningDifference(EventTime) AS delta
-FROM
-(
-    SELECT
-        EventID,
-        EventTime
-    FROM events
-    WHERE EventDate = '2016-11-24'
-    ORDER BY EventTime ASC
-    LIMIT 5
-)
-
- - -
┌─EventID─┬───────────EventTime─┬─delta─┐
-│    1106 │ 2016-11-24 00:00:04 │     0 │
-│    1107 │ 2016-11-24 00:00:05 │     1 │
-│    1108 │ 2016-11-24 00:00:05 │     0 │
-│    1109 │ 2016-11-24 00:00:09 │     4 │
-│    1110 │ 2016-11-24 00:00:10 │     1 │
-└─────────┴─────────────────────┴───────┘
-
- - -

MACNumToString(num)

-

Accepts a UInt64 number. Interprets it as a MAC address in big endian. Returns a string containing the corresponding MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form).

-

MACStringToNum(s)

-

The inverse function of MACNumToString. If the MAC address has an invalid format, it returns 0.

-

MACStringToOUI(s)

-

Accepts a MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form). Returns the first three octets as a UInt64 number. If the MAC address has an invalid format, it returns 0.

-

-

Functions for working with external dictionaries

-

For information on connecting and configuring external dictionaries, see "External dictionaries".

-

dictGetUInt8, dictGetUInt16, dictGetUInt32, dictGetUInt64

-

dictGetInt8, dictGetInt16, dictGetInt32, dictGetInt64

-

dictGetFloat32, dictGetFloat64

-

dictGetDate, dictGetDateTime

-

dictGetUUID

-

dictGetString

-

dictGetT('dict_name', 'attr_name', id)

-
    -
  • Get the value of the attr_name attribute from the dict_name dictionary using the 'id' key.dict_name and attr_name are constant strings.idmust be UInt64. -If there is no id key in the dictionary, it returns the default value specified in the dictionary description.
    • -
-

dictGetTOrDefault

-

dictGetT('dict_name', 'attr_name', id, default)

-

The same as the dictGetT functions, but the default value is taken from the function's last argument.

-

dictIsIn

-

dictIsIn('dict_name', child_id, ancestor_id)

-
    -
  • For the 'dict_name' hierarchical dictionary, finds out whether the 'child_id' key is located inside 'ancestor_id' (or matches 'ancestor_id'). Returns UInt8.
    • -
-

dictGetHierarchy

-

dictGetHierarchy('dict_name', id)

-
    -
  • For the 'dict_name' hierarchical dictionary, returns an array of dictionary keys starting from 'id' and continuing along the chain of parent elements. Returns Array(UInt64).
    • -
-

dictHas

-

dictHas('dict_name', id)

-
    -
  • Check whether the dictionary has the key. Returns a UInt8 value equal to 0 if there is no key and 1 if there is a key.
    • -
-

Functions for working with Yandex.Metrica dictionaries

-

In order for the functions below to work, the server config must specify the paths and addresses for getting all the Yandex.Metrica dictionaries. The dictionaries are loaded at the first call of any of these functions. If the reference lists can't be loaded, an exception is thrown.

-

For information about creating reference lists, see the section "Dictionaries".

-

Multiple geobases

-

ClickHouse supports working with multiple alternative geobases (regional hierarchies) simultaneously, in order to support various perspectives on which countries certain regions belong to.

-

The 'clickhouse-server' config specifies the file with the regional hierarchy::<path_to_regions_hierarchy_file>/opt/geo/regions_hierarchy.txt</path_to_regions_hierarchy_file>

-

Besides this file, it also searches for files nearby that have the _ symbol and any suffix appended to the name (before the file extension). -For example, it will also find the file /opt/geo/regions_hierarchy_ua.txt, if present.

-

ua is called the dictionary key. For a dictionary without a suffix, the key is an empty string.

-

All the dictionaries are re-loaded in runtime (once every certain number of seconds, as defined in the builtin_dictionaries_reload_interval config parameter, or once an hour by default). However, the list of available dictionaries is defined one time, when the server starts.

-

All functions for working with regions have an optional argument at the end – the dictionary key. It is referred to as the geobase. -Example:

-
regionToCountry(RegionID) – Uses the default dictionary: /opt/geo/regions_hierarchy.txt
-regionToCountry(RegionID, '') – Uses the default dictionary: /opt/geo/regions_hierarchy.txt
-regionToCountry(RegionID, 'ua') – Uses the dictionary for the 'ua' key: /opt/geo/regions_hierarchy_ua.txt
-
- - -

regionToCity(id[, geobase])

-

Accepts a UInt32 number – the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0.

-

regionToArea(id[, geobase])

-

Converts a region to an area (type 5 in the geobase). In every other way, this function is the same as 'regionToCity'.

-
SELECT DISTINCT regionToName(regionToArea(toUInt32(number), 'ua'))
-FROM system.numbers
-LIMIT 15
-
- - -
┌─regionToName(regionToArea(toUInt32(number), \'ua\'))─┐
-│                                                      │
-│ Moscow and Moscow region                             │
-│ St. Petersburg and Leningrad region                  │
-│ Belgorod region                                      │
-│ Ivanovsk region                                      │
-│ Kaluga region                                        │
-│ Kostroma region                                      │
-│ Kursk region                                         │
-│ Lipetsk region                                       │
-│ Orlov region                                         │
-│ Ryazan region                                        │
-│ Smolensk region                                      │
-│ Tambov region                                        │
-│ Tver region                                          │
-│ Tula region                                          │
-└──────────────────────────────────────────────────────┘
-
- - -

regionToDistrict(id[, geobase])

-

Converts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as 'regionToCity'.

-
SELECT DISTINCT regionToName(regionToDistrict(toUInt32(number), 'ua'))
-FROM system.numbers
-LIMIT 15
-
- - -
┌─regionToName(regionToDistrict(toUInt32(number), \'ua\'))─┐
-│                                                          │
-│ Central federal district                                 │
-│ Northwest federal district                               │
-│ South federal district                                   │
-│ North Caucases federal district                          │
-│ Privolga federal district                                │
-│ Ural federal district                                    │
-│ Siberian federal district                                │
-│ Far East federal district                                │
-│ Scotland                                                 │
-│ Faroe Islands                                            │
-│ Flemish region                                           │
-│ Brussels capital region                                  │
-│ Wallonia                                                 │
-│ Federation of Bosnia and Herzegovina                     │
-└──────────────────────────────────────────────────────────┘
-
- - -

regionToCountry(id[, geobase])

-

Converts a region to a country. In every other way, this function is the same as 'regionToCity'. -Example: regionToCountry(toUInt32(213)) = 225 converts Moscow (213) to Russia (225).

-

regionToContinent(id[, geobase])

-

Converts a region to a continent. In every other way, this function is the same as 'regionToCity'. -Example: regionToContinent(toUInt32(213)) = 10001 converts Moscow (213) to Eurasia (10001).

-

regionToPopulation(id[, geobase])

-

Gets the population for a region. -The population can be recorded in files with the geobase. See the section "External dictionaries". -If the population is not recorded for the region, it returns 0. -In the Yandex geobase, the population might be recorded for child regions, but not for parent regions.

-

regionIn(lhs, rhs[, geobase])

-

Checks whether a 'lhs' region belongs to a 'rhs' region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn't belong. -The relationship is reflexive – any region also belongs to itself.

-

regionHierarchy(id[, geobase])

-

Accepts a UInt32 number – the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain. -Example: regionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000].

-

regionToName(id[, lang])

-

Accepts a UInt32 number – the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language 'ru' is used. If the language is not supported, an exception is thrown. Returns a string – the name of the region in the corresponding language. If the region with the specified ID doesn't exist, an empty string is returned.

-

ua and uk both mean Ukrainian.

-

Functions for implementing the IN operator

-

in, notIn, globalIn, globalNotIn

-

See the section "IN operators".

-

tuple(x, y, ...), operator (x, y, ...)

-

A function that allows grouping multiple columns. -For columns with the types T1, T2, ..., it returns a Tuple(T1, T2, ...) type tuple containing these columns. There is no cost to execute the function. -Tuples are normally used as intermediate values for an argument of IN operators, or for creating a list of formal parameters of lambda functions. Tuples can't be written to a table.

-

tupleElement(tuple, n), operator x.N

-

A function that allows getting a column from a tuple. -'N' is the column index, starting from 1. N must be a constant. 'N' must be a constant. 'N' must be a strict postive integer no greater than the size of the tuple. -There is no cost to execute the function.

-

-

arrayJoin function

-

This is a very unusual function.

-

Normal functions don't change a set of rows, but just change the values in each row (map). -Aggregate functions compress a set of rows (fold or reduce). -The 'arrayJoin' function takes each row and generates a set of rows (unfold).

-

This function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array. -All the values in columns are simply copied, except the values in the column where this function is applied; it is replaced with the corresponding array value.

-

A query can use multiple arrayJoin functions. In this case, the transformation is performed multiple times.

-

Note the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities.

-

Example:

-
SELECT arrayJoin([1, 2, 3] AS src) AS dst, 'Hello', src
-
- - -
┌─dst─┬─\'Hello\'─┬─src─────┐
-│   1 │ Hello     │ [1,2,3] │
-│   2 │ Hello     │ [1,2,3] │
-│   3 │ Hello     │ [1,2,3] │
-└─────┴───────────┴─────────┘
-
- - -

-

Aggregate functions

-

Aggregate functions work in the normal way as expected by database experts.

-

ClickHouse also supports:

- -

-

Function reference

-

count()

-

Counts the number of rows. Accepts zero arguments and returns UInt64. -The syntax COUNT(DISTINCT x) is not supported. The separate uniq aggregate function exists for this purpose.

-

A SELECT count() FROM table query is not optimized, because the number of entries in the table is not stored separately. It will select some small column from the table and count the number of values in it.

-

any(x)

-

Selects the first encountered value. -The query can be executed in any order and even in a different order each time, so the result of this function is indeterminate. -To get a determinate result, you can use the 'min' or 'max' function instead of 'any'.

-

In some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.

-

When a SELECT query has the GROUP BY clause or at least one aggregate function, ClickHouse (in contrast to MySQL) requires that all expressions in the SELECT, HAVING, and ORDER BY clauses be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. To get behavior like in MySQL, you can put the other columns in the any aggregate function.

-

anyHeavy(x)

-

Selects a frequently occurring value using the heavy hitters algorithm. If there is a value that occurs more than in half the cases in each of the query's execution threads, this value is returned. Normally, the result is nondeterministic.

-
anyHeavy(column)
-
- - -

Arguments -- column – The column name.

-

Example

-

Take the OnTime data set and select any frequently occurring value in the AirlineID column.

-
SELECT anyHeavy(AirlineID) AS res
-FROM ontime
-
- - -
┌───res─┐
-│ 19690 │
-└───────┘
-
- - -

anyLast(x)

-

Selects the last value encountered. -The result is just as indeterminate as for the any function.

-

min(x)

-

Calculates the minimum.

-

max(x)

-

Calculates the maximum.

-

argMin(arg, val)

-

Calculates the 'arg' value for a minimal 'val' value. If there are several different values of 'arg' for minimal values of 'val', the first of these values encountered is output.

-

argMax(arg, val)

-

Calculates the 'arg' value for a maximum 'val' value. If there are several different values of 'arg' for maximum values of 'val', the first of these values encountered is output.

-

sum(x)

-

Calculates the sum. -Only works for numbers.

-

sumWithOverflow(x)

-

Computes the sum of the numbers, using the same data type for the result as for the input parameters. If the sum exceeds the maximum value for this data type, the function returns an error.

-

Only works for numbers.

-

sumMap(key, value)

-

Totals the 'value' array according to the keys specified in the 'key' array. -The number of elements in 'key' and 'value' must be the same for each row that is totaled. -Returns a tuple of two arrays: keys in sorted order, and values ​​summed for the corresponding keys.

-

Example:

-
CREATE TABLE sum_map(
-    date Date,
-    timeslot DateTime,
-    statusMap Nested(
-        status UInt16,
-        requests UInt64
-    )
-) ENGINE = Log;
-INSERT INTO sum_map VALUES
-    ('2000-01-01', '2000-01-01 00:00:00', [1, 2, 3], [10, 10, 10]),
-    ('2000-01-01', '2000-01-01 00:00:00', [3, 4, 5], [10, 10, 10]),
-    ('2000-01-01', '2000-01-01 00:01:00', [4, 5, 6], [10, 10, 10]),
-    ('2000-01-01', '2000-01-01 00:01:00', [6, 7, 8], [10, 10, 10]);
-SELECT
-    timeslot,
-    sumMap(statusMap.status, statusMap.requests)
-FROM sum_map
-GROUP BY timeslot
-
- - -
┌────────────timeslot─┬─sumMap(statusMap.status, statusMap.requests)─┐
-│ 2000-01-01 00:00:00 │ ([1,2,3,4,5],[10,10,20,10,10])               │
-│ 2000-01-01 00:01:00 │ ([4,5,6,7,8],[10,10,20,10,10])               │
-└─────────────────────┴──────────────────────────────────────────────┘
-
- - -

avg(x)

-

Calculates the average. -Only works for numbers. -The result is always Float64.

-

uniq(x)

-

Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.

-

Uses an adaptive sampling algorithm: for the calculation state, it uses a sample of element hash values with a size up to 65536. -This algorithm is also very accurate for data sets with low cardinality (up to 65536) and very efficient on CPU (when computing not too many of these functions, using uniq is almost as fast as using other aggregate functions).

-

The result is determinate (it doesn't depend on the order of query processing).

-

This function provides excellent accuracy even for data sets with extremely high cardinality (over 10 billion elements). It is recommended for default use.

-

uniqCombined(x)

-

Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.

-

A combination of three algorithms is used: array, hash table and HyperLogLog with an error correction table. The memory consumption is several times smaller than for the uniq function, and the accuracy is several times higher. Performance is slightly lower than for the uniq function, but sometimes it can be even higher than it, such as with distributed queries that transmit a large number of aggregation states over the network. The maximum state size is 96 KiB (HyperLogLog of 217 6-bit cells).

-

The result is determinate (it doesn't depend on the order of query processing).

-

The uniqCombined function is a good default choice for calculating the number of different values, but keep in mind that the estimation error will increase for high-cardinality data sets (200M+ elements), and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).

-

uniqHLL12(x)

-

Uses the HyperLogLog algorithm to approximate the number of different values of the argument. -212 5-bit cells are used. The size of the state is slightly more than 2.5 KB. The result is not very accurate (up to ~10% error) for small data sets (<10K elements). However, the result is fairly accurate for high-cardinality data sets (10K-100M), with a maximum error of ~1.6%. Starting from 100M, the estimation error increases, and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).

-

The result is determinate (it doesn't depend on the order of query processing).

-

We don't recommend using this function. In most cases, use the uniq or uniqCombined function.

-

uniqExact(x)

-

Calculates the number of different values of the argument, exactly. -There is no reason to fear approximations. It's better to use the uniq function. -Use the uniqExact function if you definitely need an exact result.

-

The uniqExact function uses more memory than the uniq function, because the size of the state has unbounded growth as the number of different values increases.

-

groupArray(x), groupArray(max_size)(x)

-

Creates an array of argument values. -Values can be added to the array in any (indeterminate) order.

-

The second version (with the max_size parameter) limits the size of the resulting array to max_size elements. -For example, groupArray (1) (x) is equivalent to [any (x)].

-

In some cases, you can still rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.

-

-

groupArrayInsertAt(x)

-

Inserts a value into the array in the specified position.

-

Accepts the value and position as input. If several values ​​are inserted into the same position, any of them might end up in the resulting array (the first one will be used in the case of single-threaded execution). If no value is inserted into a position, the position is assigned the default value.

-

Optional parameters:

-
    -
  • The default value for substituting in empty positions.
    • -
  • The length of the resulting array. This allows you to receive arrays of the same size for all the aggregate keys. When using this parameter, the default value must be specified.
    • -
-

groupUniqArray(x)

-

Creates an array from different argument values. Memory consumption is the same as for the uniqExact function.

-

quantile(level)(x)

-

Approximates the 'level' quantile. 'level' is a constant, a floating-point number from 0 to 1. -We recommend using a 'level' value in the range of 0.01..0.99 -Don't use a 'level' value equal to 0 or 1 – use the 'min' and 'max' functions for these cases.

-

In this function, as well as in all functions for calculating quantiles, the 'level' parameter can be omitted. In this case, it is assumed to be equal to 0.5 (in other words, the function will calculate the median).

-

Works for numbers, dates, and dates with times. -Returns: for numbers – Float64; for dates – a date; for dates with times – a date with time.

-

Uses reservoir sampling with a reservoir size up to 8192. -If necessary, the result is output with linear approximation from the two neighboring values. -This algorithm provides very low accuracy. See also: quantileTiming, quantileTDigest, quantileExact.

-

The result depends on the order of running the query, and is nondeterministic.

-

When using multiple quantile (and similar) functions with different levels in a query, the internal states are not combined (that is, the query works less efficiently than it could). In this case, use the quantiles (and similar) functions.

-

quantileDeterministic(level)(x, determinator)

-

Works the same way as the quantile function, but the result is deterministic and does not depend on the order of query execution.

-

To achieve this, the function takes a second argument – the "determinator". This is a number whose hash is used instead of a random number generator in the reservoir sampling algorithm. For the function to work correctly, the same determinator value should not occur too often. For the determinator, you can use an event ID, user ID, and so on.

-

Don't use this function for calculating timings. There is a more suitable function for this purpose: quantileTiming.

-

quantileTiming(level)(x)

-

Computes the quantile of 'level' with a fixed precision. -Works for numbers. Intended for calculating quantiles of page loading time in milliseconds.

-

If the value is greater than 30,000 (a page loading time of more than 30 seconds), the result is equated to 30,000.

-

If the total value is not more than about 5670, then the calculation is accurate.

-

Otherwise:

-
    -
  • if the time is less than 1024 ms, then the calculation is accurate.
    • -
  • otherwise the calculation is rounded to a multiple of 16 ms.
    • -
-

When passing negative values to the function, the behavior is undefined.

-

The returned value has the Float32 type. If no values were passed to the function (when using quantileTimingIf), 'nan' is returned. The purpose of this is to differentiate these instances from zeros. See the note on sorting NaNs in "ORDER BY clause".

-

The result is determinate (it doesn't depend on the order of query processing).

-

For its purpose (calculating quantiles of page loading times), using this function is more effective and the result is more accurate than for the quantile function.

-

quantileTimingWeighted(level)(x, weight)

-

Differs from the quantileTiming function in that it has a second argument, "weights". Weight is a non-negative integer. -The result is calculated as if the x value were passed weight number of times to the quantileTiming function.

-

quantileExact(level)(x)

-

Computes the quantile of 'level' exactly. To do this, all the passed values ​​are combined into an array, which is then partially sorted. Therefore, the function consumes O(n) memory, where 'n' is the number of values that were passed. However, for a small number of values, the function is very effective.

-

quantileExactWeighted(level)(x, weight)

-

Computes the quantile of 'level' exactly. In addition, each value is counted with its weight, as if it is present 'weight' times. The arguments of the function can be considered as histograms, where the value 'x' corresponds to a histogram "column" of the height 'weight', and the function itself can be considered as a summation of histograms.

-

A hash table is used as the algorithm. Because of this, if the passed values ​​are frequently repeated, the function consumes less RAM than quantileExact. You can use this function instead of quantileExact and specify the weight as 1.

-

quantileTDigest(level)(x)

-

Approximates the quantile level using the t-digest algorithm. The maximum error is 1%. Memory consumption by State is proportional to the logarithm of the number of passed values.

-

The performance of the function is lower than for quantile, quantileTiming. In terms of the ratio of State size to precision, this function is much better than quantile.

-

The result depends on the order of running the query, and is nondeterministic.

-

median(x)

-

All the quantile functions have corresponding median functions: median, medianDeterministic, medianTiming, medianTimingWeighted, medianExact, medianExactWeighted, medianTDigest. They are synonyms and their behavior is identical.

-

quantiles(level1, level2, ...)(x)

-

All the quantile functions also have corresponding quantiles functions: quantiles, quantilesDeterministic, quantilesTiming, quantilesTimingWeighted, quantilesExact, quantilesExactWeighted, quantilesTDigest. These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.

-

varSamp(x)

-

Calculates the amount Σ((x - x̅)^2) / (n - 1), where n is the sample size and x̅is the average value of x.

-

It represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount.

-

Returns Float64. When n <= 1, returns +∞.

-

varPop(x)

-

Calculates the amount Σ((x - x̅)^2) / (n - 1), where n is the sample size and x̅is the average value of x.

-

In other words, dispersion for a set of values. Returns Float64.

-

stddevSamp(x)

-

The result is equal to the square root of varSamp(x).

-

stddevPop(x)

-

The result is equal to the square root of varPop(x).

-

topK(N)(column)

-

Returns an array of the most frequent values in the specified column. The resulting array is sorted in descending order of frequency of values (not by the values themselves).

-

Implements the Filtered Space-Saving algorithm for analyzing TopK, based on the reduce-and-combine algorithm from Parallel Space Saving.

-
topK(N)(column)
-
- - -

This function doesn't provide a guaranteed result. In certain situations, errors might occur and it might return frequent values that aren't the most frequent values.

-

We recommend using the N < 10 value; performance is reduced with large N values. Maximum value of N = 65536.

-

Arguments -- 'N' is the number of values. -- ' x ' – The column.

-

Example

-

Take the OnTime data set and select the three most frequently occurring values in the AirlineID column.

-
SELECT topK(3)(AirlineID) AS res
-FROM ontime
-
- - -
┌─res─────────────────┐
-│ [19393,19790,19805] │
-└─────────────────────┘
-
- - -

covarSamp(x, y)

-

Calculates the value of Σ((x - x̅)(y - y̅)) / (n - 1).

-

Returns Float64. When n <= 1, returns +∞.

-

covarPop(x, y)

-

Calculates the value of Σ((x - x̅)(y - y̅)) / n.

-

corr(x, y)

-

Calculates the Pearson correlation coefficient: Σ((x - x̅)(y - y̅)) / sqrt(Σ((x - x̅)^2) * Σ((y - y̅)^2)).

-

-

Aggregate function combinators

-

The name of an aggregate function can have a suffix appended to it. This changes the way the aggregate function works.

-

-If

-

The suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument – a condition (Uint8 type). The aggregate function processes only the rows that trigger the condition. If the condition was not triggered even once, it returns a default value (usually zeros or empty strings).

-

Examples: sumIf(column, cond), countIf(cond), avgIf(x, cond), quantilesTimingIf(level1, level2)(x, cond), argMinIf(arg, val, cond) and so on.

-

With conditional aggregate functions, you can calculate aggregates for several conditions at once, without using subqueries and JOINs. For example, in Yandex.Metrica, conditional aggregate functions are used to implement the segment comparison functionality.

-

-Array

-

The -Array suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the 'Array(T)' type (arrays) instead of 'T' type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. When processing arrays, the aggregate function works like the original aggregate function across all array elements.

-

Example 1: sumArray(arr) - Totals all the elements of all 'arr' arrays. In this example, it could have been written more simply: sum(arraySum(arr)).

-

Example 2: uniqArray(arr) – Count the number of unique elements in all 'arr' arrays. This could be done an easier way: uniq(arrayJoin(arr)), but it's not always possible to add 'arrayJoin' to a query.

-

-If and -Array can be combined. However, 'Array' must come first, then 'If'. Examples: uniqArrayIf(arr, cond), quantilesTimingArrayIf(level1, level2)(arr, cond). Due to this order, the 'cond' argument can't be an array.

-

-State

-

If you apply this combinator, the aggregate function doesn't return the resulting value (such as the number of unique values for the 'uniq' function), but an intermediate state of the aggregation (for uniq, this is the hash table for calculating the number of unique values). This is an AggregateFunction(...) that can be used for further processing or stored in a table to finish aggregating later. See the sections "AggregatingMergeTree" and "Functions for working with intermediate aggregation states".

-

-Merge

-

If you apply this combinator, the aggregate function takes the intermediate aggregation state as an argument, combines the states to finish aggregation, and returns the resulting value.

-

-MergeState.

-

Merges the intermediate aggregation states in the same way as the -Merge combinator. However, it doesn't return the resulting value, but an intermediate aggregation state, similar to the -State combinator.

-

-ForEach

-

Converts an aggregate function for tables into an aggregate function for arrays that aggregates the corresponding array items and returns an array of results. For example, sumForEach for the arrays [1, 2], [3, 4, 5]and[6, 7]returns the result [10, 13, 5] after adding together the corresponding array items.

-

-

Parametric aggregate functions

-

Some aggregate functions can accept not only argument columns (used for compression), but a set of parameters – constants for initialization. The syntax is two pairs of brackets instead of one. The first is for parameters, and the second is for arguments.

-

sequenceMatch(pattern)(time, cond1, cond2, ...)

-

Pattern matching for event chains.

-

pattern is a string containing a pattern to match. The pattern is similar to a regular expression.

-

time is the time of the event with the DateTime type.

-

cond1, cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event.

-

The function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern. -It returns UInt8: 0 if the pattern isn't matched, or 1 if it matches.

-

Example: sequenceMatch ('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%')

-
    -
  • whether there was a chain of events in which a pageview with 'company' in the address occurred earlier than a pageview with 'cart' in the address.
    • -
-

This is a singular example. You could write it using other aggregate functions:

-
minIf(EventTime, URL LIKE '%company%') < maxIf(EventTime, URL LIKE '%cart%').
-
- - -

However, there is no such solution for more complex situations.

-

Pattern syntax:

-

(?1) refers to the condition (any number can be used in place of 1).

-

.* is any number of any events.

-

(?t>=1800) is a time condition.

-

Any quantity of any type of events is allowed over the specified time.

-

Instead of >=, the following operators can be used:<, >, <=.

-

Any number may be specified in place of 1800.

-

Events that occur during the same second can be put in the chain in any order. This may affect the result of the function.

-

sequenceCount(pattern)(time, cond1, cond2, ...)

-

Works the same way as the sequenceMatch function, but instead of returning whether there is an event chain, it returns UInt64 with the number of event chains found. -Chains are searched for without overlapping. In other words, the next chain can start only after the end of the previous one.

-

windowFunnel(window)(timestamp, cond1, cond2, cond3, ....)

-

Window funnel matching for event chains, calculates the max event level in a sliding window.

-

window is the timestamp window value, such as 3600.

-

timestamp is the time of the event with the DateTime type or UInt32 type.

-

cond1, cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event

-

Example:

-

Consider you are doing a website analytics, intend to find out the user counts clicked login button( event = 1001 ), then the user counts followed by searched the phones( event = 1003 and product = 'phone' ) , then the user counts followed by made an order ( event = 1009 ). And all event chains must be in a 3600 seconds sliding window.

-

This could be easily calculate by windowFunnel

-
SELECT
-    level,
-    count() AS c
-FROM
-(
-    SELECT
-        user_id,
-        windowFunnel(3600)(timestamp, event_id = 1001, event_id = 1003 AND product = 'phone', event_id = 1009) AS level
-    FROM trend_event
-    WHERE (event_date >= '2017-01-01') AND (event_date <= '2017-01-31')
-    GROUP BY user_id
-)
-GROUP BY level
-ORDER BY level
-
- - -

Simply, the level could only be 0,1,2,3, it means the maxium event action stage that one user could reach.

-

uniqUpTo(N)(x)

-

Calculates the number of different argument values ​​if it is less than or equal to N. If the number of different argument values is greater than N, it returns N + 1.

-

Recommended for use with small Ns, up to 10. The maximum value of N is 100.

-

For the state of an aggregate function, it uses the amount of memory equal to 1 + N * the size of one value of bytes. -For strings, it stores a non-cryptographic hash of 8 bytes. That is, the calculation is approximated for strings.

-

The function also works for several arguments.

-

It works as fast as possible, except for cases when a large N value is used and the number of unique values is slightly less than N.

-

Usage example:

-
Problem: Generate a report that shows only keywords that produced at least 5 unique users.
-Solution: Write in the GROUP BY query SearchPhrase HAVING uniqUpTo(4)(UserID) >= 5
-
- - -

Dictionaries

-

A dictionary is a mapping (key -> attributes) that can be used in a query as functions. -You can think of this as a more convenient and efficient type of JOIN with dimension tables.

-

There are built-in (internal) and add-on (external) dictionaries.

-

-

External dictionaries

-

You can add your own dictionaries from various data sources. The data source for a dictionary can be a local text or executable file, an HTTP(s) resource, or another DBMS. For more information, see "Sources for external dictionaries".

-

ClickHouse:

-
-
    -
  • Fully or partially stores dictionaries in RAM.
    • -
  • Periodically updates dictionaries and dynamically loads missing values. In other words, dictionaries can be loaded dynamically.
    • -
-
-

The configuration of external dictionaries is located in one or more files. The path to the configuration is specified in the dictionaries_config parameter.

-

Dictionaries can be loaded at server startup or at first use, depending on the dictionaries_lazy_load setting.

-

The dictionary config file has the following format:

-
<yandex>
-    <comment>An optional element with any content. Ignored by the ClickHouse server.</comment>
-
-    <!--Optional element. File name with substitutions-->
-    <include_from>/etc/metrika.xml</include_from>
-
-
-    <dictionary>
-        <!-- Dictionary configuration -->
-    </dictionary>
-
-    ...
-
-    <dictionary>
-        <!-- Dictionary configuration -->
-    </dictionary>
-</yandex>
-
- - -

You can configure any number of dictionaries in the same file. The file format is preserved even if there is only one dictionary (i.e. <yandex><dictionary> <!--configuration -> </dictionary></yandex> ).

-

See also "Functions for working with external dictionaries".

-
- -You can convert values ​​for a small dictionary by describing it in a `SELECT` query (see the [transform](#other_functions-transform) function). This functionality is not related to external dictionaries. - -
- -

-

Configuring an external dictionary

-

The dictionary configuration has the following structure:

-
<dictionary>
-    <name>dict_name</name>
-
-    <source>
-      <!-- Source configuration -->
-    </source>
-
-    <layout>
-      <!-- Memory layout configuration -->
-    </layout>
-
-    <structure>
-      <!-- Complex key configuration -->
-    </structure>
-
-    <lifetime>
-      <!-- Lifetime of dictionary in memory -->
-    </lifetime>
-</dictionary>
-
- - -
    -
  • name – The identifier that can be used to access the dictionary. Use the characters [a-zA-Z0-9_\-].
    • -
  • source — Source of the dictionary.
    • -
  • layout — Dictionary layout in memory.
    • -
  • structure — Structure of the dictionary . A key and attributes that can be retrieved by this key.
    • -
  • lifetime — Frequency of dictionary updates.
    • -
-

-

Storing dictionaries in memory

-

There are a variety of ways to store dictionaries in memory.

-

We recommend flat, hashedandcomplex_key_hashed. which provide optimal processing speed.

-

Caching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section "cache".

-

There are several ways to improve dictionary performance:

-
    -
  • Call the function for working with the dictionary after GROUP BY.
    • -
  • Mark attributes to extract as injective. An attribute is called injective if different attribute values correspond to different keys. So when GROUP BY uses a function that fetches an attribute value by the key, this function is automatically taken out of GROUP BY.
    • -
-

ClickHouse generates an exception for errors with dictionaries. Examples of errors:

-
    -
  • The dictionary being accessed could not be loaded.
    • -
  • Error querying a cached dictionary.
    • -
-

You can view the list of external dictionaries and their statuses in the system.dictionaries table.

-

The configuration looks like this:

-
<yandex>
-    <dictionary>
-        ...
-        <layout>
-            <layout_type>
-                <!-- layout settings -->
-            </layout_type>
-        </layout>
-        ...
-    </dictionary>
-</yandex>
-
- - -

-

Ways to store dictionaries in memory

- -

-

flat

-

The dictionary is completely stored in memory in the form of flat arrays. How much memory does the dictionary use? The amount is proportional to the size of the largest key (in space used).

-

The dictionary key has the UInt64 type and the value is limited to 500,000. If a larger key is discovered when creating the dictionary, ClickHouse throws an exception and does not create the dictionary.

-

All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.

-

This method provides the best performance among all available methods of storing the dictionary.

-

Configuration example:

-
<layout>
-  <flat />
-</layout>
-
- - -

-

hashed

-

The dictionary is completely stored in memory in the form of a hash table. The dictionary can contain any number of elements with any identifiers In practice, the number of keys can reach tens of millions of items.

-

All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.

-

Configuration example:

-
<layout>
-  <hashed />
-</layout>
-
- - -

-

complex_key_hashed

-

This type of storage is for use with composite keys. Similar to hashed.

-

Configuration example:

-
<layout>
-  <complex_key_hashed />
-</layout>
-
- - -

-

range_hashed

-

The dictionary is stored in memory in the form of a hash table with an ordered array of ranges and their corresponding values.

-

This storage method works the same way as hashed and allows using date/time ranges in addition to the key, if they appear in the dictionary.

-

Example: The table contains discounts for each advertiser in the format:

-
+---------------+---------------------+-------------------+--------+
-| advertiser id | discount start date | discount end date | amount |
-+===============+=====================+===================+========+
-| 123           | 2015-01-01          | 2015-01-15        | 0.15   |
-+---------------+---------------------+-------------------+--------+
-| 123           | 2015-01-16          | 2015-01-31        | 0.25   |
-+---------------+---------------------+-------------------+--------+
-| 456           | 2015-01-01          | 2015-01-15        | 0.05   |
-+---------------+---------------------+-------------------+--------+
-
- - -

To use a sample for date ranges, define the range_min and range_max elements in the structure.

-

Example:

-
<structure>
-    <id>
-        <name>Id</name>
-    </id>
-    <range_min>
-        <name>first</name>
-    </range_min>
-    <range_max>
-        <name>last</name>
-    </range_max>
-    ...
-
- - -

To work with these dictionaries, you need to pass an additional date argument to the dictGetT function:

-
dictGetT('dict_name', 'attr_name', id, date)
-
- - -

This function returns the value for the specified ids and the date range that includes the passed date.

-

Details of the algorithm:

-
    -
  • If the id is not found or a range is not found for the id, it returns the default value for the dictionary.
    • -
  • If there are overlapping ranges, you can use any.
    • -
  • If the range delimiter is NULL or an invalid date (such as 1900-01-01 or 2039-01-01), the range is left open. The range can be open on both sides.
    • -
-

Configuration example:

-
<yandex>
-        <dictionary>
-
-                ...
-
-                <layout>
-                        <range_hashed />
-                </layout>
-
-                <structure>
-                        <id>
-                                <name>Abcdef</name>
-                        </id>
-                        <range_min>
-                                <name>StartDate</name>
-                        </range_min>
-                        <range_max>
-                                <name>EndDate</name>
-                        </range_max>
-                        <attribute>
-                                <name>XXXType</name>
-                                <type>String</type>
-                                <null_value />
-                        </attribute>
-                </structure>
-
-        </dictionary>
-</yandex>
-
- - -

-

cache

-

The dictionary is stored in a cache that has a fixed number of cells. These cells contain frequently used elements.

-

When searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using SELECT attrs... FROM db.table WHERE id IN (k1, k2, ...). The received data is then written to the cache.

-

For cache dictionaries, the expiration lifetime of data in the cache can be set. If more time than lifetime has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used.

-

This is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the system.dictionaries table.

-

To improve cache performance, use a subquery with LIMIT, and call the function with the dictionary externally.

-

Supported sources: MySQL, ClickHouse, executable, HTTP.

-

Example of settings:

-
<layout>
-    <cache>
-        <!-- The size of the cache, in number of cells. Rounded up to a power of two. -->
-        <size_in_cells>1000000000</size_in_cells>
-    </cache>
-</layout>
-
- - -

Set a large enough cache size. You need to experiment to select the number of cells:

-
    -
  1. Set some value.
    2. -
  2. Run queries until the cache is completely full.
    3. -
  3. Assess memory consumption using the system.dictionaries table.
    4. -
  4. Increase or decrease the number of cells until the required memory consumption is reached.
    5. -
-
- -Do not use ClickHouse as a source, because it is slow to process queries with random reads. - -
- -

-

complex_key_cache

-

This type of storage is for use with composite keys. Similar to cache.

-

-

ip_trie

-

This type of storage is for mapping network prefixes (IP addresses) to metadata such as ASN.

-

Example: The table contains network prefixes and their corresponding AS number and country code:

-
  +-----------------+-------+--------+
-  | prefix          | asn   | cca2   |
-  +=================+=======+========+
-  | 202.79.32.0/20  | 17501 | NP     |
-  +-----------------+-------+--------+
-  | 2620:0:870::/48 | 3856  | US     |
-  +-----------------+-------+--------+
-  | 2a02:6b8:1::/48 | 13238 | RU     |
-  +-----------------+-------+--------+
-  | 2001:db8::/32   | 65536 | ZZ     |
-  +-----------------+-------+--------+
-
- - -

When using this type of layout, the structure must have a composite key.

-

Example:

-
<structure>
-    <key>
-        <attribute>
-            <name>prefix</name>
-            <type>String</type>
-        </attribute>
-    </key>
-    <attribute>
-            <name>asn</name>
-            <type>UInt32</type>
-            <null_value />
-    </attribute>
-    <attribute>
-            <name>cca2</name>
-            <type>String</type>
-            <null_value>??</null_value>
-    </attribute>
-    ...
-
- - -

The key must have only one String type attribute that contains an allowed IP prefix. Other types are not supported yet.

-

For queries, you must use the same functions (dictGetT with a tuple) as for dictionaries with composite keys:

-
dictGetT('dict_name', 'attr_name', tuple(ip))
-
- - -

The function takes either UInt32 for IPv4, or FixedString(16) for IPv6:

-
dictGetString('prefix', 'asn', tuple(IPv6StringToNum('2001:db8::1')))
-
- - -

Other types are not supported yet. The function returns the attribute for the prefix that corresponds to this IP address. If there are overlapping prefixes, the most specific one is returned.

-

Data is stored in a trie. It must completely fit into RAM.

-

-

Dictionary updates

-

ClickHouse periodically updates the dictionaries. The update interval for fully downloaded dictionaries and the invalidation interval for cached dictionaries are defined in the <lifetime> tag in seconds.

-

Dictionary updates (other than loading for first use) do not block queries. During updates, the old version of a dictionary is used. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.

-

Example of settings:

-
<dictionary>
-    ...
-    <lifetime>300</lifetime>
-    ...
-</dictionary>
-
- - -

Setting <lifetime> 0</lifetime> prevents updating dictionaries.

-

You can set a time interval for upgrades, and ClickHouse will choose a uniformly random time within this range. This is necessary in order to distribute the load on the dictionary source when upgrading on a large number of servers.

-

Example of settings:

-
<dictionary>
-    ...
-    <lifetime>
-        <min>300</min>
-        <max>360</max>
-    </lifetime>
-    ...
-</dictionary>
-
- - -

When upgrading the dictionaries, the ClickHouse server applies different logic depending on the type of source:

-
-
    -
  • For a text file, it checks the time of modification. If the time differs from the previously recorded time, the dictionary is updated.
    • -
  • For MyISAM tables, the time of modification is checked using a SHOW TABLE STATUS query.
    • -
  • Dictionaries from other sources are updated every time by default.
    • -
-
-

For MySQL (InnoDB) and ODBC sources, you can set up a query that will update the dictionaries only if they really changed, rather than each time. To do this, follow these steps:

-
-
    -
  • The dictionary table must have a field that always changes when the source data is updated.
    • -
  • The settings of the source must specify a query that retrieves the changing field. The ClickHouse server interprets the query result as a row, and if this row has changed relative to its previous state, the dictionary is updated. Specify the query in the <invalidate_query> field in the settings for the source.
    • -
-
-

Example of settings:

-
<dictionary>
-    ...
-    <odbc>
-      ...
-      <invalidate_query>SELECT update_time FROM dictionary_source where id = 1</invalidate_query>
-    </odbc>
-    ...
-</dictionary>
-
- - -

-

Sources of external dictionaries

-

An external dictionary can be connected from many different sources.

-

The configuration looks like this:

-
<yandex>
-  <dictionary>
-    ...
-    <source>
-      <source_type>
-        <!-- Source configuration -->
-      </source_type>
-    </source>
-    ...
-  </dictionary>
-  ...
-</yandex>
-
- - -

The source is configured in the source section.

-

Types of sources (source_type):

- -

-

Local file

-

Example of settings:

-
<source>
-  <file>
-    <path>/opt/dictionaries/os.tsv</path>
-    <format>TabSeparated</format>
-  </file>
-</source>
-
- - -

Setting fields:

-
    -
  • path – The absolute path to the file.
    • -
  • format – The file format. All the formats described in "Formats" are supported.
    • -
-

-

Executable file

-

Working with executable files depends on how the dictionary is stored in memory. If the dictionary is stored using cache and complex_key_cache, ClickHouse requests the necessary keys by sending a request to the executable file's STDIN.

-

Example of settings:

-
<source>
-    <executable>
-        <command>cat /opt/dictionaries/os.tsv</command>
-        <format>TabSeparated</format>
-    </executable>
-</source>
-
- - -

Setting fields:

-
    -
  • command – The absolute path to the executable file, or the file name (if the program directory is written to PATH).
    • -
  • format – The file format. All the formats described in "Formats" are supported.
    • -
-

-

HTTP(s)

-

Working with an HTTP(s) server depends on how the dictionary is stored in memory. If the dictionary is stored using cache and complex_key_cache, ClickHouse requests the necessary keys by sending a request via the POST method.

-

Example of settings:

-
<source>
-    <http>
-        <url>http://[::1]/os.tsv</url>
-        <format>TabSeparated</format>
-    </http>
-</source>
-
- - -

In order for ClickHouse to access an HTTPS resource, you must configure openSSL in the server configuration.

-

Setting fields:

-
    -
  • url – The source URL.
    • -
  • format – The file format. All the formats described in "Formats" are supported.
    • -
-

-

ODBC

-

You can use this method to connect any database that has an ODBC driver.

-

Example of settings:

-
<odbc>
-    <db>DatabaseName</db>
-    <table>TableName</table>
-    <connection_string>DSN=some_parameters</connection_string>
-    <invalidate_query>SQL_QUERY</invalidate_query>
-</odbc>
-
- - -

Setting fields:

-
    -
  • db – Name of the database. Omit it if the database name is set in the <connection_string> parameters.
    • -
  • table – Name of the table.
    • -
  • connection_string – Connection string.
    • -
  • invalidate_query – Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries.
    • -
-

Example of connecting PostgreSQL

-

Ubuntu OS.

-

Installing unixODBC and the ODBC driver for PostgreSQL:

-
sudo apt-get install -y unixodbc odbcinst odbc-postgresql
-
- - -

Configuring /etc/odbc.ini (or ~/.odbc.ini):

-
    [DEFAULT]
-    Driver = myconnection
-
-    [myconnection]
-    Description         = PostgreSQL connection to my_db
-    Driver              = PostgreSQL Unicode
-    Database            = my_db
-    Servername          = 127.0.0.1
-    UserName            = username
-    Password            = password
-    Port                = 5432
-    Protocol            = 9.3
-    ReadOnly            = No
-    RowVersioning       = No
-    ShowSystemTables    = No
-    ConnSettings        =
-
- - -

The dictionary configuration in ClickHouse:

-
<dictionary>
-    <name>table_name</name>
-    <source>
-    <odbc>
-        <!-- You can specifiy the following parameters in connection_string: -->
-        <!-- DSN=myconnection;UID=username;PWD=password;HOST=127.0.0.1;PORT=5432;DATABASE=my_db -->
-            <connection_string>DSN=myconnection</connection_string>
-            <table>postgresql_table</table>
-        </odbc>
-    </source>
-    <lifetime>
-        <min>300</min>
-        <max>360</max>
-    </lifetime>
-    <layout>
-        <hashed/>
-    </layout>
-    <structure>
-        <id>
-            <name>id</name>
-        </id>
-        <attribute>
-            <name>some_column</name>
-            <type>UInt64</type>
-            <null_value>0</null_value>
-        </attribute>
-    </structure>
-</dictionary>
-
- - -

You may need to edit odbc.ini to specify the full path to the library with the driver DRIVER=/usr/local/lib/psqlodbcw.so.

-

Example of connecting MS SQL Server

-

Ubuntu OS.

-

Installing the driver: :

-
    sudo apt-get install tdsodbc freetds-bin sqsh
-
- - -

Configuring the driver: :

-
    $ cat /etc/freetds/freetds.conf 
-    ...
-
-    [MSSQL]
-    host = 192.168.56.101
-    port = 1433
-    tds version = 7.0
-    client charset = UTF-8
-
-    $ cat /etc/odbcinst.ini 
-    ...
-
-    [FreeTDS]
-    Description     = FreeTDS
-    Driver          = /usr/lib/x86_64-linux-gnu/odbc/libtdsodbc.so
-    Setup           = /usr/lib/x86_64-linux-gnu/odbc/libtdsS.so
-    FileUsage       = 1
-    UsageCount      = 5
-
-    $ cat ~/.odbc.ini 
-    ...
-
-    [MSSQL]
-    Description     = FreeTDS
-    Driver          = FreeTDS
-    Servername      = MSSQL
-    Database        = test
-    UID             = test
-    PWD             = test
-    Port            = 1433
-
- - -

Configuring the dictionary in ClickHouse:

-
<yandex>
-    <dictionary>
-        <name>test</name>
-        <source>
-            <odbc>
-                <table>dict</table>
-                <connection_string>DSN=MSSQL;UID=test;PWD=test</connection_string>
-            </odbc>
-        </source>
-
-        <lifetime>
-            <min>300</min>
-            <max>360</max>
-        </lifetime>
-
-        <layout>
-            <flat />
-        </layout>
-
-        <structure>
-            <id>
-                <name>k</name>
-            </id>
-            <attribute>
-                <name>s</name>
-                <type>String</type>
-                <null_value></null_value>
-            </attribute>
-        </structure>
-    </dictionary>
-</yandex>
-
- - -

DBMS

-

-

MySQL

-

Example of settings:

-
<source>
-  <mysql>
-      <port>3306</port>
-      <user>clickhouse</user>
-      <password>qwerty</password>
-      <replica>
-          <host>example01-1</host>
-          <priority>1</priority>
-      </replica>
-      <replica>
-          <host>example01-2</host>
-          <priority>1</priority>
-      </replica>
-      <db>db_name</db>
-      <table>table_name</table>
-      <where>id=10</where>
-      <invalidate_query>SQL_QUERY</invalidate_query>
-  </mysql>
-</source>
-
- - -

Setting fields:

-
    -
  • -

    port – The port on the MySQL server. You can specify it for all replicas, or for each one individually (inside <replica>).

    -
    • -
  • -

    user – Name of the MySQL user. You can specify it for all replicas, or for each one individually (inside <replica>).

    -
    • -
  • -

    password – Password of the MySQL user. You can specify it for all replicas, or for each one individually (inside <replica>).

    -
    • -
  • -

    replica – Section of replica configurations. There can be multiple sections.

    -
    • -
  • replica/host – The MySQL host.
    • -
-

* replica/priority – The replica priority. When attempting to connect, ClickHouse traverses the replicas in order of priority. The lower the number, the higher the priority.

-
    -
  • -

    db – Name of the database.

    -
    • -
  • -

    table – Name of the table.

    -
    • -
  • -

    where – The selection criteria. Optional parameter.

    -
    • -
  • -

    invalidate_query – Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries.

    -
    • -
-

MySQL can be connected on a local host via sockets. To do this, set host and socket.

-

Example of settings:

-
<source>
-  <mysql>
-      <host>localhost</host>
-      <socket>/path/to/socket/file.sock</socket>
-      <user>clickhouse</user>
-      <password>qwerty</password>
-      <db>db_name</db>
-      <table>table_name</table>
-      <where>id=10</where>
-      <invalidate_query>SQL_QUERY</invalidate_query>
-  </mysql>
-</source>
-
- - -

-

ClickHouse

-

Example of settings:

-
<source>
-    <clickhouse>
-        <host>example01-01-1</host>
-        <port>9000</port>
-        <user>default</user>
-        <password></password>
-        <db>default</db>
-        <table>ids</table>
-        <where>id=10</where>
-    </clickhouse>
-</source>
-
- - -

Setting fields:

-
    -
  • host – The ClickHouse host. If it is a local host, the query is processed without any network activity. To improve fault tolerance, you can create a Distributed table and enter it in subsequent configurations.
    • -
  • port – The port on the ClickHouse server.
    • -
  • user – Name of the ClickHouse user.
    • -
  • password – Password of the ClickHouse user.
    • -
  • db – Name of the database.
    • -
  • table – Name of the table.
    • -
  • where – The selection criteria. May be omitted.
    • -
-

-

MongoDB

-

Example of settings:

-
<source>
-    <mongodb>
-        <host>localhost</host>
-        <port>27017</port>
-        <user></user>
-        <password></password>
-        <db>test</db>
-        <collection>dictionary_source</collection>
-    </mongodb>
-</source>
-
- - -

Setting fields:

-
    -
  • host – The MongoDB host.
    • -
  • port – The port on the MongoDB server.
    • -
  • user – Name of the MongoDB user.
    • -
  • password – Password of the MongoDB user.
    • -
  • db – Name of the database.
    • -
  • collection – Name of the collection.
    • -
-

-

Dictionary key and fields

-

The <structure> clause describes the dictionary key and fields available for queries.

-

Overall structure:

-
<dictionary>
-    <structure>
-        <id>
-            <name>Id</name>
-        </id>
-
-        <attribute>
-            <!-- Attribute parameters -->
-        </attribute>
-
-        ...
-
-    </structure>
-</dictionary>
-
- - -

Columns are described in the structure:

- -

-

Key

-

ClickHouse supports the following types of keys:

-
    -
  • Numeric key. UInt64. Defined in the tag <id> .
    • -
  • Composite key. Set of values of different types. Defined in the tag <key> .
    • -
-

A structure can contain either <id> or <key> .

-
- -The key doesn't need to be defined separately in attributes. - -
- -

Numeric key

-

Format: UInt64.

-

Configuration example:

-
<id>
-    <name>Id</name>
-</id>
-
- - -

Configuration fields:

-
    -
  • name – The name of the column with keys.
    • -
-

Composite key

-

The key can be a tuple from any types of fields. The layout in this case must be complex_key_hashed or complex_key_cache.

-
-A composite key can consist of a single element. This makes it possible to use a string as the key, for instance. -
- -

The key structure is set in the element <key>. Key fields are specified in the same format as the dictionary attributes. Example:

-
<structure>
-    <key>
-        <attribute>
-            <name>field1</name>
-            <type>String</type>
-        </attribute>
-        <attribute>
-            <name>field2</name>
-            <type>UInt32</type>
-        </attribute>
-        ...
-    </key>
-...
-
- - -

For a query to the dictGet* function, a tuple is passed as the key. Example: dictGetString('dict_name', 'attr_name', tuple('string for field1', num_for_field2)).

-

-

Attributes

-

Configuration example:

-
<structure>
-    ...
-    <attribute>
-        <name>Name</name>
-        <type>Type</type>
-        <null_value></null_value>
-        <expression>rand64()</expression>
-        <hierarchical>true</hierarchical>
-        <injective>true</injective>
-        <is_object_id>true</is_object_id>
-    </attribute>
-</structure>
-
- - -

Configuration fields:

-
    -
  • name – The column name.
    • -
  • type – The column type. Sets the method for interpreting data in the source. For example, for MySQL, the field might be TEXT, VARCHAR, or BLOB in the source table, but it can be uploaded as String.
    • -
  • null_value – The default value for a non-existing element. In the example, it is an empty string.
    • -
  • expression – The attribute can be an expression. The tag is not required.
    • -
  • hierarchical – Hierarchical support. Mirrored to the parent identifier. By default, false.
    • -
  • injective – Whether the id -> attribute image is injective. If true, then you can optimize the GROUP BY clause. By default, false.
    • -
  • is_object_id – Whether the query is executed for a MongoDB document by ObjectID.
    • -
-

Internal dictionaries

-

ClickHouse contains a built-in feature for working with a geobase.

-

This allows you to:

-
    -
  • Use a region's ID to get its name in the desired language.
    • -
  • Use a region's ID to get the ID of a city, area, federal district, country, or continent.
    • -
  • Check whether a region is part of another region.
    • -
  • Get a chain of parent regions.
    • -
-

All the functions support "translocality," the ability to simultaneously use different perspectives on region ownership. For more information, see the section "Functions for working with Yandex.Metrica dictionaries".

-

The internal dictionaries are disabled in the default package. -To enable them, uncomment the parameters path_to_regions_hierarchy_file and path_to_regions_names_files in the server configuration file.

-

The geobase is loaded from text files. -If you work at Yandex, you can follow these instructions to create them: -https://github.yandex-team.ru/raw/Metrika/ClickHouse_private/master/doc/create_embedded_geobase_dictionaries.txt

-

Put the regions_hierarchy*.txt files in the path_to_regions_hierarchy_file directory. This configuration parameter must contain the path to the regions_hierarchy.txt file (the default regional hierarchy), and the other files (regions_hierarchy_ua.txt) must be located in the same directory.

-

Put the regions_names_*.txt files in the path_to_regions_names_files directory.

-

You can also create these files yourself. The file format is as follows:

-

regions_hierarchy*.txt: TabSeparated (no header), columns:

-
    -
  • Region ID (UInt32)
    • -
  • Parent region ID (UInt32)
    • -
  • Region type (UInt8): 1 - continent, 3 - country, 4 - federal district, 5 - region, 6 - city; other types don't have values.
    • -
  • Population (UInt32) - Optional column.
    • -
-

regions_names_*.txt: TabSeparated (no header), columns:

-
    -
  • Region ID (UInt32)
    • -
  • Region name (String) - Can't contain tabs or line feeds, even escaped ones.
    • -
-

A flat array is used for storing in RAM. For this reason, IDs shouldn't be more than a million.

-

Dictionaries can be updated without restarting the server. However, the set of available dictionaries is not updated. -For updates, the file modification times are checked. If a file has changed, the dictionary is updated. -The interval to check for changes is configured in the 'builtin_dictionaries_reload_interval' parameter. -Dictionary updates (other than loading at first use) do not block queries. During updates, queries use the old versions of dictionaries. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.

-

We recommend periodically updating the dictionaries with the geobase. During an update, generate new files and write them to a separate location. When everything is ready, rename them to the files used by the server.

-

There are also functions for working with OS identifiers and Yandex.Metrica search engines, but they shouldn't be used.

-

Usage

-

Access rights

-

Users and access rights are set up in the user config. This is usually users.xml.

-

Users are recorded in the users section. Here is a fragment of the users.xml file:

-
<!-- Users and ACL. -->
-<users>
-    <!-- If the user name is not specified, the 'default' user is used. -->
-    <default>
-        <!-- Password could be specified in plaintext or in SHA256 (in hex format).
-
-             If you want to specify the password in plain text (not recommended), place it in the 'password' element.
-             Example: <password>qwerty</password>.
-             Password can be empty.
-
-             If you want to specify SHA256, place it in the 'password_sha256_hex' element.
-                          Example: <password_sha256_hex>65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5</password_sha256_hex>
-
-             How to generate decent password:
-             Execute: PASSWORD=$(base64 < /dev/urandom | head -c8); echo "$PASSWORD"; echo -n "$PASSWORD" | sha256sum | tr -d '-'
-             In first line will be password and in second - corresponding SHA256.
-        -->
-        <password></password>
-        <!-- A list of networks that access is allowed from.
-            Each list item has one of the following forms:
-            <ip>IP address or subnet mask. For example: 198.51.100.0/24 or 2001:DB8::/32.
-            <host> Host name. For example: example01. A DNS query is made for verification, and all addresses obtained are compared with the address of the customer.
-            <host_regexp> Regular expression for host names. For example: ^example\d\d-\d\d-\d\.yandex\.ru$
-                For verification, a DNS PTR query is made for the customer's address and a regular expression is applied to the result.
-                Then another DNS query is made for the result of the PTR query, and all received address are compared to the client address.
-                We strongly recommend that the regex ends with \.yandex\.ru$.
-
-            If you are installing ClickHouse yourself, enter:
-                <networks>
-                        <ip>::/0</ip>
-                </networks>
-        -->
-        <networks incl="networks" />
-
-        <!-- Settings profile for the user. -->
-        <profile>default</profile>
-
-        <!-- Quota for the user. -->
-        <quota>default</quota>
-    </default>
-
-    <!-- For requests from the Yandex.Metrica user interface via the API for data on specific counters. -->
-    <web>
-        <password></password>
-        <networks incl="networks" />
-        <profile>web</profile>
-        <quota>default</quota>
-        <allow_databases>
-        <database>test</database>
-        </allow_databases>
-    </web>
-</users>
-
- - -

You can see a declaration from two users: default and web. We added the web user separately.

-

The default user is chosen in cases when the username is not passed. The default user is also used for distributed query processing, if the configuration of the server or cluster doesn't specify the user and password (see the section on the Distributed engine).

-

The user that is used for exchanging information between servers combined in a cluster must not have substantial restrictions or quotas – otherwise, distributed queries will fail.

-

The password is specified in open format (not recommended) or in SHA-256. The hash isn't salted. In this regard, you should not consider these passwords as providing security against potential malicious attacks. Rather, they are necessary for protection from employees.

-

A list of networks is specified that access is allowed from. In this example, the list of networks for both users is loaded from a separate file (/etc/metrika.xml) containing the 'networks' substitution. Here is a fragment of it:

-
<yandex>
-    ...
-    <networks>
-        <ip>::/64</ip>
-        <ip>203.0.113.0/24</ip>
-        <ip>2001:DB8::/32</ip>
-        ...
-    </networks>
-</yandex>
-
- - -

We could have defined this list of networks directly in 'users.xml', or in a file in the 'users.d' directory (for more information, see the section "Configuration files").

-

The config includes comments explaining how to open access from everywhere.

-

For use in production, only specify IP elements (IP addresses and their masks), since using 'host' and 'hoost_regexp' might cause extra latency.

-

Next the user settings profile is specified (see the section "Settings profiles"). You can specify the default profile, default. The profile can have any name. You can specify the same profile for different users. The most important thing you can write in the settings profile is 'readonly' set to 1, which provides read-only access.

-

After this, the quota is defined (see the section "Quotas"). You can specify the default quota, default. It is set in the config by default so that it only counts resource usage, but does not restrict it. The quota can have any name. You can specify the same quota for different users – in this case, resource usage is calculated for each user individually.

-

In the optional <allow_databases> section, you can also specify a list of databases that the user can access. By default, all databases are available to the user. You can specify the default database. In this case, the user will receive access to the database by default.

-

Access to the system database is always allowed (since this database is used for processing queries).

-

The user can get a list of all databases and tables in them by using SHOW queries or system tables, even if access to individual databases isn't allowed.

-

Database access is not related to the readonly setting. You can't grant full access to one database and readonly access to another one.

-

-

Configuration files

-

The main server config file is config.xml. It resides in the /etc/clickhouse-server/ directory.

-

Individual settings can be overridden in the *.xmland*.conf files in the conf.d and config.d directories next to the config file.

-

The replace or remove attributes can be specified for the elements of these config files.

-

If neither is specified, it combines the contents of elements recursively, replacing values of duplicate children.

-

If replace is specified, it replaces the entire element with the specified one.

-

If remove is specified, it deletes the element.

-

The config can also define "substitutions". If an element has the incl attribute, the corresponding substitution from the file will be used as the value. By default, the path to the file with substitutions is /etc/metrika.xml. This can be changed in the include_from element in the server config. The substitution values are specified in /yandex/substitution_name elements in this file. If a substitution specified in incl does not exist, it is recorded in the log. To prevent ClickHouse from logging missing substitutions, specify the optional="true" attribute (for example, settings for macros).

-

Substitutions can also be performed from ZooKeeper. To do this, specify the attribute from_zk = "/path/to/node". The element value is replaced with the contents of the node at /path/to/node in ZooKeeper. You can also put an entire XML subtree on the ZooKeeper node and it will be fully inserted into the source element.

-

The config.xml file can specify a separate config with user settings, profiles, and quotas. The relative path to this config is set in the 'users_config' element. By default, it is users.xml. If users_config is omitted, the user settings, profiles, and quotas are specified directly in config.xml.

-

In addition, users_config may have overrides in files from the users_config.d directory (for example, users.d) and substitutions.

-

For each config file, the server also generates file-preprocessed.xml files when starting. These files contain all the completed substitutions and overrides, and they are intended for informational use. If ZooKeeper substitutions were used in the config files but ZooKeeper is not available on the server start, the server loads the configuration from the preprocessed file.

-

The server tracks changes in config files, as well as files and ZooKeeper nodes that were used when performing substitutions and overrides, and reloads the settings for users and clusters on the fly. This means that you can modify the cluster, users, and their settings without restarting the server.

-

Quotas

-

Quotas allow you to limit resource usage over a period of time, or simply track the use of resources. -Quotas are set up in the user config. This is usually 'users.xml'.

-

The system also has a feature for limiting the complexity of a single query. See the section "Restrictions on query complexity").

-

In contrast to query complexity restrictions, quotas:

-
    -
  • Place restrictions on a set of queries that can be run over a period of time, instead of limiting a single query.
    • -
  • Account for resources spent on all remote servers for distributed query processing.
    • -
-

Let's look at the section of the 'users.xml' file that defines quotas.

-
<!-- Quotas. -->
-<quotas>
-    <!-- Quota name. -->
-    <default>
-        <!-- Restrictions for a time period. You can set many intervals with different restrictions. -->
-        <interval>
-            <!-- Length of the interval. -->
-            <duration>3600</duration>
-
-            <!-- Unlimited. Just collect data for the specified time interval. -->
-            <queries>0</queries>
-            <errors>0</errors>
-            <result_rows>0</result_rows>
-            <read_rows>0</read_rows>
-            <execution_time>0</execution_time>
-        </interval>
-    </default>
-
- - -

By default, the quota just tracks resource consumption for each hour, without limiting usage. -The resource consumption calculated for each interval is output to the server log after each request.

-
<statbox>
-    <!-- Restrictions for a time period. You can set many intervals with different restrictions. -->
-    <interval>
-        <!-- Length of the interval. -->
-        <duration>3600</duration>
-
-        <queries>1000</queries>
-        <errors>100</errors>
-        <result_rows>1000000000</result_rows>
-        <read_rows>100000000000</read_rows>
-        <execution_time>900</execution_time>
-    </interval>
-
-    <interval>
-        <duration>86400</duration>
-
-        <queries>10000</queries>
-        <errors>1000</errors>
-        <result_rows>5000000000</result_rows>
-        <read_rows>500000000000</read_rows>
-        <execution_time>7200</execution_time>
-    </interval>
-</statbox>
-
- - -

For the 'statbox' quota, restrictions are set for every hour and for every 24 hours (86,400 seconds). The time interval is counted starting from an implementation-defined fixed moment in time. In other words, the 24-hour interval doesn't necessarily begin at midnight.

-

When the interval ends, all collected values are cleared. For the next hour, the quota calculation starts over.

-

Here are the amounts that can be restricted:

-

queries – The total number of requests.

-

errors – The number of queries that threw an exception.

-

result_rows – The total number of rows given as the result.

-

read_rows – The total number of source rows read from tables for running the query, on all remote servers.

-

execution_time – The total query execution time, in seconds (wall time).

-

If the limit is exceeded for at least one time interval, an exception is thrown with a text about which restriction was exceeded, for which interval, and when the new interval begins (when queries can be sent again).

-

Quotas can use the "quota key" feature in order to report on resources for multiple keys independently. Here is an example of this:

-
<!-- For the global reports designer. -->
-<web_global>
-    <!-- keyed - The quota_key "key" is passed in the query parameter,
-            and the quota is tracked separately for each key value.
-        For example, you can pass a Yandex.Metrica username as the key,
-            so the quota will be counted separately for each username.
-        Using keys makes sense only if quota_key is transmitted by the program, not by a user.
-
-        You can also write <keyed_by_ip /> so the IP address is used as the quota key.
-        (But keep in mind that users can change the IPv6 address fairly easily.)
-    -->
-    <keyed />
-
- - -

The quota is assigned to users in the 'users' section of the config. See the section "Access rights".

-

For distributed query processing, the accumulated amounts are stored on the requestor server. So if the user goes to another server, the quota there will "start over".

-

When the server is restarted, quotas are reset.

-

Usage recommendations

-

CPU

-

The SSE 4.2 instruction set must be supported. Modern processors (since 2008) support it.

-

When choosing a processor, prefer a large number of cores and slightly slower clock rate over fewer cores and a higher clock rate. -For example, 16 cores with 2600 MHz is better than 8 cores with 3600 MHz.

-

Hyper-threading

-

Don't disable hyper-threading. It helps for some queries, but not for others.

-

Turbo Boost

-

Turbo Boost is highly recommended. It significantly improves performance with a typical load. -You can use turbostat to view the CPU's actual clock rate under a load.

-

CPU scaling governor

-

Always use the performance scaling governor. The on-demand scaling governor works much worse with constantly high demand.

-
sudo echo 'performance' | tee /sys/devices/system/cpu/cpu\*/cpufreq/scaling_governor
-
- - -

CPU limitations

-

Processors can overheat. Use dmesg to see if the CPU's clock rate was limited due to overheating. -The restriction can also be set externally at the datacenter level. You can use turbostat to monitor it under a load.

-

RAM

-

For small amounts of data (up to \~200 GB compressed), it is best to use as much memory as the volume of data. -For large amounts of data and when processing interactive (online) queries, you should use a reasonable amount of RAM (128 GB or more) so the hot data subset will fit in the cache of pages. -Even for data volumes of \~50 TB per server, using 128 GB of RAM significantly improves query performance compared to 64 GB.

-

Swap file

-

Always disable the swap file. The only reason for not doing this is if you are using ClickHouse on your personal laptop.

-

Huge pages

-

Always disable transparent huge pages. It interferes with memory allocators, which leads to significant performance degradation.

-
echo 'never' | sudo tee /sys/kernel/mm/transparent_hugepage/enabled
-
- - -

Use perf top to watch the time spent in the kernel for memory management. -Permanent huge pages also do not need to be allocated.

-

Storage subsystem

-

If your budget allows you to use SSD, use SSD. -If not, use HDD. SATA HDDs 7200 RPM will do.

-

Give preference to a lot of servers with local hard drives over a smaller number of servers with attached disk shelves. -But for storing archives with rare queries, shelves will work.

-

RAID

-

When using HDD, you can combine their RAID-10, RAID-5, RAID-6 or RAID-50. -For Linux, software RAID is better (with mdadm). We don't recommend using LVM. -When creating RAID-10, select the far layout. -If your budget allows, choose RAID-10.

-

If you have more than 4 disks, use RAID-6 (preferred) or RAID-50, instead of RAID-5. -When using RAID-5, RAID-6 or RAID-50, always increase stripe_cache_size, since the default value is usually not the best choice.

-
echo 4096 | sudo tee /sys/block/md2/md/stripe_cache_size
-
- - -

Calculate the exact number from the number of devices and the block size, using the formula: 2 * num_devices * chunk_size_in_bytes / 4096.

-

A block size of 1025 KB is sufficient for all RAID configurations. -Never set the block size too small or too large.

-

You can use RAID-0 on SSD. -Regardless of RAID use, always use replication for data security.

-

Enable NCQ with a long queue. For HDD, choose the CFQ scheduler, and for SSD, choose noop. Don't reduce the 'readahead' setting. -For HDD, enable the write cache.

-

File system

-

Ext4 is the most reliable option. Set the mount options noatime, nobarrier. -XFS is also suitable, but it hasn't been as thoroughly tested with ClickHouse. -Most other file systems should also work fine. File systems with delayed allocation work better.

-

Linux kernel

-

Don't use an outdated Linux kernel. In 2015, 3.18.19 was new enough. -Consider using the kernel build from Yandex:https://github.com/yandex/smart – it provides at least a 5% performance increase.

-

Network

-

If you are using IPv6, increase the size of the route cache. -The Linux kernel prior to 3.2 had a multitude of problems with IPv6 implementation.

-

Use at least a 10 GB network, if possible. 1 Gb will also work, but it will be much worse for patching replicas with tens of terabytes of data, or for processing distributed queries with a large amount of intermediate data.

-

ZooKeeper

-

You are probably already using ZooKeeper for other purposes. You can use the same installation of ZooKeeper, if it isn't already overloaded.

-

It's best to use a fresh version of ZooKeeper – 3.4.9 or later. The version in stable Linux distributions may be outdated.

-

With the default settings, ZooKeeper is a time bomb:

-
-

The ZooKeeper server won't delete files from old snapshots and logs when using the default configuration (see autopurge), and this is the responsibility of the operator.

-
-

This bomb must be defused.

-

The ZooKeeper (3.5.1) configuration below is used in the Yandex.Metrica production environment as of May 20, 2017:

-

zoo.cfg:

-
## http://hadoop.apache.org/zookeeper/docs/current/zookeeperAdmin.html
-
-## The number of milliseconds of each tick
-tickTime=2000
-## The number of ticks that the initial
-## synchronization phase can take
-initLimit=30000
-## The number of ticks that can pass between
-## sending a request and getting an acknowledgement
-syncLimit=10
-
-maxClientCnxns=2000
-
-maxSessionTimeout=60000000
-## the directory where the snapshot is stored.
-dataDir=/opt/zookeeper/{{ cluster['name'] }}/data
-## Place the dataLogDir to a separate physical disc for better performance
-dataLogDir=/opt/zookeeper/{{ cluster['name'] }}/logs
-
-autopurge.snapRetainCount=10
-autopurge.purgeInterval=1
-
-
-## To avoid seeks ZooKeeper allocates space in the transaction log file in
-## blocks of preAllocSize kilobytes. The default block size is 64M. One reason
-## for changing the size of the blocks is to reduce the block size if snapshots
-## are taken more often. (Also, see snapCount).
-preAllocSize=131072
-
-## Clients can submit requests faster than ZooKeeper can process them,
-## especially if there are a lot of clients. To prevent ZooKeeper from running
-## out of memory due to queued requests, ZooKeeper will throttle clients so that
-## there is no more than globalOutstandingLimit outstanding requests in the
-## system. The default limit is 1,000.ZooKeeper logs transactions to a
-## transaction log. After snapCount transactions are written to a log file a
-## snapshot is started and a new transaction log file is started. The default
-## snapCount is 10,000.
-snapCount=3000000
-
-## If this option is defined, requests will be will logged to a trace file named
-## traceFile.year.month.day.
-##traceFile=
-
-## Leader accepts client connections. Default value is "yes". The leader machine
-## coordinates updates. For higher update throughput at thes slight expense of
-## read throughput the leader can be configured to not accept clients and focus
-## on coordination.
-leaderServes=yes
-
-standaloneEnabled=false
-dynamicConfigFile=/etc/zookeeper-{{ cluster['name'] }}/conf/zoo.cfg.dynamic
-
- - -

Java version:

-
Java(TM) SE Runtime Environment (build 1.8.0_25-b17)
-Java HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode)
-
- - -

JVM parameters:

-
NAME=zookeeper-{{ cluster['name'] }}
-ZOOCFGDIR=/etc/$NAME/conf
-
-## TODO this is really ugly
-## How to find out, which jars are needed?
-## seems, that log4j requires the log4j.properties file to be in the classpath
-CLASSPATH="$ZOOCFGDIR:/usr/build/classes:/usr/build/lib/*.jar:/usr/share/zookeeper/zookeeper-3.5.1-metrika.jar:/usr/share/zookeeper/slf4j-log4j12-1.7.5.jar:/usr/share/zookeeper/slf4j-api-1.7.5.jar:/usr/share/zookeeper/servlet-api-2.5-20081211.jar:/usr/share/zookeeper/netty-3.7.0.Final.jar:/usr/share/zookeeper/log4j-1.2.16.jar:/usr/share/zookeeper/jline-2.11.jar:/usr/share/zookeeper/jetty-util-6.1.26.jar:/usr/share/zookeeper/jetty-6.1.26.jar:/usr/share/zookeeper/javacc.jar:/usr/share/zookeeper/jackson-mapper-asl-1.9.11.jar:/usr/share/zookeeper/jackson-core-asl-1.9.11.jar:/usr/share/zookeeper/commons-cli-1.2.jar:/usr/src/java/lib/*.jar:/usr/etc/zookeeper"
-
-ZOOCFG="$ZOOCFGDIR/zoo.cfg"
-ZOO_LOG_DIR=/var/log/$NAME
-USER=zookeeper
-GROUP=zookeeper
-PIDDIR=/var/run/$NAME
-PIDFILE=$PIDDIR/$NAME.pid
-SCRIPTNAME=/etc/init.d/$NAME
-JAVA=/usr/bin/java
-ZOOMAIN="org.apache.zookeeper.server.quorum.QuorumPeerMain"
-ZOO_LOG4J_PROP="INFO,ROLLINGFILE"
-JMXLOCALONLY=false
-JAVA_OPTS="-Xms{{ cluster.get('xms','128M') }} \
-    -Xmx{{ cluster.get('xmx','1G') }} \
-    -Xloggc:/var/log/$NAME/zookeeper-gc.log \
-    -XX:+UseGCLogFileRotation \
-    -XX:NumberOfGCLogFiles=16 \
-    -XX:GCLogFileSize=16M \
-    -verbose:gc \
-    -XX:+PrintGCTimeStamps \
-    -XX:+PrintGCDateStamps \
-    -XX:+PrintGCDetails
-    -XX:+PrintTenuringDistribution \
-    -XX:+PrintGCApplicationStoppedTime \
-    -XX:+PrintGCApplicationConcurrentTime \
-    -XX:+PrintSafepointStatistics \
-    -XX:+UseParNewGC \
-    -XX:+UseConcMarkSweepGC \
--XX:+CMSParallelRemarkEnabled"
-
- - -

Salt init:

-
description "zookeeper-{{ cluster['name'] }} centralized coordination service"
-
-start on runlevel [2345]
-stop on runlevel [!2345]
-
-respawn
-
-limit nofile 8192 8192
-
-pre-start script
-    [ -r "/etc/zookeeper-{{ cluster['name'] }}/conf/environment" ] || exit 0
-    . /etc/zookeeper-{{ cluster['name'] }}/conf/environment
-    [ -d $ZOO_LOG_DIR ] || mkdir -p $ZOO_LOG_DIR
-    chown $USER:$GROUP $ZOO_LOG_DIR
-end script
-
-script
-    . /etc/zookeeper-{{ cluster['name'] }}/conf/environment
-    [ -r /etc/default/zookeeper ] && . /etc/default/zookeeper
-    if [ -z "$JMXDISABLE" ]; then
-        JAVA_OPTS="$JAVA_OPTS -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.local.only=$JMXLOCALONLY"
-    fi
-    exec start-stop-daemon --start -c $USER --exec $JAVA --name zookeeper-{{ cluster['name'] }} \
-        -- -cp $CLASSPATH $JAVA_OPTS -Dzookeeper.log.dir=${ZOO_LOG_DIR} \
-        -Dzookeeper.root.logger=${ZOO_LOG4J_PROP} $ZOOMAIN $ZOOCFG
-end script
-
- - -

-

Server configuration parameters

-

This section contains descriptions of server settings that cannot be changed at the session or query level.

-

These settings are stored in the config.xml file on the ClickHouse server.

-

Other settings are described in the "Settings" section.

-

Before studying the settings, read the Configuration files section and note the use of substitutions (the incl and optional attributes).

-

Server settings

-

-

builtin_dictionaries_reload_interval

-

The interval in seconds before reloading built-in dictionaries.

-

ClickHouse reloads built-in dictionaries every x seconds. This makes it possible to edit dictionaries "on the fly" without restarting the server.

-

Default value: 3600.

-

Example

-
<builtin_dictionaries_reload_interval>3600</builtin_dictionaries_reload_interval>
-
- - -

-

compression

-

Data compression settings.

-
- -Don't use it if you have just started using ClickHouse. - -
- -

The configuration looks like this:

-
<compression>
-    <case>
-      <parameters/>
-    </case>
-    ...
-</compression>
-
- - -

You can configure multiple sections <case>.

-

Block field <case>:

-
    -
  • min_part_size – The minimum size of a table part.
    • -
  • min_part_size_ratio – The ratio of the minimum size of a table part to the full size of the table.
    • -
  • method – Compression method. Acceptable values ​: lz4 or zstd(experimental).
    • -
-

ClickHouse checks min_part_size and min_part_size_ratio and processes the case blocks that match these conditions. If none of the <case> matches, ClickHouse applies the lz4 compression algorithm.

-

Example

-
<compression incl="clickhouse_compression">
-    <case>
-        <min_part_size>10000000000</min_part_size>
-        <min_part_size_ratio>0.01</min_part_size_ratio>
-        <method>zstd</method>
-    </case>
-</compression>
-
- - -

-

default_database

-

The default database.

-

To get a list of databases, use the SHOW DATABASES.

-

Example

-
<default_database>default</default_database>
-
- - -

-

default_profile

-

Default settings profile.

-

Settings profiles are located in the file specified in the parameter user_config.

-

Example

-
<default_profile>default</default_profile>
-
- - -

-

dictionaries_config

-

The path to the config file for external dictionaries.

-

Path:

-
    -
  • Specify the absolute path or the path relative to the server config file.
    • -
  • The path can contain wildcards * and ?.
    • -
-

See also "External dictionaries".

-

Example

-
<dictionaries_config>*_dictionary.xml</dictionaries_config>
-
- - -

-

dictionaries_lazy_load

-

Lazy loading of dictionaries.

-

If true, then each dictionary is created on first use. If dictionary creation failed, the function that was using the dictionary throws an exception.

-

If false, all dictionaries are created when the server starts, and if there is an error, the server shuts down.

-

The default is true.

-

Example

-
<dictionaries_lazy_load>true</dictionaries_lazy_load>
-
- - -

-

format_schema_path

-

The path to the directory with the schemes for the input data, such as schemas for the CapnProto format.

-

Example

-
  <!-- Directory containing schema files for various input formats. -->
-  <format_schema_path>format_schemas/</format_schema_path>
-
- - -

-

graphite

-

Sending data to Graphite.

-

Settings:

-
    -
  • host – The Graphite server.
    • -
  • port – The port on the Graphite server.
    • -
  • interval – The interval for sending, in seconds.
    • -
  • timeout – The timeout for sending data, in seconds.
    • -
  • root_path – Prefix for keys.
    • -
  • metrics – Sending data from a :ref:system_tables-system.metrics table.
    • -
  • events – Sending data from a :ref:system_tables-system.events table.
    • -
  • asynchronous_metrics – Sending data from a :ref:system_tables-system.asynchronous_metrics table.
    • -
-

You can configure multiple <graphite> clauses. For instance, you can use this for sending different data at different intervals.

-

Example

-
<graphite>
-    <host>localhost</host>
-    <port>42000</port>
-    <timeout>0.1</timeout>
-    <interval>60</interval>
-    <root_path>one_min</root_path>
-    <metrics>true</metrics>
-    <events>true</events>
-    <asynchronous_metrics>true</asynchronous_metrics>
-</graphite>
-
- - -

-

graphite_rollup

-

Settings for thinning data for Graphite.

-

For more information, see GraphiteMergeTree.

-

Example

-
<graphite_rollup_example>
-    <default>
-        <function>max</function>
-        <retention>
-            <age>0</age>
-            <precision>60</precision>
-        </retention>
-        <retention>
-            <age>3600</age>
-            <precision>300</precision>
-        </retention>
-        <retention>
-            <age>86400</age>
-            <precision>3600</precision>
-        </retention>
-    </default>
-</graphite_rollup_example>
-
- - -

-

http_port/https_port

-

The port for connecting to the server over HTTP(s).

-

If https_port is specified, openSSL must be configured.

-

If http_port is specified, the openSSL configuration is ignored even if it is set.

-

Example

-
<https>0000</https>
-
- - -

-

http_server_default_response

-

The page that is shown by default when you access the ClickHouse HTTP(s) server.

-

Example

-

Opens https://tabix.io/ when accessing http://localhost: http_port.

-
<http_server_default_response>
-  <![CDATA[<html ng-app="SMI2"><head><base href="http://ui.tabix.io/"></head><body><div ui-view="" class="content-ui"></div><script src="http://loader.tabix.io/master.js"></script></body></html>]]>
-</http_server_default_response>
-
- - -

-

include_from

-

The path to the file with substitutions.

-

For more information, see the section "Configuration files".

-

Example

-
<include_from>/etc/metrica.xml</include_from>
-
- - -

-

interserver_http_port

-

Port for exchanging data between ClickHouse servers.

-

Example

-
<interserver_http_port>9009</interserver_http_port>
-
- - -

-

interserver_http_host

-

The host name that can be used by other servers to access this server.

-

If omitted, it is defined in the same way as the hostname-f command.

-

Useful for breaking away from a specific network interface.

-

Example

-
<interserver_http_host>example.yandex.ru</interserver_http_host>
-
- - -

-

keep_alive_timeout

-

The number of milliseconds that ClickHouse waits for incoming requests before closing the connection.

-

Example

-
<keep_alive_timeout>3</keep_alive_timeout>
-
- - -

-

listen_host

-

Restriction on hosts that requests can come from. If you want the server to answer all of them, specify ::.

-

Examples:

-
<listen_host>::1</listen_host>
-<listen_host>127.0.0.1</listen_host>
-
- - -

-

logger

-

Logging settings.

-

Keys:

-
    -
  • level – Logging level. Acceptable values: trace, debug, information, warning, error.
    • -
  • log – The log file. Contains all the entries according to level.
    • -
  • errorlog – Error log file.
    • -
  • size – Size of the file. Applies to loganderrorlog. Once the file reaches size, ClickHouse archives and renames it, and creates a new log file in its place.
    • -
  • count – The number of archived log files that ClickHouse stores.
    • -
-

Example

-
<logger>
-    <level>trace</level>
-    <log>/var/log/clickhouse-server/clickhouse-server.log</log>
-    <errorlog>/var/log/clickhouse-server/clickhouse-server.err.log</errorlog>
-    <size>1000M</size>
-    <count>10</count>
-</logger>
-
- - -

-

macros

-

Parameter substitutions for replicated tables.

-

Can be omitted if replicated tables are not used.

-

For more information, see the section "Creating replicated tables".

-

Example

-
<macros incl="macros" optional="true" />
-
- - -

-

mark_cache_size

-

Approximate size (in bytes) of the cache of "marks" used by MergeTree engines.

-

The cache is shared for the server and memory is allocated as needed. The cache size must be at least 5368709120.

-

Example

-
<mark_cache_size>5368709120</mark_cache_size>
-
- - -

-

max_concurrent_queries

-

The maximum number of simultaneously processed requests.

-

Example

-
<max_concurrent_queries>100</max_concurrent_queries>
-
- - -

-

max_connections

-

The maximum number of inbound connections.

-

Example

-
<max_connections>4096</max_connections>
-
- - -

-

max_open_files

-

The maximum number of open files.

-

By default: maximum.

-

We recommend using this option in Mac OS X, since the getrlimit() function returns an incorrect value.

-

Example

-
<max_open_files>262144</max_open_files>
-
- - -

-

max_table_size_to_drop

-

Restriction on deleting tables.

-

If the size of a MergeTree type table exceeds max_table_size_to_drop (in bytes), you can't delete it using a DROP query.

-

If you still need to delete the table without restarting the ClickHouse server, create the <clickhouse-path>/flags/force_drop_table file and run the DROP query.

-

Default value: 50 GB.

-

The value 0 means that you can delete all tables without any restrictions.

-

Example

-
<max_table_size_to_drop>0</max_table_size_to_drop>
-
- - -

-

merge_tree

-

Fine tuning for tables in the MergeTree family.

-

For more information, see the MergeTreeSettings.h header file.

-

Example

-
<merge_tree>
-    <max_suspicious_broken_parts>5</max_suspicious_broken_parts>
-</merge_tree>
-
- - -

-

openSSL

-

SSL client/server configuration.

-

Support for SSL is provided by the libpoco library. The interface is described in the file SSLManager.h

-

Keys for server/client settings:

-
    -
  • privateKeyFile – The path to the file with the secret key of the PEM certificate. The file may contain a key and certificate at the same time.
    • -
  • certificateFile – The path to the client/server certificate file in PEM format. You can omit it if privateKeyFile contains the certificate.
    • -
  • caConfig – The path to the file or directory that contains trusted root certificates.
    • -
  • verificationMode – The method for checking the node's certificates. Details are in the description of the Context class. Possible values: none, relaxed, strict, once.
    • -
  • verificationDepth – The maximum length of the verification chain. Verification will fail if the certificate chain length exceeds the set value.
    • -
  • loadDefaultCAFile – Indicates that built-in CA certificates for OpenSSL will be used. Acceptable values: true, false. |
    • -
  • cipherList – Supported OpenSSL encryptions. For example: ALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH.
    • -
  • cacheSessions – Enables or disables caching sessions. Must be used in combination with sessionIdContext. Acceptable values: true, false.
    • -
  • sessionIdContext – A unique set of random characters that the server appends to each generated identifier. The length of the string must not exceed SSL_MAX_SSL_SESSION_ID_LENGTH. This parameter is always recommended, since it helps avoid problems both if the server caches the session and if the client requested caching. Default value: ${application.name}.
    • -
  • sessionCacheSize – The maximum number of sessions that the server caches. Default value: 1024*20. 0 – Unlimited sessions.
    • -
  • sessionTimeout – Time for caching the session on the server.
    • -
  • extendedVerification – Automatically extended verification of certificates after the session ends. Acceptable values: true, false.
    • -
  • requireTLSv1 – Require a TLSv1 connection. Acceptable values: true, false.
    • -
  • requireTLSv1_1 – Require a TLSv1.1 connection. Acceptable values: true, false.
    • -
  • requireTLSv1 – Require a TLSv1.2 connection. Acceptable values: true, false.
    • -
  • fips – Activates OpenSSL FIPS mode. Supported if the library's OpenSSL version supports FIPS.
    • -
  • privateKeyPassphraseHandler – Class (PrivateKeyPassphraseHandler subclass) that requests the passphrase for accessing the private key. For example: <privateKeyPassphraseHandler>, <name>KeyFileHandler</name>, <options><password>test</password></options>, </privateKeyPassphraseHandler>.
    • -
  • invalidCertificateHandler – Class (subclass of CertificateHandler) for verifying invalid certificates. For example: <invalidCertificateHandler> <name>ConsoleCertificateHandler</name> </invalidCertificateHandler> .
    • -
  • disableProtocols – Protocols that are not allowed to use.
    • -
  • preferServerCiphers – Preferred server ciphers on the client.
    • -
-

Example of settings:

-
<openSSL>
-    <server>
-        <!-- openssl req -subj "/CN=localhost" -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout /etc/clickhouse-server/server.key -out /etc/clickhouse-server/server.crt -->
-        <certificateFile>/etc/clickhouse-server/server.crt</certificateFile>
-        <privateKeyFile>/etc/clickhouse-server/server.key</privateKeyFile>
-        <!-- openssl dhparam -out /etc/clickhouse-server/dhparam.pem 4096 -->
-        <dhParamsFile>/etc/clickhouse-server/dhparam.pem</dhParamsFile>
-        <verificationMode>none</verificationMode>
-        <loadDefaultCAFile>true</loadDefaultCAFile>
-        <cacheSessions>true</cacheSessions>
-        <disableProtocols>sslv2,sslv3</disableProtocols>
-        <preferServerCiphers>true</preferServerCiphers>
-    </server>
-    <client>
-        <loadDefaultCAFile>true</loadDefaultCAFile>
-        <cacheSessions>true</cacheSessions>
-        <disableProtocols>sslv2,sslv3</disableProtocols>
-        <preferServerCiphers>true</preferServerCiphers>
-        <!-- Use for self-signed: <verificationMode>none</verificationMode> -->
-        <invalidCertificateHandler>
-            <!-- Use for self-signed: <name>AcceptCertificateHandler</name> -->
-            <name>RejectCertificateHandler</name>
-        </invalidCertificateHandler>
-    </client>
-</openSSL>
-
- - -

-

part_log

-

Logging events that are associated with MergeTree data. For instance, adding or merging data. You can use the log to simulate merge algorithms and compare their characteristics. You can visualize the merge process.

-

Queries are logged in the ClickHouse table, not in a separate file.

-

Columns in the log:

-
    -
  • event_time – Date of the event.
    • -
  • duration_ms – Duration of the event.
    • -
  • event_type – Type of event. 1 – new data part; 2 – merge result; 3 – data part downloaded from replica; 4 – data part deleted.
    • -
  • database_name – The name of the database.
    • -
  • table_name – Name of the table.
    • -
  • part_name – Name of the data part.
    • -
  • size_in_bytes – Size of the data part in bytes.
    • -
  • merged_from – An array of names of data parts that make up the merge (also used when downloading a merged part).
    • -
  • merge_time_ms – Time spent on the merge.
    • -
-

Use the following parameters to configure logging:

-
    -
  • database – Name of the database.
    • -
  • table – Name of the table.
    • -
  • partition_by – Sets a custom partitioning key.
    • -
  • flush_interval_milliseconds – Interval for flushing data from memory to the disk.
    • -
-

Example

-
<part_log>
-    <database>system</database>
-    <table>part_log</table>
-    <partition_by>toMonday(event_date)</partition_by>
-    <flush_interval_milliseconds>7500</flush_interval_milliseconds>
-</part_log>
-
- - -

-

path

-

The path to the directory containing data.

-
- -The end slash is mandatory. - -
- -

Example

-
<path>/var/lib/clickhouse/</path>
-
- - -

-

query_log

-

Setting for logging queries received with the log_queries=1 setting.

-

Queries are logged in the ClickHouse table, not in a separate file.

-

Use the following parameters to configure logging:

-
    -
  • database – Name of the database.
    • -
  • table – Name of the table.
    • -
  • partition_by – Sets a custom partitioning key.
    • -
  • flush_interval_milliseconds – Interval for flushing data from memory to the disk.
    • -
-

If the table doesn't exist, ClickHouse will create it. If the structure of the query log changed when the ClickHouse server was updated, the table with the old structure is renamed, and a new table is created automatically.

-

Example

-
<query_log>
-    <database>system</database>
-    <table>query_log</table>
-    <partition_by>toMonday(event_date)</partition_by>
-    <flush_interval_milliseconds>7500</flush_interval_milliseconds>
-</query_log>
-
- - -

-

remote_servers

-

Configuration of clusters used by the Distributed table engine.

-

For more information, see the section "Table engines/Distributed".

-

Example

-
<remote_servers incl="clickhouse_remote_servers" />
-
- - -

For the value of the incl attribute, see the section "Configuration files".

-

-

timezone

-

The server's time zone.

-

Specified as an IANA identifier for the UTC time zone or geographic location (for example, Africa/Abidjan).

-

The time zone is necessary for conversions between String and DateTime formats when DateTime fields are output to text format (printed on the screen or in a file), and when getting DateTime from a string. In addition, the time zone is used in functions that work with the time and date if they didn't receive the time zone in the input parameters.

-

Example

-
<timezone>Europe/Moscow</timezone>
-
- - -

-

tcp_port

-

Port for communicating with clients over the TCP protocol.

-

Example

-
<tcp_port>9000</tcp_port>
-
- - -

-

tmp_path

-

Path to temporary data for processing large queries.

-
- -The end slash is mandatory. - -
- -

Example

-
<tmp_path>/var/lib/clickhouse/tmp/</tmp_path>
-
- - -

-

uncompressed_cache_size

-

Cache size (in bytes) for uncompressed data used by table engines from the MergeTree family.

-

There is one shared cache for the server. Memory is allocated on demand. The cache is used if the option use_uncompressed_cache is enabled.

-

The uncompressed cache is advantageous for very short queries in individual cases.

-

Example

-
<uncompressed_cache_size>8589934592</uncompressed_cache_size>
-
- - -

-

users_config

-

Path to the file that contains:

-
    -
  • User configurations.
    • -
  • Access rights.
    • -
  • Settings profiles.
    • -
  • Quota settings.
    • -
-

Example

-
<users_config>users.xml</users_config>
-
- - -

-

zookeeper

-

Configuration of ZooKeeper servers.

-

ClickHouse uses ZooKeeper for storing replica metadata when using replicated tables.

-

This parameter can be omitted if replicated tables are not used.

-

For more information, see the section "Replication".

-

Example

-
<zookeeper incl="zookeeper-servers" optional="true" />
-
- - -

-

Settings

-

There are multiple ways to make all the settings described below. -Settings are configured in layers, so each subsequent layer redefines the previous settings.

-

Ways to configure settings, in order of priority:

-
    -
  • Settings in the server config file.
    • -
-

Settings from user profiles.

-
    -
  • Session settings.
    • -
-

Send SET setting=value from the ClickHouse console client in interactive mode. -Similarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to specify the session_id HTTP parameter.

-
    -
  • For a query.
    • -
  • When starting the ClickHouse console client in non-interactive mode, set the startup parameter --setting=value.
    • -
  • When using the HTTP API, pass CGI parameters (URL?setting_1=value&setting_2=value...).
    • -
-

Settings that can only be made in the server config file are not covered in this section.

-

Restrictions on query complexity

-

Restrictions on query complexity are part of the settings. -They are used in order to provide safer execution from the user interface. -Almost all the restrictions only apply to SELECTs.For distributed query processing, restrictions are applied on each server separately.

-

Restrictions on the "maximum amount of something" can take the value 0, which means "unrestricted". -Most restrictions also have an 'overflow_mode' setting, meaning what to do when the limit is exceeded. -It can take one of two values: throw or break. Restrictions on aggregation (group_by_overflow_mode) also have the value any.

-

throw – Throw an exception (default).

-

break – Stop executing the query and return the partial result, as if the source data ran out.

-

any (only for group_by_overflow_mode) – Continuing aggregation for the keys that got into the set, but don't add new keys to the set.

-

-

readonly

-

With a value of 0, you can execute any queries. -With a value of 1, you can only execute read requests (such as SELECT and SHOW). Requests for writing and changing settings (INSERT, SET) are prohibited. -With a value of 2, you can process read queries (SELECT, SHOW) and change settings (SET).

-

After enabling readonly mode, you can't disable it in the current session.

-

When using the GET method in the HTTP interface, 'readonly = 1' is set automatically. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.

-

-

max_memory_usage

-

The maximum amount of RAM to use for running a query on a single server.

-

In the default configuration file, the maximum is 10 GB.

-

The setting doesn't consider the volume of available memory or the total volume of memory on the machine. -The restriction applies to a single query within a single server. -You can use SHOW PROCESSLIST to see the current memory consumption for each query. -In addition, the peak memory consumption is tracked for each query and written to the log.

-

Memory usage is not monitored for the states of certain aggregate functions.

-

Memory usage is not fully tracked for states of the aggregate functions min, max, any, anyLast, argMin, argMax from String and Array arguments.

-

Memory consumption is also restricted by the parameters max_memory_usage_for_user and max_memory_usage_for_all_queries.

-

max_memory_usage_for_user

-

The maximum amount of RAM to use for running a user's queries on a single server.

-

Default values are defined in Settings.h. By default, the amount is not restricted (max_memory_usage_for_user = 0).

-

See also the description of max_memory_usage.

-

max_memory_usage_for_all_queries

-

The maximum amount of RAM to use for running all queries on a single server.

-

Default values are defined in Settings.h. By default, the amount is not restricted (max_memory_usage_for_all_queries = 0).

-

See also the description of max_memory_usage.

-

max_rows_to_read

-

The following restrictions can be checked on each block (instead of on each row). That is, the restrictions can be broken a little. -When running a query in multiple threads, the following restrictions apply to each thread separately.

-

Maximum number of rows that can be read from a table when running a query.

-

max_bytes_to_read

-

Maximum number of bytes (uncompressed data) that can be read from a table when running a query.

-

read_overflow_mode

-

What to do when the volume of data read exceeds one of the limits: 'throw' or 'break'. By default, throw.

-

max_rows_to_group_by

-

Maximum number of unique keys received from aggregation. This setting lets you limit memory consumption when aggregating.

-

group_by_overflow_mode

-

What to do when the number of unique keys for aggregation exceeds the limit: 'throw', 'break', or 'any'. By default, throw. -Using the 'any' value lets you run an approximation of GROUP BY. The quality of this approximation depends on the statistical nature of the data.

-

max_rows_to_sort

-

Maximum number of rows before sorting. This allows you to limit memory consumption when sorting.

-

max_bytes_to_sort

-

Maximum number of bytes before sorting.

-

sort_overflow_mode

-

What to do if the number of rows received before sorting exceeds one of the limits: 'throw' or 'break'. By default, throw.

-

max_result_rows

-

Limit on the number of rows in the result. Also checked for subqueries, and on remote servers when running parts of a distributed query.

-

max_result_bytes

-

Limit on the number of bytes in the result. The same as the previous setting.

-

result_overflow_mode

-

What to do if the volume of the result exceeds one of the limits: 'throw' or 'break'. By default, throw. -Using 'break' is similar to using LIMIT.

-

max_execution_time

-

Maximum query execution time in seconds. -At this time, it is not checked for one of the sorting stages, or when merging and finalizing aggregate functions.

-

timeout_overflow_mode

-

What to do if the query is run longer than 'max_execution_time': 'throw' or 'break'. By default, throw.

-

min_execution_speed

-

Minimal execution speed in rows per second. Checked on every data block when 'timeout_before_checking_execution_speed' expires. If the execution speed is lower, an exception is thrown.

-

timeout_before_checking_execution_speed

-

Checks that execution speed is not too slow (no less than 'min_execution_speed'), after the specified time in seconds has expired.

-

max_columns_to_read

-

Maximum number of columns that can be read from a table in a single query. If a query requires reading a greater number of columns, it throws an exception.

-

max_temporary_columns

-

Maximum number of temporary columns that must be kept in RAM at the same time when running a query, including constant columns. If there are more temporary columns than this, it throws an exception.

-

max_temporary_non_const_columns

-

The same thing as 'max_temporary_columns', but without counting constant columns. -Note that constant columns are formed fairly often when running a query, but they require approximately zero computing resources.

-

max_subquery_depth

-

Maximum nesting depth of subqueries. If subqueries are deeper, an exception is thrown. By default, 100.

-

max_pipeline_depth

-

Maximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000.

-

max_ast_depth

-

Maximum nesting depth of a query syntactic tree. If exceeded, an exception is thrown. -At this time, it isn't checked during parsing, but only after parsing the query. That is, a syntactic tree that is too deep can be created during parsing, but the query will fail. By default, 1000.

-

max_ast_elements

-

Maximum number of elements in a query syntactic tree. If exceeded, an exception is thrown. -In the same way as the previous setting, it is checked only after parsing the query. By default, 10,000.

-

max_rows_in_set

-

Maximum number of rows for a data set in the IN clause created from a subquery.

-

max_bytes_in_set

-

Maximum number of bytes (uncompressed data) used by a set in the IN clause created from a subquery.

-

set_overflow_mode

-

What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.

-

max_rows_in_distinct

-

Maximum number of different rows when using DISTINCT.

-

max_bytes_in_distinct

-

Maximum number of bytes used by a hash table when using DISTINCT.

-

distinct_overflow_mode

-

What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.

-

max_rows_to_transfer

-

Maximum number of rows that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.

-

max_bytes_to_transfer

-

Maximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.

-

transfer_overflow_mode

-

What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.

-

Settings

-

-

distributed_product_mode

-

Changes the behavior of distributed subqueries, i.e. in cases when the query contains the product of distributed tables.

-

ClickHouse applies the configuration if the subqueries on any level have a distributed table that exists on the local server and has more than one shard.

-

Restrictions:

-
    -
  • Only applied for IN and JOIN subqueries.
    • -
  • Used only if a distributed table is used in the FROM clause.
    • -
  • Not used for a table-valued remote function.
    • -
-

The possible values ​​are:

-

-

fallback_to_stale_replicas_for_distributed_queries

-

Forces a query to an out-of-date replica if updated data is not available. See "Replication".

-

ClickHouse selects the most relevant from the outdated replicas of the table.

-

Used when performing SELECT from a distributed table that points to replicated tables.

-

By default, 1 (enabled).

-

-

force_index_by_date

-

Disables query execution if the index can't be used by date.

-

Works with tables in the MergeTree family.

-

If force_index_by_date=1, ClickHouse checks whether the query has a date key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For example, the condition Date != ' 2000-01-01 ' is acceptable even when it matches all the data in the table (i.e., running the query requires a full scan). For more information about ranges of data in MergeTree tables, see "MergeTree".

-

-

force_primary_key

-

Disables query execution if indexing by the primary key is not possible.

-

Works with tables in the MergeTree family.

-

If force_primary_key=1, ClickHouse checks to see if the query has a primary key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For more information about data ranges in MergeTree tables, see "MergeTree".

-

-

fsync_metadata

-

Enable or disable fsync when writing .sql files. By default, it is enabled.

-

It makes sense to disable it if the server has millions of tiny table chunks that are constantly being created and destroyed.

-

input_format_allow_errors_num

-

Sets the maximum number of acceptable errors when reading from text formats (CSV, TSV, etc.).

-

The default value is 0.

-

Always pair it with input_format_allow_errors_ratio. To skip errors, both settings must be greater than 0.

-

If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_num, ClickHouse ignores the row and moves on to the next one.

-

If input_format_allow_errors_numis exceeded, ClickHouse throws an exception.

-

input_format_allow_errors_ratio

-

Sets the maximum percentage of errors allowed when reading from text formats (CSV, TSV, etc.). -The percentage of errors is set as a floating-point number between 0 and 1.

-

The default value is 0.

-

Always pair it with input_format_allow_errors_num. To skip errors, both settings must be greater than 0.

-

If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_ratio, ClickHouse ignores the row and moves on to the next one.

-

If input_format_allow_errors_ratio is exceeded, ClickHouse throws an exception.

-

max_block_size

-

In ClickHouse, data is processed by blocks (sets of column parts). The internal processing cycles for a single block are efficient enough, but there are noticeable expenditures on each block. max_block_size is a recommendation for what size of block (in number of rows) to load from tables. The block size shouldn't be too small, so that the expenditures on each block are still noticeable, but not too large, so that the query with LIMIT that is completed after the first block is processed quickly, so that too much memory isn't consumed when extracting a large number of columns in multiple threads, and so that at least some cache locality is preserved.

-

By default, 65,536.

-

Blocks the size of max_block_size are not always loaded from the table. If it is obvious that less data needs to be retrieved, a smaller block is processed.

-

preferred_block_size_bytes

-

Used for the same purpose as max_block_size, but it sets the recommended block size in bytes by adapting it to the number of rows in the block. -However, the block size cannot be more than max_block_size rows. -Disabled by default (set to 0). It only works when reading from MergeTree engines.

-

-

log_queries

-

Setting up query the logging.

-

Queries sent to ClickHouse with this setup are logged according to the rules in the query_log server configuration parameter.

-

Example:

-
log_queries=1
-
- - -

-

max_insert_block_size

-

The size of blocks to form for insertion into a table. -This setting only applies in cases when the server forms the blocks. -For example, for an INSERT via the HTTP interface, the server parses the data format and forms blocks of the specified size. -But when using clickhouse-client, the client parses the data itself, and the 'max_insert_block_size' setting on the server doesn't affect the size of the inserted blocks. -The setting also doesn't have a purpose when using INSERT SELECT, since data is inserted using the same blocks that are formed after SELECT.

-

By default, it is 1,048,576.

-

This is slightly more than max_block_size. The reason for this is because certain table engines (*MergeTree) form a data part on the disk for each inserted block, which is a fairly large entity. Similarly, *MergeTree tables sort data during insertion, and a large enough block size allows sorting more data in RAM.

-

-

max_replica_delay_for_distributed_queries

-

Disables lagging replicas for distributed queries. See "Replication".

-

Sets the time in seconds. If a replica lags more than the set value, this replica is not used.

-

Default value: 0 (off).

-

Used when performing SELECT from a distributed table that points to replicated tables.

-

max_threads

-

The maximum number of query processing threads

-
    -
  • excluding threads for retrieving data from remote servers (see the 'max_distributed_connections' parameter).
    • -
-

This parameter applies to threads that perform the same stages of the query processing pipeline in parallel. -For example, if reading from a table, evaluating expressions with functions, filtering with WHERE and pre-aggregating for GROUP BY can all be done in parallel using at least 'max_threads' number of threads, then 'max_threads' are used.

-

By default, 8.

-

If less than one SELECT query is normally run on a server at a time, set this parameter to a value slightly less than the actual number of processor cores.

-

For queries that are completed quickly because of a LIMIT, you can set a lower 'max_threads'. For example, if the necessary number of entries are located in every block and max_threads = 8, 8 blocks are retrieved, although it would have been enough to read just one.

-

The smaller the max_threads value, the less memory is consumed.

-

max_compress_block_size

-

The maximum size of blocks of uncompressed data before compressing for writing to a table. By default, 1,048,576 (1 MiB). If the size is reduced, the compression rate is significantly reduced, the compression and decompression speed increases slightly due to cache locality, and memory consumption is reduced. There usually isn't any reason to change this setting.

-

Don't confuse blocks for compression (a chunk of memory consisting of bytes) and blocks for query processing (a set of rows from a table).

-

min_compress_block_size

-

For MergeTree" tables. In order to reduce latency when processing queries, a block is compressed when writing the next mark if its size is at least 'min_compress_block_size'. By default, 65,536.

-

The actual size of the block, if the uncompressed data is less than 'max_compress_block_size', is no less than this value and no less than the volume of data for one mark.

-

Let's look at an example. Assume that 'index_granularity' was set to 8192 during table creation.

-

We are writing a UInt32-type column (4 bytes per value). When writing 8192 rows, the total will be 32 KB of data. Since min_compress_block_size = 65,536, a compressed block will be formed for every two marks.

-

We are writing a URL column with the String type (average size of 60 bytes per value). When writing 8192 rows, the average will be slightly less than 500 KB of data. Since this is more than 65,536, a compressed block will be formed for each mark. In this case, when reading data from the disk in the range of a single mark, extra data won't be decompressed.

-

There usually isn't any reason to change this setting.

-

max_query_size

-

The maximum part of a query that can be taken to RAM for parsing with the SQL parser. -The INSERT query also contains data for INSERT that is processed by a separate stream parser (that consumes O(1) RAM), which is not included in this restriction.

-

The default is 256 KiB.

-

interactive_delay

-

The interval in microseconds for checking whether request execution has been canceled and sending the progress.

-

By default, 100,000 (check for canceling and send progress ten times per second).

-

connect_timeout

-

receive_timeout

-

send_timeout

-

Timeouts in seconds on the socket used for communicating with the client.

-

By default, 10, 300, 300.

-

poll_interval

-

Lock in a wait loop for the specified number of seconds.

-

By default, 10.

-

max_distributed_connections

-

The maximum number of simultaneous connections with remote servers for distributed processing of a single query to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.

-

By default, 100.

-

The following parameters are only used when creating Distributed tables (and when launching a server), so there is no reason to change them at runtime.

-

distributed_connections_pool_size

-

The maximum number of simultaneous connections with remote servers for distributed processing of all queries to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.

-

By default, 128.

-

connect_timeout_with_failover_ms

-

The timeout in milliseconds for connecting to a remote server for a Distributed table engine, if the 'shard' and 'replica' sections are used in the cluster definition. -If unsuccessful, several attempts are made to connect to various replicas.

-

By default, 50.

-

connections_with_failover_max_tries

-

The maximum number of connection attempts with each replica, for the Distributed table engine.

-

By default, 3.

-

extremes

-

Whether to count extreme values (the minimums and maximums in columns of a query result). Accepts 0 or 1. By default, 0 (disabled). -For more information, see the section "Extreme values".

-

-

use_uncompressed_cache

-

Whether to use a cache of uncompressed blocks. Accepts 0 or 1. By default, 0 (disabled). -The uncompressed cache (only for tables in the MergeTree family) allows significantly reducing latency and increasing throughput when working with a large number of short queries. Enable this setting for users who send frequent short requests. Also pay attention to the 'uncompressed_cache_size' configuration parameter (only set in the config file) – the size of uncompressed cache blocks. By default, it is 8 GiB. The uncompressed cache is filled in as needed; the least-used data is automatically deleted.

-

For queries that read at least a somewhat large volume of data (one million rows or more), the uncompressed cache is disabled automatically in order to save space for truly small queries. So you can keep the 'use_uncompressed_cache' setting always set to 1.

-

replace_running_query

-

When using the HTTP interface, the 'query_id' parameter can be passed. This is any string that serves as the query identifier. -If a query from the same user with the same 'query_id' already exists at this time, the behavior depends on the 'replace_running_query' parameter.

-

0 (default) – Throw an exception (don't allow the query to run if a query with the same 'query_id' is already running).

-

1 – Cancel the old query and start running the new one.

-

Yandex.Metrica uses this parameter set to 1 for implementing suggestions for segmentation conditions. After entering the next character, if the old query hasn't finished yet, it should be canceled.

-

schema

-

This parameter is useful when you are using formats that require a schema definition, such as Cap'n Proto. The value depends on the format.

-

-

stream_flush_interval_ms

-

Works for tables with streaming in the case of a timeout, or when a thread generatesmax_insert_block_size rows.

-

The default value is 7500.

-

The smaller the value, the more often data is flushed into the table. Setting the value too low leads to poor performance.

-

-

load_balancing

-

Which replicas (among healthy replicas) to preferably send a query to (on the first attempt) for distributed processing.

-

random (default)

-

The number of errors is counted for each replica. The query is sent to the replica with the fewest errors, and if there are several of these, to any one of them. -Disadvantages: Server proximity is not accounted for; if the replicas have different data, you will also get different data.

-

nearest_hostname

-

The number of errors is counted for each replica. Every 5 minutes, the number of errors is integrally divided by 2. Thus, the number of errors is calculated for a recent time with exponential smoothing. If there is one replica with a minimal number of errors (i.e. errors occurred recently on the other replicas), the query is sent to it. If there are multiple replicas with the same minimal number of errors, the query is sent to the replica with a host name that is most similar to the server's host name in the config file (for the number of different characters in identical positions, up to the minimum length of both host names).

-

For instance, example01-01-1 and example01-01-2.yandex.ru are different in one position, while example01-01-1 and example01-02-2 differ in two places. -This method might seem a little stupid, but it doesn't use external data about network topology, and it doesn't compare IP addresses, which would be complicated for our IPv6 addresses.

-

Thus, if there are equivalent replicas, the closest one by name is preferred. -We can also assume that when sending a query to the same server, in the absence of failures, a distributed query will also go to the same servers. So even if different data is placed on the replicas, the query will return mostly the same results.

-

in_order

-

Replicas are accessed in the same order as they are specified. The number of errors does not matter. -This method is appropriate when you know exactly which replica is preferable.

-

totals_mode

-

How to calculate TOTALS when HAVING is present, as well as when max_rows_to_group_by and group_by_overflow_mode = 'any' are present. -See the section "WITH TOTALS modifier".

-

totals_auto_threshold

-

The threshold for totals_mode = 'auto'. -See the section "WITH TOTALS modifier".

-

default_sample

-

Floating-point number from 0 to 1. By default, 1. -Allows you to set the default sampling ratio for all SELECT queries. -(For tables that do not support sampling, it throws an exception.) -If set to 1, sampling is not performed by default.

-

max_parallel_replicas

-

The maximum number of replicas for each shard when executing a query. -For consistency (to get different parts of the same data split), this option only works when the sampling key is set. -Replica lag is not controlled.

-

compile

-

Enable compilation of queries. By default, 0 (disabled).

-

Compilation is only used for part of the query-processing pipeline: for the first stage of aggregation (GROUP BY). -If this portion of the pipeline was compiled, the query may run faster due to deployment of short cycles and inlining aggregate function calls. The maximum performance improvement (up to four times faster in rare cases) is seen for queries with multiple simple aggregate functions. Typically, the performance gain is insignificant. In very rare cases, it may slow down query execution.

-

min_count_to_compile

-

How many times to potentially use a compiled chunk of code before running compilation. By default, 3. -If the value is zero, then compilation runs synchronously and the query waits for the end of the compilation process before continuing execution. This can be used for testing; otherwise, use values ​​starting with 1. Compilation normally takes about 5-10 seconds. -If the value is 1 or more, compilation occurs asynchronously in a separate thread. The result will be used as soon as it is ready, including by queries that are currently running.

-

Compiled code is required for each different combination of aggregate functions used in the query and the type of keys in the GROUP BY clause. -The results of compilation are saved in the build directory in the form of .so files. There is no restriction on the number of compilation results, since they don't use very much space. Old results will be used after server restarts, except in the case of a server upgrade – in this case, the old results are deleted.

-

input_format_skip_unknown_fields

-

If the value is true, running INSERT skips input data from columns with unknown names. Otherwise, this situation will generate an exception. -It works for JSONEachRow and TSKV formats.

-

output_format_json_quote_64bit_integers

-

If the value is true, integers appear in quotes when using JSON* Int64 and UInt64 formats (for compatibility with most JavaScript implementations); otherwise, integers are output without the quotes.

-

-

format_csv_delimiter

-

The character to be considered as a delimiter in CSV data. By default, ,.

-

Settings profiles

-

A settings profile is a collection of settings grouped under the same name. Each ClickHouse user has a profile. -To apply all the settings in a profile, set profile.

-

Example:

-

Setting web profile.

-
SET profile = 'web'
-
- - -

Settings profiles are declared in the user config file. This is usually users.xml.

-

Example:

-
<!-- Settings profiles -->
-<profiles>
-    <!-- Default settings -->
-    <default>
-        <!-- The maximum number of threads when running a single query. -->
-        <max_threads>8</max_threads>
-    </default>
-
-    <!-- Settings for quries from the user interface -->
-    <web>
-        <max_rows_to_read>1000000000</max_rows_to_read>
-        <max_bytes_to_read>100000000000</max_bytes_to_read>
-
-        <max_rows_to_group_by>1000000</max_rows_to_group_by>
-        <group_by_overflow_mode>any</group_by_overflow_mode>
-
-        <max_rows_to_sort>1000000</max_rows_to_sort>
-        <max_bytes_to_sort>1000000000</max_bytes_to_sort>
-
-        <max_result_rows>100000</max_result_rows>
-        <max_result_bytes>100000000</max_result_bytes>
-        <result_overflow_mode>break</result_overflow_mode>
-
-        <max_execution_time>600</max_execution_time>
-        <min_execution_speed>1000000</min_execution_speed>
-        <timeout_before_checking_execution_speed>15</timeout_before_checking_execution_speed>
-
-        <max_columns_to_read>25</max_columns_to_read>
-        <max_temporary_columns>100</max_temporary_columns>
-        <max_temporary_non_const_columns>50</max_temporary_non_const_columns>
-
-        <max_subquery_depth>2</max_subquery_depth>
-        <max_pipeline_depth>25</max_pipeline_depth>
-        <max_ast_depth>50</max_ast_depth>
-        <max_ast_elements>100</max_ast_elements>
-
-        <readonly>1</readonly>
-    </web>
-</profiles>
-
- - -

The example specifies two profiles: default and web. The default profile has a special purpose: it must always be present and is applied when starting the server. In other words, the default profile contains default settings. The web profile is a regular profile that can be set using the SET query or using a URL parameter in an HTTP query.

-

Settings profiles can inherit from each other. To use inheritance, indicate the profile setting before the other settings that are listed in the profile.

-

ClickHouse utility

-
    -
  • clickhouse-local — Allows running SQL queries on data without stopping the ClickHouse server, similar to how awk does this.
    • -
  • clickhouse-copier — Copies (and reshards) data from one cluster to another cluster.
    • -
-

-

clickhouse-copier

-

Copies data from the tables in one cluster to tables in another (or the same) cluster.

-

You can run multiple clickhouse-copier instances on different servers to perform the same job. ZooKeeper is used for syncing the processes.

-

After starting, clickhouse-copier:

-
    -
  • Connects to ZooKeeper and receives:
    • -
  • Copying jobs.
    • -
  • -

    The state of the copying jobs.

    -
    • -
  • -

    It performs the jobs.

    -
    • -
-

Each running process chooses the "closest" shard of the source cluster and copies the data into the destination cluster, resharding the data if necessary.

-

clickhouse-copier tracks the changes in ZooKeeper and applies them on the fly.

-

To reduce network traffic, we recommend running clickhouse-copier on the same server where the source data is located.

-

Running clickhouse-copier

-

The utility should be run manually:

-
clickhouse-copier copier --daemon --config zookeeper.xml --task-path /task/path --base-dir /path/to/dir
-
- - -

Parameters:

-
    -
  • daemon — Starts clickhouse-copier in daemon mode.
    • -
  • config — The path to the zookeeper.xml file with the parameters for the connection to ZooKeeper.
    • -
  • task-path — The path to the ZooKeeper node. This node is used for syncing clickhouse-copier processes and storing tasks. Tasks are stored in $task-path/description.
    • -
  • base-dir — The path to logs and auxiliary files. When it starts, clickhouse-copier creates clickhouse-copier_YYYYMMHHSS_<PID> subdirectories in $base-dir. If this parameter is omitted, the directories are created in the directory where clickhouse-copier was launched.
    • -
-

Format of zookeeper.xml

-
<yandex>
-    <zookeeper>
-        <node index="1">
-            <host>127.0.0.1</host>
-            <port>2181</port>
-        </node>
-    </zookeeper>
-</yandex>
-
- - -

Configuration of copying tasks

-
<yandex>
-    <!-- Configuration of clusters as in an ordinary server config -->
-    <remote_servers>
-        <source_cluster>
-            <shard>
-                <internal_replication>false</internal_replication>
-                    <replica>
-                        <host>127.0.0.1</host>
-                        <port>9000</port>
-                    </replica>
-            </shard>
-            ...
-        </source_cluster>
-
-        <destination_cluster>
-        ...
-        </destination_cluster>
-    </remote_servers>
-
-    <!-- How many simultaneously active workers are possible. If you run more workers superfluous workers will sleep. -->
-    <max_workers>2</max_workers>
-
-    <!-- Setting used to fetch (pull) data from source cluster tables -->
-    <settings_pull>
-        <readonly>1</readonly>
-    </settings_pull>
-
-    <!-- Setting used to insert (push) data to destination cluster tables -->
-    <settings_push>
-        <readonly>0</readonly>
-    </settings_push>
-
-    <!-- Common setting for fetch (pull) and insert (push) operations. The copier process context also uses it.
-         They are overlaid by <settings_pull/> and <settings_push/> respectively. -->
-    <settings>
-        <connect_timeout>3</connect_timeout>
-        <!-- Sync insert is set forcibly, leave it here just in case. -->
-        <insert_distributed_sync>1</insert_distributed_sync>
-    </settings>
-
-    <!-- Copying description of tasks.
-         You can specify several table tasks in the same task description (in the same ZooKeeper node), and they will be performed         sequentially.
-    -->
-    <tables>
-        <!-- A table task that copies one table. -->
-        <table_hits>
-            <!-- Source cluster name (from the <remote_servers/> section) and tables in it that should be copied -->
-            <cluster_pull>source_cluster</cluster_pull>
-            <database_pull>test</database_pull>
-            <table_pull>hits</table_pull>
-
-            <!-- Destination cluster name and tables in which the data should be inserted -->
-            <cluster_push>destination_cluster</cluster_push>
-            <database_push>test</database_push>
-            <table_push>hits2</table_push>
-
-            <!-- Engine of destination tables.
-                 If the destination tables have not been created yet, workers create them using column definitions from source tables and the engine                 definition from here.
-
-                 NOTE: If the first worker starts to insert data and detects that the destination partition is not empty, then the partition will
-                 be dropped and refilled. Take this into account if you already have some data in destination tables. You can directly 
-                 specify partitions that should be copied in <enabled_partitions/>. They should be in quoted format like the partition column in the                 
-                 system.parts table.
-            -->
-            <engine>
-            ENGINE=ReplicatedMergeTree('/clickhouse/tables/{cluster}/{shard}/hits2', '{replica}')
-            PARTITION BY toMonday(date)
-            ORDER BY (CounterID, EventDate)
-            </engine>
-
-            <!-- Sharding key used to insert data to destination cluster -->
-            <sharding_key>jumpConsistentHash(intHash64(UserID), 2)</sharding_key>
-
-            <!-- Optional expression that filter data while pull them from source servers -->
-            <where_condition>CounterID != 0</where_condition>
-
-            <!-- This section specifies partitions that should be copied, other partition will be ignored.
-                 Partition names should have the same format as
-                 partition column of system.parts table (i.e. a quoted text).
-                 Since partition key of source and destination cluster could be different,
-                 these partition names specify destination partitions.
-
-                 Note: Although this section is optional (if it omitted, all partitions will be copied), 
-                 it is strongly recommended to specify the partitions explicitly.
-                 If you already have some partitions ready on the destination cluster, they                 
-                 will be removed at the start of the copying, because they will be interpreted                 
-                 as unfinished data from the previous copying.
-            -->
-            <enabled_partitions>
-                <partition>'2018-02-26'</partition>
-                <partition>'2018-03-05'</partition>
-                ...
-            </enabled_partitions>
-        </table_hits>
-
-        <!-- Next table to copy. It is not copied until the previous table is copying. -->
-        </table_visits>
-        ...
-        </table_visits>
-        ...
-    </tables>
-</yandex>
-
- - -

clickhouse-copier tracks the changes in /task/path/description and applies them on the fly. For instance, if you change the value of max_workers, the number of processes running tasks will also change.

-

-

clickhouse-local

-

The clickhouse-local program enables you to perform fast processing on local files that store tables, without having to deploy and configure the ClickHouse server.

-

ClickHouse Development

-

Overview of ClickHouse architecture

-

ClickHouse is a true column-oriented DBMS. Data is stored by columns, and during the execution of arrays (vectors or chunks of columns). Whenever possible, operations are dispatched on arrays, rather than on individual values. This is called "vectorized query execution," and it helps lower the cost of actual data processing.

-
-

This idea is nothing new. It dates back to the APL programming language and its descendants: A +, J, K, and Q. Array programming is used in scientific data processing. Neither is this idea something new in relational databases: for example, it is used in the Vectorwise system.

-
-

There are two different approaches for speeding up the query processing: vectorized query execution and runtime code generation. In the latter, the code is generated for every kind of query on the fly, removing all indirection and dynamic dispatch. Neither of these approaches is strictly better than the other. Runtime code generation can be better when it's fuses many operations together, thus fully utilizing CPU execution units and the pipeline. Vectorized query execution can be less practical, because it involves the temporary vectors that must be written to the cache and read back. If the temporary data does not fit in the L2 cache, this becomes an issue. But vectorized query execution more easily utilizes the SIMD capabilities of the CPU. A research paper written by our friends shows that it is better to combine both approaches. ClickHouse uses vectorized query execution and has limited initial support for runtime code.

-

Columns

-

To represent columns in memory (actually, chunks of columns), the IColumn interface is used. This interface provides helper methods for implementation of various relational operators. Almost all operations are immutable: they do not modify the original column, but create a new modified one. For example, the IColumn :: filter method accepts a filter byte mask. It is used for the WHERE and HAVING relational operators. Additional examples: the IColumn :: permute method to support ORDER BY, the IColumn :: cut method to support LIMIT, and so on.

-

Various IColumn implementations (ColumnUInt8, ColumnString and so on) are responsible for the memory layout of columns. Memory layout is usually a contiguous array. For the integer type of columns it is just one contiguous array, like std :: vector. For String and Array columns, it is two vectors: one for all array elements, placed contiguously, and a second one for offsets to the beginning of each array. There is also ColumnConst that stores just one value in memory, but looks like a column.

-

Field

-

Nevertheless, it is possible to work with individual values as well. To represent an individual value, the Field is used. Field is just a discriminated union of UInt64, Int64, Float64, String and Array. IColumn has the operator[] method to get the n-th value as a Field, and the insert method to append a Field to the end of a column. These methods are not very efficient, because they require dealing with temporary Field objects representing an individual value. There are more efficient methods, such as insertFrom, insertRangeFrom, and so on.

-

Field doesn't have enough information about a specific data type for a table. For example, UInt8, UInt16, UInt32, and UInt64 are all represented as UInt64 in a Field.

-

Leaky abstractions

-

IColumn has methods for common relational transformations of data, but they don't meet all needs. For example, ColumnUInt64 doesn't have a method to calculate the sum of two columns, and ColumnString doesn't have a method to run a substring search. These countless routines are implemented outside of IColumn.

-

Various functions on columns can be implemented in a generic, non-efficient way using IColumn methods to extract Field values, or in a specialized way using knowledge of inner memory layout of data in a specific IColumn implementation. To do this, functions are cast to a specific IColumn type and deal with internal representation directly. For example, ColumnUInt64 has the getData method that returns a reference to an internal array, then a separate routine reads or fills that array directly. In fact, we have "leaky abstractions" to allow efficient specializations of various routines.

-

Data types

-

IDataType is responsible for serialization and deserialization: for reading and writing chunks of columns or individual values in binary or text form. -IDataType directly corresponds to data types in tables. For example, there are DataTypeUInt32, DataTypeDateTime, DataTypeString and so on.

-

IDataType and IColumn are only loosely related to each other. Different data types can be represented in memory by the same IColumn implementations. For example, DataTypeUInt32 and DataTypeDateTime are both represented by ColumnUInt32 or ColumnConstUInt32. In addition, the same data type can be represented by different IColumn implementations. For example, DataTypeUInt8 can be represented by ColumnUInt8 or ColumnConstUInt8.

-

IDataType only stores metadata. For instance, DataTypeUInt8 doesn't store anything at all (except vptr) and DataTypeFixedString stores just N (the size of fixed-size strings).

-

IDataType has helper methods for various data formats. Examples are methods to serialize a value with possible quoting, to serialize a value for JSON, and to serialize a value as part of XML format. There is no direct correspondence to data formats. For example, the different data formats Pretty and TabSeparated can use the same serializeTextEscaped helper method from the IDataType interface.

-

Block

-

A Block is a container that represents a subset (chunk) of a table in memory. It is just a set of triples: (IColumn, IDataType, column name). During query execution, data is processed by Blocks. If we have a Block, we have data (in the IColumn object), we have information about its type (in IDataType) that tells us how to deal with that column, and we have the column name (either the original column name from the table, or some artificial name assigned for getting temporary results of calculations).

-

When we calculate some function over columns in a block, we add another column with its result to the block, and we don't touch columns for arguments of the function because operations are immutable. Later, unneeded columns can be removed from the block, but not modified. This is convenient for elimination of common subexpressions.

-

Blocks are created for every processed chunk of data. Note that for the same type of calculation, the column names and types remain the same for different blocks, and only column data changes. It is better to split block data from the block header, because small block sizes will have a high overhead of temporary strings for copying shared_ptrs and column names.

-

Block Streams

-

Block streams are for processing data. We use streams of blocks to read data from somewhere, perform data transformations, or write data to somewhere. IBlockInputStream has the read method to fetch the next block while available. IBlockOutputStream has the write method to push the block somewhere.

-

Streams are responsible for:

-
    -
  1. Reading or writing to a table. The table just returns a stream for reading or writing blocks.
    2. -
  2. Implementing data formats. For example, if you want to output data to a terminal in Pretty format, you create a block output stream where you push blocks, and it formats them.
    3. -
  3. Performing data transformations. Let's say you have IBlockInputStream and want to create a filtered stream. You create FilterBlockInputStream and initialize it with your stream. Then when you pull a block from FilterBlockInputStream, it pulls a block from your stream, filters it, and returns the filtered block to you. Query execution pipelines are represented this way.
    4. -
-

There are more sophisticated transformations. For example, when you pull from AggregatingBlockInputStream, it reads all data from its source, aggregates it, and then returns a stream of aggregated data for you. Another example: UnionBlockInputStream accepts many input sources in the constructor and also a number of threads. It launches multiple threads and reads from multiple sources in parallel.

-
-

Block streams use the "pull" approach to control flow: when you pull a block from the first stream, it consequently pulls the required blocks from nested streams, and the entire execution pipeline will work. Neither "pull" nor "push" is the best solution, because control flow is implicit, and that limits implementation of various features like simultaneous execution of multiple queries (merging many pipelines together). This limitation could be overcome with coroutines or just running extra threads that wait for each other. We may have more possibilities if we make control flow explicit: if we locate the logic for passing data from one calculation unit to another outside of those calculation units. Read this article for more thoughts.

-
-

We should note that the query execution pipeline creates temporary data at each step. We try to keep block size small enough so that temporary data fits in the CPU cache. With that assumption, writing and reading temporary data is almost free in comparison with other calculations. We could consider an alternative, which is to fuse many operations in the pipeline together, to make the pipeline as short as possible and remove much of the temporary data. This could be an advantage, but it also has drawbacks. For example, a split pipeline makes it easy to implement caching intermediate data, stealing intermediate data from similar queries running at the same time, and merging pipelines for similar queries.

-

Formats

-

Data formats are implemented with block streams. There are "presentational" formats only suitable for output of data to the client, such as Pretty format, which provides only IBlockOutputStream. And there are input/output formats, such as TabSeparated or JSONEachRow.

-

There are also row streams: IRowInputStream and IRowOutputStream. They allow you to pull/push data by individual rows, not by blocks. And they are only needed to simplify implementation of row-oriented formats. The wrappers BlockInputStreamFromRowInputStream and BlockOutputStreamFromRowOutputStream allow you to convert row-oriented streams to regular block-oriented streams.

-

I/O

-

For byte-oriented input/output, there are ReadBuffer and WriteBuffer abstract classes. They are used instead of C++ iostream's. Don't worry: every mature C++ project is using something other than iostream's for good reasons.

-

ReadBuffer and WriteBuffer are just a contiguous buffer and a cursor pointing to the position in that buffer. Implementations may own or not own the memory for the buffer. There is a virtual method to fill the buffer with the following data (for ReadBuffer) or to flush the buffer somewhere (for WriteBuffer). The virtual methods are rarely called.

-

Implementations of ReadBuffer/WriteBuffer are used for working with files and file descriptors and network sockets, for implementing compression (CompressedWriteBuffer is initialized with another WriteBuffer and performs compression before writing data to it), and for other purposes – the names ConcatReadBuffer, LimitReadBuffer, and HashingWriteBuffer speak for themselves.

-

Read/WriteBuffers only deal with bytes. To help with formatted input/output (for instance, to write a number in decimal format), there are functions from ReadHelpers and WriteHelpers header files.

-

Let's look at what happens when you want to write a result set in JSON format to stdout. You have a result set ready to be fetched from IBlockInputStream. You create WriteBufferFromFileDescriptor(STDOUT_FILENO) to write bytes to stdout. You create JSONRowOutputStream, initialized with that WriteBuffer, to write rows in JSON to stdout. You create BlockOutputStreamFromRowOutputStream on top of it, to represent it as IBlockOutputStream. Then you call copyData to transfer data from IBlockInputStream to IBlockOutputStream, and everything works. Internally, JSONRowOutputStream will write various JSON delimiters and call the IDataType::serializeTextJSON method with a reference to IColumn and the row number as arguments. Consequently, IDataType::serializeTextJSON will call a method from WriteHelpers.h: for example, writeText for numeric types and writeJSONString for DataTypeString.

-

Tables

-

Tables are represented by the IStorage interface. Different implementations of that interface are different table engines. Examples are StorageMergeTree, StorageMemory, and so on. Instances of these classes are just tables.

-

The most important IStorage methods are read and write. There are also alter, rename, drop, and so on. The read method accepts the following arguments: the set of columns to read from a table, the AST query to consider, and the desired number of streams to return. It returns one or multiple IBlockInputStream objects and information about the stage of data processing that was completed inside a table engine during query execution.

-

In most cases, the read method is only responsible for reading the specified columns from a table, not for any further data processing. All further data processing is done by the query interpreter and is outside the responsibility of IStorage.

-

But there are notable exceptions:

-
    -
  • The AST query is passed to the read method and the table engine can use it to derive index usage and to read less data from a table.
    • -
  • Sometimes the table engine can process data itself to a specific stage. For example, StorageDistributed can send a query to remote servers, ask them to process data to a stage where data from different remote servers can be merged, and return that preprocessed data. -The query interpreter then finishes processing the data.
    • -
-

The table's read method can return multiple IBlockInputStream objects to allow parallel data processing. These multiple block input streams can read from a table in parallel. Then you can wrap these streams with various transformations (such as expression evaluation or filtering) that can be calculated independently and create a UnionBlockInputStream on top of them, to read from multiple streams in parallel.

-

There are also TableFunctions. These are functions that return a temporary IStorage object to use in the FROM clause of a query.

-

To get a quick idea of how to implement your own table engine, look at something simple, like StorageMemory or StorageTinyLog.

-
-

As the result of the read method, IStorage returns QueryProcessingStage – information about what parts of the query were already calculated inside storage. Currently we have only very coarse granularity for that information. There is no way for the storage to say "I have already processed this part of the expression in WHERE, for this range of data". We need to work on that.

-
-

Parsers

-

A query is parsed by a hand-written recursive descent parser. For example, ParserSelectQuery just recursively calls the underlying parsers for various parts of the query. Parsers create an AST. The AST is represented by nodes, which are instances of IAST.

-
-

Parser generators are not used for historical reasons.

-
-

Interpreters

-

Interpreters are responsible for creating the query execution pipeline from an AST. There are simple interpreters, such as InterpreterExistsQueryand InterpreterDropQuery, or the more sophisticated InterpreterSelectQuery. The query execution pipeline is a combination of block input or output streams. For example, the result of interpreting the SELECT query is the IBlockInputStream to read the result set from; the result of the INSERT query is the IBlockOutputStream to write data for insertion to; and the result of interpreting the INSERT SELECT query is the IBlockInputStream that returns an empty result set on the first read, but that copies data from SELECT to INSERT at the same time.

-

InterpreterSelectQuery uses ExpressionAnalyzer and ExpressionActions machinery for query analysis and transformations. This is where most rule-based query optimizations are done. ExpressionAnalyzer is quite messy and should be rewritten: various query transformations and optimizations should be extracted to separate classes to allow modular transformations or query.

-

Functions

-

There are ordinary functions and aggregate functions. For aggregate functions, see the next section.

-

Ordinary functions don't change the number of rows – they work as if they are processing each row independently. In fact, functions are not called for individual rows, but for Block's of data to implement vectorized query execution.

-

There are some miscellaneous functions, like blockSize, rowNumberInBlock, and runningAccumulate, that exploit block processing and violate the independence of rows.

-

ClickHouse has strong typing, so implicit type conversion doesn't occur. If a function doesn't support a specific combination of types, an exception will be thrown. But functions can work (be overloaded) for many different combinations of types. For example, the plus function (to implement the + operator) works for any combination of numeric types: UInt8 + Float32, UInt16 + Int8, and so on. Also, some variadic functions can accept any number of arguments, such as the concat function.

-

Implementing a function may be slightly inconvenient because a function explicitly dispatches supported data types and supported IColumns. For example, the plus function has code generated by instantiation of a C++ template for each combination of numeric types, and for constant or non-constant left and right arguments.

-
-

This is a nice place to implement runtime code generation to avoid template code bloat. Also, it will make it possible to add fused functions like fused multiply-add, or to make multiple comparisons in one loop iteration.

-
-

Due to vectorized query execution, functions are not short-circuit. For example, if you write WHERE f(x) AND g(y), both sides will be calculated, even for rows, when f(x) is zero (except when f(x) is a zero constant expression). But if selectivity of the f(x) condition is high, and calculation of f(x) is much cheaper than g(y), it's better to implement multi-pass calculation: first calculate f(x), then filter columns by the result, and then calculate g(y) only for smaller, filtered chunks of data.

-

Aggregate Functions

-

Aggregate functions are stateful functions. They accumulate passed values into some state, and allow you to get results from that state. They are managed with the IAggregateFunction interface. States can be rather simple (the state for AggregateFunctionCount is just a single UInt64 value) or quite complex (the state of AggregateFunctionUniqCombined is a combination of a linear array, a hash table and a HyperLogLog probabilistic data structure).

-

To deal with multiple states while executing a high-cardinality GROUP BY query, states are allocated in Arena (a memory pool), or they could be allocated in any suitable piece of memory. States can have a non-trivial constructor and destructor: for example, complex aggregation states can allocate additional memory themselves. This requires some attention to creating and destroying states and properly passing their ownership, to keep track of who and when will destroy states.

-

Aggregation states can be serialized and deserialized to pass over the network during distributed query execution or to write them on disk where there is not enough RAM. They can even be stored in a table with the DataTypeAggregateFunction to allow incremental aggregation of data.

-
-

The serialized data format for aggregate function states is not versioned right now. This is ok if aggregate states are only stored temporarily. But we have the AggregatingMergeTree table engine for incremental aggregation, and people are already using it in production. This is why we should add support for backward compatibility when changing the serialized format for any aggregate function in the future.

-
-

Server

-

The server implements several different interfaces:

-
    -
  • An HTTP interface for any foreign clients.
    • -
  • A TCP interface for the native ClickHouse client and for cross-server communication during distributed query execution.
    • -
  • An interface for transferring data for replication.
    • -
-

Internally, it is just a basic multithreaded server without coroutines, fibers, etc. Since the server is not designed to process a high rate of simple queries but is intended to process a relatively low rate of complex queries, each of them can process a vast amount of data for analytics.

-

The server initializes the Context class with the necessary environment for query execution: the list of available databases, users and access rights, settings, clusters, the process list, the query log, and so on. This environment is used by interpreters.

-

We maintain full backward and forward compatibility for the server TCP protocol: old clients can talk to new servers and new clients can talk to old servers. But we don't want to maintain it eternally, and we are removing support for old versions after about one year.

-
-

For all external applications, we recommend using the HTTP interface because it is simple and easy to use. The TCP protocol is more tightly linked to internal data structures: it uses an internal format for passing blocks of data and it uses custom framing for compressed data. We haven't released a C library for that protocol because it requires linking most of the ClickHouse codebase, which is not practical.

-
-

Distributed query execution

-

Servers in a cluster setup are mostly independent. You can create a Distributed table on one or all servers in a cluster. The Distributed table does not store data itself – it only provides a "view" to all local tables on multiple nodes of a cluster. When you SELECT from a Distributed table, it rewrites that query, chooses remote nodes according to load balancing settings, and sends the query to them. The Distributed table requests remote servers to process a query just up to a stage where intermediate results from different servers can be merged. Then it receives the intermediate results and merges them. The distributed table tries to distribute as much work as possible to remote servers, and does not send much intermediate data over the network.

-
-

Things become more complicated when you have subqueries in IN or JOIN clauses and each of them uses a Distributed table. We have different strategies for execution of these queries.

-
-

There is no global query plan for distributed query execution. Each node has its own local query plan for its part of the job. We only have simple one-pass distributed query execution: we send queries for remote nodes and then merge the results. But this is not feasible for difficult queries with high cardinality GROUP BYs or with a large amount of temporary data for JOIN: in such cases, we need to "reshuffle" data between servers, which requires additional coordination. ClickHouse does not support that kind of query execution, and we need to work on it.

-

Merge Tree

-

MergeTree is a family of storage engines that supports indexing by primary key. The primary key can be an arbitary tuple of columns or expressions. Data in a MergeTree table is stored in "parts". Each part stores data in the primary key order (data is ordered lexicographically by the primary key tuple). All the table columns are stored in separate column.bin files in these parts. The files consist of compressed blocks. Each block is usually from 64 KB to 1 MB of uncompressed data, depending on the average value size. The blocks consist of column values placed contiguously one after the other. Column values are in the same order for each column (the order is defined by the primary key), so when you iterate by many columns, you get values for the corresponding rows.

-

The primary key itself is "sparse". It doesn't address each single row, but only some ranges of data. A separate primary.idx file has the value of the primary key for each N-th row, where N is called index_granularity (usually, N = 8192). Also, for each column, we have column.mrk files with "marks," which are offsets to each N-th row in the data file. Each mark is a pair: the offset in the file to the beginning of the compressed block, and the offset in the decompressed block to the beginning of data. Usually compressed blocks are aligned by marks, and the offset in the decompressed block is zero. Data for primary.idx always resides in memory and data for column.mrk files is cached.

-

When we are going to read something from a part in MergeTree, we look at primary.idx data and locate ranges that could possibly contain requested data, then look at column.mrk data and calculate offsets for where to start reading those ranges. Because of sparseness, excess data may be read. ClickHouse is not suitable for a high load of simple point queries, because the entire range with index_granularity rows must be read for each key, and the entire compressed block must be decompressed for each column. We made the index sparse because we must be able to maintain trillions of rows per single server without noticeable memory consumption for the index. Also, because the primary key is sparse, it is not unique: it cannot check the existence of the key in the table at INSERT time. You could have many rows with the same key in a table.

-

When you INSERT a bunch of data into MergeTree, that bunch is sorted by primary key order and forms a new part. To keep the number of parts relatively low, there are background threads that periodically select some parts and merge them to a single sorted part. That's why it is called MergeTree. Of course, merging leads to "write amplification". All parts are immutable: they are only created and deleted, but not modified. When SELECT is run, it holds a snapshot of the table (a set of parts). After merging, we also keep old parts for some time to make recovery after failure easier, so if we see that some merged part is probably broken, we can replace it with its source parts.

-

MergeTree is not an LSM tree because it doesn't contain "memtable" and "log": inserted data is written directly to the filesystem. This makes it suitable only to INSERT data in batches, not by individual row and not very frequently – about once per second is ok, but a thousand times a second is not. We did it this way for simplicity's sake, and because we are already inserting data in batches in our applications.

-
-

MergeTree tables can only have one (primary) index: there aren't any secondary indices. It would be nice to allow multiple physical representations under one logical table, for example, to store data in more than one physical order or even to allow representations with pre-aggregated data along with original data.

-
-

There are MergeTree engines that are doing additional work during background merges. Examples are CollapsingMergeTree and AggregatingMergeTree. This could be treated as special support for updates. Keep in mind that these are not real updates because users usually have no control over the time when background merges will be executed, and data in a MergeTree table is almost always stored in more than one part, not in completely merged form.

-

Replication

-

Replication in ClickHouse is implemented on a per-table basis. You could have some replicated and some non-replicated tables on the same server. You could also have tables replicated in different ways, such as one table with two-factor replication and another with three-factor.

-

Replication is implemented in the ReplicatedMergeTree storage engine. The path in ZooKeeper is specified as a parameter for the storage engine. All tables with the same path in ZooKeeper become replicas of each other: they synchronize their data and maintain consistency. Replicas can be added and removed dynamically simply by creating or dropping a table.

-

Replication uses an asynchronous multi-master scheme. You can insert data into any replica that has a session with ZooKeeper, and data is replicated to all other replicas asynchronously. Because ClickHouse doesn't support UPDATEs, replication is conflict-free. As there is no quorum acknowledgment of inserts, just-inserted data might be lost if one node fails.

-

Metadata for replication is stored in ZooKeeper. There is a replication log that lists what actions to do. Actions are: get part; merge parts; drop partition, etc. Each replica copies the replication log to its queue and then executes the actions from the queue. For example, on insertion, the "get part" action is created in the log, and every replica downloads that part. Merges are coordinated between replicas to get byte-identical results. All parts are merged in the same way on all replicas. To achieve this, one replica is elected as the leader, and that replica initiates merges and writes "merge parts" actions to the log.

-

Replication is physical: only compressed parts are transferred between nodes, not queries. To lower the network cost (to avoid network amplification), merges are processed on each replica independently in most cases. Large merged parts are sent over the network only in cases of significant replication lag.

-

In addition, each replica stores its state in ZooKeeper as the set of parts and its checksums. When the state on the local filesystem diverges from the reference state in ZooKeeper, the replica restores its consistency by downloading missing and broken parts from other replicas. When there is some unexpected or broken data in the local filesystem, ClickHouse does not remove it, but moves it to a separate directory and forgets it.

-
-

The ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is not elastic, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load will be uneven. This implementation gives you more control, and it is fine for relatively small clusters such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that will span its data across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically.

-
-

How to build ClickHouse on Linux

-

Build should work on Linux Ubuntu 12.04, 14.04 or newer. -With appropriate changes, it should also work on any other Linux distribution. -The build process is not intended to work on Mac OS X. -Only x86_64 with SSE 4.2 is supported. Support for AArch64 is experimental.

-

To test for SSE 4.2, do

-
grep -q sse4_2 /proc/cpuinfo && echo "SSE 4.2 supported" || echo "SSE 4.2 not supported"
-
- - -

Install Git and CMake

-
sudo apt-get install git cmake
-
- - -

Or cmake3 instead of cmake on older systems.

-

Detect the number of threads

-
export THREADS=$(grep -c ^processor /proc/cpuinfo)
-
- - -

Install GCC 7

-

There are several ways to do this.

-

Install from a PPA package

-
sudo apt-get install software-properties-common
-sudo apt-add-repository ppa:ubuntu-toolchain-r/test
-sudo apt-get update
-sudo apt-get install gcc-7 g++-7
-
- - -

Install from sources

-

Look at [https://github.com/yandex/ClickHouse/blob/master/utils/prepare-environment/install-gcc.sh]

-

Use GCC 7 for builds

-
export CC=gcc-7
-export CXX=g++-7
-
- - -

Install required libraries from packages

-
sudo apt-get install libicu-dev libreadline-dev libmysqlclient-dev libssl-dev unixodbc-dev ninja-build
-
- - -

Checkout ClickHouse sources

-

To get the latest stable version:

-
git clone -b stable --recursive git@github.com:yandex/ClickHouse.git
-## or: git clone -b stable --recursive https://github.com/yandex/ClickHouse.git
-
-cd ClickHouse
-
- - -

For development, switch to the master branch. -For the latest release candidate, switch to the testing branch.

-

Build ClickHouse

-

There are two build variants.

-

Build release package

-

Install prerequisites to build Debian packages.

-
sudo apt-get install devscripts dupload fakeroot debhelper
-
- - -

Install the most recent version of Clang.

-

Clang is embedded into the ClickHouse package and used at runtime. The minimum version is 5.0. It is optional.

-

To install clang, see utils/prepare-environment/install-clang.sh

-

You may also build ClickHouse with Clang for development purposes. -For production releases, GCC is used.

-

Run the release script:

-
rm -f ../clickhouse*.deb
-./release
-
- - -

You will find built packages in the parent directory:

-
ls -l ../clickhouse*.deb
-
- - -

Note that usage of debian packages is not required. -ClickHouse has no runtime dependencies except libc, so it could work on almost any Linux.

-

Installing freshly built packages on a development server:

-
sudo dpkg -i ../clickhouse*.deb
-sudo service clickhouse-server start
-
- - -

Build to work with code

-
mkdir build
-cd build
-cmake ..
-make -j $THREADS
-cd ..
-
- - -

To create an executable, run make clickhouse. -This will create the dbms/src/Server/clickhouse executable, which can be used with client or server arguments.

-

How to build ClickHouse on Mac OS X

-

Build should work on Mac OS X 10.12. If you're using earlier version, you can try to build ClickHouse using Gentoo Prefix and clang sl in this instruction. -With appropriate changes, it should also work on any other Linux distribution.

-

Install Homebrew

-
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
-
- - -

Install required compilers, tools, and libraries

-
brew install cmake gcc icu4c mysql openssl unixodbc libtool gettext zlib readline boost --cc=gcc-7
-
- - -

Checkout ClickHouse sources

-

To get the latest stable version:

-
git clone -b stable --recursive --depth=10 git@github.com:yandex/ClickHouse.git
-## or: git clone -b stable --recursive --depth=10 https://github.com/yandex/ClickHouse.git
-
-cd ClickHouse
-
- - -

For development, switch to the master branch. -For the latest release candidate, switch to the testing branch.

-

Build ClickHouse

-
mkdir build
-cd build
-cmake .. -DCMAKE_CXX_COMPILER=`which g++-7` -DCMAKE_C_COMPILER=`which gcc-7`
-make -j `sysctl -n hw.ncpu`
-cd ..
-
- - -

Caveats

-

If you intend to run clickhouse-server, make sure to increase the system's maxfiles variable. See MacOS.md for more details.

-

How to write C++ code

-

General recommendations

-

1. The following are recommendations, not requirements.

-

2. If you are editing code, it makes sense to follow the formatting of the existing code.

-

3. Code style is needed for consistency. Consistency makes it easier to read the code, and it also makes it easier to search the code.

-

4. Many of the rules do not have logical reasons; they are dictated by established practices.

-

Formatting

-

1. Most of the formatting will be done automatically by clang-format.

-

2. Indents are 4 spaces. Configure your development environment so that a tab adds four spaces.

-

3. A left curly bracket must be separated on a new line. (And the right one, as well.)

-
inline void readBoolText(bool & x, ReadBuffer & buf)
-{
-    char tmp = '0';
-    readChar(tmp, buf);
-    x = tmp != '0';
-}
-
- - -

4. -But if the entire function body is quite short (a single statement), you can place it entirely on one line if you wish. Place spaces around curly braces (besides the space at the end of the line).

-
inline size_t mask() const                { return buf_size() - 1; }
-inline size_t place(HashValue x) const    { return x & mask(); }
-
- - -

5. For functions, don't put spaces around brackets.

-
void reinsert(const Value & x)
-memcpy(&buf[place_value], &x, sizeof(x));
-
- - -

6. When using statements such as if, for, and while (unlike function calls), put a space before the opening bracket.

-

cpp - for (size_t i = 0; i < rows; i += storage.index_granularity)

-

7. Put spaces around binary operators (+, -, *, /, %, ...), as well as the ternary operator ?:.

-
UInt16 year = (s[0] - '0') * 1000 + (s[1] - '0') * 100 + (s[2] - '0') * 10 + (s[3] - '0');
-UInt8 month = (s[5] - '0') * 10 + (s[6] - '0');
-UInt8 day = (s[8] - '0') * 10 + (s[9] - '0');
-
- - -

8. If a line feed is entered, put the operator on a new line and increase the indent before it.

-
if (elapsed_ns)
-    message << " ("
-         << rows_read_on_server * 1000000000 / elapsed_ns << " rows/s., "
-        << bytes_read_on_server * 1000.0 / elapsed_ns << " MB/s.) ";
-
- - -

9. You can use spaces for alignment within a line, if desired.

-
dst.ClickLogID         = click.LogID;
-dst.ClickEventID       = click.EventID;
-dst.ClickGoodEvent     = click.GoodEvent;
-
- - -

10. Don't use spaces around the operators ., -> .

-

If necessary, the operator can be wrapped to the next line. In this case, the offset in front of it is increased.

-

11. Do not use a space to separate unary operators (-, +, *, &, ...) from the argument.

-

12. Put a space after a comma, but not before it. The same rule goes for a semicolon inside a for expression.

-

13. Do not use spaces to separate the [] operator.

-

14. In a template <...> expression, use a space between template and <. No spaces after < or before >.

-
template <typename TKey, typename TValue>
-struct AggregatedStatElement
-{}
-
- - -

15. In classes and structures, public, private, and protected are written on the same level as the class/struct, but all other internal elements should be deeper.

-
template <typename T>
-class MultiVersion
-{
-public:
-    /// Version of object for usage. shared_ptr manage lifetime of version.
-    using Version = std::shared_ptr<const T>;
-    ...
-}
-
- - -

16. If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace.

-

17. If the block for if, for, while... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets.

-
/// Finish write.
-for (auto & stream : streams)
-    stream.second->finalize();
-
- - -

18. There should be any spaces at the ends of lines.

-

19. Sources are UTF-8 encoded.

-

20. Non-ASCII characters can be used in string literals.

-
<< ", " << (timer.elapsed() / chunks_stats.hits) << " μsec/hit.";
-
- - -

21. Do not write multiple expressions in a single line.

-

22. Group sections of code inside functions and separate them with no more than one empty line.

-

23. Separate functions, classes, and so on with one or two empty lines.

-

24. A const (related to a value) must be written before the type name.

-
//correct
-const char * pos
-const std::string & s
-//incorrect
-char const * pos
-
- - -

25. When declaring a pointer or reference, the * and & symbols should be separated by spaces on both sides.

-
//correct
-const char * pos
-//incorrect
-const char* pos
-const char *pos
-
- - -

26. When using template types, alias them with the using keyword (except in the simplest cases).

-

In other words, the template parameters are specified only in using and aren't repeated in the code.

-

using can be declared locally, such as inside a function.

-
//correct
-using FileStreams = std::map<std::string, std::shared_ptr<Stream>>;
-FileStreams streams;
-//incorrect
-std::map<std::string, std::shared_ptr<Stream>> streams;
-
- - -

27. Do not declare several variables of different types in one statement.

-
//incorrect
-int x, *y;
-
- - -

28. Do not use C-style casts.

-
//incorrect
-std::cerr << (int)c <<; std::endl;
-//correct
-std::cerr << static_cast<int>(c) << std::endl;
-
- - -

29. In classes and structs, group members and functions separately inside each visibility scope.

-

30. For small classes and structs, it is not necessary to separate the method declaration from the implementation.

-

The same is true for small methods in any classes or structs.

-

For templated classes and structs, don't separate the method declarations from the implementation (because otherwise they must be defined in the same translation unit).

-

31. You can wrap lines at 140 characters, instead of 80.

-

32. Always use the prefix increment/decrement operators if postfix is not required.

-
for (Names::const_iterator it = column_names.begin(); it != column_names.end(); ++it)
-
- - -

Comments

-

1. Be sure to add comments for all non-trivial parts of code.

-

This is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong.

-
/** Part of piece of memory, that can be used.
-  * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading,
-  * then working_buffer will have size of only 10 bytes
-  * (working_buffer.end() will point to the position right after those 10 bytes available for read).
-*/
-
- - -

2. Comments can be as detailed as necessary.

-

3. Place comments before the code they describe. In rare cases, comments can come after the code, on the same line.

-
/** Parses and executes the query.
-*/
-void executeQuery(
-    ReadBuffer & istr, /// Where to read the query from (and data for INSERT, if applicable)
-    WriteBuffer & ostr, /// Where to write the result
-    Context & context, /// DB, tables, data types, engines, functions, aggregate functions...
-    BlockInputStreamPtr & query_plan, /// A description of query processing can be included here
-    QueryProcessingStage::Enum stage = QueryProcessingStage::Complete /// The last stage to process the SELECT query to
-    )
-
- - -

4. Comments should be written in English only.

-

5. If you are writing a library, include detailed comments explaining it in the main header file.

-

6. Do not add comments that do not provide additional information. In particular, do not leave empty comments like this:

-
/*
-* Procedure Name:
-* Original procedure name:
-* Author:
-* Date of creation:
-* Dates of modification:
-* Modification authors:
-* Original file name:
-* Purpose:
-* Intent:
-* Designation:
-* Classes used:
-* Constants:
-* Local variables:
-* Parameters:
-* Date of creation:
-* Purpose:
-*/
-
- - -

The example is borrowed from http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/.

-

7. Do not write garbage comments (author, creation date ..) at the beginning of each file.

-

8. Single-line comments begin with three slashes: /// and multi-line comments begin with /**. These comments are considered "documentation".

-

Note: You can use Doxygen to generate documentation from these comments. But Doxygen is not generally used because it is more convenient to navigate the code in the IDE.

-

9. Multi-line comments must not have empty lines at the beginning and end (except the line that closes a multi-line comment).

-

10. For commenting out code, use basic comments, not "documenting" comments.

-

11. Delete the commented out parts of the code before commiting.

-

12. Do not use profanity in comments or code.

-

13. Do not use uppercase letters. Do not use excessive punctuation.

-
/// WHAT THE FAIL???
-
- - -

14. Do not make delimeters from comments.

-
///******************************************************
-
- - -

15. Do not start discussions in comments.

-
/// Why did you do this stuff?
-
- - -

16. There's no need to write a comment at the end of a block describing what it was about.

-
/// for
-
- - -

Names

-

1. The names of variables and class members use lowercase letters with underscores.

-
size_t max_block_size;
-
- - -

2. The names of functions (methods) use camelCase beginning with a lowercase letter.

-
std::string getName() const override { return "Memory"; }
-
- - -

3. The names of classes (structures) use CamelCase beginning with an uppercase letter. Prefixes other than I are not used for interfaces.

-
class StorageMemory : public IStorage
-
- - -

4. The names of usings follow the same rules as classes, or you can add _t at the end.

-

5. Names of template type arguments for simple cases: T; T, U; T1, T2.

-

For more complex cases, either follow the rules for class names, or add the prefix T.

-
template <typename TKey, typename TValue>
-struct AggregatedStatElement
-
- - -

6. Names of template constant arguments: either follow the rules for variable names, or use N in simple cases.

-
template <bool without_www>
-struct ExtractDomain
-
- - -

7. For abstract classes (interfaces) you can add the I prefix.

-
class IBlockInputStream
-
- - -

8. If you use a variable locally, you can use the short name.

-

In other cases, use a descriptive name that conveys the meaning.

-
bool info_successfully_loaded = false;
-
- - -

9. define‘s should be in ALL_CAPS with underscores. The same is true for global constants.

-
##define MAX_SRC_TABLE_NAMES_TO_STORE 1000
-
- - -

10. File names should use the same style as their contents.

-

If a file contains a single class, name the file the same way as the class, in CamelCase.

-

If the file contains a single function, name the file the same way as the function, in camelCase.

-

11. If the name contains an abbreviation, then:

-
    -
  • For variable names, the abbreviation should use lowercase letters mysql_connection (not mySQL_connection).
    • -
  • For names of classes and functions, keep the uppercase letters in the abbreviation MySQLConnection (not MySqlConnection).
    • -
-

12. Constructor arguments that are used just to initialize the class members should be named the same way as the class members, but with an underscore at the end.

-
FileQueueProcessor(
-    const std::string & path_,
-    const std::string & prefix_,
-    std::shared_ptr<FileHandler> handler_)
-    : path(path_),
-    prefix(prefix_),
-    handler(handler_),
-    log(&Logger::get("FileQueueProcessor"))
-{
-}
-
- - -

The underscore suffix can be omitted if the argument is not used in the constructor body.

-

13. There is no difference in the names of local variables and class members (no prefixes required).

-
timer (not m_timer)
-
- - -

14. Constants in enums use CamelCase beginning with an uppercase letter. ALL_CAPS is also allowed. If the enum is not local, use enum class.

-
enum class CompressionMethod
-{
-    QuickLZ = 0,
-    LZ4     = 1,
-};
-
- - -

15. All names must be in English. Transliteration of Russian words is not allowed.

-
not Stroka
-
- - -

16. Abbreviations are acceptable if they are well known (when you can easily find the meaning of the abbreviation in Wikipedia or in a search engine).

-
`AST`, `SQL`.
-
-Not `NVDH` (some random letters)
-
- - -

Incomplete words are acceptable if the shortened version is common use.

-

You can also use an abbreviation if the full name is included next to it in the comments.

-

17. File names with C++ source code must have the .cpp extension. Header files must have the .h extension.

-

How to write code

-

1. Memory management.

-

Manual memory deallocation (delete) can only be used in library code.

-

In library code, the delete operator can only be used in destructors.

-

In application code, memory must be freed by the object that owns it.

-

Examples:

-
    -
  • The easiest way is to place an object on the stack, or make it a member of another class.
    • -
  • For a large number of small objects, use containers.
    • -
  • For automatic deallocation of a small number of objects that reside in the heap, use shared_ptr/unique_ptr.
    • -
-

2. Resource management.

-

Use RAII and see the previous point.

-

3. Error handling.

-

Use exceptions. In most cases, you only need to throw an exception, and don't need to catch it (because of RAII).

-

In offline data processing applications, it's often acceptable to not catch exceptions.

-

In servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler.

-
/// If there were no other calculations yet, do it synchronously
-if (!started)
-{
-    calculate();
-    started = true;
-}
-else    /// If the calculations are already in progress, wait for results
-    pool.wait();
-
-if (exception)
-    exception->rethrow();
-
- - -

Never hide exceptions without handling. Never just blindly put all exceptions to log.

-

Not catch (...) {}.

-

If you need to ignore some exceptions, do so only for specific ones and rethrow the rest.

-
catch (const DB::Exception & e)
-{
-    if (e.code() == ErrorCodes::UNKNOWN_AGGREGATE_FUNCTION)
-        return nullptr;
-    else
-        throw;
-}
-
- - -

When using functions with response codes or errno, always check the result and throw an exception in case of error.

-
if (0 != close(fd))
-    throwFromErrno("Cannot close file " + file_name, ErrorCodes::CANNOT_CLOSE_FILE);
-
- - -

Asserts are not used.

-

4. Exception types.

-

There is no need to use complex exception hierarchy in application code. The exception text should be understandable to a system administrator.

-

5. Throwing exceptions from destructors.

-

This is not recommended, but it is allowed.

-

Use the following options:

-
    -
  • Create a (done() or finalize()) function that will do all the work in advance that might lead to an exception. If that function was called, there should be no exceptions in the destructor later.
    • -
  • Tasks that are too complex (such as sending messages over the network) can be put in separate method that the class user will have to call before destruction.
    • -
  • If there is an exception in the destructor, it’s better to log it than to hide it (if the logger is available).
    • -
  • In simple applications, it is acceptable to rely on std::terminate (for cases of noexcept by default in C++11) to handle exceptions.
    • -
-

6. Anonymous code blocks.

-

You can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block.

-
Block block = data.in->read();
-
-{
-    std::lock_guard<std::mutex> lock(mutex);
-    data.ready = true;
-    data.block = block;
-}
-
-ready_any.set();
-
- - -

7. Multithreading.

-

For offline data processing applications:

-
    -
  • Try to get the best possible performance on a single CPU core. You can then parallelize your code if necessary.
    • -
-

In server applications:

-
    -
  • Use the thread pool to process requests. At this point, we haven't had any tasks that required userspace context switching.
    • -
-

Fork is not used for parallelization.

-

8. Synchronizing threads.

-

Often it is possible to make different threads use different memory cells (even better: different cache lines,) and to not use any thread synchronization (except joinAll).

-

If synchronization is required, in most cases, it is sufficient to use mutex under lock_guard.

-

In other cases use system synchronization primitives. Do not use busy wait.

-

Atomic operations should be used only in the simplest cases.

-

Do not try to implement lock-free data structures unless it is your primary area of expertise.

-

9. Pointers vs references.

-

In most cases, prefer references.

-

10. const.

-

Use constant references, pointers to constants, const_iterator, const methods.

-

Consider const to be default and use non-const only when necessary.

-

When passing variable by value, using const usually does not make sense.

-

11. unsigned.

-

Use unsigned, if needed.

-

12. Numeric types

-

Use UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, and size_t, ssize_t, ptrdiff_t.

-

Don't use signed/unsigned long, long long, short, signed char, unsigned char, or char types for numbers.

-

13. Passing arguments.

-

Pass complex values by reference (including std::string).

-

If a function captures ownership of an objected created in the heap, make the argument type shared_ptr or unique_ptr.

-

14. Returning values.

-

In most cases, just use return. Do not write [return std::move(res)]{.strike}.

-

If the function allocates an object on heap and returns it, use shared_ptr or unique_ptr.

-

In rare cases you might need to return the value via an argument. In this case, the argument should be a reference.

-
using AggregateFunctionPtr = std::shared_ptr<IAggregateFunction>;
-
-/** Creates an aggregate function by name.
- */
-class AggregateFunctionFactory
-{
-public:
-   AggregateFunctionFactory();
-   AggregateFunctionPtr get(const String & name, const DataTypes & argument_types) const;
-
- - -

15. namespace.

-

There is no need to use a separate namespace for application code or small libraries.

-

or small libraries.

-

For medium to large libraries, put everything in the namespace.

-

You can use the additional detail namespace in a library's .h file to hide implementation details.

-

In a .cpp file, you can use the static or anonymous namespace to hide symbols.

-

You can also use namespace for enums to prevent its names from polluting the outer namespace, but it’s better to use the enum class.

-

16. Delayed initialization.

-

If arguments are required for initialization then do not write a default constructor.

-

If later you’ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use shared_ptr/unique_ptr.

-
Loader(DB::Connection * connection_, const std::string & query, size_t max_block_size_);
-
-/// For delayed initialization
-Loader() {}
-
- - -

17. Virtual functions.

-

If the class is not intended for polymorphic use, you do not need to make functions virtual. This also applies to the destructor.

-

18. Encodings.

-

Use UTF-8 everywhere. Use std::stringandchar *. Do not use std::wstringandwchar_t.

-

19. Logging.

-

See the examples everywhere in the code.

-

Before committing, delete all meaningless and debug logging, and any other types of debug output.

-

Logging in cycles should be avoided, even on the Trace level.

-

Logs must be readable at any logging level.

-

Logging should only be used in application code, for the most part.

-

Log messages must be written in English.

-

The log should preferably be understandable for the system administrator.

-

Do not use profanity in the log.

-

Use UTF-8 encoding in the log. In rare cases you can use non-ASCII characters in the log.

-

20. I/O.

-

Don't use iostreams in internal cycles that are critical for application performance (and never use stringstream).

-

Use the DB/IO library instead.

-

21. Date and time.

-

See the DateLUT library.

-

22. include.

-

Always use #pragma once instead of include guards.

-

23. using.

-

The using namespace is not used.

-

It's fine if you are 'using' something specific, but make it local inside a class or function.

-

24. Do not use trailing return type for functions unless necessary.

-
[auto f() -&gt; void;]{.strike}
-
- - -

25. Do not declare and init variables like this:

-
auto s = std::string{"Hello"};
-
- - -

Do it like this:

-
std::string s = "Hello";
-std::string s{"Hello"};
-
- - -

26. For virtual functions, write virtual in the base class, but write override in descendent classes.

-

Unused features of C++

-

1. Virtual inheritance is not used.

-

2. Exception specifiers from C++03 are not used.

-

3. Function try block is not used, except for the main function in tests.

-

Platform

-

1. We write code for a specific platform.

-

But other things being equal, cross-platform or portable code is preferred.

-

2. The language is C++17.

-

3. The compiler is gcc. At this time (December 2017), the code is compiled using version 7.2. (It can also be compiled using clang 5.)

-

The standard library is used (implementation of libstdc++ or libc++).

-

4. OS: Linux Ubuntu, not older than Precise.

-

5. Code is written for x86_64 CPU architecture.

-

The CPU instruction set is the minimum supported set among our servers. Currently, it is SSE 4.2.

-

6. Use -Wall -Wextra -Werror compilation flags.

-

7. Use static linking with all libraries except those that are difficult to connect to statically (see the output of the ldd command).

-

8. Code is developed and debugged with release settings.

-

Tools

-

1. KDevelop is a good IDE.

-

2. For debugging, use gdb, valgrind (memcheck), strace, -fsanitize=, ..., tcmalloc_minimal_debug.

-

3. For profiling, use Linux Perf valgrind (callgrind), strace-cf.

-

4. Sources are in Git.

-

5. Compilation is managed by CMake.

-

6. Releases are in deb packages.

-

7. Commits to master must not break the build.

-

Though only selected revisions are considered workable.

-

8. Make commits as often as possible, even if the code is only partially ready.

-

Use branches for this purpose.

-

If your code is not buildable yet, exclude it from the build before pushing to master. You'll need to finish it or remove it from master within a few days.

-

9. For non-trivial changes, used branches and publish them on the server.

-

10. Unused code is removed from the repository.

-

Libraries

-

1. The C++14 standard library is used (experimental extensions are fine), as well as boost and Poco frameworks.

-

2. If necessary, you can use any well-known libraries available in the OS package.

-

If there is a good solution already available, then use it, even if it means you have to install another library.

-

(But be prepared to remove bad libraries from code.)

-

3. You can install a library that isn't in the packages, if the packages don't have what you need or have an outdated version or the wrong type of compilation.

-

4. If the library is small and doesn't have its own complex build system, put the source files in the contrib folder.

-

5. Preference is always given to libraries that are already used.

-

General recommendations

-

1. Write as little code as possible.

-

2. Try the simplest solution.

-

3. Don't write code until you know how it's going to work and how the inner loop will function.

-

4. In the simplest cases, use 'using' instead of classes or structs.

-

5. If possible, do not write copy constructors, assignment operators, destructors (other than a virtual one, if the class contains at least one virtual function), mpve-constructors and move assignment operators. In other words, the compiler-generated functions must work correctly. You can use 'default'.

-

6. Code simplification is encouraged. Reduce the size of your code where possible.

-

Additional recommendations

-

1. Explicit std:: for types from stddef.h is not recommended.

-

We recommend writing size_t instead std::size_t because it's shorter.

-

But if you prefer, std:: is acceptable.

-

2. Explicit std:: for functions from the standard C library is not recommended.

-

Write memcpy instead of std::memcpy.

-

The reason is that there are similar non-standard functions, such as memmem. We do use these functions on occasion. These functions do not exist in namespace std.

-

If you write std::memcpy instead of memcpy everywhere, then memmem without std:: will look awkward.

-

Nevertheless, std:: is allowed if you prefer it.

-

3. Using functions from C when the ones are available in the standard C++ library.

-

This is acceptable if it is more efficient.

-

For example, use memcpy instead of std::copy for copying large chunks of memory.

-

4. Multiline function arguments.

-

Any of the following wrapping styles are allowed:

-
function(
-    T1 x1,
-    T2 x2)
-
- - -
function(
-    size_t left, size_t right,
-    const & RangesInDataParts ranges,
-    size_t limit)
-
- - -
function(size_t left, size_t right,
-    const & RangesInDataParts ranges,
-    size_t limit)
-
- - -
function(size_t left, size_t right,
-        const & RangesInDataParts ranges,
-        size_t limit)
-
- - -
function(
-        size_t left,
-        size_t right,
-        const & RangesInDataParts ranges,
-        size_t limit)
-
- - -

How to run ClickHouse tests

-

The clickhouse-test utility that is used for functional testing is written using Python 2.x.It also requires you to have some third-party packages:

-
$ pip install lxml termcolor
-
- - -

In a nutshell:

-
    -
  • Put the clickhouse program to /usr/bin (or PATH)
    • -
  • Create a clickhouse-client symlink in /usr/bin pointing to clickhouse
    • -
  • Start the clickhouse server
    • -
  • cd dbms/tests/
    • -
  • Run ./clickhouse-test
    • -
-

Example usage

-

Run ./clickhouse-test --help to see available options.

-

To run tests without having to create a symlink or mess with PATH:

-
./clickhouse-test -c "../../build/dbms/src/Server/clickhouse --client"
-
- - -

To run a single test, i.e. 00395_nullable:

-
./clickhouse-test 00395
-
- - -

Roadmap

-

Q1 2018

-

New fuctionality

-
    -
  • -

    Support for UPDATE and DELETE.

    -
    • -
  • -

    Multidimensional and nested arrays.

    -
    • -
-

It can look something like this:

-
CREATE TABLE t
-(
-    x Array(Array(String)),
-    z Nested(
-        x Array(String),
-        y Nested(...))
-)
-ENGINE = MergeTree ORDER BY x
-
- - -
    -
  • External MySQL and ODBC tables.
    • -
-

External tables can be integrated into ClickHouse using external dictionaries. This new functionality is a convenient alternative to connecting external tables.

-
SELECT ...
-FROM mysql('host:port', 'db', 'table', 'user', 'password')`
-
- - -

Improvements

-
    -
  • Effective data copying between ClickHouse clusters.
    • -
-

Now you can copy data with the remote() function. For example: INSERT INTO t SELECT * FROM remote(...).

-

This operation will have improved performance.

-
    -
  • O_DIRECT for merges.
    • -
-

This will improve the performance of the OS cache and "hot" queries.

-

Q2 2018

-

New functionality

-
    -
  • -

    UPDATE/DELETE conform to the EU GDPR.

    -
    • -
  • -

    Protobuf and Parquet input and output formats.

    -
    • -
  • -

    Creating dictionaries using DDL queries.

    -
    • -
-

Currently, dictionaries that are part of the database schema are defined in external XML files. This is inconvenient and counter-intuitive. The new approach should fix it.

-
    -
  • -

    Integration with LDAP.

    -
    • -
  • -

    WITH ROLLUP and WITH CUBE for GROUP BY.

    -
    • -
  • -

    Custom encoding and compression for each column individually.

    -
    • -
-

As of now, ClickHouse supports LZ4 and ZSTD compression of columns, and compression settings are global (see the article Compression in ClickHouse). Per-column compression and encoding will provide more efficient data storage, which in turn will speed up queries.

-
    -
  • Storing data on multiple disks on the same server.
    • -
-

This functionality will make it easier to extend the disk space, since different disk systems can be used for different databases or tables. Currently, users are forced to use symbolic links if the databases and tables must be stored on a different disk.

-

Improvements

-

Many improvements and fixes are planned for the query execution system. For example:

-
    -
  • Using an index for in (subquery).
    • -
-

The index is not used right now, which reduces performance.

-
    -
  • Passing predicates from where to subqueries, and passing predicates to views.
    • -
-

The predicates must be passed, since the view is changed by the subquery. Performance is still low for view filters, and views can't use the primary key of the original table, which makes views useless for large tables.

-
    -
  • Optimizing branching operations (ternary operator, if, multiIf).
    • -
-

ClickHouse currently performs all branches, even if they aren't necessary.

-
    -
  • Using a primary key for GROUP BY and ORDER BY.
    • -
-

This will speed up certain types of queries with partially sorted data.

-

Q3-Q4 2018

-

We don't have any set plans yet, but the main projects will be:

-
    -
  • Resource pools for executing queries.
    • -
-

This will make load management more efficient.

-
    -
  • ANSI SQL JOIN syntax.
    • -
-

Improve ClickHouse compatibility with many SQL tools.

- - - - - - - -
-
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-
- - -
- -
-
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- -
- ©2016–2018 Yandex LLC -
- -
- - - -
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- - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/docs/en/single/search/search_index.json b/docs/build/docs/en/single/search/search_index.json deleted file mode 100644 index df30bbf064d..00000000000 --- a/docs/build/docs/en/single/search/search_index.json +++ /dev/null @@ -1,4179 +0,0 @@ -{ - "docs": [ - { - "location": "/index.html", - "text": "What is ClickHouse?\n\n\nClickHouse is a columnar DBMS for OLAP.\n\n\nIn a \"normal\" row-oriented DBMS, data is stored in this order:\n\n\n5123456789123456789 1 Eurobasket - Greece - Bosnia and Herzegovina - example.com 1 2011-09-01 01:03:02 6274717 1294101174 11409 612345678912345678 0 33 6 http://www.example.com/basketball/team/123/match/456789.html http://www.example.com/basketball/team/123/match/987654.html 0 1366 768 32 10 3183 0 0 13 0\\0 1 1 0 0 2011142 -1 0 0 01321 613 660 2011-09-01 08:01:17 0 0 0 0 utf-8 1466 0 0 0 5678901234567890123 277789954 0 0 0 0 0\n5234985259563631958 0 Consulting, Tax assessment, Accounting, Law 1 2011-09-01 01:03:02 6320881 2111222333 213 6458937489576391093 0 3 2 http://www.example.ru/ 0 800 600 16 10 2 153.1 0 0 10 63 1 1 0 0 2111678 000 0 588 368 240 2011-09-01 01:03:17 4 0 60310 0 windows-1251 1466 0 000 778899001 0 0 0 0 0\n...\n\n\n\n\n\nIn order words, all the values related to a row are stored next to each other.\nExamples of a row-oriented DBMS are MySQL, Postgres, MS SQL Server, and others.\n\n\nIn a column-oriented DBMS, data is stored like this:\n\n\nWatchID: 5385521489354350662 5385521490329509958 5385521489953706054 5385521490476781638 5385521490583269446 5385521490218868806 5385521491437850694 5385521491090174022 5385521490792669254 5385521490420695110 5385521491532181574 5385521491559694406 5385521491459625030 5385521492275175494 5385521492781318214 5385521492710027334 5385521492955615302 5385521493708759110 5385521494506434630 5385521493104611398\nJavaEnable: 1 0 1 0 0 0 1 0 1 1 1 1 1 1 0 1 0 0 1 1\nTitle: Yandex Announcements - Investor Relations - Yandex Yandex \u2014 Contact us \u2014 Moscow Yandex \u2014 Mission Ru Yandex \u2014 History \u2014 History of Yandex Yandex Financial Releases - Investor Relations - Yandex Yandex \u2014 Locations Yandex Board of Directors - Corporate Governance - Yandex Yandex \u2014 Technologies\nGoodEvent: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\nEventTime: 2016-05-18 05:19:20 2016-05-18 08:10:20 2016-05-18 07:38:00 2016-05-18 01:13:08 2016-05-18 00:04:06 2016-05-18 04:21:30 2016-05-18 00:34:16 2016-05-18 07:35:49 2016-05-18 11:41:59 2016-05-18 01:13:32\n\n\n\n\n\nThese examples only show the order that data is arranged in.\nThe values from different columns are stored separately, and data from the same column is stored together.\n\n\nExamples of column-oriented DBMSs: \nVertica\n, \nParaccel (Actian Matrix) (Amazon Redshift)\n, \nSybase IQ\n, \nExasol\n, \nInfobright\n, \nInfiniDB\n, \nMonetDB (VectorWise) (Actian Vector)\n, \nLucidDB\n, \nSAP HANA\n, \nGoogle Dremel\n, \nGoogle PowerDrill\n, \nDruid\n, \nkdb+\n, and so on.\n\n\nDifferent orders for storing data are better suited to different scenarios.\nThe data access scenario refers to what queries are made, how often, and in what proportion; how much data is read for each type of query \u2013 rows, columns, and bytes; the relationship between reading and updating data; the working size of the data and how locally it is used; whether transactions are used, and how isolated they are; requirements for data replication and logical integrity; requirements for latency and throughput for each type of query, and so on.\n\n\nThe higher the load on the system, the more important it is to customize the system to the scenario, and the more specific this customization becomes. There is no system that is equally well-suited to significantly different scenarios. If a system is adaptable to a wide set of scenarios, under a high load, the system will handle all the scenarios equally poorly, or will work well for just one of the scenarios.\n\n\nWe'll say that the following is true for the OLAP (online analytical processing) scenario:\n\n\n\n\nThe vast majority of requests are for read access.\n\n\nData is updated in fairly large batches (\n 1000 rows), not by single rows; or it is not updated at all.\n\n\nData is added to the DB but is not modified.\n\n\nFor reads, quite a large number of rows are extracted from the DB, but only a small subset of columns.\n\n\nTables are \"wide,\" meaning they contain a large number of columns.\n\n\nQueries are relatively rare (usually hundreds of queries per server or less per second).\n\n\nFor simple queries, latencies around 50 ms are allowed.\n\n\nColumn values are fairly small: numbers and short strings (for example, 60 bytes per URL).\n\n\nRequires high throughput when processing a single query (up to billions of rows per second per server).\n\n\nThere are no transactions.\n\n\nLow requirements for data consistency.\n\n\nThere is one large table per query. All tables are small, except for one.\n\n\nA query result is significantly smaller than the source data. In other words, data is filtered or aggregated. The result fits in a single server's RAM.\n\n\n\n\nIt is easy to see that the OLAP scenario is very different from other popular scenarios (such as OLTP or Key-Value access). So it doesn't make sense to try to use OLTP or a Key-Value DB for processing analytical queries if you want to get decent performance. For example, if you try to use MongoDB or Elliptics for analytics, you will get very poor performance compared to OLAP databases.\n\n\nColumnar-oriented databases are better suited to OLAP scenarios (at least 100 times better in processing speed for most queries), for the following reasons:\n\n\n\n\nFor I/O.\n\n\nFor an analytical query, only a small number of table columns need to be read. In a column-oriented database, you can read just the data you need. For example, if you need 5 columns out of 100, you can expect a 20-fold reduction in I/O.\n\n\nSince data is read in packets, it is easier to compress. Data in columns is also easier to compress. This further reduces the I/O volume.\n\n\nDue to the reduced I/O, more data fits in the system cache.\n\n\n\n\nFor example, the query \"count the number of records for each advertising platform\" requires reading one \"advertising platform ID\" column, which takes up 1 byte uncompressed. If most of the traffic was not from advertising platforms, you can expect at least 10-fold compression of this column. When using a quick compression algorithm, data decompression is possible at a speed of at least several gigabytes of uncompressed data per second. In other words, this query can be processed at a speed of approximately several billion rows per second on a single server. This speed is actually achieved in practice.\n\n\nExample:\n\n\nmilovidov@hostname:~$ clickhouse-client\nClickHouse client version \n0\n.0.52053.\nConnecting to localhost:9000.\nConnected to ClickHouse server version \n0\n.0.52053.\n\n:\n)\n SELECT CounterID, count\n()\n FROM hits GROUP BY CounterID ORDER BY count\n()\n DESC LIMIT \n20\n\n\nSELECT\n CounterID,\n count\n()\n\nFROM hits\nGROUP BY CounterID\nORDER BY count\n()\n DESC\nLIMIT \n20\n\n\n\u250c\u2500CounterID\u2500\u252c\u2500\u2500count\n()\n\u2500\u2510\n\u2502 \n114208\n \u2502 \n56057344\n \u2502\n\u2502 \n115080\n \u2502 \n51619590\n \u2502\n\u2502 \n3228\n \u2502 \n44658301\n \u2502\n\u2502 \n38230\n \u2502 \n42045932\n \u2502\n\u2502 \n145263\n \u2502 \n42042158\n \u2502\n\u2502 \n91244\n \u2502 \n38297270\n \u2502\n\u2502 \n154139\n \u2502 \n26647572\n \u2502\n\u2502 \n150748\n \u2502 \n24112755\n \u2502\n\u2502 \n242232\n \u2502 \n21302571\n \u2502\n\u2502 \n338158\n \u2502 \n13507087\n \u2502\n\u2502 \n62180\n \u2502 \n12229491\n \u2502\n\u2502 \n82264\n \u2502 \n12187441\n \u2502\n\u2502 \n232261\n \u2502 \n12148031\n \u2502\n\u2502 \n146272\n \u2502 \n11438516\n \u2502\n\u2502 \n168777\n \u2502 \n11403636\n \u2502\n\u2502 \n4120072\n \u2502 \n11227824\n \u2502\n\u2502 \n10938808\n \u2502 \n10519739\n \u2502\n\u2502 \n74088\n \u2502 \n9047015\n \u2502\n\u2502 \n115079\n \u2502 \n8837972\n \u2502\n\u2502 \n337234\n \u2502 \n8205961\n \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n20\n rows in set. Elapsed: \n0\n.153 sec. Processed \n1\n.00 billion rows, \n4\n.00 GB \n(\n6\n.53 billion rows/s., \n26\n.10 GB/s.\n)\n\n\n:\n)\n\n\n\n\n\n\n\n\nFor CPU.\n\n\n\n\nSince executing a query requires processing a large number of rows, it helps to dispatch all operations for entire vectors instead of for separate rows, or to implement the query engine so that there is almost no dispatching cost. If you don't do this, with any half-decent disk subsystem, the query interpreter inevitably stalls the CPU.\nIt makes sense to both store data in columns and process it, when possible, by columns.\n\n\nThere are two ways to do this:\n\n\n\n\n\n\nA vector engine. All operations are written for vectors, instead of for separate values. This means you don't need to call operations very often, and dispatching costs are negligible. Operation code contains an optimized internal cycle.\n\n\n\n\n\n\nCode generation. The code generated for the query has all the indirect calls in it.\n\n\n\n\n\n\nThis is not done in \"normal\" databases, because it doesn't make sense when running simple queries. However, there are exceptions. For example, MemSQL uses code generation to reduce latency when processing SQL queries. (For comparison, analytical DBMSs require optimization of throughput, not latency.)\n\n\nNote that for CPU efficiency, the query language must be declarative (SQL or MDX), or at least a vector (J, K). The query should only contain implicit loops, allowing for optimization.\n\n\nIntroduction\n\n\nDistinctive features of ClickHouse\n\n\nTrue column-oriented DBMS\n\n\nIn a true column-oriented DBMS, there isn't any \"garbage\" stored with the values. Among other things, this means that constant-length values must be supported, to avoid storing their length \"number\" next to the values. As an example, a billion UInt8-type values should actually consume around 1 GB uncompressed, or this will strongly affect the CPU use. It is very important to store data compactly (without any \"garbage\") even when uncompressed, since the speed of decompression (CPU usage) depends mainly on the volume of uncompressed data.\n\n\nThis is worth noting because there are systems that can store values of separate columns separately, but that can't effectively process analytical queries due to their optimization for other scenarios. Examples are HBase, BigTable, Cassandra, and HyperTable. In these systems, you will get throughput around a hundred thousand rows per second, but not hundreds of millions of rows per second.\n\n\nAlso note that ClickHouse is a DBMS, not a single database. ClickHouse allows creating tables and databases in runtime, loading data, and running queries without reconfiguring and restarting the server.\n\n\nData compression\n\n\nSome column-oriented DBMSs (InfiniDB CE and MonetDB) do not use data compression. However, data compression really improves performance.\n\n\nDisk storage of data\n\n\nMany column-oriented DBMSs (such as SAP HANA and Google PowerDrill) can only work in RAM. But even on thousands of servers, the RAM is too small for storing all the pageviews and sessions in Yandex.Metrica.\n\n\nParallel processing on multiple cores\n\n\nLarge queries are parallelized in a natural way.\n\n\nDistributed processing on multiple servers\n\n\nAlmost none of the columnar DBMSs listed above have support for distributed processing.\nIn ClickHouse, data can reside on different shards. Each shard can be a group of replicas that are used for fault tolerance. The query is processed on all the shards in parallel. This is transparent for the user.\n\n\nSQL support\n\n\nIf you are familiar with standard SQL, we can't really talk about SQL support.\nAll the functions have different names.\nHowever, this is a declarative query language based on SQL that can't be differentiated from SQL in many instances.\nJOINs are supported. Subqueries are supported in FROM, IN, and JOIN clauses, as well as scalar subqueries.\nDependent subqueries are not supported.\n\n\nVector engine\n\n\nData is not only stored by columns, but is processed by vectors (parts of columns). This allows us to achieve high CPU performance.\n\n\nReal-time data updates\n\n\nClickHouse supports primary key tables. In order to quickly perform queries on the range of the primary key, the data is sorted incrementally using the merge tree. Due to this, data can continually be added to the table. There is no locking when adding data.\n\n\nIndexes\n\n\nHaving a primary key makes it possible to extract data for specific clients (for instance, Yandex.Metrica tracking tags) for a specific time range, with low latency less than several dozen milliseconds.\n\n\nSuitable for online queries\n\n\nThis lets us use the system as the back-end for a web interface. Low latency means queries can be processed without delay, while the Yandex.Metrica interface page is loading. In other words, in online mode.\n\n\nSupport for approximated calculations\n\n\n\n\nThe system contains aggregate functions for approximated calculation of the number of various values, medians, and quantiles.\n\n\nSupports running a query based on a part (sample) of data and getting an approximated result. In this case, proportionally less data is retrieved from the disk.\n\n\nSupports running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reasonably accurate result while using fewer resources.\n\n\n\n\nData replication and support for data integrity on replicas\n\n\nUses asynchronous multimaster replication. After being written to any available replica, data is distributed to all the remaining replicas. The system maintains identical data on different replicas. Data is restored automatically after a failure, or using a \"button\" for complex cases.\nFor more information, see the section \nData replication\n.\n\n\nClickHouse features that can be considered disadvantages\n\n\n\n\nNo transactions.\n\n\nFor aggregation, query results must fit in the RAM on a single server. However, the volume of source data for a query may be indefinitely large.\n\n\nLack of full-fledged UPDATE/DELETE implementation.\n\n\n\n\nYandex.Metrica use case\n\n\nClickHouse currently powers \nYandex.Metrica\n, \nthe second largest web analytics platform in the world\n. With more than 13 trillion records in the database and more than 20 billion events daily, ClickHouse allows you generating custom reports on the fly directly from non-aggregated data.\n\n\nWe need to get custom reports based on hits and sessions, with custom segments set by the user. Data for the reports is updated in real-time. Queries must be run immediately (in online mode). We must be able to build reports for any time period. Complex aggregates must be calculated, such as the number of unique visitors.\nAt this time (April 2014), Yandex.Metrica receives approximately 12 billion events (pageviews and mouse clicks) daily. All these events must be stored in order to build custom reports. A single query may require scanning hundreds of millions of rows over a few seconds, or millions of rows in no more than a few hundred milliseconds.\n\n\nUsage in Yandex.Metrica and other Yandex services\n\n\nClickHouse is used for multiple purposes in Yandex.Metrica.\nIts main task is to build reports in online mode using non-aggregated data. It uses a cluster of 374 servers, which store over 20.3 trillion rows in the database. The volume of compressed data, without counting duplication and replication, is about 2 PB. The volume of uncompressed data (in TSV format) would be approximately 17 PB.\n\n\nClickHouse is also used for:\n\n\n\n\nStoring data for Session Replay from Yandex.Metrica.\n\n\nProcessing intermediate data.\n\n\nBuilding global reports with Analytics.\n\n\nRunning queries for debugging the Yandex.Metrica engine.\n\n\nAnalyzing logs from the API and the user interface.\n\n\n\n\nClickHouse has at least a dozen installations in other Yandex services: in search verticals, Market, Direct, business analytics, mobile development, AdFox, personal services, and others.\n\n\nAggregated and non-aggregated data\n\n\nThere is a popular opinion that in order to effectively calculate statistics, you must aggregate data, since this reduces the volume of data.\n\n\nBut data aggregation is a very limited solution, for the following reasons:\n\n\n\n\nYou must have a pre-defined list of reports the user will need.\n\n\nThe user can't make custom reports.\n\n\nWhen aggregating a large quantity of keys, the volume of data is not reduced, and aggregation is useless.\n\n\nFor a large number of reports, there are too many aggregation variations (combinatorial explosion).\n\n\nWhen aggregating keys with high cardinality (such as URLs), the volume of data is not reduced by much (less than twofold).\n\n\nFor this reason, the volume of data with aggregation might grow instead of shrink.\n\n\nUsers do not view all the reports we generate for them. A large portion of calculations are useless.\n\n\nThe logical integrity of data may be violated for various aggregations.\n\n\n\n\nIf we do not aggregate anything and work with non-aggregated data, this might actually reduce the volume of calculations.\n\n\nHowever, with aggregation, a significant part of the work is taken offline and completed relatively calmly. In contrast, online calculations require calculating as fast as possible, since the user is waiting for the result.\n\n\nYandex.Metrica has a specialized system for aggregating data called Metrage, which is used for the majority of reports.\nStarting in 2009, Yandex.Metrica also used a specialized OLAP database for non-aggregated data called OLAPServer, which was previously used for the report builder.\nOLAPServer worked well for non-aggregated data, but it had many restrictions that did not allow it to be used for all reports as desired. These included the lack of support for data types (only numbers), and the inability to incrementally update data in real-time (it could only be done by rewriting data daily). OLAPServer is not a DBMS, but a specialized DB.\n\n\nTo remove the limitations of OLAPServer and solve the problem of working with non-aggregated data for all reports, we developed the ClickHouse DBMS.\n\n\nQuestions you were afraid to ask\n\n\nWhy not use something like MapReduce?\n\n\nWe can refer to systems like map-reduce as distributed computing systems in which the reduce operation is based on distributed sorting. In this sense, they include Hadoop, and YT (YT is developed at Yandex for internal use).\n\n\nThese systems aren't appropriate for online queries due to their high latency. In other words, they can't be used as the back-end for a web interface.\nThese types of systems aren't useful for real-time data updates.\nDistributed sorting isn't the best way to perform reduce operations if the result of the operation and all the intermediate results (if there are any) are located in the RAM of a single server, which is usually the case for online queries. In such a case, a hash table is the optimal way to perform reduce operations. A common approach to optimizing map-reduce tasks is pre-aggregation (partial reduce) using a hash table in RAM. The user performs this optimization manually.\nDistributed sorting is one of the main causes of reduced performance when running simple map-reduce tasks.\n\n\nSystems like map-reduce allow executing any code on the cluster. But a declarative query language is better suited to OLAP in order to run experiments quickly. For example, Hadoop has Hive and Pig. Also consider Cloudera Impala, Shark (outdated) for Spark, and Spark SQL, Presto, and Apache Drill. Performance when running such tasks is highly sub-optimal compared to specialized systems, but relatively high latency makes it unrealistic to use these systems as the backend for a web interface.\n\n\nYT allows storing groups of columns separately. But YT can't be considered a true column-based system because it doesn't have fixed-length data types (for efficiently storing numbers without extra \"garbage\"), and also due to its lack of a vector engine. Tasks are performed in YT using custom code in streaming mode, so they cannot be optimized enough (up to hundreds of millions of rows per second per server). \"Dynamic table sorting\" is under development in YT using MergeTree, strict value typing, and a query language similar to SQL. Dynamically sorted tables are not appropriate for OLAP tasks because the data is stored by row. The YT query language is still under development, so we can't yet rely on this functionality. YT developers are considering using dynamically sorted tables in OLTP and Key-Value scenarios.\n\n\nPerformance\n\n\nAccording to internal testing results, ClickHouse shows the best performance for comparable operating scenarios among systems of its class that were available for testing. This includes the highest throughput for long queries, and the lowest latency on short queries. Testing results are shown on a separate page.\n\n\nThroughput for a single large query\n\n\nThroughput can be measured in rows per second or in megabytes per second. If the data is placed in the page cache, a query that is not too complex is processed on modern hardware at a speed of approximately 2-10 GB/s of uncompressed data on a single server (for the simplest cases, the speed may reach 30 GB/s). If data is not placed in the page cache, the speed depends on the disk subsystem and the data compression rate. For example, if the disk subsystem allows reading data at 400 MB/s, and the data compression rate is 3, the speed will be around 1.2 GB/s. To get the speed in rows per second, divide the speed in bytes per second by the total size of the columns used in the query. For example, if 10 bytes of columns are extracted, the speed will be around 100-200 million rows per second.\n\n\nThe processing speed increases almost linearly for distributed processing, but only if the number of rows resulting from aggregation or sorting is not too large.\n\n\nLatency when processing short queries\n\n\nIf a query uses a primary key and does not select too many rows to process (hundreds of thousands), and does not use too many columns, we can expect less than 50 milliseconds of latency (single digits of milliseconds in the best case) if data is placed in the page cache. Otherwise, latency is calculated from the number of seeks. If you use rotating drives, for a system that is not overloaded, the latency is calculated by this formula: seek time (10 ms) * number of columns queried * number of data parts.\n\n\nThroughput when processing a large quantity of short queries\n\n\nUnder the same conditions, ClickHouse can handle several hundred queries per second on a single server (up to several thousand in the best case). Since this scenario is not typical for analytical DBMSs, we recommend expecting a maximum of 100 queries per second.\n\n\nPerformance when inserting data\n\n\nWe recommend inserting data in packets of at least 1000 rows, or no more than a single request per second. When inserting to a MergeTree table from a tab-separated dump, the insertion speed will be from 50 to 200 MB/s. If the inserted rows are around 1 Kb in size, the speed will be from 50,000 to 200,000 rows per second. If the rows are small, the performance will be higher in rows per second (on Banner System data -\n 500,000 rows per second; on Graphite data -\n 1,000,000 rows per second). To improve performance, you can make multiple INSERT queries in parallel, and performance will increase linearly.\n\n\nGetting started\n\n\nSystem requirements\n\n\nThis is not a cross-platform system. It requires Linux Ubuntu Precise (12.04) or newer, with x86_64 architecture and support for the SSE 4.2 instruction set.\nTo check for SSE 4.2:\n\n\ngrep -q sse4_2 /proc/cpuinfo \n \necho\n \nSSE 4.2 supported\n \n||\n \necho\n \nSSE 4.2 not supported\n\n\n\n\n\n\nWe recommend using Ubuntu Trusty, Ubuntu Xenial, or Ubuntu Precise.\nThe terminal must use UTF-8 encoding (the default in Ubuntu).\n\n\nInstallation\n\n\nFor testing and development, the system can be installed on a single server or on a desktop computer.\n\n\nInstalling from packages for Debian/Ubuntu\n\n\nIn \n/etc/apt/sources.list\n (or in a separate \n/etc/apt/sources.list.d/clickhouse.list\n file), add the repository:\n\n\ndeb http://repo.yandex.ru/clickhouse/deb/stable/ main/\n\n\n\n\n\nIf you want to use the most recent test version, replace 'stable' with 'testing'.\n\n\nThen run:\n\n\nsudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4 \n# optional\n\nsudo apt-get update\nsudo apt-get install clickhouse-client clickhouse-server\n\n\n\n\n\nYou can also download and install packages manually from here: \nhttps://repo.yandex.ru/clickhouse/deb/stable/main/\n.\n\n\nClickHouse contains access restriction settings. They are located in the 'users.xml' file (next to 'config.xml').\nBy default, access is allowed from anywhere for the 'default' user, without a password. See 'user/default/networks'.\nFor more information, see the section \"Configuration files\".\n\n\nInstalling from sources\n\n\nTo compile, follow the instructions: build.md\n\n\nYou can compile packages and install them.\nYou can also use programs without installing packages.\n\n\nClient: dbms/src/Client/\nServer: dbms/src/Server/\n\n\n\n\n\nFor the server, create a catalog with data, such as:\n\n\n/opt/clickhouse/data/default/\n/opt/clickhouse/metadata/default/\n\n\n\n\n\n(Configurable in the server config.)\nRun 'chown' for the desired user.\n\n\nNote the path to logs in the server config (src/dbms/src/Server/config.xml).\n\n\nOther installation methods\n\n\nDocker image: \nhttps://hub.docker.com/r/yandex/clickhouse-server/\n\n\nRPM packages for CentOS or RHEL: \nhttps://github.com/Altinity/clickhouse-rpm-install\n\n\nGentoo overlay: \nhttps://github.com/kmeaw/clickhouse-overlay\n\n\nLaunch\n\n\nTo start the server (as a daemon), run:\n\n\nsudo service clickhouse-server start\n\n\n\n\n\nSee the logs in the \n/var/log/clickhouse-server/ directory.\n\n\nIf the server doesn't start, check the configurations in the file \n/etc/clickhouse-server/config.xml.\n\n\nYou can also launch the server from the console:\n\n\nclickhouse-server --config-file\n=\n/etc/clickhouse-server/config.xml\n\n\n\n\n\nIn this case, the log will be printed to the console, which is convenient during development.\nIf the configuration file is in the current directory, you don't need to specify the '--config-file' parameter. By default, it uses './config.xml'.\n\n\nYou can use the command-line client to connect to the server:\n\n\nclickhouse-client\n\n\n\n\n\nThe default parameters indicate connecting with localhost:9000 on behalf of the user 'default' without a password.\nThe client can be used for connecting to a remote server. Example:\n\n\nclickhouse-client --host\n=\nexample.com\n\n\n\n\n\nFor more information, see the section \"Command-line client\".\n\n\nChecking the system:\n\n\nmilovidov@hostname:~/work/metrica/src/dbms/src/Client$ ./clickhouse-client\nClickHouse client version \n0\n.0.18749.\nConnecting to localhost:9000.\nConnected to ClickHouse server version \n0\n.0.18749.\n\n:\n)\n SELECT \n1\n\n\nSELECT \n1\n\n\n\u250c\u25001\u2500\u2510\n\u2502 \n1\n \u2502\n\u2514\u2500\u2500\u2500\u2518\n\n\n1\n rows in set. Elapsed: \n0\n.003 sec.\n\n:\n)\n\n\n\n\n\n\nCongratulations, the system works!\n\n\nTo continue experimenting, you can try to download from the test data sets.\n\n\n\n\nOnTime\n\n\nThis performance test was created by Vadim Tkachenko. See:\n\n\n\n\nhttps://www.percona.com/blog/2009/10/02/analyzing-air-traffic-performance-with-infobright-and-monetdb/\n\n\nhttps://www.percona.com/blog/2009/10/26/air-traffic-queries-in-luciddb/\n\n\nhttps://www.percona.com/blog/2009/11/02/air-traffic-queries-in-infinidb-early-alpha/\n\n\nhttps://www.percona.com/blog/2014/04/21/using-apache-hadoop-and-impala-together-with-mysql-for-data-analysis/\n\n\nhttps://www.percona.com/blog/2016/01/07/apache-spark-with-air-ontime-performance-data/\n\n\nhttp://nickmakos.blogspot.ru/2012/08/analyzing-air-traffic-performance-with.html\n\n\n\n\nDownloading data:\n\n\nfor\n s in \n`\nseq \n1987\n \n2017\n`\n\n\ndo\n\n\nfor\n m in \n`\nseq \n1\n \n12\n`\n\n\ndo\n\nwget http://transtats.bts.gov/PREZIP/On_Time_On_Time_Performance_\n${\ns\n}\n_\n${\nm\n}\n.zip\n\ndone\n\n\ndone\n\n\n\n\n\n\n(from \nhttps://github.com/Percona-Lab/ontime-airline-performance/blob/master/download.sh\n )\n\n\nCreating a table:\n\n\nCREATE\n \nTABLE\n \n`\nontime\n`\n \n(\n\n \n`\nYear\n`\n \nUInt16\n,\n\n \n`\nQuarter\n`\n \nUInt8\n,\n\n \n`\nMonth\n`\n \nUInt8\n,\n\n \n`\nDayofMonth\n`\n \nUInt8\n,\n\n \n`\nDayOfWeek\n`\n \nUInt8\n,\n\n \n`\nFlightDate\n`\n \nDate\n,\n\n \n`\nUniqueCarrier\n`\n \nFixedString\n(\n7\n),\n\n \n`\nAirlineID\n`\n \nInt32\n,\n\n \n`\nCarrier\n`\n \nFixedString\n(\n2\n),\n\n \n`\nTailNum\n`\n \nString\n,\n\n \n`\nFlightNum\n`\n \nString\n,\n\n \n`\nOriginAirportID\n`\n \nInt32\n,\n\n \n`\nOriginAirportSeqID\n`\n \nInt32\n,\n\n \n`\nOriginCityMarketID\n`\n \nInt32\n,\n\n \n`\nOrigin\n`\n \nFixedString\n(\n5\n),\n\n \n`\nOriginCityName\n`\n \nString\n,\n\n \n`\nOriginState\n`\n \nFixedString\n(\n2\n),\n\n \n`\nOriginStateFips\n`\n \nString\n,\n\n \n`\nOriginStateName\n`\n \nString\n,\n\n \n`\nOriginWac\n`\n \nInt32\n,\n\n \n`\nDestAirportID\n`\n \nInt32\n,\n\n \n`\nDestAirportSeqID\n`\n \nInt32\n,\n\n \n`\nDestCityMarketID\n`\n \nInt32\n,\n\n \n`\nDest\n`\n \nFixedString\n(\n5\n),\n\n \n`\nDestCityName\n`\n \nString\n,\n\n \n`\nDestState\n`\n \nFixedString\n(\n2\n),\n\n \n`\nDestStateFips\n`\n \nString\n,\n\n \n`\nDestStateName\n`\n \nString\n,\n\n \n`\nDestWac\n`\n \nInt32\n,\n\n \n`\nCRSDepTime\n`\n \nInt32\n,\n\n \n`\nDepTime\n`\n \nInt32\n,\n\n \n`\nDepDelay\n`\n \nInt32\n,\n\n \n`\nDepDelayMinutes\n`\n \nInt32\n,\n\n \n`\nDepDel15\n`\n \nInt32\n,\n\n \n`\nDepartureDelayGroups\n`\n \nString\n,\n\n \n`\nDepTimeBlk\n`\n \nString\n,\n\n \n`\nTaxiOut\n`\n \nInt32\n,\n\n \n`\nWheelsOff\n`\n \nInt32\n,\n\n \n`\nWheelsOn\n`\n \nInt32\n,\n\n \n`\nTaxiIn\n`\n \nInt32\n,\n\n \n`\nCRSArrTime\n`\n \nInt32\n,\n\n \n`\nArrTime\n`\n \nInt32\n,\n\n \n`\nArrDelay\n`\n \nInt32\n,\n\n \n`\nArrDelayMinutes\n`\n \nInt32\n,\n\n \n`\nArrDel15\n`\n \nInt32\n,\n\n \n`\nArrivalDelayGroups\n`\n \nInt32\n,\n\n \n`\nArrTimeBlk\n`\n \nString\n,\n\n \n`\nCancelled\n`\n \nUInt8\n,\n\n \n`\nCancellationCode\n`\n \nFixedString\n(\n1\n),\n\n \n`\nDiverted\n`\n \nUInt8\n,\n\n \n`\nCRSElapsedTime\n`\n \nInt32\n,\n\n \n`\nActualElapsedTime\n`\n \nInt32\n,\n\n \n`\nAirTime\n`\n \nInt32\n,\n\n \n`\nFlights\n`\n \nInt32\n,\n\n \n`\nDistance\n`\n \nInt32\n,\n\n \n`\nDistanceGroup\n`\n \nUInt8\n,\n\n \n`\nCarrierDelay\n`\n \nInt32\n,\n\n \n`\nWeatherDelay\n`\n \nInt32\n,\n\n \n`\nNASDelay\n`\n \nInt32\n,\n\n \n`\nSecurityDelay\n`\n \nInt32\n,\n\n \n`\nLateAircraftDelay\n`\n \nInt32\n,\n\n \n`\nFirstDepTime\n`\n \nString\n,\n\n \n`\nTotalAddGTime\n`\n \nString\n,\n\n \n`\nLongestAddGTime\n`\n \nString\n,\n\n \n`\nDivAirportLandings\n`\n \nString\n,\n\n \n`\nDivReachedDest\n`\n \nString\n,\n\n \n`\nDivActualElapsedTime\n`\n \nString\n,\n\n \n`\nDivArrDelay\n`\n \nString\n,\n\n \n`\nDivDistance\n`\n \nString\n,\n\n \n`\nDiv1Airport\n`\n \nString\n,\n\n \n`\nDiv1AirportID\n`\n \nInt32\n,\n\n \n`\nDiv1AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv1WheelsOn\n`\n \nString\n,\n\n \n`\nDiv1TotalGTime\n`\n \nString\n,\n\n \n`\nDiv1LongestGTime\n`\n \nString\n,\n\n \n`\nDiv1WheelsOff\n`\n \nString\n,\n\n \n`\nDiv1TailNum\n`\n \nString\n,\n\n \n`\nDiv2Airport\n`\n \nString\n,\n\n \n`\nDiv2AirportID\n`\n \nInt32\n,\n\n \n`\nDiv2AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv2WheelsOn\n`\n \nString\n,\n\n \n`\nDiv2TotalGTime\n`\n \nString\n,\n\n \n`\nDiv2LongestGTime\n`\n \nString\n,\n\n \n`\nDiv2WheelsOff\n`\n \nString\n,\n\n \n`\nDiv2TailNum\n`\n \nString\n,\n\n \n`\nDiv3Airport\n`\n \nString\n,\n\n \n`\nDiv3AirportID\n`\n \nInt32\n,\n\n \n`\nDiv3AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv3WheelsOn\n`\n \nString\n,\n\n \n`\nDiv3TotalGTime\n`\n \nString\n,\n\n \n`\nDiv3LongestGTime\n`\n \nString\n,\n\n \n`\nDiv3WheelsOff\n`\n \nString\n,\n\n \n`\nDiv3TailNum\n`\n \nString\n,\n\n \n`\nDiv4Airport\n`\n \nString\n,\n\n \n`\nDiv4AirportID\n`\n \nInt32\n,\n\n \n`\nDiv4AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv4WheelsOn\n`\n \nString\n,\n\n \n`\nDiv4TotalGTime\n`\n \nString\n,\n\n \n`\nDiv4LongestGTime\n`\n \nString\n,\n\n \n`\nDiv4WheelsOff\n`\n \nString\n,\n\n \n`\nDiv4TailNum\n`\n \nString\n,\n\n \n`\nDiv5Airport\n`\n \nString\n,\n\n \n`\nDiv5AirportID\n`\n \nInt32\n,\n\n \n`\nDiv5AirportSeqID\n`\n \nInt32\n,\n\n \n`\nDiv5WheelsOn\n`\n \nString\n,\n\n \n`\nDiv5TotalGTime\n`\n \nString\n,\n\n \n`\nDiv5LongestGTime\n`\n \nString\n,\n\n \n`\nDiv5WheelsOff\n`\n \nString\n,\n\n \n`\nDiv5TailNum\n`\n \nString\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nFlightDate\n,\n \n(\nYear\n,\n \nFlightDate\n),\n \n8192\n)\n\n\n\n\n\n\nLoading data:\n\n\nfor\n i in *.zip\n;\n \ndo\n \necho\n \n$i\n;\n unzip -cq \n$i\n \n*.csv\n \n|\n sed \ns/\\.00//g\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO ontime FORMAT CSVWithNames\n;\n \ndone\n\n\n\n\n\n\nQueries:\n\n\nQ0.\n\n\nselect\n \navg\n(\nc1\n)\n \nfrom\n \n(\nselect\n \nYear\n,\n \nMonth\n,\n \ncount\n(\n*\n)\n \nas\n \nc1\n \nfrom\n \nontime\n \ngroup\n \nby\n \nYear\n,\n \nMonth\n);\n\n\n\n\n\n\nQ1. The number of flights per day from the year 2000 to 2008\n\n\nSELECT\n \nDayOfWeek\n,\n \ncount\n(\n*\n)\n \nAS\n \nc\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nDayOfWeek\n \nORDER\n \nBY\n \nc\n \nDESC\n;\n\n\n\n\n\n\nQ2. The number of flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008\n\n\nSELECT\n \nDayOfWeek\n,\n \ncount\n(\n*\n)\n \nAS\n \nc\n \nFROM\n \nontime\n \nWHERE\n \nDepDelay\n10\n \nAND\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nDayOfWeek\n \nORDER\n \nBY\n \nc\n \nDESC\n\n\n\n\n\n\nQ3. The number of delays by airport for 2000-2008\n\n\nSELECT\n \nOrigin\n,\n \ncount\n(\n*\n)\n \nAS\n \nc\n \nFROM\n \nontime\n \nWHERE\n \nDepDelay\n10\n \nAND\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nOrigin\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n10\n\n\n\n\n\n\nQ4. The number of delays by carrier for 2007\n\n\nSELECT\n \nCarrier\n,\n \ncount\n(\n*\n)\n \nFROM\n \nontime\n \nWHERE\n \nDepDelay\n10\n \nAND\n \nYear\n \n=\n \n2007\n \nGROUP\n \nBY\n \nCarrier\n \nORDER\n \nBY\n \ncount\n(\n*\n)\n \nDESC\n\n\n\n\n\n\nQ5. The percentage of delays by carrier for 2007\n\n\nSELECT\n \nCarrier\n,\n \nc\n,\n \nc2\n,\n \nc\n*\n1000\n/\nc2\n \nas\n \nc3\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc\n\n \nFROM\n \nontime\n\n \nWHERE\n \nDepDelay\n10\n\n \nAND\n \nYear\n=\n2007\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n\n\nANY\n \nINNER\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc2\n\n \nFROM\n \nontime\n\n \nWHERE\n \nYear\n=\n2007\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n \nUSING\n \nCarrier\n\n\nORDER\n \nBY\n \nc3\n \nDESC\n;\n\n\n\n\n\n\nBetter version of the same query:\n\n\nSELECT\n \nCarrier\n,\n \navg\n(\nDepDelay\n \n \n10\n)\n \n*\n \n1000\n \nAS\n \nc3\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2007\n \nGROUP\n \nBY\n \nCarrier\n \nORDER\n \nBY\n \nCarrier\n\n\n\n\n\n\nQ6. The previous request for a broader range of years, 2000-2008\n\n\nSELECT\n \nCarrier\n,\n \nc\n,\n \nc2\n,\n \nc\n*\n1000\n/\nc2\n \nas\n \nc3\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc\n\n \nFROM\n \nontime\n\n \nWHERE\n \nDepDelay\n10\n\n \nAND\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n\n\nANY\n \nINNER\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nCarrier\n,\n\n \ncount\n(\n*\n)\n \nAS\n \nc2\n\n \nFROM\n \nontime\n\n \nWHERE\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n\n \nGROUP\n \nBY\n \nCarrier\n\n\n)\n \nUSING\n \nCarrier\n\n\nORDER\n \nBY\n \nc3\n \nDESC\n;\n\n\n\n\n\n\nBetter version of the same query:\n\n\nSELECT\n \nCarrier\n,\n \navg\n(\nDepDelay\n \n \n10\n)\n \n*\n \n1000\n \nAS\n \nc3\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2000\n \nAND\n \nYear\n \n=\n \n2008\n \nGROUP\n \nBY\n \nCarrier\n \nORDER\n \nBY\n \nCarrier\n\n\n\n\n\n\nQ7. Percentage of flights delayed for more than 10 minutes, by year\n\n\nSELECT\n \nYear\n,\n \nc1\n/\nc2\n\n\nFROM\n\n\n(\n\n \nselect\n\n \nYear\n,\n\n \ncount\n(\n*\n)\n*\n1000\n \nas\n \nc1\n\n \nfrom\n \nontime\n\n \nWHERE\n \nDepDelay\n10\n\n \nGROUP\n \nBY\n \nYear\n\n\n)\n\n\nANY\n \nINNER\n \nJOIN\n\n\n(\n\n \nselect\n\n \nYear\n,\n\n \ncount\n(\n*\n)\n \nas\n \nc2\n\n \nfrom\n \nontime\n\n \nGROUP\n \nBY\n \nYear\n\n\n)\n \nUSING\n \n(\nYear\n)\n\n\nORDER\n \nBY\n \nYear\n\n\n\n\n\n\nBetter version of the same query:\n\n\nSELECT\n \nYear\n,\n \navg\n(\nDepDelay\n \n \n10\n)\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nYear\n \nORDER\n \nBY\n \nYear\n\n\n\n\n\n\nQ8. The most popular destinations by the number of directly connected cities for various year ranges\n\n\nSELECT\n \nDestCityName\n,\n \nuniqExact\n(\nOriginCityName\n)\n \nAS\n \nu\n \nFROM\n \nontime\n \nWHERE\n \nYear\n \n=\n \n2000\n \nand\n \nYear\n \n=\n \n2010\n \nGROUP\n \nBY\n \nDestCityName\n \nORDER\n \nBY\n \nu\n \nDESC\n \nLIMIT\n \n10\n;\n\n\n\n\n\n\nQ9.\n\n\nselect\n \nYear\n,\n \ncount\n(\n*\n)\n \nas\n \nc1\n \nfrom\n \nontime\n \ngroup\n \nby\n \nYear\n;\n\n\n\n\n\n\nQ10.\n\n\nselect\n\n \nmin\n(\nYear\n),\n \nmax\n(\nYear\n),\n \nCarrier\n,\n \ncount\n(\n*\n)\n \nas\n \ncnt\n,\n\n \nsum\n(\nArrDelayMinutes\n30\n)\n \nas\n \nflights_delayed\n,\n\n \nround\n(\nsum\n(\nArrDelayMinutes\n30\n)\n/\ncount\n(\n*\n),\n2\n)\n \nas\n \nrate\n\n\nFROM\n \nontime\n\n\nWHERE\n\n \nDayOfWeek\n \nnot\n \nin\n \n(\n6\n,\n7\n)\n \nand\n \nOriginState\n \nnot\n \nin\n \n(\nAK\n,\n \nHI\n,\n \nPR\n,\n \nVI\n)\n\n \nand\n \nDestState\n \nnot\n \nin\n \n(\nAK\n,\n \nHI\n,\n \nPR\n,\n \nVI\n)\n\n \nand\n \nFlightDate\n \n \n2010-01-01\n\n\nGROUP\n \nby\n \nCarrier\n\n\nHAVING\n \ncnt\n \n \n100000\n \nand\n \nmax\n(\nYear\n)\n \n \n1990\n\n\nORDER\n \nby\n \nrate\n \nDESC\n\n\nLIMIT\n \n1000\n;\n\n\n\n\n\n\nBonus:\n\n\nSELECT\n \navg\n(\ncnt\n)\n \nFROM\n \n(\nSELECT\n \nYear\n,\nMonth\n,\ncount\n(\n*\n)\n \nAS\n \ncnt\n \nFROM\n \nontime\n \nWHERE\n \nDepDel15\n=\n1\n \nGROUP\n \nBY\n \nYear\n,\nMonth\n)\n\n\n\nselect\n \navg\n(\nc1\n)\n \nfrom\n \n(\nselect\n \nYear\n,\nMonth\n,\ncount\n(\n*\n)\n \nas\n \nc1\n \nfrom\n \nontime\n \ngroup\n \nby\n \nYear\n,\nMonth\n)\n\n\n\nSELECT\n \nDestCityName\n,\n \nuniqExact\n(\nOriginCityName\n)\n \nAS\n \nu\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nDestCityName\n \nORDER\n \nBY\n \nu\n \nDESC\n \nLIMIT\n \n10\n;\n\n\n\nSELECT\n \nOriginCityName\n,\n \nDestCityName\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nOriginCityName\n,\n \nDestCityName\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n10\n;\n\n\n\nSELECT\n \nOriginCityName\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \nontime\n \nGROUP\n \nBY\n \nOriginCityName\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n10\n;\n\n\n\n\n\n\nNew York Taxi data\n\n\nHow to import the raw data\n\n\nSee \nhttps://github.com/toddwschneider/nyc-taxi-data\n and \nhttp://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html\n for the description of the dataset and instructions for downloading.\n\n\nDownloading will result in about 227 GB of uncompressed data in CSV files. The download takes about an hour over a 1 Gbit connection (parallel downloading from s3.amazonaws.com recovers at least half of a 1 Gbit channel).\nSome of the files might not download fully. Check the file sizes and re-download any that seem doubtful.\n\n\nSome of the files might contain invalid rows. You can fix them as follows:\n\n\nsed -E \n/(.*,){18,}/d\n data/yellow_tripdata_2010-02.csv \n data/yellow_tripdata_2010-02.csv_\nsed -E \n/(.*,){18,}/d\n data/yellow_tripdata_2010-03.csv \n data/yellow_tripdata_2010-03.csv_\nmv data/yellow_tripdata_2010-02.csv_ data/yellow_tripdata_2010-02.csv\nmv data/yellow_tripdata_2010-03.csv_ data/yellow_tripdata_2010-03.csv\n\n\n\n\n\nThen the data must be pre-processed in PostgreSQL. This will create selections of points in the polygons (to match points on the map with the boroughs of New York City) and combine all the data into a single denormalized flat table by using a JOIN. To do this, you will need to install PostgreSQL with PostGIS support.\n\n\nBe careful when running \ninitialize_database.sh\n and manually re-check that all the tables were created correctly.\n\n\nIt takes about 20-30 minutes to process each month's worth of data in PostgreSQL, for a total of about 48 hours.\n\n\nYou can check the number of downloaded rows as follows:\n\n\ntime psql nyc-taxi-data -c \nSELECT count(*) FROM trips;\n\n### count\n 1298979494\n(1 row)\n\nreal 7m9.164s\n\n\n\n\n\n(This is slightly more than 1.1 billion rows reported by Mark Litwintschik in a series of blog posts.)\n\n\nThe data in PostgreSQL uses 370 GB of space.\n\n\nExporting the data from PostgreSQL:\n\n\nCOPY\n\n\n(\n\n \nSELECT\n \ntrips\n.\nid\n,\n\n \ntrips\n.\nvendor_id\n,\n\n \ntrips\n.\npickup_datetime\n,\n\n \ntrips\n.\ndropoff_datetime\n,\n\n \ntrips\n.\nstore_and_fwd_flag\n,\n\n \ntrips\n.\nrate_code_id\n,\n\n \ntrips\n.\npickup_longitude\n,\n\n \ntrips\n.\npickup_latitude\n,\n\n \ntrips\n.\ndropoff_longitude\n,\n\n \ntrips\n.\ndropoff_latitude\n,\n\n \ntrips\n.\npassenger_count\n,\n\n \ntrips\n.\ntrip_distance\n,\n\n \ntrips\n.\nfare_amount\n,\n\n \ntrips\n.\nextra\n,\n\n \ntrips\n.\nmta_tax\n,\n\n \ntrips\n.\ntip_amount\n,\n\n \ntrips\n.\ntolls_amount\n,\n\n \ntrips\n.\nehail_fee\n,\n\n \ntrips\n.\nimprovement_surcharge\n,\n\n \ntrips\n.\ntotal_amount\n,\n\n \ntrips\n.\npayment_type\n,\n\n \ntrips\n.\ntrip_type\n,\n\n \ntrips\n.\npickup\n,\n\n \ntrips\n.\ndropoff\n,\n\n\n \ncab_types\n.\ntype\n \ncab_type\n,\n\n\n \nweather\n.\nprecipitation_tenths_of_mm\n \nrain\n,\n\n \nweather\n.\nsnow_depth_mm\n,\n\n \nweather\n.\nsnowfall_mm\n,\n\n \nweather\n.\nmax_temperature_tenths_degrees_celsius\n \nmax_temp\n,\n\n \nweather\n.\nmin_temperature_tenths_degrees_celsius\n \nmin_temp\n,\n\n \nweather\n.\naverage_wind_speed_tenths_of_meters_per_second\n \nwind\n,\n\n\n \npick_up\n.\ngid\n \npickup_nyct2010_gid\n,\n\n \npick_up\n.\nctlabel\n \npickup_ctlabel\n,\n\n \npick_up\n.\nborocode\n \npickup_borocode\n,\n\n \npick_up\n.\nboroname\n \npickup_boroname\n,\n\n \npick_up\n.\nct2010\n \npickup_ct2010\n,\n\n \npick_up\n.\nboroct2010\n \npickup_boroct2010\n,\n\n \npick_up\n.\ncdeligibil\n \npickup_cdeligibil\n,\n\n \npick_up\n.\nntacode\n \npickup_ntacode\n,\n\n \npick_up\n.\nntaname\n \npickup_ntaname\n,\n\n \npick_up\n.\npuma\n \npickup_puma\n,\n\n\n \ndrop_off\n.\ngid\n \ndropoff_nyct2010_gid\n,\n\n \ndrop_off\n.\nctlabel\n \ndropoff_ctlabel\n,\n\n \ndrop_off\n.\nborocode\n \ndropoff_borocode\n,\n\n \ndrop_off\n.\nboroname\n \ndropoff_boroname\n,\n\n \ndrop_off\n.\nct2010\n \ndropoff_ct2010\n,\n\n \ndrop_off\n.\nboroct2010\n \ndropoff_boroct2010\n,\n\n \ndrop_off\n.\ncdeligibil\n \ndropoff_cdeligibil\n,\n\n \ndrop_off\n.\nntacode\n \ndropoff_ntacode\n,\n\n \ndrop_off\n.\nntaname\n \ndropoff_ntaname\n,\n\n \ndrop_off\n.\npuma\n \ndropoff_puma\n\n \nFROM\n \ntrips\n\n \nLEFT\n \nJOIN\n \ncab_types\n\n \nON\n \ntrips\n.\ncab_type_id\n \n=\n \ncab_types\n.\nid\n\n \nLEFT\n \nJOIN\n \ncentral_park_weather_observations_raw\n \nweather\n\n \nON\n \nweather\n.\ndate\n \n=\n \ntrips\n.\npickup_datetime\n::\ndate\n\n \nLEFT\n \nJOIN\n \nnyct2010\n \npick_up\n\n \nON\n \npick_up\n.\ngid\n \n=\n \ntrips\n.\npickup_nyct2010_gid\n\n \nLEFT\n \nJOIN\n \nnyct2010\n \ndrop_off\n\n \nON\n \ndrop_off\n.\ngid\n \n=\n \ntrips\n.\ndropoff_nyct2010_gid\n\n\n)\n \nTO\n \n/opt/milovidov/nyc-taxi-data/trips.tsv\n;\n\n\n\n\n\n\nThe data snapshot is created at a speed of about 50 MB per second. While creating the snapshot, PostgreSQL reads from the disk at a speed of about 28 MB per second.\nThis takes about 5 hours. The resulting TSV file is 590612904969 bytes.\n\n\nCreate a temporary table in ClickHouse:\n\n\nCREATE\n \nTABLE\n \ntrips\n\n\n(\n\n\ntrip_id\n \nUInt32\n,\n\n\nvendor_id\n \nString\n,\n\n\npickup_datetime\n \nDateTime\n,\n\n\ndropoff_datetime\n \nNullable\n(\nDateTime\n),\n\n\nstore_and_fwd_flag\n \nNullable\n(\nFixedString\n(\n1\n)),\n\n\nrate_code_id\n \nNullable\n(\nUInt8\n),\n\n\npickup_longitude\n \nNullable\n(\nFloat64\n),\n\n\npickup_latitude\n \nNullable\n(\nFloat64\n),\n\n\ndropoff_longitude\n \nNullable\n(\nFloat64\n),\n\n\ndropoff_latitude\n \nNullable\n(\nFloat64\n),\n\n\npassenger_count\n \nNullable\n(\nUInt8\n),\n\n\ntrip_distance\n \nNullable\n(\nFloat64\n),\n\n\nfare_amount\n \nNullable\n(\nFloat32\n),\n\n\nextra\n \nNullable\n(\nFloat32\n),\n\n\nmta_tax\n \nNullable\n(\nFloat32\n),\n\n\ntip_amount\n \nNullable\n(\nFloat32\n),\n\n\ntolls_amount\n \nNullable\n(\nFloat32\n),\n\n\nehail_fee\n \nNullable\n(\nFloat32\n),\n\n\nimprovement_surcharge\n \nNullable\n(\nFloat32\n),\n\n\ntotal_amount\n \nNullable\n(\nFloat32\n),\n\n\npayment_type\n \nNullable\n(\nString\n),\n\n\ntrip_type\n \nNullable\n(\nUInt8\n),\n\n\npickup\n \nNullable\n(\nString\n),\n\n\ndropoff\n \nNullable\n(\nString\n),\n\n\ncab_type\n \nNullable\n(\nString\n),\n\n\nprecipitation\n \nNullable\n(\nUInt8\n),\n\n\nsnow_depth\n \nNullable\n(\nUInt8\n),\n\n\nsnowfall\n \nNullable\n(\nUInt8\n),\n\n\nmax_temperature\n \nNullable\n(\nUInt8\n),\n\n\nmin_temperature\n \nNullable\n(\nUInt8\n),\n\n\naverage_wind_speed\n \nNullable\n(\nUInt8\n),\n\n\npickup_nyct2010_gid\n \nNullable\n(\nUInt8\n),\n\n\npickup_ctlabel\n \nNullable\n(\nString\n),\n\n\npickup_borocode\n \nNullable\n(\nUInt8\n),\n\n\npickup_boroname\n \nNullable\n(\nString\n),\n\n\npickup_ct2010\n \nNullable\n(\nString\n),\n\n\npickup_boroct2010\n \nNullable\n(\nString\n),\n\n\npickup_cdeligibil\n \nNullable\n(\nFixedString\n(\n1\n)),\n\n\npickup_ntacode\n \nNullable\n(\nString\n),\n\n\npickup_ntaname\n \nNullable\n(\nString\n),\n\n\npickup_puma\n \nNullable\n(\nString\n),\n\n\ndropoff_nyct2010_gid\n \nNullable\n(\nUInt8\n),\n\n\ndropoff_ctlabel\n \nNullable\n(\nString\n),\n\n\ndropoff_borocode\n \nNullable\n(\nUInt8\n),\n\n\ndropoff_boroname\n \nNullable\n(\nString\n),\n\n\ndropoff_ct2010\n \nNullable\n(\nString\n),\n\n\ndropoff_boroct2010\n \nNullable\n(\nString\n),\n\n\ndropoff_cdeligibil\n \nNullable\n(\nString\n),\n\n\ndropoff_ntacode\n \nNullable\n(\nString\n),\n\n\ndropoff_ntaname\n \nNullable\n(\nString\n),\n\n\ndropoff_puma\n \nNullable\n(\nString\n)\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\n\n\n\nIt is needed for converting fields to more correct data types and, if possible, to eliminate NULLs.\n\n\ntime clickhouse-client --query=\nINSERT INTO trips FORMAT TabSeparated\n \n trips.tsv\n\nreal 75m56.214s\n\n\n\n\n\nData is read at a speed of 112-140 Mb/second.\nLoading data into a Log type table in one stream took 76 minutes.\nThe data in this table uses 142 GB.\n\n\n(Importing data directly from Postgres is also possible using \nCOPY ... TO PROGRAM\n.)\n\n\nUnfortunately, all the fields associated with the weather (precipitation...average_wind_speed) were filled with NULL. Because of this, we will remove them from the final data set.\n\n\nTo start, we'll create a table on a single server. Later we will make the table distributed.\n\n\nCreate and populate a summary table:\n\n\nCREATE TABLE trips_mergetree\nENGINE = MergeTree(pickup_date, pickup_datetime, 8192)\nAS SELECT\n\ntrip_id,\nCAST(vendor_id AS Enum8(\n1\n = 1, \n2\n = 2, \nCMT\n = 3, \nVTS\n = 4, \nDDS\n = 5, \nB02512\n = 10, \nB02598\n = 11, \nB02617\n = 12, \nB02682\n = 13, \nB02764\n = 14)) AS vendor_id,\ntoDate(pickup_datetime) AS pickup_date,\nifNull(pickup_datetime, toDateTime(0)) AS pickup_datetime,\ntoDate(dropoff_datetime) AS dropoff_date,\nifNull(dropoff_datetime, toDateTime(0)) AS dropoff_datetime,\nassumeNotNull(store_and_fwd_flag) IN (\nY\n, \n1\n, \n2\n) AS store_and_fwd_flag,\nassumeNotNull(rate_code_id) AS rate_code_id,\nassumeNotNull(pickup_longitude) AS pickup_longitude,\nassumeNotNull(pickup_latitude) AS pickup_latitude,\nassumeNotNull(dropoff_longitude) AS dropoff_longitude,\nassumeNotNull(dropoff_latitude) AS dropoff_latitude,\nassumeNotNull(passenger_count) AS passenger_count,\nassumeNotNull(trip_distance) AS trip_distance,\nassumeNotNull(fare_amount) AS fare_amount,\nassumeNotNull(extra) AS extra,\nassumeNotNull(mta_tax) AS mta_tax,\nassumeNotNull(tip_amount) AS tip_amount,\nassumeNotNull(tolls_amount) AS tolls_amount,\nassumeNotNull(ehail_fee) AS ehail_fee,\nassumeNotNull(improvement_surcharge) AS improvement_surcharge,\nassumeNotNull(total_amount) AS total_amount,\nCAST((assumeNotNull(payment_type) AS pt) IN (\nCSH\n, \nCASH\n, \nCash\n, \nCAS\n, \nCas\n, \n1\n) ? \nCSH\n : (pt IN (\nCRD\n, \nCredit\n, \nCre\n, \nCRE\n, \nCREDIT\n, \n2\n) ? \nCRE\n : (pt IN (\nNOC\n, \nNo Charge\n, \nNo\n, \n3\n) ? \nNOC\n : (pt IN (\nDIS\n, \nDispute\n, \nDis\n, \n4\n) ? \nDIS\n : \nUNK\n))) AS Enum8(\nCSH\n = 1, \nCRE\n = 2, \nUNK\n = 0, \nNOC\n = 3, \nDIS\n = 4)) AS payment_type_,\nassumeNotNull(trip_type) AS trip_type,\nifNull(toFixedString(unhex(pickup), 25), toFixedString(\n, 25)) AS pickup,\nifNull(toFixedString(unhex(dropoff), 25), toFixedString(\n, 25)) AS dropoff,\nCAST(assumeNotNull(cab_type) AS Enum8(\nyellow\n = 1, \ngreen\n = 2, \nuber\n = 3)) AS cab_type,\n\nassumeNotNull(pickup_nyct2010_gid) AS pickup_nyct2010_gid,\ntoFloat32(ifNull(pickup_ctlabel, \n0\n)) AS pickup_ctlabel,\nassumeNotNull(pickup_borocode) AS pickup_borocode,\nCAST(assumeNotNull(pickup_boroname) AS Enum8(\nManhattan\n = 1, \nQueens\n = 4, \nBrooklyn\n = 3, \n = 0, \nBronx\n = 2, \nStaten Island\n = 5)) AS pickup_boroname,\ntoFixedString(ifNull(pickup_ct2010, \n000000\n), 6) AS pickup_ct2010,\ntoFixedString(ifNull(pickup_boroct2010, \n0000000\n), 7) AS pickup_boroct2010,\nCAST(assumeNotNull(ifNull(pickup_cdeligibil, \n \n)) AS Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2)) AS pickup_cdeligibil,\ntoFixedString(ifNull(pickup_ntacode, \n0000\n), 4) AS pickup_ntacode,\n\nCAST(assumeNotNull(pickup_ntaname) AS Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195)) AS pickup_ntaname,\n\ntoUInt16(ifNull(pickup_puma, \n0\n)) AS pickup_puma,\n\nassumeNotNull(dropoff_nyct2010_gid) AS dropoff_nyct2010_gid,\ntoFloat32(ifNull(dropoff_ctlabel, \n0\n)) AS dropoff_ctlabel,\nassumeNotNull(dropoff_borocode) AS dropoff_borocode,\nCAST(assumeNotNull(dropoff_boroname) AS Enum8(\nManhattan\n = 1, \nQueens\n = 4, \nBrooklyn\n = 3, \n = 0, \nBronx\n = 2, \nStaten Island\n = 5)) AS dropoff_boroname,\ntoFixedString(ifNull(dropoff_ct2010, \n000000\n), 6) AS dropoff_ct2010,\ntoFixedString(ifNull(dropoff_boroct2010, \n0000000\n), 7) AS dropoff_boroct2010,\nCAST(assumeNotNull(ifNull(dropoff_cdeligibil, \n \n)) AS Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2)) AS dropoff_cdeligibil,\ntoFixedString(ifNull(dropoff_ntacode, \n0000\n), 4) AS dropoff_ntacode,\n\nCAST(assumeNotNull(dropoff_ntaname) AS Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195)) AS dropoff_ntaname,\n\ntoUInt16(ifNull(dropoff_puma, \n0\n)) AS dropoff_puma\n\nFROM trips\n\n\n\n\n\nThis takes 3030 seconds at a speed of about 428,000 rows per second.\nTo load it faster, you can create the table with the \nLog\n engine instead of \nMergeTree\n. In this case, the download works faster than 200 seconds.\n\n\nThe table uses 126 GB of disk space.\n\n\n:) SELECT formatReadableSize(sum(bytes)) FROM system.parts WHERE table = \ntrips_mergetree\n AND active\n\nSELECT formatReadableSize(sum(bytes))\nFROM system.parts\nWHERE (table = \ntrips_mergetree\n) AND active\n\n\u250c\u2500formatReadableSize(sum(bytes))\u2500\u2510\n\u2502 126.18 GiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nAmong other things, you can run the OPTIMIZE query on MergeTree. But it's not required, since everything will be fine without it.\n\n\nResults on single server\n\n\nQ1:\n\n\nSELECT\n \ncab_type\n,\n \ncount\n(\n*\n)\n \nFROM\n \ntrips_mergetree\n \nGROUP\n \nBY\n \ncab_type\n\n\n\n\n\n\n0.490 seconds.\n\n\nQ2:\n\n\nSELECT\n \npassenger_count\n,\n \navg\n(\ntotal_amount\n)\n \nFROM\n \ntrips_mergetree\n \nGROUP\n \nBY\n \npassenger_count\n\n\n\n\n\n\n1.224 seconds.\n\n\nQ3:\n\n\nSELECT\n \npassenger_count\n,\n \ntoYear\n(\npickup_date\n)\n \nAS\n \nyear\n,\n \ncount\n(\n*\n)\n \nFROM\n \ntrips_mergetree\n \nGROUP\n \nBY\n \npassenger_count\n,\n \nyear\n\n\n\n\n\n\n2.104 seconds.\n\n\nQ4:\n\n\nSELECT\n \npassenger_count\n,\n \ntoYear\n(\npickup_date\n)\n \nAS\n \nyear\n,\n \nround\n(\ntrip_distance\n)\n \nAS\n \ndistance\n,\n \ncount\n(\n*\n)\n\n\nFROM\n \ntrips_mergetree\n\n\nGROUP\n \nBY\n \npassenger_count\n,\n \nyear\n,\n \ndistance\n\n\nORDER\n \nBY\n \nyear\n,\n \ncount\n(\n*\n)\n \nDESC\n\n\n\n\n\n\n3.593 seconds.\n\n\nThe following server was used:\n\n\nTwo Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical kernels total,\n128 GiB RAM,\n8x6 TB HD on hardware RAID-5\n\n\nExecution time is the best of three runsBut starting from the second run, queries read data from the file system cache. No further caching occurs: the data is read out and processed in each run.\n\n\nCreating a table on three servers:\n\n\nOn each server:\n\n\nCREATE TABLE default.trips_mergetree_third ( trip_id UInt32, vendor_id Enum8(\n1\n = 1, \n2\n = 2, \nCMT\n = 3, \nVTS\n = 4, \nDDS\n = 5, \nB02512\n = 10, \nB02598\n = 11, \nB02617\n = 12, \nB02682\n = 13, \nB02764\n = 14), pickup_date Date, pickup_datetime DateTime, dropoff_date Date, dropoff_datetime DateTime, store_and_fwd_flag UInt8, rate_code_id UInt8, pickup_longitude Float64, pickup_latitude Float64, dropoff_longitude Float64, dropoff_latitude Float64, passenger_count UInt8, trip_distance Float64, fare_amount Float32, extra Float32, mta_tax Float32, tip_amount Float32, tolls_amount Float32, ehail_fee Float32, improvement_surcharge Float32, total_amount Float32, payment_type_ Enum8(\nUNK\n = 0, \nCSH\n = 1, \nCRE\n = 2, \nNOC\n = 3, \nDIS\n = 4), trip_type UInt8, pickup FixedString(25), dropoff FixedString(25), cab_type Enum8(\nyellow\n = 1, \ngreen\n = 2, \nuber\n = 3), pickup_nyct2010_gid UInt8, pickup_ctlabel Float32, pickup_borocode UInt8, pickup_boroname Enum8(\n = 0, \nManhattan\n = 1, \nBronx\n = 2, \nBrooklyn\n = 3, \nQueens\n = 4, \nStaten Island\n = 5), pickup_ct2010 FixedString(6), pickup_boroct2010 FixedString(7), pickup_cdeligibil Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2), pickup_ntacode FixedString(4), pickup_ntaname Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195), pickup_puma UInt16, dropoff_nyct2010_gid UInt8, dropoff_ctlabel Float32, dropoff_borocode UInt8, dropoff_boroname Enum8(\n = 0, \nManhattan\n = 1, \nBronx\n = 2, \nBrooklyn\n = 3, \nQueens\n = 4, \nStaten Island\n = 5), dropoff_ct2010 FixedString(6), dropoff_boroct2010 FixedString(7), dropoff_cdeligibil Enum8(\n \n = 0, \nE\n = 1, \nI\n = 2), dropoff_ntacode FixedString(4), dropoff_ntaname Enum16(\n = 0, \nAirport\n = 1, \nAllerton-Pelham Gardens\n = 2, \nAnnadale-Huguenot-Prince\\\ns Bay-Eltingville\n = 3, \nArden Heights\n = 4, \nAstoria\n = 5, \nAuburndale\n = 6, \nBaisley Park\n = 7, \nBath Beach\n = 8, \nBattery Park City-Lower Manhattan\n = 9, \nBay Ridge\n = 10, \nBayside-Bayside Hills\n = 11, \nBedford\n = 12, \nBedford Park-Fordham North\n = 13, \nBellerose\n = 14, \nBelmont\n = 15, \nBensonhurst East\n = 16, \nBensonhurst West\n = 17, \nBorough Park\n = 18, \nBreezy Point-Belle Harbor-Rockaway Park-Broad Channel\n = 19, \nBriarwood-Jamaica Hills\n = 20, \nBrighton Beach\n = 21, \nBronxdale\n = 22, \nBrooklyn Heights-Cobble Hill\n = 23, \nBrownsville\n = 24, \nBushwick North\n = 25, \nBushwick South\n = 26, \nCambria Heights\n = 27, \nCanarsie\n = 28, \nCarroll Gardens-Columbia Street-Red Hook\n = 29, \nCentral Harlem North-Polo Grounds\n = 30, \nCentral Harlem South\n = 31, \nCharleston-Richmond Valley-Tottenville\n = 32, \nChinatown\n = 33, \nClaremont-Bathgate\n = 34, \nClinton\n = 35, \nClinton Hill\n = 36, \nCo-op City\n = 37, \nCollege Point\n = 38, \nCorona\n = 39, \nCrotona Park East\n = 40, \nCrown Heights North\n = 41, \nCrown Heights South\n = 42, \nCypress Hills-City Line\n = 43, \nDUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill\n = 44, \nDouglas Manor-Douglaston-Little Neck\n = 45, \nDyker Heights\n = 46, \nEast Concourse-Concourse Village\n = 47, \nEast Elmhurst\n = 48, \nEast Flatbush-Farragut\n = 49, \nEast Flushing\n = 50, \nEast Harlem North\n = 51, \nEast Harlem South\n = 52, \nEast New York\n = 53, \nEast New York (Pennsylvania Ave)\n = 54, \nEast Tremont\n = 55, \nEast Village\n = 56, \nEast Williamsburg\n = 57, \nEastchester-Edenwald-Baychester\n = 58, \nElmhurst\n = 59, \nElmhurst-Maspeth\n = 60, \nErasmus\n = 61, \nFar Rockaway-Bayswater\n = 62, \nFlatbush\n = 63, \nFlatlands\n = 64, \nFlushing\n = 65, \nFordham South\n = 66, \nForest Hills\n = 67, \nFort Greene\n = 68, \nFresh Meadows-Utopia\n = 69, \nFt. Totten-Bay Terrace-Clearview\n = 70, \nGeorgetown-Marine Park-Bergen Beach-Mill Basin\n = 71, \nGlen Oaks-Floral Park-New Hyde Park\n = 72, \nGlendale\n = 73, \nGramercy\n = 74, \nGrasmere-Arrochar-Ft. Wadsworth\n = 75, \nGravesend\n = 76, \nGreat Kills\n = 77, \nGreenpoint\n = 78, \nGrymes Hill-Clifton-Fox Hills\n = 79, \nHamilton Heights\n = 80, \nHammels-Arverne-Edgemere\n = 81, \nHighbridge\n = 82, \nHollis\n = 83, \nHomecrest\n = 84, \nHudson Yards-Chelsea-Flatiron-Union Square\n = 85, \nHunters Point-Sunnyside-West Maspeth\n = 86, \nHunts Point\n = 87, \nJackson Heights\n = 88, \nJamaica\n = 89, \nJamaica Estates-Holliswood\n = 90, \nKensington-Ocean Parkway\n = 91, \nKew Gardens\n = 92, \nKew Gardens Hills\n = 93, \nKingsbridge Heights\n = 94, \nLaurelton\n = 95, \nLenox Hill-Roosevelt Island\n = 96, \nLincoln Square\n = 97, \nLindenwood-Howard Beach\n = 98, \nLongwood\n = 99, \nLower East Side\n = 100, \nMadison\n = 101, \nManhattanville\n = 102, \nMarble Hill-Inwood\n = 103, \nMariner\\\ns Harbor-Arlington-Port Ivory-Graniteville\n = 104, \nMaspeth\n = 105, \nMelrose South-Mott Haven North\n = 106, \nMiddle Village\n = 107, \nMidtown-Midtown South\n = 108, \nMidwood\n = 109, \nMorningside Heights\n = 110, \nMorrisania-Melrose\n = 111, \nMott Haven-Port Morris\n = 112, \nMount Hope\n = 113, \nMurray Hill\n = 114, \nMurray Hill-Kips Bay\n = 115, \nNew Brighton-Silver Lake\n = 116, \nNew Dorp-Midland Beach\n = 117, \nNew Springville-Bloomfield-Travis\n = 118, \nNorth Corona\n = 119, \nNorth Riverdale-Fieldston-Riverdale\n = 120, \nNorth Side-South Side\n = 121, \nNorwood\n = 122, \nOakland Gardens\n = 123, \nOakwood-Oakwood Beach\n = 124, \nOcean Hill\n = 125, \nOcean Parkway South\n = 126, \nOld Astoria\n = 127, \nOld Town-Dongan Hills-South Beach\n = 128, \nOzone Park\n = 129, \nPark Slope-Gowanus\n = 130, \nParkchester\n = 131, \nPelham Bay-Country Club-City Island\n = 132, \nPelham Parkway\n = 133, \nPomonok-Flushing Heights-Hillcrest\n = 134, \nPort Richmond\n = 135, \nProspect Heights\n = 136, \nProspect Lefferts Gardens-Wingate\n = 137, \nQueens Village\n = 138, \nQueensboro Hill\n = 139, \nQueensbridge-Ravenswood-Long Island City\n = 140, \nRego Park\n = 141, \nRichmond Hill\n = 142, \nRidgewood\n = 143, \nRikers Island\n = 144, \nRosedale\n = 145, \nRossville-Woodrow\n = 146, \nRugby-Remsen Village\n = 147, \nSchuylerville-Throgs Neck-Edgewater Park\n = 148, \nSeagate-Coney Island\n = 149, \nSheepshead Bay-Gerritsen Beach-Manhattan Beach\n = 150, \nSoHo-TriBeCa-Civic Center-Little Italy\n = 151, \nSoundview-Bruckner\n = 152, \nSoundview-Castle Hill-Clason Point-Harding Park\n = 153, \nSouth Jamaica\n = 154, \nSouth Ozone Park\n = 155, \nSpringfield Gardens North\n = 156, \nSpringfield Gardens South-Brookville\n = 157, \nSpuyten Duyvil-Kingsbridge\n = 158, \nSt. Albans\n = 159, \nStapleton-Rosebank\n = 160, \nStarrett City\n = 161, \nSteinway\n = 162, \nStuyvesant Heights\n = 163, \nStuyvesant Town-Cooper Village\n = 164, \nSunset Park East\n = 165, \nSunset Park West\n = 166, \nTodt Hill-Emerson Hill-Heartland Village-Lighthouse Hill\n = 167, \nTurtle Bay-East Midtown\n = 168, \nUniversity Heights-Morris Heights\n = 169, \nUpper East Side-Carnegie Hill\n = 170, \nUpper West Side\n = 171, \nVan Cortlandt Village\n = 172, \nVan Nest-Morris Park-Westchester Square\n = 173, \nWashington Heights North\n = 174, \nWashington Heights South\n = 175, \nWest Brighton\n = 176, \nWest Concourse\n = 177, \nWest Farms-Bronx River\n = 178, \nWest New Brighton-New Brighton-St. George\n = 179, \nWest Village\n = 180, \nWestchester-Unionport\n = 181, \nWesterleigh\n = 182, \nWhitestone\n = 183, \nWilliamsbridge-Olinville\n = 184, \nWilliamsburg\n = 185, \nWindsor Terrace\n = 186, \nWoodhaven\n = 187, \nWoodlawn-Wakefield\n = 188, \nWoodside\n = 189, \nYorkville\n = 190, \npark-cemetery-etc-Bronx\n = 191, \npark-cemetery-etc-Brooklyn\n = 192, \npark-cemetery-etc-Manhattan\n = 193, \npark-cemetery-etc-Queens\n = 194, \npark-cemetery-etc-Staten Island\n = 195), dropoff_puma UInt16) ENGINE = MergeTree(pickup_date, pickup_datetime, 8192)\n\n\n\n\n\nOn the source server:\n\n\nCREATE\n \nTABLE\n \ntrips_mergetree_x3\n \nAS\n \ntrips_mergetree_third\n \nENGINE\n \n=\n \nDistributed\n(\nperftest\n,\n \ndefault\n,\n \ntrips_mergetree_third\n,\n \nrand\n())\n\n\n\n\n\n\nThe following query redistributes data:\n\n\nINSERT\n \nINTO\n \ntrips_mergetree_x3\n \nSELECT\n \n*\n \nFROM\n \ntrips_mergetree\n\n\n\n\n\n\nThis takes 2454 seconds.\n\n\nOn three servers:\n\n\nQ1: 0.212 seconds.\nQ2: 0.438 seconds.\nQ3: 0.733 seconds.\nQ4: 1.241 seconds.\n\n\nNo surprises here, since the queries are scaled linearly.\n\n\nWe also have results from a cluster of 140 servers:\n\n\nQ1: 0.028 sec.\nQ2: 0.043 sec.\nQ3: 0.051 sec.\nQ4: 0.072 sec.\n\n\nIn this case, the query processing time is determined above all by network latency.\nWe ran queries using a client located in a Yandex datacenter in Finland on a cluster in Russia, which added about 20 ms of latency.\n\n\nSummary\n\n\nnodes Q1 Q2 Q3 Q4\n 1 0.490 1.224 2.104 3.593\n 3 0.212 0.438 0.733 1.241\n140 0.028 0.043 0.051 0.072\n\n\n\n\n\nAMPLab Big Data Benchmark\n\n\nSee \nhttps://amplab.cs.berkeley.edu/benchmark/\n\n\nSign up for a free account at \nhttps://aws.amazon.com\n. You will need a credit card, email and phone number.Get a new access key at \nhttps://console.aws.amazon.com/iam/home?nc2=h_m_sc#security_credential\n\n\nRun the following in the console:\n\n\nsudo apt-get install s3cmd\nmkdir tiny\n;\n \ncd\n tiny\n;\n\ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/tiny/ .\n\ncd\n ..\nmkdir 1node\n;\n \ncd\n 1node\n;\n\ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/1node/ .\n\ncd\n ..\nmkdir 5nodes\n;\n \ncd\n 5nodes\n;\n\ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/5nodes/ .\n\ncd\n ..\n\n\n\n\n\nRun the following ClickHouse queries:\n\n\nCREATE\n \nTABLE\n \nrankings_tiny\n\n\n(\n\n \npageURL\n \nString\n,\n\n \npageRank\n \nUInt32\n,\n\n \navgDuration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\nCREATE\n \nTABLE\n \nuservisits_tiny\n\n\n(\n\n \nsourceIP\n \nString\n,\n\n \ndestinationURL\n \nString\n,\n\n \nvisitDate\n \nDate\n,\n\n \nadRevenue\n \nFloat32\n,\n\n \nUserAgent\n \nString\n,\n\n \ncCode\n \nFixedString\n(\n3\n),\n\n \nlCode\n \nFixedString\n(\n6\n),\n\n \nsearchWord\n \nString\n,\n\n \nduration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nvisitDate\n,\n \nvisitDate\n,\n \n8192\n);\n\n\n\nCREATE\n \nTABLE\n \nrankings_1node\n\n\n(\n\n \npageURL\n \nString\n,\n\n \npageRank\n \nUInt32\n,\n\n \navgDuration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\nCREATE\n \nTABLE\n \nuservisits_1node\n\n\n(\n\n \nsourceIP\n \nString\n,\n\n \ndestinationURL\n \nString\n,\n\n \nvisitDate\n \nDate\n,\n\n \nadRevenue\n \nFloat32\n,\n\n \nUserAgent\n \nString\n,\n\n \ncCode\n \nFixedString\n(\n3\n),\n\n \nlCode\n \nFixedString\n(\n6\n),\n\n \nsearchWord\n \nString\n,\n\n \nduration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nvisitDate\n,\n \nvisitDate\n,\n \n8192\n);\n\n\n\nCREATE\n \nTABLE\n \nrankings_5nodes_on_single\n\n\n(\n\n \npageURL\n \nString\n,\n\n \npageRank\n \nUInt32\n,\n\n \navgDuration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\n\nCREATE\n \nTABLE\n \nuservisits_5nodes_on_single\n\n\n(\n\n \nsourceIP\n \nString\n,\n\n \ndestinationURL\n \nString\n,\n\n \nvisitDate\n \nDate\n,\n\n \nadRevenue\n \nFloat32\n,\n\n \nUserAgent\n \nString\n,\n\n \ncCode\n \nFixedString\n(\n3\n),\n\n \nlCode\n \nFixedString\n(\n6\n),\n\n \nsearchWord\n \nString\n,\n\n \nduration\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\nvisitDate\n,\n \nvisitDate\n,\n \n8192\n);\n\n\n\n\n\n\nGo back to the console:\n\n\nfor\n i in tiny/rankings/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO rankings_tiny FORMAT CSV\n;\n \ndone\n\n\nfor\n i in tiny/uservisits/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO uservisits_tiny FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 1node/rankings/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO rankings_1node FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 1node/uservisits/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO uservisits_1node FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 5nodes/rankings/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO rankings_5nodes_on_single FORMAT CSV\n;\n \ndone\n\n\nfor\n i in 5nodes/uservisits/*.deflate\n;\n \ndo\n \necho\n \n$i\n;\n zlib-flate -uncompress \n \n$i\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO uservisits_5nodes_on_single FORMAT CSV\n;\n \ndone\n\n\n\n\n\n\nQueries for obtaining data samples:\n\n\nSELECT\n \npageURL\n,\n \npageRank\n \nFROM\n \nrankings_1node\n \nWHERE\n \npageRank\n \n \n1000\n\n\n\nSELECT\n \nsubstring\n(\nsourceIP\n,\n \n1\n,\n \n8\n),\n \nsum\n(\nadRevenue\n)\n \nFROM\n \nuservisits_1node\n \nGROUP\n \nBY\n \nsubstring\n(\nsourceIP\n,\n \n1\n,\n \n8\n)\n\n\n\nSELECT\n\n \nsourceIP\n,\n\n \nsum\n(\nadRevenue\n)\n \nAS\n \ntotalRevenue\n,\n\n \navg\n(\npageRank\n)\n \nAS\n \npageRank\n\n\nFROM\n \nrankings_1node\n \nALL\n \nINNER\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nsourceIP\n,\n\n \ndestinationURL\n \nAS\n \npageURL\n,\n\n \nadRevenue\n\n \nFROM\n \nuservisits_1node\n\n \nWHERE\n \n(\nvisitDate\n \n \n1980-01-01\n)\n \nAND\n \n(\nvisitDate\n \n \n1980-04-01\n)\n\n\n)\n \nUSING\n \npageURL\n\n\nGROUP\n \nBY\n \nsourceIP\n\n\nORDER\n \nBY\n \ntotalRevenue\n \nDESC\n\n\nLIMIT\n \n1\n\n\n\n\n\n\nWikiStat\n\n\nSee: \nhttp://dumps.wikimedia.org/other/pagecounts-raw/\n\n\nCreating a table:\n\n\nCREATE\n \nTABLE\n \nwikistat\n\n\n(\n\n \ndate\n \nDate\n,\n\n \ntime\n \nDateTime\n,\n\n \nproject\n \nString\n,\n\n \nsubproject\n \nString\n,\n\n \npath\n \nString\n,\n\n \nhits\n \nUInt64\n,\n\n \nsize\n \nUInt64\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\ndate\n,\n \n(\npath\n,\n \ntime\n),\n \n8192\n);\n\n\n\n\n\n\nLoading data:\n\n\nfor\n i in \n{\n2007\n..2016\n}\n;\n \ndo\n \nfor\n j in \n{\n01\n..12\n}\n;\n \ndo\n \necho\n \n$i\n-\n$j\n \n2\n;\n curl -sSL \nhttp://dumps.wikimedia.org/other/pagecounts-raw/\n$i\n/\n$i\n-\n$j\n/\n \n|\n grep -oE \npagecounts-[0-9]+-[0-9]+\\.gz\n;\n \ndone\n;\n \ndone\n \n|\n sort \n|\n uniq \n|\n tee links.txt\ncat links.txt \n|\n \nwhile\n \nread\n link\n;\n \ndo\n wget http://dumps.wikimedia.org/other/pagecounts-raw/\n$(\necho\n \n$link\n \n|\n sed -r \ns/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1/\n)\n/\n$(\necho\n \n$link\n \n|\n sed -r \ns/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1-\\2/\n)\n/\n$link\n;\n \ndone\n\nls -1 /opt/wikistat/ \n|\n grep gz \n|\n \nwhile\n \nread\n i\n;\n \ndo\n \necho\n \n$i\n;\n gzip -cd /opt/wikistat/\n$i\n \n|\n ./wikistat-loader --time\n=\n$(\necho\n -n \n$i\n \n|\n sed -r \ns/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\\.gz/\\1-\\2-\\3 \\4-00-00/\n)\n \n|\n clickhouse-client --query\n=\nINSERT INTO wikistat FORMAT TabSeparated\n;\n \ndone\n\n\n\n\n\n\nTerabyte of click logs from Criteo\n\n\nDownload the data from \nhttp://labs.criteo.com/downloads/download-terabyte-click-logs/\n\n\nCreate a table to import the log to:\n\n\nCREATE\n \nTABLE\n \ncriteo_log\n \n(\ndate\n \nDate\n,\n \nclicked\n \nUInt8\n,\n \nint1\n \nInt32\n,\n \nint2\n \nInt32\n,\n \nint3\n \nInt32\n,\n \nint4\n \nInt32\n,\n \nint5\n \nInt32\n,\n \nint6\n \nInt32\n,\n \nint7\n \nInt32\n,\n \nint8\n \nInt32\n,\n \nint9\n \nInt32\n,\n \nint10\n \nInt32\n,\n \nint11\n \nInt32\n,\n \nint12\n \nInt32\n,\n \nint13\n \nInt32\n,\n \ncat1\n \nString\n,\n \ncat2\n \nString\n,\n \ncat3\n \nString\n,\n \ncat4\n \nString\n,\n \ncat5\n \nString\n,\n \ncat6\n \nString\n,\n \ncat7\n \nString\n,\n \ncat8\n \nString\n,\n \ncat9\n \nString\n,\n \ncat10\n \nString\n,\n \ncat11\n \nString\n,\n \ncat12\n \nString\n,\n \ncat13\n \nString\n,\n \ncat14\n \nString\n,\n \ncat15\n \nString\n,\n \ncat16\n \nString\n,\n \ncat17\n \nString\n,\n \ncat18\n \nString\n,\n \ncat19\n \nString\n,\n \ncat20\n \nString\n,\n \ncat21\n \nString\n,\n \ncat22\n \nString\n,\n \ncat23\n \nString\n,\n \ncat24\n \nString\n,\n \ncat25\n \nString\n,\n \ncat26\n \nString\n)\n \nENGINE\n \n=\n \nLog\n\n\n\n\n\n\nDownload the data:\n\n\nfor\n i in \n{\n00\n..23\n}\n;\n \ndo\n \necho\n \n$i\n;\n zcat datasets/criteo/day_\n${\ni\n#0\n}\n.gz \n|\n sed -r \ns/^/2000-01-\n${\ni\n/00/24\n}\n\\t/\n \n|\n clickhouse-client --host\n=\nexample-perftest01j --query\n=\nINSERT INTO criteo_log FORMAT TabSeparated\n;\n \ndone\n\n\n\n\n\n\nCreate a table for the converted data:\n\n\nCREATE\n \nTABLE\n \ncriteo\n\n\n(\n\n \ndate\n \nDate\n,\n\n \nclicked\n \nUInt8\n,\n\n \nint1\n \nInt32\n,\n\n \nint2\n \nInt32\n,\n\n \nint3\n \nInt32\n,\n\n \nint4\n \nInt32\n,\n\n \nint5\n \nInt32\n,\n\n \nint6\n \nInt32\n,\n\n \nint7\n \nInt32\n,\n\n \nint8\n \nInt32\n,\n\n \nint9\n \nInt32\n,\n\n \nint10\n \nInt32\n,\n\n \nint11\n \nInt32\n,\n\n \nint12\n \nInt32\n,\n\n \nint13\n \nInt32\n,\n\n \nicat1\n \nUInt32\n,\n\n \nicat2\n \nUInt32\n,\n\n \nicat3\n \nUInt32\n,\n\n \nicat4\n \nUInt32\n,\n\n \nicat5\n \nUInt32\n,\n\n \nicat6\n \nUInt32\n,\n\n \nicat7\n \nUInt32\n,\n\n \nicat8\n \nUInt32\n,\n\n \nicat9\n \nUInt32\n,\n\n \nicat10\n \nUInt32\n,\n\n \nicat11\n \nUInt32\n,\n\n \nicat12\n \nUInt32\n,\n\n \nicat13\n \nUInt32\n,\n\n \nicat14\n \nUInt32\n,\n\n \nicat15\n \nUInt32\n,\n\n \nicat16\n \nUInt32\n,\n\n \nicat17\n \nUInt32\n,\n\n \nicat18\n \nUInt32\n,\n\n \nicat19\n \nUInt32\n,\n\n \nicat20\n \nUInt32\n,\n\n \nicat21\n \nUInt32\n,\n\n \nicat22\n \nUInt32\n,\n\n \nicat23\n \nUInt32\n,\n\n \nicat24\n \nUInt32\n,\n\n \nicat25\n \nUInt32\n,\n\n \nicat26\n \nUInt32\n\n\n)\n \nENGINE\n \n=\n \nMergeTree\n(\ndate\n,\n \nintHash32\n(\nicat1\n),\n \n(\ndate\n,\n \nintHash32\n(\nicat1\n)),\n \n8192\n)\n\n\n\n\n\n\nTransform data from the raw log and put it in the second table:\n\n\nINSERT\n \nINTO\n \ncriteo\n \nSELECT\n \ndate\n,\n \nclicked\n,\n \nint1\n,\n \nint2\n,\n \nint3\n,\n \nint4\n,\n \nint5\n,\n \nint6\n,\n \nint7\n,\n \nint8\n,\n \nint9\n,\n \nint10\n,\n \nint11\n,\n \nint12\n,\n \nint13\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat1\n))\n \nAS\n \nicat1\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat2\n))\n \nAS\n \nicat2\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat3\n))\n \nAS\n \nicat3\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat4\n))\n \nAS\n \nicat4\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat5\n))\n \nAS\n \nicat5\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat6\n))\n \nAS\n \nicat6\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat7\n))\n \nAS\n \nicat7\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat8\n))\n \nAS\n \nicat8\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat9\n))\n \nAS\n \nicat9\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat10\n))\n \nAS\n \nicat10\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat11\n))\n \nAS\n \nicat11\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat12\n))\n \nAS\n \nicat12\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat13\n))\n \nAS\n \nicat13\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat14\n))\n \nAS\n \nicat14\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat15\n))\n \nAS\n \nicat15\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat16\n))\n \nAS\n \nicat16\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat17\n))\n \nAS\n \nicat17\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat18\n))\n \nAS\n \nicat18\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat19\n))\n \nAS\n \nicat19\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat20\n))\n \nAS\n \nicat20\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat21\n))\n \nAS\n \nicat21\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat22\n))\n \nAS\n \nicat22\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat23\n))\n \nAS\n \nicat23\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat24\n))\n \nAS\n \nicat24\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat25\n))\n \nAS\n \nicat25\n,\n \nreinterpretAsUInt32\n(\nunhex\n(\ncat26\n))\n \nAS\n \nicat26\n \nFROM\n \ncriteo_log\n;\n\n\n\nDROP\n \nTABLE\n \ncriteo_log\n;\n\n\n\n\n\n\nStar Schema Benchmark\n\n\nCompiling dbgen: \nhttps://github.com/vadimtk/ssb-dbgen\n\n\ngit clone git@github.com:vadimtk/ssb-dbgen.git\n\ncd\n ssb-dbgen\nmake\n\n\n\n\n\nThere will be some warnings during the process, but this is normal.\n\n\nPlace \ndbgen\n and \ndists.dss\n in any location with 800 GB of free disk space.\n\n\nGenerating data:\n\n\n./dbgen -s \n1000\n -T c\n./dbgen -s \n1000\n -T l\n\n\n\n\n\nCreating tables in ClickHouse:\n\n\nCREATE\n \nTABLE\n \nlineorder\n \n(\n\n \nLO_ORDERKEY\n \nUInt32\n,\n\n \nLO_LINENUMBER\n \nUInt8\n,\n\n \nLO_CUSTKEY\n \nUInt32\n,\n\n \nLO_PARTKEY\n \nUInt32\n,\n\n \nLO_SUPPKEY\n \nUInt32\n,\n\n \nLO_ORDERDATE\n \nDate\n,\n\n \nLO_ORDERPRIORITY\n \nString\n,\n\n \nLO_SHIPPRIORITY\n \nUInt8\n,\n\n \nLO_QUANTITY\n \nUInt8\n,\n\n \nLO_EXTENDEDPRICE\n \nUInt32\n,\n\n \nLO_ORDTOTALPRICE\n \nUInt32\n,\n\n \nLO_DISCOUNT\n \nUInt8\n,\n\n \nLO_REVENUE\n \nUInt32\n,\n\n \nLO_SUPPLYCOST\n \nUInt32\n,\n\n \nLO_TAX\n \nUInt8\n,\n\n \nLO_COMMITDATE\n \nDate\n,\n\n \nLO_SHIPMODE\n \nString\n\n\n)\nEngine\n=\nMergeTree\n(\nLO_ORDERDATE\n,(\nLO_ORDERKEY\n,\nLO_LINENUMBER\n,\nLO_ORDERDATE\n),\n8192\n);\n\n\n\nCREATE\n \nTABLE\n \ncustomer\n \n(\n\n \nC_CUSTKEY\n \nUInt32\n,\n\n \nC_NAME\n \nString\n,\n\n \nC_ADDRESS\n \nString\n,\n\n \nC_CITY\n \nString\n,\n\n \nC_NATION\n \nString\n,\n\n \nC_REGION\n \nString\n,\n\n \nC_PHONE\n \nString\n,\n\n \nC_MKTSEGMENT\n \nString\n,\n\n \nC_FAKEDATE\n \nDate\n\n\n)\nEngine\n=\nMergeTree\n(\nC_FAKEDATE\n,(\nC_CUSTKEY\n,\nC_FAKEDATE\n),\n8192\n);\n\n\n\nCREATE\n \nTABLE\n \npart\n \n(\n\n \nP_PARTKEY\n \nUInt32\n,\n\n \nP_NAME\n \nString\n,\n\n \nP_MFGR\n \nString\n,\n\n \nP_CATEGORY\n \nString\n,\n\n \nP_BRAND\n \nString\n,\n\n \nP_COLOR\n \nString\n,\n\n \nP_TYPE\n \nString\n,\n\n \nP_SIZE\n \nUInt8\n,\n\n \nP_CONTAINER\n \nString\n,\n\n \nP_FAKEDATE\n \nDate\n\n\n)\nEngine\n=\nMergeTree\n(\nP_FAKEDATE\n,(\nP_PARTKEY\n,\nP_FAKEDATE\n),\n8192\n);\n\n\n\nCREATE\n \nTABLE\n \nlineorderd\n \nAS\n \nlineorder\n \nENGINE\n \n=\n \nDistributed\n(\nperftest_3shards_1replicas\n,\n \ndefault\n,\n \nlineorder\n,\n \nrand\n());\n\n\nCREATE\n \nTABLE\n \ncustomerd\n \nAS\n \ncustomer\n \nENGINE\n \n=\n \nDistributed\n(\nperftest_3shards_1replicas\n,\n \ndefault\n,\n \ncustomer\n,\n \nrand\n());\n\n\nCREATE\n \nTABLE\n \npartd\n \nAS\n \npart\n \nENGINE\n \n=\n \nDistributed\n(\nperftest_3shards_1replicas\n,\n \ndefault\n,\n \npart\n,\n \nrand\n());\n\n\n\n\n\n\nFor testing on a single server, just use MergeTree tables.\nFor distributed testing, you need to configure the \nperftest_3shards_1replicas\n cluster in the config file.\nNext, create MergeTree tables on each server and a Distributed above them.\n\n\nDownloading data (change 'customer' to 'customerd' in the distributed version):\n\n\ncat customer.tbl \n|\n sed \ns/$/2000-01-01/\n \n|\n clickhouse-client --query \nINSERT INTO customer FORMAT CSV\n\ncat lineorder.tbl \n|\n clickhouse-client --query \nINSERT INTO lineorder FORMAT CSV\n\n\n\n\n\n\n\n\nInterfaces\n\n\nTo explore the system's capabilities, download data to tables, or make manual queries, use the clickhouse-client program.\n\n\nCommand-line client\n\n\nTo work from the command line, you can use \nclickhouse-client\n:\n\n\n$ clickhouse-client\nClickHouse client version \n0\n.0.26176.\nConnecting to localhost:9000.\nConnected to ClickHouse server version \n0\n.0.26176.\n\n:\n)\n\n\n\n\n\n\nThe client supports command-line options and configuration files. For more information, see \"\nConfiguring\n\".\n\n\nUsage\n\n\nThe client can be used in interactive and non-interactive (batch) mode.\nTo use batch mode, specify the 'query' parameter, or send data to 'stdin' (it verifies that 'stdin' is not a terminal), or both.\nSimilar to the HTTP interface, when using the 'query' parameter and sending data to 'stdin', the request is a concatenation of the 'query' parameter, a line feed, and the data in 'stdin'. This is convenient for large INSERT queries.\n\n\nExample of using the client to insert data:\n\n\necho\n -ne \n1, \nsome text\n, \n2016-08-14 00:00:00\n\\n2, \nsome more text\n, \n2016-08-14 00:00:01\n \n|\n clickhouse-client --database\n=\ntest\n --query\n=\nINSERT INTO test FORMAT CSV\n;\n\n\ncat \n_EOF | clickhouse-client --database=test --query=\nINSERT INTO test FORMAT CSV\n;\n\n\n3, \nsome text\n, \n2016-08-14 00:00:00\n\n\n4, \nsome more text\n, \n2016-08-14 00:00:01\n\n\n_EOF\n\n\ncat file.csv \n|\n clickhouse-client --database\n=\ntest\n --query\n=\nINSERT INTO test FORMAT CSV\n;\n\n\n\n\n\n\nIn batch mode, the default data format is TabSeparated. You can set the format in the FORMAT clause of the query.\n\n\nBy default, you can only process a single query in batch mode. To make multiple queries from a \"script,\" use the --multiquery parameter. This works for all queries except INSERT. Query results are output consecutively without additional separators.\nSimilarly, to process a large number of queries, you can run 'clickhouse-client' for each query. Note that it may take tens of milliseconds to launch the 'clickhouse-client' program.\n\n\nIn interactive mode, you get a command line where you can enter queries.\n\n\nIf 'multiline' is not specified (the default):To run the query, press Enter. The semicolon is not necessary at the end of the query. To enter a multiline query, enter a backslash \n\\\n before the line feed. After you press Enter, you will be asked to enter the next line of the query.\n\n\nIf multiline is specified:To run a query, end it with a semicolon and press Enter. If the semicolon was omitted at the end of the entered line, you will be asked to enter the next line of the query.\n\n\nOnly a single query is run, so everything after the semicolon is ignored.\n\n\nYou can specify \n\\G\n instead of or after the semicolon. This indicates Vertical format. In this format, each value is printed on a separate line, which is convenient for wide tables. This unusual feature was added for compatibility with the MySQL CLI.\n\n\nThe command line is based on 'readline' (and 'history' or 'libedit', or without a library, depending on the build). In other words, it uses the familiar keyboard shortcuts and keeps a history.\nThe history is written to \n~/.clickhouse-client-history\n.\n\n\nBy default, the format used is PrettyCompact. You can change the format in the FORMAT clause of the query, or by specifying \n\\G\n at the end of the query, using the \n--format\n or \n--vertical\n argument in the command line, or using the client configuration file.\n\n\nTo exit the client, press Ctrl+D (or Ctrl+C), or enter one of the following instead of a query:\"exit\", \"quit\", \"logout\", \"\u0443\u0447\u0448\u0435\", \"\u0439\u0433\u0448\u0435\", \"\u0434\u0449\u043f\u0449\u0433\u0435\", \"exit;\", \"quit;\", \"logout;\", \"\u0443\u0447\u0448\u0435\u0436\", \"\u0439\u0433\u0448\u0435\u0436\", \"\u0434\u0449\u043f\u0449\u0433\u0435\u0436\", \"q\", \"\u0439\", \"q\", \"Q\", \":q\", \"\u0439\", \"\u0419\", \"\u0416\u0439\"\n\n\nWhen processing a query, the client shows:\n\n\n\n\nProgress, which is updated no more than 10 times per second (by default). For quick queries, the progress might not have time to be displayed.\n\n\nThe formatted query after parsing, for debugging.\n\n\nThe result in the specified format.\n\n\nThe number of lines in the result, the time passed, and the average speed of query processing.\n\n\n\n\nYou can cancel a long query by pressing Ctrl+C. However, you will still need to wait a little for the server to abort the request. It is not possible to cancel a query at certain stages. If you don't wait and press Ctrl+C a second time, the client will exit.\n\n\nThe command-line client allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".\n\n\n\n\nConfiguring\n\n\nYou can pass parameters to \nclickhouse-client\n (all parameters have a default value) using:\n\n\n\n\nFrom the Command Line\n\n\n\n\nCommand-line options override the default values and settings in configuration files.\n\n\n\n\nConfiguration files.\n\n\n\n\nSettings in the configuration files override the default values.\n\n\nCommand line options\n\n\n\n\n--host, -h\n -\u2013 The server name, 'localhost' by default. You can use either the name or the IPv4 or IPv6 address.\n\n\n--port\n \u2013 The port to connect to. Default value: 9000. Note that the HTTP interface and the native interface use different ports.\n\n\n--user, -u\n \u2013 The username. Default value: default.\n\n\n--password\n \u2013 The password. Default value: empty string.\n\n\n--query, -q\n \u2013 The query to process when using non-interactive mode.\n\n\n--database, -d\n \u2013 Select the current default database. Default value: the current database from the server settings ('default' by default).\n\n\n--multiline, -m\n \u2013 If specified, allow multiline queries (do not send the query on Enter).\n\n\n--multiquery, -n\n \u2013 If specified, allow processing multiple queries separated by commas. Only works in non-interactive mode.\n\n\n--format, -f\n \u2013 Use the specified default format to output the result.\n\n\n--vertical, -E\n \u2013 If specified, use the Vertical format by default to output the result. This is the same as '--format=Vertical'. In this format, each value is printed on a separate line, which is helpful when displaying wide tables.\n\n\n--time, -t\n \u2013 If specified, print the query execution time to 'stderr' in non-interactive mode.\n\n\n--stacktrace\n \u2013 If specified, also print the stack trace if an exception occurs.\n\n\n-config-file\n \u2013 The name of the configuration file.\n\n\n\n\nConfiguration files\n\n\nclickhouse-client\n uses the first existing file of the following:\n\n\n\n\nDefined in the \n-config-file\n parameter.\n\n\n./clickhouse-client.xml\n\n\n\\~/.clickhouse-client/config.xml\n\n\n/etc/clickhouse-client/config.xml\n\n\n\n\nExample of a config file:\n\n\nconfig\n\n \nuser\nusername\n/user\n\n \npassword\npassword\n/password\n\n\n/config\n\n\n\n\n\n\nHTTP interface\n\n\nThe HTTP interface lets you use ClickHouse on any platform from any programming language. We use it for working from Java and Perl, as well as shell scripts. In other departments, the HTTP interface is used from Perl, Python, and Go. The HTTP interface is more limited than the native interface, but it has better compatibility.\n\n\nBy default, clickhouse-server listens for HTTP on port 8123 (this can be changed in the config).\nIf you make a GET / request without parameters, it returns the string \"Ok\" (with a line feed at the end). You can use this in health-check scripts.\n\n\n$ curl \nhttp://localhost:8123/\n\nOk.\n\n\n\n\n\nSend the request as a URL 'query' parameter, or as a POST. Or send the beginning of the query in the 'query' parameter, and the rest in the POST (we'll explain later why this is necessary). The size of the URL is limited to 16 KB, so keep this in mind when sending large queries.\n\n\nIf successful, you receive the 200 response code and the result in the response body.\nIf an error occurs, you receive the 500 response code and an error description text in the response body.\n\n\nWhen using the GET method, 'readonly' is set. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.\n\n\nExamples:\n\n\n$ curl \nhttp://localhost:8123/?query=SELECT%201\n\n\n1\n\n\n$ wget -O- -q \nhttp://localhost:8123/?query=SELECT 1\n\n\n1\n\n\n$ GET \nhttp://localhost:8123/?query=SELECT 1\n\n\n1\n\n\n$ \necho\n -ne \nGET /?query=SELECT%201 HTTP/1.0\\r\\n\\r\\n\n \n|\n nc localhost \n8123\n\nHTTP/1.0 \n200\n OK\nConnection: Close\nDate: Fri, \n16\n Nov \n2012\n \n19\n:21:50 GMT\n\n\n1\n\n\n\n\n\n\nAs you can see, curl is somewhat inconvenient in that spaces must be URL escaped.Although wget escapes everything itself, we don't recommend using it because it doesn't work well over HTTP 1.1 when using keep-alive and Transfer-Encoding: chunked.\n\n\n$ \necho\n \nSELECT 1\n \n|\n curl \nhttp://localhost:8123/\n --data-binary @-\n\n1\n\n\n$ \necho\n \nSELECT 1\n \n|\n curl \nhttp://localhost:8123/?query=\n --data-binary @-\n\n1\n\n\n$ \necho\n \n1\n \n|\n curl \nhttp://localhost:8123/?query=SELECT\n --data-binary @-\n\n1\n\n\n\n\n\n\nIf part of the query is sent in the parameter, and part in the POST, a line feed is inserted between these two data parts.\nExample (this won't work):\n\n\n$ \necho\n \nECT 1\n \n|\n curl \nhttp://localhost:8123/?query=SEL\n --data-binary @-\nCode: \n59\n, e.displayText\n()\n \n=\n DB::Exception: Syntax error: failed at position \n0\n: SEL\nECT \n1\n\n, expected One of: SHOW TABLES, SHOW DATABASES, SELECT, INSERT, CREATE, ATTACH, RENAME, DROP, DETACH, USE, SET, OPTIMIZE., e.what\n()\n \n=\n DB::Exception\n\n\n\n\n\nBy default, data is returned in TabSeparated format (for more information, see the \"Formats\" section).\nYou use the FORMAT clause of the query to request any other format.\n\n\n$ \necho\n \nSELECT 1 FORMAT Pretty\n \n|\n curl \nhttp://localhost:8123/?\n --data-binary @-\n\u250f\u2501\u2501\u2501\u2513\n\u2503 \n1\n \u2503\n\u2521\u2501\u2501\u2501\u2529\n\u2502 \n1\n \u2502\n\u2514\u2500\u2500\u2500\u2518\n\n\n\n\n\nThe POST method of transmitting data is necessary for INSERT queries. In this case, you can write the beginning of the query in the URL parameter, and use POST to pass the data to insert. The data to insert could be, for example, a tab-separated dump from MySQL. In this way, the INSERT query replaces LOAD DATA LOCAL INFILE from MySQL.\n\n\nExamples: Creating a table:\n\n\necho\n \nCREATE TABLE t (a UInt8) ENGINE = Memory\n \n|\n POST \nhttp://localhost:8123/\n\n\n\n\n\n\nUsing the familiar INSERT query for data insertion:\n\n\necho\n \nINSERT INTO t VALUES (1),(2),(3)\n \n|\n POST \nhttp://localhost:8123/\n\n\n\n\n\n\nData can be sent separately from the query:\n\n\necho\n \n(4),(5),(6)\n \n|\n POST \nhttp://localhost:8123/?query=INSERT INTO t VALUES\n\n\n\n\n\n\nYou can specify any data format. The 'Values' format is the same as what is used when writing INSERT INTO t VALUES:\n\n\necho\n \n(7),(8),(9)\n \n|\n POST \nhttp://localhost:8123/?query=INSERT INTO t FORMAT Values\n\n\n\n\n\n\nTo insert data from a tab-separated dump, specify the corresponding format:\n\n\necho\n -ne \n10\\n11\\n12\\n\n \n|\n POST \nhttp://localhost:8123/?query=INSERT INTO t FORMAT TabSeparated\n\n\n\n\n\n\nReading the table contents. Data is output in random order due to parallel query processing:\n\n\n$ GET \nhttp://localhost:8123/?query=SELECT a FROM t\n\n\n7\n\n\n8\n\n\n9\n\n\n10\n\n\n11\n\n\n12\n\n\n1\n\n\n2\n\n\n3\n\n\n4\n\n\n5\n\n\n6\n\n\n\n\n\n\nDeleting the table.\n\n\nPOST \nhttp://localhost:8123/?query=DROP TABLE t\n\n\n\n\n\n\nFor successful requests that don't return a data table, an empty response body is returned.\n\n\nYou can use the internal ClickHouse compression format when transmitting data. The compressed data has a non-standard format, and you will need to use the special clickhouse-compressor program to work with it (it is installed with the clickhouse-client package).\n\n\nIf you specified 'compress=1' in the URL, the server will compress the data it sends you.\nIf you specified 'decompress=1' in the URL, the server will decompress the same data that you pass in the POST method.\n\n\nIt is also possible to use the standard gzip-based HTTP compression. To send a POST request compressed using gzip, append the request header \nContent-Encoding: gzip\n.\nIn order for ClickHouse to compress the response using gzip, you must append \nAccept-Encoding: gzip\n to the request headers, and enable the ClickHouse setting \nenable_http_compression\n.\n\n\nYou can use this to reduce network traffic when transmitting a large amount of data, or for creating dumps that are immediately compressed.\n\n\nYou can use the 'database' URL parameter to specify the default database.\n\n\n$ \necho\n \nSELECT number FROM numbers LIMIT 10\n \n|\n curl \nhttp://localhost:8123/?database=system\n --data-binary @-\n\n0\n\n\n1\n\n\n2\n\n\n3\n\n\n4\n\n\n5\n\n\n6\n\n\n7\n\n\n8\n\n\n9\n\n\n\n\n\n\nBy default, the database that is registered in the server settings is used as the default database. By default, this is the database called 'default'. Alternatively, you can always specify the database using a dot before the table name.\n\n\nThe username and password can be indicated in one of two ways:\n\n\n\n\nUsing HTTP Basic Authentication. Example:\n\n\n\n\necho\n \nSELECT 1\n \n|\n curl \nhttp://user:password@localhost:8123/\n -d @-\n\n\n\n\n\n\n\nIn the 'user' and 'password' URL parameters. Example:\n\n\n\n\necho\n \nSELECT 1\n \n|\n curl \nhttp://localhost:8123/?user=user\npassword=password\n -d @-\n\n\n\n\n\nIf the user name is not indicated, the username 'default' is used. If the password is not indicated, an empty password is used.\nYou can also use the URL parameters to specify any settings for processing a single query, or entire profiles of settings. Example:\nhttp://localhost:8123/?profile=web\nmax_rows_to_read=1000000000\nquery=SELECT+1\n\n\nFor more information, see the section \"Settings\".\n\n\n$ \necho\n \nSELECT number FROM system.numbers LIMIT 10\n \n|\n curl \nhttp://localhost:8123/?\n --data-binary @-\n\n0\n\n\n1\n\n\n2\n\n\n3\n\n\n4\n\n\n5\n\n\n6\n\n\n7\n\n\n8\n\n\n9\n\n\n\n\n\n\nFor information about other parameters, see the section \"SET\".\n\n\nSimilarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to add the \nsession_id\n GET parameter to the request. You can use any string as the session ID. By default, the session is terminated after 60 seconds of inactivity. To change this timeout, modify the \ndefault_session_timeout\n setting in the server configuration, or add the \nsession_timeout\n GET parameter to the request. To check the session status, use the \nsession_check=1\n parameter. Only one query at a time can be executed within a single session.\n\n\nYou have the option to receive information about the progress of query execution in X-ClickHouse-Progress headers. To do this, enable the setting send_progress_in_http_headers.\n\n\nRunning requests don't stop automatically if the HTTP connection is lost. Parsing and data formatting are performed on the server side, and using the network might be ineffective.\nThe optional 'query_id' parameter can be passed as the query ID (any string). For more information, see the section \"Settings, replace_running_query\".\n\n\nThe optional 'quota_key' parameter can be passed as the quota key (any string). For more information, see the section \"Quotas\".\n\n\nThe HTTP interface allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".\n\n\nResponse buffering\n\n\nYou can enable response buffering on the server side. The \nbuffer_size\n and \nwait_end_of_query\n URL parameters are provided for this purpose.\n\n\nbuffer_size\n determines the number of bytes in the result to buffer in the server memory. If the result body is larger than this threshold, the buffer is written to the HTTP channel, and the remaining data is sent directly to the HTTP channel.\n\n\nTo ensure that the entire response is buffered, set \nwait_end_of_query=1\n. In this case, the data that is not stored in memory will be buffered in a temporary server file.\n\n\nExample:\n\n\ncurl -sS \nhttp://localhost:8123/?max_result_bytes=4000000\nbuffer_size=3000000\nwait_end_of_query=1\n -d \nSELECT toUInt8(number) FROM system.numbers LIMIT 9000000 FORMAT RowBinary\n\n\n\n\n\n\nUse buffering to avoid situations where a query processing error occurred after the response code and HTTP headers were sent to the client. In this situation, an error message is written at the end of the response body, and on the client side, the error can only be detected at the parsing stage.\n\n\nJDBC driver\n\n\nThere is an official JDBC driver for ClickHouse. See \nhere\n .\n\n\nNative interface (TCP)\n\n\nThe native interface is used in the \"clickhouse-client\" command-line client for interaction between servers with distributed query processing, and also in C++ programs. We will only cover the command-line client.\n\n\nLibraries from third-party developers\n\n\nThere are libraries for working with ClickHouse for:\n\n\n\n\nPython\n\n\ninfi.clickhouse_orm\n\n\nsqlalchemy-clickhouse\n\n\nclickhouse-driver\n\n\nclickhouse-client\n\n\n\n\n\n\nPHP\n\n\nclickhouse-php-client\n\n\nPhpClickHouseClient\n\n\nphpClickHouse\n\n\nclickhouse-client\n\n\n\n\n\n\nGo\n\n\nclickhouse\n\n\ngo-clickhouse\n\n\nmailrugo-clickhouse\n\n\ngolang-clickhouse\n\n\n\n\n\n\nNodeJs\n\n\nclickhouse (NodeJs)\n\n\nnode-clickhouse\n\n\n\n\n\n\nPerl\n\n\nperl-DBD-ClickHouse\n\n\nHTTP-ClickHouse\n\n\nAnyEvent-ClickHouse\n\n\n\n\n\n\nRuby\n\n\nclickhouse (Ruby)\n\n\n\n\n\n\nR\n\n\nclickhouse-r\n\n\nRClickhouse\n\n\n\n\n\n\n.NET\n\n\nClickHouse-Net\n\n\n\n\n\n\nC++\n\n\nclickhouse-cpp\n\n\n\n\n\n\nElixir\n\n\nclickhousex\n\n\nclickhouse_ecto\n\n\n\n\n\n\nJava\n\n\nclickhouse-client-java\n\n\n\n\n\n\n\n\nWe have not tested these libraries. They are listed in random order.\n\n\nVisual interfaces from third-party developers\n\n\nTabix\n\n\nWeb interface for ClickHouse in the \nTabix\n project.\n\n\nFeatures:\n\n\n\n\nWorks with ClickHouse directly from the browser, without the need to install additional software.\n\n\nQuery editor with syntax highlighting.\n\n\nAuto-completion of commands.\n\n\nTools for graphical analysis of query execution.\n\n\nColor scheme options.\n\n\n\n\nTabix documentation\n.\n\n\nHouseOps\n\n\nHouseOps\n is a unique Desktop ClickHouse Ops UI / IDE for OSX, Linux and Windows.\n\n\nFeatures:\n\n\n\n\nQuery builder;\n\n\nDatabase manangement (soon);\n\n\nUsers manangement (soon);\n\n\nReal-Time Data Analytics (soon);\n\n\nCluster/Infra monitoring (soon);\n\n\nCluster manangement (soon);\n\n\nKafka and Replicated tables monitoring (soon);\n\n\nAnd a lot of others features (soon) for you take a beautiful implementation of ClickHouse.\n\n\n\n\nQuery language\n\n\nQueries\n\n\nCREATE DATABASE\n\n\nCreating db_name databases\n\n\nCREATE\n \nDATABASE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \ndb_name\n\n\n\n\n\n\nA database\n is just a directory for tables.\nIf \nIF NOT EXISTS\n is included, the query won't return an error if the database already exists.\n\n\n\n\nCREATE TABLE\n\n\nThe \nCREATE TABLE\n query can have several forms.\n\n\nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n(\n\n \nname1\n \n[\ntype1\n]\n \n[\nDEFAULT\n|\nMATERIALIZED\n|\nALIAS\n \nexpr1\n],\n\n \nname2\n \n[\ntype2\n]\n \n[\nDEFAULT\n|\nMATERIALIZED\n|\nALIAS\n \nexpr2\n],\n\n \n...\n\n\n)\n \nENGINE\n \n=\n \nengine\n\n\n\n\n\n\nCreates a table named 'name' in the 'db' database or the current database if 'db' is not set, with the structure specified in brackets and the 'engine' engine.\nThe structure of the table is a list of column descriptions. If indexes are supported by the engine, they are indicated as parameters for the table engine.\n\n\nA column description is \nname type\n in the simplest case. Example: \nRegionID UInt32\n.\nExpressions can also be defined for default values (see below).\n\n\nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \nAS\n \n[\ndb2\n.]\nname2\n \n[\nENGINE\n \n=\n \nengine\n]\n\n\n\n\n\n\nCreates a table with the same structure as another table. You can specify a different engine for the table. If the engine is not specified, the same engine will be used as for the \ndb2.name2\n table.\n\n\nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \nENGINE\n \n=\n \nengine\n \nAS\n \nSELECT\n \n...\n\n\n\n\n\n\nCreates a table with a structure like the result of the \nSELECT\n query, with the 'engine' engine, and fills it with data from SELECT.\n\n\nIn all cases, if \nIF NOT EXISTS\n is specified, the query won't return an error if the table already exists. In this case, the query won't do anything.\n\n\nDefault values\n\n\nThe column description can specify an expression for a default value, in one of the following ways:\nDEFAULT expr\n, \nMATERIALIZED expr\n, \nALIAS expr\n.\nExample: \nURLDomain String DEFAULT domain(URL)\n.\n\n\nIf an expression for the default value is not defined, the default values will be set to zeros for numbers, empty strings for strings, empty arrays for arrays, and \n0000-00-00\n for dates or \n0000-00-00 00:00:00\n for dates with time. NULLs are not supported.\n\n\nIf the default expression is defined, the column type is optional. If there isn't an explicitly defined type, the default expression type is used. Example: \nEventDate DEFAULT toDate(EventTime)\n \u2013 the 'Date' type will be used for the 'EventDate' column.\n\n\nIf the data type and default expression are defined explicitly, this expression will be cast to the specified type using type casting functions. Example: \nHits UInt32 DEFAULT 0\n means the same thing as \nHits UInt32 DEFAULT toUInt32(0)\n.\n\n\nDefault expressions may be defined as an arbitrary expression from table constants and columns. When creating and changing the table structure, it checks that expressions don't contain loops. For INSERT, it checks that expressions are resolvable \u2013 that all columns they can be calculated from have been passed.\n\n\nDEFAULT expr\n\n\nNormal default value. If the INSERT query doesn't specify the corresponding column, it will be filled in by computing the corresponding expression.\n\n\nMATERIALIZED expr\n\n\nMaterialized expression. Such a column can't be specified for INSERT, because it is always calculated.\nFor an INSERT without a list of columns, these columns are not considered.\nIn addition, this column is not substituted when using an asterisk in a SELECT query. This is to preserve the invariant that the dump obtained using \nSELECT *\n can be inserted back into the table using INSERT without specifying the list of columns.\n\n\nALIAS expr\n\n\nSynonym. Such a column isn't stored in the table at all.\nIts values can't be inserted in a table, and it is not substituted when using an asterisk in a SELECT query.\nIt can be used in SELECTs if the alias is expanded during query parsing.\n\n\nWhen using the ALTER query to add new columns, old data for these columns is not written. Instead, when reading old data that does not have values for the new columns, expressions are computed on the fly by default. However, if running the expressions requires different columns that are not indicated in the query, these columns will additionally be read, but only for the blocks of data that need it.\n\n\nIf you add a new column to a table but later change its default expression, the values used for old data will change (for data where values were not stored on the disk). Note that when running background merges, data for columns that are missing in one of the merging parts is written to the merged part.\n\n\nIt is not possible to set default values for elements in nested data structures.\n\n\nTemporary tables\n\n\nIn all cases, if \nTEMPORARY\n is specified, a temporary table will be created. Temporary tables have the following characteristics:\n\n\n\n\nTemporary tables disappear when the session ends, including if the connection is lost.\n\n\nA temporary table is created with the Memory engine. The other table engines are not supported.\n\n\nThe DB can't be specified for a temporary table. It is created outside of databases.\n\n\nIf a temporary table has the same name as another one and a query specifies the table name without specifying the DB, the temporary table will be used.\n\n\nFor distributed query processing, temporary tables used in a query are passed to remote servers.\n\n\n\n\nIn most cases, temporary tables are not created manually, but when using external data for a query, or for distributed \n(GLOBAL) IN\n. For more information, see the appropriate sections\n\n\nDistributed DDL queries (ON CLUSTER clause)\n\n\nThe \nCREATE\n, \nDROP\n, \nALTER\n, and \nRENAME\n queries support distributed execution on a cluster.\nFor example, the following query creates the \nall_hits\n \nDistributed\n table on each host in \ncluster\n:\n\n\nCREATE\n \nTABLE\n \nIF\n \nNOT\n \nEXISTS\n \nall_hits\n \nON\n \nCLUSTER\n \ncluster\n \n(\np\n \nDate\n,\n \ni\n \nInt32\n)\n \nENGINE\n \n=\n \nDistributed\n(\ncluster\n,\n \ndefault\n,\n \nhits\n)\n\n\n\n\n\n\nIn order to run these queries correctly, each host must have the same cluster definition (to simplify syncing configs, you can use substitutions from ZooKeeper). They must also connect to the ZooKeeper servers.\nThe local version of the query will eventually be implemented on each host in the cluster, even if some hosts are currently not available. The order for executing queries within a single host is guaranteed.\n\nALTER\n queries are not yet supported for replicated tables.\n\n\nCREATE VIEW\n\n\nCREATE\n \n[\nMATERIALIZED\n]\n \nVIEW\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \n[\nTO\n[\ndb\n.]\nname\n]\n \n[\nENGINE\n \n=\n \nengine\n]\n \n[\nPOPULATE\n]\n \nAS\n \nSELECT\n \n...\n\n\n\n\n\n\nCreates a view. There are two types of views: normal and MATERIALIZED.\n\n\nWhen creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data.\n\n\nA materialized view works as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.\n\n\nNormal views don't store any data, but just perform a read from another table. In other words, a normal view is nothing more than a saved query. When reading from a view, this saved query is used as a subquery in the FROM clause.\n\n\nAs an example, assume you've created a view:\n\n\nCREATE\n \nVIEW\n \nview\n \nAS\n \nSELECT\n \n...\n\n\n\n\n\n\nand written a query:\n\n\nSELECT\n \na\n,\n \nb\n,\n \nc\n \nFROM\n \nview\n\n\n\n\n\n\nThis query is fully equivalent to using the subquery:\n\n\nSELECT\n \na\n,\n \nb\n,\n \nc\n \nFROM\n \n(\nSELECT\n \n...)\n\n\n\n\n\n\nMaterialized views store data transformed by the corresponding SELECT query.\n\n\nWhen creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data.\n\n\nA materialized view is arranged as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.\n\n\nIf you specify POPULATE, the existing table data is inserted in the view when creating it, as if making a \nCREATE TABLE ... AS SELECT ...\n . Otherwise, the query contains only the data inserted in the table after creating the view. We don't recommend using POPULATE, since data inserted in the table during the view creation will not be inserted in it.\n\n\nA \nSELECT\n query can contain \nDISTINCT\n, \nGROUP BY\n, \nORDER BY\n, \nLIMIT\n... Note that the corresponding conversions are performed independently on each block of inserted data. For example, if \nGROUP BY\n is set, data is aggregated during insertion, but only within a single packet of inserted data. The data won't be further aggregated. The exception is when using an ENGINE that independently performs data aggregation, such as \nSummingMergeTree\n.\n\n\nThe execution of \nALTER\n queries on materialized views has not been fully developed, so they might be inconvenient. If the materialized view uses the construction \nTO [db.]name\n, you can \nDETACH\n the view, run \nALTER\n for the target table, and then \nATTACH\n the previously detached (\nDETACH\n) view.\n\n\nViews look the same as normal tables. For example, they are listed in the result of the \nSHOW TABLES\n query.\n\n\nThere isn't a separate query for deleting views. To delete a view, use \nDROP TABLE\n.\n\n\nATTACH\n\n\nThis query is exactly the same as \nCREATE\n, but\n\n\n\n\ninstead of the word \nCREATE\n it uses the word \nATTACH\n.\n\n\nThe query doesn't create data on the disk, but assumes that data is already in the appropriate places, and just adds information about the table to the server.\nAfter executing an ATTACH query, the server will know about the existence of the table.\n\n\n\n\nIf the table was previously detached (\nDETACH\n), meaning that its structure is known, you can use shorthand without defining the structure.\n\n\nATTACH\n \nTABLE\n \n[\nIF\n \nNOT\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n\n\n\n\n\n\nThis query is used when starting the server. The server stores table metadata as files with \nATTACH\n queries, which it simply runs at launch (with the exception of system tables, which are explicitly created on the server).\n\n\nDROP\n\n\nThis query has two types: \nDROP DATABASE\n and \nDROP TABLE\n.\n\n\nDROP\n \nDATABASE\n \n[\nIF\n \nEXISTS\n]\n \ndb\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n\n\n\n\nDeletes all tables inside the 'db' database, then deletes the 'db' database itself.\nIf \nIF EXISTS\n is specified, it doesn't return an error if the database doesn't exist.\n\n\nDROP\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\nIF\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n\n\n\n\nDeletes the table.\nIf \nIF EXISTS\n is specified, it doesn't return an error if the table doesn't exist or the database doesn't exist.\n\n\nDETACH\n\n\nDeletes information about the 'name' table from the server. The server stops knowing about the table's existence.\n\n\nDETACH\n \nTABLE\n \n[\nIF\n \nEXISTS\n]\n \n[\ndb\n.]\nname\n\n\n\n\n\n\nThis does not delete the table's data or metadata. On the next server launch, the server will read the metadata and find out about the table again.\nSimilarly, a \"detached\" table can be re-attached using the \nATTACH\n query (with the exception of system tables, which do not have metadata stored for them).\n\n\nThere is no \nDETACH DATABASE\n query.\n\n\nRENAME\n\n\nRenames one or more tables.\n\n\nRENAME\n \nTABLE\n \n[\ndb11\n.]\nname11\n \nTO\n \n[\ndb12\n.]\nname12\n,\n \n[\ndb21\n.]\nname21\n \nTO\n \n[\ndb22\n.]\nname22\n,\n \n...\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n\n\n\n\n\n\nAll tables are renamed under global locking. Renaming tables is a light operation. If you indicated another database after TO, the table will be moved to this database. However, the directories with databases must reside in the same file system (otherwise, an error is returned).\n\n\n\n\nALTER\n\n\nThe \nALTER\n query is only supported for \n*MergeTree\n tables, as well as \nMerge\nand\nDistributed\n. The query has several variations.\n\n\nColumn manipulations\n\n\nChanging the table structure.\n\n\nALTER\n \nTABLE\n \n[\ndb\n].\nname\n \n[\nON\n \nCLUSTER\n \ncluster\n]\n \nADD\n|\nDROP\n|\nMODIFY\n \nCOLUMN\n \n...\n\n\n\n\n\n\nIn the query, specify a list of one or more comma-separated actions.\nEach action is an operation on a column.\n\n\nThe following actions are supported:\n\n\nADD\n \nCOLUMN\n \nname\n \n[\ntype\n]\n \n[\ndefault_expr\n]\n \n[\nAFTER\n \nname_after\n]\n\n\n\n\n\n\nAdds a new column to the table with the specified name, type, and \ndefault_expr\n (see the section \"Default expressions\"). If you specify \nAFTER name_after\n (the name of another column), the column is added after the specified one in the list of table columns. Otherwise, the column is added to the end of the table. Note that there is no way to add a column to the beginning of a table. For a chain of actions, 'name_after' can be the name of a column that is added in one of the previous actions.\n\n\nAdding a column just changes the table structure, without performing any actions with data. The data doesn't appear on the disk after ALTER. If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). The column appears on the disk after merging data parts (see MergeTree).\n\n\nThis approach allows us to complete the ALTER query instantly, without increasing the volume of old data.\n\n\nDROP\n \nCOLUMN\n \nname\n\n\n\n\n\n\nDeletes the column with the name 'name'.\nDeletes data from the file system. Since this deletes entire files, the query is completed almost instantly.\n\n\nMODIFY\n \nCOLUMN\n \nname\n \n[\ntype\n]\n \n[\ndefault_expr\n]\n\n\n\n\n\n\nChanges the 'name' column's type to 'type' and/or the default expression to 'default_expr'. When changing the type, values are converted as if the 'toType' function were applied to them.\n\n\nIf only the default expression is changed, the query doesn't do anything complex, and is completed almost instantly.\n\n\nChanging the column type is the only complex action \u2013 it changes the contents of files with data. For large tables, this may take a long time.\n\n\nThere are several processing stages:\n\n\n\n\nPreparing temporary (new) files with modified data.\n\n\nRenaming old files.\n\n\nRenaming the temporary (new) files to the old names.\n\n\nDeleting the old files.\n\n\n\n\nOnly the first stage takes time. If there is a failure at this stage, the data is not changed.\nIf there is a failure during one of the successive stages, data can be restored manually. The exception is if the old files were deleted from the file system but the data for the new files did not get written to the disk and was lost.\n\n\nThere is no support for changing the column type in arrays and nested data structures.\n\n\nThe \nALTER\n query lets you create and delete separate elements (columns) in nested data structures, but not whole nested data structures. To add a nested data structure, you can add columns with a name like \nname.nested_name\n and the type \nArray(T)\n. A nested data structure is equivalent to multiple array columns with a name that has the same prefix before the dot.\n\n\nThere is no support for deleting columns in the primary key or the sampling key (columns that are in the \nENGINE\n expression). Changing the type for columns that are included in the primary key is only possible if this change does not cause the data to be modified (for example, it is allowed to add values to an Enum or change a type with \nDateTime\n to \nUInt32\n).\n\n\nIf the \nALTER\n query is not sufficient for making the table changes you need, you can create a new table, copy the data to it using the \nINSERT SELECT\n query, then switch the tables using the \nRENAME\n query and delete the old table.\n\n\nThe \nALTER\n query blocks all reads and writes for the table. In other words, if a long \nSELECT\n is running at the time of the \nALTER\n query, the \nALTER\n query will wait for it to complete. At the same time, all new queries to the same table will wait while this \nALTER\n is running.\n\n\nFor tables that don't store data themselves (such as \nMerge\n and \nDistributed\n), \nALTER\n just changes the table structure, and does not change the structure of subordinate tables. For example, when running ALTER for a \nDistributed\n table, you will also need to run \nALTER\n for the tables on all remote servers.\n\n\nThe \nALTER\n query for changing columns is replicated. The instructions are saved in ZooKeeper, then each replica applies them. All \nALTER\n queries are run in the same order. The query waits for the appropriate actions to be completed on the other replicas. However, a query to change columns in a replicated table can be interrupted, and all actions will be performed asynchronously.\n\n\nManipulations with partitions and parts\n\n\nIt only works for tables in the \nMergeTree\n family. The following operations are available:\n\n\n\n\nDETACH PARTITION\n \u2013 Move a partition to the 'detached' directory and forget it.\n\n\nDROP PARTITION\n \u2013 Delete a partition.\n\n\nATTACH PART|PARTITION\n \u2013 Add a new part or partition from the \ndetached\n directory to the table.\n\n\nFREEZE PARTITION\n \u2013 Create a backup of a partition.\n\n\nFETCH PARTITION\n \u2013 Download a partition from another server.\n\n\n\n\nEach type of query is covered separately below.\n\n\nA partition in a table is data for a single calendar month. This is determined by the values of the date key specified in the table engine parameters. Each month's data is stored separately in order to simplify manipulations with this data.\n\n\nA \"part\" in the table is part of the data from a single partition, sorted by the primary key.\n\n\nYou can use the \nsystem.parts\n table to view the set of table parts and partitions:\n\n\nSELECT\n \n*\n \nFROM\n \nsystem\n.\nparts\n \nWHERE\n \nactive\n\n\n\n\n\n\nactive\n \u2013 Only count active parts. Inactive parts are, for example, source parts remaining after merging to a larger part \u2013 these parts are deleted approximately 10 minutes after merging.\n\n\nAnother way to view a set of parts and partitions is to go into the directory with table data.\nData directory: \n/var/lib/clickhouse/data/database/table/\n,where \n/var/lib/clickhouse/\n is the path to the ClickHouse data, 'database' is the database name, and 'table' is the table name. Example:\n\n\n$ ls -l /var/lib/clickhouse/data/test/visits/\ntotal \n48\n\ndrwxrwxrwx \n2\n clickhouse clickhouse \n20480\n May \n5\n \n02\n:58 20140317_20140323_2_2_0\ndrwxrwxrwx \n2\n clickhouse clickhouse \n20480\n May \n5\n \n02\n:58 20140317_20140323_4_4_0\ndrwxrwxrwx \n2\n clickhouse clickhouse \n4096\n May \n5\n \n02\n:55 detached\n-rw-rw-rw- \n1\n clickhouse clickhouse \n2\n May \n5\n \n02\n:58 increment.txt\n\n\n\n\n\nHere, \n20140317_20140323_2_2_0\n and \n20140317_20140323_4_4_0\n are the directories of data parts.\n\n\nLet's break down the name of the first part: \n20140317_20140323_2_2_0\n.\n\n\n\n\n20140317\n is the minimum date of the data in the chunk.\n\n\n20140323\n is the maximum date of the data in the chunk.\n\n\n2\n is the minimum number of the data block.\n\n\n2\n is the maximum number of the data block.\n\n\n0\n is the chunk level (the depth of the merge tree it is formed from).\n\n\n\n\nEach piece relates to a single partition and contains data for just one month.\n\n201403\n is the name of the partition. A partition is a set of parts for a single month.\n\n\nOn an operating server, you can't manually change the set of parts or their data on the file system, since the server won't know about it.\nFor non-replicated tables, you can do this when the server is stopped, but we don't recommended it.\nFor replicated tables, the set of parts can't be changed in any case.\n\n\nThe \ndetached\n directory contains parts that are not used by the server - detached from the table using the \nALTER ... DETACH\n query. Parts that are damaged are also moved to this directory, instead of deleting them. You can add, delete, or modify the data in the 'detached' directory at any time \u2013 the server won't know about this until you make the \nALTER TABLE ... ATTACH\n query.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nDETACH\n \nPARTITION\n \nname\n\n\n\n\n\n\nMove all data for partitions named 'name' to the 'detached' directory and forget about them.\nThe partition name is specified in YYYYMM format. It can be indicated in single quotes or without them.\n\n\nAfter the query is executed, you can do whatever you want with the data in the 'detached' directory \u2014 delete it from the file system, or just leave it.\n\n\nThe query is replicated \u2013 data will be moved to the 'detached' directory and forgotten on all replicas. The query can only be sent to a leader replica. To find out if a replica is a leader, perform SELECT to the 'system.replicas' system table. Alternatively, it is easier to make a query on all replicas, and all except one will throw an exception.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nDROP\n \nPARTITION\n \nname\n\n\n\n\n\n\nThe same as the \nDETACH\n operation. Deletes data from the table. Data parts will be tagged as inactive and will be completely deleted in approximately 10 minutes. The query is replicated \u2013 data will be deleted on all replicas.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nATTACH\n \nPARTITION\n|\nPART\n \nname\n\n\n\n\n\n\nAdds data to the table from the 'detached' directory.\n\n\nIt is possible to add data for an entire partition or a separate part. For a part, specify the full name of the part in single quotes.\n\n\nThe query is replicated. Each replica checks whether there is data in the 'detached' directory. If there is data, it checks the integrity, verifies that it matches the data on the server that initiated the query, and then adds it if everything is correct. If not, it downloads data from the query requestor replica, or from another replica where the data has already been added.\n\n\nSo you can put data in the 'detached' directory on one replica, and use the ALTER ... ATTACH query to add it to the table on all replicas.\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nFREEZE\n \nPARTITION\n \nname\n\n\n\n\n\n\nCreates a local backup of one or multiple partitions. The name can be the full name of the partition (for example, 201403), or its prefix (for example, 2014): then the backup will be created for all the corresponding partitions.\n\n\nThe query does the following: for a data snapshot at the time of execution, it creates hardlinks to table data in the directory \n/var/lib/clickhouse/shadow/N/...\n\n\n/var/lib/clickhouse/\n is the working ClickHouse directory from the config.\n\nN\n is the incremental number of the backup.\n\n\nThe same structure of directories is created inside the backup as inside \n/var/lib/clickhouse/\n.\nIt also performs 'chmod' for all files, forbidding writes to them.\n\n\nThe backup is created almost instantly (but first it waits for current queries to the corresponding table to finish running). At first, the backup doesn't take any space on the disk. As the system works, the backup can take disk space, as data is modified. If the backup is made for old enough data, it won't take space on the disk.\n\n\nAfter creating the backup, data from \n/var/lib/clickhouse/shadow/\n can be copied to the remote server and then deleted on the local server.\nThe entire backup process is performed without stopping the server.\n\n\nThe \nALTER ... FREEZE PARTITION\n query is not replicated. A local backup is only created on the local server.\n\n\nAs an alternative, you can manually copy data from the \n/var/lib/clickhouse/data/database/table\n directory.\nBut if you do this while the server is running, race conditions are possible when copying directories with files being added or changed, and the backup may be inconsistent. You can do this if the server isn't running \u2013 then the resulting data will be the same as after the \nALTER TABLE t FREEZE PARTITION\n query.\n\n\nALTER TABLE ... FREEZE PARTITION\n only copies data, not table metadata. To make a backup of table metadata, copy the file \n/var/lib/clickhouse/metadata/database/table.sql\n\n\nTo restore from a backup:\n\n\n\n\n\n\nUse the CREATE query to create the table if it doesn't exist. The query can be taken from an .sql file (replace \nATTACH\n in it with \nCREATE\n).\n\n\nCopy the data from the data/database/table/ directory inside the backup to the \n/var/lib/clickhouse/data/database/table/detached/ directory.\n\n\nRun \nALTER TABLE ... ATTACH PARTITION YYYYMM\n queries, where \nYYYYMM\n is the month, for every month.\n\n\n\n\n\n\nIn this way, data from the backup will be added to the table.\nRestoring from a backup doesn't require stopping the server.\n\n\nBackups and replication\n\n\nReplication provides protection from device failures. If all data disappeared on one of your replicas, follow the instructions in the \"Restoration after failure\" section to restore it.\n\n\nFor protection from device failures, you must use replication. For more information about replication, see the section \"Data replication\".\n\n\nBackups protect against human error (accidentally deleting data, deleting the wrong data or in the wrong cluster, or corrupting data).\nFor high-volume databases, it can be difficult to copy backups to remote servers. In such cases, to protect from human error, you can keep a backup on the same server (it will reside in \n/var/lib/clickhouse/shadow/\n).\n\n\nALTER\n \nTABLE\n \n[\ndb\n.]\ntable\n \nFETCH\n \nPARTITION\n \nname\n \nFROM\n \npath-in-zookeeper\n\n\n\n\n\n\nThis query only works for replicatable tables.\n\n\nIt downloads the specified partition from the shard that has its \nZooKeeper path\n specified in the \nFROM\n clause, then puts it in the \ndetached\n directory for the specified table.\n\n\nAlthough the query is called \nALTER TABLE\n, it does not change the table structure, and does not immediately change the data available in the table.\n\n\nData is placed in the \ndetached\n directory. You can use the \nALTER TABLE ... ATTACH\n query to attach the data.\n\n\nThe \nFROM\n clause specifies the path in \nZooKeeper\n. For example, \n/clickhouse/tables/01-01/visits\n.\nBefore downloading, the system checks that the partition exists and the table structure matches. The most appropriate replica is selected automatically from the healthy replicas.\n\n\nThe \nALTER ... FETCH PARTITION\n query is not replicated. The partition will be downloaded to the 'detached' directory only on the local server. Note that if after this you use the \nALTER TABLE ... ATTACH\n query to add data to the table, the data will be added on all replicas (on one of the replicas it will be added from the 'detached' directory, and on the rest it will be loaded from neighboring replicas).\n\n\nSynchronicity of ALTER queries\n\n\nFor non-replicatable tables, all \nALTER\n queries are performed synchronously. For replicatable tables, the query just adds instructions for the appropriate actions to \nZooKeeper\n, and the actions themselves are performed as soon as possible. However, the query can wait for these actions to be completed on all the replicas.\n\n\nFor \nALTER ... ATTACH|DETACH|DROP\n queries, you can use the \nreplication_alter_partitions_sync\n setting to set up waiting.\nPossible values: \n0\n \u2013 do not wait; \n1\n \u2013 only wait for own execution (default); \n2\n \u2013 wait for all.\n\n\n\n\nSHOW DATABASES\n\n\nSHOW\n \nDATABASES\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nPrints a list of all databases.\nThis query is identical to \nSELECT name FROM system.databases [INTO OUTFILE filename] [FORMAT format]\n.\n\n\nSee also the section \"Formats\".\n\n\nSHOW TABLES\n\n\nSHOW\n \n[\nTEMPORARY\n]\n \nTABLES\n \n[\nFROM\n \ndb\n]\n \n[\nLIKE\n \npattern\n]\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nDisplays a list of tables\n\n\n\n\ntables from the current database, or from the 'db' database if \"FROM db\" is specified.\n\n\nall tables, or tables whose name matches the pattern, if \"LIKE 'pattern'\" is specified.\n\n\n\n\nThis query is identical to: \nSELECT name FROM system.tables WHERE database = 'db' [AND name LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format]\n.\n\n\nSee also the section \"LIKE operator\".\n\n\nSHOW PROCESSLIST\n\n\nSHOW\n \nPROCESSLIST\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nOutputs a list of queries currently being processed, other than \nSHOW PROCESSLIST\n queries.\n\n\nPrints a table containing the columns:\n\n\nuser\n \u2013 The user who made the query. Keep in mind that for distributed processing, queries are sent to remote servers under the 'default' user. SHOW PROCESSLIST shows the username for a specific query, not for a query that this query initiated.\n\n\naddress\n \u2013 The name of the host that the query was sent from. For distributed processing, on remote servers, this is the name of the query requestor host. To track where a distributed query was originally made from, look at SHOW PROCESSLIST on the query requestor server.\n\n\nelapsed\n \u2013 The execution time, in seconds. Queries are output in order of decreasing execution time.\n\n\nrows_read\n, \nbytes_read\n \u2013 How many rows and bytes of uncompressed data were read when processing the query. For distributed processing, data is totaled from all the remote servers. This is the data used for restrictions and quotas.\n\n\nmemory_usage\n \u2013 Current RAM usage in bytes. See the setting 'max_memory_usage'.\n\n\nquery\n \u2013 The query itself. In INSERT queries, the data for insertion is not output.\n\n\nquery_id\n \u2013 The query identifier. Non-empty only if it was explicitly defined by the user. For distributed processing, the query ID is not passed to remote servers.\n\n\nThis query is identical to: \nSELECT * FROM system.processes [INTO OUTFILE filename] [FORMAT format]\n.\n\n\nTip (execute in the console):\n\n\nwatch -n1 \nclickhouse-client --query=\nSHOW PROCESSLIST\n\n\n\n\n\n\nSHOW CREATE TABLE\n\n\nSHOW\n \nCREATE\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\ndb\n.]\ntable\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nReturns a single \nString\n-type 'statement' column, which contains a single value \u2013 the \nCREATE\n query used for creating the specified table.\n\n\nDESCRIBE TABLE\n\n\nDESC\n|\nDESCRIBE\n \nTABLE\n \n[\ndb\n.]\ntable\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nReturns two \nString\n-type columns: \nname\n and \ntype\n, which indicate the names and types of columns in the specified table.\n\n\nNested data structures are output in \"expanded\" format. Each column is shown separately, with the name after a dot.\n\n\nEXISTS\n\n\nEXISTS\n \n[\nTEMPORARY\n]\n \nTABLE\n \n[\ndb\n.]\nname\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nReturns a single \nUInt8\n-type column, which contains the single value \n0\n if the table or database doesn't exist, or \n1\n if the table exists in the specified database.\n\n\nUSE\n\n\nUSE\n \ndb\n\n\n\n\n\n\nLets you set the current database for the session.\nThe current database is used for searching for tables if the database is not explicitly defined in the query with a dot before the table name.\nThis query can't be made when using the HTTP protocol, since there is no concept of a session.\n\n\nSET\n\n\nSET\n \nparam\n \n=\n \nvalue\n\n\n\n\n\n\nAllows you to set \nparam\n to \nvalue\n. You can also make all the settings from the specified settings profile in a single query. To do this, specify 'profile' as the setting name. For more information, see the section \"Settings\".\nThe setting is made for the session, or for the server (globally) if \nGLOBAL\n is specified.\nWhen making a global setting, the setting is not applied to sessions already running, including the current session. It will only be used for new sessions.\n\n\nWhen the server is restarted, global settings made using \nSET\n are lost.\nTo make settings that persist after a server restart, you can only use the server's config file.\n\n\nOPTIMIZE\n\n\nOPTIMIZE\n \nTABLE\n \n[\ndb\n.]\nname\n \n[\nPARTITION\n \npartition\n]\n \n[\nFINAL\n]\n\n\n\n\n\n\nAsks the table engine to do something for optimization.\nSupported only by \n*MergeTree\n engines, in which this query initializes a non-scheduled merge of data parts.\nIf you specify a \nPARTITION\n, only the specified partition will be optimized.\nIf you specify \nFINAL\n, optimization will be performed even when all the data is already in one part.\n\n\n\n\nINSERT\n\n\nAdding data.\n\n\nBasic query format:\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nVALUES\n \n(\nv11\n,\n \nv12\n,\n \nv13\n),\n \n(\nv21\n,\n \nv22\n,\n \nv23\n),\n \n...\n\n\n\n\n\n\nThe query can specify a list of columns to insert \n[(c1, c2, c3)]\n. In this case, the rest of the columns are filled with:\n\n\n\n\nThe values calculated from the \nDEFAULT\n expressions specified in the table definition.\n\n\nZeros and empty strings, if \nDEFAULT\n expressions are not defined.\n\n\n\n\nIf \nstrict_insert_defaults=1\n, columns that do not have \nDEFAULT\n defined must be listed in the query.\n\n\nData can be passed to the INSERT in any \nformat\n supported by ClickHouse. The format must be specified explicitly in the query:\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nFORMAT\n \nformat_name\n \ndata_set\n\n\n\n\n\n\nFor example, the following query format is identical to the basic version of INSERT ... VALUES:\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nFORMAT\n \nValues\n \n(\nv11\n,\n \nv12\n,\n \nv13\n),\n \n(\nv21\n,\n \nv22\n,\n \nv23\n),\n \n...\n\n\n\n\n\n\nClickHouse removes all spaces and one line feed (if there is one) before the data. When forming a query, we recommend putting the data on a new line after the query operators (this is important if the data begins with spaces).\n\n\nExample:\n\n\nINSERT\n \nINTO\n \nt\n \nFORMAT\n \nTabSeparated\n\n\n11\n \nHello\n,\n \nworld\n!\n\n\n22\n \nQwerty\n\n\n\n\n\n\nYou can insert data separately from the query by using the command-line client or the HTTP interface. For more information, see the section \"\nInterfaces\n\".\n\n\nInserting the results of \nSELECT\n\n\nINSERT\n \nINTO\n \n[\ndb\n.]\ntable\n \n[(\nc1\n,\n \nc2\n,\n \nc3\n)]\n \nSELECT\n \n...\n\n\n\n\n\n\nColumns are mapped according to their position in the SELECT clause. However, their names in the SELECT expression and the table for INSERT may differ. If necessary, type casting is performed.\n\n\nNone of the data formats except Values allow setting values to expressions such as \nnow()\n, \n1 + 2\n, and so on. The Values format allows limited use of expressions, but this is not recommended, because in this case inefficient code is used for their execution.\n\n\nOther queries for modifying data parts are not supported: \nUPDATE\n, \nDELETE\n, \nREPLACE\n, \nMERGE\n, \nUPSERT\n, \nINSERT UPDATE\n.\nHowever, you can delete old data using \nALTER TABLE ... DROP PARTITION\n.\n\n\nPerformance considerations\n\n\nINSERT\n sorts the input data by primary key and splits them into partitions by month. If you insert data for mixed months, it can significantly reduce the performance of the \nINSERT\n query. To avoid this:\n\n\n\n\nAdd data in fairly large batches, such as 100,000 rows at a time.\n\n\nGroup data by month before uploading it to ClickHouse.\n\n\n\n\nPerformance will not decrease if:\n\n\n\n\nData is added in real time.\n\n\nYou upload data that is usually sorted by time.\n\n\n\n\nSELECT\n\n\nData sampling.\n\n\nSELECT\n \n[\nDISTINCT\n]\n \nexpr_list\n\n \n[\nFROM\n \n[\ndb\n.]\ntable\n \n|\n \n(\nsubquery\n)\n \n|\n \ntable_function\n]\n \n[\nFINAL\n]\n\n \n[\nSAMPLE\n \nsample_coeff\n]\n\n \n[\nARRAY\n \nJOIN\n \n...]\n\n \n[\nGLOBAL\n]\n \nANY\n|\nALL\n \nINNER\n|\nLEFT\n \nJOIN\n \n(\nsubquery\n)\n|\ntable\n \nUSING\n \ncolumns_list\n\n \n[\nPREWHERE\n \nexpr\n]\n\n \n[\nWHERE\n \nexpr\n]\n\n \n[\nGROUP\n \nBY\n \nexpr_list\n]\n \n[\nWITH\n \nTOTALS\n]\n\n \n[\nHAVING\n \nexpr\n]\n\n \n[\nORDER\n \nBY\n \nexpr_list\n]\n\n \n[\nLIMIT\n \n[\nn\n,\n \n]\nm\n]\n\n \n[\nUNION\n \nALL\n \n...]\n\n \n[\nINTO\n \nOUTFILE\n \nfilename\n]\n\n \n[\nFORMAT\n \nformat\n]\n\n \n[\nLIMIT\n \nn\n \nBY\n \ncolumns\n]\n\n\n\n\n\n\nAll the clauses are optional, except for the required list of expressions immediately after SELECT.\nThe clauses below are described in almost the same order as in the query execution conveyor.\n\n\nIf the query omits the \nDISTINCT\n, \nGROUP BY\n and \nORDER BY\n clauses and the \nIN\n and \nJOIN\n subqueries, the query will be completely stream processed, using O(1) amount of RAM.\nOtherwise, the query might consume a lot of RAM if the appropriate restrictions are not specified: \nmax_memory_usage\n, \nmax_rows_to_group_by\n, \nmax_rows_to_sort\n, \nmax_rows_in_distinct\n, \nmax_bytes_in_distinct\n, \nmax_rows_in_set\n, \nmax_bytes_in_set\n, \nmax_rows_in_join\n, \nmax_bytes_in_join\n, \nmax_bytes_before_external_sort\n, \nmax_bytes_before_external_group_by\n. For more information, see the section \"Settings\". It is possible to use external sorting (saving temporary tables to a disk) and external aggregation. \nThe system does not have \"merge join\"\n.\n\n\nFROM clause\n\n\nIf the FROM clause is omitted, data will be read from the \nsystem.one\n table.\nThe 'system.one' table contains exactly one row (this table fulfills the same purpose as the DUAL table found in other DBMSs).\n\n\nThe FROM clause specifies the table to read data from, or a subquery, or a table function; ARRAY JOIN and the regular JOIN may also be included (see below).\n\n\nInstead of a table, the SELECT subquery may be specified in brackets.\nIn this case, the subquery processing pipeline will be built into the processing pipeline of an external query.\nIn contrast to standard SQL, a synonym does not need to be specified after a subquery. For compatibility, it is possible to write 'AS name' after a subquery, but the specified name isn't used anywhere.\n\n\nA table function may be specified instead of a table. For more information, see the section \"Table functions\".\n\n\nTo execute a query, all the columns listed in the query are extracted from the appropriate table. Any columns not needed for the external query are thrown out of the subqueries.\nIf a query does not list any columns (for example, SELECT count() FROM t), some column is extracted from the table anyway (the smallest one is preferred), in order to calculate the number of rows.\n\n\nThe FINAL modifier can be used only for a SELECT from a CollapsingMergeTree table. When you specify FINAL, data is selected fully \"collapsed\". Keep in mind that using FINAL leads to a selection that includes columns related to the primary key, in addition to the columns specified in the SELECT. Additionally, the query will be executed in a single stream, and data will be merged during query execution. This means that when using FINAL, the query is processed more slowly. In most cases, you should avoid using FINAL. For more information, see the section \"CollapsingMergeTree engine\".\n\n\nSAMPLE clause\n\n\nThe SAMPLE clause allows for approximated query processing. Approximated query processing is only supported by MergeTree* type tables, and only if the sampling expression was specified during table creation (see the section \"MergeTree engine\").\n\n\nSAMPLE\n has the \nformat SAMPLE k\n, where \nk\n is a decimal number from 0 to 1, or \nSAMPLE n\n, where 'n' is a sufficiently large integer.\n\n\nIn the first case, the query will be executed on 'k' percent of data. For example, \nSAMPLE 0.1\n runs the query on 10% of data.\nIn the second case, the query will be executed on a sample of no more than 'n' rows. For example, \nSAMPLE 10000000\n runs the query on a maximum of 10,000,000 rows.\n\n\nExample:\n\n\nSELECT\n\n \nTitle\n,\n\n \ncount\n()\n \n*\n \n10\n \nAS\n \nPageViews\n\n\nFROM\n \nhits_distributed\n\n\nSAMPLE\n \n0\n.\n1\n\n\nWHERE\n\n \nCounterID\n \n=\n \n34\n\n \nAND\n \ntoDate\n(\nEventDate\n)\n \n=\n \ntoDate\n(\n2013-01-29\n)\n\n \nAND\n \ntoDate\n(\nEventDate\n)\n \n=\n \ntoDate\n(\n2013-02-04\n)\n\n \nAND\n \nNOT\n \nDontCountHits\n\n \nAND\n \nNOT\n \nRefresh\n\n \nAND\n \nTitle\n \n!=\n \n\n\nGROUP\n \nBY\n \nTitle\n\n\nORDER\n \nBY\n \nPageViews\n \nDESC\n \nLIMIT\n \n1000\n\n\n\n\n\n\nIn this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value 'count()' is manually multiplied by 10.\n\n\nWhen using something like \nSAMPLE 10000000\n, there isn't any information about which relative percent of data was processed or what the aggregate functions should be multiplied by, so this method of writing is not always appropriate to the situation.\n\n\nA sample with a relative coefficient is \"consistent\": if we look at all possible data that could be in the table, a sample (when using a single sampling expression specified during table creation) with the same coefficient always selects the same subset of possible data. In other words, a sample from different tables on different servers at different times is made the same way.\n\n\nFor example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This allows using the sample in subqueries in the IN clause, as well as for manually correlating results of different queries with samples.\n\n\nARRAY JOIN clause\n\n\nAllows executing JOIN with an array or nested data structure. The intent is similar to the 'arrayJoin' function, but its functionality is broader.\n\n\nARRAY JOIN\n is essentially \nINNER JOIN\n with an array. Example:\n\n\n:) CREATE TABLE arrays_test (s String, arr Array(UInt8)) ENGINE = Memory\n\nCREATE TABLE arrays_test\n(\n s String,\n arr Array(UInt8)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.001 sec.\n\n:) INSERT INTO arrays_test VALUES (\nHello\n, [1,2]), (\nWorld\n, [3,4,5]), (\nGoodbye\n, [])\n\nINSERT INTO arrays_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM arrays_test\n\nSELECT *\nFROM arrays_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502\n\u2502 Goodbye \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, arr FROM arrays_test ARRAY JOIN arr\n\nSELECT s, arr\nFROM arrays_test\nARRAY JOIN arr\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2510\n\u2502 Hello \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502\n\u2502 World \u2502 4 \u2502\n\u2502 World \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nAn alias can be specified for an array in the ARRAY JOIN clause. In this case, an array item can be accessed by this alias, but the array itself by the original name. Example:\n\n\n:) SELECT s, arr, a FROM arrays_test ARRAY JOIN arr AS a\n\nSELECT s, arr, a\nFROM arrays_test\nARRAY JOIN arr AS a\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nMultiple arrays of the same size can be comma-separated in the ARRAY JOIN clause. In this case, JOIN is performed with them simultaneously (the direct sum, not the direct product). Example:\n\n\n:) SELECT s, arr, a, num, mapped FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(x -\n x + 1, arr) AS mapped\n\nSELECT s, arr, a, num, mapped\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(lambda(tuple(x), plus(x, 1)), arr) AS mapped\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500mapped\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 2 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 5 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 6 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n:) SELECT s, arr, a, num, arrayEnumerate(arr) FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\nSELECT s, arr, a, num, arrayEnumerate(arr)\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500arrayEnumerate(arr)\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 [1,2] \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n\n\n\n\nARRAY JOIN also works with nested data structures. Example:\n\n\n:) CREATE TABLE nested_test (s String, nest Nested(x UInt8, y UInt32)) ENGINE = Memory\n\nCREATE TABLE nested_test\n(\n s String,\n nest Nested(\n x UInt8,\n y UInt32)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.006 sec.\n\n:) INSERT INTO nested_test VALUES (\nHello\n, [1,2], [10,20]), (\nWorld\n, [3,4,5], [30,40,50]), (\nGoodbye\n, [], [])\n\nINSERT INTO nested_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM nested_test\n\nSELECT *\nFROM nested_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 Goodbye \u2502 [] \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nWhen specifying names of nested data structures in ARRAY JOIN, the meaning is the same as ARRAY JOIN with all the array elements that it consists of. Example:\n\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x, nest.y\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`, `nest.y`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nThis variation also makes sense:\n\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nAn alias may be used for a nested data structure, in order to select either the JOIN result or the source array. Example:\n\n\n:) SELECT s, n.x, n.y, nest.x, nest.y FROM nested_test ARRAY JOIN nest AS n\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest AS n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec.\n\n\n\n\n\nExample of using the arrayEnumerate function:\n\n\n:) SELECT s, n.x, n.y, nest.x, nest.y, num FROM nested_test ARRAY JOIN nest AS n, arrayEnumerate(nest.x) AS num\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`, num\nFROM nested_test\nARRAY JOIN nest AS n, arrayEnumerate(`nest.x`) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u252c\u2500num\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 1 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 2 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 3 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n\n\n\n\nThe query can only specify a single ARRAY JOIN clause.\n\n\nThe corresponding conversion can be performed before the WHERE/PREWHERE clause (if its result is needed in this clause), or after completing WHERE/PREWHERE (to reduce the volume of calculations).\n\n\nJOIN clause\n\n\nThe normal JOIN, which is not related to ARRAY JOIN described above.\n\n\n[\nGLOBAL\n]\n \nANY\n|\nALL\n \nINNER\n|\nLEFT\n \n[\nOUTER\n]\n \nJOIN\n \n(\nsubquery\n)\n|\ntable\n \nUSING\n \ncolumns_list\n\n\n\n\n\n\nPerforms joins with data from the subquery. At the beginning of query processing, the subquery specified after JOIN is run, and its result is saved in memory. Then it is read from the \"left\" table specified in the FROM clause, and while it is being read, for each of the read rows from the \"left\" table, rows are selected from the subquery results table (the \"right\" table) that meet the condition for matching the values of the columns specified in USING.\n\n\nThe table name can be specified instead of a subquery. This is equivalent to the \nSELECT * FROM table\n subquery, except in a special case when the table has the Join engine \u2013 an array prepared for joining.\n\n\nAll columns that are not needed for the JOIN are deleted from the subquery.\n\n\nThere are several types of JOINs:\n\n\nINNER\n or \nLEFT\n type:If INNER is specified, the result will contain only those rows that have a matching row in the right table.\nIf LEFT is specified, any rows in the left table that don't have matching rows in the right table will be assigned the default value - zeros or empty rows. LEFT OUTER may be written instead of LEFT; the word OUTER does not affect anything.\n\n\nANY\n or \nALL\n stringency:If \nANY\n is specified and the right table has several matching rows, only the first one found is joined.\nIf \nALL\n is specified and the right table has several matching rows, the data will be multiplied by the number of these rows.\n\n\nUsing ALL corresponds to the normal JOIN semantic from standard SQL.\nUsing ANY is optimal. If the right table has only one matching row, the results of ANY and ALL are the same. You must specify either ANY or ALL (neither of them is selected by default).\n\n\nGLOBAL\n distribution:\n\n\nWhen using a normal JOIN, the query is sent to remote servers. Subqueries are run on each of them in order to make the right table, and the join is performed with this table. In other words, the right table is formed on each server separately.\n\n\nWhen using \nGLOBAL ... JOIN\n, first the requestor server runs a subquery to calculate the right table. This temporary table is passed to each remote server, and queries are run on them using the temporary data that was transmitted.\n\n\nBe careful when using GLOBAL JOINs. For more information, see the section \"Distributed subqueries\".\n\n\nAny combination of JOINs is possible. For example, \nGLOBAL ANY LEFT OUTER JOIN\n.\n\n\nWhen running a JOIN, there is no optimization of the order of execution in relation to other stages of the query. The join (a search in the right table) is run before filtering in WHERE and before aggregation. In order to explicitly set the processing order, we recommend running a JOIN subquery with a subquery.\n\n\nExample:\n\n\nSELECT\n\n \nCounterID\n,\n\n \nhits\n,\n\n \nvisits\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nCounterID\n,\n\n \ncount\n()\n \nAS\n \nhits\n\n \nFROM\n \ntest\n.\nhits\n\n \nGROUP\n \nBY\n \nCounterID\n\n\n)\n \nANY\n \nLEFT\n \nJOIN\n\n\n(\n\n \nSELECT\n\n \nCounterID\n,\n\n \nsum\n(\nSign\n)\n \nAS\n \nvisits\n\n \nFROM\n \ntest\n.\nvisits\n\n \nGROUP\n \nBY\n \nCounterID\n\n\n)\n \nUSING\n \nCounterID\n\n\nORDER\n \nBY\n \nhits\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500CounterID\u2500\u252c\u2500\u2500\u2500hits\u2500\u252c\u2500visits\u2500\u2510\n\u2502 1143050 \u2502 523264 \u2502 13665 \u2502\n\u2502 731962 \u2502 475698 \u2502 102716 \u2502\n\u2502 722545 \u2502 337212 \u2502 108187 \u2502\n\u2502 722889 \u2502 252197 \u2502 10547 \u2502\n\u2502 2237260 \u2502 196036 \u2502 9522 \u2502\n\u2502 23057320 \u2502 147211 \u2502 7689 \u2502\n\u2502 722818 \u2502 90109 \u2502 17847 \u2502\n\u2502 48221 \u2502 85379 \u2502 4652 \u2502\n\u2502 19762435 \u2502 77807 \u2502 7026 \u2502\n\u2502 722884 \u2502 77492 \u2502 11056 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSubqueries don't allow you to set names or use them for referencing a column from a specific subquery.\nThe columns specified in USING must have the same names in both subqueries, and the other columns must be named differently. You can use aliases to change the names of columns in subqueries (the example uses the aliases 'hits' and 'visits').\n\n\nThe USING clause specifies one or more columns to join, which establishes the equality of these columns. The list of columns is set without brackets. More complex join conditions are not supported.\n\n\nThe right table (the subquery result) resides in RAM. If there isn't enough memory, you can't run a JOIN.\n\n\nOnly one JOIN can be specified in a query (on a single level). To run multiple JOINs, you can put them in subqueries.\n\n\nEach time a query is run with the same JOIN, the subquery is run again \u2013 the result is not cached. To avoid this, use the special 'Join' table engine, which is a prepared array for joining that is always in RAM. For more information, see the section \"Table engines, Join\".\n\n\nIn some cases, it is more efficient to use IN instead of JOIN.\nAmong the various types of JOINs, the most efficient is ANY LEFT JOIN, then ANY INNER JOIN. The least efficient are ALL LEFT JOIN and ALL INNER JOIN.\n\n\nIf you need a JOIN for joining with dimension tables (these are relatively small tables that contain dimension properties, such as names for advertising campaigns), a JOIN might not be very convenient due to the bulky syntax and the fact that the right table is re-accessed for every query. For such cases, there is an \"external dictionaries\" feature that you should use instead of JOIN. For more information, see the section \"External dictionaries\".\n\n\nWHERE clause\n\n\nIf there is a WHERE clause, it must contain an expression with the UInt8 type. This is usually an expression with comparison and logical operators.\nThis expression will be used for filtering data before all other transformations.\n\n\nIf indexes are supported by the database table engine, the expression is evaluated on the ability to use indexes.\n\n\nPREWHERE clause\n\n\nThis clause has the same meaning as the WHERE clause. The difference is in which data is read from the table.\nWhen using PREWHERE, first only the columns necessary for executing PREWHERE are read. Then the other columns are read that are needed for running the query, but only those blocks where the PREWHERE expression is true.\n\n\nIt makes sense to use PREWHERE if there are filtration conditions that are not suitable for indexes that are used by a minority of the columns in the query, but that provide strong data filtration. This reduces the volume of data to read.\n\n\nFor example, it is useful to write PREWHERE for queries that extract a large number of columns, but that only have filtration for a few columns.\n\n\nPREWHERE is only supported by tables from the \n*MergeTree\n family.\n\n\nA query may simultaneously specify PREWHERE and WHERE. In this case, PREWHERE precedes WHERE.\n\n\nKeep in mind that it does not make much sense for PREWHERE to only specify those columns that have an index, because when using an index, only the data blocks that match the index are read.\n\n\nIf the 'optimize_move_to_prewhere' setting is set to 1 and PREWHERE is omitted, the system uses heuristics to automatically move parts of expressions from WHERE to PREWHERE.\n\n\nGROUP BY clause\n\n\nThis is one of the most important parts of a column-oriented DBMS.\n\n\nIf there is a GROUP BY clause, it must contain a list of expressions. Each expression will be referred to here as a \"key\".\nAll the expressions in the SELECT, HAVING, and ORDER BY clauses must be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions.\n\n\nIf a query contains only table columns inside aggregate functions, the GROUP BY clause can be omitted, and aggregation by an empty set of keys is assumed.\n\n\nExample:\n\n\nSELECT\n\n \ncount\n(),\n\n \nmedian\n(\nFetchTiming\n \n \n60\n \n?\n \n60\n \n:\n \nFetchTiming\n),\n\n \ncount\n()\n \n-\n \nsum\n(\nRefresh\n)\n\n\nFROM\n \nhits\n\n\n\n\n\n\nHowever, in contrast to standard SQL, if the table doesn't have any rows (either there aren't any at all, or there aren't any after using WHERE to filter), an empty result is returned, and not the result from one of the rows containing the initial values of aggregate functions.\n\n\nAs opposed to MySQL (and conforming to standard SQL), you can't get some value of some column that is not in a key or aggregate function (except constant expressions). To work around this, you can use the 'any' aggregate function (get the first encountered value) or 'min/max'.\n\n\nExample:\n\n\nSELECT\n\n \ndomainWithoutWWW\n(\nURL\n)\n \nAS\n \ndomain\n,\n\n \ncount\n(),\n\n \nany\n(\nTitle\n)\n \nAS\n \ntitle\n \n-- getting the first occurred page header for each domain.\n\n\nFROM\n \nhits\n\n\nGROUP\n \nBY\n \ndomain\n\n\n\n\n\n\nFor every different key value encountered, GROUP BY calculates a set of aggregate function values.\n\n\nGROUP BY is not supported for array columns.\n\n\nA constant can't be specified as arguments for aggregate functions. Example: sum(1). Instead of this, you can get rid of the constant. Example: \ncount()\n.\n\n\nWITH TOTALS modifier\n\n\nIf the WITH TOTALS modifier is specified, another row will be calculated. This row will have key columns containing default values (zeros or empty lines), and columns of aggregate functions with the values calculated across all the rows (the \"total\" values).\n\n\nThis extra row is output in JSON*, TabSeparated*, and Pretty* formats, separately from the other rows. In the other formats, this row is not output.\n\n\nIn JSON* formats, this row is output as a separate 'totals' field. In TabSeparated* formats, the row comes after the main result, preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result.\n\n\nWITH TOTALS\n can be run in different ways when HAVING is present. The behavior depends on the 'totals_mode' setting.\nBy default, \ntotals_mode = 'before_having'\n. In this case, 'totals' is calculated across all rows, including the ones that don't pass through HAVING and 'max_rows_to_group_by'.\n\n\nThe other alternatives include only the rows that pass through HAVING in 'totals', and behave differently with the setting \nmax_rows_to_group_by\n and \ngroup_by_overflow_mode = 'any'\n.\n\n\nafter_having_exclusive\n \u2013 Don't include rows that didn't pass through \nmax_rows_to_group_by\n. In other words, 'totals' will have less than or the same number of rows as it would if \nmax_rows_to_group_by\n were omitted.\n\n\nafter_having_inclusive\n \u2013 Include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. In other words, 'totals' will have more than or the same number of rows as it would if \nmax_rows_to_group_by\n were omitted.\n\n\nafter_having_auto\n \u2013 Count the number of rows that passed through HAVING. If it is more than a certain amount (by default, 50%), include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. Otherwise, do not include them.\n\n\ntotals_auto_threshold\n \u2013 By default, 0.5. The coefficient for \nafter_having_auto\n.\n\n\nIf \nmax_rows_to_group_by\n and \ngroup_by_overflow_mode = 'any'\n are not used, all variations of \nafter_having\n are the same, and you can use any of them (for example, \nafter_having_auto\n).\n\n\nYou can use WITH TOTALS in subqueries, including subqueries in the JOIN clause (in this case, the respective total values are combined).\n\n\nGROUP BY in external memory\n\n\nYou can enable dumping temporary data to the disk to restrict memory usage during GROUP BY.\nThe \nmax_bytes_before_external_group_by\n setting determines the threshold RAM consumption for dumping GROUP BY temporary data to the file system. If set to 0 (the default), it is disabled.\n\n\nWhen using \nmax_bytes_before_external_group_by\n, we recommend that you set max_memory_usage about twice as high. This is necessary because there are two stages to aggregation: reading the date and forming intermediate data (1) and merging the intermediate data (2). Dumping data to the file system can only occur during stage 1. If the temporary data wasn't dumped, then stage 2 might require up to the same amount of memory as in stage 1.\n\n\nFor example, if \nmax_memory_usage\n was set to 10000000000 and you want to use external aggregation, it makes sense to set \nmax_bytes_before_external_group_by\n to 10000000000, and max_memory_usage to 20000000000. When external aggregation is triggered (if there was at least one dump of temporary data), maximum consumption of RAM is only slightly more than \nmax_bytes_before_external_group_by\n.\n\n\nWith distributed query processing, external aggregation is performed on remote servers. In order for the requestor server to use only a small amount of RAM, set \ndistributed_aggregation_memory_efficient\n to 1.\n\n\nWhen merging data flushed to the disk, as well as when merging results from remote servers when the \ndistributed_aggregation_memory_efficient\n setting is enabled, consumes up to 1/256 * the number of threads from the total amount of RAM.\n\n\nWhen external aggregation is enabled, if there was less than \nmax_bytes_before_external_group_by\n of data (i.e. data was not flushed), the query runs just as fast as without external aggregation. If any temporary data was flushed, the run time will be several times longer (approximately three times).\n\n\nIf you have an ORDER BY with a small LIMIT after GROUP BY, then the ORDER BY CLAUSE will not use significant amounts of RAM.\nBut if the ORDER BY doesn't have LIMIT, don't forget to enable external sorting (\nmax_bytes_before_external_sort\n).\n\n\nLIMIT N BY clause\n\n\nLIMIT N BY COLUMNS selects the top N rows for each group of COLUMNS. LIMIT N BY is not related to LIMIT; they can both be used in the same query. The key for LIMIT N BY can contain any number of columns or expressions.\n\n\nExample:\n\n\nSELECT\n\n \ndomainWithoutWWW\n(\nURL\n)\n \nAS\n \ndomain\n,\n\n \ndomainWithoutWWW\n(\nREFERRER_URL\n)\n \nAS\n \nreferrer\n,\n\n \ndevice_type\n,\n\n \ncount\n()\n \ncnt\n\n\nFROM\n \nhits\n\n\nGROUP\n \nBY\n \ndomain\n,\n \nreferrer\n,\n \ndevice_type\n\n\nORDER\n \nBY\n \ncnt\n \nDESC\n\n\nLIMIT\n \n5\n \nBY\n \ndomain\n,\n \ndevice_type\n\n\nLIMIT\n \n100\n\n\n\n\n\n\nThe query will select the top 5 referrers for each \ndomain, device_type\n pair, but not more than 100 rows (\nLIMIT n BY + LIMIT\n).\n\n\nHAVING clause\n\n\nAllows filtering the result received after GROUP BY, similar to the WHERE clause.\nWHERE and HAVING differ in that WHERE is performed before aggregation (GROUP BY), while HAVING is performed after it.\nIf aggregation is not performed, HAVING can't be used.\n\n\n\n\nORDER BY clause\n\n\nThe ORDER BY clause contains a list of expressions, which can each be assigned DESC or ASC (the sorting direction). If the direction is not specified, ASC is assumed. ASC is sorted in ascending order, and DESC in descending order. The sorting direction applies to a single expression, not to the entire list. Example: \nORDER BY Visits DESC, SearchPhrase\n\n\nFor sorting by String values, you can specify collation (comparison). Example: \nORDER BY SearchPhrase COLLATE 'tr'\n - for sorting by keyword in ascending order, using the Turkish alphabet, case insensitive, assuming that strings are UTF-8 encoded. COLLATE can be specified or not for each expression in ORDER BY independently. If ASC or DESC is specified, COLLATE is specified after it. When using COLLATE, sorting is always case-insensitive.\n\n\nWe only recommend using COLLATE for final sorting of a small number of rows, since sorting with COLLATE is less efficient than normal sorting by bytes.\n\n\nRows that have identical values for the list of sorting expressions are output in an arbitrary order, which can also be nondeterministic (different each time).\nIf the ORDER BY clause is omitted, the order of the rows is also undefined, and may be nondeterministic as well.\n\n\nWhen floating point numbers are sorted, NaNs are separate from the other values. Regardless of the sorting order, NaNs come at the end. In other words, for ascending sorting they are placed as if they are larger than all the other numbers, while for descending sorting they are placed as if they are smaller than the rest.\n\n\nLess RAM is used if a small enough LIMIT is specified in addition to ORDER BY. Otherwise, the amount of memory spent is proportional to the volume of data for sorting. For distributed query processing, if GROUP BY is omitted, sorting is partially done on remote servers, and the results are merged on the requestor server. This means that for distributed sorting, the volume of data to sort can be greater than the amount of memory on a single server.\n\n\nIf there is not enough RAM, it is possible to perform sorting in external memory (creating temporary files on a disk). Use the setting \nmax_bytes_before_external_sort\n for this purpose. If it is set to 0 (the default), external sorting is disabled. If it is enabled, when the volume of data to sort reaches the specified number of bytes, the collected data is sorted and dumped into a temporary file. After all data is read, all the sorted files are merged and the results are output. Files are written to the /var/lib/clickhouse/tmp/ directory in the config (by default, but you can use the 'tmp_path' parameter to change this setting).\n\n\nRunning a query may use more memory than 'max_bytes_before_external_sort'. For this reason, this setting must have a value significantly smaller than 'max_memory_usage'. As an example, if your server has 128 GB of RAM and you need to run a single query, set 'max_memory_usage' to 100 GB, and 'max_bytes_before_external_sort' to 80 GB.\n\n\nExternal sorting works much less effectively than sorting in RAM.\n\n\nSELECT clause\n\n\nThe expressions specified in the SELECT clause are analyzed after the calculations for all the clauses listed above are completed.\nMore specifically, expressions are analyzed that are above the aggregate functions, if there are any aggregate functions.\nThe aggregate functions and everything below them are calculated during aggregation (GROUP BY).\nThese expressions work as if they are applied to separate rows in the result.\n\n\nDISTINCT clause\n\n\nIf DISTINCT is specified, only a single row will remain out of all the sets of fully matching rows in the result.\nThe result will be the same as if GROUP BY were specified across all the fields specified in SELECT without aggregate functions. But there are several differences from GROUP BY:\n\n\n\n\nDISTINCT can be applied together with GROUP BY.\n\n\nWhen ORDER BY is omitted and LIMIT is defined, the query stops running immediately after the required number of different rows has been read.\n\n\nData blocks are output as they are processed, without waiting for the entire query to finish running.\n\n\n\n\nDISTINCT is not supported if SELECT has at least one array column.\n\n\nLIMIT clause\n\n\nLIMIT m allows you to select the first 'm' rows from the result.\nLIMIT n, m allows you to select the first 'm' rows from the result after skipping the first 'n' rows.\n\n\n'n' and 'm' must be non-negative integers.\n\n\nIf there isn't an ORDER BY clause that explicitly sorts results, the result may be arbitrary and nondeterministic.\n\n\nUNION ALL clause\n\n\nYou can use UNION ALL to combine any number of queries. Example:\n\n\nSELECT\n \nCounterID\n,\n \n1\n \nAS\n \ntable\n,\n \ntoInt64\n(\ncount\n())\n \nAS\n \nc\n\n \nFROM\n \ntest\n.\nhits\n\n \nGROUP\n \nBY\n \nCounterID\n\n\n\nUNION\n \nALL\n\n\n\nSELECT\n \nCounterID\n,\n \n2\n \nAS\n \ntable\n,\n \nsum\n(\nSign\n)\n \nAS\n \nc\n\n \nFROM\n \ntest\n.\nvisits\n\n \nGROUP\n \nBY\n \nCounterID\n\n \nHAVING\n \nc\n \n \n0\n\n\n\n\n\n\nOnly UNION ALL is supported. The regular UNION (UNION DISTINCT) is not supported. If you need UNION DISTINCT, you can write SELECT DISTINCT from a subquery containing UNION ALL.\n\n\nQueries that are parts of UNION ALL can be run simultaneously, and their results can be mixed together.\n\n\nThe structure of results (the number and type of columns) must match for the queries. But the column names can differ. In this case, the column names for the final result will be taken from the first query.\n\n\nQueries that are parts of UNION ALL can't be enclosed in brackets. ORDER BY and LIMIT are applied to separate queries, not to the final result. If you need to apply a conversion to the final result, you can put all the queries with UNION ALL in a subquery in the FROM clause.\n\n\nINTO OUTFILE clause\n\n\nAdd the \nINTO OUTFILE filename\n clause (where filename is a string literal) to redirect query output to the specified file.\nIn contrast to MySQL, the file is created on the client side. The query will fail if a file with the same filename already exists.\nThis functionality is available in the command-line client and clickhouse-local (a query sent via HTTP interface will fail).\n\n\nThe default output format is TabSeparated (the same as in the command-line client batch mode).\n\n\nFORMAT clause\n\n\nSpecify 'FORMAT format' to get data in any specified format.\nYou can use this for convenience, or for creating dumps.\nFor more information, see the section \"Formats\".\nIf the FORMAT clause is omitted, the default format is used, which depends on both the settings and the interface used for accessing the DB. For the HTTP interface and the command-line client in batch mode, the default format is TabSeparated. For the command-line client in interactive mode, the default format is PrettyCompact (it has attractive and compact tables).\n\n\nWhen using the command-line client, data is passed to the client in an internal efficient format. The client independently interprets the FORMAT clause of the query and formats the data itself (thus relieving the network and the server from the load).\n\n\nIN operators\n\n\nThe \nIN\n, \nNOT IN\n, \nGLOBAL IN\n, and \nGLOBAL NOT IN\n operators are covered separately, since their functionality is quite rich.\n\n\nThe left side of the operator is either a single column or a tuple.\n\n\nExamples:\n\n\nSELECT\n \nUserID\n \nIN\n \n(\n123\n,\n \n456\n)\n \nFROM\n \n...\n\n\nSELECT\n \n(\nCounterID\n,\n \nUserID\n)\n \nIN\n \n((\n34\n,\n \n123\n),\n \n(\n101500\n,\n \n456\n))\n \nFROM\n \n...\n\n\n\n\n\n\nIf the left side is a single column that is in the index, and the right side is a set of constants, the system uses the index for processing the query.\n\n\nDon't list too many values explicitly (i.e. millions). If a data set is large, put it in a temporary table (for example, see the section \"External data for query processing\"), then use a subquery.\n\n\nThe right side of the operator can be a set of constant expressions, a set of tuples with constant expressions (shown in the examples above), or the name of a database table or SELECT subquery in brackets.\n\n\nIf the right side of the operator is the name of a table (for example, \nUserID IN users\n), this is equivalent to the subquery \nUserID IN (SELECT * FROM users)\n. Use this when working with external data that is sent along with the query. For example, the query can be sent together with a set of user IDs loaded to the 'users' temporary table, which should be filtered.\n\n\nIf the right side of the operator is a table name that has the Set engine (a prepared data set that is always in RAM), the data set will not be created over again for each query.\n\n\nThe subquery may specify more than one column for filtering tuples.\nExample:\n\n\nSELECT\n \n(\nCounterID\n,\n \nUserID\n)\n \nIN\n \n(\nSELECT\n \nCounterID\n,\n \nUserID\n \nFROM\n \n...)\n \nFROM\n \n...\n\n\n\n\n\n\nThe columns to the left and right of the IN operator should have the same type.\n\n\nThe IN operator and subquery may occur in any part of the query, including in aggregate functions and lambda functions.\nExample:\n\n\nSELECT\n\n \nEventDate\n,\n\n \navg\n(\nUserID\n \nIN\n\n \n(\n\n \nSELECT\n \nUserID\n\n \nFROM\n \ntest\n.\nhits\n\n \nWHERE\n \nEventDate\n \n=\n \ntoDate\n(\n2014-03-17\n)\n\n \n))\n \nAS\n \nratio\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \nEventDate\n\n\nORDER\n \nBY\n \nEventDate\n \nASC\n\n\n\n\n\n\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500ratio\u2500\u2510\n\u2502 2014-03-17 \u2502 1 \u2502\n\u2502 2014-03-18 \u2502 0.807696 \u2502\n\u2502 2014-03-19 \u2502 0.755406 \u2502\n\u2502 2014-03-20 \u2502 0.723218 \u2502\n\u2502 2014-03-21 \u2502 0.697021 \u2502\n\u2502 2014-03-22 \u2502 0.647851 \u2502\n\u2502 2014-03-23 \u2502 0.648416 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nFor each day after March 17th, count the percentage of pageviews made by users who visited the site on March 17th.\nA subquery in the IN clause is always run just one time on a single server. There are no dependent subqueries.\n\n\n\n\nDistributed subqueries\n\n\nThere are two options for IN-s with subqueries (similar to JOINs): normal \nIN\n / \nOIN\n and \nIN GLOBAL\n / \nGLOBAL JOIN\n. They differ in how they are run for distributed query processing.\n\n\n\n\nRemember that the algorithms described below may work differently depending on the [settings](#settings-distributed_product_mode) `distributed_product_mode` setting.\n\n\n\n\n\nWhen using the regular IN, the query is sent to remote servers, and each of them runs the subqueries in the \nIN\n or \nJOIN\n clause.\n\n\nWhen using \nGLOBAL IN\n / \nGLOBAL JOINs\n, first all the subqueries are run for \nGLOBAL IN\n / \nGLOBAL JOINs\n, and the results are collected in temporary tables. Then the temporary tables are sent to each remote server, where the queries are run using this temporary data.\n\n\nFor a non-distributed query, use the regular \nIN\n / \nJOIN\n.\n\n\nBe careful when using subqueries in the \nIN\n / \nJOIN\n clauses for distributed query processing.\n\n\nLet's look at some examples. Assume that each server in the cluster has a normal \nlocal_table\n. Each server also has a \ndistributed_table\n table with the \nDistributed\n type, which looks at all the servers in the cluster.\n\n\nFor a query to the \ndistributed_table\n, the query will be sent to all the remote servers and run on them using the \nlocal_table\n.\n\n\nFor example, the query\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n\n\n\n\n\n\nwill be sent to all remote servers as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n\n\n\n\n\n\nand run on each of them in parallel, until it reaches the stage where intermediate results can be combined. Then the intermediate results will be returned to the requestor server and merged on it, and the final result will be sent to the client.\n\n\nNow let's examine a query with IN:\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\n\n\nCalculation of the intersection of audiences of two sites.\n\n\n\n\nThis query will be sent to all remote servers as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nIn other words, the data set in the IN clause will be collected on each server independently, only across the data that is stored locally on each of the servers.\n\n\nThis will work correctly and optimally if you are prepared for this case and have spread data across the cluster servers such that the data for a single UserID resides entirely on a single server. In this case, all the necessary data will be available locally on each server. Otherwise, the result will be inaccurate. We refer to this variation of the query as \"local IN\".\n\n\nTo correct how the query works when data is spread randomly across the cluster servers, you could specify \ndistributed_table\n inside a subquery. The query would look like this:\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nThis query will be sent to all remote servers as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nThe subquery will begin running on each remote server. Since the subquery uses a distributed table, the subquery that is on each remote server will be resent to every remote server as\n\n\nSELECT\n \nUserID\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n\n\n\n\n\n\nFor example, if you have a cluster of 100 servers, executing the entire query will require 10,000 elementary requests, which is generally considered unacceptable.\n\n\nIn such cases, you should always use GLOBAL IN instead of IN. Let's look at how it works for the query\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nGLOBAL\n \nIN\n \n(\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n)\n\n\n\n\n\n\nThe requestor server will run the subquery\n\n\nSELECT\n \nUserID\n \nFROM\n \ndistributed_table\n \nWHERE\n \nCounterID\n \n=\n \n34\n\n\n\n\n\n\nand the result will be put in a temporary table in RAM. Then the request will be sent to each remote server as\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \nlocal_table\n \nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nUserID\n \nGLOBAL\n \nIN\n \n_data1\n\n\n\n\n\n\nand the temporary table \n_data1\n will be sent to every remote server with the query (the name of the temporary table is implementation-defined).\n\n\nThis is more optimal than using the normal IN. However, keep the following points in mind:\n\n\n\n\nWhen creating a temporary table, data is not made unique. To reduce the volume of data transmitted over the network, specify DISTINCT in the subquery. (You don't need to do this for a normal IN.)\n\n\nThe temporary table will be sent to all the remote servers. Transmission does not account for network topology. For example, if 10 remote servers reside in a datacenter that is very remote in relation to the requestor server, the data will be sent 10 times over the channel to the remote datacenter. Try to avoid large data sets when using GLOBAL IN.\n\n\nWhen transmitting data to remote servers, restrictions on network bandwidth are not configurable. You might overload the network.\n\n\nTry to distribute data across servers so that you don't need to use GLOBAL IN on a regular basis.\n\n\nIf you need to use GLOBAL IN often, plan the location of the ClickHouse cluster so that a single group of replicas resides in no more than one data center with a fast network between them, so that a query can be processed entirely within a single data center.\n\n\n\n\nIt also makes sense to specify a local table in the \nGLOBAL IN\n clause, in case this local table is only available on the requestor server and you want to use data from it on remote servers.\n\n\nExtreme values\n\n\nIn addition to results, you can also get minimum and maximum values for the results columns. To do this, set the \nextremes\n setting to 1. Minimums and maximums are calculated for numeric types, dates, and dates with times. For other columns, the default values are output.\n\n\nAn extra two rows are calculated \u2013 the minimums and maximums, respectively. These extra two rows are output in JSON*, TabSeparated*, and Pretty* formats, separate from the other rows. They are not output for other formats.\n\n\nIn JSON* formats, the extreme values are output in a separate 'extremes' field. In TabSeparated* formats, the row comes after the main result, and after 'totals' if present. It is preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result, and after 'totals' if present.\n\n\nExtreme values are calculated for rows that have passed through LIMIT. However, when using 'LIMIT offset, size', the rows before 'offset' are included in 'extremes'. In stream requests, the result may also include a small number of rows that passed through LIMIT.\n\n\nNotes\n\n\nThe \nGROUP BY\n and \nORDER BY\n clauses do not support positional arguments. This contradicts MySQL, but conforms to standard SQL.\nFor example, \nGROUP BY 1, 2\n will be interpreted as grouping by constants (i.e. aggregation of all rows into one).\n\n\nYou can use synonyms (\nAS\n aliases) in any part of a query.\n\n\nYou can put an asterisk in any part of a query instead of an expression. When the query is analyzed, the asterisk is expanded to a list of all table columns (excluding the \nMATERIALIZED\n and \nALIAS\n columns). There are only a few cases when using an asterisk is justified:\n\n\n\n\nWhen creating a table dump.\n\n\nFor tables containing just a few columns, such as system tables.\n\n\nFor getting information about what columns are in a table. In this case, set \nLIMIT 1\n. But it is better to use the \nDESC TABLE\n query.\n\n\nWhen there is strong filtration on a small number of columns using \nPREWHERE\n.\n\n\nIn subqueries (since columns that aren't needed for the external query are excluded from subqueries).\n\n\n\n\nIn all other cases, we don't recommend using the asterisk, since it only gives you the drawbacks of a columnar DBMS instead of the advantages. In other words using the asterisk is not recommended.\n\n\nKILL QUERY\n\n\nKILL\n \nQUERY\n\n \nWHERE\n \nwhere\n \nexpression\n \nto\n \nSELECT\n \nFROM\n \nsystem\n.\nprocesses\n \nquery\n\n \n[\nSYNC\n|\nASYNC\n|\nTEST\n]\n\n \n[\nFORMAT\n \nformat\n]\n\n\n\n\n\n\nAttempts to forcibly terminate the currently running queries.\nThe queries to terminate are selected from the system.processes table using the criteria defined in the \nWHERE\n clause of the \nKILL\n query.\n\n\nExamples:\n\n\n-- Forcibly terminates all queries with the specified query_id:\n\n\nKILL\n \nQUERY\n \nWHERE\n \nquery_id\n=\n2-857d-4a57-9ee0-327da5d60a90\n\n\n\n-- Synchronously terminates all queries run by \nusername\n:\n\n\nKILL\n \nQUERY\n \nWHERE\n \nuser\n=\nusername\n \nSYNC\n\n\n\n\n\n\nRead-only users can only stop their own queries.\n\n\nBy default, the asynchronous version of queries is used (\nASYNC\n), which doesn't wait for confirmation that queries have stopped.\n\n\nThe synchronous version (\nSYNC\n) waits for all queries to stop and displays information about each process as it stops.\nThe response contains the \nkill_status\n column, which can take the following values:\n\n\n\n\n'finished' \u2013 The query was terminated successfully.\n\n\n'waiting' \u2013 Waiting for the query to end after sending it a signal to terminate.\n\n\nThe other values \u200b\u200bexplain why the query can't be stopped.\n\n\n\n\nA test query (\nTEST\n) only checks the user's rights and displays a list of queries to stop.\n\n\nSyntax\n\n\nThere are two types of parsers in the system: the full SQL parser (a recursive descent parser), and the data format parser (a fast stream parser).\nIn all cases except the INSERT query, only the full SQL parser is used.\nThe INSERT query uses both parsers:\n\n\nINSERT\n \nINTO\n \nt\n \nVALUES\n \n(\n1\n,\n \nHello, world\n),\n \n(\n2\n,\n \nabc\n),\n \n(\n3\n,\n \ndef\n)\n\n\n\n\n\n\nThe \nINSERT INTO t VALUES\n fragment is parsed by the full parser, and the data \n(1, 'Hello, world'), (2, 'abc'), (3, 'def')\n is parsed by the fast stream parser.\nData can have any format. When a query is received, the server calculates no more than \nmax_query_size\n bytes of the request in RAM (by default, 1 MB), and the rest is stream parsed.\nThis means the system doesn't have problems with large INSERT queries, like MySQL does.\n\n\nWhen using the Values format in an INSERT query, it may seem that data is parsed the same as expressions in a SELECT query, but this is not true. The Values format is much more limited.\n\n\nNext we will cover the full parser. For more information about format parsers, see the section \"Formats\".\n\n\nSpaces\n\n\nThere may be any number of space symbols between syntactical constructions (including the beginning and end of a query). Space symbols include the space, tab, line feed, CR, and form feed.\n\n\nComments\n\n\nSQL-style and C-style comments are supported.\nSQL-style comments: from \n--\n to the end of the line. The space after \n--\n can be omitted.\nComments in C-style: from \n/*\n to \n*/\n. These comments can be multiline. Spaces are not required here, either.\n\n\nKeywords\n\n\nKeywords (such as \nSELECT\n) are not case-sensitive. Everything else (column names, functions, and so on), in contrast to standard SQL, is case-sensitive. Keywords are not reserved (they are just parsed as keywords in the corresponding context).\n\n\nIdentifiers\n\n\nIdentifiers (column names, functions, and data types) can be quoted or non-quoted.\nNon-quoted identifiers start with a Latin letter or underscore, and continue with a Latin letter, underscore, or number. In other words, they must match the regex \n^[a-zA-Z_][0-9a-zA-Z_]*$\n. Examples: \nx, _1, X_y__Z123_.\n\n\nQuoted identifiers are placed in reversed quotation marks \n`id`\n (the same as in MySQL), and can indicate any set of bytes (non-empty). In addition, symbols (for example, the reverse quotation mark) inside this type of identifier can be backslash-escaped. Escaping rules are the same as for string literals (see below).\nWe recommend using identifiers that do not need to be quoted.\n\n\nLiterals\n\n\nThere are numeric literals, string literals, and compound literals.\n\n\nNumeric literals\n\n\nA numeric literal tries to be parsed:\n\n\n\n\nFirst as a 64-bit signed number, using the 'strtoull' function.\n\n\nIf unsuccessful, as a 64-bit unsigned number, using the 'strtoll' function.\n\n\nIf unsuccessful, as a floating-point number using the 'strtod' function.\n\n\nOtherwise, an error is returned.\n\n\n\n\nThe corresponding value will have the smallest type that the value fits in.\nFor example, 1 is parsed as UInt8, but 256 is parsed as UInt16. For more information, see \"Data types\".\n\n\nExamples: \n1\n, \n18446744073709551615\n, \n0xDEADBEEF\n, \n01\n, \n0.1\n, \n1e100\n, \n-1e-100\n, \ninf\n, \nnan\n.\n\n\nString literals\n\n\nOnly string literals in single quotes are supported. The enclosed characters can be backslash-escaped. The following escape sequences have a corresponding special value: \n\\b\n, \n\\f\n, \n\\r\n, \n\\n\n, \n\\t\n, \n\\0\n, \n\\a\n, \n\\v\n, \n\\xHH\n. In all other cases, escape sequences in the format \n\\c\n, where \"c\" is any character, are converted to \"c\". This means that you can use the sequences \n\\'\nand\n\\\\\n. The value will have the String type.\n\n\nThe minimum set of characters that you need to escape in string literals: \n'\n and \n\\\n.\n\n\nCompound literals\n\n\nConstructions are supported for arrays: \n[1, 2, 3]\n and tuples: \n(1, 'Hello, world!', 2)\n..\nActually, these are not literals, but expressions with the array creation operator and the tuple creation operator, respectively.\nFor more information, see the section \"Operators2\".\nAn array must consist of at least one item, and a tuple must have at least two items.\nTuples have a special purpose for use in the IN clause of a SELECT query. Tuples can be obtained as the result of a query, but they can't be saved to a database (with the exception of Memory-type tables).\n\n\nFunctions\n\n\nFunctions are written like an identifier with a list of arguments (possibly empty) in brackets. In contrast to standard SQL, the brackets are required, even for an empty arguments list. Example: \nnow()\n.\nThere are regular and aggregate functions (see the section \"Aggregate functions\"). Some aggregate functions can contain two lists of arguments in brackets. Example: \nquantile (0.9) (x)\n. These aggregate functions are called \"parametric\" functions, and the arguments in the first list are called \"parameters\". The syntax of aggregate functions without parameters is the same as for regular functions.\n\n\nOperators\n\n\nOperators are converted to their corresponding functions during query parsing, taking their priority and associativity into account.\nFor example, the expression \n1 + 2 * 3 + 4\n is transformed to \nplus(plus(1, multiply(2, 3)), 4)\n.\nFor more information, see the section \"Operators\" below.\n\n\nData types and database table engines\n\n\nData types and table engines in the \nCREATE\n query are written the same way as identifiers or functions. In other words, they may or may not contain an arguments list in brackets. For more information, see the sections \"Data types,\" \"Table engines,\" and \"CREATE\".\n\n\nSynonyms\n\n\nIn the SELECT query, expressions can specify synonyms using the AS keyword. Any expression is placed to the left of AS. The identifier name for the synonym is placed to the right of AS. As opposed to standard SQL, synonyms are not only declared on the top level of expressions:\n\n\nSELECT\n \n(\n1\n \nAS\n \nn\n)\n \n+\n \n2\n,\n \nn\n\n\n\n\n\n\nIn contrast to standard SQL, synonyms can be used in all parts of a query, not just \nSELECT\n.\n\n\nAsterisk\n\n\nIn a \nSELECT\n query, an asterisk can replace the expression. For more information, see the section \"SELECT\".\n\n\nExpressions\n\n\nAn expression is a function, identifier, literal, application of an operator, expression in brackets, subquery, or asterisk. It can also contain a synonym.\nA list of expressions is one or more expressions separated by commas.\nFunctions and operators, in turn, can have expressions as arguments.\n\n\nTable engines\n\n\nThe table engine (type of table) determines:\n\n\n\n\nHow and where data is stored: where to write it to, and where to read it from.\n\n\nWhich queries are supported, and how.\n\n\nConcurrent data access.\n\n\nUse of indexes, if present.\n\n\nWhether multithreaded request execution is possible.\n\n\nData replication.\n\n\n\n\nWhen reading data, the engine is only required to extract the necessary set of columns. However, in some cases, the query may be partially processed inside the table engine.\n\n\nNote that for most serious tasks, you should use engines from the \nMergeTree\n family.\n\n\nTinyLog\n\n\nThe simplest table engine, which stores data on a disk.\nEach column is stored in a separate compressed file.\nWhen writing, data is appended to the end of files.\n\n\nConcurrent data access is not restricted in any way:\n\n\n\n\nIf you are simultaneously reading from a table and writing to it in a different query, the read operation will complete with an error.\n\n\nIf you are writing to a table in multiple queries simultaneously, the data will be broken.\n\n\n\n\nThe typical way to use this table is write-once: first just write the data one time, then read it as many times as needed.\nQueries are executed in a single stream. In other words, this engine is intended for relatively small tables (recommended up to 1,000,000 rows).\nIt makes sense to use this table engine if you have many small tables, since it is simpler than the Log engine (fewer files need to be opened).\nThe situation when you have a large number of small tables guarantees poor productivity, but may already be used when working with another DBMS, and you may find it easier to switch to using TinyLog types of tables.\n\nIndexes are not supported.\n\n\nIn Yandex.Metrica, TinyLog tables are used for intermediary data that is processed in small batches.\n\n\nLog\n\n\nLog differs from TinyLog in that a small file of \"marks\" resides with the column files. These marks are written on every data block and contain offsets that indicate where to start reading the file in order to skip the specified number of rows. This makes it possible to read table data in multiple threads.\nFor concurrent data access, the read operations can be performed simultaneously, while write operations block reads and each other.\nThe Log engine does not support indexes. Similarly, if writing to a table failed, the table is broken, and reading from it returns an error. The Log engine is appropriate for temporary data, write-once tables, and for testing or demonstration purposes.\n\n\nMemory\n\n\nThe Memory engine stores data in RAM, in uncompressed form. Data is stored in exactly the same form as it is received when read. In other words, reading from this table is completely free.\nConcurrent data access is synchronized. Locks are short: read and write operations don't block each other.\nIndexes are not supported. Reading is parallelized.\nMaximal productivity (over 10 GB/sec) is reached on simple queries, because there is no reading from the disk, decompressing, or deserializing data. (We should note that in many cases, the productivity of the MergeTree engine is almost as high.)\nWhen restarting a server, data disappears from the table and the table becomes empty.\nNormally, using this table engine is not justified. However, it can be used for tests, and for tasks where maximum speed is required on a relatively small number of rows (up to approximately 100,000,000).\n\n\nThe Memory engine is used by the system for temporary tables with external query data (see the section \"External data for processing a query\"), and for implementing GLOBAL IN (see the section \"IN operators\").\n\n\n\n\nMergeTree\n\n\nThe MergeTree engine supports an index by primary key and by date, and provides the possibility to update data in real time.\nThis is the most advanced table engine in ClickHouse. Don't confuse it with the Merge engine.\n\n\nThe engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity.\n\n\nExample without sampling support.\n\n\nMergeTree(EventDate, (CounterID, EventDate), 8192)\n\n\n\n\n\nExample with sampling support.\n\n\nMergeTree(EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID)), 8192)\n\n\n\n\n\nA MergeTree table must have a separate column containing the date. Here, it is the EventDate column. The date column must have the 'Date' type (not 'DateTime').\n\n\nThe primary key may be a tuple from any expressions (usually this is just a tuple of columns), or a single expression.\n\n\nThe sampling expression (optional) can be any expression. It must also be present in the primary key. The example uses a hash of user IDs to pseudo-randomly disperse data in the table for each CounterID and EventDate. In other words, when using the SAMPLE clause in a query, you get an evenly pseudo-random sample of data for a subset of users.\n\n\nThe table is implemented as a set of parts. Each part is sorted by the primary key. In addition, each part has the minimum and maximum date assigned. When inserting in the table, a new sorted part is created. The merge process is periodically initiated in the background. When merging, several parts are selected (usually the smallest ones) and then merged into one large sorted part.\n\n\nIn other words, incremental sorting occurs when inserting to the table. Merging is implemented so that the table always consists of a small number of sorted parts, and the merge itself doesn't do too much work.\n\n\nDuring insertion, data belonging to different months is separated into different parts. The parts that correspond to different months are never combined. The purpose of this is to provide local data modification (for ease in backups).\n\n\nParts are combined up to a certain size threshold, so there aren't any merges that are too long.\n\n\nFor each part, an index file is also written. The index file contains the primary key value for every 'index_granularity' row in the table. In other words, this is an abbreviated index of sorted data.\n\n\nFor columns, \"marks\" are also written to each 'index_granularity' row so that data can be read in a specific range.\n\n\nWhen reading from a table, the SELECT query is analyzed for whether indexes can be used.\nAn index can be used if the WHERE or PREWHERE clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has IN or LIKE with a fixed prefix on columns or expressions that are in the primary key or partitioning key, or on certain partially repetitive functions of these columns, or logical relationships of these expressions.\n\n\nThus, it is possible to quickly run queries on one or many ranges of the primary key. In this example, queries will be fast when run for a specific tracking tag; for a specific tag and date range; for a specific tag and date; for multiple tags with a date range, and so on.\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \nEventDate\n \n=\n \ntoDate\n(\nnow\n())\n \nAND\n \nCounterID\n \n=\n \n34\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \nEventDate\n \n=\n \ntoDate\n(\nnow\n())\n \nAND\n \n(\nCounterID\n \n=\n \n34\n \nOR\n \nCounterID\n \n=\n \n42\n)\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \n((\nEventDate\n \n=\n \ntoDate\n(\n2014-01-01\n)\n \nAND\n \nEventDate\n \n=\n \ntoDate\n(\n2014-01-31\n))\n \nOR\n \nEventDate\n \n=\n \ntoDate\n(\n2014-05-01\n))\n \nAND\n \nCounterID\n \nIN\n \n(\n101500\n,\n \n731962\n,\n \n160656\n)\n \nAND\n \n(\nCounterID\n \n=\n \n101500\n \nOR\n \nEventDate\n \n!=\n \ntoDate\n(\n2014-05-01\n))\n\n\n\n\n\n\nAll of these cases will use the index by date and by primary key. The index is used even for complex expressions. Reading from the table is organized so that using the index can't be slower than a full scan.\n\n\nIn this example, the index can't be used.\n\n\nSELECT\n \ncount\n()\n \nFROM\n \ntable\n \nWHERE\n \nCounterID\n \n=\n \n34\n \nOR\n \nURL\n \nLIKE\n \n%upyachka%\n\n\n\n\n\n\nTo check whether ClickHouse can use the index when executing the query, use the settings \nforce_index_by_date\nand\nforce_primary_key\n.\n\n\nThe index by date only allows reading those parts that contain dates from the desired range. However, a data part may contain data for many dates (up to an entire month), while within a single part the data is ordered by the primary key, which might not contain the date as the first column. Because of this, using a query with only a date condition that does not specify the primary key prefix will cause more data to be read than for a single date.\n\n\nFor concurrent table access, we use multi-versioning. In other words, when a table is simultaneously read and updated, data is read from a set of parts that is current at the time of the query. There are no lengthy locks. Inserts do not get in the way of read operations.\n\n\nReading from a table is automatically parallelized.\n\n\nThe \nOPTIMIZE\n query is supported, which calls an extra merge step.\n\n\nYou can use a single large table and continually add data to it in small chunks \u2013 this is what MergeTree is intended for.\n\n\nData replication is possible for all types of tables in the MergeTree family (see the section \"Data replication\").\n\n\n\n\nCustom partitioning key\n\n\nStarting with version 1.1.54310, you can create tables in the MergeTree family with any partitioning expression (not only partitioning by month).\n\n\nThe partition key can be an expression from the table columns, or a tuple of such expressions (similar to the primary key). The partition key can be omitted. When creating a table, specify the partition key in the ENGINE description with the new syntax:\n\n\nENGINE [=] Name(...) [PARTITION BY expr] [ORDER BY expr] [SAMPLE BY expr] [SETTINGS name=value, ...]\n\n\n\n\n\nFor MergeTree tables, the partition expression is specified after \nPARTITION BY\n, the primary key after \nORDER BY\n, the sampling key after \nSAMPLE BY\n, and \nSETTINGS\n can specify \nindex_granularity\n (optional; the default value is 8192), as well as other settings from \nMergeTreeSettings.h\n. The other engine parameters are specified in parentheses after the engine name, as previously. Example:\n\n\nENGINE\n \n=\n \nReplicatedCollapsingMergeTree\n(\n/clickhouse/tables/name\n,\n \nreplica1\n,\n \nSign\n)\n\n \nPARTITION\n \nBY\n \n(\ntoMonday\n(\nStartDate\n),\n \nEventType\n)\n\n \nORDER\n \nBY\n \n(\nCounterID\n,\n \nStartDate\n,\n \nintHash32\n(\nUserID\n))\n\n \nSAMPLE\n \nBY\n \nintHash32\n(\nUserID\n)\n\n\n\n\n\n\nThe traditional partitioning by month is expressed as \ntoYYYYMM(date_column)\n.\n\n\nYou can't convert an old-style table to a table with custom partitions (only via INSERT SELECT).\n\n\nAfter this table is created, merge will only work for data parts that have the same value for the partitioning expression. Note: This means that you shouldn't make overly granular partitions (more than about a thousand partitions), or SELECT will perform poorly.\n\n\nTo specify a partition in ALTER PARTITION commands, specify the value of the partition expression (or a tuple). Constants and constant expressions are supported. Example:\n\n\nALTER\n \nTABLE\n \ntable\n \nDROP\n \nPARTITION\n \n(\ntoMonday\n(\ntoday\n()),\n \n1\n)\n\n\n\n\n\n\nDeletes the partition for the current week with event type 1. The same is true for the OPTIMIZE query. To specify the only partition in a non-partitioned table, specify \nPARTITION tuple()\n.\n\n\nNote: For old-style tables, the partition can be specified either as a number \n201710\n or a string \n'201710'\n. The syntax for the new style of tables is stricter with types (similar to the parser for the VALUES input format). In addition, ALTER TABLE FREEZE PARTITION uses exact match for new-style tables (not prefix match).\n\n\nIn the \nsystem.parts\n table, the \npartition\n column specifies the value of the partition expression to use in ALTER queries (if quotas are removed). The \nname\n column should specify the name of the data part that has a new format.\n\n\nWas: \n20140317_20140323_2_2_0\n (minimum date - maximum date - minimum block number - maximum block number - level).\n\n\nNow: \n201403_2_2_0\n (partition ID - minimum block number - maximum block number - level).\n\n\nThe partition ID is its string identifier (human-readable, if possible) that is used for the names of data parts in the file system and in ZooKeeper. You can specify it in ALTER queries in place of the partition key. Example: Partition key \ntoYYYYMM(EventDate)\n; ALTER can specify either \nPARTITION 201710\n or \nPARTITION ID '201710'\n.\n\n\nFor more examples, see the tests \n00502_custom_partitioning_local\n and \n00502_custom_partitioning_replicated_zookeeper\n.\n\n\nReplacingMergeTree\n\n\nThis engine table differs from \nMergeTree\n in that it removes duplicate entries with the same primary key value.\n\n\nThe last optional parameter for the table engine is the version column. When merging, it reduces all rows with the same primary key value to just one row. If the version column is specified, it leaves the row with the highest version; otherwise, it leaves the last row.\n\n\nThe version column must have a type from the \nUInt\n family, \nDate\n, or \nDateTime\n.\n\n\nReplacingMergeTree\n(\nEventDate\n,\n \n(\nOrderID\n,\n \nEventDate\n,\n \nBannerID\n,\n \n...),\n \n8192\n,\n \nver\n)\n\n\n\n\n\n\nNote that data is only deduplicated during merges. Merging occurs in the background at an unknown time, so you can't plan for it. Some of the data may remain unprocessed. Although you can run an unscheduled merge using the OPTIMIZE query, don't count on using it, because the OPTIMIZE query will read and write a large amount of data.\n\n\nThus, \nReplacingMergeTree\n is suitable for clearing out duplicate data in the background in order to save space, but it doesn't guarantee the absence of duplicates.\n\n\nThis engine is not used in Yandex.Metrica, but it has been applied in other Yandex projects.\n\n\nSummingMergeTree\n\n\nThis engine differs from \nMergeTree\n in that it totals data while merging.\n\n\nSummingMergeTree\n(\nEventDate\n,\n \n(\nOrderID\n,\n \nEventDate\n,\n \nBannerID\n,\n \n...),\n \n8192\n)\n\n\n\n\n\n\nThe columns to total are implicit. When merging, all rows with the same primary key value (in the example, OrderId, EventDate, BannerID, ...) have their values totaled in numeric columns that are not part of the primary key.\n\n\nSummingMergeTree\n(\nEventDate\n,\n \n(\nOrderID\n,\n \nEventDate\n,\n \nBannerID\n,\n \n...),\n \n8192\n,\n \n(\nShows\n,\n \nClicks\n,\n \nCost\n,\n \n...))\n\n\n\n\n\n\nThe columns to total are set explicitly (the last parameter \u2013 Shows, Clicks, Cost, ...). When merging, all rows with the same primary key value have their values totaled in the specified columns. The specified columns also must be numeric and must not be part of the primary key.\n\n\nIf the values were null in all of these columns, the row is deleted. (The exception is cases when the data part would not have any rows left in it.)\n\n\nFor the other rows that are not part of the primary key, the first value that occurs is selected when merging.\n\n\nSummation is not performed for a read operation. If it is necessary, write the appropriate GROUP BY.\n\n\nIn addition, a table can have nested data structures that are processed in a special way.\nIf the name of a nested table ends in 'Map' and it contains at least two columns that meet the following criteria:\n\n\n\n\nThe first table is numeric ((U)IntN, Date, DateTime), which we'll refer to as the 'key'.\n\n\nThe other columns are arithmetic ((U)IntN, Float32/64), which we'll refer to as '(values...)'. Then this nested table is interpreted as a mapping of key =\n (values...), and when merging its rows, the elements of two data sets are merged by 'key' with a summation of the corresponding (values...).\n\n\n\n\nExamples:\n\n\n[(1, 100)] + [(2, 150)] -\n [(1, 100), (2, 150)]\n[(1, 100)] + [(1, 150)] -\n [(1, 250)]\n[(1, 100)] + [(1, 150), (2, 150)] -\n [(1, 250), (2, 150)]\n[(1, 100), (2, 150)] + [(1, -100)] -\n [(2, 150)]\n\n\n\n\n\nFor aggregation of Map, use the function sumMap(key, value).\n\n\nFor nested data structures, you don't need to specify the columns as a list of columns for totaling.\n\n\nThis table engine is not particularly useful. Remember that when saving just pre-aggregated data, you lose some of the system's advantages.\n\n\nAggregatingMergeTree\n\n\nThis engine differs from \nMergeTree\n in that the merge combines the states of aggregate functions stored in the table for rows with the same primary key value.\n\n\nFor this to work, it uses the \nAggregateFunction\n data type, as well as \n-State\n and \n-Merge\n modifiers for aggregate functions. Let's examine it more closely.\n\n\nThere is an \nAggregateFunction\n data type. It is a parametric data type. As parameters, the name of the aggregate function is passed, then the types of its arguments.\n\n\nExamples:\n\n\nCREATE\n \nTABLE\n \nt\n\n\n(\n\n \ncolumn1\n \nAggregateFunction\n(\nuniq\n,\n \nUInt64\n),\n\n \ncolumn2\n \nAggregateFunction\n(\nanyIf\n,\n \nString\n,\n \nUInt8\n),\n\n \ncolumn3\n \nAggregateFunction\n(\nquantiles\n(\n0\n.\n5\n,\n \n0\n.\n9\n),\n \nUInt64\n)\n\n\n)\n \nENGINE\n \n=\n \n...\n\n\n\n\n\n\nThis type of column stores the state of an aggregate function.\n\n\nTo get this type of value, use aggregate functions with the \nState\n suffix.\n\n\nExample:\n\nuniqState(UserID), quantilesState(0.5, 0.9)(SendTiming)\n\n\nIn contrast to the corresponding \nuniq\n and \nquantiles\n functions, these functions return the state, rather than the prepared value. In other words, they return an \nAggregateFunction\n type value.\n\n\nAn \nAggregateFunction\n type value can't be output in Pretty formats. In other formats, these types of values are output as implementation-specific binary data. The \nAggregateFunction\n type values are not intended for output or saving in a dump.\n\n\nThe only useful thing you can do with \nAggregateFunction\n type values is combine the states and get a result, which essentially means to finish aggregation. Aggregate functions with the 'Merge' suffix are used for this purpose.\nExample: \nuniqMerge(UserIDState), where UserIDState has the AggregateFunction\n type.\n\n\nIn other words, an aggregate function with the 'Merge' suffix takes a set of states, combines them, and returns the result.\nAs an example, these two queries return the same result:\n\n\nSELECT\n \nuniq\n(\nUserID\n)\n \nFROM\n \ntable\n\n\n\nSELECT\n \nuniqMerge\n(\nstate\n)\n \nFROM\n \n(\nSELECT\n \nuniqState\n(\nUserID\n)\n \nAS\n \nstate\n \nFROM\n \ntable\n \nGROUP\n \nBY\n \nRegionID\n)\n\n\n\n\n\n\nThere is an \nAggregatingMergeTree\n engine. Its job during a merge is to combine the states of aggregate functions from different table rows with the same primary key value.\n\n\nYou can't use a normal INSERT to insert a row in a table containing \nAggregateFunction\n columns, because you can't explicitly define the \nAggregateFunction\n value. Instead, use \nINSERT SELECT\n with \n-State\n aggregate functions for inserting data.\n\n\nWith SELECT from an \nAggregatingMergeTree\n table, use GROUP BY and aggregate functions with the '-Merge' modifier in order to complete data aggregation.\n\n\nYou can use \nAggregatingMergeTree\n tables for incremental data aggregation, including for aggregated materialized views.\n\n\nExample:\n\n\nCreate an \nAggregatingMergeTree\n materialized view that watches the \ntest.visits\n table:\n\n\nCREATE\n \nMATERIALIZED\n \nVIEW\n \ntest\n.\nbasic\n\n\nENGINE\n \n=\n \nAggregatingMergeTree\n(\nStartDate\n,\n \n(\nCounterID\n,\n \nStartDate\n),\n \n8192\n)\n\n\nAS\n \nSELECT\n\n \nCounterID\n,\n\n \nStartDate\n,\n\n \nsumState\n(\nSign\n)\n \nAS\n \nVisits\n,\n\n \nuniqState\n(\nUserID\n)\n \nAS\n \nUsers\n\n\nFROM\n \ntest\n.\nvisits\n\n\nGROUP\n \nBY\n \nCounterID\n,\n \nStartDate\n;\n\n\n\n\n\n\nInsert data in the \ntest.visits\n table. Data will also be inserted in the view, where it will be aggregated:\n\n\nINSERT\n \nINTO\n \ntest\n.\nvisits\n \n...\n\n\n\n\n\n\nPerform \nSELECT\n from the view using \nGROUP BY\n in order to complete data aggregation:\n\n\nSELECT\n\n \nStartDate\n,\n\n \nsumMerge\n(\nVisits\n)\n \nAS\n \nVisits\n,\n\n \nuniqMerge\n(\nUsers\n)\n \nAS\n \nUsers\n\n\nFROM\n \ntest\n.\nbasic\n\n\nGROUP\n \nBY\n \nStartDate\n\n\nORDER\n \nBY\n \nStartDate\n;\n\n\n\n\n\n\nYou can create a materialized view like this and assign a normal view to it that finishes data aggregation.\n\n\nNote that in most cases, using \nAggregatingMergeTree\n is not justified, since queries can be run efficiently enough on non-aggregated data.\n\n\nCollapsingMergeTree\n\n\nThis engine is used specifically for Yandex.Metrica.\n\n\nIt differs from \nMergeTree\n in that it allows automatic deletion, or \"collapsing\" certain pairs of rows when merging.\n\n\nYandex.Metrica has normal logs (such as hit logs) and change logs. Change logs are used for incrementally calculating statistics on data that is constantly changing. Examples are the log of session changes, or logs of changes to user histories. Sessions are constantly changing in Yandex.Metrica. For example, the number of hits per session increases. We refer to changes in any object as a pair (?old values, ?new values). Old values may be missing if the object was created. New values may be missing if the object was deleted. If the object was changed, but existed previously and was not deleted, both values are present. In the change log, one or two entries are made for each change. Each entry contains all the attributes that the object has, plus a special attribute for differentiating between the old and new values. When objects change, only the new entries are added to the change log, and the existing ones are not touched.\n\n\nThe change log makes it possible to incrementally calculate almost any statistics. To do this, we need to consider \"new\" rows with a plus sign, and \"old\" rows with a minus sign. In other words, incremental calculation is possible for all statistics whose algebraic structure contains an operation for taking the inverse of an element. This is true of most statistics. We can also calculate \"idempotent\" statistics, such as the number of unique visitors, since the unique visitors are not deleted when making changes to sessions.\n\n\nThis is the main concept that allows Yandex.Metrica to work in real time.\n\n\nCollapsingMergeTree accepts an additional parameter - the name of an Int8-type column that contains the row's \"sign\". Example:\n\n\nCollapsingMergeTree\n(\nEventDate\n,\n \n(\nCounterID\n,\n \nEventDate\n,\n \nintHash32\n(\nUniqID\n),\n \nVisitID\n),\n \n8192\n,\n \nSign\n)\n\n\n\n\n\n\nHere, \nSign\n is a column containing -1 for \"old\" values and 1 for \"new\" values.\n\n\nWhen merging, each group of consecutive identical primary key values (columns for sorting data) is reduced to no more than one row with the column value 'sign_column = -1' (the \"negative row\") and no more than one row with the column value 'sign_column = 1' (the \"positive row\"). In other words, entries from the change log are collapsed.\n\n\nIf the number of positive and negative rows matches, the first negative row and the last positive row are written.\nIf there is one more positive row than negative rows, only the last positive row is written.\nIf there is one more negative row than positive rows, only the first negative row is written.\nOtherwise, there will be a logical error and none of the rows will be written. (A logical error can occur if the same section of the log was accidentally inserted more than once. The error is just recorded in the server log, and the merge continues.)\n\n\nThus, collapsing should not change the results of calculating statistics.\nChanges are gradually collapsed so that in the end only the last value of almost every object is left.\nCompared to MergeTree, the CollapsingMergeTree engine allows a multifold reduction of data volume.\n\n\nThere are several ways to get completely \"collapsed\" data from a \nCollapsingMergeTree\n table:\n\n\n\n\nWrite a query with GROUP BY and aggregate functions that accounts for the sign. For example, to calculate quantity, write 'sum(Sign)' instead of 'count()'. To calculate the sum of something, write 'sum(Sign * x)' instead of 'sum(x)', and so on, and also add 'HAVING sum(Sign) \n 0'. Not all amounts can be calculated this way. For example, the aggregate functions 'min' and 'max' can't be rewritten.\n\n\nIf you must extract data without aggregation (for example, to check whether rows are present whose newest values match certain conditions), you can use the FINAL modifier for the FROM clause. This approach is significantly less efficient.\n\n\n\n\n\n\nGraphiteMergeTree\n\n\nThis engine is designed for rollup (thinning and aggregating/averaging) \nGraphite\n data. It may be helpful to developers who want to use ClickHouse as a data store for Graphite.\n\n\nGraphite stores full data in ClickHouse, and data can be retrieved in the following ways:\n\n\n\n\nWithout thinning.\n\n\n\n\nUses the \nMergeTree\n engine.\n\n\n\n\nWith thinning.\n\n\n\n\nUsing the \nGraphiteMergeTree\n engine.\n\n\nThe engine inherits properties from MergeTree. The settings for thinning data are defined by the \ngraphite_rollup\n parameter in the server configuration.\n\n\nUsing the engine\n\n\nThe Graphite data table must contain the following fields at minimum:\n\n\n\n\nPath\n \u2013 The metric name (Graphite sensor).\n\n\nTime\n \u2013 The time for measuring the metric.\n\n\nValue\n \u2013 The value of the metric at the time set in Time.\n\n\nVersion\n \u2013 Determines which value of the metric with the same Path and Time will remain in the database.\n\n\n\n\nRollup pattern:\n\n\npattern\n regexp\n function\n age -\n precision\n ...\npattern\n ...\ndefault\n function\n age -\n precision\n ...\n\n\n\n\n\nWhen processing a record, ClickHouse will check the rules in the \npattern\nclause. If the metric name matches the \nregexp\n, the rules from \npattern\n are applied; otherwise, the rules from \ndefault\n are used.\n\n\nFields in the pattern.\n\n\n\n\nage\n \u2013 The minimum age of the data in seconds.\n\n\nfunction\n \u2013 The name of the aggregating function to apply to data whose age falls within the range \n[age, age + precision]\n.\n\n\nprecision\n\u2013 How precisely to define the age of the data in seconds.\n\n\nregexp\n\u2013 A pattern for the metric name.\n\n\n\n\nExample of settings:\n\n\ngraphite_rollup\n\n \npattern\n\n \nregexp\nclick_cost\n/regexp\n\n \nfunction\nany\n/function\n\n \nretention\n\n \nage\n0\n/age\n\n \nprecision\n5\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n86400\n/age\n\n \nprecision\n60\n/precision\n\n \n/retention\n\n \n/pattern\n\n \ndefault\n\n \nfunction\nmax\n/function\n\n \nretention\n\n \nage\n0\n/age\n\n \nprecision\n60\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n3600\n/age\n\n \nprecision\n300\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n86400\n/age\n\n \nprecision\n3600\n/precision\n\n \n/retention\n\n \n/default\n\n\n/graphite_rollup\n\n\n\n\n\n\n\n\nData replication\n\n\nReplication is only supported for tables in the MergeTree family:\n\n\n\n\nReplicatedMergeTree\n\n\nReplicatedSummingMergeTree\n\n\nReplicatedReplacingMergeTree\n\n\nReplicatedAggregatingMergeTree\n\n\nReplicatedCollapsingMergeTree\n\n\nReplicatedGraphiteMergeTree\n\n\n\n\nReplication works at the level of an individual table, not the entire server. A server can store both replicated and non-replicated tables at the same time.\n\n\nReplication does not depend on sharding. Each shard has its own independent replication.\n\n\nCompressed data is replicated for \nINSERT\n and \nALTER\n queries (see the description of the \nALTER\n query).\n\n\nCREATE\n, \nDROP\n, \nATTACH\n, \nDETACH\n and \nRENAME\n queries are executed on a single server and are not replicated:\n\n\n\n\nThe CREATE TABLE\n query creates a new replicatable table on the server where the query is run. If this table already exists on other servers, it adds a new replica.\n\n\nThe DROP TABLE\n query deletes the replica located on the server where the query is run.\n\n\nThe RENAME\n query renames the table on one of the replicas. In other words, replicated tables can have different names on different replicas.\n\n\n\n\nTo use replication, set the addresses of the ZooKeeper cluster in the config file. Example:\n\n\nzookeeper\n\n \nnode\n \nindex=\n1\n\n \nhost\nexample1\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n \nnode\n \nindex=\n2\n\n \nhost\nexample2\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n \nnode\n \nindex=\n3\n\n \nhost\nexample3\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n\n/zookeeper\n\n\n\n\n\n\nUse ZooKeeper version 3.4.5 or later.\n\n\nYou can specify any existing ZooKeeper cluster and the system will use a directory on it for its own data (the directory is specified when creating a replicatable table).\n\n\nIf ZooKeeper isn't set in the config file, you can't create replicated tables, and any existing replicated tables will be read-only.\n\n\nZooKeeper is not used in \nSELECT\n queries because replication does not affect the performance of \nSELECT\n and queries run just as fast as they do for non-replicated tables. When querying distributed replicated tables, ClickHouse behavior is controlled by the settings \nmax_replica_delay_for_distributed_queries\n and \nfallback_to_stale_replicas_for_distributed_queries\n.\n\n\nFor each \nINSERT\n query, approximately ten entries are added to ZooKeeper through several transactions. (To be more precise, this is for each inserted block of data; an INSERT query contains one block or one block per \nmax_insert_block_size = 1048576\n rows.) This leads to slightly longer latencies for \nINSERT\n compared to non-replicated tables. But if you follow the recommendations to insert data in batches of no more than one \nINSERT\n per second, it doesn't create any problems. The entire ClickHouse cluster used for coordinating one ZooKeeper cluster has a total of several hundred \nINSERTs\n per second. The throughput on data inserts (the number of rows per second) is just as high as for non-replicated data.\n\n\nFor very large clusters, you can use different ZooKeeper clusters for different shards. However, this hasn't proven necessary on the Yandex.Metrica cluster (approximately 300 servers).\n\n\nReplication is asynchronous and multi-master. \nINSERT\n queries (as well as \nALTER\n) can be sent to any available server. Data is inserted on the server where the query is run, and then it is copied to the other servers. Because it is asynchronous, recently inserted data appears on the other replicas with some latency. If part of the replicas are not available, the data is written when they become available. If a replica is available, the latency is the amount of time it takes to transfer the block of compressed data over the network.\n\n\nBy default, an INSERT query waits for confirmation of writing the data from only one replica. If the data was successfully written to only one replica and the server with this replica ceases to exist, the stored data will be lost. Tp enable getting confirmation of data writes from multiple replicas, use the \ninsert_quorum\n option.\n\n\nEach block of data is written atomically. The INSERT query is divided into blocks up to \nmax_insert_block_size = 1048576\n rows. In other words, if the \nINSERT\n query has less than 1048576 rows, it is made atomically.\n\n\nData blocks are deduplicated. For multiple writes of the same data block (data blocks of the same size containing the same rows in the same order), the block is only written once. The reason for this is in case of network failures when the client application doesn't know if the data was written to the DB, so the \nINSERT\n query can simply be repeated. It doesn't matter which replica INSERTs were sent to with identical data. \nINSERTs\n are idempotent. Deduplication parameters are controlled by \nmerge_tree\n server settings.\n\n\nDuring replication, only the source data to insert is transferred over the network. Further data transformation (merging) is coordinated and performed on all the replicas in the same way. This minimizes network usage, which means that replication works well when replicas reside in different datacenters. (Note that duplicating data in different datacenters is the main goal of replication.)\n\n\nYou can have any number of replicas of the same data. Yandex.Metrica uses double replication in production. Each server uses RAID-5 or RAID-6, and RAID-10 in some cases. This is a relatively reliable and convenient solution.\n\n\nThe system monitors data synchronicity on replicas and is able to recover after a failure. Failover is automatic (for small differences in data) or semi-automatic (when data differs too much, which may indicate a configuration error).\n\n\n\n\nCreating replicated tables\n\n\nThe \nReplicated\n prefix is added to the table engine name. For example:\nReplicatedMergeTree\n.\n\n\nTwo parameters are also added in the beginning of the parameters list \u2013 the path to the table in ZooKeeper, and the replica name in ZooKeeper.\n\n\nExample:\n\n\nReplicatedMergeTree(\n/clickhouse/tables/{layer}-{shard}/hits\n, \n{replica}\n, EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID), EventTime), 8192)\n\n\n\n\n\nAs the example shows, these parameters can contain substitutions in curly brackets. The substituted values are taken from the 'macros' section of the config file. Example:\n\n\nmacros\n\n \nlayer\n05\n/layer\n\n \nshard\n02\n/shard\n\n \nreplica\nexample05-02-1.yandex.ru\n/replica\n\n\n/macros\n\n\n\n\n\n\nThe path to the table in ZooKeeper should be unique for each replicated table. Tables on different shards should have different paths.\nIn this case, the path consists of the following parts:\n\n\n/clickhouse/tables/\n is the common prefix. We recommend using exactly this one.\n\n\n{layer}-{shard}\n is the shard identifier. In this example it consists of two parts, since the Yandex.Metrica cluster uses bi-level sharding. For most tasks, you can leave just the {shard} substitution, which will be expanded to the shard identifier.\n\n\nhits\n is the name of the node for the table in ZooKeeper. It is a good idea to make it the same as the table name. It is defined explicitly, because in contrast to the table name, it doesn't change after a RENAME query.\n\n\nThe replica name identifies different replicas of the same table. You can use the server name for this, as in the example. The name only needs to be unique within each shard.\n\n\nYou can define the parameters explicitly instead of using substitutions. This might be convenient for testing and for configuring small clusters. However, you can't use distributed DDL queries (\nON CLUSTER\n) in this case.\n\n\nWhen working with large clusters, we recommend using substitutions because they reduce the probability of error.\n\n\nRun the \nCREATE TABLE\n query on each replica. This query creates a new replicated table, or adds a new replica to an existing one.\n\n\nIf you add a new replica after the table already contains some data on other replicas, the data will be copied from the other replicas to the new one after running the query. In other words, the new replica syncs itself with the others.\n\n\nTo delete a replica, run \nDROP TABLE\n. However, only one replica is deleted \u2013 the one that resides on the server where you run the query.\n\n\nRecovery after failures\n\n\nIf ZooKeeper is unavailable when a server starts, replicated tables switch to read-only mode. The system periodically attempts to connect to ZooKeeper.\n\n\nIf ZooKeeper is unavailable during an \nINSERT\n, or an error occurs when interacting with ZooKeeper, an exception is thrown.\n\n\nAfter connecting to ZooKeeper, the system checks whether the set of data in the local file system matches the expected set of data (ZooKeeper stores this information). If there are minor inconsistencies, the system resolves them by syncing data with the replicas.\n\n\nIf the system detects broken data parts (with the wrong size of files) or unrecognized parts (parts written to the file system but not recorded in ZooKeeper), it moves them to the 'detached' subdirectory (they are not deleted). Any missing parts are copied from the replicas.\n\n\nNote that ClickHouse does not perform any destructive actions such as automatically deleting a large amount of data.\n\n\nWhen the server starts (or establishes a new session with ZooKeeper), it only checks the quantity and sizes of all files. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a \nSELECT\n query. The query throws an exception about a non-matching checksum or size of a compressed block. In this case, data parts are added to the verification queue and copied from the replicas if necessary.\n\n\nIf the local set of data differs too much from the expected one, a safety mechanism is triggered. The server enters this in the log and refuses to launch. The reason for this is that this case may indicate a configuration error, such as if a replica on a shard was accidentally configured like a replica on a different shard. However, the thresholds for this mechanism are set fairly low, and this situation might occur during normal failure recovery. In this case, data is restored semi-automatically - by \"pushing a button\".\n\n\nTo start recovery, create the node \n/path_to_table/replica_name/flags/force_restore_data\n in ZooKeeper with any content, or run the command to restore all replicated tables:\n\n\nsudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data\n\n\n\n\n\nThen restart the server. On start, the server deletes these flags and starts recovery.\n\n\nRecovery after complete data loss\n\n\nIf all data and metadata disappeared from one of the servers, follow these steps for recovery:\n\n\n\n\nInstall ClickHouse on the server. Define substitutions correctly in the config file that contains the shard identifier and replicas, if you use them.\n\n\nIf you had unreplicated tables that must be manually duplicated on the servers, copy their data from a replica (in the directory \n/var/lib/clickhouse/data/db_name/table_name/\n).\n\n\nCopy table definitions located in \n/var/lib/clickhouse/metadata/\n from a replica. If a shard or replica identifier is defined explicitly in the table definitions, correct it so that it corresponds to this replica. (Alternatively, start the server and make all the \nATTACH TABLE\n queries that should have been in the .sql files in \n/var/lib/clickhouse/metadata/\n.)\n\n\nTo start recovery, create the ZooKeeper node \n/path_to_table/replica_name/flags/force_restore_data\n with any content, or run the command to restore all replicated tables: \nsudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data\n\n\n\n\nThen start the server (restart, if it is already running). Data will be downloaded from replicas.\n\n\nAn alternative recovery option is to delete information about the lost replica from ZooKeeper (\n/path_to_table/replica_name\n), then create the replica again as described in \"\nCreating replicatable tables\n\".\n\n\nThere is no restriction on network bandwidth during recovery. Keep this in mind if you are restoring many replicas at once.\n\n\nConverting from MergeTree to ReplicatedMergeTree\n\n\nWe use the term \nMergeTree\n to refer to all table engines in the \nMergeTree family\n, the same as for \nReplicatedMergeTree\n.\n\n\nIf you had a \nMergeTree\n table that was manually replicated, you can convert it to a replicatable table. You might need to do this if you have already collected a large amount of data in a \nMergeTree\n table and now you want to enable replication.\n\n\nIf the data differs on various replicas, first sync it, or delete this data on all the replicas except one.\n\n\nRename the existing MergeTree table, then create a \nReplicatedMergeTree\n table with the old name.\nMove the data from the old table to the 'detached' subdirectory inside the directory with the new table data (\n/var/lib/clickhouse/data/db_name/table_name/\n).\nThen run \nALTER TABLE ATTACH PARTITION\n on one of the replicas to add these data parts to the working set.\n\n\nConverting from ReplicatedMergeTree to MergeTree\n\n\nCreate a MergeTree table with a different name. Move all the data from the directory with the \nReplicatedMergeTree\n table data to the new table's data directory. Then delete the \nReplicatedMergeTree\n table and restart the server.\n\n\nIf you want to get rid of a \nReplicatedMergeTree\n table without launching the server:\n\n\n\n\nDelete the corresponding \n.sql\n file in the metadata directory (\n/var/lib/clickhouse/metadata/\n).\n\n\nDelete the corresponding path in ZooKeeper (\n/path_to_table/replica_name\n).\n\n\n\n\nAfter this, you can launch the server, create a \nMergeTree\n table, move the data to its directory, and then restart the server.\n\n\nRecovery when metadata in the ZooKeeper cluster is lost or damaged\n\n\nIf the data in ZooKeeper was lost or damaged, you can save data by moving it to an unreplicated table as described above.\n\n\nIf exactly the same parts exist on the other replicas, they are added to the working set on them. If not, the parts are downloaded from the replica that has them.\n\n\n\n\nDistributed\n\n\nThe Distributed engine does not store data itself\n, but allows distributed query processing on multiple servers.\nReading is automatically parallelized. During a read, the table indexes on remote servers are used, if there are any.\nThe Distributed engine accepts parameters: the cluster name in the server's config file, the name of a remote database, the name of a remote table, and (optionally) a sharding key.\nExample:\n\n\nDistributed(logs, default, hits[, sharding_key])\n\n\n\n\n\nData will be read from all servers in the 'logs' cluster, from the default.hits table located on every server in the cluster.\nData is not only read, but is partially processed on the remote servers (to the extent that this is possible).\nFor example, for a query with GROUP BY, data will be aggregated on remote servers, and the intermediate states of aggregate functions will be sent to the requestor server. Then data will be further aggregated.\n\n\nInstead of the database name, you can use a constant expression that returns a string. For example: currentDatabase().\n\n\nlogs \u2013 The cluster name in the server's config file.\n\n\nClusters are set like this:\n\n\nremote_servers\n\n \nlogs\n\n \nshard\n\n \n!-- Optional. Shard weight when writing data. Default: 1. --\n\n \nweight\n1\n/weight\n\n \n!-- Optional. Whether to write data to just one of the replicas. Default: false (write data to all replicas). --\n\n \ninternal_replication\nfalse\n/internal_replication\n\n \nreplica\n\n \nhost\nexample01-01-1\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \nreplica\n\n \nhost\nexample01-01-2\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \n/shard\n\n \nshard\n\n \nweight\n2\n/weight\n\n \ninternal_replication\nfalse\n/internal_replication\n\n \nreplica\n\n \nhost\nexample01-02-1\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \nreplica\n\n \nhost\nexample01-02-2\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \n/shard\n\n \n/logs\n\n\n/remote_servers\n\n\n\n\n\n\nHere a cluster is defined with the name 'logs' that consists of two shards, each of which contains two replicas.\nShards refer to the servers that contain different parts of the data (in order to read all the data, you must access all the shards).\nReplicas are duplicating servers (in order to read all the data, you can access the data on any one of the replicas).\n\n\nThe parameters \nhost\n, \nport\n, and optionally \nuser\n and \npassword\n are specified for each server:\n\n\n: - \nhost\n \u2013 The address of the remote server. You can use either the domain or the IPv4 or IPv6 address. If you specify the domain, the server makes a DNS request when it starts, and the result is stored as long as the server is running. If the DNS request fails, the server doesn't start. If you change the DNS record, restart the server.\n- \nport\n\u2013 The TCP port for messenger activity ('tcp_port' in the config, usually set to 9000). Do not confuse it with http_port.\n- \nuser\n\u2013 Name of the user for connecting to a remote server. Default value: default. This user must have access to connect to the specified server. Access is configured in the users.xml file. For more information, see the section \"Access rights\".\n- \npassword\n \u2013 The password for connecting to a remote server (not masked). Default value: empty string.\n\n\nWhen specifying replicas, one of the available replicas will be selected for each of the shards when reading. You can configure the algorithm for load balancing (the preference for which replica to access) \u2013 see the 'load_balancing' setting.\nIf the connection with the server is not established, there will be an attempt to connect with a short timeout. If the connection failed, the next replica will be selected, and so on for all the replicas. If the connection attempt failed for all the replicas, the attempt will be repeated the same way, several times.\nThis works in favor of resiliency, but does not provide complete fault tolerance: a remote server might accept the connection, but might not work, or work poorly.\n\n\nYou can specify just one of the shards (in this case, query processing should be called remote, rather than distributed) or up to any number of shards. In each shard, you can specify from one to any number of replicas. You can specify a different number of replicas for each shard.\n\n\nYou can specify as many clusters as you wish in the configuration.\n\n\nTo view your clusters, use the 'system.clusters' table.\n\n\nThe Distributed engine allows working with a cluster like a local server. However, the cluster is inextensible: you must write its configuration in the server config file (even better, for all the cluster's servers).\n\n\nThere is no support for Distributed tables that look at other Distributed tables (except in cases when a Distributed table only has one shard). As an alternative, make the Distributed table look at the \"final\" tables.\n\n\nThe Distributed engine requires writing clusters to the config file. Clusters from the config file are updated on the fly, without restarting the server. If you need to send a query to an unknown set of shards and replicas each time, you don't need to create a Distributed table \u2013 use the 'remote' table function instead. See the section \"Table functions\".\n\n\nThere are two methods for writing data to a cluster:\n\n\nFirst, you can define which servers to write which data to, and perform the write directly on each shard. In other words, perform INSERT in the tables that the distributed table \"looks at\".\nThis is the most flexible solution \u2013 you can use any sharding scheme, which could be non-trivial due to the requirements of the subject area.\nThis is also the most optimal solution, since data can be written to different shards completely independently.\n\n\nSecond, you can perform INSERT in a Distributed table. In this case, the table will distribute the inserted data across servers itself.\nIn order to write to a Distributed table, it must have a sharding key set (the last parameter). In addition, if there is only one shard, the write operation works without specifying the sharding key, since it doesn't have any meaning in this case.\n\n\nEach shard can have a weight defined in the config file. By default, the weight is equal to one. Data is distributed across shards in the amount proportional to the shard weight. For example, if there are two shards and the first has a weight of 9 while the second has a weight of 10, the first will be sent 9 / 19 parts of the rows, and the second will be sent 10 / 19.\n\n\nEach shard can have the 'internal_replication' parameter defined in the config file.\n\n\nIf this parameter is set to 'true', the write operation selects the first healthy replica and writes data to it. Use this alternative if the Distributed table \"looks at\" replicated tables. In other words, if the table where data will be written is going to replicate them itself.\n\n\nIf it is set to 'false' (the default), data is written to all replicas. In essence, this means that the Distributed table replicates data itself. This is worse than using replicated tables, because the consistency of replicas is not checked, and over time they will contain slightly different data.\n\n\nTo select the shard that a row of data is sent to, the sharding expression is analyzed, and its remainder is taken from dividing it by the total weight of the shards. The row is sent to the shard that corresponds to the half-interval of the remainders from 'prev_weight' to 'prev_weights + weight', where 'prev_weights' is the total weight of the shards with the smallest number, and 'weight' is the weight of this shard. For example, if there are two shards, and the first has a weight of 9 while the second has a weight of 10, the row will be sent to the first shard for the remainders from the range [0, 9), and to the second for the remainders from the range [9, 19).\n\n\nThe sharding expression can be any expression from constants and table columns that returns an integer. For example, you can use the expression 'rand()' for random distribution of data, or 'UserID' for distribution by the remainder from dividing the user's ID (then the data of a single user will reside on a single shard, which simplifies running IN and JOIN by users). If one of the columns is not distributed evenly enough, you can wrap it in a hash function: intHash64(UserID).\n\n\nA simple remainder from division is a limited solution for sharding and isn't always appropriate. It works for medium and large volumes of data (dozens of servers), but not for very large volumes of data (hundreds of servers or more). In the latter case, use the sharding scheme required by the subject area, rather than using entries in Distributed tables.\n\n\nSELECT queries are sent to all the shards, and work regardless of how data is distributed across the shards (they can be distributed completely randomly). When you add a new shard, you don't have to transfer the old data to it. You can write new data with a heavier weight \u2013 the data will be distributed slightly unevenly, but queries will work correctly and efficiently.\n\n\nYou should be concerned about the sharding scheme in the following cases:\n\n\n\n\nQueries are used that require joining data (IN or JOIN) by a specific key. If data is sharded by this key, you can use local IN or JOIN instead of GLOBAL IN or GLOBAL JOIN, which is much more efficient.\n\n\nA large number of servers is used (hundreds or more) with a large number of small queries (queries of individual clients - websites, advertisers, or partners). In order for the small queries to not affect the entire cluster, it makes sense to locate data for a single client on a single shard. Alternatively, as we've done in Yandex.Metrica, you can set up bi-level sharding: divide the entire cluster into \"layers\", where a layer may consist of multiple shards. Data for a single client is located on a single layer, but shards can be added to a layer as necessary, and data is randomly distributed within them. Distributed tables are created for each layer, and a single shared distributed table is created for global queries.\n\n\n\n\nData is written asynchronously. For an INSERT to a Distributed table, the data block is just written to the local file system. The data is sent to the remote servers in the background as soon as possible. You should check whether data is sent successfully by checking the list of files (data waiting to be sent) in the table directory: /var/lib/clickhouse/data/database/table/.\n\n\nIf the server ceased to exist or had a rough restart (for example, after a device failure) after an INSERT to a Distributed table, the inserted data might be lost. If a damaged data part is detected in the table directory, it is transferred to the 'broken' subdirectory and no longer used.\n\n\nWhen the max_parallel_replicas option is enabled, query processing is parallelized across all replicas within a single shard. For more information, see the section \"Settings, max_parallel_replicas\".\n\n\n\n\nDictionary\n\n\nThe \nDictionary\n engine displays the dictionary data as a ClickHouse table.\n\n\nAs an example, consider a dictionary of \nproducts\n with the following configuration:\n\n\ndictionaries\n\n\ndictionary\n\n \nname\nproducts\n/name\n\n \nsource\n\n \nodbc\n\n \ntable\nproducts\n/table\n\n \nconnection_string\nDSN=some-db-server\n/connection_string\n\n \n/odbc\n\n \n/source\n\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n \nlayout\n\n \nflat/\n\n \n/layout\n\n \nstructure\n\n \nid\n\n \nname\nproduct_id\n/name\n\n \n/id\n\n \nattribute\n\n \nname\ntitle\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n/null_value\n\n \n/attribute\n\n \n/structure\n\n\n/dictionary\n\n\n/dictionaries\n\n\n\n\n\n\nQuery the dictionary data:\n\n\nselect\n \nname\n,\n \ntype\n,\n \nkey\n,\n \nattribute\n.\nnames\n,\n \nattribute\n.\ntypes\n,\n \nbytes_allocated\n,\n \nelement_count\n,\nsource\n \nfrom\n \nsystem\n.\ndictionaries\n \nwhere\n \nname\n \n=\n \nproducts\n;\n \n\n\nSELECT\n\n \nname\n,\n\n \ntype\n,\n\n \nkey\n,\n\n \nattribute\n.\nnames\n,\n\n \nattribute\n.\ntypes\n,\n\n \nbytes_allocated\n,\n\n \nelement_count\n,\n\n \nsource\n\n\nFROM\n \nsystem\n.\ndictionaries\n\n\nWHERE\n \nname\n \n=\n \nproducts\n\n\n\n\n\n\n\u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u252c\u2500type\u2500\u252c\u2500key\u2500\u2500\u2500\u2500\u252c\u2500attribute.names\u2500\u252c\u2500attribute.types\u2500\u252c\u2500bytes_allocated\u2500\u252c\u2500element_count\u2500\u252c\u2500source\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 products \u2502 Flat \u2502 UInt64 \u2502 [\ntitle\n] \u2502 [\nString\n] \u2502 23065376 \u2502 175032 \u2502 ODBC: .products \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nYou can use the \ndictGet*\n function to get the dictionary data in this format.\n\n\nThis view isn't helpful when you need to get raw data, or when performing a \nJOIN\n operation. For these cases, you can use the \nDictionary\n engine, which displays the dictionary data in a table.\n\n\nSyntax:\n\n\nCREATE TABLE %table_name% (%fields%) engine = Dictionary(%dictionary_name%)`\n\n\n\n\n\nUsage example:\n\n\ncreate\n \ntable\n \nproducts\n \n(\nproduct_id\n \nUInt64\n,\n \ntitle\n \nString\n)\n \nEngine\n \n=\n \nDictionary\n(\nproducts\n);\n\n\n\nCREATE\n \nTABLE\n \nproducts\n\n\n(\n\n \nproduct_id\n \nUInt64\n,\n\n \ntitle\n \nString\n,\n\n\n)\n\n\nENGINE\n \n=\n \nDictionary\n(\nproducts\n)\n\n\n\n\n\n\nOk.\n\n0 rows in set. Elapsed: 0.004 sec.\n\n\n\n\n\nTake a look at what's in the table.\n\n\nselect\n \n*\n \nfrom\n \nproducts\n \nlimit\n \n1\n;\n\n\n\nSELECT\n \n*\n\n\nFROM\n \nproducts\n\n\nLIMIT\n \n1\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500product_id\u2500\u252c\u2500title\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 152689 \u2502 Some item \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n1 rows in set. Elapsed: 0.006 sec.\n\n\n\n\n\nMerge\n\n\nThe Merge engine (not to be confused with \nMergeTree\n) does not store data itself, but allows reading from any number of other tables simultaneously.\nReading is automatically parallelized. Writing to a table is not supported. When reading, the indexes of tables that are actually being read are used, if they exist.\nThe Merge engine accepts parameters: the database name and a regular expression for tables.\n\n\nExample:\n\n\nMerge(hits, \n^WatchLog\n)\n\n\n\n\n\nData will be read from the tables in the 'hits' database that have names that match the regular expression '\n^WatchLog\n'.\n\n\nInstead of the database name, you can use a constant expression that returns a string. For example, \ncurrentDatabase()\n.\n\n\nRegular expressions \u2014 \nre2\n (supports a subset of PCRE), case-sensitive.\nSee the notes about escaping symbols in regular expressions in the \"match\" section.\n\n\nWhen selecting tables to read, the Merge table itself will not be selected, even if it matches the regex. This is to avoid loops.\nIt is possible to create two Merge tables that will endlessly try to read each others' data, but this is not a good idea.\n\n\nThe typical way to use the Merge engine is for working with a large number of TinyLog tables as if with a single table.\n\n\nVirtual columns\n\n\nVirtual columns are columns that are provided by the table engine, regardless of the table definition. In other words, these columns are not specified in CREATE TABLE, but they are accessible for SELECT.\n\n\nVirtual columns differ from normal columns in the following ways:\n\n\n\n\nThey are not specified in table definitions.\n\n\nData can't be added to them with INSERT.\n\n\nWhen using INSERT without specifying the list of columns, virtual columns are ignored.\n\n\nThey are not selected when using the asterisk (\nSELECT *\n).\n\n\nVirtual columns are not shown in \nSHOW CREATE TABLE\n and \nDESC TABLE\n queries.\n\n\n\n\nA Merge type table contains a virtual _table column with the String type. (If the table already has a _table column, the virtual column is named _table1, and if it already has _table1, it is named _table2, and so on.) It contains the name of the table that data was read from.\n\n\nIf the WHERE or PREWHERE clause contains conditions for the '_table' column that do not depend on other table columns (as one of the conjunction elements, or as an entire expression), these conditions are used as an index. The conditions are performed on a data set of table names to read data from, and the read operation will be performed from only those tables that the condition was triggered on.\n\n\nBuffer\n\n\nBuffers the data to write in RAM, periodically flushing it to another table. During the read operation, data is read from the buffer and the other table simultaneously.\n\n\nBuffer(database, table, num_layers, min_time, max_time, min_rows, max_rows, min_bytes, max_bytes)\n\n\n\n\n\nEngine parameters:database, table \u2013 The table to flush data to. Instead of the database name, you can use a constant expression that returns a string.num_layers \u2013 Parallelism layer. Physically, the table will be represented as 'num_layers' of independent buffers. Recommended value: 16.min_time, max_time, min_rows, max_rows, min_bytes, and max_bytes are conditions for flushing data from the buffer.\n\n\nData is flushed from the buffer and written to the destination table if all the 'min' conditions or at least one 'max' condition are met.min_time, max_time \u2013 Condition for the time in seconds from the moment of the first write to the buffer.min_rows, max_rows \u2013 Condition for the number of rows in the buffer.min_bytes, max_bytes \u2013 Condition for the number of bytes in the buffer.\n\n\nDuring the write operation, data is inserted to a 'num_layers' number of random buffers. Or, if the data part to insert is large enough (greater than 'max_rows' or 'max_bytes'), it is written directly to the destination table, omitting the buffer.\n\n\nThe conditions for flushing the data are calculated separately for each of the 'num_layers' buffers. For example, if num_layers = 16 and max_bytes = 100000000, the maximum RAM consumption is 1.6 GB.\n\n\nExample:\n\n\nCREATE\n \nTABLE\n \nmerge\n.\nhits_buffer\n \nAS\n \nmerge\n.\nhits\n \nENGINE\n \n=\n \nBuffer\n(\nmerge\n,\n \nhits\n,\n \n16\n,\n \n10\n,\n \n100\n,\n \n10000\n,\n \n1000000\n,\n \n10000000\n,\n \n100000000\n)\n\n\n\n\n\n\nCreating a 'merge.hits_buffer' table with the same structure as 'merge.hits' and using the Buffer engine. When writing to this table, data is buffered in RAM and later written to the 'merge.hits' table. 16 buffers are created. The data in each of them is flushed if either 100 seconds have passed, or one million rows have been written, or 100 MB of data have been written; or if simultaneously 10 seconds have passed and 10,000 rows and 10 MB of data have been written. For example, if just one row has been written, after 100 seconds it will be flushed, no matter what. But if many rows have been written, the data will be flushed sooner.\n\n\nWhen the server is stopped, with DROP TABLE or DETACH TABLE, buffer data is also flushed to the destination table.\n\n\nYou can set empty strings in single quotation marks for the database and table name. This indicates the absence of a destination table. In this case, when the data flush conditions are reached, the buffer is simply cleared. This may be useful for keeping a window of data in memory.\n\n\nWhen reading from a Buffer table, data is processed both from the buffer and from the destination table (if there is one).\nNote that the Buffer tables does not support an index. In other words, data in the buffer is fully scanned, which might be slow for large buffers. (For data in a subordinate table, the index that it supports will be used.)\n\n\nIf the set of columns in the Buffer table doesn't match the set of columns in a subordinate table, a subset of columns that exist in both tables is inserted.\n\n\nIf the types don't match for one of the columns in the Buffer table and a subordinate table, an error message is entered in the server log and the buffer is cleared.\nThe same thing happens if the subordinate table doesn't exist when the buffer is flushed.\n\n\nIf you need to run ALTER for a subordinate table and the Buffer table, we recommend first deleting the Buffer table, running ALTER for the subordinate table, then creating the Buffer table again.\n\n\nIf the server is restarted abnormally, the data in the buffer is lost.\n\n\nPREWHERE, FINAL and SAMPLE do not work correctly for Buffer tables. These conditions are passed to the destination table, but are not used for processing data in the buffer. Because of this, we recommend only using the Buffer table for writing, while reading from the destination table.\n\n\nWhen adding data to a Buffer, one of the buffers is locked. This causes delays if a read operation is simultaneously being performed from the table.\n\n\nData that is inserted to a Buffer table may end up in the subordinate table in a different order and in different blocks. Because of this, a Buffer table is difficult to use for writing to a CollapsingMergeTree correctly. To avoid problems, you can set 'num_layers' to 1.\n\n\nIf the destination table is replicated, some expected characteristics of replicated tables are lost when writing to a Buffer table. The random changes to the order of rows and sizes of data parts cause data deduplication to quit working, which means it is not possible to have a reliable 'exactly once' write to replicated tables.\n\n\nDue to these disadvantages, we can only recommend using a Buffer table in rare cases.\n\n\nA Buffer table is used when too many INSERTs are received from a large number of servers over a unit of time and data can't be buffered before insertion, which means the INSERTs can't run fast enough.\n\n\nNote that it doesn't make sense to insert data one row at a time, even for Buffer tables. This will only produce a speed of a few thousand rows per second, while inserting larger blocks of data can produce over a million rows per second (see the section \"Performance\").\n\n\nFile(InputFormat)\n\n\nThe data source is a file that stores data in one of the supported input formats (TabSeparated, Native, etc.).\n\n\nNull\n\n\nWhen writing to a Null table, data is ignored. When reading from a Null table, the response is empty.\n\n\nHowever, you can create a materialized view on a Null table. So the data written to the table will end up in the view.\n\n\nSet\n\n\nA data set that is always in RAM. It is intended for use on the right side of the IN operator (see the section \"IN operators\").\n\n\nYou can use INSERT to insert data in the table. New elements will be added to the data set, while duplicates will be ignored.\nBut you can't perform SELECT from the table. The only way to retrieve data is by using it in the right half of the IN operator.\n\n\nData is always located in RAM. For INSERT, the blocks of inserted data are also written to the directory of tables on the disk. When starting the server, this data is loaded to RAM. In other words, after restarting, the data remains in place.\n\n\nFor a rough server restart, the block of data on the disk might be lost or damaged. In the latter case, you may need to manually delete the file with damaged data.\n\n\nJoin\n\n\nA prepared data structure for JOIN that is always located in RAM.\n\n\nJoin(ANY|ALL, LEFT|INNER, k1[, k2, ...])\n\n\n\n\n\nEngine parameters: \nANY|ALL\n \u2013 strictness; \nLEFT|INNER\n \u2013 type.\nThese parameters are set without quotes and must match the JOIN that the table will be used for. k1, k2, ... are the key columns from the USING clause that the join will be made on.\n\n\nThe table can't be used for GLOBAL JOINs.\n\n\nYou can use INSERT to add data to the table, similar to the Set engine. For ANY, data for duplicated keys will be ignored. For ALL, it will be counted. You can't perform SELECT directly from the table. The only way to retrieve data is to use it as the \"right-hand\" table for JOIN.\n\n\nStoring data on the disk is the same as for the Set engine.\n\n\nView\n\n\nUsed for implementing views (for more information, see the \nCREATE VIEW query\n). It does not store data, but only stores the specified \nSELECT\n query. When reading from a table, it runs this query (and deletes all unnecessary columns from the query).\n\n\nMaterializedView\n\n\nUsed for implementing materialized views (for more information, see the \nCREATE TABLE\n) query. For storing data, it uses a different engine that was specified when creating the view. When reading from a table, it just uses this engine.\n\n\nKafka\n\n\nThis engine works with \nApache Kafka\n.\n\n\nKafka lets you:\n\n\n\n\nPublish or subscribe to data flows.\n\n\nOrganize fault-tolerant storage.\n\n\nProcess streams as they become available.\n\n\n\n\nKafka(broker_list, topic_list, group_name, format[, schema, num_consumers])\n\n\n\n\n\nParameters:\n\n\n\n\nbroker_list\n \u2013 A comma-separated list of brokers (\nlocalhost:9092\n).\n\n\ntopic_list\n \u2013 A list of Kafka topics (\nmy_topic\n).\n\n\ngroup_name\n \u2013 A group of Kafka consumers (\ngroup1\n). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere.\n\n\n--format\n \u2013 Message format. Uses the same notation as the SQL \nFORMAT\n function, such as \nJSONEachRow\n. For more information, see the \"Formats\" section.\n\n\nschema\n \u2013 An optional parameter that must be used if the format requires a schema definition. For example, \nCap'n Proto\n requires the path to the schema file and the name of the root \nschema.capnp:Message\n object.\n\n\nnum_consumers\n \u2013 The number of consumers per table. Default: \n1\n. Specify more consumers if the throughput of one consumer is insufficient. The total number of consumers should not exceed the number of partitions in the topic, since only one consumer can be assigned per partition.\n\n\n\n\nExample:\n\n\n \nCREATE\n \nTABLE\n \nqueue\n \n(\n\n \ntimestamp\n \nUInt64\n,\n\n \nlevel\n \nString\n,\n\n \nmessage\n \nString\n\n \n)\n \nENGINE\n \n=\n \nKafka\n(\nlocalhost:9092\n,\n \ntopic\n,\n \ngroup1\n,\n \nJSONEachRow\n);\n\n\n \nSELECT\n \n*\n \nFROM\n \nqueue\n \nLIMIT\n \n5\n;\n\n\n\n\n\n\nThe delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name.\n\n\nGroups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. Read more about this at \nhttp://kafka.apache.org/intro\n.\n\n\nSELECT\n is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. To do this:\n\n\n\n\nUse the engine to create a Kafka consumer and consider it a data stream.\n\n\nCreate a table with the desired structure.\n\n\nCreate a materialized view that converts data from the engine and puts it into a previously created table.\n\n\n\n\nWhen the \nMATERIALIZED VIEW\n joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using \nSELECT\n\n\nExample:\n\n\n \nCREATE\n \nTABLE\n \nqueue\n \n(\n\n \ntimestamp\n \nUInt64\n,\n\n \nlevel\n \nString\n,\n\n \nmessage\n \nString\n\n \n)\n \nENGINE\n \n=\n \nKafka\n(\nlocalhost:9092\n,\n \ntopic\n,\n \ngroup1\n,\n \nJSONEachRow\n);\n\n\n \nCREATE\n \nTABLE\n \ndaily\n \n(\n\n \nday\n \nDate\n,\n\n \nlevel\n \nString\n,\n\n \ntotal\n \nUInt64\n\n \n)\n \nENGINE\n \n=\n \nSummingMergeTree\n(\nday\n,\n \n(\nday\n,\n \nlevel\n),\n \n8192\n);\n\n\n \nCREATE\n \nMATERIALIZED\n \nVIEW\n \nconsumer\n \nTO\n \ndaily\n\n \nAS\n \nSELECT\n \ntoDate\n(\ntoDateTime\n(\ntimestamp\n))\n \nAS\n \nday\n,\n \nlevel\n,\n \ncount\n()\n \nas\n \ntotal\n\n \nFROM\n \nqueue\n \nGROUP\n \nBY\n \nday\n,\n \nlevel\n;\n\n\n \nSELECT\n \nlevel\n,\n \nsum\n(\ntotal\n)\n \nFROM\n \ndaily\n \nGROUP\n \nBY\n \nlevel\n;\n\n\n\n\n\n\nTo improve performance, received messages are grouped into blocks the size of \nmax_insert_block_size\n. If the block wasn't formed within \nstream_flush_interval_ms\n milliseconds, the data will be flushed to the table regardless of the completeness of the block.\n\n\nTo stop receiving topic data or to change the conversion logic, detach the materialized view:\n\n\n DETACH TABLE consumer;\n ATTACH MATERIALIZED VIEW consumer;\n\n\n\n\n\nIf you want to change the target table by using \nALTER\nmaterialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view.\n\n\nConfiguration\n\n\nSimilar to GraphiteMergeTree, the Kafka engine supports extended configuration using the ClickHouse config file. There are two configuration keys that you can use: global (\nkafka\n) and topic-level (\nkafka_topic_*\n). The global configuration is applied first, and the topic-level configuration is second (if it exists).\n\n\n \n!-- Global configuration options for all tables of Kafka engine type --\n\n \nkafka\n\n \ndebug\ncgrp\n/debug\n\n \nauto_offset_reset\nsmallest\n/auto_offset_reset\n\n \n/kafka\n\n\n \n!-- Configuration specific for topic \nlogs\n --\n\n \nkafka_topic_logs\n\n \nretry_backoff_ms\n250\n/retry_backoff_ms\n\n \nfetch_min_bytes\n100000\n/fetch_min_bytes\n\n \n/kafka_topic_logs\n\n\n\n\n\n\nFor a list of possible configuration options, see the \nlibrdkafka configuration reference\n. Use the underscore (\n_\n) instead of a dot in the ClickHouse configuration. For example, \ncheck.crcs=true\n will be \ncheck_crcs\ntrue\n/check_crcs\n.\n\n\n\n\nMySQL\n\n\nThe MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server.\n\n\nThe engine takes 4 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example:\n\n\nMySQL(\nhost:port\n, \ndatabase\n, \ntable\n, \nuser\n, \npassword\n);\n\n\n\n\n\nAt this time, simple WHERE clauses such as \n=, !=, \n, \n=, \n, \n=\n are executed on the MySQL server.\n\n\nThe rest of the conditions and the LIMIT sampling constraint are executed in ClickHouse only after the query to MySQL finishes.\n\n\nExternal data for query processing\n\n\nClickHouse allows sending a server the data that is needed for processing a query, together with a SELECT query. This data is put in a temporary table (see the section \"Temporary tables\") and can be used in the query (for example, in IN operators).\n\n\nFor example, if you have a text file with important user identifiers, you can upload it to the server along with a query that uses filtration by this list.\n\n\nIf you need to run more than one query with a large volume of external data, don't use this feature. It is better to upload the data to the DB ahead of time.\n\n\nExternal data can be uploaded using the command-line client (in non-interactive mode), or using the HTTP interface.\n\n\nIn the command-line client, you can specify a parameters section in the format\n\n\n--external --file\n=\n... \n[\n--name\n=\n...\n]\n \n[\n--format\n=\n...\n]\n \n[\n--types\n=\n...\n|\n--structure\n=\n...\n]\n\n\n\n\n\n\nYou may have multiple sections like this, for the number of tables being transmitted.\n\n\n--external\n \u2013 Marks the beginning of a clause.\n\n--file\n \u2013 Path to the file with the table dump, or -, which refers to stdin.\nOnly a single table can be retrieved from stdin.\n\n\nThe following parameters are optional: \n--name\n\u2013 Name of the table. If omitted, _data is used.\n\n--format\n \u2013 Data format in the file. If omitted, TabSeparated is used.\n\n\nOne of the following parameters is required:\n--types\n \u2013 A list of comma-separated column types. For example: \nUInt64,String\n. The columns will be named _1, _2, ...\n\n--structure\n\u2013 The table structure in the format\nUserID UInt64\n, \nURL String\n. Defines the column names and types.\n\n\nThe files specified in 'file' will be parsed by the format specified in 'format', using the data types specified in 'types' or 'structure'. The table will be uploaded to the server and accessible there as a temporary table with the name in 'name'.\n\n\nExamples:\n\n\necho\n -ne \n1\\n2\\n3\\n\n \n|\n clickhouse-client --query\n=\nSELECT count() FROM test.visits WHERE TraficSourceID IN _data\n --external --file\n=\n- --types\n=\nInt8\n\n849897\n\ncat /etc/passwd \n|\n sed \ns/:/\\t/g\n \n|\n clickhouse-client --query\n=\nSELECT shell, count() AS c FROM passwd GROUP BY shell ORDER BY c DESC\n --external --file\n=\n- --name\n=\npasswd --structure\n=\nlogin String, unused String, uid UInt16, gid UInt16, comment String, home String, shell String\n\n/bin/sh \n20\n\n/bin/false \n5\n\n/bin/bash \n4\n\n/usr/sbin/nologin \n1\n\n/bin/sync \n1\n\n\n\n\n\n\nWhen using the HTTP interface, external data is passed in the multipart/form-data format. Each table is transmitted as a separate file. The table name is taken from the file name. The 'query_string' is passed the parameters 'name_format', 'name_types', and 'name_structure', where 'name' is the name of the table that these parameters correspond to. The meaning of the parameters is the same as when using the command-line client.\n\n\nExample:\n\n\ncat /etc/passwd \n|\n sed \ns/:/\\t/g\n \n passwd.tsv\n\ncurl -F \npasswd=@passwd.tsv;\n \nhttp://localhost:8123/?query=SELECT+shell,+count()+AS+c+FROM+passwd+GROUP+BY+shell+ORDER+BY+c+DESC\npasswd_structure=login+String,+unused+String,+uid+UInt16,+gid+UInt16,+comment+String,+home+String,+shell+String\n\n/bin/sh \n20\n\n/bin/false \n5\n\n/bin/bash \n4\n\n/usr/sbin/nologin \n1\n\n/bin/sync \n1\n\n\n\n\n\n\nFor distributed query processing, the temporary tables are sent to all the remote servers.\n\n\nSystem tables\n\n\nSystem tables are used for implementing part of the system's functionality, and for providing access to information about how the system is working.\nYou can't delete a system table (but you can perform DETACH).\nSystem tables don't have files with data on the disk or files with metadata. The server creates all the system tables when it starts.\nSystem tables are read-only.\nThey are located in the 'system' database.\n\n\nsystem.one\n\n\nThis table contains a single row with a single 'dummy' UInt8 column containing the value 0.\nThis table is used if a SELECT query doesn't specify the FROM clause.\nThis is similar to the DUAL table found in other DBMSs.\n\n\nsystem.numbers\n\n\nThis table contains a single UInt64 column named 'number' that contains almost all the natural numbers starting from zero.\nYou can use this table for tests, or if you need to do a brute force search.\nReads from this table are not parallelized.\n\n\nsystem.numbers_mt\n\n\nThe same as 'system.numbers' but reads are parallelized. The numbers can be returned in any order.\nUsed for tests.\n\n\nsystem.databases\n\n\nThis table contains a single String column called 'name' \u2013 the name of a database.\nEach database that the server knows about has a corresponding entry in the table.\nThis system table is used for implementing the \nSHOW DATABASES\n query.\n\n\nsystem.tables\n\n\nThis table contains the String columns 'database', 'name', and 'engine'.\nThe table also contains three virtual columns: metadata_modification_time (DateTime type), create_table_query, and engine_full (String type).\nEach table that the server knows about is entered in the 'system.tables' table.\nThis system table is used for implementing SHOW TABLES queries.\n\n\nsystem.columns\n\n\nContains information about the columns in all tables.\nYou can use this table to get information similar to \nDESCRIBE TABLE\n, but for multiple tables at once.\n\n\ndatabase String - Name of the database the table is located in.\ntable String - Table name.\nname String - Column name.\ntype String - Column type.\ndefault_type String - Expression type (DEFAULT, MATERIALIZED, ALIAS) for the default value, or an empty string if it is not defined.\ndefault_expression String - Expression for the default value, or an empty string if it is not defined.\n\n\n\n\n\nsystem.parts\n\n\nContains information about parts of a table in the \nMergeTree\n family.\n\n\nEach row describes one part of the data.\n\n\nColumns:\n\n\n\n\npartition (String) \u2013 The partition name. YYYYMM format. To learn what a partition is, see the description of the \nALTER\n query.\n\n\nname (String) \u2013 Name of the data part.\n\n\nactive (UInt8) \u2013 Indicates whether the part is active. If a part is active, it is used in a table; otherwise, it will be deleted. Inactive data parts remain after merging.\n\n\nmarks (UInt64) \u2013 The number of marks. To get the approximate number of rows in a data part, multiply \nmarks\n by the index granularity (usually 8192).\n\n\nmarks_size (UInt64) \u2013 The size of the file with marks.\n\n\nrows (UInt64) \u2013 The number of rows.\n\n\nbytes (UInt64) \u2013 The number of bytes when compressed.\n\n\nmodification_time (DateTime) \u2013 The modification time of the directory with the data part. This usually corresponds to the time of data part creation.|\n\n\nremove_time (DateTime) \u2013 The time when the data part became inactive.\n\n\nrefcount (UInt32) \u2013 The number of places where the data part is used. A value greater than 2 indicates that the data part is used in queries or merges.\n\n\nmin_date (Date) \u2013 The minimum value of the date key in the data part.\n\n\nmax_date (Date) \u2013 The maximum value of the date key in the data part.\n\n\nmin_block_number (UInt64) \u2013 The minimum number of data parts that make up the current part after merging.\n\n\nmax_block_number (UInt64) \u2013 The maximum number of data parts that make up the current part after merging.\n\n\nlevel (UInt32) \u2013 Depth of the merge tree. If a merge was not performed, \nlevel=0\n.\n\n\nprimary_key_bytes_in_memory (UInt64) \u2013 The amount of memory (in bytes) used by primary key values.\n\n\nprimary_key_bytes_in_memory_allocated (UInt64) \u2013 The amount of memory (in bytes) reserved for primary key values.\n\n\ndatabase (String) \u2013 Name of the database.\n\n\ntable (String) \u2013 Name of the table.\n\n\nengine (String) \u2013 Name of the table engine without parameters.\n\n\n\n\nsystem.processes\n\n\nThis system table is used for implementing the \nSHOW PROCESSLIST\n query.\nColumns:\n\n\nuser String \u2013 Name of the user who made the request. For distributed query processing, this is the user who helped the requestor server send the query to this server, not the user who made the distributed request on the requestor server.\n\naddress String \u2013 The IP address that the query was made from. The same is true for distributed query processing.\n\nelapsed Float64 \u2013 The time in seconds since request execution started.\n\nrows_read UInt64 \u2013 The number of rows read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nbytes_read UInt64 \u2013 The number of uncompressed bytes read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nUInt64 total_rows_approx \u2013 The approximate total number of rows that must be read. For distributed processing, on the requestor server, this is the total for all remote servers. It can be updated during request processing, when new sources to process become known.\n\nmemory_usage UInt64 \u2013 Memory consumption by the query. It might not include some types of dedicated memory.\n\nquery String \u2013 The query text. For INSERT, it doesn\nt include the data to insert.\n\nquery_id \u2013 Query ID, if defined.\n\n\n\n\n\nsystem.merges\n\n\nContains information about merges currently in process for tables in the MergeTree family.\n\n\nColumns:\n\n\n\n\ndatabase String\n \u2014 Name of the database the table is located in.\n\n\ntable String\n \u2014 Name of the table.\n\n\nelapsed Float64\n \u2014 Time in seconds since the merge started.\n\n\nprogress Float64\n \u2014 Percent of progress made, from 0 to 1.\n\n\nnum_parts UInt64\n \u2014 Number of parts to merge.\n\n\nresult_part_name String\n \u2014 Name of the part that will be formed as the result of the merge.\n\n\ntotal_size_bytes_compressed UInt64\n \u2014 Total size of compressed data in the parts being merged.\n\n\ntotal_size_marks UInt64\n \u2014 Total number of marks in the parts being merged.\n\n\nbytes_read_uncompressed UInt64\n \u2014 Amount of bytes read, decompressed.\n\n\nrows_read UInt64\n \u2014 Number of rows read.\n\n\nbytes_written_uncompressed UInt64\n \u2014 Amount of bytes written, uncompressed.\n\n\nrows_written UInt64\n \u2014 Number of rows written.\n\n\n\n\n\n\nsystem.events\n\n\nContains information about the number of events that have occurred in the system. This is used for profiling and monitoring purposes.\nExample: The number of processed SELECT queries.\nColumns: 'event String' \u2013 the event name, and 'value UInt64' \u2013 the quantity.\n\n\n\n\nsystem.metrics\n\n\n\n\nsystem.asynchronous_metrics\n\n\nContain metrics used for profiling and monitoring.\nThey usually reflect the number of events currently in the system, or the total resources consumed by the system.\nExample: The number of SELECT queries currently running; the amount of memory in use.\nsystem.asynchronous_metrics\nand\nsystem.metrics\n differ in their sets of metrics and how they are calculated.\n\n\nsystem.replicas\n\n\nContains information and status for replicated tables residing on the local server.\nThis table can be used for monitoring. The table contains a row for every Replicated* table.\n\n\nExample:\n\n\nSELECT\n \n*\n\n\nFROM\n \nsystem\n.\nreplicas\n\n\nWHERE\n \ntable\n \n=\n \nvisits\n\n\nFORMAT\n \nVertical\n\n\n\n\n\n\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ndatabase: merge\ntable: visits\nengine: ReplicatedCollapsingMergeTree\nis_leader: 1\nis_readonly: 0\nis_session_expired: 0\nfuture_parts: 1\nparts_to_check: 0\nzookeeper_path: /clickhouse/tables/01-06/visits\nreplica_name: example01-06-1.yandex.ru\nreplica_path: /clickhouse/tables/01-06/visits/replicas/example01-06-1.yandex.ru\ncolumns_version: 9\nqueue_size: 1\ninserts_in_queue: 0\nmerges_in_queue: 1\nlog_max_index: 596273\nlog_pointer: 596274\ntotal_replicas: 2\nactive_replicas: 2\n\n\n\n\n\nColumns:\n\n\ndatabase: database name\ntable: table name\nengine: table engine name\n\nis_leader: whether the replica is the leader\n\nOnly one replica at a time can be the leader. The leader is responsible for selecting background merges to perform.\nNote that writes can be performed to any replica that is available and has a session in ZK, regardless of whether it is a leader.\n\nis_readonly: Whether the replica is in read-only mode.\nThis mode is turned on if the config doesn\nt have sections with ZK, if an unknown error occurred when reinitializing sessions in ZK, and during session reinitialization in ZK.\n\nis_session_expired: Whether the ZK session expired.\nBasically, the same thing as is_readonly.\n\nfuture_parts: The number of data parts that will appear as the result of INSERTs or merges that haven\nt been done yet. \n\nparts_to_check: The number of data parts in the queue for verification.\nA part is put in the verification queue if there is suspicion that it might be damaged.\n\nzookeeper_path: The path to the table data in ZK. \nreplica_name: Name of the replica in ZK. Different replicas of the same table have different names. \nreplica_path: The path to the replica data in ZK. The same as concatenating zookeeper_path/replicas/replica_path.\n\ncolumns_version: Version number of the table structure.\nIndicates how many times ALTER was performed. If replicas have different versions, it means some replicas haven\nt made all of the ALTERs yet.\n\nqueue_size: Size of the queue for operations waiting to be performed.\nOperations include inserting blocks of data, merges, and certain other actions.\nNormally coincides with future_parts.\n\ninserts_in_queue: Number of inserts of blocks of data that need to be made.\nInsertions are usually replicated fairly quickly. If the number is high, something is wrong.\n\nmerges_in_queue: The number of merges waiting to be made. \nSometimes merges are lengthy, so this value may be greater than zero for a long time.\n\nThe next 4 columns have a non-null value only if the ZK session is active.\n\nlog_max_index: Maximum entry number in the log of general activity.\nlog_pointer: Maximum entry number in the log of general activity that the replica copied to its execution queue, plus one.\nIf log_pointer is much smaller than log_max_index, something is wrong.\n\ntotal_replicas: Total number of known replicas of this table.\nactive_replicas: Number of replicas of this table that have a ZK session (the number of active replicas).\n\n\n\n\n\nIf you request all the columns, the table may work a bit slowly, since several reads from ZK are made for each row.\nIf you don't request the last 4 columns (log_max_index, log_pointer, total_replicas, active_replicas), the table works quickly.\n\n\nFor example, you can check that everything is working correctly like this:\n\n\nSELECT\n\n \ndatabase\n,\n\n \ntable\n,\n\n \nis_leader\n,\n\n \nis_readonly\n,\n\n \nis_session_expired\n,\n\n \nfuture_parts\n,\n\n \nparts_to_check\n,\n\n \ncolumns_version\n,\n\n \nqueue_size\n,\n\n \ninserts_in_queue\n,\n\n \nmerges_in_queue\n,\n\n \nlog_max_index\n,\n\n \nlog_pointer\n,\n\n \ntotal_replicas\n,\n\n \nactive_replicas\n\n\nFROM\n \nsystem\n.\nreplicas\n\n\nWHERE\n\n \nis_readonly\n\n \nOR\n \nis_session_expired\n\n \nOR\n \nfuture_parts\n \n \n20\n\n \nOR\n \nparts_to_check\n \n \n10\n\n \nOR\n \nqueue_size\n \n \n20\n\n \nOR\n \ninserts_in_queue\n \n \n10\n\n \nOR\n \nlog_max_index\n \n-\n \nlog_pointer\n \n \n10\n\n \nOR\n \ntotal_replicas\n \n \n2\n\n \nOR\n \nactive_replicas\n \n \ntotal_replicas\n\n\n\n\n\n\nIf this query doesn't return anything, it means that everything is fine.\n\n\nsystem.dictionaries\n\n\nContains information about external dictionaries.\n\n\nColumns:\n\n\n\n\nname String\n \u2013 Dictionary name.\n\n\ntype String\n \u2013 Dictionary type: Flat, Hashed, Cache.\n\n\norigin String\n \u2013 Path to the config file where the dictionary is described.\n\n\nattribute.names Array(String)\n \u2013 Array of attribute names provided by the dictionary.\n\n\nattribute.types Array(String)\n \u2013 Corresponding array of attribute types provided by the dictionary.\n\n\nhas_hierarchy UInt8\n \u2013 Whether the dictionary is hierarchical.\n\n\nbytes_allocated UInt64\n \u2013 The amount of RAM used by the dictionary.\n\n\nhit_rate Float64\n \u2013 For cache dictionaries, the percent of usage for which the value was in the cache.\n\n\nelement_count UInt64\n \u2013 The number of items stored in the dictionary.\n\n\nload_factor Float64\n \u2013 The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table).\n\n\ncreation_time DateTime\n \u2013 Time spent for the creation or last successful reload of the dictionary.\n\n\nlast_exception String\n \u2013 Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created.\n\n\nsource String\n \u2013 Text describing the data source for the dictionary.\n\n\n\n\nNote that the amount of memory used by the dictionary is not proportional to the number of items stored in it. So for flat and cached dictionaries, all the memory cells are pre-assigned, regardless of how full the dictionary actually is.\n\n\nsystem.clusters\n\n\nContains information about clusters available in the config file and the servers in them.\nColumns:\n\n\ncluster String \u2013 Cluster name.\nshard_num UInt32 \u2013 Number of a shard in the cluster, starting from 1.\nshard_weight UInt32 \u2013 Relative weight of a shard when writing data.\nreplica_num UInt32 \u2013 Number of a replica in the shard, starting from 1.\nhost_name String \u2013 Host name as specified in the config.\nhost_address String \u2013 Host\ns IP address obtained from DNS.\nport UInt16 \u2013 The port used to access the server.\nuser String \u2013 The username to use for connecting to the server.\n\n\n\n\n\nsystem.functions\n\n\nContains information about normal and aggregate functions.\n\n\nColumns:\n\n\n\n\nname\n (\nString\n) \u2013 Function name.\n\n\nis_aggregate\n (\nUInt8\n) \u2013 Whether it is an aggregate function.\n\n\n\n\nsystem.settings\n\n\nContains information about settings that are currently in use.\nI.e. used for executing the query you are using to read from the system.settings table).\n\n\nColumns:\n\n\nname String \u2013 Setting name.\nvalue String \u2013 Setting value.\nchanged UInt8 - Whether the setting was explicitly defined in the config or explicitly changed.\n\n\n\n\n\nExample:\n\n\nSELECT\n \n*\n\n\nFROM\n \nsystem\n.\nsettings\n\n\nWHERE\n \nchanged\n\n\n\n\n\n\n\u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500value\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500changed\u2500\u2510\n\u2502 max_threads \u2502 8 \u2502 1 \u2502\n\u2502 use_uncompressed_cache \u2502 0 \u2502 1 \u2502\n\u2502 load_balancing \u2502 random \u2502 1 \u2502\n\u2502 max_memory_usage \u2502 10000000000 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nsystem.zookeeper\n\n\nAllows reading data from the ZooKeeper cluster defined in the config.\nThe query must have a 'path' equality condition in the WHERE clause. This is the path in ZooKeeper for the children that you want to get data for.\n\n\nThe query \nSELECT * FROM system.zookeeper WHERE path = '/clickhouse'\n outputs data for all children on the \n/clickhouse\n node.\nTo output data for all root nodes, write path = '/'.\nIf the path specified in 'path' doesn't exist, an exception will be thrown.\n\n\nColumns:\n\n\n\n\nname String\n \u2014 Name of the node.\n\n\npath String\n \u2014 Path to the node.\n\n\nvalue String\n \u2014 Value of the node.\n\n\ndataLength Int32\n \u2014 Size of the value.\n\n\nnumChildren Int32\n \u2014 Number of children.\n\n\nczxid Int64\n \u2014 ID of the transaction that created the node.\n\n\nmzxid Int64\n \u2014 ID of the transaction that last changed the node.\n\n\npzxid Int64\n \u2014 ID of the transaction that last added or removed children.\n\n\nctime DateTime\n \u2014 Time of node creation.\n\n\nmtime DateTime\n \u2014 Time of the last node modification.\n\n\nversion Int32\n \u2014 Node version - the number of times the node was changed.\n\n\ncversion Int32\n \u2014 Number of added or removed children.\n\n\naversion Int32\n \u2014 Number of changes to ACL.\n\n\nephemeralOwner Int64\n \u2014 For ephemeral nodes, the ID of the session that owns this node.\n\n\n\n\nExample:\n\n\nSELECT\n \n*\n\n\nFROM\n \nsystem\n.\nzookeeper\n\n\nWHERE\n \npath\n \n=\n \n/clickhouse/tables/01-08/visits/replicas\n\n\nFORMAT\n \nVertical\n\n\n\n\n\n\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-1.yandex.ru\nvalue:\nczxid: 932998691229\nmzxid: 932998691229\nctime: 2015-03-27 16:49:51\nmtime: 2015-03-27 16:49:51\nversion: 0\ncversion: 47\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021031383\npath: /clickhouse/tables/01-08/visits/replicas\n\nRow 2:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-2.yandex.ru\nvalue:\nczxid: 933002738135\nmzxid: 933002738135\nctime: 2015-03-27 16:57:01\nmtime: 2015-03-27 16:57:01\nversion: 0\ncversion: 37\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021252247\npath: /clickhouse/tables/01-08/visits/replicas\n\n\n\n\n\nTable functions\n\n\nTable functions can be specified in the FROM clause instead of the database and table names.\nTable functions can only be used if 'readonly' is not set.\nTable functions aren't related to other functions.\n\n\n\n\nremote\n\n\nAllows you to access remote servers without creating a \nDistributed\n table.\n\n\nSignatures:\n\n\nremote\n(\naddresses_expr\n,\n \ndb\n,\n \ntable\n[,\n \nuser\n[,\n \npassword\n]])\n\n\nremote\n(\naddresses_expr\n,\n \ndb\n.\ntable\n[,\n \nuser\n[,\n \npassword\n]])\n\n\n\n\n\n\naddresses_expr\n \u2013 An expression that generates addresses of remote servers. This may be just one server address. The server address is \nhost:port\n, or just \nhost\n. The host can be specified as the server name, or as the IPv4 or IPv6 address. An IPv6 address is specified in square brackets. The port is the TCP port on the remote server. If the port is omitted, it uses \ntcp_port\n from the server's config file (by default, 9000).\n\n\n\n\nThe port is required for an IPv6 address.\n\n\n\n\n\nExamples:\n\n\nexample01-01-1\nexample01-01-1:9000\nlocalhost\n127.0.0.1\n[::]:9000\n[2a02:6b8:0:1111::11]:9000\n\n\n\n\n\nMultiple addresses can be comma-separated. In this case, ClickHouse will use distributed processing, so it will send the query to all specified addresses (like to shards with different data).\n\n\nExample:\n\n\nexample01-01-1,example01-02-1\n\n\n\n\n\nPart of the expression can be specified in curly brackets. The previous example can be written as follows:\n\n\nexample01-0{1,2}-1\n\n\n\n\n\nCurly brackets can contain a range of numbers separated by two dots (non-negative integers). In this case, the range is expanded to a set of values that generate shard addresses. If the first number starts with zero, the values are formed with the same zero alignment. The previous example can be written as follows:\n\n\nexample01-{01..02}-1\n\n\n\n\n\nIf you have multiple pairs of curly brackets, it generates the direct product of the corresponding sets.\n\n\nAddresses and parts of addresses in curly brackets can be separated by the pipe symbol (|). In this case, the corresponding sets of addresses are interpreted as replicas, and the query will be sent to the first healthy replica. However, the replicas are iterated in the order currently set in the \nload_balancing\n setting.\n\n\nExample:\n\n\nexample01-{01..02}-{1|2}\n\n\n\n\n\nThis example specifies two shards that each have two replicas.\n\n\nThe number of addresses generated is limited by a constant. Right now this is 1000 addresses.\n\n\nUsing the \nremote\n table function is less optimal than creating a \nDistributed\n table, because in this case, the server connection is re-established for every request. In addition, if host names are set, the names are resolved, and errors are not counted when working with various replicas. When processing a large number of queries, always create the \nDistributed\n table ahead of time, and don't use the \nremote\n table function.\n\n\nThe \nremote\n table function can be useful in the following cases:\n\n\n\n\nAccessing a specific server for data comparison, debugging, and testing.\n\n\nQueries between various ClickHouse clusters for research purposes.\n\n\nInfrequent distributed requests that are made manually.\n\n\nDistributed requests where the set of servers is re-defined each time.\n\n\n\n\nIf the user is not specified, \ndefault\n is used.\nIf the password is not specified, an empty password is used.\n\n\nmerge\n\n\nmerge(db_name, 'tables_regexp')\n \u2013 Creates a temporary Merge table. For more information, see the section \"Table engines, Merge\".\n\n\nThe table structure is taken from the first table encountered that matches the regular expression.\n\n\nnumbers\n\n\nnumbers(N)\n \u2013 Returns a table with the single 'number' column (UInt64) that contains integers from 0 to N-1.\n\n\nSimilar to the \nsystem.numbers\n table, it can be used for testing and generating successive values.\n\n\nThe following two queries are equivalent:\n\n\nSELECT\n \n*\n \nFROM\n \nnumbers\n(\n10\n);\n\n\nSELECT\n \n*\n \nFROM\n \nsystem\n.\nnumbers\n \nLIMIT\n \n10\n;\n\n\n\n\n\n\nExamples:\n\n\n-- Generate a sequence of dates from 2010-01-01 to 2010-12-31\n\n\nselect\n \ntoDate\n(\n2010-01-01\n)\n \n+\n \nnumber\n \nas\n \nd\n \nFROM\n \nnumbers\n(\n365\n);\n\n\n\n\n\n\n\n\nFormats\n\n\nThe format determines how data is returned to you after SELECTs (how it is written and formatted by the server), and how it is accepted for INSERTs (how it is read and parsed by the server).\n\n\nTabSeparated\n\n\nIn TabSeparated format, data is written by row. Each row contains values separated by tabs. Each value is follow by a tab, except the last value in the row, which is followed by a line feed. Strictly Unix line feeds are assumed everywhere. The last row also must contain a line feed at the end. Values are written in text format, without enclosing quotation marks, and with special characters escaped.\n\n\nInteger numbers are written in decimal form. Numbers can contain an extra \"+\" character at the beginning (ignored when parsing, and not recorded when formatting). Non-negative numbers can't contain the negative sign. When reading, it is allowed to parse an empty string as a zero, or (for signed types) a string consisting of just a minus sign as a zero. Numbers that do not fit into the corresponding data type may be parsed as a different number, without an error message.\n\n\nFloating-point numbers are written in decimal form. The dot is used as the decimal separator. Exponential entries are supported, as are 'inf', '+inf', '-inf', and 'nan'. An entry of floating-point numbers may begin or end with a decimal point.\nDuring formatting, accuracy may be lost on floating-point numbers.\nDuring parsing, it is not strictly required to read the nearest machine-representable number.\n\n\nDates are written in YYYY-MM-DD format and parsed in the same format, but with any characters as separators.\nDates with times are written in the format YYYY-MM-DD hh:mm:ss and parsed in the same format, but with any characters as separators.\nThis all occurs in the system time zone at the time the client or server starts (depending on which one formats data). For dates with times, daylight saving time is not specified. So if a dump has times during daylight saving time, the dump does not unequivocally match the data, and parsing will select one of the two times.\nDuring a read operation, incorrect dates and dates with times can be parsed with natural overflow or as null dates and times, without an error message.\n\n\nAs an exception, parsing dates with times is also supported in Unix timestamp format, if it consists of exactly 10 decimal digits. The result is not time zone-dependent. The formats YYYY-MM-DD hh:mm:ss and NNNNNNNNNN are differentiated automatically.\n\n\nStrings are output with backslash-escaped special characters. The following escape sequences are used for output: \n\\b\n, \n\\f\n, \n\\r\n, \n\\n\n, \n\\t\n, \n\\0\n, \n\\'\n, \n\\\\\n. Parsing also supports the sequences \n\\a\n, \n\\v\n, and \n\\xHH\n (hex escape sequences) and any \n\\c\n sequences, where \nc\n is any character (these sequences are converted to \nc\n). Thus, reading data supports formats where a line feed can be written as \n\\n\n or \n\\\n, or as a line feed. For example, the string \nHello world\n with a line feed between the words instead of a space can be parsed in any of the following variations:\n\n\nHello\\nworld\n\nHello\\\nworld\n\n\n\n\n\nThe second variant is supported because MySQL uses it when writing tab-separated dumps.\n\n\nThe minimum set of characters that you need to escape when passing data in TabSeparated format: tab, line feed (LF) and backslash.\n\n\nOnly a small set of symbols are escaped. You can easily stumble onto a string value that your terminal will ruin in output.\n\n\nArrays are written as a list of comma-separated values in square brackets. Number items in the array are fomratted as normally, but dates, dates with times, and strings are written in single quotes with the same escaping rules as above.\n\n\nThe TabSeparated format is convenient for processing data using custom programs and scripts. It is used by default in the HTTP interface, and in the command-line client's batch mode. This format also allows transferring data between different DBMSs. For example, you can get a dump from MySQL and upload it to ClickHouse, or vice versa.\n\n\nThe TabSeparated format supports outputting total values (when using WITH TOTALS) and extreme values (when 'extremes' is set to 1). In these cases, the total values and extremes are output after the main data. The main result, total values, and extremes are separated from each other by an empty line. Example:\n\n\nSELECT\n \nEventDate\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n \nGROUP\n \nBY\n \nEventDate\n \nWITH\n \nTOTALS\n \nORDER\n \nBY\n \nEventDate\n \nFORMAT\n \nTabSeparated\n``\n\n\n\n\n\n\n2014-03-17 1406958\n2014-03-18 1383658\n2014-03-19 1405797\n2014-03-20 1353623\n2014-03-21 1245779\n2014-03-22 1031592\n2014-03-23 1046491\n\n0000-00-00 8873898\n\n2014-03-17 1031592\n2014-03-23 1406958\n\n\n\n\n\nThis format is also available under the name \nTSV\n.\n\n\nTabSeparatedRaw\n\n\nDiffers from \nTabSeparated\n format in that the rows are written without escaping.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\n\n\nThis format is also available under the name \nTSVRaw\n.\n\n\nTabSeparatedWithNames\n\n\nDiffers from the \nTabSeparated\n format in that the column names are written in the first row.\nDuring parsing, the first row is completely ignored. You can't use column names to determine their position or to check their correctness.\n(Support for parsing the header row may be added in the future.)\n\n\nThis format is also available under the name \nTSVWithNames\n.\n\n\nTabSeparatedWithNamesAndTypes\n\n\nDiffers from the \nTabSeparated\n format in that the column names are written to the first row, while the column types are in the second row.\nDuring parsing, the first and second rows are completely ignored.\n\n\nThis format is also available under the name \nTSVWithNamesAndTypes\n.\n\n\nCSV\n\n\nComma Separated Values format (\nRFC\n).\n\n\nWhen formatting, rows are enclosed in double quotes. A double quote inside a string is output as two double quotes in a row. There are no other rules for escaping characters. Date and date-time are enclosed in double quotes. Numbers are output without quotes. Values \u200b\u200bare separated by a delimiter\n. Rows are separated using the Unix line feed (LF). Arrays are serialized in CSV as follows: first the array is serialized to a string as in TabSeparated format, and then the resulting string is output to CSV in double quotes. Tuples in CSV format are serialized as separate columns (that is, their nesting in the tuple is lost).\n\n\nBy default \u2014 \n,\n. See a \nformat_csv_delimiter\n setting for additional info.\n\n\nWhen parsing, all values can be parsed either with or without quotes. Both double and single quotes are supported. Rows can also be arranged without quotes. In this case, they are parsed up to a delimiter or line feed (CR or LF). In violation of the RFC, when parsing rows without quotes, the leading and trailing spaces and tabs are ignored. For the line feed, Unix (LF), Windows (CR LF) and Mac OS Classic (CR LF) are all supported.\n\n\nThe CSV format supports the output of totals and extremes the same way as \nTabSeparated\n.\n\n\nCSVWithNames\n\n\nAlso prints the header row, similar to \nTabSeparatedWithNames\n.\n\n\nValues\n\n\nPrints every row in brackets. Rows are separated by commas. There is no comma after the last row. The values inside the brackets are also comma-separated. Numbers are output in decimal format without quotes. Arrays are output in square brackets. Strings, dates, and dates with times are output in quotes. Escaping rules and parsing are similar to the TabSeparated format. During formatting, extra spaces aren't inserted, but during parsing, they are allowed and skipped (except for spaces inside array values, which are not allowed).\n\n\nThe minimum set of characters that you need to escape when passing data in Values \u200b\u200bformat: single quotes and backslashes.\n\n\nThis is the format that is used in \nINSERT INTO t VALUES ...\n, but you can also use it for formatting query results.\n\n\nVertical\n\n\nPrints each value on a separate line with the column name specified. This format is convenient for printing just one or a few rows, if each row consists of a large number of columns.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\n\n\nVerticalRaw\n\n\nDiffers from \nVertical\n format in that the rows are not escaped.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\n\n\nExamples:\n\n\n:) SHOW CREATE TABLE geonames FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nstatement: CREATE TABLE default.geonames ( geonameid UInt32, date Date DEFAULT CAST(\n2017-12-08\n AS Date)) ENGINE = MergeTree(date, geonameid, 8192)\n\n:) SELECT \nstring with \\\nquotes\\\n and \\t with some special \\n characters\n AS test FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with \nquotes\n and with some special\n characters\n\n\n\n\n\nCompare with the Vertical format:\n\n\n:) SELECT \nstring with \\\nquotes\\\n and \\t with some special \\n characters\n AS test FORMAT Vertical;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with \\\nquotes\\\n and \\t with some special \\n characters\n\n\n\n\n\nJSON\n\n\nOutputs data in JSON format. Besides data tables, it also outputs column names and types, along with some additional information: the total number of output rows, and the number of rows that could have been output if there weren't a LIMIT. Example:\n\n\nSELECT\n \nSearchPhrase\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n \nGROUP\n \nBY\n \nSearchPhrase\n \nWITH\n \nTOTALS\n \nORDER\n \nBY\n \nc\n \nDESC\n \nLIMIT\n \n5\n \nFORMAT\n \nJSON\n\n\n\n\n\n\n{\n\n \nmeta\n:\n\n \n[\n\n \n{\n\n \nname\n:\n \nSearchPhrase\n,\n\n \ntype\n:\n \nString\n\n \n},\n\n \n{\n\n \nname\n:\n \nc\n,\n\n \ntype\n:\n \nUInt64\n\n \n}\n\n \n],\n\n\n \ndata\n:\n\n \n[\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n8267016\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nbathroom interior design\n,\n\n \nc\n:\n \n2166\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nyandex\n,\n\n \nc\n:\n \n1655\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nspring 2014 fashion\n,\n\n \nc\n:\n \n1549\n\n \n},\n\n \n{\n\n \nSearchPhrase\n:\n \nfreeform photos\n,\n\n \nc\n:\n \n1480\n\n \n}\n\n \n],\n\n\n \ntotals\n:\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n8873898\n\n \n},\n\n\n \nextremes\n:\n\n \n{\n\n \nmin\n:\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n1480\n\n \n},\n\n \nmax\n:\n\n \n{\n\n \nSearchPhrase\n:\n \n,\n\n \nc\n:\n \n8267016\n\n \n}\n\n \n},\n\n\n \nrows\n:\n \n5\n,\n\n\n \nrows_before_limit_at_least\n:\n \n141137\n\n\n}\n\n\n\n\n\n\nThe JSON is compatible with JavaScript. To ensure this, some characters are additionally escaped: the slash \n/\n is escaped as \n\\/\n; alternative line breaks \nU+2028\n and \nU+2029\n, which break some browsers, are escaped as \n\\uXXXX\n. ASCII control characters are escaped: backspace, form feed, line feed, carriage return, and horizontal tab are replaced with \n\\b\n, \n\\f\n, \n\\n\n, \n\\r\n, \n\\t\n , as well as the remaining bytes in the 00-1F range using \n\\uXXXX\n sequences. Invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences. For compatibility with JavaScript, Int64 and UInt64 integers are enclosed in double quotes by default. To remove the quotes, you can set the configuration parameter output_format_json_quote_64bit_integers to 0.\n\n\nrows\n \u2013 The total number of output rows.\n\n\nrows_before_limit_at_least\n The minimal number of rows there would have been without LIMIT. Output only if the query contains LIMIT.\nIf the query contains GROUP BY, rows_before_limit_at_least is the exact number of rows there would have been without a LIMIT.\n\n\ntotals\n \u2013 Total values (when using WITH TOTALS).\n\n\nextremes\n \u2013 Extreme values (when extremes is set to 1).\n\n\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the JSONEachRow format.\n\n\nJSONCompact\n\n\nDiffers from JSON only in that data rows are output in arrays, not in objects.\n\n\nExample:\n\n\n{\n\n \nmeta\n:\n\n \n[\n\n \n{\n\n \nname\n:\n \nSearchPhrase\n,\n\n \ntype\n:\n \nString\n\n \n},\n\n \n{\n\n \nname\n:\n \nc\n,\n\n \ntype\n:\n \nUInt64\n\n \n}\n\n \n],\n\n\n \ndata\n:\n\n \n[\n\n \n[\n,\n \n8267016\n],\n\n \n[\nbathroom interior design\n,\n \n2166\n],\n\n \n[\nyandex\n,\n \n1655\n],\n\n \n[\nspring 2014 fashion\n,\n \n1549\n],\n\n \n[\nfreeform photos\n,\n \n1480\n]\n\n \n],\n\n\n \ntotals\n:\n \n[\n,\n8873898\n],\n\n\n \nextremes\n:\n\n \n{\n\n \nmin\n:\n \n[\n,\n1480\n],\n\n \nmax\n:\n \n[\n,\n8267016\n]\n\n \n},\n\n\n \nrows\n:\n \n5\n,\n\n\n \nrows_before_limit_at_least\n:\n \n141137\n\n\n}\n\n\n\n\n\n\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the \nJSONEachRow\n format.\n\n\nJSONEachRow\n\n\nOutputs data as separate JSON objects for each row (newline delimited JSON).\n\n\n{\nSearchPhrase\n:\n,\ncount()\n:\n8267016\n}\n\n\n{\nSearchPhrase\n:\nbathroom interior design\n,\ncount()\n:\n2166\n}\n\n\n{\nSearchPhrase\n:\nyandex\n,\ncount()\n:\n1655\n}\n\n\n{\nSearchPhrase\n:\nspring 2014 fashion\n,\ncount()\n:\n1549\n}\n\n\n{\nSearchPhrase\n:\nfreeform photo\n,\ncount()\n:\n1480\n}\n\n\n{\nSearchPhrase\n:\nangelina jolie\n,\ncount()\n:\n1245\n}\n\n\n{\nSearchPhrase\n:\nomsk\n,\ncount()\n:\n1112\n}\n\n\n{\nSearchPhrase\n:\nphotos of dog breeds\n,\ncount()\n:\n1091\n}\n\n\n{\nSearchPhrase\n:\ncurtain design\n,\ncount()\n:\n1064\n}\n\n\n{\nSearchPhrase\n:\nbaku\n,\ncount()\n:\n1000\n}\n\n\n\n\n\n\nUnlike the JSON format, there is no substitution of invalid UTF-8 sequences. Any set of bytes can be output in the rows. This is necessary so that data can be formatted without losing any information. Values are escaped in the same way as for JSON.\n\n\nFor parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Whitespace between elements is ignored. If a comma is placed after the objects, it is ignored. Objects don't necessarily have to be separated by new lines.\n\n\nTSKV\n\n\nSimilar to TabSeparated, but outputs a value in name=value format. Names are escaped the same way as in TabSeparated format, and the = symbol is also escaped.\n\n\nSearchPhrase= count()=8267016\nSearchPhrase=bathroom interior design count()=2166\nSearchPhrase=yandex count()=1655\nSearchPhrase=spring 2014 fashion count()=1549\nSearchPhrase=freeform photos count()=1480\nSearchPhrase=angelina jolia count()=1245\nSearchPhrase=omsk count()=1112\nSearchPhrase=photos of dog breeds count()=1091\nSearchPhrase=curtain design count()=1064\nSearchPhrase=baku count()=1000\n\n\n\n\n\nWhen there is a large number of small columns, this format is ineffective, and there is generally no reason to use it. It is used in some departments of Yandex.\n\n\nBoth data output and parsing are supported in this format. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults.\n\n\nParsing allows the presence of the additional field \ntskv\n without the equal sign or a value. This field is ignored.\n\n\nPretty\n\n\nOutputs data as Unicode-art tables, also using ANSI-escape sequences for setting colors in the terminal.\nA full grid of the table is drawn, and each row occupies two lines in the terminal.\nEach result block is output as a separate table. This is necessary so that blocks can be output without buffering results (buffering would be necessary in order to pre-calculate the visible width of all the values).\nTo avoid dumping too much data to the terminal, only the first 10,000 rows are printed. If the number of rows is greater than or equal to 10,000, the message \"Showed first 10 000\" is printed.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\n\n\nThe Pretty format supports outputting total values (when using WITH TOTALS) and extremes (when 'extremes' is set to 1). In these cases, total values and extreme values are output after the main data, in separate tables. Example (shown for the PrettyCompact format):\n\n\nSELECT\n \nEventDate\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n \nGROUP\n \nBY\n \nEventDate\n \nWITH\n \nTOTALS\n \nORDER\n \nBY\n \nEventDate\n \nFORMAT\n \nPrettyCompact\n\n\n\n\n\n\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1406958 \u2502\n\u2502 2014-03-18 \u2502 1383658 \u2502\n\u2502 2014-03-19 \u2502 1405797 \u2502\n\u2502 2014-03-20 \u2502 1353623 \u2502\n\u2502 2014-03-21 \u2502 1245779 \u2502\n\u2502 2014-03-22 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1046491 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nTotals:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 0000-00-00 \u2502 8873898 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nExtremes:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1406958 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nPrettyCompact\n\n\nDiffers from \nPretty\n in that the grid is drawn between rows and the result is more compact.\nThis format is used by default in the command-line client in interactive mode.\n\n\nPrettyCompactMonoBlock\n\n\nDiffers from \nPrettyCompact\n in that up to 10,000 rows are buffered, then output as a single table, not by blocks.\n\n\nPrettyNoEscapes\n\n\nDiffers from Pretty in that ANSI-escape sequences aren't used. This is necessary for displaying this format in a browser, as well as for using the 'watch' command-line utility.\n\n\nExample:\n\n\nwatch -n1 \nclickhouse-client --query=\nSELECT * FROM system.events FORMAT PrettyCompactNoEscapes\n\n\n\n\n\n\nYou can use the HTTP interface for displaying in the browser.\n\n\nPrettyCompactNoEscapes\n\n\nThe same as the previous setting.\n\n\nPrettySpaceNoEscapes\n\n\nThe same as the previous setting.\n\n\nPrettySpace\n\n\nDiffers from \nPrettyCompact\n in that whitespace (space characters) is used instead of the grid.\n\n\nRowBinary\n\n\nFormats and parses data by row in binary format. Rows and values are listed consecutively, without separators.\nThis format is less efficient than the Native format, since it is row-based.\n\n\nIntegers use fixed-length little endian representation. For example, UInt64 uses 8 bytes.\nDateTime is represented as UInt32 containing the Unix timestamp as the value.\nDate is represented as a UInt16 object that contains the number of days since 1970-01-01 as the value.\nString is represented as a varint length (unsigned \nLEB128\n), followed by the bytes of the string.\nFixedString is represented simply as a sequence of bytes.\n\n\nArray is represented as a varint length (unsigned \nLEB128\n), followed by successive elements of the array.\n\n\nNative\n\n\nThe most efficient format. Data is written and read by blocks in binary format. For each block, the number of rows, number of columns, column names and types, and parts of columns in this block are recorded one after another. In other words, this format is \"columnar\" \u2013 it doesn't convert columns to rows. This is the format used in the native interface for interaction between servers, for using the command-line client, and for C++ clients.\n\n\nYou can use this format to quickly generate dumps that can only be read by the ClickHouse DBMS. It doesn't make sense to work with this format yourself.\n\n\nNull\n\n\nNothing is output. However, the query is processed, and when using the command-line client, data is transmitted to the client. This is used for tests, including productivity testing.\nObviously, this format is only appropriate for output, not for parsing.\n\n\nXML\n\n\nXML format is suitable only for output, not for parsing. Example:\n\n\n?xml version=\n1.0\n encoding=\nUTF-8\n ?\n\n\nresult\n\n \nmeta\n\n \ncolumns\n\n \ncolumn\n\n \nname\nSearchPhrase\n/name\n\n \ntype\nString\n/type\n\n \n/column\n\n \ncolumn\n\n \nname\ncount()\n/name\n\n \ntype\nUInt64\n/type\n\n \n/column\n\n \n/columns\n\n \n/meta\n\n \ndata\n\n \nrow\n\n \nSearchPhrase\n/SearchPhrase\n\n \nfield\n8267016\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nbathroom interior design\n/SearchPhrase\n\n \nfield\n2166\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nyandex\n/SearchPhrase\n\n \nfield\n1655\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nspring 2014 fashion\n/SearchPhrase\n\n \nfield\n1549\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nfreeform photos\n/SearchPhrase\n\n \nfield\n1480\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nangelina jolie\n/SearchPhrase\n\n \nfield\n1245\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nomsk\n/SearchPhrase\n\n \nfield\n1112\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nphotos of dog breeds\n/SearchPhrase\n\n \nfield\n1091\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\ncurtain design\n/SearchPhrase\n\n \nfield\n1064\n/field\n\n \n/row\n\n \nrow\n\n \nSearchPhrase\nbaku\n/SearchPhrase\n\n \nfield\n1000\n/field\n\n \n/row\n\n \n/data\n\n \nrows\n10\n/rows\n\n \nrows_before_limit_at_least\n141137\n/rows_before_limit_at_least\n\n\n/result\n\n\n\n\n\n\nIf the column name does not have an acceptable format, just 'field' is used as the element name. In general, the XML structure follows the JSON structure.\nJust as for JSON, invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences.\n\n\nIn string values, the characters \n and \n are escaped as \n and \n.\n\n\nArrays are output as \narray\nelem\nHello\n/elem\nelem\nWorld\n/elem\n...\n/array\n,\nand tuples as \ntuple\nelem\nHello\n/elem\nelem\nWorld\n/elem\n...\n/tuple\n.\n\n\n\n\nCapnProto\n\n\nCap'n Proto is a binary message format similar to Protocol Buffers and Thrift, but not like JSON or MessagePack.\n\n\nCap'n Proto messages are strictly typed and not self-describing, meaning they need an external schema description. The schema is applied on the fly and cached for each query.\n\n\nSELECT\n \nSearchPhrase\n,\n \ncount\n()\n \nAS\n \nc\n \nFROM\n \ntest\n.\nhits\n\n \nGROUP\n \nBY\n \nSearchPhrase\n \nFORMAT\n \nCapnProto\n \nSETTINGS\n \nschema\n \n=\n \nschema:Message\n\n\n\n\n\n\nWhere \nschema.capnp\n looks like this:\n\n\nstruct\n \nMessage\n \n{\n\n \nSearchPhrase\n \n@0\n \n:\nText\n;\n\n \nc\n \n@1\n \n:\nUint64\n;\n\n\n}\n\n\n\n\n\n\nSchema files are in the file that is located in the directory specified in \n format_schema_path\n in the server configuration.\n\n\nDeserialization is effective and usually doesn't increase the system load.\n\n\n\n\nData types\n\n\nClickHouse can store various types of data in table cells.\n\n\nThis section describes the supported data types and special considerations when using and/or implementing them, if any.\n\n\nUInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64\n\n\nFixed-length integers, with or without a sign.\n\n\nInt ranges\n\n\n\n\nInt8 - [-128 : 127]\n\n\nInt16 - [-32768 : 32767]\n\n\nInt32 - [-2147483648 : 2147483647]\n\n\nInt64 - [-9223372036854775808 : 9223372036854775807]\n\n\n\n\nUint ranges\n\n\n\n\nUInt8 - [0 : 255]\n\n\nUInt16 - [0 : 65535]\n\n\nUInt32 - [0 : 4294967295]\n\n\nUInt64 - [0 : 18446744073709551615]\n\n\n\n\nFloat32, Float64\n\n\nFloating point numbers\n.\n\n\nTypes are equivalent to types of C:\n\n\n\n\nFloat32\n - \nfloat\n\n\nFloat64\n - \ndouble\n\n\n\n\nWe recommend that you store data in integer form whenever possible. For example, convert fixed precision numbers to integer values, such as monetary amounts or page load times in milliseconds.\n\n\nUsing floating-point numbers\n\n\n\n\nComputations with floating-point numbers might produce a rounding error.\n\n\n\n\nSELECT\n \n1\n \n-\n \n0\n.\n9\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500minus(1, 0.9)\u2500\u2510\n\u2502 0.09999999999999998 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\nThe result of the calculation depends on the calculation method (the processor type and architecture of the computer system).\n\n\nFloating-point calculations might result in numbers such as infinity (\nInf\n) and \"not-a-number\" (\nNaN\n). This should be taken into account when processing the results of calculations.\n\n\nWhen reading floating point numbers from rows, the result might not be the nearest machine-representable number.\n\n\n\n\nNaN and Inf\n\n\nIn contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers:\n\n\n\n\nInf\n \u2013 Infinity.\n\n\n\n\nSELECT\n \n0\n.\n5\n \n/\n \n0\n\n\n\n\n\n\n\u250c\u2500divide(0.5, 0)\u2500\u2510\n\u2502 inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\n-Inf\n \u2013 Negative infinity.\n\n\n\n\nSELECT\n \n-\n0\n.\n5\n \n/\n \n0\n\n\n\n\n\n\n\u250c\u2500divide(-0.5, 0)\u2500\u2510\n\u2502 -inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\nNaN\n \u2013 Not a number.\n\n\n\n\nSELECT 0 / 0\n\n\n\n\n\n\u250c\u2500divide(0, 0)\u2500\u2510\n\u2502 nan \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSee the rules for \nNaN\n sorting in the section \nORDER BY clause\n.\n\n\nBoolean values\n\n\nThere isn't a separate type for boolean values. They use the UInt8 type, restricted to the values 0 or 1.\n\n\nString\n\n\nStrings of an arbitrary length. The length is not limited. The value can contain an arbitrary set of bytes, including null bytes.\nThe String type replaces the types VARCHAR, BLOB, CLOB, and others from other DBMSs.\n\n\nEncodings\n\n\nClickHouse doesn't have the concept of encodings. Strings can contain an arbitrary set of bytes, which are stored and output as-is.\nIf you need to store texts, we recommend using UTF-8 encoding. At the very least, if your terminal uses UTF-8 (as recommended), you can read and write your values without making conversions.\nSimilarly, certain functions for working with strings have separate variations that work under the assumption that the string contains a set of bytes representing a UTF-8 encoded text.\nFor example, the 'length' function calculates the string length in bytes, while the 'lengthUTF8' function calculates the string length in Unicode code points, assuming that the value is UTF-8 encoded.\n\n\nFixedString(N)\n\n\nA fixed-length string of N bytes (not characters or code points). N must be a strictly positive natural number.\nWhen the server reads a string that contains fewer bytes (such as when parsing INSERT data), the string is padded to N bytes by appending null bytes at the right.\nWhen the server reads a string that contains more bytes, an error message is returned.\nWhen the server writes a string (such as when outputting the result of a SELECT query), null bytes are not trimmed off of the end of the string, but are output.\nNote that this behavior differs from MySQL behavior for the CHAR type (where strings are padded with spaces, and the spaces are removed for output).\n\n\nFewer functions can work with the FixedString(N) type than with String, so it is less convenient to use.\n\n\nDate\n\n\nA date. Stored in two bytes as the number of days since 1970-01-01 (unsigned). Allows storing values from just after the beginning of the Unix Epoch to the upper threshold defined by a constant at the compilation stage (currently, this is until the year 2106, but the final fully-supported year is 2105).\nThe minimum value is output as 0000-00-00.\n\n\nThe date is stored without the time zone.\n\n\nDateTime\n\n\nDate with time. Stored in four bytes as a Unix timestamp (unsigned). Allows storing values in the same range as for the Date type. The minimal value is output as 0000-00-00 00:00:00.\nThe time is stored with accuracy up to one second (without leap seconds).\n\n\nTime zones\n\n\nThe date with time is converted from text (divided into component parts) to binary and back, using the system's time zone at the time the client or server starts. In text format, information about daylight savings is lost.\n\n\nBy default, the client switches to the timezone of the server when it connects. You can change this behavior by enabling the client command-line option \n--use_client_time_zone\n.\n\n\nSupports only those time zones that never had the time differ from UTC for a partial number of hours (without leap seconds) over the entire time range you will be working with.\n\n\nSo when working with a textual date (for example, when saving text dumps), keep in mind that there may be ambiguity during changes for daylight savings time, and there may be problems matching data if the time zone changed.\n\n\nEnum\n\n\nEnum8 or Enum16. A finite set of string values that can be stored more efficiently than the \nString\n data type.\n\n\nExample:\n\n\nEnum8(\nhello\n = 1, \nworld\n = 2)\n\n\n\n\n\n\n\nA data type with two possible values: 'hello' and 'world'.\n\n\n\n\nEach of the values is assigned a number in the range \n-128 ... 127\n for \nEnum8\n or in the range \n-32768 ... 32767\n for \nEnum16\n. All the strings and numbers must be different. An empty string is allowed. If this type is specified (in a table definition), numbers can be in an arbitrary order. However, the order does not matter.\n\n\nIn RAM, this type of column is stored in the same way as \nInt8\n or \nInt16\n of the corresponding numerical values.\nWhen reading in text form, ClickHouse parses the value as a string and searches for the corresponding string from the set of Enum values. If it is not found, an exception is thrown. When reading in text format, the string is read and the corresponding numeric value is looked up. An exception will be thrown if it is not found.\nWhen writing in text form, it writes the value as the corresponding string. If column data contains garbage (numbers that are not from the valid set), an exception is thrown. When reading and writing in binary form, it works the same way as for Int8 and Int16 data types.\nThe implicit default value is the value with the lowest number.\n\n\nDuring \nORDER BY\n, \nGROUP BY\n, \nIN\n, \nDISTINCT\n and so on, Enums behave the same way as the corresponding numbers. For example, ORDER BY sorts them numerically. Equality and comparison operators work the same way on Enums as they do on the underlying numeric values.\n\n\nEnum values cannot be compared with numbers. Enums can be compared to a constant string. If the string compared to is not a valid value for the Enum, an exception will be thrown. The IN operator is supported with the Enum on the left hand side and a set of strings on the right hand side. The strings are the values of the corresponding Enum.\n\n\nMost numeric and string operations are not defined for Enum values, e.g. adding a number to an Enum or concatenating a string to an Enum.\nHowever, the Enum has a natural \ntoString\n function that returns its string value.\n\n\nEnum values are also convertible to numeric types using the \ntoT\n function, where T is a numeric type. When T corresponds to the enum\u2019s underlying numeric type, this conversion is zero-cost.\nThe Enum type can be changed without cost using ALTER, if only the set of values is changed. It is possible to both add and remove members of the Enum using ALTER (removing is safe only if the removed value has never been used in the table). As a safeguard, changing the numeric value of a previously defined Enum member will throw an exception.\n\n\nUsing ALTER, it is possible to change an Enum8 to an Enum16 or vice versa, just like changing an Int8 to Int16.\n\n\nArray(T)\n\n\nAn array of elements of type T. The T type can be any type, including an array.\nWe don't recommend using multidimensional arrays, because they are not well supported (for example, you can't store multidimensional arrays in tables with a MergeTree engine).\n\n\nAggregateFunction(name, types_of_arguments...)\n\n\nThe intermediate state of an aggregate function. To get it, use aggregate functions with the '-State' suffix. For more information, see \"AggregatingMergeTree\".\n\n\nTuple(T1, T2, ...)\n\n\nTuples can't be written to tables (other than Memory tables). They are used for temporary column grouping. Columns can be grouped when an IN expression is used in a query, and for specifying certain formal parameters of lambda functions. For more information, see \"IN operators\" and \"Higher order functions\".\n\n\nTuples can be output as the result of running a query. In this case, for text formats other than JSON*, values are comma-separated in brackets. In JSON* formats, tuples are output as arrays (in square brackets).\n\n\nNested data structures\n\n\nNested(Name1 Type1, Name2 Type2, ...)\n\n\nA nested data structure is like a nested table. The parameters of a nested data structure \u2013 the column names and types \u2013 are specified the same way as in a CREATE query. Each table row can correspond to any number of rows in a nested data structure.\n\n\nExample:\n\n\nCREATE\n \nTABLE\n \ntest\n.\nvisits\n\n\n(\n\n \nCounterID\n \nUInt32\n,\n\n \nStartDate\n \nDate\n,\n\n \nSign\n \nInt8\n,\n\n \nIsNew\n \nUInt8\n,\n\n \nVisitID\n \nUInt64\n,\n\n \nUserID\n \nUInt64\n,\n\n \n...\n\n \nGoals\n \nNested\n\n \n(\n\n \nID\n \nUInt32\n,\n\n \nSerial\n \nUInt32\n,\n\n \nEventTime\n \nDateTime\n,\n\n \nPrice\n \nInt64\n,\n\n \nOrderID\n \nString\n,\n\n \nCurrencyID\n \nUInt32\n\n \n),\n\n \n...\n\n\n)\n \nENGINE\n \n=\n \nCollapsingMergeTree\n(\nStartDate\n,\n \nintHash32\n(\nUserID\n),\n \n(\nCounterID\n,\n \nStartDate\n,\n \nintHash32\n(\nUserID\n),\n \nVisitID\n),\n \n8192\n,\n \nSign\n)\n\n\n\n\n\n\nThis example declares the \nGoals\n nested data structure, which contains data about conversions (goals reached). Each row in the 'visits' table can correspond to zero or any number of conversions.\n\n\nOnly a single nesting level is supported. Columns of nested structures containing arrays are equivalent to multidimensional arrays, so they have limited support (there is no support for storing these columns in tables with the MergeTree engine).\n\n\nIn most cases, when working with a nested data structure, its individual columns are specified. To do this, the column names are separated by a dot. These columns make up an array of matching types. All the column arrays of a single nested data structure have the same length.\n\n\nExample:\n\n\nSELECT\n\n \nGoals\n.\nID\n,\n\n \nGoals\n.\nEventTime\n\n\nFROM\n \ntest\n.\nvisits\n\n\nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nlength\n(\nGoals\n.\nID\n)\n \n \n5\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500Goals.ID\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500Goals.EventTime\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1073752,591325,591325] \u2502 [\n2014-03-17 16:38:10\n,\n2014-03-17 16:38:48\n,\n2014-03-17 16:42:27\n] \u2502\n\u2502 [1073752] \u2502 [\n2014-03-17 00:28:25\n] \u2502\n\u2502 [1073752] \u2502 [\n2014-03-17 10:46:20\n] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [\n2014-03-17 13:59:20\n,\n2014-03-17 22:17:55\n,\n2014-03-17 22:18:07\n,\n2014-03-17 22:18:51\n] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [1073752,591325,591325] \u2502 [\n2014-03-17 11:37:06\n,\n2014-03-17 14:07:47\n,\n2014-03-17 14:36:21\n] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [591325,1073752] \u2502 [\n2014-03-17 00:46:05\n,\n2014-03-17 00:46:05\n] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [\n2014-03-17 13:28:33\n,\n2014-03-17 13:30:26\n,\n2014-03-17 18:51:21\n,\n2014-03-17 18:51:45\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIt is easiest to think of a nested data structure as a set of multiple column arrays of the same length.\n\n\nThe only place where a SELECT query can specify the name of an entire nested data structure instead of individual columns is the ARRAY JOIN clause. For more information, see \"ARRAY JOIN clause\". Example:\n\n\nSELECT\n\n \nGoal\n.\nID\n,\n\n \nGoal\n.\nEventTime\n\n\nFROM\n \ntest\n.\nvisits\n\n\nARRAY\n \nJOIN\n \nGoals\n \nAS\n \nGoal\n\n\nWHERE\n \nCounterID\n \n=\n \n101500\n \nAND\n \nlength\n(\nGoals\n.\nID\n)\n \n \n5\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500Goal.ID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500Goal.EventTime\u2500\u2510\n\u2502 1073752 \u2502 2014-03-17 16:38:10 \u2502\n\u2502 591325 \u2502 2014-03-17 16:38:48 \u2502\n\u2502 591325 \u2502 2014-03-17 16:42:27 \u2502\n\u2502 1073752 \u2502 2014-03-17 00:28:25 \u2502\n\u2502 1073752 \u2502 2014-03-17 10:46:20 \u2502\n\u2502 1073752 \u2502 2014-03-17 13:59:20 \u2502\n\u2502 591325 \u2502 2014-03-17 22:17:55 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:07 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:51 \u2502\n\u2502 1073752 \u2502 2014-03-17 11:37:06 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nYou can't perform SELECT for an entire nested data structure. You can only explicitly list individual columns that are part of it.\n\n\nFor an INSERT query, you should pass all the component column arrays of a nested data structure separately (as if they were individual column arrays). During insertion, the system checks that they have the same length.\n\n\nFor a DESCRIBE query, the columns in a nested data structure are listed separately in the same way.\n\n\nThe ALTER query is very limited for elements in a nested data structure.\n\n\nSpecial data types\n\n\nSpecial data type values can't be saved to a table or output in results, but are used as the intermediate result of running a query.\n\n\nExpression\n\n\nUsed for representing lambda expressions in high-order functions.\n\n\nSet\n\n\nUsed for the right half of an IN expression.\n\n\nOperators\n\n\nAll operators are transformed to the corresponding functions at the query parsing stage, in accordance with their precedence and associativity.\nGroups of operators are listed in order of priority (the higher it is in the list, the earlier the operator is connected to its arguments).\n\n\nAccess operators\n\n\na[N]\n Access to an element of an array; \narrayElement(a, N) function\n.\n\n\na.N\n \u2013 Access to a tuble element; \ntupleElement(a, N)\n function.\n\n\nNumeric negation operator\n\n\n-a\n \u2013 The \nnegate (a)\n function.\n\n\nMultiplication and division operators\n\n\na * b\n \u2013 The \nmultiply (a, b) function.\n\n\na / b\n \u2013 The \ndivide(a, b) function.\n\n\na % b\n \u2013 The \nmodulo(a, b) function.\n\n\nAddition and subtraction operators\n\n\na + b\n \u2013 The \nplus(a, b) function.\n\n\na - b\n \u2013 The \nminus(a, b) function.\n\n\nComparison operators\n\n\na = b\n \u2013 The \nequals(a, b) function.\n\n\na == b\n \u2013 The \nequals(a, b) function.\n\n\na != b\n \u2013 The \nnotEquals(a, b) function.\n\n\na \n b\n \u2013 The \nnotEquals(a, b) function.\n\n\na \n= b\n \u2013 The \nlessOrEquals(a, b) function.\n\n\na \n= b\n \u2013 The \ngreaterOrEquals(a, b) function.\n\n\na \n b\n \u2013 The \nless(a, b) function.\n\n\na \n b\n \u2013 The \ngreater(a, b) function.\n\n\na LIKE s\n \u2013 The \nlike(a, b) function.\n\n\na NOT LIKE s\n \u2013 The \nnotLike(a, b) function.\n\n\na BETWEEN b AND c\n \u2013 The same as \na \n= b AND a \n= c.\n\n\nOperators for working with data sets\n\n\nSee the section \"IN operators\".\n\n\na IN ...\n \u2013 The \nin(a, b) function\n\n\na NOT IN ...\n \u2013 The \nnotIn(a, b) function.\n\n\na GLOBAL IN ...\n \u2013 The \nglobalIn(a, b) function.\n\n\na GLOBAL NOT IN ...\n \u2013 The \nglobalNotIn(a, b) function.\n\n\nLogical negation operator\n\n\nNOT a\n The \nnot(a) function.\n\n\nLogical AND operator\n\n\na AND b\n \u2013 The\nand(a, b) function.\n\n\nLogical OR operator\n\n\na OR b\n \u2013 The \nor(a, b) function.\n\n\nConditional operator\n\n\na ? b : c\n \u2013 The \nif(a, b, c) function.\n\n\nNote:\n\n\nThe conditional operator calculates the values of b and c, then checks whether condition a is met, and then returns the corresponding value. If \"b\" or \"c\" is an arrayJoin() function, each row will be replicated regardless of the \"a\" condition.\n\n\nConditional expression\n\n\nCASE\n \n[\nx\n]\n\n \nWHEN\n \na\n \nTHEN\n \nb\n\n \n[\nWHEN\n \n...\n \nTHEN\n \n...]\n\n \nELSE\n \nc\n\n\nEND\n\n\n\n\n\n\nIf \"x\" is specified, then transform(x, [a, ...], [b, ...], c). Otherwise \u2013 multiIf(a, b, ..., c).\n\n\nConcatenation operator\n\n\ns1 || s2\n \u2013 The \nconcat(s1, s2) function.\n\n\nLambda creation operator\n\n\nx -\n expr\n \u2013 The \nlambda(x, expr) function.\n\n\nThe following operators do not have a priority, since they are brackets:\n\n\nArray creation operator\n\n\n[x1, ...]\n \u2013 The \narray(x1, ...) function.\n\n\nTuple creation operator\n\n\n(x1, x2, ...)\n \u2013 The \ntuple(x2, x2, ...) function.\n\n\nAssociativity\n\n\nAll binary operators have left associativity. For example, \n1 + 2 + 3\n is transformed to \nplus(plus(1, 2), 3)\n.\nSometimes this doesn't work the way you expect. For example, \nSELECT 4 \n 2 \n 3\n will result in 0.\n\n\nFor efficiency, the \nand\n and \nor\n functions accept any number of arguments. The corresponding chains of \nAND\n and \nOR\n operators are transformed to a single call of these functions.\n\n\nFunctions\n\n\nThere are at least* two types of functions - regular functions (they are just called \"functions\") and aggregate functions. These are completely different concepts. Regular functions work as if they are applied to each row separately (for each row, the result of the function doesn't depend on the other rows). Aggregate functions accumulate a set of values from various rows (i.e. they depend on the entire set of rows).\n\n\nIn this section we discuss regular functions. For aggregate functions, see the section \"Aggregate functions\".\n\n\n* - There is a third type of function that the 'arrayJoin' function belongs to; table functions can also be mentioned separately.*\n\n\nStrong typing\n\n\nIn contrast to standard SQL, ClickHouse has strong typing. In other words, it doesn't make implicit conversions between types. Each function works for a specific set of types. This means that sometimes you need to use type conversion functions.\n\n\nCommon subexpression elimination\n\n\nAll expressions in a query that have the same AST (the same record or same result of syntactic parsing) are considered to have identical values. Such expressions are concatenated and executed once. Identical subqueries are also eliminated this way.\n\n\nTypes of results\n\n\nAll functions return a single return as the result (not several values, and not zero values). The type of result is usually defined only by the types of arguments, not by the values. Exceptions are the tupleElement function (the a.N operator), and the toFixedString function.\n\n\nConstants\n\n\nFor simplicity, certain functions can only work with constants for some arguments. For example, the right argument of the LIKE operator must be a constant.\nAlmost all functions return a constant for constant arguments. The exception is functions that generate random numbers.\nThe 'now' function returns different values for queries that were run at different times, but the result is considered a constant, since constancy is only important within a single query.\nA constant expression is also considered a constant (for example, the right half of the LIKE operator can be constructed from multiple constants).\n\n\nFunctions can be implemented in different ways for constant and non-constant arguments (different code is executed). But the results for a constant and for a true column containing only the same value should match each other.\n\n\nConstancy\n\n\nFunctions can't change the values of their arguments \u2013 any changes are returned as the result. Thus, the result of calculating separate functions does not depend on the order in which the functions are written in the query.\n\n\nError handling\n\n\nSome functions might throw an exception if the data is invalid. In this case, the query is canceled and an error text is returned to the client. For distributed processing, when an exception occurs on one of the servers, the other servers also attempt to abort the query.\n\n\nEvaluation of argument expressions\n\n\nIn almost all programming languages, one of the arguments might not be evaluated for certain operators. This is usually the operators \n, \n||\n, and \n?:\n.\nBut in ClickHouse, arguments of functions (operators) are always evaluated. This is because entire parts of columns are evaluated at once, instead of calculating each row separately.\n\n\nPerforming functions for distributed query processing\n\n\nFor distributed query processing, as many stages of query processing as possible are performed on remote servers, and the rest of the stages (merging intermediate results and everything after that) are performed on the requestor server.\n\n\nThis means that functions can be performed on different servers.\nFor example, in the query \nSELECT f(sum(g(x))) FROM distributed_table GROUP BY h(y),\n\n\n\n\nif a \ndistributed_table\n has at least two shards, the functions 'g' and 'h' are performed on remote servers, and the function 'f' is performed on the requestor server.\n\n\nif a \ndistributed_table\n has only one shard, all the 'f', 'g', and 'h' functions are performed on this shard's server.\n\n\n\n\nThe result of a function usually doesn't depend on which server it is performed on. However, sometimes this is important.\nFor example, functions that work with dictionaries use the dictionary that exists on the server they are running on.\nAnother example is the \nhostName\n function, which returns the name of the server it is running on in order to make \nGROUP BY\n by servers in a \nSELECT\n query.\n\n\nIf a function in a query is performed on the requestor server, but you need to perform it on remote servers, you can wrap it in an 'any' aggregate function or add it to a key in \nGROUP BY\n.\n\n\nArithmetic functions\n\n\nFor all arithmetic functions, the result type is calculated as the smallest number type that the result fits in, if there is such a type. The minimum is taken simultaneously based on the number of bits, whether it is signed, and whether it floats. If there are not enough bits, the highest bit type is taken.\n\n\nExample:\n\n\nSELECT\n \ntoTypeName\n(\n0\n),\n \ntoTypeName\n(\n0\n \n+\n \n0\n),\n \ntoTypeName\n(\n0\n \n+\n \n0\n \n+\n \n0\n),\n \ntoTypeName\n(\n0\n \n+\n \n0\n \n+\n \n0\n \n+\n \n0\n)\n\n\n\n\n\n\n\u250c\u2500toTypeName(0)\u2500\u252c\u2500toTypeName(plus(0, 0))\u2500\u252c\u2500toTypeName(plus(plus(0, 0), 0))\u2500\u252c\u2500toTypeName(plus(plus(plus(0, 0), 0), 0))\u2500\u2510\n\u2502 UInt8 \u2502 UInt16 \u2502 UInt32 \u2502 UInt64 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nArithmetic functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.\n\n\nOverflow is produced the same way as in C++.\n\n\nplus(a, b), a + b operator\n\n\nCalculates the sum of the numbers.\nYou can also add integer numbers with a date or date and time. In the case of a date, adding an integer means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds.\n\n\nminus(a, b), a - b operator\n\n\nCalculates the difference. The result is always signed.\n\n\nYou can also calculate integer numbers from a date or date with time. The idea is the same \u2013 see above for 'plus'.\n\n\nmultiply(a, b), a * b operator\n\n\nCalculates the product of the numbers.\n\n\ndivide(a, b), a / b operator\n\n\nCalculates the quotient of the numbers. The result type is always a floating-point type.\nIt is not integer division. For integer division, use the 'intDiv' function.\nWhen dividing by zero you get 'inf', '-inf', or 'nan'.\n\n\nintDiv(a, b)\n\n\nCalculates the quotient of the numbers. Divides into integers, rounding down (by the absolute value).\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nintDivOrZero(a, b)\n\n\nDiffers from 'intDiv' in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nmodulo(a, b), a % b operator\n\n\nCalculates the remainder after division.\nIf arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion.\nThe remainder is taken in the same sense as in C++. Truncated division is used for negative numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nnegate(a), -a operator\n\n\nCalculates a number with the reverse sign. The result is always signed.\n\n\nabs(a)\n\n\nCalculates the absolute value of the number (a). That is, if a \n 0, it returns -a. For unsigned types it doesn't do anything. For signed integer types, it returns an unsigned number.\n\n\ngcd(a, b)\n\n\nReturns the greatest common divisor of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nlcm(a, b)\n\n\nReturns the least common multiple of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\n\n\nComparison functions\n\n\nComparison functions always return 0 or 1 (Uint8).\n\n\nThe following types can be compared:\n\n\n\n\nnumbers\n\n\nstrings and fixed strings\n\n\ndates\n\n\ndates with times\n\n\n\n\nwithin each group, but not between different groups.\n\n\nFor example, you can't compare a date with a string. You have to use a function to convert the string to a date, or vice versa.\n\n\nStrings are compared by bytes. A shorter string is smaller than all strings that start with it and that contain at least one more character.\n\n\nNote. Up until version 1.1.54134, signed and unsigned numbers were compared the same way as in C++. In other words, you could get an incorrect result in cases like SELECT 9223372036854775807 \n -1. This behavior changed in version 1.1.54134 and is now mathematically correct.\n\n\nequals, a = b and a == b operator\n\n\nnotEquals, a ! operator= b and a \n b\n\n\nless, \n operator\n\n\ngreater, \n operator\n\n\nlessOrEquals, \n= operator\n\n\ngreaterOrEquals, \n= operator\n\n\nLogical functions\n\n\nLogical functions accept any numeric types, but return a UInt8 number equal to 0 or 1.\n\n\nZero as an argument is considered \"false,\" while any non-zero value is considered \"true\".\n\n\nand, AND operator\n\n\nor, OR operator\n\n\nnot, NOT operator\n\n\nxor\n\n\n\n\nType conversion functions\n\n\ntoUInt8, toUInt16, toUInt32, toUInt64\n\n\ntoInt8, toInt16, toInt32, toInt64\n\n\ntoFloat32, toFloat64\n\n\ntoUInt8OrZero, toUInt16OrZero, toUInt32OrZero, toUInt64OrZero, toInt8OrZero, toInt16OrZero, toInt32OrZero, toInt64OrZero, toFloat32OrZero, toFloat64OrZero\n\n\ntoDate, toDateTime\n\n\ntoString\n\n\nFunctions for converting between numbers, strings (but not fixed strings), dates, and dates with times.\nAll these functions accept one argument.\n\n\nWhen converting to or from a string, the value is formatted or parsed using the same rules as for the TabSeparated format (and almost all other text formats). If the string can't be parsed, an exception is thrown and the request is canceled.\n\n\nWhen converting dates to numbers or vice versa, the date corresponds to the number of days since the beginning of the Unix epoch.\nWhen converting dates with times to numbers or vice versa, the date with time corresponds to the number of seconds since the beginning of the Unix epoch.\n\n\nThe date and date-with-time formats for the toDate/toDateTime functions are defined as follows:\n\n\nYYYY-MM-DD\nYYYY-MM-DD hh:mm:ss\n\n\n\n\n\nAs an exception, if converting from UInt32, Int32, UInt64, or Int64 numeric types to Date, and if the number is greater than or equal to 65536, the number is interpreted as a Unix timestamp (and not as the number of days) and is rounded to the date. This allows support for the common occurrence of writing 'toDate(unix_timestamp)', which otherwise would be an error and would require writing the more cumbersome 'toDate(toDateTime(unix_timestamp))'.\n\n\nConversion between a date and date with time is performed the natural way: by adding a null time or dropping the time.\n\n\nConversion between numeric types uses the same rules as assignments between different numeric types in C++.\n\n\nAdditionally, the toString function of the DateTime argument can take a second String argument containing the name of the time zone. Example: \nAsia/Yekaterinburg\n In this case, the time is formatted according to the specified time zone.\n\n\nSELECT\n\n \nnow\n()\n \nAS\n \nnow_local\n,\n\n \ntoString\n(\nnow\n(),\n \nAsia/Yekaterinburg\n)\n \nAS\n \nnow_yekat\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500now_local\u2500\u252c\u2500now_yekat\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 00:11:21 \u2502 2016-06-15 02:11:21 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nAlso see the \ntoUnixTimestamp\n function.\n\n\ntoFixedString(s, N)\n\n\nConverts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant.\nIf the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown.\n\n\ntoStringCutToZero(s)\n\n\nAccepts a String or FixedString argument. Returns the String with the content truncated at the first zero byte found.\n\n\nExample:\n\n\nSELECT\n \ntoFixedString\n(\nfoo\n,\n \n8\n)\n \nAS\n \ns\n,\n \ntoStringCutToZero\n(\ns\n)\n \nAS\n \ns_cut\n\n\n\n\n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0\\0\\0\\0\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n \ntoFixedString\n(\nfoo\\0bar\n,\n \n8\n)\n \nAS\n \ns\n,\n \ntoStringCutToZero\n(\ns\n)\n \nAS\n \ns_cut\n\n\n\n\n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0bar\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nreinterpretAsUInt8, reinterpretAsUInt16, reinterpretAsUInt32, reinterpretAsUInt64\n\n\nreinterpretAsInt8, reinterpretAsInt16, reinterpretAsInt32, reinterpretAsInt64\n\n\nreinterpretAsFloat32, reinterpretAsFloat64\n\n\nreinterpretAsDate, reinterpretAsDateTime\n\n\nThese functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\n\n\nreinterpretAsString\n\n\nThis function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long.\n\n\nCAST(x, t)\n\n\nConverts 'x' to the 't' data type. The syntax CAST(x AS t) is also supported.\n\n\nExample:\n\n\nSELECT\n\n \n2016-06-15 23:00:00\n \nAS\n \ntimestamp\n,\n\n \nCAST\n(\ntimestamp\n \nAS\n \nDateTime\n)\n \nAS\n \ndatetime\n,\n\n \nCAST\n(\ntimestamp\n \nAS\n \nDate\n)\n \nAS\n \ndate\n,\n\n \nCAST\n(\ntimestamp\n,\n \nString\n)\n \nAS\n \nstring\n,\n\n \nCAST\n(\ntimestamp\n,\n \nFixedString(22)\n)\n \nAS\n \nfixed_string\n\n\n\n\n\n\n\u250c\u2500timestamp\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500datetime\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500date\u2500\u252c\u2500string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500fixed_string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00\\0\\0\\0 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nConversion to FixedString (N) only works for arguments of type String or FixedString (N).\n\n\nFunctions for working with dates and times\n\n\nSupport for time zones\n\n\nAll functions for working with the date and time that have a logical use for the time zone can accept a second optional time zone argument. Example: Asia/Yekaterinburg. In this case, they use the specified time zone instead of the local (default) one.\n\n\nSELECT\n\n \ntoDateTime\n(\n2016-06-15 23:00:00\n)\n \nAS\n \ntime\n,\n\n \ntoDate\n(\ntime\n)\n \nAS\n \ndate_local\n,\n\n \ntoDate\n(\ntime\n,\n \nAsia/Yekaterinburg\n)\n \nAS\n \ndate_yekat\n,\n\n \ntoString\n(\ntime\n,\n \nUS/Samoa\n)\n \nAS\n \ntime_samoa\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500time\u2500\u252c\u2500date_local\u2500\u252c\u2500date_yekat\u2500\u252c\u2500time_samoa\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-16 \u2502 2016-06-15 09:00:00 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nOnly time zones that differ from UTC by a whole number of hours are supported.\n\n\ntoYear\n\n\nConverts a date or date with time to a UInt16 number containing the year number (AD).\n\n\ntoMonth\n\n\nConverts a date or date with time to a UInt8 number containing the month number (1-12).\n\n\ntoDayOfMonth\n\n\n-Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31).\n\n\ntoDayOfWeek\n\n\nConverts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7).\n\n\ntoHour\n\n\nConverts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23).\nThis function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true \u2013 even in Moscow the clocks were twice changed at a different time).\n\n\ntoMinute\n\n\nConverts a date with time to a UInt8 number containing the number of the minute of the hour (0-59).\n\n\ntoSecond\n\n\nConverts a date with time to a UInt8 number containing the number of the second in the minute (0-59).\nLeap seconds are not accounted for.\n\n\ntoMonday\n\n\nRounds down a date or date with time to the nearest Monday.\nReturns the date.\n\n\ntoStartOfMonth\n\n\nRounds down a date or date with time to the first day of the month.\nReturns the date.\n\n\ntoStartOfQuarter\n\n\nRounds down a date or date with time to the first day of the quarter.\nThe first day of the quarter is either 1 January, 1 April, 1 July, or 1 October.\nReturns the date.\n\n\ntoStartOfYear\n\n\nRounds down a date or date with time to the first day of the year.\nReturns the date.\n\n\ntoStartOfMinute\n\n\nRounds down a date with time to the start of the minute.\n\n\ntoStartOfFiveMinute\n\n\nRounds down a date with time to the start of the hour.\n\n\ntoStartOfFifteenMinutes\n\n\nRounds down the date with time to the start of the fifteen-minute interval.\n\n\nNote: If you need to round a date with time to any other number of seconds, minutes, or hours, you can convert it into a number by using the toUInt32 function, then round the number using intDiv and multiplication, and convert it back using the toDateTime function.\n\n\ntoStartOfHour\n\n\nRounds down a date with time to the start of the hour.\n\n\ntoStartOfDay\n\n\nRounds down a date with time to the start of the day.\n\n\ntoTime\n\n\nConverts a date with time to a certain fixed date, while preserving the time.\n\n\ntoRelativeYearNum\n\n\nConverts a date with time or date to the number of the year, starting from a certain fixed point in the past.\n\n\ntoRelativeMonthNum\n\n\nConverts a date with time or date to the number of the month, starting from a certain fixed point in the past.\n\n\ntoRelativeWeekNum\n\n\nConverts a date with time or date to the number of the week, starting from a certain fixed point in the past.\n\n\ntoRelativeDayNum\n\n\nConverts a date with time or date to the number of the day, starting from a certain fixed point in the past.\n\n\ntoRelativeHourNum\n\n\nConverts a date with time or date to the number of the hour, starting from a certain fixed point in the past.\n\n\ntoRelativeMinuteNum\n\n\nConverts a date with time or date to the number of the minute, starting from a certain fixed point in the past.\n\n\ntoRelativeSecondNum\n\n\nConverts a date with time or date to the number of the second, starting from a certain fixed point in the past.\n\n\nnow\n\n\nAccepts zero arguments and returns the current time at one of the moments of request execution.\nThis function returns a constant, even if the request took a long time to complete.\n\n\ntoday\n\n\nAccepts zero arguments and returns the current date at one of the moments of request execution.\nThe same as 'toDate(now())'.\n\n\nyesterday\n\n\nAccepts zero arguments and returns yesterday's date at one of the moments of request execution.\nThe same as 'today() - 1'.\n\n\ntimeSlot\n\n\nRounds the time to the half hour.\nThis function is specific to Yandex.Metrica, since half an hour is the minimum amount of time for breaking a session into two sessions if a tracking tag shows a single user's consecutive pageviews that differ in time by strictly more than this amount. This means that tuples (the tag ID, user ID, and time slot) can be used to search for pageviews that are included in the corresponding session.\n\n\ntimeSlots(StartTime, Duration)\n\n\nFor a time interval starting at 'StartTime' and continuing for 'Duration' seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour.\nFor example, \ntimeSlots(toDateTime('2012-01-01 12:20:00'), 600) = [toDateTime('2012-01-01 12:00:00'), toDateTime('2012-01-01 12:30:00')]\n.\nThis is necessary for searching for pageviews in the corresponding session.\n\n\nFunctions for working with strings\n\n\nempty\n\n\nReturns 1 for an empty string or 0 for a non-empty string.\nThe result type is UInt8.\nA string is considered non-empty if it contains at least one byte, even if this is a space or a null byte.\nThe function also works for arrays.\n\n\nnotEmpty\n\n\nReturns 0 for an empty string or 1 for a non-empty string.\nThe result type is UInt8.\nThe function also works for arrays.\n\n\nlength\n\n\nReturns the length of a string in bytes (not in characters, and not in code points).\nThe result type is UInt64.\nThe function also works for arrays.\n\n\nlengthUTF8\n\n\nReturns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\nThe result type is UInt64.\n\n\nlower\n\n\nConverts ASCII Latin symbols in a string to lowercase.\n\n\nupper\n\n\nConverts ASCII Latin symbols in a string to uppercase.\n\n\nlowerUTF8\n\n\nConverts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.\n\n\nupperUTF8\n\n\nConverts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.\n\n\nreverse\n\n\nReverses the string (as a sequence of bytes).\n\n\nreverseUTF8\n\n\nReverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn't throw an exception).\n\n\nconcat(s1, s2, ...)\n\n\nConcatenates the strings listed in the arguments, without a separator.\n\n\nsubstring(s, offset, length)\n\n\nReturns a substring starting with the byte from the 'offset' index that is 'length' bytes long. Character indexing starts from one (as in standard SQL). The 'offset' and 'length' arguments must be constants.\n\n\nsubstringUTF8(s, offset, length)\n\n\nThe same as 'substring', but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\n\n\nappendTrailingCharIfAbsent(s, c)\n\n\nIf the 's' string is non-empty and does not contain the 'c' character at the end, it appends the 'c' character to the end.\n\n\nconvertCharset(s, from, to)\n\n\nReturns the string 's' that was converted from the encoding in 'from' to the encoding in 'to'.\n\n\nFunctions for searching strings\n\n\nThe search is case-sensitive in all these functions.\nThe search substring or regular expression must be a constant in all these functions.\n\n\nposition(haystack, needle)\n\n\nSearch for the \nneedle\n substring in the \nhaystack\n string.\nReturns the position (in bytes) of the found substring, starting from 1, or returns 0 if the substring was not found.\n\n\nFor case-insensitive search use \npositionCaseInsensitive\n function.\n\n\npositionUTF8(haystack, needle)\n\n\nThe same as \nposition\n, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\n\n\nFor case-insensitive search use \npositionCaseInsensitiveUTF8\n function.\n\n\nmatch(haystack, pattern)\n\n\nChecks whether the string matches the 'pattern' regular expression. A re2 regular expression.\nReturns 0 if it doesn't match, or 1 if it matches.\n\n\nNote that the backslash symbol (\n\\\n) is used for escaping in the regular expression. The same symbol is used for escaping in string literals. So in order to escape the symbol in a regular expression, you must write two backslashes (\\) in a string literal.\n\n\nThe regular expression works with the string as if it is a set of bytes. The regular expression can't contain null bytes.\nFor patterns to search for substrings in a string, it is better to use LIKE or 'position', since they work much faster.\n\n\nextract(haystack, pattern)\n\n\nExtracts a fragment of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. If the regex doesn't contain subpatterns, it takes the fragment that matches the entire regex. Otherwise, it takes the fragment that matches the first subpattern.\n\n\nextractAll(haystack, pattern)\n\n\nExtracts all the fragments of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex. In general, the behavior is the same as the 'extract' function (it takes the first subpattern, or the entire expression if there isn't a subpattern).\n\n\nlike(haystack, pattern), haystack LIKE pattern operator\n\n\nChecks whether a string matches a simple regular expression.\nThe regular expression can contain the metasymbols \n%\n and \n_\n.\n\n\n``% indicates any quantity of any bytes (including zero characters).\n\n\n_\n indicates any one byte.\n\n\nUse the backslash (\n\\\n) for escaping metasymbols. See the note on escaping in the description of the 'match' function.\n\n\nFor regular expressions like \n%needle%\n, the code is more optimal and works as fast as the \nposition\n function.\nFor other regular expressions, the code is the same as for the 'match' function.\n\n\nnotLike(haystack, pattern), haystack NOT LIKE pattern operator\n\n\nThe same thing as 'like', but negative.\n\n\nFunctions for searching and replacing in strings\n\n\nreplaceOne(haystack, pattern, replacement)\n\n\nReplaces the first occurrence, if it exists, of the 'pattern' substring in 'haystack' with the 'replacement' substring.\nHereafter, 'pattern' and 'replacement' must be constants.\n\n\nreplaceAll(haystack, pattern, replacement)\n\n\nReplaces all occurrences of the 'pattern' substring in 'haystack' with the 'replacement' substring.\n\n\nreplaceRegexpOne(haystack, pattern, replacement)\n\n\nReplacement using the 'pattern' regular expression. A re2 regular expression.\nReplaces only the first occurrence, if it exists.\nA pattern can be specified as 'replacement'. This pattern can include substitutions \n\\0-\\9\n.\nThe substitution \n\\0\n includes the entire regular expression. Substitutions \n\\1-\\9\n correspond to the subpattern numbers.To use the \n\\\n character in a template, escape it using \n\\\n.\nAlso keep in mind that a string literal requires an extra escape.\n\n\nExample 1. Converting the date to American format:\n\n\nSELECT\n \nDISTINCT\n\n \nEventDate\n,\n\n \nreplaceRegexpOne\n(\ntoString\n(\nEventDate\n),\n \n(\\\\d{4})-(\\\\d{2})-(\\\\d{2})\n,\n \n\\\\2/\\\\3/\\\\1\n)\n \nAS\n \nres\n\n\nFROM\n \ntest\n.\nhits\n\n\nLIMIT\n \n7\n\n\nFORMAT\n \nTabSeparated\n\n\n\n\n\n\n2014-03-17 03/17/2014\n2014-03-18 03/18/2014\n2014-03-19 03/19/2014\n2014-03-20 03/20/2014\n2014-03-21 03/21/2014\n2014-03-22 03/22/2014\n2014-03-23 03/23/2014\n\n\n\n\n\nExample 2. Copying a string ten times:\n\n\nSELECT\n \nreplaceRegexpOne\n(\nHello, World!\n,\n \n.*\n,\n \n\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nreplaceRegexpAll(haystack, pattern, replacement)\n\n\nThis does the same thing, but replaces all the occurrences. Example:\n\n\nSELECT\n \nreplaceRegexpAll\n(\nHello, World!\n,\n \n.\n,\n \n\\\\0\\\\0\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 HHeelllloo,, WWoorrlldd!! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nAs an exception, if a regular expression worked on an empty substring, the replacement is not made more than once.\nExample:\n\n\nSELECT\n \nreplaceRegexpAll\n(\nHello, World!\n,\n \n^\n,\n \nhere: \n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 here: Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nConditional functions\n\n\nif(cond, then, else), cond ? operator then : else\n\n\nReturns 'then' if cond !or 'else' if cond = 0.'cond' must be UInt 8, and 'then' and 'else' must be a type that has the smallest common type.\n\n\nMathematical functions\n\n\nAll the functions return a Float64 number. The accuracy of the result is close to the maximum precision possible, but the result might not coincide with the machine representable number nearest to the corresponding real number.\n\n\ne()\n\n\nReturns a Float64 number close to the e number.\n\n\npi()\n\n\nReturns a Float64 number close to \u03c0.\n\n\nexp(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the exponent of the argument.\n\n\nlog(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the natural logarithm of the argument.\n\n\nexp2(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to 2^x.\n\n\nlog2(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument.\n\n\nexp10(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to 10^x.\n\n\nlog10(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument.\n\n\nsqrt(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the square root of the argument.\n\n\ncbrt(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to the cubic root of the argument.\n\n\nerf(x)\n\n\nIf 'x' is non-negative, then erf(x / \u03c3\u221a2)\n is the probability that a random variable having a normal distribution with standard deviation '\u03c3' takes the value that is separated from the expected value by more than 'x'.\n\n\nExample (three sigma rule):\n\n\nSELECT\n \nerf\n(\n3\n \n/\n \nsqrt\n(\n2\n))\n\n\n\n\n\n\n\u250c\u2500erf(divide(3, sqrt(2)))\u2500\u2510\n\u2502 0.9973002039367398 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nerfc(x)\n\n\nAccepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large 'x' values.\n\n\nlgamma(x)\n\n\nThe logarithm of the gamma function.\n\n\ntgamma(x)\n\n\nGamma function.\n\n\nsin(x)\n\n\nThe sine.\n\n\ncos(x)\n\n\nThe cosine.\n\n\ntan(x)\n\n\nThe tangent.\n\n\nasin(x)\n\n\nThe arc sine.\n\n\nacos(x)\n\n\nThe arc cosine.\n\n\natan(x)\n\n\nThe arc tangent.\n\n\npow(x, y)\n\n\nAccepts two numeric arguments and returns a Float64 number close to x^y.\n\n\nRounding functions\n\n\nfloor(x[, N])\n\n\nReturns the largest round number that is less than or equal to x. A round number is a multiple of 1/10N, or the nearest number of the appropriate data type if 1 / 10N isn't exact.\n'N' is an integer constant, optional parameter. By default it is zero, which means to round to an integer.\n'N' may be negative.\n\n\nExamples: \nfloor(123.45, 1) = 123.4, floor(123.45, -1) = 120.\n\n\nx\n is any numeric type. The result is a number of the same type.\nFor integer arguments, it makes sense to round with a negative 'N' value (for non-negative 'N', the function doesn't do anything).\nIf rounding causes overflow (for example, floor(-128, -1)), an implementation-specific result is returned.\n\n\nceil(x[, N])\n\n\nReturns the smallest round number that is greater than or equal to 'x'. In every other way, it is the same as the 'floor' function (see above).\n\n\nround(x[, N])\n\n\nReturns the round number nearest to 'num', which may be less than, greater than, or equal to 'x'.If 'x' is exactly in the middle between the nearest round numbers, one of them is returned (implementation-specific).\nThe number '-0.' may or may not be considered round (implementation-specific).\nIn every other way, this function is the same as 'floor' and 'ceil' described above.\n\n\nroundToExp2(num)\n\n\nAccepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two.\n\n\nroundDuration(num)\n\n\nAccepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length\n\n\nroundAge(num)\n\n\nAccepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to a number from the set: 18, 25, 35, 45, 55. This function is specific to Yandex.Metrica and used for implementing the report on user age.\n\n\nFunctions for working with arrays\n\n\nempty\n\n\nReturns 1 for an empty array, or 0 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.\n\n\nnotEmpty\n\n\nReturns 0 for an empty array, or 1 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.\n\n\nlength\n\n\nReturns the number of items in the array.\nThe result type is UInt64.\nThe function also works for strings.\n\n\nemptyArrayUInt8, emptyArrayUInt16, emptyArrayUInt32, emptyArrayUInt64\n\n\nemptyArrayInt8, emptyArrayInt16, emptyArrayInt32, emptyArrayInt64\n\n\nemptyArrayFloat32, emptyArrayFloat64\n\n\nemptyArrayDate, emptyArrayDateTime\n\n\nemptyArrayString\n\n\nAccepts zero arguments and returns an empty array of the appropriate type.\n\n\nemptyArrayToSingle\n\n\nAccepts an empty array and returns a one-element array that is equal to the default value.\n\n\nrange(N)\n\n\nReturns an array of numbers from 0 to N-1.\nJust in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block.\n\n\narray(x1, ...), operator [x1, ...]\n\n\nCreates an array from the function arguments.\nThe arguments must be constants and have types that have the smallest common type. At least one argument must be passed, because otherwise it isn't clear which type of array to create. That is, you can't use this function to create an empty array (to do that, use the 'emptyArray*' function described above).\nReturns an 'Array(T)' type result, where 'T' is the smallest common type out of the passed arguments.\n\n\narrayConcat\n\n\nCombines arrays passed as arguments.\n\n\narrayConcat(arrays)\n\n\n\n\n\nArguments\n\n\n\n\narrays\n \u2013 Arrays of comma-separated \n[values]\n.\n\n\n\n\nExample\n\n\nSELECT\n \narrayConcat\n([\n1\n,\n \n2\n],\n \n[\n3\n,\n \n4\n],\n \n[\n5\n,\n \n6\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,3,4,5,6] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayElement(arr, n), operator arr[n]\n\n\nGet the element with the index 'n' from the array 'arr'.'n' must be any integer type.\nIndexes in an array begin from one.\nNegative indexes are supported. In this case, it selects the corresponding element numbered from the end. For example, 'arr[-1]' is the last item in the array.\n\n\nIf the index falls outside of the bounds of an array, it returns some default value (0 for numbers, an empty string for strings, etc.).\n\n\nhas(arr, elem)\n\n\nChecks whether the 'arr' array has the 'elem' element.\nReturns 0 if the the element is not in the array, or 1 if it is.\n\n\nindexOf(arr, x)\n\n\nReturns the index of the 'x' element (starting from 1) if it is in the array, or 0 if it is not.\n\n\ncountEqual(arr, x)\n\n\nReturns the number of elements in the array equal to x. Equivalent to arrayCount (elem-\n elem = x, arr).\n\n\narrayEnumerate(arr)\n\n\nReturns the array [1, 2, 3, ..., length (arr) ]\n\n\nThis function is normally used with ARRAY JOIN. It allows counting something just once for each array after applying ARRAY JOIN. Example:\n\n\nSELECT\n\n \ncount\n()\n \nAS\n \nReaches\n,\n\n \ncountIf\n(\nnum\n \n=\n \n1\n)\n \nAS\n \nHits\n\n\nFROM\n \ntest\n.\nhits\n\n\nARRAY\n \nJOIN\n\n \nGoalsReached\n,\n\n \narrayEnumerate\n(\nGoalsReached\n)\n \nAS\n \nnum\n\n\nWHERE\n \nCounterID\n \n=\n \n160656\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIn this example, Reaches is the number of conversions (the strings received after applying ARRAY JOIN), and Hits is the number of pageviews (strings before ARRAY JOIN). In this particular case, you can get the same result in an easier way:\n\n\nSELECT\n\n \nsum\n(\nlength\n(\nGoalsReached\n))\n \nAS\n \nReaches\n,\n\n \ncount\n()\n \nAS\n \nHits\n\n\nFROM\n \ntest\n.\nhits\n\n\nWHERE\n \n(\nCounterID\n \n=\n \n160656\n)\n \nAND\n \nnotEmpty\n(\nGoalsReached\n)\n\n\n\n\n\n\n\u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nThis function can also be used in higher-order functions. For example, you can use it to get array indexes for elements that match a condition.\n\n\narrayEnumerateUniq(arr, ...)\n\n\nReturns an array the same size as the source array, indicating for each element what its position is among elements with the same value.\nFor example: arrayEnumerateUniq([10, 20, 10, 30]) = [1, 1, 2, 1].\n\n\nThis function is useful when using ARRAY JOIN and aggregation of array elements.\nExample:\n\n\nSELECT\n\n \nGoals\n.\nID\n \nAS\n \nGoalID\n,\n\n \nsum\n(\nSign\n)\n \nAS\n \nReaches\n,\n\n \nsumIf\n(\nSign\n,\n \nnum\n \n=\n \n1\n)\n \nAS\n \nVisits\n\n\nFROM\n \ntest\n.\nvisits\n\n\nARRAY\n \nJOIN\n\n \nGoals\n,\n\n \narrayEnumerateUniq\n(\nGoals\n.\nID\n)\n \nAS\n \nnum\n\n\nWHERE\n \nCounterID\n \n=\n \n160656\n\n\nGROUP\n \nBY\n \nGoalID\n\n\nORDER\n \nBY\n \nReaches\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500\u2500GoalID\u2500\u252c\u2500Reaches\u2500\u252c\u2500Visits\u2500\u2510\n\u2502 53225 \u2502 3214 \u2502 1097 \u2502\n\u2502 2825062 \u2502 3188 \u2502 1097 \u2502\n\u2502 56600 \u2502 2803 \u2502 488 \u2502\n\u2502 1989037 \u2502 2401 \u2502 365 \u2502\n\u2502 2830064 \u2502 2396 \u2502 910 \u2502\n\u2502 1113562 \u2502 2372 \u2502 373 \u2502\n\u2502 3270895 \u2502 2262 \u2502 812 \u2502\n\u2502 1084657 \u2502 2262 \u2502 345 \u2502\n\u2502 56599 \u2502 2260 \u2502 799 \u2502\n\u2502 3271094 \u2502 2256 \u2502 812 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIn this example, each goal ID has a calculation of the number of conversions (each element in the Goals nested data structure is a goal that was reached, which we refer to as a conversion) and the number of sessions. Without ARRAY JOIN, we would have counted the number of sessions as sum(Sign). But in this particular case, the rows were multiplied by the nested Goals structure, so in order to count each session one time after this, we apply a condition to the value of the arrayEnumerateUniq(Goals.ID) function.\n\n\nThe arrayEnumerateUniq function can take multiple arrays of the same size as arguments. In this case, uniqueness is considered for tuples of elements in the same positions in all the arrays.\n\n\nSELECT\n \narrayEnumerateUniq\n([\n1\n,\n \n1\n,\n \n1\n,\n \n2\n,\n \n2\n,\n \n2\n],\n \n[\n1\n,\n \n1\n,\n \n2\n,\n \n1\n,\n \n1\n,\n \n2\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,1,1,2,1] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nThis is necessary when using ARRAY JOIN with a nested data structure and further aggregation across multiple elements in this structure.\n\n\narrayPopBack\n\n\nRemoves the last item from the array.\n\n\narrayPopBack(array)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\n\n\nExample\n\n\nSELECT\n \narrayPopBack\n([\n1\n,\n \n2\n,\n \n3\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [1,2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayPopFront\n\n\nRemoves the first item from the array.\n\n\narrayPopFront(array)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\n\n\nExample\n\n\nSELECT\n \narrayPopFront\n([\n1\n,\n \n2\n,\n \n3\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayPushBack\n\n\nAdds one item to the end of the array.\n\n\narrayPushBack(array, single_value)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\nsingle_value\n \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the \nsingle_value\n type for the data type of the array. For more information about ClickHouse data types, read the section \"\nData types\n\".\n\n\n\n\nExample\n\n\nSELECT\n \narrayPushBack\n([\na\n],\n \nb\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\na\n,\nb\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayPushFront\n\n\nAdds one element to the beginning of the array.\n\n\narrayPushFront(array, single_value)\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array.\n\n\nsingle_value\n \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the \nsingle_value\n type for the data type of the array. For more information about ClickHouse data types, read the section \"\nData types\n\".\n\n\n\n\nExample\n\n\nSELECT\n \narrayPushBack\n([\nb\n],\n \na\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\na\n,\nb\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narraySlice\n\n\nReturns a slice of the array.\n\n\narraySlice(array, offset[, length])\n\n\n\n\n\nArguments\n\n\n\n\narray\n \u2013 Array of data.\n\n\noffset\n \u2013 Indent from the edge of the array. A positive value indicates an offset on the left, and a negative value is an indent on the right. Numbering of the array items begins with 1.\n\n\nlength\n - The length of the required slice. If you specify a negative value, the function returns an open slice \n[offset, array_length - length)\n. If you omit the value, the function returns the slice \n[offset, the_end_of_array]\n.\n\n\n\n\nExample\n\n\nSELECT\n \narraySlice\n([\n1\n,\n \n2\n,\n \n3\n,\n \n4\n,\n \n5\n],\n \n2\n,\n \n3\n)\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [2,3,4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayUniq(arr, ...)\n\n\nIf one argument is passed, it counts the number of different elements in the array.\nIf multiple arguments are passed, it counts the number of different tuples of elements at corresponding positions in multiple arrays.\n\n\nIf you want to get a list of unique items in an array, you can use arrayReduce('groupUniqArray', arr).\n\n\narrayJoin(arr)\n\n\nA special function. See the section \n\"ArrayJoin function\"\n.\n\n\nFunctions for splitting and merging strings and arrays\n\n\nsplitByChar(separator, s)\n\n\nSplits a string into substrings separated by 'separator'.'separator' must be a string constant consisting of exactly one character.\nReturns an array of selected substrings. Empty substrings may be selected if the separator occurs at the beginning or end of the string, or if there are multiple consecutive separators.\n\n\nsplitByString(separator, s)\n\n\nThe same as above, but it uses a string of multiple characters as the separator. The string must be non-empty.\n\n\narrayStringConcat(arr[, separator])\n\n\nConcatenates the strings listed in the array with the separator.'separator' is an optional parameter: a constant string, set to an empty string by default.\nReturns the string.\n\n\nalphaTokens(s)\n\n\nSelects substrings of consecutive bytes from the ranges a-z and A-Z.Returns an array of substrings.\n\n\nBit functions\n\n\nBit functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64.\n\n\nThe result type is an integer with bits equal to the maximum bits of its arguments. If at least one of the arguments is signed, the result is a signed number. If an argument is a floating-point number, it is cast to Int64.\n\n\nbitAnd(a, b)\n\n\nbitOr(a, b)\n\n\nbitXor(a, b)\n\n\nbitNot(a)\n\n\nbitShiftLeft(a, b)\n\n\nbitShiftRight(a, b)\n\n\nHash functions\n\n\nHash functions can be used for deterministic pseudo-random shuffling of elements.\n\n\nhalfMD5\n\n\nCalculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian.\nAccepts a String-type argument. Returns UInt64.\nThis function works fairly slowly (5 million short strings per second per processor core).\nIf you don't need MD5 in particular, use the 'sipHash64' function instead.\n\n\nMD5\n\n\nCalculates the MD5 from a string and returns the resulting set of bytes as FixedString(16).\nIf you don't need MD5 in particular, but you need a decent cryptographic 128-bit hash, use the 'sipHash128' function instead.\nIf you want to get the same result as output by the md5sum utility, use lower(hex(MD5(s))).\n\n\nsipHash64\n\n\nCalculates SipHash from a string.\nAccepts a String-type argument. Returns UInt64.\nSipHash is a cryptographic hash function. It works at least three times faster than MD5.\nFor more information, see the link: \nhttps://131002.net/siphash/\n\n\nsipHash128\n\n\nCalculates SipHash from a string.\nAccepts a String-type argument. Returns FixedString(16).\nDiffers from sipHash64 in that the final xor-folding state is only done up to 128 bytes.\n\n\ncityHash64\n\n\nCalculates CityHash64 from a string or a similar hash function for any number of any type of arguments.\nFor String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality.\nFor other types of arguments, a decent implementation-specific fast non-cryptographic hash function is used.\nIf multiple arguments are passed, the function is calculated using the same rules and chain combinations using the CityHash combinator.\nFor example, you can compute the checksum of an entire table with accuracy up to the row order: \nSELECT sum(cityHash64(*)) FROM table\n.\n\n\nintHash32\n\n\nCalculates a 32-bit hash code from any type of integer.\nThis is a relatively fast non-cryptographic hash function of average quality for numbers.\n\n\nintHash64\n\n\nCalculates a 64-bit hash code from any type of integer.\nIt works faster than intHash32. Average quality.\n\n\nSHA1\n\n\nSHA224\n\n\nSHA256\n\n\nCalculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32).\nThe function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million).\nWe recommend using this function only in cases when you need a specific hash function and you can't select it.\nEven in these cases, we recommend applying the function offline and pre-calculating values when inserting them into the table, instead of applying it in SELECTS.\n\n\nURLHash(url[, N])\n\n\nA fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization.\n\nURLHash(s)\n \u2013 Calculates a hash from a string without one of the trailing symbols \n/\n,\n?\n or \n#\n at the end, if present.\n\nURLHash(s, N)\n \u2013 Calculates a hash from a string up to the N level in the URL hierarchy, without one of the trailing symbols \n/\n,\n?\n or \n#\n at the end, if present.\nLevels are the same as in URLHierarchy. This function is specific to Yandex.Metrica.\n\n\nFunctions for generating pseudo-random numbers\n\n\nNon-cryptographic generators of pseudo-random numbers are used.\n\n\nAll the functions accept zero arguments or one argument.\nIf an argument is passed, it can be any type, and its value is not used for anything.\nThe only purpose of this argument is to prevent common subexpression elimination, so that two different instances of the same function return different columns with different random numbers.\n\n\nrand\n\n\nReturns a pseudo-random UInt32 number, evenly distributed among all UInt32-type numbers.\nUses a linear congruential generator.\n\n\nrand64\n\n\nReturns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers.\nUses a linear congruential generator.\n\n\nEncoding functions\n\n\nhex\n\n\nAccepts arguments of types: \nString\n, \nunsigned integer\n, \nDate\n, or \nDateTime\n. Returns a string containing the argument's hexadecimal representation. Uses uppercase letters \nA-F\n. Does not use \n0x\n prefixes or \nh\n suffixes. For strings, all bytes are simply encoded as two hexadecimal numbers. Numbers are converted to big endian (\"human readable\") format. For numbers, older zeros are trimmed, but only by entire bytes. For example, \nhex (1) = '01'\n. \nDate\n is encoded as the number of days since the beginning of the Unix epoch. \nDateTime\n is encoded as the number of seconds since the beginning of the Unix epoch.\n\n\nunhex(str)\n\n\nAccepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits does not have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn't thrown).\nIf you want to convert the result to a number, you can use the 'reverse' and 'reinterpretAsType' functions.\n\n\nUUIDStringToNum(str)\n\n\nAccepts a string containing 36 characters in the format \n123e4567-e89b-12d3-a456-426655440000\n, and returns it as a set of bytes in a FixedString(16).\n\n\nUUIDNumToString(str)\n\n\nAccepts a FixedString(16) value. Returns a string containing 36 characters in text format.\n\n\nbitmaskToList(num)\n\n\nAccepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order.\n\n\nbitmaskToArray(num)\n\n\nAccepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order.\n\n\nFunctions for working with URLs\n\n\nAll these functions don't follow the RFC. They are maximally simplified for improved performance.\n\n\nFunctions that extract part of a URL\n\n\nIf there isn't anything similar in a URL, an empty string is returned.\n\n\nprotocol\n\n\nReturns the protocol. Examples: http, ftp, mailto, magnet...\n\n\ndomain\n\n\nGets the domain.\n\n\ndomainWithoutWWW\n\n\nReturns the domain and removes no more than one 'www.' from the beginning of it, if present.\n\n\ntopLevelDomain\n\n\nReturns the top-level domain. Example: .ru.\n\n\nfirstSignificantSubdomain\n\n\nReturns the \"first significant subdomain\". This is a non-standard concept specific to Yandex.Metrica. The first significant subdomain is a second-level domain if it is 'com', 'net', 'org', or 'co'. Otherwise, it is a third-level domain. For example, firstSignificantSubdomain ('\nhttps://news.yandex.ru/\n') = 'yandex ', firstSignificantSubdomain ('\nhttps://news.yandex.com.tr/\n') = 'yandex '. The list of \"insignificant\" second-level domains and other implementation details may change in the future.\n\n\ncutToFirstSignificantSubdomain\n\n\nReturns the part of the domain that includes top-level subdomains up to the \"first significant subdomain\" (see the explanation above).\n\n\nFor example, \ncutToFirstSignificantSubdomain('https://news.yandex.com.tr/') = 'yandex.com.tr'\n.\n\n\npath\n\n\nReturns the path. Example: \n/top/news.html\n The path does not include the query string.\n\n\npathFull\n\n\nThe same as above, but including query string and fragment. Example: /top/news.html?page=2#comments\n\n\nqueryString\n\n\nReturns the query string. Example: page=1\nlr=213. query-string does not include the initial question mark, as well as # and everything after #.\n\n\nfragment\n\n\nReturns the fragment identifier. fragment does not include the initial hash symbol.\n\n\nqueryStringAndFragment\n\n\nReturns the query string and fragment identifier. Example: page=1#29390.\n\n\nextractURLParameter(URL, name)\n\n\nReturns the value of the 'name' parameter in the URL, if present. Otherwise, an empty string. If there are many parameters with this name, it returns the first occurrence. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.\n\n\nextractURLParameters(URL)\n\n\nReturns an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way.\n\n\nextractURLParameterNames(URL)\n\n\nReturns an array of name strings corresponding to the names of URL parameters. The values are not decoded in any way.\n\n\nURLHierarchy(URL)\n\n\nReturns an array containing the URL, truncated at the end by the symbols /,? in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:\n\n\nURLPathHierarchy(URL)\n\n\nThe same as above, but without the protocol and host in the result. The / element (root) is not included. Example: the function is used to implement tree reports the URL in Yandex. Metric.\n\n\nURLPathHierarchy(\nhttps://example.com/browse/CONV-6788\n) =\n[\n \n/browse/\n,\n \n/browse/CONV-6788\n\n]\n\n\n\n\n\ndecodeURLComponent(URL)\n\n\nReturns the decoded URL.\nExample:\n\n\nSELECT\n \ndecodeURLComponent\n(\nhttp://127.0.0.1:8123/?query=SELECT%201%3B\n)\n \nAS\n \nDecodedURL\n;\n\n\n\n\n\n\n\u250c\u2500DecodedURL\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 http://127.0.0.1:8123/?query=SELECT 1; \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nFunctions that remove part of a URL.\n\n\nIf the URL doesn't have anything similar, the URL remains unchanged.\n\n\ncutWWW\n\n\nRemoves no more than one 'www.' from the beginning of the URL's domain, if present.\n\n\ncutQueryString\n\n\nRemoves query string. The question mark is also removed.\n\n\ncutFragment\n\n\nRemoves the fragment identifier. The number sign is also removed.\n\n\ncutQueryStringAndFragment\n\n\nRemoves the query string and fragment identifier. The question mark and number sign are also removed.\n\n\ncutURLParameter(URL, name)\n\n\nRemoves the 'name' URL parameter, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.\n\n\nFunctions for working with IP addresses\n\n\nIPv4NumToString(num)\n\n\nTakes a UInt32 number. Interprets it as an IPv4 address in big endian. Returns a string containing the corresponding IPv4 address in the format A.B.C.d (dot-separated numbers in decimal form).\n\n\nIPv4StringToNum(s)\n\n\nThe reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0.\n\n\nIPv4NumToStringClassC(num)\n\n\nSimilar to IPv4NumToString, but using xxx instead of the last octet.\n\n\nExample:\n\n\nSELECT\n\n \nIPv4NumToStringClassC\n(\nClientIP\n)\n \nAS\n \nk\n,\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \nk\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500k\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 83.149.9.xxx \u2502 26238 \u2502\n\u2502 217.118.81.xxx \u2502 26074 \u2502\n\u2502 213.87.129.xxx \u2502 25481 \u2502\n\u2502 83.149.8.xxx \u2502 24984 \u2502\n\u2502 217.118.83.xxx \u2502 22797 \u2502\n\u2502 78.25.120.xxx \u2502 22354 \u2502\n\u2502 213.87.131.xxx \u2502 21285 \u2502\n\u2502 78.25.121.xxx \u2502 20887 \u2502\n\u2502 188.162.65.xxx \u2502 19694 \u2502\n\u2502 83.149.48.xxx \u2502 17406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSince using 'xxx' is highly unusual, this may be changed in the future. We recommend that you don't rely on the exact format of this fragment.\n\n\nIPv6NumToString(x)\n\n\nAccepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format.\nIPv6-mapped IPv4 addresses are output in the format ::ffff:111.222.33.44. Examples:\n\n\nSELECT\n \nIPv6NumToString\n(\ntoFixedString\n(\nunhex\n(\n2A0206B8000000000000000000000011\n),\n \n16\n))\n \nAS\n \naddr\n\n\n\n\n\n\n\u250c\u2500addr\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2a02:6b8::11 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n\n \nIPv6NumToString\n(\nClientIP6\n \nAS\n \nk\n),\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \nhits_all\n\n\nWHERE\n \nEventDate\n \n=\n \ntoday\n()\n \nAND\n \nsubstring\n(\nClientIP6\n,\n \n1\n,\n \n12\n)\n \n!=\n \nunhex\n(\n00000000000000000000FFFF\n)\n\n\nGROUP\n \nBY\n \nk\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500IPv6NumToString(ClientIP6)\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2a02:2168:aaa:bbbb::2 \u2502 24695 \u2502\n\u2502 2a02:2698:abcd:abcd:abcd:abcd:8888:5555 \u2502 22408 \u2502\n\u2502 2a02:6b8:0:fff::ff \u2502 16389 \u2502\n\u2502 2a01:4f8:111:6666::2 \u2502 16016 \u2502\n\u2502 2a02:2168:888:222::1 \u2502 15896 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:222 \u2502 14774 \u2502\n\u2502 2a02:8109:eee:ee:eeee:eeee:eeee:eeee \u2502 14443 \u2502\n\u2502 2a02:810b:8888:888:8888:8888:8888:8888 \u2502 14345 \u2502\n\u2502 2a02:6b8:0:444:4444:4444:4444:4444 \u2502 14279 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:ffff \u2502 13880 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n\n \nIPv6NumToString\n(\nClientIP6\n \nAS\n \nk\n),\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \nhits_all\n\n\nWHERE\n \nEventDate\n \n=\n \ntoday\n()\n\n\nGROUP\n \nBY\n \nk\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500IPv6NumToString(ClientIP6)\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 ::ffff:94.26.111.111 \u2502 747440 \u2502\n\u2502 ::ffff:37.143.222.4 \u2502 529483 \u2502\n\u2502 ::ffff:5.166.111.99 \u2502 317707 \u2502\n\u2502 ::ffff:46.38.11.77 \u2502 263086 \u2502\n\u2502 ::ffff:79.105.111.111 \u2502 186611 \u2502\n\u2502 ::ffff:93.92.111.88 \u2502 176773 \u2502\n\u2502 ::ffff:84.53.111.33 \u2502 158709 \u2502\n\u2502 ::ffff:217.118.11.22 \u2502 154004 \u2502\n\u2502 ::ffff:217.118.11.33 \u2502 148449 \u2502\n\u2502 ::ffff:217.118.11.44 \u2502 148243 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nIPv6StringToNum(s)\n\n\nThe reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes.\nHEX can be uppercase or lowercase.\n\n\nFunctions for working with JSON\n\n\nIn Yandex.Metrica, JSON is transmitted by users as session parameters. There are some special functions for working with this JSON. (Although in most of the cases, the JSONs are additionally pre-processed, and the resulting values are put in separate columns in their processed format.) All these functions are based on strong assumptions about what the JSON can be, but they try to do as little as possible to get the job done.\n\n\nThe following assumptions are made:\n\n\n\n\nThe field name (function argument) must be a constant.\n\n\nThe field name is somehow canonically encoded in JSON. For example: \nvisitParamHas('{\"abc\":\"def\"}', 'abc') = 1\n, but \nvisitParamHas('{\"\\\\u0061\\\\u0062\\\\u0063\":\"def\"}', 'abc') = 0\n\n\nFields are searched for on any nesting level, indiscriminately. If there are multiple matching fields, the first occurrence is used.\n\n\nThe JSON doesn't have space characters outside of string literals.\n\n\n\n\nvisitParamHas(params, name)\n\n\nChecks whether there is a field with the 'name' name.\n\n\nvisitParamExtractUInt(params, name)\n\n\nParses UInt64 from the value of the field named 'name'. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn't exist, or it exists but doesn't contain a number, it returns 0.\n\n\nvisitParamExtractInt(params, name)\n\n\nThe same as for Int64.\n\n\nvisitParamExtractFloat(params, name)\n\n\nThe same as for Float64.\n\n\nvisitParamExtractBool(params, name)\n\n\nParses a true/false value. The result is UInt8.\n\n\nvisitParamExtractRaw(params, name)\n\n\nReturns the value of a field, including separators.\n\n\nExamples:\n\n\nvisitParamExtractRaw(\n{\nabc\n:\n\\\\n\\\\u0000\n}\n, \nabc\n) = \n\\\\n\\\\u0000\n\nvisitParamExtractRaw(\n{\nabc\n:{\ndef\n:[1,2,3]}}\n, \nabc\n) = \n{\ndef\n:[1,2,3]}\n\n\n\n\n\n\nvisitParamExtractString(params, name)\n\n\nParses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string.\n\n\nExamples:\n\n\nvisitParamExtractString(\n{\nabc\n:\n\\\\n\\\\u0000\n}\n, \nabc\n) = \n\\n\\0\n\nvisitParamExtractString(\n{\nabc\n:\n\\\\u263a\n}\n, \nabc\n) = \n\u263a\n\nvisitParamExtractString(\n{\nabc\n:\n\\\\u263\n}\n, \nabc\n) = \n\nvisitParamExtractString(\n{\nabc\n:\nhello}\n, \nabc\n) = \n\n\n\n\n\n\nThere is currently no support for code points in the format \n\\uXXXX\\uYYYY\n that are not from the basic multilingual plane (they are converted to CESU-8 instead of UTF-8).\n\n\nHigher-order functions\n\n\n-\n operator, lambda(params, expr) function\n\n\nAllows describing a lambda function for passing to a higher-order function. The left side of the arrow has a formal parameter, which is any ID, or multiple formal parameters \u2013 any IDs in a tuple. The right side of the arrow has an expression that can use these formal parameters, as well as any table columns.\n\n\nExamples: \nx -\n 2 * x, str -\n str != Referer.\n\n\nHigher-order functions can only accept lambda functions as their functional argument.\n\n\nA lambda function that accepts multiple arguments can be passed to a higher-order function. In this case, the higher-order function is passed several arrays of identical length that these arguments will correspond to.\n\n\nFor all functions other than 'arrayMap' and 'arrayFilter', the first argument (the lambda function) can be omitted. In this case, identical mapping is assumed.\n\n\narrayMap(func, arr1, ...)\n\n\nReturns an array obtained from the original application of the 'func' function to each element in the 'arr' array.\n\n\narrayFilter(func, arr1, ...)\n\n\nReturns an array containing only the elements in 'arr1' for which 'func' returns something other than 0.\n\n\nExamples:\n\n\nSELECT\n \narrayFilter\n(\nx\n \n-\n \nx\n \nLIKE\n \n%World%\n,\n \n[\nHello\n,\n \nabc World\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\nabc World\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nSELECT\n\n \narrayFilter\n(\n\n \n(\ni\n,\n \nx\n)\n \n-\n \nx\n \nLIKE\n \n%World%\n,\n\n \narrayEnumerate\n(\narr\n),\n\n \n[\nHello\n,\n \nabc World\n]\n \nAS\n \narr\n)\n\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2510\n\u2502 [2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayCount([func,] arr1, ...)\n\n\nReturns the number of elements in the arr array for which func returns something other than 0. If 'func' is not specified, it returns the number of non-zero elements in the array.\n\n\narrayExists([func,] arr1, ...)\n\n\nReturns 1 if there is at least one element in 'arr' for which 'func' returns something other than 0. Otherwise, it returns 0.\n\n\narrayAll([func,] arr1, ...)\n\n\nReturns 1 if 'func' returns something other than 0 for all the elements in 'arr'. Otherwise, it returns 0.\n\n\narraySum([func,] arr1, ...)\n\n\nReturns the sum of the 'func' values. If the function is omitted, it just returns the sum of the array elements.\n\n\narrayFirst(func, arr1, ...)\n\n\nReturns the first element in the 'arr1' array for which 'func' returns something other than 0.\n\n\narrayFirstIndex(func, arr1, ...)\n\n\nReturns the index of the first element in the 'arr1' array for which 'func' returns something other than 0.\n\n\narrayCumSum([func,] arr1, ...)\n\n\nReturns an array of partial sums of elements in the source array (a running sum). If the \nfunc\n function is specified, then the values of the array elements are converted by this function before summing.\n\n\nExample:\n\n\nSELECT\n \narrayCumSum\n([\n1\n,\n \n1\n,\n \n1\n,\n \n1\n])\n \nAS\n \nres\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1, 2, 3, 4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narraySort([func,] arr1, ...)\n\n\nReturns an array as result of sorting the elements of \narr1\n in ascending order. If the \nfunc\n function is specified, sorting order is determined by the result of the function \nfunc\n applied to the elements of array (arrays) \n\n\nThe \nSchwartzian transform\n is used to impove sorting efficiency.\n\n\nExample:\n\n\nSELECT\n \narraySort\n((\nx\n,\n \ny\n)\n \n-\n \ny\n,\n \n[\nhello\n,\n \nworld\n],\n \n[\n2\n,\n \n1\n]);\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [\nworld\n, \nhello\n] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\narrayReverseSort([func,] arr1, ...)\n\n\nReturns an array as result of sorting the elements of \narr1\n in descending order. If the \nfunc\n function is specified, sorting order is determined by the result of the function \nfunc\n applied to the elements of array (arrays) \n\n\nOther functions\n\n\nhostName()\n\n\nReturns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server.\n\n\nvisibleWidth(x)\n\n\nCalculates the approximate width when outputting values to the console in text format (tab-separated).\nThis function is used by the system for implementing Pretty formats.\n\n\ntoTypeName(x)\n\n\nReturns a string containing the type name of the passed argument.\n\n\nblockSize()\n\n\nGets the size of the block.\nIn ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for.\n\n\nmaterialize(x)\n\n\nTurns a constant into a full column containing just one value.\nIn ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior.\n\n\nignore(...)\n\n\nAccepts any arguments and always returns 0.\nHowever, the argument is still evaluated. This can be used for benchmarks.\n\n\nsleep(seconds)\n\n\nSleeps 'seconds' seconds on each data block. You can specify an integer or a floating-point number.\n\n\ncurrentDatabase()\n\n\nReturns the name of the current database.\nYou can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database.\n\n\nisFinite(x)\n\n\nAccepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0.\n\n\nisInfinite(x)\n\n\nAccepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. Note that 0 is returned for a NaN.\n\n\nisNaN(x)\n\n\nAccepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0.\n\n\nhasColumnInTable(['hostname'[, 'username'[, 'password']],] 'database', 'table', 'column')\n\n\nAccepts constant strings: database name, table name, and column name. Returns a UInt8 constant expression equal to 1 if there is a column, otherwise 0. If the hostname parameter is set, the test will run on a remote server.\nThe function throws an exception if the table does not exist.\nFor elements in a nested data structure, the function checks for the existence of a column. For the nested data structure itself, the function returns 0.\n\n\nbar\n\n\nAllows building a unicode-art diagram.\n\n\nbar (x, min, max, width)\n draws a band with a width proportional to \n(x - min)\n and equal to \nwidth\n characters when \nx = max\n.\n\n\nParameters:\n\n\n\n\nx\n \u2013 Value to display.\n\n\nmin, max\n \u2013 Integer constants. The value must fit in Int64.\n\n\nwidth\n \u2013 Constant, positive number, may be a fraction.\n\n\n\n\nThe band is drawn with accuracy to one eighth of a symbol.\n\n\nExample:\n\n\nSELECT\n\n \ntoHour\n(\nEventTime\n)\n \nAS\n \nh\n,\n\n \ncount\n()\n \nAS\n \nc\n,\n\n \nbar\n(\nc\n,\n \n0\n,\n \n600000\n,\n \n20\n)\n \nAS\n \nbar\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \nh\n\n\nORDER\n \nBY\n \nh\n \nASC\n\n\n\n\n\n\n\u250c\u2500\u2500h\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u252c\u2500bar\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 0 \u2502 292907 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 1 \u2502 180563 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 2 \u2502 114861 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 3 \u2502 85069 \u2502 \u2588\u2588\u258b \u2502\n\u2502 4 \u2502 68543 \u2502 \u2588\u2588\u258e \u2502\n\u2502 5 \u2502 78116 \u2502 \u2588\u2588\u258c \u2502\n\u2502 6 \u2502 113474 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 7 \u2502 170678 \u2502 \u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 8 \u2502 278380 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 9 \u2502 391053 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 10 \u2502 457681 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 11 \u2502 493667 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 12 \u2502 509641 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 13 \u2502 522947 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 14 \u2502 539954 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 15 \u2502 528460 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c \u2502\n\u2502 16 \u2502 539201 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 17 \u2502 523539 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 18 \u2502 506467 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 19 \u2502 520915 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 20 \u2502 521665 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 21 \u2502 542078 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 22 \u2502 493642 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 23 \u2502 400397 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2514\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\ntransform\n\n\nTransforms a value according to the explicitly defined mapping of some elements to other ones.\nThere are two variations of this function:\n\n\n\n\ntransform(x, array_from, array_to, default)\n\n\n\n\nx\n \u2013 What to transform.\n\n\narray_from\n \u2013 Constant array of values for converting.\n\n\narray_to\n \u2013 Constant array of values to convert the values in 'from' to.\n\n\ndefault\n \u2013 Which value to use if 'x' is not equal to any of the values in 'from'.\n\n\narray_from\n and \narray_to\n \u2013 Arrays of the same size.\n\n\nTypes:\n\n\ntransform(T, Array(T), Array(U), U) -\n U\n\n\nT\n and \nU\n can be numeric, string, or Date or DateTime types.\nWhere the same letter is indicated (T or U), for numeric types these might not be matching types, but types that have a common type.\nFor example, the first argument can have the Int64 type, while the second has the Array(Uint16) type.\n\n\nIf the 'x' value is equal to one of the elements in the 'array_from' array, it returns the existing element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'default'. If there are multiple matching elements in 'array_from', it returns one of the matches.\n\n\nExample:\n\n\nSELECT\n\n \ntransform\n(\nSearchEngineID\n,\n \n[\n2\n,\n \n3\n],\n \n[\nYandex\n,\n \nGoogle\n],\n \nOther\n)\n \nAS\n \ntitle\n,\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \ntest\n.\nhits\n\n\nWHERE\n \nSearchEngineID\n \n!=\n \n0\n\n\nGROUP\n \nBY\n \ntitle\n\n\nORDER\n \nBY\n \nc\n \nDESC\n\n\n\n\n\n\n\u250c\u2500title\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 Yandex \u2502 498635 \u2502\n\u2502 Google \u2502 229872 \u2502\n\u2502 Other \u2502 104472 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\ntransform(x, array_from, array_to)\n\n\n\n\nDiffers from the first variation in that the 'default' argument is omitted.\nIf the 'x' value is equal to one of the elements in the 'array_from' array, it returns the matching element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'x'.\n\n\nTypes:\n\n\ntransform(T, Array(T), Array(T)) -\n T\n\n\nExample:\n\n\nSELECT\n\n \ntransform\n(\ndomain\n(\nReferer\n),\n \n[\nyandex.ru\n,\n \ngoogle.ru\n,\n \nvk.com\n],\n \n[\nwww.yandex\n,\n \nexample.com\n])\n \nAS\n \ns\n,\n\n \ncount\n()\n \nAS\n \nc\n\n\nFROM\n \ntest\n.\nhits\n\n\nGROUP\n \nBY\n \ndomain\n(\nReferer\n)\n\n\nORDER\n \nBY\n \ncount\n()\n \nDESC\n\n\nLIMIT\n \n10\n\n\n\n\n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 \u2502 2906259 \u2502\n\u2502 www.yandex \u2502 867767 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 313599 \u2502\n\u2502 mail.yandex.ru \u2502 107147 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 100355 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 65040 \u2502\n\u2502 news.yandex.ru \u2502 64515 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.net \u2502 59141 \u2502\n\u2502 example.com \u2502 57316 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nformatReadableSize(x)\n\n\nAccepts the size (number of bytes). Returns a rounded size with a suffix (KiB, MiB, etc.) as a string.\n\n\nExample:\n\n\nSELECT\n\n \narrayJoin\n([\n1\n,\n \n1024\n,\n \n1024\n*\n1024\n,\n \n192851925\n])\n \nAS\n \nfilesize_bytes\n,\n\n \nformatReadableSize\n(\nfilesize_bytes\n)\n \nAS\n \nfilesize\n\n\n\n\n\n\n\u250c\u2500filesize_bytes\u2500\u252c\u2500filesize\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 1.00 B \u2502\n\u2502 1024 \u2502 1.00 KiB \u2502\n\u2502 1048576 \u2502 1.00 MiB \u2502\n\u2502 192851925 \u2502 183.92 MiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nleast(a, b)\n\n\nReturns the smallest value from a and b.\n\n\ngreatest(a, b)\n\n\nReturns the largest value of a and b.\n\n\nuptime()\n\n\nReturns the server's uptime in seconds.\n\n\nversion()\n\n\nReturns the version of the server as a string.\n\n\nrowNumberInAllBlocks()\n\n\nReturns the ordinal number of the row in the data block. This function only considers the affected data blocks.\n\n\nrunningDifference(x)\n\n\nCalculates the difference between successive row values \u200b\u200bin the data block.\nReturns 0 for the first row and the difference from the previous row for each subsequent row.\n\n\nThe result of the function depends on the affected data blocks and the order of data in the block.\nIf you make a subquery with ORDER BY and call the function from outside the subquery, you can get the expected result.\n\n\nExample:\n\n\nSELECT\n\n \nEventID\n,\n\n \nEventTime\n,\n\n \nrunningDifference\n(\nEventTime\n)\n \nAS\n \ndelta\n\n\nFROM\n\n\n(\n\n \nSELECT\n\n \nEventID\n,\n\n \nEventTime\n\n \nFROM\n \nevents\n\n \nWHERE\n \nEventDate\n \n=\n \n2016-11-24\n\n \nORDER\n \nBY\n \nEventTime\n \nASC\n\n \nLIMIT\n \n5\n\n\n)\n\n\n\n\n\n\n\u250c\u2500EventID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500EventTime\u2500\u252c\u2500delta\u2500\u2510\n\u2502 1106 \u2502 2016-11-24 00:00:04 \u2502 0 \u2502\n\u2502 1107 \u2502 2016-11-24 00:00:05 \u2502 1 \u2502\n\u2502 1108 \u2502 2016-11-24 00:00:05 \u2502 0 \u2502\n\u2502 1109 \u2502 2016-11-24 00:00:09 \u2502 4 \u2502\n\u2502 1110 \u2502 2016-11-24 00:00:10 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nMACNumToString(num)\n\n\nAccepts a UInt64 number. Interprets it as a MAC address in big endian. Returns a string containing the corresponding MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form).\n\n\nMACStringToNum(s)\n\n\nThe inverse function of MACNumToString. If the MAC address has an invalid format, it returns 0.\n\n\nMACStringToOUI(s)\n\n\nAccepts a MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form). Returns the first three octets as a UInt64 number. If the MAC address has an invalid format, it returns 0.\n\n\n\n\nFunctions for working with external dictionaries\n\n\nFor information on connecting and configuring external dictionaries, see \"\nExternal dictionaries\n\".\n\n\ndictGetUInt8, dictGetUInt16, dictGetUInt32, dictGetUInt64\n\n\ndictGetInt8, dictGetInt16, dictGetInt32, dictGetInt64\n\n\ndictGetFloat32, dictGetFloat64\n\n\ndictGetDate, dictGetDateTime\n\n\ndictGetUUID\n\n\ndictGetString\n\n\ndictGetT('dict_name', 'attr_name', id)\n\n\n\n\nGet the value of the attr_name attribute from the dict_name dictionary using the 'id' key.\ndict_name\n and \nattr_name\n are constant strings.\nid\nmust be UInt64.\nIf there is no \nid\n key in the dictionary, it returns the default value specified in the dictionary description.\n\n\n\n\ndictGetTOrDefault\n\n\ndictGetT('dict_name', 'attr_name', id, default)\n\n\nThe same as the \ndictGetT\n functions, but the default value is taken from the function's last argument.\n\n\ndictIsIn\n\n\ndictIsIn('dict_name', child_id, ancestor_id)\n\n\n\n\nFor the 'dict_name' hierarchical dictionary, finds out whether the 'child_id' key is located inside 'ancestor_id' (or matches 'ancestor_id'). Returns UInt8.\n\n\n\n\ndictGetHierarchy\n\n\ndictGetHierarchy('dict_name', id)\n\n\n\n\nFor the 'dict_name' hierarchical dictionary, returns an array of dictionary keys starting from 'id' and continuing along the chain of parent elements. Returns Array(UInt64).\n\n\n\n\ndictHas\n\n\ndictHas('dict_name', id)\n\n\n\n\nCheck whether the dictionary has the key. Returns a UInt8 value equal to 0 if there is no key and 1 if there is a key.\n\n\n\n\nFunctions for working with Yandex.Metrica dictionaries\n\n\nIn order for the functions below to work, the server config must specify the paths and addresses for getting all the Yandex.Metrica dictionaries. The dictionaries are loaded at the first call of any of these functions. If the reference lists can't be loaded, an exception is thrown.\n\n\nFor information about creating reference lists, see the section \"Dictionaries\".\n\n\nMultiple geobases\n\n\nClickHouse supports working with multiple alternative geobases (regional hierarchies) simultaneously, in order to support various perspectives on which countries certain regions belong to.\n\n\nThe 'clickhouse-server' config specifies the file with the regional hierarchy::\npath_to_regions_hierarchy_file\n/opt/geo/regions_hierarchy.txt\n/path_to_regions_hierarchy_file\n\n\nBesides this file, it also searches for files nearby that have the _ symbol and any suffix appended to the name (before the file extension).\nFor example, it will also find the file \n/opt/geo/regions_hierarchy_ua.txt\n, if present.\n\n\nua\n is called the dictionary key. For a dictionary without a suffix, the key is an empty string.\n\n\nAll the dictionaries are re-loaded in runtime (once every certain number of seconds, as defined in the builtin_dictionaries_reload_interval config parameter, or once an hour by default). However, the list of available dictionaries is defined one time, when the server starts.\n\n\nAll functions for working with regions have an optional argument at the end \u2013 the dictionary key. It is referred to as the geobase.\nExample:\n\n\nregionToCountry(RegionID) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, \n) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, \nua\n) \u2013 Uses the dictionary for the \nua\n key: /opt/geo/regions_hierarchy_ua.txt\n\n\n\n\n\nregionToCity(id[, geobase])\n\n\nAccepts a UInt32 number \u2013 the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0.\n\n\nregionToArea(id[, geobase])\n\n\nConverts a region to an area (type 5 in the geobase). In every other way, this function is the same as 'regionToCity'.\n\n\nSELECT\n \nDISTINCT\n \nregionToName\n(\nregionToArea\n(\ntoUInt32\n(\nnumber\n),\n \nua\n))\n\n\nFROM\n \nsystem\n.\nnumbers\n\n\nLIMIT\n \n15\n\n\n\n\n\n\n\u250c\u2500regionToName(regionToArea(toUInt32(number), \\\nua\\\n))\u2500\u2510\n\u2502 \u2502\n\u2502 Moscow and Moscow region \u2502\n\u2502 St. Petersburg and Leningrad region \u2502\n\u2502 Belgorod region \u2502\n\u2502 Ivanovsk region \u2502\n\u2502 Kaluga region \u2502\n\u2502 Kostroma region \u2502\n\u2502 Kursk region \u2502\n\u2502 Lipetsk region \u2502\n\u2502 Orlov region \u2502\n\u2502 Ryazan region \u2502\n\u2502 Smolensk region \u2502\n\u2502 Tambov region \u2502\n\u2502 Tver region \u2502\n\u2502 Tula region \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nregionToDistrict(id[, geobase])\n\n\nConverts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as 'regionToCity'.\n\n\nSELECT\n \nDISTINCT\n \nregionToName\n(\nregionToDistrict\n(\ntoUInt32\n(\nnumber\n),\n \nua\n))\n\n\nFROM\n \nsystem\n.\nnumbers\n\n\nLIMIT\n \n15\n\n\n\n\n\n\n\u250c\u2500regionToName(regionToDistrict(toUInt32(number), \\\nua\\\n))\u2500\u2510\n\u2502 \u2502\n\u2502 Central federal district \u2502\n\u2502 Northwest federal district \u2502\n\u2502 South federal district \u2502\n\u2502 North Caucases federal district \u2502\n\u2502 Privolga federal district \u2502\n\u2502 Ural federal district \u2502\n\u2502 Siberian federal district \u2502\n\u2502 Far East federal district \u2502\n\u2502 Scotland \u2502\n\u2502 Faroe Islands \u2502\n\u2502 Flemish region \u2502\n\u2502 Brussels capital region \u2502\n\u2502 Wallonia \u2502\n\u2502 Federation of Bosnia and Herzegovina \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nregionToCountry(id[, geobase])\n\n\nConverts a region to a country. In every other way, this function is the same as 'regionToCity'.\nExample: \nregionToCountry(toUInt32(213)) = 225\n converts Moscow (213) to Russia (225).\n\n\nregionToContinent(id[, geobase])\n\n\nConverts a region to a continent. In every other way, this function is the same as 'regionToCity'.\nExample: \nregionToContinent(toUInt32(213)) = 10001\n converts Moscow (213) to Eurasia (10001).\n\n\nregionToPopulation(id[, geobase])\n\n\nGets the population for a region.\nThe population can be recorded in files with the geobase. See the section \"External dictionaries\".\nIf the population is not recorded for the region, it returns 0.\nIn the Yandex geobase, the population might be recorded for child regions, but not for parent regions.\n\n\nregionIn(lhs, rhs[, geobase])\n\n\nChecks whether a 'lhs' region belongs to a 'rhs' region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn't belong.\nThe relationship is reflexive \u2013 any region also belongs to itself.\n\n\nregionHierarchy(id[, geobase])\n\n\nAccepts a UInt32 number \u2013 the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain.\nExample: \nregionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000]\n.\n\n\nregionToName(id[, lang])\n\n\nAccepts a UInt32 number \u2013 the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language 'ru' is used. If the language is not supported, an exception is thrown. Returns a string \u2013 the name of the region in the corresponding language. If the region with the specified ID doesn't exist, an empty string is returned.\n\n\nua\n and \nuk\n both mean Ukrainian.\n\n\nFunctions for implementing the IN operator\n\n\nin, notIn, globalIn, globalNotIn\n\n\nSee the section \"IN operators\".\n\n\ntuple(x, y, ...), operator (x, y, ...)\n\n\nA function that allows grouping multiple columns.\nFor columns with the types T1, T2, ..., it returns a Tuple(T1, T2, ...) type tuple containing these columns. There is no cost to execute the function.\nTuples are normally used as intermediate values for an argument of IN operators, or for creating a list of formal parameters of lambda functions. Tuples can't be written to a table.\n\n\ntupleElement(tuple, n), operator x.N\n\n\nA function that allows getting a column from a tuple.\n'N' is the column index, starting from 1. N must be a constant. 'N' must be a constant. 'N' must be a strict postive integer no greater than the size of the tuple.\nThere is no cost to execute the function.\n\n\n\n\narrayJoin function\n\n\nThis is a very unusual function.\n\n\nNormal functions don't change a set of rows, but just change the values in each row (map).\nAggregate functions compress a set of rows (fold or reduce).\nThe 'arrayJoin' function takes each row and generates a set of rows (unfold).\n\n\nThis function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array.\nAll the values in columns are simply copied, except the values in the column where this function is applied; it is replaced with the corresponding array value.\n\n\nA query can use multiple \narrayJoin\n functions. In this case, the transformation is performed multiple times.\n\n\nNote the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities.\n\n\nExample:\n\n\nSELECT\n \narrayJoin\n([\n1\n,\n \n2\n,\n \n3\n]\n \nAS\n \nsrc\n)\n \nAS\n \ndst\n,\n \nHello\n,\n \nsrc\n\n\n\n\n\n\n\u250c\u2500dst\u2500\u252c\u2500\\\nHello\\\n\u2500\u252c\u2500src\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 2 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 3 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\n\n\nAggregate functions\n\n\nAggregate functions work in the \nnormal\n way as expected by database experts.\n\n\nClickHouse also supports:\n\n\n\n\nParametric aggregate functions\n, which accept other parameters in addition to columns.\n\n\nCombinators\n, which change the behavior of aggregate functions.\n\n\n\n\n\n\nFunction reference\n\n\ncount()\n\n\nCounts the number of rows. Accepts zero arguments and returns UInt64.\nThe syntax \nCOUNT(DISTINCT x)\n is not supported. The separate \nuniq\n aggregate function exists for this purpose.\n\n\nA \nSELECT count() FROM table\n query is not optimized, because the number of entries in the table is not stored separately. It will select some small column from the table and count the number of values in it.\n\n\nany(x)\n\n\nSelects the first encountered value.\nThe query can be executed in any order and even in a different order each time, so the result of this function is indeterminate.\nTo get a determinate result, you can use the 'min' or 'max' function instead of 'any'.\n\n\nIn some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.\n\n\nWhen a \nSELECT\n query has the \nGROUP BY\n clause or at least one aggregate function, ClickHouse (in contrast to MySQL) requires that all expressions in the \nSELECT\n, \nHAVING\n, and \nORDER BY\n clauses be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. To get behavior like in MySQL, you can put the other columns in the \nany\n aggregate function.\n\n\nanyHeavy(x)\n\n\nSelects a frequently occurring value using the \nheavy hitters\n algorithm. If there is a value that occurs more than in half the cases in each of the query's execution threads, this value is returned. Normally, the result is nondeterministic.\n\n\nanyHeavy(column)\n\n\n\n\n\nArguments\n\n- \ncolumn\n \u2013 The column name.\n\n\nExample\n\n\nTake the \nOnTime\n data set and select any frequently occurring value in the \nAirlineID\n column.\n\n\nSELECT\n \nanyHeavy\n(\nAirlineID\n)\n \nAS\n \nres\n\n\nFROM\n \nontime\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500res\u2500\u2510\n\u2502 19690 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\nanyLast(x)\n\n\nSelects the last value encountered.\nThe result is just as indeterminate as for the \nany\n function.\n\n\nmin(x)\n\n\nCalculates the minimum.\n\n\nmax(x)\n\n\nCalculates the maximum.\n\n\nargMin(arg, val)\n\n\nCalculates the 'arg' value for a minimal 'val' value. If there are several different values of 'arg' for minimal values of 'val', the first of these values encountered is output.\n\n\nargMax(arg, val)\n\n\nCalculates the 'arg' value for a maximum 'val' value. If there are several different values of 'arg' for maximum values of 'val', the first of these values encountered is output.\n\n\nsum(x)\n\n\nCalculates the sum.\nOnly works for numbers.\n\n\nsumWithOverflow(x)\n\n\nComputes the sum of the numbers, using the same data type for the result as for the input parameters. If the sum exceeds the maximum value for this data type, the function returns an error.\n\n\nOnly works for numbers.\n\n\nsumMap(key, value)\n\n\nTotals the 'value' array according to the keys specified in the 'key' array.\nThe number of elements in 'key' and 'value' must be the same for each row that is totaled.\nReturns a tuple of two arrays: keys in sorted order, and values \u200b\u200bsummed for the corresponding keys.\n\n\nExample:\n\n\nCREATE\n \nTABLE\n \nsum_map\n(\n\n \ndate\n \nDate\n,\n\n \ntimeslot\n \nDateTime\n,\n\n \nstatusMap\n \nNested\n(\n\n \nstatus\n \nUInt16\n,\n\n \nrequests\n \nUInt64\n\n \n)\n\n\n)\n \nENGINE\n \n=\n \nLog\n;\n\n\nINSERT\n \nINTO\n \nsum_map\n \nVALUES\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:00:00\n,\n \n[\n1\n,\n \n2\n,\n \n3\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]),\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:00:00\n,\n \n[\n3\n,\n \n4\n,\n \n5\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]),\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:01:00\n,\n \n[\n4\n,\n \n5\n,\n \n6\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]),\n\n \n(\n2000-01-01\n,\n \n2000-01-01 00:01:00\n,\n \n[\n6\n,\n \n7\n,\n \n8\n],\n \n[\n10\n,\n \n10\n,\n \n10\n]);\n\n\nSELECT\n\n \ntimeslot\n,\n\n \nsumMap\n(\nstatusMap\n.\nstatus\n,\n \nstatusMap\n.\nrequests\n)\n\n\nFROM\n \nsum_map\n\n\nGROUP\n \nBY\n \ntimeslot\n\n\n\n\n\n\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500timeslot\u2500\u252c\u2500sumMap(statusMap.status, statusMap.requests)\u2500\u2510\n\u2502 2000-01-01 00:00:00 \u2502 ([1,2,3,4,5],[10,10,20,10,10]) \u2502\n\u2502 2000-01-01 00:01:00 \u2502 ([4,5,6,7,8],[10,10,20,10,10]) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\navg(x)\n\n\nCalculates the average.\nOnly works for numbers.\nThe result is always Float64.\n\n\nuniq(x)\n\n\nCalculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.\n\n\nUses an adaptive sampling algorithm: for the calculation state, it uses a sample of element hash values with a size up to 65536.\nThis algorithm is also very accurate for data sets with low cardinality (up to 65536) and very efficient on CPU (when computing not too many of these functions, using \nuniq\n is almost as fast as using other aggregate functions).\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nThis function provides excellent accuracy even for data sets with extremely high cardinality (over 10 billion elements). It is recommended for default use.\n\n\nuniqCombined(x)\n\n\nCalculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments.\n\n\nA combination of three algorithms is used: array, hash table and \nHyperLogLog\n with an error correction table. The memory consumption is several times smaller than for the \nuniq\n function, and the accuracy is several times higher. Performance is slightly lower than for the \nuniq\n function, but sometimes it can be even higher than it, such as with distributed queries that transmit a large number of aggregation states over the network. The maximum state size is 96 KiB (HyperLogLog of 217 6-bit cells).\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nThe \nuniqCombined\n function is a good default choice for calculating the number of different values, but keep in mind that the estimation error will increase for high-cardinality data sets (200M+ elements), and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).\n\n\nuniqHLL12(x)\n\n\nUses the \nHyperLogLog\n algorithm to approximate the number of different values of the argument.\n212 5-bit cells are used. The size of the state is slightly more than 2.5 KB. The result is not very accurate (up to ~10% error) for small data sets (\n10K elements). However, the result is fairly accurate for high-cardinality data sets (10K-100M), with a maximum error of ~1.6%. Starting from 100M, the estimation error increases, and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nWe don't recommend using this function. In most cases, use the \nuniq\n or \nuniqCombined\n function.\n\n\nuniqExact(x)\n\n\nCalculates the number of different values of the argument, exactly.\nThere is no reason to fear approximations. It's better to use the \nuniq\n function.\nUse the \nuniqExact\n function if you definitely need an exact result.\n\n\nThe \nuniqExact\n function uses more memory than the \nuniq\n function, because the size of the state has unbounded growth as the number of different values increases.\n\n\ngroupArray(x), groupArray(max_size)(x)\n\n\nCreates an array of argument values.\nValues can be added to the array in any (indeterminate) order.\n\n\nThe second version (with the \nmax_size\n parameter) limits the size of the resulting array to \nmax_size\n elements.\nFor example, \ngroupArray (1) (x)\n is equivalent to \n[any (x)]\n.\n\n\nIn some cases, you can still rely on the order of execution. This applies to cases when \nSELECT\n comes from a subquery that uses \nORDER BY\n.\n\n\n\n\ngroupArrayInsertAt(x)\n\n\nInserts a value into the array in the specified position.\n\n\nAccepts the value and position as input. If several values \u200b\u200bare inserted into the same position, any of them might end up in the resulting array (the first one will be used in the case of single-threaded execution). If no value is inserted into a position, the position is assigned the default value.\n\n\nOptional parameters:\n\n\n\n\nThe default value for substituting in empty positions.\n\n\nThe length of the resulting array. This allows you to receive arrays of the same size for all the aggregate keys. When using this parameter, the default value must be specified.\n\n\n\n\ngroupUniqArray(x)\n\n\nCreates an array from different argument values. Memory consumption is the same as for the \nuniqExact\n function.\n\n\nquantile(level)(x)\n\n\nApproximates the 'level' quantile. 'level' is a constant, a floating-point number from 0 to 1.\nWe recommend using a 'level' value in the range of 0.01..0.99\nDon't use a 'level' value equal to 0 or 1 \u2013 use the 'min' and 'max' functions for these cases.\n\n\nIn this function, as well as in all functions for calculating quantiles, the 'level' parameter can be omitted. In this case, it is assumed to be equal to 0.5 (in other words, the function will calculate the median).\n\n\nWorks for numbers, dates, and dates with times.\nReturns: for numbers \u2013 Float64; for dates \u2013 a date; for dates with times \u2013 a date with time.\n\n\nUses \nreservoir sampling\n with a reservoir size up to 8192.\nIf necessary, the result is output with linear approximation from the two neighboring values.\nThis algorithm provides very low accuracy. See also: \nquantileTiming\n, \nquantileTDigest\n, \nquantileExact\n.\n\n\nThe result depends on the order of running the query, and is nondeterministic.\n\n\nWhen using multiple \nquantile\n (and similar) functions with different levels in a query, the internal states are not combined (that is, the query works less efficiently than it could). In this case, use the \nquantiles\n (and similar) functions.\n\n\nquantileDeterministic(level)(x, determinator)\n\n\nWorks the same way as the \nquantile\n function, but the result is deterministic and does not depend on the order of query execution.\n\n\nTo achieve this, the function takes a second argument \u2013 the \"determinator\". This is a number whose hash is used instead of a random number generator in the reservoir sampling algorithm. For the function to work correctly, the same determinator value should not occur too often. For the determinator, you can use an event ID, user ID, and so on.\n\n\nDon't use this function for calculating timings. There is a more suitable function for this purpose: \nquantileTiming\n.\n\n\nquantileTiming(level)(x)\n\n\nComputes the quantile of 'level' with a fixed precision.\nWorks for numbers. Intended for calculating quantiles of page loading time in milliseconds.\n\n\nIf the value is greater than 30,000 (a page loading time of more than 30 seconds), the result is equated to 30,000.\n\n\nIf the total value is not more than about 5670, then the calculation is accurate.\n\n\nOtherwise:\n\n\n\n\nif the time is less than 1024 ms, then the calculation is accurate.\n\n\notherwise the calculation is rounded to a multiple of 16 ms.\n\n\n\n\nWhen passing negative values to the function, the behavior is undefined.\n\n\nThe returned value has the Float32 type. If no values were passed to the function (when using \nquantileTimingIf\n), 'nan' is returned. The purpose of this is to differentiate these instances from zeros. See the note on sorting NaNs in \"ORDER BY clause\".\n\n\nThe result is determinate (it doesn't depend on the order of query processing).\n\n\nFor its purpose (calculating quantiles of page loading times), using this function is more effective and the result is more accurate than for the \nquantile\n function.\n\n\nquantileTimingWeighted(level)(x, weight)\n\n\nDiffers from the \nquantileTiming\n function in that it has a second argument, \"weights\". Weight is a non-negative integer.\nThe result is calculated as if the \nx\n value were passed \nweight\n number of times to the \nquantileTiming\n function.\n\n\nquantileExact(level)(x)\n\n\nComputes the quantile of 'level' exactly. To do this, all the passed values \u200b\u200bare combined into an array, which is then partially sorted. Therefore, the function consumes O(n) memory, where 'n' is the number of values that were passed. However, for a small number of values, the function is very effective.\n\n\nquantileExactWeighted(level)(x, weight)\n\n\nComputes the quantile of 'level' exactly. In addition, each value is counted with its weight, as if it is present 'weight' times. The arguments of the function can be considered as histograms, where the value 'x' corresponds to a histogram \"column\" of the height 'weight', and the function itself can be considered as a summation of histograms.\n\n\nA hash table is used as the algorithm. Because of this, if the passed values \u200b\u200bare frequently repeated, the function consumes less RAM than \nquantileExact\n. You can use this function instead of \nquantileExact\n and specify the weight as 1.\n\n\nquantileTDigest(level)(x)\n\n\nApproximates the quantile level using the \nt-digest\n algorithm. The maximum error is 1%. Memory consumption by State is proportional to the logarithm of the number of passed values.\n\n\nThe performance of the function is lower than for \nquantile\n, \nquantileTiming\n. In terms of the ratio of State size to precision, this function is much better than \nquantile\n.\n\n\nThe result depends on the order of running the query, and is nondeterministic.\n\n\nmedian(x)\n\n\nAll the quantile functions have corresponding median functions: \nmedian\n, \nmedianDeterministic\n, \nmedianTiming\n, \nmedianTimingWeighted\n, \nmedianExact\n, \nmedianExactWeighted\n, \nmedianTDigest\n. They are synonyms and their behavior is identical.\n\n\nquantiles(level1, level2, ...)(x)\n\n\nAll the quantile functions also have corresponding quantiles functions: \nquantiles\n, \nquantilesDeterministic\n, \nquantilesTiming\n, \nquantilesTimingWeighted\n, \nquantilesExact\n, \nquantilesExactWeighted\n, \nquantilesTDigest\n. These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.\n\n\nvarSamp(x)\n\n\nCalculates the amount \n\u03a3((x - x\u0305)^2) / (n - 1)\n, where \nn\n is the sample size and \nx\u0305\nis the average value of \nx\n.\n\n\nIt represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount.\n\n\nReturns \nFloat64\n. When \nn \n= 1\n, returns \n+\u221e\n.\n\n\nvarPop(x)\n\n\nCalculates the amount \n\u03a3((x - x\u0305)^2) / (n - 1)\n, where \nn\n is the sample size and \nx\u0305\nis the average value of \nx\n.\n\n\nIn other words, dispersion for a set of values. Returns \nFloat64\n.\n\n\nstddevSamp(x)\n\n\nThe result is equal to the square root of \nvarSamp(x)\n.\n\n\nstddevPop(x)\n\n\nThe result is equal to the square root of \nvarPop(x)\n.\n\n\ntopK(N)(column)\n\n\nReturns an array of the most frequent values in the specified column. The resulting array is sorted in descending order of frequency of values (not by the values themselves).\n\n\nImplements the \nFiltered Space-Saving\n algorithm for analyzing TopK, based on the reduce-and-combine algorithm from \nParallel Space Saving\n.\n\n\ntopK(N)(column)\n\n\n\n\n\nThis function doesn't provide a guaranteed result. In certain situations, errors might occur and it might return frequent values that aren't the most frequent values.\n\n\nWe recommend using the \nN \n 10\n value; performance is reduced with large \nN\n values. Maximum value of \nN = 65536\n.\n\n\nArguments\n\n- 'N' is the number of values.\n- ' x ' \u2013 The column.\n\n\nExample\n\n\nTake the \nOnTime\n data set and select the three most frequently occurring values in the \nAirlineID\n column.\n\n\nSELECT\n \ntopK\n(\n3\n)(\nAirlineID\n)\n \nAS\n \nres\n\n\nFROM\n \nontime\n\n\n\n\n\n\n\u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [19393,19790,19805] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\n\n\n\ncovarSamp(x, y)\n\n\nCalculates the value of \n\u03a3((x - x\u0305)(y - y\u0305)) / (n - 1)\n.\n\n\nReturns Float64. When \nn \n= 1\n, returns +\u221e.\n\n\ncovarPop(x, y)\n\n\nCalculates the value of \n\u03a3((x - x\u0305)(y - y\u0305)) / n\n.\n\n\ncorr(x, y)\n\n\nCalculates the Pearson correlation coefficient: \n\u03a3((x - x\u0305)(y - y\u0305)) / sqrt(\u03a3((x - x\u0305)^2) * \u03a3((y - y\u0305)^2))\n.\n\n\n\n\nAggregate function combinators\n\n\nThe name of an aggregate function can have a suffix appended to it. This changes the way the aggregate function works.\n\n\n-If\n\n\nThe suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument \u2013 a condition (Uint8 type). The aggregate function processes only the rows that trigger the condition. If the condition was not triggered even once, it returns a default value (usually zeros or empty strings).\n\n\nExamples: \nsumIf(column, cond)\n, \ncountIf(cond)\n, \navgIf(x, cond)\n, \nquantilesTimingIf(level1, level2)(x, cond)\n, \nargMinIf(arg, val, cond)\n and so on.\n\n\nWith conditional aggregate functions, you can calculate aggregates for several conditions at once, without using subqueries and \nJOIN\ns. For example, in Yandex.Metrica, conditional aggregate functions are used to implement the segment comparison functionality.\n\n\n-Array\n\n\nThe -Array suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the 'Array(T)' type (arrays) instead of 'T' type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. When processing arrays, the aggregate function works like the original aggregate function across all array elements.\n\n\nExample 1: \nsumArray(arr)\n - Totals all the elements of all 'arr' arrays. In this example, it could have been written more simply: \nsum(arraySum(arr))\n.\n\n\nExample 2: \nuniqArray(arr)\n \u2013 Count the number of unique elements in all 'arr' arrays. This could be done an easier way: \nuniq(arrayJoin(arr))\n, but it's not always possible to add 'arrayJoin' to a query.\n\n\n-If and -Array can be combined. However, 'Array' must come first, then 'If'. Examples: \nuniqArrayIf(arr, cond)\n, \nquantilesTimingArrayIf(level1, level2)(arr, cond)\n. Due to this order, the 'cond' argument can't be an array.\n\n\n-State\n\n\nIf you apply this combinator, the aggregate function doesn't return the resulting value (such as the number of unique values for the 'uniq' function), but an intermediate state of the aggregation (for \nuniq\n, this is the hash table for calculating the number of unique values). This is an AggregateFunction(...) that can be used for further processing or stored in a table to finish aggregating later. See the sections \"AggregatingMergeTree\" and \"Functions for working with intermediate aggregation states\".\n\n\n-Merge\n\n\nIf you apply this combinator, the aggregate function takes the intermediate aggregation state as an argument, combines the states to finish aggregation, and returns the resulting value.\n\n\n-MergeState.\n\n\nMerges the intermediate aggregation states in the same way as the -Merge combinator. However, it doesn't return the resulting value, but an intermediate aggregation state, similar to the -State combinator.\n\n\n-ForEach\n\n\nConverts an aggregate function for tables into an aggregate function for arrays that aggregates the corresponding array items and returns an array of results. For example, \nsumForEach\n for the arrays \n[1, 2]\n, \n[3, 4, 5]\nand\n[6, 7]\nreturns the result \n[10, 13, 5]\n after adding together the corresponding array items.\n\n\n\n\nParametric aggregate functions\n\n\nSome aggregate functions can accept not only argument columns (used for compression), but a set of parameters \u2013 constants for initialization. The syntax is two pairs of brackets instead of one. The first is for parameters, and the second is for arguments.\n\n\nsequenceMatch(pattern)(time, cond1, cond2, ...)\n\n\nPattern matching for event chains.\n\n\npattern\n is a string containing a pattern to match. The pattern is similar to a regular expression.\n\n\ntime\n is the time of the event with the DateTime type.\n\n\ncond1\n, \ncond2\n ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event.\n\n\nThe function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern.\nIt returns UInt8: 0 if the pattern isn't matched, or 1 if it matches.\n\n\nExample: \nsequenceMatch ('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%')\n\n\n\n\nwhether there was a chain of events in which a pageview with 'company' in the address occurred earlier than a pageview with 'cart' in the address.\n\n\n\n\nThis is a singular example. You could write it using other aggregate functions:\n\n\nminIf(EventTime, URL LIKE \n%company%\n) \n maxIf(EventTime, URL LIKE \n%cart%\n).\n\n\n\n\n\nHowever, there is no such solution for more complex situations.\n\n\nPattern syntax:\n\n\n(?1)\n refers to the condition (any number can be used in place of 1).\n\n\n.*\n is any number of any events.\n\n\n(?t\n=1800)\n is a time condition.\n\n\nAny quantity of any type of events is allowed over the specified time.\n\n\nInstead of \n=\n, the following operators can be used:\n, \n, \n=\n.\n\n\nAny number may be specified in place of 1800.\n\n\nEvents that occur during the same second can be put in the chain in any order. This may affect the result of the function.\n\n\nsequenceCount(pattern)(time, cond1, cond2, ...)\n\n\nWorks the same way as the sequenceMatch function, but instead of returning whether there is an event chain, it returns UInt64 with the number of event chains found.\nChains are searched for without overlapping. In other words, the next chain can start only after the end of the previous one.\n\n\nwindowFunnel(window)(timestamp, cond1, cond2, cond3, ....)\n\n\nWindow funnel matching for event chains, calculates the max event level in a sliding window.\n\n\nwindow\n is the timestamp window value, such as 3600.\n\n\ntimestamp\n is the time of the event with the DateTime type or UInt32 type.\n\n\ncond1\n, \ncond2\n ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event\n\n\nExample: \n\n\nConsider you are doing a website analytics, intend to find out the user counts clicked login button( event = 1001 ), then the user counts followed by searched the phones( event = 1003 and product = 'phone' ) , then the user counts followed by made an order ( event = 1009 ). And all event chains must be in a 3600 seconds sliding window. \n\n\nThis could be easily calculate by \nwindowFunnel\n\n\nSELECT\n level,\n count() AS c\nFROM\n(\n SELECT\n user_id,\n windowFunnel(3600)(timestamp, event_id = 1001, event_id = 1003 AND product = \nphone\n, event_id = 1009) AS level\n FROM trend_event\n WHERE (event_date \n= \n2017-01-01\n) AND (event_date \n= \n2017-01-31\n)\n GROUP BY user_id\n)\nGROUP BY level\nORDER BY level\n\n\n\n\n\nSimply, the level could only be 0,1,2,3, it means the maxium event action stage that one user could reach.\n\n\nuniqUpTo(N)(x)\n\n\nCalculates the number of different argument values \u200b\u200bif it is less than or equal to N. If the number of different argument values is greater than N, it returns N + 1.\n\n\nRecommended for use with small Ns, up to 10. The maximum value of N is 100.\n\n\nFor the state of an aggregate function, it uses the amount of memory equal to 1 + N * the size of one value of bytes.\nFor strings, it stores a non-cryptographic hash of 8 bytes. That is, the calculation is approximated for strings.\n\n\nThe function also works for several arguments.\n\n\nIt works as fast as possible, except for cases when a large N value is used and the number of unique values is slightly less than N.\n\n\nUsage example:\n\n\nProblem: Generate a report that shows only keywords that produced at least 5 unique users.\nSolution: Write in the GROUP BY query SearchPhrase HAVING uniqUpTo(4)(UserID) \n= 5\n\n\n\n\n\nDictionaries\n\n\nA dictionary\n is a mapping (key \n-\n attributes) that can be used in a query as functions.\nYou can think of this as a more convenient and efficient type of JOIN with dimension tables.\n\n\nThere are built-in (internal) and add-on (external) dictionaries.\n\n\n\n\nExternal dictionaries\n\n\nYou can add your own dictionaries from various data sources. The data source for a dictionary can be a local text or executable file, an HTTP(s) resource, or another DBMS. For more information, see \"\nSources for external dictionaries\n\".\n\n\nClickHouse:\n\n\n\n\n\n\nFully or partially stores dictionaries in RAM.\n\n\nPeriodically updates dictionaries and dynamically loads missing values. In other words, dictionaries can be loaded dynamically.\n\n\n\n\n\n\nThe configuration of external dictionaries is located in one or more files. The path to the configuration is specified in the \ndictionaries_config\n parameter.\n\n\nDictionaries can be loaded at server startup or at first use, depending on the \ndictionaries_lazy_load\n setting.\n\n\nThe dictionary config file has the following format:\n\n\nyandex\n\n \ncomment\nAn optional element with any content. Ignored by the ClickHouse server.\n/comment\n\n\n \n!--Optional element. File name with substitutions--\n\n \ninclude_from\n/etc/metrika.xml\n/include_from\n\n\n\n \ndictionary\n\n \n!-- Dictionary configuration --\n\n \n/dictionary\n\n\n ...\n\n \ndictionary\n\n \n!-- Dictionary configuration --\n\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\nYou can \nconfigure\n any number of dictionaries in the same file. The file format is preserved even if there is only one dictionary (i.e. \nyandex\ndictionary\n \n!--configuration -\n \n/dictionary\n/yandex\n ).\n\n\nSee also \"\nFunctions for working with external dictionaries\n\".\n\n\n\n\nYou can convert values \u200b\u200bfor a small dictionary by describing it in a `SELECT` query (see the [transform](#other_functions-transform) function). This functionality is not related to external dictionaries.\n\n\n\n\n\n\n\nConfiguring an external dictionary\n\n\nThe dictionary configuration has the following structure:\n\n\ndictionary\n\n \nname\ndict_name\n/name\n\n\n \nsource\n\n \n!-- Source configuration --\n\n \n/source\n\n\n \nlayout\n\n \n!-- Memory layout configuration --\n\n \n/layout\n\n\n \nstructure\n\n \n!-- Complex key configuration --\n\n \n/structure\n\n\n \nlifetime\n\n \n!-- Lifetime of dictionary in memory --\n\n \n/lifetime\n\n\n/dictionary\n\n\n\n\n\n\n\n\nname \u2013 The identifier that can be used to access the dictionary. Use the characters \n[a-zA-Z0-9_\\-]\n.\n\n\nsource\n \u2014 Source of the dictionary.\n\n\nlayout\n \u2014 Dictionary layout in memory.\n\n\nstructure\n \u2014 Structure of the dictionary . A key and attributes that can be retrieved by this key.\n\n\nlifetime\n \u2014 Frequency of dictionary updates.\n\n\n\n\n\n\nStoring dictionaries in memory\n\n\nThere are a \nvariety of ways\n to store dictionaries in memory.\n\n\nWe recommend \nflat\n, \nhashed\nand\ncomplex_key_hashed\n. which provide optimal processing speed.\n\n\nCaching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section \"\ncache\n\".\n\n\nThere are several ways to improve dictionary performance:\n\n\n\n\nCall the function for working with the dictionary after \nGROUP BY\n.\n\n\nMark attributes to extract as injective. An attribute is called injective if different attribute values correspond to different keys. So when \nGROUP BY\n uses a function that fetches an attribute value by the key, this function is automatically taken out of \nGROUP BY\n.\n\n\n\n\nClickHouse generates an exception for errors with dictionaries. Examples of errors:\n\n\n\n\nThe dictionary being accessed could not be loaded.\n\n\nError querying a \ncached\n dictionary.\n\n\n\n\nYou can view the list of external dictionaries and their statuses in the \nsystem.dictionaries\n table.\n\n\nThe configuration looks like this:\n\n\nyandex\n\n \ndictionary\n\n ...\n \nlayout\n\n \nlayout_type\n\n \n!-- layout settings --\n\n \n/layout_type\n\n \n/layout\n\n ...\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\n\n\nWays to store dictionaries in memory\n\n\n\n\nflat\n\n\nhashed\n\n\ncache\n\n\nrange_hashed\n\n\ncomplex_key_hashed\n\n\ncomplex_key_cache\n\n\nip_trie\n\n\n\n\n\n\nflat\n\n\nThe dictionary is completely stored in memory in the form of flat arrays. How much memory does the dictionary use? The amount is proportional to the size of the largest key (in space used).\n\n\nThe dictionary key has the \nUInt64\n type and the value is limited to 500,000. If a larger key is discovered when creating the dictionary, ClickHouse throws an exception and does not create the dictionary.\n\n\nAll types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.\n\n\nThis method provides the best performance among all available methods of storing the dictionary.\n\n\nConfiguration example:\n\n\nlayout\n\n \nflat\n \n/\n\n\n/layout\n\n\n\n\n\n\n\n\nhashed\n\n\nThe dictionary is completely stored in memory in the form of a hash table. The dictionary can contain any number of elements with any identifiers In practice, the number of keys can reach tens of millions of items.\n\n\nAll types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.\n\n\nConfiguration example:\n\n\nlayout\n\n \nhashed\n \n/\n\n\n/layout\n\n\n\n\n\n\n\n\ncomplex_key_hashed\n\n\nThis type of storage is for use with composite \nkeys\n. Similar to \nhashed\n.\n\n\nConfiguration example:\n\n\nlayout\n\n \ncomplex_key_hashed\n \n/\n\n\n/layout\n\n\n\n\n\n\n\n\nrange_hashed\n\n\nThe dictionary is stored in memory in the form of a hash table with an ordered array of ranges and their corresponding values.\n\n\nThis storage method works the same way as hashed and allows using date/time ranges in addition to the key, if they appear in the dictionary.\n\n\nExample: The table contains discounts for each advertiser in the format:\n\n\n+---------------+---------------------+-------------------+--------+\n| advertiser id | discount start date | discount end date | amount |\n+===============+=====================+===================+========+\n| 123 | 2015-01-01 | 2015-01-15 | 0.15 |\n+---------------+---------------------+-------------------+--------+\n| 123 | 2015-01-16 | 2015-01-31 | 0.25 |\n+---------------+---------------------+-------------------+--------+\n| 456 | 2015-01-01 | 2015-01-15 | 0.05 |\n+---------------+---------------------+-------------------+--------+\n\n\n\n\n\nTo use a sample for date ranges, define the \nrange_min\n and \nrange_max\n elements in the \nstructure\n.\n\n\nExample:\n\n\nstructure\n\n \nid\n\n \nname\nId\n/name\n\n \n/id\n\n \nrange_min\n\n \nname\nfirst\n/name\n\n \n/range_min\n\n \nrange_max\n\n \nname\nlast\n/name\n\n \n/range_max\n\n ...\n\n\n\n\n\nTo work with these dictionaries, you need to pass an additional date argument to the \ndictGetT\n function:\n\n\ndictGetT(\ndict_name\n, \nattr_name\n, id, date)\n\n\n\n\n\nThis function returns the value for the specified \nid\ns and the date range that includes the passed date.\n\n\nDetails of the algorithm:\n\n\n\n\nIf the \nid\n is not found or a range is not found for the \nid\n, it returns the default value for the dictionary.\n\n\nIf there are overlapping ranges, you can use any.\n\n\nIf the range delimiter is \nNULL\n or an invalid date (such as 1900-01-01 or 2039-01-01), the range is left open. The range can be open on both sides.\n\n\n\n\nConfiguration example:\n\n\nyandex\n\n \ndictionary\n\n\n ...\n\n \nlayout\n\n \nrange_hashed\n \n/\n\n \n/layout\n\n\n \nstructure\n\n \nid\n\n \nname\nAbcdef\n/name\n\n \n/id\n\n \nrange_min\n\n \nname\nStartDate\n/name\n\n \n/range_min\n\n \nrange_max\n\n \nname\nEndDate\n/name\n\n \n/range_max\n\n \nattribute\n\n \nname\nXXXType\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n \n/\n\n \n/attribute\n\n \n/structure\n\n\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\n\n\ncache\n\n\nThe dictionary is stored in a cache that has a fixed number of cells. These cells contain frequently used elements.\n\n\nWhen searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using \nSELECT attrs... FROM db.table WHERE id IN (k1, k2, ...)\n. The received data is then written to the cache.\n\n\nFor cache dictionaries, the expiration \nlifetime\n of data in the cache can be set. If more time than \nlifetime\n has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used.\n\n\nThis is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the \nsystem.dictionaries\n table.\n\n\nTo improve cache performance, use a subquery with \nLIMIT\n, and call the function with the dictionary externally.\n\n\nSupported \nsources\n: MySQL, ClickHouse, executable, HTTP.\n\n\nExample of settings:\n\n\nlayout\n\n \ncache\n\n \n!-- The size of the cache, in number of cells. Rounded up to a power of two. --\n\n \nsize_in_cells\n1000000000\n/size_in_cells\n\n \n/cache\n\n\n/layout\n\n\n\n\n\n\nSet a large enough cache size. You need to experiment to select the number of cells:\n\n\n\n\nSet some value.\n\n\nRun queries until the cache is completely full.\n\n\nAssess memory consumption using the \nsystem.dictionaries\n table.\n\n\nIncrease or decrease the number of cells until the required memory consumption is reached.\n\n\n\n\n\n\nDo not use ClickHouse as a source, because it is slow to process queries with random reads.\n\n\n\n\n\n\n\ncomplex_key_cache\n\n\nThis type of storage is for use with composite \nkeys\n. Similar to \ncache\n.\n\n\n\n\nip_trie\n\n\nThis type of storage is for mapping network prefixes (IP addresses) to metadata such as ASN.\n\n\nExample: The table contains network prefixes and their corresponding AS number and country code:\n\n\n +-----------------+-------+--------+\n | prefix | asn | cca2 |\n +=================+=======+========+\n | 202.79.32.0/20 | 17501 | NP |\n +-----------------+-------+--------+\n | 2620:0:870::/48 | 3856 | US |\n +-----------------+-------+--------+\n | 2a02:6b8:1::/48 | 13238 | RU |\n +-----------------+-------+--------+\n | 2001:db8::/32 | 65536 | ZZ |\n +-----------------+-------+--------+\n\n\n\n\n\nWhen using this type of layout, the structure must have a composite key.\n\n\nExample:\n\n\nstructure\n\n \nkey\n\n \nattribute\n\n \nname\nprefix\n/name\n\n \ntype\nString\n/type\n\n \n/attribute\n\n \n/key\n\n \nattribute\n\n \nname\nasn\n/name\n\n \ntype\nUInt32\n/type\n\n \nnull_value\n \n/\n\n \n/attribute\n\n \nattribute\n\n \nname\ncca2\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n??\n/null_value\n\n \n/attribute\n\n ...\n\n\n\n\n\nThe key must have only one String type attribute that contains an allowed IP prefix. Other types are not supported yet.\n\n\nFor queries, you must use the same functions (\ndictGetT\n with a tuple) as for dictionaries with composite keys:\n\n\ndictGetT(\ndict_name\n, \nattr_name\n, tuple(ip))\n\n\n\n\n\nThe function takes either \nUInt32\n for IPv4, or \nFixedString(16)\n for IPv6:\n\n\ndictGetString(\nprefix\n, \nasn\n, tuple(IPv6StringToNum(\n2001:db8::1\n)))\n\n\n\n\n\nOther types are not supported yet. The function returns the attribute for the prefix that corresponds to this IP address. If there are overlapping prefixes, the most specific one is returned.\n\n\nData is stored in a \ntrie\n. It must completely fit into RAM.\n\n\n\n\nDictionary updates\n\n\nClickHouse periodically updates the dictionaries. The update interval for fully downloaded dictionaries and the invalidation interval for cached dictionaries are defined in the \nlifetime\n tag in seconds.\n\n\nDictionary updates (other than loading for first use) do not block queries. During updates, the old version of a dictionary is used. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.\n\n\nExample of settings:\n\n\ndictionary\n\n ...\n \nlifetime\n300\n/lifetime\n\n ...\n\n/dictionary\n\n\n\n\n\n\nSetting \nlifetime\n 0\n/lifetime\n prevents updating dictionaries.\n\n\nYou can set a time interval for upgrades, and ClickHouse will choose a uniformly random time within this range. This is necessary in order to distribute the load on the dictionary source when upgrading on a large number of servers.\n\n\nExample of settings:\n\n\ndictionary\n\n ...\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n ...\n\n/dictionary\n\n\n\n\n\n\nWhen upgrading the dictionaries, the ClickHouse server applies different logic depending on the type of \n source\n:\n\n\n\n\n\n\nFor a text file, it checks the time of modification. If the time differs from the previously recorded time, the dictionary is updated.\n\n\nFor MyISAM tables, the time of modification is checked using a \nSHOW TABLE STATUS\n query.\n\n\nDictionaries from other sources are updated every time by default.\n\n\n\n\n\n\nFor MySQL (InnoDB) and ODBC sources, you can set up a query that will update the dictionaries only if they really changed, rather than each time. To do this, follow these steps:\n\n\n\n\n\n\nThe dictionary table must have a field that always changes when the source data is updated.\n\n\nThe settings of the source must specify a query that retrieves the changing field. The ClickHouse server interprets the query result as a row, and if this row has changed relative to its previous state, the dictionary is updated. Specify the query in the \ninvalidate_query\n field in the settings for the \nsource\n.\n\n\n\n\n\n\nExample of settings:\n\n\ndictionary\n\n ...\n \nodbc\n\n ...\n \ninvalidate_query\nSELECT update_time FROM dictionary_source where id = 1\n/invalidate_query\n\n \n/odbc\n\n ...\n\n/dictionary\n\n\n\n\n\n\n\n\nSources of external dictionaries\n\n\nAn external dictionary can be connected from many different sources.\n\n\nThe configuration looks like this:\n\n\nyandex\n\n \ndictionary\n\n ...\n \nsource\n\n \nsource_type\n\n \n!-- Source configuration --\n\n \n/source_type\n\n \n/source\n\n ...\n \n/dictionary\n\n ...\n\n/yandex\n\n\n\n\n\n\nThe source is configured in the \nsource\n section.\n\n\nTypes of sources (\nsource_type\n):\n\n\n\n\nLocal file\n\n\nExecutable file\n\n\nHTTP(s)\n\n\nODBC\n\n\nDBMS\n\n\nMySQL\n\n\nClickHouse\n\n\nMongoDB\n\n\n\n\n\n\nLocal file\n\n\nExample of settings:\n\n\nsource\n\n \nfile\n\n \npath\n/opt/dictionaries/os.tsv\n/path\n\n \nformat\nTabSeparated\n/format\n\n \n/file\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\npath\n \u2013 The absolute path to the file.\n\n\nformat\n \u2013 The file format. All the formats described in \"\nFormats\n\" are supported.\n\n\n\n\n\n\nExecutable file\n\n\nWorking with executable files depends on \nhow the dictionary is stored in memory\n. If the dictionary is stored using \ncache\n and \ncomplex_key_cache\n, ClickHouse requests the necessary keys by sending a request to the executable file's \nSTDIN\n.\n\n\nExample of settings:\n\n\nsource\n\n \nexecutable\n\n \ncommand\ncat /opt/dictionaries/os.tsv\n/command\n\n \nformat\nTabSeparated\n/format\n\n \n/executable\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\ncommand\n \u2013 The absolute path to the executable file, or the file name (if the program directory is written to \nPATH\n).\n\n\nformat\n \u2013 The file format. All the formats described in \"\nFormats\n\" are supported.\n\n\n\n\n\n\nHTTP(s)\n\n\nWorking with an HTTP(s) server depends on \nhow the dictionary is stored in memory\n. If the dictionary is stored using \ncache\n and \ncomplex_key_cache\n, ClickHouse requests the necessary keys by sending a request via the \nPOST\n method.\n\n\nExample of settings:\n\n\nsource\n\n \nhttp\n\n \nurl\nhttp://[::1]/os.tsv\n/url\n\n \nformat\nTabSeparated\n/format\n\n \n/http\n\n\n/source\n\n\n\n\n\n\nIn order for ClickHouse to access an HTTPS resource, you must \nconfigure openSSL\n in the server configuration.\n\n\nSetting fields:\n\n\n\n\nurl\n \u2013 The source URL.\n\n\nformat\n \u2013 The file format. All the formats described in \"\nFormats\n\" are supported.\n\n\n\n\n\n\nODBC\n\n\nYou can use this method to connect any database that has an ODBC driver.\n\n\nExample of settings:\n\n\nodbc\n\n \ndb\nDatabaseName\n/db\n\n \ntable\nTableName\n/table\n\n \nconnection_string\nDSN=some_parameters\n/connection_string\n\n \ninvalidate_query\nSQL_QUERY\n/invalidate_query\n\n\n/odbc\n\n\n\n\n\n\nSetting fields:\n\n\n\n\ndb\n \u2013 Name of the database. Omit it if the database name is set in the \nconnection_string\n parameters.\n\n\ntable\n \u2013 Name of the table.\n\n\nconnection_string\n \u2013 Connection string.\n\n\ninvalidate_query\n \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section \nUpdating dictionaries\n.\n\n\n\n\nExample of connecting PostgreSQL\n\n\nUbuntu OS.\n\n\nInstalling unixODBC and the ODBC driver for PostgreSQL:\n\n\nsudo apt-get install -y unixodbc odbcinst odbc-postgresql\n\n\n\n\n\nConfiguring \n/etc/odbc.ini\n (or \n~/.odbc.ini\n):\n\n\n [DEFAULT]\n Driver = myconnection\n\n [myconnection]\n Description = PostgreSQL connection to my_db\n Driver = PostgreSQL Unicode\n Database = my_db\n Servername = 127.0.0.1\n UserName = username\n Password = password\n Port = 5432\n Protocol = 9.3\n ReadOnly = No\n RowVersioning = No\n ShowSystemTables = No\n ConnSettings =\n\n\n\n\n\nThe dictionary configuration in ClickHouse:\n\n\ndictionary\n\n \nname\ntable_name\n/name\n\n \nsource\n\n \nodbc\n\n \n!-- You can specifiy the following parameters in connection_string: --\n\n \n!-- DSN=myconnection;UID=username;PWD=password;HOST=127.0.0.1;PORT=5432;DATABASE=my_db --\n\n \nconnection_string\nDSN=myconnection\n/connection_string\n\n \ntable\npostgresql_table\n/table\n\n \n/odbc\n\n \n/source\n\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n \nlayout\n\n \nhashed/\n\n \n/layout\n\n \nstructure\n\n \nid\n\n \nname\nid\n/name\n\n \n/id\n\n \nattribute\n\n \nname\nsome_column\n/name\n\n \ntype\nUInt64\n/type\n\n \nnull_value\n0\n/null_value\n\n \n/attribute\n\n \n/structure\n\n\n/dictionary\n\n\n\n\n\n\nYou may need to edit \nodbc.ini\n to specify the full path to the library with the driver \nDRIVER=/usr/local/lib/psqlodbcw.so\n.\n\n\nExample of connecting MS SQL Server\n\n\nUbuntu OS.\n\n\nInstalling the driver: :\n\n\n sudo apt-get install tdsodbc freetds-bin sqsh\n\n\n\n\n\nConfiguring the driver: :\n\n\n $ cat /etc/freetds/freetds.conf \n ...\n\n [MSSQL]\n host = 192.168.56.101\n port = 1433\n tds version = 7.0\n client charset = UTF-8\n\n $ cat /etc/odbcinst.ini \n ...\n\n [FreeTDS]\n Description = FreeTDS\n Driver = /usr/lib/x86_64-linux-gnu/odbc/libtdsodbc.so\n Setup = /usr/lib/x86_64-linux-gnu/odbc/libtdsS.so\n FileUsage = 1\n UsageCount = 5\n\n $ cat ~/.odbc.ini \n ...\n\n [MSSQL]\n Description = FreeTDS\n Driver = FreeTDS\n Servername = MSSQL\n Database = test\n UID = test\n PWD = test\n Port = 1433\n\n\n\n\n\nConfiguring the dictionary in ClickHouse:\n\n\nyandex\n\n \ndictionary\n\n \nname\ntest\n/name\n\n \nsource\n\n \nodbc\n\n \ntable\ndict\n/table\n\n \nconnection_string\nDSN=MSSQL;UID=test;PWD=test\n/connection_string\n\n \n/odbc\n\n \n/source\n\n\n \nlifetime\n\n \nmin\n300\n/min\n\n \nmax\n360\n/max\n\n \n/lifetime\n\n\n \nlayout\n\n \nflat\n \n/\n\n \n/layout\n\n\n \nstructure\n\n \nid\n\n \nname\nk\n/name\n\n \n/id\n\n \nattribute\n\n \nname\ns\n/name\n\n \ntype\nString\n/type\n\n \nnull_value\n/null_value\n\n \n/attribute\n\n \n/structure\n\n \n/dictionary\n\n\n/yandex\n\n\n\n\n\n\nDBMS\n\n\n\n\nMySQL\n\n\nExample of settings:\n\n\nsource\n\n \nmysql\n\n \nport\n3306\n/port\n\n \nuser\nclickhouse\n/user\n\n \npassword\nqwerty\n/password\n\n \nreplica\n\n \nhost\nexample01-1\n/host\n\n \npriority\n1\n/priority\n\n \n/replica\n\n \nreplica\n\n \nhost\nexample01-2\n/host\n\n \npriority\n1\n/priority\n\n \n/replica\n\n \ndb\ndb_name\n/db\n\n \ntable\ntable_name\n/table\n\n \nwhere\nid=10\n/where\n\n \ninvalidate_query\nSQL_QUERY\n/invalidate_query\n\n \n/mysql\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\n\n\nport\n \u2013 The port on the MySQL server. You can specify it for all replicas, or for each one individually (inside \nreplica\n).\n\n\n\n\n\n\nuser\n \u2013 Name of the MySQL user. You can specify it for all replicas, or for each one individually (inside \nreplica\n).\n\n\n\n\n\n\npassword\n \u2013 Password of the MySQL user. You can specify it for all replicas, or for each one individually (inside \nreplica\n).\n\n\n\n\n\n\nreplica\n \u2013 Section of replica configurations. There can be multiple sections.\n\n\n\n\nreplica/host\n \u2013 The MySQL host.\n\n\n\n\n* \nreplica/priority\n \u2013 The replica priority. When attempting to connect, ClickHouse traverses the replicas in order of priority. The lower the number, the higher the priority.\n\n\n\n\n\n\ndb\n \u2013 Name of the database.\n\n\n\n\n\n\ntable\n \u2013 Name of the table.\n\n\n\n\n\n\nwhere\n \u2013 The selection criteria. Optional parameter.\n\n\n\n\n\n\ninvalidate_query\n \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section \nUpdating dictionaries\n.\n\n\n\n\n\n\nMySQL can be connected on a local host via sockets. To do this, set \nhost\n and \nsocket\n.\n\n\nExample of settings:\n\n\nsource\n\n \nmysql\n\n \nhost\nlocalhost\n/host\n\n \nsocket\n/path/to/socket/file.sock\n/socket\n\n \nuser\nclickhouse\n/user\n\n \npassword\nqwerty\n/password\n\n \ndb\ndb_name\n/db\n\n \ntable\ntable_name\n/table\n\n \nwhere\nid=10\n/where\n\n \ninvalidate_query\nSQL_QUERY\n/invalidate_query\n\n \n/mysql\n\n\n/source\n\n\n\n\n\n\n\n\nClickHouse\n\n\nExample of settings:\n\n\nsource\n\n \nclickhouse\n\n \nhost\nexample01-01-1\n/host\n\n \nport\n9000\n/port\n\n \nuser\ndefault\n/user\n\n \npassword\n/password\n\n \ndb\ndefault\n/db\n\n \ntable\nids\n/table\n\n \nwhere\nid=10\n/where\n\n \n/clickhouse\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\nhost\n \u2013 The ClickHouse host. If it is a local host, the query is processed without any network activity. To improve fault tolerance, you can create a \nDistributed\n table and enter it in subsequent configurations.\n\n\nport\n \u2013 The port on the ClickHouse server.\n\n\nuser\n \u2013 Name of the ClickHouse user.\n\n\npassword\n \u2013 Password of the ClickHouse user.\n\n\ndb\n \u2013 Name of the database.\n\n\ntable\n \u2013 Name of the table.\n\n\nwhere\n \u2013 The selection criteria. May be omitted.\n\n\n\n\n\n\nMongoDB\n\n\nExample of settings:\n\n\nsource\n\n \nmongodb\n\n \nhost\nlocalhost\n/host\n\n \nport\n27017\n/port\n\n \nuser\n/user\n\n \npassword\n/password\n\n \ndb\ntest\n/db\n\n \ncollection\ndictionary_source\n/collection\n\n \n/mongodb\n\n\n/source\n\n\n\n\n\n\nSetting fields:\n\n\n\n\nhost\n \u2013 The MongoDB host.\n\n\nport\n \u2013 The port on the MongoDB server.\n\n\nuser\n \u2013 Name of the MongoDB user.\n\n\npassword\n \u2013 Password of the MongoDB user.\n\n\ndb\n \u2013 Name of the database.\n\n\ncollection\n \u2013 Name of the collection.\n\n\n\n\n\n\nDictionary key and fields\n\n\nThe \nstructure\n clause describes the dictionary key and fields available for queries.\n\n\nOverall structure:\n\n\ndictionary\n\n \nstructure\n\n \nid\n\n \nname\nId\n/name\n\n \n/id\n\n\n \nattribute\n\n \n!-- Attribute parameters --\n\n \n/attribute\n\n\n ...\n\n \n/structure\n\n\n/dictionary\n\n\n\n\n\n\nColumns are described in the structure:\n\n\n\n\nid\n - \nkey column\n.\n\n\nattribute\n - \ndata column\n. There can be a large number of columns.\n\n\n\n\n\n\nKey\n\n\nClickHouse supports the following types of keys:\n\n\n\n\nNumeric key. UInt64. Defined in the tag \nid\n .\n\n\nComposite key. Set of values of different types. Defined in the tag \nkey\n .\n\n\n\n\nA structure can contain either \nid\n or \nkey\n .\n\n\n\n\nThe key doesn't need to be defined separately in attributes.\n\n\n\n\n\nNumeric key\n\n\nFormat: \nUInt64\n.\n\n\nConfiguration example:\n\n\nid\n\n \nname\nId\n/name\n\n\n/id\n\n\n\n\n\n\nConfiguration fields:\n\n\n\n\nname \u2013 The name of the column with keys.\n\n\n\n\nComposite key\n\n\nThe key can be a \ntuple\n from any types of fields. The \nlayout\n in this case must be \ncomplex_key_hashed\n or \ncomplex_key_cache\n.\n\n\n\nA composite key can consist of a single element. This makes it possible to use a string as the key, for instance.\n\n\n\n\nThe key structure is set in the element \nkey\n. Key fields are specified in the same format as the dictionary \nattributes\n. Example:\n\n\nstructure\n\n \nkey\n\n \nattribute\n\n \nname\nfield1\n/name\n\n \ntype\nString\n/type\n\n \n/attribute\n\n \nattribute\n\n \nname\nfield2\n/name\n\n \ntype\nUInt32\n/type\n\n \n/attribute\n\n ...\n \n/key\n\n...\n\n\n\n\n\nFor a query to the \ndictGet*\n function, a tuple is passed as the key. Example: \ndictGetString('dict_name', 'attr_name', tuple('string for field1', num_for_field2))\n.\n\n\n\n\nAttributes\n\n\nConfiguration example:\n\n\nstructure\n\n ...\n \nattribute\n\n \nname\nName\n/name\n\n \ntype\nType\n/type\n\n \nnull_value\n/null_value\n\n \nexpression\nrand64()\n/expression\n\n \nhierarchical\ntrue\n/hierarchical\n\n \ninjective\ntrue\n/injective\n\n \nis_object_id\ntrue\n/is_object_id\n\n \n/attribute\n\n\n/structure\n\n\n\n\n\n\nConfiguration fields:\n\n\n\n\nname\n \u2013 The column name.\n\n\ntype\n \u2013 The column type. Sets the method for interpreting data in the source. For example, for MySQL, the field might be \nTEXT\n, \nVARCHAR\n, or \nBLOB\n in the source table, but it can be uploaded as \nString\n.\n\n\nnull_value\n \u2013 The default value for a non-existing element. In the example, it is an empty string.\n\n\nexpression\n \u2013 The attribute can be an expression. The tag is not required.\n\n\nhierarchical\n \u2013 Hierarchical support. Mirrored to the parent identifier. By default, \nfalse\n.\n\n\ninjective\n \u2013 Whether the \nid -\n attribute\n image is injective. If \ntrue\n, then you can optimize the \nGROUP BY\n clause. By default, \nfalse\n.\n\n\nis_object_id\n \u2013 Whether the query is executed for a MongoDB document by \nObjectID\n.\n\n\n\n\nInternal dictionaries\n\n\nClickHouse contains a built-in feature for working with a geobase.\n\n\nThis allows you to:\n\n\n\n\nUse a region's ID to get its name in the desired language.\n\n\nUse a region's ID to get the ID of a city, area, federal district, country, or continent.\n\n\nCheck whether a region is part of another region.\n\n\nGet a chain of parent regions.\n\n\n\n\nAll the functions support \"translocality,\" the ability to simultaneously use different perspectives on region ownership. For more information, see the section \"Functions for working with Yandex.Metrica dictionaries\".\n\n\nThe internal dictionaries are disabled in the default package.\nTo enable them, uncomment the parameters \npath_to_regions_hierarchy_file\n and \npath_to_regions_names_files\n in the server configuration file.\n\n\nThe geobase is loaded from text files.\nIf you work at Yandex, you can follow these instructions to create them:\n\nhttps://github.yandex-team.ru/raw/Metrika/ClickHouse_private/master/doc/create_embedded_geobase_dictionaries.txt\n\n\nPut the regions_hierarchy*.txt files in the path_to_regions_hierarchy_file directory. This configuration parameter must contain the path to the regions_hierarchy.txt file (the default regional hierarchy), and the other files (regions_hierarchy_ua.txt) must be located in the same directory.\n\n\nPut the \nregions_names_*.txt\n files in the path_to_regions_names_files directory.\n\n\nYou can also create these files yourself. The file format is as follows:\n\n\nregions_hierarchy*.txt\n: TabSeparated (no header), columns:\n\n\n\n\nRegion ID (UInt32)\n\n\nParent region ID (UInt32)\n\n\nRegion type (UInt8): 1 - continent, 3 - country, 4 - federal district, 5 - region, 6 - city; other types don't have values.\n\n\nPopulation (UInt32) - Optional column.\n\n\n\n\nregions_names_*.txt\n: TabSeparated (no header), columns:\n\n\n\n\nRegion ID (UInt32)\n\n\nRegion name (String) - Can't contain tabs or line feeds, even escaped ones.\n\n\n\n\nA flat array is used for storing in RAM. For this reason, IDs shouldn't be more than a million.\n\n\nDictionaries can be updated without restarting the server. However, the set of available dictionaries is not updated.\nFor updates, the file modification times are checked. If a file has changed, the dictionary is updated.\nThe interval to check for changes is configured in the 'builtin_dictionaries_reload_interval' parameter.\nDictionary updates (other than loading at first use) do not block queries. During updates, queries use the old versions of dictionaries. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries.\n\n\nWe recommend periodically updating the dictionaries with the geobase. During an update, generate new files and write them to a separate location. When everything is ready, rename them to the files used by the server.\n\n\nThere are also functions for working with OS identifiers and Yandex.Metrica search engines, but they shouldn't be used.\n\n\nUsage\n\n\nAccess rights\n\n\nUsers and access rights are set up in the user config. This is usually \nusers.xml\n.\n\n\nUsers are recorded in the \nusers\n section. Here is a fragment of the \nusers.xml\n file:\n\n\n!-- Users and ACL. --\n\n\nusers\n\n \n!-- If the user name is not specified, the \ndefault\n user is used. --\n\n \ndefault\n\n \n!-- Password could be specified in plaintext or in SHA256 (in hex format).\n\n\n\n If you want to specify the password in plain text (not recommended), place it in the \npassword\n element.\n\n\n Example: \npassword\nqwerty\n/password\n.\n\n\n Password can be empty.\n\n\n\n If you want to specify SHA256, place it in the \npassword_sha256_hex\n element.\n\n\n Example: \npassword_sha256_hex\n65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5\n/password_sha256_hex\n\n\n\n How to generate decent password:\n\n\n Execute: PASSWORD=$(base64 \n /dev/urandom | head -c8); echo \n$PASSWORD\n; echo -n \n$PASSWORD\n | sha256sum | tr -d \n-\n\n\n In first line will be password and in second - corresponding SHA256.\n\n\n --\n\n \npassword\n/password\n\n \n!-- A list of networks that access is allowed from.\n\n\n Each list item has one of the following forms:\n\n\n \nip\nIP address or subnet mask. For example: 198.51.100.0/24 or 2001:DB8::/32.\n\n\n \nhost\n Host name. For example: example01. A DNS query is made for verification, and all addresses obtained are compared with the address of the customer.\n\n\n \nhost_regexp\n Regular expression for host names. For example: ^example\\d\\d-\\d\\d-\\d\\.yandex\\.ru$\n\n\n For verification, a DNS PTR query is made for the customer\ns address and a regular expression is applied to the result.\n\n\n Then another DNS query is made for the result of the PTR query, and all received address are compared to the client address.\n\n\n We strongly recommend that the regex ends with \\.yandex\\.ru$.\n\n\n\n If you are installing ClickHouse yourself, enter:\n\n\n \nnetworks\n\n\n \nip\n::/0\n/ip\n\n\n \n/networks\n\n\n --\n\n \nnetworks\n \nincl=\nnetworks\n \n/\n\n\n \n!-- Settings profile for the user. --\n\n \nprofile\ndefault\n/profile\n\n\n \n!-- Quota for the user. --\n\n \nquota\ndefault\n/quota\n\n \n/default\n\n\n \n!-- For requests from the Yandex.Metrica user interface via the API for data on specific counters. --\n\n \nweb\n\n \npassword\n/password\n\n \nnetworks\n \nincl=\nnetworks\n \n/\n\n \nprofile\nweb\n/profile\n\n \nquota\ndefault\n/quota\n\n \nallow_databases\n\n \ndatabase\ntest\n/database\n\n \n/allow_databases\n\n \n/web\n\n\n/users\n\n\n\n\n\n\nYou can see a declaration from two users: \ndefault\n and \nweb\n. We added the \nweb\n user separately.\n\n\nThe \ndefault\n user is chosen in cases when the username is not passed. The \ndefault\n user is also used for distributed query processing, if the configuration of the server or cluster doesn't specify the \nuser\n and \npassword\n (see the section on the \nDistributed\n engine).\n\n\nThe user that is used for exchanging information between servers combined in a cluster must not have substantial restrictions or quotas \u2013 otherwise, distributed queries will fail.\n\n\nThe password is specified in open format (not recommended) or in SHA-256. The hash isn't salted. In this regard, you should not consider these passwords as providing security against potential malicious attacks. Rather, they are necessary for protection from employees.\n\n\nA list of networks is specified that access is allowed from. In this example, the list of networks for both users is loaded from a separate file (/etc/metrika.xml) containing the 'networks' substitution. Here is a fragment of it:\n\n\nyandex\n\n ...\n \nnetworks\n\n \nip\n::/64\n/ip\n\n \nip\n203.0.113.0/24\n/ip\n\n \nip\n2001:DB8::/32\n/ip\n\n ...\n \n/networks\n\n\n/yandex\n\n\n\n\n\n\nWe could have defined this list of networks directly in 'users.xml', or in a file in the 'users.d' directory (for more information, see the section \"Configuration files\").\n\n\nThe config includes comments explaining how to open access from everywhere.\n\n\nFor use in production, only specify IP elements (IP addresses and their masks), since using 'host' and 'hoost_regexp' might cause extra latency.\n\n\nNext the user settings profile is specified (see the section \"Settings profiles\"). You can specify the default profile, \ndefault\n. The profile can have any name. You can specify the same profile for different users. The most important thing you can write in the settings profile is 'readonly' set to 1, which provides read-only access.\n\n\nAfter this, the quota is defined (see the section \"Quotas\"). You can specify the default quota, \ndefault\n. It is set in the config by default so that it only counts resource usage, but does not restrict it. The quota can have any name. You can specify the same quota for different users \u2013 in this case, resource usage is calculated for each user individually.\n\n\nIn the optional \nallow_databases\n section, you can also specify a list of databases that the user can access. By default, all databases are available to the user. You can specify the \ndefault\n database. In this case, the user will receive access to the database by default.\n\n\nAccess to the \nsystem\n database is always allowed (since this database is used for processing queries).\n\n\nThe user can get a list of all databases and tables in them by using \nSHOW\n queries or system tables, even if access to individual databases isn't allowed.\n\n\nDatabase access is not related to the \nreadonly\n setting. You can't grant full access to one database and \nreadonly\n access to another one.\n\n\n\n\nConfiguration files\n\n\nThe main server config file is \nconfig.xml\n. It resides in the \n/etc/clickhouse-server/\n directory.\n\n\nIndividual settings can be overridden in the \n*.xml\nand\n*.conf\n files in the \nconf.d\n and \nconfig.d\n directories next to the config file.\n\n\nThe \nreplace\n or \nremove\n attributes can be specified for the elements of these config files.\n\n\nIf neither is specified, it combines the contents of elements recursively, replacing values of duplicate children.\n\n\nIf \nreplace\n is specified, it replaces the entire element with the specified one.\n\n\nIf \nremove\n is specified, it deletes the element.\n\n\nThe config can also define \"substitutions\". If an element has the \nincl\n attribute, the corresponding substitution from the file will be used as the value. By default, the path to the file with substitutions is \n/etc/metrika.xml\n. This can be changed in the \ninclude_from\n element in the server config. The substitution values are specified in \n/yandex/substitution_name\n elements in this file. If a substitution specified in \nincl\n does not exist, it is recorded in the log. To prevent ClickHouse from logging missing substitutions, specify the \noptional=\"true\"\n attribute (for example, settings for \nmacros\n).\n\n\nSubstitutions can also be performed from ZooKeeper. To do this, specify the attribute \nfrom_zk = \"/path/to/node\"\n. The element value is replaced with the contents of the node at \n/path/to/node\n in ZooKeeper. You can also put an entire XML subtree on the ZooKeeper node and it will be fully inserted into the source element.\n\n\nThe \nconfig.xml\n file can specify a separate config with user settings, profiles, and quotas. The relative path to this config is set in the 'users_config' element. By default, it is \nusers.xml\n. If \nusers_config\n is omitted, the user settings, profiles, and quotas are specified directly in \nconfig.xml\n.\n\n\nIn addition, \nusers_config\n may have overrides in files from the \nusers_config.d\n directory (for example, \nusers.d\n) and substitutions.\n\n\nFor each config file, the server also generates \nfile-preprocessed.xml\n files when starting. These files contain all the completed substitutions and overrides, and they are intended for informational use. If ZooKeeper substitutions were used in the config files but ZooKeeper is not available on the server start, the server loads the configuration from the preprocessed file.\n\n\nThe server tracks changes in config files, as well as files and ZooKeeper nodes that were used when performing substitutions and overrides, and reloads the settings for users and clusters on the fly. This means that you can modify the cluster, users, and their settings without restarting the server.\n\n\nQuotas\n\n\nQuotas allow you to limit resource usage over a period of time, or simply track the use of resources.\nQuotas are set up in the user config. This is usually 'users.xml'.\n\n\nThe system also has a feature for limiting the complexity of a single query. See the section \"Restrictions on query complexity\").\n\n\nIn contrast to query complexity restrictions, quotas:\n\n\n\n\nPlace restrictions on a set of queries that can be run over a period of time, instead of limiting a single query.\n\n\nAccount for resources spent on all remote servers for distributed query processing.\n\n\n\n\nLet's look at the section of the 'users.xml' file that defines quotas.\n\n\n!-- Quotas. --\n\n\nquotas\n\n \n!-- Quota name. --\n\n \ndefault\n\n \n!-- Restrictions for a time period. You can set many intervals with different restrictions. --\n\n \ninterval\n\n \n!-- Length of the interval. --\n\n \nduration\n3600\n/duration\n\n\n \n!-- Unlimited. Just collect data for the specified time interval. --\n\n \nqueries\n0\n/queries\n\n \nerrors\n0\n/errors\n\n \nresult_rows\n0\n/result_rows\n\n \nread_rows\n0\n/read_rows\n\n \nexecution_time\n0\n/execution_time\n\n \n/interval\n\n \n/default\n\n\n\n\n\n\nBy default, the quota just tracks resource consumption for each hour, without limiting usage.\nThe resource consumption calculated for each interval is output to the server log after each request.\n\n\nstatbox\n\n \n!-- Restrictions for a time period. You can set many intervals with different restrictions. --\n\n \ninterval\n\n \n!-- Length of the interval. --\n\n \nduration\n3600\n/duration\n\n\n \nqueries\n1000\n/queries\n\n \nerrors\n100\n/errors\n\n \nresult_rows\n1000000000\n/result_rows\n\n \nread_rows\n100000000000\n/read_rows\n\n \nexecution_time\n900\n/execution_time\n\n \n/interval\n\n\n \ninterval\n\n \nduration\n86400\n/duration\n\n\n \nqueries\n10000\n/queries\n\n \nerrors\n1000\n/errors\n\n \nresult_rows\n5000000000\n/result_rows\n\n \nread_rows\n500000000000\n/read_rows\n\n \nexecution_time\n7200\n/execution_time\n\n \n/interval\n\n\n/statbox\n\n\n\n\n\n\nFor the 'statbox' quota, restrictions are set for every hour and for every 24 hours (86,400 seconds). The time interval is counted starting from an implementation-defined fixed moment in time. In other words, the 24-hour interval doesn't necessarily begin at midnight.\n\n\nWhen the interval ends, all collected values are cleared. For the next hour, the quota calculation starts over.\n\n\nHere are the amounts that can be restricted:\n\n\nqueries\n \u2013 The total number of requests.\n\n\nerrors\n \u2013 The number of queries that threw an exception.\n\n\nresult_rows\n \u2013 The total number of rows given as the result.\n\n\nread_rows\n \u2013 The total number of source rows read from tables for running the query, on all remote servers.\n\n\nexecution_time\n \u2013 The total query execution time, in seconds (wall time).\n\n\nIf the limit is exceeded for at least one time interval, an exception is thrown with a text about which restriction was exceeded, for which interval, and when the new interval begins (when queries can be sent again).\n\n\nQuotas can use the \"quota key\" feature in order to report on resources for multiple keys independently. Here is an example of this:\n\n\n!-- For the global reports designer. --\n\n\nweb_global\n\n \n!-- keyed - The quota_key \nkey\n is passed in the query parameter,\n\n\n and the quota is tracked separately for each key value.\n\n\n For example, you can pass a Yandex.Metrica username as the key,\n\n\n so the quota will be counted separately for each username.\n\n\n Using keys makes sense only if quota_key is transmitted by the program, not by a user.\n\n\n\n You can also write \nkeyed_by_ip /\n so the IP address is used as the quota key.\n\n\n (But keep in mind that users can change the IPv6 address fairly easily.)\n\n\n --\n\n \nkeyed\n \n/\n\n\n\n\n\n\nThe quota is assigned to users in the 'users' section of the config. See the section \"Access rights\".\n\n\nFor distributed query processing, the accumulated amounts are stored on the requestor server. So if the user goes to another server, the quota there will \"start over\".\n\n\nWhen the server is restarted, quotas are reset.\n\n\nUsage recommendations\n\n\nCPU\n\n\nThe SSE 4.2 instruction set must be supported. Modern processors (since 2008) support it.\n\n\nWhen choosing a processor, prefer a large number of cores and slightly slower clock rate over fewer cores and a higher clock rate.\nFor example, 16 cores with 2600 MHz is better than 8 cores with 3600 MHz.\n\n\nHyper-threading\n\n\nDon't disable hyper-threading. It helps for some queries, but not for others.\n\n\nTurbo Boost\n\n\nTurbo Boost is highly recommended. It significantly improves performance with a typical load.\nYou can use \nturbostat\n to view the CPU's actual clock rate under a load.\n\n\nCPU scaling governor\n\n\nAlways use the \nperformance\n scaling governor. The \non-demand\n scaling governor works much worse with constantly high demand.\n\n\nsudo \necho\n \nperformance\n \n|\n tee /sys/devices/system/cpu/cpu\n\\*\n/cpufreq/scaling_governor\n\n\n\n\n\nCPU limitations\n\n\nProcessors can overheat. Use \ndmesg\n to see if the CPU's clock rate was limited due to overheating.\nThe restriction can also be set externally at the datacenter level. You can use \nturbostat\n to monitor it under a load.\n\n\nRAM\n\n\nFor small amounts of data (up to \\~200 GB compressed), it is best to use as much memory as the volume of data.\nFor large amounts of data and when processing interactive (online) queries, you should use a reasonable amount of RAM (128 GB or more) so the hot data subset will fit in the cache of pages.\nEven for data volumes of \\~50 TB per server, using 128 GB of RAM significantly improves query performance compared to 64 GB.\n\n\nSwap file\n\n\nAlways disable the swap file. The only reason for not doing this is if you are using ClickHouse on your personal laptop.\n\n\nHuge pages\n\n\nAlways disable transparent huge pages. It interferes with memory allocators, which leads to significant performance degradation.\n\n\necho\n \nnever\n \n|\n sudo tee /sys/kernel/mm/transparent_hugepage/enabled\n\n\n\n\n\nUse \nperf top\n to watch the time spent in the kernel for memory management.\nPermanent huge pages also do not need to be allocated.\n\n\nStorage subsystem\n\n\nIf your budget allows you to use SSD, use SSD.\nIf not, use HDD. SATA HDDs 7200 RPM will do.\n\n\nGive preference to a lot of servers with local hard drives over a smaller number of servers with attached disk shelves.\nBut for storing archives with rare queries, shelves will work.\n\n\nRAID\n\n\nWhen using HDD, you can combine their RAID-10, RAID-5, RAID-6 or RAID-50.\nFor Linux, software RAID is better (with \nmdadm\n). We don't recommend using LVM.\nWhen creating RAID-10, select the \nfar\n layout.\nIf your budget allows, choose RAID-10.\n\n\nIf you have more than 4 disks, use RAID-6 (preferred) or RAID-50, instead of RAID-5.\nWhen using RAID-5, RAID-6 or RAID-50, always increase stripe_cache_size, since the default value is usually not the best choice.\n\n\necho\n \n4096\n \n|\n sudo tee /sys/block/md2/md/stripe_cache_size\n\n\n\n\n\nCalculate the exact number from the number of devices and the block size, using the formula: \n2 * num_devices * chunk_size_in_bytes / 4096\n.\n\n\nA block size of 1025 KB is sufficient for all RAID configurations.\nNever set the block size too small or too large.\n\n\nYou can use RAID-0 on SSD.\nRegardless of RAID use, always use replication for data security.\n\n\nEnable NCQ with a long queue. For HDD, choose the CFQ scheduler, and for SSD, choose noop. Don't reduce the 'readahead' setting.\nFor HDD, enable the write cache.\n\n\nFile system\n\n\nExt4 is the most reliable option. Set the mount options \nnoatime, nobarrier\n.\nXFS is also suitable, but it hasn't been as thoroughly tested with ClickHouse.\nMost other file systems should also work fine. File systems with delayed allocation work better.\n\n\nLinux kernel\n\n\nDon't use an outdated Linux kernel. In 2015, 3.18.19 was new enough.\nConsider using the kernel build from Yandex:\nhttps://github.com/yandex/smart\n \u2013 it provides at least a 5% performance increase.\n\n\nNetwork\n\n\nIf you are using IPv6, increase the size of the route cache.\nThe Linux kernel prior to 3.2 had a multitude of problems with IPv6 implementation.\n\n\nUse at least a 10 GB network, if possible. 1 Gb will also work, but it will be much worse for patching replicas with tens of terabytes of data, or for processing distributed queries with a large amount of intermediate data.\n\n\nZooKeeper\n\n\nYou are probably already using ZooKeeper for other purposes. You can use the same installation of ZooKeeper, if it isn't already overloaded.\n\n\nIt's best to use a fresh version of ZooKeeper \u2013 3.4.9 or later. The version in stable Linux distributions may be outdated.\n\n\nWith the default settings, ZooKeeper is a time bomb:\n\n\n\n\nThe ZooKeeper server won't delete files from old snapshots and logs when using the default configuration (see autopurge), and this is the responsibility of the operator.\n\n\n\n\nThis bomb must be defused.\n\n\nThe ZooKeeper (3.5.1) configuration below is used in the Yandex.Metrica production environment as of May 20, 2017:\n\n\nzoo.cfg:\n\n\n## http://hadoop.apache.org/zookeeper/docs/current/zookeeperAdmin.html\n\n\n\n## The number of milliseconds of each tick\n\n\ntickTime\n=\n2000\n\n\n## The number of ticks that the initial\n\n\n## synchronization phase can take\n\n\ninitLimit\n=\n30000\n\n\n## The number of ticks that can pass between\n\n\n## sending a request and getting an acknowledgement\n\n\nsyncLimit\n=\n10\n\n\n\nmaxClientCnxns\n=\n2000\n\n\n\nmaxSessionTimeout\n=\n60000000\n\n\n## the directory where the snapshot is stored.\n\n\ndataDir\n=\n/opt/zookeeper/\n{{\n cluster\n[\nname\n]\n \n}}\n/data\n\n## Place the dataLogDir to a separate physical disc for better performance\n\n\ndataLogDir\n=\n/opt/zookeeper/\n{{\n cluster\n[\nname\n]\n \n}}\n/logs\n\nautopurge.snapRetainCount\n=\n10\n\nautopurge.purgeInterval\n=\n1\n\n\n\n\n## To avoid seeks ZooKeeper allocates space in the transaction log file in\n\n\n## blocks of preAllocSize kilobytes. The default block size is 64M. One reason\n\n\n## for changing the size of the blocks is to reduce the block size if snapshots\n\n\n## are taken more often. (Also, see snapCount).\n\n\npreAllocSize\n=\n131072\n\n\n\n## Clients can submit requests faster than ZooKeeper can process them,\n\n\n## especially if there are a lot of clients. To prevent ZooKeeper from running\n\n\n## out of memory due to queued requests, ZooKeeper will throttle clients so that\n\n\n## there is no more than globalOutstandingLimit outstanding requests in the\n\n\n## system. The default limit is 1,000.ZooKeeper logs transactions to a\n\n\n## transaction log. After snapCount transactions are written to a log file a\n\n\n## snapshot is started and a new transaction log file is started. The default\n\n\n## snapCount is 10,000.\n\n\nsnapCount\n=\n3000000\n\n\n\n## If this option is defined, requests will be will logged to a trace file named\n\n\n## traceFile.year.month.day.\n\n\n##traceFile=\n\n\n\n## Leader accepts client connections. Default value is \nyes\n. The leader machine\n\n\n## coordinates updates. For higher update throughput at thes slight expense of\n\n\n## read throughput the leader can be configured to not accept clients and focus\n\n\n## on coordination.\n\n\nleaderServes\n=\nyes\n\n\nstandaloneEnabled\n=\nfalse\n\n\ndynamicConfigFile\n=\n/etc/zookeeper-\n{{\n cluster\n[\nname\n]\n \n}}\n/conf/zoo.cfg.dynamic\n\n\n\n\n\nJava version:\n\n\nJava(TM) SE Runtime Environment (build 1.8.0_25-b17)\nJava HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode)\n\n\n\n\n\nJVM parameters:\n\n\nNAME\n=\nzookeeper-\n{{\n cluster\n[\nname\n]\n \n}}\n\n\nZOOCFGDIR\n=\n/etc/\n$NAME\n/conf\n\n\n## TODO this is really ugly\n\n\n## How to find out, which jars are needed?\n\n\n## seems, that log4j requires the log4j.properties file to be in the classpath\n\n\nCLASSPATH\n=\n$ZOOCFGDIR\n:/usr/build/classes:/usr/build/lib/*.jar:/usr/share/zookeeper/zookeeper-3.5.1-metrika.jar:/usr/share/zookeeper/slf4j-log4j12-1.7.5.jar:/usr/share/zookeeper/slf4j-api-1.7.5.jar:/usr/share/zookeeper/servlet-api-2.5-20081211.jar:/usr/share/zookeeper/netty-3.7.0.Final.jar:/usr/share/zookeeper/log4j-1.2.16.jar:/usr/share/zookeeper/jline-2.11.jar:/usr/share/zookeeper/jetty-util-6.1.26.jar:/usr/share/zookeeper/jetty-6.1.26.jar:/usr/share/zookeeper/javacc.jar:/usr/share/zookeeper/jackson-mapper-asl-1.9.11.jar:/usr/share/zookeeper/jackson-core-asl-1.9.11.jar:/usr/share/zookeeper/commons-cli-1.2.jar:/usr/src/java/lib/*.jar:/usr/etc/zookeeper\n\n\n\nZOOCFG\n=\n$ZOOCFGDIR\n/zoo.cfg\n\n\nZOO_LOG_DIR\n=\n/var/log/\n$NAME\n\n\nUSER\n=\nzookeeper\n\nGROUP\n=\nzookeeper\n\nPIDDIR\n=\n/var/run/\n$NAME\n\n\nPIDFILE\n=\n$PIDDIR\n/\n$NAME\n.pid\n\nSCRIPTNAME\n=\n/etc/init.d/\n$NAME\n\n\nJAVA\n=\n/usr/bin/java\n\nZOOMAIN\n=\norg.apache.zookeeper.server.quorum.QuorumPeerMain\n\n\nZOO_LOG4J_PROP\n=\nINFO,ROLLINGFILE\n\n\nJMXLOCALONLY\n=\nfalse\n\n\nJAVA_OPTS\n=\n-Xms{{ cluster.get(\nxms\n,\n128M\n) }} \\\n\n\n -Xmx{{ cluster.get(\nxmx\n,\n1G\n) }} \\\n\n\n -Xloggc:/var/log/\n$NAME\n/zookeeper-gc.log \\\n\n\n -XX:+UseGCLogFileRotation \\\n\n\n -XX:NumberOfGCLogFiles=16 \\\n\n\n -XX:GCLogFileSize=16M \\\n\n\n -verbose:gc \\\n\n\n -XX:+PrintGCTimeStamps \\\n\n\n -XX:+PrintGCDateStamps \\\n\n\n -XX:+PrintGCDetails\n\n\n -XX:+PrintTenuringDistribution \\\n\n\n -XX:+PrintGCApplicationStoppedTime \\\n\n\n -XX:+PrintGCApplicationConcurrentTime \\\n\n\n -XX:+PrintSafepointStatistics \\\n\n\n -XX:+UseParNewGC \\\n\n\n -XX:+UseConcMarkSweepGC \\\n\n\n-XX:+CMSParallelRemarkEnabled\n\n\n\n\n\n\nSalt init:\n\n\ndescription \nzookeeper-{{ cluster[\nname\n] }} centralized coordination service\n\n\nstart on runlevel [2345]\nstop on runlevel [!2345]\n\nrespawn\n\nlimit nofile 8192 8192\n\npre-start script\n [ -r \n/etc/zookeeper-{{ cluster[\nname\n] }}/conf/environment\n ] || exit 0\n . /etc/zookeeper-{{ cluster[\nname\n] }}/conf/environment\n [ -d $ZOO_LOG_DIR ] || mkdir -p $ZOO_LOG_DIR\n chown $USER:$GROUP $ZOO_LOG_DIR\nend script\n\nscript\n . /etc/zookeeper-{{ cluster[\nname\n] }}/conf/environment\n [ -r /etc/default/zookeeper ] \n . /etc/default/zookeeper\n if [ -z \n$JMXDISABLE\n ]; then\n JAVA_OPTS=\n$JAVA_OPTS -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.local.only=$JMXLOCALONLY\n\n fi\n exec start-stop-daemon --start -c $USER --exec $JAVA --name zookeeper-{{ cluster[\nname\n] }} \\\n -- -cp $CLASSPATH $JAVA_OPTS -Dzookeeper.log.dir=${ZOO_LOG_DIR} \\\n -Dzookeeper.root.logger=${ZOO_LOG4J_PROP} $ZOOMAIN $ZOOCFG\nend script\n\n\n\n\n\n\n\nServer configuration parameters\n\n\nThis section contains descriptions of server settings that cannot be changed at the session or query level.\n\n\nThese settings are stored in the \nconfig.xml\n file on the ClickHouse server.\n\n\nOther settings are described in the \"\nSettings\n\" section.\n\n\nBefore studying the settings, read the \nConfiguration files\n section and note the use of substitutions (the \nincl\n and \noptional\n attributes).\n\n\nServer settings\n\n\n\n\nbuiltin_dictionaries_reload_interval\n\n\nThe interval in seconds before reloading built-in dictionaries.\n\n\nClickHouse reloads built-in dictionaries every x seconds. This makes it possible to edit dictionaries \"on the fly\" without restarting the server.\n\n\nDefault value: 3600.\n\n\nExample\n\n\nbuiltin_dictionaries_reload_interval\n3600\n/builtin_dictionaries_reload_interval\n\n\n\n\n\n\n\n\ncompression\n\n\nData compression settings.\n\n\n\n\nDon't use it if you have just started using ClickHouse.\n\n\n\n\n\nThe configuration looks like this:\n\n\ncompression\n\n \ncase\n\n \nparameters/\n\n \n/case\n\n ...\n\n/compression\n\n\n\n\n\n\nYou can configure multiple sections \ncase\n.\n\n\nBlock field \ncase\n:\n\n\n\n\nmin_part_size\n \u2013 The minimum size of a table part.\n\n\nmin_part_size_ratio\n \u2013 The ratio of the minimum size of a table part to the full size of the table.\n\n\nmethod\n \u2013 Compression method. Acceptable values \u200b: \nlz4\n or \nzstd\n(experimental).\n\n\n\n\nClickHouse checks \nmin_part_size\n and \nmin_part_size_ratio\n and processes the \ncase\n blocks that match these conditions. If none of the \ncase\n matches, ClickHouse applies the \nlz4\n compression algorithm.\n\n\nExample\n\n\ncompression\n \nincl=\nclickhouse_compression\n\n \ncase\n\n \nmin_part_size\n10000000000\n/min_part_size\n\n \nmin_part_size_ratio\n0.01\n/min_part_size_ratio\n\n \nmethod\nzstd\n/method\n\n \n/case\n\n\n/compression\n\n\n\n\n\n\n\n\ndefault_database\n\n\nThe default database.\n\n\nTo get a list of databases, use the \nSHOW DATABASES\n.\n\n\nExample\n\n\ndefault_database\ndefault\n/default_database\n\n\n\n\n\n\n\n\ndefault_profile\n\n\nDefault settings profile.\n\n\nSettings profiles are located in the file specified in the parameter \nuser_config\n.\n\n\nExample\n\n\ndefault_profile\ndefault\n/default_profile\n\n\n\n\n\n\n\n\ndictionaries_config\n\n\nThe path to the config file for external dictionaries.\n\n\nPath:\n\n\n\n\nSpecify the absolute path or the path relative to the server config file.\n\n\nThe path can contain wildcards * and ?.\n\n\n\n\nSee also \"\nExternal dictionaries\n\".\n\n\nExample\n\n\ndictionaries_config\n*_dictionary.xml\n/dictionaries_config\n\n\n\n\n\n\n\n\ndictionaries_lazy_load\n\n\nLazy loading of dictionaries.\n\n\nIf \ntrue\n, then each dictionary is created on first use. If dictionary creation failed, the function that was using the dictionary throws an exception.\n\n\nIf \nfalse\n, all dictionaries are created when the server starts, and if there is an error, the server shuts down.\n\n\nThe default is \ntrue\n.\n\n\nExample\n\n\ndictionaries_lazy_load\ntrue\n/dictionaries_lazy_load\n\n\n\n\n\n\n\n\nformat_schema_path\n\n\nThe path to the directory with the schemes for the input data, such as schemas for the \nCapnProto\n format.\n\n\nExample\n\n\n \n!-- Directory containing schema files for various input formats. --\n\n \nformat_schema_path\nformat_schemas/\n/format_schema_path\n\n\n\n\n\n\n\n\ngraphite\n\n\nSending data to \nGraphite\n.\n\n\nSettings:\n\n\n\n\nhost \u2013 The Graphite server.\n\n\nport \u2013 The port on the Graphite server.\n\n\ninterval \u2013 The interval for sending, in seconds.\n\n\ntimeout \u2013 The timeout for sending data, in seconds.\n\n\nroot_path \u2013 Prefix for keys.\n\n\nmetrics \u2013 Sending data from a :ref:\nsystem_tables-system.metrics\n table.\n\n\nevents \u2013 Sending data from a :ref:\nsystem_tables-system.events\n table.\n\n\nasynchronous_metrics \u2013 Sending data from a :ref:\nsystem_tables-system.asynchronous_metrics\n table.\n\n\n\n\nYou can configure multiple \ngraphite\n clauses. For instance, you can use this for sending different data at different intervals.\n\n\nExample\n\n\ngraphite\n\n \nhost\nlocalhost\n/host\n\n \nport\n42000\n/port\n\n \ntimeout\n0.1\n/timeout\n\n \ninterval\n60\n/interval\n\n \nroot_path\none_min\n/root_path\n\n \nmetrics\ntrue\n/metrics\n\n \nevents\ntrue\n/events\n\n \nasynchronous_metrics\ntrue\n/asynchronous_metrics\n\n\n/graphite\n\n\n\n\n\n\n\n\ngraphite_rollup\n\n\nSettings for thinning data for Graphite.\n\n\nFor more information, see \nGraphiteMergeTree\n.\n\n\nExample\n\n\ngraphite_rollup_example\n\n \ndefault\n\n \nfunction\nmax\n/function\n\n \nretention\n\n \nage\n0\n/age\n\n \nprecision\n60\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n3600\n/age\n\n \nprecision\n300\n/precision\n\n \n/retention\n\n \nretention\n\n \nage\n86400\n/age\n\n \nprecision\n3600\n/precision\n\n \n/retention\n\n \n/default\n\n\n/graphite_rollup_example\n\n\n\n\n\n\n\n\nhttp_port/https_port\n\n\nThe port for connecting to the server over HTTP(s).\n\n\nIf \nhttps_port\n is specified, \nopenSSL\n must be configured.\n\n\nIf \nhttp_port\n is specified, the openSSL configuration is ignored even if it is set.\n\n\nExample\n\n\nhttps\n0000\n/https\n\n\n\n\n\n\n\n\nhttp_server_default_response\n\n\nThe page that is shown by default when you access the ClickHouse HTTP(s) server.\n\n\nExample\n\n\nOpens \nhttps://tabix.io/\n when accessing \nhttp://localhost: http_port\n.\n\n\nhttp_server_default_response\n\n \n![CDATA[\nhtml ng-app=\nSMI2\nhead\nbase href=\nhttp://ui.tabix.io/\n/head\nbody\ndiv ui-view=\n class=\ncontent-ui\n/div\nscript src=\nhttp://loader.tabix.io/master.js\n/script\n/body\n/html\n]]\n\n\n/http_server_default_response\n\n\n\n\n\n\n\n\ninclude_from\n\n\nThe path to the file with substitutions.\n\n\nFor more information, see the section \"\nConfiguration files\n\".\n\n\nExample\n\n\ninclude_from\n/etc/metrica.xml\n/include_from\n\n\n\n\n\n\n\n\ninterserver_http_port\n\n\nPort for exchanging data between ClickHouse servers.\n\n\nExample\n\n\ninterserver_http_port\n9009\n/interserver_http_port\n\n\n\n\n\n\n\n\ninterserver_http_host\n\n\nThe host name that can be used by other servers to access this server.\n\n\nIf omitted, it is defined in the same way as the \nhostname-f\n command.\n\n\nUseful for breaking away from a specific network interface.\n\n\nExample\n\n\ninterserver_http_host\nexample.yandex.ru\n/interserver_http_host\n\n\n\n\n\n\n\n\nkeep_alive_timeout\n\n\nThe number of milliseconds that ClickHouse waits for incoming requests before closing the connection.\n\n\nExample\n\n\nkeep_alive_timeout\n3\n/keep_alive_timeout\n\n\n\n\n\n\n\n\nlisten_host\n\n\nRestriction on hosts that requests can come from. If you want the server to answer all of them, specify \n::\n.\n\n\nExamples:\n\n\nlisten_host\n::1\n/listen_host\n\n\nlisten_host\n127.0.0.1\n/listen_host\n\n\n\n\n\n\n\n\nlogger\n\n\nLogging settings.\n\n\nKeys:\n\n\n\n\nlevel \u2013 Logging level. Acceptable values: \ntrace\n, \ndebug\n, \ninformation\n, \nwarning\n, \nerror\n.\n\n\nlog \u2013 The log file. Contains all the entries according to \nlevel\n.\n\n\nerrorlog \u2013 Error log file.\n\n\nsize \u2013 Size of the file. Applies to \nlog\nand\nerrorlog\n. Once the file reaches \nsize\n, ClickHouse archives and renames it, and creates a new log file in its place.\n\n\ncount \u2013 The number of archived log files that ClickHouse stores.\n\n\n\n\nExample\n\n\nlogger\n\n \nlevel\ntrace\n/level\n\n \nlog\n/var/log/clickhouse-server/clickhouse-server.log\n/log\n\n \nerrorlog\n/var/log/clickhouse-server/clickhouse-server.err.log\n/errorlog\n\n \nsize\n1000M\n/size\n\n \ncount\n10\n/count\n\n\n/logger\n\n\n\n\n\n\n\n\nmacros\n\n\nParameter substitutions for replicated tables.\n\n\nCan be omitted if replicated tables are not used.\n\n\nFor more information, see the section \"\nCreating replicated tables\n\".\n\n\nExample\n\n\nmacros\n \nincl=\nmacros\n \noptional=\ntrue\n \n/\n\n\n\n\n\n\n\n\nmark_cache_size\n\n\nApproximate size (in bytes) of the cache of \"marks\" used by \nMergeTree\n engines.\n\n\nThe cache is shared for the server and memory is allocated as needed. The cache size must be at least 5368709120.\n\n\nExample\n\n\nmark_cache_size\n5368709120\n/mark_cache_size\n\n\n\n\n\n\n\n\nmax_concurrent_queries\n\n\nThe maximum number of simultaneously processed requests.\n\n\nExample\n\n\nmax_concurrent_queries\n100\n/max_concurrent_queries\n\n\n\n\n\n\n\n\nmax_connections\n\n\nThe maximum number of inbound connections.\n\n\nExample\n\n\nmax_connections\n4096\n/max_connections\n\n\n\n\n\n\n\n\nmax_open_files\n\n\nThe maximum number of open files.\n\n\nBy default: \nmaximum\n.\n\n\nWe recommend using this option in Mac OS X, since the \ngetrlimit()\n function returns an incorrect value.\n\n\nExample\n\n\nmax_open_files\n262144\n/max_open_files\n\n\n\n\n\n\n\n\nmax_table_size_to_drop\n\n\nRestriction on deleting tables.\n\n\nIf the size of a \nMergeTree\n type table exceeds \nmax_table_size_to_drop\n (in bytes), you can't delete it using a DROP query.\n\n\nIf you still need to delete the table without restarting the ClickHouse server, create the \nclickhouse-path\n/flags/force_drop_table\n file and run the DROP query.\n\n\nDefault value: 50 GB.\n\n\nThe value 0 means that you can delete all tables without any restrictions.\n\n\nExample\n\n\nmax_table_size_to_drop\n0\n/max_table_size_to_drop\n\n\n\n\n\n\n\n\nmerge_tree\n\n\nFine tuning for tables in the \n MergeTree\n family.\n\n\nFor more information, see the MergeTreeSettings.h header file.\n\n\nExample\n\n\nmerge_tree\n\n \nmax_suspicious_broken_parts\n5\n/max_suspicious_broken_parts\n\n\n/merge_tree\n\n\n\n\n\n\n\n\nopenSSL\n\n\nSSL client/server configuration.\n\n\nSupport for SSL is provided by the \nlibpoco\n library. The interface is described in the file \nSSLManager.h\n\n\nKeys for server/client settings:\n\n\n\n\nprivateKeyFile \u2013 The path to the file with the secret key of the PEM certificate. The file may contain a key and certificate at the same time.\n\n\ncertificateFile \u2013 The path to the client/server certificate file in PEM format. You can omit it if \nprivateKeyFile\n contains the certificate.\n\n\ncaConfig \u2013 The path to the file or directory that contains trusted root certificates.\n\n\nverificationMode \u2013 The method for checking the node's certificates. Details are in the description of the \nContext\n class. Possible values: \nnone\n, \nrelaxed\n, \nstrict\n, \nonce\n.\n\n\nverificationDepth \u2013 The maximum length of the verification chain. Verification will fail if the certificate chain length exceeds the set value.\n\n\nloadDefaultCAFile \u2013 Indicates that built-in CA certificates for OpenSSL will be used. Acceptable values: \ntrue\n, \nfalse\n. |\n\n\ncipherList \u2013 Supported OpenSSL encryptions. For example: \nALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH\n.\n\n\ncacheSessions \u2013 Enables or disables caching sessions. Must be used in combination with \nsessionIdContext\n. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nsessionIdContext \u2013 A unique set of random characters that the server appends to each generated identifier. The length of the string must not exceed \nSSL_MAX_SSL_SESSION_ID_LENGTH\n. This parameter is always recommended, since it helps avoid problems both if the server caches the session and if the client requested caching. Default value: \n${application.name}\n.\n\n\nsessionCacheSize \u2013 The maximum number of sessions that the server caches. Default value: 1024*20. 0 \u2013 Unlimited sessions.\n\n\nsessionTimeout \u2013 Time for caching the session on the server.\n\n\nextendedVerification \u2013 Automatically extended verification of certificates after the session ends. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nrequireTLSv1 \u2013 Require a TLSv1 connection. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nrequireTLSv1_1 \u2013 Require a TLSv1.1 connection. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nrequireTLSv1 \u2013 Require a TLSv1.2 connection. Acceptable values: \ntrue\n, \nfalse\n.\n\n\nfips \u2013 Activates OpenSSL FIPS mode. Supported if the library's OpenSSL version supports FIPS.\n\n\nprivateKeyPassphraseHandler \u2013 Class (PrivateKeyPassphraseHandler subclass) that requests the passphrase for accessing the private key. For example: \nprivateKeyPassphraseHandler\n, \nname\nKeyFileHandler\n/name\n, \noptions\npassword\ntest\n/password\n/options\n, \n/privateKeyPassphraseHandler\n.\n\n\ninvalidCertificateHandler \u2013 Class (subclass of CertificateHandler) for verifying invalid certificates. For example: \ninvalidCertificateHandler\n \nname\nConsoleCertificateHandler\n/name\n \n/invalidCertificateHandler\n .\n\n\ndisableProtocols \u2013 Protocols that are not allowed to use.\n\n\npreferServerCiphers \u2013 Preferred server ciphers on the client.\n\n\n\n\nExample of settings:\n\n\nopenSSL\n\n \nserver\n\n \n!-- openssl req -subj \n/CN=localhost\n -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout /etc/clickhouse-server/server.key -out /etc/clickhouse-server/server.crt --\n\n \ncertificateFile\n/etc/clickhouse-server/server.crt\n/certificateFile\n\n \nprivateKeyFile\n/etc/clickhouse-server/server.key\n/privateKeyFile\n\n \n!-- openssl dhparam -out /etc/clickhouse-server/dhparam.pem 4096 --\n\n \ndhParamsFile\n/etc/clickhouse-server/dhparam.pem\n/dhParamsFile\n\n \nverificationMode\nnone\n/verificationMode\n\n \nloadDefaultCAFile\ntrue\n/loadDefaultCAFile\n\n \ncacheSessions\ntrue\n/cacheSessions\n\n \ndisableProtocols\nsslv2,sslv3\n/disableProtocols\n\n \npreferServerCiphers\ntrue\n/preferServerCiphers\n\n \n/server\n\n \nclient\n\n \nloadDefaultCAFile\ntrue\n/loadDefaultCAFile\n\n \ncacheSessions\ntrue\n/cacheSessions\n\n \ndisableProtocols\nsslv2,sslv3\n/disableProtocols\n\n \npreferServerCiphers\ntrue\n/preferServerCiphers\n\n \n!-- Use for self-signed: \nverificationMode\nnone\n/verificationMode\n --\n\n \ninvalidCertificateHandler\n\n \n!-- Use for self-signed: \nname\nAcceptCertificateHandler\n/name\n --\n\n \nname\nRejectCertificateHandler\n/name\n\n \n/invalidCertificateHandler\n\n \n/client\n\n\n/openSSL\n\n\n\n\n\n\n\n\npart_log\n\n\nLogging events that are associated with \nMergeTree\n data. For instance, adding or merging data. You can use the log to simulate merge algorithms and compare their characteristics. You can visualize the merge process.\n\n\nQueries are logged in the ClickHouse table, not in a separate file.\n\n\nColumns in the log:\n\n\n\n\nevent_time \u2013 Date of the event.\n\n\nduration_ms \u2013 Duration of the event.\n\n\nevent_type \u2013 Type of event. 1 \u2013 new data part; 2 \u2013 merge result; 3 \u2013 data part downloaded from replica; 4 \u2013 data part deleted.\n\n\ndatabase_name \u2013 The name of the database.\n\n\ntable_name \u2013 Name of the table.\n\n\npart_name \u2013 Name of the data part.\n\n\nsize_in_bytes \u2013 Size of the data part in bytes.\n\n\nmerged_from \u2013 An array of names of data parts that make up the merge (also used when downloading a merged part).\n\n\nmerge_time_ms \u2013 Time spent on the merge.\n\n\n\n\nUse the following parameters to configure logging:\n\n\n\n\ndatabase \u2013 Name of the database.\n\n\ntable \u2013 Name of the table.\n\n\npartition_by \u2013 Sets a \ncustom partitioning key\n.\n\n\nflush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk.\n\n\n\n\nExample\n\n\npart_log\n\n \ndatabase\nsystem\n/database\n\n \ntable\npart_log\n/table\n\n \npartition_by\ntoMonday(event_date)\n/partition_by\n\n \nflush_interval_milliseconds\n7500\n/flush_interval_milliseconds\n\n\n/part_log\n\n\n\n\n\n\n\n\npath\n\n\nThe path to the directory containing data.\n\n\n\n\nThe end slash is mandatory.\n\n\n\n\n\nExample\n\n\npath\n/var/lib/clickhouse/\n/path\n\n\n\n\n\n\n\n\nquery_log\n\n\nSetting for logging queries received with the \nlog_queries=1\n setting.\n\n\nQueries are logged in the ClickHouse table, not in a separate file.\n\n\nUse the following parameters to configure logging:\n\n\n\n\ndatabase \u2013 Name of the database.\n\n\ntable \u2013 Name of the table.\n\n\npartition_by \u2013 Sets a \ncustom partitioning key\n.\n\n\nflush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk.\n\n\n\n\nIf the table doesn't exist, ClickHouse will create it. If the structure of the query log changed when the ClickHouse server was updated, the table with the old structure is renamed, and a new table is created automatically.\n\n\nExample\n\n\nquery_log\n\n \ndatabase\nsystem\n/database\n\n \ntable\nquery_log\n/table\n\n \npartition_by\ntoMonday(event_date)\n/partition_by\n\n \nflush_interval_milliseconds\n7500\n/flush_interval_milliseconds\n\n\n/query_log\n\n\n\n\n\n\n\n\nremote_servers\n\n\nConfiguration of clusters used by the Distributed table engine.\n\n\nFor more information, see the section \"\nTable engines/Distributed\n\".\n\n\nExample\n\n\nremote_servers\n \nincl=\nclickhouse_remote_servers\n \n/\n\n\n\n\n\n\nFor the value of the \nincl\n attribute, see the section \"\nConfiguration files\n\".\n\n\n\n\ntimezone\n\n\nThe server's time zone.\n\n\nSpecified as an IANA identifier for the UTC time zone or geographic location (for example, Africa/Abidjan).\n\n\nThe time zone is necessary for conversions between String and DateTime formats when DateTime fields are output to text format (printed on the screen or in a file), and when getting DateTime from a string. In addition, the time zone is used in functions that work with the time and date if they didn't receive the time zone in the input parameters.\n\n\nExample\n\n\ntimezone\nEurope/Moscow\n/timezone\n\n\n\n\n\n\n\n\ntcp_port\n\n\nPort for communicating with clients over the TCP protocol.\n\n\nExample\n\n\ntcp_port\n9000\n/tcp_port\n\n\n\n\n\n\n\n\ntmp_path\n\n\nPath to temporary data for processing large queries.\n\n\n\n\nThe end slash is mandatory.\n\n\n\n\n\nExample\n\n\ntmp_path\n/var/lib/clickhouse/tmp/\n/tmp_path\n\n\n\n\n\n\n\n\nuncompressed_cache_size\n\n\nCache size (in bytes) for uncompressed data used by table engines from the \nMergeTree\n family.\n\n\nThere is one shared cache for the server. Memory is allocated on demand. The cache is used if the option \nuse_uncompressed_cache\n is enabled.\n\n\nThe uncompressed cache is advantageous for very short queries in individual cases.\n\n\nExample\n\n\nuncompressed_cache_size\n8589934592\n/uncompressed_cache_size\n\n\n\n\n\n\n\n\nusers_config\n\n\nPath to the file that contains:\n\n\n\n\nUser configurations.\n\n\nAccess rights.\n\n\nSettings profiles.\n\n\nQuota settings.\n\n\n\n\nExample\n\n\nusers_config\nusers.xml\n/users_config\n\n\n\n\n\n\n\n\nzookeeper\n\n\nConfiguration of ZooKeeper servers.\n\n\nClickHouse uses ZooKeeper for storing replica metadata when using replicated tables.\n\n\nThis parameter can be omitted if replicated tables are not used.\n\n\nFor more information, see the section \"\nReplication\n\".\n\n\nExample\n\n\nzookeeper\n \nincl=\nzookeeper-servers\n \noptional=\ntrue\n \n/\n\n\n\n\n\n\n\n\nSettings\n\n\nThere are multiple ways to make all the settings described below.\nSettings are configured in layers, so each subsequent layer redefines the previous settings.\n\n\nWays to configure settings, in order of priority:\n\n\n\n\nSettings in the server config file.\n\n\n\n\nSettings from user profiles.\n\n\n\n\nSession settings.\n\n\n\n\nSend \nSET setting=value\n from the ClickHouse console client in interactive mode.\nSimilarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to specify the \nsession_id\n HTTP parameter.\n\n\n\n\nFor a query.\n\n\nWhen starting the ClickHouse console client in non-interactive mode, set the startup parameter \n--setting=value\n.\n\n\nWhen using the HTTP API, pass CGI parameters (\nURL?setting_1=value\nsetting_2=value...\n).\n\n\n\n\nSettings that can only be made in the server config file are not covered in this section.\n\n\nRestrictions on query complexity\n\n\nRestrictions on query complexity are part of the settings.\nThey are used in order to provide safer execution from the user interface.\nAlmost all the restrictions only apply to SELECTs.For distributed query processing, restrictions are applied on each server separately.\n\n\nRestrictions on the \"maximum amount of something\" can take the value 0, which means \"unrestricted\".\nMost restrictions also have an 'overflow_mode' setting, meaning what to do when the limit is exceeded.\nIt can take one of two values: \nthrow\n or \nbreak\n. Restrictions on aggregation (group_by_overflow_mode) also have the value \nany\n.\n\n\nthrow\n \u2013 Throw an exception (default).\n\n\nbreak\n \u2013 Stop executing the query and return the partial result, as if the source data ran out.\n\n\nany (only for group_by_overflow_mode)\n \u2013 Continuing aggregation for the keys that got into the set, but don't add new keys to the set.\n\n\n\n\nreadonly\n\n\nWith a value of 0, you can execute any queries.\nWith a value of 1, you can only execute read requests (such as SELECT and SHOW). Requests for writing and changing settings (INSERT, SET) are prohibited.\nWith a value of 2, you can process read queries (SELECT, SHOW) and change settings (SET).\n\n\nAfter enabling readonly mode, you can't disable it in the current session.\n\n\nWhen using the GET method in the HTTP interface, 'readonly = 1' is set automatically. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.\n\n\n\n\nmax_memory_usage\n\n\nThe maximum amount of RAM to use for running a query on a single server.\n\n\nIn the default configuration file, the maximum is 10 GB.\n\n\nThe setting doesn't consider the volume of available memory or the total volume of memory on the machine.\nThe restriction applies to a single query within a single server.\nYou can use \nSHOW PROCESSLIST\n to see the current memory consumption for each query.\nIn addition, the peak memory consumption is tracked for each query and written to the log.\n\n\nMemory usage is not monitored for the states of certain aggregate functions.\n\n\nMemory usage is not fully tracked for states of the aggregate functions \nmin\n, \nmax\n, \nany\n, \nanyLast\n, \nargMin\n, \nargMax\n from \nString\n and \nArray\n arguments.\n\n\nMemory consumption is also restricted by the parameters \nmax_memory_usage_for_user\n and \nmax_memory_usage_for_all_queries\n.\n\n\nmax_memory_usage_for_user\n\n\nThe maximum amount of RAM to use for running a user's queries on a single server.\n\n\nDefault values are defined in \nSettings.h\n. By default, the amount is not restricted (\nmax_memory_usage_for_user = 0\n).\n\n\nSee also the description of \nmax_memory_usage\n.\n\n\nmax_memory_usage_for_all_queries\n\n\nThe maximum amount of RAM to use for running all queries on a single server.\n\n\nDefault values are defined in \nSettings.h\n. By default, the amount is not restricted (\nmax_memory_usage_for_all_queries = 0\n).\n\n\nSee also the description of \nmax_memory_usage\n.\n\n\nmax_rows_to_read\n\n\nThe following restrictions can be checked on each block (instead of on each row). That is, the restrictions can be broken a little.\nWhen running a query in multiple threads, the following restrictions apply to each thread separately.\n\n\nMaximum number of rows that can be read from a table when running a query.\n\n\nmax_bytes_to_read\n\n\nMaximum number of bytes (uncompressed data) that can be read from a table when running a query.\n\n\nread_overflow_mode\n\n\nWhat to do when the volume of data read exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_rows_to_group_by\n\n\nMaximum number of unique keys received from aggregation. This setting lets you limit memory consumption when aggregating.\n\n\ngroup_by_overflow_mode\n\n\nWhat to do when the number of unique keys for aggregation exceeds the limit: 'throw', 'break', or 'any'. By default, throw.\nUsing the 'any' value lets you run an approximation of GROUP BY. The quality of this approximation depends on the statistical nature of the data.\n\n\nmax_rows_to_sort\n\n\nMaximum number of rows before sorting. This allows you to limit memory consumption when sorting.\n\n\nmax_bytes_to_sort\n\n\nMaximum number of bytes before sorting.\n\n\nsort_overflow_mode\n\n\nWhat to do if the number of rows received before sorting exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_result_rows\n\n\nLimit on the number of rows in the result. Also checked for subqueries, and on remote servers when running parts of a distributed query.\n\n\nmax_result_bytes\n\n\nLimit on the number of bytes in the result. The same as the previous setting.\n\n\nresult_overflow_mode\n\n\nWhat to do if the volume of the result exceeds one of the limits: 'throw' or 'break'. By default, throw.\nUsing 'break' is similar to using LIMIT.\n\n\nmax_execution_time\n\n\nMaximum query execution time in seconds.\nAt this time, it is not checked for one of the sorting stages, or when merging and finalizing aggregate functions.\n\n\ntimeout_overflow_mode\n\n\nWhat to do if the query is run longer than 'max_execution_time': 'throw' or 'break'. By default, throw.\n\n\nmin_execution_speed\n\n\nMinimal execution speed in rows per second. Checked on every data block when 'timeout_before_checking_execution_speed' expires. If the execution speed is lower, an exception is thrown.\n\n\ntimeout_before_checking_execution_speed\n\n\nChecks that execution speed is not too slow (no less than 'min_execution_speed'), after the specified time in seconds has expired.\n\n\nmax_columns_to_read\n\n\nMaximum number of columns that can be read from a table in a single query. If a query requires reading a greater number of columns, it throws an exception.\n\n\nmax_temporary_columns\n\n\nMaximum number of temporary columns that must be kept in RAM at the same time when running a query, including constant columns. If there are more temporary columns than this, it throws an exception.\n\n\nmax_temporary_non_const_columns\n\n\nThe same thing as 'max_temporary_columns', but without counting constant columns.\nNote that constant columns are formed fairly often when running a query, but they require approximately zero computing resources.\n\n\nmax_subquery_depth\n\n\nMaximum nesting depth of subqueries. If subqueries are deeper, an exception is thrown. By default, 100.\n\n\nmax_pipeline_depth\n\n\nMaximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000.\n\n\nmax_ast_depth\n\n\nMaximum nesting depth of a query syntactic tree. If exceeded, an exception is thrown.\nAt this time, it isn't checked during parsing, but only after parsing the query. That is, a syntactic tree that is too deep can be created during parsing, but the query will fail. By default, 1000.\n\n\nmax_ast_elements\n\n\nMaximum number of elements in a query syntactic tree. If exceeded, an exception is thrown.\nIn the same way as the previous setting, it is checked only after parsing the query. By default, 10,000.\n\n\nmax_rows_in_set\n\n\nMaximum number of rows for a data set in the IN clause created from a subquery.\n\n\nmax_bytes_in_set\n\n\nMaximum number of bytes (uncompressed data) used by a set in the IN clause created from a subquery.\n\n\nset_overflow_mode\n\n\nWhat to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_rows_in_distinct\n\n\nMaximum number of different rows when using DISTINCT.\n\n\nmax_bytes_in_distinct\n\n\nMaximum number of bytes used by a hash table when using DISTINCT.\n\n\ndistinct_overflow_mode\n\n\nWhat to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nmax_rows_to_transfer\n\n\nMaximum number of rows that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.\n\n\nmax_bytes_to_transfer\n\n\nMaximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.\n\n\ntransfer_overflow_mode\n\n\nWhat to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.\n\n\nSettings\n\n\n\n\ndistributed_product_mode\n\n\nChanges the behavior of \ndistributed subqueries\n, i.e. in cases when the query contains the product of distributed tables.\n\n\nClickHouse applies the configuration if the subqueries on any level have a distributed table that exists on the local server and has more than one shard.\n\n\nRestrictions:\n\n\n\n\nOnly applied for IN and JOIN subqueries.\n\n\nUsed only if a distributed table is used in the FROM clause.\n\n\nNot used for a table-valued \n remote\n function.\n\n\n\n\nThe possible values \u200b\u200bare:\n\n\n\n\nfallback_to_stale_replicas_for_distributed_queries\n\n\nForces a query to an out-of-date replica if updated data is not available. See \"\nReplication\n\".\n\n\nClickHouse selects the most relevant from the outdated replicas of the table.\n\n\nUsed when performing \nSELECT\n from a distributed table that points to replicated tables.\n\n\nBy default, 1 (enabled).\n\n\n\n\nforce_index_by_date\n\n\nDisables query execution if the index can't be used by date.\n\n\nWorks with tables in the MergeTree family.\n\n\nIf \nforce_index_by_date=1\n, ClickHouse checks whether the query has a date key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For example, the condition \nDate != ' 2000-01-01 '\n is acceptable even when it matches all the data in the table (i.e., running the query requires a full scan). For more information about ranges of data in MergeTree tables, see \"\nMergeTree\n\".\n\n\n\n\nforce_primary_key\n\n\nDisables query execution if indexing by the primary key is not possible.\n\n\nWorks with tables in the MergeTree family.\n\n\nIf \nforce_primary_key=1\n, ClickHouse checks to see if the query has a primary key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For more information about data ranges in MergeTree tables, see \"\nMergeTree\n\".\n\n\n\n\nfsync_metadata\n\n\nEnable or disable fsync when writing .sql files. By default, it is enabled.\n\n\nIt makes sense to disable it if the server has millions of tiny table chunks that are constantly being created and destroyed.\n\n\ninput_format_allow_errors_num\n\n\nSets the maximum number of acceptable errors when reading from text formats (CSV, TSV, etc.).\n\n\nThe default value is 0.\n\n\nAlways pair it with \ninput_format_allow_errors_ratio\n. To skip errors, both settings must be greater than 0.\n\n\nIf an error occurred while reading rows but the error counter is still less than \ninput_format_allow_errors_num\n, ClickHouse ignores the row and moves on to the next one.\n\n\nIf \ninput_format_allow_errors_num\nis exceeded, ClickHouse throws an exception.\n\n\ninput_format_allow_errors_ratio\n\n\nSets the maximum percentage of errors allowed when reading from text formats (CSV, TSV, etc.).\nThe percentage of errors is set as a floating-point number between 0 and 1.\n\n\nThe default value is 0.\n\n\nAlways pair it with \ninput_format_allow_errors_num\n. To skip errors, both settings must be greater than 0.\n\n\nIf an error occurred while reading rows but the error counter is still less than \ninput_format_allow_errors_ratio\n, ClickHouse ignores the row and moves on to the next one.\n\n\nIf \ninput_format_allow_errors_ratio\n is exceeded, ClickHouse throws an exception.\n\n\nmax_block_size\n\n\nIn ClickHouse, data is processed by blocks (sets of column parts). The internal processing cycles for a single block are efficient enough, but there are noticeable expenditures on each block. \nmax_block_size\n is a recommendation for what size of block (in number of rows) to load from tables. The block size shouldn't be too small, so that the expenditures on each block are still noticeable, but not too large, so that the query with LIMIT that is completed after the first block is processed quickly, so that too much memory isn't consumed when extracting a large number of columns in multiple threads, and so that at least some cache locality is preserved.\n\n\nBy default, 65,536.\n\n\nBlocks the size of \nmax_block_size\n are not always loaded from the table. If it is obvious that less data needs to be retrieved, a smaller block is processed.\n\n\npreferred_block_size_bytes\n\n\nUsed for the same purpose as \nmax_block_size\n, but it sets the recommended block size in bytes by adapting it to the number of rows in the block.\nHowever, the block size cannot be more than \nmax_block_size\n rows.\nDisabled by default (set to 0). It only works when reading from MergeTree engines.\n\n\n\n\nlog_queries\n\n\nSetting up query the logging.\n\n\nQueries sent to ClickHouse with this setup are logged according to the rules in the \nquery_log\n server configuration parameter.\n\n\nExample\n:\n\n\nlog_queries=1\n\n\n\n\n\n\n\nmax_insert_block_size\n\n\nThe size of blocks to form for insertion into a table.\nThis setting only applies in cases when the server forms the blocks.\nFor example, for an INSERT via the HTTP interface, the server parses the data format and forms blocks of the specified size.\nBut when using clickhouse-client, the client parses the data itself, and the 'max_insert_block_size' setting on the server doesn't affect the size of the inserted blocks.\nThe setting also doesn't have a purpose when using INSERT SELECT, since data is inserted using the same blocks that are formed after SELECT.\n\n\nBy default, it is 1,048,576.\n\n\nThis is slightly more than \nmax_block_size\n. The reason for this is because certain table engines (\n*MergeTree\n) form a data part on the disk for each inserted block, which is a fairly large entity. Similarly, \n*MergeTree\n tables sort data during insertion, and a large enough block size allows sorting more data in RAM.\n\n\n\n\nmax_replica_delay_for_distributed_queries\n\n\nDisables lagging replicas for distributed queries. See \"\nReplication\n\".\n\n\nSets the time in seconds. If a replica lags more than the set value, this replica is not used.\n\n\nDefault value: 0 (off).\n\n\nUsed when performing \nSELECT\n from a distributed table that points to replicated tables.\n\n\nmax_threads\n\n\nThe maximum number of query processing threads\n\n\n\n\nexcluding threads for retrieving data from remote servers (see the 'max_distributed_connections' parameter).\n\n\n\n\nThis parameter applies to threads that perform the same stages of the query processing pipeline in parallel.\nFor example, if reading from a table, evaluating expressions with functions, filtering with WHERE and pre-aggregating for GROUP BY can all be done in parallel using at least 'max_threads' number of threads, then 'max_threads' are used.\n\n\nBy default, 8.\n\n\nIf less than one SELECT query is normally run on a server at a time, set this parameter to a value slightly less than the actual number of processor cores.\n\n\nFor queries that are completed quickly because of a LIMIT, you can set a lower 'max_threads'. For example, if the necessary number of entries are located in every block and max_threads = 8, 8 blocks are retrieved, although it would have been enough to read just one.\n\n\nThe smaller the \nmax_threads\n value, the less memory is consumed.\n\n\nmax_compress_block_size\n\n\nThe maximum size of blocks of uncompressed data before compressing for writing to a table. By default, 1,048,576 (1 MiB). If the size is reduced, the compression rate is significantly reduced, the compression and decompression speed increases slightly due to cache locality, and memory consumption is reduced. There usually isn't any reason to change this setting.\n\n\nDon't confuse blocks for compression (a chunk of memory consisting of bytes) and blocks for query processing (a set of rows from a table).\n\n\nmin_compress_block_size\n\n\nFor \nMergeTree\n\" tables. In order to reduce latency when processing queries, a block is compressed when writing the next mark if its size is at least 'min_compress_block_size'. By default, 65,536.\n\n\nThe actual size of the block, if the uncompressed data is less than 'max_compress_block_size', is no less than this value and no less than the volume of data for one mark.\n\n\nLet's look at an example. Assume that 'index_granularity' was set to 8192 during table creation.\n\n\nWe are writing a UInt32-type column (4 bytes per value). When writing 8192 rows, the total will be 32 KB of data. Since min_compress_block_size = 65,536, a compressed block will be formed for every two marks.\n\n\nWe are writing a URL column with the String type (average size of 60 bytes per value). When writing 8192 rows, the average will be slightly less than 500 KB of data. Since this is more than 65,536, a compressed block will be formed for each mark. In this case, when reading data from the disk in the range of a single mark, extra data won't be decompressed.\n\n\nThere usually isn't any reason to change this setting.\n\n\nmax_query_size\n\n\nThe maximum part of a query that can be taken to RAM for parsing with the SQL parser.\nThe INSERT query also contains data for INSERT that is processed by a separate stream parser (that consumes O(1) RAM), which is not included in this restriction.\n\n\nThe default is 256 KiB.\n\n\ninteractive_delay\n\n\nThe interval in microseconds for checking whether request execution has been canceled and sending the progress.\n\n\nBy default, 100,000 (check for canceling and send progress ten times per second).\n\n\nconnect_timeout\n\n\nreceive_timeout\n\n\nsend_timeout\n\n\nTimeouts in seconds on the socket used for communicating with the client.\n\n\nBy default, 10, 300, 300.\n\n\npoll_interval\n\n\nLock in a wait loop for the specified number of seconds.\n\n\nBy default, 10.\n\n\nmax_distributed_connections\n\n\nThe maximum number of simultaneous connections with remote servers for distributed processing of a single query to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.\n\n\nBy default, 100.\n\n\nThe following parameters are only used when creating Distributed tables (and when launching a server), so there is no reason to change them at runtime.\n\n\ndistributed_connections_pool_size\n\n\nThe maximum number of simultaneous connections with remote servers for distributed processing of all queries to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster.\n\n\nBy default, 128.\n\n\nconnect_timeout_with_failover_ms\n\n\nThe timeout in milliseconds for connecting to a remote server for a Distributed table engine, if the 'shard' and 'replica' sections are used in the cluster definition.\nIf unsuccessful, several attempts are made to connect to various replicas.\n\n\nBy default, 50.\n\n\nconnections_with_failover_max_tries\n\n\nThe maximum number of connection attempts with each replica, for the Distributed table engine.\n\n\nBy default, 3.\n\n\nextremes\n\n\nWhether to count extreme values (the minimums and maximums in columns of a query result). Accepts 0 or 1. By default, 0 (disabled).\nFor more information, see the section \"Extreme values\".\n\n\n\n\nuse_uncompressed_cache\n\n\nWhether to use a cache of uncompressed blocks. Accepts 0 or 1. By default, 0 (disabled).\nThe uncompressed cache (only for tables in the MergeTree family) allows significantly reducing latency and increasing throughput when working with a large number of short queries. Enable this setting for users who send frequent short requests. Also pay attention to the 'uncompressed_cache_size' configuration parameter (only set in the config file) \u2013 the size of uncompressed cache blocks. By default, it is 8 GiB. The uncompressed cache is filled in as needed; the least-used data is automatically deleted.\n\n\nFor queries that read at least a somewhat large volume of data (one million rows or more), the uncompressed cache is disabled automatically in order to save space for truly small queries. So you can keep the 'use_uncompressed_cache' setting always set to 1.\n\n\nreplace_running_query\n\n\nWhen using the HTTP interface, the 'query_id' parameter can be passed. This is any string that serves as the query identifier.\nIf a query from the same user with the same 'query_id' already exists at this time, the behavior depends on the 'replace_running_query' parameter.\n\n\n0\n (default) \u2013 Throw an exception (don't allow the query to run if a query with the same 'query_id' is already running).\n\n\n1\n \u2013 Cancel the old query and start running the new one.\n\n\nYandex.Metrica uses this parameter set to 1 for implementing suggestions for segmentation conditions. After entering the next character, if the old query hasn't finished yet, it should be canceled.\n\n\nschema\n\n\nThis parameter is useful when you are using formats that require a schema definition, such as \nCap'n Proto\n. The value depends on the format.\n\n\n\n\nstream_flush_interval_ms\n\n\nWorks for tables with streaming in the case of a timeout, or when a thread generates\nmax_insert_block_size\n rows.\n\n\nThe default value is 7500.\n\n\nThe smaller the value, the more often data is flushed into the table. Setting the value too low leads to poor performance.\n\n\n\n\nload_balancing\n\n\nWhich replicas (among healthy replicas) to preferably send a query to (on the first attempt) for distributed processing.\n\n\nrandom (default)\n\n\nThe number of errors is counted for each replica. The query is sent to the replica with the fewest errors, and if there are several of these, to any one of them.\nDisadvantages: Server proximity is not accounted for; if the replicas have different data, you will also get different data.\n\n\nnearest_hostname\n\n\nThe number of errors is counted for each replica. Every 5 minutes, the number of errors is integrally divided by 2. Thus, the number of errors is calculated for a recent time with exponential smoothing. If there is one replica with a minimal number of errors (i.e. errors occurred recently on the other replicas), the query is sent to it. If there are multiple replicas with the same minimal number of errors, the query is sent to the replica with a host name that is most similar to the server's host name in the config file (for the number of different characters in identical positions, up to the minimum length of both host names).\n\n\nFor instance, example01-01-1 and example01-01-2.yandex.ru are different in one position, while example01-01-1 and example01-02-2 differ in two places.\nThis method might seem a little stupid, but it doesn't use external data about network topology, and it doesn't compare IP addresses, which would be complicated for our IPv6 addresses.\n\n\nThus, if there are equivalent replicas, the closest one by name is preferred.\nWe can also assume that when sending a query to the same server, in the absence of failures, a distributed query will also go to the same servers. So even if different data is placed on the replicas, the query will return mostly the same results.\n\n\nin_order\n\n\nReplicas are accessed in the same order as they are specified. The number of errors does not matter.\nThis method is appropriate when you know exactly which replica is preferable.\n\n\ntotals_mode\n\n\nHow to calculate TOTALS when HAVING is present, as well as when max_rows_to_group_by and group_by_overflow_mode = 'any' are present.\nSee the section \"WITH TOTALS modifier\".\n\n\ntotals_auto_threshold\n\n\nThe threshold for \ntotals_mode = 'auto'\n.\nSee the section \"WITH TOTALS modifier\".\n\n\ndefault_sample\n\n\nFloating-point number from 0 to 1. By default, 1.\nAllows you to set the default sampling ratio for all SELECT queries.\n(For tables that do not support sampling, it throws an exception.)\nIf set to 1, sampling is not performed by default.\n\n\nmax_parallel_replicas\n\n\nThe maximum number of replicas for each shard when executing a query.\nFor consistency (to get different parts of the same data split), this option only works when the sampling key is set.\nReplica lag is not controlled.\n\n\ncompile\n\n\nEnable compilation of queries. By default, 0 (disabled).\n\n\nCompilation is only used for part of the query-processing pipeline: for the first stage of aggregation (GROUP BY).\nIf this portion of the pipeline was compiled, the query may run faster due to deployment of short cycles and inlining aggregate function calls. The maximum performance improvement (up to four times faster in rare cases) is seen for queries with multiple simple aggregate functions. Typically, the performance gain is insignificant. In very rare cases, it may slow down query execution.\n\n\nmin_count_to_compile\n\n\nHow many times to potentially use a compiled chunk of code before running compilation. By default, 3.\nIf the value is zero, then compilation runs synchronously and the query waits for the end of the compilation process before continuing execution. This can be used for testing; otherwise, use values \u200b\u200bstarting with 1. Compilation normally takes about 5-10 seconds.\nIf the value is 1 or more, compilation occurs asynchronously in a separate thread. The result will be used as soon as it is ready, including by queries that are currently running.\n\n\nCompiled code is required for each different combination of aggregate functions used in the query and the type of keys in the GROUP BY clause.\nThe results of compilation are saved in the build directory in the form of .so files. There is no restriction on the number of compilation results, since they don't use very much space. Old results will be used after server restarts, except in the case of a server upgrade \u2013 in this case, the old results are deleted.\n\n\ninput_format_skip_unknown_fields\n\n\nIf the value is true, running INSERT skips input data from columns with unknown names. Otherwise, this situation will generate an exception.\nIt works for JSONEachRow and TSKV formats.\n\n\noutput_format_json_quote_64bit_integers\n\n\nIf the value is true, integers appear in quotes when using JSON* Int64 and UInt64 formats (for compatibility with most JavaScript implementations); otherwise, integers are output without the quotes.\n\n\n\n\nformat_csv_delimiter\n\n\nThe character to be considered as a delimiter in CSV data. By default, \n,\n.\n\n\nSettings profiles\n\n\nA settings profile is a collection of settings grouped under the same name. Each ClickHouse user has a profile.\nTo apply all the settings in a profile, set \nprofile\n.\n\n\nExample:\n\n\nSetting \nweb\n profile.\n\n\nSET\n \nprofile\n \n=\n \nweb\n\n\n\n\n\n\nSettings profiles are declared in the user config file. This is usually \nusers.xml\n.\n\n\nExample:\n\n\n!-- Settings profiles --\n\n\nprofiles\n\n \n!-- Default settings --\n\n \ndefault\n\n \n!-- The maximum number of threads when running a single query. --\n\n \nmax_threads\n8\n/max_threads\n\n \n/default\n\n\n \n!-- Settings for quries from the user interface --\n\n \nweb\n\n \nmax_rows_to_read\n1000000000\n/max_rows_to_read\n\n \nmax_bytes_to_read\n100000000000\n/max_bytes_to_read\n\n\n \nmax_rows_to_group_by\n1000000\n/max_rows_to_group_by\n\n \ngroup_by_overflow_mode\nany\n/group_by_overflow_mode\n\n\n \nmax_rows_to_sort\n1000000\n/max_rows_to_sort\n\n \nmax_bytes_to_sort\n1000000000\n/max_bytes_to_sort\n\n\n \nmax_result_rows\n100000\n/max_result_rows\n\n \nmax_result_bytes\n100000000\n/max_result_bytes\n\n \nresult_overflow_mode\nbreak\n/result_overflow_mode\n\n\n \nmax_execution_time\n600\n/max_execution_time\n\n \nmin_execution_speed\n1000000\n/min_execution_speed\n\n \ntimeout_before_checking_execution_speed\n15\n/timeout_before_checking_execution_speed\n\n\n \nmax_columns_to_read\n25\n/max_columns_to_read\n\n \nmax_temporary_columns\n100\n/max_temporary_columns\n\n \nmax_temporary_non_const_columns\n50\n/max_temporary_non_const_columns\n\n\n \nmax_subquery_depth\n2\n/max_subquery_depth\n\n \nmax_pipeline_depth\n25\n/max_pipeline_depth\n\n \nmax_ast_depth\n50\n/max_ast_depth\n\n \nmax_ast_elements\n100\n/max_ast_elements\n\n\n \nreadonly\n1\n/readonly\n\n \n/web\n\n\n/profiles\n\n\n\n\n\n\nThe example specifies two profiles: \ndefault\n and \nweb\n. The \ndefault\n profile has a special purpose: it must always be present and is applied when starting the server. In other words, the \ndefault\n profile contains default settings. The \nweb\n profile is a regular profile that can be set using the \nSET\n query or using a URL parameter in an HTTP query.\n\n\nSettings profiles can inherit from each other. To use inheritance, indicate the \nprofile\n setting before the other settings that are listed in the profile.\n\n\nClickHouse utility\n\n\n\n\nclickhouse-local\n \u2014 Allows running SQL queries on data without stopping the ClickHouse server, similar to how \nawk\n does this.\n\n\nclickhouse-copier\n \u2014 Copies (and reshards) data from one cluster to another cluster.\n\n\n\n\n\n\nclickhouse-copier\n\n\nCopies data from the tables in one cluster to tables in another (or the same) cluster.\n\n\nYou can run multiple \nclickhouse-copier\n instances on different servers to perform the same job. ZooKeeper is used for syncing the processes.\n\n\nAfter starting, \nclickhouse-copier\n:\n\n\n\n\nConnects to ZooKeeper and receives:\n\n\nCopying jobs.\n\n\n\n\nThe state of the copying jobs.\n\n\n\n\n\n\nIt performs the jobs.\n\n\n\n\n\n\nEach running process chooses the \"closest\" shard of the source cluster and copies the data into the destination cluster, resharding the data if necessary.\n\n\nclickhouse-copier\n tracks the changes in ZooKeeper and applies them on the fly.\n\n\nTo reduce network traffic, we recommend running \nclickhouse-copier\n on the same server where the source data is located.\n\n\nRunning clickhouse-copier\n\n\nThe utility should be run manually:\n\n\nclickhouse-copier copier --daemon --config zookeeper.xml --task-path /task/path --base-dir /path/to/dir\n\n\n\n\n\nParameters:\n\n\n\n\ndaemon\n \u2014 Starts \nclickhouse-copier\n in daemon mode.\n\n\nconfig\n \u2014 The path to the \nzookeeper.xml\n file with the parameters for the connection to ZooKeeper.\n\n\ntask-path\n \u2014 The path to the ZooKeeper node. This node is used for syncing \nclickhouse-copier\n processes and storing tasks. Tasks are stored in \n$task-path/description\n.\n\n\nbase-dir\n \u2014 The path to logs and auxiliary files. When it starts, \nclickhouse-copier\n creates \nclickhouse-copier_YYYYMMHHSS_\nPID\n subdirectories in \n$base-dir\n. If this parameter is omitted, the directories are created in the directory where \nclickhouse-copier\n was launched.\n\n\n\n\nFormat of zookeeper.xml\n\n\nyandex\n\n \nzookeeper\n\n \nnode\n \nindex=\n1\n\n \nhost\n127.0.0.1\n/host\n\n \nport\n2181\n/port\n\n \n/node\n\n \n/zookeeper\n\n\n/yandex\n\n\n\n\n\n\nConfiguration of copying tasks\n\n\nyandex\n\n \n!-- Configuration of clusters as in an ordinary server config --\n\n \nremote_servers\n\n \nsource_cluster\n\n \nshard\n\n \ninternal_replication\nfalse\n/internal_replication\n\n \nreplica\n\n \nhost\n127.0.0.1\n/host\n\n \nport\n9000\n/port\n\n \n/replica\n\n \n/shard\n\n ...\n \n/source_cluster\n\n\n \ndestination_cluster\n\n ...\n \n/destination_cluster\n\n \n/remote_servers\n\n\n \n!-- How many simultaneously active workers are possible. If you run more workers superfluous workers will sleep. --\n\n \nmax_workers\n2\n/max_workers\n\n\n \n!-- Setting used to fetch (pull) data from source cluster tables --\n\n \nsettings_pull\n\n \nreadonly\n1\n/readonly\n\n \n/settings_pull\n\n\n \n!-- Setting used to insert (push) data to destination cluster tables --\n\n \nsettings_push\n\n \nreadonly\n0\n/readonly\n\n \n/settings_push\n\n\n \n!-- Common setting for fetch (pull) and insert (push) operations. The copier process context also uses it.\n\n\n They are overlaid by \nsettings_pull/\n and \nsettings_push/\n respectively. --\n\n \nsettings\n\n \nconnect_timeout\n3\n/connect_timeout\n\n \n!-- Sync insert is set forcibly, leave it here just in case. --\n\n \ninsert_distributed_sync\n1\n/insert_distributed_sync\n\n \n/settings\n\n\n \n!-- Copying description of tasks.\n\n\n You can specify several table tasks in the same task description (in the same ZooKeeper node), and they will be performed sequentially.\n\n\n --\n\n \ntables\n\n \n!-- A table task that copies one table. --\n\n \ntable_hits\n\n \n!-- Source cluster name (from the \nremote_servers/\n section) and tables in it that should be copied --\n\n \ncluster_pull\nsource_cluster\n/cluster_pull\n\n \ndatabase_pull\ntest\n/database_pull\n\n \ntable_pull\nhits\n/table_pull\n\n\n \n!-- Destination cluster name and tables in which the data should be inserted --\n\n \ncluster_push\ndestination_cluster\n/cluster_push\n\n \ndatabase_push\ntest\n/database_push\n\n \ntable_push\nhits2\n/table_push\n\n\n \n!-- Engine of destination tables.\n\n\n If the destination tables have not been created yet, workers create them using column definitions from source tables and the engine definition from here.\n\n\n\n NOTE: If the first worker starts to insert data and detects that the destination partition is not empty, then the partition will\n\n\n be dropped and refilled. Take this into account if you already have some data in destination tables. You can directly \n\n\n specify partitions that should be copied in \nenabled_partitions/\n. They should be in quoted format like the partition column in the \n\n\n system.parts table.\n\n\n --\n\n \nengine\n\n ENGINE=ReplicatedMergeTree(\n/clickhouse/tables/{cluster}/{shard}/hits2\n, \n{replica}\n)\n PARTITION BY toMonday(date)\n ORDER BY (CounterID, EventDate)\n \n/engine\n\n\n \n!-- Sharding key used to insert data to destination cluster --\n\n \nsharding_key\njumpConsistentHash(intHash64(UserID), 2)\n/sharding_key\n\n\n \n!-- Optional expression that filter data while pull them from source servers --\n\n \nwhere_condition\nCounterID != 0\n/where_condition\n\n\n \n!-- This section specifies partitions that should be copied, other partition will be ignored.\n\n\n Partition names should have the same format as\n\n\n partition column of system.parts table (i.e. a quoted text).\n\n\n Since partition key of source and destination cluster could be different,\n\n\n these partition names specify destination partitions.\n\n\n\n Note: Although this section is optional (if it omitted, all partitions will be copied), \n\n\n it is strongly recommended to specify the partitions explicitly.\n\n\n If you already have some partitions ready on the destination cluster, they \n\n\n will be removed at the start of the copying, because they will be interpreted \n\n\n as unfinished data from the previous copying.\n\n\n --\n\n \nenabled_partitions\n\n \npartition\n2018-02-26\n/partition\n\n \npartition\n2018-03-05\n/partition\n\n ...\n \n/enabled_partitions\n\n \n/table_hits\n\n\n \n!-- Next table to copy. It is not copied until the previous table is copying. --\n\n \n/table_visits\n\n ...\n \n/table_visits\n\n ...\n \n/tables\n\n\n/yandex\n\n\n\n\n\n\nclickhouse-copier\n tracks the changes in \n/task/path/description\n and applies them on the fly. For instance, if you change the value of \nmax_workers\n, the number of processes running tasks will also change.\n\n\n\n\nclickhouse-local\n\n\nThe \nclickhouse-local\n program enables you to perform fast processing on local files that store tables, without having to deploy and configure the ClickHouse server.\n\n\nClickHouse Development\n\n\nOverview of ClickHouse architecture\n\n\nClickHouse is a true column-oriented DBMS. Data is stored by columns, and during the execution of arrays (vectors or chunks of columns). Whenever possible, operations are dispatched on arrays, rather than on individual values. This is called \"vectorized query execution,\" and it helps lower the cost of actual data processing.\n\n\n\n\nThis idea is nothing new. It dates back to the \nAPL\n programming language and its descendants: \nA +\n, \nJ\n, \nK\n, and \nQ\n. Array programming is used in scientific data processing. Neither is this idea something new in relational databases: for example, it is used in the \nVectorwise\n system.\n\n\n\n\nThere are two different approaches for speeding up the query processing: vectorized query execution and runtime code generation. In the latter, the code is generated for every kind of query on the fly, removing all indirection and dynamic dispatch. Neither of these approaches is strictly better than the other. Runtime code generation can be better when it's fuses many operations together, thus fully utilizing CPU execution units and the pipeline. Vectorized query execution can be less practical, because it involves the temporary vectors that must be written to the cache and read back. If the temporary data does not fit in the L2 cache, this becomes an issue. But vectorized query execution more easily utilizes the SIMD capabilities of the CPU. A \nresearch paper\n written by our friends shows that it is better to combine both approaches. ClickHouse uses vectorized query execution and has limited initial support for runtime code.\n\n\nColumns\n\n\nTo represent columns in memory (actually, chunks of columns), the \nIColumn\n interface is used. This interface provides helper methods for implementation of various relational operators. Almost all operations are immutable: they do not modify the original column, but create a new modified one. For example, the \nIColumn :: filter\n method accepts a filter byte mask. It is used for the \nWHERE\n and \nHAVING\n relational operators. Additional examples: the \nIColumn :: permute\n method to support \nORDER BY\n, the \nIColumn :: cut\n method to support \nLIMIT\n, and so on.\n\n\nVarious \nIColumn\n implementations (\nColumnUInt8\n, \nColumnString\n and so on) are responsible for the memory layout of columns. Memory layout is usually a contiguous array. For the integer type of columns it is just one contiguous array, like \nstd :: vector\n. For \nString\n and \nArray\n columns, it is two vectors: one for all array elements, placed contiguously, and a second one for offsets to the beginning of each array. There is also \nColumnConst\n that stores just one value in memory, but looks like a column.\n\n\nField\n\n\nNevertheless, it is possible to work with individual values as well. To represent an individual value, the \nField\n is used. \nField\n is just a discriminated union of \nUInt64\n, \nInt64\n, \nFloat64\n, \nString\n and \nArray\n. \nIColumn\n has the \noperator[]\n method to get the n-th value as a \nField\n, and the \ninsert\n method to append a \nField\n to the end of a column. These methods are not very efficient, because they require dealing with temporary \nField\n objects representing an individual value. There are more efficient methods, such as \ninsertFrom\n, \ninsertRangeFrom\n, and so on.\n\n\nField\n doesn't have enough information about a specific data type for a table. For example, \nUInt8\n, \nUInt16\n, \nUInt32\n, and \nUInt64\n are all represented as \nUInt64\n in a \nField\n.\n\n\nLeaky abstractions\n\n\nIColumn\n has methods for common relational transformations of data, but they don't meet all needs. For example, \nColumnUInt64\n doesn't have a method to calculate the sum of two columns, and \nColumnString\n doesn't have a method to run a substring search. These countless routines are implemented outside of \nIColumn\n.\n\n\nVarious functions on columns can be implemented in a generic, non-efficient way using \nIColumn\n methods to extract \nField\n values, or in a specialized way using knowledge of inner memory layout of data in a specific \nIColumn\n implementation. To do this, functions are cast to a specific \nIColumn\n type and deal with internal representation directly. For example, \nColumnUInt64\n has the \ngetData\n method that returns a reference to an internal array, then a separate routine reads or fills that array directly. In fact, we have \"leaky abstractions\" to allow efficient specializations of various routines.\n\n\nData types\n\n\nIDataType\n is responsible for serialization and deserialization: for reading and writing chunks of columns or individual values in binary or text form.\n\nIDataType\n directly corresponds to data types in tables. For example, there are \nDataTypeUInt32\n, \nDataTypeDateTime\n, \nDataTypeString\n and so on.\n\n\nIDataType\n and \nIColumn\n are only loosely related to each other. Different data types can be represented in memory by the same \nIColumn\n implementations. For example, \nDataTypeUInt32\n and \nDataTypeDateTime\n are both represented by \nColumnUInt32\n or \nColumnConstUInt32\n. In addition, the same data type can be represented by different \nIColumn\n implementations. For example, \nDataTypeUInt8\n can be represented by \nColumnUInt8\n or \nColumnConstUInt8\n.\n\n\nIDataType\n only stores metadata. For instance, \nDataTypeUInt8\n doesn't store anything at all (except vptr) and \nDataTypeFixedString\n stores just \nN\n (the size of fixed-size strings).\n\n\nIDataType\n has helper methods for various data formats. Examples are methods to serialize a value with possible quoting, to serialize a value for JSON, and to serialize a value as part of XML format. There is no direct correspondence to data formats. For example, the different data formats \nPretty\n and \nTabSeparated\n can use the same \nserializeTextEscaped\n helper method from the \nIDataType\n interface.\n\n\nBlock\n\n\nA \nBlock\n is a container that represents a subset (chunk) of a table in memory. It is just a set of triples: \n(IColumn, IDataType, column name)\n. During query execution, data is processed by \nBlock\ns. If we have a \nBlock\n, we have data (in the \nIColumn\n object), we have information about its type (in \nIDataType\n) that tells us how to deal with that column, and we have the column name (either the original column name from the table, or some artificial name assigned for getting temporary results of calculations).\n\n\nWhen we calculate some function over columns in a block, we add another column with its result to the block, and we don't touch columns for arguments of the function because operations are immutable. Later, unneeded columns can be removed from the block, but not modified. This is convenient for elimination of common subexpressions.\n\n\nBlocks are created for every processed chunk of data. Note that for the same type of calculation, the column names and types remain the same for different blocks, and only column data changes. It is better to split block data from the block header, because small block sizes will have a high overhead of temporary strings for copying shared_ptrs and column names.\n\n\nBlock Streams\n\n\nBlock streams are for processing data. We use streams of blocks to read data from somewhere, perform data transformations, or write data to somewhere. \nIBlockInputStream\n has the \nread\n method to fetch the next block while available. \nIBlockOutputStream\n has the \nwrite\n method to push the block somewhere.\n\n\nStreams are responsible for:\n\n\n\n\nReading or writing to a table. The table just returns a stream for reading or writing blocks.\n\n\nImplementing data formats. For example, if you want to output data to a terminal in \nPretty\n format, you create a block output stream where you push blocks, and it formats them.\n\n\nPerforming data transformations. Let's say you have \nIBlockInputStream\n and want to create a filtered stream. You create \nFilterBlockInputStream\n and initialize it with your stream. Then when you pull a block from \nFilterBlockInputStream\n, it pulls a block from your stream, filters it, and returns the filtered block to you. Query execution pipelines are represented this way.\n\n\n\n\nThere are more sophisticated transformations. For example, when you pull from \nAggregatingBlockInputStream\n, it reads all data from its source, aggregates it, and then returns a stream of aggregated data for you. Another example: \nUnionBlockInputStream\n accepts many input sources in the constructor and also a number of threads. It launches multiple threads and reads from multiple sources in parallel.\n\n\n\n\nBlock streams use the \"pull\" approach to control flow: when you pull a block from the first stream, it consequently pulls the required blocks from nested streams, and the entire execution pipeline will work. Neither \"pull\" nor \"push\" is the best solution, because control flow is implicit, and that limits implementation of various features like simultaneous execution of multiple queries (merging many pipelines together). This limitation could be overcome with coroutines or just running extra threads that wait for each other. We may have more possibilities if we make control flow explicit: if we locate the logic for passing data from one calculation unit to another outside of those calculation units. Read this \narticle\n for more thoughts.\n\n\n\n\nWe should note that the query execution pipeline creates temporary data at each step. We try to keep block size small enough so that temporary data fits in the CPU cache. With that assumption, writing and reading temporary data is almost free in comparison with other calculations. We could consider an alternative, which is to fuse many operations in the pipeline together, to make the pipeline as short as possible and remove much of the temporary data. This could be an advantage, but it also has drawbacks. For example, a split pipeline makes it easy to implement caching intermediate data, stealing intermediate data from similar queries running at the same time, and merging pipelines for similar queries.\n\n\nFormats\n\n\nData formats are implemented with block streams. There are \"presentational\" formats only suitable for output of data to the client, such as \nPretty\n format, which provides only \nIBlockOutputStream\n. And there are input/output formats, such as \nTabSeparated\n or \nJSONEachRow\n.\n\n\nThere are also row streams: \nIRowInputStream\n and \nIRowOutputStream\n. They allow you to pull/push data by individual rows, not by blocks. And they are only needed to simplify implementation of row-oriented formats. The wrappers \nBlockInputStreamFromRowInputStream\n and \nBlockOutputStreamFromRowOutputStream\n allow you to convert row-oriented streams to regular block-oriented streams.\n\n\nI/O\n\n\nFor byte-oriented input/output, there are \nReadBuffer\n and \nWriteBuffer\n abstract classes. They are used instead of C++ \niostream\n's. Don't worry: every mature C++ project is using something other than \niostream\n's for good reasons.\n\n\nReadBuffer\n and \nWriteBuffer\n are just a contiguous buffer and a cursor pointing to the position in that buffer. Implementations may own or not own the memory for the buffer. There is a virtual method to fill the buffer with the following data (for \nReadBuffer\n) or to flush the buffer somewhere (for \nWriteBuffer\n). The virtual methods are rarely called.\n\n\nImplementations of \nReadBuffer\n/\nWriteBuffer\n are used for working with files and file descriptors and network sockets, for implementing compression (\nCompressedWriteBuffer\n is initialized with another WriteBuffer and performs compression before writing data to it), and for other purposes \u2013 the names \nConcatReadBuffer\n, \nLimitReadBuffer\n, and \nHashingWriteBuffer\n speak for themselves.\n\n\nRead/WriteBuffers only deal with bytes. To help with formatted input/output (for instance, to write a number in decimal format), there are functions from \nReadHelpers\n and \nWriteHelpers\n header files.\n\n\nLet's look at what happens when you want to write a result set in \nJSON\n format to stdout. You have a result set ready to be fetched from \nIBlockInputStream\n. You create \nWriteBufferFromFileDescriptor(STDOUT_FILENO)\n to write bytes to stdout. You create \nJSONRowOutputStream\n, initialized with that \nWriteBuffer\n, to write rows in \nJSON\n to stdout. You create \nBlockOutputStreamFromRowOutputStream\n on top of it, to represent it as \nIBlockOutputStream\n. Then you call \ncopyData\n to transfer data from \nIBlockInputStream\n to \nIBlockOutputStream\n, and everything works. Internally, \nJSONRowOutputStream\n will write various JSON delimiters and call the \nIDataType::serializeTextJSON\n method with a reference to \nIColumn\n and the row number as arguments. Consequently, \nIDataType::serializeTextJSON\n will call a method from \nWriteHelpers.h\n: for example, \nwriteText\n for numeric types and \nwriteJSONString\n for \nDataTypeString\n.\n\n\nTables\n\n\nTables are represented by the \nIStorage\n interface. Different implementations of that interface are different table engines. Examples are \nStorageMergeTree\n, \nStorageMemory\n, and so on. Instances of these classes are just tables.\n\n\nThe most important \nIStorage\n methods are \nread\n and \nwrite\n. There are also \nalter\n, \nrename\n, \ndrop\n, and so on. The \nread\n method accepts the following arguments: the set of columns to read from a table, the \nAST\n query to consider, and the desired number of streams to return. It returns one or multiple \nIBlockInputStream\n objects and information about the stage of data processing that was completed inside a table engine during query execution.\n\n\nIn most cases, the read method is only responsible for reading the specified columns from a table, not for any further data processing. All further data processing is done by the query interpreter and is outside the responsibility of \nIStorage\n.\n\n\nBut there are notable exceptions:\n\n\n\n\nThe AST query is passed to the \nread\n method and the table engine can use it to derive index usage and to read less data from a table.\n\n\nSometimes the table engine can process data itself to a specific stage. For example, \nStorageDistributed\n can send a query to remote servers, ask them to process data to a stage where data from different remote servers can be merged, and return that preprocessed data.\nThe query interpreter then finishes processing the data.\n\n\n\n\nThe table's \nread\n method can return multiple \nIBlockInputStream\n objects to allow parallel data processing. These multiple block input streams can read from a table in parallel. Then you can wrap these streams with various transformations (such as expression evaluation or filtering) that can be calculated independently and create a \nUnionBlockInputStream\n on top of them, to read from multiple streams in parallel.\n\n\nThere are also \nTableFunction\ns. These are functions that return a temporary \nIStorage\n object to use in the \nFROM\n clause of a query.\n\n\nTo get a quick idea of how to implement your own table engine, look at something simple, like \nStorageMemory\n or \nStorageTinyLog\n.\n\n\n\n\nAs the result of the \nread\n method, \nIStorage\n returns \nQueryProcessingStage\n \u2013 information about what parts of the query were already calculated inside storage. Currently we have only very coarse granularity for that information. There is no way for the storage to say \"I have already processed this part of the expression in WHERE, for this range of data\". We need to work on that.\n\n\n\n\nParsers\n\n\nA query is parsed by a hand-written recursive descent parser. For example, \nParserSelectQuery\n just recursively calls the underlying parsers for various parts of the query. Parsers create an \nAST\n. The \nAST\n is represented by nodes, which are instances of \nIAST\n.\n\n\n\n\nParser generators are not used for historical reasons.\n\n\n\n\nInterpreters\n\n\nInterpreters are responsible for creating the query execution pipeline from an \nAST\n. There are simple interpreters, such as \nInterpreterExistsQuery\nand \nInterpreterDropQuery\n, or the more sophisticated \nInterpreterSelectQuery\n. The query execution pipeline is a combination of block input or output streams. For example, the result of interpreting the \nSELECT\n query is the \nIBlockInputStream\n to read the result set from; the result of the INSERT query is the \nIBlockOutputStream\n to write data for insertion to; and the result of interpreting the \nINSERT SELECT\n query is the \nIBlockInputStream\n that returns an empty result set on the first read, but that copies data from \nSELECT\n to \nINSERT\n at the same time.\n\n\nInterpreterSelectQuery\n uses \nExpressionAnalyzer\n and \nExpressionActions\n machinery for query analysis and transformations. This is where most rule-based query optimizations are done. \nExpressionAnalyzer\n is quite messy and should be rewritten: various query transformations and optimizations should be extracted to separate classes to allow modular transformations or query.\n\n\nFunctions\n\n\nThere are ordinary functions and aggregate functions. For aggregate functions, see the next section.\n\n\nOrdinary functions don't change the number of rows \u2013 they work as if they are processing each row independently. In fact, functions are not called for individual rows, but for \nBlock\n's of data to implement vectorized query execution.\n\n\nThere are some miscellaneous functions, like \nblockSize\n, \nrowNumberInBlock\n, and \nrunningAccumulate\n, that exploit block processing and violate the independence of rows.\n\n\nClickHouse has strong typing, so implicit type conversion doesn't occur. If a function doesn't support a specific combination of types, an exception will be thrown. But functions can work (be overloaded) for many different combinations of types. For example, the \nplus\n function (to implement the \n+\n operator) works for any combination of numeric types: \nUInt8\n + \nFloat32\n, \nUInt16\n + \nInt8\n, and so on. Also, some variadic functions can accept any number of arguments, such as the \nconcat\n function.\n\n\nImplementing a function may be slightly inconvenient because a function explicitly dispatches supported data types and supported \nIColumns\n. For example, the \nplus\n function has code generated by instantiation of a C++ template for each combination of numeric types, and for constant or non-constant left and right arguments.\n\n\n\n\nThis is a nice place to implement runtime code generation to avoid template code bloat. Also, it will make it possible to add fused functions like fused multiply-add, or to make multiple comparisons in one loop iteration.\n\n\n\n\nDue to vectorized query execution, functions are not short-circuit. For example, if you write \nWHERE f(x) AND g(y)\n, both sides will be calculated, even for rows, when \nf(x)\n is zero (except when \nf(x)\n is a zero constant expression). But if selectivity of the \nf(x)\n condition is high, and calculation of \nf(x)\n is much cheaper than \ng(y)\n, it's better to implement multi-pass calculation: first calculate \nf(x)\n, then filter columns by the result, and then calculate \ng(y)\n only for smaller, filtered chunks of data.\n\n\nAggregate Functions\n\n\nAggregate functions are stateful functions. They accumulate passed values into some state, and allow you to get results from that state. They are managed with the \nIAggregateFunction\n interface. States can be rather simple (the state for \nAggregateFunctionCount\n is just a single \nUInt64\n value) or quite complex (the state of \nAggregateFunctionUniqCombined\n is a combination of a linear array, a hash table and a \nHyperLogLog\n probabilistic data structure).\n\n\nTo deal with multiple states while executing a high-cardinality \nGROUP BY\n query, states are allocated in \nArena\n (a memory pool), or they could be allocated in any suitable piece of memory. States can have a non-trivial constructor and destructor: for example, complex aggregation states can allocate additional memory themselves. This requires some attention to creating and destroying states and properly passing their ownership, to keep track of who and when will destroy states.\n\n\nAggregation states can be serialized and deserialized to pass over the network during distributed query execution or to write them on disk where there is not enough RAM. They can even be stored in a table with the \nDataTypeAggregateFunction\n to allow incremental aggregation of data.\n\n\n\n\nThe serialized data format for aggregate function states is not versioned right now. This is ok if aggregate states are only stored temporarily. But we have the \nAggregatingMergeTree\n table engine for incremental aggregation, and people are already using it in production. This is why we should add support for backward compatibility when changing the serialized format for any aggregate function in the future.\n\n\n\n\nServer\n\n\nThe server implements several different interfaces:\n\n\n\n\nAn HTTP interface for any foreign clients.\n\n\nA TCP interface for the native ClickHouse client and for cross-server communication during distributed query execution.\n\n\nAn interface for transferring data for replication.\n\n\n\n\nInternally, it is just a basic multithreaded server without coroutines, fibers, etc. Since the server is not designed to process a high rate of simple queries but is intended to process a relatively low rate of complex queries, each of them can process a vast amount of data for analytics.\n\n\nThe server initializes the \nContext\n class with the necessary environment for query execution: the list of available databases, users and access rights, settings, clusters, the process list, the query log, and so on. This environment is used by interpreters.\n\n\nWe maintain full backward and forward compatibility for the server TCP protocol: old clients can talk to new servers and new clients can talk to old servers. But we don't want to maintain it eternally, and we are removing support for old versions after about one year.\n\n\n\n\nFor all external applications, we recommend using the HTTP interface because it is simple and easy to use. The TCP protocol is more tightly linked to internal data structures: it uses an internal format for passing blocks of data and it uses custom framing for compressed data. We haven't released a C library for that protocol because it requires linking most of the ClickHouse codebase, which is not practical.\n\n\n\n\nDistributed query execution\n\n\nServers in a cluster setup are mostly independent. You can create a \nDistributed\n table on one or all servers in a cluster. The \nDistributed\n table does not store data itself \u2013 it only provides a \"view\" to all local tables on multiple nodes of a cluster. When you SELECT from a \nDistributed\n table, it rewrites that query, chooses remote nodes according to load balancing settings, and sends the query to them. The \nDistributed\n table requests remote servers to process a query just up to a stage where intermediate results from different servers can be merged. Then it receives the intermediate results and merges them. The distributed table tries to distribute as much work as possible to remote servers, and does not send much intermediate data over the network.\n\n\n\n\nThings become more complicated when you have subqueries in IN or JOIN clauses and each of them uses a \nDistributed\n table. We have different strategies for execution of these queries.\n\n\n\n\nThere is no global query plan for distributed query execution. Each node has its own local query plan for its part of the job. We only have simple one-pass distributed query execution: we send queries for remote nodes and then merge the results. But this is not feasible for difficult queries with high cardinality GROUP BYs or with a large amount of temporary data for JOIN: in such cases, we need to \"reshuffle\" data between servers, which requires additional coordination. ClickHouse does not support that kind of query execution, and we need to work on it.\n\n\nMerge Tree\n\n\nMergeTree\n is a family of storage engines that supports indexing by primary key. The primary key can be an arbitary tuple of columns or expressions. Data in a \nMergeTree\n table is stored in \"parts\". Each part stores data in the primary key order (data is ordered lexicographically by the primary key tuple). All the table columns are stored in separate \ncolumn.bin\n files in these parts. The files consist of compressed blocks. Each block is usually from 64 KB to 1 MB of uncompressed data, depending on the average value size. The blocks consist of column values placed contiguously one after the other. Column values are in the same order for each column (the order is defined by the primary key), so when you iterate by many columns, you get values for the corresponding rows.\n\n\nThe primary key itself is \"sparse\". It doesn't address each single row, but only some ranges of data. A separate \nprimary.idx\n file has the value of the primary key for each N-th row, where N is called \nindex_granularity\n (usually, N = 8192). Also, for each column, we have \ncolumn.mrk\n files with \"marks,\" which are offsets to each N-th row in the data file. Each mark is a pair: the offset in the file to the beginning of the compressed block, and the offset in the decompressed block to the beginning of data. Usually compressed blocks are aligned by marks, and the offset in the decompressed block is zero. Data for \nprimary.idx\n always resides in memory and data for \ncolumn.mrk\n files is cached.\n\n\nWhen we are going to read something from a part in \nMergeTree\n, we look at \nprimary.idx\n data and locate ranges that could possibly contain requested data, then look at \ncolumn.mrk\n data and calculate offsets for where to start reading those ranges. Because of sparseness, excess data may be read. ClickHouse is not suitable for a high load of simple point queries, because the entire range with \nindex_granularity\n rows must be read for each key, and the entire compressed block must be decompressed for each column. We made the index sparse because we must be able to maintain trillions of rows per single server without noticeable memory consumption for the index. Also, because the primary key is sparse, it is not unique: it cannot check the existence of the key in the table at INSERT time. You could have many rows with the same key in a table.\n\n\nWhen you \nINSERT\n a bunch of data into \nMergeTree\n, that bunch is sorted by primary key order and forms a new part. To keep the number of parts relatively low, there are background threads that periodically select some parts and merge them to a single sorted part. That's why it is called \nMergeTree\n. Of course, merging leads to \"write amplification\". All parts are immutable: they are only created and deleted, but not modified. When SELECT is run, it holds a snapshot of the table (a set of parts). After merging, we also keep old parts for some time to make recovery after failure easier, so if we see that some merged part is probably broken, we can replace it with its source parts.\n\n\nMergeTree\n is not an LSM tree because it doesn't contain \"memtable\" and \"log\": inserted data is written directly to the filesystem. This makes it suitable only to INSERT data in batches, not by individual row and not very frequently \u2013 about once per second is ok, but a thousand times a second is not. We did it this way for simplicity's sake, and because we are already inserting data in batches in our applications.\n\n\n\n\nMergeTree tables can only have one (primary) index: there aren't any secondary indices. It would be nice to allow multiple physical representations under one logical table, for example, to store data in more than one physical order or even to allow representations with pre-aggregated data along with original data.\n\n\n\n\nThere are MergeTree engines that are doing additional work during background merges. Examples are \nCollapsingMergeTree\n and \nAggregatingMergeTree\n. This could be treated as special support for updates. Keep in mind that these are not real updates because users usually have no control over the time when background merges will be executed, and data in a \nMergeTree\n table is almost always stored in more than one part, not in completely merged form.\n\n\nReplication\n\n\nReplication in ClickHouse is implemented on a per-table basis. You could have some replicated and some non-replicated tables on the same server. You could also have tables replicated in different ways, such as one table with two-factor replication and another with three-factor.\n\n\nReplication is implemented in the \nReplicatedMergeTree\n storage engine. The path in \nZooKeeper\n is specified as a parameter for the storage engine. All tables with the same path in \nZooKeeper\n become replicas of each other: they synchronize their data and maintain consistency. Replicas can be added and removed dynamically simply by creating or dropping a table.\n\n\nReplication uses an asynchronous multi-master scheme. You can insert data into any replica that has a session with \nZooKeeper\n, and data is replicated to all other replicas asynchronously. Because ClickHouse doesn't support UPDATEs, replication is conflict-free. As there is no quorum acknowledgment of inserts, just-inserted data might be lost if one node fails.\n\n\nMetadata for replication is stored in ZooKeeper. There is a replication log that lists what actions to do. Actions are: get part; merge parts; drop partition, etc. Each replica copies the replication log to its queue and then executes the actions from the queue. For example, on insertion, the \"get part\" action is created in the log, and every replica downloads that part. Merges are coordinated between replicas to get byte-identical results. All parts are merged in the same way on all replicas. To achieve this, one replica is elected as the leader, and that replica initiates merges and writes \"merge parts\" actions to the log.\n\n\nReplication is physical: only compressed parts are transferred between nodes, not queries. To lower the network cost (to avoid network amplification), merges are processed on each replica independently in most cases. Large merged parts are sent over the network only in cases of significant replication lag.\n\n\nIn addition, each replica stores its state in ZooKeeper as the set of parts and its checksums. When the state on the local filesystem diverges from the reference state in ZooKeeper, the replica restores its consistency by downloading missing and broken parts from other replicas. When there is some unexpected or broken data in the local filesystem, ClickHouse does not remove it, but moves it to a separate directory and forgets it.\n\n\n\n\nThe ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is not elastic, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load will be uneven. This implementation gives you more control, and it is fine for relatively small clusters such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that will span its data across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically.\n\n\n\n\nHow to build ClickHouse on Linux\n\n\nBuild should work on Linux Ubuntu 12.04, 14.04 or newer.\nWith appropriate changes, it should also work on any other Linux distribution.\nThe build process is not intended to work on Mac OS X.\nOnly x86_64 with SSE 4.2 is supported. Support for AArch64 is experimental.\n\n\nTo test for SSE 4.2, do\n\n\ngrep -q sse4_2 /proc/cpuinfo \n \necho\n \nSSE 4.2 supported\n \n||\n \necho\n \nSSE 4.2 not supported\n\n\n\n\n\n\nInstall Git and CMake\n\n\nsudo apt-get install git cmake\n\n\n\n\n\nOr cmake3 instead of cmake on older systems.\n\n\nDetect the number of threads\n\n\nexport\n \nTHREADS\n=\n$(\ngrep -c ^processor /proc/cpuinfo\n)\n\n\n\n\n\n\nInstall GCC 7\n\n\nThere are several ways to do this.\n\n\nInstall from a PPA package\n\n\nsudo apt-get install software-properties-common\nsudo apt-add-repository ppa:ubuntu-toolchain-r/test\nsudo apt-get update\nsudo apt-get install gcc-7 g++-7\n\n\n\n\n\nInstall from sources\n\n\nLook at [https://github.com/yandex/ClickHouse/blob/master/utils/prepare-environment/install-gcc.sh]\n\n\nUse GCC 7 for builds\n\n\nexport\n \nCC\n=\ngcc-7\n\nexport\n \nCXX\n=\ng++-7\n\n\n\n\n\nInstall required libraries from packages\n\n\nsudo apt-get install libicu-dev libreadline-dev libmysqlclient-dev libssl-dev unixodbc-dev ninja-build\n\n\n\n\n\nCheckout ClickHouse sources\n\n\nTo get the latest stable version:\n\n\ngit clone -b stable --recursive git@github.com:yandex/ClickHouse.git\n\n## or: git clone -b stable --recursive https://github.com/yandex/ClickHouse.git\n\n\n\ncd\n ClickHouse\n\n\n\n\n\nFor development, switch to the \nmaster\n branch.\nFor the latest release candidate, switch to the \ntesting\n branch.\n\n\nBuild ClickHouse\n\n\nThere are two build variants.\n\n\nBuild release package\n\n\nInstall prerequisites to build Debian packages.\n\n\nsudo apt-get install devscripts dupload fakeroot debhelper\n\n\n\n\n\nInstall the most recent version of Clang.\n\n\nClang is embedded into the ClickHouse package and used at runtime. The minimum version is 5.0. It is optional.\n\n\nTo install clang, see \nutils/prepare-environment/install-clang.sh\n\n\nYou may also build ClickHouse with Clang for development purposes.\nFor production releases, GCC is used.\n\n\nRun the release script:\n\n\nrm -f ../clickhouse*.deb\n./release\n\n\n\n\n\nYou will find built packages in the parent directory:\n\n\nls -l ../clickhouse*.deb\n\n\n\n\n\nNote that usage of debian packages is not required.\nClickHouse has no runtime dependencies except libc, so it could work on almost any Linux.\n\n\nInstalling freshly built packages on a development server:\n\n\nsudo dpkg -i ../clickhouse*.deb\nsudo service clickhouse-server start\n\n\n\n\n\nBuild to work with code\n\n\nmkdir build\n\ncd\n build\ncmake ..\nmake -j \n$THREADS\n\n\ncd\n ..\n\n\n\n\n\nTo create an executable, run \nmake clickhouse\n.\nThis will create the \ndbms/src/Server/clickhouse\n executable, which can be used with \nclient\n or \nserver\n arguments.\n\n\nHow to build ClickHouse on Mac OS X\n\n\nBuild should work on Mac OS X 10.12. If you're using earlier version, you can try to build ClickHouse using Gentoo Prefix and clang sl in this instruction.\nWith appropriate changes, it should also work on any other Linux distribution.\n\n\nInstall Homebrew\n\n\n/usr/bin/ruby -e \n$(\ncurl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install\n)\n\n\n\n\n\n\nInstall required compilers, tools, and libraries\n\n\nbrew install cmake gcc icu4c mysql openssl unixodbc libtool gettext zlib readline boost --cc\n=\ngcc-7\n\n\n\n\n\nCheckout ClickHouse sources\n\n\nTo get the latest stable version:\n\n\ngit clone -b stable --recursive --depth\n=\n10\n git@github.com:yandex/ClickHouse.git\n\n## or: git clone -b stable --recursive --depth=10 https://github.com/yandex/ClickHouse.git\n\n\n\ncd\n ClickHouse\n\n\n\n\n\nFor development, switch to the \nmaster\n branch.\nFor the latest release candidate, switch to the \ntesting\n branch.\n\n\nBuild ClickHouse\n\n\nmkdir build\n\ncd\n build\ncmake .. -DCMAKE_CXX_COMPILER\n=\n`\nwhich g++-7\n`\n -DCMAKE_C_COMPILER\n=\n`\nwhich gcc-7\n`\n\nmake -j \n`\nsysctl -n hw.ncpu\n`\n\n\ncd\n ..\n\n\n\n\n\nCaveats\n\n\nIf you intend to run clickhouse-server, make sure to increase the system's maxfiles variable. See \nMacOS.md\n for more details.\n\n\nHow to write C++ code\n\n\nGeneral recommendations\n\n\n1.\n The following are recommendations, not requirements.\n\n\n2.\n If you are editing code, it makes sense to follow the formatting of the existing code.\n\n\n3.\n Code style is needed for consistency. Consistency makes it easier to read the code, and it also makes it easier to search the code.\n\n\n4.\n Many of the rules do not have logical reasons; they are dictated by established practices.\n\n\nFormatting\n\n\n1.\n Most of the formatting will be done automatically by \nclang-format\n.\n\n\n2.\n Indents are 4 spaces. Configure your development environment so that a tab adds four spaces.\n\n\n3.\n A left curly bracket must be separated on a new line. (And the right one, as well.)\n\n\ninline\n \nvoid\n \nreadBoolText\n(\nbool\n \n \nx\n,\n \nReadBuffer\n \n \nbuf\n)\n\n\n{\n\n \nchar\n \ntmp\n \n=\n \n0\n;\n\n \nreadChar\n(\ntmp\n,\n \nbuf\n);\n\n \nx\n \n=\n \ntmp\n \n!=\n \n0\n;\n\n\n}\n\n\n\n\n\n\n4.\n\nBut if the entire function body is quite short (a single statement), you can place it entirely on one line if you wish. Place spaces around curly braces (besides the space at the end of the line).\n\n\ninline\n \nsize_t\n \nmask\n()\n \nconst\n \n{\n \nreturn\n \nbuf_size\n()\n \n-\n \n1\n;\n \n}\n\n\ninline\n \nsize_t\n \nplace\n(\nHashValue\n \nx\n)\n \nconst\n \n{\n \nreturn\n \nx\n \n \nmask\n();\n \n}\n\n\n\n\n\n\n5.\n For functions, don't put spaces around brackets.\n\n\nvoid\n \nreinsert\n(\nconst\n \nValue\n \n \nx\n)\n\n\nmemcpy\n(\nbuf\n[\nplace_value\n],\n \nx\n,\n \nsizeof\n(\nx\n));\n\n\n\n\n\n\n6.\n When using statements such as \nif\n, \nfor\n, and \nwhile\n (unlike function calls), put a space before the opening bracket.\n\n\ncpp\n for (size_t i = 0; i \n rows; i += storage.index_granularity)\n\n\n7.\n Put spaces around binary operators (\n+\n, \n-\n, \n*\n, \n/\n, \n%\n, ...), as well as the ternary operator \n?:\n.\n\n\nUInt16\n \nyear\n \n=\n \n(\ns\n[\n0\n]\n \n-\n \n0\n)\n \n*\n \n1000\n \n+\n \n(\ns\n[\n1\n]\n \n-\n \n0\n)\n \n*\n \n100\n \n+\n \n(\ns\n[\n2\n]\n \n-\n \n0\n)\n \n*\n \n10\n \n+\n \n(\ns\n[\n3\n]\n \n-\n \n0\n);\n\n\nUInt8\n \nmonth\n \n=\n \n(\ns\n[\n5\n]\n \n-\n \n0\n)\n \n*\n \n10\n \n+\n \n(\ns\n[\n6\n]\n \n-\n \n0\n);\n\n\nUInt8\n \nday\n \n=\n \n(\ns\n[\n8\n]\n \n-\n \n0\n)\n \n*\n \n10\n \n+\n \n(\ns\n[\n9\n]\n \n-\n \n0\n);\n\n\n\n\n\n\n8.\n If a line feed is entered, put the operator on a new line and increase the indent before it.\n\n\nif\n \n(\nelapsed_ns\n)\n\n \nmessage\n \n \n (\n\n \n \nrows_read_on_server\n \n*\n \n1000000000\n \n/\n \nelapsed_ns\n \n \n rows/s., \n\n \n \nbytes_read_on_server\n \n*\n \n1000.0\n \n/\n \nelapsed_ns\n \n \n MB/s.) \n;\n\n\n\n\n\n\n9.\n You can use spaces for alignment within a line, if desired.\n\n\ndst\n.\nClickLogID\n \n=\n \nclick\n.\nLogID\n;\n\n\ndst\n.\nClickEventID\n \n=\n \nclick\n.\nEventID\n;\n\n\ndst\n.\nClickGoodEvent\n \n=\n \nclick\n.\nGoodEvent\n;\n\n\n\n\n\n\n10.\n Don't use spaces around the operators \n.\n, \n-\n .\n\n\nIf necessary, the operator can be wrapped to the next line. In this case, the offset in front of it is increased.\n\n\n11.\n Do not use a space to separate unary operators (\n-\n, \n+\n, \n*\n, \n, ...) from the argument.\n\n\n12.\n Put a space after a comma, but not before it. The same rule goes for a semicolon inside a for expression.\n\n\n13.\n Do not use spaces to separate the \n[]\n operator.\n\n\n14.\n In a \ntemplate \n...\n expression, use a space between \ntemplate\n and \n. No spaces after \n or before \n.\n\n\ntemplate\n \ntypename\n \nTKey\n,\n \ntypename\n \nTValue\n\n\nstruct\n \nAggregatedStatElement\n\n\n{}\n\n\n\n\n\n\n15.\n In classes and structures, public, private, and protected are written on the same level as the \nclass/struct\n, but all other internal elements should be deeper.\n\n\ntemplate\n \ntypename\n \nT\n\n\nclass\n \nMultiVersion\n\n\n{\n\n\npublic\n:\n\n \n/// Version of object for usage. shared_ptr manage lifetime of version.\n\n \nusing\n \nVersion\n \n=\n \nstd\n::\nshared_ptr\nconst\n \nT\n;\n\n \n...\n\n\n}\n\n\n\n\n\n\n16.\n If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace.\n\n\n17.\n If the block for \nif\n, \nfor\n, \nwhile\n... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets.\n\n\n/// Finish write.\n\n\nfor\n \n(\nauto\n \n \nstream\n \n:\n \nstreams\n)\n\n \nstream\n.\nsecond\n-\nfinalize\n();\n\n\n\n\n\n\n18.\n There should be any spaces at the ends of lines.\n\n\n19.\n Sources are UTF-8 encoded.\n\n\n20.\n Non-ASCII characters can be used in string literals.\n\n\n \n, \n \n \n(\ntimer\n.\nelapsed\n()\n \n/\n \nchunks_stats\n.\nhits\n)\n \n \n \u03bcsec/hit.\n;\n\n\n\n\n\n\n21.\n Do not write multiple expressions in a single line.\n\n\n22.\n Group sections of code inside functions and separate them with no more than one empty line.\n\n\n23.\n Separate functions, classes, and so on with one or two empty lines.\n\n\n24.\n A \nconst\n (related to a value) must be written before the type name.\n\n\n//correct\n\n\nconst\n \nchar\n \n*\n \npos\n\n\nconst\n \nstd\n::\nstring\n \n \ns\n\n\n//incorrect\n\n\nchar\n \nconst\n \n*\n \npos\n\n\n\n\n\n\n25.\n When declaring a pointer or reference, the \n*\n and \n symbols should be separated by spaces on both sides.\n\n\n//correct\n\n\nconst\n \nchar\n \n*\n \npos\n\n\n//incorrect\n\n\nconst\n \nchar\n*\n \npos\n\n\nconst\n \nchar\n \n*\npos\n\n\n\n\n\n\n26.\n When using template types, alias them with the \nusing\n keyword (except in the simplest cases).\n\n\nIn other words, the template parameters are specified only in \nusing\n and aren't repeated in the code.\n\n\nusing\n can be declared locally, such as inside a function.\n\n\n//correct\n\n\nusing\n \nFileStreams\n \n=\n \nstd\n::\nmap\nstd\n::\nstring\n,\n \nstd\n::\nshared_ptr\nStream\n;\n\n\nFileStreams\n \nstreams\n;\n\n\n//incorrect\n\n\nstd\n::\nmap\nstd\n::\nstring\n,\n \nstd\n::\nshared_ptr\nStream\n \nstreams\n;\n\n\n\n\n\n\n27.\n Do not declare several variables of different types in one statement.\n\n\n//incorrect\n\n\nint\n \nx\n,\n \n*\ny\n;\n\n\n\n\n\n\n28.\n Do not use C-style casts.\n\n\n//incorrect\n\n\nstd\n::\ncerr\n \n \n(\nint\n)\nc\n \n;\n \nstd\n::\nendl\n;\n\n\n//correct\n\n\nstd\n::\ncerr\n \n \nstatic_cast\nint\n(\nc\n)\n \n \nstd\n::\nendl\n;\n\n\n\n\n\n\n29.\n In classes and structs, group members and functions separately inside each visibility scope.\n\n\n30.\n For small classes and structs, it is not necessary to separate the method declaration from the implementation.\n\n\nThe same is true for small methods in any classes or structs.\n\n\nFor templated classes and structs, don't separate the method declarations from the implementation (because otherwise they must be defined in the same translation unit).\n\n\n31.\n You can wrap lines at 140 characters, instead of 80.\n\n\n32.\n Always use the prefix increment/decrement operators if postfix is not required.\n\n\nfor\n \n(\nNames\n::\nconst_iterator\n \nit\n \n=\n \ncolumn_names\n.\nbegin\n();\n \nit\n \n!=\n \ncolumn_names\n.\nend\n();\n \n++\nit\n)\n\n\n\n\n\n\nComments\n\n\n1.\n Be sure to add comments for all non-trivial parts of code.\n\n\nThis is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong.\n\n\n/** Part of piece of memory, that can be used.\n\n\n * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading,\n\n\n * then working_buffer will have size of only 10 bytes\n\n\n * (working_buffer.end() will point to the position right after those 10 bytes available for read).\n\n\n*/\n\n\n\n\n\n\n2.\n Comments can be as detailed as necessary.\n\n\n3.\n Place comments before the code they describe. In rare cases, comments can come after the code, on the same line.\n\n\n/** Parses and executes the query.\n\n\n*/\n\n\nvoid\n \nexecuteQuery\n(\n\n \nReadBuffer\n \n \nistr\n,\n \n/// Where to read the query from (and data for INSERT, if applicable)\n\n \nWriteBuffer\n \n \nostr\n,\n \n/// Where to write the result\n\n \nContext\n \n \ncontext\n,\n \n/// DB, tables, data types, engines, functions, aggregate functions...\n\n \nBlockInputStreamPtr\n \n \nquery_plan\n,\n \n/// A description of query processing can be included here\n\n \nQueryProcessingStage\n::\nEnum\n \nstage\n \n=\n \nQueryProcessingStage\n::\nComplete\n \n/// The last stage to process the SELECT query to\n\n \n)\n\n\n\n\n\n\n4.\n Comments should be written in English only.\n\n\n5.\n If you are writing a library, include detailed comments explaining it in the main header file.\n\n\n6.\n Do not add comments that do not provide additional information. In particular, do not leave empty comments like this:\n\n\n/*\n\n\n* Procedure Name:\n\n\n* Original procedure name:\n\n\n* Author:\n\n\n* Date of creation:\n\n\n* Dates of modification:\n\n\n* Modification authors:\n\n\n* Original file name:\n\n\n* Purpose:\n\n\n* Intent:\n\n\n* Designation:\n\n\n* Classes used:\n\n\n* Constants:\n\n\n* Local variables:\n\n\n* Parameters:\n\n\n* Date of creation:\n\n\n* Purpose:\n\n\n*/\n\n\n\n\n\n\nThe example is borrowed from \nhttp://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/\n.\n\n\n7.\n Do not write garbage comments (author, creation date ..) at the beginning of each file.\n\n\n8.\n Single-line comments begin with three slashes: \n///\n and multi-line comments begin with \n/**\n. These comments are considered \"documentation\".\n\n\nNote: You can use Doxygen to generate documentation from these comments. But Doxygen is not generally used because it is more convenient to navigate the code in the IDE.\n\n\n9.\n Multi-line comments must not have empty lines at the beginning and end (except the line that closes a multi-line comment).\n\n\n10.\n For commenting out code, use basic comments, not \"documenting\" comments.\n\n\n11.\n Delete the commented out parts of the code before commiting.\n\n\n12.\n Do not use profanity in comments or code.\n\n\n13.\n Do not use uppercase letters. Do not use excessive punctuation.\n\n\n/// WHAT THE FAIL???\n\n\n\n\n\n\n14.\n Do not make delimeters from comments.\n\n\n///******************************************************\n\n\n\n\n\n15.\n Do not start discussions in comments.\n\n\n/// Why did you do this stuff?\n\n\n\n\n\n16.\n There's no need to write a comment at the end of a block describing what it was about.\n\n\n/// for\n\n\n\n\n\nNames\n\n\n1.\n The names of variables and class members use lowercase letters with underscores.\n\n\nsize_t\n \nmax_block_size\n;\n\n\n\n\n\n\n2.\n The names of functions (methods) use camelCase beginning with a lowercase letter.\n\n\nstd\n::\nstring\n \ngetName\n()\n \nconst\n \noverride\n \n{\n \nreturn\n \nMemory\n;\n \n}\n\n\n\n\n\n\n3.\n The names of classes (structures) use CamelCase beginning with an uppercase letter. Prefixes other than I are not used for interfaces.\n\n\nclass\n \nStorageMemory\n \n:\n \npublic\n \nIStorage\n\n\n\n\n\n\n4.\n The names of usings follow the same rules as classes, or you can add _t at the end.\n\n\n5.\n Names of template type arguments for simple cases: T; T, U; T1, T2.\n\n\nFor more complex cases, either follow the rules for class names, or add the prefix T.\n\n\ntemplate\n \ntypename\n \nTKey\n,\n \ntypename\n \nTValue\n\n\nstruct\n \nAggregatedStatElement\n\n\n\n\n\n\n6.\n Names of template constant arguments: either follow the rules for variable names, or use N in simple cases.\n\n\ntemplate\n \nbool\n \nwithout_www\n\n\nstruct\n \nExtractDomain\n\n\n\n\n\n\n7.\n For abstract classes (interfaces) you can add the I prefix.\n\n\nclass\n \nIBlockInputStream\n\n\n\n\n\n\n8.\n If you use a variable locally, you can use the short name.\n\n\nIn other cases, use a descriptive name that conveys the meaning.\n\n\nbool\n \ninfo_successfully_loaded\n \n=\n \nfalse\n;\n\n\n\n\n\n\n9.\n \ndefine\n\u2018s should be in ALL_CAPS with underscores. The same is true for global constants.\n\n\n##define MAX_SRC_TABLE_NAMES_TO_STORE 1000\n\n\n\n\n\n\n10.\n File names should use the same style as their contents.\n\n\nIf a file contains a single class, name the file the same way as the class, in CamelCase.\n\n\nIf the file contains a single function, name the file the same way as the function, in camelCase.\n\n\n11.\n If the name contains an abbreviation, then:\n\n\n\n\nFor variable names, the abbreviation should use lowercase letters \nmysql_connection\n (not \nmySQL_connection\n).\n\n\nFor names of classes and functions, keep the uppercase letters in the abbreviation \nMySQLConnection\n (not \nMySqlConnection\n).\n\n\n\n\n12.\n Constructor arguments that are used just to initialize the class members should be named the same way as the class members, but with an underscore at the end.\n\n\nFileQueueProcessor\n(\n\n \nconst\n \nstd\n::\nstring\n \n \npath_\n,\n\n \nconst\n \nstd\n::\nstring\n \n \nprefix_\n,\n\n \nstd\n::\nshared_ptr\nFileHandler\n \nhandler_\n)\n\n \n:\n \npath\n(\npath_\n),\n\n \nprefix\n(\nprefix_\n),\n\n \nhandler\n(\nhandler_\n),\n\n \nlog\n(\nLogger\n::\nget\n(\nFileQueueProcessor\n))\n\n\n{\n\n\n}\n\n\n\n\n\n\nThe underscore suffix can be omitted if the argument is not used in the constructor body.\n\n\n13.\n There is no difference in the names of local variables and class members (no prefixes required).\n\n\ntimer\n \n(\nnot\n \nm_timer\n)\n\n\n\n\n\n\n14.\n Constants in enums use CamelCase beginning with an uppercase letter. ALL_CAPS is also allowed. If the enum is not local, use enum class.\n\n\nenum\n \nclass\n \nCompressionMethod\n\n\n{\n\n \nQuickLZ\n \n=\n \n0\n,\n\n \nLZ4\n \n=\n \n1\n,\n\n\n};\n\n\n\n\n\n\n15.\n All names must be in English. Transliteration of Russian words is not allowed.\n\n\nnot\n \nStroka\n\n\n\n\n\n\n16.\n Abbreviations are acceptable if they are well known (when you can easily find the meaning of the abbreviation in Wikipedia or in a search engine).\n\n\n`AST`, `SQL`.\n\nNot `NVDH` (some random letters)\n\n\n\n\n\nIncomplete words are acceptable if the shortened version is common use.\n\n\nYou can also use an abbreviation if the full name is included next to it in the comments.\n\n\n17.\n File names with C++ source code must have the \n.cpp\n extension. Header files must have the \n.h\n extension.\n\n\nHow to write code\n\n\n1.\n Memory management.\n\n\nManual memory deallocation (delete) can only be used in library code.\n\n\nIn library code, the delete operator can only be used in destructors.\n\n\nIn application code, memory must be freed by the object that owns it.\n\n\nExamples:\n\n\n\n\nThe easiest way is to place an object on the stack, or make it a member of another class.\n\n\nFor a large number of small objects, use containers.\n\n\nFor automatic deallocation of a small number of objects that reside in the heap, use shared_ptr/unique_ptr.\n\n\n\n\n2.\n Resource management.\n\n\nUse RAII and see the previous point.\n\n\n3.\n Error handling.\n\n\nUse exceptions. In most cases, you only need to throw an exception, and don't need to catch it (because of RAII).\n\n\nIn offline data processing applications, it's often acceptable to not catch exceptions.\n\n\nIn servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler.\n\n\n/// If there were no other calculations yet, do it synchronously\n\n\nif\n \n(\n!\nstarted\n)\n\n\n{\n\n \ncalculate\n();\n\n \nstarted\n \n=\n \ntrue\n;\n\n\n}\n\n\nelse\n \n/// If the calculations are already in progress, wait for results\n\n \npool\n.\nwait\n();\n\n\n\nif\n \n(\nexception\n)\n\n \nexception\n-\nrethrow\n();\n\n\n\n\n\n\nNever hide exceptions without handling. Never just blindly put all exceptions to log.\n\n\nNot \ncatch (...) {}\n.\n\n\nIf you need to ignore some exceptions, do so only for specific ones and rethrow the rest.\n\n\ncatch\n \n(\nconst\n \nDB\n::\nException\n \n \ne\n)\n\n\n{\n\n \nif\n \n(\ne\n.\ncode\n()\n \n==\n \nErrorCodes\n::\nUNKNOWN_AGGREGATE_FUNCTION\n)\n\n \nreturn\n \nnullptr\n;\n\n \nelse\n\n \nthrow\n;\n\n\n}\n\n\n\n\n\n\nWhen using functions with response codes or errno, always check the result and throw an exception in case of error.\n\n\nif\n \n(\n0\n \n!=\n \nclose\n(\nfd\n))\n\n \nthrowFromErrno\n(\nCannot close file \n \n+\n \nfile_name\n,\n \nErrorCodes\n::\nCANNOT_CLOSE_FILE\n);\n\n\n\n\n\n\nAsserts are not used.\n\n\n4.\n Exception types.\n\n\nThere is no need to use complex exception hierarchy in application code. The exception text should be understandable to a system administrator.\n\n\n5.\n Throwing exceptions from destructors.\n\n\nThis is not recommended, but it is allowed.\n\n\nUse the following options:\n\n\n\n\nCreate a (done() or finalize()) function that will do all the work in advance that might lead to an exception. If that function was called, there should be no exceptions in the destructor later.\n\n\nTasks that are too complex (such as sending messages over the network) can be put in separate method that the class user will have to call before destruction.\n\n\nIf there is an exception in the destructor, it\u2019s better to log it than to hide it (if the logger is available).\n\n\nIn simple applications, it is acceptable to rely on std::terminate (for cases of noexcept by default in C++11) to handle exceptions.\n\n\n\n\n6.\n Anonymous code blocks.\n\n\nYou can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block.\n\n\nBlock\n \nblock\n \n=\n \ndata\n.\nin\n-\nread\n();\n\n\n\n{\n\n \nstd\n::\nlock_guard\nstd\n::\nmutex\n \nlock\n(\nmutex\n);\n\n \ndata\n.\nready\n \n=\n \ntrue\n;\n\n \ndata\n.\nblock\n \n=\n \nblock\n;\n\n\n}\n\n\n\nready_any\n.\nset\n();\n\n\n\n\n\n\n7.\n Multithreading.\n\n\nFor offline data processing applications:\n\n\n\n\nTry to get the best possible performance on a single CPU core. You can then parallelize your code if necessary.\n\n\n\n\nIn server applications:\n\n\n\n\nUse the thread pool to process requests. At this point, we haven't had any tasks that required userspace context switching.\n\n\n\n\nFork is not used for parallelization.\n\n\n8.\n Synchronizing threads.\n\n\nOften it is possible to make different threads use different memory cells (even better: different cache lines,) and to not use any thread synchronization (except joinAll).\n\n\nIf synchronization is required, in most cases, it is sufficient to use mutex under lock_guard.\n\n\nIn other cases use system synchronization primitives. Do not use busy wait.\n\n\nAtomic operations should be used only in the simplest cases.\n\n\nDo not try to implement lock-free data structures unless it is your primary area of expertise.\n\n\n9.\n Pointers vs references.\n\n\nIn most cases, prefer references.\n\n\n10.\n const.\n\n\nUse constant references, pointers to constants, \nconst_iterator\n, \nconst\n methods.\n\n\nConsider \nconst\n to be default and use non-const only when necessary.\n\n\nWhen passing variable by value, using \nconst\n usually does not make sense.\n\n\n11.\n unsigned.\n\n\nUse \nunsigned\n, if needed.\n\n\n12.\n Numeric types\n\n\nUse \nUInt8\n, \nUInt16\n, \nUInt32\n, \nUInt64\n, \nInt8\n, \nInt16\n, \nInt32\n, \nInt64\n, and \nsize_t\n, \nssize_t\n, \nptrdiff_t\n.\n\n\nDon't use \nsigned/unsigned long\n, \nlong long\n, \nshort\n, \nsigned char\n, \nunsigned char\n, or \nchar\n types for numbers.\n\n\n13.\n Passing arguments.\n\n\nPass complex values by reference (including \nstd::string\n).\n\n\nIf a function captures ownership of an objected created in the heap, make the argument type \nshared_ptr\n or \nunique_ptr\n.\n\n\n14.\n Returning values.\n\n\nIn most cases, just use return. Do not write \n[return std::move(res)]{.strike}\n.\n\n\nIf the function allocates an object on heap and returns it, use \nshared_ptr\n or \nunique_ptr\n.\n\n\nIn rare cases you might need to return the value via an argument. In this case, the argument should be a reference.\n\n\nusing\n \nAggregateFunctionPtr\n \n=\n \nstd\n::\nshared_ptr\nIAggregateFunction\n;\n\n\n\n/** Creates an aggregate function by name.\n\n\n */\n\n\nclass\n \nAggregateFunctionFactory\n\n\n{\n\n\npublic\n:\n\n \nAggregateFunctionFactory\n();\n\n \nAggregateFunctionPtr\n \nget\n(\nconst\n \nString\n \n \nname\n,\n \nconst\n \nDataTypes\n \n \nargument_types\n)\n \nconst\n;\n\n\n\n\n\n\n15.\n namespace.\n\n\nThere is no need to use a separate namespace for application code or small libraries.\n\n\nor small libraries.\n\n\nFor medium to large libraries, put everything in the namespace.\n\n\nYou can use the additional detail namespace in a library's \n.h\n file to hide implementation details.\n\n\nIn a \n.cpp\n file, you can use the static or anonymous namespace to hide symbols.\n\n\nYou can also use namespace for enums to prevent its names from polluting the outer namespace, but it\u2019s better to use the enum class.\n\n\n16.\n Delayed initialization.\n\n\nIf arguments are required for initialization then do not write a default constructor.\n\n\nIf later you\u2019ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use \nshared_ptr/unique_ptr\n.\n\n\nLoader\n(\nDB\n::\nConnection\n \n*\n \nconnection_\n,\n \nconst\n \nstd\n::\nstring\n \n \nquery\n,\n \nsize_t\n \nmax_block_size_\n);\n\n\n\n/// For delayed initialization\n\n\nLoader\n()\n \n{}\n\n\n\n\n\n\n17.\n Virtual functions.\n\n\nIf the class is not intended for polymorphic use, you do not need to make functions virtual. This also applies to the destructor.\n\n\n18.\n Encodings.\n\n\nUse UTF-8 everywhere. Use \nstd::string\nand\nchar *\n. Do not use \nstd::wstring\nand\nwchar_t\n.\n\n\n19.\n Logging.\n\n\nSee the examples everywhere in the code.\n\n\nBefore committing, delete all meaningless and debug logging, and any other types of debug output.\n\n\nLogging in cycles should be avoided, even on the Trace level.\n\n\nLogs must be readable at any logging level.\n\n\nLogging should only be used in application code, for the most part.\n\n\nLog messages must be written in English.\n\n\nThe log should preferably be understandable for the system administrator.\n\n\nDo not use profanity in the log.\n\n\nUse UTF-8 encoding in the log. In rare cases you can use non-ASCII characters in the log.\n\n\n20.\n I/O.\n\n\nDon't use iostreams in internal cycles that are critical for application performance (and never use stringstream).\n\n\nUse the DB/IO library instead.\n\n\n21.\n Date and time.\n\n\nSee the \nDateLUT\n library.\n\n\n22.\n include.\n\n\nAlways use \n#pragma once\n instead of include guards.\n\n\n23.\n using.\n\n\nThe \nusing namespace\n is not used.\n\n\nIt's fine if you are 'using' something specific, but make it local inside a class or function.\n\n\n24.\n Do not use trailing return type for functions unless necessary.\n\n\n[auto f() -\ngt; void;]{.strike}\n\n\n\n\n\n25.\n Do not declare and init variables like this:\n\n\nauto\n \ns\n \n=\n \nstd\n::\nstring\n{\nHello\n};\n\n\n\n\n\n\nDo it like this:\n\n\nstd\n::\nstring\n \ns\n \n=\n \nHello\n;\n\n\nstd\n::\nstring\n \ns\n{\nHello\n};\n\n\n\n\n\n\n26.\n For virtual functions, write \nvirtual\n in the base class, but write \noverride\n in descendent classes.\n\n\nUnused features of C++\n\n\n1.\n Virtual inheritance is not used.\n\n\n2.\n Exception specifiers from C++03 are not used.\n\n\n3.\n Function try block is not used, except for the main function in tests.\n\n\nPlatform\n\n\n1.\n We write code for a specific platform.\n\n\nBut other things being equal, cross-platform or portable code is preferred.\n\n\n2.\n The language is C++17.\n\n\n3.\n The compiler is \ngcc\n. At this time (December 2017), the code is compiled using version 7.2. (It can also be compiled using clang 5.)\n\n\nThe standard library is used (implementation of \nlibstdc++\n or \nlibc++\n).\n\n\n4.\n OS: Linux Ubuntu, not older than Precise.\n\n\n5.\n Code is written for x86_64 CPU architecture.\n\n\nThe CPU instruction set is the minimum supported set among our servers. Currently, it is SSE 4.2.\n\n\n6.\n Use \n-Wall -Wextra -Werror\n compilation flags.\n\n\n7.\n Use static linking with all libraries except those that are difficult to connect to statically (see the output of the \nldd\n command).\n\n\n8.\n Code is developed and debugged with release settings.\n\n\nTools\n\n\n1.\n \nKDevelop\n is a good IDE.\n\n\n2.\n For debugging, use \ngdb\n, \nvalgrind\n (\nmemcheck\n), \nstrace\n, \n-fsanitize=\n, ..., \ntcmalloc_minimal_debug\n.\n\n\n3.\n For profiling, use Linux Perf \nvalgrind\n (\ncallgrind\n), \nstrace-cf\n.\n\n\n4.\n Sources are in Git.\n\n\n5.\n Compilation is managed by \nCMake\n.\n\n\n6.\n Releases are in \ndeb\n packages.\n\n\n7.\n Commits to master must not break the build.\n\n\nThough only selected revisions are considered workable.\n\n\n8.\n Make commits as often as possible, even if the code is only partially ready.\n\n\nUse branches for this purpose.\n\n\nIf your code is not buildable yet, exclude it from the build before pushing to master. You'll need to finish it or remove it from master within a few days.\n\n\n9.\n For non-trivial changes, used branches and publish them on the server.\n\n\n10.\n Unused code is removed from the repository.\n\n\nLibraries\n\n\n1.\n The C++14 standard library is used (experimental extensions are fine), as well as boost and Poco frameworks.\n\n\n2.\n If necessary, you can use any well-known libraries available in the OS package.\n\n\nIf there is a good solution already available, then use it, even if it means you have to install another library.\n\n\n(But be prepared to remove bad libraries from code.)\n\n\n3.\n You can install a library that isn't in the packages, if the packages don't have what you need or have an outdated version or the wrong type of compilation.\n\n\n4.\n If the library is small and doesn't have its own complex build system, put the source files in the contrib folder.\n\n\n5.\n Preference is always given to libraries that are already used.\n\n\nGeneral recommendations\n\n\n1.\n Write as little code as possible.\n\n\n2.\n Try the simplest solution.\n\n\n3.\n Don't write code until you know how it's going to work and how the inner loop will function.\n\n\n4.\n In the simplest cases, use 'using' instead of classes or structs.\n\n\n5.\n If possible, do not write copy constructors, assignment operators, destructors (other than a virtual one, if the class contains at least one virtual function), mpve-constructors and move assignment operators. In other words, the compiler-generated functions must work correctly. You can use 'default'.\n\n\n6.\n Code simplification is encouraged. Reduce the size of your code where possible.\n\n\nAdditional recommendations\n\n\n1.\n Explicit \nstd::\n for types from \nstddef.h\n is not recommended.\n\n\nWe recommend writing \nsize_t\n instead \nstd::size_t\n because it's shorter.\n\n\nBut if you prefer, \nstd::\n is acceptable.\n\n\n2.\n Explicit \nstd::\n for functions from the standard C library is not recommended.\n\n\nWrite \nmemcpy\n instead of \nstd::memcpy\n.\n\n\nThe reason is that there are similar non-standard functions, such as \nmemmem\n. We do use these functions on occasion. These functions do not exist in namespace \nstd\n.\n\n\nIf you write \nstd::memcpy\n instead of \nmemcpy\n everywhere, then \nmemmem\n without \nstd::\n will look awkward.\n\n\nNevertheless, \nstd::\n is allowed if you prefer it.\n\n\n3.\n Using functions from C when the ones are available in the standard C++ library.\n\n\nThis is acceptable if it is more efficient.\n\n\nFor example, use \nmemcpy\n instead of \nstd::copy\n for copying large chunks of memory.\n\n\n4.\n Multiline function arguments.\n\n\nAny of the following wrapping styles are allowed:\n\n\nfunction\n(\n\n \nT1\n \nx1\n,\n\n \nT2\n \nx2\n)\n\n\n\n\n\n\nfunction\n(\n\n \nsize_t\n \nleft\n,\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)\n\n\n\n\n\n\nfunction\n(\nsize_t\n \nleft\n,\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)\n\n\n\n\n\n\nfunction\n(\nsize_t\n \nleft\n,\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)\n\n\n\n\n\n\nfunction\n(\n\n \nsize_t\n \nleft\n,\n\n \nsize_t\n \nright\n,\n\n \nconst\n \n \nRangesInDataParts\n \nranges\n,\n\n \nsize_t\n \nlimit\n)\n\n\n\n\n\n\nHow to run ClickHouse tests\n\n\nThe \nclickhouse-test\n utility that is used for functional testing is written using Python 2.x.It also requires you to have some third-party packages:\n\n\n$ pip install lxml termcolor\n\n\n\n\n\nIn a nutshell:\n\n\n\n\nPut the \nclickhouse\n program to \n/usr/bin\n (or \nPATH\n)\n\n\nCreate a \nclickhouse-client\n symlink in \n/usr/bin\n pointing to \nclickhouse\n\n\nStart the \nclickhouse\n server\n\n\ncd dbms/tests/\n\n\nRun \n./clickhouse-test\n\n\n\n\nExample usage\n\n\nRun \n./clickhouse-test --help\n to see available options.\n\n\nTo run tests without having to create a symlink or mess with \nPATH\n:\n\n\n./clickhouse-test -c \n../../build/dbms/src/Server/clickhouse --client\n\n\n\n\n\n\nTo run a single test, i.e. \n00395_nullable\n:\n\n\n./clickhouse-test \n00395\n\n\n\n\n\n\nRoadmap\n\n\nQ1 2018\n\n\nNew fuctionality\n\n\n\n\n\n\nSupport for \nUPDATE\n and \nDELETE\n.\n\n\n\n\n\n\nMultidimensional and nested arrays.\n\n\n\n\n\n\nIt can look something like this:\n\n\nCREATE\n \nTABLE\n \nt\n\n\n(\n\n \nx\n \nArray\n(\nArray\n(\nString\n)),\n\n \nz\n \nNested\n(\n\n \nx\n \nArray\n(\nString\n),\n\n \ny\n \nNested\n(...))\n\n\n)\n\n\nENGINE\n \n=\n \nMergeTree\n \nORDER\n \nBY\n \nx\n\n\n\n\n\n\n\n\nExternal MySQL and ODBC tables.\n\n\n\n\nExternal tables can be integrated into ClickHouse using external dictionaries. This new functionality is a convenient alternative to connecting external tables.\n\n\nSELECT\n \n...\n\n\nFROM\n \nmysql\n(\nhost:port\n,\n \ndb\n,\n \ntable\n,\n \nuser\n,\n \npassword\n)\n`\n\n\n\n\n\n\nImprovements\n\n\n\n\nEffective data copying between ClickHouse clusters.\n\n\n\n\nNow you can copy data with the remote() function. For example: \nINSERT INTO t SELECT * FROM remote(...)\n.\n\n\nThis operation will have improved performance.\n\n\n\n\nO_DIRECT for merges.\n\n\n\n\nThis will improve the performance of the OS cache and \"hot\" queries.\n\n\nQ2 2018\n\n\nNew functionality\n\n\n\n\n\n\nUPDATE/DELETE conform to the EU GDPR.\n\n\n\n\n\n\nProtobuf and Parquet input and output formats.\n\n\n\n\n\n\nCreating dictionaries using DDL queries.\n\n\n\n\n\n\nCurrently, dictionaries that are part of the database schema are defined in external XML files. This is inconvenient and counter-intuitive. The new approach should fix it.\n\n\n\n\n\n\nIntegration with LDAP.\n\n\n\n\n\n\nWITH ROLLUP and WITH CUBE for GROUP BY.\n\n\n\n\n\n\nCustom encoding and compression for each column individually.\n\n\n\n\n\n\nAs of now, ClickHouse supports LZ4 and ZSTD compression of columns, and compression settings are global (see the article \nCompression in ClickHouse\n). Per-column compression and encoding will provide more efficient data storage, which in turn will speed up queries.\n\n\n\n\nStoring data on multiple disks on the same server.\n\n\n\n\nThis functionality will make it easier to extend the disk space, since different disk systems can be used for different databases or tables. Currently, users are forced to use symbolic links if the databases and tables must be stored on a different disk.\n\n\nImprovements\n\n\nMany improvements and fixes are planned for the query execution system. For example:\n\n\n\n\nUsing an index for \nin (subquery)\n.\n\n\n\n\nThe index is not used right now, which reduces performance.\n\n\n\n\nPassing predicates from \nwhere\n to subqueries, and passing predicates to views.\n\n\n\n\nThe predicates must be passed, since the view is changed by the subquery. Performance is still low for view filters, and views can't use the primary key of the original table, which makes views useless for large tables.\n\n\n\n\nOptimizing branching operations (ternary operator, if, multiIf).\n\n\n\n\nClickHouse currently performs all branches, even if they aren't necessary.\n\n\n\n\nUsing a primary key for GROUP BY and ORDER BY.\n\n\n\n\nThis will speed up certain types of queries with partially sorted data.\n\n\nQ3-Q4 2018\n\n\nWe don't have any set plans yet, but the main projects will be:\n\n\n\n\nResource pools for executing queries.\n\n\n\n\nThis will make load management more efficient.\n\n\n\n\nANSI SQL JOIN syntax.\n\n\n\n\nImprove ClickHouse compatibility with many SQL tools.", - "title": "Documentation" - }, - { - "location": "/index.html#what-is-clickhouse", - "text": "ClickHouse is a columnar DBMS for OLAP. In a \"normal\" row-oriented DBMS, data is stored in this order: 5123456789123456789 1 Eurobasket - Greece - Bosnia and Herzegovina - example.com 1 2011-09-01 01:03:02 6274717 1294101174 11409 612345678912345678 0 33 6 http://www.example.com/basketball/team/123/match/456789.html http://www.example.com/basketball/team/123/match/987654.html 0 1366 768 32 10 3183 0 0 13 0\\0 1 1 0 0 2011142 -1 0 0 01321 613 660 2011-09-01 08:01:17 0 0 0 0 utf-8 1466 0 0 0 5678901234567890123 277789954 0 0 0 0 0\n5234985259563631958 0 Consulting, Tax assessment, Accounting, Law 1 2011-09-01 01:03:02 6320881 2111222333 213 6458937489576391093 0 3 2 http://www.example.ru/ 0 800 600 16 10 2 153.1 0 0 10 63 1 1 0 0 2111678 000 0 588 368 240 2011-09-01 01:03:17 4 0 60310 0 windows-1251 1466 0 000 778899001 0 0 0 0 0\n... In order words, all the values related to a row are stored next to each other.\nExamples of a row-oriented DBMS are MySQL, Postgres, MS SQL Server, and others. In a column-oriented DBMS, data is stored like this: WatchID: 5385521489354350662 5385521490329509958 5385521489953706054 5385521490476781638 5385521490583269446 5385521490218868806 5385521491437850694 5385521491090174022 5385521490792669254 5385521490420695110 5385521491532181574 5385521491559694406 5385521491459625030 5385521492275175494 5385521492781318214 5385521492710027334 5385521492955615302 5385521493708759110 5385521494506434630 5385521493104611398\nJavaEnable: 1 0 1 0 0 0 1 0 1 1 1 1 1 1 0 1 0 0 1 1\nTitle: Yandex Announcements - Investor Relations - Yandex Yandex \u2014 Contact us \u2014 Moscow Yandex \u2014 Mission Ru Yandex \u2014 History \u2014 History of Yandex Yandex Financial Releases - Investor Relations - Yandex Yandex \u2014 Locations Yandex Board of Directors - Corporate Governance - Yandex Yandex \u2014 Technologies\nGoodEvent: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\nEventTime: 2016-05-18 05:19:20 2016-05-18 08:10:20 2016-05-18 07:38:00 2016-05-18 01:13:08 2016-05-18 00:04:06 2016-05-18 04:21:30 2016-05-18 00:34:16 2016-05-18 07:35:49 2016-05-18 11:41:59 2016-05-18 01:13:32 These examples only show the order that data is arranged in.\nThe values from different columns are stored separately, and data from the same column is stored together. Examples of column-oriented DBMSs: Vertica , Paraccel (Actian Matrix) (Amazon Redshift) , Sybase IQ , Exasol , Infobright , InfiniDB , MonetDB (VectorWise) (Actian Vector) , LucidDB , SAP HANA , Google Dremel , Google PowerDrill , Druid , kdb+ , and so on. Different orders for storing data are better suited to different scenarios.\nThe data access scenario refers to what queries are made, how often, and in what proportion; how much data is read for each type of query \u2013 rows, columns, and bytes; the relationship between reading and updating data; the working size of the data and how locally it is used; whether transactions are used, and how isolated they are; requirements for data replication and logical integrity; requirements for latency and throughput for each type of query, and so on. The higher the load on the system, the more important it is to customize the system to the scenario, and the more specific this customization becomes. There is no system that is equally well-suited to significantly different scenarios. If a system is adaptable to a wide set of scenarios, under a high load, the system will handle all the scenarios equally poorly, or will work well for just one of the scenarios. We'll say that the following is true for the OLAP (online analytical processing) scenario: The vast majority of requests are for read access. Data is updated in fairly large batches ( 1000 rows), not by single rows; or it is not updated at all. Data is added to the DB but is not modified. For reads, quite a large number of rows are extracted from the DB, but only a small subset of columns. Tables are \"wide,\" meaning they contain a large number of columns. Queries are relatively rare (usually hundreds of queries per server or less per second). For simple queries, latencies around 50 ms are allowed. Column values are fairly small: numbers and short strings (for example, 60 bytes per URL). Requires high throughput when processing a single query (up to billions of rows per second per server). There are no transactions. Low requirements for data consistency. There is one large table per query. All tables are small, except for one. A query result is significantly smaller than the source data. In other words, data is filtered or aggregated. The result fits in a single server's RAM. It is easy to see that the OLAP scenario is very different from other popular scenarios (such as OLTP or Key-Value access). So it doesn't make sense to try to use OLTP or a Key-Value DB for processing analytical queries if you want to get decent performance. For example, if you try to use MongoDB or Elliptics for analytics, you will get very poor performance compared to OLAP databases. Columnar-oriented databases are better suited to OLAP scenarios (at least 100 times better in processing speed for most queries), for the following reasons: For I/O. For an analytical query, only a small number of table columns need to be read. In a column-oriented database, you can read just the data you need. For example, if you need 5 columns out of 100, you can expect a 20-fold reduction in I/O. Since data is read in packets, it is easier to compress. Data in columns is also easier to compress. This further reduces the I/O volume. Due to the reduced I/O, more data fits in the system cache. For example, the query \"count the number of records for each advertising platform\" requires reading one \"advertising platform ID\" column, which takes up 1 byte uncompressed. If most of the traffic was not from advertising platforms, you can expect at least 10-fold compression of this column. When using a quick compression algorithm, data decompression is possible at a speed of at least several gigabytes of uncompressed data per second. In other words, this query can be processed at a speed of approximately several billion rows per second on a single server. This speed is actually achieved in practice. Example: milovidov@hostname:~$ clickhouse-client\nClickHouse client version 0 .0.52053.\nConnecting to localhost:9000.\nConnected to ClickHouse server version 0 .0.52053.\n\n: ) SELECT CounterID, count () FROM hits GROUP BY CounterID ORDER BY count () DESC LIMIT 20 \n\nSELECT\n CounterID,\n count () \nFROM hits\nGROUP BY CounterID\nORDER BY count () DESC\nLIMIT 20 \n\n\u250c\u2500CounterID\u2500\u252c\u2500\u2500count () \u2500\u2510\n\u2502 114208 \u2502 56057344 \u2502\n\u2502 115080 \u2502 51619590 \u2502\n\u2502 3228 \u2502 44658301 \u2502\n\u2502 38230 \u2502 42045932 \u2502\n\u2502 145263 \u2502 42042158 \u2502\n\u2502 91244 \u2502 38297270 \u2502\n\u2502 154139 \u2502 26647572 \u2502\n\u2502 150748 \u2502 24112755 \u2502\n\u2502 242232 \u2502 21302571 \u2502\n\u2502 338158 \u2502 13507087 \u2502\n\u2502 62180 \u2502 12229491 \u2502\n\u2502 82264 \u2502 12187441 \u2502\n\u2502 232261 \u2502 12148031 \u2502\n\u2502 146272 \u2502 11438516 \u2502\n\u2502 168777 \u2502 11403636 \u2502\n\u2502 4120072 \u2502 11227824 \u2502\n\u2502 10938808 \u2502 10519739 \u2502\n\u2502 74088 \u2502 9047015 \u2502\n\u2502 115079 \u2502 8837972 \u2502\n\u2502 337234 \u2502 8205961 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 20 rows in set. Elapsed: 0 .153 sec. Processed 1 .00 billion rows, 4 .00 GB ( 6 .53 billion rows/s., 26 .10 GB/s. ) \n\n: ) For CPU. Since executing a query requires processing a large number of rows, it helps to dispatch all operations for entire vectors instead of for separate rows, or to implement the query engine so that there is almost no dispatching cost. If you don't do this, with any half-decent disk subsystem, the query interpreter inevitably stalls the CPU.\nIt makes sense to both store data in columns and process it, when possible, by columns. There are two ways to do this: A vector engine. All operations are written for vectors, instead of for separate values. This means you don't need to call operations very often, and dispatching costs are negligible. Operation code contains an optimized internal cycle. Code generation. The code generated for the query has all the indirect calls in it. This is not done in \"normal\" databases, because it doesn't make sense when running simple queries. However, there are exceptions. For example, MemSQL uses code generation to reduce latency when processing SQL queries. (For comparison, analytical DBMSs require optimization of throughput, not latency.) Note that for CPU efficiency, the query language must be declarative (SQL or MDX), or at least a vector (J, K). The query should only contain implicit loops, allowing for optimization.", - "title": "What is ClickHouse?" - }, - { - "location": "/index.html#introduction", - "text": "", - "title": "Introduction" - }, - { - "location": "/index.html#distinctive-features-of-clickhouse", - "text": "", - "title": "Distinctive features of ClickHouse" - }, - { - "location": "/index.html#true-column-oriented-dbms", - "text": "In a true column-oriented DBMS, there isn't any \"garbage\" stored with the values. Among other things, this means that constant-length values must be supported, to avoid storing their length \"number\" next to the values. As an example, a billion UInt8-type values should actually consume around 1 GB uncompressed, or this will strongly affect the CPU use. It is very important to store data compactly (without any \"garbage\") even when uncompressed, since the speed of decompression (CPU usage) depends mainly on the volume of uncompressed data. This is worth noting because there are systems that can store values of separate columns separately, but that can't effectively process analytical queries due to their optimization for other scenarios. Examples are HBase, BigTable, Cassandra, and HyperTable. In these systems, you will get throughput around a hundred thousand rows per second, but not hundreds of millions of rows per second. Also note that ClickHouse is a DBMS, not a single database. ClickHouse allows creating tables and databases in runtime, loading data, and running queries without reconfiguring and restarting the server.", - "title": "True column-oriented DBMS" - }, - { - "location": "/index.html#data-compression", - "text": "Some column-oriented DBMSs (InfiniDB CE and MonetDB) do not use data compression. However, data compression really improves performance.", - "title": "Data compression" - }, - { - "location": "/index.html#disk-storage-of-data", - "text": "Many column-oriented DBMSs (such as SAP HANA and Google PowerDrill) can only work in RAM. But even on thousands of servers, the RAM is too small for storing all the pageviews and sessions in Yandex.Metrica.", - "title": "Disk storage of data" - }, - { - "location": "/index.html#parallel-processing-on-multiple-cores", - "text": "Large queries are parallelized in a natural way.", - "title": "Parallel processing on multiple cores" - }, - { - "location": "/index.html#distributed-processing-on-multiple-servers", - "text": "Almost none of the columnar DBMSs listed above have support for distributed processing.\nIn ClickHouse, data can reside on different shards. Each shard can be a group of replicas that are used for fault tolerance. The query is processed on all the shards in parallel. This is transparent for the user.", - "title": "Distributed processing on multiple servers" - }, - { - "location": "/index.html#sql-support", - "text": "If you are familiar with standard SQL, we can't really talk about SQL support.\nAll the functions have different names.\nHowever, this is a declarative query language based on SQL that can't be differentiated from SQL in many instances.\nJOINs are supported. Subqueries are supported in FROM, IN, and JOIN clauses, as well as scalar subqueries.\nDependent subqueries are not supported.", - "title": "SQL support" - }, - { - "location": "/index.html#vector-engine", - "text": "Data is not only stored by columns, but is processed by vectors (parts of columns). This allows us to achieve high CPU performance.", - "title": "Vector engine" - }, - { - "location": "/index.html#real-time-data-updates", - "text": "ClickHouse supports primary key tables. In order to quickly perform queries on the range of the primary key, the data is sorted incrementally using the merge tree. Due to this, data can continually be added to the table. There is no locking when adding data.", - "title": "Real-time data updates" - }, - { - "location": "/index.html#indexes", - "text": "Having a primary key makes it possible to extract data for specific clients (for instance, Yandex.Metrica tracking tags) for a specific time range, with low latency less than several dozen milliseconds.", - "title": "Indexes" - }, - { - "location": "/index.html#suitable-for-online-queries", - "text": "This lets us use the system as the back-end for a web interface. Low latency means queries can be processed without delay, while the Yandex.Metrica interface page is loading. In other words, in online mode.", - "title": "Suitable for online queries" - }, - { - "location": "/index.html#support-for-approximated-calculations", - "text": "The system contains aggregate functions for approximated calculation of the number of various values, medians, and quantiles. Supports running a query based on a part (sample) of data and getting an approximated result. In this case, proportionally less data is retrieved from the disk. Supports running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reasonably accurate result while using fewer resources.", - "title": "Support for approximated calculations" - }, - { - "location": "/index.html#data-replication-and-support-for-data-integrity-on-replicas", - "text": "Uses asynchronous multimaster replication. After being written to any available replica, data is distributed to all the remaining replicas. The system maintains identical data on different replicas. Data is restored automatically after a failure, or using a \"button\" for complex cases.\nFor more information, see the section Data replication .", - "title": "Data replication and support for data integrity on replicas" - }, - { - "location": "/index.html#clickhouse-features-that-can-be-considered-disadvantages", - "text": "No transactions. For aggregation, query results must fit in the RAM on a single server. However, the volume of source data for a query may be indefinitely large. Lack of full-fledged UPDATE/DELETE implementation.", - "title": "ClickHouse features that can be considered disadvantages" - }, - { - "location": "/index.html#yandexmetrica-use-case", - "text": "ClickHouse currently powers Yandex.Metrica , the second largest web analytics platform in the world . With more than 13 trillion records in the database and more than 20 billion events daily, ClickHouse allows you generating custom reports on the fly directly from non-aggregated data. We need to get custom reports based on hits and sessions, with custom segments set by the user. Data for the reports is updated in real-time. Queries must be run immediately (in online mode). We must be able to build reports for any time period. Complex aggregates must be calculated, such as the number of unique visitors.\nAt this time (April 2014), Yandex.Metrica receives approximately 12 billion events (pageviews and mouse clicks) daily. All these events must be stored in order to build custom reports. A single query may require scanning hundreds of millions of rows over a few seconds, or millions of rows in no more than a few hundred milliseconds.", - "title": "Yandex.Metrica use case" - }, - { - "location": "/index.html#usage-in-yandexmetrica-and-other-yandex-services", - "text": "ClickHouse is used for multiple purposes in Yandex.Metrica.\nIts main task is to build reports in online mode using non-aggregated data. It uses a cluster of 374 servers, which store over 20.3 trillion rows in the database. The volume of compressed data, without counting duplication and replication, is about 2 PB. The volume of uncompressed data (in TSV format) would be approximately 17 PB. ClickHouse is also used for: Storing data for Session Replay from Yandex.Metrica. Processing intermediate data. Building global reports with Analytics. Running queries for debugging the Yandex.Metrica engine. Analyzing logs from the API and the user interface. ClickHouse has at least a dozen installations in other Yandex services: in search verticals, Market, Direct, business analytics, mobile development, AdFox, personal services, and others.", - "title": "Usage in Yandex.Metrica and other Yandex services" - }, - { - "location": "/index.html#aggregated-and-non-aggregated-data", - "text": "There is a popular opinion that in order to effectively calculate statistics, you must aggregate data, since this reduces the volume of data. But data aggregation is a very limited solution, for the following reasons: You must have a pre-defined list of reports the user will need. The user can't make custom reports. When aggregating a large quantity of keys, the volume of data is not reduced, and aggregation is useless. For a large number of reports, there are too many aggregation variations (combinatorial explosion). When aggregating keys with high cardinality (such as URLs), the volume of data is not reduced by much (less than twofold). For this reason, the volume of data with aggregation might grow instead of shrink. Users do not view all the reports we generate for them. A large portion of calculations are useless. The logical integrity of data may be violated for various aggregations. If we do not aggregate anything and work with non-aggregated data, this might actually reduce the volume of calculations. However, with aggregation, a significant part of the work is taken offline and completed relatively calmly. In contrast, online calculations require calculating as fast as possible, since the user is waiting for the result. Yandex.Metrica has a specialized system for aggregating data called Metrage, which is used for the majority of reports.\nStarting in 2009, Yandex.Metrica also used a specialized OLAP database for non-aggregated data called OLAPServer, which was previously used for the report builder.\nOLAPServer worked well for non-aggregated data, but it had many restrictions that did not allow it to be used for all reports as desired. These included the lack of support for data types (only numbers), and the inability to incrementally update data in real-time (it could only be done by rewriting data daily). OLAPServer is not a DBMS, but a specialized DB. To remove the limitations of OLAPServer and solve the problem of working with non-aggregated data for all reports, we developed the ClickHouse DBMS.", - "title": "Aggregated and non-aggregated data" - }, - { - "location": "/index.html#questions-you-were-afraid-to-ask", - "text": "", - "title": "Questions you were afraid to ask" - }, - { - "location": "/index.html#why-not-use-something-like-mapreduce", - "text": "We can refer to systems like map-reduce as distributed computing systems in which the reduce operation is based on distributed sorting. In this sense, they include Hadoop, and YT (YT is developed at Yandex for internal use). These systems aren't appropriate for online queries due to their high latency. In other words, they can't be used as the back-end for a web interface.\nThese types of systems aren't useful for real-time data updates.\nDistributed sorting isn't the best way to perform reduce operations if the result of the operation and all the intermediate results (if there are any) are located in the RAM of a single server, which is usually the case for online queries. In such a case, a hash table is the optimal way to perform reduce operations. A common approach to optimizing map-reduce tasks is pre-aggregation (partial reduce) using a hash table in RAM. The user performs this optimization manually.\nDistributed sorting is one of the main causes of reduced performance when running simple map-reduce tasks. Systems like map-reduce allow executing any code on the cluster. But a declarative query language is better suited to OLAP in order to run experiments quickly. For example, Hadoop has Hive and Pig. Also consider Cloudera Impala, Shark (outdated) for Spark, and Spark SQL, Presto, and Apache Drill. Performance when running such tasks is highly sub-optimal compared to specialized systems, but relatively high latency makes it unrealistic to use these systems as the backend for a web interface. YT allows storing groups of columns separately. But YT can't be considered a true column-based system because it doesn't have fixed-length data types (for efficiently storing numbers without extra \"garbage\"), and also due to its lack of a vector engine. Tasks are performed in YT using custom code in streaming mode, so they cannot be optimized enough (up to hundreds of millions of rows per second per server). \"Dynamic table sorting\" is under development in YT using MergeTree, strict value typing, and a query language similar to SQL. Dynamically sorted tables are not appropriate for OLAP tasks because the data is stored by row. The YT query language is still under development, so we can't yet rely on this functionality. YT developers are considering using dynamically sorted tables in OLTP and Key-Value scenarios.", - "title": "Why not use something like MapReduce?" - }, - { - "location": "/index.html#performance", - "text": "According to internal testing results, ClickHouse shows the best performance for comparable operating scenarios among systems of its class that were available for testing. This includes the highest throughput for long queries, and the lowest latency on short queries. Testing results are shown on a separate page.", - "title": "Performance" - }, - { - "location": "/index.html#throughput-for-a-single-large-query", - "text": "Throughput can be measured in rows per second or in megabytes per second. If the data is placed in the page cache, a query that is not too complex is processed on modern hardware at a speed of approximately 2-10 GB/s of uncompressed data on a single server (for the simplest cases, the speed may reach 30 GB/s). If data is not placed in the page cache, the speed depends on the disk subsystem and the data compression rate. For example, if the disk subsystem allows reading data at 400 MB/s, and the data compression rate is 3, the speed will be around 1.2 GB/s. To get the speed in rows per second, divide the speed in bytes per second by the total size of the columns used in the query. For example, if 10 bytes of columns are extracted, the speed will be around 100-200 million rows per second. The processing speed increases almost linearly for distributed processing, but only if the number of rows resulting from aggregation or sorting is not too large.", - "title": "Throughput for a single large query" - }, - { - "location": "/index.html#latency-when-processing-short-queries", - "text": "If a query uses a primary key and does not select too many rows to process (hundreds of thousands), and does not use too many columns, we can expect less than 50 milliseconds of latency (single digits of milliseconds in the best case) if data is placed in the page cache. Otherwise, latency is calculated from the number of seeks. If you use rotating drives, for a system that is not overloaded, the latency is calculated by this formula: seek time (10 ms) * number of columns queried * number of data parts.", - "title": "Latency when processing short queries" - }, - { - "location": "/index.html#throughput-when-processing-a-large-quantity-of-short-queries", - "text": "Under the same conditions, ClickHouse can handle several hundred queries per second on a single server (up to several thousand in the best case). Since this scenario is not typical for analytical DBMSs, we recommend expecting a maximum of 100 queries per second.", - "title": "Throughput when processing a large quantity of short queries" - }, - { - "location": "/index.html#performance-when-inserting-data", - "text": "We recommend inserting data in packets of at least 1000 rows, or no more than a single request per second. When inserting to a MergeTree table from a tab-separated dump, the insertion speed will be from 50 to 200 MB/s. If the inserted rows are around 1 Kb in size, the speed will be from 50,000 to 200,000 rows per second. If the rows are small, the performance will be higher in rows per second (on Banner System data - 500,000 rows per second; on Graphite data - 1,000,000 rows per second). To improve performance, you can make multiple INSERT queries in parallel, and performance will increase linearly.", - "title": "Performance when inserting data" - }, - { - "location": "/index.html#getting-started", - "text": "", - "title": "Getting started" - }, - { - "location": "/index.html#system-requirements", - "text": "This is not a cross-platform system. It requires Linux Ubuntu Precise (12.04) or newer, with x86_64 architecture and support for the SSE 4.2 instruction set.\nTo check for SSE 4.2: grep -q sse4_2 /proc/cpuinfo echo SSE 4.2 supported || echo SSE 4.2 not supported We recommend using Ubuntu Trusty, Ubuntu Xenial, or Ubuntu Precise.\nThe terminal must use UTF-8 encoding (the default in Ubuntu).", - "title": "System requirements" - }, - { - "location": "/index.html#installation", - "text": "For testing and development, the system can be installed on a single server or on a desktop computer.", - "title": "Installation" - }, - { - "location": "/index.html#installing-from-packages-for-debianubuntu", - "text": "In /etc/apt/sources.list (or in a separate /etc/apt/sources.list.d/clickhouse.list file), add the repository: deb http://repo.yandex.ru/clickhouse/deb/stable/ main/ If you want to use the most recent test version, replace 'stable' with 'testing'. Then run: sudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4 # optional \nsudo apt-get update\nsudo apt-get install clickhouse-client clickhouse-server You can also download and install packages manually from here: https://repo.yandex.ru/clickhouse/deb/stable/main/ . ClickHouse contains access restriction settings. They are located in the 'users.xml' file (next to 'config.xml').\nBy default, access is allowed from anywhere for the 'default' user, without a password. See 'user/default/networks'.\nFor more information, see the section \"Configuration files\".", - "title": "Installing from packages for Debian/Ubuntu" - }, - { - "location": "/index.html#installing-from-sources", - "text": "To compile, follow the instructions: build.md You can compile packages and install them.\nYou can also use programs without installing packages. Client: dbms/src/Client/\nServer: dbms/src/Server/ For the server, create a catalog with data, such as: /opt/clickhouse/data/default/\n/opt/clickhouse/metadata/default/ (Configurable in the server config.)\nRun 'chown' for the desired user. Note the path to logs in the server config (src/dbms/src/Server/config.xml).", - "title": "Installing from sources" - }, - { - "location": "/index.html#other-installation-methods", - "text": "Docker image: https://hub.docker.com/r/yandex/clickhouse-server/ RPM packages for CentOS or RHEL: https://github.com/Altinity/clickhouse-rpm-install Gentoo overlay: https://github.com/kmeaw/clickhouse-overlay", - "title": "Other installation methods" - }, - { - "location": "/index.html#launch", - "text": "To start the server (as a daemon), run: sudo service clickhouse-server start See the logs in the /var/log/clickhouse-server/ directory. If the server doesn't start, check the configurations in the file /etc/clickhouse-server/config.xml. You can also launch the server from the console: clickhouse-server --config-file = /etc/clickhouse-server/config.xml In this case, the log will be printed to the console, which is convenient during development.\nIf the configuration file is in the current directory, you don't need to specify the '--config-file' parameter. By default, it uses './config.xml'. You can use the command-line client to connect to the server: clickhouse-client The default parameters indicate connecting with localhost:9000 on behalf of the user 'default' without a password.\nThe client can be used for connecting to a remote server. Example: clickhouse-client --host = example.com For more information, see the section \"Command-line client\". Checking the system: milovidov@hostname:~/work/metrica/src/dbms/src/Client$ ./clickhouse-client\nClickHouse client version 0 .0.18749.\nConnecting to localhost:9000.\nConnected to ClickHouse server version 0 .0.18749.\n\n: ) SELECT 1 \n\nSELECT 1 \n\n\u250c\u25001\u2500\u2510\n\u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2518 1 rows in set. Elapsed: 0 .003 sec.\n\n: ) Congratulations, the system works! To continue experimenting, you can try to download from the test data sets.", - "title": "Launch" - }, - { - "location": "/index.html#ontime", - "text": "This performance test was created by Vadim Tkachenko. See: https://www.percona.com/blog/2009/10/02/analyzing-air-traffic-performance-with-infobright-and-monetdb/ https://www.percona.com/blog/2009/10/26/air-traffic-queries-in-luciddb/ https://www.percona.com/blog/2009/11/02/air-traffic-queries-in-infinidb-early-alpha/ https://www.percona.com/blog/2014/04/21/using-apache-hadoop-and-impala-together-with-mysql-for-data-analysis/ https://www.percona.com/blog/2016/01/07/apache-spark-with-air-ontime-performance-data/ http://nickmakos.blogspot.ru/2012/08/analyzing-air-traffic-performance-with.html Downloading data: for s in ` seq 1987 2017 ` do for m in ` seq 1 12 ` do \nwget http://transtats.bts.gov/PREZIP/On_Time_On_Time_Performance_ ${ s } _ ${ m } .zip done done (from https://github.com/Percona-Lab/ontime-airline-performance/blob/master/download.sh ) Creating a table: CREATE TABLE ` ontime ` ( \n ` Year ` UInt16 , \n ` Quarter ` UInt8 , \n ` Month ` UInt8 , \n ` DayofMonth ` UInt8 , \n ` DayOfWeek ` UInt8 , \n ` FlightDate ` Date , \n ` UniqueCarrier ` FixedString ( 7 ), \n ` AirlineID ` Int32 , \n ` Carrier ` FixedString ( 2 ), \n ` TailNum ` String , \n ` FlightNum ` String , \n ` OriginAirportID ` Int32 , \n ` OriginAirportSeqID ` Int32 , \n ` OriginCityMarketID ` Int32 , \n ` Origin ` FixedString ( 5 ), \n ` OriginCityName ` String , \n ` OriginState ` FixedString ( 2 ), \n ` OriginStateFips ` String , \n ` OriginStateName ` String , \n ` OriginWac ` Int32 , \n ` DestAirportID ` Int32 , \n ` DestAirportSeqID ` Int32 , \n ` DestCityMarketID ` Int32 , \n ` Dest ` FixedString ( 5 ), \n ` DestCityName ` String , \n ` DestState ` FixedString ( 2 ), \n ` DestStateFips ` String , \n ` DestStateName ` String , \n ` DestWac ` Int32 , \n ` CRSDepTime ` Int32 , \n ` DepTime ` Int32 , \n ` DepDelay ` Int32 , \n ` DepDelayMinutes ` Int32 , \n ` DepDel15 ` Int32 , \n ` DepartureDelayGroups ` String , \n ` DepTimeBlk ` String , \n ` TaxiOut ` Int32 , \n ` WheelsOff ` Int32 , \n ` WheelsOn ` Int32 , \n ` TaxiIn ` Int32 , \n ` CRSArrTime ` Int32 , \n ` ArrTime ` Int32 , \n ` ArrDelay ` Int32 , \n ` ArrDelayMinutes ` Int32 , \n ` ArrDel15 ` Int32 , \n ` ArrivalDelayGroups ` Int32 , \n ` ArrTimeBlk ` String , \n ` Cancelled ` UInt8 , \n ` CancellationCode ` FixedString ( 1 ), \n ` Diverted ` UInt8 , \n ` CRSElapsedTime ` Int32 , \n ` ActualElapsedTime ` Int32 , \n ` AirTime ` Int32 , \n ` Flights ` Int32 , \n ` Distance ` Int32 , \n ` DistanceGroup ` UInt8 , \n ` CarrierDelay ` Int32 , \n ` WeatherDelay ` Int32 , \n ` NASDelay ` Int32 , \n ` SecurityDelay ` Int32 , \n ` LateAircraftDelay ` Int32 , \n ` FirstDepTime ` String , \n ` TotalAddGTime ` String , \n ` LongestAddGTime ` String , \n ` DivAirportLandings ` String , \n ` DivReachedDest ` String , \n ` DivActualElapsedTime ` String , \n ` DivArrDelay ` String , \n ` DivDistance ` String , \n ` Div1Airport ` String , \n ` Div1AirportID ` Int32 , \n ` Div1AirportSeqID ` Int32 , \n ` Div1WheelsOn ` String , \n ` Div1TotalGTime ` String , \n ` Div1LongestGTime ` String , \n ` Div1WheelsOff ` String , \n ` Div1TailNum ` String , \n ` Div2Airport ` String , \n ` Div2AirportID ` Int32 , \n ` Div2AirportSeqID ` Int32 , \n ` Div2WheelsOn ` String , \n ` Div2TotalGTime ` String , \n ` Div2LongestGTime ` String , \n ` Div2WheelsOff ` String , \n ` Div2TailNum ` String , \n ` Div3Airport ` String , \n ` Div3AirportID ` Int32 , \n ` Div3AirportSeqID ` Int32 , \n ` Div3WheelsOn ` String , \n ` Div3TotalGTime ` String , \n ` Div3LongestGTime ` String , \n ` Div3WheelsOff ` String , \n ` Div3TailNum ` String , \n ` Div4Airport ` String , \n ` Div4AirportID ` Int32 , \n ` Div4AirportSeqID ` Int32 , \n ` Div4WheelsOn ` String , \n ` Div4TotalGTime ` String , \n ` Div4LongestGTime ` String , \n ` Div4WheelsOff ` String , \n ` Div4TailNum ` String , \n ` Div5Airport ` String , \n ` Div5AirportID ` Int32 , \n ` Div5AirportSeqID ` Int32 , \n ` Div5WheelsOn ` String , \n ` Div5TotalGTime ` String , \n ` Div5LongestGTime ` String , \n ` Div5WheelsOff ` String , \n ` Div5TailNum ` String ) ENGINE = MergeTree ( FlightDate , ( Year , FlightDate ), 8192 ) Loading data: for i in *.zip ; do echo $i ; unzip -cq $i *.csv | sed s/\\.00//g | clickhouse-client --host = example-perftest01j --query = INSERT INTO ontime FORMAT CSVWithNames ; done Queries: Q0. select avg ( c1 ) from ( select Year , Month , count ( * ) as c1 from ontime group by Year , Month ); Q1. The number of flights per day from the year 2000 to 2008 SELECT DayOfWeek , count ( * ) AS c FROM ontime WHERE Year = 2000 AND Year = 2008 GROUP BY DayOfWeek ORDER BY c DESC ; Q2. The number of flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008 SELECT DayOfWeek , count ( * ) AS c FROM ontime WHERE DepDelay 10 AND Year = 2000 AND Year = 2008 GROUP BY DayOfWeek ORDER BY c DESC Q3. The number of delays by airport for 2000-2008 SELECT Origin , count ( * ) AS c FROM ontime WHERE DepDelay 10 AND Year = 2000 AND Year = 2008 GROUP BY Origin ORDER BY c DESC LIMIT 10 Q4. The number of delays by carrier for 2007 SELECT Carrier , count ( * ) FROM ontime WHERE DepDelay 10 AND Year = 2007 GROUP BY Carrier ORDER BY count ( * ) DESC Q5. The percentage of delays by carrier for 2007 SELECT Carrier , c , c2 , c * 1000 / c2 as c3 FROM ( \n SELECT \n Carrier , \n count ( * ) AS c \n FROM ontime \n WHERE DepDelay 10 \n AND Year = 2007 \n GROUP BY Carrier ) ANY INNER JOIN ( \n SELECT \n Carrier , \n count ( * ) AS c2 \n FROM ontime \n WHERE Year = 2007 \n GROUP BY Carrier ) USING Carrier ORDER BY c3 DESC ; Better version of the same query: SELECT Carrier , avg ( DepDelay 10 ) * 1000 AS c3 FROM ontime WHERE Year = 2007 GROUP BY Carrier ORDER BY Carrier Q6. The previous request for a broader range of years, 2000-2008 SELECT Carrier , c , c2 , c * 1000 / c2 as c3 FROM ( \n SELECT \n Carrier , \n count ( * ) AS c \n FROM ontime \n WHERE DepDelay 10 \n AND Year = 2000 AND Year = 2008 \n GROUP BY Carrier ) ANY INNER JOIN ( \n SELECT \n Carrier , \n count ( * ) AS c2 \n FROM ontime \n WHERE Year = 2000 AND Year = 2008 \n GROUP BY Carrier ) USING Carrier ORDER BY c3 DESC ; Better version of the same query: SELECT Carrier , avg ( DepDelay 10 ) * 1000 AS c3 FROM ontime WHERE Year = 2000 AND Year = 2008 GROUP BY Carrier ORDER BY Carrier Q7. Percentage of flights delayed for more than 10 minutes, by year SELECT Year , c1 / c2 FROM ( \n select \n Year , \n count ( * ) * 1000 as c1 \n from ontime \n WHERE DepDelay 10 \n GROUP BY Year ) ANY INNER JOIN ( \n select \n Year , \n count ( * ) as c2 \n from ontime \n GROUP BY Year ) USING ( Year ) ORDER BY Year Better version of the same query: SELECT Year , avg ( DepDelay 10 ) FROM ontime GROUP BY Year ORDER BY Year Q8. The most popular destinations by the number of directly connected cities for various year ranges SELECT DestCityName , uniqExact ( OriginCityName ) AS u FROM ontime WHERE Year = 2000 and Year = 2010 GROUP BY DestCityName ORDER BY u DESC LIMIT 10 ; Q9. select Year , count ( * ) as c1 from ontime group by Year ; Q10. select \n min ( Year ), max ( Year ), Carrier , count ( * ) as cnt , \n sum ( ArrDelayMinutes 30 ) as flights_delayed , \n round ( sum ( ArrDelayMinutes 30 ) / count ( * ), 2 ) as rate FROM ontime WHERE \n DayOfWeek not in ( 6 , 7 ) and OriginState not in ( AK , HI , PR , VI ) \n and DestState not in ( AK , HI , PR , VI ) \n and FlightDate 2010-01-01 GROUP by Carrier HAVING cnt 100000 and max ( Year ) 1990 ORDER by rate DESC LIMIT 1000 ; Bonus: SELECT avg ( cnt ) FROM ( SELECT Year , Month , count ( * ) AS cnt FROM ontime WHERE DepDel15 = 1 GROUP BY Year , Month ) select avg ( c1 ) from ( select Year , Month , count ( * ) as c1 from ontime group by Year , Month ) SELECT DestCityName , uniqExact ( OriginCityName ) AS u FROM ontime GROUP BY DestCityName ORDER BY u DESC LIMIT 10 ; SELECT OriginCityName , DestCityName , count () AS c FROM ontime GROUP BY OriginCityName , DestCityName ORDER BY c DESC LIMIT 10 ; SELECT OriginCityName , count () AS c FROM ontime GROUP BY OriginCityName ORDER BY c DESC LIMIT 10 ;", - "title": "OnTime" - }, - { - "location": "/index.html#new-york-taxi-data", - "text": "", - "title": "New York Taxi data" - }, - { - "location": "/index.html#how-to-import-the-raw-data", - "text": "See https://github.com/toddwschneider/nyc-taxi-data and http://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html for the description of the dataset and instructions for downloading. Downloading will result in about 227 GB of uncompressed data in CSV files. The download takes about an hour over a 1 Gbit connection (parallel downloading from s3.amazonaws.com recovers at least half of a 1 Gbit channel).\nSome of the files might not download fully. Check the file sizes and re-download any that seem doubtful. Some of the files might contain invalid rows. You can fix them as follows: sed -E /(.*,){18,}/d data/yellow_tripdata_2010-02.csv data/yellow_tripdata_2010-02.csv_\nsed -E /(.*,){18,}/d data/yellow_tripdata_2010-03.csv data/yellow_tripdata_2010-03.csv_\nmv data/yellow_tripdata_2010-02.csv_ data/yellow_tripdata_2010-02.csv\nmv data/yellow_tripdata_2010-03.csv_ data/yellow_tripdata_2010-03.csv Then the data must be pre-processed in PostgreSQL. This will create selections of points in the polygons (to match points on the map with the boroughs of New York City) and combine all the data into a single denormalized flat table by using a JOIN. To do this, you will need to install PostgreSQL with PostGIS support. Be careful when running initialize_database.sh and manually re-check that all the tables were created correctly. It takes about 20-30 minutes to process each month's worth of data in PostgreSQL, for a total of about 48 hours. You can check the number of downloaded rows as follows: time psql nyc-taxi-data -c SELECT count(*) FROM trips; \n### count\n 1298979494\n(1 row)\n\nreal 7m9.164s (This is slightly more than 1.1 billion rows reported by Mark Litwintschik in a series of blog posts.) The data in PostgreSQL uses 370 GB of space. Exporting the data from PostgreSQL: COPY ( \n SELECT trips . id , \n trips . vendor_id , \n trips . pickup_datetime , \n trips . dropoff_datetime , \n trips . store_and_fwd_flag , \n trips . rate_code_id , \n trips . pickup_longitude , \n trips . pickup_latitude , \n trips . dropoff_longitude , \n trips . dropoff_latitude , \n trips . passenger_count , \n trips . trip_distance , \n trips . fare_amount , \n trips . extra , \n trips . mta_tax , \n trips . tip_amount , \n trips . tolls_amount , \n trips . ehail_fee , \n trips . improvement_surcharge , \n trips . total_amount , \n trips . payment_type , \n trips . trip_type , \n trips . pickup , \n trips . dropoff , \n\n cab_types . type cab_type , \n\n weather . precipitation_tenths_of_mm rain , \n weather . snow_depth_mm , \n weather . snowfall_mm , \n weather . max_temperature_tenths_degrees_celsius max_temp , \n weather . min_temperature_tenths_degrees_celsius min_temp , \n weather . average_wind_speed_tenths_of_meters_per_second wind , \n\n pick_up . gid pickup_nyct2010_gid , \n pick_up . ctlabel pickup_ctlabel , \n pick_up . borocode pickup_borocode , \n pick_up . boroname pickup_boroname , \n pick_up . ct2010 pickup_ct2010 , \n pick_up . boroct2010 pickup_boroct2010 , \n pick_up . cdeligibil pickup_cdeligibil , \n pick_up . ntacode pickup_ntacode , \n pick_up . ntaname pickup_ntaname , \n pick_up . puma pickup_puma , \n\n drop_off . gid dropoff_nyct2010_gid , \n drop_off . ctlabel dropoff_ctlabel , \n drop_off . borocode dropoff_borocode , \n drop_off . boroname dropoff_boroname , \n drop_off . ct2010 dropoff_ct2010 , \n drop_off . boroct2010 dropoff_boroct2010 , \n drop_off . cdeligibil dropoff_cdeligibil , \n drop_off . ntacode dropoff_ntacode , \n drop_off . ntaname dropoff_ntaname , \n drop_off . puma dropoff_puma \n FROM trips \n LEFT JOIN cab_types \n ON trips . cab_type_id = cab_types . id \n LEFT JOIN central_park_weather_observations_raw weather \n ON weather . date = trips . pickup_datetime :: date \n LEFT JOIN nyct2010 pick_up \n ON pick_up . gid = trips . pickup_nyct2010_gid \n LEFT JOIN nyct2010 drop_off \n ON drop_off . gid = trips . dropoff_nyct2010_gid ) TO /opt/milovidov/nyc-taxi-data/trips.tsv ; The data snapshot is created at a speed of about 50 MB per second. While creating the snapshot, PostgreSQL reads from the disk at a speed of about 28 MB per second.\nThis takes about 5 hours. The resulting TSV file is 590612904969 bytes. Create a temporary table in ClickHouse: CREATE TABLE trips ( trip_id UInt32 , vendor_id String , pickup_datetime DateTime , dropoff_datetime Nullable ( DateTime ), store_and_fwd_flag Nullable ( FixedString ( 1 )), rate_code_id Nullable ( UInt8 ), pickup_longitude Nullable ( Float64 ), pickup_latitude Nullable ( Float64 ), dropoff_longitude Nullable ( Float64 ), dropoff_latitude Nullable ( Float64 ), passenger_count Nullable ( UInt8 ), trip_distance Nullable ( Float64 ), fare_amount Nullable ( Float32 ), extra Nullable ( Float32 ), mta_tax Nullable ( Float32 ), tip_amount Nullable ( Float32 ), tolls_amount Nullable ( Float32 ), ehail_fee Nullable ( Float32 ), improvement_surcharge Nullable ( Float32 ), total_amount Nullable ( Float32 ), payment_type Nullable ( String ), trip_type Nullable ( UInt8 ), pickup Nullable ( String ), dropoff Nullable ( String ), cab_type Nullable ( String ), precipitation Nullable ( UInt8 ), snow_depth Nullable ( UInt8 ), snowfall Nullable ( UInt8 ), max_temperature Nullable ( UInt8 ), min_temperature Nullable ( UInt8 ), average_wind_speed Nullable ( UInt8 ), pickup_nyct2010_gid Nullable ( UInt8 ), pickup_ctlabel Nullable ( String ), pickup_borocode Nullable ( UInt8 ), pickup_boroname Nullable ( String ), pickup_ct2010 Nullable ( String ), pickup_boroct2010 Nullable ( String ), pickup_cdeligibil Nullable ( FixedString ( 1 )), pickup_ntacode Nullable ( String ), pickup_ntaname Nullable ( String ), pickup_puma Nullable ( String ), dropoff_nyct2010_gid Nullable ( UInt8 ), dropoff_ctlabel Nullable ( String ), dropoff_borocode Nullable ( UInt8 ), dropoff_boroname Nullable ( String ), dropoff_ct2010 Nullable ( String ), dropoff_boroct2010 Nullable ( String ), dropoff_cdeligibil Nullable ( String ), dropoff_ntacode Nullable ( String ), dropoff_ntaname Nullable ( String ), dropoff_puma Nullable ( String ) ) ENGINE = Log ; It is needed for converting fields to more correct data types and, if possible, to eliminate NULLs. time clickhouse-client --query= INSERT INTO trips FORMAT TabSeparated trips.tsv\n\nreal 75m56.214s Data is read at a speed of 112-140 Mb/second.\nLoading data into a Log type table in one stream took 76 minutes.\nThe data in this table uses 142 GB. (Importing data directly from Postgres is also possible using COPY ... TO PROGRAM .) Unfortunately, all the fields associated with the weather (precipitation...average_wind_speed) were filled with NULL. Because of this, we will remove them from the final data set. To start, we'll create a table on a single server. Later we will make the table distributed. Create and populate a summary table: CREATE TABLE trips_mergetree\nENGINE = MergeTree(pickup_date, pickup_datetime, 8192)\nAS SELECT\n\ntrip_id,\nCAST(vendor_id AS Enum8( 1 = 1, 2 = 2, CMT = 3, VTS = 4, DDS = 5, B02512 = 10, B02598 = 11, B02617 = 12, B02682 = 13, B02764 = 14)) AS vendor_id,\ntoDate(pickup_datetime) AS pickup_date,\nifNull(pickup_datetime, toDateTime(0)) AS pickup_datetime,\ntoDate(dropoff_datetime) AS dropoff_date,\nifNull(dropoff_datetime, toDateTime(0)) AS dropoff_datetime,\nassumeNotNull(store_and_fwd_flag) IN ( Y , 1 , 2 ) AS store_and_fwd_flag,\nassumeNotNull(rate_code_id) AS rate_code_id,\nassumeNotNull(pickup_longitude) AS pickup_longitude,\nassumeNotNull(pickup_latitude) AS pickup_latitude,\nassumeNotNull(dropoff_longitude) AS dropoff_longitude,\nassumeNotNull(dropoff_latitude) AS dropoff_latitude,\nassumeNotNull(passenger_count) AS passenger_count,\nassumeNotNull(trip_distance) AS trip_distance,\nassumeNotNull(fare_amount) AS fare_amount,\nassumeNotNull(extra) AS extra,\nassumeNotNull(mta_tax) AS mta_tax,\nassumeNotNull(tip_amount) AS tip_amount,\nassumeNotNull(tolls_amount) AS tolls_amount,\nassumeNotNull(ehail_fee) AS ehail_fee,\nassumeNotNull(improvement_surcharge) AS improvement_surcharge,\nassumeNotNull(total_amount) AS total_amount,\nCAST((assumeNotNull(payment_type) AS pt) IN ( CSH , CASH , Cash , CAS , Cas , 1 ) ? CSH : (pt IN ( CRD , Credit , Cre , CRE , CREDIT , 2 ) ? CRE : (pt IN ( NOC , No Charge , No , 3 ) ? NOC : (pt IN ( DIS , Dispute , Dis , 4 ) ? DIS : UNK ))) AS Enum8( CSH = 1, CRE = 2, UNK = 0, NOC = 3, DIS = 4)) AS payment_type_,\nassumeNotNull(trip_type) AS trip_type,\nifNull(toFixedString(unhex(pickup), 25), toFixedString( , 25)) AS pickup,\nifNull(toFixedString(unhex(dropoff), 25), toFixedString( , 25)) AS dropoff,\nCAST(assumeNotNull(cab_type) AS Enum8( yellow = 1, green = 2, uber = 3)) AS cab_type,\n\nassumeNotNull(pickup_nyct2010_gid) AS pickup_nyct2010_gid,\ntoFloat32(ifNull(pickup_ctlabel, 0 )) AS pickup_ctlabel,\nassumeNotNull(pickup_borocode) AS pickup_borocode,\nCAST(assumeNotNull(pickup_boroname) AS Enum8( Manhattan = 1, Queens = 4, Brooklyn = 3, = 0, Bronx = 2, Staten Island = 5)) AS pickup_boroname,\ntoFixedString(ifNull(pickup_ct2010, 000000 ), 6) AS pickup_ct2010,\ntoFixedString(ifNull(pickup_boroct2010, 0000000 ), 7) AS pickup_boroct2010,\nCAST(assumeNotNull(ifNull(pickup_cdeligibil, )) AS Enum8( = 0, E = 1, I = 2)) AS pickup_cdeligibil,\ntoFixedString(ifNull(pickup_ntacode, 0000 ), 4) AS pickup_ntacode,\n\nCAST(assumeNotNull(pickup_ntaname) AS Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195)) AS pickup_ntaname,\n\ntoUInt16(ifNull(pickup_puma, 0 )) AS pickup_puma,\n\nassumeNotNull(dropoff_nyct2010_gid) AS dropoff_nyct2010_gid,\ntoFloat32(ifNull(dropoff_ctlabel, 0 )) AS dropoff_ctlabel,\nassumeNotNull(dropoff_borocode) AS dropoff_borocode,\nCAST(assumeNotNull(dropoff_boroname) AS Enum8( Manhattan = 1, Queens = 4, Brooklyn = 3, = 0, Bronx = 2, Staten Island = 5)) AS dropoff_boroname,\ntoFixedString(ifNull(dropoff_ct2010, 000000 ), 6) AS dropoff_ct2010,\ntoFixedString(ifNull(dropoff_boroct2010, 0000000 ), 7) AS dropoff_boroct2010,\nCAST(assumeNotNull(ifNull(dropoff_cdeligibil, )) AS Enum8( = 0, E = 1, I = 2)) AS dropoff_cdeligibil,\ntoFixedString(ifNull(dropoff_ntacode, 0000 ), 4) AS dropoff_ntacode,\n\nCAST(assumeNotNull(dropoff_ntaname) AS Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195)) AS dropoff_ntaname,\n\ntoUInt16(ifNull(dropoff_puma, 0 )) AS dropoff_puma\n\nFROM trips This takes 3030 seconds at a speed of about 428,000 rows per second.\nTo load it faster, you can create the table with the Log engine instead of MergeTree . In this case, the download works faster than 200 seconds. The table uses 126 GB of disk space. :) SELECT formatReadableSize(sum(bytes)) FROM system.parts WHERE table = trips_mergetree AND active\n\nSELECT formatReadableSize(sum(bytes))\nFROM system.parts\nWHERE (table = trips_mergetree ) AND active\n\n\u250c\u2500formatReadableSize(sum(bytes))\u2500\u2510\n\u2502 126.18 GiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Among other things, you can run the OPTIMIZE query on MergeTree. But it's not required, since everything will be fine without it.", - "title": "How to import the raw data" - }, - { - "location": "/index.html#results-on-single-server", - "text": "Q1: SELECT cab_type , count ( * ) FROM trips_mergetree GROUP BY cab_type 0.490 seconds. Q2: SELECT passenger_count , avg ( total_amount ) FROM trips_mergetree GROUP BY passenger_count 1.224 seconds. Q3: SELECT passenger_count , toYear ( pickup_date ) AS year , count ( * ) FROM trips_mergetree GROUP BY passenger_count , year 2.104 seconds. Q4: SELECT passenger_count , toYear ( pickup_date ) AS year , round ( trip_distance ) AS distance , count ( * ) FROM trips_mergetree GROUP BY passenger_count , year , distance ORDER BY year , count ( * ) DESC 3.593 seconds. The following server was used: Two Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical kernels total,\n128 GiB RAM,\n8x6 TB HD on hardware RAID-5 Execution time is the best of three runsBut starting from the second run, queries read data from the file system cache. No further caching occurs: the data is read out and processed in each run. Creating a table on three servers: On each server: CREATE TABLE default.trips_mergetree_third ( trip_id UInt32, vendor_id Enum8( 1 = 1, 2 = 2, CMT = 3, VTS = 4, DDS = 5, B02512 = 10, B02598 = 11, B02617 = 12, B02682 = 13, B02764 = 14), pickup_date Date, pickup_datetime DateTime, dropoff_date Date, dropoff_datetime DateTime, store_and_fwd_flag UInt8, rate_code_id UInt8, pickup_longitude Float64, pickup_latitude Float64, dropoff_longitude Float64, dropoff_latitude Float64, passenger_count UInt8, trip_distance Float64, fare_amount Float32, extra Float32, mta_tax Float32, tip_amount Float32, tolls_amount Float32, ehail_fee Float32, improvement_surcharge Float32, total_amount Float32, payment_type_ Enum8( UNK = 0, CSH = 1, CRE = 2, NOC = 3, DIS = 4), trip_type UInt8, pickup FixedString(25), dropoff FixedString(25), cab_type Enum8( yellow = 1, green = 2, uber = 3), pickup_nyct2010_gid UInt8, pickup_ctlabel Float32, pickup_borocode UInt8, pickup_boroname Enum8( = 0, Manhattan = 1, Bronx = 2, Brooklyn = 3, Queens = 4, Staten Island = 5), pickup_ct2010 FixedString(6), pickup_boroct2010 FixedString(7), pickup_cdeligibil Enum8( = 0, E = 1, I = 2), pickup_ntacode FixedString(4), pickup_ntaname Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195), pickup_puma UInt16, dropoff_nyct2010_gid UInt8, dropoff_ctlabel Float32, dropoff_borocode UInt8, dropoff_boroname Enum8( = 0, Manhattan = 1, Bronx = 2, Brooklyn = 3, Queens = 4, Staten Island = 5), dropoff_ct2010 FixedString(6), dropoff_boroct2010 FixedString(7), dropoff_cdeligibil Enum8( = 0, E = 1, I = 2), dropoff_ntacode FixedString(4), dropoff_ntaname Enum16( = 0, Airport = 1, Allerton-Pelham Gardens = 2, Annadale-Huguenot-Prince\\ s Bay-Eltingville = 3, Arden Heights = 4, Astoria = 5, Auburndale = 6, Baisley Park = 7, Bath Beach = 8, Battery Park City-Lower Manhattan = 9, Bay Ridge = 10, Bayside-Bayside Hills = 11, Bedford = 12, Bedford Park-Fordham North = 13, Bellerose = 14, Belmont = 15, Bensonhurst East = 16, Bensonhurst West = 17, Borough Park = 18, Breezy Point-Belle Harbor-Rockaway Park-Broad Channel = 19, Briarwood-Jamaica Hills = 20, Brighton Beach = 21, Bronxdale = 22, Brooklyn Heights-Cobble Hill = 23, Brownsville = 24, Bushwick North = 25, Bushwick South = 26, Cambria Heights = 27, Canarsie = 28, Carroll Gardens-Columbia Street-Red Hook = 29, Central Harlem North-Polo Grounds = 30, Central Harlem South = 31, Charleston-Richmond Valley-Tottenville = 32, Chinatown = 33, Claremont-Bathgate = 34, Clinton = 35, Clinton Hill = 36, Co-op City = 37, College Point = 38, Corona = 39, Crotona Park East = 40, Crown Heights North = 41, Crown Heights South = 42, Cypress Hills-City Line = 43, DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill = 44, Douglas Manor-Douglaston-Little Neck = 45, Dyker Heights = 46, East Concourse-Concourse Village = 47, East Elmhurst = 48, East Flatbush-Farragut = 49, East Flushing = 50, East Harlem North = 51, East Harlem South = 52, East New York = 53, East New York (Pennsylvania Ave) = 54, East Tremont = 55, East Village = 56, East Williamsburg = 57, Eastchester-Edenwald-Baychester = 58, Elmhurst = 59, Elmhurst-Maspeth = 60, Erasmus = 61, Far Rockaway-Bayswater = 62, Flatbush = 63, Flatlands = 64, Flushing = 65, Fordham South = 66, Forest Hills = 67, Fort Greene = 68, Fresh Meadows-Utopia = 69, Ft. Totten-Bay Terrace-Clearview = 70, Georgetown-Marine Park-Bergen Beach-Mill Basin = 71, Glen Oaks-Floral Park-New Hyde Park = 72, Glendale = 73, Gramercy = 74, Grasmere-Arrochar-Ft. Wadsworth = 75, Gravesend = 76, Great Kills = 77, Greenpoint = 78, Grymes Hill-Clifton-Fox Hills = 79, Hamilton Heights = 80, Hammels-Arverne-Edgemere = 81, Highbridge = 82, Hollis = 83, Homecrest = 84, Hudson Yards-Chelsea-Flatiron-Union Square = 85, Hunters Point-Sunnyside-West Maspeth = 86, Hunts Point = 87, Jackson Heights = 88, Jamaica = 89, Jamaica Estates-Holliswood = 90, Kensington-Ocean Parkway = 91, Kew Gardens = 92, Kew Gardens Hills = 93, Kingsbridge Heights = 94, Laurelton = 95, Lenox Hill-Roosevelt Island = 96, Lincoln Square = 97, Lindenwood-Howard Beach = 98, Longwood = 99, Lower East Side = 100, Madison = 101, Manhattanville = 102, Marble Hill-Inwood = 103, Mariner\\ s Harbor-Arlington-Port Ivory-Graniteville = 104, Maspeth = 105, Melrose South-Mott Haven North = 106, Middle Village = 107, Midtown-Midtown South = 108, Midwood = 109, Morningside Heights = 110, Morrisania-Melrose = 111, Mott Haven-Port Morris = 112, Mount Hope = 113, Murray Hill = 114, Murray Hill-Kips Bay = 115, New Brighton-Silver Lake = 116, New Dorp-Midland Beach = 117, New Springville-Bloomfield-Travis = 118, North Corona = 119, North Riverdale-Fieldston-Riverdale = 120, North Side-South Side = 121, Norwood = 122, Oakland Gardens = 123, Oakwood-Oakwood Beach = 124, Ocean Hill = 125, Ocean Parkway South = 126, Old Astoria = 127, Old Town-Dongan Hills-South Beach = 128, Ozone Park = 129, Park Slope-Gowanus = 130, Parkchester = 131, Pelham Bay-Country Club-City Island = 132, Pelham Parkway = 133, Pomonok-Flushing Heights-Hillcrest = 134, Port Richmond = 135, Prospect Heights = 136, Prospect Lefferts Gardens-Wingate = 137, Queens Village = 138, Queensboro Hill = 139, Queensbridge-Ravenswood-Long Island City = 140, Rego Park = 141, Richmond Hill = 142, Ridgewood = 143, Rikers Island = 144, Rosedale = 145, Rossville-Woodrow = 146, Rugby-Remsen Village = 147, Schuylerville-Throgs Neck-Edgewater Park = 148, Seagate-Coney Island = 149, Sheepshead Bay-Gerritsen Beach-Manhattan Beach = 150, SoHo-TriBeCa-Civic Center-Little Italy = 151, Soundview-Bruckner = 152, Soundview-Castle Hill-Clason Point-Harding Park = 153, South Jamaica = 154, South Ozone Park = 155, Springfield Gardens North = 156, Springfield Gardens South-Brookville = 157, Spuyten Duyvil-Kingsbridge = 158, St. Albans = 159, Stapleton-Rosebank = 160, Starrett City = 161, Steinway = 162, Stuyvesant Heights = 163, Stuyvesant Town-Cooper Village = 164, Sunset Park East = 165, Sunset Park West = 166, Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill = 167, Turtle Bay-East Midtown = 168, University Heights-Morris Heights = 169, Upper East Side-Carnegie Hill = 170, Upper West Side = 171, Van Cortlandt Village = 172, Van Nest-Morris Park-Westchester Square = 173, Washington Heights North = 174, Washington Heights South = 175, West Brighton = 176, West Concourse = 177, West Farms-Bronx River = 178, West New Brighton-New Brighton-St. George = 179, West Village = 180, Westchester-Unionport = 181, Westerleigh = 182, Whitestone = 183, Williamsbridge-Olinville = 184, Williamsburg = 185, Windsor Terrace = 186, Woodhaven = 187, Woodlawn-Wakefield = 188, Woodside = 189, Yorkville = 190, park-cemetery-etc-Bronx = 191, park-cemetery-etc-Brooklyn = 192, park-cemetery-etc-Manhattan = 193, park-cemetery-etc-Queens = 194, park-cemetery-etc-Staten Island = 195), dropoff_puma UInt16) ENGINE = MergeTree(pickup_date, pickup_datetime, 8192) On the source server: CREATE TABLE trips_mergetree_x3 AS trips_mergetree_third ENGINE = Distributed ( perftest , default , trips_mergetree_third , rand ()) The following query redistributes data: INSERT INTO trips_mergetree_x3 SELECT * FROM trips_mergetree This takes 2454 seconds. On three servers: Q1: 0.212 seconds.\nQ2: 0.438 seconds.\nQ3: 0.733 seconds.\nQ4: 1.241 seconds. No surprises here, since the queries are scaled linearly. We also have results from a cluster of 140 servers: Q1: 0.028 sec.\nQ2: 0.043 sec.\nQ3: 0.051 sec.\nQ4: 0.072 sec. In this case, the query processing time is determined above all by network latency.\nWe ran queries using a client located in a Yandex datacenter in Finland on a cluster in Russia, which added about 20 ms of latency.", - "title": "Results on single server" - }, - { - "location": "/index.html#summary", - "text": "nodes Q1 Q2 Q3 Q4\n 1 0.490 1.224 2.104 3.593\n 3 0.212 0.438 0.733 1.241\n140 0.028 0.043 0.051 0.072", - "title": "Summary" - }, - { - "location": "/index.html#amplab-big-data-benchmark", - "text": "See https://amplab.cs.berkeley.edu/benchmark/ Sign up for a free account at https://aws.amazon.com . You will need a credit card, email and phone number.Get a new access key at https://console.aws.amazon.com/iam/home?nc2=h_m_sc#security_credential Run the following in the console: sudo apt-get install s3cmd\nmkdir tiny ; cd tiny ; \ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/tiny/ . cd ..\nmkdir 1node ; cd 1node ; \ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/1node/ . cd ..\nmkdir 5nodes ; cd 5nodes ; \ns3cmd sync s3://big-data-benchmark/pavlo/text-deflate/5nodes/ . cd .. Run the following ClickHouse queries: CREATE TABLE rankings_tiny ( \n pageURL String , \n pageRank UInt32 , \n avgDuration UInt32 ) ENGINE = Log ; CREATE TABLE uservisits_tiny ( \n sourceIP String , \n destinationURL String , \n visitDate Date , \n adRevenue Float32 , \n UserAgent String , \n cCode FixedString ( 3 ), \n lCode FixedString ( 6 ), \n searchWord String , \n duration UInt32 ) ENGINE = MergeTree ( visitDate , visitDate , 8192 ); CREATE TABLE rankings_1node ( \n pageURL String , \n pageRank UInt32 , \n avgDuration UInt32 ) ENGINE = Log ; CREATE TABLE uservisits_1node ( \n sourceIP String , \n destinationURL String , \n visitDate Date , \n adRevenue Float32 , \n UserAgent String , \n cCode FixedString ( 3 ), \n lCode FixedString ( 6 ), \n searchWord String , \n duration UInt32 ) ENGINE = MergeTree ( visitDate , visitDate , 8192 ); CREATE TABLE rankings_5nodes_on_single ( \n pageURL String , \n pageRank UInt32 , \n avgDuration UInt32 ) ENGINE = Log ; CREATE TABLE uservisits_5nodes_on_single ( \n sourceIP String , \n destinationURL String , \n visitDate Date , \n adRevenue Float32 , \n UserAgent String , \n cCode FixedString ( 3 ), \n lCode FixedString ( 6 ), \n searchWord String , \n duration UInt32 ) ENGINE = MergeTree ( visitDate , visitDate , 8192 ); Go back to the console: for i in tiny/rankings/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO rankings_tiny FORMAT CSV ; done for i in tiny/uservisits/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO uservisits_tiny FORMAT CSV ; done for i in 1node/rankings/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO rankings_1node FORMAT CSV ; done for i in 1node/uservisits/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO uservisits_1node FORMAT CSV ; done for i in 5nodes/rankings/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO rankings_5nodes_on_single FORMAT CSV ; done for i in 5nodes/uservisits/*.deflate ; do echo $i ; zlib-flate -uncompress $i | clickhouse-client --host = example-perftest01j --query = INSERT INTO uservisits_5nodes_on_single FORMAT CSV ; done Queries for obtaining data samples: SELECT pageURL , pageRank FROM rankings_1node WHERE pageRank 1000 SELECT substring ( sourceIP , 1 , 8 ), sum ( adRevenue ) FROM uservisits_1node GROUP BY substring ( sourceIP , 1 , 8 ) SELECT \n sourceIP , \n sum ( adRevenue ) AS totalRevenue , \n avg ( pageRank ) AS pageRank FROM rankings_1node ALL INNER JOIN ( \n SELECT \n sourceIP , \n destinationURL AS pageURL , \n adRevenue \n FROM uservisits_1node \n WHERE ( visitDate 1980-01-01 ) AND ( visitDate 1980-04-01 ) ) USING pageURL GROUP BY sourceIP ORDER BY totalRevenue DESC LIMIT 1", - "title": "AMPLab Big Data Benchmark" - }, - { - "location": "/index.html#wikistat", - "text": "See: http://dumps.wikimedia.org/other/pagecounts-raw/ Creating a table: CREATE TABLE wikistat ( \n date Date , \n time DateTime , \n project String , \n subproject String , \n path String , \n hits UInt64 , \n size UInt64 ) ENGINE = MergeTree ( date , ( path , time ), 8192 ); Loading data: for i in { 2007 ..2016 } ; do for j in { 01 ..12 } ; do echo $i - $j 2 ; curl -sSL http://dumps.wikimedia.org/other/pagecounts-raw/ $i / $i - $j / | grep -oE pagecounts-[0-9]+-[0-9]+\\.gz ; done ; done | sort | uniq | tee links.txt\ncat links.txt | while read link ; do wget http://dumps.wikimedia.org/other/pagecounts-raw/ $( echo $link | sed -r s/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1/ ) / $( echo $link | sed -r s/pagecounts-([0-9]{4})([0-9]{2})[0-9]{2}-[0-9]+\\.gz/\\1-\\2/ ) / $link ; done \nls -1 /opt/wikistat/ | grep gz | while read i ; do echo $i ; gzip -cd /opt/wikistat/ $i | ./wikistat-loader --time = $( echo -n $i | sed -r s/pagecounts-([0-9]{4})([0-9]{2})([0-9]{2})-([0-9]{2})([0-9]{2})([0-9]{2})\\.gz/\\1-\\2-\\3 \\4-00-00/ ) | clickhouse-client --query = INSERT INTO wikistat FORMAT TabSeparated ; done", - "title": "WikiStat" - }, - { - "location": "/index.html#terabyte-of-click-logs-from-criteo", - "text": "Download the data from http://labs.criteo.com/downloads/download-terabyte-click-logs/ Create a table to import the log to: CREATE TABLE criteo_log ( date Date , clicked UInt8 , int1 Int32 , int2 Int32 , int3 Int32 , int4 Int32 , int5 Int32 , int6 Int32 , int7 Int32 , int8 Int32 , int9 Int32 , int10 Int32 , int11 Int32 , int12 Int32 , int13 Int32 , cat1 String , cat2 String , cat3 String , cat4 String , cat5 String , cat6 String , cat7 String , cat8 String , cat9 String , cat10 String , cat11 String , cat12 String , cat13 String , cat14 String , cat15 String , cat16 String , cat17 String , cat18 String , cat19 String , cat20 String , cat21 String , cat22 String , cat23 String , cat24 String , cat25 String , cat26 String ) ENGINE = Log Download the data: for i in { 00 ..23 } ; do echo $i ; zcat datasets/criteo/day_ ${ i #0 } .gz | sed -r s/^/2000-01- ${ i /00/24 } \\t/ | clickhouse-client --host = example-perftest01j --query = INSERT INTO criteo_log FORMAT TabSeparated ; done Create a table for the converted data: CREATE TABLE criteo ( \n date Date , \n clicked UInt8 , \n int1 Int32 , \n int2 Int32 , \n int3 Int32 , \n int4 Int32 , \n int5 Int32 , \n int6 Int32 , \n int7 Int32 , \n int8 Int32 , \n int9 Int32 , \n int10 Int32 , \n int11 Int32 , \n int12 Int32 , \n int13 Int32 , \n icat1 UInt32 , \n icat2 UInt32 , \n icat3 UInt32 , \n icat4 UInt32 , \n icat5 UInt32 , \n icat6 UInt32 , \n icat7 UInt32 , \n icat8 UInt32 , \n icat9 UInt32 , \n icat10 UInt32 , \n icat11 UInt32 , \n icat12 UInt32 , \n icat13 UInt32 , \n icat14 UInt32 , \n icat15 UInt32 , \n icat16 UInt32 , \n icat17 UInt32 , \n icat18 UInt32 , \n icat19 UInt32 , \n icat20 UInt32 , \n icat21 UInt32 , \n icat22 UInt32 , \n icat23 UInt32 , \n icat24 UInt32 , \n icat25 UInt32 , \n icat26 UInt32 ) ENGINE = MergeTree ( date , intHash32 ( icat1 ), ( date , intHash32 ( icat1 )), 8192 ) Transform data from the raw log and put it in the second table: INSERT INTO criteo SELECT date , clicked , int1 , int2 , int3 , int4 , int5 , int6 , int7 , int8 , int9 , int10 , int11 , int12 , int13 , reinterpretAsUInt32 ( unhex ( cat1 )) AS icat1 , reinterpretAsUInt32 ( unhex ( cat2 )) AS icat2 , reinterpretAsUInt32 ( unhex ( cat3 )) AS icat3 , reinterpretAsUInt32 ( unhex ( cat4 )) AS icat4 , reinterpretAsUInt32 ( unhex ( cat5 )) AS icat5 , reinterpretAsUInt32 ( unhex ( cat6 )) AS icat6 , reinterpretAsUInt32 ( unhex ( cat7 )) AS icat7 , reinterpretAsUInt32 ( unhex ( cat8 )) AS icat8 , reinterpretAsUInt32 ( unhex ( cat9 )) AS icat9 , reinterpretAsUInt32 ( unhex ( cat10 )) AS icat10 , reinterpretAsUInt32 ( unhex ( cat11 )) AS icat11 , reinterpretAsUInt32 ( unhex ( cat12 )) AS icat12 , reinterpretAsUInt32 ( unhex ( cat13 )) AS icat13 , reinterpretAsUInt32 ( unhex ( cat14 )) AS icat14 , reinterpretAsUInt32 ( unhex ( cat15 )) AS icat15 , reinterpretAsUInt32 ( unhex ( cat16 )) AS icat16 , reinterpretAsUInt32 ( unhex ( cat17 )) AS icat17 , reinterpretAsUInt32 ( unhex ( cat18 )) AS icat18 , reinterpretAsUInt32 ( unhex ( cat19 )) AS icat19 , reinterpretAsUInt32 ( unhex ( cat20 )) AS icat20 , reinterpretAsUInt32 ( unhex ( cat21 )) AS icat21 , reinterpretAsUInt32 ( unhex ( cat22 )) AS icat22 , reinterpretAsUInt32 ( unhex ( cat23 )) AS icat23 , reinterpretAsUInt32 ( unhex ( cat24 )) AS icat24 , reinterpretAsUInt32 ( unhex ( cat25 )) AS icat25 , reinterpretAsUInt32 ( unhex ( cat26 )) AS icat26 FROM criteo_log ; DROP TABLE criteo_log ;", - "title": "Terabyte of click logs from Criteo" - }, - { - "location": "/index.html#star-schema-benchmark", - "text": "Compiling dbgen: https://github.com/vadimtk/ssb-dbgen git clone git@github.com:vadimtk/ssb-dbgen.git cd ssb-dbgen\nmake There will be some warnings during the process, but this is normal. Place dbgen and dists.dss in any location with 800 GB of free disk space. Generating data: ./dbgen -s 1000 -T c\n./dbgen -s 1000 -T l Creating tables in ClickHouse: CREATE TABLE lineorder ( \n LO_ORDERKEY UInt32 , \n LO_LINENUMBER UInt8 , \n LO_CUSTKEY UInt32 , \n LO_PARTKEY UInt32 , \n LO_SUPPKEY UInt32 , \n LO_ORDERDATE Date , \n LO_ORDERPRIORITY String , \n LO_SHIPPRIORITY UInt8 , \n LO_QUANTITY UInt8 , \n LO_EXTENDEDPRICE UInt32 , \n LO_ORDTOTALPRICE UInt32 , \n LO_DISCOUNT UInt8 , \n LO_REVENUE UInt32 , \n LO_SUPPLYCOST UInt32 , \n LO_TAX UInt8 , \n LO_COMMITDATE Date , \n LO_SHIPMODE String ) Engine = MergeTree ( LO_ORDERDATE ,( LO_ORDERKEY , LO_LINENUMBER , LO_ORDERDATE ), 8192 ); CREATE TABLE customer ( \n C_CUSTKEY UInt32 , \n C_NAME String , \n C_ADDRESS String , \n C_CITY String , \n C_NATION String , \n C_REGION String , \n C_PHONE String , \n C_MKTSEGMENT String , \n C_FAKEDATE Date ) Engine = MergeTree ( C_FAKEDATE ,( C_CUSTKEY , C_FAKEDATE ), 8192 ); CREATE TABLE part ( \n P_PARTKEY UInt32 , \n P_NAME String , \n P_MFGR String , \n P_CATEGORY String , \n P_BRAND String , \n P_COLOR String , \n P_TYPE String , \n P_SIZE UInt8 , \n P_CONTAINER String , \n P_FAKEDATE Date ) Engine = MergeTree ( P_FAKEDATE ,( P_PARTKEY , P_FAKEDATE ), 8192 ); CREATE TABLE lineorderd AS lineorder ENGINE = Distributed ( perftest_3shards_1replicas , default , lineorder , rand ()); CREATE TABLE customerd AS customer ENGINE = Distributed ( perftest_3shards_1replicas , default , customer , rand ()); CREATE TABLE partd AS part ENGINE = Distributed ( perftest_3shards_1replicas , default , part , rand ()); For testing on a single server, just use MergeTree tables.\nFor distributed testing, you need to configure the perftest_3shards_1replicas cluster in the config file.\nNext, create MergeTree tables on each server and a Distributed above them. Downloading data (change 'customer' to 'customerd' in the distributed version): cat customer.tbl | sed s/$/2000-01-01/ | clickhouse-client --query INSERT INTO customer FORMAT CSV \ncat lineorder.tbl | clickhouse-client --query INSERT INTO lineorder FORMAT CSV", - "title": "Star Schema Benchmark" - }, - { - "location": "/index.html#interfaces", - "text": "To explore the system's capabilities, download data to tables, or make manual queries, use the clickhouse-client program.", - "title": "Interfaces" - }, - { - "location": "/index.html#command-line-client", - "text": "To work from the command line, you can use clickhouse-client : $ clickhouse-client\nClickHouse client version 0 .0.26176.\nConnecting to localhost:9000.\nConnected to ClickHouse server version 0 .0.26176.\n\n: ) The client supports command-line options and configuration files. For more information, see \" Configuring \".", - "title": "Command-line client" - }, - { - "location": "/index.html#usage", - "text": "The client can be used in interactive and non-interactive (batch) mode.\nTo use batch mode, specify the 'query' parameter, or send data to 'stdin' (it verifies that 'stdin' is not a terminal), or both.\nSimilar to the HTTP interface, when using the 'query' parameter and sending data to 'stdin', the request is a concatenation of the 'query' parameter, a line feed, and the data in 'stdin'. This is convenient for large INSERT queries. Example of using the client to insert data: echo -ne 1, some text , 2016-08-14 00:00:00 \\n2, some more text , 2016-08-14 00:00:01 | clickhouse-client --database = test --query = INSERT INTO test FORMAT CSV ; \n\ncat _EOF | clickhouse-client --database=test --query= INSERT INTO test FORMAT CSV ; 3, some text , 2016-08-14 00:00:00 4, some more text , 2016-08-14 00:00:01 _EOF \n\ncat file.csv | clickhouse-client --database = test --query = INSERT INTO test FORMAT CSV ; In batch mode, the default data format is TabSeparated. You can set the format in the FORMAT clause of the query. By default, you can only process a single query in batch mode. To make multiple queries from a \"script,\" use the --multiquery parameter. This works for all queries except INSERT. Query results are output consecutively without additional separators.\nSimilarly, to process a large number of queries, you can run 'clickhouse-client' for each query. Note that it may take tens of milliseconds to launch the 'clickhouse-client' program. In interactive mode, you get a command line where you can enter queries. If 'multiline' is not specified (the default):To run the query, press Enter. The semicolon is not necessary at the end of the query. To enter a multiline query, enter a backslash \\ before the line feed. After you press Enter, you will be asked to enter the next line of the query. If multiline is specified:To run a query, end it with a semicolon and press Enter. If the semicolon was omitted at the end of the entered line, you will be asked to enter the next line of the query. Only a single query is run, so everything after the semicolon is ignored. You can specify \\G instead of or after the semicolon. This indicates Vertical format. In this format, each value is printed on a separate line, which is convenient for wide tables. This unusual feature was added for compatibility with the MySQL CLI. The command line is based on 'readline' (and 'history' or 'libedit', or without a library, depending on the build). In other words, it uses the familiar keyboard shortcuts and keeps a history.\nThe history is written to ~/.clickhouse-client-history . By default, the format used is PrettyCompact. You can change the format in the FORMAT clause of the query, or by specifying \\G at the end of the query, using the --format or --vertical argument in the command line, or using the client configuration file. To exit the client, press Ctrl+D (or Ctrl+C), or enter one of the following instead of a query:\"exit\", \"quit\", \"logout\", \"\u0443\u0447\u0448\u0435\", \"\u0439\u0433\u0448\u0435\", \"\u0434\u0449\u043f\u0449\u0433\u0435\", \"exit;\", \"quit;\", \"logout;\", \"\u0443\u0447\u0448\u0435\u0436\", \"\u0439\u0433\u0448\u0435\u0436\", \"\u0434\u0449\u043f\u0449\u0433\u0435\u0436\", \"q\", \"\u0439\", \"q\", \"Q\", \":q\", \"\u0439\", \"\u0419\", \"\u0416\u0439\" When processing a query, the client shows: Progress, which is updated no more than 10 times per second (by default). For quick queries, the progress might not have time to be displayed. The formatted query after parsing, for debugging. The result in the specified format. The number of lines in the result, the time passed, and the average speed of query processing. You can cancel a long query by pressing Ctrl+C. However, you will still need to wait a little for the server to abort the request. It is not possible to cancel a query at certain stages. If you don't wait and press Ctrl+C a second time, the client will exit. The command-line client allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".", - "title": "Usage" - }, - { - "location": "/index.html#configuring", - "text": "You can pass parameters to clickhouse-client (all parameters have a default value) using: From the Command Line Command-line options override the default values and settings in configuration files. Configuration files. Settings in the configuration files override the default values.", - "title": "Configuring" - }, - { - "location": "/index.html#command-line-options", - "text": "--host, -h -\u2013 The server name, 'localhost' by default. You can use either the name or the IPv4 or IPv6 address. --port \u2013 The port to connect to. Default value: 9000. Note that the HTTP interface and the native interface use different ports. --user, -u \u2013 The username. Default value: default. --password \u2013 The password. Default value: empty string. --query, -q \u2013 The query to process when using non-interactive mode. --database, -d \u2013 Select the current default database. Default value: the current database from the server settings ('default' by default). --multiline, -m \u2013 If specified, allow multiline queries (do not send the query on Enter). --multiquery, -n \u2013 If specified, allow processing multiple queries separated by commas. Only works in non-interactive mode. --format, -f \u2013 Use the specified default format to output the result. --vertical, -E \u2013 If specified, use the Vertical format by default to output the result. This is the same as '--format=Vertical'. In this format, each value is printed on a separate line, which is helpful when displaying wide tables. --time, -t \u2013 If specified, print the query execution time to 'stderr' in non-interactive mode. --stacktrace \u2013 If specified, also print the stack trace if an exception occurs. -config-file \u2013 The name of the configuration file.", - "title": "Command line options" - }, - { - "location": "/index.html#configuration-files", - "text": "clickhouse-client uses the first existing file of the following: Defined in the -config-file parameter. ./clickhouse-client.xml \\~/.clickhouse-client/config.xml /etc/clickhouse-client/config.xml Example of a config file: config \n user username /user \n password password /password /config", - "title": "Configuration files" - }, - { - "location": "/index.html#http-interface", - "text": "The HTTP interface lets you use ClickHouse on any platform from any programming language. We use it for working from Java and Perl, as well as shell scripts. In other departments, the HTTP interface is used from Perl, Python, and Go. The HTTP interface is more limited than the native interface, but it has better compatibility. By default, clickhouse-server listens for HTTP on port 8123 (this can be changed in the config).\nIf you make a GET / request without parameters, it returns the string \"Ok\" (with a line feed at the end). You can use this in health-check scripts. $ curl http://localhost:8123/ \nOk. Send the request as a URL 'query' parameter, or as a POST. Or send the beginning of the query in the 'query' parameter, and the rest in the POST (we'll explain later why this is necessary). The size of the URL is limited to 16 KB, so keep this in mind when sending large queries. If successful, you receive the 200 response code and the result in the response body.\nIf an error occurs, you receive the 500 response code and an error description text in the response body. When using the GET method, 'readonly' is set. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter. Examples: $ curl http://localhost:8123/?query=SELECT%201 1 \n\n$ wget -O- -q http://localhost:8123/?query=SELECT 1 1 \n\n$ GET http://localhost:8123/?query=SELECT 1 1 \n\n$ echo -ne GET /?query=SELECT%201 HTTP/1.0\\r\\n\\r\\n | nc localhost 8123 \nHTTP/1.0 200 OK\nConnection: Close\nDate: Fri, 16 Nov 2012 19 :21:50 GMT 1 As you can see, curl is somewhat inconvenient in that spaces must be URL escaped.Although wget escapes everything itself, we don't recommend using it because it doesn't work well over HTTP 1.1 when using keep-alive and Transfer-Encoding: chunked. $ echo SELECT 1 | curl http://localhost:8123/ --data-binary @- 1 \n\n$ echo SELECT 1 | curl http://localhost:8123/?query= --data-binary @- 1 \n\n$ echo 1 | curl http://localhost:8123/?query=SELECT --data-binary @- 1 If part of the query is sent in the parameter, and part in the POST, a line feed is inserted between these two data parts.\nExample (this won't work): $ echo ECT 1 | curl http://localhost:8123/?query=SEL --data-binary @-\nCode: 59 , e.displayText () = DB::Exception: Syntax error: failed at position 0 : SEL\nECT 1 \n, expected One of: SHOW TABLES, SHOW DATABASES, SELECT, INSERT, CREATE, ATTACH, RENAME, DROP, DETACH, USE, SET, OPTIMIZE., e.what () = DB::Exception By default, data is returned in TabSeparated format (for more information, see the \"Formats\" section).\nYou use the FORMAT clause of the query to request any other format. $ echo SELECT 1 FORMAT Pretty | curl http://localhost:8123/? --data-binary @-\n\u250f\u2501\u2501\u2501\u2513\n\u2503 1 \u2503\n\u2521\u2501\u2501\u2501\u2529\n\u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2518 The POST method of transmitting data is necessary for INSERT queries. In this case, you can write the beginning of the query in the URL parameter, and use POST to pass the data to insert. The data to insert could be, for example, a tab-separated dump from MySQL. In this way, the INSERT query replaces LOAD DATA LOCAL INFILE from MySQL. Examples: Creating a table: echo CREATE TABLE t (a UInt8) ENGINE = Memory | POST http://localhost:8123/ Using the familiar INSERT query for data insertion: echo INSERT INTO t VALUES (1),(2),(3) | POST http://localhost:8123/ Data can be sent separately from the query: echo (4),(5),(6) | POST http://localhost:8123/?query=INSERT INTO t VALUES You can specify any data format. The 'Values' format is the same as what is used when writing INSERT INTO t VALUES: echo (7),(8),(9) | POST http://localhost:8123/?query=INSERT INTO t FORMAT Values To insert data from a tab-separated dump, specify the corresponding format: echo -ne 10\\n11\\n12\\n | POST http://localhost:8123/?query=INSERT INTO t FORMAT TabSeparated Reading the table contents. Data is output in random order due to parallel query processing: $ GET http://localhost:8123/?query=SELECT a FROM t 7 8 9 10 11 12 1 2 3 4 5 6 Deleting the table. POST http://localhost:8123/?query=DROP TABLE t For successful requests that don't return a data table, an empty response body is returned. You can use the internal ClickHouse compression format when transmitting data. The compressed data has a non-standard format, and you will need to use the special clickhouse-compressor program to work with it (it is installed with the clickhouse-client package). If you specified 'compress=1' in the URL, the server will compress the data it sends you.\nIf you specified 'decompress=1' in the URL, the server will decompress the same data that you pass in the POST method. It is also possible to use the standard gzip-based HTTP compression. To send a POST request compressed using gzip, append the request header Content-Encoding: gzip .\nIn order for ClickHouse to compress the response using gzip, you must append Accept-Encoding: gzip to the request headers, and enable the ClickHouse setting enable_http_compression . You can use this to reduce network traffic when transmitting a large amount of data, or for creating dumps that are immediately compressed. You can use the 'database' URL parameter to specify the default database. $ echo SELECT number FROM numbers LIMIT 10 | curl http://localhost:8123/?database=system --data-binary @- 0 1 2 3 4 5 6 7 8 9 By default, the database that is registered in the server settings is used as the default database. By default, this is the database called 'default'. Alternatively, you can always specify the database using a dot before the table name. The username and password can be indicated in one of two ways: Using HTTP Basic Authentication. Example: echo SELECT 1 | curl http://user:password@localhost:8123/ -d @- In the 'user' and 'password' URL parameters. Example: echo SELECT 1 | curl http://localhost:8123/?user=user password=password -d @- If the user name is not indicated, the username 'default' is used. If the password is not indicated, an empty password is used.\nYou can also use the URL parameters to specify any settings for processing a single query, or entire profiles of settings. Example:\nhttp://localhost:8123/?profile=web max_rows_to_read=1000000000 query=SELECT+1 For more information, see the section \"Settings\". $ echo SELECT number FROM system.numbers LIMIT 10 | curl http://localhost:8123/? --data-binary @- 0 1 2 3 4 5 6 7 8 9 For information about other parameters, see the section \"SET\". Similarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to add the session_id GET parameter to the request. You can use any string as the session ID. By default, the session is terminated after 60 seconds of inactivity. To change this timeout, modify the default_session_timeout setting in the server configuration, or add the session_timeout GET parameter to the request. To check the session status, use the session_check=1 parameter. Only one query at a time can be executed within a single session. You have the option to receive information about the progress of query execution in X-ClickHouse-Progress headers. To do this, enable the setting send_progress_in_http_headers. Running requests don't stop automatically if the HTTP connection is lost. Parsing and data formatting are performed on the server side, and using the network might be ineffective.\nThe optional 'query_id' parameter can be passed as the query ID (any string). For more information, see the section \"Settings, replace_running_query\". The optional 'quota_key' parameter can be passed as the quota key (any string). For more information, see the section \"Quotas\". The HTTP interface allows passing external data (external temporary tables) for querying. For more information, see the section \"External data for query processing\".", - "title": "HTTP interface" - }, - { - "location": "/index.html#response-buffering", - "text": "You can enable response buffering on the server side. The buffer_size and wait_end_of_query URL parameters are provided for this purpose. buffer_size determines the number of bytes in the result to buffer in the server memory. If the result body is larger than this threshold, the buffer is written to the HTTP channel, and the remaining data is sent directly to the HTTP channel. To ensure that the entire response is buffered, set wait_end_of_query=1 . In this case, the data that is not stored in memory will be buffered in a temporary server file. Example: curl -sS http://localhost:8123/?max_result_bytes=4000000 buffer_size=3000000 wait_end_of_query=1 -d SELECT toUInt8(number) FROM system.numbers LIMIT 9000000 FORMAT RowBinary Use buffering to avoid situations where a query processing error occurred after the response code and HTTP headers were sent to the client. In this situation, an error message is written at the end of the response body, and on the client side, the error can only be detected at the parsing stage.", - "title": "Response buffering" - }, - { - "location": "/index.html#jdbc-driver", - "text": "There is an official JDBC driver for ClickHouse. See here .", - "title": "JDBC driver" - }, - { - "location": "/index.html#native-interface-tcp", - "text": "The native interface is used in the \"clickhouse-client\" command-line client for interaction between servers with distributed query processing, and also in C++ programs. We will only cover the command-line client.", - "title": "Native interface (TCP)" - }, - { - "location": "/index.html#libraries-from-third-party-developers", - "text": "There are libraries for working with ClickHouse for: Python infi.clickhouse_orm sqlalchemy-clickhouse clickhouse-driver clickhouse-client PHP clickhouse-php-client PhpClickHouseClient phpClickHouse clickhouse-client Go clickhouse go-clickhouse mailrugo-clickhouse golang-clickhouse NodeJs clickhouse (NodeJs) node-clickhouse Perl perl-DBD-ClickHouse HTTP-ClickHouse AnyEvent-ClickHouse Ruby clickhouse (Ruby) R clickhouse-r RClickhouse .NET ClickHouse-Net C++ clickhouse-cpp Elixir clickhousex clickhouse_ecto Java clickhouse-client-java We have not tested these libraries. They are listed in random order.", - "title": "Libraries from third-party developers" - }, - { - "location": "/index.html#visual-interfaces-from-third-party-developers", - "text": "", - "title": "Visual interfaces from third-party developers" - }, - { - "location": "/index.html#tabix", - "text": "Web interface for ClickHouse in the Tabix project.", - "title": "Tabix" - }, - { - "location": "/index.html#features", - "text": "Works with ClickHouse directly from the browser, without the need to install additional software. Query editor with syntax highlighting. Auto-completion of commands. Tools for graphical analysis of query execution. Color scheme options. Tabix documentation .", - "title": "Features:" - }, - { - "location": "/index.html#houseops", - "text": "HouseOps is a unique Desktop ClickHouse Ops UI / IDE for OSX, Linux and Windows.", - "title": "HouseOps" - }, - { - "location": "/index.html#features_1", - "text": "Query builder; Database manangement (soon); Users manangement (soon); Real-Time Data Analytics (soon); Cluster/Infra monitoring (soon); Cluster manangement (soon); Kafka and Replicated tables monitoring (soon); And a lot of others features (soon) for you take a beautiful implementation of ClickHouse.", - "title": "Features:" - }, - { - "location": "/index.html#query-language", - "text": "", - "title": "Query language" - }, - { - "location": "/index.html#queries", - "text": "", - "title": "Queries" - }, - { - "location": "/index.html#create-database", - "text": "Creating db_name databases CREATE DATABASE [ IF NOT EXISTS ] db_name A database is just a directory for tables.\nIf IF NOT EXISTS is included, the query won't return an error if the database already exists.", - "title": "CREATE DATABASE" - }, - { - "location": "/index.html#create-table", - "text": "The CREATE TABLE query can have several forms. CREATE [ TEMPORARY ] TABLE [ IF NOT EXISTS ] [ db .] name [ ON CLUSTER cluster ] ( \n name1 [ type1 ] [ DEFAULT | MATERIALIZED | ALIAS expr1 ], \n name2 [ type2 ] [ DEFAULT | MATERIALIZED | ALIAS expr2 ], \n ... ) ENGINE = engine Creates a table named 'name' in the 'db' database or the current database if 'db' is not set, with the structure specified in brackets and the 'engine' engine.\nThe structure of the table is a list of column descriptions. If indexes are supported by the engine, they are indicated as parameters for the table engine. A column description is name type in the simplest case. Example: RegionID UInt32 .\nExpressions can also be defined for default values (see below). CREATE [ TEMPORARY ] TABLE [ IF NOT EXISTS ] [ db .] name AS [ db2 .] name2 [ ENGINE = engine ] Creates a table with the same structure as another table. You can specify a different engine for the table. If the engine is not specified, the same engine will be used as for the db2.name2 table. CREATE [ TEMPORARY ] TABLE [ IF NOT EXISTS ] [ db .] name ENGINE = engine AS SELECT ... Creates a table with a structure like the result of the SELECT query, with the 'engine' engine, and fills it with data from SELECT. In all cases, if IF NOT EXISTS is specified, the query won't return an error if the table already exists. In this case, the query won't do anything.", - "title": "CREATE TABLE" - }, - { - "location": "/index.html#default-values", - "text": "The column description can specify an expression for a default value, in one of the following ways: DEFAULT expr , MATERIALIZED expr , ALIAS expr .\nExample: URLDomain String DEFAULT domain(URL) . If an expression for the default value is not defined, the default values will be set to zeros for numbers, empty strings for strings, empty arrays for arrays, and 0000-00-00 for dates or 0000-00-00 00:00:00 for dates with time. NULLs are not supported. If the default expression is defined, the column type is optional. If there isn't an explicitly defined type, the default expression type is used. Example: EventDate DEFAULT toDate(EventTime) \u2013 the 'Date' type will be used for the 'EventDate' column. If the data type and default expression are defined explicitly, this expression will be cast to the specified type using type casting functions. Example: Hits UInt32 DEFAULT 0 means the same thing as Hits UInt32 DEFAULT toUInt32(0) . Default expressions may be defined as an arbitrary expression from table constants and columns. When creating and changing the table structure, it checks that expressions don't contain loops. For INSERT, it checks that expressions are resolvable \u2013 that all columns they can be calculated from have been passed. DEFAULT expr Normal default value. If the INSERT query doesn't specify the corresponding column, it will be filled in by computing the corresponding expression. MATERIALIZED expr Materialized expression. Such a column can't be specified for INSERT, because it is always calculated.\nFor an INSERT without a list of columns, these columns are not considered.\nIn addition, this column is not substituted when using an asterisk in a SELECT query. This is to preserve the invariant that the dump obtained using SELECT * can be inserted back into the table using INSERT without specifying the list of columns. ALIAS expr Synonym. Such a column isn't stored in the table at all.\nIts values can't be inserted in a table, and it is not substituted when using an asterisk in a SELECT query.\nIt can be used in SELECTs if the alias is expanded during query parsing. When using the ALTER query to add new columns, old data for these columns is not written. Instead, when reading old data that does not have values for the new columns, expressions are computed on the fly by default. However, if running the expressions requires different columns that are not indicated in the query, these columns will additionally be read, but only for the blocks of data that need it. If you add a new column to a table but later change its default expression, the values used for old data will change (for data where values were not stored on the disk). Note that when running background merges, data for columns that are missing in one of the merging parts is written to the merged part. It is not possible to set default values for elements in nested data structures.", - "title": "Default values" - }, - { - "location": "/index.html#temporary-tables", - "text": "In all cases, if TEMPORARY is specified, a temporary table will be created. Temporary tables have the following characteristics: Temporary tables disappear when the session ends, including if the connection is lost. A temporary table is created with the Memory engine. The other table engines are not supported. The DB can't be specified for a temporary table. It is created outside of databases. If a temporary table has the same name as another one and a query specifies the table name without specifying the DB, the temporary table will be used. For distributed query processing, temporary tables used in a query are passed to remote servers. In most cases, temporary tables are not created manually, but when using external data for a query, or for distributed (GLOBAL) IN . For more information, see the appropriate sections", - "title": "Temporary tables" - }, - { - "location": "/index.html#distributed-ddl-queries-on-cluster-clause", - "text": "The CREATE , DROP , ALTER , and RENAME queries support distributed execution on a cluster.\nFor example, the following query creates the all_hits Distributed table on each host in cluster : CREATE TABLE IF NOT EXISTS all_hits ON CLUSTER cluster ( p Date , i Int32 ) ENGINE = Distributed ( cluster , default , hits ) In order to run these queries correctly, each host must have the same cluster definition (to simplify syncing configs, you can use substitutions from ZooKeeper). They must also connect to the ZooKeeper servers.\nThe local version of the query will eventually be implemented on each host in the cluster, even if some hosts are currently not available. The order for executing queries within a single host is guaranteed. ALTER queries are not yet supported for replicated tables.", - "title": "Distributed DDL queries (ON CLUSTER clause)" - }, - { - "location": "/index.html#create-view", - "text": "CREATE [ MATERIALIZED ] VIEW [ IF NOT EXISTS ] [ db .] name [ TO [ db .] name ] [ ENGINE = engine ] [ POPULATE ] AS SELECT ... Creates a view. There are two types of views: normal and MATERIALIZED. When creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data. A materialized view works as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view. Normal views don't store any data, but just perform a read from another table. In other words, a normal view is nothing more than a saved query. When reading from a view, this saved query is used as a subquery in the FROM clause. As an example, assume you've created a view: CREATE VIEW view AS SELECT ... and written a query: SELECT a , b , c FROM view This query is fully equivalent to using the subquery: SELECT a , b , c FROM ( SELECT ...) Materialized views store data transformed by the corresponding SELECT query. When creating a materialized view, you must specify ENGINE \u2013 the table engine for storing data. A materialized view is arranged as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view. If you specify POPULATE, the existing table data is inserted in the view when creating it, as if making a CREATE TABLE ... AS SELECT ... . Otherwise, the query contains only the data inserted in the table after creating the view. We don't recommend using POPULATE, since data inserted in the table during the view creation will not be inserted in it. A SELECT query can contain DISTINCT , GROUP BY , ORDER BY , LIMIT ... Note that the corresponding conversions are performed independently on each block of inserted data. For example, if GROUP BY is set, data is aggregated during insertion, but only within a single packet of inserted data. The data won't be further aggregated. The exception is when using an ENGINE that independently performs data aggregation, such as SummingMergeTree . The execution of ALTER queries on materialized views has not been fully developed, so they might be inconvenient. If the materialized view uses the construction TO [db.]name , you can DETACH the view, run ALTER for the target table, and then ATTACH the previously detached ( DETACH ) view. Views look the same as normal tables. For example, they are listed in the result of the SHOW TABLES query. There isn't a separate query for deleting views. To delete a view, use DROP TABLE .", - "title": "CREATE VIEW" - }, - { - "location": "/index.html#attach", - "text": "This query is exactly the same as CREATE , but instead of the word CREATE it uses the word ATTACH . The query doesn't create data on the disk, but assumes that data is already in the appropriate places, and just adds information about the table to the server.\nAfter executing an ATTACH query, the server will know about the existence of the table. If the table was previously detached ( DETACH ), meaning that its structure is known, you can use shorthand without defining the structure. ATTACH TABLE [ IF NOT EXISTS ] [ db .] name This query is used when starting the server. The server stores table metadata as files with ATTACH queries, which it simply runs at launch (with the exception of system tables, which are explicitly created on the server).", - "title": "ATTACH" - }, - { - "location": "/index.html#drop", - "text": "This query has two types: DROP DATABASE and DROP TABLE . DROP DATABASE [ IF EXISTS ] db [ ON CLUSTER cluster ] Deletes all tables inside the 'db' database, then deletes the 'db' database itself.\nIf IF EXISTS is specified, it doesn't return an error if the database doesn't exist. DROP [ TEMPORARY ] TABLE [ IF EXISTS ] [ db .] name [ ON CLUSTER cluster ] Deletes the table.\nIf IF EXISTS is specified, it doesn't return an error if the table doesn't exist or the database doesn't exist.", - "title": "DROP" - }, - { - "location": "/index.html#detach", - "text": "Deletes information about the 'name' table from the server. The server stops knowing about the table's existence. DETACH TABLE [ IF EXISTS ] [ db .] name This does not delete the table's data or metadata. On the next server launch, the server will read the metadata and find out about the table again.\nSimilarly, a \"detached\" table can be re-attached using the ATTACH query (with the exception of system tables, which do not have metadata stored for them). There is no DETACH DATABASE query.", - "title": "DETACH" - }, - { - "location": "/index.html#rename", - "text": "Renames one or more tables. RENAME TABLE [ db11 .] name11 TO [ db12 .] name12 , [ db21 .] name21 TO [ db22 .] name22 , ... [ ON CLUSTER cluster ] All tables are renamed under global locking. Renaming tables is a light operation. If you indicated another database after TO, the table will be moved to this database. However, the directories with databases must reside in the same file system (otherwise, an error is returned).", - "title": "RENAME" - }, - { - "location": "/index.html#alter", - "text": "The ALTER query is only supported for *MergeTree tables, as well as Merge and Distributed . The query has several variations.", - "title": "ALTER" - }, - { - "location": "/index.html#column-manipulations", - "text": "Changing the table structure. ALTER TABLE [ db ]. name [ ON CLUSTER cluster ] ADD | DROP | MODIFY COLUMN ... In the query, specify a list of one or more comma-separated actions.\nEach action is an operation on a column. The following actions are supported: ADD COLUMN name [ type ] [ default_expr ] [ AFTER name_after ] Adds a new column to the table with the specified name, type, and default_expr (see the section \"Default expressions\"). If you specify AFTER name_after (the name of another column), the column is added after the specified one in the list of table columns. Otherwise, the column is added to the end of the table. Note that there is no way to add a column to the beginning of a table. For a chain of actions, 'name_after' can be the name of a column that is added in one of the previous actions. Adding a column just changes the table structure, without performing any actions with data. The data doesn't appear on the disk after ALTER. If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). If the data is missing for a column when reading from the table, it is filled in with default values (by performing the default expression if there is one, or using zeros or empty strings). The column appears on the disk after merging data parts (see MergeTree). This approach allows us to complete the ALTER query instantly, without increasing the volume of old data. DROP COLUMN name Deletes the column with the name 'name'.\nDeletes data from the file system. Since this deletes entire files, the query is completed almost instantly. MODIFY COLUMN name [ type ] [ default_expr ] Changes the 'name' column's type to 'type' and/or the default expression to 'default_expr'. When changing the type, values are converted as if the 'toType' function were applied to them. If only the default expression is changed, the query doesn't do anything complex, and is completed almost instantly. Changing the column type is the only complex action \u2013 it changes the contents of files with data. For large tables, this may take a long time. There are several processing stages: Preparing temporary (new) files with modified data. Renaming old files. Renaming the temporary (new) files to the old names. Deleting the old files. Only the first stage takes time. If there is a failure at this stage, the data is not changed.\nIf there is a failure during one of the successive stages, data can be restored manually. The exception is if the old files were deleted from the file system but the data for the new files did not get written to the disk and was lost. There is no support for changing the column type in arrays and nested data structures. The ALTER query lets you create and delete separate elements (columns) in nested data structures, but not whole nested data structures. To add a nested data structure, you can add columns with a name like name.nested_name and the type Array(T) . A nested data structure is equivalent to multiple array columns with a name that has the same prefix before the dot. There is no support for deleting columns in the primary key or the sampling key (columns that are in the ENGINE expression). Changing the type for columns that are included in the primary key is only possible if this change does not cause the data to be modified (for example, it is allowed to add values to an Enum or change a type with DateTime to UInt32 ). If the ALTER query is not sufficient for making the table changes you need, you can create a new table, copy the data to it using the INSERT SELECT query, then switch the tables using the RENAME query and delete the old table. The ALTER query blocks all reads and writes for the table. In other words, if a long SELECT is running at the time of the ALTER query, the ALTER query will wait for it to complete. At the same time, all new queries to the same table will wait while this ALTER is running. For tables that don't store data themselves (such as Merge and Distributed ), ALTER just changes the table structure, and does not change the structure of subordinate tables. For example, when running ALTER for a Distributed table, you will also need to run ALTER for the tables on all remote servers. The ALTER query for changing columns is replicated. The instructions are saved in ZooKeeper, then each replica applies them. All ALTER queries are run in the same order. The query waits for the appropriate actions to be completed on the other replicas. However, a query to change columns in a replicated table can be interrupted, and all actions will be performed asynchronously.", - "title": "Column manipulations" - }, - { - "location": "/index.html#manipulations-with-partitions-and-parts", - "text": "It only works for tables in the MergeTree family. The following operations are available: DETACH PARTITION \u2013 Move a partition to the 'detached' directory and forget it. DROP PARTITION \u2013 Delete a partition. ATTACH PART|PARTITION \u2013 Add a new part or partition from the detached directory to the table. FREEZE PARTITION \u2013 Create a backup of a partition. FETCH PARTITION \u2013 Download a partition from another server. Each type of query is covered separately below. A partition in a table is data for a single calendar month. This is determined by the values of the date key specified in the table engine parameters. Each month's data is stored separately in order to simplify manipulations with this data. A \"part\" in the table is part of the data from a single partition, sorted by the primary key. You can use the system.parts table to view the set of table parts and partitions: SELECT * FROM system . parts WHERE active active \u2013 Only count active parts. Inactive parts are, for example, source parts remaining after merging to a larger part \u2013 these parts are deleted approximately 10 minutes after merging. Another way to view a set of parts and partitions is to go into the directory with table data.\nData directory: /var/lib/clickhouse/data/database/table/ ,where /var/lib/clickhouse/ is the path to the ClickHouse data, 'database' is the database name, and 'table' is the table name. Example: $ ls -l /var/lib/clickhouse/data/test/visits/\ntotal 48 \ndrwxrwxrwx 2 clickhouse clickhouse 20480 May 5 02 :58 20140317_20140323_2_2_0\ndrwxrwxrwx 2 clickhouse clickhouse 20480 May 5 02 :58 20140317_20140323_4_4_0\ndrwxrwxrwx 2 clickhouse clickhouse 4096 May 5 02 :55 detached\n-rw-rw-rw- 1 clickhouse clickhouse 2 May 5 02 :58 increment.txt Here, 20140317_20140323_2_2_0 and 20140317_20140323_4_4_0 are the directories of data parts. Let's break down the name of the first part: 20140317_20140323_2_2_0 . 20140317 is the minimum date of the data in the chunk. 20140323 is the maximum date of the data in the chunk. 2 is the minimum number of the data block. 2 is the maximum number of the data block. 0 is the chunk level (the depth of the merge tree it is formed from). Each piece relates to a single partition and contains data for just one month. 201403 is the name of the partition. A partition is a set of parts for a single month. On an operating server, you can't manually change the set of parts or their data on the file system, since the server won't know about it.\nFor non-replicated tables, you can do this when the server is stopped, but we don't recommended it.\nFor replicated tables, the set of parts can't be changed in any case. The detached directory contains parts that are not used by the server - detached from the table using the ALTER ... DETACH query. Parts that are damaged are also moved to this directory, instead of deleting them. You can add, delete, or modify the data in the 'detached' directory at any time \u2013 the server won't know about this until you make the ALTER TABLE ... ATTACH query. ALTER TABLE [ db .] table DETACH PARTITION name Move all data for partitions named 'name' to the 'detached' directory and forget about them.\nThe partition name is specified in YYYYMM format. It can be indicated in single quotes or without them. After the query is executed, you can do whatever you want with the data in the 'detached' directory \u2014 delete it from the file system, or just leave it. The query is replicated \u2013 data will be moved to the 'detached' directory and forgotten on all replicas. The query can only be sent to a leader replica. To find out if a replica is a leader, perform SELECT to the 'system.replicas' system table. Alternatively, it is easier to make a query on all replicas, and all except one will throw an exception. ALTER TABLE [ db .] table DROP PARTITION name The same as the DETACH operation. Deletes data from the table. Data parts will be tagged as inactive and will be completely deleted in approximately 10 minutes. The query is replicated \u2013 data will be deleted on all replicas. ALTER TABLE [ db .] table ATTACH PARTITION | PART name Adds data to the table from the 'detached' directory. It is possible to add data for an entire partition or a separate part. For a part, specify the full name of the part in single quotes. The query is replicated. Each replica checks whether there is data in the 'detached' directory. If there is data, it checks the integrity, verifies that it matches the data on the server that initiated the query, and then adds it if everything is correct. If not, it downloads data from the query requestor replica, or from another replica where the data has already been added. So you can put data in the 'detached' directory on one replica, and use the ALTER ... ATTACH query to add it to the table on all replicas. ALTER TABLE [ db .] table FREEZE PARTITION name Creates a local backup of one or multiple partitions. The name can be the full name of the partition (for example, 201403), or its prefix (for example, 2014): then the backup will be created for all the corresponding partitions. The query does the following: for a data snapshot at the time of execution, it creates hardlinks to table data in the directory /var/lib/clickhouse/shadow/N/... /var/lib/clickhouse/ is the working ClickHouse directory from the config. N is the incremental number of the backup. The same structure of directories is created inside the backup as inside /var/lib/clickhouse/ .\nIt also performs 'chmod' for all files, forbidding writes to them. The backup is created almost instantly (but first it waits for current queries to the corresponding table to finish running). At first, the backup doesn't take any space on the disk. As the system works, the backup can take disk space, as data is modified. If the backup is made for old enough data, it won't take space on the disk. After creating the backup, data from /var/lib/clickhouse/shadow/ can be copied to the remote server and then deleted on the local server.\nThe entire backup process is performed without stopping the server. The ALTER ... FREEZE PARTITION query is not replicated. A local backup is only created on the local server. As an alternative, you can manually copy data from the /var/lib/clickhouse/data/database/table directory.\nBut if you do this while the server is running, race conditions are possible when copying directories with files being added or changed, and the backup may be inconsistent. You can do this if the server isn't running \u2013 then the resulting data will be the same as after the ALTER TABLE t FREEZE PARTITION query. ALTER TABLE ... FREEZE PARTITION only copies data, not table metadata. To make a backup of table metadata, copy the file /var/lib/clickhouse/metadata/database/table.sql To restore from a backup: Use the CREATE query to create the table if it doesn't exist. The query can be taken from an .sql file (replace ATTACH in it with CREATE ). Copy the data from the data/database/table/ directory inside the backup to the /var/lib/clickhouse/data/database/table/detached/ directory. Run ALTER TABLE ... ATTACH PARTITION YYYYMM queries, where YYYYMM is the month, for every month. In this way, data from the backup will be added to the table.\nRestoring from a backup doesn't require stopping the server.", - "title": "Manipulations with partitions and parts" - }, - { - "location": "/index.html#backups-and-replication", - "text": "Replication provides protection from device failures. If all data disappeared on one of your replicas, follow the instructions in the \"Restoration after failure\" section to restore it. For protection from device failures, you must use replication. For more information about replication, see the section \"Data replication\". Backups protect against human error (accidentally deleting data, deleting the wrong data or in the wrong cluster, or corrupting data).\nFor high-volume databases, it can be difficult to copy backups to remote servers. In such cases, to protect from human error, you can keep a backup on the same server (it will reside in /var/lib/clickhouse/shadow/ ). ALTER TABLE [ db .] table FETCH PARTITION name FROM path-in-zookeeper This query only works for replicatable tables. It downloads the specified partition from the shard that has its ZooKeeper path specified in the FROM clause, then puts it in the detached directory for the specified table. Although the query is called ALTER TABLE , it does not change the table structure, and does not immediately change the data available in the table. Data is placed in the detached directory. You can use the ALTER TABLE ... ATTACH query to attach the data. The FROM clause specifies the path in ZooKeeper . For example, /clickhouse/tables/01-01/visits .\nBefore downloading, the system checks that the partition exists and the table structure matches. The most appropriate replica is selected automatically from the healthy replicas. The ALTER ... FETCH PARTITION query is not replicated. The partition will be downloaded to the 'detached' directory only on the local server. Note that if after this you use the ALTER TABLE ... ATTACH query to add data to the table, the data will be added on all replicas (on one of the replicas it will be added from the 'detached' directory, and on the rest it will be loaded from neighboring replicas).", - "title": "Backups and replication" - }, - { - "location": "/index.html#synchronicity-of-alter-queries", - "text": "For non-replicatable tables, all ALTER queries are performed synchronously. For replicatable tables, the query just adds instructions for the appropriate actions to ZooKeeper , and the actions themselves are performed as soon as possible. However, the query can wait for these actions to be completed on all the replicas. For ALTER ... ATTACH|DETACH|DROP queries, you can use the replication_alter_partitions_sync setting to set up waiting.\nPossible values: 0 \u2013 do not wait; 1 \u2013 only wait for own execution (default); 2 \u2013 wait for all.", - "title": "Synchronicity of ALTER queries" - }, - { - "location": "/index.html#show-databases", - "text": "SHOW DATABASES [ INTO OUTFILE filename ] [ FORMAT format ] Prints a list of all databases.\nThis query is identical to SELECT name FROM system.databases [INTO OUTFILE filename] [FORMAT format] . See also the section \"Formats\".", - "title": "SHOW DATABASES" - }, - { - "location": "/index.html#show-tables", - "text": "SHOW [ TEMPORARY ] TABLES [ FROM db ] [ LIKE pattern ] [ INTO OUTFILE filename ] [ FORMAT format ] Displays a list of tables tables from the current database, or from the 'db' database if \"FROM db\" is specified. all tables, or tables whose name matches the pattern, if \"LIKE 'pattern'\" is specified. This query is identical to: SELECT name FROM system.tables WHERE database = 'db' [AND name LIKE 'pattern'] [INTO OUTFILE filename] [FORMAT format] . See also the section \"LIKE operator\".", - "title": "SHOW TABLES" - }, - { - "location": "/index.html#show-processlist", - "text": "SHOW PROCESSLIST [ INTO OUTFILE filename ] [ FORMAT format ] Outputs a list of queries currently being processed, other than SHOW PROCESSLIST queries. Prints a table containing the columns: user \u2013 The user who made the query. Keep in mind that for distributed processing, queries are sent to remote servers under the 'default' user. SHOW PROCESSLIST shows the username for a specific query, not for a query that this query initiated. address \u2013 The name of the host that the query was sent from. For distributed processing, on remote servers, this is the name of the query requestor host. To track where a distributed query was originally made from, look at SHOW PROCESSLIST on the query requestor server. elapsed \u2013 The execution time, in seconds. Queries are output in order of decreasing execution time. rows_read , bytes_read \u2013 How many rows and bytes of uncompressed data were read when processing the query. For distributed processing, data is totaled from all the remote servers. This is the data used for restrictions and quotas. memory_usage \u2013 Current RAM usage in bytes. See the setting 'max_memory_usage'. query \u2013 The query itself. In INSERT queries, the data for insertion is not output. query_id \u2013 The query identifier. Non-empty only if it was explicitly defined by the user. For distributed processing, the query ID is not passed to remote servers. This query is identical to: SELECT * FROM system.processes [INTO OUTFILE filename] [FORMAT format] . Tip (execute in the console): watch -n1 clickhouse-client --query= SHOW PROCESSLIST", - "title": "SHOW PROCESSLIST" - }, - { - "location": "/index.html#show-create-table", - "text": "SHOW CREATE [ TEMPORARY ] TABLE [ db .] table [ INTO OUTFILE filename ] [ FORMAT format ] Returns a single String -type 'statement' column, which contains a single value \u2013 the CREATE query used for creating the specified table.", - "title": "SHOW CREATE TABLE" - }, - { - "location": "/index.html#describe-table", - "text": "DESC | DESCRIBE TABLE [ db .] table [ INTO OUTFILE filename ] [ FORMAT format ] Returns two String -type columns: name and type , which indicate the names and types of columns in the specified table. Nested data structures are output in \"expanded\" format. Each column is shown separately, with the name after a dot.", - "title": "DESCRIBE TABLE" - }, - { - "location": "/index.html#exists", - "text": "EXISTS [ TEMPORARY ] TABLE [ db .] name [ INTO OUTFILE filename ] [ FORMAT format ] Returns a single UInt8 -type column, which contains the single value 0 if the table or database doesn't exist, or 1 if the table exists in the specified database.", - "title": "EXISTS" - }, - { - "location": "/index.html#use", - "text": "USE db Lets you set the current database for the session.\nThe current database is used for searching for tables if the database is not explicitly defined in the query with a dot before the table name.\nThis query can't be made when using the HTTP protocol, since there is no concept of a session.", - "title": "USE" - }, - { - "location": "/index.html#set", - "text": "SET param = value Allows you to set param to value . You can also make all the settings from the specified settings profile in a single query. To do this, specify 'profile' as the setting name. For more information, see the section \"Settings\".\nThe setting is made for the session, or for the server (globally) if GLOBAL is specified.\nWhen making a global setting, the setting is not applied to sessions already running, including the current session. It will only be used for new sessions. When the server is restarted, global settings made using SET are lost.\nTo make settings that persist after a server restart, you can only use the server's config file.", - "title": "SET" - }, - { - "location": "/index.html#optimize", - "text": "OPTIMIZE TABLE [ db .] name [ PARTITION partition ] [ FINAL ] Asks the table engine to do something for optimization.\nSupported only by *MergeTree engines, in which this query initializes a non-scheduled merge of data parts.\nIf you specify a PARTITION , only the specified partition will be optimized.\nIf you specify FINAL , optimization will be performed even when all the data is already in one part.", - "title": "OPTIMIZE" - }, - { - "location": "/index.html#insert", - "text": "Adding data. Basic query format: INSERT INTO [ db .] table [( c1 , c2 , c3 )] VALUES ( v11 , v12 , v13 ), ( v21 , v22 , v23 ), ... The query can specify a list of columns to insert [(c1, c2, c3)] . In this case, the rest of the columns are filled with: The values calculated from the DEFAULT expressions specified in the table definition. Zeros and empty strings, if DEFAULT expressions are not defined. If strict_insert_defaults=1 , columns that do not have DEFAULT defined must be listed in the query. Data can be passed to the INSERT in any format supported by ClickHouse. The format must be specified explicitly in the query: INSERT INTO [ db .] table [( c1 , c2 , c3 )] FORMAT format_name data_set For example, the following query format is identical to the basic version of INSERT ... VALUES: INSERT INTO [ db .] table [( c1 , c2 , c3 )] FORMAT Values ( v11 , v12 , v13 ), ( v21 , v22 , v23 ), ... ClickHouse removes all spaces and one line feed (if there is one) before the data. When forming a query, we recommend putting the data on a new line after the query operators (this is important if the data begins with spaces). Example: INSERT INTO t FORMAT TabSeparated 11 Hello , world ! 22 Qwerty You can insert data separately from the query by using the command-line client or the HTTP interface. For more information, see the section \" Interfaces \".", - "title": "INSERT" - }, - { - "location": "/index.html#inserting-the-results-of-select", - "text": "INSERT INTO [ db .] table [( c1 , c2 , c3 )] SELECT ... Columns are mapped according to their position in the SELECT clause. However, their names in the SELECT expression and the table for INSERT may differ. If necessary, type casting is performed. None of the data formats except Values allow setting values to expressions such as now() , 1 + 2 , and so on. The Values format allows limited use of expressions, but this is not recommended, because in this case inefficient code is used for their execution. Other queries for modifying data parts are not supported: UPDATE , DELETE , REPLACE , MERGE , UPSERT , INSERT UPDATE .\nHowever, you can delete old data using ALTER TABLE ... DROP PARTITION .", - "title": "Inserting the results of SELECT" - }, - { - "location": "/index.html#performance-considerations", - "text": "INSERT sorts the input data by primary key and splits them into partitions by month. If you insert data for mixed months, it can significantly reduce the performance of the INSERT query. To avoid this: Add data in fairly large batches, such as 100,000 rows at a time. Group data by month before uploading it to ClickHouse. Performance will not decrease if: Data is added in real time. You upload data that is usually sorted by time.", - "title": "Performance considerations" - }, - { - "location": "/index.html#select", - "text": "Data sampling. SELECT [ DISTINCT ] expr_list \n [ FROM [ db .] table | ( subquery ) | table_function ] [ FINAL ] \n [ SAMPLE sample_coeff ] \n [ ARRAY JOIN ...] \n [ GLOBAL ] ANY | ALL INNER | LEFT JOIN ( subquery ) | table USING columns_list \n [ PREWHERE expr ] \n [ WHERE expr ] \n [ GROUP BY expr_list ] [ WITH TOTALS ] \n [ HAVING expr ] \n [ ORDER BY expr_list ] \n [ LIMIT [ n , ] m ] \n [ UNION ALL ...] \n [ INTO OUTFILE filename ] \n [ FORMAT format ] \n [ LIMIT n BY columns ] All the clauses are optional, except for the required list of expressions immediately after SELECT.\nThe clauses below are described in almost the same order as in the query execution conveyor. If the query omits the DISTINCT , GROUP BY and ORDER BY clauses and the IN and JOIN subqueries, the query will be completely stream processed, using O(1) amount of RAM.\nOtherwise, the query might consume a lot of RAM if the appropriate restrictions are not specified: max_memory_usage , max_rows_to_group_by , max_rows_to_sort , max_rows_in_distinct , max_bytes_in_distinct , max_rows_in_set , max_bytes_in_set , max_rows_in_join , max_bytes_in_join , max_bytes_before_external_sort , max_bytes_before_external_group_by . For more information, see the section \"Settings\". It is possible to use external sorting (saving temporary tables to a disk) and external aggregation. The system does not have \"merge join\" .", - "title": "SELECT" - }, - { - "location": "/index.html#from-clause", - "text": "If the FROM clause is omitted, data will be read from the system.one table.\nThe 'system.one' table contains exactly one row (this table fulfills the same purpose as the DUAL table found in other DBMSs). The FROM clause specifies the table to read data from, or a subquery, or a table function; ARRAY JOIN and the regular JOIN may also be included (see below). Instead of a table, the SELECT subquery may be specified in brackets.\nIn this case, the subquery processing pipeline will be built into the processing pipeline of an external query.\nIn contrast to standard SQL, a synonym does not need to be specified after a subquery. For compatibility, it is possible to write 'AS name' after a subquery, but the specified name isn't used anywhere. A table function may be specified instead of a table. For more information, see the section \"Table functions\". To execute a query, all the columns listed in the query are extracted from the appropriate table. Any columns not needed for the external query are thrown out of the subqueries.\nIf a query does not list any columns (for example, SELECT count() FROM t), some column is extracted from the table anyway (the smallest one is preferred), in order to calculate the number of rows. The FINAL modifier can be used only for a SELECT from a CollapsingMergeTree table. When you specify FINAL, data is selected fully \"collapsed\". Keep in mind that using FINAL leads to a selection that includes columns related to the primary key, in addition to the columns specified in the SELECT. Additionally, the query will be executed in a single stream, and data will be merged during query execution. This means that when using FINAL, the query is processed more slowly. In most cases, you should avoid using FINAL. For more information, see the section \"CollapsingMergeTree engine\".", - "title": "FROM clause" - }, - { - "location": "/index.html#sample-clause", - "text": "The SAMPLE clause allows for approximated query processing. Approximated query processing is only supported by MergeTree* type tables, and only if the sampling expression was specified during table creation (see the section \"MergeTree engine\"). SAMPLE has the format SAMPLE k , where k is a decimal number from 0 to 1, or SAMPLE n , where 'n' is a sufficiently large integer. In the first case, the query will be executed on 'k' percent of data. For example, SAMPLE 0.1 runs the query on 10% of data.\nIn the second case, the query will be executed on a sample of no more than 'n' rows. For example, SAMPLE 10000000 runs the query on a maximum of 10,000,000 rows. Example: SELECT \n Title , \n count () * 10 AS PageViews FROM hits_distributed SAMPLE 0 . 1 WHERE \n CounterID = 34 \n AND toDate ( EventDate ) = toDate ( 2013-01-29 ) \n AND toDate ( EventDate ) = toDate ( 2013-02-04 ) \n AND NOT DontCountHits \n AND NOT Refresh \n AND Title != GROUP BY Title ORDER BY PageViews DESC LIMIT 1000 In this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value 'count()' is manually multiplied by 10. When using something like SAMPLE 10000000 , there isn't any information about which relative percent of data was processed or what the aggregate functions should be multiplied by, so this method of writing is not always appropriate to the situation. A sample with a relative coefficient is \"consistent\": if we look at all possible data that could be in the table, a sample (when using a single sampling expression specified during table creation) with the same coefficient always selects the same subset of possible data. In other words, a sample from different tables on different servers at different times is made the same way. For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This allows using the sample in subqueries in the IN clause, as well as for manually correlating results of different queries with samples.", - "title": "SAMPLE clause" - }, - { - "location": "/index.html#array-join-clause", - "text": "Allows executing JOIN with an array or nested data structure. The intent is similar to the 'arrayJoin' function, but its functionality is broader. ARRAY JOIN is essentially INNER JOIN with an array. Example: :) CREATE TABLE arrays_test (s String, arr Array(UInt8)) ENGINE = Memory\n\nCREATE TABLE arrays_test\n(\n s String,\n arr Array(UInt8)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.001 sec.\n\n:) INSERT INTO arrays_test VALUES ( Hello , [1,2]), ( World , [3,4,5]), ( Goodbye , [])\n\nINSERT INTO arrays_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM arrays_test\n\nSELECT *\nFROM arrays_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502\n\u2502 Goodbye \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, arr FROM arrays_test ARRAY JOIN arr\n\nSELECT s, arr\nFROM arrays_test\nARRAY JOIN arr\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2510\n\u2502 Hello \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502\n\u2502 World \u2502 4 \u2502\n\u2502 World \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. An alias can be specified for an array in the ARRAY JOIN clause. In this case, an array item can be accessed by this alias, but the array itself by the original name. Example: :) SELECT s, arr, a FROM arrays_test ARRAY JOIN arr AS a\n\nSELECT s, arr, a\nFROM arrays_test\nARRAY JOIN arr AS a\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. Multiple arrays of the same size can be comma-separated in the ARRAY JOIN clause. In this case, JOIN is performed with them simultaneously (the direct sum, not the direct product). Example: :) SELECT s, arr, a, num, mapped FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(x - x + 1, arr) AS mapped\n\nSELECT s, arr, a, num, mapped\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(lambda(tuple(x), plus(x, 1)), arr) AS mapped\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500mapped\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 2 \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 3 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 4 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 5 \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 6 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec.\n\n:) SELECT s, arr, a, num, arrayEnumerate(arr) FROM arrays_test ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\nSELECT s, arr, a, num, arrayEnumerate(arr)\nFROM arrays_test\nARRAY JOIN arr AS a, arrayEnumerate(arr) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500arr\u2500\u2500\u2500\u2500\u2500\u252c\u2500a\u2500\u252c\u2500num\u2500\u252c\u2500arrayEnumerate(arr)\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 1 \u2502 1 \u2502 [1,2] \u2502\n\u2502 Hello \u2502 [1,2] \u2502 2 \u2502 2 \u2502 [1,2] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 3 \u2502 1 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 4 \u2502 2 \u2502 [1,2,3] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 5 \u2502 3 \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec. ARRAY JOIN also works with nested data structures. Example: :) CREATE TABLE nested_test (s String, nest Nested(x UInt8, y UInt32)) ENGINE = Memory\n\nCREATE TABLE nested_test\n(\n s String,\n nest Nested(\n x UInt8,\n y UInt32)\n) ENGINE = Memory\n\nOk.\n\n0 rows in set. Elapsed: 0.006 sec.\n\n:) INSERT INTO nested_test VALUES ( Hello , [1,2], [10,20]), ( World , [3,4,5], [30,40,50]), ( Goodbye , [], [])\n\nINSERT INTO nested_test VALUES\n\nOk.\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT * FROM nested_test\n\nSELECT *\nFROM nested_test\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 Goodbye \u2502 [] \u2502 [] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n3 rows in set. Elapsed: 0.001 sec.\n\n:) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. When specifying names of nested data structures in ARRAY JOIN, the meaning is the same as ARRAY JOIN with all the array elements that it consists of. Example: :) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x, nest.y\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`, `nest.y`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. This variation also makes sense: :) SELECT s, nest.x, nest.y FROM nested_test ARRAY JOIN nest.x\n\nSELECT s, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN `nest.x`\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500nest.x\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. An alias may be used for a nested data structure, in order to select either the JOIN result or the source array. Example: :) SELECT s, n.x, n.y, nest.x, nest.y FROM nested_test ARRAY JOIN nest AS n\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`\nFROM nested_test\nARRAY JOIN nest AS n\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.001 sec. Example of using the arrayEnumerate function: :) SELECT s, n.x, n.y, nest.x, nest.y, num FROM nested_test ARRAY JOIN nest AS n, arrayEnumerate(nest.x) AS num\n\nSELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`, num\nFROM nested_test\nARRAY JOIN nest AS n, arrayEnumerate(`nest.x`) AS num\n\n\u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u252c\u2500n.x\u2500\u252c\u2500n.y\u2500\u252c\u2500nest.x\u2500\u2500\u252c\u2500nest.y\u2500\u2500\u2500\u2500\u2500\u252c\u2500num\u2500\u2510\n\u2502 Hello \u2502 1 \u2502 10 \u2502 [1,2] \u2502 [10,20] \u2502 1 \u2502\n\u2502 Hello \u2502 2 \u2502 20 \u2502 [1,2] \u2502 [10,20] \u2502 2 \u2502\n\u2502 World \u2502 3 \u2502 30 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 1 \u2502\n\u2502 World \u2502 4 \u2502 40 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 2 \u2502\n\u2502 World \u2502 5 \u2502 50 \u2502 [3,4,5] \u2502 [30,40,50] \u2502 3 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n5 rows in set. Elapsed: 0.002 sec. The query can only specify a single ARRAY JOIN clause. The corresponding conversion can be performed before the WHERE/PREWHERE clause (if its result is needed in this clause), or after completing WHERE/PREWHERE (to reduce the volume of calculations).", - "title": "ARRAY JOIN clause" - }, - { - "location": "/index.html#join-clause", - "text": "The normal JOIN, which is not related to ARRAY JOIN described above. [ GLOBAL ] ANY | ALL INNER | LEFT [ OUTER ] JOIN ( subquery ) | table USING columns_list Performs joins with data from the subquery. At the beginning of query processing, the subquery specified after JOIN is run, and its result is saved in memory. Then it is read from the \"left\" table specified in the FROM clause, and while it is being read, for each of the read rows from the \"left\" table, rows are selected from the subquery results table (the \"right\" table) that meet the condition for matching the values of the columns specified in USING. The table name can be specified instead of a subquery. This is equivalent to the SELECT * FROM table subquery, except in a special case when the table has the Join engine \u2013 an array prepared for joining. All columns that are not needed for the JOIN are deleted from the subquery. There are several types of JOINs: INNER or LEFT type:If INNER is specified, the result will contain only those rows that have a matching row in the right table.\nIf LEFT is specified, any rows in the left table that don't have matching rows in the right table will be assigned the default value - zeros or empty rows. LEFT OUTER may be written instead of LEFT; the word OUTER does not affect anything. ANY or ALL stringency:If ANY is specified and the right table has several matching rows, only the first one found is joined.\nIf ALL is specified and the right table has several matching rows, the data will be multiplied by the number of these rows. Using ALL corresponds to the normal JOIN semantic from standard SQL.\nUsing ANY is optimal. If the right table has only one matching row, the results of ANY and ALL are the same. You must specify either ANY or ALL (neither of them is selected by default). GLOBAL distribution: When using a normal JOIN, the query is sent to remote servers. Subqueries are run on each of them in order to make the right table, and the join is performed with this table. In other words, the right table is formed on each server separately. When using GLOBAL ... JOIN , first the requestor server runs a subquery to calculate the right table. This temporary table is passed to each remote server, and queries are run on them using the temporary data that was transmitted. Be careful when using GLOBAL JOINs. For more information, see the section \"Distributed subqueries\". Any combination of JOINs is possible. For example, GLOBAL ANY LEFT OUTER JOIN . When running a JOIN, there is no optimization of the order of execution in relation to other stages of the query. The join (a search in the right table) is run before filtering in WHERE and before aggregation. In order to explicitly set the processing order, we recommend running a JOIN subquery with a subquery. Example: SELECT \n CounterID , \n hits , \n visits FROM ( \n SELECT \n CounterID , \n count () AS hits \n FROM test . hits \n GROUP BY CounterID ) ANY LEFT JOIN ( \n SELECT \n CounterID , \n sum ( Sign ) AS visits \n FROM test . visits \n GROUP BY CounterID ) USING CounterID ORDER BY hits DESC LIMIT 10 \u250c\u2500CounterID\u2500\u252c\u2500\u2500\u2500hits\u2500\u252c\u2500visits\u2500\u2510\n\u2502 1143050 \u2502 523264 \u2502 13665 \u2502\n\u2502 731962 \u2502 475698 \u2502 102716 \u2502\n\u2502 722545 \u2502 337212 \u2502 108187 \u2502\n\u2502 722889 \u2502 252197 \u2502 10547 \u2502\n\u2502 2237260 \u2502 196036 \u2502 9522 \u2502\n\u2502 23057320 \u2502 147211 \u2502 7689 \u2502\n\u2502 722818 \u2502 90109 \u2502 17847 \u2502\n\u2502 48221 \u2502 85379 \u2502 4652 \u2502\n\u2502 19762435 \u2502 77807 \u2502 7026 \u2502\n\u2502 722884 \u2502 77492 \u2502 11056 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Subqueries don't allow you to set names or use them for referencing a column from a specific subquery.\nThe columns specified in USING must have the same names in both subqueries, and the other columns must be named differently. You can use aliases to change the names of columns in subqueries (the example uses the aliases 'hits' and 'visits'). The USING clause specifies one or more columns to join, which establishes the equality of these columns. The list of columns is set without brackets. More complex join conditions are not supported. The right table (the subquery result) resides in RAM. If there isn't enough memory, you can't run a JOIN. Only one JOIN can be specified in a query (on a single level). To run multiple JOINs, you can put them in subqueries. Each time a query is run with the same JOIN, the subquery is run again \u2013 the result is not cached. To avoid this, use the special 'Join' table engine, which is a prepared array for joining that is always in RAM. For more information, see the section \"Table engines, Join\". In some cases, it is more efficient to use IN instead of JOIN.\nAmong the various types of JOINs, the most efficient is ANY LEFT JOIN, then ANY INNER JOIN. The least efficient are ALL LEFT JOIN and ALL INNER JOIN. If you need a JOIN for joining with dimension tables (these are relatively small tables that contain dimension properties, such as names for advertising campaigns), a JOIN might not be very convenient due to the bulky syntax and the fact that the right table is re-accessed for every query. For such cases, there is an \"external dictionaries\" feature that you should use instead of JOIN. For more information, see the section \"External dictionaries\".", - "title": "JOIN clause" - }, - { - "location": "/index.html#where-clause", - "text": "If there is a WHERE clause, it must contain an expression with the UInt8 type. This is usually an expression with comparison and logical operators.\nThis expression will be used for filtering data before all other transformations. If indexes are supported by the database table engine, the expression is evaluated on the ability to use indexes.", - "title": "WHERE clause" - }, - { - "location": "/index.html#prewhere-clause", - "text": "This clause has the same meaning as the WHERE clause. The difference is in which data is read from the table.\nWhen using PREWHERE, first only the columns necessary for executing PREWHERE are read. Then the other columns are read that are needed for running the query, but only those blocks where the PREWHERE expression is true. It makes sense to use PREWHERE if there are filtration conditions that are not suitable for indexes that are used by a minority of the columns in the query, but that provide strong data filtration. This reduces the volume of data to read. For example, it is useful to write PREWHERE for queries that extract a large number of columns, but that only have filtration for a few columns. PREWHERE is only supported by tables from the *MergeTree family. A query may simultaneously specify PREWHERE and WHERE. In this case, PREWHERE precedes WHERE. Keep in mind that it does not make much sense for PREWHERE to only specify those columns that have an index, because when using an index, only the data blocks that match the index are read. If the 'optimize_move_to_prewhere' setting is set to 1 and PREWHERE is omitted, the system uses heuristics to automatically move parts of expressions from WHERE to PREWHERE.", - "title": "PREWHERE clause" - }, - { - "location": "/index.html#group-by-clause", - "text": "This is one of the most important parts of a column-oriented DBMS. If there is a GROUP BY clause, it must contain a list of expressions. Each expression will be referred to here as a \"key\".\nAll the expressions in the SELECT, HAVING, and ORDER BY clauses must be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. If a query contains only table columns inside aggregate functions, the GROUP BY clause can be omitted, and aggregation by an empty set of keys is assumed. Example: SELECT \n count (), \n median ( FetchTiming 60 ? 60 : FetchTiming ), \n count () - sum ( Refresh ) FROM hits However, in contrast to standard SQL, if the table doesn't have any rows (either there aren't any at all, or there aren't any after using WHERE to filter), an empty result is returned, and not the result from one of the rows containing the initial values of aggregate functions. As opposed to MySQL (and conforming to standard SQL), you can't get some value of some column that is not in a key or aggregate function (except constant expressions). To work around this, you can use the 'any' aggregate function (get the first encountered value) or 'min/max'. Example: SELECT \n domainWithoutWWW ( URL ) AS domain , \n count (), \n any ( Title ) AS title -- getting the first occurred page header for each domain. FROM hits GROUP BY domain For every different key value encountered, GROUP BY calculates a set of aggregate function values. GROUP BY is not supported for array columns. A constant can't be specified as arguments for aggregate functions. Example: sum(1). Instead of this, you can get rid of the constant. Example: count() .", - "title": "GROUP BY clause" - }, - { - "location": "/index.html#with-totals-modifier", - "text": "If the WITH TOTALS modifier is specified, another row will be calculated. This row will have key columns containing default values (zeros or empty lines), and columns of aggregate functions with the values calculated across all the rows (the \"total\" values). This extra row is output in JSON*, TabSeparated*, and Pretty* formats, separately from the other rows. In the other formats, this row is not output. In JSON* formats, this row is output as a separate 'totals' field. In TabSeparated* formats, the row comes after the main result, preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result. WITH TOTALS can be run in different ways when HAVING is present. The behavior depends on the 'totals_mode' setting.\nBy default, totals_mode = 'before_having' . In this case, 'totals' is calculated across all rows, including the ones that don't pass through HAVING and 'max_rows_to_group_by'. The other alternatives include only the rows that pass through HAVING in 'totals', and behave differently with the setting max_rows_to_group_by and group_by_overflow_mode = 'any' . after_having_exclusive \u2013 Don't include rows that didn't pass through max_rows_to_group_by . In other words, 'totals' will have less than or the same number of rows as it would if max_rows_to_group_by were omitted. after_having_inclusive \u2013 Include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. In other words, 'totals' will have more than or the same number of rows as it would if max_rows_to_group_by were omitted. after_having_auto \u2013 Count the number of rows that passed through HAVING. If it is more than a certain amount (by default, 50%), include all the rows that didn't pass through 'max_rows_to_group_by' in 'totals'. Otherwise, do not include them. totals_auto_threshold \u2013 By default, 0.5. The coefficient for after_having_auto . If max_rows_to_group_by and group_by_overflow_mode = 'any' are not used, all variations of after_having are the same, and you can use any of them (for example, after_having_auto ). You can use WITH TOTALS in subqueries, including subqueries in the JOIN clause (in this case, the respective total values are combined).", - "title": "WITH TOTALS modifier" - }, - { - "location": "/index.html#group-by-in-external-memory", - "text": "You can enable dumping temporary data to the disk to restrict memory usage during GROUP BY.\nThe max_bytes_before_external_group_by setting determines the threshold RAM consumption for dumping GROUP BY temporary data to the file system. If set to 0 (the default), it is disabled. When using max_bytes_before_external_group_by , we recommend that you set max_memory_usage about twice as high. This is necessary because there are two stages to aggregation: reading the date and forming intermediate data (1) and merging the intermediate data (2). Dumping data to the file system can only occur during stage 1. If the temporary data wasn't dumped, then stage 2 might require up to the same amount of memory as in stage 1. For example, if max_memory_usage was set to 10000000000 and you want to use external aggregation, it makes sense to set max_bytes_before_external_group_by to 10000000000, and max_memory_usage to 20000000000. When external aggregation is triggered (if there was at least one dump of temporary data), maximum consumption of RAM is only slightly more than max_bytes_before_external_group_by . With distributed query processing, external aggregation is performed on remote servers. In order for the requestor server to use only a small amount of RAM, set distributed_aggregation_memory_efficient to 1. When merging data flushed to the disk, as well as when merging results from remote servers when the distributed_aggregation_memory_efficient setting is enabled, consumes up to 1/256 * the number of threads from the total amount of RAM. When external aggregation is enabled, if there was less than max_bytes_before_external_group_by of data (i.e. data was not flushed), the query runs just as fast as without external aggregation. If any temporary data was flushed, the run time will be several times longer (approximately three times). If you have an ORDER BY with a small LIMIT after GROUP BY, then the ORDER BY CLAUSE will not use significant amounts of RAM.\nBut if the ORDER BY doesn't have LIMIT, don't forget to enable external sorting ( max_bytes_before_external_sort ).", - "title": "GROUP BY in external memory" - }, - { - "location": "/index.html#limit-n-by-clause", - "text": "LIMIT N BY COLUMNS selects the top N rows for each group of COLUMNS. LIMIT N BY is not related to LIMIT; they can both be used in the same query. The key for LIMIT N BY can contain any number of columns or expressions. Example: SELECT \n domainWithoutWWW ( URL ) AS domain , \n domainWithoutWWW ( REFERRER_URL ) AS referrer , \n device_type , \n count () cnt FROM hits GROUP BY domain , referrer , device_type ORDER BY cnt DESC LIMIT 5 BY domain , device_type LIMIT 100 The query will select the top 5 referrers for each domain, device_type pair, but not more than 100 rows ( LIMIT n BY + LIMIT ).", - "title": "LIMIT N BY clause" - }, - { - "location": "/index.html#having-clause", - "text": "Allows filtering the result received after GROUP BY, similar to the WHERE clause.\nWHERE and HAVING differ in that WHERE is performed before aggregation (GROUP BY), while HAVING is performed after it.\nIf aggregation is not performed, HAVING can't be used.", - "title": "HAVING clause" - }, - { - "location": "/index.html#order-by-clause", - "text": "The ORDER BY clause contains a list of expressions, which can each be assigned DESC or ASC (the sorting direction). If the direction is not specified, ASC is assumed. ASC is sorted in ascending order, and DESC in descending order. The sorting direction applies to a single expression, not to the entire list. Example: ORDER BY Visits DESC, SearchPhrase For sorting by String values, you can specify collation (comparison). Example: ORDER BY SearchPhrase COLLATE 'tr' - for sorting by keyword in ascending order, using the Turkish alphabet, case insensitive, assuming that strings are UTF-8 encoded. COLLATE can be specified or not for each expression in ORDER BY independently. If ASC or DESC is specified, COLLATE is specified after it. When using COLLATE, sorting is always case-insensitive. We only recommend using COLLATE for final sorting of a small number of rows, since sorting with COLLATE is less efficient than normal sorting by bytes. Rows that have identical values for the list of sorting expressions are output in an arbitrary order, which can also be nondeterministic (different each time).\nIf the ORDER BY clause is omitted, the order of the rows is also undefined, and may be nondeterministic as well. When floating point numbers are sorted, NaNs are separate from the other values. Regardless of the sorting order, NaNs come at the end. In other words, for ascending sorting they are placed as if they are larger than all the other numbers, while for descending sorting they are placed as if they are smaller than the rest. Less RAM is used if a small enough LIMIT is specified in addition to ORDER BY. Otherwise, the amount of memory spent is proportional to the volume of data for sorting. For distributed query processing, if GROUP BY is omitted, sorting is partially done on remote servers, and the results are merged on the requestor server. This means that for distributed sorting, the volume of data to sort can be greater than the amount of memory on a single server. If there is not enough RAM, it is possible to perform sorting in external memory (creating temporary files on a disk). Use the setting max_bytes_before_external_sort for this purpose. If it is set to 0 (the default), external sorting is disabled. If it is enabled, when the volume of data to sort reaches the specified number of bytes, the collected data is sorted and dumped into a temporary file. After all data is read, all the sorted files are merged and the results are output. Files are written to the /var/lib/clickhouse/tmp/ directory in the config (by default, but you can use the 'tmp_path' parameter to change this setting). Running a query may use more memory than 'max_bytes_before_external_sort'. For this reason, this setting must have a value significantly smaller than 'max_memory_usage'. As an example, if your server has 128 GB of RAM and you need to run a single query, set 'max_memory_usage' to 100 GB, and 'max_bytes_before_external_sort' to 80 GB. External sorting works much less effectively than sorting in RAM.", - "title": "ORDER BY clause" - }, - { - "location": "/index.html#select-clause", - "text": "The expressions specified in the SELECT clause are analyzed after the calculations for all the clauses listed above are completed.\nMore specifically, expressions are analyzed that are above the aggregate functions, if there are any aggregate functions.\nThe aggregate functions and everything below them are calculated during aggregation (GROUP BY).\nThese expressions work as if they are applied to separate rows in the result.", - "title": "SELECT clause" - }, - { - "location": "/index.html#distinct-clause", - "text": "If DISTINCT is specified, only a single row will remain out of all the sets of fully matching rows in the result.\nThe result will be the same as if GROUP BY were specified across all the fields specified in SELECT without aggregate functions. But there are several differences from GROUP BY: DISTINCT can be applied together with GROUP BY. When ORDER BY is omitted and LIMIT is defined, the query stops running immediately after the required number of different rows has been read. Data blocks are output as they are processed, without waiting for the entire query to finish running. DISTINCT is not supported if SELECT has at least one array column.", - "title": "DISTINCT clause" - }, - { - "location": "/index.html#limit-clause", - "text": "LIMIT m allows you to select the first 'm' rows from the result.\nLIMIT n, m allows you to select the first 'm' rows from the result after skipping the first 'n' rows. 'n' and 'm' must be non-negative integers. If there isn't an ORDER BY clause that explicitly sorts results, the result may be arbitrary and nondeterministic.", - "title": "LIMIT clause" - }, - { - "location": "/index.html#union-all-clause", - "text": "You can use UNION ALL to combine any number of queries. Example: SELECT CounterID , 1 AS table , toInt64 ( count ()) AS c \n FROM test . hits \n GROUP BY CounterID UNION ALL SELECT CounterID , 2 AS table , sum ( Sign ) AS c \n FROM test . visits \n GROUP BY CounterID \n HAVING c 0 Only UNION ALL is supported. The regular UNION (UNION DISTINCT) is not supported. If you need UNION DISTINCT, you can write SELECT DISTINCT from a subquery containing UNION ALL. Queries that are parts of UNION ALL can be run simultaneously, and their results can be mixed together. The structure of results (the number and type of columns) must match for the queries. But the column names can differ. In this case, the column names for the final result will be taken from the first query. Queries that are parts of UNION ALL can't be enclosed in brackets. ORDER BY and LIMIT are applied to separate queries, not to the final result. If you need to apply a conversion to the final result, you can put all the queries with UNION ALL in a subquery in the FROM clause.", - "title": "UNION ALL clause" - }, - { - "location": "/index.html#into-outfile-clause", - "text": "Add the INTO OUTFILE filename clause (where filename is a string literal) to redirect query output to the specified file.\nIn contrast to MySQL, the file is created on the client side. The query will fail if a file with the same filename already exists.\nThis functionality is available in the command-line client and clickhouse-local (a query sent via HTTP interface will fail). The default output format is TabSeparated (the same as in the command-line client batch mode).", - "title": "INTO OUTFILE clause" - }, - { - "location": "/index.html#format-clause", - "text": "Specify 'FORMAT format' to get data in any specified format.\nYou can use this for convenience, or for creating dumps.\nFor more information, see the section \"Formats\".\nIf the FORMAT clause is omitted, the default format is used, which depends on both the settings and the interface used for accessing the DB. For the HTTP interface and the command-line client in batch mode, the default format is TabSeparated. For the command-line client in interactive mode, the default format is PrettyCompact (it has attractive and compact tables). When using the command-line client, data is passed to the client in an internal efficient format. The client independently interprets the FORMAT clause of the query and formats the data itself (thus relieving the network and the server from the load).", - "title": "FORMAT clause" - }, - { - "location": "/index.html#in-operators", - "text": "The IN , NOT IN , GLOBAL IN , and GLOBAL NOT IN operators are covered separately, since their functionality is quite rich. The left side of the operator is either a single column or a tuple. Examples: SELECT UserID IN ( 123 , 456 ) FROM ... SELECT ( CounterID , UserID ) IN (( 34 , 123 ), ( 101500 , 456 )) FROM ... If the left side is a single column that is in the index, and the right side is a set of constants, the system uses the index for processing the query. Don't list too many values explicitly (i.e. millions). If a data set is large, put it in a temporary table (for example, see the section \"External data for query processing\"), then use a subquery. The right side of the operator can be a set of constant expressions, a set of tuples with constant expressions (shown in the examples above), or the name of a database table or SELECT subquery in brackets. If the right side of the operator is the name of a table (for example, UserID IN users ), this is equivalent to the subquery UserID IN (SELECT * FROM users) . Use this when working with external data that is sent along with the query. For example, the query can be sent together with a set of user IDs loaded to the 'users' temporary table, which should be filtered. If the right side of the operator is a table name that has the Set engine (a prepared data set that is always in RAM), the data set will not be created over again for each query. The subquery may specify more than one column for filtering tuples.\nExample: SELECT ( CounterID , UserID ) IN ( SELECT CounterID , UserID FROM ...) FROM ... The columns to the left and right of the IN operator should have the same type. The IN operator and subquery may occur in any part of the query, including in aggregate functions and lambda functions.\nExample: SELECT \n EventDate , \n avg ( UserID IN \n ( \n SELECT UserID \n FROM test . hits \n WHERE EventDate = toDate ( 2014-03-17 ) \n )) AS ratio FROM test . hits GROUP BY EventDate ORDER BY EventDate ASC \u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500ratio\u2500\u2510\n\u2502 2014-03-17 \u2502 1 \u2502\n\u2502 2014-03-18 \u2502 0.807696 \u2502\n\u2502 2014-03-19 \u2502 0.755406 \u2502\n\u2502 2014-03-20 \u2502 0.723218 \u2502\n\u2502 2014-03-21 \u2502 0.697021 \u2502\n\u2502 2014-03-22 \u2502 0.647851 \u2502\n\u2502 2014-03-23 \u2502 0.648416 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 For each day after March 17th, count the percentage of pageviews made by users who visited the site on March 17th.\nA subquery in the IN clause is always run just one time on a single server. There are no dependent subqueries.", - "title": "IN operators" - }, - { - "location": "/index.html#distributed-subqueries", - "text": "There are two options for IN-s with subqueries (similar to JOINs): normal IN / OIN and IN GLOBAL / GLOBAL JOIN . They differ in how they are run for distributed query processing. \n\nRemember that the algorithms described below may work differently depending on the [settings](#settings-distributed_product_mode) `distributed_product_mode` setting. When using the regular IN, the query is sent to remote servers, and each of them runs the subqueries in the IN or JOIN clause. When using GLOBAL IN / GLOBAL JOINs , first all the subqueries are run for GLOBAL IN / GLOBAL JOINs , and the results are collected in temporary tables. Then the temporary tables are sent to each remote server, where the queries are run using this temporary data. For a non-distributed query, use the regular IN / JOIN . Be careful when using subqueries in the IN / JOIN clauses for distributed query processing. Let's look at some examples. Assume that each server in the cluster has a normal local_table . Each server also has a distributed_table table with the Distributed type, which looks at all the servers in the cluster. For a query to the distributed_table , the query will be sent to all the remote servers and run on them using the local_table . For example, the query SELECT uniq ( UserID ) FROM distributed_table will be sent to all remote servers as SELECT uniq ( UserID ) FROM local_table and run on each of them in parallel, until it reaches the stage where intermediate results can be combined. Then the intermediate results will be returned to the requestor server and merged on it, and the final result will be sent to the client. Now let's examine a query with IN: SELECT uniq ( UserID ) FROM distributed_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM local_table WHERE CounterID = 34 ) Calculation of the intersection of audiences of two sites. This query will be sent to all remote servers as SELECT uniq ( UserID ) FROM local_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM local_table WHERE CounterID = 34 ) In other words, the data set in the IN clause will be collected on each server independently, only across the data that is stored locally on each of the servers. This will work correctly and optimally if you are prepared for this case and have spread data across the cluster servers such that the data for a single UserID resides entirely on a single server. In this case, all the necessary data will be available locally on each server. Otherwise, the result will be inaccurate. We refer to this variation of the query as \"local IN\". To correct how the query works when data is spread randomly across the cluster servers, you could specify distributed_table inside a subquery. The query would look like this: SELECT uniq ( UserID ) FROM distributed_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM distributed_table WHERE CounterID = 34 ) This query will be sent to all remote servers as SELECT uniq ( UserID ) FROM local_table WHERE CounterID = 101500 AND UserID IN ( SELECT UserID FROM distributed_table WHERE CounterID = 34 ) The subquery will begin running on each remote server. Since the subquery uses a distributed table, the subquery that is on each remote server will be resent to every remote server as SELECT UserID FROM local_table WHERE CounterID = 34 For example, if you have a cluster of 100 servers, executing the entire query will require 10,000 elementary requests, which is generally considered unacceptable. In such cases, you should always use GLOBAL IN instead of IN. Let's look at how it works for the query SELECT uniq ( UserID ) FROM distributed_table WHERE CounterID = 101500 AND UserID GLOBAL IN ( SELECT UserID FROM distributed_table WHERE CounterID = 34 ) The requestor server will run the subquery SELECT UserID FROM distributed_table WHERE CounterID = 34 and the result will be put in a temporary table in RAM. Then the request will be sent to each remote server as SELECT uniq ( UserID ) FROM local_table WHERE CounterID = 101500 AND UserID GLOBAL IN _data1 and the temporary table _data1 will be sent to every remote server with the query (the name of the temporary table is implementation-defined). This is more optimal than using the normal IN. However, keep the following points in mind: When creating a temporary table, data is not made unique. To reduce the volume of data transmitted over the network, specify DISTINCT in the subquery. (You don't need to do this for a normal IN.) The temporary table will be sent to all the remote servers. Transmission does not account for network topology. For example, if 10 remote servers reside in a datacenter that is very remote in relation to the requestor server, the data will be sent 10 times over the channel to the remote datacenter. Try to avoid large data sets when using GLOBAL IN. When transmitting data to remote servers, restrictions on network bandwidth are not configurable. You might overload the network. Try to distribute data across servers so that you don't need to use GLOBAL IN on a regular basis. If you need to use GLOBAL IN often, plan the location of the ClickHouse cluster so that a single group of replicas resides in no more than one data center with a fast network between them, so that a query can be processed entirely within a single data center. It also makes sense to specify a local table in the GLOBAL IN clause, in case this local table is only available on the requestor server and you want to use data from it on remote servers.", - "title": "Distributed subqueries" - }, - { - "location": "/index.html#extreme-values", - "text": "In addition to results, you can also get minimum and maximum values for the results columns. To do this, set the extremes setting to 1. Minimums and maximums are calculated for numeric types, dates, and dates with times. For other columns, the default values are output. An extra two rows are calculated \u2013 the minimums and maximums, respectively. These extra two rows are output in JSON*, TabSeparated*, and Pretty* formats, separate from the other rows. They are not output for other formats. In JSON* formats, the extreme values are output in a separate 'extremes' field. In TabSeparated* formats, the row comes after the main result, and after 'totals' if present. It is preceded by an empty row (after the other data). In Pretty* formats, the row is output as a separate table after the main result, and after 'totals' if present. Extreme values are calculated for rows that have passed through LIMIT. However, when using 'LIMIT offset, size', the rows before 'offset' are included in 'extremes'. In stream requests, the result may also include a small number of rows that passed through LIMIT.", - "title": "Extreme values" - }, - { - "location": "/index.html#notes", - "text": "The GROUP BY and ORDER BY clauses do not support positional arguments. This contradicts MySQL, but conforms to standard SQL.\nFor example, GROUP BY 1, 2 will be interpreted as grouping by constants (i.e. aggregation of all rows into one). You can use synonyms ( AS aliases) in any part of a query. You can put an asterisk in any part of a query instead of an expression. When the query is analyzed, the asterisk is expanded to a list of all table columns (excluding the MATERIALIZED and ALIAS columns). There are only a few cases when using an asterisk is justified: When creating a table dump. For tables containing just a few columns, such as system tables. For getting information about what columns are in a table. In this case, set LIMIT 1 . But it is better to use the DESC TABLE query. When there is strong filtration on a small number of columns using PREWHERE . In subqueries (since columns that aren't needed for the external query are excluded from subqueries). In all other cases, we don't recommend using the asterisk, since it only gives you the drawbacks of a columnar DBMS instead of the advantages. In other words using the asterisk is not recommended.", - "title": "Notes" - }, - { - "location": "/index.html#kill-query", - "text": "KILL QUERY \n WHERE where expression to SELECT FROM system . processes query \n [ SYNC | ASYNC | TEST ] \n [ FORMAT format ] Attempts to forcibly terminate the currently running queries.\nThe queries to terminate are selected from the system.processes table using the criteria defined in the WHERE clause of the KILL query. Examples: -- Forcibly terminates all queries with the specified query_id: KILL QUERY WHERE query_id = 2-857d-4a57-9ee0-327da5d60a90 -- Synchronously terminates all queries run by username : KILL QUERY WHERE user = username SYNC Read-only users can only stop their own queries. By default, the asynchronous version of queries is used ( ASYNC ), which doesn't wait for confirmation that queries have stopped. The synchronous version ( SYNC ) waits for all queries to stop and displays information about each process as it stops.\nThe response contains the kill_status column, which can take the following values: 'finished' \u2013 The query was terminated successfully. 'waiting' \u2013 Waiting for the query to end after sending it a signal to terminate. The other values \u200b\u200bexplain why the query can't be stopped. A test query ( TEST ) only checks the user's rights and displays a list of queries to stop.", - "title": "KILL QUERY" - }, - { - "location": "/index.html#syntax", - "text": "There are two types of parsers in the system: the full SQL parser (a recursive descent parser), and the data format parser (a fast stream parser).\nIn all cases except the INSERT query, only the full SQL parser is used.\nThe INSERT query uses both parsers: INSERT INTO t VALUES ( 1 , Hello, world ), ( 2 , abc ), ( 3 , def ) The INSERT INTO t VALUES fragment is parsed by the full parser, and the data (1, 'Hello, world'), (2, 'abc'), (3, 'def') is parsed by the fast stream parser.\nData can have any format. When a query is received, the server calculates no more than max_query_size bytes of the request in RAM (by default, 1 MB), and the rest is stream parsed.\nThis means the system doesn't have problems with large INSERT queries, like MySQL does. When using the Values format in an INSERT query, it may seem that data is parsed the same as expressions in a SELECT query, but this is not true. The Values format is much more limited. Next we will cover the full parser. For more information about format parsers, see the section \"Formats\".", - "title": "Syntax" - }, - { - "location": "/index.html#spaces", - "text": "There may be any number of space symbols between syntactical constructions (including the beginning and end of a query). Space symbols include the space, tab, line feed, CR, and form feed.", - "title": "Spaces" - }, - { - "location": "/index.html#comments", - "text": "SQL-style and C-style comments are supported.\nSQL-style comments: from -- to the end of the line. The space after -- can be omitted.\nComments in C-style: from /* to */ . These comments can be multiline. Spaces are not required here, either.", - "title": "Comments" - }, - { - "location": "/index.html#keywords", - "text": "Keywords (such as SELECT ) are not case-sensitive. Everything else (column names, functions, and so on), in contrast to standard SQL, is case-sensitive. Keywords are not reserved (they are just parsed as keywords in the corresponding context).", - "title": "Keywords" - }, - { - "location": "/index.html#identifiers", - "text": "Identifiers (column names, functions, and data types) can be quoted or non-quoted.\nNon-quoted identifiers start with a Latin letter or underscore, and continue with a Latin letter, underscore, or number. In other words, they must match the regex ^[a-zA-Z_][0-9a-zA-Z_]*$ . Examples: x, _1, X_y__Z123_. Quoted identifiers are placed in reversed quotation marks `id` (the same as in MySQL), and can indicate any set of bytes (non-empty). In addition, symbols (for example, the reverse quotation mark) inside this type of identifier can be backslash-escaped. Escaping rules are the same as for string literals (see below).\nWe recommend using identifiers that do not need to be quoted.", - "title": "Identifiers" - }, - { - "location": "/index.html#literals", - "text": "There are numeric literals, string literals, and compound literals.", - "title": "Literals" - }, - { - "location": "/index.html#numeric-literals", - "text": "A numeric literal tries to be parsed: First as a 64-bit signed number, using the 'strtoull' function. If unsuccessful, as a 64-bit unsigned number, using the 'strtoll' function. If unsuccessful, as a floating-point number using the 'strtod' function. Otherwise, an error is returned. The corresponding value will have the smallest type that the value fits in.\nFor example, 1 is parsed as UInt8, but 256 is parsed as UInt16. For more information, see \"Data types\". Examples: 1 , 18446744073709551615 , 0xDEADBEEF , 01 , 0.1 , 1e100 , -1e-100 , inf , nan .", - "title": "Numeric literals" - }, - { - "location": "/index.html#string-literals", - "text": "Only string literals in single quotes are supported. The enclosed characters can be backslash-escaped. The following escape sequences have a corresponding special value: \\b , \\f , \\r , \\n , \\t , \\0 , \\a , \\v , \\xHH . In all other cases, escape sequences in the format \\c , where \"c\" is any character, are converted to \"c\". This means that you can use the sequences \\' and \\\\ . The value will have the String type. The minimum set of characters that you need to escape in string literals: ' and \\ .", - "title": "String literals" - }, - { - "location": "/index.html#compound-literals", - "text": "Constructions are supported for arrays: [1, 2, 3] and tuples: (1, 'Hello, world!', 2) ..\nActually, these are not literals, but expressions with the array creation operator and the tuple creation operator, respectively.\nFor more information, see the section \"Operators2\".\nAn array must consist of at least one item, and a tuple must have at least two items.\nTuples have a special purpose for use in the IN clause of a SELECT query. Tuples can be obtained as the result of a query, but they can't be saved to a database (with the exception of Memory-type tables).", - "title": "Compound literals" - }, - { - "location": "/index.html#functions", - "text": "Functions are written like an identifier with a list of arguments (possibly empty) in brackets. In contrast to standard SQL, the brackets are required, even for an empty arguments list. Example: now() .\nThere are regular and aggregate functions (see the section \"Aggregate functions\"). Some aggregate functions can contain two lists of arguments in brackets. Example: quantile (0.9) (x) . These aggregate functions are called \"parametric\" functions, and the arguments in the first list are called \"parameters\". The syntax of aggregate functions without parameters is the same as for regular functions.", - "title": "Functions" - }, - { - "location": "/index.html#operators", - "text": "Operators are converted to their corresponding functions during query parsing, taking their priority and associativity into account.\nFor example, the expression 1 + 2 * 3 + 4 is transformed to plus(plus(1, multiply(2, 3)), 4) .\nFor more information, see the section \"Operators\" below.", - "title": "Operators" - }, - { - "location": "/index.html#data-types-and-database-table-engines", - "text": "Data types and table engines in the CREATE query are written the same way as identifiers or functions. In other words, they may or may not contain an arguments list in brackets. For more information, see the sections \"Data types,\" \"Table engines,\" and \"CREATE\".", - "title": "Data types and database table engines" - }, - { - "location": "/index.html#synonyms", - "text": "In the SELECT query, expressions can specify synonyms using the AS keyword. Any expression is placed to the left of AS. The identifier name for the synonym is placed to the right of AS. As opposed to standard SQL, synonyms are not only declared on the top level of expressions: SELECT ( 1 AS n ) + 2 , n In contrast to standard SQL, synonyms can be used in all parts of a query, not just SELECT .", - "title": "Synonyms" - }, - { - "location": "/index.html#asterisk", - "text": "In a SELECT query, an asterisk can replace the expression. For more information, see the section \"SELECT\".", - "title": "Asterisk" - }, - { - "location": "/index.html#expressions", - "text": "An expression is a function, identifier, literal, application of an operator, expression in brackets, subquery, or asterisk. It can also contain a synonym.\nA list of expressions is one or more expressions separated by commas.\nFunctions and operators, in turn, can have expressions as arguments.", - "title": "Expressions" - }, - { - "location": "/index.html#table-engines", - "text": "The table engine (type of table) determines: How and where data is stored: where to write it to, and where to read it from. Which queries are supported, and how. Concurrent data access. Use of indexes, if present. Whether multithreaded request execution is possible. Data replication. When reading data, the engine is only required to extract the necessary set of columns. However, in some cases, the query may be partially processed inside the table engine. Note that for most serious tasks, you should use engines from the MergeTree family.", - "title": "Table engines" - }, - { - "location": "/index.html#tinylog", - "text": "The simplest table engine, which stores data on a disk.\nEach column is stored in a separate compressed file.\nWhen writing, data is appended to the end of files. Concurrent data access is not restricted in any way: If you are simultaneously reading from a table and writing to it in a different query, the read operation will complete with an error. If you are writing to a table in multiple queries simultaneously, the data will be broken. The typical way to use this table is write-once: first just write the data one time, then read it as many times as needed.\nQueries are executed in a single stream. In other words, this engine is intended for relatively small tables (recommended up to 1,000,000 rows).\nIt makes sense to use this table engine if you have many small tables, since it is simpler than the Log engine (fewer files need to be opened).\nThe situation when you have a large number of small tables guarantees poor productivity, but may already be used when working with another DBMS, and you may find it easier to switch to using TinyLog types of tables. Indexes are not supported. In Yandex.Metrica, TinyLog tables are used for intermediary data that is processed in small batches.", - "title": "TinyLog" - }, - { - "location": "/index.html#log", - "text": "Log differs from TinyLog in that a small file of \"marks\" resides with the column files. These marks are written on every data block and contain offsets that indicate where to start reading the file in order to skip the specified number of rows. This makes it possible to read table data in multiple threads.\nFor concurrent data access, the read operations can be performed simultaneously, while write operations block reads and each other.\nThe Log engine does not support indexes. Similarly, if writing to a table failed, the table is broken, and reading from it returns an error. The Log engine is appropriate for temporary data, write-once tables, and for testing or demonstration purposes.", - "title": "Log" - }, - { - "location": "/index.html#memory", - "text": "The Memory engine stores data in RAM, in uncompressed form. Data is stored in exactly the same form as it is received when read. In other words, reading from this table is completely free.\nConcurrent data access is synchronized. Locks are short: read and write operations don't block each other.\nIndexes are not supported. Reading is parallelized.\nMaximal productivity (over 10 GB/sec) is reached on simple queries, because there is no reading from the disk, decompressing, or deserializing data. (We should note that in many cases, the productivity of the MergeTree engine is almost as high.)\nWhen restarting a server, data disappears from the table and the table becomes empty.\nNormally, using this table engine is not justified. However, it can be used for tests, and for tasks where maximum speed is required on a relatively small number of rows (up to approximately 100,000,000). The Memory engine is used by the system for temporary tables with external query data (see the section \"External data for processing a query\"), and for implementing GLOBAL IN (see the section \"IN operators\").", - "title": "Memory" - }, - { - "location": "/index.html#mergetree", - "text": "The MergeTree engine supports an index by primary key and by date, and provides the possibility to update data in real time.\nThis is the most advanced table engine in ClickHouse. Don't confuse it with the Merge engine. The engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity. Example without sampling support. MergeTree(EventDate, (CounterID, EventDate), 8192) Example with sampling support. MergeTree(EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID)), 8192) A MergeTree table must have a separate column containing the date. Here, it is the EventDate column. The date column must have the 'Date' type (not 'DateTime'). The primary key may be a tuple from any expressions (usually this is just a tuple of columns), or a single expression. The sampling expression (optional) can be any expression. It must also be present in the primary key. The example uses a hash of user IDs to pseudo-randomly disperse data in the table for each CounterID and EventDate. In other words, when using the SAMPLE clause in a query, you get an evenly pseudo-random sample of data for a subset of users. The table is implemented as a set of parts. Each part is sorted by the primary key. In addition, each part has the minimum and maximum date assigned. When inserting in the table, a new sorted part is created. The merge process is periodically initiated in the background. When merging, several parts are selected (usually the smallest ones) and then merged into one large sorted part. In other words, incremental sorting occurs when inserting to the table. Merging is implemented so that the table always consists of a small number of sorted parts, and the merge itself doesn't do too much work. During insertion, data belonging to different months is separated into different parts. The parts that correspond to different months are never combined. The purpose of this is to provide local data modification (for ease in backups). Parts are combined up to a certain size threshold, so there aren't any merges that are too long. For each part, an index file is also written. The index file contains the primary key value for every 'index_granularity' row in the table. In other words, this is an abbreviated index of sorted data. For columns, \"marks\" are also written to each 'index_granularity' row so that data can be read in a specific range. When reading from a table, the SELECT query is analyzed for whether indexes can be used.\nAn index can be used if the WHERE or PREWHERE clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has IN or LIKE with a fixed prefix on columns or expressions that are in the primary key or partitioning key, or on certain partially repetitive functions of these columns, or logical relationships of these expressions. Thus, it is possible to quickly run queries on one or many ranges of the primary key. In this example, queries will be fast when run for a specific tracking tag; for a specific tag and date range; for a specific tag and date; for multiple tags with a date range, and so on. SELECT count () FROM table WHERE EventDate = toDate ( now ()) AND CounterID = 34 SELECT count () FROM table WHERE EventDate = toDate ( now ()) AND ( CounterID = 34 OR CounterID = 42 ) SELECT count () FROM table WHERE (( EventDate = toDate ( 2014-01-01 ) AND EventDate = toDate ( 2014-01-31 )) OR EventDate = toDate ( 2014-05-01 )) AND CounterID IN ( 101500 , 731962 , 160656 ) AND ( CounterID = 101500 OR EventDate != toDate ( 2014-05-01 )) All of these cases will use the index by date and by primary key. The index is used even for complex expressions. Reading from the table is organized so that using the index can't be slower than a full scan. In this example, the index can't be used. SELECT count () FROM table WHERE CounterID = 34 OR URL LIKE %upyachka% To check whether ClickHouse can use the index when executing the query, use the settings force_index_by_date and force_primary_key . The index by date only allows reading those parts that contain dates from the desired range. However, a data part may contain data for many dates (up to an entire month), while within a single part the data is ordered by the primary key, which might not contain the date as the first column. Because of this, using a query with only a date condition that does not specify the primary key prefix will cause more data to be read than for a single date. For concurrent table access, we use multi-versioning. In other words, when a table is simultaneously read and updated, data is read from a set of parts that is current at the time of the query. There are no lengthy locks. Inserts do not get in the way of read operations. Reading from a table is automatically parallelized. The OPTIMIZE query is supported, which calls an extra merge step. You can use a single large table and continually add data to it in small chunks \u2013 this is what MergeTree is intended for. Data replication is possible for all types of tables in the MergeTree family (see the section \"Data replication\").", - "title": "MergeTree" - }, - { - "location": "/index.html#custom-partitioning-key", - "text": "Starting with version 1.1.54310, you can create tables in the MergeTree family with any partitioning expression (not only partitioning by month). The partition key can be an expression from the table columns, or a tuple of such expressions (similar to the primary key). The partition key can be omitted. When creating a table, specify the partition key in the ENGINE description with the new syntax: ENGINE [=] Name(...) [PARTITION BY expr] [ORDER BY expr] [SAMPLE BY expr] [SETTINGS name=value, ...] For MergeTree tables, the partition expression is specified after PARTITION BY , the primary key after ORDER BY , the sampling key after SAMPLE BY , and SETTINGS can specify index_granularity (optional; the default value is 8192), as well as other settings from MergeTreeSettings.h . The other engine parameters are specified in parentheses after the engine name, as previously. Example: ENGINE = ReplicatedCollapsingMergeTree ( /clickhouse/tables/name , replica1 , Sign ) \n PARTITION BY ( toMonday ( StartDate ), EventType ) \n ORDER BY ( CounterID , StartDate , intHash32 ( UserID )) \n SAMPLE BY intHash32 ( UserID ) The traditional partitioning by month is expressed as toYYYYMM(date_column) . You can't convert an old-style table to a table with custom partitions (only via INSERT SELECT). After this table is created, merge will only work for data parts that have the same value for the partitioning expression. Note: This means that you shouldn't make overly granular partitions (more than about a thousand partitions), or SELECT will perform poorly. To specify a partition in ALTER PARTITION commands, specify the value of the partition expression (or a tuple). Constants and constant expressions are supported. Example: ALTER TABLE table DROP PARTITION ( toMonday ( today ()), 1 ) Deletes the partition for the current week with event type 1. The same is true for the OPTIMIZE query. To specify the only partition in a non-partitioned table, specify PARTITION tuple() . Note: For old-style tables, the partition can be specified either as a number 201710 or a string '201710' . The syntax for the new style of tables is stricter with types (similar to the parser for the VALUES input format). In addition, ALTER TABLE FREEZE PARTITION uses exact match for new-style tables (not prefix match). In the system.parts table, the partition column specifies the value of the partition expression to use in ALTER queries (if quotas are removed). The name column should specify the name of the data part that has a new format. Was: 20140317_20140323_2_2_0 (minimum date - maximum date - minimum block number - maximum block number - level). Now: 201403_2_2_0 (partition ID - minimum block number - maximum block number - level). The partition ID is its string identifier (human-readable, if possible) that is used for the names of data parts in the file system and in ZooKeeper. You can specify it in ALTER queries in place of the partition key. Example: Partition key toYYYYMM(EventDate) ; ALTER can specify either PARTITION 201710 or PARTITION ID '201710' . For more examples, see the tests 00502_custom_partitioning_local and 00502_custom_partitioning_replicated_zookeeper .", - "title": "Custom partitioning key" - }, - { - "location": "/index.html#replacingmergetree", - "text": "This engine table differs from MergeTree in that it removes duplicate entries with the same primary key value. The last optional parameter for the table engine is the version column. When merging, it reduces all rows with the same primary key value to just one row. If the version column is specified, it leaves the row with the highest version; otherwise, it leaves the last row. The version column must have a type from the UInt family, Date , or DateTime . ReplacingMergeTree ( EventDate , ( OrderID , EventDate , BannerID , ...), 8192 , ver ) Note that data is only deduplicated during merges. Merging occurs in the background at an unknown time, so you can't plan for it. Some of the data may remain unprocessed. Although you can run an unscheduled merge using the OPTIMIZE query, don't count on using it, because the OPTIMIZE query will read and write a large amount of data. Thus, ReplacingMergeTree is suitable for clearing out duplicate data in the background in order to save space, but it doesn't guarantee the absence of duplicates. This engine is not used in Yandex.Metrica, but it has been applied in other Yandex projects.", - "title": "ReplacingMergeTree" - }, - { - "location": "/index.html#summingmergetree", - "text": "This engine differs from MergeTree in that it totals data while merging. SummingMergeTree ( EventDate , ( OrderID , EventDate , BannerID , ...), 8192 ) The columns to total are implicit. When merging, all rows with the same primary key value (in the example, OrderId, EventDate, BannerID, ...) have their values totaled in numeric columns that are not part of the primary key. SummingMergeTree ( EventDate , ( OrderID , EventDate , BannerID , ...), 8192 , ( Shows , Clicks , Cost , ...)) The columns to total are set explicitly (the last parameter \u2013 Shows, Clicks, Cost, ...). When merging, all rows with the same primary key value have their values totaled in the specified columns. The specified columns also must be numeric and must not be part of the primary key. If the values were null in all of these columns, the row is deleted. (The exception is cases when the data part would not have any rows left in it.) For the other rows that are not part of the primary key, the first value that occurs is selected when merging. Summation is not performed for a read operation. If it is necessary, write the appropriate GROUP BY. In addition, a table can have nested data structures that are processed in a special way.\nIf the name of a nested table ends in 'Map' and it contains at least two columns that meet the following criteria: The first table is numeric ((U)IntN, Date, DateTime), which we'll refer to as the 'key'. The other columns are arithmetic ((U)IntN, Float32/64), which we'll refer to as '(values...)'. Then this nested table is interpreted as a mapping of key = (values...), and when merging its rows, the elements of two data sets are merged by 'key' with a summation of the corresponding (values...). Examples: [(1, 100)] + [(2, 150)] - [(1, 100), (2, 150)]\n[(1, 100)] + [(1, 150)] - [(1, 250)]\n[(1, 100)] + [(1, 150), (2, 150)] - [(1, 250), (2, 150)]\n[(1, 100), (2, 150)] + [(1, -100)] - [(2, 150)] For aggregation of Map, use the function sumMap(key, value). For nested data structures, you don't need to specify the columns as a list of columns for totaling. This table engine is not particularly useful. Remember that when saving just pre-aggregated data, you lose some of the system's advantages.", - "title": "SummingMergeTree" - }, - { - "location": "/index.html#aggregatingmergetree", - "text": "This engine differs from MergeTree in that the merge combines the states of aggregate functions stored in the table for rows with the same primary key value. For this to work, it uses the AggregateFunction data type, as well as -State and -Merge modifiers for aggregate functions. Let's examine it more closely. There is an AggregateFunction data type. It is a parametric data type. As parameters, the name of the aggregate function is passed, then the types of its arguments. Examples: CREATE TABLE t ( \n column1 AggregateFunction ( uniq , UInt64 ), \n column2 AggregateFunction ( anyIf , String , UInt8 ), \n column3 AggregateFunction ( quantiles ( 0 . 5 , 0 . 9 ), UInt64 ) ) ENGINE = ... This type of column stores the state of an aggregate function. To get this type of value, use aggregate functions with the State suffix. Example: uniqState(UserID), quantilesState(0.5, 0.9)(SendTiming) In contrast to the corresponding uniq and quantiles functions, these functions return the state, rather than the prepared value. In other words, they return an AggregateFunction type value. An AggregateFunction type value can't be output in Pretty formats. In other formats, these types of values are output as implementation-specific binary data. The AggregateFunction type values are not intended for output or saving in a dump. The only useful thing you can do with AggregateFunction type values is combine the states and get a result, which essentially means to finish aggregation. Aggregate functions with the 'Merge' suffix are used for this purpose.\nExample: uniqMerge(UserIDState), where UserIDState has the AggregateFunction type. In other words, an aggregate function with the 'Merge' suffix takes a set of states, combines them, and returns the result.\nAs an example, these two queries return the same result: SELECT uniq ( UserID ) FROM table SELECT uniqMerge ( state ) FROM ( SELECT uniqState ( UserID ) AS state FROM table GROUP BY RegionID ) There is an AggregatingMergeTree engine. Its job during a merge is to combine the states of aggregate functions from different table rows with the same primary key value. You can't use a normal INSERT to insert a row in a table containing AggregateFunction columns, because you can't explicitly define the AggregateFunction value. Instead, use INSERT SELECT with -State aggregate functions for inserting data. With SELECT from an AggregatingMergeTree table, use GROUP BY and aggregate functions with the '-Merge' modifier in order to complete data aggregation. You can use AggregatingMergeTree tables for incremental data aggregation, including for aggregated materialized views. Example: Create an AggregatingMergeTree materialized view that watches the test.visits table: CREATE MATERIALIZED VIEW test . basic ENGINE = AggregatingMergeTree ( StartDate , ( CounterID , StartDate ), 8192 ) AS SELECT \n CounterID , \n StartDate , \n sumState ( Sign ) AS Visits , \n uniqState ( UserID ) AS Users FROM test . visits GROUP BY CounterID , StartDate ; Insert data in the test.visits table. Data will also be inserted in the view, where it will be aggregated: INSERT INTO test . visits ... Perform SELECT from the view using GROUP BY in order to complete data aggregation: SELECT \n StartDate , \n sumMerge ( Visits ) AS Visits , \n uniqMerge ( Users ) AS Users FROM test . basic GROUP BY StartDate ORDER BY StartDate ; You can create a materialized view like this and assign a normal view to it that finishes data aggregation. Note that in most cases, using AggregatingMergeTree is not justified, since queries can be run efficiently enough on non-aggregated data.", - "title": "AggregatingMergeTree" - }, - { - "location": "/index.html#collapsingmergetree", - "text": "This engine is used specifically for Yandex.Metrica. It differs from MergeTree in that it allows automatic deletion, or \"collapsing\" certain pairs of rows when merging. Yandex.Metrica has normal logs (such as hit logs) and change logs. Change logs are used for incrementally calculating statistics on data that is constantly changing. Examples are the log of session changes, or logs of changes to user histories. Sessions are constantly changing in Yandex.Metrica. For example, the number of hits per session increases. We refer to changes in any object as a pair (?old values, ?new values). Old values may be missing if the object was created. New values may be missing if the object was deleted. If the object was changed, but existed previously and was not deleted, both values are present. In the change log, one or two entries are made for each change. Each entry contains all the attributes that the object has, plus a special attribute for differentiating between the old and new values. When objects change, only the new entries are added to the change log, and the existing ones are not touched. The change log makes it possible to incrementally calculate almost any statistics. To do this, we need to consider \"new\" rows with a plus sign, and \"old\" rows with a minus sign. In other words, incremental calculation is possible for all statistics whose algebraic structure contains an operation for taking the inverse of an element. This is true of most statistics. We can also calculate \"idempotent\" statistics, such as the number of unique visitors, since the unique visitors are not deleted when making changes to sessions. This is the main concept that allows Yandex.Metrica to work in real time. CollapsingMergeTree accepts an additional parameter - the name of an Int8-type column that contains the row's \"sign\". Example: CollapsingMergeTree ( EventDate , ( CounterID , EventDate , intHash32 ( UniqID ), VisitID ), 8192 , Sign ) Here, Sign is a column containing -1 for \"old\" values and 1 for \"new\" values. When merging, each group of consecutive identical primary key values (columns for sorting data) is reduced to no more than one row with the column value 'sign_column = -1' (the \"negative row\") and no more than one row with the column value 'sign_column = 1' (the \"positive row\"). In other words, entries from the change log are collapsed. If the number of positive and negative rows matches, the first negative row and the last positive row are written.\nIf there is one more positive row than negative rows, only the last positive row is written.\nIf there is one more negative row than positive rows, only the first negative row is written.\nOtherwise, there will be a logical error and none of the rows will be written. (A logical error can occur if the same section of the log was accidentally inserted more than once. The error is just recorded in the server log, and the merge continues.) Thus, collapsing should not change the results of calculating statistics.\nChanges are gradually collapsed so that in the end only the last value of almost every object is left.\nCompared to MergeTree, the CollapsingMergeTree engine allows a multifold reduction of data volume. There are several ways to get completely \"collapsed\" data from a CollapsingMergeTree table: Write a query with GROUP BY and aggregate functions that accounts for the sign. For example, to calculate quantity, write 'sum(Sign)' instead of 'count()'. To calculate the sum of something, write 'sum(Sign * x)' instead of 'sum(x)', and so on, and also add 'HAVING sum(Sign) 0'. Not all amounts can be calculated this way. For example, the aggregate functions 'min' and 'max' can't be rewritten. If you must extract data without aggregation (for example, to check whether rows are present whose newest values match certain conditions), you can use the FINAL modifier for the FROM clause. This approach is significantly less efficient.", - "title": "CollapsingMergeTree" - }, - { - "location": "/index.html#graphitemergetree", - "text": "This engine is designed for rollup (thinning and aggregating/averaging) Graphite data. It may be helpful to developers who want to use ClickHouse as a data store for Graphite. Graphite stores full data in ClickHouse, and data can be retrieved in the following ways: Without thinning. Uses the MergeTree engine. With thinning. Using the GraphiteMergeTree engine. The engine inherits properties from MergeTree. The settings for thinning data are defined by the graphite_rollup parameter in the server configuration.", - "title": "GraphiteMergeTree" - }, - { - "location": "/index.html#using-the-engine", - "text": "The Graphite data table must contain the following fields at minimum: Path \u2013 The metric name (Graphite sensor). Time \u2013 The time for measuring the metric. Value \u2013 The value of the metric at the time set in Time. Version \u2013 Determines which value of the metric with the same Path and Time will remain in the database. Rollup pattern: pattern\n regexp\n function\n age - precision\n ...\npattern\n ...\ndefault\n function\n age - precision\n ... When processing a record, ClickHouse will check the rules in the pattern clause. If the metric name matches the regexp , the rules from pattern are applied; otherwise, the rules from default are used. Fields in the pattern. age \u2013 The minimum age of the data in seconds. function \u2013 The name of the aggregating function to apply to data whose age falls within the range [age, age + precision] . precision \u2013 How precisely to define the age of the data in seconds. regexp \u2013 A pattern for the metric name. Example of settings: graphite_rollup \n pattern \n regexp click_cost /regexp \n function any /function \n retention \n age 0 /age \n precision 5 /precision \n /retention \n retention \n age 86400 /age \n precision 60 /precision \n /retention \n /pattern \n default \n function max /function \n retention \n age 0 /age \n precision 60 /precision \n /retention \n retention \n age 3600 /age \n precision 300 /precision \n /retention \n retention \n age 86400 /age \n precision 3600 /precision \n /retention \n /default /graphite_rollup", - "title": "Using the engine" - }, - { - "location": "/index.html#data-replication", - "text": "Replication is only supported for tables in the MergeTree family: ReplicatedMergeTree ReplicatedSummingMergeTree ReplicatedReplacingMergeTree ReplicatedAggregatingMergeTree ReplicatedCollapsingMergeTree ReplicatedGraphiteMergeTree Replication works at the level of an individual table, not the entire server. A server can store both replicated and non-replicated tables at the same time. Replication does not depend on sharding. Each shard has its own independent replication. Compressed data is replicated for INSERT and ALTER queries (see the description of the ALTER query). CREATE , DROP , ATTACH , DETACH and RENAME queries are executed on a single server and are not replicated: The CREATE TABLE query creates a new replicatable table on the server where the query is run. If this table already exists on other servers, it adds a new replica. The DROP TABLE query deletes the replica located on the server where the query is run. The RENAME query renames the table on one of the replicas. In other words, replicated tables can have different names on different replicas. To use replication, set the addresses of the ZooKeeper cluster in the config file. Example: zookeeper \n node index= 1 \n host example1 /host \n port 2181 /port \n /node \n node index= 2 \n host example2 /host \n port 2181 /port \n /node \n node index= 3 \n host example3 /host \n port 2181 /port \n /node /zookeeper Use ZooKeeper version 3.4.5 or later. You can specify any existing ZooKeeper cluster and the system will use a directory on it for its own data (the directory is specified when creating a replicatable table). If ZooKeeper isn't set in the config file, you can't create replicated tables, and any existing replicated tables will be read-only. ZooKeeper is not used in SELECT queries because replication does not affect the performance of SELECT and queries run just as fast as they do for non-replicated tables. When querying distributed replicated tables, ClickHouse behavior is controlled by the settings max_replica_delay_for_distributed_queries and fallback_to_stale_replicas_for_distributed_queries . For each INSERT query, approximately ten entries are added to ZooKeeper through several transactions. (To be more precise, this is for each inserted block of data; an INSERT query contains one block or one block per max_insert_block_size = 1048576 rows.) This leads to slightly longer latencies for INSERT compared to non-replicated tables. But if you follow the recommendations to insert data in batches of no more than one INSERT per second, it doesn't create any problems. The entire ClickHouse cluster used for coordinating one ZooKeeper cluster has a total of several hundred INSERTs per second. The throughput on data inserts (the number of rows per second) is just as high as for non-replicated data. For very large clusters, you can use different ZooKeeper clusters for different shards. However, this hasn't proven necessary on the Yandex.Metrica cluster (approximately 300 servers). Replication is asynchronous and multi-master. INSERT queries (as well as ALTER ) can be sent to any available server. Data is inserted on the server where the query is run, and then it is copied to the other servers. Because it is asynchronous, recently inserted data appears on the other replicas with some latency. If part of the replicas are not available, the data is written when they become available. If a replica is available, the latency is the amount of time it takes to transfer the block of compressed data over the network. By default, an INSERT query waits for confirmation of writing the data from only one replica. If the data was successfully written to only one replica and the server with this replica ceases to exist, the stored data will be lost. Tp enable getting confirmation of data writes from multiple replicas, use the insert_quorum option. Each block of data is written atomically. The INSERT query is divided into blocks up to max_insert_block_size = 1048576 rows. In other words, if the INSERT query has less than 1048576 rows, it is made atomically. Data blocks are deduplicated. For multiple writes of the same data block (data blocks of the same size containing the same rows in the same order), the block is only written once. The reason for this is in case of network failures when the client application doesn't know if the data was written to the DB, so the INSERT query can simply be repeated. It doesn't matter which replica INSERTs were sent to with identical data. INSERTs are idempotent. Deduplication parameters are controlled by merge_tree server settings. During replication, only the source data to insert is transferred over the network. Further data transformation (merging) is coordinated and performed on all the replicas in the same way. This minimizes network usage, which means that replication works well when replicas reside in different datacenters. (Note that duplicating data in different datacenters is the main goal of replication.) You can have any number of replicas of the same data. Yandex.Metrica uses double replication in production. Each server uses RAID-5 or RAID-6, and RAID-10 in some cases. This is a relatively reliable and convenient solution. The system monitors data synchronicity on replicas and is able to recover after a failure. Failover is automatic (for small differences in data) or semi-automatic (when data differs too much, which may indicate a configuration error).", - "title": "Data replication" - }, - { - "location": "/index.html#creating-replicated-tables", - "text": "The Replicated prefix is added to the table engine name. For example: ReplicatedMergeTree . Two parameters are also added in the beginning of the parameters list \u2013 the path to the table in ZooKeeper, and the replica name in ZooKeeper. Example: ReplicatedMergeTree( /clickhouse/tables/{layer}-{shard}/hits , {replica} , EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID), EventTime), 8192) As the example shows, these parameters can contain substitutions in curly brackets. The substituted values are taken from the 'macros' section of the config file. Example: macros \n layer 05 /layer \n shard 02 /shard \n replica example05-02-1.yandex.ru /replica /macros The path to the table in ZooKeeper should be unique for each replicated table. Tables on different shards should have different paths.\nIn this case, the path consists of the following parts: /clickhouse/tables/ is the common prefix. We recommend using exactly this one. {layer}-{shard} is the shard identifier. In this example it consists of two parts, since the Yandex.Metrica cluster uses bi-level sharding. For most tasks, you can leave just the {shard} substitution, which will be expanded to the shard identifier. hits is the name of the node for the table in ZooKeeper. It is a good idea to make it the same as the table name. It is defined explicitly, because in contrast to the table name, it doesn't change after a RENAME query. The replica name identifies different replicas of the same table. You can use the server name for this, as in the example. The name only needs to be unique within each shard. You can define the parameters explicitly instead of using substitutions. This might be convenient for testing and for configuring small clusters. However, you can't use distributed DDL queries ( ON CLUSTER ) in this case. When working with large clusters, we recommend using substitutions because they reduce the probability of error. Run the CREATE TABLE query on each replica. This query creates a new replicated table, or adds a new replica to an existing one. If you add a new replica after the table already contains some data on other replicas, the data will be copied from the other replicas to the new one after running the query. In other words, the new replica syncs itself with the others. To delete a replica, run DROP TABLE . However, only one replica is deleted \u2013 the one that resides on the server where you run the query.", - "title": "Creating replicated tables" - }, - { - "location": "/index.html#recovery-after-failures", - "text": "If ZooKeeper is unavailable when a server starts, replicated tables switch to read-only mode. The system periodically attempts to connect to ZooKeeper. If ZooKeeper is unavailable during an INSERT , or an error occurs when interacting with ZooKeeper, an exception is thrown. After connecting to ZooKeeper, the system checks whether the set of data in the local file system matches the expected set of data (ZooKeeper stores this information). If there are minor inconsistencies, the system resolves them by syncing data with the replicas. If the system detects broken data parts (with the wrong size of files) or unrecognized parts (parts written to the file system but not recorded in ZooKeeper), it moves them to the 'detached' subdirectory (they are not deleted). Any missing parts are copied from the replicas. Note that ClickHouse does not perform any destructive actions such as automatically deleting a large amount of data. When the server starts (or establishes a new session with ZooKeeper), it only checks the quantity and sizes of all files. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a SELECT query. The query throws an exception about a non-matching checksum or size of a compressed block. In this case, data parts are added to the verification queue and copied from the replicas if necessary. If the local set of data differs too much from the expected one, a safety mechanism is triggered. The server enters this in the log and refuses to launch. The reason for this is that this case may indicate a configuration error, such as if a replica on a shard was accidentally configured like a replica on a different shard. However, the thresholds for this mechanism are set fairly low, and this situation might occur during normal failure recovery. In this case, data is restored semi-automatically - by \"pushing a button\". To start recovery, create the node /path_to_table/replica_name/flags/force_restore_data in ZooKeeper with any content, or run the command to restore all replicated tables: sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data Then restart the server. On start, the server deletes these flags and starts recovery.", - "title": "Recovery after failures" - }, - { - "location": "/index.html#recovery-after-complete-data-loss", - "text": "If all data and metadata disappeared from one of the servers, follow these steps for recovery: Install ClickHouse on the server. Define substitutions correctly in the config file that contains the shard identifier and replicas, if you use them. If you had unreplicated tables that must be manually duplicated on the servers, copy their data from a replica (in the directory /var/lib/clickhouse/data/db_name/table_name/ ). Copy table definitions located in /var/lib/clickhouse/metadata/ from a replica. If a shard or replica identifier is defined explicitly in the table definitions, correct it so that it corresponds to this replica. (Alternatively, start the server and make all the ATTACH TABLE queries that should have been in the .sql files in /var/lib/clickhouse/metadata/ .) To start recovery, create the ZooKeeper node /path_to_table/replica_name/flags/force_restore_data with any content, or run the command to restore all replicated tables: sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data Then start the server (restart, if it is already running). Data will be downloaded from replicas. An alternative recovery option is to delete information about the lost replica from ZooKeeper ( /path_to_table/replica_name ), then create the replica again as described in \" Creating replicatable tables \". There is no restriction on network bandwidth during recovery. Keep this in mind if you are restoring many replicas at once.", - "title": "Recovery after complete data loss" - }, - { - "location": "/index.html#converting-from-mergetree-to-replicatedmergetree", - "text": "We use the term MergeTree to refer to all table engines in the MergeTree family , the same as for ReplicatedMergeTree . If you had a MergeTree table that was manually replicated, you can convert it to a replicatable table. You might need to do this if you have already collected a large amount of data in a MergeTree table and now you want to enable replication. If the data differs on various replicas, first sync it, or delete this data on all the replicas except one. Rename the existing MergeTree table, then create a ReplicatedMergeTree table with the old name.\nMove the data from the old table to the 'detached' subdirectory inside the directory with the new table data ( /var/lib/clickhouse/data/db_name/table_name/ ).\nThen run ALTER TABLE ATTACH PARTITION on one of the replicas to add these data parts to the working set.", - "title": "Converting from MergeTree to ReplicatedMergeTree" - }, - { - "location": "/index.html#converting-from-replicatedmergetree-to-mergetree", - "text": "Create a MergeTree table with a different name. Move all the data from the directory with the ReplicatedMergeTree table data to the new table's data directory. Then delete the ReplicatedMergeTree table and restart the server. If you want to get rid of a ReplicatedMergeTree table without launching the server: Delete the corresponding .sql file in the metadata directory ( /var/lib/clickhouse/metadata/ ). Delete the corresponding path in ZooKeeper ( /path_to_table/replica_name ). After this, you can launch the server, create a MergeTree table, move the data to its directory, and then restart the server.", - "title": "Converting from ReplicatedMergeTree to MergeTree" - }, - { - "location": "/index.html#recovery-when-metadata-in-the-zookeeper-cluster-is-lost-or-damaged", - "text": "If the data in ZooKeeper was lost or damaged, you can save data by moving it to an unreplicated table as described above. If exactly the same parts exist on the other replicas, they are added to the working set on them. If not, the parts are downloaded from the replica that has them.", - "title": "Recovery when metadata in the ZooKeeper cluster is lost or damaged" - }, - { - "location": "/index.html#distributed", - "text": "The Distributed engine does not store data itself , but allows distributed query processing on multiple servers.\nReading is automatically parallelized. During a read, the table indexes on remote servers are used, if there are any.\nThe Distributed engine accepts parameters: the cluster name in the server's config file, the name of a remote database, the name of a remote table, and (optionally) a sharding key.\nExample: Distributed(logs, default, hits[, sharding_key]) Data will be read from all servers in the 'logs' cluster, from the default.hits table located on every server in the cluster.\nData is not only read, but is partially processed on the remote servers (to the extent that this is possible).\nFor example, for a query with GROUP BY, data will be aggregated on remote servers, and the intermediate states of aggregate functions will be sent to the requestor server. Then data will be further aggregated. Instead of the database name, you can use a constant expression that returns a string. For example: currentDatabase(). logs \u2013 The cluster name in the server's config file. Clusters are set like this: remote_servers \n logs \n shard \n !-- Optional. Shard weight when writing data. Default: 1. -- \n weight 1 /weight \n !-- Optional. Whether to write data to just one of the replicas. Default: false (write data to all replicas). -- \n internal_replication false /internal_replication \n replica \n host example01-01-1 /host \n port 9000 /port \n /replica \n replica \n host example01-01-2 /host \n port 9000 /port \n /replica \n /shard \n shard \n weight 2 /weight \n internal_replication false /internal_replication \n replica \n host example01-02-1 /host \n port 9000 /port \n /replica \n replica \n host example01-02-2 /host \n port 9000 /port \n /replica \n /shard \n /logs /remote_servers Here a cluster is defined with the name 'logs' that consists of two shards, each of which contains two replicas.\nShards refer to the servers that contain different parts of the data (in order to read all the data, you must access all the shards).\nReplicas are duplicating servers (in order to read all the data, you can access the data on any one of the replicas). The parameters host , port , and optionally user and password are specified for each server: : - host \u2013 The address of the remote server. You can use either the domain or the IPv4 or IPv6 address. If you specify the domain, the server makes a DNS request when it starts, and the result is stored as long as the server is running. If the DNS request fails, the server doesn't start. If you change the DNS record, restart the server.\n- port \u2013 The TCP port for messenger activity ('tcp_port' in the config, usually set to 9000). Do not confuse it with http_port.\n- user \u2013 Name of the user for connecting to a remote server. Default value: default. This user must have access to connect to the specified server. Access is configured in the users.xml file. For more information, see the section \"Access rights\".\n- password \u2013 The password for connecting to a remote server (not masked). Default value: empty string. When specifying replicas, one of the available replicas will be selected for each of the shards when reading. You can configure the algorithm for load balancing (the preference for which replica to access) \u2013 see the 'load_balancing' setting.\nIf the connection with the server is not established, there will be an attempt to connect with a short timeout. If the connection failed, the next replica will be selected, and so on for all the replicas. If the connection attempt failed for all the replicas, the attempt will be repeated the same way, several times.\nThis works in favor of resiliency, but does not provide complete fault tolerance: a remote server might accept the connection, but might not work, or work poorly. You can specify just one of the shards (in this case, query processing should be called remote, rather than distributed) or up to any number of shards. In each shard, you can specify from one to any number of replicas. You can specify a different number of replicas for each shard. You can specify as many clusters as you wish in the configuration. To view your clusters, use the 'system.clusters' table. The Distributed engine allows working with a cluster like a local server. However, the cluster is inextensible: you must write its configuration in the server config file (even better, for all the cluster's servers). There is no support for Distributed tables that look at other Distributed tables (except in cases when a Distributed table only has one shard). As an alternative, make the Distributed table look at the \"final\" tables. The Distributed engine requires writing clusters to the config file. Clusters from the config file are updated on the fly, without restarting the server. If you need to send a query to an unknown set of shards and replicas each time, you don't need to create a Distributed table \u2013 use the 'remote' table function instead. See the section \"Table functions\". There are two methods for writing data to a cluster: First, you can define which servers to write which data to, and perform the write directly on each shard. In other words, perform INSERT in the tables that the distributed table \"looks at\".\nThis is the most flexible solution \u2013 you can use any sharding scheme, which could be non-trivial due to the requirements of the subject area.\nThis is also the most optimal solution, since data can be written to different shards completely independently. Second, you can perform INSERT in a Distributed table. In this case, the table will distribute the inserted data across servers itself.\nIn order to write to a Distributed table, it must have a sharding key set (the last parameter). In addition, if there is only one shard, the write operation works without specifying the sharding key, since it doesn't have any meaning in this case. Each shard can have a weight defined in the config file. By default, the weight is equal to one. Data is distributed across shards in the amount proportional to the shard weight. For example, if there are two shards and the first has a weight of 9 while the second has a weight of 10, the first will be sent 9 / 19 parts of the rows, and the second will be sent 10 / 19. Each shard can have the 'internal_replication' parameter defined in the config file. If this parameter is set to 'true', the write operation selects the first healthy replica and writes data to it. Use this alternative if the Distributed table \"looks at\" replicated tables. In other words, if the table where data will be written is going to replicate them itself. If it is set to 'false' (the default), data is written to all replicas. In essence, this means that the Distributed table replicates data itself. This is worse than using replicated tables, because the consistency of replicas is not checked, and over time they will contain slightly different data. To select the shard that a row of data is sent to, the sharding expression is analyzed, and its remainder is taken from dividing it by the total weight of the shards. The row is sent to the shard that corresponds to the half-interval of the remainders from 'prev_weight' to 'prev_weights + weight', where 'prev_weights' is the total weight of the shards with the smallest number, and 'weight' is the weight of this shard. For example, if there are two shards, and the first has a weight of 9 while the second has a weight of 10, the row will be sent to the first shard for the remainders from the range [0, 9), and to the second for the remainders from the range [9, 19). The sharding expression can be any expression from constants and table columns that returns an integer. For example, you can use the expression 'rand()' for random distribution of data, or 'UserID' for distribution by the remainder from dividing the user's ID (then the data of a single user will reside on a single shard, which simplifies running IN and JOIN by users). If one of the columns is not distributed evenly enough, you can wrap it in a hash function: intHash64(UserID). A simple remainder from division is a limited solution for sharding and isn't always appropriate. It works for medium and large volumes of data (dozens of servers), but not for very large volumes of data (hundreds of servers or more). In the latter case, use the sharding scheme required by the subject area, rather than using entries in Distributed tables. SELECT queries are sent to all the shards, and work regardless of how data is distributed across the shards (they can be distributed completely randomly). When you add a new shard, you don't have to transfer the old data to it. You can write new data with a heavier weight \u2013 the data will be distributed slightly unevenly, but queries will work correctly and efficiently. You should be concerned about the sharding scheme in the following cases: Queries are used that require joining data (IN or JOIN) by a specific key. If data is sharded by this key, you can use local IN or JOIN instead of GLOBAL IN or GLOBAL JOIN, which is much more efficient. A large number of servers is used (hundreds or more) with a large number of small queries (queries of individual clients - websites, advertisers, or partners). In order for the small queries to not affect the entire cluster, it makes sense to locate data for a single client on a single shard. Alternatively, as we've done in Yandex.Metrica, you can set up bi-level sharding: divide the entire cluster into \"layers\", where a layer may consist of multiple shards. Data for a single client is located on a single layer, but shards can be added to a layer as necessary, and data is randomly distributed within them. Distributed tables are created for each layer, and a single shared distributed table is created for global queries. Data is written asynchronously. For an INSERT to a Distributed table, the data block is just written to the local file system. The data is sent to the remote servers in the background as soon as possible. You should check whether data is sent successfully by checking the list of files (data waiting to be sent) in the table directory: /var/lib/clickhouse/data/database/table/. If the server ceased to exist or had a rough restart (for example, after a device failure) after an INSERT to a Distributed table, the inserted data might be lost. If a damaged data part is detected in the table directory, it is transferred to the 'broken' subdirectory and no longer used. When the max_parallel_replicas option is enabled, query processing is parallelized across all replicas within a single shard. For more information, see the section \"Settings, max_parallel_replicas\".", - "title": "Distributed" - }, - { - "location": "/index.html#dictionary", - "text": "The Dictionary engine displays the dictionary data as a ClickHouse table. As an example, consider a dictionary of products with the following configuration: dictionaries dictionary \n name products /name \n source \n odbc \n table products /table \n connection_string DSN=some-db-server /connection_string \n /odbc \n /source \n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n layout \n flat/ \n /layout \n structure \n id \n name product_id /name \n /id \n attribute \n name title /name \n type String /type \n null_value /null_value \n /attribute \n /structure /dictionary /dictionaries Query the dictionary data: select name , type , key , attribute . names , attribute . types , bytes_allocated , element_count , source from system . dictionaries where name = products ; SELECT \n name , \n type , \n key , \n attribute . names , \n attribute . types , \n bytes_allocated , \n element_count , \n source FROM system . dictionaries WHERE name = products \u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u252c\u2500type\u2500\u252c\u2500key\u2500\u2500\u2500\u2500\u252c\u2500attribute.names\u2500\u252c\u2500attribute.types\u2500\u252c\u2500bytes_allocated\u2500\u252c\u2500element_count\u2500\u252c\u2500source\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 products \u2502 Flat \u2502 UInt64 \u2502 [ title ] \u2502 [ String ] \u2502 23065376 \u2502 175032 \u2502 ODBC: .products \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 You can use the dictGet* function to get the dictionary data in this format. This view isn't helpful when you need to get raw data, or when performing a JOIN operation. For these cases, you can use the Dictionary engine, which displays the dictionary data in a table. Syntax: CREATE TABLE %table_name% (%fields%) engine = Dictionary(%dictionary_name%)` Usage example: create table products ( product_id UInt64 , title String ) Engine = Dictionary ( products ); CREATE TABLE products ( \n product_id UInt64 , \n title String , ) ENGINE = Dictionary ( products ) Ok.\n\n0 rows in set. Elapsed: 0.004 sec. Take a look at what's in the table. select * from products limit 1 ; SELECT * FROM products LIMIT 1 \u250c\u2500\u2500\u2500\u2500product_id\u2500\u252c\u2500title\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 152689 \u2502 Some item \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n1 rows in set. Elapsed: 0.006 sec.", - "title": "Dictionary" - }, - { - "location": "/index.html#merge", - "text": "The Merge engine (not to be confused with MergeTree ) does not store data itself, but allows reading from any number of other tables simultaneously.\nReading is automatically parallelized. Writing to a table is not supported. When reading, the indexes of tables that are actually being read are used, if they exist.\nThe Merge engine accepts parameters: the database name and a regular expression for tables. Example: Merge(hits, ^WatchLog ) Data will be read from the tables in the 'hits' database that have names that match the regular expression ' ^WatchLog '. Instead of the database name, you can use a constant expression that returns a string. For example, currentDatabase() . Regular expressions \u2014 re2 (supports a subset of PCRE), case-sensitive.\nSee the notes about escaping symbols in regular expressions in the \"match\" section. When selecting tables to read, the Merge table itself will not be selected, even if it matches the regex. This is to avoid loops.\nIt is possible to create two Merge tables that will endlessly try to read each others' data, but this is not a good idea. The typical way to use the Merge engine is for working with a large number of TinyLog tables as if with a single table.", - "title": "Merge" - }, - { - "location": "/index.html#virtual-columns", - "text": "Virtual columns are columns that are provided by the table engine, regardless of the table definition. In other words, these columns are not specified in CREATE TABLE, but they are accessible for SELECT. Virtual columns differ from normal columns in the following ways: They are not specified in table definitions. Data can't be added to them with INSERT. When using INSERT without specifying the list of columns, virtual columns are ignored. They are not selected when using the asterisk ( SELECT * ). Virtual columns are not shown in SHOW CREATE TABLE and DESC TABLE queries. A Merge type table contains a virtual _table column with the String type. (If the table already has a _table column, the virtual column is named _table1, and if it already has _table1, it is named _table2, and so on.) It contains the name of the table that data was read from. If the WHERE or PREWHERE clause contains conditions for the '_table' column that do not depend on other table columns (as one of the conjunction elements, or as an entire expression), these conditions are used as an index. The conditions are performed on a data set of table names to read data from, and the read operation will be performed from only those tables that the condition was triggered on.", - "title": "Virtual columns" - }, - { - "location": "/index.html#buffer", - "text": "Buffers the data to write in RAM, periodically flushing it to another table. During the read operation, data is read from the buffer and the other table simultaneously. Buffer(database, table, num_layers, min_time, max_time, min_rows, max_rows, min_bytes, max_bytes) Engine parameters:database, table \u2013 The table to flush data to. Instead of the database name, you can use a constant expression that returns a string.num_layers \u2013 Parallelism layer. Physically, the table will be represented as 'num_layers' of independent buffers. Recommended value: 16.min_time, max_time, min_rows, max_rows, min_bytes, and max_bytes are conditions for flushing data from the buffer. Data is flushed from the buffer and written to the destination table if all the 'min' conditions or at least one 'max' condition are met.min_time, max_time \u2013 Condition for the time in seconds from the moment of the first write to the buffer.min_rows, max_rows \u2013 Condition for the number of rows in the buffer.min_bytes, max_bytes \u2013 Condition for the number of bytes in the buffer. During the write operation, data is inserted to a 'num_layers' number of random buffers. Or, if the data part to insert is large enough (greater than 'max_rows' or 'max_bytes'), it is written directly to the destination table, omitting the buffer. The conditions for flushing the data are calculated separately for each of the 'num_layers' buffers. For example, if num_layers = 16 and max_bytes = 100000000, the maximum RAM consumption is 1.6 GB. Example: CREATE TABLE merge . hits_buffer AS merge . hits ENGINE = Buffer ( merge , hits , 16 , 10 , 100 , 10000 , 1000000 , 10000000 , 100000000 ) Creating a 'merge.hits_buffer' table with the same structure as 'merge.hits' and using the Buffer engine. When writing to this table, data is buffered in RAM and later written to the 'merge.hits' table. 16 buffers are created. The data in each of them is flushed if either 100 seconds have passed, or one million rows have been written, or 100 MB of data have been written; or if simultaneously 10 seconds have passed and 10,000 rows and 10 MB of data have been written. For example, if just one row has been written, after 100 seconds it will be flushed, no matter what. But if many rows have been written, the data will be flushed sooner. When the server is stopped, with DROP TABLE or DETACH TABLE, buffer data is also flushed to the destination table. You can set empty strings in single quotation marks for the database and table name. This indicates the absence of a destination table. In this case, when the data flush conditions are reached, the buffer is simply cleared. This may be useful for keeping a window of data in memory. When reading from a Buffer table, data is processed both from the buffer and from the destination table (if there is one).\nNote that the Buffer tables does not support an index. In other words, data in the buffer is fully scanned, which might be slow for large buffers. (For data in a subordinate table, the index that it supports will be used.) If the set of columns in the Buffer table doesn't match the set of columns in a subordinate table, a subset of columns that exist in both tables is inserted. If the types don't match for one of the columns in the Buffer table and a subordinate table, an error message is entered in the server log and the buffer is cleared.\nThe same thing happens if the subordinate table doesn't exist when the buffer is flushed. If you need to run ALTER for a subordinate table and the Buffer table, we recommend first deleting the Buffer table, running ALTER for the subordinate table, then creating the Buffer table again. If the server is restarted abnormally, the data in the buffer is lost. PREWHERE, FINAL and SAMPLE do not work correctly for Buffer tables. These conditions are passed to the destination table, but are not used for processing data in the buffer. Because of this, we recommend only using the Buffer table for writing, while reading from the destination table. When adding data to a Buffer, one of the buffers is locked. This causes delays if a read operation is simultaneously being performed from the table. Data that is inserted to a Buffer table may end up in the subordinate table in a different order and in different blocks. Because of this, a Buffer table is difficult to use for writing to a CollapsingMergeTree correctly. To avoid problems, you can set 'num_layers' to 1. If the destination table is replicated, some expected characteristics of replicated tables are lost when writing to a Buffer table. The random changes to the order of rows and sizes of data parts cause data deduplication to quit working, which means it is not possible to have a reliable 'exactly once' write to replicated tables. Due to these disadvantages, we can only recommend using a Buffer table in rare cases. A Buffer table is used when too many INSERTs are received from a large number of servers over a unit of time and data can't be buffered before insertion, which means the INSERTs can't run fast enough. Note that it doesn't make sense to insert data one row at a time, even for Buffer tables. This will only produce a speed of a few thousand rows per second, while inserting larger blocks of data can produce over a million rows per second (see the section \"Performance\").", - "title": "Buffer" - }, - { - "location": "/index.html#fileinputformat", - "text": "The data source is a file that stores data in one of the supported input formats (TabSeparated, Native, etc.).", - "title": "File(InputFormat)" - }, - { - "location": "/index.html#null", - "text": "When writing to a Null table, data is ignored. When reading from a Null table, the response is empty. However, you can create a materialized view on a Null table. So the data written to the table will end up in the view.", - "title": "Null" - }, - { - "location": "/index.html#set_1", - "text": "A data set that is always in RAM. It is intended for use on the right side of the IN operator (see the section \"IN operators\"). You can use INSERT to insert data in the table. New elements will be added to the data set, while duplicates will be ignored.\nBut you can't perform SELECT from the table. The only way to retrieve data is by using it in the right half of the IN operator. Data is always located in RAM. For INSERT, the blocks of inserted data are also written to the directory of tables on the disk. When starting the server, this data is loaded to RAM. In other words, after restarting, the data remains in place. For a rough server restart, the block of data on the disk might be lost or damaged. In the latter case, you may need to manually delete the file with damaged data.", - "title": "Set" - }, - { - "location": "/index.html#join", - "text": "A prepared data structure for JOIN that is always located in RAM. Join(ANY|ALL, LEFT|INNER, k1[, k2, ...]) Engine parameters: ANY|ALL \u2013 strictness; LEFT|INNER \u2013 type.\nThese parameters are set without quotes and must match the JOIN that the table will be used for. k1, k2, ... are the key columns from the USING clause that the join will be made on. The table can't be used for GLOBAL JOINs. You can use INSERT to add data to the table, similar to the Set engine. For ANY, data for duplicated keys will be ignored. For ALL, it will be counted. You can't perform SELECT directly from the table. The only way to retrieve data is to use it as the \"right-hand\" table for JOIN. Storing data on the disk is the same as for the Set engine.", - "title": "Join" - }, - { - "location": "/index.html#view", - "text": "Used for implementing views (for more information, see the CREATE VIEW query ). It does not store data, but only stores the specified SELECT query. When reading from a table, it runs this query (and deletes all unnecessary columns from the query).", - "title": "View" - }, - { - "location": "/index.html#materializedview", - "text": "Used for implementing materialized views (for more information, see the CREATE TABLE ) query. For storing data, it uses a different engine that was specified when creating the view. When reading from a table, it just uses this engine.", - "title": "MaterializedView" - }, - { - "location": "/index.html#kafka", - "text": "This engine works with Apache Kafka . Kafka lets you: Publish or subscribe to data flows. Organize fault-tolerant storage. Process streams as they become available. Kafka(broker_list, topic_list, group_name, format[, schema, num_consumers]) Parameters: broker_list \u2013 A comma-separated list of brokers ( localhost:9092 ). topic_list \u2013 A list of Kafka topics ( my_topic ). group_name \u2013 A group of Kafka consumers ( group1 ). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere. --format \u2013 Message format. Uses the same notation as the SQL FORMAT function, such as JSONEachRow . For more information, see the \"Formats\" section. schema \u2013 An optional parameter that must be used if the format requires a schema definition. For example, Cap'n Proto requires the path to the schema file and the name of the root schema.capnp:Message object. num_consumers \u2013 The number of consumers per table. Default: 1 . Specify more consumers if the throughput of one consumer is insufficient. The total number of consumers should not exceed the number of partitions in the topic, since only one consumer can be assigned per partition. Example: CREATE TABLE queue ( \n timestamp UInt64 , \n level String , \n message String \n ) ENGINE = Kafka ( localhost:9092 , topic , group1 , JSONEachRow ); \n\n SELECT * FROM queue LIMIT 5 ; The delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name. Groups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. Read more about this at http://kafka.apache.org/intro . SELECT is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. To do this: Use the engine to create a Kafka consumer and consider it a data stream. Create a table with the desired structure. Create a materialized view that converts data from the engine and puts it into a previously created table. When the MATERIALIZED VIEW joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using SELECT Example: CREATE TABLE queue ( \n timestamp UInt64 , \n level String , \n message String \n ) ENGINE = Kafka ( localhost:9092 , topic , group1 , JSONEachRow ); \n\n CREATE TABLE daily ( \n day Date , \n level String , \n total UInt64 \n ) ENGINE = SummingMergeTree ( day , ( day , level ), 8192 ); \n\n CREATE MATERIALIZED VIEW consumer TO daily \n AS SELECT toDate ( toDateTime ( timestamp )) AS day , level , count () as total \n FROM queue GROUP BY day , level ; \n\n SELECT level , sum ( total ) FROM daily GROUP BY level ; To improve performance, received messages are grouped into blocks the size of max_insert_block_size . If the block wasn't formed within stream_flush_interval_ms milliseconds, the data will be flushed to the table regardless of the completeness of the block. To stop receiving topic data or to change the conversion logic, detach the materialized view: DETACH TABLE consumer;\n ATTACH MATERIALIZED VIEW consumer; If you want to change the target table by using ALTER materialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view.", - "title": "Kafka" - }, - { - "location": "/index.html#configuration", - "text": "Similar to GraphiteMergeTree, the Kafka engine supports extended configuration using the ClickHouse config file. There are two configuration keys that you can use: global ( kafka ) and topic-level ( kafka_topic_* ). The global configuration is applied first, and the topic-level configuration is second (if it exists). !-- Global configuration options for all tables of Kafka engine type -- \n kafka \n debug cgrp /debug \n auto_offset_reset smallest /auto_offset_reset \n /kafka \n\n !-- Configuration specific for topic logs -- \n kafka_topic_logs \n retry_backoff_ms 250 /retry_backoff_ms \n fetch_min_bytes 100000 /fetch_min_bytes \n /kafka_topic_logs For a list of possible configuration options, see the librdkafka configuration reference . Use the underscore ( _ ) instead of a dot in the ClickHouse configuration. For example, check.crcs=true will be check_crcs true /check_crcs .", - "title": "Configuration" - }, - { - "location": "/index.html#mysql", - "text": "The MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server. The engine takes 4 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example: MySQL( host:port , database , table , user , password ); At this time, simple WHERE clauses such as =, !=, , =, , = are executed on the MySQL server. The rest of the conditions and the LIMIT sampling constraint are executed in ClickHouse only after the query to MySQL finishes.", - "title": "MySQL" - }, - { - "location": "/index.html#external-data-for-query-processing", - "text": "ClickHouse allows sending a server the data that is needed for processing a query, together with a SELECT query. This data is put in a temporary table (see the section \"Temporary tables\") and can be used in the query (for example, in IN operators). For example, if you have a text file with important user identifiers, you can upload it to the server along with a query that uses filtration by this list. If you need to run more than one query with a large volume of external data, don't use this feature. It is better to upload the data to the DB ahead of time. External data can be uploaded using the command-line client (in non-interactive mode), or using the HTTP interface. In the command-line client, you can specify a parameters section in the format --external --file = ... [ --name = ... ] [ --format = ... ] [ --types = ... | --structure = ... ] You may have multiple sections like this, for the number of tables being transmitted. --external \u2013 Marks the beginning of a clause. --file \u2013 Path to the file with the table dump, or -, which refers to stdin.\nOnly a single table can be retrieved from stdin. The following parameters are optional: --name \u2013 Name of the table. If omitted, _data is used. --format \u2013 Data format in the file. If omitted, TabSeparated is used. One of the following parameters is required: --types \u2013 A list of comma-separated column types. For example: UInt64,String . The columns will be named _1, _2, ... --structure \u2013 The table structure in the format UserID UInt64 , URL String . Defines the column names and types. The files specified in 'file' will be parsed by the format specified in 'format', using the data types specified in 'types' or 'structure'. The table will be uploaded to the server and accessible there as a temporary table with the name in 'name'. Examples: echo -ne 1\\n2\\n3\\n | clickhouse-client --query = SELECT count() FROM test.visits WHERE TraficSourceID IN _data --external --file = - --types = Int8 849897 \ncat /etc/passwd | sed s/:/\\t/g | clickhouse-client --query = SELECT shell, count() AS c FROM passwd GROUP BY shell ORDER BY c DESC --external --file = - --name = passwd --structure = login String, unused String, uid UInt16, gid UInt16, comment String, home String, shell String \n/bin/sh 20 \n/bin/false 5 \n/bin/bash 4 \n/usr/sbin/nologin 1 \n/bin/sync 1 When using the HTTP interface, external data is passed in the multipart/form-data format. Each table is transmitted as a separate file. The table name is taken from the file name. The 'query_string' is passed the parameters 'name_format', 'name_types', and 'name_structure', where 'name' is the name of the table that these parameters correspond to. The meaning of the parameters is the same as when using the command-line client. Example: cat /etc/passwd | sed s/:/\\t/g passwd.tsv\n\ncurl -F passwd=@passwd.tsv; http://localhost:8123/?query=SELECT+shell,+count()+AS+c+FROM+passwd+GROUP+BY+shell+ORDER+BY+c+DESC passwd_structure=login+String,+unused+String,+uid+UInt16,+gid+UInt16,+comment+String,+home+String,+shell+String \n/bin/sh 20 \n/bin/false 5 \n/bin/bash 4 \n/usr/sbin/nologin 1 \n/bin/sync 1 For distributed query processing, the temporary tables are sent to all the remote servers.", - "title": "External data for query processing" - }, - { - "location": "/index.html#system-tables", - "text": "System tables are used for implementing part of the system's functionality, and for providing access to information about how the system is working.\nYou can't delete a system table (but you can perform DETACH).\nSystem tables don't have files with data on the disk or files with metadata. The server creates all the system tables when it starts.\nSystem tables are read-only.\nThey are located in the 'system' database.", - "title": "System tables" - }, - { - "location": "/index.html#systemone", - "text": "This table contains a single row with a single 'dummy' UInt8 column containing the value 0.\nThis table is used if a SELECT query doesn't specify the FROM clause.\nThis is similar to the DUAL table found in other DBMSs.", - "title": "system.one" - }, - { - "location": "/index.html#systemnumbers", - "text": "This table contains a single UInt64 column named 'number' that contains almost all the natural numbers starting from zero.\nYou can use this table for tests, or if you need to do a brute force search.\nReads from this table are not parallelized.", - "title": "system.numbers" - }, - { - "location": "/index.html#systemnumbers_mt", - "text": "The same as 'system.numbers' but reads are parallelized. The numbers can be returned in any order.\nUsed for tests.", - "title": "system.numbers_mt" - }, - { - "location": "/index.html#systemdatabases", - "text": "This table contains a single String column called 'name' \u2013 the name of a database.\nEach database that the server knows about has a corresponding entry in the table.\nThis system table is used for implementing the SHOW DATABASES query.", - "title": "system.databases" - }, - { - "location": "/index.html#systemtables", - "text": "This table contains the String columns 'database', 'name', and 'engine'.\nThe table also contains three virtual columns: metadata_modification_time (DateTime type), create_table_query, and engine_full (String type).\nEach table that the server knows about is entered in the 'system.tables' table.\nThis system table is used for implementing SHOW TABLES queries.", - "title": "system.tables" - }, - { - "location": "/index.html#systemcolumns", - "text": "Contains information about the columns in all tables.\nYou can use this table to get information similar to DESCRIBE TABLE , but for multiple tables at once. database String - Name of the database the table is located in.\ntable String - Table name.\nname String - Column name.\ntype String - Column type.\ndefault_type String - Expression type (DEFAULT, MATERIALIZED, ALIAS) for the default value, or an empty string if it is not defined.\ndefault_expression String - Expression for the default value, or an empty string if it is not defined.", - "title": "system.columns" - }, - { - "location": "/index.html#systemparts", - "text": "Contains information about parts of a table in the MergeTree family. Each row describes one part of the data. Columns: partition (String) \u2013 The partition name. YYYYMM format. To learn what a partition is, see the description of the ALTER query. name (String) \u2013 Name of the data part. active (UInt8) \u2013 Indicates whether the part is active. If a part is active, it is used in a table; otherwise, it will be deleted. Inactive data parts remain after merging. marks (UInt64) \u2013 The number of marks. To get the approximate number of rows in a data part, multiply marks by the index granularity (usually 8192). marks_size (UInt64) \u2013 The size of the file with marks. rows (UInt64) \u2013 The number of rows. bytes (UInt64) \u2013 The number of bytes when compressed. modification_time (DateTime) \u2013 The modification time of the directory with the data part. This usually corresponds to the time of data part creation.| remove_time (DateTime) \u2013 The time when the data part became inactive. refcount (UInt32) \u2013 The number of places where the data part is used. A value greater than 2 indicates that the data part is used in queries or merges. min_date (Date) \u2013 The minimum value of the date key in the data part. max_date (Date) \u2013 The maximum value of the date key in the data part. min_block_number (UInt64) \u2013 The minimum number of data parts that make up the current part after merging. max_block_number (UInt64) \u2013 The maximum number of data parts that make up the current part after merging. level (UInt32) \u2013 Depth of the merge tree. If a merge was not performed, level=0 . primary_key_bytes_in_memory (UInt64) \u2013 The amount of memory (in bytes) used by primary key values. primary_key_bytes_in_memory_allocated (UInt64) \u2013 The amount of memory (in bytes) reserved for primary key values. database (String) \u2013 Name of the database. table (String) \u2013 Name of the table. engine (String) \u2013 Name of the table engine without parameters.", - "title": "system.parts" - }, - { - "location": "/index.html#systemprocesses", - "text": "This system table is used for implementing the SHOW PROCESSLIST query.\nColumns: user String \u2013 Name of the user who made the request. For distributed query processing, this is the user who helped the requestor server send the query to this server, not the user who made the distributed request on the requestor server.\n\naddress String \u2013 The IP address that the query was made from. The same is true for distributed query processing.\n\nelapsed Float64 \u2013 The time in seconds since request execution started.\n\nrows_read UInt64 \u2013 The number of rows read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nbytes_read UInt64 \u2013 The number of uncompressed bytes read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.\n\nUInt64 total_rows_approx \u2013 The approximate total number of rows that must be read. For distributed processing, on the requestor server, this is the total for all remote servers. It can be updated during request processing, when new sources to process become known.\n\nmemory_usage UInt64 \u2013 Memory consumption by the query. It might not include some types of dedicated memory.\n\nquery String \u2013 The query text. For INSERT, it doesn t include the data to insert.\n\nquery_id \u2013 Query ID, if defined.", - "title": "system.processes" - }, - { - "location": "/index.html#systemmerges", - "text": "Contains information about merges currently in process for tables in the MergeTree family. Columns: database String \u2014 Name of the database the table is located in. table String \u2014 Name of the table. elapsed Float64 \u2014 Time in seconds since the merge started. progress Float64 \u2014 Percent of progress made, from 0 to 1. num_parts UInt64 \u2014 Number of parts to merge. result_part_name String \u2014 Name of the part that will be formed as the result of the merge. total_size_bytes_compressed UInt64 \u2014 Total size of compressed data in the parts being merged. total_size_marks UInt64 \u2014 Total number of marks in the parts being merged. bytes_read_uncompressed UInt64 \u2014 Amount of bytes read, decompressed. rows_read UInt64 \u2014 Number of rows read. bytes_written_uncompressed UInt64 \u2014 Amount of bytes written, uncompressed. rows_written UInt64 \u2014 Number of rows written.", - "title": "system.merges" - }, - { - "location": "/index.html#systemevents", - "text": "Contains information about the number of events that have occurred in the system. This is used for profiling and monitoring purposes.\nExample: The number of processed SELECT queries.\nColumns: 'event String' \u2013 the event name, and 'value UInt64' \u2013 the quantity.", - "title": "system.events" - }, - { - "location": "/index.html#systemmetrics", - "text": "", - "title": "system.metrics" - }, - { - "location": "/index.html#systemasynchronous_metrics", - "text": "Contain metrics used for profiling and monitoring.\nThey usually reflect the number of events currently in the system, or the total resources consumed by the system.\nExample: The number of SELECT queries currently running; the amount of memory in use. system.asynchronous_metrics and system.metrics differ in their sets of metrics and how they are calculated.", - "title": "system.asynchronous_metrics" - }, - { - "location": "/index.html#systemreplicas", - "text": "Contains information and status for replicated tables residing on the local server.\nThis table can be used for monitoring. The table contains a row for every Replicated* table. Example: SELECT * FROM system . replicas WHERE table = visits FORMAT Vertical Row 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ndatabase: merge\ntable: visits\nengine: ReplicatedCollapsingMergeTree\nis_leader: 1\nis_readonly: 0\nis_session_expired: 0\nfuture_parts: 1\nparts_to_check: 0\nzookeeper_path: /clickhouse/tables/01-06/visits\nreplica_name: example01-06-1.yandex.ru\nreplica_path: /clickhouse/tables/01-06/visits/replicas/example01-06-1.yandex.ru\ncolumns_version: 9\nqueue_size: 1\ninserts_in_queue: 0\nmerges_in_queue: 1\nlog_max_index: 596273\nlog_pointer: 596274\ntotal_replicas: 2\nactive_replicas: 2 Columns: database: database name\ntable: table name\nengine: table engine name\n\nis_leader: whether the replica is the leader\n\nOnly one replica at a time can be the leader. The leader is responsible for selecting background merges to perform.\nNote that writes can be performed to any replica that is available and has a session in ZK, regardless of whether it is a leader.\n\nis_readonly: Whether the replica is in read-only mode.\nThis mode is turned on if the config doesn t have sections with ZK, if an unknown error occurred when reinitializing sessions in ZK, and during session reinitialization in ZK.\n\nis_session_expired: Whether the ZK session expired.\nBasically, the same thing as is_readonly.\n\nfuture_parts: The number of data parts that will appear as the result of INSERTs or merges that haven t been done yet. \n\nparts_to_check: The number of data parts in the queue for verification.\nA part is put in the verification queue if there is suspicion that it might be damaged.\n\nzookeeper_path: The path to the table data in ZK. \nreplica_name: Name of the replica in ZK. Different replicas of the same table have different names. \nreplica_path: The path to the replica data in ZK. The same as concatenating zookeeper_path/replicas/replica_path.\n\ncolumns_version: Version number of the table structure.\nIndicates how many times ALTER was performed. If replicas have different versions, it means some replicas haven t made all of the ALTERs yet.\n\nqueue_size: Size of the queue for operations waiting to be performed.\nOperations include inserting blocks of data, merges, and certain other actions.\nNormally coincides with future_parts.\n\ninserts_in_queue: Number of inserts of blocks of data that need to be made.\nInsertions are usually replicated fairly quickly. If the number is high, something is wrong.\n\nmerges_in_queue: The number of merges waiting to be made. \nSometimes merges are lengthy, so this value may be greater than zero for a long time.\n\nThe next 4 columns have a non-null value only if the ZK session is active.\n\nlog_max_index: Maximum entry number in the log of general activity.\nlog_pointer: Maximum entry number in the log of general activity that the replica copied to its execution queue, plus one.\nIf log_pointer is much smaller than log_max_index, something is wrong.\n\ntotal_replicas: Total number of known replicas of this table.\nactive_replicas: Number of replicas of this table that have a ZK session (the number of active replicas). If you request all the columns, the table may work a bit slowly, since several reads from ZK are made for each row.\nIf you don't request the last 4 columns (log_max_index, log_pointer, total_replicas, active_replicas), the table works quickly. For example, you can check that everything is working correctly like this: SELECT \n database , \n table , \n is_leader , \n is_readonly , \n is_session_expired , \n future_parts , \n parts_to_check , \n columns_version , \n queue_size , \n inserts_in_queue , \n merges_in_queue , \n log_max_index , \n log_pointer , \n total_replicas , \n active_replicas FROM system . replicas WHERE \n is_readonly \n OR is_session_expired \n OR future_parts 20 \n OR parts_to_check 10 \n OR queue_size 20 \n OR inserts_in_queue 10 \n OR log_max_index - log_pointer 10 \n OR total_replicas 2 \n OR active_replicas total_replicas If this query doesn't return anything, it means that everything is fine.", - "title": "system.replicas" - }, - { - "location": "/index.html#systemdictionaries", - "text": "Contains information about external dictionaries. Columns: name String \u2013 Dictionary name. type String \u2013 Dictionary type: Flat, Hashed, Cache. origin String \u2013 Path to the config file where the dictionary is described. attribute.names Array(String) \u2013 Array of attribute names provided by the dictionary. attribute.types Array(String) \u2013 Corresponding array of attribute types provided by the dictionary. has_hierarchy UInt8 \u2013 Whether the dictionary is hierarchical. bytes_allocated UInt64 \u2013 The amount of RAM used by the dictionary. hit_rate Float64 \u2013 For cache dictionaries, the percent of usage for which the value was in the cache. element_count UInt64 \u2013 The number of items stored in the dictionary. load_factor Float64 \u2013 The filled percentage of the dictionary (for a hashed dictionary, it is the filled percentage of the hash table). creation_time DateTime \u2013 Time spent for the creation or last successful reload of the dictionary. last_exception String \u2013 Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created. source String \u2013 Text describing the data source for the dictionary. Note that the amount of memory used by the dictionary is not proportional to the number of items stored in it. So for flat and cached dictionaries, all the memory cells are pre-assigned, regardless of how full the dictionary actually is.", - "title": "system.dictionaries" - }, - { - "location": "/index.html#systemclusters", - "text": "Contains information about clusters available in the config file and the servers in them.\nColumns: cluster String \u2013 Cluster name.\nshard_num UInt32 \u2013 Number of a shard in the cluster, starting from 1.\nshard_weight UInt32 \u2013 Relative weight of a shard when writing data.\nreplica_num UInt32 \u2013 Number of a replica in the shard, starting from 1.\nhost_name String \u2013 Host name as specified in the config.\nhost_address String \u2013 Host s IP address obtained from DNS.\nport UInt16 \u2013 The port used to access the server.\nuser String \u2013 The username to use for connecting to the server.", - "title": "system.clusters" - }, - { - "location": "/index.html#systemfunctions", - "text": "Contains information about normal and aggregate functions. Columns: name ( String ) \u2013 Function name. is_aggregate ( UInt8 ) \u2013 Whether it is an aggregate function.", - "title": "system.functions" - }, - { - "location": "/index.html#systemsettings", - "text": "Contains information about settings that are currently in use.\nI.e. used for executing the query you are using to read from the system.settings table). Columns: name String \u2013 Setting name.\nvalue String \u2013 Setting value.\nchanged UInt8 - Whether the setting was explicitly defined in the config or explicitly changed. Example: SELECT * FROM system . settings WHERE changed \u250c\u2500name\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500value\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500changed\u2500\u2510\n\u2502 max_threads \u2502 8 \u2502 1 \u2502\n\u2502 use_uncompressed_cache \u2502 0 \u2502 1 \u2502\n\u2502 load_balancing \u2502 random \u2502 1 \u2502\n\u2502 max_memory_usage \u2502 10000000000 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "system.settings" - }, - { - "location": "/index.html#systemzookeeper", - "text": "Allows reading data from the ZooKeeper cluster defined in the config.\nThe query must have a 'path' equality condition in the WHERE clause. This is the path in ZooKeeper for the children that you want to get data for. The query SELECT * FROM system.zookeeper WHERE path = '/clickhouse' outputs data for all children on the /clickhouse node.\nTo output data for all root nodes, write path = '/'.\nIf the path specified in 'path' doesn't exist, an exception will be thrown. Columns: name String \u2014 Name of the node. path String \u2014 Path to the node. value String \u2014 Value of the node. dataLength Int32 \u2014 Size of the value. numChildren Int32 \u2014 Number of children. czxid Int64 \u2014 ID of the transaction that created the node. mzxid Int64 \u2014 ID of the transaction that last changed the node. pzxid Int64 \u2014 ID of the transaction that last added or removed children. ctime DateTime \u2014 Time of node creation. mtime DateTime \u2014 Time of the last node modification. version Int32 \u2014 Node version - the number of times the node was changed. cversion Int32 \u2014 Number of added or removed children. aversion Int32 \u2014 Number of changes to ACL. ephemeralOwner Int64 \u2014 For ephemeral nodes, the ID of the session that owns this node. Example: SELECT * FROM system . zookeeper WHERE path = /clickhouse/tables/01-08/visits/replicas FORMAT Vertical Row 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-1.yandex.ru\nvalue:\nczxid: 932998691229\nmzxid: 932998691229\nctime: 2015-03-27 16:49:51\nmtime: 2015-03-27 16:49:51\nversion: 0\ncversion: 47\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021031383\npath: /clickhouse/tables/01-08/visits/replicas\n\nRow 2:\n\u2500\u2500\u2500\u2500\u2500\u2500\nname: example01-08-2.yandex.ru\nvalue:\nczxid: 933002738135\nmzxid: 933002738135\nctime: 2015-03-27 16:57:01\nmtime: 2015-03-27 16:57:01\nversion: 0\ncversion: 37\naversion: 0\nephemeralOwner: 0\ndataLength: 0\nnumChildren: 7\npzxid: 987021252247\npath: /clickhouse/tables/01-08/visits/replicas", - "title": "system.zookeeper" - }, - { - "location": "/index.html#table-functions", - "text": "Table functions can be specified in the FROM clause instead of the database and table names.\nTable functions can only be used if 'readonly' is not set.\nTable functions aren't related to other functions.", - "title": "Table functions" - }, - { - "location": "/index.html#remote", - "text": "Allows you to access remote servers without creating a Distributed table. Signatures: remote ( addresses_expr , db , table [, user [, password ]]) remote ( addresses_expr , db . table [, user [, password ]]) addresses_expr \u2013 An expression that generates addresses of remote servers. This may be just one server address. The server address is host:port , or just host . The host can be specified as the server name, or as the IPv4 or IPv6 address. An IPv6 address is specified in square brackets. The port is the TCP port on the remote server. If the port is omitted, it uses tcp_port from the server's config file (by default, 9000). \n\nThe port is required for an IPv6 address. Examples: example01-01-1\nexample01-01-1:9000\nlocalhost\n127.0.0.1\n[::]:9000\n[2a02:6b8:0:1111::11]:9000 Multiple addresses can be comma-separated. In this case, ClickHouse will use distributed processing, so it will send the query to all specified addresses (like to shards with different data). Example: example01-01-1,example01-02-1 Part of the expression can be specified in curly brackets. The previous example can be written as follows: example01-0{1,2}-1 Curly brackets can contain a range of numbers separated by two dots (non-negative integers). In this case, the range is expanded to a set of values that generate shard addresses. If the first number starts with zero, the values are formed with the same zero alignment. The previous example can be written as follows: example01-{01..02}-1 If you have multiple pairs of curly brackets, it generates the direct product of the corresponding sets. Addresses and parts of addresses in curly brackets can be separated by the pipe symbol (|). In this case, the corresponding sets of addresses are interpreted as replicas, and the query will be sent to the first healthy replica. However, the replicas are iterated in the order currently set in the load_balancing setting. Example: example01-{01..02}-{1|2} This example specifies two shards that each have two replicas. The number of addresses generated is limited by a constant. Right now this is 1000 addresses. Using the remote table function is less optimal than creating a Distributed table, because in this case, the server connection is re-established for every request. In addition, if host names are set, the names are resolved, and errors are not counted when working with various replicas. When processing a large number of queries, always create the Distributed table ahead of time, and don't use the remote table function. The remote table function can be useful in the following cases: Accessing a specific server for data comparison, debugging, and testing. Queries between various ClickHouse clusters for research purposes. Infrequent distributed requests that are made manually. Distributed requests where the set of servers is re-defined each time. If the user is not specified, default is used.\nIf the password is not specified, an empty password is used.", - "title": "remote" - }, - { - "location": "/index.html#merge_1", - "text": "merge(db_name, 'tables_regexp') \u2013 Creates a temporary Merge table. For more information, see the section \"Table engines, Merge\". The table structure is taken from the first table encountered that matches the regular expression.", - "title": "merge" - }, - { - "location": "/index.html#numbers", - "text": "numbers(N) \u2013 Returns a table with the single 'number' column (UInt64) that contains integers from 0 to N-1. Similar to the system.numbers table, it can be used for testing and generating successive values. The following two queries are equivalent: SELECT * FROM numbers ( 10 ); SELECT * FROM system . numbers LIMIT 10 ; Examples: -- Generate a sequence of dates from 2010-01-01 to 2010-12-31 select toDate ( 2010-01-01 ) + number as d FROM numbers ( 365 );", - "title": "numbers" - }, - { - "location": "/index.html#formats", - "text": "The format determines how data is returned to you after SELECTs (how it is written and formatted by the server), and how it is accepted for INSERTs (how it is read and parsed by the server).", - "title": "Formats" - }, - { - "location": "/index.html#tabseparated", - "text": "In TabSeparated format, data is written by row. Each row contains values separated by tabs. Each value is follow by a tab, except the last value in the row, which is followed by a line feed. Strictly Unix line feeds are assumed everywhere. The last row also must contain a line feed at the end. Values are written in text format, without enclosing quotation marks, and with special characters escaped. Integer numbers are written in decimal form. Numbers can contain an extra \"+\" character at the beginning (ignored when parsing, and not recorded when formatting). Non-negative numbers can't contain the negative sign. When reading, it is allowed to parse an empty string as a zero, or (for signed types) a string consisting of just a minus sign as a zero. Numbers that do not fit into the corresponding data type may be parsed as a different number, without an error message. Floating-point numbers are written in decimal form. The dot is used as the decimal separator. Exponential entries are supported, as are 'inf', '+inf', '-inf', and 'nan'. An entry of floating-point numbers may begin or end with a decimal point.\nDuring formatting, accuracy may be lost on floating-point numbers.\nDuring parsing, it is not strictly required to read the nearest machine-representable number. Dates are written in YYYY-MM-DD format and parsed in the same format, but with any characters as separators.\nDates with times are written in the format YYYY-MM-DD hh:mm:ss and parsed in the same format, but with any characters as separators.\nThis all occurs in the system time zone at the time the client or server starts (depending on which one formats data). For dates with times, daylight saving time is not specified. So if a dump has times during daylight saving time, the dump does not unequivocally match the data, and parsing will select one of the two times.\nDuring a read operation, incorrect dates and dates with times can be parsed with natural overflow or as null dates and times, without an error message. As an exception, parsing dates with times is also supported in Unix timestamp format, if it consists of exactly 10 decimal digits. The result is not time zone-dependent. The formats YYYY-MM-DD hh:mm:ss and NNNNNNNNNN are differentiated automatically. Strings are output with backslash-escaped special characters. The following escape sequences are used for output: \\b , \\f , \\r , \\n , \\t , \\0 , \\' , \\\\ . Parsing also supports the sequences \\a , \\v , and \\xHH (hex escape sequences) and any \\c sequences, where c is any character (these sequences are converted to c ). Thus, reading data supports formats where a line feed can be written as \\n or \\ , or as a line feed. For example, the string Hello world with a line feed between the words instead of a space can be parsed in any of the following variations: Hello\\nworld\n\nHello\\\nworld The second variant is supported because MySQL uses it when writing tab-separated dumps. The minimum set of characters that you need to escape when passing data in TabSeparated format: tab, line feed (LF) and backslash. Only a small set of symbols are escaped. You can easily stumble onto a string value that your terminal will ruin in output. Arrays are written as a list of comma-separated values in square brackets. Number items in the array are fomratted as normally, but dates, dates with times, and strings are written in single quotes with the same escaping rules as above. The TabSeparated format is convenient for processing data using custom programs and scripts. It is used by default in the HTTP interface, and in the command-line client's batch mode. This format also allows transferring data between different DBMSs. For example, you can get a dump from MySQL and upload it to ClickHouse, or vice versa. The TabSeparated format supports outputting total values (when using WITH TOTALS) and extreme values (when 'extremes' is set to 1). In these cases, the total values and extremes are output after the main data. The main result, total values, and extremes are separated from each other by an empty line. Example: SELECT EventDate , count () AS c FROM test . hits GROUP BY EventDate WITH TOTALS ORDER BY EventDate FORMAT TabSeparated `` 2014-03-17 1406958\n2014-03-18 1383658\n2014-03-19 1405797\n2014-03-20 1353623\n2014-03-21 1245779\n2014-03-22 1031592\n2014-03-23 1046491\n\n0000-00-00 8873898\n\n2014-03-17 1031592\n2014-03-23 1406958 This format is also available under the name TSV .", - "title": "TabSeparated" - }, - { - "location": "/index.html#tabseparatedraw", - "text": "Differs from TabSeparated format in that the rows are written without escaping.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). This format is also available under the name TSVRaw .", - "title": "TabSeparatedRaw" - }, - { - "location": "/index.html#tabseparatedwithnames", - "text": "Differs from the TabSeparated format in that the column names are written in the first row.\nDuring parsing, the first row is completely ignored. You can't use column names to determine their position or to check their correctness.\n(Support for parsing the header row may be added in the future.) This format is also available under the name TSVWithNames .", - "title": "TabSeparatedWithNames" - }, - { - "location": "/index.html#tabseparatedwithnamesandtypes", - "text": "Differs from the TabSeparated format in that the column names are written to the first row, while the column types are in the second row.\nDuring parsing, the first and second rows are completely ignored. This format is also available under the name TSVWithNamesAndTypes .", - "title": "TabSeparatedWithNamesAndTypes" - }, - { - "location": "/index.html#csv", - "text": "Comma Separated Values format ( RFC ). When formatting, rows are enclosed in double quotes. A double quote inside a string is output as two double quotes in a row. There are no other rules for escaping characters. Date and date-time are enclosed in double quotes. Numbers are output without quotes. Values \u200b\u200bare separated by a delimiter . Rows are separated using the Unix line feed (LF). Arrays are serialized in CSV as follows: first the array is serialized to a string as in TabSeparated format, and then the resulting string is output to CSV in double quotes. Tuples in CSV format are serialized as separate columns (that is, their nesting in the tuple is lost). By default \u2014 , . See a format_csv_delimiter setting for additional info. When parsing, all values can be parsed either with or without quotes. Both double and single quotes are supported. Rows can also be arranged without quotes. In this case, they are parsed up to a delimiter or line feed (CR or LF). In violation of the RFC, when parsing rows without quotes, the leading and trailing spaces and tabs are ignored. For the line feed, Unix (LF), Windows (CR LF) and Mac OS Classic (CR LF) are all supported. The CSV format supports the output of totals and extremes the same way as TabSeparated .", - "title": "CSV" - }, - { - "location": "/index.html#csvwithnames", - "text": "Also prints the header row, similar to TabSeparatedWithNames .", - "title": "CSVWithNames" - }, - { - "location": "/index.html#values", - "text": "Prints every row in brackets. Rows are separated by commas. There is no comma after the last row. The values inside the brackets are also comma-separated. Numbers are output in decimal format without quotes. Arrays are output in square brackets. Strings, dates, and dates with times are output in quotes. Escaping rules and parsing are similar to the TabSeparated format. During formatting, extra spaces aren't inserted, but during parsing, they are allowed and skipped (except for spaces inside array values, which are not allowed). The minimum set of characters that you need to escape when passing data in Values \u200b\u200bformat: single quotes and backslashes. This is the format that is used in INSERT INTO t VALUES ... , but you can also use it for formatting query results.", - "title": "Values" - }, - { - "location": "/index.html#vertical", - "text": "Prints each value on a separate line with the column name specified. This format is convenient for printing just one or a few rows, if each row consists of a large number of columns.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).", - "title": "Vertical" - }, - { - "location": "/index.html#verticalraw", - "text": "Differs from Vertical format in that the rows are not escaped.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). Examples: :) SHOW CREATE TABLE geonames FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\nstatement: CREATE TABLE default.geonames ( geonameid UInt32, date Date DEFAULT CAST( 2017-12-08 AS Date)) ENGINE = MergeTree(date, geonameid, 8192)\n\n:) SELECT string with \\ quotes\\ and \\t with some special \\n characters AS test FORMAT VerticalRaw;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with quotes and with some special\n characters Compare with the Vertical format: :) SELECT string with \\ quotes\\ and \\t with some special \\n characters AS test FORMAT Vertical;\nRow 1:\n\u2500\u2500\u2500\u2500\u2500\u2500\ntest: string with \\ quotes\\ and \\t with some special \\n characters", - "title": "VerticalRaw" - }, - { - "location": "/index.html#json", - "text": "Outputs data in JSON format. Besides data tables, it also outputs column names and types, along with some additional information: the total number of output rows, and the number of rows that could have been output if there weren't a LIMIT. Example: SELECT SearchPhrase , count () AS c FROM test . hits GROUP BY SearchPhrase WITH TOTALS ORDER BY c DESC LIMIT 5 FORMAT JSON { \n meta : \n [ \n { \n name : SearchPhrase , \n type : String \n }, \n { \n name : c , \n type : UInt64 \n } \n ], \n\n data : \n [ \n { \n SearchPhrase : , \n c : 8267016 \n }, \n { \n SearchPhrase : bathroom interior design , \n c : 2166 \n }, \n { \n SearchPhrase : yandex , \n c : 1655 \n }, \n { \n SearchPhrase : spring 2014 fashion , \n c : 1549 \n }, \n { \n SearchPhrase : freeform photos , \n c : 1480 \n } \n ], \n\n totals : \n { \n SearchPhrase : , \n c : 8873898 \n }, \n\n extremes : \n { \n min : \n { \n SearchPhrase : , \n c : 1480 \n }, \n max : \n { \n SearchPhrase : , \n c : 8267016 \n } \n }, \n\n rows : 5 , \n\n rows_before_limit_at_least : 141137 } The JSON is compatible with JavaScript. To ensure this, some characters are additionally escaped: the slash / is escaped as \\/ ; alternative line breaks U+2028 and U+2029 , which break some browsers, are escaped as \\uXXXX . ASCII control characters are escaped: backspace, form feed, line feed, carriage return, and horizontal tab are replaced with \\b , \\f , \\n , \\r , \\t , as well as the remaining bytes in the 00-1F range using \\uXXXX sequences. Invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences. For compatibility with JavaScript, Int64 and UInt64 integers are enclosed in double quotes by default. To remove the quotes, you can set the configuration parameter output_format_json_quote_64bit_integers to 0. rows \u2013 The total number of output rows. rows_before_limit_at_least The minimal number of rows there would have been without LIMIT. Output only if the query contains LIMIT.\nIf the query contains GROUP BY, rows_before_limit_at_least is the exact number of rows there would have been without a LIMIT. totals \u2013 Total values (when using WITH TOTALS). extremes \u2013 Extreme values (when extremes is set to 1). This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the JSONEachRow format.", - "title": "JSON" - }, - { - "location": "/index.html#jsoncompact", - "text": "Differs from JSON only in that data rows are output in arrays, not in objects. Example: { \n meta : \n [ \n { \n name : SearchPhrase , \n type : String \n }, \n { \n name : c , \n type : UInt64 \n } \n ], \n\n data : \n [ \n [ , 8267016 ], \n [ bathroom interior design , 2166 ], \n [ yandex , 1655 ], \n [ spring 2014 fashion , 1549 ], \n [ freeform photos , 1480 ] \n ], \n\n totals : [ , 8873898 ], \n\n extremes : \n { \n min : [ , 1480 ], \n max : [ , 8267016 ] \n }, \n\n rows : 5 , \n\n rows_before_limit_at_least : 141137 } This format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table).\nSee also the JSONEachRow format.", - "title": "JSONCompact" - }, - { - "location": "/index.html#jsoneachrow", - "text": "Outputs data as separate JSON objects for each row (newline delimited JSON). { SearchPhrase : , count() : 8267016 } { SearchPhrase : bathroom interior design , count() : 2166 } { SearchPhrase : yandex , count() : 1655 } { SearchPhrase : spring 2014 fashion , count() : 1549 } { SearchPhrase : freeform photo , count() : 1480 } { SearchPhrase : angelina jolie , count() : 1245 } { SearchPhrase : omsk , count() : 1112 } { SearchPhrase : photos of dog breeds , count() : 1091 } { SearchPhrase : curtain design , count() : 1064 } { SearchPhrase : baku , count() : 1000 } Unlike the JSON format, there is no substitution of invalid UTF-8 sequences. Any set of bytes can be output in the rows. This is necessary so that data can be formatted without losing any information. Values are escaped in the same way as for JSON. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Whitespace between elements is ignored. If a comma is placed after the objects, it is ignored. Objects don't necessarily have to be separated by new lines.", - "title": "JSONEachRow" - }, - { - "location": "/index.html#tskv", - "text": "Similar to TabSeparated, but outputs a value in name=value format. Names are escaped the same way as in TabSeparated format, and the = symbol is also escaped. SearchPhrase= count()=8267016\nSearchPhrase=bathroom interior design count()=2166\nSearchPhrase=yandex count()=1655\nSearchPhrase=spring 2014 fashion count()=1549\nSearchPhrase=freeform photos count()=1480\nSearchPhrase=angelina jolia count()=1245\nSearchPhrase=omsk count()=1112\nSearchPhrase=photos of dog breeds count()=1091\nSearchPhrase=curtain design count()=1064\nSearchPhrase=baku count()=1000 When there is a large number of small columns, this format is ineffective, and there is generally no reason to use it. It is used in some departments of Yandex. Both data output and parsing are supported in this format. For parsing, any order is supported for the values of different columns. It is acceptable for some values to be omitted \u2013 they are treated as equal to their default values. In this case, zeros and blank rows are used as default values. Complex values that could be specified in the table are not supported as defaults. Parsing allows the presence of the additional field tskv without the equal sign or a value. This field is ignored.", - "title": "TSKV" - }, - { - "location": "/index.html#pretty", - "text": "Outputs data as Unicode-art tables, also using ANSI-escape sequences for setting colors in the terminal.\nA full grid of the table is drawn, and each row occupies two lines in the terminal.\nEach result block is output as a separate table. This is necessary so that blocks can be output without buffering results (buffering would be necessary in order to pre-calculate the visible width of all the values).\nTo avoid dumping too much data to the terminal, only the first 10,000 rows are printed. If the number of rows is greater than or equal to 10,000, the message \"Showed first 10 000\" is printed.\nThis format is only appropriate for outputting a query result, but not for parsing (retrieving data to insert in a table). The Pretty format supports outputting total values (when using WITH TOTALS) and extremes (when 'extremes' is set to 1). In these cases, total values and extreme values are output after the main data, in separate tables. Example (shown for the PrettyCompact format): SELECT EventDate , count () AS c FROM test . hits GROUP BY EventDate WITH TOTALS ORDER BY EventDate FORMAT PrettyCompact \u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1406958 \u2502\n\u2502 2014-03-18 \u2502 1383658 \u2502\n\u2502 2014-03-19 \u2502 1405797 \u2502\n\u2502 2014-03-20 \u2502 1353623 \u2502\n\u2502 2014-03-21 \u2502 1245779 \u2502\n\u2502 2014-03-22 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1046491 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nTotals:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 0000-00-00 \u2502 8873898 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nExtremes:\n\u250c\u2500\u2500EventDate\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2014-03-17 \u2502 1031592 \u2502\n\u2502 2014-03-23 \u2502 1406958 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "Pretty" - }, - { - "location": "/index.html#prettycompact", - "text": "Differs from Pretty in that the grid is drawn between rows and the result is more compact.\nThis format is used by default in the command-line client in interactive mode.", - "title": "PrettyCompact" - }, - { - "location": "/index.html#prettycompactmonoblock", - "text": "Differs from PrettyCompact in that up to 10,000 rows are buffered, then output as a single table, not by blocks.", - "title": "PrettyCompactMonoBlock" - }, - { - "location": "/index.html#prettynoescapes", - "text": "Differs from Pretty in that ANSI-escape sequences aren't used. This is necessary for displaying this format in a browser, as well as for using the 'watch' command-line utility. Example: watch -n1 clickhouse-client --query= SELECT * FROM system.events FORMAT PrettyCompactNoEscapes You can use the HTTP interface for displaying in the browser.", - "title": "PrettyNoEscapes" - }, - { - "location": "/index.html#prettycompactnoescapes", - "text": "The same as the previous setting.", - "title": "PrettyCompactNoEscapes" - }, - { - "location": "/index.html#prettyspacenoescapes", - "text": "The same as the previous setting.", - "title": "PrettySpaceNoEscapes" - }, - { - "location": "/index.html#prettyspace", - "text": "Differs from PrettyCompact in that whitespace (space characters) is used instead of the grid.", - "title": "PrettySpace" - }, - { - "location": "/index.html#rowbinary", - "text": "Formats and parses data by row in binary format. Rows and values are listed consecutively, without separators.\nThis format is less efficient than the Native format, since it is row-based. Integers use fixed-length little endian representation. For example, UInt64 uses 8 bytes.\nDateTime is represented as UInt32 containing the Unix timestamp as the value.\nDate is represented as a UInt16 object that contains the number of days since 1970-01-01 as the value.\nString is represented as a varint length (unsigned LEB128 ), followed by the bytes of the string.\nFixedString is represented simply as a sequence of bytes. Array is represented as a varint length (unsigned LEB128 ), followed by successive elements of the array.", - "title": "RowBinary" - }, - { - "location": "/index.html#native", - "text": "The most efficient format. Data is written and read by blocks in binary format. For each block, the number of rows, number of columns, column names and types, and parts of columns in this block are recorded one after another. In other words, this format is \"columnar\" \u2013 it doesn't convert columns to rows. This is the format used in the native interface for interaction between servers, for using the command-line client, and for C++ clients. You can use this format to quickly generate dumps that can only be read by the ClickHouse DBMS. It doesn't make sense to work with this format yourself.", - "title": "Native" - }, - { - "location": "/index.html#null_1", - "text": "Nothing is output. However, the query is processed, and when using the command-line client, data is transmitted to the client. This is used for tests, including productivity testing.\nObviously, this format is only appropriate for output, not for parsing.", - "title": "Null" - }, - { - "location": "/index.html#xml", - "text": "XML format is suitable only for output, not for parsing. Example: ?xml version= 1.0 encoding= UTF-8 ? result \n meta \n columns \n column \n name SearchPhrase /name \n type String /type \n /column \n column \n name count() /name \n type UInt64 /type \n /column \n /columns \n /meta \n data \n row \n SearchPhrase /SearchPhrase \n field 8267016 /field \n /row \n row \n SearchPhrase bathroom interior design /SearchPhrase \n field 2166 /field \n /row \n row \n SearchPhrase yandex /SearchPhrase \n field 1655 /field \n /row \n row \n SearchPhrase spring 2014 fashion /SearchPhrase \n field 1549 /field \n /row \n row \n SearchPhrase freeform photos /SearchPhrase \n field 1480 /field \n /row \n row \n SearchPhrase angelina jolie /SearchPhrase \n field 1245 /field \n /row \n row \n SearchPhrase omsk /SearchPhrase \n field 1112 /field \n /row \n row \n SearchPhrase photos of dog breeds /SearchPhrase \n field 1091 /field \n /row \n row \n SearchPhrase curtain design /SearchPhrase \n field 1064 /field \n /row \n row \n SearchPhrase baku /SearchPhrase \n field 1000 /field \n /row \n /data \n rows 10 /rows \n rows_before_limit_at_least 141137 /rows_before_limit_at_least /result If the column name does not have an acceptable format, just 'field' is used as the element name. In general, the XML structure follows the JSON structure.\nJust as for JSON, invalid UTF-8 sequences are changed to the replacement character \ufffd so the output text will consist of valid UTF-8 sequences. In string values, the characters and are escaped as and . Arrays are output as array elem Hello /elem elem World /elem ... /array ,\nand tuples as tuple elem Hello /elem elem World /elem ... /tuple .", - "title": "XML" - }, - { - "location": "/index.html#capnproto", - "text": "Cap'n Proto is a binary message format similar to Protocol Buffers and Thrift, but not like JSON or MessagePack. Cap'n Proto messages are strictly typed and not self-describing, meaning they need an external schema description. The schema is applied on the fly and cached for each query. SELECT SearchPhrase , count () AS c FROM test . hits \n GROUP BY SearchPhrase FORMAT CapnProto SETTINGS schema = schema:Message Where schema.capnp looks like this: struct Message { \n SearchPhrase @0 : Text ; \n c @1 : Uint64 ; } Schema files are in the file that is located in the directory specified in format_schema_path in the server configuration. Deserialization is effective and usually doesn't increase the system load.", - "title": "CapnProto" - }, - { - "location": "/index.html#data-types", - "text": "ClickHouse can store various types of data in table cells. This section describes the supported data types and special considerations when using and/or implementing them, if any.", - "title": "Data types" - }, - { - "location": "/index.html#uint8-uint16-uint32-uint64-int8-int16-int32-int64", - "text": "Fixed-length integers, with or without a sign.", - "title": "UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64" - }, - { - "location": "/index.html#int-ranges", - "text": "Int8 - [-128 : 127] Int16 - [-32768 : 32767] Int32 - [-2147483648 : 2147483647] Int64 - [-9223372036854775808 : 9223372036854775807]", - "title": "Int ranges" - }, - { - "location": "/index.html#uint-ranges", - "text": "UInt8 - [0 : 255] UInt16 - [0 : 65535] UInt32 - [0 : 4294967295] UInt64 - [0 : 18446744073709551615]", - "title": "Uint ranges" - }, - { - "location": "/index.html#float32-float64", - "text": "Floating point numbers . Types are equivalent to types of C: Float32 - float Float64 - double We recommend that you store data in integer form whenever possible. For example, convert fixed precision numbers to integer values, such as monetary amounts or page load times in milliseconds.", - "title": "Float32, Float64" - }, - { - "location": "/index.html#using-floating-point-numbers", - "text": "Computations with floating-point numbers might produce a rounding error. SELECT 1 - 0 . 9 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500minus(1, 0.9)\u2500\u2510\n\u2502 0.09999999999999998 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 The result of the calculation depends on the calculation method (the processor type and architecture of the computer system). Floating-point calculations might result in numbers such as infinity ( Inf ) and \"not-a-number\" ( NaN ). This should be taken into account when processing the results of calculations. When reading floating point numbers from rows, the result might not be the nearest machine-representable number.", - "title": "Using floating-point numbers" - }, - { - "location": "/index.html#nan-and-inf", - "text": "In contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers: Inf \u2013 Infinity. SELECT 0 . 5 / 0 \u250c\u2500divide(0.5, 0)\u2500\u2510\n\u2502 inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 -Inf \u2013 Negative infinity. SELECT - 0 . 5 / 0 \u250c\u2500divide(-0.5, 0)\u2500\u2510\n\u2502 -inf \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 NaN \u2013 Not a number. SELECT 0 / 0 \u250c\u2500divide(0, 0)\u2500\u2510\n\u2502 nan \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 See the rules for NaN sorting in the section ORDER BY clause .", - "title": "NaN and Inf" - }, - { - "location": "/index.html#boolean-values", - "text": "There isn't a separate type for boolean values. They use the UInt8 type, restricted to the values 0 or 1.", - "title": "Boolean values" - }, - { - "location": "/index.html#string", - "text": "Strings of an arbitrary length. The length is not limited. The value can contain an arbitrary set of bytes, including null bytes.\nThe String type replaces the types VARCHAR, BLOB, CLOB, and others from other DBMSs.", - "title": "String" - }, - { - "location": "/index.html#encodings", - "text": "ClickHouse doesn't have the concept of encodings. Strings can contain an arbitrary set of bytes, which are stored and output as-is.\nIf you need to store texts, we recommend using UTF-8 encoding. At the very least, if your terminal uses UTF-8 (as recommended), you can read and write your values without making conversions.\nSimilarly, certain functions for working with strings have separate variations that work under the assumption that the string contains a set of bytes representing a UTF-8 encoded text.\nFor example, the 'length' function calculates the string length in bytes, while the 'lengthUTF8' function calculates the string length in Unicode code points, assuming that the value is UTF-8 encoded.", - "title": "Encodings" - }, - { - "location": "/index.html#fixedstringn", - "text": "A fixed-length string of N bytes (not characters or code points). N must be a strictly positive natural number.\nWhen the server reads a string that contains fewer bytes (such as when parsing INSERT data), the string is padded to N bytes by appending null bytes at the right.\nWhen the server reads a string that contains more bytes, an error message is returned.\nWhen the server writes a string (such as when outputting the result of a SELECT query), null bytes are not trimmed off of the end of the string, but are output.\nNote that this behavior differs from MySQL behavior for the CHAR type (where strings are padded with spaces, and the spaces are removed for output). Fewer functions can work with the FixedString(N) type than with String, so it is less convenient to use.", - "title": "FixedString(N)" - }, - { - "location": "/index.html#date", - "text": "A date. Stored in two bytes as the number of days since 1970-01-01 (unsigned). Allows storing values from just after the beginning of the Unix Epoch to the upper threshold defined by a constant at the compilation stage (currently, this is until the year 2106, but the final fully-supported year is 2105).\nThe minimum value is output as 0000-00-00. The date is stored without the time zone.", - "title": "Date" - }, - { - "location": "/index.html#datetime", - "text": "Date with time. Stored in four bytes as a Unix timestamp (unsigned). Allows storing values in the same range as for the Date type. The minimal value is output as 0000-00-00 00:00:00.\nThe time is stored with accuracy up to one second (without leap seconds).", - "title": "DateTime" - }, - { - "location": "/index.html#time-zones", - "text": "The date with time is converted from text (divided into component parts) to binary and back, using the system's time zone at the time the client or server starts. In text format, information about daylight savings is lost. By default, the client switches to the timezone of the server when it connects. You can change this behavior by enabling the client command-line option --use_client_time_zone . Supports only those time zones that never had the time differ from UTC for a partial number of hours (without leap seconds) over the entire time range you will be working with. So when working with a textual date (for example, when saving text dumps), keep in mind that there may be ambiguity during changes for daylight savings time, and there may be problems matching data if the time zone changed.", - "title": "Time zones" - }, - { - "location": "/index.html#enum", - "text": "Enum8 or Enum16. A finite set of string values that can be stored more efficiently than the String data type. Example: Enum8( hello = 1, world = 2) A data type with two possible values: 'hello' and 'world'. Each of the values is assigned a number in the range -128 ... 127 for Enum8 or in the range -32768 ... 32767 for Enum16 . All the strings and numbers must be different. An empty string is allowed. If this type is specified (in a table definition), numbers can be in an arbitrary order. However, the order does not matter. In RAM, this type of column is stored in the same way as Int8 or Int16 of the corresponding numerical values.\nWhen reading in text form, ClickHouse parses the value as a string and searches for the corresponding string from the set of Enum values. If it is not found, an exception is thrown. When reading in text format, the string is read and the corresponding numeric value is looked up. An exception will be thrown if it is not found.\nWhen writing in text form, it writes the value as the corresponding string. If column data contains garbage (numbers that are not from the valid set), an exception is thrown. When reading and writing in binary form, it works the same way as for Int8 and Int16 data types.\nThe implicit default value is the value with the lowest number. During ORDER BY , GROUP BY , IN , DISTINCT and so on, Enums behave the same way as the corresponding numbers. For example, ORDER BY sorts them numerically. Equality and comparison operators work the same way on Enums as they do on the underlying numeric values. Enum values cannot be compared with numbers. Enums can be compared to a constant string. If the string compared to is not a valid value for the Enum, an exception will be thrown. The IN operator is supported with the Enum on the left hand side and a set of strings on the right hand side. The strings are the values of the corresponding Enum. Most numeric and string operations are not defined for Enum values, e.g. adding a number to an Enum or concatenating a string to an Enum.\nHowever, the Enum has a natural toString function that returns its string value. Enum values are also convertible to numeric types using the toT function, where T is a numeric type. When T corresponds to the enum\u2019s underlying numeric type, this conversion is zero-cost.\nThe Enum type can be changed without cost using ALTER, if only the set of values is changed. It is possible to both add and remove members of the Enum using ALTER (removing is safe only if the removed value has never been used in the table). As a safeguard, changing the numeric value of a previously defined Enum member will throw an exception. Using ALTER, it is possible to change an Enum8 to an Enum16 or vice versa, just like changing an Int8 to Int16.", - "title": "Enum" - }, - { - "location": "/index.html#arrayt", - "text": "An array of elements of type T. The T type can be any type, including an array.\nWe don't recommend using multidimensional arrays, because they are not well supported (for example, you can't store multidimensional arrays in tables with a MergeTree engine).", - "title": "Array(T)" - }, - { - "location": "/index.html#aggregatefunctionname-types_of_arguments", - "text": "The intermediate state of an aggregate function. To get it, use aggregate functions with the '-State' suffix. For more information, see \"AggregatingMergeTree\".", - "title": "AggregateFunction(name, types_of_arguments...)" - }, - { - "location": "/index.html#tuplet1-t2", - "text": "Tuples can't be written to tables (other than Memory tables). They are used for temporary column grouping. Columns can be grouped when an IN expression is used in a query, and for specifying certain formal parameters of lambda functions. For more information, see \"IN operators\" and \"Higher order functions\". Tuples can be output as the result of running a query. In this case, for text formats other than JSON*, values are comma-separated in brackets. In JSON* formats, tuples are output as arrays (in square brackets).", - "title": "Tuple(T1, T2, ...)" - }, - { - "location": "/index.html#nested-data-structures", - "text": "", - "title": "Nested data structures" - }, - { - "location": "/index.html#nestedname1-type1-name2-type2", - "text": "A nested data structure is like a nested table. The parameters of a nested data structure \u2013 the column names and types \u2013 are specified the same way as in a CREATE query. Each table row can correspond to any number of rows in a nested data structure. Example: CREATE TABLE test . visits ( \n CounterID UInt32 , \n StartDate Date , \n Sign Int8 , \n IsNew UInt8 , \n VisitID UInt64 , \n UserID UInt64 , \n ... \n Goals Nested \n ( \n ID UInt32 , \n Serial UInt32 , \n EventTime DateTime , \n Price Int64 , \n OrderID String , \n CurrencyID UInt32 \n ), \n ... ) ENGINE = CollapsingMergeTree ( StartDate , intHash32 ( UserID ), ( CounterID , StartDate , intHash32 ( UserID ), VisitID ), 8192 , Sign ) This example declares the Goals nested data structure, which contains data about conversions (goals reached). Each row in the 'visits' table can correspond to zero or any number of conversions. Only a single nesting level is supported. Columns of nested structures containing arrays are equivalent to multidimensional arrays, so they have limited support (there is no support for storing these columns in tables with the MergeTree engine). In most cases, when working with a nested data structure, its individual columns are specified. To do this, the column names are separated by a dot. These columns make up an array of matching types. All the column arrays of a single nested data structure have the same length. Example: SELECT \n Goals . ID , \n Goals . EventTime FROM test . visits WHERE CounterID = 101500 AND length ( Goals . ID ) 5 LIMIT 10 \u250c\u2500Goals.ID\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500Goals.EventTime\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1073752,591325,591325] \u2502 [ 2014-03-17 16:38:10 , 2014-03-17 16:38:48 , 2014-03-17 16:42:27 ] \u2502\n\u2502 [1073752] \u2502 [ 2014-03-17 00:28:25 ] \u2502\n\u2502 [1073752] \u2502 [ 2014-03-17 10:46:20 ] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [ 2014-03-17 13:59:20 , 2014-03-17 22:17:55 , 2014-03-17 22:18:07 , 2014-03-17 22:18:51 ] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [1073752,591325,591325] \u2502 [ 2014-03-17 11:37:06 , 2014-03-17 14:07:47 , 2014-03-17 14:36:21 ] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [] \u2502 [] \u2502\n\u2502 [591325,1073752] \u2502 [ 2014-03-17 00:46:05 , 2014-03-17 00:46:05 ] \u2502\n\u2502 [1073752,591325,591325,591325] \u2502 [ 2014-03-17 13:28:33 , 2014-03-17 13:30:26 , 2014-03-17 18:51:21 , 2014-03-17 18:51:45 ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 It is easiest to think of a nested data structure as a set of multiple column arrays of the same length. The only place where a SELECT query can specify the name of an entire nested data structure instead of individual columns is the ARRAY JOIN clause. For more information, see \"ARRAY JOIN clause\". Example: SELECT \n Goal . ID , \n Goal . EventTime FROM test . visits ARRAY JOIN Goals AS Goal WHERE CounterID = 101500 AND length ( Goals . ID ) 5 LIMIT 10 \u250c\u2500Goal.ID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500Goal.EventTime\u2500\u2510\n\u2502 1073752 \u2502 2014-03-17 16:38:10 \u2502\n\u2502 591325 \u2502 2014-03-17 16:38:48 \u2502\n\u2502 591325 \u2502 2014-03-17 16:42:27 \u2502\n\u2502 1073752 \u2502 2014-03-17 00:28:25 \u2502\n\u2502 1073752 \u2502 2014-03-17 10:46:20 \u2502\n\u2502 1073752 \u2502 2014-03-17 13:59:20 \u2502\n\u2502 591325 \u2502 2014-03-17 22:17:55 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:07 \u2502\n\u2502 591325 \u2502 2014-03-17 22:18:51 \u2502\n\u2502 1073752 \u2502 2014-03-17 11:37:06 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 You can't perform SELECT for an entire nested data structure. You can only explicitly list individual columns that are part of it. For an INSERT query, you should pass all the component column arrays of a nested data structure separately (as if they were individual column arrays). During insertion, the system checks that they have the same length. For a DESCRIBE query, the columns in a nested data structure are listed separately in the same way. The ALTER query is very limited for elements in a nested data structure.", - "title": "Nested(Name1 Type1, Name2 Type2, ...)" - }, - { - "location": "/index.html#special-data-types", - "text": "Special data type values can't be saved to a table or output in results, but are used as the intermediate result of running a query.", - "title": "Special data types" - }, - { - "location": "/index.html#expression", - "text": "Used for representing lambda expressions in high-order functions.", - "title": "Expression" - }, - { - "location": "/index.html#set_2", - "text": "Used for the right half of an IN expression.", - "title": "Set" - }, - { - "location": "/index.html#operators_1", - "text": "All operators are transformed to the corresponding functions at the query parsing stage, in accordance with their precedence and associativity.\nGroups of operators are listed in order of priority (the higher it is in the list, the earlier the operator is connected to its arguments).", - "title": "Operators" - }, - { - "location": "/index.html#access-operators", - "text": "a[N] Access to an element of an array; arrayElement(a, N) function . a.N \u2013 Access to a tuble element; tupleElement(a, N) function.", - "title": "Access operators" - }, - { - "location": "/index.html#numeric-negation-operator", - "text": "-a \u2013 The negate (a) function.", - "title": "Numeric negation operator" - }, - { - "location": "/index.html#multiplication-and-division-operators", - "text": "a * b \u2013 The multiply (a, b) function. a / b \u2013 The divide(a, b) function. a % b \u2013 The modulo(a, b) function.", - "title": "Multiplication and division operators" - }, - { - "location": "/index.html#addition-and-subtraction-operators", - "text": "a + b \u2013 The plus(a, b) function. a - b \u2013 The minus(a, b) function.", - "title": "Addition and subtraction operators" - }, - { - "location": "/index.html#comparison-operators", - "text": "a = b \u2013 The equals(a, b) function. a == b \u2013 The equals(a, b) function. a != b \u2013 The notEquals(a, b) function. a b \u2013 The notEquals(a, b) function. a = b \u2013 The lessOrEquals(a, b) function. a = b \u2013 The greaterOrEquals(a, b) function. a b \u2013 The less(a, b) function. a b \u2013 The greater(a, b) function. a LIKE s \u2013 The like(a, b) function. a NOT LIKE s \u2013 The notLike(a, b) function. a BETWEEN b AND c \u2013 The same as a = b AND a = c.", - "title": "Comparison operators" - }, - { - "location": "/index.html#operators-for-working-with-data-sets", - "text": "See the section \"IN operators\". a IN ... \u2013 The in(a, b) function a NOT IN ... \u2013 The notIn(a, b) function. a GLOBAL IN ... \u2013 The globalIn(a, b) function. a GLOBAL NOT IN ... \u2013 The globalNotIn(a, b) function.", - "title": "Operators for working with data sets" - }, - { - "location": "/index.html#logical-negation-operator", - "text": "NOT a The not(a) function.", - "title": "Logical negation operator" - }, - { - "location": "/index.html#logical-and-operator", - "text": "a AND b \u2013 The and(a, b) function.", - "title": "Logical AND operator" - }, - { - "location": "/index.html#logical-or-operator", - "text": "a OR b \u2013 The or(a, b) function.", - "title": "Logical OR operator" - }, - { - "location": "/index.html#conditional-operator", - "text": "a ? b : c \u2013 The if(a, b, c) function. Note: The conditional operator calculates the values of b and c, then checks whether condition a is met, and then returns the corresponding value. If \"b\" or \"c\" is an arrayJoin() function, each row will be replicated regardless of the \"a\" condition.", - "title": "Conditional operator" - }, - { - "location": "/index.html#conditional-expression", - "text": "CASE [ x ] \n WHEN a THEN b \n [ WHEN ... THEN ...] \n ELSE c END If \"x\" is specified, then transform(x, [a, ...], [b, ...], c). Otherwise \u2013 multiIf(a, b, ..., c).", - "title": "Conditional expression" - }, - { - "location": "/index.html#concatenation-operator", - "text": "s1 || s2 \u2013 The concat(s1, s2) function.", - "title": "Concatenation operator" - }, - { - "location": "/index.html#lambda-creation-operator", - "text": "x - expr \u2013 The lambda(x, expr) function. The following operators do not have a priority, since they are brackets:", - "title": "Lambda creation operator" - }, - { - "location": "/index.html#array-creation-operator", - "text": "[x1, ...] \u2013 The array(x1, ...) function.", - "title": "Array creation operator" - }, - { - "location": "/index.html#tuple-creation-operator", - "text": "(x1, x2, ...) \u2013 The tuple(x2, x2, ...) function.", - "title": "Tuple creation operator" - }, - { - "location": "/index.html#associativity", - "text": "All binary operators have left associativity. For example, 1 + 2 + 3 is transformed to plus(plus(1, 2), 3) .\nSometimes this doesn't work the way you expect. For example, SELECT 4 2 3 will result in 0. For efficiency, the and and or functions accept any number of arguments. The corresponding chains of AND and OR operators are transformed to a single call of these functions.", - "title": "Associativity" - }, - { - "location": "/index.html#functions_1", - "text": "There are at least* two types of functions - regular functions (they are just called \"functions\") and aggregate functions. These are completely different concepts. Regular functions work as if they are applied to each row separately (for each row, the result of the function doesn't depend on the other rows). Aggregate functions accumulate a set of values from various rows (i.e. they depend on the entire set of rows). In this section we discuss regular functions. For aggregate functions, see the section \"Aggregate functions\". * - There is a third type of function that the 'arrayJoin' function belongs to; table functions can also be mentioned separately.*", - "title": "Functions" - }, - { - "location": "/index.html#strong-typing", - "text": "In contrast to standard SQL, ClickHouse has strong typing. In other words, it doesn't make implicit conversions between types. Each function works for a specific set of types. This means that sometimes you need to use type conversion functions.", - "title": "Strong typing" - }, - { - "location": "/index.html#common-subexpression-elimination", - "text": "All expressions in a query that have the same AST (the same record or same result of syntactic parsing) are considered to have identical values. Such expressions are concatenated and executed once. Identical subqueries are also eliminated this way.", - "title": "Common subexpression elimination" - }, - { - "location": "/index.html#types-of-results", - "text": "All functions return a single return as the result (not several values, and not zero values). The type of result is usually defined only by the types of arguments, not by the values. Exceptions are the tupleElement function (the a.N operator), and the toFixedString function.", - "title": "Types of results" - }, - { - "location": "/index.html#constants", - "text": "For simplicity, certain functions can only work with constants for some arguments. For example, the right argument of the LIKE operator must be a constant.\nAlmost all functions return a constant for constant arguments. The exception is functions that generate random numbers.\nThe 'now' function returns different values for queries that were run at different times, but the result is considered a constant, since constancy is only important within a single query.\nA constant expression is also considered a constant (for example, the right half of the LIKE operator can be constructed from multiple constants). Functions can be implemented in different ways for constant and non-constant arguments (different code is executed). But the results for a constant and for a true column containing only the same value should match each other.", - "title": "Constants" - }, - { - "location": "/index.html#constancy", - "text": "Functions can't change the values of their arguments \u2013 any changes are returned as the result. Thus, the result of calculating separate functions does not depend on the order in which the functions are written in the query.", - "title": "Constancy" - }, - { - "location": "/index.html#error-handling", - "text": "Some functions might throw an exception if the data is invalid. In this case, the query is canceled and an error text is returned to the client. For distributed processing, when an exception occurs on one of the servers, the other servers also attempt to abort the query.", - "title": "Error handling" - }, - { - "location": "/index.html#evaluation-of-argument-expressions", - "text": "In almost all programming languages, one of the arguments might not be evaluated for certain operators. This is usually the operators , || , and ?: .\nBut in ClickHouse, arguments of functions (operators) are always evaluated. This is because entire parts of columns are evaluated at once, instead of calculating each row separately.", - "title": "Evaluation of argument expressions" - }, - { - "location": "/index.html#performing-functions-for-distributed-query-processing", - "text": "For distributed query processing, as many stages of query processing as possible are performed on remote servers, and the rest of the stages (merging intermediate results and everything after that) are performed on the requestor server. This means that functions can be performed on different servers.\nFor example, in the query SELECT f(sum(g(x))) FROM distributed_table GROUP BY h(y), if a distributed_table has at least two shards, the functions 'g' and 'h' are performed on remote servers, and the function 'f' is performed on the requestor server. if a distributed_table has only one shard, all the 'f', 'g', and 'h' functions are performed on this shard's server. The result of a function usually doesn't depend on which server it is performed on. However, sometimes this is important.\nFor example, functions that work with dictionaries use the dictionary that exists on the server they are running on.\nAnother example is the hostName function, which returns the name of the server it is running on in order to make GROUP BY by servers in a SELECT query. If a function in a query is performed on the requestor server, but you need to perform it on remote servers, you can wrap it in an 'any' aggregate function or add it to a key in GROUP BY .", - "title": "Performing functions for distributed query processing" - }, - { - "location": "/index.html#arithmetic-functions", - "text": "For all arithmetic functions, the result type is calculated as the smallest number type that the result fits in, if there is such a type. The minimum is taken simultaneously based on the number of bits, whether it is signed, and whether it floats. If there are not enough bits, the highest bit type is taken. Example: SELECT toTypeName ( 0 ), toTypeName ( 0 + 0 ), toTypeName ( 0 + 0 + 0 ), toTypeName ( 0 + 0 + 0 + 0 ) \u250c\u2500toTypeName(0)\u2500\u252c\u2500toTypeName(plus(0, 0))\u2500\u252c\u2500toTypeName(plus(plus(0, 0), 0))\u2500\u252c\u2500toTypeName(plus(plus(plus(0, 0), 0), 0))\u2500\u2510\n\u2502 UInt8 \u2502 UInt16 \u2502 UInt32 \u2502 UInt64 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Arithmetic functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64. Overflow is produced the same way as in C++.", - "title": "Arithmetic functions" - }, - { - "location": "/index.html#plusa-b-a-b-operator", - "text": "Calculates the sum of the numbers.\nYou can also add integer numbers with a date or date and time. In the case of a date, adding an integer means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds.", - "title": "plus(a, b), a + b operator" - }, - { - "location": "/index.html#minusa-b-a-b-operator", - "text": "Calculates the difference. The result is always signed. You can also calculate integer numbers from a date or date with time. The idea is the same \u2013 see above for 'plus'.", - "title": "minus(a, b), a - b operator" - }, - { - "location": "/index.html#multiplya-b-a-42-b-operator", - "text": "Calculates the product of the numbers.", - "title": "multiply(a, b), a * b operator" - }, - { - "location": "/index.html#dividea-b-a-b-operator", - "text": "Calculates the quotient of the numbers. The result type is always a floating-point type.\nIt is not integer division. For integer division, use the 'intDiv' function.\nWhen dividing by zero you get 'inf', '-inf', or 'nan'.", - "title": "divide(a, b), a / b operator" - }, - { - "location": "/index.html#intdiva-b", - "text": "Calculates the quotient of the numbers. Divides into integers, rounding down (by the absolute value).\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", - "title": "intDiv(a, b)" - }, - { - "location": "/index.html#intdivorzeroa-b", - "text": "Differs from 'intDiv' in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one.", - "title": "intDivOrZero(a, b)" - }, - { - "location": "/index.html#moduloa-b-a-b-operator", - "text": "Calculates the remainder after division.\nIf arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion.\nThe remainder is taken in the same sense as in C++. Truncated division is used for negative numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", - "title": "modulo(a, b), a % b operator" - }, - { - "location": "/index.html#negatea-a-operator", - "text": "Calculates a number with the reverse sign. The result is always signed.", - "title": "negate(a), -a operator" - }, - { - "location": "/index.html#absa", - "text": "Calculates the absolute value of the number (a). That is, if a 0, it returns -a. For unsigned types it doesn't do anything. For signed integer types, it returns an unsigned number.", - "title": "abs(a)" - }, - { - "location": "/index.html#gcda-b", - "text": "Returns the greatest common divisor of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", - "title": "gcd(a, b)" - }, - { - "location": "/index.html#lcma-b", - "text": "Returns the least common multiple of the numbers.\nAn exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.", - "title": "lcm(a, b)" - }, - { - "location": "/index.html#comparison-functions", - "text": "Comparison functions always return 0 or 1 (Uint8). The following types can be compared: numbers strings and fixed strings dates dates with times within each group, but not between different groups. For example, you can't compare a date with a string. You have to use a function to convert the string to a date, or vice versa. Strings are compared by bytes. A shorter string is smaller than all strings that start with it and that contain at least one more character. Note. Up until version 1.1.54134, signed and unsigned numbers were compared the same way as in C++. In other words, you could get an incorrect result in cases like SELECT 9223372036854775807 -1. This behavior changed in version 1.1.54134 and is now mathematically correct.", - "title": "Comparison functions" - }, - { - "location": "/index.html#equals-a-b-and-a-b-operator", - "text": "", - "title": "equals, a = b and a == b operator" - }, - { - "location": "/index.html#notequals-a-operator-b-and-a-b", - "text": "", - "title": "notEquals, a ! operator= b and a <> b" - }, - { - "location": "/index.html#less-operator", - "text": "", - "title": "less, < operator" - }, - { - "location": "/index.html#greater-operator", - "text": "", - "title": "greater, > operator" - }, - { - "location": "/index.html#lessorequals-operator", - "text": "", - "title": "lessOrEquals, <= operator" - }, - { - "location": "/index.html#greaterorequals-operator", - "text": "", - "title": "greaterOrEquals, >= operator" - }, - { - "location": "/index.html#logical-functions", - "text": "Logical functions accept any numeric types, but return a UInt8 number equal to 0 or 1. Zero as an argument is considered \"false,\" while any non-zero value is considered \"true\".", - "title": "Logical functions" - }, - { - "location": "/index.html#and-and-operator", - "text": "", - "title": "and, AND operator" - }, - { - "location": "/index.html#or-or-operator", - "text": "", - "title": "or, OR operator" - }, - { - "location": "/index.html#not-not-operator", - "text": "", - "title": "not, NOT operator" - }, - { - "location": "/index.html#xor", - "text": "", - "title": "xor" - }, - { - "location": "/index.html#type-conversion-functions", - "text": "", - "title": "Type conversion functions" - }, - { - "location": "/index.html#touint8-touint16-touint32-touint64", - "text": "", - "title": "toUInt8, toUInt16, toUInt32, toUInt64" - }, - { - "location": "/index.html#toint8-toint16-toint32-toint64", - "text": "", - "title": "toInt8, toInt16, toInt32, toInt64" - }, - { - "location": "/index.html#tofloat32-tofloat64", - "text": "", - "title": "toFloat32, toFloat64" - }, - { - "location": "/index.html#touint8orzero-touint16orzero-touint32orzero-touint64orzero-toint8orzero-toint16orzero-toint32orzero-toint64orzero-tofloat32orzero-tofloat64orzero", - "text": "", - "title": "toUInt8OrZero, toUInt16OrZero, toUInt32OrZero, toUInt64OrZero, toInt8OrZero, toInt16OrZero, toInt32OrZero, toInt64OrZero, toFloat32OrZero, toFloat64OrZero" - }, - { - "location": "/index.html#todate-todatetime", - "text": "", - "title": "toDate, toDateTime" - }, - { - "location": "/index.html#tostring", - "text": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times.\nAll these functions accept one argument. When converting to or from a string, the value is formatted or parsed using the same rules as for the TabSeparated format (and almost all other text formats). If the string can't be parsed, an exception is thrown and the request is canceled. When converting dates to numbers or vice versa, the date corresponds to the number of days since the beginning of the Unix epoch.\nWhen converting dates with times to numbers or vice versa, the date with time corresponds to the number of seconds since the beginning of the Unix epoch. The date and date-with-time formats for the toDate/toDateTime functions are defined as follows: YYYY-MM-DD\nYYYY-MM-DD hh:mm:ss As an exception, if converting from UInt32, Int32, UInt64, or Int64 numeric types to Date, and if the number is greater than or equal to 65536, the number is interpreted as a Unix timestamp (and not as the number of days) and is rounded to the date. This allows support for the common occurrence of writing 'toDate(unix_timestamp)', which otherwise would be an error and would require writing the more cumbersome 'toDate(toDateTime(unix_timestamp))'. Conversion between a date and date with time is performed the natural way: by adding a null time or dropping the time. Conversion between numeric types uses the same rules as assignments between different numeric types in C++. Additionally, the toString function of the DateTime argument can take a second String argument containing the name of the time zone. Example: Asia/Yekaterinburg In this case, the time is formatted according to the specified time zone. SELECT \n now () AS now_local , \n toString ( now (), Asia/Yekaterinburg ) AS now_yekat \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500now_local\u2500\u252c\u2500now_yekat\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 00:11:21 \u2502 2016-06-15 02:11:21 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Also see the toUnixTimestamp function.", - "title": "toString" - }, - { - "location": "/index.html#tofixedstrings-n", - "text": "Converts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant.\nIf the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown.", - "title": "toFixedString(s, N)" - }, - { - "location": "/index.html#tostringcuttozeros", - "text": "Accepts a String or FixedString argument. Returns the String with the content truncated at the first zero byte found. Example: SELECT toFixedString ( foo , 8 ) AS s , toStringCutToZero ( s ) AS s_cut \u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0\\0\\0\\0\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT toFixedString ( foo\\0bar , 8 ) AS s , toStringCutToZero ( s ) AS s_cut \u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500s_cut\u2500\u2510\n\u2502 foo\\0bar\\0 \u2502 foo \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "toStringCutToZero(s)" - }, - { - "location": "/index.html#reinterpretasuint8-reinterpretasuint16-reinterpretasuint32-reinterpretasuint64", - "text": "", - "title": "reinterpretAsUInt8, reinterpretAsUInt16, reinterpretAsUInt32, reinterpretAsUInt64" - }, - { - "location": "/index.html#reinterpretasint8-reinterpretasint16-reinterpretasint32-reinterpretasint64", - "text": "", - "title": "reinterpretAsInt8, reinterpretAsInt16, reinterpretAsInt32, reinterpretAsInt64" - }, - { - "location": "/index.html#reinterpretasfloat32-reinterpretasfloat64", - "text": "", - "title": "reinterpretAsFloat32, reinterpretAsFloat64" - }, - { - "location": "/index.html#reinterpretasdate-reinterpretasdatetime", - "text": "These functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.", - "title": "reinterpretAsDate, reinterpretAsDateTime" - }, - { - "location": "/index.html#reinterpretasstring", - "text": "This function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long.", - "title": "reinterpretAsString" - }, - { - "location": "/index.html#castx-t", - "text": "Converts 'x' to the 't' data type. The syntax CAST(x AS t) is also supported. Example: SELECT \n 2016-06-15 23:00:00 AS timestamp , \n CAST ( timestamp AS DateTime ) AS datetime , \n CAST ( timestamp AS Date ) AS date , \n CAST ( timestamp , String ) AS string , \n CAST ( timestamp , FixedString(22) ) AS fixed_string \u250c\u2500timestamp\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500datetime\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500date\u2500\u252c\u2500string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500fixed_string\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-15 23:00:00 \u2502 2016-06-15 23:00:00\\0\\0\\0 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Conversion to FixedString (N) only works for arguments of type String or FixedString (N).", - "title": "CAST(x, t)" - }, - { - "location": "/index.html#functions-for-working-with-dates-and-times", - "text": "Support for time zones All functions for working with the date and time that have a logical use for the time zone can accept a second optional time zone argument. Example: Asia/Yekaterinburg. In this case, they use the specified time zone instead of the local (default) one. SELECT \n toDateTime ( 2016-06-15 23:00:00 ) AS time , \n toDate ( time ) AS date_local , \n toDate ( time , Asia/Yekaterinburg ) AS date_yekat , \n toString ( time , US/Samoa ) AS time_samoa \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500time\u2500\u252c\u2500date_local\u2500\u252c\u2500date_yekat\u2500\u252c\u2500time_samoa\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2016-06-15 23:00:00 \u2502 2016-06-15 \u2502 2016-06-16 \u2502 2016-06-15 09:00:00 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Only time zones that differ from UTC by a whole number of hours are supported.", - "title": "Functions for working with dates and times" - }, - { - "location": "/index.html#toyear", - "text": "Converts a date or date with time to a UInt16 number containing the year number (AD).", - "title": "toYear" - }, - { - "location": "/index.html#tomonth", - "text": "Converts a date or date with time to a UInt8 number containing the month number (1-12).", - "title": "toMonth" - }, - { - "location": "/index.html#todayofmonth", - "text": "-Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31).", - "title": "toDayOfMonth" - }, - { - "location": "/index.html#todayofweek", - "text": "Converts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7).", - "title": "toDayOfWeek" - }, - { - "location": "/index.html#tohour", - "text": "Converts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23).\nThis function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true \u2013 even in Moscow the clocks were twice changed at a different time).", - "title": "toHour" - }, - { - "location": "/index.html#tominute", - "text": "Converts a date with time to a UInt8 number containing the number of the minute of the hour (0-59).", - "title": "toMinute" - }, - { - "location": "/index.html#tosecond", - "text": "Converts a date with time to a UInt8 number containing the number of the second in the minute (0-59).\nLeap seconds are not accounted for.", - "title": "toSecond" - }, - { - "location": "/index.html#tomonday", - "text": "Rounds down a date or date with time to the nearest Monday.\nReturns the date.", - "title": "toMonday" - }, - { - "location": "/index.html#tostartofmonth", - "text": "Rounds down a date or date with time to the first day of the month.\nReturns the date.", - "title": "toStartOfMonth" - }, - { - "location": "/index.html#tostartofquarter", - "text": "Rounds down a date or date with time to the first day of the quarter.\nThe first day of the quarter is either 1 January, 1 April, 1 July, or 1 October.\nReturns the date.", - "title": "toStartOfQuarter" - }, - { - "location": "/index.html#tostartofyear", - "text": "Rounds down a date or date with time to the first day of the year.\nReturns the date.", - "title": "toStartOfYear" - }, - { - "location": "/index.html#tostartofminute", - "text": "Rounds down a date with time to the start of the minute.", - "title": "toStartOfMinute" - }, - { - "location": "/index.html#tostartoffiveminute", - "text": "Rounds down a date with time to the start of the hour.", - "title": "toStartOfFiveMinute" - }, - { - "location": "/index.html#tostartoffifteenminutes", - "text": "Rounds down the date with time to the start of the fifteen-minute interval. Note: If you need to round a date with time to any other number of seconds, minutes, or hours, you can convert it into a number by using the toUInt32 function, then round the number using intDiv and multiplication, and convert it back using the toDateTime function.", - "title": "toStartOfFifteenMinutes" - }, - { - "location": "/index.html#tostartofhour", - "text": "Rounds down a date with time to the start of the hour.", - "title": "toStartOfHour" - }, - { - "location": "/index.html#tostartofday", - "text": "Rounds down a date with time to the start of the day.", - "title": "toStartOfDay" - }, - { - "location": "/index.html#totime", - "text": "Converts a date with time to a certain fixed date, while preserving the time.", - "title": "toTime" - }, - { - "location": "/index.html#torelativeyearnum", - "text": "Converts a date with time or date to the number of the year, starting from a certain fixed point in the past.", - "title": "toRelativeYearNum" - }, - { - "location": "/index.html#torelativemonthnum", - "text": "Converts a date with time or date to the number of the month, starting from a certain fixed point in the past.", - "title": "toRelativeMonthNum" - }, - { - "location": "/index.html#torelativeweeknum", - "text": "Converts a date with time or date to the number of the week, starting from a certain fixed point in the past.", - "title": "toRelativeWeekNum" - }, - { - "location": "/index.html#torelativedaynum", - "text": "Converts a date with time or date to the number of the day, starting from a certain fixed point in the past.", - "title": "toRelativeDayNum" - }, - { - "location": "/index.html#torelativehournum", - "text": "Converts a date with time or date to the number of the hour, starting from a certain fixed point in the past.", - "title": "toRelativeHourNum" - }, - { - "location": "/index.html#torelativeminutenum", - "text": "Converts a date with time or date to the number of the minute, starting from a certain fixed point in the past.", - "title": "toRelativeMinuteNum" - }, - { - "location": "/index.html#torelativesecondnum", - "text": "Converts a date with time or date to the number of the second, starting from a certain fixed point in the past.", - "title": "toRelativeSecondNum" - }, - { - "location": "/index.html#now", - "text": "Accepts zero arguments and returns the current time at one of the moments of request execution.\nThis function returns a constant, even if the request took a long time to complete.", - "title": "now" - }, - { - "location": "/index.html#today", - "text": "Accepts zero arguments and returns the current date at one of the moments of request execution.\nThe same as 'toDate(now())'.", - "title": "today" - }, - { - "location": "/index.html#yesterday", - "text": "Accepts zero arguments and returns yesterday's date at one of the moments of request execution.\nThe same as 'today() - 1'.", - "title": "yesterday" - }, - { - "location": "/index.html#timeslot", - "text": "Rounds the time to the half hour.\nThis function is specific to Yandex.Metrica, since half an hour is the minimum amount of time for breaking a session into two sessions if a tracking tag shows a single user's consecutive pageviews that differ in time by strictly more than this amount. This means that tuples (the tag ID, user ID, and time slot) can be used to search for pageviews that are included in the corresponding session.", - "title": "timeSlot" - }, - { - "location": "/index.html#timeslotsstarttime-duration", - "text": "For a time interval starting at 'StartTime' and continuing for 'Duration' seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour.\nFor example, timeSlots(toDateTime('2012-01-01 12:20:00'), 600) = [toDateTime('2012-01-01 12:00:00'), toDateTime('2012-01-01 12:30:00')] .\nThis is necessary for searching for pageviews in the corresponding session.", - "title": "timeSlots(StartTime, Duration)" - }, - { - "location": "/index.html#functions-for-working-with-strings", - "text": "", - "title": "Functions for working with strings" - }, - { - "location": "/index.html#empty", - "text": "Returns 1 for an empty string or 0 for a non-empty string.\nThe result type is UInt8.\nA string is considered non-empty if it contains at least one byte, even if this is a space or a null byte.\nThe function also works for arrays.", - "title": "empty" - }, - { - "location": "/index.html#notempty", - "text": "Returns 0 for an empty string or 1 for a non-empty string.\nThe result type is UInt8.\nThe function also works for arrays.", - "title": "notEmpty" - }, - { - "location": "/index.html#length", - "text": "Returns the length of a string in bytes (not in characters, and not in code points).\nThe result type is UInt64.\nThe function also works for arrays.", - "title": "length" - }, - { - "location": "/index.html#lengthutf8", - "text": "Returns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).\nThe result type is UInt64.", - "title": "lengthUTF8" - }, - { - "location": "/index.html#lower", - "text": "Converts ASCII Latin symbols in a string to lowercase.", - "title": "lower" - }, - { - "location": "/index.html#upper", - "text": "Converts ASCII Latin symbols in a string to uppercase.", - "title": "upper" - }, - { - "location": "/index.html#lowerutf8", - "text": "Converts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.", - "title": "lowerUTF8" - }, - { - "location": "/index.html#upperutf8", - "text": "Converts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text.\nIt doesn't detect the language. So for Turkish the result might not be exactly correct.\nIf the length of the UTF-8 byte sequence is different for upper and lower case of a code point, the result may be incorrect for this code point.\nIf the string contains a set of bytes that is not UTF-8, then the behavior is undefined.", - "title": "upperUTF8" - }, - { - "location": "/index.html#reverse", - "text": "Reverses the string (as a sequence of bytes).", - "title": "reverse" - }, - { - "location": "/index.html#reverseutf8", - "text": "Reverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn't throw an exception).", - "title": "reverseUTF8" - }, - { - "location": "/index.html#concats1-s2", - "text": "Concatenates the strings listed in the arguments, without a separator.", - "title": "concat(s1, s2, ...)" - }, - { - "location": "/index.html#substrings-offset-length", - "text": "Returns a substring starting with the byte from the 'offset' index that is 'length' bytes long. Character indexing starts from one (as in standard SQL). The 'offset' and 'length' arguments must be constants.", - "title": "substring(s, offset, length)" - }, - { - "location": "/index.html#substringutf8s-offset-length", - "text": "The same as 'substring', but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception).", - "title": "substringUTF8(s, offset, length)" - }, - { - "location": "/index.html#appendtrailingcharifabsents-c", - "text": "If the 's' string is non-empty and does not contain the 'c' character at the end, it appends the 'c' character to the end.", - "title": "appendTrailingCharIfAbsent(s, c)" - }, - { - "location": "/index.html#convertcharsets-from-to", - "text": "Returns the string 's' that was converted from the encoding in 'from' to the encoding in 'to'.", - "title": "convertCharset(s, from, to)" - }, - { - "location": "/index.html#functions-for-searching-strings", - "text": "The search is case-sensitive in all these functions.\nThe search substring or regular expression must be a constant in all these functions.", - "title": "Functions for searching strings" - }, - { - "location": "/index.html#positionhaystack-needle", - "text": "Search for the needle substring in the haystack string.\nReturns the position (in bytes) of the found substring, starting from 1, or returns 0 if the substring was not found. For case-insensitive search use positionCaseInsensitive function.", - "title": "position(haystack, needle)" - }, - { - "location": "/index.html#positionutf8haystack-needle", - "text": "The same as position , but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn't throw an exception). For case-insensitive search use positionCaseInsensitiveUTF8 function.", - "title": "positionUTF8(haystack, needle)" - }, - { - "location": "/index.html#matchhaystack-pattern", - "text": "Checks whether the string matches the 'pattern' regular expression. A re2 regular expression.\nReturns 0 if it doesn't match, or 1 if it matches. Note that the backslash symbol ( \\ ) is used for escaping in the regular expression. The same symbol is used for escaping in string literals. So in order to escape the symbol in a regular expression, you must write two backslashes (\\) in a string literal. The regular expression works with the string as if it is a set of bytes. The regular expression can't contain null bytes.\nFor patterns to search for substrings in a string, it is better to use LIKE or 'position', since they work much faster.", - "title": "match(haystack, pattern)" - }, - { - "location": "/index.html#extracthaystack-pattern", - "text": "Extracts a fragment of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. If the regex doesn't contain subpatterns, it takes the fragment that matches the entire regex. Otherwise, it takes the fragment that matches the first subpattern.", - "title": "extract(haystack, pattern)" - }, - { - "location": "/index.html#extractallhaystack-pattern", - "text": "Extracts all the fragments of a string using a regular expression. If 'haystack' doesn't match the 'pattern' regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex. In general, the behavior is the same as the 'extract' function (it takes the first subpattern, or the entire expression if there isn't a subpattern).", - "title": "extractAll(haystack, pattern)" - }, - { - "location": "/index.html#likehaystack-pattern-haystack-like-pattern-operator", - "text": "Checks whether a string matches a simple regular expression.\nThe regular expression can contain the metasymbols % and _ . ``% indicates any quantity of any bytes (including zero characters). _ indicates any one byte. Use the backslash ( \\ ) for escaping metasymbols. See the note on escaping in the description of the 'match' function. For regular expressions like %needle% , the code is more optimal and works as fast as the position function.\nFor other regular expressions, the code is the same as for the 'match' function.", - "title": "like(haystack, pattern), haystack LIKE pattern operator" - }, - { - "location": "/index.html#notlikehaystack-pattern-haystack-not-like-pattern-operator", - "text": "The same thing as 'like', but negative.", - "title": "notLike(haystack, pattern), haystack NOT LIKE pattern operator" - }, - { - "location": "/index.html#functions-for-searching-and-replacing-in-strings", - "text": "", - "title": "Functions for searching and replacing in strings" - }, - { - "location": "/index.html#replaceonehaystack-pattern-replacement", - "text": "Replaces the first occurrence, if it exists, of the 'pattern' substring in 'haystack' with the 'replacement' substring.\nHereafter, 'pattern' and 'replacement' must be constants.", - "title": "replaceOne(haystack, pattern, replacement)" - }, - { - "location": "/index.html#replaceallhaystack-pattern-replacement", - "text": "Replaces all occurrences of the 'pattern' substring in 'haystack' with the 'replacement' substring.", - "title": "replaceAll(haystack, pattern, replacement)" - }, - { - "location": "/index.html#replaceregexponehaystack-pattern-replacement", - "text": "Replacement using the 'pattern' regular expression. A re2 regular expression.\nReplaces only the first occurrence, if it exists.\nA pattern can be specified as 'replacement'. This pattern can include substitutions \\0-\\9 .\nThe substitution \\0 includes the entire regular expression. Substitutions \\1-\\9 correspond to the subpattern numbers.To use the \\ character in a template, escape it using \\ .\nAlso keep in mind that a string literal requires an extra escape. Example 1. Converting the date to American format: SELECT DISTINCT \n EventDate , \n replaceRegexpOne ( toString ( EventDate ), (\\\\d{4})-(\\\\d{2})-(\\\\d{2}) , \\\\2/\\\\3/\\\\1 ) AS res FROM test . hits LIMIT 7 FORMAT TabSeparated 2014-03-17 03/17/2014\n2014-03-18 03/18/2014\n2014-03-19 03/19/2014\n2014-03-20 03/20/2014\n2014-03-21 03/21/2014\n2014-03-22 03/22/2014\n2014-03-23 03/23/2014 Example 2. Copying a string ten times: SELECT replaceRegexpOne ( Hello, World! , .* , \\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\0 ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World!Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "replaceRegexpOne(haystack, pattern, replacement)" - }, - { - "location": "/index.html#replaceregexpallhaystack-pattern-replacement", - "text": "This does the same thing, but replaces all the occurrences. Example: SELECT replaceRegexpAll ( Hello, World! , . , \\\\0\\\\0 ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 HHeelllloo,, WWoorrlldd!! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 As an exception, if a regular expression worked on an empty substring, the replacement is not made more than once.\nExample: SELECT replaceRegexpAll ( Hello, World! , ^ , here: ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 here: Hello, World! \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "replaceRegexpAll(haystack, pattern, replacement)" - }, - { - "location": "/index.html#conditional-functions", - "text": "", - "title": "Conditional functions" - }, - { - "location": "/index.html#ifcond-then-else-cond-operator-then-else", - "text": "Returns 'then' if cond !or 'else' if cond = 0.'cond' must be UInt 8, and 'then' and 'else' must be a type that has the smallest common type.", - "title": "if(cond, then, else), cond ? operator then : else" - }, - { - "location": "/index.html#mathematical-functions", - "text": "All the functions return a Float64 number. The accuracy of the result is close to the maximum precision possible, but the result might not coincide with the machine representable number nearest to the corresponding real number.", - "title": "Mathematical functions" - }, - { - "location": "/index.html#e", - "text": "Returns a Float64 number close to the e number.", - "title": "e()" - }, - { - "location": "/index.html#pi", - "text": "Returns a Float64 number close to \u03c0.", - "title": "pi()" - }, - { - "location": "/index.html#expx", - "text": "Accepts a numeric argument and returns a Float64 number close to the exponent of the argument.", - "title": "exp(x)" - }, - { - "location": "/index.html#logx", - "text": "Accepts a numeric argument and returns a Float64 number close to the natural logarithm of the argument.", - "title": "log(x)" - }, - { - "location": "/index.html#exp2x", - "text": "Accepts a numeric argument and returns a Float64 number close to 2^x.", - "title": "exp2(x)" - }, - { - "location": "/index.html#log2x", - "text": "Accepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument.", - "title": "log2(x)" - }, - { - "location": "/index.html#exp10x", - "text": "Accepts a numeric argument and returns a Float64 number close to 10^x.", - "title": "exp10(x)" - }, - { - "location": "/index.html#log10x", - "text": "Accepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument.", - "title": "log10(x)" - }, - { - "location": "/index.html#sqrtx", - "text": "Accepts a numeric argument and returns a Float64 number close to the square root of the argument.", - "title": "sqrt(x)" - }, - { - "location": "/index.html#cbrtx", - "text": "Accepts a numeric argument and returns a Float64 number close to the cubic root of the argument.", - "title": "cbrt(x)" - }, - { - "location": "/index.html#erfx", - "text": "If 'x' is non-negative, then erf(x / \u03c3\u221a2) is the probability that a random variable having a normal distribution with standard deviation '\u03c3' takes the value that is separated from the expected value by more than 'x'. Example (three sigma rule): SELECT erf ( 3 / sqrt ( 2 )) \u250c\u2500erf(divide(3, sqrt(2)))\u2500\u2510\n\u2502 0.9973002039367398 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "erf(x)" - }, - { - "location": "/index.html#erfcx", - "text": "Accepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large 'x' values.", - "title": "erfc(x)" - }, - { - "location": "/index.html#lgammax", - "text": "The logarithm of the gamma function.", - "title": "lgamma(x)" - }, - { - "location": "/index.html#tgammax", - "text": "Gamma function.", - "title": "tgamma(x)" - }, - { - "location": "/index.html#sinx", - "text": "The sine.", - "title": "sin(x)" - }, - { - "location": "/index.html#cosx", - "text": "The cosine.", - "title": "cos(x)" - }, - { - "location": "/index.html#tanx", - "text": "The tangent.", - "title": "tan(x)" - }, - { - "location": "/index.html#asinx", - "text": "The arc sine.", - "title": "asin(x)" - }, - { - "location": "/index.html#acosx", - "text": "The arc cosine.", - "title": "acos(x)" - }, - { - "location": "/index.html#atanx", - "text": "The arc tangent.", - "title": "atan(x)" - }, - { - "location": "/index.html#powx-y", - "text": "Accepts two numeric arguments and returns a Float64 number close to x^y.", - "title": "pow(x, y)" - }, - { - "location": "/index.html#rounding-functions", - "text": "", - "title": "Rounding functions" - }, - { - "location": "/index.html#floorx91-n93", - "text": "Returns the largest round number that is less than or equal to x. A round number is a multiple of 1/10N, or the nearest number of the appropriate data type if 1 / 10N isn't exact.\n'N' is an integer constant, optional parameter. By default it is zero, which means to round to an integer.\n'N' may be negative. Examples: floor(123.45, 1) = 123.4, floor(123.45, -1) = 120. x is any numeric type. The result is a number of the same type.\nFor integer arguments, it makes sense to round with a negative 'N' value (for non-negative 'N', the function doesn't do anything).\nIf rounding causes overflow (for example, floor(-128, -1)), an implementation-specific result is returned.", - "title": "floor(x[, N])" - }, - { - "location": "/index.html#ceilx91-n93", - "text": "Returns the smallest round number that is greater than or equal to 'x'. In every other way, it is the same as the 'floor' function (see above).", - "title": "ceil(x[, N])" - }, - { - "location": "/index.html#roundx91-n93", - "text": "Returns the round number nearest to 'num', which may be less than, greater than, or equal to 'x'.If 'x' is exactly in the middle between the nearest round numbers, one of them is returned (implementation-specific).\nThe number '-0.' may or may not be considered round (implementation-specific).\nIn every other way, this function is the same as 'floor' and 'ceil' described above.", - "title": "round(x[, N])" - }, - { - "location": "/index.html#roundtoexp2num", - "text": "Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two.", - "title": "roundToExp2(num)" - }, - { - "location": "/index.html#rounddurationnum", - "text": "Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length", - "title": "roundDuration(num)" - }, - { - "location": "/index.html#roundagenum", - "text": "Accepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to a number from the set: 18, 25, 35, 45, 55. This function is specific to Yandex.Metrica and used for implementing the report on user age.", - "title": "roundAge(num)" - }, - { - "location": "/index.html#functions-for-working-with-arrays", - "text": "", - "title": "Functions for working with arrays" - }, - { - "location": "/index.html#empty_1", - "text": "Returns 1 for an empty array, or 0 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.", - "title": "empty" - }, - { - "location": "/index.html#notempty_1", - "text": "Returns 0 for an empty array, or 1 for a non-empty array.\nThe result type is UInt8.\nThe function also works for strings.", - "title": "notEmpty" - }, - { - "location": "/index.html#length_1", - "text": "Returns the number of items in the array.\nThe result type is UInt64.\nThe function also works for strings.", - "title": "length" - }, - { - "location": "/index.html#emptyarrayuint8-emptyarrayuint16-emptyarrayuint32-emptyarrayuint64", - "text": "", - "title": "emptyArrayUInt8, emptyArrayUInt16, emptyArrayUInt32, emptyArrayUInt64" - }, - { - "location": "/index.html#emptyarrayint8-emptyarrayint16-emptyarrayint32-emptyarrayint64", - "text": "", - "title": "emptyArrayInt8, emptyArrayInt16, emptyArrayInt32, emptyArrayInt64" - }, - { - "location": "/index.html#emptyarrayfloat32-emptyarrayfloat64", - "text": "", - "title": "emptyArrayFloat32, emptyArrayFloat64" - }, - { - "location": "/index.html#emptyarraydate-emptyarraydatetime", - "text": "", - "title": "emptyArrayDate, emptyArrayDateTime" - }, - { - "location": "/index.html#emptyarraystring", - "text": "Accepts zero arguments and returns an empty array of the appropriate type.", - "title": "emptyArrayString" - }, - { - "location": "/index.html#emptyarraytosingle", - "text": "Accepts an empty array and returns a one-element array that is equal to the default value.", - "title": "emptyArrayToSingle" - }, - { - "location": "/index.html#rangen", - "text": "Returns an array of numbers from 0 to N-1.\nJust in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block.", - "title": "range(N)" - }, - { - "location": "/index.html#arrayx1-operator-91x1-93", - "text": "Creates an array from the function arguments.\nThe arguments must be constants and have types that have the smallest common type. At least one argument must be passed, because otherwise it isn't clear which type of array to create. That is, you can't use this function to create an empty array (to do that, use the 'emptyArray*' function described above).\nReturns an 'Array(T)' type result, where 'T' is the smallest common type out of the passed arguments.", - "title": "array(x1, ...), operator [x1, ...]" - }, - { - "location": "/index.html#arrayconcat", - "text": "Combines arrays passed as arguments. arrayConcat(arrays) Arguments arrays \u2013 Arrays of comma-separated [values] . Example SELECT arrayConcat ([ 1 , 2 ], [ 3 , 4 ], [ 5 , 6 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,3,4,5,6] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayConcat" - }, - { - "location": "/index.html#arrayelementarr-n-operator-arrn", - "text": "Get the element with the index 'n' from the array 'arr'.'n' must be any integer type.\nIndexes in an array begin from one.\nNegative indexes are supported. In this case, it selects the corresponding element numbered from the end. For example, 'arr[-1]' is the last item in the array. If the index falls outside of the bounds of an array, it returns some default value (0 for numbers, an empty string for strings, etc.).", - "title": "arrayElement(arr, n), operator arr[n]" - }, - { - "location": "/index.html#hasarr-elem", - "text": "Checks whether the 'arr' array has the 'elem' element.\nReturns 0 if the the element is not in the array, or 1 if it is.", - "title": "has(arr, elem)" - }, - { - "location": "/index.html#indexofarr-x", - "text": "Returns the index of the 'x' element (starting from 1) if it is in the array, or 0 if it is not.", - "title": "indexOf(arr, x)" - }, - { - "location": "/index.html#countequalarr-x", - "text": "Returns the number of elements in the array equal to x. Equivalent to arrayCount (elem- elem = x, arr).", - "title": "countEqual(arr, x)" - }, - { - "location": "/index.html#arrayenumeratearr", - "text": "Returns the array [1, 2, 3, ..., length (arr) ] This function is normally used with ARRAY JOIN. It allows counting something just once for each array after applying ARRAY JOIN. Example: SELECT \n count () AS Reaches , \n countIf ( num = 1 ) AS Hits FROM test . hits ARRAY JOIN \n GoalsReached , \n arrayEnumerate ( GoalsReached ) AS num WHERE CounterID = 160656 LIMIT 10 \u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 In this example, Reaches is the number of conversions (the strings received after applying ARRAY JOIN), and Hits is the number of pageviews (strings before ARRAY JOIN). In this particular case, you can get the same result in an easier way: SELECT \n sum ( length ( GoalsReached )) AS Reaches , \n count () AS Hits FROM test . hits WHERE ( CounterID = 160656 ) AND notEmpty ( GoalsReached ) \u250c\u2500Reaches\u2500\u252c\u2500\u2500Hits\u2500\u2510\n\u2502 95606 \u2502 31406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 This function can also be used in higher-order functions. For example, you can use it to get array indexes for elements that match a condition.", - "title": "arrayEnumerate(arr)" - }, - { - "location": "/index.html#arrayenumerateuniqarr", - "text": "Returns an array the same size as the source array, indicating for each element what its position is among elements with the same value.\nFor example: arrayEnumerateUniq([10, 20, 10, 30]) = [1, 1, 2, 1]. This function is useful when using ARRAY JOIN and aggregation of array elements.\nExample: SELECT \n Goals . ID AS GoalID , \n sum ( Sign ) AS Reaches , \n sumIf ( Sign , num = 1 ) AS Visits FROM test . visits ARRAY JOIN \n Goals , \n arrayEnumerateUniq ( Goals . ID ) AS num WHERE CounterID = 160656 GROUP BY GoalID ORDER BY Reaches DESC LIMIT 10 \u250c\u2500\u2500GoalID\u2500\u252c\u2500Reaches\u2500\u252c\u2500Visits\u2500\u2510\n\u2502 53225 \u2502 3214 \u2502 1097 \u2502\n\u2502 2825062 \u2502 3188 \u2502 1097 \u2502\n\u2502 56600 \u2502 2803 \u2502 488 \u2502\n\u2502 1989037 \u2502 2401 \u2502 365 \u2502\n\u2502 2830064 \u2502 2396 \u2502 910 \u2502\n\u2502 1113562 \u2502 2372 \u2502 373 \u2502\n\u2502 3270895 \u2502 2262 \u2502 812 \u2502\n\u2502 1084657 \u2502 2262 \u2502 345 \u2502\n\u2502 56599 \u2502 2260 \u2502 799 \u2502\n\u2502 3271094 \u2502 2256 \u2502 812 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 In this example, each goal ID has a calculation of the number of conversions (each element in the Goals nested data structure is a goal that was reached, which we refer to as a conversion) and the number of sessions. Without ARRAY JOIN, we would have counted the number of sessions as sum(Sign). But in this particular case, the rows were multiplied by the nested Goals structure, so in order to count each session one time after this, we apply a condition to the value of the arrayEnumerateUniq(Goals.ID) function. The arrayEnumerateUniq function can take multiple arrays of the same size as arguments. In this case, uniqueness is considered for tuples of elements in the same positions in all the arrays. SELECT arrayEnumerateUniq ([ 1 , 1 , 1 , 2 , 2 , 2 ], [ 1 , 1 , 2 , 1 , 1 , 2 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1,2,1,1,2,1] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 This is necessary when using ARRAY JOIN with a nested data structure and further aggregation across multiple elements in this structure.", - "title": "arrayEnumerateUniq(arr, ...)" - }, - { - "location": "/index.html#arraypopback", - "text": "Removes the last item from the array. arrayPopBack(array) Arguments array \u2013 Array. Example SELECT arrayPopBack ([ 1 , 2 , 3 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [1,2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayPopBack" - }, - { - "location": "/index.html#arraypopfront", - "text": "Removes the first item from the array. arrayPopFront(array) Arguments array \u2013 Array. Example SELECT arrayPopFront ([ 1 , 2 , 3 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2510\n\u2502 [2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayPopFront" - }, - { - "location": "/index.html#arraypushback", - "text": "Adds one item to the end of the array. arrayPushBack(array, single_value) Arguments array \u2013 Array. single_value \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section \" Data types \". Example SELECT arrayPushBack ([ a ], b ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ a , b ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayPushBack" - }, - { - "location": "/index.html#arraypushfront", - "text": "Adds one element to the beginning of the array. arrayPushFront(array, single_value) Arguments array \u2013 Array. single_value \u2013 A single value. Only numbers can be added to an array with numbers, and only strings can be added to an array of strings. When adding numbers, ClickHouse automatically sets the single_value type for the data type of the array. For more information about ClickHouse data types, read the section \" Data types \". Example SELECT arrayPushBack ([ b ], a ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ a , b ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayPushFront" - }, - { - "location": "/index.html#arrayslice", - "text": "Returns a slice of the array. arraySlice(array, offset[, length]) Arguments array \u2013 Array of data. offset \u2013 Indent from the edge of the array. A positive value indicates an offset on the left, and a negative value is an indent on the right. Numbering of the array items begins with 1. length - The length of the required slice. If you specify a negative value, the function returns an open slice [offset, array_length - length) . If you omit the value, the function returns the slice [offset, the_end_of_array] . Example SELECT arraySlice ([ 1 , 2 , 3 , 4 , 5 ], 2 , 3 ) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [2,3,4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arraySlice" - }, - { - "location": "/index.html#arrayuniqarr", - "text": "If one argument is passed, it counts the number of different elements in the array.\nIf multiple arguments are passed, it counts the number of different tuples of elements at corresponding positions in multiple arrays. If you want to get a list of unique items in an array, you can use arrayReduce('groupUniqArray', arr).", - "title": "arrayUniq(arr, ...)" - }, - { - "location": "/index.html#arrayjoinarr", - "text": "A special function. See the section \"ArrayJoin function\" .", - "title": "arrayJoin(arr)" - }, - { - "location": "/index.html#functions-for-splitting-and-merging-strings-and-arrays", - "text": "", - "title": "Functions for splitting and merging strings and arrays" - }, - { - "location": "/index.html#splitbycharseparator-s", - "text": "Splits a string into substrings separated by 'separator'.'separator' must be a string constant consisting of exactly one character.\nReturns an array of selected substrings. Empty substrings may be selected if the separator occurs at the beginning or end of the string, or if there are multiple consecutive separators.", - "title": "splitByChar(separator, s)" - }, - { - "location": "/index.html#splitbystringseparator-s", - "text": "The same as above, but it uses a string of multiple characters as the separator. The string must be non-empty.", - "title": "splitByString(separator, s)" - }, - { - "location": "/index.html#arraystringconcatarr91-separator93", - "text": "Concatenates the strings listed in the array with the separator.'separator' is an optional parameter: a constant string, set to an empty string by default.\nReturns the string.", - "title": "arrayStringConcat(arr[, separator])" - }, - { - "location": "/index.html#alphatokenss", - "text": "Selects substrings of consecutive bytes from the ranges a-z and A-Z.Returns an array of substrings.", - "title": "alphaTokens(s)" - }, - { - "location": "/index.html#bit-functions", - "text": "Bit functions work for any pair of types from UInt8, UInt16, UInt32, UInt64, Int8, Int16, Int32, Int64, Float32, or Float64. The result type is an integer with bits equal to the maximum bits of its arguments. If at least one of the arguments is signed, the result is a signed number. If an argument is a floating-point number, it is cast to Int64.", - "title": "Bit functions" - }, - { - "location": "/index.html#bitanda-b", - "text": "", - "title": "bitAnd(a, b)" - }, - { - "location": "/index.html#bitora-b", - "text": "", - "title": "bitOr(a, b)" - }, - { - "location": "/index.html#bitxora-b", - "text": "", - "title": "bitXor(a, b)" - }, - { - "location": "/index.html#bitnota", - "text": "", - "title": "bitNot(a)" - }, - { - "location": "/index.html#bitshiftlefta-b", - "text": "", - "title": "bitShiftLeft(a, b)" - }, - { - "location": "/index.html#bitshiftrighta-b", - "text": "", - "title": "bitShiftRight(a, b)" - }, - { - "location": "/index.html#hash-functions", - "text": "Hash functions can be used for deterministic pseudo-random shuffling of elements.", - "title": "Hash functions" - }, - { - "location": "/index.html#halfmd5", - "text": "Calculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian.\nAccepts a String-type argument. Returns UInt64.\nThis function works fairly slowly (5 million short strings per second per processor core).\nIf you don't need MD5 in particular, use the 'sipHash64' function instead.", - "title": "halfMD5" - }, - { - "location": "/index.html#md5", - "text": "Calculates the MD5 from a string and returns the resulting set of bytes as FixedString(16).\nIf you don't need MD5 in particular, but you need a decent cryptographic 128-bit hash, use the 'sipHash128' function instead.\nIf you want to get the same result as output by the md5sum utility, use lower(hex(MD5(s))).", - "title": "MD5" - }, - { - "location": "/index.html#siphash64", - "text": "Calculates SipHash from a string.\nAccepts a String-type argument. Returns UInt64.\nSipHash is a cryptographic hash function. It works at least three times faster than MD5.\nFor more information, see the link: https://131002.net/siphash/", - "title": "sipHash64" - }, - { - "location": "/index.html#siphash128", - "text": "Calculates SipHash from a string.\nAccepts a String-type argument. Returns FixedString(16).\nDiffers from sipHash64 in that the final xor-folding state is only done up to 128 bytes.", - "title": "sipHash128" - }, - { - "location": "/index.html#cityhash64", - "text": "Calculates CityHash64 from a string or a similar hash function for any number of any type of arguments.\nFor String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality.\nFor other types of arguments, a decent implementation-specific fast non-cryptographic hash function is used.\nIf multiple arguments are passed, the function is calculated using the same rules and chain combinations using the CityHash combinator.\nFor example, you can compute the checksum of an entire table with accuracy up to the row order: SELECT sum(cityHash64(*)) FROM table .", - "title": "cityHash64" - }, - { - "location": "/index.html#inthash32", - "text": "Calculates a 32-bit hash code from any type of integer.\nThis is a relatively fast non-cryptographic hash function of average quality for numbers.", - "title": "intHash32" - }, - { - "location": "/index.html#inthash64", - "text": "Calculates a 64-bit hash code from any type of integer.\nIt works faster than intHash32. Average quality.", - "title": "intHash64" - }, - { - "location": "/index.html#sha1", - "text": "", - "title": "SHA1" - }, - { - "location": "/index.html#sha224", - "text": "", - "title": "SHA224" - }, - { - "location": "/index.html#sha256", - "text": "Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32).\nThe function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million).\nWe recommend using this function only in cases when you need a specific hash function and you can't select it.\nEven in these cases, we recommend applying the function offline and pre-calculating values when inserting them into the table, instead of applying it in SELECTS.", - "title": "SHA256" - }, - { - "location": "/index.html#urlhashurl91-n93", - "text": "A fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization. URLHash(s) \u2013 Calculates a hash from a string without one of the trailing symbols / , ? or # at the end, if present. URLHash(s, N) \u2013 Calculates a hash from a string up to the N level in the URL hierarchy, without one of the trailing symbols / , ? or # at the end, if present.\nLevels are the same as in URLHierarchy. This function is specific to Yandex.Metrica.", - "title": "URLHash(url[, N])" - }, - { - "location": "/index.html#functions-for-generating-pseudo-random-numbers", - "text": "Non-cryptographic generators of pseudo-random numbers are used. All the functions accept zero arguments or one argument.\nIf an argument is passed, it can be any type, and its value is not used for anything.\nThe only purpose of this argument is to prevent common subexpression elimination, so that two different instances of the same function return different columns with different random numbers.", - "title": "Functions for generating pseudo-random numbers" - }, - { - "location": "/index.html#rand", - "text": "Returns a pseudo-random UInt32 number, evenly distributed among all UInt32-type numbers.\nUses a linear congruential generator.", - "title": "rand" - }, - { - "location": "/index.html#rand64", - "text": "Returns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers.\nUses a linear congruential generator.", - "title": "rand64" - }, - { - "location": "/index.html#encoding-functions", - "text": "", - "title": "Encoding functions" - }, - { - "location": "/index.html#hex", - "text": "Accepts arguments of types: String , unsigned integer , Date , or DateTime . Returns a string containing the argument's hexadecimal representation. Uses uppercase letters A-F . Does not use 0x prefixes or h suffixes. For strings, all bytes are simply encoded as two hexadecimal numbers. Numbers are converted to big endian (\"human readable\") format. For numbers, older zeros are trimmed, but only by entire bytes. For example, hex (1) = '01' . Date is encoded as the number of days since the beginning of the Unix epoch. DateTime is encoded as the number of seconds since the beginning of the Unix epoch.", - "title": "hex" - }, - { - "location": "/index.html#unhexstr", - "text": "Accepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits does not have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn't thrown).\nIf you want to convert the result to a number, you can use the 'reverse' and 'reinterpretAsType' functions.", - "title": "unhex(str)" - }, - { - "location": "/index.html#uuidstringtonumstr", - "text": "Accepts a string containing 36 characters in the format 123e4567-e89b-12d3-a456-426655440000 , and returns it as a set of bytes in a FixedString(16).", - "title": "UUIDStringToNum(str)" - }, - { - "location": "/index.html#uuidnumtostringstr", - "text": "Accepts a FixedString(16) value. Returns a string containing 36 characters in text format.", - "title": "UUIDNumToString(str)" - }, - { - "location": "/index.html#bitmasktolistnum", - "text": "Accepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order.", - "title": "bitmaskToList(num)" - }, - { - "location": "/index.html#bitmasktoarraynum", - "text": "Accepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order.", - "title": "bitmaskToArray(num)" - }, - { - "location": "/index.html#functions-for-working-with-urls", - "text": "All these functions don't follow the RFC. They are maximally simplified for improved performance.", - "title": "Functions for working with URLs" - }, - { - "location": "/index.html#functions-that-extract-part-of-a-url", - "text": "If there isn't anything similar in a URL, an empty string is returned.", - "title": "Functions that extract part of a URL" - }, - { - "location": "/index.html#protocol", - "text": "Returns the protocol. Examples: http, ftp, mailto, magnet...", - "title": "protocol" - }, - { - "location": "/index.html#domain", - "text": "Gets the domain.", - "title": "domain" - }, - { - "location": "/index.html#domainwithoutwww", - "text": "Returns the domain and removes no more than one 'www.' from the beginning of it, if present.", - "title": "domainWithoutWWW" - }, - { - "location": "/index.html#topleveldomain", - "text": "Returns the top-level domain. Example: .ru.", - "title": "topLevelDomain" - }, - { - "location": "/index.html#firstsignificantsubdomain", - "text": "Returns the \"first significant subdomain\". This is a non-standard concept specific to Yandex.Metrica. The first significant subdomain is a second-level domain if it is 'com', 'net', 'org', or 'co'. Otherwise, it is a third-level domain. For example, firstSignificantSubdomain (' https://news.yandex.ru/ ') = 'yandex ', firstSignificantSubdomain (' https://news.yandex.com.tr/ ') = 'yandex '. The list of \"insignificant\" second-level domains and other implementation details may change in the future.", - "title": "firstSignificantSubdomain" - }, - { - "location": "/index.html#cuttofirstsignificantsubdomain", - "text": "Returns the part of the domain that includes top-level subdomains up to the \"first significant subdomain\" (see the explanation above). For example, cutToFirstSignificantSubdomain('https://news.yandex.com.tr/') = 'yandex.com.tr' .", - "title": "cutToFirstSignificantSubdomain" - }, - { - "location": "/index.html#path", - "text": "Returns the path. Example: /top/news.html The path does not include the query string.", - "title": "path" - }, - { - "location": "/index.html#pathfull", - "text": "The same as above, but including query string and fragment. Example: /top/news.html?page=2#comments", - "title": "pathFull" - }, - { - "location": "/index.html#querystring", - "text": "Returns the query string. Example: page=1 lr=213. query-string does not include the initial question mark, as well as # and everything after #.", - "title": "queryString" - }, - { - "location": "/index.html#fragment", - "text": "Returns the fragment identifier. fragment does not include the initial hash symbol.", - "title": "fragment" - }, - { - "location": "/index.html#querystringandfragment", - "text": "Returns the query string and fragment identifier. Example: page=1#29390.", - "title": "queryStringAndFragment" - }, - { - "location": "/index.html#extracturlparameterurl-name", - "text": "Returns the value of the 'name' parameter in the URL, if present. Otherwise, an empty string. If there are many parameters with this name, it returns the first occurrence. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.", - "title": "extractURLParameter(URL, name)" - }, - { - "location": "/index.html#extracturlparametersurl", - "text": "Returns an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way.", - "title": "extractURLParameters(URL)" - }, - { - "location": "/index.html#extracturlparameternamesurl", - "text": "Returns an array of name strings corresponding to the names of URL parameters. The values are not decoded in any way.", - "title": "extractURLParameterNames(URL)" - }, - { - "location": "/index.html#urlhierarchyurl", - "text": "Returns an array containing the URL, truncated at the end by the symbols /,? in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:", - "title": "URLHierarchy(URL)" - }, - { - "location": "/index.html#urlpathhierarchyurl", - "text": "The same as above, but without the protocol and host in the result. The / element (root) is not included. Example: the function is used to implement tree reports the URL in Yandex. Metric. URLPathHierarchy( https://example.com/browse/CONV-6788 ) =\n[\n /browse/ ,\n /browse/CONV-6788 \n]", - "title": "URLPathHierarchy(URL)" - }, - { - "location": "/index.html#decodeurlcomponenturl", - "text": "Returns the decoded URL.\nExample: SELECT decodeURLComponent ( http://127.0.0.1:8123/?query=SELECT%201%3B ) AS DecodedURL ; \u250c\u2500DecodedURL\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 http://127.0.0.1:8123/?query=SELECT 1; \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "decodeURLComponent(URL)" - }, - { - "location": "/index.html#functions-that-remove-part-of-a-url", - "text": "If the URL doesn't have anything similar, the URL remains unchanged.", - "title": "Functions that remove part of a URL." - }, - { - "location": "/index.html#cutwww", - "text": "Removes no more than one 'www.' from the beginning of the URL's domain, if present.", - "title": "cutWWW" - }, - { - "location": "/index.html#cutquerystring", - "text": "Removes query string. The question mark is also removed.", - "title": "cutQueryString" - }, - { - "location": "/index.html#cutfragment", - "text": "Removes the fragment identifier. The number sign is also removed.", - "title": "cutFragment" - }, - { - "location": "/index.html#cutquerystringandfragment", - "text": "Removes the query string and fragment identifier. The question mark and number sign are also removed.", - "title": "cutQueryStringAndFragment" - }, - { - "location": "/index.html#cuturlparameterurl-name", - "text": "Removes the 'name' URL parameter, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.", - "title": "cutURLParameter(URL, name)" - }, - { - "location": "/index.html#functions-for-working-with-ip-addresses", - "text": "", - "title": "Functions for working with IP addresses" - }, - { - "location": "/index.html#ipv4numtostringnum", - "text": "Takes a UInt32 number. Interprets it as an IPv4 address in big endian. Returns a string containing the corresponding IPv4 address in the format A.B.C.d (dot-separated numbers in decimal form).", - "title": "IPv4NumToString(num)" - }, - { - "location": "/index.html#ipv4stringtonums", - "text": "The reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0.", - "title": "IPv4StringToNum(s)" - }, - { - "location": "/index.html#ipv4numtostringclasscnum", - "text": "Similar to IPv4NumToString, but using xxx instead of the last octet. Example: SELECT \n IPv4NumToStringClassC ( ClientIP ) AS k , \n count () AS c FROM test . hits GROUP BY k ORDER BY c DESC LIMIT 10 \u250c\u2500k\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 83.149.9.xxx \u2502 26238 \u2502\n\u2502 217.118.81.xxx \u2502 26074 \u2502\n\u2502 213.87.129.xxx \u2502 25481 \u2502\n\u2502 83.149.8.xxx \u2502 24984 \u2502\n\u2502 217.118.83.xxx \u2502 22797 \u2502\n\u2502 78.25.120.xxx \u2502 22354 \u2502\n\u2502 213.87.131.xxx \u2502 21285 \u2502\n\u2502 78.25.121.xxx \u2502 20887 \u2502\n\u2502 188.162.65.xxx \u2502 19694 \u2502\n\u2502 83.149.48.xxx \u2502 17406 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 Since using 'xxx' is highly unusual, this may be changed in the future. We recommend that you don't rely on the exact format of this fragment.", - "title": "IPv4NumToStringClassC(num)" - }, - { - "location": "/index.html#ipv6numtostringx", - "text": "Accepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format.\nIPv6-mapped IPv4 addresses are output in the format ::ffff:111.222.33.44. Examples: SELECT IPv6NumToString ( toFixedString ( unhex ( 2A0206B8000000000000000000000011 ), 16 )) AS addr \u250c\u2500addr\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 2a02:6b8::11 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT \n IPv6NumToString ( ClientIP6 AS k ), \n count () AS c FROM hits_all WHERE EventDate = today () AND substring ( ClientIP6 , 1 , 12 ) != unhex ( 00000000000000000000FFFF ) GROUP BY k ORDER BY c DESC LIMIT 10 \u250c\u2500IPv6NumToString(ClientIP6)\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 2a02:2168:aaa:bbbb::2 \u2502 24695 \u2502\n\u2502 2a02:2698:abcd:abcd:abcd:abcd:8888:5555 \u2502 22408 \u2502\n\u2502 2a02:6b8:0:fff::ff \u2502 16389 \u2502\n\u2502 2a01:4f8:111:6666::2 \u2502 16016 \u2502\n\u2502 2a02:2168:888:222::1 \u2502 15896 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:222 \u2502 14774 \u2502\n\u2502 2a02:8109:eee:ee:eeee:eeee:eeee:eeee \u2502 14443 \u2502\n\u2502 2a02:810b:8888:888:8888:8888:8888:8888 \u2502 14345 \u2502\n\u2502 2a02:6b8:0:444:4444:4444:4444:4444 \u2502 14279 \u2502\n\u2502 2a01:7e00::ffff:ffff:ffff:ffff \u2502 13880 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT \n IPv6NumToString ( ClientIP6 AS k ), \n count () AS c FROM hits_all WHERE EventDate = today () GROUP BY k ORDER BY c DESC LIMIT 10 \u250c\u2500IPv6NumToString(ClientIP6)\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 ::ffff:94.26.111.111 \u2502 747440 \u2502\n\u2502 ::ffff:37.143.222.4 \u2502 529483 \u2502\n\u2502 ::ffff:5.166.111.99 \u2502 317707 \u2502\n\u2502 ::ffff:46.38.11.77 \u2502 263086 \u2502\n\u2502 ::ffff:79.105.111.111 \u2502 186611 \u2502\n\u2502 ::ffff:93.92.111.88 \u2502 176773 \u2502\n\u2502 ::ffff:84.53.111.33 \u2502 158709 \u2502\n\u2502 ::ffff:217.118.11.22 \u2502 154004 \u2502\n\u2502 ::ffff:217.118.11.33 \u2502 148449 \u2502\n\u2502 ::ffff:217.118.11.44 \u2502 148243 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "IPv6NumToString(x)" - }, - { - "location": "/index.html#ipv6stringtonums", - "text": "The reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes.\nHEX can be uppercase or lowercase.", - "title": "IPv6StringToNum(s)" - }, - { - "location": "/index.html#functions-for-working-with-json", - "text": "In Yandex.Metrica, JSON is transmitted by users as session parameters. There are some special functions for working with this JSON. (Although in most of the cases, the JSONs are additionally pre-processed, and the resulting values are put in separate columns in their processed format.) All these functions are based on strong assumptions about what the JSON can be, but they try to do as little as possible to get the job done. The following assumptions are made: The field name (function argument) must be a constant. The field name is somehow canonically encoded in JSON. For example: visitParamHas('{\"abc\":\"def\"}', 'abc') = 1 , but visitParamHas('{\"\\\\u0061\\\\u0062\\\\u0063\":\"def\"}', 'abc') = 0 Fields are searched for on any nesting level, indiscriminately. If there are multiple matching fields, the first occurrence is used. The JSON doesn't have space characters outside of string literals.", - "title": "Functions for working with JSON" - }, - { - "location": "/index.html#visitparamhasparams-name", - "text": "Checks whether there is a field with the 'name' name.", - "title": "visitParamHas(params, name)" - }, - { - "location": "/index.html#visitparamextractuintparams-name", - "text": "Parses UInt64 from the value of the field named 'name'. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn't exist, or it exists but doesn't contain a number, it returns 0.", - "title": "visitParamExtractUInt(params, name)" - }, - { - "location": "/index.html#visitparamextractintparams-name", - "text": "The same as for Int64.", - "title": "visitParamExtractInt(params, name)" - }, - { - "location": "/index.html#visitparamextractfloatparams-name", - "text": "The same as for Float64.", - "title": "visitParamExtractFloat(params, name)" - }, - { - "location": "/index.html#visitparamextractboolparams-name", - "text": "Parses a true/false value. The result is UInt8.", - "title": "visitParamExtractBool(params, name)" - }, - { - "location": "/index.html#visitparamextractrawparams-name", - "text": "Returns the value of a field, including separators. Examples: visitParamExtractRaw( { abc : \\\\n\\\\u0000 } , abc ) = \\\\n\\\\u0000 \nvisitParamExtractRaw( { abc :{ def :[1,2,3]}} , abc ) = { def :[1,2,3]}", - "title": "visitParamExtractRaw(params, name)" - }, - { - "location": "/index.html#visitparamextractstringparams-name", - "text": "Parses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string. Examples: visitParamExtractString( { abc : \\\\n\\\\u0000 } , abc ) = \\n\\0 \nvisitParamExtractString( { abc : \\\\u263a } , abc ) = \u263a \nvisitParamExtractString( { abc : \\\\u263 } , abc ) = \nvisitParamExtractString( { abc : hello} , abc ) = There is currently no support for code points in the format \\uXXXX\\uYYYY that are not from the basic multilingual plane (they are converted to CESU-8 instead of UTF-8).", - "title": "visitParamExtractString(params, name)" - }, - { - "location": "/index.html#higher-order-functions", - "text": "", - "title": "Higher-order functions" - }, - { - "location": "/index.html#-operator-lambdaparams-expr-function", - "text": "Allows describing a lambda function for passing to a higher-order function. The left side of the arrow has a formal parameter, which is any ID, or multiple formal parameters \u2013 any IDs in a tuple. The right side of the arrow has an expression that can use these formal parameters, as well as any table columns. Examples: x - 2 * x, str - str != Referer. Higher-order functions can only accept lambda functions as their functional argument. A lambda function that accepts multiple arguments can be passed to a higher-order function. In this case, the higher-order function is passed several arrays of identical length that these arguments will correspond to. For all functions other than 'arrayMap' and 'arrayFilter', the first argument (the lambda function) can be omitted. In this case, identical mapping is assumed.", - "title": "-> operator, lambda(params, expr) function" - }, - { - "location": "/index.html#arraymapfunc-arr1", - "text": "Returns an array obtained from the original application of the 'func' function to each element in the 'arr' array.", - "title": "arrayMap(func, arr1, ...)" - }, - { - "location": "/index.html#arrayfilterfunc-arr1", - "text": "Returns an array containing only the elements in 'arr1' for which 'func' returns something other than 0. Examples: SELECT arrayFilter ( x - x LIKE %World% , [ Hello , abc World ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ abc World ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 SELECT \n arrayFilter ( \n ( i , x ) - x LIKE %World% , \n arrayEnumerate ( arr ), \n [ Hello , abc World ] AS arr ) \n AS res \u250c\u2500res\u2500\u2510\n\u2502 [2] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayFilter(func, arr1, ...)" - }, - { - "location": "/index.html#arraycount91func93-arr1", - "text": "Returns the number of elements in the arr array for which func returns something other than 0. If 'func' is not specified, it returns the number of non-zero elements in the array.", - "title": "arrayCount([func,] arr1, ...)" - }, - { - "location": "/index.html#arrayexists91func93-arr1", - "text": "Returns 1 if there is at least one element in 'arr' for which 'func' returns something other than 0. Otherwise, it returns 0.", - "title": "arrayExists([func,] arr1, ...)" - }, - { - "location": "/index.html#arrayall91func93-arr1", - "text": "Returns 1 if 'func' returns something other than 0 for all the elements in 'arr'. Otherwise, it returns 0.", - "title": "arrayAll([func,] arr1, ...)" - }, - { - "location": "/index.html#arraysum91func93-arr1", - "text": "Returns the sum of the 'func' values. If the function is omitted, it just returns the sum of the array elements.", - "title": "arraySum([func,] arr1, ...)" - }, - { - "location": "/index.html#arrayfirstfunc-arr1", - "text": "Returns the first element in the 'arr1' array for which 'func' returns something other than 0.", - "title": "arrayFirst(func, arr1, ...)" - }, - { - "location": "/index.html#arrayfirstindexfunc-arr1", - "text": "Returns the index of the first element in the 'arr1' array for which 'func' returns something other than 0.", - "title": "arrayFirstIndex(func, arr1, ...)" - }, - { - "location": "/index.html#arraycumsum91func93-arr1", - "text": "Returns an array of partial sums of elements in the source array (a running sum). If the func function is specified, then the values of the array elements are converted by this function before summing. Example: SELECT arrayCumSum ([ 1 , 1 , 1 , 1 ]) AS res \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [1, 2, 3, 4] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayCumSum([func,] arr1, ...)" - }, - { - "location": "/index.html#arraysort91func93-arr1", - "text": "Returns an array as result of sorting the elements of arr1 in ascending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays) The Schwartzian transform is used to impove sorting efficiency. Example: SELECT arraySort (( x , y ) - y , [ hello , world ], [ 2 , 1 ]); \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [ world , hello ] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arraySort([func,] arr1, ...)" - }, - { - "location": "/index.html#arrayreversesort91func93-arr1", - "text": "Returns an array as result of sorting the elements of arr1 in descending order. If the func function is specified, sorting order is determined by the result of the function func applied to the elements of array (arrays)", - "title": "arrayReverseSort([func,] arr1, ...)" - }, - { - "location": "/index.html#other-functions", - "text": "", - "title": "Other functions" - }, - { - "location": "/index.html#hostname", - "text": "Returns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server.", - "title": "hostName()" - }, - { - "location": "/index.html#visiblewidthx", - "text": "Calculates the approximate width when outputting values to the console in text format (tab-separated).\nThis function is used by the system for implementing Pretty formats.", - "title": "visibleWidth(x)" - }, - { - "location": "/index.html#totypenamex", - "text": "Returns a string containing the type name of the passed argument.", - "title": "toTypeName(x)" - }, - { - "location": "/index.html#blocksize", - "text": "Gets the size of the block.\nIn ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for.", - "title": "blockSize()" - }, - { - "location": "/index.html#materializex", - "text": "Turns a constant into a full column containing just one value.\nIn ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior.", - "title": "materialize(x)" - }, - { - "location": "/index.html#ignore", - "text": "Accepts any arguments and always returns 0.\nHowever, the argument is still evaluated. This can be used for benchmarks.", - "title": "ignore(...)" - }, - { - "location": "/index.html#sleepseconds", - "text": "Sleeps 'seconds' seconds on each data block. You can specify an integer or a floating-point number.", - "title": "sleep(seconds)" - }, - { - "location": "/index.html#currentdatabase", - "text": "Returns the name of the current database.\nYou can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database.", - "title": "currentDatabase()" - }, - { - "location": "/index.html#isfinitex", - "text": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0.", - "title": "isFinite(x)" - }, - { - "location": "/index.html#isinfinitex", - "text": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. Note that 0 is returned for a NaN.", - "title": "isInfinite(x)" - }, - { - "location": "/index.html#isnanx", - "text": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0.", - "title": "isNaN(x)" - }, - { - "location": "/index.html#hascolumnintable91hostname91-username91-password939393-database-table-column", - "text": "Accepts constant strings: database name, table name, and column name. Returns a UInt8 constant expression equal to 1 if there is a column, otherwise 0. If the hostname parameter is set, the test will run on a remote server.\nThe function throws an exception if the table does not exist.\nFor elements in a nested data structure, the function checks for the existence of a column. For the nested data structure itself, the function returns 0.", - "title": "hasColumnInTable(['hostname'[, 'username'[, 'password']],] 'database', 'table', 'column')" - }, - { - "location": "/index.html#bar", - "text": "Allows building a unicode-art diagram. bar (x, min, max, width) draws a band with a width proportional to (x - min) and equal to width characters when x = max . Parameters: x \u2013 Value to display. min, max \u2013 Integer constants. The value must fit in Int64. width \u2013 Constant, positive number, may be a fraction. The band is drawn with accuracy to one eighth of a symbol. Example: SELECT \n toHour ( EventTime ) AS h , \n count () AS c , \n bar ( c , 0 , 600000 , 20 ) AS bar FROM test . hits GROUP BY h ORDER BY h ASC \u250c\u2500\u2500h\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u252c\u2500bar\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 0 \u2502 292907 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 1 \u2502 180563 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 2 \u2502 114861 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 3 \u2502 85069 \u2502 \u2588\u2588\u258b \u2502\n\u2502 4 \u2502 68543 \u2502 \u2588\u2588\u258e \u2502\n\u2502 5 \u2502 78116 \u2502 \u2588\u2588\u258c \u2502\n\u2502 6 \u2502 113474 \u2502 \u2588\u2588\u2588\u258b \u2502\n\u2502 7 \u2502 170678 \u2502 \u2588\u2588\u2588\u2588\u2588\u258b \u2502\n\u2502 8 \u2502 278380 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 9 \u2502 391053 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 10 \u2502 457681 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 11 \u2502 493667 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 12 \u2502 509641 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 13 \u2502 522947 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 14 \u2502 539954 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 15 \u2502 528460 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c \u2502\n\u2502 16 \u2502 539201 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 17 \u2502 523539 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 18 \u2502 506467 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a \u2502\n\u2502 19 \u2502 520915 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2502 20 \u2502 521665 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 21 \u2502 542078 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2502\n\u2502 22 \u2502 493642 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d \u2502\n\u2502 23 \u2502 400397 \u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e \u2502\n\u2514\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "bar" - }, - { - "location": "/index.html#transform", - "text": "Transforms a value according to the explicitly defined mapping of some elements to other ones.\nThere are two variations of this function: transform(x, array_from, array_to, default) x \u2013 What to transform. array_from \u2013 Constant array of values for converting. array_to \u2013 Constant array of values to convert the values in 'from' to. default \u2013 Which value to use if 'x' is not equal to any of the values in 'from'. array_from and array_to \u2013 Arrays of the same size. Types: transform(T, Array(T), Array(U), U) - U T and U can be numeric, string, or Date or DateTime types.\nWhere the same letter is indicated (T or U), for numeric types these might not be matching types, but types that have a common type.\nFor example, the first argument can have the Int64 type, while the second has the Array(Uint16) type. If the 'x' value is equal to one of the elements in the 'array_from' array, it returns the existing element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'default'. If there are multiple matching elements in 'array_from', it returns one of the matches. Example: SELECT \n transform ( SearchEngineID , [ 2 , 3 ], [ Yandex , Google ], Other ) AS title , \n count () AS c FROM test . hits WHERE SearchEngineID != 0 GROUP BY title ORDER BY c DESC \u250c\u2500title\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 Yandex \u2502 498635 \u2502\n\u2502 Google \u2502 229872 \u2502\n\u2502 Other \u2502 104472 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 transform(x, array_from, array_to) Differs from the first variation in that the 'default' argument is omitted.\nIf the 'x' value is equal to one of the elements in the 'array_from' array, it returns the matching element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'x'. Types: transform(T, Array(T), Array(T)) - T Example: SELECT \n transform ( domain ( Referer ), [ yandex.ru , google.ru , vk.com ], [ www.yandex , example.com ]) AS s , \n count () AS c FROM test . hits GROUP BY domain ( Referer ) ORDER BY count () DESC LIMIT 10 \u250c\u2500s\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500c\u2500\u2510\n\u2502 \u2502 2906259 \u2502\n\u2502 www.yandex \u2502 867767 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 313599 \u2502\n\u2502 mail.yandex.ru \u2502 107147 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 100355 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588.ru \u2502 65040 \u2502\n\u2502 news.yandex.ru \u2502 64515 \u2502\n\u2502 \u2588\u2588\u2588\u2588\u2588\u2588.net \u2502 59141 \u2502\n\u2502 example.com \u2502 57316 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "transform" - }, - { - "location": "/index.html#formatreadablesizex", - "text": "Accepts the size (number of bytes). Returns a rounded size with a suffix (KiB, MiB, etc.) as a string. Example: SELECT \n arrayJoin ([ 1 , 1024 , 1024 * 1024 , 192851925 ]) AS filesize_bytes , \n formatReadableSize ( filesize_bytes ) AS filesize \u250c\u2500filesize_bytes\u2500\u252c\u2500filesize\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 1.00 B \u2502\n\u2502 1024 \u2502 1.00 KiB \u2502\n\u2502 1048576 \u2502 1.00 MiB \u2502\n\u2502 192851925 \u2502 183.92 MiB \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "formatReadableSize(x)" - }, - { - "location": "/index.html#leasta-b", - "text": "Returns the smallest value from a and b.", - "title": "least(a, b)" - }, - { - "location": "/index.html#greatesta-b", - "text": "Returns the largest value of a and b.", - "title": "greatest(a, b)" - }, - { - "location": "/index.html#uptime", - "text": "Returns the server's uptime in seconds.", - "title": "uptime()" - }, - { - "location": "/index.html#version", - "text": "Returns the version of the server as a string.", - "title": "version()" - }, - { - "location": "/index.html#rownumberinallblocks", - "text": "Returns the ordinal number of the row in the data block. This function only considers the affected data blocks.", - "title": "rowNumberInAllBlocks()" - }, - { - "location": "/index.html#runningdifferencex", - "text": "Calculates the difference between successive row values \u200b\u200bin the data block.\nReturns 0 for the first row and the difference from the previous row for each subsequent row. The result of the function depends on the affected data blocks and the order of data in the block.\nIf you make a subquery with ORDER BY and call the function from outside the subquery, you can get the expected result. Example: SELECT \n EventID , \n EventTime , \n runningDifference ( EventTime ) AS delta FROM ( \n SELECT \n EventID , \n EventTime \n FROM events \n WHERE EventDate = 2016-11-24 \n ORDER BY EventTime ASC \n LIMIT 5 ) \u250c\u2500EventID\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500EventTime\u2500\u252c\u2500delta\u2500\u2510\n\u2502 1106 \u2502 2016-11-24 00:00:04 \u2502 0 \u2502\n\u2502 1107 \u2502 2016-11-24 00:00:05 \u2502 1 \u2502\n\u2502 1108 \u2502 2016-11-24 00:00:05 \u2502 0 \u2502\n\u2502 1109 \u2502 2016-11-24 00:00:09 \u2502 4 \u2502\n\u2502 1110 \u2502 2016-11-24 00:00:10 \u2502 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "runningDifference(x)" - }, - { - "location": "/index.html#macnumtostringnum", - "text": "Accepts a UInt64 number. Interprets it as a MAC address in big endian. Returns a string containing the corresponding MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form).", - "title": "MACNumToString(num)" - }, - { - "location": "/index.html#macstringtonums", - "text": "The inverse function of MACNumToString. If the MAC address has an invalid format, it returns 0.", - "title": "MACStringToNum(s)" - }, - { - "location": "/index.html#macstringtoouis", - "text": "Accepts a MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form). Returns the first three octets as a UInt64 number. If the MAC address has an invalid format, it returns 0.", - "title": "MACStringToOUI(s)" - }, - { - "location": "/index.html#functions-for-working-with-external-dictionaries", - "text": "For information on connecting and configuring external dictionaries, see \" External dictionaries \".", - "title": "Functions for working with external dictionaries" - }, - { - "location": "/index.html#dictgetuint8-dictgetuint16-dictgetuint32-dictgetuint64", - "text": "", - "title": "dictGetUInt8, dictGetUInt16, dictGetUInt32, dictGetUInt64" - }, - { - "location": "/index.html#dictgetint8-dictgetint16-dictgetint32-dictgetint64", - "text": "", - "title": "dictGetInt8, dictGetInt16, dictGetInt32, dictGetInt64" - }, - { - "location": "/index.html#dictgetfloat32-dictgetfloat64", - "text": "", - "title": "dictGetFloat32, dictGetFloat64" - }, - { - "location": "/index.html#dictgetdate-dictgetdatetime", - "text": "", - "title": "dictGetDate, dictGetDateTime" - }, - { - "location": "/index.html#dictgetuuid", - "text": "", - "title": "dictGetUUID" - }, - { - "location": "/index.html#dictgetstring", - "text": "dictGetT('dict_name', 'attr_name', id) Get the value of the attr_name attribute from the dict_name dictionary using the 'id' key. dict_name and attr_name are constant strings. id must be UInt64.\nIf there is no id key in the dictionary, it returns the default value specified in the dictionary description.", - "title": "dictGetString" - }, - { - "location": "/index.html#dictgettordefault", - "text": "dictGetT('dict_name', 'attr_name', id, default) The same as the dictGetT functions, but the default value is taken from the function's last argument.", - "title": "dictGetTOrDefault" - }, - { - "location": "/index.html#dictisin", - "text": "dictIsIn('dict_name', child_id, ancestor_id) For the 'dict_name' hierarchical dictionary, finds out whether the 'child_id' key is located inside 'ancestor_id' (or matches 'ancestor_id'). Returns UInt8.", - "title": "dictIsIn" - }, - { - "location": "/index.html#dictgethierarchy", - "text": "dictGetHierarchy('dict_name', id) For the 'dict_name' hierarchical dictionary, returns an array of dictionary keys starting from 'id' and continuing along the chain of parent elements. Returns Array(UInt64).", - "title": "dictGetHierarchy" - }, - { - "location": "/index.html#dicthas", - "text": "dictHas('dict_name', id) Check whether the dictionary has the key. Returns a UInt8 value equal to 0 if there is no key and 1 if there is a key.", - "title": "dictHas" - }, - { - "location": "/index.html#functions-for-working-with-yandexmetrica-dictionaries", - "text": "In order for the functions below to work, the server config must specify the paths and addresses for getting all the Yandex.Metrica dictionaries. The dictionaries are loaded at the first call of any of these functions. If the reference lists can't be loaded, an exception is thrown. For information about creating reference lists, see the section \"Dictionaries\".", - "title": "Functions for working with Yandex.Metrica dictionaries" - }, - { - "location": "/index.html#multiple-geobases", - "text": "ClickHouse supports working with multiple alternative geobases (regional hierarchies) simultaneously, in order to support various perspectives on which countries certain regions belong to. The 'clickhouse-server' config specifies the file with the regional hierarchy:: path_to_regions_hierarchy_file /opt/geo/regions_hierarchy.txt /path_to_regions_hierarchy_file Besides this file, it also searches for files nearby that have the _ symbol and any suffix appended to the name (before the file extension).\nFor example, it will also find the file /opt/geo/regions_hierarchy_ua.txt , if present. ua is called the dictionary key. For a dictionary without a suffix, the key is an empty string. All the dictionaries are re-loaded in runtime (once every certain number of seconds, as defined in the builtin_dictionaries_reload_interval config parameter, or once an hour by default). However, the list of available dictionaries is defined one time, when the server starts. All functions for working with regions have an optional argument at the end \u2013 the dictionary key. It is referred to as the geobase.\nExample: regionToCountry(RegionID) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, ) \u2013 Uses the default dictionary: /opt/geo/regions_hierarchy.txt\nregionToCountry(RegionID, ua ) \u2013 Uses the dictionary for the ua key: /opt/geo/regions_hierarchy_ua.txt", - "title": "Multiple geobases" - }, - { - "location": "/index.html#regiontocityid-geobase", - "text": "Accepts a UInt32 number \u2013 the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0.", - "title": "regionToCity(id[, geobase])" - }, - { - "location": "/index.html#regiontoareaid91-geobase93", - "text": "Converts a region to an area (type 5 in the geobase). In every other way, this function is the same as 'regionToCity'. SELECT DISTINCT regionToName ( regionToArea ( toUInt32 ( number ), ua )) FROM system . numbers LIMIT 15 \u250c\u2500regionToName(regionToArea(toUInt32(number), \\ ua\\ ))\u2500\u2510\n\u2502 \u2502\n\u2502 Moscow and Moscow region \u2502\n\u2502 St. Petersburg and Leningrad region \u2502\n\u2502 Belgorod region \u2502\n\u2502 Ivanovsk region \u2502\n\u2502 Kaluga region \u2502\n\u2502 Kostroma region \u2502\n\u2502 Kursk region \u2502\n\u2502 Lipetsk region \u2502\n\u2502 Orlov region \u2502\n\u2502 Ryazan region \u2502\n\u2502 Smolensk region \u2502\n\u2502 Tambov region \u2502\n\u2502 Tver region \u2502\n\u2502 Tula region \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "regionToArea(id[, geobase])" - }, - { - "location": "/index.html#regiontodistrictid-geobase", - "text": "Converts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as 'regionToCity'. SELECT DISTINCT regionToName ( regionToDistrict ( toUInt32 ( number ), ua )) FROM system . numbers LIMIT 15 \u250c\u2500regionToName(regionToDistrict(toUInt32(number), \\ ua\\ ))\u2500\u2510\n\u2502 \u2502\n\u2502 Central federal district \u2502\n\u2502 Northwest federal district \u2502\n\u2502 South federal district \u2502\n\u2502 North Caucases federal district \u2502\n\u2502 Privolga federal district \u2502\n\u2502 Ural federal district \u2502\n\u2502 Siberian federal district \u2502\n\u2502 Far East federal district \u2502\n\u2502 Scotland \u2502\n\u2502 Faroe Islands \u2502\n\u2502 Flemish region \u2502\n\u2502 Brussels capital region \u2502\n\u2502 Wallonia \u2502\n\u2502 Federation of Bosnia and Herzegovina \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "regionToDistrict(id[, geobase])" - }, - { - "location": "/index.html#regiontocountryid-geobase", - "text": "Converts a region to a country. In every other way, this function is the same as 'regionToCity'.\nExample: regionToCountry(toUInt32(213)) = 225 converts Moscow (213) to Russia (225).", - "title": "regionToCountry(id[, geobase])" - }, - { - "location": "/index.html#regiontocontinentid-geobase", - "text": "Converts a region to a continent. In every other way, this function is the same as 'regionToCity'.\nExample: regionToContinent(toUInt32(213)) = 10001 converts Moscow (213) to Eurasia (10001).", - "title": "regionToContinent(id[, geobase])" - }, - { - "location": "/index.html#regiontopopulationid-geobase", - "text": "Gets the population for a region.\nThe population can be recorded in files with the geobase. See the section \"External dictionaries\".\nIf the population is not recorded for the region, it returns 0.\nIn the Yandex geobase, the population might be recorded for child regions, but not for parent regions.", - "title": "regionToPopulation(id[, geobase])" - }, - { - "location": "/index.html#regioninlhs-rhs-geobase", - "text": "Checks whether a 'lhs' region belongs to a 'rhs' region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn't belong.\nThe relationship is reflexive \u2013 any region also belongs to itself.", - "title": "regionIn(lhs, rhs[, geobase])" - }, - { - "location": "/index.html#regionhierarchyid91-geobase93", - "text": "Accepts a UInt32 number \u2013 the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain.\nExample: regionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000] .", - "title": "regionHierarchy(id[, geobase])" - }, - { - "location": "/index.html#regiontonameid91-lang93", - "text": "Accepts a UInt32 number \u2013 the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language 'ru' is used. If the language is not supported, an exception is thrown. Returns a string \u2013 the name of the region in the corresponding language. If the region with the specified ID doesn't exist, an empty string is returned. ua and uk both mean Ukrainian.", - "title": "regionToName(id[, lang])" - }, - { - "location": "/index.html#functions-for-implementing-the-in-operator", - "text": "", - "title": "Functions for implementing the IN operator" - }, - { - "location": "/index.html#in-notin-globalin-globalnotin", - "text": "See the section \"IN operators\".", - "title": "in, notIn, globalIn, globalNotIn" - }, - { - "location": "/index.html#tuplex-y-operator-x-y", - "text": "A function that allows grouping multiple columns.\nFor columns with the types T1, T2, ..., it returns a Tuple(T1, T2, ...) type tuple containing these columns. There is no cost to execute the function.\nTuples are normally used as intermediate values for an argument of IN operators, or for creating a list of formal parameters of lambda functions. Tuples can't be written to a table.", - "title": "tuple(x, y, ...), operator (x, y, ...)" - }, - { - "location": "/index.html#tupleelementtuple-n-operator-xn", - "text": "A function that allows getting a column from a tuple.\n'N' is the column index, starting from 1. N must be a constant. 'N' must be a constant. 'N' must be a strict postive integer no greater than the size of the tuple.\nThere is no cost to execute the function.", - "title": "tupleElement(tuple, n), operator x.N" - }, - { - "location": "/index.html#arrayjoin-function", - "text": "This is a very unusual function. Normal functions don't change a set of rows, but just change the values in each row (map).\nAggregate functions compress a set of rows (fold or reduce).\nThe 'arrayJoin' function takes each row and generates a set of rows (unfold). This function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array.\nAll the values in columns are simply copied, except the values in the column where this function is applied; it is replaced with the corresponding array value. A query can use multiple arrayJoin functions. In this case, the transformation is performed multiple times. Note the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities. Example: SELECT arrayJoin ([ 1 , 2 , 3 ] AS src ) AS dst , Hello , src \u250c\u2500dst\u2500\u252c\u2500\\ Hello\\ \u2500\u252c\u2500src\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 1 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 2 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2502 3 \u2502 Hello \u2502 [1,2,3] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "arrayJoin function" - }, - { - "location": "/index.html#aggregate-functions", - "text": "Aggregate functions work in the normal way as expected by database experts. ClickHouse also supports: Parametric aggregate functions , which accept other parameters in addition to columns. Combinators , which change the behavior of aggregate functions.", - "title": "Aggregate functions" - }, - { - "location": "/index.html#function-reference", - "text": "", - "title": "Function reference" - }, - { - "location": "/index.html#count", - "text": "Counts the number of rows. Accepts zero arguments and returns UInt64.\nThe syntax COUNT(DISTINCT x) is not supported. The separate uniq aggregate function exists for this purpose. A SELECT count() FROM table query is not optimized, because the number of entries in the table is not stored separately. It will select some small column from the table and count the number of values in it.", - "title": "count()" - }, - { - "location": "/index.html#anyx", - "text": "Selects the first encountered value.\nThe query can be executed in any order and even in a different order each time, so the result of this function is indeterminate.\nTo get a determinate result, you can use the 'min' or 'max' function instead of 'any'. In some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY. When a SELECT query has the GROUP BY clause or at least one aggregate function, ClickHouse (in contrast to MySQL) requires that all expressions in the SELECT , HAVING , and ORDER BY clauses be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. To get behavior like in MySQL, you can put the other columns in the any aggregate function.", - "title": "any(x)" - }, - { - "location": "/index.html#anyheavyx", - "text": "Selects a frequently occurring value using the heavy hitters algorithm. If there is a value that occurs more than in half the cases in each of the query's execution threads, this value is returned. Normally, the result is nondeterministic. anyHeavy(column) Arguments \n- column \u2013 The column name. Example Take the OnTime data set and select any frequently occurring value in the AirlineID column. SELECT anyHeavy ( AirlineID ) AS res FROM ontime \u250c\u2500\u2500\u2500res\u2500\u2510\n\u2502 19690 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "anyHeavy(x)" - }, - { - "location": "/index.html#anylastx", - "text": "Selects the last value encountered.\nThe result is just as indeterminate as for the any function.", - "title": "anyLast(x)" - }, - { - "location": "/index.html#minx", - "text": "Calculates the minimum.", - "title": "min(x)" - }, - { - "location": "/index.html#maxx", - "text": "Calculates the maximum.", - "title": "max(x)" - }, - { - "location": "/index.html#argminarg-val", - "text": "Calculates the 'arg' value for a minimal 'val' value. If there are several different values of 'arg' for minimal values of 'val', the first of these values encountered is output.", - "title": "argMin(arg, val)" - }, - { - "location": "/index.html#argmaxarg-val", - "text": "Calculates the 'arg' value for a maximum 'val' value. If there are several different values of 'arg' for maximum values of 'val', the first of these values encountered is output.", - "title": "argMax(arg, val)" - }, - { - "location": "/index.html#sumx", - "text": "Calculates the sum.\nOnly works for numbers.", - "title": "sum(x)" - }, - { - "location": "/index.html#sumwithoverflowx", - "text": "Computes the sum of the numbers, using the same data type for the result as for the input parameters. If the sum exceeds the maximum value for this data type, the function returns an error. Only works for numbers.", - "title": "sumWithOverflow(x)" - }, - { - "location": "/index.html#summapkey-value", - "text": "Totals the 'value' array according to the keys specified in the 'key' array.\nThe number of elements in 'key' and 'value' must be the same for each row that is totaled.\nReturns a tuple of two arrays: keys in sorted order, and values \u200b\u200bsummed for the corresponding keys. Example: CREATE TABLE sum_map ( \n date Date , \n timeslot DateTime , \n statusMap Nested ( \n status UInt16 , \n requests UInt64 \n ) ) ENGINE = Log ; INSERT INTO sum_map VALUES \n ( 2000-01-01 , 2000-01-01 00:00:00 , [ 1 , 2 , 3 ], [ 10 , 10 , 10 ]), \n ( 2000-01-01 , 2000-01-01 00:00:00 , [ 3 , 4 , 5 ], [ 10 , 10 , 10 ]), \n ( 2000-01-01 , 2000-01-01 00:01:00 , [ 4 , 5 , 6 ], [ 10 , 10 , 10 ]), \n ( 2000-01-01 , 2000-01-01 00:01:00 , [ 6 , 7 , 8 ], [ 10 , 10 , 10 ]); SELECT \n timeslot , \n sumMap ( statusMap . status , statusMap . requests ) FROM sum_map GROUP BY timeslot \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500timeslot\u2500\u252c\u2500sumMap(statusMap.status, statusMap.requests)\u2500\u2510\n\u2502 2000-01-01 00:00:00 \u2502 ([1,2,3,4,5],[10,10,20,10,10]) \u2502\n\u2502 2000-01-01 00:01:00 \u2502 ([4,5,6,7,8],[10,10,20,10,10]) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "sumMap(key, value)" - }, - { - "location": "/index.html#avgx", - "text": "Calculates the average.\nOnly works for numbers.\nThe result is always Float64.", - "title": "avg(x)" - }, - { - "location": "/index.html#uniqx", - "text": "Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments. Uses an adaptive sampling algorithm: for the calculation state, it uses a sample of element hash values with a size up to 65536.\nThis algorithm is also very accurate for data sets with low cardinality (up to 65536) and very efficient on CPU (when computing not too many of these functions, using uniq is almost as fast as using other aggregate functions). The result is determinate (it doesn't depend on the order of query processing). This function provides excellent accuracy even for data sets with extremely high cardinality (over 10 billion elements). It is recommended for default use.", - "title": "uniq(x)" - }, - { - "location": "/index.html#uniqcombinedx", - "text": "Calculates the approximate number of different values of the argument. Works for numbers, strings, dates, date-with-time, and for multiple arguments and tuple arguments. A combination of three algorithms is used: array, hash table and HyperLogLog with an error correction table. The memory consumption is several times smaller than for the uniq function, and the accuracy is several times higher. Performance is slightly lower than for the uniq function, but sometimes it can be even higher than it, such as with distributed queries that transmit a large number of aggregation states over the network. The maximum state size is 96 KiB (HyperLogLog of 217 6-bit cells). The result is determinate (it doesn't depend on the order of query processing). The uniqCombined function is a good default choice for calculating the number of different values, but keep in mind that the estimation error will increase for high-cardinality data sets (200M+ elements), and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).", - "title": "uniqCombined(x)" - }, - { - "location": "/index.html#uniqhll12x", - "text": "Uses the HyperLogLog algorithm to approximate the number of different values of the argument.\n212 5-bit cells are used. The size of the state is slightly more than 2.5 KB. The result is not very accurate (up to ~10% error) for small data sets ( 10K elements). However, the result is fairly accurate for high-cardinality data sets (10K-100M), with a maximum error of ~1.6%. Starting from 100M, the estimation error increases, and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements). The result is determinate (it doesn't depend on the order of query processing). We don't recommend using this function. In most cases, use the uniq or uniqCombined function.", - "title": "uniqHLL12(x)" - }, - { - "location": "/index.html#uniqexactx", - "text": "Calculates the number of different values of the argument, exactly.\nThere is no reason to fear approximations. It's better to use the uniq function.\nUse the uniqExact function if you definitely need an exact result. The uniqExact function uses more memory than the uniq function, because the size of the state has unbounded growth as the number of different values increases.", - "title": "uniqExact(x)" - }, - { - "location": "/index.html#grouparrayx-grouparraymax_sizex", - "text": "Creates an array of argument values.\nValues can be added to the array in any (indeterminate) order. The second version (with the max_size parameter) limits the size of the resulting array to max_size elements.\nFor example, groupArray (1) (x) is equivalent to [any (x)] . In some cases, you can still rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY .", - "title": "groupArray(x), groupArray(max_size)(x)" - }, - { - "location": "/index.html#grouparrayinsertatx", - "text": "Inserts a value into the array in the specified position. Accepts the value and position as input. If several values \u200b\u200bare inserted into the same position, any of them might end up in the resulting array (the first one will be used in the case of single-threaded execution). If no value is inserted into a position, the position is assigned the default value. Optional parameters: The default value for substituting in empty positions. The length of the resulting array. This allows you to receive arrays of the same size for all the aggregate keys. When using this parameter, the default value must be specified.", - "title": "groupArrayInsertAt(x)" - }, - { - "location": "/index.html#groupuniqarrayx", - "text": "Creates an array from different argument values. Memory consumption is the same as for the uniqExact function.", - "title": "groupUniqArray(x)" - }, - { - "location": "/index.html#quantilelevelx", - "text": "Approximates the 'level' quantile. 'level' is a constant, a floating-point number from 0 to 1.\nWe recommend using a 'level' value in the range of 0.01..0.99\nDon't use a 'level' value equal to 0 or 1 \u2013 use the 'min' and 'max' functions for these cases. In this function, as well as in all functions for calculating quantiles, the 'level' parameter can be omitted. In this case, it is assumed to be equal to 0.5 (in other words, the function will calculate the median). Works for numbers, dates, and dates with times.\nReturns: for numbers \u2013 Float64; for dates \u2013 a date; for dates with times \u2013 a date with time. Uses reservoir sampling with a reservoir size up to 8192.\nIf necessary, the result is output with linear approximation from the two neighboring values.\nThis algorithm provides very low accuracy. See also: quantileTiming , quantileTDigest , quantileExact . The result depends on the order of running the query, and is nondeterministic. When using multiple quantile (and similar) functions with different levels in a query, the internal states are not combined (that is, the query works less efficiently than it could). In this case, use the quantiles (and similar) functions.", - "title": "quantile(level)(x)" - }, - { - "location": "/index.html#quantiledeterministiclevelx-determinator", - "text": "Works the same way as the quantile function, but the result is deterministic and does not depend on the order of query execution. To achieve this, the function takes a second argument \u2013 the \"determinator\". This is a number whose hash is used instead of a random number generator in the reservoir sampling algorithm. For the function to work correctly, the same determinator value should not occur too often. For the determinator, you can use an event ID, user ID, and so on. Don't use this function for calculating timings. There is a more suitable function for this purpose: quantileTiming .", - "title": "quantileDeterministic(level)(x, determinator)" - }, - { - "location": "/index.html#quantiletiminglevelx", - "text": "Computes the quantile of 'level' with a fixed precision.\nWorks for numbers. Intended for calculating quantiles of page loading time in milliseconds. If the value is greater than 30,000 (a page loading time of more than 30 seconds), the result is equated to 30,000. If the total value is not more than about 5670, then the calculation is accurate. Otherwise: if the time is less than 1024 ms, then the calculation is accurate. otherwise the calculation is rounded to a multiple of 16 ms. When passing negative values to the function, the behavior is undefined. The returned value has the Float32 type. If no values were passed to the function (when using quantileTimingIf ), 'nan' is returned. The purpose of this is to differentiate these instances from zeros. See the note on sorting NaNs in \"ORDER BY clause\". The result is determinate (it doesn't depend on the order of query processing). For its purpose (calculating quantiles of page loading times), using this function is more effective and the result is more accurate than for the quantile function.", - "title": "quantileTiming(level)(x)" - }, - { - "location": "/index.html#quantiletimingweightedlevelx-weight", - "text": "Differs from the quantileTiming function in that it has a second argument, \"weights\". Weight is a non-negative integer.\nThe result is calculated as if the x value were passed weight number of times to the quantileTiming function.", - "title": "quantileTimingWeighted(level)(x, weight)" - }, - { - "location": "/index.html#quantileexactlevelx", - "text": "Computes the quantile of 'level' exactly. To do this, all the passed values \u200b\u200bare combined into an array, which is then partially sorted. Therefore, the function consumes O(n) memory, where 'n' is the number of values that were passed. However, for a small number of values, the function is very effective.", - "title": "quantileExact(level)(x)" - }, - { - "location": "/index.html#quantileexactweightedlevelx-weight", - "text": "Computes the quantile of 'level' exactly. In addition, each value is counted with its weight, as if it is present 'weight' times. The arguments of the function can be considered as histograms, where the value 'x' corresponds to a histogram \"column\" of the height 'weight', and the function itself can be considered as a summation of histograms. A hash table is used as the algorithm. Because of this, if the passed values \u200b\u200bare frequently repeated, the function consumes less RAM than quantileExact . You can use this function instead of quantileExact and specify the weight as 1.", - "title": "quantileExactWeighted(level)(x, weight)" - }, - { - "location": "/index.html#quantiletdigestlevelx", - "text": "Approximates the quantile level using the t-digest algorithm. The maximum error is 1%. Memory consumption by State is proportional to the logarithm of the number of passed values. The performance of the function is lower than for quantile , quantileTiming . In terms of the ratio of State size to precision, this function is much better than quantile . The result depends on the order of running the query, and is nondeterministic.", - "title": "quantileTDigest(level)(x)" - }, - { - "location": "/index.html#medianx", - "text": "All the quantile functions have corresponding median functions: median , medianDeterministic , medianTiming , medianTimingWeighted , medianExact , medianExactWeighted , medianTDigest . They are synonyms and their behavior is identical.", - "title": "median(x)" - }, - { - "location": "/index.html#quantileslevel1-level2-x", - "text": "All the quantile functions also have corresponding quantiles functions: quantiles , quantilesDeterministic , quantilesTiming , quantilesTimingWeighted , quantilesExact , quantilesExactWeighted , quantilesTDigest . These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.", - "title": "quantiles(level1, level2, ...)(x)" - }, - { - "location": "/index.html#varsampx", - "text": "Calculates the amount \u03a3((x - x\u0305)^2) / (n - 1) , where n is the sample size and x\u0305 is the average value of x . It represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount. Returns Float64 . When n = 1 , returns +\u221e .", - "title": "varSamp(x)" - }, - { - "location": "/index.html#varpopx", - "text": "Calculates the amount \u03a3((x - x\u0305)^2) / (n - 1) , where n is the sample size and x\u0305 is the average value of x . In other words, dispersion for a set of values. Returns Float64 .", - "title": "varPop(x)" - }, - { - "location": "/index.html#stddevsampx", - "text": "The result is equal to the square root of varSamp(x) .", - "title": "stddevSamp(x)" - }, - { - "location": "/index.html#stddevpopx", - "text": "The result is equal to the square root of varPop(x) .", - "title": "stddevPop(x)" - }, - { - "location": "/index.html#topkncolumn", - "text": "Returns an array of the most frequent values in the specified column. The resulting array is sorted in descending order of frequency of values (not by the values themselves). Implements the Filtered Space-Saving algorithm for analyzing TopK, based on the reduce-and-combine algorithm from Parallel Space Saving . topK(N)(column) This function doesn't provide a guaranteed result. In certain situations, errors might occur and it might return frequent values that aren't the most frequent values. We recommend using the N 10 value; performance is reduced with large N values. Maximum value of N = 65536 . Arguments \n- 'N' is the number of values.\n- ' x ' \u2013 The column. Example Take the OnTime data set and select the three most frequently occurring values in the AirlineID column. SELECT topK ( 3 )( AirlineID ) AS res FROM ontime \u250c\u2500res\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 [19393,19790,19805] \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518", - "title": "topK(N)(column)" - }, - { - "location": "/index.html#covarsampx-y", - "text": "Calculates the value of \u03a3((x - x\u0305)(y - y\u0305)) / (n - 1) . Returns Float64. When n = 1 , returns +\u221e.", - "title": "covarSamp(x, y)" - }, - { - "location": "/index.html#covarpopx-y", - "text": "Calculates the value of \u03a3((x - x\u0305)(y - y\u0305)) / n .", - "title": "covarPop(x, y)" - }, - { - "location": "/index.html#corrx-y", - "text": "Calculates the Pearson correlation coefficient: \u03a3((x - x\u0305)(y - y\u0305)) / sqrt(\u03a3((x - x\u0305)^2) * \u03a3((y - y\u0305)^2)) .", - "title": "corr(x, y)" - }, - { - "location": "/index.html#aggregate-function-combinators", - "text": "The name of an aggregate function can have a suffix appended to it. This changes the way the aggregate function works.", - "title": "Aggregate function combinators" - }, - { - "location": "/index.html#-if", - "text": "The suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument \u2013 a condition (Uint8 type). The aggregate function processes only the rows that trigger the condition. If the condition was not triggered even once, it returns a default value (usually zeros or empty strings). Examples: sumIf(column, cond) , countIf(cond) , avgIf(x, cond) , quantilesTimingIf(level1, level2)(x, cond) , argMinIf(arg, val, cond) and so on. With conditional aggregate functions, you can calculate aggregates for several conditions at once, without using subqueries and JOIN s. For example, in Yandex.Metrica, conditional aggregate functions are used to implement the segment comparison functionality.", - "title": "-If" - }, - { - "location": "/index.html#-array", - "text": "The -Array suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the 'Array(T)' type (arrays) instead of 'T' type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. When processing arrays, the aggregate function works like the original aggregate function across all array elements. Example 1: sumArray(arr) - Totals all the elements of all 'arr' arrays. In this example, it could have been written more simply: sum(arraySum(arr)) . Example 2: uniqArray(arr) \u2013 Count the number of unique elements in all 'arr' arrays. This could be done an easier way: uniq(arrayJoin(arr)) , but it's not always possible to add 'arrayJoin' to a query. -If and -Array can be combined. However, 'Array' must come first, then 'If'. Examples: uniqArrayIf(arr, cond) , quantilesTimingArrayIf(level1, level2)(arr, cond) . Due to this order, the 'cond' argument can't be an array.", - "title": "-Array" - }, - { - "location": "/index.html#-state", - "text": "If you apply this combinator, the aggregate function doesn't return the resulting value (such as the number of unique values for the 'uniq' function), but an intermediate state of the aggregation (for uniq , this is the hash table for calculating the number of unique values). This is an AggregateFunction(...) that can be used for further processing or stored in a table to finish aggregating later. See the sections \"AggregatingMergeTree\" and \"Functions for working with intermediate aggregation states\".", - "title": "-State" - }, - { - "location": "/index.html#-merge", - "text": "If you apply this combinator, the aggregate function takes the intermediate aggregation state as an argument, combines the states to finish aggregation, and returns the resulting value.", - "title": "-Merge" - }, - { - "location": "/index.html#-mergestate", - "text": "Merges the intermediate aggregation states in the same way as the -Merge combinator. However, it doesn't return the resulting value, but an intermediate aggregation state, similar to the -State combinator.", - "title": "-MergeState." - }, - { - "location": "/index.html#-foreach", - "text": "Converts an aggregate function for tables into an aggregate function for arrays that aggregates the corresponding array items and returns an array of results. For example, sumForEach for the arrays [1, 2] , [3, 4, 5] and [6, 7] returns the result [10, 13, 5] after adding together the corresponding array items.", - "title": "-ForEach" - }, - { - "location": "/index.html#parametric-aggregate-functions", - "text": "Some aggregate functions can accept not only argument columns (used for compression), but a set of parameters \u2013 constants for initialization. The syntax is two pairs of brackets instead of one. The first is for parameters, and the second is for arguments.", - "title": "Parametric aggregate functions" - }, - { - "location": "/index.html#sequencematchpatterntime-cond1-cond2", - "text": "Pattern matching for event chains. pattern is a string containing a pattern to match. The pattern is similar to a regular expression. time is the time of the event with the DateTime type. cond1 , cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event. The function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern.\nIt returns UInt8: 0 if the pattern isn't matched, or 1 if it matches. Example: sequenceMatch ('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%') whether there was a chain of events in which a pageview with 'company' in the address occurred earlier than a pageview with 'cart' in the address. This is a singular example. You could write it using other aggregate functions: minIf(EventTime, URL LIKE %company% ) maxIf(EventTime, URL LIKE %cart% ). However, there is no such solution for more complex situations. Pattern syntax: (?1) refers to the condition (any number can be used in place of 1). .* is any number of any events. (?t =1800) is a time condition. Any quantity of any type of events is allowed over the specified time. Instead of = , the following operators can be used: , , = . Any number may be specified in place of 1800. Events that occur during the same second can be put in the chain in any order. This may affect the result of the function.", - "title": "sequenceMatch(pattern)(time, cond1, cond2, ...)" - }, - { - "location": "/index.html#sequencecountpatterntime-cond1-cond2", - "text": "Works the same way as the sequenceMatch function, but instead of returning whether there is an event chain, it returns UInt64 with the number of event chains found.\nChains are searched for without overlapping. In other words, the next chain can start only after the end of the previous one.", - "title": "sequenceCount(pattern)(time, cond1, cond2, ...)" - }, - { - "location": "/index.html#windowfunnelwindowtimestamp-cond1-cond2-cond3", - "text": "Window funnel matching for event chains, calculates the max event level in a sliding window. window is the timestamp window value, such as 3600. timestamp is the time of the event with the DateTime type or UInt32 type. cond1 , cond2 ... is from one to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event Example: Consider you are doing a website analytics, intend to find out the user counts clicked login button( event = 1001 ), then the user counts followed by searched the phones( event = 1003 and product = 'phone' ) , then the user counts followed by made an order ( event = 1009 ). And all event chains must be in a 3600 seconds sliding window. This could be easily calculate by windowFunnel SELECT\n level,\n count() AS c\nFROM\n(\n SELECT\n user_id,\n windowFunnel(3600)(timestamp, event_id = 1001, event_id = 1003 AND product = phone , event_id = 1009) AS level\n FROM trend_event\n WHERE (event_date = 2017-01-01 ) AND (event_date = 2017-01-31 )\n GROUP BY user_id\n)\nGROUP BY level\nORDER BY level Simply, the level could only be 0,1,2,3, it means the maxium event action stage that one user could reach.", - "title": "windowFunnel(window)(timestamp, cond1, cond2, cond3, ....)" - }, - { - "location": "/index.html#uniquptonx", - "text": "Calculates the number of different argument values \u200b\u200bif it is less than or equal to N. If the number of different argument values is greater than N, it returns N + 1. Recommended for use with small Ns, up to 10. The maximum value of N is 100. For the state of an aggregate function, it uses the amount of memory equal to 1 + N * the size of one value of bytes.\nFor strings, it stores a non-cryptographic hash of 8 bytes. That is, the calculation is approximated for strings. The function also works for several arguments. It works as fast as possible, except for cases when a large N value is used and the number of unique values is slightly less than N. Usage example: Problem: Generate a report that shows only keywords that produced at least 5 unique users.\nSolution: Write in the GROUP BY query SearchPhrase HAVING uniqUpTo(4)(UserID) = 5", - "title": "uniqUpTo(N)(x)" - }, - { - "location": "/index.html#dictionaries", - "text": "A dictionary is a mapping (key - attributes) that can be used in a query as functions.\nYou can think of this as a more convenient and efficient type of JOIN with dimension tables. There are built-in (internal) and add-on (external) dictionaries.", - "title": "Dictionaries" - }, - { - "location": "/index.html#external-dictionaries", - "text": "You can add your own dictionaries from various data sources. The data source for a dictionary can be a local text or executable file, an HTTP(s) resource, or another DBMS. For more information, see \" Sources for external dictionaries \". ClickHouse: Fully or partially stores dictionaries in RAM. Periodically updates dictionaries and dynamically loads missing values. In other words, dictionaries can be loaded dynamically. The configuration of external dictionaries is located in one or more files. The path to the configuration is specified in the dictionaries_config parameter. Dictionaries can be loaded at server startup or at first use, depending on the dictionaries_lazy_load setting. The dictionary config file has the following format: yandex \n comment An optional element with any content. Ignored by the ClickHouse server. /comment \n\n !--Optional element. File name with substitutions-- \n include_from /etc/metrika.xml /include_from \n\n\n dictionary \n !-- Dictionary configuration -- \n /dictionary \n\n ...\n\n dictionary \n !-- Dictionary configuration -- \n /dictionary /yandex You can configure any number of dictionaries in the same file. The file format is preserved even if there is only one dictionary (i.e. yandex dictionary !--configuration - /dictionary /yandex ). See also \" Functions for working with external dictionaries \". \n\nYou can convert values \u200b\u200bfor a small dictionary by describing it in a `SELECT` query (see the [transform](#other_functions-transform) function). This functionality is not related to external dictionaries.", - "title": "External dictionaries" - }, - { - "location": "/index.html#configuring-an-external-dictionary", - "text": "The dictionary configuration has the following structure: dictionary \n name dict_name /name \n\n source \n !-- Source configuration -- \n /source \n\n layout \n !-- Memory layout configuration -- \n /layout \n\n structure \n !-- Complex key configuration -- \n /structure \n\n lifetime \n !-- Lifetime of dictionary in memory -- \n /lifetime /dictionary name \u2013 The identifier that can be used to access the dictionary. Use the characters [a-zA-Z0-9_\\-] . source \u2014 Source of the dictionary. layout \u2014 Dictionary layout in memory. structure \u2014 Structure of the dictionary . A key and attributes that can be retrieved by this key. lifetime \u2014 Frequency of dictionary updates.", - "title": "Configuring an external dictionary" - }, - { - "location": "/index.html#storing-dictionaries-in-memory", - "text": "There are a variety of ways to store dictionaries in memory. We recommend flat , hashed and complex_key_hashed . which provide optimal processing speed. Caching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section \" cache \". There are several ways to improve dictionary performance: Call the function for working with the dictionary after GROUP BY . Mark attributes to extract as injective. An attribute is called injective if different attribute values correspond to different keys. So when GROUP BY uses a function that fetches an attribute value by the key, this function is automatically taken out of GROUP BY . ClickHouse generates an exception for errors with dictionaries. Examples of errors: The dictionary being accessed could not be loaded. Error querying a cached dictionary. You can view the list of external dictionaries and their statuses in the system.dictionaries table. The configuration looks like this: yandex \n dictionary \n ...\n layout \n layout_type \n !-- layout settings -- \n /layout_type \n /layout \n ...\n /dictionary /yandex", - "title": "Storing dictionaries in memory" - }, - { - "location": "/index.html#ways-to-store-dictionaries-in-memory", - "text": "flat hashed cache range_hashed complex_key_hashed complex_key_cache ip_trie", - "title": "Ways to store dictionaries in memory" - }, - { - "location": "/index.html#flat", - "text": "The dictionary is completely stored in memory in the form of flat arrays. How much memory does the dictionary use? The amount is proportional to the size of the largest key (in space used). The dictionary key has the UInt64 type and the value is limited to 500,000. If a larger key is discovered when creating the dictionary, ClickHouse throws an exception and does not create the dictionary. All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety. This method provides the best performance among all available methods of storing the dictionary. Configuration example: layout \n flat / /layout", - "title": "flat" - }, - { - "location": "/index.html#hashed", - "text": "The dictionary is completely stored in memory in the form of a hash table. The dictionary can contain any number of elements with any identifiers In practice, the number of keys can reach tens of millions of items. All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety. Configuration example: layout \n hashed / /layout", - "title": "hashed" - }, - { - "location": "/index.html#complex_key_hashed", - "text": "This type of storage is for use with composite keys . Similar to hashed . Configuration example: layout \n complex_key_hashed / /layout", - "title": "complex_key_hashed" - }, - { - "location": "/index.html#range_hashed", - "text": "The dictionary is stored in memory in the form of a hash table with an ordered array of ranges and their corresponding values. This storage method works the same way as hashed and allows using date/time ranges in addition to the key, if they appear in the dictionary. Example: The table contains discounts for each advertiser in the format: +---------------+---------------------+-------------------+--------+\n| advertiser id | discount start date | discount end date | amount |\n+===============+=====================+===================+========+\n| 123 | 2015-01-01 | 2015-01-15 | 0.15 |\n+---------------+---------------------+-------------------+--------+\n| 123 | 2015-01-16 | 2015-01-31 | 0.25 |\n+---------------+---------------------+-------------------+--------+\n| 456 | 2015-01-01 | 2015-01-15 | 0.05 |\n+---------------+---------------------+-------------------+--------+ To use a sample for date ranges, define the range_min and range_max elements in the structure . Example: structure \n id \n name Id /name \n /id \n range_min \n name first /name \n /range_min \n range_max \n name last /name \n /range_max \n ... To work with these dictionaries, you need to pass an additional date argument to the dictGetT function: dictGetT( dict_name , attr_name , id, date) This function returns the value for the specified id s and the date range that includes the passed date. Details of the algorithm: If the id is not found or a range is not found for the id , it returns the default value for the dictionary. If there are overlapping ranges, you can use any. If the range delimiter is NULL or an invalid date (such as 1900-01-01 or 2039-01-01), the range is left open. The range can be open on both sides. Configuration example: yandex \n dictionary \n\n ...\n\n layout \n range_hashed / \n /layout \n\n structure \n id \n name Abcdef /name \n /id \n range_min \n name StartDate /name \n /range_min \n range_max \n name EndDate /name \n /range_max \n attribute \n name XXXType /name \n type String /type \n null_value / \n /attribute \n /structure \n\n /dictionary /yandex", - "title": "range_hashed" - }, - { - "location": "/index.html#cache", - "text": "The dictionary is stored in a cache that has a fixed number of cells. These cells contain frequently used elements. When searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using SELECT attrs... FROM db.table WHERE id IN (k1, k2, ...) . The received data is then written to the cache. For cache dictionaries, the expiration lifetime of data in the cache can be set. If more time than lifetime has passed since loading the data in a cell, the cell's value is not used, and it is re-requested the next time it needs to be used. This is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the system.dictionaries table. To improve cache performance, use a subquery with LIMIT , and call the function with the dictionary externally. Supported sources : MySQL, ClickHouse, executable, HTTP. Example of settings: layout \n cache \n !-- The size of the cache, in number of cells. Rounded up to a power of two. -- \n size_in_cells 1000000000 /size_in_cells \n /cache /layout Set a large enough cache size. You need to experiment to select the number of cells: Set some value. Run queries until the cache is completely full. Assess memory consumption using the system.dictionaries table. Increase or decrease the number of cells until the required memory consumption is reached. \n\nDo not use ClickHouse as a source, because it is slow to process queries with random reads.", - "title": "cache" - }, - { - "location": "/index.html#complex_key_cache", - "text": "This type of storage is for use with composite keys . Similar to cache .", - "title": "complex_key_cache" - }, - { - "location": "/index.html#ip_trie", - "text": "This type of storage is for mapping network prefixes (IP addresses) to metadata such as ASN. Example: The table contains network prefixes and their corresponding AS number and country code: +-----------------+-------+--------+\n | prefix | asn | cca2 |\n +=================+=======+========+\n | 202.79.32.0/20 | 17501 | NP |\n +-----------------+-------+--------+\n | 2620:0:870::/48 | 3856 | US |\n +-----------------+-------+--------+\n | 2a02:6b8:1::/48 | 13238 | RU |\n +-----------------+-------+--------+\n | 2001:db8::/32 | 65536 | ZZ |\n +-----------------+-------+--------+ When using this type of layout, the structure must have a composite key. Example: structure \n key \n attribute \n name prefix /name \n type String /type \n /attribute \n /key \n attribute \n name asn /name \n type UInt32 /type \n null_value / \n /attribute \n attribute \n name cca2 /name \n type String /type \n null_value ?? /null_value \n /attribute \n ... The key must have only one String type attribute that contains an allowed IP prefix. Other types are not supported yet. For queries, you must use the same functions ( dictGetT with a tuple) as for dictionaries with composite keys: dictGetT( dict_name , attr_name , tuple(ip)) The function takes either UInt32 for IPv4, or FixedString(16) for IPv6: dictGetString( prefix , asn , tuple(IPv6StringToNum( 2001:db8::1 ))) Other types are not supported yet. The function returns the attribute for the prefix that corresponds to this IP address. If there are overlapping prefixes, the most specific one is returned. Data is stored in a trie . It must completely fit into RAM.", - "title": "ip_trie" - }, - { - "location": "/index.html#dictionary-updates", - "text": "ClickHouse periodically updates the dictionaries. The update interval for fully downloaded dictionaries and the invalidation interval for cached dictionaries are defined in the lifetime tag in seconds. Dictionary updates (other than loading for first use) do not block queries. During updates, the old version of a dictionary is used. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries. Example of settings: dictionary \n ...\n lifetime 300 /lifetime \n ... /dictionary Setting lifetime 0 /lifetime prevents updating dictionaries. You can set a time interval for upgrades, and ClickHouse will choose a uniformly random time within this range. This is necessary in order to distribute the load on the dictionary source when upgrading on a large number of servers. Example of settings: dictionary \n ...\n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n ... /dictionary When upgrading the dictionaries, the ClickHouse server applies different logic depending on the type of source : For a text file, it checks the time of modification. If the time differs from the previously recorded time, the dictionary is updated. For MyISAM tables, the time of modification is checked using a SHOW TABLE STATUS query. Dictionaries from other sources are updated every time by default. For MySQL (InnoDB) and ODBC sources, you can set up a query that will update the dictionaries only if they really changed, rather than each time. To do this, follow these steps: The dictionary table must have a field that always changes when the source data is updated. The settings of the source must specify a query that retrieves the changing field. The ClickHouse server interprets the query result as a row, and if this row has changed relative to its previous state, the dictionary is updated. Specify the query in the invalidate_query field in the settings for the source . Example of settings: dictionary \n ...\n odbc \n ...\n invalidate_query SELECT update_time FROM dictionary_source where id = 1 /invalidate_query \n /odbc \n ... /dictionary", - "title": "Dictionary updates" - }, - { - "location": "/index.html#sources-of-external-dictionaries", - "text": "An external dictionary can be connected from many different sources. The configuration looks like this: yandex \n dictionary \n ...\n source \n source_type \n !-- Source configuration -- \n /source_type \n /source \n ...\n /dictionary \n ... /yandex The source is configured in the source section. Types of sources ( source_type ): Local file Executable file HTTP(s) ODBC DBMS MySQL ClickHouse MongoDB", - "title": "Sources of external dictionaries" - }, - { - "location": "/index.html#local-file", - "text": "Example of settings: source \n file \n path /opt/dictionaries/os.tsv /path \n format TabSeparated /format \n /file /source Setting fields: path \u2013 The absolute path to the file. format \u2013 The file format. All the formats described in \" Formats \" are supported.", - "title": "Local file" - }, - { - "location": "/index.html#executable-file", - "text": "Working with executable files depends on how the dictionary is stored in memory . If the dictionary is stored using cache and complex_key_cache , ClickHouse requests the necessary keys by sending a request to the executable file's STDIN . Example of settings: source \n executable \n command cat /opt/dictionaries/os.tsv /command \n format TabSeparated /format \n /executable /source Setting fields: command \u2013 The absolute path to the executable file, or the file name (if the program directory is written to PATH ). format \u2013 The file format. All the formats described in \" Formats \" are supported.", - "title": "Executable file" - }, - { - "location": "/index.html#https", - "text": "Working with an HTTP(s) server depends on how the dictionary is stored in memory . If the dictionary is stored using cache and complex_key_cache , ClickHouse requests the necessary keys by sending a request via the POST method. Example of settings: source \n http \n url http://[::1]/os.tsv /url \n format TabSeparated /format \n /http /source In order for ClickHouse to access an HTTPS resource, you must configure openSSL in the server configuration. Setting fields: url \u2013 The source URL. format \u2013 The file format. All the formats described in \" Formats \" are supported.", - "title": "HTTP(s)" - }, - { - "location": "/index.html#odbc", - "text": "You can use this method to connect any database that has an ODBC driver. Example of settings: odbc \n db DatabaseName /db \n table TableName /table \n connection_string DSN=some_parameters /connection_string \n invalidate_query SQL_QUERY /invalidate_query /odbc Setting fields: db \u2013 Name of the database. Omit it if the database name is set in the connection_string parameters. table \u2013 Name of the table. connection_string \u2013 Connection string. invalidate_query \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries .", - "title": "ODBC" - }, - { - "location": "/index.html#example-of-connecting-postgresql", - "text": "Ubuntu OS. Installing unixODBC and the ODBC driver for PostgreSQL: sudo apt-get install -y unixodbc odbcinst odbc-postgresql Configuring /etc/odbc.ini (or ~/.odbc.ini ): [DEFAULT]\n Driver = myconnection\n\n [myconnection]\n Description = PostgreSQL connection to my_db\n Driver = PostgreSQL Unicode\n Database = my_db\n Servername = 127.0.0.1\n UserName = username\n Password = password\n Port = 5432\n Protocol = 9.3\n ReadOnly = No\n RowVersioning = No\n ShowSystemTables = No\n ConnSettings = The dictionary configuration in ClickHouse: dictionary \n name table_name /name \n source \n odbc \n !-- You can specifiy the following parameters in connection_string: -- \n !-- DSN=myconnection;UID=username;PWD=password;HOST=127.0.0.1;PORT=5432;DATABASE=my_db -- \n connection_string DSN=myconnection /connection_string \n table postgresql_table /table \n /odbc \n /source \n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n layout \n hashed/ \n /layout \n structure \n id \n name id /name \n /id \n attribute \n name some_column /name \n type UInt64 /type \n null_value 0 /null_value \n /attribute \n /structure /dictionary You may need to edit odbc.ini to specify the full path to the library with the driver DRIVER=/usr/local/lib/psqlodbcw.so .", - "title": "Example of connecting PostgreSQL" - }, - { - "location": "/index.html#example-of-connecting-ms-sql-server", - "text": "Ubuntu OS. Installing the driver: : sudo apt-get install tdsodbc freetds-bin sqsh Configuring the driver: : $ cat /etc/freetds/freetds.conf \n ...\n\n [MSSQL]\n host = 192.168.56.101\n port = 1433\n tds version = 7.0\n client charset = UTF-8\n\n $ cat /etc/odbcinst.ini \n ...\n\n [FreeTDS]\n Description = FreeTDS\n Driver = /usr/lib/x86_64-linux-gnu/odbc/libtdsodbc.so\n Setup = /usr/lib/x86_64-linux-gnu/odbc/libtdsS.so\n FileUsage = 1\n UsageCount = 5\n\n $ cat ~/.odbc.ini \n ...\n\n [MSSQL]\n Description = FreeTDS\n Driver = FreeTDS\n Servername = MSSQL\n Database = test\n UID = test\n PWD = test\n Port = 1433 Configuring the dictionary in ClickHouse: yandex \n dictionary \n name test /name \n source \n odbc \n table dict /table \n connection_string DSN=MSSQL;UID=test;PWD=test /connection_string \n /odbc \n /source \n\n lifetime \n min 300 /min \n max 360 /max \n /lifetime \n\n layout \n flat / \n /layout \n\n structure \n id \n name k /name \n /id \n attribute \n name s /name \n type String /type \n null_value /null_value \n /attribute \n /structure \n /dictionary /yandex", - "title": "Example of connecting MS SQL Server" - }, - { - "location": "/index.html#dbms", - "text": "", - "title": "DBMS" - }, - { - "location": "/index.html#mysql_1", - "text": "Example of settings: source \n mysql \n port 3306 /port \n user clickhouse /user \n password qwerty /password \n replica \n host example01-1 /host \n priority 1 /priority \n /replica \n replica \n host example01-2 /host \n priority 1 /priority \n /replica \n db db_name /db \n table table_name /table \n where id=10 /where \n invalidate_query SQL_QUERY /invalidate_query \n /mysql /source Setting fields: port \u2013 The port on the MySQL server. You can specify it for all replicas, or for each one individually (inside replica ). user \u2013 Name of the MySQL user. You can specify it for all replicas, or for each one individually (inside replica ). password \u2013 Password of the MySQL user. You can specify it for all replicas, or for each one individually (inside replica ). replica \u2013 Section of replica configurations. There can be multiple sections. replica/host \u2013 The MySQL host. * replica/priority \u2013 The replica priority. When attempting to connect, ClickHouse traverses the replicas in order of priority. The lower the number, the higher the priority. db \u2013 Name of the database. table \u2013 Name of the table. where \u2013 The selection criteria. Optional parameter. invalidate_query \u2013 Query for checking the dictionary status. Optional parameter. Read more in the section Updating dictionaries . MySQL can be connected on a local host via sockets. To do this, set host and socket . Example of settings: source \n mysql \n host localhost /host \n socket /path/to/socket/file.sock /socket \n user clickhouse /user \n password qwerty /password \n db db_name /db \n table table_name /table \n where id=10 /where \n invalidate_query SQL_QUERY /invalidate_query \n /mysql /source", - "title": "MySQL" - }, - { - "location": "/index.html#clickhouse", - "text": "Example of settings: source \n clickhouse \n host example01-01-1 /host \n port 9000 /port \n user default /user \n password /password \n db default /db \n table ids /table \n where id=10 /where \n /clickhouse /source Setting fields: host \u2013 The ClickHouse host. If it is a local host, the query is processed without any network activity. To improve fault tolerance, you can create a Distributed table and enter it in subsequent configurations. port \u2013 The port on the ClickHouse server. user \u2013 Name of the ClickHouse user. password \u2013 Password of the ClickHouse user. db \u2013 Name of the database. table \u2013 Name of the table. where \u2013 The selection criteria. May be omitted.", - "title": "ClickHouse" - }, - { - "location": "/index.html#mongodb", - "text": "Example of settings: source \n mongodb \n host localhost /host \n port 27017 /port \n user /user \n password /password \n db test /db \n collection dictionary_source /collection \n /mongodb /source Setting fields: host \u2013 The MongoDB host. port \u2013 The port on the MongoDB server. user \u2013 Name of the MongoDB user. password \u2013 Password of the MongoDB user. db \u2013 Name of the database. collection \u2013 Name of the collection.", - "title": "MongoDB" - }, - { - "location": "/index.html#dictionary-key-and-fields", - "text": "The structure clause describes the dictionary key and fields available for queries. Overall structure: dictionary \n structure \n id \n name Id /name \n /id \n\n attribute \n !-- Attribute parameters -- \n /attribute \n\n ...\n\n /structure /dictionary Columns are described in the structure: id - key column . attribute - data column . There can be a large number of columns.", - "title": "Dictionary key and fields" - }, - { - "location": "/index.html#key", - "text": "ClickHouse supports the following types of keys: Numeric key. UInt64. Defined in the tag id . Composite key. Set of values of different types. Defined in the tag key . A structure can contain either id or key . \n\nThe key doesn't need to be defined separately in attributes.", - "title": "Key" - }, - { - "location": "/index.html#numeric-key", - "text": "Format: UInt64 . Configuration example: id \n name Id /name /id Configuration fields: name \u2013 The name of the column with keys.", - "title": "Numeric key" - }, - { - "location": "/index.html#composite-key", - "text": "The key can be a tuple from any types of fields. The layout in this case must be complex_key_hashed or complex_key_cache . \nA composite key can consist of a single element. This makes it possible to use a string as the key, for instance. The key structure is set in the element key . Key fields are specified in the same format as the dictionary attributes . Example: structure \n key \n attribute \n name field1 /name \n type String /type \n /attribute \n attribute \n name field2 /name \n type UInt32 /type \n /attribute \n ...\n /key \n... For a query to the dictGet* function, a tuple is passed as the key. Example: dictGetString('dict_name', 'attr_name', tuple('string for field1', num_for_field2)) .", - "title": "Composite key" - }, - { - "location": "/index.html#attributes", - "text": "Configuration example: structure \n ...\n attribute \n name Name /name \n type Type /type \n null_value /null_value \n expression rand64() /expression \n hierarchical true /hierarchical \n injective true /injective \n is_object_id true /is_object_id \n /attribute /structure Configuration fields: name \u2013 The column name. type \u2013 The column type. Sets the method for interpreting data in the source. For example, for MySQL, the field might be TEXT , VARCHAR , or BLOB in the source table, but it can be uploaded as String . null_value \u2013 The default value for a non-existing element. In the example, it is an empty string. expression \u2013 The attribute can be an expression. The tag is not required. hierarchical \u2013 Hierarchical support. Mirrored to the parent identifier. By default, false . injective \u2013 Whether the id - attribute image is injective. If true , then you can optimize the GROUP BY clause. By default, false . is_object_id \u2013 Whether the query is executed for a MongoDB document by ObjectID .", - "title": "Attributes" - }, - { - "location": "/index.html#internal-dictionaries", - "text": "ClickHouse contains a built-in feature for working with a geobase. This allows you to: Use a region's ID to get its name in the desired language. Use a region's ID to get the ID of a city, area, federal district, country, or continent. Check whether a region is part of another region. Get a chain of parent regions. All the functions support \"translocality,\" the ability to simultaneously use different perspectives on region ownership. For more information, see the section \"Functions for working with Yandex.Metrica dictionaries\". The internal dictionaries are disabled in the default package.\nTo enable them, uncomment the parameters path_to_regions_hierarchy_file and path_to_regions_names_files in the server configuration file. The geobase is loaded from text files.\nIf you work at Yandex, you can follow these instructions to create them: https://github.yandex-team.ru/raw/Metrika/ClickHouse_private/master/doc/create_embedded_geobase_dictionaries.txt Put the regions_hierarchy*.txt files in the path_to_regions_hierarchy_file directory. This configuration parameter must contain the path to the regions_hierarchy.txt file (the default regional hierarchy), and the other files (regions_hierarchy_ua.txt) must be located in the same directory. Put the regions_names_*.txt files in the path_to_regions_names_files directory. You can also create these files yourself. The file format is as follows: regions_hierarchy*.txt : TabSeparated (no header), columns: Region ID (UInt32) Parent region ID (UInt32) Region type (UInt8): 1 - continent, 3 - country, 4 - federal district, 5 - region, 6 - city; other types don't have values. Population (UInt32) - Optional column. regions_names_*.txt : TabSeparated (no header), columns: Region ID (UInt32) Region name (String) - Can't contain tabs or line feeds, even escaped ones. A flat array is used for storing in RAM. For this reason, IDs shouldn't be more than a million. Dictionaries can be updated without restarting the server. However, the set of available dictionaries is not updated.\nFor updates, the file modification times are checked. If a file has changed, the dictionary is updated.\nThe interval to check for changes is configured in the 'builtin_dictionaries_reload_interval' parameter.\nDictionary updates (other than loading at first use) do not block queries. During updates, queries use the old versions of dictionaries. If an error occurs during an update, the error is written to the server log, and queries continue using the old version of dictionaries. We recommend periodically updating the dictionaries with the geobase. During an update, generate new files and write them to a separate location. When everything is ready, rename them to the files used by the server. There are also functions for working with OS identifiers and Yandex.Metrica search engines, but they shouldn't be used.", - "title": "Internal dictionaries" - }, - { - "location": "/index.html#usage_1", - "text": "", - "title": "Usage" - }, - { - "location": "/index.html#access-rights", - "text": "Users and access rights are set up in the user config. This is usually users.xml . Users are recorded in the users section. Here is a fragment of the users.xml file: !-- Users and ACL. -- users \n !-- If the user name is not specified, the default user is used. -- \n default \n !-- Password could be specified in plaintext or in SHA256 (in hex format). If you want to specify the password in plain text (not recommended), place it in the password element. Example: password qwerty /password . Password can be empty. If you want to specify SHA256, place it in the password_sha256_hex element. Example: password_sha256_hex 65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5 /password_sha256_hex How to generate decent password: Execute: PASSWORD=$(base64 /dev/urandom | head -c8); echo $PASSWORD ; echo -n $PASSWORD | sha256sum | tr -d - In first line will be password and in second - corresponding SHA256. -- \n password /password \n !-- A list of networks that access is allowed from. Each list item has one of the following forms: ip IP address or subnet mask. For example: 198.51.100.0/24 or 2001:DB8::/32. host Host name. For example: example01. A DNS query is made for verification, and all addresses obtained are compared with the address of the customer. host_regexp Regular expression for host names. For example: ^example\\d\\d-\\d\\d-\\d\\.yandex\\.ru$ For verification, a DNS PTR query is made for the customer s address and a regular expression is applied to the result. Then another DNS query is made for the result of the PTR query, and all received address are compared to the client address. We strongly recommend that the regex ends with \\.yandex\\.ru$. If you are installing ClickHouse yourself, enter: networks ip ::/0 /ip /networks -- \n networks incl= networks / \n\n !-- Settings profile for the user. -- \n profile default /profile \n\n !-- Quota for the user. -- \n quota default /quota \n /default \n\n !-- For requests from the Yandex.Metrica user interface via the API for data on specific counters. -- \n web \n password /password \n networks incl= networks / \n profile web /profile \n quota default /quota \n allow_databases \n database test /database \n /allow_databases \n /web /users You can see a declaration from two users: default and web . We added the web user separately. The default user is chosen in cases when the username is not passed. The default user is also used for distributed query processing, if the configuration of the server or cluster doesn't specify the user and password (see the section on the Distributed engine). The user that is used for exchanging information between servers combined in a cluster must not have substantial restrictions or quotas \u2013 otherwise, distributed queries will fail. The password is specified in open format (not recommended) or in SHA-256. The hash isn't salted. In this regard, you should not consider these passwords as providing security against potential malicious attacks. Rather, they are necessary for protection from employees. A list of networks is specified that access is allowed from. In this example, the list of networks for both users is loaded from a separate file (/etc/metrika.xml) containing the 'networks' substitution. Here is a fragment of it: yandex \n ...\n networks \n ip ::/64 /ip \n ip 203.0.113.0/24 /ip \n ip 2001:DB8::/32 /ip \n ...\n /networks /yandex We could have defined this list of networks directly in 'users.xml', or in a file in the 'users.d' directory (for more information, see the section \"Configuration files\"). The config includes comments explaining how to open access from everywhere. For use in production, only specify IP elements (IP addresses and their masks), since using 'host' and 'hoost_regexp' might cause extra latency. Next the user settings profile is specified (see the section \"Settings profiles\"). You can specify the default profile, default . The profile can have any name. You can specify the same profile for different users. The most important thing you can write in the settings profile is 'readonly' set to 1, which provides read-only access. After this, the quota is defined (see the section \"Quotas\"). You can specify the default quota, default . It is set in the config by default so that it only counts resource usage, but does not restrict it. The quota can have any name. You can specify the same quota for different users \u2013 in this case, resource usage is calculated for each user individually. In the optional allow_databases section, you can also specify a list of databases that the user can access. By default, all databases are available to the user. You can specify the default database. In this case, the user will receive access to the database by default. Access to the system database is always allowed (since this database is used for processing queries). The user can get a list of all databases and tables in them by using SHOW queries or system tables, even if access to individual databases isn't allowed. Database access is not related to the readonly setting. You can't grant full access to one database and readonly access to another one.", - "title": "Access rights" - }, - { - "location": "/index.html#configuration-files_1", - "text": "The main server config file is config.xml . It resides in the /etc/clickhouse-server/ directory. Individual settings can be overridden in the *.xml and *.conf files in the conf.d and config.d directories next to the config file. The replace or remove attributes can be specified for the elements of these config files. If neither is specified, it combines the contents of elements recursively, replacing values of duplicate children. If replace is specified, it replaces the entire element with the specified one. If remove is specified, it deletes the element. The config can also define \"substitutions\". If an element has the incl attribute, the corresponding substitution from the file will be used as the value. By default, the path to the file with substitutions is /etc/metrika.xml . This can be changed in the include_from element in the server config. The substitution values are specified in /yandex/substitution_name elements in this file. If a substitution specified in incl does not exist, it is recorded in the log. To prevent ClickHouse from logging missing substitutions, specify the optional=\"true\" attribute (for example, settings for macros ). Substitutions can also be performed from ZooKeeper. To do this, specify the attribute from_zk = \"/path/to/node\" . The element value is replaced with the contents of the node at /path/to/node in ZooKeeper. You can also put an entire XML subtree on the ZooKeeper node and it will be fully inserted into the source element. The config.xml file can specify a separate config with user settings, profiles, and quotas. The relative path to this config is set in the 'users_config' element. By default, it is users.xml . If users_config is omitted, the user settings, profiles, and quotas are specified directly in config.xml . In addition, users_config may have overrides in files from the users_config.d directory (for example, users.d ) and substitutions. For each config file, the server also generates file-preprocessed.xml files when starting. These files contain all the completed substitutions and overrides, and they are intended for informational use. If ZooKeeper substitutions were used in the config files but ZooKeeper is not available on the server start, the server loads the configuration from the preprocessed file. The server tracks changes in config files, as well as files and ZooKeeper nodes that were used when performing substitutions and overrides, and reloads the settings for users and clusters on the fly. This means that you can modify the cluster, users, and their settings without restarting the server.", - "title": "Configuration files" - }, - { - "location": "/index.html#quotas", - "text": "Quotas allow you to limit resource usage over a period of time, or simply track the use of resources.\nQuotas are set up in the user config. This is usually 'users.xml'. The system also has a feature for limiting the complexity of a single query. See the section \"Restrictions on query complexity\"). In contrast to query complexity restrictions, quotas: Place restrictions on a set of queries that can be run over a period of time, instead of limiting a single query. Account for resources spent on all remote servers for distributed query processing. Let's look at the section of the 'users.xml' file that defines quotas. !-- Quotas. -- quotas \n !-- Quota name. -- \n default \n !-- Restrictions for a time period. You can set many intervals with different restrictions. -- \n interval \n !-- Length of the interval. -- \n duration 3600 /duration \n\n !-- Unlimited. Just collect data for the specified time interval. -- \n queries 0 /queries \n errors 0 /errors \n result_rows 0 /result_rows \n read_rows 0 /read_rows \n execution_time 0 /execution_time \n /interval \n /default By default, the quota just tracks resource consumption for each hour, without limiting usage.\nThe resource consumption calculated for each interval is output to the server log after each request. statbox \n !-- Restrictions for a time period. You can set many intervals with different restrictions. -- \n interval \n !-- Length of the interval. -- \n duration 3600 /duration \n\n queries 1000 /queries \n errors 100 /errors \n result_rows 1000000000 /result_rows \n read_rows 100000000000 /read_rows \n execution_time 900 /execution_time \n /interval \n\n interval \n duration 86400 /duration \n\n queries 10000 /queries \n errors 1000 /errors \n result_rows 5000000000 /result_rows \n read_rows 500000000000 /read_rows \n execution_time 7200 /execution_time \n /interval /statbox For the 'statbox' quota, restrictions are set for every hour and for every 24 hours (86,400 seconds). The time interval is counted starting from an implementation-defined fixed moment in time. In other words, the 24-hour interval doesn't necessarily begin at midnight. When the interval ends, all collected values are cleared. For the next hour, the quota calculation starts over. Here are the amounts that can be restricted: queries \u2013 The total number of requests. errors \u2013 The number of queries that threw an exception. result_rows \u2013 The total number of rows given as the result. read_rows \u2013 The total number of source rows read from tables for running the query, on all remote servers. execution_time \u2013 The total query execution time, in seconds (wall time). If the limit is exceeded for at least one time interval, an exception is thrown with a text about which restriction was exceeded, for which interval, and when the new interval begins (when queries can be sent again). Quotas can use the \"quota key\" feature in order to report on resources for multiple keys independently. Here is an example of this: !-- For the global reports designer. -- web_global \n !-- keyed - The quota_key key is passed in the query parameter, and the quota is tracked separately for each key value. For example, you can pass a Yandex.Metrica username as the key, so the quota will be counted separately for each username. Using keys makes sense only if quota_key is transmitted by the program, not by a user. You can also write keyed_by_ip / so the IP address is used as the quota key. (But keep in mind that users can change the IPv6 address fairly easily.) -- \n keyed / The quota is assigned to users in the 'users' section of the config. See the section \"Access rights\". For distributed query processing, the accumulated amounts are stored on the requestor server. So if the user goes to another server, the quota there will \"start over\". When the server is restarted, quotas are reset.", - "title": "Quotas" - }, - { - "location": "/index.html#usage-recommendations", - "text": "", - "title": "Usage recommendations" - }, - { - "location": "/index.html#cpu", - "text": "The SSE 4.2 instruction set must be supported. Modern processors (since 2008) support it. When choosing a processor, prefer a large number of cores and slightly slower clock rate over fewer cores and a higher clock rate.\nFor example, 16 cores with 2600 MHz is better than 8 cores with 3600 MHz.", - "title": "CPU" - }, - { - "location": "/index.html#hyper-threading", - "text": "Don't disable hyper-threading. It helps for some queries, but not for others.", - "title": "Hyper-threading" - }, - { - "location": "/index.html#turbo-boost", - "text": "Turbo Boost is highly recommended. It significantly improves performance with a typical load.\nYou can use turbostat to view the CPU's actual clock rate under a load.", - "title": "Turbo Boost" - }, - { - "location": "/index.html#cpu-scaling-governor", - "text": "Always use the performance scaling governor. The on-demand scaling governor works much worse with constantly high demand. sudo echo performance | tee /sys/devices/system/cpu/cpu \\* /cpufreq/scaling_governor", - "title": "CPU scaling governor" - }, - { - "location": "/index.html#cpu-limitations", - "text": "Processors can overheat. Use dmesg to see if the CPU's clock rate was limited due to overheating.\nThe restriction can also be set externally at the datacenter level. You can use turbostat to monitor it under a load.", - "title": "CPU limitations" - }, - { - "location": "/index.html#ram", - "text": "For small amounts of data (up to \\~200 GB compressed), it is best to use as much memory as the volume of data.\nFor large amounts of data and when processing interactive (online) queries, you should use a reasonable amount of RAM (128 GB or more) so the hot data subset will fit in the cache of pages.\nEven for data volumes of \\~50 TB per server, using 128 GB of RAM significantly improves query performance compared to 64 GB.", - "title": "RAM" - }, - { - "location": "/index.html#swap-file", - "text": "Always disable the swap file. The only reason for not doing this is if you are using ClickHouse on your personal laptop.", - "title": "Swap file" - }, - { - "location": "/index.html#huge-pages", - "text": "Always disable transparent huge pages. It interferes with memory allocators, which leads to significant performance degradation. echo never | sudo tee /sys/kernel/mm/transparent_hugepage/enabled Use perf top to watch the time spent in the kernel for memory management.\nPermanent huge pages also do not need to be allocated.", - "title": "Huge pages" - }, - { - "location": "/index.html#storage-subsystem", - "text": "If your budget allows you to use SSD, use SSD.\nIf not, use HDD. SATA HDDs 7200 RPM will do. Give preference to a lot of servers with local hard drives over a smaller number of servers with attached disk shelves.\nBut for storing archives with rare queries, shelves will work.", - "title": "Storage subsystem" - }, - { - "location": "/index.html#raid", - "text": "When using HDD, you can combine their RAID-10, RAID-5, RAID-6 or RAID-50.\nFor Linux, software RAID is better (with mdadm ). We don't recommend using LVM.\nWhen creating RAID-10, select the far layout.\nIf your budget allows, choose RAID-10. If you have more than 4 disks, use RAID-6 (preferred) or RAID-50, instead of RAID-5.\nWhen using RAID-5, RAID-6 or RAID-50, always increase stripe_cache_size, since the default value is usually not the best choice. echo 4096 | sudo tee /sys/block/md2/md/stripe_cache_size Calculate the exact number from the number of devices and the block size, using the formula: 2 * num_devices * chunk_size_in_bytes / 4096 . A block size of 1025 KB is sufficient for all RAID configurations.\nNever set the block size too small or too large. You can use RAID-0 on SSD.\nRegardless of RAID use, always use replication for data security. Enable NCQ with a long queue. For HDD, choose the CFQ scheduler, and for SSD, choose noop. Don't reduce the 'readahead' setting.\nFor HDD, enable the write cache.", - "title": "RAID" - }, - { - "location": "/index.html#file-system", - "text": "Ext4 is the most reliable option. Set the mount options noatime, nobarrier .\nXFS is also suitable, but it hasn't been as thoroughly tested with ClickHouse.\nMost other file systems should also work fine. File systems with delayed allocation work better.", - "title": "File system" - }, - { - "location": "/index.html#linux-kernel", - "text": "Don't use an outdated Linux kernel. In 2015, 3.18.19 was new enough.\nConsider using the kernel build from Yandex: https://github.com/yandex/smart \u2013 it provides at least a 5% performance increase.", - "title": "Linux kernel" - }, - { - "location": "/index.html#network", - "text": "If you are using IPv6, increase the size of the route cache.\nThe Linux kernel prior to 3.2 had a multitude of problems with IPv6 implementation. Use at least a 10 GB network, if possible. 1 Gb will also work, but it will be much worse for patching replicas with tens of terabytes of data, or for processing distributed queries with a large amount of intermediate data.", - "title": "Network" - }, - { - "location": "/index.html#zookeeper", - "text": "You are probably already using ZooKeeper for other purposes. You can use the same installation of ZooKeeper, if it isn't already overloaded. It's best to use a fresh version of ZooKeeper \u2013 3.4.9 or later. The version in stable Linux distributions may be outdated. With the default settings, ZooKeeper is a time bomb: The ZooKeeper server won't delete files from old snapshots and logs when using the default configuration (see autopurge), and this is the responsibility of the operator. This bomb must be defused. The ZooKeeper (3.5.1) configuration below is used in the Yandex.Metrica production environment as of May 20, 2017: zoo.cfg: ## http://hadoop.apache.org/zookeeper/docs/current/zookeeperAdmin.html ## The number of milliseconds of each tick tickTime = 2000 ## The number of ticks that the initial ## synchronization phase can take initLimit = 30000 ## The number of ticks that can pass between ## sending a request and getting an acknowledgement syncLimit = 10 maxClientCnxns = 2000 maxSessionTimeout = 60000000 ## the directory where the snapshot is stored. dataDir = /opt/zookeeper/ {{ cluster [ name ] }} /data ## Place the dataLogDir to a separate physical disc for better performance dataLogDir = /opt/zookeeper/ {{ cluster [ name ] }} /logs\n\nautopurge.snapRetainCount = 10 \nautopurge.purgeInterval = 1 ## To avoid seeks ZooKeeper allocates space in the transaction log file in ## blocks of preAllocSize kilobytes. The default block size is 64M. One reason ## for changing the size of the blocks is to reduce the block size if snapshots ## are taken more often. (Also, see snapCount). preAllocSize = 131072 ## Clients can submit requests faster than ZooKeeper can process them, ## especially if there are a lot of clients. To prevent ZooKeeper from running ## out of memory due to queued requests, ZooKeeper will throttle clients so that ## there is no more than globalOutstandingLimit outstanding requests in the ## system. The default limit is 1,000.ZooKeeper logs transactions to a ## transaction log. After snapCount transactions are written to a log file a ## snapshot is started and a new transaction log file is started. The default ## snapCount is 10,000. snapCount = 3000000 ## If this option is defined, requests will be will logged to a trace file named ## traceFile.year.month.day. ##traceFile= ## Leader accepts client connections. Default value is yes . The leader machine ## coordinates updates. For higher update throughput at thes slight expense of ## read throughput the leader can be configured to not accept clients and focus ## on coordination. leaderServes = yes standaloneEnabled = false dynamicConfigFile = /etc/zookeeper- {{ cluster [ name ] }} /conf/zoo.cfg.dynamic Java version: Java(TM) SE Runtime Environment (build 1.8.0_25-b17)\nJava HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode) JVM parameters: NAME = zookeeper- {{ cluster [ name ] }} ZOOCFGDIR = /etc/ $NAME /conf ## TODO this is really ugly ## How to find out, which jars are needed? ## seems, that log4j requires the log4j.properties file to be in the classpath CLASSPATH = $ZOOCFGDIR :/usr/build/classes:/usr/build/lib/*.jar:/usr/share/zookeeper/zookeeper-3.5.1-metrika.jar:/usr/share/zookeeper/slf4j-log4j12-1.7.5.jar:/usr/share/zookeeper/slf4j-api-1.7.5.jar:/usr/share/zookeeper/servlet-api-2.5-20081211.jar:/usr/share/zookeeper/netty-3.7.0.Final.jar:/usr/share/zookeeper/log4j-1.2.16.jar:/usr/share/zookeeper/jline-2.11.jar:/usr/share/zookeeper/jetty-util-6.1.26.jar:/usr/share/zookeeper/jetty-6.1.26.jar:/usr/share/zookeeper/javacc.jar:/usr/share/zookeeper/jackson-mapper-asl-1.9.11.jar:/usr/share/zookeeper/jackson-core-asl-1.9.11.jar:/usr/share/zookeeper/commons-cli-1.2.jar:/usr/src/java/lib/*.jar:/usr/etc/zookeeper ZOOCFG = $ZOOCFGDIR /zoo.cfg ZOO_LOG_DIR = /var/log/ $NAME USER = zookeeper GROUP = zookeeper PIDDIR = /var/run/ $NAME PIDFILE = $PIDDIR / $NAME .pid SCRIPTNAME = /etc/init.d/ $NAME JAVA = /usr/bin/java ZOOMAIN = org.apache.zookeeper.server.quorum.QuorumPeerMain ZOO_LOG4J_PROP = INFO,ROLLINGFILE JMXLOCALONLY = false JAVA_OPTS = -Xms{{ cluster.get( xms , 128M ) }} \\ -Xmx{{ cluster.get( xmx , 1G ) }} \\ -Xloggc:/var/log/ $NAME /zookeeper-gc.log \\ -XX:+UseGCLogFileRotation \\ -XX:NumberOfGCLogFiles=16 \\ -XX:GCLogFileSize=16M \\ -verbose:gc \\ -XX:+PrintGCTimeStamps \\ -XX:+PrintGCDateStamps \\ -XX:+PrintGCDetails -XX:+PrintTenuringDistribution \\ -XX:+PrintGCApplicationStoppedTime \\ -XX:+PrintGCApplicationConcurrentTime \\ -XX:+PrintSafepointStatistics \\ -XX:+UseParNewGC \\ -XX:+UseConcMarkSweepGC \\ -XX:+CMSParallelRemarkEnabled Salt init: description zookeeper-{{ cluster[ name ] }} centralized coordination service \n\nstart on runlevel [2345]\nstop on runlevel [!2345]\n\nrespawn\n\nlimit nofile 8192 8192\n\npre-start script\n [ -r /etc/zookeeper-{{ cluster[ name ] }}/conf/environment ] || exit 0\n . /etc/zookeeper-{{ cluster[ name ] }}/conf/environment\n [ -d $ZOO_LOG_DIR ] || mkdir -p $ZOO_LOG_DIR\n chown $USER:$GROUP $ZOO_LOG_DIR\nend script\n\nscript\n . /etc/zookeeper-{{ cluster[ name ] }}/conf/environment\n [ -r /etc/default/zookeeper ] . /etc/default/zookeeper\n if [ -z $JMXDISABLE ]; then\n JAVA_OPTS= $JAVA_OPTS -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.local.only=$JMXLOCALONLY \n fi\n exec start-stop-daemon --start -c $USER --exec $JAVA --name zookeeper-{{ cluster[ name ] }} \\\n -- -cp $CLASSPATH $JAVA_OPTS -Dzookeeper.log.dir=${ZOO_LOG_DIR} \\\n -Dzookeeper.root.logger=${ZOO_LOG4J_PROP} $ZOOMAIN $ZOOCFG\nend script", - "title": "ZooKeeper" - }, - { - "location": "/index.html#server-configuration-parameters", - "text": "This section contains descriptions of server settings that cannot be changed at the session or query level. These settings are stored in the config.xml file on the ClickHouse server. Other settings are described in the \" Settings \" section. Before studying the settings, read the Configuration files section and note the use of substitutions (the incl and optional attributes).", - "title": "Server configuration parameters" - }, - { - "location": "/index.html#server-settings", - "text": "", - "title": "Server settings" - }, - { - "location": "/index.html#builtin_dictionaries_reload_interval", - "text": "The interval in seconds before reloading built-in dictionaries. ClickHouse reloads built-in dictionaries every x seconds. This makes it possible to edit dictionaries \"on the fly\" without restarting the server. Default value: 3600. Example builtin_dictionaries_reload_interval 3600 /builtin_dictionaries_reload_interval", - "title": "builtin_dictionaries_reload_interval" - }, - { - "location": "/index.html#compression", - "text": "Data compression settings. \n\nDon't use it if you have just started using ClickHouse. The configuration looks like this: compression \n case \n parameters/ \n /case \n ... /compression You can configure multiple sections case . Block field case : min_part_size \u2013 The minimum size of a table part. min_part_size_ratio \u2013 The ratio of the minimum size of a table part to the full size of the table. method \u2013 Compression method. Acceptable values \u200b: lz4 or zstd (experimental). ClickHouse checks min_part_size and min_part_size_ratio and processes the case blocks that match these conditions. If none of the case matches, ClickHouse applies the lz4 compression algorithm. Example compression incl= clickhouse_compression \n case \n min_part_size 10000000000 /min_part_size \n min_part_size_ratio 0.01 /min_part_size_ratio \n method zstd /method \n /case /compression", - "title": "compression" - }, - { - "location": "/index.html#default_database", - "text": "The default database. To get a list of databases, use the SHOW DATABASES . Example default_database default /default_database", - "title": "default_database" - }, - { - "location": "/index.html#default_profile", - "text": "Default settings profile. Settings profiles are located in the file specified in the parameter user_config . Example default_profile default /default_profile", - "title": "default_profile" - }, - { - "location": "/index.html#dictionaries_config", - "text": "The path to the config file for external dictionaries. Path: Specify the absolute path or the path relative to the server config file. The path can contain wildcards * and ?. See also \" External dictionaries \". Example dictionaries_config *_dictionary.xml /dictionaries_config", - "title": "dictionaries_config" - }, - { - "location": "/index.html#dictionaries_lazy_load", - "text": "Lazy loading of dictionaries. If true , then each dictionary is created on first use. If dictionary creation failed, the function that was using the dictionary throws an exception. If false , all dictionaries are created when the server starts, and if there is an error, the server shuts down. The default is true . Example dictionaries_lazy_load true /dictionaries_lazy_load", - "title": "dictionaries_lazy_load" - }, - { - "location": "/index.html#format_schema_path", - "text": "The path to the directory with the schemes for the input data, such as schemas for the CapnProto format. Example !-- Directory containing schema files for various input formats. -- \n format_schema_path format_schemas/ /format_schema_path", - "title": "format_schema_path" - }, - { - "location": "/index.html#graphite", - "text": "Sending data to Graphite . Settings: host \u2013 The Graphite server. port \u2013 The port on the Graphite server. interval \u2013 The interval for sending, in seconds. timeout \u2013 The timeout for sending data, in seconds. root_path \u2013 Prefix for keys. metrics \u2013 Sending data from a :ref: system_tables-system.metrics table. events \u2013 Sending data from a :ref: system_tables-system.events table. asynchronous_metrics \u2013 Sending data from a :ref: system_tables-system.asynchronous_metrics table. You can configure multiple graphite clauses. For instance, you can use this for sending different data at different intervals. Example graphite \n host localhost /host \n port 42000 /port \n timeout 0.1 /timeout \n interval 60 /interval \n root_path one_min /root_path \n metrics true /metrics \n events true /events \n asynchronous_metrics true /asynchronous_metrics /graphite", - "title": "graphite" - }, - { - "location": "/index.html#graphite_rollup", - "text": "Settings for thinning data for Graphite. For more information, see GraphiteMergeTree . Example graphite_rollup_example \n default \n function max /function \n retention \n age 0 /age \n precision 60 /precision \n /retention \n retention \n age 3600 /age \n precision 300 /precision \n /retention \n retention \n age 86400 /age \n precision 3600 /precision \n /retention \n /default /graphite_rollup_example", - "title": "graphite_rollup" - }, - { - "location": "/index.html#http_porthttps_port", - "text": "The port for connecting to the server over HTTP(s). If https_port is specified, openSSL must be configured. If http_port is specified, the openSSL configuration is ignored even if it is set. Example https 0000 /https", - "title": "http_port/https_port" - }, - { - "location": "/index.html#http_server_default_response", - "text": "The page that is shown by default when you access the ClickHouse HTTP(s) server. Example Opens https://tabix.io/ when accessing http://localhost: http_port . http_server_default_response \n ![CDATA[ html ng-app= SMI2 head base href= http://ui.tabix.io/ /head body div ui-view= class= content-ui /div script src= http://loader.tabix.io/master.js /script /body /html ]] /http_server_default_response", - "title": "http_server_default_response" - }, - { - "location": "/index.html#include_from", - "text": "The path to the file with substitutions. For more information, see the section \" Configuration files \". Example include_from /etc/metrica.xml /include_from", - "title": "include_from" - }, - { - "location": "/index.html#interserver_http_port", - "text": "Port for exchanging data between ClickHouse servers. Example interserver_http_port 9009 /interserver_http_port", - "title": "interserver_http_port" - }, - { - "location": "/index.html#interserver_http_host", - "text": "The host name that can be used by other servers to access this server. If omitted, it is defined in the same way as the hostname-f command. Useful for breaking away from a specific network interface. Example interserver_http_host example.yandex.ru /interserver_http_host", - "title": "interserver_http_host" - }, - { - "location": "/index.html#keep_alive_timeout", - "text": "The number of milliseconds that ClickHouse waits for incoming requests before closing the connection. Example keep_alive_timeout 3 /keep_alive_timeout", - "title": "keep_alive_timeout" - }, - { - "location": "/index.html#listen_host", - "text": "Restriction on hosts that requests can come from. If you want the server to answer all of them, specify :: . Examples: listen_host ::1 /listen_host listen_host 127.0.0.1 /listen_host", - "title": "listen_host" - }, - { - "location": "/index.html#logger", - "text": "Logging settings. Keys: level \u2013 Logging level. Acceptable values: trace , debug , information , warning , error . log \u2013 The log file. Contains all the entries according to level . errorlog \u2013 Error log file. size \u2013 Size of the file. Applies to log and errorlog . Once the file reaches size , ClickHouse archives and renames it, and creates a new log file in its place. count \u2013 The number of archived log files that ClickHouse stores. Example logger \n level trace /level \n log /var/log/clickhouse-server/clickhouse-server.log /log \n errorlog /var/log/clickhouse-server/clickhouse-server.err.log /errorlog \n size 1000M /size \n count 10 /count /logger", - "title": "logger" - }, - { - "location": "/index.html#macros", - "text": "Parameter substitutions for replicated tables. Can be omitted if replicated tables are not used. For more information, see the section \" Creating replicated tables \". Example macros incl= macros optional= true /", - "title": "macros" - }, - { - "location": "/index.html#mark_cache_size", - "text": "Approximate size (in bytes) of the cache of \"marks\" used by MergeTree engines. The cache is shared for the server and memory is allocated as needed. The cache size must be at least 5368709120. Example mark_cache_size 5368709120 /mark_cache_size", - "title": "mark_cache_size" - }, - { - "location": "/index.html#max_concurrent_queries", - "text": "The maximum number of simultaneously processed requests. Example max_concurrent_queries 100 /max_concurrent_queries", - "title": "max_concurrent_queries" - }, - { - "location": "/index.html#max_connections", - "text": "The maximum number of inbound connections. Example max_connections 4096 /max_connections", - "title": "max_connections" - }, - { - "location": "/index.html#max_open_files", - "text": "The maximum number of open files. By default: maximum . We recommend using this option in Mac OS X, since the getrlimit() function returns an incorrect value. Example max_open_files 262144 /max_open_files", - "title": "max_open_files" - }, - { - "location": "/index.html#max_table_size_to_drop", - "text": "Restriction on deleting tables. If the size of a MergeTree type table exceeds max_table_size_to_drop (in bytes), you can't delete it using a DROP query. If you still need to delete the table without restarting the ClickHouse server, create the clickhouse-path /flags/force_drop_table file and run the DROP query. Default value: 50 GB. The value 0 means that you can delete all tables without any restrictions. Example max_table_size_to_drop 0 /max_table_size_to_drop", - "title": "max_table_size_to_drop" - }, - { - "location": "/index.html#merge_tree", - "text": "Fine tuning for tables in the MergeTree family. For more information, see the MergeTreeSettings.h header file. Example merge_tree \n max_suspicious_broken_parts 5 /max_suspicious_broken_parts /merge_tree", - "title": "merge_tree" - }, - { - "location": "/index.html#openssl", - "text": "SSL client/server configuration. Support for SSL is provided by the libpoco library. The interface is described in the file SSLManager.h Keys for server/client settings: privateKeyFile \u2013 The path to the file with the secret key of the PEM certificate. The file may contain a key and certificate at the same time. certificateFile \u2013 The path to the client/server certificate file in PEM format. You can omit it if privateKeyFile contains the certificate. caConfig \u2013 The path to the file or directory that contains trusted root certificates. verificationMode \u2013 The method for checking the node's certificates. Details are in the description of the Context class. Possible values: none , relaxed , strict , once . verificationDepth \u2013 The maximum length of the verification chain. Verification will fail if the certificate chain length exceeds the set value. loadDefaultCAFile \u2013 Indicates that built-in CA certificates for OpenSSL will be used. Acceptable values: true , false . | cipherList \u2013 Supported OpenSSL encryptions. For example: ALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH . cacheSessions \u2013 Enables or disables caching sessions. Must be used in combination with sessionIdContext . Acceptable values: true , false . sessionIdContext \u2013 A unique set of random characters that the server appends to each generated identifier. The length of the string must not exceed SSL_MAX_SSL_SESSION_ID_LENGTH . This parameter is always recommended, since it helps avoid problems both if the server caches the session and if the client requested caching. Default value: ${application.name} . sessionCacheSize \u2013 The maximum number of sessions that the server caches. Default value: 1024*20. 0 \u2013 Unlimited sessions. sessionTimeout \u2013 Time for caching the session on the server. extendedVerification \u2013 Automatically extended verification of certificates after the session ends. Acceptable values: true , false . requireTLSv1 \u2013 Require a TLSv1 connection. Acceptable values: true , false . requireTLSv1_1 \u2013 Require a TLSv1.1 connection. Acceptable values: true , false . requireTLSv1 \u2013 Require a TLSv1.2 connection. Acceptable values: true , false . fips \u2013 Activates OpenSSL FIPS mode. Supported if the library's OpenSSL version supports FIPS. privateKeyPassphraseHandler \u2013 Class (PrivateKeyPassphraseHandler subclass) that requests the passphrase for accessing the private key. For example: privateKeyPassphraseHandler , name KeyFileHandler /name , options password test /password /options , /privateKeyPassphraseHandler . invalidCertificateHandler \u2013 Class (subclass of CertificateHandler) for verifying invalid certificates. For example: invalidCertificateHandler name ConsoleCertificateHandler /name /invalidCertificateHandler . disableProtocols \u2013 Protocols that are not allowed to use. preferServerCiphers \u2013 Preferred server ciphers on the client. Example of settings: openSSL \n server \n !-- openssl req -subj /CN=localhost -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout /etc/clickhouse-server/server.key -out /etc/clickhouse-server/server.crt -- \n certificateFile /etc/clickhouse-server/server.crt /certificateFile \n privateKeyFile /etc/clickhouse-server/server.key /privateKeyFile \n !-- openssl dhparam -out /etc/clickhouse-server/dhparam.pem 4096 -- \n dhParamsFile /etc/clickhouse-server/dhparam.pem /dhParamsFile \n verificationMode none /verificationMode \n loadDefaultCAFile true /loadDefaultCAFile \n cacheSessions true /cacheSessions \n disableProtocols sslv2,sslv3 /disableProtocols \n preferServerCiphers true /preferServerCiphers \n /server \n client \n loadDefaultCAFile true /loadDefaultCAFile \n cacheSessions true /cacheSessions \n disableProtocols sslv2,sslv3 /disableProtocols \n preferServerCiphers true /preferServerCiphers \n !-- Use for self-signed: verificationMode none /verificationMode -- \n invalidCertificateHandler \n !-- Use for self-signed: name AcceptCertificateHandler /name -- \n name RejectCertificateHandler /name \n /invalidCertificateHandler \n /client /openSSL", - "title": "openSSL" - }, - { - "location": "/index.html#part_log", - "text": "Logging events that are associated with MergeTree data. For instance, adding or merging data. You can use the log to simulate merge algorithms and compare their characteristics. You can visualize the merge process. Queries are logged in the ClickHouse table, not in a separate file. Columns in the log: event_time \u2013 Date of the event. duration_ms \u2013 Duration of the event. event_type \u2013 Type of event. 1 \u2013 new data part; 2 \u2013 merge result; 3 \u2013 data part downloaded from replica; 4 \u2013 data part deleted. database_name \u2013 The name of the database. table_name \u2013 Name of the table. part_name \u2013 Name of the data part. size_in_bytes \u2013 Size of the data part in bytes. merged_from \u2013 An array of names of data parts that make up the merge (also used when downloading a merged part). merge_time_ms \u2013 Time spent on the merge. Use the following parameters to configure logging: database \u2013 Name of the database. table \u2013 Name of the table. partition_by \u2013 Sets a custom partitioning key . flush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk. Example part_log \n database system /database \n table part_log /table \n partition_by toMonday(event_date) /partition_by \n flush_interval_milliseconds 7500 /flush_interval_milliseconds /part_log", - "title": "part_log" - }, - { - "location": "/index.html#path_1", - "text": "The path to the directory containing data. \n\nThe end slash is mandatory. Example path /var/lib/clickhouse/ /path", - "title": "path" - }, - { - "location": "/index.html#query_log", - "text": "Setting for logging queries received with the log_queries=1 setting. Queries are logged in the ClickHouse table, not in a separate file. Use the following parameters to configure logging: database \u2013 Name of the database. table \u2013 Name of the table. partition_by \u2013 Sets a custom partitioning key . flush_interval_milliseconds \u2013 Interval for flushing data from memory to the disk. If the table doesn't exist, ClickHouse will create it. If the structure of the query log changed when the ClickHouse server was updated, the table with the old structure is renamed, and a new table is created automatically. Example query_log \n database system /database \n table query_log /table \n partition_by toMonday(event_date) /partition_by \n flush_interval_milliseconds 7500 /flush_interval_milliseconds /query_log", - "title": "query_log" - }, - { - "location": "/index.html#remote_servers", - "text": "Configuration of clusters used by the Distributed table engine. For more information, see the section \" Table engines/Distributed \". Example remote_servers incl= clickhouse_remote_servers / For the value of the incl attribute, see the section \" Configuration files \".", - "title": "remote_servers" - }, - { - "location": "/index.html#timezone", - "text": "The server's time zone. Specified as an IANA identifier for the UTC time zone or geographic location (for example, Africa/Abidjan). The time zone is necessary for conversions between String and DateTime formats when DateTime fields are output to text format (printed on the screen or in a file), and when getting DateTime from a string. In addition, the time zone is used in functions that work with the time and date if they didn't receive the time zone in the input parameters. Example timezone Europe/Moscow /timezone", - "title": "timezone" - }, - { - "location": "/index.html#tcp_port", - "text": "Port for communicating with clients over the TCP protocol. Example tcp_port 9000 /tcp_port", - "title": "tcp_port" - }, - { - "location": "/index.html#tmp_path", - "text": "Path to temporary data for processing large queries. \n\nThe end slash is mandatory. Example tmp_path /var/lib/clickhouse/tmp/ /tmp_path", - "title": "tmp_path" - }, - { - "location": "/index.html#uncompressed_cache_size", - "text": "Cache size (in bytes) for uncompressed data used by table engines from the MergeTree family. There is one shared cache for the server. Memory is allocated on demand. The cache is used if the option use_uncompressed_cache is enabled. The uncompressed cache is advantageous for very short queries in individual cases. Example uncompressed_cache_size 8589934592 /uncompressed_cache_size", - "title": "uncompressed_cache_size" - }, - { - "location": "/index.html#users_config", - "text": "Path to the file that contains: User configurations. Access rights. Settings profiles. Quota settings. Example users_config users.xml /users_config", - "title": "users_config" - }, - { - "location": "/index.html#zookeeper_1", - "text": "Configuration of ZooKeeper servers. ClickHouse uses ZooKeeper for storing replica metadata when using replicated tables. This parameter can be omitted if replicated tables are not used. For more information, see the section \" Replication \". Example zookeeper incl= zookeeper-servers optional= true /", - "title": "zookeeper" - }, - { - "location": "/index.html#settings", - "text": "There are multiple ways to make all the settings described below.\nSettings are configured in layers, so each subsequent layer redefines the previous settings. Ways to configure settings, in order of priority: Settings in the server config file. Settings from user profiles. Session settings. Send SET setting=value from the ClickHouse console client in interactive mode.\nSimilarly, you can use ClickHouse sessions in the HTTP protocol. To do this, you need to specify the session_id HTTP parameter. For a query. When starting the ClickHouse console client in non-interactive mode, set the startup parameter --setting=value . When using the HTTP API, pass CGI parameters ( URL?setting_1=value setting_2=value... ). Settings that can only be made in the server config file are not covered in this section.", - "title": "Settings" - }, - { - "location": "/index.html#restrictions-on-query-complexity", - "text": "Restrictions on query complexity are part of the settings.\nThey are used in order to provide safer execution from the user interface.\nAlmost all the restrictions only apply to SELECTs.For distributed query processing, restrictions are applied on each server separately. Restrictions on the \"maximum amount of something\" can take the value 0, which means \"unrestricted\".\nMost restrictions also have an 'overflow_mode' setting, meaning what to do when the limit is exceeded.\nIt can take one of two values: throw or break . Restrictions on aggregation (group_by_overflow_mode) also have the value any . throw \u2013 Throw an exception (default). break \u2013 Stop executing the query and return the partial result, as if the source data ran out. any (only for group_by_overflow_mode) \u2013 Continuing aggregation for the keys that got into the set, but don't add new keys to the set.", - "title": "Restrictions on query complexity" - }, - { - "location": "/index.html#readonly", - "text": "With a value of 0, you can execute any queries.\nWith a value of 1, you can only execute read requests (such as SELECT and SHOW). Requests for writing and changing settings (INSERT, SET) are prohibited.\nWith a value of 2, you can process read queries (SELECT, SHOW) and change settings (SET). After enabling readonly mode, you can't disable it in the current session. When using the GET method in the HTTP interface, 'readonly = 1' is set automatically. In other words, for queries that modify data, you can only use the POST method. You can send the query itself either in the POST body, or in the URL parameter.", - "title": "readonly" - }, - { - "location": "/index.html#max_memory_usage", - "text": "The maximum amount of RAM to use for running a query on a single server. In the default configuration file, the maximum is 10 GB. The setting doesn't consider the volume of available memory or the total volume of memory on the machine.\nThe restriction applies to a single query within a single server.\nYou can use SHOW PROCESSLIST to see the current memory consumption for each query.\nIn addition, the peak memory consumption is tracked for each query and written to the log. Memory usage is not monitored for the states of certain aggregate functions. Memory usage is not fully tracked for states of the aggregate functions min , max , any , anyLast , argMin , argMax from String and Array arguments. Memory consumption is also restricted by the parameters max_memory_usage_for_user and max_memory_usage_for_all_queries .", - "title": "max_memory_usage" - }, - { - "location": "/index.html#max_memory_usage_for_user", - "text": "The maximum amount of RAM to use for running a user's queries on a single server. Default values are defined in Settings.h . By default, the amount is not restricted ( max_memory_usage_for_user = 0 ). See also the description of max_memory_usage .", - "title": "max_memory_usage_for_user" - }, - { - "location": "/index.html#max_memory_usage_for_all_queries", - "text": "The maximum amount of RAM to use for running all queries on a single server. Default values are defined in Settings.h . By default, the amount is not restricted ( max_memory_usage_for_all_queries = 0 ). See also the description of max_memory_usage .", - "title": "max_memory_usage_for_all_queries" - }, - { - "location": "/index.html#max_rows_to_read", - "text": "The following restrictions can be checked on each block (instead of on each row). That is, the restrictions can be broken a little.\nWhen running a query in multiple threads, the following restrictions apply to each thread separately. Maximum number of rows that can be read from a table when running a query.", - "title": "max_rows_to_read" - }, - { - "location": "/index.html#max_bytes_to_read", - "text": "Maximum number of bytes (uncompressed data) that can be read from a table when running a query.", - "title": "max_bytes_to_read" - }, - { - "location": "/index.html#read_overflow_mode", - "text": "What to do when the volume of data read exceeds one of the limits: 'throw' or 'break'. By default, throw.", - "title": "read_overflow_mode" - }, - { - "location": "/index.html#max_rows_to_group_by", - "text": "Maximum number of unique keys received from aggregation. This setting lets you limit memory consumption when aggregating.", - "title": "max_rows_to_group_by" - }, - { - "location": "/index.html#group_by_overflow_mode", - "text": "What to do when the number of unique keys for aggregation exceeds the limit: 'throw', 'break', or 'any'. By default, throw.\nUsing the 'any' value lets you run an approximation of GROUP BY. The quality of this approximation depends on the statistical nature of the data.", - "title": "group_by_overflow_mode" - }, - { - "location": "/index.html#max_rows_to_sort", - "text": "Maximum number of rows before sorting. This allows you to limit memory consumption when sorting.", - "title": "max_rows_to_sort" - }, - { - "location": "/index.html#max_bytes_to_sort", - "text": "Maximum number of bytes before sorting.", - "title": "max_bytes_to_sort" - }, - { - "location": "/index.html#sort_overflow_mode", - "text": "What to do if the number of rows received before sorting exceeds one of the limits: 'throw' or 'break'. By default, throw.", - "title": "sort_overflow_mode" - }, - { - "location": "/index.html#max_result_rows", - "text": "Limit on the number of rows in the result. Also checked for subqueries, and on remote servers when running parts of a distributed query.", - "title": "max_result_rows" - }, - { - "location": "/index.html#max_result_bytes", - "text": "Limit on the number of bytes in the result. The same as the previous setting.", - "title": "max_result_bytes" - }, - { - "location": "/index.html#result_overflow_mode", - "text": "What to do if the volume of the result exceeds one of the limits: 'throw' or 'break'. By default, throw.\nUsing 'break' is similar to using LIMIT.", - "title": "result_overflow_mode" - }, - { - "location": "/index.html#max_execution_time", - "text": "Maximum query execution time in seconds.\nAt this time, it is not checked for one of the sorting stages, or when merging and finalizing aggregate functions.", - "title": "max_execution_time" - }, - { - "location": "/index.html#timeout_overflow_mode", - "text": "What to do if the query is run longer than 'max_execution_time': 'throw' or 'break'. By default, throw.", - "title": "timeout_overflow_mode" - }, - { - "location": "/index.html#min_execution_speed", - "text": "Minimal execution speed in rows per second. Checked on every data block when 'timeout_before_checking_execution_speed' expires. If the execution speed is lower, an exception is thrown.", - "title": "min_execution_speed" - }, - { - "location": "/index.html#timeout_before_checking_execution_speed", - "text": "Checks that execution speed is not too slow (no less than 'min_execution_speed'), after the specified time in seconds has expired.", - "title": "timeout_before_checking_execution_speed" - }, - { - "location": "/index.html#max_columns_to_read", - "text": "Maximum number of columns that can be read from a table in a single query. If a query requires reading a greater number of columns, it throws an exception.", - "title": "max_columns_to_read" - }, - { - "location": "/index.html#max_temporary_columns", - "text": "Maximum number of temporary columns that must be kept in RAM at the same time when running a query, including constant columns. If there are more temporary columns than this, it throws an exception.", - "title": "max_temporary_columns" - }, - { - "location": "/index.html#max_temporary_non_const_columns", - "text": "The same thing as 'max_temporary_columns', but without counting constant columns.\nNote that constant columns are formed fairly often when running a query, but they require approximately zero computing resources.", - "title": "max_temporary_non_const_columns" - }, - { - "location": "/index.html#max_subquery_depth", - "text": "Maximum nesting depth of subqueries. If subqueries are deeper, an exception is thrown. By default, 100.", - "title": "max_subquery_depth" - }, - { - "location": "/index.html#max_pipeline_depth", - "text": "Maximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000.", - "title": "max_pipeline_depth" - }, - { - "location": "/index.html#max_ast_depth", - "text": "Maximum nesting depth of a query syntactic tree. If exceeded, an exception is thrown.\nAt this time, it isn't checked during parsing, but only after parsing the query. That is, a syntactic tree that is too deep can be created during parsing, but the query will fail. By default, 1000.", - "title": "max_ast_depth" - }, - { - "location": "/index.html#max_ast_elements", - "text": "Maximum number of elements in a query syntactic tree. If exceeded, an exception is thrown.\nIn the same way as the previous setting, it is checked only after parsing the query. By default, 10,000.", - "title": "max_ast_elements" - }, - { - "location": "/index.html#max_rows_in_set", - "text": "Maximum number of rows for a data set in the IN clause created from a subquery.", - "title": "max_rows_in_set" - }, - { - "location": "/index.html#max_bytes_in_set", - "text": "Maximum number of bytes (uncompressed data) used by a set in the IN clause created from a subquery.", - "title": "max_bytes_in_set" - }, - { - "location": "/index.html#set_overflow_mode", - "text": "What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.", - "title": "set_overflow_mode" - }, - { - "location": "/index.html#max_rows_in_distinct", - "text": "Maximum number of different rows when using DISTINCT.", - "title": "max_rows_in_distinct" - }, - { - "location": "/index.html#max_bytes_in_distinct", - "text": "Maximum number of bytes used by a hash table when using DISTINCT.", - "title": "max_bytes_in_distinct" - }, - { - "location": "/index.html#distinct_overflow_mode", - "text": "What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.", - "title": "distinct_overflow_mode" - }, - { - "location": "/index.html#max_rows_to_transfer", - "text": "Maximum number of rows that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.", - "title": "max_rows_to_transfer" - }, - { - "location": "/index.html#max_bytes_to_transfer", - "text": "Maximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.", - "title": "max_bytes_to_transfer" - }, - { - "location": "/index.html#transfer_overflow_mode", - "text": "What to do when the amount of data exceeds one of the limits: 'throw' or 'break'. By default, throw.", - "title": "transfer_overflow_mode" - }, - { - "location": "/index.html#settings_1", - "text": "", - "title": "Settings" - }, - { - "location": "/index.html#distributed_product_mode", - "text": "Changes the behavior of distributed subqueries , i.e. in cases when the query contains the product of distributed tables. ClickHouse applies the configuration if the subqueries on any level have a distributed table that exists on the local server and has more than one shard. Restrictions: Only applied for IN and JOIN subqueries. Used only if a distributed table is used in the FROM clause. Not used for a table-valued remote function. The possible values \u200b\u200bare:", - "title": "distributed_product_mode" - }, - { - "location": "/index.html#fallback_to_stale_replicas_for_distributed_queries", - "text": "Forces a query to an out-of-date replica if updated data is not available. See \" Replication \". ClickHouse selects the most relevant from the outdated replicas of the table. Used when performing SELECT from a distributed table that points to replicated tables. By default, 1 (enabled).", - "title": "fallback_to_stale_replicas_for_distributed_queries" - }, - { - "location": "/index.html#force_index_by_date", - "text": "Disables query execution if the index can't be used by date. Works with tables in the MergeTree family. If force_index_by_date=1 , ClickHouse checks whether the query has a date key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For example, the condition Date != ' 2000-01-01 ' is acceptable even when it matches all the data in the table (i.e., running the query requires a full scan). For more information about ranges of data in MergeTree tables, see \" MergeTree \".", - "title": "force_index_by_date" - }, - { - "location": "/index.html#force_primary_key", - "text": "Disables query execution if indexing by the primary key is not possible. Works with tables in the MergeTree family. If force_primary_key=1 , ClickHouse checks to see if the query has a primary key condition that can be used for restricting data ranges. If there is no suitable condition, it throws an exception. However, it does not check whether the condition actually reduces the amount of data to read. For more information about data ranges in MergeTree tables, see \" MergeTree \".", - "title": "force_primary_key" - }, - { - "location": "/index.html#fsync_metadata", - "text": "Enable or disable fsync when writing .sql files. By default, it is enabled. It makes sense to disable it if the server has millions of tiny table chunks that are constantly being created and destroyed.", - "title": "fsync_metadata" - }, - { - "location": "/index.html#input_format_allow_errors_num", - "text": "Sets the maximum number of acceptable errors when reading from text formats (CSV, TSV, etc.). The default value is 0. Always pair it with input_format_allow_errors_ratio . To skip errors, both settings must be greater than 0. If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_num , ClickHouse ignores the row and moves on to the next one. If input_format_allow_errors_num is exceeded, ClickHouse throws an exception.", - "title": "input_format_allow_errors_num" - }, - { - "location": "/index.html#input_format_allow_errors_ratio", - "text": "Sets the maximum percentage of errors allowed when reading from text formats (CSV, TSV, etc.).\nThe percentage of errors is set as a floating-point number between 0 and 1. The default value is 0. Always pair it with input_format_allow_errors_num . To skip errors, both settings must be greater than 0. If an error occurred while reading rows but the error counter is still less than input_format_allow_errors_ratio , ClickHouse ignores the row and moves on to the next one. If input_format_allow_errors_ratio is exceeded, ClickHouse throws an exception.", - "title": "input_format_allow_errors_ratio" - }, - { - "location": "/index.html#max_block_size", - "text": "In ClickHouse, data is processed by blocks (sets of column parts). The internal processing cycles for a single block are efficient enough, but there are noticeable expenditures on each block. max_block_size is a recommendation for what size of block (in number of rows) to load from tables. The block size shouldn't be too small, so that the expenditures on each block are still noticeable, but not too large, so that the query with LIMIT that is completed after the first block is processed quickly, so that too much memory isn't consumed when extracting a large number of columns in multiple threads, and so that at least some cache locality is preserved. By default, 65,536. Blocks the size of max_block_size are not always loaded from the table. If it is obvious that less data needs to be retrieved, a smaller block is processed.", - "title": "max_block_size" - }, - { - "location": "/index.html#preferred_block_size_bytes", - "text": "Used for the same purpose as max_block_size , but it sets the recommended block size in bytes by adapting it to the number of rows in the block.\nHowever, the block size cannot be more than max_block_size rows.\nDisabled by default (set to 0). It only works when reading from MergeTree engines.", - "title": "preferred_block_size_bytes" - }, - { - "location": "/index.html#log_queries", - "text": "Setting up query the logging. Queries sent to ClickHouse with this setup are logged according to the rules in the query_log server configuration parameter. Example : log_queries=1", - "title": "log_queries" - }, - { - "location": "/index.html#max_insert_block_size", - "text": "The size of blocks to form for insertion into a table.\nThis setting only applies in cases when the server forms the blocks.\nFor example, for an INSERT via the HTTP interface, the server parses the data format and forms blocks of the specified size.\nBut when using clickhouse-client, the client parses the data itself, and the 'max_insert_block_size' setting on the server doesn't affect the size of the inserted blocks.\nThe setting also doesn't have a purpose when using INSERT SELECT, since data is inserted using the same blocks that are formed after SELECT. By default, it is 1,048,576. This is slightly more than max_block_size . The reason for this is because certain table engines ( *MergeTree ) form a data part on the disk for each inserted block, which is a fairly large entity. Similarly, *MergeTree tables sort data during insertion, and a large enough block size allows sorting more data in RAM.", - "title": "max_insert_block_size" - }, - { - "location": "/index.html#max_replica_delay_for_distributed_queries", - "text": "Disables lagging replicas for distributed queries. See \" Replication \". Sets the time in seconds. If a replica lags more than the set value, this replica is not used. Default value: 0 (off). Used when performing SELECT from a distributed table that points to replicated tables.", - "title": "max_replica_delay_for_distributed_queries" - }, - { - "location": "/index.html#max_threads", - "text": "The maximum number of query processing threads excluding threads for retrieving data from remote servers (see the 'max_distributed_connections' parameter). This parameter applies to threads that perform the same stages of the query processing pipeline in parallel.\nFor example, if reading from a table, evaluating expressions with functions, filtering with WHERE and pre-aggregating for GROUP BY can all be done in parallel using at least 'max_threads' number of threads, then 'max_threads' are used. By default, 8. If less than one SELECT query is normally run on a server at a time, set this parameter to a value slightly less than the actual number of processor cores. For queries that are completed quickly because of a LIMIT, you can set a lower 'max_threads'. For example, if the necessary number of entries are located in every block and max_threads = 8, 8 blocks are retrieved, although it would have been enough to read just one. The smaller the max_threads value, the less memory is consumed.", - "title": "max_threads" - }, - { - "location": "/index.html#max_compress_block_size", - "text": "The maximum size of blocks of uncompressed data before compressing for writing to a table. By default, 1,048,576 (1 MiB). If the size is reduced, the compression rate is significantly reduced, the compression and decompression speed increases slightly due to cache locality, and memory consumption is reduced. There usually isn't any reason to change this setting. Don't confuse blocks for compression (a chunk of memory consisting of bytes) and blocks for query processing (a set of rows from a table).", - "title": "max_compress_block_size" - }, - { - "location": "/index.html#min_compress_block_size", - "text": "For MergeTree \" tables. In order to reduce latency when processing queries, a block is compressed when writing the next mark if its size is at least 'min_compress_block_size'. By default, 65,536. The actual size of the block, if the uncompressed data is less than 'max_compress_block_size', is no less than this value and no less than the volume of data for one mark. Let's look at an example. Assume that 'index_granularity' was set to 8192 during table creation. We are writing a UInt32-type column (4 bytes per value). When writing 8192 rows, the total will be 32 KB of data. Since min_compress_block_size = 65,536, a compressed block will be formed for every two marks. We are writing a URL column with the String type (average size of 60 bytes per value). When writing 8192 rows, the average will be slightly less than 500 KB of data. Since this is more than 65,536, a compressed block will be formed for each mark. In this case, when reading data from the disk in the range of a single mark, extra data won't be decompressed. There usually isn't any reason to change this setting.", - "title": "min_compress_block_size" - }, - { - "location": "/index.html#max_query_size", - "text": "The maximum part of a query that can be taken to RAM for parsing with the SQL parser.\nThe INSERT query also contains data for INSERT that is processed by a separate stream parser (that consumes O(1) RAM), which is not included in this restriction. The default is 256 KiB.", - "title": "max_query_size" - }, - { - "location": "/index.html#interactive_delay", - "text": "The interval in microseconds for checking whether request execution has been canceled and sending the progress. By default, 100,000 (check for canceling and send progress ten times per second).", - "title": "interactive_delay" - }, - { - "location": "/index.html#connect_timeout", - "text": "", - "title": "connect_timeout" - }, - { - "location": "/index.html#receive_timeout", - "text": "", - "title": "receive_timeout" - }, - { - "location": "/index.html#send_timeout", - "text": "Timeouts in seconds on the socket used for communicating with the client. By default, 10, 300, 300.", - "title": "send_timeout" - }, - { - "location": "/index.html#poll_interval", - "text": "Lock in a wait loop for the specified number of seconds. By default, 10.", - "title": "poll_interval" - }, - { - "location": "/index.html#max_distributed_connections", - "text": "The maximum number of simultaneous connections with remote servers for distributed processing of a single query to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster. By default, 100. The following parameters are only used when creating Distributed tables (and when launching a server), so there is no reason to change them at runtime.", - "title": "max_distributed_connections" - }, - { - "location": "/index.html#distributed_connections_pool_size", - "text": "The maximum number of simultaneous connections with remote servers for distributed processing of all queries to a single Distributed table. We recommend setting a value no less than the number of servers in the cluster. By default, 128.", - "title": "distributed_connections_pool_size" - }, - { - "location": "/index.html#connect_timeout_with_failover_ms", - "text": "The timeout in milliseconds for connecting to a remote server for a Distributed table engine, if the 'shard' and 'replica' sections are used in the cluster definition.\nIf unsuccessful, several attempts are made to connect to various replicas. By default, 50.", - "title": "connect_timeout_with_failover_ms" - }, - { - "location": "/index.html#connections_with_failover_max_tries", - "text": "The maximum number of connection attempts with each replica, for the Distributed table engine. By default, 3.", - "title": "connections_with_failover_max_tries" - }, - { - "location": "/index.html#extremes", - "text": "Whether to count extreme values (the minimums and maximums in columns of a query result). Accepts 0 or 1. By default, 0 (disabled).\nFor more information, see the section \"Extreme values\".", - "title": "extremes" - }, - { - "location": "/index.html#use_uncompressed_cache", - "text": "Whether to use a cache of uncompressed blocks. Accepts 0 or 1. By default, 0 (disabled).\nThe uncompressed cache (only for tables in the MergeTree family) allows significantly reducing latency and increasing throughput when working with a large number of short queries. Enable this setting for users who send frequent short requests. Also pay attention to the 'uncompressed_cache_size' configuration parameter (only set in the config file) \u2013 the size of uncompressed cache blocks. By default, it is 8 GiB. The uncompressed cache is filled in as needed; the least-used data is automatically deleted. For queries that read at least a somewhat large volume of data (one million rows or more), the uncompressed cache is disabled automatically in order to save space for truly small queries. So you can keep the 'use_uncompressed_cache' setting always set to 1.", - "title": "use_uncompressed_cache" - }, - { - "location": "/index.html#replace_running_query", - "text": "When using the HTTP interface, the 'query_id' parameter can be passed. This is any string that serves as the query identifier.\nIf a query from the same user with the same 'query_id' already exists at this time, the behavior depends on the 'replace_running_query' parameter. 0 (default) \u2013 Throw an exception (don't allow the query to run if a query with the same 'query_id' is already running). 1 \u2013 Cancel the old query and start running the new one. Yandex.Metrica uses this parameter set to 1 for implementing suggestions for segmentation conditions. After entering the next character, if the old query hasn't finished yet, it should be canceled.", - "title": "replace_running_query" - }, - { - "location": "/index.html#schema", - "text": "This parameter is useful when you are using formats that require a schema definition, such as Cap'n Proto . The value depends on the format.", - "title": "schema" - }, - { - "location": "/index.html#stream_flush_interval_ms", - "text": "Works for tables with streaming in the case of a timeout, or when a thread generates max_insert_block_size rows. The default value is 7500. The smaller the value, the more often data is flushed into the table. Setting the value too low leads to poor performance.", - "title": "stream_flush_interval_ms" - }, - { - "location": "/index.html#load_balancing", - "text": "Which replicas (among healthy replicas) to preferably send a query to (on the first attempt) for distributed processing.", - "title": "load_balancing" - }, - { - "location": "/index.html#random-default", - "text": "The number of errors is counted for each replica. The query is sent to the replica with the fewest errors, and if there are several of these, to any one of them.\nDisadvantages: Server proximity is not accounted for; if the replicas have different data, you will also get different data.", - "title": "random (default)" - }, - { - "location": "/index.html#nearest_hostname", - "text": "The number of errors is counted for each replica. Every 5 minutes, the number of errors is integrally divided by 2. Thus, the number of errors is calculated for a recent time with exponential smoothing. If there is one replica with a minimal number of errors (i.e. errors occurred recently on the other replicas), the query is sent to it. If there are multiple replicas with the same minimal number of errors, the query is sent to the replica with a host name that is most similar to the server's host name in the config file (for the number of different characters in identical positions, up to the minimum length of both host names). For instance, example01-01-1 and example01-01-2.yandex.ru are different in one position, while example01-01-1 and example01-02-2 differ in two places.\nThis method might seem a little stupid, but it doesn't use external data about network topology, and it doesn't compare IP addresses, which would be complicated for our IPv6 addresses. Thus, if there are equivalent replicas, the closest one by name is preferred.\nWe can also assume that when sending a query to the same server, in the absence of failures, a distributed query will also go to the same servers. So even if different data is placed on the replicas, the query will return mostly the same results.", - "title": "nearest_hostname" - }, - { - "location": "/index.html#in_order", - "text": "Replicas are accessed in the same order as they are specified. The number of errors does not matter.\nThis method is appropriate when you know exactly which replica is preferable.", - "title": "in_order" - }, - { - "location": "/index.html#totals_mode", - "text": "How to calculate TOTALS when HAVING is present, as well as when max_rows_to_group_by and group_by_overflow_mode = 'any' are present.\nSee the section \"WITH TOTALS modifier\".", - "title": "totals_mode" - }, - { - "location": "/index.html#totals_auto_threshold", - "text": "The threshold for totals_mode = 'auto' .\nSee the section \"WITH TOTALS modifier\".", - "title": "totals_auto_threshold" - }, - { - "location": "/index.html#default_sample", - "text": "Floating-point number from 0 to 1. By default, 1.\nAllows you to set the default sampling ratio for all SELECT queries.\n(For tables that do not support sampling, it throws an exception.)\nIf set to 1, sampling is not performed by default.", - "title": "default_sample" - }, - { - "location": "/index.html#max_parallel_replicas", - "text": "The maximum number of replicas for each shard when executing a query.\nFor consistency (to get different parts of the same data split), this option only works when the sampling key is set.\nReplica lag is not controlled.", - "title": "max_parallel_replicas" - }, - { - "location": "/index.html#compile", - "text": "Enable compilation of queries. By default, 0 (disabled). Compilation is only used for part of the query-processing pipeline: for the first stage of aggregation (GROUP BY).\nIf this portion of the pipeline was compiled, the query may run faster due to deployment of short cycles and inlining aggregate function calls. The maximum performance improvement (up to four times faster in rare cases) is seen for queries with multiple simple aggregate functions. Typically, the performance gain is insignificant. In very rare cases, it may slow down query execution.", - "title": "compile" - }, - { - "location": "/index.html#min_count_to_compile", - "text": "How many times to potentially use a compiled chunk of code before running compilation. By default, 3.\nIf the value is zero, then compilation runs synchronously and the query waits for the end of the compilation process before continuing execution. This can be used for testing; otherwise, use values \u200b\u200bstarting with 1. Compilation normally takes about 5-10 seconds.\nIf the value is 1 or more, compilation occurs asynchronously in a separate thread. The result will be used as soon as it is ready, including by queries that are currently running. Compiled code is required for each different combination of aggregate functions used in the query and the type of keys in the GROUP BY clause.\nThe results of compilation are saved in the build directory in the form of .so files. There is no restriction on the number of compilation results, since they don't use very much space. Old results will be used after server restarts, except in the case of a server upgrade \u2013 in this case, the old results are deleted.", - "title": "min_count_to_compile" - }, - { - "location": "/index.html#input_format_skip_unknown_fields", - "text": "If the value is true, running INSERT skips input data from columns with unknown names. Otherwise, this situation will generate an exception.\nIt works for JSONEachRow and TSKV formats.", - "title": "input_format_skip_unknown_fields" - }, - { - "location": "/index.html#output_format_json_quote_64bit_integers", - "text": "If the value is true, integers appear in quotes when using JSON* Int64 and UInt64 formats (for compatibility with most JavaScript implementations); otherwise, integers are output without the quotes.", - "title": "output_format_json_quote_64bit_integers" - }, - { - "location": "/index.html#format_csv_delimiter", - "text": "The character to be considered as a delimiter in CSV data. By default, , .", - "title": "format_csv_delimiter" - }, - { - "location": "/index.html#settings-profiles", - "text": "A settings profile is a collection of settings grouped under the same name. Each ClickHouse user has a profile.\nTo apply all the settings in a profile, set profile . Example: Setting web profile. SET profile = web Settings profiles are declared in the user config file. This is usually users.xml . Example: !-- Settings profiles -- profiles \n !-- Default settings -- \n default \n !-- The maximum number of threads when running a single query. -- \n max_threads 8 /max_threads \n /default \n\n !-- Settings for quries from the user interface -- \n web \n max_rows_to_read 1000000000 /max_rows_to_read \n max_bytes_to_read 100000000000 /max_bytes_to_read \n\n max_rows_to_group_by 1000000 /max_rows_to_group_by \n group_by_overflow_mode any /group_by_overflow_mode \n\n max_rows_to_sort 1000000 /max_rows_to_sort \n max_bytes_to_sort 1000000000 /max_bytes_to_sort \n\n max_result_rows 100000 /max_result_rows \n max_result_bytes 100000000 /max_result_bytes \n result_overflow_mode break /result_overflow_mode \n\n max_execution_time 600 /max_execution_time \n min_execution_speed 1000000 /min_execution_speed \n timeout_before_checking_execution_speed 15 /timeout_before_checking_execution_speed \n\n max_columns_to_read 25 /max_columns_to_read \n max_temporary_columns 100 /max_temporary_columns \n max_temporary_non_const_columns 50 /max_temporary_non_const_columns \n\n max_subquery_depth 2 /max_subquery_depth \n max_pipeline_depth 25 /max_pipeline_depth \n max_ast_depth 50 /max_ast_depth \n max_ast_elements 100 /max_ast_elements \n\n readonly 1 /readonly \n /web /profiles The example specifies two profiles: default and web . The default profile has a special purpose: it must always be present and is applied when starting the server. In other words, the default profile contains default settings. The web profile is a regular profile that can be set using the SET query or using a URL parameter in an HTTP query. Settings profiles can inherit from each other. To use inheritance, indicate the profile setting before the other settings that are listed in the profile.", - "title": "Settings profiles" - }, - { - "location": "/index.html#clickhouse-utility", - "text": "clickhouse-local \u2014 Allows running SQL queries on data without stopping the ClickHouse server, similar to how awk does this. clickhouse-copier \u2014 Copies (and reshards) data from one cluster to another cluster.", - "title": "ClickHouse utility" - }, - { - "location": "/index.html#clickhouse-copier", - "text": "Copies data from the tables in one cluster to tables in another (or the same) cluster. You can run multiple clickhouse-copier instances on different servers to perform the same job. ZooKeeper is used for syncing the processes. After starting, clickhouse-copier : Connects to ZooKeeper and receives: Copying jobs. The state of the copying jobs. It performs the jobs. Each running process chooses the \"closest\" shard of the source cluster and copies the data into the destination cluster, resharding the data if necessary. clickhouse-copier tracks the changes in ZooKeeper and applies them on the fly. To reduce network traffic, we recommend running clickhouse-copier on the same server where the source data is located.", - "title": "clickhouse-copier" - }, - { - "location": "/index.html#running-clickhouse-copier", - "text": "The utility should be run manually: clickhouse-copier copier --daemon --config zookeeper.xml --task-path /task/path --base-dir /path/to/dir Parameters: daemon \u2014 Starts clickhouse-copier in daemon mode. config \u2014 The path to the zookeeper.xml file with the parameters for the connection to ZooKeeper. task-path \u2014 The path to the ZooKeeper node. This node is used for syncing clickhouse-copier processes and storing tasks. Tasks are stored in $task-path/description . base-dir \u2014 The path to logs and auxiliary files. When it starts, clickhouse-copier creates clickhouse-copier_YYYYMMHHSS_ PID subdirectories in $base-dir . If this parameter is omitted, the directories are created in the directory where clickhouse-copier was launched.", - "title": "Running clickhouse-copier" - }, - { - "location": "/index.html#format-of-zookeeperxml", - "text": "yandex \n zookeeper \n node index= 1 \n host 127.0.0.1 /host \n port 2181 /port \n /node \n /zookeeper /yandex", - "title": "Format of zookeeper.xml" - }, - { - "location": "/index.html#configuration-of-copying-tasks", - "text": "yandex \n !-- Configuration of clusters as in an ordinary server config -- \n remote_servers \n source_cluster \n shard \n internal_replication false /internal_replication \n replica \n host 127.0.0.1 /host \n port 9000 /port \n /replica \n /shard \n ...\n /source_cluster \n\n destination_cluster \n ...\n /destination_cluster \n /remote_servers \n\n !-- How many simultaneously active workers are possible. If you run more workers superfluous workers will sleep. -- \n max_workers 2 /max_workers \n\n !-- Setting used to fetch (pull) data from source cluster tables -- \n settings_pull \n readonly 1 /readonly \n /settings_pull \n\n !-- Setting used to insert (push) data to destination cluster tables -- \n settings_push \n readonly 0 /readonly \n /settings_push \n\n !-- Common setting for fetch (pull) and insert (push) operations. The copier process context also uses it. They are overlaid by settings_pull/ and settings_push/ respectively. -- \n settings \n connect_timeout 3 /connect_timeout \n !-- Sync insert is set forcibly, leave it here just in case. -- \n insert_distributed_sync 1 /insert_distributed_sync \n /settings \n\n !-- Copying description of tasks. You can specify several table tasks in the same task description (in the same ZooKeeper node), and they will be performed sequentially. -- \n tables \n !-- A table task that copies one table. -- \n table_hits \n !-- Source cluster name (from the remote_servers/ section) and tables in it that should be copied -- \n cluster_pull source_cluster /cluster_pull \n database_pull test /database_pull \n table_pull hits /table_pull \n\n !-- Destination cluster name and tables in which the data should be inserted -- \n cluster_push destination_cluster /cluster_push \n database_push test /database_push \n table_push hits2 /table_push \n\n !-- Engine of destination tables. If the destination tables have not been created yet, workers create them using column definitions from source tables and the engine definition from here. NOTE: If the first worker starts to insert data and detects that the destination partition is not empty, then the partition will be dropped and refilled. Take this into account if you already have some data in destination tables. You can directly specify partitions that should be copied in enabled_partitions/ . They should be in quoted format like the partition column in the system.parts table. -- \n engine \n ENGINE=ReplicatedMergeTree( /clickhouse/tables/{cluster}/{shard}/hits2 , {replica} )\n PARTITION BY toMonday(date)\n ORDER BY (CounterID, EventDate)\n /engine \n\n !-- Sharding key used to insert data to destination cluster -- \n sharding_key jumpConsistentHash(intHash64(UserID), 2) /sharding_key \n\n !-- Optional expression that filter data while pull them from source servers -- \n where_condition CounterID != 0 /where_condition \n\n !-- This section specifies partitions that should be copied, other partition will be ignored. Partition names should have the same format as partition column of system.parts table (i.e. a quoted text). Since partition key of source and destination cluster could be different, these partition names specify destination partitions. Note: Although this section is optional (if it omitted, all partitions will be copied), it is strongly recommended to specify the partitions explicitly. If you already have some partitions ready on the destination cluster, they will be removed at the start of the copying, because they will be interpreted as unfinished data from the previous copying. -- \n enabled_partitions \n partition 2018-02-26 /partition \n partition 2018-03-05 /partition \n ...\n /enabled_partitions \n /table_hits \n\n !-- Next table to copy. It is not copied until the previous table is copying. -- \n /table_visits \n ...\n /table_visits \n ...\n /tables /yandex clickhouse-copier tracks the changes in /task/path/description and applies them on the fly. For instance, if you change the value of max_workers , the number of processes running tasks will also change.", - "title": "Configuration of copying tasks" - }, - { - "location": "/index.html#clickhouse-local", - "text": "The clickhouse-local program enables you to perform fast processing on local files that store tables, without having to deploy and configure the ClickHouse server.", - "title": "clickhouse-local" - }, - { - "location": "/index.html#clickhouse-development", - "text": "", - "title": "ClickHouse Development" - }, - { - "location": "/index.html#overview-of-clickhouse-architecture", - "text": "ClickHouse is a true column-oriented DBMS. Data is stored by columns, and during the execution of arrays (vectors or chunks of columns). Whenever possible, operations are dispatched on arrays, rather than on individual values. This is called \"vectorized query execution,\" and it helps lower the cost of actual data processing. This idea is nothing new. It dates back to the APL programming language and its descendants: A + , J , K , and Q . Array programming is used in scientific data processing. Neither is this idea something new in relational databases: for example, it is used in the Vectorwise system. There are two different approaches for speeding up the query processing: vectorized query execution and runtime code generation. In the latter, the code is generated for every kind of query on the fly, removing all indirection and dynamic dispatch. Neither of these approaches is strictly better than the other. Runtime code generation can be better when it's fuses many operations together, thus fully utilizing CPU execution units and the pipeline. Vectorized query execution can be less practical, because it involves the temporary vectors that must be written to the cache and read back. If the temporary data does not fit in the L2 cache, this becomes an issue. But vectorized query execution more easily utilizes the SIMD capabilities of the CPU. A research paper written by our friends shows that it is better to combine both approaches. ClickHouse uses vectorized query execution and has limited initial support for runtime code.", - "title": "Overview of ClickHouse architecture" - }, - { - "location": "/index.html#columns", - "text": "To represent columns in memory (actually, chunks of columns), the IColumn interface is used. This interface provides helper methods for implementation of various relational operators. Almost all operations are immutable: they do not modify the original column, but create a new modified one. For example, the IColumn :: filter method accepts a filter byte mask. It is used for the WHERE and HAVING relational operators. Additional examples: the IColumn :: permute method to support ORDER BY , the IColumn :: cut method to support LIMIT , and so on. Various IColumn implementations ( ColumnUInt8 , ColumnString and so on) are responsible for the memory layout of columns. Memory layout is usually a contiguous array. For the integer type of columns it is just one contiguous array, like std :: vector . For String and Array columns, it is two vectors: one for all array elements, placed contiguously, and a second one for offsets to the beginning of each array. There is also ColumnConst that stores just one value in memory, but looks like a column.", - "title": "Columns" - }, - { - "location": "/index.html#field", - "text": "Nevertheless, it is possible to work with individual values as well. To represent an individual value, the Field is used. Field is just a discriminated union of UInt64 , Int64 , Float64 , String and Array . IColumn has the operator[] method to get the n-th value as a Field , and the insert method to append a Field to the end of a column. These methods are not very efficient, because they require dealing with temporary Field objects representing an individual value. There are more efficient methods, such as insertFrom , insertRangeFrom , and so on. Field doesn't have enough information about a specific data type for a table. For example, UInt8 , UInt16 , UInt32 , and UInt64 are all represented as UInt64 in a Field .", - "title": "Field" - }, - { - "location": "/index.html#leaky-abstractions", - "text": "IColumn has methods for common relational transformations of data, but they don't meet all needs. For example, ColumnUInt64 doesn't have a method to calculate the sum of two columns, and ColumnString doesn't have a method to run a substring search. These countless routines are implemented outside of IColumn . Various functions on columns can be implemented in a generic, non-efficient way using IColumn methods to extract Field values, or in a specialized way using knowledge of inner memory layout of data in a specific IColumn implementation. To do this, functions are cast to a specific IColumn type and deal with internal representation directly. For example, ColumnUInt64 has the getData method that returns a reference to an internal array, then a separate routine reads or fills that array directly. In fact, we have \"leaky abstractions\" to allow efficient specializations of various routines.", - "title": "Leaky abstractions" - }, - { - "location": "/index.html#data-types_1", - "text": "IDataType is responsible for serialization and deserialization: for reading and writing chunks of columns or individual values in binary or text form. IDataType directly corresponds to data types in tables. For example, there are DataTypeUInt32 , DataTypeDateTime , DataTypeString and so on. IDataType and IColumn are only loosely related to each other. Different data types can be represented in memory by the same IColumn implementations. For example, DataTypeUInt32 and DataTypeDateTime are both represented by ColumnUInt32 or ColumnConstUInt32 . In addition, the same data type can be represented by different IColumn implementations. For example, DataTypeUInt8 can be represented by ColumnUInt8 or ColumnConstUInt8 . IDataType only stores metadata. For instance, DataTypeUInt8 doesn't store anything at all (except vptr) and DataTypeFixedString stores just N (the size of fixed-size strings). IDataType has helper methods for various data formats. Examples are methods to serialize a value with possible quoting, to serialize a value for JSON, and to serialize a value as part of XML format. There is no direct correspondence to data formats. For example, the different data formats Pretty and TabSeparated can use the same serializeTextEscaped helper method from the IDataType interface.", - "title": "Data types" - }, - { - "location": "/index.html#block", - "text": "A Block is a container that represents a subset (chunk) of a table in memory. It is just a set of triples: (IColumn, IDataType, column name) . During query execution, data is processed by Block s. If we have a Block , we have data (in the IColumn object), we have information about its type (in IDataType ) that tells us how to deal with that column, and we have the column name (either the original column name from the table, or some artificial name assigned for getting temporary results of calculations). When we calculate some function over columns in a block, we add another column with its result to the block, and we don't touch columns for arguments of the function because operations are immutable. Later, unneeded columns can be removed from the block, but not modified. This is convenient for elimination of common subexpressions. Blocks are created for every processed chunk of data. Note that for the same type of calculation, the column names and types remain the same for different blocks, and only column data changes. It is better to split block data from the block header, because small block sizes will have a high overhead of temporary strings for copying shared_ptrs and column names.", - "title": "Block" - }, - { - "location": "/index.html#block-streams", - "text": "Block streams are for processing data. We use streams of blocks to read data from somewhere, perform data transformations, or write data to somewhere. IBlockInputStream has the read method to fetch the next block while available. IBlockOutputStream has the write method to push the block somewhere. Streams are responsible for: Reading or writing to a table. The table just returns a stream for reading or writing blocks. Implementing data formats. For example, if you want to output data to a terminal in Pretty format, you create a block output stream where you push blocks, and it formats them. Performing data transformations. Let's say you have IBlockInputStream and want to create a filtered stream. You create FilterBlockInputStream and initialize it with your stream. Then when you pull a block from FilterBlockInputStream , it pulls a block from your stream, filters it, and returns the filtered block to you. Query execution pipelines are represented this way. There are more sophisticated transformations. For example, when you pull from AggregatingBlockInputStream , it reads all data from its source, aggregates it, and then returns a stream of aggregated data for you. Another example: UnionBlockInputStream accepts many input sources in the constructor and also a number of threads. It launches multiple threads and reads from multiple sources in parallel. Block streams use the \"pull\" approach to control flow: when you pull a block from the first stream, it consequently pulls the required blocks from nested streams, and the entire execution pipeline will work. Neither \"pull\" nor \"push\" is the best solution, because control flow is implicit, and that limits implementation of various features like simultaneous execution of multiple queries (merging many pipelines together). This limitation could be overcome with coroutines or just running extra threads that wait for each other. We may have more possibilities if we make control flow explicit: if we locate the logic for passing data from one calculation unit to another outside of those calculation units. Read this article for more thoughts. We should note that the query execution pipeline creates temporary data at each step. We try to keep block size small enough so that temporary data fits in the CPU cache. With that assumption, writing and reading temporary data is almost free in comparison with other calculations. We could consider an alternative, which is to fuse many operations in the pipeline together, to make the pipeline as short as possible and remove much of the temporary data. This could be an advantage, but it also has drawbacks. For example, a split pipeline makes it easy to implement caching intermediate data, stealing intermediate data from similar queries running at the same time, and merging pipelines for similar queries.", - "title": "Block Streams" - }, - { - "location": "/index.html#formats_1", - "text": "Data formats are implemented with block streams. There are \"presentational\" formats only suitable for output of data to the client, such as Pretty format, which provides only IBlockOutputStream . And there are input/output formats, such as TabSeparated or JSONEachRow . There are also row streams: IRowInputStream and IRowOutputStream . They allow you to pull/push data by individual rows, not by blocks. And they are only needed to simplify implementation of row-oriented formats. The wrappers BlockInputStreamFromRowInputStream and BlockOutputStreamFromRowOutputStream allow you to convert row-oriented streams to regular block-oriented streams.", - "title": "Formats" - }, - { - "location": "/index.html#io", - "text": "For byte-oriented input/output, there are ReadBuffer and WriteBuffer abstract classes. They are used instead of C++ iostream 's. Don't worry: every mature C++ project is using something other than iostream 's for good reasons. ReadBuffer and WriteBuffer are just a contiguous buffer and a cursor pointing to the position in that buffer. Implementations may own or not own the memory for the buffer. There is a virtual method to fill the buffer with the following data (for ReadBuffer ) or to flush the buffer somewhere (for WriteBuffer ). The virtual methods are rarely called. Implementations of ReadBuffer / WriteBuffer are used for working with files and file descriptors and network sockets, for implementing compression ( CompressedWriteBuffer is initialized with another WriteBuffer and performs compression before writing data to it), and for other purposes \u2013 the names ConcatReadBuffer , LimitReadBuffer , and HashingWriteBuffer speak for themselves. Read/WriteBuffers only deal with bytes. To help with formatted input/output (for instance, to write a number in decimal format), there are functions from ReadHelpers and WriteHelpers header files. Let's look at what happens when you want to write a result set in JSON format to stdout. You have a result set ready to be fetched from IBlockInputStream . You create WriteBufferFromFileDescriptor(STDOUT_FILENO) to write bytes to stdout. You create JSONRowOutputStream , initialized with that WriteBuffer , to write rows in JSON to stdout. You create BlockOutputStreamFromRowOutputStream on top of it, to represent it as IBlockOutputStream . Then you call copyData to transfer data from IBlockInputStream to IBlockOutputStream , and everything works. Internally, JSONRowOutputStream will write various JSON delimiters and call the IDataType::serializeTextJSON method with a reference to IColumn and the row number as arguments. Consequently, IDataType::serializeTextJSON will call a method from WriteHelpers.h : for example, writeText for numeric types and writeJSONString for DataTypeString .", - "title": "I/O" - }, - { - "location": "/index.html#tables", - "text": "Tables are represented by the IStorage interface. Different implementations of that interface are different table engines. Examples are StorageMergeTree , StorageMemory , and so on. Instances of these classes are just tables. The most important IStorage methods are read and write . There are also alter , rename , drop , and so on. The read method accepts the following arguments: the set of columns to read from a table, the AST query to consider, and the desired number of streams to return. It returns one or multiple IBlockInputStream objects and information about the stage of data processing that was completed inside a table engine during query execution. In most cases, the read method is only responsible for reading the specified columns from a table, not for any further data processing. All further data processing is done by the query interpreter and is outside the responsibility of IStorage . But there are notable exceptions: The AST query is passed to the read method and the table engine can use it to derive index usage and to read less data from a table. Sometimes the table engine can process data itself to a specific stage. For example, StorageDistributed can send a query to remote servers, ask them to process data to a stage where data from different remote servers can be merged, and return that preprocessed data.\nThe query interpreter then finishes processing the data. The table's read method can return multiple IBlockInputStream objects to allow parallel data processing. These multiple block input streams can read from a table in parallel. Then you can wrap these streams with various transformations (such as expression evaluation or filtering) that can be calculated independently and create a UnionBlockInputStream on top of them, to read from multiple streams in parallel. There are also TableFunction s. These are functions that return a temporary IStorage object to use in the FROM clause of a query. To get a quick idea of how to implement your own table engine, look at something simple, like StorageMemory or StorageTinyLog . As the result of the read method, IStorage returns QueryProcessingStage \u2013 information about what parts of the query were already calculated inside storage. Currently we have only very coarse granularity for that information. There is no way for the storage to say \"I have already processed this part of the expression in WHERE, for this range of data\". We need to work on that.", - "title": "Tables" - }, - { - "location": "/index.html#parsers", - "text": "A query is parsed by a hand-written recursive descent parser. For example, ParserSelectQuery just recursively calls the underlying parsers for various parts of the query. Parsers create an AST . The AST is represented by nodes, which are instances of IAST . Parser generators are not used for historical reasons.", - "title": "Parsers" - }, - { - "location": "/index.html#interpreters", - "text": "Interpreters are responsible for creating the query execution pipeline from an AST . There are simple interpreters, such as InterpreterExistsQuery and InterpreterDropQuery , or the more sophisticated InterpreterSelectQuery . The query execution pipeline is a combination of block input or output streams. For example, the result of interpreting the SELECT query is the IBlockInputStream to read the result set from; the result of the INSERT query is the IBlockOutputStream to write data for insertion to; and the result of interpreting the INSERT SELECT query is the IBlockInputStream that returns an empty result set on the first read, but that copies data from SELECT to INSERT at the same time. InterpreterSelectQuery uses ExpressionAnalyzer and ExpressionActions machinery for query analysis and transformations. This is where most rule-based query optimizations are done. ExpressionAnalyzer is quite messy and should be rewritten: various query transformations and optimizations should be extracted to separate classes to allow modular transformations or query.", - "title": "Interpreters" - }, - { - "location": "/index.html#functions_2", - "text": "There are ordinary functions and aggregate functions. For aggregate functions, see the next section. Ordinary functions don't change the number of rows \u2013 they work as if they are processing each row independently. In fact, functions are not called for individual rows, but for Block 's of data to implement vectorized query execution. There are some miscellaneous functions, like blockSize , rowNumberInBlock , and runningAccumulate , that exploit block processing and violate the independence of rows. ClickHouse has strong typing, so implicit type conversion doesn't occur. If a function doesn't support a specific combination of types, an exception will be thrown. But functions can work (be overloaded) for many different combinations of types. For example, the plus function (to implement the + operator) works for any combination of numeric types: UInt8 + Float32 , UInt16 + Int8 , and so on. Also, some variadic functions can accept any number of arguments, such as the concat function. Implementing a function may be slightly inconvenient because a function explicitly dispatches supported data types and supported IColumns . For example, the plus function has code generated by instantiation of a C++ template for each combination of numeric types, and for constant or non-constant left and right arguments. This is a nice place to implement runtime code generation to avoid template code bloat. Also, it will make it possible to add fused functions like fused multiply-add, or to make multiple comparisons in one loop iteration. Due to vectorized query execution, functions are not short-circuit. For example, if you write WHERE f(x) AND g(y) , both sides will be calculated, even for rows, when f(x) is zero (except when f(x) is a zero constant expression). But if selectivity of the f(x) condition is high, and calculation of f(x) is much cheaper than g(y) , it's better to implement multi-pass calculation: first calculate f(x) , then filter columns by the result, and then calculate g(y) only for smaller, filtered chunks of data.", - "title": "Functions" - }, - { - "location": "/index.html#aggregate-functions_1", - "text": "Aggregate functions are stateful functions. They accumulate passed values into some state, and allow you to get results from that state. They are managed with the IAggregateFunction interface. States can be rather simple (the state for AggregateFunctionCount is just a single UInt64 value) or quite complex (the state of AggregateFunctionUniqCombined is a combination of a linear array, a hash table and a HyperLogLog probabilistic data structure). To deal with multiple states while executing a high-cardinality GROUP BY query, states are allocated in Arena (a memory pool), or they could be allocated in any suitable piece of memory. States can have a non-trivial constructor and destructor: for example, complex aggregation states can allocate additional memory themselves. This requires some attention to creating and destroying states and properly passing their ownership, to keep track of who and when will destroy states. Aggregation states can be serialized and deserialized to pass over the network during distributed query execution or to write them on disk where there is not enough RAM. They can even be stored in a table with the DataTypeAggregateFunction to allow incremental aggregation of data. The serialized data format for aggregate function states is not versioned right now. This is ok if aggregate states are only stored temporarily. But we have the AggregatingMergeTree table engine for incremental aggregation, and people are already using it in production. This is why we should add support for backward compatibility when changing the serialized format for any aggregate function in the future.", - "title": "Aggregate Functions" - }, - { - "location": "/index.html#server", - "text": "The server implements several different interfaces: An HTTP interface for any foreign clients. A TCP interface for the native ClickHouse client and for cross-server communication during distributed query execution. An interface for transferring data for replication. Internally, it is just a basic multithreaded server without coroutines, fibers, etc. Since the server is not designed to process a high rate of simple queries but is intended to process a relatively low rate of complex queries, each of them can process a vast amount of data for analytics. The server initializes the Context class with the necessary environment for query execution: the list of available databases, users and access rights, settings, clusters, the process list, the query log, and so on. This environment is used by interpreters. We maintain full backward and forward compatibility for the server TCP protocol: old clients can talk to new servers and new clients can talk to old servers. But we don't want to maintain it eternally, and we are removing support for old versions after about one year. For all external applications, we recommend using the HTTP interface because it is simple and easy to use. The TCP protocol is more tightly linked to internal data structures: it uses an internal format for passing blocks of data and it uses custom framing for compressed data. We haven't released a C library for that protocol because it requires linking most of the ClickHouse codebase, which is not practical.", - "title": "Server" - }, - { - "location": "/index.html#distributed-query-execution", - "text": "Servers in a cluster setup are mostly independent. You can create a Distributed table on one or all servers in a cluster. The Distributed table does not store data itself \u2013 it only provides a \"view\" to all local tables on multiple nodes of a cluster. When you SELECT from a Distributed table, it rewrites that query, chooses remote nodes according to load balancing settings, and sends the query to them. The Distributed table requests remote servers to process a query just up to a stage where intermediate results from different servers can be merged. Then it receives the intermediate results and merges them. The distributed table tries to distribute as much work as possible to remote servers, and does not send much intermediate data over the network. Things become more complicated when you have subqueries in IN or JOIN clauses and each of them uses a Distributed table. We have different strategies for execution of these queries. There is no global query plan for distributed query execution. Each node has its own local query plan for its part of the job. We only have simple one-pass distributed query execution: we send queries for remote nodes and then merge the results. But this is not feasible for difficult queries with high cardinality GROUP BYs or with a large amount of temporary data for JOIN: in such cases, we need to \"reshuffle\" data between servers, which requires additional coordination. ClickHouse does not support that kind of query execution, and we need to work on it.", - "title": "Distributed query execution" - }, - { - "location": "/index.html#merge-tree", - "text": "MergeTree is a family of storage engines that supports indexing by primary key. The primary key can be an arbitary tuple of columns or expressions. Data in a MergeTree table is stored in \"parts\". Each part stores data in the primary key order (data is ordered lexicographically by the primary key tuple). All the table columns are stored in separate column.bin files in these parts. The files consist of compressed blocks. Each block is usually from 64 KB to 1 MB of uncompressed data, depending on the average value size. The blocks consist of column values placed contiguously one after the other. Column values are in the same order for each column (the order is defined by the primary key), so when you iterate by many columns, you get values for the corresponding rows. The primary key itself is \"sparse\". It doesn't address each single row, but only some ranges of data. A separate primary.idx file has the value of the primary key for each N-th row, where N is called index_granularity (usually, N = 8192). Also, for each column, we have column.mrk files with \"marks,\" which are offsets to each N-th row in the data file. Each mark is a pair: the offset in the file to the beginning of the compressed block, and the offset in the decompressed block to the beginning of data. Usually compressed blocks are aligned by marks, and the offset in the decompressed block is zero. Data for primary.idx always resides in memory and data for column.mrk files is cached. When we are going to read something from a part in MergeTree , we look at primary.idx data and locate ranges that could possibly contain requested data, then look at column.mrk data and calculate offsets for where to start reading those ranges. Because of sparseness, excess data may be read. ClickHouse is not suitable for a high load of simple point queries, because the entire range with index_granularity rows must be read for each key, and the entire compressed block must be decompressed for each column. We made the index sparse because we must be able to maintain trillions of rows per single server without noticeable memory consumption for the index. Also, because the primary key is sparse, it is not unique: it cannot check the existence of the key in the table at INSERT time. You could have many rows with the same key in a table. When you INSERT a bunch of data into MergeTree , that bunch is sorted by primary key order and forms a new part. To keep the number of parts relatively low, there are background threads that periodically select some parts and merge them to a single sorted part. That's why it is called MergeTree . Of course, merging leads to \"write amplification\". All parts are immutable: they are only created and deleted, but not modified. When SELECT is run, it holds a snapshot of the table (a set of parts). After merging, we also keep old parts for some time to make recovery after failure easier, so if we see that some merged part is probably broken, we can replace it with its source parts. MergeTree is not an LSM tree because it doesn't contain \"memtable\" and \"log\": inserted data is written directly to the filesystem. This makes it suitable only to INSERT data in batches, not by individual row and not very frequently \u2013 about once per second is ok, but a thousand times a second is not. We did it this way for simplicity's sake, and because we are already inserting data in batches in our applications. MergeTree tables can only have one (primary) index: there aren't any secondary indices. It would be nice to allow multiple physical representations under one logical table, for example, to store data in more than one physical order or even to allow representations with pre-aggregated data along with original data. There are MergeTree engines that are doing additional work during background merges. Examples are CollapsingMergeTree and AggregatingMergeTree . This could be treated as special support for updates. Keep in mind that these are not real updates because users usually have no control over the time when background merges will be executed, and data in a MergeTree table is almost always stored in more than one part, not in completely merged form.", - "title": "Merge Tree" - }, - { - "location": "/index.html#replication", - "text": "Replication in ClickHouse is implemented on a per-table basis. You could have some replicated and some non-replicated tables on the same server. You could also have tables replicated in different ways, such as one table with two-factor replication and another with three-factor. Replication is implemented in the ReplicatedMergeTree storage engine. The path in ZooKeeper is specified as a parameter for the storage engine. All tables with the same path in ZooKeeper become replicas of each other: they synchronize their data and maintain consistency. Replicas can be added and removed dynamically simply by creating or dropping a table. Replication uses an asynchronous multi-master scheme. You can insert data into any replica that has a session with ZooKeeper , and data is replicated to all other replicas asynchronously. Because ClickHouse doesn't support UPDATEs, replication is conflict-free. As there is no quorum acknowledgment of inserts, just-inserted data might be lost if one node fails. Metadata for replication is stored in ZooKeeper. There is a replication log that lists what actions to do. Actions are: get part; merge parts; drop partition, etc. Each replica copies the replication log to its queue and then executes the actions from the queue. For example, on insertion, the \"get part\" action is created in the log, and every replica downloads that part. Merges are coordinated between replicas to get byte-identical results. All parts are merged in the same way on all replicas. To achieve this, one replica is elected as the leader, and that replica initiates merges and writes \"merge parts\" actions to the log. Replication is physical: only compressed parts are transferred between nodes, not queries. To lower the network cost (to avoid network amplification), merges are processed on each replica independently in most cases. Large merged parts are sent over the network only in cases of significant replication lag. In addition, each replica stores its state in ZooKeeper as the set of parts and its checksums. When the state on the local filesystem diverges from the reference state in ZooKeeper, the replica restores its consistency by downloading missing and broken parts from other replicas. When there is some unexpected or broken data in the local filesystem, ClickHouse does not remove it, but moves it to a separate directory and forgets it. The ClickHouse cluster consists of independent shards, and each shard consists of replicas. The cluster is not elastic, so after adding a new shard, data is not rebalanced between shards automatically. Instead, the cluster load will be uneven. This implementation gives you more control, and it is fine for relatively small clusters such as tens of nodes. But for clusters with hundreds of nodes that we are using in production, this approach becomes a significant drawback. We should implement a table engine that will span its data across the cluster with dynamically replicated regions that could be split and balanced between clusters automatically.", - "title": "Replication" - }, - { - "location": "/index.html#how-to-build-clickhouse-on-linux", - "text": "Build should work on Linux Ubuntu 12.04, 14.04 or newer.\nWith appropriate changes, it should also work on any other Linux distribution.\nThe build process is not intended to work on Mac OS X.\nOnly x86_64 with SSE 4.2 is supported. Support for AArch64 is experimental. To test for SSE 4.2, do grep -q sse4_2 /proc/cpuinfo echo SSE 4.2 supported || echo SSE 4.2 not supported", - "title": "How to build ClickHouse on Linux" - }, - { - "location": "/index.html#install-git-and-cmake", - "text": "sudo apt-get install git cmake Or cmake3 instead of cmake on older systems.", - "title": "Install Git and CMake" - }, - { - "location": "/index.html#detect-the-number-of-threads", - "text": "export THREADS = $( grep -c ^processor /proc/cpuinfo )", - "title": "Detect the number of threads" - }, - { - "location": "/index.html#install-gcc-7", - "text": "There are several ways to do this.", - "title": "Install GCC 7" - }, - { - "location": "/index.html#install-from-a-ppa-package", - "text": "sudo apt-get install software-properties-common\nsudo apt-add-repository ppa:ubuntu-toolchain-r/test\nsudo apt-get update\nsudo apt-get install gcc-7 g++-7", - "title": "Install from a PPA package" - }, - { - "location": "/index.html#install-from-sources", - "text": "Look at [https://github.com/yandex/ClickHouse/blob/master/utils/prepare-environment/install-gcc.sh]", - "title": "Install from sources" - }, - { - "location": "/index.html#use-gcc-7-for-builds", - "text": "export CC = gcc-7 export CXX = g++-7", - "title": "Use GCC 7 for builds" - }, - { - "location": "/index.html#install-required-libraries-from-packages", - "text": "sudo apt-get install libicu-dev libreadline-dev libmysqlclient-dev libssl-dev unixodbc-dev ninja-build", - "title": "Install required libraries from packages" - }, - { - "location": "/index.html#checkout-clickhouse-sources", - "text": "To get the latest stable version: git clone -b stable --recursive git@github.com:yandex/ClickHouse.git ## or: git clone -b stable --recursive https://github.com/yandex/ClickHouse.git cd ClickHouse For development, switch to the master branch.\nFor the latest release candidate, switch to the testing branch.", - "title": "Checkout ClickHouse sources" - }, - { - "location": "/index.html#build-clickhouse", - "text": "There are two build variants.", - "title": "Build ClickHouse" - }, - { - "location": "/index.html#build-release-package", - "text": "Install prerequisites to build Debian packages. sudo apt-get install devscripts dupload fakeroot debhelper Install the most recent version of Clang. Clang is embedded into the ClickHouse package and used at runtime. The minimum version is 5.0. It is optional. To install clang, see utils/prepare-environment/install-clang.sh You may also build ClickHouse with Clang for development purposes.\nFor production releases, GCC is used. Run the release script: rm -f ../clickhouse*.deb\n./release You will find built packages in the parent directory: ls -l ../clickhouse*.deb Note that usage of debian packages is not required.\nClickHouse has no runtime dependencies except libc, so it could work on almost any Linux. Installing freshly built packages on a development server: sudo dpkg -i ../clickhouse*.deb\nsudo service clickhouse-server start", - "title": "Build release package" - }, - { - "location": "/index.html#build-to-work-with-code", - "text": "mkdir build cd build\ncmake ..\nmake -j $THREADS cd .. To create an executable, run make clickhouse .\nThis will create the dbms/src/Server/clickhouse executable, which can be used with client or server arguments.", - "title": "Build to work with code" - }, - { - "location": "/index.html#how-to-build-clickhouse-on-mac-os-x", - "text": "Build should work on Mac OS X 10.12. If you're using earlier version, you can try to build ClickHouse using Gentoo Prefix and clang sl in this instruction.\nWith appropriate changes, it should also work on any other Linux distribution.", - "title": "How to build ClickHouse on Mac OS X" - }, - { - "location": "/index.html#install-homebrew", - "text": "/usr/bin/ruby -e $( curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install )", - "title": "Install Homebrew" - }, - { - "location": "/index.html#install-required-compilers-tools-and-libraries", - "text": "brew install cmake gcc icu4c mysql openssl unixodbc libtool gettext zlib readline boost --cc = gcc-7", - "title": "Install required compilers, tools, and libraries" - }, - { - "location": "/index.html#checkout-clickhouse-sources_1", - "text": "To get the latest stable version: git clone -b stable --recursive --depth = 10 git@github.com:yandex/ClickHouse.git ## or: git clone -b stable --recursive --depth=10 https://github.com/yandex/ClickHouse.git cd ClickHouse For development, switch to the master branch.\nFor the latest release candidate, switch to the testing branch.", - "title": "Checkout ClickHouse sources" - }, - { - "location": "/index.html#build-clickhouse_1", - "text": "mkdir build cd build\ncmake .. -DCMAKE_CXX_COMPILER = ` which g++-7 ` -DCMAKE_C_COMPILER = ` which gcc-7 ` \nmake -j ` sysctl -n hw.ncpu ` cd ..", - "title": "Build ClickHouse" - }, - { - "location": "/index.html#caveats", - "text": "If you intend to run clickhouse-server, make sure to increase the system's maxfiles variable. See MacOS.md for more details.", - "title": "Caveats" - }, - { - "location": "/index.html#how-to-write-c-code", - "text": "", - "title": "How to write C++ code" - }, - { - "location": "/index.html#general-recommendations", - "text": "1. The following are recommendations, not requirements. 2. If you are editing code, it makes sense to follow the formatting of the existing code. 3. Code style is needed for consistency. Consistency makes it easier to read the code, and it also makes it easier to search the code. 4. Many of the rules do not have logical reasons; they are dictated by established practices.", - "title": "General recommendations" - }, - { - "location": "/index.html#formatting", - "text": "1. Most of the formatting will be done automatically by clang-format . 2. Indents are 4 spaces. Configure your development environment so that a tab adds four spaces. 3. A left curly bracket must be separated on a new line. (And the right one, as well.) inline void readBoolText ( bool x , ReadBuffer buf ) { \n char tmp = 0 ; \n readChar ( tmp , buf ); \n x = tmp != 0 ; } 4. \nBut if the entire function body is quite short (a single statement), you can place it entirely on one line if you wish. Place spaces around curly braces (besides the space at the end of the line). inline size_t mask () const { return buf_size () - 1 ; } inline size_t place ( HashValue x ) const { return x mask (); } 5. For functions, don't put spaces around brackets. void reinsert ( const Value x ) memcpy ( buf [ place_value ], x , sizeof ( x )); 6. When using statements such as if , for , and while (unlike function calls), put a space before the opening bracket. cpp\n for (size_t i = 0; i rows; i += storage.index_granularity) 7. Put spaces around binary operators ( + , - , * , / , % , ...), as well as the ternary operator ?: . UInt16 year = ( s [ 0 ] - 0 ) * 1000 + ( s [ 1 ] - 0 ) * 100 + ( s [ 2 ] - 0 ) * 10 + ( s [ 3 ] - 0 ); UInt8 month = ( s [ 5 ] - 0 ) * 10 + ( s [ 6 ] - 0 ); UInt8 day = ( s [ 8 ] - 0 ) * 10 + ( s [ 9 ] - 0 ); 8. If a line feed is entered, put the operator on a new line and increase the indent before it. if ( elapsed_ns ) \n message ( \n rows_read_on_server * 1000000000 / elapsed_ns rows/s., \n bytes_read_on_server * 1000.0 / elapsed_ns MB/s.) ; 9. You can use spaces for alignment within a line, if desired. dst . ClickLogID = click . LogID ; dst . ClickEventID = click . EventID ; dst . ClickGoodEvent = click . GoodEvent ; 10. Don't use spaces around the operators . , - . If necessary, the operator can be wrapped to the next line. In this case, the offset in front of it is increased. 11. Do not use a space to separate unary operators ( - , + , * , , ...) from the argument. 12. Put a space after a comma, but not before it. The same rule goes for a semicolon inside a for expression. 13. Do not use spaces to separate the [] operator. 14. In a template ... expression, use a space between template and . No spaces after or before . template typename TKey , typename TValue struct AggregatedStatElement {} 15. In classes and structures, public, private, and protected are written on the same level as the class/struct , but all other internal elements should be deeper. template typename T class MultiVersion { public : \n /// Version of object for usage. shared_ptr manage lifetime of version. \n using Version = std :: shared_ptr const T ; \n ... } 16. If the same namespace is used for the entire file, and there isn't anything else significant, an offset is not necessary inside namespace. 17. If the block for if , for , while ... expressions consists of a single statement, you don't need to use curly brackets. Place the statement on a separate line, instead. The same is true for a nested if, for, while... statement. But if the inner statement contains curly brackets or else, the external block should be written in curly brackets. /// Finish write. for ( auto stream : streams ) \n stream . second - finalize (); 18. There should be any spaces at the ends of lines. 19. Sources are UTF-8 encoded. 20. Non-ASCII characters can be used in string literals. , ( timer . elapsed () / chunks_stats . hits ) \u03bcsec/hit. ; 21. Do not write multiple expressions in a single line. 22. Group sections of code inside functions and separate them with no more than one empty line. 23. Separate functions, classes, and so on with one or two empty lines. 24. A const (related to a value) must be written before the type name. //correct const char * pos const std :: string s //incorrect char const * pos 25. When declaring a pointer or reference, the * and symbols should be separated by spaces on both sides. //correct const char * pos //incorrect const char * pos const char * pos 26. When using template types, alias them with the using keyword (except in the simplest cases). In other words, the template parameters are specified only in using and aren't repeated in the code. using can be declared locally, such as inside a function. //correct using FileStreams = std :: map std :: string , std :: shared_ptr Stream ; FileStreams streams ; //incorrect std :: map std :: string , std :: shared_ptr Stream streams ; 27. Do not declare several variables of different types in one statement. //incorrect int x , * y ; 28. Do not use C-style casts. //incorrect std :: cerr ( int ) c ; std :: endl ; //correct std :: cerr static_cast int ( c ) std :: endl ; 29. In classes and structs, group members and functions separately inside each visibility scope. 30. For small classes and structs, it is not necessary to separate the method declaration from the implementation. The same is true for small methods in any classes or structs. For templated classes and structs, don't separate the method declarations from the implementation (because otherwise they must be defined in the same translation unit). 31. You can wrap lines at 140 characters, instead of 80. 32. Always use the prefix increment/decrement operators if postfix is not required. for ( Names :: const_iterator it = column_names . begin (); it != column_names . end (); ++ it )", - "title": "Formatting" - }, - { - "location": "/index.html#comments_1", - "text": "1. Be sure to add comments for all non-trivial parts of code. This is very important. Writing the comment might help you realize that the code isn't necessary, or that it is designed wrong. /** Part of piece of memory, that can be used. * For example, if internal_buffer is 1MB, and there was only 10 bytes loaded to buffer from file for reading, * then working_buffer will have size of only 10 bytes * (working_buffer.end() will point to the position right after those 10 bytes available for read). */ 2. Comments can be as detailed as necessary. 3. Place comments before the code they describe. In rare cases, comments can come after the code, on the same line. /** Parses and executes the query. */ void executeQuery ( \n ReadBuffer istr , /// Where to read the query from (and data for INSERT, if applicable) \n WriteBuffer ostr , /// Where to write the result \n Context context , /// DB, tables, data types, engines, functions, aggregate functions... \n BlockInputStreamPtr query_plan , /// A description of query processing can be included here \n QueryProcessingStage :: Enum stage = QueryProcessingStage :: Complete /// The last stage to process the SELECT query to \n ) 4. Comments should be written in English only. 5. If you are writing a library, include detailed comments explaining it in the main header file. 6. Do not add comments that do not provide additional information. In particular, do not leave empty comments like this: /* * Procedure Name: * Original procedure name: * Author: * Date of creation: * Dates of modification: * Modification authors: * Original file name: * Purpose: * Intent: * Designation: * Classes used: * Constants: * Local variables: * Parameters: * Date of creation: * Purpose: */ The example is borrowed from http://home.tamk.fi/~jaalto/course/coding-style/doc/unmaintainable-code/ . 7. Do not write garbage comments (author, creation date ..) at the beginning of each file. 8. Single-line comments begin with three slashes: /// and multi-line comments begin with /** . These comments are considered \"documentation\". Note: You can use Doxygen to generate documentation from these comments. But Doxygen is not generally used because it is more convenient to navigate the code in the IDE. 9. Multi-line comments must not have empty lines at the beginning and end (except the line that closes a multi-line comment). 10. For commenting out code, use basic comments, not \"documenting\" comments. 11. Delete the commented out parts of the code before commiting. 12. Do not use profanity in comments or code. 13. Do not use uppercase letters. Do not use excessive punctuation. /// WHAT THE FAIL??? 14. Do not make delimeters from comments. ///****************************************************** 15. Do not start discussions in comments. /// Why did you do this stuff? 16. There's no need to write a comment at the end of a block describing what it was about. /// for", - "title": "Comments" - }, - { - "location": "/index.html#names", - "text": "1. The names of variables and class members use lowercase letters with underscores. size_t max_block_size ; 2. The names of functions (methods) use camelCase beginning with a lowercase letter. std :: string getName () const override { return Memory ; } 3. The names of classes (structures) use CamelCase beginning with an uppercase letter. Prefixes other than I are not used for interfaces. class StorageMemory : public IStorage 4. The names of usings follow the same rules as classes, or you can add _t at the end. 5. Names of template type arguments for simple cases: T; T, U; T1, T2. For more complex cases, either follow the rules for class names, or add the prefix T. template typename TKey , typename TValue struct AggregatedStatElement 6. Names of template constant arguments: either follow the rules for variable names, or use N in simple cases. template bool without_www struct ExtractDomain 7. For abstract classes (interfaces) you can add the I prefix. class IBlockInputStream 8. If you use a variable locally, you can use the short name. In other cases, use a descriptive name that conveys the meaning. bool info_successfully_loaded = false ; 9. define \u2018s should be in ALL_CAPS with underscores. The same is true for global constants. ##define MAX_SRC_TABLE_NAMES_TO_STORE 1000 10. File names should use the same style as their contents. If a file contains a single class, name the file the same way as the class, in CamelCase. If the file contains a single function, name the file the same way as the function, in camelCase. 11. If the name contains an abbreviation, then: For variable names, the abbreviation should use lowercase letters mysql_connection (not mySQL_connection ). For names of classes and functions, keep the uppercase letters in the abbreviation MySQLConnection (not MySqlConnection ). 12. Constructor arguments that are used just to initialize the class members should be named the same way as the class members, but with an underscore at the end. FileQueueProcessor ( \n const std :: string path_ , \n const std :: string prefix_ , \n std :: shared_ptr FileHandler handler_ ) \n : path ( path_ ), \n prefix ( prefix_ ), \n handler ( handler_ ), \n log ( Logger :: get ( FileQueueProcessor )) { } The underscore suffix can be omitted if the argument is not used in the constructor body. 13. There is no difference in the names of local variables and class members (no prefixes required). timer ( not m_timer ) 14. Constants in enums use CamelCase beginning with an uppercase letter. ALL_CAPS is also allowed. If the enum is not local, use enum class. enum class CompressionMethod { \n QuickLZ = 0 , \n LZ4 = 1 , }; 15. All names must be in English. Transliteration of Russian words is not allowed. not Stroka 16. Abbreviations are acceptable if they are well known (when you can easily find the meaning of the abbreviation in Wikipedia or in a search engine). `AST`, `SQL`.\n\nNot `NVDH` (some random letters) Incomplete words are acceptable if the shortened version is common use. You can also use an abbreviation if the full name is included next to it in the comments. 17. File names with C++ source code must have the .cpp extension. Header files must have the .h extension.", - "title": "Names" - }, - { - "location": "/index.html#how-to-write-code", - "text": "1. Memory management. Manual memory deallocation (delete) can only be used in library code. In library code, the delete operator can only be used in destructors. In application code, memory must be freed by the object that owns it. Examples: The easiest way is to place an object on the stack, or make it a member of another class. For a large number of small objects, use containers. For automatic deallocation of a small number of objects that reside in the heap, use shared_ptr/unique_ptr. 2. Resource management. Use RAII and see the previous point. 3. Error handling. Use exceptions. In most cases, you only need to throw an exception, and don't need to catch it (because of RAII). In offline data processing applications, it's often acceptable to not catch exceptions. In servers that handle user requests, it's usually enough to catch exceptions at the top level of the connection handler. /// If there were no other calculations yet, do it synchronously if ( ! started ) { \n calculate (); \n started = true ; } else /// If the calculations are already in progress, wait for results \n pool . wait (); if ( exception ) \n exception - rethrow (); Never hide exceptions without handling. Never just blindly put all exceptions to log. Not catch (...) {} . If you need to ignore some exceptions, do so only for specific ones and rethrow the rest. catch ( const DB :: Exception e ) { \n if ( e . code () == ErrorCodes :: UNKNOWN_AGGREGATE_FUNCTION ) \n return nullptr ; \n else \n throw ; } When using functions with response codes or errno, always check the result and throw an exception in case of error. if ( 0 != close ( fd )) \n throwFromErrno ( Cannot close file + file_name , ErrorCodes :: CANNOT_CLOSE_FILE ); Asserts are not used. 4. Exception types. There is no need to use complex exception hierarchy in application code. The exception text should be understandable to a system administrator. 5. Throwing exceptions from destructors. This is not recommended, but it is allowed. Use the following options: Create a (done() or finalize()) function that will do all the work in advance that might lead to an exception. If that function was called, there should be no exceptions in the destructor later. Tasks that are too complex (such as sending messages over the network) can be put in separate method that the class user will have to call before destruction. If there is an exception in the destructor, it\u2019s better to log it than to hide it (if the logger is available). In simple applications, it is acceptable to rely on std::terminate (for cases of noexcept by default in C++11) to handle exceptions. 6. Anonymous code blocks. You can create a separate code block inside a single function in order to make certain variables local, so that the destructors are called when exiting the block. Block block = data . in - read (); { \n std :: lock_guard std :: mutex lock ( mutex ); \n data . ready = true ; \n data . block = block ; } ready_any . set (); 7. Multithreading. For offline data processing applications: Try to get the best possible performance on a single CPU core. You can then parallelize your code if necessary. In server applications: Use the thread pool to process requests. At this point, we haven't had any tasks that required userspace context switching. Fork is not used for parallelization. 8. Synchronizing threads. Often it is possible to make different threads use different memory cells (even better: different cache lines,) and to not use any thread synchronization (except joinAll). If synchronization is required, in most cases, it is sufficient to use mutex under lock_guard. In other cases use system synchronization primitives. Do not use busy wait. Atomic operations should be used only in the simplest cases. Do not try to implement lock-free data structures unless it is your primary area of expertise. 9. Pointers vs references. In most cases, prefer references. 10. const. Use constant references, pointers to constants, const_iterator , const methods. Consider const to be default and use non-const only when necessary. When passing variable by value, using const usually does not make sense. 11. unsigned. Use unsigned , if needed. 12. Numeric types Use UInt8 , UInt16 , UInt32 , UInt64 , Int8 , Int16 , Int32 , Int64 , and size_t , ssize_t , ptrdiff_t . Don't use signed/unsigned long , long long , short , signed char , unsigned char , or char types for numbers. 13. Passing arguments. Pass complex values by reference (including std::string ). If a function captures ownership of an objected created in the heap, make the argument type shared_ptr or unique_ptr . 14. Returning values. In most cases, just use return. Do not write [return std::move(res)]{.strike} . If the function allocates an object on heap and returns it, use shared_ptr or unique_ptr . In rare cases you might need to return the value via an argument. In this case, the argument should be a reference. using AggregateFunctionPtr = std :: shared_ptr IAggregateFunction ; /** Creates an aggregate function by name. */ class AggregateFunctionFactory { public : \n AggregateFunctionFactory (); \n AggregateFunctionPtr get ( const String name , const DataTypes argument_types ) const ; 15. namespace. There is no need to use a separate namespace for application code or small libraries. or small libraries. For medium to large libraries, put everything in the namespace. You can use the additional detail namespace in a library's .h file to hide implementation details. In a .cpp file, you can use the static or anonymous namespace to hide symbols. You can also use namespace for enums to prevent its names from polluting the outer namespace, but it\u2019s better to use the enum class. 16. Delayed initialization. If arguments are required for initialization then do not write a default constructor. If later you\u2019ll need to delay initialization, you can add a default constructor that will create an invalid object. Or, for a small number of objects, you can use shared_ptr/unique_ptr . Loader ( DB :: Connection * connection_ , const std :: string query , size_t max_block_size_ ); /// For delayed initialization Loader () {} 17. Virtual functions. If the class is not intended for polymorphic use, you do not need to make functions virtual. This also applies to the destructor. 18. Encodings. Use UTF-8 everywhere. Use std::string and char * . Do not use std::wstring and wchar_t . 19. Logging. See the examples everywhere in the code. Before committing, delete all meaningless and debug logging, and any other types of debug output. Logging in cycles should be avoided, even on the Trace level. Logs must be readable at any logging level. Logging should only be used in application code, for the most part. Log messages must be written in English. The log should preferably be understandable for the system administrator. Do not use profanity in the log. Use UTF-8 encoding in the log. In rare cases you can use non-ASCII characters in the log. 20. I/O. Don't use iostreams in internal cycles that are critical for application performance (and never use stringstream). Use the DB/IO library instead. 21. Date and time. See the DateLUT library. 22. include. Always use #pragma once instead of include guards. 23. using. The using namespace is not used. It's fine if you are 'using' something specific, but make it local inside a class or function. 24. Do not use trailing return type for functions unless necessary. [auto f() - gt; void;]{.strike} 25. Do not declare and init variables like this: auto s = std :: string { Hello }; Do it like this: std :: string s = Hello ; std :: string s { Hello }; 26. For virtual functions, write virtual in the base class, but write override in descendent classes.", - "title": "How to write code" - }, - { - "location": "/index.html#unused-features-of-c", - "text": "1. Virtual inheritance is not used. 2. Exception specifiers from C++03 are not used. 3. Function try block is not used, except for the main function in tests.", - "title": "Unused features of C++" - }, - { - "location": "/index.html#platform", - "text": "1. We write code for a specific platform. But other things being equal, cross-platform or portable code is preferred. 2. The language is C++17. 3. The compiler is gcc . At this time (December 2017), the code is compiled using version 7.2. (It can also be compiled using clang 5.) The standard library is used (implementation of libstdc++ or libc++ ). 4. OS: Linux Ubuntu, not older than Precise. 5. Code is written for x86_64 CPU architecture. The CPU instruction set is the minimum supported set among our servers. Currently, it is SSE 4.2. 6. Use -Wall -Wextra -Werror compilation flags. 7. Use static linking with all libraries except those that are difficult to connect to statically (see the output of the ldd command). 8. Code is developed and debugged with release settings.", - "title": "Platform" - }, - { - "location": "/index.html#tools", - "text": "1. KDevelop is a good IDE. 2. For debugging, use gdb , valgrind ( memcheck ), strace , -fsanitize= , ..., tcmalloc_minimal_debug . 3. For profiling, use Linux Perf valgrind ( callgrind ), strace-cf . 4. Sources are in Git. 5. Compilation is managed by CMake . 6. Releases are in deb packages. 7. Commits to master must not break the build. Though only selected revisions are considered workable. 8. Make commits as often as possible, even if the code is only partially ready. Use branches for this purpose. If your code is not buildable yet, exclude it from the build before pushing to master. You'll need to finish it or remove it from master within a few days. 9. For non-trivial changes, used branches and publish them on the server. 10. Unused code is removed from the repository.", - "title": "Tools" - }, - { - "location": "/index.html#libraries", - "text": "1. The C++14 standard library is used (experimental extensions are fine), as well as boost and Poco frameworks. 2. If necessary, you can use any well-known libraries available in the OS package. If there is a good solution already available, then use it, even if it means you have to install another library. (But be prepared to remove bad libraries from code.) 3. You can install a library that isn't in the packages, if the packages don't have what you need or have an outdated version or the wrong type of compilation. 4. If the library is small and doesn't have its own complex build system, put the source files in the contrib folder. 5. Preference is always given to libraries that are already used.", - "title": "Libraries" - }, - { - "location": "/index.html#general-recommendations_1", - "text": "1. Write as little code as possible. 2. Try the simplest solution. 3. Don't write code until you know how it's going to work and how the inner loop will function. 4. In the simplest cases, use 'using' instead of classes or structs. 5. If possible, do not write copy constructors, assignment operators, destructors (other than a virtual one, if the class contains at least one virtual function), mpve-constructors and move assignment operators. In other words, the compiler-generated functions must work correctly. You can use 'default'. 6. Code simplification is encouraged. Reduce the size of your code where possible.", - "title": "General recommendations" - }, - { - "location": "/index.html#additional-recommendations", - "text": "1. Explicit std:: for types from stddef.h is not recommended. We recommend writing size_t instead std::size_t because it's shorter. But if you prefer, std:: is acceptable. 2. Explicit std:: for functions from the standard C library is not recommended. Write memcpy instead of std::memcpy . The reason is that there are similar non-standard functions, such as memmem . We do use these functions on occasion. These functions do not exist in namespace std . If you write std::memcpy instead of memcpy everywhere, then memmem without std:: will look awkward. Nevertheless, std:: is allowed if you prefer it. 3. Using functions from C when the ones are available in the standard C++ library. This is acceptable if it is more efficient. For example, use memcpy instead of std::copy for copying large chunks of memory. 4. Multiline function arguments. Any of the following wrapping styles are allowed: function ( \n T1 x1 , \n T2 x2 ) function ( \n size_t left , size_t right , \n const RangesInDataParts ranges , \n size_t limit ) function ( size_t left , size_t right , \n const RangesInDataParts ranges , \n size_t limit ) function ( size_t left , size_t right , \n const RangesInDataParts ranges , \n size_t limit ) function ( \n size_t left , \n size_t right , \n const RangesInDataParts ranges , \n size_t limit )", - "title": "Additional recommendations" - }, - { - "location": "/index.html#how-to-run-clickhouse-tests", - "text": "The clickhouse-test utility that is used for functional testing is written using Python 2.x.It also requires you to have some third-party packages: $ pip install lxml termcolor In a nutshell: Put the clickhouse program to /usr/bin (or PATH ) Create a clickhouse-client symlink in /usr/bin pointing to clickhouse Start the clickhouse server cd dbms/tests/ Run ./clickhouse-test", - "title": "How to run ClickHouse tests" - }, - { - "location": "/index.html#example-usage", - "text": "Run ./clickhouse-test --help to see available options. To run tests without having to create a symlink or mess with PATH : ./clickhouse-test -c ../../build/dbms/src/Server/clickhouse --client To run a single test, i.e. 00395_nullable : ./clickhouse-test 00395", - "title": "Example usage" - }, - { - "location": "/index.html#roadmap", - "text": "", - "title": "Roadmap" - }, - { - "location": "/index.html#q1-2018", - "text": "", - "title": "Q1 2018" - }, - { - "location": "/index.html#new-fuctionality", - "text": "Support for UPDATE and DELETE . Multidimensional and nested arrays. It can look something like this: CREATE TABLE t ( \n x Array ( Array ( String )), \n z Nested ( \n x Array ( String ), \n y Nested (...)) ) ENGINE = MergeTree ORDER BY x External MySQL and ODBC tables. External tables can be integrated into ClickHouse using external dictionaries. This new functionality is a convenient alternative to connecting external tables. SELECT ... FROM mysql ( host:port , db , table , user , password ) `", - "title": "New fuctionality" - }, - { - "location": "/index.html#improvements", - "text": "Effective data copying between ClickHouse clusters. Now you can copy data with the remote() function. For example: INSERT INTO t SELECT * FROM remote(...) . This operation will have improved performance. O_DIRECT for merges. This will improve the performance of the OS cache and \"hot\" queries.", - "title": "Improvements" - }, - { - "location": "/index.html#q2-2018", - "text": "", - "title": "Q2 2018" - }, - { - "location": "/index.html#new-functionality", - "text": "UPDATE/DELETE conform to the EU GDPR. Protobuf and Parquet input and output formats. Creating dictionaries using DDL queries. Currently, dictionaries that are part of the database schema are defined in external XML files. This is inconvenient and counter-intuitive. The new approach should fix it. Integration with LDAP. WITH ROLLUP and WITH CUBE for GROUP BY. Custom encoding and compression for each column individually. As of now, ClickHouse supports LZ4 and ZSTD compression of columns, and compression settings are global (see the article Compression in ClickHouse ). Per-column compression and encoding will provide more efficient data storage, which in turn will speed up queries. Storing data on multiple disks on the same server. This functionality will make it easier to extend the disk space, since different disk systems can be used for different databases or tables. Currently, users are forced to use symbolic links if the databases and tables must be stored on a different disk.", - "title": "New functionality" - }, - { - "location": "/index.html#improvements_1", - "text": "Many improvements and fixes are planned for the query execution system. For example: Using an index for in (subquery) . The index is not used right now, which reduces performance. Passing predicates from where to subqueries, and passing predicates to views. The predicates must be passed, since the view is changed by the subquery. Performance is still low for view filters, and views can't use the primary key of the original table, which makes views useless for large tables. Optimizing branching operations (ternary operator, if, multiIf). ClickHouse currently performs all branches, even if they aren't necessary. Using a primary key for GROUP BY and ORDER BY. This will speed up certain types of queries with partially sorted data.", - "title": "Improvements" - }, - { - "location": "/index.html#q3-q4-2018", - "text": "We don't have any set plans yet, but the main projects will be: Resource pools for executing queries. This will make load management more efficient. ANSI SQL JOIN syntax. Improve ClickHouse compatibility with many SQL tools.", - "title": "Q3-Q4 2018" - } - ] -} \ No newline at end of file diff --git a/docs/build/docs/en/single/sitemap.xml b/docs/build/docs/en/single/sitemap.xml deleted file mode 100644 index f9f9a9431a8..00000000000 --- a/docs/build/docs/en/single/sitemap.xml +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - /index.html - 2018-05-13 - daily - - - - \ No newline at end of file diff --git a/docs/build/docs/en/sitemap.xml b/docs/build/docs/en/sitemap.xml deleted file mode 100644 index 72393e66e2f..00000000000 --- a/docs/build/docs/en/sitemap.xml +++ /dev/null @@ -1,988 +0,0 @@ - - - - - - / - 2018-05-13 - daily - - - - - - - /introduction/distinctive_features/ - 2018-05-13 - daily - - - - /introduction/features_considered_disadvantages/ - 2018-05-13 - daily - - - - /introduction/ya_metrika_task/ - 2018-05-13 - daily - - - - /introduction/possible_silly_questions/ - 2018-05-13 - daily - - - - /introduction/performance/ - 2018-05-13 - daily - - - - - 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System tables are used for implementing part of the system's functionality, and for providing access to information about how the system is working. -You can't delete a system table (but you can perform DETACH). -System tables don't have files with data on the disk or files with metadata. The server creates all the system tables when it starts. -System tables are read-only. -They are located in the 'system' database.

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Contain metrics used for profiling and monitoring. -They usually reflect the number of events currently in the system, or the total resources consumed by the system. -Example: The number of SELECT queries currently running; the amount of memory in use.system.asynchronous_metricsandsystem.metrics differ in their sets of metrics and how they are calculated.

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system.clusters

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Contains information about clusters available in the config file and the servers in them. -Columns:

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cluster String      – Cluster name.
-shard_num UInt32    – Number of a shard in the cluster, starting from 1.
-shard_weight UInt32 – Relative weight of a shard when writing data.
-replica_num UInt32  – Number of a replica in the shard, starting from 1.
-host_name String    – Host name as specified in the config.
-host_address String – Host's IP address obtained from DNS.
-port UInt16         – The port used to access the server.
-user String         – The username to use for connecting to the server.
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Contains information about the columns in all tables. -You can use this table to get information similar to DESCRIBE TABLE, but for multiple tables at once.

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database String           - Name of the database the table is located in.
-table String              - Table name.
-name String               - Column name.
-type String               - Column type.
-default_type String       - Expression type (DEFAULT, MATERIALIZED, ALIAS) for the default value, or an empty string if it is not defined.
-default_expression String - Expression for the default value, or an empty string if it is not defined.
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This table contains a single String column called 'name' – the name of a database. -Each database that the server knows about has a corresponding entry in the table. -This system table is used for implementing the SHOW DATABASES query.

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Contains information about external dictionaries.

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  • hit_rate Float64 – For cache dictionaries, the percent of usage for which the value was in the cache.
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  • element_count UInt64 – The number of items stored in the dictionary.
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  • creation_time DateTime – Time spent for the creation or last successful reload of the dictionary.
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  • last_exception String – Text of an error that occurred when creating or reloading the dictionary, if the dictionary couldn't be created.
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  • source String – Text describing the data source for the dictionary.
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Contains information about the number of events that have occurred in the system. This is used for profiling and monitoring purposes. -Example: The number of processed SELECT queries. -Columns: 'event String' – the event name, and 'value UInt64' – the quantity.

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system.functions

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Contains information about normal and aggregate functions.

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system.merges

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Contains information about merges currently in process for tables in the MergeTree family.

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  • database String — Name of the database the table is located in.
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  • table String — Name of the table.
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  • progress Float64 — Percent of progress made, from 0 to 1.
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  • num_parts UInt64 — Number of parts to merge.
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  • result_part_name String — Name of the part that will be formed as the result of the merge.
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  • total_size_bytes_compressed UInt64 — Total size of compressed data in the parts being merged.
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  • total_size_marks UInt64 — Total number of marks in the parts being merged.
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  • bytes_read_uncompressed UInt64 — Amount of bytes read, decompressed.
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  • rows_read UInt64 — Number of rows read.
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  • bytes_written_uncompressed UInt64 — Amount of bytes written, uncompressed.
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This table contains a single UInt64 column named 'number' that contains almost all the natural numbers starting from zero. -You can use this table for tests, or if you need to do a brute force search. -Reads from this table are not parallelized.

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The same as 'system.numbers' but reads are parallelized. The numbers can be returned in any order. -Used for tests.

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This table contains a single row with a single 'dummy' UInt8 column containing the value 0. -This table is used if a SELECT query doesn't specify the FROM clause. -This is similar to the DUAL table found in other DBMSs.

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system.parts

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Contains information about parts of a table in the MergeTree family.

-

Each row describes one part of the data.

-

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-
    -
  • partition (String) – The partition name. YYYYMM format. To learn what a partition is, see the description of the ALTER query.
    • -
  • name (String) – Name of the data part.
    • -
  • active (UInt8) – Indicates whether the part is active. If a part is active, it is used in a table; otherwise, it will be deleted. Inactive data parts remain after merging.
    • -
  • marks (UInt64) – The number of marks. To get the approximate number of rows in a data part, multiply marks by the index granularity (usually 8192).
    • -
  • marks_size (UInt64) – The size of the file with marks.
    • -
  • rows (UInt64) – The number of rows.
    • -
  • bytes (UInt64) – The number of bytes when compressed.
    • -
  • modification_time (DateTime) – The modification time of the directory with the data part. This usually corresponds to the time of data part creation.|
    • -
  • remove_time (DateTime) – The time when the data part became inactive.
    • -
  • refcount (UInt32) – The number of places where the data part is used. A value greater than 2 indicates that the data part is used in queries or merges.
    • -
  • min_date (Date) – The minimum value of the date key in the data part.
    • -
  • max_date (Date) – The maximum value of the date key in the data part.
    • -
  • min_block_number (UInt64) – The minimum number of data parts that make up the current part after merging.
    • -
  • max_block_number (UInt64) – The maximum number of data parts that make up the current part after merging.
    • -
  • level (UInt32) – Depth of the merge tree. If a merge was not performed, level=0.
    • -
  • primary_key_bytes_in_memory (UInt64) – The amount of memory (in bytes) used by primary key values.
    • -
  • primary_key_bytes_in_memory_allocated (UInt64) – The amount of memory (in bytes) reserved for primary key values.
    • -
  • database (String) – Name of the database.
    • -
  • table (String) – Name of the table.
    • -
  • engine (String) – Name of the table engine without parameters.
    • -
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This system table is used for implementing the SHOW PROCESSLIST query. -Columns:

-
user String              – Name of the user who made the request. For distributed query processing, this is the user who helped the requestor server send the query to this server, not the user who made the distributed request on the requestor server.
-
-address String           – The IP address that the query was made from. The same is true for distributed query processing.
-
-elapsed Float64          –  The time in seconds since request execution started.
-
-rows_read UInt64         – The number of rows read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.
-
-bytes_read UInt64        – The number of uncompressed bytes read from the table. For distributed processing, on the requestor server, this is the total for all remote servers.
-
-UInt64 total_rows_approx – The approximate total number of rows that must be read. For distributed processing, on the requestor server, this is the total for all remote servers. It can be updated during request processing, when new sources to process become known.
-
-memory_usage UInt64 – Memory consumption by the query. It might not include some types of dedicated memory.
-
-query String – The query text. For INSERT, it doesn't include the data to insert.
-
-query_id – Query ID, if defined.
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system.replicas

-

Contains information and status for replicated tables residing on the local server. -This table can be used for monitoring. The table contains a row for every Replicated* table.

-

Example:

-
SELECT *
-FROM system.replicas
-WHERE table = 'visits'
-FORMAT Vertical
-
- - -
Row 1:
-──────
-database:           merge
-table:              visits
-engine:             ReplicatedCollapsingMergeTree
-is_leader:          1
-is_readonly:        0
-is_session_expired: 0
-future_parts:       1
-parts_to_check:     0
-zookeeper_path:     /clickhouse/tables/01-06/visits
-replica_name:       example01-06-1.yandex.ru
-replica_path:       /clickhouse/tables/01-06/visits/replicas/example01-06-1.yandex.ru
-columns_version:    9
-queue_size:         1
-inserts_in_queue:   0
-merges_in_queue:    1
-log_max_index:      596273
-log_pointer:        596274
-total_replicas:     2
-active_replicas:    2
-
- - -

Columns:

-
database:           database name
-table:              table name
-engine:             table engine name
-
-is_leader:          whether the replica is the leader
-
-Only one replica at a time can be the leader. The leader is responsible for selecting background merges to perform.
-Note that writes can be performed to any replica that is available and has a session in ZK, regardless of whether it is a leader.
-
-is_readonly:        Whether the replica is in read-only mode.
-This mode is turned on if the config doesn't have sections with ZK, if an unknown error occurred when reinitializing sessions in ZK, and during session reinitialization in ZK.
-
-is_session_expired: Whether the ZK session expired.
-Basically, the same thing as is_readonly.
-
-future_parts: The number of data parts that will appear as the result of INSERTs or merges that haven't been done yet. 
-
-parts_to_check: The number of data parts in the queue for verification.
-A part is put in the verification queue if there is suspicion that it might be damaged.
-
-zookeeper_path: The path to the table data in ZK. 
-replica_name: Name of the replica in ZK. Different replicas of the same table have different names. 
-replica_path: The path to the replica data in ZK. The same as concatenating zookeeper_path/replicas/replica_path.
-
-columns_version: Version number of the table structure.
-Indicates how many times ALTER was performed. If replicas have different versions, it means some replicas haven't made all of the ALTERs yet.
-
-queue_size:         Size of the queue for operations waiting to be performed.
-Operations include inserting blocks of data, merges, and certain other actions.
-Normally coincides with future_parts.
-
-inserts_in_queue: Number of inserts of blocks of data that need to be made.
-Insertions are usually replicated fairly quickly. If the number is high, something is wrong.
-
-merges_in_queue: The number of merges waiting to be made. 
-Sometimes merges are lengthy, so this value may be greater than zero for a long time.
-
-The next 4 columns have a non-null value only if the ZK session is active.
-
-log_max_index:     Maximum entry number in the log of general activity.
-log_pointer:        Maximum entry number in the log of general activity that the replica copied to its execution queue, plus one.
-If log_pointer is much smaller than log_max_index, something is wrong.
-
-total_replicas:     Total number of known replicas of this table.
-active_replicas:    Number of replicas of this table that have a ZK session (the number of active replicas).
-
- - -

If you request all the columns, the table may work a bit slowly, since several reads from ZK are made for each row. -If you don't request the last 4 columns (log_max_index, log_pointer, total_replicas, active_replicas), the table works quickly.

-

For example, you can check that everything is working correctly like this:

-
SELECT
-    database,
-    table,
-    is_leader,
-    is_readonly,
-    is_session_expired,
-    future_parts,
-    parts_to_check,
-    columns_version,
-    queue_size,
-    inserts_in_queue,
-    merges_in_queue,
-    log_max_index,
-    log_pointer,
-    total_replicas,
-    active_replicas
-FROM system.replicas
-WHERE
-       is_readonly
-    OR is_session_expired
-    OR future_parts > 20
-    OR parts_to_check > 10
-    OR queue_size > 20
-    OR inserts_in_queue > 10
-    OR log_max_index - log_pointer > 10
-    OR total_replicas < 2
-    OR active_replicas < total_replicas
-
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If this query doesn't return anything, it means that everything is fine.

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Contains information about settings that are currently in use. -I.e. used for executing the query you are using to read from the system.settings table).

-

Columns:

-
name String   – Setting name.
-value String  – Setting value.
-changed UInt8 - Whether the setting was explicitly defined in the config or explicitly changed.
-
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-FROM system.settings
-WHERE changed
-
- - -
┌─name───────────────────┬─value───────┬─changed─┐
-│ max_threads            │ 8           │       1 │
-│ use_uncompressed_cache │ 0           │       1 │
-│ load_balancing         │ random      │       1 │
-│ max_memory_usage       │ 10000000000 │       1 │
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system.tables

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This table contains the String columns 'database', 'name', and 'engine'. -The table also contains three virtual columns: metadata_modification_time (DateTime type), create_table_query, and engine_full (String type). -Each table that the server knows about is entered in the 'system.tables' table. -This system table is used for implementing SHOW TABLES queries.

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system.zookeeper

-

Allows reading data from the ZooKeeper cluster defined in the config. -The query must have a 'path' equality condition in the WHERE clause. This is the path in ZooKeeper for the children that you want to get data for.

-

The query SELECT * FROM system.zookeeper WHERE path = '/clickhouse' outputs data for all children on the /clickhouse node. -To output data for all root nodes, write path = '/'. -If the path specified in 'path' doesn't exist, an exception will be thrown.

-

Columns:

-
    -
  • name String — Name of the node.
    • -
  • path String — Path to the node.
    • -
  • value String — Value of the node.
    • -
  • dataLength Int32 — Size of the value.
    • -
  • numChildren Int32 — Number of children.
    • -
  • czxid Int64 — ID of the transaction that created the node.
    • -
  • mzxid Int64 — ID of the transaction that last changed the node.
    • -
  • pzxid Int64 — ID of the transaction that last added or removed children.
    • -
  • ctime DateTime — Time of node creation.
    • -
  • mtime DateTime — Time of the last node modification.
    • -
  • version Int32 — Node version - the number of times the node was changed.
    • -
  • cversion Int32 — Number of added or removed children.
    • -
  • aversion Int32 — Number of changes to ACL.
    • -
  • ephemeralOwner Int64 — For ephemeral nodes, the ID of the session that owns this node.
    • -
-

Example:

-
SELECT *
-FROM system.zookeeper
-WHERE path = '/clickhouse/tables/01-08/visits/replicas'
-FORMAT Vertical
-
- - -
Row 1:
-──────
-name:           example01-08-1.yandex.ru
-value:
-czxid:          932998691229
-mzxid:          932998691229
-ctime:          2015-03-27 16:49:51
-mtime:          2015-03-27 16:49:51
-version:        0
-cversion:       47
-aversion:       0
-ephemeralOwner: 0
-dataLength:     0
-numChildren:    7
-pzxid:          987021031383
-path:           /clickhouse/tables/01-08/visits/replicas
-
-Row 2:
-──────
-name:           example01-08-2.yandex.ru
-value:
-czxid:          933002738135
-mzxid:          933002738135
-ctime:          2015-03-27 16:57:01
-mtime:          2015-03-27 16:57:01
-version:        0
-cversion:       37
-aversion:       0
-ephemeralOwner: 0
-dataLength:     0
-numChildren:    7
-pzxid:          987021252247
-path:           /clickhouse/tables/01-08/visits/replicas
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AggregatingMergeTree

-

This engine differs from MergeTree in that the merge combines the states of aggregate functions stored in the table for rows with the same primary key value.

-

For this to work, it uses the AggregateFunction data type, as well as -State and -Merge modifiers for aggregate functions. Let's examine it more closely.

-

There is an AggregateFunction data type. It is a parametric data type. As parameters, the name of the aggregate function is passed, then the types of its arguments.

-

Examples:

-
CREATE TABLE t
-(
-    column1 AggregateFunction(uniq, UInt64),
-    column2 AggregateFunction(anyIf, String, UInt8),
-    column3 AggregateFunction(quantiles(0.5, 0.9), UInt64)
-) ENGINE = ...
-
- - -

This type of column stores the state of an aggregate function.

-

To get this type of value, use aggregate functions with the State suffix.

-

Example: -uniqState(UserID), quantilesState(0.5, 0.9)(SendTiming)

-

In contrast to the corresponding uniq and quantiles functions, these functions return the state, rather than the prepared value. In other words, they return an AggregateFunction type value.

-

An AggregateFunction type value can't be output in Pretty formats. In other formats, these types of values are output as implementation-specific binary data. The AggregateFunction type values are not intended for output or saving in a dump.

-

The only useful thing you can do with AggregateFunction type values is combine the states and get a result, which essentially means to finish aggregation. Aggregate functions with the 'Merge' suffix are used for this purpose. -Example: uniqMerge(UserIDState), where UserIDState has the AggregateFunction type.

-

In other words, an aggregate function with the 'Merge' suffix takes a set of states, combines them, and returns the result. -As an example, these two queries return the same result:

-
SELECT uniq(UserID) FROM table
-
-SELECT uniqMerge(state) FROM (SELECT uniqState(UserID) AS state FROM table GROUP BY RegionID)
-
- - -

There is an AggregatingMergeTree engine. Its job during a merge is to combine the states of aggregate functions from different table rows with the same primary key value.

-

You can't use a normal INSERT to insert a row in a table containing AggregateFunction columns, because you can't explicitly define the AggregateFunction value. Instead, use INSERT SELECT with -State aggregate functions for inserting data.

-

With SELECT from an AggregatingMergeTree table, use GROUP BY and aggregate functions with the '-Merge' modifier in order to complete data aggregation.

-

You can use AggregatingMergeTree tables for incremental data aggregation, including for aggregated materialized views.

-

Example:

-

Create an AggregatingMergeTree materialized view that watches the test.visits table:

-
CREATE MATERIALIZED VIEW test.basic
-ENGINE = AggregatingMergeTree(StartDate, (CounterID, StartDate), 8192)
-AS SELECT
-    CounterID,
-    StartDate,
-    sumState(Sign)    AS Visits,
-    uniqState(UserID) AS Users
-FROM test.visits
-GROUP BY CounterID, StartDate;
-
- - -

Insert data in the test.visits table. Data will also be inserted in the view, where it will be aggregated:

-
INSERT INTO test.visits ...
-
- - -

Perform SELECT from the view using GROUP BY in order to complete data aggregation:

-
SELECT
-    StartDate,
-    sumMerge(Visits) AS Visits,
-    uniqMerge(Users) AS Users
-FROM test.basic
-GROUP BY StartDate
-ORDER BY StartDate;
-
- - -

You can create a materialized view like this and assign a normal view to it that finishes data aggregation.

-

Note that in most cases, using AggregatingMergeTree is not justified, since queries can be run efficiently enough on non-aggregated data.

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-
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Buffers the data to write in RAM, periodically flushing it to another table. During the read operation, data is read from the buffer and the other table simultaneously.

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Buffer(database, table, num_layers, min_time, max_time, min_rows, max_rows, min_bytes, max_bytes)
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Engine parameters:database, table – The table to flush data to. Instead of the database name, you can use a constant expression that returns a string.num_layers – Parallelism layer. Physically, the table will be represented as 'num_layers' of independent buffers. Recommended value: 16.min_time, max_time, min_rows, max_rows, min_bytes, and max_bytes are conditions for flushing data from the buffer.

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Data is flushed from the buffer and written to the destination table if all the 'min' conditions or at least one 'max' condition are met.min_time, max_time – Condition for the time in seconds from the moment of the first write to the buffer.min_rows, max_rows – Condition for the number of rows in the buffer.min_bytes, max_bytes – Condition for the number of bytes in the buffer.

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During the write operation, data is inserted to a 'num_layers' number of random buffers. Or, if the data part to insert is large enough (greater than 'max_rows' or 'max_bytes'), it is written directly to the destination table, omitting the buffer.

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The conditions for flushing the data are calculated separately for each of the 'num_layers' buffers. For example, if num_layers = 16 and max_bytes = 100000000, the maximum RAM consumption is 1.6 GB.

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Example:

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CREATE TABLE merge.hits_buffer AS merge.hits ENGINE = Buffer(merge, hits, 16, 10, 100, 10000, 1000000, 10000000, 100000000)
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Creating a 'merge.hits_buffer' table with the same structure as 'merge.hits' and using the Buffer engine. When writing to this table, data is buffered in RAM and later written to the 'merge.hits' table. 16 buffers are created. The data in each of them is flushed if either 100 seconds have passed, or one million rows have been written, or 100 MB of data have been written; or if simultaneously 10 seconds have passed and 10,000 rows and 10 MB of data have been written. For example, if just one row has been written, after 100 seconds it will be flushed, no matter what. But if many rows have been written, the data will be flushed sooner.

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When the server is stopped, with DROP TABLE or DETACH TABLE, buffer data is also flushed to the destination table.

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You can set empty strings in single quotation marks for the database and table name. This indicates the absence of a destination table. In this case, when the data flush conditions are reached, the buffer is simply cleared. This may be useful for keeping a window of data in memory.

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When reading from a Buffer table, data is processed both from the buffer and from the destination table (if there is one). -Note that the Buffer tables does not support an index. In other words, data in the buffer is fully scanned, which might be slow for large buffers. (For data in a subordinate table, the index that it supports will be used.)

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If the set of columns in the Buffer table doesn't match the set of columns in a subordinate table, a subset of columns that exist in both tables is inserted.

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If the types don't match for one of the columns in the Buffer table and a subordinate table, an error message is entered in the server log and the buffer is cleared. -The same thing happens if the subordinate table doesn't exist when the buffer is flushed.

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If you need to run ALTER for a subordinate table and the Buffer table, we recommend first deleting the Buffer table, running ALTER for the subordinate table, then creating the Buffer table again.

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If the server is restarted abnormally, the data in the buffer is lost.

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PREWHERE, FINAL and SAMPLE do not work correctly for Buffer tables. These conditions are passed to the destination table, but are not used for processing data in the buffer. Because of this, we recommend only using the Buffer table for writing, while reading from the destination table.

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When adding data to a Buffer, one of the buffers is locked. This causes delays if a read operation is simultaneously being performed from the table.

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Data that is inserted to a Buffer table may end up in the subordinate table in a different order and in different blocks. Because of this, a Buffer table is difficult to use for writing to a CollapsingMergeTree correctly. To avoid problems, you can set 'num_layers' to 1.

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If the destination table is replicated, some expected characteristics of replicated tables are lost when writing to a Buffer table. The random changes to the order of rows and sizes of data parts cause data deduplication to quit working, which means it is not possible to have a reliable 'exactly once' write to replicated tables.

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Due to these disadvantages, we can only recommend using a Buffer table in rare cases.

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A Buffer table is used when too many INSERTs are received from a large number of servers over a unit of time and data can't be buffered before insertion, which means the INSERTs can't run fast enough.

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Note that it doesn't make sense to insert data one row at a time, even for Buffer tables. This will only produce a speed of a few thousand rows per second, while inserting larger blocks of data can produce over a million rows per second (see the section "Performance").

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CollapsingMergeTree

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This engine is used specifically for Yandex.Metrica.

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It differs from MergeTree in that it allows automatic deletion, or "collapsing" certain pairs of rows when merging.

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Yandex.Metrica has normal logs (such as hit logs) and change logs. Change logs are used for incrementally calculating statistics on data that is constantly changing. Examples are the log of session changes, or logs of changes to user histories. Sessions are constantly changing in Yandex.Metrica. For example, the number of hits per session increases. We refer to changes in any object as a pair (?old values, ?new values). Old values may be missing if the object was created. New values may be missing if the object was deleted. If the object was changed, but existed previously and was not deleted, both values are present. In the change log, one or two entries are made for each change. Each entry contains all the attributes that the object has, plus a special attribute for differentiating between the old and new values. When objects change, only the new entries are added to the change log, and the existing ones are not touched.

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The change log makes it possible to incrementally calculate almost any statistics. To do this, we need to consider "new" rows with a plus sign, and "old" rows with a minus sign. In other words, incremental calculation is possible for all statistics whose algebraic structure contains an operation for taking the inverse of an element. This is true of most statistics. We can also calculate "idempotent" statistics, such as the number of unique visitors, since the unique visitors are not deleted when making changes to sessions.

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This is the main concept that allows Yandex.Metrica to work in real time.

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CollapsingMergeTree accepts an additional parameter - the name of an Int8-type column that contains the row's "sign". Example:

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CollapsingMergeTree(EventDate, (CounterID, EventDate, intHash32(UniqID), VisitID), 8192, Sign)
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Here, Sign is a column containing -1 for "old" values and 1 for "new" values.

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When merging, each group of consecutive identical primary key values (columns for sorting data) is reduced to no more than one row with the column value 'sign_column = -1' (the "negative row") and no more than one row with the column value 'sign_column = 1' (the "positive row"). In other words, entries from the change log are collapsed.

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If the number of positive and negative rows matches, the first negative row and the last positive row are written. -If there is one more positive row than negative rows, only the last positive row is written. -If there is one more negative row than positive rows, only the first negative row is written. -Otherwise, there will be a logical error and none of the rows will be written. (A logical error can occur if the same section of the log was accidentally inserted more than once. The error is just recorded in the server log, and the merge continues.)

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Thus, collapsing should not change the results of calculating statistics. -Changes are gradually collapsed so that in the end only the last value of almost every object is left. -Compared to MergeTree, the CollapsingMergeTree engine allows a multifold reduction of data volume.

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There are several ways to get completely "collapsed" data from a CollapsingMergeTree table:

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  1. Write a query with GROUP BY and aggregate functions that accounts for the sign. For example, to calculate quantity, write 'sum(Sign)' instead of 'count()'. To calculate the sum of something, write 'sum(Sign * x)' instead of 'sum(x)', and so on, and also add 'HAVING sum(Sign) > 0'. Not all amounts can be calculated this way. For example, the aggregate functions 'min' and 'max' can't be rewritten.
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  2. If you must extract data without aggregation (for example, to check whether rows are present whose newest values match certain conditions), you can use the FINAL modifier for the FROM clause. This approach is significantly less efficient.
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Custom partitioning key

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Starting with version 1.1.54310, you can create tables in the MergeTree family with any partitioning expression (not only partitioning by month).

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The partition key can be an expression from the table columns, or a tuple of such expressions (similar to the primary key). The partition key can be omitted. When creating a table, specify the partition key in the ENGINE description with the new syntax:

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ENGINE [=] Name(...) [PARTITION BY expr] [ORDER BY expr] [SAMPLE BY expr] [SETTINGS name=value, ...]
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For MergeTree tables, the partition expression is specified after PARTITION BY, the primary key after ORDER BY, the sampling key after SAMPLE BY, and SETTINGS can specify index_granularity (optional; the default value is 8192), as well as other settings from MergeTreeSettings.h. The other engine parameters are specified in parentheses after the engine name, as previously. Example:

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ENGINE = ReplicatedCollapsingMergeTree('/clickhouse/tables/name', 'replica1', Sign)
-    PARTITION BY (toMonday(StartDate), EventType)
-    ORDER BY (CounterID, StartDate, intHash32(UserID))
-    SAMPLE BY intHash32(UserID)
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The traditional partitioning by month is expressed as toYYYYMM(date_column).

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You can't convert an old-style table to a table with custom partitions (only via INSERT SELECT).

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After this table is created, merge will only work for data parts that have the same value for the partitioning expression. Note: This means that you shouldn't make overly granular partitions (more than about a thousand partitions), or SELECT will perform poorly.

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To specify a partition in ALTER PARTITION commands, specify the value of the partition expression (or a tuple). Constants and constant expressions are supported. Example:

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ALTER TABLE table DROP PARTITION (toMonday(today()), 1)
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Deletes the partition for the current week with event type 1. The same is true for the OPTIMIZE query. To specify the only partition in a non-partitioned table, specify PARTITION tuple().

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Note: For old-style tables, the partition can be specified either as a number 201710 or a string '201710'. The syntax for the new style of tables is stricter with types (similar to the parser for the VALUES input format). In addition, ALTER TABLE FREEZE PARTITION uses exact match for new-style tables (not prefix match).

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In the system.parts table, the partition column specifies the value of the partition expression to use in ALTER queries (if quotas are removed). The name column should specify the name of the data part that has a new format.

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Was: 20140317_20140323_2_2_0 (minimum date - maximum date - minimum block number - maximum block number - level).

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Now: 201403_2_2_0 (partition ID - minimum block number - maximum block number - level).

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The partition ID is its string identifier (human-readable, if possible) that is used for the names of data parts in the file system and in ZooKeeper. You can specify it in ALTER queries in place of the partition key. Example: Partition key toYYYYMM(EventDate); ALTER can specify either PARTITION 201710 or PARTITION ID '201710'.

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For more examples, see the tests 00502_custom_partitioning_local and 00502_custom_partitioning_replicated_zookeeper.

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Dictionary

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The Dictionary engine displays the dictionary data as a ClickHouse table.

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As an example, consider a dictionary of products with the following configuration:

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<dictionaries>
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-        <name>products</name>
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-            <odbc>
-                <table>products</table>
-                <connection_string>DSN=some-db-server</connection_string>
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select name, type, key, attribute.names, attribute.types, bytes_allocated, element_count,source from system.dictionaries where name = 'products';                     
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┌─name─────┬─type─┬─key────┬─attribute.names─┬─attribute.types─┬─bytes_allocated─┬─element_count─┬─source──────────┐
-│ products │ Flat │ UInt64 │ ['title']       │ ['String']      │        23065376 │        175032 │ ODBC: .products │
-└──────────┴──────┴────────┴─────────────────┴─────────────────┴─────────────────┴───────────────┴─────────────────┘
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You can use the dictGet* function to get the dictionary data in this format.

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This view isn't helpful when you need to get raw data, or when performing a JOIN operation. For these cases, you can use the Dictionary engine, which displays the dictionary data in a table.

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Syntax:

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CREATE TABLE %table_name% (%fields%) engine = Dictionary(%dictionary_name%)`
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Usage example:

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create table products (product_id UInt64, title String) Engine = Dictionary(products);
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-CREATE TABLE products
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┌────product_id─┬─title───────────┐
-│        152689 │ Some item       │
-└───────────────┴─────────────────┘
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Distributed

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The Distributed engine does not store data itself, but allows distributed query processing on multiple servers. -Reading is automatically parallelized. During a read, the table indexes on remote servers are used, if there are any. -The Distributed engine accepts parameters: the cluster name in the server's config file, the name of a remote database, the name of a remote table, and (optionally) a sharding key. -Example:

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Distributed(logs, default, hits[, sharding_key])
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Data will be read from all servers in the 'logs' cluster, from the default.hits table located on every server in the cluster. -Data is not only read, but is partially processed on the remote servers (to the extent that this is possible). -For example, for a query with GROUP BY, data will be aggregated on remote servers, and the intermediate states of aggregate functions will be sent to the requestor server. Then data will be further aggregated.

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Instead of the database name, you can use a constant expression that returns a string. For example: currentDatabase().

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logs – The cluster name in the server's config file.

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Clusters are set like this:

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<remote_servers>
-    <logs>
-        <shard>
-            <!-- Optional. Shard weight when writing data. Default: 1. -->
-            <weight>1</weight>
-            <!-- Optional. Whether to write data to just one of the replicas. Default: false (write data to all replicas). -->
-            <internal_replication>false</internal_replication>
-            <replica>
-                <host>example01-01-1</host>
-                <port>9000</port>
-            </replica>
-            <replica>
-                <host>example01-01-2</host>
-                <port>9000</port>
-            </replica>
-        </shard>
-        <shard>
-            <weight>2</weight>
-            <internal_replication>false</internal_replication>
-            <replica>
-                <host>example01-02-1</host>
-                <port>9000</port>
-            </replica>
-            <replica>
-                <host>example01-02-2</host>
-                <port>9000</port>
-            </replica>
-        </shard>
-    </logs>
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Here a cluster is defined with the name 'logs' that consists of two shards, each of which contains two replicas. -Shards refer to the servers that contain different parts of the data (in order to read all the data, you must access all the shards). -Replicas are duplicating servers (in order to read all the data, you can access the data on any one of the replicas).

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The parameters host, port, and optionally user and password are specified for each server:

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: - host – The address of the remote server. You can use either the domain or the IPv4 or IPv6 address. If you specify the domain, the server makes a DNS request when it starts, and the result is stored as long as the server is running. If the DNS request fails, the server doesn't start. If you change the DNS record, restart the server. -- port– The TCP port for messenger activity ('tcp_port' in the config, usually set to 9000). Do not confuse it with http_port. -- user– Name of the user for connecting to a remote server. Default value: default. This user must have access to connect to the specified server. Access is configured in the users.xml file. For more information, see the section "Access rights". -- password – The password for connecting to a remote server (not masked). Default value: empty string.

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When specifying replicas, one of the available replicas will be selected for each of the shards when reading. You can configure the algorithm for load balancing (the preference for which replica to access) – see the 'load_balancing' setting. -If the connection with the server is not established, there will be an attempt to connect with a short timeout. If the connection failed, the next replica will be selected, and so on for all the replicas. If the connection attempt failed for all the replicas, the attempt will be repeated the same way, several times. -This works in favor of resiliency, but does not provide complete fault tolerance: a remote server might accept the connection, but might not work, or work poorly.

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You can specify just one of the shards (in this case, query processing should be called remote, rather than distributed) or up to any number of shards. In each shard, you can specify from one to any number of replicas. You can specify a different number of replicas for each shard.

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You can specify as many clusters as you wish in the configuration.

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To view your clusters, use the 'system.clusters' table.

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The Distributed engine allows working with a cluster like a local server. However, the cluster is inextensible: you must write its configuration in the server config file (even better, for all the cluster's servers).

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There is no support for Distributed tables that look at other Distributed tables (except in cases when a Distributed table only has one shard). As an alternative, make the Distributed table look at the "final" tables.

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The Distributed engine requires writing clusters to the config file. Clusters from the config file are updated on the fly, without restarting the server. If you need to send a query to an unknown set of shards and replicas each time, you don't need to create a Distributed table – use the 'remote' table function instead. See the section "Table functions".

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There are two methods for writing data to a cluster:

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First, you can define which servers to write which data to, and perform the write directly on each shard. In other words, perform INSERT in the tables that the distributed table "looks at". -This is the most flexible solution – you can use any sharding scheme, which could be non-trivial due to the requirements of the subject area. -This is also the most optimal solution, since data can be written to different shards completely independently.

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Second, you can perform INSERT in a Distributed table. In this case, the table will distribute the inserted data across servers itself. -In order to write to a Distributed table, it must have a sharding key set (the last parameter). In addition, if there is only one shard, the write operation works without specifying the sharding key, since it doesn't have any meaning in this case.

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Each shard can have a weight defined in the config file. By default, the weight is equal to one. Data is distributed across shards in the amount proportional to the shard weight. For example, if there are two shards and the first has a weight of 9 while the second has a weight of 10, the first will be sent 9 / 19 parts of the rows, and the second will be sent 10 / 19.

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Each shard can have the 'internal_replication' parameter defined in the config file.

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If this parameter is set to 'true', the write operation selects the first healthy replica and writes data to it. Use this alternative if the Distributed table "looks at" replicated tables. In other words, if the table where data will be written is going to replicate them itself.

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If it is set to 'false' (the default), data is written to all replicas. In essence, this means that the Distributed table replicates data itself. This is worse than using replicated tables, because the consistency of replicas is not checked, and over time they will contain slightly different data.

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To select the shard that a row of data is sent to, the sharding expression is analyzed, and its remainder is taken from dividing it by the total weight of the shards. The row is sent to the shard that corresponds to the half-interval of the remainders from 'prev_weight' to 'prev_weights + weight', where 'prev_weights' is the total weight of the shards with the smallest number, and 'weight' is the weight of this shard. For example, if there are two shards, and the first has a weight of 9 while the second has a weight of 10, the row will be sent to the first shard for the remainders from the range [0, 9), and to the second for the remainders from the range [9, 19).

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The sharding expression can be any expression from constants and table columns that returns an integer. For example, you can use the expression 'rand()' for random distribution of data, or 'UserID' for distribution by the remainder from dividing the user's ID (then the data of a single user will reside on a single shard, which simplifies running IN and JOIN by users). If one of the columns is not distributed evenly enough, you can wrap it in a hash function: intHash64(UserID).

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A simple remainder from division is a limited solution for sharding and isn't always appropriate. It works for medium and large volumes of data (dozens of servers), but not for very large volumes of data (hundreds of servers or more). In the latter case, use the sharding scheme required by the subject area, rather than using entries in Distributed tables.

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SELECT queries are sent to all the shards, and work regardless of how data is distributed across the shards (they can be distributed completely randomly). When you add a new shard, you don't have to transfer the old data to it. You can write new data with a heavier weight – the data will be distributed slightly unevenly, but queries will work correctly and efficiently.

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You should be concerned about the sharding scheme in the following cases:

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    -
  • Queries are used that require joining data (IN or JOIN) by a specific key. If data is sharded by this key, you can use local IN or JOIN instead of GLOBAL IN or GLOBAL JOIN, which is much more efficient.
    • -
  • A large number of servers is used (hundreds or more) with a large number of small queries (queries of individual clients - websites, advertisers, or partners). In order for the small queries to not affect the entire cluster, it makes sense to locate data for a single client on a single shard. Alternatively, as we've done in Yandex.Metrica, you can set up bi-level sharding: divide the entire cluster into "layers", where a layer may consist of multiple shards. Data for a single client is located on a single layer, but shards can be added to a layer as necessary, and data is randomly distributed within them. Distributed tables are created for each layer, and a single shared distributed table is created for global queries.
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Data is written asynchronously. For an INSERT to a Distributed table, the data block is just written to the local file system. The data is sent to the remote servers in the background as soon as possible. You should check whether data is sent successfully by checking the list of files (data waiting to be sent) in the table directory: /var/lib/clickhouse/data/database/table/.

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If the server ceased to exist or had a rough restart (for example, after a device failure) after an INSERT to a Distributed table, the inserted data might be lost. If a damaged data part is detected in the table directory, it is transferred to the 'broken' subdirectory and no longer used.

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When the max_parallel_replicas option is enabled, query processing is parallelized across all replicas within a single shard. For more information, see the section "Settings, max_parallel_replicas".

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ClickHouse allows sending a server the data that is needed for processing a query, together with a SELECT query. This data is put in a temporary table (see the section "Temporary tables") and can be used in the query (for example, in IN operators).

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External data can be uploaded using the command-line client (in non-interactive mode), or using the HTTP interface.

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--external – Marks the beginning of a clause. ---file – Path to the file with the table dump, or -, which refers to stdin. -Only a single table can be retrieved from stdin.

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The files specified in 'file' will be parsed by the format specified in 'format', using the data types specified in 'types' or 'structure'. The table will be uploaded to the server and accessible there as a temporary table with the name in 'name'.

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echo -ne "1\n2\n3\n" | clickhouse-client --query="SELECT count() FROM test.visits WHERE TraficSourceID IN _data" --external --file=- --types=Int8
-849897
-cat /etc/passwd | sed 's/:/\t/g' | clickhouse-client --query="SELECT shell, count() AS c FROM passwd GROUP BY shell ORDER BY c DESC" --external --file=- --name=passwd --structure='login String, unused String, uid UInt16, gid UInt16, comment String, home String, shell String'
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When using the HTTP interface, external data is passed in the multipart/form-data format. Each table is transmitted as a separate file. The table name is taken from the file name. The 'query_string' is passed the parameters 'name_format', 'name_types', and 'name_structure', where 'name' is the name of the table that these parameters correspond to. The meaning of the parameters is the same as when using the command-line client.

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For distributed query processing, the temporary tables are sent to all the remote servers.

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The data source is a file that stores data in one of the supported input formats (TabSeparated, Native, etc.).

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GraphiteMergeTree

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This engine is designed for rollup (thinning and aggregating/averaging) Graphite data. It may be helpful to developers who want to use ClickHouse as a data store for Graphite.

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Graphite stores full data in ClickHouse, and data can be retrieved in the following ways:

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Using the GraphiteMergeTree engine.

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Using the engine

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Rollup pattern:

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When processing a record, ClickHouse will check the rules in the patternclause. If the metric name matches the regexp, the rules from pattern are applied; otherwise, the rules from default are used.

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Fields in the pattern.

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The table engine (type of table) determines:

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A prepared data structure for JOIN that is always located in RAM.

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Engine parameters: ANY|ALL – strictness; LEFT|INNER – type. -These parameters are set without quotes and must match the JOIN that the table will be used for. k1, k2, ... are the key columns from the USING clause that the join will be made on.

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The table can't be used for GLOBAL JOINs.

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You can use INSERT to add data to the table, similar to the Set engine. For ANY, data for duplicated keys will be ignored. For ALL, it will be counted. You can't perform SELECT directly from the table. The only way to retrieve data is to use it as the "right-hand" table for JOIN.

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This engine works with Apache Kafka.

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The delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name.

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Groups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. Read more about this at http://kafka.apache.org/intro.

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SELECT is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. To do this:

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When the MATERIALIZED VIEW joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using SELECT

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To improve performance, received messages are grouped into blocks the size of max_insert_block_size. If the block wasn't formed within stream_flush_interval_ms milliseconds, the data will be flushed to the table regardless of the completeness of the block.

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To stop receiving topic data or to change the conversion logic, detach the materialized view:

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If you want to change the target table by using ALTERmaterialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view.

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Configuration

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Similar to GraphiteMergeTree, the Kafka engine supports extended configuration using the ClickHouse config file. There are two configuration keys that you can use: global (kafka) and topic-level (kafka_topic_*). The global configuration is applied first, and the topic-level configuration is second (if it exists).

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  <!--  Global configuration options for all tables of Kafka engine type -->
-  <kafka>
-    <debug>cgrp</debug>
-    <auto_offset_reset>smallest</auto_offset_reset>
-  </kafka>
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-  <kafka_topic_logs>
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For a list of possible configuration options, see the librdkafka configuration reference. Use the underscore (_) instead of a dot in the ClickHouse configuration. For example, check.crcs=true will be <check_crcs>true</check_crcs>.

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Log differs from TinyLog in that a small file of "marks" resides with the column files. These marks are written on every data block and contain offsets that indicate where to start reading the file in order to skip the specified number of rows. This makes it possible to read table data in multiple threads. -For concurrent data access, the read operations can be performed simultaneously, while write operations block reads and each other. -The Log engine does not support indexes. Similarly, if writing to a table failed, the table is broken, and reading from it returns an error. The Log engine is appropriate for temporary data, write-once tables, and for testing or demonstration purposes.

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Used for implementing materialized views (for more information, see the CREATE TABLE) query. For storing data, it uses a different engine that was specified when creating the view. When reading from a table, it just uses this engine.

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The Memory engine stores data in RAM, in uncompressed form. Data is stored in exactly the same form as it is received when read. In other words, reading from this table is completely free. -Concurrent data access is synchronized. Locks are short: read and write operations don't block each other. -Indexes are not supported. Reading is parallelized. -Maximal productivity (over 10 GB/sec) is reached on simple queries, because there is no reading from the disk, decompressing, or deserializing data. (We should note that in many cases, the productivity of the MergeTree engine is almost as high.) -When restarting a server, data disappears from the table and the table becomes empty. -Normally, using this table engine is not justified. However, it can be used for tests, and for tasks where maximum speed is required on a relatively small number of rows (up to approximately 100,000,000).

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The Memory engine is used by the system for temporary tables with external query data (see the section "External data for processing a query"), and for implementing GLOBAL IN (see the section "IN operators").

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The Merge engine (not to be confused with MergeTree) does not store data itself, but allows reading from any number of other tables simultaneously. -Reading is automatically parallelized. Writing to a table is not supported. When reading, the indexes of tables that are actually being read are used, if they exist. -The Merge engine accepts parameters: the database name and a regular expression for tables.

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Data will be read from the tables in the 'hits' database that have names that match the regular expression '^WatchLog'.

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Regular expressions — re2 (supports a subset of PCRE), case-sensitive. -See the notes about escaping symbols in regular expressions in the "match" section.

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Virtual columns

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Virtual columns are columns that are provided by the table engine, regardless of the table definition. In other words, these columns are not specified in CREATE TABLE, but they are accessible for SELECT.

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A Merge type table contains a virtual _table column with the String type. (If the table already has a _table column, the virtual column is named _table1, and if it already has _table1, it is named _table2, and so on.) It contains the name of the table that data was read from.

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If the WHERE or PREWHERE clause contains conditions for the '_table' column that do not depend on other table columns (as one of the conjunction elements, or as an entire expression), these conditions are used as an index. The conditions are performed on a data set of table names to read data from, and the read operation will be performed from only those tables that the condition was triggered on.

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MergeTree

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The MergeTree engine supports an index by primary key and by date, and provides the possibility to update data in real time. -This is the most advanced table engine in ClickHouse. Don't confuse it with the Merge engine.

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The engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity.

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Example without sampling support.

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MergeTree(EventDate, (CounterID, EventDate), 8192)
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Example with sampling support.

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MergeTree(EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID)), 8192)
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A MergeTree table must have a separate column containing the date. Here, it is the EventDate column. The date column must have the 'Date' type (not 'DateTime').

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The primary key may be a tuple from any expressions (usually this is just a tuple of columns), or a single expression.

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The sampling expression (optional) can be any expression. It must also be present in the primary key. The example uses a hash of user IDs to pseudo-randomly disperse data in the table for each CounterID and EventDate. In other words, when using the SAMPLE clause in a query, you get an evenly pseudo-random sample of data for a subset of users.

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The table is implemented as a set of parts. Each part is sorted by the primary key. In addition, each part has the minimum and maximum date assigned. When inserting in the table, a new sorted part is created. The merge process is periodically initiated in the background. When merging, several parts are selected (usually the smallest ones) and then merged into one large sorted part.

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In other words, incremental sorting occurs when inserting to the table. Merging is implemented so that the table always consists of a small number of sorted parts, and the merge itself doesn't do too much work.

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During insertion, data belonging to different months is separated into different parts. The parts that correspond to different months are never combined. The purpose of this is to provide local data modification (for ease in backups).

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Parts are combined up to a certain size threshold, so there aren't any merges that are too long.

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For each part, an index file is also written. The index file contains the primary key value for every 'index_granularity' row in the table. In other words, this is an abbreviated index of sorted data.

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For columns, "marks" are also written to each 'index_granularity' row so that data can be read in a specific range.

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When reading from a table, the SELECT query is analyzed for whether indexes can be used. -An index can be used if the WHERE or PREWHERE clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has IN or LIKE with a fixed prefix on columns or expressions that are in the primary key or partitioning key, or on certain partially repetitive functions of these columns, or logical relationships of these expressions.

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Thus, it is possible to quickly run queries on one or many ranges of the primary key. In this example, queries will be fast when run for a specific tracking tag; for a specific tag and date range; for a specific tag and date; for multiple tags with a date range, and so on.

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SELECT count() FROM table WHERE EventDate = toDate(now()) AND CounterID = 34
-SELECT count() FROM table WHERE EventDate = toDate(now()) AND (CounterID = 34 OR CounterID = 42)
-SELECT count() FROM table WHERE ((EventDate >= toDate('2014-01-01') AND EventDate <= toDate('2014-01-31')) OR EventDate = toDate('2014-05-01')) AND CounterID IN (101500, 731962, 160656) AND (CounterID = 101500 OR EventDate != toDate('2014-05-01'))
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All of these cases will use the index by date and by primary key. The index is used even for complex expressions. Reading from the table is organized so that using the index can't be slower than a full scan.

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In this example, the index can't be used.

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SELECT count() FROM table WHERE CounterID = 34 OR URL LIKE '%upyachka%'
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To check whether ClickHouse can use the index when executing the query, use the settings force_index_by_dateandforce_primary_key.

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The index by date only allows reading those parts that contain dates from the desired range. However, a data part may contain data for many dates (up to an entire month), while within a single part the data is ordered by the primary key, which might not contain the date as the first column. Because of this, using a query with only a date condition that does not specify the primary key prefix will cause more data to be read than for a single date.

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For concurrent table access, we use multi-versioning. In other words, when a table is simultaneously read and updated, data is read from a set of parts that is current at the time of the query. There are no lengthy locks. Inserts do not get in the way of read operations.

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Reading from a table is automatically parallelized.

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The OPTIMIZE query is supported, which calls an extra merge step.

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You can use a single large table and continually add data to it in small chunks – this is what MergeTree is intended for.

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Data replication is possible for all types of tables in the MergeTree family (see the section "Data replication").

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MySQL

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The MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server.

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The engine takes 4 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example:

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MySQL('host:port', 'database', 'table', 'user', 'password');
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At this time, simple WHERE clauses such as =, !=, >, >=, <, <= are executed on the MySQL server.

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The rest of the conditions and the LIMIT sampling constraint are executed in ClickHouse only after the query to MySQL finishes.

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When writing to a Null table, data is ignored. When reading from a Null table, the response is empty.

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However, you can create a materialized view on a Null table. So the data written to the table will end up in the view.

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ReplacingMergeTree

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This engine table differs from MergeTree in that it removes duplicate entries with the same primary key value.

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The last optional parameter for the table engine is the version column. When merging, it reduces all rows with the same primary key value to just one row. If the version column is specified, it leaves the row with the highest version; otherwise, it leaves the last row.

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The version column must have a type from the UInt family, Date, or DateTime.

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ReplacingMergeTree(EventDate, (OrderID, EventDate, BannerID, ...), 8192, ver)
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Note that data is only deduplicated during merges. Merging occurs in the background at an unknown time, so you can't plan for it. Some of the data may remain unprocessed. Although you can run an unscheduled merge using the OPTIMIZE query, don't count on using it, because the OPTIMIZE query will read and write a large amount of data.

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Thus, ReplacingMergeTree is suitable for clearing out duplicate data in the background in order to save space, but it doesn't guarantee the absence of duplicates.

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This engine is not used in Yandex.Metrica, but it has been applied in other Yandex projects.

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Data replication

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Replication is only supported for tables in the MergeTree family:

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Replication works at the level of an individual table, not the entire server. A server can store both replicated and non-replicated tables at the same time.

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Replication does not depend on sharding. Each shard has its own independent replication.

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Compressed data is replicated for INSERT and ALTER queries (see the description of the ALTER query).

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CREATE, DROP, ATTACH, DETACH and RENAME queries are executed on a single server and are not replicated:

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To use replication, set the addresses of the ZooKeeper cluster in the config file. Example:

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<zookeeper>
-    <node index="1">
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Use ZooKeeper version 3.4.5 or later.

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You can specify any existing ZooKeeper cluster and the system will use a directory on it for its own data (the directory is specified when creating a replicatable table).

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If ZooKeeper isn't set in the config file, you can't create replicated tables, and any existing replicated tables will be read-only.

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ZooKeeper is not used in SELECT queries because replication does not affect the performance of SELECT and queries run just as fast as they do for non-replicated tables. When querying distributed replicated tables, ClickHouse behavior is controlled by the settings max_replica_delay_for_distributed_queries and fallback_to_stale_replicas_for_distributed_queries.

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For each INSERT query, approximately ten entries are added to ZooKeeper through several transactions. (To be more precise, this is for each inserted block of data; an INSERT query contains one block or one block per max_insert_block_size = 1048576 rows.) This leads to slightly longer latencies for INSERT compared to non-replicated tables. But if you follow the recommendations to insert data in batches of no more than one INSERT per second, it doesn't create any problems. The entire ClickHouse cluster used for coordinating one ZooKeeper cluster has a total of several hundred INSERTs per second. The throughput on data inserts (the number of rows per second) is just as high as for non-replicated data.

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For very large clusters, you can use different ZooKeeper clusters for different shards. However, this hasn't proven necessary on the Yandex.Metrica cluster (approximately 300 servers).

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Replication is asynchronous and multi-master. INSERT queries (as well as ALTER) can be sent to any available server. Data is inserted on the server where the query is run, and then it is copied to the other servers. Because it is asynchronous, recently inserted data appears on the other replicas with some latency. If part of the replicas are not available, the data is written when they become available. If a replica is available, the latency is the amount of time it takes to transfer the block of compressed data over the network.

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By default, an INSERT query waits for confirmation of writing the data from only one replica. If the data was successfully written to only one replica and the server with this replica ceases to exist, the stored data will be lost. Tp enable getting confirmation of data writes from multiple replicas, use the insert_quorum option.

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Each block of data is written atomically. The INSERT query is divided into blocks up to max_insert_block_size = 1048576 rows. In other words, if the INSERT query has less than 1048576 rows, it is made atomically.

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Data blocks are deduplicated. For multiple writes of the same data block (data blocks of the same size containing the same rows in the same order), the block is only written once. The reason for this is in case of network failures when the client application doesn't know if the data was written to the DB, so the INSERT query can simply be repeated. It doesn't matter which replica INSERTs were sent to with identical data. INSERTs are idempotent. Deduplication parameters are controlled by merge_tree server settings.

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During replication, only the source data to insert is transferred over the network. Further data transformation (merging) is coordinated and performed on all the replicas in the same way. This minimizes network usage, which means that replication works well when replicas reside in different datacenters. (Note that duplicating data in different datacenters is the main goal of replication.)

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You can have any number of replicas of the same data. Yandex.Metrica uses double replication in production. Each server uses RAID-5 or RAID-6, and RAID-10 in some cases. This is a relatively reliable and convenient solution.

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The system monitors data synchronicity on replicas and is able to recover after a failure. Failover is automatic (for small differences in data) or semi-automatic (when data differs too much, which may indicate a configuration error).

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Creating replicated tables

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The Replicated prefix is added to the table engine name. For example:ReplicatedMergeTree.

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Two parameters are also added in the beginning of the parameters list – the path to the table in ZooKeeper, and the replica name in ZooKeeper.

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Example:

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ReplicatedMergeTree('/clickhouse/tables/{layer}-{shard}/hits', '{replica}', EventDate, intHash32(UserID), (CounterID, EventDate, intHash32(UserID), EventTime), 8192)
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As the example shows, these parameters can contain substitutions in curly brackets. The substituted values are taken from the 'macros' section of the config file. Example:

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<macros>
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-    <shard>02</shard>
-    <replica>example05-02-1.yandex.ru</replica>
-</macros>
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The path to the table in ZooKeeper should be unique for each replicated table. Tables on different shards should have different paths. -In this case, the path consists of the following parts:

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/clickhouse/tables/ is the common prefix. We recommend using exactly this one.

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{layer}-{shard} is the shard identifier. In this example it consists of two parts, since the Yandex.Metrica cluster uses bi-level sharding. For most tasks, you can leave just the {shard} substitution, which will be expanded to the shard identifier.

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hits is the name of the node for the table in ZooKeeper. It is a good idea to make it the same as the table name. It is defined explicitly, because in contrast to the table name, it doesn't change after a RENAME query.

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The replica name identifies different replicas of the same table. You can use the server name for this, as in the example. The name only needs to be unique within each shard.

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You can define the parameters explicitly instead of using substitutions. This might be convenient for testing and for configuring small clusters. However, you can't use distributed DDL queries (ON CLUSTER) in this case.

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When working with large clusters, we recommend using substitutions because they reduce the probability of error.

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Run the CREATE TABLE query on each replica. This query creates a new replicated table, or adds a new replica to an existing one.

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If you add a new replica after the table already contains some data on other replicas, the data will be copied from the other replicas to the new one after running the query. In other words, the new replica syncs itself with the others.

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To delete a replica, run DROP TABLE. However, only one replica is deleted – the one that resides on the server where you run the query.

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Recovery after failures

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If ZooKeeper is unavailable when a server starts, replicated tables switch to read-only mode. The system periodically attempts to connect to ZooKeeper.

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If ZooKeeper is unavailable during an INSERT, or an error occurs when interacting with ZooKeeper, an exception is thrown.

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After connecting to ZooKeeper, the system checks whether the set of data in the local file system matches the expected set of data (ZooKeeper stores this information). If there are minor inconsistencies, the system resolves them by syncing data with the replicas.

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If the system detects broken data parts (with the wrong size of files) or unrecognized parts (parts written to the file system but not recorded in ZooKeeper), it moves them to the 'detached' subdirectory (they are not deleted). Any missing parts are copied from the replicas.

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Note that ClickHouse does not perform any destructive actions such as automatically deleting a large amount of data.

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When the server starts (or establishes a new session with ZooKeeper), it only checks the quantity and sizes of all files. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a SELECT query. The query throws an exception about a non-matching checksum or size of a compressed block. In this case, data parts are added to the verification queue and copied from the replicas if necessary.

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If the local set of data differs too much from the expected one, a safety mechanism is triggered. The server enters this in the log and refuses to launch. The reason for this is that this case may indicate a configuration error, such as if a replica on a shard was accidentally configured like a replica on a different shard. However, the thresholds for this mechanism are set fairly low, and this situation might occur during normal failure recovery. In this case, data is restored semi-automatically - by "pushing a button".

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To start recovery, create the node /path_to_table/replica_name/flags/force_restore_data in ZooKeeper with any content, or run the command to restore all replicated tables:

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sudo -u clickhouse touch /var/lib/clickhouse/flags/force_restore_data
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Then restart the server. On start, the server deletes these flags and starts recovery.

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Recovery after complete data loss

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If all data and metadata disappeared from one of the servers, follow these steps for recovery:

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Then start the server (restart, if it is already running). Data will be downloaded from replicas.

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An alternative recovery option is to delete information about the lost replica from ZooKeeper (/path_to_table/replica_name), then create the replica again as described in "Creating replicatable tables".

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There is no restriction on network bandwidth during recovery. Keep this in mind if you are restoring many replicas at once.

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Converting from MergeTree to ReplicatedMergeTree

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We use the term MergeTree to refer to all table engines in the MergeTree family, the same as for ReplicatedMergeTree.

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If you had a MergeTree table that was manually replicated, you can convert it to a replicatable table. You might need to do this if you have already collected a large amount of data in a MergeTree table and now you want to enable replication.

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If the data differs on various replicas, first sync it, or delete this data on all the replicas except one.

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Rename the existing MergeTree table, then create a ReplicatedMergeTree table with the old name. -Move the data from the old table to the 'detached' subdirectory inside the directory with the new table data (/var/lib/clickhouse/data/db_name/table_name/). -Then run ALTER TABLE ATTACH PARTITION on one of the replicas to add these data parts to the working set.

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Converting from ReplicatedMergeTree to MergeTree

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Create a MergeTree table with a different name. Move all the data from the directory with the ReplicatedMergeTree table data to the new table's data directory. Then delete the ReplicatedMergeTree table and restart the server.

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If you want to get rid of a ReplicatedMergeTree table without launching the server:

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After this, you can launch the server, create a MergeTree table, move the data to its directory, and then restart the server.

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Recovery when metadata in the ZooKeeper cluster is lost or damaged

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If the data in ZooKeeper was lost or damaged, you can save data by moving it to an unreplicated table as described above.

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If exactly the same parts exist on the other replicas, they are added to the working set on them. If not, the parts are downloaded from the replica that has them.

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A data set that is always in RAM. It is intended for use on the right side of the IN operator (see the section "IN operators").

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You can use INSERT to insert data in the table. New elements will be added to the data set, while duplicates will be ignored. -But you can't perform SELECT from the table. The only way to retrieve data is by using it in the right half of the IN operator.

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Data is always located in RAM. For INSERT, the blocks of inserted data are also written to the directory of tables on the disk. When starting the server, this data is loaded to RAM. In other words, after restarting, the data remains in place.

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This engine differs from MergeTree in that it totals data while merging.

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SummingMergeTree(EventDate, (OrderID, EventDate, BannerID, ...), 8192)
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The columns to total are implicit. When merging, all rows with the same primary key value (in the example, OrderId, EventDate, BannerID, ...) have their values totaled in numeric columns that are not part of the primary key.

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SummingMergeTree(EventDate, (OrderID, EventDate, BannerID, ...), 8192, (Shows, Clicks, Cost, ...))
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The columns to total are set explicitly (the last parameter – Shows, Clicks, Cost, ...). When merging, all rows with the same primary key value have their values totaled in the specified columns. The specified columns also must be numeric and must not be part of the primary key.

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If the values were null in all of these columns, the row is deleted. (The exception is cases when the data part would not have any rows left in it.)

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For the other rows that are not part of the primary key, the first value that occurs is selected when merging.

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Summation is not performed for a read operation. If it is necessary, write the appropriate GROUP BY.

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    • -
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[(1, 100)] + [(2, 150)] -> [(1, 100), (2, 150)]
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-[(1, 100)] + [(1, 150), (2, 150)] -> [(1, 250), (2, 150)]
-[(1, 100), (2, 150)] + [(1, -100)] -> [(2, 150)]
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For nested data structures, you don't need to specify the columns as a list of columns for totaling.

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This table engine is not particularly useful. Remember that when saving just pre-aggregated data, you lose some of the system's advantages.

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The simplest table engine, which stores data on a disk. -Each column is stored in a separate compressed file. -When writing, data is appended to the end of files.

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The typical way to use this table is write-once: first just write the data one time, then read it as many times as needed. -Queries are executed in a single stream. In other words, this engine is intended for relatively small tables (recommended up to 1,000,000 rows). -It makes sense to use this table engine if you have many small tables, since it is simpler than the Log engine (fewer files need to be opened). -The situation when you have a large number of small tables guarantees poor productivity, but may already be used when working with another DBMS, and you may find it easier to switch to using TinyLog types of tables. -Indexes are not supported.

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In Yandex.Metrica, TinyLog tables are used for intermediary data that is processed in small batches.

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Used for implementing views (for more information, see the CREATE VIEW query). It does not store data, but only stores the specified SELECT query. When reading from a table, it runs this query (and deletes all unnecessary columns from the query).

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Table functions can be specified in the FROM clause instead of the database and table names. -Table functions can only be used if 'readonly' is not set. -Table functions aren't related to other functions.

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merge(db_name, 'tables_regexp') – Creates a temporary Merge table. For more information, see the section "Table engines, Merge".

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The table structure is taken from the first table encountered that matches the regular expression.

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numbers(N) – Returns a table with the single 'number' column (UInt64) that contains integers from 0 to N-1.

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Similar to the system.numbers table, it can be used for testing and generating successive values.

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The following two queries are equivalent:

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SELECT * FROM numbers(10);
-SELECT * FROM system.numbers LIMIT 10;
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-- Generate a sequence of dates from 2010-01-01 to 2010-12-31
-select toDate('2010-01-01') + number as d FROM numbers(365);
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remote

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Allows you to access remote servers without creating a Distributed table.

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Signatures:

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remote('addresses_expr', db, table[, 'user'[, 'password']])
-remote('addresses_expr', db.table[, 'user'[, 'password']])
-
- - -

addresses_expr – An expression that generates addresses of remote servers. This may be just one server address. The server address is host:port, or just host. The host can be specified as the server name, or as the IPv4 or IPv6 address. An IPv6 address is specified in square brackets. The port is the TCP port on the remote server. If the port is omitted, it uses tcp_port from the server's config file (by default, 9000).

-
- -The port is required for an IPv6 address. - -
- -

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example01-01-1
-example01-01-1:9000
-localhost
-127.0.0.1
-[::]:9000
-[2a02:6b8:0:1111::11]:9000
-
- - -

Multiple addresses can be comma-separated. In this case, ClickHouse will use distributed processing, so it will send the query to all specified addresses (like to shards with different data).

-

Example:

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example01-01-1,example01-02-1
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Part of the expression can be specified in curly brackets. The previous example can be written as follows:

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example01-0{1,2}-1
-
- - -

Curly brackets can contain a range of numbers separated by two dots (non-negative integers). In this case, the range is expanded to a set of values that generate shard addresses. If the first number starts with zero, the values are formed with the same zero alignment. The previous example can be written as follows:

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example01-{01..02}-1
-
- - -

If you have multiple pairs of curly brackets, it generates the direct product of the corresponding sets.

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Addresses and parts of addresses in curly brackets can be separated by the pipe symbol (|). In this case, the corresponding sets of addresses are interpreted as replicas, and the query will be sent to the first healthy replica. However, the replicas are iterated in the order currently set in the load_balancing setting.

-

Example:

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example01-{01..02}-{1|2}
-
- - -

This example specifies two shards that each have two replicas.

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The number of addresses generated is limited by a constant. Right now this is 1000 addresses.

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Using the remote table function is less optimal than creating a Distributed table, because in this case, the server connection is re-established for every request. In addition, if host names are set, the names are resolved, and errors are not counted when working with various replicas. When processing a large number of queries, always create the Distributed table ahead of time, and don't use the remote table function.

-

The remote table function can be useful in the following cases:

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    -
  • Accessing a specific server for data comparison, debugging, and testing.
    • -
  • Queries between various ClickHouse clusters for research purposes.
    • -
  • Infrequent distributed requests that are made manually.
    • -
  • Distributed requests where the set of servers is re-defined each time.
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If the user is not specified, default is used. -If the password is not specified, an empty password is used.

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clickhouse-copier

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Copies data from the tables in one cluster to tables in another (or the same) cluster.

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You can run multiple clickhouse-copier instances on different servers to perform the same job. ZooKeeper is used for syncing the processes.

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After starting, clickhouse-copier:

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clickhouse-copier tracks the changes in ZooKeeper and applies them on the fly.

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To reduce network traffic, we recommend running clickhouse-copier on the same server where the source data is located.

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Running clickhouse-copier

-

The utility should be run manually:

-
clickhouse-copier copier --daemon --config zookeeper.xml --task-path /task/path --base-dir /path/to/dir
-
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Parameters:

-
    -
  • daemon — Starts clickhouse-copier in daemon mode.
    • -
  • config — The path to the zookeeper.xml file with the parameters for the connection to ZooKeeper.
    • -
  • task-path — The path to the ZooKeeper node. This node is used for syncing clickhouse-copier processes and storing tasks. Tasks are stored in $task-path/description.
    • -
  • base-dir — The path to logs and auxiliary files. When it starts, clickhouse-copier creates clickhouse-copier_YYYYMMHHSS_<PID> subdirectories in $base-dir. If this parameter is omitted, the directories are created in the directory where clickhouse-copier was launched.
    • -
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Format of zookeeper.xml

-
<yandex>
-    <zookeeper>
-        <node index="1">
-            <host>127.0.0.1</host>
-            <port>2181</port>
-        </node>
-    </zookeeper>
-</yandex>
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Configuration of copying tasks

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<yandex>
-    <!-- Configuration of clusters as in an ordinary server config -->
-    <remote_servers>
-        <source_cluster>
-            <shard>
-                <internal_replication>false</internal_replication>
-                    <replica>
-                        <host>127.0.0.1</host>
-                        <port>9000</port>
-                    </replica>
-            </shard>
-            ...
-        </source_cluster>
-
-        <destination_cluster>
-        ...
-        </destination_cluster>
-    </remote_servers>
-
-    <!-- How many simultaneously active workers are possible. If you run more workers superfluous workers will sleep. -->
-    <max_workers>2</max_workers>
-
-    <!-- Setting used to fetch (pull) data from source cluster tables -->
-    <settings_pull>
-        <readonly>1</readonly>
-    </settings_pull>
-
-    <!-- Setting used to insert (push) data to destination cluster tables -->
-    <settings_push>
-        <readonly>0</readonly>
-    </settings_push>
-
-    <!-- Common setting for fetch (pull) and insert (push) operations. The copier process context also uses it.
-         They are overlaid by <settings_pull/> and <settings_push/> respectively. -->
-    <settings>
-        <connect_timeout>3</connect_timeout>
-        <!-- Sync insert is set forcibly, leave it here just in case. -->
-        <insert_distributed_sync>1</insert_distributed_sync>
-    </settings>
-
-    <!-- Copying description of tasks.
-         You can specify several table tasks in the same task description (in the same ZooKeeper node), and they will be performed         sequentially.
-    -->
-    <tables>
-        <!-- A table task that copies one table. -->
-        <table_hits>
-            <!-- Source cluster name (from the <remote_servers/> section) and tables in it that should be copied -->
-            <cluster_pull>source_cluster</cluster_pull>
-            <database_pull>test</database_pull>
-            <table_pull>hits</table_pull>
-
-            <!-- Destination cluster name and tables in which the data should be inserted -->
-            <cluster_push>destination_cluster</cluster_push>
-            <database_push>test</database_push>
-            <table_push>hits2</table_push>
-
-            <!-- Engine of destination tables.
-                 If the destination tables have not been created yet, workers create them using column definitions from source tables and the engine                 definition from here.
-
-                 NOTE: If the first worker starts to insert data and detects that the destination partition is not empty, then the partition will
-                 be dropped and refilled. Take this into account if you already have some data in destination tables. You can directly 
-                 specify partitions that should be copied in <enabled_partitions/>. They should be in quoted format like the partition column in the                 
-                 system.parts table.
-            -->
-            <engine>
-            ENGINE=ReplicatedMergeTree('/clickhouse/tables/{cluster}/{shard}/hits2', '{replica}')
-            PARTITION BY toMonday(date)
-            ORDER BY (CounterID, EventDate)
-            </engine>
-
-            <!-- Sharding key used to insert data to destination cluster -->
-            <sharding_key>jumpConsistentHash(intHash64(UserID), 2)</sharding_key>
-
-            <!-- Optional expression that filter data while pull them from source servers -->
-            <where_condition>CounterID != 0</where_condition>
-
-            <!-- This section specifies partitions that should be copied, other partition will be ignored.
-                 Partition names should have the same format as
-                 partition column of system.parts table (i.e. a quoted text).
-                 Since partition key of source and destination cluster could be different,
-                 these partition names specify destination partitions.
-
-                 Note: Although this section is optional (if it omitted, all partitions will be copied), 
-                 it is strongly recommended to specify the partitions explicitly.
-                 If you already have some partitions ready on the destination cluster, they                 
-                 will be removed at the start of the copying, because they will be interpreted                 
-                 as unfinished data from the previous copying.
-            -->
-            <enabled_partitions>
-                <partition>'2018-02-26'</partition>
-                <partition>'2018-03-05'</partition>
-                ...
-            </enabled_partitions>
-        </table_hits>
-
-        <!-- Next table to copy. It is not copied until the previous table is copying. -->
-        </table_visits>
-        ...
-        </table_visits>
-        ...
-    </tables>
-</yandex>
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clickhouse-copier tracks the changes in /task/path/description and applies them on the fly. For instance, if you change the value of max_workers, the number of processes running tasks will also change.

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The clickhouse-local program enables you to perform fast processing on local files that store tables, without having to deploy and configure the ClickHouse server.

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- - - - - - - - - - - \ No newline at end of file From 904382056591b823aad48f281320d13db04556e3 Mon Sep 17 00:00:00 2001 From: alexey-milovidov Date: Sun, 13 May 2018 12:31:13 +0300 Subject: [PATCH 172/231] Update AggregateFunctionWindowFunnel.h --- .../AggregateFunctions/AggregateFunctionWindowFunnel.h | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h b/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h index ed1628c3bf8..a5abc9be724 100644 --- a/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h +++ b/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h @@ -89,7 +89,7 @@ struct AggregateFunctionWindowFunnelData void sort() { if (!sorted) - { + { std::sort(std::begin(events_list), std::end(events_list), Comparator{}); sorted = true; } @@ -113,6 +113,8 @@ struct AggregateFunctionWindowFunnelData size_t size; readBinary(size, buf); + + /// TODO Protection against huge size events_list.clear(); events_list.resize(size); @@ -149,7 +151,8 @@ private: // The Algorithm complexity is O(n). UInt8 getEventLevel(const AggregateFunctionWindowFunnelData & data) const { - if(data.size() == 0) return 0; + if (data.size() == 0) + return 0; if (events_size == 1) return 1; @@ -190,7 +193,7 @@ public: throw Exception{"Illegal type " + time_arg->getName() + " of first argument of aggregate function " + getName() + ", must be DateTime or UInt32"}; - if(arguments.size() - 1 > AggregateFunctionWindowFunnelData::max_events) + if (arguments.size() - 1 > AggregateFunctionWindowFunnelData::max_events) throw Exception{"Aggregate function " + getName() + " supports up to " + toString(AggregateFunctionWindowFunnelData::max_events) + " event arguments.", ErrorCodes::TOO_MANY_ARGUMENTS_FOR_FUNCTION}; From b096f95cb85622bf8412a3aeb74b763d16e26e5f Mon Sep 17 00:00:00 2001 From: alexey-milovidov Date: Sun, 13 May 2018 12:32:07 +0300 Subject: [PATCH 173/231] Update parametric_functions.md --- docs/en/agg_functions/parametric_functions.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/en/agg_functions/parametric_functions.md b/docs/en/agg_functions/parametric_functions.md index 05ea5c9a642..6def6191371 100644 --- a/docs/en/agg_functions/parametric_functions.md +++ b/docs/en/agg_functions/parametric_functions.md @@ -50,7 +50,7 @@ Events that occur during the same second can be put in the chain in any order. T Works the same way as the sequenceMatch function, but instead of returning whether there is an event chain, it returns UInt64 with the number of event chains found. Chains are searched for without overlapping. In other words, the next chain can start only after the end of the previous one. -## windowFunnel(window)(timestamp, cond1, cond2, cond3, ....) +## windowFunnel(window)(timestamp, cond1, cond2, cond3, ...) Window funnel matching for event chains, calculates the max event level in a sliding window. @@ -62,7 +62,7 @@ Window funnel matching for event chains, calculates the max event level in a sli Example: -Consider you are doing a website analytics, intend to find out the user counts clicked login button( event = 1001 ), then the user counts followed by searched the phones( event = 1003 and product = 'phone' ) , then the user counts followed by made an order ( event = 1009 ). And all event chains must be in a 3600 seconds sliding window. +Consider you are doing a website analytics, intend to find out the user counts clicked login button (event = 1001), then the user counts followed by searched the phones( event = 1003 and product = 'phone'), then the user counts followed by made an order (event = 1009). And all event chains must be in a 3600 seconds sliding window. This could be easily calculate by `windowFunnel` @@ -83,7 +83,7 @@ GROUP BY level ORDER BY level ``` -Simply, the level value could only be 0,1,2,3, it means the maxium event action stage that one user could reach. +Simply, the level value could only be 0, 1, 2, 3, it means the maxium event action stage that one user could reach. ## uniqUpTo(N)(x) From 8690d596b05e29b00e1089269e95edc0fade7b43 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Sun, 13 May 2018 12:32:56 +0300 Subject: [PATCH 174/231] Miscellaneous [#CLICKHOUSE-2] --- dbms/src/Interpreters/InterpreterCreateQuery.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/dbms/src/Interpreters/InterpreterCreateQuery.cpp b/dbms/src/Interpreters/InterpreterCreateQuery.cpp index 0b4caa95157..ef48b12e2e8 100644 --- a/dbms/src/Interpreters/InterpreterCreateQuery.cpp +++ b/dbms/src/Interpreters/InterpreterCreateQuery.cpp @@ -50,7 +50,6 @@ namespace ErrorCodes extern const int EMPTY_LIST_OF_COLUMNS_PASSED; extern const int INCORRECT_QUERY; extern const int ENGINE_REQUIRED; - extern const int TABLE_METADATA_ALREADY_EXISTS; extern const int UNKNOWN_DATABASE_ENGINE; extern const int DUPLICATE_COLUMN; extern const int READONLY; From cecffcd5abd0016ba5f8915b4ba126350270c83a Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Sun, 13 May 2018 12:36:51 +0300 Subject: [PATCH 175/231] Apply ./utils/check-style/fix-style #2352 --- .../AggregateFunctionWindowFunnel.h | 108 +++++++++--------- 1 file changed, 56 insertions(+), 52 deletions(-) diff --git a/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h b/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h index a5abc9be724..ec8569a2923 100644 --- a/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h +++ b/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h @@ -1,23 +1,22 @@ #pragma once -#include -#include -#include -#include #include -#include -#include +#include +#include #include +#include +#include +#include +#include #include -#include #include +#include #include namespace DB { - namespace ErrorCodes { extern const int NUMBER_OF_ARGUMENTS_DOESNT_MATCH; @@ -39,8 +38,8 @@ struct AggregateFunctionWindowFunnelData using TimestampEvent = std::pair; static constexpr size_t bytes_on_stack = 64; - using TimestampEvents = PODArray, bytes_on_stack>>; - + using TimestampEvents = PODArray, bytes_on_stack>>; + using Comparator = ComparePairFirst; bool sorted = true; @@ -113,7 +112,7 @@ struct AggregateFunctionWindowFunnelData size_t size; readBinary(size, buf); - + /// TODO Protection against huge size events_list.clear(); @@ -129,7 +128,6 @@ struct AggregateFunctionWindowFunnelData events_list.emplace_back(timestamp, event); } } - }; /** Calculates the max event level in a sliding window. @@ -138,11 +136,12 @@ struct AggregateFunctionWindowFunnelData * Usage: * - windowFunnel(window)(timestamp, cond1, cond2, cond3, ....) */ -class AggregateFunctionWindowFunnel final : public IAggregateFunctionDataHelper +class AggregateFunctionWindowFunnel final + : public IAggregateFunctionDataHelper { private: UInt32 window; - UInt8 events_size; + UInt8 events_size; // Loop through the entire events_list, update the event timestamp value @@ -161,54 +160,59 @@ private: // events_timestamp stores the timestamp that lastest level 1 happen. // timestamp defaults to -1, which unsigned timestamp value never meet std::vector events_timestamp(events_size, -1); - for(const auto i : ext::range(0, data.size())) + for (const auto i : ext::range(0, data.size())) { const auto & timestamp = (data.events_list)[i].first; const auto & event_idx = (data.events_list)[i].second - 1; - if(event_idx == 0) + if (event_idx == 0) events_timestamp[0] = timestamp; - else if(events_timestamp[event_idx - 1] >= 0 && timestamp <= events_timestamp[event_idx - 1] + window) + else if (events_timestamp[event_idx - 1] >= 0 && timestamp <= events_timestamp[event_idx - 1] + window) { events_timestamp[event_idx] = events_timestamp[event_idx - 1]; - if(event_idx + 1 == events_size) return events_size; + if (event_idx + 1 == events_size) + return events_size; } } - for(size_t event = events_timestamp.size(); event > 0; --event) + for (size_t event = events_timestamp.size(); event > 0; --event) { - if(events_timestamp[event - 1] >= 0) return event; + if (events_timestamp[event - 1] >= 0) + return event; } return 0; } public: - - String getName() const override { return "windowFunnel"; } + String getName() const override + { + return "windowFunnel"; + } AggregateFunctionWindowFunnel(const DataTypes & arguments, const Array & params) { DataTypePtr windowType = arguments[0]; const auto time_arg = arguments.front().get(); - if (!typeid_cast(time_arg) && !typeid_cast(time_arg) ) - throw Exception{"Illegal type " + time_arg->getName() + " of first argument of aggregate function " - + getName() + ", must be DateTime or UInt32"}; + if (!typeid_cast(time_arg) && !typeid_cast(time_arg)) + throw Exception{"Illegal type " + time_arg->getName() + " of first argument of aggregate function " + getName() + + ", must be DateTime or UInt32"}; - if (arguments.size() - 1 > AggregateFunctionWindowFunnelData::max_events) - throw Exception{"Aggregate function " + getName() + " supports up to " + - toString(AggregateFunctionWindowFunnelData::max_events) + " event arguments.", - ErrorCodes::TOO_MANY_ARGUMENTS_FOR_FUNCTION}; + if (arguments.size() - 1 > AggregateFunctionWindowFunnelData::max_events) + throw Exception{"Aggregate function " + getName() + " supports up to " + toString(AggregateFunctionWindowFunnelData::max_events) + + " event arguments.", + ErrorCodes::TOO_MANY_ARGUMENTS_FOR_FUNCTION}; - for(const auto i : ext::range(1, arguments.size())) - { - auto cond_arg = arguments[i].get(); - if (!typeid_cast(cond_arg)) - throw Exception{"Illegal type " + cond_arg->getName() + " of argument " + toString(i + 1) + - " of aggregate function " + getName() + ", must be UInt8", - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; - } + for (const auto i : ext::range(1, arguments.size())) + { + auto cond_arg = arguments[i].get(); + if (!typeid_cast(cond_arg)) + throw Exception{"Illegal type " + cond_arg->getName() + " of argument " + toString(i + 1) + " of aggregate function " + + getName() + ", must be UInt8", + ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; + } - if (params.size() != 1) - throw Exception("Aggregate function " + getName() + " requires exactly 1 args(timestamp_window).", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); + if (params.size() != 1) + throw Exception("Aggregate function " + getName() + " requires exactly 1 args(timestamp_window).", + ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); events_size = arguments.size() - 1; window = params[0].safeGet(); @@ -223,20 +227,18 @@ public: void add(AggregateDataPtr place, const IColumn ** columns, const size_t row_num, Arena *) const override { UInt8 event_level = 0; - for(const auto i : ext::range(1, events_size + 1)) + for (const auto i : ext::range(1, events_size + 1)) { - auto event = static_cast *>(columns[i])->getData()[row_num]; - if(event){ - event_level = i; - break; - } + auto event = static_cast *>(columns[i])->getData()[row_num]; + if (event) + { + event_level = i; + break; + } } - if(event_level) + if (event_level) { - this->data(place).add( - static_cast *>(columns[0])->getData()[row_num], - event_level - ); + this->data(place).add(static_cast *>(columns[0])->getData()[row_num], event_level); } } @@ -260,7 +262,9 @@ public: static_cast(to).getData().push_back(getEventLevel(this->data(place))); } - const char * getHeaderFilePath() const override { return __FILE__; } + const char * getHeaderFilePath() const override + { + return __FILE__; + } }; - } From 1fa8a9a6dfbbba6c8cbf4603df4b90c333019968 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Sun, 13 May 2018 13:05:34 +0300 Subject: [PATCH 176/231] Fixed unclean code #2352 --- .../AggregateFunctionWindowFunnel.cpp | 10 +++++++-- .../AggregateFunctionWindowFunnel.h | 22 +++++-------------- 2 files changed, 14 insertions(+), 18 deletions(-) diff --git a/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.cpp b/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.cpp index 164693e1873..4b822296386 100644 --- a/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.cpp +++ b/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.cpp @@ -12,9 +12,15 @@ namespace AggregateFunctionPtr createAggregateFunctionWindowFunnel(const std::string & name, const DataTypes & arguments, const Array & params) { + if (params.size() != 1) + throw Exception{"Aggregate function " + name + " requires exactly one parameter.", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH}; + + if (arguments.size() < 2) + throw Exception("Aggregate function " + name + " requires one timestamp argument and at least one event condition.", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); + + if (arguments.size() > AggregateFunctionWindowFunnelData::max_events + 1) + throw Exception("Too many event arguments for aggregate function " + name, ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); - if (params.size() <= 0 || params.size() > 32) - throw Exception("Aggregate function " + name + " requires (1, 32] event ids.", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); return std::make_shared(arguments, params); } diff --git a/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h b/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h index ec8569a2923..4ad0400d160 100644 --- a/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h +++ b/dbms/src/AggregateFunctions/AggregateFunctionWindowFunnel.h @@ -157,13 +157,13 @@ private: const_cast(data).sort(); - // events_timestamp stores the timestamp that lastest level 1 happen. + // events_timestamp stores the timestamp that latest i-th level event happen withing time window after previous level event. // timestamp defaults to -1, which unsigned timestamp value never meet std::vector events_timestamp(events_size, -1); - for (const auto i : ext::range(0, data.size())) + for (const auto & pair : data.events_list) { - const auto & timestamp = (data.events_list)[i].first; - const auto & event_idx = (data.events_list)[i].second - 1; + const auto & timestamp = pair.first; + const auto & event_idx = pair.second - 1; if (event_idx == 0) events_timestamp[0] = timestamp; else if (events_timestamp[event_idx - 1] >= 0 && timestamp <= events_timestamp[event_idx - 1] + window) @@ -189,18 +189,11 @@ public: AggregateFunctionWindowFunnel(const DataTypes & arguments, const Array & params) { - DataTypePtr windowType = arguments[0]; - const auto time_arg = arguments.front().get(); if (!typeid_cast(time_arg) && !typeid_cast(time_arg)) throw Exception{"Illegal type " + time_arg->getName() + " of first argument of aggregate function " + getName() + ", must be DateTime or UInt32"}; - if (arguments.size() - 1 > AggregateFunctionWindowFunnelData::max_events) - throw Exception{"Aggregate function " + getName() + " supports up to " + toString(AggregateFunctionWindowFunnelData::max_events) - + " event arguments.", - ErrorCodes::TOO_MANY_ARGUMENTS_FOR_FUNCTION}; - for (const auto i : ext::range(1, arguments.size())) { auto cond_arg = arguments[i].get(); @@ -210,12 +203,8 @@ public: ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT}; } - if (params.size() != 1) - throw Exception("Aggregate function " + getName() + " requires exactly 1 args(timestamp_window).", - ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); - events_size = arguments.size() - 1; - window = params[0].safeGet(); + window = params.at(0).safeGet(); } @@ -267,4 +256,5 @@ public: return __FILE__; } }; + } From 157e3339b568399f6a0951a39430bba2ec8a11a3 Mon Sep 17 00:00:00 2001 From: proller Date: Sun, 13 May 2018 13:34:29 +0300 Subject: [PATCH 177/231] Build fixes (#2350) * Try fix travis * fix * Fix clickhouse-local shared-split link * fix * fix * fix * Build fixes * Fix tinfo * fix * tinfo -> termcap * termcap fix * Better llvm version detect * fix --- cmake/find_llvm.cmake | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/cmake/find_llvm.cmake b/cmake/find_llvm.cmake index 57700df0c0e..a2006e37c64 100644 --- a/cmake/find_llvm.cmake +++ b/cmake/find_llvm.cmake @@ -10,10 +10,18 @@ if (ENABLE_EMBEDDED_COMPILER) if (NOT USE_INTERNAL_LLVM_LIBRARY) set (LLVM_PATHS "/usr/local/lib/llvm") - if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang") - find_package(LLVM CONFIG PATHS ${LLVM_PATHS}) + if (LLVM_VERSION) + find_package(LLVM ${LLVM_VERSION} CONFIG PATHS ${LLVM_PATHS}) + elseif (CMAKE_CXX_COMPILER_ID STREQUAL "Clang") + find_package(LLVM ${CMAKE_CXX_COMPILER_VERSION} CONFIG PATHS ${LLVM_PATHS}) else () - find_package(LLVM 5 CONFIG PATHS ${LLVM_PATHS}) + find_package (LLVM 6 CONFIG PATHS ${LLVM_PATHS}) + if (NOT LLVM_FOUND) + find_package (LLVM 5 CONFIG PATHS ${LLVM_PATHS}) + endif () + if (NOT LLVM_FOUND) + find_package (LLVM 7 CONFIG PATHS ${LLVM_PATHS}) + endif () endif () if (LLVM_FOUND) From 589899ac25089132ffa78365d289675807416345 Mon Sep 17 00:00:00 2001 From: alexey-milovidov Date: Mon, 14 May 2018 02:12:30 +0300 Subject: [PATCH 178/231] Added scripts for various CI tasks (in progress). (#2356) * Added initial scripts for CI [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] * CI: development [#CLICKHOUSE-2] --- ci/README.md | 130 +++++++++++++++++++++ ci/build-clang-from-sources.sh | 37 ++++++ ci/build-debian-packages.sh | 8 ++ ci/build-gcc-from-sources.sh | 47 ++++++++ ci/build-normal.sh | 22 ++++ ci/check-docker.sh | 7 ++ ci/check-syntax.sh | 21 ++++ ci/create-sources-tarball.sh | 10 ++ ci/default-config | 65 +++++++++++ ci/docker-multiarch/LICENSE | 21 ++++ ci/docker-multiarch/README.md | 53 +++++++++ ci/docker-multiarch/update.sh | 96 +++++++++++++++ ci/get-sources.sh | 18 +++ ci/install-compiler-from-packages.sh | 29 +++++ ci/install-compiler-from-sources.sh | 12 ++ ci/install-libraries.sh | 12 ++ ci/jobs/quick-build/config | 12 ++ ci/jobs/quick-build/run.sh | 18 +++ ci/prepare-docker-image-ubuntu.sh | 23 ++++ ci/prepare-toolchain.sh | 14 +++ ci/prepare-vagrant-image-freebsd.sh | 13 +++ ci/run-clickhouse-from-binaries.sh | 18 +++ ci/run-with-docker.sh | 6 + ci/vagrant-freebsd/.gitignore | 1 + ci/vagrant-freebsd/Vagrantfile | 3 + docker/builder/Dockerfile | 2 +- utils/prepare-environment/install-clang.sh | 48 -------- utils/prepare-environment/install-gcc.sh | 40 ------- 28 files changed, 697 insertions(+), 89 deletions(-) create mode 100644 ci/README.md create mode 100755 ci/build-clang-from-sources.sh create mode 100755 ci/build-debian-packages.sh create mode 100755 ci/build-gcc-from-sources.sh create mode 100755 ci/build-normal.sh create mode 100755 ci/check-docker.sh create mode 100755 ci/check-syntax.sh create mode 100755 ci/create-sources-tarball.sh create mode 100644 ci/default-config create mode 100644 ci/docker-multiarch/LICENSE create mode 100644 ci/docker-multiarch/README.md create mode 100755 ci/docker-multiarch/update.sh create mode 100755 ci/get-sources.sh create mode 100755 ci/install-compiler-from-packages.sh create mode 100755 ci/install-compiler-from-sources.sh create mode 100755 ci/install-libraries.sh create mode 100644 ci/jobs/quick-build/config create mode 100755 ci/jobs/quick-build/run.sh create mode 100755 ci/prepare-docker-image-ubuntu.sh create mode 100755 ci/prepare-toolchain.sh create mode 100755 ci/prepare-vagrant-image-freebsd.sh create mode 100755 ci/run-clickhouse-from-binaries.sh create mode 100755 ci/run-with-docker.sh create mode 100644 ci/vagrant-freebsd/.gitignore create mode 100644 ci/vagrant-freebsd/Vagrantfile delete mode 100755 utils/prepare-environment/install-clang.sh delete mode 100755 utils/prepare-environment/install-gcc.sh diff --git a/ci/README.md b/ci/README.md new file mode 100644 index 00000000000..5dc8b11f1e3 --- /dev/null +++ b/ci/README.md @@ -0,0 +1,130 @@ +### Build and test ClickHouse on various plaforms + +Quick and dirty scripts. + +Usage example: +``` +./run-with-docker.sh ubuntu:bionic jobs/quick-build/run.sh +``` + +Look at `default_config` and `jobs/quick-build/config` + +Various possible options. We are not going to automate testing all of them. + +##### CPU architectures: +- x86_64; +- AArch64. + +x86_64 is the main CPU architecture. We also have minimal support for AArch64. + +##### Operating systems: +- Linux; +- FreeBSD. + +We also target Mac OS X, but it's more difficult to test. +Linux is the main. FreeBSD is also supported as production OS. +Mac OS is intended only for development and have minimal support: client should work, server should just start. + +##### Linux distributions: +For build: +- Ubuntu Bionic; +- Ubuntu Trusty. + +For run: +- Ubuntu Hardy; +- CentOS 5 + +We should support almost any Linux to run ClickHouse. That's why we test also on old distributions. + +##### How to obtain sources: +- use sources from local working copy; +- clone sources from github; +- download source tarball. + +##### Compilers: +- gcc-7; +- gcc-8; +- clang-6; +- clang-svn. + +##### Compiler installation: +- from OS packages; +- build from sources. + +##### C++ standard library implementation: +- libc++; +- libstdc++ with C++11 ABI; +- libstdc++ with old ABI. + +When building with clang, libc++ is used. When building with gcc, we choose libstdc++ with C++11 ABI. + +##### Linkers: +- ldd; +- gold; + +When building with clang on x86_64, ldd is used. Otherwise we use gold. + +##### Build types: +- RelWithDebInfo; +- Debug; +- ASan; +- TSan. + +##### Build types, extra: +- -g0 for quick build; +- enable test coverage; +- debug tcmalloc. + +##### What to build: +- only `clickhouse` target; +- all targets; +- debian packages; + +We also have intent to build RPM and simple tgz packages. + +##### Where to get third-party libraries: +- from contrib directory (submodules); +- from OS packages. + +The only production option is to use libraries from contrib directory. +Using libraries from OS packages is discouraged, but we also support this option. + +##### Linkage types: +- static; +- shared; + +Static linking is the only option for production usage. +We also have support for shared linking, but it is indended only for developers. + +##### Make tools: +- make; +- ninja. + +##### Installation options: +- run built `clickhouse` binary directly; +- install from packages. + +##### How to obtain packages: +- build them; +- download from repository. + +##### Sanity checks: +- check that clickhouse binary has no dependencies on unexpected shared libraries; +- check that source code have no style violations. + +##### Tests: +- Functional tests; +- Integration tests; +- Unit tests; +- Simple sh/reference tests; +- Performance tests (note that they require predictable computing power); +- Tests for external dictionaries (should be moved to integration tests); +- Jepsen like tests for quorum inserts (not yet available in opensource). + +##### Tests extra: +- Run functional tests with Valgrind. + +##### Static analyzers: +- CppCheck; +- clang-tidy; +- Coverity. diff --git a/ci/build-clang-from-sources.sh b/ci/build-clang-from-sources.sh new file mode 100755 index 00000000000..afadfd22f1d --- /dev/null +++ b/ci/build-clang-from-sources.sh @@ -0,0 +1,37 @@ +#!/usr/bin/env bash +set -e -x + +source default-config + +$SUDO apt-get install -y subversion +apt-cache search cmake3 | grep -P '^cmake3 ' && $SUDO apt-get -y install cmake3 || $SUDO apt-get -y install cmake + +mkdir "${WORKSPACE}/llvm" + +svn co "http://llvm.org/svn/llvm-project/llvm/${CLANG_SOURCES_BRANCH}" "${WORKSPACE}/llvm/llvm" +svn co "http://llvm.org/svn/llvm-project/cfe/${CLANG_SOURCES_BRANCH}" "${WORKSPACE}/llvm/llvm/tools/clang" +svn co "http://llvm.org/svn/llvm-project/lld/${CLANG_SOURCES_BRANCH}" "${WORKSPACE}/llvm/llvm/tools/lld" +svn co "http://llvm.org/svn/llvm-project/polly/${CLANG_SOURCES_BRANCH}" "${WORKSPACE}/llvm/llvm/tools/polly" +svn co "http://llvm.org/svn/llvm-project/clang-tools-extra/${CLANG_SOURCES_BRANCH}" "${WORKSPACE}/llvm/llvm/tools/clang/tools/extra" +svn co "http://llvm.org/svn/llvm-project/compiler-rt/${CLANG_SOURCES_BRANCH}" "${WORKSPACE}/llvm/llvm/projects/compiler-rt" +svn co "http://llvm.org/svn/llvm-project/libcxx/${CLANG_SOURCES_BRANCH}" "${WORKSPACE}/llvm/llvm/projects/libcxx" +svn co "http://llvm.org/svn/llvm-project/libcxxabi/${CLANG_SOURCES_BRANCH}" "${WORKSPACE}/llvm/llvm/projects/libcxxabi" + +mkdir "${WORKSPACE}/llvm/build" +cd "${WORKSPACE}/llvm/build" + +# NOTE You must build LLVM with the same ABI as ClickHouse. +# For example, if you compile ClickHouse with libc++, you must add +# -D LLVM_ENABLE_LIBCXX=1 +# to the line below. + +cmake -D CMAKE_BUILD_TYPE:STRING=Release ../llvm + +make -j $THREADS +$SUDO make install +hash clang + +cd ../../.. + +export CC=clang +export CXX=clang++ diff --git a/ci/build-debian-packages.sh b/ci/build-debian-packages.sh new file mode 100755 index 00000000000..8726b675c4d --- /dev/null +++ b/ci/build-debian-packages.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash +set -e -x + +source default-config + +[[ -d "${WORKSPACE}/sources" ]] || die "Run get-sources.sh first" + +./sources/release diff --git a/ci/build-gcc-from-sources.sh b/ci/build-gcc-from-sources.sh new file mode 100755 index 00000000000..b41ac0365bd --- /dev/null +++ b/ci/build-gcc-from-sources.sh @@ -0,0 +1,47 @@ +#!/usr/bin/env bash +set -e -x + +source default-config + +$SUDO apt-get install -y curl + +if [[ "${GCC_SOURCES_VERSION}" == "latest" ]]; then + GCC_SOURCES_VERSION=$(curl -sSL https://ftpmirror.gnu.org/gcc/ | grep -oE 'gcc-[0-9]+(\.[0-9]+)+' | sort -Vr | head -n1) +fi + +GCC_VERSION_SHORT=$(echo "$GCC_SOURCES_VERSION" | grep -oE '[0-9]' | head -n1) + +echo "Will download ${GCC_SOURCES_VERSION} (short version: $GCC_VERSION_SHORT)." + +THREADS=$(grep -c ^processor /proc/cpuinfo) + +mkdir "${WORKSPACE}/gcc" +pushd "${WORKSPACE}/gcc" + +wget https://ftpmirror.gnu.org/gcc/${GCC_SOURCES_VERSION}/${GCC_SOURCES_VERSION}.tar.xz +tar xf ${GCC_SOURCES_VERSION}.tar.xz +pushd ${GCC_SOURCES_VERSION} +./contrib/download_prerequisites +popd +mkdir gcc-build +pushd gcc-build +../${GCC_SOURCES_VERSION}/configure --enable-languages=c,c++ --disable-multilib +make -j $THREADS +$SUDO make install + +popd +popd + +$SUDO ln -sf /usr/local/bin/gcc /usr/local/bin/gcc-${GCC_GCC_SOURCES_VERSION_SHORT} +$SUDO ln -sf /usr/local/bin/g++ /usr/local/bin/g++-${GCC_GCC_SOURCES_VERSION_SHORT} +$SUDO ln -sf /usr/local/bin/gcc /usr/local/bin/cc +$SUDO ln -sf /usr/local/bin/g++ /usr/local/bin/c++ + +echo '/usr/local/lib64' | $SUDO tee /etc/ld.so.conf.d/10_local-lib64.conf +$SUDO ldconfig + +hash gcc g++ +gcc --version + +export CC=gcc +export CXX=g++ diff --git a/ci/build-normal.sh b/ci/build-normal.sh new file mode 100755 index 00000000000..e165489cc9d --- /dev/null +++ b/ci/build-normal.sh @@ -0,0 +1,22 @@ +#!/usr/bin/env bash +set -e -x + +source default-config + +[[ -d "${WORKSPACE}/sources" ]] || die "Run get-sources.sh first" + +mkdir -p "${WORKSPACE}/build" +pushd "${WORKSPACE}/build" + +if [[ "${ENABLE_EMBEDDED_COMPILER}" == 1 ]]; then + [[ "$USE_LLVM_LIBRARIES_FROM_SYSTEM" == 0 ]] && CMAKE_FLAGS="$CMAKE_FLAGS -D USE_INTERNAL_LLVM_LIBRARY=1" + [[ "$USE_LLVM_LIBRARIES_FROM_SYSTEM" != 0 ]] && CMAKE_FLAGS="$CMAKE_FLAGS -D USE_INTERNAL_LLVM_LIBRARY=0" +fi + +cmake -D CMAKE_BUILD_TYPE=${BUILD_TYPE} -D ENABLE_EMBEDDED_COMPILER=${ENABLE_EMBEDDED_COMPILER} $CMAKE_FLAGS ../sources + +[[ "$BUILD_TARGETS" != 'all' ]] && BUILD_TARGETS_STRING="--target $BUILD_TARGETS" + +cmake --build . $BUILD_TARGETS_STRING -- -j $THREADS + +popd diff --git a/ci/check-docker.sh b/ci/check-docker.sh new file mode 100755 index 00000000000..e4ffb11f643 --- /dev/null +++ b/ci/check-docker.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env bash +set -e -x + +source default-config + +command -v docker > /dev/null || die "You need to install Docker" +docker ps > /dev/null || die "You need to have access to Docker: run '$SUDO usermod -aG docker $USER' and relogin" diff --git a/ci/check-syntax.sh b/ci/check-syntax.sh new file mode 100755 index 00000000000..d212c4b6e27 --- /dev/null +++ b/ci/check-syntax.sh @@ -0,0 +1,21 @@ +#!/usr/bin/env bash +set -e -x + +source default-config + +# NOTE: It will argue about +# fatal error: re2_st/re2.h: No such file or directory +# due to generated headers. + +$SUDO apt-get install -y jq + +[[ -d "${WORKSPACE}/sources" ]] || die "Run get-sources.sh first" + +mkdir -p "${WORKSPACE}/build" +pushd "${WORKSPACE}/build" + +cmake -D CMAKE_BUILD_TYPE=Debug $CMAKE_FLAGS ../sources +jq --raw-output '.[] | .command' compile_commands.json | grep -v -P -- '-c .+/contrib/' | sed -r -e 's/-o\s+\S+/-fsyntax-only/' > syntax-commands +xargs --arg-file=syntax-commands --max-procs=$THREADS --replace /bin/sh -c "{}" + +popd diff --git a/ci/create-sources-tarball.sh b/ci/create-sources-tarball.sh new file mode 100755 index 00000000000..bfbbf61e556 --- /dev/null +++ b/ci/create-sources-tarball.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash +set -e -x + +source default-config + +if [[ -d "${WORKSPACE}/sources" ]]; then + tar -c -z -f "${WORKSPACE}/sources.tar.gz" --directory "${WORKSPACE}/sources" . +else + die "Run get-sources first" +fi diff --git a/ci/default-config b/ci/default-config new file mode 100644 index 00000000000..7837b1fe57d --- /dev/null +++ b/ci/default-config @@ -0,0 +1,65 @@ +#!/usr/bin/env bash +set -e -x + +if [[ -z "$INITIALIZED" ]]; then + +INITIALIZED=1 + +SCRIPTPATH=$(pwd) +WORKSPACE=${SCRIPTPATH}/workspace +PROJECT_ROOT=$(cd $SCRIPTPATH/.. && pwd) + +# All scripts take no arguments. All arguments must be in config. + +# get-sources +SOURCES_METHOD=local # clone, local, tarball +SOURCES_CLONE_URL="https://github.com/yandex/ClickHouse.git" +SOURCES_BRANCH="master" +SOURCES_COMMIT=HEAD # do checkout of this commit after clone + +# prepare-toolchain +COMPILER=gcc # gcc, clang +COMPILER_INSTALL_METHOD=packages # packages, sources +COMPILER_PACKAGE_VERSION=7 # or 6.0 for clang + +# install-compiler-from-sources +CLANG_SOURCES_BRANCH=trunk # or tags/RELEASE_600/final +GCC_SOURCES_VERSION=latest # or gcc-7.1.0 + +# install-libraries +USE_LLVM_LIBRARIES_FROM_SYSTEM=0 # 0 or 1 +ENABLE_EMBEDDED_COMPILER=1 + +# build +BUILD_METHOD=normal # normal, debian +BUILD_TARGETS=clickhouse # tagtet name, all; only for "normal" +BUILD_TYPE=RelWithDebInfo # RelWithDebInfo, Debug, ASan, TSan +CMAKE_FLAGS="" + +# prepare-docker-image-ubuntu +DOCKER_UBUNTU_VERSION=bionic +DOCKER_UBUNTU_ARCH=arm64 # How the architecture is named in a tarball at https://partner-images.canonical.com/core/ +DOCKER_UBUNTU_QUEMU_ARCH=aarch64 # How the architecture is named in QEMU +DOCKER_UBUNTU_TAG_ARCH=arm64 # How the architecture is named in Docker +DOCKER_UBUNTU_QEMU_VER=v2.9.1 +DOCKER_UBUNTU_REPO=multiarch/ubuntu-core + +THREADS=$(grep -c ^processor /proc/cpuinfo || nproc || sysctl -a | grep -F 'hw.ncpu') + +# All scripts should return 0 in case of success, 1 in case of permanent error, +# 2 in case of temporary error, any other code in case of permanent error. +function die { + echo ${1:-Error} + exit ${2:1} +} + +[[ $EUID -ne 0 ]] && SUDO=sudo + +command -v apt-get && $SUDO apt-get update + +# Configuration parameters may be overriden with CONFIG environment variable pointing to config file. +[[ -n "$CONFIG" ]] && source $CONFIG + +mkdir -p $WORKSPACE + +fi diff --git a/ci/docker-multiarch/LICENSE b/ci/docker-multiarch/LICENSE new file mode 100644 index 00000000000..60d0ea8fa82 --- /dev/null +++ b/ci/docker-multiarch/LICENSE @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright (c) 2016 Multiarch + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/ci/docker-multiarch/README.md b/ci/docker-multiarch/README.md new file mode 100644 index 00000000000..9cc30c2dca8 --- /dev/null +++ b/ci/docker-multiarch/README.md @@ -0,0 +1,53 @@ +Source: https://github.com/multiarch/ubuntu-core +Commit: 3972a7794b40a965615abd710759d3ed439c9a55 + +# :earth_africa: ubuntu-core + +![](https://raw.githubusercontent.com/multiarch/dockerfile/master/logo.jpg) + +Multiarch Ubuntu images for Docker. + +Based on https://github.com/tianon/docker-brew-ubuntu-core/ + +* `multiarch/ubuntu-core` on [Docker Hub](https://hub.docker.com/r/multiarch/ubuntu-core/) +* [Available tags](https://hub.docker.com/r/multiarch/ubuntu-core/tags/) + +## Usage + +Once you need to configure binfmt-support on your Docker host. +This works locally or remotely (i.e using boot2docker or swarm). + +```console +# configure binfmt-support on the Docker host (works locally or remotely, i.e: using boot2docker) +$ docker run --rm --privileged multiarch/qemu-user-static:register --reset +``` + +Then you can run an `armhf` image from your `x86_64` Docker host. + +```console +$ docker run -it --rm multiarch/ubuntu-core:armhf-wily +root@a0818570f614:/# uname -a +Linux a0818570f614 4.1.13-boot2docker #1 SMP Fri Nov 20 19:05:50 UTC 2015 armv7l armv7l armv7l GNU/Linux +root@a0818570f614:/# exit +``` + +Or an `x86_64` image from your `x86_64` Docker host, directly, without qemu emulation. + +```console +$ docker run -it --rm multiarch/ubuntu-core:amd64-wily +root@27fe384370c9:/# uname -a +Linux 27fe384370c9 4.1.13-boot2docker #1 SMP Fri Nov 20 19:05:50 UTC 2015 x86_64 x86_64 x86_64 GNU/Linux +root@27fe384370c9:/# +``` + +It also works for `arm64` + +```console +$ docker run -it --rm multiarch/ubuntu-core:arm64-wily +root@723fb9f184fa:/# uname -a +Linux 723fb9f184fa 4.1.13-boot2docker #1 SMP Fri Nov 20 19:05:50 UTC 2015 aarch64 aarch64 aarch64 GNU/Linux +``` + +## License + +MIT diff --git a/ci/docker-multiarch/update.sh b/ci/docker-multiarch/update.sh new file mode 100755 index 00000000000..6e3e18047de --- /dev/null +++ b/ci/docker-multiarch/update.sh @@ -0,0 +1,96 @@ +#!/usr/bin/env bash +set -e -x + +# A POSIX variable +OPTIND=1 # Reset in case getopts has been used previously in the shell. + +while getopts "a:v:q:u:d:t:" opt; do + case "$opt" in + a) ARCH=$OPTARG + ;; + v) VERSION=$OPTARG + ;; + q) QEMU_ARCH=$OPTARG + ;; + u) QEMU_VER=$OPTARG + ;; + d) DOCKER_REPO=$OPTARG + ;; + t) TAG_ARCH=$OPTARG + ;; + esac +done + +thisTarBase="ubuntu-$VERSION-core-cloudimg-$ARCH" +thisTar="$thisTarBase-root.tar.gz" +baseUrl="https://partner-images.canonical.com/core/$VERSION" + + +# install qemu-user-static +if [ -n "${QEMU_ARCH}" ]; then + if [ ! -f x86_64_qemu-${QEMU_ARCH}-static.tar.gz ]; then + wget -N https://github.com/multiarch/qemu-user-static/releases/download/${QEMU_VER}/x86_64_qemu-${QEMU_ARCH}-static.tar.gz + fi + tar -xvf x86_64_qemu-${QEMU_ARCH}-static.tar.gz -C $ROOTFS/usr/bin/ +fi + + +# get the image +if \ + wget -q --spider "$baseUrl/current" \ + && wget -q --spider "$baseUrl/current/$thisTar" \ + ; then + baseUrl+='/current' +fi +wget -qN "$baseUrl/"{{MD5,SHA{1,256}}SUMS{,.gpg},"$thisTarBase.manifest",'unpacked/build-info.txt'} || true +wget -N "$baseUrl/$thisTar" + +# check checksum +if [ -f SHA256SUMS ]; then + sha256sum="$(sha256sum "$thisTar" | cut -d' ' -f1)" + if ! grep -q "$sha256sum" SHA256SUMS; then + echo >&2 "error: '$thisTar' has invalid SHA256" + exit 1 + fi +fi + +cat > Dockerfile <<-EOF + FROM scratch + ADD $thisTar / + ENV ARCH=${ARCH} UBUNTU_SUITE=${VERSION} DOCKER_REPO=${DOCKER_REPO} +EOF + +# add qemu-user-static binary +if [ -n "${QEMU_ARCH}" ]; then + cat >> Dockerfile <> Dockerfile <<-EOF + # a few minor docker-specific tweaks + # see https://github.com/docker/docker/blob/master/contrib/mkimage/debootstrap + RUN echo '#!/bin/sh' > /usr/sbin/policy-rc.d \\ + && echo 'exit 101' >> /usr/sbin/policy-rc.d \\ + && chmod +x /usr/sbin/policy-rc.d \\ + && dpkg-divert --local --rename --add /sbin/initctl \\ + && cp -a /usr/sbin/policy-rc.d /sbin/initctl \\ + && sed -i 's/^exit.*/exit 0/' /sbin/initctl \\ + && echo 'force-unsafe-io' > /etc/dpkg/dpkg.cfg.d/docker-apt-speedup \\ + && echo 'DPkg::Post-Invoke { "rm -f /var/cache/apt/archives/*.deb /var/cache/apt/archives/partial/*.deb /var/cache/apt/*.bin || true"; };' > /etc/apt/apt.conf.d/docker-clean \\ + && echo 'APT::Update::Post-Invoke { "rm -f /var/cache/apt/archives/*.deb /var/cache/apt/archives/partial/*.deb /var/cache/apt/*.bin || true"; };' >> /etc/apt/apt.conf.d/docker-clean \\ + && echo 'Dir::Cache::pkgcache ""; Dir::Cache::srcpkgcache "";' >> /etc/apt/apt.conf.d/docker-clean \\ + && echo 'Acquire::Languages "none";' > /etc/apt/apt.conf.d/docker-no-languages \\ + && echo 'Acquire::GzipIndexes "true"; Acquire::CompressionTypes::Order:: "gz";' > /etc/apt/apt.conf.d/docker-gzip-indexes + + # enable the universe + RUN sed -i 's/^#\s*\(deb.*universe\)$/\1/g' /etc/apt/sources.list + + # overwrite this with 'CMD []' in a dependent Dockerfile + CMD ["/bin/bash"] +EOF + +docker build -t "${DOCKER_REPO}:${TAG_ARCH}-${VERSION}" . +docker run --rm "${DOCKER_REPO}:${TAG_ARCH}-${VERSION}" /bin/bash -ec "echo Hello from Ubuntu!" diff --git a/ci/get-sources.sh b/ci/get-sources.sh new file mode 100755 index 00000000000..f09f8c3c812 --- /dev/null +++ b/ci/get-sources.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash +set -e -x + +source default-config + +if [[ "$SOURCES_METHOD" == "clone" ]]; then + $SUDO apt-get install -y git + SOURCES_DIR="${WORKSPACE}/sources" + mkdir -p "${SOURCES_DIR}" + git clone --recursive --branch "$SOURCES_BRANCH" "$SOURCES_CLONE_URL" "${SOURCES_DIR}" + pushd "${SOURCES_DIR}" + git checkout "$SOURCES_COMMIT" + popd +elif [[ "$SOURCES_METHOD" == "local" ]]; then + ln -f -s "${PROJECT_ROOT}" "${WORKSPACE}/sources" +else + die "Unknown SOURCES_METHOD" +fi diff --git a/ci/install-compiler-from-packages.sh b/ci/install-compiler-from-packages.sh new file mode 100755 index 00000000000..c46f09219e7 --- /dev/null +++ b/ci/install-compiler-from-packages.sh @@ -0,0 +1,29 @@ +#!/usr/bin/env bash +set -e -x + +source default-config + +# TODO Non debian systems +# TODO Install from PPA on older Ubuntu + +if [ -f '/etc/lsb-release' ]; then + source /etc/lsb-release + if [[ "$DISTRIB_ID" == "Ubuntu" ]]; then + if [[ "$COMPILER" == "gcc" ]]; then + $SUDO apt-get -y install gcc-${COMPILER_PACKAGE_VERSION} g++-${COMPILER_PACKAGE_VERSION} + export CC=gcc-${COMPILER_PACKAGE_VERSION} + export CXX=g++-${COMPILER_PACKAGE_VERSION} + elif [[ "$COMPILER" == "clang" ]]; then + [[ $(uname -m) == "x86_64" ]] && LLD="lld-${COMPILER_PACKAGE_VERSION}" + $SUDO apt-get -y install clang-${COMPILER_PACKAGE_VERSION} "$LLD" libc++-dev libc++abi-dev + export CC=clang-${COMPILER_PACKAGE_VERSION} + export CXX=clang++-${COMPILER_PACKAGE_VERSION} + else + die "Unknown compiler specified" + fi + else + die "Unknown Linux variant" + fi +else + die "Unknown OS" +fi diff --git a/ci/install-compiler-from-sources.sh b/ci/install-compiler-from-sources.sh new file mode 100755 index 00000000000..235898ed300 --- /dev/null +++ b/ci/install-compiler-from-sources.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -e -x + +source default-config + +if [[ "$COMPILER" == "gcc" ]]; then + . build-gcc-from-sources.sh +elif [[ "$COMPILER" == "clang" ]]; then + . build-clang-from-sources.sh +else + die "Unknown COMPILER" +fi diff --git a/ci/install-libraries.sh b/ci/install-libraries.sh new file mode 100755 index 00000000000..7070083d57e --- /dev/null +++ b/ci/install-libraries.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -e -x + +source default-config + +# TODO Non-debian systems + +$SUDO apt-get -y install libssl-dev libicu-dev libreadline-dev libmysqlclient-dev unixodbc-dev + +if [[ "$ENABLE_EMBEDDED_COMPILER" == 1 && "$USE_LLVM_LIBRARIES_FROM_SYSTEM" == 1 ]]; then + $SUDO apt-get -y install liblld-5.0-dev libclang-5.0-dev +fi diff --git a/ci/jobs/quick-build/config b/ci/jobs/quick-build/config new file mode 100644 index 00000000000..c45d9690c7a --- /dev/null +++ b/ci/jobs/quick-build/config @@ -0,0 +1,12 @@ +SOURCES_METHOD=local +COMPILER=clang +COMPILER_INSTALL_METHOD=packages +COMPILER_PACKAGE_VERSION=6.0 +USE_LLVM_LIBRARIES_FROM_SYSTEM=0 +BUILD_METHOD=normal +BUILD_TARGETS=clickhouse +BUILD_TYPE=Debug +ENABLE_EMBEDDED_COMPILER=0 +CMAKE_FLAGS="-D CMAKE_C_FLAGS_ADD=-g0 -D CMAKE_CXX_FLAGS_ADD=-g0 -D ENABLE_TCMALLOC=0 -D ENABLE_CAPNP=0 -D ENABLE_RDKAFKA=0 -D ENABLE_UNWIND=0 -D ENABLE_ICU=0" + +# TODO it doesn't build with -D ENABLE_NETSSL=0 -D ENABLE_MONGODB=0 -D ENABLE_MYSQL=0 -D ENABLE_DATA_ODBC=0 diff --git a/ci/jobs/quick-build/run.sh b/ci/jobs/quick-build/run.sh new file mode 100755 index 00000000000..0872b685e7c --- /dev/null +++ b/ci/jobs/quick-build/run.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash +set -e -x + +# How to run: +# From "ci" directory: +# jobs/quick-build/run.sh +# or: +# ./run-with-docker.sh ubuntu:bionic jobs/quick-build/run.sh + +CONFIG="$(dirname $0)"/config +cd "$(dirname $0)"/../.. + +. default-config + +. get-sources.sh +. prepare-toolchain.sh +. install-libraries.sh +. build-normal.sh diff --git a/ci/prepare-docker-image-ubuntu.sh b/ci/prepare-docker-image-ubuntu.sh new file mode 100755 index 00000000000..1b3d3bd18f6 --- /dev/null +++ b/ci/prepare-docker-image-ubuntu.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env bash +set -e -x + +source default-config + +./check-docker.sh + +# http://fl47l1n3.net/2015/12/24/binfmt/ +$SUDO apt-get -y install qemu-user-static + +pushd docker-multiarch + +$SUDO ./update.sh \ + -a "$DOCKER_UBUNTU_ARCH" \ + -v "$DOCKER_UBUNTU_VERSION" \ + -q "$DOCKER_UBUNTU_QUEMU_ARCH" \ + -u "$DOCKER_UBUNTU_QEMU_VER" \ + -d "$DOCKER_UBUNTU_REPO" \ + -t "$DOCKER_UBUNTU_TAG_ARCH" + +docker run --rm --privileged multiarch/qemu-user-static:register + +popd diff --git a/ci/prepare-toolchain.sh b/ci/prepare-toolchain.sh new file mode 100755 index 00000000000..74aa4f1142f --- /dev/null +++ b/ci/prepare-toolchain.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -e -x + +source default-config + +apt-cache search cmake3 | grep -P '^cmake3 ' && $SUDO apt-get -y install cmake3 || $SUDO apt-get -y install cmake + +if [[ "$COMPILER_INSTALL_METHOD" == "packages" ]]; then + . install-compiler-from-packages.sh; +elif [[ "$COMPILER_INSTALL_METHOD" == "sources" ]]; then + . install-compiler-from-sources.sh +else + die "Unknown COMPILER_INSTALL_METHOD" +fi diff --git a/ci/prepare-vagrant-image-freebsd.sh b/ci/prepare-vagrant-image-freebsd.sh new file mode 100755 index 00000000000..81d021ca31f --- /dev/null +++ b/ci/prepare-vagrant-image-freebsd.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env bash +set -e -x + +source default-config + +$SUDO apt-get -y install vagrant virtualbox + +pushd "vagrant-freebsd" +vagrant up +vagrant ssh-config > vagrant-ssh +ssh -F vagrant-ssh default 'uname -a' +scp -F vagrant-ssh -r ../../ci default:~ +popd diff --git a/ci/run-clickhouse-from-binaries.sh b/ci/run-clickhouse-from-binaries.sh new file mode 100755 index 00000000000..f16d840316a --- /dev/null +++ b/ci/run-clickhouse-from-binaries.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash +set -e -x + +# Usage example: +# ./run-with-docker.sh centos:centos6 ./run-clickhouse-from-binaries.sh + +source default-config + +SERVER_BIN="${WORKSPACE}/build/dbms/src/Server/clickhouse" +SERVER_CONF="${WORKSPACE}/sources/dbms/src/Server/config.xml" +SERVER_DATADIR="${WORKSPACE}/clickhouse" + +[[ -x "$SERVER_BIN" ]] || die "Run build-normal.sh first" +[[ -r "$SERVER_CONF" ]] || die "Run get-sources.sh first" + +mkdir -p "${SERVER_DATADIR}" + +$SERVER_BIN server --config-file "$SERVER_CONF" --pid-file="${WORKSPACE}/clickhouse.pid" -- --path "$SERVER_DATADIR" diff --git a/ci/run-with-docker.sh b/ci/run-with-docker.sh new file mode 100755 index 00000000000..238907bb5dd --- /dev/null +++ b/ci/run-with-docker.sh @@ -0,0 +1,6 @@ +#!/usr/bin/env bash +set -e -x + +PROJECT_ROOT="$(cd "$(dirname "$0")/.."; pwd -P)" +[[ -n "$CONFIG" ]] && DOCKER_ENV="--env=CONFIG" +docker run -t --network=host --mount=type=bind,source=${PROJECT_ROOT},destination=/ClickHouse --workdir=/ClickHouse/ci $DOCKER_ENV "$1" "$2" diff --git a/ci/vagrant-freebsd/.gitignore b/ci/vagrant-freebsd/.gitignore new file mode 100644 index 00000000000..8000dd9db47 --- /dev/null +++ b/ci/vagrant-freebsd/.gitignore @@ -0,0 +1 @@ +.vagrant diff --git a/ci/vagrant-freebsd/Vagrantfile b/ci/vagrant-freebsd/Vagrantfile new file mode 100644 index 00000000000..c01ae5fa6e2 --- /dev/null +++ b/ci/vagrant-freebsd/Vagrantfile @@ -0,0 +1,3 @@ +Vagrant.configure("2") do |config| + config.vm.box = "generic/freebsd11" +end diff --git a/docker/builder/Dockerfile b/docker/builder/Dockerfile index 65180f4daef..6413d1a3e67 100644 --- a/docker/builder/Dockerfile +++ b/docker/builder/Dockerfile @@ -3,7 +3,7 @@ FROM ubuntu:17.10 RUN apt-get update -y && \ apt-get install -y \ cmake pkg-config gcc-7 g++-7 \ - liblld-5.0-dev libclang-5.0-dev liblld-5.0 \ + liblld-5.0-dev libclang-5.0-dev \ libssl-dev libicu-dev libreadline-dev libmysqlclient-dev unixodbc-dev # For tests: bash expect python python-lxml python-termcolor curl perl sudo tzdata diff --git a/utils/prepare-environment/install-clang.sh b/utils/prepare-environment/install-clang.sh deleted file mode 100755 index d754650f858..00000000000 --- a/utils/prepare-environment/install-clang.sh +++ /dev/null @@ -1,48 +0,0 @@ -#!/usr/bin/env bash - -set -e - -BRANCH=trunk -#BRANCH=tags/RELEASE_500/final - -THREADS=$(grep -c ^processor /proc/cpuinfo) - -cd ~ -sudo apt-get install -y subversion cmake - -mkdir llvm -cd llvm -svn co "http://llvm.org/svn/llvm-project/llvm/${BRANCH}" llvm - -cd llvm/tools -svn co "http://llvm.org/svn/llvm-project/cfe/${BRANCH}" clang -svn co "http://llvm.org/svn/llvm-project/lld/${BRANCH}" lld -svn co "http://llvm.org/svn/llvm-project/polly/${BRANCH}" polly - -cd clang/tools -svn co "http://llvm.org/svn/llvm-project/clang-tools-extra/${BRANCH}" extra - -git clone https://github.com/include-what-you-use/include-what-you-use.git -echo 'add_subdirectory(include-what-you-use)' >> CMakeLists.txt -sudo apt-get install libncurses5-dev - -cd ../../../.. -cd llvm/projects/ -svn co "http://llvm.org/svn/llvm-project/compiler-rt/${BRANCH}" compiler-rt -svn co "http://llvm.org/svn/llvm-project/libcxx/${BRANCH}" libcxx -svn co "http://llvm.org/svn/llvm-project/libcxxabi/${BRANCH}" libcxxabi - -cd ../.. -mkdir build -cd build/ - -# NOTE You must build LLVM with the same ABI as ClickHouse. -# For example, if you compile ClickHouse with libc++, you must add -# -D LLVM_ENABLE_LIBCXX=1 -# to the line below. - -cmake -D CMAKE_BUILD_TYPE:STRING=Release ../llvm - -make -j $THREADS -sudo make install -hash clang diff --git a/utils/prepare-environment/install-gcc.sh b/utils/prepare-environment/install-gcc.sh deleted file mode 100755 index 441f297c448..00000000000 --- a/utils/prepare-environment/install-gcc.sh +++ /dev/null @@ -1,40 +0,0 @@ -#!/usr/bin/env bash - -set -e - -sudo apt-get install -y curl - -VERSION=$(curl -sSL https://ftpmirror.gnu.org/gcc/ | grep -oE 'gcc-[0-9]+(\.[0-9]+)+' | sort -Vr | head -n1) #' -#VERSION=gcc-7.1.0 - -VERSION_SHORT=$(echo "$VERSION" | grep -oE '[0-9]' | head -n1) - -echo "Will download ${VERSION} (short version: $VERSION_SHORT)." - -THREADS=$(grep -c ^processor /proc/cpuinfo) - -cd ~ -mkdir gcc -cd gcc - -wget https://ftpmirror.gnu.org/gcc/${VERSION}/${VERSION}.tar.xz -tar xf ${VERSION}.tar.xz -cd ${VERSION} -./contrib/download_prerequisites -cd .. -mkdir gcc-build -cd gcc-build -../${VERSION}/configure --enable-languages=c,c++ --disable-multilib -make -j $THREADS -sudo make install - -sudo ln -sf /usr/local/bin/gcc /usr/local/bin/gcc-${VERSION_SHORT} -sudo ln -sf /usr/local/bin/g++ /usr/local/bin/g++-${VERSION_SHORT} -sudo ln -sf /usr/local/bin/gcc /usr/local/bin/cc -sudo ln -sf /usr/local/bin/g++ /usr/local/bin/c++ - -echo "/usr/local/lib64" | sudo tee /etc/ld.so.conf.d/10_local-lib64.conf -sudo ldconfig - -hash gcc g++ -gcc --version From fa59cbc8718343f339ad8510754daac27f97cf02 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 02:20:57 +0300 Subject: [PATCH 179/231] CI: development [#CLICKHOUSE-2] --- ci/build-clang-from-sources.sh | 1 + ci/check-syntax.sh | 7 +++---- ci/prepare-toolchain.sh | 1 + 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/ci/build-clang-from-sources.sh b/ci/build-clang-from-sources.sh index afadfd22f1d..64898c5fdc3 100755 --- a/ci/build-clang-from-sources.sh +++ b/ci/build-clang-from-sources.sh @@ -3,6 +3,7 @@ set -e -x source default-config +# TODO Non debian systems $SUDO apt-get install -y subversion apt-cache search cmake3 | grep -P '^cmake3 ' && $SUDO apt-get -y install cmake3 || $SUDO apt-get -y install cmake diff --git a/ci/check-syntax.sh b/ci/check-syntax.sh index d212c4b6e27..069862f0e41 100755 --- a/ci/check-syntax.sh +++ b/ci/check-syntax.sh @@ -3,10 +3,6 @@ set -e -x source default-config -# NOTE: It will argue about -# fatal error: re2_st/re2.h: No such file or directory -# due to generated headers. - $SUDO apt-get install -y jq [[ -d "${WORKSPACE}/sources" ]] || die "Run get-sources.sh first" @@ -15,6 +11,9 @@ mkdir -p "${WORKSPACE}/build" pushd "${WORKSPACE}/build" cmake -D CMAKE_BUILD_TYPE=Debug $CMAKE_FLAGS ../sources + +make re2_st # Generated headers + jq --raw-output '.[] | .command' compile_commands.json | grep -v -P -- '-c .+/contrib/' | sed -r -e 's/-o\s+\S+/-fsyntax-only/' > syntax-commands xargs --arg-file=syntax-commands --max-procs=$THREADS --replace /bin/sh -c "{}" diff --git a/ci/prepare-toolchain.sh b/ci/prepare-toolchain.sh index 74aa4f1142f..f90cb4fca4d 100755 --- a/ci/prepare-toolchain.sh +++ b/ci/prepare-toolchain.sh @@ -3,6 +3,7 @@ set -e -x source default-config +# TODO Non debian systems apt-cache search cmake3 | grep -P '^cmake3 ' && $SUDO apt-get -y install cmake3 || $SUDO apt-get -y install cmake if [[ "$COMPILER_INSTALL_METHOD" == "packages" ]]; then From fc1fa366d294b91a90134e74fa6d3f30bdb29504 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 02:32:03 +0300 Subject: [PATCH 180/231] CI: development [#CLICKHOUSE-2] --- ci/check-syntax.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ci/check-syntax.sh b/ci/check-syntax.sh index 069862f0e41..c5043ff512c 100755 --- a/ci/check-syntax.sh +++ b/ci/check-syntax.sh @@ -12,7 +12,7 @@ pushd "${WORKSPACE}/build" cmake -D CMAKE_BUILD_TYPE=Debug $CMAKE_FLAGS ../sources -make re2_st # Generated headers +make -j $THREADS re2_st # Generated headers jq --raw-output '.[] | .command' compile_commands.json | grep -v -P -- '-c .+/contrib/' | sed -r -e 's/-o\s+\S+/-fsyntax-only/' > syntax-commands xargs --arg-file=syntax-commands --max-procs=$THREADS --replace /bin/sh -c "{}" From d57c118fdfb7107efa5362804b774a687527ff5d Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 03:58:58 +0300 Subject: [PATCH 181/231] Successfully managed to build ClickHouse on AArch64 in Docker with QEMU [#CLICKHOUSE-2] --- ci/README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/ci/README.md b/ci/README.md index 5dc8b11f1e3..6eeb35c1c25 100644 --- a/ci/README.md +++ b/ci/README.md @@ -7,6 +7,12 @@ Usage example: ./run-with-docker.sh ubuntu:bionic jobs/quick-build/run.sh ``` +Another example, check build on ARM 64: +``` +./prepare-docker-image-ubuntu.sh +./run-with-docker.sh multiarch/ubuntu-core:arm64-bionic jobs/quick-build/run.sh +``` + Look at `default_config` and `jobs/quick-build/config` Various possible options. We are not going to automate testing all of them. From 6ba8cb922c9799539b92836216199a531c5af1b2 Mon Sep 17 00:00:00 2001 From: Nikolai Kochetov Date: Mon, 14 May 2018 13:48:50 +0300 Subject: [PATCH 182/231] Fix usage of columns from header in SummingSortedBlockInputStream. #2273 --- .../SummingSortedBlockInputStream.cpp | 2 +- .../00625_summing_merge_tree_merge.reference | 0 .../00625_summing_merge_tree_merge.sql | 29 +++++++++++++++++++ 3 files changed, 30 insertions(+), 1 deletion(-) create mode 100644 dbms/tests/queries/0_stateless/00625_summing_merge_tree_merge.reference create mode 100644 dbms/tests/queries/0_stateless/00625_summing_merge_tree_merge.sql diff --git a/dbms/src/DataStreams/SummingSortedBlockInputStream.cpp b/dbms/src/DataStreams/SummingSortedBlockInputStream.cpp index e914b8f8b65..62ece4a1545 100644 --- a/dbms/src/DataStreams/SummingSortedBlockInputStream.cpp +++ b/dbms/src/DataStreams/SummingSortedBlockInputStream.cpp @@ -263,7 +263,7 @@ Block SummingSortedBlockInputStream::readImpl() size_t tuple_size = desc.column_numbers.size(); MutableColumns tuple_columns(tuple_size); for (size_t i = 0; i < tuple_size; ++i) - tuple_columns[i] = header.safeGetByPosition(desc.column_numbers[i]).column->assumeMutable(); + tuple_columns[i] = header.safeGetByPosition(desc.column_numbers[i]).column->cloneEmpty(); desc.merged_column = ColumnTuple::create(std::move(tuple_columns)); } diff --git a/dbms/tests/queries/0_stateless/00625_summing_merge_tree_merge.reference b/dbms/tests/queries/0_stateless/00625_summing_merge_tree_merge.reference new file mode 100644 index 00000000000..e69de29bb2d diff --git a/dbms/tests/queries/0_stateless/00625_summing_merge_tree_merge.sql b/dbms/tests/queries/0_stateless/00625_summing_merge_tree_merge.sql new file mode 100644 index 00000000000..dd629b7bb15 --- /dev/null +++ b/dbms/tests/queries/0_stateless/00625_summing_merge_tree_merge.sql @@ -0,0 +1,29 @@ +DROP TABLE IF EXISTS test.tab ; + +CREATE TABLE test.tab +( + date Date, + key UInt32, + testMap Nested( + k UInt16, + v UInt64) +) +ENGINE = SummingMergeTree(date, (date, key), 1); + +INSERT INTO test.tab SELECT + today(), + number, + [toUInt16(number)], + [number] +FROM system.numbers +LIMIT 8190; + +INSERT INTO test.tab SELECT + today(), + number + 8190, + [toUInt16(number)], + [number + 8190] +FROM system.numbers +LIMIT 10; + +OPTIMIZE TABLE test.tab; From 7deceb578392c128f2d7ae56e2cf5c8a7d629d3c Mon Sep 17 00:00:00 2001 From: proller Date: Mon, 14 May 2018 14:52:41 +0300 Subject: [PATCH 183/231] Build fixes --- dbms/CMakeLists.txt | 6 +++++- dbms/src/Server/Compiler-5.0.0/CMakeLists.txt | 3 +++ dbms/src/Server/Compiler-6.0.0/CMakeLists.txt | 3 +++ dbms/src/Server/Compiler-7.0.0/CMakeLists.txt | 3 +++ 4 files changed, 14 insertions(+), 1 deletion(-) diff --git a/dbms/CMakeLists.txt b/dbms/CMakeLists.txt index 2dee7937e8d..da199163994 100644 --- a/dbms/CMakeLists.txt +++ b/dbms/CMakeLists.txt @@ -103,7 +103,11 @@ if (USE_EMBEDDED_COMPILER) llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) if (TERMCAP_LIBRARY) list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) - endif() + endif () + if (LTDL_LIBRARY) + list(APPEND REQUIRED_LLVM_LIBRARIES ${LTDL_LIBRARY}) + endif () + target_link_libraries (dbms ${REQUIRED_LLVM_LIBRARIES}) target_include_directories (dbms BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) endif () diff --git a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt index 2fa435f451f..076eef6921d 100644 --- a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt @@ -12,6 +12,9 @@ llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) if (TERMCAP_LIBRARY) list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) endif () +if (LTDL_LIBRARY) + list(APPEND REQUIRED_LLVM_LIBRARIES ${LTDL_LIBRARY}) +endif () message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") diff --git a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt index 481b0cc39e7..23c7ea61c31 100644 --- a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt @@ -12,6 +12,9 @@ llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) if (TERMCAP_LIBRARY) list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) endif () +if (LTDL_LIBRARY) + list(APPEND REQUIRED_LLVM_LIBRARIES ${LTDL_LIBRARY}) +endif () message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") diff --git a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt index a72f09b3a58..809f8604366 100644 --- a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt @@ -12,6 +12,9 @@ llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) if (TERMCAP_LIBRARY) list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) endif () +if (LTDL_LIBRARY) + list(APPEND REQUIRED_LLVM_LIBRARIES ${LTDL_LIBRARY}) +endif () message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") From d25338582db8b20f9080449912c55dba01c28c21 Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Fri, 30 Mar 2018 19:25:26 +0300 Subject: [PATCH 184/231] Speedup partition check, add more preformance output. [#CLICKHOUSE-2] Faster partition check. Added more debug info. --- dbms/src/DataStreams/copyData.cpp | 50 +++++++++++++++++-- dbms/src/Server/ClusterCopier.cpp | 47 ++++++++++------- .../DistributedBlockOutputStream.cpp | 31 +++++++++--- .../DistributedBlockOutputStream.h | 2 + .../task_month_to_week_description.xml | 1 + 5 files changed, 105 insertions(+), 26 deletions(-) diff --git a/dbms/src/DataStreams/copyData.cpp b/dbms/src/DataStreams/copyData.cpp index 106f2f5033e..5730ed52c4e 100644 --- a/dbms/src/DataStreams/copyData.cpp +++ b/dbms/src/DataStreams/copyData.cpp @@ -1,6 +1,9 @@ #include #include #include +#include +#include +#include namespace DB @@ -22,8 +25,29 @@ void copyDataImpl(IBlockInputStream & from, IBlockOutputStream & to, TCancelCall from.readPrefix(); to.writePrefix(); - while (Block block = from.read()) + size_t num_blocks = 0; + double total_blocks_time = 0; + size_t slowest_block_number = 0; + double slowest_block_time = 0; + + while (true) { + Stopwatch watch; + Block block = from.read(); + double elapsed = watch.elapsedSeconds(); + + if (num_blocks == 0 || elapsed > slowest_block_time) + { + slowest_block_number = num_blocks; + slowest_block_time = elapsed; + } + + total_blocks_time += elapsed; + ++num_blocks; + + if (!block) + break; + if (is_cancelled()) break; @@ -47,8 +71,28 @@ void copyDataImpl(IBlockInputStream & from, IBlockOutputStream & to, TCancelCall if (is_cancelled()) return; - from.readSuffix(); - to.writeSuffix(); + auto log = &Poco::Logger::get("copyData"); + bool print_dbg = num_blocks > 2; + + if (print_dbg) + { + LOG_DEBUG(log, "Read " << num_blocks << " blocks. It took " << std::fixed << total_blocks_time << " seconds, average " + << std::fixed << total_blocks_time / num_blocks * 1000 << " ms, the slowest block #" << slowest_block_number + << " was read for " << std::fixed << slowest_block_time * 1000 << " ms "); + } + + { + Stopwatch watch; + to.writeSuffix(); + if (num_blocks > 1) + LOG_DEBUG(log, "It took " << std::fixed << watch.elapsedSeconds() << " for writeSuffix()"); + } + { + Stopwatch watch; + from.readSuffix(); + if (num_blocks > 1) + LOG_DEBUG(log, "It took " << std::fixed << watch.elapsedSeconds() << " seconds for readSuffix()"); + } } diff --git a/dbms/src/Server/ClusterCopier.cpp b/dbms/src/Server/ClusterCopier.cpp index c0d9c95b427..5699465e860 100644 --- a/dbms/src/Server/ClusterCopier.cpp +++ b/dbms/src/Server/ClusterCopier.cpp @@ -699,15 +699,19 @@ void DB::TaskCluster::reloadSettings(const Poco::Util::AbstractConfiguration & c if (config.has(prefix + "settings_push")) settings_push.loadSettingsFromConfig(prefix + "settings_push", config); - /// Override important settings - settings_pull.load_balancing = LoadBalancing::NEAREST_HOSTNAME; - settings_pull.readonly = 1; - settings_pull.max_threads = 1; - settings_pull.max_block_size = settings_pull.max_block_size.changed ? settings_pull.max_block_size.value : 8192UL; - settings_pull.preferred_block_size_bytes = 0; + auto set_default_value = [] (auto && setting, auto && default_value) + { + setting = setting.changed ? setting.value : default_value; + }; - settings_push.insert_distributed_timeout = 0; + /// Override important settings + settings_pull.readonly = 1; settings_push.insert_distributed_sync = 1; + set_default_value(settings_pull.load_balancing, LoadBalancing::NEAREST_HOSTNAME); + set_default_value(settings_pull.max_threads, 1); + set_default_value(settings_pull.max_block_size, 8192UL); + set_default_value(settings_pull.preferred_block_size_bytes, 0); + set_default_value(settings_push.insert_distributed_timeout, 0); } @@ -1097,22 +1101,27 @@ protected: status_paths.emplace_back(task_shard_partition.getShardStatusPath()); } - zkutil::Stat stat; std::vector zxid1, zxid2; try { - // Check that state is Finished and remember zxid + std::vector get_futures; for (const String & path : status_paths) + get_futures.emplace_back(zookeeper->asyncGet(path)); + + // Check that state is Finished and remember zxid + for (auto & future : get_futures) { - TaskStateWithOwner status = TaskStateWithOwner::fromString(zookeeper->get(path, &stat)); + auto res = future.get(); + + TaskStateWithOwner status = TaskStateWithOwner::fromString(res.value); if (status.state != TaskState::Finished) { - LOG_INFO(log, "The task " << path << " is being rewritten by " << status.owner - << ". Partition will be rechecked"); + LOG_INFO(log, "The task " << res.value << " is being rewritten by " << status.owner << ". Partition will be rechecked"); return false; } - zxid1.push_back(stat.pzxid); + + zxid1.push_back(res.stat.pzxid); } // Check that partition is not dirty @@ -1122,11 +1131,15 @@ protected: return false; } + get_futures.clear(); + for (const String & path : status_paths) + get_futures.emplace_back(zookeeper->asyncGet(path)); + // Remember zxid of states again - for (const auto & path : status_paths) + for (auto & future : get_futures) { - zookeeper->exists(path, &stat); - zxid2.push_back(stat.pzxid); + auto res = future.get(); + zxid2.push_back(res.stat.pzxid); } } catch (const zkutil::KeeperException & e) @@ -1664,7 +1677,7 @@ protected: BlockIO io_select = InterpreterFactory::get(query_select_ast, context_select)->execute(); BlockIO io_insert = InterpreterFactory::get(query_insert_ast, context_insert)->execute(); - input = std::make_shared(io_select.in); + input = io_select.in; output = io_insert.out; } diff --git a/dbms/src/Storages/Distributed/DistributedBlockOutputStream.cpp b/dbms/src/Storages/Distributed/DistributedBlockOutputStream.cpp index d739badba88..2ff2174c751 100644 --- a/dbms/src/Storages/Distributed/DistributedBlockOutputStream.cpp +++ b/dbms/src/Storages/Distributed/DistributedBlockOutputStream.cpp @@ -28,6 +28,7 @@ #include #include +#include #include #include @@ -353,35 +354,53 @@ void DistributedBlockOutputStream::writeSync(const Block & block) inserted_blocks += 1; inserted_rows += block.rows(); + last_block_finish_time = time(nullptr); } void DistributedBlockOutputStream::writeSuffix() { + auto log_performance = [this] () + { + double elapsed = watch.elapsedSeconds(); + LOG_DEBUG(log, "It took " << std::fixed << std::setprecision(1) << elapsed << " sec. to insert " << inserted_blocks << " blocks" + << ", " << std::fixed << std::setprecision(1) << inserted_rows / elapsed << " rows per second" + << ". " << getCurrentStateDescription()); + }; + if (insert_sync && pool) { + auto format_ts = [] (time_t ts) { + WriteBufferFromOwnString wb; + writeDateTimeText(ts, wb); + return wb.str(); + }; + + LOG_DEBUG(log, "Writing suffix, the last block was at " << format_ts(last_block_finish_time)); + finished_jobs_count = 0; for (auto & shard_jobs : per_shard_jobs) for (JobReplica & job : shard_jobs.replicas_jobs) { if (job.stream) - pool->schedule([&job] () { job.stream->writeSuffix(); }); + { + pool->schedule([&job] () { + job.stream->writeSuffix(); + }); + } } try { pool->wait(); + log_performance(); } catch (Exception & exception) { + log_performance(); exception.addMessage(getCurrentStateDescription()); throw; } - - double elapsed = watch.elapsedSeconds(); - LOG_DEBUG(log, "It took " << std::fixed << std::setprecision(1) << elapsed << " sec. to insert " << inserted_blocks << " blocks" - << ", " << std::fixed << std::setprecision(1) << inserted_rows / elapsed << " rows per second" - << ". " << getCurrentStateDescription()); } } diff --git a/dbms/src/Storages/Distributed/DistributedBlockOutputStream.h b/dbms/src/Storages/Distributed/DistributedBlockOutputStream.h index 6b3349eb16d..32f3a49f549 100644 --- a/dbms/src/Storages/Distributed/DistributedBlockOutputStream.h +++ b/dbms/src/Storages/Distributed/DistributedBlockOutputStream.h @@ -12,6 +12,7 @@ #include #include + namespace Poco { class Logger; @@ -93,6 +94,7 @@ private: std::optional pool; ThrottlerPtr throttler; String query_string; + time_t last_block_finish_time = 0; struct JobReplica { diff --git a/dbms/tests/integration/test_cluster_copier/task_month_to_week_description.xml b/dbms/tests/integration/test_cluster_copier/task_month_to_week_description.xml index 5e7c614d2b7..e212d1a3d04 100644 --- a/dbms/tests/integration/test_cluster_copier/task_month_to_week_description.xml +++ b/dbms/tests/integration/test_cluster_copier/task_month_to_week_description.xml @@ -6,6 +6,7 @@ 1 + 2 From 61705acd69987bec08f0d7c53b0a882be48a19d1 Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Mon, 9 Apr 2018 20:25:37 +0300 Subject: [PATCH 185/231] Speedup initialization and fixed a bug. [#CLICKHOUSE-2] --- dbms/src/Server/ClusterCopier.cpp | 31 ++++++++++++++++++++++--------- 1 file changed, 22 insertions(+), 9 deletions(-) diff --git a/dbms/src/Server/ClusterCopier.cpp b/dbms/src/Server/ClusterCopier.cpp index 5699465e860..64a1249ae0c 100644 --- a/dbms/src/Server/ClusterCopier.cpp +++ b/dbms/src/Server/ClusterCopier.cpp @@ -1043,8 +1043,12 @@ protected: String workers_path = getWorkersPath(); String current_worker_path = getCurrentWorkerNodePath(); + size_t num_bad_version_errors = 0; + while (true) { + updateConfigIfNeeded(); + zkutil::Stat stat; zookeeper->get(workers_version_path, &stat); auto version = stat.version; @@ -1054,6 +1058,12 @@ protected: { LOG_DEBUG(log, "Too many workers (" << stat.numChildren << ", maximum " << task_cluster->max_workers << ")" << ". Postpone processing " << description); + + if (unprioritized) + current_sleep_time = std::min(max_sleep_time, current_sleep_time + default_sleep_time); + + std::this_thread::sleep_for(current_sleep_time); + num_bad_version_errors = 0; } else { @@ -1068,18 +1078,18 @@ protected: if (code == ZooKeeperImpl::ZooKeeper::ZBADVERSION) { - LOG_DEBUG(log, "A concurrent worker has just been added, will check free worker slots again"); + ++num_bad_version_errors; + + /// Try to make fast retries + if (num_bad_version_errors > 3) + { + LOG_DEBUG(log, "A concurrent worker has just been added, will check free worker slots again"); + std::this_thread::sleep_for(default_sleep_time); + } } else throw zkutil::KeeperException(code); } - - if (unprioritized) - current_sleep_time = std::min(max_sleep_time, current_sleep_time + default_sleep_time); - - std::this_thread::sleep_for(current_sleep_time); - - updateConfigIfNeeded(); } } @@ -1291,6 +1301,9 @@ protected: bool tryProcessTable(TaskTable & task_table) { + /// An heuristic: if previous shard is already done, then check next one without sleeps due to max_workers constraint + bool previous_shard_is_instantly_finished = false; + /// Process each partition that is present in cluster for (const String & partition_name : task_table.ordered_partition_names) { @@ -1302,7 +1315,6 @@ protected: Stopwatch watch; TasksShard expected_shards; size_t num_failed_shards = 0; - bool previous_shard_is_instantly_finished = false; ++cluster_partition.total_tries; @@ -1337,6 +1349,7 @@ protected: else { /// We have already checked that partition, but did not discover it + previous_shard_is_instantly_finished = true; continue; } } From e8b94b89e9067b900f7a2cd3b59fec9c5bac4742 Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Tue, 8 May 2018 15:56:32 +0300 Subject: [PATCH 186/231] Removed dbg output, fixed test. [#CLICKHOUSE-2] --- contrib/llvm | 2 +- contrib/poco | 2 +- dbms/src/Common/ZooKeeper/ZooKeeper.h | 21 +++++--- dbms/src/DataStreams/copyData.cpp | 50 ++----------------- dbms/src/Server/ClusterCopier.cpp | 30 +++++------ dbms/src/Server/ClusterCopier.h | 1 + .../DistributedBlockOutputStream.cpp | 10 ---- .../DistributedBlockOutputStream.h | 1 - dbms/tests/integration/helpers/cluster.py | 2 +- .../integration/test_cluster_copier/test.py | 2 +- 10 files changed, 36 insertions(+), 85 deletions(-) diff --git a/contrib/llvm b/contrib/llvm index 163def21781..6b3975cf38d 160000 --- a/contrib/llvm +++ b/contrib/llvm @@ -1 +1 @@ -Subproject commit 163def217817c90fb982a6daf384744d8472b92b +Subproject commit 6b3975cf38d5c9436e1311b7e54ad93ef1a9aa9c diff --git a/contrib/poco b/contrib/poco index 3a2d0a833a2..2d5a158303a 160000 --- a/contrib/poco +++ b/contrib/poco @@ -1 +1 @@ -Subproject commit 3a2d0a833a22ef5e1164a9ada54e3253cb038904 +Subproject commit 2d5a158303adf9d47b980cdcfdb26cee1460704e diff --git a/dbms/src/Common/ZooKeeper/ZooKeeper.h b/dbms/src/Common/ZooKeeper/ZooKeeper.h index 340d0dc2b2c..bbcf82fb2c2 100644 --- a/dbms/src/Common/ZooKeeper/ZooKeeper.h +++ b/dbms/src/Common/ZooKeeper/ZooKeeper.h @@ -181,26 +181,31 @@ public: /// /// Future should not be destroyed before the result is gotten. - std::future asyncGet(const std::string & path); + using FutureGet = std::future; + FutureGet asyncGet(const std::string & path); - std::future asyncTryGet(const std::string & path); + FutureGet asyncTryGet(const std::string & path); - std::future asyncExists(const std::string & path); + using FutureExists = std::future ; + FutureExists asyncExists(const std::string & path); - std::future asyncGetChildren(const std::string & path); + using FutureGetChildren = std::future; + FutureGetChildren asyncGetChildren(const std::string & path); - std::future asyncRemove(const std::string & path, int32_t version = -1); + using FutureRemove = std::future; + FutureRemove asyncRemove(const std::string & path, int32_t version = -1); /// Doesn't throw in the following cases: /// * The node doesn't exist /// * The versions do not match /// * The node has children - std::future asyncTryRemove(const std::string & path, int32_t version = -1); + FutureRemove asyncTryRemove(const std::string & path, int32_t version = -1); - std::future asyncMulti(const Requests & ops); + using FutureMulti = std::future; + FutureMulti asyncMulti(const Requests & ops); /// Like the previous one but don't throw any exceptions on future.get() - std::future tryAsyncMulti(const Requests & ops); + FutureMulti tryAsyncMulti(const Requests & ops); static std::string error2string(int32_t code); diff --git a/dbms/src/DataStreams/copyData.cpp b/dbms/src/DataStreams/copyData.cpp index 5730ed52c4e..106f2f5033e 100644 --- a/dbms/src/DataStreams/copyData.cpp +++ b/dbms/src/DataStreams/copyData.cpp @@ -1,9 +1,6 @@ #include #include #include -#include -#include -#include namespace DB @@ -25,29 +22,8 @@ void copyDataImpl(IBlockInputStream & from, IBlockOutputStream & to, TCancelCall from.readPrefix(); to.writePrefix(); - size_t num_blocks = 0; - double total_blocks_time = 0; - size_t slowest_block_number = 0; - double slowest_block_time = 0; - - while (true) + while (Block block = from.read()) { - Stopwatch watch; - Block block = from.read(); - double elapsed = watch.elapsedSeconds(); - - if (num_blocks == 0 || elapsed > slowest_block_time) - { - slowest_block_number = num_blocks; - slowest_block_time = elapsed; - } - - total_blocks_time += elapsed; - ++num_blocks; - - if (!block) - break; - if (is_cancelled()) break; @@ -71,28 +47,8 @@ void copyDataImpl(IBlockInputStream & from, IBlockOutputStream & to, TCancelCall if (is_cancelled()) return; - auto log = &Poco::Logger::get("copyData"); - bool print_dbg = num_blocks > 2; - - if (print_dbg) - { - LOG_DEBUG(log, "Read " << num_blocks << " blocks. It took " << std::fixed << total_blocks_time << " seconds, average " - << std::fixed << total_blocks_time / num_blocks * 1000 << " ms, the slowest block #" << slowest_block_number - << " was read for " << std::fixed << slowest_block_time * 1000 << " ms "); - } - - { - Stopwatch watch; - to.writeSuffix(); - if (num_blocks > 1) - LOG_DEBUG(log, "It took " << std::fixed << watch.elapsedSeconds() << " for writeSuffix()"); - } - { - Stopwatch watch; - from.readSuffix(); - if (num_blocks > 1) - LOG_DEBUG(log, "It took " << std::fixed << watch.elapsedSeconds() << " seconds for readSuffix()"); - } + from.readSuffix(); + to.writeSuffix(); } diff --git a/dbms/src/Server/ClusterCopier.cpp b/dbms/src/Server/ClusterCopier.cpp index 64a1249ae0c..f1c0cf99a5c 100644 --- a/dbms/src/Server/ClusterCopier.cpp +++ b/dbms/src/Server/ClusterCopier.cpp @@ -1084,7 +1084,9 @@ protected: if (num_bad_version_errors > 3) { LOG_DEBUG(log, "A concurrent worker has just been added, will check free worker slots again"); - std::this_thread::sleep_for(default_sleep_time); + std::chrono::milliseconds random_sleep_time(std::uniform_int_distribution(1, 1000)(task_cluster->random_engine)); + std::this_thread::sleep_for(random_sleep_time); + num_bad_version_errors = 0; } } else @@ -1115,7 +1117,7 @@ protected: try { - std::vector get_futures; + std::vector get_futures; for (const String & path : status_paths) get_futures.emplace_back(zookeeper->asyncGet(path)); @@ -1124,10 +1126,10 @@ protected: { auto res = future.get(); - TaskStateWithOwner status = TaskStateWithOwner::fromString(res.value); + TaskStateWithOwner status = TaskStateWithOwner::fromString(res.data); if (status.state != TaskState::Finished) { - LOG_INFO(log, "The task " << res.value << " is being rewritten by " << status.owner << ". Partition will be rechecked"); + LOG_INFO(log, "The task " << res.data << " is being rewritten by " << status.owner << ". Partition will be rechecked"); return false; } @@ -1362,12 +1364,12 @@ protected: expected_shards.emplace_back(shard); /// Do not sleep if there is a sequence of already processed shards to increase startup - bool sleep_before_execution = !previous_shard_is_instantly_finished && shard->priority.is_remote; + bool is_unprioritized_task = !previous_shard_is_instantly_finished && shard->priority.is_remote; PartitionTaskStatus task_status = PartitionTaskStatus::Error; bool was_error = false; for (size_t try_num = 0; try_num < max_shard_partition_tries; ++try_num) { - task_status = tryProcessPartitionTask(partition, sleep_before_execution); + task_status = tryProcessPartitionTask(partition, is_unprioritized_task); /// Exit if success if (task_status == PartitionTaskStatus::Finished) @@ -1453,13 +1455,13 @@ protected: Error, }; - PartitionTaskStatus tryProcessPartitionTask(ShardPartition & task_partition, bool sleep_before_execution) + PartitionTaskStatus tryProcessPartitionTask(ShardPartition & task_partition, bool is_unprioritized_task) { PartitionTaskStatus res; try { - res = processPartitionTaskImpl(task_partition, sleep_before_execution); + res = processPartitionTaskImpl(task_partition, is_unprioritized_task); } catch (...) { @@ -1480,7 +1482,7 @@ protected: return res; } - PartitionTaskStatus processPartitionTaskImpl(ShardPartition & task_partition, bool sleep_before_execution) + PartitionTaskStatus processPartitionTaskImpl(ShardPartition & task_partition, bool is_unprioritized_task) { TaskShard & task_shard = task_partition.task_shard; TaskTable & task_table = task_shard.task_table; @@ -1519,7 +1521,7 @@ protected: }; /// Load balancing - auto worker_node_holder = createTaskWorkerNodeAndWaitIfNeed(zookeeper, current_task_status_path, sleep_before_execution); + auto worker_node_holder = createTaskWorkerNodeAndWaitIfNeed(zookeeper, current_task_status_path, is_unprioritized_task); LOG_DEBUG(log, "Processing " << current_task_status_path); @@ -1534,7 +1536,7 @@ protected: } catch (...) { - tryLogCurrentException(log, "An error occurred while clean partition"); + tryLogCurrentException(log, "An error occurred when clean partition"); } return PartitionTaskStatus::Error; @@ -1653,8 +1655,7 @@ protected: bool inject_fault = false; if (copy_fault_probability > 0) { - std::uniform_real_distribution<> get_urand(0, 1); - double value = get_urand(task_table.task_cluster.random_engine); + double value = std::uniform_real_distribution<>(0, 1)(task_table.task_cluster.random_engine); inject_fault = value < copy_fault_probability; } @@ -2179,8 +2180,7 @@ void ClusterCopierApp::mainImpl() context->addDatabase(default_database, std::make_shared(default_database)); context->setCurrentDatabase(default_database); - std::unique_ptr copier = std::make_unique(task_path, host_id, default_database, *context); - + auto copier = std::make_unique(task_path, host_id, default_database, *context); copier->setSafeMode(is_safe_mode); copier->setCopyFaultProbability(copy_fault_probability); copier->init(); diff --git a/dbms/src/Server/ClusterCopier.h b/dbms/src/Server/ClusterCopier.h index 347a1d8f645..501e65b0a88 100644 --- a/dbms/src/Server/ClusterCopier.h +++ b/dbms/src/Server/ClusterCopier.h @@ -1,5 +1,6 @@ #pragma once #include +#include /* clickhouse cluster copier util * Copies tables data from one cluster to new tables of other (possibly the same) cluster in distributed fault-tolerant manner. diff --git a/dbms/src/Storages/Distributed/DistributedBlockOutputStream.cpp b/dbms/src/Storages/Distributed/DistributedBlockOutputStream.cpp index 2ff2174c751..d17b62bb36e 100644 --- a/dbms/src/Storages/Distributed/DistributedBlockOutputStream.cpp +++ b/dbms/src/Storages/Distributed/DistributedBlockOutputStream.cpp @@ -28,7 +28,6 @@ #include #include -#include #include #include @@ -354,7 +353,6 @@ void DistributedBlockOutputStream::writeSync(const Block & block) inserted_blocks += 1; inserted_rows += block.rows(); - last_block_finish_time = time(nullptr); } @@ -370,14 +368,6 @@ void DistributedBlockOutputStream::writeSuffix() if (insert_sync && pool) { - auto format_ts = [] (time_t ts) { - WriteBufferFromOwnString wb; - writeDateTimeText(ts, wb); - return wb.str(); - }; - - LOG_DEBUG(log, "Writing suffix, the last block was at " << format_ts(last_block_finish_time)); - finished_jobs_count = 0; for (auto & shard_jobs : per_shard_jobs) for (JobReplica & job : shard_jobs.replicas_jobs) diff --git a/dbms/src/Storages/Distributed/DistributedBlockOutputStream.h b/dbms/src/Storages/Distributed/DistributedBlockOutputStream.h index 32f3a49f549..b4f830082ec 100644 --- a/dbms/src/Storages/Distributed/DistributedBlockOutputStream.h +++ b/dbms/src/Storages/Distributed/DistributedBlockOutputStream.h @@ -94,7 +94,6 @@ private: std::optional pool; ThrottlerPtr throttler; String query_string; - time_t last_block_finish_time = 0; struct JobReplica { diff --git a/dbms/tests/integration/helpers/cluster.py b/dbms/tests/integration/helpers/cluster.py index c27a0b94f5d..52003b1d010 100644 --- a/dbms/tests/integration/helpers/cluster.py +++ b/dbms/tests/integration/helpers/cluster.py @@ -125,7 +125,7 @@ class ClickHouseCluster: self.run_kazoo_commands_with_retries(command, repeats=5) # Uncomment for debugging - # print ' '.join(self.base_cmd + ['up', '--no-recreate']) + #print ' '.join(self.base_cmd + ['up', '--no-recreate']) subprocess.check_call(self.base_cmd + ['up', '-d', '--no-recreate']) diff --git a/dbms/tests/integration/test_cluster_copier/test.py b/dbms/tests/integration/test_cluster_copier/test.py index 593ce7a807f..1bc06fda310 100644 --- a/dbms/tests/integration/test_cluster_copier/test.py +++ b/dbms/tests/integration/test_cluster_copier/test.py @@ -154,7 +154,7 @@ class Task_test_block_size: ddl_check_query(instance, """ CREATE TABLE test_block_size ON CLUSTER shard_0_0 (partition Date, d UInt64) - ENGINE=ReplicatedMergeTree('/clickhouse/tables/cluster_{cluster}/{shard}/a', '{replica}') + ENGINE=ReplicatedMergeTree('/clickhouse/tables/cluster_{cluster}/{shard}/test_block_size', '{replica}') ORDER BY d""", 2) instance.query("INSERT INTO test_block_size SELECT toDate(0) AS partition, number as d FROM system.numbers LIMIT {}".format(self.rows)) From fb7e6350dbdc95c6edf14943f28830ac3cff0b4c Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Mon, 14 May 2018 17:12:33 +0300 Subject: [PATCH 187/231] Fixed disabled stderr output. Better configs for clickhouse-copier. [#CLICKHOUSE-2] --- dbms/src/Server/ClusterCopier.cpp | 53 ++++++++----------------------- dbms/src/Server/ClusterCopier.h | 4 ++- libs/libdaemon/src/BaseDaemon.cpp | 4 +-- 3 files changed, 18 insertions(+), 43 deletions(-) diff --git a/dbms/src/Server/ClusterCopier.cpp b/dbms/src/Server/ClusterCopier.cpp index f1c0cf99a5c..2cf6088b494 100644 --- a/dbms/src/Server/ClusterCopier.cpp +++ b/dbms/src/Server/ClusterCopier.cpp @@ -2057,8 +2057,6 @@ private: void ClusterCopierApp::initialize(Poco::Util::Application & self) { - Poco::Util::ServerApplication::initialize(self); - is_help = config().has("help"); if (is_help) return; @@ -2080,11 +2078,17 @@ void ClusterCopierApp::initialize(Poco::Util::Application & self) process_path = Poco::Path(base_dir + "/clickhouse-copier_" + process_id).absolute().toString(); Poco::File(process_path).createDirectories(); - setupLogging(); + /// Override variables for BaseDaemon + if (config().has("log-level")) + config().setString("logger.level", config().getString("log-level")); - std::string stderr_path = process_path + "/stderr"; - if (!freopen(stderr_path.c_str(), "a+", stderr)) - throw Poco::OpenFileException("Cannot attach stderr to " + stderr_path); + if (config().has("base-dir") || !config().has("logger.log")) + config().setString("logger.log", process_path + "/log.log"); + + if (config().has("base-dir") || !config().has("logger.errorlog")) + config().setString("logger.errorlog", process_path + "/log.err.log"); + + Base::initialize(self); } @@ -2102,10 +2106,8 @@ void ClusterCopierApp::handleHelp(const std::string &, const std::string &) void ClusterCopierApp::defineOptions(Poco::Util::OptionSet & options) { - Poco::Util::ServerApplication::defineOptions(options); + Base::defineOptions(options); - options.addOption(Poco::Util::Option("config-file", "c", "path to config file with ZooKeeper config", true) - .argument("config-file").binding("config-file")); options.addOption(Poco::Util::Option("task-path", "", "path to task in ZooKeeper") .argument("task-path").binding("task-path")); options.addOption(Poco::Util::Option("safe-mode", "", "disables ALTER DROP PARTITION in case of errors") @@ -2123,40 +2125,11 @@ void ClusterCopierApp::defineOptions(Poco::Util::OptionSet & options) } -void ClusterCopierApp::setupLogging() -{ - Poco::AutoPtr split_channel(new Poco::SplitterChannel); - - Poco::AutoPtr log_file_channel(new Poco::FileChannel); - log_file_channel->setProperty("path", process_path + "/log.log"); - split_channel->addChannel(log_file_channel); - log_file_channel->open(); - - if (!config().getBool("application.runAsDaemon", true)) - { - Poco::AutoPtr console_channel(new Poco::ConsoleChannel); - split_channel->addChannel(console_channel); - console_channel->open(); - } - - Poco::AutoPtr formatter = new OwnPatternFormatter(nullptr); - formatter->setProperty("times", "local"); - Poco::AutoPtr formatting_channel(new Poco::FormattingChannel(formatter)); - formatting_channel->setChannel(split_channel); - split_channel->open(); - - Poco::Logger::root().setChannel(formatting_channel); - Poco::Logger::root().setLevel(log_level); -} - - void ClusterCopierApp::mainImpl() { - ConfigurationPtr zookeeper_configuration(new Poco::Util::XMLConfiguration(config_xml_path)); - auto log = &logger(); - StatusFile status_file(process_path + "/status"); + auto log = &logger(); LOG_INFO(log, "Starting clickhouse-copier (" << "id " << process_id << ", " << "host_id " << host_id << ", " @@ -2166,7 +2139,7 @@ void ClusterCopierApp::mainImpl() auto context = std::make_unique(Context::createGlobal()); SCOPE_EXIT(context->shutdown()); - context->setConfig(zookeeper_configuration); + context->setConfig(loaded_config.configuration); context->setGlobalContext(*context); context->setApplicationType(Context::ApplicationType::LOCAL); context->setPath(process_path); diff --git a/dbms/src/Server/ClusterCopier.h b/dbms/src/Server/ClusterCopier.h index 501e65b0a88..89f45df8686 100644 --- a/dbms/src/Server/ClusterCopier.h +++ b/dbms/src/Server/ClusterCopier.h @@ -53,7 +53,7 @@ namespace DB { -class ClusterCopierApp : public Poco::Util::ServerApplication +class ClusterCopierApp : public BaseDaemon { public: @@ -67,6 +67,8 @@ public: private: + using Base = BaseDaemon; + void mainImpl(); void setupLogging(); diff --git a/libs/libdaemon/src/BaseDaemon.cpp b/libs/libdaemon/src/BaseDaemon.cpp index ff1fbabceb1..01fd377b66e 100644 --- a/libs/libdaemon/src/BaseDaemon.cpp +++ b/libs/libdaemon/src/BaseDaemon.cpp @@ -700,7 +700,7 @@ void BaseDaemon::buildLoggers(Poco::Util::AbstractConfiguration & config) return; config_logger = current_logger; - bool is_daemon = config.getBool("application.runAsDaemon", false); + bool is_daemon = config.getBool("application.runAsDaemon", true); // Split log and error log. Poco::AutoPtr split = new SplitterChannel; @@ -883,7 +883,7 @@ void BaseDaemon::initialize(Application & self) config().add(map_config, PRIO_APPLICATION - 100); } - bool is_daemon = config().getBool("application.runAsDaemon", false); + bool is_daemon = config().getBool("application.runAsDaemon", true); if (is_daemon) { From aa1552ebf2fd08e26049561e9538bd9b3496dda5 Mon Sep 17 00:00:00 2001 From: proller Date: Mon, 14 May 2018 20:25:32 +0300 Subject: [PATCH 188/231] Build fixes --- cmake/find_llvm.cmake | 2 +- dbms/CMakeLists.txt | 4 +--- dbms/src/Server/Compiler-5.0.0/CMakeLists.txt | 5 ++--- dbms/src/Server/Compiler-6.0.0/CMakeLists.txt | 4 +--- dbms/src/Server/Compiler-7.0.0/CMakeLists.txt | 5 ++--- debian/pbuilder-hooks/A00ccache | 1 + 6 files changed, 8 insertions(+), 13 deletions(-) diff --git a/cmake/find_llvm.cmake b/cmake/find_llvm.cmake index a2006e37c64..22195c85f2f 100644 --- a/cmake/find_llvm.cmake +++ b/cmake/find_llvm.cmake @@ -1,5 +1,5 @@ option (ENABLE_EMBEDDED_COMPILER "Set to TRUE to enable support for 'compile' option for query execution" 1) -option (USE_INTERNAL_LLVM_LIBRARY "Use bundled or system LLVM library. Default: system library for quicker developer builds." 0) +option (USE_INTERNAL_LLVM_LIBRARY "Use bundled or system LLVM library. Default: system library for quicker developer builds." ${APPLE}) if (ENABLE_EMBEDDED_COMPILER) if (USE_INTERNAL_LLVM_LIBRARY AND NOT EXISTS "${ClickHouse_SOURCE_DIR}/contrib/llvm/llvm/CMakeLists.txt") diff --git a/dbms/CMakeLists.txt b/dbms/CMakeLists.txt index da199163994..45695beb9c1 100644 --- a/dbms/CMakeLists.txt +++ b/dbms/CMakeLists.txt @@ -104,9 +104,7 @@ if (USE_EMBEDDED_COMPILER) if (TERMCAP_LIBRARY) list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) endif () - if (LTDL_LIBRARY) - list(APPEND REQUIRED_LLVM_LIBRARIES ${LTDL_LIBRARY}) - endif () + list(APPEND REQUIRED_LLVM_LIBRARIES ${CMAKE_DL_LIBS}) target_link_libraries (dbms ${REQUIRED_LLVM_LIBRARIES}) target_include_directories (dbms BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) diff --git a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt index 076eef6921d..5a29d3bd8ae 100644 --- a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt @@ -12,9 +12,8 @@ llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) if (TERMCAP_LIBRARY) list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) endif () -if (LTDL_LIBRARY) - list(APPEND REQUIRED_LLVM_LIBRARIES ${LTDL_LIBRARY}) -endif () +list(APPEND REQUIRED_LLVM_LIBRARIES ${CMAKE_DL_LIBS}) + message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") diff --git a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt index 23c7ea61c31..d7123ea3f07 100644 --- a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt @@ -12,9 +12,7 @@ llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) if (TERMCAP_LIBRARY) list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) endif () -if (LTDL_LIBRARY) - list(APPEND REQUIRED_LLVM_LIBRARIES ${LTDL_LIBRARY}) -endif () +list(APPEND REQUIRED_LLVM_LIBRARIES ${CMAKE_DL_LIBS}) message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") diff --git a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt index 809f8604366..15f6234dfa9 100644 --- a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt @@ -12,9 +12,8 @@ llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) if (TERMCAP_LIBRARY) list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) endif () -if (LTDL_LIBRARY) - list(APPEND REQUIRED_LLVM_LIBRARIES ${LTDL_LIBRARY}) -endif () +list(APPEND REQUIRED_LLVM_LIBRARIES ${CMAKE_DL_LIBS}) + message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") diff --git a/debian/pbuilder-hooks/A00ccache b/debian/pbuilder-hooks/A00ccache index a5d1d33b428..b8bf8d579c0 100755 --- a/debian/pbuilder-hooks/A00ccache +++ b/debian/pbuilder-hooks/A00ccache @@ -12,5 +12,6 @@ if [ -n "$CCACHE_DIR" ]; then chmod -R a+rwx $CCACHE_DIR || true fi +df -h ccache --show-stats ccache -M ${CCACHE_SIZE:=32G} From 2a6d037eab50f1ceb1734c71a5bade44f599e39d Mon Sep 17 00:00:00 2001 From: proller Date: Mon, 14 May 2018 20:28:26 +0300 Subject: [PATCH 189/231] Build fixes (#2360) --- cmake/find_llvm.cmake | 2 +- dbms/CMakeLists.txt | 4 +--- dbms/src/Server/Compiler-5.0.0/CMakeLists.txt | 5 ++--- dbms/src/Server/Compiler-6.0.0/CMakeLists.txt | 4 +--- dbms/src/Server/Compiler-7.0.0/CMakeLists.txt | 5 ++--- debian/pbuilder-hooks/A00ccache | 1 + 6 files changed, 8 insertions(+), 13 deletions(-) diff --git a/cmake/find_llvm.cmake b/cmake/find_llvm.cmake index a2006e37c64..22195c85f2f 100644 --- a/cmake/find_llvm.cmake +++ b/cmake/find_llvm.cmake @@ -1,5 +1,5 @@ option (ENABLE_EMBEDDED_COMPILER "Set to TRUE to enable support for 'compile' option for query execution" 1) -option (USE_INTERNAL_LLVM_LIBRARY "Use bundled or system LLVM library. Default: system library for quicker developer builds." 0) +option (USE_INTERNAL_LLVM_LIBRARY "Use bundled or system LLVM library. Default: system library for quicker developer builds." ${APPLE}) if (ENABLE_EMBEDDED_COMPILER) if (USE_INTERNAL_LLVM_LIBRARY AND NOT EXISTS "${ClickHouse_SOURCE_DIR}/contrib/llvm/llvm/CMakeLists.txt") diff --git a/dbms/CMakeLists.txt b/dbms/CMakeLists.txt index da199163994..45695beb9c1 100644 --- a/dbms/CMakeLists.txt +++ b/dbms/CMakeLists.txt @@ -104,9 +104,7 @@ if (USE_EMBEDDED_COMPILER) if (TERMCAP_LIBRARY) list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) endif () - if (LTDL_LIBRARY) - list(APPEND REQUIRED_LLVM_LIBRARIES ${LTDL_LIBRARY}) - endif () + list(APPEND REQUIRED_LLVM_LIBRARIES ${CMAKE_DL_LIBS}) target_link_libraries (dbms ${REQUIRED_LLVM_LIBRARIES}) target_include_directories (dbms BEFORE PUBLIC ${LLVM_INCLUDE_DIRS}) diff --git a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt index 076eef6921d..5a29d3bd8ae 100644 --- a/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-5.0.0/CMakeLists.txt @@ -12,9 +12,8 @@ llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) if (TERMCAP_LIBRARY) list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) endif () -if (LTDL_LIBRARY) - list(APPEND REQUIRED_LLVM_LIBRARIES ${LTDL_LIBRARY}) -endif () +list(APPEND REQUIRED_LLVM_LIBRARIES ${CMAKE_DL_LIBS}) + message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") diff --git a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt index 23c7ea61c31..d7123ea3f07 100644 --- a/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-6.0.0/CMakeLists.txt @@ -12,9 +12,7 @@ llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) if (TERMCAP_LIBRARY) list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) endif () -if (LTDL_LIBRARY) - list(APPEND REQUIRED_LLVM_LIBRARIES ${LTDL_LIBRARY}) -endif () +list(APPEND REQUIRED_LLVM_LIBRARIES ${CMAKE_DL_LIBS}) message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") diff --git a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt index 809f8604366..15f6234dfa9 100644 --- a/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt +++ b/dbms/src/Server/Compiler-7.0.0/CMakeLists.txt @@ -12,9 +12,8 @@ llvm_map_components_to_libnames(REQUIRED_LLVM_LIBRARIES all) if (TERMCAP_LIBRARY) list(APPEND REQUIRED_LLVM_LIBRARIES ${TERMCAP_LIBRARY}) endif () -if (LTDL_LIBRARY) - list(APPEND REQUIRED_LLVM_LIBRARIES ${LTDL_LIBRARY}) -endif () +list(APPEND REQUIRED_LLVM_LIBRARIES ${CMAKE_DL_LIBS}) + message(STATUS "Using LLVM ${LLVM_VERSION}: ${LLVM_INCLUDE_DIRS} : ${REQUIRED_LLVM_LIBRARIES}") diff --git a/debian/pbuilder-hooks/A00ccache b/debian/pbuilder-hooks/A00ccache index a5d1d33b428..b8bf8d579c0 100755 --- a/debian/pbuilder-hooks/A00ccache +++ b/debian/pbuilder-hooks/A00ccache @@ -12,5 +12,6 @@ if [ -n "$CCACHE_DIR" ]; then chmod -R a+rwx $CCACHE_DIR || true fi +df -h ccache --show-stats ccache -M ${CCACHE_SIZE:=32G} From d81adc243da781c651b5ec54040f41482d2450a1 Mon Sep 17 00:00:00 2001 From: proller Date: Mon, 14 May 2018 20:30:30 +0300 Subject: [PATCH 190/231] ci fixes --- ci/build-normal.sh | 3 +++ ci/install-libraries.sh | 2 +- ci/jobs/quick-build/config | 4 +--- ci/run-with-docker.sh | 3 +++ cmake/find_poco.cmake | 2 +- contrib/CMakeLists.txt | 13 +++++++++---- 6 files changed, 18 insertions(+), 9 deletions(-) diff --git a/ci/build-normal.sh b/ci/build-normal.sh index e165489cc9d..aa77a1ad28b 100755 --- a/ci/build-normal.sh +++ b/ci/build-normal.sh @@ -3,6 +3,9 @@ set -e -x source default-config +ccache -M 32G +ccache -s + [[ -d "${WORKSPACE}/sources" ]] || die "Run get-sources.sh first" mkdir -p "${WORKSPACE}/build" diff --git a/ci/install-libraries.sh b/ci/install-libraries.sh index 7070083d57e..9b59e3284eb 100755 --- a/ci/install-libraries.sh +++ b/ci/install-libraries.sh @@ -5,7 +5,7 @@ source default-config # TODO Non-debian systems -$SUDO apt-get -y install libssl-dev libicu-dev libreadline-dev libmysqlclient-dev unixodbc-dev +$SUDO apt-get -y install libssl-dev libicu-dev libreadline-dev libmariadbclient-dev unixodbc-dev ccache if [[ "$ENABLE_EMBEDDED_COMPILER" == 1 && "$USE_LLVM_LIBRARIES_FROM_SYSTEM" == 1 ]]; then $SUDO apt-get -y install liblld-5.0-dev libclang-5.0-dev diff --git a/ci/jobs/quick-build/config b/ci/jobs/quick-build/config index c45d9690c7a..f21ab545c02 100644 --- a/ci/jobs/quick-build/config +++ b/ci/jobs/quick-build/config @@ -7,6 +7,4 @@ BUILD_METHOD=normal BUILD_TARGETS=clickhouse BUILD_TYPE=Debug ENABLE_EMBEDDED_COMPILER=0 -CMAKE_FLAGS="-D CMAKE_C_FLAGS_ADD=-g0 -D CMAKE_CXX_FLAGS_ADD=-g0 -D ENABLE_TCMALLOC=0 -D ENABLE_CAPNP=0 -D ENABLE_RDKAFKA=0 -D ENABLE_UNWIND=0 -D ENABLE_ICU=0" - -# TODO it doesn't build with -D ENABLE_NETSSL=0 -D ENABLE_MONGODB=0 -D ENABLE_MYSQL=0 -D ENABLE_DATA_ODBC=0 +CMAKE_FLAGS="-D CMAKE_C_FLAGS_ADD=-g0 -D CMAKE_CXX_FLAGS_ADD=-g0 -D ENABLE_TCMALLOC=0 -D ENABLE_CAPNP=0 -D ENABLE_RDKAFKA=0 -D ENABLE_UNWIND=0 -D ENABLE_ICU=0 -D DISABLE_POCO_SSL=1 -D OPENSSL_FOUND=0 -D ENABLE_NETSSL=0 -D ENABLE_CRYPTO=0 -D Poco_NetSSL_FOUND=0 -D ENABLE_MONGODB=0 -D ENABLE_MYSQL=0 -D ENABLE_DATA_ODBC=0" diff --git a/ci/run-with-docker.sh b/ci/run-with-docker.sh index 238907bb5dd..158961dd5da 100755 --- a/ci/run-with-docker.sh +++ b/ci/run-with-docker.sh @@ -1,6 +1,9 @@ #!/usr/bin/env bash set -e -x +mkdir -p /var/cache/ccache +DOCKER_ENV+=" --mount=type=bind,source=/var/cache/ccache,destination=/ccache -e CCACHE_DIR=/ccache " + PROJECT_ROOT="$(cd "$(dirname "$0")/.."; pwd -P)" [[ -n "$CONFIG" ]] && DOCKER_ENV="--env=CONFIG" docker run -t --network=host --mount=type=bind,source=${PROJECT_ROOT},destination=/ClickHouse --workdir=/ClickHouse/ci $DOCKER_ENV "$1" "$2" diff --git a/cmake/find_poco.cmake b/cmake/find_poco.cmake index b46e722c94b..ca13f0b9718 100644 --- a/cmake/find_poco.cmake +++ b/cmake/find_poco.cmake @@ -84,7 +84,7 @@ elseif (NOT MISSING_INTERNAL_POCO_LIBRARY) endif () # TODO! fix internal ssl - if (OPENSSL_FOUND AND NOT USE_INTERNAL_SSL_LIBRARY) + if (OPENSSL_FOUND AND NOT USE_INTERNAL_SSL_LIBRARY AND NOT DISABLE_POCO_SSL) set (Poco_NetSSL_FOUND 1) set (Poco_NetSSL_LIBRARY PocoNetSSL) set (Poco_Crypto_LIBRARY PocoCrypto) diff --git a/contrib/CMakeLists.txt b/contrib/CMakeLists.txt index 104db478ef0..6a03c483cc3 100644 --- a/contrib/CMakeLists.txt +++ b/contrib/CMakeLists.txt @@ -128,7 +128,7 @@ if (USE_INTERNAL_POCO_LIBRARY) set (_save ${ENABLE_TESTS}) set (ENABLE_TESTS 0) set (CMAKE_DISABLE_FIND_PACKAGE_ZLIB 1) - if (USE_INTERNAL_SSL_LIBRARY) + if (USE_INTERNAL_SSL_LIBRARY OR DISABLE_POCO_SSL) set (DISABLE_INTERNAL_OPENSSL 1 CACHE INTERNAL "") set (ENABLE_NETSSL 0 CACHE INTERNAL "") # TODO! set (ENABLE_CRYPTO 0 CACHE INTERNAL "") # TODO! @@ -141,9 +141,14 @@ if (USE_INTERNAL_POCO_LIBRARY) set (ENABLE_TESTS ${_save}) set (CMAKE_CXX_FLAGS ${save_CMAKE_CXX_FLAGS}) set (CMAKE_C_FLAGS ${save_CMAKE_C_FLAGS}) - if (OPENSSL_FOUND AND TARGET Crypto) - # Bug in poco https://github.com/pocoproject/poco/pull/2100 found on macos - target_include_directories(Crypto PUBLIC ${OPENSSL_INCLUDE_DIR}) + + if (DISABLE_POCO_SSL) + set (Poco_NetSSL_FOUND 0) + else() + if (OPENSSL_FOUND AND TARGET Crypto) + # Bug in poco https://github.com/pocoproject/poco/pull/2100 found on macos + target_include_directories(Crypto PUBLIC ${OPENSSL_INCLUDE_DIR}) + endif () endif () endif () From fb911f7cfaaee1057bc0c00cd2a0f01fbc7c989e Mon Sep 17 00:00:00 2001 From: proller Date: Mon, 14 May 2018 21:36:01 +0300 Subject: [PATCH 191/231] Allow force disable libs: poco netssl poco mongodb, poco data odbc --- ci/jobs/quick-build/config | 3 +- cmake/find_poco.cmake | 34 +++++++++++++------ contrib/CMakeLists.txt | 12 +++---- dbms/CMakeLists.txt | 9 +++-- dbms/src/Client/Connection.cpp | 4 +-- dbms/src/Common/config.h.in | 8 ++--- dbms/src/Common/config_build.cpp.in | 8 ++--- .../Dictionaries/DictionarySourceFactory.cpp | 12 +++---- .../Dictionaries/MongoDBBlockInputStream.cpp | 2 +- .../Dictionaries/MongoDBDictionarySource.cpp | 2 +- dbms/src/IO/HTTPCommon.cpp | 4 +-- dbms/src/IO/ReadWriteBufferFromHTTP.cpp | 4 +-- dbms/src/Server/Server.cpp | 10 +++--- dbms/src/Storages/registerStorages.cpp | 4 +-- dbms/src/TableFunctions/CMakeLists.txt | 4 +-- dbms/src/TableFunctions/TableFunctionODBC.cpp | 2 +- dbms/src/TableFunctions/TableFunctionODBC.h | 2 +- .../TableFunctions/registerTableFunctions.cpp | 4 +-- 18 files changed, 67 insertions(+), 61 deletions(-) diff --git a/ci/jobs/quick-build/config b/ci/jobs/quick-build/config index f21ab545c02..46d3c98c7fb 100644 --- a/ci/jobs/quick-build/config +++ b/ci/jobs/quick-build/config @@ -7,4 +7,5 @@ BUILD_METHOD=normal BUILD_TARGETS=clickhouse BUILD_TYPE=Debug ENABLE_EMBEDDED_COMPILER=0 -CMAKE_FLAGS="-D CMAKE_C_FLAGS_ADD=-g0 -D CMAKE_CXX_FLAGS_ADD=-g0 -D ENABLE_TCMALLOC=0 -D ENABLE_CAPNP=0 -D ENABLE_RDKAFKA=0 -D ENABLE_UNWIND=0 -D ENABLE_ICU=0 -D DISABLE_POCO_SSL=1 -D OPENSSL_FOUND=0 -D ENABLE_NETSSL=0 -D ENABLE_CRYPTO=0 -D Poco_NetSSL_FOUND=0 -D ENABLE_MONGODB=0 -D ENABLE_MYSQL=0 -D ENABLE_DATA_ODBC=0" +CMAKE_FLAGS="-D CMAKE_C_FLAGS_ADD=-g0 -D CMAKE_CXX_FLAGS_ADD=-g0 -D ENABLE_TCMALLOC=0 -D ENABLE_CAPNP=0 -D ENABLE_RDKAFKA=0 -D ENABLE_UNWIND=0 -D ENABLE_ICU=0" +CMAKE_FLAGS+=" -D ENABLE_POCO_MONGODB=0 -D ENABLE_POCO_NETSSL=0 -D ENABLE_MYSQL=0 -D ENABLE_POCO_ODBC=0" diff --git a/cmake/find_poco.cmake b/cmake/find_poco.cmake index ca13f0b9718..e09c7428720 100644 --- a/cmake/find_poco.cmake +++ b/cmake/find_poco.cmake @@ -8,8 +8,21 @@ if (NOT EXISTS "${ClickHouse_SOURCE_DIR}/contrib/poco/CMakeLists.txt") set (MISSING_INTERNAL_POCO_LIBRARY 1) endif () +set (POCO_COMPONENTS Net XML SQL Data) +if (NOT DEFINED ENABLE_POCO_NETSSL OR ENABLE_POCO_NETSSL) + list (APPEND POCO_COMPONENTS Crypto NetSSL) +endif () +if (NOT DEFINED ENABLE_POCO_MONGODB OR ENABLE_POCO_MONGODB) + list (APPEND POCO_COMPONENTS MongoDB) +endif () +# TODO: after new poco release with SQL library rename ENABLE_POCO_ODBC -> ENABLE_POCO_SQLODBC +if (NOT DEFINED ENABLE_POCO_ODBC OR ENABLE_POCO_ODBC) + list (APPEND POCO_COMPONENTS DataODBC) + #list (APPEND POCO_COMPONENTS SQLODBC) # future +endif () + if (NOT USE_INTERNAL_POCO_LIBRARY) - find_package (Poco COMPONENTS Net NetSSL XML SQL Data Crypto DataODBC MongoDB) + find_package (Poco COMPONENTS ${POCO_COMPONENTS}) endif () if (Poco_INCLUDE_DIRS AND Poco_Foundation_LIBRARY) @@ -46,13 +59,12 @@ elseif (NOT MISSING_INTERNAL_POCO_LIBRARY) "${ClickHouse_SOURCE_DIR}/contrib/poco/Util/include/" ) - if (NOT DEFINED POCO_ENABLE_MONGODB OR POCO_ENABLE_MONGODB) - set (Poco_MongoDB_FOUND 1) + if (NOT DEFINED ENABLE_POCO_MONGODB OR ENABLE_POCO_MONGODB) + set (USE_POCO_MONGODB 1) set (Poco_MongoDB_LIBRARY PocoMongoDB) set (Poco_MongoDB_INCLUDE_DIRS "${ClickHouse_SOURCE_DIR}/contrib/poco/MongoDB/include/") endif () - if (EXISTS "${ClickHouse_SOURCE_DIR}/contrib/poco/SQL/ODBC/include/") set (Poco_SQL_FOUND 1) set (Poco_SQL_LIBRARY PocoSQL) @@ -60,8 +72,8 @@ elseif (NOT MISSING_INTERNAL_POCO_LIBRARY) "${ClickHouse_SOURCE_DIR}/contrib/poco/SQL/include" "${ClickHouse_SOURCE_DIR}/contrib/poco/Data/include" ) - if (ODBC_FOUND) - set (Poco_SQLODBC_FOUND 1) + if ((NOT DEFINED ENABLE_POCO_ODBC OR ENABLE_POCO_ODBC) AND ODBC_FOUND) + set (USE_POCO_SQLODBC 1) set (Poco_SQLODBC_INCLUDE_DIRS "${ClickHouse_SOURCE_DIR}/contrib/poco/SQL/ODBC/include/" "${ClickHouse_SOURCE_DIR}/contrib/poco/Data/ODBC/include/" @@ -73,8 +85,8 @@ elseif (NOT MISSING_INTERNAL_POCO_LIBRARY) set (Poco_Data_FOUND 1) set (Poco_Data_INCLUDE_DIRS "${ClickHouse_SOURCE_DIR}/contrib/poco/Data/include") set (Poco_Data_LIBRARY PocoData) - if (ODBC_FOUND) - set (Poco_DataODBC_FOUND 1) + if ((NOT DEFINED ENABLE_POCO_ODBC OR ENABLE_POCO_ODBC) AND ODBC_FOUND) + set (USE_POCO_DATAODBC 1) set (Poco_DataODBC_INCLUDE_DIRS "${ClickHouse_SOURCE_DIR}/contrib/poco/Data/ODBC/include/" ${ODBC_INCLUDE_DIRECTORIES} @@ -84,8 +96,8 @@ elseif (NOT MISSING_INTERNAL_POCO_LIBRARY) endif () # TODO! fix internal ssl - if (OPENSSL_FOUND AND NOT USE_INTERNAL_SSL_LIBRARY AND NOT DISABLE_POCO_SSL) - set (Poco_NetSSL_FOUND 1) + if (OPENSSL_FOUND AND NOT USE_INTERNAL_SSL_LIBRARY AND (NOT DEFINED ENABLE_POCO_NETSSL OR ENABLE_POCO_NETSSL)) + set (USE_POCO_NETSSL 1) set (Poco_NetSSL_LIBRARY PocoNetSSL) set (Poco_Crypto_LIBRARY PocoCrypto) endif () @@ -103,7 +115,7 @@ elseif (NOT MISSING_INTERNAL_POCO_LIBRARY) set (Poco_XML_LIBRARY PocoXML) endif () -message(STATUS "Using Poco: ${Poco_INCLUDE_DIRS} : ${Poco_Foundation_LIBRARY},${Poco_Util_LIBRARY},${Poco_Net_LIBRARY},${Poco_NetSSL_LIBRARY},${Poco_XML_LIBRARY},${Poco_Data_LIBRARY},${Poco_DataODBC_LIBRARY},${Poco_MongoDB_LIBRARY}; MongoDB=${Poco_MongoDB_FOUND}, DataODBC=${Poco_DataODBC_FOUND}, NetSSL=${Poco_NetSSL_FOUND}") +message(STATUS "Using Poco: ${Poco_INCLUDE_DIRS} : ${Poco_Foundation_LIBRARY},${Poco_Util_LIBRARY},${Poco_Net_LIBRARY},${Poco_NetSSL_LIBRARY},${Poco_XML_LIBRARY},${Poco_Data_LIBRARY},${Poco_DataODBC_LIBRARY},${Poco_MongoDB_LIBRARY}; MongoDB=${USE_POCO_MONGODB}, DataODBC=${Poco_DataODBC_FOUND}, NetSSL=${USE_POCO_NETSSL}") # How to make sutable poco: # use branch: diff --git a/contrib/CMakeLists.txt b/contrib/CMakeLists.txt index 6a03c483cc3..2966d5b26f8 100644 --- a/contrib/CMakeLists.txt +++ b/contrib/CMakeLists.txt @@ -128,7 +128,7 @@ if (USE_INTERNAL_POCO_LIBRARY) set (_save ${ENABLE_TESTS}) set (ENABLE_TESTS 0) set (CMAKE_DISABLE_FIND_PACKAGE_ZLIB 1) - if (USE_INTERNAL_SSL_LIBRARY OR DISABLE_POCO_SSL) + if (USE_INTERNAL_SSL_LIBRARY OR (DEFINED ENABLE_POCO_NETSSL AND NOT ENABLE_POCO_NETSSL)) set (DISABLE_INTERNAL_OPENSSL 1 CACHE INTERNAL "") set (ENABLE_NETSSL 0 CACHE INTERNAL "") # TODO! set (ENABLE_CRYPTO 0 CACHE INTERNAL "") # TODO! @@ -142,13 +142,9 @@ if (USE_INTERNAL_POCO_LIBRARY) set (CMAKE_CXX_FLAGS ${save_CMAKE_CXX_FLAGS}) set (CMAKE_C_FLAGS ${save_CMAKE_C_FLAGS}) - if (DISABLE_POCO_SSL) - set (Poco_NetSSL_FOUND 0) - else() - if (OPENSSL_FOUND AND TARGET Crypto) - # Bug in poco https://github.com/pocoproject/poco/pull/2100 found on macos - target_include_directories(Crypto PUBLIC ${OPENSSL_INCLUDE_DIR}) - endif () + if (OPENSSL_FOUND AND TARGET Crypto AND (NOT DEFINED ENABLE_POCO_NETSSL OR ENABLE_POCO_NETSSL)) + # Bug in poco https://github.com/pocoproject/poco/pull/2100 found on macos + target_include_directories(Crypto PUBLIC ${OPENSSL_INCLUDE_DIR}) endif () endif () diff --git a/dbms/CMakeLists.txt b/dbms/CMakeLists.txt index 45695beb9c1..1f921beeda6 100644 --- a/dbms/CMakeLists.txt +++ b/dbms/CMakeLists.txt @@ -170,7 +170,7 @@ if (NOT USE_INTERNAL_BOOST_LIBRARY) target_include_directories (clickhouse_common_io BEFORE PUBLIC ${Boost_INCLUDE_DIRS}) endif () -if (Poco_SQLODBC_FOUND) +if (USE_POCO_SQLODBC) target_link_libraries (clickhouse_common_io ${Poco_SQL_LIBRARY}) target_link_libraries (dbms ${Poco_SQLODBC_LIBRARY} ${Poco_SQL_LIBRARY}) if (NOT USE_INTERNAL_POCO_LIBRARY) @@ -184,7 +184,7 @@ if (Poco_Data_FOUND AND NOT USE_INTERNAL_POCO_LIBRARY) target_include_directories (dbms PRIVATE ${Poco_Data_INCLUDE_DIRS}) endif() -if (Poco_DataODBC_FOUND) +if (USE_POCO_DATAODBC) target_link_libraries (clickhouse_common_io ${Poco_Data_LIBRARY}) target_link_libraries (dbms ${Poco_DataODBC_LIBRARY}) if (NOT USE_INTERNAL_POCO_LIBRARY) @@ -192,12 +192,11 @@ if (Poco_DataODBC_FOUND) endif() endif() - -if (Poco_MongoDB_FOUND) +if (USE_POCO_MONGODB) target_link_libraries (dbms ${Poco_MongoDB_LIBRARY}) endif() -if (Poco_NetSSL_FOUND) +if (USE_POCO_NETSSL) target_link_libraries (clickhouse_common_io ${Poco_NetSSL_LIBRARY}) endif() diff --git a/dbms/src/Client/Connection.cpp b/dbms/src/Client/Connection.cpp index 16f5bde4f71..9cd6e29986d 100644 --- a/dbms/src/Client/Connection.cpp +++ b/dbms/src/Client/Connection.cpp @@ -21,7 +21,7 @@ #include #include -#if Poco_NetSSL_FOUND +#if USE_POCO_NETSSL #include #endif @@ -57,7 +57,7 @@ void Connection::connect() if (static_cast(secure)) { -#if Poco_NetSSL_FOUND +#if USE_POCO_NETSSL socket = std::make_unique(); #else throw Exception{"tcp_secure protocol is disabled because poco library was built without NetSSL support.", ErrorCodes::SUPPORT_IS_DISABLED}; diff --git a/dbms/src/Common/config.h.in b/dbms/src/Common/config.h.in index f4d155de2c8..f037a62d36e 100644 --- a/dbms/src/Common/config.h.in +++ b/dbms/src/Common/config.h.in @@ -10,7 +10,7 @@ #cmakedefine01 USE_CAPNP #cmakedefine01 USE_EMBEDDED_COMPILER #cmakedefine01 LLVM_HAS_RTTI -#cmakedefine01 Poco_SQLODBC_FOUND -#cmakedefine01 Poco_DataODBC_FOUND -#cmakedefine01 Poco_MongoDB_FOUND -#cmakedefine01 Poco_NetSSL_FOUND +#cmakedefine01 USE_POCO_SQLODBC +#cmakedefine01 USE_POCO_DATAODBC +#cmakedefine01 USE_POCO_MONGODB +#cmakedefine01 USE_POCO_NETSSL diff --git a/dbms/src/Common/config_build.cpp.in b/dbms/src/Common/config_build.cpp.in index fee7c868384..9e1114668a6 100644 --- a/dbms/src/Common/config_build.cpp.in +++ b/dbms/src/Common/config_build.cpp.in @@ -35,10 +35,10 @@ const char * auto_config_build[] "USE_VECTORCLASS", "@USE_VECTORCLASS@", "USE_RDKAFKA", "@USE_RDKAFKA@", "USE_CAPNP", "@USE_CAPNP@", - "USE_Poco_SQLODBC", "@Poco_SQLODBC_FOUND@", - "USE_Poco_DataODBC", "@Poco_DataODBC_FOUND@", - "USE_Poco_MongoDB", "@Poco_MongoDB_FOUND@", - "USE_Poco_NetSSL", "@Poco_NetSSL_FOUND@", + "USE_POCO_SQLODBC", "@USE_POCO_SQLODBC@", + "USE_POCO_DATAODBC", "@USE_POCO_DATAODBC@", + "USE_POCO_MONGODB", "@USE_POCO_MONGODB@", + "USE_POCO_NETSSL", "@USE_POCO_NETSSL@", nullptr, nullptr }; diff --git a/dbms/src/Dictionaries/DictionarySourceFactory.cpp b/dbms/src/Dictionaries/DictionarySourceFactory.cpp index e77a1189233..963a51c7923 100644 --- a/dbms/src/Dictionaries/DictionarySourceFactory.cpp +++ b/dbms/src/Dictionaries/DictionarySourceFactory.cpp @@ -16,10 +16,10 @@ #include #include -#if Poco_MongoDB_FOUND +#if USE_POCO_MONGODB #include #endif -#if Poco_SQLODBC_FOUND || Poco_DataODBC_FOUND +#if USE_POCO_SQLODBC || USE_POCO_DATAODBC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wunused-parameter" #include @@ -88,7 +88,7 @@ Block createSampleBlock(const DictionaryStructure & dict_struct) DictionarySourceFactory::DictionarySourceFactory() : log(&Poco::Logger::get("DictionarySourceFactory")) { -#if Poco_SQLODBC_FOUND || Poco_DataODBC_FOUND +#if USE_POCO_SQLODBC || USE_POCO_DATAODBC Poco::Data::ODBC::Connector::registerConnector(); #endif } @@ -139,7 +139,7 @@ DictionarySourcePtr DictionarySourceFactory::create( } else if ("mongodb" == source_type) { -#if Poco_MongoDB_FOUND +#if USE_POCO_MONGODB return std::make_unique(dict_struct, config, config_prefix + ".mongodb", sample_block); #else throw Exception{"Dictionary source of type `mongodb` is disabled because poco library was built without mongodb support.", @@ -148,7 +148,7 @@ DictionarySourcePtr DictionarySourceFactory::create( } else if ("odbc" == source_type) { -#if Poco_SQLODBC_FOUND || Poco_DataODBC_FOUND +#if USE_POCO_SQLODBC || USE_POCO_DATAODBC return std::make_unique(dict_struct, config, config_prefix + ".odbc", sample_block, context); #else throw Exception{"Dictionary source of type `odbc` is disabled because poco library was built without ODBC support.", @@ -168,7 +168,7 @@ DictionarySourcePtr DictionarySourceFactory::create( if (dict_struct.has_expressions) throw Exception{"Dictionary source of type `http` does not support attribute expressions", ErrorCodes::LOGICAL_ERROR}; -#if Poco_NetSSL_FOUND +#if USE_POCO_NETSSL // Used for https queries std::call_once(ssl_init_once, SSLInit); #endif diff --git a/dbms/src/Dictionaries/MongoDBBlockInputStream.cpp b/dbms/src/Dictionaries/MongoDBBlockInputStream.cpp index 20c2d655d85..02fc75976ab 100644 --- a/dbms/src/Dictionaries/MongoDBBlockInputStream.cpp +++ b/dbms/src/Dictionaries/MongoDBBlockInputStream.cpp @@ -1,5 +1,5 @@ #include -#if Poco_MongoDB_FOUND +#if USE_POCO_MONGODB #include #include diff --git a/dbms/src/Dictionaries/MongoDBDictionarySource.cpp b/dbms/src/Dictionaries/MongoDBDictionarySource.cpp index 348e415b201..340d5b81a5b 100644 --- a/dbms/src/Dictionaries/MongoDBDictionarySource.cpp +++ b/dbms/src/Dictionaries/MongoDBDictionarySource.cpp @@ -1,5 +1,5 @@ #include -#if Poco_MongoDB_FOUND +#if USE_POCO_MONGODB #include #pragma GCC diagnostic push diff --git a/dbms/src/IO/HTTPCommon.cpp b/dbms/src/IO/HTTPCommon.cpp index 7ca9058896f..84ee03b679d 100644 --- a/dbms/src/IO/HTTPCommon.cpp +++ b/dbms/src/IO/HTTPCommon.cpp @@ -1,7 +1,7 @@ #include #include -#if Poco_NetSSL_FOUND +#if USE_POCO_NETSSL #include #include #include @@ -30,7 +30,7 @@ std::once_flag ssl_init_once; void SSLInit() { // http://stackoverflow.com/questions/18315472/https-request-in-c-using-poco -#if Poco_NetSSL_FOUND +#if USE_POCO_NETSSL Poco::Net::initializeSSL(); #endif } diff --git a/dbms/src/IO/ReadWriteBufferFromHTTP.cpp b/dbms/src/IO/ReadWriteBufferFromHTTP.cpp index 0ec26f684f1..dfd3cfbdbde 100644 --- a/dbms/src/IO/ReadWriteBufferFromHTTP.cpp +++ b/dbms/src/IO/ReadWriteBufferFromHTTP.cpp @@ -9,7 +9,7 @@ #include #include -#if Poco_NetSSL_FOUND +#if USE_POCO_NETSSL #include #endif @@ -36,7 +36,7 @@ ReadWriteBufferFromHTTP::ReadWriteBufferFromHTTP(const Poco::URI & uri, session { std::unique_ptr( -#if Poco_NetSSL_FOUND +#if USE_POCO_NETSSL is_ssl ? new Poco::Net::HTTPSClientSession : #endif new Poco::Net::HTTPClientSession) diff --git a/dbms/src/Server/Server.cpp b/dbms/src/Server/Server.cpp index 918ea19c92e..6cc6f09799b 100644 --- a/dbms/src/Server/Server.cpp +++ b/dbms/src/Server/Server.cpp @@ -26,6 +26,7 @@ #include #include #include +#include #include #include #include @@ -38,12 +39,9 @@ #include "StatusFile.h" #include "TCPHandlerFactory.h" -#if Poco_NetSSL_FOUND +#if USE_POCO_NETSSL #include #include -#include - - #endif namespace CurrentMetrics @@ -431,7 +429,7 @@ int Server::main(const std::vector & /*args*/) /// HTTPS if (config().has("https_port")) { -#if Poco_NetSSL_FOUND +#if USE_POCO_NETSSL std::call_once(ssl_init_once, SSLInit); Poco::Net::SecureServerSocket socket; @@ -471,7 +469,7 @@ int Server::main(const std::vector & /*args*/) /// TCP with SSL if (config().has("tcp_port_secure")) { -#if Poco_NetSSL_FOUND +#if USE_POCO_NETSSL Poco::Net::SecureServerSocket socket; auto address = socket_bind_listen(socket, listen_host, config().getInt("tcp_port_secure"), /* secure = */ true); socket.setReceiveTimeout(settings.receive_timeout); diff --git a/dbms/src/Storages/registerStorages.cpp b/dbms/src/Storages/registerStorages.cpp index 6f140d92562..651146eee99 100644 --- a/dbms/src/Storages/registerStorages.cpp +++ b/dbms/src/Storages/registerStorages.cpp @@ -23,7 +23,7 @@ void registerStorageJoin(StorageFactory & factory); void registerStorageView(StorageFactory & factory); void registerStorageMaterializedView(StorageFactory & factory); -#if Poco_SQLODBC_FOUND || Poco_DataODBC_FOUND +#if USE_POCO_SQLODBC || USE_POCO_DATAODBC void registerStorageODBC(StorageFactory & factory); #endif @@ -56,7 +56,7 @@ void registerStorages() registerStorageView(factory); registerStorageMaterializedView(factory); - #if Poco_SQLODBC_FOUND || Poco_DataODBC_FOUND + #if USE_POCO_SQLODBC || USE_POCO_DATAODBC registerStorageODBC(factory); #endif diff --git a/dbms/src/TableFunctions/CMakeLists.txt b/dbms/src/TableFunctions/CMakeLists.txt index 4fef8cf3978..b4ba1191ba0 100644 --- a/dbms/src/TableFunctions/CMakeLists.txt +++ b/dbms/src/TableFunctions/CMakeLists.txt @@ -7,12 +7,12 @@ list(REMOVE_ITEM clickhouse_table_functions_headers ITableFunction.h TableFuncti add_library(clickhouse_table_functions ${clickhouse_table_functions_sources}) target_link_libraries(clickhouse_table_functions clickhouse_storages_system dbms ${Poco_Foundation_LIBRARY}) -if (Poco_SQLODBC_FOUND) +if (USE_POCO_SQLODBC) target_link_libraries (clickhouse_table_functions ${Poco_SQLODBC_LIBRARY}) target_include_directories (clickhouse_table_functions PRIVATE ${ODBC_INCLUDE_DIRECTORIES} ${Poco_SQLODBC_INCLUDE_DIRS}) endif () -if (Poco_DataODBC_FOUND) +if (USE_POCO_DATAODBC) target_link_libraries (clickhouse_table_functions ${Poco_DataODBC_LIBRARY}) target_include_directories (clickhouse_table_functions PRIVATE ${ODBC_INCLUDE_DIRECTORIES} ${Poco_DataODBC_INCLUDE_DIRS}) endif () diff --git a/dbms/src/TableFunctions/TableFunctionODBC.cpp b/dbms/src/TableFunctions/TableFunctionODBC.cpp index 333ab0e9c6b..75f73146485 100644 --- a/dbms/src/TableFunctions/TableFunctionODBC.cpp +++ b/dbms/src/TableFunctions/TableFunctionODBC.cpp @@ -1,6 +1,6 @@ #include -#if Poco_SQLODBC_FOUND || Poco_DataODBC_FOUND +#if USE_POCO_SQLODBC || USE_POCO_DATAODBC #include #include diff --git a/dbms/src/TableFunctions/TableFunctionODBC.h b/dbms/src/TableFunctions/TableFunctionODBC.h index eb06a8c5097..ce0ded30555 100644 --- a/dbms/src/TableFunctions/TableFunctionODBC.h +++ b/dbms/src/TableFunctions/TableFunctionODBC.h @@ -1,7 +1,7 @@ #pragma once #include -#if Poco_SQLODBC_FOUND || Poco_DataODBC_FOUND +#if USE_POCO_SQLODBC || USE_POCO_DATAODBC #include diff --git a/dbms/src/TableFunctions/registerTableFunctions.cpp b/dbms/src/TableFunctions/registerTableFunctions.cpp index 776ea491921..0858b44cbb0 100644 --- a/dbms/src/TableFunctions/registerTableFunctions.cpp +++ b/dbms/src/TableFunctions/registerTableFunctions.cpp @@ -13,7 +13,7 @@ void registerTableFunctionNumbers(TableFunctionFactory & factory); void registerTableFunctionCatBoostPool(TableFunctionFactory & factory); void registerTableFunctionFile(TableFunctionFactory & factory); -#if Poco_SQLODBC_FOUND || Poco_DataODBC_FOUND +#if USE_POCO_SQLODBC || USE_POCO_DATAODBC void registerTableFunctionODBC(TableFunctionFactory & factory); #endif @@ -33,7 +33,7 @@ void registerTableFunctions() registerTableFunctionCatBoostPool(factory); registerTableFunctionFile(factory); -#if Poco_SQLODBC_FOUND || Poco_DataODBC_FOUND +#if USE_POCO_SQLODBC || USE_POCO_DATAODBC registerTableFunctionODBC(factory); #endif From a98d33f77fca314e7459742d1740b56e1b302e65 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 21:49:51 +0300 Subject: [PATCH 192/231] CI: added support to build on FreeBSD under Vagrant [#CLICKHOUSE-2] --- ci/README.md | 50 ++++++----- ci/build-clang-from-sources.sh | 4 +- ci/build-gcc-from-sources.sh | 2 +- ci/check-syntax.sh | 2 +- ci/default-config | 4 +- ci/get-sources.sh | 4 +- ci/install-compiler-from-packages.sh | 31 +++---- ci/install-libraries.sh | 10 ++- ci/install-os-packages.sh | 120 +++++++++++++++++++++++++++ ci/jobs/quick-build/README.md | 5 ++ ci/jobs/quick-build/config | 12 --- ci/jobs/quick-build/run.sh | 15 ++++ ci/prepare-docker-image-ubuntu.sh | 2 +- ci/prepare-toolchain.sh | 5 +- ci/prepare-vagrant-image-freebsd.sh | 2 +- ci/run-with-vagrant.sh | 13 +++ 16 files changed, 211 insertions(+), 70 deletions(-) create mode 100755 ci/install-os-packages.sh create mode 100644 ci/jobs/quick-build/README.md delete mode 100644 ci/jobs/quick-build/config create mode 100755 ci/run-with-vagrant.sh diff --git a/ci/README.md b/ci/README.md index 6eeb35c1c25..733cbce80c9 100644 --- a/ci/README.md +++ b/ci/README.md @@ -1,4 +1,4 @@ -### Build and test ClickHouse on various plaforms +## Build and test ClickHouse on various plaforms Quick and dirty scripts. @@ -13,17 +13,23 @@ Another example, check build on ARM 64: ./run-with-docker.sh multiarch/ubuntu-core:arm64-bionic jobs/quick-build/run.sh ``` -Look at `default_config` and `jobs/quick-build/config` +Another example, check build on FreeBSD: +``` +./prepare-vagrant-image-freebsd.sh +./run-with-vagrant.sh freebsd jobs/quick-build/run.sh +``` + +Look at `default_config` and `jobs/quick-build/run.sh` Various possible options. We are not going to automate testing all of them. -##### CPU architectures: +#### CPU architectures: - x86_64; - AArch64. x86_64 is the main CPU architecture. We also have minimal support for AArch64. -##### Operating systems: +#### Operating systems: - Linux; - FreeBSD. @@ -31,7 +37,7 @@ We also target Mac OS X, but it's more difficult to test. Linux is the main. FreeBSD is also supported as production OS. Mac OS is intended only for development and have minimal support: client should work, server should just start. -##### Linux distributions: +#### Linux distributions: For build: - Ubuntu Bionic; - Ubuntu Trusty. @@ -42,83 +48,83 @@ For run: We should support almost any Linux to run ClickHouse. That's why we test also on old distributions. -##### How to obtain sources: +#### How to obtain sources: - use sources from local working copy; - clone sources from github; - download source tarball. -##### Compilers: +#### Compilers: - gcc-7; - gcc-8; - clang-6; - clang-svn. -##### Compiler installation: +#### Compiler installation: - from OS packages; - build from sources. -##### C++ standard library implementation: +#### C++ standard library implementation: - libc++; - libstdc++ with C++11 ABI; - libstdc++ with old ABI. When building with clang, libc++ is used. When building with gcc, we choose libstdc++ with C++11 ABI. -##### Linkers: +#### Linkers: - ldd; - gold; When building with clang on x86_64, ldd is used. Otherwise we use gold. -##### Build types: +#### Build types: - RelWithDebInfo; - Debug; - ASan; - TSan. -##### Build types, extra: +#### Build types, extra: - -g0 for quick build; - enable test coverage; - debug tcmalloc. -##### What to build: +#### What to build: - only `clickhouse` target; - all targets; - debian packages; We also have intent to build RPM and simple tgz packages. -##### Where to get third-party libraries: +#### Where to get third-party libraries: - from contrib directory (submodules); - from OS packages. The only production option is to use libraries from contrib directory. Using libraries from OS packages is discouraged, but we also support this option. -##### Linkage types: +#### Linkage types: - static; - shared; Static linking is the only option for production usage. We also have support for shared linking, but it is indended only for developers. -##### Make tools: +#### Make tools: - make; - ninja. -##### Installation options: +#### Installation options: - run built `clickhouse` binary directly; - install from packages. -##### How to obtain packages: +#### How to obtain packages: - build them; - download from repository. -##### Sanity checks: +#### Sanity checks: - check that clickhouse binary has no dependencies on unexpected shared libraries; - check that source code have no style violations. -##### Tests: +#### Tests: - Functional tests; - Integration tests; - Unit tests; @@ -127,10 +133,10 @@ We also have support for shared linking, but it is indended only for developers. - Tests for external dictionaries (should be moved to integration tests); - Jepsen like tests for quorum inserts (not yet available in opensource). -##### Tests extra: +#### Tests extra: - Run functional tests with Valgrind. -##### Static analyzers: +#### Static analyzers: - CppCheck; - clang-tidy; - Coverity. diff --git a/ci/build-clang-from-sources.sh b/ci/build-clang-from-sources.sh index 64898c5fdc3..7e3793c8148 100755 --- a/ci/build-clang-from-sources.sh +++ b/ci/build-clang-from-sources.sh @@ -4,8 +4,8 @@ set -e -x source default-config # TODO Non debian systems -$SUDO apt-get install -y subversion -apt-cache search cmake3 | grep -P '^cmake3 ' && $SUDO apt-get -y install cmake3 || $SUDO apt-get -y install cmake +./install-os-packages.sh svn +./install-os-packages.sh cmake mkdir "${WORKSPACE}/llvm" diff --git a/ci/build-gcc-from-sources.sh b/ci/build-gcc-from-sources.sh index b41ac0365bd..0734b22335a 100755 --- a/ci/build-gcc-from-sources.sh +++ b/ci/build-gcc-from-sources.sh @@ -3,7 +3,7 @@ set -e -x source default-config -$SUDO apt-get install -y curl +./install-os-packages.sh curl if [[ "${GCC_SOURCES_VERSION}" == "latest" ]]; then GCC_SOURCES_VERSION=$(curl -sSL https://ftpmirror.gnu.org/gcc/ | grep -oE 'gcc-[0-9]+(\.[0-9]+)+' | sort -Vr | head -n1) diff --git a/ci/check-syntax.sh b/ci/check-syntax.sh index c5043ff512c..e95e38346d6 100755 --- a/ci/check-syntax.sh +++ b/ci/check-syntax.sh @@ -3,7 +3,7 @@ set -e -x source default-config -$SUDO apt-get install -y jq +./install-os-packages.sh jq [[ -d "${WORKSPACE}/sources" ]] || die "Run get-sources.sh first" diff --git a/ci/default-config b/ci/default-config index 7837b1fe57d..7cc22758d54 100644 --- a/ci/default-config +++ b/ci/default-config @@ -9,7 +9,7 @@ SCRIPTPATH=$(pwd) WORKSPACE=${SCRIPTPATH}/workspace PROJECT_ROOT=$(cd $SCRIPTPATH/.. && pwd) -# All scripts take no arguments. All arguments must be in config. +# Almost all scripts take no arguments. Arguments should be in config. # get-sources SOURCES_METHOD=local # clone, local, tarball @@ -55,7 +55,7 @@ function die { [[ $EUID -ne 0 ]] && SUDO=sudo -command -v apt-get && $SUDO apt-get update +./install-os-packages.sh prepare # Configuration parameters may be overriden with CONFIG environment variable pointing to config file. [[ -n "$CONFIG" ]] && source $CONFIG diff --git a/ci/get-sources.sh b/ci/get-sources.sh index f09f8c3c812..ee57b0ec27d 100755 --- a/ci/get-sources.sh +++ b/ci/get-sources.sh @@ -4,12 +4,12 @@ set -e -x source default-config if [[ "$SOURCES_METHOD" == "clone" ]]; then - $SUDO apt-get install -y git + ./install-os-packages.sh git SOURCES_DIR="${WORKSPACE}/sources" mkdir -p "${SOURCES_DIR}" git clone --recursive --branch "$SOURCES_BRANCH" "$SOURCES_CLONE_URL" "${SOURCES_DIR}" pushd "${SOURCES_DIR}" - git checkout "$SOURCES_COMMIT" + git checkout --recurse-submodules "$SOURCES_COMMIT" popd elif [[ "$SOURCES_METHOD" == "local" ]]; then ln -f -s "${PROJECT_ROOT}" "${WORKSPACE}/sources" diff --git a/ci/install-compiler-from-packages.sh b/ci/install-compiler-from-packages.sh index c46f09219e7..53909435a06 100755 --- a/ci/install-compiler-from-packages.sh +++ b/ci/install-compiler-from-packages.sh @@ -3,27 +3,20 @@ set -e -x source default-config -# TODO Non debian systems # TODO Install from PPA on older Ubuntu -if [ -f '/etc/lsb-release' ]; then - source /etc/lsb-release - if [[ "$DISTRIB_ID" == "Ubuntu" ]]; then - if [[ "$COMPILER" == "gcc" ]]; then - $SUDO apt-get -y install gcc-${COMPILER_PACKAGE_VERSION} g++-${COMPILER_PACKAGE_VERSION} - export CC=gcc-${COMPILER_PACKAGE_VERSION} - export CXX=g++-${COMPILER_PACKAGE_VERSION} - elif [[ "$COMPILER" == "clang" ]]; then - [[ $(uname -m) == "x86_64" ]] && LLD="lld-${COMPILER_PACKAGE_VERSION}" - $SUDO apt-get -y install clang-${COMPILER_PACKAGE_VERSION} "$LLD" libc++-dev libc++abi-dev - export CC=clang-${COMPILER_PACKAGE_VERSION} - export CXX=clang++-${COMPILER_PACKAGE_VERSION} - else - die "Unknown compiler specified" - fi - else - die "Unknown Linux variant" +./install-os-packages.sh ${COMPILER}-${COMPILER_PACKAGE_VERSION} + +if [[ "$COMPILER" == "gcc" ]]; then + if command -v gcc-${COMPILER_PACKAGE_VERSION}; then export CC=gcc-${COMPILER_PACKAGE_VERSION} CXX=g++-${COMPILER_PACKAGE_VERSION}; + elif command -v gcc${COMPILER_PACKAGE_VERSION}; then export CC=gcc${COMPILER_PACKAGE_VERSION} CXX=g++${COMPILER_PACKAGE_VERSION}; + elif command -v gcc; then export CC=gcc CXX=g++; + fi +elif [[ "$COMPILER" == "clang" ]]; then + if command -v clang-${COMPILER_PACKAGE_VERSION}; then export CC=clang-${COMPILER_PACKAGE_VERSION} CXX=clang++-${COMPILER_PACKAGE_VERSION}; + elif command -v clang${COMPILER_PACKAGE_VERSION}; then export CC=clang${COMPILER_PACKAGE_VERSION} CXX=clang++${COMPILER_PACKAGE_VERSION}; + elif command -v clang; then export CC=clang CXX=clang++; fi else - die "Unknown OS" + die "Unknown compiler specified" fi diff --git a/ci/install-libraries.sh b/ci/install-libraries.sh index 7070083d57e..8f881c2db61 100755 --- a/ci/install-libraries.sh +++ b/ci/install-libraries.sh @@ -3,10 +3,12 @@ set -e -x source default-config -# TODO Non-debian systems - -$SUDO apt-get -y install libssl-dev libicu-dev libreadline-dev libmysqlclient-dev unixodbc-dev +./install-os-packages.sh libssl-dev +./install-os-packages.sh libicu-dev +./install-os-packages.sh libreadline-dev +./install-os-packages.sh libmysqlclient-dev +./install-os-packages.sh libunixodbc-dev if [[ "$ENABLE_EMBEDDED_COMPILER" == 1 && "$USE_LLVM_LIBRARIES_FROM_SYSTEM" == 1 ]]; then - $SUDO apt-get -y install liblld-5.0-dev libclang-5.0-dev + ./install-os-packages.sh llvm-libs-5.0 fi diff --git a/ci/install-os-packages.sh b/ci/install-os-packages.sh new file mode 100755 index 00000000000..46502094507 --- /dev/null +++ b/ci/install-os-packages.sh @@ -0,0 +1,120 @@ +#!/usr/bin/env bash +set -e -x + +# Dispatches package installation on various OS and distributives + +WHAT=$1 + +[[ $EUID -ne 0 ]] && SUDO=sudo + +command -v apt-get && PACKAGE_MANAGER=apt +command -v yum && PACKAGE_MANAGER=yum +command -v pkg && PACKAGE_MANAGER=pkg + + +case $PACKAGE_MANAGER in + apt) + case $WHAT in + prepare) + $SUDO apt-get update + ;; + svn) + $SUDO apt-get install -y subversion + ;; + gcc*) + $SUDO apt-get install -y $WHAT ${WHAT/cc/++} + ;; + clang*) + $SUDO apt-get install -y $WHAT libc++-dev libc++abi-dev + [[ $(uname -m) == "x86_64" ]] && $SUDO apt-get install -y ${WHAT/clang/lld} + ;; + git) + $SUDO apt-get install -y git + ;; + cmake) + $SUDO apt-get install -y cmake3 || $SUDO apt-get install -y cmake + ;; + curl) + $SUDO apt-get install -y curl + ;; + jq) + $SUDO apt-get install -y jq + ;; + libssl-dev) + $SUDO apt-get install -y libssl-dev + ;; + libicu-dev) + $SUDO apt-get install -y libicu-dev + ;; + libreadline-dev) + $SUDO apt-get install -y libreadline-dev + ;; + libunixodbc-dev) + $SUDO apt-get install -y unixodbc-dev + ;; + libmysqlclient-dev) + $SUDO apt-get install -y libmysqlclient-dev + ;; + llvm-libs*) + $SUDO apt-get -y install ${WHAT/llvm-libs/liblld}-dev ${WHAT/llvm-libs/libclang}-dev + ;; + qemu-user-static) + $SUDO apt-get install -y quemu-user-static + ;; + vagrant-virtualbox) + $SUDO apt-get install -y vagrant virtualbox + ;; + *) + echo "Unknown package"; exit 1; + ;; + esac + ;; + pkg) + case $WHAT in + prepare) + ;; + svn) + $SUDO pkg install -y subversion + ;; + gcc*) + $SUDO pkg install -y ${WHAT/-/} + ;; + clang*) + $SUDO pkg install -y clang-devel + ;; + git) + $SUDO pkg install -y git + ;; + cmake) + $SUDO pkg install -y cmake + ;; + curl) + $SUDO pkg install -y curl + ;; + jq) + $SUDO pkg install -y jq + ;; + libssl-dev) + $SUDO pkg install -y openssl + ;; + libicu-dev) + $SUDO pkg install -y icu + ;; + libreadline-dev) + $SUDO pkg install -y readline + ;; + libunixodbc-dev) + $SUDO pkg install -y unixODBC + ;; + libmysqlclient-dev) + $SUDO pkg install -y mysql57-client + ;; + *) + echo "Unknown package"; exit 1; + ;; + esac + ;; + *) + echo "Unknown distributive"; exit 1; + ;; +esac diff --git a/ci/jobs/quick-build/README.md b/ci/jobs/quick-build/README.md new file mode 100644 index 00000000000..803acae0f93 --- /dev/null +++ b/ci/jobs/quick-build/README.md @@ -0,0 +1,5 @@ +## Build with debug mode and without many libraries + +This job is intended as first check that build is not broken on wide variety of platforms. + +Results of this build are not intended for production usage. diff --git a/ci/jobs/quick-build/config b/ci/jobs/quick-build/config deleted file mode 100644 index c45d9690c7a..00000000000 --- a/ci/jobs/quick-build/config +++ /dev/null @@ -1,12 +0,0 @@ -SOURCES_METHOD=local -COMPILER=clang -COMPILER_INSTALL_METHOD=packages -COMPILER_PACKAGE_VERSION=6.0 -USE_LLVM_LIBRARIES_FROM_SYSTEM=0 -BUILD_METHOD=normal -BUILD_TARGETS=clickhouse -BUILD_TYPE=Debug -ENABLE_EMBEDDED_COMPILER=0 -CMAKE_FLAGS="-D CMAKE_C_FLAGS_ADD=-g0 -D CMAKE_CXX_FLAGS_ADD=-g0 -D ENABLE_TCMALLOC=0 -D ENABLE_CAPNP=0 -D ENABLE_RDKAFKA=0 -D ENABLE_UNWIND=0 -D ENABLE_ICU=0" - -# TODO it doesn't build with -D ENABLE_NETSSL=0 -D ENABLE_MONGODB=0 -D ENABLE_MYSQL=0 -D ENABLE_DATA_ODBC=0 diff --git a/ci/jobs/quick-build/run.sh b/ci/jobs/quick-build/run.sh index 0872b685e7c..744511e602b 100755 --- a/ci/jobs/quick-build/run.sh +++ b/ci/jobs/quick-build/run.sh @@ -12,6 +12,21 @@ cd "$(dirname $0)"/../.. . default-config +SOURCES_METHOD=local +COMPILER=clang +COMPILER_INSTALL_METHOD=packages +COMPILER_PACKAGE_VERSION=6.0 +USE_LLVM_LIBRARIES_FROM_SYSTEM=0 +BUILD_METHOD=normal +BUILD_TARGETS=clickhouse +BUILD_TYPE=Debug +ENABLE_EMBEDDED_COMPILER=0 + +CMAKE_FLAGS="-D CMAKE_C_FLAGS_ADD=-g0 -D CMAKE_CXX_FLAGS_ADD=-g0 -D ENABLE_TCMALLOC=0 -D ENABLE_CAPNP=0 -D ENABLE_RDKAFKA=0 -D ENABLE_UNWIND=0 -D ENABLE_ICU=0" +# TODO it doesn't build with -D ENABLE_NETSSL=0 -D ENABLE_MONGODB=0 -D ENABLE_MYSQL=0 -D ENABLE_DATA_ODBC=0 + +[[ $(uname) == "FreeBSD" ]] && COMPILER_PACKAGE_VERSION=devel + . get-sources.sh . prepare-toolchain.sh . install-libraries.sh diff --git a/ci/prepare-docker-image-ubuntu.sh b/ci/prepare-docker-image-ubuntu.sh index 1b3d3bd18f6..2880d7fc1e6 100755 --- a/ci/prepare-docker-image-ubuntu.sh +++ b/ci/prepare-docker-image-ubuntu.sh @@ -6,7 +6,7 @@ source default-config ./check-docker.sh # http://fl47l1n3.net/2015/12/24/binfmt/ -$SUDO apt-get -y install qemu-user-static +./install-os-packages.sh qemu-user-static pushd docker-multiarch diff --git a/ci/prepare-toolchain.sh b/ci/prepare-toolchain.sh index f90cb4fca4d..4718a854860 100755 --- a/ci/prepare-toolchain.sh +++ b/ci/prepare-toolchain.sh @@ -3,11 +3,10 @@ set -e -x source default-config -# TODO Non debian systems -apt-cache search cmake3 | grep -P '^cmake3 ' && $SUDO apt-get -y install cmake3 || $SUDO apt-get -y install cmake +./install-os-packages.sh cmake if [[ "$COMPILER_INSTALL_METHOD" == "packages" ]]; then - . install-compiler-from-packages.sh; + . install-compiler-from-packages.sh elif [[ "$COMPILER_INSTALL_METHOD" == "sources" ]]; then . install-compiler-from-sources.sh else diff --git a/ci/prepare-vagrant-image-freebsd.sh b/ci/prepare-vagrant-image-freebsd.sh index 81d021ca31f..6b5cfdd619c 100755 --- a/ci/prepare-vagrant-image-freebsd.sh +++ b/ci/prepare-vagrant-image-freebsd.sh @@ -3,7 +3,7 @@ set -e -x source default-config -$SUDO apt-get -y install vagrant virtualbox +./install-os-packages.sh vagrant-virtualbox pushd "vagrant-freebsd" vagrant up diff --git a/ci/run-with-vagrant.sh b/ci/run-with-vagrant.sh new file mode 100755 index 00000000000..67275c248d0 --- /dev/null +++ b/ci/run-with-vagrant.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env bash +set -e -x + +[[ -r "vagrant-${1}/vagrant-ssh" ]] || die "Run prepare-vagrant-image-... first." + +pushd vagrant-$1 + +shopt -s extglob +vagrant ssh -c "mkdir ClickHouse" +scp -F vagrant-ssh -r ../../../ClickHouse/!(*build*) default:~/ClickHouse +vagrant ssh -c "cd ClickHouse/ci; $2" + +popd From 0d89ef7ed79fd916d89610bdd89065fdf648fc3f Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 21:54:40 +0300 Subject: [PATCH 193/231] Addition to prev. revision [#CLICKHOUSE-2] --- ci/install-os-packages.sh | 2 +- ci/run-with-vagrant.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/ci/install-os-packages.sh b/ci/install-os-packages.sh index 46502094507..b06d7d2d7c4 100755 --- a/ci/install-os-packages.sh +++ b/ci/install-os-packages.sh @@ -59,7 +59,7 @@ case $PACKAGE_MANAGER in $SUDO apt-get -y install ${WHAT/llvm-libs/liblld}-dev ${WHAT/llvm-libs/libclang}-dev ;; qemu-user-static) - $SUDO apt-get install -y quemu-user-static + $SUDO apt-get install -y qemu-user-static ;; vagrant-virtualbox) $SUDO apt-get install -y vagrant virtualbox diff --git a/ci/run-with-vagrant.sh b/ci/run-with-vagrant.sh index 67275c248d0..6726f1bfdcf 100755 --- a/ci/run-with-vagrant.sh +++ b/ci/run-with-vagrant.sh @@ -6,7 +6,7 @@ set -e -x pushd vagrant-$1 shopt -s extglob -vagrant ssh -c "mkdir ClickHouse" +vagrant ssh -c "mkdir -p ClickHouse" scp -F vagrant-ssh -r ../../../ClickHouse/!(*build*) default:~/ClickHouse vagrant ssh -c "cd ClickHouse/ci; $2" From a693cb333355a6e494075d5719f72347b7956089 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 21:57:07 +0300 Subject: [PATCH 194/231] CI: added support to build on FreeBSD under Vagrant [#CLICKHOUSE-2] --- ci/jobs/quick-build/run.sh | 1 - 1 file changed, 1 deletion(-) diff --git a/ci/jobs/quick-build/run.sh b/ci/jobs/quick-build/run.sh index 744511e602b..c4aadbc3862 100755 --- a/ci/jobs/quick-build/run.sh +++ b/ci/jobs/quick-build/run.sh @@ -7,7 +7,6 @@ set -e -x # or: # ./run-with-docker.sh ubuntu:bionic jobs/quick-build/run.sh -CONFIG="$(dirname $0)"/config cd "$(dirname $0)"/../.. . default-config From 6b9160665b218960c79ac7e8343ead7b8f6373fe Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 22:03:19 +0300 Subject: [PATCH 195/231] Addition to prev. revision [#CLICKHOUSE-2] --- ci/default-config | 2 +- ci/install-os-packages.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/ci/default-config b/ci/default-config index 7cc22758d54..26e82ddcceb 100644 --- a/ci/default-config +++ b/ci/default-config @@ -44,7 +44,7 @@ DOCKER_UBUNTU_TAG_ARCH=arm64 # How the architecture is named in Docker DOCKER_UBUNTU_QEMU_VER=v2.9.1 DOCKER_UBUNTU_REPO=multiarch/ubuntu-core -THREADS=$(grep -c ^processor /proc/cpuinfo || nproc || sysctl -a | grep -F 'hw.ncpu') +THREADS=$(grep -c ^processor /proc/cpuinfo || nproc || sysctl -a | grep -F 'hw.ncpu' | grep -oE '[0-9]+') # All scripts should return 0 in case of success, 1 in case of permanent error, # 2 in case of temporary error, any other code in case of permanent error. diff --git a/ci/install-os-packages.sh b/ci/install-os-packages.sh index b06d7d2d7c4..23a6b1a49ff 100755 --- a/ci/install-os-packages.sh +++ b/ci/install-os-packages.sh @@ -104,7 +104,7 @@ case $PACKAGE_MANAGER in $SUDO pkg install -y readline ;; libunixodbc-dev) - $SUDO pkg install -y unixODBC + $SUDO pkg install -y unixODBC libltdl ;; libmysqlclient-dev) $SUDO pkg install -y mysql57-client From 8d74ea3dba7350927dfc32a50b1587bafb2b01a3 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 22:13:00 +0300 Subject: [PATCH 196/231] Addition to prev. revision [#CLICKHOUSE-2] --- ci/install-os-packages.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ci/install-os-packages.sh b/ci/install-os-packages.sh index 23a6b1a49ff..54bc5782754 100755 --- a/ci/install-os-packages.sh +++ b/ci/install-os-packages.sh @@ -26,7 +26,7 @@ case $PACKAGE_MANAGER in ;; clang*) $SUDO apt-get install -y $WHAT libc++-dev libc++abi-dev - [[ $(uname -m) == "x86_64" ]] && $SUDO apt-get install -y ${WHAT/clang/lld} + [[ $(uname -m) == "x86_64" ]] && $SUDO apt-get install -y ${WHAT/clang/lld} || true ;; git) $SUDO apt-get install -y git From 3943cfb11e16a12d15f8f517924f58badde55509 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 22:14:37 +0300 Subject: [PATCH 197/231] Addition to prev. revision [#CLICKHOUSE-2] --- dbms/src/Interpreters/DNSCacheUpdater.h | 3 +++ 1 file changed, 3 insertions(+) diff --git a/dbms/src/Interpreters/DNSCacheUpdater.h b/dbms/src/Interpreters/DNSCacheUpdater.h index 4c1939d2f8e..ad57f37b5f6 100644 --- a/dbms/src/Interpreters/DNSCacheUpdater.h +++ b/dbms/src/Interpreters/DNSCacheUpdater.h @@ -1,5 +1,8 @@ #pragma once + #include +#include +#include namespace DB From b4ef4a13a5a2d5cff30851cdb5a87bfc3ec5b4e4 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 22:15:46 +0300 Subject: [PATCH 198/231] Addition to prev. revision [#CLICKHOUSE-2] --- ci/run-with-vagrant.sh | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ci/run-with-vagrant.sh b/ci/run-with-vagrant.sh index 6726f1bfdcf..6d7b10ea410 100755 --- a/ci/run-with-vagrant.sh +++ b/ci/run-with-vagrant.sh @@ -5,7 +5,9 @@ set -e -x pushd vagrant-$1 +shopt -s dotglob shopt -s extglob + vagrant ssh -c "mkdir -p ClickHouse" scp -F vagrant-ssh -r ../../../ClickHouse/!(*build*) default:~/ClickHouse vagrant ssh -c "cd ClickHouse/ci; $2" From 3000d399f935500c273a185a0e9ff66695bcaf80 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 22:23:32 +0300 Subject: [PATCH 199/231] Addition to prev. revision [#CLICKHOUSE-2] --- ci/install-os-packages.sh | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/ci/install-os-packages.sh b/ci/install-os-packages.sh index 54bc5782754..e3e7e88044a 100755 --- a/ci/install-os-packages.sh +++ b/ci/install-os-packages.sh @@ -52,11 +52,11 @@ case $PACKAGE_MANAGER in libunixodbc-dev) $SUDO apt-get install -y unixodbc-dev ;; - libmysqlclient-dev) - $SUDO apt-get install -y libmysqlclient-dev + libmariadbclient-dev) + $SUDO apt-get install -y libmariadbclient-dev ;; llvm-libs*) - $SUDO apt-get -y install ${WHAT/llvm-libs/liblld}-dev ${WHAT/llvm-libs/libclang}-dev + $SUDO apt-get install -y ${WHAT/llvm-libs/liblld}-dev ${WHAT/llvm-libs/libclang}-dev ;; qemu-user-static) $SUDO apt-get install -y qemu-user-static @@ -106,8 +106,8 @@ case $PACKAGE_MANAGER in libunixodbc-dev) $SUDO pkg install -y unixODBC libltdl ;; - libmysqlclient-dev) - $SUDO pkg install -y mysql57-client + libmariadbclient-dev) + $SUDO pkg install -y mariadb102-client ;; *) echo "Unknown package"; exit 1; From 90cb2f45e578c91eee5b311f2bf35fe654e15401 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 22:29:07 +0300 Subject: [PATCH 200/231] Addition to prev. revision [#CLICKHOUSE-2] --- ci/prepare-vagrant-image-freebsd.sh | 1 - 1 file changed, 1 deletion(-) diff --git a/ci/prepare-vagrant-image-freebsd.sh b/ci/prepare-vagrant-image-freebsd.sh index 6b5cfdd619c..16c5e58c7c5 100755 --- a/ci/prepare-vagrant-image-freebsd.sh +++ b/ci/prepare-vagrant-image-freebsd.sh @@ -9,5 +9,4 @@ pushd "vagrant-freebsd" vagrant up vagrant ssh-config > vagrant-ssh ssh -F vagrant-ssh default 'uname -a' -scp -F vagrant-ssh -r ../../ci default:~ popd From 22dfdfc7e5f64a789b7faa0b92cedf3311702c87 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 22:38:11 +0300 Subject: [PATCH 201/231] Partially reverted bad modification #2363 --- ci/build-normal.sh | 3 --- 1 file changed, 3 deletions(-) diff --git a/ci/build-normal.sh b/ci/build-normal.sh index aa77a1ad28b..e165489cc9d 100755 --- a/ci/build-normal.sh +++ b/ci/build-normal.sh @@ -3,9 +3,6 @@ set -e -x source default-config -ccache -M 32G -ccache -s - [[ -d "${WORKSPACE}/sources" ]] || die "Run get-sources.sh first" mkdir -p "${WORKSPACE}/build" From 33c0b15615d4e7566eb84d5d34106cdd028c47e2 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 22:39:54 +0300 Subject: [PATCH 202/231] Addition to prev. revision [#CLICKHOUSE-2] --- ci/run-with-vagrant.sh | 1 - 1 file changed, 1 deletion(-) diff --git a/ci/run-with-vagrant.sh b/ci/run-with-vagrant.sh index 6d7b10ea410..74ad3e99ead 100755 --- a/ci/run-with-vagrant.sh +++ b/ci/run-with-vagrant.sh @@ -5,7 +5,6 @@ set -e -x pushd vagrant-$1 -shopt -s dotglob shopt -s extglob vagrant ssh -c "mkdir -p ClickHouse" From 7edae472820614d1ff17e9aa5f34f1348c072397 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 22:46:38 +0300 Subject: [PATCH 203/231] Addition to prev. revision [#CLICKHOUSE-2] --- ci/run-with-vagrant.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ci/run-with-vagrant.sh b/ci/run-with-vagrant.sh index 74ad3e99ead..2568292847f 100755 --- a/ci/run-with-vagrant.sh +++ b/ci/run-with-vagrant.sh @@ -8,7 +8,7 @@ pushd vagrant-$1 shopt -s extglob vagrant ssh -c "mkdir -p ClickHouse" -scp -F vagrant-ssh -r ../../../ClickHouse/!(*build*) default:~/ClickHouse +scp -F vagrant-ssh -r ../../!(*build*) default:~/ClickHouse vagrant ssh -c "cd ClickHouse/ci; $2" popd From b453e2dde958096d226c39b333ea8b539b2377b2 Mon Sep 17 00:00:00 2001 From: topvisor Date: Mon, 14 May 2018 22:58:49 +0300 Subject: [PATCH 204/231] file() function's description (#2361) * Create file.md * Update file.md * Update file.md * Update settings.md * Update file.md * Update settings.md * Update file.md * Update file.md * Update settings.md * Update file.md * Update settings.md * Update file.md * Update settings.md * Update settings.md * Update file.md * Update file.md * Update file.md * Update file.md * Update file.md * Update file.md * Update settings.md * Update file.md * Update settings.md * Update settings.md * Update settings.md * Create file.md * Update mkdocs_ru.yml * Update mkdocs_en.yml * Update settings.md * Update settings.md * Update mkdocs_en.yml * Update mkdocs_ru.yml * Update mkdocs_en.yml * Update mkdocs_en.yml * Update file.md * Update file.md --- docs/en/operations/server_settings/settings.md | 10 ++++++++++ docs/en/table_functions/file.md | 18 ++++++++++++++++++ docs/mkdocs_en.yml | 3 ++- docs/mkdocs_ru.yml | 3 ++- docs/ru/operations/server_settings/settings.md | 10 ++++++++++ docs/ru/table_functions/file.md | 18 ++++++++++++++++++ 6 files changed, 60 insertions(+), 2 deletions(-) create mode 100644 docs/en/table_functions/file.md create mode 100644 docs/ru/table_functions/file.md diff --git a/docs/en/operations/server_settings/settings.md b/docs/en/operations/server_settings/settings.md index 39858c27e72..99419345600 100644 --- a/docs/en/operations/server_settings/settings.md +++ b/docs/en/operations/server_settings/settings.md @@ -657,6 +657,16 @@ The uncompressed cache is advantageous for very short queries in individual case 8589934592 ``` +## user_files_path + +A catalog with user files. Used in a [file()](../../table_functions/file.md#table_functions-file) table function. + +**Example** + +```xml +/var/lib/clickhouse/user_files/ +``` + ## users_config diff --git a/docs/en/table_functions/file.md b/docs/en/table_functions/file.md new file mode 100644 index 00000000000..2760f7e56c2 --- /dev/null +++ b/docs/en/table_functions/file.md @@ -0,0 +1,18 @@ + + +# file + +`file(path, format, structure)` - returns a table created from a path file with a format type, with columns specified in structure. + +path - a relative path to a file from [user_files_path](../operations/server_settings/settings.md#user_files_path). + +format - file [format](../formats/index.md). + +structure - table structure in 'UserID UInt64, URL String' format. Determines column names and types. + +**Example** + +```sql +-- getting the first 10 lines of a table that contains 3 columns of UInt32 type from a CSV file +SELECT * FROM file('test.csv', 'CSV', 'column1 UInt32, column2 UInt32, column3 UInt32') LIMIT 10 +``` diff --git a/docs/mkdocs_en.yml b/docs/mkdocs_en.yml index 08209f90550..566557dd095 100644 --- a/docs/mkdocs_en.yml +++ b/docs/mkdocs_en.yml @@ -122,9 +122,10 @@ pages: - 'Table functions': - 'Introduction': 'table_functions/index.md' - - 'remote': 'table_functions/remote.md' + - 'file': 'table_functions/file.md' - 'merge': 'table_functions/merge.md' - 'numbers': 'table_functions/numbers.md' + - 'remote': 'table_functions/remote.md' - 'Formats': - 'Introduction': 'formats/index.md' diff --git a/docs/mkdocs_ru.yml b/docs/mkdocs_ru.yml index 2e8eae30640..8207ebe5f53 100644 --- a/docs/mkdocs_ru.yml +++ b/docs/mkdocs_ru.yml @@ -122,9 +122,10 @@ pages: - 'Табличные функции': - 'Введение': 'table_functions/index.md' - - 'remote': 'table_functions/remote.md' + - 'file': 'table_functions/file.md' - 'merge': 'table_functions/merge.md' - 'numbers': 'table_functions/numbers.md' + - 'remote': 'table_functions/remote.md' - 'Форматы': - 'Введение': 'formats/index.md' diff --git a/docs/ru/operations/server_settings/settings.md b/docs/ru/operations/server_settings/settings.md index a96f3df3666..9398803c4f5 100644 --- a/docs/ru/operations/server_settings/settings.md +++ b/docs/ru/operations/server_settings/settings.md @@ -660,6 +660,16 @@ ClickHouse проверит условия `min_part_size` и `min_part_size_rat 8589934592 ``` +## user_files_path + +Каталог с пользовательскими файлами. Используется в табличной функции [file()](../../table_functions/file.md#table_functions-file). + +**Пример** + +```xml +/var/lib/clickhouse/user_files/ +``` + ## users_config diff --git a/docs/ru/table_functions/file.md b/docs/ru/table_functions/file.md new file mode 100644 index 00000000000..88bb201eb9b --- /dev/null +++ b/docs/ru/table_functions/file.md @@ -0,0 +1,18 @@ + + +# file + +`file(path, format, structure)` - возвращает таблицу со столбцами, указанными в structure, созданную из файла path типа format. + +path - относительный путь до файла от [user_files_path](../operations/server_settings/settings.md#user_files_path). + +format - [формат](../formats/index.md) файла. + +structure - структура таблицы в форме 'UserID UInt64, URL String'. Определяет имена и типы столбцов. + +**Пример** + +```sql +-- получение первых 10 строк таблицы, состоящей из трёх колонок типа UInt32 из CSV файла +SELECT * FROM file('test.csv', 'CSV', 'column1 UInt32, column2 UInt32, column3 UInt32') LIMIT 10 +``` From 28a4513ec3160accb348b3c4a7ff616d45b10c68 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Mon, 14 May 2018 23:41:50 +0300 Subject: [PATCH 205/231] Addition to prev. revision [#CLICKHOUSE-2] --- ci/install-os-packages.sh | 2 ++ ci/run-with-vagrant.sh | 2 +- cmake/find_execinfo.cmake | 3 +++ 3 files changed, 6 insertions(+), 1 deletion(-) diff --git a/ci/install-os-packages.sh b/ci/install-os-packages.sh index e3e7e88044a..9198f64852d 100755 --- a/ci/install-os-packages.sh +++ b/ci/install-os-packages.sh @@ -78,9 +78,11 @@ case $PACKAGE_MANAGER in ;; gcc*) $SUDO pkg install -y ${WHAT/-/} + export COMPILER_PATH=/usr/local/bin # Otherwise ld cannot link some binaries. ;; clang*) $SUDO pkg install -y clang-devel + export COMPILER_PATH=/usr/local/bin ;; git) $SUDO pkg install -y git diff --git a/ci/run-with-vagrant.sh b/ci/run-with-vagrant.sh index 2568292847f..620d38071eb 100755 --- a/ci/run-with-vagrant.sh +++ b/ci/run-with-vagrant.sh @@ -8,7 +8,7 @@ pushd vagrant-$1 shopt -s extglob vagrant ssh -c "mkdir -p ClickHouse" -scp -F vagrant-ssh -r ../../!(*build*) default:~/ClickHouse +scp -q -F vagrant-ssh -r ../../!(*build*) default:~/ClickHouse vagrant ssh -c "cd ClickHouse/ci; $2" popd diff --git a/cmake/find_execinfo.cmake b/cmake/find_execinfo.cmake index 6d7428a166f..05dd72dbb3d 100644 --- a/cmake/find_execinfo.cmake +++ b/cmake/find_execinfo.cmake @@ -1,6 +1,9 @@ if (ARCH_FREEBSD) find_library (EXECINFO_LIBRARY execinfo) + find_library (ELF_LIBRARY elf) message (STATUS "Using execinfo: ${EXECINFO_LIBRARY}") + message (STATUS "Using elf: ${ELF_LIBRARY}") else () set (EXECINFO_LIBRARY "") + set (ELF_LIBRARY "") endif () From 3e79e8974f664b1992dbff56cd56b70f74d66cde Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Tue, 15 May 2018 00:37:56 +0300 Subject: [PATCH 206/231] Addition to prev. revision [#CLICKHOUSE-2] --- ci/install-os-packages.sh | 2 -- ci/jobs/quick-build/run.sh | 2 +- dbms/CMakeLists.txt | 1 + libs/libdaemon/CMakeLists.txt | 2 +- 4 files changed, 3 insertions(+), 4 deletions(-) diff --git a/ci/install-os-packages.sh b/ci/install-os-packages.sh index 9198f64852d..e3e7e88044a 100755 --- a/ci/install-os-packages.sh +++ b/ci/install-os-packages.sh @@ -78,11 +78,9 @@ case $PACKAGE_MANAGER in ;; gcc*) $SUDO pkg install -y ${WHAT/-/} - export COMPILER_PATH=/usr/local/bin # Otherwise ld cannot link some binaries. ;; clang*) $SUDO pkg install -y clang-devel - export COMPILER_PATH=/usr/local/bin ;; git) $SUDO pkg install -y git diff --git a/ci/jobs/quick-build/run.sh b/ci/jobs/quick-build/run.sh index fab36079b61..5fe57457645 100755 --- a/ci/jobs/quick-build/run.sh +++ b/ci/jobs/quick-build/run.sh @@ -23,7 +23,7 @@ ENABLE_EMBEDDED_COMPILER=0 CMAKE_FLAGS="-D CMAKE_C_FLAGS_ADD=-g0 -D CMAKE_CXX_FLAGS_ADD=-g0 -D ENABLE_TCMALLOC=0 -D ENABLE_CAPNP=0 -D ENABLE_RDKAFKA=0 -D ENABLE_UNWIND=0 -D ENABLE_ICU=0 -D ENABLE_POCO_MONGODB=0 -D ENABLE_POCO_NETSSL=0 -D ENABLE_POCO_ODBC=0 -D ENABLE_MYSQL=0" -[[ $(uname) == "FreeBSD" ]] && COMPILER_PACKAGE_VERSION=devel +[[ $(uname) == "FreeBSD" ]] && COMPILER_PACKAGE_VERSION=devel && export COMPILER_PATH=/usr/local/bin . get-sources.sh . prepare-toolchain.sh diff --git a/dbms/CMakeLists.txt b/dbms/CMakeLists.txt index 1f921beeda6..bb83e67cd0a 100644 --- a/dbms/CMakeLists.txt +++ b/dbms/CMakeLists.txt @@ -147,6 +147,7 @@ target_link_libraries (clickhouse_common_io ${Poco_Data_LIBRARY} ${ZLIB_LIBRARIES} ${EXECINFO_LIBRARY} + ${ELF_LIBRARY} ${Boost_SYSTEM_LIBRARY} ${CMAKE_DL_LIBS} ) diff --git a/libs/libdaemon/CMakeLists.txt b/libs/libdaemon/CMakeLists.txt index 6f31c4e5b38..a0e4e7d2733 100644 --- a/libs/libdaemon/CMakeLists.txt +++ b/libs/libdaemon/CMakeLists.txt @@ -17,4 +17,4 @@ endif () target_include_directories (daemon PUBLIC include) target_include_directories (daemon PRIVATE ${ClickHouse_SOURCE_DIR}/libs/libpocoext/include) -target_link_libraries (daemon clickhouse_common_io clickhouse_common_config ${EXECINFO_LIBRARY}) +target_link_libraries (daemon clickhouse_common_io clickhouse_common_config ${EXECINFO_LIBRARY} ${ELF_LIBRARY}) From b746ba2115d14c6557ba8372c09f2d70d3411f86 Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Tue, 15 May 2018 01:41:19 +0300 Subject: [PATCH 207/231] Add ru changelog for 1.1.54381 --- CHANGELOG_RU.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/CHANGELOG_RU.md b/CHANGELOG_RU.md index d6b0c1e1ddb..c05d70b0eef 100644 --- a/CHANGELOG_RU.md +++ b/CHANGELOG_RU.md @@ -1,3 +1,8 @@ +# ClickHouse release 1.1.54381, 2018-05-14 + +## Исправление ошибок: +* Исправлена ошибка, приводящая к "утеканию" метаданных в ZooKeeper при потере соединения с сервером ZooKeeper. + # ClickHouse release 1.1.54380, 2018-04-21 ## Новые возможности: From 9a2fc55123104343530a1ab079af5ead5b5386db Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Tue, 15 May 2018 01:43:43 +0300 Subject: [PATCH 208/231] Add en changelog for 1.1.54381 --- CHANGELOG.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index b5a9928e8ff..f59e58846d3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,8 @@ +# ClickHouse release 1.1.54381, 2018-05-14 + +## Bug fixes: +* Fixed a nodes leak in ZooKeeper when ClickHouse loses connection to ZooKeeper server. + # ClickHouse release 1.1.54380, 2018-04-21 ## New features: From 2511a4784b3de21734cbdb93e5f1ccb4cb53df69 Mon Sep 17 00:00:00 2001 From: sundy-li <543950155@qq.com> Date: Thu, 10 May 2018 17:23:38 +0800 Subject: [PATCH 209/231] Support data write to StorageMySQL table --- dbms/src/Interpreters/Settings.h | 2 + dbms/src/Storages/StorageMySQL.cpp | 101 +++++++++++++++++++++++++++++ dbms/src/Storages/StorageMySQL.h | 2 + 3 files changed, 105 insertions(+) diff --git a/dbms/src/Interpreters/Settings.h b/dbms/src/Interpreters/Settings.h index 0d0e47c598c..ff275938ad2 100644 --- a/dbms/src/Interpreters/Settings.h +++ b/dbms/src/Interpreters/Settings.h @@ -87,6 +87,8 @@ struct Settings \ M(SettingBool, merge_tree_uniform_read_distribution, true, "Distribute read from MergeTree over threads evenly, ensuring stable average execution time of each thread within one read operation.") \ \ + M(SettingUInt64, mysql_max_rows_to_insert, 65536, "The maximum number of rows in MySQL batch insertion of the MySQL storage engine") \ + \ M(SettingUInt64, optimize_min_equality_disjunction_chain_length, 3, "The minimum length of the expression `expr = x1 OR ... expr = xN` for optimization ") \ \ M(SettingUInt64, min_bytes_to_use_direct_io, 0, "The minimum number of bytes for input/output operations is bypassing the page cache. 0 - disabled.") \ diff --git a/dbms/src/Storages/StorageMySQL.cpp b/dbms/src/Storages/StorageMySQL.cpp index 3c63ebe4ca5..4961398d62c 100644 --- a/dbms/src/Storages/StorageMySQL.cpp +++ b/dbms/src/Storages/StorageMySQL.cpp @@ -1,12 +1,20 @@ #include #if USE_MYSQL + #include #include #include #include +#include +#include +#include +#include #include +#include +#include #include +#include namespace DB @@ -56,6 +64,99 @@ BlockInputStreams StorageMySQL::read( } +class StorageMySQLBlockOutputStream : public IBlockOutputStream +{ +public: + explicit StorageMySQLBlockOutputStream( + const StorageMySQL & storage, + const std::string & remote_database_name, + const std::string & remote_table_name , + const mysqlxx::PoolWithFailover::Entry & entry, + const size_t & mysql_max_rows_to_insert) + : sample_block{storage.getSampleBlock()} + , remote_database_name{remote_database_name} + , remote_table_name{remote_table_name} + , entry{entry} + , max_batch_rows{mysql_max_rows_to_insert} + { + } + + Block getHeader() const override { return sample_block; } + + void write(const Block & block) override + { + auto blocks = splitBlocks(block, max_batch_rows); + mysqlxx::Transaction trans(entry); + try + { + for(const Block & batch_data : blocks) + { + writeBlockData(batch_data); + } + trans.commit(); + } + catch(...) + { + trans.rollback(); + throw; + } + } + + void writeBlockData(const Block & block) + { + WriteBufferFromOwnString sqlbuf; + sqlbuf << "INSERT INTO " << remote_database_name << "." << remote_table_name << " ( " << block.dumpNames() << " ) VALUES "; + + auto writer = std::make_shared(std::make_shared(sqlbuf), sample_block); + writer->write(block); + sqlbuf << ";"; + + auto query = this->entry->query(sqlbuf.str()); + query.execute(); + } + + Blocks splitBlocks(const Block & block, const size_t & max_rows) const + { + const size_t splited_block_size = ceil(block.rows() * 1.0 / max_rows); + Blocks splitted_blocks(splited_block_size); + + for (size_t idx = 0; idx < splited_block_size; ++idx) + splitted_blocks[idx] = block.cloneEmpty(); + + const size_t columns = block.columns(); + const size_t rows = block.rows(); + size_t offsets = 0; + size_t limits = max_batch_rows; + for (size_t idx = 0; idx < splited_block_size; ++idx) + { + // For last batch, limits should be the remain size + if(idx == splited_block_size - 1) limits = rows - offsets; + for(size_t col_idx = 0; col_idx < columns; ++col_idx) + { + splitted_blocks[idx].getByPosition(col_idx).column = block.getByPosition(col_idx).column->cut(offsets, limits); + } + offsets += max_batch_rows; + } + + return splitted_blocks; + } + + +private: + Block sample_block; + std::string remote_database_name; + std::string remote_table_name; + mysqlxx::PoolWithFailover::Entry entry; + size_t max_batch_rows; +}; + + +BlockOutputStreamPtr StorageMySQL::write( + const ASTPtr & /*query*/, const Settings & settings) +{ + return std::make_shared(*this, remote_database_name, remote_table_name, pool.Get(), settings.mysql_max_rows_to_insert); +} + void registerStorageMySQL(StorageFactory & factory) { factory.registerStorage("MySQL", [](const StorageFactory::Arguments & args) diff --git a/dbms/src/Storages/StorageMySQL.h b/dbms/src/Storages/StorageMySQL.h index 9e2b233283e..bf733abeca1 100644 --- a/dbms/src/Storages/StorageMySQL.h +++ b/dbms/src/Storages/StorageMySQL.h @@ -37,6 +37,8 @@ public: size_t max_block_size, unsigned num_streams) override; + BlockOutputStreamPtr write(const ASTPtr & query, const Settings & settings) override; + private: std::string name; From 763f1fda96d6adab5765f54ad395c22150d71626 Mon Sep 17 00:00:00 2001 From: sundy-li <543950155@qq.com> Date: Fri, 11 May 2018 12:15:22 +0800 Subject: [PATCH 210/231] Avoid Excessive copy when block is small enough && fix some code --- dbms/src/Storages/StorageMySQL.cpp | 25 ++++++++++++------- dbms/src/Storages/StorageMySQL.h | 5 +++- .../src/TableFunctions/TableFunctionMySQL.cpp | 3 ++- 3 files changed, 22 insertions(+), 11 deletions(-) diff --git a/dbms/src/Storages/StorageMySQL.cpp b/dbms/src/Storages/StorageMySQL.cpp index 4961398d62c..a86b39442f5 100644 --- a/dbms/src/Storages/StorageMySQL.cpp +++ b/dbms/src/Storages/StorageMySQL.cpp @@ -7,9 +7,11 @@ #include #include #include +#include #include #include #include +#include #include #include #include @@ -26,17 +28,18 @@ namespace ErrorCodes } -StorageMySQL::StorageMySQL( - const std::string & name, +StorageMySQL::StorageMySQL(const std::string & name, mysqlxx::Pool && pool, const std::string & remote_database_name, const std::string & remote_table_name, - const ColumnsDescription & columns_) + const ColumnsDescription & columns_, + const Context & context) : IStorage{columns_} , name(name) , remote_database_name(remote_database_name) , remote_table_name(remote_table_name) , pool(std::move(pool)) + , context(context) { } @@ -73,7 +76,7 @@ public: const std::string & remote_table_name , const mysqlxx::PoolWithFailover::Entry & entry, const size_t & mysql_max_rows_to_insert) - : sample_block{storage.getSampleBlock()} + : storage{storage} , remote_database_name{remote_database_name} , remote_table_name{remote_table_name} , entry{entry} @@ -81,7 +84,7 @@ public: { } - Block getHeader() const override { return sample_block; } + Block getHeader() const override { return storage.getSampleBlock(); } void write(const Block & block) override { @@ -105,9 +108,9 @@ public: void writeBlockData(const Block & block) { WriteBufferFromOwnString sqlbuf; - sqlbuf << "INSERT INTO " << remote_database_name << "." << remote_table_name << " ( " << block.dumpNames() << " ) VALUES "; + sqlbuf << "INSERT INTO `" << remote_database_name << "`.`" << remote_table_name << "` ( " << block.dumpNames() << " ) VALUES "; - auto writer = std::make_shared(std::make_shared(sqlbuf), sample_block); + auto writer = FormatFactory().getOutput("Values", sqlbuf, storage.getSampleBlock(), storage.context); writer->write(block); sqlbuf << ";"; @@ -117,6 +120,9 @@ public: Blocks splitBlocks(const Block & block, const size_t & max_rows) const { + // Avoid Excessive copy when block is small enough + if(block.rows() <= max_rows) return Blocks{std::move(block)}; + const size_t splited_block_size = ceil(block.rows() * 1.0 / max_rows); Blocks splitted_blocks(splited_block_size); @@ -143,7 +149,7 @@ public: private: - Block sample_block; + const StorageMySQL & storage; std::string remote_database_name; std::string remote_table_name; mysqlxx::PoolWithFailover::Entry entry; @@ -186,7 +192,8 @@ void registerStorageMySQL(StorageFactory & factory) std::move(pool), remote_database, remote_table, - args.columns); + args.columns, + args.context); }); } diff --git a/dbms/src/Storages/StorageMySQL.h b/dbms/src/Storages/StorageMySQL.h index bf733abeca1..0c3ce55286a 100644 --- a/dbms/src/Storages/StorageMySQL.h +++ b/dbms/src/Storages/StorageMySQL.h @@ -24,7 +24,8 @@ public: mysqlxx::Pool && pool, const std::string & remote_database_name, const std::string & remote_table_name, - const ColumnsDescription & columns); + const ColumnsDescription & columns, + const Context & context); std::string getName() const override { return "MySQL"; } std::string getTableName() const override { return name; } @@ -40,6 +41,7 @@ public: BlockOutputStreamPtr write(const ASTPtr & query, const Settings & settings) override; private: + friend class StorageMySQLBlockOutputStream; std::string name; std::string remote_database_name; @@ -47,6 +49,7 @@ private: mysqlxx::Pool pool; + const Context & context; }; } diff --git a/dbms/src/TableFunctions/TableFunctionMySQL.cpp b/dbms/src/TableFunctions/TableFunctionMySQL.cpp index 1f7839ada86..45202d3125d 100644 --- a/dbms/src/TableFunctions/TableFunctionMySQL.cpp +++ b/dbms/src/TableFunctions/TableFunctionMySQL.cpp @@ -152,7 +152,8 @@ StoragePtr TableFunctionMySQL::executeImpl(const ASTPtr & ast_function, const Co std::move(pool), database_name, table_name, - ColumnsDescription{columns}); + ColumnsDescription{columns}, + context); res->startup(); return res; From 200076b593825483e8161920c076eb8daf2b4479 Mon Sep 17 00:00:00 2001 From: sundy-li <543950155@qq.com> Date: Sun, 13 May 2018 10:34:49 +0800 Subject: [PATCH 211/231] ADD replace_query, on_duplicate_clause config for StorageMySQL && add docs --- dbms/src/Storages/StorageMySQL.cpp | 24 +++++++++++++++---- dbms/src/Storages/StorageMySQL.h | 4 ++++ .../src/TableFunctions/TableFunctionMySQL.cpp | 15 +++++++++--- docs/en/table_engines/mysql.md | 8 +++++-- 4 files changed, 42 insertions(+), 9 deletions(-) diff --git a/dbms/src/Storages/StorageMySQL.cpp b/dbms/src/Storages/StorageMySQL.cpp index a86b39442f5..0fb6aa8eca0 100644 --- a/dbms/src/Storages/StorageMySQL.cpp +++ b/dbms/src/Storages/StorageMySQL.cpp @@ -32,12 +32,16 @@ StorageMySQL::StorageMySQL(const std::string & name, mysqlxx::Pool && pool, const std::string & remote_database_name, const std::string & remote_table_name, + const bool & replace_query, + const std::string & on_duplicate_clause, const ColumnsDescription & columns_, const Context & context) : IStorage{columns_} , name(name) , remote_database_name(remote_database_name) , remote_table_name(remote_table_name) + , replace_query{replace_query} + , on_duplicate_clause{on_duplicate_clause} , pool(std::move(pool)) , context(context) { @@ -108,10 +112,15 @@ public: void writeBlockData(const Block & block) { WriteBufferFromOwnString sqlbuf; - sqlbuf << "INSERT INTO `" << remote_database_name << "`.`" << remote_table_name << "` ( " << block.dumpNames() << " ) VALUES "; + // If both `replace_query` and `on_duplicate_clause` are specified, only use the `on_duplicate_clause`. + sqlbuf << ( (storage.replace_query && storage.on_duplicate_clause.empty()) ? "REPLACE" : "INSERT"); + sqlbuf << " INTO `" << remote_database_name << "`.`" << remote_table_name << "`" + << " ( " << block.dumpNames() << " ) VALUES "; auto writer = FormatFactory().getOutput("Values", sqlbuf, storage.getSampleBlock(), storage.context); writer->write(block); + if(!storage.on_duplicate_clause.empty()) + sqlbuf << " ON DUPLICATE KEY " << storage.on_duplicate_clause; sqlbuf << ";"; auto query = this->entry->query(sqlbuf.str()); @@ -169,12 +178,12 @@ void registerStorageMySQL(StorageFactory & factory) { ASTs & engine_args = args.engine_args; - if (engine_args.size() != 5) + if (engine_args.size() < 5 || engine_args.size() > 7) throw Exception( - "Storage MySQL requires exactly 5 parameters: MySQL('host:port', database, table, 'user', 'password').", + "Storage MySQL requires 5-7 parameters: MySQL('host:port', database, table, 'user', 'password'[, replace_query, 'on_duplicate_clause' ]).", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); - for (size_t i = 0; i < 5; ++i) + for (size_t i = 0; i < engine_args.size(); ++i) engine_args[i] = evaluateConstantExpressionOrIdentifierAsLiteral(engine_args[i], args.local_context); /// 3306 is the default MySQL port. @@ -187,11 +196,18 @@ void registerStorageMySQL(StorageFactory & factory) mysqlxx::Pool pool(remote_database, parsed_host_port.first, username, password, parsed_host_port.second); + bool replace_query = false; + std::string on_duplicate_clause; + if(engine_args.size() >= 6) replace_query = static_cast(*engine_args[5]).value.safeGet() > 0; + if(engine_args.size() == 7) on_duplicate_clause = static_cast(*engine_args[6]).value.safeGet(); + return StorageMySQL::create( args.table_name, std::move(pool), remote_database, remote_table, + replace_query, + on_duplicate_clause, args.columns, args.context); }); diff --git a/dbms/src/Storages/StorageMySQL.h b/dbms/src/Storages/StorageMySQL.h index 0c3ce55286a..eb9f91fb425 100644 --- a/dbms/src/Storages/StorageMySQL.h +++ b/dbms/src/Storages/StorageMySQL.h @@ -24,6 +24,8 @@ public: mysqlxx::Pool && pool, const std::string & remote_database_name, const std::string & remote_table_name, + const bool & replace_query, + const std::string & on_duplicate_clause, const ColumnsDescription & columns, const Context & context); @@ -46,6 +48,8 @@ private: std::string remote_database_name; std::string remote_table_name; + bool replace_query; + std::string on_duplicate_clause; mysqlxx::Pool pool; diff --git a/dbms/src/TableFunctions/TableFunctionMySQL.cpp b/dbms/src/TableFunctions/TableFunctionMySQL.cpp index 45202d3125d..04f61394c63 100644 --- a/dbms/src/TableFunctions/TableFunctionMySQL.cpp +++ b/dbms/src/TableFunctions/TableFunctionMySQL.cpp @@ -89,11 +89,11 @@ StoragePtr TableFunctionMySQL::executeImpl(const ASTPtr & ast_function, const Co ASTs & args = typeid_cast(*args_func.arguments).children; - if (args.size() != 5) - throw Exception("Table function 'mysql' requires exactly 5 arguments: host:port, database name, table name, username and password", + if (args.size() < 5 || args.size() > 7) + throw Exception("Storage MySQL requires 5-7 parameters: MySQL('host:port', database, table, 'user', 'password'[, replace_query, 'on_duplicate_clause' ]).", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); - for (size_t i = 0; i < 5; ++i) + for (size_t i = 0; i < args.size(); ++i) args[i] = evaluateConstantExpressionOrIdentifierAsLiteral(args[i], context); std::string host_port = static_cast(*args[0]).value.safeGet(); @@ -102,6 +102,13 @@ StoragePtr TableFunctionMySQL::executeImpl(const ASTPtr & ast_function, const Co std::string user_name = static_cast(*args[3]).value.safeGet(); std::string password = static_cast(*args[4]).value.safeGet(); + bool replace_query = false; + std::string on_duplicate_clause; + if(args.size() >= 6) + replace_query = static_cast(*args[5]).value.safeGet() > 0; + if(args.size() == 7) + on_duplicate_clause = static_cast(*args[6]).value.safeGet(); + /// 3306 is the default MySQL port number auto parsed_host_port = parseAddress(host_port, 3306); @@ -152,6 +159,8 @@ StoragePtr TableFunctionMySQL::executeImpl(const ASTPtr & ast_function, const Co std::move(pool), database_name, table_name, + replace_query, + on_duplicate_clause, ColumnsDescription{columns}, context); diff --git a/docs/en/table_engines/mysql.md b/docs/en/table_engines/mysql.md index 42a0e2d0c1b..769c989a8a4 100644 --- a/docs/en/table_engines/mysql.md +++ b/docs/en/table_engines/mysql.md @@ -4,13 +4,17 @@ The MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server. -The engine takes 4 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example: +The engine takes 5 - 7 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example: ```text -MySQL('host:port', 'database', 'table', 'user', 'password'); +MySQL('host:port', 'database', 'table', 'user', 'password'[, replace_query, 'on_duplicate_clause' ]); ``` At this time, simple WHERE clauses such as ```=, !=, >, >=, <, <=``` are executed on the MySQL server. The rest of the conditions and the LIMIT sampling constraint are executed in ClickHouse only after the query to MySQL finishes. +If `replace_query` is specified to 1, then `INSERT INTO` query to this table would be transformed to `REPLACE INTO`. +If `on_duplicate_clause` is specified, eg `update impression = values(impression) + impression`, it would add `on_duplicate_clause` to the end of the MySQL insert sql. +If both `replace_query` and `on_duplicate_clause` are specified, only the `on_duplicate_clause` will work. + From 5d91b4f2fdc962a78f6fb2ca706a71140d265cd9 Mon Sep 17 00:00:00 2001 From: sundy-li <543950155@qq.com> Date: Mon, 14 May 2018 19:00:22 +0800 Subject: [PATCH 212/231] fix some bugs, fix some code styles --- dbms/src/Storages/StorageMySQL.cpp | 167 ++++++++++-------- dbms/src/Storages/StorageMySQL.h | 2 +- .../src/TableFunctions/TableFunctionMySQL.cpp | 13 +- 3 files changed, 104 insertions(+), 78 deletions(-) diff --git a/dbms/src/Storages/StorageMySQL.cpp b/dbms/src/Storages/StorageMySQL.cpp index 0fb6aa8eca0..cd9e0295de0 100644 --- a/dbms/src/Storages/StorageMySQL.cpp +++ b/dbms/src/Storages/StorageMySQL.cpp @@ -25,6 +25,7 @@ namespace DB namespace ErrorCodes { extern const int NUMBER_OF_ARGUMENTS_DOESNT_MATCH; + extern const int BAD_ARGUMENTS; } @@ -32,7 +33,7 @@ StorageMySQL::StorageMySQL(const std::string & name, mysqlxx::Pool && pool, const std::string & remote_database_name, const std::string & remote_table_name, - const bool & replace_query, + const bool replace_query, const std::string & on_duplicate_clause, const ColumnsDescription & columns_, const Context & context) @@ -74,12 +75,11 @@ BlockInputStreams StorageMySQL::read( class StorageMySQLBlockOutputStream : public IBlockOutputStream { public: - explicit StorageMySQLBlockOutputStream( - const StorageMySQL & storage, - const std::string & remote_database_name, - const std::string & remote_table_name , - const mysqlxx::PoolWithFailover::Entry & entry, - const size_t & mysql_max_rows_to_insert) + explicit StorageMySQLBlockOutputStream(const StorageMySQL & storage, + const std::string & remote_database_name, + const std::string & remote_table_name , + const mysqlxx::PoolWithFailover::Entry & entry, + const size_t & mysql_max_rows_to_insert) : storage{storage} , remote_database_name{remote_database_name} , remote_table_name{remote_table_name} @@ -88,81 +88,95 @@ public: { } - Block getHeader() const override { return storage.getSampleBlock(); } + Block getHeader() const override { return storage.getSampleBlock(); } - void write(const Block & block) override - { - auto blocks = splitBlocks(block, max_batch_rows); - mysqlxx::Transaction trans(entry); - try - { - for(const Block & batch_data : blocks) - { - writeBlockData(batch_data); - } - trans.commit(); - } - catch(...) - { - trans.rollback(); - throw; - } - } + void write(const Block & block) override + { + auto blocks = splitBlocks(block, max_batch_rows); + mysqlxx::Transaction trans(entry); + try + { + for (const Block & batch_data : blocks) + { + writeBlockData(batch_data); + } + trans.commit(); + } + catch(...) + { + trans.rollback(); + throw; + } + } - void writeBlockData(const Block & block) - { - WriteBufferFromOwnString sqlbuf; - // If both `replace_query` and `on_duplicate_clause` are specified, only use the `on_duplicate_clause`. - sqlbuf << ( (storage.replace_query && storage.on_duplicate_clause.empty()) ? "REPLACE" : "INSERT"); - sqlbuf << " INTO `" << remote_database_name << "`.`" << remote_table_name << "`" - << " ( " << block.dumpNames() << " ) VALUES "; + void writeBlockData(const Block & block) + { + WriteBufferFromOwnString sqlbuf; + sqlbuf << (storage.replace_query ? "REPLACE" : "INSERT") << " INTO "; + sqlbuf << backQuoteIfNeed(remote_database_name) << "." << backQuoteIfNeed(remote_table_name); + sqlbuf << " ( " << dumpNamesWithBackQuote(block) << " ) VALUES "; - auto writer = FormatFactory().getOutput("Values", sqlbuf, storage.getSampleBlock(), storage.context); - writer->write(block); - if(!storage.on_duplicate_clause.empty()) - sqlbuf << " ON DUPLICATE KEY " << storage.on_duplicate_clause; - sqlbuf << ";"; + auto writer = FormatFactory().getOutput("Values", sqlbuf, storage.getSampleBlock(), storage.context); + writer->write(block); - auto query = this->entry->query(sqlbuf.str()); - query.execute(); - } + if (!storage.on_duplicate_clause.empty()) + sqlbuf << " ON DUPLICATE KEY " << storage.on_duplicate_clause; - Blocks splitBlocks(const Block & block, const size_t & max_rows) const - { - // Avoid Excessive copy when block is small enough - if(block.rows() <= max_rows) return Blocks{std::move(block)}; + sqlbuf << ";"; - const size_t splited_block_size = ceil(block.rows() * 1.0 / max_rows); - Blocks splitted_blocks(splited_block_size); + auto query = this->entry->query(sqlbuf.str()); + query.execute(); + } - for (size_t idx = 0; idx < splited_block_size; ++idx) - splitted_blocks[idx] = block.cloneEmpty(); + Blocks splitBlocks(const Block & block, const size_t & max_rows) const + { + /// Avoid Excessive copy when block is small enough + if (block.rows() <= max_rows) + return Blocks{std::move(block)}; - const size_t columns = block.columns(); - const size_t rows = block.rows(); - size_t offsets = 0; - size_t limits = max_batch_rows; - for (size_t idx = 0; idx < splited_block_size; ++idx) - { - // For last batch, limits should be the remain size - if(idx == splited_block_size - 1) limits = rows - offsets; - for(size_t col_idx = 0; col_idx < columns; ++col_idx) - { - splitted_blocks[idx].getByPosition(col_idx).column = block.getByPosition(col_idx).column->cut(offsets, limits); - } - offsets += max_batch_rows; - } + const size_t splited_block_size = ceil(block.rows() * 1.0 / max_rows); + Blocks splitted_blocks(splited_block_size); - return splitted_blocks; - } + for (size_t idx = 0; idx < splited_block_size; ++idx) + splitted_blocks[idx] = block.cloneEmpty(); + + const size_t columns = block.columns(); + const size_t rows = block.rows(); + size_t offsets = 0; + size_t limits = max_batch_rows; + for (size_t idx = 0; idx < splited_block_size; ++idx) + { + /// For last batch, limits should be the remain size + if (idx == splited_block_size - 1) limits = rows - offsets; + for (size_t col_idx = 0; col_idx < columns; ++col_idx) + { + splitted_blocks[idx].getByPosition(col_idx).column = block.getByPosition(col_idx).column->cut(offsets, limits); + } + offsets += max_batch_rows; + } + + return splitted_blocks; + } + + std::string dumpNamesWithBackQuote(const Block & block) const + { + WriteBufferFromOwnString out; + for (auto it = block.begin(); it != block.end(); ++it) + { + if (it != block.begin()) + out << ", "; + out << backQuoteIfNeed(it->name); + } + return out.str(); + } private: - const StorageMySQL & storage; - std::string remote_database_name; - std::string remote_table_name; - mysqlxx::PoolWithFailover::Entry entry; - size_t max_batch_rows; + const StorageMySQL & storage; + std::string remote_database_name; + std::string remote_table_name; + mysqlxx::PoolWithFailover::Entry entry; + size_t max_batch_rows; }; @@ -180,7 +194,7 @@ void registerStorageMySQL(StorageFactory & factory) if (engine_args.size() < 5 || engine_args.size() > 7) throw Exception( - "Storage MySQL requires 5-7 parameters: MySQL('host:port', database, table, 'user', 'password'[, replace_query, 'on_duplicate_clause' ]).", + "Storage MySQL requires 5-7 parameters: MySQL('host:port', database, table, 'user', 'password'[, replace_query, 'on_duplicate_clause']).", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); for (size_t i = 0; i < engine_args.size(); ++i) @@ -198,8 +212,15 @@ void registerStorageMySQL(StorageFactory & factory) bool replace_query = false; std::string on_duplicate_clause; - if(engine_args.size() >= 6) replace_query = static_cast(*engine_args[5]).value.safeGet() > 0; - if(engine_args.size() == 7) on_duplicate_clause = static_cast(*engine_args[6]).value.safeGet(); + if (engine_args.size() >= 6) + replace_query = static_cast(*engine_args[5]).value.safeGet() > 0; + if (engine_args.size() == 7) + on_duplicate_clause = static_cast(*engine_args[6]).value.safeGet(); + + if (replace_query && !on_duplicate_clause.empty()) + throw Exception( + "Only one of 'replace_query' and 'on_duplicate_clause' can be specified, or none of them", + ErrorCodes::BAD_ARGUMENTS); return StorageMySQL::create( args.table_name, diff --git a/dbms/src/Storages/StorageMySQL.h b/dbms/src/Storages/StorageMySQL.h index eb9f91fb425..52197d54ae0 100644 --- a/dbms/src/Storages/StorageMySQL.h +++ b/dbms/src/Storages/StorageMySQL.h @@ -24,7 +24,7 @@ public: mysqlxx::Pool && pool, const std::string & remote_database_name, const std::string & remote_table_name, - const bool & replace_query, + const bool replace_query, const std::string & on_duplicate_clause, const ColumnsDescription & columns, const Context & context); diff --git a/dbms/src/TableFunctions/TableFunctionMySQL.cpp b/dbms/src/TableFunctions/TableFunctionMySQL.cpp index 04f61394c63..3201270d0bf 100644 --- a/dbms/src/TableFunctions/TableFunctionMySQL.cpp +++ b/dbms/src/TableFunctions/TableFunctionMySQL.cpp @@ -29,8 +29,8 @@ namespace DB namespace ErrorCodes { - extern const int LOGICAL_ERROR; extern const int NUMBER_OF_ARGUMENTS_DOESNT_MATCH; + extern const int BAD_ARGUMENTS;; } @@ -90,7 +90,7 @@ StoragePtr TableFunctionMySQL::executeImpl(const ASTPtr & ast_function, const Co ASTs & args = typeid_cast(*args_func.arguments).children; if (args.size() < 5 || args.size() > 7) - throw Exception("Storage MySQL requires 5-7 parameters: MySQL('host:port', database, table, 'user', 'password'[, replace_query, 'on_duplicate_clause' ]).", + throw Exception("Table function 'mysql' requires 5-7 parameters: MySQL('host:port', database, table, 'user', 'password'[, replace_query, 'on_duplicate_clause']).", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); for (size_t i = 0; i < args.size(); ++i) @@ -104,11 +104,16 @@ StoragePtr TableFunctionMySQL::executeImpl(const ASTPtr & ast_function, const Co bool replace_query = false; std::string on_duplicate_clause; - if(args.size() >= 6) + if (args.size() >= 6) replace_query = static_cast(*args[5]).value.safeGet() > 0; - if(args.size() == 7) + if (args.size() == 7) on_duplicate_clause = static_cast(*args[6]).value.safeGet(); + if (replace_query && !on_duplicate_clause.empty()) + throw Exception( + "Only one of 'replace_query' and 'on_duplicate_clause' can be specified, or none of them", + ErrorCodes::BAD_ARGUMENTS); + /// 3306 is the default MySQL port number auto parsed_host_port = parseAddress(host_port, 3306); From 8a5990fc7449aba2dbbc70f5a30bb88c8137c14e Mon Sep 17 00:00:00 2001 From: sundy-li <543950155@qq.com> Date: Mon, 14 May 2018 19:10:07 +0800 Subject: [PATCH 213/231] add integration test_storage_mysql --- dbms/src/Dictionaries/MySQLBlockInputStream.h | 2 +- dbms/tests/integration/README.md | 2 +- dbms/tests/integration/helpers/cluster.py | 37 +++++-- .../helpers/docker_compose_mysql.yml | 9 ++ .../test_storage_mysql/__init__.py | 0 .../configs/remote_servers.xml | 12 +++ .../integration/test_storage_mysql/test.py | 98 +++++++++++++++++++ docs/en/table_engines/mysql.md | 7 +- 8 files changed, 152 insertions(+), 15 deletions(-) create mode 100644 dbms/tests/integration/helpers/docker_compose_mysql.yml create mode 100644 dbms/tests/integration/test_storage_mysql/__init__.py create mode 100644 dbms/tests/integration/test_storage_mysql/configs/remote_servers.xml create mode 100644 dbms/tests/integration/test_storage_mysql/test.py diff --git a/dbms/src/Dictionaries/MySQLBlockInputStream.h b/dbms/src/Dictionaries/MySQLBlockInputStream.h index 6e72f4eb3cf..9e760cd28f8 100644 --- a/dbms/src/Dictionaries/MySQLBlockInputStream.h +++ b/dbms/src/Dictionaries/MySQLBlockInputStream.h @@ -21,7 +21,7 @@ public: String getName() const override { return "MySQL"; } - Block getHeader() const override { return description.sample_block; }; + Block getHeader() const override { return description.sample_block; } private: Block readImpl() override; diff --git a/dbms/tests/integration/README.md b/dbms/tests/integration/README.md index cc704022e79..bf0d184f134 100644 --- a/dbms/tests/integration/README.md +++ b/dbms/tests/integration/README.md @@ -14,7 +14,7 @@ Don't use Docker from your system repository. * [pip](https://pypi.python.org/pypi/pip). To install: `sudo apt-get install python-pip` * [py.test](https://docs.pytest.org/) testing framework. To install: `sudo -H pip install pytest` -* [docker-compose](https://docs.docker.com/compose/) and additional python libraries. To install: `sudo -H pip install docker-compose docker dicttoxml kazoo` +* [docker-compose](https://docs.docker.com/compose/) and additional python libraries. To install: `sudo -H pip install docker-compose docker dicttoxml kazoo PyMySQL` If you want to run the tests under a non-privileged user, you must add this user to `docker` group: `sudo usermod -aG docker $USER` and re-login. (You must close all your sessions (for example, restart your computer)) diff --git a/dbms/tests/integration/helpers/cluster.py b/dbms/tests/integration/helpers/cluster.py index 52003b1d010..ffcce52c07e 100644 --- a/dbms/tests/integration/helpers/cluster.py +++ b/dbms/tests/integration/helpers/cluster.py @@ -48,15 +48,17 @@ class ClickHouseCluster: self.base_cmd = ['docker-compose', '--project-directory', self.base_dir, '--project-name', self.project_name] self.base_zookeeper_cmd = None + self.base_mysql_cmd = [] self.pre_zookeeper_commands = [] self.instances = {} self.with_zookeeper = False - + self.with_mysql = False + self.docker_client = None self.is_up = False - def add_instance(self, name, config_dir=None, main_configs=[], user_configs=[], macroses={}, with_zookeeper=False, + def add_instance(self, name, config_dir=None, main_configs=[], user_configs=[], macroses={}, with_zookeeper=False, with_mysql=False, clickhouse_path_dir=None, hostname=None): """Add an instance to the cluster. @@ -75,7 +77,7 @@ class ClickHouseCluster: instance = ClickHouseInstance( self, self.base_dir, name, config_dir, main_configs, user_configs, macroses, with_zookeeper, - self.zookeeper_config_path, self.base_configs_dir, self.server_bin_path, clickhouse_path_dir, hostname=hostname) + self.zookeeper_config_path, with_mysql, self.base_configs_dir, self.server_bin_path, clickhouse_path_dir, hostname=hostname) self.instances[name] = instance self.base_cmd.extend(['--file', instance.docker_compose_path]) @@ -84,6 +86,12 @@ class ClickHouseCluster: self.base_cmd.extend(['--file', p.join(HELPERS_DIR, 'docker_compose_zookeeper.yml')]) self.base_zookeeper_cmd = ['docker-compose', '--project-directory', self.base_dir, '--project-name', self.project_name, '--file', p.join(HELPERS_DIR, 'docker_compose_zookeeper.yml')] + + if with_mysql and not self.with_mysql: + self.with_mysql = True + self.base_cmd.extend(['--file', p.join(HELPERS_DIR, 'docker_compose_mysql.yml')]) + self.base_mysql_cmd = ['docker-compose', '--project-directory', self.base_dir, '--project-name', + self.project_name, '--file', p.join(HELPERS_DIR, 'docker_compose_mysql.yml')] return instance @@ -124,6 +132,9 @@ class ClickHouseCluster: for command in self.pre_zookeeper_commands: self.run_kazoo_commands_with_retries(command, repeats=5) + if self.with_mysql and self.base_mysql_cmd: + subprocess.check_call(self.base_mysql_cmd + ['up', '-d', '--no-recreate']) + # Uncomment for debugging #print ' '.join(self.base_cmd + ['up', '--no-recreate']) @@ -138,6 +149,7 @@ class ClickHouseCluster: instance.client = Client(instance.ip_address, command=self.client_bin_path) + self.is_up = True @@ -201,7 +213,7 @@ services: class ClickHouseInstance: def __init__( self, cluster, base_path, name, custom_config_dir, custom_main_configs, custom_user_configs, macroses, - with_zookeeper, zookeeper_config_path, base_configs_dir, server_bin_path, clickhouse_path_dir, hostname=None): + with_zookeeper, zookeeper_config_path, with_mysql, base_configs_dir, server_bin_path, clickhouse_path_dir, hostname=None): self.name = name self.base_cmd = cluster.base_cmd[:] @@ -220,6 +232,8 @@ class ClickHouseInstance: self.base_configs_dir = base_configs_dir self.server_bin_path = server_bin_path + self.with_mysql = with_mysql + self.path = p.join(self.cluster.instances_dir, name) self.docker_compose_path = p.join(self.path, 'docker_compose.yml') @@ -269,7 +283,6 @@ class ClickHouseInstance: while True: status = self.get_docker_handle().status - if status == 'exited': raise Exception("Instance `{}' failed to start. Container status: {}".format(self.name, status)) @@ -356,9 +369,15 @@ class ClickHouseInstance: logs_dir = p.abspath(p.join(self.path, 'logs')) os.mkdir(logs_dir) - depends_on = '[]' + depends_on = [] + + if self.with_mysql: + depends_on.append("mysql1") + if self.with_zookeeper: - depends_on = '["zoo1", "zoo2", "zoo3"]' + depends_on.append("zoo1") + depends_on.append("zoo2") + depends_on.append("zoo3") with open(self.docker_compose_path, 'w') as docker_compose: docker_compose.write(DOCKER_COMPOSE_TEMPLATE.format( @@ -368,9 +387,9 @@ class ClickHouseInstance: binary_path=self.server_bin_path, configs_dir=configs_dir, config_d_dir=config_d_dir, - db_dir=db_dir, + db_dir=db_dir, logs_dir=logs_dir, - depends_on=depends_on)) + depends_on=str(depends_on))) def destroy_dir(self): diff --git a/dbms/tests/integration/helpers/docker_compose_mysql.yml b/dbms/tests/integration/helpers/docker_compose_mysql.yml new file mode 100644 index 00000000000..6106b588f76 --- /dev/null +++ b/dbms/tests/integration/helpers/docker_compose_mysql.yml @@ -0,0 +1,9 @@ +version: '2' +services: + mysql1: + image: mysql:5.7 + restart: always + environment: + MYSQL_ROOT_PASSWORD: clickhouse + ports: + - 3308:3306 diff --git a/dbms/tests/integration/test_storage_mysql/__init__.py b/dbms/tests/integration/test_storage_mysql/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/dbms/tests/integration/test_storage_mysql/configs/remote_servers.xml b/dbms/tests/integration/test_storage_mysql/configs/remote_servers.xml new file mode 100644 index 00000000000..de8e5865f12 --- /dev/null +++ b/dbms/tests/integration/test_storage_mysql/configs/remote_servers.xml @@ -0,0 +1,12 @@ + + + + + + node1 + 9000 + + + + + diff --git a/dbms/tests/integration/test_storage_mysql/test.py b/dbms/tests/integration/test_storage_mysql/test.py new file mode 100644 index 00000000000..97aca105c74 --- /dev/null +++ b/dbms/tests/integration/test_storage_mysql/test.py @@ -0,0 +1,98 @@ +from contextlib import contextmanager + +import pytest + +## sudo -H pip install PyMySQL +import pymysql.cursors + +from helpers.cluster import ClickHouseCluster + +cluster = ClickHouseCluster(__file__) + +node1 = cluster.add_instance('node1', main_configs=['configs/remote_servers.xml'], with_mysql = True) +create_table_sql_template = """ + CREATE TABLE `clickhouse`.`{}` ( + `id` int(11) NOT NULL, + `name` varchar(50) NOT NULL, + `age` int NOT NULL default 0, + `money` int NOT NULL default 0, + PRIMARY KEY (`id`)) ENGINE=InnoDB; + """ + +@pytest.fixture(scope="module") +def started_cluster(): + try: + cluster.start() + + conn = get_mysql_conn() + ## create mysql db and table + create_mysql_db(conn, 'clickhouse') + yield cluster + + finally: + cluster.shutdown() + + +def test_insert_select(started_cluster): + table_name = 'test_insert_select' + conn = get_mysql_conn() + create_mysql_table(conn, table_name) + + node1.query(''' +CREATE TABLE {}(id UInt32, name String, age UInt32, money UInt32) ENGINE = MySQL('mysql1:3306', 'clickhouse', '{}', 'root', 'clickhouse'); +'''.format(table_name, table_name)) + node1.query("INSERT INTO {}(id, name, money) select number, concat('name_', toString(number)), 3 from numbers(10000) ".format(table_name)) + assert node1.query("SELECT count() FROM {}".format(table_name)).rstrip() == '10000' + assert node1.query("SELECT sum(money) FROM {}".format(table_name)).rstrip() == '30000' + conn.close() + + +def test_replace_select(started_cluster): + table_name = 'test_replace_select' + conn = get_mysql_conn() + create_mysql_table(conn, table_name) + + node1.query(''' +CREATE TABLE {}(id UInt32, name String, age UInt32, money UInt32) ENGINE = MySQL('mysql1:3306', 'clickhouse', '{}', 'root', 'clickhouse', 1); +'''.format(table_name, table_name)) + node1.query("INSERT INTO {}(id, name, money) select number, concat('name_', toString(number)), 3 from numbers(10000) ".format(table_name)) + node1.query("INSERT INTO {}(id, name, money) select number, concat('name_', toString(number)), 3 from numbers(10000) ".format(table_name)) + assert node1.query("SELECT count() FROM {}".format(table_name)).rstrip() == '10000' + assert node1.query("SELECT sum(money) FROM {}".format(table_name)).rstrip() == '30000' + conn.close() + + +def test_insert_on_duplicate_select(started_cluster): + table_name = 'test_insert_on_duplicate_select' + conn = get_mysql_conn() + create_mysql_table(conn, table_name) + + node1.query(''' +CREATE TABLE {}(id UInt32, name String, age UInt32, money UInt32) ENGINE = MySQL('mysql1:3306', 'clickhouse', '{}', 'root', 'clickhouse', 0, 'update money = money + values(money)'); +'''.format(table_name, table_name)) + node1.query("INSERT INTO {}(id, name, money) select number, concat('name_', toString(number)), 3 from numbers(10000) ".format(table_name)) + node1.query("INSERT INTO {}(id, name, money) select number, concat('name_', toString(number)), 3 from numbers(10000) ".format(table_name)) + assert node1.query("SELECT count() FROM {}".format(table_name)).rstrip() == '10000' + assert node1.query("SELECT sum(money) FROM {}".format(table_name)).rstrip() == '60000' + conn.close() + + +def get_mysql_conn(): + conn = pymysql.connect(user='root', password='clickhouse', host='127.0.0.1', port=3308) + return conn + +def create_mysql_db(conn, name): + with conn.cursor() as cursor: + cursor.execute( + "CREATE DATABASE {} DEFAULT CHARACTER SET 'utf8'".format(name)) + +def create_mysql_table(conn, tableName): + with conn.cursor() as cursor: + cursor.execute(create_table_sql_template.format(tableName)) + + +if __name__ == '__main__': + with contextmanager(started_cluster)() as cluster: + for name, instance in cluster.instances.items(): + print name, instance.ip_address + raw_input("Cluster created, press any key to destroy...") diff --git a/docs/en/table_engines/mysql.md b/docs/en/table_engines/mysql.md index 769c989a8a4..dafd5b5fc3a 100644 --- a/docs/en/table_engines/mysql.md +++ b/docs/en/table_engines/mysql.md @@ -4,10 +4,10 @@ The MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server. -The engine takes 5 - 7 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password. Example: +The engine takes 5-7 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password; wheter to use replace query; the on duplcate clause. Example: ```text -MySQL('host:port', 'database', 'table', 'user', 'password'[, replace_query, 'on_duplicate_clause' ]); +MySQL('host:port', 'database', 'table', 'user', 'password'[, replace_query, 'on_duplicate_clause']); ``` At this time, simple WHERE clauses such as ```=, !=, >, >=, <, <=``` are executed on the MySQL server. @@ -16,5 +16,4 @@ The rest of the conditions and the LIMIT sampling constraint are executed in Cli If `replace_query` is specified to 1, then `INSERT INTO` query to this table would be transformed to `REPLACE INTO`. If `on_duplicate_clause` is specified, eg `update impression = values(impression) + impression`, it would add `on_duplicate_clause` to the end of the MySQL insert sql. -If both `replace_query` and `on_duplicate_clause` are specified, only the `on_duplicate_clause` will work. - +Notice that only one of 'replace_query' and 'on_duplicate_clause' can be specified, or none of them. From 04c734d7851c9ff54dc4ddfddca848862414e492 Mon Sep 17 00:00:00 2001 From: sundy-li <543950155@qq.com> Date: Mon, 14 May 2018 19:14:49 +0800 Subject: [PATCH 214/231] delete unused space --- dbms/tests/integration/helpers/cluster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dbms/tests/integration/helpers/cluster.py b/dbms/tests/integration/helpers/cluster.py index ffcce52c07e..4242fa8fa62 100644 --- a/dbms/tests/integration/helpers/cluster.py +++ b/dbms/tests/integration/helpers/cluster.py @@ -387,7 +387,7 @@ class ClickHouseInstance: binary_path=self.server_bin_path, configs_dir=configs_dir, config_d_dir=config_d_dir, - db_dir=db_dir, + db_dir=db_dir, logs_dir=logs_dir, depends_on=str(depends_on))) From 9ef0a771e3e0b92141c716a8880a1878ed629f69 Mon Sep 17 00:00:00 2001 From: alexey-milovidov Date: Tue, 15 May 2018 02:59:58 +0300 Subject: [PATCH 215/231] Update TableFunctionMySQL.cpp --- dbms/src/TableFunctions/TableFunctionMySQL.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dbms/src/TableFunctions/TableFunctionMySQL.cpp b/dbms/src/TableFunctions/TableFunctionMySQL.cpp index 3201270d0bf..cccfb76dd80 100644 --- a/dbms/src/TableFunctions/TableFunctionMySQL.cpp +++ b/dbms/src/TableFunctions/TableFunctionMySQL.cpp @@ -30,7 +30,7 @@ namespace DB namespace ErrorCodes { extern const int NUMBER_OF_ARGUMENTS_DOESNT_MATCH; - extern const int BAD_ARGUMENTS;; + extern const int BAD_ARGUMENTS; } From 0eace5ffe242cebd8f86f9429a554e01b370f4d0 Mon Sep 17 00:00:00 2001 From: alexey-milovidov Date: Tue, 15 May 2018 03:02:19 +0300 Subject: [PATCH 216/231] Update mysql.md --- docs/en/table_engines/mysql.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/en/table_engines/mysql.md b/docs/en/table_engines/mysql.md index dafd5b5fc3a..c9b90d2e253 100644 --- a/docs/en/table_engines/mysql.md +++ b/docs/en/table_engines/mysql.md @@ -4,7 +4,7 @@ The MySQL engine allows you to perform SELECT queries on data that is stored on a remote MySQL server. -The engine takes 5-7 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password; wheter to use replace query; the on duplcate clause. Example: +The engine takes 5-7 parameters: the server address (host and port); the name of the database; the name of the table; the user's name; the user's password; whether to use replace query; the on duplcate clause. Example: ```text MySQL('host:port', 'database', 'table', 'user', 'password'[, replace_query, 'on_duplicate_clause']); From a0fa49c44ac03dcdc1c730a3103437aa0dff6e88 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Tue, 15 May 2018 04:16:19 +0300 Subject: [PATCH 217/231] Reverted submodules --- contrib/llvm | 2 +- contrib/poco | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/contrib/llvm b/contrib/llvm index 6b3975cf38d..163def21781 160000 --- a/contrib/llvm +++ b/contrib/llvm @@ -1 +1 @@ -Subproject commit 6b3975cf38d5c9436e1311b7e54ad93ef1a9aa9c +Subproject commit 163def217817c90fb982a6daf384744d8472b92b diff --git a/contrib/poco b/contrib/poco index 2d5a158303a..3a2d0a833a2 160000 --- a/contrib/poco +++ b/contrib/poco @@ -1 +1 @@ -Subproject commit 2d5a158303adf9d47b980cdcfdb26cee1460704e +Subproject commit 3a2d0a833a22ef5e1164a9ada54e3253cb038904 From 651bac519c5537a762baa252853781340ad61f3d Mon Sep 17 00:00:00 2001 From: Nikolai Kochetov Date: Mon, 14 May 2018 18:05:45 +0300 Subject: [PATCH 218/231] Fixed element types for explicit set in IN function. Fixed element types for explicit set in IN function. [#CLICKHOUSE-3730] --- dbms/src/Interpreters/ExpressionAnalyzer.cpp | 120 +++++++++---------- 1 file changed, 58 insertions(+), 62 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionAnalyzer.cpp b/dbms/src/Interpreters/ExpressionAnalyzer.cpp index 378b4d805b1..5402684c640 100644 --- a/dbms/src/Interpreters/ExpressionAnalyzer.cpp +++ b/dbms/src/Interpreters/ExpressionAnalyzer.cpp @@ -58,7 +58,9 @@ #include #include #include +#include #include "ProjectionManipulation.h" +#include "evaluateConstantExpression.h" namespace DB @@ -1645,82 +1647,76 @@ void ExpressionAnalyzer::makeExplicitSet(const ASTFunction * node, const Block & if (args.children.size() != 2) throw Exception("Wrong number of arguments passed to function in", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH); - const ASTPtr & arg = args.children.at(1); + const ASTPtr & left_arg = args.children.at(0); + const ASTPtr & right_arg = args.children.at(1); + const DataTypePtr & left_arg_type = sample_block.getByName(left_arg->getColumnName()).type; + + std::function getTupleDepth; + getTupleDepth = [&getTupleDepth](const DataTypePtr & type) -> size_t + { + if (auto tuple_type = typeid_cast(type.get())) + return 1 + (tuple_type->getElements().empty() ? 0 : getTupleDepth(tuple_type->getElements().at(0))); + + return 0; + }; + + auto getTupleDepthFromAst = [&getTupleDepth](const ASTPtr & node) -> size_t + { + size_t depth = 0; + ASTPtr element = node; + + auto ast_function = typeid_cast(node.get()); + if (ast_function && ast_function->name == "tuple" && !ast_function->arguments->children.empty()) + { + ++depth; + element = ast_function->arguments->children.at(0); + } + + std::pair value_raw = evaluateConstantExpression(element, context); + return depth + getTupleDepth(value_raw.second); + }; + + size_t left_tuple_depth = getTupleDepth(left_arg_type); + size_t right_tuple_depth = getTupleDepthFromAst(right_arg); DataTypes set_element_types; - const ASTPtr & left_arg = args.children.at(0); + ASTPtr elements_ast = nullptr; - const ASTFunction * left_arg_tuple = typeid_cast(left_arg.get()); - - /** NOTE If tuple in left hand side specified non-explicitly - * Example: identity((a, b)) IN ((1, 2), (3, 4)) - * instead of (a, b)) IN ((1, 2), (3, 4)) - * then set creation doesn't work correctly. - */ - if (left_arg_tuple && left_arg_tuple->name == "tuple") + if (left_tuple_depth > 0) { - for (const auto & arg : left_arg_tuple->arguments->children) - set_element_types.push_back(sample_block.getByName(arg->getColumnName()).type); + auto left_tuple_type = static_cast(left_arg_type.get()); + set_element_types = left_tuple_type->getElements(); } else - { - DataTypePtr left_type = sample_block.getByName(left_arg->getColumnName()).type; - set_element_types.push_back(left_type); - } + set_element_types.push_back(left_arg_type); - /// The case `x in (1, 2)` distinguishes from the case `x in 1` (also `x in (1)`). - bool single_value = false; - ASTPtr elements_ast = arg; - - if (ASTFunction * set_func = typeid_cast(arg.get())) - { - if (set_func->name == "tuple") - { - if (set_func->arguments->children.empty()) - { - /// Empty set. - elements_ast = set_func->arguments; - } - else - { - /// Distinguish the case `(x, y) in ((1, 2), (3, 4))` from the case `(x, y) in (1, 2)`. - ASTFunction * any_element = typeid_cast(set_func->arguments->children.at(0).get()); - if (set_element_types.size() >= 2 && (!any_element || any_element->name != "tuple")) - single_value = true; - else - elements_ast = set_func->arguments; - } - } - else - { - if (set_element_types.size() >= 2) - throw Exception("Incorrect type of 2nd argument for function " + node->name - + ". Must be subquery or set of " + toString(set_element_types.size()) + "-element tuples.", - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); - - single_value = true; - } - } - else if (typeid_cast(arg.get())) - { - single_value = true; - } - else - { - throw Exception("Incorrect type of 2nd argument for function " + node->name + ". Must be subquery or set of values.", - ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); - } - - if (single_value) + /// 1 in 1; (1, 2) in (1, 2); identity(tuple(tuple(tuple(1)))) in tuple(tuple(tuple(1))); etc. + if (left_tuple_depth == right_tuple_depth) { ASTPtr exp_list = std::make_shared(); - exp_list->children.push_back(elements_ast); + exp_list->children.push_back(right_arg); elements_ast = exp_list; } + /// 1 in (1, 2); (1, 2) in ((1, 2), (3, 4)); etc. + else if (left_tuple_depth + 1 == right_tuple_depth) + { + ASTFunction * set_func = typeid_cast(right_arg.get()); + + if (!set_func || set_func->name != "tuple") + throw Exception("Incorrect type of 2nd argument for function " + node->name + + ". Must be subquery or set of elements with type " + left_arg_type->getName() + ".", + ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); + + elements_ast = set_func->arguments; + } + else + throw Exception("Invalid types for IN function: " + + left_arg_type->getName() + " and " + right_arg_type->getName() + ".", + ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT); SetPtr set = std::make_shared(SizeLimits(settings.max_rows_in_set, settings.max_bytes_in_set, settings.set_overflow_mode)); set->createFromAST(set_element_types, elements_ast, context, create_ordered_set); - prepared_sets[arg.get()] = std::move(set); + prepared_sets[right_arg.get()] = std::move(set); } From e6adcfaad5a5007c862ca6e2fc402afdc0390c43 Mon Sep 17 00:00:00 2001 From: Nikolai Kochetov Date: Mon, 14 May 2018 18:47:38 +0300 Subject: [PATCH 219/231] Fixed element types for explicit set in IN function. [#CLICKHOUSE-3730] --- dbms/src/Functions/FunctionsMiscellaneous.cpp | 2 +- dbms/src/Interpreters/ExpressionAnalyzer.cpp | 48 +++++++++---------- 2 files changed, 23 insertions(+), 27 deletions(-) diff --git a/dbms/src/Functions/FunctionsMiscellaneous.cpp b/dbms/src/Functions/FunctionsMiscellaneous.cpp index d599e9bf1ec..ca35888a4b9 100644 --- a/dbms/src/Functions/FunctionsMiscellaneous.cpp +++ b/dbms/src/Functions/FunctionsMiscellaneous.cpp @@ -755,7 +755,7 @@ public: tuple = typeid_cast(materialized_tuple.get()); } - if (tuple) + if (tuple && type_tuple->getElements().size() != 1) { const Columns & tuple_columns = tuple->getColumns(); const DataTypes & tuple_types = type_tuple->getElements(); diff --git a/dbms/src/Interpreters/ExpressionAnalyzer.cpp b/dbms/src/Interpreters/ExpressionAnalyzer.cpp index 5402684c640..b50780d339c 100644 --- a/dbms/src/Interpreters/ExpressionAnalyzer.cpp +++ b/dbms/src/Interpreters/ExpressionAnalyzer.cpp @@ -1649,7 +1649,23 @@ void ExpressionAnalyzer::makeExplicitSet(const ASTFunction * node, const Block & const ASTPtr & left_arg = args.children.at(0); const ASTPtr & right_arg = args.children.at(1); + + auto getTupleTypeFromAst = [this](const ASTPtr & node) -> DataTypePtr + { + auto ast_function = typeid_cast(node.get()); + if (ast_function && ast_function->name == "tuple" && !ast_function->arguments->children.empty()) + { + /// Won't parse all values of outer tuple. + auto element = ast_function->arguments->children.at(0); + std::pair value_raw = evaluateConstantExpression(element, context); + return std::make_shared(DataTypes({value_raw.second})); + } + + return evaluateConstantExpression(node, context).second; + }; + const DataTypePtr & left_arg_type = sample_block.getByName(left_arg->getColumnName()).type; + const DataTypePtr & right_arg_type = getTupleTypeFromAst(right_arg); std::function getTupleDepth; getTupleDepth = [&getTupleDepth](const DataTypePtr & type) -> size_t @@ -1660,35 +1676,15 @@ void ExpressionAnalyzer::makeExplicitSet(const ASTFunction * node, const Block & return 0; }; - auto getTupleDepthFromAst = [&getTupleDepth](const ASTPtr & node) -> size_t - { - size_t depth = 0; - ASTPtr element = node; - - auto ast_function = typeid_cast(node.get()); - if (ast_function && ast_function->name == "tuple" && !ast_function->arguments->children.empty()) - { - ++depth; - element = ast_function->arguments->children.at(0); - } - - std::pair value_raw = evaluateConstantExpression(element, context); - return depth + getTupleDepth(value_raw.second); - }; - size_t left_tuple_depth = getTupleDepth(left_arg_type); - size_t right_tuple_depth = getTupleDepthFromAst(right_arg); + size_t right_tuple_depth = getTupleDepth(right_arg_type); - DataTypes set_element_types; - ASTPtr elements_ast = nullptr; - - if (left_tuple_depth > 0) - { - auto left_tuple_type = static_cast(left_arg_type.get()); + DataTypes set_element_types = {left_arg_type}; + auto left_tuple_type = typeid_cast(left_arg_type.get()); + if (left_tuple_type && left_tuple_type->getElements().size() != 1) set_element_types = left_tuple_type->getElements(); - } - else - set_element_types.push_back(left_arg_type); + + ASTPtr elements_ast = nullptr; /// 1 in 1; (1, 2) in (1, 2); identity(tuple(tuple(tuple(1)))) in tuple(tuple(tuple(1))); etc. if (left_tuple_depth == right_tuple_depth) From aaad77a6024257447d19e6fee1840979d5a04c0e Mon Sep 17 00:00:00 2001 From: Nikolai Kochetov Date: Tue, 15 May 2018 12:32:54 +0300 Subject: [PATCH 220/231] Fixed unnecessary creation of prepared set for function arguments other than second for in or global in. [#CLICKHOUSE-3730] --- dbms/src/Interpreters/ExpressionAnalyzer.cpp | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionAnalyzer.cpp b/dbms/src/Interpreters/ExpressionAnalyzer.cpp index b50780d339c..a46c9de3a20 100644 --- a/dbms/src/Interpreters/ExpressionAnalyzer.cpp +++ b/dbms/src/Interpreters/ExpressionAnalyzer.cpp @@ -2081,8 +2081,10 @@ void ExpressionAnalyzer::getActionsImpl(const ASTPtr & ast, bool no_subqueries, /// If the function has an argument-lambda expression, you need to determine its type before the recursive call. bool has_lambda_arguments = false; - for (auto & child : node->arguments->children) + for (size_t arg = 0; arg < node->arguments->children.size(); ++arg) { + auto & child = node->arguments->children[arg]; + ASTFunction * lambda = typeid_cast(child.get()); if (lambda && lambda->name == "lambda") { @@ -2100,7 +2102,7 @@ void ExpressionAnalyzer::getActionsImpl(const ASTPtr & ast, bool no_subqueries, /// Select the name in the next cycle. argument_names.emplace_back(); } - else if (prepared_sets.count(child.get())) + else if (prepared_sets.count(child.get()) && functionIsInOrGlobalInOperator(node->name) && arg == 1) { ColumnWithTypeAndName column; column.type = std::make_shared(); @@ -2196,9 +2198,9 @@ void ExpressionAnalyzer::getActionsImpl(const ASTPtr & ast, bool no_subqueries, Names captured; Names required = lambda_actions->getRequiredColumns(); - for (size_t j = 0; j < required.size(); ++j) - if (findColumn(required[j], lambda_arguments) == lambda_arguments.end()) - captured.push_back(required[j]); + for (const auto & required_arg : required) + if (findColumn(required_arg, lambda_arguments) == lambda_arguments.end()) + captured.push_back(required_arg); /// We can not name `getColumnName()`, /// because it does not uniquely define the expression (the types of arguments can be different). @@ -2218,9 +2220,9 @@ void ExpressionAnalyzer::getActionsImpl(const ASTPtr & ast, bool no_subqueries, if (only_consts) { - for (size_t i = 0; i < argument_names.size(); ++i) + for (const auto & argument_name : argument_names) { - if (!actions_stack.getSampleBlock().has(argument_names[i])) + if (!actions_stack.getSampleBlock().has(argument_name)) { arguments_present = false; break; From 6715b945d9d6dd2a7fc4f1fc05a0434da1b8ae41 Mon Sep 17 00:00:00 2001 From: Nikolai Kochetov Date: Tue, 15 May 2018 13:44:54 +0300 Subject: [PATCH 221/231] Allow using of function which returs tuple for IN. [#CLICKHOUSE-3730] --- dbms/src/Interpreters/ExpressionAnalyzer.cpp | 4 ++-- dbms/src/Interpreters/Set.cpp | 20 +++++++++++++++++--- 2 files changed, 19 insertions(+), 5 deletions(-) diff --git a/dbms/src/Interpreters/ExpressionAnalyzer.cpp b/dbms/src/Interpreters/ExpressionAnalyzer.cpp index a46c9de3a20..c7810666da8 100644 --- a/dbms/src/Interpreters/ExpressionAnalyzer.cpp +++ b/dbms/src/Interpreters/ExpressionAnalyzer.cpp @@ -32,6 +32,8 @@ #include #include #include +#include +#include #include #include @@ -59,8 +61,6 @@ #include #include #include -#include "ProjectionManipulation.h" -#include "evaluateConstantExpression.h" namespace DB diff --git a/dbms/src/Interpreters/Set.cpp b/dbms/src/Interpreters/Set.cpp index 925479e05e1..9fb95128ad1 100644 --- a/dbms/src/Interpreters/Set.cpp +++ b/dbms/src/Interpreters/Set.cpp @@ -208,6 +208,7 @@ void Set::createFromAST(const DataTypes & types, ASTPtr node, const Context & co MutableColumns columns = header.cloneEmptyColumns(); + DataTypePtr tuple_type; Row tuple_values; ASTExpressionList & list = typeid_cast(*node); for (auto & elem : list.children) @@ -221,10 +222,22 @@ void Set::createFromAST(const DataTypes & types, ASTPtr node, const Context & co } else if (ASTFunction * func = typeid_cast(elem.get())) { + Field function_result; + const TupleBackend * tuple = nullptr; if (func->name != "tuple") - throw Exception("Incorrect element of set. Must be tuple.", ErrorCodes::INCORRECT_ELEMENT_OF_SET); + { + if (!tuple_type) + tuple_type = std::make_shared(types); - size_t tuple_size = func->arguments->children.size(); + function_result = extractValueFromNode(elem, *tuple_type, context); + if (function_result.getType() != Field::Types::Tuple) + throw Exception("Invalid type of set. Expected tuple, got " + String(function_result.getTypeName()), + ErrorCodes::INCORRECT_ELEMENT_OF_SET); + + tuple = &function_result.get().t; + } + + size_t tuple_size = tuple ? tuple->size() : func->arguments->children.size(); if (tuple_size != num_columns) throw Exception("Incorrect size of tuple in set: " + toString(tuple_size) + " instead of " + toString(num_columns), ErrorCodes::INCORRECT_ELEMENT_OF_SET); @@ -235,7 +248,8 @@ void Set::createFromAST(const DataTypes & types, ASTPtr node, const Context & co size_t i = 0; for (; i < tuple_size; ++i) { - Field value = extractValueFromNode(func->arguments->children[i], *types[i], context); + Field value = tuple ? (*tuple)[i] + : extractValueFromNode(func->arguments->children[i], *types[i], context); /// If at least one of the elements of the tuple has an impossible (outside the range of the type) value, then the entire tuple too. if (value.isNull()) From 664171a0031963428036a1457a6be3a524eaf559 Mon Sep 17 00:00:00 2001 From: Nikolai Kochetov Date: Tue, 15 May 2018 15:31:52 +0300 Subject: [PATCH 222/231] Added test for in syntax. --- .../0_stateless/00626_in_syntax.reference | 38 ++++++++++++++++ .../queries/0_stateless/00626_in_syntax.sql | 44 +++++++++++++++++++ 2 files changed, 82 insertions(+) create mode 100644 dbms/tests/queries/0_stateless/00626_in_syntax.reference create mode 100644 dbms/tests/queries/0_stateless/00626_in_syntax.sql diff --git a/dbms/tests/queries/0_stateless/00626_in_syntax.reference b/dbms/tests/queries/0_stateless/00626_in_syntax.reference new file mode 100644 index 00000000000..3e4db78953a --- /dev/null +++ b/dbms/tests/queries/0_stateless/00626_in_syntax.reference @@ -0,0 +1,38 @@ +1 +1 +1 +1 +1 +1 +- +1 +1 +1 +1 +1 +1 +1 +- +0 +0 +1 +0 +1 +1 +- +0 +1 +1 +1 +0 +1 +0 +1 +1 +0 +- +1 +1 +1 +- +(1,2) ((1,2),(3,4)) 1 1 diff --git a/dbms/tests/queries/0_stateless/00626_in_syntax.sql b/dbms/tests/queries/0_stateless/00626_in_syntax.sql new file mode 100644 index 00000000000..e4777f7fe61 --- /dev/null +++ b/dbms/tests/queries/0_stateless/00626_in_syntax.sql @@ -0,0 +1,44 @@ +select (1, 2) in tuple((1, 2)); +select (1, 2) in ((1, 2), (3, 4)); +select ((1, 2), (3, 4)) in ((1, 2), (3, 4)); +select ((1, 2), (3, 4)) in (((1, 2), (3, 4))); +select ((1, 2), (3, 4)) in tuple(((1, 2), (3, 4))); +select ((1, 2), (3, 4)) in (((1, 2), (3, 4)), ((5, 6), (7, 8))); + +select '-'; +select 1 in 1; +select 1 in tuple(1); +select tuple(1) in tuple(1); +select tuple(1) in tuple(tuple(1)); +select tuple(tuple(1)) in tuple(tuple(1)); +select tuple(tuple(1)) in tuple(tuple(tuple(1))); +select tuple(tuple(tuple(1))) in tuple(tuple(tuple(1))); + +select '-'; +select 1 in Null; +select 1 in tuple(Null); +select 1 in tuple(Null, 1); +select tuple(1) in tuple(tuple(Null)); +select tuple(1) in tuple(tuple(Null), tuple(1)); +select tuple(tuple(Null), tuple(1)) in tuple(tuple(Null), tuple(1)); + +select '-'; +select 1 in (1 + 1, 1 - 1); +select 1 in (0 + 1, 1, toInt8(sin(5))); +select (0 + 1, 1, toInt8(sin(5))) in (0 + 1, 1, toInt8(sin(5))); +select identity(tuple(1)) in (tuple(1), tuple(2)); +select identity(tuple(1)) in (tuple(0), tuple(2)); +select identity(tuple(1)) in (identity(tuple(1)), tuple(2)); +select identity(tuple(1)) in (identity(tuple(0)), tuple(2)); +select identity(tuple(1)) in (identity(tuple(1)), identity(tuple(2))); +select identity(tuple(1)) in (identity(tuple(1)), identity(identity(tuple(2)))); +select identity(tuple(1)) in (identity(tuple(0)), identity(identity(tuple(2)))); + +select '-'; +select identity((1, 2)) in (1, 2); +select identity((1, 2)) in ((1, 2), (3, 4)); +select identity((1, 2)) in ((1, 2), identity((3, 4))); + +select '-'; +select (1,2) as x, ((1,2),(3,4)) as y, 1 in x, x in y; + From 31c4a51ae4019a592a182f2b5f499c76fd33d3cb Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Tue, 15 May 2018 19:22:00 +0300 Subject: [PATCH 223/231] Reverted bad changes. [#CLICKHOUSE-2] --- libs/libdaemon/src/BaseDaemon.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/libs/libdaemon/src/BaseDaemon.cpp b/libs/libdaemon/src/BaseDaemon.cpp index 01fd377b66e..ff1fbabceb1 100644 --- a/libs/libdaemon/src/BaseDaemon.cpp +++ b/libs/libdaemon/src/BaseDaemon.cpp @@ -700,7 +700,7 @@ void BaseDaemon::buildLoggers(Poco::Util::AbstractConfiguration & config) return; config_logger = current_logger; - bool is_daemon = config.getBool("application.runAsDaemon", true); + bool is_daemon = config.getBool("application.runAsDaemon", false); // Split log and error log. Poco::AutoPtr split = new SplitterChannel; @@ -883,7 +883,7 @@ void BaseDaemon::initialize(Application & self) config().add(map_config, PRIO_APPLICATION - 100); } - bool is_daemon = config().getBool("application.runAsDaemon", true); + bool is_daemon = config().getBool("application.runAsDaemon", false); if (is_daemon) { From 7d1078d992a3a4854c6f3de9204a73982d86992e Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Tue, 15 May 2018 20:05:13 +0300 Subject: [PATCH 224/231] Add config key to force stderr redirecting. [#CLICKHOUSE-2] --- libs/libdaemon/src/BaseDaemon.cpp | 41 ++++++++++++++++--------------- 1 file changed, 21 insertions(+), 20 deletions(-) diff --git a/libs/libdaemon/src/BaseDaemon.cpp b/libs/libdaemon/src/BaseDaemon.cpp index ff1fbabceb1..e9415f60455 100644 --- a/libs/libdaemon/src/BaseDaemon.cpp +++ b/libs/libdaemon/src/BaseDaemon.cpp @@ -943,29 +943,30 @@ void BaseDaemon::initialize(Application & self) if (!log_path.empty()) log_path = Poco::Path(log_path).setFileName("").toString(); - if (is_daemon) + /** Redirect stdout, stderr to separate files in the log directory (or in the specified file). + * Some libraries write to stderr in case of errors in debug mode, + * and this output makes sense even if the program is run in daemon mode. + * We have to do it before buildLoggers, for errors on logger initialization will be written to these files. + * If logger.stderr is specified then stderr will be forcibly redirected to that file. + */ + if (!log_path.empty() && is_daemon || config().has("logger.stderr")) { - /** Redirect stdout, stderr to separate files in the log directory. - * Some libraries write to stderr in case of errors in debug mode, - * and this output makes sense even if the program is run in daemon mode. - * We have to do it before buildLoggers, for errors on logger initialization will be written to these files. - */ - if (!log_path.empty()) - { - std::string stdout_path = log_path + "/stdout"; - if (!freopen(stdout_path.c_str(), "a+", stdout)) - throw Poco::OpenFileException("Cannot attach stdout to " + stdout_path); - - std::string stderr_path = log_path + "/stderr"; - if (!freopen(stderr_path.c_str(), "a+", stderr)) - throw Poco::OpenFileException("Cannot attach stderr to " + stderr_path); - } - - /// Create pid file. - if (is_daemon && config().has("pid")) - pid.seed(config().getString("pid")); + std::string stderr_path = config().getString("logger.stderr", log_path + "/stderr"); + if (!freopen(stderr_path.c_str(), "a+", stderr)) + throw Poco::OpenFileException("Cannot attach stderr to " + stderr_path); } + if (!log_path.empty() && is_daemon || config().has("logger.stdout")) + { + std::string stdout_path = config().getString("logger.stdout", log_path + "/stdout"); + if (!freopen(stdout_path.c_str(), "a+", stdout)) + throw Poco::OpenFileException("Cannot attach stdout to " + stdout_path); + } + + /// Create pid file. + if (is_daemon && config().has("pid")) + pid.seed(config().getString("pid")); + /// Change path for logging. if (!log_path.empty()) { From 9b681eb97469afcc23d487c6d6606c6143b3a32f Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Tue, 15 May 2018 21:10:50 +0300 Subject: [PATCH 225/231] Suppress compiler warnings. [#CLICKHOUSE-2] --- libs/libdaemon/src/BaseDaemon.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/libs/libdaemon/src/BaseDaemon.cpp b/libs/libdaemon/src/BaseDaemon.cpp index e9415f60455..0d2b53fd9bc 100644 --- a/libs/libdaemon/src/BaseDaemon.cpp +++ b/libs/libdaemon/src/BaseDaemon.cpp @@ -949,14 +949,14 @@ void BaseDaemon::initialize(Application & self) * We have to do it before buildLoggers, for errors on logger initialization will be written to these files. * If logger.stderr is specified then stderr will be forcibly redirected to that file. */ - if (!log_path.empty() && is_daemon || config().has("logger.stderr")) + if ((!log_path.empty() && is_daemon) || config().has("logger.stderr")) { std::string stderr_path = config().getString("logger.stderr", log_path + "/stderr"); if (!freopen(stderr_path.c_str(), "a+", stderr)) throw Poco::OpenFileException("Cannot attach stderr to " + stderr_path); } - if (!log_path.empty() && is_daemon || config().has("logger.stdout")) + if ((!log_path.empty() && is_daemon) || config().has("logger.stdout")) { std::string stdout_path = config().getString("logger.stdout", log_path + "/stdout"); if (!freopen(stdout_path.c_str(), "a+", stdout)) From 412c154045a119f618e206180c6e455c944b1f79 Mon Sep 17 00:00:00 2001 From: Vitaliy Lyudvichenko Date: Tue, 15 May 2018 21:25:54 +0300 Subject: [PATCH 226/231] Better configs for an integration test. [#CLICKHOUSE-2] --- dbms/src/Common/Config/ConfigProcessor.cpp | 18 ++++++++++-------- .../configs/config-copier.xml | 11 +++++++++++ .../integration/test_cluster_copier/test.py | 2 +- 3 files changed, 22 insertions(+), 9 deletions(-) create mode 100644 dbms/tests/integration/test_cluster_copier/configs/config-copier.xml diff --git a/dbms/src/Common/Config/ConfigProcessor.cpp b/dbms/src/Common/Config/ConfigProcessor.cpp index 54f9ca58823..e25c5faa412 100644 --- a/dbms/src/Common/Config/ConfigProcessor.cpp +++ b/dbms/src/Common/Config/ConfigProcessor.cpp @@ -369,15 +369,17 @@ ConfigProcessor::Files ConfigProcessor::getConfigMergeFiles(const std::string & Files files; Poco::Path merge_dir_path(config_path); - merge_dir_path.setExtension("d"); + std::set merge_dirs; - std::vector merge_dirs; - merge_dirs.push_back(merge_dir_path.toString()); - if (merge_dir_path.getBaseName() != "conf") - { - merge_dir_path.setBaseName("conf"); - merge_dirs.push_back(merge_dir_path.toString()); - } + /// Add path_to_config/config_name.d dir + merge_dir_path.setExtension("d"); + merge_dirs.insert(merge_dir_path.toString()); + /// Add path_to_config/conf.d dir + merge_dir_path.setBaseName("conf"); + merge_dirs.insert(merge_dir_path.toString()); + /// Add path_to_config/config.d dir + merge_dir_path.setBaseName("config"); + merge_dirs.insert(merge_dir_path.toString()); for (const std::string & merge_dir_name : merge_dirs) { diff --git a/dbms/tests/integration/test_cluster_copier/configs/config-copier.xml b/dbms/tests/integration/test_cluster_copier/configs/config-copier.xml new file mode 100644 index 00000000000..1248d295c09 --- /dev/null +++ b/dbms/tests/integration/test_cluster_copier/configs/config-copier.xml @@ -0,0 +1,11 @@ + + + trace + /var/log/clickhouse-server/copier/log.log + /var/log/clickhouse-server/copier/log.err.log + 1000M + 10 + /var/log/clickhouse-server/copier/stderr + /var/log/clickhouse-server/copier/stdout + + \ No newline at end of file diff --git a/dbms/tests/integration/test_cluster_copier/test.py b/dbms/tests/integration/test_cluster_copier/test.py index 1bc06fda310..a19fa8231cf 100644 --- a/dbms/tests/integration/test_cluster_copier/test.py +++ b/dbms/tests/integration/test_cluster_copier/test.py @@ -183,7 +183,7 @@ def execute_task(task, cmd_options): copiers_exec_ids = [] cmd = ['/usr/bin/clickhouse', 'copier', - '--config', '/etc/clickhouse-server/config-preprocessed.xml', + '--config', '/etc/clickhouse-server/config-copier.xml', '--task-path', zk_task_path, '--base-dir', '/var/log/clickhouse-server/copier'] cmd += cmd_options From fb91bba27933fe715ddefaab95f18fa9adbfa3b2 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Wed, 16 May 2018 03:34:56 +0300 Subject: [PATCH 227/231] Miscellaneous [#CLICKHOUSE-2] --- dbms/src/Columns/ColumnTuple.cpp | 8 ++++---- dbms/src/Common/FieldVisitors.cpp | 4 ++-- dbms/src/Core/Field.cpp | 4 ++-- dbms/src/DataTypes/DataTypeTuple.cpp | 4 ++-- dbms/src/IO/WriteBufferFromString.h | 1 - dbms/src/Interpreters/Set.cpp | 2 +- debian/clickhouse-server.init | 7 +++++-- libs/libcommon/include/common/strong_typedef.h | 4 +++- release | 5 ++++- utils/travis/normal.sh | 10 +++++----- utils/travis/pbuilder.sh | 2 +- 11 files changed, 29 insertions(+), 22 deletions(-) diff --git a/dbms/src/Columns/ColumnTuple.cpp b/dbms/src/Columns/ColumnTuple.cpp index 558b1685276..7eaebcdb95d 100644 --- a/dbms/src/Columns/ColumnTuple.cpp +++ b/dbms/src/Columns/ColumnTuple.cpp @@ -74,7 +74,7 @@ void ColumnTuple::get(size_t n, Field & res) const { const size_t tuple_size = columns.size(); res = Tuple(TupleBackend(tuple_size)); - TupleBackend & res_arr = DB::get(res).t; + TupleBackend & res_arr = DB::get(res).toUnderType(); for (const auto i : ext::range(0, tuple_size)) columns[i]->get(n, res_arr[i]); } @@ -91,7 +91,7 @@ void ColumnTuple::insertData(const char *, size_t) void ColumnTuple::insert(const Field & x) { - const TupleBackend & tuple = DB::get(x).t; + const TupleBackend & tuple = DB::get(x).toUnderType(); const size_t tuple_size = columns.size(); if (tuple.size() != tuple_size) @@ -309,8 +309,8 @@ void ColumnTuple::getExtremes(Field & min, Field & max) const min = Tuple(TupleBackend(tuple_size)); max = Tuple(TupleBackend(tuple_size)); - auto & min_backend = min.get().t; - auto & max_backend = max.get().t; + auto & min_backend = min.get().toUnderType(); + auto & max_backend = max.get().toUnderType(); for (const auto i : ext::range(0, tuple_size)) columns[i]->getExtremes(min_backend[i], max_backend[i]); diff --git a/dbms/src/Common/FieldVisitors.cpp b/dbms/src/Common/FieldVisitors.cpp index 2243a838a99..3132a7412ca 100644 --- a/dbms/src/Common/FieldVisitors.cpp +++ b/dbms/src/Common/FieldVisitors.cpp @@ -61,7 +61,7 @@ String FieldVisitorDump::operator() (const Array & x) const String FieldVisitorDump::operator() (const Tuple & x_def) const { - auto & x = x_def.t; + auto & x = x_def.toUnderType(); WriteBufferFromOwnString wb; wb.write("Tuple_(", 7); @@ -124,7 +124,7 @@ String FieldVisitorToString::operator() (const Array & x) const String FieldVisitorToString::operator() (const Tuple & x_def) const { - auto & x = x_def.t; + auto & x = x_def.toUnderType(); WriteBufferFromOwnString wb; writeChar('(', wb); diff --git a/dbms/src/Core/Field.cpp b/dbms/src/Core/Field.cpp index a5213c70515..1e14689d856 100644 --- a/dbms/src/Core/Field.cpp +++ b/dbms/src/Core/Field.cpp @@ -143,7 +143,7 @@ namespace DB { inline void readBinary(Tuple & x_def, ReadBuffer & buf) { - auto & x = x_def.t; + auto & x = x_def.toUnderType(); size_t size; DB::readBinary(size, buf); @@ -214,7 +214,7 @@ namespace DB void writeBinary(const Tuple & x_def, WriteBuffer & buf) { - auto & x = x_def.t; + auto & x = x_def.toUnderType(); const size_t size = x.size(); DB::writeBinary(size, buf); diff --git a/dbms/src/DataTypes/DataTypeTuple.cpp b/dbms/src/DataTypes/DataTypeTuple.cpp index 2da9f0f6ecf..3bcd815686f 100644 --- a/dbms/src/DataTypes/DataTypeTuple.cpp +++ b/dbms/src/DataTypes/DataTypeTuple.cpp @@ -98,7 +98,7 @@ static inline const IColumn & extractElementColumn(const IColumn & column, size_ void DataTypeTuple::serializeBinary(const Field & field, WriteBuffer & ostr) const { - const auto & tuple = get(field).t; + const auto & tuple = get(field).toUnderType(); for (const auto & idx_elem : ext::enumerate(elems)) idx_elem.second->serializeBinary(tuple[idx_elem.first], ostr); } @@ -107,7 +107,7 @@ void DataTypeTuple::deserializeBinary(Field & field, ReadBuffer & istr) const { const size_t size = elems.size(); field = Tuple(TupleBackend(size)); - TupleBackend & tuple = get(field).t; + TupleBackend & tuple = get(field).toUnderType(); for (const auto i : ext::range(0, size)) elems[i]->deserializeBinary(tuple[i], istr); } diff --git a/dbms/src/IO/WriteBufferFromString.h b/dbms/src/IO/WriteBufferFromString.h index fe1be32266f..fcb7eea9484 100644 --- a/dbms/src/IO/WriteBufferFromString.h +++ b/dbms/src/IO/WriteBufferFromString.h @@ -63,7 +63,6 @@ namespace detail /// Creates the string by itself and allows to get it. class WriteBufferFromOwnString : public detail::StringHolder, public WriteBufferFromString { - public: WriteBufferFromOwnString() : WriteBufferFromString(value) {} diff --git a/dbms/src/Interpreters/Set.cpp b/dbms/src/Interpreters/Set.cpp index 9fb95128ad1..2654182201e 100644 --- a/dbms/src/Interpreters/Set.cpp +++ b/dbms/src/Interpreters/Set.cpp @@ -234,7 +234,7 @@ void Set::createFromAST(const DataTypes & types, ASTPtr node, const Context & co throw Exception("Invalid type of set. Expected tuple, got " + String(function_result.getTypeName()), ErrorCodes::INCORRECT_ELEMENT_OF_SET); - tuple = &function_result.get().t; + tuple = &function_result.get().toUnderType(); } size_t tuple_size = tuple ? tuple->size() : func->arguments->children.size(); diff --git a/debian/clickhouse-server.init b/debian/clickhouse-server.init index 8d0a75f573e..e92b3e281df 100755 --- a/debian/clickhouse-server.init +++ b/debian/clickhouse-server.init @@ -29,6 +29,9 @@ RETVAL=0 CLICKHOUSE_PIDDIR=/var/run/$PROGRAM CLICKHOUSE_PIDFILE="$CLICKHOUSE_PIDDIR/$PROGRAM.pid" +# Some systems lack "flock" +command -v flock >/dev/null && FLOCK=flock + # Override defaults from optional config file test -f /etc/default/clickhouse && . /etc/default/clickhouse @@ -165,7 +168,7 @@ start() if ! is_running; then # Lock should not be held while running child process, so we release the lock. Note: obviously, there is race condition. # But clickhouse-server has protection from simultaneous runs with same data directory. - su -s $SHELL ${CLICKHOUSE_USER} -c "flock -u 9; exec -a \"$PROGRAM\" \"$BINDIR/$PROGRAM\" --daemon --pid-file=\"$CLICKHOUSE_PIDFILE\" --config-file=\"$CLICKHOUSE_CONFIG\"" + su -s $SHELL ${CLICKHOUSE_USER} -c "$FLOCK -u 9; exec -a \"$PROGRAM\" \"$BINDIR/$PROGRAM\" --daemon --pid-file=\"$CLICKHOUSE_PIDFILE\" --config-file=\"$CLICKHOUSE_CONFIG\"" EXIT_STATUS=$? if [ $EXIT_STATUS -ne 0 ]; then break @@ -345,7 +348,7 @@ esac ( - if flock -n 9; then + if $FLOCK -n 9; then main "$@" else echo "Init script is already running" && exit 1 diff --git a/libs/libcommon/include/common/strong_typedef.h b/libs/libcommon/include/common/strong_typedef.h index 095dd6436b8..83e7ee5e08a 100644 --- a/libs/libcommon/include/common/strong_typedef.h +++ b/libs/libcommon/include/common/strong_typedef.h @@ -11,9 +11,11 @@ struct StrongTypedef : boost::totally_ordered1< StrongTypedef , boost::totally_ordered2< StrongTypedef, T> > { +private: using Self = StrongTypedef; T t; +public: template ::type> explicit StrongTypedef(const T & t_) : t(t_) {}; template ::type> @@ -34,7 +36,7 @@ struct StrongTypedef template ::type> Self & operator=(T && rhs) { t = std::move(rhs); return *this;} - operator const T & () const {return t; } + operator const T & () const { return t; } operator T & () { return t; } bool operator==(const Self & rhs) const { return t == rhs.t; } diff --git a/release b/release index 0e785aa0886..a0da085aa75 100755 --- a/release +++ b/release @@ -82,7 +82,10 @@ elif [[ $BUILD_TYPE == 'debug' ]]; then VERSION_POSTFIX+=+$BUILD_TYPE fi -CMAKE_FLAGS=" $LIBTCMALLOC_OPTS -DCMAKE_BUILD_TYPE=$CMAKE_BUILD_TYPE $CMAKE_FLAGS" +CMAKE_FLAGS=" $LIBTCMALLOC_OPTS -D CMAKE_BUILD_TYPE=$CMAKE_BUILD_TYPE $CMAKE_FLAGS" + +[[ "$CMAKE_FLAGS" =~ "USE_INTERNAL_LLVM_LIBRARY" ]] || CMAKE_FLAGS=" -D USE_INTERNAL_LLVM_LIBRARY=1 $CMAKE_FLAGS" + export CMAKE_FLAGS export EXTRAPACKAGES diff --git a/utils/travis/normal.sh b/utils/travis/normal.sh index 573b486762f..bd54d11a097 100755 --- a/utils/travis/normal.sh +++ b/utils/travis/normal.sh @@ -22,15 +22,15 @@ date mkdir -p build cd build -cmake $CUR_DIR/../.. -DCMAKE_CXX_COMPILER=`which $DEB_CXX $CXX` -DCMAKE_C_COMPILER=`which $DEB_CC $CC` \ +cmake $CUR_DIR/../.. -D CMAKE_CXX_COMPILER=`which $DEB_CXX $CXX` -D CMAKE_C_COMPILER=`which $DEB_CC $CC` \ `# Does not optimize to speedup build, skip debug info to use less disk` \ - -DCMAKE_C_FLAGS_ADD="-O0 -g0" -DCMAKE_CXX_FLAGS_ADD="-O0 -g0" \ + -D CMAKE_C_FLAGS_ADD="-O0 -g0" -D CMAKE_CXX_FLAGS_ADD="-O0 -g0" \ `# ignore ccache disabler on trusty` \ - -DCMAKE_C_COMPILER_LAUNCHER=`which ccache` -DCMAKE_CXX_COMPILER_LAUNCHER=`which ccache` \ + -D CMAKE_C_COMPILER_LAUNCHER=`which ccache` -D CMAKE_CXX_COMPILER_LAUNCHER=`which ccache` \ `# Use all possible contrib libs from system` \ - -DUNBUNDLED=1 \ + -D UNBUNDLED=1 \ `# Disable all features` \ - -DENABLE_CAPNP=0 -DENABLE_RDKAFKA=0 -DENABLE_EMBEDDED_COMPILER=0 -DENABLE_TCMALLOC=0 -DENABLE_UNWIND=0 -DENABLE_MYSQL=0 $CMAKE_FLAGS \ + -D ENABLE_CAPNP=0 -D ENABLE_RDKAFKA=0 -D ENABLE_EMBEDDED_COMPILER=0 -D ENABLE_TCMALLOC=0 -D ENABLE_UNWIND=0 -D ENABLE_MYSQL=0 -D USE_INTERNAL_LLVM_LIBRARY=0 $CMAKE_FLAGS \ && make -j `nproc || grep -c ^processor /proc/cpuinfo || sysctl -n hw.ncpu || echo 4` clickhouse-bundle \ `# Skip tests:` \ `# 00281 requires internal compiler` \ diff --git a/utils/travis/pbuilder.sh b/utils/travis/pbuilder.sh index dee1176d840..a496428c6af 100755 --- a/utils/travis/pbuilder.sh +++ b/utils/travis/pbuilder.sh @@ -24,7 +24,7 @@ env TEST_RUN=${TEST_RUN=1} \ DEB_CC=${DEB_CC=$CC} DEB_CXX=${DEB_CXX=$CXX} \ CCACHE_SIZE=${CCACHE_SIZE:=4G} \ `# Disable all features` \ - CMAKE_FLAGS="-DCMAKE_BUILD_TYPE=Debug -DUNBUNDLED=1 -DENABLE_UNWIND=0 -DENABLE_MYSQL=0 -DENABLE_CAPNP=0 -DENABLE_RDKAFKA=0 -DCMAKE_C_FLAGS_ADD='-O0 -g0' -DCMAKE_CXX_FLAGS_ADD='-O0 -g0' $CMAKE_FLAGS" \ + CMAKE_FLAGS="-D CMAKE_BUILD_TYPE=Debug -D UNBUNDLED=1 -D ENABLE_UNWIND=0 -D ENABLE_MYSQL=0 -D ENABLE_CAPNP=0 -D ENABLE_RDKAFKA=0 -D USE_INTERNAL_LLVM_LIBRARY=0 -D CMAKE_C_FLAGS_ADD='-O0 -g0' -D CMAKE_CXX_FLAGS_ADD='-O0 -g0' $CMAKE_FLAGS" \ `# Use all possible contrib libs from system` \ `# psmisc - killall` \ EXTRAPACKAGES="psmisc clang-5.0 lld-5.0 liblld-5.0-dev libclang-5.0-dev liblld-5.0 libc++abi-dev libc++-dev libboost-program-options-dev libboost-system-dev libboost-filesystem-dev libboost-thread-dev zlib1g-dev liblz4-dev libdouble-conversion-dev libsparsehash-dev librdkafka-dev libpoco-dev libsparsehash-dev libgoogle-perftools-dev libzstd-dev libre2-dev $EXTRAPACKAGES" \ From 3b97d3938f5e0608e54434e0d8dcdea9383b3325 Mon Sep 17 00:00:00 2001 From: Alexey Zatelepin Date: Fri, 2 Feb 2018 19:02:43 +0300 Subject: [PATCH 228/231] alter delete skeleton [#CLICKHOUSE-3688] --- .../Interpreters/InterpreterAlterQuery.cpp | 27 ++++++++++---- dbms/src/Interpreters/InterpreterAlterQuery.h | 5 ++- dbms/src/Parsers/ASTAlterQuery.cpp | 9 +++++ dbms/src/Parsers/ASTAlterQuery.h | 5 +++ dbms/src/Parsers/ParserAlterQuery.cpp | 10 ++++++ dbms/src/Parsers/ParserAlterQuery.h | 1 + dbms/src/Storages/IStorage.h | 7 ++++ dbms/src/Storages/MutationCommands.h | 35 +++++++++++++++++++ 8 files changed, 91 insertions(+), 8 deletions(-) create mode 100644 dbms/src/Storages/MutationCommands.h diff --git a/dbms/src/Interpreters/InterpreterAlterQuery.cpp b/dbms/src/Interpreters/InterpreterAlterQuery.cpp index bc7861ad41c..4e160e0262d 100644 --- a/dbms/src/Interpreters/InterpreterAlterQuery.cpp +++ b/dbms/src/Interpreters/InterpreterAlterQuery.cpp @@ -50,7 +50,14 @@ BlockIO InterpreterAlterQuery::execute() AlterCommands alter_commands; PartitionCommands partition_commands; - parseAlter(alter.parameters, alter_commands, partition_commands); + MutationCommands mutation_commands; + parseAlter(alter.parameters, alter_commands, partition_commands, mutation_commands); + + if (!mutation_commands.commands.empty()) + { + /// TODO: validate + table->mutate(mutation_commands, context); + } partition_commands.validate(table.get()); for (const PartitionCommand & command : partition_commands) @@ -79,18 +86,20 @@ BlockIO InterpreterAlterQuery::execute() } } - if (alter_commands.empty()) - return {}; - - alter_commands.validate(table.get(), context); - table->alter(alter_commands, database_name, table_name, context); + if (!alter_commands.empty()) + { + alter_commands.validate(table.get(), context); + table->alter(alter_commands, database_name, table_name, context); + } return {}; } void InterpreterAlterQuery::parseAlter( const ASTAlterQuery::ParameterContainer & params_container, - AlterCommands & out_alter_commands, PartitionCommands & out_partition_commands) + AlterCommands & out_alter_commands, + PartitionCommands & out_partition_commands, + MutationCommands & out_mutation_commands) { const DataTypeFactory & data_type_factory = DataTypeFactory::instance(); @@ -186,6 +195,10 @@ void InterpreterAlterQuery::parseAlter( { out_partition_commands.emplace_back(PartitionCommand::freezePartition(params.partition, params.with_name)); } + else if (params.type == ASTAlterQuery::DELETE) + { + out_mutation_commands.commands.emplace_back(MutationCommand::delete_(params.predicate)); + } else throw Exception("Wrong parameter type in ALTER query", ErrorCodes::LOGICAL_ERROR); } diff --git a/dbms/src/Interpreters/InterpreterAlterQuery.h b/dbms/src/Interpreters/InterpreterAlterQuery.h index 011459a1d32..7a1d00c10f1 100644 --- a/dbms/src/Interpreters/InterpreterAlterQuery.h +++ b/dbms/src/Interpreters/InterpreterAlterQuery.h @@ -2,6 +2,7 @@ #include #include +#include #include #include #include @@ -102,7 +103,9 @@ private: const Context & context; static void parseAlter(const ASTAlterQuery::ParameterContainer & params, - AlterCommands & out_alter_commands, PartitionCommands & out_partition_commands); + AlterCommands & out_alter_commands, + PartitionCommands & out_partition_commands, + MutationCommands & out_mutation_commands); }; } diff --git a/dbms/src/Parsers/ASTAlterQuery.cpp b/dbms/src/Parsers/ASTAlterQuery.cpp index 6b439e83dda..dc3cb357235 100644 --- a/dbms/src/Parsers/ASTAlterQuery.cpp +++ b/dbms/src/Parsers/ASTAlterQuery.cpp @@ -17,7 +17,9 @@ void ASTAlterQuery::Parameters::clone(Parameters & p) const p = *this; if (col_decl) p.col_decl = col_decl->clone(); if (column) p.column = column->clone(); + if (primary_key) p.primary_key = primary_key->clone(); if (partition) p.partition = partition->clone(); + if (predicate) p.predicate = predicate->clone(); } void ASTAlterQuery::addParameters(const Parameters & params) @@ -31,6 +33,8 @@ void ASTAlterQuery::addParameters(const Parameters & params) children.push_back(params.partition); if (params.primary_key) children.push_back(params.primary_key); + if (params.predicate) + children.push_back(params.predicate); } /** Get the text that identifies this element. */ @@ -150,6 +154,11 @@ void ASTAlterQuery::formatQueryImpl(const FormatSettings & settings, FormatState << " " << std::quoted(p.with_name, '\''); } } + else if (p.type == ASTAlterQuery::DELETE) + { + settings.ostr << (settings.hilite ? hilite_keyword : "") << indent_str << "DELETE WHERE " << (settings.hilite ? hilite_none : ""); + p.predicate->formatImpl(settings, state, frame); + } else throw Exception("Unexpected type of ALTER", ErrorCodes::UNEXPECTED_AST_STRUCTURE); diff --git a/dbms/src/Parsers/ASTAlterQuery.h b/dbms/src/Parsers/ASTAlterQuery.h index dc1c4dde849..0d52da5a79b 100644 --- a/dbms/src/Parsers/ASTAlterQuery.h +++ b/dbms/src/Parsers/ASTAlterQuery.h @@ -31,6 +31,8 @@ public: FETCH_PARTITION, FREEZE_PARTITION, + DELETE, + NO_TYPE, }; @@ -59,6 +61,9 @@ public: */ ASTPtr partition; + /// For DELETE WHERE: the predicate that filters the rows to delete. + ASTPtr predicate; + bool detach = false; /// true for DETACH PARTITION bool part = false; /// true for ATTACH PART diff --git a/dbms/src/Parsers/ParserAlterQuery.cpp b/dbms/src/Parsers/ParserAlterQuery.cpp index dbd1805e7b1..baea5b9e433 100644 --- a/dbms/src/Parsers/ParserAlterQuery.cpp +++ b/dbms/src/Parsers/ParserAlterQuery.cpp @@ -33,6 +33,8 @@ bool ParserAlterQuery::parseImpl(Pos & pos, ASTPtr & node, Expected & expected) ParserKeyword s_with("WITH"); ParserKeyword s_name("NAME"); + ParserKeyword s_delete_where("DELETE WHERE"); + ParserToken s_dot(TokenType::Dot); ParserToken s_comma(TokenType::Comma); @@ -41,6 +43,7 @@ bool ParserAlterQuery::parseImpl(Pos & pos, ASTPtr & node, Expected & expected) ParserCompoundColumnDeclaration parser_col_decl; ParserPartition parser_partition; ParserStringLiteral parser_string_literal; + ParserExpression exp_elem; ASTPtr table; ASTPtr database; @@ -206,6 +209,13 @@ bool ParserAlterQuery::parseImpl(Pos & pos, ASTPtr & node, Expected & expected) params.type = ASTAlterQuery::MODIFY_PRIMARY_KEY; } + else if (s_delete_where.ignore(pos, expected)) + { + if (!exp_elem.parse(pos, params.predicate, expected)) + return false; + + params.type = ASTAlterQuery::DELETE; + } else return false; diff --git a/dbms/src/Parsers/ParserAlterQuery.h b/dbms/src/Parsers/ParserAlterQuery.h index 42b9b23ea99..03c23c6f47f 100644 --- a/dbms/src/Parsers/ParserAlterQuery.h +++ b/dbms/src/Parsers/ParserAlterQuery.h @@ -16,6 +16,7 @@ namespace DB * [DROP|DETACH|ATTACH PARTITION|PART partition, ...] * [FETCH PARTITION partition FROM ...] * [FREEZE PARTITION] + * [DELETE WHERE ...] */ class ParserAlterQuery : public IParserBase { diff --git a/dbms/src/Storages/IStorage.h b/dbms/src/Storages/IStorage.h index 74db0604147..2a8cff88fb2 100644 --- a/dbms/src/Storages/IStorage.h +++ b/dbms/src/Storages/IStorage.h @@ -40,6 +40,7 @@ using StorageWeakPtr = std::weak_ptr; struct Settings; class AlterCommands; +struct MutationCommands; /** Does not allow changing the table description (including rename and delete the table). @@ -260,6 +261,12 @@ public: throw Exception("Method optimize is not supported by storage " + getName(), ErrorCodes::NOT_IMPLEMENTED); } + /// Mutate the table contents + virtual void mutate(const MutationCommands &, const Context &) + { + throw Exception("Mutations are not supported by storage " + getName(), ErrorCodes::NOT_IMPLEMENTED); + } + /** If the table have to do some complicated work on startup, * that must be postponed after creation of table object * (like launching some background threads), diff --git a/dbms/src/Storages/MutationCommands.h b/dbms/src/Storages/MutationCommands.h new file mode 100644 index 00000000000..b35fd6d1b06 --- /dev/null +++ b/dbms/src/Storages/MutationCommands.h @@ -0,0 +1,35 @@ +#pragma once + +#include + + +namespace DB +{ + +struct MutationCommand +{ + enum Type + { + EMPTY, /// Not used. + DELETE, + }; + + Type type = EMPTY; + + ASTPtr predicate; + + static MutationCommand delete_(const ASTPtr & predicate) + { + MutationCommand res; + res.type = DELETE; + res.predicate = predicate; + return res; + } +}; + +struct MutationCommands +{ + std::vector commands; +}; + +} From d31b897ba796e3d3096dc7066d42aeb29e25ee7b Mon Sep 17 00:00:00 2001 From: Alexey Zatelepin Date: Tue, 15 May 2018 15:56:14 +0300 Subject: [PATCH 229/231] validate mutation commands [#CLICKHOUSE-3688] --- dbms/src/Columns/FilterDescription.cpp | 15 +++++++++ dbms/src/Columns/FilterDescription.h | 6 ++++ .../Interpreters/InterpreterAlterQuery.cpp | 10 +++--- dbms/src/Interpreters/InterpreterAlterQuery.h | 2 +- dbms/src/Storages/AlterCommands.cpp | 6 ++-- dbms/src/Storages/AlterCommands.h | 2 +- dbms/src/Storages/MutationCommands.cpp | 32 +++++++++++++++++++ dbms/src/Storages/MutationCommands.h | 5 +++ 8 files changed, 68 insertions(+), 10 deletions(-) create mode 100644 dbms/src/Storages/MutationCommands.cpp diff --git a/dbms/src/Columns/FilterDescription.cpp b/dbms/src/Columns/FilterDescription.cpp index 3d3e3f5ffea..b4779648b82 100644 --- a/dbms/src/Columns/FilterDescription.cpp +++ b/dbms/src/Columns/FilterDescription.cpp @@ -4,6 +4,7 @@ #include #include #include +#include namespace DB @@ -81,4 +82,18 @@ FilterDescription::FilterDescription(const IColumn & column) ErrorCodes::ILLEGAL_TYPE_OF_COLUMN_FOR_FILTER); } + +void checkColumnCanBeUsedAsFilter(const ColumnWithTypeAndName & column_elem) +{ + ConstantFilterDescription const_filter; + if (column_elem.column) + const_filter = ConstantFilterDescription(*column_elem.column); + + if (!const_filter.always_false && !const_filter.always_true) + { + auto column = column_elem.column ? column_elem.column : column_elem.type->createColumn(); + FilterDescription filter(*column); + } +} + } diff --git a/dbms/src/Columns/FilterDescription.h b/dbms/src/Columns/FilterDescription.h index 0c9c4c217ff..89474ea523c 100644 --- a/dbms/src/Columns/FilterDescription.h +++ b/dbms/src/Columns/FilterDescription.h @@ -29,4 +29,10 @@ struct FilterDescription explicit FilterDescription(const IColumn & column); }; + +struct ColumnWithTypeAndName; + +/// Will throw an exception if column_elem is cannot be used as a filter column. +void checkColumnCanBeUsedAsFilter(const ColumnWithTypeAndName & column_elem); + } diff --git a/dbms/src/Interpreters/InterpreterAlterQuery.cpp b/dbms/src/Interpreters/InterpreterAlterQuery.cpp index 4e160e0262d..0a28910a1d1 100644 --- a/dbms/src/Interpreters/InterpreterAlterQuery.cpp +++ b/dbms/src/Interpreters/InterpreterAlterQuery.cpp @@ -55,11 +55,11 @@ BlockIO InterpreterAlterQuery::execute() if (!mutation_commands.commands.empty()) { - /// TODO: validate + mutation_commands.validate(*table, context); table->mutate(mutation_commands, context); } - partition_commands.validate(table.get()); + partition_commands.validate(*table); for (const PartitionCommand & command : partition_commands) { switch (command.type) @@ -88,7 +88,7 @@ BlockIO InterpreterAlterQuery::execute() if (!alter_commands.empty()) { - alter_commands.validate(table.get(), context); + alter_commands.validate(*table, context); table->alter(alter_commands, database_name, table_name, context); } @@ -205,7 +205,7 @@ void InterpreterAlterQuery::parseAlter( } -void InterpreterAlterQuery::PartitionCommands::validate(const IStorage * table) +void InterpreterAlterQuery::PartitionCommands::validate(const IStorage & table) { for (const PartitionCommand & command : *this) { @@ -213,7 +213,7 @@ void InterpreterAlterQuery::PartitionCommands::validate(const IStorage * table) { String column_name = command.column_name.safeGet(); - if (!table->getColumns().hasPhysical(column_name)) + if (!table.getColumns().hasPhysical(column_name)) { throw Exception("Wrong column name. Cannot find column " + column_name + " to clear it from partition", DB::ErrorCodes::ILLEGAL_COLUMN); diff --git a/dbms/src/Interpreters/InterpreterAlterQuery.h b/dbms/src/Interpreters/InterpreterAlterQuery.h index 7a1d00c10f1..106d1271a52 100644 --- a/dbms/src/Interpreters/InterpreterAlterQuery.h +++ b/dbms/src/Interpreters/InterpreterAlterQuery.h @@ -95,7 +95,7 @@ private: class PartitionCommands : public std::vector { public: - void validate(const IStorage * table); + void validate(const IStorage & table); }; ASTPtr query_ptr; diff --git a/dbms/src/Storages/AlterCommands.cpp b/dbms/src/Storages/AlterCommands.cpp index f30dc9df53a..cea0a6b68eb 100644 --- a/dbms/src/Storages/AlterCommands.cpp +++ b/dbms/src/Storages/AlterCommands.cpp @@ -171,10 +171,10 @@ void AlterCommands::apply(ColumnsDescription & columns_description) const columns_description = std::move(new_columns_description); } -void AlterCommands::validate(IStorage * table, const Context & context) +void AlterCommands::validate(const IStorage & table, const Context & context) { - auto all_columns = table->getColumns().getAll(); - auto defaults = table->getColumns().defaults; + auto all_columns = table.getColumns().getAll(); + auto defaults = table.getColumns().defaults; std::vector> defaulted_columns{}; diff --git a/dbms/src/Storages/AlterCommands.h b/dbms/src/Storages/AlterCommands.h index 306d5cec54c..796f48eea1a 100644 --- a/dbms/src/Storages/AlterCommands.h +++ b/dbms/src/Storages/AlterCommands.h @@ -62,7 +62,7 @@ class AlterCommands : public std::vector public: void apply(ColumnsDescription & columns_description) const; - void validate(IStorage * table, const Context & context); + void validate(const IStorage & table, const Context & context); }; } diff --git a/dbms/src/Storages/MutationCommands.cpp b/dbms/src/Storages/MutationCommands.cpp new file mode 100644 index 00000000000..2f97914d5ac --- /dev/null +++ b/dbms/src/Storages/MutationCommands.cpp @@ -0,0 +1,32 @@ +#include +#include +#include +#include + + +namespace DB +{ + +void MutationCommands::validate(const IStorage & table, const Context & context) +{ + auto all_columns = table.getColumns().getAll(); + + for (const MutationCommand & command : commands) + { + switch (command.type) + { + case MutationCommand::DELETE: + { + auto actions = ExpressionAnalyzer(command.predicate, context, {}, all_columns).getActions(true); + const ColumnWithTypeAndName & predicate_column = actions->getSampleBlock().getByName( + command.predicate->getColumnName()); + checkColumnCanBeUsedAsFilter(predicate_column); + break; + } + default: + throw Exception("Bad mutation type: " + toString(command.type), ErrorCodes::LOGICAL_ERROR); + } + } +} + +} diff --git a/dbms/src/Storages/MutationCommands.h b/dbms/src/Storages/MutationCommands.h index b35fd6d1b06..5c1b99f44d4 100644 --- a/dbms/src/Storages/MutationCommands.h +++ b/dbms/src/Storages/MutationCommands.h @@ -6,6 +6,9 @@ namespace DB { +class IStorage; +class Context; + struct MutationCommand { enum Type @@ -30,6 +33,8 @@ struct MutationCommand struct MutationCommands { std::vector commands; + + void validate(const IStorage & table, const Context & context); }; } From 3ffa269c0c52caac07cf0cdd9fdbbbbb87721550 Mon Sep 17 00:00:00 2001 From: Alexey Milovidov Date: Thu, 17 May 2018 03:26:29 +0300 Subject: [PATCH 230/231] Fixed build [#CLICKHOUSE-2] --- libs/libcommon/src/tests/gtest_strong_typedef.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/libs/libcommon/src/tests/gtest_strong_typedef.cpp b/libs/libcommon/src/tests/gtest_strong_typedef.cpp index 082fd623dec..6bf2f1eaad1 100644 --- a/libs/libcommon/src/tests/gtest_strong_typedef.cpp +++ b/libs/libcommon/src/tests/gtest_strong_typedef.cpp @@ -35,18 +35,18 @@ TEST(StrongTypedefSuite, CopyAndMoveCtor) Int a(1); Int b(2); a = b; - EXPECT_EQ(a.t, 2); + EXPECT_EQ(a.toUnderType(), 2); STRONG_TYPEDEF(std::unique_ptr, IntPtr); { IntPtr ptr; ptr = IntPtr(std::make_unique(3)); - EXPECT_EQ(*ptr.t, 3); + EXPECT_EQ(*ptr.toUnderType(), 3); } { IntPtr ptr(std::make_unique(3)); - EXPECT_EQ(*ptr.t, 3); + EXPECT_EQ(*ptr.toUnderType(), 3); } } From 276663f8997051d6f9d76f87203dba6a79cd4235 Mon Sep 17 00:00:00 2001 From: Derek Perkins Date: Thu, 17 May 2018 05:17:16 -0600 Subject: [PATCH 231/231] fix typo in Changelog (#2377) --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index f59e58846d3..8c01d9601f3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -21,7 +21,7 @@ * Fixed an error in ZooKeeper client library which led to watches loses, freezing of distributed DDL queue and slowing replication queue if non-empty `chroot` prefix is used in ZooKeeper configuration. ## Backward incompatible changes: -* Removed support of expressions like `(a, b) IN (SELECT (a, b))` (instead of them you can use their equivalent `(a, b) IN (SELECT a, b)`). In previous releases, these expressions led to undermined data filtering or caused errors. +* Removed support of expressions like `(a, b) IN (SELECT (a, b))` (instead of them you can use their equivalent `(a, b) IN (SELECT a, b)`). In previous releases, these expressions led to undetermined data filtering or caused errors. # ClickHouse release 1.1.54378, 2018-04-16 ## New features: